# mypy: allow-untyped-defs
import copy
import warnings
from itertools import chain
from typing import Any, Dict, List, Optional, Sequence, Tuple

import torch
import torch.utils._pytree as pytree
from torch._export.utils import _check_input_constraints_for_graph
from torch.export.unflatten import _assign_attr, _AttrKind
from torch.fx.graph import _PyTreeCodeGen, _PyTreeInfo

from ._remove_effect_tokens_pass import _remove_effect_tokens
from ._tree_utils import reorder_kwargs
from .exported_program import (
    ExportedProgram,
    ExportGraphSignature,
    InputKind,
    OutputKind,
)


def _check_inputs_match(args, kwargs, in_spec: pytree.TreeSpec) -> List:
    reordered_kwargs = reorder_kwargs(kwargs, in_spec)
    flat_args_with_path, received_spec = pytree.tree_flatten_with_path(
        (args, reordered_kwargs)
    )

    if received_spec != in_spec:
        raise ValueError(  # noqa: B904
            "Trying to flatten user inputs with exported input tree spec: \n"
            f"{in_spec}\n"
            "but actually got inputs with tree spec of: \n"
            f"{received_spec}.\n"
            "Please check that the inputs have the same number of args "
            "and kwargs as the ones you used when tracing."
        )

    return flat_args_with_path


@torch._dynamo.disable
def _check_input_constraints_pre_hook(self, args, kwargs):
    if not self.validate_inputs:
        return

    flat_args_with_path = _check_inputs_match(args, kwargs, self._in_spec)

    _check_input_constraints_for_graph(
        [node for node in self.graph.nodes if node.op == "placeholder"],
        flat_args_with_path,
        self.range_constraints,
    )


def _unlift_inputs_as_getattr(
    gm: torch.fx.GraphModule,
    lifted_inputs: Sequence[Optional[str]],
) -> Tuple[Dict[str, torch.fx.Node], Dict[str, torch.fx.Node]]:
    """
    Unlift inputs referring to params/buffers/constants as getattr nodes in the
    graph
    """
    unlifted_name_to_node = {}
    input_name_to_node = {}

    placeholder_nodes = [node for node in gm.graph.nodes if node.op == "placeholder"]
    assert len(lifted_inputs) == len(placeholder_nodes)
    for input_node, lifted_node in zip(placeholder_nodes, lifted_inputs):
        if lifted_node is None:
            input_name_to_node[input_node.name] = input_node

        else:
            with gm.graph.inserting_after(input_node):
                getattr_node = gm.graph.get_attr(lifted_node)
                input_node.replace_all_uses_with(getattr_node)
                metadata = input_node.meta
                gm.graph.erase_node(input_node)
                getattr_node.meta = metadata
                unlifted_name_to_node[lifted_node] = getattr_node

    return unlifted_name_to_node, input_name_to_node


def _insert_copy_for_mutations(
    gm: torch.fx.GraphModule,
    mutated_outputs: Sequence[Optional[str]],
    unlifted_name_to_node: Dict[str, torch.fx.Node],
    input_name_to_node: Dict[str, torch.fx.Node],
) -> None:
    """
    Find the all the buffers and inputs that were mutated and insert copy_
    operators to reflect mutations.
    """
    output_node = None
    for node in gm.graph.nodes:
        if node.op == "output":
            output_node = node
            break
    assert output_node is not None
    outputs = pytree.tree_flatten(output_node.args)[0]
    assert len(outputs) == len(mutated_outputs)

    user_output_nodes = []
    return_nodes_to_copy = {}
    for return_node, mutated_node_name in zip(outputs, mutated_outputs):
        if mutated_node_name is None:
            user_output_nodes.append(return_node)
            continue

        if mutated_node_name in unlifted_name_to_node:
            mutated_node = unlifted_name_to_node[mutated_node_name]
        elif mutated_node_name in input_name_to_node:
            mutated_node = input_name_to_node[mutated_node_name]
        else:
            raise RuntimeError(
                f"Could not find {mutated_node_name} in either buffer or input nodes"
            )

        with gm.graph.inserting_before(output_node):
            copy_node = gm.graph.call_function(
                torch.ops.aten.copy_.default, (mutated_node, return_node)
            )
            return_nodes_to_copy[return_node] = copy_node

    output_args = [
        return_nodes_to_copy[node] if node in return_nodes_to_copy else node
        for node in user_output_nodes
    ]
    with gm.graph.inserting_before(output_node):
        # Only return user outputs
        new_output = gm.graph.output(tuple(output_args))
        output_node.replace_all_uses_with(new_output)
        gm.graph.erase_node(output_node)
        new_output.name = output_node.name
        new_output.meta.update(output_node.meta)


def _get_codegen(
    in_spec: pytree.TreeSpec,
    out_spec: Optional[pytree.TreeSpec],
    forward_arg_names: Optional[List[str]] = None,
) -> _PyTreeCodeGen:
    """
    Create the codegen for the graph module based on the in/out specs
    """
    if forward_arg_names:
        names = forward_arg_names
    else:
        if (
            in_spec.type == tuple
            and in_spec.num_children == 2
            and in_spec.children_specs[0].type == tuple
            and in_spec.children_specs[1].type == dict
        ):
            # if in_spec contains the args (tuple) and kwargs (dict)
            names = [f"arg_{i}" for i in range(in_spec.children_specs[0].num_children)]
            # add kwarg names
            names.extend(in_spec.children_specs[1].context)
        else:
            names = [f"arg_{i}" for i in range(in_spec.num_children)]

    return _PyTreeCodeGen(
        _PyTreeInfo(
            names,
            in_spec,
            out_spec,
        )
    )


def _unlift(
    gm: torch.fx.GraphModule,
    lifted_inputs: Sequence[Optional[str]],
    mutated_outputs: Sequence[Optional[str]],
    in_spec: pytree.TreeSpec,
    out_spec: Optional[pytree.TreeSpec],
    state_dict: Dict[str, Any],
    constants: Dict[str, Any],
    forward_arg_names: Optional[List[str]] = None,
):
    """
    Args:
        lifted_inputs: A list matching the graph module's input nodes. For
        an input node that is referring to a lifted parameter/buffer, this
        list will contain the fqn the corresponding attribute. Otherwise, this
        list will contain None. This is used to unlift the lifted parameters as
        get_attr nodes.

        mutated_outputs: A list matching the graph module's output nodes. For
        an output node that is referring to a mutated buffer or user input, this
        list will contain the name of the corresponding buffer or user input
        that needs to be mutated. Otherwise, this list will contain None. This
        is used to re-insert an inplace copy_ operator to copy the mutated
        values back to the original node.
    """
    unlifted_name_to_node, input_name_to_node = _unlift_inputs_as_getattr(
        gm, lifted_inputs
    )
    _insert_copy_for_mutations(
        gm, mutated_outputs, unlifted_name_to_node, input_name_to_node
    )
    gm.graph._codegen = _get_codegen(in_spec, out_spec, forward_arg_names)
    gm.graph.lint()
    gm.recompile()
    return gm


def _register_attrs_to_new_gm(
    new_gm: torch.fx.GraphModule,
    graph_signature: ExportGraphSignature,
    state_dict: Dict[str, Any],
    constants: Dict[str, Any],
) -> None:
    non_persistent_buffers = set(graph_signature.non_persistent_buffers)
    for name in graph_signature.buffers:
        if name in non_persistent_buffers:
            persistent = False
            value = constants[name]
        else:
            persistent = True
            value = state_dict[name]
        _assign_attr(
            value, new_gm, name, attr_kind=_AttrKind.BUFFER, persistent=persistent
        )
    for name in graph_signature.parameters:
        value = state_dict[name]
        _assign_attr(
            value,
            new_gm,
            name,
            attr_kind=_AttrKind.PARAMETER,
        )

    # Technically this doesn't account for the aliased multiple constants but
    # it is ok because we have a seperate pass later in the stack that populates
    # the final gm.
    for name in chain(
        graph_signature.lifted_custom_objs, graph_signature.lifted_tensor_constants
    ):
        value = constants[name]
        _assign_attr(
            value,
            new_gm,
            name,
            attr_kind=_AttrKind.CONSTANT,
        )


class _StatefulGraphModuleFactory(type):
    """
    Metaclass that ensures a private constructor for _StatefulGraphModule
    """

    def __call__(cls, *args, **kwargs):
        raise TypeError(
            f"{cls.__module__}.{cls.__qualname__} has no public constructor. "
        )

    def _create(cls, root, graph, range_constraints=None):
        return super().__call__(
            root,
            graph,
            range_constraints=range_constraints,
        )


class _StatefulGraphModule(torch.fx.GraphModule, metaclass=_StatefulGraphModuleFactory):
    def __init__(self, root, graph, range_constraints=None):
        super().__init__(root, graph)
        # Need to fix up non-persistent buffers.
        self.range_constraints = range_constraints or []
        self.validate_inputs = True


def _create_stateful_graph_module(
    plain_graph_module: torch.fx.GraphModule,
    range_constraints,
    # TODO(suo) this should not be optional, but is since we still have
    # capture_pre_autograd_graph grr
    ep: Optional[ExportedProgram] = None,
) -> _StatefulGraphModule:
    stateful_gm = _StatefulGraphModule._create(
        plain_graph_module,
        plain_graph_module.graph,
        range_constraints=range_constraints,
    )

    stateful_gm.register_forward_pre_hook(
        _check_input_constraints_pre_hook, with_kwargs=True
    )

    if ep is None:
        return stateful_gm

    # When we have a constant that has requires_grad=True, we need to detach it
    # when we unlift as the tensors that require gradients should be registered
    # via parameters. But this is problematic when we have aliasing two constants
    # because when we call detach, they will become different tensors. This dict
    # keeps track of this logic.
    original_tensor_to_detached_tensor = {}

    # Fix up lifted tensor constants.
    # fx.GraphModule() constructor silently turns a constant attribute of plain_graph_module
    # into a buffer in stateful_gm and creates an inconsistency with graph_signature.
    # We fix this by de-registering these buffers in lifted_tensor_constants
    # and call _assign_attr(attr_kind=CONSTANT) to register them as constants.
    for constant_fqn in ep.graph_signature.lifted_tensor_constants:
        # Sometimes, the constant can require gradient, this is probably a bug in user code,
        # e.g. `self.const = torch.randn(2, 2, requires_grad=True)`.
        # We call detach on the constant_val since they're tensor contants and we don't need to
        # compute their gradients anyway.
        # Users should properly register it as parameter if they want it to require gradient.
        buffer = stateful_gm.get_buffer(constant_fqn)
        if buffer.requires_grad:
            warnings.warn(
                f"A model attribute `{constant_fqn}` requires gradient. "
                f"but it's not properly registered as a parameter. "
                f"torch.export will detach it and treat it as a constant tensor "
                f"but please register it as parameter instead."
            )
            detached_buffer = buffer.detach()
            original_tensor_to_detached_tensor[buffer] = detached_buffer
            buffer = detached_buffer
        *prefix, field = constant_fqn.rsplit(".")
        submod = torch.fx.graph_module._get_attr_via_attr_list(stateful_gm, prefix)
        delattr(submod, field)
        _assign_attr(buffer, stateful_gm, constant_fqn, attr_kind=_AttrKind.CONSTANT)

    # Constants are not preserved well when we create a new GraphModule unlike param/buffers
    for const_name, value in ep.constants.items():
        if not torch.fx.graph_module._has_attr(stateful_gm, const_name):
            if isinstance(value, torch.Tensor):
                if value.requires_grad:
                    warnings.warn(
                        f"A model attribute `{const_name}` requires gradient "
                        f"but it's not properly registered as a parameter. "
                        f"torch.export will detach it and treat it as a constant tensor "
                        f"but please register it as parameter instead."
                    )
                    if value in original_tensor_to_detached_tensor:
                        value = original_tensor_to_detached_tensor[value]
                    else:
                        detached_value = value.detach()
                        original_tensor_to_detached_tensor[value] = detached_value
                        value = detached_value
            _assign_attr(
                value,
                stateful_gm,
                const_name,
                attr_kind=_AttrKind.CONSTANT,
            )

    # Fix up non-persistent buffers. torch.fx does not distinguish between
    # persistent and non-persistent buffers, so we must restore that distinction
    # here.
    for buffer in ep.graph_signature.non_persistent_buffers:
        _assign_attr(
            plain_graph_module.get_buffer(buffer),
            stateful_gm,
            buffer,
            attr_kind=_AttrKind.BUFFER,
            persistent=False,
        )

    return stateful_gm


def _unlift_exported_program_lifted_states(ep: ExportedProgram) -> torch.nn.Module:
    # TODO T206340015
    if ep.verifiers[0].dialect != "TRAINING":
        ep = _remove_effect_tokens(ep)
    new_gm = torch.fx.GraphModule(ep.graph_module, copy.deepcopy(ep.graph))
    _register_attrs_to_new_gm(new_gm, ep.graph_signature, ep.state_dict, ep.constants)
    forward_arg_names = (
        sig.forward_arg_names if (sig := ep.module_call_graph[0].signature) else None
    )
    lifted_inputs: List[Optional[str]] = [
        (
            in_spec.target
            if in_spec.kind
            in (
                InputKind.BUFFER,
                InputKind.CONSTANT_TENSOR,
                InputKind.PARAMETER,
                InputKind.CUSTOM_OBJ,
            )
            else None
        )
        for in_spec in ep.graph_signature.input_specs
    ]

    mutated_outputs: List[Optional[str]] = [
        (
            out_spec.target
            if out_spec.kind
            in (OutputKind.BUFFER_MUTATION, OutputKind.USER_INPUT_MUTATION)
            else None
        )
        for out_spec in ep.graph_signature.output_specs
    ]

    new_gm = _unlift(
        new_gm,
        lifted_inputs,
        mutated_outputs,
        ep.call_spec.in_spec,
        ep.call_spec.out_spec,
        ep.state_dict,
        ep.constants,
        forward_arg_names=forward_arg_names,
    )
    unlift_gm = _create_stateful_graph_module(new_gm, ep.range_constraints, ep)
    unlift_gm.meta.update(ep.graph_module.meta)
    return unlift_gm
