from contextlib import contextmanager

import torch
import torch._custom_ops
from torch._C import DispatchKey
from torch._higher_order_ops.strict_mode import strict_mode
from torch._higher_order_ops.utils import autograd_not_implemented
from torch._ops import HigherOrderOperator
from torch._subclasses.fake_tensor import FakeTensorMode
from torch.fx.experimental.proxy_tensor import ProxyTorchDispatchMode, track_tensor_tree
from torch.utils import _pytree as pytree


_export_tracepoint = HigherOrderOperator("_export_tracepoint")


@_export_tracepoint.py_impl(ProxyTorchDispatchMode)
def export_tracepoint_dispatch_mode(mode, *args, **kwargs):
    if not mode.enable_tracing:
        return _export_tracepoint(*args, **kwargs)
    p_args, p_kwargs = pytree.tree_map(mode.tracer.unwrap_proxy, (args, kwargs))
    proxy = mode.tracer.create_proxy(
        "call_function", _export_tracepoint, p_args, p_kwargs
    )
    return track_tensor_tree(args, proxy, constant=None, tracer=mode.tracer)


@_export_tracepoint.py_impl(FakeTensorMode)
def export_tracepoint_fake_tensor_mode(mode, *args, **kwargs):
    with mode:
        return args


@_export_tracepoint.py_functionalize_impl
def export_tracepoint_functional(ctx, *args, **kwargs):
    unwrapped_args = ctx.unwrap_tensors(args)
    unwrapped_kwargs = ctx.unwrap_tensors(kwargs)

    with ctx.redispatch_to_next():
        out = _export_tracepoint(*unwrapped_args, **unwrapped_kwargs)
        return ctx.wrap_tensors(out)


_export_tracepoint.py_impl(DispatchKey.Autograd)(
    autograd_not_implemented(_export_tracepoint, deferred_error=True)
)


@_export_tracepoint.py_impl(DispatchKey.CPU)
def export_tracepoint_cpu(*args, **kwargs):
    return args


def _wrap_submodule(mod, path, module_call_specs):
    assert isinstance(mod, torch.nn.Module)
    assert path != ""
    submodule = mod
    for name in path.split("."):
        if not hasattr(submodule, name):
            raise RuntimeError(f"Couldn't find submodule at path {path}")
        submodule = getattr(submodule, name)

    def update_module_call_signatures(path, in_spec, out_spec):
        if path in module_call_specs:
            assert module_call_specs[path]["in_spec"] == in_spec
            assert module_call_specs[path]["out_spec"] == out_spec
        module_call_specs[path] = {"in_spec": in_spec, "out_spec": out_spec}

    def check_flattened(flat_args):
        for a in flat_args:
            if not (isinstance(a, (torch.Tensor, str, int, float, bool)) or a is None):
                raise AssertionError(
                    f"Only Tensors or scalars are supported as pytree flattened inputs, got: {a}"
                )

    def pre_hook(module, args, kwargs):
        flat_args, in_spec = pytree.tree_flatten((args, kwargs))
        check_flattened(flat_args)
        flat_args = _export_tracepoint(*flat_args, kind="module_call_inputs", path=path)
        args, kwargs = pytree.tree_unflatten(flat_args, in_spec)
        return args, kwargs

    def post_hook(module, args, kwargs, res):
        _, in_spec = pytree.tree_flatten((args, kwargs))
        flat_res, out_spec = pytree.tree_flatten(res)
        check_flattened(flat_res)
        flat_res = _export_tracepoint(*flat_res, kind="module_call_outputs", path=path)
        update_module_call_signatures(path, in_spec, out_spec)
        return pytree.tree_unflatten(flat_res, out_spec)

    pre_handle = submodule.register_forward_pre_hook(pre_hook, with_kwargs=True)
    post_handle = submodule.register_forward_hook(post_hook, with_kwargs=True)
    return pre_handle, post_handle


@contextmanager
def _wrap_submodules(f, preserve_signature, module_call_signatures):
    handles = []

    try:
        for path in preserve_signature:
            handles.extend(_wrap_submodule(f, path, module_call_signatures))
        yield
    finally:
        for handle in handles:
            handle.remove()


def _mark_strict_experimental(cls):
    def call(self, *args):
        return strict_mode(self, args)

    cls.__call__ = call
    return cls