import torch
from collections import OrderedDict
import weakref
import warnings
import functools
from typing import Any


class RemovableHandle(object):
    """A handle which provides the capability to remove a hook."""

    id: int
    next_id: int = 0

    def __init__(self, hooks_dict: Any) -> None:
        self.hooks_dict_ref = weakref.ref(hooks_dict)
        self.id = RemovableHandle.next_id
        RemovableHandle.next_id += 1

    def remove(self) -> None:
        hooks_dict = self.hooks_dict_ref()
        if hooks_dict is not None and self.id in hooks_dict:
            del hooks_dict[self.id]

    def __getstate__(self):
        return (self.hooks_dict_ref(), self.id)

    def __setstate__(self, state) -> None:
        if state[0] is None:
            # create a dead reference
            self.hooks_dict_ref = weakref.ref(OrderedDict())
        else:
            self.hooks_dict_ref = weakref.ref(state[0])
        self.id = state[1]
        RemovableHandle.next_id = max(RemovableHandle.next_id, self.id + 1)

    def __enter__(self) -> 'RemovableHandle':
        return self

    def __exit__(self, type: Any, value: Any, tb: Any) -> None:
        self.remove()


def unserializable_hook(f):
    """
    Decorator which marks a function as an unserializable hook.
    This suppresses warnings that would otherwise arise if you attempt
    to serialize a tensor that has a hook.
    """
    f.__torch_unserializable__ = True
    return f


def warn_if_has_hooks(tensor):
    if tensor._backward_hooks:
        for k in tensor._backward_hooks:
            hook = tensor._backward_hooks[k]
            if not hasattr(k, "__torch_unserializable__"):
                warnings.warn("backward hook {} on tensor will not be "
                              "serialized.  If this is expected, you can "
                              "decorate the function with @torch.utils.hooks.unserializable_hook "
                              "to suppress this warning".format(repr(hook)))

class BackwardHook(object):
    """
    A wrapper class to implement nn.Module backward hooks.
    It handles:
      - Ignoring non-Tensor inputs and replacing them by None before calling the user hook
      - Generating the proper Node to capture a set of Tensor's gradients
      - Linking the gradients captures for the outputs with the gradients captured for the input
      - Calling the user hook once both output and input gradients are available
    """

    def __init__(self, module, user_hooks):
        self.user_hooks = user_hooks
        self.module = module

        self.grad_outputs = None
        self.n_outputs = -1
        self.output_tensors_index = None
        self.n_inputs = -1
        self.input_tensors_index = None

    def _pack_with_none(self, indices, values, size):
        res = [None] * size
        for idx, val in zip(indices, values):
            res[idx] = val

        return tuple(res)

    def _unpack_none(self, indices, values):
        res = []
        for idx in indices:
            res.append(values[idx])

        return tuple(res)

    def _set_user_hook(self, grad_fn, user_hook):
        @functools.wraps(user_hook)
        def hook(grad_input, _):
            if self.grad_outputs is None:
                raise RuntimeError("Module backward hook for grad_input is called before "
                                   "the grad_output one. This happens because the gradient "
                                   "in your nn.Module flows to the Module's input without "
                                   "passing through the Module's output. Make sure that the "
                                   "output depends on the input and that the loss is computed "
                                   "based on the output.")

            grad_input = self._pack_with_none(self.input_tensors_index, grad_input, self.n_inputs)
            res = user_hook(self.module, grad_input, self.grad_outputs)
            if res is None:
                return res

            if len(res) != len(grad_input):
                raise RuntimeError("Backward hook returned an invalid number of grad_input, "
                                   "got {}, but expected {}".format(len(res), len(grad_input)))
            return self._unpack_none(self.input_tensors_index, res)
        grad_fn.register_hook(hook)

    def _apply_on_tensors(self, fn, args):
        # Can be used to apply the given function to the tensors contained in the
        # args. Will return updated args and the tensors indices
        tensors_idx = []
        tensors = []

        requires_grad = False
        for i, arg in enumerate(args):
            if isinstance(arg, torch.Tensor):
                tensors_idx.append(i)
                tensors.append(arg)
                requires_grad |= arg.requires_grad

        if not (requires_grad and torch.is_grad_enabled()):
            return args, None

        new_tensors = torch.nn.modules._functions.BackwardHookFunction.apply(*tensors)
        if len(new_tensors) == 0:
            raise RuntimeError("Cannot set Module backward hook for a Module with no input Tensors.")

        grad_fns = [t.grad_fn for t in new_tensors if t.grad_fn is not None and t.grad_fn.name() == "BackwardHookFunctionBackward"]
        if len(grad_fns) == 0:
            raise RuntimeError("Error while setting up backward hooks. Please open "
                               "an issue with a code sample to reproduce this.")

        fn(grad_fns[0])

        arg_list = list(args)
        for idx, val in zip(tensors_idx, new_tensors):
            arg_list[idx] = val

        return tuple(arg_list), tensors_idx

    def setup_input_hook(self, args):
        def fn(grad_fn):
            for hook in self.user_hooks:
                self._set_user_hook(grad_fn, hook)

        res, input_idx = self._apply_on_tensors(fn, args)
        self.n_inputs = len(args)
        self.input_tensors_index = input_idx
        return res

    def setup_output_hook(self, args):
        def fn(grad_fn):
            def hook(_, grad_output):
                self.grad_outputs = self._pack_with_none(self.output_tensors_index,
                                                         grad_output,
                                                         self.n_outputs)

                # Special case if no input required gradients, this hook should call the user
                # hook directly
                if self.input_tensors_index is None:
                    grad_inputs = self._pack_with_none([], [], self.n_inputs)
                    for user_hook in self.user_hooks:
                        res = user_hook(self.module, grad_inputs, self.grad_outputs)
                        if res is not None and not (isinstance(res, tuple) and all(el is None for el in res)):
                            raise RuntimeError("Backward hook for Modules where no input requires "
                                               "gradient should always return None or None for all gradients.")

            grad_fn.register_hook(hook)

        is_tuple = True
        if not isinstance(args, tuple):
            args = (args,)
            is_tuple = False

        res, output_idx = self._apply_on_tensors(fn, args)
        self.n_outputs = len(args)
        self.output_tensors_index = output_idx

        if not is_tuple:
            res = res[0]
        return res