import warnings
from collections import OrderedDict, abc as container_abcs
from itertools import chain, islice
import operator

import torch
from .module import Module
from ..parameter import Parameter
from torch._jit_internal import _copy_to_script_wrapper

from typing import Any, Dict, Iterable, Iterator, Mapping, Optional, overload, Tuple, TypeVar, Union

__all__ = ['Container', 'Sequential', 'ModuleList', 'ModuleDict', 'ParameterList', 'ParameterDict']

T = TypeVar('T', bound=Module)


# Copied from torch.nn.modules.module, required for a cusom __repr__ for ModuleList
def _addindent(s_, numSpaces):
    s = s_.split('\n')
    # don't do anything for single-line stuff
    if len(s) == 1:
        return s_
    first = s.pop(0)
    s = [(numSpaces * ' ') + line for line in s]
    s = '\n'.join(s)
    s = first + '\n' + s
    return s


class Container(Module):

    def __init__(self, **kwargs: Any) -> None:
        super().__init__()
        # DeprecationWarning is ignored by default <sigh>
        warnings.warn("nn.Container is deprecated. All of it's functionality "
                      "is now implemented in nn.Module. Subclass that instead.")
        for key, value in kwargs.items():
            self.add_module(key, value)


class Sequential(Module):
    r"""A sequential container.
    Modules will be added to it in the order they are passed in the
    constructor. Alternatively, an ``OrderedDict`` of modules can be
    passed in. The ``forward()`` method of ``Sequential`` accepts any
    input and forwards it to the first module it contains. It then
    "chains" outputs to inputs sequentially for each subsequent module,
    finally returning the output of the last module.

    The value a ``Sequential`` provides over manually calling a sequence
    of modules is that it allows treating the whole container as a
    single module, such that performing a transformation on the
    ``Sequential`` applies to each of the modules it stores (which are
    each a registered submodule of the ``Sequential``).

    What's the difference between a ``Sequential`` and a
    :class:`torch.nn.ModuleList`? A ``ModuleList`` is exactly what it
    sounds like--a list for storing ``Module`` s! On the other hand,
    the layers in a ``Sequential`` are connected in a cascading way.

    Example::

        # Using Sequential to create a small model. When `model` is run,
        # input will first be passed to `Conv2d(1,20,5)`. The output of
        # `Conv2d(1,20,5)` will be used as the input to the first
        # `ReLU`; the output of the first `ReLU` will become the input
        # for `Conv2d(20,64,5)`. Finally, the output of
        # `Conv2d(20,64,5)` will be used as input to the second `ReLU`
        model = nn.Sequential(
                  nn.Conv2d(1,20,5),
                  nn.ReLU(),
                  nn.Conv2d(20,64,5),
                  nn.ReLU()
                )

        # Using Sequential with OrderedDict. This is functionally the
        # same as the above code
        model = nn.Sequential(OrderedDict([
                  ('conv1', nn.Conv2d(1,20,5)),
                  ('relu1', nn.ReLU()),
                  ('conv2', nn.Conv2d(20,64,5)),
                  ('relu2', nn.ReLU())
                ]))
    """

    _modules: Dict[str, Module]  # type: ignore[assignment]

    @overload
    def __init__(self, *args: Module) -> None:
        ...

    @overload
    def __init__(self, arg: 'OrderedDict[str, Module]') -> None:
        ...

    def __init__(self, *args):
        super().__init__()
        if len(args) == 1 and isinstance(args[0], OrderedDict):
            for key, module in args[0].items():
                self.add_module(key, module)
        else:
            for idx, module in enumerate(args):
                self.add_module(str(idx), module)

    def _get_item_by_idx(self, iterator, idx) -> T:
        """Get the idx-th item of the iterator"""
        size = len(self)
        idx = operator.index(idx)
        if not -size <= idx < size:
            raise IndexError('index {} is out of range'.format(idx))
        idx %= size
        return next(islice(iterator, idx, None))

    @_copy_to_script_wrapper
    def __getitem__(self, idx: Union[slice, int]) -> Union['Sequential', T]:
        if isinstance(idx, slice):
            return self.__class__(OrderedDict(list(self._modules.items())[idx]))
        else:
            return self._get_item_by_idx(self._modules.values(), idx)

    def __setitem__(self, idx: int, module: Module) -> None:
        key: str = self._get_item_by_idx(self._modules.keys(), idx)
        return setattr(self, key, module)

    def __delitem__(self, idx: Union[slice, int]) -> None:
        if isinstance(idx, slice):
            for key in list(self._modules.keys())[idx]:
                delattr(self, key)
        else:
            key = self._get_item_by_idx(self._modules.keys(), idx)
            delattr(self, key)
        # To preserve numbering
        str_indices = [str(i) for i in range(len(self._modules))]
        self._modules = OrderedDict(list(zip(str_indices, self._modules.values())))

    @_copy_to_script_wrapper
    def __len__(self) -> int:
        return len(self._modules)

    def __add__(self, other) -> 'Sequential':
        if isinstance(other, Sequential):
            ret = Sequential()
            for layer in self:
                ret.append(layer)
            for layer in other:
                ret.append(layer)
            return ret
        else:
            raise ValueError('add operator supports only objects '
                             'of Sequential class, but {} is given.'.format(
                                 str(type(other))))

    def pop(self, key: Union[int, slice]) -> Module:
        v = self[key]
        del self[key]
        return v

    def __iadd__(self, other) -> 'Sequential':
        if isinstance(other, Sequential):
            offset = len(self)
            for i, module in enumerate(other):
                self.add_module(str(i + offset), module)
            return self
        else:
            raise ValueError('add operator supports only objects '
                             'of Sequential class, but {} is given.'.format(
                                 str(type(other))))

    def __mul__(self, other: int) -> 'Sequential':
        if not isinstance(other, int):
            raise TypeError(f"unsupported operand type(s) for *: {type(self)} and {type(other)}")
        elif (other <= 0):
            raise ValueError(f"Non-positive multiplication factor {other} for {type(self)}")
        else:
            combined = Sequential()
            offset = 0
            for _ in range(other):
                for module in self:
                    combined.add_module(str(offset), module)
                    offset += 1
            return combined

    def __rmul__(self, other: int) -> 'Sequential':
        return self.__mul__(other)

    def __imul__(self, other: int) -> 'Sequential':
        if not isinstance(other, int):
            raise TypeError(f"unsupported operand type(s) for *: {type(self)} and {type(other)}")
        elif (other <= 0):
            raise ValueError(f"Non-positive multiplication factor {other} for {type(self)}")
        else:
            len_original = len(self)
            offset = len(self)
            for _ in range(other - 1):
                for i in range(len_original):
                    self.add_module(str(i + offset), self._modules[str(i)])
                offset += len_original
            return self

    @_copy_to_script_wrapper
    def __dir__(self):
        keys = super().__dir__()
        keys = [key for key in keys if not key.isdigit()]
        return keys

    @_copy_to_script_wrapper
    def __iter__(self) -> Iterator[Module]:
        return iter(self._modules.values())

    # NB: We can't really type check this function as the type of input
    # may change dynamically (as is tested in
    # TestScript.test_sequential_intermediary_types).  Cannot annotate
    # with Any as TorchScript expects a more precise type
    def forward(self, input):
        for module in self:
            input = module(input)
        return input

    def append(self, module: Module) -> 'Sequential':
        r"""Appends a given module to the end.

        Args:
            module (nn.Module): module to append
        """
        self.add_module(str(len(self)), module)
        return self

    def insert(self, index: int, module: Module) -> 'Sequential':
        if not isinstance(module, Module):
            raise AssertionError(
                'module should be of type: {}'.format(Module))
        n = len(self._modules)
        if not (-n <= index <= n):
            raise IndexError(
                'Index out of range: {}'.format(index))
        if index < 0:
            index += n
        for i in range(n, index, -1):
            self._modules[str(i)] = self._modules[str(i - 1)]
        self._modules[str(index)] = module
        return self

    def extend(self, sequential) -> 'Sequential':
        for layer in sequential:
            self.append(layer)
        return self


class ModuleList(Module):
    r"""Holds submodules in a list.

    :class:`~torch.nn.ModuleList` can be indexed like a regular Python list, but
    modules it contains are properly registered, and will be visible by all
    :class:`~torch.nn.Module` methods.

    Args:
        modules (iterable, optional): an iterable of modules to add

    Example::

        class MyModule(nn.Module):
            def __init__(self):
                super().__init__()
                self.linears = nn.ModuleList([nn.Linear(10, 10) for i in range(10)])

            def forward(self, x):
                # ModuleList can act as an iterable, or be indexed using ints
                for i, l in enumerate(self.linears):
                    x = self.linears[i // 2](x) + l(x)
                return x
    """

    _modules: Dict[str, Module]  # type: ignore[assignment]

    def __init__(self, modules: Optional[Iterable[Module]] = None) -> None:
        super().__init__()
        if modules is not None:
            self += modules

    def _get_abs_string_index(self, idx):
        """Get the absolute index for the list of modules"""
        idx = operator.index(idx)
        if not (-len(self) <= idx < len(self)):
            raise IndexError('index {} is out of range'.format(idx))
        if idx < 0:
            idx += len(self)
        return str(idx)

    @_copy_to_script_wrapper
    def __getitem__(self, idx: Union[int, slice]) -> Union[Module, 'ModuleList']:
        if isinstance(idx, slice):
            return self.__class__(list(self._modules.values())[idx])
        else:
            return self._modules[self._get_abs_string_index(idx)]

    def __setitem__(self, idx: int, module: Module) -> None:
        idx = self._get_abs_string_index(idx)
        return setattr(self, str(idx), module)

    def __delitem__(self, idx: Union[int, slice]) -> None:
        if isinstance(idx, slice):
            for k in range(len(self._modules))[idx]:
                delattr(self, str(k))
        else:
            delattr(self, self._get_abs_string_index(idx))
        # To preserve numbering, self._modules is being reconstructed with modules after deletion
        str_indices = [str(i) for i in range(len(self._modules))]
        self._modules = OrderedDict(list(zip(str_indices, self._modules.values())))

    @_copy_to_script_wrapper
    def __len__(self) -> int:
        return len(self._modules)

    @_copy_to_script_wrapper
    def __iter__(self) -> Iterator[Module]:
        return iter(self._modules.values())

    def __iadd__(self, modules: Iterable[Module]) -> 'ModuleList':
        return self.extend(modules)

    def __add__(self, other: Iterable[Module]) -> 'ModuleList':
        combined = ModuleList()
        for i, module in enumerate(chain(self, other)):
            combined.add_module(str(i), module)
        return combined

    def __repr__(self):
        """A custom repr for ModuleList that compresses repeated module representations"""
        list_of_reprs = [repr(item) for item in self]
        if len(list_of_reprs) == 0:
            return self._get_name() + '()'

        start_end_indices = [[0, 0]]
        repeated_blocks = [list_of_reprs[0]]
        for i, r in enumerate(list_of_reprs[1:], 1):
            if r == repeated_blocks[-1]:
                start_end_indices[-1][1] += 1
                continue

            start_end_indices.append([i, i])
            repeated_blocks.append(r)

        lines = []
        main_str = self._get_name() + '('