from typing import Optional

import torch
from torch import Tensor
from torch.nn.parameter import Parameter

from .module import Module
from .. import functional as F
from .. import init


class Embedding(Module):
    r"""A simple lookup table that stores embeddings of a fixed dictionary and size.

    This module is often used to store word embeddings and retrieve them using indices.
    The input to the module is a list of indices, and the output is the corresponding
    word embeddings.

    Args:
        num_embeddings (int): size of the dictionary of embeddings
        embedding_dim (int): the size of each embedding vector
        padding_idx (int, optional): If given, pads the output with the embedding vector at :attr:`padding_idx`
                                         (initialized to zeros) whenever it encounters the index.
        max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
                                    is renormalized to have norm :attr:`max_norm`.
        norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
        scale_grad_by_freq (boolean, optional): If given, this will scale gradients by the inverse of frequency of
                                                the words in the mini-batch. Default ``False``.
        sparse (bool, optional): If ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor.
                                 See Notes for more details regarding sparse gradients.

    Attributes:
        weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
                         initialized from :math:`\mathcal{N}(0, 1)`

    Shape:
        - Input: :math:`(*)`, IntTensor or LongTensor of arbitrary shape containing the indices to extract
        - Output: :math:`(*, H)`, where `*` is the input shape and :math:`H=\text{embedding\_dim}`

    .. note::
        Keep in mind that only a limited number of optimizers support
        sparse gradients: currently it's :class:`optim.SGD` (`CUDA` and `CPU`),
        :class:`optim.SparseAdam` (`CUDA` and `CPU`) and :class:`optim.Adagrad` (`CPU`)

    .. note::
        With :attr:`padding_idx` set, the embedding vector at
        :attr:`padding_idx` is initialized to all zeros. However, note that this
        vector can be modified afterwards, e.g., using a customized
        initialization method, and thus changing the vector used to pad the
        output. The gradient for this vector from :class:`~torch.nn.Embedding`
        is always zero.

    .. note::
        When :attr:`max_norm` is not ``None``, :class:`Embedding`'s forward method will modify the
        :attr:`weight` tensor in-place. Since tensors needed for gradient computations cannot be
        modified in-place, performing a differentiable operation on ``Embedding.weight`` before
        calling :class:`Embedding`'s forward method requires cloning ``Embedding.weight`` when
        :attr:`max_norm` is not ``None``. For example::

            n, d, m = 3, 5, 7
            embedding = nn.Embedding(n, d, max_norm=True)
            W = torch.randn((m, d), requires_grad=True)
            idx = torch.tensor([1, 2])
            a = embedding.weight.clone() @ W.t()  # weight must be cloned for this to be differentiable
            b = embedding(idx) @ W.t()  # modifies weight in-place
            out = (a.unsqueeze(0) + b.unsqueeze(1))
            loss = out.sigmoid().prod()
            loss.backward()

    Examples::

        >>> # an Embedding module containing 10 tensors of size 3
        >>> embedding = nn.Embedding(10, 3)
        >>> # a batch of 2 samples of 4 indices each
        >>> input = torch.LongTensor([[1,2,4,5],[4,3,2,9]])
        >>> embedding(input)
        tensor([[[-0.0251, -1.6902,  0.7172],
                 [-0.6431,  0.0748,  0.6969],
                 [ 1.4970,  1.3448, -0.9685],
                 [-0.3677, -2.7265, -0.1685]],

                [[ 1.4970,  1.3448, -0.9685],
                 [ 0.4362, -0.4004,  0.9400],
                 [-0.6431,  0.0748,  0.6969],
                 [ 0.9124, -2.3616,  1.1151]]])


        >>> # example with padding_idx
        >>> embedding = nn.Embedding(10, 3, padding_idx=0)
        >>> input = torch.LongTensor([[0,2,0,5]])
        >>> embedding(input)
        tensor([[[ 0.0000,  0.0000,  0.0000],
                 [ 0.1535, -2.0309,  0.9315],
                 [ 0.0000,  0.0000,  0.0000],
                 [-0.1655,  0.9897,  0.0635]]])
    """
    __constants__ = ['num_embeddings', 'embedding_dim', 'padding_idx', 'max_norm',
                     'norm_type', 'scale_grad_by_freq', 'sparse']

    num_embeddings: int
    embedding_dim: int
    padding_idx: Optional[int]
    max_norm: Optional[float]
    norm_type: float
    scale_grad_by_freq: bool
    weight: Tensor
    sparse: bool

    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None,
                 max_norm: Optional[float] = None, norm_type: float = 2., scale_grad_by_freq: bool = False,
                 sparse: bool = False, _weight: Optional[Tensor] = None) -> None:
        super(Embedding, self).__init__()
        self.num_embeddings = num_embeddings
        self.embedding_dim = embedding_dim
        if padding_idx is not None:
            if padding_idx > 0:
                assert padding_idx < self.num_embeddings, 'Padding_idx must be within num_embeddings'
            elif padding_idx < 0:
                assert padding_idx >= -self.num_embeddings, 'Padding_idx must be within num_embeddings'
                padding_idx = self.num_embeddings + padding_idx
        self.padding_idx = padding_idx
        self.max_norm = max_norm
        self.norm_type = norm_type
        self.scale_grad_by_freq = scale_grad_by_freq
        if _weight is None:
            self.weight = Parameter(torch.Tensor(num_embeddings, embedding_dim))
            self.reset_parameters()
        else:
            assert list(_weight.shape) == [num_embeddings, embedding_dim], \
                'Shape of weight does not match num_embeddings and embedding_dim'
            self.weight = Parameter(_weight)
            self._fill_padding_idx_with_zero()
        self.sparse = sparse

    def reset_parameters(self) -> None:
        init.normal_(self.weight)
        self._fill_padding_idx_with_zero()

    def _fill_padding_idx_with_zero(self) -> None:
        if self.padding_idx is not None:
            with torch.no_grad():
                self.weight[self.padding_idx].fill_(0)

    def forward(self, input: Tensor) -> Tensor:
        return F.embedding(
            input, self.weight, self.padding_idx, self.max_norm,
            self.norm_type, self.scale_grad_by_freq, self.sparse)

    def extra_repr(self) -> str:
        s = '{num_embeddings}, {embedding_dim}'
        if self.padding_idx is not None:
            s += ', padding_idx={padding_idx}'
        if self.max_norm is not None:
            s += ', max_norm={max_norm}'
        if self.norm_type != 2:
            s += ', norm_type={norm_type}'
        if self.scale_grad_by_freq is not False:
            s += ', scale_grad_by_freq={scale_grad_by_freq}'
        if self.sparse is not False:
            s += ', sparse=True'
        return s.format(**self.__dict__)

    @classmethod
    def from_pretrained(cls, embeddings, freeze=True, padding_idx=None,
                        max_norm=None, norm_type=2., scale_grad_by_freq=False,
                        sparse=False):
        r"""Creates Embedding instance from given 2-dimensional FloatTensor.

        Args:
            embeddings (Tensor): FloatTensor containing weights for the Embedding.
                First dimension is being passed to Embedding as ``num_embeddings``, second as ``embedding_dim``.
            freeze (boolean, optional): If ``True``, the tensor does not get updated in the learning process.
                Equivalent to ``embedding.weight.requires_grad = False``. Default: ``True``
            padding_idx (int, optional): See module initialization documentation.
            max_norm (float, optional): See module initialization documentation.
            norm_type (float, optional): See module initialization documentation. Default ``2``.
            scale_grad_by_freq (boolean, optional): See module initialization documentation. Default ``False``.
            sparse (bool, optional): See module initialization documentation.

        Examples::

            >>> # FloatTensor containing pretrained weights
            >>> weight = torch.FloatTensor([[1, 2.3, 3], [4, 5.1, 6.3]])
            >>> embedding = nn.Embedding.from_pretrained(weight)
            >>> # Get embeddings for index 1
            >>> input = torch.LongTensor([1])
            >>> embedding(input)
            tensor([[ 4.0000,  5.1000,  6.3000]])
        """
        assert embeddings.dim() == 2, \
            'Embeddings parameter is expected to be 2-dimensional'
        rows, cols = embeddings.shape
        embedding = cls(
            num_embeddings=rows,
            embedding_dim=cols,
            _weight=embeddings,
            padding_idx=padding_idx,
            max_norm=max_norm,
            norm_type=norm_type,
            scale_grad_by_freq=scale_grad_by_freq,
            sparse=sparse)
        embedding.weight.requires_grad = not freeze
        return embedding


class EmbeddingBag(Module):
    r"""Computes sums or means of 'bags' of embeddings, without instantiating the
    intermediate embeddings.

    For bags of constant length and no :attr:`per_sample_weights` and 2D inputs, this class

        * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``,
        * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``,
        * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``.

    However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these
    operations.

    EmbeddingBag also supports per-sample weights as an argument to the forward
    pass. This scales the output of the Embedding before performing a weighted
    reduction as specified by ``mode``. If :attr:`per_sample_weights`` is passed, the
    only supported ``mode`` is ``"sum"``, which computes a weighted sum according to
    :attr:`per_sample_weights`.

    Args:
        num_embeddings (int): size of the dictionary of embeddings
        embedding_dim (int): the size of each embedding vector
        max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
                                    is renormalized to have norm :attr:`max_norm`.
        norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
        scale_grad_by_freq (boolean, optional): if given, this will scale gradients by the inverse of frequency of
                                                the words in the mini-batch. Default ``False``.
                                                Note: this option is not supported when ``mode="max"``.
        mode (string, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag.
                                 ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights`
                                 into consideration. ``"mean"`` computes the average of the values
                                 in the bag, ``"max"`` computes the max value over each bag.
                                 Default: ``"mean"``
        sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See
                                 Notes for more details regarding sparse gradients. Note: this option is not
                                 supported when ``mode="max"``.
        include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element
                                      is equivalent to the size of `indices`. This matches the CSR format.

    Attributes:
        weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`
                         initialized from :math:`\mathcal{N}(0, 1)`.

    Inputs: :attr:`input` (IntTensor or LongTensor), :attr:`offsets` (IntTensor or LongTensor, optional), and
        :attr:`per_index_weights` (Tensor, optional)

        - :attr:`input` and :attr:`offsets` have to be of the same type, either int or long

        - If :attr:`input` is 2D of shape `(B, N)`,

          it will be treated as ``B`` bags (sequences) each of fixed length ``N``, and
          this will return ``B`` values aggregated in a way depending on the :attr:`mode`.
          :attr:`offsets` is ignored and required to be ``None`` in this case.

        - If :attr:`input` is 1D of shape `(N)`,

          it will be treated as a concatenation of multiple bags (sequences).
          :attr:`offsets` is required to be a 1D tensor containing the
          starting index positions of each bag in :attr:`input`. Therefore,
          for :attr:`offsets` of shape `(B)`, :attr:`input` will be viewed as
          having ``B`` bags. Empty bags (i.e., having 0-length) will have
          returned vectors filled by zeros.

        per_sample_weights (Tensor, optional): a tensor of float / double weights, or None
            to indicate all weights should be taken to be ``1``. If specified, :attr:`per_sample_weights`
            must have exactly the same shape as input and is treated as having the same
            :attr:`offsets`, if those are not ``None``. Only supported for ``mode='sum'``.


    Output shape: `(B, embedding_dim)`

    Examples::

        >>> # an Embedding module containing 10 tensors of size 3
        >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum')
        >>> # a batch of 2 samples of 4 indices each
        >>> input = torch.LongTensor([1,2,4,5,4,3,2,9])
        >>> offsets = torch.LongTensor([0,4])
        >>> embedding_sum(input, offsets)
        tensor([[-0.8861, -5.4350, -0.0523],
                [ 1.1306, -2.5798, -1.0044]])
    """
    __constants__ = ['num_embeddings', 'embedding_dim', 'max_norm', 'norm_type',
                     'scale_grad_by_freq', 'mode', 'sparse', 'include_last_offset']

    num_embeddings: int
    embedding_dim: int
    max_norm: Optional[float]
    norm_type: float
    scale_grad_by_freq: bool
    weight: Tensor
    mode: str
    sparse: bool
    include_last_offset: bool

    def __init__(self, num_embeddings: int, embedding_dim: int,
                 max_norm: Optional[float] = None, norm_type: float = 2., scale_grad_by_freq: bool = False,
                 mode: str = 'mean', sparse: bool = False, _weight: Optional[Tensor] = None,
                 include_last_offset: bool = False) -> None:
        super(EmbeddingBag, self).__init__()
        self.num_embeddings = num_embeddings
        self.embedding_dim = embedding_dim
        self.max_norm = max_norm
        self.norm_type = norm_type
        self.scale_grad_by_freq = scale_grad_by_freq
        if _weight is None:
            self.weight = Parameter(torch.Tensor(num_embeddings, embedding_dim))
            self.reset_parameters()
        else:
            assert list(_weight.shape) == [num_embeddings, embedding_dim], \
                'Shape of weight does not match num_embeddings and embedding_dim'
            self.weight = Parameter(_weight)
        self.mode = mode
        self.sparse = sparse
        self.include_last_offset = include_last_offset

    def reset_parameters(self) -> None:
        init.normal_(self.weight)

    def forward(self, input: Tensor, offsets: Optional[Tensor] = None, per_sample_weights: Optional[Tensor] = None) -> Tensor:
        return F.embedding_bag(input, self.weight, offsets,
                               self.max_norm, self.norm_type,
                               self.scale_grad_by_freq, self.mode, self.sparse,
                               per_sample_weights, self.include_last_offset)

    def extra_repr(self) -> str:
        s = '{num_embeddings}, {embedding_dim}'
        if self.max_norm is not None:
            s += ', max_norm={max_norm}'
        if self.norm_type != 2:
            s += ', norm_type={norm_type}'
        if self.scale_grad_by_freq is not False:
            s += ', scale_grad_by_freq={scale_grad_by_freq}'
        s += ', mode={mode}'
        return s.format(**self.__dict__)

    @classmethod
    def from_pretrained(cls, embeddings: Tensor, freeze: bool = True, max_norm: Optional[float] = None,
                        norm_type: float = 2., scale_grad_by_freq: bool = False,
                        mode: str = 'mean', sparse: bool = False, include_last_offset: bool = False) -> 'EmbeddingBag':