import torch
from torch.nn.parameter import Parameter

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


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, will renormalize the embeddings to always have a norm lesser than this
        norm_type (float, optional): The p of the p-norm to compute for the max_norm option
        scale_grad_by_freq (bool, optional): if given, this will scale gradients by the frequency of
                                                the words in the mini-batch.
        sparse (bool, optional): if ``True``, gradient w.r.t. 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)

    Shape:
        - Input: LongTensor of arbitrary shape containing the indices to extract
        - Output: `(*, embedding_dim)`, where `*` is the input shape

    .. 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.

    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]]])
    """

    def __init__(self, num_embeddings, embedding_dim, padding_idx=None,
                 max_norm=None, norm_type=2, scale_grad_by_freq=False,
                 sparse=False, _weight=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.sparse = sparse

    def reset_parameters(self):
        self.weight.data.normal_(0, 1)
        if self.padding_idx is not None:
            self.weight.data[self.padding_idx].fill_(0)

    def forward(self, input):
        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):
        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):
        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``

        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)
        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,
        * nn.EmbeddingBag with `mode=sum` is equivalent to nn.Embedding followed by `torch.sum(dim=1)`
        * with `mode=mean` is equivalent to nn.Embedding followed by `torch.mean(dim=1)`

    However, nn.EmbeddingBag is much more time and memory efficient than using a chain of these
    operations.

    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, will renormalize the embeddings to always have a norm lesser than this
        norm_type (float, optional): The p of the p-norm to compute for the max_norm option
        scale_grad_by_freq (bool, optional): if given, this will scale gradients by the frequency of
                                                the words in the dictionary.
        mode (string, optional): 'sum' | 'mean'. Specifies the way to reduce the bag. Default: 'mean'
        sparse (bool, optional): if ``True``, gradient w.r.t. 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)

    Inputs: input, offsets
        - **input** (``N`` or ``B x N``): LongTensor containing the indices of the embeddings
                                to extract. When `input` is 1D Tensor of shape `N`,
                                an `offsets` Tensor is given, that contains the
                                starting position of each new sequence in the
                                mini-batch.
        - **offsets** (``B`` or ``None``): LongTensor containing the starting positions of
                                   each sample in a mini-batch of variable length
                                   sequences. If `input` is 2D (``B x N``), then offsets
                                   does not need to be given, as the `input` is
                                   treated as a mini-batch of fixed length sequences
                                   of length `N` each.


    Shape:
        - Input: LongTensor `N`, N = number of embeddings to extract
                 (or) LongTensor ``B x N``, B = number of sequences in mini-batch,
                                        N = number of embeddings per sequence
        - Offsets: LongTensor `B`, B = number of bags. The values are the
                   offsets in `input` for each bag, i.e. the cumsum of lengths.
                   Offsets is not given if Input is 2D ``B x N`` Tensor,
                   the input is considered to be of fixed-length sequences
        - Output: `(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]])
    """

    def __init__(self, num_embeddings, embedding_dim,
                 max_norm=None, norm_type=2, scale_grad_by_freq=False,
                 mode='mean', sparse=False):
        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
        self.weight = Parameter(torch.Tensor(num_embeddings, embedding_dim))
        self.mode = mode
        self.sparse = sparse

        self.reset_parameters()

    def reset_parameters(self):
        self.weight.data.normal_(0, 1)

    def forward(self, input, offsets=None):
        return F.embedding_bag(self.weight, input, offsets,
                               self.max_norm, self.norm_type,
                               self.scale_grad_by_freq, self.mode, self.sparse)

    def extra_repr(self):
        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__)

# TODO: SparseLinear
