# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.

import math

import torch
import torch.nn as nn
import torch.onnx.operators

from fairseq import utils


class SinusoidalPositionalEmbedding(nn.Module):
    """This module produces sinusoidal positional embeddings of any length.

    Padding symbols are ignored.
    """

    def __init__(self, embedding_dim, padding_idx, init_size=1024):
        super().__init__()
        self.embedding_dim = embedding_dim
        self.padding_idx = padding_idx
        self.weights = SinusoidalPositionalEmbedding.get_embedding(
            init_size,
            embedding_dim,
            padding_idx,
        )
        self.onnx_trace = False
        self.register_buffer('_float_tensor', torch.FloatTensor(1))

    def prepare_for_onnx_export_(self):
        self.onnx_trace = True

    @staticmethod
    def get_embedding(num_embeddings, embedding_dim, padding_idx=None):
        """Build sinusoidal embeddings.

        This matches the implementation in tensor2tensor, but differs slightly
        from the description in Section 3.5 of "Attention Is All You Need".
        """
        half_dim = embedding_dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
        emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
        if embedding_dim % 2 == 1:
            # zero pad
            emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
        if padding_idx is not None:
            emb[padding_idx, :] = 0
        return emb

    def forward(self, input, incremental_state=None, timestep=None):
        """Input is expected to be of size [bsz x seqlen]."""
        bsz, seq_len = torch.onnx.operators.shape_as_tensor(input)
        max_pos = self.padding_idx + 1 + seq_len
        if self.weights is None or max_pos > self.weights.size(0):
            # recompute/expand embeddings if needed
            self.weights = SinusoidalPositionalEmbedding.get_embedding(
                max_pos,
                self.embedding_dim,
                self.padding_idx,
            )
        self.weights = self.weights.type_as(self._float_tensor)

        if incremental_state is not None:
            # positions is the same for every token when decoding a single step
            pos = (timestep.int() + 1).long() if timestep is not None else seq_len
            if self.onnx_trace:
                return self.weights[self.padding_idx + pos, :].unsqueeze(1).repeat(bsz, 1, 1)
            return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1)

        positions = utils.make_positions(input, self.padding_idx, onnx_trace=self.onnx_trace)
        if self.onnx_trace:
            flat_embeddings = self.weights.detach().index_select(0, positions.view(-1))
            embedding_shape = torch.cat((bsz.view(1), seq_len.view(1), torch.LongTensor([-1])))
            embeddings = torch.onnx.operators.reshape_from_tensor_shape(flat_embeddings, embedding_shape)
            return embeddings
        return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach()

    def max_positions(self):
        """Maximum number of supported positions."""
        return int(1e5)  # an arbitrary large number
