#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# Copyright 2019 Mobvoi Inc. All Rights Reserved.
# Author: di.wu@mobvoi.com (DI WU)
"""Encoder self-attention layer definition."""

from typing import Optional, Tuple

import torch
from torch import nn


class TransformerEncoderLayer(nn.Module):
    """Encoder layer module.

    Args:
        size (int): Input dimension.
        self_attn (torch.nn.Module): Self-attention module instance.
            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
            instance can be used as the argument.
        feed_forward (torch.nn.Module): Feed-forward module instance.
            `PositionwiseFeedForward`, instance can be used as the argument.
        dropout_rate (float): Dropout rate.
        normalize_before (bool):
            True: use layer_norm before each sub-block.
            False: to use layer_norm after each sub-block.
        concat_after (bool): Whether to concat attention layer's input and
            output.
            True: x -> x + linear(concat(x, att(x)))
            False: x -> x + att(x)

    """
    def __init__(
        self,
        size: int,
        self_attn: torch.nn.Module,
        feed_forward: torch.nn.Module,
        dropout_rate: float,
        normalize_before: bool = True,
        concat_after: bool = False,
    ):
        """Construct an EncoderLayer object."""
        super().__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        self.norm1 = nn.LayerNorm(size, eps=1e-12)
        self.norm2 = nn.LayerNorm(size, eps=1e-12)
        self.dropout = nn.Dropout(dropout_rate)
        self.size = size
        self.normalize_before = normalize_before
        self.concat_after = concat_after
        # concat_linear may be not used in forward fuction,
        # but will be saved in the *.pt
        self.concat_linear = nn.Linear(size + size, size)

    def forward(
        self,
        x: torch.Tensor,
        mask: torch.Tensor,
        pos_emb: torch.Tensor,
        mask_pad: Optional[torch.Tensor] = None,
        output_cache: Optional[torch.Tensor] = None,
        cnn_cache: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """Compute encoded features.

        Args:
            x (torch.Tensor): Input tensor (#batch, time, size).
            mask (torch.Tensor): Mask tensor for the input (#batch, time).
            pos_emb (torch.Tensor): just for interface compatibility
                to ConformerEncoderLayer
            mask_pad (torch.Tensor): does not used in transformer layer,
                just for unified api with conformer.
            output_cache (torch.Tensor): Cache tensor of the output
                (#batch, time2, size), time2 < time in x.
            cnn_cache (torch.Tensor): not used here, it's for interface
                compatibility to ConformerEncoderLayer
        Returns:
            torch.Tensor: Output tensor (#batch, time, size).
            torch.Tensor: Mask tensor (#batch, time).

        """
        residual = x
        if self.normalize_before:
            x = self.norm1(x)

        if output_cache is None:
            x_q = x
        else:
            assert output_cache.size(0) == x.size(0)
            assert output_cache.size(2) == self.size
            assert output_cache.size(1) < x.size(1)
            chunk = x.size(1) - output_cache.size(1)
            x_q = x[:, -chunk:, :]
            residual = residual[:, -chunk:, :]
            mask = mask[:, -chunk:, :]

        if self.concat_after:
            x_concat = torch.cat((x, self.self_attn(x_q, x, x, mask)), dim=-1)
            x = residual + self.concat_linear(x_concat)
        else:
            x = residual + self.dropout(self.self_attn(x_q, x, x, mask))
        if not self.normalize_before:
            x = self.norm1(x)

        residual = x
        if self.normalize_before:
            x = self.norm2(x)
        x = residual + self.dropout(self.feed_forward(x))
        if not self.normalize_before:
            x = self.norm2(x)

        if output_cache is not None:
            x = torch.cat([output_cache, x], dim=1)

        fake_cnn_cache = torch.tensor([0.0], dtype=x.dtype, device=x.device)
        return x, mask, fake_cnn_cache


class ConformerEncoderLayer(nn.Module):
    """Encoder layer module.
    Args:
        size (int): Input dimension.
        self_attn (torch.nn.Module): Self-attention module instance.
            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
            instance can be used as the argument.
        feed_forward (torch.nn.Module): Feed-forward module instance.
            `PositionwiseFeedForward` instance can be used as the argument.
        feed_forward_macaron (torch.nn.Module): Additional feed-forward module
             instance.
            `PositionwiseFeedForward` instance can be used as the argument.
        conv_module (torch.nn.Module): Convolution module instance.
            `ConvlutionModule` instance can be used as the argument.
        dropout_rate (float): Dropout rate.
        normalize_before (bool):
            True: use layer_norm before each sub-block.
            False: use layer_norm after each sub-block.
        concat_after (bool): Whether to concat attention layer's input and
            output.
            True: x -> x + linear(concat(x, att(x)))
            False: x -> x + att(x)
    """
    def __init__(
        self,
        size: int,
        self_attn: torch.nn.Module,
        feed_forward: Optional[nn.Module] = None,
        feed_forward_macaron: Optional[nn.Module] = None,
        conv_module: Optional[nn.Module] = None,
        dropout_rate: float = 0.1,
        normalize_before: bool = True,
        concat_after: bool = False,
    ):
        """Construct an EncoderLayer object."""
        super().__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        self.feed_forward_macaron = feed_forward_macaron
        self.conv_module = conv_module
        self.norm_ff = nn.LayerNorm(size, eps=1e-12)  # for the FNN module
        self.norm_mha = nn.LayerNorm(size, eps=1e-12)  # for the MHA module
        if feed_forward_macaron is not None:
            self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-12)
            self.ff_scale = 0.5
        else:
            self.ff_scale = 1.0
        if self.conv_module is not None:
            self.norm_conv = nn.LayerNorm(size,
                                          eps=1e-12)  # for the CNN module
            self.norm_final = nn.LayerNorm(
                size, eps=1e-12)  # for the final output of the block
        self.dropout = nn.Dropout(dropout_rate)
        self.size = size
        self.normalize_before = normalize_before
        self.concat_after = concat_after
        self.concat_linear = nn.Linear(size + size, size)

    def forward(
        self,
        x: torch.Tensor,
        mask: torch.Tensor,
        pos_emb: torch.Tensor,
        mask_pad: Optional[torch.Tensor] = None,
        output_cache: Optional[torch.Tensor] = None,
        cnn_cache: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """Compute encoded features.

        Args:
            x (torch.Tensor): (#batch, time, size)
            mask (torch.Tensor): Mask tensor for the input (#batch, time，time).
            pos_emb (torch.Tensor): positional encoding, must not be None
                for ConformerEncoderLayer.
            mask_pad (torch.Tensor): batch padding mask used for conv module.
                (#batch, 1，time)
            output_cache (torch.Tensor): Cache tensor of the output
                (#batch, time2, size), time2 < time in x.
            cnn_cache (torch.Tensor): Convolution cache in conformer layer
        Returns:
            torch.Tensor: Output tensor (#batch, time, size).
            torch.Tensor: Mask tensor (#batch, time).
        """

        # whether to use macaron style
        if self.feed_forward_macaron is not None:
            residual = x
            if self.normalize_before:
                x = self.norm_ff_macaron(x)
            x = residual + self.ff_scale * self.dropout(
                self.feed_forward_macaron(x))
            if not self.normalize_before:
                x = self.norm_ff_macaron(x)

        # multi-headed self-attention module
        residual = x
        if self.normalize_before:
            x = self.norm_mha(x)

        if output_cache is None:
            x_q = x
        else:
            assert output_cache.size(0) == x.size(0)
            assert output_cache.size(2) == self.size
            assert output_cache.size(1) < x.size(1)
            chunk = x.size(1) - output_cache.size(1)
            x_q = x[:, -chunk:, :]
            residual = residual[:, -chunk:, :]
            mask = mask[:, -chunk:, :]

        x_att = self.self_attn(x_q, x, x, mask, pos_emb)
        if self.concat_after:
            x_concat = torch.cat((x, x_att), dim=-1)
            x = residual + self.concat_linear(x_concat)
        else:
            x = residual + self.dropout(x_att)
        if not self.normalize_before:
            x = self.norm_mha(x)

        # convolution module
        # Fake new cnn cache here, and then change it in conv_module
        new_cnn_cache = torch.tensor([0.0], dtype=x.dtype, device=x.device)
        if self.conv_module is not None:
            residual = x
            if self.normalize_before:
                x = self.norm_conv(x)
            x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
            x = residual + self.dropout(x)

            if not self.normalize_before:
                x = self.norm_conv(x)

        # feed forward module
        residual = x
        if self.normalize_before:
            x = self.norm_ff(x)

        x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
        if not self.normalize_before:
            x = self.norm_ff(x)

        if self.conv_module is not None:
            x = self.norm_final(x)

        if output_cache is not None:
            x = torch.cat([output_cache, x], dim=1)

        return x, mask, new_cnn_cache