# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
# Adapted by Florian Lux 2021

"""
Layer modules for FFT block in FastSpeech (Feed-forward Transformer).
"""

import torch


class MultiLayeredConv1d(torch.nn.Module):
    """
    Multi-layered conv1d for Transformer block.

    This is a module of multi-layered conv1d designed
    to replace positionwise feed-forward network
    in Transformer block, which is introduced in
    `FastSpeech: Fast, Robust and Controllable Text to Speech`_.

    .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
        https://arxiv.org/pdf/1905.09263.pdf
    """

    def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
        """
        Initialize MultiLayeredConv1d module.

        Args:
            in_chans (int): Number of input channels.
            hidden_chans (int): Number of hidden channels.
            kernel_size (int): Kernel size of conv1d.
            dropout_rate (float): Dropout rate.
        """
        super(MultiLayeredConv1d, self).__init__()
        self.w_1 = torch.nn.Conv1d(in_chans, hidden_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, )
        self.w_2 = torch.nn.Conv1d(hidden_chans, in_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, )
        self.dropout = torch.nn.Dropout(dropout_rate)

    def forward(self, x):
        """
        Calculate forward propagation.

        Args:
            x (torch.Tensor): Batch of input tensors (B, T, in_chans).

        Returns:
            torch.Tensor: Batch of output tensors (B, T, hidden_chans).
        """
        x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
        return self.w_2(self.dropout(x).transpose(-1, 1)).transpose(-1, 1)


class Conv1dLinear(torch.nn.Module):
    """
    Conv1D + Linear for Transformer block.

    A variant of MultiLayeredConv1d, which replaces second conv-layer to linear.
    """

    def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
        """
        Initialize Conv1dLinear module.

        Args:
            in_chans (int): Number of input channels.
            hidden_chans (int): Number of hidden channels.
            kernel_size (int): Kernel size of conv1d.
            dropout_rate (float): Dropout rate.
        """
        super(Conv1dLinear, self).__init__()
        self.w_1 = torch.nn.Conv1d(in_chans, hidden_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, )
        self.w_2 = torch.nn.Linear(hidden_chans, in_chans)
        self.dropout = torch.nn.Dropout(dropout_rate)

    def forward(self, x):
        """
        Calculate forward propagation.

        Args:
            x (torch.Tensor): Batch of input tensors (B, T, in_chans).

        Returns:
            torch.Tensor: Batch of output tensors (B, T, hidden_chans).
        """
        x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
        return self.w_2(self.dropout(x))