text: # BSD 3-Clause License
  #
  # Copyright (c) 2018, NVIDIA Corporation
  # Copyright (c) 2019, Guanlong Zhao
  # All rights reserved.
  #
  # Redistribution and use in source and binary forms, with or without
  # modification, are permitted provided that the following conditions are met:
  #
  # * Redistributions of source code must retain the above copyright notice, this
  #   list of conditions and the following disclaimer.
  #
  # * Redistributions in binary form must reproduce the above copyright notice,
  #   this list of conditions and the following disclaimer in the documentation
  #   and/or other materials provided with the distribution.
  #
  # * Neither the name of the copyright holder nor the names of its
  #   contributors may be used to endorse or promote products derived from
  #   this software without specific prior written permission.
  #
  # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
  # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
  # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
  # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
  # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
  # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
  # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
  # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
  # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  
  """Modified from https://github.com/NVIDIA/tacotron2"""
  
  import torch
  from torch.autograd import Variable
  from torch import nn
  from torch.nn import functional as F
  from common.layers import ConvNorm, LinearNorm
  from common.utils import to_gpu, get_mask_from_lengths,\
      get_mask_from_lengths_window_and_time_step
  from common.fp16_optimizer import fp32_to_fp16, fp16_to_fp32
  
  
  class LocationLayer(nn.Module):
      def __init__(self, attention_n_filters, attention_kernel_size,
                   attention_dim):
          super(LocationLayer, self).__init__()
          padding = int((attention_kernel_size - 1) / 2)
          self.location_conv = ConvNorm(2, attention_n_filters,
                                        kernel_size=attention_kernel_size,
                                        padding=padding, bias=False, stride=1,
                                        dilation=1)
          self.location_dense = LinearNorm(attention_n_filters, attention_dim,
                                           bias=False, w_init_gain='tanh')
  
      def forward(self, attention_weights_cat):
          processed_attention = self.location_conv(attention_weights_cat)
          processed_attention = processed_attention.transpose(1, 2)
          processed_attention = self.location_dense(processed_attention)
          return processed_attention
  
  
  class Attention(nn.Module):
      def __init__(self, attention_rnn_dim, embedding_dim, attention_dim,
                   attention_location_n_filters, attention_location_kernel_size):
          super(Attention, self).__init__()
          self.query_layer = LinearNorm(attention_rnn_dim, attention_dim,
                                        bias=False, w_init_gain='tanh')
          self.memory_layer = LinearNorm(embedding_dim, attention_dim, bias=False,
                                         w_init_gain='tanh')
          self.v = LinearNorm(attention_dim, 1, bias=False)
          self.location_layer = LocationLayer(attention_location_n_filters,
                                              attention_location_kernel_size,
                                              attention_dim)
          self.score_mask_value = -float("inf")
          self.not_so_small_mask = -1000
  
      def get_alignment_energies(self, query, processed_memory,
                                 attention_weights_cat):
          """
          PARAMS
          ------
          query: decoder output (batch, n_mel_channels * n_frames_per_step)
          processed_memory: processed encoder outputs (B, T_in, attention_dim)
          attention_weights_cat: cumulative and prev. att weights (B, 2, max_time)
  
          RETURNS
          -------
          alignment (batch, max_time)
          """
  
          processed_query = self.query_layer(query.unsqueeze(1))
          processed_attention_weights = self.location_layer(attention_weights_cat)
          energies = self.v(torch.tanh(
              processed_query + processed_attention_weights + processed_memory))
  
          energies = energies.squeeze(-1)
          return energies
  
      def forward(self, attention_hidden_state, memory, processed_memory,
                  attention_weights_cat, mask):
          """
          PARAMS
          ------
          attention_hidden_state: attention rnn last output
          memory: encoder outputs
          processed_memory: processed encoder outputs
          attention_weights_cat: previous and cummulative attention weights
          mask: binary mask for padded data
          """
          alignment = self.get_alignment_energies(
              attention_hidden_state, processed_memory, attention_weights_cat)
  
          if mask is not None:
              alignment.data.masked_fill_(mask, self.score_mask_value)
  
          attention_weights = F.softmax(alignment, dim=1)
          attention_context = torch.bmm(attention_weights.unsqueeze(1), memory)
          attention_context = attention_context.squeeze(1)
  
          return attention_context, attention_weights
  
  
  class Prenet(nn.Module):
      def __init__(self, in_dim, sizes):
          super(Prenet, self).__init__()
          in_sizes = [in_dim] + sizes[:-1]
          self.layers = nn.ModuleList(
              [LinearNorm(in_size, out_size, bias=False)
               for (in_size, out_size) in zip(in_sizes, sizes)])
  
      def forward(self, x):
          for linear in self.layers:
              x = F.dropout(F.relu(linear(x)), p=0.5, training=True)
          return x
  
  
  class Postnet(nn.Module):
      """Postnet
          - Five 1-d convolution with 512 channels and kernel size 5
      """
  
      def __init__(self, hparams):
          super(Postnet, self).__init__()
          self.convolutions = nn.ModuleList()
  
          self.convolutions.append(
              nn.Sequential(
                  ConvNorm(hparams.n_acoustic_feat_dims,
                           hparams.postnet_embedding_dim,
                           kernel_size=hparams.postnet_kernel_size, stride=1,
                           padding=int((hparams.postnet_kernel_size - 1) / 2),
                           dilation=1, w_init_gain='tanh'),
                  nn.BatchNorm1d(hparams.postnet_embedding_dim))
          )
  
          for i in range(1, hparams.postnet_n_convolutions - 1):
              self.convolutions.append(
                  nn.Sequential(
                      ConvNorm(hparams.postnet_embedding_dim,
                               hparams.postnet_embedding_dim,
                               kernel_size=hparams.postnet_kernel_size, stride=1,
                               padding=int((hparams.postnet_kernel_size - 1) / 2),
                               dilation=1, w_init_gain='tanh'),
                      nn.BatchNorm1d(hparams.postnet_embedding_dim))
              )
  
          self.convolutions.append(
              nn.Sequential(
                  ConvNorm(hparams.postnet_embedding_dim,
                           hparams.n_acoustic_feat_dims,
                           kernel_size=hparams.postnet_kernel_size, stride=1,
                           padding=int((hparams.postnet_kernel_size - 1) / 2),
                           dilation=1, w_init_gain='linear'),
                  nn.BatchNorm1d(hparams.n_acoustic_feat_dims))
              )
  
      def forward(self, x):
          for i in range(len(self.convolutions) - 1):
              x = F.dropout(torch.tanh(self.convolutions[i](x)), 0.5,
                            self.training)
          x = F.dropout(self.convolutions[-1](x), 0.5, self.training)
  
          return x
  
  
  class Encoder(nn.Module):
      """Encoder module:
          - Three 1-d convolution banks
          - Bidirectional LSTM
      """
      def __init__(self, hparams):
          super(Encoder, self).__init__()
  
          self.prenet = Prenet(hparams.n_symbols,
                               [hparams.symbols_embedding_dim,
                                hparams.symbols_embedding_dim])
  
          convolutions = []
          for _ in range(hparams.encoder_n_convolutions):
              conv_layer = nn.Sequential(
                  ConvNorm(hparams.encoder_embedding_dim,
                           hparams.encoder_embedding_dim,
                           kernel_size=hparams.encoder_kernel_size, stride=1,
                           padding=int((hparams.encoder_kernel_size - 1) / 2),
                           dilation=1, w_init_gain='relu'),
                  nn.BatchNorm1d(hparams.encoder_embedding_dim))
              convolutions.append(conv_layer)
          self.convolutions = nn.ModuleList(convolutions)
  
          self.lstm = nn.LSTM(hparams.encoder_embedding_dim,
                              int(hparams.encoder_embedding_dim / 2), 1,
                              batch_first=True, bidirectional=True)
  
      def forward(self, x, input_lengths):
          # x: (B, D, T) -> (B, T, D) -> (B, D, T)
          x = self.prenet(x.transpose(1, 2)).transpose(1, 2)
  
          for conv in self.convolutions:
              x = F.dropout(F.relu(conv(x)), 0.5, self.training)
  
          x = x.transpose(1, 2)
  
          # pytorch tensor are not reversible, hence the conversion
          input_lengths = input_lengths.cpu().numpy()
          x = nn.utils.rnn.pack_padded_sequence(
              x, input_lengths, batch_first=True)
  
          self.lstm.flatten_parameters()
          outputs, _ = self.lstm(x)
  
          outputs, _ = nn.utils.rnn.pad_packed_sequence(
              outputs, batch_first=True)
  
  
          return outputs
  
      def inference(self, x):
          # x: (B, D, T) -> (B, T, D) -> (B, D, T)
          x = self.prenet(x.transpose(1, 2)).transpose(1, 2)
  
          for conv in self.convolutions:
              x = F.dropout(F.relu(conv(x)), 0.5, self.training)
  
          x = x.transpose(1, 2)
  
          self.lstm.flatten_parameters()
          outputs, _ = self.lstm(x)
  
          return outputs
  
  
  class Decoder(nn.Module):
      def __init__(self, hparams):
          super(Decoder, self).__init__()
          self.n_acoustic_feat_dims = hparams.n_acoustic_feat_dims
          self.encoder_embedding_dim = hparams.encoder_embedding_dim
          self.attention_rnn_dim = hparams.attention_rnn_dim
          self.decoder_rnn_dim = hparams.decoder_rnn_dim
          self.prenet_dim = hparams.prenet_dim
          self.max_decoder_steps = hparams.max_decoder_steps
          self.gate_threshold = hparams.gate_threshold
          self.p_attention_dropout = hparams.p_attention_dropout
          self.p_decoder_dropout = hparams.p_decoder_dropout
          self.attention_window_size = hparams.attention_window_size
          self.prenet = Prenet(hparams.n_acoustic_feat_dims,
                               [hparams.prenet_dim, hparams.prenet_dim])
  
          self.attention_rnn = nn.LSTMCell(
              hparams.prenet_dim + hparams.encoder_embedding_dim,
              hparams.attention_rnn_dim)
  
          self.attention_layer = Attention(
              hparams.attention_rnn_dim, hparams.encoder_embedding_dim,
              hparams.attention_dim, hparams.attention_location_n_filters,
              hparams.attention_location_kernel_size)
  
          self.decoder_rnn = nn.LSTMCell(
              hparams.attention_rnn_dim + hparams.encoder_embedding_dim,
              hparams.decoder_rnn_dim, 1)
  
          self.linear_projection = LinearNorm(
              hparams.decoder_rnn_dim + hparams.encoder_embedding_dim,
              hparams.n_acoustic_feat_dims)
  
          self.gate_layer = LinearNorm(
              hparams.decoder_rnn_dim + hparams.encoder_embedding_dim, 1,
              bias=True, w_init_gain='sigmoid')
  
      def get_go_frame(self, memory):
          """ Gets all zeros frames to use as first decoder input
          PARAMS
          ------
          memory: decoder outputs
  
          RETURNS
          -------
          decoder_input: all zeros frames
          """
          B = memory.size(0)
          decoder_input = Variable(memory.data.new(
              B, self.n_acoustic_feat_dims).zero_())
          return decoder_input
  
      def initialize_decoder_states(self, memory, mask):
          """ Initializes attention rnn states, decoder rnn states, attention
          weights, attention cumulative weights, attention context, stores memory
          and stores processed memory
          PARAMS
          ------
          memory: Encoder outputs
          mask: Mask for padded data if training, expects None for inference
          """
          B = memory.size(0)
          MAX_TIME = memory.size(1)
  
          self.attention_hidden = Variable(memory.data.new(
              B, self.attention_rnn_dim).zero_())
          self.attention_cell = Variable(memory.data.new(
              B, self.attention_rnn_dim).zero_())
  
          self.decoder_hidden = Variable(memory.data.new(
              B, self.decoder_rnn_dim).zero_())
          self.decoder_cell = Variable(memory.data.new(
              B, self.decoder_rnn_dim).zero_())
  
          self.attention_weights = Variable(memory.data.new(
              B, MAX_TIME).zero_())
          self.attention_weights_cum = Variable(memory.data.new(
              B, MAX_TIME).zero_())
          self.attention_context = Variable(memory.data.new(
              B, self.encoder_embedding_dim).zero_())
  
          self.memory = memory
          self.processed_memory = self.attention_layer.memory_layer(memory)
          self.mask = mask
  
      def parse_decoder_inputs(self, decoder_inputs):
          """ Prepares decoder inputs, i.e. acoustic outputs
          PARAMS
          ------
          decoder_inputs: inputs used for teacher-forced training,
          i.e. acoustic-feats.
  
          RETURNS
          -------
          inputs: processed decoder inputs
  
          """
          # (B, n_acoustic_feat_dims, T_out) -> (B, T_out, n_acoustic_feat_dims)
          decoder_inputs = decoder_inputs.transpose(1, 2)
          decoder_inputs = decoder_inputs.view(
              decoder_inputs.size(0),
              int(decoder_inputs.size(1)), -1)
          # (B, T_out, n_acoustic_feat_dims) -> (T_out, B, n_acoustic_feat_dims)
          decoder_inputs = decoder_inputs.transpose(0, 1)
          return decoder_inputs
  
      def parse_decoder_outputs(self, acoustic_outputs, gate_outputs, alignments):
          """ Prepares decoder outputs for output
          PARAMS
          ------
          acoustic_outputs:
          gate_outputs: gate output energies
          alignments:
  
          RETURNS
          -------
          acoustic_outputs:
          gate_outpust: gate output energies
          alignments:
          """
          # (T_out, B) -> (B, T_out)
          alignments = torch.stack(alignments).transpose(0, 1)
          # (T_out, B) -> (B, T_out)
          gate_outputs = torch.stack(gate_outputs).transpose(0, 1)
          gate_outputs = gate_outputs.contiguous()
          # (T_out, B, n_acoustic_feat_dims) -> (B, T_out, n_acoustic_feat_dims)
          acoustic_outputs = torch.stack(acoustic_outputs).transpose(0, 1).contiguous()
          # decouple frames per step
          acoustic_outputs = acoustic_outputs.view(
              acoustic_outputs.size(0), -1, self.n_acoustic_feat_dims)
          # (B, T_out, n_acoustic_feat_dims) -> (B, n_acoustic_feat_dims, T_out)
          acoustic_outputs = acoustic_outputs.transpose(1, 2)
  
          return acoustic_outputs, gate_outputs, alignments
  
      def decode(self, decoder_input, attention_windowed_mask=None):
          """ Decoder step using stored states, attention and memory
          PARAMS
          ------
          decoder_input: previous acoustic output
          attention_window: B*T mask.
  
          RETURNS
          -------
          decoder_output:
          gate_output: gate output energies
          attention_weights:
          """
          cell_input = torch.cat((decoder_input, self.attention_context), -1)
          self.attention_hidden, self.attention_cell = self.attention_rnn(
              cell_input, (self.attention_hidden, self.attention_cell))
          self.attention_hidden = F.dropout(
              self.attention_hidden, self.p_attention_dropout, self.training)
          self.attention_cell = F.dropout(
              self.attention_cell, self.p_attention_dropout, self.training)
  
          attention_weights_cat = torch.cat(
              (self.attention_weights.unsqueeze(1),
               self.attention_weights_cum.unsqueeze(1)), dim=1)
  
          if attention_windowed_mask is None:
              self.attention_context, self.attention_weights = \
                  self.attention_layer(self.attention_hidden, self.memory,
                                       self.processed_memory,
                                       attention_weights_cat, self.mask)
          else:
              self.attention_context, self.attention_weights = \
                  self.attention_layer(self.attention_hidden, self.memory,
                                       self.processed_memory,
                                       attention_weights_cat,
                                       attention_windowed_mask)
  
          self.attention_weights_cum += self.attention_weights
          decoder_input = torch.cat(
              (self.attention_hidden, self.attention_context), -1)
          self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
              decoder_input, (self.decoder_hidden, self.decoder_cell))
          # self.decoder_hidden, self.decoder_cell = self.decoder_rnn2(
          #     self.decoder_hidden, (self.decoder_hidden, self.decoder_cell))
          self.decoder_hidden = F.dropout(
              self.decoder_hidden, self.p_decoder_dropout, self.training)
          self.decoder_cell = F.dropout(
              self.decoder_cell, self.p_decoder_dropout, self.training)
  
          decoder_hidden_attention_context = torch.cat(
              (self.decoder_hidden, self.attention_context), dim=1)
          decoder_output = self.linear_projection(
              decoder_hidden_attention_context)
  
          gate_prediction = self.gate_layer(decoder_hidden_attention_context)
          return decoder_output, gate_prediction, self.attention_weights
  
      def forward(self, memory, decoder_inputs, memory_lengths):
          """ Decoder forward pass for training
          PARAMS
          ------
          memory: Encoder outputs
          decoder_inputs: Decoder inputs for teacher forcing. i.e. acoustic-feats.
          memory_lengths: Encoder output lengths for attention masking.
  
          RETURNS
          -------
          acoustic_outputs: acoustic outputs from the decoder
          gate_outputs: gate outputs from the decoder
          alignments: sequence of attention weights from the decoder
          """
  
          decoder_input = self.get_go_frame(memory).unsqueeze(0)
          decoder_inputs = self.parse_decoder_inputs(decoder_inputs)
          decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0)
          decoder_inputs = self.prenet(decoder_inputs)
  
          self.initialize_decoder_states(
              memory, mask=~get_mask_from_lengths(memory_lengths))
  
          acoustic_outputs, gate_outputs, alignments = [], [], []
          while len(acoustic_outputs) < decoder_inputs.size(0) - 1:
              decoder_input = decoder_inputs[len(acoustic_outputs)]
  
              if self.attention_window_size is not None:
                  time_step = len(acoustic_outputs)
                  attention_windowed_mask = \
                      get_mask_from_lengths_window_and_time_step(
                          memory_lengths, self.attention_window_size, time_step)
              else:
                  attention_windowed_mask = None
              acoustic_output, gate_output, attention_weights = self.decode(
                  decoder_input, attention_windowed_mask)
              acoustic_outputs += [acoustic_output.squeeze(1)]
              gate_outputs += [gate_output.squeeze()]
              alignments += [attention_weights]
  
          acoustic_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
              acoustic_outputs, gate_outputs, alignments)
  
          return acoustic_outputs, gate_outputs, alignments
  
      def inference(self, memory, memory_lengths):
          """ Decoder inference
          PARAMS
          ------
          memory: Encoder outputs
  
          RETURNS
          -------
          acoustic_outputs: acoustic outputs from the decoder
          gate_outputs: gate outputs from the decoder
          alignments: sequence of attention weights from the decoder
          """
          decoder_input = self.get_go_frame(memory)
  
          self.initialize_decoder_states(memory, mask=None)
  
          acoustic_outputs, gate_outputs, alignments = [], [], []
          while True:
              decoder_input = self.prenet(decoder_input)
  
              if self.attention_window_size is not None:
                  time_step = len(acoustic_outputs)
                  attention_windowed_mask = \
                      get_mask_from_lengths_window_and_time_step(
                          memory_lengths, self.attention_window_size, time_step)
              else:
                  attention_windowed_mask = None
  
              acoustic_output, gate_output, alignment = self.decode(
                  decoder_input, attention_windowed_mask)
  
              acoustic_outputs += [acoustic_output.squeeze(1)]
              gate_outputs += [gate_output]
              alignments += [alignment]
  
              if torch.sigmoid(gate_output.data) > self.gate_threshold:
                  break
              elif len(acoustic_outputs) == self.max_decoder_steps:
                  print("Warning! Reached max decoder steps")
                  break
  
              decoder_input = acoustic_output
  
          acoustic_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
              acoustic_outputs, gate_outputs, alignments)
  
          return acoustic_outputs, gate_outputs, alignments
  
  
  class Tacotron2(nn.Module):
      def __init__(self, hparams):
          super(Tacotron2, self).__init__()
          self.mask_padding = hparams.mask_padding
          self.fp16_run = hparams.fp16_run
          self.n_acoustic_feat_dims = hparams.n_acoustic_feat_dims
          self.encoder = Encoder(hparams)
          self.decoder = Decoder(hparams)
          self.postnet = Postnet(hparams)
  
      def parse_batch(self, batch):
          ppg_padded, input_lengths, acoustic_padded, gate_padded,\
              output_lengths, dvec = batch
          ppg_padded = to_gpu(ppg_padded).float()
          input_lengths = to_gpu(input_lengths).long()
          max_len = torch.max(input_lengths.data).item()
          acoustic_padded = to_gpu(acoustic_padded).float()
          gate_padded = to_gpu(gate_padded).float()
          output_lengths = to_gpu(output_lengths).long()
          dvec = to_gpu(dvec).float()
  
          return ((ppg_padded, input_lengths, acoustic_padded, max_len,
                   output_lengths, dvec),
                  (acoustic_padded, gate_padded))
  
      def parse_input(self, inputs):
          inputs = fp32_to_fp16(inputs) if self.fp16_run else inputs
          return inputs
  
      def parse_output(self, outputs, output_lengths=None):
          if self.mask_padding and output_lengths is not None:
              mask = ~get_mask_from_lengths(output_lengths)
              mask = mask.expand(self.n_acoustic_feat_dims, mask.size(0),
                                 mask.size(1))
              mask = mask.permute(1, 0, 2)
  
              outputs[0].data.masked_fill_(mask, 0.0)
              outputs[1].data.masked_fill_(mask, 0.0)
              outputs[2].data.masked_fill_(mask[:, 0, :], 1e3)  # gate energies
  
          outputs = fp16_to_fp32(outputs) if self.fp16_run else outputs
          return outputs
  
      def forward(self, inputs):
          inputs, input_lengths, targets, max_len, \
              output_lengths, dvec = self.parse_input(inputs)
          input_lengths, output_lengths = input_lengths.data, output_lengths.data
  
          # inputs: (B, D, T)
          encoder_outputs = self.encoder(inputs, input_lengths)
  
          # dvec
          padded_dvec = torch.FloatTensor(encoder_outputs.size(0), encoder_outputs.size(1), dvec.size(1)).cuda()
          padded_dvec.zero_()
          for i in range(len(input_lengths)):
              padded_dvec[i, :input_lengths[i], :] = dvec[i].unsqueeze(0).repeat(input_lengths[i], 1)
  
          acoustic_outputs, gate_outputs, alignments = self.decoder(
              encoder_outputs, targets, memory_lengths=input_lengths)
  
          acoustic_outputs_postnet = self.postnet(acoustic_outputs)
          acoustic_outputs_postnet = acoustic_outputs + acoustic_outputs_postnet
  
          return self.parse_output([acoustic_outputs, acoustic_outputs_postnet,
                                    gate_outputs, alignments], output_lengths)
  
      def inference(self, inputs):
          inputs = self.parse_input(inputs)
          input_lengths = torch.cuda.LongTensor([t.shape[1] for t in inputs])
          encoder_outputs = self.encoder.inference(inputs)
          acoustic_outputs, gate_outputs, alignments = self.decoder.inference(
              encoder_outputs, input_lengths)
  
          acoustic_outputs_postnet = self.postnet(acoustic_outputs)
          acoustic_outputs_postnet = acoustic_outputs + acoustic_outputs_postnet
  
          outputs = self.parse_output([acoustic_outputs, acoustic_outputs_postnet,
                                       gate_outputs, alignments])
  
          return outputs
  