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  from math import sqrt
  import torch
  from torch.autograd import Variable
  from torch import nn
  from torch.nn import functional as F
  import sys
  from os.path import abspath, dirname
  # enabling modules discovery from global entrypoint
  sys.path.append(abspath(dirname(__file__)+'/../'))
  from common.layers import ConvNorm, LinearNorm
  from common.utils import to_gpu, get_mask_from_lengths
  
  
  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")
  
      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, inference=False):
          if inference:
              for linear in self.layers:
                  x = F.relu(linear(x))
                  x0 = x[0].unsqueeze(0)
                  mask = Variable(torch.bernoulli(x0.data.new(x0.data.size()).fill_(0.5)))
                  mask = mask.expand(x.size(0), x.size(1))
                  x = x*mask*2
          else:
              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, n_mel_channels, postnet_embedding_dim,
                   postnet_kernel_size, postnet_n_convolutions):
          super(Postnet, self).__init__()
          self.convolutions = nn.ModuleList()
  
          self.convolutions.append(
              nn.Sequential(
                  ConvNorm(n_mel_channels, postnet_embedding_dim,
                           kernel_size=postnet_kernel_size, stride=1,
                           padding=int((postnet_kernel_size - 1) / 2),
                           dilation=1, w_init_gain='tanh'),
                  nn.BatchNorm1d(postnet_embedding_dim))
          )
  
          for i in range(1, postnet_n_convolutions - 1):
              self.convolutions.append(
                  nn.Sequential(
                      ConvNorm(postnet_embedding_dim,
                               postnet_embedding_dim,
                               kernel_size=postnet_kernel_size, stride=1,
                               padding=int((postnet_kernel_size - 1) / 2),
                               dilation=1, w_init_gain='tanh'),
                      nn.BatchNorm1d(postnet_embedding_dim))
              )
  
          self.convolutions.append(
              nn.Sequential(
                  ConvNorm(postnet_embedding_dim, n_mel_channels,
                           kernel_size=postnet_kernel_size, stride=1,
                           padding=int((postnet_kernel_size - 1) / 2),
                           dilation=1, w_init_gain='linear'),
                  nn.BatchNorm1d(n_mel_channels))
          )
  
      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, encoder_n_convolutions,
                   encoder_embedding_dim, encoder_kernel_size):
          super(Encoder, self).__init__()
  
          convolutions = []
          for _ in range(encoder_n_convolutions):
              conv_layer = nn.Sequential(
                  ConvNorm(encoder_embedding_dim,
                           encoder_embedding_dim,
                           kernel_size=encoder_kernel_size, stride=1,
                           padding=int((encoder_kernel_size - 1) / 2),
                           dilation=1, w_init_gain='relu'),
                  nn.BatchNorm1d(encoder_embedding_dim))
              convolutions.append(conv_layer)
  
          self.convolutions = nn.ModuleList(convolutions)
          self.lstm = nn.LSTM(encoder_embedding_dim,
                              int(encoder_embedding_dim / 2), 1,
                              batch_first=True, bidirectional=True)
  
      def forward(self, x, input_lengths):
          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 infer(self, x):
          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, n_mel_channels, n_frames_per_step,
                   encoder_embedding_dim, attention_dim,
                   attention_location_n_filters,
                   attention_location_kernel_size,
                   attention_rnn_dim, decoder_rnn_dim,
                   prenet_dim, max_decoder_steps, gate_threshold,
                   p_attention_dropout, p_decoder_dropout,
                   early_stopping):
          super(Decoder, self).__init__()
          self.n_mel_channels = n_mel_channels
          self.n_frames_per_step = n_frames_per_step
          self.encoder_embedding_dim = encoder_embedding_dim
          self.attention_rnn_dim = attention_rnn_dim
          self.decoder_rnn_dim = decoder_rnn_dim
          self.prenet_dim = prenet_dim
          self.max_decoder_steps = max_decoder_steps
          self.gate_threshold = gate_threshold
          self.p_attention_dropout = p_attention_dropout
          self.p_decoder_dropout = p_decoder_dropout
          self.early_stopping = early_stopping
  
          self.prenet = Prenet(
              n_mel_channels,
              [prenet_dim, prenet_dim])
  
          self.attention_rnn = nn.LSTMCell(
              prenet_dim + encoder_embedding_dim,
              attention_rnn_dim)
  
          self.attention_layer = Attention(
              attention_rnn_dim, encoder_embedding_dim,
              attention_dim, attention_location_n_filters,
              attention_location_kernel_size)
  
          self.decoder_rnn = nn.LSTMCell(
              attention_rnn_dim + encoder_embedding_dim,
              decoder_rnn_dim, 1)
  
          self.linear_projection = LinearNorm(
              decoder_rnn_dim + encoder_embedding_dim,
              n_mel_channels * n_frames_per_step)
  
          self.gate_layer = LinearNorm(
              decoder_rnn_dim + 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_mel_channels * self.n_frames_per_step).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. mel outputs
          PARAMS
          ------
          decoder_inputs: inputs used for teacher-forced training, i.e. mel-specs
  
          RETURNS
          -------
          inputs: processed decoder inputs
  
          """
          # (B, n_mel_channels, T_out) -> (B, T_out, n_mel_channels)
  
          decoder_inputs = decoder_inputs.transpose(1, 2).contiguous()
          decoder_inputs = decoder_inputs.view(
              decoder_inputs.size(0),
              int(decoder_inputs.size(1)/self.n_frames_per_step), -1)
          # (B, T_out, n_mel_channels) -> (T_out, B, n_mel_channels)
          decoder_inputs = decoder_inputs.transpose(0, 1)
  
  
          return decoder_inputs
  
      def parse_decoder_outputs(self, mel_outputs, gate_outputs, alignments):
          """ Prepares decoder outputs for output
          PARAMS
          ------
          mel_outputs:
          gate_outputs: gate output energies
          alignments:
  
          RETURNS
          -------
          mel_outputs:
          gate_outputs: 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_mel_channels) -> (B, T_out, n_mel_channels)
          mel_outputs = torch.stack(mel_outputs).transpose(0, 1).contiguous()
          # decouple frames per step
          mel_outputs = mel_outputs.view(
              mel_outputs.size(0), -1, self.n_mel_channels)
          # (B, T_out, n_mel_channels) -> (B, n_mel_channels, T_out)
          mel_outputs = mel_outputs.transpose(1, 2)
  
          return mel_outputs, gate_outputs, alignments
  
  
  
      def decode(self, decoder_input):
          """ Decoder step using stored states, attention and memory
          PARAMS
          ------
          decoder_input: previous mel output
  
          RETURNS
          -------
          mel_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)
  
          attention_weights_cat = torch.cat(
              (self.attention_weights.unsqueeze(1),
               self.attention_weights_cum.unsqueeze(1)), dim=1)
          self.attention_context, self.attention_weights = self.attention_layer(
              self.attention_hidden, self.memory, self.processed_memory,
              attention_weights_cat, self.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 = F.dropout(
              self.decoder_hidden, 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. mel-specs
          memory_lengths: Encoder output lengths for attention masking.
  
          RETURNS
          -------
          mel_outputs: mel 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_input_frames = []
          z = int(decoder_inputs.size(2) / self.n_frames_per_step)
          for i in range(self.n_frames_per_step):
              decoder_input_frames.append(self.prenet(decoder_inputs[:, :, i*z:(i+1)*z]))
  
          self.initialize_decoder_states(
              memory, mask=~get_mask_from_lengths(memory_lengths))
  
          mel_outputs, gate_outputs, alignments = [], [], []
  
          while len(mel_outputs) < decoder_input_frames[0].size(0) - 1:
              for input_frame in decoder_input_frames:
                  decoder_input = input_frame[len(mel_outputs)]
  
                  mel_output, gate_output, attention_weights = self.decode(
                      decoder_input)
                  gate_outputs += [gate_output.squeeze() if memory.shape[0] > 1 else gate_output]
                  alignments += [attention_weights]
  
              mel_outputs += [mel_output.squeeze(1)]
  
          mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
                  mel_outputs, gate_outputs, alignments)
  
          return mel_outputs, gate_outputs, alignments
  
      def infer(self, memory):
          """ Decoder inference
          PARAMS
          ------
          memory: Encoder outputs
  
          RETURNS
          -------
          mel_outputs: mel 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)
  
          mel_lengths = torch.zeros([memory.size(0)], dtype=torch.int32).cuda()
          not_finished = torch.ones([memory.size(0)], dtype=torch.int32).cuda()
          mel_outputs, gate_outputs, alignments = [], [], []
          z = int(decoder_input.size(1) / self.n_frames_per_step)
          while True:
              decoder_input_frames = []
              for i in range(self.n_frames_per_step):
                  decoder_input_frames.append(decoder_input[:, i * z:(i + 1) * z])
  
              for input_frame in decoder_input_frames:
                  mel_output, gate_output, alignment = self.decode(self.prenet(input_frame))
                  gate_outputs += [gate_output]
                  alignments += [alignment]
  
              mel_outputs += [mel_output.squeeze(1)]
  
              dec = torch.le(torch.sigmoid(gate_output.data),
                             self.gate_threshold).to(torch.int32).squeeze(1)
  
              not_finished = not_finished*dec
              mel_lengths += not_finished
  
              if self.early_stopping and torch.sum(not_finished) == 0:
                  break
              if len(mel_outputs) == self.max_decoder_steps:
                  print("Warning! Reached max decoder steps")
                  break
  
              decoder_input = mel_output
  
          mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
              mel_outputs, gate_outputs, alignments)
  
          return mel_outputs, gate_outputs, alignments
  
  
  class Tacotron2(nn.Module):
      def __init__(self, mask_padding, n_mel_channels,
                   n_symbols, symbols_embedding_dim, n_speakers, speakers_embedding_dim,
                   use_emotions, n_emotions, emotions_embedding_dim,
                   encoder_kernel_size, encoder_n_convolutions, encoder_embedding_dim,
                   attention_rnn_dim, attention_dim, attention_location_n_filters,
                   attention_location_kernel_size, n_frames_per_step,
                   decoder_rnn_dim, prenet_dim, max_decoder_steps, gate_threshold,
                   p_attention_dropout, p_decoder_dropout,
                   postnet_embedding_dim, postnet_kernel_size,
                   postnet_n_convolutions, decoder_no_early_stopping, **kwargs):
          super(Tacotron2, self).__init__()
  
          self.mask_padding = mask_padding
          self.n_mel_channels = n_mel_channels
          self.n_frames_per_step = n_frames_per_step
  
          self.symbols_embedding = nn.Embedding(
              n_symbols, symbols_embedding_dim)
          std = sqrt(2.0 / (n_symbols + symbols_embedding_dim))
          val = sqrt(3.0) * std  # uniform bounds for std
          self.symbols_embedding.weight.data.uniform_(-val, val)
  
          self.speakers_embedding = nn.Embedding(n_speakers, speakers_embedding_dim)
          torch.nn.init.xavier_uniform_(self.speakers_embedding.weight)
  
          self.encoder = Encoder(encoder_n_convolutions,
                                 encoder_embedding_dim,
                                 encoder_kernel_size)
  
          encoder_out_embedding_dim = encoder_embedding_dim + speakers_embedding_dim
  
          self.use_emotions = use_emotions
          if self.use_emotions:
              self.emotions_embedding = nn.Embedding(n_emotions, emotions_embedding_dim)
              torch.nn.init.xavier_uniform_(self.emotions_embedding.weight)
              encoder_out_embedding_dim += emotions_embedding_dim
  
          self.decoder = Decoder(n_mel_channels, n_frames_per_step,
                                 encoder_out_embedding_dim, attention_dim,
                                 attention_location_n_filters,
                                 attention_location_kernel_size,
                                 attention_rnn_dim, decoder_rnn_dim,
                                 prenet_dim, max_decoder_steps,
                                 gate_threshold, p_attention_dropout,
                                 p_decoder_dropout,
                                 not decoder_no_early_stopping)
  
          self.postnet = Postnet(n_mel_channels, postnet_embedding_dim,
                                 postnet_kernel_size,
                                 postnet_n_convolutions)
  
      def parse_batch(self, batch):
          text_padded, input_lengths, mel_padded, gate_padded, \
              output_lengths, speaker_ids, emotion_ids = batch
          text_padded = to_gpu(text_padded).long()
          input_lengths = to_gpu(input_lengths).long()
          max_len = torch.max(input_lengths.data).item()
          mel_padded = to_gpu(mel_padded).float()
          gate_padded = to_gpu(gate_padded).float()
          output_lengths = to_gpu(output_lengths).long()
          speaker_ids = to_gpu(speaker_ids).long()
          emotion_ids = to_gpu(emotion_ids).long()
  
          return ((text_padded, input_lengths, mel_padded, max_len, output_lengths, speaker_ids, emotion_ids),
                  (mel_padded, gate_padded))
  
      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_mel_channels, 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
  
          return outputs
  
      def forward(self, inputs):
          # Parse inputs
          inputs, input_lengths, targets, max_len, output_lengths, speaker_ids, emotion_ids = inputs
          input_lengths, output_lengths = input_lengths.data, output_lengths.data
  
          # Outputs
          outputs = []
  
          # Get symbols encoder outputs
          embedded_inputs = self.symbols_embedding(inputs).transpose(1, 2)
          encoder_outputs = self.encoder(embedded_inputs, input_lengths)
          outputs.append(encoder_outputs)
  
          # Extract speaker embeddings
          speaker_ids = speaker_ids.unsqueeze(1)
          embedded_speakers = self.speakers_embedding(speaker_ids)
          embedded_speakers = embedded_speakers.expand(-1, max_len, -1)
          outputs.append(embedded_speakers)
  
          # Extract emotion embeddings
          if self.use_emotions:
              emotion_ids = emotion_ids.unsqueeze(1)
              embedded_emotions = self.emotions_embedding(emotion_ids)
              embedded_emotions = embedded_emotions.expand(-1, max_len, -1)
              outputs.append(embedded_emotions)
          # Combine all embeddings embeddings
          merged_outputs = torch.cat(outputs, -1)
  
          mel_outputs, gate_outputs, alignments = self.decoder(
              merged_outputs, targets, memory_lengths=input_lengths)
  
          mel_outputs_postnet = self.postnet(mel_outputs)
          mel_outputs_postnet = mel_outputs + mel_outputs_postnet
  
          return self.parse_output(
              [mel_outputs, mel_outputs_postnet, gate_outputs, alignments],
              output_lengths)
  
      def infer(self, input, speaker_id, emotion_id=None):
          # Outputs
          outputs = []
  
          # Get symbols encoder output
          embedded_input = self.symbols_embedding(input).transpose(1, 2)
          encoder_output = self.encoder.infer(embedded_input)
          outputs.append(encoder_output)
  
          # Get speaker embedding
          speaker_id = speaker_id.unsqueeze(1)
          embedded_speaker = self.speakers_embedding(speaker_id)
          embedded_speaker = embedded_speaker.expand(-1, encoder_output.shape[1], -1)
          outputs.append(embedded_speaker)
  
          # Extract emotion embeddings
          if self.use_emotions:
              emotion_id = emotion_id.unsqueeze(1)
              embedded_emotion = self.emotions_embedding(emotion_id)
              embedded_emotion = embedded_emotion.expand(-1, encoder_output.shape[1], -1)
              outputs.append(embedded_emotion)
  
          # Merge embeddings
          merged_outputs = torch.cat(outputs, -1)
  
          # Decode
          mel_outputs, gate_outputs, alignments = self.decoder.infer(
              merged_outputs)
  
          # Post
          mel_outputs_postnet = self.postnet(mel_outputs)
          mel_outputs_postnet = mel_outputs + mel_outputs_postnet
  
          # Parse
          outputs = self.parse_output(
              [mel_outputs, mel_outputs_postnet, gate_outputs, alignments])
  
          return outputs
  
apis: torch.sum torch.nn.init.xavier_uniform_ torch.stack torch.nn.utils.rnn.pack_padded_sequence torch.nn.functional.dropout torch.nn.BatchNorm1d torch.nn.functional.softmax torch.nn.Embedding torch.nn.LSTMCell torch.nn.ModuleList torch.tanh torch.max torch.sigmoid torch.cat torch.nn.utils.rnn.pad_packed_sequence