text: #!/usr/bin/env python3
  # -*- coding: utf-8 -*-
  
  # Copyright 2020 Johns Hopkins University (Shinji Watanabe)
  #                Northwestern Polytechnical University (Pengcheng Guo)
  #  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
  
  """Encoder self-attention layer definition."""
  
  import torch
  
  from torch import nn
  
  from muskit.layers.transformer.layer_norm import LayerNorm
  
  
  class EncoderLayer(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`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
              can be used as the argument.
          feed_forward_macaron (torch.nn.Module): Additional feed-forward module instance.
              `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` 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): Whether to use layer_norm before the first block.
          concat_after (bool): Whether to concat attention layer's input and output.
              if True, additional linear will be applied.
              i.e. x -> x + linear(concat(x, att(x)))
              if False, no additional linear will be applied. i.e. x -> x + att(x)
          stochastic_depth_rate (float): Proability to skip this layer.
              During training, the layer may skip residual computation and return input
              as-is with given probability.
      """
  
      def __init__(
          self,
          size,
          self_attn,
          feed_forward,
          feed_forward_macaron,
          conv_module,
          dropout_rate,
          normalize_before=True,
          concat_after=False,
          stochastic_depth_rate=0.0,
      ):
          """Construct an EncoderLayer object."""
          super(EncoderLayer, self).__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 = LayerNorm(size)  # for the FNN module
          self.norm_mha = LayerNorm(size)  # for the MHA module
          if feed_forward_macaron is not None:
              self.norm_ff_macaron = LayerNorm(size)
              self.ff_scale = 0.5
          else:
              self.ff_scale = 1.0
          if self.conv_module is not None:
              self.norm_conv = LayerNorm(size)  # for the CNN module
              self.norm_final = LayerNorm(size)  # 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
          if self.concat_after:
              self.concat_linear = nn.Linear(size + size, size)
          self.stochastic_depth_rate = stochastic_depth_rate
  
      def forward(self, x_input, mask, cache=None):
          """Compute encoded features.
          Args:
              x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
                  - w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
                  - w/o pos emb: Tensor (#batch, time, size).
              mask (torch.Tensor): Mask tensor for the input (#batch, time).
              cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
          Returns:
              torch.Tensor: Output tensor (#batch, time, size).
              torch.Tensor: Mask tensor (#batch, time).
          """
          if isinstance(x_input, tuple):
              x, pos_emb = x_input[0], x_input[1]
          else:
              x, pos_emb = x_input, None
  
          skip_layer = False
          # with stochastic depth, residual connection `x + f(x)` becomes
          # `x <- x + 1 / (1 - p) * f(x)` at training time.
          stoch_layer_coeff = 1.0
          if self.training and self.stochastic_depth_rate > 0:
              skip_layer = torch.rand(1).item() < self.stochastic_depth_rate
              stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate)
  
          if skip_layer:
              if cache is not None:
                  x = torch.cat([cache, x], dim=1)
              if pos_emb is not None:
                  return (x, pos_emb), mask
              return x, mask
  
          # 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 + stoch_layer_coeff * 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 cache is None:
              x_q = x
          else:
              assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
              x_q = x[:, -1:, :]
              residual = residual[:, -1:, :]
              mask = None if mask is None else mask[:, -1:, :]
  
          if pos_emb is not None:
              x_att = self.self_attn(x_q, x, x, pos_emb, mask)
          else:
              x_att = self.self_attn(x_q, x, x, mask)
  
          if self.concat_after:
              x_concat = torch.cat((x, x_att), dim=-1)
              x = residual + stoch_layer_coeff * self.concat_linear(x_concat)
          else:
              x = residual + stoch_layer_coeff * self.dropout(x_att)
          if not self.normalize_before:
              x = self.norm_mha(x)
  
          # convolution module
          if self.conv_module is not None:
              residual = x
              if self.normalize_before:
                  x = self.norm_conv(x)
              x = residual + stoch_layer_coeff * self.dropout(self.conv_module(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 + stoch_layer_coeff * 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 cache is not None:
              x = torch.cat([cache, x], dim=1)
  
          if pos_emb is not None:
              return (x, pos_emb), mask
  
          return x, mask
  