text: # Copyright 2019 Shigeki Karita
  #  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
  
  """Encoder definition."""
  from typing import Optional
  from typing import Tuple
  
  import torch
  from typeguard import check_argument_types
  
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
  from espnet.nets.pytorch_backend.transformer.encoder_layer import EncoderLayer
  from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
  from espnet.nets.pytorch_backend.transformer.multi_layer_conv import Conv1dLinear
  from espnet.nets.pytorch_backend.transformer.multi_layer_conv import MultiLayeredConv1d
  from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import (
      PositionwiseFeedForward,  # noqa: H301
  )
  from espnet.nets.pytorch_backend.transformer.repeat import repeat
  from espnet.nets.pytorch_backend.transformer.subsampling import Conv2dSubsampling
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  
  
  class TransformerEncoder(AbsEncoder):
      """Transformer encoder module.
  
      Args:
          input_size: input dim
          output_size: dimension of attention
          attention_heads: the number of heads of multi head attention
          linear_units: the number of units of position-wise feed forward
          num_blocks: the number of decoder blocks
          dropout_rate: dropout rate
          attention_dropout_rate: dropout rate in attention
          positional_dropout_rate: dropout rate after adding positional encoding
          input_layer: input layer type
          pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
          normalize_before: whether to use layer_norm before the first block
          concat_after: 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)
          positionwise_layer_type: linear of conv1d
          positionwise_conv_kernel_size: kernel size of positionwise conv1d layer
          padding_idx: padding_idx for input_layer=embed
      """
  
      def __init__(
          self,
          input_size: int,
          output_size: int = 256,
          attention_heads: int = 4,
          linear_units: int = 2048,
          num_blocks: int = 6,
          dropout_rate: float = 0.1,
          positional_dropout_rate: float = 0.1,
          attention_dropout_rate: float = 0.0,
          input_layer: Optional[str] = "conv2d",
          pos_enc_class=PositionalEncoding,
          normalize_before: bool = True,
          concat_after: bool = False,
          positionwise_layer_type: str = "linear",
          positionwise_conv_kernel_size: int = 1,
          padding_idx: int = -1,
      ):
          assert check_argument_types()
          super().__init__()
          self._output_size = output_size
  
          if input_layer == "linear":
              self.embed = torch.nn.Sequential(
                  torch.nn.Linear(input_size, output_size),
                  torch.nn.LayerNorm(output_size),
                  torch.nn.Dropout(dropout_rate),
                  torch.nn.ReLU(),
                  pos_enc_class(output_size, positional_dropout_rate),
              )
          elif input_layer == "conv2d":
              self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
          elif input_layer == "embed":
              self.embed = torch.nn.Sequential(
                  torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
                  pos_enc_class(output_size, positional_dropout_rate),
              )
          elif input_layer is None:
              self.embed = torch.nn.Sequential(
                  pos_enc_class(output_size, positional_dropout_rate)
              )
          else:
              raise ValueError("unknown input_layer: " + input_layer)
          self.normalize_before = normalize_before
          if positionwise_layer_type == "linear":
              positionwise_layer = PositionwiseFeedForward
              positionwise_layer_args = (
                  output_size,
                  linear_units,
                  dropout_rate,
              )
          elif positionwise_layer_type == "conv1d":
              positionwise_layer = MultiLayeredConv1d
              positionwise_layer_args = (
                  output_size,
                  linear_units,
                  positionwise_conv_kernel_size,
                  dropout_rate,
              )
          elif positionwise_layer_type == "conv1d-linear":
              positionwise_layer = Conv1dLinear
              positionwise_layer_args = (
                  output_size,
                  linear_units,
                  positionwise_conv_kernel_size,
                  dropout_rate,
              )
          else:
              raise NotImplementedError("Support only linear or conv1d.")
          self.encoders = repeat(
              num_blocks,
              lambda lnum: EncoderLayer(
                  output_size,
                  MultiHeadedAttention(
                      attention_heads, output_size, attention_dropout_rate
                  ),
                  positionwise_layer(*positionwise_layer_args),
                  dropout_rate,
                  normalize_before,
                  concat_after,
              ),
          )
          if self.normalize_before:
              self.after_norm = LayerNorm(output_size)
  
      def output_size(self) -> int:
          return self._output_size
  
      def forward(
          self,
          xs_pad: torch.Tensor,
          ilens: torch.Tensor,
          prev_states: torch.Tensor = None,
      ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
          """Embed positions in tensor.
  
          Args:
              xs_pad: input tensor (B, L, D)
              ilens: input length (B)
              prev_states: Not to be used now.
          Returns:
              position embedded tensor and mask
          """
          masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
  
          if isinstance(self.embed, Conv2dSubsampling):
              xs_pad, masks = self.embed(xs_pad, masks)
          else:
              xs_pad = self.embed(xs_pad)
          xs_pad, masks = self.encoders(xs_pad, masks)
          if self.normalize_before:
              xs_pad = self.after_norm(xs_pad)
  
          olens = masks.squeeze(1).sum(1)
          return xs_pad, olens, None
  