text: # Copyright (c) Facebook, Inc. and its affiliates.
  #
  # This source code is licensed under the MIT license found in the
  # LICENSE file in the root directory of this source tree.
  
  from typing import Optional, Tuple
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from fairseq.modules import (
      LayerNorm,
      MultiheadAttention,
      PositionalEmbedding,
      TransformerSentenceEncoderLayer,
  )
  
  
  def init_bert_params(module):
      """
      Initialize the weights specific to the BERT Model.
      This overrides the default initializations depending on the specified arguments.
          1. If normal_init_linear_weights is set then weights of linear
             layer will be initialized using the normal distribution and
             bais will be set to the specified value.
          2. If normal_init_embed_weights is set then weights of embedding
             layer will be initialized using the normal distribution.
          3. If normal_init_proj_weights is set then weights of
             in_project_weight for MultiHeadAttention initialized using
             the normal distribution (to be validated).
      """
  
      if isinstance(module, nn.Linear):
          module.weight.data.normal_(mean=0.0, std=0.02)
          if module.bias is not None:
              module.bias.data.zero_()
      if isinstance(module, nn.Embedding):
          module.weight.data.normal_(mean=0.0, std=0.02)
          module.weight.data[module.padding_idx].zero_()
      if isinstance(module, MultiheadAttention):
          module.in_proj_weight.data.normal_(mean=0.0, std=0.02)
  
  
  class TransformerSentenceEncoder(nn.Module):
      """
      Implementation for a Bi-directional Transformer based Sentence Encoder used
      in BERT/XLM style pre-trained models.
  
      This first computes the token embedding using the token embedding matrix,
      position embeddings (if specified) and segment embeddings
      (if specified). After applying the specified number of
      TransformerEncoderLayers, it outputs all the internal states of the
      encoder as well as the final representation associated with the first
      token (usually CLS token).
  
      Input:
          - tokens: B x T matrix representing sentences
          - segment_labels: B x T matrix representing segment label for tokens
  
      Output:
          - a tuple of the following:
              - a list of internal model states used to compute the
                predictions where each tensor has shape B x T x C
              - sentence representation associated with first input token
                in format B x C.
      """
  
      def __init__(
          self,
          padding_idx: int,
          vocab_size: int,
          num_encoder_layers: int = 6,
          embedding_dim: int = 768,
          ffn_embedding_dim: int = 3072,
          num_attention_heads: int = 8,
          dropout: float = 0.1,
          attention_dropout: float = 0.1,
          activation_dropout: float = 0.1,
          max_seq_len: int = 256,
          num_segments: int = 2,
          use_position_embeddings: bool = True,
          offset_positions_by_padding: bool = True,
          encoder_normalize_before: bool = False,
          apply_bert_init: bool = False,
          activation_fn: str = "relu",
          learned_pos_embedding: bool = True,
          add_bias_kv: bool = False,
          add_zero_attn: bool = False,
          embed_scale: float = None,
          freeze_embeddings: bool = False,
          n_trans_layers_to_freeze: int = 0,
          export: bool = False,
          bert: bool = False,
      ) -> None:
  
          super().__init__()
          self.padding_idx = padding_idx
          self.vocab_size = vocab_size
          self.dropout = dropout
          self.max_seq_len = max_seq_len
          self.embedding_dim = embedding_dim
          self.num_segments = num_segments
          self.use_position_embeddings = use_position_embeddings
          self.apply_bert_init = apply_bert_init
          self.learned_pos_embedding = learned_pos_embedding
          self.bert = bert
  
          self.embed_tokens = nn.Embedding(
              self.vocab_size, self.embedding_dim, self.padding_idx
          )
          self.embed_scale = embed_scale
          if bert:
              self.num_segments = 2
          self.segment_embeddings = (
              nn.Embedding(self.num_segments, self.embedding_dim, padding_idx=None)
              if self.num_segments > 0
              else None
          )
          self.embed_positions = (
              PositionalEmbedding(
                  self.max_seq_len,
                  self.embedding_dim,
                  padding_idx=(self.padding_idx if offset_positions_by_padding else None),
                  learned=self.learned_pos_embedding,
                  bert_pos_emb=bert,
              )
              if self.use_position_embeddings
              else None
          )
  
          self.layers = nn.ModuleList(
              [
                  TransformerSentenceEncoderLayer(
                      embedding_dim=self.embedding_dim,
                      ffn_embedding_dim=ffn_embedding_dim,
                      num_attention_heads=num_attention_heads,
                      dropout=self.dropout,
                      attention_dropout=attention_dropout,
                      activation_dropout=activation_dropout,
                      activation_fn=activation_fn,
                      add_bias_kv=add_bias_kv,
                      add_zero_attn=add_zero_attn,
                      export=export,
                  )
                  for _ in range(num_encoder_layers)
              ]
          )
  
          if encoder_normalize_before:
              if bert:
                  self.emb_layer_norm = LayerNorm(self.embedding_dim, export=export, eps=1e-12)
              else:
                  self.emb_layer_norm = LayerNorm(self.embedding_dim, export=export)
          else:
              self.emb_layer_norm = None
  
          # Apply initialization of model params after building the model
          if self.apply_bert_init:
              self.apply(init_bert_params)
  
          def freeze_module_params(m):
              if m is not None:
                  for p in m.parameters():
                      p.requires_grad = False
  
          if freeze_embeddings:
              freeze_module_params(self.embed_tokens)
              freeze_module_params(self.segment_embeddings)
              freeze_module_params(self.embed_positions)
              freeze_module_params(self.emb_layer_norm)
  
          for layer in range(n_trans_layers_to_freeze):
              freeze_module_params(self.layers[layer])
  
      def forward(
          self,
          tokens: torch.Tensor,
          segment_labels: torch.Tensor = None,
          last_state_only: bool = False,
          positions: Optional[torch.Tensor] = None,
      ) -> Tuple[torch.Tensor, torch.Tensor]:
  
          # compute padding mask. This is needed for multi-head attention
          padding_mask = tokens.eq(self.padding_idx)
          if not padding_mask.any():
              padding_mask = None
  
          x = self.embed_tokens(tokens)
  
          if self.embed_scale is not None:
              x *= self.embed_scale
  
          if self.embed_positions is not None:
              if self.bert and positions is None:
                  positions = torch.arange(tokens.size(1), dtype=torch.long, device=tokens.device)
                  positions = positions.unsqueeze(0).expand(tokens.size())
              x += self.embed_positions(tokens, positions=positions)
          
          if self.bert:
              if self.segment_embeddings is not None:
                  if segment_labels is None:
                      segment_labels = torch.zeros(tokens.size(), dtype=torch.long, device=tokens.device)
                  x += self.segment_embeddings(segment_labels)
          else:
              if self.segment_embeddings is not None and segment_labels is not None:
                  x += self.segment_embeddings(segment_labels)
  
          if self.emb_layer_norm is not None:
              x = self.emb_layer_norm(x)
  
          x = F.dropout(x, p=self.dropout, training=self.training)
  
          # account for padding while computing the representation
          if padding_mask is not None:
              x *= 1 - padding_mask.unsqueeze(-1).type_as(x)
  
          # B x T x C -> T x B x C
          x = x.transpose(0, 1)
  
          inner_states = []
          if not last_state_only:
              inner_states.append(x)
  
          for layer in self.layers:
              x, _ = layer(x, self_attn_padding_mask=padding_mask)
              if not last_state_only:
                  inner_states.append(x)
  
          # T x B x C -> B x T x C
          x = x.transpose(0, 1)
  
          sentence_rep = x[:, 0, :]
  
          if last_state_only:
              inner_states = [x]
  
          return inner_states, sentence_rep
  