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.
  
  import math
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from fairseq import options, utils
  from fairseq.models import (
      FairseqEncoder,
      FairseqIncrementalDecoder,
      FairseqEncoderDecoderModel,
      register_model,
      register_model_architecture,
  )
  from fairseq.modules import (
      AdaptiveSoftmax,
      LayerNorm,
      PositionalEmbedding,
      SinusoidalPositionalEmbedding,
      TransformerDecoderLayer,
      TransformerEncoderLayer,
      SDEembedding,
  )
  
  DEFAULT_MAX_SOURCE_POSITIONS = 1024
  DEFAULT_MAX_TARGET_POSITIONS = 1024
  
  
  @register_model('transformer')
  class TransformerModel(FairseqEncoderDecoderModel):
      """
      Transformer model from `"Attention Is All You Need" (Vaswani, et al, 2017)
      <https://arxiv.org/abs/1706.03762>`_.
  
      Args:
          encoder (TransformerEncoder): the encoder
          decoder (TransformerDecoder): the decoder
  
      The Transformer model provides the following named architectures and
      command-line arguments:
  
      .. argparse::
          :ref: fairseq.models.transformer_parser
          :prog:
      """
  
      @classmethod
      def hub_models(cls):
          # fmt: off
          return {
              'transformer.wmt14.en-fr': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2',
              'transformer.wmt16.en-de': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2',
              'transformer.wmt18.en-de': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz',
              'transformer.wmt19.en-de': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz',
              'transformer.wmt19.en-ru': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz',
              'transformer.wmt19.de-en': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz',
              'transformer.wmt19.ru-en': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz',
              'transformer.wmt19.en-de.single_model': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.single_model.tar.gz',
              'transformer.wmt19.en-ru.single_model': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.single_model.tar.gz',
              'transformer.wmt19.de-en.single_model': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.single_model.tar.gz',
              'transformer.wmt19.ru-en.single_model': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.single_model.tar.gz',
          }
          # fmt: on
  
      def __init__(self, encoder, decoder, args):
          super().__init__(encoder, decoder, args)
  
      @staticmethod
      def add_args(parser):
          """Add model-specific arguments to the parser."""
          # fmt: off
          parser.add_argument('--activation-fn',
                              choices=utils.get_available_activation_fns(),
                              help='activation function to use')
          parser.add_argument('--dropout', type=float, metavar='D',
                              help='dropout probability')
          parser.add_argument('--attention-dropout', type=float, metavar='D',
                              help='dropout probability for attention weights')
          parser.add_argument('--activation-dropout', '--relu-dropout', type=float, metavar='D',
                              help='dropout probability after activation in FFN.')
          parser.add_argument('--encoder-embed-path', type=str, metavar='STR',
                              help='path to pre-trained encoder embedding')
          parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
                              help='encoder embedding dimension')
          parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='N',
                              help='encoder embedding dimension for FFN')
          parser.add_argument('--encoder-layers', type=int, metavar='N',
                              help='num encoder layers')
          parser.add_argument('--encoder-attention-heads', type=int, metavar='N',
                              help='num encoder attention heads')
          parser.add_argument('--encoder-normalize-before', action='store_true',
                              help='apply layernorm before each encoder block')
          parser.add_argument('--encoder-learned-pos', action='store_true',
                              help='use learned positional embeddings in the encoder')
          parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
                              help='path to pre-trained decoder embedding')
          parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
                              help='decoder embedding dimension')
          parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N',
                              help='decoder embedding dimension for FFN')
          parser.add_argument('--decoder-layers', type=int, metavar='N',
                              help='num decoder layers')
          parser.add_argument('--decoder-attention-heads', type=int, metavar='N',
                              help='num decoder attention heads')
          parser.add_argument('--decoder-learned-pos', action='store_true',
                              help='use learned positional embeddings in the decoder')
          parser.add_argument('--decoder-normalize-before', action='store_true',
                              help='apply layernorm before each decoder block')
          parser.add_argument('--share-decoder-input-output-embed', action='store_true',
                              help='share decoder input and output embeddings')
          parser.add_argument('--share-all-embeddings', action='store_true',
                              help='share encoder, decoder and output embeddings'
                                   ' (requires shared dictionary and embed dim)')
          parser.add_argument('--no-token-positional-embeddings', default=False, action='store_true',
                              help='if set, disables positional embeddings (outside self attention)')
          parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
                              help='comma separated list of adaptive softmax cutoff points. '
                                   'Must be used with adaptive_loss criterion'),
          parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D',
                              help='sets adaptive softmax dropout for the tail projections')
          # fmt: on
  
      @classmethod
      def build_model(cls, args, task):
          """Build a new model instance."""
  
          # make sure all arguments are present in older models
          base_architecture(args)
  
          if not hasattr(args, 'max_source_positions'):
              args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS
          if not hasattr(args, 'max_target_positions'):
              args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS
  
          src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
  
          def build_embedding(dictionary, embed_dim, path=None, sde=False):
              num_embeddings = len(dictionary)
              padding_idx = dictionary.pad()
              if sde:
                  emb = SDEembedding(char_vsize=num_embeddings, d_vec=embed_dim, padding_idx=padding_idx)
              else:
                  emb = Embedding(num_embeddings, embed_dim, padding_idx, fix_norm=args.fix_norm)
              # if provided, load from preloaded dictionaries
              if path:
                  embed_dict = utils.parse_embedding(path)
                  utils.load_embedding(embed_dict, dictionary, emb)
              return emb
  
          if args.share_all_embeddings:
              if src_dict != tgt_dict:
                  raise ValueError('--share-all-embeddings requires a joined dictionary')
              if args.encoder_embed_dim != args.decoder_embed_dim:
                  raise ValueError(
                      '--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim')
              if args.decoder_embed_path and (
                      args.decoder_embed_path != args.encoder_embed_path):
                  raise ValueError('--share-all-embeddings not compatible with --decoder-embed-path')
              encoder_embed_tokens = build_embedding(
                  src_dict, args.encoder_embed_dim, args.encoder_embed_path, sde=args.sde,
              )
              decoder_embed_tokens = encoder_embed_tokens
              args.share_decoder_input_output_embed = True
          else:
              encoder_embed_tokens = build_embedding(
                  src_dict, args.encoder_embed_dim, args.encoder_embed_path, sde=args.sde,
              )
              decoder_embed_tokens = build_embedding(
                  tgt_dict, args.decoder_embed_dim, args.decoder_embed_path
              )
  
          encoder = cls.build_encoder(args, src_dict, encoder_embed_tokens)
          decoder = cls.build_decoder(args, tgt_dict, decoder_embed_tokens)
          return TransformerModel(encoder, decoder, args)
  
      @classmethod
      def build_encoder(cls, args, src_dict, embed_tokens):
          return TransformerEncoder(args, src_dict, embed_tokens)
  
      @classmethod
      def build_decoder(cls, args, tgt_dict, embed_tokens):
          return TransformerDecoder(args, tgt_dict, embed_tokens)
  
  
  class TransformerEncoder(FairseqEncoder):
      """
      Transformer encoder consisting of *args.encoder_layers* layers. Each layer
      is a :class:`TransformerEncoderLayer`.
  
      Args:
          args (argparse.Namespace): parsed command-line arguments
          dictionary (~fairseq.data.Dictionary): encoding dictionary
          embed_tokens (torch.nn.Embedding): input embedding
      """
  
      def __init__(self, args, dictionary, embed_tokens):
          super().__init__(dictionary)
          self.register_buffer('version', torch.Tensor([3]))
  
          self.dropout = args.dropout
  
          embed_dim = embed_tokens.embedding_dim
          self.padding_idx = embed_tokens.padding_idx
          self.max_source_positions = args.max_source_positions
  
          self.embed_tokens = embed_tokens
          self.embed_scale = math.sqrt(embed_dim)
          self.embed_positions = PositionalEmbedding(
              args.max_source_positions, embed_dim, self.padding_idx,
              learned=args.encoder_learned_pos,
          ) if not args.no_token_positional_embeddings else None
          
          self.sde = args.sde
          self.layers = nn.ModuleList([])
          self.layers.extend([
              TransformerEncoderLayer(args)
              for i in range(args.encoder_layers)
          ])
  
          if args.scale_norm:
              scale = embed_dim**0.5    
          else:
              scale = None
  
          if args.encoder_normalize_before:
              self.layer_norm = LayerNorm(embed_dim, scale=scale)
          else:
              self.layer_norm = None
  
      def forward(self, src_tokens, src_lengths):
          """
          Args:
              src_tokens (LongTensor): tokens in the source language of shape
                  `(batch, src_len)`
              src_lengths (torch.LongTensor): lengths of each source sentence of
                  shape `(batch)`
  
          Returns:
              dict:
                  - **encoder_out** (Tensor): the last encoder layer's output of
                    shape `(src_len, batch, embed_dim)`
                  - **encoder_padding_mask** (ByteTensor): the positions of
                    padding elements of shape `(batch, src_len)`
          """
          # embed tokens and positions
          # embed_tokens = torch.nn.functional.normalize(self.embed_tokens, dim=-1) if self.args.fix_norm else self.embed_tokens
          x = self.embed_scale * self.embed_tokens(src_tokens)
  
          # compute padding mask
          if self.sde:
              seqlen = x.size(1)
              encoder_padding_mask = []
              for s in src_tokens:
                  encoder_padding_mask.append([0 for _ in range(len(s))] + [1 for _ in range(seqlen-len(s))])
              encoder_padding_mask = torch.tensor(encoder_padding_mask, device=x.device).byte()
          else:
              encoder_padding_mask = src_tokens.eq(self.padding_idx)
  
          if self.embed_positions is not None:
              if self.sde:
                  padding_src = encoder_padding_mask.clone().masked_fill(encoder_padding_mask, self.padding_idx)
                  x += self.embed_positions(padding_src)
              else:
                  x += self.embed_positions(src_tokens)
          x = F.dropout(x, p=self.dropout, training=self.training)
  
          # B x T x C -> T x B x C
          x = x.transpose(0, 1)
  
          if not encoder_padding_mask.any():
              encoder_padding_mask = None
  
          # encoder layers
          for layer in self.layers:
              x = layer(x, encoder_padding_mask)
  
          if self.layer_norm:
              x = self.layer_norm(x)
  
          return {
              'encoder_out': x,  # T x B x C
              'encoder_padding_mask': encoder_padding_mask,  # B x T
          }
  
      def reorder_encoder_out(self, encoder_out, new_order):
          """
          Reorder encoder output according to *new_order*.
  
          Args:
              encoder_out: output from the ``forward()`` method
              new_order (LongTensor): desired order
  
          Returns:
              *encoder_out* rearranged according to *new_order*
          """
          if encoder_out['encoder_out'] is not None:
              encoder_out['encoder_out'] = \
                  encoder_out['encoder_out'].index_select(1, new_order)
          if encoder_out['encoder_padding_mask'] is not None:
              encoder_out['encoder_padding_mask'] = \
                  encoder_out['encoder_padding_mask'].index_select(0, new_order)
          return encoder_out
  
      def max_positions(self):
          """Maximum input length supported by the encoder."""
          if self.embed_positions is None:
              return self.max_source_positions
          return min(self.max_source_positions, self.embed_positions.max_positions())
  
      def upgrade_state_dict_named(self, state_dict, name):
          """Upgrade a (possibly old) state dict for new versions of fairseq."""
          if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
              weights_key = '{}.embed_positions.weights'.format(name)
              if weights_key in state_dict:
                  del state_dict[weights_key]
              state_dict['{}.embed_positions._float_tensor'.format(name)] = torch.FloatTensor(1)
          for i in range(len(self.layers)):
              # update layer norms
              self.layers[i].upgrade_state_dict_named(state_dict, "{}.layers.{}".format(name, i))
  
          version_key = '{}.version'.format(name)
          if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) < 2:
              # earlier checkpoints did not normalize after the stack of layers
              self.layer_norm = None
              self.normalize = False
              state_dict[version_key] = torch.Tensor([1])
          return state_dict
  
  
  class TransformerDecoder(FairseqIncrementalDecoder):
      """
      Transformer decoder consisting of *args.decoder_layers* layers. Each layer
      is a :class:`TransformerDecoderLayer`.
  
      Args:
          args (argparse.Namespace): parsed command-line arguments
          dictionary (~fairseq.data.Dictionary): decoding dictionary
          embed_tokens (torch.nn.Embedding): output embedding
          no_encoder_attn (bool, optional): whether to attend to encoder outputs
              (default: False).
      """
  
      def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
          super().__init__(dictionary)
          self.register_buffer('version', torch.Tensor([3]))
  
          self.dropout = args.dropout
          self.share_input_output_embed = args.share_decoder_input_output_embed
  
          input_embed_dim = embed_tokens.embedding_dim
          embed_dim = args.decoder_embed_dim
          self.output_embed_dim = args.decoder_output_dim
  
          padding_idx = embed_tokens.padding_idx
          self.max_target_positions = args.max_target_positions
  
          self.embed_tokens = embed_tokens
          self.embed_scale = math.sqrt(embed_dim)  # todo: try with input_embed_dim
  
          self.project_in_dim = Linear(input_embed_dim, embed_dim, bias=False) if embed_dim != input_embed_dim else None
  
          self.embed_positions = PositionalEmbedding(
              args.max_target_positions, embed_dim, padding_idx,
              learned=args.decoder_learned_pos,
          ) if not args.no_token_positional_embeddings else None
  
          self.layers = nn.ModuleList([])
          self.layers.extend([
              TransformerDecoderLayer(args, no_encoder_attn)
              for _ in range(args.decoder_layers)
          ])
  
          self.adaptive_softmax = None
  
          self.project_out_dim = Linear(embed_dim, self.output_embed_dim, bias=False) \
              if embed_dim != self.output_embed_dim and not args.tie_adaptive_weights else None
  
          if args.adaptive_softmax_cutoff is not None:
              self.adaptive_softmax = AdaptiveSoftmax(
                  len(dictionary),
                  self.output_embed_dim,
                  options.eval_str_list(args.adaptive_softmax_cutoff, type=int),
                  dropout=args.adaptive_softmax_dropout,
                  adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None,
                  factor=args.adaptive_softmax_factor,
                  tie_proj=args.tie_adaptive_proj,
              )
          elif not self.share_input_output_embed:
              self.embed_out = nn.Parameter(torch.Tensor(len(dictionary), self.output_embed_dim))
              nn.init.normal_(self.embed_out, mean=0, std=self.output_embed_dim ** -0.5)
          
          if args.scale_norm:
              scale = embed_dim**0.5    
          else:
              scale = None
  
          if args.decoder_normalize_before and not getattr(args, 'no_decoder_final_norm', False):
              self.layer_norm = LayerNorm(embed_dim, scale=scale)
          else:
              self.layer_norm = None
  
      def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused):
          """
          Args:
              prev_output_tokens (LongTensor): previous decoder outputs of shape
                  `(batch, tgt_len)`, for teacher forcing
              encoder_out (Tensor, optional): output from the encoder, used for
                  encoder-side attention
              incremental_state (dict): dictionary used for storing state during
                  :ref:`Incremental decoding`
  
          Returns:
              tuple:
                  - the decoder's output of shape `(batch, tgt_len, vocab)`
                  - a dictionary with any model-specific outputs
          """
          x, extra = self.extract_features(prev_output_tokens, encoder_out, incremental_state)
          x = self.output_layer(x)
          return x, extra
  
      def extract_features(self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused):
          """
          Similar to *forward* but only return features.
  
          Returns:
              tuple:
                  - the decoder's features of shape `(batch, tgt_len, embed_dim)`
                  - a dictionary with any model-specific outputs
          """
          # embed positions
          positions = self.embed_positions(
              prev_output_tokens,
              incremental_state=incremental_state,
          ) if self.embed_positions is not None else None
  
          if incremental_state is not None:
              prev_output_tokens = prev_output_tokens[:, -1:]
              if positions is not None:
                  positions = positions[:, -1:]
  
          # embed tokens and positions
          x = self.embed_scale * self.embed_tokens(prev_output_tokens)
  
          if self.project_in_dim is not None:
              x = self.project_in_dim(x)
  
          if positions is not None:
              x += positions
          x = F.dropout(x, p=self.dropout, training=self.training)
  
          # B x T x C -> T x B x C
          x = x.transpose(0, 1)
          attn = None
  
          inner_states = [x]
  
          # decoder layers
          for layer in self.layers:
              x, attn = layer(
                  x,
                  encoder_out['encoder_out'] if encoder_out is not None else None,
                  encoder_out['encoder_padding_mask'] if encoder_out is not None else None,
                  incremental_state,
                  self_attn_mask=self.buffered_future_mask(x) if incremental_state is None else None,
              )
              inner_states.append(x)
  
          if self.layer_norm:
              x = self.layer_norm(x)
  
          # T x B x C -> B x T x C
          x = x.transpose(0, 1)
  
          if self.project_out_dim is not None:
              x = self.project_out_dim(x)
  
          return x, {'attn': attn, 'inner_states': inner_states}
  
      def output_layer(self, features, **kwargs):
          """Project features to the vocabulary size."""
          if self.adaptive_softmax is None:
              # project back to size of vocabulary
              if self.share_input_output_embed:
                  return F.linear(features, self.embed_tokens.weight)
              else:
                  return F.linear(features, self.embed_out)
          else:
              return features
  
      def max_positions(self):
          """Maximum output length supported by the decoder."""
          if self.embed_positions is None:
              return self.max_target_positions
          return min(self.max_target_positions, self.embed_positions.max_positions())
  
      def buffered_future_mask(self, tensor):
          dim = tensor.size(0)
          if not hasattr(self, '_future_mask') or self._future_mask is None or self._future_mask.device != tensor.device or self._future_mask.size(0) < dim:
              self._future_mask = torch.triu(utils.fill_with_neg_inf(tensor.new(dim, dim)), 1)
          return self._future_mask[:dim, :dim]
  
      def upgrade_state_dict_named(self, state_dict, name):
          """Upgrade a (possibly old) state dict for new versions of fairseq."""
          if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
              weights_key = '{}.embed_positions.weights'.format(name)
              if weights_key in state_dict:
                  del state_dict[weights_key]
              state_dict['{}.embed_positions._float_tensor'.format(name)] = torch.FloatTensor(1)
  
          for i in range(len(self.layers)):
              # update layer norms
              layer_norm_map = {
                  '0': 'self_attn_layer_norm',
                  '1': 'encoder_attn_layer_norm',
                  '2': 'final_layer_norm'
              }
              for old, new in layer_norm_map.items():
                  for m in ('weight', 'bias'):
                      k = '{}.layers.{}.layer_norms.{}.{}'.format(name, i, old, m)
                      if k in state_dict:
                          state_dict['{}.layers.{}.{}.{}'.format(name, i, new, m)] = state_dict[k]
                          del state_dict[k]
  
          version_key = '{}.version'.format(name)
          if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) <= 2:
              # earlier checkpoints did not normalize after the stack of layers
              self.layer_norm = None
              self.normalize = False
              state_dict[version_key] = torch.Tensor([1])
  
          return state_dict
  
  
  def Embedding(num_embeddings, embedding_dim, padding_idx, fix_norm=None):
      m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx, max_norm=fix_norm)
      if fix_norm is not None:
          d = 0.01
          nn.init.uniform_(m.weight, a=-d, b=d)
      else:
          nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
      nn.init.constant_(m.weight[padding_idx], 0)
      return m
  
  
  def Linear(in_features, out_features, bias=True):
      m = nn.Linear(in_features, out_features, bias)
      nn.init.xavier_uniform_(m.weight)
      if bias:
          nn.init.constant_(m.bias, 0.)
      return m
  
  
  @register_model_architecture('transformer', 'transformer')
  def base_architecture(args):
      args.encoder_embed_path = getattr(args, 'encoder_embed_path', None)
      args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
      args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 2048)
      args.encoder_layers = getattr(args, 'encoder_layers', 6)
      args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 8)
      args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
      args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', False)
      args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
      args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', args.encoder_embed_dim)
      args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', args.encoder_ffn_embed_dim)
      args.decoder_layers = getattr(args, 'decoder_layers', 6)
      args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
      args.decoder_normalize_before = getattr(args, 'decoder_normalize_before', False)
      args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False)
      args.attention_dropout = getattr(args, 'attention_dropout', 0.)
      args.activation_dropout = getattr(args, 'activation_dropout', 0.)
      args.activation_fn = getattr(args, 'activation_fn', 'relu')
      args.dropout = getattr(args, 'dropout', 0.1)
      args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
      args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
      args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
      args.share_all_embeddings = getattr(args, 'share_all_embeddings', False)
      args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False)
      args.adaptive_input = getattr(args, 'adaptive_input', False)
  
      args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim)
      args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim)
  
  
  @register_model_architecture('transformer', 'transformer_iwslt_de_en')
  def transformer_iwslt_de_en(args):
      args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
      args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 1024)
      args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4)
      args.encoder_layers = getattr(args, 'encoder_layers', 6)
      args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
      args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 1024)
      args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
      args.decoder_layers = getattr(args, 'decoder_layers', 6)
      base_architecture(args)
  
  
  @register_model_architecture('transformer', 'transformer_wmt_en_de')
  def transformer_wmt_en_de(args):
      base_architecture(args)
  
  
  # parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017)
  @register_model_architecture('transformer', 'transformer_vaswani_wmt_en_de_big')
  def transformer_vaswani_wmt_en_de_big(args):
      args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
      args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096)
      args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)
      args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
      args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1024)
      args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 4096)
      args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
      args.dropout = getattr(args, 'dropout', 0.3)
      base_architecture(args)
  
  
  @register_model_architecture('transformer', 'transformer_vaswani_wmt_en_fr_big')
  def transformer_vaswani_wmt_en_fr_big(args):
      args.dropout = getattr(args, 'dropout', 0.1)
      transformer_vaswani_wmt_en_de_big(args)
  
  
  @register_model_architecture('transformer', 'transformer_wmt_en_de_big')
  def transformer_wmt_en_de_big(args):
      args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
      transformer_vaswani_wmt_en_de_big(args)
  
  
  # default parameters used in tensor2tensor implementation
  @register_model_architecture('transformer', 'transformer_wmt_en_de_big_t2t')
  def transformer_wmt_en_de_big_t2t(args):
      args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', True)
      args.decoder_normalize_before = getattr(args, 'decoder_normalize_before', True)
      args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
      args.activation_dropout = getattr(args, 'activation_dropout', 0.1)
      transformer_vaswani_wmt_en_de_big(args)
  