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
  from packaging import version
  
  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,
  )
  
  from torch_scatter import scatter_mean
  
  from ..modules.transformer_layer import TransformerEncoderLayer, TransformerDecoderLayer
  from .attention_masks import get_cross_attention_mask, get_cross_attention_mask_heads
  from .graphmp_attention_masks import get_graph_self_attn_mask
  
  
  DEFAULT_MAX_SOURCE_POSITIONS = 1024
  DEFAULT_MAX_TARGET_POSITIONS = 1024
  
  
  @register_model('transformer_tgt_pointer_graphmp')
  class TransformerTgtPointerGraphMPModel(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):
          super().__init__(encoder, decoder)
  
      @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', type=int, default=0,
                              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')
          # Use stack transformer
          parser.add_argument('--encode-state-machine', type=bool,
                              help='controls encoding of stack and buffer')
          # control BERT backprop
          parser.add_argument('--bert-backprop', action='store_true',
                              help='Backpropagate through BERT', default=False)
          parser.add_argument('--no-bert-precompute', action='store_true',
                              help='Compute BERT on the fly (debugging)',
                              default=False)
          parser.add_argument('--pretrained-embed-dim', type=int,
                              help='Pretrained embeddings size',
                              default=768)
          # additional
          # NOTE do not set default values here; if set, make sure they are consistent with the arch registry
          #      since when loading model (e.g. saved pre some argument additions), the default values will be used first
          #      then the arch registry
          parser.add_argument('--apply-tgt-vocab-masks', type=int,
                              help='whether to apply target (actions) vocabulary mask for output')
          parser.add_argument('--apply-tgt-actnode-masks', type=int,
                              help='whether to apply target (actions) node mask for pointer')
          parser.add_argument('--apply-tgt-src-align', type=int,
                              help='whether to apply target source alignment to guide the cross attention')
          parser.add_argument('--apply-tgt-input-src', type=int,
                              help='whether to apply target input to include source token embeddings for better '
                                   'representations of the graph nodes')
          # additional: tgt src alignment masks for decoder cross-attention
          parser.add_argument('--tgt-src-align-layers', nargs='*', type=int,
                              help='target source alignment in decoder cross-attention: which layers to use')
          parser.add_argument('--tgt-src-align-heads', type=int,
                              help='target source alignment in decoder cross-attention: how many heads per layer to use')
          parser.add_argument('--tgt-src-align-focus', nargs='*', type=str,
                              help='target source alignment in decoder cross-attention: what to focus per head')
          # additional: pointer distribution from decoder self-attentions
          parser.add_argument('--pointer-dist-decoder-selfattn-layers', nargs='*', type=int,
                              help='pointer distribution from decoder self-attention: which layers to use')
          parser.add_argument('--pointer-dist-decoder-selfattn-heads', type=int,
                              help='pointer distribution from decoder self-attention: how many heads per layer to use')
          parser.add_argument('--pointer-dist-decoder-selfattn-avg', type=int,
                              help='pointer distribution from decoder self-attention: whether to use the average '
                                   'self-attention each layer (arithmetic mean); if set to 0, geometric mean is used; '
                                   'if set to -1, no average is used and all the pointer distributions are used to '
                                   'compute the loss')
          parser.add_argument('--pointer-dist-decoder-selfattn-infer', type=int,
                              help='pointer distribution from decoder self-attention: at inference, which layer to use')
          # additional: combine source token embeddings into action embeddings for decoder input for node representation
          parser.add_argument('--tgt-input-src-emb', type=str, choices=['raw', 'bot', 'top'],
                              help='target input to include aligned source tokens: where to take the source embeddings; '
                                   '"raw": raw RoBERTa embeddings from the very beginning; '
                                   '"bot": bottom source embeddings before the encoder; '
                                   '"top": top source embeddings after the encoder')
          parser.add_argument('--tgt-input-src-backprop', type=int,
                              help='target input to include aligned source tokens: whether to back prop through the '
                                   'source embeddings')
          parser.add_argument('--tgt-input-src-combine', type=str, choices=['cat', 'add'],
                              help='target input to include aligned source tokens: how to combine the source token '
                                   'embeddings and the action embeddings')
          # additional: graph structure encoding into the decoder self-attention
          parser.add_argument('--tgt-graph-layers', nargs='*', type=int,
                              help='target graph structure encoding in decoder self-attention: which layers to use')
          parser.add_argument('--tgt-graph-heads', type=int,
                              help='target graph structure encoding in decoder self-attention: how many heads per layer')
          parser.add_argument('--tgt-graph-mask', type=str, choices=['1prev', 'allprev', '1prev_in', 'allprev_in',
                                                                     '1prev_1in1out', 'allprev_1in1out'],
                              help='target graph structure encoding in decoder self-attention: how to set the graph mask')
          # additional: factored embeddings for the target actions
          parser.add_argument('--tgt-factored-emb-out', type=int,
                              help='whether to set target output embeddings to be factored')
          # 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)
  
          # user specific: make sure all arguments are present in older models during development
          transformer_pointer(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):
              num_embeddings = len(dictionary)
              padding_idx = dictionary.pad()
              emb = Embedding(num_embeddings, embed_dim, padding_idx)
              # 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
              )
              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
              )
              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 cls(encoder, decoder)
  
      @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)
  
      def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs):
          """
          Run the forward pass for an encoder-decoder model.
          First feed a batch of source tokens through the encoder. Then, feed the
          encoder output and previous decoder outputs (i.e., input feeding/teacher
          forcing) to the decoder to produce the next outputs::
              encoder_out = self.encoder(src_tokens, src_lengths)
              return self.decoder(prev_output_tokens, encoder_out)
          Args:
              src_tokens (LongTensor): tokens in the source language of shape
                  `(batch, src_len)`
              src_lengths (LongTensor): source sentence lengths of shape `(batch)`
              prev_output_tokens (LongTensor): previous decoder outputs of shape
                  `(batch, tgt_len)`, for input feeding/teacher forcing
          Returns:
              tuple:
                  - the decoder's output of shape `(batch, tgt_len, vocab)`
                  - a dictionary with any model-specific outputs
          """
          encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
          decoder_out = self.decoder(prev_output_tokens, encoder_out=encoder_out, **kwargs)
          return decoder_out
  
  
  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
          self.bert_backprop = args.bert_backprop
          self.no_bert_precompute = args.no_bert_precompute
  
          # backprop needs on the fly extraction
          if self.bert_backprop or self.no_bert_precompute:
              roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
              roberta.cuda()
              self.roberta = roberta
              # if args.no_bert_precompute:
              #    # Set BERT to purely evaluation mode
              #    self.roberta.eval()
  
          embed_dim = embed_tokens.embedding_dim
          self.padding_idx = embed_tokens.padding_idx
          self.max_source_positions = args.max_source_positions
  
          # BERT embeddings as input
          input_embed_dim = args.pretrained_embed_dim
          self.subspace = Linear(input_embed_dim, embed_dim, bias=False)
  
          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.layers = nn.ModuleList([])
          self.layers.extend([
              TransformerEncoderLayer(args)
              for i in range(args.encoder_layers)
          ])
  
          if args.encoder_normalize_before:
              self.layer_norm = LayerNorm(embed_dim)
          else:
              self.layer_norm = None
  
          # copying the arguments for the separate model in decoding to use
          self.args = args
  
      def forward(self, src_tokens, src_lengths, memory, memory_pos, source_fix_emb, src_wordpieces, src_wp2w, **unused):
          """
          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
          # x = self.embed_scale * self.embed_tokens(src_tokens)
          # if self.embed_positions is not None:
          #     x += self.embed_positions(src_tokens)
  
          if self.bert_backprop or self.no_bert_precompute:
              # extract roberta on the fly
              last_layer = self.roberta.extract_features(src_wordpieces)
              # remove sentence start
              bsize, max_len, emb_size = last_layer.shape
              mask = (src_wordpieces != 0).unsqueeze(2).expand(last_layer.shape)
              last_layer = last_layer[mask].view((bsize, max_len - 1, emb_size))
              # remove sentence end
              last_layer = last_layer[:, :-1, :]
              # apply scatter, src_wp2w was inverted in pre-processing to use
              # scatter's left side padding . We need to flip the result.
              source_fix_emb2 = scatter_mean(
                  last_layer,
                  src_wp2w.unsqueeze(2),
                  dim=1
              )
              source_fix_emb2 = source_fix_emb2.flip(1)
              # Remove extra padding
              source_fix_emb2 = source_fix_emb2[:, -src_tokens.shape[1]:, :]
  
              # do not backprop for on-the-fly computing
              if self.no_bert_precompute:
                  bert_embeddings = source_fix_emb2.detach()
              else:
                  bert_embeddings = source_fix_emb2
  
              # DEBUG: check precomputed and on the fly sufficiently close
              # abs(source_fix_emb2 - source_fix_emb).max()
          else:
              # use pre-extracted roberta
              bert_embeddings = source_fix_emb
  
          x = self.subspace(bert_embeddings)
  
          if self.args.apply_tgt_input_src and self.args.tgt_input_src_emb == 'bot':
              src_embs = x    # size (B, T, C)
  
          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)
  
          # compute padding mask
          encoder_padding_mask = src_tokens.eq(self.padding_idx)
          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)
  
          if self.args.apply_tgt_input_src:
              if self.args.tgt_input_src_emb == 'top':
                  src_embs = x.transpose(0, 1)
              elif self.args.tgt_input_src_emb == 'raw':
                  src_embs = bert_embeddings
              elif self.args.tgt_input_src_emb == 'bot':
                  pass    # already dealt with above
              else:
                  raise NotImplementedError
          else:
              src_embs = None
  
          return {
              'encoder_out': x,  # T x B x C
              'encoder_padding_mask': encoder_padding_mask,  # B x T
              'src_embs': src_embs,  # B x T x C
          }
  
      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)
          if encoder_out['src_embs'] is not None:
              encoder_out['src_embs'] = encoder_out['src_embs'].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
  
          # controls the use of stack transformer
          self.encode_state_machine = args.encode_state_machine
  
          if self.encode_state_machine:
              # positions of buffer and stack for each time step
              self.embed_stack_positions = PositionalEmbedding(
                  args.max_target_positions, args.decoder_embed_dim,
                  padding_idx, learned=args.decoder_learned_pos,
              )
  
          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:
              if not args.tgt_factored_emb_out:
                  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)
              else:
                  # factored embeddings for target actions on the decoder output side
                  from ..modules.factored_embeddings import FactoredEmbeddings
                  self.factored_embeddings = FactoredEmbeddings(dictionary, self.output_embed_dim)
                  self.dictionary_arange = torch.arange(len(dictionary)).unsqueeze(0)
  
          if args.decoder_normalize_before and not getattr(args, 'no_decoder_final_norm', False):
              self.layer_norm = LayerNorm(embed_dim)
          else:
              self.layer_norm = None
  
          # copying the arguments for the separate model in decoding to use
          self.args = args
  
          # target input to include source token embeddings
          if self.args.apply_tgt_input_src:
              assert self.args.tgt_input_src_emb != 'raw', 'Not implemented yet'
              if self.args.tgt_input_src_combine == 'cat':
                  self.combine_src_embs = Linear(input_embed_dim + args.encoder_embed_dim, input_embed_dim, bias=False)
  
      def forward(self, prev_output_tokens, encoder_out, memory=None, memory_pos=None,
                  incremental_state=None, logits_mask=None, logits_indices=None,
                  # AMR actions states
                  tgt_vocab_masks=None, tgt_actnode_masks=None, tgt_src_cursors=None,
                  # graph structure
                  tgt_actedge_masks=None, tgt_actedge_1stnode_masks=None,
                  tgt_actedge_indexes=None, tgt_actedge_cur_node_indexes=None, tgt_actedge_cur_1stnode_indexes=None,
                  tgt_actedge_pre_node_indexes=None, tgt_actedge_directions=None,
                  # graph structure for connecting with all previous nodes
                  tgt_actedge_allpre_indexes=None, tgt_actedge_allpre_pre_node_indexes=None,
                  tgt_actedge_allpre_directions=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
          """
          # breakpoint()
  
          graph_self_attn_mask = get_graph_self_attn_mask(
              tgt_actedge_masks=tgt_actedge_masks,
              tgt_actedge_1stnode_masks=tgt_actedge_1stnode_masks,
              tgt_actedge_indexes=tgt_actedge_indexes,
              tgt_actedge_cur_node_indexes=tgt_actedge_cur_node_indexes,
              tgt_actedge_cur_1stnode_indexes=tgt_actedge_cur_1stnode_indexes,
              tgt_actedge_pre_node_indexes=tgt_actedge_pre_node_indexes,
              tgt_actedge_directions=tgt_actedge_directions,
              # graph structure for connecting with all previous nodes
              tgt_actedge_allpre_indexes=tgt_actedge_allpre_indexes,
              tgt_actedge_allpre_pre_node_indexes=tgt_actedge_allpre_pre_node_indexes,
              tgt_actedge_allpre_directions=tgt_actedge_allpre_directions,
              # mask generation control
              mask_num_heads=self.args.tgt_graph_heads,
              num_heads=self.layers[0].self_attn.num_heads,
              tgt_graph_mask=self.args.tgt_graph_mask
              )
  
          x, extra = self.extract_features(
              prev_output_tokens,
              memory,
              memory_pos,
              encoder_out,
              incremental_state,
              tgt_src_cursors=tgt_src_cursors,
              tgt_actnode_masks=tgt_actnode_masks,
              # graph structure: decoder self-attention mask
              graph_self_attn_mask=graph_self_attn_mask
          )
          x = self.output_layer(
              x,
              logits_mask=logits_mask,
              logits_indices=logits_indices,
              tgt_vocab_masks=tgt_vocab_masks,
          )
  
          # DEBUG: (consumes time)
          # if (x != x).any():
          #    import pdb; pdb.set_trace()
          #    print()
  
          return x, extra
  
      def extract_features(self, prev_output_tokens, memory, memory_pos,
                           encoder_out=None, incremental_state=None,
                           tgt_src_cursors=None, tgt_actnode_masks=None,
                           graph_self_attn_mask=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:
              # It needs only the last auto-regressive element. Rest is cached.
              prev_output_tokens = prev_output_tokens[:, -1:]
              if positions is not None:
                  positions = positions[:, -1:]
              # TODO this is a hacky way of ignoring these two
              memory = memory[:, :, -1:] if memory is not None else None
              memory_pos = memory_pos[:, :, -1:] if memory_pos is not None else None
              # graph structure mask on the decoder self-attention: take out the last row of (tgt_len, tgt_len)
              # for only one step
              if graph_self_attn_mask is not None:
                  graph_self_attn_mask = (graph_self_attn_mask[0][:, -1, :].unsqueeze(1),
                                          graph_self_attn_mask[1][:, -1].unsqueeze(1))
  
          # embed tokens and positions
          x = self.embed_scale * self.embed_tokens(prev_output_tokens)
  
          # ========== combine the corresponding source token embeddings with the action embeddings as input ==========
          if self.args.apply_tgt_input_src:
              # 1) take out the source embeddings
              src_embs = encoder_out['src_embs']    # size (batch_size, src_max_len, encoder_emb_dim)
              if not self.args.tgt_input_src_backprop:
                  src_embs = src_embs.detach()
  
              # 2) align the source embeddings to the tgt input actions
              assert tgt_src_cursors is not None
              tgt_src_index = tgt_src_cursors.clone()    # size (bsz, tgt_max_len)
              if encoder_out['encoder_padding_mask'] is not None:
                  src_num_pads = encoder_out['encoder_padding_mask'].sum(dim=1, keepdim=True)
                  tgt_src_index = tgt_src_index + src_num_pads    # NOTE this is key to left padding!
  
              tgt_src_index = tgt_src_index.unsqueeze(-1).repeat(1, 1, src_embs.size(-1))
              # or
              # tgt_src_index = tgt_src_index.unsqueeze(-1).expand(-1, -1, src_embs.size(-1))
              src_embs = torch.gather(src_embs, 1, tgt_src_index)
              # size (bsz, tgt_max_len, src_embs.size(-1))
  
              # 3) combine the action embeddings with the aligned source token embeddings
              if self.args.tgt_input_src_combine == 'cat':
                  x = self.combine_src_embs(torch.cat([src_embs, x], dim=-1))
              elif self.args.tgt_input_src_combine == 'add':
                  x = src_embs + x
              else:
                  raise NotImplementedError
          # ===========================================================================================================
  
          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
          attn_all = []
  
          inner_states = [x]
  
          # ========== alignment guidance in the cross-attention: get the mask ==========
          if self.args.apply_tgt_src_align:
              assert tgt_src_cursors is not None
              cross_attention_mask = get_cross_attention_mask_heads(tgt_src_cursors,
                                                                    encoder_out['encoder_out'].size(0),
                                                                    encoder_out['encoder_padding_mask'],
                                                                    self.args.tgt_src_align_focus,
                                                                    self.args.tgt_src_align_heads,
                                                                    self.layers[0].encoder_attn.num_heads)
          else:
              cross_attention_mask = None
          # ==============================================================================
  
          # TODO there are some problems with the pointer mask
          #      attention mask dimension is (bsz * num_heads, target_size, source_size)
          #      where for each batch, head dimension comes first
          #      BUT here, the pointer mask assumes the batch dimension comes first, since it only generates
          #      the number of heads needed for pointer and stacks them
  
          # ========== pointer distribution (decoder self-attention) mask ==========
          if self.args.apply_tgt_actnode_masks:
              assert tgt_actnode_masks is not None
              if self.args.shift_pointer_value:
                  tgt_actnode_masks[:, 1:] = tgt_actnode_masks[:, :-1]
                  tgt_actnode_masks[:, 0] = 0
              ptr_self_attn_mask = tgt_actnode_masks.unsqueeze(dim=1).repeat(1, tgt_actnode_masks.size(1), 1)
              ptr_self_attn_mask = ptr_self_attn_mask.unsqueeze(dim=1).repeat(
                  1, self.args.pointer_dist_decoder_selfattn_heads, 1, 1).view(
                      -1, tgt_actnode_masks.size(1), tgt_actnode_masks.size(1))
              # NOTE need to include the causal mask as well in case some rows are completely masked out
              # in which case we need to do the post mask
              ptr_self_attn_mask &= (self.buffered_future_mask(x) != -float('inf')).byte().unsqueeze(dim=0)
              # NOTE when one row out of bsz * num_heads (tgt_max_len, src_max_len) masks is full zeros, after softmax the
              # distribution will be all "nan"s, which will cause problem when calculating gradients.
              # Thus, we mask these positions after softmax
              ptr_self_attn_mask_post_softmax = ptr_self_attn_mask.new_ones(*ptr_self_attn_mask.size()[:2], 1,
                                                                            dtype=torch.float)
              ptr_self_attn_mask_post_softmax[ptr_self_attn_mask.sum(dim=2) == 0] = 0
              # we need to modify the pre-softmax as well, since after we get nan, multiplying by 0 is still nan
              ptr_self_attn_mask[(ptr_self_attn_mask.sum(dim=2, keepdim=True) == 0).
                                 repeat(1, 1, tgt_actnode_masks.size(1))] = 1
              # NOTE must use torch.bool for mask for PyTorch >= 1.2, otherwise there will be problems around ~mask
              # for compatibility of PyTorch 1.1
              if version.parse(torch.__version__) < version.parse('1.2.0'):
                  ptr_self_attn_mask = (ptr_self_attn_mask, ptr_self_attn_mask_post_softmax)
              else:
                  ptr_self_attn_mask = (ptr_self_attn_mask.to(torch.bool), ptr_self_attn_mask_post_softmax)
          else:
              ptr_self_attn_mask = None
          # ========================================================================
          # import pdb; pdb.set_trace()
  
          # breakpoint()
  
          # TODO tgt_src_align_layers are not really controlled!!!
  
          # decoder layers
          for layer_index, layer in enumerate(self.layers):
  
              # Encode state of state machine as attention masks and encoded
              # token positions changing for each target action
              if self.encode_state_machine is None:
                  head_attention_masks = None
                  head_positions = None
              else:
                  raise NotImplementedError('Deprecated: use stack-transformer branch')
  
              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,
                  head_attention_masks=head_attention_masks,
                  head_positions=head_positions,
                  cross_attention_mask=(cross_attention_mask
                                        if layer_index in self.args.tgt_src_align_layers
                                        else None),
                  ptr_self_attn_mask=(ptr_self_attn_mask
                                      if layer_index in self.args.pointer_dist_decoder_selfattn_layers
                                      else None),
                  graph_self_attn_mask=(graph_self_attn_mask
                                        if layer_index in self.args.tgt_graph_layers
                                        else None)
              )
              inner_states.append(x)
  
              if layer_index not in self.args.pointer_dist_decoder_selfattn_layers:
                  continue
  
              # attn is tgt self-attention of size (bsz, num_heads, tgt_len, tgt_len) with future masks
              if self.args.pointer_dist_decoder_selfattn_heads == 1:
                  attn = attn[:, 0, :, :]
                  attn_all.append(attn)
              else:
                  attn = attn[:, :self.args.pointer_dist_decoder_selfattn_heads, :, :]
                  if self.args.pointer_dist_decoder_selfattn_avg == 1:
                      # arithmetic mean
                      attn = attn.sum(dim=1) / self.args.pointer_dist_decoder_selfattn_heads
                      attn_all.append(attn)
                  elif self.args.pointer_dist_decoder_selfattn_avg == 0:
                      # geometric mean
                      attn = attn.prod(dim=1).pow(1 / self.args.pointer_dist_decoder_selfattn_heads)
                      # TODO there is an nan bug when backward for the above power
                      attn_all.append(attn)
                  elif self.args.pointer_dist_decoder_selfattn_avg == -1:
                      # no mean
                      pointer_dists = list(map(lambda x: x.squeeze(1),
                                               torch.chunk(attn, self.args.pointer_dist_decoder_selfattn_heads, dim=1)))
                      # for decoding: using a single pointer distribution
                      attn = attn.prod(dim=1).pow(1 / self.args.pointer_dist_decoder_selfattn_heads)
                      attn_all.extend(pointer_dists)
                  else:
                      raise ValueError
  
          # for decoding: which pointer distribution to use
          attn = attn_all[self.args.pointer_dist_decoder_selfattn_layers.index(
              self.args.pointer_dist_decoder_selfattn_infer)]
  
          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)
  
          # NOTE here 'attn' is used for inference pointer prediction, 'attn_all' is used for loss calculation
          # TODO change the names to be more straightforward, such as 'pointer_dist_infer', 'pointer_dist_list'
          # TODO add teacher forcing; this will change the backward behavior
          return x, {'attn': attn, 'inner_states': inner_states, 'attn_all': attn_all}
  
      def output_layer(self, features, logits_mask=None, logits_indices=None,
                       tgt_vocab_masks=None,
                       **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:
                  emb_weights = self.embed_tokens.weight
              else:
                  if not self.args.tgt_factored_emb_out:
                      emb_weights = self.embed_out
                  else:
                      # factored embeddings for target actions on the decoder output side
                      emb_weights = self.factored_embeddings(self.dictionary_arange).squeeze(0)
              if logits_indices:
  
                  # indices of active logits
                  indices = torch.tensor(list(logits_indices.keys()))
                  # compute only active logits
                  # (batch_size, target_size, target_emb_size)
                  active_output = F.linear(features, emb_weights[indices, :])
                  # forbid masked elements
                  active_output[logits_mask == 0] = float("-Inf")
                  # assign output
                  emb_size = emb_weights.shape[0]
                  batch_size, target_size, _ = features.shape
                  out_shape = (batch_size, target_size, emb_size)
                  output = features.new_ones(out_shape) * float("-Inf")
                  output[:, :, indices] = active_output
  
              else:
                  output = F.linear(features, emb_weights)
  
              # TODO fix this when decoding
              # if args is not None and args.apply_tgt_vocab_masks:
              # import pdb; pdb.set_trace()
              if self.args.apply_tgt_vocab_masks:
                  assert tgt_vocab_masks is not None
                  output[tgt_vocab_masks == 0] = float('-inf')
          else:
              assert not logits_mask
              output = features
  
          return output
  
      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):
      m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
      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)
  
  
  # @register_model_architecture('transformer', 'transformer_2x2')
  # def transformer_2x2(args):
  #     args.encode_state_machine = getattr(args, 'encode_state_machine', None)
  #     #
  #     args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
  #     args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
  #     args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4)
  #     args.encoder_layers = getattr(args, 'encoder_layers', 2)
  #     args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
  #     args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
  #     args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
  #     args.decoder_layers = getattr(args, 'decoder_layers', 2)
  #     base_architecture(args)
  
  
  # @register_model_architecture('transformer', 'transformer_6x6')
  # def transformer_6x6(args):
  #     args.encode_state_machine = getattr(args, 'encode_state_machine', None)
  #     #
  #     args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
  #     args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
  #     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', 256)
  #     args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
  #     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_3x8')
  # def transformer_3x8(args):
  #     args.encode_state_machine = getattr(args, 'encode_state_machine', None)
  #     #
  #     args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
  #     args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
  #     args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4)
  #     args.encoder_layers = getattr(args, 'encoder_layers', 3)
  #     args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
  #     args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
  #     args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
  #     args.decoder_layers = getattr(args, 'decoder_layers', 8)
  #     base_architecture(args)
  
  
  # # Stack-Transformer code
  
  
  # @register_model_architecture('transformer', 'stack_transformer_2x2_layer0')
  # def stack_transformer_2x2_layer0(args):
  #     args.encode_state_machine = getattr(args, 'encode_state_machine', "layer0")
  #     #
  #     args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
  #     args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
  #     args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4)
  #     args.encoder_layers = getattr(args, 'encoder_layers', 2)
  #     args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
  #     args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
  #     args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
  #     args.decoder_layers = getattr(args, 'decoder_layers', 2)
  #     base_architecture(args)
  
  
  # @register_model_architecture('transformer', 'stack_transformer_6x6_layer0')
  # def stack_transformer_6x6_layer0(args):
  #     args.encode_state_machine = getattr(args, 'encode_state_machine', "layer0")
  #     #
  #     args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
  #     args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
  #     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', 256)
  #     args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
  #     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', 'stack_transformer_2x2_nopos_layer0')
  # def stack_transformer_2x2_nopos_layer0(args):
  #     args.encode_state_machine = getattr(args, 'encode_state_machine', "layer0_nopos")
  #     #
  #     args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
  #     args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
  #     args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4)
  #     args.encoder_layers = getattr(args, 'encoder_layers', 2)
  #     args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
  #     args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
  #     args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
  #     args.decoder_layers = getattr(args, 'decoder_layers', 2)
  #     base_architecture(args)
  
  
  # @register_model_architecture('transformer', 'stack_transformer_2x2_nopos')
  # def stack_transformer_2x2_nopos(args):
  #     args.encode_state_machine = getattr(args, 'encode_state_machine', "all-layers_nopos")
  #     #
  #     args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
  #     args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
  #     args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4)
  #     args.encoder_layers = getattr(args, 'encoder_layers', 2)
  #     args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
  #     args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
  #     args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
  #     args.decoder_layers = getattr(args, 'decoder_layers', 2)
  #     base_architecture(args)
  
  
  # @register_model_architecture('transformer', 'stack_transformer_6x6')
  # def stack_transformer_6x6(args):
  #     args.encode_state_machine = getattr(args, 'encode_state_machine', "all-layers")
  #     #
  #     args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
  #     args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
  #     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', 256)
  #     args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
  #     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', 'stack_transformer_6x6_nopos')
  # def stack_transformer_6x6_nopos(args):
  #     args.encode_state_machine = getattr(args, 'encode_state_machine', "all-layers_nopos")
  #     #
  #     args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
  #     args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
  #     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', 256)
  #     args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
  #     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', 'stack_transformer_6x6_tops_nopos')
  # def stack_transformer_6x6_tops_nopos(args):
  #     args.encode_state_machine = getattr(args, 'encode_state_machine', "stack_top_nopos")
  #     #
  #     args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
  #     args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
  #     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', 256)
  #     args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
  #     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', 'stack_transformer_6x6_only_buffer_nopos')
  # def stack_transformer_6x6_only_buffer_nopos(args):
  #     args.encode_state_machine = getattr(args, 'encode_state_machine', "only_buffer_nopos")
  #     #
  #     args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
  #     args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
  #     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', 256)
  #     args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
  #     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', 'stack_transformer_6x6_only_stack_nopos')
  # def stack_transformer_6x6_only_stack_nopos(args):
  #     args.encode_state_machine = getattr(args, 'encode_state_machine', "only_stack_nopos")
  #     #
  #     args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
  #     args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
  #     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', 256)
  #     args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
  #     args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
  #     args.decoder_layers = getattr(args, 'decoder_layers', 6)
  #     base_architecture(args)
  
  
  # pointer transformer
  
  
  @register_model_architecture('transformer_tgt_pointer_graphmp', 'transformer_tgt_pointer_graphmp')
  def transformer_pointer(args):
      # args.encode_state_machine = getattr(args, 'encode_state_machine', "stack_top_nopos")
      args.encode_state_machine = getattr(args, 'encode_state_machine', None)
      #
      args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
      args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
      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', 256)
      args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
      args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
      args.decoder_layers = getattr(args, 'decoder_layers', 6)
      # additional control of whether to use various action states information in the model
      args.apply_tgt_vocab_masks = getattr(args, 'apply_tgt_vocab_masks', 0)
      args.apply_tgt_actnode_masks = getattr(args, 'apply_tgt_actnode_masks', 0)
      args.apply_tgt_src_align = getattr(args, 'apply_tgt_src_align', 1)
      args.apply_tgt_input_src = getattr(args, 'apply_tgt_input_src', 0)
      # target source alignment masks for decoder cross-attention
      args.tgt_src_align_layers = getattr(args, 'tgt_src_align_layers', list(range(args.decoder_layers)))
      args.tgt_src_align_heads = getattr(args, 'tgt_src_align_heads', 1)
      args.tgt_src_align_focus = getattr(args, 'tgt_src_align_focus', ['p0c1n0'])
      # pointer distribution from decoder self-attention
      args.pointer_dist_decoder_selfattn_layers = getattr(args, 'pointer_dist_decoder_selfattn_layers',
                                                          list(range(args.decoder_layers)))
      args.pointer_dist_decoder_selfattn_heads = getattr(args, 'pointer_dist_decoder_selfattn_heads',
                                                         args.decoder_attention_heads)
      args.pointer_dist_decoder_selfattn_avg = getattr(args, 'pointer_dist_decoder_selfattn_avg', 1)
      args.pointer_dist_decoder_selfattn_infer = getattr(args, 'pointer_dist_decoder_selfattn_infer',
                                                         args.pointer_dist_decoder_selfattn_layers[-1])
      # combine source token embeddings into action embeddings for decoder input for node representation
      args.tgt_input_src_emb = getattr(args, 'tgt_input_src_emb', 'top')
      args.tgt_input_src_backprop = getattr(args, 'tgt_input_src_backprop', 1)
      args.tgt_input_src_combine = getattr(args, 'tgt_input_src_combine', 'cat')
      # graph structure encoding into the decoder self-attention
      args.tgt_graph_layers = getattr(args, 'tgt_graph_layers', list(range(args.decoder_layers)))
      args.tgt_graph_heads = getattr(args, 'tgt_graph_heads', 1)
      args.tgt_graph_mask = getattr(args, 'tgt_graph_mask', '1prev')
      # target factored embeddings
      args.tgt_factored_emb_out = getattr(args, 'tgt_factored_emb_out', 0)
  
      # process some of the args for compatibility issue with legacy versions
      if isinstance(args.tgt_src_align_focus, str):
          assert len(args.tgt_src_align_focus) == 4, 'legacy version has "p-n-" format'
          args.tgt_src_align_focus = [args.tgt_src_align_focus[:2] + 'c1' + args.tgt_src_align_focus[-2:]]
      elif isinstance(args.tgt_src_align_focus, list):
          if len(args.tgt_src_align_focus) == 1 and len(args.tgt_src_align_focus[0]) == 4:
              args.tgt_src_align_focus[0] = args.tgt_src_align_focus[0][:2] + 'c1' + args.tgt_src_align_focus[0][-2:]
      else:
          raise TypeError
  
      base_architecture(args)
  
  
  @register_model_architecture('transformer_tgt_pointer_graphmp', 'transformer_tgt_pointer_graphmp_3x3')
  def transformer_pointer(args):
      # args.encode_state_machine = getattr(args, 'encode_state_machine', "stack_top_nopos")
      args.encode_state_machine = getattr(args, 'encode_state_machine', None)
      #
      args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
      args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
      args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4)
      args.encoder_layers = getattr(args, 'encoder_layers', 3)
      args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
      args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
      args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
      args.decoder_layers = getattr(args, 'decoder_layers', 3)
      # additional control of whether to use various action states information in the model
      args.apply_tgt_vocab_masks = getattr(args, 'apply_tgt_vocab_masks', 0)
      args.apply_tgt_actnode_masks = getattr(args, 'apply_tgt_actnode_masks', 0)
      args.apply_tgt_src_align = getattr(args, 'apply_tgt_src_align', 1)
      args.apply_tgt_input_src = getattr(args, 'apply_tgt_input_src', 0)
      # target source alignment masks for decoder cross-attention
      args.tgt_src_align_layers = getattr(args, 'tgt_src_align_layers', list(range(args.decoder_layers)))
      args.tgt_src_align_heads = getattr(args, 'tgt_src_align_heads', 1)
      args.tgt_src_align_focus = getattr(args, 'tgt_src_align_focus', ['p0c1n0'])
      # pointer distribution from decoder self-attention
      args.pointer_dist_decoder_selfattn_layers = getattr(args, 'pointer_dist_decoder_selfattn_layers',
                                                          list(range(args.decoder_layers)))
      args.pointer_dist_decoder_selfattn_heads = getattr(args, 'pointer_dist_decoder_selfattn_heads',
                                                         args.decoder_attention_heads)
      args.pointer_dist_decoder_selfattn_avg = getattr(args, 'pointer_dist_decoder_selfattn_avg', 1)
      args.pointer_dist_decoder_selfattn_infer = getattr(args, 'pointer_dist_decoder_selfattn_infer',
                                                         args.pointer_dist_decoder_selfattn_layers[-1])
      # combine source token embeddings into action embeddings for decoder input for node representation
      args.tgt_input_src_emb = getattr(args, 'tgt_input_src_emb', 'top')
      args.tgt_input_src_backprop = getattr(args, 'tgt_input_src_backprop', 1)
      args.tgt_input_src_combine = getattr(args, 'tgt_input_src_combine', 'cat')
      # graph structure encoding into the decoder self-attention
      args.tgt_graph_layers = getattr(args, 'tgt_graph_layers', list(range(args.decoder_layers)))
      args.tgt_graph_heads = getattr(args, 'tgt_graph_heads', 1)
      args.tgt_graph_mask = getattr(args, 'tgt_graph_mask', '1prev')
      # target factored embeddings
      args.tgt_factored_emb_out = getattr(args, 'tgt_factored_emb_out', 0)
  
      # process some of the args for compatibility issue with legacy versions
      if isinstance(args.tgt_src_align_focus, str):
          assert len(args.tgt_src_align_focus) == 4, 'legacy version has "p-n-" format'
          args.tgt_src_align_focus = [args.tgt_src_align_focus[:2] + 'c1' + args.tgt_src_align_focus[-2:]]
      elif isinstance(args.tgt_src_align_focus, list):
          if len(args.tgt_src_align_focus) == 1 and len(args.tgt_src_align_focus[0]) == 4:
              args.tgt_src_align_focus[0] = args.tgt_src_align_focus[0][:2] + 'c1' + args.tgt_src_align_focus[0][-2:]
      else:
          raise TypeError
  
      base_architecture(args)
  
  
  @register_model_architecture('transformer_tgt_pointer_graphmp', 'transformer_tgt_pointer_graphmp_2x2')
  def transformer_pointer(args):
      # args.encode_state_machine = getattr(args, 'encode_state_machine', "stack_top_nopos")
      args.encode_state_machine = getattr(args, 'encode_state_machine', None)
      #
      args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
      args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
      args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4)
      args.encoder_layers = getattr(args, 'encoder_layers', 2)
      args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
      args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
      args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
      args.decoder_layers = getattr(args, 'decoder_layers', 2)
      # additional control of whether to use various action states information in the model
      args.apply_tgt_vocab_masks = getattr(args, 'apply_tgt_vocab_masks', 0)
      args.apply_tgt_actnode_masks = getattr(args, 'apply_tgt_actnode_masks', 0)
      args.apply_tgt_src_align = getattr(args, 'apply_tgt_src_align', 1)
      args.apply_tgt_input_src = getattr(args, 'apply_tgt_input_src', 0)
      # target source alignment masks for decoder cross-attention
      args.tgt_src_align_layers = getattr(args, 'tgt_src_align_layers', list(range(args.decoder_layers)))
      args.tgt_src_align_heads = getattr(args, 'tgt_src_align_heads', 1)
      args.tgt_src_align_focus = getattr(args, 'tgt_src_align_focus', ['p0c1n0'])
      # pointer distribution from decoder self-attention
      args.pointer_dist_decoder_selfattn_layers = getattr(args, 'pointer_dist_decoder_selfattn_layers',
                                                          list(range(args.decoder_layers)))
      args.pointer_dist_decoder_selfattn_heads = getattr(args, 'pointer_dist_decoder_selfattn_heads',
                                                         args.decoder_attention_heads)
      args.pointer_dist_decoder_selfattn_avg = getattr(args, 'pointer_dist_decoder_selfattn_avg', 1)
      args.pointer_dist_decoder_selfattn_infer = getattr(args, 'pointer_dist_decoder_selfattn_infer',
                                                         args.pointer_dist_decoder_selfattn_layers[-1])
      # combine source token embeddings into action embeddings for decoder input for node representation
      args.tgt_input_src_emb = getattr(args, 'tgt_input_src_emb', 'top')
      args.tgt_input_src_backprop = getattr(args, 'tgt_input_src_backprop', 1)
      args.tgt_input_src_combine = getattr(args, 'tgt_input_src_combine', 'cat')
      # graph structure encoding into the decoder self-attention
      args.tgt_graph_layers = getattr(args, 'tgt_graph_layers', list(range(args.decoder_layers)))
      args.tgt_graph_heads = getattr(args, 'tgt_graph_heads', 1)
      args.tgt_graph_mask = getattr(args, 'tgt_graph_mask', '1prev')
      # target factored embeddings
      args.tgt_factored_emb_out = getattr(args, 'tgt_factored_emb_out', 0)
  
      # process some of the args for compatibility issue with legacy versions
      if isinstance(args.tgt_src_align_focus, str):
          assert len(args.tgt_src_align_focus) == 4, 'legacy version has "p-n-" format'
          args.tgt_src_align_focus = [args.tgt_src_align_focus[:2] + 'c1' + args.tgt_src_align_focus[-2:]]
      elif isinstance(args.tgt_src_align_focus, list):
          if len(args.tgt_src_align_focus) == 1 and len(args.tgt_src_align_focus[0]) == 4:
              args.tgt_src_align_focus[0] = args.tgt_src_align_focus[0][:2] + 'c1' + args.tgt_src_align_focus[0][-2:]
      else:
          raise TypeError
  
      base_architecture(args)
  
  
  @register_model_architecture('transformer_tgt_pointer_graphmp', 'transformer_tgt_pointer_graphmp_1x3')
  def transformer_pointer(args):
      # args.encode_state_machine = getattr(args, 'encode_state_machine', "stack_top_nopos")
      args.encode_state_machine = getattr(args, 'encode_state_machine', None)
      #
      args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
      args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
      args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4)
      args.encoder_layers = getattr(args, 'encoder_layers', 1)
      args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
      args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
      args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
      args.decoder_layers = getattr(args, 'decoder_layers', 3)
      # additional control of whether to use various action states information in the model
      args.apply_tgt_vocab_masks = getattr(args, 'apply_tgt_vocab_masks', 0)
      args.apply_tgt_actnode_masks = getattr(args, 'apply_tgt_actnode_masks', 0)
      args.apply_tgt_src_align = getattr(args, 'apply_tgt_src_align', 1)
      args.apply_tgt_input_src = getattr(args, 'apply_tgt_input_src', 0)
      # target source alignment masks for decoder cross-attention
      args.tgt_src_align_layers = getattr(args, 'tgt_src_align_layers', list(range(args.decoder_layers)))
      args.tgt_src_align_heads = getattr(args, 'tgt_src_align_heads', 1)
      args.tgt_src_align_focus = getattr(args, 'tgt_src_align_focus', ['p0c1n0'])
      # pointer distribution from decoder self-attention
      args.pointer_dist_decoder_selfattn_layers = getattr(args, 'pointer_dist_decoder_selfattn_layers',
                                                          list(range(args.decoder_layers)))
      args.pointer_dist_decoder_selfattn_heads = getattr(args, 'pointer_dist_decoder_selfattn_heads',
                                                         args.decoder_attention_heads)
      args.pointer_dist_decoder_selfattn_avg = getattr(args, 'pointer_dist_decoder_selfattn_avg', 1)
      args.pointer_dist_decoder_selfattn_infer = getattr(args, 'pointer_dist_decoder_selfattn_infer',
                                                         args.pointer_dist_decoder_selfattn_layers[-1])
      # combine source token embeddings into action embeddings for decoder input for node representation
      args.tgt_input_src_emb = getattr(args, 'tgt_input_src_emb', 'top')
      args.tgt_input_src_backprop = getattr(args, 'tgt_input_src_backprop', 1)
      args.tgt_input_src_combine = getattr(args, 'tgt_input_src_combine', 'cat')
      # graph structure encoding into the decoder self-attention
      args.tgt_graph_layers = getattr(args, 'tgt_graph_layers', list(range(args.decoder_layers)))
      args.tgt_graph_heads = getattr(args, 'tgt_graph_heads', 1)
      args.tgt_graph_mask = getattr(args, 'tgt_graph_mask', '1prev')
      # target factored embeddings
      args.tgt_factored_emb_out = getattr(args, 'tgt_factored_emb_out', 0)
  
      # process some of the args for compatibility issue with legacy versions
      if isinstance(args.tgt_src_align_focus, str):
          assert len(args.tgt_src_align_focus) == 4, 'legacy version has "p-n-" format'
          args.tgt_src_align_focus = [args.tgt_src_align_focus[:2] + 'c1' + args.tgt_src_align_focus[-2:]]
      elif isinstance(args.tgt_src_align_focus, list):
          if len(args.tgt_src_align_focus) == 1 and len(args.tgt_src_align_focus[0]) == 4:
              args.tgt_src_align_focus[0] = args.tgt_src_align_focus[0][:2] + 'c1' + args.tgt_src_align_focus[0][-2:]
      else:
          raise TypeError
  
      base_architecture(args)
  
  
  @register_model_architecture('transformer_tgt_pointer_graphmp', 'transformer_tgt_pointer_graphmp_3x3_2h')
  def transformer_pointer(args):
      # args.encode_state_machine = getattr(args, 'encode_state_machine', "stack_top_nopos")
      args.encode_state_machine = getattr(args, 'encode_state_machine', None)
      #
      args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
      args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
      args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 2)
      args.encoder_layers = getattr(args, 'encoder_layers', 3)
      args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
      args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
      args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 2)
      args.decoder_layers = getattr(args, 'decoder_layers', 3)
      # additional control of whether to use various action states information in the model
      args.apply_tgt_vocab_masks = getattr(args, 'apply_tgt_vocab_masks', 0)
      args.apply_tgt_actnode_masks = getattr(args, 'apply_tgt_actnode_masks', 0)
      args.apply_tgt_src_align = getattr(args, 'apply_tgt_src_align', 1)
      args.apply_tgt_input_src = getattr(args, 'apply_tgt_input_src', 0)
      # target source alignment masks for decoder cross-attention
      args.tgt_src_align_layers = getattr(args, 'tgt_src_align_layers', list(range(args.decoder_layers)))
      args.tgt_src_align_heads = getattr(args, 'tgt_src_align_heads', 1)
      args.tgt_src_align_focus = getattr(args, 'tgt_src_align_focus', ['p0c1n0'])
      # pointer distribution from decoder self-attention
      args.pointer_dist_decoder_selfattn_layers = getattr(args, 'pointer_dist_decoder_selfattn_layers',
                                                          list(range(args.decoder_layers)))
      args.pointer_dist_decoder_selfattn_heads = getattr(args, 'pointer_dist_decoder_selfattn_heads',
                                                         args.decoder_attention_heads)
      args.pointer_dist_decoder_selfattn_avg = getattr(args, 'pointer_dist_decoder_selfattn_avg', 1)
      args.pointer_dist_decoder_selfattn_infer = getattr(args, 'pointer_dist_decoder_selfattn_infer',
                                                         args.pointer_dist_decoder_selfattn_layers[-1])
      # combine source token embeddings into action embeddings for decoder input for node representation
      args.tgt_input_src_emb = getattr(args, 'tgt_input_src_emb', 'top')
      args.tgt_input_src_backprop = getattr(args, 'tgt_input_src_backprop', 1)
      args.tgt_input_src_combine = getattr(args, 'tgt_input_src_combine', 'cat')
      # graph structure encoding into the decoder self-attention
      args.tgt_graph_layers = getattr(args, 'tgt_graph_layers', list(range(args.decoder_layers)))
      args.tgt_graph_heads = getattr(args, 'tgt_graph_heads', 1)
      args.tgt_graph_mask = getattr(args, 'tgt_graph_mask', '1prev')
      # target factored embeddings
      args.tgt_factored_emb_out = getattr(args, 'tgt_factored_emb_out', 0)
  
      # process some of the args for compatibility issue with legacy versions
      if isinstance(args.tgt_src_align_focus, str):
          assert len(args.tgt_src_align_focus) == 4, 'legacy version has "p-n-" format'
          args.tgt_src_align_focus = [args.tgt_src_align_focus[:2] + 'c1' + args.tgt_src_align_focus[-2:]]
      elif isinstance(args.tgt_src_align_focus, list):
          if len(args.tgt_src_align_focus) == 1 and len(args.tgt_src_align_focus[0]) == 4:
              args.tgt_src_align_focus[0] = args.tgt_src_align_focus[0][:2] + 'c1' + args.tgt_src_align_focus[0][-2:]
      else:
          raise TypeError
  
      base_architecture(args)
  
  
  @register_model_architecture('transformer_tgt_pointer_graphmp', 'transformer_tgt_pointer_graphmp_3x3_3h')
  def transformer_pointer(args):
      # args.encode_state_machine = getattr(args, 'encode_state_machine', "stack_top_nopos")
      args.encode_state_machine = getattr(args, 'encode_state_machine', None)
      #
      args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 192)
      args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 512)
      args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 3)
      args.encoder_layers = getattr(args, 'encoder_layers', 3)
      args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 192)
      args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 512)
      args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 3)
      args.decoder_layers = getattr(args, 'decoder_layers', 3)
      # additional control of whether to use various action states information in the model
      args.apply_tgt_vocab_masks = getattr(args, 'apply_tgt_vocab_masks', 0)
      args.apply_tgt_actnode_masks = getattr(args, 'apply_tgt_actnode_masks', 0)
      args.apply_tgt_src_align = getattr(args, 'apply_tgt_src_align', 1)
      args.apply_tgt_input_src = getattr(args, 'apply_tgt_input_src', 0)
      # target source alignment masks for decoder cross-attention
      args.tgt_src_align_layers = getattr(args, 'tgt_src_align_layers', list(range(args.decoder_layers)))
      args.tgt_src_align_heads = getattr(args, 'tgt_src_align_heads', 1)
      args.tgt_src_align_focus = getattr(args, 'tgt_src_align_focus', ['p0c1n0'])
      # pointer distribution from decoder self-attention
      args.pointer_dist_decoder_selfattn_layers = getattr(args, 'pointer_dist_decoder_selfattn_layers',
                                                          list(range(args.decoder_layers)))
      args.pointer_dist_decoder_selfattn_heads = getattr(args, 'pointer_dist_decoder_selfattn_heads',
                                                         args.decoder_attention_heads)
      args.pointer_dist_decoder_selfattn_avg = getattr(args, 'pointer_dist_decoder_selfattn_avg', 1)
      args.pointer_dist_decoder_selfattn_infer = getattr(args, 'pointer_dist_decoder_selfattn_infer',
                                                         args.pointer_dist_decoder_selfattn_layers[-1])
      # combine source token embeddings into action embeddings for decoder input for node representation
      args.tgt_input_src_emb = getattr(args, 'tgt_input_src_emb', 'top')
      args.tgt_input_src_backprop = getattr(args, 'tgt_input_src_backprop', 1)
      args.tgt_input_src_combine = getattr(args, 'tgt_input_src_combine', 'cat')
      # graph structure encoding into the decoder self-attention
      args.tgt_graph_layers = getattr(args, 'tgt_graph_layers', list(range(args.decoder_layers)))
      args.tgt_graph_heads = getattr(args, 'tgt_graph_heads', 1)
      args.tgt_graph_mask = getattr(args, 'tgt_graph_mask', '1prev')
      # target factored embeddings
      args.tgt_factored_emb_out = getattr(args, 'tgt_factored_emb_out', 0)
  
      # process some of the args for compatibility issue with legacy versions
      if isinstance(args.tgt_src_align_focus, str):
          assert len(args.tgt_src_align_focus) == 4, 'legacy version has "p-n-" format'
          args.tgt_src_align_focus = [args.tgt_src_align_focus[:2] + 'c1' + args.tgt_src_align_focus[-2:]]
      elif isinstance(args.tgt_src_align_focus, list):
          if len(args.tgt_src_align_focus) == 1 and len(args.tgt_src_align_focus[0]) == 4:
              args.tgt_src_align_focus[0] = args.tgt_src_align_focus[0][:2] + 'c1' + args.tgt_src_align_focus[0][-2:]
      else:
          raise TypeError
  
      base_architecture(args)
  