code: import math
  from typing import Optional, Tuple
  
  from overrides import overrides
  
  import torch
  import torch.nn.functional as F
  from transformers import XLNetConfig
  
  from allennlp.data.tokenizers import PretrainedTransformerTokenizer
  from allennlp.modules.token_embedders.token_embedder import TokenEmbedder
  from allennlp.nn.util import batched_index_select
  
  
  @TokenEmbedder.register("pretrained_transformer")
  class PretrainedTransformerEmbedder(TokenEmbedder):
      """
      Uses a pretrained model from `transformers` as a `TokenEmbedder`.
  
      Registered as a `TokenEmbedder` with name "pretrained_transformer".
  
      # Parameters
  
      model_name : `str`
          The name of the `transformers` model to use. Should be the same as the corresponding
          `PretrainedTransformerIndexer`.
      max_length : `int`, optional (default = `None`)
          If positive, folds input token IDs into multiple segments of this length, pass them
          through the transformer model independently, and concatenate the final representations.
          Should be set to the same value as the `max_length` option on the
          `PretrainedTransformerIndexer`.
      sub_module: `str`, optional (default = `None`)
          The name of a submodule of the transformer to be used as the embedder. Some transformers naturally act
          as embedders such as BERT. However, other models consist of encoder and decoder, in which case we just
          want to use the encoder.
      train_parameters: `bool`, optional (default = `True`)
          If this is `True`, the transformer weights get updated during training.
      """
  
      def __init__(
          self,
          model_name: str,
          *,
          max_length: int = None,
          sub_module: str = None,
          train_parameters: bool = True,
          override_weights_file: Optional[str] = None,
          override_weights_strip_prefix: Optional[str] = None
      ) -> None:
          super().__init__()
          from allennlp.common import cached_transformers
  
          self.transformer_model = cached_transformers.get(
              model_name, True, override_weights_file, override_weights_strip_prefix
          )
          self.config = self.transformer_model.config
          if sub_module:
              assert hasattr(self.transformer_model, sub_module)
              self.transformer_model = getattr(self.transformer_model, sub_module)
          self._max_length = max_length
          # I'm not sure if this works for all models; open an issue on github if you find a case
          # where it doesn't work.
          self.output_dim = self.config.hidden_size
  
          tokenizer = PretrainedTransformerTokenizer(model_name)
          self._num_added_start_tokens = len(tokenizer.single_sequence_start_tokens)
          self._num_added_end_tokens = len(tokenizer.single_sequence_end_tokens)
          self._num_added_tokens = self._num_added_start_tokens + self._num_added_end_tokens
  
          if not train_parameters:
              for param in self.transformer_model.parameters():
                  param.requires_grad = False
  
      @overrides
      def get_output_dim(self):
          return self.output_dim
  
      def _number_of_token_type_embeddings(self):
          if isinstance(self.config, XLNetConfig):
              return 3  # XLNet has 3 type ids
          elif hasattr(self.config, "type_vocab_size"):
              return self.config.type_vocab_size
          else:
              return 0
  
      @overrides
      def forward(
          self,
          token_ids: torch.LongTensor,
          mask: torch.BoolTensor,
          type_ids: Optional[torch.LongTensor] = None,
          segment_concat_mask: Optional[torch.BoolTensor] = None,
      ) -> torch.Tensor:  # type: ignore
          """
          # Parameters
  
          token_ids: `torch.LongTensor`
              Shape: `[batch_size, num_wordpieces if max_length is None else num_segment_concat_wordpieces]`.
              num_segment_concat_wordpieces is num_wordpieces plus special tokens inserted in the
              middle, e.g. the length of: "[CLS] A B C [SEP] [CLS] D E F [SEP]" (see indexer logic).
          mask: `torch.BoolTensor`
              Shape: [batch_size, num_wordpieces].
          type_ids: `Optional[torch.LongTensor]`
              Shape: `[batch_size, num_wordpieces if max_length is None else num_segment_concat_wordpieces]`.
          segment_concat_mask: `Optional[torch.BoolTensor]`
              Shape: `[batch_size, num_segment_concat_wordpieces]`.
  
          # Returns
  
          `torch.Tensor`
              Shape: `[batch_size, num_wordpieces, embedding_size]`.
  
          """
          # Some of the huggingface transformers don't support type ids at all and crash when you supply
          # them. For others, you can supply a tensor of zeros, and if you don't, they act as if you did.
          # There is no practical difference to the caller, so here we pretend that one case is the same
          # as another case.
          if type_ids is not None:
              max_type_id = type_ids.max()
              if max_type_id == 0:
                  type_ids = None
              else:
                  if max_type_id >= self._number_of_token_type_embeddings():
                      raise ValueError("Found type ids too large for the chosen transformer model.")
                  assert token_ids.shape == type_ids.shape
  
          fold_long_sequences = self._max_length is not None and token_ids.size(1) > self._max_length
          if fold_long_sequences:
              batch_size, num_segment_concat_wordpieces = token_ids.size()
              token_ids, segment_concat_mask, type_ids = self._fold_long_sequences(
                  token_ids, segment_concat_mask, type_ids
              )
  
          transformer_mask = segment_concat_mask if self._max_length is not None else mask
          # Shape: [batch_size, num_wordpieces, embedding_size],
          # or if self._max_length is not None:
          # [batch_size * num_segments, self._max_length, embedding_size]
  
          # We call this with kwargs because some of the huggingface models don't have the
          # token_type_ids parameter and fail even when it's given as None.
          # Also, as of transformers v2.5.1, they are taking FloatTensor masks.
          parameters = {"input_ids": token_ids, "attention_mask": transformer_mask.float()}
          if type_ids is not None:
              parameters["token_type_ids"] = type_ids
          embeddings = self.transformer_model(**parameters)[0]
  
          if fold_long_sequences:
              embeddings = self._unfold_long_sequences(
                  embeddings, segment_concat_mask, batch_size, num_segment_concat_wordpieces
              )
  
          return embeddings
  
      def _fold_long_sequences(
          self,
          token_ids: torch.LongTensor,
          mask: torch.BoolTensor,
          type_ids: Optional[torch.LongTensor] = None,
      ) -> Tuple[torch.LongTensor, torch.LongTensor, Optional[torch.LongTensor]]:
          """
          We fold 1D sequences (for each element in batch), returned by `PretrainedTransformerIndexer`
          that are in reality multiple segments concatenated together, to 2D tensors, e.g.
  
          [ [CLS] A B C [SEP] [CLS] D E [SEP] ]
          -> [ [ [CLS] A B C [SEP] ], [ [CLS] D E [SEP] [PAD] ] ]
          The [PAD] positions can be found in the returned `mask`.
  
          # Parameters
  
          token_ids: `torch.LongTensor`
              Shape: `[batch_size, num_segment_concat_wordpieces]`.
              num_segment_concat_wordpieces is num_wordpieces plus special tokens inserted in the
              middle, i.e. the length of: "[CLS] A B C [SEP] [CLS] D E F [SEP]" (see indexer logic).
          mask: `torch.BoolTensor`
              Shape: `[batch_size, num_segment_concat_wordpieces]`.
              The mask for the concatenated segments of wordpieces. The same as `segment_concat_mask`
              in `forward()`.
          type_ids: `Optional[torch.LongTensor]`
              Shape: [batch_size, num_segment_concat_wordpieces].
  
          # Returns:
  
          token_ids: `torch.LongTensor`
              Shape: [batch_size * num_segments, self._max_length].
          mask: `torch.BoolTensor`
              Shape: [batch_size * num_segments, self._max_length].
          """
          num_segment_concat_wordpieces = token_ids.size(1)
          num_segments = math.ceil(num_segment_concat_wordpieces / self._max_length)
          padded_length = num_segments * self._max_length
          length_to_pad = padded_length - num_segment_concat_wordpieces
  
          def fold(tensor):  # Shape: [batch_size, num_segment_concat_wordpieces]
              # Shape: [batch_size, num_segments * self._max_length]
              tensor = F.pad(tensor, [0, length_to_pad], value=0)
              # Shape: [batch_size * num_segments, self._max_length]
              return tensor.reshape(-1, self._max_length)
  
          return fold(token_ids), fold(mask), fold(type_ids) if type_ids is not None else None
  
      def _unfold_long_sequences(
          self,
          embeddings: torch.FloatTensor,
          mask: torch.BoolTensor,
          batch_size: int,
          num_segment_concat_wordpieces: int,
      ) -> torch.FloatTensor:
          """
          We take 2D segments of a long sequence and flatten them out to get the whole sequence
          representation while remove unnecessary special tokens.
  
          [ [ [CLS]_emb A_emb B_emb C_emb [SEP]_emb ], [ [CLS]_emb D_emb E_emb [SEP]_emb [PAD]_emb ] ]
          -> [ [CLS]_emb A_emb B_emb C_emb D_emb E_emb [SEP]_emb ]
  
          We truncate the start and end tokens for all segments, recombine the segments,
          and manually add back the start and end tokens.
  
          # Parameters
  
          embeddings: `torch.FloatTensor`
              Shape: [batch_size * num_segments, self._max_length, embedding_size].
          mask: `torch.BoolTensor`
              Shape: [batch_size * num_segments, self._max_length].
              The mask for the concatenated segments of wordpieces. The same as `segment_concat_mask`
              in `forward()`.
          batch_size: `int`
          num_segment_concat_wordpieces: `int`
              The length of the original "[ [CLS] A B C [SEP] [CLS] D E F [SEP] ]", i.e.
              the original `token_ids.size(1)`.
  
          # Returns:
  
          embeddings: `torch.FloatTensor`
              Shape: [batch_size, self._num_wordpieces, embedding_size].
          """
  
          def lengths_to_mask(lengths, max_len, device):
              return torch.arange(max_len, device=device).expand(
                  lengths.size(0), max_len
              ) < lengths.unsqueeze(1)
  
          device = embeddings.device
          num_segments = int(embeddings.size(0) / batch_size)
          embedding_size = embeddings.size(2)
  
          # We want to remove all segment-level special tokens but maintain sequence-level ones
          num_wordpieces = num_segment_concat_wordpieces - (num_segments - 1) * self._num_added_tokens
  
          embeddings = embeddings.reshape(batch_size, num_segments * self._max_length, embedding_size)
          mask = mask.reshape(batch_size, num_segments * self._max_length)
          # We assume that all 1s in the mask precede all 0s, and add an assert for that.
          # Open an issue on GitHub if this breaks for you.
          # Shape: (batch_size,)
          seq_lengths = mask.sum(-1)
          if not (lengths_to_mask(seq_lengths, mask.size(1), device) == mask).all():
              raise ValueError(
                  "Long sequence splitting only supports masks with all 1s preceding all 0s."
              )
          # Shape: (batch_size, self._num_added_end_tokens); this is a broadcast op
          end_token_indices = (
              seq_lengths.unsqueeze(-1) - torch.arange(self._num_added_end_tokens, device=device) - 1
          )
  
          # Shape: (batch_size, self._num_added_start_tokens, embedding_size)
          start_token_embeddings = embeddings[:, : self._num_added_start_tokens, :]
          # Shape: (batch_size, self._num_added_end_tokens, embedding_size)
          end_token_embeddings = batched_index_select(embeddings, end_token_indices)
  
          embeddings = embeddings.reshape(batch_size, num_segments, self._max_length, embedding_size)
          embeddings = embeddings[
              :, :, self._num_added_start_tokens : -self._num_added_end_tokens, :
          ]  # truncate segment-level start/end tokens
          embeddings = embeddings.reshape(batch_size, -1, embedding_size)  # flatten
  
          # Now try to put end token embeddings back which is a little tricky.
  
          # The number of segment each sequence spans, excluding padding. Mimicking ceiling operation.
          # Shape: (batch_size,)
          num_effective_segments = (seq_lengths + self._max_length - 1) / self._max_length
          # The number of indices that end tokens should shift back.
          num_removed_non_end_tokens = (
              num_effective_segments * self._num_added_tokens - self._num_added_end_tokens
          )
          # Shape: (batch_size, self._num_added_end_tokens)
          end_token_indices -= num_removed_non_end_tokens.unsqueeze(-1)
          assert (end_token_indices >= self._num_added_start_tokens).all()
          # Add space for end embeddings
          embeddings = torch.cat([embeddings, torch.zeros_like(end_token_embeddings)], 1)
          # Add end token embeddings back
          embeddings.scatter_(
              1, end_token_indices.unsqueeze(-1).expand_as(end_token_embeddings), end_token_embeddings
          )
  
          # Now put back start tokens. We can do this before putting back end tokens, but then
          # we need to change `num_removed_non_end_tokens` a little.
          embeddings = torch.cat([start_token_embeddings, embeddings], 1)
  
          # Truncate to original length
          embeddings = embeddings[:, :num_wordpieces, :]
          return embeddings
  
apis: torch.nn.functional.pad torch.arange torch.cat torch.zeros_like