class_docstr: Construct a "Fast" BPE tokenizer for HerBERT (backed by HuggingFace's *tokenizers* library).
  
  Peculiarities:
  
  - uses BERT's pre-tokenizer: BertPreTokenizer splits tokens on spaces, and also on punctuation. Each occurrence of
    a punctuation character will be treated separately.
  
  This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the methods. Users should refer to the
  superclass for more information regarding methods.
  
  Args:
      vocab_file (`str`):
          Path to the vocabulary file.
      merges_file (`str`):
          Path to the merges file.
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docstr: Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
  special tokens using the tokenizer `prepare_for_model` method.
  
  Args:
      token_ids_0 (`List[int]`):
          List of IDs.
      token_ids_1 (`List[int]`, *optional*):
          Optional second list of IDs for sequence pairs.
      already_has_special_tokens (`bool`, *optional*, defaults to `False`):
          Whether or not the token list is already formatted with special tokens for the model.
  
  Returns:
      `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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code: def get_special_tokens_mask(
      self,
      token_ids_0: List[int],
      token_ids_1: Optional[List[int]] = None,
      already_has_special_tokens: bool = False,
  ) -> List[int]:
      """
      Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
      special tokens using the tokenizer `prepare_for_model` method.
  
      Args:
          token_ids_0 (`List[int]`):
              List of IDs.
          token_ids_1 (`List[int]`, *optional*):
              Optional second list of IDs for sequence pairs.
          already_has_special_tokens (`bool`, *optional*, defaults to `False`):
              Whether or not the token list is already formatted with special tokens for the model.
  
      Returns:
          `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
      """
      if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_0=token_ids_0,
              token_ids_1=token_ids_1,
              already_has_special_tokens=True,
          )
  
      if token_ids_1 is None:
          return [1] + ([0] * len(token_ids_0)) + [1]
      return (
          [1]
          + ([0] * len(token_ids_0))
          + [1]
          + ([0] * len(token_ids_1))
          + [1]
      )
  
file_path: transformers/src/transformers/models/herbert/tokenization_herbert_fast.py
imports: from typing import List, Optional, Tuple
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_herbert import HerbertTokenizer
label_desc: Use image node_id 3 for calling the HerbertTokenizerFast obj's underlying member method code with example usage: obj.get_special_tokens_mask(token_ids_0, token_ids_1, already_has_special_tokens) and returns: unknown, super, unknown