class_docstr: Construct an Whisper tokenizer.
  
  This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to
  the superclass for more information regarding such methods.
  
   Args:
      vocab_file (`str`):
          Path to the vocabulary file.
      merges_file (`str`):
          Path to the merges file.
      normalizer_file (`str`, *optional*, defaults to `None`):
          Path to the normalizer_file file.
      errors (`str`, *optional*, defaults to `"replace"`):
          Paradigm to follow when decoding bytes to UTF-8. See
          [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
      unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
          The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
          token instead.
      bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
          The beginning of sequence token.
      eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
          The end of sequence token.
      add_prefix_space (`bool`, *optional*, defaults to `False`):
          Whether or not to add an initial space to the input. This allows to treat the leading word just as any
          other word.
      add_bos_token (`bool`, *optional*, defaults to `False`):
          Whether or not to add an initial <|endoftext|> to the input. This allows to treat the leading word just as
          any other word.
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code: def vocab_size(self) -> int:
      return len(self.encoder)
  
imports: import json
  import os
  from typing import List, Optional, Tuple, Union
  import regex
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  from .english_normalizer import EnglishTextNormalizer
label_desc: Use image node_id 3 for calling the WhisperTokenizer obj's underlying member method code with example usage: obj.vocab_size() and returns: len