class_docstr: Construct a CLIP tokenizer. Based on byte-level Byte-Pair-Encoding.
  
  This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
  this superclass for more information regarding those methods.
  
  Args:
      vocab_file (`str`):
          Path to the vocabulary file.
      merges_file (`str`):
          Path to the merges 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 `"<|startoftext|>"`):
          The beginning of sequence token.
      eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
          The end of sequence token.
      pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
          The token used for padding, for example when batching sequences of different lengths.
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code: def vocab_size(self):
      return len(self.encoder)
  
imports: import json
  import os
  import unicodedata
  from functools import lru_cache
  from typing import List, Optional, Tuple
  import regex
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
  from ...utils import logging
label_desc: Use image node_id 2 for calling the CLIPTokenizer obj's underlying member method code with example usage: obj.vocab_size() and returns: len