class_docstr: Construct a "fast" CLIP tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
  Byte-Pair-Encoding.
  
  This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
  refer to this superclass for more information regarding those methods.
  
  Args:
      vocab_file (`str`, *optional*):
          Path to the vocabulary file.
      merges_file (`str`, *optional*):
          Path to the merges file.
      tokenizer_file (`str`, *optional*):
          The path to a tokenizer file to use instead of the vocab file.
      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.
docstr: Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
  adding special tokens. A CLIP sequence has the following format:
  
  - single sequence: `<|startoftext|> X <|endoftext|>`
  
  Pairs of sequences are not the expected use case, but they will be handled without a separator.
  
  Args:
      token_ids_0 (`List[int]`):
          List of IDs to which the special tokens will be added.
      token_ids_1 (`List[int]`, *optional*):
          Optional second list of IDs for sequence pairs.
  
  Returns:
      `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
code: def build_inputs_with_special_tokens(
      self,
      token_ids_0: List[int],
      token_ids_1: Optional[List[int]] = None,
  ) -> List[int]:
      """
      Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
      adding special tokens. A CLIP sequence has the following format:
  
      - single sequence: `<|startoftext|> X <|endoftext|>`
  
      Pairs of sequences are not the expected use case, but they will be handled without a separator.
  
      Args:
          token_ids_0 (`List[int]`):
              List of IDs to which the special tokens will be added.
          token_ids_1 (`List[int]`, *optional*):
              Optional second list of IDs for sequence pairs.
  
      Returns:
          `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
      """
      bos_token = [self.bos_token_id]
      eos_token = [self.eos_token_id]
  
      if token_ids_1 is None:
          return bos_token + token_ids_0 + eos_token
      return (
          bos_token
          + token_ids_0
          + eos_token
          + eos_token
          + token_ids_1
          + eos_token
      )
  
file_path: transformers/src/transformers/models/clip/tokenization_clip_fast.py
imports: from typing import List, Optional, Tuple
  from tokenizers import pre_tokenizers
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_clip import CLIPTokenizer
label_desc: Use image node_id 3 for calling the CLIPTokenizerFast obj's underlying member method code with example usage: obj.build_inputs_with_special_tokens(token_ids_0, token_ids_1) and returns: unknown, unknown