class_docstr: Construct a "fast" BlenderbotSmall tokenizer (backed by HuggingFace's *tokenizers* library).
  
  Args:
      vocab_file (`str`):
          Path to the vocabulary file.
class_docstr_tok: ["Construct","a","``","fast","''","BlenderbotSmall","tokenizer","(","backed","by","HuggingFace","'s","*","tokenizers","*","library",")",".","Args",":","vocab_file","(","`","str","`",")",":","Path","to","the","vocabulary","file","."]
code: def __init__(
      self,
      vocab_file=None,
      merges_file=None,
      unk_token="<|endoftext|>",
      bos_token="<|endoftext|>",
      eos_token="<|endoftext|>",
      add_prefix_space=False,
      trim_offsets=True,
      **kwargs,
  ):
      super().__init__(
          ByteLevelBPETokenizer(
              vocab=vocab_file,
              merges=merges_file,
              add_prefix_space=add_prefix_space,
              trim_offsets=trim_offsets,
          ),
          bos_token=bos_token,
          eos_token=eos_token,
          unk_token=unk_token,
          **kwargs,
      )
      self.add_prefix_space = add_prefix_space
  
file_path: transformers/src/transformers/models/blenderbot_small/tokenization_blenderbot_small_fast.py
imports: from typing import List, Optional
  from tokenizers import ByteLevelBPETokenizer
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
label_desc: Use image node_id 1 to create a new BlenderbotSmallTokenizerFast object from inherited base classes: PreTrainedTokenizerFast with example: obj = BlenderbotSmallTokenizerFast(vocab_file, merges_file, unk_token, bos_token, eos_token, add_prefix_space, trim_offsets)