class_docstr: Constructs a Wav2Vec2CTC 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`):
          File containing the vocabulary.
      bos_token (`str`, *optional*, defaults to `"<s>"`):
          The beginning of sentence token.
      eos_token (`str`, *optional*, defaults to `"</s>"`):
          The end of sentence token.
      unk_token (`str`, *optional*, defaults to `"<unk>"`):
          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.
      pad_token (`str`, *optional*, defaults to `"<pad>"`):
          The token used for padding, for example when batching sequences of different lengths.
      word_delimiter_token (`str`, *optional*, defaults to `"|"`):
          The token used for defining the end of a word.
      do_lower_case (`bool`, *optional*, defaults to `False`):
          Whether or not to accept lowercase input and lowercase the output when decoding.
  
      **kwargs
          Additional keyword arguments passed along to [`PreTrainedTokenizer`]
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code: def __init__(
      self,
      vocab_file,
      bos_token="<s>",
      eos_token="</s>",
      unk_token="<unk>",
      pad_token="<pad>",
      word_delimiter_token="|",
      replace_word_delimiter_char=" ",
      do_lower_case=False,
      **kwargs,
  ):
      super().__init__(
          unk_token=unk_token,
          bos_token=bos_token,
          eos_token=eos_token,
          pad_token=pad_token,
          do_lower_case=do_lower_case,
          word_delimiter_token=word_delimiter_token,
          replace_word_delimiter_char=replace_word_delimiter_char,
          **kwargs,
      )
  
      self._word_delimiter_token = word_delimiter_token
  
      self.do_lower_case = do_lower_case
      self.replace_word_delimiter_char = replace_word_delimiter_char
  
      with open(vocab_file, encoding="utf-8") as vocab_handle:
          self.encoder = json.load(vocab_handle)
      self.decoder = {v: k for k, v in self.encoder.items()}
  
      # make sure that tokens made of several
      # characters are not split at tokenization
      for token in self.encoder.keys():
          if len(token) > 1:
              self.unique_no_split_tokens.append(token)
  
      self._create_trie(self.unique_no_split_tokens)
  
imports: import json
  import os
  import sys
  import warnings
  from dataclasses import dataclass
  from itertools import groupby
  from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
  import numpy
  from ...tokenization_utils import PreTrainedTokenizer, _insert_one_token_to_ordered_list
  from ...tokenization_utils_base import AddedToken, BatchEncoding
  from ...utils import ModelOutput, PaddingStrategy, TensorType, add_end_docstrings, is_flax_available, is_tf_available, is_torch_available, logging, to_py_obj
label_desc: Use image node_id 1 to create a new Wav2Vec2CTCTokenizer object from inherited base classes: PreTrainedTokenizer with example: obj = Wav2Vec2CTCTokenizer(vocab_file, bos_token, eos_token, unk_token, pad_token, word_delimiter_token, replace_word_delimiter_char, do_lower_case)