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`]
class_docstr_tok: ["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","can","not","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","`","]"]
docstr: Convert a list of lists of token ids into a list of strings by calling decode.
  
  Args:
      sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
          List of tokenized input ids. Can be obtained using the `__call__` method.
      skip_special_tokens (`bool`, *optional*, defaults to `False`):
          Whether or not to remove special tokens in the decoding.
      clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
          Whether or not to clean up the tokenization spaces.
      output_char_offsets (`bool`, *optional*, defaults to `False`):
          Whether or not to output character offsets. Character offsets can be used in combination with the
          sampling rate and model downsampling rate to compute the time-stamps of transcribed characters.
  
          <Tip>
  
          Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make
          use of `output_char_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched
          output.
  
          </Tip>
  
      output_word_offsets (`bool`, *optional*, defaults to `False`):
          Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate
          and model downsampling rate to compute the time-stamps of transcribed words.
  
          <Tip>
  
          Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make
          use of `output_word_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched
          output.
  
          </Tip>
  
      kwargs (additional keyword arguments, *optional*):
          Will be passed to the underlying model specific decode method.
  
  Returns:
      `List[str]` or [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`]: The list of decoded
      sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when
      `output_char_offsets == True` or `output_word_offsets == True`.
docstr_tok: ["Convert","a","list","of","lists","of","token","ids","into","a","list","of","strings","by","calling","decode",".","Args",":","sequences","(","`","Union","[","List","[","int","]",",","List","[","List","[","int","]","]",",","np.ndarray",",","torch.Tensor",",","tf.Tensor","]","`",")",":","List","of","tokenized","input","ids",".","Can","be","obtained","using","the","`","__call__","`","method",".","skip_special_tokens","(","`","bool","`",",","*","optional","*",",","defaults","to","`","False","`",")",":","Whether","or","not","to","remove","special","tokens","in","the","decoding",".","clean_up_tokenization_spaces","(","`","bool","`",",","*","optional","*",",","defaults","to","`","True","`",")",":","Whether","or","not","to","clean","up","the","tokenization","spaces",".","output_char_offsets","(","`","bool","`",",","*","optional","*",",","defaults","to","`","False","`",")",":","Whether","or","not","to","output","character","offsets",".","Character","offsets","can","be","used","in","combination","with","the","sampling","rate","and","model","downsampling","rate","to","compute","the","time-stamps","of","transcribed","characters",".","<","Tip",">","Please","take","a","look","at","the","Example","of","[","`","~Wav2Vec2CTCTokenizer.decode","`","]","to","better","understand","how","to","make","use","of","`","output_char_offsets","`",".","[","`","~Wav2Vec2CTCTokenizer.batch_decode","`","]","works","the","same","way","with","batched","output",".","<","\/Tip",">","output_word_offsets","(","`","bool","`",",","*","optional","*",",","defaults","to","`","False","`",")",":","Whether","or","not","to","output","word","offsets",".","Word","offsets","can","be","used","in","combination","with","the","sampling","rate","and","model","downsampling","rate","to","compute","the","time-stamps","of","transcribed","words",".","<","Tip",">","Please","take","a","look","at","the","Example","of","[","`","~Wav2Vec2CTCTokenizer.decode","`","]","to","better","understand","how","to","make","use","of","`","output_word_offsets","`",".","[","`","~Wav2Vec2CTCTokenizer.batch_decode","`","]","works","the","same","way","with","batched","output",".","<","\/Tip",">","kwargs","(","additional","keyword","arguments",",","*","optional","*",")",":","Will","be","passed","to","the","underlying","model","specific","decode","method",".","Returns",":","`","List","[","str","]","`","or","[","`","~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput","`","]",":","The","list","of","decoded","sentences",".","Will","be","a","[","`","~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput","`","]","when","`","output_char_offsets","==","True","`","or","`","output_word_offsets","==","True","`","."]
code: def batch_decode(
      self,
      sequences: Union[
          List[int],
          List[List[int]],
          "np.ndarray",
          "torch.Tensor",
          "tf.Tensor",
      ],
      skip_special_tokens: bool = False,
      clean_up_tokenization_spaces: bool = True,
      output_char_offsets: bool = False,
      output_word_offsets: bool = False,
      **kwargs,
  ) -> List[str]:
      """
      Convert a list of lists of token ids into a list of strings by calling decode.
  
      Args:
          sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
              List of tokenized input ids. Can be obtained using the `__call__` method.
          skip_special_tokens (`bool`, *optional*, defaults to `False`):
              Whether or not to remove special tokens in the decoding.
          clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
              Whether or not to clean up the tokenization spaces.
          output_char_offsets (`bool`, *optional*, defaults to `False`):
              Whether or not to output character offsets. Character offsets can be used in combination with the
              sampling rate and model downsampling rate to compute the time-stamps of transcribed characters.
  
              <Tip>
  
              Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make
              use of `output_char_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched
              output.
  
              </Tip>
  
          output_word_offsets (`bool`, *optional*, defaults to `False`):
              Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate
              and model downsampling rate to compute the time-stamps of transcribed words.
  
              <Tip>
  
              Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make
              use of `output_word_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched
              output.
  
              </Tip>
  
          kwargs (additional keyword arguments, *optional*):
              Will be passed to the underlying model specific decode method.
  
      Returns:
          `List[str]` or [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`]: The list of decoded
          sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when
          `output_char_offsets == True` or `output_word_offsets == True`.
      """
      batch_decoded = [
          self.decode(
              seq,
              skip_special_tokens=skip_special_tokens,
              clean_up_tokenization_spaces=clean_up_tokenization_spaces,
              output_char_offsets=output_char_offsets,
              output_word_offsets=output_word_offsets,
              **kwargs,
          )
          for seq in sequences
      ]
      if output_char_offsets or output_word_offsets:
          # transform list of dicts to dict of lists
          return Wav2Vec2CTCTokenizerOutput(
              {
                  k: [d[k] for d in batch_decoded]
                  for k in batch_decoded[0]
              }
          )
  
      return batch_decoded
  
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 16 for calling the Wav2Vec2CTCTokenizer obj's underlying member method code with example usage: obj.batch_decode(sequences, skip_special_tokens, clean_up_tokenization_spaces, output_char_offsets, output_word_offsets) and returns: batch_decoded, Wav2Vec2CTCTokenizerOutput