class_docstr: Constructs a Wav2Vec2PhonemeCTC 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.
      do_phonemize (`bool`, *optional*, defaults to `True`):
          Whether the tokenizer should phonetize the input or not. Only if a sequence of phonemes is passed to the
          tokenizer, `do_phonemize` should be set to `False`.
      phonemizer_lang (`str`, *optional*, defaults to `"en-us"`):
          The language of the phoneme set to which the tokenizer should phonetize the input text to.
      phonemizer_backend (`str`, *optional*. defaults to `"espeak"`):
          The backend phonetization library that shall be used by the phonemizer library. Defaults to `espeak-ng`.
          See the [phonemizer package](https://github.com/bootphon/phonemizer#readme). for more information.
  
      **kwargs
          Additional keyword arguments passed along to [`PreTrainedTokenizer`]
docstr: Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
  tokens and clean up tokenization spaces.
  
  Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
  
  Args:
      token_ids (`Union[int, 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 [`~models.wav2vec2.tokenization_wav2vec2.decode`] to better
          understand how to make use of `output_word_offsets`.
          [`~model.wav2vec2_phoneme.tokenization_wav2vec2_phoneme.batch_decode`] works the same way with
          phonemes.
  
          </Tip>
  
      kwargs (additional keyword arguments, *optional*):
          Will be passed to the underlying model specific decode method.
  
  Returns:
      `str` or [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`]: The decoded
      sentence. Will be a [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`]
      when `output_char_offsets == True`.
code: def decode(
      self,
      token_ids: Union[
          int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"
      ],
      skip_special_tokens: bool = False,
      clean_up_tokenization_spaces: bool = True,
      output_char_offsets: bool = False,
      **kwargs,
  ) -> str:
      """
      Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
      tokens and clean up tokenization spaces.
  
      Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
  
      Args:
          token_ids (`Union[int, 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 [`~models.wav2vec2.tokenization_wav2vec2.decode`] to better
              understand how to make use of `output_word_offsets`.
              [`~model.wav2vec2_phoneme.tokenization_wav2vec2_phoneme.batch_decode`] works the same way with
              phonemes.
  
              </Tip>
  
          kwargs (additional keyword arguments, *optional*):
              Will be passed to the underlying model specific decode method.
  
      Returns:
          `str` or [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`]: The decoded
          sentence. Will be a [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`]
          when `output_char_offsets == True`.
      """
      # Convert inputs to python lists
      token_ids = to_py_obj(token_ids)
  
      return self._decode(
          token_ids=token_ids,
          skip_special_tokens=skip_special_tokens,
          clean_up_tokenization_spaces=clean_up_tokenization_spaces,
          output_char_offsets=output_char_offsets,
          **kwargs,
      )
  
imports: import json
  import os
  import sys
  from dataclasses import dataclass
  from itertools import groupby
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
  import numpy
  from ...tokenization_utils import PreTrainedTokenizer, _insert_one_token_to_ordered_list
  from ...tokenization_utils_base import AddedToken
  from ...utils import ModelOutput, is_flax_available, is_tf_available, is_torch_available, logging, requires_backends, to_py_obj
label_desc: Use image node_id 21 for calling the Wav2Vec2PhonemeCTCTokenizer obj's underlying member method code with example usage: obj.decode(token_ids, skip_special_tokens, clean_up_tokenization_spaces, output_char_offsets) and returns: self