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: Performs any necessary transformations before tokenization.
  
  This method should pop the arguments from kwargs and return the remaining `kwargs` as well. We test the
  `kwargs` at the end of the encoding process to be sure all the arguments have been used.
  
  Args:
      text (`str`):
          The text to prepare.
      is_split_into_words (`bool`, *optional*, defaults to `False`):
          Whether or not the input is already pre-tokenized (e.g., split into words). If set to `True`, the
          tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
          which it will tokenize. This is useful for NER or token classification.
      phonemizer_lang (`str`, *optional*):
          The language of the phoneme set to which the tokenizer should phonetize the input text to.
      do_phonemize (`bool`, *optional*):
          Whether the tokenizer should phonetize the input text or not. Only if a sequence of phonemes is passed
          to the tokenizer, `do_phonemize` should be set to `False`.
  
  
  Returns:
      `Tuple[str, Dict[str, Any]]`: The prepared text and the unused kwargs.
code: def prepare_for_tokenization(
      self,
      text: str,
      is_split_into_words: bool = False,
      phonemizer_lang: Optional[str] = None,
      do_phonemize: Optional[bool] = None,
  ) -> Tuple[str, Dict[str, Any]]:
      """
      Performs any necessary transformations before tokenization.
  
      This method should pop the arguments from kwargs and return the remaining `kwargs` as well. We test the
      `kwargs` at the end of the encoding process to be sure all the arguments have been used.
  
      Args:
          text (`str`):
              The text to prepare.
          is_split_into_words (`bool`, *optional*, defaults to `False`):
              Whether or not the input is already pre-tokenized (e.g., split into words). If set to `True`, the
              tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
              which it will tokenize. This is useful for NER or token classification.
          phonemizer_lang (`str`, *optional*):
              The language of the phoneme set to which the tokenizer should phonetize the input text to.
          do_phonemize (`bool`, *optional*):
              Whether the tokenizer should phonetize the input text or not. Only if a sequence of phonemes is passed
              to the tokenizer, `do_phonemize` should be set to `False`.
  
  
      Returns:
          `Tuple[str, Dict[str, Any]]`: The prepared text and the unused kwargs.
      """
      if is_split_into_words:
          text = " " + text
  
      # set whether tokenizer should phonemize or not
      if do_phonemize is not None:
          self.do_phonemize = do_phonemize
  
      # set the correct phonemizer language
      if phonemizer_lang is not None:
          self.phonemizer_lang = phonemizer_lang
          self.init_backend(phonemizer_lang)
  
      return (text, {})
  
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
  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 6 for calling the Wav2Vec2PhonemeCTCTokenizer obj's underlying member method code with example usage: obj.prepare_for_tokenization(text, is_split_into_words, phonemizer_lang, do_phonemize) and returns: text,  dict