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, {})

        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.
        
dpkd/transformers/src/transformers/models/wav2vec2_phoneme/tokenization_wav2vec2_phoneme.py