src/transformers/models/deberta_v2/tokenization_deberta_v2.py/DebertaV2Tokenizer/__init__
class DebertaV2Tokenizer:    def __init__(
        self,
        vocab_file,
        do_lower_case=False,
        split_by_punct=False,
        bos_token="[CLS]",
        eos_token="[SEP]",
        unk_token="[UNK]",
        sep_token="[SEP]",
        pad_token="[PAD]",
        cls_token="[CLS]",
        mask_token="[MASK]",
        sp_model_kwargs: Optional[Dict[str, Any]] = None,
        **kwargs,
    ) -> None:
        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            do_lower_case=do_lower_case,
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            sep_token=sep_token,
            pad_token=pad_token,
            cls_token=cls_token,
            mask_token=mask_token,
            split_by_punct=split_by_punct,
            sp_model_kwargs=self.sp_model_kwargs,
            **kwargs,
        )

        if not os.path.isfile(vocab_file):
            raise ValueError(
                f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
                " model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
            )
        self.do_lower_case = do_lower_case
        self.split_by_punct = split_by_punct
        self.vocab_file = vocab_file
        self._tokenizer = SPMTokenizer(
            vocab_file, self.all_special_tokens, split_by_punct=split_by_punct, sp_model_kwargs=self.sp_model_kwargs
        )