]`) -- A dictionary with, as keys, the `short-cut-names`
          of the pretrained models, and as associated values, the maximum length of the sequence inputs of this model,
          or `None` if the model has no maximum input size.
        - **pretrained_init_configuration** (`Dict[str, Dict[str, Any]]`) -- A dictionary with, as keys, the
          `short-cut-names` of the pretrained models, and as associated values, a dictionary of specific arguments to
          pass to the `__init__` method of the tokenizer class for this pretrained model when loading the tokenizer
          with the [`~tokenization_utils_base.PreTrainedTokenizerBase.from_pretrained`] method.
        - **model_input_names** (`List[str]`) -- A list of inputs expected in the forward pass of the model.
        - **padding_side** (`str`) -- The default value for the side on which the model should have padding applied.
          Should be `'right'` or `'left'`.
        - **truncation_side** (`str`) -- The default value for the side on which the model should have truncation
          applied. Should be `'right'` or `'left'`.

    Args:
        model_max_length (`int`, *optional*):
            The maximum length (in number of tokens) for the inputs to the transformer model. When the tokenizer is
            loaded with [`~tokenization_utils_base.PreTrainedTokenizerBase.from_pretrained`], this will be set to the
            value stored for the associated model in `max_model_input_sizes` (see above). If no value is provided, will
            default to VERY_LARGE_INTEGER (`int(1e30)`).
        padding_side (`str`, *optional*):
            The side on which the model should have padding applied. Should be selected between ['right', 'left'].
            Default value is picked from the class attribute of the same name.
        truncation_side (`str`, *optional*):
            The side on which the model should have truncation applied. Should be selected between ['right', 'left'].
            Default value is picked from the class attribute of the same name.
        model_input_names (`List[string]`, *optional*):
            The list of inputs accepted by the forward pass of the model (like `"token_type_ids"` or
            `"attention_mask"`). Default value is picked from the class attribute of the same name.
        bos_token (`str` or `tokenizers.AddedToken`, *optional*):
            A special token representing the beginning of a sentence. Will be associated to `self.bos_token` and
            `self.bos_token_id`.
        eos_token (`str` or `tokenizers.AddedToken`, *optional*):
            A special token representing the end of a sentence. Will be associated to `self.eos_token` and
            `self.eos_token_id`.
        unk_token (`str` or `tokenizers.AddedToken`, *optional*):
            A special token representing an out-of-vocabulary token. Will be associated to `self.unk_token` and
            `self.unk_token_id`.
        sep_token (`str` or `tokenizers.AddedToken`, *optional*):
            A special token separating two different sentences in the same input (used by BERT for instance). Will be
            associated to `self.sep_token` and `self.sep_token_id`.
        pad_token (`str` or `tokenizers.AddedToken`, *optional*):
            A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
            attention mechanisms or loss computation. Will be associated to `self.pad_token` and `self.pad_token_id`.
        cls_token (`str` or `tokenizers.AddedToken`, *optional*):
            A special token representing the class of the input (used by BERT for instance). Will be associated to
            `self.cls_token` and `self.cls_token_id`.
        mask_token (`str` or `tokenizers.AddedToken`, *optional*):
            A special token representing a masked token (used by masked-language modeling pretraining objectives, like
            BERT). Will be associated to `self.mask_token` and `self.mask_token_id`.
        additional_special_tokens (tuple or list of `str` or `tokenizers.AddedToken`, *optional*):
            A tuple or a list of additional special tokens. Add them here to ensure they won't be split by the
            tokenization process. Will be associated to `self.additional_special_tokens` and
            `self.additional_special_tokens_ids`.
        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
            Whether or not the model should cleanup the spaces that were added when splitting the input text during the
            tokenization process.
        split_special_tokens (`bool`, *optional*, defaults to `False`):
            Whether or not the special tokens should be split during the tokenization process. The default behavior is
            to not split special tokens. This means that if `<s>` is the `bos_token`, then `tokenizer.tokenize("<s>") =
            ['<s>`]. Otherwise, if `split_special_tokens=True`, then `tokenizer.tokenize("<s>")` will be give `['<',
            's', '>']`. This argument is only supported for `slow` tokenizers for the moment.
c