def __call__(
        self,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        text_pair: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
        text_target: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        text_pair_target: Optional[
            Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]
        ] = None,
        padding: Union[bool, str, PaddingStrategy] = True,
        pad_to_multiple_of: Optional[int] = 2,
        src_lang: Optional[str] = None,
        tgt_lang: Optional[str] = None,
        **kwargs,
    ):
        """
        Args:
            text (`str`, `List[str]`, `List[List[str]]`, *optional*):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            text_pair (`str`, `List[str]`, `List[List[str]]`, *optional*):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            text_target (`str`, `List[str]`, `List[List[str]]`, *optional*):
                The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a
                list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized),
                you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            text_pair_target (`str`, `List[str]`, `List[List[str]]`, *optional*):
                The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a
                list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized),
                you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
                 Select a strategy to pad the returned sequences (according to the model's padding side and padding
                 index) among:

                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
                  sequence if provided).
                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
                  acceptable input length for the model if that argument is not provided.
                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
                  lengths).
            pad_to_multiple_of (`int`, *optional*):
                If set will pad the sequence to a multiple of the provided value.

                This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
                `>= 7.5` (Volta).
            src_lang (`str`, *optional*):
                A string representing the source language. If not specified, the last `src_lang` specified (either
                during initialization or when calling this tokenizer) will be used.
            tgt_lang (`str`, *optional*):
                A string representing the target language. If not specified, the last `tgt_lang` specified (either
                during initialization or when calling this tokenizer) will be used.
            kwargs (*optional*):
                Remaining dictionary of keyword arguments that will be passed to [`PreTrainedTokenizer.__call__`].
        """
        if src_lang is not None:
            self.src_lang = src_lang
        if tgt_lang is not None:
            self.tgt_lang = tgt_lang

        output = super().__call__(
            text=text,
            text_pair=text_pair,
            text_target=text_target,
            text_pair_target=text_pair_target,
            padding=padding,
            pad_to_multiple_of=pad_to_multiple_of,
            **kwargs,
        )

        return BatchEncoding(output, tensor_type=kwargs.get("return_tensors"))

        Args:
            text (`str`, `List[str]`, `List[List[str]]`, *optional*):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            text_pair (`str`, `List[str]`, `List[List[str]]`, *optional*):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            text_target (`str`, `List[str]`, `List[List[str]]`, *optional*):
                The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a
                list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized),
                you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            text_pair_target (`str`, `List[str]`, `List[List[str]]`, *optional*):
                The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a
                list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized),
                you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
                 Select a strategy to pad the returned sequences (according to the model's padding side and padding
                 index) among:

                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
                  sequence if provided).
                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
                  acceptable input length for the model if that argument is not provided.
                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
                  lengths).
            pad_to_multiple_of (`int`, *optional*):
                If set will pad the sequence to a multiple of the provided value.

                This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
                `>= 7.5` (Volta).
            src_lang (`str`, *optional*):
                A string representing the source language. If not specified, the last `src_lang` specified (either
                during initialization or when calling this tokenizer) will be used.
            tgt_lang (`str`, *optional*):
                A string representing the target language. If not specified, the last `tgt_lang` specified (either
                during initialization or when calling this tokenizer) will be used.
            kwargs (*optional*):
                Remaining dictionary of keyword arguments that will be passed to [`PreTrainedTokenizer.__call__`].
        
dpkd/transformers/src/transformers/models/seamless_m4t/tokenization_seamless_m4t.py