def __call__(self, text=None, audios=None, src_lang=None, tgt_lang=None, **kwargs):
        """
        Main method to prepare for the model one or several sequences(s) and audio(s). This method forwards the `text`
        and `kwargs` arguments to SeamlessM4TTokenizerFast's [`~SeamlessM4TTokenizerFast.__call__`] if `text` is not
        `None` to encode the text. To prepare the audio(s), this method forwards the `audios` and `kwrags` arguments to
        SeamlessM4TFeatureExtractor's [`~SeamlessM4TFeatureExtractor.__call__`] if `audios` is not `None`. Please refer
        to the doctsring of the above two methods for more information.

        Args:
            text (`str`, `List[str]`, `List[List[str]]`):
                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).
            audios (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The audio or batch of audios to be prepared. Each audio can be NumPy array or PyTorch tensor. In case
                of a NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels,
                and T the sample length of the audio.
            src_lang (`str`, *optional*):
                The language code of the input texts/audios. If not specified, the last `src_lang` specified will be
                used.
            tgt_lang (`str`, *optional*):
                The code of the target language. If not specified, the last `tgt_lang` specified will be used.
            kwargs (*optional*):
                Remaining dictionary of keyword arguments that will be passed to the feature extractor and/or the
                tokenizer.
        Returns:
            [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:

            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
              `None`).
            - **input_features** -- Audio input features to be fed to a model. Returned when `audios` is not `None`.
        """
        sampling_rate = kwargs.pop("sampling_rate", None)

        if text is None and audios is None:
            raise ValueError("You have to specify either text or audios. Both cannot be none.")
        elif text is not None and audios is not None:
            raise ValueError(
                "Text and audios are mututally exclusive when passed to `SeamlessM4T`. Specify one or another."
            )
        elif text is not None:
            if tgt_lang is not None:
                self.tokenizer.tgt_lang = tgt_lang
            if src_lang is not None:
                self.tokenizer.src_lang = src_lang
            encoding = self.tokenizer(text, **kwargs)

            return encoding

        else:
            encoding = self.feature_extractor(audios, sampling_rate=sampling_rate, **kwargs)
            return encoding

        Main method to prepare for the model one or several sequences(s) and audio(s). This method forwards the `text`
        and `kwargs` arguments to SeamlessM4TTokenizerFast's [`~SeamlessM4TTokenizerFast.__call__`] if `text` is not
        `None` to encode the text. To prepare the audio(s), this method forwards the `audios` and `kwrags` arguments to
        SeamlessM4TFeatureExtractor's [`~SeamlessM4TFeatureExtractor.__call__`] if `audios` is not `None`. Please refer
        to the doctsring of the above two methods for more information.

        Args:
            text (`str`, `List[str]`, `List[List[str]]`):
                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).
            audios (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The audio or batch of audios to be prepared. Each audio can be NumPy array or PyTorch tensor. In case
                of a NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels,
                and T the sample length of the audio.
            src_lang (`str`, *optional*):
                The language code of the input texts/audios. If not specified, the last `src_lang` specified will be
                used.
            tgt_lang (`str`, *optional*):
                The code of the target language. If not specified, the last `tgt_lang` specified will be used.
            kwargs (*optional*):
                Remaining dictionary of keyword arguments that will be passed to the feature extractor and/or the
                tokenizer.
        Returns:
            [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:

            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
              `None`).
            - **input_features** -- Audio input features to be fed to a model. Returned when `audios` is not `None`.
        
dpkd/transformers/src/transformers/models/seamless_m4t/processing_seamless_m4t.py