def prepare_seq2seq_batch(
        self,
        src_texts: List[str],
        tgt_texts: Optional[List[str]] = None,
        max_length: Optional[int] = None,
        max_target_length: Optional[int] = None,
        padding: str = "longest",
        return_tensors: str = None,
        truncation: bool = True,
        **kwargs,
    ) -> BatchEncoding:
        """
        Prepare model inputs for translation. For best performance, translate one sentence at a time.

        Arguments:
            src_texts (`List[str]`):
                List of documents to summarize or source language texts.
            tgt_texts (`list`, *optional*):
                List of summaries or target language texts.
            max_length (`int`, *optional*):
                Controls the maximum length for encoder inputs (documents to summarize or source language texts) If
                left unset or set to `None`, this will use the predefined model maximum length if a maximum length is
                required by one of the truncation/padding parameters. If the model has no specific maximum input length
                (like XLNet) truncation/padding to a maximum length will be deactivated.
            max_target_length (`int`, *optional*):
                Controls the maximum length of decoder inputs (target language texts or summaries) If left unset or set
                to `None`, this will use the max_length value.
            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
                Activates and controls padding. Accepts the following values:

                - `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).
            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
            truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `True`):
                Activates and controls truncation. Accepts the following values:

                - `True` or `'longest_first'`: Truncate 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. This will
                  truncate token by token, removing a token from the longest sequence in the pair if a pair of
                  sequences (or a batch of pairs) is provided.
                - `'only_first'`: Truncate 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. This will only
                  truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
                - `'only_second'`: Truncate 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. This will only
                  truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
                - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
                  greater than the model maximum admissible input size).
            **kwargs:
                Additional keyword arguments passed along to `self.__call__`.

        Return:
            [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:

            - **input_ids** -- List of token ids to be fed to the encoder.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model.
            - **labels** -- List of token ids for tgt_texts.

            The full set of keys `[input_ids, attention_mask, labels]`, will only be returned if tgt_texts is passed.
            Otherwise, input_ids, attention_mask will be the only keys.
        """
        # docstyle-ignore
        formatted_warning = """
`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of HuggingFace Transformers. Use the regular
`__call__` method to prepare your inputs and targets.

Here is a short example:

model_inputs = tokenizer(src_texts, text_target=tgt_texts, ...)

If you either need to use different keyword arguments for the source and target texts, you should do two calls like
this:

model_inputs = tokenizer(src_texts, ...)
labels = tokenizer(text_target=tgt_texts, ...)
model_inputs["labels"] = labels["input_ids"]

See the documentation of your specific tokenizer for more details on the specific arguments to the tokenizer of choice.
For a more complete example, see the implementation of `prepare_seq2seq_batch`.
"""
        warnings.warn(formatted_warning, FutureWarning)
        # mBART-specific kwargs that should be ignored by other models.
        kwargs.pop("src_lang", None)
        kwargs.pop("tgt_lang", None)
        if max_length is None:
            max_length = self.model_max_length
        model_inputs = self(
            src_texts,
            add_special_tokens=True,
            return_tensors=return_tensors,
            max_length=max_length,
            padding=padding,
            truncation=truncation,
            **kwargs,
        )
        if tgt_texts is None:
            return model_inputs
        # Process tgt_texts
        if max_target_length is None:
            max_target_length = max_length
        with self.as_target_tokenizer():
            labels = self(
                tgt_texts,
                add_special_tokens=True,
                return_tensors=return_tensors,
                padding=padding,
                max_length=max_target_length,
                truncation=truncation,
                **kwargs,
            )
        model_inputs["labels"] = labels["input_ids"]
        return model_inputs

        Prepare model inputs for translation. For best performance, translate one sentence at a time.

        Arguments:
            src_texts (`List[str]`):
                List of documents to summarize or source language texts.
            tgt_texts (`list`, *optional*):
                List of summaries or target language texts.
            max_length (`int`, *optional*):
                Controls the maximum length for encoder inputs (documents to summarize or source language texts) If
                left unset or set to `None`, this will use the predefined model maximum length if a maximum length is
                required by one of the truncation/padding parameters. If the model has no specific maximum input length
                (like XLNet) truncation/padding to a maximum length will be deactivated.
            max_target_length (`int`, *optional*):
                Controls the maximum length of decoder inputs (target language texts or summaries) If left unset or set
                to `None`, this will use the max_length value.
            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
                Activates and controls padding. Accepts the following values:

                - `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).
            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
            truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `True`):
                Activates and controls truncation. Accepts the following values:

                - `True` or `'longest_first'`: Truncate 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. This will
                  truncate token by token, removing a token from the longest sequence in the pair if a pair of
                  sequences (or a batch of pairs) is provided.
                - `'only_first'`: Truncate 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. This will only
                  truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
                - `'only_second'`: Truncate 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. This will only
                  truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
                - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
                  greater than the model maximum admissible input size).
            **kwargs:
                Additional keyword arguments passed along to `self.__call__`.

        Return:
            [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:

            - **input_ids** -- List of token ids to be fed to the encoder.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model.
            - **labels** -- List of token ids for tgt_texts.

            The full set of keys `[input_ids, attention_mask, labels]`, will only be returned if tgt_texts is passed.
            Otherwise, input_ids, attention_mask will be the only keys.
        
dpkd/transformers/src/transformers/tokenization_utils_base.py