def _parse_and_tokenize(
        self, sequence_pairs, padding=True, add_special_tokens=True, truncation=TruncationStrategy.ONLY_FIRST, **kwargs
    ):
        """
        Parse arguments and tokenize only_first so that hypothesis (label) is not truncated
        """
        return_tensors = self.framework
        if self.tokenizer.pad_token is None:
            # Override for tokenizers not supporting padding
            logger.error(
                "Tokenizer was not supporting padding necessary for zero-shot, attempting to use "
                " `pad_token=eos_token`"
            )
            self.tokenizer.pad_token = self.tokenizer.eos_token
        try:
            inputs = self.tokenizer(
                sequence_pairs,
                add_special_tokens=add_special_tokens,
                return_tensors=return_tensors,
                padding=padding,
                truncation=truncation,
            )
        except Exception as e:
            if "too short" in str(e):
                # tokenizers might yell that we want to truncate
                # to a value that is not even reached by the input.
                # In that case we don't want to truncate.
                # It seems there's not a really better way to catch that
                # exception.

                inputs = self.tokenizer(
                    sequence_pairs,
                    add_special_tokens=add_special_tokens,
                    return_tensors=return_tensors,
                    padding=padding,
                    truncation=TruncationStrategy.DO_NOT_TRUNCATE,
                )
            else:
                raise e

        return inputs

        Parse arguments and tokenize only_first so that hypothesis (label) is not truncated
        
dpkd/transformers/src/transformers/pipelines/zero_shot_classification.py