def __call__(self, texts_a: List[str]) -> Tuple[Any, List[int]]:
        """Tokenize and find special tokens positions.
        Args:
            texts_a: list of texts,
        Returns:
            batch of :class:`transformers.data.processors.utils.InputFeatures` with subtokens, subtoken ids, \
                subtoken mask, segment mask, or tuple of batch of InputFeatures and Batch of subtokens
            batch of indices of special token ids in input ids sequence
        """
        # in case of iterator's strange behaviour
        if isinstance(texts_a, tuple):
            texts_a = list(texts_a)
        if self.do_lower_case:
            texts_a = [text.lower() for text in texts_a]
        lengths = []
        input_ids_batch = []
        for text_a in texts_a:
            encoding = self.tokenizer.encode_plus(
                text_a, add_special_tokens=True, pad_to_max_length=True, return_attention_mask=True)
            input_ids = encoding["input_ids"]
            input_ids_batch.append(input_ids)
            lengths.append(len(input_ids))

        max_length = min(max(lengths), self.max_seq_length)
        input_features = self.tokenizer(text=texts_a,
                                        add_special_tokens=True,
                                        max_length=max_length,
                                        padding='max_length',
                                        return_attention_mask=True,
                                        truncation=True,
                                        return_tensors='pt')
        special_tokens_pos = []
        for input_ids_list in input_ids_batch:
            found_n = -1
            for n, input_id in enumerate(input_ids_list):
                if input_id == self.special_token_id:
                    found_n = n
                    break
            if found_n == -1:
                found_n = 0
            special_tokens_pos.append(found_n)

        if self.return_special_tokens_pos:
            return input_features, special_tokens_pos
        else:
            return input_features
Tokenize and find special tokens positions.
        Args:
            texts_a: list of texts,
        Returns:
            batch of :class:`transformers.data.processors.utils.InputFeatures` with subtokens, subtoken ids,                 subtoken mask, segment mask, or tuple of batch of InputFeatures and Batch of subtokens
            batch of indices of special token ids in input ids sequence
        
deeppavlov/models/preprocessors/torch_transformers_preprocessor.py