def _compatible_inputs()

in eland/ml/pytorch/transformers.py [0:0]

• 30 lines of code
• 4 McCabe index (conditional complexity)

    def _compatible_inputs(self) -> Tuple[Tensor, ...]:
        inputs = self._prepare_inputs()

        # Add params when not provided by the tokenizer (e.g. DistilBERT), to conform to BERT interface
        if "token_type_ids" not in inputs:
            inputs["token_type_ids"] = torch.zeros(
                inputs["input_ids"].size(1), dtype=torch.long
            )
        if isinstance(
            self._tokenizer,
            (
                transformers.BartTokenizer,
                transformers.MPNetTokenizer,
                transformers.RobertaTokenizer,
                transformers.XLMRobertaTokenizer,
            ),
        ):
            return (inputs["input_ids"], inputs["attention_mask"])

        if isinstance(self._tokenizer, transformers.DebertaV2Tokenizer):
            return (
                inputs["input_ids"],
                inputs["attention_mask"],
                inputs["token_type_ids"],
            )

        position_ids = torch.arange(inputs["input_ids"].size(1), dtype=torch.long)
        inputs["position_ids"] = position_ids
        return (
            inputs["input_ids"],
            inputs["attention_mask"],
            inputs["token_type_ids"],
            inputs["position_ids"],
        )