src/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py/Speech2Text2SinusoidalPositionalEmbedding/create_position_ids_from_input_ids
class Speech2Text2SinusoidalPositionalEmbedding:    def create_position_ids_from_input_ids(
        self, input_ids: torch.Tensor, padding_idx: int, past_key_values_length: Optional[int] = 0
    ):
        """
        Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
        symbols are ignored. This is modified from fairseq's `utils.make_positions`.

        Args:
            x: torch.Tensor x:
        Returns: torch.Tensor
        """
        # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
        mask = input_ids.ne(padding_idx).int()
        incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
        return incremental_indices.long() + padding_idx