src/transformers/models/blenderbot/modeling_tf_blenderbot.py/TFBlenderbotForConditionalGeneration/prepare_inputs_for_generation
class TFBlenderbotForConditionalGeneration:    def prepare_inputs_for_generation(
        self,
        decoder_input_ids,
        past_key_values=None,
        attention_mask=None,
        decoder_attention_mask=None,
        head_mask=None,
        decoder_head_mask=None,
        cross_attn_head_mask=None,
        use_cache=None,
        encoder_outputs=None,
        **kwargs,
    ):
        # cut decoder_input_ids if past_key_values is used
        if past_key_values is not None:
            decoder_input_ids = decoder_input_ids[:, -1:]

        if decoder_attention_mask is not None:  # xla
            decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:]
        elif past_key_values is not None:  # no xla + past_key_values
            decoder_position_ids = past_key_values[0][0].shape[2]
        else:  # no xla + no past_key_values
            decoder_position_ids = tf.range(decoder_input_ids.shape[1])

        return {
            "input_ids": None,  # encoder_outputs is defined. input_ids not needed
            "encoder_outputs": encoder_outputs,
            "past_key_values": past_key_values,
            "decoder_input_ids": decoder_input_ids,
            "attention_mask": attention_mask,
            "decoder_attention_mask": decoder_attention_mask,
            "decoder_position_ids": decoder_position_ids,
            "head_mask": head_mask,
            "decoder_head_mask": decoder_head_mask,
            "cross_attn_head_mask": cross_attn_head_mask,
            "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
        }