def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.BoolTensor] = None,
        input_values: Optional[torch.FloatTensor] = None,
        padding_mask: Optional[torch.BoolTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.BoolTensor] = None,
        encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
        past_key_values: Tuple[Tuple[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **kwargs,
    ) -> Union[Tuple, Seq2SeqLMOutput]:
        r"""
        Returns:

        Examples:
        ```python
        >>> from transformers import AutoProcessor, MusicgenForConditionalGeneration
        >>> import torch

        >>> processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
        >>> model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")

        >>> inputs = processor(
        ...     text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"],
        ...     padding=True,
        ...     return_tensors="pt",
        ... )

        >>> pad_token_id = model.generation_config.pad_token_id
        >>> decoder_input_ids = (
        ...     torch.ones((inputs.input_ids.shape[0] * model.decoder.num_codebooks, 1), dtype=torch.long)
        ...     * pad_token_id
        ... )

        >>> logits = model(**inputs, decoder_input_ids=decoder_input_ids).logits
        >>> logits.shape  # (bsz * num_codebooks, tgt_len, vocab_size)
        torch.Size([8, 1, 2048])
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        kwargs_text_encoder = {
            argument[len("text_encoder_")]: value
            for argument, value in kwargs.items()
            if argument.startswith("text_encoder_")
        }

        kwargs_audio_encoder = {
            argument[len("audio_encoder_")]: value
            for argument, value in kwargs.items()
            if argument.startswith("audio_encoder_")
        }

        kwargs_decoder = {
            argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
        }

        if encoder_outputs is None:
            encoder_outputs = self.text_encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                inputs_embeds=inputs_embeds,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                **kwargs_text_encoder,
            )
        elif isinstance(encoder_outputs, tuple):
            encoder_outputs = BaseModelOutput(*encoder_outputs)

        encoder_hidden_states = encoder_outputs[0]

        # optionally project encoder_hidden_states
        if (
            self.text_encoder.config.hidden_size != self.decoder.config.hidden_size
            and self.decoder.config.cross_attention_hidden_size is None
        ):
            encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)

        if attention_mask is not None:
            encoder_hidden_states = encoder_hidden_states * attention_mask[..., None]

        if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
            decoder_input_ids = shift_tokens_right(
                labels, self.config.pad_token_id, self.config.decoder_start_token_id
            )

        elif decoder_input_ids is None and decoder_inputs_embeds is None:
            audio_encoder_outputs = self.audio_encoder(
                input_values=input_values,
                padding_mask=padding_mask,
                **kwargs_audio_encoder,
            )
            audio_codes = audio_encoder_outputs.audio_codes
            frames, bsz, codebooks, seq_len = audio_codes.shape
            if frames != 1:
                raise ValueError(
                    f"Expected 1 frame in the audio code outputs, got {frames} frames. Ensure chunking is "
                    "disabled by setting `chunk_length=None` in the audio encoder."
                )

            if self.config.audio_channels == 2 and audio_codes.shape[2] == self.decoder.num_codebooks // 2:
                # mono input through encodec that we convert to stereo
                audio_codes = audio_codes.repeat_interleave(2, dim=2)

            decoder_input_ids = audio_codes[0, ...].reshape(bsz * self.decoder.num_codebooks, seq_len)

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            use_cache=use_cache,
            past_key_values=past_key_values,
            return_dict=return_dict,
            **kwargs_decoder,
        )

        loss = None
        if labels is not None:
            logits = decoder_outputs.logits if return_dict else decoder_outputs[0]
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))

        if not return_dict:
            if loss is not None:
                return (loss,) + decoder_outputs + encoder_outputs
            else:
                return decoder_outputs + encoder_outputs

        return Seq2SeqLMOutput(
            loss=loss,
            logits=decoder_outputs.logits,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )

        Returns:

        Examples:
        ```python
        >>> from transformers import AutoProcessor, MusicgenForConditionalGeneration
        >>> import torch

        >>> processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
        >>> model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")

        >>> inputs = processor(
        ...     text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"],
        ...     padding=True,
        ...     return_tensors="pt",
        ... )

        >>> pad_token_id = model.generation_config.pad_token_id
        >>> decoder_input_ids = (
        ...     torch.ones((inputs.input_ids.shape[0] * model.decoder.num_codebooks, 1), dtype=torch.long)
        ...     * pad_token_id
        ... )

        >>> logits = model(**inputs, decoder_input_ids=decoder_input_ids).logits
        >>> logits.shape  # (bsz * num_codebooks, tgt_len, vocab_size)
        torch.Size([8, 1, 2048])
        ```
dpkd/transformers/src/transformers/models/musicgen/modeling_musicgen.py