modeling_musicgen.py

# coding=utf-8
# Copyright 2023 Meta AI and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch Musicgen model."""
import copy
import inspect
import math
import random
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss

from ...activations import ACT2FN
from ...generation.configuration_utils import GenerationConfig
from ...generation.logits_process import ClassifierFreeGuidanceLogitsProcessor, LogitsProcessorList
from ...generation.stopping_criteria import StoppingCriteriaList
from ...modeling_attn_mask_utils import (
    _prepare_4d_attention_mask,
    _prepare_4d_attention_mask_for_sdpa,
    _prepare_4d_causal_attention_mask,
    _prepare_4d_causal_attention_mask_for_sdpa,
)
from ...modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPastAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
    ModelOutput,
    Seq2SeqLMOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_flash_attn_2_available,
    is_flash_attn_greater_or_equal_2_10,
    logging,
    replace_return_docstrings,
)
from ..auto.configuration_auto import AutoConfig
from ..auto.modeling_auto import AutoModel
from .configuration_musicgen import MusicgenConfig, MusicgenDecoderConfig


if is_flash_attn_2_available():
    from flash_attn import flash_attn_func, flash_attn_varlen_func
    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa

if TYPE_CHECKING:
    from ...generation.streamers import BaseStreamer

logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "MusicgenConfig"
_CHECKPOINT_FOR_DOC = "facebook/musicgen-small"


# Copied from transformers.models.llama.modeling_llama._get_unpad_data
def _get_unpad_data(attention_mask):
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
    return (
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
    )


@dataclass
class MusicgenUnconditionalInput(ModelOutput):
    """
    Args:
        encoder_outputs  (`Tuple[torch.FloatTensor]` of length 1, with tensor shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the text encoder model.
        attention_mask (`torch.LongTensor`)  of shape `(batch_size, sequence_length)`, *optional*):
            Encoder attention mask to avoid performing attention on padding token indices. Mask values selected in `[0,
            1]`: 1 for tokens that are **not masked**, 0 for tokens that are **masked**.
        guidance_scale (`float`, *optional*):
            Guidance scale for classifier free guidance, setting the balance between the conditional logits (predicted
            from the prompts) and the unconditional logits (predicted without prompts).
    """

    encoder_outputs: Tuple[torch.FloatTensor] = None
    attention_mask: torch.LongTensor = None
    guidance_scale: float = None


def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
    """
    Shift input ids one token to the right.
    """
    # transpose to get (bsz, num_codebooks, seq_len)
    input_ids = input_ids.transpose(1, 2)
    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
    shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
    if decoder_start_token_id is None:
        raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.")
    shifted_input_ids[..., 0] = decoder_start_token_id

    if pad_token_id is None:
        raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.")
    # replace possible -100 values in labels by `pad_token_id`
    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)

    return shifted_input_ids


class MusicgenSinusoidalPositionalEmbedding(nn.Module):
    """This module produces sinusoidal positional embeddings of any length."""

    def __init__(self, num_positions: int, embedding_dim: int):
        super().__init__()
        self.embedding_dim = embedding_dim
        self.make_weights(num_positions, embedding_dim)

    def make_weights(self, num_embeddings: int, embedding_dim: int):
        emb_weights = self.get_embedding(num_embeddings, embedding_dim)
        if hasattr(self, "weights"):
            # in forward put the weights on the correct dtype and device of the param
            emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)

        self.weights = nn.Parameter(emb_weights)
        self.weights.requires_grad = False
        self.weights.detach_()

    @staticmethod
    def get_embedding(num_embeddings: int, embedding_dim: int):
        """
        Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the
        description in Section 3.5 of "Attention Is All You Need".
        """
        half_dim = embedding_dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb)
        emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0)
        emb = torch.cat([torch.cos(emb), torch.sin(emb)], dim=1).view(num_embeddings, -1)
        if embedding_dim % 2 == 1:
            # zero pad
            emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
        return emb.to(torch.get_default_dtype())

    @torch.no_grad()
    def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0):
        bsz, codebooks, seq_len = input_ids.size()
        # Create the position ids from the input token ids.
        position_ids = (torch.arange(seq_len) + past_key_values_length).to(input_ids.device)
        # expand embeddings if needed
        if seq_len > self.weights.size(0):
            self.make_weights(seq_len + self.offset, self.embedding_dim)
        return self.weights.index_select(0, position_ids.view(-1)).detach()


# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Musicgen
class MusicgenAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        dropout: float = 0.0,
        is_decoder: bool = False,
        bias: bool = True,
        is_causal: bool = False,
        config: Optional[MusicgenConfig] = None,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        self.config = config

        if (self.head_dim * num_heads) != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
                f" and `num_heads`: {num_heads})."
            )
        self.scaling = self.head_dim**-0.5
        self.is_decoder = is_decoder
        self.is_causal = is_causal

        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)

    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def forward(
        self,
        hidden_states: torch.Tensor,
        key_value_states: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel"""

        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None

        bsz, tgt_len, _ = hidden_states.size()

        # get query proj
        query_states = self.q_proj(hidden_states) * self.scaling
        # get key, value proj
        # `past_key_value[0].shape[2] == key_value_states.shape[1]`
        # is checking that the `sequence_length` of the `past_key_value` is the same as
        # the provided `key_value_states` to support prefix tuning
        if (
            is_cross_attention
            and past_key_value is not None
            and past_key_value[0].shape[2] == key_value_states.shape[1]
        ):
            # reuse k,v, cross_attentions
            key_states = past_key_value[0]
            value_states = past_key_value[1]
        elif is_cross_attention:
            # cross_attentions
            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
        elif past_key_value is not None:
            # reuse k, v, self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)
        else:
            # self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)

        if self.is_decoder:
            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_states, value_states)

        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
        key_states = key_states.reshape(*proj_shape)
        value_states = value_states.reshape(*proj_shape)

        src_len = key_states.size(1)
        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))

        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
            raise ValueError(
                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
                f" {attn_weights.size()}"
            )

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
                )
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

        attn_weights = nn.functional.softmax(attn_weights, dim=-1)

        if layer_head_mask is not None:
            if layer_head_mask.size() != (self.num_heads,):
                raise ValueError(
                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
                    f" {layer_head_mask.size()}"
                )
            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

        if output_attentions:
            # this operation is a bit awkward, but it's required to
            # make sure that attn_weights keeps its gradient.
            # In order to do so, attn_weights have to be reshaped
            # twice and have to be reused in the following
            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
        else:
            attn_weights_reshaped = None

        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

        attn_output = torch.bmm(attn_probs, value_states)

        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
        attn_output = attn_output.transpose(1, 2)

        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
        # partitioned across GPUs when using tensor-parallelism.
        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)

        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights_reshaped, past_key_value


# Copied from transformers.models.bart.modeling_bart.BartFlashAttention2 with Bart->Musicgen
class MusicgenFlashAttention2(MusicgenAttention):
    """
    Musicgen flash attention module. This module inherits from `MusicgenAttention` as the weights of the module stays
    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
    flash attention and deal with padding tokens in case the input contains any of them.
    """

    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()

    def _reshape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        key_value_states: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        # MusicgenFlashAttention2 attention does not support output_attentions
        if output_attentions:
            raise ValueError("MusicgenFlashAttention2 attention does not support output_attentions")

        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None

        bsz, q_len, _ = hidden_states.size()

        # get query proj
        query_states = self._reshape(self.q_proj(hidden_states), -1, bsz)
        # get key, value proj
        # `past_key_value[0].shape[2] == key_value_states.shape[1]`
        # is checking that the `sequence_length` of the `past_key_value` is the same as
        # the provided `key_value_states` to support prefix tuning
        if (
            is_cross_attention
            and past_key_value is not None
            and past_key_value[0].shape[2] == key_value_states.shape[1]
        ):
            # reuse k,v, cross_attentions
            key_states = past_key_value[0].transpose(1, 2)
            value_states = past_key_value[1].transpose(1, 2)
        elif is_cross_attention:
            # cross_attentions
            key_states = self._reshape(self.k_proj(key_value_states), -1, bsz)
            value_states = self._reshape(self.v_proj(key_value_states), -1, bsz)
        elif past_key_value is not None:
            # reuse k, v, self_attention
            key_states = self._reshape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._reshape(self.v_proj(hidden_states), -1, bsz)
            key_states = torch.cat([past_key_value[0].transpose(1, 2), key_states], dim=1)
            value_states = torch.cat([past_key_value[1].transpose(1, 2), value_states], dim=1)
        else:
            # self_attention
            key_states = self._reshape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._reshape(self.v_proj(hidden_states), -1, bsz)

        if self.is_decoder:
            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_states.transpose(1, 2), value_states.transpose(1, 2))

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[0].shape[-2]

        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
        # therefore the input hidden states gets silently casted in float32. Hence, we need
        # cast them back in the correct dtype just to be sure everything works as expected.
        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
        # in fp32. (LlamaRMSNorm handles it correctly)

        input_dtype = query_states.dtype
        if input_dtype == torch.float32:
            if torch.is_autocast_enabled():
                target_dtype = torch.get_autocast_gpu_dtype()
            # Handle the case where the model is quantized
            elif hasattr(self.config, "_pre_quantization_dtype"):
                target_dtype = self.config._pre_quantization_dtype
            else:
                target_dtype = self.q_proj.weight.dtype

            logger.warning_once(
                f"The input hidden states seems to be silently casted in float32, this might be related to"
                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
                f" {target_dtype}."
            )

            query_states = query_states.to(target_dtype)
            key_states = key_states.to(target_dtype)
            value_states = value_states.to(target_dtype)

        attn_output = self._flash_attention_forward(
            query_states, key_states, value_states, attention_mask, q_len, dropout=self.dropout
        )

        attn_output = attn_output.reshape(bsz, q_len, -1)
        attn_output = self.out_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value

    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
    def _flash_attention_forward(
        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
    ):
        """
        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
        first unpad the input, then computes the attention scores and pad the final attention scores.
        Args:
            query_states (`torch.Tensor`):
                Input query states to be passed to Flash Attention API
            key_states (`torch.Tensor`):
                Input key states to be passed to Flash Attention API
            value_states (`torch.Tensor`):
                Input value states to be passed to Flash Attention API
            attention_mask (`torch.Tensor`):
                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
                position of padding tokens and 1 for the position of non-padding tokens.
            dropout (`float`):
                Attention dropout
            softmax_scale (`float`, *optional*):
                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
        """
        if not self._flash_attn_uses_top_left_mask:
            causal = self.is_causal
        else:
            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
            causal = self.is_causal and query_length != 1

        # Contains at least one padding token in the sequence
        if attention_mask is not None:
            batch_size = query_states.shape[0]
            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
                query_states, key_states, value_states, attention_mask, query_length
            )

            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

            attn_output_unpad = flash_attn_varlen_func(
                query_states,
                key_states,
                value_states,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_k=cu_seqlens_k,
                max_seqlen_q=max_seqlen_in_batch_q,
                max_seqlen_k=max_seqlen_in_batch_k,
                dropout_p=dropout,
                softmax_scale=softmax_scale,
                causal=causal,
            )

            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
        else:
            attn_output = flash_attn_func(
                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
            )

        return attn_output

    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape

        key_layer = index_first_axis(
            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
        )
        value_layer = index_first_axis(
            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
        )
        if query_length == kv_seq_len:
            query_layer = index_first_axis(
                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
            )
            cu_seqlens_q = cu_seqlens_k
            max_seqlen_in_batch_q = max_seqlen_in_batch_k
            indices_q = indices_k
        elif query_length == 1:
            max_seqlen_in_batch_q = 1
            cu_seqlens_q = torch.arange(
                batch_size + 1, dtype=torch.int32, device=query_layer.device
            )  # There is a memcpy here, that is very bad.
            indices_q = cu_seqlens_q[:-1]
            query_layer = query_layer.squeeze(1)
        else:
            # The -q_len: slice assumes left padding.
            attention_mask = attention_mask[:, -query_length:]
            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)

        return (
            query_layer,
            key_layer,
            value_layer,
            indices_q,
            (cu_seqlens_q, cu_seqlens_k),
            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
        )


# Copied from transformers.models.bart.modeling_bart.BartSdpaAttention with Bart->Musicgen
class MusicgenSdpaAttention(MusicgenAttention):
    def forward(
        self,
        hidden_states: torch.Tensor,
        key_value_states: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel"""
        if output_attentions or layer_head_mask is not None:
            # TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once this is implemented.
            logger.warning_once(
                "MusicgenModel is using MusicgenSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True` or `layer_head_mask` not None. Falling back to the manual attention"
                ' implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
            )
            return super().forward(
                hidden_states,
                key_value_states=key_value_states,
                past_key_value=past_key_value,
                attention_mask=attention_mask,
                layer_head_mask=layer_head_mask,
                output_attentions=output_attentions,
            )

        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None

        bsz, tgt_len, _ = hidden_states.size()

        # get query proj
        query_states = self.q_proj(hidden_states)
        # get key, value proj
        # `past_key_value[0].shape[2] == key_value_states.shape[1]`
        # is checking that the `sequence_length` of the `past_key_value` is the same as
        # the provided `key_value_states` to support prefix tuning
        if (
            is_cross_attention
            and past_key_value is not None
            and past_key_value[0].shape[2] == key_value_states.shape[1]
        ):
            # reuse k,v, cross_attentions
            key_states = past_key_value[0]
            value_states = past_key_value[1]
        elif is_cross_attention:
            # cross_attentions
            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
        elif past_key_value is not None:
            # reuse k, v, self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)
        else:
            # self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)

        if self.is_decoder:
            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_states, value_states)

        query_states = self._shape(query_states, tgt_len, bsz)

        # NOTE: SDPA with memory-efficient backend is currently (torch==2.1.2) bugged when using non-contiguous inputs and a custom attn_mask,
        # but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577
        attn_output = torch.nn.functional.scaled_dot_product_attention(
            query_states,
            key_states,
            value_states,
            attn_mask=attention_mask,
            dropout_p=self.dropout if self.training else 0.0,
            # The tgt_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case tgt_len == 1.
            is_causal=self.is_causal and attention_mask is None and tgt_len > 1,
        )

        if attn_output.size() != (bsz, self.num_heads, tgt_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.transpose(1, 2)

        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
        # partitioned across GPUs when using tensor-parallelism.
        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)

        attn_output = self.out_proj(attn_output)

        return attn_output, None, past_key_value


MUSICGEN_ATTENTION_CLASSES = {
    "eager": MusicgenAttention,
    "sdpa": MusicgenSdpaAttention,
    "flash_attention_2": MusicgenFlashAttention2,
}


class MusicgenDecoderLayer(nn.Module):
    def __init__(self, config: MusicgenDecoderConfig):
        super().__init__()
        self.embed_dim = config.hidden_size

        self.self_attn = MUSICGEN_ATTENTION_CLASSES[config._attn_implementation](
            embed_dim=self.embed_dim,
            num_heads=config.num_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
            bias=False,
            is_causal=True,
            config=config,
        )
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout

        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.encoder_attn = MUSICGEN_ATTENTION_CLASSES[config._attn_implementation](
            self.embed_dim,
            config.num_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
            bias=False,
            config=config,
        )
        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim, bias=False)
        self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim, bias=False)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)

    # Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer.forward
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = True,
    ) -> torch.Tensor:
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            encoder_hidden_states (`torch.FloatTensor`):
                cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
            encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
                `(encoder_attention_heads,)`.
            cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
                size `(decoder_attention_heads,)`.
            past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        # Self Attention
        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        # add present self-attn cache to positions 1,2 of present_key_value tuple
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            past_key_value=self_attn_past_key_value,
            attention_mask=attention_mask,
            layer_head_mask=layer_head_mask,
            output_attentions=output_attentions,
        )
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states

        # Cross-Attention Block
        cross_attn_present_key_value = None
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states
            hidden_states = self.encoder_attn_layer_norm(hidden_states)

            # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
            hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
                hidden_states=hidden_states,
                key_value_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                layer_head_mask=cross_attn_layer_head_mask,
                past_key_value=cross_attn_past_key_value,
                output_attentions=output_attentions,
            )
            hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
            hidden_states = residual + hidden_states

            # add cross-attn to positions 3,4 of present_key_value tuple
            present_key_value = present_key_value + cross_attn_present_key_value

        # Fully Connected
        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.fc2(hidden_states)
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights, cross_attn_weights)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


class MusicgenPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = MusicgenDecoderConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["MusicgenDecoderLayer", "MusicgenAttention"]
    _supports_flash_attn_2 = True
    _supports_sdpa = True

    def _init_weights(self, module):
        std = self.config.initializer_factor
        if isinstance(module, (nn.Linear, nn.Conv1d)):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()


MUSICGEN_START_DOCSTRING = r"""

    The Musicgen model was proposed in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by
    Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez. It is an
    encoder decoder transformer trained on the task of conditional music generation

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`MusicgenConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

MUSICGEN_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary, corresponding to the sequence of audio codes.

            Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes,
            such as with the [`EncodecModel`]. See [`EncodecModel.encode`] for details.

            [What are decoder input IDs?](../glossary#decoder-input-ids)

            <Tip warning={true}>

            The `decoder_input_ids` will automatically be converted from shape `(batch_size * num_codebooks,
            target_sequence_length)` to `(batch_size, num_codebooks, target_sequence_length)` in the forward pass. If
            you obtain audio codes from an audio encoding model, such as [`EncodecModel`], ensure that the number of
            frames is equal to 1, and that you reshape the audio codes from `(frames, batch_size, num_codebooks,
            target_sequence_length)` to `(batch_size * num_codebooks, target_sequence_length)` prior to passing them as
            `decoder_input_ids`.

            </Tip>

        decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.
        head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
            1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
            input (see `past_key_values`). This is useful if you want more control over how to convert
            `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.

            If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
            of `inputs_embeds`.
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length, num_codebooks)`, *optional*):
            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

MUSICGEN_DECODER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, sequence_length)`):
            Indices of input sequence tokens in the vocabulary, corresponding to the sequence of audio codes.

            Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes,
            such as with the [`EncodecModel`]. See [`EncodecModel.encode`] for details.

            [What are input IDs?](../glossary#input-ids)

            <Tip warning={true}>

            The `input_ids` will automatically be converted from shape `(batch_size * num_codebooks,
            target_sequence_length)` to `(batch_size, num_codebooks, target_sequence_length)` in the forward pass. If
            you obtain audio codes from an audio encoding model, such as [`EncodecModel`], ensure that the number of
            frames is equal to 1, and that you reshape the audio codes from `(frames, batch_size, num_codebooks,
            target_sequence_length)` to `(batch_size * num_codebooks, target_sequence_length)` prior to passing them as
            `input_ids`.

            </Tip>

        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of
            the decoder.
        encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
            Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
            selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
            cross-attention on hidden heads. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


class MusicgenDecoder(MusicgenPreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MusicgenDecoderLayer`]
    """

    def __init__(self, config: MusicgenDecoderConfig):
        super().__init__(config)
        self.dropout = config.dropout
        self.layerdrop = config.layerdrop
        self.max_target_positions = config.max_position_embeddings
        self.d_model = config.hidden_size
        self.num_codebooks = config.num_codebooks
        self.embed_scale = math.sqrt(config.hidden_size) if config.scale_embedding else 1.0

        embed_dim = config.vocab_size + 1
        self.embed_tokens = nn.ModuleList(
            [nn.Embedding(embed_dim, config.hidden_size) for _ in range(config.num_codebooks)]
        )

        self.embed_positions = MusicgenSinusoidalPositionalEmbedding(
            config.max_position_embeddings,
            config.hidden_size,
        )

        self.layers = nn.ModuleList([MusicgenDecoderLayer(config) for _ in range(config.num_hidden_layers)])
        self.layer_norm = nn.LayerNorm(config.hidden_size)
        self.attn_implementation = config._attn_implementation

        self.gradient_checkpointing = False
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    @add_start_docstrings_to_model_forward(MUSICGEN_DECODER_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            # (bsz * codebooks, seq_len) -> (bsz, codebooks, seq_len)
            input = input_ids.reshape(-1, self.num_codebooks, input_ids.shape[-1])
            bsz, num_codebooks, seq_len = input.shape
            input_shape = (bsz, seq_len)
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
            input = inputs_embeds[:, :, -1:]
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

        # past_key_values_length
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

        if inputs_embeds is None:
            inputs_embeds = sum([self.embed_tokens[codebook](input[:, codebook]) for codebook in range(num_codebooks)])

        if self.attn_implementation == "flash_attention_2":
            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
        elif self.attn_implementation == "sdpa" and head_mask is None and not output_attentions:
            # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
            # the manual implementation that requires a 4D causal mask in all cases.
            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                attention_mask,
                input_shape,
                inputs_embeds,
                past_key_values_length,
            )
        else:
            attention_mask = _prepare_4d_causal_attention_mask(
                attention_mask, input_shape, inputs_embeds, past_key_values_length
            )

        # expand encoder attention mask
        if encoder_hidden_states is not None and encoder_attention_mask is not None:
            if self.attn_implementation == "flash_attention_2":
                encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
            elif self.attn_implementation == "sdpa" and cross_attn_head_mask is None and not output_attentions:
                # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
                # the manual implementation that requires a 4D causal mask in all cases.
                # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
                encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(
                    encoder_attention_mask,
                    inputs_embeds.dtype,
                    tgt_len=input_shape[-1],
                )
            else:
                # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
                encoder_attention_mask = _prepare_4d_attention_mask(
                    encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
                )

        # embed positions
        positions = self.embed_positions(input, past_key_values_length)

        hidden_states = inputs_embeds + positions.to(inputs_embeds.device)

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing`. Setting `use_cache=False`..."
                )
                use_cache = False

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
        next_decoder_cache = () if use_cache else None

        # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
        for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
            if attn_mask is not None:
                if attn_mask.size()[0] != len(self.layers):
                    raise ValueError(
                        f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
                        f" {attn_mask.size()[0]}."
                    )
        for idx, decoder_layer in enumerate(self.layers):
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            if output_hidden_states:
                all_hidden_states += (hidden_states,)
            dropout_probability = random.uniform(0, 1)
            if self.training and (dropout_probability < self.layerdrop):
                continue

            past_key_value = past_key_values[idx] if past_key_values is not None else None

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.forward,
                    hidden_states,
                    attention_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    head_mask[idx] if head_mask is not None else None,
                    cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
                    None,
                    output_attentions,
                    use_cache,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_attention_mask,
                    layer_head_mask=(head_mask[idx] if head_mask is not None else None),
                    cross_attn_layer_head_mask=(
                        cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
                    ),
                    past_key_value=past_key_value,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                )
            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

                if encoder_hidden_states is not None:
                    all_cross_attentions += (layer_outputs[2],)

        hidden_states = self.layer_norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if not return_dict:
            return tuple(
                v
                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
                if v is not None
            )
        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            cross_attentions=all_cross_attentions,
        )


@add_start_docstrings(
    "The bare Musicgen decoder model outputting raw hidden-states without any specific head on top.",
    MUSICGEN_START_DOCSTRING,
)
class MusicgenModel(MusicgenPreTrainedModel):
    def __init__(self, config: MusicgenDecoderConfig):
        super().__init__(config)
        self.decoder = MusicgenDecoder(config)
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.decoder.embed_tokens

    def set_input_embeddings(self, value):
        self.decoder.embed_tokens = value

    def get_decoder(self):
        return self.decoder

    @add_start_docstrings_to_model_forward(MUSICGEN_DECODER_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
        decoder_outputs = self.decoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            encoder_attention_mask=encoder_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            head_mask=head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if not return_dict:
            return decoder_outputs

        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=decoder_outputs.last_hidden_state,
            past_key_values=decoder_outputs.past_key_values,
            hidden_states=decoder_outputs.hidden_states,
            attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
        )


@add_start_docstrings(
    "The MusicGen decoder model with a language modelling head on top.",
    MUSICGEN_START_DOCSTRING,
)
class MusicgenForCausalLM(MusicgenPreTrainedModel):
    def __init__(self, config: MusicgenDecoderConfig):
        super().__init__(config)

        self.model = MusicgenModel(config)

        self.num_codebooks = config.num_codebooks
        self.lm_heads = nn.ModuleList(
            [nn.Linear(config.hidden_size, config.vocab_size, bias=False) for _ in range(config.num_codebooks)]
        )

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.decoder.embed_tokens

    def set_input_embeddings(self, value):
        self.model.decoder.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_heads

    def set_output_embeddings(self, new_embeddings):
        self.lm_heads = new_embeddings

    def set_decoder(self, decoder):
        self.model.decoder = decoder

    def get_decoder(self):
        return self.model.decoder

    @add_start_docstrings_to_model_forward(MUSICGEN_DECODER_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        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,
    ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length, num_codebooks)`, *optional*):
            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
        Returns:
        """

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if (labels is not None) and (input_ids is None and inputs_embeds is None):
            input_ids = shift_tokens_right(labels, self.config.pad_token_id, self.config.bos_token_id)

        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            head_mask=head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]

        lm_logits = torch.stack([head(hidden_states) for head in self.lm_heads], dim=1)

        loss = None
        if labels is not None:
            # since encoder hidden states have been concatenated to the decoder hidden states,
            # we take the last timestamps corresponding to labels
            logits = lm_logits[:, :, -labels.shape[1] :]

            loss_fct = CrossEntropyLoss()
            loss = torch.zeros([], device=self.device)

            # per codebook cross-entropy
            # -100 labels are ignored
            labels = labels.masked_fill(labels == self.config.pad_token_id, -100)

            # per codebook cross-entropy
            # ref: https://github.com/facebookresearch/audiocraft/blob/69fea8b290ad1b4b40d28f92d1dfc0ab01dbab85/audiocraft/solvers/musicgen.py#L242-L243
            for codebook in range(self.config.num_codebooks):
                codebook_logits = logits[:, codebook].contiguous().view(-1, logits.shape[-1])
                codebook_labels = labels[..., codebook].contiguous().view(-1)
                loss += loss_fct(codebook_logits, codebook_labels)

            loss = loss / self.config.num_codebooks

        # (bsz, num_codebooks, seq_len, vocab_size) -> (bsz * num_codebooks, seq_len, vocab_size)
        lm_logits = lm_logits.reshape(-1, *lm_logits.shape[2:])

        if not return_dict:
            output = (lm_logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return CausalLMOutputWithCrossAttentions(
            loss=loss,
            logits=lm_logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        head_mask=None,
        cross_attn_head_mask=None,
        past_key_values=None,
        use_cache=True,
        delay_pattern_mask=None,
        guidance_scale=None,
        **kwargs,
    ):
        if delay_pattern_mask is None:
            input_ids, delay_pattern_mask = self.build_delay_pattern_mask(
                input_ids,
                pad_token_id=self.generation_config.pad_token_id,
                max_length=self.generation_config.max_length,
            )

        # apply the delay pattern mask
        input_ids = self.apply_delay_pattern_mask(input_ids, delay_pattern_mask)

        if guidance_scale is not None and guidance_scale > 1:
            # for classifier free guidance we need to replicate the decoder args across the batch dim (we'll split these
            # before sampling)
            input_ids = input_ids.repeat((2, 1))
            if attention_mask is not None:
                attention_mask = attention_mask.repeat((2, 1))

        if past_key_values is not None:
            input_ids = input_ids[:, -1:]

        return {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "encoder_hidden_states": encoder_hidden_states,
            "encoder_attention_mask": encoder_attention_mask,
            "head_mask": head_mask,
            "cross_attn_head_mask": cross_attn_head_mask,
            "past_key_values": past_key_values,
            "use_cache": use_cache,
        }

    def build_delay_pattern_mask(self, input_ids: torch.LongTensor, pad_token_id: int, max_length: int = None):
        """Build a delayed pattern mask to the input_ids. Each codebook is offset by the previous codebook by
        one, giving a delayed pattern mask at the start of sequence and end of sequence. Take the example where there
        are 4 codebooks and a max sequence length of 8, we have the delayed pattern mask of shape `(codebooks,
        seq_len)`:
        - [P, -1, -1, -1, -1, P, P, P]
        - [P, P, -1, -1, -1, -1, P, P]
        - [P, P, P, -1, -1, -1, -1, P]
        - [P, P, P, P, -1, -1, -1, -1]
        where P is the special padding token id and -1 indicates that the token is valid for prediction. If we include
        a prompt (decoder input ids), the -1 positions indicate where new tokens should be predicted. Otherwise, the
        mask is set to the value in the prompt:
        - [P, a, b, -1, -1, P, P, P]
        - [P, P, c, d, -1, -1, P, P]
        - [P, P, P, e, f, -1, -1, P]
        - [P, P, P, P, g, h, -1, -1]
        where a-h indicate the input prompt (decoder input ids) that are offset by 1. Now, we only override the -1
        tokens in our prediction.
        """
        # (bsz * num_codebooks, seq_len) -> (bsz, num_codebooks, seq_len)
        input_ids = input_ids.reshape(-1, self.num_codebooks, input_ids.shape[-1])
        bsz, num_codebooks, seq_len = input_ids.shape

        max_length = max_length if max_length is not None else self.generation_config.max_length
        input_ids_shifted = (
            torch.ones((bsz, num_codebooks, max_length), dtype=torch.long, device=input_ids.device) * -1
        )

        channel_codebooks = num_codebooks // 2 if self.config.audio_channels == 2 else num_codebooks
        # we only apply the mask if we have a large enough seq len - otherwise we return as is
        if max_length < 2 * channel_codebooks - 1:
            return input_ids.reshape(bsz * num_codebooks, -1), input_ids_shifted.reshape(bsz * num_codebooks, -1)

        # fill the shifted ids with the prompt entries, offset by the codebook idx
        for codebook in range(channel_codebooks):
            if self.config.audio_channels == 1:
                # mono channel - loop over the codebooks one-by-one
                input_ids_shifted[:, codebook, codebook : seq_len + codebook] = input_ids[:, codebook]
            else:
                # left/right channels are interleaved in the generated codebooks, so handle one then the other
                input_ids_shifted[:, 2 * codebook, codebook : seq_len + codebook] = input_ids[:, 2 * codebook]
                input_ids_shifted[:, 2 * codebook + 1, codebook : seq_len + codebook] = input_ids[:, 2 * codebook + 1]

        # construct a pattern mask that indicates the positions of padding tokens for each codebook
        # first fill the upper triangular part (the EOS padding)
        delay_pattern = torch.triu(
            torch.ones((channel_codebooks, max_length), dtype=torch.bool), diagonal=max_length - channel_codebooks + 1
        )
        # then fill the lower triangular part (the BOS padding)
        delay_pattern = delay_pattern + torch.tril(torch.ones((channel_codebooks, max_length), dtype=torch.bool))

        if self.config.audio_channels == 2:
            # for left/right channel we need to duplicate every row of the pattern mask in an interleaved fashion
            delay_pattern = delay_pattern.repeat_interleave(2, dim=0)

        mask = ~delay_pattern.to(input_ids.device)
        input_ids = mask * input_ids_shifted + ~mask * pad_token_id

        # find the first position to start generating - this is the first place we have the -1 token
        # and will always be in the first codebook (since it has no codebook offset)
        first_codebook_ids = input_ids[:, 0, :]
        start_ids = (first_codebook_ids == -1).nonzero()[:, 1]
        if len(start_ids) > 0:
            first_start_id = min(start_ids)
        else:
            # we have no tokens that need to be filled - return entire matrix of input ids
            first_start_id = seq_len

        # (bsz * num_codebooks, seq_len) -> (bsz, num_codebooks, seq_len)
        pattern_mask = input_ids.reshape(bsz * num_codebooks, -1)
        input_ids = input_ids[..., :first_start_id].reshape(bsz * num_codebooks, -1)
        return input_ids, pattern_mask

    @staticmethod
    def apply_delay_pattern_mask(input_ids, decoder_pad_token_mask):
        """Apply a delay pattern mask to the decoder input ids, only preserving predictions where
        the mask is set to -1, and otherwise setting to the value detailed in the mask."""
        seq_len = input_ids.shape[-1]
        decoder_pad_token_mask = decoder_pad_token_mask[..., :seq_len]
        input_ids = torch.where(decoder_pad_token_mask == -1, input_ids, decoder_pad_token_mask)
        return input_ids

    @torch.no_grad()
    def generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        generation_config: Optional[GenerationConfig] = None,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        synced_gpus: Optional[bool] = None,
        streamer: Optional["BaseStreamer"] = None,
        **kwargs,
    ):
        """

        Generates sequences of token ids for models with a language modeling head.

        <Tip warning={true}>

        Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
        model's default generation configuration. You can override any `generation_config` by passing the corresponding
        parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`.

        For an overview of generation strategies and code examples, check out the [following
        guide](./generation_strategies).

        </Tip>

        Parameters:
            inputs (`torch.Tensor` of varying shape depending on the modality, *optional*):
                The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
                method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
                should be in the format `input_ids`. For encoder-decoder models *inputs* can represent any of
                `input_ids`, `input_values`, `input_features`, or `pixel_values`.
            generation_config (`~generation.GenerationConfig`, *optional*):
                The generation configuration to be used as base parametrization for the generation call. `**kwargs`
                passed to generate matching the attributes of `generation_config` will override them. If
                `generation_config` is not provided, the default will be used, which had the following loading
                priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
                configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
                default values, whose documentation should be checked to parameterize generation.
            logits_processor (`LogitsProcessorList`, *optional*):
                Custom logits processors that complement the default logits processors built from arguments and
                generation config. If a logit processor is passed that is already created with the arguments or a
                generation config an error is thrown. This feature is intended for advanced users.
            stopping_criteria (`StoppingCriteriaList`, *optional*):
                Custom stopping criteria that complement the default stopping criteria built from arguments and a
                generation config. If a stopping criteria is passed that is already created with the arguments or a
                generation config an error is thrown. This feature is intended for advanced users.
            synced_gpus (`bool`, *optional*, defaults to `False`):
                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
            streamer (`BaseStreamer`, *optional*):
                Streamer object that will be used to stream the generated sequences. Generated tokens are passed
                through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
            kwargs (`Dict[str, Any]`, *optional*):
                Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
                forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
                specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.

        Return:
            [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True`
            or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`.

                If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
                [`~utils.ModelOutput`] types are:

                    - [`~generation.GenerateDecoderOnlyOutput`],
                    - [`~generation.GenerateBeamDecoderOnlyOutput`]

                If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
                [`~utils.ModelOutput`] types are:

                    - [`~generation.GenerateEncoderDecoderOutput`],
                    - [`~generation.GenerateBeamEncoderDecoderOutput`]
        """
        # 1. Handle `generation_config` and kwargs that might update it, and validate the resulting objects
        if generation_config is None:
            generation_config = self.generation_config

        generation_config = copy.deepcopy(generation_config)
        model_kwargs = generation_config.update(**kwargs)  # All unused kwargs must be model kwargs
        generation_config.validate()
        self._validate_model_kwargs(model_kwargs.copy())

        # 2. Set generation parameters if not already defined
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()

        if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
            if model_kwargs.get("attention_mask", None) is None:
                logger.warning(
                    "The attention mask and the pad token id were not set. As a consequence, you may observe "
                    "unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
                )
            eos_token_id = generation_config.eos_token_id
            if isinstance(eos_token_id, list):
                eos_token_id = eos_token_id[0]
            logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
            generation_config.pad_token_id = eos_token_id

        # 3. Define model inputs
        # inputs_tensor has to be defined
        # model_input_name is defined if model-specific keyword input is passed
        # otherwise model_input_name is None
        # all model-specific keyword inputs are removed from `model_kwargs`
        input_ids, model_input_name, model_kwargs = self._prepare_model_inputs(
            inputs, generation_config.bos_token_id, model_kwargs
        )
        batch_size = input_ids.shape[0] // self.num_codebooks

        # 4. Define other model kwargs
        model_kwargs["use_cache"] = generation_config.use_cache
        model_kwargs["guidance_scale"] = generation_config.guidance_scale

        requires_attention_mask = "encoder_outputs" not in model_kwargs
        if model_kwargs.get("attention_mask", None) is None and requires_attention_mask:
            model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
                input_ids, generation_config.pad_token_id, generation_config.eos_token_id
            )

        # 5. Prepare `max_length` depending on other stopping criteria.
        input_ids_seq_length = input_ids.shape[-1]
        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
        if has_default_max_length and generation_config.max_new_tokens is None and generation_config.max_length == 20:
            logger.warning(
                f"Using the model-agnostic default `max_length` (={generation_config.max_length}) "
                "to control the generation length.  recommend setting `max_new_tokens` to control the maximum length of the generation."
            )
        elif generation_config.max_new_tokens is not None:
            if not has_default_max_length:
                logger.warning(
                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
                    "Please refer to the documentation for more information. "
                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
                )
            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length

        if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
            raise ValueError(
                f"Unfeasible length constraints: the minimum length ({generation_config.min_length}) is larger than"
                f" the maximum length ({generation_config.max_length})"
            )
        if input_ids_seq_length >= generation_config.max_length:
            logger.warning(
                f"Input length of decoder_input_ids is {input_ids_seq_length}, but `max_length` is set to"
                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
                " increasing `max_new_tokens`."
            )

        # 6. Prepare `input_ids` which will be used for auto-regressive generation
        # Build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to MusicGen)
        input_ids, delay_pattern_mask = self.build_delay_pattern_mask(
            input_ids,
            pad_token_id=generation_config.decoder_start_token_id,
            max_length=generation_config.max_length,
        )

        if streamer is not None:
            streamer.put(input_ids.cpu())

        # stash the delay mask so that we don't have to recompute it in each forward pass
        model_kwargs["delay_pattern_mask"] = delay_pattern_mask

        # 7. determine generation mode
        is_greedy_gen_mode = (
            (generation_config.num_beams == 1)
            and (generation_config.num_beam_groups == 1)
            and generation_config.do_sample is False
        )
        is_sample_gen_mode = (
            (generation_config.num_beams == 1)
            and (generation_config.num_beam_groups == 1)
            and generation_config.do_sample is True
        )

        # 8. prepare batched CFG externally (to enable coexistance with the unbatched CFG)
        if generation_config.guidance_scale is not None and generation_config.guidance_scale > 1:
            logits_processor.append(ClassifierFreeGuidanceLogitsProcessor(generation_config.guidance_scale))
            generation_config.guidance_scale = None

        # 9. prepare distribution pre_processing samplers
        logits_processor = self._get_logits_processor(
            generation_config=generation_config,
            input_ids_seq_length=input_ids_seq_length,
            encoder_input_ids=input_ids,
            prefix_allowed_tokens_fn=None,
            logits_processor=logits_processor,
        )

        # 10. prepare stopping criteria
        stopping_criteria = self._get_stopping_criteria(
            generation_config=generation_config, stopping_criteria=stopping_criteria
        )

        if is_greedy_gen_mode:
            if generation_config.num_return_sequences > 1:
                raise ValueError(
                    "num_return_sequences has to be 1 when doing greedy search, "
                    f"but is {generation_config.num_return_sequences}."
                )

            # 11. run greedy search
            outputs = self._sample(
                input_ids,
                logits_processor=logits_processor,
                stopping_criteria=stopping_criteria,
                generation_config=generation_config,
                synced_gpus=synced_gpus,
                streamer=streamer,
                **model_kwargs,
            )

        elif is_sample_gen_mode:
            # 11. prepare logits warper
            logits_warper = self._get_logits_warper(generation_config)

            # expand input_ids with `num_return_sequences` additional sequences per batch
            input_ids, model_kwargs = self._expand_inputs_for_generation(
                input_ids=input_ids,
                expand_size=generation_config.num_return_sequences,
                **model_kwargs,
            )

            # 12. run sample
            outputs = self._sample(
                input_ids,
                logits_processor=logits_processor,
                logits_warper=logits_warper,
                stopping_criteria=stopping_criteria,
                generation_config=generation_config,
                synced_gpus=synced_gpus,
                streamer=streamer,
                **model_kwargs,
            )

        else:
            raise ValueError(
                "Got incompatible mode for generation, should be one of greedy or sampling. "
                "Ensure that beam search is de-activated by setting `num_beams=1` and `num_beam_groups=1`."
            )

        if generation_config.return_dict_in_generate:
            output_ids = outputs.sequences
        else:
            output_ids = outputs

        # apply the pattern mask to the final ids
        output_ids = self.apply_delay_pattern_mask(output_ids, model_kwargs["delay_pattern_mask"])

        # revert the pattern delay mask by filtering the pad token id
        output_ids = output_ids[output_ids != generation_config.pad_token_id].reshape(
            batch_size, self.num_codebooks, -1
        )

        if generation_config.return_dict_in_generate:
            outputs.sequences = output_ids
            return outputs
        else:
            return output_ids


@add_start_docstrings(
    "The composite MusicGen model with a text encoder, audio encoder and Musicgen decoder, "
    "for music generation tasks with one or both of text and audio prompts.",
    MUSICGEN_START_DOCSTRING,
)
class MusicgenForConditionalGeneration(PreTrainedModel):
    config_class = MusicgenConfig
    base_model_prefix = "encoder_decoder"
    main_input_name = "input_ids"
    supports_gradient_checkpointing = True
    _supports_flash_attn_2 = True
    _supports_sdpa = True

    def __init__(
        self,
        config: Optional[MusicgenConfig] = None,
        text_encoder: Optional[PreTrainedModel] = None,
        audio_encoder: Optional[PreTrainedModel] = None,
        decoder: Optional[MusicgenForCausalLM] = None,
    ):
        if config is None and (text_encoder is None or audio_encoder is None or decoder is None):
            raise ValueError(
                "Either a configuration has to be provided, or all three of text encoder, audio encoder and MusicGen decoder."
            )
        if config is None:
            config = MusicgenConfig.from_sub_models_config(text_encoder.config, audio_encoder.config, decoder.config)
        else:
            if not isinstance(config, self.config_class):
                raise ValueError(f"Config: {config} has to be of type {self.config_class}")

        if config.decoder.cross_attention_hidden_size is not None:
            if config.decoder.cross_attention_hidden_size != config.text_encoder.hidden_size:
                raise ValueError(
                    "If `cross_attention_hidden_size` is specified in the MusicGen decoder's configuration, it has to be equal"
                    f" to the text encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for"
                    f" `config.decoder.cross_attention_hidden_size` and {config.text_encoder.hidden_size} for"
                    " `config.text_encoder.hidden_size`."
                )

        # initialize with config
        super().__init__(config)

        if text_encoder is None:
            from ..auto.modeling_auto import AutoModelForTextEncoding

            text_encoder = AutoModelForTextEncoding.from_config(config.text_encoder)

        if audio_encoder is None:
            from ..auto.modeling_auto import AutoModel

            audio_encoder = AutoModel.from_config(config.audio_encoder)

        if decoder is None:
            decoder = MusicgenForCausalLM(config.decoder)

        self.text_encoder = text_encoder
        self.audio_encoder = audio_encoder
        self.decoder = decoder

        if self.text_encoder.config.to_dict() != self.config.text_encoder.to_dict():
            logger.warning(
                f"Config of the text_encoder: {self.text_encoder.__class__} is overwritten by shared text_encoder config:"
                f" {self.config.text_encoder}"
            )
        if self.audio_encoder.config.to_dict() != self.config.audio_encoder.to_dict():
            logger.warning(
                f"Config of the audio_encoder: {self.audio_encoder.__class__} is overwritten by shared audio_encoder config:"
                f" {self.config.audio_encoder}"
            )
        if self.decoder.config.to_dict() != self.config.decoder.to_dict():
            logger.warning(
                f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config:"
                f" {self.config.decoder}"
            )

        # make sure that the individual model's config refers to the shared config
        # so that the updates to the config will be synced
        self.text_encoder.config = self.config.text_encoder
        self.audio_encoder.config = self.config.audio_encoder
        self.decoder.config = self.config.decoder

        # text encoder outputs might need to be projected to different dimension for decoder
        if (
            self.text_encoder.config.hidden_size != self.decoder.config.hidden_size
            and self.decoder.config.cross_attention_hidden_size is None
        ):
            self.enc_to_dec_proj = nn.Linear(self.text_encoder.config.hidden_size, self.decoder.config.hidden_size)

        if self.text_encoder.get_output_embeddings() is not None:
            raise ValueError(
                f"The encoder {self.text_encoder} should not have a LM Head. Please use a model without and LM Head"
            )

        decoder_signature = set(inspect.signature(self.decoder.forward).parameters.keys())
        if "encoder_hidden_states" not in decoder_signature:
            raise ValueError(
                "The selected decoder is not prepared for the encoder hidden states to be passed. Please see the "
                "following discussion on GitHub: https://github.com/huggingface/transformers/issues/23350"
            )

        # tie text encoder, decoder weights if config set accordingly
        self.tie_weights()

    def tie_weights(self):
        # tie text encoder & decoder if needed
        if self.config.tie_encoder_decoder:
            # tie text encoder and decoder base model
            decoder_base_model_prefix = self.decoder.base_model_prefix
            tied_weights = self._tie_encoder_decoder_weights(
                self.text_encoder,
                self.decoder._modules[decoder_base_model_prefix],
                self.decoder.base_model_prefix,
                "text_encoder",
            )
            # Setting a dynamic variable instead of `_tied_weights_keys` because it's a class
            # attributed not an instance member, therefore modifying it will modify the entire class
            # Leading to issues on subsequent calls by different tests or subsequent calls.
            self._dynamic_tied_weights_keys = tied_weights

    def get_audio_encoder(self):
        return self.audio_encoder

    def get_text_encoder(self):
        return self.text_encoder

    def get_encoder(self):
        # get the text encoder to compute the encoder hidden-states for generation
        return self.get_text_encoder()

    def get_decoder(self):
        return self.decoder

    def get_input_embeddings(self):
        return self.text_encoder.get_input_embeddings()

    def get_output_embeddings(self):
        return self.decoder.get_output_embeddings()

    def set_output_embeddings(self, new_embeddings):
        return self.decoder.set_output_embeddings(new_embeddings)

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Example:

        ```python
        >>> from transformers import MusicgenForConditionalGeneration

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

        # At the moment fast initialization is not supported for composite models
        if kwargs.get("_fast_init", False):
            logger.warning(
                "Fast initialization is currently not supported for MusicgenForConditionalGeneration. "
                "Falling back to slow initialization..."
            )
        kwargs["_fast_init"] = False

        return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)

    @classmethod
    def from_sub_models_pretrained(
        cls,
        text_encoder_pretrained_model_name_or_path: str = None,
        audio_encoder_pretrained_model_name_or_path: str = None,
        decoder_pretrained_model_name_or_path: str = None,
        *model_args,
        **kwargs,
    ) -> PreTrainedModel:
        r"""
        Instantiate a text encoder, an audio encoder, and a MusicGen decoder from one, two or three base classes of the
        library from pretrained model checkpoints.


        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
        the model, you need to first set it back in training mode with `model.train()`.

        Params:
            text_encoder_pretrained_model_name_or_path (`str`, *optional*):
                Information necessary to initiate the text encoder. Can be either:

                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
                    - A path to a *directory* containing model weights saved using
                      [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.

            audio_encoder_pretrained_model_name_or_path (`str`, *optional*):
                Information necessary to initiate the audio encoder. Can be either:

                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
                    - A path to a *directory* containing model weights saved using
                      [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.

            decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to `None`):
                Information necessary to initiate the decoder. Can be either:

                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
                    - A path to a *directory* containing model weights saved using
                      [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.

            model_args (remaining positional arguments, *optional*):
                All remaining positional arguments will be passed to the underlying model's `__init__` method.

            kwargs (remaining dictionary of keyword arguments, *optional*):
                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
                `output_attentions=True`).

                - To update the text encoder configuration, use the prefix *text_encoder_* for each configuration
                  parameter.
                - To update the audio encoder configuration, use the prefix *audio_encoder_* for each configuration
                  parameter.
                - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
                - To update the parent model configuration, do not use a prefix for each configuration parameter.

                Behaves differently depending on whether a `config` is provided or automatically loaded.

        Example:

        ```python
        >>> from transformers import MusicgenForConditionalGeneration

        >>> # initialize a musicgen model from a t5 text encoder, encodec audio encoder, and musicgen decoder
        >>> model = MusicgenForConditionalGeneration.from_sub_models_pretrained(
        ...     text_encoder_pretrained_model_name_or_path="google-t5/t5-base",
        ...     audio_encoder_pretrained_model_name_or_path="facebook/encodec_24khz",
        ...     decoder_pretrained_model_name_or_path="facebook/musicgen-small",
        ... )
        >>> # saving model after fine-tuning
        >>> model.save_pretrained("./musicgen-ft")
        >>> # load fine-tuned model
        >>> model = MusicgenForConditionalGeneration.from_pretrained("./musicgen-ft")
        ```"""

        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_")
        }

        # remove text encoder, audio encoder and decoder kwargs from kwargs
        for key in kwargs_text_encoder.keys():
            del kwargs["text_encoder_" + key]
        for key in kwargs_audio_encoder.keys():
            del kwargs["audio_encoder_" + key]
        for key in kwargs_decoder.keys():
            del kwargs["decoder_" + key]

        # Load and initialize the encoder and decoder
        # The distinction between encoder and decoder at the model level is made
        # by the value of the flag `is_decoder` that we need to set correctly.
        text_encoder = kwargs_text_encoder.pop("model", None)
        if text_encoder is None:
            if text_encoder_pretrained_model_name_or_path is None:
                raise ValueError(
                    "If `text_encoder_model` is not defined as an argument, a `text_encoder_pretrained_model_name_or_path` has "
                    "to be defined."
                )

            if "config" not in kwargs_text_encoder:
                encoder_config, kwargs_text_encoder = AutoConfig.from_pretrained(
                    text_encoder_pretrained_model_name_or_path, **kwargs_text_encoder, return_unused_kwargs=True
                )

                if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
                    logger.info(
                        f"Initializing {text_encoder_pretrained_model_name_or_path} as a text_encoder model "
                        "from a decoder model. Cross-attention and casual mask are disabled."
                    )
                    encoder_config.is_decoder = False
                    encoder_config.add_cross_attention = False

                kwargs_text_encoder["config"] = encoder_config

            text_encoder = AutoModel.from_pretrained(
                text_encoder_pretrained_model_name_or_path, *model_args, **kwargs_text_encoder
            )

        audio_encoder = kwargs_audio_encoder.pop("model", None)
        if audio_encoder is None:
            if audio_encoder_pretrained_model_name_or_path is None:
                raise ValueError(
                    "If `audio_encoder_model` is not defined as an argument, an `audio_encoder_pretrained_model_name_or_path` has "
                    "to be defined."
                )

            if "config" not in kwargs_audio_encoder:
                encoder_config, kwargs_audio_encoder = AutoConfig.from_pretrained(
                    audio_encoder_pretrained_model_name_or_path, **kwargs_audio_encoder, return_unused_kwargs=True
                )

                if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
                    logger.info(
                        f"Initializing {audio_encoder_pretrained_model_name_or_path} as an audio_encoder model "
                        "from a decoder model. Cross-attention and casual mask are disabled."
                    )
                    encoder_config.is_decoder = False
                    encoder_config.add_cross_attention = False

                kwargs_audio_encoder["config"] = encoder_config

            audio_encoder = AutoModel.from_pretrained(
                audio_encoder_pretrained_model_name_or_path, *model_args, **kwargs_audio_encoder
            )

        decoder = kwargs_decoder.pop("model", None)
        if decoder is None:
            if decoder_pretrained_model_name_or_path is None:
                raise ValueError(
                    "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has "
                    "to be defined."
                )

            if "config" not in kwargs_decoder:
                decoder_config, kwargs_decoder = AutoConfig.from_pretrained(
                    decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True
                )

                if isinstance(decoder_config, MusicgenConfig):
                    decoder_config = decoder_config.decoder

                if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
                    logger.info(
                        f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention"
                        f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if"
                        f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
                    )
                    decoder_config.is_decoder = True
                    decoder_config.add_cross_attention = True

                kwargs_decoder["config"] = decoder_config

            if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False:
                logger.warning(
                    f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. "
                    f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, "
                    "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` "
                    "passed to `.from_sub_models_pretrained(...)` are set to `True` or do not pass a "
                    "`decoder_config` to `.from_sub_models_pretrained(...)`"
                )

            decoder = MusicgenForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)

        # instantiate config with corresponding kwargs
        config = MusicgenConfig.from_sub_models_config(
            text_encoder.config, audio_encoder.config, decoder.config, **kwargs
        )
        return cls(text_encoder=text_encoder, audio_encoder=audio_encoder, decoder=decoder, config=config)

    @add_start_docstrings_to_model_forward(MUSICGEN_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
    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.decoder.pad_token_id, self.config.decoder.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.decoder.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,
            labels=labels,
            **kwargs_decoder,
        )

        if not return_dict:
            return decoder_outputs + encoder_outputs

        return Seq2SeqLMOutput(
            loss=decoder_outputs.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,
        )

    def prepare_inputs_for_generation(
        self,
        decoder_input_ids,
        past_key_values=None,
        attention_mask=None,
        head_mask=None,
        decoder_attention_mask=None,
        decoder_head_mask=None,
        cross_attn_head_mask=None,
        use_cache=None,
        encoder_outputs=None,
        decoder_delay_pattern_mask=None,
        guidance_scale=None,
        **kwargs,
    ):
        if decoder_delay_pattern_mask is None:
            decoder_input_ids, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask(
                decoder_input_ids,
                self.generation_config.pad_token_id,
                max_length=self.generation_config.max_length,
            )

        # apply the delay pattern mask
        decoder_input_ids = self.decoder.apply_delay_pattern_mask(decoder_input_ids, decoder_delay_pattern_mask)

        if guidance_scale is not None and guidance_scale > 1:
            # for classifier free guidance we need to replicate the decoder args across the batch dim (we'll split these
            # before sampling)
            decoder_input_ids = decoder_input_ids.repeat((2, 1))
            if decoder_attention_mask is not None:
                decoder_attention_mask = decoder_attention_mask.repeat((2, 1))

        if past_key_values is not None:
            past_length = past_key_values[0][0].shape[2]

            # Some generation methods already pass only the last input ID
            if decoder_input_ids.shape[1] > past_length:
                remove_prefix_length = past_length
            else:
                # Default to old behavior: keep only final ID
                remove_prefix_length = decoder_input_ids.shape[1] - 1

            decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]

        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,
            "head_mask": head_mask,
            "decoder_head_mask": decoder_head_mask,
            "cross_attn_head_mask": cross_attn_head_mask,
            "use_cache": use_cache,
        }

    def _prepare_decoder_input_ids_for_generation(
        self,
        batch_size: int,
        model_input_name: str,
        model_kwargs: Dict[str, torch.Tensor],
        decoder_start_token_id: int = None,
        bos_token_id: int = None,
        device: torch.device = None,
    ) -> Tuple[torch.LongTensor, Dict[str, torch.Tensor]]:
        """Prepares `decoder_input_ids` for generation with encoder-decoder models"""

        # 1. Check whether the user has defined `decoder_input_ids` manually. To facilitate in terms of input naming,
        # we also allow the user to pass it under `input_ids`, if the encoder does not use it as the main input.
        if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
            decoder_input_ids = model_kwargs.pop("decoder_input_ids")
        elif "input_ids" in model_kwargs and model_input_name != "input_ids":
            decoder_input_ids = model_kwargs.pop("input_ids")
        else:
            decoder_input_ids = None

        # 2. Encoder-decoder models expect the `decoder_input_ids` to start with a special token. Let's ensure that.
        decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
        if device is None:
            device = self.device
        decoder_input_ids_start = (
            torch.ones((batch_size * self.decoder.num_codebooks, 1), dtype=torch.long, device=device)
            * decoder_start_token_id
        )

        # no user input -> use decoder_start_token_id as decoder_input_ids
        if decoder_input_ids is None:
            decoder_input_ids = decoder_input_ids_start

        # user input but doesn't start with decoder_start_token_id -> prepend decoder_start_token_id (and adjust
        # decoder_attention_mask if provided)
        elif (decoder_input_ids[..., 0] != decoder_start_token_id).all().item():
            decoder_input_ids = torch.cat([decoder_input_ids_start, decoder_input_ids], dim=-1)
            if "decoder_attention_mask" in model_kwargs:
                decoder_attention_mask = model_kwargs["decoder_attention_mask"]
                decoder_attention_mask = torch.cat(
                    (torch.ones_like(decoder_attention_mask)[:, :1], decoder_attention_mask),
                    dim=-1,
                )
                model_kwargs["decoder_attention_mask"] = decoder_attention_mask

        return decoder_input_ids, model_kwargs

    def _prepare_text_encoder_kwargs_for_generation(
        self,
        inputs_tensor: torch.Tensor,
        model_kwargs,
        model_input_name: Optional[str],
        generation_config: GenerationConfig,
    ) -> Dict[str, Any]:
        # 1. get text encoder
        encoder = self.get_text_encoder()
        # Compatibility with Accelerate big model inference: we need the encoder to outputs stuff on the same device
        # as the inputs.
        if hasattr(encoder, "_hf_hook"):
            encoder._hf_hook.io_same_device = True

        # 2. Prepare encoder args and encoder kwargs from model kwargs.
        irrelevant_prefix = ["decoder_", "cross_attn", "use_cache"]
        encoder_kwargs = {
            argument: value
            for argument, value in model_kwargs.items()
            if not any(argument.startswith(p) for p in irrelevant_prefix)
        }
        encoder_signature = set(inspect.signature(encoder.forward).parameters)
        encoder_accepts_wildcard = "kwargs" in encoder_signature or "model_kwargs" in encoder_signature
        if not encoder_accepts_wildcard:
            encoder_kwargs = {
                argument: value for argument, value in encoder_kwargs.items() if argument in encoder_signature
            }
        encoder_kwargs["output_attentions"] = generation_config.output_attentions
        encoder_kwargs["output_hidden_states"] = generation_config.output_hidden_states
        guidance_scale = generation_config.guidance_scale

        # 3. make sure that encoder returns `ModelOutput`
        model_input_name = model_input_name if model_input_name is not None else self.text_encoder.main_input_name
        encoder_kwargs["return_dict"] = True
        encoder_kwargs[model_input_name] = inputs_tensor
        last_hidden_state = encoder(**encoder_kwargs).last_hidden_state

        # for classifier free guidance we need to add a 'null' input to our encoder hidden states
        if guidance_scale is not None and guidance_scale > 1:
            last_hidden_state = torch.concatenate([last_hidden_state, torch.zeros_like(last_hidden_state)], dim=0)
            if "attention_mask" in model_kwargs:
                model_kwargs["attention_mask"] = torch.concatenate(
                    [model_kwargs["attention_mask"], torch.zeros_like(model_kwargs["attention_mask"])], dim=0
                )

        model_kwargs["encoder_outputs"] = BaseModelOutput(last_hidden_state=last_hidden_state)

        return model_kwargs

    def _prepare_audio_encoder_kwargs_for_generation(
        self, input_values, model_kwargs, model_input_name: Optional[str] = None
    ):
        # 1. get audio encoder
        encoder = self.get_audio_encoder()
        # Compatibility with Accelerate big model inference: we need the encoder to outputs stuff on the same device
        # as the inputs.
        if hasattr(encoder, "_hf_hook"):
            encoder._hf_hook.io_same_device = True

        # 2. Prepare encoder args and encoder kwargs from model kwargs.
        irrelevant_prefix = ["decoder_", "cross_attn", "use_cache"]
        encoder_kwargs = {
            argument: value
            for argument, value in model_kwargs.items()
            if not any(argument.startswith(p) for p in irrelevant_prefix)
        }
        encoder_signature = set(inspect.signature(encoder.forward).parameters)
        encoder_accepts_wildcard = "kwargs" in encoder_signature or "model_kwargs" in encoder_signature
        if not encoder_accepts_wildcard:
            encoder_kwargs = {
                argument: value for argument, value in encoder_kwargs.items() if argument in encoder_signature
            }

        # 3. make sure that encoder returns `ModelOutput`
        model_input_name = model_input_name if model_input_name is not None else self.audio_encoder.main_input_name
        encoder_kwargs["return_dict"] = True

        if self.decoder.config.audio_channels == 1:
            encoder_kwargs[model_input_name] = input_values
            audio_encoder_outputs = encoder.encode(**encoder_kwargs)
            audio_codes = audio_encoder_outputs.audio_codes
            audio_scales = audio_encoder_outputs.audio_scales

            frames, bsz, codebooks, seq_len = audio_codes.shape

        else:
            if input_values.shape[1] != 2:
                raise ValueError(
                    f"Expected stereo audio (2-channels) but example has {input_values.shape[1]} channel."
                )

            encoder_kwargs[model_input_name] = input_values[:, :1, :]
            audio_encoder_outputs_left = encoder.encode(**encoder_kwargs)
            audio_codes_left = audio_encoder_outputs_left.audio_codes
            audio_scales_left = audio_encoder_outputs_left.audio_scales

            encoder_kwargs[model_input_name] = input_values[:, 1:, :]
            audio_encoder_outputs_right = encoder.encode(**encoder_kwargs)
            audio_codes_right = audio_encoder_outputs_right.audio_codes
            audio_scales_right = audio_encoder_outputs_right.audio_scales

            frames, bsz, codebooks, seq_len = audio_codes_left.shape
            # copy alternating left/right channel codes into stereo codebook
            audio_codes = audio_codes_left.new_ones((frames, bsz, 2 * codebooks, seq_len))

            audio_codes[:, :, ::2, :] = audio_codes_left
            audio_codes[:, :, 1::2, :] = audio_codes_right

            if audio_scales_left != [None] or audio_scales_right != [None]:
                audio_scales = torch.stack([audio_scales_left, audio_scales_right], dim=1)
            else:
                audio_scales = [None] * bsz

        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."
            )

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

        model_kwargs["decoder_input_ids"] = decoder_input_ids
        model_kwargs["audio_scales"] = audio_scales
        return model_kwargs

    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
        return shift_tokens_right(labels, self.config.decoder.pad_token_id, self.config.decoder.bos_token_id)

    def resize_token_embeddings(self, *args, **kwargs):
        raise NotImplementedError(
            "Resizing the embedding layers via the EncoderDecoderModel directly is not supported. Please use the"
            " respective methods of the wrapped objects (model.encoder.resize_token_embeddings(...) or"
            " model.decoder.resize_token_embeddings(...))"
        )

    def freeze_audio_encoder(self):
        """
        Freeze the audio encoder weights.
        """
        for param in self.audio_encoder.parameters():
            param.requires_grad = False
        self.audio_encoder._requires_grad = False

    def freeze_text_encoder(self):
        """
        Freeze the text encoder weights.
        """
        for param in self.text_encoder.parameters():
            param.requires_grad = False
        self.text_encoder._requires_grad = False

    def _maybe_initialize_input_ids_for_generation(
        self,
        inputs: Optional[torch.Tensor] = None,
        bos_token_id: Optional[int] = None,
        model_kwargs: Optional[Dict[str, torch.Tensor]] = None,
    ) -> torch.LongTensor:
        """Initializes input ids for generation, if necessary."""
        if inputs is not None:
            return inputs

        encoder_outputs = model_kwargs.get("encoder_outputs")
        if encoder_outputs is not None:
            # make dummy input_ids with value -100, as a sanity check ensuring that they won't be used for encoding
            shape = encoder_outputs[0].size()[:-1]
            return torch.ones(shape, dtype=torch.long, device=self.device) * -100

        if bos_token_id is None:
            raise ValueError("`bos_token_id` has to be defined when no `input_ids` are provided.")

        # If there is some tensor in `model_kwargs`, we can infer the batch size from it. This is helpful with
        # soft-prompting or in multimodal implementations built on top of decoder-only language models.
        batch_size = 1
        for value in model_kwargs.values():
            if isinstance(value, torch.Tensor):
                batch_size = value.shape[0]
                break
        return torch.ones((batch_size, 1), dtype=torch.long, device=self.device) * bos_token_id

    def _get_decoder_start_token_id(
        self, decoder_start_token_id: Union[int, List[int]] = None, bos_token_id: int = None
    ) -> int:
        decoder_start_token_id = (
            decoder_start_token_id
            if decoder_start_token_id is not None
            else self.generation_config.decoder_start_token_id
        )
        bos_token_id = bos_token_id if bos_token_id is not None else self.generation_config.bos_token_id

        if decoder_start_token_id is not None:
            return decoder_start_token_id
        elif bos_token_id is not None:
            return bos_token_id
        raise ValueError(
            "`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
        )

    @torch.no_grad()
    def generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        generation_config: Optional[GenerationConfig] = None,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        synced_gpus: Optional[bool] = None,
        streamer: Optional["BaseStreamer"] = None,
        **kwargs,
    ):
        """

        Generates sequences of token ids for models with a language modeling head.

        <Tip warning={true}>

        Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
        model's default generation configuration. You can override any `generation_config` by passing the corresponding
        parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`.

        For an overview of generation strategies and code examples, check out the [following
        guide](./generation_strategies).

        </Tip>

        Parameters:
            inputs (`torch.Tensor` of varying shape depending on the modality, *optional*):
                The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
                method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
                should be in the format `input_ids`. For encoder-decoder models *inputs* can represent any of
                `input_ids`, `input_values`, `input_features`, or `pixel_values`.
            generation_config (`~generation.GenerationConfig`, *optional*):
                The generation configuration to be used as base parametrization for the generation call. `**kwargs`
                passed to generate matching the attributes of `generation_config` will override them. If
                `generation_config` is not provided, the default will be used, which had the following loading
                priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
                configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
                default values, whose documentation should be checked to parameterize generation.
            logits_processor (`LogitsProcessorList`, *optional*):
                Custom logits processors that complement the default logits processors built from arguments and
                generation config. If a logit processor is passed that is already created with the arguments or a
                generation config an error is thrown. This feature is intended for advanced users.
            stopping_criteria (`StoppingCriteriaList`, *optional*):
                Custom stopping criteria that complement the default stopping criteria built from arguments and a
                generation config. If a stopping criteria is passed that is already created with the arguments or a
                generation config an error is thrown. This feature is intended for advanced users.
            synced_gpus (`bool`, *optional*, defaults to `False`):
                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
            streamer (`BaseStreamer`, *optional*):
                Streamer object that will be used to stream the generated sequences. Generated tokens are passed
                through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
            kwargs (`Dict[str, Any]`, *optional*):
                Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
                forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
                specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.

        Return:
            [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True`
            or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`.

                If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
                [`~utils.ModelOutput`] types are:

                    - [`~generation.GenerateDecoderOnlyOutput`],
                    - [`~generation.GenerateBeamDecoderOnlyOutput`]

                If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
                [`~utils.ModelOutput`] types are:

                    - [`~generation.GenerateEncoderDecoderOutput`],
                    - [`~generation.GenerateBeamEncoderDecoderOutput`]
        """
        # 1. Handle `generation_config` and kwargs that might update it, and validate the resulting objects
        if generation_config is None:
            generation_config = self.generation_config

        generation_config = copy.deepcopy(generation_config)
        model_kwargs = generation_config.update(**kwargs)  # All unused kwargs must be model kwargs
        generation_config.validate()
        self._validate_model_kwargs(model_kwargs.copy())

        if model_kwargs.get("encoder_outputs") is not None and type(model_kwargs["encoder_outputs"]) == tuple:
            # wrap the unconditional outputs as a BaseModelOutput for compatibility with the rest of generate
            model_kwargs["encoder_outputs"] = BaseModelOutput(last_hidden_state=model_kwargs["encoder_outputs"][0])

        # 2. Set generation parameters if not already defined
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()

        if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
            if model_kwargs.get("attention_mask", None) is None:
                logger.warning(
                    "The attention mask and the pad token id were not set. As a consequence, you may observe "
                    "unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
                )
            eos_token_id = generation_config.eos_token_id
            if isinstance(eos_token_id, list):
                eos_token_id = eos_token_id[0]
            logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
            generation_config.pad_token_id = eos_token_id

        # 3. Define model inputs
        # inputs_tensor has to be defined
        # model_input_name is defined if model-specific keyword input is passed
        # otherwise model_input_name is None
        # all model-specific keyword inputs are removed from `model_kwargs`
        inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
            inputs, generation_config.bos_token_id, model_kwargs
        )
        batch_size = inputs_tensor.shape[0]

        # 4. Define other model kwargs
        model_kwargs["use_cache"] = generation_config.use_cache
        model_kwargs["guidance_scale"] = generation_config.guidance_scale

        requires_attention_mask = "encoder_outputs" not in model_kwargs

        if model_kwargs.get("attention_mask", None) is None and requires_attention_mask:
            model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
                inputs_tensor, generation_config.pad_token_id, generation_config.eos_token_id
            )

        if "encoder_outputs" not in model_kwargs:
            # encoder_outputs are created and added to `model_kwargs`
            model_kwargs = self._prepare_text_encoder_kwargs_for_generation(
                inputs_tensor, model_kwargs, model_input_name, generation_config
            )

        if "decoder_input_ids" not in model_kwargs and "input_values" in model_kwargs:
            model_kwargs = self._prepare_audio_encoder_kwargs_for_generation(
                model_kwargs["input_values"],
                model_kwargs,
            )

        # 5. Prepare `input_ids` which will be used for auto-regressive generation
        input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation(
            batch_size=batch_size,
            model_input_name=model_input_name,
            model_kwargs=model_kwargs,
            decoder_start_token_id=generation_config.decoder_start_token_id,
            bos_token_id=generation_config.bos_token_id,
            device=inputs_tensor.device,
        )

        # 6. Prepare `max_length` depending on other stopping criteria.
        input_ids_seq_length = input_ids.shape[-1]
        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
        if has_default_max_length and generation_config.max_new_tokens is None:
            logger.warning(
                f"Using the model-agnostic default `max_length` (={generation_config.max_length}) "
                "to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation."
            )
        elif generation_config.max_new_tokens is not None:
            if not has_default_max_length:
                logger.warning(
                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
                    "Please refer to the documentation for more information. "
                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
                )
            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length

        if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
            raise ValueError(
                f"Unfeasible length constraints: the minimum length ({generation_config.min_length}) is larger than"
                f" the maximum length ({generation_config.max_length})"
            )
        if input_ids_seq_length >= generation_config.max_length:
            logger.warning(
                f"Input length of decoder_input_ids is {input_ids_seq_length}, but `max_length` is set to"
                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
                " increasing `max_new_tokens`."
            )

        # build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to MusicGen)
        input_ids, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask(
            input_ids,
            pad_token_id=generation_config.decoder_start_token_id,
            max_length=generation_config.max_length,
        )
        # stash the delay mask so that we don't have to recompute in each forward pass
        model_kwargs["decoder_delay_pattern_mask"] = decoder_delay_pattern_mask

        # input_ids are ready to be placed on the streamer (if used)
        if streamer is not None:
            streamer.put(input_ids.cpu())

        # 7. determine generation mode
        is_greedy_gen_mode = (
            (generation_config.num_beams == 1)
            and (generation_config.num_beam_groups == 1)
            and generation_config.do_sample is False
        )
        is_sample_gen_mode = (
            (generation_config.num_beams == 1)
            and (generation_config.num_beam_groups == 1)
            and generation_config.do_sample is True
        )

        # 8. prepare batched CFG externally (to enable coexistance with the unbatched CFG)
        if generation_config.guidance_scale is not None and generation_config.guidance_scale > 1:
            logits_processor.append(ClassifierFreeGuidanceLogitsProcessor(generation_config.guidance_scale))
            generation_config.guidance_scale = None

        # 9. prepare distribution pre_processing samplers
        logits_processor = self._get_logits_processor(
            generation_config=generation_config,
            input_ids_seq_length=input_ids_seq_length,
            encoder_input_ids=inputs_tensor,
            prefix_allowed_tokens_fn=None,
            logits_processor=logits_processor,
        )

        # 10. prepare stopping criteria
        stopping_criteria = self._get_stopping_criteria(
            generation_config=generation_config, stopping_criteria=stopping_criteria
        )

        if is_greedy_gen_mode:
            if generation_config.num_return_sequences > 1:
                raise ValueError(
                    "num_return_sequences has to be 1 when doing greedy search, "
                    f"but is {generation_config.num_return_sequences}."
                )

            # 11. run greedy search
            outputs = self._sample(
                input_ids,
                logits_processor=logits_processor,
                stopping_criteria=stopping_criteria,
                generation_config=generation_config,
                synced_gpus=synced_gpus,
                streamer=streamer,
                **model_kwargs,
            )

        elif is_sample_gen_mode:
            # 11. prepare logits warper
            logits_warper = self._get_logits_warper(generation_config)

            # expand input_ids with `num_return_sequences` additional sequences per batch
            input_ids, model_kwargs = self._expand_inputs_for_generation(
                input_ids=input_ids,
                expand_size=generation_config.num_return_sequences,
                is_encoder_decoder=self.config.is_encoder_decoder,
                **model_kwargs,
            )

            # 12. run sample
            outputs = self._sample(
                input_ids,
                logits_processor=logits_processor,
                logits_warper=logits_warper,
                stopping_criteria=stopping_criteria,
                generation_config=generation_config,
                synced_gpus=synced_gpus,
                streamer=streamer,
                **model_kwargs,
            )

        else:
            raise ValueError(
                "Got incompatible mode for generation, should be one of greedy or sampling. "
                "Ensure that beam search is de-activated by setting `num_beams=1` and `num_beam_groups=1`."
            )

        if generation_config.return_dict_in_generate:
            output_ids = outputs.sequences
        else:
            output_ids = outputs

        # apply the pattern mask to the final ids
        output_ids = self.decoder.apply_delay_pattern_mask(output_ids, model_kwargs["decoder_delay_pattern_mask"])

        # revert the pattern delay mask by filtering the pad token id
        output_ids = output_ids[output_ids != generation_config.pad_token_id].reshape(
            batch_size, self.decoder.num_codebooks, -1
        )

        # append the frame dimension back to the audio codes
        output_ids = output_ids[None, ...]

        audio_scales = model_kwargs.get("audio_scales")
        if audio_scales is None:
            audio_scales = [None] * batch_size

        if self.decoder.config.audio_channels == 1:
            output_values = self.audio_encoder.decode(
                output_ids,
                audio_scales=audio_scales,
            ).audio_values
        else:
            codec_outputs_left = self.audio_encoder.decode(output_ids[:, :, ::2, :], audio_scales=audio_scales)
            output_values_left = codec_outputs_left.audio_values

            codec_outputs_right = self.audio_encoder.decode(output_ids[:, :, 1::2, :], audio_scales=audio_scales)
            output_values_right = codec_outputs_right.audio_values

            output_values = torch.cat([output_values_left, output_values_right], dim=1)

        if generation_config.return_dict_in_generate:
            outputs.sequences = output_values
            return outputs
        else:
            return output_values

    def get_unconditional_inputs(self, num_samples=1):
        """
        Helper function to get null inputs for unconditional generation, enabling the model to be used without the
        feature extractor or tokenizer.

        Args:
            num_samples (int, *optional*):
                Number of audio samples to unconditionally generate.
            max_new_tokens (int, *optional*):
                Number of tokens to generate for each sample. More tokens means longer audio samples, at the expense of
                longer inference (since more audio tokens need to be generated per sample).

        Example:
        ```python
        >>> from transformers import MusicgenForConditionalGeneration

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

        >>> # get the unconditional (or 'null') inputs for the model
        >>> unconditional_inputs = model.get_unconditional_inputs(num_samples=1)
        >>> audio_samples = model.generate(**unconditional_inputs, max_new_tokens=256)
        ```"""
        last_hidden_state = torch.zeros(
            (num_samples, 1, self.config.text_encoder.hidden_size), device=self.device, dtype=self.dtype
        )

        attention_mask = torch.zeros((num_samples, 1), device=self.device, dtype=torch.long)

        return MusicgenUnconditionalInput(
            encoder_outputs=(last_hidden_state,),
            attention_mask=attention_mask,
            guidance_scale=1.0,
        )