modeling_parler_tts.py

# coding=utf-8
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" PyTorch ParlerTTS model."""
import copy
import inspect
import math
import random
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, List

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss
from transformers import AutoConfig, AutoModel, AutoModelForTextEncoding
from transformers.activations import ACT2FN
from transformers.cache_utils import (
    Cache,
    DynamicCache,
    EncoderDecoderCache,
    SlidingWindowCache,
    StaticCache,
)
from transformers.generation.configuration_utils import GenerationConfig, GenerationMode
from transformers.generation.logits_process import LogitsProcessorList
from transformers.generation.stopping_criteria import StoppingCriteriaList
from transformers.modeling_attn_mask_utils import (
    AttentionMaskConverter,
    _prepare_4d_attention_mask,
    _prepare_4d_attention_mask_for_sdpa,
)
from transformers.modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPastAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
    ModelOutput,
    Seq2SeqLMOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
    is_torchdynamo_compiling,
)
from transformers.utils.import_utils import is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10

from .configuration_parler_tts import ParlerTTSConfig, ParlerTTSDecoderConfig
from .dac_wrapper import DACConfig, DACModel

from importlib.metadata import version
from packaging.version import Version

is_dac_integrated_to_transformers = Version(version("transformers")) > Version("4.44.2dev")
if not is_dac_integrated_to_transformers:
    AutoConfig.register("dac", DACConfig)
else:
    AutoConfig.register("dac_on_the_hub", DACConfig)

AutoModel.register(DACConfig, DACModel)

if TYPE_CHECKING:
    from transformers.generation.streamers import BaseStreamer

logger = logging.get_logger(__name__)


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
else:
    logger.warn("Flash attention 2 is not installed")

_CONFIG_FOR_DOC = "ParlerTTSConfig"
_CHECKPOINT_FOR_DOC = "parler-tts/parler-tts-mini-v1"

MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "parler-tts/parler-tts-mini-v1",
    # See all ParlerTTS models at https://huggingface.co/models?filter=parler_tts
]


NEED_SETUP_CACHE_CLASSES_MAPPING = {"static": StaticCache, "sliding_window": SlidingWindowCache}



@dataclass
class ParlerTTSSeq2SeqLMOutput(ModelOutput):
    """
    Base class for sequence-to-sequence language models outputs.

    Args:
        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
            Language modeling loss.
        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        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.
        decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
        decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
            self-attention heads.
        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
            weighted average in the cross-attention heads.
        encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder of the model.
        encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
        encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
            self-attention heads.
    """

    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
    decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
    cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
    encoder_last_hidden_state: Optional[torch.FloatTensor] = None
    encoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    encoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
    per_codebook_losses: Optional[List[torch.FloatTensor]] = None

@dataclass
class ParlerTTSCausalLMOutputWithCrossAttentions(ModelOutput):
    """
    Base class for causal language model (or autoregressive) outputs.

    Args:
        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
            Language modeling loss (for next-token prediction).
        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Cross attentions weights after the attention softmax, used to compute the weighted average in the
            cross-attention heads.
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `torch.FloatTensor` tuples of length `config.n_layers`, with each tuple containing the cached key,
            value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
            setting. Only relevant if `config.is_decoder = True`.

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

    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
    cross_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
    per_codebook_losses: Optional[List[torch.FloatTensor]] = None

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


def build_delay_pattern_mask(
    input_ids: torch.LongTensor, bos_token_id: int, pad_token_id: int, max_length: int, num_codebooks: int
):
    """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)`:
    - [B, -1, -1, -1, -1, P, P, P]
    - [B, B, -1, -1, -1, -1, P, P]
    - [B, B, B, -1, -1, -1, -1, P]
    - [B, B, B, B, -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:
    - [B, a, b, -1, -1, P, P, P]
    - [B, B, c, d, -1, -1, P, P]
    - [B, B, B, e, f, -1, -1, P]
    - [B, B, B, B, 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, num_codebooks, input_ids.shape[-1])
    bsz, num_codebooks, seq_len = input_ids.shape

    input_ids_shifted = torch.ones((bsz, num_codebooks, max_length), dtype=torch.long, device=input_ids.device) * -1

    # we only apply the mask if we have a large enough seq len - otherwise we return as is
    if max_length < 2 * num_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(num_codebooks):
        # mono channel - loop over the codebooks one-by-one
        input_ids_shifted[:, codebook, codebook : seq_len + codebook] = input_ids[:, codebook]

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

    bos_mask = ~(bos_delay_pattern).to(input_ids.device)
    eos_mask = ~(eos_delay_pattern).to(input_ids.device)
    mask = ~(bos_delay_pattern + eos_delay_pattern).to(input_ids.device)
    input_ids = mask * input_ids_shifted + ~bos_mask * bos_token_id + ~eos_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


# Copied from transformers.models.llama.modeling_llama.repeat_kv
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


@dataclass
class ParlerTTSUnconditionalInput(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**.
    """

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


# Copied from transformers.models.encoder_decoder.modeling_encoder_decoder.shift_tokens_right
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.
    """
    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


# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenSinusoidalPositionalEmbedding with Musicgen->ParlerTTS
class ParlerTTSSinusoidalPositionalEmbedding(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, seq_len, _ = input_ids.size()
        # Create the position ids from the input token ids.
        position_ids = torch.arange(seq_len, device=input_ids.device) + past_key_values_length
        # 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.llama.modeling_llama.LlamaRotaryEmbedding with Llama->ParlerTTS
class ParlerTTSRotaryEmbedding(nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
        super().__init__()
        self.scaling_factor = scaling_factor
        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        # For BC we register cos and sin cached
        self.max_seq_len_cached = max_position_embeddings
        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
        t = t / self.scaling_factor
        freqs = torch.outer(t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("_cos_cached", emb.cos().to(torch.get_default_dtype()), persistent=False)
        self.register_buffer("_sin_cached", emb.sin().to(torch.get_default_dtype()), persistent=False)

    # Ignore copy
    @torch.no_grad()
    def forward(self, device_type, position_ids):
        # x: [bs, num_attention_heads, seq_len, head_size]
        inv_freq_expanded = self.inv_freq[None, :, None].expand(position_ids.shape[0], -1, 1)
        position_ids_expanded = position_ids[:, None, :]
        # Force float32 since bfloat16 loses precision on long contexts
        # See https://github.com/huggingface/transformers/pull/29285
        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
        with torch.autocast(device_type=device_type, enabled=False):
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos()
            sin = emb.sin()
        return cos, sin


def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(x, cos, sin, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        x (`torch.Tensor`): The tensor over which to apply the rope embeddings
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    x_embed = (x * cos) + (rotate_half(x) * sin)
    return x_embed


class ParlerTTSAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper. Modified to use GQA and MQA."""

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

        if layer_idx is None and is_decoder:
            logger.warning_once(
                f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
                "will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
                "when creating this class."
            )
        self.layer_idx = layer_idx

        self.k_proj = nn.Linear(embed_dim, self.num_key_value_heads * self.head_dim, bias=bias)
        self.v_proj = nn.Linear(embed_dim, self.num_key_value_heads * self.head_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)

        self.rope_embeddings = rope_embeddings

    def _shape_query(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 _shape_key_value(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_key_value_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[EncoderDecoderCache] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cos: Optional[torch.LongTensor] = None,
        sin: Optional[torch.LongTensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> 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.shape[:2]

        # get query proj
        query_states = self.q_proj(hidden_states) * self.scaling
        query_states = self._shape_query(query_states, tgt_len, bsz)
        if self.rope_embeddings:
            query_states = apply_rotary_pos_emb(query_states, cos, sin)

        if past_key_value is not None:
            is_updated = past_key_value.is_updated.get(self.layer_idx)
            if is_cross_attention:
                # after the first generated id, we can subsequently re-use all key/value_states from cache
                past_key_value.is_updated[self.layer_idx] = True
                past_key_value = past_key_value.cross_attention_cache
            else:
                past_key_value = past_key_value.self_attention_cache

        # use key_value_states if cross attention
        current_states = key_value_states if key_value_states is not None else hidden_states
        if is_cross_attention and past_key_value and is_updated:
            # reuse k,v, cross_attentions
            key_states = past_key_value.key_cache[self.layer_idx]
            value_states = past_key_value.value_cache[self.layer_idx]
        else:
            key_states = self._shape_key_value(self.k_proj(current_states), -1, bsz)
            value_states = self._shape_key_value(self.v_proj(current_states), -1, bsz)

            if not is_cross_attention:
                # cached key states already have rope applied - only apply to new state
                key_states = apply_rotary_pos_emb(key_states, cos, sin) if self.rope_embeddings else key_states

            if past_key_value is not None:
                # save all key/value_states to cache to be re-used for fast auto-regressive generation
                cache_position = cache_position if not is_cross_attention else None
                key_states, value_states = past_key_value.update(
                    key_states, value_states, self.layer_idx, {"cache_position": cache_position}
                )

        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))

        if attention_mask is not None:  # no matter the length, we just slice it
            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
            attn_weights = attn_weights + causal_mask

        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

        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
        attn_output = torch.matmul(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.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, past_key_value


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,
    )


# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenFlashAttention2 with Musicgen->ParlerTTS
class ParlerTTSFlashAttention2(ParlerTTSAttention):
    """
    ParlerTTS flash attention module. This module inherits from `ParlerTTSAttention` 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 forward(
        self,
        hidden_states: torch.Tensor,
        key_value_states: Optional[torch.Tensor] = None,
        past_key_value: Optional[EncoderDecoderCache] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cos: Optional[torch.LongTensor] = None,
        sin: Optional[torch.LongTensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        # ParlerTTSFlashAttention2 attention does not support output_attentions
        if isinstance(past_key_value, StaticCache):
            raise ValueError(
                "The `static` cache implementation is not compatible with `attn_implementation='flash_attention_2'`. "
                "Use `attn_implementation='sdpa'` in the meantime, and open an issue at https://github.com/huggingface/transformers"
            )
        # 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.shape[:2]

        # get query proj
        query_states = self.q_proj(hidden_states).view(bsz, tgt_len, self.num_heads, self.head_dim)

        if self.rope_embeddings:
            query_states = apply_rotary_pos_emb(query_states, cos, sin, unsqueeze_dim=2)

        if past_key_value is not None:
            is_updated = past_key_value.is_updated.get(self.layer_idx)
            if is_cross_attention:
                # after the first generated id, we can subsequently re-use all key/value_states from cache
                past_key_value.is_updated[self.layer_idx] = True
                past_key_value = past_key_value.cross_attention_cache
            else:
                past_key_value = past_key_value.self_attention_cache

        # use key_value_states if cross attention
        current_states = key_value_states if key_value_states is not None else hidden_states
        if is_cross_attention and past_key_value and is_updated:
            # reuse k,v, cross_attentions
            key_states = past_key_value.key_cache[self.layer_idx]
            value_states = past_key_value.value_cache[self.layer_idx]
        else:
            key_states = self._shape_key_value(self.k_proj(current_states), -1, bsz)
            value_states = self._shape_key_value(self.v_proj(current_states), -1, bsz)

            if not is_cross_attention and self.rope_embeddings:
                # cached key states already have rope applied - only apply to new state
                key_states = apply_rotary_pos_emb(key_states, cos, sin)

            if past_key_value is not None:
                # save all key/value_states to cache to be re-used for fast auto-regressive generation
                cache_position = cache_position if not is_cross_attention else None
                key_states, value_states = past_key_value.update(
                    key_states, value_states, self.layer_idx, {"cache_position": cache_position}
                )

        # # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]
        # #  We would need to refactor the KV cache to be able to avoid many of these transpose/reshape/view.
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 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)

        if query_states.dtype == torch.float32 or value_states.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, tgt_len, dropout=self.dropout
        )

        attn_output = attn_output.reshape(bsz, tgt_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 ParlerTTSSdpaAttention(ParlerTTSAttention):
    def forward(
        self,
        hidden_states: torch.Tensor,
        key_value_states: Optional[torch.Tensor] = None,
        past_key_value: Optional[EncoderDecoderCache] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cos: Optional[torch.LongTensor] = None,
        sin: Optional[torch.LongTensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> 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(
                "ParlerTTSModel is using ParlerTTSSdpaAttention, 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,
                cache_position=cache_position,
            )

        # 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.shape[:2]

        # get query proj
        query_states = self.q_proj(hidden_states)
        query_states = self._shape_query(query_states, tgt_len, bsz)

        if self.rope_embeddings:
            query_states = apply_rotary_pos_emb(query_states, cos, sin)

        if past_key_value is not None:
            is_updated = past_key_value.is_updated.get(self.layer_idx)
            if is_cross_attention:
                # after the first generated id, we can subsequently re-use all key/value_states from cache
                past_key_value.is_updated[self.layer_idx] = True
                past_key_value = past_key_value.cross_attention_cache
            else:
                past_key_value = past_key_value.self_attention_cache

        # use key_value_states if cross attention
        current_states = key_value_states if key_value_states is not None else hidden_states
        if is_cross_attention and past_key_value and is_updated:
            # reuse k,v, cross_attentions
            key_states = past_key_value.key_cache[self.layer_idx]
            value_states = past_key_value.value_cache[self.layer_idx]
        else:
            key_states = self._shape_key_value(self.k_proj(current_states), -1, bsz)
            value_states = self._shape_key_value(self.v_proj(current_states), -1, bsz)

            if not is_cross_attention and self.rope_embeddings:
                # cached key states already have rope applied - only apply to new state
                key_states = apply_rotary_pos_emb(key_states, cos, sin)

            if past_key_value is not None:
                # save all key/value_states to cache to be re-used for fast auto-regressive generation
                cache_position = cache_position if not is_cross_attention else None
                key_states, value_states = past_key_value.update(
                    key_states, value_states, self.layer_idx, {"cache_position": cache_position}
                )

        causal_mask = attention_mask
        if attention_mask is not None:  # no matter the length, we just slice it
            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]

        # repeat k/v heads if n_kv_heads < n_heads
        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
        # 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 = True if self.is_causal and causal_mask is None and tgt_len > 1 else False

        # 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=causal_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=is_causal,
        )

        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


PARLERTTS_ATTENTION_CLASSES = {
    "eager": ParlerTTSAttention,
    "sdpa": ParlerTTSSdpaAttention,
    "flash_attention_2": ParlerTTSFlashAttention2,
}


class ParlerTTSDecoderLayer(nn.Module):
    def __init__(self, config: ParlerTTSDecoderConfig, layer_idx: int = None):
        super().__init__()
        self.embed_dim = config.hidden_size

        self.self_attn = PARLERTTS_ATTENTION_CLASSES[config._attn_implementation](
            embed_dim=self.embed_dim,
            num_heads=config.num_attention_heads,
            num_key_value_heads=config.num_key_value_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
            is_causal=True,
            bias=False,
            rope_embeddings=config.rope_embeddings,
            layer_idx=layer_idx,
            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)
        cross_attn_implementation = config._attn_implementation
        if config.cross_attention_implementation_strategy == "always_eager":
            cross_attn_implementation = "eager"
        elif config.cross_attention_implementation_strategy == "always_sdpa":
            cross_attn_implementation = "sdpa"
        self.encoder_attn = PARLERTTS_ATTENTION_CLASSES[cross_attn_implementation](
            self.embed_dim,
            config.num_attention_heads,
            num_key_value_heads=config.num_cross_attention_key_value_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
            bias=False,
            rope_embeddings=config.rope_embeddings,
            layer_idx=layer_idx,
            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)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        cos: Optional[torch.LongTensor] = None,
        sin: Optional[torch.LongTensor] = 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[EncoderDecoderCache] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = True,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> 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.
            position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
                config.n_positions - 1]`.
            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
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            past_key_value=past_key_value,
            attention_mask=attention_mask,
            cos=cos,
            sin=sin,
            layer_head_mask=layer_head_mask,
            output_attentions=output_attentions,
            cache_position=cache_position,
        )
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        hidden_states = residual + hidden_states

        # Cross-Attention Block
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states
            hidden_states = self.encoder_attn_layer_norm(hidden_states)
            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,
                cos=cos,
                sin=sin,
                layer_head_mask=cross_attn_layer_head_mask,
                past_key_value=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 1 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


# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenPreTrainedModel with Musicgen->ParlerTTS
class ParlerTTSPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = ParlerTTSDecoderConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _no_split_modules = ["ParlerTTSDecoderLayer", "ParlerTTSAttention"]
    _supports_cache_class = True
    _supports_static_cache = 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 ParlerTTS 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 ([`ParlerTTSConfig`]): 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.
            TODO: it's passed through enc_to_dec_proj and optionnally we concat the prompt hidden states in certain cases.
        past_key_values (`EncoderDecoderCache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
            Pre-computed hidden-states that can be used to speed up auto-regressive (sequential) decoding. There are
            four sets of pre-computed hidden-states: key and values states in the self-attention blocks (2) and
            in the cross-attention blocks (2). The `past_key_values` are returned when `use_cache=True` is passed or
            when `config.use_cache=True`

            Two formats are allowed:
            - An [`~cache_utils.EncoderDecoderCache`] instance;
            - 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)`.

            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`.
        prompt_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input prompt 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)
        prompt_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding prompt 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)
        prompt_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `prompt_input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `prompt_input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
        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.
        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
            Indices depicting the position of the input sequence tokens in the sequence. It is used to update the cache
            in the correct position and to infer the complete sequence length.
"""

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)
        prompt_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
            Sequence of prompt hidden-states at the output of the initial embedding layer. Concatenated to the input embeds.
        prompt_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
            Mask to avoid performing cross-attention on padding tokens indices of prompt 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 ParlerTTSDecoder(ParlerTTSPreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`ParlerTTSDecoderLayer`]
    """

    def __init__(self, config: ParlerTTSDecoderConfig):
        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

        # TODO(YL): actually doesn't need the +1 if initialized correctly. Too late to change now.
        embed_dim = config.vocab_size + 1  # + 1 for pad token id
        self.embed_tokens = nn.ModuleList(
            [nn.Embedding(embed_dim, config.hidden_size) for _ in range(config.num_codebooks)]
        )

        self.rope_embeddings = config.rope_embeddings
        if not config.rope_embeddings:
            self.embed_positions = ParlerTTSSinusoidalPositionalEmbedding(
                config.max_position_embeddings,
                config.hidden_size,
            )
        else:
            self.rotary_emb = ParlerTTSRotaryEmbedding(
                config.hidden_size // config.num_attention_heads,
                max_position_embeddings=config.max_position_embeddings,
                base=config.rope_theta,
            )
        self.layers = nn.ModuleList(
            [ParlerTTSDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.layer_norm = nn.LayerNorm(config.hidden_size)
        self.attn_implementation = config._attn_implementation
        encoder_attn_implementation = config._attn_implementation
        if config.cross_attention_implementation_strategy is not None:
            encoder_attn_implementation = (
                "sdpa" if config.cross_attention_implementation_strategy == "always_sdpa" else "eager"
            )
        self.encoder_attn_implementation = encoder_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,
        prompt_hidden_states: Optional[torch.FloatTensor] = None,
        prompt_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,
        position_ids: 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,
        cache_position=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")

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

        prepended_sequence_length = 0
        # if prompt_hidden_states, fuse to inputs_embeds and update input shape
        if prompt_hidden_states is not None:
            prepended_sequence_length = prompt_hidden_states.shape[-2]
            inputs_embeds = torch.cat([prompt_hidden_states, inputs_embeds], dim=1)

        return_legacy_cache = False
        return_self_attention_cache = False
        if use_cache or past_key_values is not None:
            if isinstance(past_key_values, Cache) and not isinstance(past_key_values, EncoderDecoderCache):
                return_self_attention_cache = True
                past_key_values = EncoderDecoderCache(past_key_values, DynamicCache())
            elif not isinstance(past_key_values, EncoderDecoderCache):
                return_legacy_cache = True
                logger.warning_once(
                    "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.43.0. "
                    "You should pass an instance of `EncoderDecoderCache` instead, e.g. "
                    "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
                )
                past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)

        past_key_values_length = 0
        if cache_position is not None:
            past_key_values_length = cache_position[0]
        elif past_key_values is not None:
            past_key_values_length = past_key_values.get_seq_length()

        if cache_position is None:
            cache_position = torch.arange(
                past_key_values_length, past_key_values_length + input_shape[1] + prepended_sequence_length, device=inputs_embeds.device
            )

        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        # NOTE: 1. As it is, the masked ids from the prompt will still count in the positions embeddings
        # NOTE: 2. we want to concatenate the prompt attention mask and the decoder attention mask
        # i.i.f `prompt_cross_attention=False`. ParlerTTSForConditionalGeneration's taking care of setting
        # `prompt_attention_mask=None`
        if prompt_attention_mask is not None and attention_mask is not None:
            attention_mask = torch.cat([prompt_attention_mask, attention_mask], dim=1)
        elif prompt_attention_mask is not None:
            logger.warning_once(
                "`prompt_attention_mask` is specified but `attention_mask` is not. A full `attention_mask` will be created. Make sure this is the intended behaviour."
            )
            if past_key_values_length == 0:
                attention_mask = torch.cat(
                    [
                        prompt_attention_mask,
                        torch.ones(input_shape, device=self.device, dtype=prompt_attention_mask.dtype),
                    ],
                    dim=1,
                )
            else:
                # In the generation case of `prompt_cross_attention=True`, we need to recreate an attention mask from scratch
                # to be able to prepend the prompt attention mask.
                # Since we generate token per token, we can recompute the generated length from the information we have.
                generated_length = past_key_values_length - prompt_attention_mask.shape[1] + 1
                attention_mask = torch.cat(
                    [
                        prompt_attention_mask,
                        torch.ones(
                            (input_shape[0], generated_length), device=self.device, dtype=prompt_attention_mask.dtype
                        ),
                    ],
                    dim=1,
                )

        input_shape = inputs_embeds.size()[:-1]
        cos, sin = None, None

        if not self.rope_embeddings:
            # embed positions
            # TODO: As it is, the masked ids from the prompt will still count in the positions embeddings
            # maybe should modify position embeddings
            positions = self.embed_positions(inputs_embeds, past_key_values_length)
            hidden_states = inputs_embeds + positions.to(inputs_embeds.device)
        else:
            hidden_states = inputs_embeds

            if position_ids is None:
                if attention_mask is not None:
                    # masked ids will **not** count in the position embeddings
                    position_ids = attention_mask.long().cumsum(-1) - 1
                    position_ids.masked_fill_(attention_mask == 0, 1)
                else:
                    position_ids = torch.arange(
                        past_key_values_length,
                        input_shape[1] + past_key_values_length,
                        dtype=torch.long,
                        device=inputs_embeds.device,
                    )
                    position_ids = position_ids.unsqueeze(0)

                # Some generation methods already pass only the last input ID
                if position_ids.shape[1] > input_shape[1]:
                    position_ids = position_ids[:, -input_shape[1] :]

            cos, sin = self.rotary_emb(hidden_states.device.type, position_ids)
            cos, sin = cos.to(hidden_states.dtype), sin.to(hidden_states.dtype)

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

        causal_mask = self._update_causal_mask(
            attention_mask,
            inputs_embeds,
            cache_position,
            past_key_values.self_attention_cache if past_key_values is not None else None,
            output_attentions,
        )

        if encoder_hidden_states is not None and encoder_attention_mask is not None:
            if self.encoder_attn_implementation == "flash_attention_2":
                encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
            elif self.encoder_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]
                )

        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

        # 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

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.forward,
                    hidden_states,
                    causal_mask,
                    cos,
                    sin,
                    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,
                    cache_position,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=causal_mask,
                    cos=cos,
                    sin=sin,
                    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_values if use_cache else None,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                    cache_position=cache_position,
                )
            hidden_states = layer_outputs[0]

            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 = past_key_values if use_cache else None
        if return_self_attention_cache:
            next_cache = past_key_values.self_attention_cache
        if return_legacy_cache:
            next_cache = past_key_values.to_legacy_cache()
        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,
        )

    # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
    def _update_causal_mask(
        self,
        attention_mask: torch.Tensor,
        input_tensor: torch.Tensor,
        cache_position: torch.Tensor,
        past_key_values: Cache,
        output_attentions: bool,
    ):
        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
        # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
        # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
        # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114

        if self.config._attn_implementation == "flash_attention_2":
            if attention_mask is not None and 0.0 in attention_mask:
                return attention_mask
            return None

        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
        # to infer the attention mask.
        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        using_static_cache = isinstance(past_key_values, StaticCache)

        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
            if AttentionMaskConverter._ignore_causal_mask_sdpa(
                attention_mask,
                inputs_embeds=input_tensor,
                past_key_values_length=past_seen_tokens,
                is_training=self.training,
            ):
                return None

        dtype, device = input_tensor.dtype, input_tensor.device
        min_dtype = torch.finfo(dtype).min
        sequence_length = input_tensor.shape[1]
        if using_static_cache:
            target_length = past_key_values.get_max_length()
        else:
            target_length = (
                attention_mask.shape[-1]
                if isinstance(attention_mask, torch.Tensor)
                else past_seen_tokens + sequence_length + 1
            )

        if attention_mask is not None and attention_mask.dim() == 4:
            # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
            if attention_mask.max() != 0:
                raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
            causal_mask = attention_mask
        else:
            causal_mask = torch.full(
                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
            )
            if sequence_length != 1:
                causal_mask = torch.triu(causal_mask, diagonal=1)
            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
            causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
            if attention_mask is not None:
                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
                mask_length = attention_mask.shape[-1]
                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
                padding_mask = padding_mask == 0
                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
                    padding_mask, min_dtype
                )
        if (
            self.config._attn_implementation == "sdpa"
            and attention_mask is not None
            and attention_mask.device.type == "cuda"
            and not output_attentions
        ):
            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
            # Details: https://github.com/pytorch/pytorch/issues/110213
            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)

        return causal_mask


@add_start_docstrings(
    "The bare ParlerTTS decoder model outputting raw hidden-states without any specific head on top.",
    MUSICGEN_START_DOCSTRING,
)
# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenModel with Musicgen->ParlerTTS
class ParlerTTSModel(ParlerTTSPreTrainedModel):
    def __init__(self, config: ParlerTTSDecoderConfig):
        super().__init__(config)
        self.decoder = ParlerTTSDecoder(config)
        self.config = 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,
        position_ids: Optional[torch.LongTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.LongTensor] = None,
        prompt_hidden_states: Optional[torch.FloatTensor] = None,
        prompt_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[Union[EncoderDecoderCache, 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,
        cache_position: Optional[torch.LongTensor] = 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,
            position_ids=position_ids,
            encoder_attention_mask=encoder_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            prompt_hidden_states=prompt_hidden_states,
            prompt_attention_mask=prompt_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,
            cache_position=cache_position,
        )

        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 Parler-TTS decoder model with a language modelling head on top.",
    MUSICGEN_START_DOCSTRING,
)
class ParlerTTSForCausalLM(ParlerTTSPreTrainedModel):
    def __init__(self, config: ParlerTTSDecoderConfig):
        super().__init__(config)

        self.model = ParlerTTSModel(config)

        self.num_codebooks = config.num_codebooks
        self.vocab_size = config.vocab_size
        self.num_codebooks = config.num_codebooks
        
        self.use_fused_lm_heads = config.use_fused_lm_heads
        if self.use_fused_lm_heads:
            self.lm_heads = nn.Linear(config.hidden_size, config.vocab_size * config.num_codebooks, bias=False)
        else:
            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=ParlerTTSCausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.LongTensor] = None,
        prompt_hidden_states: Optional[torch.FloatTensor] = None,
        prompt_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,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple, ParlerTTSCausalLMOutputWithCrossAttentions]:
        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

        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            prompt_hidden_states=prompt_hidden_states,
            prompt_attention_mask=prompt_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,
            cache_position=cache_position,
        )

        hidden_states = outputs[0]

        if self.use_fused_lm_heads:
            lm_logits = self.lm_heads(hidden_states).view(hidden_states.shape[0], -1, self.num_codebooks, self.vocab_size).transpose(1,2)
        else:
            lm_logits = torch.stack([head(hidden_states) for head in self.lm_heads], dim=1)

        loss = None
        per_codebook_losses = None
        if labels is not None:
            codebook_weights = self.config.codebook_weights
            # since encoder hidden states have concatenated to hidden states, take the last hidden states corresponding to labels
            logits = lm_logits[:, :, -labels.shape[1] :]

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

            # (bsz, vocab_size, seq_len, num_codebooks), (bsz, seq_len, num_codebooks)
            labels = labels.masked_fill(labels == self.config.bos_token_id, -100)

            # we use every codebooks token AND one single EOS at the end of each codebooks
            mask = (input_ids.transpose(1, 2) != self.config.eos_token_id) & ((labels != -100))

            # per codebook cross-entropy
            for codebook in range(self.config.num_codebooks):
                codebook_logits = logits[:, codebook].contiguous().view(-1, logits.shape[-1])
                codebook_mask = mask[..., codebook].contiguous().view(-1)
                codebook_labels = labels[..., codebook].contiguous().view(-1)

                codebook_loss = loss_fct(codebook_logits[codebook_mask], codebook_labels[codebook_mask])
                per_codebook_losses.append(codebook_loss)

                if codebook_weights is not None:
                    codebook_loss = codebook_loss*codebook_weights[codebook]
                    
                loss += codebook_loss

            if codebook_weights is not None:
                loss = loss / sum(codebook_weights)
            else:
                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 + (per_codebook_losses, )) if loss is not None else output

        return ParlerTTSCausalLMOutputWithCrossAttentions(
            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,
            per_codebook_losses=per_codebook_losses,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        prompt_hidden_states=None,
        prompt_attention_mask=None,
        head_mask=None,
        cross_attn_head_mask=None,
        past_key_values=None,
        use_cache=True,
        delay_pattern_mask=None,
        cache_position=None,
        inputs_embeds=None,
        **kwargs,
    ):
        if delay_pattern_mask is None:
            input_ids, delay_pattern_mask = self.build_delay_pattern_mask(
                input_ids,
                bos_token_id=self.generation_config.bos_token_id,
                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)

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)

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

            # we only want to use prompt signal in the 1st generation step but keeping the attention mask
            prompt_hidden_states = None

        return {
            "input_ids": input_ids.contiguous(), # `contiguous()` needed for compilation use cases
            "attention_mask": attention_mask,
            "position_ids": position_ids,
            "encoder_hidden_states": encoder_hidden_states,
            "encoder_attention_mask": encoder_attention_mask,
            "prompt_hidden_states": prompt_hidden_states,
            "prompt_attention_mask": prompt_attention_mask,
            "head_mask": head_mask,
            "cross_attn_head_mask": cross_attn_head_mask,
            "past_key_values": past_key_values,
            "use_cache": use_cache,
            "cache_position": cache_position,
            "inputs_embeds": inputs_embeds,
        }

    # Ignore copy
    def build_delay_pattern_mask(
        self, input_ids: torch.LongTensor, bos_token_id: int, 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)`:
        - [B, -1, -1, -1, -1, P, P, P]
        - [B, B, -1, -1, -1, -1, P, P]
        - [B, B, B, -1, -1, -1, -1, P]
        - [B, B, B, B, -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:
        - [B, a, b, -1, -1, P, P, P]
        - [B, B, c, d, -1, -1, P, P]
        - [B, B, B, e, f, -1, -1, P]
        - [B, B, B, B, 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.
        """
        max_length = max_length if max_length is not None else self.generation_config.max_length
        return build_delay_pattern_mask(input_ids, bos_token_id, pad_token_id, max_length, self.num_codebooks)

    @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."""
        return apply_delay_pattern_mask(input_ids, decoder_pad_token_mask)

    @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()

        requires_attention_mask = "encoder_outputs" not in model_kwargs
        kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None

        # 3. Define model inputs`
        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
        self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=input_ids.device)

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

        if model_kwargs.get("attention_mask", None) is None and requires_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_length = input_ids.shape[-1]
        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
        has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None
        generation_config = self._prepare_generated_length(
            generation_config=generation_config,
            has_default_max_length=has_default_max_length,
            has_default_min_length=has_default_min_length,
            model_input_name=model_input_name,
            inputs_tensor=input_ids,
            input_ids_length=input_ids_length,
        )

        # 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 Parler-TTS)
        input_ids, delay_pattern_mask = self.build_delay_pattern_mask(
            input_ids,
            pad_token_id=generation_config._decoder_start_token_tensor,
            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 distribution pre_processing samplers
        logits_processor = self._get_logits_processor(
            generation_config=generation_config,
            input_ids_seq_length=input_ids_length,
            encoder_input_ids=input_ids,
            prefix_allowed_tokens_fn=None,
            logits_processor=logits_processor,
            device=input_ids.device,
        )

        # 9. 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}."
                )

            # 10. 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:
            # 10. prepare logits warper
            logits_warper = self._get_logits_warper(generation_config, device=input_ids.device)

            # 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,
            )

            # 11. 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 eos and bos token ids from the delay pattern mask
        _, mask = self.build_delay_pattern_mask(
            input_ids,
            bos_token_id=generation_config.bos_token_id,
            pad_token_id=generation_config.pad_token_id,
            max_length=output_ids.shape[1],
        )

        mask = (mask != generation_config._bos_token_tensor) & (mask != generation_config._pad_token_tensor)
        output_ids = output_ids[mask].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 Parler-TTS model with a text encoder, audio encoder and ParlerTTS decoder, "
    "for music generation tasks with one or both of text and audio prompts.",
    MUSICGEN_START_DOCSTRING,
)
class ParlerTTSForConditionalGeneration(PreTrainedModel):
    config_class = ParlerTTSConfig
    base_model_prefix = "encoder_decoder"
    main_input_name = "input_ids"
    supports_gradient_checkpointing = True
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_cache_class = True
    _supports_static_cache = True

    def __init__(
        self,
        config: Optional[ParlerTTSConfig] = None,
        text_encoder: Optional[PreTrainedModel] = None,
        audio_encoder: Optional[PreTrainedModel] = None,
        decoder: Optional[ParlerTTSForCausalLM] = 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 Parler-TTS decoder."
            )
        if config is None:
            config = ParlerTTSConfig.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 Parler-TTS 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 transformers.models.auto.modeling_auto import AutoModelForTextEncoding

            text_encoder = AutoModelForTextEncoding.from_config(config.text_encoder)

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

            audio_encoder = AutoModel.from_config(config.audio_encoder)

        if decoder is None:
            decoder = ParlerTTSForCausalLM._from_config(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.config.text_encoder._attn_implementation = self.text_encoder.config._attn_implementation
        self.config.audio_encoder._attn_implementation = self.audio_encoder.config._attn_implementation
        self.config.decoder._attn_implementation = self.decoder.config._attn_implementation
        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)

        # prompt embeddings
        self.embed_prompts = nn.Embedding(config.vocab_size, self.decoder.config.hidden_size)

        self.prompt_cross_attention = config.prompt_cross_attention
        if config.prompt_cross_attention:
            self.embed_positions = ParlerTTSSinusoidalPositionalEmbedding(
                config.decoder.max_position_embeddings,
                config.decoder.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"
            )

        audio_encoder_signature = set(inspect.signature(self.audio_encoder.decode).parameters.keys())
        self.use_audio_scales = "audio_scales" in audio_encoder_signature

        self.use_4dim_audio_codes = False
        audio_type = audio_encoder.config.model_type
        if audio_type in {"encodec", "dac_on_the_hub"} or (audio_type == "dac" and not is_dac_integrated_to_transformers):
            self.use_4dim_audio_codes = True 
 
        # Initialize projection and embedding layers and tie text encoder and decoder weights if set accordingly
        self.post_init()

    def _init_weights(self, module):
        std = self.decoder.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_()

    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
            self._tie_encoder_decoder_weights(
                self.text_encoder, self.decoder._modules[decoder_base_model_prefix], self.decoder.base_model_prefix
            )

    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 parler_tts import ParlerTTSForConditionalGeneration

        >>> model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1")
        ```"""

        # 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 ParlerTTSForConditionalGeneration. "
                "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 Parler-TTS 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.
                      Valid model ids can be located at the root-level, like `t5-base`, or namespaced under a user or
                      organization name, like `google/flan-t5-base.
                    - 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.
                      Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
                      user or organization name, like `facebook/encodec_24khz`.
                    - 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.
                      Valid model ids can be located at the root-level, like `gpt2`, or namespaced under a user or
                      organization name, like `parler-tts/parler-tts-mini-v1`.
                    - 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 parler_tts import ParlerTTSForConditionalGeneration

        >>> # initialize a parler_tts model from a t5 text encoder, encodec audio encoder, and parler_tts decoder
        >>> model = ParlerTTSForConditionalGeneration.from_sub_models_pretrained(
        ...     text_encoder_pretrained_model_name_or_path="t5-base",
        ...     audio_encoder_pretrained_model_name_or_path="facebook/encodec_24khz",
        ...     decoder_pretrained_model_name_or_path="parler-tts/parler-tts-mini-v1",
        ... )
        >>> # saving model after fine-tuning
        >>> model.save_pretrained("./parler_tts-ft")
        >>> # load fine-tuned model
        >>> model = ParlerTTSForConditionalGeneration.from_pretrained("./parler_tts-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 = AutoModelForTextEncoding.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 = ParlerTTSDecoderConfig.from_pretrained(
                    decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True
                )

                if isinstance(decoder_config, ParlerTTSConfig):
                    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 = ParlerTTSForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)

        # instantiate config with corresponding kwargs
        config = ParlerTTSConfig.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=ParlerTTSSeq2SeqLMOutput, 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: Optional[Union[EncoderDecoderCache, Tuple[torch.FloatTensor]]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        prompt_input_ids: Optional[torch.FloatTensor] = None,
        prompt_attention_mask: Optional[torch.LongTensor] = None,
        prompt_hidden_states: Optional[torch.FloatTensor] = None,
        decoder_position_ids: Optional[torch.LongTensor] = 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,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> Union[Tuple, ParlerTTSSeq2SeqLMOutput]:
        r"""
        Returns:

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

        >>> processor = AutoProcessor.from_pretrained("parler-tts/parler-tts-mini-v1")
        >>> model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1")

        >>> 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 prompt_hidden_states is None:
            if prompt_input_ids is not None:
                prompt_hidden_states = self.embed_prompts(prompt_input_ids)

        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,
            )
            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 prompt_hidden_states is not None and self.prompt_cross_attention:
                # add sinusoidal positional embedding
                positions = self.embed_positions(prompt_hidden_states, 0)
                prompt_hidden_states = prompt_hidden_states + positions.to(prompt_hidden_states.device)

                if prompt_attention_mask is not None and attention_mask is None:
                    attention_mask = torch.ones(
                        encoder_hidden_states.shape[:2], device=self.device, dtype=prompt_attention_mask.dtype
                    )
                elif attention_mask is not None and prompt_attention_mask is None:
                    prompt_attention_mask = torch.ones(
                        prompt_hidden_states.shape[:2], device=self.device, dtype=attention_mask.dtype
                    )

                # concatenate text description states with prompt description states
                encoder_hidden_states = torch.cat([encoder_hidden_states, prompt_hidden_states], dim=1)
                if prompt_attention_mask is not None:
                    attention_mask = torch.cat([attention_mask, prompt_attention_mask], dim=1)

                prompt_hidden_states = None
                prompt_attention_mask = None

            encoder_outputs["last_hidden_state"] = encoder_hidden_states

        elif isinstance(encoder_outputs, tuple):
            encoder_outputs = BaseModelOutput(*encoder_outputs)

        encoder_hidden_states = encoder_outputs.last_hidden_state

        if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
            decoder_input_ids = shift_tokens_right(
                labels, self.config.pad_token_id, self.config.decoder_start_token_id
            ).transpose(1, 2)

        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,
            position_ids=decoder_position_ids,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=attention_mask,
            prompt_hidden_states=prompt_hidden_states,
            prompt_attention_mask=prompt_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,
            cache_position=cache_position,
            **kwargs_decoder,
        )

        if not return_dict:
            return decoder_outputs + (encoder_hidden_states,)

        return ParlerTTSSeq2SeqLMOutput(
            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,
            per_codebook_losses=decoder_outputs.per_codebook_losses,
        )

    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,
        prompt_hidden_states=None,
        prompt_attention_mask=None,
        cross_attn_head_mask=None,
        use_cache=None,
        encoder_outputs=None,
        decoder_delay_pattern_mask=None,
        cache_position=None,
        inputs_embeds=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,
                bos_token_id=self.generation_config.bos_token_id,
                pad_token_id=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)

        past_length = 0
        if past_key_values is not None:
            if isinstance(past_key_values, EncoderDecoderCache):
                past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
                if past_key_values.get_seq_length() > 0:
                    # we only want to use prompt signal in the 1st generation step
                    prompt_hidden_states = None
            else:
                past_length = past_key_values[0][0].shape[2]
                # we only want to use prompt signal in the 1st generation step
                prompt_hidden_states = None

            # 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:]

        if cache_position is None:
            cache_position = torch.arange(
                past_length, past_length + decoder_input_ids.shape[1], device=decoder_input_ids.device
            )
        elif use_cache:
            cur_len = decoder_input_ids.shape[1]
            if prompt_hidden_states is not None and not self.prompt_cross_attention:
                # meaning we are in 1st generation step and prompt_hidden_state will be prepended
                cur_len += prompt_hidden_states.shape[1]

            cache_position = cache_position[-cur_len:]

        if decoder_attention_mask is None and prompt_attention_mask is not None:
            input = decoder_input_ids.reshape(-1, self.decoder.num_codebooks, decoder_input_ids.shape[-1])
            bsz, _, seq_len = input.shape
            input_shape = (bsz, seq_len)

            past_key_values_length = 0
            if cache_position is not None:
                past_key_values_length = cache_position[0]
            elif past_key_values is not None:
                past_key_values_length = past_key_values.get_seq_length()

            logger.warning_once(
                "`prompt_attention_mask` is specified but `attention_mask` is not. A full `attention_mask` will be created. Make sure this is the intended behaviour."
            )
            if past_key_values is None or (
                isinstance(past_key_values, EncoderDecoderCache) and past_key_values.get_seq_length() == 0
            ):
                decoder_attention_mask = torch.ones(input_shape, device=self.device, dtype=decoder_input_ids.dtype)
            elif prompt_attention_mask is not None:
                # In the generation case of `prompt_cross_attention=True`, we need to recreate an attention mask from scratch
                # to be able to prepend the prompt attention mask.
                # Since we generate token per token, we can recompute the generated length from the information we have.
                generated_length = past_key_values_length - prompt_attention_mask.shape[1] + 1
                decoder_attention_mask = torch.ones(
                    (input_shape[0], generated_length), device=self.device, dtype=prompt_attention_mask.dtype
                )

        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.contiguous(),
            "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,
            "prompt_hidden_states": prompt_hidden_states,
            "prompt_attention_mask": prompt_attention_mask,
            "use_cache": use_cache,
            "cache_position": cache_position,
            "inputs_embeds": inputs_embeds,
        }

    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

        if not self.prompt_cross_attention:
            prompt_hidden_states = model_kwargs["prompt_hidden_states"]
            num_codebooks = self.decoder.num_codebooks
            input = decoder_input_ids.reshape(-1, num_codebooks, decoder_input_ids.shape[-1])
            inputs_embeds = sum(
                [
                    self.decoder.model.decoder.embed_tokens[codebook](input[:, codebook])
                    for codebook in range(num_codebooks)
                ]
            )
            inputs_embeds = torch.cat([prompt_hidden_states, inputs_embeds], dim=1)
            model_kwargs["inputs_embeds"] = inputs_embeds

        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", "prompt_", "use_cache", "labels"]
        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

        # 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

        # we optionnally project last_hidden_state to avoid recomputing every time
        encoder_hidden_states = last_hidden_state
        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 model_kwargs["attention_mask"] is not None:
            encoder_hidden_states = encoder_hidden_states * model_kwargs["attention_mask"][..., None]

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

        return model_kwargs

    def _prepare_prompt_kwargs_for_generation(self, prompt_input_ids, model_kwargs):
        prompt_hidden_states = self.embed_prompts(prompt_input_ids)

        if self.prompt_cross_attention:
            # add sinusoidal positional embedding
            positions = self.embed_positions(prompt_hidden_states, 0)
            prompt_hidden_states = prompt_hidden_states + positions.to(prompt_hidden_states.device)

            attention_mask = model_kwargs.get("attention_mask", None)
            prompt_attention_mask = model_kwargs.get("prompt_attention_mask", None)
            encoder_hidden_states = model_kwargs["encoder_outputs"].last_hidden_state

            if prompt_attention_mask is not None and attention_mask is None:
                attention_mask = torch.ones(
                    encoder_hidden_states.shape[:2], device=self.device, dtype=prompt_attention_mask.dtype
                )
            elif attention_mask is not None and prompt_attention_mask is None:
                prompt_attention_mask = torch.ones(
                    prompt_hidden_states.shape[:2], device=self.device, dtype=attention_mask.dtype
                )

            # concatenate text description states with prompt description states
            encoder_hidden_states = torch.cat([encoder_hidden_states, prompt_hidden_states], dim=1)
            if prompt_attention_mask is not None:
                attention_mask = torch.cat([attention_mask, prompt_attention_mask], dim=1)

            model_kwargs["encoder_outputs"].last_hidden_state = encoder_hidden_states
            model_kwargs["attention_mask"] = attention_mask

            # in this case, since we already concatenated the prompt hidden states and attention mask, we don't need them anymore.
            model_kwargs["prompt_hidden_states"] = None
            model_kwargs["prompt_attention_mask"] = None
        else:
            model_kwargs["prompt_hidden_states"] = prompt_hidden_states
            # we're keeping the prompt attention mask because it has to be prepended to the decoder attention mask on the fly
        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 "num_quantizers" in encoder_signature:
            encoder_kwargs["num_quantizers"] = self.config.decoder.num_codebooks
        elif "num_codebooks" in encoder_signature:
            encoder_kwargs["num_codebooks"] = self.config.decoder.num_codebooks
        elif "n_quantizers" in encoder_signature:
            encoder_kwargs["n_quantizers"] = self.config.decoder.num_codebooks

        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.get("audio_scales")

        if audio_codes.ndim == 3:
            bsz, codebooks, seq_len = audio_codes.shape
            decoder_input_ids = audio_codes.reshape(bsz * self.decoder.num_codebooks, seq_len)
        else:
            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."
                )

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

        model_kwargs["decoder_input_ids"] = decoder_input_ids
        if audio_scales is not None:
            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
        ).transpose(1, 2)

    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 _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."
        )

    def _get_cache(self, cache_implementation: str, max_batch_size: int, max_cache_len: int, model_kwargs) -> Cache:
        """
        Sets a cache for `generate`, that will persist across calls. A new cache will only be initialized a
        new `generate` call requires a larger cache.

        Returns the resulting cache object.
        """
        cache_cls: Cache = NEED_SETUP_CACHE_CLASSES_MAPPING[cache_implementation]
        requires_cross_attention_cache = (
            self.config.is_encoder_decoder or model_kwargs.get("encoder_outputs") is not None
        )

        if hasattr(self, "_cache"):
            cache_to_check = self._cache.self_attention_cache if requires_cross_attention_cache else self._cache

        if cache_implementation == "sliding_window":
            max_cache_len = min(self.config.sliding_window, max_cache_len)

        need_new_cache = (
            not hasattr(self, "_cache")
            or (not isinstance(cache_to_check, cache_cls))
            or cache_to_check.max_batch_size != max_batch_size
            or cache_to_check.max_cache_len < max_cache_len
        )

        if requires_cross_attention_cache and hasattr(self, "_cache"):
            need_new_cache = (
                need_new_cache
                or self._cache.cross_attention_cache.max_cache_len != model_kwargs["encoder_outputs"][0].shape[1]
            )

        if need_new_cache:
            if hasattr(self.config, "_pre_quantization_dtype"):
                cache_dtype = self.config._pre_quantization_dtype
            else:
                cache_dtype = self.dtype
            cache_kwargs = {
                "config": self.config.decoder,
                "max_batch_size": max_batch_size,
                "max_cache_len": max_cache_len,
                "device": self.device,
                "dtype": cache_dtype,
            }
            self._cache = cache_cls(**cache_kwargs)
            if requires_cross_attention_cache:
                encoder_kwargs = cache_kwargs.copy()
                encoder_kwargs["max_cache_len"] = model_kwargs["encoder_outputs"][0].shape[1]
                config_cross_attention_cache = copy.deepcopy(self.config.decoder)
                config_cross_attention_cache.update(
                    {"num_key_value_heads": self.config.decoder.num_cross_attention_key_value_heads}
                )
                encoder_kwargs["config"] = config_cross_attention_cache
                self._cache = EncoderDecoderCache(self._cache, cache_cls(**encoder_kwargs))
        else:
            self._cache.reset()
        return self._cache

    def freeze_encoders(self, freeze_text_encoder=True):
        if freeze_text_encoder:
            for param in self.text_encoder.parameters():
                param.requires_grad = False
            self.text_encoder._requires_grad = False

        for param in self.audio_encoder.parameters():
            param.requires_grad = False
        self.audio_encoder._requires_grad = False

    @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()

        requires_attention_mask = "encoder_outputs" not in model_kwargs
        kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None

        # 3. Define model inputs
        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]
        self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=inputs_tensor.device)

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

        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_tensor, generation_config._eos_token_tensor
            )

        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 "prompt_hidden_states" not in model_kwargs and "prompt_input_ids" in model_kwargs:
            # `prompt_hidden_states` are created and added to `model_kwargs`
            model_kwargs = self._prepare_prompt_kwargs_for_generation(
                model_kwargs["prompt_input_ids"],
                model_kwargs,
            )

        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_tensor,
            bos_token_id=generation_config._bos_token_tensor,
            device=inputs_tensor.device,
        )

        # 6. Prepare `max_length` depending on other stopping criteria.
        input_ids_length = input_ids.shape[-1]
        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
        has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None
        generation_config = self._prepare_generated_length(
            generation_config=generation_config,
            has_default_max_length=has_default_max_length,
            has_default_min_length=has_default_min_length,
            model_input_name=model_input_name,
            inputs_tensor=inputs_tensor,
            input_ids_length=input_ids_length,
        )

        if generation_config.cache_implementation is not None and model_kwargs.get("past_key_values") is not None:
            raise ValueError(
                "Passing both `cache_implementation` (used to initialize certain caches) and `past_key_values` (a "
                "Cache object) is unsupported. Please use only one of the two."
            )
        elif generation_config.cache_implementation is not None:
            if generation_config.cache_implementation in NEED_SETUP_CACHE_CLASSES_MAPPING:
                if generation_config.cache_implementation == "static" and not self._supports_static_cache:
                    raise ValueError(
                        "This model does not support `cache_implementation='static'`. Please check the following "
                        "issue: https://github.com/huggingface/transformers/issues/28981"
                    )
                if not self.prompt_cross_attention:
                    # when we prepend prompt_hidden_state to inputs_embeds, max_cache_len needs to be actualised
                    # generation_config.max_length has already been increased by input_ids_length which is
                    # already counted in input_embeds_seq_length so we remove it
                    input_embeds_seq_length = model_kwargs["inputs_embeds"].shape[1]
                    max_cache_len = generation_config.max_length + input_embeds_seq_length - input_ids_length
                else:
                    max_cache_len = self.generation_config.max_length
                model_kwargs["past_key_values"] = self._get_cache(
                    generation_config.cache_implementation,
                    getattr(generation_config, "num_beams", 1) * batch_size,
                    max_cache_len,
                    model_kwargs,
                )
            elif generation_config.cache_implementation == "quantized":
                raise ValueError(
                    "This model does not support the quantized cache. If you want your model to support quantized "
                    "cache, please open an issue on the Parler-TTS repository https://github.com/huggingface/parler-tts"
                )
        # Use DynamicCache() instance by default. This will avoid back and forth from legacy format that
        # keeps copying the cache thus using much more memory
        elif generation_config.cache_implementation is None and self._supports_default_dynamic_cache():
            past = model_kwargs.get("past_key_values", None)
            requires_cross_attention_cache = (
                self.config.is_encoder_decoder or model_kwargs.get("encoder_outputs") is not None
            )
            if past is None:
                model_kwargs["past_key_values"] = (
                    DynamicCache()
                    if not requires_cross_attention_cache
                    else EncoderDecoderCache(DynamicCache(), DynamicCache())
                )
            elif isinstance(past, tuple):
                model_kwargs["past_key_values"] = (
                    DynamicCache.from_legacy_cache(past)
                    if not requires_cross_attention_cache
                    else EncoderDecoderCache.from_legacy_cache(past)
                )

        # build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to Parler-TTS)
        delayed_input_ids, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask(
            input_ids,
            bos_token_id=generation_config._bos_token_tensor,
            pad_token_id=generation_config._pad_token_tensor,
            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(delayed_input_ids.cpu())

        # 7. determine generation mode
        generation_mode = generation_config.get_generation_mode()

        # 8. prepare distribution pre_processing samplers
        logits_processor = self._get_logits_processor(
            generation_config=generation_config,
            input_ids_seq_length=input_ids_length,
            encoder_input_ids=inputs_tensor,
            prefix_allowed_tokens_fn=None,
            logits_processor=logits_processor,
            device=delayed_input_ids.device,
        )

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

        if generation_mode in (GenerationMode.SAMPLE, GenerationMode.GREEDY_SEARCH):
            # expand input_ids with `num_return_sequences` additional sequences per batch
            delayed_input_ids, model_kwargs = self._expand_inputs_for_generation(
                input_ids=delayed_input_ids,
                expand_size=generation_config.num_return_sequences,
                is_encoder_decoder=self.config.is_encoder_decoder,
                **model_kwargs,
            )

            # 10. run sample
            outputs = self._sample(
                delayed_input_ids,
                logits_processor=logits_processor,
                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 eos and bos token ids from the delay pattern mask
        _, mask = self.decoder.build_delay_pattern_mask(
            input_ids,
            bos_token_id=generation_config.bos_token_id,
            pad_token_id=generation_config.pad_token_id,
            max_length=output_ids.shape[1],
        )

        mask = (mask != generation_config.bos_token_id) & (mask != generation_config.pad_token_id)
        output_ids = output_ids[mask].reshape(batch_size, self.decoder.num_codebooks, -1)

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

        audio_decode_kwargs = {}
        if self.use_audio_scales:
            audio_scales = model_kwargs.get("audio_scales")
            if audio_scales is None:
                audio_scales = [None] * batch_size
            audio_decode_kwargs["audio_scales"] = audio_scales

        
        if not self.use_4dim_audio_codes:
            # remove chunk dim
            output_ids = output_ids.squeeze(0)
            
            
        decode_sequentially = (
            generation_config.bos_token_id in output_ids
            or generation_config.pad_token_id in output_ids
            or generation_config.eos_token_id in output_ids
        )
        if not decode_sequentially:
            output_values = self.audio_encoder.decode(
                audio_codes=output_ids,
                **audio_decode_kwargs,
            ).audio_values.squeeze(1)
            output_lengths = [audio.shape[0] for audio in output_values]
        else:
            output_values = []
            for sample_id in range(batch_size):
                sample = output_ids[:, sample_id] if self.use_4dim_audio_codes else output_ids[sample_id]
                sample_mask = (sample >= self.audio_encoder.config.codebook_size)
                sample_mask = (sample_mask.sum(dim=(0, 1)) == 0) if self.use_4dim_audio_codes else (sample_mask.sum(dim=0) == 0)
                single_audio_decode_kwargs = {}
                if self.use_audio_scales:
                    single_audio_decode_kwargs["audio_scales"] = [audio_decode_kwargs["audio_scales"][sample_id]]
                if sample_mask.sum() > 0:
                    sample = sample[:, :, sample_mask] if self.use_4dim_audio_codes else sample[:, sample_mask]
                    sample = self.audio_encoder.decode(audio_codes=sample[None, ...], **single_audio_decode_kwargs).audio_values
                    sample = sample if sample.ndim == 3 else sample.unsqueeze(0)
                    output_values.append(sample.transpose(0, 2))
                else:
                    output_values.append(torch.zeros((1, 1, 1)).to(self.device))
            output_lengths = [audio.shape[0] for audio in output_values]
            output_values = (
                torch.nn.utils.rnn.pad_sequence(output_values, batch_first=True, padding_value=0)
                .squeeze(-1)
                .squeeze(-1)
            )
        if generation_config.return_dict_in_generate:
            outputs["audios_length"] = output_lengths
            outputs.sequences = output_values
            return outputs
        else:
            return output_values

    def _get_initial_cache_position(self, input_ids, model_kwargs):
        """Calculates `cache_position` for the pre-fill stage based on `input_ids` and optionally past length"""
        # `torch.compile`-friendly `torch.arange` from a shape -- the lines below are equivalent to `torch.arange`
        if "inputs_embeds" in model_kwargs:
            cache_position = torch.ones_like(model_kwargs["inputs_embeds"][0, :, 0], dtype=torch.int64).cumsum(0) - 1
        else:
            cache_position = torch.ones_like(input_ids[0, :], dtype=torch.int64).cumsum(0) - 1

        past_length = 0
        if model_kwargs.get("past_key_values") is not None:
            cache = model_kwargs["past_key_values"]
            past_length = 0
            if not isinstance(cache, Cache):
                past_length = cache[0][0].shape[2]
            elif hasattr(cache, "get_seq_length") and cache.get_seq_length() is not None:
                past_length = cache.get_seq_length()

            # TODO(joao): this is not torch.compile-friendly, find a work-around. If the cache is not empty,
            # end-to-end compilation will yield bad results because `cache_position` will be incorrect.
            if not is_torchdynamo_compiling():
                cache_position = cache_position[past_length:]

        model_kwargs["cache_position"] = cache_position
        return model_kwargs