# coding=utf-8
# Copyright 2023 The Fairseq Authors, Microsoft Research, and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch SpeechT5 model."""

import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, L1Loss

from ...activations import ACT2FN
from ...integrations.deepspeed import is_deepspeed_zero3_enabled
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
from ...modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPastAndCrossAttentions,
    Seq2SeqLMOutput,
    Seq2SeqModelOutput,
    Seq2SeqSpectrogramOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_speecht5 import SpeechT5Config, SpeechT5HifiGanConfig


logger = logging.get_logger(__name__)


_HIDDEN_STATES_START_POSITION = 1

# General docstring
_CONFIG_FOR_DOC = "SpeechT5Config"


from ..deprecated._archive_maps import SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST  # noqa: F401, E402


# Copied from transformers.models.bart.modeling_bart.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()
    shifted_input_ids[:, 0] = decoder_start_token_id

    if pad_token_id is None:
        raise ValueError("self.model.config.pad_token_id has to be defined.")
    # 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


def shift_spectrograms_right(input_values: torch.Tensor, reduction_factor: int = 1):
    """
    Shift input spectrograms one timestep to the right. Also applies the reduction factor to the sequence length.
    """
    # thin out frames for reduction factor
    if reduction_factor > 1:
        input_values = input_values[:, reduction_factor - 1 :: reduction_factor]

    shifted_input_values = input_values.new_zeros(input_values.shape)
    shifted_input_values[:, 1:] = input_values[:, :-1].clone()

    # replace possible -100 values in labels by zeros
    shifted_input_values.masked_fill_(shifted_input_values == -100.0, 0.0)

    return shifted_input_values


# Copied from transformers.models.wav2vec2.modeling_wav2vec2._compute_mask_indices
def _compute_mask_indices(
    shape: Tuple[int, int],
    mask_prob: float,
    mask_length: int,
    attention_mask: Optional[torch.LongTensor] = None,
    min_masks: int = 0,
) -> np.ndarray:
    """
    Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for
    ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on
    CPU as part of the preprocessing during training.

    Args:
        shape: The shape for which to compute masks. This should be of a tuple of size 2 where
               the first element is the batch size and the second element is the length of the axis to span.
        mask_prob:  The percentage of the whole axis (between 0 and 1) which will be masked. The number of
                    independently generated mask spans of length `mask_length` is computed by
                    `mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the
                    actual percentage will be smaller.
        mask_length: size of the mask
        min_masks: minimum number of masked spans
        attention_mask: A (right-padded) attention mask which independently shortens the feature axis of
                        each batch dimension.
    """
    batch_size, sequence_length = shape

    if mask_length < 1:
        raise ValueError("`mask_length` has to be bigger than 0.")

    if mask_length > sequence_length:
        raise ValueError(
            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}"
            f" and `sequence_length`: {sequence_length}`"
        )

    # epsilon is used for probabilistic rounding
    epsilon = np.random.rand(1).item()

    def compute_num_masked_span(input_length):
        """Given input length, compute how many spans should be masked"""
        num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
        num_masked_span = max(num_masked_span, min_masks)

        # make sure num masked span <= sequence_length
        if num_masked_span * mask_length > sequence_length:
            num_masked_span = sequence_length // mask_length

        # make sure num_masked span is also <= input_length - (mask_length - 1)
        if input_length - (mask_length - 1) < num_masked_span:
            num_masked_span = max(input_length - (mask_length - 1), 0)

        return num_masked_span

    # compute number of masked spans in batch
    input_lengths = (
        attention_mask.sum(-1).detach().tolist()
        if attention_mask is not None
        else [sequence_length for _ in range(batch_size)]
    )

    # SpecAugment mask to fill
    spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool)
    spec_aug_mask_idxs = []

    max_num_masked_span = compute_num_masked_span(sequence_length)

    if max_num_masked_span == 0:
        return spec_aug_mask

    for input_length in input_lengths:
        # compute num of masked spans for this input
        num_masked_span = compute_num_masked_span(input_length)

        # get random indices to mask
        spec_aug_mask_idx = np.random.choice(
            np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False
        )

        # pick first sampled index that will serve as a dummy index to pad vector
        # to ensure same dimension for all batches due to probabilistic rounding
        # Picking first sample just pads those vectors twice.
        if len(spec_aug_mask_idx) == 0:
            # this case can only happen if `input_length` is strictly smaller then
            # `sequence_length` in which case the last token has to be a padding
            # token which we can use as a dummy mask id
            dummy_mask_idx = sequence_length - 1
        else:
            dummy_mask_idx = spec_aug_mask_idx[0]

        spec_aug_mask_idx = np.concatenate(
            [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
        )
        spec_aug_mask_idxs.append(spec_aug_mask_idx)

    spec_aug_mask_idxs = np.array(spec_aug_mask_idxs)

    # expand masked indices to masked spans
    spec_aug_mask_idxs = np.broadcast_to(
        spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length)
    )
    spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length)

    # add offset to the starting indexes so that indexes now create a span
    offsets = np.arange(mask_length)[None, None, :]
    offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape(
        batch_size, max_num_masked_span * mask_length
    )
    spec_aug_mask_idxs = spec_aug_mask_idxs + offsets

    # ensure that we cannot have indices larger than sequence_length
    if spec_aug_mask_idxs.max() > sequence_length - 1:
        spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1

    # scatter indices to mask
    np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)

    return spec_aug_mask


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2NoLayerNormConvLayer with Wav2Vec2->SpeechT5
class SpeechT5NoLayerNormConvLayer(nn.Module):
    def __init__(self, config, layer_id=0):
        super().__init__()
        self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
        self.out_conv_dim = config.conv_dim[layer_id]

        self.conv = nn.Conv1d(
            self.in_conv_dim,
            self.out_conv_dim,
            kernel_size=config.conv_kernel[layer_id],
            stride=config.conv_stride[layer_id],
            bias=config.conv_bias,
        )
        self.activation = ACT2FN[config.feat_extract_activation]

    def forward(self, hidden_states):
        hidden_states = self.conv(hidden_states)
        hidden_states = self.activation(hidden_states)
        return hidden_states


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2LayerNormConvLayer with Wav2Vec2->SpeechT5
class SpeechT5LayerNormConvLayer(nn.Module):
    def __init__(self, config, layer_id=0):
        super().__init__()
        self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
        self.out_conv_dim = config.conv_dim[layer_id]

        self.conv = nn.Conv1d(
            self.in_conv_dim,
            self.out_conv_dim,
            kernel_size=config.conv_kernel[layer_id],
            stride=config.conv_stride[layer_id],
            bias=config.conv_bias,
        )
        self.layer_norm = nn.LayerNorm(self.out_conv_dim, elementwise_affine=True)
        self.activation = ACT2FN[config.feat_extract_activation]

    def forward(self, hidden_states):
        hidden_states = self.conv(hidden_states)

        hidden_states = hidden_states.transpose(-2, -1)
        hidden_states = self.layer_norm(hidden_states)
        hidden_states = hidden_states.transpose(-2, -1)

        hidden_states = self.activation(hidden_states)
        return hidden_states


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2GroupNormConvLayer with Wav2Vec2->SpeechT5
class SpeechT5GroupNormConvLayer(nn.Module):
    def __init__(self, config, layer_id=0):
        super().__init__()
        self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
        self.out_conv_dim = config.conv_dim[layer_id]

        self.conv = nn.Conv1d(
            self.in_conv_dim,
            self.out_conv_dim,
            kernel_size=config.conv_kernel[layer_id],
            stride=config.conv_stride[layer_id],
            bias=config.conv_bias,
        )
        self.activation = ACT2FN[config.feat_extract_activation]

        self.layer_norm = nn.GroupNorm(num_groups=self.out_conv_dim, num_channels=self.out_conv_dim, affine=True)

    def forward(self, hidden_states):
        hidden_states = self.conv(hidden_states)
        hidden_states = self.layer_norm(hidden_states)
        hidden_states = self.activation(hidden_states)
        return hidden_states


# Copied from transformers.models.speech_to_text.modeling_speech_to_text.Speech2TextSinusoidalPositionalEmbedding with Speech2Text->SpeechT5
class SpeechT5SinusoidalPositionalEmbedding(nn.Module):
    """This module produces sinusoidal positional embeddings of any length."""

    def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
        super().__init__()
        self.offset = 2
        self.embedding_dim = embedding_dim
        self.padding_idx = padding_idx
        self.make_weights(num_positions + self.offset, embedding_dim, padding_idx)

    def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
        emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx)
        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, padding_idx: Optional[int] = None):
        """
        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.sin(emb), torch.cos(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)
        if padding_idx is not None:
            emb[padding_idx, :] = 0
        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. Any padded tokens remain padded.
        position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length).to(
            input_ids.device
        )

        # expand embeddings if needed
        max_pos = self.padding_idx + 1 + seq_len
        if max_pos > self.weights.size(0):
            self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)

        return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach()

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

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


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2PositionalConvEmbedding with Wav2Vec2->SpeechT5
class SpeechT5PositionalConvEmbedding(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.conv = nn.Conv1d(
            config.hidden_size,
            config.hidden_size,
            kernel_size=config.num_conv_pos_embeddings,
            padding=config.num_conv_pos_embeddings // 2,
            groups=config.num_conv_pos_embedding_groups,
        )

        weight_norm = nn.utils.weight_norm
        if hasattr(nn.utils.parametrizations, "weight_norm"):
            weight_norm = nn.utils.parametrizations.weight_norm

        if is_deepspeed_zero3_enabled():
            import deepspeed

            with deepspeed.zero.GatheredParameters(self.conv.weight, modifier_rank=0):
                self.conv = weight_norm(self.conv, name="weight", dim=2)
            deepspeed.zero.register_external_parameter(self, self.conv.weight_v)
            deepspeed.zero.register_external_parameter(self, self.conv.weight_g)
        else:
            self.conv = weight_norm(self.conv, name="weight", dim=2)

        self.padding = SpeechT5SamePadLayer(config.num_conv_pos_embeddings)
        self.activation = ACT2FN[config.feat_extract_activation]

    def forward(self, hidden_states):
        hidden_states = hidden_states.transpose(1, 2)

        hidden_states = self.conv(hidden_states)
        hidden_states = self.padding(hidden_states)
        hidden_states = self.activation(hidden_states)

        hidden_states = hidden_states.transpose(1, 2)
        return hidden_states


class SpeechT5ScaledPositionalEncoding(nn.Module):
    """
    Scaled positional encoding, see §3.2 in https://arxiv.org/abs/1809.08895
    """

    def __init__(self, dropout, dim, max_len=5000):
        pe = torch.zeros(max_len, dim)
        position = torch.arange(0, max_len).unsqueeze(1)
        div_term = torch.exp((torch.arange(0, dim, 2, dtype=torch.int64).float() * -(math.log(10000.0) / dim)))
        pe[:, 0::2] = torch.sin(position.float() * div_term)
        pe[:, 1::2] = torch.cos(position.float() * div_term)
        pe = pe.unsqueeze(0)
        super().__init__()
        self.register_buffer("pe", pe, persistent=False)
        self.dropout = nn.Dropout(p=dropout)
        self.dim = dim
        self.alpha = torch.nn.Parameter(torch.tensor(1.0))

    def forward(self, emb):
        emb = emb + self.alpha * self.pe[:, : emb.size(1)]
        emb = self.dropout(emb)
        return emb


class SpeechT5RelativePositionalEncoding(torch.nn.Module):
    def __init__(self, dim, max_length=1000):
        super().__init__()
        self.dim = dim
        self.max_length = max_length
        self.pe_k = torch.nn.Embedding(2 * max_length, dim)

    def forward(self, hidden_states):
        seq_len = hidden_states.shape[1]
        pos_seq = torch.arange(0, seq_len).long().to(hidden_states.device)
        pos_seq = pos_seq[:, None] - pos_seq[None, :]

        pos_seq[pos_seq < -self.max_length] = -self.max_length
        pos_seq[pos_seq >= self.max_length] = self.max_length - 1
        pos_seq = pos_seq + self.max_length

        return self.pe_k(pos_seq)


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2SamePadLayer with Wav2Vec2->SpeechT5
class SpeechT5SamePadLayer(nn.Module):
    def __init__(self, num_conv_pos_embeddings):
        super().__init__()
        self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0

    def forward(self, hidden_states):
        if self.num_pad_remove > 0:
            hidden_states = hidden_states[:, :, : -self.num_pad_remove]
        return hidden_states


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureEncoder with Wav2Vec2->SpeechT5
class SpeechT5FeatureEncoder(nn.Module):
    """Construct the features from raw audio waveform"""

    def __init__(self, config):
        super().__init__()

        if config.feat_extract_norm == "group":
            conv_layers = [SpeechT5GroupNormConvLayer(config, layer_id=0)] + [
                SpeechT5NoLayerNormConvLayer(config, layer_id=i + 1) for i in range(config.num_feat_extract_layers - 1)
            ]
        elif config.feat_extract_norm == "layer":
            conv_layers = [
                SpeechT5LayerNormConvLayer(config, layer_id=i) for i in range(config.num_feat_extract_layers)
            ]
        else:
            raise ValueError(
                f"`config.feat_extract_norm` is {config.feat_extract_norm}, but has to be one of ['group', 'layer']"
            )
        self.conv_layers = nn.ModuleList(conv_layers)
        self.gradient_checkpointing = False
        self._requires_grad = True

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

    def forward(self, input_values):
        hidden_states = input_values[:, None]

        # make sure hidden_states require grad for gradient_checkpointing
        if self._requires_grad and self.training:
            hidden_states.requires_grad = True

        for conv_layer in self.conv_layers:
            if self._requires_grad and self.gradient_checkpointing and self.training:
                hidden_states = self._gradient_checkpointing_func(
                    conv_layer.__call__,
                    hidden_states,
                )
            else:
                hidden_states = conv_layer(hidden_states)

        return hidden_states


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureProjection with Wav2Vec2->SpeechT5
class SpeechT5FeatureProjection(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps)
        self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
        self.dropout = nn.Dropout(config.feat_proj_dropout)

    def forward(self, hidden_states):
        # non-projected hidden states are needed for quantization
        norm_hidden_states = self.layer_norm(hidden_states)
        hidden_states = self.projection(norm_hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states, norm_hidden_states


class SpeechT5SpeechEncoderPrenet(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.feature_encoder = SpeechT5FeatureEncoder(config)
        self.feature_projection = SpeechT5FeatureProjection(config)

        # model only needs masking vector if mask prob is > 0.0
        if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0:
            self.masked_spec_embed = nn.Parameter(torch.Tensor(config.hidden_size).uniform_())

        self.pos_conv_embed = SpeechT5PositionalConvEmbedding(config)
        self.pos_sinusoidal_embed = SpeechT5SinusoidalPositionalEmbedding(
            config.max_speech_positions + config.pad_token_id + 1,
            config.hidden_size,
            config.pad_token_id,
        )

    def freeze_feature_encoder(self):
        self.feature_encoder._freeze_parameters()

    def forward(
        self,
        input_values: torch.Tensor,
        attention_mask: Optional[torch.LongTensor] = None,
        mask_time_indices: Optional[torch.FloatTensor] = None,
    ):
        extract_features = self.feature_encoder(input_values)
        extract_features = extract_features.transpose(1, 2)

        if attention_mask is not None:
            # compute reduced attention_mask corresponding to feature vectors
            attention_mask = self._get_feature_vector_attention_mask(
                extract_features.shape[1],
                attention_mask,
            )

        hidden_states, extract_features = self.feature_projection(extract_features)
        hidden_states = self._mask_hidden_states(
            hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
        )

        positional_conv_embedding = self.pos_conv_embed(hidden_states)
        hidden_states = hidden_states + positional_conv_embedding

        if attention_mask is not None:
            padding_mask = attention_mask.ne(1).long()
        else:
            padding_mask = torch.zeros(hidden_states.shape[:2], dtype=torch.long, device=hidden_states.device)

        positional_sinusoidal_embeddings = self.pos_sinusoidal_embed(padding_mask)
        hidden_states = hidden_states + positional_sinusoidal_embeddings

        return hidden_states, attention_mask

    # Copied from transformers.models.unispeech.modeling_unispeech.UniSpeechPreTrainedModel._get_feature_vector_attention_mask
    def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor):
        # Effectively attention_mask.sum(-1), but not inplace to be able to run
        # on inference mode.
        non_padded_lengths = attention_mask.cumsum(dim=-1)[:, -1]
        output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths).to(torch.long)
        batch_size = attention_mask.shape[0]

        attention_mask = torch.zeros(
            (batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device
        )
        # these two operations makes sure that all values before the output lengths idxs are attended to
        attention_mask[(torch.arange(attention_mask.shape[0], device=attention_mask.device), output_lengths - 1)] = 1
        attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()
        return attention_mask

    # Copied from transformers.models.unispeech.modeling_unispeech.UniSpeechPreTrainedModel._get_feat_extract_output_lengths
    def _get_feat_extract_output_lengths(self, input_lengths: Union[torch.LongTensor, int]):
        """
        Computes the output length of the convolutional layers
        """

        def _conv_out_length(input_length, kernel_size, stride):
            # 1D convolutional layer output length formula taken
            # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
            return torch.div(input_length - kernel_size, stride, rounding_mode="floor") + 1

        for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
            input_lengths = _conv_out_length(input_lengths, kernel_size, stride)

        return input_lengths

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model._mask_hidden_states
    def _mask_hidden_states(
        self,
        hidden_states: torch.FloatTensor,
        mask_time_indices: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
    ):
        """
        Masks extracted features along time axis and/or along feature axis according to
        [SpecAugment](https://arxiv.org/abs/1904.08779).
        """

        # `config.apply_spec_augment` can set masking to False
        if not getattr(self.config, "apply_spec_augment", True):
            return hidden_states

        # generate indices & apply SpecAugment along time axis
        batch_size, sequence_length, hidden_size = hidden_states.size()

        if mask_time_indices is not None:
            # apply SpecAugment along time axis with given mask_time_indices
            hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)
        elif self.config.mask_time_prob > 0 and self.training:
            mask_time_indices = _compute_mask_indices(
                (batch_size, sequence_length),
                mask_prob=self.config.mask_time_prob,
                mask_length=self.config.mask_time_length,
                attention_mask=attention_mask,
                min_masks=self.config.mask_time_min_masks,
            )
            mask_time_indices = torch.tensor(mask_time_indices, device=hidden_states.device, dtype=torch.bool)
            hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)

        if self.config.mask_feature_prob > 0 and self.training:
            # generate indices & apply SpecAugment along feature axis
            mask_feature_indices = _compute_mask_indices(
                (batch_size, hidden_size),
                mask_prob=self.config.mask_feature_prob,
                mask_length=self.config.mask_feature_length,
                min_masks=self.config.mask_feature_min_masks,
            )
            mask_feature_indices = torch.tensor(mask_feature_indices, device=hidden_states.device, dtype=torch.bool)
            mask_feature_indices = mask_feature_indices[:, None].expand(-1, sequence_length, -1)
            hidden_states[mask_feature_indices] = 0

        return hidden_states


class SpeechT5SpeechDecoderPrenet(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config

        self.layers = nn.ModuleList(
            [
                nn.Linear(
                    config.num_mel_bins if i == 0 else config.speech_decoder_prenet_units,
                    config.speech_decoder_prenet_units,
                )
                for i in range(config.speech_decoder_prenet_layers)
            ]
        )

        self.final_layer = nn.Linear(config.speech_decoder_prenet_units, config.hidden_size)
        self.encode_positions = SpeechT5ScaledPositionalEncoding(
            config.positional_dropout,
            config.hidden_size,
            config.max_speech_positions,
        )
        self.speaker_embeds_layer = nn.Linear(config.speaker_embedding_dim + config.hidden_size, config.hidden_size)

    def _consistent_dropout(self, inputs_embeds, p):
        mask = torch.bernoulli(inputs_embeds[0], p=p)
        all_masks = mask.unsqueeze(0).repeat(inputs_embeds.size(0), 1, 1)
        return torch.where(all_masks == 1, inputs_embeds, 0) * 1 / (1 - p)

    def forward(
        self,
        input_values: torch.Tensor,
        speaker_embeddings: Optional[torch.Tensor] = None,
    ):
        # Dropout is always applied, even when evaluating. See §2.2 in https://arxiv.org/abs/1712.05884.

        inputs_embeds = input_values
        for layer in self.layers:
            inputs_embeds = nn.functional.relu(layer(inputs_embeds))
            inputs_embeds = self._consistent_dropout(inputs_embeds, self.config.speech_decoder_prenet_dropout)

        inputs_embeds = self.final_layer(inputs_embeds)
        inputs_embeds = self.encode_positions(inputs_embeds)

        if speaker_embeddings is not None:
            speaker_embeddings = nn.functional.normalize(speaker_embeddings)
            speaker_embeddings = speaker_embeddings.unsqueeze(1).expand(-1, inputs_embeds.size(1), -1)
            inputs_embeds = torch.cat([inputs_embeds, speaker_embeddings], dim=-1)
            inputs_embeds = nn.functional.relu(self.speaker_embeds_layer(inputs_embeds))

        return inputs_embeds


class SpeechT5BatchNormConvLayer(nn.Module):
    def __init__(self, config, layer_id=0):
        super().__init__()

        if layer_id == 0:
            in_conv_dim = config.num_mel_bins
        else:
            in_conv_dim = config.speech_decoder_postnet_units

        if layer_id == config.speech_decoder_postnet_layers - 1:
            out_conv_dim = config.num_mel_bins
        else:
            out_conv_dim = config.speech_decoder_postnet_units

        self.conv = nn.Conv1d(
            in_conv_dim,
            out_conv_dim,
            kernel_size=config.speech_decoder_postnet_kernel,
            stride=1,
            padding=(config.speech_decoder_postnet_kernel - 1) // 2,
            bias=False,
        )
        self.batch_norm = nn.BatchNorm1d(out_conv_dim)

        if layer_id < config.speech_decoder_postnet_layers - 1:
            self.activation = nn.Tanh()
        else:
            self.activation = None

        self.dropout = nn.Dropout(config.speech_decoder_postnet_dropout)

    def forward(self, hidden_states):
        hidden_states = self.conv(hidden_states)
        hidden_states = self.batch_norm(hidden_states)
        if self.activation is not None:
            hidden_states = self.activation(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states


class SpeechT5SpeechDecoderPostnet(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config

        self.feat_out = nn.Linear(config.hidden_size, config.num_mel_bins * config.reduction_factor)
        self.prob_out = nn.Linear(config.hidden_size, config.reduction_factor)

        self.layers = nn.ModuleList(
            [SpeechT5BatchNormConvLayer(config, i) for i in range(config.speech_decoder_postnet_layers)]
        )

    def forward(self, hidden_states: torch.Tensor):
        outputs_before_postnet = self.feat_out(hidden_states).view(hidden_states.size(0), -1, self.config.num_mel_bins)
        outputs_after_postnet = self.postnet(outputs_before_postnet)
        logits = self.prob_out(hidden_states).view(hidden_states.size(0), -1)
        return outputs_before_postnet, outputs_after_postnet, logits

    def postnet(self, hidden_states: torch.Tensor):
        layer_output = hidden_states.transpose(1, 2)
        for layer in self.layers:
            layer_output = layer(layer_output)
        return hidden_states + layer_output.transpose(1, 2)


class SpeechT5TextEncoderPrenet(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
        self.encode_positions = SpeechT5ScaledPositionalEncoding(
            config.positional_dropout,
            config.hidden_size,
            config.max_text_positions,
        )

    def get_input_embeddings(self):
        return self.embed_tokens

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

    def forward(self, input_ids: torch.Tensor):
        inputs_embeds = self.embed_tokens(input_ids)
        inputs_embeds = self.encode_positions(inputs_embeds)
        return inputs_embeds


class SpeechT5TextDecoderPrenet(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.dropout = nn.Dropout(config.positional_dropout)
        self.embed_scale = math.sqrt(config.hidden_size) if config.scale_embedding else 1.0

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)

        self.embed_positions = SpeechT5SinusoidalPositionalEmbedding(
            config.max_text_positions + config.pad_token_id + 1,
            config.hidden_size,
            config.pad_token_id,
        )

    def get_input_embeddings(self):
        return self.embed_tokens

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

    def forward(
        self,
        input_ids: torch.Tensor,
        attention_mask: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
    ):
        if input_ids is not None:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        else:
            raise ValueError("You have to specify `decoder_input_ids`")

        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
        positions = self.embed_positions(input_ids, past_key_values_length)

        inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
        inputs_embeds += positions
        inputs_embeds = self.dropout(inputs_embeds)

        return inputs_embeds, attention_mask


class SpeechT5TextDecoderPostnet(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

    def forward(self, hidden_states: torch.Tensor):
        return self.lm_head(hidden_states)

    def get_output_embeddings(self):
        return self.lm_head

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


class SpeechT5Attention(nn.Module):
    """
    Multi-headed attention from 'Attention Is All You Need' paper with relative position bias (see
    https://aclanthology.org/N18-2074.pdf)
    """

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        dropout: float = 0.0,
        is_decoder: bool = False,
        bias: bool = True,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads

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

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

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

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

        bsz, tgt_len, _ = hidden_states.size()

        # get query proj
        query_states = self.q_proj(hidden_states) * self.scaling
        # get key, value proj
        if is_cross_attention and past_key_value is not None:
            # reuse k,v, cross_attentions
            key_states = past_key_value[0]
            value_states = past_key_value[1]
        elif is_cross_attention:
            # cross_attentions
            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
        elif past_key_value is not None:
            # reuse k, v, self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)
        else:
            # self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)

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

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

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

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

        # relative attention bias
        if position_bias is not None:
            reshape_q = query_states.contiguous().view(bsz * self.num_heads, -1, self.head_dim).transpose(0, 1)
            rel_pos_bias = torch.matmul(reshape_q, position_bias.transpose(-2, -1))
            rel_pos_bias = rel_pos_bias.transpose(0, 1).view(
                bsz * self.num_heads, position_bias.size(0), position_bias.size(1)
            )
            attn_weights += rel_pos_bias

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

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

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

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

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

        attn_output = torch.bmm(attn_probs, value_states)

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

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

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

        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights_reshaped, past_key_value


class SpeechT5FeedForward(nn.Module):
    def __init__(self, config, intermediate_size):
        super().__init__()
        self.intermediate_dropout = nn.Dropout(config.activation_dropout)

        self.intermediate_dense = nn.Linear(config.hidden_size, intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

        self.output_dense = nn.Linear(intermediate_size, config.hidden_size)
        self.output_dropout = nn.Dropout(config.hidden_dropout)

    def forward(self, hidden_states):
        hidden_states = self.intermediate_dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        hidden_states = self.intermediate_dropout(hidden_states)

        hidden_states = self.output_dense(hidden_states)
        hidden_states = self.output_dropout(hidden_states)
        return hidden_states


class SpeechT5EncoderLayer(nn.Module):
    def __init__(self, config: SpeechT5Config):
        super().__init__()
        self.attention = SpeechT5Attention(
            embed_dim=config.hidden_size,
            num_heads=config.encoder_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=False,
        )
        self.dropout = nn.Dropout(config.hidden_dropout)
        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.feed_forward = SpeechT5FeedForward(config, config.encoder_ffn_dim)
        self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        position_bias: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ):
        """
        Args:
            hidden_states (`torch.FloatTensor`):
                input to the layer of shape `(batch, seq_len, hidden_size)`
            attention_mask (`torch.FloatTensor`):
                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
                `(config.encoder_attention_heads,)`.
            position_bias (`torch.FloatTensor`):
                relative position embeddings of size `(seq_len, seq_len, hidden_size // encoder_attention_heads)`
            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, attn_weights, _ = self.attention(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            layer_head_mask=layer_head_mask,
            position_bias=position_bias,
            output_attentions=output_attentions,
        )

        hidden_states = self.dropout(hidden_states)
        hidden_states = residual + hidden_states

        hidden_states = self.layer_norm(hidden_states)
        hidden_states = hidden_states + self.feed_forward(hidden_states)
        hidden_states = self.final_layer_norm(hidden_states)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs


class SpeechT5DecoderLayer(nn.Module):
    def __init__(self, config: SpeechT5Config):
        super().__init__()
        self.self_attn = SpeechT5Attention(
            embed_dim=config.hidden_size,
            num_heads=config.decoder_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
        )
        self.dropout = nn.Dropout(config.hidden_dropout)
        self.self_attn_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

        self.encoder_attn = SpeechT5Attention(
            config.hidden_size,
            config.decoder_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
        )
        self.encoder_attn_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

        self.feed_forward = SpeechT5FeedForward(config, config.decoder_ffn_dim)
        self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = True,
    ):
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, hidden_size)`
            attention_mask (`torch.FloatTensor`): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            encoder_hidden_states (`torch.FloatTensor`):
                cross attention input to the layer of shape `(batch, seq_len, hidden_size)`
            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

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

        # Cross-Attention Block
        cross_attn_present_key_value = None
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states

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

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

        # Fully Connected
        hidden_states = hidden_states + self.feed_forward(hidden_states)
        hidden_states = self.final_layer_norm(hidden_states)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights, cross_attn_weights)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


class SpeechT5PreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = SpeechT5Config
    base_model_prefix = "speecht5"
    main_input_name = "input_values"
    supports_gradient_checkpointing = True

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, SpeechT5PositionalConvEmbedding):
            nn.init.normal_(
                module.conv.weight,
                mean=0,
                std=2 * math.sqrt(1 / (module.conv.kernel_size[0] * module.conv.in_channels)),
            )
            nn.init.constant_(module.conv.bias, 0)
        elif isinstance(module, SpeechT5FeatureProjection):
            k = math.sqrt(1 / module.projection.in_features)
            nn.init.uniform_(module.projection.weight, a=-k, b=k)
            nn.init.uniform_(module.projection.bias, a=-k, b=k)
        elif isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        elif isinstance(module, nn.Conv1d):
            nn.init.kaiming_normal_(module.weight)
            if module.bias is not None:
                k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0]))
                nn.init.uniform_(module.bias, a=-k, b=k)
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()


class SpeechT5Encoder(SpeechT5PreTrainedModel):
    """
    Transformer encoder consisting of *config.encoder_layers* layers. Each layer is a [`SpeechT5EncoderLayer`].
    """

    def __init__(self, config: SpeechT5Config):
        super().__init__(config)
        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout)
        self.layerdrop = config.encoder_layerdrop

        self.layers = nn.ModuleList([SpeechT5EncoderLayer(config) for _ in range(config.encoder_layers)])

        self.embed_positions = SpeechT5RelativePositionalEncoding(
            config.hidden_size // config.encoder_attention_heads, config.encoder_max_relative_position
        )

        self.gradient_checkpointing = False

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

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutput]:
        """
        Args:
            hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, feature_size)`):
                Features extracted from the speech or text input by the encoder prenet.
            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing convolution and 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)
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_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**.

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

        # expand attention_mask
        if attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)

        hidden_states = self.layer_norm(hidden_states)
        hidden_states = self.dropout(hidden_states)

        position_bias = self.embed_positions(hidden_states)

        deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()

        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        # check if head_mask has a correct number of layers specified if desired
        if head_mask is not None:
            if head_mask.size()[0] != len(self.layers):
                raise ValueError(
                    f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
                    f" {head_mask.size()[0]}."
                )

        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            skip_the_layer = False
            if self.training:
                dropout_probability = torch.rand([])
                skip_the_layer = dropout_probability < self.layerdrop

            if not skip_the_layer or deepspeed_zero3_is_enabled:
                # under deepspeed zero3 all gpus must run in sync
                if self.gradient_checkpointing and self.training:
                    layer_outputs = self._gradient_checkpointing_func(
                        encoder_layer.__call__,
                        hidden_states,
                        attention_mask,
                        (head_mask[idx] if head_mask is not None else None),
                        position_bias,
                        output_attentions,
                    )
                else:
                    layer_outputs = encoder_layer(
                        hidden_states,
                        attention_mask=attention_mask,
                        position_bias=position_bias,
                        layer_head_mask=(head_mask[idx] if head_mask is not None else None),
                        output_attentions=output_attentions,
                    )
                hidden_states = layer_outputs[0]

            if skip_the_layer:
                layer_outputs = (None, None)

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

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)

        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


class SpeechT5EncoderWithSpeechPrenet(SpeechT5PreTrainedModel):
    """
    Wrapper around SpeechT5Encoder that applies SpeechT5SpeechEncoderPrenet to convert the audio waveform data to
    hidden features.
    """

    def __init__(self, config: SpeechT5Config):
        super().__init__(config)
        self.prenet = SpeechT5SpeechEncoderPrenet(config)
        self.wrapped_encoder = SpeechT5Encoder(config)

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

    def forward(
        self,
        input_values: torch.FloatTensor,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutput]:
        hidden_states, attention_mask = self.prenet(input_values, attention_mask)

        outputs = self.wrapped_encoder(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        return outputs


class SpeechT5EncoderWithTextPrenet(SpeechT5PreTrainedModel):
    """
    Wrapper around SpeechT5Encoder that applies SpeechT5TextEncoderPrenet to convert the input_ids to hidden features.
    """

    def __init__(self, config: SpeechT5Config):
        super().__init__(config)
        self.prenet = SpeechT5TextEncoderPrenet(config)
        self.wrapped_encoder = SpeechT5Encoder(config)

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

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

    def set_input_embeddings(self, value):
        self.prenet.set_input_embeddings(value)

    def forward(
        self,
        input_values: torch.FloatTensor,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutput]:
        hidden_states = self.prenet(input_values)

        outputs = self.wrapped_encoder(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        return outputs


class SpeechT5EncoderWithoutPrenet(SpeechT5PreTrainedModel):
    """
    This wrapper class is a helper class to correctly load pretrained checkpoints when used in combination with
    [`SpeechT5Model`].
    """

    def __init__(self, config: SpeechT5Config):
        super().__init__(config)
        self.wrapped_encoder = SpeechT5Encoder(config)

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

    def forward(
        self,
        input_values: torch.FloatTensor,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutput]:
        return self.wrapped_encoder(
            hidden_states=input_values,
            attention_mask=attention_mask,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )


class SpeechT5Decoder(SpeechT5PreTrainedModel):
    """
    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`SpeechT5DecoderLayer`]
    """

    def __init__(self, config: SpeechT5Config):
        super().__init__(config)
        self.layerdrop = config.decoder_layerdrop

        self.layers = nn.ModuleList([SpeechT5DecoderLayer(config) for _ in range(config.decoder_layers)])

        self.gradient_checkpointing = False

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

    def forward(
        self,
        hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
        r"""
        Args:
            hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, feature_size)`):
                Features extracted from the speech or text input by the decoder prenet.
            attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

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

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

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

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

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

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

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

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

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

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

        input_shape = hidden_states.size()[:-1]

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

        attention_mask = _prepare_4d_causal_attention_mask(
            attention_mask, input_shape, hidden_states, past_key_values_length
        )

        # expand encoder attention mask
        if encoder_hidden_states is not None and encoder_attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            encoder_attention_mask = _prepare_4d_attention_mask(
                encoder_attention_mask, hidden_states.dtype, tgt_len=input_shape[-1]
            )

        deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()

        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_attentions = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
        next_decoder_cache = () if use_cache else None

        # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
        for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
            if attn_mask is not None:
                if attn_mask.size()[0] != (len(self.layers)):
                    raise ValueError(
                        f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
                        f" {head_mask.size()[0]}."
                    )

        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            skip_the_layer = False
            if self.training:
                dropout_probability = torch.rand([])
                skip_the_layer = dropout_probability < self.layerdrop
            if skip_the_layer and not deepspeed_zero3_is_enabled:
                continue

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

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

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

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

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

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if not return_dict:
            return tuple(
                v
                for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions, 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_attentions,
            cross_attentions=all_cross_attentions,
        )


class SpeechT5DecoderWithSpeechPrenet(SpeechT5PreTrainedModel):
    """
    Wrapper around SpeechT5Decoder that applies SpeechT5SpeechDecoderPrenet to convert log-mel filterbanks to hidden
    features.
    """

    def __init__(self, config: SpeechT5Config):
        super().__init__(config)
        self.prenet = SpeechT5SpeechDecoderPrenet(config)
        self.wrapped_decoder = SpeechT5Decoder(config)

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

    def forward(
        self,
        input_values: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.LongTensor] = None,
        speaker_embeddings: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
        decoder_hidden_states = self.prenet(input_values, speaker_embeddings)

        outputs = self.wrapped_decoder(
            hidden_states=decoder_hidden_states,
            attention_mask=attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            head_mask=head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        return outputs


class SpeechT5DecoderWithTextPrenet(SpeechT5PreTrainedModel):
    """
    Wrapper around SpeechT5Decoder that applies SpeechT5TextDecoderPrenet to convert input tokens to hidden features.
    """

    def __init__(self, config: SpeechT5Config):
        super().__init__(config)
        self.prenet = SpeechT5TextDecoderPrenet(config)
        self.wrapped_decoder = SpeechT5Decoder(config)

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

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

    def set_input_embeddings(self, value):
        self.prenet.set_input_embeddings(value)

    def forward(
        self,
        input_values: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
        decoder_hidden_states, attention_mask = self.prenet(input_values, attention_mask, past_key_values)

        outputs = self.wrapped_decoder(
            hidden_states=decoder_hidden_states,
            attention_mask=attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            head_mask=head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        return outputs


class SpeechT5DecoderWithoutPrenet(SpeechT5PreTrainedModel):
    """
    This wrapper class is a helper class to correctly load pretrained checkpoints when used in combination with
    [`SpeechT5Model`].
    """

    def __init__(self, config: SpeechT5Config):
        super().__init__(config)
        self.wrapped_decoder = SpeechT5Decoder(config)

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

    def forward(
        self,
        input_values: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        encoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
        outputs = self.wrapped_decoder(
            hidden_states=input_values,
            attention_mask=attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            head_mask=head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        return outputs


class SpeechT5GuidedMultiheadAttentionLoss(nn.Module):
    """
    Guided attention loss from the paper [Efficiently Trainable Text-to-Speech System Based on Deep Convolutional
    Networks with Guided Attention](https://arxiv.org/abs/1710.08969), adapted for multi-head attention.
    """

    def __init__(self, config: SpeechT5Config):
        super().__init__()
        self.sigma = config.guided_attention_loss_sigma
        self.scale = config.guided_attention_loss_scale

    def forward(
        self, attentions: torch.FloatTensor, input_masks: torch.BoolTensor, output_masks: torch.BoolTensor
    ) -> torch.Tensor:
        """
        Compute the attention loss.

        Args:
            attentions (`torch.FloatTensor` of shape `(batch_size, layers * heads, output_sequence_length, input_sequence_length)`):
                Batch of multi-head attention weights
            input_masks (`torch.BoolTensor` of shape `(batch_size, input_sequence_length)`):
                Input attention mask as booleans.
            output_masks (`torch.BoolTensor` of shape `(batch_size, output_sequence_length)`):
                Target attention mask as booleans.

        Returns:
            `torch.Tensor` with the loss value
        """
        guided_attn_masks = self._make_guided_attention_masks(input_masks, output_masks, attentions.device)
        masks = output_masks.unsqueeze(-1) & input_masks.unsqueeze(-2)
        masks = masks.to(attentions.device).unsqueeze(1)

        losses = guided_attn_masks * attentions
        loss = torch.mean(losses.masked_select(masks))
        return self.scale * loss

    def _make_guided_attention_masks(self, input_masks, output_masks, device):
        input_lengths = input_masks.sum(-1)
        output_lengths = output_masks.sum(-1)

        guided_attn_masks = torch.zeros((len(input_masks), output_masks.shape[1], input_masks.shape[1]), device=device)

        for idx, (ilen, olen) in enumerate(zip(input_lengths, output_lengths)):
            guided_attn_masks[idx, :olen, :ilen] = self._make_guided_attention_mask(ilen, olen, self.sigma, device)

        return guided_attn_masks.unsqueeze(1)

    @staticmethod
    def _make_guided_attention_mask(input_length, output_length, sigma, device):
        grid_y, grid_x = torch.meshgrid(
            torch.arange(input_length, device=device),
            torch.arange(output_length, device=device),
            indexing="xy",
        )
        grid_x = grid_x.float() / output_length
        grid_y = grid_y.float() / input_length
        return 1.0 - torch.exp(-((grid_y - grid_x) ** 2) / (2 * (sigma**2)))


class SpeechT5SpectrogramLoss(nn.Module):
    """
    Loss computation used by SpeechT5ForTextToSpeech.
    """

    def __init__(self, config: SpeechT5Config):
        super().__init__()
        self.use_guided_attention_loss = config.use_guided_attention_loss
        self.guided_attention_loss_num_heads = config.guided_attention_loss_num_heads
        self.reduction_factor = config.reduction_factor

        self.l1_criterion = L1Loss()
        self.bce_criterion = BCEWithLogitsLoss(pos_weight=torch.tensor(5.0))

        if self.use_guided_attention_loss:
            self.attn_criterion = SpeechT5GuidedMultiheadAttentionLoss(config)

    def forward(
        self,
        attention_mask: torch.LongTensor,
        outputs_before_postnet: torch.FloatTensor,
        outputs_after_postnet: torch.FloatTensor,
        logits: torch.FloatTensor,
        labels: torch.FloatTensor,
        cross_attentions: Optional[torch.FloatTensor] = None,
    ) -> torch.Tensor:
        padding_mask = labels != -100.0

        # mask out the padded portions
        labels = labels.masked_select(padding_mask)
        outputs_before_postnet = outputs_before_postnet.masked_select(padding_mask)
        outputs_after_postnet = outputs_after_postnet.masked_select(padding_mask)

        # spectrogram loss
        l1_loss = self.l1_criterion(outputs_after_postnet, labels) + self.l1_criterion(outputs_before_postnet, labels)

        # construct stop labels from the padding mask
        masks = padding_mask[:, :, 0]
        stop_labels = torch.cat([~masks * 1.0, torch.ones(masks.size(0), 1).to(masks.device)], dim=1)
        stop_labels = stop_labels[:, 1:].masked_select(masks)
        logits = logits.masked_select(masks)

        # stop token loss
        bce_loss = self.bce_criterion(logits, stop_labels)

        # combined loss
        loss = l1_loss + bce_loss

        # guided attention loss
        if self.use_guided_attention_loss:
            attn = torch.cat([x[:, : self.guided_attention_loss_num_heads] for x in cross_attentions], dim=1)
            input_masks = attention_mask == 1
            output_masks = padding_mask[:, :, 0]
            if self.reduction_factor > 1:
                output_masks = output_masks[:, self.reduction_factor - 1 :: self.reduction_factor]
            attn_loss = self.attn_criterion(attn, input_masks, output_masks)
            loss += attn_loss

        return loss


SPEECHT5_BASE_START_DOCSTRING = r"""
    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 ([`SpeechT5Config`]):
            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.
        encoder ([`SpeechT5EncoderWithSpeechPrenet`] or [`SpeechT5EncoderWithTextPrenet`] or `None`):
            The Transformer encoder module that applies the appropiate speech or text encoder prenet. If `None`,
            [`SpeechT5EncoderWithoutPrenet`] will be used and the `input_values` are assumed to be hidden states.
        decoder ([`SpeechT5DecoderWithSpeechPrenet`] or [`SpeechT5DecoderWithTextPrenet`] or `None`):
            The Transformer decoder module that applies the appropiate speech or text decoder prenet. If `None`,
            [`SpeechT5DecoderWithoutPrenet`] will be used and the `decoder_input_values` are assumed to be hidden
            states.
"""


SPEECHT5_START_DOCSTRING = r"""
    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 ([`SpeechT5Config`]):
            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.
"""


SPEECHT5_INPUTS_DOCSTRING = r"""
    Args:
        attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing convolution and 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)

            <Tip warning={true}>

            `attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask ==
            True`. For all models whose processor has `config.return_attention_mask == False`, `attention_mask` should
            **not** be passed to avoid degraded performance when doing batched inference. For such models
            `input_values` should simply be padded with 0 and passed without `attention_mask`. Be aware that these
            models also yield slightly different results depending on whether `input_values` is padded or not.

            </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_values`. Causal mask will
            also be used by default.

            If you want to change padding behavior, you should read [`SpeechT5Decoder._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.

        head_mask (`torch.FloatTensor` 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.FloatTensor` 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. Mask values selected in `[0, 1]`:

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

        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.

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

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

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_values` (those
            that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_values` of shape `(batch_size, sequence_length)`. decoder_inputs_embeds (`torch.FloatTensor`
            of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing
            `decoder_input_values` 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_values` 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.
"""


@add_start_docstrings(
    "The bare SpeechT5 Encoder-Decoder Model outputting raw hidden-states without any specific pre- or post-nets.",
    SPEECHT5_BASE_START_DOCSTRING,
)
class SpeechT5Model(SpeechT5PreTrainedModel):
    def __init__(
        self,
        config: SpeechT5Config,
        encoder: Optional[nn.Module] = None,
        decoder: Optional[nn.Module] = None,
    ):
        super().__init__(config)
        self.config = config
        self.encoder = SpeechT5EncoderWithoutPrenet(config) if encoder is None else encoder
        self.decoder = SpeechT5DecoderWithoutPrenet(config) if decoder is None else decoder

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

    def get_input_embeddings(self):
        if isinstance(self.encoder, SpeechT5EncoderWithTextPrenet):
            return self.encoder.get_input_embeddings()
        if isinstance(self.decoder, SpeechT5DecoderWithTextPrenet):
            return self.decoder.get_input_embeddings()
        return None

    def set_input_embeddings(self, value):
        if isinstance(self.encoder, SpeechT5EncoderWithTextPrenet):
            self.encoder.set_input_embeddings(value)
        if isinstance(self.decoder, SpeechT5DecoderWithTextPrenet):
            self.decoder.set_input_embeddings(value)

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    def freeze_feature_encoder(self):
        """
        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
        not be updated during training.
        """
        if isinstance(self.encoder, SpeechT5EncoderWithSpeechPrenet):
            self.encoder.prenet.freeze_feature_encoder()

    @add_start_docstrings_to_model_forward(SPEECHT5_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_values: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        decoder_input_values: Optional[torch.Tensor] = None,
        decoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        decoder_head_mask: Optional[torch.FloatTensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        use_cache: Optional[bool] = None,
        speaker_embeddings: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
        r"""
        input_values (`torch.Tensor` of shape `(batch_size, sequence_length)`):
            Depending on which encoder is being used, the `input_values` are either: float values of the input raw
            speech waveform, or indices of input sequence tokens in the vocabulary, or hidden states.

        decoder_input_values (`torch.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Depending on which decoder is being used, the `decoder_input_values` are either: float values of log-mel
            filterbank features extracted from the raw speech waveform, or indices of decoder input sequence tokens in
            the vocabulary, or hidden states.

        speaker_embeddings (`torch.FloatTensor` of shape `(batch_size, config.speaker_embedding_dim)`, *optional*):
            Tensor containing the speaker embeddings.

        Returns:
        """
        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

        # Encode if needed (training, first prediction pass)
        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                input_values=input_values,
                attention_mask=attention_mask,
                head_mask=head_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )
        # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
            encoder_outputs = BaseModelOutput(
                last_hidden_state=encoder_outputs[0],
                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
            )

        # downsample encoder attention mask (only for encoders with speech input)
        if attention_mask is not None and isinstance(self.encoder, SpeechT5EncoderWithSpeechPrenet):
            encoder_attention_mask = self.encoder.prenet._get_feature_vector_attention_mask(
                encoder_outputs[0].shape[1], attention_mask
            )
        else:
            encoder_attention_mask = attention_mask

        if isinstance(self.decoder, SpeechT5DecoderWithSpeechPrenet):
            decoder_args = {"speaker_embeddings": speaker_embeddings}
        else:
            decoder_args = {}

        decoder_outputs = self.decoder(
            input_values=decoder_input_values,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_outputs[0],
            encoder_attention_mask=encoder_attention_mask,
            head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            **decoder_args,
        )

        if not return_dict:
            return decoder_outputs + encoder_outputs

        return Seq2SeqModelOutput(
            last_hidden_state=decoder_outputs.last_hidden_state,
            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,
        )


@add_start_docstrings(
    """SpeechT5 Model with a speech encoder and a text decoder.""",
    SPEECHT5_START_DOCSTRING,
)
class SpeechT5ForSpeechToText(SpeechT5PreTrainedModel):
    _tied_weights_keys = ["text_decoder_postnet.lm_head.weight"]

    def __init__(self, config: SpeechT5Config):
        super().__init__(config)

        if config.vocab_size is None:
            raise ValueError(
                f"You are trying to instantiate {self.__class__} with a configuration that does not define the"
                " vocabulary size of the language model head. Please instantiate the model as follows:"
                " `SpeechT5ForSpeechToText.from_pretrained(..., vocab_size=vocab_size)`. or define `vocab_size` of"
                " your model's configuration."
            )

        speech_encoder = SpeechT5EncoderWithSpeechPrenet(config)
        text_decoder = SpeechT5DecoderWithTextPrenet(config)
        self.speecht5 = SpeechT5Model(config, speech_encoder, text_decoder)

        self.text_decoder_postnet = SpeechT5TextDecoderPostnet(config)

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

    def get_encoder(self):
        return self.speecht5.get_encoder()

    def get_decoder(self):
        return self.speecht5.get_decoder()

    def freeze_feature_encoder(self):
        """
        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
        not be updated during training.
        """
        self.get_encoder().prenet.freeze_feature_encoder()

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

    def set_output_embeddings(self, new_embeddings):
        self.text_decoder_postnet.set_output_embeddings(new_embeddings)

    @add_start_docstrings_to_model_forward(SPEECHT5_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_values: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        decoder_head_mask: Optional[torch.FloatTensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple, Seq2SeqLMOutput]:
        r"""
        input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
            Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file
            into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (*pip install
            soundfile*). To prepare the array into `input_values`, the [`SpeechT5Processor`] should be used for padding
            and conversion into a tensor of type `torch.FloatTensor`. See [`SpeechT5Processor.__call__`] for details.

        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary.

            Indices can be obtained using [`SpeechT5Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

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

            SpeechT5 uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If
            `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).

        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]`
            or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is
            only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

            Label indices can be obtained using [`SpeechT5Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

        Returns:

        Example:

        ```python
        >>> from transformers import SpeechT5Processor, SpeechT5ForSpeechToText
        >>> from datasets import load_dataset

        >>> dataset = load_dataset(
        ...     "hf-internal-testing/librispeech_asr_demo", "clean", split="validation"
        ... )  # doctest: +IGNORE_RESULT
        >>> dataset = dataset.sort("id")
        >>> sampling_rate = dataset.features["audio"].sampling_rate

        >>> processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_asr")
        >>> model = SpeechT5ForSpeechToText.from_pretrained("microsoft/speecht5_asr")

        >>> # audio file is decoded on the fly
        >>> inputs = processor(audio=dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
        >>> predicted_ids = model.generate(**inputs, max_length=100)

        >>> # transcribe speech
        >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
        >>> transcription[0]
        'mister quilter is the apostle of the middle classes and we are glad to welcome his gospel'
        ```

        ```python
        >>> inputs["labels"] = processor(text_target=dataset[0]["text"], return_tensors="pt").input_ids

        >>> # compute loss
        >>> loss = model(**inputs).loss
        >>> round(loss.item(), 2)
        19.68
        ```
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

        outputs = self.speecht5(
            input_values=input_values,
            attention_mask=attention_mask,
            decoder_input_values=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
            head_mask=head_mask,
            decoder_head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            encoder_outputs=encoder_outputs,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=True,
        )

        logits = self.text_decoder_postnet(outputs[0])

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))

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

        return Seq2SeqLMOutput(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            decoder_hidden_states=outputs.decoder_hidden_states,
            decoder_attentions=outputs.decoder_attentions,
            cross_attentions=outputs.cross_attentions,
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
            encoder_hidden_states=outputs.encoder_hidden_states,
            encoder_attentions=outputs.encoder_attentions,
        )

    def prepare_inputs_for_generation(
        self,
        decoder_input_ids,
        past_key_values=None,
        attention_mask=None,
        head_mask=None,
        decoder_head_mask=None,
        cross_attn_head_mask=None,
        use_cache=None,
        encoder_outputs=None,
        **kwargs,
    ):
        # cut decoder_input_ids if past is used
        if past_key_values is not None:
            past_length = past_key_values[0][0].shape[2]

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

            decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]

        return {
            "encoder_outputs": encoder_outputs,
            "past_key_values": past_key_values,
            "decoder_input_ids": decoder_input_ids,
            "attention_mask": attention_mask,
            "head_mask": head_mask,
            "decoder_head_mask": decoder_head_mask,
            "cross_attn_head_mask": cross_attn_head_mask,
            "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
        }

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
            )
        return reordered_past


def _generate_speech(
    model: SpeechT5PreTrainedModel,
    input_values: torch.FloatTensor,
    speaker_embeddings: Optional[torch.FloatTensor] = None,
    attention_mask: Optional[torch.LongTensor] = None,
    threshold: float = 0.5,
    minlenratio: float = 0.0,
    maxlenratio: float = 20.0,
    vocoder: Optional[nn.Module] = None,
    output_cross_attentions: bool = False,
    return_output_lengths: bool = False,
) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, torch.FloatTensor]]:
    if speaker_embeddings is None:
        raise ValueError(
            """`speaker_embeddings` must be specified. For example, you can use a speaker embeddings by following
                    the code snippet provided in this link:
                    https://huggingface.co/datasets/Matthijs/cmu-arctic-xvectors
                    """
        )

    if attention_mask is None:
        encoder_attention_mask = 1 - (input_values == model.config.pad_token_id).int()
    else:
        encoder_attention_mask = attention_mask

    bsz = input_values.size(0)

    encoder_out = model.speecht5.encoder(
        input_values=input_values,
        attention_mask=encoder_attention_mask,
        return_dict=True,
    )

    encoder_last_hidden_state = encoder_out.last_hidden_state

    # downsample encoder attention mask
    if isinstance(model.speecht5.encoder, SpeechT5EncoderWithSpeechPrenet):
        encoder_attention_mask = model.speecht5.encoder.prenet._get_feature_vector_attention_mask(
            encoder_out[0].shape[1], encoder_attention_mask
        )

    maxlen = int(encoder_last_hidden_state.size(1) * maxlenratio / model.config.reduction_factor)
    minlen = int(encoder_last_hidden_state.size(1) * minlenratio / model.config.reduction_factor)

    # Start the output sequence with a mel spectrum that is all zeros.
    output_sequence = encoder_last_hidden_state.new_zeros(bsz, 1, model.config.num_mel_bins)

    spectrogram = []
    cross_attentions = []
    past_key_values = None
    idx = 0
    result_spectrogram = {}

    while True:
        idx += 1

        # Run the decoder prenet on the entire output sequence.
        decoder_hidden_states = model.speecht5.decoder.prenet(output_sequence, speaker_embeddings)
        # Run the decoder layers on the last element of the prenet output.
        decoder_out = model.speecht5.decoder.wrapped_decoder(
            hidden_states=decoder_hidden_states[:, -1:],
            attention_mask=None,
            encoder_hidden_states=encoder_last_hidden_state,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            use_cache=True,
            output_attentions=output_cross_attentions,
            return_dict=True,
        )

        if output_cross_attentions:
            cross_attentions.append(torch.cat(decoder_out.cross_attentions, dim=0))

        last_decoder_output = decoder_out.last_hidden_state.squeeze(1)
        past_key_values = decoder_out.past_key_values

        # Predict the new mel spectrum for this step in the sequence.
        spectrum = model.speech_decoder_postnet.feat_out(last_decoder_output)
        spectrum = spectrum.view(bsz, model.config.reduction_factor, model.config.num_mel_bins)
        spectrogram.append(spectrum)

        # Extend the output sequence with the new mel spectrum.
        new_spectrogram = spectrum[:, -1, :].view(bsz, 1, model.config.num_mel_bins)
        output_sequence = torch.cat((output_sequence, new_spectrogram), dim=1)
        # Predict the probability that this is the stop token.
        prob = torch.sigmoid(model.speech_decoder_postnet.prob_out(last_decoder_output))

        if idx < minlen:
            continue
        else:
            # If the generation loop is less than maximum length time, check the ones in the batch that have met
            # the prob threshold. Otherwise, assume all have met thresholds and fill other spectrograms for the batch.
            if idx < maxlen:
                meet_thresholds = torch.sum(prob, dim=-1) >= threshold
                meet_indexes = torch.where(meet_thresholds)[0].tolist()
            else:
                meet_indexes = range(len(prob))
            meet_indexes = [i for i in meet_indexes if i not in result_spectrogram]
            if len(meet_indexes) > 0:
                spectrograms = torch.stack(spectrogram)
                spectrograms = spectrograms.transpose(0, 1).flatten(1, 2)
                spectrograms = model.speech_decoder_postnet.postnet(spectrograms)
                for meet_index in meet_indexes:
                    result_spectrogram[meet_index] = spectrograms[meet_index]
            if len(result_spectrogram) >= bsz:
                break
    spectrograms = [result_spectrogram[i] for i in range(len(result_spectrogram))]
    if not return_output_lengths:
        spectrogram = spectrograms[0] if bsz == 1 else torch.nn.utils.rnn.pad_sequence(spectrograms, batch_first=True)
        if vocoder is not None:
            outputs = vocoder(spectrogram)
        else:
            outputs = spectrogram
        if output_cross_attentions:
            cross_attentions = torch.cat(cross_attentions, dim=2)
            if bsz > 1:
                cross_attentions = cross_attentions.view(
                    bsz, int(cross_attentions.size(0) / bsz), *cross_attentions.size()[-3:]
                )
            outputs = (outputs, cross_attentions)
    else:
        # batched return values should also include the spectrogram/waveform lengths
        spectrogram_lengths = []
        for i in range(bsz):
            spectrogram_lengths.append(spectrograms[i].size(0))
        if vocoder is None:
            spectrograms = torch.nn.utils.rnn.pad_sequence(spectrograms, batch_first=True)
            outputs = (spectrograms, spectrogram_lengths)
        else:
            waveforms = []
            spectrograms = torch.nn.utils.rnn.pad_sequence(spectrograms, batch_first=True)
            waveforms = vocoder(spectrograms)
            waveform_lengths = [int(waveforms.size(1) / max(spectrogram_lengths)) * i for i in spectrogram_lengths]
            outputs = (waveforms, waveform_lengths)
        if output_cross_attentions:
            cross_attentions = torch.cat(cross_attentions, dim=2)
            cross_attentions = cross_attentions.view(
                bsz, int(cross_attentions.size(0) / bsz), *cross_attentions.size()[-3:]
            )
            outputs = (*outputs, cross_attentions)
    return outputs


@add_start_docstrings(
    """SpeechT5 Model with a text encoder and a speech decoder.""",
    SPEECHT5_START_DOCSTRING,
)
class SpeechT5ForTextToSpeech(SpeechT5PreTrainedModel):
    main_input_name = "input_ids"

    def __init__(self, config: SpeechT5Config):
        super().__init__(config)

        if config.vocab_size is None:
            raise ValueError(
                f"You are trying to instantiate {self.__class__} with a configuration that does not define the"
                " vocabulary size of the language model head. Please instantiate the model as follows:"
                " `SpeechT5ForTextToSpeech.from_pretrained(..., vocab_size=vocab_size)`. or define `vocab_size` of"
                " your model's configuration."
            )

        text_encoder = SpeechT5EncoderWithTextPrenet(config)
        speech_decoder = SpeechT5DecoderWithSpeechPrenet(config)
        self.speecht5 = SpeechT5Model(config, text_encoder, speech_decoder)

        self.speech_decoder_postnet = SpeechT5SpeechDecoderPostnet(config)

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

    def get_encoder(self):
        return self.speecht5.get_encoder()

    def get_decoder(self):
        return self.speecht5.get_decoder()

    @add_start_docstrings_to_model_forward(SPEECHT5_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqSpectrogramOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        decoder_input_values: Optional[torch.FloatTensor] = None,
        decoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        decoder_head_mask: Optional[torch.FloatTensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        speaker_embeddings: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.FloatTensor] = None,
        stop_labels: Optional[torch.Tensor] = None,
    ) -> Union[Tuple, Seq2SeqSpectrogramOutput]:
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`SpeechT5Tokenizer`]. See [`~PreTrainedTokenizer.encode`] and
            [`~PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        decoder_input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_mel_bins)`):
            Float values of input mel spectrogram.

            SpeechT5 uses an all-zero spectrum as the starting token for `decoder_input_values` generation. If
            `past_key_values` is used, optionally only the last `decoder_input_values` have to be input (see
            `past_key_values`).
        speaker_embeddings (`torch.FloatTensor` of shape `(batch_size, config.speaker_embedding_dim)`, *optional*):
            Tensor containing the speaker embeddings.
        labels (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_mel_bins)`, *optional*):
            Float values of target mel spectrogram. Timesteps set to `-100.0` are ignored (masked) for the loss
            computation. Spectrograms can be obtained using [`SpeechT5Processor`]. See [`SpeechT5Processor.__call__`]
            for details.

        Returns:

        Example:

        ```python
        >>> from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan, set_seed
        >>> import torch

        >>> processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
        >>> model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
        >>> vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")

        >>> inputs = processor(text="Hello, my dog is cute", return_tensors="pt")
        >>> speaker_embeddings = torch.zeros((1, 512))  # or load xvectors from a file

        >>> set_seed(555)  # make deterministic

        >>> # generate speech
        >>> speech = model.generate(inputs["input_ids"], speaker_embeddings=speaker_embeddings, vocoder=vocoder)
        >>> speech.shape
        torch.Size([15872])
        ```
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if labels is not None:
            if decoder_input_values is None:
                decoder_input_values = shift_spectrograms_right(labels, self.config.reduction_factor)
            if self.config.use_guided_attention_loss:
                output_attentions = True

        outputs = self.speecht5(
            input_values=input_ids,
            attention_mask=attention_mask,
            decoder_input_values=decoder_input_values,
            decoder_attention_mask=decoder_attention_mask,
            head_mask=head_mask,
            decoder_head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            encoder_outputs=encoder_outputs,
            past_key_values=past_key_values,
            use_cache=use_cache,
            speaker_embeddings=speaker_embeddings,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=True,
        )

        outputs_before_postnet, outputs_after_postnet, logits = self.speech_decoder_postnet(outputs[0])

        loss = None
        if labels is not None:
            criterion = SpeechT5SpectrogramLoss(self.config)
            loss = criterion(
                attention_mask,
                outputs_before_postnet,
                outputs_after_postnet,
                logits,
                labels,
                outputs.cross_attentions,
            )

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

        return Seq2SeqSpectrogramOutput(
            loss=loss,
            spectrogram=outputs_after_postnet,
            past_key_values=outputs.past_key_values,
            decoder_hidden_states=outputs.decoder_hidden_states,
            decoder_attentions=outputs.decoder_attentions,
            cross_attentions=outputs.cross_attentions,
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
            encoder_hidden_states=outputs.encoder_hidden_states,
            encoder_attentions=outputs.encoder_attentions,
        )

    @torch.no_grad()
    def generate(
        self,
        input_ids: torch.LongTensor,
        attention_mask: Optional[torch.LongTensor] = None,
        speaker_embeddings: Optional[torch.FloatTensor] = None,
        threshold: float = 0.5,
        minlenratio: float = 0.0,
        maxlenratio: float = 20.0,
        vocoder: Optional[nn.Module] = None,
        output_cross_attentions: bool = False,
        return_output_lengths: bool = False,
        **kwargs,
    ) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, torch.FloatTensor]]:
        r"""
        Converts a sequence of input tokens into a sequence of mel spectrograms, which are subsequently turned into a
        speech waveform using a vocoder.

        Args:
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary.

                Indices can be obtained using [`SpeechT5Tokenizer`]. See [`~PreTrainedTokenizer.encode`] and
                [`~PreTrainedTokenizer.__call__`] for details.

                [What are input IDs?](../glossary#input-ids)
            attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Attention mask from the tokenizer, required for batched inference to signal to the model where to
                ignore padded tokens from the input_ids.
            speaker_embeddings (`torch.FloatTensor` of shape `(batch_size, config.speaker_embedding_dim)`, *optional*):
                Tensor containing the speaker embeddings.
            threshold (`float`, *optional*, defaults to 0.5):
                The generated sequence ends when the predicted stop token probability exceeds this value.
            minlenratio (`float`, *optional*, defaults to 0.0):
                Used to calculate the minimum required length for the output sequence.
            maxlenratio (`float`, *optional*, defaults to 20.0):
                Used to calculate the maximum allowed length for the output sequence.
            vocoder (`nn.Module`, *optional*):
                The vocoder that converts the mel spectrogram into a speech waveform. If `None`, the output is the mel
                spectrogram.
            output_cross_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of the decoder's cross-attention layers.
            return_output_lengths (`bool`, *optional*, defaults to `False`):
                Whether or not to return the concrete spectrogram/waveform lengths.

        Returns:
            `tuple(torch.FloatTensor)` comprising various elements depending on the inputs:
            - when `return_output_lengths` is False
                - **spectrogram** (*optional*, returned when no `vocoder` is provided) `torch.FloatTensor` of shape
                `(output_sequence_length, config.num_mel_bins)` -- The predicted log-mel spectrogram.
                - **waveform** (*optional*, returned when a `vocoder` is provided) `torch.FloatTensor` of shape
                `(num_frames,)` -- The predicted speech waveform.
                - **cross_attentions** (*optional*, returned when `output_cross_attentions` is `True`)
                `torch.FloatTensor` of shape `(config.decoder_layers, config.decoder_attention_heads,
                output_sequence_length, input_sequence_length)` -- The outputs of the decoder's cross-attention layers.
            - when `return_output_lengths` is True
                - **spectrograms** (*optional*, returned when no `vocoder` is provided) `torch.FloatTensor` of shape
                `(batch_size, output_sequence_length, config.num_mel_bins)` -- The predicted log-mel spectrograms that
                are padded to the maximum length.
                - **spectrogram_lengths** (*optional*, returned when no `vocoder` is provided) `List[Int]` -- A list of
                all the concrete lengths for each spectrogram.
                - **waveforms** (*optional*, returned when a `vocoder` is provided) `torch.FloatTensor` of shape
                `(batch_size, num_frames)` -- The predicted speech waveforms that are padded to the maximum length.
                - **waveform_lengths** (*optional*, returned when a `vocoder` is provided) `List[Int]` -- A list of all
                the concrete lengths for each waveform.
                - **cross_attentions** (*optional*, returned when `output_cross_attentions` is `True`)
                `torch.FloatTensor` of shape `(batch_size, config.decoder_layers, config.decoder_attention_heads,
                output_sequence_length, input_sequence_length)` -- The outputs of the decoder's cross-attention layers.
        """
        if speaker_embeddings is not None:
            batch_size = input_ids.size(0)
            if speaker_embeddings.size(0) != batch_size:
                if speaker_embeddings.size(0) == 1:
                    speaker_embeddings = speaker_embeddings.repeat(batch_size, 1)
                else:
                    raise ValueError(
                        "The first dimension of speaker_embeddings must be either 1 or the same as batch_size."
                    )

        return _generate_speech(
            self,
            input_ids,
            speaker_embeddings,
            attention_mask,
            threshold,
            minlenratio,
            maxlenratio,
            vocoder,
            output_cross_attentions,
            return_output_lengths,
        )

    @torch.no_grad()
    def generate_speech(
        self,
        input_ids: torch.LongTensor,
        speaker_embeddings: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        threshold: float = 0.5,
        minlenratio: float = 0.0,
        maxlenratio: float = 20.0,
        vocoder: Optional[nn.Module] = None,
        output_cross_attentions: bool = False,
        return_output_lengths: bool = False,
    ) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, torch.FloatTensor]]:
        r"""
        Converts a sequence of input tokens into a sequence of mel spectrograms, which are subsequently turned into a
        speech waveform using a vocoder.

        Args:
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary.

                Indices can be obtained using [`SpeechT5Tokenizer`]. See [`~PreTrainedTokenizer.encode`] and
                [`~PreTrainedTokenizer.__call__`] for details.

                [What are input IDs?](../glossary#input-ids)
            speaker_embeddings (`torch.FloatTensor` of shape `(batch_size, config.speaker_embedding_dim)`, *optional*):
                Tensor containing the speaker embeddings.
            attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing convolution and 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)
            threshold (`float`, *optional*, defaults to 0.5):
                The generated sequence ends when the predicted stop token probability exceeds this value.
            minlenratio (`float`, *optional*, defaults to 0.0):
                Used to calculate the minimum required length for the output sequence.
            maxlenratio (`float`, *optional*, defaults to 20.0):
                Used to calculate the maximum allowed length for the output sequence.
            vocoder (`nn.Module`, *optional*, defaults to `None`):
                The vocoder that converts the mel spectrogram into a speech waveform. If `None`, the output is the mel
                spectrogram.
            output_cross_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of the decoder's cross-attention layers.
            return_output_lengths (`bool`, *optional*, defaults to `False`):
                Whether or not to return the concrete spectrogram/waveform lengths.

        Returns:
            `tuple(torch.FloatTensor)` comprising various elements depending on the inputs:
            - when `return_output_lengths` is False
                - **spectrogram** (*optional*, returned when no `vocoder` is provided) `torch.FloatTensor` of shape
                `(output_sequence_length, config.num_mel_bins)` -- The predicted log-mel spectrogram.
                - **waveform** (*optional*, returned when a `vocoder` is provided) `torch.FloatTensor` of shape
                `(num_frames,)` -- The predicted speech waveform.
                - **cross_attentions** (*optional*, returned when `output_cross_attentions` is `True`)
                `torch.FloatTensor` of shape `(config.decoder_layers, config.decoder_attention_heads,
                output_sequence_length, input_sequence_length)` -- The outputs of the decoder's cross-attention layers.
            - when `return_output_lengths` is True
                - **spectrograms** (*optional*, returned when no `vocoder` is provided) `torch.FloatTensor` of shape
                `(batch_size, output_sequence_length, config.num_mel_bins)` -- The predicted log-mel spectrograms that
                are padded to the maximum length.
                - **spectrogram_lengths** (*optional*, returned when no `vocoder` is provided) `List[Int]` -- A list of
                all the concrete lengths for each spectrogram.
                - **waveforms** (*optional*, returned when a `vocoder` is provided) `torch.FloatTensor` of shape
                `(batch_size, num_frames)` -- The predicted speech waveforms that are padded to the maximum length.
                - **waveform_lengths** (*optional*, returned when a `vocoder` is provided) `List[Int]` -- A list of all
                the concrete lengths for each waveform.
                - **cross_attentions** (*optional*, returned when `output_cross_attentions` is `True`)
                `torch.FloatTensor` of shape `(batch_size, config.decoder_layers, config.decoder_attention_heads,
                output_sequence_length, input_sequence_length)` -- The outputs of the decoder's cross-attention layers.
        """
        if speaker_embeddings is not None:
            batch_size = input_ids.size(0)
            if speaker_embeddings.size(0) != batch_size:
                if speaker_embeddings.size(0) == 1:
                    speaker_embeddings = speaker_embeddings.repeat(batch_size, 1)
                else:
                    raise ValueError(
                        "The first dimension of speaker_embeddings must be either 1 or the same as batch size."
                    )

        return _generate_speech(
            self,
            input_ids,
            speaker_embeddings,
            attention_mask,
            threshold,
            minlenratio,
            maxlenratio,
            vocoder,
            output_cross_attentions,
            return_output_lengths,
        )


@add_start_docstrings(
    """SpeechT5 Model with a speech encoder and a speech decoder.""",
    SPEECHT5_START_DOCSTRING,
)
class SpeechT5ForSpeechToSpeech(SpeechT5PreTrainedModel):
    def __init__(self, config: SpeechT5Config):
        super().__init__(config)

        speech_encoder = SpeechT5EncoderWithSpeechPrenet(config)
        speech_decoder = SpeechT5DecoderWithSpeechPrenet(config)
        self.speecht5 = SpeechT5Model(config, speech_encoder, speech_decoder)

        self.speech_decoder_postnet = SpeechT5SpeechDecoderPostnet(config)

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

    def get_encoder(self):
        return self.speecht5.get_encoder()

    def get_decoder(self):
        return self.speecht5.get_decoder()

    def freeze_feature_encoder(self):
        """
        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
        not be updated during training.
        """
        self.get_encoder().prenet.freeze_feature_encoder()

    @add_start_docstrings_to_model_forward(SPEECHT5_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqSpectrogramOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_values: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        decoder_input_values: Optional[torch.FloatTensor] = None,
        decoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        decoder_head_mask: Optional[torch.FloatTensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        speaker_embeddings: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.FloatTensor] = None,
        stop_labels: Optional[torch.Tensor] = None,
    ) -> Union[Tuple, Seq2SeqSpectrogramOutput]:
        r"""
        input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
            Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file
            into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (*pip install
            soundfile*). To prepare the array into `input_values`, the [`SpeechT5Processor`] should be used for padding
            and conversion into a tensor of type `torch.FloatTensor`. See [`SpeechT5Processor.__call__`] for details.
        decoder_input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_mel_bins)`):
            Float values of input mel spectrogram.

            SpeechT5 uses an all-zero spectrum as the starting token for `decoder_input_values` generation. If
            `past_key_values` is used, optionally only the last `decoder_input_values` have to be input (see
            `past_key_values`).
        speaker_embeddings (`torch.FloatTensor` of shape `(batch_size, config.speaker_embedding_dim)`, *optional*):
            Tensor containing the speaker embeddings.
        labels (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_mel_bins)`, *optional*):
            Float values of target mel spectrogram. Spectrograms can be obtained using [`SpeechT5Processor`]. See
            [`SpeechT5Processor.__call__`] for details.

        Returns:

        Example:

        ```python
        >>> from transformers import SpeechT5Processor, SpeechT5ForSpeechToSpeech, SpeechT5HifiGan, set_seed
        >>> from datasets import load_dataset
        >>> import torch

        >>> dataset = load_dataset(
        ...     "hf-internal-testing/librispeech_asr_demo", "clean", split="validation"
        ... )  # doctest: +IGNORE_RESULT
        >>> dataset = dataset.sort("id")
        >>> sampling_rate = dataset.features["audio"].sampling_rate

        >>> processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_vc")
        >>> model = SpeechT5ForSpeechToSpeech.from_pretrained("microsoft/speecht5_vc")
        >>> vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")

        >>> # audio file is decoded on the fly
        >>> inputs = processor(audio=dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")

        >>> speaker_embeddings = torch.zeros((1, 512))  # or load xvectors from a file

        >>> set_seed(555)  # make deterministic

        >>> # generate speech
        >>> speech = model.generate_speech(inputs["input_values"], speaker_embeddings, vocoder=vocoder)
        >>> speech.shape
        torch.Size([77824])
        ```
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if labels is not None:
            if decoder_input_values is None:
                decoder_input_values = shift_spectrograms_right(labels, self.config.reduction_factor)

        outputs = self.speecht5(
            input_values=input_values,
            attention_mask=attention_mask,
            decoder_input_values=decoder_input_values,
            decoder_attention_mask=decoder_attention_mask,
            head_mask=head_mask,
            decoder_head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            encoder_outputs=encoder_outputs,
            past_key_values=past_key_values,
            use_cache=use_cache,
            speaker_embeddings=speaker_embeddings,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=True,
        )

        _, spectrogram, logits = self.speech_decoder_postnet(outputs[0])

        loss = None

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

        return Seq2SeqSpectrogramOutput(
            loss=loss,
            spectrogram=spectrogram,
            past_key_values=outputs.past_key_values,
            decoder_hidden_states=outputs.decoder_hidden_states,
            decoder_attentions=outputs.decoder_attentions,
            cross_attentions=outputs.cross_attentions,
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
            encoder_hidden_states=outputs.encoder_hidden_states,
            encoder_attentions=outputs.encoder_attentions,
        )

    @torch.no_grad()
    def generate_speech(
        self,
        input_values: torch.FloatTensor,
        speaker_embeddings: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        threshold: float = 0.5,
        minlenratio: float = 0.0,
        maxlenratio: float = 20.0,
        vocoder: Optional[nn.Module] = None,
        output_cross_attentions: bool = False,
        return_output_lengths: bool = False,
    ) -> torch.FloatTensor:
        r"""
        Converts a raw speech waveform into a sequence of mel spectrograms, which are subsequently turned back into a
        speech waveform using a vocoder.

        Args:
            input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
                Float values of input raw speech waveform.

                Values can be obtained by loading a *.flac* or *.wav* audio file into an array of type `List[float]` or
                a `numpy.ndarray`, *e.g.* via the soundfile library (*pip install soundfile*). To prepare the array
                into `input_values`, the [`SpeechT5Processor`] should be used for padding and conversion into a tensor
                of type `torch.FloatTensor`. See [`SpeechT5Processor.__call__`] for details.
            speaker_embeddings (`torch.FloatTensor` of shape `(batch_size, config.speaker_embedding_dim)`, *optional*):
                Tensor containing the speaker embeddings.
            attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing convolution and 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)
            threshold (`float`, *optional*, defaults to 0.5):
                The generated sequence ends when the predicted stop token probability exceeds this value.
            minlenratio (`float`, *optional*, defaults to 0.0):
                Used to calculate the minimum required length for the output sequence.
            maxlenratio (`float`, *optional*, defaults to 20.0):
                Used to calculate the maximum allowed length for the output sequence.
            vocoder (`nn.Module`, *optional*, defaults to `None`):
                The vocoder that converts the mel spectrogram into a speech waveform. If `None`, the output is the mel
                spectrogram.
            output_cross_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of the decoder's cross-attention layers.
            return_output_lengths (`bool`, *optional*, defaults to `False`):
                Whether or not to return the concrete spectrogram/waveform lengths.

        Returns:
            `tuple(torch.FloatTensor)` comprising various elements depending on the inputs:
            - when `return_output_lengths` is False
                - **spectrogram** (*optional*, returned when no `vocoder` is provided) `torch.FloatTensor` of shape
                `(output_sequence_length, config.num_mel_bins)` -- The predicted log-mel spectrogram.
                - **waveform** (*optional*, returned when a `vocoder` is provided) `torch.FloatTensor` of shape
                `(num_frames,)` -- The predicted speech waveform.
                - **cross_attentions** (*optional*, returned when `output_cross_attentions` is `True`)
                `torch.FloatTensor` of shape `(config.decoder_layers, config.decoder_attention_heads,
                output_sequence_length, input_sequence_length)` -- The outputs of the decoder's cross-attention layers.
            - when `return_output_lengths` is True
                - **spectrograms** (*optional*, returned when no `vocoder` is provided) `torch.FloatTensor` of shape
                `(batch_size, output_sequence_length, config.num_mel_bins)` -- The predicted log-mel spectrograms that
                are padded to the maximum length.
                - **spectrogram_lengths** (*optional*, returned when no `vocoder` is provided) `List[Int]` -- A list of
                all the concrete lengths for each spectrogram.
                - **waveforms** (*optional*, returned when a `vocoder` is provided) `torch.FloatTensor` of shape
                `(batch_size, num_frames)` -- The predicted speech waveforms that are padded to the maximum length.
                - **waveform_lengths** (*optional*, returned when a `vocoder` is provided) `List[Int]` -- A list of all
                the concrete lengths for each waveform.
                - **cross_attentions** (*optional*, returned when `output_cross_attentions` is `True`)
                `torch.FloatTensor` of shape `(batch_size, config.decoder_layers, config.decoder_attention_heads,
                output_sequence_length, input_sequence_length)` -- The outputs of the decoder's cross-attention layers.
        """
        if speaker_embeddings is None:
            speaker_embeddings = torch.zeros((1, 512), device=input_values.device)

        return _generate_speech(
            self,
            input_values,
            speaker_embeddings,
            attention_mask,
            threshold,
            minlenratio,
            maxlenratio,
            vocoder,
            output_cross_attentions,
            return_output_lengths,
        )


HIFIGAN_START_DOCSTRING = r"""
    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 ([`SpeechT5HifiGanConfig`]):
            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.
"""


class HifiGanResidualBlock(nn.Module):
    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), leaky_relu_slope=0.1):
        super().__init__()
        self.leaky_relu_slope = leaky_relu_slope

        self.convs1 = nn.ModuleList(
            [
                nn.Conv1d(
                    channels,
                    channels,
                    kernel_size,
                    stride=1,
                    dilation=dilation[i],
                    padding=self.get_padding(kernel_size, dilation[i]),
                )
                for i in range(len(dilation))
            ]
        )
        self.convs2 = nn.ModuleList(
            [
                nn.Conv1d(
                    channels,
                    channels,
                    kernel_size,
                    stride=1,
                    dilation=1,
                    padding=self.get_padding(kernel_size, 1),
                )
                for _ in range(len(dilation))
            ]
        )

    def get_padding(self, kernel_size, dilation=1):
        return (kernel_size * dilation - dilation) // 2

    def apply_weight_norm(self):
        for layer in self.convs1:
            nn.utils.weight_norm(layer)
        for layer in self.convs2:
            nn.utils.weight_norm(layer)

    def remove_weight_norm(self):
        for layer in self.convs1:
            nn.utils.remove_weight_norm(layer)
        for layer in self.convs2:
            nn.utils.remove_weight_norm(layer)

    def forward(self, hidden_states):
        for conv1, conv2 in zip(self.convs1, self.convs2):
            residual = hidden_states
            hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope)
            hidden_states = conv1(hidden_states)
            hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope)
            hidden_states = conv2(hidden_states)
            hidden_states = hidden_states + residual
        return hidden_states


@add_start_docstrings(
    """HiFi-GAN vocoder.""",
    HIFIGAN_START_DOCSTRING,
)
class SpeechT5HifiGan(PreTrainedModel):
    config_class = SpeechT5HifiGanConfig
    main_input_name = "spectrogram"

    def __init__(self, config: SpeechT5HifiGanConfig):
        super().__init__(config)
        self.num_kernels = len(config.resblock_kernel_sizes)
        self.num_upsamples = len(config.upsample_rates)
        self.conv_pre = nn.Conv1d(
            config.model_in_dim,
            config.upsample_initial_channel,
            kernel_size=7,
            stride=1,
            padding=3,
        )

        self.upsampler = nn.ModuleList()
        for i, (upsample_rate, kernel_size) in enumerate(zip(config.upsample_rates, config.upsample_kernel_sizes)):
            self.upsampler.append(
                nn.ConvTranspose1d(
                    config.upsample_initial_channel // (2**i),
                    config.upsample_initial_channel // (2 ** (i + 1)),
                    kernel_size=kernel_size,
                    stride=upsample_rate,
                    padding=(kernel_size - upsample_rate) // 2,
                )
            )

        self.resblocks = nn.ModuleList()
        for i in range(len(self.upsampler)):
            channels = config.upsample_initial_channel // (2 ** (i + 1))
            for kernel_size, dilation in zip(config.resblock_kernel_sizes, config.resblock_dilation_sizes):
                self.resblocks.append(HifiGanResidualBlock(channels, kernel_size, dilation, config.leaky_relu_slope))

        self.conv_post = nn.Conv1d(channels, 1, kernel_size=7, stride=1, padding=3)

        self.register_buffer("mean", torch.zeros(config.model_in_dim))
        self.register_buffer("scale", torch.ones(config.model_in_dim))

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

    def _init_weights(self, module):
        """Initialize the weights."""
        if isinstance(module, (nn.Linear, nn.Conv1d)):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()

    def apply_weight_norm(self):
        nn.utils.weight_norm(self.conv_pre)
        for layer in self.upsampler:
            nn.utils.weight_norm(layer)
        for layer in self.resblocks:
            layer.apply_weight_norm()
        nn.utils.weight_norm(self.conv_post)

    def remove_weight_norm(self):
        nn.utils.remove_weight_norm(self.conv_pre)
        for layer in self.upsampler:
            nn.utils.remove_weight_norm(layer)
        for layer in self.resblocks:
            layer.remove_weight_norm()
        nn.utils.remove_weight_norm(self.conv_post)

    def forward(self, spectrogram: torch.FloatTensor) -> torch.FloatTensor:
        r"""
        Converts a log-mel spectrogram into a speech waveform. Passing a batch of log-mel spectrograms returns a batch
        of speech waveforms. Passing a single, un-batched log-mel spectrogram returns a single, un-batched speech
        waveform.

        Args:
            spectrogram (`torch.FloatTensor`):
                Tensor containing the log-mel spectrograms. Can be batched and of shape `(batch_size, sequence_length,
                config.model_in_dim)`, or un-batched and of shape `(sequence_length, config.model_in_dim)`.

        Returns:
            `torch.FloatTensor`: Tensor containing the speech waveform. If the input spectrogram is batched, will be of
            shape `(batch_size, num_frames,)`. If un-batched, will be of shape `(num_frames,)`.
        """
        if self.config.normalize_before:
            spectrogram = (spectrogram - self.mean) / self.scale

        is_batched = spectrogram.dim() == 3
        if not is_batched:
            spectrogram = spectrogram.unsqueeze(0)

        hidden_states = spectrogram.transpose(2, 1)

        hidden_states = self.conv_pre(hidden_states)
        for i in range(self.num_upsamples):
            hidden_states = nn.functional.leaky_relu(hidden_states, self.config.leaky_relu_slope)
            hidden_states = self.upsampler[i](hidden_states)

            res_state = self.resblocks[i * self.num_kernels](hidden_states)
            for j in range(1, self.num_kernels):
                res_state += self.resblocks[i * self.num_kernels + j](hidden_states)
            hidden_states = res_state / self.num_kernels

        hidden_states = nn.functional.leaky_relu(hidden_states)
        hidden_states = self.conv_post(hidden_states)
        hidden_states = torch.tanh(hidden_states)

        if not is_batched:
            # remove batch dim and collapse tensor to 1-d audio waveform
            waveform = hidden_states.squeeze(0).transpose(1, 0).view(-1)
        else:
            # remove seq-len dim since this collapses to 1
            waveform = hidden_states.squeeze(1)

        return waveform
