modeling_data2vec_audio.py

# coding=utf-8
# Copyright 2021 The Fairseq Authors 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 Data2VecAudio model."""

import math
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss

from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput, TokenClassifierOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import torch_int_div
from ...utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
)
from .configuration_data2vec_audio import Data2VecAudioConfig


logger = logging.get_logger(__name__)


_HIDDEN_STATES_START_POSITION = 2

# General docstring
_CONFIG_FOR_DOC = "Data2VecAudioConfig"
_PROCESSOR_FOR_DOC = "Wav2Vec2Processor"

# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/data2vec-audio-base-960h"
_EXPECTED_OUTPUT_SHAPE = [1, 292, 768]

# CTC docstring
_CTC_EXPECTED_OUTPUT = "'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'"
_CTC_EXPECTED_LOSS = 66.95

# Audio class docstring
_FEAT_EXTRACTOR_FOR_DOC = "Wav2Vec2FeatureExtractor"
_SEQ_CLASS_CHECKPOINT = "hf-internal-testing/tiny-random-data2vec-seq-class"
_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_1'"
_SEQ_CLASS_EXPECTED_LOSS = 0.69

# Frame class docstring
_FRAME_CLASS_CHECKPOINT = "hf-internal-testing/tiny-random-data2vec-audio-frame"
_FRAME_EXPECTED_OUTPUT = [1, 1]

# Speaker Verification docstring
_XVECTOR_CHECKPOINT = "hf-internal-testing/tiny-random-data2vec-xvector"
_XVECTOR_EXPECTED_OUTPUT = 1.0


DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/data2vec-audio-base",
    "facebook/data2vec-audio-base-10m",
    "facebook/data2vec-audio-base-100h",
    "facebook/data2vec-audio-base-960h",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
]


@dataclass
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2BaseModelOutput with Wav2Vec2->Data2VecAudio
class Data2VecAudioBaseModelOutput(ModelOutput):
    """
    Output type of [`Data2VecAudioBaseModelOutput`], with potential hidden states and attentions.

    Args:
        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        extract_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, conv_dim[-1])`):
            Sequence of extracted feature vectors of the last convolutional layer of the model.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
            shape `(batch_size, sequence_length, hidden_size)`.

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

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

    last_hidden_state: torch.FloatTensor = None
    extract_features: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None


@dataclass
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.XVectorOutput with Wav2Vec2->Data2VecAudio
class XVectorOutput(ModelOutput):
    """
    Output type of [`Data2VecAudioForXVector`].

    Args:
        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
            Classification loss.
        logits (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`):
            Classification hidden states before AMSoftmax.
        embeddings (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`):
            Utterance embeddings used for vector similarity-based retrieval.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
            shape `(batch_size, sequence_length, hidden_size)`.

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

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

    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    embeddings: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None


# 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=np.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 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


class Data2VecAudioConvLayer(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.Wav2Vec2SamePadLayer with Wav2Vec2->Data2VecAudio
class Data2VecAudioPadLayer(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


class Data2VecAudioPositionalConvLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.conv = nn.Conv1d(
            config.hidden_size,
            config.hidden_size,
            kernel_size=config.conv_pos_kernel_size,
            padding=config.conv_pos_kernel_size // 2,
            groups=config.num_conv_pos_embedding_groups,
        )

        self.padding = Data2VecAudioPadLayer(config.conv_pos_kernel_size)
        self.activation = ACT2FN[config.feat_extract_activation]
        # no learnable parameters
        self.layer_norm = nn.LayerNorm(config.hidden_size, elementwise_affine=False)

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

        hidden_states = hidden_states.transpose(1, 2)
        hidden_states = self.layer_norm(hidden_states)
        hidden_states = hidden_states.transpose(1, 2)
        hidden_states = self.activation(hidden_states)
        return hidden_states


class Data2VecAudioPositionalConvEmbedding(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.layers = nn.ModuleList(
            [Data2VecAudioPositionalConvLayer(config) for _ in range(config.num_conv_pos_embeddings)]
        )

    def forward(self, hidden_states):
        hidden_states = hidden_states.transpose(1, 2)
        for layer in self.layers:
            hidden_states = layer(hidden_states)
        hidden_states = hidden_states.transpose(1, 2)
        return hidden_states


class Data2VecAudioFeatureEncoder(nn.Module):
    """Construct the features from raw audio waveform"""

    def __init__(self, config):
        super().__init__()
        self.conv_layers = nn.ModuleList(
            [Data2VecAudioConvLayer(config, layer_id=i) for i in range(config.num_feat_extract_layers)]
        )
        self.gradient_checkpointing = False
        self._requires_grad = True

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureEncoder._freeze_parameters
    def _freeze_parameters(self):
        for param in self.parameters():
            param.requires_grad = False
        self._requires_grad = False

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureEncoder.forward
    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:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs)

                    return custom_forward

                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(conv_layer),
                    hidden_states,
                )
            else:
                hidden_states = conv_layer(hidden_states)

        return hidden_states


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureProjection with Wav2Vec2->Data2VecAudio
class Data2VecAudioFeatureProjection(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


# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Data2VecAudio
class Data2VecAudioAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        dropout: float = 0.0,
        is_decoder: bool = False,
        bias: bool = True,
    ):
        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,
        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 {attn_weights.size()}"
            )

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

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

        if layer_head_mask is not None:
            if layer_head_mask.size() != (self.num_heads,):
                raise ValueError(
                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {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 {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


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeedForward with Wav2Vec2->Data2VecAudio
class Data2VecAudioFeedForward(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.intermediate_dropout = nn.Dropout(config.activation_dropout)

        self.intermediate_dense = nn.Linear(config.hidden_size, config.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(config.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


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2EncoderLayer with Wav2Vec2->Data2VecAudio
class Data2VecAudioEncoderLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.attention = Data2VecAudioAttention(
            embed_dim=config.hidden_size,
            num_heads=config.num_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 = Data2VecAudioFeedForward(config)
        self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states, attention_mask=None, output_attentions=False):
        attn_residual = hidden_states
        hidden_states, attn_weights, _ = self.attention(
            hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
        )
        hidden_states = self.dropout(hidden_states)
        hidden_states = attn_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


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Encoder with Wav2Vec2->Data2VecAudio
class Data2VecAudioEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.pos_conv_embed = Data2VecAudioPositionalConvEmbedding(config)
        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout)
        self.layers = nn.ModuleList([Data2VecAudioEncoderLayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        if attention_mask is not None:
            # make sure padded tokens output 0
            hidden_states[~attention_mask] = 0.0

            # extend attention_mask
            attention_mask = (1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype)) * -10000.0
            attention_mask = attention_mask.expand(
                attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]
            )

        position_embeddings = self.pos_conv_embed(hidden_states)
        hidden_states = hidden_states + position_embeddings
        hidden_states = self.layer_norm(hidden_states)
        hidden_states = self.dropout(hidden_states)

        deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()

        for layer in 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)
            dropout_probability = np.random.uniform(0, 1)

            skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False
            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:
                    # create gradient checkpointing function
                    def create_custom_forward(module):
                        def custom_forward(*inputs):
                            return module(*inputs, output_attentions)

                        return custom_forward

                    layer_outputs = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(layer),
                        hidden_states,
                        attention_mask,
                    )
                else:
                    layer_outputs = layer(
                        hidden_states, attention_mask=attention_mask, 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,
        )


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Adapter with Wav2Vec2->Data2VecAudio
class Data2VecAudioAdapter(nn.Module):
    def __init__(self, config):
        super().__init__()

        # feature dim might need to be down-projected
        if config.output_hidden_size != config.hidden_size:
            self.proj = nn.Linear(config.hidden_size, config.output_hidden_size)
            self.proj_layer_norm = nn.LayerNorm(config.output_hidden_size)
        else:
            self.proj = self.proj_layer_norm = None

        self.layers = nn.ModuleList(Data2VecAudioAdapterLayer(config) for _ in range(config.num_adapter_layers))
        self.layerdrop = config.layerdrop

    def forward(self, hidden_states):
        # down project hidden_states if necessary
        if self.proj is not None and self.proj_layer_norm is not None:
            hidden_states = self.proj(hidden_states)
            hidden_states = self.proj_layer_norm(hidden_states)

        hidden_states = hidden_states.transpose(1, 2)

        for layer in self.layers:
            layerdrop_prob = np.random.random()
            if not self.training or (layerdrop_prob > self.layerdrop):
                hidden_states = layer(hidden_states)

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


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2AdapterLayer with Wav2Vec2->Data2VecAudio
class Data2VecAudioAdapterLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.conv = nn.Conv1d(
            config.output_hidden_size,
            2 * config.output_hidden_size,
            config.adapter_kernel_size,
            stride=config.adapter_stride,
            padding=1,
        )

    def forward(self, hidden_states):
        hidden_states = self.conv(hidden_states)
        hidden_states = nn.functional.glu(hidden_states, dim=1)

        return hidden_states


class Data2VecAudioPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = Data2VecAudioConfig
    base_model_prefix = "data2vec_audio"
    main_input_name = "input_values"
    _keys_to_ignore_on_load_missing = [r"position_ids"]
    supports_gradient_checkpointing = True

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, Data2VecAudioFeatureProjection):
            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, Data2VecAudioPositionalConvLayer):
            nn.init.constant_(module.conv.bias, 0)
        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)):
            if module.bias is not None:
                module.bias.data.zero_()
            if module.weight is not None:
                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)

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2PreTrainedModel._get_feat_extract_output_lengths with
    def _get_feat_extract_output_lengths(
        self, input_lengths: Union[torch.LongTensor, int], add_adapter: Optional[bool] = None
    ):
        """
        Computes the output length of the convolutional layers
        """

        add_adapter = self.config.add_adapter if add_adapter is None else add_adapter

        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_int_div(input_length - kernel_size, stride) + 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)

        if add_adapter:
            for _ in range(self.config.num_adapter_layers):
                input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride)

        return input_lengths

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2PreTrainedModel._get_feature_vector_attention_mask
    def _get_feature_vector_attention_mask(
        self, feature_vector_length: int, attention_mask: torch.LongTensor, add_adapter=None
    ):
        # 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, add_adapter=add_adapter)
        output_lengths = output_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

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (Data2VecAudioEncoder, Data2VecAudioFeatureEncoder)):
            module.gradient_checkpointing = value


DATA2VEC_AUDIO_START_DOCSTRING = r"""
    Data2VecAudio was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
    Language](https://arxiv.org/pdf/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and
    Michael Auli.

    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 etc.).

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

    Parameters:
        config ([`Data2VecAudioConfig`]): 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.
"""


DATA2VEC_AUDIO_INPUTS_DOCSTRING = r"""
    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 [`Wav2Vec2Processor`] should be used for padding
            and conversion into a tensor of type *torch.FloatTensor*. See [`Wav2Vec2Processor.__call__`] for details.
        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`, such as
            [data2vec-audio-base](https://huggingface.co/facebook/data2vec-audio-base-960h), `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>

        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 Data2VecAudio Model transformer outputting raw hidden-states without any specific head on top.",
    DATA2VEC_AUDIO_START_DOCSTRING,
)
class Data2VecAudioModel(Data2VecAudioPreTrainedModel):
    def __init__(self, config: Data2VecAudioConfig):
        super().__init__(config)
        self.config = config
        self.feature_extractor = Data2VecAudioFeatureEncoder(config)
        self.feature_projection = Data2VecAudioFeatureProjection(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.FloatTensor(config.hidden_size).uniform_())

        self.encoder = Data2VecAudioEncoder(config)

        self.adapter = Data2VecAudioAdapter(config) if config.add_adapter else None

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

    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.feature_extractor._freeze_parameters()

    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

    @add_start_docstrings_to_model_forward(DATA2VEC_AUDIO_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        processor_class=_PROCESSOR_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=Data2VecAudioBaseModelOutput,
        config_class=_CONFIG_FOR_DOC,
        modality="audio",
        expected_output=_EXPECTED_OUTPUT_SHAPE,
    )
    def forward(
        self,
        input_values: Optional[torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
        mask_time_indices: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, Data2VecAudioBaseModelOutput]:
        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

        extract_features = self.feature_extractor(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, add_adapter=False
            )

        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
        )

        encoder_outputs = self.encoder(
            hidden_states,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = encoder_outputs[0]

        if self.adapter is not None:
            hidden_states = self.adapter(hidden_states)

        if not return_dict:
            return (hidden_states, extract_features) + encoder_outputs[1:]

        return Data2VecAudioBaseModelOutput(
            last_hidden_state=hidden_states,
            extract_features=extract_features,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


@add_start_docstrings(
    """Data2VecAudio Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""",
    DATA2VEC_AUDIO_START_DOCSTRING,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC with Wav2Vec2->Data2VecAudio, wav2vec2->data2vec_audio, WAV_2_VEC_2->DATA2VEC_AUDIO
class Data2VecAudioForCTC(Data2VecAudioPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.data2vec_audio = Data2VecAudioModel(config)
        self.dropout = nn.Dropout(config.final_dropout)

        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: `Data2VecAudioForCTC.from_pretrained(..., vocab_size=vocab_size)`. "
                "or define `vocab_size` of your model's configuration."
            )
        output_hidden_size = (
            config.output_hidden_size if hasattr(config, "add_adapter") and config.add_adapter else config.hidden_size
        )
        self.lm_head = nn.Linear(output_hidden_size, config.vocab_size)

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

    def freeze_feature_extractor(self):
        """
        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
        not be updated during training.
        """
        warnings.warn(
            "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5."
            "Please use the equivalent `freeze_feature_encoder` method instead.",
            FutureWarning,
        )
        self.freeze_feature_encoder()

    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.data2vec_audio.feature_extractor._freeze_parameters()

    @add_start_docstrings_to_model_forward(DATA2VEC_AUDIO_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        processor_class=_PROCESSOR_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=CausalLMOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output=_CTC_EXPECTED_OUTPUT,
        expected_loss=_CTC_EXPECTED_LOSS,
    )
    def forward(
        self,
        input_values: Optional[torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[torch.Tensor] = None,
    ) -> Union[Tuple, CausalLMOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
            Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
            the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
            All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
            config.vocab_size - 1]`.
        """

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

        outputs = self.data2vec_audio(
            input_values,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        hidden_states = self.dropout(hidden_states)

        logits = self.lm_head(hidden_states)

        loss = None
        if labels is not None:

            if labels.max() >= self.config.vocab_size:
                raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")

            # retrieve loss input_lengths from attention_mask
            attention_mask = (
                attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long)
            )
            input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)

            # assuming that padded tokens are filled with -100
            # when not being attended to
            labels_mask = labels >= 0
            target_lengths = labels_mask.sum(-1)
            flattened_targets = labels.masked_select(labels_mask)

            # ctc_loss doesn't support fp16
            log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)

            with torch.backends.cudnn.flags(enabled=False):
                loss = nn.functional.ctc_loss(
                    log_probs,
                    flattened_targets,
                    input_lengths,
                    target_lengths,
                    blank=self.config.pad_token_id,
                    reduction=self.config.ctc_loss_reduction,
                    zero_infinity=self.config.ctc_zero_infinity,
                )

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

        return CausalLMOutput(
            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
        )


@add_start_docstrings(
    """
    Data2VecAudio Model with a sequence classification head on top (a linear layer over the pooled output) for tasks
    like SUPERB Keyword Spotting.
    """,
    DATA2VEC_AUDIO_START_DOCSTRING,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification with Wav2Vec2->Data2VecAudio, wav2vec2->data2vec_audio, WAV_2_VEC_2->DATA2VEC_AUDIO
class Data2VecAudioForSequenceClassification(Data2VecAudioPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        if hasattr(config, "add_adapter") and config.add_adapter:
            raise ValueError(
                "Sequence classification does not support the use of Data2VecAudio adapters (config.add_adapter=True)"
            )
        self.data2vec_audio = Data2VecAudioModel(config)
        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
        if config.use_weighted_layer_sum:
            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
        self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size)
        self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels)

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

    def freeze_feature_extractor(self):
        """
        Calling this function will disable the gradient computation for the feature encoder so that its parameters will
        not be updated during training.
        """
        warnings.warn(
            "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5."
            "Please use the equivalent `freeze_feature_encoder` method instead.",
            FutureWarning,
        )
        self.freeze_feature_encoder()

    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.data2vec_audio.feature_extractor._freeze_parameters()

    def freeze_base_model(self):
        """
        Calling this function will disable the gradient computation for the base model so that its parameters will not
        be updated during training. Only the classification head will be updated.
        """
        for param in self.data2vec_audio.parameters():
            param.requires_grad = False

    @add_start_docstrings_to_model_forward(DATA2VEC_AUDIO_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
        checkpoint=_SEQ_CLASS_CHECKPOINT,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
        modality="audio",
        expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
        expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
    )
    def forward(
        self,
        input_values: Optional[torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[torch.Tensor] = None,
    ) -> Union[Tuple, SequenceClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states

        outputs = self.data2vec_audio(
            input_values,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if self.config.use_weighted_layer_sum:
            hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
            hidden_states = torch.stack(hidden_states, dim=1)
            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
        else:
            hidden_states = outputs[0]

        hidden_states = self.projector(hidden_states)
        if attention_mask is None:
            pooled_output = hidden_states.mean(dim=1)
        else:
            padding_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask)
            hidden_states[~padding_mask] = 0.0
            pooled_output = hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)

        logits = self.classifier(pooled_output)

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

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

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    Data2VecAudio Model with a frame classification head on top for tasks like Speaker Diarization.
    """,
    DATA2VEC_AUDIO_START_DOCSTRING,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForAudioFrameClassification with Wav2Vec2->Data2VecAudio, wav2vec2->data2vec_audio, WAV_2_VEC_2->DATA2VEC_AUDIO
class Data2VecAudioForAudioFrameClassification(Data2VecAudioPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        if hasattr(config, "add_adapter") and config.add_adapter:
            raise ValueError(
                "Audio frame classification does not support the use of Data2VecAudio adapters (config.add_adapter=True)"
            )
        self.data2vec_audio = Data2VecAudioModel(config)
        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
        if config.use_weighted_layer_sum:
            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        self.init_weights()

    def freeze_feature_extractor(self):
        """
        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
        not be updated during training.
        """
        warnings.warn(
            "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5."
            "Please use the equivalent `freeze_feature_encoder` method instead.",
            FutureWarning,
        )
        self.freeze_feature_encoder()

    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.data2vec_audio.feature_extractor._freeze_parameters()

    def freeze_base_model(self):
        """
        Calling this function will disable the gradient computation for the base model so that its parameters will not
        be updated during training. Only the classification head will be updated.
        """
        for param in self.data2vec_audio.parameters():
            param.requires_grad = False

    @add_start_docstrings_to_model_forward(DATA2VEC_AUDIO_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
        checkpoint=_FRAME_CLASS_CHECKPOINT,
        output_type=TokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
        modality="audio",
        expected_output=_FRAME_EXPECTED_OUTPUT,
    )
    def forward(
        self,
        input_values: Optional[torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, TokenClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states

        outputs = self.data2vec_audio(
            input_values,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if self.config.use_weighted_layer_sum:
            hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
            hidden_states = torch.stack(hidden_states, dim=1)
            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
        else:
            hidden_states = outputs[0]

        logits = self.classifier(hidden_states)

        if not return_dict:
            output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
            return output

        return TokenClassifierOutput(
            loss=None,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.AMSoftmaxLoss
class AMSoftmaxLoss(nn.Module):
    def __init__(self, input_dim, num_labels, scale=30.0, margin=0.4):
        super(AMSoftmaxLoss, self).__init__()
        self.scale = scale
        self.margin = margin
        self.num_labels = num_labels
        self.weight = nn.Parameter(torch.randn(input_dim, num_labels), requires_grad=True)
        self.loss = nn.CrossEntropyLoss()

    def forward(self, hidden_states, labels):
        labels = labels.flatten()
        weight = nn.functional.normalize(self.weight, dim=0)
        hidden_states = nn.functional.normalize(hidden_states, dim=1)
        cos_theta = torch.mm(hidden_states, weight)
        psi = cos_theta - self.margin

        onehot = nn.functional.one_hot(labels, self.num_labels)
        logits = self.scale * torch.where(onehot.bool(), psi, cos_theta)
        loss = self.loss(logits, labels)

        return loss


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.TDNNLayer
class TDNNLayer(nn.Module):
    def __init__(self, config, layer_id=0):
        super().__init__()
        self.in_conv_dim = config.tdnn_dim[layer_id - 1] if layer_id > 0 else config.tdnn_dim[layer_id]
        self.out_conv_dim = config.tdnn_dim[layer_id]
        self.kernel_size = config.tdnn_kernel[layer_id]
        self.dilation = config.tdnn_dilation[layer_id]

        self.kernel = nn.Linear(self.in_conv_dim * self.kernel_size, self.out_conv_dim)
        self.activation = nn.ReLU()

    def forward(self, hidden_states):
        hidden_states = hidden_states.unsqueeze(1)
        hidden_states = nn.functional.unfold(
            hidden_states,
            (self.kernel_size, self.in_conv_dim),
            stride=(1, self.in_conv_dim),
            dilation=(self.dilation, 1),
        )
        hidden_states = hidden_states.transpose(1, 2)
        hidden_states = self.kernel(hidden_states)

        hidden_states = self.activation(hidden_states)
        return hidden_states


@add_start_docstrings(
    """
    Data2VecAudio Model with an XVector feature extraction head on top for tasks like Speaker Verification.
    """,
    DATA2VEC_AUDIO_START_DOCSTRING,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForXVector with Wav2Vec2->Data2VecAudio, wav2vec2->data2vec_audio, WAV_2_VEC_2->DATA2VEC_AUDIO
class Data2VecAudioForXVector(Data2VecAudioPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.data2vec_audio = Data2VecAudioModel(config)
        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
        if config.use_weighted_layer_sum:
            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
        self.projector = nn.Linear(config.hidden_size, config.tdnn_dim[0])

        tdnn_layers = [TDNNLayer(config, i) for i in range(len(config.tdnn_dim))]
        self.tdnn = nn.ModuleList(tdnn_layers)

        self.feature_extractor = nn.Linear(config.tdnn_dim[-1] * 2, config.xvector_output_dim)
        self.classifier = nn.Linear(config.xvector_output_dim, config.xvector_output_dim)

        self.objective = AMSoftmaxLoss(config.xvector_output_dim, config.num_labels)

        self.init_weights()

    def freeze_feature_extractor(self):
        """
        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
        not be updated during training.
        """
        warnings.warn(
            "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5."
            "Please use the equivalent `freeze_feature_encoder` method instead.",
            FutureWarning,
        )
        self.freeze_feature_encoder()

    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.data2vec_audio.feature_extractor._freeze_parameters()

    def freeze_base_model(self):
        """
        Calling this function will disable the gradient computation for the base model so that its parameters will not
        be updated during training. Only the classification head will be updated.
        """
        for param in self.data2vec_audio.parameters():
            param.requires_grad = False

    def _get_tdnn_output_lengths(self, input_lengths: Union[torch.LongTensor, int]):
        """
        Computes the output length of the TDNN 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 (input_length - kernel_size) // stride + 1

        for kernel_size in self.config.tdnn_kernel:
            input_lengths = _conv_out_length(input_lengths, kernel_size, 1)

        return input_lengths

    @add_start_docstrings_to_model_forward(DATA2VEC_AUDIO_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        processor_class=_FEAT_EXTRACTOR_FOR_DOC,
        checkpoint=_XVECTOR_CHECKPOINT,
        output_type=XVectorOutput,
        config_class=_CONFIG_FOR_DOC,
        modality="audio",
        expected_output=_XVECTOR_EXPECTED_OUTPUT,
    )
    def forward(
        self,
        input_values: Optional[torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[torch.Tensor] = None,
    ) -> Union[Tuple, XVectorOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states

        outputs = self.data2vec_audio(
            input_values,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if self.config.use_weighted_layer_sum:
            hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
            hidden_states = torch.stack(hidden_states, dim=1)
            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
        else:
            hidden_states = outputs[0]

        hidden_states = self.projector(hidden_states)

        for tdnn_layer in self.tdnn:
            hidden_states = tdnn_layer(hidden_states)

        # Statistic Pooling
        if attention_mask is None:
            mean_features = hidden_states.mean(dim=1)
            std_features = hidden_states.std(dim=1)
        else:
            feat_extract_output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(dim=1))
            tdnn_output_lengths = self._get_tdnn_output_lengths(feat_extract_output_lengths)
            mean_features = []
            std_features = []
            for i, length in enumerate(tdnn_output_lengths):
                mean_features.append(hidden_states[i, :length].mean(dim=0))
                std_features.append(hidden_states[i, :length].std(dim=0))
            mean_features = torch.stack(mean_features)
            std_features = torch.stack(std_features)
        statistic_pooling = torch.cat([mean_features, std_features], dim=-1)

        output_embeddings = self.feature_extractor(statistic_pooling)
        logits = self.classifier(output_embeddings)

        loss = None
        if labels is not None:
            loss = self.objective(logits, labels)

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

        return XVectorOutput(
            loss=loss,
            logits=logits,
            embeddings=output_embeddings,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )