# coding=utf-8
# Copyright 2022 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 Wav2Vec2-Conformer model."""

import math
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 ...integrations.deepspeed import is_deepspeed_zero3_enabled
from ...modeling_outputs import (
    BaseModelOutput,
    CausalLMOutput,
    SequenceClassifierOutput,
    TokenClassifierOutput,
    Wav2Vec2BaseModelOutput,
    XVectorOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from .configuration_wav2vec2_conformer import Wav2Vec2ConformerConfig


logger = logging.get_logger(__name__)


_HIDDEN_STATES_START_POSITION = 2

# General docstring
_CONFIG_FOR_DOC = "Wav2Vec2ConformerConfig"

# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/wav2vec2-conformer-rope-large-960h-ft"
_EXPECTED_OUTPUT_SHAPE = [1, 292, 1024]

# 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 = 64.21


WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/wav2vec2-conformer-rel-pos-large",
    # See all Wav2Vec2Conformer models at https://huggingface.co/models?filter=wav2vec2-conformer
]


@dataclass
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerForPreTrainingOutput(ModelOutput):
    """
    Output type of [`Wav2Vec2ConformerForPreTraining`], with potential hidden states and attentions.

    Args:
        loss (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`):
            Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the [official
            paper](https://arxiv.org/pdf/2006.11477.pdf) . (classification) loss.
        projected_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`):
            Hidden-states of the model projected to *config.proj_codevector_dim* that can be used to predict the masked
            projected quantized states.
        projected_quantized_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`):
            Quantized extracted feature vectors projected to *config.proj_codevector_dim* representing the positive
            target vectors for contrastive loss.
        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.
        contrastive_loss (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`):
            The contrastive loss (L_m) as stated in the [official paper](https://arxiv.org/pdf/2006.11477.pdf) .
        diversity_loss (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`):
            The diversity loss (L_d) as stated in the [official paper](https://arxiv.org/pdf/2006.11477.pdf) .
    """

    loss: Optional[torch.FloatTensor] = None
    projected_states: torch.FloatTensor = None
    projected_quantized_states: torch.FloatTensor = None
    codevector_perplexity: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    contrastive_loss: Optional[torch.FloatTensor] = None
    diversity_loss: Optional[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=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._sample_negative_indices
def _sample_negative_indices(
    features_shape: Tuple, num_negatives: int, mask_time_indices: Optional[np.ndarray] = None
):
    """
    Sample `num_negatives` vectors from feature vectors.
    """
    batch_size, sequence_length = features_shape

    # generate indices of the positive vectors themselves, repeat them `num_negatives` times
    sequence_length_range = np.arange(sequence_length)

    # get `num_negatives` random vector indices from the same utterance
    sampled_negative_indices = np.zeros(shape=(batch_size, sequence_length, num_negatives), dtype=np.int32)

    mask_time_indices = (
        mask_time_indices.astype(bool) if mask_time_indices is not None else np.ones(features_shape, dtype=bool)
    )

    for batch_idx in range(batch_size):
        high = mask_time_indices[batch_idx].sum() - 1
        mapped_masked_indices = sequence_length_range[mask_time_indices[batch_idx]]

        feature_indices = np.broadcast_to(np.arange(high + 1)[:, None], (high + 1, num_negatives))
        sampled_indices = np.random.randint(0, high, size=(high + 1, num_negatives))
        # avoid sampling the same positive vector, but keep the distribution uniform
        sampled_indices[sampled_indices >= feature_indices] += 1

        # remap to actual indices
        sampled_negative_indices[batch_idx][mask_time_indices[batch_idx]] = mapped_masked_indices[sampled_indices]

        # correct for batch size
        sampled_negative_indices[batch_idx] += batch_idx * sequence_length

    return sampled_negative_indices


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2NoLayerNormConvLayer with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerNoLayerNormConvLayer(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->Wav2Vec2Conformer
class Wav2Vec2ConformerLayerNormConvLayer(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->Wav2Vec2Conformer
class Wav2Vec2ConformerGroupNormConvLayer(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.wav2vec2.modeling_wav2vec2.Wav2Vec2PositionalConvEmbedding with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerPositionalConvEmbedding(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 = Wav2Vec2ConformerSamePadLayer(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 Wav2Vec2ConformerRotaryPositionalEmbedding(nn.Module):
    """Rotary positional embedding
    Reference : https://blog.eleuther.ai/rotary-embeddings/ Paper: https://arxiv.org/pdf/2104.09864.pdf
    """

    def __init__(self, config):
        super().__init__()
        dim = config.hidden_size // config.num_attention_heads
        base = config.rotary_embedding_base

        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer("inv_freq", inv_freq)
        self.cached_sequence_length = None
        self.cached_rotary_positional_embedding = None

    def forward(self, hidden_states):
        sequence_length = hidden_states.shape[1]

        if sequence_length == self.cached_sequence_length and self.cached_rotary_positional_embedding is not None:
            return self.cached_rotary_positional_embedding

        self.cached_sequence_length = sequence_length
        time_stamps = torch.arange(sequence_length).type_as(self.inv_freq)
        freqs = torch.einsum("i,j->ij", time_stamps, self.inv_freq)
        embeddings = torch.cat((freqs, freqs), dim=-1)

        cos_embeddings = embeddings.cos()[:, None, None, :]
        sin_embeddings = embeddings.sin()[:, None, None, :]
        self.cached_rotary_positional_embedding = torch.stack([cos_embeddings, sin_embeddings])
        return self.cached_rotary_positional_embedding


class Wav2Vec2ConformerRelPositionalEmbedding(nn.Module):
    """Relative positional encoding module."""

    def __init__(self, config):
        super().__init__()
        self.max_len = config.max_source_positions
        self.d_model = config.hidden_size
        self.pe = None
        self.extend_pe(torch.tensor(0.0).expand(1, self.max_len))

    def extend_pe(self, x):
        # Reset the positional encodings
        if self.pe is not None:
            # self.pe contains both positive and negative parts
            # the length of self.pe is 2 * input_len - 1
            if self.pe.size(1) >= x.size(1) * 2 - 1:
                if self.pe.dtype != x.dtype or self.pe.device != x.device:
                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                return
        # Suppose `i` is the position of query vector and `j` is the
        # position of key vector. We use positive relative positions when keys
        # are to the left (i>j) and negative relative positions otherwise (i<j).
        pe_positive = torch.zeros(x.size(1), self.d_model)
        pe_negative = torch.zeros(x.size(1), self.d_model)
        position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
        div_term = torch.exp(
            torch.arange(0, self.d_model, 2, dtype=torch.float32) * -(math.log(10000.0) / self.d_model)
        )
        pe_positive[:, 0::2] = torch.sin(position * div_term)
        pe_positive[:, 1::2] = torch.cos(position * div_term)
        pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
        pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)

        # Reverse the order of positive indices and concat both positive and
        # negative indices. This is used to support the shifting trick
        # as in https://arxiv.org/abs/1901.02860
        pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
        pe_negative = pe_negative[1:].unsqueeze(0)
        pe = torch.cat([pe_positive, pe_negative], dim=1)
        self.pe = pe.to(device=x.device, dtype=x.dtype)

    def forward(self, hidden_states: torch.Tensor):
        self.extend_pe(hidden_states)
        start_idx = self.pe.size(1) // 2 - hidden_states.size(1) + 1
        end_idx = self.pe.size(1) // 2 + hidden_states.size(1)
        relative_position_embeddings = self.pe[:, start_idx:end_idx]

        return relative_position_embeddings


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2SamePadLayer with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerSamePadLayer(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->Wav2Vec2Conformer
class Wav2Vec2ConformerFeatureEncoder(nn.Module):
    """Construct the features from raw audio waveform"""

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

        if config.feat_extract_norm == "group":
            conv_layers = [Wav2Vec2ConformerGroupNormConvLayer(config, layer_id=0)] + [
                Wav2Vec2ConformerNoLayerNormConvLayer(config, layer_id=i + 1)
                for i in range(config.num_feat_extract_layers - 1)
            ]
        elif config.feat_extract_norm == "layer":
            conv_layers = [
                Wav2Vec2ConformerLayerNormConvLayer(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:

                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->Wav2Vec2Conformer
class Wav2Vec2ConformerFeatureProjection(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.wav2vec2.modeling_wav2vec2.Wav2Vec2FeedForward with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerFeedForward(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


class Wav2Vec2ConformerConvolutionModule(nn.Module):
    """Convolution block used in the conformer block"""

    def __init__(self, config):
        super().__init__()
        if (config.conv_depthwise_kernel_size - 1) % 2 == 1:
            raise ValueError("`config.conv_depthwise_kernel_size` should be a odd number for 'SAME' padding")
        self.layer_norm = nn.LayerNorm(config.hidden_size)
        self.pointwise_conv1 = torch.nn.Conv1d(
            config.hidden_size,
            2 * config.hidden_size,
            kernel_size=1,
            stride=1,
            padding=0,
            bias=False,
        )
        self.glu = torch.nn.GLU(dim=1)
        self.depthwise_conv = torch.nn.Conv1d(
            config.hidden_size,
            config.hidden_size,
            config.conv_depthwise_kernel_size,
            stride=1,
            padding=(config.conv_depthwise_kernel_size - 1) // 2,
            groups=config.hidden_size,
            bias=False,
        )
        self.batch_norm = torch.nn.BatchNorm1d(config.hidden_size)
        self.activation = ACT2FN[config.hidden_act]
        self.pointwise_conv2 = torch.nn.Conv1d(
            config.hidden_size,
            config.hidden_size,
            kernel_size=1,
            stride=1,
            padding=0,
            bias=False,
        )
        self.dropout = torch.nn.Dropout(config.conformer_conv_dropout)

    def forward(self, hidden_states):
        hidden_states = self.layer_norm(hidden_states)
        # exchange the temporal dimension and the feature dimension
        hidden_states = hidden_states.transpose(1, 2)

        # GLU mechanism
        # => (batch, 2*channel, dim)
        hidden_states = self.pointwise_conv1(hidden_states)
        # => (batch, channel, dim)
        hidden_states = self.glu(hidden_states)

        # 1D Depthwise Conv
        hidden_states = self.depthwise_conv(hidden_states)
        hidden_states = self.batch_norm(hidden_states)
        hidden_states = self.activation(hidden_states)

        hidden_states = self.pointwise_conv2(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = hidden_states.transpose(1, 2)
        return hidden_states


class Wav2Vec2ConformerSelfAttention(nn.Module):
    """Construct an Wav2Vec2ConformerSelfAttention object.
    Can be enhanced with rotary or relative position embeddings.
    """

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

        self.head_size = config.hidden_size // config.num_attention_heads
        self.num_heads = config.num_attention_heads
        self.position_embeddings_type = config.position_embeddings_type

        self.linear_q = nn.Linear(config.hidden_size, config.hidden_size)
        self.linear_k = nn.Linear(config.hidden_size, config.hidden_size)
        self.linear_v = nn.Linear(config.hidden_size, config.hidden_size)
        self.linear_out = nn.Linear(config.hidden_size, config.hidden_size)

        self.dropout = nn.Dropout(p=config.attention_dropout)

        if self.position_embeddings_type == "relative":
            # linear transformation for positional encoding
            self.linear_pos = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
            # these two learnable bias are used in matrix c and matrix d
            # as described in https://arxiv.org/abs/1901.02860 Section 3.3
            self.pos_bias_u = nn.Parameter(torch.zeros(self.num_heads, self.head_size))
            self.pos_bias_v = nn.Parameter(torch.zeros(self.num_heads, self.head_size))

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        relative_position_embeddings: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        # self-attention mechanism
        batch_size, sequence_length, hidden_size = hidden_states.size()

        # make sure query/key states can be != value states
        query_key_states = hidden_states
        value_states = hidden_states

        if self.position_embeddings_type == "rotary":
            if relative_position_embeddings is None:
                raise ValueError(
                    "`relative_position_embeddings` has to be defined when `self.position_embeddings_type == 'rotary'"
                )
            query_key_states = self._apply_rotary_embedding(query_key_states, relative_position_embeddings)

        # project query_key_states and value_states
        query = self.linear_q(query_key_states).view(batch_size, -1, self.num_heads, self.head_size)
        key = self.linear_k(query_key_states).view(batch_size, -1, self.num_heads, self.head_size)
        value = self.linear_v(value_states).view(batch_size, -1, self.num_heads, self.head_size)

        # => (batch, head, time1, d_k)
        query = query.transpose(1, 2)
        key = key.transpose(1, 2)
        value = value.transpose(1, 2)

        if self.position_embeddings_type == "relative":
            if relative_position_embeddings is None:
                raise ValueError(
                    "`relative_position_embeddings` has to be defined when `self.position_embeddings_type =="
                    " 'relative'"
                )
            # apply relative_position_embeddings to qk scores
            # as proposed in Transformer_XL: https://arxiv.org/abs/1901.02860
            scores = self._apply_relative_embeddings(
                query=query, key=key, relative_position_embeddings=relative_position_embeddings
            )
        else:
            scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.head_size)

        # apply attention_mask if necessary
        if attention_mask is not None:
            scores = scores + attention_mask

        # => (batch, head, time1, time2)
        probs = torch.softmax(scores, dim=-1)
        probs = self.dropout(probs)

        # => (batch, head, time1, d_k)
        hidden_states = torch.matmul(probs, value)

        # => (batch, time1, hidden_size)
        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_size)
        hidden_states = self.linear_out(hidden_states)

        return hidden_states, probs

    def _apply_rotary_embedding(self, hidden_states, relative_position_embeddings):
        batch_size, sequence_length, hidden_size = hidden_states.size()
        hidden_states = hidden_states.view(batch_size, sequence_length, self.num_heads, self.head_size)

        cos = relative_position_embeddings[0, :sequence_length, ...]
        sin = relative_position_embeddings[1, :sequence_length, ...]

        # rotate hidden_states with rotary embeddings
        hidden_states = hidden_states.transpose(0, 1)
        rotated_states_begin = hidden_states[..., : self.head_size // 2]
        rotated_states_end = hidden_states[..., self.head_size // 2 :]
        rotated_states = torch.cat((-rotated_states_end, rotated_states_begin), dim=rotated_states_begin.ndim - 1)
        hidden_states = (hidden_states * cos) + (rotated_states * sin)
        hidden_states = hidden_states.transpose(0, 1)

        hidden_states = hidden_states.view(batch_size, sequence_length, self.num_heads * self.head_size)

        return hidden_states

    def _apply_relative_embeddings(self, query, key, relative_position_embeddings):
        # 1. project positional embeddings
        # => (batch, head, 2*time1-1, d_k)
        proj_relative_position_embeddings = self.linear_pos(relative_position_embeddings)
        proj_relative_position_embeddings = proj_relative_position_embeddings.view(
            relative_position_embeddings.size(0), -1, self.num_heads, self.head_size
        )
        proj_relative_position_embeddings = proj_relative_position_embeddings.transpose(1, 2)
        proj_relative_position_embeddings = proj_relative_position_embeddings.transpose(2, 3)

        # 2. Add bias to query
        # => (batch, head, time1, d_k)
        query = query.transpose(1, 2)
        q_with_bias_u = (query + self.pos_bias_u).transpose(1, 2)
        q_with_bias_v = (query + self.pos_bias_v).transpose(1, 2)

        # 3. attention score: first compute matrix a and matrix c
        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
        # => (batch, head, time1, time2)
        scores_ac = torch.matmul(q_with_bias_u, key.transpose(-2, -1))

        # 4. then compute matrix b and matrix d
        # => (batch, head, time1, 2*time1-1)
        scores_bd = torch.matmul(q_with_bias_v, proj_relative_position_embeddings)

        # 5. shift matrix b and matrix d
        zero_pad = torch.zeros((*scores_bd.size()[:3], 1), device=scores_bd.device, dtype=scores_bd.dtype)
        scores_bd_padded = torch.cat([zero_pad, scores_bd], dim=-1)
        scores_bd_padded_shape = scores_bd.size()[:2] + (scores_bd.shape[3] + 1, scores_bd.shape[2])
        scores_bd_padded = scores_bd_padded.view(*scores_bd_padded_shape)
        scores_bd = scores_bd_padded[:, :, 1:].view_as(scores_bd)
        scores_bd = scores_bd[:, :, :, : scores_bd.size(-1) // 2 + 1]

        # 6. sum matrices
        # => (batch, head, time1, time2)
        scores = (scores_ac + scores_bd) / math.sqrt(self.head_size)

        return scores


class Wav2Vec2ConformerEncoderLayer(nn.Module):
    """Conformer block based on https://arxiv.org/abs/2005.08100."""

    def __init__(self, config):
        super().__init__()
        embed_dim = config.hidden_size
        dropout = config.attention_dropout

        # Feed-forward 1
        self.ffn1_layer_norm = nn.LayerNorm(embed_dim)
        self.ffn1 = Wav2Vec2ConformerFeedForward(config)

        # Self-Attention
        self.self_attn_layer_norm = nn.LayerNorm(embed_dim)
        self.self_attn_dropout = torch.nn.Dropout(dropout)
        self.self_attn = Wav2Vec2ConformerSelfAttention(config)

        # Conformer Convolution
        self.conv_module = Wav2Vec2ConformerConvolutionModule(config)

        # Feed-forward 2
        self.ffn2_layer_norm = nn.LayerNorm(embed_dim)
        self.ffn2 = Wav2Vec2ConformerFeedForward(config)
        self.final_layer_norm = nn.LayerNorm(embed_dim)

    def forward(
        self,
        hidden_states,
        attention_mask: Optional[torch.Tensor] = None,
        relative_position_embeddings: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ):
        hidden_states = hidden_states

        # 1. Feed-Forward 1 layer
        residual = hidden_states
        hidden_states = self.ffn1_layer_norm(hidden_states)
        hidden_states = self.ffn1(hidden_states)
        hidden_states = hidden_states * 0.5 + residual
        residual = hidden_states

        # 2. Self-Attention layer
        hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states, attn_weigts = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            relative_position_embeddings=relative_position_embeddings,
            output_attentions=output_attentions,
        )
        hidden_states = self.self_attn_dropout(hidden_states)
        hidden_states = hidden_states + residual

        # 3. Convolutional Layer
        residual = hidden_states
        hidden_states = self.conv_module(hidden_states)
        hidden_states = residual + hidden_states

        # 4. Feed-Forward 2 Layer
        residual = hidden_states
        hidden_states = self.ffn2_layer_norm(hidden_states)
        hidden_states = self.ffn2(hidden_states)
        hidden_states = hidden_states * 0.5 + residual
        hidden_states = self.final_layer_norm(hidden_states)

        return hidden_states, attn_weigts


class Wav2Vec2ConformerEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config

        if config.position_embeddings_type == "relative":
            self.embed_positions = Wav2Vec2ConformerRelPositionalEmbedding(config)
        elif config.position_embeddings_type == "rotary":
            self.embed_positions = Wav2Vec2ConformerRotaryPositionalEmbedding(config)
        else:
            self.embed_positions = None

        self.pos_conv_embed = Wav2Vec2ConformerPositionalConvEmbedding(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([Wav2Vec2ConformerEncoderLayer(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)
            attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min
            attention_mask = attention_mask.expand(
                attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]
            )

        hidden_states = self.dropout(hidden_states)

        if self.embed_positions is not None:
            relative_position_embeddings = self.embed_positions(hidden_states)
        else:
            relative_position_embeddings = None

        deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()

        for i, 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)
            dropout_probability = torch.rand([])

            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,
                        relative_position_embeddings,
                    )
                else:
                    layer_outputs = layer(
                        hidden_states,
                        attention_mask=attention_mask,
                        relative_position_embeddings=relative_position_embeddings,
                        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],)

        hidden_states = self.layer_norm(hidden_states)
        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.Wav2Vec2GumbelVectorQuantizer with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerGumbelVectorQuantizer(nn.Module):
    """
    Vector quantization using gumbel softmax. See `[CATEGORICAL REPARAMETERIZATION WITH
    GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information.
    """

    def __init__(self, config):
        super().__init__()
        self.num_groups = config.num_codevector_groups
        self.num_vars = config.num_codevectors_per_group

        if config.codevector_dim % self.num_groups != 0:
            raise ValueError(
                f"`config.codevector_dim {config.codevector_dim} must be divisible "
                f"by `config.num_codevector_groups` {self.num_groups} for concatenation"
            )

        # storage for codebook variables (codewords)
        self.codevectors = nn.Parameter(
            torch.FloatTensor(1, self.num_groups * self.num_vars, config.codevector_dim // self.num_groups)
        )
        self.weight_proj = nn.Linear(config.conv_dim[-1], self.num_groups * self.num_vars)

        # can be decayed for training
        self.temperature = 2

    @staticmethod
    def _compute_perplexity(probs, mask=None):
        if mask is not None:
            mask_extended = mask.flatten()[:, None, None].expand(probs.shape)
            probs = torch.where(mask_extended, probs, torch.zeros_like(probs))
            marginal_probs = probs.sum(dim=0) / mask.sum()
        else:
            marginal_probs = probs.mean(dim=0)

        perplexity = torch.exp(-torch.sum(marginal_probs * torch.log(marginal_probs + 1e-7), dim=-1)).sum()
        return perplexity

    def forward(self, hidden_states, mask_time_indices=None):
        batch_size, sequence_length, hidden_size = hidden_states.shape

        # project to codevector dim
        hidden_states = self.weight_proj(hidden_states)
        hidden_states = hidden_states.view(batch_size * sequence_length * self.num_groups, -1)

        if self.training:
            # sample code vector probs via gumbel in differentiateable way
            codevector_probs = nn.functional.gumbel_softmax(
                hidden_states.float(), tau=self.temperature, hard=True
            ).type_as(hidden_states)

            # compute perplexity
            codevector_soft_dist = torch.softmax(
                hidden_states.view(batch_size * sequence_length, self.num_groups, -1).float(), dim=-1
            )
            perplexity = self._compute_perplexity(codevector_soft_dist, mask_time_indices)
        else:
            # take argmax in non-differentiable way
            # comptute hard codevector distribution (one hot)
            codevector_idx = hidden_states.argmax(dim=-1)
            codevector_probs = hidden_states.new_zeros(hidden_states.shape).scatter_(
                -1, codevector_idx.view(-1, 1), 1.0
            )
            codevector_probs = codevector_probs.view(batch_size * sequence_length, self.num_groups, -1)

            perplexity = self._compute_perplexity(codevector_probs, mask_time_indices)

        codevector_probs = codevector_probs.view(batch_size * sequence_length, -1)
        # use probs to retrieve codevectors
        codevectors_per_group = codevector_probs.unsqueeze(-1) * self.codevectors
        codevectors = codevectors_per_group.view(batch_size * sequence_length, self.num_groups, self.num_vars, -1)
        codevectors = codevectors.sum(-2).view(batch_size, sequence_length, -1)

        return codevectors, perplexity


# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Adapter with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerAdapter(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(Wav2Vec2ConformerAdapterLayer(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->Wav2Vec2Conformer
class Wav2Vec2ConformerAdapterLayer(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 Wav2Vec2ConformerPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = Wav2Vec2ConformerConfig
    base_model_prefix = "wav2vec2_conformer"
    main_input_name = "input_values"
    supports_gradient_checkpointing = True

    def _init_weights(self, module):
        """Initialize the weights"""
        # Wav2Vec2ForPreTraining last 2 linear layers need standard Linear init.
        if isinstance(module, Wav2Vec2ConformerForPreTraining):
            module.project_hid.reset_parameters()
            module.project_q.reset_parameters()
            module.project_hid._is_hf_initialized = True
            module.project_q._is_hf_initialized = True
        # gumbel softmax requires special init
        elif isinstance(module, Wav2Vec2ConformerGumbelVectorQuantizer):
            module.weight_proj.weight.data.normal_(mean=0.0, std=1)
            module.weight_proj.bias.data.zero_()
            nn.init.uniform_(module.codevectors)
        elif isinstance(module, Wav2Vec2ConformerSelfAttention):
            if hasattr(module, "pos_bias_u"):
                nn.init.xavier_uniform_(module.pos_bias_u)
            if hasattr(module, "pos_bias_v"):
                nn.init.xavier_uniform_(module.pos_bias_v)
        elif isinstance(module, Wav2Vec2ConformerPositionalConvEmbedding):
            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, Wav2Vec2ConformerFeatureProjection):
            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)

    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.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)

        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

    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, (Wav2Vec2ConformerEncoder, Wav2Vec2ConformerFeatureEncoder)):
            module.gradient_checkpointing = value


WAV2VEC2_CONFORMER_START_DOCSTRING = r"""
    Wav2Vec2Conformer was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
    Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, 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 [nn.Module](https://pytorch.org/docs/stable/nn.html#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 ([`Wav2Vec2ConformerConfig`]): 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.
"""


WAV2VEC2_CONFORMER_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 [`AutoProcessor`] 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
            [wav2vec2-conformer-rel-pos-large](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large),
            `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 Wav2Vec2Conformer Model transformer outputting raw hidden-states without any specific head on top.",
    WAV2VEC2_CONFORMER_START_DOCSTRING,
)
class Wav2Vec2ConformerModel(Wav2Vec2ConformerPreTrainedModel):
    def __init__(self, config: Wav2Vec2ConformerConfig):
        super().__init__(config)
        self.config = config
        self.feature_extractor = Wav2Vec2ConformerFeatureEncoder(config)
        self.feature_projection = Wav2Vec2ConformerFeatureProjection(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 = Wav2Vec2ConformerEncoder(config)

        self.adapter = Wav2Vec2ConformerAdapter(config) if config.add_adapter else None

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

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model.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.feature_extractor._freeze_parameters()

    # 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

    @add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=Wav2Vec2BaseModelOutput,
        config_class=_CONFIG_FOR_DOC,
        modality="audio",
        expected_output=_EXPECTED_OUTPUT_SHAPE,
    )
    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model.forward with wav2vec2->wav2vec2_conformer
    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, Wav2Vec2BaseModelOutput]:
        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 Wav2Vec2BaseModelOutput(
            last_hidden_state=hidden_states,
            extract_features=extract_features,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


@add_start_docstrings(
    """Wav2Vec2Conformer Model with a quantizer and `VQ` head on top.""", WAV2VEC2_CONFORMER_START_DOCSTRING
)
class Wav2Vec2ConformerForPreTraining(Wav2Vec2ConformerPreTrainedModel):
    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTraining.__init__ with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer
    def __init__(self, config: Wav2Vec2ConformerConfig):
        super().__init__(config)
        self.wav2vec2_conformer = Wav2Vec2ConformerModel(config)
        self.dropout_features = nn.Dropout(config.feat_quantizer_dropout)

        self.quantizer = Wav2Vec2ConformerGumbelVectorQuantizer(config)

        self.project_hid = nn.Linear(config.hidden_size, config.proj_codevector_dim)
        self.project_q = nn.Linear(config.codevector_dim, config.proj_codevector_dim)

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

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTraining.set_gumbel_temperature
    def set_gumbel_temperature(self, temperature: int):
        """
        Set the Gumbel softmax temperature to a given value. Only necessary for training
        """
        self.quantizer.temperature = temperature

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTraining.freeze_feature_encoder with wav2vec2->wav2vec2_conformer
    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.wav2vec2_conformer.feature_extractor._freeze_parameters()

    @staticmethod
    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTraining.compute_contrastive_logits
    def compute_contrastive_logits(
        target_features: torch.FloatTensor,
        negative_features: torch.FloatTensor,
        predicted_features: torch.FloatTensor,
        temperature: int = 0.1,
    ):
        """
        Compute logits for contrastive loss based using cosine similarity as the distance measure between
        `[positive_feature, negative_features]` and `[predicted_features]`. Additionally, temperature can be applied.
        """
        target_features = torch.cat([target_features, negative_features], dim=0)

        logits = torch.cosine_similarity(predicted_features.float(), target_features.float(), dim=-1).type_as(
            target_features
        )

        # apply temperature
        logits = logits / temperature
        return logits

    @add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Wav2Vec2ConformerForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTraining.forward with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer,wav2vec2_conformer-base->wav2vec2-conformer-rel-pos-large
    def forward(
        self,
        input_values: Optional[torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
        mask_time_indices: Optional[torch.BoolTensor] = None,
        sampled_negative_indices: Optional[torch.BoolTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, Wav2Vec2ConformerForPreTrainingOutput]:
        r"""
        mask_time_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
            masked extracted features in *config.proj_codevector_dim* space.
        sampled_negative_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_negatives)`, *optional*):
            Indices indicating which quantized target vectors are used as negative sampled vectors in contrastive loss.
            Required input for pre-training.

        Returns:

        Example:

        ```python
        >>> import torch
        >>> from transformers import AutoFeatureExtractor, Wav2Vec2ConformerForPreTraining
        >>> from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import (
        ...     _compute_mask_indices,
        ...     _sample_negative_indices,
        ... )
        >>> from datasets import load_dataset

        >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large")
        >>> model = Wav2Vec2ConformerForPreTraining.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large")

        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        >>> input_values = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt").input_values  # Batch size 1

        >>> # compute masked indices
        >>> batch_size, raw_sequence_length = input_values.shape
        >>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length).item()
        >>> mask_time_indices = _compute_mask_indices(
        ...     shape=(batch_size, sequence_length), mask_prob=0.2, mask_length=2
        ... )
        >>> sampled_negative_indices = _sample_negative_indices(
        ...     features_shape=(batch_size, sequence_length),
        ...     num_negatives=model.config.num_negatives,
        ...     mask_time_indices=mask_time_indices,
        ... )
        >>> mask_time_indices = torch.tensor(data=mask_time_indices, device=input_values.device, dtype=torch.long)
        >>> sampled_negative_indices = torch.tensor(
        ...     data=sampled_negative_indices, device=input_values.device, dtype=torch.long
        ... )

        >>> with torch.no_grad():
        ...     outputs = model(input_values, mask_time_indices=mask_time_indices)

        >>> # compute cosine similarity between predicted (=projected_states) and target (=projected_quantized_states)
        >>> cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-1)

        >>> # show that cosine similarity is much higher than random
        >>> cosine_sim[mask_time_indices.to(torch.bool)].mean() > 0.5
        tensor(True)

        >>> # for contrastive loss training model should be put into train mode
        >>> model = model.train()
        >>> loss = model(
        ...     input_values, mask_time_indices=mask_time_indices, sampled_negative_indices=sampled_negative_indices
        ... ).loss
        ```"""

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

        if mask_time_indices is not None:
            mask_time_indices = mask_time_indices.to(torch.bool)

        outputs = self.wav2vec2_conformer(
            input_values,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            mask_time_indices=mask_time_indices,
            return_dict=return_dict,
        )

        # 1. project all transformed features (including masked) to final vq dim
        transformer_features = self.project_hid(outputs[0])

        # 2. quantize all (unmasked) extracted features and project to final vq dim
        extract_features = self.dropout_features(outputs[1])

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

        quantized_features, codevector_perplexity = self.quantizer(
            extract_features, mask_time_indices=mask_time_indices
        )
        quantized_features = self.project_q(quantized_features)

        loss = contrastive_loss = diversity_loss = None
        if sampled_negative_indices is not None:
            batch_size, sequence_length, hidden_size = quantized_features.shape

            # for training, we sample negatives
            # 3. sample K negatives (distractors) quantized states for contrastive loss
            # if attention_mask is passed, make sure that padded feature vectors cannot be sampled
            # sample negative quantized vectors BTC => (BxT)C
            negative_quantized_features = quantized_features.view(-1, hidden_size)[
                sampled_negative_indices.long().view(-1)
            ]
            negative_quantized_features = negative_quantized_features.view(
                batch_size, sequence_length, -1, hidden_size
            ).permute(2, 0, 1, 3)

            # 4. compute logits, corresponding to `logs = sim(c_t, [q_t, \sim{q}_t]) / \kappa`
            # of equation (3) in https://arxiv.org/pdf/2006.11477.pdf
            logits = self.compute_contrastive_logits(
                quantized_features[None, :],
                negative_quantized_features,
                transformer_features,
                self.config.contrastive_logits_temperature,
            )

            # 5. if a negative vector is identical to the positive (i.e. when codebook utilization is low),
            # its cosine similarity will be masked
            neg_is_pos = (quantized_features == negative_quantized_features).all(-1)

            if neg_is_pos.any():
                logits[1:][neg_is_pos] = float("-inf")

            # 6. compute contrastive loss \mathbf{L}_m = cross_entropy(logs) =
            # -log(exp(sim(c_t, q_t)/\kappa) / \sum_{\sim{q}} exp(sim(c_t, \sim{q})/\kappa))
            logits = logits.transpose(0, 2).reshape(-1, logits.size(0))
            target = ((1 - mask_time_indices.long()) * -100).transpose(0, 1).flatten()

            contrastive_loss = nn.functional.cross_entropy(logits.float(), target, reduction="sum")
            # 7. compute diversity loss: \mathbf{L}_d
            num_codevectors = self.config.num_codevectors_per_group * self.config.num_codevector_groups
            diversity_loss = ((num_codevectors - codevector_perplexity) / num_codevectors) * mask_time_indices.sum()

            # 8. \mathbf{L} = \mathbf{L}_m + \alpha * \mathbf{L}_d
            loss = contrastive_loss + self.config.diversity_loss_weight * diversity_loss

        if not return_dict:
            if loss is not None:
                return (loss, transformer_features, quantized_features, codevector_perplexity) + outputs[2:]
            return (transformer_features, quantized_features, codevector_perplexity) + outputs[2:]

        return Wav2Vec2ConformerForPreTrainingOutput(
            loss=loss,
            projected_states=transformer_features,
            projected_quantized_states=quantized_features,
            codevector_perplexity=codevector_perplexity,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            contrastive_loss=contrastive_loss,
            diversity_loss=diversity_loss,
        )


@add_start_docstrings(
    """Wav2Vec2Conformer Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""",
    WAV2VEC2_CONFORMER_START_DOCSTRING,
)
class Wav2Vec2ConformerForCTC(Wav2Vec2ConformerPreTrainedModel):
    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.__init__ with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer
    def __init__(self, config, target_lang: Optional[str] = None):
        super().__init__(config)

        self.wav2vec2_conformer = Wav2Vec2ConformerModel(config)
        self.dropout = nn.Dropout(config.final_dropout)

        self.target_lang = target_lang

        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: `Wav2Vec2ConformerForCTC.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()

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.freeze_feature_encoder with wav2vec2->wav2vec2_conformer
    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.wav2vec2_conformer.feature_extractor._freeze_parameters()

    @add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=CausalLMOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output=_CTC_EXPECTED_OUTPUT,
        expected_loss=_CTC_EXPECTED_LOSS,
    )
    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.forward with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer
    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.wav2vec2_conformer(
            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(
    """
    Wav2Vec2Conformer Model with a sequence classification head on top (a linear layer over the pooled output) for
    tasks like SUPERB Keyword Spotting.
    """,
    WAV2VEC2_CONFORMER_START_DOCSTRING,
)
class Wav2Vec2ConformerForSequenceClassification(Wav2Vec2ConformerPreTrainedModel):
    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.__init__ with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer
    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 Wav2Vec2Conformer adapters (config.add_adapter=True)"
            )
        self.wav2vec2_conformer = Wav2Vec2ConformerModel(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()

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_feature_encoder with wav2vec2->wav2vec2_conformer
    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.wav2vec2_conformer.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.wav2vec2_conformer.parameters():
            param.requires_grad = False

    @add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
        modality="audio",
    )
    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.forward with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer,WAV_2_VEC_2->WAV2VEC2_CONFORMER
    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.wav2vec2_conformer(
            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(
    """
    Wav2Vec2Conformer Model with a frame classification head on top for tasks like Speaker Diarization.
    """,
    WAV2VEC2_CONFORMER_START_DOCSTRING,
)
class Wav2Vec2ConformerForAudioFrameClassification(Wav2Vec2ConformerPreTrainedModel):
    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForAudioFrameClassification.__init__ with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer,WAV_2_VEC_2->WAV2VEC2_CONFORMER
    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 Wav2Vec2Conformer adapters (config.add_adapter=True)"
            )
        self.wav2vec2_conformer = Wav2Vec2ConformerModel(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.num_labels = config.num_labels

        self.init_weights()

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForAudioFrameClassification.freeze_feature_encoder with wav2vec2->wav2vec2_conformer
    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.wav2vec2_conformer.feature_extractor._freeze_parameters()

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForAudioFrameClassification.freeze_base_model with wav2vec2->wav2vec2_conformer
    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.wav2vec2_conformer.parameters():
            param.requires_grad = False

    @add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
        modality="audio",
    )
    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForAudioFrameClassification.forward with wav2vec2->wav2vec2_conformer
    def forward(
        self,
        input_values: Optional[torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
        labels: 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.wav2vec2_conformer(
            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)

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

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

        return TokenClassifierOutput(
            loss=loss,
            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(
    """
    Wav2Vec2Conformer Model with an XVector feature extraction head on top for tasks like Speaker Verification.
    """,
    WAV2VEC2_CONFORMER_START_DOCSTRING,
)
class Wav2Vec2ConformerForXVector(Wav2Vec2ConformerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.wav2vec2_conformer = Wav2Vec2ConformerModel(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()

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForXVector.freeze_feature_encoder with wav2vec2->wav2vec2_conformer
    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.wav2vec2_conformer.feature_extractor._freeze_parameters()

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForXVector.freeze_base_model with wav2vec2->wav2vec2_conformer
    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.wav2vec2_conformer.parameters():
            param.requires_grad = False

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForXVector._get_tdnn_output_lengths with wav2vec2->wav2vec2_conformer
    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(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=XVectorOutput,
        config_class=_CONFIG_FOR_DOC,
        modality="audio",
    )
    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForXVector.forward with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer,WAV_2_VEC_2->WAV2VEC2_CONFORMER
    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.wav2vec2_conformer(
            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,
        )
