text: # coding=utf-8
  # Copyright 2021 The Fairseq Authors and the HuggingFace Inc. team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """ PyTorch Wav2Vec2 model. """
  
  import warnings
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import numpy as np
  import torch
  import torch.utils.checkpoint
  from torch import nn
  
  from ...activations import ACT2FN
  from ...deepspeed import is_deepspeed_zero3_enabled
  from ...file_utils import (
      ModelOutput,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      replace_return_docstrings,
  )
  from ...modeling_outputs import BaseModelOutput, CausalLMOutput, MaskedLMOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_wav2vec2 import Wav2Vec2Config
  
  
  logger = logging.get_logger(__name__)
  
  _CONFIG_FOR_DOC = "Wav2Vec2Config"
  _CHECKPOINT_FOR_DOC = "facebook/wav2vec2-base-960h"
  
  WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "facebook/wav2vec2-base-960h",
      "facebook/wav2vec2-large-960h",
      "facebook/wav2vec2-large-960h-lv60",
      "facebook/wav2vec2-large-960h-lv60-self",
      # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
  ]
  
  
  @dataclass
  class Wav2Vec2BaseModelOutput(ModelOutput):
      """
      Output type of :class:`~transformers.Wav2Vec2BaseModelOutput`, with potential hidden states and attentions.
  
      Args:
          last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
              Sequence of hidden-states at the output of the last layer of the model.
          extract_features (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, conv_dim[-1])`):
              Sequence of extracted feature vectors of the last convolutional layer of the model.
          hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      last_hidden_state: torch.FloatTensor = None
      extract_features: torch.FloatTensor = None
      hidden_states: Optional[Tuple[torch.FloatTensor]] = None
      attentions: Optional[Tuple[torch.FloatTensor]] = None
      extract_features2: Optional[torch.FloatTensor] = None
      hidden_states2: Optional[torch.FloatTensor] = None
  
  
  @dataclass
  class Wav2Vec2ForPreTrainingOutput(ModelOutput):
      """
      Output type of :class:`~transformers.Wav2Vec2ForPreTrainingOutput`, with potential hidden states and attentions.
  
      Args:
          loss (`optional`, returned when model is in train mode, ``torch.FloatTensor`` of shape :obj:`(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 (:obj:`torch.FloatTensor` of shape :obj:`(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 (:obj:`torch.FloatTensor` of shape :obj:`(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 (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
              Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
              of shape :obj:`(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
              Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
              sequence_length, sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      loss: Optional[torch.FloatTensor] = None
      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
  
  
  def _compute_mask_indices(
      shape: Tuple[int, int],
      mask_prob: float,
      mask_length: int,
      device: torch.device,
      attention_mask: Optional[torch.tensor] = None,
      min_masks: int = 0,
  ) -> torch.tensor:
      """
      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>`__.
  
      Args:
          shape: the the shape for which to compute masks.
              should be of size 2 where first element is batch size and 2nd is timesteps
          mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
              number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
              however due to overlaps, the actual number will be smaller (unless no_overlap is True)
          mask_length: size of the mask
          min_masks: minimum number of masked spans
  
      """
      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} and `sequence_length`: {sequence_length}`"
          )
  
      # compute number of masked spans in batch
      num_masked_spans = int(mask_prob * sequence_length / mask_length + torch.rand((1,)).item())
      num_masked_spans = max(num_masked_spans, min_masks)
  
      # make sure num masked indices <= sequence_length
      if num_masked_spans * mask_length > sequence_length:
          num_masked_spans = sequence_length // mask_length
  
      # SpecAugment mask to fill
      spec_aug_mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
  
      # uniform distribution to sample from, make sure that offset samples are < sequence_length
      uniform_dist = torch.ones((batch_size, sequence_length - (mask_length - 1)), device=device)
  
      # get random indices to mask
      spec_aug_mask_idxs = torch.multinomial(uniform_dist, num_masked_spans)
  
      # expand masked indices to masked spans
      spec_aug_mask_idxs = (
          spec_aug_mask_idxs.unsqueeze(dim=-1)
          .expand((batch_size, num_masked_spans, mask_length))
          .reshape(batch_size, num_masked_spans * mask_length)
      )
      offsets = (
          torch.arange(mask_length, device=device)[None, None, :]
          .expand((batch_size, num_masked_spans, mask_length))
          .reshape(batch_size, num_masked_spans * mask_length)
      )
      spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
  
      # scatter indices to mask
      spec_aug_mask = spec_aug_mask.scatter(1, spec_aug_mask_idxs, True)
  
      if attention_mask is not None:
          # make sure padded input ids cannot be masked
          spec_aug_mask = torch.where(attention_mask.bool(), spec_aug_mask, False)
  
      return spec_aug_mask
  
  
  class Wav2Vec2NoLayerNormConvLayer(nn.Module):
      def __init__(self, config, layer_id=0):
          super().__init__()
          self.in_conv_dim = config.conv_dim[layer_id] 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
  
  
  class Wav2Vec2LayerNormConvLayer(nn.Module):
      def __init__(self, config, layer_id=0):
          super().__init__()
          self.in_conv_dim = config.conv_dim[layer_id] 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
  
  
  class Wav2Vec2GroupNormConvLayer(nn.Module):
      def __init__(self, config, layer_id=0):
          super().__init__()
          self.in_conv_dim = config.conv_dim[layer_id] 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
  
  
  class Wav2Vec2PositionalConvEmbedding(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,
          )
  
          if is_deepspeed_zero3_enabled():
              import deepspeed
  
              with deepspeed.zero.GatheredParameters(self.conv.weight, modifier_rank=0):
                  self.conv = nn.utils.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 = nn.utils.weight_norm(self.conv, name="weight", dim=2)
  
          self.padding = Wav2Vec2SamePadLayer(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 Wav2Vec2SamePadLayer(nn.Module):
      def __init__(self, num_conv_pos_embeddings):
          super().__init__()
          self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
  
      def forward(self, hidden_states):
          if self.num_pad_remove > 0:
              hidden_states = hidden_states[:, :, : -self.num_pad_remove]
          return hidden_states
  
  
  class Wav2Vec2FeatureExtractor(nn.Module):
      """Construct the featurs from raw audio waveform"""
  
      def __init__(self, config):
          super().__init__()
  
          if config.feat_extract_norm == "group":
              conv_layers = [Wav2Vec2GroupNormConvLayer(config, layer_id=0)] + [
                  Wav2Vec2NoLayerNormConvLayer(config, layer_id=i + 1) for i in range(config.num_feat_extract_layers - 1)
              ]
          elif config.feat_extract_norm == "layer":
              conv_layers = [
                  Wav2Vec2LayerNormConvLayer(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)
  
      def _freeze_parameters(self):
          for param in self.parameters():
              param.requires_grad = False
  
      def forward(self, input_values):
          hidden_states = input_values[:, None]
          for conv_layer in self.conv_layers:
              hidden_states = conv_layer(hidden_states)
  
          return hidden_states
  
  
  class Wav2Vec2FeatureProjection(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps)
          self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
          self.dropout = nn.Dropout(config.feat_proj_dropout)
  
      def forward(self, hidden_states):
          # non-projected hidden states are needed for quantization
          norm_hidden_states = self.layer_norm(hidden_states)
          hidden_states = self.projection(norm_hidden_states)
          hidden_states = self.dropout(hidden_states)
          return hidden_states, norm_hidden_states
  
  
  # Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Wav2Vec2
  class Wav2Vec2Attention(nn.Module):
      """Multi-headed attention from 'Attention Is All You Need' paper"""
  
      def __init__(
          self,
          embed_dim: int,
          num_heads: int,
          dropout: float = 0.0,
          is_decoder: bool = False,
          bias: bool = True,
      ):
          super().__init__()
          self.embed_dim = embed_dim
          self.num_heads = num_heads
          self.dropout = dropout
          self.head_dim = embed_dim // num_heads
          assert (
              self.head_dim * num_heads == self.embed_dim
          ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {num_heads})."
          self.scaling = self.head_dim ** -0.5
          self.is_decoder = is_decoder
  
          self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
          self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
          self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
          self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
  
      def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
          return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          key_value_states: Optional[torch.Tensor] = None,
          past_key_value: Optional[Tuple[torch.Tensor]] = None,
          attention_mask: Optional[torch.Tensor] = None,
          layer_head_mask: Optional[torch.Tensor] = None,
          output_attentions: bool = False,
      ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
          """Input shape: Batch x Time x Channel"""
  
          # if key_value_states are provided this layer is used as a cross-attention layer
          # for the decoder
          is_cross_attention = key_value_states is not None
          bsz, tgt_len, embed_dim = hidden_states.size()
  
          # get query proj
          query_states = self.q_proj(hidden_states) * self.scaling
          # get key, value proj
          if is_cross_attention and past_key_value is not None:
              # reuse k,v, cross_attentions
              key_states = past_key_value[0]
              value_states = past_key_value[1]
          elif is_cross_attention:
              # cross_attentions
              key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
              value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
          elif past_key_value is not None:
              # reuse k, v, self_attention
              key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
              value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
              key_states = torch.cat([past_key_value[0], key_states], dim=2)
              value_states = torch.cat([past_key_value[1], value_states], dim=2)
          else:
              # self_attention
              key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
              value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
  
          if self.is_decoder:
              # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
              # Further calls to cross_attention layer can then reuse all cross-attention
              # key/value_states (first "if" case)
              # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
              # all previous decoder key/value_states. Further calls to uni-directional self-attention
              # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
              # if encoder bi-directional self-attention `past_key_value` is always `None`
              past_key_value = (key_states, value_states)
  
          proj_shape = (bsz * self.num_heads, -1, self.head_dim)
          query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
          key_states = key_states.view(*proj_shape)
          value_states = value_states.view(*proj_shape)
  
          src_len = key_states.size(1)
          attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
  
          if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
              raise ValueError(
                  f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
              )
  
          if attention_mask is not None:
              if attention_mask.size() != (bsz, 1, tgt_len, src_len):
                  raise ValueError(
                      f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
                  )
              attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
              attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
  
          attn_weights = nn.functional.softmax(attn_weights, dim=-1)
  
          if layer_head_mask is not None:
              if layer_head_mask.size() != (self.num_heads,):
                  raise ValueError(
                      f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
                  )
              attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
              attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
  
          if output_attentions:
              # this operation is a bit awkward, but it's required to
              # make sure that attn_weights keeps its gradient.
              # In order to do so, attn_weights have to be reshaped
              # twice and have to be reused in the following
              attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
              attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
          else:
              attn_weights_reshaped = None
  
          attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
  
          attn_output = torch.bmm(attn_probs, value_states)
  
          if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
              raise ValueError(
                  f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
              )
  
          attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
          attn_output = attn_output.transpose(1, 2)
          attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
  
          attn_output = self.out_proj(attn_output)
  
          return attn_output, attn_weights_reshaped, past_key_value
  
  
  class Wav2Vec2FeedForward(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 Wav2Vec2EncoderLayer(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.attention = Wav2Vec2Attention(
              embed_dim=config.hidden_size,
              num_heads=config.num_attention_heads,
              dropout=config.attention_dropout,
              is_decoder=False,
          )
          self.dropout = nn.Dropout(config.hidden_dropout)
          self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.feed_forward = Wav2Vec2FeedForward(config)
          self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
  
      def forward(self, hidden_states, attention_mask=None, output_attentions=False):
          attn_residual = hidden_states
          hidden_states, attn_weights, _ = self.attention(
              hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
          )
          hidden_states = self.dropout(hidden_states)
          hidden_states = attn_residual + hidden_states
  
          hidden_states = self.layer_norm(hidden_states)
          hidden_states = hidden_states + self.feed_forward(hidden_states)
          hidden_states = self.final_layer_norm(hidden_states)
  
          outputs = (hidden_states,)
  
          if output_attentions:
              outputs += (attn_weights,)
  
          return outputs
  
  
  class Wav2Vec2EncoderLayerStableLayerNorm(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.attention = Wav2Vec2Attention(
              embed_dim=config.hidden_size,
              num_heads=config.num_attention_heads,
              dropout=config.attention_dropout,
              is_decoder=False,
          )
          self.dropout = nn.Dropout(config.hidden_dropout)
          self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.feed_forward = Wav2Vec2FeedForward(config)
          self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
  
      def forward(self, hidden_states, attention_mask=None, output_attentions=False):
          attn_residual = hidden_states
          hidden_states = self.layer_norm(hidden_states)
          hidden_states, attn_weights, _ = self.attention(
              hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
          )
          hidden_states = self.dropout(hidden_states)
          hidden_states = attn_residual + hidden_states
          hidden_states = hidden_states + self.feed_forward(self.final_layer_norm(hidden_states))
  
          outputs = (hidden_states,)
  
          if output_attentions:
              outputs += (attn_weights,)
  
          return outputs
  
  
  class Wav2Vec2Encoder(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.config = config
          self.pos_conv_embed = Wav2Vec2PositionalConvEmbedding(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([Wav2Vec2EncoderLayer(config) for _ in range(config.num_hidden_layers)])
  
      def forward(
          self,
          hidden_states,
          attention_mask=None,
          output_attentions=False,
          output_hidden_states=False,
          return_dict=True,
      ):
          all_hidden_states = () if output_hidden_states else None
          all_self_attentions = () if output_attentions else None
  
          if attention_mask is not None:
              # make sure padded tokens output 0
              hidden_states[~attention_mask] = 0.0
  
              # extend attention_mask
              attention_mask = (1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype)) * -10000.0
              attention_mask = attention_mask.expand(
                  attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]
              )
  
          position_embeddings = self.pos_conv_embed(hidden_states)
          hidden_states = hidden_states + position_embeddings
          hidden_states = self.layer_norm(hidden_states)
          hidden_states = self.dropout(hidden_states)
  
          deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()
  
          for layer in self.layers:
              if output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_states,)
  
              # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
              dropout_probability = np.random.uniform(0, 1)
  
              skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False
              if not skip_the_layer or deepspeed_zero3_is_enabled:
                  # under deepspeed zero3 all gpus must run in sync
                  if getattr(self.config, "gradient_checkpointing", False) and self.training:
                      # create gradient checkpointing function
                      def create_custom_forward(module):
                          def custom_forward(*inputs):
                              return module(*inputs, output_attentions)
  
                          return custom_forward
  
                      layer_outputs = torch.utils.checkpoint.checkpoint(
                          create_custom_forward(layer),
                          hidden_states,
                          attention_mask,
                      )
                  else:
                      layer_outputs = layer(
                          hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
                      )
                  hidden_states = layer_outputs[0]
  
              if skip_the_layer:
                  layer_outputs = (None, None)
  
              if output_attentions:
                  all_self_attentions = all_self_attentions + (layer_outputs[1],)
  
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          if not return_dict:
              return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
          return BaseModelOutput(
              last_hidden_state=hidden_states,
              hidden_states=all_hidden_states,
              attentions=all_self_attentions,
          )
  
  
  class Wav2Vec2EncoderStableLayerNorm(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.config = config
          self.pos_conv_embed = Wav2Vec2PositionalConvEmbedding(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(
              [Wav2Vec2EncoderLayerStableLayerNorm(config) for _ in range(config.num_hidden_layers)]
          )
  
      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 are not attended to
              hidden_states[~attention_mask] = 0
  
              # extend attention_mask
              attention_mask = (1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype)) * -10000.0
              attention_mask = attention_mask.expand(
                  attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]
              )
  
          position_embeddings = self.pos_conv_embed(hidden_states)
          hidden_states = hidden_states + position_embeddings
          hidden_states = self.dropout(hidden_states)
  
          deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()
  
          for layer in self.layers:
              if output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_states,)
  
              # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
              dropout_probability = np.random.uniform(0, 1)
  
              skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False
              if not skip_the_layer or deepspeed_zero3_is_enabled:
                  # under deepspeed zero3 all gpus must run in sync
                  # XXX: could optimize this like synced_gpus in generate_utils but not sure if it's worth the code complication
                  if getattr(self.config, "gradient_checkpointing", False) and self.training:
                      # create gradient checkpointing function
                      def create_custom_forward(module):
                          def custom_forward(*inputs):
                              return module(*inputs, output_attentions)
  
                          return custom_forward
  
                      layer_outputs = torch.utils.checkpoint.checkpoint(
                          create_custom_forward(layer),
                          hidden_states,
                          attention_mask,
                      )
                  else:
                      layer_outputs = layer(
                          hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
                      )
                  hidden_states = layer_outputs[0]
  
              if skip_the_layer:
                  layer_outputs = (None, None)
  
              if output_attentions:
                  all_self_attentions = all_self_attentions + (layer_outputs[1],)
  
          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,
          )
  
  
  class Wav2Vec2GumbelVectorQuantizer(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
  
          assert (
              config.codevector_dim % self.num_groups == 0
          ), f"`config.codevector_dim {config.codevector_dim} must be divisible 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 = 1
  
      def set_temperature(self, temperature: int):
          self.temperature = temperature
  
      @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)
              .sum(-2)
              .view(batch_size, sequence_length, -1)
          )
  
          return codevectors, perplexity
  
  
  class Wav2Vec2PreTrainedModel(PreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = Wav2Vec2Config
      base_model_prefix = "wav2vec2"
      _keys_to_ignore_on_load_missing = [r"position_ids"]
  
      def _init_weights(self, module):
          """Initialize the weights"""
          # gumbel softmax requires special init
          if isinstance(module, Wav2Vec2GumbelVectorQuantizer):
              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, nn.Linear):
              # Slightly different from the TF version which uses truncated_normal for initialization
              # cf https://github.com/pytorch/pytorch/pull/5617
              module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
          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.data)
  
          if isinstance(module, (nn.Linear, nn.Conv1d)) and module.bias is not None:
              module.bias.data.zero_()
  
      def _get_feat_extract_output_lengths(self, input_lengths: Union[torch.LongTensor, int]):
          """
          Computes the output length of the convolutional layers
          """
  
          def _conv_out_length(input_length, kernel_size, stride):
              # 1D convolutional layer output length formula taken
              # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
              return (input_length - kernel_size) // stride + 1
  
          for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
              input_lengths = _conv_out_length(input_lengths, kernel_size, stride)
  
          return input_lengths
  
      def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor):
          output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).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
  
  
  WAV_2_VEC_2_START_DOCSTRING = r"""
      Wav2Vec2 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 :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
      methods the library implements for all its model (such as downloading or saving etc.).
  
      This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class. Use
      it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
      behavior.
  
      Parameters:
          config (:class:`~transformers.Wav2Vec2Config`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
              weights.
  """
  
  
  WAV_2_VEC_2_INPUTS_DOCSTRING = r"""
      Args:
          input_values (:obj:`torch.FloatTensor` of shape :obj:`(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 :class:`~transformers.Wav2Vec2Processor` should
              be used for padding and conversion into a tensor of type `torch.FloatTensor`. See
              :meth:`transformers.Wav2Vec2Processor.__call__` for details.
          attention_mask (:obj:`torch.LongTensor` of shape :obj:`(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.html#attention-mask>`__
  
              .. warning::
                  :obj:`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-base
                  <https://huggingface.co/facebook/wav2vec2-base-960h>`__, :obj:`attention_mask` should **not** be passed
                  to avoid degraded performance when doing batched inference. For such models :obj:`input_values` should
                  simply be padded with 0 and passed without :obj:`attention_mask`. Be aware that these models also yield
                  slightly different results depending on whether :obj:`input_values` is padded or not.
  
          output_attentions (:obj:`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 (:obj:`bool`, `optional`):
              Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
              more detail.
          return_dict (:obj:`bool`, `optional`):
              Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
  """
  
  
  @add_start_docstrings(
      "The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top.",
      WAV_2_VEC_2_START_DOCSTRING,
  )
  class Wav2Vec2Model(Wav2Vec2PreTrainedModel):
      def __init__(self, config: Wav2Vec2Config):
          super().__init__(config)
          self.config = config
          self.feature_extractor = Wav2Vec2FeatureExtractor(config)
          self.feature_projection = Wav2Vec2FeatureProjection(config)
  
          self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
  
          if config.do_stable_layer_norm:
              self.encoder = Wav2Vec2EncoderStableLayerNorm(config)
          else:
              self.encoder = Wav2Vec2Encoder(config)
  
          self.init_weights()
  
      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,
                  device=hidden_states.device,
                  attention_mask=attention_mask,
                  min_masks=2,
              )
              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,
                  device=hidden_states.device,
                  attention_mask=attention_mask,
              )
              hidden_states[mask_feature_indices[:, None].expand(-1, sequence_length, -1)] = 0
  
          return hidden_states
  
      @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_values,
          attention_mask=None,
          mask_time_indices=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          """
  
          Returns:
  
          Example::
  
              >>> from transformers import Wav2Vec2Processor, Wav2Vec2Model
              >>> from datasets import load_dataset
              >>> import soundfile as sf
  
              >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
              >>> model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
  
              >>> def map_to_array(batch):
              >>>     speech, _ = sf.read(batch["file"])
              >>>     batch["speech"] = speech
              >>>     return batch
  
              >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
              >>> ds = ds.map(map_to_array)
  
              >>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
              >>> hidden_states = model(input_values).last_hidden_state
          """
          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)
          extract_features2 = extract_features
          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)
  
          hidden_states, extract_features = self.feature_projection(extract_features)
          hidden_states2 = hidden_states
          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 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,
              hidden_states2=hidden_states2,
              extract_features2=extract_features2,
          )
  
  
  @add_start_docstrings("""Wav2Vec2 Model with a quantizer and `VQ` head on top. """, WAV_2_VEC_2_START_DOCSTRING)
  class Wav2Vec2ForPreTraining(Wav2Vec2PreTrainedModel):
      def __init__(self, config: Wav2Vec2Config):
          super().__init__(config)
          self.wav2vec2 = Wav2Vec2Model(config)
          self.dropout_features = nn.Dropout(config.feat_quantizer_dropout)
  
          self.quantizer = Wav2Vec2GumbelVectorQuantizer(config)
          self.project_q = nn.Linear(config.codevector_dim, config.proj_codevector_dim)
          self.project_hid = nn.Linear(config.hidden_size, config.proj_codevector_dim)
  
          self.init_weights()
  
      def set_gumbel_temperature(self, temperature: int):
          """
          Set the Gumbel softmax temperature to a given value. Only necessary for training
          """
          return self.quantizer.set_temperature(temperature)
  
      def freeze_feature_extractor(self):
          """
          Calling this function will disable the gradient computation for the feature extractor so that its parameters
          will not be updated during training.
          """
          self.wav2vec2.feature_extractor._freeze_parameters()
  
      @staticmethod
      def _sample_negatives(
          features: torch.FloatTensor, num_negatives: int, attention_mask: Optional[torch.LongTensor] = None
      ):
          """
          Sample `num_negatives` vectors from feature vectors.
          """
          batch_size, sequence_length, hidden_size = features.shape
          if sequence_length <= 1:
              raise ValueError(
                  f"`features should have `sequence_length` > 1, but are of shape (batch_size, sequence_length, hidden_size) = ({batch_size, sequence_length, hidden_size})."
              )
  
          features = features.view(-1, hidden_size)  # BTC => (BxT)C
  
          with torch.no_grad():
              # get `num_negatives` random vector indices from the same utterance
              sampled_negative_indices = []
              for batch_idx in range(batch_size):
                  high = attention_mask[batch_idx].sum() - 1 if attention_mask is not None else sequence_length - 1
                  sampled_indices_slice = torch.randint(
                      0, high, size=(num_negatives * sequence_length,), device=features.device
                  )
                  sampled_negative_indices.append(sampled_indices_slice)
  
              sampled_negative_indices = torch.stack(sampled_negative_indices)
  
              # generate indices of the positive vectors themselves, repeat them `num_negatives` times
              feature_indices = (
                  torch.arange(sequence_length, device=features.device)[:, None]
                  .expand(sequence_length, num_negatives)
                  .flatten()
              )
  
              # avoid sampling the same positive vector, but keep the distribution uniform
              sampled_negative_indices[sampled_negative_indices >= feature_indices] += 1
  
          # correct for batch size
          for batch_idx in range(1, batch_size):
              sampled_negative_indices[batch_idx] += batch_idx * sequence_length
  
          # take negative vectors from sampled indices
          sampled_negatives = features[sampled_negative_indices.view(-1)]
          sampled_negatives = sampled_negatives.view(batch_size, sequence_length, num_negatives, hidden_size).permute(
              2, 0, 1, 3
          )
  
          return sampled_negatives
  
      @staticmethod
      def compute_contrastive_logits(
          target_features: torch.FloatTensor,
          negative_features: torch.FloatTensor,
          predicted_features: torch.FloatTensor,
          temperature: int = 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(WAV_2_VEC_2_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=Wav2Vec2ForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_values,
          attention_mask=None,
          mask_time_indices=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
          r"""
          mask_time_indices (:obj:`torch.BoolTensor` of shape :obj:`(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.
  
          Returns:
  
          Example::
  
              >>> import torch
              >>> from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForPreTraining
              >>> from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices
              >>> from datasets import load_dataset
              >>> import soundfile as sf
  
              >>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("patrickvonplaten/wav2vec2-base")
              >>> model = Wav2Vec2ForPreTraining.from_pretrained("patrickvonplaten/wav2vec2-base")
  
  
              >>> def map_to_array(batch):
              ...     speech, _ = sf.read(batch["file"])
              ...     batch["speech"] = speech
              ...     return batch
  
  
              >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
              >>> ds = ds.map(map_to_array)
  
              >>> input_values = feature_extractor(ds["speech"][0], 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)
              >>> mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=0.2, mask_length=2, device=model.device)
  
              >>> 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
              >>> assert cosine_sim[mask_time_indices].mean() > 0.5
  
              >>> # for contrastive loss training model should be put into train mode
              >>> model.train()
              >>> loss = model(input_values, mask_time_indices=mask_time_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(
              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])
          quantized_features, codevector_perplexity = self.quantizer(extract_features, mask_time_indices)
          quantized_features = self.project_q(quantized_features)
  
          loss = None
          if self.training:
              # 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
              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)
  
              negative_quantized_features = self._sample_negatives(
                  quantized_features, self.config.num_negatives, attention_mask=attention_mask
              )
  
              # 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))
              preds = 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(preds.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
  
              # 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 Wav2Vec2ForPreTrainingOutput(
              loss=loss,
              projected_states=transformer_features,
              projected_quantized_states=quantized_features,
              codevector_perplexity=codevector_perplexity,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  @add_start_docstrings("""Wav2Vec2 Model with a `language modeling` head on top. """, WAV_2_VEC_2_START_DOCSTRING)
  class Wav2Vec2ForMaskedLM(Wav2Vec2PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          warnings.warn(
              "The class `Wav2Vec2ForMaskedLM` is deprecated. Please use `Wav2Vec2ForCTC` instead.", FutureWarning
          )
  
          self.wav2vec2 = Wav2Vec2Model(config)
          self.dropout = nn.Dropout(config.final_dropout)
          self.lm_head = nn.Linear(config.hidden_size, config.vocab_size)
  
          self.init_weights()
  
      @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=Wav2Vec2BaseModelOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_values,
          attention_mask=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
              TODO(PVP): Fill out when adding training
  
          Returns:
  
          Example::
  
              >>> from transformers import Wav2Vec2Processor, Wav2Vec2Model
              >>> from datasets import load_dataset
              >>> import soundfile as sf
  
              >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
              >>> model = Wav2Vec2ForMaskedLM.from_pretrained("facebook/wav2vec2-base-960h")
  
              >>> def map_to_array(batch):
              >>>     speech, _ = sf.read(batch["file"])
              >>>     batch["speech"] = speech
              >>>     return batch
  
              >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
              >>> ds = ds.map(map_to_array)
  
              >>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
              >>> logits = model(input_values).logits
  
              >>> predicted_ids = torch.argmax(logits, dim=-1)
              >>> transcription = processor.decode(predicted_ids[0])
          """
  
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.wav2vec2(
              input_values,
              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)
  
          if not return_dict:
              output = (logits,) + outputs[2:]
              return output
  
          return MaskedLMOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
  
  
  @add_start_docstrings(
      """Wav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC). """,
      WAV_2_VEC_2_START_DOCSTRING,
  )
  class Wav2Vec2ForCTC(Wav2Vec2PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.wav2vec2 = Wav2Vec2Model(config)
          self.dropout = nn.Dropout(config.final_dropout)
          self.lm_head = nn.Linear(config.hidden_size, config.vocab_size)
  
          self.init_weights()
  
      def freeze_feature_extractor(self):
          """
          Calling this function will disable the gradient computation for the feature extractor so that its parameter
          will not be updated during training.
          """
          self.wav2vec2.feature_extractor._freeze_parameters()
  
      @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=CausalLMOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_values,
          attention_mask=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
      ):
          r"""
          labels (:obj:`torch.LongTensor` of shape :obj:`(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]``.
  
          Returns:
  
          Example::
  
              >>> import torch
              >>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
              >>> from datasets import load_dataset
              >>> import soundfile as sf
  
              >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
              >>> model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
  
              >>> def map_to_array(batch):
              >>>     speech, _ = sf.read(batch["file"])
              >>>     batch["speech"] = speech
              >>>     return batch
  
              >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
              >>> ds = ds.map(map_to_array)
  
              >>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
              >>> logits = model(input_values).logits
              >>> predicted_ids = torch.argmax(logits, dim=-1)
  
              >>> transcription = processor.decode(predicted_ids[0])
  
              >>> # compute loss
              >>> target_transcription = "A MAN SAID TO THE UNIVERSE SIR I EXIST"
  
              >>> # wrap processor as target processor to encode labels
              >>> with processor.as_target_processor():
              >>>     labels = processor(target_transcription, return_tensors="pt").input_ids
  
              >>> loss = model(input_values, labels=labels).loss
          """
  
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.wav2vec2(
              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[2:]
              return ((loss,) + output) if loss is not None else output
  
          return CausalLMOutput(
              loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions,
              extract_features2 = outputs.extract_features2,
              hidden_states2 = outputs.hidden_states2,
  
          )
  
  # In[]
  
  class Raadar2VecLayerNorm2DConvLayer(nn.Module):
      def __init__(self, config: Wav2Vec2Config, layer_id=0):
          super().__init__()
          self.in_conv_ch = config.r_conv2_ch[layer_id] if layer_id > 0 else 3
          self.out_conv_ch = config.r_conv2_ch[layer_id]
  
          self.conv = nn.Conv2d(
              self.in_conv_ch,
              self.out_conv_ch,
              kernel_size=config.r_conv2_kernel[layer_id],
              stride=config.r_conv2_stride[layer_id],
              padding=config.r_conv2_padding[layer_id],
              bias=config.r_conv2_bias,
          )
          self.activation = ACT2FN[config.feat_extract_activation]
  
          self.layer_norm = nn.GroupNorm(1, self.out_conv_ch, 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
  
  class Radar2VecNoLayerNorm2DConvLayer(nn.Module):
      def __init__(self, config, layer_id=0):
          super().__init__()
          self.in_conv_dim = config.r_conv2_ch[layer_id-1] 
          self.out_conv_dim = config.r_conv2_ch[layer_id]
  
          self.conv = nn.Conv2d(
              self.in_conv_dim,
              self.out_conv_dim,
              kernel_size=config.r_conv2_kernel[layer_id],
              stride=config.r_conv2_stride[layer_id],
              padding=config.r_conv2_padding[layer_id],
              bias=config.conv_bias,
          )
          
          self.activation = ACT2FN[config.feat_extract_activation]
          self.pool = nn.MaxPool2d(kernel_size=(config.r_conv2_pool[layer_id],1), stride=(2,1),padding=(0,0))
  
      def forward(self, hidden_states):        
          hidden_states = self.conv(hidden_states)
          hidden_states = self.activation(hidden_states)
          hidden_states = self.pool(hidden_states)
          return hidden_states
  
  class Radar2VecNoLayerNormPool2DConvLayer(nn.Module):
      def __init__(self, config, layer_id=0):
          super().__init__()
          self.in_conv_dim = config.r_conv2_ch[layer_id-1] 
          self.out_conv_dim = config.r_conv2_ch[layer_id]
  
          self.conv = nn.Conv2d(
              self.in_conv_dim,
              self.out_conv_dim,
              kernel_size=config.r_conv2_kernel[layer_id],
              stride=config.r_conv2_stride[layer_id],
              padding=config.r_conv2_padding[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        
  
  class Radar2VecNoLayerNorm1DConvLayer(nn.Module):
      def __init__(self, config, layer_id=0):
          super().__init__()
          self.in_conv_dim = config.r_conv1_dim[layer_id] 
          self.out_conv_dim = config.conv_dim[layer_id]
  
          self.conv = nn.Conv1d(
              self.in_conv_dim,
              self.out_conv_dim,
              kernel_size=config.r_conv1_kernel[layer_id],
              stride=config.r_conv1_stride[layer_id],
              padding=config.r_conv1_padding[layer_id],
              bias=config.conv_bias,
          )
          self.activation = ACT2FN[config.feat_extract_activation]
  
      def forward(self, hidden_states):
          if hidden_states.size(2) == 1:
              hidden_states = hidden_states.squeeze(2)
          
          hidden_states = self.conv(hidden_states)
          hidden_states = self.activation(hidden_states)
          return hidden_states
  
  class Radar2VecFeatureExtractor(nn.Module):
      """Construct the featurs from raw audio waveform"""
  
      def __init__(self, config: Wav2Vec2Config):
          super().__init__()
  
          conv_layers = [Raadar2VecLayerNorm2DConvLayer(config, layer_id=0)] + [
              Radar2VecNoLayerNorm2DConvLayer(config, layer_id=i + 1) for i in range(config.num_2d_layers - 2)
          ] + [Radar2VecNoLayerNormPool2DConvLayer(config, layer_id = config.num_2d_layers-1)
          ] + [Radar2VecNoLayerNorm1DConvLayer(config, layer_id=i) for i in range(config.num_1d_layers)]
          
          self.conv_layers = nn.ModuleList(conv_layers)
  
      def _freeze_parameters(self):
          for param in self.parameters():
              param.requires_grad = False
  
      def forward(self, input_values):
          hidden_states = input_values
          for conv_layer in self.conv_layers:
              hidden_states = conv_layer(hidden_states)
          return hidden_states
  
  @add_start_docstrings(
      "The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top.",
      WAV_2_VEC_2_START_DOCSTRING,
  )
  class Radar2VecModel(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.config = config
          self.feature_extractor = Radar2VecFeatureExtractor(config)
          # self.layer_nom = self.layer_norm = nn.LayerNorm(config.r_conv1_dim[-1], eps=config.layer_norm_eps)
          # self.feature_projection = Wav2Vec2FeatureProjection(config)
          
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(m.weight)
              elif isinstance(m, (nn.LayerNorm, nn.GroupNorm)):
                  # nn.init.constant_(m.weight, 1)
                  # nn.init.constant_(m.bias, 0)
                  m.bias.data.zero_()
                  m.weight.data.fill_(1.0)
          
  
      @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
      # @replace_return_docstrings(output_type=None, config_class=_CONFIG_FOR_DOC)
      # @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_values,
          
      ):
          extract_features = self.feature_extractor(input_values)
          extract_features = extract_features.transpose(1, 2)
          hidden_states = 0
          # extract_features = self.layer_nom(extract_features)
          # hidden_states, extract_features = self.feature_projection(extract_features)
  
          return Wav2Vec2BaseModelOutput(
              last_hidden_state=hidden_states,
              extract_features=extract_features,
              hidden_states=hidden_states,
          )
  
  
  
  @add_start_docstrings(
      "The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top.",
      WAV_2_VEC_2_START_DOCSTRING,
  )
  class Wav2Vec2ModelForRadar(Wav2Vec2PreTrainedModel):
      def __init__(self, config: Wav2Vec2Config):
          super().__init__(config)
          self.config = config
          self.feature_extractor = Radar2VecFeatureExtractor(config) #Wav2Vec2FeatureExtractor(config)
          self.feature_projection = Wav2Vec2FeatureProjection(config)
  
          self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
  
          if config.do_stable_layer_norm:
              self.encoder = Wav2Vec2EncoderStableLayerNorm(config)
          else:
              self.encoder = Wav2Vec2Encoder(config)
  
          self.init_weights()
  
      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,
                  device=hidden_states.device,
                  attention_mask=attention_mask,
                  min_masks=2,
              )
              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,
                  device=hidden_states.device,
                  attention_mask=attention_mask,
              )
              hidden_states[mask_feature_indices[:, None].expand(-1, sequence_length, -1)] = 0
  
          return hidden_states
  
      @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
      # @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_values,
          attention_mask=None,
          mask_time_indices=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
      ):
         
          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)
  
          # extract_features = input_values
          # hidden_states = input_values
          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)
  
          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 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(
      """Wav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC). """,
      WAV_2_VEC_2_START_DOCSTRING,
  )
  class Radar2Vec(Wav2Vec2PreTrainedModel):
      def __init__(self, config):
          super().__init__(config)
  
          self.wav2vec2 = Wav2Vec2ModelForRadar(config)
          self.dropout = nn.Dropout(config.final_dropout)
          self.lm_head = nn.Linear(config.hidden_size, config.vocab_size)
  
          self.init_weights()
  
      def freeze_feature_extractor(self):
          """
          Calling this function will disable the gradient computation for the feature extractor so that its parameter
          will not be updated during training.
          """
          self.wav2vec2.feature_extractor._freeze_parameters()
  
      @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
      # @replace_return_docstrings(output_type=CausalLMOutput, config_class=_CONFIG_FOR_DOC)
      def forward(
          self,
          input_values,
          attention_mask=None,
          output_attentions=None,
          output_hidden_states=None,
          return_dict=None,
          labels=None,
      ):
  
          return_dict = return_dict if return_dict is not None else self.config.use_return_dict
  
          outputs = self.wav2vec2(
              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[2:]
              return ((loss,) + output) if loss is not None else output
  
          return CausalLMOutput(
              loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
          )
  
  