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.
  """ TensorFlow Wav2Vec2 model."""
  
  import inspect
  import warnings
  from collections.abc import Mapping
  from dataclasses import dataclass
  from typing import Any, Dict, Optional, Tuple, Union
  
  import numpy as np
  import tensorflow as tf
  
  from ...activations_tf import get_tf_activation
  from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput
  from ...modeling_tf_utils import TFPreTrainedModel, booleans_processing, get_initializer, keras_serializable
  from ...tf_utils import shape_list, stable_softmax
  from ...utils import (
      ModelOutput,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      logging,
      replace_return_docstrings,
  )
  from .configuration_wav2vec2 import Wav2Vec2Config
  
  
  logger = logging.get_logger(__name__)
  
  
  _HIDDEN_STATES_START_POSITION = 2
  
  _CHECKPOINT_FOR_DOC = "facebook/wav2vec2-base-960h"
  _CONFIG_FOR_DOC = "Wav2Vec2Config"
  _TOKENIZER_FOR_DOC = "Wav2Vec2Tokenizer"
  
  TF_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
  ]
  
  LARGE_NEGATIVE = -1e8
  
  
  @dataclass
  class TFWav2Vec2BaseModelOutput(ModelOutput):
      """
      Output type of [`TFWav2Vec2BaseModelOutput`], with potential hidden states and attentions.
  
      Args:
          last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
              Sequence of hidden-states at the output of the last layer of the model.
          extract_features (`tf.Tensor` of shape `(batch_size, sequence_length, conv_dim[-1])`):
              Sequence of extracted feature vectors of the last convolutional layer of the model.
          hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
              Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
              Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
              sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      last_hidden_state: tf.Tensor = None
      extract_features: tf.Tensor = None
      hidden_states: Optional[Tuple[tf.Tensor]] = None
      attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  def input_values_processing(func, config, input_values, **kwargs):
      """
      Process the input of each TensorFlow model including the booleans. In case of a list of symbolic inputs, each input
      has to be named accordingly to the parameters name, i.e. `input_values = tf.keras.Input(shape=(128,),
      dtype='float32', name="input_values")` otherwise the order of the tensors will not be guaranteed during the
      training.
  
      Args:
          func (`callable`):
              The callable function of the TensorFlow model.
          config ([`PretrainedConfig`]):
              The config of the running model.
          **kwargs:
              The inputs of the model.
  
      Returns:
          Two lists, one for the missing layers, and another one for the unexpected layers.
      """
      signature = dict(inspect.signature(func).parameters)
      signature.pop("kwargs", None)
      signature.pop("self", None)
      parameter_names = list(signature.keys())
      output = {}
      allowed_types = (tf.Tensor, bool, int, ModelOutput, tuple, list, dict, np.ndarray)
  
      for k, v in kwargs.items():
          if isinstance(v, allowed_types) or v is None:
              output[k] = v
          else:
              raise ValueError(f"Data of type {type(v)} is not allowed only {allowed_types} is accepted for {k}.")
  
      if isinstance(input_values, (tuple, list)):
          for i, input in enumerate(input_values):
              # EagerTensors don't allow to use the .name property so we check for a real Tensor
              if type(input) == tf.Tensor:
                  # Tensor names have always the pattern `name:id` then we check only the
                  # `name` part
                  tensor_name = input.name.split(":")[0]
  
                  if tensor_name in parameter_names:
                      output[tensor_name] = input
                  else:
                      output[parameter_names[i]] = input
              elif isinstance(input, allowed_types) or input is None:
                  output[parameter_names[i]] = input
              else:
                  raise ValueError(
                      f"Data of type {type(input)} is not allowed only {allowed_types} is accepted for"
                      f" {parameter_names[i]}."
                  )
      elif isinstance(input_values, Mapping):
          if "inputs" in input_values:
              warnings.warn(
                  "The `inputs` argument is deprecated and will be removed in a future version, use `input_values`"
                  " instead.",
                  FutureWarning,
              )
  
              output["input_values"] = input_values.pop("inputs")
  
          if "decoder_cached_states" in input_values:
              warnings.warn(
                  "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use"
                  " `past_key_values` instead.",
                  FutureWarning,
              )
              output["past_key_values"] = input_values.pop("decoder_cached_states")
  
          for k, v in dict(input_values).items():
              if isinstance(v, allowed_types) or v is None:
                  output[k] = v
              elif k not in parameter_names and "args" not in parameter_names:
                  logger.warning(
                      f"The parameter {k} does not belongs to the parameter list {parameter_names} and will be ignored."
                  )
                  continue
              else:
                  raise ValueError(f"Data of type {type(v)} is not allowed only {allowed_types} is accepted for {k}.")
      else:
          if isinstance(input_values, tf.Tensor) or input_values is None:
              output[parameter_names[0]] = input_values
          else:
              raise ValueError(
                  f"Data of type {type(input_values)} is not allowed only {allowed_types} is accepted for"
                  f" {parameter_names[0]}."
              )
  
      for name in parameter_names:
          if name not in list(output.keys()) and name != "args":
              output[name] = kwargs.pop(name, signature[name].default)
  
      # When creating a SavedModel TF calls the method with LayerCall.__call__(args, **kwargs)
      # So to respect the proper output we have to add this exception
      if "args" in output:
          if output["args"] is not None and type(output["args"]) == tf.Tensor:
              tensor_name = output["args"].name.split(":")[0]
              output[tensor_name] = output["args"]
          else:
              # `args` in this case is always the first parameter, then `input_values`
              output["input_values"] = output["args"]
  
          del output["args"]
  
      if "kwargs" in output:
          del output["kwargs"]
  
      boolean_dict = {
          k: v
          for k, v in output.items()
          if k in ["return_dict", "output_attentions", "output_hidden_states", "use_cache"]
      }
  
      output.update(booleans_processing(config=config, **boolean_dict))
  
      return output
  
  
  def _sample_without_replacement(distribution, num_samples):
      """
      Categorical sampling without replacement is currently not implemented. The gumbel-max trick will do for now - see
      https://github.com/tensorflow/tensorflow/issues/9260 for more info
      """
      z = -tf.math.log(tf.random.uniform(shape_list(distribution), 0, 1))
      _, indices = tf.nn.top_k(distribution + z, num_samples)
      return indices
  
  
  def _scatter_values_on_batch_indices(values, batch_indices, output_shape):
      """
      Scatter function as in PyTorch with indices in format (batch_dim, indixes)
      """
      indices_shape = shape_list(batch_indices)
      # broadcast batch dim to indices_shape
      broad_casted_batch_dims = tf.reshape(
          tf.broadcast_to(tf.expand_dims(tf.range(indices_shape[0]), axis=-1), indices_shape), [1, -1]
      )
      # transform batch_indices to pair_indices
      pair_indices = tf.transpose(tf.concat([broad_casted_batch_dims, tf.reshape(batch_indices, [1, -1])], 0))
      # scatter values to pair indices
      return tf.scatter_nd(pair_indices, tf.reshape(values, [-1]), output_shape)
  
  
  def _compute_mask_indices(
      shape: Tuple[int, int],
      mask_prob: float,
      mask_length: int,
      min_masks: int = 0,
  ) -> tf.Tensor:
      """
      Computes random mask spans for a given shape
  
      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
          attention_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
          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
  
      Adapted from [fairseq's
      data_utils.py](https://github.com/pytorch/fairseq/blob/e0788f7007a8473a76db573985031f3c94201e79/fairseq/data/data_utils.py#L376).
      """
      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"
              f" `sequence_length`: {sequence_length}`"
          )
      # compute number of masked spans in batch
      num_masked_spans = int(mask_prob * sequence_length / mask_length + tf.random.uniform((1,)))
      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 = tf.zeros((batch_size, sequence_length), dtype=tf.int32)
  
      # uniform distribution to sample from, make sure that offset samples are < sequence_length
      uniform_dist = tf.ones((batch_size, sequence_length - (mask_length - 1)))
  
      # get random indices to mask
      spec_aug_mask_idxs = _sample_without_replacement(uniform_dist, num_masked_spans)
  
      # expand masked indices to masked spans
      spec_aug_mask_idxs = tf.expand_dims(spec_aug_mask_idxs, -1)
      spec_aug_mask_idxs = tf.tile(spec_aug_mask_idxs, (1, 1, mask_length))
      spec_aug_mask_idxs = tf.reshape(spec_aug_mask_idxs, (batch_size, num_masked_spans * mask_length))
  
      offsets = tf.range(mask_length)[tf.newaxis, tf.newaxis, :]
      offsets = tf.tile(offsets, (batch_size, num_masked_spans, 1))
      offsets = tf.reshape(offsets, (batch_size, num_masked_spans * mask_length))
  
      spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
  
      # scatter indices to mask
      spec_aug_mask = _scatter_values_on_batch_indices(
          tf.ones_like(spec_aug_mask_idxs), spec_aug_mask_idxs, spec_aug_mask.shape
      )
  
      return spec_aug_mask
  
  
  def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None, past_key_values_length: int = 0):
      """
      Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
      """
      src_len = shape_list(mask)[1]
      tgt_len = tgt_len if tgt_len is not None else src_len
      one_cst = tf.constant(1.0)
      mask = tf.cast(mask, dtype=one_cst.dtype)
      expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
  
      return (one_cst - expanded_mask) * LARGE_NEGATIVE
  
  
  class TFWav2Vec2GroupNorm(tf.keras.layers.Layer):
      """
      From tensorflow-addons https://www.tensorflow.org/addons/api_docs/python/tfa/layers/GroupNormalization
      """
  
      def __init__(
          self,
          groups: int = 32,
          axis: int = -1,
          epsilon: float = 1e-3,
          center: bool = True,
          scale: bool = True,
          beta_initializer: tf.keras.initializers.Initializer = "zeros",
          gamma_initializer: tf.keras.initializers.Initializer = "ones",
          beta_regularizer: tf.keras.regularizers.Regularizer = None,
          gamma_regularizer: tf.keras.regularizers.Regularizer = None,
          beta_constraint: tf.keras.constraints.Constraint = None,
          gamma_constraint: tf.keras.constraints.Constraint = None,
          **kwargs,
      ):
          super().__init__(**kwargs)
          self.supports_masking = True
          self.groups = groups
          self.axis = axis
          self.epsilon = epsilon
          self.center = center
          self.scale = scale
          self.beta_initializer = tf.keras.initializers.get(beta_initializer)
          self.gamma_initializer = tf.keras.initializers.get(gamma_initializer)
          self.beta_regularizer = tf.keras.regularizers.get(beta_regularizer)
          self.gamma_regularizer = tf.keras.regularizers.get(gamma_regularizer)
          self.beta_constraint = tf.keras.constraints.get(beta_constraint)
          self.gamma_constraint = tf.keras.constraints.get(gamma_constraint)
          self._check_axis()
  
      def build(self, input_shape):
  
          self._check_if_input_shape_is_none(input_shape)
          self._set_number_of_groups_for_instance_norm(input_shape)
          self._check_size_of_dimensions(input_shape)
          self._create_input_spec(input_shape)
  
          self._add_gamma_weight(input_shape)
          self._add_beta_weight(input_shape)
          self.built = True
          super().build(input_shape)
  
      def call(self, inputs):
  
          input_shape = tf.keras.backend.int_shape(inputs)
          tensor_input_shape = tf.shape(inputs)
  
          reshaped_inputs, group_shape = self._reshape_into_groups(inputs, input_shape, tensor_input_shape)
  
          normalized_inputs = self._apply_normalization(reshaped_inputs, input_shape)
  
          is_instance_norm = (input_shape[self.axis] // self.groups) == 1
          if not is_instance_norm:
              outputs = tf.reshape(normalized_inputs, tensor_input_shape)
          else:
              outputs = normalized_inputs
  
          return outputs
  
      def get_config(self):
          config = {
              "groups": self.groups,
              "axis": self.axis,
              "epsilon": self.epsilon,
              "center": self.center,
              "scale": self.scale,
              "beta_initializer": tf.keras.initializers.serialize(self.beta_initializer),
              "gamma_initializer": tf.keras.initializers.serialize(self.gamma_initializer),
              "beta_regularizer": tf.keras.regularizers.serialize(self.beta_regularizer),
              "gamma_regularizer": tf.keras.regularizers.serialize(self.gamma_regularizer),
              "beta_constraint": tf.keras.constraints.serialize(self.beta_constraint),
              "gamma_constraint": tf.keras.constraints.serialize(self.gamma_constraint),
          }
          base_config = super().get_config()
          return {**base_config, **config}
  
      def compute_output_shape(self, input_shape):
          return input_shape
  
      def _reshape_into_groups(self, inputs, input_shape, tensor_input_shape):
  
          group_shape = [tensor_input_shape[i] for i in range(len(input_shape))]
          is_instance_norm = (input_shape[self.axis] // self.groups) == 1
          if not is_instance_norm:
              group_shape[self.axis] = input_shape[self.axis] // self.groups
              group_shape.insert(self.axis, self.groups)
              group_shape = tf.stack(group_shape)
              reshaped_inputs = tf.reshape(inputs, group_shape)
              return reshaped_inputs, group_shape
          else:
              return inputs, group_shape
  
      def _apply_normalization(self, reshaped_inputs, input_shape):
  
          group_shape = tf.keras.backend.int_shape(reshaped_inputs)
          group_reduction_axes = list(range(1, len(group_shape)))
          is_instance_norm = (input_shape[self.axis] // self.groups) == 1
          if not is_instance_norm:
              axis = -2 if self.axis == -1 else self.axis - 1
          else:
              axis = -1 if self.axis == -1 else self.axis - 1
          group_reduction_axes.pop(axis)
  
          mean, variance = tf.nn.moments(reshaped_inputs, group_reduction_axes, keepdims=True)
  
          gamma, beta = self._get_reshaped_weights(input_shape)
          normalized_inputs = tf.nn.batch_normalization(
              reshaped_inputs,
              mean=mean,
              variance=variance,
              scale=gamma,
              offset=beta,
              variance_epsilon=self.epsilon,
          )
          return normalized_inputs
  
      def _get_reshaped_weights(self, input_shape):
          broadcast_shape = self._create_broadcast_shape(input_shape)
          gamma = None
          beta = None
          if self.scale:
              gamma = tf.reshape(self.gamma, broadcast_shape)
  
          if self.center:
              beta = tf.reshape(self.beta, broadcast_shape)
          return gamma, beta
  
      def _check_if_input_shape_is_none(self, input_shape):
          dim = input_shape[self.axis]
          if dim is None:
              raise ValueError(
                  "Axis "
                  + str(self.axis)
                  + " of input tensor should have a defined dimension but the layer received an input with shape "
                  + str(input_shape)
                  + "."
              )
  
      def _set_number_of_groups_for_instance_norm(self, input_shape):
          dim = input_shape[self.axis]
  
          if self.groups == -1:
              self.groups = dim
  
      def _check_size_of_dimensions(self, input_shape):
  
          dim = input_shape[self.axis]
          if dim < self.groups:
              raise ValueError(
                  "Number of groups ("
                  + str(self.groups)
                  + ") cannot be more than the number of channels ("
                  + str(dim)
                  + ")."
              )
  
          if dim % self.groups != 0:
              raise ValueError(
                  "Number of groups ("
                  + str(self.groups)
                  + ") must be a multiple of the number of channels ("
                  + str(dim)
                  + ")."
              )
  
      def _check_axis(self):
  
          if self.axis == 0:
              raise ValueError(
                  "You are trying to normalize your batch axis. Do you want to use tf.layer.batch_normalization instead"
              )
  
      def _create_input_spec(self, input_shape):
  
          dim = input_shape[self.axis]
          self.input_spec = tf.keras.layers.InputSpec(ndim=len(input_shape), axes={self.axis: dim})
  
      def _add_gamma_weight(self, input_shape):
  
          dim = input_shape[self.axis]
          shape = (dim,)
  
          if self.scale:
              self.gamma = self.add_weight(
                  shape=shape,
                  name="gamma",
                  initializer=self.gamma_initializer,
                  regularizer=self.gamma_regularizer,
                  constraint=self.gamma_constraint,
              )
          else:
              self.gamma = None
  
      def _add_beta_weight(self, input_shape):
  
          dim = input_shape[self.axis]
          shape = (dim,)
  
          if self.center:
              self.beta = self.add_weight(
                  shape=shape,
                  name="beta",
                  initializer=self.beta_initializer,
                  regularizer=self.beta_regularizer,
                  constraint=self.beta_constraint,
              )
          else:
              self.beta = None
  
      def _create_broadcast_shape(self, input_shape):
          broadcast_shape = [1] * len(input_shape)
          is_instance_norm = (input_shape[self.axis] // self.groups) == 1
          if not is_instance_norm:
              broadcast_shape[self.axis] = input_shape[self.axis] // self.groups
              broadcast_shape.insert(self.axis, self.groups)
          else:
              broadcast_shape[self.axis] = self.groups
          return broadcast_shape
  
  
  class TFWav2Vec2WeightNormConv1D(tf.keras.layers.Conv1D):
      """Adapted from https://www.tensorflow.org/probability/api_docs/python/tfp/layers/weight_norm/WeightNorm"""
  
      def __init__(self, filters, kernel_size, groups, explicit_padding, **kwargs):
          super().__init__(
              filters=filters,
              kernel_size=kernel_size,
              groups=groups,
              padding="valid",
              use_bias=True,
              bias_initializer="he_normal",
              **kwargs,
          )
          self.explicit_padding = explicit_padding
          self.filter_axis = 2
          self.initialized = False
          self.kernel_norm_axes = tf.constant([0, 1])
  
      def _init_norm(self):
          """Set the norm of the weight vector."""
          kernel_norm = tf.sqrt(tf.reduce_sum(tf.square(self.weight_v), axis=self.kernel_norm_axes))
          self.weight_g.assign(kernel_norm[:, tf.newaxis, tf.newaxis])
  
      def _normalize_kernel(self):
          """Generate normalized weights."""
          kernel = tf.nn.l2_normalize(self.weight_v, axis=self.kernel_norm_axes) * tf.transpose(self.weight_g)
          self.kernel = tf.transpose(kernel)
  
      def build(self, input_shape):
          if not self.built:
              input_shape = input_shape.as_list()
              # Conv1D output shapes are checked at build time since TF 2.7, so we need to account for padding
              input_shape[-2] += self.explicit_padding * 2
              super().build(input_shape)
  
              self.kernel = tf.Variable(tf.transpose(self.kernel), name="weight_v", trainable=True)
              self.weight_v = self.kernel
  
              self.weight_g = self.add_weight(
                  name="weight_g",
                  shape=(int(self.weight_v.shape[self.filter_axis]), 1, 1),
                  initializer="ones",
                  dtype=self.weight_v.dtype,
                  trainable=True,
              )
              self.bias = self.add_weight(name="bias", shape=(self.filters,), initializer="zeros", trainable=True)
  
      def call(self, inputs):
          if not self.initialized:
              self._init_norm()
              self.initialized = True
  
          self._normalize_kernel()
  
          padded_inputs = tf.pad(inputs, ((0, 0), (self.explicit_padding, self.explicit_padding), (0, 0)))
          output = super().call(padded_inputs)
  
          return output
  
  
  class TFWav2Vec2NoLayerNormConvLayer(tf.keras.layers.Layer):
      def __init__(self, config: Wav2Vec2Config, layer_id: int = 0, **kwargs: Any) -> None:
          super().__init__(**kwargs)
          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 = tf.keras.layers.Conv1D(
              filters=self.out_conv_dim,
              kernel_size=config.conv_kernel[layer_id],
              strides=config.conv_stride[layer_id],
              use_bias=config.conv_bias,
              name="conv",
          )
          self.activation = get_tf_activation(config.feat_extract_activation)
  
      def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
          hidden_states = self.conv(hidden_states)
          hidden_states = self.activation(hidden_states)
          return hidden_states
  
  
  class TFWav2Vec2LayerNormConvLayer(tf.keras.layers.Layer):
      def __init__(self, config: Wav2Vec2Config, layer_id: int = 0, **kwargs: Any) -> None:
          super().__init__(**kwargs)
          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 = tf.keras.layers.Conv1D(
              filters=self.out_conv_dim,
              kernel_size=config.conv_kernel[layer_id],
              strides=config.conv_stride[layer_id],
              use_bias=config.conv_bias,
              name="conv",
          )
          self.layer_norm = tf.keras.layers.LayerNormalization(name="layer_norm", epsilon=config.layer_norm_eps)
          self.activation = get_tf_activation(config.feat_extract_activation)
  
      def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
          hidden_states = self.conv(hidden_states)
          hidden_states = self.layer_norm(hidden_states)
          hidden_states = self.activation(hidden_states)
          return hidden_states
  
  
  class TFWav2Vec2GroupNormConvLayer(tf.keras.layers.Layer):
      def __init__(self, config: Wav2Vec2Config, layer_id: int = 0, **kwargs: Any) -> None:
          super().__init__(**kwargs)
          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 = tf.keras.layers.Conv1D(
              filters=self.out_conv_dim,
              kernel_size=config.conv_kernel[layer_id],
              strides=config.conv_stride[layer_id],
              use_bias=config.conv_bias,
              name="conv",
          )
          self.activation = get_tf_activation(config.feat_extract_activation)
          self.layer_norm = TFWav2Vec2GroupNorm(
              groups=self.out_conv_dim, epsilon=config.layer_norm_eps, name="layer_norm"
          )
  
      def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
          hidden_states = self.conv(hidden_states)
          hidden_states = self.layer_norm(hidden_states)
          hidden_states = self.activation(hidden_states)
          return hidden_states
  
  
  class TFWav2Vec2PositionalConvEmbedding(tf.keras.layers.Layer):
      def __init__(self, config: Wav2Vec2Config, **kwargs: Any) -> None:
          super().__init__(**kwargs)
          self.conv = TFWav2Vec2WeightNormConv1D(
              filters=config.hidden_size,
              kernel_size=config.num_conv_pos_embeddings,
              groups=config.num_conv_pos_embedding_groups,
              explicit_padding=config.num_conv_pos_embeddings // 2,
              name="conv",
          )
          self.padding = TFWav2Vec2SamePadLayer(config.num_conv_pos_embeddings)
          self.activation = get_tf_activation(config.feat_extract_activation)
  
      def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
          hidden_states = self.conv(hidden_states)
          hidden_states = self.padding(hidden_states)
          hidden_states = self.activation(hidden_states)
          return hidden_states
  
  
  class TFWav2Vec2SamePadLayer(tf.keras.layers.Layer):
      def __init__(self, num_conv_pos_embeddings, **kwargs):
          super().__init__(**kwargs)
          self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
  
      def call(self, hidden_states):
          if self.num_pad_remove > 0:
              hidden_states = hidden_states[:, : -self.num_pad_remove, :]
          return hidden_states
  
  
  class TFWav2Vec2FeatureEncoder(tf.keras.layers.Layer):
      def __init__(self, config: Wav2Vec2Config, **kwargs: Any) -> None:
          super().__init__(**kwargs)
  
          if config.feat_extract_norm == "group":
              conv_layers = [TFWav2Vec2GroupNormConvLayer(config, layer_id=0, name=f"conv_layers.{0}")] + [
                  TFWav2Vec2NoLayerNormConvLayer(config, layer_id=i + 1, name=f"conv_layers.{i+1}")
                  for i in range(config.num_feat_extract_layers - 1)
              ]
          elif config.feat_extract_norm == "layer":
              conv_layers = [
                  TFWav2Vec2LayerNormConvLayer(config, layer_id=i, name=f"conv_layers.{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 = conv_layers
  
      def call(self, input_values):
          hidden_states = tf.expand_dims(input_values, -1)
          for conv_layer in self.conv_layers:
              hidden_states = conv_layer(hidden_states)
          return hidden_states
  
  
  class TFWav2Vec2FeatureExtractor(TFWav2Vec2FeatureEncoder):
      def __init__(self, config, **kwargs):
          super().__init__(config, **kwargs)
          warnings.warn(
              f"The class `{self.__class__.__name__}` has been depreciated "
              "and will be removed in Transformers v5. "
              f"Use `{self.__class__.__bases__[0].__name__}` instead.",
              FutureWarning,
          )
  
  
  class TFWav2Vec2FeatureProjection(tf.keras.layers.Layer):
      def __init__(self, config: Wav2Vec2Config, **kwargs):
          super().__init__(**kwargs)
  
          self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
          self.projection = tf.keras.layers.Dense(
              units=config.hidden_size,
              kernel_initializer=get_initializer(config.initializer_range),
              bias_initializer="zeros",
              name="projection",
          )
          self.dropout = tf.keras.layers.Dropout(rate=config.feat_proj_dropout)
  
      def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
          norm_hidden_states = self.layer_norm(hidden_states)
          hidden_states = self.projection(norm_hidden_states)
          hidden_states = self.dropout(hidden_states, training=training)
          return hidden_states, norm_hidden_states
  
  
  # Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with TFBart->TFWav2Vec2
  class TFWav2Vec2Attention(tf.keras.layers.Layer):
      """Multi-headed attention from "Attention Is All You Need"""
  
      def __init__(
          self,
          embed_dim: int,
          num_heads: int,
          dropout: float = 0.0,
          is_decoder: bool = False,
          bias: bool = True,
          **kwargs,
      ):
          super().__init__(**kwargs)
          self.embed_dim = embed_dim
  
          self.num_heads = num_heads
          self.dropout = tf.keras.layers.Dropout(dropout)
          self.head_dim = embed_dim // num_heads
          if (self.head_dim * num_heads) != self.embed_dim:
              raise ValueError(
                  f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
                  f" and `num_heads`: {num_heads})."
              )
          self.scaling = self.head_dim**-0.5
          self.is_decoder = is_decoder
  
          self.k_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
          self.q_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
          self.v_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
          self.out_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
  
      def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
          return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))
  
      def call(
          self,
          hidden_states: tf.Tensor,
          key_value_states: Optional[tf.Tensor] = None,
          past_key_value: Optional[Tuple[Tuple[tf.Tensor]]] = None,
          attention_mask: Optional[tf.Tensor] = None,
          layer_head_mask: Optional[tf.Tensor] = None,
          training: Optional[bool] = False,
      ) -> Tuple[tf.Tensor, Optional[tf.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 = shape_list(hidden_states)
  
          # 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 = tf.concat([past_key_value[0], key_states], axis=2)
              value_states = tf.concat([past_key_value[1], value_states], axis=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(tf.Tensor, tf.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(tf.Tensor, tf.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 = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
          key_states = tf.reshape(key_states, proj_shape)
          value_states = tf.reshape(value_states, proj_shape)
  
          src_len = shape_list(key_states)[1]
          attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
  
          # The tf.debugging asserts are not compliant with XLA then they
          # have to be disabled in other modes than eager.
          if tf.executing_eagerly():
              tf.debugging.assert_equal(
                  shape_list(attn_weights),
                  [bsz * self.num_heads, tgt_len, src_len],
                  message=(
                      f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
                      f" {shape_list(attn_weights)}"
                  ),
              )
  
          if attention_mask is not None:
              # The tf.debugging asserts are not compliant with XLA then they
              # have to be disabled in other modes than eager.
              if tf.executing_eagerly():
                  tf.debugging.assert_equal(
                      shape_list(attention_mask),
                      [bsz, 1, tgt_len, src_len],
                      message=(
                          f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
                          f" {shape_list(attention_mask)}"
                      ),
                  )
  
              attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype)
              attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
              attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
  
          attn_weights = stable_softmax(attn_weights, axis=-1)
  
          if layer_head_mask is not None:
              # The tf.debugging asserts are not compliant with XLA then they
              # have to be disabled in other modes than eager.
              if tf.executing_eagerly():
                  tf.debugging.assert_equal(
                      shape_list(layer_head_mask),
                      [self.num_heads],
                      message=(
                          f"Head mask for a single layer should be of size {(self.num_heads)}, but is"
                          f" {shape_list(layer_head_mask)}"
                      ),
                  )
  
              attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(
                  attn_weights, (bsz, self.num_heads, tgt_len, src_len)
              )
              attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
  
          attn_probs = self.dropout(attn_weights, training=training)
          attn_output = tf.matmul(attn_probs, value_states)
  
          # The tf.debugging asserts are not compliant with XLA then they
          # have to be disabled in other modes than eager.
          if tf.executing_eagerly():
              tf.debugging.assert_equal(
                  shape_list(attn_output),
                  [bsz * self.num_heads, tgt_len, self.head_dim],
                  message=(
                      f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
                      f" {shape_list(attn_output)}"
                  ),
              )
  
          attn_output = tf.transpose(
              tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3)
          )
          attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
  
          attn_output = self.out_proj(attn_output)
          attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
  
          return attn_output, attn_weights, past_key_value
  
  
  class TFWav2Vec2FeedForward(tf.keras.layers.Layer):
      def __init__(self, config: Wav2Vec2Config, **kwargs):
          super().__init__(**kwargs)
  
          self.intermediate_dropout = tf.keras.layers.Dropout(config.activation_dropout)
  
          self.intermediate_dense = tf.keras.layers.Dense(
              units=config.intermediate_size,
              kernel_initializer=get_initializer(config.initializer_range),
              bias_initializer="zeros",
              name="intermediate_dense",
          )
          self.intermediate_act_fn = get_tf_activation(config.hidden_act)
  
          self.output_dense = tf.keras.layers.Dense(
              units=config.hidden_size,
              kernel_initializer=get_initializer(config.initializer_range),
              bias_initializer="zeros",
              name="output_dense",
          )
          self.output_dropout = tf.keras.layers.Dropout(config.hidden_dropout)
  
      def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
          hidden_states = self.intermediate_dense(hidden_states)
          hidden_states = self.intermediate_act_fn(hidden_states)
          hidden_states = self.intermediate_dropout(hidden_states, training=training)
  
          hidden_states = self.output_dense(hidden_states)
          hidden_states = self.output_dropout(hidden_states, training=training)
          return hidden_states
  
  
  class TFWav2Vec2EncoderLayer(tf.keras.layers.Layer):
      def __init__(self, config: Wav2Vec2Config, **kwargs):
          super().__init__(**kwargs)
          self.attention = TFWav2Vec2Attention(
              embed_dim=config.hidden_size,
              num_heads=config.num_attention_heads,
              dropout=config.attention_dropout,
              is_decoder=False,
              name="attention",
          )
          self.dropout = tf.keras.layers.Dropout(config.hidden_dropout)
          self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
          self.feed_forward = TFWav2Vec2FeedForward(config, name="feed_forward")
          self.final_layer_norm = tf.keras.layers.LayerNormalization(
              epsilon=config.layer_norm_eps, name="final_layer_norm"
          )
  
      def call(
          self,
          hidden_states: tf.Tensor,
          attention_mask: Optional[tf.Tensor] = None,
          output_attentions: Optional[bool] = False,
          training: bool = False,
      ) -> Tuple[tf.Tensor]:
          attn_residual = hidden_states
          hidden_states, attn_weights, _ = self.attention(
              hidden_states, attention_mask=attention_mask, training=training
          )
          hidden_states = self.dropout(hidden_states, training=training)
          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 TFWav2Vec2EncoderLayerStableLayerNorm(tf.keras.layers.Layer):
      def __init__(self, config: Wav2Vec2Config, **kwargs):
          super().__init__(**kwargs)
          self.attention = TFWav2Vec2Attention(
              embed_dim=config.hidden_size,
              num_heads=config.num_attention_heads,
              dropout=config.attention_dropout,
              is_decoder=False,
              name="attention",
          )
          self.dropout = tf.keras.layers.Dropout(config.hidden_dropout)
          self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
          self.feed_forward = TFWav2Vec2FeedForward(config, name="feed_forward")
          self.final_layer_norm = tf.keras.layers.LayerNormalization(
              epsilon=config.layer_norm_eps, name="final_layer_norm"
          )
  
      def call(
          self,
          hidden_states: tf.Tensor,
          attention_mask: Optional[tf.Tensor] = None,
          output_attentions: Optional[bool] = False,
          training: bool = False,
      ) -> Tuple[tf.Tensor]:
          attn_residual = hidden_states
          hidden_states = self.layer_norm(hidden_states)
          hidden_states, attn_weights, _ = self.attention(
              hidden_states, attention_mask=attention_mask, training=training
          )
          hidden_states = self.dropout(hidden_states, training=training)
          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 TFWav2Vec2Encoder(tf.keras.layers.Layer):
      def __init__(self, config: Wav2Vec2Config, **kwargs):
          super().__init__(**kwargs)
          self.config = config
          self.pos_conv_embed = TFWav2Vec2PositionalConvEmbedding(config, name="pos_conv_embed")
          self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
          self.dropout = tf.keras.layers.Dropout(config.hidden_dropout)
          self.layer = [TFWav2Vec2EncoderLayer(config, name=f"layers.{i}") for i in range(config.num_hidden_layers)]
  
      def call(
          self,
          hidden_states: tf.Tensor,
          attention_mask: Optional[tf.Tensor] = None,
          output_attentions: Optional[bool] = False,
          output_hidden_states: Optional[bool] = False,
          return_dict: Optional[bool] = True,
          training: Optional[bool] = False,
      ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
          all_hidden_states = () if output_hidden_states else None
          all_self_attentions = () if output_attentions else None
  
          if attention_mask is not None:
              hidden_states = hidden_states * tf.expand_dims(attention_mask, -1)
              attention_mask = _expand_mask(attention_mask)
          else:
              attention_mask = None
  
          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, training=training)
  
          for i, layer_module in enumerate(self.layer):
              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)
              if training and (dropout_probability < self.config.layerdrop):  # skip the layer
                  continue
  
              layer_outputs = layer_module(
                  hidden_states=hidden_states,
                  attention_mask=attention_mask,
                  output_attentions=output_attentions,
                  training=training,
              )
              hidden_states = layer_outputs[0]
  
              if output_attentions:
                  all_self_attentions = all_self_attentions + (layer_outputs[1],)
  
          # Add last layer
          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 TFBaseModelOutput(
              last_hidden_state=hidden_states,
              hidden_states=all_hidden_states,
              attentions=all_self_attentions,
          )
  
  
  class TFWav2Vec2EncoderStableLayerNorm(tf.keras.layers.Layer):
      def __init__(self, config: Wav2Vec2Config, **kwargs):
          super().__init__(**kwargs)
          self.config = config
          self.pos_conv_embed = TFWav2Vec2PositionalConvEmbedding(config, name="pos_conv_embed")
          self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
          self.dropout = tf.keras.layers.Dropout(config.hidden_dropout)
          self.layer = [
              TFWav2Vec2EncoderLayerStableLayerNorm(config, name=f"layers.{i}") for i in range(config.num_hidden_layers)
          ]
  
      def call(
          self,
          hidden_states: tf.Tensor,
          attention_mask: Optional[tf.Tensor] = None,
          output_attentions: Optional[bool] = False,
          output_hidden_states: Optional[bool] = False,
          return_dict: Optional[bool] = True,
          training: Optional[bool] = False,
      ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
          all_hidden_states = () if output_hidden_states else None
          all_self_attentions = () if output_attentions else None
  
          if attention_mask is not None:
              hidden_states = hidden_states * tf.expand_dims(attention_mask, -1)
              attention_mask = _expand_mask(attention_mask)
          else:
              attention_mask = None
  
          position_embeddings = self.pos_conv_embed(hidden_states)
          hidden_states = hidden_states + position_embeddings
          hidden_states = self.dropout(hidden_states, training=training)
  
          for i, layer_module in enumerate(self.layer):
              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)
              if training and (dropout_probability < self.config.layerdrop):  # skip the layer
                  continue
  
              layer_outputs = layer_module(
                  hidden_states=hidden_states,
                  attention_mask=attention_mask,
                  output_attentions=output_attentions,
                  training=training,
              )
              hidden_states = layer_outputs[0]
  
              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 TFBaseModelOutput(
              last_hidden_state=hidden_states,
              hidden_states=all_hidden_states,
              attentions=all_self_attentions,
          )
  
  
  @keras_serializable
  class TFWav2Vec2MainLayer(tf.keras.layers.Layer):
      config_class = Wav2Vec2Config
  
      def __init__(self, config: Wav2Vec2Config, **kwargs):
          super().__init__(**kwargs)
          self.config = config
          self.feature_extractor = TFWav2Vec2FeatureEncoder(config, name="feature_extractor")
          self.feature_projection = TFWav2Vec2FeatureProjection(config, name="feature_projection")
  
          if config.do_stable_layer_norm:
              self.encoder = TFWav2Vec2EncoderStableLayerNorm(config, name="encoder")
          else:
              self.encoder = TFWav2Vec2Encoder(config, name="encoder")
  
      def build(self, input_shape: tf.TensorShape):
          self.masked_spec_embed = self.add_weight(
              shape=(self.config.hidden_size,), initializer="uniform", trainable=True, name="masked_spec_embed"
          )
  
          super().build(input_shape)
  
      def _get_feat_extract_output_lengths(self, input_lengths: tf.Tensor):
          """
          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 _mask_hidden_states(self, hidden_states: tf.Tensor, mask_time_indices: Optional[tf.Tensor] = None):
          """
          Masks extracted features along time axis and/or along feature axis according to
          [SpecAugment](https://arxiv.org/abs/1904.08779).
          """
          batch_size, sequence_length, hidden_size = shape_list(hidden_states)
  
          # `config.apply_spec_augment` can set masking to False
          if not getattr(self.config, "apply_spec_augment", True):
              return hidden_states
  
          if mask_time_indices is not None:
              # apply SpecAugment along time axis with given mask_time_indices
              hidden_states = tf.where(
                  tf.cast(mask_time_indices[:, :, tf.newaxis], tf.bool),
                  self.masked_spec_embed[tf.newaxis, tf.newaxis, :],
                  hidden_states,
              )
  
          elif self.config.mask_time_prob > 0:
              # generate indices & apply SpecAugment along time axis
              mask_time_indices = _compute_mask_indices(
                  (batch_size, sequence_length),
                  mask_prob=self.config.mask_time_prob,
                  mask_length=self.config.mask_time_length,
                  min_masks=2,
              )
              hidden_states = tf.where(
                  tf.cast(mask_time_indices[:, :, tf.newaxis], tf.bool),
                  self.masked_spec_embed[tf.newaxis, tf.newaxis, :],
                  hidden_states,
              )
  
          # apply SpecAugment along feature axis
          if self.config.mask_feature_prob > 0:
              mask_feature_indices = _compute_mask_indices(
                  (batch_size, hidden_size),
                  mask_prob=self.config.mask_feature_prob,
                  mask_length=self.config.mask_feature_length,
              )
              hidden_states = tf.where(mask_feature_indices[:, tf.newaxis, :], hidden_states, 0)
  
          return hidden_states
  
      def call(
          self,
          input_values: tf.Tensor,
          attention_mask: Optional[tf.Tensor] = None,
          token_type_ids: Optional[tf.Tensor] = None,
          position_ids: Optional[tf.Tensor] = None,
          head_mask: Optional[tf.Tensor] = None,
          inputs_embeds: Optional[tf.Tensor] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          training: bool = False,
          **kwargs: Any,
      ):
          inputs = input_values_processing(
              func=self.call,
              config=self.config,
              input_values=input_values,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              training=training,
              kwargs_call=kwargs,
          )
  
          extract_features = self.feature_extractor(
              tf.cast(inputs["input_values"], tf.float32), training=inputs["training"]
          )
          # extract_features = tf.transpose(extract_features, perm=(0, 2, 1))
  
          if inputs["attention_mask"] is not None:
              # compute real output lengths according to convolution formula
              output_lengths = self._get_feat_extract_output_lengths(tf.reduce_sum(inputs["attention_mask"], -1))
  
              attention_mask = tf.sequence_mask(
                  output_lengths, maxlen=shape_list(extract_features)[1], dtype=extract_features.dtype
              )
  
          hidden_states, extract_features = self.feature_projection(extract_features, training=inputs["training"])
  
          mask_time_indices = kwargs.get("mask_time_indices", None)
          if inputs["training"]:
              hidden_states = self._mask_hidden_states(hidden_states, mask_time_indices=mask_time_indices)
  
          encoder_outputs = self.encoder(
              hidden_states,
              attention_mask=attention_mask,
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
          hidden_states = encoder_outputs[0]
  
          if not inputs["return_dict"]:
              return (hidden_states, extract_features) + encoder_outputs[1:]
  
          return TFWav2Vec2BaseModelOutput(
              last_hidden_state=hidden_states,
              extract_features=extract_features,
              hidden_states=encoder_outputs.hidden_states,
              attentions=encoder_outputs.attentions,
          )
  
  
  class TFWav2Vec2PreTrainedModel(TFPreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = Wav2Vec2Config
      base_model_prefix = "wav2vec2"
      main_input_name = "input_values"
  
      @property
      def dummy_inputs(self) -> Dict[str, tf.Tensor]:
          pad_token = 0.0
          input_values = tf.convert_to_tensor(np.random.rand(1, 16000), tf.float32)
          dummy_inputs = {
              "input_values": input_values,
              "attention_mask": tf.cast(tf.not_equal(input_values, pad_token), tf.float32),
          }
          return dummy_inputs
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          logger.warning(
              f"\n{self.__class__.__name__} has backpropagation operations that are NOT supported on CPU. If you wish "
              "to train/fine-tine this model, you need a GPU or a TPU"
          )
  
      @tf.function
      def serving(self, inputs):
          output = self.call(input_values=inputs, training=False)
  
          return self.serving_output(output)
  
  
  WAV_2_VEC_2_START_DOCSTRING = r"""
  
      This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
      library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
      etc.)
  
      This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
      as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
      behavior.
  
      <Tip>
  
      TF 2.0 models accepts two formats as inputs:
  
      - having all inputs as keyword arguments (like PyTorch models), or
      - having all inputs as a list, tuple or dict in the first positional arguments.
  
      This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the
      tensors in the first argument of the model call function: `model(inputs)`.
  
      If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
      first positional argument :
  
      - a single Tensor with `input_values` only and nothing else: `model(inputs_ids)`
      - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
      `model([input_values, attention_mask])` or `model([input_values, attention_mask, token_type_ids])`
      - a dictionary with one or several input Tensors associated to the input names given in the docstring:
      `model({"input_values": input_values, "token_type_ids": token_type_ids})`
  
      </Tip>
  
      Args:
          config ([`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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
  """
  
  WAV_2_VEC_2_INPUTS_DOCSTRING = r"""
      Args:
          input_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
              Indices of input sequence tokens in the vocabulary.
  
              Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
              [`PreTrainedTokenizer.encode`] for details.
  
              [What are input IDs?](../glossary#input-ids)
          attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
              Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              [What are attention masks?](../glossary#attention-mask)
          token_type_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
              Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
              1]`:
  
              - 0 corresponds to a *sentence A* token,
              - 1 corresponds to a *sentence B* token.
  
              [What are token type IDs?](../glossary#token-type-ids)
          position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
              Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
              config.max_position_embeddings - 1]`.
  
              [What are position IDs?](../glossary#position-ids)
          head_mask (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
              Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
              Optionally, instead of passing `input_values` you can choose to directly pass an embedded representation.
              This is useful if you want more control over how to convert `input_values` indices into associated vectors
              than the model's internal embedding lookup matrix.
          output_attentions (`bool`, *optional*):
              Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
              tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
              config will be used instead.
          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. This argument can be used only in eager mode, in graph mode the value in the config will be
              used instead.
          return_dict (`bool`, *optional*):
              Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
              eager mode, in graph mode the value will always be set to True.
          training (`bool`, *optional*, defaults to `False``):
              Whether or not to use the model in training mode (some modules like dropout modules have different
              behaviors between training and evaluation).
  """
  
  
  @add_start_docstrings(
      "The bare TFWav2Vec2 Model transformer outputing raw hidden-states without any specific head on top.",
      WAV_2_VEC_2_START_DOCSTRING,
  )
  class TFWav2Vec2Model(TFWav2Vec2PreTrainedModel):
      def __init__(self, config: Wav2Vec2Config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.config = config
          self.wav2vec2 = TFWav2Vec2MainLayer(config, name="wav2vec2")
  
      @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=TFBaseModelOutput, config_class=_CONFIG_FOR_DOC)
      def call(
          self,
          input_values: tf.Tensor,
          attention_mask: Optional[tf.Tensor] = None,
          token_type_ids: Optional[tf.Tensor] = None,
          position_ids: Optional[tf.Tensor] = None,
          head_mask: Optional[tf.Tensor] = None,
          inputs_embeds: Optional[tf.Tensor] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          training: bool = False,
      ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
          """
  
          Returns:
  
          Example:
  
          ```python
          >>> from transformers import Wav2Vec2Processor, TFWav2Vec2Model
          >>> from datasets import load_dataset
          >>> import soundfile as sf
  
          >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
          >>> model = TFWav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
  
  
          >>> def map_to_array(batch):
          ...     speech, _ = sf.read(batch["file"])
          ...     batch["speech"] = speech
          ...     return batch
  
  
          >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
          >>> ds = ds.map(map_to_array)
  
          >>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values  # Batch size 1
          >>> hidden_states = model(input_values).last_hidden_state
          ```"""
  
          inputs = input_values_processing(
              func=self.call,
              config=self.config,
              input_values=input_values,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              training=training,
          )
  
          inputs["output_hidden_states"] = (
              inputs["output_hidden_states"] if inputs["output_hidden_states"] else self.config.output_hidden_states
          )
          inputs["output_attentions"] = (
              inputs["output_attentions"] if inputs["output_attentions"] else self.config.output_attentions
          )
          inputs["return_dict"] = inputs["return_dict"] if inputs["return_dict"] else self.config.return_dict
  
          outputs = self.wav2vec2(
              input_values=inputs["input_values"],
              attention_mask=inputs["attention_mask"],
              token_type_ids=inputs["token_type_ids"],
              position_ids=inputs["position_ids"],
              head_mask=inputs["head_mask"],
              inputs_embeds=inputs["inputs_embeds"],
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
  
          return outputs
  
      def serving_output(self, output):
          hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
          attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
  
          return TFWav2Vec2BaseModelOutput(
              last_hidden_state=output.last_hidden_state,
              extract_features=output.extract_features,
              hidden_states=hs,
              attentions=attns,
          )
  
  
  @add_start_docstrings(
      """TFWav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""",
      WAV_2_VEC_2_START_DOCSTRING,
  )
  class TFWav2Vec2ForCTC(TFWav2Vec2PreTrainedModel):
      def __init__(self, config: Wav2Vec2Config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
  
          self.wav2vec2 = TFWav2Vec2MainLayer(config, name="wav2vec2")
          self.dropout = tf.keras.layers.Dropout(config.final_dropout)
          self.lm_head = tf.keras.layers.Dense(config.vocab_size, name="lm_head")
  
      def freeze_feature_extractor(self):
          """
          Calling this function will disable the gradient computation for the feature encoder so that its parameters will
          not be updated during training.
          """
          warnings.warn(
              "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5."
              "Please use the equivalent `freeze_feature_encoder` method instead.",
              FutureWarning,
          )
          self.freeze_feature_encoder()
  
      def freeze_feature_encoder(self):
          """
          Calling this function will disable the gradient computation for the feature encoder so that its parameter will
          not be updated during training.
          """
          self.wav2vec2.feature_extractor.trainable = False
  
      @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=TFCausalLMOutput, config_class=_CONFIG_FOR_DOC)
      def call(
          self,
          input_values: tf.Tensor,
          attention_mask: Optional[tf.Tensor] = None,
          token_type_ids: Optional[tf.Tensor] = None,
          position_ids: Optional[tf.Tensor] = None,
          head_mask: Optional[tf.Tensor] = None,
          inputs_embeds: Optional[tf.Tensor] = None,
          output_attentions: Optional[bool] = None,
          labels: Optional[tf.Tensor] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          training: Optional[bool] = False,
      ) -> Union[TFCausalLMOutput, Tuple[tf.Tensor]]:
          r"""
          labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
              Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
              config.vocab_size]` (see `input_values` docstring) Tokens with indices set to `-100` are ignored (masked),
              the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
  
          Returns:
  
          Example:
  
          ```python
          >>> import tensorflow as tf
          >>> from transformers import Wav2Vec2Processor, TFWav2Vec2ForCTC
          >>> from datasets import load_dataset
          >>> import soundfile as sf
  
          >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
          >>> model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
  
  
          >>> def map_to_array(batch):
          ...     speech, _ = sf.read(batch["file"])
          ...     batch["speech"] = speech
          ...     return batch
  
  
          >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
          >>> ds = ds.map(map_to_array)
  
          >>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values  # Batch size 1
          >>> logits = model(input_values).logits
          >>> predicted_ids = tf.argmax(logits, axis=-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(transcription, return_tensors="tf").input_ids
  
          >>> loss = model(input_values, labels=labels).loss
          ```"""
          inputs = input_values_processing(
              func=self.call,
              config=self.config,
              input_values=input_values,
              attention_mask=attention_mask,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              training=training,
          )
  
          outputs = self.wav2vec2(
              input_values=inputs["input_values"],
              attention_mask=inputs["attention_mask"],
              token_type_ids=inputs["token_type_ids"],
              position_ids=inputs["position_ids"],
              head_mask=inputs["head_mask"],
              inputs_embeds=inputs["inputs_embeds"],
              output_attentions=inputs["output_attentions"],
              output_hidden_states=inputs["output_hidden_states"],
              return_dict=inputs["return_dict"],
              training=inputs["training"],
          )
          hidden_states = outputs[0]
          hidden_states = self.dropout(hidden_states, training=inputs["training"])
  
          logits = self.lm_head(hidden_states)
  
          if labels is not None:
  
              if tf.reduce_max(labels) >= self.config.vocab_size:
                  raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
  
              attention_mask = (
                  inputs["attention_mask"]
                  if inputs["attention_mask"] is not None
                  else tf.ones_like(inputs["input_values"], dtype=tf.float32)
              )
              input_lengths = self.wav2vec2._get_feat_extract_output_lengths(tf.reduce_sum(attention_mask, axis=-1))
  
              # assuming that padded tokens are filled with -100
              # when not being attended to
              labels_mask = tf.cast(labels >= 0, tf.int32)
              target_lengths = tf.reduce_sum(labels_mask, axis=-1)
  
              loss = tf.nn.ctc_loss(
                  logits=logits,
                  labels=labels,
                  logit_length=input_lengths,
                  label_length=target_lengths,
                  blank_index=self.config.pad_token_id,
                  logits_time_major=False,
              )
  
              if self.config.ctc_loss_reduction == "sum":
                  loss = tf.reduce_sum(loss)
              if self.config.ctc_loss_reduction == "mean":
                  loss = tf.reduce_mean(loss)
          else:
              loss = None
  
          if not inputs["return_dict"]:
              output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
              return ((loss,) + output) if loss is not None else output
  
          return TFCausalLMOutput(
              loss=loss,
              logits=logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
      def serving_output(self, output: TFCausalLMOutput) -> TFCausalLMOutput:
          hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
          attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
          return TFCausalLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
  