class_docstr: This is the configuration class to store the configuration of a [`SEWDModel`]. It is used to instantiate a SEW-D
  model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
  defaults will yield a similar configuration to that of the SEW-D
  [asapp/sew-d-tiny-100k](https://huggingface.co/asapp/sew-d-tiny-100k) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  
  Args:
      vocab_size (`int`, *optional*, defaults to 32):
          Vocabulary size of the SEW-D model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`SEWD`].
      hidden_size (`int`, *optional*, defaults to 768):
          Dimensionality of the encoder layers and the pooler layer.
      num_hidden_layers (`int`, *optional*, defaults to 12):
          Number of hidden layers in the Transformer encoder.
      num_attention_heads (`int`, *optional*, defaults to 12):
          Number of attention heads for each attention layer in the Transformer encoder.
      intermediate_size (`int`, *optional*, defaults to 3072):
          Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
      squeeze_factor (`int`, *optional*, defaults to 2):
          Sequence length downsampling factor after the encoder and upsampling factor after the transformer.
      max_position_embeddings (`int`, *optional*, defaults to 512):
          The maximum sequence length that this model might ever be used with. Typically set this to something large
          just in case (e.g., 512 or 1024 or 2048).
      position_buckets (`int`, *optional*, defaults to 256):
          The maximum size of relative position embeddings.
      share_att_key (`bool`, *optional*, defaults to `True`):
          Whether to share attention key with c2p and p2c.
      relative_attention (`bool`, *optional*, defaults to `True`):
          Whether to use relative position encoding.
      pos_att_type (`Tuple[str]`, *optional*, defaults to `("p2c", "c2p")`):
          The type of relative position attention, it can be a combination of `("p2c", "c2p")`, e.g. `("p2c")`,
          `("p2c", "c2p")`, `("p2c", "c2p")`.
      norm_rel_ebd (`str`, *optional*, defaults to `"layer_norm"`):
          Whether to use layer norm in relative embedding (`"layer_norm"` if yes)
      hidden_act (`str` or `function`, *optional*, defaults to `"gelu_python"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"selu"`, `"gelu_python"` and `"gelu_new"` are supported.
      hidden_dropout (`float`, *optional*, defaults to 0.1):
          Deprecated. Not used by the model and will be removed in a future version.
      activation_dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      attention_dropout (`float`, *optional*, defaults to 0.1):
          The dropout ratio for the attention probabilities.
      final_dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability for the final projection layer of [`SEWDForCTC`].
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      layer_norm_eps (`float`, *optional*, defaults to 1e-7):
          The epsilon used by the layer normalization layers in the transformer encoder.
      feature_layer_norm_eps (`float`, *optional*, defaults to 1e-5):
          The epsilon used by the layer normalization after the feature encoder.
      feat_extract_norm (`str`, *optional*, defaults to `"group"`):
          The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
          normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
          convolutional layers.
      feat_proj_dropout (`float`, *optional*, defaults to 0.0):
          The dropout probability for output of the feature encoder.
      feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the 1D convolutional layers of the feature
          extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
      conv_dim (`Tuple[int]` or `List[int]`, *optional*, defaults to `(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)`):
          A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
          feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
      conv_stride (`Tuple[int]` or `List[int]`, *optional*, defaults to `(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)`):
          A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
          of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
      conv_kernel (`Tuple[int]` or `List[int]`, *optional*, defaults to `(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)`):
          A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
          length of *conv_kernel* defines the number of convolutional layers and has to match the length of
          *conv_dim*.
      conv_bias (`bool`, *optional*, defaults to `False`):
          Whether the 1D convolutional layers have a bias.
      num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
          Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
          embeddings layer.
      num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
          Number of groups of 1D convolutional positional embeddings layer.
      apply_spec_augment (`bool`, *optional*, defaults to `True`):
          Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
          [SpecAugment: A Simple Data Augmentation Method for Automatic Speech
          Recognition](https://arxiv.org/abs/1904.08779).
      mask_time_prob (`float`, *optional*, defaults to 0.05):
          Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
          procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
          reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
          masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
          actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`.
      mask_time_length (`int`, *optional*, defaults to 10):
          Length of vector span along the time axis.
      mask_time_min_masks (`int`, *optional*, defaults to 2),:
          The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
          irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
          mask_time_min_masks''
      mask_feature_prob (`float`, *optional*, defaults to 0.0):
          Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
          masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
          the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
          span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
          may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
          True`.
      mask_feature_length (`int`, *optional*, defaults to 10):
          Length of vector span along the feature axis.
      mask_feature_min_masks (`int`, *optional*, defaults to 0),:
          The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
          step, irrespectively of `mask_feature_prob`. Only relevant if
          ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
      diversity_loss_weight (`int`, *optional*, defaults to 0.1):
          The weight of the codebook diversity loss component.
      ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
          Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
          instance of [`SEWDForCTC`].
      ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
          Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
          occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
          of [`SEWDForCTC`].
      use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
          Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
          instance of [`Wav2Vec2ForSequenceClassification`].
      classifier_proj_size (`int`, *optional*, defaults to 256):
          Dimensionality of the projection before token mean-pooling for classification.
  
  Example:
  
  ```python
  >>> from transformers import SEWDConfig, SEWDModel
  
  >>> # Initializing a SEW-D asapp/sew-d-tiny-100k style configuration
  >>> configuration = SEWDConfig()
  
  >>> # Initializing a model (with random weights) from the asapp/sew-d-tiny-100k style configuration
  >>> model = SEWDModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def hidden_dropout(self):
      logger.warning_once(
          "hidden_dropout is not used by the model and will be removed as config attribute in v4.35"
      )
      return self._hidden_dropout
  
file_path: transformers/src/transformers/models/sew_d/configuration_sew_d.py
imports: import functools
  import operator
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 3 for calling the SEWDConfig obj's underlying member method code with example usage: obj.hidden_dropout() and returns: self