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.
      position_biased_input (`bool`, *optional*, defaults to `False`):
          Whether to add absolute position embedding to content embedding.
      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):
          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 __init__(
      self,
      vocab_size=32,
      hidden_size=768,
      num_hidden_layers=12,
      num_attention_heads=12,
      intermediate_size=3072,
      squeeze_factor=2,
      max_position_embeddings=512,
      position_buckets=256,
      share_att_key=True,
      relative_attention=True,
      position_biased_input=False,
      pos_att_type=("p2c", "c2p"),
      norm_rel_ebd="layer_norm",
      hidden_act="gelu_python",
      hidden_dropout=0.1,
      activation_dropout=0.1,
      attention_dropout=0.1,
      feat_proj_dropout=0.0,
      final_dropout=0.1,
      layerdrop=0.1,
      initializer_range=0.02,
      layer_norm_eps=1e-7,
      feature_layer_norm_eps=1e-5,
      feat_extract_norm="group",
      feat_extract_activation="gelu",
      conv_dim=(
          64,
          128,
          128,
          128,
          128,
          256,
          256,
          256,
          256,
          512,
          512,
          512,
          512,
      ),
      conv_stride=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1),
      conv_kernel=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1),
      conv_bias=False,
      num_conv_pos_embeddings=128,
      num_conv_pos_embedding_groups=16,
      apply_spec_augment=True,
      mask_time_prob=0.05,
      mask_time_length=10,
      mask_time_min_masks=2,
      mask_feature_prob=0.0,
      mask_feature_length=10,
      mask_feature_min_masks=0,
      ctc_loss_reduction="mean",
      ctc_zero_infinity=False,
      use_weighted_layer_sum=False,
      classifier_proj_size=256,
      pad_token_id=0,
      bos_token_id=1,
      eos_token_id=2,
      **kwargs,
  ):
      super().__init__(
          **kwargs,
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
      )
      self.hidden_size = hidden_size
      self.feat_extract_norm = feat_extract_norm
      self.feat_extract_activation = feat_extract_activation
      self.conv_dim = list(conv_dim)
      self.conv_stride = list(conv_stride)
      self.conv_kernel = list(conv_kernel)
      self.conv_bias = conv_bias
      self.num_conv_pos_embeddings = num_conv_pos_embeddings
      self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
      self.num_feat_extract_layers = len(self.conv_dim)
      self.num_hidden_layers = num_hidden_layers
      self.intermediate_size = intermediate_size
      self.squeeze_factor = squeeze_factor
      self.max_position_embeddings = max_position_embeddings
      self.position_buckets = position_buckets
      self.share_att_key = share_att_key
      self.relative_attention = relative_attention
      self.norm_rel_ebd = norm_rel_ebd
      self.position_biased_input = position_biased_input
      self.pos_att_type = list(pos_att_type)
      self.hidden_act = hidden_act
      self.num_attention_heads = num_attention_heads
      self.hidden_dropout = hidden_dropout
      self.attention_dropout = attention_dropout
      self.activation_dropout = activation_dropout
      self.feat_proj_dropout = feat_proj_dropout
      self.final_dropout = final_dropout
      self.layerdrop = layerdrop
      self.layer_norm_eps = layer_norm_eps
      self.feature_layer_norm_eps = feature_layer_norm_eps
      self.initializer_range = initializer_range
      self.vocab_size = vocab_size
  
      if (
          (len(self.conv_stride) != self.num_feat_extract_layers)
          or (len(self.conv_kernel) != self.num_feat_extract_layers)
          or (len(self.conv_dim) != self.num_feat_extract_layers)
      ):
          raise ValueError(
              "Configuration for convolutional layers is incorrect."
              "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
              f"but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)"
              f"= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`."
          )
  
      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      self.apply_spec_augment = apply_spec_augment
      self.mask_time_prob = mask_time_prob
      self.mask_time_length = mask_time_length
      self.mask_time_min_masks = mask_time_min_masks
      self.mask_feature_prob = mask_feature_prob
      self.mask_feature_length = mask_feature_length
      self.mask_feature_min_masks = mask_feature_min_masks
  
      # ctc loss
      self.ctc_loss_reduction = ctc_loss_reduction
      self.ctc_zero_infinity = ctc_zero_infinity
  
      # sequence classification
      self.use_weighted_layer_sum = use_weighted_layer_sum
      self.classifier_proj_size = classifier_proj_size
  
file_path: H2O/h2o_flexgen/benchmark/third_party/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 1 to create a new SEWDConfig object from inherited base classes: PretrainedConfig with example: obj = SEWDConfig(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, squeeze_factor, max_position_embeddings, position_buckets, share_att_key, relative_attention, position_biased_input, pos_att_type, norm_rel_ebd, hidden_act, hidden_dropout, activation_dropout, attention_dropout, feat_proj_dropout, final_dropout, layerdrop, initializer_range, layer_norm_eps, feature_layer_norm_eps, feat_extract_norm, feat_extract_activation, conv_dim, conv_stride, conv_kernel, conv_bias, num_conv_pos_embeddings, num_conv_pos_embedding_groups, apply_spec_augment, mask_time_prob, mask_time_length, mask_time_min_masks, mask_feature_prob, mask_feature_length, mask_feature_min_masks, ctc_loss_reduction, ctc_zero_infinity, use_weighted_layer_sum, classifier_proj_size, pad_token_id, bos_token_id, eos_token_id)