class_docstr: This is the configuration class to store the configuration of a [`HubertModel`]. It is used to instantiate an
  Hubert 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 Hubert
  [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) 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 Hubert model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`HubertModel`]. Vocabulary size of the model. Defines the different
          tokens that can be represented by the *inputs_ids* passed to the forward method of [`HubertModel`].
      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.
      hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"selu"` 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 probabilitiy for the final projection layer of [`Wav2Vec2ForCTC`].
      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-12):
          The epsilon used by the layer normalization layers.
      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_proj_layer_norm (`bool`, *optional*, defaults to `True`):
          Whether to apply LayerNorm to the 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]`, *optional*, defaults to `(512, 512, 512, 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]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
          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]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
          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.
      do_stable_layer_norm (`bool`, *optional*, defaults to `False`):
          Whether do apply *stable* layer norm architecture of the Transformer encoder. `do_stable_layer_norm is
          True` corresponds to applying layer norm before the attention layer, whereas `do_stable_layer_norm is
          False` corresponds to applying layer norm after the attention 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''
      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 [`HubertForCTC`].
      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 [`HubertForCTC`].
      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 [`HubertForSequenceClassification`].
      classifier_proj_size (`int`, *optional*, defaults to 256):
          Dimensionality of the projection before token mean-pooling for classification.
  
  Example:
  
  ```python
  >>> from transformers import HubertModel, HubertConfig
  
  >>> # Initializing a Hubert facebook/hubert-base-ls960 style configuration
  >>> configuration = HubertConfig()
  
  >>> # Initializing a model from the facebook/hubert-base-ls960 style configuration
  >>> model = HubertModel(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,
      hidden_act="gelu",
      hidden_dropout=0.1,
      activation_dropout=0.1,
      attention_dropout=0.1,
      feat_proj_layer_norm=True,
      feat_proj_dropout=0.0,
      final_dropout=0.1,
      layerdrop=0.1,
      initializer_range=0.02,
      layer_norm_eps=1e-5,
      feat_extract_norm="group",
      feat_extract_activation="gelu",
      conv_dim=(512, 512, 512, 512, 512, 512, 512),
      conv_stride=(5, 2, 2, 2, 2, 2, 2),
      conv_kernel=(10, 3, 3, 3, 3, 2, 2),
      conv_bias=False,
      num_conv_pos_embeddings=128,
      num_conv_pos_embedding_groups=16,
      do_stable_layer_norm=False,
      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="sum",
      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.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_layer_norm = feat_proj_layer_norm
      self.feat_proj_dropout = feat_proj_dropout
      self.final_dropout = final_dropout
      self.layerdrop = layerdrop
      self.layer_norm_eps = layer_norm_eps
      self.initializer_range = initializer_range
      self.vocab_size = vocab_size
      self.do_stable_layer_norm = do_stable_layer_norm
      self.use_weighted_layer_sum = use_weighted_layer_sum
      self.classifier_proj_size = classifier_proj_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)`, but is `len(config.conv_dim) ="
              f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
              f" `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
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/hubert/configuration_hubert.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 HubertConfig object from inherited base classes: PretrainedConfig with example: obj = HubertConfig(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout, activation_dropout, attention_dropout, feat_proj_layer_norm, feat_proj_dropout, final_dropout, layerdrop, initializer_range, 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, do_stable_layer_norm, 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)