class_docstr: This is the configuration class to store the configuration of a [`BeitModel`]. It is used to instantiate an BEiT
  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 BEiT
  [microsoft/beit-base-patch16-224-pt22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k) architecture.
  
  Args:
      vocab_size (`int`, *optional*, defaults to 8092):
          Vocabulary size of the BEiT model. Defines the number of different image tokens that can be used during
          pre-training.
      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_prob (`float`, *optional*, defaults to 0.0):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
          The dropout ratio for the attention probabilities.
      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.
      image_size (`int`, *optional*, defaults to 224):
          The size (resolution) of each image.
      patch_size (`int`, *optional*, defaults to 16):
          The size (resolution) of each patch.
      num_channels (`int`, *optional*, defaults to 3):
          The number of input channels.
      use_mask_token (`bool`, *optional*, defaults to `False`):
          Whether to use a mask token for masked image modeling.
      use_absolute_position_embeddings (`bool`, *optional*, defaults to `False`):
          Whether to use BERT-style absolute position embeddings.
      use_relative_position_bias (`bool`, *optional*, defaults to `False`):
          Whether to use T5-style relative position embeddings in the self-attention layers.
      use_shared_relative_position_bias (`bool`, *optional*, defaults to `False`):
          Whether to use the same relative position embeddings across all self-attention layers of the Transformer.
      layer_scale_init_value (`float`, *optional*, defaults to 0.1):
          Scale to use in the self-attention layers. 0.1 for base, 1e-5 for large. Set 0 to disable layer scale.
      drop_path_rate (`float`, *optional*, defaults to 0.1):
          Stochastic depth rate per sample (when applied in the main path of residual layers).
      use_mean_pooling (`bool`, *optional*, defaults to `True`):
          Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the
          CLS token, before applying the classification head.
      out_indices (`List[int]`, *optional*, defaults to `[3, 5, 7, 11]`):
          Indices of the feature maps to use for semantic segmentation.
      pool_scales (`Tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`):
          Pooling scales used in Pooling Pyramid Module applied on the last feature map.
      use_auxiliary_head (`bool`, *optional*, defaults to `True`):
          Whether to use an auxiliary head during training.
      auxiliary_loss_weight (`float`, *optional*, defaults to 0.4):
          Weight of the cross-entropy loss of the auxiliary head.
      auxiliary_channels (`int`, *optional*, defaults to 256):
          Number of channels to use in the auxiliary head.
      auxiliary_num_convs (`int`, *optional*, defaults to 1):
          Number of convolutional layers to use in the auxiliary head.
      auxiliary_concat_input (`bool`, *optional*, defaults to `False`):
          Whether to concatenate the output of the auxiliary head with the input before the classification layer.
      semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
          The index that is ignored by the loss function of the semantic segmentation model.
  
  Example:
  
  ```python
  >>> from transformers import BeitConfig, BeitModel
  
  >>> # Initializing a BEiT beit-base-patch16-224-pt22k style configuration
  >>> configuration = BeitConfig()
  
  >>> # Initializing a model (with random weights) from the beit-base-patch16-224-pt22k style configuration
  >>> model = BeitModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      vocab_size=8192,
      hidden_size=768,
      num_hidden_layers=12,
      num_attention_heads=12,
      intermediate_size=3072,
      hidden_act="gelu",
      hidden_dropout_prob=0.0,
      attention_probs_dropout_prob=0.0,
      initializer_range=0.02,
      layer_norm_eps=1e-12,
      is_encoder_decoder=False,
      image_size=224,
      patch_size=16,
      num_channels=3,
      use_mask_token=False,
      use_absolute_position_embeddings=False,
      use_relative_position_bias=False,
      use_shared_relative_position_bias=False,
      layer_scale_init_value=0.1,
      drop_path_rate=0.1,
      use_mean_pooling=True,
      out_indices=[3, 5, 7, 11],
      pool_scales=[1, 2, 3, 6],
      use_auxiliary_head=True,
      auxiliary_loss_weight=0.4,
      auxiliary_channels=256,
      auxiliary_num_convs=1,
      auxiliary_concat_input=False,
      semantic_loss_ignore_index=255,
      **kwargs,
  ):
      super().__init__(**kwargs)
  
      self.vocab_size = vocab_size
      self.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.intermediate_size = intermediate_size
      self.hidden_act = hidden_act
      self.hidden_dropout_prob = hidden_dropout_prob
      self.attention_probs_dropout_prob = attention_probs_dropout_prob
      self.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
  
      self.image_size = image_size
      self.patch_size = patch_size
      self.num_channels = num_channels
      self.use_mask_token = use_mask_token
      self.use_absolute_position_embeddings = (
          use_absolute_position_embeddings
      )
      self.use_relative_position_bias = use_relative_position_bias
      self.use_shared_relative_position_bias = (
          use_shared_relative_position_bias
      )
      self.layer_scale_init_value = layer_scale_init_value
      self.drop_path_rate = drop_path_rate
      self.use_mean_pooling = use_mean_pooling
      # decode head attributes (semantic segmentation)
      self.out_indices = out_indices
      self.pool_scales = pool_scales
      # auxiliary head attributes (semantic segmentation)
      self.use_auxiliary_head = use_auxiliary_head
      self.auxiliary_loss_weight = auxiliary_loss_weight
      self.auxiliary_channels = auxiliary_channels
      self.auxiliary_num_convs = auxiliary_num_convs
      self.auxiliary_concat_input = auxiliary_concat_input
      self.semantic_loss_ignore_index = semantic_loss_ignore_index
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/beit/configuration_beit.py
imports: from collections import OrderedDict
  from typing import Mapping
  from packaging import version
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new BeitConfig object from inherited base classes: PretrainedConfig with example: obj = BeitConfig(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, initializer_range, layer_norm_eps, is_encoder_decoder, image_size, patch_size, num_channels, use_mask_token, use_absolute_position_embeddings, use_relative_position_bias, use_shared_relative_position_bias, layer_scale_init_value, drop_path_rate, use_mean_pooling, out_indices, pool_scales, use_auxiliary_head, auxiliary_loss_weight, auxiliary_channels, auxiliary_num_convs, auxiliary_concat_input, semantic_loss_ignore_index)