class_docstr: This is the configuration class to store the configuration of a [`GPT2Model`] or a [`TFGPT2Model`]. It is used to
  instantiate a GPT-2 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 GPT-2
  [gpt2](https://huggingface.co/gpt2) 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 50257):
          Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`GPT2Model`] or [`TFGPT2Model`].
      n_positions (`int`, *optional*, defaults to 1024):
          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).
      n_embd (`int`, *optional*, defaults to 768):
          Dimensionality of the embeddings and hidden states.
      n_layer (`int`, *optional*, defaults to 12):
          Number of hidden layers in the Transformer encoder.
      n_head (`int`, *optional*, defaults to 12):
          Number of attention heads for each attention layer in the Transformer encoder.
      n_inner (`int`, *optional*, defaults to None):
          Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
      activation_function (`str`, *optional*, defaults to `"gelu"`):
          Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
      resid_pdrop (`float`, *optional*, defaults to 0.1):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      embd_pdrop (`int`, *optional*, defaults to 0.1):
          The dropout ratio for the embeddings.
      attn_pdrop (`float`, *optional*, defaults to 0.1):
          The dropout ratio for the attention.
      layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
          The epsilon to use in the layer normalization layers.
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      summary_type (`string`, *optional*, defaults to `"cls_index"`):
          Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
          [`TFGPT2DoubleHeadsModel`].
  
          Has to be one of the following options:
  
              - `"last"`: Take the last token hidden state (like XLNet).
              - `"first"`: Take the first token hidden state (like BERT).
              - `"mean"`: Take the mean of all tokens hidden states.
              - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
              - `"attn"`: Not implemented now, use multi-head attention.
      summary_use_proj (`bool`, *optional*, defaults to `True`):
          Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
          [`TFGPT2DoubleHeadsModel`].
  
          Whether or not to add a projection after the vector extraction.
      summary_activation (`str`, *optional*):
          Argument used when doing sequence summary. Used in for the multiple choice head in
          [`GPT2DoubleHeadsModel`].
  
          Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
      summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
          Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
          [`TFGPT2DoubleHeadsModel`].
  
          Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
      summary_first_dropout (`float`, *optional*, defaults to 0.1):
          Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
          [`TFGPT2DoubleHeadsModel`].
  
          The dropout ratio to be used after the projection and activation.
      scale_attn_weights (`bool`, *optional*, defaults to `True`):
          Scale attention weights by dividing by sqrt(hidden_size)..
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether or not the model should return the last key/values attentions (not used by all models).
      scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
          Whether to additionally scale attention weights by `1 / layer_idx + 1`.
      reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
          Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
          dot-product/softmax to float() when training with mixed precision.
  
  Example:
  
  ```python
  >>> from transformers import GPT2Config, GPT2Model
  
  >>> # Initializing a GPT2 configuration
  >>> configuration = GPT2Config()
  
  >>> # Initializing a model (with random weights) from the configuration
  >>> model = GPT2Model(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      vocab_size=50257,
      n_positions=1024,
      n_embd=768,
      n_layer=12,
      n_head=12,
      n_inner=None,
      activation_function="gelu_new",
      resid_pdrop=0.1,
      embd_pdrop=0.1,
      attn_pdrop=0.1,
      layer_norm_epsilon=1e-5,
      initializer_range=0.02,
      summary_type="cls_index",
      summary_use_proj=True,
      summary_activation=None,
      summary_proj_to_labels=True,
      summary_first_dropout=0.1,
      scale_attn_weights=True,
      use_cache=True,
      bos_token_id=50256,
      eos_token_id=50256,
      scale_attn_by_inverse_layer_idx=False,
      reorder_and_upcast_attn=False,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.n_positions = n_positions
      self.n_embd = n_embd
      self.n_layer = n_layer
      self.n_head = n_head
      self.n_inner = n_inner
      self.activation_function = activation_function
      self.resid_pdrop = resid_pdrop
      self.embd_pdrop = embd_pdrop
      self.attn_pdrop = attn_pdrop
      self.layer_norm_epsilon = layer_norm_epsilon
      self.initializer_range = initializer_range
      self.summary_type = summary_type
      self.summary_use_proj = summary_use_proj
      self.summary_activation = summary_activation
      self.summary_first_dropout = summary_first_dropout
      self.summary_proj_to_labels = summary_proj_to_labels
      self.scale_attn_weights = scale_attn_weights
      self.use_cache = use_cache
      self.scale_attn_by_inverse_layer_idx = (
          scale_attn_by_inverse_layer_idx
      )
      self.reorder_and_upcast_attn = reorder_and_upcast_attn
  
      self.bos_token_id = bos_token_id
      self.eos_token_id = eos_token_id
  
      super().__init__(
          bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/gpt2/configuration_gpt2.py
imports: from collections import OrderedDict
  from typing import Any, List, Mapping, Optional
  from transformers import PreTrainedTokenizer, TensorType, is_torch_available
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast, PatchingSpec
  from ...utils import logging
label_desc: Use image node_id 1 to create a new GPT2Config object from inherited base classes: PretrainedConfig with example: obj = GPT2Config(vocab_size, n_positions, n_embd, n_layer, n_head, n_inner, activation_function, resid_pdrop, embd_pdrop, attn_pdrop, layer_norm_epsilon, initializer_range, summary_type, summary_use_proj, summary_activation, summary_proj_to_labels, summary_first_dropout, scale_attn_weights, use_cache, bos_token_id, eos_token_id, scale_attn_by_inverse_layer_idx, reorder_and_upcast_attn)