code: 
  '''simple docstring'''
  
  
  
  from collections import OrderedDict
  from typing import Any, List, Mapping, Optional
  
  from ... import PreTrainedTokenizer, TensorType, is_torch_available
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast, PatchingSpec
  from ...utils import logging
  
  
  __A          =    logging.get_logger(__name__)
  
  __A          =    {
      "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
      "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
      "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
      "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
      "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
  }
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							int       		=  """gpt2"""
  			lowerCamelCase							:							Optional[int]       		=  ["""past_key_values"""]
  			lowerCamelCase							:							Any       		=  {
  			    """hidden_size""": """n_embd""",
  			    """max_position_embeddings""": """n_positions""",
  			    """num_attention_heads""": """n_head""",
  			    """num_hidden_layers""": """n_layer""",
  			}
  
  
  
  			def __init__(					self       ,     lowerCamelCase__=50_257       ,     lowerCamelCase__=1_024       ,     lowerCamelCase__=768       ,     lowerCamelCase__=12       ,     lowerCamelCase__=12       ,     lowerCamelCase__=None       ,     lowerCamelCase__="gelu_new"       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=1e-5       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__="cls_index"       ,     lowerCamelCase__=True       ,     lowerCamelCase__=None       ,     lowerCamelCase__=True       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=50_256       ,     lowerCamelCase__=50_256       ,     lowerCamelCase__=False       ,     lowerCamelCase__=False       ,     **lowerCamelCase__       ,     )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       vocab_size
  									lowercase__							 =       n_positions
  									lowercase__							 =       n_embd
  									lowercase__							 =       n_layer
  									lowercase__							 =       n_head
  									lowercase__							 =       n_inner
  									lowercase__							 =       activation_function
  									lowercase__							 =       resid_pdrop
  									lowercase__							 =       embd_pdrop
  									lowercase__							 =       attn_pdrop
  									lowercase__							 =       layer_norm_epsilon
  									lowercase__							 =       initializer_range
  									lowercase__							 =       summary_type
  									lowercase__							 =       summary_use_proj
  									lowercase__							 =       summary_activation
  									lowercase__							 =       summary_first_dropout
  									lowercase__							 =       summary_proj_to_labels
  									lowercase__							 =       scale_attn_weights
  									lowercase__							 =       use_cache
  									lowercase__							 =       scale_attn_by_inverse_layer_idx
  									lowercase__							 =       reorder_and_upcast_attn
  
  									lowercase__							 =       bos_token_id
  									lowercase__							 =       eos_token_id
  
  									super().__init__(bos_token_id=lowerCamelCase__       ,     eos_token_id=lowerCamelCase__       ,     **lowerCamelCase__       )
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = "default"       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = False       ,     )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().__init__(lowerCamelCase__       ,     task=lowerCamelCase__       ,     patching_specs=lowerCamelCase__       ,     use_past=lowerCamelCase__       )
  									if not getattr(self._config       ,     """pad_token_id"""       ,     lowerCamelCase__       ):
  															# TODO: how to do that better?
  															lowercase__							 =       0
  
  
  
  			@property
  			def A__ (					self       )			->		Mapping[str, Mapping[int, str]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}       )
  									if self.use_past:
  															self.fill_with_past_key_values_(lowerCamelCase__       ,     direction="""inputs"""       )
  															lowercase__							 =       {0: """batch""", 1: """past_sequence + sequence"""}
  									else:
  															lowercase__							 =       {0: """batch""", 1: """sequence"""}
  
  									return common_inputs
  
  
  
  			@property
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self._config.n_layer
  
  
  
  			@property
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self._config.n_head
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = -1       ,     lowerCamelCase__ = -1       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = None       ,     )			->		Mapping[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       super(lowerCamelCase__       ,     self       ).generate_dummy_inputs(
  									    lowerCamelCase__       ,     batch_size=lowerCamelCase__       ,     seq_length=lowerCamelCase__       ,     is_pair=lowerCamelCase__       ,     framework=lowerCamelCase__       )
  
  									# We need to order the input in the way they appears in the forward()
  									lowercase__							 =       OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}       )
  
  									# Need to add the past_keys
  									if self.use_past:
  															if not is_torch_available():
  																					raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
  															else:
  																					import torch
  
  																					lowercase__ ,						lowercase__							 =       common_inputs["""input_ids"""].shape
  																					# Not using the same length for past_key_values
  																					lowercase__							 =       seqlen + 2
  																					lowercase__							 =       (
  																					    batch,
  																					    self.num_attention_heads,
  																					    past_key_values_length,
  																					    self._config.hidden_size // self.num_attention_heads,
  																					)
  																					lowercase__							 =       [
  																					    (torch.zeros(lowerCamelCase__       ), torch.zeros(lowerCamelCase__       )) for _ in range(self.num_layers       )
  																					]
  
  									lowercase__							 =       common_inputs["""attention_mask"""]
  									if self.use_past:
  															lowercase__							 =       ordered_inputs["""attention_mask"""].dtype
  															lowercase__							 =       torch.cat(
  															    [ordered_inputs["""attention_mask"""], torch.ones(lowerCamelCase__       ,     lowerCamelCase__       ,     dtype=lowerCamelCase__       )]       ,     dim=1       )
  
  									return ordered_inputs
  
  
  
  			@property
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return 13
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  def  _A		(							lowercase__	):
  
  						# validate:
  						if not isinstance(lowercase__		,		lowercase__	) or not all(
  						    isinstance(lowercase__		,		lowercase__	) and item >= 0 for item in citations	):
  												raise ValueError("""The citations should be a list of non negative integers."""	)
  
  						citations.sort()
  						lowercase__							 =       len(lowercase__	)
  
  						for i in range(lowercase__	):
  												if citations[len_citations - 1 - i] <= i:
  																		return i
  
  						return len_citations
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
  
  
  
  
  