code: 
  '''simple docstring'''
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __A          =    logging.get_logger(__name__)
  
  __A          =    {
      # pcqm4mv1 now deprecated
      """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
      # See all Graphormer models at https://huggingface.co/models?filter=graphormer
  }
  
  
  
  
  
  class 			A							(       _SCREAMING_SNAKE_CASE				):
  			lowerCamelCase							:							List[Any]       		=  """graphormer"""
  			lowerCamelCase							:							str       		=  ["""past_key_values"""]
  
  
  
  			def __init__(					self       ,     lowerCamelCase__ = 1       ,     lowerCamelCase__ = 512 * 9       ,     lowerCamelCase__ = 512 * 3       ,     lowerCamelCase__ = 512       ,     lowerCamelCase__ = 512       ,     lowerCamelCase__ = 512       ,     lowerCamelCase__ = 128       ,     lowerCamelCase__ = 5       ,     lowerCamelCase__ = 1_024       ,     lowerCamelCase__ = "multi_hop"       ,     lowerCamelCase__ = 512       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = 12       ,     lowerCamelCase__ = 768       ,     lowerCamelCase__ = 768       ,     lowerCamelCase__ = 32       ,     lowerCamelCase__ = 0.1       ,     lowerCamelCase__ = 0.1       ,     lowerCamelCase__ = 0.0       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = "gelu"       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = 0       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = 0.0       ,     lowerCamelCase__ = 8       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = True       ,     lowerCamelCase__ = True       ,     lowerCamelCase__=0       ,     lowerCamelCase__=1       ,     lowerCamelCase__=2       ,     **lowerCamelCase__       ,     )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       num_classes
  									lowercase__							 =       num_atoms
  									lowercase__							 =       num_in_degree
  									lowercase__							 =       num_out_degree
  									lowercase__							 =       num_edges
  									lowercase__							 =       num_spatial
  									lowercase__							 =       num_edge_dis
  									lowercase__							 =       edge_type
  									lowercase__							 =       multi_hop_max_dist
  									lowercase__							 =       spatial_pos_max
  									lowercase__							 =       max_nodes
  									lowercase__							 =       num_hidden_layers
  									lowercase__							 =       embedding_dim
  									lowercase__							 =       embedding_dim
  									lowercase__							 =       ffn_embedding_dim
  									lowercase__							 =       num_attention_heads
  									lowercase__							 =       dropout
  									lowercase__							 =       attention_dropout
  									lowercase__							 =       layerdrop
  									lowercase__							 =       encoder_normalize_before
  									lowercase__							 =       pre_layernorm
  									lowercase__							 =       apply_graphormer_init
  									lowercase__							 =       activation_fn
  									lowercase__							 =       embed_scale
  									lowercase__							 =       freeze_embeddings
  									lowercase__							 =       num_trans_layers_to_freeze
  									lowercase__							 =       share_input_output_embed
  									lowercase__							 =       traceable
  									lowercase__							 =       q_noise
  									lowercase__							 =       qn_block_size
  
  									# These parameters are here for future extensions
  									# atm, the model only supports self attention
  									lowercase__							 =       kdim
  									lowercase__							 =       vdim
  									lowercase__							 =       self_attention
  									lowercase__							 =       bias
  
  									super().__init__(
  									    pad_token_id=__A       ,     bos_token_id=__A       ,     eos_token_id=__A       ,     **__A       ,     )
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()
  
  
  
  
  
  
  