
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_  :       Union[str, Any]					      =     logging.get_logger(__name__)

lowerCamelCase_  :       List[str]					      =     {
    # 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 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  Dict    					=       """graphormer"""
			__UpperCamelCase					:  Any    					=       ["""past_key_values"""]

			def __init__(				self       :				List[str]  ,		snake_case_       :				int = 1  ,		snake_case_       :				int = 512 * 9  ,		snake_case_       :				int = 512 * 3  ,		snake_case_       :				int = 512  ,		snake_case_       :				int = 512  ,		snake_case_       :				int = 512  ,		snake_case_       :				int = 128  ,		snake_case_       :				int = 5  ,		snake_case_       :				int = 1024  ,		snake_case_       :				str = "multi_hop"  ,		snake_case_       :				int = 512  ,		snake_case_       :				bool = False  ,		snake_case_       :				int = 12  ,		snake_case_       :				int = 768  ,		snake_case_       :				int = 768  ,		snake_case_       :				int = 32  ,		snake_case_       :				float = 0.1  ,		snake_case_       :				float = 0.1  ,		snake_case_       :				float = 0.0  ,		snake_case_       :				bool = False  ,		snake_case_       :				bool = False  ,		snake_case_       :				bool = False  ,		snake_case_       :				str = "gelu"  ,		snake_case_       :				float = None  ,		snake_case_       :				bool = False  ,		snake_case_       :				int = 0  ,		snake_case_       :				bool = False  ,		snake_case_       :				float = 0.0  ,		snake_case_       :				int = 8  ,		snake_case_       :				int = None  ,		snake_case_       :				int = None  ,		snake_case_       :				bool = True  ,		snake_case_       :				bool = True  ,		snake_case_       :				Optional[Any]=0  ,		snake_case_       :				Union[str, Any]=1  ,		snake_case_       :				List[Any]=2  ,		**snake_case_       :				str  ,		):
							UpperCamelCase_:							Optional[int]		=  num_classes
							UpperCamelCase_:							Optional[int]		=  num_atoms
							UpperCamelCase_:							Tuple		=  num_in_degree
							UpperCamelCase_:							Any		=  num_out_degree
							UpperCamelCase_:							Dict		=  num_edges
							UpperCamelCase_:							List[Any]		=  num_spatial
							UpperCamelCase_:							int		=  num_edge_dis
							UpperCamelCase_:							str		=  edge_type
							UpperCamelCase_:							Any		=  multi_hop_max_dist
							UpperCamelCase_:							List[str]		=  spatial_pos_max
							UpperCamelCase_:							int		=  max_nodes
							UpperCamelCase_:							Optional[Any]		=  num_hidden_layers
							UpperCamelCase_:							Optional[int]		=  embedding_dim
							UpperCamelCase_:							int		=  embedding_dim
							UpperCamelCase_:							int		=  ffn_embedding_dim
							UpperCamelCase_:							Union[str, Any]		=  num_attention_heads
							UpperCamelCase_:							Dict		=  dropout
							UpperCamelCase_:							Optional[int]		=  attention_dropout
							UpperCamelCase_:							Dict		=  layerdrop
							UpperCamelCase_:							List[str]		=  encoder_normalize_before
							UpperCamelCase_:							List[Any]		=  pre_layernorm
							UpperCamelCase_:							str		=  apply_graphormer_init
							UpperCamelCase_:							Union[str, Any]		=  activation_fn
							UpperCamelCase_:							Union[str, Any]		=  embed_scale
							UpperCamelCase_:							Any		=  freeze_embeddings
							UpperCamelCase_:							str		=  num_trans_layers_to_freeze
							UpperCamelCase_:							Optional[Any]		=  share_input_output_embed
							UpperCamelCase_:							Optional[Any]		=  traceable
							UpperCamelCase_:							Optional[Any]		=  q_noise
							UpperCamelCase_:							List[str]		=  qn_block_size

							# These parameters are here for future extensions
							# atm, the model only supports self attention
							UpperCamelCase_:							Tuple		=  kdim
							UpperCamelCase_:							int		=  vdim
							UpperCamelCase_:							Optional[Any]		=  self_attention
							UpperCamelCase_:							str		=  bias

							super().__init__(
							    pad_token_id=snake_case_  ,		bos_token_id=snake_case_  ,		eos_token_id=snake_case_  ,		**snake_case_  ,		)








def 				A__    (  lowerCamelCase = 20  )     ->		int:

				try:
								UpperCamelCase_:							int		=  int(lowerCamelCase  )
				except (TypeError, ValueError):
								raise TypeError("""Parameter n must be int or castable to int."""  )
				if n <= 0:
								raise ValueError("""Parameter n must be greater than or equal to one."""  )
				UpperCamelCase_:							Any		=  0
				while 1:
								i += n * (n - 1)
								UpperCamelCase_:							List[Any]		=  0
								for j in range(2   ,			lowerCamelCase  ):
												if i % j != 0:
																UpperCamelCase_:							Union[str, Any]		=  1
																break
								if nfound == 0:
												if i == 0:
																UpperCamelCase_:							List[str]		=  1
												return i
				return None


if __name__ == "__main__":
							print(F"""{solution() = }""")






