



def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= str(_lowerCAmelCase).strip()
					if not oct_string:
										raise ValueError("Empty string was passed to the function")
					UpperCamelCase_			= oct_string[0] == "-"
					if is_negative:
										UpperCamelCase_			= oct_string[1:]
					if not oct_string.isdigit() or not all(0 <= int(_lowerCAmelCase) <= 7 for char in oct_string):
										raise ValueError("Non-octal value was passed to the function")
					UpperCamelCase_			= 0
					for char in oct_string:
										UpperCamelCase_			= 8 * decimal_number + int(_lowerCAmelCase)
					if is_negative:
										UpperCamelCase_			= -decimal_number
					return decimal_number


if __name__ == "__main__":
	from doctest import testmod

	testmod()







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


__UpperCAmelCase			:  List[str]										=				logging.get_logger(__name__)

__UpperCAmelCase			:  int										=				{
    # 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        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	"""graphormer"""
       lowerCAmelCase__ 						=	["""past_key_values"""]




       def __init__(    self					:       List[Any]				,							A					:       int = 1				,							A					:       int = 512 * 9				,							A					:       int = 512 * 3				,							A					:       int = 512				,							A					:       int = 512				,							A					:       int = 512				,							A					:       int = 128				,							A					:       int = 5				,							A					:       int = 1_024				,							A					:       str = "multi_hop"				,							A					:       int = 512				,							A					:       bool = False				,							A					:       int = 12				,							A					:       int = 768				,							A					:       int = 768				,							A					:       int = 32				,							A					:       float = 0.1				,							A					:       float = 0.1				,							A					:       float = 0.0				,							A					:       bool = False				,							A					:       bool = False				,							A					:       bool = False				,							A					:       str = "gelu"				,							A					:       float = None				,							A					:       bool = False				,							A					:       int = 0				,							A					:       bool = False				,							A					:       float = 0.0				,							A					:       int = 8				,							A					:       int = None				,							A					:       int = None				,							A					:       bool = True				,							A					:       bool = True				,							A					:       Any=0				,							A					:       Optional[int]=1				,							A					:       Any=2				,							**A					:       Optional[Any]				,							):
         __snake_case:		List[str]					    =		num_classes
         __snake_case:		int					    =		num_atoms
         __snake_case:		List[str]					    =		num_in_degree
         __snake_case:		str					    =		num_out_degree
         __snake_case:		Tuple					    =		num_edges
         __snake_case:		Optional[int]					    =		num_spatial
         __snake_case:		int					    =		num_edge_dis
         __snake_case:		Tuple					    =		edge_type
         __snake_case:		str					    =		multi_hop_max_dist
         __snake_case:		List[str]					    =		spatial_pos_max
         __snake_case:		Optional[Any]					    =		max_nodes
         __snake_case:		Optional[int]					    =		num_hidden_layers
         __snake_case:		Union[str, Any]					    =		embedding_dim
         __snake_case:		Union[str, Any]					    =		embedding_dim
         __snake_case:		Optional[int]					    =		ffn_embedding_dim
         __snake_case:		List[str]					    =		num_attention_heads
         __snake_case:		Any					    =		dropout
         __snake_case:		Dict					    =		attention_dropout
         __snake_case:		Any					    =		layerdrop
         __snake_case:		Dict					    =		encoder_normalize_before
         __snake_case:		Any					    =		pre_layernorm
         __snake_case:		Optional[int]					    =		apply_graphormer_init
         __snake_case:		List[str]					    =		activation_fn
         __snake_case:		Optional[int]					    =		embed_scale
         __snake_case:		Dict					    =		freeze_embeddings
         __snake_case:		Any					    =		num_trans_layers_to_freeze
         __snake_case:		int					    =		share_input_output_embed
         __snake_case:		Dict					    =		traceable
         __snake_case:		str					    =		q_noise
         __snake_case:		Union[str, Any]					    =		qn_block_size

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

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






