



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


__UpperCAmelCase    	=  logging.get_logger(__name__)

__UpperCAmelCase    	=  {
    # 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 						lowerCamelCase__			(		_a ):
						_lowerCAmelCase             =			'''graphormer'''
						_lowerCAmelCase             =			['''past_key_values''']


						def __init__(			self    :	List[str]   ,							_a    :	int = 1   ,							_a    :	int = 5_1_2 * 9   ,							_a    :	int = 5_1_2 * 3   ,							_a    :	int = 5_1_2   ,							_a    :	int = 5_1_2   ,							_a    :	int = 5_1_2   ,							_a    :	int = 1_2_8   ,							_a    :	int = 5   ,							_a    :	int = 1_0_2_4   ,							_a    :	str = "multi_hop"   ,							_a    :	int = 5_1_2   ,							_a    :	bool = False   ,							_a    :	int = 1_2   ,							_a    :	int = 7_6_8   ,							_a    :	int = 7_6_8   ,							_a    :	int = 3_2   ,							_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    :	List[Any]=0   ,							_a    :	Optional[Any]=1   ,							_a    :	Union[str, Any]=2   ,							**_a    :	List[str]   ,							):
													a__:							Optional[int]									=num_classes
													a__:							Dict									=num_atoms
													a__:							Tuple									=num_in_degree
													a__:							List[str]									=num_out_degree
													a__:							List[Any]									=num_edges
													a__:							Union[str, Any]									=num_spatial
													a__:							List[str]									=num_edge_dis
													a__:							Optional[Any]									=edge_type
													a__:							List[str]									=multi_hop_max_dist
													a__:							Dict									=spatial_pos_max
													a__:							Dict									=max_nodes
													a__:							Any									=num_hidden_layers
													a__:							int									=embedding_dim
													a__:							Any									=embedding_dim
													a__:							Tuple									=ffn_embedding_dim
													a__:							Union[str, Any]									=num_attention_heads
													a__:							Dict									=dropout
													a__:							int									=attention_dropout
													a__:							Optional[Any]									=layerdrop
													a__:							Optional[int]									=encoder_normalize_before
													a__:							str									=pre_layernorm
													a__:							int									=apply_graphormer_init
													a__:							List[str]									=activation_fn
													a__:							int									=embed_scale
													a__:							List[Any]									=freeze_embeddings
													a__:							Union[str, Any]									=num_trans_layers_to_freeze
													a__:							Optional[Any]									=share_input_output_embed
													a__:							str									=traceable
													a__:							List[Any]									=q_noise
													a__:							Dict									=qn_block_size

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

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










from typing import Callable





def     __lowerCamelCase							(   __magic_name__						:	Callable    ):

							def wrapper(__magic_name__						:	Optional[Any]    ):
														a__:							List[Any]									=function_with_docstring.__doc__
														return target_function

							return wrapper





