

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 A__     (			A			):



 """simple docstring"""







 _lowercase  :       Any  			=    '''graphormer'''
 _lowercase  :       str  			=    ['''past_key_values''']

 def __init__(		self			:						Any  ,    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_			:						str=1  ,    A_			:						str=2  ,    **A_			:						Union[str, Any]  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[Any]	   =     num_classes
      _lowerCAmelCase :       Any	   =     num_atoms
      _lowerCAmelCase :       Tuple	   =     num_in_degree
      _lowerCAmelCase :       Optional[Any]	   =     num_out_degree
      _lowerCAmelCase :       str	   =     num_edges
      _lowerCAmelCase :       Tuple	   =     num_spatial
      _lowerCAmelCase :       Tuple	   =     num_edge_dis
      _lowerCAmelCase :       str	   =     edge_type
      _lowerCAmelCase :       int	   =     multi_hop_max_dist
      _lowerCAmelCase :       List[Any]	   =     spatial_pos_max
      _lowerCAmelCase :       Any	   =     max_nodes
      _lowerCAmelCase :       Any	   =     num_hidden_layers
      _lowerCAmelCase :       Optional[Any]	   =     embedding_dim
      _lowerCAmelCase :       Union[str, Any]	   =     embedding_dim
      _lowerCAmelCase :       Union[str, Any]	   =     ffn_embedding_dim
      _lowerCAmelCase :       Dict	   =     num_attention_heads
      _lowerCAmelCase :       Optional[Any]	   =     dropout
      _lowerCAmelCase :       Optional[int]	   =     attention_dropout
      _lowerCAmelCase :       Optional[Any]	   =     layerdrop
      _lowerCAmelCase :       Optional[int]	   =     encoder_normalize_before
      _lowerCAmelCase :       Optional[int]	   =     pre_layernorm
      _lowerCAmelCase :       Tuple	   =     apply_graphormer_init
      _lowerCAmelCase :       str	   =     activation_fn
      _lowerCAmelCase :       Union[str, Any]	   =     embed_scale
      _lowerCAmelCase :       Optional[Any]	   =     freeze_embeddings
      _lowerCAmelCase :       List[str]	   =     num_trans_layers_to_freeze
      _lowerCAmelCase :       List[str]	   =     share_input_output_embed
      _lowerCAmelCase :       int	   =     traceable
      _lowerCAmelCase :       List[str]	   =     q_noise
      _lowerCAmelCase :       List[Any]	   =     qn_block_size

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

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









def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[int]:

     """simple docstring"""







     if power < 0:
          return -1
     base %= mod
     _lowerCAmelCase :       Tuple	   =     1

     while power > 0:
          if power & 1:
               _lowerCAmelCase :       Optional[Any]	   =     (result * base) % mod
          _lowerCAmelCase :       int	   =     power >> 1
          _lowerCAmelCase :       Optional[int]	   =     (base * base) % mod

     return result







def 						_snake_case		(     )							->   Optional[int]:

     """simple docstring"""







     print(modular_exponential(3		,				200		,				13					)					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  main()






