





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


_UpperCamelCase       :				Any           =			logging.get_logger(__name__)

_UpperCamelCase       :				Union[str, Any]           =			{
    # 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):

  """simple docstring"""







  lowerCamelCase__     	=       '''graphormer'''
  lowerCamelCase__     	=       ['''past_key_values''']

  def __init__(   self    ,							UpperCAmelCase = 1    ,							UpperCAmelCase = 5_12 * 9    ,							UpperCAmelCase = 5_12 * 3    ,							UpperCAmelCase = 5_12    ,							UpperCAmelCase = 5_12    ,							UpperCAmelCase = 5_12    ,							UpperCAmelCase = 1_28    ,							UpperCAmelCase = 5    ,							UpperCAmelCase = 10_24    ,							UpperCAmelCase = "multi_hop"    ,							UpperCAmelCase = 5_12    ,							UpperCAmelCase = False    ,							UpperCAmelCase = 12    ,							UpperCAmelCase = 7_68    ,							UpperCAmelCase = 7_68    ,							UpperCAmelCase = 32    ,							UpperCAmelCase = 0.1    ,							UpperCAmelCase = 0.1    ,							UpperCAmelCase = 0.0    ,							UpperCAmelCase = False    ,							UpperCAmelCase = False    ,							UpperCAmelCase = False    ,							UpperCAmelCase = "gelu"    ,							UpperCAmelCase = None    ,							UpperCAmelCase = False    ,							UpperCAmelCase = 0    ,							UpperCAmelCase = False    ,							UpperCAmelCase = 0.0    ,							UpperCAmelCase = 8    ,							UpperCAmelCase = None    ,							UpperCAmelCase = None    ,							UpperCAmelCase = True    ,							UpperCAmelCase = True    ,							UpperCAmelCase=0    ,							UpperCAmelCase=1    ,							UpperCAmelCase=2    ,							**UpperCAmelCase    ,							)->   Optional[int]:
     __A    =  num_classes
     __A    =  num_atoms
     __A    =  num_in_degree
     __A    =  num_out_degree
     __A    =  num_edges
     __A    =  num_spatial
     __A    =  num_edge_dis
     __A    =  edge_type
     __A    =  multi_hop_max_dist
     __A    =  spatial_pos_max
     __A    =  max_nodes
     __A    =  num_hidden_layers
     __A    =  embedding_dim
     __A    =  embedding_dim
     __A    =  ffn_embedding_dim
     __A    =  num_attention_heads
     __A    =  dropout
     __A    =  attention_dropout
     __A    =  layerdrop
     __A    =  encoder_normalize_before
     __A    =  pre_layernorm
     __A    =  apply_graphormer_init
     __A    =  activation_fn
     __A    =  embed_scale
     __A    =  freeze_embeddings
     __A    =  num_trans_layers_to_freeze
     __A    =  share_input_output_embed
     __A    =  traceable
     __A    =  q_noise
     __A    =  qn_block_size

     # These parameters are here for future extensions
     # atm, the model only supports self attention
     __A    =  kdim
     __A    =  vdim
     __A    =  self_attention
     __A    =  bias

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








from __future__ import annotations
def  __UpperCamelCase    (						snake_case      )    ->		bool:


   '''simple docstring'''







   __A    =  42
   __A    =  list(snake_case      )
   __A   ,		__A    =  _vector[:-1], int(_vector[-1]      )
   __A    =  [int(snake_case      ) for digit in __vector]

   vector.reverse()
   for i, digit in enumerate(snake_case      ):
      if i & 1 == 0:
         __A    =  digit * 2
         if doubled > 9:
            doubled -= 9
         check_digit += doubled
      else:
         check_digit += digit

   return check_digit % 1_0 == 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    assert is_luhn("""79927398713""")
    assert not is_luhn("""79927398714""")

