UpperCAmelCase							   =      (1, 153, 370, 371, 1634, 24678051, 115132219018763992565095597973971522401)
UpperCAmelCase							   =      (-153, -1, 0, 1.2, 200, "A", [], {}, None)







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	bool:
     if not isinstance(lowerCAmelCase_						,      lowerCAmelCase_       ) or n < 1:
          return False

     # Initialization of sum and number of digits.
     a  :    Any				=						0
     a  :    List[Any]				=						0
     a  :    List[Any]				=						n
     # Calculation of digits of the number
     while temp > 0:
          number_of_digits += 1
          temp //= 1_0
     # Dividing number into separate digits and find Armstrong number
     a  :    Optional[int]				=						n
     while temp > 0:
          a  :    Dict				=						temp % 1_0
          total += rem**number_of_digits
          temp //= 1_0
     return n == total







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	bool:
     if not isinstance(lowerCAmelCase_						,      lowerCAmelCase_       ) or n < 1:
          return False

     # Init a "histogram" of the digits
     a  :    str				=						[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
     a  :    int				=						0
     a  :    Optional[Any]				=						0
     a  :    List[str]				=						n
     while temp > 0:
          a			,							a  :    Optional[int]				=						divmod(lowerCAmelCase_						,      1_0       )
          digit_histogram[rem] += 1
          digit_total += 1

     for cnt, i in zip(lowerCAmelCase_						,      range(len(lowerCAmelCase_       )       )       ):
          total += cnt * i**digit_total

     return n == total







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	bool:
     if not isinstance(lowerCAmelCase_						,      lowerCAmelCase_       ) or n < 1:
          return False
     a  :    int				=						len(str(lowerCAmelCase_       )       )  # the power that all digits will be raised to
     # check if sum of each digit multiplied expo times is equal to number
     return n == sum(int(lowerCAmelCase_       ) ** expo for i in str(lowerCAmelCase_       )       )







def 	UpperCAmelCase       (		)	->	Optional[Any]:
     a  :    List[Any]				=						int(input('''Enter an integer to see if it is an Armstrong number: '''       ).strip()       )
     print(f"""{num} is {'' if armstrong_number(lowerCAmelCase_       ) else 'not '}an Armstrong number."""       )
     print(f"""{num} is {'' if narcissistic_number(lowerCAmelCase_       ) else 'not '}an Armstrong number."""       )
     print(f"""{num} is {'' if pluperfect_number(lowerCAmelCase_       ) else 'not '}an Armstrong number."""       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()
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 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						int 			=   'graphormer'
   UpperCamelCase_				:						Tuple 			=   ['past_key_values']
   def __init__(    self							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = 5_12 * 9							,     lowerCAmelCase_ = 5_12 * 3							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 1_28							,     lowerCAmelCase_ = 5							,     lowerCAmelCase_ = 10_24							,     lowerCAmelCase_ = "multi_hop"							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 12							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 32							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = "gelu"							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = 8							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = True							,     lowerCAmelCase_=0							,     lowerCAmelCase_=1							,     lowerCAmelCase_=2							,     **lowerCAmelCase_							,     )	->Optional[int]:
        a  :    List[Any]				=						num_classes
        a  :    Any				=						num_atoms
        a  :    Dict				=						num_in_degree
        a  :    int				=						num_out_degree
        a  :    Dict				=						num_edges
        a  :    Tuple				=						num_spatial
        a  :    Dict				=						num_edge_dis
        a  :    Optional[int]				=						edge_type
        a  :    List[Any]				=						multi_hop_max_dist
        a  :    Optional[int]				=						spatial_pos_max
        a  :    str				=						max_nodes
        a  :    Optional[Any]				=						num_hidden_layers
        a  :    Any				=						embedding_dim
        a  :    Dict				=						embedding_dim
        a  :    Dict				=						ffn_embedding_dim
        a  :    List[Any]				=						num_attention_heads
        a  :    List[str]				=						dropout
        a  :    int				=						attention_dropout
        a  :    Optional[Any]				=						layerdrop
        a  :    str				=						encoder_normalize_before
        a  :    Optional[int]				=						pre_layernorm
        a  :    Tuple				=						apply_graphormer_init
        a  :    int				=						activation_fn
        a  :    Any				=						embed_scale
        a  :    Optional[int]				=						freeze_embeddings
        a  :    Tuple				=						num_trans_layers_to_freeze
        a  :    List[str]				=						share_input_output_embed
        a  :    List[Any]				=						traceable
        a  :    Optional[Any]				=						q_noise
        a  :    Dict				=						qn_block_size

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

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