

"""simple docstring"""




__A	:       List[str]        =						(1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
__A	:       Any        =						(-1_53, -1, 0, 1.2, 2_00, 'A', [], {}, None)
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					if not isinstance(__a     ,				__a ) or n < 1:
										return False

					# Initialization of sum and number of digits.
					A     =   0
					A     =   0
					A     =   n
					# Calculation of digits of the number
					while temp > 0:
										number_of_digits += 1
										temp //= 10
					# Dividing number into separate digits and find Armstrong number
					A     =   n
					while temp > 0:
										A     =   temp % 10
										total += rem**number_of_digits
										temp //= 10
					return n == total
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					if not isinstance(__a     ,				__a ) or n < 1:
										return False

					# Init a "histogram" of the digits
					A     =   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
					A     =   0
					A     =   0
					A     =   n
					while temp > 0:
										A     =   divmod(__a     ,				10 )
										digit_histogram[rem] += 1
										digit_total += 1

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

					return n == total
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					if not isinstance(__a     ,				__a ) or n < 1:
										return False
					A     =   len(str(__a ) )  # 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(__a ) ** expo for i in str(__a ) )
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A     =   int(input("Enter an integer to see if it is an Armstrong number: " ).strip() )
					print(F"""{num} is {"" if armstrong_number(__a ) else "not "}an Armstrong number.""" )
					print(F"""{num} is {"" if narcissistic_number(__a ) else "not "}an Armstrong number.""" )
					print(F"""{num} is {"" if pluperfect_number(__a ) else "not "}an Armstrong number.""" )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()












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


_SCREAMING_SNAKE_CASE   						=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   						=		{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Optional[Any]			   =							"nystromformer"






						def __init__(   self   :   List[str]		, _A   :   Optional[Any]=30000		, _A   :   str=768		, _A   :   Optional[int]=12		, _A   :   Dict=12		, _A   :   Any=3072		, _A   :   str="gelu_new"		, _A   :   Optional[Any]=0.1		, _A   :   int=0.1		, _A   :   Dict=510		, _A   :   List[Any]=2		, _A   :   Optional[Any]=64		, _A   :   Union[str, Any]=64		, _A   :   List[Any]=65		, _A   :   Dict=False		, _A   :   str=0.0_2		, _A   :   Dict=1E-5		, _A   :   List[Any]=1		, _A   :   Optional[int]=0		, _A   :   List[Any]=2		, **_A   :   int		, )				->     int:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						vocab_size
							snake_case_		:      str           =						max_position_embeddings
							snake_case_		:      Tuple           =						hidden_size
							snake_case_		:      Union[str, Any]           =						num_hidden_layers
							snake_case_		:      str           =						num_attention_heads
							snake_case_		:      Optional[Any]           =						intermediate_size
							snake_case_		:      str           =						hidden_act
							snake_case_		:      str           =						hidden_dropout_prob
							snake_case_		:      Optional[Any]           =						attention_probs_dropout_prob
							snake_case_		:      Union[str, Any]           =						initializer_range
							snake_case_		:      Union[str, Any]           =						type_vocab_size
							snake_case_		:      Dict           =						segment_means_seq_len
							snake_case_		:      Tuple           =						num_landmarks
							snake_case_		:      Any           =						conv_kernel_size
							snake_case_		:      List[str]           =						inv_coeff_init_option
							snake_case_		:      str           =						layer_norm_eps
							super().__init__(pad_token_id=_A		, bos_token_id=_A		, eos_token_id=_A		, **_A						)

