





import torch
from torch import autograd

class 			A_ (autograd.Function    ):
	@staticmethod
	def   _lowercase       (					_A							,      _A							,      _A							,      _A	):






				'''simple docstring'''







				UpperCAmelCase     	=       inputs.numel()
				UpperCAmelCase     	=       int(0.0_05 * nb_elems	) + 1
				if sigmoid:
							UpperCAmelCase     	=       (torch.sigmoid(_A	) > threshold).type(inputs.type()	)
				else:
							UpperCAmelCase     	=       (inputs > threshold).type(inputs.type()	)
				if mask.sum() < nb_min:
							# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
							UpperCAmelCase     	=       inputs.flatten().kthvalue(max(nb_elems - nb_min							,      1	)	).values
							UpperCAmelCase     	=       (inputs > k_threshold).type(inputs.type()	)
				return mask







	@staticmethod
	def   _lowercase       (					_A							,      _A	):






				'''simple docstring'''







				return gradOutput, None, None

class 			A_ (autograd.Function    ):
	@staticmethod
	def   _lowercase       (					_A							,      _A							,      _A	):






				'''simple docstring'''







				UpperCAmelCase     	=       inputs.clone()
				UpperCAmelCase	,    UpperCAmelCase     	=       inputs.flatten().sort(descending=_A	)
				UpperCAmelCase     	=       int(threshold * inputs.numel()	)

				# flat_out and mask access the same memory.
				UpperCAmelCase     	=       mask.flatten()
				UpperCAmelCase     	=       0
				UpperCAmelCase     	=       1
				return mask







	@staticmethod
	def   _lowercase       (					_A							,      _A	):






				'''simple docstring'''







				return gradOutput, None

class 			A_ (a_    ):
	@staticmethod
	def   _lowercase       (					_A							,      _A	):






				'''simple docstring'''







				UpperCAmelCase     	=       inputs.clone()
				UpperCAmelCase	,    UpperCAmelCase     	=       inputs.abs().flatten().sort(descending=_A	)
				UpperCAmelCase     	=       int(threshold * inputs.numel()	)

				# flat_out and mask access the same memory.
				UpperCAmelCase     	=       mask.flatten()
				UpperCAmelCase     	=       0
				UpperCAmelCase     	=       1
				return mask










from __future__ import annotations





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__				)						->       bool:







			'''simple docstring'''

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

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

			return check_digit % 10 == 0


if __name__ == "__main__":
				import doctest

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



