
"""simple docstring"""




import torch
from torch import autograd
class SCREAMING_SNAKE_CASE       (      autograd.Function					):






	'''simple docstring'''







	@staticmethod
	def    _UpperCamelCase							(  __magic_name__ :Dict       ,    __magic_name__ :torch.tensor       ,    __magic_name__ :float       ,    __magic_name__ :bool			)			->   List[str]:

			'''simple docstring'''






			a__   		=     inputs.numel()
			a__   		=     int(0.005 * nb_elems			) + 1
			if sigmoid:
					a__   		=     (torch.sigmoid(__magic_name__			) > threshold).type(inputs.type()			)
			else:
					a__   		=     (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
					a__   		=     inputs.flatten().kthvalue(max(nb_elems - nb_min       ,    1			)			).values
					a__   		=     (inputs > k_threshold).type(inputs.type()			)
			return mask






	@staticmethod
	def    _UpperCamelCase							(  __magic_name__ :Optional[Any]       ,    __magic_name__ :Optional[Any]			)			->   Tuple:

			'''simple docstring'''






			return gradOutput, None, None

class SCREAMING_SNAKE_CASE       (      autograd.Function					):






	'''simple docstring'''







	@staticmethod
	def    _UpperCamelCase							(  __magic_name__ :List[str]       ,    __magic_name__ :torch.tensor       ,    __magic_name__ :float			)			->   str:

			'''simple docstring'''






			a__   		=     inputs.clone()
			a__		,	a__   		=     inputs.flatten().sort(descending=__magic_name__			)
			a__   		=     int(threshold * inputs.numel()			)

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






	@staticmethod
	def    _UpperCamelCase							(  __magic_name__ :Tuple       ,    __magic_name__ :Union[str, Any]			)			->   Optional[Any]:

			'''simple docstring'''






			return gradOutput, None

class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	@staticmethod
	def    _UpperCamelCase							(  __magic_name__ :torch.tensor       ,    __magic_name__ :float			)			->   Optional[Any]:

			'''simple docstring'''






			a__   		=     inputs.clone()
			a__		,	a__   		=     inputs.abs().flatten().sort(descending=__magic_name__			)
			a__   		=     int(threshold * inputs.numel()			)

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






"""simple docstring"""




def __snake_case (      UpperCamelCase = 10**6    )       ->    int:


		"""simple docstring"""





		if n <= 0:
				raise ValueError('''Please enter an integer greater than 0'''    )

		a__   		=     list(range(n + 1    )    )
		for number in range(2						,					n + 1    ):
				if phi[number] == number:
						phi[number] -= 1
						for multiple in range(number * 2						,					n + 1						,					UpperCamelCase    ):
								a__   		=     (phi[multiple] // number) * (number - 1)

		a__   		=     1
		for number in range(1						,					n + 1    ):
				if (answer / phi[answer]) < (number / phi[number]):
						a__   		=     number

		return answer


if __name__ == "__main__":
					print(solution())




