





import torch
from torch import autograd





class 						_A( autograd.Function       ):


    """simple docstring"""


    @staticmethod
    def  UpperCAmelCase_		(      _A					, _A					, _A					, _A					):
         __A     :						Any          =    inputs.numel()
         __A     :						List[Any]          =    int(0.0_0_5 * nb_elems					) + 1
         if sigmoid:
              __A     :						Dict          =    (torch.sigmoid(__SCREAMING_SNAKE_CASE					) > threshold).type(inputs.type()					)
         else:
              __A     :						List[str]          =    (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     :						int          =    inputs.flatten().kthvalue(max(nb_elems - nb_min					, 1					)					).values
              __A     :						int          =    (inputs > k_threshold).type(inputs.type()					)
         return mask





    @staticmethod
    def  UpperCAmelCase_		(      _A					, _A					):
         return gradOutput, None, None





class 						_A( autograd.Function       ):


    """simple docstring"""


    @staticmethod
    def  UpperCAmelCase_		(      _A					, _A					, _A					):
         __A     :						Dict          =    inputs.clone()
         __A   ,    __A     :						str          =    inputs.flatten().sort(descending=__SCREAMING_SNAKE_CASE					)
         __A     :						Tuple          =    int(threshold * inputs.numel()					)

         # flat_out and mask access the same memory.
         __A     :						Tuple          =    mask.flatten()
         __A     :						List[str]          =    0
         __A     :						List[str]          =    1
         return mask





    @staticmethod
    def  UpperCAmelCase_		(      _A					, _A					):
         return gradOutput, None





class 						_A( UpperCamelCase_       ):


    """simple docstring"""


    @staticmethod
    def  UpperCAmelCase_		(      _A					, _A					):
         __A     :						Union[str, Any]          =    inputs.clone()
         __A   ,    __A     :						Union[str, Any]          =    inputs.abs().flatten().sort(descending=__SCREAMING_SNAKE_CASE					)
         __A     :						Dict          =    int(threshold * inputs.numel()					)

         # flat_out and mask access the same memory.
         __A     :						str          =    mask.flatten()
         __A     :						List[str]          =    0
         __A     :						int          =    1
         return mask











from __future__ import annotations

import math
import random







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				list[int]:
					__A     :						int          =    d * e - 1
					__A     :						List[str]          =    0
					__A     :						Optional[Any]          =    0
					while p == 0:
										__A     :						List[Any]          =    random.randint(2 ,						n - 1  )
										__A     :						Optional[int]          =    k
										while True:
															if t % 2 == 0:
																				__A     :						Tuple          =    t // 2
																				__A     :						List[str]          =    (g**t) % n
																				__A     :						List[Any]          =    math.gcd(x - 1 ,						a  )
																				if x > 1 and y > 1:
																									__A     :						List[str]          =    y
																									__A     :						str          =    n // y
																									break  # find the correct factors
															else:
																				break  # t is not divisible by 2, break and choose another g
					return sorted([p, q]  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




