


import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class 				__SCREAMING_SNAKE_CASE   (			nn.Linear   ):


      def __init__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__ = True							,						SCREAMING_SNAKE_CASE__ = "constant"							,						SCREAMING_SNAKE_CASE__ = 0.0							,						SCREAMING_SNAKE_CASE__ = "topK"							,						):
            super(SCREAMING_SNAKE_CASE__							,						self		).__init__(in_features=SCREAMING_SNAKE_CASE__							,						out_features=SCREAMING_SNAKE_CASE__							,						bias=SCREAMING_SNAKE_CASE__		)
            assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
            lowercase			:   Any					   =		pruning_method

            if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
                  lowercase			:   Dict					   =		mask_scale
                  lowercase			:   Tuple					   =		mask_init
                  lowercase			:   Dict					   =		nn.Parameter(torch.empty(self.weight.size()		)		)
                  self.init_mask()


      def        __lowerCamelCase			(							self		):
            if self.mask_init == "constant":
                  init.constant_(self.mask_scores							,						val=self.mask_scale		)
            elif self.mask_init == "uniform":
                  init.uniform_(self.mask_scores							,						a=-self.mask_scale							,						b=self.mask_scale		)
            elif self.mask_init == "kaiming":
                  init.kaiming_uniform_(self.mask_scores							,						a=math.sqrt(5		)		)






      def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
            # Get the mask
            if self.pruning_method == "topK":
                  lowercase			:   Optional[Any]					   =		TopKBinarizer.apply(self.mask_scores							,						SCREAMING_SNAKE_CASE__		)
            elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
                  lowercase			:   List[str]					   =		'''sigmoied''' in self.pruning_method
                  lowercase			:   Optional[Any]					   =		ThresholdBinarizer.apply(self.mask_scores							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)
            elif self.pruning_method == "magnitude":
                  lowercase			:   Any					   =		MagnitudeBinarizer.apply(self.weight							,						SCREAMING_SNAKE_CASE__		)
            elif self.pruning_method == "l0":
                  lowercase   ,			lowercase   ,			lowercase			:   int					   =		-0.1, 1.1, 2 / 3
                  if self.training:
                        lowercase			:   Tuple					   =		torch.zeros_like(self.mask_scores		).uniform_().clamp(0.0001							,						0.9999		)
                        lowercase			:   Tuple					   =		torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b		)
                  else:
                        lowercase			:   Optional[int]					   =		torch.sigmoid(self.mask_scores		)
                  lowercase			:   Dict					   =		s * (r - l) + l
                  lowercase			:   List[str]					   =		s_bar.clamp(min=0.0							,						max=1.0		)
            # Mask weights with computed mask
            lowercase			:   Union[str, Any]					   =		mask * self.weight
            # Compute output (linear layer) with masked weights
            return nn.functional.linear(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						self.bias		)





import math




def 			__lowercase	(						_UpperCamelCase     )    ->Union[str, Any]:







      """simple docstring"""

      lowercase			:   List[Any]					   =		0
      if n % 2 == 0:
            count += 1
            while n % 2 == 0:
                  lowercase			:   Any					   =		int(n / 2     )
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

      lowercase			:   Optional[int]					   =		3

      while i <= int(math.sqrt(_UpperCamelCase     )     ):
            if n % i == 0:
                  count += 1
                  while n % i == 0:
                        lowercase			:   Optional[Any]					   =		int(n / i     )
            lowercase			:   str					   =		i + 2

      # this condition checks the prime
      # number n is greater than 2

      if n > 2:
            count += 1
      return count


if __name__ == "__main__":
  __a   				=      51_24_21_83
  print(F'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
  print(F'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')

