code: 
  
  
  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		)
  
  
style_context: 
  
  
  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}''')
  
  