




'''simple docstring'''






import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






class 					lowercase_			(	nn.Linear       ):





     def __init__(    self    :				Union[str, Any]    ,       __lowerCamelCase    :				int    ,       __lowerCamelCase    :				int    ,       __lowerCamelCase    :				bool = True    ,       __lowerCamelCase    :				str = "constant"    ,       __lowerCamelCase    :				float = 0.0    ,       __lowerCamelCase    :				str = "topK"    ,       ):
           super(__lowerCamelCase    ,       self ).__init__(in_features=__lowerCamelCase    ,       out_features=__lowerCamelCase    ,       bias=__lowerCamelCase )
           assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
           snake_case__					:      Optional[Any]  			=						pruning_method

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





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           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    :				Tuple    ,       __lowerCamelCase    :				torch.tensor    ,       __lowerCamelCase    :				float ):
           # Get the mask
           if self.pruning_method == "topK":
                 snake_case__					:      List[Any]  			=						TopKBinarizer.apply(self.mask_scores    ,       __lowerCamelCase )
           elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
                 snake_case__					:      Any  			=						'sigmoied' in self.pruning_method
                 snake_case__					:      int  			=						ThresholdBinarizer.apply(self.mask_scores    ,       __lowerCamelCase    ,       __lowerCamelCase )
           elif self.pruning_method == "magnitude":
                 snake_case__					:      Dict  			=						MagnitudeBinarizer.apply(self.weight    ,       __lowerCamelCase )
           elif self.pruning_method == "l0":
                 snake_case__			,       snake_case__			,       snake_case__					:      Optional[Any]  			=						-0.1, 1.1, 2 / 3
                 if self.training:
                       snake_case__					:      Union[str, Any]  			=						torch.zeros_like(self.mask_scores ).uniform_().clamp(0.0_0_0_1    ,       0.9_9_9_9 )
                       snake_case__					:      Optional[int]  			=						torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b )
                 else:
                       snake_case__					:      str  			=						torch.sigmoid(self.mask_scores )
                 snake_case__					:      int  			=						s * (r - l) + l
                 snake_case__					:      int  			=						s_bar.clamp(min=0.0    ,       max=1.0 )
           # Mask weights with computed mask
           snake_case__					:      List[str]  			=						mask * self.weight
           # Compute output (linear layer) with masked weights
           return nn.functional.linear(__lowerCamelCase    ,       __lowerCamelCase    ,       self.bias )






'''simple docstring'''






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE = 1000		)						->      int:
      snake_case__					:      List[str]  			=						0
      snake_case__					:      int  			=						(n - 1) // 3
      total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
      snake_case__					:      Optional[Any]  			=						(n - 1) // 5
      total += ((terms) * (10 + (terms - 1) * 5)) // 2
      snake_case__					:      Tuple  			=						(n - 1) // 15
      total -= ((terms) * (30 + (terms - 1) * 15)) // 2
      return total


if __name__ == "__main__":
      print(F'{solution() = }')
