



"""simple docstring"""






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  ):


					'''simple docstring'''





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

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



					def 						snake_case			(			self				:    Union[str, Any]	)->   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 						snake_case			(			self				:    int,    lowerCamelCase				:    torch.tensor,    lowerCamelCase				:    float	)->   Union[str, Any]:
								# Get the mask
								if self.pruning_method == "topK":
											lowerCamelCase__						:						Dict						      =TopKBinarizer.apply(self.mask_scores,    lowerCamelCase	)
								elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
											lowerCamelCase__						:						int						      ='''sigmoied''' in self.pruning_method
											lowerCamelCase__						:						Optional[Any]						      =ThresholdBinarizer.apply(self.mask_scores,    lowerCamelCase,    lowerCamelCase	)
								elif self.pruning_method == "magnitude":
											lowerCamelCase__						:						Optional[Any]						      =MagnitudeBinarizer.apply(self.weight,    lowerCamelCase	)
								elif self.pruning_method == "l0":
											lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__						:						str						      =-0.1, 1.1, 2 / 3
											if self.training:
														lowerCamelCase__						:						Any						      =torch.zeros_like(self.mask_scores	).uniform_().clamp(0.0_001,    0.9_999	)
														lowerCamelCase__						:						List[str]						      =torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b	)
											else:
														lowerCamelCase__						:						List[Any]						      =torch.sigmoid(self.mask_scores	)
											lowerCamelCase__						:						Optional[int]						      =s * (r - l) + l
											lowerCamelCase__						:						List[Any]						      =s_bar.clamp(min=0.0,    max=1.0	)
								# Mask weights with computed mask
								lowerCamelCase__						:						int						      =mask * self.weight
								# Compute output (linear layer) with masked weights
								return nn.functional.linear(lowerCamelCase,    lowerCamelCase,    self.bias	)









def        a( A			:       int					,	A			:       int					,	A			:       int			)						->			float:
					"""simple docstring"""





					if common_ratio == 1:
										# Formula for sum if common ratio is 1
										return num_of_terms * first_term

					# Formula for finding sum of n terms of a GeometricProgression
					return (first_term / (1 - common_ratio)) * (1 - common_ratio**num_of_terms)
