






import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer





class __lowerCamelCase					( nn.Linear):





	"""simple docstring"""






	def __init__(				self			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase = True			,       UpperCAmelCase = "constant"			,       UpperCAmelCase = 0.0			,       UpperCAmelCase = "topK"			,       ):
							"""simple docstring"""







							super(UpperCAmelCase			,       self       ).__init__(in_features=UpperCAmelCase			,       out_features=UpperCAmelCase			,       bias=UpperCAmelCase       )
							assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
							_UpperCAmelCase							   =     pruning_method

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

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							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 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase       ):
							"""simple docstring"""







							if self.pruning_method == "topK":
													_UpperCAmelCase							   =     TopKBinarizer.apply(self.mask_scores			,       UpperCAmelCase       )
							elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
													_UpperCAmelCase							   =     'sigmoied' in self.pruning_method
													_UpperCAmelCase							   =     ThresholdBinarizer.apply(self.mask_scores			,       UpperCAmelCase			,       UpperCAmelCase       )
							elif self.pruning_method == "magnitude":
													_UpperCAmelCase							   =     MagnitudeBinarizer.apply(self.weight			,       UpperCAmelCase       )
							elif self.pruning_method == "l0":
													_UpperCAmelCase   ,					_UpperCAmelCase   ,					_UpperCAmelCase							   =     -0.1, 1.1, 2 / 3
													if self.training:
																			_UpperCAmelCase							   =     torch.zeros_like(self.mask_scores       ).uniform_().clamp(0.00_01			,       0.99_99       )
																			_UpperCAmelCase							   =     torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b       )
													else:
																			_UpperCAmelCase							   =     torch.sigmoid(self.mask_scores       )
													_UpperCAmelCase							   =     s * (r - l) + l
													_UpperCAmelCase							   =     s_bar.clamp(min=0.0			,       max=1.0       )
							# Mask weights with computed mask
							_UpperCAmelCase							   =     mask * self.weight
							# Compute output (linear layer) with masked weights
							return nn.functional.linear(UpperCAmelCase			,       UpperCAmelCase			,       self.bias       )








from __future__ import annotations


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						)->    list[float | int]:





						"""simple docstring"""



						if not all((nth_term, start_term_a, common_ratio_r)      ):
												return []
						_UpperCAmelCase							   =     []
						_UpperCAmelCase							   =     1
						_UpperCAmelCase							   =     common_ratio_r
						for _ in range(int(__lowerCAmelCase      )      ):
												if not series:
																		series.append(__lowerCAmelCase      )
												else:
																		power += 1
																		series.append(float(start_term_a * multiple      )      )
																		_UpperCAmelCase							   =     pow(float(__lowerCAmelCase      )					,						__lowerCAmelCase      )
						return series


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		_a       =    float(input('''Enter the last number (n term) of the Geometric Series'''))
		_a       =    float(input('''Enter the starting term (a) of the Geometric Series'''))
		_a       =    float(
		    input('''Enter the common ratio between two terms (r) of the Geometric Series''')
		)
		print('''Formula of Geometric Series => a + ar + ar^2 ... +ar^n''')
		print(geometric_series(nth_term, start_term_a, common_ratio_r))
