



"""simple docstring"""


def 				_lowerCAmelCase   (     lowercase_	,			lowercase_		):
						UpperCAmelCase    =				[]
						UpperCAmelCase    =				len(lowercase_		)
						for i in range(lowercase_		):
												UpperCAmelCase    =				[]
												for j in range(lowercase_		):
																		UpperCAmelCase    =				0
																		for k in range(lowercase_		):
																								UpperCAmelCase    =				val + matrix_a[i][k] * matrix_b[k][j]
																		list_a.append(lowercase_		)
												matrix_c.append(lowercase_		)
						return matrix_c

def 				_lowerCAmelCase   (     lowercase_		):
						return [[int(row == column		) for column in range(lowercase_		)] for row in range(lowercase_		)]

def 				_lowerCAmelCase   (     lowercase_		):
						if n <= 1:
												return n
						UpperCAmelCase    =				identity(2		)
						UpperCAmelCase    =				[[1, 1], [1, 0]]
						UpperCAmelCase    =				n - 1
						while n > 0:
												if n % 2 == 1:
																		UpperCAmelCase    =				multiply(lowercase_	,			lowercase_		)
												UpperCAmelCase    =				multiply(lowercase_	,			lowercase_		)
												UpperCAmelCase    =				int(n / 2		)
						return res_matrix[0][0]

def 				_lowerCAmelCase   (     lowercase_		):
						if n <= 1:
												return n
						UpperCAmelCase    =				0
						UpperCAmelCase    =				1
						for _ in range(2	,			n + 1		):
												UpperCAmelCase   ,					UpperCAmelCase    =				fiba, fiba + fiba
						return fiba

def 				_lowerCAmelCase   (     ):
						for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
												UpperCAmelCase    =				int(''.join(c for c in ordinal if c in '0123456789'		)		)  # 1000th --> 1000
												print(
												    F"""{ordinal} fibonacci number using matrix exponentiation is """
												    F"""{nth_fibonacci_matrix(lowercase_		)} and using bruteforce is """
												    F"""{nth_fibonacci_bruteforce(lowercase_		)}\n"""		)
						# from timeit import timeit
						# print(timeit("nth_fibonacci_matrix(1000000)",
						#              "from main import nth_fibonacci_matrix", number=5))
						# print(timeit("nth_fibonacci_bruteforce(1000000)",
						#              "from main import nth_fibonacci_bruteforce", number=5))
						# 2.3342058970001744
						# 57.256506615000035


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()










"""simple docstring"""


import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class    A_    (  nn.Linear       ):



						"""simple docstring"""






						def __init__(      self				:List[str]     ,						lowercase_				:int     ,						lowercase_				:int     ,						lowercase_				:bool = True     ,						lowercase_				:str = "constant"     ,						lowercase_				:float = 0.0     ,						lowercase_				:str = "topK"     ,						)       ->    Union[str, Any]:
												super(lowercase_     ,						self      ).__init__(in_features=lowercase_     ,						out_features=lowercase_     ,						bias=lowercase_      )
												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				:Optional[Any]      )       ->    Dict:
												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				:Dict     ,						lowercase_				:torch.tensor     ,						lowercase_				:float      )       ->    List[str]:
												# Get the mask
												if self.pruning_method == "topK":
																		UpperCAmelCase    =				TopKBinarizer.apply(self.mask_scores     ,						lowercase_      )
												elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																		UpperCAmelCase    =				'sigmoied' in self.pruning_method
																		UpperCAmelCase    =				ThresholdBinarizer.apply(self.mask_scores     ,						lowercase_     ,						lowercase_      )
												elif self.pruning_method == "magnitude":
																		UpperCAmelCase    =				MagnitudeBinarizer.apply(self.weight     ,						lowercase_      )
												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(0.0001     ,						0.9999      )
																								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(lowercase_     ,						lowercase_     ,						self.bias      )





