




'''simple docstring'''



import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer





class 			lowerCAmelCase      (       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"			,     )					->     Optional[Any]:



            '''simple docstring'''




            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"]
            __snake_case			    =     pruning_method

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


       def 					lowerCAmelCase					(      self     )					->     List[str]:



            '''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 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     Any:



            '''simple docstring'''




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












'''simple docstring'''



def 	_UpperCamelCase       (_lowerCamelCase							:   list[list[int]]      ,		_lowerCamelCase							:   list[list[int]]		)->      list[list[int]]:




     '''simple docstring'''





     __snake_case			    =     []
     __snake_case			    =     len(_lowerCamelCase		)
     for i in range(_lowerCamelCase		):
          __snake_case			    =     []
          for j in range(_lowerCamelCase		):
               __snake_case			    =     0
               for k in range(_lowerCamelCase		):
                    __snake_case			    =     val + matrix_a[i][k] * matrix_b[k][j]
               list_a.append(_lowerCamelCase		)
          matrix_c.append(_lowerCamelCase		)
     return matrix_c







def 	_UpperCamelCase       (_lowerCamelCase							:   int		)->      list[list[int]]:




     '''simple docstring'''





     return [[int(row == column		) for column in range(_lowerCamelCase		)] for row in range(_lowerCamelCase		)]







def 	_UpperCamelCase       (_lowerCamelCase							:   int		)->      int:




     '''simple docstring'''





     if n <= 1:
          return n
     __snake_case			    =     identity(2		)
     __snake_case			    =     [[1, 1], [1, 0]]
     __snake_case			    =     n - 1
     while n > 0:
          if n % 2 == 1:
               __snake_case			    =     multiply(_lowerCamelCase      ,		_lowerCamelCase		)
          __snake_case			    =     multiply(_lowerCamelCase      ,		_lowerCamelCase		)
          __snake_case			    =     int(n / 2		)
     return res_matrix[0][0]







def 	_UpperCamelCase       (_lowerCamelCase							:   int		)->      int:




     '''simple docstring'''





     if n <= 1:
          return n
     __snake_case			    =     0
     __snake_case			    =     1
     for _ in range(2      ,		n + 1		):
          __snake_case    ,			__snake_case			    =     fiba, fiba + fiba
     return fiba







def 	_UpperCamelCase       ()->      None:




     '''simple docstring'''





     for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
          __snake_case			    =     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(_lowerCamelCase		)} and using bruteforce is '''
              f'''{nth_fibonacci_bruteforce(_lowerCamelCase		)}\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()






