




'''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[int]:



            '''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[Any]:



            '''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  )      ->  List[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'''



from typing import Any

import numpy as np







def 			_UpperCamelCase (_lowerCamelCase							:     np.ndarray	)->   bool:




     '''simple docstring'''





     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[1]

     # If matrix not symmetric, exit right away.
     if np.allclose(_lowerCamelCase   ,	matrix.T	) is False:
          return False

     # Get eigenvalues and eignevectors for a symmetric matrix.
     __snake_case					,    __snake_case    					=  np.linalg.eigh(_lowerCamelCase	)

     # Check sign of all eigenvalues.
     # np.all returns a value of type np.bool_
     return bool(np.all(eigen_values > 0	)	)







def 			_UpperCamelCase (_lowerCamelCase							:     int	)->   Any:




     '''simple docstring'''





     __snake_case    					=  np.random.randn(_lowerCamelCase   ,	_lowerCamelCase	)
     __snake_case    					=  np.dot(_lowerCamelCase   ,	random_matrix.T	)
     assert _is_matrix_spd(_lowerCamelCase	)
     return spd_matrix







def 			_UpperCamelCase (_lowerCamelCase							:     np.ndarray   ,	_lowerCamelCase							:     np.ndarray   ,	_lowerCamelCase							:     int = 10_00   ,	_lowerCamelCase							:     float = 1E-8   ,	)->   Any:




     '''simple docstring'''





     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[1]
     assert np.shape(_lowerCamelCase	)[0] == np.shape(_lowerCamelCase	)[0]
     assert _is_matrix_spd(_lowerCamelCase	)

     # Initialize solution guess, residual, search direction.
     __snake_case    					=  np.zeros((np.shape(_lowerCamelCase	)[0], 1)	)
     __snake_case    					=  np.copy(_lowerCamelCase	)
     __snake_case    					=  np.copy(_lowerCamelCase	)

     # Set initial errors in solution guess and residual.
     __snake_case    					=  1E9
     __snake_case    					=  1E9
     __snake_case    					=  1E9

     # Set iteration counter to threshold number of iterations.
     __snake_case    					=  0

     while error > tol:
          # Save this value so we only calculate the matrix-vector product once.
          __snake_case    					=  np.dot(_lowerCamelCase   ,	_lowerCamelCase	)

          # The main algorithm.

          # Update search direction magnitude.
          __snake_case    					=  np.dot(ra.T   ,	_lowerCamelCase	) / np.dot(pa.T   ,	_lowerCamelCase	)
          # Update solution guess.
          __snake_case    					=  xa + alpha * pa
          # Calculate new residual.
          __snake_case    					=  ra - alpha * w
          # Calculate new Krylov subspace scale.
          __snake_case    					=  np.dot(r.T   ,	_lowerCamelCase	) / np.dot(ra.T   ,	_lowerCamelCase	)
          # Calculate new A conjuage search direction.
          __snake_case    					=  r + beta * pa

          # Calculate errors.
          __snake_case    					=  np.linalg.norm(r - ra	)
          __snake_case    					=  np.linalg.norm(x - xa	)
          __snake_case    					=  np.maximum(_lowerCamelCase   ,	_lowerCamelCase	)

          # Update variables.
          __snake_case    					=  np.copy(_lowerCamelCase	)
          __snake_case    					=  np.copy(_lowerCamelCase	)
          __snake_case    					=  np.copy(_lowerCamelCase	)

          # Update number of iterations.
          iterations += 1
          if iterations > max_iterations:
               break

     return x







def 			_UpperCamelCase ()->   None:




     '''simple docstring'''





     __snake_case    					=  3
     __snake_case    					=  _create_spd_matrix(_lowerCamelCase	)
     __snake_case    					=  np.random.randn(_lowerCamelCase   ,	1	)
     __snake_case    					=  np.dot(_lowerCamelCase   ,	_lowerCamelCase	)

     # Numpy solution.
     __snake_case    					=  np.linalg.solve(_lowerCamelCase   ,	_lowerCamelCase	)

     # Our implementation.
     __snake_case    					=  conjugate_gradient(_lowerCamelCase   ,	_lowerCamelCase	)

     # Ensure both solutions are close to x_true (and therefore one another).
     assert np.linalg.norm(x_numpy - x_true	) <= 1E-6
     assert np.linalg.norm(x_conjugate_gradient - x_true	) <= 1E-6


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     test_conjugate_gradient()






