code: 
  
  import math
  import os
  
  import cva
  import numpy as np
  
  A_		:			int										=   255.0
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     float:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			np.mean((original - contrast) ** 2      )
      if mse == 0:
          return 1_0_0
  
      return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(SCREAMING_SNAKE_CASE      )      )
  
  
  def       __a       (   )      ->     None:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE      )      )
      # Loading images (original image and compressed image)
      __UpperCAmelCase									=			cva.imread(os.path.join(SCREAMING_SNAKE_CASE       ,   '''image_data/original_image.png'''      )      )
      __UpperCAmelCase									=			cva.imread(os.path.join(SCREAMING_SNAKE_CASE       ,   '''image_data/compressed_image.png'''      )       ,   1      )
  
      __UpperCAmelCase									=			cva.imread(os.path.join(SCREAMING_SNAKE_CASE       ,   '''image_data/PSNR-example-base.png'''      )      )
      __UpperCAmelCase									=			cva.imread(
          os.path.join(SCREAMING_SNAKE_CASE       ,   '''image_data/PSNR-example-comp-10.jpg'''      )       ,   1      )
  
      # Value expected: 29.73dB
      print('''-- First Test --'''      )
      print(f'''PSNR value is {peak_signal_to_noise_ratio(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )} dB'''      )
  
      # # Value expected: 31.53dB (Wikipedia Example)
      print('''\n-- Second Test --'''      )
      print(f'''PSNR value is {peak_signal_to_noise_ratio(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )} dB'''      )
  
  
  if __name__ == "__main__":
         main()
  
  
  
  
  
style_context: 
  
  import numpy as np
  
  
  def       __a       (   SCREAMING_SNAKE_CASE      )      ->     Tuple:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase					,     __UpperCAmelCase									=			a.shape
      __UpperCAmelCase									=			min(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
      __UpperCAmelCase									=			np.eye(SCREAMING_SNAKE_CASE      )
      __UpperCAmelCase									=			a.copy()
  
      for k in range(t - 1      ):
          # select a column of modified matrix A':
          __UpperCAmelCase									=			r[k:, [k]]
          # construct first basis vector
          __UpperCAmelCase									=			np.zeros_like(SCREAMING_SNAKE_CASE      )
          __UpperCAmelCase									=			1.0
          # determine scaling factor
          __UpperCAmelCase									=			np.linalg.norm(SCREAMING_SNAKE_CASE      )
          # construct vector v for Householder reflection
          __UpperCAmelCase									=			x + np.sign(x[0]      ) * alpha * ea
          v /= np.linalg.norm(SCREAMING_SNAKE_CASE      )
  
          # construct the Householder matrix
          __UpperCAmelCase									=			np.eye(m - k      ) - 2.0 * v @ v.T
          # pad with ones and zeros as necessary
          __UpperCAmelCase									=			np.block([[np.eye(SCREAMING_SNAKE_CASE      ), np.zeros((k, m - k)      )], [np.zeros((m - k, k)      ), q_k]]      )
  
          __UpperCAmelCase									=			q @ q_k.T
          __UpperCAmelCase									=			q_k @ r
  
      return q, r
  
  
  if __name__ == "__main__":
         import doctest
  
         doctest.testmod()
  
  
  
  
  