code: 
  
  import math
  import os
  
  import cva
  import numpy as np
  
  lowerCamelCase     =      255.0
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				float			,   _A   :				float							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=np.mean((original - contrast) ** 2							)
    if mse == 0:
      return 1_00
  
    return 20 * math.logaa(PIXEL_MAX / math.sqrt(UpperCamelCase__							)							)
  
  
  
  
  def     UpperCAmelCase__       (    ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=os.path.dirname(os.path.realpath(UpperCamelCase__							)							)
    # Loading images (original image and compressed image)
    a__						=cva.imread(os.path.join(UpperCamelCase__			,   '''image_data/original_image.png'''							)							)
    a__						=cva.imread(os.path.join(UpperCamelCase__			,   '''image_data/compressed_image.png'''							)			,   1							)
  
    a__						=cva.imread(os.path.join(UpperCamelCase__			,   '''image_data/PSNR-example-base.png'''							)							)
    a__						=cva.imread(
        os.path.join(UpperCamelCase__			,   '''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(UpperCamelCase__			,   UpperCamelCase__							)} dB"""							)
  
    # # Value expected: 31.53dB (Wikipedia Example)
    print('''\n-- Second Test --'''							)
    print(F"""PSNR value is {peak_signal_to_noise_ratio(UpperCamelCase__			,   UpperCamelCase__							)} dB"""							)
  
  
  if __name__ == "__main__":
    main()
  
style_context: 
  
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  
  
  @dataclass
  # Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			Union[List[PIL.Image.Image], np.ndarray]
     lowerCamelCase__				:			Optional[List[bool]]
  
  
  if is_transformers_available() and is_torch_available():
         from .modeling_roberta_series import RobertaSeriesModelWithTransformation
         from .pipeline_alt_diffusion import AltDiffusionPipeline
         from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
  