


'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()
class   __lowerCAmelCase						(unittest.TestCase					):





      '''simple docstring'''






      @property
      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







             torch.manual_seed(0			)
             lowercase__				      =			UNetaDModel(
                 block_out_channels=(32, 64)       ,						layers_per_block=2       ,						sample_size=32       ,						in_channels=3       ,						out_channels=3       ,						down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')       ,						up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')       ,						)
             return model



      def 				UpperCamelCase__		(self   :   List[str]			):

             '''simple docstring'''







             lowercase__				      =			'''cpu'''
             lowercase__				      =			self.dummy_uncond_unet
             lowercase__				      =			DDPMScheduler()

             lowercase__				      =			DDPMPipeline(unet=__SCREAMING_SNAKE_CASE       ,						scheduler=__SCREAMING_SNAKE_CASE			)
             ddpm.to(__SCREAMING_SNAKE_CASE			)
             ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

             lowercase__				      =			torch.Generator(device=__SCREAMING_SNAKE_CASE			).manual_seed(0			)
             lowercase__				      =			ddpm(generator=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=2       ,						output_type='''numpy'''			).images

             lowercase__				      =			torch.Generator(device=__SCREAMING_SNAKE_CASE			).manual_seed(0			)
             lowercase__				      =			ddpm(generator=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=2       ,						output_type='''numpy'''       ,						return_dict=__SCREAMING_SNAKE_CASE			)[0]

             lowercase__				      =			image[0, -3:, -3:, -1]
             lowercase__				      =			image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             lowercase__				      =			np.array(
                 [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.0_00, 1.1_99E-03, 2.6_48E-04, 5.1_01E-04]			)

             assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2






      def 				UpperCamelCase__		(self   :   str			):

             '''simple docstring'''







             lowercase__				      =			self.dummy_uncond_unet
             lowercase__				      =			DDPMScheduler(prediction_type='''sample'''			)

             lowercase__				      =			DDPMPipeline(unet=__SCREAMING_SNAKE_CASE       ,						scheduler=__SCREAMING_SNAKE_CASE			)
             ddpm.to(__SCREAMING_SNAKE_CASE			)
             ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

             lowercase__				      =			torch.manual_seed(0			)
             lowercase__				      =			ddpm(generator=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=2       ,						output_type='''numpy'''			).images

             lowercase__				      =			torch.manual_seed(0			)
             lowercase__				      =			ddpm(generator=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=2       ,						output_type='''numpy'''			)[0]

             lowercase__				      =			image[0, -3:, -3:, -1]
             lowercase__				      =			image_eps[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             lowercase__				      =			1E-2 if torch_device != '''mps''' else 3E-2
             assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance






@slow
@require_torch_gpu
class   __lowerCAmelCase						(unittest.TestCase					):





      '''simple docstring'''






      def 				UpperCamelCase__		(self   :   Tuple			):

             '''simple docstring'''







             lowercase__				      =			'''google/ddpm-cifar10-32'''

             lowercase__				      =			UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
             lowercase__				      =			DDPMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE			)

             lowercase__				      =			DDPMPipeline(unet=__SCREAMING_SNAKE_CASE       ,						scheduler=__SCREAMING_SNAKE_CASE			)
             ddpm.to(__SCREAMING_SNAKE_CASE			)
             ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

             lowercase__				      =			torch.manual_seed(0			)
             lowercase__				      =			ddpm(generator=__SCREAMING_SNAKE_CASE       ,						output_type='''numpy'''			).images

             lowercase__				      =			image[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             lowercase__				      =			np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85]			)
             assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2








'''simple docstring'''

import math
import os

import cva
import numpy as np

lowerCamelCase							:       Optional[Any]  						=     2_5_5.0

def _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	float:






       """simple docstring"""







       lowercase__				      =			np.mean((original - contrast) ** 2	)
       if mse == 0:
              return 100

       return 20 * math.logaa(PIXEL_MAX / math.sqrt(A	)	)

def _SCREAMING_SNAKE_CASE				()	->	None:






       """simple docstring"""







       lowercase__				      =			os.path.dirname(os.path.realpath(A	)	)
       # Loading images (original image and compressed image)
       lowercase__				      =			cva.imread(os.path.join(A  ,			'''image_data/original_image.png'''	)	)
       lowercase__				      =			cva.imread(os.path.join(A  ,			'''image_data/compressed_image.png'''	)  ,			1	)

       lowercase__				      =			cva.imread(os.path.join(A  ,			'''image_data/PSNR-example-base.png'''	)	)
       lowercase__				      =			cva.imread(
           os.path.join(A  ,			'''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(A  ,			A	)} dB"	)

       # # Value expected: 31.53dB (Wikipedia Example)
       print('''\n-- Second Test --'''	)
       print(f"PSNR value is {peak_signal_to_noise_ratio(A  ,			A	)} dB"	)


if __name__ == "__main__":
 main()




