





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




						def __init__(   self				,							a_				,							a_     ):
							super().__init__()
							self.register_modules(unet=a_				,							scheduler=a_     )




						@torch.no_grad()
						def __call__(   self				,							a_ = 1				,							a_ = None				,							a_ = 50				,							a_ = "pil"				,							a_ = True				,							**a_				,							):
							lowerCamelCase_			:							Optional[int]      =       torch.randn(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,							generator=a_				,							)
							lowerCamelCase_			:							str      =       image.to(self.device     )

							# set step values
							self.scheduler.set_timesteps(a_     )

							for t in self.progress_bar(self.scheduler.timesteps     ):
								# 1. predict noise model_output
								lowerCamelCase_			:							Tuple      =       self.unet(a_				,							a_     ).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								lowerCamelCase_			:							Optional[int]      =       self.scheduler.step(a_				,							a_				,							a_     ).prev_sample

							lowerCamelCase_			:							List[str]      =       (image / 2 + 0.5).clamp(0				,							1     )
							lowerCamelCase_			:							List[str]      =       image.cpu().permute(0				,							2				,							3				,							1     ).numpy()
							if output_type == "pil":
								lowerCamelCase_			:							int      =       self.numpy_to_pil(a_     )

							if not return_dict:
								return (image,), "This is a local test"

							return ImagePipelineOutput(images=a_     ), "This is a local test"





import math
import os

import cva
import numpy as np

__magic_name__       					=      2_55.0







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							List[Any]      =       np.mean((original - contrast) ** 2)
	if mse == 0:
		return 100

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







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       os.path.dirname(os.path.realpath(lowerCAmelCase_))
	# Loading images (original image and compressed image)
	lowerCamelCase_			:							Optional[int]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/original_image.png"))
	lowerCamelCase_			:							List[str]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/compressed_image.png")      ,					1)

	lowerCamelCase_			:							str      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-base.png"))
	lowerCamelCase_			:							int      =       cva.imread(
	    os.path.join(lowerCAmelCase_      ,					"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(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")

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


if __name__ == "__main__":
							main()

