"""simple docstring"""







from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION






class 							__snake_case(					snake_case_				,	snake_case_	):


    '''simple docstring'''





    def __init__(  self	):




         '''simple docstring'''



         super().__init__()

         self.register_buffer('watermark_image'					,       torch.zeros((62, 62, 4)	)	)
         __A					:					Any								=		None

    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase=None	):




         '''simple docstring'''



         __A					:					Optional[Any]								=		images[0].height
         __A					:					str								=		images[0].width

         __A					:					Optional[int]								=		sample_size or h

         __A					:					Optional[int]								=		min(h / sample_size					,       w / sample_size	)
         __A					:					List[str]								=		(int(h / coef	), int(w / coef	)) if coef < 1 else (h, w)

         __A					:					List[Any]								=		1024**2, img_w * img_h
         __A					:					Dict								=		(Sa / Sa) ** 0.5
         __A					:					Optional[Any]								=		int(K * 62	), img_w - int(14 * K	), img_h - int(14 * K	)

         if self.watermark_image_as_pil is None:
              __A					:					Union[str, Any]								=		self.watermark_image.to(torch.uinta	).cpu().numpy()
              __A					:					Any								=		Image.fromarray(SCREAMING_SNAKE_CASE_					,       mode='RGBA'	)
              __A					:					List[str]								=		watermark_image

         __A					:					Union[str, Any]								=		self.watermark_image_as_pil.resize(
             (wm_size, wm_size)					,       PIL_INTERPOLATION['bicubic']					,       reducing_gap=SCREAMING_SNAKE_CASE_	)

         for pil_img in images:
              pil_img.paste(SCREAMING_SNAKE_CASE_					,       box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)					,       mask=wm_img.split()[-1]	)

         return images









"""simple docstring"""





import math
import os

import cva
import numpy as np

_lowercase						   =     2_55.0

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


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

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

def 		lowerCAmelCase				(			):
						'''simple docstring'''


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

						snake_case__							:       Optional[Any]				      =       cva.imread(os.path.join(lowerCamelCase   ,      '''image_data/PSNR-example-base.png'''		)		)
						snake_case__							:       Tuple				      =       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()


