"""simple docstring"""






from pathlib import Path

import numpy as np
from PIL import Image


def 			UpperCAmelCase__       (				lowerCAmelCase__     :np.ndarray		) ->    np.ndarray:
      '''simple docstring'''


      lowercase							,							lowercase							,							lowercase														=	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
      return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b


def 			UpperCAmelCase__       (				lowerCAmelCase__     :np.ndarray		) ->    np.ndarray:
      '''simple docstring'''


      return (gray > 1_2_7) & (gray <= 2_5_5)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :np.ndarray					,		lowerCAmelCase__     :np.ndarray		) ->    np.ndarray:
      '''simple docstring'''


      lowercase														=	np.zeros_like(lowerCAmelCase__		)
      lowercase														=	np.zeros(
          (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)		)

      # Copy image to padded image
      lowercase														=	image

      # Iterate over image & apply kernel
      for x in range(image.shape[1]		):
            for y in range(image.shape[0]		):
                  lowercase														=	(
                      kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                  ).sum()
                  lowercase														=	int(summation > 0		)
      return output


if __name__ == "__main__":
       # read original image
       __lowerCAmelCase    :       Tuple					     =Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
       __lowerCAmelCase    :       int					     =np.array(Image.open(lena_path))
       # kernel to be applied
       __lowerCAmelCase    :       List[Any]					     =np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
       __lowerCAmelCase    :       List[str]					     =dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
       # Save the output image
       __lowerCAmelCase    :       Optional[Any]					     =Image.fromarray(output).convert("""RGB""")
       pil_img.save("""result_dilation.png""")




"""simple docstring"""






import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__lowerCAmelCase    :       Union[str, Any]					     =0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__lowerCAmelCase    :       Optional[int]					     =[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	WATERMARK_BITS
            lowercase														=	WatermarkEncoder()

            self.encoder.set_watermark("""bits"""		,       self.watermark						)







      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if images.shape[-1] < 256:
                  return images

            lowercase														=	(255 * (images / 2 + 0.5)).cpu().permute(0		,       2		,       3		,       1						).float().numpy()

            lowercase														=	[self.encoder.encode(__lowerCAmelCase		,       """dwtDct"""						) for image in images]

            lowercase														=	torch.from_numpy(np.array(__lowerCAmelCase						)						).permute(0		,       3		,       1		,       2						)

            lowercase														=	torch.clamp(2 * (images / 255 - 0.5)		,       min=-1.0		,       max=1.0						)
            return images



