




import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey






class 	A	:
     """simple docstring"""

     def __init__(			self     :      Any		,     A__     :      Any		,     A__     :      int	) ->				Optional[int]:
           lowerCamelCase__					     = 0
           # max greyscale value for #FFFFFF
           lowerCamelCase__					     = int(self.get_greyscale(2_5_5		,     2_5_5		,     2_5_5	)	)

           if not self.min_threshold < threshold < self.max_threshold:
                 lowerCamelCase__					     = F"""Factor value should be from 0 to {self.max_threshold}"""
                 raise ValueError(A__	)

           lowerCamelCase__					     = input_img
           lowerCamelCase__					     = threshold
           lowerCamelCase__     ,						lowerCamelCase__					     = self.input_img.shape[1], self.input_img.shape[0]

           # error table size (+4 columns and +1 row) greater than input image because of
           # lack of if statements
           lowerCamelCase__					     = [
               [0 for _ in range(self.height + 4	)] for __ in range(self.width + 1	)
           ]
           lowerCamelCase__					     = np.ones((self.width, self.height, 3)		,     np.uinta	) * 2_5_5




     @classmethod
     def        __snake_case      (			cls     :      Dict		,     A__     :      int		,     A__     :      int		,     A__     :      int	) ->				float:
           return 0.114 * blue + 0.587 * green + 0.2126 * red




     def        __snake_case      (			self     :      Union[str, Any]	) ->				None:
           for y in range(self.height	):
                 for x in range(self.width	):
                       lowerCamelCase__					     = int(self.get_greyscale(*self.input_img[y][x]	)	)
                       if self.threshold > greyscale + self.error_table[y][x]:
                             lowerCamelCase__					     = (0, 0, 0)
                             lowerCamelCase__					     = greyscale + self.error_table[x][y]
                       else:
                             lowerCamelCase__					     = (2_5_5, 2_5_5, 2_5_5)
                             lowerCamelCase__					     = greyscale + self.error_table[x][y] - 2_5_5
                       self.error_table[y][x + 1] += int(8 / 3_2 * current_error	)
                       self.error_table[y][x + 2] += int(4 / 3_2 * current_error	)
                       self.error_table[y + 1][x] += int(8 / 3_2 * current_error	)
                       self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error	)
                       self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error	)
                       self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error	)
                       self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error	)


if __name__ == "__main__":
      # create Burke's instances with original images in greyscale
      __SCREAMING_SNAKE_CASE			:      Optional[Any]      			=[
          Burkes(imread("""image_data/lena.jpg""", 1), threshold)
          for threshold in (1, 126, 130, 140)
      ]

      for burkes in burkes_instances:
            burkes.process()

      for burkes in burkes_instances:
            imshow(
                F"""Original image with dithering threshold: {burkes.threshold}""",
                burkes.output_img,
            )

      waitKey(0)
      destroyAllWindows()





from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int	) ->				Union[str, Any]:
           super().__init__()
           self.register_modules(unet=A__		,     scheduler=A__	)




     @torch.no_grad()
     def __call__(			self     :      int		,     A__     :      int = 1		,     A__     :      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     A__     :      int = 1_0_0_0		,     A__     :      Optional[str] = "pil"		,     A__     :      bool = True		,     ) ->				Union[ImagePipelineOutput, Tuple]:
           # Sample gaussian noise to begin loop
           if isinstance(self.unet.config.sample_size		,     A__	):
                 lowerCamelCase__					     = (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
           else:
                 lowerCamelCase__					     = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

           if self.device.type == "mps":
                 # randn does not work reproducibly on mps
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__	)
                 lowerCamelCase__					     = image.to(self.device	)
           else:
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__		,     device=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__					     = self.unet(A__		,     A__	).sample

                 # 2. compute previous image: x_t -> x_t-1
                 lowerCamelCase__					     = self.scheduler.step(A__		,     A__		,     A__		,     generator=A__	).prev_sample

           lowerCamelCase__					     = (image / 2 + 0.5).clamp(0		,     1	)
           lowerCamelCase__					     = image.cpu().permute(0		,     2		,     3		,     1	).numpy()
           if output_type == "pil":
                 lowerCamelCase__					     = self.numpy_to_pil(A__	)

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=A__	)