






'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class    _snake_case   :

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	Any:
										a_									=   0
										# max greyscale value for #FFFFFF
										a_									=   int(self.get_greyscale(255   , 255   , 255     )     )

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

										a_									=   input_img
										a_									=   threshold
										a_							,					a_									=   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
										a_									=   [
										    [0 for _ in range(self.height + 4     )] for __ in range(self.width + 1     )
										]
										a_									=   np.ones((self.width, self.height, 3)   , np.uinta     ) * 255




					@classmethod
					def  __SCREAMING_SNAKE_CASE (     cls   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	float:
										return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	None:
										for y in range(self.height     ):
															for x in range(self.width     ):
																				a_									=   int(self.get_greyscale(*self.input_img[y][x]     )     )
																				if self.threshold > greyscale + self.error_table[y][x]:
																									a_									=   (0, 0, 0)
																									a_									=   greyscale + self.error_table[x][y]
																				else:
																									a_									=   (255, 255, 255)
																									a_									=   greyscale + self.error_table[x][y] - 255
																				self.error_table[y][x + 1] += int(8 / 32 * current_error     )
																				self.error_table[y][x + 2] += int(4 / 32 * current_error     )
																				self.error_table[y + 1][x] += int(8 / 32 * current_error     )
																				self.error_table[y + 1][x + 1] += int(4 / 32 * current_error     )
																				self.error_table[y + 1][x + 2] += int(2 / 32 * current_error     )
																				self.error_table[y + 1][x - 1] += int(4 / 32 * current_error     )
																				self.error_table[y + 1][x - 2] += int(2 / 32 * current_error     )


if __name__ == "__main__":
						# create Burke's instances with original images in greyscale
						__lowerCAmelCase							=[
						    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()









'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      42
					_UpperCamelCase		 =      42




					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	int:
										super().__init__()

										a_									=   PNDMScheduler.from_config(scheduler.config     )

										self.register_modules(unet=UpperCAmelCase__   , scheduler=UpperCAmelCase__     )




					@torch.no_grad()
					def __call__(     self   , UpperCAmelCase__ = 1   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = None   , UpperCAmelCase__ = "pil"   , UpperCAmelCase__ = True   , **UpperCAmelCase__   , )      ->	Union[ImagePipelineOutput, Tuple]:
										a_									=   randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   , generator=UpperCAmelCase__   , device=self.device   , )

										self.scheduler.set_timesteps(UpperCAmelCase__     )
										for t in self.progress_bar(self.scheduler.timesteps     ):
															a_									=   self.unet(UpperCAmelCase__   , UpperCAmelCase__     ).sample

															a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

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

										if not return_dict:
															return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__     )

