



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    {
				    """sld_warmup_steps""": 15,
				    """sld_guidance_scale""": 20,
				    """sld_threshold""": 0.0,
				    """sld_momentum_scale""": 0.0,
				    """sld_mom_beta""": 0.0,
				}
				lowercase__     =    {
				    """sld_warmup_steps""": 10,
				    """sld_guidance_scale""": 10_00,
				    """sld_threshold""": 0.0_1,
				    """sld_momentum_scale""": 0.3,
				    """sld_mom_beta""": 0.4,
				}
				lowercase__     =    {
				    """sld_warmup_steps""": 7,
				    """sld_guidance_scale""": 20_00,
				    """sld_threshold""": 0.0_2_5,
				    """sld_momentum_scale""": 0.5,
				    """sld_mom_beta""": 0.7,
				}
				lowercase__     =    {
				    """sld_warmup_steps""": 0,
				    """sld_guidance_scale""": 50_00,
				    """sld_threshold""": 1.0,
				    """sld_momentum_scale""": 0.5,
				    """sld_mom_beta""": 0.7,
				}




@dataclass
class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    42
				lowercase__     =    42
				lowercase__     =    42
				lowercase__     =    42


if is_transformers_available() and is_torch_available():
	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
	from .safety_checker import SafeStableDiffusionSafetyChecker




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







class     __lowerCamelCase     :


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = 0
								# max greyscale value for #FFFFFF
								lowercase_			 = int(self.get_greyscale(255						,       255						,       255  )  )

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

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




							@classmethod
							def 				A__      (  cls						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       float:






								'''simple docstring'''






								return 0.114 * blue + 0.587 * green + 0.2126 * red




							def 				A__      (  self  )     ->       None:






								'''simple docstring'''






								for y in range(self.height  ):
									for x in range(self.width  ):
										lowercase_			 = int(self.get_greyscale(*self.input_img[y][x]  )  )
										if self.threshold > greyscale + self.error_table[y][x]:
											lowercase_			 = (0, 0, 0)
											lowercase_			 = greyscale + self.error_table[x][y]
										else:
											lowercase_			 = (255, 255, 255)
											lowercase_			 = 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
							SCREAMING_SNAKE_CASE__       			=				[
							    Burkes(imread("""image_data/lena.jpg""", 1), threshold)
							    for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
							]

							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()

