

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





class 			snake_case_     :







			'''simple docstring'''

			def __init__(  self				:       Tuple							,     _UpperCamelCase				:       str							,     _UpperCamelCase				:       int							)   ->str:
									snake_case_     =			0
									# max greyscale value for #FFFFFF
									snake_case_     =			int(self.get_greyscale(2_5_5							,     2_5_5							,     2_5_5							)							)

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

									snake_case_     =			input_img
									snake_case_     =			threshold
									snake_case_,					snake_case_     =			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
									snake_case_     =			[
									    [0 for _ in range(self.height + 4							)] for __ in range(self.width + 1							)
									]
									snake_case_     =			np.ones((self.width, self.height, 3)							,     np.uinta							) * 2_5_5







			@classmethod
			def        snake_case__(  cls				:       List[Any]							,     _UpperCamelCase				:       int							,     _UpperCamelCase				:       int							,     _UpperCamelCase				:       int							)   ->float:
									return 0.114 * blue + 0.587 * green + 0.2126 * red







			def        snake_case__(  self				:       str							)   ->None:
									for y in range(self.height							):
															for x in range(self.width							):
																					snake_case_     =			int(self.get_greyscale(*self.input_img[y][x]							)							)
																					if self.threshold > greyscale + self.error_table[y][x]:
																											snake_case_     =			(0, 0, 0)
																											snake_case_     =			greyscale + self.error_table[x][y]
																					else:
																											snake_case_     =			(2_5_5, 2_5_5, 2_5_5)
																											snake_case_     =			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
					lowerCAmelCase_ 			=			[
					    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
					    for threshold in (1, 1_26, 1_30, 1_40)
					]

					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 Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


lowerCAmelCase_ 			=			logging.get_logger(__name__)  # pylint: disable=invalid-name





class 			snake_case_     (	__A      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     str		    =       ["safety_checker", "feature_extractor"]







			def __init__(  self				:       Any							,     _UpperCamelCase				:       AutoencoderKL							,     _UpperCamelCase				:       CLIPTextModel							,     _UpperCamelCase				:       CLIPTokenizer							,     _UpperCamelCase				:       UNetaDConditionModel							,     _UpperCamelCase				:       Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]							,     _UpperCamelCase				:       StableDiffusionSafetyChecker							,     _UpperCamelCase				:       CLIPImageProcessor							,     _UpperCamelCase				:       bool = True							,     )   ->Any:
									super().__init__()
									if hasattr(scheduler.config							,     '''steps_offset'''							) and scheduler.config.steps_offset != 1:
															snake_case_     =			(
															    f'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
															    f''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
															    '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
															    ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
															    ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
															    ''' file'''
															)
															deprecate('''steps_offset!=1'''							,     '''1.0.0'''							,     _UpperCamelCase							,     standard_warn=_UpperCamelCase							)
															snake_case_     =			dict(scheduler.config							)
															snake_case_     =			1
															snake_case_     =			FrozenDict(_UpperCamelCase							)

									self.register_modules(
									    vae=_UpperCamelCase							,     text_encoder=_UpperCamelCase							,     tokenizer=_UpperCamelCase							,     unet=_UpperCamelCase							,     scheduler=_UpperCamelCase							,     safety_checker=_UpperCamelCase							,     feature_extractor=_UpperCamelCase							,     )
									self.register_to_config(requires_safety_checker=_UpperCamelCase							)







			@property
			def        snake_case__(  self				:       int							)   ->Dict[str, Any]:
									return {k: getattr(self							,     _UpperCamelCase							) for k in self.config.keys() if not k.startswith('''_'''							)}







			def        snake_case__(  self				:       Dict							,     _UpperCamelCase				:       Optional[Union[str, int]] = "auto"							)   ->Union[str, Any]:
									if slice_size == "auto":
															# half the attention head size is usually a good trade-off between
															# speed and memory
															snake_case_     =			self.unet.config.attention_head_dim // 2
									self.unet.set_attention_slice(_UpperCamelCase							)







			def        snake_case__(  self				:       str							)   ->Tuple:
									self.enable_attention_slicing(_UpperCamelCase							)







			@torch.no_grad()
			def        snake_case__(  self				:       Tuple							,     _UpperCamelCase				:       Union[str, List[str]]							,     _UpperCamelCase				:       Union[torch.FloatTensor, PIL.Image.Image]							,     _UpperCamelCase				:       Union[torch.FloatTensor, PIL.Image.Image]							,     _UpperCamelCase				:       float = 0.8							,     _UpperCamelCase				:       Optional[int] = 5_0							,     _UpperCamelCase				:       Optional[float] = 7.5							,     _UpperCamelCase				:       Optional[Union[str, List[str]]] = None							,     _UpperCamelCase				:       Optional[int] = 1							,     _UpperCamelCase				:       Optional[float] = 0.0							,     _UpperCamelCase				:       Optional[torch.Generator] = None							,     _UpperCamelCase				:       Optional[str] = "pil"							,     _UpperCamelCase				:       bool = True							,     _UpperCamelCase				:       Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,     _UpperCamelCase				:       int = 1							,     )   ->Dict:
									# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
									return StableDiffusionInpaintPipelineLegacy(**self.components							)(
									    prompt=_UpperCamelCase							,     image=_UpperCamelCase							,     mask_image=_UpperCamelCase							,     strength=_UpperCamelCase							,     num_inference_steps=_UpperCamelCase							,     guidance_scale=_UpperCamelCase							,     negative_prompt=_UpperCamelCase							,     num_images_per_prompt=_UpperCamelCase							,     eta=_UpperCamelCase							,     generator=_UpperCamelCase							,     output_type=_UpperCamelCase							,     return_dict=_UpperCamelCase							,     callback=_UpperCamelCase							,     )







			@torch.no_grad()
			def        snake_case__(  self				:       Optional[Any]							,     _UpperCamelCase				:       Union[str, List[str]]							,     _UpperCamelCase				:       Union[torch.FloatTensor, PIL.Image.Image]							,     _UpperCamelCase				:       float = 0.8							,     _UpperCamelCase				:       Optional[int] = 5_0							,     _UpperCamelCase				:       Optional[float] = 7.5							,     _UpperCamelCase				:       Optional[Union[str, List[str]]] = None							,     _UpperCamelCase				:       Optional[int] = 1							,     _UpperCamelCase				:       Optional[float] = 0.0							,     _UpperCamelCase				:       Optional[torch.Generator] = None							,     _UpperCamelCase				:       Optional[str] = "pil"							,     _UpperCamelCase				:       bool = True							,     _UpperCamelCase				:       Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,     _UpperCamelCase				:       int = 1							,     **_UpperCamelCase				:       Tuple							,     )   ->Union[str, Any]:
									# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
									return StableDiffusionImgaImgPipeline(**self.components							)(
									    prompt=_UpperCamelCase							,     image=_UpperCamelCase							,     strength=_UpperCamelCase							,     num_inference_steps=_UpperCamelCase							,     guidance_scale=_UpperCamelCase							,     negative_prompt=_UpperCamelCase							,     num_images_per_prompt=_UpperCamelCase							,     eta=_UpperCamelCase							,     generator=_UpperCamelCase							,     output_type=_UpperCamelCase							,     return_dict=_UpperCamelCase							,     callback=_UpperCamelCase							,     callback_steps=_UpperCamelCase							,     )







			@torch.no_grad()
			def        snake_case__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       Union[str, List[str]]							,     _UpperCamelCase				:       int = 5_1_2							,     _UpperCamelCase				:       int = 5_1_2							,     _UpperCamelCase				:       int = 5_0							,     _UpperCamelCase				:       float = 7.5							,     _UpperCamelCase				:       Optional[Union[str, List[str]]] = None							,     _UpperCamelCase				:       Optional[int] = 1							,     _UpperCamelCase				:       float = 0.0							,     _UpperCamelCase				:       Optional[torch.Generator] = None							,     _UpperCamelCase				:       Optional[torch.FloatTensor] = None							,     _UpperCamelCase				:       Optional[str] = "pil"							,     _UpperCamelCase				:       bool = True							,     _UpperCamelCase				:       Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,     _UpperCamelCase				:       int = 1							,     )   ->List[Any]:
									# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
									return StableDiffusionPipeline(**self.components							)(
									    prompt=_UpperCamelCase							,     height=_UpperCamelCase							,     width=_UpperCamelCase							,     num_inference_steps=_UpperCamelCase							,     guidance_scale=_UpperCamelCase							,     negative_prompt=_UpperCamelCase							,     num_images_per_prompt=_UpperCamelCase							,     eta=_UpperCamelCase							,     generator=_UpperCamelCase							,     latents=_UpperCamelCase							,     output_type=_UpperCamelCase							,     return_dict=_UpperCamelCase							,     callback=_UpperCamelCase							,     callback_steps=_UpperCamelCase							,     )