
"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

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





class       snake_case_       (    __lowerCAmelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Dict   			=					42
	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					42


	def __init__(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					super().__init__()
					self.register_modules(unet=_UpperCamelCase  ,					scheduler=_UpperCamelCase						)






	@torch.no_grad()
	def __call__(      self  ,					__a = 1  ,					__a = 2000  ,					__a = None  ,					__a = "pil"  ,					__a = True  ,					**__a  ,					):


					"""simple docstring"""

					A__							 =       self.unet.config.sample_size
					A__							 =       (batch_size, 3, img_size, img_size)

					A__							 =       self.unet

					A__							 =       randn_tensor(_UpperCamelCase  ,					generator=_UpperCamelCase						) * self.scheduler.init_noise_sigma
					A__							 =       sample.to(self.device						)

					self.scheduler.set_timesteps(_UpperCamelCase						)
					self.scheduler.set_sigmas(_UpperCamelCase						)

					for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
									A__							 =       self.scheduler.sigmas[i] * torch.ones(shape[0]  ,					device=self.device						)

									# correction step
									for _ in range(self.scheduler.config.correct_steps						):
													A__							 =       self.unet(_UpperCamelCase  ,					_UpperCamelCase						).sample
													A__							 =       self.scheduler.step_correct(_UpperCamelCase  ,					_UpperCamelCase  ,					generator=_UpperCamelCase						).prev_sample

									# prediction step
									A__							 =       model(_UpperCamelCase  ,					_UpperCamelCase						).sample
									A__							 =       self.scheduler.step_pred(_UpperCamelCase  ,					_UpperCamelCase  ,					_UpperCamelCase  ,					generator=_UpperCamelCase						)

									A__							 =       output.prev_sample, output.prev_sample_mean

					A__							 =       sample_mean.clamp(0  ,					1						)
					A__							 =       sample.cpu().permute(0  ,					2  ,					3  ,					1						).numpy()
					if output_type == "pil":
									A__							 =       self.numpy_to_pil(_UpperCamelCase						)

					if not return_dict:
									return (sample,)

					return ImagePipelineOutput(images=_UpperCamelCase						)










"""simple docstring"""






from PIL import Image



def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__ ,			A__							 =       image.size
				A__							 =       0
				A__							 =       image.load()
				for i in range(lowerCAmelCase__				):
								for j in range(lowerCAmelCase__				):
												A__							 =       pixels[j, i]
												mean += pixel
				mean //= width * height

				for j in range(lowerCAmelCase__				):
								for i in range(lowerCAmelCase__				):
												A__							 =       255 if pixels[i, j] > mean else 0
				return image


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE						:						Dict					   =       mean_threshold(Image.open('''path_to_image''').convert('''L'''))
					image.save('''output_image_path''')


