


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       _UpperCamelCase    (					a__				):

				"""simple docstring"""







				lowerCAmelCase 		= 42
				lowerCAmelCase 		= 42

				def __init__(						self				,   a__				,   a__   )	->				Optional[int]:
					super().__init__()
					self.register_modules(unet=lowercase__				,   scheduler=lowercase__   )

				@torch.no_grad()
				def __call__(						self				,   a__ = 1				,   a__ = 2000				,   a__ = None				,   a__ = "pil"				,   a__ = True				,   **a__				,   )	->				Union[ImagePipelineOutput, Tuple]:
					A								=				self.unet.config.sample_size
					A								=				(batch_size, 3, img_size, img_size)

					A								=				self.unet

					A								=				randn_tensor(lowercase__				,   generator=lowercase__   ) * self.scheduler.init_noise_sigma
					A								=				sample.to(self.device   )

					self.scheduler.set_timesteps(lowercase__   )
					self.scheduler.set_sigmas(lowercase__   )

					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(lowercase__				,   lowercase__   ).sample
							A								=				self.scheduler.step_correct(lowercase__				,   lowercase__				,   generator=lowercase__   ).prev_sample

						# prediction step
						A								=				model(lowercase__				,   lowercase__   ).sample
						A								=				self.scheduler.step_pred(lowercase__				,   lowercase__				,   lowercase__				,   generator=lowercase__   )

						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(lowercase__   )

					if not return_dict:
						return (sample,)

					return ImagePipelineOutput(images=lowercase__   )



def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   bool:





	"""simple docstring"""







	return str(UpperCamelCase__						) == str(UpperCamelCase__						)[::-1]





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   int:





	"""simple docstring"""







	return int(UpperCamelCase__						) + int(str(UpperCamelCase__						)[::-1]						)





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 1_00_00						) ->   int:





	"""simple docstring"""







	A								=				[]
	for num in range(1							,					UpperCamelCase__						):
		A								=				0
		A								=				num
		while iterations < 50:
			A								=				sum_reverse(UpperCamelCase__						)
			iterations += 1
			if is_palindrome(UpperCamelCase__						):
				break
		else:
			lychrel_nums.append(UpperCamelCase__						)
	return len(UpperCamelCase__						)


if __name__ == "__main__":
				print(f'''{solution() = }''')



