





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 			__lowerCAmelCase				(					_UpperCamelCase			):


	'''simple docstring'''





	def __init__(	self:	List[Any],				lowerCamelCase_:	int,				lowerCamelCase_:	List[Any] ):
			super().__init__()
			self.register_modules(unet=lowerCamelCase_,				scheduler=lowerCamelCase_ )





	@torch.no_grad()
	def __call__(	self:	List[Any],				lowerCamelCase_:	int = 1,				lowerCamelCase_:	Optional[torch.Generator] = None,				lowerCamelCase_:	int = 50,				lowerCamelCase_:	Optional[str] = "pil",				lowerCamelCase_:	bool = True,				**lowerCamelCase_:	List[str],				):
			lowercase__				:			str            =		torch.randn(
			    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),				generator=lowerCamelCase_,				)
			lowercase__				:			Dict            =		image.to(self.device )

			# set step values
			self.scheduler.set_timesteps(lowerCamelCase_ )

			for t in self.progress_bar(self.scheduler.timesteps ):
					# 1. predict noise model_output
					lowercase__				:			Dict            =		self.unet(lowerCamelCase_,				lowerCamelCase_ ).sample

					# 2. predict previous mean of image x_t-1 and add variance depending on eta
					# eta corresponds to η in paper and should be between [0, 1]
					# do x_t -> x_t-1
					lowercase__				:			Optional[Any]            =		self.scheduler.step(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_ ).prev_sample

			lowercase__				:			Any            =		(image / 2 + 0.5).clamp(0,				1 )
			lowercase__				:			Tuple            =		image.cpu().permute(0,				2,				3,				1 ).numpy()
			if output_type == "pil":
					lowercase__				:			int            =		self.numpy_to_pil(lowerCamelCase_ )

			if not return_dict:
					return (image,), "This is a local test"

			return ImagePipelineOutput(images=lowerCamelCase_ ), "This is a local test"









def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   list )      ->       list:




		'''simple docstring'''
		lowercase__				:			Any            =		len(_lowercase )
		for cycle_start in range(0      ,						array_len - 1 ):
				lowercase__				:			List[str]            =		array[cycle_start]

				lowercase__				:			str            =		cycle_start
				for i in range(cycle_start + 1      ,						_lowercase ):
						if array[i] < item:
								pos += 1

				if pos == cycle_start:
						continue

				while item == array[pos]:
						pos += 1

				lowercase__      ,							lowercase__				:			Optional[Any]            =		item, array[pos]
				while pos != cycle_start:
						lowercase__				:			str            =		cycle_start
						for i in range(cycle_start + 1      ,						_lowercase ):
								if array[i] < item:
										pos += 1

						while item == array[pos]:
								pos += 1

						lowercase__      ,							lowercase__				:			Tuple            =		item, array[pos]

		return array


if __name__ == "__main__":
			assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
			assert cycle_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]


