
'''simple docstring'''



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def __init__(   self       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



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




						@torch.no_grad()
						def __call__(   self       ,  _lowercase = 1       ,  _lowercase = None       ,  _lowercase = 50       ,  _lowercase = "pil"       ,  _lowercase = True       ,  **_lowercase       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					torch.randn(
												    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,  generator=_lowercase       ,  )
												_lowerCAmelCase    		=					image.to(self.device )

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

												for t in self.progress_bar(self.scheduler.timesteps ):
																		# 1. predict noise model_output
																		_lowerCAmelCase    		=					self.unet(_lowercase       ,  _lowercase ).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
																		_lowerCAmelCase    		=					self.scheduler.step(_lowercase       ,  _lowercase       ,  _lowercase ).prev_sample

												_lowerCAmelCase    		=					(image / 2 + 0.5).clamp(0       ,  1 )
												_lowerCAmelCase    		=					image.cpu().permute(0       ,  2       ,  3       ,  1 ).numpy()
												if output_type == "pil":
																		_lowerCAmelCase    		=					self.numpy_to_pil(_lowercase )

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

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







'''simple docstring'''






from __future__ import annotations







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase = None    ,	_lowerCamelCase = None ) ->				None:



			"""simple docstring"""


			if start is None:
						__snake_case					:      Optional[Any]							      =     0

			if end is None:
						__snake_case					:      Optional[Any]							      =     len(_lowerCamelCase ) - 1

			if start >= end:
						return

			__snake_case					:      Tuple							      =     (start + end) // 2

			slowsort(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )
			slowsort(_lowerCamelCase    ,	mid + 1    ,	_lowerCamelCase )

			if sequence[end] < sequence[mid]:
						__snake_case      ,     __snake_case					:      str							      =     sequence[mid], sequence[end]

			slowsort(_lowerCamelCase    ,	_lowerCamelCase    ,	end - 1 )


if __name__ == "__main__":
	from doctest import testmod

	testmod()


