




'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

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



class    A						(					SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				A       =							42
				A       =							42
				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			Optional[Any]:
							super().__init__()

							__UpperCamelCase :     Dict										=  PNDMScheduler.from_config(scheduler.config     )

							self.register_modules(unet=_UpperCAmelCase					,    scheduler=_UpperCAmelCase     )





				@torch.no_grad()
				def __call__(self					,    _UpperCAmelCase = 1					,    _UpperCAmelCase = 5_0					,    _UpperCAmelCase = None					,    _UpperCAmelCase = "pil"					,    _UpperCAmelCase = True					,    **_UpperCAmelCase					,    )      ->			Union[ImagePipelineOutput, Tuple]:
							__UpperCamelCase :     int										=  randn_tensor(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,    generator=_UpperCAmelCase					,    device=self.device					,    )

							self.scheduler.set_timesteps(_UpperCAmelCase     )
							for t in self.progress_bar(self.scheduler.timesteps     ):
										__UpperCamelCase :     Union[str, Any]										=  self.unet(_UpperCAmelCase					,    _UpperCAmelCase     ).sample

										__UpperCamelCase :     Optional[int]										=  self.scheduler.step(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     ).prev_sample

							__UpperCamelCase :     List[str]										=  (image / 2 + 0.5).clamp(0					,    1     )
							__UpperCamelCase :     List[Any]										=  image.cpu().permute(0					,    2					,    3					,    1     ).numpy()
							if output_type == "pil":
										__UpperCamelCase :     Optional[Any]										=  self.numpy_to_pil(_UpperCAmelCase     )

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=_UpperCAmelCase     )









'''simple docstring'''






def      __lowerCAmelCase   (       snake_case__		):

			for insert_index, insert_value in enumerate(collection[1:]		):
						__UpperCamelCase :     Union[str, Any]										=  insert_index
						while insert_index >= 0 and insert_value < collection[insert_index]:
									__UpperCamelCase :     int										=  collection[insert_index]
									insert_index -= 1
						if insert_index != temp_index:
									__UpperCamelCase :     Tuple										=  insert_value
			return collection


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

				testmod()

				_lowerCAmelCase       =   input('''Enter numbers separated by a comma:\n''').strip()
				_lowerCAmelCase       =   [int(item) for item in user_input.split(''',''')]
				print(f'{insertion_sort(unsorted) = }')



