



'''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 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		UNetaDModel
			_A				:		PNDMScheduler

			def __init__(							self					:			List[Any]							,					lowerCAmelCase__					:			UNetaDModel							,					lowerCAmelCase__					:			PNDMScheduler						):





					"""simple docstring"""






					super().__init__()

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		PNDMScheduler.from_config(scheduler.config						)

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



			@torch.no_grad()
			def __call__(							self					:			Tuple							,					lowerCAmelCase__					:			int = 1							,					lowerCAmelCase__					:			int = 5_0							,					lowerCAmelCase__					:			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					lowerCAmelCase__					:			Optional[str] = "pil"							,					lowerCAmelCase__					:			bool = True							,					**lowerCAmelCase__					:			List[str]							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		str   	=		randn_tensor(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,					generator=lowerCAmelCase__							,					device=self.device							,					)

					self.scheduler.set_timesteps(lowerCAmelCase__						)
					for t in self.progress_bar(self.scheduler.timesteps						):
							__SCREAMING_SNAKE_CASE	:		int   	=		self.unet(lowerCAmelCase__							,					lowerCAmelCase__						).sample

							__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.scheduler.step(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						).prev_sample

					__SCREAMING_SNAKE_CASE	:		int   	=		(image / 2 + 0.5).clamp(0							,					1						)
					__SCREAMING_SNAKE_CASE	:		Any   	=		image.cpu().permute(0							,					2							,					3							,					1						).numpy()
					if output_type == "pil":
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.numpy_to_pil(lowerCAmelCase__						)

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=lowerCAmelCase__						)




'''simple docstring'''




from __future__ import annotations


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int | float] ):
		if len(_lowerCamelCase ) == 0:
				raise ValueError("""find_min() arg is an empty sequence""" )
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		nums[0]
		for num in nums:
				__SCREAMING_SNAKE_CASE	:		int   	=		min(_lowerCamelCase	,				_lowerCamelCase )
		return min_num


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)