
'''simple docstring'''







def       _a		(						_SCREAMING_SNAKE_CASE		: float	):
						return -num if num < 0 else num



def       _a		(						_SCREAMING_SNAKE_CASE		: list[int]	):
						if len(_SCREAMING_SNAKE_CASE	) == 0:
												raise ValueError("abs_min() arg is an empty sequence"	)
						_SCREAMING_SNAKE_CASE					=			x[0]
						for i in x:
												if abs_val(_SCREAMING_SNAKE_CASE	) < abs_val(_SCREAMING_SNAKE_CASE	):
																		_SCREAMING_SNAKE_CASE					=			i
						return j



def       _a		(						_SCREAMING_SNAKE_CASE		: list[int]	):
						if len(_SCREAMING_SNAKE_CASE	) == 0:
												raise ValueError("abs_max() arg is an empty sequence"	)
						_SCREAMING_SNAKE_CASE					=			x[0]
						for i in x:
												if abs(_SCREAMING_SNAKE_CASE	) > abs(_SCREAMING_SNAKE_CASE	):
																		_SCREAMING_SNAKE_CASE					=			i
						return j



def       _a		(						_SCREAMING_SNAKE_CASE		: list[int]	):
						if len(_SCREAMING_SNAKE_CASE	) == 0:
												raise ValueError("abs_max_sort() arg is an empty sequence"	)
						return sorted(_SCREAMING_SNAKE_CASE						,      key=_SCREAMING_SNAKE_CASE	)[-1]



def       _a		(						):
						assert abs_val(0	) == 0
						assert abs_val(34	) == 34
						assert abs_val(-1000_0000_0000	) == 1000_0000_0000

						_SCREAMING_SNAKE_CASE					=			[-3, -1, 2, -11]
						assert abs_max(_SCREAMING_SNAKE_CASE	) == -11
						assert abs_max_sort(_SCREAMING_SNAKE_CASE	) == -11
						assert abs_min(_SCREAMING_SNAKE_CASE	) == -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						test_abs_val()
						print(abs_val(-34))  # --> 34

'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								super().__init__()
								self.register_modules(unet=UpperCamelCase       ,    scheduler=UpperCamelCase   )




		@torch.no_grad()
		def __call__(					self       ,    UpperCamelCase = 1       ,    UpperCamelCase = None       ,    UpperCamelCase = 1_000       ,    UpperCamelCase = "pil"       ,    UpperCamelCase = True       ,    ):
								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			(
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
								else:
														_SCREAMING_SNAKE_CASE					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if self.device.type == "mps":
														# randn does not work reproducibly on mps
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			image.to(self.device   )
								else:
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase       ,    device=self.device   )

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

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

														# 2. compute previous image: x_t -> x_t-1
														_SCREAMING_SNAKE_CASE					=			self.scheduler.step(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    generator=UpperCamelCase   ).prev_sample

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

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=UpperCamelCase   )