

import math
from timeit import timeit







def     A_   (					_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Tuple							= 0
						SCREAMING_SNAKE_CASE_:				int							= abs(_UpperCAmelCase					)
						while True:
												SCREAMING_SNAKE_CASE_:				Optional[int]							= n // 10
												digits += 1
												if n == 0:
																		break
						return digits







def     A_   (					_UpperCAmelCase					):
						return 1 if n == 0 else math.floor(math.log(abs(_UpperCAmelCase					)       ,			10					) + 1					)







def     A_   (					_UpperCAmelCase					):
						return len(str(abs(_UpperCAmelCase					)					)					)







def     A_   (					):
						from collections.abc import Callable

						def benchmark_a_function(_UpperCAmelCase       ,			_UpperCAmelCase					) -> None:
												SCREAMING_SNAKE_CASE_:				Optional[int]							= f"{func.__name__}({value})"
												SCREAMING_SNAKE_CASE_:				Any							= timeit(f"__main__.{call}"       ,			setup="import __main__"					)
												print(f"{call}: {func(_UpperCAmelCase					)} -- {timing} seconds"					)

						for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
												for func in (num_digits, num_digits_fast, num_digits_faster):
																		benchmark_a_function(_UpperCAmelCase       ,			_UpperCAmelCase					)
												print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()









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 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							UNetaDModel
			_UpperCAmelCase     :							PNDMScheduler



			def __init__(				self						:  Optional[int]     ,		lowerCAmelCase__						:  UNetaDModel     ,		lowerCAmelCase__						:  PNDMScheduler):
									super().__init__()

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

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




			@torch.no_grad()
			def __call__(				self						:  int     ,		lowerCAmelCase__						:  int = 1     ,		lowerCAmelCase__						:  int = 50     ,		lowerCAmelCase__						:  Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		lowerCAmelCase__						:  Optional[str] = "pil"     ,		lowerCAmelCase__						:  bool = True     ,		**lowerCAmelCase__						:  str     ,		):
									SCREAMING_SNAKE_CASE_:				Dict							= 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_:				Union[str, Any]							= self.unet(lowerCAmelCase__     ,		lowerCAmelCase__).sample

															SCREAMING_SNAKE_CASE_:				Any							= self.scheduler.step(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__).prev_sample

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

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowerCAmelCase__)






