






import inspect
from typing import List, Optional, Tuple, Union

import torch

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




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				List[Any]:
								super().__init__()
								self.register_modules(vqvae=_SCREAMING_SNAKE_CASE		,				unet=_SCREAMING_SNAKE_CASE		,				scheduler=_SCREAMING_SNAKE_CASE				)






						@torch.no_grad()
						def __call__(	self		,				_SCREAMING_SNAKE_CASE = 1		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = 0.0		,				_SCREAMING_SNAKE_CASE = 50		,				_SCREAMING_SNAKE_CASE = "pil"		,				_SCREAMING_SNAKE_CASE = True		,				**_SCREAMING_SNAKE_CASE		,				) ->				Union[Tuple, ImagePipelineOutput]:
								snake_case_				:							List[str]						 =       randn_tensor(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,				generator=_SCREAMING_SNAKE_CASE		,				)
								snake_case_				:							int						 =       latents.to(self.device				)

								# scale the initial noise by the standard deviation required by the scheduler
								snake_case_				:							Optional[Any]						 =       latents * self.scheduler.init_noise_sigma

								self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE				)

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								snake_case_				:							List[str]						 =       "eta" in set(inspect.signature(self.scheduler.step				).parameters.keys()				)

								snake_case_				:							Tuple						 =       {}
								if accepts_eta:
										snake_case_				:							Dict						 =       eta

								for t in self.progress_bar(self.scheduler.timesteps				):
										snake_case_				:							Any						 =       self.scheduler.scale_model_input(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										# predict the noise residual
										snake_case_				:							Tuple						 =       self.unet(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				).sample
										# compute the previous noisy sample x_t -> x_t-1
										snake_case_				:							Any						 =       self.scheduler.step(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				).prev_sample

								# decode the image latents with the VAE
								snake_case_				:							Dict						 =       self.vqvae.decode(_SCREAMING_SNAKE_CASE				).sample

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

								if not return_dict:
										return (image,)

								return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE				)








def      lowerCAmelCase__	(    _a       :			int    ):
		snake_case_				:							str						 =       1
		for i in range(1  ,      num + 1    ):
				fact *= i
		return fact

def      lowerCAmelCase__	(    _a       :			int    ):
		snake_case_				:							List[str]						 =       0
		while number > 0:
				snake_case_				:							Dict						 =       number % 10
				sum_of_digits += last_digit
				snake_case_				:							List[Any]						 =       number // 10  # Removing the last_digit from the given number
		return sum_of_digits

def      lowerCAmelCase__	(    _a       :			int = 1_00    ):
		snake_case_				:							Optional[Any]						 =       factorial(_a    )
		snake_case_				:							Optional[int]						 =       split_and_add(_a    )
		return result


if __name__ == "__main__":
							print(solution(int(input('''Enter the Number: ''').strip())))
