



"""simple docstring"""

import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference
@slow
@require_torch_gpu
class 						lowerCAmelCase__	(  unittest.TestCase   ):


	'''simple docstring'''







	def  _lowerCAmelCase    (			self							:					Dict						)	->  Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    "runwayml/stable-diffusion-v1-5"
					SCREAMING_SNAKE_CASE	:       int 							=    TextToVideoZeroPipeline.from_pretrained(_SCREAMING_SNAKE_CASE    ,					torch_dtype=torch.floataa						).to('cuda'						)
					SCREAMING_SNAKE_CASE	:       List[str] 							=    DDIMScheduler.from_config(pipe.scheduler.config						)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    torch.Generator(device='cuda'						).manual_seed(0						)

					SCREAMING_SNAKE_CASE	:       Any 							=    "A bear is playing a guitar on Times Square"
					SCREAMING_SNAKE_CASE	:       Any 							=    pipe(prompt=_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE						).images

					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    load_pt(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'						)

					assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)










"""simple docstring"""

def    __snake_case       (  __A			:	int = 100    )    ->					int:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       Any 							=    1
				SCREAMING_SNAKE_CASE	:       Optional[int] 							=    0
				for i in range(1       ,		num + 1    ):
								fact *= i

				for j in str(__A    ):
								result += int(__A    )

				return result


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





