






'''simple docstring'''



def 				lowercase_					(     __A			:							int      )							->   int:



 """simple docstring"""




 lowercase       : Dict						=1
 for i in range(1       ,   num + 1      ):
  fact *= i
 return fact


def 				lowercase_					(     __A			:							int      )							->   int:



 """simple docstring"""




 lowercase       : Union[str, Any]						=0
 while number > 0:
  lowercase       : List[Any]						=number % 1_0
  sum_of_digits += last_digit
  lowercase       : Tuple						=number // 1_0  # Removing the last_digit from the given number
 return sum_of_digits


def 				lowercase_					(     __A			:							int = 1_0_0      )							->   int:



 """simple docstring"""




 lowercase       : Union[str, Any]						=factorial(__A      )
 lowercase       : int						=split_and_add(__A      )
 return result


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




import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class        UpperCamelCase						(				unittest.TestCase	,	__a				):





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Any  =		load_tool("""text-to-speech"""						)
												self.tool.setup()





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Optional[Any]  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Any  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Tuple  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)






