


import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
	import torch


@require_torch
@require_optimum
@slow
class 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				"hf-internal-testing/tiny-random-t5"
								SCREAMING_SNAKE_CASE__     							=				AutoTokenizer.from_pretrained(__UpperCamelCase     )
								SCREAMING_SNAKE_CASE__     							=				AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase     )

								SCREAMING_SNAKE_CASE__     							=				tokenizer("This is me"       ,		return_tensors="pt"     )

								SCREAMING_SNAKE_CASE__     							=				model.to_bettertransformer()

								self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()     )     )

								SCREAMING_SNAKE_CASE__     							=				model.generate(**__UpperCamelCase     )

								SCREAMING_SNAKE_CASE__     							=				model.reverse_bettertransformer()

								self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()     )     )

								with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(__UpperCamelCase     )

												SCREAMING_SNAKE_CASE__     							=				AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase     )

												self.assertFalse(
												    any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()     )     )

												SCREAMING_SNAKE_CASE__     							=				model_reloaded.generate(**__UpperCamelCase     )
												self.assertTrue(torch.allclose(__UpperCamelCase       ,		__UpperCamelCase     )     )






				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				"hf-internal-testing/tiny-random-t5"
								SCREAMING_SNAKE_CASE__     							=				AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase     )

								SCREAMING_SNAKE_CASE__     							=				model.to_bettertransformer()

								with tempfile.TemporaryDirectory() as tmpdirname:
												with self.assertRaises(__UpperCamelCase     ):
																model.save_pretrained(__UpperCamelCase     )

												SCREAMING_SNAKE_CASE__     							=				model.reverse_bettertransformer()
												model.save_pretrained(__UpperCamelCase     )




"""simple docstring"""






from math import factorial


def __lowercase     (      lowerCamelCase_  :      int = 100  ):
				return sum(int(lowerCamelCase_  ) for x in str(factorial(lowerCamelCase_  )  )  )


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