




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class   A(						unittest.TestCase   ):



 '''simple docstring'''


 @slow
 def 				a__ (				self					:  Dict  )   ->	Optional[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'						,							return_dict=A_  ).to(A_  )
       lowerCamelCase_		     =    AutoTokenizer.from_pretrained('google/mt5-small'  )

       lowerCamelCase_		     =    tokenizer('Hello there'						,							return_tensors='pt'  ).input_ids
       lowerCamelCase_		     =    tokenizer('Hi I am'						,							return_tensors='pt'  ).input_ids

       lowerCamelCase_		     =    model(input_ids.to(A_  )						,							labels=labels.to(A_  )  ).loss
       lowerCamelCase_		     =    -(labels.shape[-1] * loss.item())

       lowerCamelCase_		     =    -84.9127
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE  ) < 1E-4  )









def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       int = 10_00     ):


      '''simple docstring'''



      lowerCamelCase_		     =    2**power
      lowerCamelCase_		     =    0
      while n:
            lowerCamelCase_     ,				lowerCamelCase_		     =    r + n % 10, n // 10
      return r


if __name__ == "__main__":
 print(solution(int(str(input()).strip())))



