




'''simple docstring'''





import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
   import optax
   from flax.training.common_utils import onehot

   from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
   from transformers.models.ta.modeling_flax_ta import shift_tokens_right

@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class   _a							(		unittest.TestCase				):






     @slow
     def 							_lowercase      (					self       )				->     Optional[Any]:
          _snake_case				= FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"       )
          _snake_case				= AutoTokenizer.from_pretrained("google/mt5-small"       )

          _snake_case				= tokenizer("Hello there"   ,return_tensors="np"       ).input_ids
          _snake_case				= tokenizer("Hi I am"   ,return_tensors="np"       ).input_ids

          _snake_case				= shift_tokens_right(_SCREAMING_SNAKE_CASE   ,model.config.pad_token_id   ,model.config.decoder_start_token_id       )

          _snake_case				= model(_SCREAMING_SNAKE_CASE   ,decoder_input_ids=_SCREAMING_SNAKE_CASE       ).logits
          _snake_case				= optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE   ,onehot(_SCREAMING_SNAKE_CASE   ,logits.shape[-1]       )       ).mean()

          _snake_case				= -(labels.shape[-1] * loss.item())

          _snake_case				= -8_4.9_1_2_7
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1e-4       )








'''simple docstring'''





from math import factorial





def       __a      (  _UpperCamelCase:       int = 100       )		->					int:

     """simple docstring"""




     return sum(map(_UpperCamelCase   ,		str(factorial(_UpperCamelCase       )       )       )       )


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


