


'''simple docstring'''


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE=5							):




     '''simple docstring'''





     assert masked_input.count("<mask>"							) == 1
     __A	:       List[str]      =							torch.tensor(tokenizer.encode(SCREAMING_SNAKE_CASE       ,					add_special_tokens=SCREAMING_SNAKE_CASE							)							).unsqueeze(0							)  # Batch size 1
     __A	:       Union[str, Any]      =							model(SCREAMING_SNAKE_CASE							)[0]  # The last hidden-state is the first element of the output tuple
     __A	:       Any      =							(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
     __A	:       Union[str, Any]      =							logits[0, masked_index, :]
     __A	:       Any      =							logits.softmax(dim=0							)
     __A  ,__A	:       Tuple      =							prob.topk(k=SCREAMING_SNAKE_CASE       ,					dim=0							)
     __A	:       List[str]      =							" ".join(
         [tokenizer.convert_ids_to_tokens(indices[i].item()							) for i in range(len(SCREAMING_SNAKE_CASE							)							)]							)
     __A	:       str      =							tokenizer.mask_token
     __A	:       Tuple      =							[]
     for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "							)							):
          __A	:       Tuple      =							predicted_token_bpe.replace("\u2581"       ,					" "							)
          if " {0}".format(SCREAMING_SNAKE_CASE							) in masked_input:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(" {0}".format(SCREAMING_SNAKE_CASE							)       ,					SCREAMING_SNAKE_CASE							),
                       values[index].item(),
                       predicted_token,
                   )							)
          else:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							),
                       values[index].item(),
                       predicted_token,
                   )							)
     return topk_filled_outputs


_UpperCamelCase 							=  CamembertTokenizer.from_pretrained("""camembert-base""")
_UpperCamelCase 							=  CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

_UpperCamelCase 							=  """Le camembert est <mask> :)"""






print(fill_mask(masked_input, model, tokenizer, topk=3))





'''simple docstring'''


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[str]      =							1
     __A	:       Union[str, Any]      =							2
     while i * i <= n:
          __A	:       List[Any]      =							0
          while n % i == 0:
               n //= i
               multiplicity += 1
          n_divisors *= multiplicity + 1
          i += 1
     if n > 1:
          n_divisors *= 2
     return n_divisors


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[Any]      =							1
     __A	:       Dict      =							1

     while True:
          i += 1
          t_num += i

          if count_divisors(SCREAMING_SNAKE_CASE							) > 500:
               break

     return t_num


if __name__ == "__main__":
      print(solution())

