





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	Dict    ,							lowerCAmelCase:	Any    ,							lowerCAmelCase:	Union[str, Any]=5				)->		int:
  # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
  assert masked_input.count('<mask>'				) == 1
  _snake_case   :				Any														=	torch.tensor(tokenizer.encode(lowerCAmelCase    ,							add_special_tokens=lowerCAmelCase				)				).unsqueeze(0				)  # Batch size 1
  _snake_case   :				Optional[Any]														=	model(lowerCAmelCase				)[0]  # The last hidden-state is the first element of the output tuple
  _snake_case   :				Union[str, Any]														=	(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
  _snake_case   :				List[Any]														=	logits[0, masked_index, :]
  _snake_case   :				Tuple														=	logits.softmax(dim=0				)
  _snake_case							,					_snake_case   :				Tuple														=	prob.topk(k=lowerCAmelCase    ,							dim=0				)
  _snake_case   :				List[Any]														=	' '.join(
      [tokenizer.convert_ids_to_tokens(indices[i].item()				) for i in range(len(lowerCAmelCase				)				)]				)
  _snake_case   :				Tuple														=	tokenizer.mask_token
  _snake_case   :				List[Any]														=	[]
  for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' '				)				):
    _snake_case   :				Dict														=	predicted_token_bpe.replace('\u2581'    ,							' '				)
    if " {0}".format(lowerCAmelCase				) in masked_input:
      topk_filled_outputs.append(
          (
              masked_input.replace(' {0}'.format(lowerCAmelCase				)    ,							lowerCAmelCase				),
              values[index].item(),
              predicted_token,
          )				)
    else:
      topk_filled_outputs.append(
          (
              masked_input.replace(lowerCAmelCase    ,							lowerCAmelCase				),
              values[index].item(),
              predicted_token,
          )				)
  return topk_filled_outputs


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

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







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











from functools import reduce

lowerCAmelCase_						   =    (
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)


def     lowerCamelCase_	(      lowerCAmelCase:	str = N				)->		int:
  return max(
      # mypy cannot properly interpret reduce
      int(reduce(lambda lowerCAmelCase    ,							lowerCAmelCase				: str(int(lowerCAmelCase				) * int(lowerCAmelCase				)				)    ,							n[i : i + 13]				)				)
      for i in range(len(lowerCAmelCase				) - 12				)				)


if __name__ == "__main__":
      print(F"""{solution() = }""")




