


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=5       ):
  # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
  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_      :				List[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_      :				Optional[Any]		     =					logits[0, masked_index, :]
  A_      :				List[str]		     =					logits.softmax(dim=0       )
  A_     ,       A_      :				Union[str, Any]		     =					prob.topk(k=SCREAMING_SNAKE_CASE ,			dim=0       )
  A_      :				Any		     =					''' '''.join(
      [tokenizer.convert_ids_to_tokens(indices[i].item()       ) for i in range(len(SCREAMING_SNAKE_CASE       )       )]       )
  A_      :				str		     =					tokenizer.mask_token
  A_      :				Optional[Any]		     =					[]
  for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''       )       ):
    A_      :				Optional[int]		     =					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))








def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if not nums:  # Makes sure that the list is not empty
    raise ValueError('''List is empty'''       )

  A_      :				Optional[int]		     =					sum(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )  # Calculate the average
  return sum(abs(x - average       ) for x in nums       ) / len(SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




