"""simple docstring"""




import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





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


_a				:		Any	   =       CamembertTokenizer.from_pretrained('camembert-base')
_a				:		Any	   =       CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

_a				:		str	   =       'Le camembert est <mask> :)'






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





"""simple docstring"""




from __future__ import annotations





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	list     )	->				float:
  if not nums:
    raise ValueError("""List is empty"""     )
  return sum(_lowerCamelCase     ) / len(_lowerCamelCase     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




