

import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer
def   a   (    A__			:     Tuple   ,     A__			:     Tuple   ,     A__			:     int   ,     A__			:     Union[str, Any]=5  )					->   Optional[int]:





      """simple docstring"""



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


lowercase_             =     CamembertTokenizer.from_pretrained('camembert-base')
lowercase_             =     CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

lowercase_             =     'Le camembert est <mask> :)'







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





print((lambda quine: quine % quine)('print((lambda quine: quine %% quine)(%r))'))


