


'''simple docstring'''






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def 		_A			(lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:Tuple			,  lowerCAmelCase__					:List[Any]			,  lowerCAmelCase__					:Any=5						)				->					str:




     '''simple docstring'''



     assert masked_input.count('<mask>'						) == 1
     _a      =							torch.tensor(tokenizer.encode(lowerCAmelCase__			,  add_special_tokens=lowerCAmelCase__						)						).unsqueeze(0						)  # Batch size 1
     _a      =							model(lowerCAmelCase__						)[0]  # The last hidden-state is the first element of the output tuple
     _a      =							(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
     _a      =							logits[0, masked_index, :]
     _a      =							logits.softmax(dim=0						)
     _a ,				_a      =							prob.topk(k=lowerCAmelCase__			,  dim=0						)
     _a      =							' '.join(
         [tokenizer.convert_ids_to_tokens(indices[i].item()						) for i in range(len(lowerCAmelCase__						)						)]						)
     _a      =							tokenizer.mask_token
     _a      =							[]
     for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' '						)						):
          _a      =							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_   :      str				    =      CamembertTokenizer.from_pretrained("camembert-base")
a_   :      List[str]				    =      CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

a_   :      int				    =      "Le camembert est <mask> :)"






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





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:list						)				->					float:




     '''simple docstring'''



     _a      =							0
     while len(lowerCAmelCase__						) > 1:
          _a      =							0
          # Consider two files with minimum cost to be merged
          for _ in range(2						):
               _a      =							files.index(min(lowerCAmelCase__						)						)
               temp += files[min_index]
               files.pop(lowerCAmelCase__						)
          files.append(lowerCAmelCase__						)
          optimal_merge_cost += temp
     return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()

