





"""simple docstring"""






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=5							)		->       str:






       """simple docstring"""




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


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))













"""simple docstring"""






def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       list:






       """simple docstring"""




       _UpperCAmelCase     =     False
       while is_sorted is False:  # Until all the indices are traversed keep looping
              _UpperCAmelCase     =     True
              for i in range(0,len(SCREAMING_SNAKE_CASE							) - 1,2							):  # iterating over all even indices
                     if input_list[i] > input_list[i + 1]:
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     input_list[i + 1], input_list[i]
                            # swapping if elements not in order
                            _UpperCAmelCase     =     False

              for i in range(1,len(SCREAMING_SNAKE_CASE							) - 1,2							):  # iterating over all odd indices
                     if input_list[i] > input_list[i + 1]:
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     input_list[i + 1], input_list[i]
                            # swapping if elements not in order
                            _UpperCAmelCase     =     False
       return input_list


if __name__ == "__main__":
    print('''Enter list to be sorted''')
    lowerCAmelCase_			=							[int(x) for x in input().split()]
    # inputing elements of the list in one line
    lowerCAmelCase_			=							odd_even_sort(input_list)
    print('''The sorted list is''')
    print(sorted_list)






