

import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[Any]	,     SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    List[Any]	,     SCREAMING_SNAKE_CASE_		:    Dict=5			):



     '''simple docstring'''





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


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

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

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




def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if not all(char in '01' for char in bin_string			):
          raise ValueError('Non-binary value was passed to the function'			)
     if not bin_string:
          raise ValueError('Empty string was passed to the function'			)
     lowercase__    :   Union[str, Any]      =       ''
     while len(SCREAMING_SNAKE_CASE_			) % 3 != 0:
          lowercase__    :   List[str]      =       '0' + bin_string
     lowercase__    :   Any      =       [
         bin_string[index : index + 3]
         for index in range(len(SCREAMING_SNAKE_CASE_			)			)
         if index % 3 == 0
     ]
     for bin_group in bin_string_in_3_list:
          lowercase__    :   str      =       0
          for index, val in enumerate(SCREAMING_SNAKE_CASE_			):
               oct_val += int(2 ** (2 - index) * int(SCREAMING_SNAKE_CASE_			)			)
          oct_string += str(SCREAMING_SNAKE_CASE_			)
     return oct_string


if __name__ == "__main__":
       from doctest import testmod

       testmod()

