




'''simple docstring'''



import torch

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


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 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			str  )			->			str:
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				(
       first_str_length if first_str_length > second_str_length else second_str_length
   )
   _SCREAMING_SNAKE_CASE	=				[]
   for char_count in range(__A  ):
      if char_count < first_str_length:
         output_list.append(first_str[char_count]  )
      if char_count < second_str_length:
         output_list.append(second_str[char_count]  )
   return "".join(__A  )


if __name__ == "__main__":
 print(alternative_string_arrange('AB', 'XYZ'), end=' ')

