




import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     Optional[int]      ,      __UpperCAmelCase						:     List[Any]      ,      __UpperCAmelCase						:     Optional[int]=5							) ->			int:
      # 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(__UpperCAmelCase      ,      add_special_tokens=__UpperCAmelCase							)							).unsqueeze(0							)  # Batch size 1
      SCREAMING_SNAKE_CASE_     						=      model(__UpperCAmelCase							)[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=__UpperCAmelCase      ,      dim=0							)
      SCREAMING_SNAKE_CASE_     						=      ' '.join(
          [tokenizer.convert_ids_to_tokens(indices[i].item()							) for i in range(len(__UpperCAmelCase							)							)]							)
      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(__UpperCAmelCase							) in masked_input:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(' {0}'.format(__UpperCAmelCase							)      ,      __UpperCAmelCase							),
                          values[index].item(),
                          predicted_token,
                      )							)
            else:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(__UpperCAmelCase      ,      __UpperCAmelCase							),
                          values[index].item(),
                          predicted_token,
                      )							)
      return topk_filled_outputs


lowerCamelCase__			:       Dict         =							CamembertTokenizer.from_pretrained('camembert-base')
lowerCamelCase__			:       Union[str, Any]         =							CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

lowerCamelCase__			:       List[str]         =							'Le camembert est <mask> :)'





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





def  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     int							) ->			int:
      return int((input_a, input_a).count(0							) != 0							)

def  UpperCAmelCase_							(				) ->			None:
      assert nand_gate(0      ,      0							) == 1
      assert nand_gate(0      ,      1							) == 1
      assert nand_gate(1      ,      0							) == 1
      assert nand_gate(1      ,      1							) == 0


if __name__ == "__main__":
   print(nand_gate(0, 0))
   print(nand_gate(0, 1))
   print(nand_gate(1, 0))
   print(nand_gate(1, 1))