



'''simple docstring'''




from __future__ import annotations



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Dict	=     0.00
			A_	:						List[str]	=     0
			for resistor in resistors:
						if resistor <= 0:
									A_	:						List[Any]	=     f'Resistor at index {index} has a negative or zero value!'
									raise ValueError(__snake_case    )
						first_sum += 1 / float(__snake_case    )
						index += 1
			return 1 / first_sum



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						int	=     0.00
			A_	:						Optional[int]	=     0
			for resistor in resistors:
						sum_r += resistor
						if resistor < 0:
									A_	:						Any	=     f'Resistor at index {index} has a negative value!'
									raise ValueError(__snake_case    )
						index += 1
			return sum_r


if __name__ == "__main__":
						import doctest

						doctest.testmod()

"""simple docstring"""





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case=5	)							->		Union[str, Any]:


					"""simple docstring"""





					assert masked_input.count('''<mask>'''	) == 1
					_UpperCamelCase							 =  torch.tensor(tokenizer.encode(__snake_case,     add_special_tokens=__snake_case	)	).unsqueeze(0	)  # Batch size 1
					_UpperCamelCase							 =  model(__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=__snake_case,     dim=0	)
					_UpperCamelCase							 =  ''' '''.join(
					    [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(__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(__snake_case	) in masked_input:
															topk_filled_outputs.append(
															    (
															        masked_input.replace(''' {0}'''.format(__snake_case	),     __snake_case	),
															        values[index].item(),
															        predicted_token,
															    )	)
										else:
															topk_filled_outputs.append(
															    (
															        masked_input.replace(__snake_case,     __snake_case	),
															        values[index].item(),
															        predicted_token,
															    )	)
					return topk_filled_outputs


_a														=		CamembertTokenizer.from_pretrained("""camembert-base""")
_a														=		CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

_a														=		"""Le camembert est <mask> :)"""
print(fill_mask(masked_input, model, tokenizer, topk=3))






