



'''simple docstring'''

import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=5  )			->      Any:
		# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
		assert masked_input.count('<mask>'  ) == 1
		_a     :      Tuple        =							torch.tensor(tokenizer.encode(lowerCAmelCase_						,			add_special_tokens=lowerCAmelCase_  )  ).unsqueeze(0  )  # Batch size 1
		_a     :      Union[str, Any]        =							model(lowerCAmelCase_  )[0]  # The last hidden-state is the first element of the output tuple
		_a     :      int        =							(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
		_a     :      str        =							logits[0, masked_index, :]
		_a     :      int        =							logits.softmax(dim=0  )
		_a       ,					_a     :      Dict        =							prob.topk(k=lowerCAmelCase_						,			dim=0  )
		_a     :      str        =							' '.join(
		    [tokenizer.convert_ids_to_tokens(indices[i].item()  ) for i in range(len(lowerCAmelCase_  )  )]  )
		_a     :      Optional[int]        =							tokenizer.mask_token
		_a     :      Tuple        =							[]
		for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' '  )  ):
				_a     :      Optional[Any]        =							predicted_token_bpe.replace('\u2581'						,			' '  )
				if " {0}".format(lowerCAmelCase_  ) in masked_input:
						topk_filled_outputs.append(
						    (
						        masked_input.replace(' {0}'.format(lowerCAmelCase_  )						,			lowerCAmelCase_  ),
						        values[index].item(),
						        predicted_token,
						    )  )
				else:
						topk_filled_outputs.append(
						    (
						        masked_input.replace(lowerCAmelCase_						,			lowerCAmelCase_  ),
						        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     (      )			->      Tuple:
		for n in range(1						,			1000000  ):
				yield n * (n + 1) // 2



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      List[Any]:
		_a     :      Any        =							1
		_a     :      Tuple        =							2
		while i * i <= n:
				_a     :      Tuple        =							0
				while n % i == 0:
						n //= i
						multiplicity += 1
				divisors_count *= multiplicity + 1
				i += 1
		if n > 1:
				divisors_count *= 2
		return divisors_count



def      __lowerCamelCase     (      )			->      str:
		return next(i for i in triangle_number_generator() if count_divisors(lowerCAmelCase_  ) > 500  )


if __name__ == "__main__":
							print(solution())




