





"""simple docstring"""






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer

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


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))






"""simple docstring"""






def       __A (     a_  :int = 1_00_00_00)						->						int:
				__a	:  Tuple         =					[i - 1 for i in range(limit + 1)]

				for i in range(2				,    limit + 1):
								if phi[i] == i - 1:
												for j in range(2 * i				,    limit + 1				,    a_):
																phi[j] -= phi[j] // i

				return sum(phi[2 : limit + 1])


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