
"""simple docstring"""





def       lowerCamelCase__		(			)							->		str:


					"""simple docstring"""





					_UpperCamelCase							 =  0
					for i in range(1,     10_01	):
										total += i**i
					return str(__snake_case	)[-10:]


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








"""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))






