


"""simple docstring"""




import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





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


__magic_name__						: Tuple    =				CamembertTokenizer.from_pretrained('camembert-base')
__magic_name__						: Optional[Any]    =				CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

__magic_name__						: Optional[int]    =				'Le camembert est <mask> :)'


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








"""simple docstring"""




def       a_ (	lowercase__ :int = 10**9     ):
			__lowerCamelCase          =      1
			__lowerCamelCase          =      2

			__lowerCamelCase          =      0
			__lowerCamelCase          =      0
			__lowerCamelCase          =      0
			while perimeter <= max_perimeter:
						perimeters_sum += perimeter

						prev_value += 2 * value
						value += prev_value

						__lowerCamelCase          =      2 * value + 2 if i % 2 == 0 else 2 * value - 2
						i += 1

			return perimeters_sum


if __name__ == "__main__":
					print(f"""{solution() = }""")




