






"""simple docstring"""
import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






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


_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"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   prime_factors(snake_case__					)
	if is_square_free(snake_case__					):
		return -1 if len(snake_case__					) % 2 else 1
	return 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()

