


import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

_lowercase       :      List[Any]	=	3





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   int:





	"""simple docstring"""







	print("""Generating primitive root of p"""						)
	while True:
		A								=				random.randrange(3							,					UpperCamelCase__						)
		if pow(UpperCamelCase__							,					2							,					UpperCamelCase__						) == 1:
			continue
		if pow(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						) == 1:
			continue
		return g





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   tuple[tuple[int, int, int, int], tuple[int, int]]:





	"""simple docstring"""







	print("""Generating prime p..."""						)
	A								=				rabin_miller.generate_large_prime(UpperCamelCase__						)  # select large prime number.
	A								=				primitive_root(UpperCamelCase__						)  # one primitive root on modulo p.
	A								=				random.randrange(3							,					UpperCamelCase__						)  # private_key -> have to be greater than 2 for safety.
	A								=				cryptomath.find_mod_inverse(pow(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)							,					UpperCamelCase__						)

	A								=				(key_size, e_a, e_a, p)
	A								=				(key_size, d)

	return public_key, private_key





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     int						) ->   None:





	"""simple docstring"""







	if os.path.exists(f'{name}_pubkey.txt'						) or os.path.exists(f'{name}_privkey.txt'						):
		print("""\nWARNING:"""						)
		print(
		    f'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
		    """Use a different name or delete these files and re-run this program."""						)
		sys.exit()

	A					,			A								=				generate_key(UpperCamelCase__						)
	print(f'\nWriting public key to file {name}_pubkey.txt...'						)
	with open(f'{name}_pubkey.txt'							,					"""w"""						) as fo:
		fo.write(f'{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}'						)

	print(f'Writing private key to file {name}_privkey.txt...'						)
	with open(f'{name}_privkey.txt'							,					"""w"""						) as fo:
		fo.write(f'{private_key[0]},{private_key[1]}'						)





def   _lowerCAmelCase				(   ) ->   None:





	"""simple docstring"""







	print("""Making key files..."""						)
	make_key_files("""elgamal"""							,					20_48						)
	print("""Key files generation successful"""						)


if __name__ == "__main__":
				main()







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any=5						) ->   Optional[Any]:





	"""simple docstring"""







	assert masked_input.count("""<mask>"""						) == 1
	A								=				torch.tensor(tokenizer.encode(UpperCamelCase__							,					add_special_tokens=UpperCamelCase__						)						).unsqueeze(0						)  # Batch size 1
	A								=				model(UpperCamelCase__						)[0]  # The last hidden-state is the first element of the output tuple
	A								=				(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
	A								=				logits[0, masked_index, :]
	A								=				logits.softmax(dim=0						)
	A					,			A								=				prob.topk(k=UpperCamelCase__							,					dim=0						)
	A								=				""" """.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()						) for i in range(len(UpperCamelCase__						)						)]						)
	A								=				tokenizer.mask_token
	A								=				[]
	for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """						)						):
		A								=				predicted_token_bpe.replace("""\u2581"""							,					""" """						)
		if " {0}".format(UpperCamelCase__						) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(""" {0}""".format(UpperCamelCase__						)							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(UpperCamelCase__							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
	return topk_filled_outputs


_lowercase       :      Optional[int]	=	CamembertTokenizer.from_pretrained("camembert-base")
_lowercase       :      int	=	CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

_lowercase       :      Optional[int]	=	"Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))



