
import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

lowercase					:							Any   =      3







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	print('Generating primitive root of p'			)
	while True:
		_snake_case     		=	random.randrange(3    ,       __A			)
		if pow(__A    ,       2    ,       __A			) == 1:
			continue
		if pow(__A    ,       __A    ,       __A			) == 1:
			continue
		return g







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			tuple[tuple[int, int, int, int], tuple[int, int]]:
	print('Generating prime p...'			)
	_snake_case     		=	rabin_miller.generate_large_prime(__A			)  # select large prime number.
	_snake_case     		=	primitive_root(__A			)  # one primitive root on modulo p.
	_snake_case     		=	random.randrange(3    ,       __A			)  # private_key -> have to be greater than 2 for safety.
	_snake_case     		=	cryptomath.find_mod_inverse(pow(__A    ,       __A    ,       __A			)    ,       __A			)

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

	return public_key, private_key







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			None:
	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()

	_snake_case		,				_snake_case     		=	generate_key(__A			)
	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        SCREAMING_SNAKE_CASE__			(     ) ->			None:
	print('Making key files...'			)
	make_key_files('elgamal'    ,       2_048			)
	print('Key files generation successful'			)


if __name__ == "__main__":
					main()



'''simple docstring'''







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer







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


lowercase					:							str   =      CamembertTokenizer.from_pretrained("camembert-base")
lowercase					:							str   =      CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowercase					:							Tuple   =      "Le camembert est <mask> :)"





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

