






import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812



def  a__ ()		-> None:
				print('''Making key files...'''   )
				make_key_files('''rsa'''					,  1024   )
				print('''Key files generation successful.'''   )



def  a__ (__lowercase							:int   )		-> tuple[tuple[int, int], tuple[int, int]]:
				print('''Generating prime p...'''   )
				_A	:				Tuple   =      rabinMiller.generate_large_prime(__lowercase   )
				print('''Generating prime q...'''   )
				_A	:				int   =      rabinMiller.generate_large_prime(__lowercase   )
				_A	:				List[Any]   =      p * q

				print('''Generating e that is relatively prime to (p - 1) * (q - 1)...'''   )
				while True:
								_A	:				Any   =      random.randrange(2 ** (key_size - 1)					,  2 ** (key_size)   )
								if cryptoMath.gcd(__lowercase					,  (p - 1) * (q - 1)   ) == 1:
												break

				print('''Calculating d that is mod inverse of e...'''   )
				_A	:				Optional[Any]   =      cryptoMath.find_mod_inverse(__lowercase					,  (p - 1) * (q - 1)   )

				_A	:				Dict   =      (n, e)
				_A	:				Dict   =      (n, d)
				return (public_key, private_key)



def  a__ (__lowercase							:str					,  __lowercase							:int   )		-> 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()

				_A  ,				_A	:				Tuple   =      generate_key(__lowercase   )
				print(f"""\nWriting public key to file {name}_pubkey.txt..."""   )
				with open(f"""{name}_pubkey.txt"""					,  '''w'''   ) as out_file:
								out_file.write(f"""{key_size},{public_key[0]},{public_key[1]}"""   )

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


if __name__ == "__main__":
	main()











from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_UpperCamelCase			:	List[str]								=HfArgumentParser(InitializationArguments)
_UpperCamelCase			:	Dict								=parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_UpperCamelCase			:	List[Any]								=AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_UpperCamelCase			:	str								={
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
_UpperCamelCase			:	int								=AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_UpperCamelCase			:	List[str]								=AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)



