





import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

lowerCamelCase_						: str         =  3







def        A__					(						lowerCamelCase						)  ->	int:
				print("""Generating primitive root of p"""						)
				while True:
								UpperCamelCase_:							Any	=						random.randrange(3     ,    lowerCamelCase						)
								if pow(lowerCamelCase     ,    2     ,    lowerCamelCase						) == 1:
												continue
								if pow(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						) == 1:
												continue
								return g







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

				UpperCamelCase_:							Optional[Any]	=						(key_size, e_a, e_a, p)
				UpperCamelCase_:							Optional[int]	=						(key_size, d)

				return public_key, private_key







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	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()

				UpperCamelCase_:							str	=						generate_key(lowerCamelCase						)
				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        A__					(						)  ->	None:
				print("""Making key files..."""						)
				make_key_files("""elgamal"""     ,    20_48						)
				print("""Key files generation successful"""						)


if __name__ == "__main__":
							main()








import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				# Initialise PyTorch model
				UpperCamelCase_:							List[Any]	=						TaConfig.from_json_file(lowerCamelCase						)
				print(F'''Building PyTorch model from configuration: {config}'''						)
				UpperCamelCase_:							Any	=						TaForConditionalGeneration(lowerCamelCase						)

				# Load weights from tf checkpoint
				load_tf_weights_in_ta(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
				model.save_pretrained(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: Tuple         =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
							)
							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							lowerCamelCase_						: Union[str, Any]         =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






