




import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

snake_case_  :      List[Any]		=		3



def        __a							(  __UpperCAmelCase		:						int      ) ->					Tuple:
							"""simple docstring"""




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



def        __a							(  __UpperCAmelCase		:						int      ) ->					Optional[Any]:
							"""simple docstring"""




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

							lowerCamelCase_			:    List[Any]   						=      (key_size, e_a, e_a, p)
							lowerCamelCase_			:    Optional[Any]   						=      (key_size, d)

							return public_key, private_key



def        __a							(  __UpperCAmelCase		:						str   ,		__UpperCAmelCase		:						int      ) ->					str:
							"""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()

							lowerCamelCase_						,						lowerCamelCase_			:    Union[str, Any]   						=      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        __a							(  ) ->					Dict:
							"""simple docstring"""




							print("Making key files..."      )
							make_key_files("elgamal"   ,		2048      )
							print("Key files generation successful"      )


if __name__ == "__main__":
	main()


'''simple docstring'''


import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor





def    lowerCAmelCase       (		UpperCamelCase__					: dict							):


	"""simple docstring"""
	return (data["data"], data["target"])





def    lowerCAmelCase       (		UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: np.ndarray							):


	"""simple docstring"""
	__UpperCAmelCase      					=      XGBRegressor(verbosity=0     ,       random_state=4_2							)
	xgb.fit(UpperCamelCase__     ,       UpperCamelCase__							)
	# Predict target for test data
	__UpperCAmelCase      					=      xgb.predict(UpperCamelCase__							)
	__UpperCAmelCase      					=      predictions.reshape(len(UpperCamelCase__							)     ,       1							)
	return predictions





def    lowerCAmelCase       (		):


	"""simple docstring"""
	__UpperCAmelCase      					=      fetch_california_housing()
	__UpperCAmelCase					,      __UpperCAmelCase      					=      data_handling(UpperCamelCase__							)
	__UpperCAmelCase					,      __UpperCAmelCase					,      __UpperCAmelCase					,      __UpperCAmelCase      					=      train_test_split(
	    UpperCamelCase__     ,       UpperCamelCase__     ,       test_size=0.25     ,       random_state=1							)
	__UpperCAmelCase      					=      xgboost(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
	# Error printing
	print(f"""Mean Absolute Error : {mean_absolute_error(UpperCamelCase__     ,       UpperCamelCase__							)}"""							)
	print(f"""Mean Square Error  : {mean_squared_error(UpperCamelCase__     ,       UpperCamelCase__							)}"""							)


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)
					main()
