


"""simple docstring"""
import random
import sys

from . import cryptomath_module as cryptomath

SCREAMING_SNAKE_CASE					:       Tuple							   =							(
    r""" !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`"""
    r"""abcdefghijklmnopqrstuvwxyz{|}~"""
)






def 						lowercase				(	_snake_case		: Dict	,  _snake_case		: Optional[int]	,  _snake_case		: Any	)	->List[Any]:
	"""simple docstring"""





	if mode == "encrypt":
		if key_a == 1:
			sys.exit(
			    '''The affine cipher becomes weak when key '''
			    '''A is set to 1. Choose different key'''	)
		if key_b == 0:
			sys.exit(
			    '''The affine cipher becomes weak when key '''
			    '''B is set to 0. Choose different key'''	)
	if key_a < 0 or key_b < 0 or key_b > len(_snake_case	) - 1:
		sys.exit(
		    '''Key A must be greater than 0 and key B must '''
		    f"""be between 0 and {len(_snake_case	) - 1}."""	)
	if cryptomath.gcd(_snake_case	,  len(_snake_case	)	) != 1:
		sys.exit(
		    f"""Key A {key_a} and the symbol set size {len(_snake_case	)} """
		    '''are not relatively prime. Choose a different key.'''	)






def 						lowercase				(	_snake_case		: Optional[Any]	,  _snake_case		: Optional[Any]	)	->Union[str, Any]:
	"""simple docstring"""





	__snake_case      :		List[Any]         =							divmod(_snake_case	,  len(_snake_case	)	)
	check_keys(_snake_case	,  _snake_case	,  '''encrypt'''	)
	__snake_case      :		List[Any]         =							''
	for symbol in message:
		if symbol in SYMBOLS:
			__snake_case      :		List[Any]         =							SYMBOLS.find(_snake_case	)
			cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(_snake_case	)]
		else:
			cipher_text += symbol
	return cipher_text






def 						lowercase				(	_snake_case		: List[Any]	,  _snake_case		: Dict	)	->str:
	"""simple docstring"""





	__snake_case      :		Optional[int]         =							divmod(_snake_case	,  len(_snake_case	)	)
	check_keys(_snake_case	,  _snake_case	,  '''decrypt'''	)
	__snake_case      :		List[str]         =							''
	__snake_case      :		Dict         =							cryptomath.find_mod_inverse(_snake_case	,  len(_snake_case	)	)
	for symbol in message:
		if symbol in SYMBOLS:
			__snake_case      :		Tuple         =							SYMBOLS.find(_snake_case	)
			plain_text += SYMBOLS[
			    (sym_index - key_b) * mod_inverse_of_key_a % len(_snake_case	)
			]
		else:
			plain_text += symbol
	return plain_text






def 						lowercase				(	)	->Any:
	"""simple docstring"""





	while True:
		__snake_case      :		List[str]         =							random.randint(2	,  len(_snake_case	)	)
		__snake_case      :		int         =							random.randint(2	,  len(_snake_case	)	)
		if cryptomath.gcd(_snake_case	,  len(_snake_case	)	) == 1 and key_b % len(_snake_case	) != 0:
			return key_b * len(_snake_case	) + key_b






def 						lowercase				(	)	->List[Any]:
	"""simple docstring"""





	__snake_case      :		int         =							input('''Enter message: '''	).strip()
	__snake_case      :		Any         =							int(input('''Enter key [2000 - 9000]: '''	).strip()	)
	__snake_case      :		int         =							input('''Encrypt/Decrypt [E/D]: '''	).strip().lower()

	if mode.startswith('''e'''	):
		__snake_case      :		int         =							'encrypt'
		__snake_case      :		int         =							encrypt_message(_snake_case	,  _snake_case	)
	elif mode.startswith('''d'''	):
		__snake_case      :		List[str]         =							'decrypt'
		__snake_case      :		Dict         =							decrypt_message(_snake_case	,  _snake_case	)
	print(f"""\n{mode.title()}ed text: \n{translated}"""	)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			# main()










'''simple docstring'''







import matplotlib.pyplot as plt
import numpy as np







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:str										=      len(SCREAMING_SNAKE_CASE     )  # Number of training samples
		SCREAMING_SNAKE_CASE_						:List[Any]										=      np.eye(SCREAMING_SNAKE_CASE     )  # Initialize weights as identity matrix
		for j in range(SCREAMING_SNAKE_CASE     ):
				SCREAMING_SNAKE_CASE_						:Tuple										=      point - x_train[j]
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.exp(diff @ diff.T / (-2.0 * tau**2)     )

		return weights







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:int										=      weight_matrix(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:str										=      np.linalg.inv(x_train.T @ weight_mat @ x_train     ) @ (
		    x_train.T @ weight_mat @ y_train.T
		)

		return weight







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:int										=      np.zeros(len(SCREAMING_SNAKE_CASE     )     )  # Initialize array of predictions
		for i, item in enumerate(SCREAMING_SNAKE_CASE     ):
				SCREAMING_SNAKE_CASE_						:Dict										=      item @ local_weight(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )

		return y_pred







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		import seaborn as sns

		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      sns.load_dataset(SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Tuple										=      np.array(data[x_name]     )
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.array(data[y_name]     )

		SCREAMING_SNAKE_CASE_						:str										=      np.ones(len(SCREAMING_SNAKE_CASE     )     )

		# pairing elements of one and x_data
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.column_stack((one, x_data)     )

		return x_train, x_data, y_data







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		):
		SCREAMING_SNAKE_CASE_						:Tuple										=      np.sort(SCREAMING_SNAKE_CASE ,		axis=0     )
		plt.scatter(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		color='blue'     )
		plt.plot(
		    x_train_sorted[:, 1] ,		preds[x_train[:, 1].argsort(0     )] ,		color='yellow' ,		linewidth=5 ,		)
		plt.title('Local Weighted Regression'     )
		plt.xlabel(SCREAMING_SNAKE_CASE     )
		plt.ylabel(SCREAMING_SNAKE_CASE     )
		plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Demo with a dataset from the seaborn module
							SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__     : str				=   load_data("""tips""", """total_bill""", """tip""")
							SCREAMING_SNAKE_CASE__     : str				=   local_weight_regression(training_data_x, tip, 5)
							plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")
