


'''simple docstring'''






import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
							__A	:	List[str]     =     pd.read_csv('sample_data.csv', header=None)
							__A	:	Optional[Any]     =     df.shape[:1][0]
							# If you're using some other dataset input the target column
							__A	:	List[str]     =     df.iloc[:, 1:2]
							__A	:	int     =     actual_data.values.reshape(len_data, 1)
							__A	:	str     =     MinMaxScaler().fit_transform(actual_data)
							__A	:	Union[str, Any]     =     10
							__A	:	Dict     =     5
							__A	:	Dict     =     20
							__A	:	Optional[int]     =     len_data - periods * look_back
							__A	:	Any     =     actual_data[:division]
							__A	:	int     =     actual_data[division - look_back :]
							__A,   __A	:	Optional[Any]     =     [], []
							__A,   __A	:	List[str]     =     [], []

							for i in range(0, len(train_data) - forward_days - look_back + 1):
														train_x.append(train_data[i : i + look_back])
														train_y.append(train_data[i + look_back : i + look_back + forward_days])
							for i in range(0, len(test_data) - forward_days - look_back + 1):
														test_x.append(test_data[i : i + look_back])
														test_y.append(test_data[i + look_back : i + look_back + forward_days])
							__A	:	int     =     np.array(train_x)
							__A	:	int     =     np.array(test_x)
							__A	:	Union[str, Any]     =     np.array([list(i.ravel()) for i in train_y])
							__A	:	Dict     =     np.array([list(i.ravel()) for i in test_y])

							__A	:	str     =     Sequential()
							model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
							model.add(LSTM(64, input_shape=(128, 1)))
							model.add(Dense(forward_days))
							model.compile(loss='mean_squared_error', optimizer='adam')
							__A	:	List[Any]     =     model.fit(
							    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
							)
							__A	:	str     =     model.predict(x_test)



'''simple docstring'''






import numpy as np
import qiskit





def      UpperCAmelCase      (		lowerCamelCase_  :int = 8				,      lowerCamelCase_  :int | None = None ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						np.random.default_rng(seed=lowerCamelCase_ )

		# Roughly 25% of the qubits will contribute to the key.
		# So we take more than we need.
		snake_case_		:							Union[str, Any]			       =						6 * key_len
		# Measurement basis for Alice's qubits.
		snake_case_		:							Tuple			       =						rng.integers(2				,      size=lowerCamelCase_ )
		# The set of states Alice will prepare.
		snake_case_		:							Tuple			       =						rng.integers(2				,      size=lowerCamelCase_ )
		# Measurement basis for Bob's qubits.
		snake_case_		:							List[Any]			       =						rng.integers(2				,      size=lowerCamelCase_ )

		# Quantum Circuit to simulate BB84
		snake_case_		:							Optional[int]			       =						qiskit.QuantumCircuit(lowerCamelCase_				,      name="""BB84""" )

		# Alice prepares her qubits according to rules above.
		for index, _ in enumerate(lowerCamelCase_ ):
				if alice_state[index] == 1:
						bbaa_circ.x(lowerCamelCase_ )
				if alice_basis[index] == 1:
						bbaa_circ.h(lowerCamelCase_ )
		bbaa_circ.barrier()

		# Bob measures the received qubits according to rules above.
		for index, _ in enumerate(lowerCamelCase_ ):
				if bob_basis[index] == 1:
						bbaa_circ.h(lowerCamelCase_ )

		bbaa_circ.barrier()
		bbaa_circ.measure_all()

		# Simulate the quantum circuit.
		snake_case_		:							Union[str, Any]			       =						qiskit.Aer.get_backend("""aer_simulator""" )
		# We only need to run one shot because the key is unique.
		# Multiple shots will produce the same key.
		snake_case_		:							Tuple			       =						qiskit.execute(lowerCamelCase_				,      lowerCamelCase_				,      shots=1				,      seed_simulator=lowerCamelCase_ )
		# Returns the result of measurement.
		snake_case_		:							Tuple			       =						job.result().get_counts(lowerCamelCase_ ).most_frequent()

		# Extracting the generated key from the simulation results.
		# Only keep measurement results where Alice and Bob chose the same basis.
		snake_case_		:							Optional[Any]			       =						"""""".join(
		    [
		        result_bit
		        for alice_basis_bit, bob_basis_bit, result_bit in zip(
		            lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_ )
		        if alice_basis_bit == bob_basis_bit
		    ] )

		# Get final key. Pad with 0 if too short, otherwise truncate.
		snake_case_		:							str			       =						gen_key[:key_len] if len(lowerCamelCase_ ) >= key_len else gen_key.ljust(lowerCamelCase_				,      """0""" )
		return key


if __name__ == "__main__":
							print(F'The generated key is : {bbaa(8, seed=0)}')
							from doctest import testmod

							testmod()