import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
from qiskit import Aer, execute, QuantumCircuit, transpile, IBMQ
from qiskit.circuit.library import ZZFeatureMap
import pqcrypto.kyber as kyber
from scipy.integrate import odeint
import matplotlib.pyplot as plt
from qiskit.providers.aer import QasmSimulator
from qiskit_machine_learning.algorithms.classifiers import VQC
from qiskit_machine_learning.circuit.library import TwoLayerQNN
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from qiskit.algorithms.optimizers import COBYLA
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.decomposition import PCA
import seaborn as sns

# Define the neural network module
class QuantumEnhancedNN(nn.Module):
    def init(self, input_dim, hidden_dim, output_dim):
        super(QuantumEnhancedNN, self).init()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_dim, output_dim)
    
    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out

# Simulate time series data
def generate_time_series(n_timesteps, n_series):
    np.random.seed(0)
    data = np.random.randn(n_series, n_timesteps)
    return data

# Preprocess the data into features and labels
def preprocess(data):
    X = data[:, :-1]
    y = data[:, -1]
    return X, y

# Perform feature map encoding to prepare the data for quantum processing
def encode_features(X, feature_map):
    quantum_backend = Aer.get_backend(‘statevector_simulator’)
    quantum_states = []
    for x in X:
        qc = QuantumCircuit(X.shape[1])
        qc.compose(feature_map.bind_parameters(x), inplace=True)
        result = execute(qc, quantum_backend).result()
        state = result.get_statevector()
        quantum_states.append(state)
    return np.array(quantum_states)

# Define the Lorenz chaotic system
def chaotic_system(state, t, sigma, rho, beta):
    x, y, z = state
    dxdt = sigma * (y - x)
    dydt = x * (rho - z) - y
    dzdt = x * y - beta * z
    return [dxdt, dydt, dzdt]

# Train the model
def train_model(model, dataloader, criterion, optimizer, num_epochs):
    model.train()
    for epoch in range(num_epochs):
        for inputs, labels in dataloader:
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

# Generate key pair and encrypt message using Kyber
def generate_keypair_and_encrypt(message):
    public_key, private_key = kyber.generate_keypair()
    cipher_text = kyber.encrypt(public_key, message.encode())
    decrypted_message = kyber.decrypt(private_key, cipher_text).decode()
    return decrypted_message

# Simulate a quantum circuit 
def simulate_quantum_circuit():
    qc = QuantumCircuit(5)
    qc.h(range(5))
    qc.cx(0, 1)
    qc.cx(1, 2)
    qc.cx(2, 3)
    qc.cx(3, 4)
    qc.measure_all()
    sim = QasmSimulator()
    job = execute(qc, sim, shots=1024)
    result = job.result()
    counts = result.get_counts(qc)
    return counts

# Train and evaluate the secure, chaotic hybrid model
def train_evaluate_secure_chaotic_model(model, X_train, y_train, X_test, y_test):
    public_key, private_key = kyber.generate_keypair()
    encrypted_message = kyber.encrypt(public_key, “Training data transmission”.encode())
    decrypted_message = kyber.decrypt(private_key, encrypted_message).decode()
    if decrypted_message == “Training data transmission”:
        train_model(model, dataloader, criterion, optimizer, num_epochs=200)
        model.eval()
        with torch.no_grad():
            test_predictions = model(X_test)
            test_loss = criterion(test_predictions, y_test)
        return test_predictions, test_loss

# Quantum Variant Classification
def quantum_variant_classification(X, y):
    X_scaled = StandardScaler().fit_transform(X)
    X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=0)
    feature_dim = X_train.shape[1]
    qnn = TwoLayerQNN(feature_dim=feature_dim, num_qubits=feature_dim, quantum_instance=Aer.get_backend(‘qasm_simulator’))
    vqc = VQC(optimizer=COBYLA(), feature_map=qnn.feature_map, quantum_instance=Aer.get_backend(‘qasm_simulator’))
    vqc.fit(X_train, y_train)
    train_accuracy = vqc.score(X_train, y_train)
    test_accuracy = vqc.score(X_test, y_test)
    return train_accuracy, test_accuracy

# Classical ML Integration (Random Forest, PCA)
def classical_ml_integration(X, y):
    pca = PCA(n_components=10)
    X_pca = pca.fit_transform(X)
    rf = RandomForestClassifier(n_estimators=100)
    X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=0)
    rf.fit(X_train, y_train)
    train_accuracy = rf.score(X_train, y_train)
    test_accuracy = rf.score(X_test, y_test)
    return train_accuracy, test_accuracy

# Additional Complexity Integration with LSTM
class LSTMModel(nn.Module):
    def init(self, input_dim, hidden_dim, num_layers, output_dim):
        super(LSTMModel, self).init()
        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)
    
    def forward(self, x):
        h0 = torch.zeros(lstm_num_layers, x.size(0), lstm_hidden_dim).requires_grad_()
        c0 = torch.zeros(lstm_num_layers, x.size(0), lstm_hidden_dim).requires_grad_()
        out, _ = self.lstm(x, (h0.detach(), c0.detach()))
        out = self.fc(out[:, -1, :])
        return out

class ExtendedHybridModel(nn.Module):
    def init(self, input_dim, hidden_dim, lstm_out_dim, output_dim):
        super(ExtendedHybridModel, self).init()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.relu = nn.ReLU()
        self.lstm = LSTMModel(input_dim, lstm_hidden_dim, lstm_num_layers, lstm_out_dim)
        self.fc2 = nn.Linear(hidden_dim + lstm_out_dim, output_dim)
        
    def forward(self, x):
        fc_out = self.fc1(x)
        lstm_out = self.lstm(x.unsqueeze(1))
        concat = torch.cat((fc_out, lstm_out), 1)
        out = self.fc2(concat)
        return out
    
def additional_complexity_integration(X, y):
    lstm_input_dim = 128
    lstm_hidden_dim = 64
    lstm_num_layers = 2
    lstm_output_dim = 1
    extended_model = ExtendedHybridModel(input_dim, hidden_dim, lstm_output_dim, output_dim)
    criterion = nn.MSELoss()
    optimizer = optim.Adam(extended_model.parameters(), lr=0.001)
    lstm_X_tensor = torch.tensor(X_real, dtype=torch.float32).view(-1, 1, X_real.shape[1])
    train_model(extended_model, dataloader, criterion, optimizer, num_epochs=300)
    with torch.no_grad():
        extended_predictions = extended_model(lstm_X_tensor)
    return extended_predictions

# Enhanced Security Mechanisms
def enhance_security_mechanisms():
    def enhanced_encryption(input_text):
        public_key, private_key = kyber.generate_keypair()
        cipher_text = kyber.encrypt(public_key, input_text.encode())
        decrypted_message = kyber.decrypt(private_key, cipher_text).decode()
        return cipher_text, decrypted_message
    message = “Top secret astrophysical discovery”
    encrypted_message, decrypted_message = enhanced_encryption(message)
    return encrypted_message, decrypted_message

# Extended Visualization Integration
def extended_visualization_integration(predictions, y, chaotic_solution):
    fig, ax = plt.subplots(4, 1, figsize=(25, 30))
    ax[0].plot(y, label=‘Actual’)
    ax[0].plot(predictions.numpy(), label=“Predicted”)
    ax[0].legend()
    ax[0].set_title(“Hybrid Quantum-CNN Predictions”)
    ax[1].plot(chaotic_solution[:, 0], chaotic_solution[:, 1], chaotic_solution[:, 2])
    ax[1].set_title(“Chaotic System: Extended Lorenz Attractor”)
    ax[2].plot(y, label=‘Actual’)
    ax[2].plot(train_predictions.numpy(), label=“Predicted Extended Model”)
    ax[2].legend()
    ax[2].set_title(“Extended Model Predictions with Enhanced Complexity”)
    ax[3].bar(list(counts.keys()), list(counts.values()))
    ax[3].set_title(“Quantum Circuit Simulation Results”)
    plt.show()

# Extended security mechanisms
encrypted_msg, decrypted_msg = enhance_security_mechanisms()
print(f"Enhanced Encryption: {encrypted_msg}\nDecrypted Message: {decrypted_msg}“)

# Data visualization for feature importance
def feature_importance_visualization(X, y):
    pca = PCA(n_components=10)
    X_pca = pca.fit_transform(X)
    rf = RandomForestClassifier(n_estimators=100)
    X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=0)
    rf.fit(X_train, y_train)
    importances = rf.feature_importances_
    sns.barplot(x=list(range(len(importances))), y=importances)
    plt.title(“Feature Importance from Random Forest”)
    plt.show()

# Main evaluation function
def evaluate_model_and_plot(fig_ttl):
    feature_dim = 99
    data = generate_time_series(feature_dim + 1, 3000)
    X, y = preprocess(data)
    quantum_states = encode_features(X, feature_map)
    X_real = np.real(quantum_states)
    input_dim = X_real.shape[1]
    hidden_dim = 128
    output_dim = 1
    extended_model = ExtendedHybridModel(input_dim, hidden_dim, lstm_output_dim, output_dim)
    criterion = nn.MSELoss()
    optimizer = optim.Adam(extended_model.parameters(), lr=0.0005)
    X_tensor = torch.tensor(X_real, dtype=torch.float32)
    y_tensor = torch.tensor(y, dtype=torch.float32).view(-1, 1)
    dataset = TensorDataset(X_tensor, y_tensor)
    dataloader = DataLoader(dataset, batch_size=64, shuffle=True)
    train_model(extended_model, dataloader, criterion, optimizer, num_epochs=500)
    with torch.no_grad():
        predictions = extended_model(X_tensor.view(-1, 1, X_tensor.shape[1]))
    initial_state = [1.0, 1.0, 1.0]
    time_points = np.linspace(0, 400, 40000)
    solution = odeint(chaotic_system, initial_state, time_points, args=(11.0, 31.0, 7.0/2.5))
    counts = simulate_quantum_circuit()
    
    # Visualization
    fig, ax = plt.subplots(3, 1, figsize=(30, 40))
    ax[0].plot(y, label=‘Actual’)
    ax[0].plot(predictions.numpy(), label=f”{fig_ttl} predictions")
    ax[0].legend()
    ax[0].set_title(“Enhanced Model Predictions”)
    ax[1].plot(solution[:, 0], solution[:, 1], solution[:, 2])
    ax[1].set_title(“Ultimate Chaotic System Simulation”)
    ax[2].bar(list(counts.keys()), list(counts.values()))
    ax[2].set_title(“Quantum Circuit Simulation Results”)
    print(f"Enhanced Encryption: {encrypted_msg}\nDecrypted Message: {decrypted_msg}“)
    plt.show()

# Feature importance visualization
def main():
    IBMQ.load_account()
    provider = IBMQ.get_provider(hub=‘ibm-q’)
    least_busy_device = provider.backends(filters=lambda x: x.configuration().n_qubits >= 5 and not x.configuration().simulator and x.status().operational())[0]
    feature_dim = 99
    feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=3, entanglement=‘linear’)
    data = generate_time_series(feature_dim + 1, 2000)
    X, y = preprocess(data)
    quantum_states = encode_features(X, feature_map)
    X_real = np.real(quantum_states)
    input_dim = X_real.shape[1]
    hidden_dim = 128
    output_dim = 1
    model = QuantumEnhancedNN(input_dim, hidden_dim, output_dim)
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)
    X_tensor = torch.tensor(X_real, dtype=torch.float32)
    y_tensor = torch.tensor(y, dtype=torch.float32).view(-1, 1)
    dataset = TensorDataset(X_tensor, y_tensor)
    dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
    train_predictions, test_loss = train_evaluate_secure_chaotic_model(model, X_tensor, y_tensor, X_tensor, y_tensor)
    initial_state = [0.0, 1.0, 1.05]
    time_points = np.linspace(0, 150, 15000)
    solution = odeint(chaotic_system, initial_state, time_points, args=(10.0, 28.0, 8.0/3.0))
    
    # Additional complexity integration and extended visualization
    extended_predictions = additional_complexity_integration(X, y)
    extended_visualization_integration(extended_predictions, y, solution)
    
    # Quantum classification and visualization
    extra_data, extra_labels = ad_hoc_data(training_size=20, test_size=10, n=2, gap=0.3, plot_data=False)[0:2]
    X_scaled = StandardScaler().fit_transform(extra_data)
    train_accuracy, test_accuracy = quantum_variant_classification(X_scaled, extra_labels)
    
    # Classical Ml integration
    rf_train_acc, rf_test_acc = classical_ml_integration(X_scaled, extra_labels)
    
    # Final visualization
    evaluate_model_and_plot(“extended”)
    print(f"Quantum Classification - Train Accuracy: {train_accuracy:.4f}, Test Accuracy: {test_accuracy:.4f}”)
    print(f"Classical ML (Random Forest) - Train Accuracy: {rf_train_acc:.4f}, Test Accuracy: {rf_test_acc:.4f}")

    # Visualize feature importance
    feature_importance_visualization(X, y)
    
if name == ‘main’:
    main()