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
from sklearn.linear_model import LogisticRegression
import seaborn as sns

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

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

def preprocess(data):
    X = data[:, :-1]
    y = data[:, -1]
    return X, y

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)

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]

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()

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

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

def train_evaluate_secure_chaotic_model(model, dataloader, X_test, y_test):
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)
    
    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

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_map=None, 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

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(self.lstm.num_layers, x.size(0), self.lstm.hidden_dim).requires_grad_()
        c0 = torch.zeros(self.lstm.num_layers, x.size(0), self.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 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

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(“Extended Hybrid Model Predictions with LSTM”)
    
    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(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()

def classic_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

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 execution flow
feature_dim = 99
feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=3, entanglement=‘linear’)
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, hidden_dim, output_dim = X_real.shape[1], 128, 1
lstm_hidden_dim, lstm_num_layers, lstm_output_dim = 64, 2, 1
criterion = nn.MSELoss()

# Initialize and train the extended model
extended_model = ExtendedHybridModel(input_dim, hidden_dim, lstm_output_dim, output_dim)
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)

# Final predictions
with torch.no_grad():
    predictions = extended_model(X_tensor.view(-1, 1, X_tensor.shape[1]))

# Simulate chaotic system
initial_state, time_points = [1.0, 1.0, 1.0], np.linspace(0, 400, 40000)
solution = odeint(chaotic_system, initial_state, time_points, args=(11.0, 31.0, 7.0/2.5))

# Quantum circuit simulation
counts = simulate_quantum_circuit()

# Quantum variant classification
extra_data, extra_labels = ad_hoc_data(training_size=100, test_size=50, n=5, gap=0.7, plot_data=False)[0:2]
X_scaled = StandardScaler().fit_transform(extra_data)
train_accuracy, test_accuracy = quantum_variant_classification(X_scaled, extra_labels)

# Classic ML integration
rf_train_acc, rf_test_acc = classic_ml_integration(X_scaled, extra_labels)

# Enhanced encryption demonstration
encrypted_msg, decrypted_msg = enhance_security_mechanisms()

# Visualization and result output
fig, ax = plt.subplots(3, 1, figsize=(30, 40))
ax[0].plot(y, label=‘Actual’)
ax[0].plot(predictions.numpy(), label=“Final Extended Model 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}“)
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}")

plt.show()

# Feature importance visualization
feature_importance_visualization(X_scaled, extra_labels)