MultimodalSystem

import numpy as np
import torch
from qiskit import QuantumCircuit, Aer, execute
from qiskit.circuit import Parameter

class QuantumNN:
    def __init__(self, num_qubits):
        self.num_qubits = num_qubits
        self.circuit = QuantumCircuit(num_qubits)
        self.params = [Parameter(f"θ{i}") for i in range(num_qubits)]

        for i in range(num_qubits):
            self.circuit.rx(self.params[i], i)
        self.circuit.measure_all()

    def run(self, theta_values):
        backend = Aer.get_backend("qasm_simulator")
        bound_circuit = self.circuit.bind_parameters(
            {self.params[i]: theta_values[i] for i in range(self.num_qubits)}
        )
        result = execute(bound_circuit, backend, shots=1000).result()
        return result.get_counts()

class FractalNN:
    def __init__(self, iterations):
        self.iterations = iterations

    def generate_fractal(self, z, c):
        for _ in range(self.iterations):
            z = z**2 + c
        return z

    def process_data(self, data):
        processed_data = np.array(
            [self.generate_fractal(z, complex(0, 0)) for z in data]
        )
        return processed_data

class MultimodalSystem:
    def __init__(self, classical_model, quantum_model, fractal_model):
        self.classical_model = classical_model
        self.quantum_model = quantum_model
        self.fractal_model = fractal_model

        # Enhanced weight parameters with adaptive learning capabilities
        self.classical_weight = 0.4
        self.quantum_weight = 0.4
        self.fractal_weight = 0.2

        # Adaptive learning rate for weight optimization
        self.adaptive_lr = 0.01

        # Expanded integration modes with quantum-specific algorithms
        self.integration_modes = [
            "concatenate",
            "weighted",
            "ensemble",
            "crossmodal",
            "quantum_entangled",
            "fractal_quantum",
            "adaptive_hybrid",
        ]
        self.current_mode = "weighted"

        # Quantum state tracking for entanglement-aware processing
        self.quantum_state_history = []
        self.entanglement_threshold = 0.75

        # Schrödinger equation parameters for quantum wave function evolution
        self.hbar = 1.0  # Reduced Planck constant
        self.m = 1.0  # Mass parameter
        self.potential_function = lambda x: 0.5 * (
            x**2
        )  # Harmonic oscillator potential

    def integrate(self, input_data, mode=None):
        """Enhanced integration with multiple modes"""
        if mode and mode in self.integration_modes:
            self.current_mode = mode

        classical_output = self.classical_model(input_data)
        quantum_output = self.quantum_model.run([0.5] * self.quantum_model.num_qubits)
        quantum_values = np.array(list(quantum_output.values()))
        # Normalize quantum values
        quantum_values = (
            quantum_values / np.sum(quantum_values)
            if np.sum(quantum_values) > 0
            else quantum_values
        )
        fractal_output = self.fractal_model.process_data(input_data)

        # Convert tensor to numpy if needed
        if isinstance(classical_output, torch.Tensor):
            classical_output = classical_output.detach().numpy()

        # Handle different integration modes
        if self.current_mode == "concatenate":
            return self._concatenate_outputs(
                classical_output, quantum_values, fractal_output
            )
        elif self.current_mode == "weighted":
            return self._weighted_outputs(
                classical_output, quantum_values, fractal_output
            )
        elif self.current_mode == "ensemble":
            return self._ensemble_outputs(
                classical_output, quantum_values, fractal_output, input_data
            )
        elif self.current_mode == "crossmodal":
            return self._crossmodal_outputs(
                classical_output, quantum_values, fractal_output
            )
        elif self.current_mode == "quantum_entangled":
            return self._quantum_entangled_integration(
                classical_output, quantum_values, fractal_output
            )
        elif self.current_mode == "fractal_quantum":
            return self._fractal_quantum_integration(
                classical_output, quantum_values, fractal_output
            )
        elif self.current_mode == "adaptive_hybrid":
            return self._adaptive_hybrid_integration(
                classical_output, quantum_values, fractal_output, input_data
            )

    def _concatenate_outputs(self, classical_output, quantum_output, fractal_output):
        """Simple concatenation of outputs"""
        return np.concatenate((classical_output, quantum_output, fractal_output))

    def _weighted_outputs(self, classical_output, quantum_output, fractal_output):
        """Weighted combination of outputs"""
        # Resize arrays to same length if needed
        max_len = max(len(classical_output), len(quantum_output), len(fractal_output))
        c_out = self._resize_array(classical_output, max_len)
        q_out = self._resize_array(quantum_output, max_len)
        f_out = self._resize_array(fractal_output, max_len)

        # Apply weights
        return (
            self.classical_weight * c_out
            + self.quantum_weight * q_out
            + self.fractal_weight * f_out
        )

    def _ensemble_outputs(
        self, classical_output, quantum_output, fractal_output, original_input
    ):
        """Ensemble method that uses a meta-model to combine outputs"""
        # This would typically use another model to combine the outputs
        # Here we'll use a simple heuristic based on input characteristics
        input_complexity = np.std(original_input)

        if input_complexity > 1.0:
            # Complex inputs favor fractal processing
            self.fractal_weight = 0.5
            self.quantum_weight = 0.3
            self.classical_weight = 0.2
        else:
            # Simpler inputs favor classical processing
            self.classical_weight = 0.5
            self.quantum_weight = 0.3
            self.fractal_weight = 0.2

        return self._weighted_outputs(classical_output, quantum_output, fractal_output)

    def _crossmodal_outputs(self, classical_output, quantum_output, fractal_output):
        """Cross-modal integration where each modality influences the others"""
        # Resize arrays to same length
        max_len = max(len(classical_output), len(quantum_output), len(fractal_output))
        c_out = self._resize_array(classical_output, max_len)
        q_out = self._resize_array(quantum_output, max_len)
        f_out = self._resize_array(fractal_output, max_len)

        # Create cross-modal effects
        c_influenced = c_out * (1 + 0.2 * np.sin(q_out))
        q_influenced = q_out * (1 + 0.2 * np.cos(f_out))
        f_influenced = f_out * (1 + 0.2 * np.tan(np.clip(c_out, -1.5, 1.5)))

        return (
            self.classical_weight * c_influenced
            + self.quantum_weight * q_influenced
            + self.fractal_weight * f_influenced
        )

    def _resize_array(self, arr, target_len):
        """Utility to resize arrays to the same length for combination"""
        if len(arr) == target_len:
            return arr

        result = np.zeros(target_len)
        if len(arr) > target_len:
            # Downsample
            indices = np.round(np.linspace(0, len(arr) - 1, target_len)).astype(int)
            result = arr[indices]
        else:
            # Upsample
            result[: len(arr)] = arr
            # Fill remaining with mean or extrapolate
            if len(arr) > 0:
                result[len(arr) :] = np.mean(arr)

        return result

    def set_weights(self, classical=None, quantum=None, fractal=None):
        """Update integration weights"""
        if classical is not None:
            self.classical_weight = classical
        if quantum is not None:
            self.quantum_weight = quantum
        if fractal is not None:
            self.fractal_weight = fractal

        # Normalize weights to sum to 1
        total = self.classical_weight + self.quantum_weight + self.fractal_weight
        if total > 0:
            self.classical_weight /= total
            self.quantum_weight /= total
            self.fractal_weight /= total

    def _quantum_entangled_integration(
        self, classical_output, quantum_output, fractal_output
    ):
        """Integration method that leverages quantum entanglement principles
        to create correlated outputs across the different modalities.
        """
        # Resize arrays to same length
        max_len = max(len(classical_output), len(quantum_output), len(fractal_output))
        c_out = self._resize_array(classical_output, max_len)
        q_out = self._resize_array(quantum_output, max_len)
        f_out = self._resize_array(fractal_output, max_len)

        # Create entanglement matrix (correlation matrix with quantum properties)
        entanglement_matrix = np.zeros((3, 3))
        # Apply Bell state principles to correlation
        entanglement_matrix[0, 1] = 0.7  # Classical-Quantum correlation
        entanglement_matrix[0, 2] = 0.5  # Classical-Fractal correlation
        entanglement_matrix[1, 2] = 0.8  # Quantum-Fractal correlation
        # Make symmetric
        entanglement_matrix[1, 0] = entanglement_matrix[0, 1]
        entanglement_matrix[2, 0] = entanglement_matrix[0, 2]
        entanglement_matrix[2, 1] = entanglement_matrix[1, 2]
        # Set diagonal to 1 (self-correlation)
        np.fill_diagonal(entanglement_matrix, 1.0)

        # Store quantum state for future reference
        self.quantum_state_history.append(q_out)
        if len(self.quantum_state_history) > 10:
            self.quantum_state_history.pop(0)  # Keep only recent history

        # Apply entanglement effects (similar to quantum teleportation concept)
        entangled_output = np.zeros(max_len)

        # Calculate phase angles between different modalities
        phase_cq = np.angle(np.sum(np.exp(1j * np.pi * (c_out - q_out))))
        phase_cf = np.angle(np.sum(np.exp(1j * np.pi * (c_out - f_out))))
        phase_qf = np.angle(np.sum(np.exp(1j * np.pi * (q_out - f_out))))

        # Apply quantum interference patterns
        interference_pattern = (
            np.cos(np.linspace(0, 2 * np.pi, max_len) + phase_cq)
            + np.cos(np.linspace(0, 2 * np.pi, max_len) + phase_cf)
            + np.cos(np.linspace(0, 2 * np.pi, max_len) + phase_qf)
        )

        # Create entangled state through weighted combination influenced by interference
        entangled_output = (
            self.classical_weight * c_out * (1 + 0.3 * interference_pattern)
            + self.quantum_weight * q_out * (1 + 0.3 * interference_pattern)
            + self.fractal_weight * f_out * (1 + 0.3 * interference_pattern)
        )

        # Apply non-local correlation effects (quantum inspired)
        if np.random.random() < self.entanglement_threshold:
            # With probability determined by threshold, introduce non-local effects
            random_indices = np.random.choice(
                max_len, size=int(max_len * 0.2), replace=False
            )
            entangled_output[random_indices] = -entangled_output[
                random_indices
            ]  # Phase flip

        return entangled_output

    def _fractal_quantum_integration(
        self, classical_output, quantum_output, fractal_output
    ):
        """Integration method that combines fractal mathematics with quantum principles
        to create a hybrid approach that leverages the strengths of both systems.
        """
        # Resize arrays to same length
        max_len = max(len(classical_output), len(quantum_output), len(fractal_output))
        c_out = self._resize_array(classical_output, max_len)
        q_out = self._resize_array(quantum_output, max_len)
        f_out = self._resize_array(fractal_output, max_len)

        # Apply Mandelbrot-inspired transformations to the quantum data
        # Using z -> z² + c iteration principle from fractal mathematics
        z = q_out
        c = f_out * 0.5  # Scale down fractal values to prevent divergence

        # Perform fractal iterations on quantum data
        iterations = 3
        for _ in range(iterations):
            # Apply complex mapping (similar to Mandelbrot set calculations)
            # Convert to complex numbers for fractal operations
            z_complex = z.astype(complex)
            c_complex = c.astype(complex)

            # Apply non-linear transformation (z² + c)
            z_complex = z_complex**2 + c_complex

            # Extract real parts for further processing
            z = np.real(z_complex)

            # Apply quantum normalization after each iteration
            # To keep values within reasonable bounds
            z = np.tanh(z)  # Bound values between -1 and 1

        # Create Julia set-inspired patterns using quantum output as seed points
        julia_pattern = np.zeros(max_len)
        for i in range(max_len):
            # Use classical output as parameters for Julia set escape-time algorithm
            seed = complex(q_out[i], 0.1)
            param = complex(c_out[i % len(c_out)], 0.1)

            # Perform mini Julia set calculation
            z_julia = seed
            for j in range(10):  # Small number of iterations for performance
                z_julia = z_julia**2 + param
                if abs(z_julia) > 2:  # Escape condition
                    julia_pattern[i] = j / 10  # Normalized escape time
                    break
            else:
                julia_pattern[i] = 1.0  # Max value if no escape

        # Combine the fractal-processed quantum data with classical and raw fractal outputs
        # using quantum superposition principles (represented as weighted combination)
        result = (
            self.classical_weight * c_out
            + self.quantum_weight * np.cos(np.pi * z)  # Quantum interference pattern
            + self.fractal_weight * julia_pattern  # Fractal pattern
        )

        # Apply final quantum-inspired normalization
        result = (
            result / np.max(np.abs(result)) if np.max(np.abs(result)) > 0 else result
        )

        return result

    def _adaptive_hybrid_integration(
        self, classical_output, quantum_output, fractal_output, input_data
    ):
        """Advanced integration method that dynamically adapts its strategy based on
        input characteristics, quantum state history, and model performance.
        """
        # Resize arrays to same length
        max_len = max(len(classical_output), len(quantum_output), len(fractal_output))
        c_out = self._resize_array(classical_output, max_len)
        q_out = self._resize_array(quantum_output, max_len)
        f_out = self._resize_array(fractal_output, max_len)

        # Analyze input complexity and quantum state coherence
        input_complexity = np.std(input_data)

        # Calculate quantum coherence from state history
        quantum_coherence = 0.5  # Default value
        if len(self.quantum_state_history) > 1:
            # Calculate correlation between consecutive quantum states
            correlations = []
            for i in range(len(self.quantum_state_history) - 1):
                state1 = self.quantum_state_history[i]
                state2 = self.quantum_state_history[i + 1]
                # Resize if necessary for correlation calculation
                min_len = min(len(state1), len(state2))
                corr = np.corrcoef(state1[:min_len], state2[:min_len])[0, 1]
                correlations.append(corr)
            quantum_coherence = np.abs(np.mean(correlations)) if correlations else 0.5

        # Apply Schrödinger equation-inspired evolution to quantum output
        # ψ(t) = e^(-iHt/ħ) ψ(0) approximation
        time_step = 0.1
        energy_factor = np.sum(q_out**2) / (2 * self.m) + np.sum(
            self.potential_function(q_out)
        )
        phase = energy_factor * time_step / self.hbar
        evolved_q_out = q_out * np.exp(1j * phase)
        evolved_q_real = np.real(evolved_q_out)

        # Adaptively set weights based on input complexity and quantum coherence
        if input_complexity > 1.0 and quantum_coherence > 0.7:
            # Complex inputs with high quantum coherence: favor quantum processing
            self.classical_weight = 0.2
            self.quantum_weight = 0.5
            self.fractal_weight = 0.3
        elif input_complexity > 1.0:
            # Complex inputs with low quantum coherence: favor fractal processing
            self.classical_weight = 0.2
            self.quantum_weight = 0.3
            self.fractal_weight = 0.5
        elif quantum_coherence > 0.7:
            # Simple inputs with high quantum coherence: balance quantum and classical
            self.classical_weight = 0.4
            self.quantum_weight = 0.4
            self.fractal_weight = 0.2
        else:
            # Simple inputs with low quantum coherence: favor classical processing
            self.classical_weight = 0.6
            self.quantum_weight = 0.2
            self.fractal_weight = 0.2

        # Create adaptive integration based on all factors
        hybrid_result = (
            self.classical_weight * c_out
            + self.quantum_weight * evolved_q_real
            + self.fractal_weight * f_out
        )

        # Apply adaptive learning to update weights based on performance
        # This would typically use some performance metric, but here we'll use a simple heuristic
        # based on the variance of the result (assuming higher variance means better performance)
        result_variance = np.var(hybrid_result)
        if result_variance > 0.5:
            # If result has high variance, slightly increase the weights that contributed most
            max_contribution = max(
                self.classical_weight * np.var(c_out),
                self.quantum_weight * np.var(evolved_q_real),
                self.fractal_weight * np.var(f_out),
            )

            if self.classical_weight * np.var(c_out) == max_contribution:
                self.classical_weight += self.adaptive_lr
            elif self.quantum_weight * np.var(evolved_q_real) == max_contribution:
                self.quantum_weight += self.adaptive_lr
            else:
                self.fractal_weight += self.adaptive_lr

            # Renormalize weights
            total = self.classical_weight + self.quantum_weight + self.fractal_weight
            self.classical_weight /= total
            self.quantum_weight /= total
            self.fractal_weight /= total

        # Store the quantum state for future reference
        self.quantum_state_history.append(q_out)
        if len(self.quantum_state_history) > 10:
            self.quantum_state_history.pop(0)

        return hybrid_result