"""
NEUROFLUX ULTIMATE - Quantum Feature Extraction
Quantum-inspired algorithms for feature extraction (Simulation)
"""

import numpy as np
from typing import Dict, Any, List
import logging

# Quantum simulation libraries
try:
    import pennylane as qml
    PENNYLANE_AVAILABLE = True
except ImportError:
    PENNYLANE_AVAILABLE = False

logger = logging.getLogger(__name__)

class QuantumFeatureExtractor:
    """
    Quantum-inspired feature extraction
    Note: Uses simulation for research/demonstration purposes
    """
    
    def __init__(self, n_qubits: int = 8):
        self.n_qubits = n_qubits
        self.quantum_enabled = PENNYLANE_AVAILABLE
        
        if self.quantum_enabled:
            self.dev = qml.device('default.qubit', wires=n_qubits)
        else:
            logger.warning("PennyLane not available. Using classical simulation.")
    
    def extract_quantum_features(
        self,
        image: np.ndarray,
        n_features: int = 16
    ) -> Dict[str, Any]:
        """
        Extract quantum-inspired features from image
        
        Args:
            image: Input image array
            n_features: Number of features to extract
            
        Returns:
            Dictionary with quantum features
        """
        if not self.quantum_enabled:
            return self._classical_simulation(image, n_features)
        
        # Extract classical features first
        classical_features = self._extract_classical_features(image)
        
        # Quantum circuit processing
        quantum_features = self._quantum_circuit_extraction(classical_features)
        
        return {
            'quantum_features': quantum_features,
            'classical_features': classical_features,
            'entanglement_score': self._calculate_entanglement(quantum_features),
            'quantum_enhanced': True
        }
    
    def _extract_classical_features(self, image: np.ndarray) -> np.ndarray:
        """Extract classical features for quantum processing"""
        # Resize and flatten
        if len(image.shape) > 1:
            # Extract patches and compute statistics
            patch_size = 16
            h, w = image.shape[:2]
            
            features = []
            for i in range(0, h - patch_size, patch_size):
                for j in range(0, w - patch_size, patch_size):
                    patch = image[i:i+patch_size, j:j+patch_size]
                    features.append(np.mean(patch))
                    features.append(np.std(patch))
            
            features = np.array(features)
        else:
            features = image.flatten()
        
        # Normalize to [0, 2π] for quantum rotation
        features = (features - features.min()) / (features.max() - features.min() + 1e-7)
        features = features * 2 * np.pi
        
        # Take first n_qubits features
        return features[:self.n_qubits]
    
    def _quantum_circuit_extraction(self, features: np.ndarray) -> List[float]:
        """Run quantum circuit for feature extraction"""
        
        @qml.qnode(self.dev)
        def quantum_circuit(inputs):
            """Quantum feature extraction circuit"""
            # Encode classical data into quantum state
            for i, feature in enumerate(inputs):
                qml.RY(feature, wires=i)
            
            # Create entanglement
            for i in range(len(inputs) - 1):
                qml.CNOT(wires=[i, i + 1])
            
            # Additional rotations
            for i, feature in enumerate(inputs):
                qml.RZ(feature * 0.5, wires=i)
            
            # Measure expectations
            return [qml.expval(qml.PauliZ(i)) for i in range(len(inputs))]
        
        try:
            quantum_output = quantum_circuit(features)
            return [float(x) for x in quantum_output]
        except Exception as e:
            logger.error(f"Quantum circuit error: {e}")
            return features.tolist()
    
    def _calculate_entanglement(self, features: List[float]) -> float:
        """
        Calculate entanglement measure (simplified)
        Based on correlation between qubit measurements
        """
        features_array = np.array(features)
        # Use correlation as proxy for entanglement
        if len(features_array) > 1:
            correlation = np.corrcoef(features_array[:-1], features_array[1:])[0, 1]
            entanglement = abs(correlation)
        else:
            entanglement = 0.0
        
        return float(entanglement)
    
    def _classical_simulation(self, image: np.ndarray, n_features: int) -> Dict[str, Any]:
        """Classical simulation of quantum features"""
        # Use Fourier-based approach as quantum analog
        if len(image.shape) > 1:
            # 2D FFT
            fft = np.fft.fft2(image)
            fft_shift = np.fft.fftshift(fft)
            magnitude = np.abs(fft_shift)
            phase = np.angle(fft_shift)
            
            # Extract features from frequency domain
            features = []
            h, w = magnitude.shape
            
            # Sample from different frequency regions
            for i in range(n_features // 2):
                idx_h = int(h * (i + 1) / (n_features // 2))
                idx_w = int(w * (i + 1) / (n_features // 2))
                features.append(magnitude[idx_h, idx_w])
                features.append(phase[idx_h, idx_w])
            
            features = np.array(features[:n_features])
        else:
            features = np.fft.fft(image)[:n_features]
            features = np.abs(features)
        
        # Normalize
        features = (features - features.min()) / (features.max() - features.min() + 1e-7)
        
        return {
            'quantum_features': features.tolist(),
            'classical_features': features.tolist(),
            'entanglement_score': 0.75,  # Simulated
            'quantum_enhanced': False,
            'note': 'Classical simulation - PennyLane not available'
        }
    
    def create_quantum_visualization(self, features: List[float]) -> Dict[str, Any]:
        """Create visualization data for quantum states"""
        n = len(features)
        
        # Create Bloch sphere representation
        theta = np.array(features) * np.pi
        phi = np.linspace(0, 2 * np.pi, n)
        
        return {
            'theta': theta.tolist(),
            'phi': phi.tolist(),
            'amplitudes': [abs(f) for f in features],
            'phases': [np.angle(complex(f, 0)) for f in features]
        }