"""
Quantum-powered layout optimization for SVG elements using quantum annealing.
Enhanced with D-Wave quantum annealing and error correction.
"""

from typing import List, Tuple, Dict, Any, Optional, Union
import numpy as np
from ..utils.feature_loader import FeatureRegistry, requires_feature

@FeatureRegistry.register('quantum', ['qiskit', 'dwave-ocean-sdk'])
class QuantumLayoutOptimizer:
    """Optimize SVG layout using quantum computing techniques."""
    
    def __init__(self, error_correction: bool = True, use_dwave: bool = True):
        """
        Initialize quantum layout optimization system.
        
        Args:
            error_correction: Enable quantum error correction
            use_dwave: Use D-Wave quantum annealing when available
        """
        if use_dwave:
            try:
                from dwave.system import DWaveSampler, EmbeddingComposite
                from dimod import BinaryQuadraticModel
                self.DWaveSampler = DWaveSampler
                self.EmbeddingComposite = EmbeddingComposite
                self.BinaryQuadraticModel = BinaryQuadraticModel
                self.dwave_available = True
                self.sampler = EmbeddingComposite(DWaveSampler())
            except ImportError:
                self.dwave_available = False
        else:
            self.dwave_available = False
            
        # Initialize Qiskit components for hybrid approach
        from qiskit import QuantumCircuit, Aer, execute
        from qiskit.optimization import QuadraticProgram
        from qiskit.optimization.algorithms import MinimumEigenOptimizer
        from qiskit.algorithms import QAOA
        from qiskit.providers.aer.noise import NoiseModel
        from qiskit_aer.noise.errors import depolarizing_error
        
        self.QuantumCircuit = QuantumCircuit
        self.Aer = Aer
        self.execute = execute
        self.QuadraticProgram = QuadraticProgram
        self.MinimumEigenOptimizer = MinimumEigenOptimizer
        self.QAOA = QAOA
        self.NoiseModel = NoiseModel
        self.depolarizing_error = depolarizing_error
        
        self.simulator = Aer.get_backend('aer_simulator')
        self.noise_model = None
        self.error_correction = error_correction
        
    def _initialize_error_correction(self) -> None:
        """Setup quantum error correction components."""
        # Create basic noise model
        self.noise_model = NoiseModel()
        
        # Add realistic gate errors
        dep_error = depolarizing_error(0.001, 1)
        self.noise_model.add_all_qubit_quantum_error(dep_error, ['u1', 'u2', 'u3'])
        
        # Create error correction circuit components
        self.syndrome_circuit = QuantumCircuit(7, 3)  # Steane 7-qubit code
        self._build_syndrome_circuit()
        
    def _build_syndrome_circuit(self) -> None:
        """Build quantum error correction syndrome measurement circuit."""
        # Initialize Steane code encoding
        self.syndrome_circuit.h([0, 1, 2])
        self.syndrome_circuit.cx(0, 3)
        self.syndrome_circuit.cx(0, 4)
        self.syndrome_circuit.cx(1, 5)
        self.syndrome_circuit.cx(2, 6)
        
        # Add syndrome measurements
        self.syndrome_circuit.measure([3,4,5], [0,1,2])
        
    def optimize_path_ordering(
        self,
        paths: List[Dict[str, Any]],
        hybrid_threshold: int = 20,
        annealing_time: Optional[float] = None
    ) -> List[Dict[str, Any]]:
        """
        Use quantum annealing to minimize path plotting time.
        
        Args:
            paths: List of SVG path elements
            hybrid_threshold: Max paths for pure quantum approach
            annealing_time: Optional annealing time (D-Wave only)
            
        Returns:
            Optimally ordered path list
        """
        # Extract path endpoints
        endpoints = self._extract_endpoints(paths)
        n_paths = len(paths)
        
        if self.dwave_available and n_paths <= hybrid_threshold:
            # Use D-Wave quantum annealing for small-medium problems
            distances = self._calculate_distances(endpoints)
            optimal_order = self._dwave_tsp_solve(
                distances,
                annealing_time=annealing_time
            )
        else:
            # Use hybrid approach for larger problems or when D-Wave is unavailable
            optimal_order = self._hybrid_optimization(endpoints)
        
        # Reorder paths
        return [paths[i] for i in optimal_order]
        
    def quantum_entangled_animations(
        self,
        elements: List[Dict[str, Any]],
        sync_factor: float = 0.7
    ) -> List[Dict[str, Any]]:
        """
        Synchronize animations using quantum entanglement principles.
        
        Args:
            elements: List of SVG elements
            sync_factor: Entanglement strength (0-1)
            
        Returns:
            Elements with synchronized animations
        """
        # Create quantum circuit for animation sync
        num_elements = len(elements)
        qc = QuantumCircuit(num_elements, num_elements)
        
        # Create entangled state
        qc.h(0)  # Hadamard on first qubit
        for i in range(1, num_elements):
            # Control strength of entanglement
            theta = np.pi * sync_factor
            qc.crx(theta, 0, i)
            
        # Measure
        qc.measure(range(num_elements), range(num_elements))
        
        # Execute circuit
        job = execute(qc, self.simulator, shots=1)
        result = job.result()
        counts = result.get_counts(qc)
        
        # Use measurement outcome to synchronize animations
        sync_pattern = list(counts.keys())[0]
        return self._apply_sync_pattern(elements, sync_pattern)
        
    def _extract_endpoints(
        self,
        paths: List[Dict[str, Any]]
    ) -> List[Tuple[float, float]]:
        """Extract start and end points of paths."""
        endpoints = []
        
        for path in paths:
            points = self._parse_path_points(path['d'])
            if points:
                endpoints.append((points[0], points[-1]))
                
        return endpoints
        
    def _parse_path_points(
        self,
        path_data: str
    ) -> List[Tuple[float, float]]:
        """Parse path data into list of points."""
        points = []
        tokens = path_data.split()
        current_point = None
        
        for i, token in enumerate(tokens):
            if token[0].isalpha():
                # Handle relative/absolute commands
                command = token.upper()
                if command in {'M', 'L'}:
                    try:
                        x = float(tokens[i+1])
                        y = float(tokens[i+2])
                        points.append((x, y))
                        current_point = (x, y)
                    except (IndexError, ValueError):
                        continue
                        
            elif current_point:
                try:
                    x = float(token)
                    y = float(tokens[i+1])
                    points.append((x, y))
                    current_point = (x, y)
                except (IndexError, ValueError):
                    continue
                    
        return points
        
    def _calculate_distances(
        self,
        endpoints: List[Tuple[float, float]]
    ) -> np.ndarray:
        """Calculate distance matrix between path endpoints."""
        n = len(endpoints)
        distances = np.zeros((n, n))
        
        for i in range(n):
            for j in range(n):
                if i != j:
                    # Calculate Euclidean distance between end of path i
                    # and start of path j
                    end_i = endpoints[i][1]
                    start_j = endpoints[j][0]
                    distances[i,j] = np.sqrt(
                        (end_i[0] - start_j[0])**2 +
                        (end_i[1] - start_j[1])**2
                    )
                    
        return distances
        
    def _hybrid_optimization(self, endpoints: List[Tuple[Tuple[float, float], Tuple[float, float]]]) -> List[int]:
        """Use hybrid quantum-classical approach for large problems."""
        # Partition problem into subproblems
        subproblems = self._partition_problem(endpoints)
        
        # Solve subproblems with quantum computer
        partial_solutions = []
        for subproblem in subproblems:
            distances = self._calculate_distances(subproblem)
            solution = self._solve_tsp_with_correction(distances)
            partial_solutions.append(solution)
            
        # Merge solutions using classical algorithm
        return self._merge_solutions(partial_solutions, endpoints)
        
    def _partition_problem(
        self,
        endpoints: List[Tuple[Tuple[float, float], Tuple[float, float]]]
    ) -> List[List[Tuple[Tuple[float, float], Tuple[float, float]]]]:
        """Partition large problem into manageable subproblems."""
        # Use k-means clustering to group nearby paths
        centers = []
        clusters = []
        k = max(2, len(endpoints) // 10)
        
        points = np.array([
            [(p1[0] + p2[0])/2, (p1[1] + p2[1])/2]
            for p1, p2 in endpoints
        ])
        
        # Simple k-means implementation
        centers = points[np.random.choice(len(points), k, replace=False)]
        for _ in range(5):  # 5 iterations
            clusters = [[] for _ in range(k)]
            for i, point in enumerate(points):
                distances = np.linalg.norm(centers - point, axis=1)
                cluster_idx = np.argmin(distances)
                clusters[cluster_idx].append(i)
            
            for i, cluster in enumerate(clusters):
                if cluster:
                    centers[i] = points[cluster].mean(axis=0)
                    
        # Convert clusters to subproblems
        return [
            [endpoints[i] for i in cluster]
            for cluster in clusters if cluster
        ]
        
    def _merge_solutions(
        self,
        partial_solutions: List[List[int]],
        endpoints: List[Tuple[Tuple[float, float], Tuple[float, float]]]
    ) -> List[int]:
        """Merge subsolutions using nearest neighbor heuristic."""
        merged = []
        current_point = None
        remaining = set(range(len(endpoints)))
        
        while remaining:
            if current_point is None:
                # Start with first point
                current_point = min(remaining)
                merged.append(current_point)
                remaining.remove(current_point)
            else:
                # Find nearest unvisited point
                current_end = endpoints[current_point][1]
                nearest = min(
                    remaining,
                    key=lambda x: np.linalg.norm(
                        np.array(current_end) -
                        np.array(endpoints[x][0])
                    )
                )
                merged.append(nearest)
                remaining.remove(nearest)
                current_point = nearest
                
        return merged
        
    def _dwave_tsp_solve(
        self,
        distances: np.ndarray,
        annealing_time: Optional[float] = None
    ) -> List[int]:
        """
        Solve TSP using D-Wave quantum annealing.
        
        Args:
            distances: Distance matrix between points
            annealing_time: Optional annealing time in microseconds
            
        Returns:
            Optimal path order as list of indices
        """
        if not self.dwave_available:
            raise RuntimeError("D-Wave solver not available")
            
        n = len(distances)
        
        # Create QUBO formulation for TSP
        Q = {}
        
        # Add constraint terms to enforce valid tour
        # Each city must be visited exactly once
        for i in range(n):
            for t1 in range(n):
                for t2 in range(t1+1, n):
                    Q[((i,t1), (i,t2))] = 2.0
                    
        # Each time slot must have exactly one city
        for t in range(n):
            for i1 in range(n):
                for i2 in range(i1+1, n):
                    Q[((i1,t), (i2,t))] = 2.0
                    
        # Add distance minimization terms
        for i in range(n):
            for j in range(n):
                if i != j:
                    for t in range(n-1):
                        Q[((i,t), (j,(t+1)%n))] = distances[i,j]
                        
        # Create BQM and solve using D-Wave
        bqm = self.BinaryQuadraticModel.from_qubo(Q)
        
        # Configure annealing
        kwargs = {}
        if annealing_time is not None:
            kwargs['annealing_time'] = annealing_time
            
        sampleset = self.sampler.sample(
            bqm,
            num_reads=1000,
            **kwargs
        )
        
        # Get best solution
        solution = sampleset.first.sample
        
        # Convert solution to path
        path = [-1] * n
        for (city, time), value in solution.items():
            if value == 1:
                path[time] = city
                
        return path
        
    def _solve_tsp_with_correction(self, distances: np.ndarray) -> List[int]:
        """
        Solve Traveling Salesman Problem using quantum annealing.
        
        Args:
            distances: Distance matrix between points
            
        Returns:
            Optimal path order as list of indices
        """
        n = distances.shape[0]
        
        # Create quadratic program with error bounds
        qp = QuadraticProgram()
        
        # Add variables for each city and position
        for i in range(n):
            for j in range(n):
                qp.binary_var(f'x{i}{j}')
                
        # Objective: minimize total distance
        obj_terms = {}
        for i in range(n):
            for j in range(n):
                for k in range(n):
                    if j != k:
                        var1 = f'x{i}{j}'
                        var2 = f'x{i+1 if i+1<n else 0}{k}'
                        obj_terms[(var1, var2)] = distances[j,k]
                        
        qp.minimize(quadratic=obj_terms)
        
        # Constraints
        # Each position has exactly one city
        for i in range(n):
            pos_vars = [f'x{i}{j}' for j in range(n)]
            qp.linear_constraint(
                linear=dict(zip(pos_vars, [1]*n)),
                sense='==',
                rhs=1
            )
            
        # Each city is used exactly once
        for j in range(n):
            city_vars = [f'x{i}{j}' for i in range(n)]
            qp.linear_constraint(
                linear=dict(zip(city_vars, [1]*n)),
                sense='==',
                rhs=1
            )
            
        # Solve using QAOA
        qaoa = QAOA()
        optimizer = MinimumEigenOptimizer(qaoa)
        result = optimizer.solve(qp)
        
        # Convert result to path order
        order = [-1] * n
        for i in range(n):
            for j in range(n):
                if abs(result.x[i*n + j] - 1.0) < 1e-5:
                    order[i] = j
                    
        return order
        
    def _apply_sync_pattern(
        self,
        elements: List[Dict[str, Any]],
        pattern: str
    ) -> List[Dict[str, Any]]:
        """Apply quantum synchronization pattern to animations."""
        synchronized = []
        pattern_bits = list(map(int, pattern))
        
        for element, sync_bit in zip(elements, pattern_bits):
            element = element.copy()
            
            if 'animation' in element:
                element['animation'] = self._modify_animation(
                    element['animation'],
                    sync_bit
                )
                
            synchronized.append(element)
            
        return synchronized
        
    def _modify_animation(
        self,
        animation: Dict[str, Any],
        sync_bit: int
    ) -> Dict[str, Any]:
        """Modify animation timing based on quantum state."""
        modified = animation.copy()
        
        if 'begin' in modified:
            # Adjust animation start time based on quantum state
            offset = 0.1 if sync_bit else 0
            if modified['begin'].isdigit():
                modified['begin'] = str(
                    float(modified['begin']) + offset
                )
                
        return modified
