from typing import Dict, Any
import numpy as np
try:
    from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
    from qiskit.providers.aer import QasmSimulator
    QISKIT_AVAILABLE = True
except ImportError:
    QISKIT_AVAILABLE = False

class QuantumRenderer:
    """
    Handles quantum-enhanced SVG rendering operations.
    """
    
    def __init__(self):
        """Initialize quantum rendering system."""
        self.simulator = None
        self.quantum_state = None
        self.cuda_enabled = False
        self._check_requirements()

    def _check_requirements(self) -> None:
        """Verify quantum computing requirements are met."""
        if not QISKIT_AVAILABLE:
            raise ImportError(
                "Quantum features require qiskit. Install with: pip install qiskit"
            )
        self.simulator = QasmSimulator()

    def initialize_quantum_state(self) -> None:
        """Initialize the quantum state for rendering."""
        # Create a quantum circuit for path optimization
        qreg = QuantumRegister(3, 'q')  # 3 qubits for basic path operations
        creg = ClassicalRegister(3, 'c')
        circuit = QuantumCircuit(qreg, creg)
        
        # Initialize superposition
        circuit.h(qreg)  # Apply Hadamard gates
        circuit.measure(qreg, creg)
        
        # Execute circuit
        job = self.simulator.run(circuit, shots=1000)
        self.quantum_state = job.result().get_counts()

    def process_attributes(self, attrs: Dict[str, Any]) -> Dict[str, Any]:
        """
        Process SVG attributes using quantum optimization.
        
        Args:
            attrs: Original SVG attributes
            
        Returns:
            Processed attributes with quantum enhancements
        """
        if not self.quantum_state:
            self.initialize_quantum_state()

        processed_attrs = attrs.copy()
        
        # Handle quantum-specific attributes
        if 'quantum-entanglement' in attrs:
            entanglement = float(attrs['quantum-entanglement'])
            processed_attrs = self._apply_quantum_transformation(
                processed_attrs, 
                entanglement
            )

        return processed_attrs

    def _apply_quantum_transformation(
        self, 
        attrs: Dict[str, Any], 
        entanglement: float
    ) -> Dict[str, Any]:
        """
        Apply quantum transformations to SVG attributes.
        
        Args:
            attrs: Original attributes
            entanglement: Quantum entanglement factor (0-1)
            
        Returns:
            Transformed attributes
        """
        if 'd' in attrs:  # Transform path data
            path_data = attrs['d']
            transformed_path = self._quantum_path_optimization(
                path_data, 
                entanglement
            )
            attrs['d'] = transformed_path

        return attrs

    def _quantum_path_optimization(
        self, 
        path_data: str, 
        entanglement: float
    ) -> str:
        """
        Optimize SVG path using quantum algorithms.
        
        Args:
            path_data: Original SVG path data
            entanglement: Quantum entanglement factor
            
        Returns:
            Optimized path data
        """
        # Create quantum circuit for path optimization
        qreg = QuantumRegister(4, 'q')
        creg = ClassicalRegister(4, 'c')
        circuit = QuantumCircuit(qreg, creg)

        # Apply quantum gates based on entanglement
        circuit.rx(entanglement * np.pi, qreg[0])
        circuit.ry(entanglement * np.pi, qreg[1])
        circuit.cx(qreg[0], qreg[1])
        
        circuit.measure(qreg, creg)
        
        # Execute circuit
        job = self.simulator.run(circuit, shots=100)
        result = job.result().get_counts()
        
        # Use quantum results to optimize path
        # This is a simplified example - real implementation would use
        # more sophisticated quantum algorithms for path optimization
        optimized_path = self._apply_quantum_results(path_data, result)
        
        return optimized_path

    def _apply_quantum_results(
        self, 
        path_data: str, 
        quantum_results: Dict[str, int]
    ) -> str:
        """
        Apply quantum computation results to path data.
        
        Args:
            path_data: Original path data
            quantum_results: Results from quantum circuit
            
        Returns:
            Modified path data
        """
        # Convert quantum results to optimization parameters
        total_shots = sum(quantum_results.values())
        quantum_factor = max(
            quantum_results.values()
        ) / total_shots
        
        # Simple path optimization example
        # Real implementation would use more sophisticated algorithms
        commands = path_data.split()
        optimized_commands = []
        
        for cmd in commands:
            if cmd[0].isalpha():  # Command letter
                optimized_commands.append(cmd)
            else:  # Coordinate value
                try:
                    value = float(cmd)
                    # Apply quantum-inspired transformation
                    optimized_value = value * (1 + (quantum_factor - 0.5) * 0.1)
                    optimized_commands.append(f"{optimized_value:.2f}")
                except ValueError:
                    optimized_commands.append(cmd)
        
        return ' '.join(optimized_commands)

    def enable_cuda(self) -> None:
        """Enable CUDA acceleration for quantum computations."""
        try:
            import numba.cuda
            self.cuda_enabled = True
            # Initialize CUDA-specific quantum operations
            if self.quantum_state is not None:
                self._initialize_cuda_quantum_state()
        except ImportError:
            raise RuntimeError("CUDA acceleration requires numba")

    def _initialize_cuda_quantum_state(self) -> None:
        """Initialize quantum state with CUDA acceleration."""
        if not self.cuda_enabled:
            return
            
        # Initialize CUDA-accelerated quantum state
        # This would involve custom CUDA kernels for quantum simulation
        # Placeholder for actual CUDA implementation
        pass
