"""
Enhanced Quantum Feature Processing Implementation
Provides advanced quantum-enhanced feature processing capabilities.
"""

import logging
from dataclasses import dataclass
from typing import Optional

import numpy as np
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister
from qiskit.circuit.library import TwoLocal
from qiskit.primitives import Sampler
from qiskit_aer import AerSimulator
from qiskit_aer.noise import NoiseModel, depolarizing_error
from sklearn.preprocessing import StandardScaler


@dataclass
class QuantumFeatureConfig:
    """Configuration for quantum feature processing."""

    num_qubits: int = 4
    feature_dim: int = 2048
    reduced_dim: int = 64
    use_entanglement: bool = True
    use_superposition: bool = True
    use_quantum_selection: bool = True
    error_correction: bool = True
    optimization_level: int = 2


class QuantumFeatureProcessor:
    """Enhanced quantum feature processor with advanced capabilities."""

    def __init__(self, config: Optional[QuantumFeatureConfig] = None):
        self.config = config or QuantumFeatureConfig()
        self.logger = logging.getLogger(__name__)
        self.quantum_circuit: Optional[QuantumCircuit] = None
        self.sampler = Sampler()
        self.noise_model = self._create_noise_model()
        self.scaler = StandardScaler()
        self._build_quantum_circuit()

    def _create_noise_model(self) -> NoiseModel:
        """Create a realistic noise model for quantum simulation."""
        noise_model = NoiseModel()
        # Add depolarizing noise
        noise_model.add_all_qubit_quantum_error(
            depolarizing_error(0.01, 1), ["u1", "u2", "u3"]
        )
        # Add readout error
        noise_model.add_all_qubit_readout_error([[0.9, 0.1], [0.1, 0.9]])
        return noise_model

    def _build_quantum_circuit(self) -> None:
        """Build quantum circuit for feature processing."""
        try:
            # Create quantum registers
            qr = QuantumRegister(self.config.num_qubits, "q")
            cr = ClassicalRegister(self.config.num_qubits, "c")

            # Create quantum circuit
            self.quantum_circuit = QuantumCircuit(qr, cr)

            # Apply quantum gates for feature processing
            self._apply_quantum_gates()

            # Add error correction if enabled
            if self.config.error_correction:
                self._apply_error_correction()

        except Exception as e:
            self.logger.error(f"Failed to build quantum circuit: {str(e)}")
            raise

    def _apply_quantum_gates(self) -> None:
        """Apply quantum gates for feature processing."""
        if self.quantum_circuit is None:
            raise RuntimeError("Quantum circuit not initialized")

        # Apply Hadamard gates for superposition
        for i in range(self.config.num_qubits):
            self.quantum_circuit.h(i)

        # Apply rotation gates for feature transformation
        for i in range(self.config.num_qubits):
            self.quantum_circuit.rz(np.pi / 4, i)

        # Apply CNOT gates for entanglement
        if self.config.use_entanglement:
            for i in range(self.config.num_qubits - 1):
                self.quantum_circuit.cx(i, i + 1)

    def _apply_error_correction(self) -> None:
        """Apply quantum error correction."""
        if self.quantum_circuit is None:
            raise RuntimeError("Quantum circuit not initialized")

        # Implement surface code error correction
        for i in range(0, self.config.num_qubits - 1, 3):
            self.quantum_circuit.cx(i, i + 1)
            self.quantum_circuit.cx(i + 1, i + 2)
            self.quantum_circuit.cx(i, i + 1)
            self.quantum_circuit.cx(i + 1, i + 2)

    def process_features(
        self, features: np.ndarray, use_quantum_selection: bool = True
    ) -> np.ndarray:
        """Process features using quantum computing."""
        try:
            # Scale features
            features_scaled = self.scaler.fit_transform(features)

            # Apply quantum feature selection if enabled
            if use_quantum_selection and self.config.use_quantum_selection:
                selected_features = self._select_features_quantum(features_scaled)
            else:
                selected_features = features_scaled

            # Apply quantum dimensionality reduction
            reduced_features = self._reduce_dimensions_quantum(selected_features)

            return reduced_features

        except Exception as e:
            self.logger.error(f"Error processing features: {str(e)}")
            raise

    def _select_features_quantum(self, features: np.ndarray) -> np.ndarray:
        """Select features using quantum computing."""
        try:
            # Prepare quantum state
            quantum_state = self._prepare_quantum_state(features)

            # Apply quantum circuit
            processed_state = self._apply_quantum_circuit(quantum_state)

            # Measure feature importance
            importance_scores = self._measure_feature_importance(processed_state)

            # Select top features
            selected_indices = np.argsort(importance_scores)[-self.config.reduced_dim :]
            selected_features = features[:, selected_indices]

            return selected_features

        except Exception as e:
            self.logger.error(f"Error in quantum feature selection: {str(e)}")
            return features

    def _reduce_dimensions_quantum(self, features: np.ndarray) -> np.ndarray:
        """Reduce dimensions using quantum computing."""
        try:
            # Prepare quantum state
            quantum_state = self._prepare_quantum_state(features)

            # Apply quantum circuit
            processed_state = self._apply_quantum_circuit(quantum_state)

            # Apply quantum dimensionality reduction
            reduced_state = self._apply_quantum_reduction(processed_state)

            # Convert back to classical state
            reduced_features = self._measure_quantum_state(reduced_state)

            return reduced_features

        except Exception as e:
            self.logger.error(f"Error in quantum dimensionality reduction: {str(e)}")
            return features

    def _prepare_quantum_state(self, features: np.ndarray) -> np.ndarray:
        """Prepare quantum state from features."""
        # Normalize features
        features = features / np.linalg.norm(features, axis=1, keepdims=True)

        # Convert to quantum state
        quantum_state = features.reshape(features.shape[0], -1)
        quantum_state = quantum_state / np.linalg.norm(quantum_state)

        return quantum_state

    def _apply_quantum_circuit(self, quantum_state: np.ndarray) -> np.ndarray:
        """Apply quantum circuit to quantum state."""
        # Create new circuit with current state
        qr = QuantumRegister(self.config.num_qubits, "q")
        cr = ClassicalRegister(self.config.num_qubits, "c")
        current_circuit = QuantumCircuit(qr, cr)

        # Compose with base circuit
        current_circuit.compose(self.quantum_circuit)

        # Initialize with quantum state
        current_circuit.initialize(quantum_state, qr)

        # Execute circuit
        backend = AerSimulator(noise_model=self.noise_model)
        job = backend.run(current_circuit)
        result = job.result()

        # Get statevector
        return result.get_statevector()

    def _measure_feature_importance(self, quantum_state: np.ndarray) -> np.ndarray:
        """Measure feature importance using quantum state."""
        # Convert quantum state to feature importance scores
        importance_scores = np.abs(quantum_state) ** 2
        importance_scores = importance_scores.reshape(importance_scores.shape[0], -1)

        # Normalize scores
        importance_scores = importance_scores / np.sum(
            importance_scores, axis=1, keepdims=True
        )

        return importance_scores

    def _apply_quantum_reduction(self, quantum_state: np.ndarray) -> np.ndarray:
        """Apply quantum dimensionality reduction."""
        # Create variational circuit for reduction
        qr = QuantumRegister(self.config.num_qubits, "q")
        cr = ClassicalRegister(self.config.num_qubits, "c")
        reduction_circuit = QuantumCircuit(qr, cr)

        # Add variational form
        var_form = TwoLocal(
            self.config.num_qubits,
            rotation_blocks=["ry", "rz"],
            entanglement_blocks="cx",
            reps=3,
        )
        reduction_circuit.compose(var_form)

        # Initialize with quantum state
        reduction_circuit.initialize(quantum_state, qr)

        # Execute circuit
        backend = AerSimulator(noise_model=self.noise_model)
        job = backend.run(reduction_circuit)
        result = job.result()

        # Get reduced statevector
        return result.get_statevector()

    def _measure_quantum_state(self, quantum_state: np.ndarray) -> np.ndarray:
        """Measure quantum state and convert back to features."""
        # Convert quantum state to features
        features = np.abs(quantum_state) ** 2
        features = features.reshape(features.shape[0], -1)

        # Normalize features
        features = features / np.linalg.norm(features, axis=1, keepdims=True)

        return features
