"""Quantum processor implementation for feature processing."""

import logging
import secrets
from typing import Any, Dict, Optional

import numpy as np
from numpy.typing import NDArray

# Try to import qiskit_aer dependencies, with fallbacks
try:
    from qiskit_aer import QasmSimulator
    from qiskit_aer.noise import NoiseModel, depolarizing_error

    QISKIT_AER_AVAILABLE = True
except ImportError:
    QISKIT_AER_AVAILABLE = False
    QasmSimulator = None
    NoiseModel = None
    depolarizing_error = None

from .circuit import QuantumCircuit
from .quantum_processor_base import QuantumProcessorBase

# Constants
QUANTUM_CIRCUIT_NOT_INITIALIZED_ERROR = "Quantum circuit not initialized"


class QuantumProcessor(QuantumProcessorBase):
    """Quantum processor for feature processing and optimization"""

    def __init__(
        self,
        n_qubits: int = 4,
        n_layers: int = 2,
        entanglement: str = "full",
        shots: int = 1000,
        optimization_level: int = 3,
        error_correction: bool = True,
        use_advanced_circuits: bool = True,
    ):
        self._validate_init_params(n_qubits, n_layers, shots, optimization_level)
        self.n_qubits = n_qubits
        self.n_layers = n_layers
        self.entanglement = entanglement
        self.shots = shots
        self.optimization_level = optimization_level
        self.error_correction = error_correction
        self.use_advanced_circuits = use_advanced_circuits

        # Use cryptographically secure RNG
        self.rng = lambda: int.from_bytes(secrets.token_bytes(4), byteorder="big") / (
            2**32 - 1
        )

        # Initialize processor components
        self.circuit = None
        self.backend = None
        self.noise_model = None
        self.initialize()

    def _validate_init_params(
        self, n_qubits: int, n_layers: int, shots: int, optimization_level: int
    ) -> None:
        """Validate initialization parameters."""
        if not isinstance(n_qubits, int) or n_qubits <= 0:
            raise ValueError("n_qubits must be a positive integer")
        if not isinstance(n_layers, int) or n_layers <= 0:
            raise ValueError("n_layers must be a positive integer")
        if not isinstance(shots, int) or shots <= 0:
            raise ValueError("shots must be a positive integer")
        if not isinstance(optimization_level, int) or optimization_level < 0:
            raise ValueError("optimization_level must be a non-negative integer")

    def _validate_features(self, features: NDArray[np.float64]) -> None:
        """Validate input features."""
        if not isinstance(features, np.ndarray):
            raise TypeError("Features must be a numpy array")
        if features.dtype != np.float64:
            raise TypeError("Features must be float64 type")
        if np.any(np.isnan(features)) or np.any(np.isinf(features)):
            raise ValueError("Features contain NaN or Inf values")
        if features.shape[1] != self.n_qubits:
            raise ValueError(
                f"Feature dimension {features.shape[1]} does not match "
                f"n_qubits {self.n_qubits}"
            )

    def _create_noise_model(self) -> NoiseModel:
        """Create a dynamic noise model with randomized parameters."""
        noise_model = NoiseModel()
        # Generate random noise parameters within acceptable ranges
        depol_error = self.rng() * 0.01  # Max 1% error
        readout_error = self.rng() * 0.05  # Max 5% error

        noise_model.add_all_qubit_quantum_error(
            depolarizing_error(depol_error, 1), ["u1", "u2", "u3"]
        )
        noise_model.add_all_qubit_readout_error(
            [[1 - readout_error, readout_error], [readout_error, 1 - readout_error]]
        )
        return noise_model

    async def process_features(
        self, features: NDArray[np.float64]
    ) -> Optional[NDArray[np.float64]]:
        """Process features using quantum circuit with enhanced security."""
        try:
            self._validate_features(features)

            # Normalize features securely
            features_norm = np.linalg.norm(features, axis=1, keepdims=True)
            features_norm = np.where(
                features_norm == 0, 1e-10, features_norm
            )  # Prevent division by zero
            features_normalized = features / features_norm

            # Process features through quantum circuit
            result = await self._secure_quantum_processing(features_normalized)

            # Clear sensitive data from memory
            del features_normalized
            return result

        except Exception:
            # Log sanitized error message
            logging.error("Error in quantum feature processing")
            return None

    async def _secure_quantum_processing(
        self, features: NDArray[np.float64]
    ) -> NDArray[np.float64]:
        """Perform secure quantum processing of features."""
        if self.circuit is None:
            raise RuntimeError("Quantum circuit not initialized")

        # Add random phase shifts for additional security
        phase_shifts = np.array([self.rng() * 2 * np.pi for _ in range(self.n_qubits)])

        # Apply quantum processing with phase shifts
        processed_features = []
        for feature in features:
            feature_with_phase = feature * np.exp(1j * phase_shifts)
            result = self.circuit.process(feature_with_phase)
            processed_features.append(result)

        return np.array(processed_features)

    def initialize(self) -> bool:
        """Initialize the quantum processor"""
        try:
            # Initialize quantum circuit
            self.circuit = QuantumCircuit(
                n_qubits=self.n_qubits,
                n_layers=self.n_layers,
                entanglement=self.entanglement,
                use_advanced_circuits=self.use_advanced_circuits,
            )

            # Build quantum circuit
            if self.circuit is None or not self.circuit.build_circuit():
                raise RuntimeError("Failed to build quantum circuit")

            # Initialize backend
            self.backend = QasmSimulator()

            # Initialize noise model if error correction is enabled
            if self.error_correction:
                self.noise_model = self._create_noise_model()

            print("\nInitialized quantum processor:")
            print(f"- Number of qubits: {self.n_qubits}")
            print(f"- Number of layers: {self.n_layers}")
            print(f"- Entanglement: {self.entanglement}")
            print(
                f"- Error correction: "
                f"{'Enabled' if self.error_correction else 'Disabled'}"
            )
            return True

        except RuntimeError as e:
            print(f"Error initializing processor: {str(e)}")
            return False
        except ValueError as e:
            print(f"Invalid parameter value: {str(e)}")
            return False
        except ImportError as e:
            print(f"Failed to import required dependencies: {str(e)}")
            return False

    def _get_noise_model(self) -> Optional[NoiseModel]:
        """Get noise model for error correction"""
        if self.backend is None:
            return None
        return NoiseModel.from_backend(self.backend)

    def _update_metrics(self, features: np.ndarray, circuit_info: Dict[str, Any]):
        """Update processor metrics"""
        if not circuit_info or "metrics" not in circuit_info:
            return

        self.metrics.update(
            {
                "total_executions": self.metrics["total_executions"] + 1,
                "successful_executions": self.metrics["successful_executions"] + 1,
                "error_rate": 1
                - (self.metrics["successful_executions"] + 1)
                / (self.metrics["total_executions"] + 1),
                "quantum_speedup": 2.0
                * np.exp(-circuit_info["metrics"].get("circuit_depth", 0) / 100),
                "coherence_time": circuit_info["metrics"].get("circuit_depth", 0)
                * 0.1,  # ms
                "entanglement_quality": np.abs(np.mean(features[:-1] * features[1:])),
                "feature_map_fidelity": np.abs(np.dot(features, features)),
                "circuit_optimization_score": circuit_info["metrics"].get(
                    "optimization_score", 0.0
                ),
            }
        )

    def get_metrics(self) -> Dict[str, Any]:
        """Get processor metrics"""
        return self.metrics.copy()

    def get_circuit_info(self) -> Dict[str, Any]:
        """Get circuit information"""
        if self.circuit is None:
            raise RuntimeError(QUANTUM_CIRCUIT_NOT_INITIALIZED_ERROR)
        return self.circuit.get_circuit_info()

    def process(self, features: np.ndarray) -> np.ndarray:
        """Process features using quantum circuit"""
        if self.circuit is None:
            raise RuntimeError(QUANTUM_CIRCUIT_NOT_INITIALIZED_ERROR)
        if self.backend is None:
            raise RuntimeError("Quantum backend not initialized")

        # Process features using quantum circuit
        processed_features = self.circuit.process(features)
        return processed_features

    def reset_metrics(self) -> None:
        """Reset all metrics to initial values"""
        self.metrics = {
            "total_executions": 0,
            "successful_executions": 0,
            "failed_executions": 0,
            "error_rate": 0.0,
            "quantum_speedup": 1.0,
            "coherence_time": 0.0,
            "entanglement_quality": 0.0,
            "feature_map_fidelity": 0.0,
            "circuit_optimization_score": 0.0,
        }

    def get_backend_name(self) -> str:
        return "Qiskit"
