"""Advanced XGBoost training with quantum enhancements."""

import logging
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple

import numpy as np
import xgboost as xgb

logger = logging.getLogger(__name__)


@dataclass
class TrainingConfig:
    """Configuration for XGBoost training"""

    n_estimators: int = 1000
    learning_rate: float = 0.01
    max_depth: int = 6
    min_child_weight: int = 1
    subsample: float = 0.8
    colsample_bytree: float = 0.8
    objective: str = "binary:logistic"
    tree_method: str = "hist"
    use_gpu: bool = True
    early_stopping_rounds: int = 50


@dataclass
class QuantumConfig:
    """Configuration for quantum enhancements"""

    n_qubits: int = 4
    n_layers: int = 2
    entanglement: str = "linear"
    shots: int = 1000
    optimization_level: int = 3
    error_correction: bool = True


@dataclass
class SecurityConfig:
    """Configuration for security features"""

    encryption: bool = False
    secure_training: bool = False
    differential_privacy: bool = False
    privacy_budget: float = 1.0


class AdvancedModelTrainer:
    """Advanced XGBoost trainer with quantum enhancements and security features."""

    def __init__(
        self,
        training_config: Optional[Dict] = None,
        quantum_config: Optional[Dict] = None,
        security_config: Optional[Dict] = None,
    ):
        # Convert dict configs to dataclass instances
        self.training_config = TrainingConfig(**(training_config or {}))
        self.quantum_config = QuantumConfig(**(quantum_config or {}))
        self.security_config = SecurityConfig(**(security_config or {}))

        # Initialize XGBoost model
        self.model = None
        self.feature_importance = None

        # Initialize logging
        self._setup_logging()

    def _setup_logging(self):
        """Setup logging configuration"""
        logging.basicConfig(
            level=logging.INFO,
            format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
        )
        self.logger = logging.getLogger(__name__)

    async def train(self, features: np.ndarray, labels: np.ndarray) -> Dict[str, float]:
        """Train the model with quantum enhancements and security measures."""
        try:
            # Apply security measures
            features_secure, labels_secure = self._apply_security_measures(
                features, labels
            )

            # Apply quantum enhancements
            features_enhanced = await self._apply_quantum_enhancements(features_secure)

            # Train model
            self.model = xgb.XGBClassifier(
                n_estimators=self.training_config.n_estimators,
                learning_rate=self.training_config.learning_rate,
                max_depth=self.training_config.max_depth,
                min_child_weight=self.training_config.min_child_weight,
                subsample=self.training_config.subsample,
                colsample_bytree=self.training_config.colsample_bytree,
                objective=self.training_config.objective,
                tree_method=self.training_config.tree_method,
                use_gpu=self.training_config.use_gpu,
            )

            # Train with early stopping
            self.model.fit(
                features_enhanced,
                labels_secure,
                eval_set=[(features_enhanced, labels_secure)],
                early_stopping_rounds=self.training_config.early_stopping_rounds,
                verbose=False,
            )

            # Calculate metrics
            metrics = self._calculate_metrics(features_enhanced, labels_secure)

            # Store feature importance
            self.feature_importance = self.model.feature_importances_

            return metrics

        except Exception as e:
            self.logger.error(f"Training failed: {str(e)}")
            raise

    async def predict(self, features: np.ndarray) -> np.ndarray:
        """Make predictions using the trained model."""
        try:
            if self.model is None:
                raise ValueError("Model has not been trained yet")

            # Apply quantum enhancements
            features_enhanced = await self._apply_quantum_enhancements(features)

            # Make predictions
            predictions = self.model.predict(features_enhanced)

            return predictions

        except Exception as e:
            self.logger.error(f"Prediction failed: {str(e)}")
            raise

    async def _apply_quantum_enhancements(self, features: np.ndarray) -> np.ndarray:
        """Apply quantum enhancements to features"""
        # Placeholder for quantum feature processing
        # This would be implemented with actual quantum circuits
        return features

    def _apply_security_measures(
        self, features: np.ndarray, labels: np.ndarray
    ) -> Tuple[np.ndarray, np.ndarray]:
        """Apply security measures to data"""
        if self.security_config.differential_privacy:
            # Add noise for differential privacy
            rng = np.random.default_rng(seed=42)  # Fixed seed for reproducibility
            noise = rng.normal(
                0, 1 / self.security_config.privacy_budget, features.shape
            )
            features = features + noise

        return features, labels

    def _calculate_metrics(
        self,
        features: np.ndarray,
        labels: np.ndarray,
        eval_set: Optional[List[Tuple[np.ndarray, np.ndarray]]] = None,
    ) -> Dict:
        """Calculate training metrics"""
        metrics = {}

        # Training metrics
        if not self.model:
            raise ValueError("Model not initialized")
        predictions = self.model.predict(features)
        metrics["accuracy"] = np.mean(predictions == labels)

        # Validation metrics if eval_set is provided
        if eval_set:
            if not hasattr(self.model, "evals_result"):
                raise ValueError("Model does not support evaluation results")
            val_metrics = self.model.evals_result()
            metrics.update(
                {
                    "val_logloss": val_metrics["validation_0"]["logloss"][-1],
                    "val_auc": val_metrics["validation_0"]["auc"][-1],
                }
            )

        return metrics
