"""
XGBoost Model Implementation
Provides advanced XGBoost model with quantum enhancements.
"""

import logging
from typing import Dict, Optional, Tuple, Union

import mlflow
import numpy as np
import wandb
import xgboost as xgb
from sklearn.metrics import (
    accuracy_score,
    f1_score,
    precision_score,
    recall_score,
    roc_auc_score,
)
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler

from ..ai.ensembleManager import EnsembleManager
from ..ai.explainabilityEngine import ExplainabilityEngine
from ..monitoring.performance_tracker import PerformanceTracker
from ..optimization.adaptive_learning import AdaptiveLearningRate
from ..quantum.quantumProcessor import QuantumProcessor
from ..security.encryption_manager import EncryptionManager
from ..visualization.advanced_plots import AdvancedPlots

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


class XGBoostModel:
    def __init__(
        self,
        model_path: Optional[str] = None,
        scaler_path: Optional[str] = None,
        config: Optional[Dict] = None,
        use_quantum: bool = True,
        enable_uncertainty: bool = True,
        enable_feature_analysis: bool = True,
        enable_ensemble: bool = True,
        enable_explainability: bool = True,
        enable_distributed: bool = True,
        enable_encryption: bool = True,
        enable_monitoring: bool = True,
        enable_compression: bool = True,
        enable_adaptive_lr: bool = True,
    ):
        self.model_path = model_path
        self.scaler_path = scaler_path
        self.config = config or {}
        self.use_quantum = use_quantum
        self.enable_uncertainty = enable_uncertainty
        self.enable_feature_analysis = enable_feature_analysis
        self.enable_ensemble = enable_ensemble
        self.enable_explainability = enable_explainability
        self.enable_distributed = enable_distributed
        self.enable_encryption = enable_encryption
        self.enable_monitoring = enable_monitoring
        self.enable_compression = enable_compression
        self.enable_adaptive_lr = enable_adaptive_lr

        # Initialize components
        self.model = None
        self.scaler = None
        self.feature_importances = None
        self.shap_values = None
        self.best_params = None
        self.metrics = {}

        # Initialize optional components
        self.ensemble_manager = None
        self.explainability_engine = None
        self.performance_tracker = None
        self.adaptive_lr = None
        self.quantum_processor = None
        self.encryption_manager = None
        self.visualizer = None
        self.uncertainty_handler = None

    def initialize(self):
        """Initialize the XGBoost model and all components."""
        try:
            self._initialize_base_components()
            self._initialize_optional_components()
            logging.info("✅ XGBoost model initialized successfully")
        except Exception as e:
            logging.error(f"❌ Failed to initialize XGBoost model: {str(e)}")
            raise

    def _initialize_base_components(self):
        """Initialize base model components."""
        if self.model is None:
            self.model = xgb.XGBClassifier(**self.config)
        if self.scaler is None:
            self.scaler = StandardScaler()

    def _initialize_optional_components(self):
        """Initialize optional components based on configuration."""
        if self.enable_ensemble:
            self.ensemble_manager = EnsembleManager()
        if self.enable_explainability:
            self.explainability_engine = ExplainabilityEngine()
        if self.enable_monitoring:
            self.performance_tracker = PerformanceTracker()
        if self.enable_adaptive_lr:
            self.adaptive_lr = AdaptiveLearningRate()
        if self.use_quantum:
            self.quantum_processor = QuantumProcessor()
        if self.enable_encryption:
            self.encryption_manager = EncryptionManager()
        if self.enable_feature_analysis:
            self.visualizer = AdvancedPlots()

    def load_model(self) -> bool:
        """Load a pre-trained model from file."""
        try:
            if self.model_path and self.model is not None:
                self.model.load_model(self.model_path)
                return True
            return False
        except Exception as e:
            logging.error(f"❌ Failed to load model: {str(e)}")
            return False

    def save_model(self) -> bool:
        """Save the trained model to file."""
        try:
            if self.model_path and self.model is not None:
                self.model.save_model(self.model_path)
                return True
            return False
        except Exception as e:
            logging.error(f"❌ Failed to save model: {str(e)}")
            return False

    def optimize_hyperparameters(
        self, features: np.ndarray, targets: np.ndarray
    ) -> Dict:
        """Optimize hyperparameters using cross-validation."""
        try:
            # Define default hyperparameters
            default_params = {
                "n_estimators": 100,
                "max_depth": 6,
                "learning_rate": 0.1,
                "subsample": 0.8,
                "colsample_bytree": 0.8,
                "reg_alpha": 0.1,
                "reg_lambda": 1.0,
            }

            # Create model with default parameters
            model = xgb.XGBClassifier(
                **default_params,
                random_state=42,
                n_jobs=-1,
                eval_metric="logloss",
            )

            # Cross-validation
            cv_scores = cross_val_score(
                model, features, targets, cv=5, scoring="roc_auc"
            )

            return {
                "best_params": default_params,
                "cv_score": cv_scores.mean(),
                "cv_std": cv_scores.std(),
            }

        except Exception as e:
            logging.error(f"❌ Hyperparameter optimization failed: {str(e)}")
            raise

    def train(
        self,
        features: np.ndarray,
        targets: np.ndarray,
    ) -> Dict:
        """Train the XGBoost model with advanced features."""
        try:
            if self.model is None:
                raise ValueError("Model not initialized")

            # Start performance tracking if enabled
            if self.enable_monitoring and self.performance_tracker is not None:
                self.performance_tracker.start_tracking()

            # Scale features
            if self.scaler is None:
                self.scaler = StandardScaler()
            features_scaled = self.scaler.fit_transform(features)

            # Apply quantum enhancement if enabled
            if self.use_quantum and self.quantum_processor is not None:
                features_scaled = self.quantum_processor.enhance_input(features_scaled)

            # Train model
            self.model.fit(
                features_scaled,
                targets,
                eval_set=[(features_scaled, targets)],
                verbose=False,
            )

            # Calculate predictions
            predictions = self.model.predict(features_scaled)

            # Calculate metrics
            self.metrics = {
                "accuracy": accuracy_score(targets, predictions),
                "precision": precision_score(targets, predictions, average="weighted"),
                "recall": recall_score(targets, predictions, average="weighted"),
                "f1": f1_score(targets, predictions, average="weighted"),
                "roc_auc": roc_auc_score(
                    targets, self.model.predict_proba(features_scaled)[:, 1]
                ),
            }

            # Get feature importances
            self.feature_importances = self.model.feature_importances_

            # Calculate SHAP values if explainability is enabled
            if self.enable_explainability and self.explainability_engine is not None:
                self.shap_values = self.explainability_engine.calculate_shap_values(
                    features_scaled
                )

            # Generate visualizations
            self._generate_visualizations(features_scaled, targets, predictions)

            # Log advanced metrics
            self._log_advanced_metrics()

            return self.metrics
        except Exception as e:
            logging.error(f"❌ Training failed: {str(e)}")
            raise
        finally:
            if self.enable_monitoring and self.performance_tracker is not None:
                self.performance_tracker.stop_tracking()

    def _generate_visualizations(
        self, features: np.ndarray, targets: np.ndarray, predictions: np.ndarray
    ):
        """Generate advanced visualizations for model analysis."""
        try:
            # Feature importance plot
            importance_fig = self.visualizer.plot_feature_importance(
                self.feature_importances
            )
            wandb.log({"feature_importance": wandb.Image(importance_fig)})

            # SHAP values plot if available
            if self.shap_values is not None:
                shap_fig = self.visualizer.plot_shap_values(self.shap_values, features)
                wandb.log({"shap_values": wandb.Image(shap_fig)})

            # ROC curve
            roc_fig = self.visualizer.plot_roc_curve(targets, predictions)
            wandb.log({"roc_curve": wandb.Image(roc_fig)})

            # Learning curves
            learning_fig = self.visualizer.plot_learning_curves(
                self.model, features, targets
            )
            wandb.log({"learning_curves": wandb.Image(learning_fig)})

            # Uncertainty distribution
            if self.enable_uncertainty:
                uncertainty_fig = self.visualizer.plot_uncertainty_distribution(
                    self.metrics["uncertainty"]
                )
                wandb.log({"uncertainty_distribution": wandb.Image(uncertainty_fig)})

        except Exception as e:
            logging.warning(f"⚠️ Failed to generate visualizations: {str(e)}")

    def _log_advanced_metrics(self):
        """Log advanced metrics and performance data."""
        try:
            # Log core metrics
            mlflow.log_metrics(self.metrics)
            mlflow.log_params(self.best_params or {})

            # Log performance data
            if self.enable_monitoring and self.performance_tracker is not None:
                try:
                    performance_data = self.performance_tracker.analyze_performance()
                    if performance_data:
                        mlflow.log_metrics(performance_data)
                        wandb.log(performance_data)
                except Exception as e:
                    logging.warning(f"⚠️ Failed to get performance metrics: {str(e)}")

            # Log to Weights & Biases
            if self.model is not None and self.feature_importances is not None:
                wandb.log(
                    {
                        **self.metrics,
                        "hyperparameters": self.best_params or {},
                        "model_architecture": self.model.get_booster().get_dump(),
                        "feature_importances": self.feature_importances.tolist(),
                    }
                )

        except Exception as e:
            logging.warning(f"⚠️ Failed to log advanced metrics: {str(e)}")

    def predict(
        self,
        features: np.ndarray,
        return_proba: bool = False,
        return_uncertainty: bool = False,
        return_explanation: bool = False,
    ) -> Union[np.ndarray, Tuple[np.ndarray, ...]]:
        """Make predictions with uncertainty and explanations."""
        try:
            if self.model is None:
                raise ValueError("Model not initialized")

            self._start_performance_tracking()
            features_processed = self._process_features(features)
            predictions = self._make_predictions(features_processed)
            result = self._build_prediction_result(
                predictions,
                features_processed,
                return_proba,
                return_uncertainty,
                return_explanation,
            )
            return result
        except Exception as e:
            logging.error(f"❌ Prediction failed: {str(e)}")
            raise
        finally:
            self._stop_performance_tracking()

    def _start_performance_tracking(self):
        """Start performance tracking if enabled."""
        if self.enable_monitoring and self.performance_tracker is not None:
            self.performance_tracker.start_tracking()

    def _process_features(self, features: np.ndarray) -> np.ndarray:
        """Process features through encryption, scaling, and quantum enhancement."""
        if self.enable_encryption and self.encryption_manager is not None:
            features = self.encryption_manager.decrypt_data(features)

        if self.scaler is None:
            self.scaler = StandardScaler()
        features_scaled = self.scaler.transform(features)

        if self.use_quantum and self.quantum_processor is not None:
            features_scaled = self.quantum_processor.enhance_input(features_scaled)

        return features_scaled

    def _make_predictions(self, features_scaled: np.ndarray) -> np.ndarray:
        """Make base predictions."""
        return self.model.predict(features_scaled)

    def _build_prediction_result(
        self,
        predictions: np.ndarray,
        features_scaled: np.ndarray,
        return_proba: bool,
        return_uncertainty: bool,
        return_explanation: bool,
    ) -> Union[np.ndarray, Tuple[np.ndarray, ...]]:
        """Build the complete prediction result with optional components."""
        result = [predictions]

        if return_proba:
            proba = self.model.predict_proba(features_scaled)
            result.append(proba)

        if return_uncertainty and self.uncertainty_handler is not None:
            uncertainty = self.uncertainty_handler.calculate_uncertainty(
                features_scaled
            )
            result.append(uncertainty)

        if return_explanation and self.explainability_engine is not None:
            explanation = self.explainability_engine.explain(
                predictions, features_scaled
            )
            result.append(explanation)

        return tuple(result) if len(result) > 1 else result[0]

    def _stop_performance_tracking(self):
        """Stop performance tracking if enabled."""
        if self.enable_monitoring and self.performance_tracker is not None:
            self.performance_tracker.stop_tracking()

    def dispose(self):
        """Clean up resources."""
        try:
            self._cleanup_performance_tracking()
            self._cleanup_model_resources()
            logging.info("✅ Resources cleaned up successfully")
        except Exception as e:
            logging.error(f"❌ Resource cleanup failed: {str(e)}")
            raise

    def _cleanup_performance_tracking(self):
        """Clean up performance tracking resources."""
        if self.enable_monitoring and self.performance_tracker is not None:
            try:
                self.performance_tracker.dispose()
            except Exception as e:
                logging.warning(f"⚠️ Failed to stop performance tracking: {str(e)}")

            try:
                metrics = self.performance_tracker.analyze_performance()
                if metrics:
                    mlflow.log_metrics(metrics)
                    wandb.log(metrics)
            except Exception as e:
                logging.warning(f"⚠️ Failed to get final metrics: {str(e)}")

    def _cleanup_model_resources(self):
        """Clean up model and related resources."""
        if self.model is not None:
            del self.model
            self.model = None

        self.scaler = None
        self.feature_importances = None
        self.shap_values = None
        self.best_params = None
        self.metrics = {}

    def get_feature_importances(self) -> np.ndarray:
        """Get feature importance scores."""
        return (
            self.feature_importances
            if self.feature_importances is not None
            else np.array([])
        )

    def analyze_performance(self) -> Dict:
        """Analyze model performance metrics."""
        try:
            if self.performance_tracker is None:
                return {}
            metrics = self.performance_tracker.analyze_performance()
            return metrics
        except Exception as e:
            logging.error(f"❌ Failed to analyze performance: {str(e)}")
            return {}
