import asyncio
import logging
import os
from concurrent.futures import ThreadPoolExecutor
from typing import Dict, Optional, Tuple, Union

import joblib
import mlflow
import numpy as np
import optuna
import wandb
import xgboost as xgb
from sklearn.preprocessing import StandardScaler

from ..ai.ensembleManager import EnsembleManager
from ..ai.explainabilityEngine import ExplainabilityEngine
from ..ai.featureAnalyzer import FeatureAnalyzer
from ..ai.uncertaintyHandler import UncertaintyHandler
from ..compression.model_compressor import ModelCompressor
from ..distributed.training_manager import DistributedTrainingManager
from ..monitoring.performance_tracker import PerformanceTracker
from ..optimization.adaptive_learning import AdaptiveLearningRate
from ..optimization.quantum_optimizer import QuantumOptimizer
from ..quantum.quantumProcessor import QuantumProcessor
from ..security.encryption_manager import EncryptionManager
from ..visualization.advanced_plots import AdvancedVisualizer

# 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.model: Optional[xgb.XGBClassifier] = None
        self.scaler: Optional[StandardScaler] = None
        self.feature_importances = None
        self.best_params = None
        self.metrics: Dict = {}
        self.shap_values = None

        # Initialize advanced components
        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

        # Core components
        self.quantum_processor: Optional[QuantumProcessor] = (
            QuantumProcessor() if use_quantum else None
        )
        self.feature_analyzer: Optional[FeatureAnalyzer] = (
            FeatureAnalyzer() if enable_feature_analysis else None
        )
        self.uncertainty_handler: Optional[UncertaintyHandler] = (
            UncertaintyHandler() if enable_uncertainty else None
        )
        self.ensemble_manager: Optional[EnsembleManager] = (
            EnsembleManager() if enable_ensemble else None
        )
        self.explainability_engine: Optional[ExplainabilityEngine] = (
            ExplainabilityEngine() if enable_explainability else None
        )

        # Advanced components
        self.visualizer = AdvancedVisualizer()
        self.quantum_optimizer = QuantumOptimizer() if use_quantum else None
        self.distributed_manager: Optional[DistributedTrainingManager] = (
            DistributedTrainingManager() if enable_distributed else None
        )
        self.encryption_manager = EncryptionManager() if enable_encryption else None
        self.performance_tracker: Optional[PerformanceTracker] = (
            PerformanceTracker() if enable_monitoring else None
        )
        self.model_compressor: Optional[ModelCompressor] = (
            ModelCompressor() if enable_compression else None
        )
        self.adaptive_lr: Optional[AdaptiveLearningRate] = (
            AdaptiveLearningRate() if enable_adaptive_lr else None
        )
        self.executor = ThreadPoolExecutor(max_workers=4)

        # Initialize wandb if configured
        if self.config.get("use_wandb", False):
            wandb.init(project="bleu-ai", config=self.config)

        logger.info("XGBoost model initialized with configuration")

    async def initialize(self):
        """Initialize all components with advanced capabilities."""
        try:
            # Initialize core components
            init_tasks = []
            if self.quantum_processor:
                init_tasks.append(self.quantum_processor.initialize())
            if self.feature_analyzer:
                init_tasks.append(self.feature_analyzer.initialize())
            if self.uncertainty_handler:
                init_tasks.append(self.uncertainty_handler.initialize())
            if self.ensemble_manager:
                init_tasks.append(self.ensemble_manager.initialize())
            if self.explainability_engine:
                init_tasks.append(self.explainability_engine.initialize())

            # Initialize advanced components
            if self.quantum_optimizer:
                init_tasks.append(self.quantum_optimizer.initialize())
            if self.distributed_manager:
                init_tasks.append(self.distributed_manager.initialize())
            if self.encryption_manager:
                init_tasks.append(self.encryption_manager.initialize())
            if self.performance_tracker:
                init_tasks.append(self.performance_tracker.initialize())
            if self.model_compressor:
                init_tasks.append(self.model_compressor.initialize())
            if self.adaptive_lr:
                init_tasks.append(self.adaptive_lr.initialize())

            await asyncio.gather(*init_tasks)
            logging.info("✅ All components initialized successfully")
        except Exception as e:
            logging.error(f"❌ Failed to initialize components: {str(e)}")
            raise

    def load_model(self) -> bool:
        """Load the XGBoost model and scaler."""
        try:
            if self.model_path and os.path.exists(self.model_path):
                self.model = joblib.load(self.model_path)
                logging.info("✅ Model loaded successfully")
            if self.scaler_path and os.path.exists(self.scaler_path):
                self.scaler = joblib.load(self.scaler_path)
                logging.info("✅ Scaler loaded successfully")
            return True
        except Exception as e:
            logging.error(f"❌ Failed to load model: {str(e)}")
            return False

    def save_model(self) -> bool:
        """Save the XGBoost model and scaler."""
        try:
            if self.model:
                joblib.dump(self.model, self.model_path)
                logging.info(f"✅ Model saved to {self.model_path}")
            if self.scaler:
                joblib.dump(self.scaler, self.scaler_path)
                logging.info(f"✅ Scaler saved to {self.scaler_path}")
            return True
        except Exception as e:
            logging.error(f"❌ Failed to save model: {str(e)}")
            return False

    async def optimize_hyperparameters(
        self, X: np.ndarray, y: np.ndarray, n_trials: int = 20
    ) -> Dict:
        """Optimize XGBoost hyperparameters using quantum-enhanced Optuna."""
        try:
            if self.use_quantum and self.quantum_optimizer is not None:
                # Use quantum optimization for hyperparameter search
                self.best_params = await self.quantum_optimizer.optimize(X, y, n_trials)
            else:
                # Use classical optimization
                study = optuna.create_study(direction="maximize")
                study.optimize(self._objective, n_trials=n_trials)
                self.best_params = study.best_params

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

    async def train(
        self,
        X: np.ndarray,
        y: np.ndarray,
        use_optimization: bool = True,
        n_trials: int = 20,
    ) -> Dict:
        """Train the model with advanced features."""
        try:
            if self.enable_monitoring and self.performance_tracker is not None:
                await self.performance_tracker.startTracking()

            # Train model
            self.model = xgb.XGBClassifier(**self.config)
            self.model.fit(X, y)

            # Generate predictions
            y_pred = self.model.predict(X)

            # Generate visualizations
            await self._generate_visualizations(X, y, y_pred)

            # Log advanced metrics
            await 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:
                await self.performance_tracker.stopTracking()

    async def _generate_visualizations(
        self, X: np.ndarray, y: np.ndarray, y_pred: np.ndarray
    ):
        """Generate advanced visualizations for model analysis."""
        try:
            # Feature importance plot
            importance_fig = await 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 = await self.visualizer.plot_shap_values(self.shap_values, X)
                wandb.log({"shap_values": wandb.Image(shap_fig)})

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

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

            # Uncertainty distribution
            if self.enable_uncertainty:
                uncertainty_fig = await 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)}")

    async 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 = (
                        await self.performance_tracker.analyzePerformance()
                    )
                    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)}")

    async def predict(
        self,
        X: 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")

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

            # Decrypt data if enabled
            if self.enable_encryption and self.encryption_manager is not None:
                X = await self.encryption_manager.decrypt_data(X)

            # Scale features
            if self.scaler is None:
                self.scaler = StandardScaler()
            X_scaled = self.scaler.transform(X)

            # Apply quantum enhancement if enabled
            if self.use_quantum and self.quantum_processor is not None:
                X_scaled = await self.quantum_processor.enhanceInput(X_scaled)

            # Make predictions
            predictions = self.model.predict(X_scaled)
            result = [predictions]

            # Add probability predictions if requested
            if return_proba:
                proba = self.model.predict_proba(X_scaled)
                result.append(proba)

            # Add uncertainty if requested
            if return_uncertainty and self.uncertainty_handler is not None:
                uncertainty = await self.uncertainty_handler.calculateUncertainty(
                    X_scaled
                )
                result.append(uncertainty)

            # Add explanations if requested
            if return_explanation and self.explainability_engine is not None:
                explanation = await self.explainability_engine.explain(
                    predictions, X_scaled
                )
                result.append(explanation)

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

        except Exception as e:
            logging.error(f"❌ Prediction failed: {str(e)}")
            raise
        finally:
            if self.enable_monitoring and self.performance_tracker is not None:
                await self.performance_tracker.stopTracking()

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

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

            # Clean up model resources
            if self.model is not None:
                del self.model
                self.model = None

            # Clean up other resources
            self.scaler = None
            self.feature_importances = None
            self.shap_values = None
            self.best_params = None
            self.metrics = {}

            logging.info("✅ Resources cleaned up successfully")

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

    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([])
        )

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