"""
Enhanced XGBoost Training Script
This script implements advanced training features including quantum computing,
distributed training, and advanced optimization techniques.
"""

import logging
import pickle
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import ray
import xgboost as xgb
from sklearn.metrics import accuracy_score, f1_score, roc_auc_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler

from .features.feature_processor import FeatureProcessor
from .quantum.quantum_processor import QuantumProcessor

# Constants
MODEL_NOT_INITIALIZED_ERROR = "Model not initialized"

# Configure logging
logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)


@dataclass
class TrainingConfig:
    """Configuration for advanced 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
    gamma: float = 0.1
    reg_alpha: float = 0.1
    reg_lambda: float = 1.0
    tree_method: str = "hist"
    gpu_id: int = 0
    use_gpu: bool = True
    num_workers: int = 4
    batch_size: int = 1024
    early_stopping_rounds: int = 50
    eval_metric: Optional[List[str]] = None
    objective: str = "binary:logistic"
    random_state: int = 42
    n_jobs: int = -1


@dataclass
class QuantumConfig:
    """Configuration for quantum feature processing"""

    n_qubits: int = 4
    n_layers: int = 2
    entanglement: str = "linear"
    shots: int = 1000
    backend: str = "qiskit"
    optimization_level: int = 3


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

    encryption_key: Optional[str] = None
    model_signature: Optional[str] = None
    access_control: bool = True
    audit_logging: bool = True
    tamper_detection: bool = True


class AdvancedDataProcessor:
    """Advanced data processing with quantum enhancement"""

    def __init__(self, quantum_config: QuantumConfig):
        self.quantum_config = quantum_config
        self.scaler = StandardScaler()
        self.feature_importance = None

    def process_data(
        self, features: np.ndarray, labels: Optional[np.ndarray] = None
    ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
        """Process input data with quantum enhancements"""
        if features is None:
            raise ValueError("Input features cannot be None")

        # Scale features
        features_scaled = self.scaler.fit_transform(features)

        # Apply quantum processing
        features_quantum = self._apply_quantum_processing(features_scaled)

        # Final processing
        features_processed = self._post_process(features_quantum)

        return features_processed, labels

    def _apply_quantum_processing(self, features: np.ndarray) -> np.ndarray:
        """Apply quantum feature processing"""
        try:
            from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister

            qr = QuantumRegister(self.quantum_config.n_qubits)
            cr = ClassicalRegister(self.quantum_config.n_qubits)
            circuit = QuantumCircuit(qr, cr)

            # Apply quantum gates
            for i in range(self.quantum_config.n_qubits):
                circuit.h(qr[i])
                circuit.rz(np.pi / 4, qr[i])

            # Add entanglement
            if self.quantum_config.entanglement == "linear":
                for i in range(self.quantum_config.n_qubits - 1):
                    circuit.cx(qr[i], qr[i + 1])

            # Process features
            quantum_features = np.zeros(
                (features.shape[0], 2**self.quantum_config.n_qubits)
            )
            for i in range(features.shape[0]):
                state = self._classical_to_quantum(features[i])
                quantum_features[i] = state

            return quantum_features

        except ImportError:
            logger.warning("Qiskit not available, skipping quantum processing")
            return np.zeros((features.shape[0], 2**self.quantum_config.n_qubits))

    def _classical_to_quantum(self, x: np.ndarray) -> np.ndarray:
        """Convert classical data to quantum state"""
        # Implement quantum state preparation
        return x


class AdvancedModelTrainer:
    """Advanced model trainer with distributed training and optimization"""

    def __init__(
        self,
        config: Optional[Dict[str, Any]] = None,
        eval_metric: Optional[List[str]] = None,
    ):
        """Initialize XGBoost trainer."""
        self.config = config or {}
        self.eval_metric = eval_metric or ["logloss", "auc"]
        self.model: Optional[xgb.XGBClassifier] = None
        self.scaler: Optional[StandardScaler] = None
        self.feature_processor: Optional[FeatureProcessor] = None
        self.quantum_processor: Optional[QuantumProcessor] = None
        self.initialized = False

        # Initialize Ray for distributed training
        if not ray.is_initialized():
            ray.init(ignore_reinit_error=True)

    async def preprocess_features(self, features: np.ndarray) -> np.ndarray:
        """Preprocess features for training."""
        try:
            if features is None or len(features) == 0:
                raise ValueError("Empty or None features provided")

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

            features_scaled = self.scaler.fit_transform(features)

            # Apply quantum processing if enabled
            if self.config.get("quantum_processing", False):
                if self.quantum_processor is None:
                    self.quantum_processor = QuantumProcessor()
                features_quantum = await self.quantum_processor.process(features_scaled)
            else:
                features_quantum = features_scaled

            # Apply feature processing
            if self.feature_processor is None:
                self.feature_processor = FeatureProcessor()
            features_processed = await self.feature_processor.process(features_quantum)

            return features_processed
        except Exception as e:
            logger.error(f"Feature preprocessing error: {e}")
            raise

    async def train(
        self,
        features_train: np.ndarray,
        features_val: np.ndarray,
        labels_train: np.ndarray,
        labels_val: np.ndarray,
    ) -> Dict[str, Any]:
        """Train XGBoost model."""
        try:
            # Preprocess features
            features_train_processed = await self.preprocess_features(features_train)
            features_val_processed = await self.preprocess_features(features_val)

            # Initialize model if not already done
            if self.model is None:
                self.model = xgb.XGBClassifier(**self.config.get("model_params", {}))

            # Train model
            self.model.fit(
                features_train_processed,
                labels_train,
                eval_set=[(features_val_processed, labels_val)],
                eval_metric=self.eval_metric,
                **self.config.get("fit_params", {}),
            )

            # Get evaluation results
            results = self.model.evals_result()

            return {
                "model": self.model,
                "scaler": self.scaler,
                "feature_processor": self.feature_processor,
                "quantum_processor": self.quantum_processor,
                "eval_results": results,
            }
        except Exception as e:
            logger.error(f"Training error: {e}")
            raise

    async def predict(
        self, features: np.ndarray, return_proba: bool = False
    ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
        """Make predictions using the trained model"""
        if not self.model:
            raise ValueError(MODEL_NOT_INITIALIZED_ERROR)

        # Preprocess features
        features_processed = await self.preprocess_features(features)

        # Make predictions
        if return_proba:
            predictions = self.model.predict_proba(features_processed)
            return predictions, predictions[:, 1]
        else:
            predictions = self.model.predict(features_processed)
            return predictions

    async def save_model(self, path: str) -> bool:
        """Save the trained model to disk"""
        if not self.model:
            raise ValueError(MODEL_NOT_INITIALIZED_ERROR)

        # Save model
        self.model.save_raw(path)

        # Save processors
        processors = {
            "scaler": self.scaler,
            "feature_processor": self.feature_processor,
            "quantum_processor": self.quantum_processor,
        }

        with open(f"{path}_processors.pkl", "wb") as f:
            pickle.dump(processors, f)

        return True

    async def load_model(self, path: str) -> bool:
        """Load a trained model from disk"""
        if not self.model:
            raise ValueError(MODEL_NOT_INITIALIZED_ERROR)

        # Load model
        self.model = xgb.XGBClassifier()
        self.model.load_raw(path)

        # Load processors
        with open(f"{path}_processors.pkl", "rb") as f:
            processors = pickle.load(f)
            self.scaler = processors["scaler"]
            self.feature_processor = processors["feature_processor"]
            self.quantum_processor = processors["quantum_processor"]

        return True

    async def cross_validate(
        self, features: np.ndarray, labels: np.ndarray, n_splits: int = 5
    ) -> Dict[str, float]:
        """Perform cross-validation."""
        try:
            if not self.model:
                raise ValueError(MODEL_NOT_INITIALIZED_ERROR)

            # Initialize k-fold cross validation
            kf = KFold(n_splits=n_splits, shuffle=True, random_state=42)

            # Store results
            results = {"accuracy": [], "auc": [], "f1": []}

            # Perform cross-validation
            for train_idx, val_idx in kf.split(features):
                features_train_fold = features[train_idx]
                features_val_fold = features[val_idx]
                labels_train_fold = labels[train_idx]
                labels_val_fold = labels[val_idx]

                # Train and evaluate
                await self.train(
                    features_train_fold,
                    features_val_fold,
                    labels_train_fold,
                    labels_val_fold,
                )

                # Get predictions
                predictions, probabilities = await self.predict(
                    features_val_fold, return_proba=True
                )

                # Calculate metrics
                results["accuracy"].append(accuracy_score(labels_val_fold, predictions))
                results["auc"].append(roc_auc_score(labels_val_fold, probabilities))
                results["f1"].append(f1_score(labels_val_fold, predictions))

            # Calculate mean metrics
            return {metric: np.mean(values) for metric, values in results.items()}
        except Exception as e:
            logger.error(f"Cross-validation error: {e}")
            raise

    async def optimize_hyperparameters(
        self,
        features: np.ndarray,
        labels: np.ndarray,
        param_grid: Optional[Dict[str, List[Any]]] = None,
    ) -> Dict[str, Any]:
        """Optimize model hyperparameters."""
        try:
            if not self.model:
                raise ValueError(MODEL_NOT_INITIALIZED_ERROR)

            # Default parameter grid
            if param_grid is None:
                param_grid = {
                    "max_depth": [3, 5, 7],
                    "learning_rate": [0.01, 0.1, 0.3],
                    "n_estimators": [100, 200, 300],
                    "min_child_weight": [1, 3, 5],
                    "subsample": [0.8, 0.9, 1.0],
                    "colsample_bytree": [0.8, 0.9, 1.0],
                }

            # Initialize random number generator
            rng = np.random.default_rng(42)

            # Perform random search
            n_iter = 20
            best_score = float("-inf")
            best_params = None

            for _ in range(n_iter):
                # Sample parameters
                params = {
                    param: rng.choice(values) for param, values in param_grid.items()
                }

                # Update model parameters
                self.model.set_params(**params)

                # Perform cross-validation
                cv_results = await self.cross_validate(features, labels)

                # Update best parameters if better score
                if cv_results["auc"] > best_score:
                    best_score = cv_results["auc"]
                    best_params = params

            # Update model with best parameters
            self.model.set_params(**best_params)

            return {"best_params": best_params, "best_score": best_score}
        except Exception as e:
            logger.error(f"Hyperparameter optimization error: {e}")
            raise


async def main():
    # Generate sample data
    rng = np.random.default_rng(42)
    n_samples = 1000
    n_features = 10

    # Generate random features
    features = rng.normal(0, 1, (n_samples, n_features))
    labels = rng.integers(0, 2, n_samples)

    # Split data
    indices = rng.permutation(n_samples)
    split_idx = int(0.8 * n_samples)
    features_train = features[indices[:split_idx]]
    features_val = features[indices[split_idx:]]
    labels_train = labels[indices[:split_idx]]
    labels_val = labels[indices[split_idx:]]

    # Initialize configurations
    training_config = TrainingConfig()

    # Initialize trainer
    trainer = AdvancedModelTrainer(config=training_config.__dict__)

    # Train model
    metrics = await trainer.train(
        features_train=features_train,
        features_val=features_val,
        labels_train=labels_train,
        labels_val=labels_val,
    )

    return metrics


if __name__ == "__main__":
    import asyncio

    loop = asyncio.get_event_loop()
    loop.run_until_complete(main())
    loop.close()
