"""Enhanced XGBoost implementation with quantum capabilities."""

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

import numpy as np
import xgboost as xgb
from sklearn.metrics import accuracy_score, roc_auc_score

from ..quantum.circuit import QuantumCircuit
from ..quantum.processor import QuantumProcessor


@dataclass
class PerformanceConfig:
    """Configuration for performance optimization"""

    tree_method: str = "hist"
    n_jobs: int = -1
    gpu_id: Optional[int] = None
    predictor: str = "auto"
    grow_policy: str = "depthwise"
    max_bin: int = 256
    max_leaves: int = 0
    sampling_method: str = "uniform"
    version: str = "1.1.4"


class EnhancedXGBoost:
    """Enhanced XGBoost model with quantum computing capabilities"""

    def __init__(
        self,
        quantum_config: Optional[Dict[str, Any]] = None,
        performance_config: Optional[Dict[str, Any]] = None,
    ):
        # Convert dict configs to dataclass instances
        self.quantum_config = quantum_config or {}
        self.performance_config = PerformanceConfig(**(performance_config or {}))

        # Initialize quantum components
        self.quantum_processor = QuantumProcessor()
        self.quantum_circuit = QuantumCircuit()

        # Initialize XGBoost model
        self.model: Optional[xgb.XGBClassifier] = None
        self.feature_importance: Optional[Dict[str, float]] = None
        self.metrics: Dict[str, float] = {}

        # Store feature dimensions
        self.n_features: Optional[int] = None
        self.n_quantum_features: Optional[int] = None

    def _ensure_model_exists(self) -> None:
        """Ensure model is initialized before use"""
        if self.model is None:
            raise ValueError("Model has not been trained yet")

    def _safe_predict(
        self, features: np.ndarray, return_proba: bool = False
    ) -> np.ndarray:
        """Safely make predictions with proper error handling"""
        self._ensure_model_exists()
        model = cast(
            xgb.XGBClassifier, self.model
        )  # Cast to correct type after None check

        if return_proba:
            if not hasattr(model, "predict_proba"):
                raise ValueError("Model does not support probability predictions")
            return model.predict_proba(features)
        return model.predict(features)

    def _safe_save(self, path: str) -> None:
        """Safely save model with proper error handling"""
        self._ensure_model_exists()
        model = cast(
            xgb.XGBClassifier, self.model
        )  # Cast to correct type after None check

        if not hasattr(model, "save_raw"):
            raise ValueError("Model does not support raw saving")
        model.save_raw(path)

    def _safe_tolist(self, array: Optional[np.ndarray]) -> Optional[List]:
        """Safely convert numpy array to list with proper error handling"""
        if array is None:
            return None
        return array.tolist()

    async def fit(
        self,
        features: np.ndarray,
        labels: np.ndarray,
        eval_set: Optional[List[Tuple[np.ndarray, np.ndarray]]] = None,
    ) -> Dict[str, float]:
        """Train the enhanced XGBoost model"""
        try:
            # Store feature dimensions
            self.n_features = features.shape[1]

            # Process features with quantum enhancement
            features_enhanced = await self._enhance_features(features)

            # Process validation set if provided
            if eval_set:
                eval_set_enhanced = []
                for val_features, val_labels in eval_set:
                    if val_features.shape[1] != self.n_features:
                        raise ValueError(
                            f"Validation set feature shape mismatch, expected: {self.n_features}, got {val_features.shape[1]}"
                        )
                    val_features_enhanced = await self._enhance_features(val_features)
                    eval_set_enhanced.append((val_features_enhanced, val_labels))
            else:
                eval_set_enhanced = None

            # Train XGBoost model
            self.model = xgb.XGBClassifier(
                **self.quantum_config.get("xgb_params", {}),
                use_label_encoder=False,
                eval_metric=["logloss", "auc"],
            )

            if self.model is None:
                raise ValueError("Failed to initialize XGBoost model")

            self.model.fit(
                features_enhanced, labels, eval_set=eval_set_enhanced, verbose=False
            )

            # Calculate metrics
            metrics = self._calculate_metrics(
                features_enhanced, labels, eval_set_enhanced
            )
            self.metrics = metrics

            # Store feature importance
            if hasattr(self.model, "feature_importances_"):
                self.feature_importance = dict(
                    zip(
                        [f"feature_{i}" for i in range(features.shape[1])],
                        self.model.feature_importances_,
                    )
                )

            return metrics

        except Exception as e:
            logging.error(f"Error during training: {str(e)}")
            raise

    async def predict(
        self, features: np.ndarray, return_proba: bool = False
    ) -> np.ndarray:
        """Make predictions using the enhanced model"""
        try:
            self._ensure_model_exists()

            if features.shape[1] != self.n_features:
                raise ValueError(
                    f"Feature shape mismatch, expected: {self.n_features}, got {features.shape[1]}"
                )

            # Process features with quantum enhancement
            features_enhanced = await self._enhance_features(features)

            # Get predictions using safe prediction method
            predictions = self._safe_predict(features_enhanced, return_proba)

            return predictions

        except Exception as e:
            logging.error(f"Error during prediction: {str(e)}")
            raise

    async def _enhance_features(self, features: np.ndarray) -> np.ndarray:
        """Enhance features using quantum processing"""
        try:
            if not hasattr(self.quantum_processor, "process_features"):
                raise ValueError(
                    "Quantum processor does not support feature processing"
                )

            # Process features with quantum circuit
            features_quantum = await self.quantum_processor.process_features(features)

            # Combine original and quantum features
            features_enhanced = np.hstack([features, features_quantum])

            return features_enhanced

        except Exception as e:
            logging.error(f"Error during feature enhancement: {str(e)}")
            raise

    def _calculate_metrics(
        self,
        features: np.ndarray,
        labels: np.ndarray,
        eval_set: Optional[List[Tuple[np.ndarray, np.ndarray]]] = None,
    ) -> Dict[str, float]:
        """Calculate model metrics"""
        if self.model is None:
            raise ValueError("Model has not been trained yet")

        metrics = {}

        # Training metrics
        labels_pred = self.model.predict(features)
        metrics["train_accuracy"] = accuracy_score(labels, labels_pred)

        if len(np.unique(labels)) == 2:  # Binary classification
            if hasattr(self.model, "predict_proba"):
                labels_pred_proba = self.model.predict_proba(features)[:, 1]
                metrics["train_auc"] = roc_auc_score(labels, labels_pred_proba)

        # Validation metrics
        if eval_set:
            val_features, val_labels = eval_set[0]
            val_labels_pred = self.model.predict(val_features)
            metrics["val_accuracy"] = accuracy_score(val_labels, val_labels_pred)

            if len(np.unique(val_labels)) == 2:  # Binary classification
                if hasattr(self.model, "predict_proba"):
                    val_labels_pred_proba = self.model.predict_proba(val_features)[:, 1]
                    metrics["val_auc"] = roc_auc_score(
                        val_labels, val_labels_pred_proba
                    )

        return metrics

    def get_feature_importance(self) -> Dict[str, float]:
        """Get feature importance scores"""
        if self.feature_importance is None:
            raise ValueError(
                "Model has not been trained yet or does not support feature importance"
            )
        return self.feature_importance.copy()

    async def optimize_hyperparameters(
        self, features: np.ndarray, labels: np.ndarray, n_trials: int = 100
    ) -> Dict[str, Any]:
        """Optimize hyperparameters using quantum-enhanced search"""
        try:
            if self.n_features is None:
                self.n_features = features.shape[1]

            # Process features with quantum enhancement
            features_enhanced = await self._enhance_features(features)

            # Define parameter search space
            param_space = {
                "max_depth": (3, 10),
                "learning_rate": (0.01, 0.3),
                "n_estimators": (100, 1000),
                "min_child_weight": (1, 7),
                "subsample": (0.6, 0.9),
                "colsample_bytree": (0.6, 0.9),
            }

            # Initialize best parameters and score
            best_params = None
            best_score = float("-inf")

            # Optimization loop
            for _ in range(n_trials):
                # Sample parameters
                params = {
                    name: np.random.uniform(low, high)
                    for name, (low, high) in param_space.items()
                }

                # Train model with sampled parameters
                model = xgb.XGBClassifier(
                    **params,
                    **self.quantum_config.get("xgb_params", {}),
                    use_label_encoder=False,
                )

                if model is None:
                    continue

                model.fit(features_enhanced, labels, verbose=False)

                # Evaluate model
                score = accuracy_score(labels, model.predict(features_enhanced))

                # Update best parameters if better score found
                if score > best_score:
                    best_score = score
                    best_params = params.copy()

            if best_params is None:
                raise ValueError("Failed to find optimal parameters")

            return {"best_params": best_params, "best_score": best_score}

        except Exception as e:
            logging.error(f"Error during hyperparameter optimization: {str(e)}")
            raise
