"""
Advanced Decision Tree Implementation with Quantum Enhancement
Copyright (c) 2024, Bleu.js
"""

from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple, Union

import mlflow
import numpy as np
import optuna
import ray
import structlog
from mlflow.tracking import MlflowClient
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister
from qiskit_machine_learning.neural_networks import CircuitQNN
from ray import tune
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler


@dataclass
class QuantumConfig:
    """Configuration for quantum enhancement features."""

    num_qubits: int = 4
    entanglement: str = "full"
    reps: int = 2
    shots: int = 1000
    optimization_level: int = 3
    use_quantum_memory: bool = True
    use_quantum_attention: bool = True


@dataclass
class ModelConfig:
    """Configuration for the advanced decision tree model."""

    max_depth: int = 10
    min_samples_split: int = 2
    min_samples_leaf: int = 1
    max_features: int = 100
    n_estimators: int = 100
    use_quantum_enhancement: bool = True
    enable_uncertainty_handling: bool = True
    enable_feature_analysis: bool = True
    enable_ensemble: bool = True
    enable_explainability: bool = True
    quantum_config: Optional[QuantumConfig] = None
    optimization_target: str = "accuracy"
    use_hyperparameter_tuning: bool = True
    enable_distributed_training: bool = True


class AdvancedDecisionTree:
    """
    Enhanced Decision Tree with quantum capabilities, advanced ML features,
    and distributed computing support.
    """

    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.logger = structlog.get_logger()
        self.model = None
        self.quantum_enhancer = None
        self.uncertainty_handler = None
        self.feature_analyzer = None
        self.ensemble_manager = None
        self.explainability_engine = None
        self.scaler = StandardScaler()
        self.metrics = {
            "accuracy": 0.0,
            "uncertainty": 0.0,
            "feature_importance": [],
            "ensemble_diversity": 0.0,
            "explainability_score": 0.0,
            "quantum_advantage": 0.0,
        }
        self.feature_importance = {}
        self.tree_structure = {}

        # Initialize MLflow tracking
        self.mlflow_client = MlflowClient()
        self.experiment_name = "advanced_decision_tree"
        mlflow.set_experiment(self.experiment_name)

        # Initialize Ray for distributed computing if enabled
        if config["enable_distributed_training"]:
            if not ray.is_initialized():
                ray.init(ignore_reinit_error=True)

    async def initialize(self) -> None:
        """Initialize all components of the advanced decision tree."""
        self.logger.info("initializing_advanced_decision_tree")

        try:
            # Initialize quantum enhancement if enabled
            if self.config["use_quantum_enhancement"]:
                await self._initialize_quantum_enhancer()

            # Initialize uncertainty handler
            if self.config["enable_uncertainty_handling"]:
                await self._initialize_uncertainty_handler()

            # Initialize feature analyzer
            if self.config["enable_feature_analysis"]:
                await self._initialize_feature_analyzer()

            # Initialize ensemble manager
            if self.config["enable_ensemble"]:
                await self._initialize_ensemble_manager()

            # Initialize explainability engine
            if self.config["enable_explainability"]:
                await self._initialize_explainability_engine()

            self.logger.info("advanced_decision_tree_initialized")

        except Exception as e:
            self.logger.error("initialization_failed", error=str(e))
            raise

    async def _initialize_quantum_enhancer(self) -> None:
        """Initialize quantum enhancement components."""
        if not self.config["quantum_config"]:
            self.config["quantum_config"] = QuantumConfig()

        # Create quantum circuit
        qr = QuantumRegister(self.config["quantum_config"].num_qubits, "q")
        cr = ClassicalRegister(self.config["quantum_config"].num_qubits, "c")
        circuit = QuantumCircuit(qr, cr)

        # Add quantum gates
        for i in range(self.config["quantum_config"].reps):
            for j in range(self.config["quantum_config"].num_qubits):
                circuit.h(qr[j])
                circuit.rz(np.random.random(), qr[j])
                circuit.cx(
                    qr[j], qr[(j + 1) % self.config["quantum_config"].num_qubits]
                )

        # Create QNN
        self.quantum_enhancer = CircuitQNN(
            circuit=circuit,
            input_params=[],
            weight_params=[],
            sampling_probabilities=None,
            sparse=False,
        )

    async def train(self, features: np.ndarray, labels: np.ndarray) -> None:
        """Train the decision tree model."""
        self._validate_features(features)
        self._validate_labels(labels)
        self.model = self._build_tree(features, labels)
        self.feature_importance = self._calculate_feature_importance()
        self.tree_structure = self._extract_tree_structure()

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

    def _build_tree(self, features: np.ndarray, labels: np.ndarray) -> Dict[str, Any]:
        """Build the decision tree recursively."""
        if len(np.unique(labels)) == 1:
            return {"type": "leaf", "value": labels[0]}

        best_split = self._find_best_split(features, labels)
        if best_split is None:
            return {"type": "leaf", "value": np.mean(labels)}

        left_mask = features[:, best_split["feature"]] <= best_split["threshold"]
        right_mask = ~left_mask

        return {
            "type": "node",
            "feature": best_split["feature"],
            "threshold": best_split["threshold"],
            "left": self._build_tree(features[left_mask], labels[left_mask]),
            "right": self._build_tree(features[right_mask], labels[right_mask]),
        }

    async def _tune_hyperparameters(
        self,
        features: np.ndarray,
        labels: np.ndarray,
        validation_data: Optional[Tuple[np.ndarray, np.ndarray]] = None,
    ) -> Dict:
        """Perform hyperparameter tuning using Optuna."""

        def objective(trial):
            params = {
                "max_depth": trial.suggest_int("max_depth", 3, 20),
                "min_samples_split": trial.suggest_int("min_samples_split", 2, 10),
                "min_samples_leaf": trial.suggest_int("min_samples_leaf", 1, 5),
                "max_features": trial.suggest_int(
                    "max_features", 10, features.shape[1]
                ),
                "n_estimators": trial.suggest_int("n_estimators", 50, 200),
            }

            model = RandomForestClassifier(**params)
            model.fit(features, labels)

            if validation_data:
                X_val, y_val = validation_data
                score = model.score(X_val, y_val)
            else:
                score = model.score(features, labels)

            return score

        study = optuna.create_study(direction="maximize")
        study.optimize(objective, n_trials=50)

        return study.best_params

    async def _distributed_train(
        self,
        features: np.ndarray,
        labels: np.ndarray,
        validation_data: Optional[Tuple[np.ndarray, np.ndarray]] = None,
    ) -> None:
        """Train the model using distributed computing with Ray."""

        def train_model(config):
            model = RandomForestClassifier(**config)
            model.fit(features, labels)
            return model

        # Define search space
        search_space = {
            "max_depth": tune.randint(3, 20),
            "min_samples_split": tune.randint(2, 10),
            "min_samples_leaf": tune.randint(1, 5),
            "max_features": tune.randint(10, features.shape[1]),
            "n_estimators": tune.randint(50, 200),
        }

        # Run distributed training
        analysis = tune.run(
            train_model,
            config=search_space,
            num_samples=50,
            resources_per_trial={"cpu": 2},
        )

        # Get best model
        best_config = analysis.get_best_config(metric="mean_accuracy")
        self.model = RandomForestClassifier(**best_config)
        self.model.fit(features, labels)

    async def _local_train(
        self,
        features: np.ndarray,
        labels: np.ndarray,
        validation_data: Optional[Tuple[np.ndarray, np.ndarray]] = None,
    ) -> None:
        """Train the model locally."""
        self.model = RandomForestClassifier(
            max_depth=self.config["max_depth"],
            min_samples_split=self.config["min_samples_split"],
            min_samples_leaf=self.config["min_samples_leaf"],
            max_features=self.config["max_features"],
            n_estimators=self.config["n_estimators"],
        )

        self.model.fit(features, labels)

        if validation_data:
            X_val, y_val = validation_data
            self.metrics["accuracy"] = self.model.score(X_val, y_val)
        else:
            self.metrics["accuracy"] = self.model.score(features, labels)

    async def predict(
        self, features: np.ndarray, return_uncertainty: bool = False
    ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
        """
        Make predictions with optional uncertainty estimation.
        """
        self._validate_features(features)
        if self.model is None:
            raise ValueError("Model not trained. Call train() first.")

        # Scale features
        X_scaled = self.scaler.transform(features)

        # Apply quantum enhancement if enabled
        if self.config["use_quantum_enhancement"] and self.quantum_enhancer:
            X_scaled, _ = await self.quantum_enhancer.enhance(X_scaled, None)

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

        if return_uncertainty and self.config["enable_uncertainty_handling"]:
            uncertainty = await self.uncertainty_handler.calculate_uncertainty(X_scaled)
            return predictions, uncertainty

        return predictions

    async def get_feature_importance(self) -> np.ndarray:
        """Get feature importance scores."""
        if self.model is None:
            raise ValueError("Model not trained. Call train() first.")

        return self.model.feature_importances_

    async def get_explanations(self, features: np.ndarray) -> Dict:
        """Get model explanations for predictions."""
        if not self.config["enable_explainability"]:
            raise ValueError("Explainability not enabled in model configuration.")

        return await self.explainability_engine.generate_explanation(
            self.model, features
        )

    async def save_model(self, path: str) -> None:
        """Save the model and its components."""
        import joblib

        model_data = {
            "model": self.model,
            "scaler": self.scaler,
            "config": self.config,
            "metrics": self.metrics,
        }

        joblib.dump(model_data, path)
        self.logger.info("model_saved", path=path)

    async def load_model(self, path: str) -> None:
        """Load a saved model and its components."""
        import joblib

        model_data = joblib.load(path)
        self.model = model_data["model"]
        self.scaler = model_data["scaler"]
        self.config = model_data["config"]
        self.metrics = model_data["metrics"]

        self.logger.info("model_loaded", path=path)

    def get_model_info(self) -> Dict[str, Any]:
        """Get model information."""
        return {
            "n_trees": self.n_trees,
            "max_depth": self.max_depth,
            "learning_rate": self.learning_rate,
            "feature_importance": self.feature_importance,
            "metrics": self.metrics,
        }

    def get_feature_importance(self) -> Dict[str, float]:
        """Get feature importance scores."""
        return self.feature_importance

    def get_metrics(self) -> Dict[str, float]:
        """Get model metrics."""
        return self.metrics

    def _apply_noise(self, features: np.ndarray) -> np.ndarray:
        """Apply noise to features for robustness."""
        rng = np.random.default_rng(seed=42)  # Fixed seed for reproducibility
        noise = rng.normal(0, 0.1, features.shape)
        return features + noise

    def _validate_features(self, features: np.ndarray) -> None:
        """Validate input features."""
        if features is None or len(features) == 0:
            raise ValueError("Features cannot be empty")

    def _validate_labels(self, labels: np.ndarray) -> None:
        """Validate input labels."""
        if labels is None or len(labels) == 0:
            raise ValueError("Labels cannot be empty")

    def _split_data(
        self, features: np.ndarray, labels: np.ndarray
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
        """Split data into training and validation sets."""
        rng = np.random.default_rng(seed=42)  # Fixed seed for reproducibility
        indices = rng.permutation(len(features))
        split_idx = int(0.8 * len(features))
        train_features = features[indices[:split_idx]]
        train_labels = labels[indices[:split_idx]]
        val_features = features[indices[split_idx:]]
        val_labels = labels[indices[split_idx:]]
        return train_features, train_labels, val_features, val_labels

    def _scale_features(self, features: np.ndarray) -> np.ndarray:
        """Scale features using standardization."""
        scaled_features = (features - np.mean(features, axis=0)) / np.std(
            features, axis=0
        )
        return scaled_features
