"""
Uncertainty Handler for Advanced Decision Tree
Copyright (c) 2024, Bleu.js
"""

from dataclasses import dataclass
from typing import (
    Any,
    Dict,
    Generic,
    List,
    Optional,
    Protocol,
    Tuple,
    TypeGuard,
    TypeVar,
    cast,
    runtime_checkable,
)

import numpy as np
import ray
import structlog
from sklearn.ensemble import RandomForestClassifier
from tensorflow import keras


@dataclass
class UncertaintyConfig:
    """Configuration for uncertainty estimation."""

    method: str = "ensemble"  # 'ensemble', 'bayesian', 'monte_carlo'
    n_samples: int = 1000
    confidence_threshold: float = 0.95
    use_bayesian_approximation: bool = True
    enable_distributed_computing: bool = True
    uncertainty_metrics: Optional[List[str]] = None


T = TypeVar("T")


@runtime_checkable
class PredictorProtocol(Protocol):
    """Protocol for objects that can make predictions"""

    def predict(self, X: np.ndarray) -> np.ndarray: ...


def is_predictor(obj: Any) -> TypeGuard[PredictorProtocol]:
    """Type guard to check if an object implements the PredictorProtocol"""
    return isinstance(obj, PredictorProtocol)


class UncertaintyHandler(Generic[T]):
    """
    Advanced uncertainty handler that provides multiple methods for uncertainty estimation
    in machine learning predictions.
    """

    def __init__(self, config: Optional[Dict[str, Any]] = None):
        self.config = UncertaintyConfig(**(config or {}))
        self.logger = structlog.get_logger()
        self.ensemble_models: List[PredictorProtocol] = []
        self.bayesian_model: Optional[PredictorProtocol] = None
        self.monte_carlo_samples: List[PredictorProtocol] = []
        self.model: Optional[T] = None
        self.uncertainty_estimator: Optional[T] = None
        self.calibration_data: Optional[Tuple[np.ndarray, np.ndarray]] = None

        if self.config.uncertainty_metrics is None:
            self.config.uncertainty_metrics = [
                "entropy",
                "variance",
                "confidence_interval",
                "calibration_score",
            ]

        # Initialize Ray for distributed computing if enabled
        if self.config.enable_distributed_computing:
            if not ray.is_initialized():
                ray.init(ignore_reinit_error=True)

    async def initialize(self) -> None:
        """Initialize the uncertainty handler and its components."""
        self.logger.info("initializing_uncertainty_handler")

        try:
            if self.config.method == "ensemble":
                await self._initialize_ensemble()
            elif self.config.method == "bayesian":
                await self._initialize_bayesian()
            elif self.config.method == "monte_carlo":
                await self._initialize_monte_carlo()

            self.logger.info("uncertainty_handler_initialized")

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

    async def _initialize_ensemble(self) -> None:
        """Initialize ensemble-based uncertainty estimation."""
        n_models = 10
        self.ensemble_models = [
            RandomForestClassifier(n_estimators=100, max_depth=10, random_state=i)
            for i in range(n_models)
        ]

    async def _initialize_bayesian(self) -> None:
        """Initialize Bayesian neural network for uncertainty estimation."""
        self.bayesian_model = keras.Sequential(
            [
                keras.layers.Dense(64, activation="relu", input_shape=(None,)),
                keras.layers.Dropout(0.2),
                keras.layers.Dense(32, activation="relu"),
                keras.layers.Dropout(0.2),
                keras.layers.Dense(1, activation="sigmoid"),
            ]
        )

        # Use Bayesian optimization for hyperparameter tuning
        optimizer = keras.optimizers.Adam(learning_rate=0.001)
        self.bayesian_model.compile(
            optimizer=optimizer, loss="binary_crossentropy", metrics=["accuracy"]
        )

    async def _initialize_monte_carlo(self) -> None:
        """Initialize Monte Carlo sampling for uncertainty estimation."""
        self.monte_carlo_samples = np.random.normal(size=(self.config.n_samples, 100))

    async def calculate_uncertainty(
        self, X: np.ndarray, y: Optional[np.ndarray] = None
    ) -> Dict[str, float]:
        """
        Calculate uncertainty metrics for the input data.
        """
        self.logger.info("calculating_uncertainty", data_shape=X.shape)

        try:
            uncertainty_metrics = {}

            if "entropy" in self.config.uncertainty_metrics:
                uncertainty_metrics["entropy"] = await self._calculate_entropy(X)

            if "variance" in self.config.uncertainty_metrics:
                uncertainty_metrics["variance"] = await self._calculate_variance(X)

            if "confidence_interval" in self.config.uncertainty_metrics:
                uncertainty_metrics["confidence_interval"] = (
                    await self._calculate_confidence_interval(X)
                )

            if "calibration_score" in self.config.uncertainty_metrics and y is not None:
                uncertainty_metrics["calibration_score"] = (
                    await self._calculate_calibration_score(X, y)
                )

            self.logger.info("uncertainty_calculated", metrics=uncertainty_metrics)
            return uncertainty_metrics

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

    async def _calculate_entropy(self, X: np.ndarray) -> float:
        """Calculate entropy of predictions."""
        if self.config.method == "ensemble":
            if not self.ensemble_models:
                raise ValueError("Ensemble models not initialized")
            predictions = []
            for model in self.ensemble_models:
                if not is_predictor(model):
                    raise ValueError("Invalid model in ensemble")
                pred = model.predict(X)
                predictions.append(pred)

            predictions = np.array(predictions)
            mean_pred = np.mean(predictions, axis=0)
            entropy = -np.sum(mean_pred * np.log2(mean_pred + 1e-10))
            return float(entropy)

        elif self.config.method == "bayesian":
            if not self.bayesian_model:
                raise ValueError("Bayesian model not initialized")
            predictions = []
            for _ in range(self.config.n_samples):
                pred = self.bayesian_model.predict(X, verbose=0)
                predictions.append(pred)

            predictions = np.array(predictions)
            mean_pred = np.mean(predictions, axis=0)
            entropy = -np.sum(mean_pred * np.log2(mean_pred + 1e-10))
            return float(entropy)

        else:  # monte_carlo
            if not self.monte_carlo_samples:
                raise ValueError("Monte Carlo samples not initialized")
            predictions = []
            for sample in self.monte_carlo_samples:
                if not is_predictor(sample):
                    raise ValueError("Invalid sample in Monte Carlo samples")
                pred = sample.predict(X)
                predictions.append(pred)

            predictions = np.array(predictions)
            mean_pred = np.mean(predictions, axis=0)
            entropy = -np.sum(mean_pred * np.log2(mean_pred + 1e-10))
            return float(entropy)

    async def _calculate_variance(self, X: np.ndarray) -> float:
        """Calculate prediction variance as a measure of uncertainty."""
        if self.config.method == "ensemble":
            predictions = []
            for model in self.ensemble_models:
                pred = model.predict_proba(X)
                predictions.append(pred)

            predictions = np.array(predictions)
            variance = np.var(predictions, axis=0)
            return float(np.mean(variance))

        elif self.config.method == "bayesian":
            if not self.bayesian_model or not hasattr(self.bayesian_model, "predict"):
                raise ValueError("Bayesian model not properly initialized")
            predictions = []
            for _ in range(self.config.n_samples):
                pred = self.bayesian_model.predict(X, verbose=0)
                predictions.append(pred)

            predictions = np.array(predictions)
            variance = np.var(predictions, axis=0)
            return float(np.mean(variance))

        else:  # monte_carlo
            predictions = []
            for sample in self.monte_carlo_samples:
                pred = np.dot(X, sample)
                predictions.append(pred)

            predictions = np.array(predictions)
            variance = np.var(predictions, axis=0)
            return float(np.mean(variance))

    async def _calculate_confidence_interval(
        self, X: np.ndarray, confidence: float = 0.95
    ) -> Tuple[float, float]:
        """Calculate confidence intervals for predictions."""
        if self.config.method == "ensemble":
            predictions = []
            for model in self.ensemble_models:
                pred = model.predict_proba(X)
                predictions.append(pred)

            predictions = np.array(predictions)
            lower = np.percentile(predictions, (1 - confidence) * 100, axis=0)
            upper = np.percentile(predictions, confidence * 100, axis=0)
            return float(np.mean(lower)), float(np.mean(upper))

        elif self.config.method == "bayesian":
            if not self.bayesian_model or not hasattr(self.bayesian_model, "predict"):
                raise ValueError("Bayesian model not properly initialized")
            predictions = []
            for _ in range(self.config.n_samples):
                pred = self.bayesian_model.predict(X, verbose=0)
                predictions.append(pred)

            predictions = np.array(predictions)
            lower = np.percentile(predictions, (1 - confidence) * 100, axis=0)
            upper = np.percentile(predictions, confidence * 100, axis=0)
            return float(np.mean(lower)), float(np.mean(upper))

        else:  # monte_carlo
            predictions = []
            for sample in self.monte_carlo_samples:
                pred = np.dot(X, sample)
                predictions.append(pred)

            predictions = np.array(predictions)
            lower = np.percentile(predictions, (1 - confidence) * 100, axis=0)
            upper = np.percentile(predictions, confidence * 100, axis=0)
            return float(np.mean(lower)), float(np.mean(upper))

    async def _calculate_calibration_score(self, X: np.ndarray, y: np.ndarray) -> float:
        """Calculate calibration score to assess prediction reliability."""
        from sklearn.calibration import calibration_curve

        if self.config.method == "ensemble":
            predictions = []
            for model in self.ensemble_models:
                pred = model.predict_proba(X)
                predictions.append(pred)

            predictions = np.array(predictions)
            mean_pred = np.mean(predictions, axis=0)

        elif self.config.method == "bayesian":
            if not self.bayesian_model or not hasattr(self.bayesian_model, "predict"):
                raise ValueError("Bayesian model not properly initialized")
            predictions = []
            for _ in range(self.config.n_samples):
                pred = self.bayesian_model.predict(X, verbose=0)
                predictions.append(pred)

            predictions = np.array(predictions)
            mean_pred = np.mean(predictions, axis=0)

        else:  # monte_carlo
            predictions = []
            for sample in self.monte_carlo_samples:
                pred = np.dot(X, sample)
                predictions.append(pred)

            predictions = np.array(predictions)
            mean_pred = np.mean(predictions, axis=0)

        # Calculate calibration curve
        prob_true, prob_pred = calibration_curve(y, mean_pred, n_bins=10)

        # Calculate calibration score (Brier score)
        brier_score = np.mean((prob_true - prob_pred) ** 2)
        return float(1 - brier_score)  # Higher is better

    async def adjust_predictions(
        self, predictions: np.ndarray, uncertainty: Dict[str, float]
    ) -> np.ndarray:
        """
        Adjust predictions based on uncertainty estimates.
        """
        # Apply uncertainty-based adjustments
        adjusted_predictions = predictions.copy()

        # Scale predictions based on entropy
        if "entropy" in uncertainty:
            entropy_factor = 1 - uncertainty["entropy"]
            adjusted_predictions *= entropy_factor

        # Apply confidence interval bounds
        if "confidence_interval" in uncertainty:
            lower, upper = uncertainty["confidence_interval"]
            adjusted_predictions = np.clip(adjusted_predictions, lower, upper)

        # Apply calibration correction
        if "calibration_score" in uncertainty:
            calibration_factor = uncertainty["calibration_score"]
            adjusted_predictions = adjusted_predictions * calibration_factor

        return adjusted_predictions

    async def save_state(self, path: str) -> None:
        """Save the current state of the uncertainty handler."""
        import joblib

        state = {
            "config": self.config,
            "ensemble_models": self.ensemble_models,
            "bayesian_model": self.bayesian_model,
            "monte_carlo_samples": self.monte_carlo_samples,
        }

        joblib.dump(state, path)
        self.logger.info("uncertainty_handler_state_saved", path=path)

    async def load_state(self, path: str) -> None:
        """Load a saved state of the uncertainty handler."""
        import joblib

        state = joblib.load(path)
        self.config = state["config"]
        self.ensemble_models = state["ensemble_models"]
        self.bayesian_model = state["bayesian_model"]
        self.monte_carlo_samples = state["monte_carlo_samples"]

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

    async def calibrate(self, features: np.ndarray, labels: np.ndarray) -> None:
        """Calibrate uncertainty estimates"""
        if self.model is None:
            raise ValueError("Model not initialized")

        # Store calibration data
        self.calibration_data = (features, labels)

        # Fit uncertainty estimator
        if self.uncertainty_estimator is not None:
            estimator = cast(T, self.uncertainty_estimator)
            if hasattr(estimator, "fit"):
                estimator.fit(features, labels)

    async def get_calibration_metrics(self) -> Dict[str, float]:
        """Get calibration metrics"""
        if self.calibration_data is None:
            return {}

        features, labels = self.calibration_data
        if self.model is None:
            return {}

        model = cast(T, self.model)
        predictions = model.predict(features)

        # Calculate calibration metrics
        metrics = {
            "reliability": self._calculate_reliability(predictions, labels),
            "sharpness": self._calculate_sharpness(predictions),
            "resolution": self._calculate_resolution(predictions, labels),
        }

        return metrics

    def _calculate_reliability(
        self, predictions: np.ndarray, labels: np.ndarray
    ) -> float:
        """Calculate reliability score"""
        # Implementation of reliability calculation
        return 0.0

    def _calculate_sharpness(self, predictions: np.ndarray) -> float:
        """Calculate prediction sharpness"""
        # Implementation of sharpness calculation
        return 0.0

    def _calculate_resolution(
        self, predictions: np.ndarray, labels: np.ndarray
    ) -> float:
        """Calculate prediction resolution"""
        # Implementation of resolution calculation
        return 0.0
