from typing import Dict, Any, Optional
import numpy as np

class MockAutoML:
    def __init__(self, model_params: Dict[str, Any] = None):
        self.model_params = model_params or {}

    def fit(self, X: np.ndarray, y: np.ndarray):
        # Simulate training process
        pass

    def evaluate(self, X: np.ndarray, y: np.ndarray) -> Dict[str, float]:
        # Return mock evaluation metrics
        return {'accuracy': 0.85, 'f1_score': 0.83}

    def predict(self, X: np.ndarray) -> np.ndarray:
        # Return mock predictions
        return np.random.randint(0, 2, size=X.shape[0])

class NeuNetSIntegration:
    def __init__(self):
        self.model = None

    def train_model(self, X: np.ndarray, y: np.ndarray, model_params: Optional[Dict[str, Any]] = None):
        """
        Train a mock NeuNetS model.

        Args:
            X (np.ndarray): Input features
            y (np.ndarray): Target values
            model_params (Optional[Dict[str, Any]]): Parameters for the mock NeuNetS model

        Returns:
            MockAutoML object
        """
        if model_params is None:
            model_params = {}

        self.model = MockAutoML(model_params)
        self.model.fit(X, y)
        return self.model

    def evaluate_model(self, X: np.ndarray, y: np.ndarray) -> Dict[str, float]:
        """
        Evaluate the trained mock NeuNetS model.

        Args:
            X (np.ndarray): Input features
            y (np.ndarray): True target values

        Returns:
            Dict[str, float]: Evaluation metrics
        """
        if self.model is None:
            raise ValueError("Model not trained. Call train_model() first.")

        return self.model.evaluate(X, y)

    def predict(self, X: np.ndarray) -> np.ndarray:
        """
        Make predictions using the trained mock NeuNetS model.

        Args:
            X (np.ndarray): Input features

        Returns:
            np.ndarray: Predicted values
        """
        if self.model is None:
            raise ValueError("Model not trained. Call train_model() first.")

        return self.model.predict(X)
