import numpy as np
from typing import List, Tuple

def compute_metrics(conf_matrix: np.ndarray) -> Tuple[List[float], float, List[float], float]:
    """
    Calculates metrics IoU and F1 from confusion matrix
    """
    num_classes = conf_matrix.shape[0]
    ious, f1s = [], []
    for c in range(num_classes):
        tp = conf_matrix[c, c]
        fp = conf_matrix[:, c].sum() - tp
        fn = conf_matrix[c, :].sum() - tp
        union = tp + fp + fn
        iou = tp / union if union else float('nan')
        f1 = 2 * tp / (2 * tp + fp + fn) if (2*tp + fp + fn) else 0.0
        ious.append(iou)
        f1s.append(f1)
    miou = np.nanmean(ious)
    mf1  = float(np.mean(f1s))
    return ious, miou, f1s, mf1