"""Threshold-independent and threshold-dependent verification metrics."""

import numpy as np
from numpy.typing import ArrayLike
from sklearn.metrics import det_curve, roc_curve


def verification_metrics(labels: ArrayLike, scores: ArrayLike) -> dict[str, object]:
    """Compute FAR/FRR curves, ROC, DET and the approximate EER.

    Labels use 0 for forgery and 1 for genuine. Scores are lower for more
    genuine-looking samples.
    """
    y = np.asarray(labels, dtype=np.int8)
    values = np.asarray(scores, dtype=np.float64)
    if y.ndim != 1 or values.ndim != 1 or len(y) != len(values) or len(y) == 0:
        raise ValueError("labels and scores must be non-empty 1D arrays of equal length")
    if not np.isin(y, [0, 1]).all() or len(np.unique(y)) != 2:
        raise ValueError("labels must contain both classes, encoded as 0=forgery and 1=genuine")

    fpr, tpr, roc_thresholds = roc_curve(y, -values)
    false_accept, false_reject, det_thresholds = det_curve(y, -values)
    fnr = 1.0 - tpr
    crossing = int(np.argmin(np.abs(fpr - fnr)))
    return {
        "far": fpr,
        "frr": fnr,
        "roc_fpr": fpr,
        "roc_tpr": tpr,
        "roc_thresholds": -roc_thresholds,
        "det_fpr": false_accept,
        "det_fnr": false_reject,
        "det_thresholds": -det_thresholds,
        "eer": float((fpr[crossing] + fnr[crossing]) / 2.0),
        "eer_threshold": float(-roc_thresholds[crossing]),
    }