"""Metrics and error attribution for the real LLM NCM evaluation (sections 8-9)."""


def metrics(rows) -> dict:
    """rows: [{gold_in_candidates, selected, gold, confidence, invalid}]"""
    total = len(rows)
    correct = sum(1 for row in rows if not row["invalid"] and row["selected"] == row["gold"])
    wrong = sum(1 for row in rows if not row["invalid"] and row["selected"] is not None and row["selected"] != row["gold"])
    abstain = sum(1 for row in rows if not row["invalid"] and row["selected"] is None)
    invalid = sum(1 for row in rows if row["invalid"])
    gold_in = sum(1 for row in rows if row["gold_in_candidates"])
    answered = correct + wrong
    high_conf = [row for row in rows if not row["invalid"] and row["confidence"] >= 0.95 and row["selected"] is not None]
    high_conf_wrong = sum(1 for row in high_conf if row["selected"] != row["gold"])
    return {
        "TOTAL": total,
        "CORRECT": correct,
        "WRONG": wrong,
        "ABSTAIN": abstain,
        "INVALID": invalid,
        "GOLD_IN_CANDIDATES": gold_in,
        "ACCURACY_ALL": round(correct / total, 4) if total else None,
        "SELECTION_ACCURACY_WHEN_GOLD_AVAILABLE": round(correct / answered, 4) if answered else None,
        "PRECISION_WHEN_HIGH_CONFIDENCE": round(sum(1 for row in high_conf if row["selected"] == row["gold"]) / len(high_conf), 4) if high_conf else None,
        "HIGH_CONFIDENCE_WRONG_RATE": round(high_conf_wrong / len(high_conf), 4) if high_conf else None,
        "ABSTENTION_RATE": round(abstain / total, 4) if total else None,
    }


def attribution(rows) -> dict:
    return {
        "GOLD_NOT_IN_CANDIDATES": sum(1 for row in rows if not row["gold_in_candidates"]),
        "GOLD_IN_CANDIDATES_BUT_WRONG": sum(1 for row in rows if row["gold_in_candidates"] and not row["invalid"] and row["selected"] is not None and row["selected"] != row["gold"]),
        "GOLD_IN_CANDIDATES_BUT_ABSTAIN": sum(1 for row in rows if row["gold_in_candidates"] and not row["invalid"] and row["selected"] is None),
        "INVALID_OUTPUT": sum(1 for row in rows if row["invalid"]),
    }


_BUCKETS = ((">=0.99", 0.99, 1.01), ("0.95-0.9899", 0.95, 0.99), ("0.90-0.9499", 0.90, 0.95), ("0.80-0.8999", 0.80, 0.90), ("<0.80", -0.01, 0.80))


def confidence_buckets(rows) -> dict:
    out = {name: {"total": 0, "correct": 0} for name, _, _ in _BUCKETS}
    for row in rows:
        if row["invalid"] or row["selected"] is None:
            continue
        for name, low, high in _BUCKETS:
            if low <= row["confidence"] < high:
                out[name]["total"] += 1
                if row["selected"] == row["gold"]:
                    out[name]["correct"] += 1
                break
    return out