import csv
import json
from collections import Counter

VALID_LABELS = {"0", "1", "2"}

POP_MAP = {"none": 0, "minor": 1, "major": 2}
DEV_MAP = {"low": 0, "medium": 1, "high": 2}
SITE_MAP = {"low": 0, "medium": 1, "high": 2}
FRAG_MAP = {"low": 0, "medium": 1, "high": 2}

REQUIRED_GOLD_COLS = [
    "id",
    "population_shift",
    "protocol_deviation_rate",
    "site_variance_level",
    "endpoint_fragility",
    "label",
]

def _norm(x) -> str:
    return str(x).strip().lower()

def _map_val(x, m, name: str) -> int:
    s = _norm(x)
    if s not in m:
        raise ValueError(f"Bad {name} value: {x}")
    return m[s]

def _validate_gold_row(r: dict):
    for c in REQUIRED_GOLD_COLS:
        if c not in r:
            raise ValueError(f"Missing column: {c}")
    lab = str(r["label"]).strip()
    if lab not in VALID_LABELS:
        raise ValueError(f"Bad label: {r['label']}")

    _map_val(r["population_shift"], POP_MAP, "population_shift")
    _map_val(r["protocol_deviation_rate"], DEV_MAP, "protocol_deviation_rate")
    _map_val(r["site_variance_level"], SITE_MAP, "site_variance_level")
    _map_val(r["endpoint_fragility"], FRAG_MAP, "endpoint_fragility")

def _extract_pred_label(pred_row: dict) -> str:
    v = pred_row.get("label") or pred_row.get("prediction") or pred_row.get("output") or ""
    s = str(v).strip()

    if s in VALID_LABELS:
        return s

    # allow JSON {"label":"2"}
    if s.startswith("{") and s.endswith("}"):
        try:
            obj = json.loads(s)
            if isinstance(obj, dict) and "label" in obj:
                lab = str(obj["label"]).strip()
                if lab in VALID_LABELS:
                    return lab
        except Exception:
            pass

    return "invalid"

def _rule_pred(g: dict) -> str:
    pop = _map_val(g["population_shift"], POP_MAP, "population_shift")
    dev = _map_val(g["protocol_deviation_rate"], DEV_MAP, "protocol_deviation_rate")
    site = _map_val(g["site_variance_level"], SITE_MAP, "site_variance_level")
    frag = _map_val(g["endpoint_fragility"], FRAG_MAP, "endpoint_fragility")

    # collapse when fragility is high plus any other high strain
    if frag == 2 and (pop == 2 or dev == 2 or site == 2):
        return "2"

    # also collapse when three nodes are high/major/medium-high combined
    highish = sum([
        1 if pop >= 1 else 0,
        1 if dev >= 1 else 0,
        1 if site >= 1 else 0,
        1 if frag >= 1 else 0,
    ])
    if highish >= 4 and (dev == 2 or site == 2):
        return "2"

    # coherent only when all nodes low/none
    if pop == 0 and dev == 0 and site == 0 and frag == 0:
        return "0"

    return "1"

def _risk_score(g: dict) -> float:
    pop = _map_val(g["population_shift"], POP_MAP, "population_shift")
    dev = _map_val(g["protocol_deviation_rate"], DEV_MAP, "protocol_deviation_rate")
    site = _map_val(g["site_variance_level"], SITE_MAP, "site_variance_level")
    frag = _map_val(g["endpoint_fragility"], FRAG_MAP, "endpoint_fragility")

    # fragility weighs double
    raw = pop + dev + site + (2 * frag)
    # max raw = 2+2+2+4 = 10
    return raw / 10.0

def run_scorer(preds_csv_path: str, gold_csv_path: str):
    with open(gold_csv_path, newline="", encoding="utf-8") as gf:
        gold_rows = list(csv.DictReader(gf))

    for r in gold_rows:
        _validate_gold_row(r)

    with open(preds_csv_path, newline="", encoding="utf-8") as pf:
        pred_rows = list(csv.DictReader(pf))

    pred_by_id = {str(r.get("id")).strip(): r for r in pred_rows if r.get("id") is not None}

    total = 0
    correct = 0
    confusion = Counter()
    errors = []
    missing_ids = []

    for g in gold_rows:
        gid = str(g["id"]).strip()
        gold = str(g["label"]).strip()

        pr = pred_by_id.get(gid)
        if pr is None:
            pred = "missing"
            missing_ids.append(gid)
        else:
            pred = _extract_pred_label(pr)

        confusion[(gold, pred)] += 1

        if pred == gold:
            correct += 1
        else:
            errors.append({
                "id": gid,
                "gold": gold,
                "pred": pred,
                "rule_pred": _rule_pred(g),
                "risk_score": round(_risk_score(g), 4),
            })

        total += 1

    report = {
        "n": total,
        "accuracy": (correct / total) if total else 0.0,
        "confusion": {f"{k[0]}->{k[1]}": v for k, v in confusion.items()},
        "avg_risk_score": round(sum(_risk_score(r) for r in gold_rows) / max(1, len(gold_rows)), 4),
        "errors_sample": errors[:25],
        "missing_ids": missing_ids[:50],
    }
    return report

if __name__ == "__main__":
    import argparse
    p = argparse.ArgumentParser()
    p.add_argument("--preds_csv", required=True)
    p.add_argument("--gold_csv", required=True)
    args = p.parse_args()

    print(json.dumps(run_scorer(args.preds_csv, args.gold_csv), indent=2))