from dataclasses import dataclass
from typing import Dict, Any, List
import re

REQ = [
    "combination_coherence_score",
    "dominant_response_basin",
    "cross_system_alignment_vector",
    "early_incoherence_flags",
    "net_functional_shift",
]

@dataclass
class ScoreResult:
    score: float
    details: Dict[str, Any]

def _f(p: str, key: str):
    m = re.search(rf"{key}\s*[:=]\s*(0\.\d+|1\.0)\b", p)
    return float(m.group(1)) if m else None

def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
    p = (prediction or "").lower()
    words_ok = len(p.split()) <= 950
    hits = sum(1 for k in REQ if k in p)

    coh = _f(p, "combination_coherence_score")
    num_ok = int(coh is not None and 0 <= coh <= 1)

    basin_ok = int("dominant_response_basin" in p)
    vec_ok = int("cross_system_alignment_vector" in p and ":" in p)
    flags_ok = int("early_incoherence_flags" in p)
    shift_ok = int("net_functional_shift" in p)

    raw = (
        0.18 * int(words_ok) +
        0.42 * (hits / len(REQ)) +
        0.18 * num_ok +
        0.08 * basin_ok +
        0.07 * vec_ok +
        0.03 * flags_ok +
        0.04 * shift_ok
    )
    return ScoreResult(score=min(1.0, raw), details={"id": sample.get("id"), "hits": hits})

def aggregate(results: List[ScoreResult]) -> Dict[str, Any]:
    if not results:
        return {"mean": 0.0, "n": 0}
    return {"mean": sum(r.score for r in results) / len(results), "n": len(results)}