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

REQ = [
    "phase_space_coherence_index",
    "baseline_dispersion_envelope",
    "command_response_lag_profile",
    "control_energy_efficiency",
    "baseline_confidence",
]

@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 _env_ok(p: str):
    m = re.search(r"baseline_dispersion_envelope\s*[:=]\s*(0\.\d+|1\.0)\s*-\s*(0\.\d+|1\.0)", p)
    if not m:
        return False
    lo = float(m.group(1))
    hi = float(m.group(2))
    return 0.0 <= lo <= hi <= 1.0

def _lag_ok(p: str):
    return re.search(r"command_response_lag_profile\s*[:=]\s*p50\s*\d+\s*ms;\s*p95\s*\d+\s*ms", p) is not 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)

    idx = _f(p, "phase_space_coherence_index")
    energy = _f(p, "control_energy_efficiency")
    conf = _f(p, "baseline_confidence")

    numeric_ok = int(
        idx is not None and 0.0 <= idx <= 1.0 and
        energy is not None and 0.0 <= energy <= 1.0 and
        conf is not None and 0.0 <= conf <= 1.0
    )

    env_ok = int(_env_ok(p))
    lag_ok = int(_lag_ok(p))
    has_surface = int("surface" in p or "aileron" in p or "elevator" in p or "rudder" in p)

    raw = (
        0.15 * int(words_ok) +
        0.45 * (hits / len(REQ)) +
        0.22 * numeric_ok +
        0.08 * env_ok +
        0.06 * lag_ok +
        0.04 * has_surface
    )

    return ScoreResult(score=min(1.0, raw), details={"id": sample.get("id"), "hits": hits, "env_ok": env_ok})

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)}