#!/usr/bin/env python3
"""slim 电池端到端验收（纯离线）：
  1. 四次 glm_53 运行信号分解对比（全量 verify / rerun / conc2 / slim）——定位 score 差来源
  2. slim 样本 top-1 归因 vs 9 参考（主验收判据）
  3. 错误普查 + 与历次样本的 meanJSD
"""
import json
import sys
from collections import Counter

from pathlib import Path  # noqa: E402

sys.path.insert(0, str(Path(__file__).resolve().parents[2]))  # 仓库根/site-packages, 使 evalharness 包可导入
from evalharness.fingerprint.engine import (build_d_normalized, distributions_by_cell,  # noqa: E402
                    jsd_bits, load_reference)

BFD = "/tmp/bfd"
R = str(Path(__file__).resolve().parent / 'references')
MODELS = [
    ("deepseek_v4_flash", "deepseek_v4_flash_fusion_reference.json"),
    ("deepseek_v4_flash_0731", "deepseek_v4_flash_0731_fusion_reference.json"),
    ("deepseek_v4_pro", "deepseek_v4_pro_fusion_reference.json"),
    ("glm_51", "glm_51_fusion_reference.json"),
    ("glm_52", "glm52_vectron_fusion_reference.json"),
    ("glm_53", "glm53_fusion_reference.json"),
    ("kimi_k2_6", "kimi_k2_6_fusion_reference.json"),
    ("kimi_k2_7code", "kimi_k2_7code_fusion_reference.json"),
    ("kimi_k3", "kimi_k3_fusion_reference.json"),
]
NAMES = [m[0] for m in MODELS]

# ---------- 1. 四次运行信号分解 ----------
RUNS = [("全量verify", f"{BFD}/glm_53/verify.json"),
        ("全量rerun", f"{BFD}/glm_53/rerun/verify_rerun.json"),
        ("全量conc2", f"{BFD}/glm_53/conc2/verify_conc2.json"),
        ("slim并发2", f"{BFD}/glm_53/slim/verify_slim.json")]
print("【信号分解】s_dist 由 ratio=meanJSD/max(split_half,0.02) 阶梯量化")
print(f"{'运行':12s} {'score':>7s} {'verdict':>15s} {'s_dist':>7s} {'meanJSD':>8s} "
      f"{'splitH':>7s} {'ratio':>6s} {'cells':>5s} {'s_idn':>6s} {'s_meta':>6s}")
for name, path in RUNS:
    r = json.load(open(path))
    dist = r["signals"]["dist"]
    idn = r["signals"].get("identity", {})
    met = r["signals"].get("meta", {})
    print(f"{name:12s} {r['score']:7.4f} {r['verdict']:>15s} "
          f"{dist.get('s_dist', 0):7.3f} {dist.get('mean_jsd') or 0:8.4f} "
          f"{(dist.get('split_half') or 0):7.4f} {dist.get('relative_ratio') or 0:6.2f} "
          f"{dist.get('comparable_cells'):5d} {idn.get('s_idn', 0):6.3f} {met.get('s_meta', 0):6.3f}")
print()

# ---------- 2. slim 样本验收 ----------
recs = [json.loads(l) for l in open(f"{BFD}/glm_53/slim/raw_answers.jsonl")]
errs = [r for r in recs if r.get("error")]
ec = Counter()
for r in errs:
    e = str(r["error"])
    for code in ("400", "402", "429", "500", "502", "503", "504", "timeout"):
        if code in e.lower():
            ec[code] += 1
            break
    else:
        ec[e[:30]] += 1
print(f"slim 记录 {len(recs)}（期望434），错误 {len(errs)}: {dict(ec) or '无'}")

d_new = distributions_by_cell(build_d_normalized(recs))
refs = {n: load_reference(f"{R}/{rf}")["cells"] for n, rf in MODELS}


def score(dist, ref):
    js = []
    for c in set(dist) & set(ref):
        a, b = dist[c], ref[c]
        if sum(a.values()) >= 10 and sum(b.values()) >= 10:
            js.append(jsd_bits(a, b))
    return (sum(js) / len(js) if js else None), len(js)


vals = {n: score(d_new, refs[n])[0] for n in NAMES}
best = min(vals, key=vals.get)
sib = min(v for n, v in vals.items() if n.startswith("glm") and n != "glm_53")
print(f"top-1 归因: {best} {'✓ 正确' if best == 'glm_53' else '✗ 混淆!'}  "
      f"own={vals['glm_53']:.4f}  同族间距={sib - vals['glm_53']:+.4f}  "
      f"可比cell={score(d_new, refs['glm_53'])[1]}(期望16)")

prior = {}
for tag, p in [("样本1(全量verify)", f"{BFD}/glm_53/raw_answers.jsonl"),
               ("样本2(rerun)", f"{BFD}/glm_53/rerun/raw_answers.jsonl"),
               ("样本3(conc2)", f"{BFD}/glm_53/conc2/raw_answers.jsonl")]:
    prior[tag] = distributions_by_cell(build_d_normalized(
        [json.loads(l) for l in open(p)]))
for tag, d in prior.items():
    js = [jsd_bits(d_new[c], d[c]) for c in set(d_new) & set(d)
          if sum(d_new[c].values()) >= 10 and sum(d[c].values()) >= 10]
    print(f"  vs {tag}: meanJSD={sum(js) / len(js):.4f} ({len(js)} cell)")
tot_v = sum(1 for s in build_d_normalized(recs) if s["cat"] == "valid")
print(f"D 层 valid: {tot_v}/400")