#!/usr/bin/env python3
"""并发2实验评估：错误普查 + 指纹稳定性（纯离线，零 API）。

对照：
  - 串行 rerun 基线: 2079s, 688/691, p50=2110ms, meanJSD=0.0836, score=0.7318
  - 并发2 (本次):    677s,  689/691, p50=1404ms, meanJSD=0.0881, score=0.7167
稳定性锚点：串行样本间 test-retest meanJSD ≈ 0.068
"""
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", "DS"),
    ("deepseek_v4_flash_0731", "deepseek_v4_flash_0731_fusion_reference.json", "DS"),
    ("deepseek_v4_pro", "deepseek_v4_pro_fusion_reference.json", "DS"),
    ("glm_51", "glm_51_fusion_reference.json", "GLM"),
    ("glm_52", "glm52_vectron_fusion_reference.json", "GLM"),
    ("glm_53", "glm53_fusion_reference.json", "GLM"),
    ("kimi_k2_6", "kimi_k2_6_fusion_reference.json", "Kimi"),
    ("kimi_k2_7code", "kimi_k2_7code_fusion_reference.json", "Kimi"),
    ("kimi_k3", "kimi_k3_fusion_reference.json", "Kimi"),
]
NAMES = [m[0] for m in MODELS]
FAMILY = {m[0]: m[2] for m in MODELS}

recs = [json.loads(l) for l in open(f"{BFD}/glm_53/conc2/raw_answers.jsonl")]
errs = [r for r in recs if r.get("error")]
print(f"记录 {len(recs)}，错误 {len(errs)} ({len(errs) / len(recs) * 100:.1f}%)")
ec = Counter()
for r in errs:
    e = str(r["error"])
    for code in ("400", "401", "402", "403", "429", "500", "502", "503", "504", "timeout"):
        if code in e.lower():
            ec[code] += 1
            break
    else:
        ec[e[:50]] += 1
for k, v in ec.items():
    print(f"  {k}: {v}")

conc2 = build_d_normalized(recs)
d2 = distributions_by_cell(conc2)
with open(f"{BFD}/glm_53/raw_answers.jsonl") as f:
    s1 = distributions_by_cell(build_d_normalized([json.loads(l) for l in f]))
with open(f"{BFD}/glm_53/rerun/raw_answers.jsonl") as f:
    s2 = distributions_by_cell(build_d_normalized([json.loads(l) for l in f]))
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 = {r: score(d2, refs[r])[0] for r in NAMES}
own = vals["glm_53"]
best = min(vals, key=vals.get)
sib = min(v for r, v in vals.items() if FAMILY[r] == "GLM" and r != "glm_53")
n_cells = score(d2, refs["glm_53"])[1]
print(f"\ntop-1 归因: {best} {'✓ 正确' if best == 'glm_53' else '✗ 混淆!'}  "
      f"own={own:.4f}  同族间距={sib - own:+.4f}  可比cell={n_cells}")

j1 = [jsd_bits(d2[c], s1[c]) for c in set(d2) & set(s1)
      if sum(d2[c].values()) >= 10 and sum(s1[c].values()) >= 10]
j2 = [jsd_bits(d2[c], s2[c]) for c in set(d2) & set(s2)
      if sum(d2[c].values()) >= 10 and sum(s2[c].values()) >= 10]
print(f"vs 串行样本1(原verify): meanJSD={sum(j1) / len(j1):.4f} ({len(j1)} cell)")
print(f"vs 串行样本2(rerun):    meanJSD={sum(j2) / len(j2):.4f} ({len(j2)} cell)")
print("锚点: 串行样本间 test-retest meanJSD≈0.068 —— 并发样本若同量级即不扰动指纹")

tot_v = sum(1 for s in conc2 if s["cat"] == "valid")
print(f"\nD 层 valid: {tot_v}/650（串行 rerun 基线 549/650）")