"""汇总配对置信区间、历史官方证据与BC/特征错位探针，不连接模拟器。"""
from pathlib import Path
import json
import hashlib
import numpy as np
import torch

import verify_rl_practice
from mcm26b.rl.env import LocalizationEnv
from mcm26b.rl.features import build_observation


def main():
    root = Path('output/rl/review-20260911')
    records = json.loads((root/'evaluation/validation/final/results.json').read_text(encoding='utf-8'))
    rng = np.random.default_rng(20260911)
    comparisons = {}
    for problem in (3,4):
        part = [r for r in records['rows'] if r['problem']==problem]
        for label in ('baseline','legacy'):
            baseline = {r['case']:r for r in part if r['variant']==label}
            selected = sorted((r for r in part if r['variant']=='revised'),key=lambda r:r['case'])
            a = np.array([baseline[r['case']]['virtual_s'] for r in selected])
            b = np.array([r['virtual_s'] for r in selected])
            samples = rng.integers(0,len(a),(10000,len(a)))
            changes = (b[samples].mean(axis=1)/a[samples].mean(axis=1)-1)*100
            comparisons[f'q{problem}-revised-vs-{label}'] = dict(
                relative_change_pct=(b.mean()/a.mean()-1)*100,
                paired_bootstrap_95_pct=np.quantile(changes,[.025,.975]).tolist(),
                faster_cases=int(sum(b<a)),runs=len(a))
    # 重新计算原始官方日志的检查，派生报告单独保存，原始归档不写入。
    verify_rl_practice.save_json = lambda path,value: None
    official = [verify_rl_practice.audit(p.parent) for p in sorted(
        Path('output/rl/official-practice').rglob('*.result.json'))]
    env = LocalizationEnv(handoff_probability=0,boundary_probability=0)
    fixed,_ = env.reset(seed=1)
    # 复现旧版把整个BeliefState作为单频道对象传入的错误。
    wrong = build_observation(env._belief.candidate_set(env._channel),env._client.state.position,
                              env._channel,env.config,env.problem,env._blocked,env._belief,
                              virtual_time_s=env._client.state.virtual_time_s)
    features = dict(mismatched_indices=np.flatnonzero(fixed!=wrong).tolist(),
                    fixed_history_count=float(fixed[7]),old_history_count=float(wrong[7]))
    env.close()
    torch.set_num_threads(1)
    torch.manual_seed(0)
    bc = {}
    for problem in (3,4):
        # 使用旧检查点和原始专家输入，比对原ReLU网络与拷贝后的Tanh网络。
        checkpoint = torch.load(f'output/rl/bc/q{problem}_bc.pt',map_location='cpu',weights_only=True)
        data = np.load(f'output/rl/bc/q{problem}_expert.npz',allow_pickle=False)
        x = torch.from_numpy(data['obs'][::max(1,len(data['obs'])//1000)].copy())
        def network(activation):
            net = torch.nn.Sequential(torch.nn.Linear(401,128),activation(),
                                      torch.nn.Linear(128,128),activation(),torch.nn.Linear(128,60))
            net.load_state_dict({k.removeprefix('net.'):v for k,v in checkpoint['state_dict'].items()})
            return net
        with torch.no_grad():
            before,after=network(torch.nn.ReLU)(x),network(torch.nn.Tanh)(x)
        bc[f'q{problem}'] = dict(samples=len(x),
            different_argmax_fraction=float((before.argmax(1)!=after.argmax(1)).float().mean()),
            max_logit_difference=float((before-after).abs().max()))
    report = dict(comparisons=comparisons,official=official,feature_probe=features,bc_probe=bc,
                  limitations='置信区间仅重采样固定模型的场景，不覆盖训练随机种子不确定性。',
                  code_sha256={str(p):hashlib.sha256(p.read_bytes()).hexdigest()
                               for p in sorted(Path('src/mcm26b/rl').glob('*.py'))})
    (root/'review_evidence.json').write_text(json.dumps(report,ensure_ascii=False,indent=2),encoding='utf-8')
    print(json.dumps(dict(comparisons=comparisons,feature_probe=features,bc_probe=bc,
                         official=[dict(case=r['case_code'],passed=r['passed']) for r in official]),indent=2))


if __name__=='__main__':
    main()