"""云端评判式晋升表（T-X5，采纳 cortiq promotion.rs，与 conformal 双闸门）。

定位：conformal 阈值给出「分对率 >= 1-α」的统计保证（单条查询粒度），
本表在其之上给出「某类任务（label）可由本地档接管」的**节奏自动化**：

- 观察：人工核验 verdict（approve=ok）等质量信号按 label 累计通过率；
- 晋升：n_total >= n_min 且 通过率 >= promote_lb 且 soak 浸泡期满足
  -> state=promoted（此后 grader 对该 label 的 T2 决策可升级 T1）；
- 退化：promoted 期间通过率 < demote_lb -> demoted（自动回退分级路径）；
- 一票否决：tier=T3（高复杂度/仓库级）观察不进通过率统计，且若已晋升
  立即降级——与 cortiq "HIGH tier escalation is served by the cloud" 同构。

灰度纪律（沿用 D-G7）：collect 数据不足（n_min 未满）不得晋升；
全部状态迁移可由 T-X4 的决策留痕审计。
"""
from __future__ import annotations

import time
from typing import Any, Dict, Optional


def promotion_label(consumer: str, domain: str = "") -> str:
    """晋升粒度：消费方×领域（domain 缺省归 general；调用方可传更细粒度）。"""
    return f"{consumer or 'default'}:{domain or 'general'}"


class PromotionTable:
    """标签晋升状态机（candidate / promoted / demoted；持久化于 sense_promotion）。"""

    def __init__(self, store, now=None, n_min: int = 20, promote_lb: float = 0.95,
                 soak_days: float = 3.0, demote_lb: float = 0.85,
                 high_tier_veto: bool = True):
        self.store = store
        self._now = now or (lambda: time.time())
        self.n_min = max(1, int(n_min))
        self.promote_lb = float(promote_lb)
        self.demote_lb = float(demote_lb)
        self.soak_s = float(soak_days) * 86400.0
        self.high_tier_veto = bool(high_tier_veto)

    def observe(self, label: str, ok: bool, tier: str = "",
                ts: Optional[float] = None) -> Dict[str, Any]:
        """记录一次质量信号并推进状态机；返回迁移后的行。"""
        ts = float(ts) if ts is not None else float(self._now())
        row = self.store.get_promotion(label) or {
            "label": label, "n_total": 0, "n_ok": 0, "state": "candidate",
            "first_ts": int(ts), "promoted_ts": 0, "last_ts": 0}

        # 一票否决：T3 观察不进通过率统计；已晋升者立即降级
        if self.high_tier_veto and str(tier).upper() == "T3":
            row["last_ts"] = int(ts)
            if row["state"] == "promoted":
                row["state"] = "demoted"
                row["promoted_ts"] = 0
            self.store.upsert_promotion(row)
            return row

        row["n_total"] = int(row["n_total"]) + 1
        row["n_ok"] = int(row["n_ok"]) + (1 if ok else 0)
        row["last_ts"] = int(ts)
        rate = row["n_ok"] / row["n_total"] if row["n_total"] else 0.0
        soaked = (ts - int(row["first_ts"])) >= self.soak_s

        if row["state"] == "promoted":
            if row["n_total"] >= self.n_min and rate < self.demote_lb:
                row["state"] = "demoted"        # 退化自动降级
                row["promoted_ts"] = 0
        else:
            # candidate / demoted 共用同一晋升门（降级后可凭数据恢复）
            if row["n_total"] >= self.n_min and rate >= self.promote_lb and soaked:
                row["state"] = "promoted"
                row["promoted_ts"] = int(ts)
        self.store.upsert_promotion(row)
        return row

    def is_promoted(self, label: str) -> bool:
        row = self.store.get_promotion(label)
        return bool(row) and row["state"] == "promoted"

    def snapshot(self, label: str) -> Optional[Dict[str, Any]]:
        return self.store.get_promotion(label)