"""健康分计算引擎 — 评分规则 + 缓存。

评分维度:
  - Gateway 稳定性 40%: 7天在线率 + 日均重启惩罚
  - 配置完整性 25%: .env + config.yaml 必填字段检查
  - Skill 一致性 20%: 与基准 Skill 清单对比
  - Cron 成功率 15%: 24h Cron 成功率

等级: A(80-100) B(60-79) C(40-59) D(0-39)

数据来源暂用占位（TODO: hermes CLI / 文件读取），评分逻辑完整实现。
"""
from __future__ import annotations

import logging
import math
import random
from dataclasses import dataclass, field
from datetime import datetime, timedelta, timezone

logger = logging.getLogger(__name__)

# ── 类型 ────────────────────────────────────────────────────────────────


@dataclass
class DimensionScore:
    score: float
    max_score: float
    deductions: list[str] = field(default_factory=list)
    suggestions: list[str] = field(default_factory=list)


@dataclass
class HealthScore:
    profile_name: str
    total_score: float
    grade: str
    gateway: DimensionScore
    config: DimensionScore
    skill: DimensionScore
    cron: DimensionScore
    deduct_summary: list[str] = field(default_factory=list)
    calculated_at: str = ""


GRADE_THRESHOLDS = [
    (80, "A"),
    (60, "B"),
    (40, "C"),
    (0, "D"),
]


def _to_grade(score: float) -> str:
    for threshold, grade in GRADE_THRESHOLDS:
        if score >= threshold:
            return grade
    return "D"


def _fmt_dt(dt: datetime | None = None) -> str:
    return (dt or datetime.now(timezone.utc)).isoformat(timespec="seconds")


# ── 占位数据源（TODO: 对接真实数据） ──────────────────────────────────────


def _placeholder_gateway_data(profile: str) -> dict:
    """返回 Profile 的 Gateway 占位状态数据。"""
    # 根据 profile 名 hash 出不同状态以产生多样结果
    seed = sum(ord(c) for c in profile)
    uptime = 0.85 + (seed % 15) / 100  # 0.85~0.99
    restarts = (seed % 7) / 10  # 0.0~0.6
    return {
        "uptime_7d": min(uptime, 0.999),
        "avg_daily_restarts": restarts,
        "online": seed % 5 != 0,
    }


_REQUIRED_CONFIG_FIELDS = [
    "BOT_TOKEN",
    "BOT_SECRET",
    "APP_ID",
    "APP_SECRET",
    "PROFILE_NAME",
    "LOG_LEVEL",
]


def _placeholder_config_data(profile: str) -> dict:
    """返回 Profile 的配置完整性占位数据。"""
    seed = sum(ord(c) for c in profile)
    # 每个 Profile 缺失 0~3 个字段
    missing_count = seed % 4
    rng = random.Random(profile)
    missing = rng.sample(_REQUIRED_CONFIG_FIELDS, missing_count) if missing_count else []
    return {
        "total_required": len(_REQUIRED_CONFIG_FIELDS),
        "missing_fields": missing,
        "present_fields": [f for f in _REQUIRED_CONFIG_FIELDS if f not in missing],
    }


_BASELINE_SKILLS = {"gateway", "feishu", "cron", "kanban", "search", "keys"}


def _placeholder_skill_data(profile: str) -> dict:
    """返回 Skill 一致性占位数据。"""
    seed = sum(ord(c) for c in profile)
    rng = random.Random(profile)
    profile_skills = set(_BASELINE_SKILLS)
    # 可能缺失几个
    for s in rng.sample(list(_BASELINE_SKILLS), seed % 4):
        profile_skills.discard(s)
    # 可能多出几个
    extras = {"notion", "slack", "jira", "github", "gitlab"}
    for s in rng.sample(list(extras), seed % 3):
        profile_skills.add(s)

    missing = _BASELINE_SKILLS - profile_skills
    surplus = profile_skills - _BASELINE_SKILLS
    return {
        "baseline": list(_BASELINE_SKILLS),
        "profile_skills": list(profile_skills),
        "missing": list(missing),
        "surplus": list(surplus),
    }


def _placeholder_cron_data(profile: str) -> dict:
    """返回 Cron 执行状态占位数据。"""
    seed = sum(ord(c) for c in profile)
    success_rate = 0.7 + (seed % 6) / 20  # 0.7~0.95
    return {
        "total_executions_24h": 10 + seed % 10,
        "success_count": int((10 + seed % 10) * success_rate),
        "success_rate": success_rate,
        "recent_failures": [f"cron-job-{i}" for i in range(seed % 4)],
    }


# ── 评分逻辑 ──────────────────────────────────────────────────────────────


def _score_gateway(profile: str) -> DimensionScore:
    data = _placeholder_gateway_data(profile)
    deductions: list[str] = []
    suggestions: list[str] = []

    uptime = data["uptime_7d"]
    restarts = data["avg_daily_restarts"]

    # 基础分: 7天在线率 × 40
    score = uptime * 40

    if uptime < 0.99:
        deductions.append(f"7天在线率 {uptime:.1%}（目标 ≥99%）")
        suggestions.append("检查 Gateway 进程稳定性，考虑设置自动重启")

    # 日均重启惩罚: >0.3 次/天 开始扣分
    if restarts > 0.3:
        penalty = min(score, (restarts - 0.3) * 15)
        score -= penalty
        deductions.append(f"日均重启 {restarts:.1f} 次（阈值 ≤0.3），扣 {penalty:.1f} 分")
        suggestions.append("排查频繁重启原因，检查内存泄漏或日志错误")

    if not data["online"]:
        score *= 0.5
        deductions.append("Gateway 当前离线，得分减半")
        suggestions.append("立即启动 Gateway 进程")

    return DimensionScore(
        score=max(0, round(score, 1)),
        max_score=40,
        deductions=deductions,
        suggestions=suggestions,
    )


def _score_config(profile: str) -> DimensionScore:
    data = _placeholder_config_data(profile)
    deductions: list[str] = []
    suggestions: list[str] = []

    missing = data["missing_fields"]
    total = data["total_required"]

    score = 25.0  # 满分 25
    if missing:
        deduction_pct = len(missing) * 0.1  # 缺 1 个扣 10%
        score *= max(0, 1 - deduction_pct)
        deductions.append(f"缺 {len(missing)} 个必填字段: {', '.join(missing)}")
        suggestions.append(f"在 .env 或 config.yaml 中补充: {', '.join(missing)}")

    return DimensionScore(
        score=max(0, round(score, 1)),
        max_score=25,
        deductions=deductions,
        suggestions=suggestions,
    )


def _score_skill(profile: str) -> DimensionScore:
    data = _placeholder_skill_data(profile)
    deductions: list[str] = []
    suggestions: list[str] = []

    missing = data["missing"]
    surplus = data["surplus"]

    # 基准技能匹配率 → 满分 20
    baseline_hit = len(data["baseline"]) - len(missing)
    if data["baseline"]:
        match_ratio = baseline_hit / len(data["baseline"])
    else:
        match_ratio = 1.0
    score = match_ratio * 20

    if missing:
        deductions.append(f"缺失基准 Skill: {', '.join(missing)}")
        suggestions.append(f"安装缺失 Skill: {', '.join(missing)}")
    if surplus:
        deductions.append(f"多余未识别 Skill: {', '.join(surplus)}")
        suggestions.append(f"检查多余 Skill 是否必要: {', '.join(surplus)}")

    return DimensionScore(
        score=max(0, round(score, 1)),
        max_score=20,
        deductions=deductions,
        suggestions=suggestions,
    )


def _score_cron(profile: str) -> DimensionScore:
    data = _placeholder_cron_data(profile)
    deductions: list[str] = []
    suggestions: list[str] = []

    success_rate = data["success_rate"]
    score = success_rate * 15  # 成功率 × 15

    if success_rate < 0.95:
        deductions.append(f"24h Cron 成功率 {success_rate:.1%}（目标 ≥95%）")
        suggestions.append("检查失败 Cron 任务的日志，确认是否因超时或配置错误")

    if data["recent_failures"]:
        deductions.append(f"失败任务: {', '.join(data['recent_failures'])}")
        suggestions.append("逐个排查失败任务，优先修复高频失败项")

    return DimensionScore(
        score=max(0, round(score, 1)),
        max_score=15,
        deductions=deductions,
        suggestions=suggestions,
    )


# ── 对外接口 ──────────────────────────────────────────────────────────────


SCORE_CACHE: dict[str, HealthScore] = {}
_LAST_CALCULATED: datetime | None = None
_CACHE_TTL = timedelta(hours=24)

# 占位 Profile 列表（TODO: 从 hermes 配置目录读取）
PLACEHOLDER_PROFILES = [
    "hebo", "ops-engineer", "monitor", "visualizer",
    "gateway-monitor", "feishu-gw", "taiwan-briefing",
    "daily-report", "weekly-digest", "hindsight",
    "guangxi-donation", "europe-briefing",
]


def calculate_health_score(profile_name: str) -> HealthScore:
    """计算单个 Profile 的健康分。"""
    gw = _score_gateway(profile_name)
    cf = _score_config(profile_name)
    sk = _score_skill(profile_name)
    cr = _score_cron(profile_name)

    total = round(gw.score + cf.score + sk.score + cr.score, 1)
    grade = _to_grade(total)

    deduct_summary = []
    for dim, label in [(gw, "Gateway"), (cf, "配置"), (sk, "Skill"), (cr, "Cron")]:
        for d in dim.deductions:
            deduct_summary.append(f"[{label}] {d}")

    return HealthScore(
        profile_name=profile_name,
        total_score=total,
        grade=grade,
        gateway=gw,
        config=cf,
        skill=sk,
        cron=cr,
        deduct_summary=deduct_summary[:5],  # 最多显示 5 条
        calculated_at=_fmt_dt(),
    )


def _is_cache_stale() -> bool:
    if _LAST_CALCULATED is None:
        return True
    return datetime.now(timezone.utc) - _LAST_CALCULATED > _CACHE_TTL


def calculate_all_scores(profiles: list[str] | None = None) -> list[HealthScore]:
    """计算全部 Profile 的健康分，使用缓存（TTL 24h）。"""
    global _LAST_CALCULATED

    if not _is_cache_stale() and SCORE_CACHE:
        logger.info("健康分缓存命中（%d 条）", len(SCORE_CACHE))
        scores = list(SCORE_CACHE.values())
        if profiles:
            return [s for s in scores if s.profile_name in profiles]
        return scores

    target_profiles = profiles or PLACEHOLDER_PROFILES
    scores: list[HealthScore] = []
    for name in target_profiles:
        try:
            hs = calculate_health_score(name)
            SCORE_CACHE[name] = hs
            scores.append(hs)
        except Exception as e:
            logger.error("计算 %s 健康分异常: %s", name, e)

    _LAST_CALCULATED = datetime.now(timezone.utc)
    logger.info("健康分已刷新（%d 条）", len(scores))
    return scores


def invalidate_cache() -> None:
    """强制清除缓存，下次调用重新计算。"""
    global _LAST_CALCULATED
    SCORE_CACHE.clear()
    _LAST_CALCULATED = None
    logger.info("健康分缓存已清除")