"""
AWOOOI AIOps Phase 3 — Playbook Evolver Agent（知識演化官）
==========================================================
職責：Playbook 自動合併、低信任封存、知識庫精化

觸發方式：定時（每日凌晨）或手動呼叫 `run_evolver()`

三大功能：
1. 低信任封存 — trust_score < 0.1 → DEPRECATED（自動退場）
2. 休眠封存 — 30 天未使用 AND trust_score < 0.5 → DEPRECATED
3. 相似合併 — Jaccard 症狀相似度 > 0.9 → 合併為一（保留 trust 較高者）

設計原則：
- 純靜態規則（不依賴 LLM）— 保證確定性
- 熔斷保護：單筆操作失敗不影響其他 Playbook
- best-effort 審計：合併/封存寫 logger.info
- feature flag 保護：AIOPS_P3_EVOLVER_ENABLED=False 時靜默跳過

ADR-083: Phase 3 學習閉環重建
2026-04-15 ogt + Claude Sonnet 4.6（亞太）: Phase 3 初始建立
"""

from __future__ import annotations

import asyncio
from dataclasses import dataclass, field
from datetime import timedelta

import structlog

from src.models.playbook import Playbook, PlaybookSource, PlaybookStatus
from src.utils.timezone import now_taipei

logger = structlog.get_logger(__name__)

# ── 閾值常數 ────────────────────────────────────────────────────────────────
TRUST_ARCHIVE_THRESHOLD = 0.1       # trust_score < 此值 → 封存
DORMANT_TRUST_THRESHOLD = 0.5       # 休眠封存：trust < 此值 AND 30d 未用
DORMANT_DAYS = 30                   # 休眠天數
MERGE_SIMILARITY_THRESHOLD = 0.9    # 症狀相似度 > 此值 → 合併候選
MAX_MERGE_PER_RUN = 10              # 單次合併上限（防止操作過多）


# ─────────────────────────────────────────────────────────────────────────────
# Result Types
# ─────────────────────────────────────────────────────────────────────────────

@dataclass
class EvolverReport:
    """Evolver 執行報告"""
    archived_count: int = 0
    merged_count: int = 0
    skipped_count: int = 0
    archived_ids: list[str] = field(default_factory=list)
    merged_pairs: list[tuple[str, str]] = field(default_factory=list)  # (deprecated_id, kept_id)
    errors: list[str] = field(default_factory=list)

    @property
    def total_affected(self) -> int:
        return self.archived_count + self.merged_count


# ─────────────────────────────────────────────────────────────────────────────
# Main Entry Point
# ─────────────────────────────────────────────────────────────────────────────

async def run_evolver(force: bool = False) -> EvolverReport:
    """
    執行 Evolver Agent 全流程。

    Args:
        force: True 時跳過 feature flag 檢查（供管理員手動觸發端點使用）

    Returns:
        EvolverReport（包含所有操作結果）

    Raises:
        不拋出 — 所有錯誤內部吸收，最壞情況返回空報告
    """
    from src.core.feature_flags import aiops_flags

    if not force and not aiops_flags.AIOPS_P3_EVOLVER_ENABLED:
        logger.debug("evolver_skipped", reason="AIOPS_P3_EVOLVER_ENABLED=False")
        return EvolverReport()

    report = EvolverReport()

    try:
        playbooks = await _fetch_all_active_playbooks()
        if not playbooks:
            logger.info("evolver_no_playbooks")
            return report

        # Step 1: 低信任封存
        await _archive_low_trust(playbooks, report)

        # Step 2: 休眠封存（剩餘 APPROVED/DRAFT 中挑）
        remaining = [p for p in playbooks if p.status not in (PlaybookStatus.DEPRECATED,)]
        await _archive_dormant(remaining, report)

        # Step 3: 相似合併
        still_active = [p for p in remaining if p.status not in (PlaybookStatus.DEPRECATED,)]
        await _merge_similar(still_active, report)

        logger.info(
            "evolver_done",
            archived=report.archived_count,
            merged=report.merged_count,
            skipped=report.skipped_count,
            errors=len(report.errors),
        )

    except Exception:
        logger.exception("evolver_fatal")

    return report


# ─────────────────────────────────────────────────────────────────────────────
# Step Implementations
# ─────────────────────────────────────────────────────────────────────────────

async def _archive_low_trust(playbooks: list[Playbook], report: EvolverReport) -> None:
    """Step 1: trust_score < 0.1 → DEPRECATED（自動退場）"""
    from src.services.playbook_service import get_playbook_service

    service = get_playbook_service()

    for pb in playbooks:
        if pb.status == PlaybookStatus.DEPRECATED:
            continue
        if pb.source == PlaybookSource.YAML_RULE:
            continue  # yaml_rule playbooks 由 seeder 管理，不受 trust 封存，保護自動修復鏈路
        if pb.trust_score < TRUST_ARCHIVE_THRESHOLD:
            try:
                await service.update_with_validation(
                    pb.playbook_id,
                    {"status": PlaybookStatus.DEPRECATED.value},
                )
                logger.info(
                    "evolver_archived_low_trust",
                    playbook_id=pb.playbook_id,
                    playbook_name=pb.name,
                    trust_score=pb.trust_score,
                )
                report.archived_count += 1
                report.archived_ids.append(pb.playbook_id)
                # 原地更新 status 避免後續步驟重複處理
                pb.status = PlaybookStatus.DEPRECATED
            except Exception as e:
                report.errors.append(f"archive_low_trust:{pb.playbook_id}:{e}")
                logger.warning(
                    "evolver_archive_failed",
                    playbook_id=pb.playbook_id,
                    error=str(e),
                )


async def _archive_dormant(playbooks: list[Playbook], report: EvolverReport) -> None:
    """Step 2: 30d 未使用 AND trust < 0.5 → DEPRECATED（休眠退場）"""
    from src.services.playbook_service import get_playbook_service

    service = get_playbook_service()
    cutoff = now_taipei() - timedelta(days=DORMANT_DAYS)

    for pb in playbooks:
        if pb.status == PlaybookStatus.DEPRECATED:
            continue
        if pb.source == PlaybookSource.YAML_RULE:
            continue  # yaml_rule playbooks 由 seeder 管理，不受休眠封存，保護自動修復鏈路
        if pb.last_used_at is None:
            # 從未使用過 — 只在 trust 低於閾值時封存
            if pb.trust_score >= DORMANT_TRUST_THRESHOLD:
                report.skipped_count += 1
                continue
        elif pb.last_used_at > cutoff:
            # 30 天內有使用 — 不封存
            report.skipped_count += 1
            continue

        if pb.trust_score >= DORMANT_TRUST_THRESHOLD:
            # trust 夠高 — 保留休眠 Playbook
            report.skipped_count += 1
            continue

        try:
            await service.update_with_validation(
                pb.playbook_id,
                {"status": PlaybookStatus.DEPRECATED.value},
            )
            logger.info(
                "evolver_archived_dormant",
                playbook_id=pb.playbook_id,
                playbook_name=pb.name,
                trust_score=pb.trust_score,
                last_used_at=str(pb.last_used_at),
            )
            report.archived_count += 1
            report.archived_ids.append(pb.playbook_id)
            pb.status = PlaybookStatus.DEPRECATED
        except Exception as e:
            report.errors.append(f"archive_dormant:{pb.playbook_id}:{e}")
            logger.warning(
                "evolver_dormant_archive_failed",
                playbook_id=pb.playbook_id,
                error=str(e),
            )


async def _merge_similar(playbooks: list[Playbook], report: EvolverReport) -> None:
    """
    Step 3: 症狀 Jaccard 相似度 > 0.9 → 合併為一

    策略：保留 trust_score 較高的那筆，將較差的標記 DEPRECATED
    合併次數上限 MAX_MERGE_PER_RUN，避免單次操作影響太多。
    """
    from src.services.playbook_service import get_playbook_service

    service = get_playbook_service()
    merged_set: set[str] = set()  # 已合併（或被合併）的 playbook_id

    active = [p for p in playbooks if p.status not in (PlaybookStatus.DEPRECATED,)]
    merge_count = 0

    for i, pb_a in enumerate(active):
        if pb_a.playbook_id in merged_set:
            continue
        if merge_count >= MAX_MERGE_PER_RUN:
            break

        for pb_b in active[i + 1:]:
            if pb_b.playbook_id in merged_set:
                continue
            if merge_count >= MAX_MERGE_PER_RUN:
                break

            sim = _compute_symptom_similarity(pb_a, pb_b)
            if sim < MERGE_SIMILARITY_THRESHOLD:
                continue

            # 相似度 >= 0.9 → 合併
            # 保留 trust 較高的，封存較差的
            keep, drop = (
                (pb_a, pb_b) if pb_a.trust_score >= pb_b.trust_score else (pb_b, pb_a)
            )

            try:
                # 把 drop 的來源 incident 合入 keep
                merged_source_ids = list(
                    set(keep.source_incident_ids) | set(drop.source_incident_ids)
                )
                await service.update_with_validation(
                    keep.playbook_id,
                    {"source_incident_ids": merged_source_ids},
                )
                # 封存被合併的
                await service.update_with_validation(
                    drop.playbook_id,
                    {"status": PlaybookStatus.DEPRECATED.value},
                )
                logger.info(
                    "evolver_merged",
                    kept_id=keep.playbook_id,
                    dropped_id=drop.playbook_id,
                    similarity=f"{sim:.2f}",
                    kept_trust=keep.trust_score,
                    dropped_trust=drop.trust_score,
                )
                merged_set.add(drop.playbook_id)
                report.merged_count += 1
                report.merged_pairs.append((drop.playbook_id, keep.playbook_id))
                drop.status = PlaybookStatus.DEPRECATED
                merge_count += 1
            except Exception as e:
                report.errors.append(f"merge:{keep.playbook_id}+{drop.playbook_id}:{e}")
                logger.warning(
                    "evolver_merge_failed",
                    keep_id=keep.playbook_id,
                    drop_id=drop.playbook_id,
                    error=str(e),
                )


# ─────────────────────────────────────────────────────────────────────────────
# Helpers
# ─────────────────────────────────────────────────────────────────────────────

async def _fetch_all_active_playbooks() -> list[Playbook]:
    """
    抓取所有非 DEPRECATED 的 Playbook（用於 Evolver 掃描）。

    2026-04-24 ogt + Claude Sonnet 4.6: 改為分兩次 query（DRAFT + APPROVED）
    原本 list_playbooks(limit=500) 不傳 status → 把 294 筆 deprecated 全載入記憶體，
    隨 deprecated 累積效能惡化；改用兩次 status 過濾查詢，只拉需要的資料。
    """
    try:
        from src.models.playbook import PlaybookStatus
        from src.services.playbook_service import get_playbook_service

        service = get_playbook_service()

        draft_playbooks, _ = await service.list_playbooks(
            status=PlaybookStatus.DRAFT, limit=500
        )
        approved_playbooks, _ = await service.list_playbooks(
            status=PlaybookStatus.APPROVED, limit=500
        )
        return draft_playbooks + approved_playbooks
    except Exception as e:
        logger.warning("evolver_fetch_playbooks_failed", error=str(e))
        return []


def _compute_symptom_similarity(pb_a: Playbook, pb_b: Playbook) -> float:
    """
    計算兩個 Playbook 症狀模式的 Jaccard 相似度。

    組合三維度：
    - alert_names Jaccard（權重 0.5）
    - keywords Jaccard（權重 0.3）
    - affected_services Jaccard（權重 0.2）
    """
    from src.utils.similarity import calculate_jaccard_similarity

    sp_a = pb_a.symptom_pattern
    sp_b = pb_b.symptom_pattern

    alert_sim = calculate_jaccard_similarity(
        set(sp_a.alert_names), set(sp_b.alert_names)
    )
    keyword_sim = calculate_jaccard_similarity(
        set(sp_a.keywords), set(sp_b.keywords)
    )

    # affected_services 為空 = 通用型 Playbook → 視為完全相符
    if not sp_a.affected_services and not sp_b.affected_services:
        service_sim = 1.0
    elif not sp_a.affected_services or not sp_b.affected_services:
        service_sim = 0.5  # 一個通用一個特定 → 中等
    else:
        service_sim = calculate_jaccard_similarity(
            set(sp_a.affected_services), set(sp_b.affected_services)
        )

    return 0.5 * alert_sim + 0.3 * keyword_sim + 0.2 * service_sim


# ─────────────────────────────────────────────────────────────────────────────
# Singleton / Scheduling Hook
# ─────────────────────────────────────────────────────────────────────────────

async def schedule_daily_evolver() -> None:
    """
    供 startup 或 APScheduler 呼叫的每日 Evolver 觸發點。

    呼叫方式（main.py lifespan 或 scheduler）：
        asyncio.create_task(schedule_daily_evolver())
    """
    from src.core.feature_flags import aiops_flags

    if not aiops_flags.AIOPS_P3_EVOLVER_ENABLED:
        return

    logger.info("evolver_daily_scheduled")
    try:
        report = await run_evolver()
        logger.info(
            "evolver_daily_done",
            archived=report.archived_count,
            merged=report.merged_count,
        )
    except Exception:
        logger.exception("evolver_daily_failed")


DAILY_INTERVAL_SEC = 86_400  # 24h


async def run_evolver_loop() -> None:
    """
    無限迴圈：每 24 小時執行一次 Evolver Agent。
    在 main.py startup 以 asyncio.create_task 掛載。

    ADR-083 Phase 3: Evolver 每日掃描（L4×D3 + L5×D2）
    2026-04-15 ogt + Claude Sonnet 4.6（亞太）: Phase 3 初始建立
    """
    while True:
        try:
            await schedule_daily_evolver()
        except Exception as e:
            logger.error("evolver_loop_error", error=str(e))

        await asyncio.sleep(DAILY_INTERVAL_SEC)