"""
AWOOOI AIOps Phase 1 — 執行後驗證器
=====================================
每次 AI 修復動作執行後，主動用 MCP 抓取環境後狀態，
與 EvidenceSnapshot.pre_execution_state 對比，
判斷修復是否真的有效。

驗證結果三態：
  - "success"   — 問題已解決（Pod Running / 指標恢復正常）
  - "degraded"  — 部分改善但未完全恢復
  - "failed"    — 執行後狀態比執行前更差，或完全未改善
  - "timeout"   — 驗證超時（MCP 無法回應）

驗證結果用途：
  1. 填入 EvidenceSnapshot.verification_result（Phase 3 學習閉環基礎）
  2. 傳給 learning_service 更新 Playbook EWMA trust_score
  3. 觸發 Reviewer Agent 的 rollback 決策（Phase 2）

設計原則：
  - 執行後等待 warm-up period（預設 10s），讓 K8s controller 有時間收斂
  - 超時不 raise，標記 "timeout" 並繼續流程
  - 不阻塞原始執行路徑（await，但結果不影響執行本身是否成功）

W2 PR-V1: SelfHealingValidator 串接 (2026-04-28 ogt + Claude Sonnet 4.6)
  - ENABLE_SELF_HEALING_VALIDATOR=True 時，verify() 完成後呼叫 assess_self_healing()
  - self_healing_score < 0.5 → Telegram 警示 rollback 提案（不自動執行）
  - 驗證失敗不阻塞主流程（try/except 全包）

ADR-081: PreDecisionInvestigator + EvidenceSnapshot
MASTER §3.1 L6×D1
2026-04-15 ogt + Claude Sonnet 4.6 (亞太): Phase 1 初始建立
"""

from __future__ import annotations

import asyncio
import re
from typing import TYPE_CHECKING, Any

import structlog

from src.db.base import get_db_context
from src.plugins.mcp.gateway import GatewayContext, McpGateway
from src.plugins.mcp.registry import AuditedMCPToolProvider
from src.services.evidence_snapshot import EvidenceSnapshot
from src.services.mcp_audit_context import with_mcp_audit_context
from src.services.mcp_tool_registry import RegisteredTool, SensorDimension, get_mcp_tool_registry
from src.services.sanitization_service import sanitize, sanitize_dict_values
# W2 PR-V1: 頂層 import 讓測試 patch 路徑固定（延遲 import 無法被 patch）
# ENABLE_SELF_HEALING_VALIDATOR=False 時此 import 不影響效能（純 python 模組）
from src.services import self_healing_validator as _shv_module

if TYPE_CHECKING:
    from src.models.incident import Incident

logger = structlog.get_logger(__name__)

# 執行後等待收斂時間（秒）— K8s controller 需要時間處理重啟/滾動更新
POST_EXEC_WARMUP_SEC = 10.0

# 驗證超時（秒）
VERIFY_TIMEOUT_SEC = 30.0

# MCP 單工具超時（秒）
TOOL_TIMEOUT_SEC = 8.0


class PostExecutionVerifier:
    """
    執行後環境狀態驗證器。

    在 approval_execution.py 的 execute_approved_action() 中，
    執行動作後呼叫 verify()，取得驗證結果並補填 EvidenceSnapshot。

    Usage:
        verifier = get_post_execution_verifier()
        result = await verifier.verify(
            incident=incident,
            snapshot=pre_decision_snapshot,
            action_taken="restart_service:awoooi-api",
        )
        # result: "success" | "degraded" | "failed" | "timeout"
    """

    def __init__(self) -> None:
        self._registry = get_mcp_tool_registry()

    async def verify(
        self,
        incident: "Incident",
        snapshot: EvidenceSnapshot | None,
        action_taken: str,
        warmup_sec: float = POST_EXEC_WARMUP_SEC,
    ) -> str:
        """
        執行後驗證。

        Args:
            incident: 原始 Incident（用於取 labels 定位資源）
            snapshot: 執行前的 EvidenceSnapshot（取 pre_execution_state 作基準線）
            action_taken: 執行的動作描述（例如 "restart_service:awoooi-api"）
            warmup_sec: 等待 K8s 收斂的秒數

        Returns:
            str: "success" | "degraded" | "failed" | "timeout"
        """
        incident_id = _get_incident_id(incident)

        logger.info(
            "verifier_start",
            incident_id=incident_id,
            action=action_taken,
            warmup_sec=warmup_sec,
        )

        # 1. 等待收斂
        if warmup_sec > 0:
            await asyncio.sleep(warmup_sec)

        # 2. 抓後狀態
        try:
            post_state = await asyncio.wait_for(
                self._collect_post_state(incident),
                timeout=VERIFY_TIMEOUT_SEC,
            )
        except asyncio.TimeoutError:
            logger.warning("verifier_timeout", incident_id=incident_id)
            if snapshot:
                await _update_snapshot(snapshot, {}, "timeout")
            else:
                await _persist_fallback_snapshot(
                    incident=incident,
                    post_state={},
                    result="timeout",
                    action_taken=action_taken,
                )
            return "timeout"
        except Exception:
            logger.exception("verifier_collect_error", incident_id=incident_id)
            if snapshot:
                await _update_snapshot(snapshot, {}, "failed")
            else:
                await _persist_fallback_snapshot(
                    incident=incident,
                    post_state={},
                    result="failed",
                    action_taken=action_taken,
                )
            return "failed"

        # 3. 對比前後狀態
        pre_state = snapshot.pre_execution_state if snapshot else None
        result = _assess_recovery(pre_state, post_state, action_taken)

        # 4. 更新 EvidenceSnapshot
        if snapshot:
            await _update_snapshot(snapshot, post_state, result)
        else:
            await _persist_fallback_snapshot(
                incident=incident,
                post_state=post_state,
                result=result,
                action_taken=action_taken,
            )

        logger.info(
            "verifier_done",
            incident_id=incident_id,
            result=result,
            action=action_taken,
        )

        # 5. W2 PR-V1: SelfHealingValidator 串接（ENABLE_SELF_HEALING_VALIDATOR gate）
        # 在 post_state 已補填後評估自愈品質，不阻塞主流程
        # 外層 try/except 確保任何 validator 失敗不影響 verify() 返回值
        try:
            await _run_self_healing_validator(
                incident_id=incident_id,
                snapshot=snapshot,
                pre_state=pre_state,
                post_state=post_state,
                verification_result=result,
                action_taken=action_taken,
            )
        except Exception:
            logger.warning(
                "self_healing_validator_uncaught",
                incident_id=incident_id,
                exc_info=True,
            )

        return result

    async def capture_pre_execution_state(
        self,
        incident: "Incident",
        snapshot: EvidenceSnapshot,
    ) -> None:
        """
        執行前快照當前狀態，寫入 snapshot.pre_execution_state。

        在 approval_execution.py 的動作執行「之前」呼叫。
        """
        incident_id = _get_incident_id(incident)
        try:
            state = await asyncio.wait_for(
                self._collect_post_state(incident),  # 同樣的抓取邏輯
                timeout=TOOL_TIMEOUT_SEC,
            )
            snapshot.pre_execution_state = state
            try:
                await snapshot.update_pre_execution(state)
            except Exception as exc:
                logger.warning(
                    "verifier_pre_state_persist_failed",
                    incident_id=incident_id,
                    snapshot_id=snapshot.snapshot_id,
                    error=str(exc),
                )
            logger.debug("verifier_pre_state_captured", incident_id=incident_id)
        except Exception:
            logger.warning("verifier_pre_state_failed", incident_id=incident_id)
            snapshot.pre_execution_state = {}
            try:
                await snapshot.update_pre_execution({})
            except Exception as exc:
                logger.warning(
                    "verifier_empty_pre_state_persist_failed",
                    incident_id=incident_id,
                    snapshot_id=snapshot.snapshot_id,
                    error=str(exc),
                )

    async def _collect_post_state(self, incident: "Incident") -> dict[str, Any]:
        """
        蒐集執行後環境狀態（K8s Pod 狀態 + 關鍵指標）。

        只選 D1（K8s 狀態）和 D3（指標）作為驗證基準線，
        其他感官維度（日誌、拓撲等）在驗證時不必要。
        """
        state: dict[str, Any] = {}
        alertname = _get_alertname(incident)
        labels = _get_labels(incident)

        # 取 D1 + D3 工具
        all_tools = self._registry.suggest_tools(alertname=alertname, incident_labels=labels)
        verify_tools = [
            t for t in all_tools
            if any(d in (SensorDimension.D1_K8S_STATE, SensorDimension.D3_METRICS)
                   for d in t.dimensions)
        ]

        params = {
            "namespace": labels.get("namespace", "awoooi-prod"),
            "pod_name": labels.get("pod", labels.get("name", "")),
            "deployment": labels.get("deployment", ""),
            "host": labels.get("instance", "").split(":")[0] or labels.get("host", ""),
            "container_name": _extract_container_name(labels),
            "filter_name": _extract_container_name(labels),
            "query": _build_prometheus_query(alertname, labels),
        }

        async def _call_one(reg) -> tuple[str, Any]:
            try:
                audited_params = with_mcp_audit_context(
                    params,
                    session_id=f"incident:{_get_incident_id(incident)}:post_execution",
                    incident_id=_get_incident_id(incident),
                    flywheel_node="verify",
                    agent_role="post_execution_verifier",
                )
                result = await asyncio.wait_for(
                    self._execute_tool(
                        reg=reg,
                        tool_name=reg.tool.name,
                        audited_params=audited_params,
                        incident_id=_get_incident_id(incident),
                    ),
                    timeout=TOOL_TIMEOUT_SEC,
                )
                if result.success and result.output:
                    return reg.tool.name, result.output
            except Exception:
                pass
            return reg.tool.name, None

        results = await asyncio.gather(*[_call_one(t) for t in verify_tools])
        for tool_name, output in results:
            if output is not None:
                if isinstance(output, dict):
                    state[tool_name] = sanitize_dict_values(output, f"post_state.{tool_name}")
                else:
                    state[tool_name] = {"raw": sanitize(str(output), f"post_state.{tool_name}")}

        return state

    async def _execute_tool(
        self,
        reg: RegisteredTool,
        tool_name: str,
        audited_params: dict[str, Any],
        incident_id: str,
    ):
        """Route production post-execution sensors through AwoooP MCP Gateway.

        Raw providers are still used by unit tests and manual injections. In
        production the registry wraps providers in `AuditedMCPToolProvider`, and
        those calls must leave first-class gateway audit rows just like the
        pre-decision sense path.
        """
        if not isinstance(reg.provider, AuditedMCPToolProvider):
            return await reg.provider.execute(tool_name, audited_params)

        async with get_db_context("awoooi") as db:
            ctx = GatewayContext(
                project_id="awoooi",
                agent_id="post_execution_verifier",
                tool_name=tool_name,
                trace_id=incident_id,
                is_shadow=True,
                environment={"env": "prod"},
                required_scope="read",
            )
            return await McpGateway(db).call(ctx, audited_params)


# ─────────────────────────────────────────────────────────────────────────────
# W2 PR-V1: SelfHealingValidator 串接
# 2026-04-28 ogt + Claude Sonnet 4.6: C6 飛輪斷鏈修復
# ─────────────────────────────────────────────────────────────────────────────

async def _run_self_healing_validator(
    incident_id: str,
    snapshot: EvidenceSnapshot | None,
    pre_state: dict[str, Any] | None,
    post_state: dict[str, Any],
    verification_result: str,
    action_taken: str,
) -> None:
    """
    SelfHealingValidator 串接入口。

    Feature gate: ENABLE_SELF_HEALING_VALIDATOR（預設 False）。
    驗證失敗全程 try/except 保護，不影響主流程。

    評估後：
    - 補填 snapshot.self_healing_score + self_healing_detail
    - score < 0.5 → 發送 Telegram rollback 提案警示
    """
    try:
        from src.core.config import get_settings
        _settings = get_settings()
        if not _settings.ENABLE_SELF_HEALING_VALIDATOR:
            return

        assessment = _shv_module.assess_self_healing(
            pre_state=pre_state,
            post_state=post_state,
            verification_result=verification_result,
            action_taken=action_taken,
        )
        score: float = assessment["score"]

        logger.info(
            "self_healing_assessed",
            incident_id=incident_id,
            score=score,
            regressions=assessment.get("regressions", []),
            root_cause_cleared=assessment.get("root_cause_cleared"),
            detail=assessment.get("detail"),
        )

        # 補填 EvidenceSnapshot
        if snapshot:
            try:
                await snapshot.update_self_healing(score=score, detail=assessment)
            except Exception as _snap_err:
                logger.warning(
                    "self_healing_snapshot_update_failed",
                    incident_id=incident_id,
                    error=str(_snap_err),
                )

        # score < 0.5 → Telegram rollback 提案警示
        if score < 0.5:
            await _send_rollback_proposal_alert(
                incident_id=incident_id,
                score=score,
                assessment=assessment,
                action_taken=action_taken,
            )

    except Exception:
        logger.warning(
            "self_healing_validator_error",
            incident_id=incident_id,
            exc_info=True,
        )


async def _send_rollback_proposal_alert(
    incident_id: str,
    score: float,
    assessment: dict[str, Any],
    action_taken: str,
) -> None:
    """
    自愈品質分數 < 0.5 時，發送 Telegram rollback 提案警示。

    不自動執行 rollback，僅通知人工評估。
    """
    try:
        from src.core.config import get_settings
        from src.services.telegram_gateway import get_telegram_gateway
        _settings = get_settings()
        gateway = get_telegram_gateway()

        regressions = assessment.get("regressions", [])
        reg_str = ", ".join(regressions[:5]) if regressions else "無"
        root_cleared = "是" if assessment.get("root_cause_cleared") else "否"

        text = (
            f"⚠️ <b>自愈品質警示 — 建議人工評估 Rollback</b>\n"
            f"Incident: <code>{incident_id}</code>\n"
            f"動作: <code>{action_taken[:120]}</code>\n"
            f"自愈分數: <b>{score:.2f}</b> (門檻 0.5)\n"
            f"Root Cause 解除: {root_cleared}\n"
            f"Regression 信號: {reg_str}\n"
            f"<i>此為提案，不會自動執行 Rollback</i>"
        )

        target_chat_id = _settings.SRE_GROUP_CHAT_ID
        await gateway._send_request(
            "sendMessage",
            {
                "chat_id": target_chat_id,
                "text": text,
                "parse_mode": "HTML",
            },
        )
        logger.info(
            "rollback_proposal_sent",
            incident_id=incident_id,
            score=score,
        )
    except Exception:
        logger.warning(
            "rollback_proposal_send_failed",
            incident_id=incident_id,
            exc_info=True,
        )


# ─────────────────────────────────────────────────────────────────────────────
# Recovery Assessment
# ─────────────────────────────────────────────────────────────────────────────

def _assess_recovery(
    pre_state: dict[str, Any] | None,
    post_state: dict[str, Any],
    action_taken: str,
) -> str:
    """
    評估修復效果。

    Phase 1 使用啟發式規則（基於 K8s Pod 狀態字串判斷）。
    Phase 4 將改用動態基線（Holt-Winters 偏差量），不再用靜態閾值。

    Heuristics（Phase 1 版本）：
    - post_state 含 Running → success
    - post_state 含 CrashLoopBackOff / Error / OOMKilled → failed
    - post_state 為空（MCP 無回應）→ degraded
    - pre_state 與 post_state 完全相同 → degraded（未改變）
    """
    if not post_state:
        return "degraded"

    # 轉為字串做啟發式掃描
    post_str = str(post_state).lower()
    pre_str = str(pre_state).lower() if pre_state else ""

    # 失敗信號（Gate 1 fix: 移除裸 "error" — 會誤觸 error_rate/error_count 等指標 key）
    # "error" 作為 K8s ContainerState reason 由 "failed" Pod phase 間接覆蓋
    failure_signals = ["crashloopbackoff", "oomkilled", "oomkill", "failed"]
    if any(sig in post_str for sig in failure_signals):
        return "failed"

    # 成功信號
    success_signals = [
        "running",
        "ready",
        "1/1",
        "2/2",
        "3/3",
        "healthy",
        "successfully rolled out",
        "'success': true",
        '"success": true',
    ]
    has_success_signal = any(sig in post_str for sig in success_signals)
    if not has_success_signal and _docker_state_indicates_running(post_str):
        has_success_signal = True

    if has_success_signal:
        if _is_observe_only_action(action_taken):
            return "degraded"
        # 但如果 pre_state 已經是 running，可能是無效操作
        if pre_str and any(sig in pre_str for sig in success_signals):
            # 如果執行的是 restart，即使 pre/post 都 Running 也算 success
            if "restart" in action_taken.lower() or "delete" in action_taken.lower():
                return "success"
            return "degraded"
        return "success"

    # 前後無變化
    if pre_str and post_str == pre_str:
        return "degraded"

    return "degraded"


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

def _get_incident_id(incident: "Incident") -> str:
    return incident.incident_id if hasattr(incident, "incident_id") else str(incident.id)


def _get_alertname(incident: "Incident") -> str:
    if incident.signals:
        signal = incident.signals[0]
        labels = signal.labels or {}
        return labels.get("alertname", "") or getattr(signal, "alert_name", "")
    return ""


def _get_labels(incident: "Incident") -> dict[str, Any]:
    if incident.signals:
        return incident.signals[0].labels or {}
    return {}


def _extract_container_name(labels: dict[str, Any]) -> str:
    """Resolve Docker container target labels for post-execution SSH sensors."""
    for key in ("filter_name", "container_name", "container", "resource", "name"):
        value = str(labels.get(key) or "").strip()
        if value and "{" not in value and "}" not in value:
            return value
    return ""


def _is_observe_only_action(action_taken: str) -> bool:
    """Return true when the executed step collected evidence but did not mutate state."""
    lowered = (action_taken or "").lower()
    observe_tokens = (
        "mcp:ssh_diagnose",
        "ssh_diagnose",
        "docker stats",
        "ps aux",
        "free -h",
        "df -h",
    )
    mutation_tokens = (
        "restart",
        "delete",
        "rollout",
        "scale",
        "patch",
        "apply",
        "prune",
        "truncate",
        "clear",
    )
    return any(token in lowered for token in observe_tokens) and not any(
        token in lowered for token in mutation_tokens
    )


def _docker_state_indicates_running(post_str: str) -> bool:
    """Recognize Docker-specific healthy/running output without matching uptime."""
    if not any(token in post_str for token in ("docker ps", "docker inspect", "docker stats")):
        return False
    return bool(
        re.search(r'\bup\s+\d', post_str)
        or re.search(r'["\']status["\']\s*:\s*["\']running["\']', post_str)
        or re.search(r'["\']running["\']\s*:\s*true', post_str)
    )


def _build_prometheus_query(alertname: str, labels: dict[str, Any]) -> str:
    """Build a non-empty PromQL probe for post-execution metric sensors."""

    alert = (alertname or "").lower()
    namespace = _safe_label_value(str(labels.get("namespace") or "awoooi-prod"))
    pod_name = _safe_label_value(str(labels.get("pod") or labels.get("name") or ""))
    host = _safe_label_value(str(labels.get("host") or _short_instance(labels.get("instance")) or ""))
    container = _safe_label_value(str(
        labels.get("container_name")
        or labels.get("container")
        or labels.get("name")
        or ""
    ))

    if alert.startswith("dockercontainer"):
        selector = _selector({
            "host": host,
            "container_name": container,
        })
        if "memory" in alert:
            return (
                f"docker_container_memory_usage_bytes{selector} / "
                f"docker_container_memory_limit_bytes{selector}"
            )
        if "restart" in alert:
            return (
                f"increase(docker_container_inspect_restart_count{selector}[15m]) "
                f"or increase(docker_container_restart_count{selector}[15m])"
            )
        if "cpu" in alert:
            return f"docker_container_cpu_cores{selector}"
        return f"docker_container_info{selector}"

    if any(key in alert for key in ("host", "node")):
        instance_selector = _selector({"instance": str(labels.get("instance") or "")})
        if "memory" in alert or "oom" in alert:
            return f"node_memory_MemAvailable_bytes{instance_selector}"
        if "disk" in alert or "storage" in alert:
            return f"node_filesystem_avail_bytes{instance_selector}"
        if "load" in alert or "cpu" in alert:
            return f"node_load5{instance_selector}"
        return f"up{instance_selector}"

    pod_filter = f',pod=~"{pod_name}.*"' if pod_name else ""
    if any(key in alert for key in ("memory", "mem", "oom")):
        return (
            f'avg(container_memory_working_set_bytes{{namespace="{namespace}"{pod_filter}}}) '
            "/ 1048576"
        )
    if any(key in alert for key in ("cpu", "load", "throttl")):
        return f'avg(rate(container_cpu_usage_seconds_total{{namespace="{namespace}"{pod_filter}}}[5m]))'
    if any(key in alert for key in ("crash", "restart", "backoff")):
        return f'sum(increase(kube_pod_container_status_restarts_total{{namespace="{namespace}"}}[15m]))'
    if any(key in alert for key in ("http", "error", "5xx", "probe", "down", "unhealthy")):
        return "1 - avg(probe_success)"
    return f'up{{namespace="{namespace}"}}'


def _selector(labels: dict[str, str]) -> str:
    parts = [f'{key}="{value}"' for key, value in labels.items() if value]
    return "{" + ",".join(parts) + "}" if parts else ""


def _short_instance(instance: Any) -> str:
    raw = str(instance or "")
    if raw.count(":") == 1:
        return raw.rsplit(":", 1)[0]
    return raw


def _safe_label_value(value: str) -> str:
    cleaned = (value or "").strip()
    if re.fullmatch(r"[a-zA-Z0-9_.:-]{1,128}", cleaned):
        return cleaned
    return ""


async def _update_snapshot(
    snapshot: EvidenceSnapshot,
    post_state: dict[str, Any],
    result: str,
) -> None:
    """補填 EvidenceSnapshot 的 post_execution_state + verification_result。"""
    try:
        await snapshot.update_post_execution(post_state, result)
    except Exception:
        logger.exception("verifier_snapshot_update_failed", snapshot_id=snapshot.snapshot_id)


async def _persist_fallback_snapshot(
    *,
    incident: "Incident",
    post_state: dict[str, Any],
    result: str,
    action_taken: str,
) -> None:
    """
    Persist verifier outcome even when the pre-decision snapshot is unavailable.

    Live T14 evidence showed auto_repair rows with verifier decisions in logs but
    NULL incident_evidence.verification_result because verify(snapshot=None) had
    no durable target. This fallback makes the verification gate auditable without
    pretending a pre-execution baseline existed.
    """
    incident_id = _get_incident_id(incident)
    try:
        snapshot = EvidenceSnapshot(incident_id=incident_id)
        snapshot.post_execution_state = post_state
        snapshot.verification_result = result
        snapshot.matched_playbook_id = _extract_playbook_id(action_taken)
        snapshot.sensors_attempted = 1
        snapshot.sensors_succeeded = 1 if post_state else 0
        snapshot.mcp_health = {"post_execution_verifier": bool(post_state)}
        snapshot.evidence_summary = (
            "[PostExecutionVerifier] fallback verification snapshot; "
            f"action={action_taken[:160]}; result={result}; "
            "pre_execution_state=missing"
        )
        await snapshot.save()
        logger.info(
            "verifier_fallback_snapshot_saved",
            incident_id=incident_id,
            snapshot_id=snapshot.snapshot_id,
            result=result,
            action=action_taken,
        )
    except Exception:
        logger.warning(
            "verifier_fallback_snapshot_save_failed",
            incident_id=incident_id,
            result=result,
            exc_info=True,
        )


def _extract_playbook_id(action_taken: str) -> str | None:
    for prefix in ("auto_repair_playbook:", "auto_repair:"):
        if action_taken.startswith(prefix):
            playbook_id = action_taken.removeprefix(prefix).split(":", 1)[0].strip()
            return playbook_id or None
    return None


# ─────────────────────────────────────────────────────────────────────────────
# Singleton
# ─────────────────────────────────────────────────────────────────────────────

_verifier: PostExecutionVerifier | None = None


def get_post_execution_verifier() -> PostExecutionVerifier:
    """取得 PostExecutionVerifier Singleton。"""
    global _verifier
    if _verifier is None:
        _verifier = PostExecutionVerifier()
    return _verifier