"""
Blast Radius Agent - 影響範圍分析專家
======================================

職責:
- 評估操作的影響範圍
- 識別受影響的服務和依賴
- 估計使用者影響人數
- 回傳影響等級 (low/medium/high/critical)

符合 ADR-009 BlastRadiusAgent 規範
"""

import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Any

import structlog

from src.agents.base import AgentResult, AgentStatus, BaseAgent

logger = structlog.get_logger(__name__)


# =============================================================================
# Blast Radius Types
# =============================================================================


class ImpactLevel(str, Enum):
    """影響等級"""
    LOW = "low"           # 單一服務，<100 用戶
    MEDIUM = "medium"     # 2-5 服務，100-1000 用戶
    HIGH = "high"         # 5-10 服務，1000-10000 用戶
    CRITICAL = "critical" # >10 服務，>10000 用戶或核心服務


@dataclass
class AffectedService:
    """受影響服務"""
    name: str
    impact_type: str  # direct, indirect, transitive
    confidence: float
    reason: str

    def to_dict(self) -> dict[str, Any]:
        return {
            "name": self.name,
            "impact_type": self.impact_type,
            "confidence": self.confidence,
            "reason": self.reason,
        }


@dataclass
class BlastRadiusResult(AgentResult):
    """
    BlastRadiusAgent 分析結果

    額外欄位:
    - impact_level: 影響等級 (low/medium/high/critical)
    - affected_services: 受影響服務列表
    - estimated_users: 估計影響用戶數
    - dependency_chain: 依賴鏈
    - recovery_time_estimate: 預估恢復時間 (分鐘)
    """
    impact_level: ImpactLevel = ImpactLevel.LOW
    affected_services: list[AffectedService] = field(default_factory=list)
    estimated_users: int = 0
    dependency_chain: list[str] = field(default_factory=list)
    recovery_time_estimate: int = 0

    def to_dict(self) -> dict[str, Any]:
        """轉換為 dict"""
        base = super().to_dict()
        base.update({
            "impact_level": self.impact_level.value,
            "affected_services": [s.to_dict() for s in self.affected_services],
            "estimated_users": self.estimated_users,
            "dependency_chain": self.dependency_chain,
            "recovery_time_estimate": self.recovery_time_estimate,
        })
        return base


# =============================================================================
# Service Dependency Graph (簡化版)
# =============================================================================


# AWOOOI 服務依賴圖 (簡化版，實際應從 GraphRAG 讀取)
SERVICE_DEPENDENCIES: dict[str, dict[str, Any]] = {
    # === Core Services ===
    "api": {
        "dependencies": ["postgres", "redis", "openclaw"],
        "dependents": ["web", "telegram-gateway"],
        "criticality": "critical",
        "estimated_users": 5000,
    },
    "web": {
        "dependencies": ["api"],
        "dependents": [],
        "criticality": "high",
        "estimated_users": 3000,
    },
    "openclaw": {
        "dependencies": ["redis", "ollama"],
        "dependents": ["api"],
        "criticality": "critical",
        "estimated_users": 5000,
    },

    # === Infrastructure ===
    "postgres": {
        "dependencies": [],
        "dependents": ["api", "signoz"],
        "criticality": "critical",
        "estimated_users": 10000,
    },
    "redis": {
        "dependencies": [],
        "dependents": ["api", "openclaw", "signal-worker"],
        "criticality": "critical",
        "estimated_users": 8000,
    },
    "ollama": {
        "dependencies": [],
        "dependents": ["openclaw"],
        "criticality": "high",
        "estimated_users": 2000,
    },

    # === Workers ===
    "signal-worker": {
        "dependencies": ["redis", "api"],
        "dependents": [],
        "criticality": "medium",
        "estimated_users": 500,
    },
    "telegram-gateway": {
        "dependencies": ["api"],
        "dependents": [],
        "criticality": "medium",
        "estimated_users": 1000,
    },

    # === Observability ===
    "signoz": {
        "dependencies": ["postgres"],
        "dependents": [],
        "criticality": "low",
        "estimated_users": 100,
    },
    "prometheus": {
        "dependencies": [],
        "dependents": [],
        "criticality": "low",
        "estimated_users": 50,
    },
}


class BlastRadiusAgent(BaseAgent[BlastRadiusResult]):
    """
    影響範圍分析專家 Agent

    分析流程:
    1. 識別直接影響的服務
    2. 遍歷依賴圖找出間接影響
    3. 計算總影響用戶數
    4. 判定影響等級

    使用方式:
    ```python
    agent = BlastRadiusAgent()
    result = await agent.analyze({
        "target_service": "api",
        "action": "kubectl rollout restart",
        "namespace": "awoooi-prod",
    })
    print(result.impact_level)  # ImpactLevel.CRITICAL
    ```
    """

    AGENT_NAME = "blast-radius"
    AGENT_DESCRIPTION = "影響範圍分析師，評估相依服務與影響範圍"
    AGENT_TOOLS = ["Read", "Glob", "Grep"]

    def __init__(
        self,
        timeout_sec: float = 30.0,
        dependency_graph: dict[str, dict[str, Any]] | None = None,
    ):
        """
        初始化 BlastRadiusAgent

        Args:
            timeout_sec: 執行超時時間
            dependency_graph: 自訂依賴圖 (測試用)
        """
        super().__init__(timeout_sec)
        self.dependency_graph = dependency_graph or SERVICE_DEPENDENCIES

    async def analyze(self, context: dict[str, Any]) -> BlastRadiusResult:
        """
        執行影響範圍分析

        Args:
            context: 分析上下文
                - target_service: 目標服務 (可以是列表)
                - action: 執行的操作
                - namespace: 命名空間

        Returns:
            BlastRadiusResult 包含影響等級和詳細分析
        """
        start_time = time.time()

        self.logger.info(
            "blast_radius_analysis_start",
            target=context.get("target_service"),
            action=context.get("action", "")[:50],
        )

        try:
            # 取得目標服務列表
            target_services = context.get("target_service", [])
            if isinstance(target_services, str):
                target_services = [target_services]

            # 分析每個目標服務的影響
            all_affected: list[AffectedService] = []
            total_users = 0
            dependency_chain: list[str] = []

            for target in target_services:
                affected, users, chain = self._analyze_service_impact(target)
                all_affected.extend(affected)
                total_users = max(total_users, users)  # 取最大值避免重複計算
                dependency_chain.extend(chain)

            # 去重
            seen_services = set()
            unique_affected: list[AffectedService] = []
            for svc in all_affected:
                if svc.name not in seen_services:
                    seen_services.add(svc.name)
                    unique_affected.append(svc)

            # 判定影響等級
            impact_level = self._calculate_impact_level(
                len(unique_affected),
                total_users,
                unique_affected,
            )

            # 估計恢復時間
            recovery_time = self._estimate_recovery_time(impact_level, len(unique_affected))

            latency_ms = int((time.time() - start_time) * 1000)

            # 生成分析摘要
            analysis = self._generate_analysis(
                impact_level,
                len(unique_affected),
                total_users,
            )

            result = BlastRadiusResult(
                agent_name=self.AGENT_NAME,
                status=AgentStatus.SUCCESS,
                confidence=0.85,  # 基於依賴圖的信心分數
                analysis=analysis,
                latency_ms=latency_ms,
                impact_level=impact_level,
                affected_services=unique_affected,
                estimated_users=total_users,
                dependency_chain=list(set(dependency_chain)),
                recovery_time_estimate=recovery_time,
            )

            self.logger.info(
                "blast_radius_analysis_complete",
                impact_level=impact_level.value,
                affected_count=len(unique_affected),
                estimated_users=total_users,
                latency_ms=latency_ms,
            )

            return result

        except Exception as e:
            latency_ms = int((time.time() - start_time) * 1000)

            self.logger.exception(
                "blast_radius_analysis_error",
                error=str(e),
            )

            return BlastRadiusResult(
                agent_name=self.AGENT_NAME,
                status=AgentStatus.FAILED,
                confidence=0.0,
                analysis=f"分析失敗: {str(e)}",
                latency_ms=latency_ms,
                error=str(e),
                impact_level=ImpactLevel.CRITICAL,  # 失敗時假設最大影響
            )

    def _analyze_service_impact(
        self,
        target_service: str,
    ) -> tuple[list[AffectedService], int, list[str]]:
        """
        分析單一服務的影響

        Returns:
            (受影響服務列表, 估計用戶數, 依賴鏈)
        """
        affected: list[AffectedService] = []
        visited: set[str] = set()
        dependency_chain: list[str] = []
        total_users = 0

        # 標準化服務名稱
        target_key = self._normalize_service_name(target_service)

        if target_key not in self.dependency_graph:
            # 未知服務，假設中等影響
            affected.append(AffectedService(
                name=target_service,
                impact_type="direct",
                confidence=0.5,
                reason="未知服務，無法確定依賴關係",
            ))
            return affected, 1000, [target_service]

        # 1. 直接影響 (目標服務本身)
        target_info = self.dependency_graph[target_key]
        affected.append(AffectedService(
            name=target_key,
            impact_type="direct",
            confidence=1.0,
            reason="目標服務",
        ))
        total_users += target_info.get("estimated_users", 0)
        dependency_chain.append(target_key)
        visited.add(target_key)

        # 2. 依賴此服務的上游 (dependents)
        self._find_dependents(
            target_key,
            affected,
            visited,
            dependency_chain,
            depth=0,
            max_depth=3,
        )

        # 計算總用戶數
        for svc in affected:
            if svc.name in self.dependency_graph:
                total_users += self.dependency_graph[svc.name].get("estimated_users", 0)

        return affected, total_users, dependency_chain

    def _find_dependents(
        self,
        service: str,
        affected: list[AffectedService],
        visited: set[str],
        chain: list[str],
        depth: int,
        max_depth: int,
    ) -> None:
        """遞迴查找依賴此服務的上游"""
        if depth >= max_depth:
            return

        if service not in self.dependency_graph:
            return

        dependents = self.dependency_graph[service].get("dependents", [])

        for dep in dependents:
            if dep in visited:
                continue

            visited.add(dep)
            chain.append(dep)

            impact_type = "indirect" if depth == 0 else "transitive"
            confidence = 0.9 - (depth * 0.1)

            affected.append(AffectedService(
                name=dep,
                impact_type=impact_type,
                confidence=confidence,
                reason=f"依賴 {service}",
            ))

            # 遞迴查找
            self._find_dependents(
                dep,
                affected,
                visited,
                chain,
                depth + 1,
                max_depth,
            )

    def _normalize_service_name(self, service: str) -> str:
        """標準化服務名稱"""
        # 移除常見後綴
        service = service.lower()
        for suffix in ["-deployment", "-svc", "-service", "-pod"]:
            if service.endswith(suffix):
                service = service[: -len(suffix)]

        # 處理常見別名
        aliases = {
            "awoooi-api": "api",
            "awoooi-web": "web",
            "nginx": "web",
            "frontend": "web",
            "backend": "api",
            "database": "postgres",
            "db": "postgres",
            "cache": "redis",
        }

        return aliases.get(service, service)

    def _calculate_impact_level(
        self,
        service_count: int,
        user_count: int,
        affected: list[AffectedService],
    ) -> ImpactLevel:
        """計算影響等級"""
        # 檢查是否有 critical 服務
        has_critical = any(
            svc.name in self.dependency_graph
            and self.dependency_graph[svc.name].get("criticality") == "critical"
            for svc in affected
        )

        if has_critical or service_count > 10 or user_count > 10000:
            return ImpactLevel.CRITICAL

        if service_count > 5 or user_count > 1000:
            return ImpactLevel.HIGH

        if service_count > 2 or user_count > 100:
            return ImpactLevel.MEDIUM

        return ImpactLevel.LOW

    def _estimate_recovery_time(
        self,
        impact_level: ImpactLevel,
        service_count: int,
    ) -> int:
        """估計恢復時間 (分鐘)"""
        base_time = {
            ImpactLevel.LOW: 5,
            ImpactLevel.MEDIUM: 15,
            ImpactLevel.HIGH: 30,
            ImpactLevel.CRITICAL: 60,
        }

        # 每多一個服務增加 5 分鐘
        return base_time[impact_level] + (service_count * 5)

    def _generate_analysis(
        self,
        impact_level: ImpactLevel,
        service_count: int,
        user_count: int,
    ) -> str:
        """生成分析摘要"""
        level_desc = {
            ImpactLevel.LOW: "低影響",
            ImpactLevel.MEDIUM: "中等影響",
            ImpactLevel.HIGH: "高影響",
            ImpactLevel.CRITICAL: "嚴重影響",
        }

        return (
            f"{level_desc[impact_level]}: "
            f"影響 {service_count} 個服務，預估 {user_count:,} 用戶受影響"
        )

    def _build_prompt(self, context: dict[str, Any]) -> str:
        """建構 LLM Prompt (Phase 9.4 擴展)"""
        return f"""你是 AWOOOI 的影響範圍分析師。
分析以下操作的影響範圍：

目標服務: {context.get("target_service", "N/A")}
操作: {context.get("action", "N/A")}
命名空間: {context.get("namespace", "N/A")}

評估:
1. 直接影響的服務
2. 間接相依的服務
3. 使用者影響人數估計

輸出 JSON:
```json
{{
  "impact_level": "low|medium|high|critical",
  "affected_services": [
    {{"name": "...", "impact_type": "direct|indirect", "reason": "..."}}
  ],
  "estimated_users": 0,
  "dependency_chain": ["service1", "service2"],
  "analysis": "一句話摘要",
  "confidence": 0-1
}}
```"""

    def _parse_response(self, response: str) -> dict[str, Any]:
        """解析 LLM 回應"""
        return self._extract_json(response)