"""
AI Decision Models - Phase 2 Structured Output
===============================================
CAI-101: OpenClaw AI 結構化輸出模型

防禦性工程鐵律:
- 絕對禁止 LLM 輸出無法解析的自由文本
- 必須強制 JSON 格式 + Pydantic 驗證
- blast_radius 為 REQUIRED 欄位，不可遺漏
"""

from enum import Enum

from pydantic import BaseModel, Field, field_validator


class SuggestedAction(str, Enum):
    """
    AI 建議操作類型

    必須與 executor.OperationType 對應

    2026-04-17 ogt + Claude Sonnet 4.6: 新增 INVESTIGATE/APPLY_HPA/TUNE_RESOURCES
    根本原因: prompts.py 列了 6 個值，但 enum 只有 4 個
    → NemoTron 輸出 "investigate" → Pydantic 爆炸 → analysis_result = None → 全部 fallback
    """
    RESTART_DEPLOYMENT = "RESTART_DEPLOYMENT"
    DELETE_POD = "DELETE_POD"
    SCALE_DEPLOYMENT = "SCALE_DEPLOYMENT"
    APPLY_HPA = "APPLY_HPA"
    TUNE_RESOURCES = "TUNE_RESOURCES"
    INVESTIGATE = "INVESTIGATE"   # 調查診斷，不下執行指令
    OBSERVE = "OBSERVE"           # 觀察等待
    NO_ACTION = "NO_ACTION"       # 無需處理


class AIRiskLevel(str, Enum):
    """AI 風險評估等級"""
    LOW = "low"
    MEDIUM = "medium"
    CRITICAL = "critical"


class AIDataImpact(str, Enum):
    """AI 資料影響評估"""
    NONE = "NONE"
    READ_ONLY = "READ_ONLY"
    WRITE = "WRITE"
    DESTRUCTIVE = "DESTRUCTIVE"


class AIBlastRadius(BaseModel):
    """
    爆炸半徑分析 (REQUIRED - 符合 API 契約)

    此物件為必填，LLM 輸出必須包含完整結構
    """
    affected_pods: int = Field(
        ...,
        ge=0,
        description="受影響的 Pod 數量",
    )
    estimated_downtime: str = Field(
        ...,
        description="預估停機時間 (例如: '~30s', '~2 min', '0')",
    )
    related_services: list[str] = Field(
        default_factory=list,
        description="相關受影響服務",
    )
    data_impact: AIDataImpact = Field(
        default=AIDataImpact.NONE,
        description="資料影響程度",
    )

    @field_validator("data_impact", mode="before")
    @classmethod
    def normalize_data_impact(cls, v):
        """正規化 data_impact (LLM 可能輸出小寫)"""
        if isinstance(v, str):
            return v.upper()
        return v


class OpenClawDecision(BaseModel):
    """
    OpenClaw AI 決策輸出 (強制結構化)

    LLM 必須輸出此格式的 JSON，否則視為解析失敗。
    blast_radius 為 REQUIRED 欄位！
    """
    # === 基本操作欄位 ===
    suggested_action: SuggestedAction = Field(
        ...,
        description="建議執行的操作類型",
    )

    target_resource: str = Field(
        ...,
        description="目標資源名稱 (e.g., 'harbor', 'grafana')",
    )
    namespace: str = Field(
        default="default",
        description="Kubernetes namespace",
    )
    # 2026-03-29 ogt: 允許 None，LLM 可能返回 null
    kubectl_command: str | None = Field(
        default="",
        description="具體的 kubectl 指令",
    )

    @field_validator("kubectl_command", mode="before")
    @classmethod
    def normalize_kubectl_command(cls, v):
        """將 null 轉換為空字串"""
        return v if v is not None else ""

    # === 風險評估欄位 ===
    risk_level: AIRiskLevel = Field(
        ...,
        description="風險等級評估",
    )

    # === REQUIRED: 爆炸半徑 (符合 API 契約) ===
    blast_radius: AIBlastRadius = Field(
        ...,
        description="爆炸半徑分析 - REQUIRED",
    )

    # === 分析說明欄位 ===
    action_title: str = Field(
        default="",
        description="操作標題 (繁體中文)",
    )
    description: str = Field(
        default="",
        description="根本原因分析說明 (繁體中文)",
    )
    reasoning: str = Field(
        default="",
        description="給人類主管看的決策理由 (繁體中文)",
    )
    deviation_analysis: str = Field(
        default="",
        description="基準線偏差分析 (例如：CPU 85% 超出基準線 45% 達 +4σ)",
    )

    # === 信心度與影響範圍 ===
    # 2026-03-29 ogt: 移除預設值，強制 LLM 必須輸出真實信心分數
    # 如果 LLM 沒有輸出 confidence，解析時會補 0.5 並標記為 COLLAB
    confidence: float = Field(
        ...,  # REQUIRED - 不允許預設值
        ge=0.0,
        le=1.0,
        description="決策信心度 (0-1) - LLM 必須計算",
    )
    affected_services: list[str] = Field(
        default_factory=list,
        description="可能受影響的相關服務",
    )

    # === v6.0 AI 仲裁欄位 ===
    primary_responsibility: str = Field(
        default="COLLAB",
        description="主要責任團隊 (FE/BE/INFRA/DB/COLLAB)",
    )
    responsibility_reasoning: str = Field(
        default="",
        description="責任判定理由",
    )
    secondary_teams: list[str] = Field(
        default_factory=list,
        description="需協助的其他團隊",
    )

    # === v7.0 調優建議與 SignOz 整合 ===
    optimization_suggestions: list[dict] = Field(
        default_factory=list,
        description="預防性調優建議 (含 kubectl 指令)",
    )
    signoz_correlation: str = Field(
        default="",
        description="SignOz 指標與告警的關聯分析",
    )

    @field_validator("risk_level", mode="before")
    @classmethod
    def normalize_risk_level(cls, v):
        """正規化 risk_level (處理 LLM 可能輸出的非標準值)"""
        if isinstance(v, str):
            mapping = {
                "high": "critical",
                "severe": "critical",
                "warning": "medium",
                "normal": "low",
                "safe": "low",
            }
            return mapping.get(v.lower(), v.lower())
        return v

    @field_validator("suggested_action", mode="before")
    @classmethod
    def normalize_suggested_action(cls, v):
        """
        正規化 suggested_action：大小寫 + 別名映射 + 未知值 fallback

        2026-04-17 ogt + Claude Sonnet 4.6（亞太）:
        根本原因: NemoTron 輸出 "investigate"(小寫) → Pydantic 拒絕 → analysis_result = None
        舊版只做 uppercase，未知值仍爆 → 修復為: 先 uppercase，再別名映射，最後 fallback NO_ACTION
        """
        if isinstance(v, str):
            normalized = v.upper().replace("-", "_").replace(" ", "_")
            # 別名映射 (LLM 可能輸出非正式名稱)
            alias_map = {
                "DIAGNOSE": "INVESTIGATE",
                "DEBUG": "INVESTIGATE",
                "MONITOR": "OBSERVE",
                "WATCH": "OBSERVE",
                "TUNE": "TUNE_RESOURCES",
                "HPA": "APPLY_HPA",
            }
            normalized = alias_map.get(normalized, normalized)
            # 未知值 fallback NO_ACTION，絕不讓 Pydantic 爆炸導致 analysis_result = None
            try:
                SuggestedAction(normalized)
                return normalized
            except ValueError:
                return "NO_ACTION"
        return v


class OpenClawAnalysisRequest(BaseModel):
    """分析請求"""
    force_refresh: bool = Field(
        default=False,
        description="強制重新抓取監控數據",
    )


class OpenClawAnalysisResponse(BaseModel):
    """分析回應"""
    success: bool
    message: str
    decision: OpenClawDecision | None = None
    approval_created: bool = Field(
        default=False,
        description="是否已建立待簽核卡片",
    )
    approval_id: str | None = Field(
        default=None,
        description="建立的 ApprovalRecord ID",
    )
    ai_provider: str = Field(
        default="unknown",
        description="使用的 AI 提供者 (ollama/gemini/claude)",
    )
    raw_llm_response: str | None = Field(
        default=None,
        description="LLM 原始回應 (debug 用)",
    )