"""LLM 结构化输出的确定性规则 fallback。

该类不是对真实模型的替代，只用于本地开发和测试时提供稳定输出。
真实 LLM client 需要实现相同方法。
"""

from __future__ import annotations

import re
from typing import Any

from pam_deploy_graph.constants import GLOBAL_ACTION_SEQUENCE, REQUIRED_PARAMS
from pam_deploy_graph.models import (
    ExecutionStrategy,
    LlmDeployPlan,
    LlmIntentResult,
    LlmParamResult,
)

KEY_ALIASES = {
    "home_base_url": "HOME_BASE_URL",
    "HOME_BASE_URL": "HOME_BASE_URL",
    "client_id": "CLIENT_ID",
    "CLIENT_ID": "CLIENT_ID",
    "client_secret": "CLIENT_SECRET",
    "CLIENT_SECRET": "CLIENT_SECRET",
    "airportCode": "AIRPORT_CODE",
    "AIRPORT_CODE": "AIRPORT_CODE",
    "applicationName": "APP_NAME",
    "APP_NAME": "APP_NAME",
    "moduleName": "MODULE_NAME",
    "MODULE_NAME": "MODULE_NAME",
    "versionNumber": "VERSION_NUMBER",
    "VERSION_NUMBER": "VERSION_NUMBER",
    "zipFilePath": "ZIP_FILE_PATH",
    "ZIP_FILE_PATH": "ZIP_FILE_PATH",
    "actionType": "ACTION_TYPE",
    "ACTION_TYPE": "ACTION_TYPE",
    "timeOut": "TIMEOUT",
    "TIMEOUT": "TIMEOUT",
    "logName": "LOG_NAME",
    "LOG_NAME": "LOG_NAME",
}


class RuleBasedLlmClient:
    """基于规则的轻量 LLM client fallback。"""

    def understand_request(self, text: str) -> LlmIntentResult:
        """用关键词规则识别用户意图和执行策略偏好。"""
        lowered = text.lower()
        reasons: list[str] = []
        intent = "deploy"

        if any(word in lowered for word in ("用法", "怎么用", "生成脚本", "给我脚本", "usage")):
            intent = "show_usage"
            reasons.append("用户在询问脚本用法或脚本生成")
        elif any(word in lowered for word in ("预演", "计划", "不执行", "不要动环境", "dry-run", "preview")):
            intent = "preview"
            reasons.append("用户要求只预演或不触碰环境")
        elif any(word in lowered for word in ("在线ip", "在线 ip", "查询ip", "查询 ip", "node", "工作站")):
            intent = "query_node_ips"
            reasons.append("用户要求查询 Node 或在线工作站")
        elif any(word in lowered for word in ("回滚", "rollback")):
            intent = "rollback"
            reasons.append("用户要求回滚")
        else:
            reasons.append("默认识别为部署请求")

        mode_preference = "未指定"
        strategy_preference = "未指定"
        if any(word in lowered for word in ("mcp", "在线执行", "直接在线")):
            mode_preference = "MCP"
            strategy_preference = "hybrid_node_mcp"
            reasons.append("用户倾向 MCP；PAM_HOME 仍需脚本 action")
        if any(word in lowered for word in ("脚本", "离线", "script", "shell", "powershell")):
            mode_preference = "API脚本"
            strategy_preference = "script_only"
            reasons.append("用户倾向脚本或离线执行")
        if intent == "preview":
            strategy_preference = strategy_preference if strategy_preference != "未指定" else "hybrid_node_mcp"

        return LlmIntentResult(
            intent=intent,  # type: ignore[arg-type]
            mode_preference=mode_preference,  # type: ignore[arg-type]
            strategy_preference=strategy_preference,  # type: ignore[arg-type]
            confidence=0.72 if intent != "deploy" else 0.6,
            reasons=reasons,
        )

    def extract_params(self, text: str, base_params: dict[str, Any] | None = None) -> LlmParamResult:
        """从 key=value、中文短语和 IP 地址中抽取参数。"""
        params = dict(base_params or {})
        params.update(self._extract_key_values(text))
        params.update(self._extract_chinese_patterns(text))

        control: dict[str, Any] = {}
        ips = re.findall(r"\b(?:\d{1,3}\.){3}\d{1,3}\b", text)
        if ips:
            control["user_specified_ips"] = ips

        missing = [key for key in REQUIRED_PARAMS if not params.get(key)]
        sensitive = [key for key in ("CLIENT_SECRET", "CLIENT_ID") if params.get(key)]
        return LlmParamResult(
            extracted_params=params,
            extracted_control=control,
            missing_required_params=missing,
            sensitive_fields_present=sensitive,
        )

    def generate_plan(
        self,
        *,
        params: dict[str, Any],
        intent: str,
        strategy: ExecutionStrategy,
    ) -> LlmDeployPlan:
        """生成确定性的部署计划和风险提示。"""
        if strategy == "hybrid_node_mcp":
            strategy_text = "PAM_HOME 使用脚本 action，PAM_NODE 使用 MCP"
        elif strategy == "script_only":
            strategy_text = "全部 action 使用脚本 action"
        else:
            strategy_text = "全部 action 使用 fake runner"

        summary = (
            f"计划处理 {params.get('AIRPORT_CODE', '-')}/"
            f"{params.get('APP_NAME', '-')}/"
            f"{params.get('MODULE_NAME', '-')}/"
            f"{params.get('VERSION_NUMBER', '-')}，执行策略为 {strategy_text}。"
        )
        risk_notes = [
            "真实部署前必须确认参数。",
            "发布版本、创建下载任务、升级和回滚属于高风险动作。",
            "回滚只能在用户确认后执行。",
        ]
        if strategy == "hybrid_node_mcp":
            risk_notes.append("PAM_HOME 当前没有 MCP 能力，HOME 阶段仍会调用脚本 action。")

        return LlmDeployPlan(
            summary=summary,
            risk_notes=risk_notes,
            planned_actions=list(GLOBAL_ACTION_SEQUENCE),
            requires_confirmation=intent in ("deploy", "query_node_ips", "rollback"),
            execution_strategy=strategy,
        )

    def _extract_key_values(self, text: str) -> dict[str, str]:
        """抽取 KEY=VALUE 形式的参数。"""
        params: dict[str, str] = {}
        for match in re.finditer(r"([A-Za-z_][A-Za-z0-9_]*)\s*=\s*([^\s,;]+)", text):
            raw_key, value = match.groups()
            key = KEY_ALIASES.get(raw_key)
            if key:
                params[key] = value.strip()
        return params

    def _extract_chinese_patterns(self, text: str) -> dict[str, str]:
        """抽取常见中文描述中的部署参数。"""
        patterns = {
            "AIRPORT_CODE": r"(?:机场|三字码)\s*[:：]?\s*([A-Z]{3})",
            "APP_NAME": r"(?:应用|应用名)\s*[:：]?\s*([A-Za-z0-9_.-]+)",
            "MODULE_NAME": r"(?:模块|模块名)\s*[:：]?\s*([A-Za-z0-9_.-]+)",
            "VERSION_NUMBER": r"(?:版本|版本号)\s*[:：]?\s*([A-Za-z0-9_.-]+)",
            "ZIP_FILE_PATH": r"(?:包|软件包|zip)\s*[:：]?\s*([A-Za-z]:[\\/][^\s,;]+|/[^\s,;]+)",
        }
        params: dict[str, str] = {}
        for key, pattern in patterns.items():
            match = re.search(pattern, text)
            if match:
                params[key] = match.group(1)
        return params