"""
multica_proxy.py — multica CLI 调用代理

封装 multica CLI 调用，自动带缓存和限流保护。
各 Agent 心跳脚本中用 multica_proxy 替代直接 subprocess.run(["multica",...])

依赖：rate_limiter.py（CacheManager, RequestScheduler, CoordinatedPoller）

作者：陆怀瑾（COO）
日期：2026-06-23
"""

import os
import sys
import json
import subprocess
import hashlib
from typing import Any, Dict, Optional

# 确保能找到 rate_limiter
_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
if _SCRIPT_DIR not in sys.path:
    sys.path.insert(0, _SCRIPT_DIR)

from rate_limiter import CacheManager, RequestScheduler, CoordinatedPoller, Priority

# BIZ-104 Phase ④ pre-trim 钩子（2026-08-31 集成）
# 仅在 multica comment reply 路径上接入 pre-trim，避免全部 CLI 调用都走精简
try:
    from pre_trim_multica_comment import pre_trim_for_multica_comment as _pre_trim
    _PRE_TRIM_AVAILABLE = True
except ImportError:
    _PRE_TRIM_AVAILABLE = False

# ============================================================================
# 全局单例
# ============================================================================

_cache = CacheManager()
_scheduler: Optional[RequestScheduler] = None
_poller: Optional[CoordinatedPoller] = None


def _get_scheduler() -> RequestScheduler:
    """获取或创建调度器单例"""
    global _scheduler
    if _scheduler is None:
        _scheduler = RequestScheduler(rate=40/60, capacity=40, enable_cache=True)
        _scheduler.start()
    return _scheduler


def _get_poller() -> CoordinatedPoller:
    """获取或创建统一轮询器单例"""
    global _poller
    if _poller is None:
        _poller = CoordinatedPoller(_get_scheduler(), poll_interval=15*60)
    return _poller


# ============================================================================
# 缓存查询辅助
# ============================================================================

def _make_cache_key(cmd: list) -> str:
    """为 CLI 命令生成缓存键"""
    return hashlib.md5(json.dumps(cmd, sort_keys=True).encode()).hexdigest()


def _cache_category(cmd: list) -> str:
    """根据命令推断缓存类别"""
    cmd_str = " ".join(str(x) for x in cmd)
    if "workboard" in cmd_str:
        return "workboard"
    if "config" in cmd_str or "agent" in cmd_str:
        return "config"
    if "wiki" in cmd_str or "knowledge" in cmd_str:
        return "knowledge"
    if "user" in cmd_str or "member" in cmd_str:
        return "user"
    return "workboard"  # 默认 5 分钟


# ============================================================================
# 核心代理函数
# ============================================================================

# OpenClaw 工作区 ID（全局常量）
# 用于所有 multica CLI 调用，确保隔离会话也能正确查询
_WORKSPACE_ID = "54344e11-6bb2-4d95-a5e5-c8b075a07cea"


def _inject_workspace_id(cmd: list) -> list:
    """自动注入 workspace-id 到 multica CLI 命令"""
    if len(cmd) >= 2 and cmd[0] == "multica" and "--workspace-id" not in cmd:
        # 插入在命令和子命令之后、标志之前
        insert_idx = 1
        while insert_idx < len(cmd) and not cmd[insert_idx].startswith("--"):
            insert_idx += 1
        new_cmd = cmd[:insert_idx] + ["--workspace-id", _WORKSPACE_ID] + cmd[insert_idx:]
        return new_cmd
    return cmd


def run_multica(cmd: list, use_cache: bool = True, timeout: int = 30) -> Dict[str, Any]:
    """
    执行 multica CLI 命令（带缓存和限流）

    参数：
        cmd: 命令列表，如 ["multica", "issue", "list", "--output", "json"]
        use_cache: 是否使用缓存
        timeout: 超时时间（秒）

    返回：
        {"success": bool, "data": Any, "from_cache": bool, "error": str|None}
    """
    # 自动注入 workspace-id，确保隔离会话正确查询
    cmd = _inject_workspace_id(cmd)
    category = _cache_category(cmd)

    # 1. 尝试从缓存获取
    if use_cache:
        cached = _cache.get(category, cmd)
        if cached is not None:
            return {"success": True, "data": cached, "from_cache": True, "error": None}

    # 2. 执行 CLI 命令
    try:
        result = subprocess.run(
            cmd,
            capture_output=True,
            text=True,
            timeout=timeout
        )

        if result.returncode != 0:
            error_msg = result.stderr.strip() or f"Exit code {result.returncode}"
            return {"success": False, "data": None, "from_cache": False, "error": error_msg}

        # 尝试解析 JSON
        try:
            data = json.loads(result.stdout)
        except json.JSONDecodeError:
            data = result.stdout.strip()

        # 3. 写入缓存
        if use_cache:
            _cache.set(category, cmd, data)

        return {"success": True, "data": data, "from_cache": False, "error": None}

    except subprocess.TimeoutExpired:
        return {"success": False, "data": None, "from_cache": False, "error": f"Command timed out after {timeout}s"}
    except Exception as e:
        return {"success": False, "data": None, "from_cache": False, "error": str(e)}


def run_openclaw_workboard(cmd: list, use_cache: bool = True, timeout: int = 30) -> Dict[str, Any]:
    """
    执行 openclaw workboard CLI 命令（带缓存）

    参数同 run_multica
    """
    return run_multica(cmd, use_cache=use_cache, timeout=timeout)


# ============================================================================
# 便捷函数：心跳脚本中直接替换
# ============================================================================

def multica_issue_list_my_todo(assignee_id: str) -> Dict[str, Any]:
    """
    获取分配给我的待办 Issue 列表
    替代: multica issue list --assignee-id <id> --status todo --output json
    """
    return run_multica([
        "multica", "issue", "list",
        "--assignee-id", assignee_id,
        "--status", "todo",
        "--output", "json"
    ])


def multica_issue_list_in_progress() -> Dict[str, Any]:
    """
    获取所有进行中的 Issue 列表（超时检测用）
    替代: multica issue list --status in_progress --output json
    """
    return run_multica([
        "multica", "issue", "list",
        "--status", "in_progress",
        "--output", "json"
    ])


def multica_issue_get(issue_id: str) -> Dict[str, Any]:
    """
    获取单个 Issue 详情
    替代: multica issue get <id> --output json
    """
    return run_multica([
        "multica", "issue", "get",
        issue_id,
        "--output", "json"
    ])


def openclaw_workboard_list() -> Dict[str, Any]:
    """
    获取 WorkBoard 卡片列表
    替代: openclaw workboard list --json
    """
    return run_multica([
        "openclaw", "workboard", "list", "--json"
    ])


def openclaw_workboard_read(card_id: str) -> Dict[str, Any]:
    """
    获取单个 WorkBoard 卡片
    替代: openclaw workboard read <id> --json
    """
    return run_multica([
        "openclaw", "workboard", "read", card_id, "--json"
    ])


# ============================================================================
# BIZ-104 Phase ④：multica issue comment reply 集成 pre-trim
# 背景：multica 0.4.35 二进制解析 openclaw stdout 时，长 markdown 被吞。
# 绕路：所有 multica issue comment reply 路径必走 pre-trim（≤4KB）。
# ============================================================================

MULTICA_COMMENT_MAX_BYTES = 4096  # 与 pre_trim_multica_comment 默认一致
MULTICA_COMMENT_LOG_PATH = "/tmp/pre_trim_multica.log"


def multica_issue_comment_add(
    issue_id: str,
    content: str,
    parent: Optional[str] = None,
    attachment: Optional[str] = None,
    content_file: Optional[str] = None,
    use_pre_trim: bool = True,
    max_bytes: int = MULTICA_COMMENT_MAX_BYTES,
    log_path: str = MULTICA_COMMENT_LOG_PATH,
) -> Dict[str, Any]:
    """
    发送 multica issue comment reply (BIZ-104 Phase ④ 集成)

    参数：
        issue_id: 目标 Issue ID
        content: 完整 markdown 内容（会被 pre-trim）
        parent: 父 comment ID（线程回复）
        attachment: 附件路径
        content_file: 如果提供，从文件读取 content （不会被 pre-trim，适用于大文件场景）
        use_pre_trim: 是否启用 pre-trim（默认 True）
        max_bytes: pre-trim 字节上限（默认 4 KB）
        log_path: 原 output 落盘路径

    返回：run_multica() 标准结果字典
    """
    # 优先使用 content_file（外部已规范格式）
    if content_file:
        return run_multica([
            "multica", "issue", "comment", "add",
            issue_id,
            "--content-file", content_file,
            *([ "--parent", parent] if parent else []),
            *([ "--attachment", attachment] if attachment else []),
        ])

    # 否则对 content 字段走 pre-trim
    if use_pre_trim and _PRE_TRIM_AVAILABLE:
        pre = _pre_trim(content, max_bytes=max_bytes, log_path=log_path)
        trimmed = pre["trimmed"]
        # 提预精简元数据到 stderr 便于追踪（不入 content，避免污染评论）
        if pre["truncated"]:
            print(
                f"[multica_proxy] BIZ-104 pre-trim 触发: "
                f"{pre['original_bytes']}B → {pre['trimmed_bytes']}B, "
                f"actions={pre['actions']}",
                file=sys.stderr,
            )
        final_content = trimmed
    else:
        final_content = content

    # --content 必须用临时文件路径（multica CLI MUL-2904）
    # 写入临时文件 + 清理
    import tempfile
    fd, tmp_path = tempfile.mkstemp(suffix=".md", prefix="multica_comment_")
    try:
        with os.fdopen(fd, "w", encoding="utf-8") as f:
            f.write(final_content)
        return run_multica([
            "multica", "issue", "comment", "add",
            issue_id,
            "--content-file", tmp_path,
            *([ "--parent", parent] if parent else []),
            *([ "--attachment", attachment] if attachment else []),
        ])
    finally:
        try:
            os.remove(tmp_path)
        except OSError:
            pass


# ============================================================================
# 缓存管理
# ============================================================================

def get_cache_stats() -> Dict[str, Any]:
    """获取缓存统计"""
    return _cache.get_stats()


def clear_cache(category: Optional[str] = None) -> int:
    """
    清理缓存
    参数：
        category: 指定类别清理，None 表示全部清理
    返回：清理条目数
    """
    if category:
        return _cache.clear_expired()
    else:
        count = len(_cache._cache)
        _cache.clear()
        return count


# ============================================================================
# 统一轮询器（仅 COO 使用）
# ============================================================================

def start_coordinated_poller() -> CoordinatedPoller:
    """
    启动 COO 统一轮询器
    仅 COO Agent 调用此函数
    """
    poller = _get_poller()
    if not poller._running:
        poller.start()
    return poller


def subscribe_to_poller(callback) -> None:
    """
    订阅 COO 统一轮询结果
    其他 Agent 调用此函数，不再各自调 multica CLI
    """
    _get_poller().subscribe(callback)


def get_poller_status() -> Dict[str, Any]:
    """获取轮询器状态"""
    poller = _get_poller()
    return {
        "running": poller._running,
        "poll_interval": poller.poll_interval,
        "subscriber_count": len(poller._subscribers)
    }


# ============================================================================
# 健康检查
# ============================================================================

def health_check() -> Dict[str, Any]:
    """检查 multica_proxy 健康状态"""
    scheduler = _get_scheduler()
    return {
        "status": "ok",
        "cache": get_cache_stats(),
        "scheduler": scheduler.get_status(),
        "poller": get_poller_status()
    }


# ============================================================================
# 测试
# ============================================================================

if __name__ == "__main__":
    print("=== multica_proxy 健康检查 ===")
    print(json.dumps(health_check(), indent=2, ensure_ascii=False))

    print("\n=== 测试缓存 ===")
    # 第一次调用（无缓存）
    result1 = run_multica(["echo", "test1"], use_cache=True)
    print(f"第1次: from_cache={result1['from_cache']}")

    # 第二次调用（应命中缓存）
    result2 = run_multica(["echo", "test1"], use_cache=True)
    print(f"第2次: from_cache={result2['from_cache']}")

    print("\n测试完成")