#  Copyright 2026 zhaoxi826
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.

import ray


@ray.remote
class WorkerCluster:
    """
    工作集群 Actor：管理和调度所有的 worker_individual
    设计理念：按需加载，内存 LRU 淘汰，避免 Actor 爆炸
    """

    def __init__(self, db_actor, max_capacity: int = 200):
        self.db = db_actor
        self.max_capacity = max_capacity
        # 核心：LRU 活跃 Agent 缓存池
        self._active_workers: OrderedDict[str, BaseWorkerIndividual] = OrderedDict()
        self.status = "running"

    async def _recruit_worker(self, agent_id: str) -> BaseWorkerIndividual:
        """内部方法：招聘/唤醒一个具体的 Agent 对象"""

        # 1. 尝试从缓存直接命中
        if agent_id in self._active_workers:
            self._active_workers.move_to_end(agent_id)  # 标记为最近使用
            return self._active_workers[agent_id]

        # 2. 缓存未命中，去数据库拉取 Agent 档案配置
        # agent_config = await self.db.get_agent_config.remote(agent_id)

        # 模拟从数据库取出的配置数据
        agent_config = {
            "agent_id": agent_id,
            "type": "skill",  # 取决于数据库里的设定：ordinary, skill, special
            "prompt": "你是一个资深架构师..."
        }

        if not agent_config:
            raise ValueError(f"无法唤醒 Agent {agent_id}：数据库中不存在该档案")

        # 3. 工厂模式：根据类型动态装配不同量级的 Individual
        worker_type = agent_config.get("type", "ordinary")
        if worker_type == "skill":
            worker = SkillIndividual(agent_config)
        elif worker_type == "special":
            worker = SpecialIndividual(agent_config)
        else:
            worker = OrdinaryIndividual(agent_config)

        # 4. 放入内存池，如果爆满则淘汰最老的那个
        self._active_workers[agent_id] = worker
        if len(self._active_workers) > self.max_capacity:
            evicted_id, _ = self._active_workers.popitem(last=False)
            print(f"[WorkerCluster] 内存池满，休眠老化 Agent: {evicted_id}")

        return worker

    async def execute_task(self, agent_id: str, task_event: dict) -> dict:
        """
        对外暴露的唯一干活接口。
        task_event 应该包含所有的上下文（Context、历史记忆、本次指令）
        """
        try:
            # 1. 获取工作实体（秒级热启动或毫秒级缓存命中）
            worker = await self._recruit_worker(agent_id)

            # 2. 注入上下文并执行
            # 这里的 run 方法内部不保存状态，所有记忆都从 task_event 传入
            start_time = time.time()
            result = await worker.run(task_event)
            cost_time = time.time() - start_time

            # 3. 封装标准回包
            return {
                "success": True,
                "agent_id": agent_id,
                "data": result,
                "metrics": {"cost_time_sec": round(cost_time, 2)}
            }

        except Exception as e:
            # 异常隔离：一个 Agent 报错，绝对不能把整个 Cluster 搞崩
            return {
                "success": False,
                "agent_id": agent_id,
                "error": str(e)
            }

    def get_cluster_metrics(self):
        """监控探针：用于查看当前集群负载"""
        return {
            "active_worker_count": len(self._active_workers),
            "max_capacity": self.max_capacity,
            "cached_agent_ids": list(self._active_workers.keys())
        }