"""
OASIS Twitter模拟预设脚本
此脚本读取配置文件中的参数来执行模拟，实现全程自动化

功能特性:
- 完成模拟后不立即关闭环境，进入等待命令模式
- 支持通过IPC接收Interview命令
- 支持单个Agent采访和批量采访
- 支持远程关闭环境命令

使用方式:
    python run_twitter_simulation.py --config /path/to/simulation_config.json
    python run_twitter_simulation.py --config /path/to/simulation_config.json --no-wait  # 完成后立即关闭
"""

import argparse
import asyncio
import json
import logging
import os
import random
import sys
import sqlite3
from datetime import datetime
from typing import Dict, Any, List, Optional

# 添加项目路径
_scripts_dir = os.path.dirname(os.path.abspath(__file__))
_backend_dir = os.path.abspath(os.path.join(_scripts_dir, '..'))
_project_root = os.path.abspath(os.path.join(_backend_dir, '..'))
sys.path.insert(0, _scripts_dir)
sys.path.insert(0, _backend_dir)

# 加载项目根目录的 .env 文件（包含 LLM_API_KEY 等配置）
from dotenv import load_dotenv
_env_file = os.path.join(_project_root, '.env')
if os.path.exists(_env_file):
    load_dotenv(_env_file)
else:
    _backend_env = os.path.join(_backend_dir, '.env')
    if os.path.exists(_backend_env):
        load_dotenv(_backend_env)


import re


class UnicodeFormatter(logging.Formatter):
    """自定义格式化器，将 Unicode 转义序列转换为可读字符"""
    
    UNICODE_ESCAPE_PATTERN = re.compile(r'\\u([0-9a-fA-F]{4})')
    
    def format(self, record):
        result = super().format(record)
        
        def replace_unicode(match):
            try:
                return chr(int(match.group(1), 16))
            except (ValueError, OverflowError):
                return match.group(0)
        
        return self.UNICODE_ESCAPE_PATTERN.sub(replace_unicode, result)


class MaxTokensWarningFilter(logging.Filter):
    """过滤掉 camel-ai 关于 max_tokens 的警告（我们故意不设置 max_tokens，让模型自行决定）"""
    
    def filter(self, record):
        # 过滤掉包含 max_tokens 警告的日志
        if "max_tokens" in record.getMessage() and "Invalid or missing" in record.getMessage():
            return False
        return True


# 在模块加载时立即添加过滤器，确保在 camel 代码执行前生效
logging.getLogger().addFilter(MaxTokensWarningFilter())


def setup_oasis_logging(log_dir: str):
    """配置 OASIS 的日志，使用固定名称的日志文件"""
    os.makedirs(log_dir, exist_ok=True)
    
    # 清理旧的日志文件
    for f in os.listdir(log_dir):
        old_log = os.path.join(log_dir, f)
        if os.path.isfile(old_log) and f.endswith('.log'):
            try:
                os.remove(old_log)
            except OSError:
                pass
    
    formatter = UnicodeFormatter("%(levelname)s - %(asctime)s - %(name)s - %(message)s")
    
    loggers_config = {
        "social.agent": os.path.join(log_dir, "social.agent.log"),
        "social.twitter": os.path.join(log_dir, "social.twitter.log"),
        "social.rec": os.path.join(log_dir, "social.rec.log"),
        "oasis.env": os.path.join(log_dir, "oasis.env.log"),
        "table": os.path.join(log_dir, "table.log"),
    }
    
    for logger_name, log_file in loggers_config.items():
        logger = logging.getLogger(logger_name)
        logger.setLevel(logging.DEBUG)
        logger.handlers.clear()
        file_handler = logging.FileHandler(log_file, encoding='utf-8', mode='w')
        file_handler.setLevel(logging.DEBUG)
        file_handler.setFormatter(formatter)
        logger.addHandler(file_handler)
        logger.propagate = False


try:
    from camel.models import ModelFactory
    from camel.types import ModelPlatformType
    import oasis
    from oasis import (
        ActionType,
        LLMAction,
        ManualAction,
        generate_twitter_agent_graph
    )
except ImportError as e:
    print(f"错误: 缺少依赖 {e}")
    print("请先安装: pip install oasis-ai camel-ai")
    sys.exit(1)


# IPC相关常量
IPC_COMMANDS_DIR = "ipc_commands"
IPC_RESPONSES_DIR = "ipc_responses"
ENV_STATUS_FILE = "env_status.json"

class CommandType:
    """命令类型常量"""
    INTERVIEW = "interview"
    BATCH_INTERVIEW = "batch_interview"
    CLOSE_ENV = "close_env"


class IPCHandler:
    """IPC命令处理器"""
    
    def __init__(self, simulation_dir: str, env, agent_graph):
        self.simulation_dir = simulation_dir
        self.env = env
        self.agent_graph = agent_graph
        self.commands_dir = os.path.join(simulation_dir, IPC_COMMANDS_DIR)
        self.responses_dir = os.path.join(simulation_dir, IPC_RESPONSES_DIR)
        self.status_file = os.path.join(simulation_dir, ENV_STATUS_FILE)
        self._running = True
        
        # 确保目录存在
        os.makedirs(self.commands_dir, exist_ok=True)
        os.makedirs(self.responses_dir, exist_ok=True)
    
    def update_status(self, status: str):
        """更新环境状态"""
        with open(self.status_file, 'w', encoding='utf-8') as f:
            json.dump({
                "status": status,
                "timestamp": datetime.now().isoformat()
            }, f, ensure_ascii=False, indent=2)
    
    def poll_command(self) -> Optional[Dict[str, Any]]:
        """轮询获取待处理命令"""
        if not os.path.exists(self.commands_dir):
            return None
        
        # 获取命令文件（按时间排序）
        command_files = []
        for filename in os.listdir(self.commands_dir):
            if filename.endswith('.json'):
                filepath = os.path.join(self.commands_dir, filename)
                command_files.append((filepath, os.path.getmtime(filepath)))
        
        command_files.sort(key=lambda x: x[1])
        
        for filepath, _ in command_files:
            try:
                with open(filepath, 'r', encoding='utf-8') as f:
                    return json.load(f)
            except (json.JSONDecodeError, OSError):
                continue
        
        return None
    
    def send_response(self, command_id: str, status: str, result: Dict = None, error: str = None):
        """发送响应"""
        response = {
            "command_id": command_id,
            "status": status,
            "result": result,
            "error": error,
            "timestamp": datetime.now().isoformat()
        }
        
        response_file = os.path.join(self.responses_dir, f"{command_id}.json")
        with open(response_file, 'w', encoding='utf-8') as f:
            json.dump(response, f, ensure_ascii=False, indent=2)
        
        # 删除命令文件
        command_file = os.path.join(self.commands_dir, f"{command_id}.json")
        try:
            os.remove(command_file)
        except OSError:
            pass
    
    async def handle_interview(self, command_id: str, agent_id: int, prompt: str) -> bool:
        """
        处理单个Agent采访命令
        
        Returns:
            True 表示成功，False 表示失败
        """
        try:
            # 获取Agent
            agent = self.agent_graph.get_agent(agent_id)
            
            # 创建Interview动作
            interview_action = ManualAction(
                action_type=ActionType.INTERVIEW,
                action_args={"prompt": prompt}
            )
            
            # 执行Interview
            actions = {agent: interview_action}
            await self.env.step(actions)
            
            # 从数据库获取结果
            result = self._get_interview_result(agent_id)
            
            self.send_response(command_id, "completed", result=result)
            print(f"  Interview完成: agent_id={agent_id}")
            return True
            
        except Exception as e:
            error_msg = str(e)
            print(f"  Interview失败: agent_id={agent_id}, error={error_msg}")
            self.send_response(command_id, "failed", error=error_msg)
            return False
    
    async def handle_batch_interview(self, command_id: str, interviews: List[Dict]) -> bool:
        """
        处理批量采访命令
        
        Args:
            interviews: [{"agent_id": int, "prompt": str}, ...]
        """
        try:
            # 构建动作字典
            actions = {}
            agent_prompts = {}  # 记录每个agent的prompt
            
            for interview in interviews:
                agent_id = interview.get("agent_id")
                prompt = interview.get("prompt", "")
                
                try:
                    agent = self.agent_graph.get_agent(agent_id)
                    actions[agent] = ManualAction(
                        action_type=ActionType.INTERVIEW,
                        action_args={"prompt": prompt}
                    )
                    agent_prompts[agent_id] = prompt
                except Exception as e:
                    print(f"  警告: 无法获取Agent {agent_id}: {e}")
            
            if not actions:
                self.send_response(command_id, "failed", error="没有有效的Agent")
                return False
            
            # 执行批量Interview
            await self.env.step(actions)
            
            # 获取所有结果
            results = {}
            for agent_id in agent_prompts.keys():
                result = self._get_interview_result(agent_id)
                results[agent_id] = result
            
            self.send_response(command_id, "completed", result={
                "interviews_count": len(results),
                "results": results
            })
            print(f"  批量Interview完成: {len(results)} 个Agent")
            return True
            
        except Exception as e:
            error_msg = str(e)
            print(f"  批量Interview失败: {error_msg}")
            self.send_response(command_id, "failed", error=error_msg)
            return False
    
    def _get_interview_result(self, agent_id: int) -> Dict[str, Any]:
        """从数据库获取最新的Interview结果"""
        db_path = os.path.join(self.simulation_dir, "twitter_simulation.db")
        
        result = {
            "agent_id": agent_id,
            "response": None,
            "timestamp": None
        }
        
        if not os.path.exists(db_path):
            return result
        
        try:
            conn = sqlite3.connect(db_path)
            cursor = conn.cursor()
            
            # 查询最新的Interview记录
            cursor.execute("""
                SELECT user_id, info, created_at
                FROM trace
                WHERE action = ? AND user_id = ?
                ORDER BY created_at DESC
                LIMIT 1
            """, (ActionType.INTERVIEW.value, agent_id))
            
            row = cursor.fetchone()
            if row:
                user_id, info_json, created_at = row
                try:
                    info = json.loads(info_json) if info_json else {}
                    result["response"] = info.get("response", info)
                    result["timestamp"] = created_at
                except json.JSONDecodeError:
                    result["response"] = info_json
            
            conn.close()
            
        except Exception as e:
            print(f"  读取Interview结果失败: {e}")
        
        return result
    
    async def process_commands(self) -> bool:
        """
        处理所有待处理命令
        
        Returns:
            True 表示继续运行，False 表示应该退出
        """
        command = self.poll_command()
        if not command:
            return True
        
        command_id = command.get("command_id")
        command_type = command.get("command_type")
        args = command.get("args", {})
        
        print(f"\n收到IPC命令: {command_type}, id={command_id}")
        
        if command_type == CommandType.INTERVIEW:
            await self.handle_interview(
                command_id,
                args.get("agent_id", 0),
                args.get("prompt", "")
            )
            return True
            
        elif command_type == CommandType.BATCH_INTERVIEW:
            await self.handle_batch_interview(
                command_id,
                args.get("interviews", [])
            )
            return True
            
        elif command_type == CommandType.CLOSE_ENV:
            print("收到关闭环境命令")
            self.send_response(command_id, "completed", result={"message": "环境即将关闭"})
            return False
        
        else:
            self.send_response(command_id, "failed", error=f"未知命令类型: {command_type}")
            return True


class TwitterSimulationRunner:
    """Twitter模拟运行器"""
    
    # Twitter可用动作（不包含INTERVIEW，INTERVIEW只能通过ManualAction手动触发）
    AVAILABLE_ACTIONS = [
        ActionType.CREATE_POST,
        ActionType.LIKE_POST,
        ActionType.REPOST,
        ActionType.FOLLOW,
        ActionType.DO_NOTHING,
        ActionType.QUOTE_POST,
    ]
    
    def __init__(self, config_path: str, wait_for_commands: bool = True):
        """
        初始化模拟运行器
        
        Args:
            config_path: 配置文件路径 (simulation_config.json)
            wait_for_commands: 模拟完成后是否等待命令（默认True）
        """
        self.config_path = config_path
        self.config = self._load_config()
        self.simulation_dir = os.path.dirname(config_path)
        self.wait_for_commands = wait_for_commands
        self.env = None
        self.agent_graph = None
        self.ipc_handler = None
        
    def _load_config(self) -> Dict[str, Any]:
        """加载配置文件"""
        with open(self.config_path, 'r', encoding='utf-8') as f:
            return json.load(f)
    
    def _get_profile_path(self) -> str:
        """获取Profile文件路径（OASIS Twitter使用CSV格式）"""
        return os.path.join(self.simulation_dir, "twitter_profiles.csv")
    
    def _get_db_path(self) -> str:
        """获取数据库路径"""
        return os.path.join(self.simulation_dir, "twitter_simulation.db")
    
    def _create_model(self):
        """
        创建LLM模型
        
        统一使用项目根目录 .env 文件中的配置（优先级最高）：
        - LLM_API_KEY: API密钥
        - LLM_BASE_URL: API基础URL
        - LLM_MODEL_NAME: 模型名称
        """
        # 优先从 .env 读取配置
        llm_api_key = os.environ.get("LLM_API_KEY", "")
        llm_base_url = os.environ.get("LLM_BASE_URL", "")
        llm_model = os.environ.get("LLM_MODEL_NAME", "")
        
        # 如果 .env 中没有，则使用 config 作为备用
        if not llm_model:
            llm_model = self.config.get("llm_model", "gpt-4o-mini")
        
        # 设置 camel-ai 所需的环境变量
        if llm_api_key:
            os.environ["OPENAI_API_KEY"] = llm_api_key
        
        if not os.environ.get("OPENAI_API_KEY"):
            raise ValueError("缺少 API Key 配置，请在项目根目录 .env 文件中设置 LLM_API_KEY")
        
        if llm_base_url:
            os.environ["OPENAI_API_BASE_URL"] = llm_base_url
        
        print(f"LLM配置: model={llm_model}, base_url={llm_base_url[:40] if llm_base_url else '默认'}...")
        
        return ModelFactory.create(
            model_platform=ModelPlatformType.OPENAI,
            model_type=llm_model,
        )
    
    def _get_active_agents_for_round(
        self, 
        env, 
        current_hour: int,
        round_num: int
    ) -> List:
        """
        根据时间和配置决定本轮激活哪些Agent
        
        Args:
            env: OASIS环境
            current_hour: 当前模拟小时（0-23）
            round_num: 当前轮数
            
        Returns:
            激活的Agent列表
        """
        time_config = self.config.get("time_config", {})
        agent_configs = self.config.get("agent_configs", [])
        
        # 基础激活数量
        base_min = time_config.get("agents_per_hour_min", 5)
        base_max = time_config.get("agents_per_hour_max", 20)
        
        # 根据时段调整
        peak_hours = time_config.get("peak_hours", [9, 10, 11, 14, 15, 20, 21, 22])
        off_peak_hours = time_config.get("off_peak_hours", [0, 1, 2, 3, 4, 5])
        
        if current_hour in peak_hours:
            multiplier = time_config.get("peak_activity_multiplier", 1.5)
        elif current_hour in off_peak_hours:
            multiplier = time_config.get("off_peak_activity_multiplier", 0.3)
        else:
            multiplier = 1.0
        
        target_count = int(random.uniform(base_min, base_max) * multiplier)
        
        # 根据每个Agent的配置计算激活概率
        candidates = []
        for cfg in agent_configs:
            agent_id = cfg.get("agent_id", 0)
            active_hours = cfg.get("active_hours", list(range(8, 23)))
            activity_level = cfg.get("activity_level", 0.5)
            
            # 检查是否在活跃时间
            if current_hour not in active_hours:
                continue
            
            # 根据活跃度计算概率
            if random.random() < activity_level:
                candidates.append(agent_id)
        
        # 随机选择
        selected_ids = random.sample(
            candidates, 
            min(target_count, len(candidates))
        ) if candidates else []
        
        # 转换为Agent对象
        active_agents = []
        for agent_id in selected_ids:
            try:
                agent = env.agent_graph.get_agent(agent_id)
                active_agents.append((agent_id, agent))
            except Exception:
                pass
        
        return active_agents
    
    async def run(self, max_rounds: int = None):
        """运行Twitter模拟
        
        Args:
            max_rounds: 最大模拟轮数（可选，用于截断过长的模拟）
        """
        print("=" * 60)
        print("OASIS Twitter模拟")
        print(f"配置文件: {self.config_path}")
        print(f"模拟ID: {self.config.get('simulation_id', 'unknown')}")
        print(f"等待命令模式: {'启用' if self.wait_for_commands else '禁用'}")
        print("=" * 60)
        
        # 加载时间配置
        time_config = self.config.get("time_config", {})
        total_hours = time_config.get("total_simulation_hours", 72)
        minutes_per_round = time_config.get("minutes_per_round", 30)
        
        # 计算总轮数
        total_rounds = (total_hours * 60) // minutes_per_round
        
        # 如果指定了最大轮数，则截断
        if max_rounds is not None and max_rounds > 0:
            original_rounds = total_rounds
            total_rounds = min(total_rounds, max_rounds)
            if total_rounds < original_rounds:
                print(f"\n轮数已截断: {original_rounds} -> {total_rounds} (max_rounds={max_rounds})")
        
        print(f"\n模拟参数:")
        print(f"  - 总模拟时长: {total_hours}小时")
        print(f"  - 每轮时间: {minutes_per_round}分钟")
        print(f"  - 总轮数: {total_rounds}")
        if max_rounds:
            print(f"  - 最大轮数限制: {max_rounds}")
        print(f"  - Agent数量: {len(self.config.get('agent_configs', []))}")
        
        # 创建模型
        print("\n初始化LLM模型...")
        model = self._create_model()
        
        # 加载Agent图
        print("加载Agent Profile...")
        profile_path = self._get_profile_path()
        if not os.path.exists(profile_path):
            print(f"错误: Profile文件不存在: {profile_path}")
            return
        
        self.agent_graph = await generate_twitter_agent_graph(
            profile_path=profile_path,
            model=model,
            available_actions=self.AVAILABLE_ACTIONS,
        )
        
        # 数据库路径
        db_path = self._get_db_path()
        if os.path.exists(db_path):
            os.remove(db_path)
            print(f"已删除旧数据库: {db_path}")
        
        # 创建环境
        print("创建OASIS环境...")
        self.env = oasis.make(
            agent_graph=self.agent_graph,
            platform=oasis.DefaultPlatformType.TWITTER,
            database_path=db_path,
            semaphore=30,  # 限制最大并发 LLM 请求数，防止 API 过载
        )
        
        await self.env.reset()
        print("环境初始化完成\n")
        
        # 初始化IPC处理器
        self.ipc_handler = IPCHandler(self.simulation_dir, self.env, self.agent_graph)
        self.ipc_handler.update_status("running")
        
        # 执行初始事件
        event_config = self.config.get("event_config", {})
        initial_posts = event_config.get("initial_posts", [])
        
        if initial_posts:
            print(f"执行初始事件 ({len(initial_posts)}条初始帖子)...")
            initial_actions = {}
            for post in initial_posts:
                agent_id = post.get("poster_agent_id", 0)
                content = post.get("content", "")
                try:
                    agent = self.env.agent_graph.get_agent(agent_id)
                    initial_actions[agent] = ManualAction(
                        action_type=ActionType.CREATE_POST,
                        action_args={"content": content}
                    )
                except Exception as e:
                    print(f"  警告: 无法为Agent {agent_id}创建初始帖子: {e}")
            
            if initial_actions:
                await self.env.step(initial_actions)
                print(f"  已发布 {len(initial_actions)} 条初始帖子")
        
        # 主模拟循环
        print("\n开始模拟循环...")
        start_time = datetime.now()
        
        for round_num in range(total_rounds):
            # 计算当前模拟时间
            simulated_minutes = round_num * minutes_per_round
            simulated_hour = (simulated_minutes // 60) % 24
            simulated_day = simulated_minutes // (60 * 24) + 1
            
            # 获取本轮激活的Agent
            active_agents = self._get_active_agents_for_round(
                self.env, simulated_hour, round_num
            )
            
            if not active_agents:
                continue
            
            # 构建动作
            actions = {
                agent: LLMAction()
                for _, agent in active_agents
            }
            
            # 执行动作
            await self.env.step(actions)
            
            # 打印进度
            if (round_num + 1) % 10 == 0 or round_num == 0:
                elapsed = (datetime.now() - start_time).total_seconds()
                progress = (round_num + 1) / total_rounds * 100
                print(f"  [Day {simulated_day}, {simulated_hour:02d}:00] "
                      f"Round {round_num + 1}/{total_rounds} ({progress:.1f}%) "
                      f"- {len(active_agents)} agents active "
                      f"- elapsed: {elapsed:.1f}s")
        
        total_elapsed = (datetime.now() - start_time).total_seconds()
        print(f"\n模拟循环完成!")
        print(f"  - 总耗时: {total_elapsed:.1f}秒")
        print(f"  - 数据库: {db_path}")
        
        # 是否进入等待命令模式
        if self.wait_for_commands:
            print("\n" + "=" * 60)
            print("进入等待命令模式 - 环境保持运行")
            print("支持的命令: interview, batch_interview, close_env")
            print("=" * 60)
            
            self.ipc_handler.update_status("alive")
            
            # 等待命令循环
            try:
                while True:
                    should_continue = await self.ipc_handler.process_commands()
                    if not should_continue:
                        break
                    await asyncio.sleep(0.5)  # 轮询间隔
            except KeyboardInterrupt:
                print("\n收到中断信号")
            except Exception as e:
                print(f"\n命令处理出错: {e}")
            
            print("\n关闭环境...")
        
        # 关闭环境
        self.ipc_handler.update_status("stopped")
        await self.env.close()
        
        print("环境已关闭")
        print("=" * 60)


async def main():
    parser = argparse.ArgumentParser(description='OASIS Twitter模拟')
    parser.add_argument(
        '--config', 
        type=str, 
        required=True,
        help='配置文件路径 (simulation_config.json)'
    )
    parser.add_argument(
        '--max-rounds',
        type=int,
        default=None,
        help='最大模拟轮数（可选，用于截断过长的模拟）'
    )
    parser.add_argument(
        '--no-wait',
        action='store_true',
        default=False,
        help='模拟完成后立即关闭环境，不进入等待命令模式'
    )
    
    args = parser.parse_args()
    
    if not os.path.exists(args.config):
        print(f"错误: 配置文件不存在: {args.config}")
        sys.exit(1)
    
    # 初始化日志配置（使用固定文件名，清理旧日志）
    simulation_dir = os.path.dirname(args.config) or "."
    setup_oasis_logging(os.path.join(simulation_dir, "log"))
    
    runner = TwitterSimulationRunner(
        config_path=args.config,
        wait_for_commands=not args.no_wait
    )
    await runner.run(max_rounds=args.max_rounds)


if __name__ == "__main__":
    asyncio.run(main())