#!/usr/bin/env python3
"""
多项目管理器：处理多个知识库项目的合并
"""

import os
import shutil
import json
import logging
from pathlib import Path
from typing import List, Dict, Optional
from datetime import datetime

# Configure logger
logger = logging.getLogger('app')

from utils.file_utils import get_document_preview
from utils import settings


def generate_robot_directory_tree(robot_dir: str, robot_id: str, max_depth: int = 3) -> str:
    """
    生成机器人项目的目录树结构
    
    Args:
        robot_dir: 机器人项目目录路径
        robot_id: 机器人ID
        max_depth: 最大深度
        
    Returns:
        str: 目录树字符串
    """
    def _build_tree(path: str, prefix: str = "", is_last: bool = True, depth: int = 0) -> List[str]:
        if depth > max_depth:
            return []
        
        lines = []
        try:
            entries = sorted(os.listdir(path))
            # 分离目录和文件
            dirs = [e for e in entries if os.path.isdir(os.path.join(path, e)) and not e.startswith('.')]
            files = [e for e in entries if os.path.isfile(os.path.join(path, e)) and not e.startswith('.')]
            
            entries = dirs + files
            
            for i, entry in enumerate(entries):
                entry_path = os.path.join(path, entry)
                is_dir = os.path.isdir(entry_path)
                is_last_entry = i == len(entries) - 1
                
                # 选择适当的树形符号
                if is_last_entry:
                    connector = "└── "
                    new_prefix = prefix + "    "
                else:
                    connector = "├── "
                    new_prefix = prefix + "│   "
                
                # 添加条目行
                line = prefix + connector + entry
                if is_dir:
                    line += "/"
                lines.append(line)
                
                # 递归添加子目录
                if is_dir and depth < max_depth:
                    sub_lines = _build_tree(entry_path, new_prefix, is_last_entry, depth + 1)
                    lines.extend(sub_lines)
                    
        except PermissionError:
            lines.append(prefix + "└── [Permission Denied]")
        except Exception as e:
            lines.append(prefix + "└── [Error: " + str(e) + "]")
        
        return lines
    
    # 从dataset目录开始构建树
    dataset_dir = os.path.join(robot_dir, "dataset")
    tree_lines = []
    
    if not os.path.exists(dataset_dir):
        return "└── [No dataset directory found]"
    
    try:
        entries = sorted(os.listdir(dataset_dir))
        dirs = [e for e in entries if os.path.isdir(os.path.join(dataset_dir, e)) and not e.startswith('.')]
        files = [e for e in entries if os.path.isfile(os.path.join(dataset_dir, e)) and not e.startswith('.')]
        
        entries = dirs + files
        
        if not entries:
            tree_lines.append("└── [Empty dataset directory]")
        else:
            for i, entry in enumerate(entries):
                entry_path = os.path.join(dataset_dir, entry)
                is_dir = os.path.isdir(entry_path)
                is_last_entry = i == len(entries) - 1
                
                if is_last_entry:
                    connector = "└── "
                    prefix = "    "
                else:
                    connector = "├── "
                    prefix = "│   "
                
                line = connector + entry
                if is_dir:
                    line += "/"
                tree_lines.append(line)
                
                # 递归添加子目录
                if is_dir:
                    sub_lines = _build_tree(entry_path, prefix, is_last_entry, 1)
                    tree_lines.extend(sub_lines)
                
    except Exception as e:
        tree_lines.append(f"└── [Error generating tree: {str(e)}]")
    
    return "\n".join(tree_lines)


def get_unique_folder_name(target_dir: Path, original_name: str) -> str:
    """
    获取唯一的文件夹名称，如果存在重名则添加数字后缀
    
    Args:
        target_dir: 目标目录
        original_name: 原始文件夹名称
        
    Returns:
        str: 唯一的文件夹名称
    """
    if not (target_dir / original_name).exists():
        return original_name
    
    # 存在重名，添加数字后缀
    counter = 1
    while True:
        new_name = f"{original_name}_{counter}"
        if not (target_dir / new_name).exists():
            return new_name
        counter += 1


def copy_dataset_folder(source_project_id: str, target_dataset_dir: Path, folder_name: str, project_path: Path) -> Dict:
    """
    复制单个项目的dataset文件夹到目标目录
    
    Args:
        source_project_id: 源项目ID
        target_dataset_dir: 目标dataset目录
        folder_name: 要复制的文件夹名称
        project_path: 项目路径
        
    Returns:
        Dict: 复制结果
    """
    result = {
        "success": False,
        "source_path": "",
        "target_path": "",
        "original_folder_name": folder_name,
        "final_folder_name": folder_name,
        "error": None
    }
    
    try:
        source_folder = project_path / "data" / source_project_id / "dataset" / folder_name
        result["source_path"] = str(source_folder)
        
        if not source_folder.exists():
            result["error"] = f"Source folder does not exist: {source_folder}"
            return result
        
        # 处理重名冲突
        unique_folder_name = get_unique_folder_name(target_dataset_dir, folder_name)
        result["final_folder_name"] = unique_folder_name
        
        target_folder = target_dataset_dir / unique_folder_name
        result["target_path"] = str(target_folder)
        
        # 复制文件夹
        shutil.copytree(source_folder, target_folder)
        result["success"] = True
        
        logger.info(f"  Copied: {source_folder} -> {target_folder}")
        
    except Exception as e:
        result["error"] = str(e)
        logger.error(f"  Error copying {folder_name}: {str(e)}")
    
    return result


def generate_robot_readme(robot_id: str, dataset_ids: List[str], copy_results: List[Dict], project_path: Path) -> str:
    """
    生成机器人项目的README.md文件
    
    Args:
        robot_id: 机器人ID
        dataset_ids: 源项目ID列表
        copy_results: 复制结果列表
        
    Returns:
        str: README.md文件路径
    """
    readme_path = project_path / "robot" / robot_id / "README.md"
    readme_path.parent.mkdir(parents=True, exist_ok=True)
    
    robot_dir = project_path / "robot" / robot_id
    
    # 统计信息
    total_folders = len(copy_results)
    successful_copies = sum(1 for r in copy_results if r["success"])
    failed_copies = total_folders - successful_copies
    
    # 按源项目分组
    project_groups = {}
    for result in copy_results:
        if result["success"]:
            source_project = result["source_path"].split("/")[2]  # projects/data/{project_id}/dataset/...
            if source_project not in project_groups:
                project_groups[source_project] = []
            project_groups[source_project].append(result)

    readme_content = "\n## 目录结构\n\n"
    # 生成实际目录树
    readme_content += "```\n"
    readme_content += generate_robot_directory_tree(str(robot_dir), robot_id)
    readme_content += "\n```\n\n"
    
    readme_content += "## 数据集详情\n\n"
    
    dataset_dir = robot_dir / "dataset"
    if not dataset_dir.exists():
        readme_content += "No dataset files available.\n"
    else:
        # 获取所有文档目录
        doc_dirs = []
        try:
            for item in sorted(os.listdir(dataset_dir)):
                item_path = dataset_dir / item
                if item_path.is_dir():
                    doc_dirs.append(item)
        except Exception as e:
            logger.error(f"Error listing dataset directories: {str(e)}")
        
        if not doc_dirs:
            readme_content += "No document directories found.\n"
        else:
            # 按源项目分组显示文档
            for project_id, folders in project_groups.items():
                for folder in folders:
                    folder_name = folder["final_folder_name"]
                    doc_path = dataset_dir / folder_name
                    
                    readme_content += f"#### {folder_name}\n\n"
                    readme_content += f"**Files:**\n"
                    
                    # 检查文件存在性
                    document_file = doc_path / "document.txt"
                    pagination_file = doc_path / "pagination.txt"
                    embeddings_file = doc_path / "embedding.pkl"
                    
                    readme_content += f"- `{folder_name}/document.txt`"
                    if document_file.exists():
                        readme_content += " ✓"
                    readme_content += "\n"
                    
                    readme_content += f"- `{folder_name}/pagination.txt`"
                    if pagination_file.exists():
                        readme_content += " ✓"
                    readme_content += "\n"
                    
                    readme_content += f"- `{folder_name}/embedding.pkl`"
                    if embeddings_file.exists():
                        readme_content += " ✓"
                    readme_content += "\n\n"
                    
                    # 添加文档预览
                    if document_file.exists():
                        readme_content += f"**内容预览 (前10行):**\n\n```\n"
                        preview = get_document_preview(str(document_file), 10)
                        readme_content += preview
                        readme_content += "\n```\n\n"
                    else:
                        readme_content += f"**内容预览:** 不可用\n\n"
                    
                    # 显示重命名信息
                    original_name = folder["original_folder_name"]
                    if original_name != folder_name:
                        readme_content += f"**原始名称:** `{original_name}` → `{folder_name}`\n\n"
                    
                    readme_content += "---\n\n"

    
    # 写入README文件
    with open(readme_path, 'w', encoding='utf-8') as f:
        f.write(readme_content)
    
    logger.info(f"Generated README: {readme_path}")
    return str(readme_path)


def _get_robot_dir(project_path: Path, bot_id: str) -> Path:
    return project_path / "robot" / bot_id


def should_rebuild_robot_project(dataset_ids: List[str], bot_id: str, project_path: Path) -> bool:
    """
    检查是否需要重建机器人项目
    1. 检查机器人项目是否存在
    2. 检查是否有新增的dataset_id
    3. 检查processing_log.json文件是否更新

    Args:
        dataset_ids: 源项目ID列表
        bot_id: 机器人ID
        project_path: 项目路径

    Returns:
        bool: 是否需要重建
    """
    robot_dir = _get_robot_dir(project_path, bot_id)
    
    # 如果机器人项目不存在，需要创建
    if not robot_dir.exists():
            logger.info(f"Robot project does not exist, need to create: {bot_id}")
            return True
    
    # 检查机器人项目的配置信息
    config_file = robot_dir / "robot_config.json"
    if not config_file.exists():
            logger.info(f"Robot config file not found, need to rebuild: {bot_id}")
            return True
    
    # 读取配置信息
    try:
        with open(config_file, 'r', encoding='utf-8') as f:
            config = json.load(f)
        cached_dataset_ids = set(config.get("dataset_ids", []))
    except Exception as e:
            logger.error(f"Error reading robot config: {e}, need to rebuild")
            return True
    
    # 检查dataset_ids是否有变化
    current_dataset_ids = set(dataset_ids)
    
    # 如果有新增的dataset_id
    new_ids = current_dataset_ids - cached_dataset_ids
    if new_ids:
            logger.info(f"Found new dataset_ids: {new_ids}, need to rebuild")
            return True
    
    # 如果有删除的dataset_id
    removed_ids = cached_dataset_ids - current_dataset_ids
    if removed_ids:
            logger.info(f"Removed dataset_ids: {removed_ids}, need to rebuild")
            return True
    
    # 获取机器人项目的最后修改时间
    robot_mod_time = robot_dir.stat().st_mtime
    
    # 检查每个源项目的processing_log.json文件
    for source_project_id in dataset_ids:
        log_file = project_path / "data" / source_project_id / "processing_log.json"
        
        if not log_file.exists():
                logger.info(f"Processing log file not found for project {source_project_id}, will rebuild")
                return True
        
        log_mod_time = log_file.stat().st_mtime
        
        # 如果任何一个processing_log.json文件比机器人项目新，需要重建
        if log_mod_time > robot_mod_time:
                logger.info(f"Processing log updated for project {source_project_id}, need to rebuild")
                return True
    
        logger.info(f"Robot project {bot_id} is up to date, no rebuild needed")
    return False


def create_robot_project(dataset_ids: List[str], bot_id: str, force_rebuild: bool = False, project_path: Path = Path("projects"), skills: Optional[List[str]] = None, robot_type: str = "catalog_agent") -> str:
    """
    创建机器人项目，合并多个源项目的dataset文件夹

    Args:
        dataset_ids: 源项目ID列表
        bot_id: 机器人ID
        force_rebuild: 是否强制重建
        skills: 技能文件名列表（如 ["rag-retrieve", "device_controller.zip"]）
        robot_type: 机器人类型 (catalog_agent, deep_agent 等)

    Returns:
        str: 机器人项目目录路径
    """
    # 如果 skills 为空或 None，且 robot_type 是 catalog_agent 或 deep_agent，默认加载 rag-retrieve
    if not skills and robot_type in ("catalog_agent", "deep_agent"):
        skills = ["rag-retrieve"]
        logger.info(f"No skills provided, using default skill 'rag-retrieve' for {robot_type}")

    logger.info(f"Creating robot project: {bot_id} from sources: {dataset_ids}, skills: {skills}")

    # 检查是否需要重建
    if not force_rebuild and not should_rebuild_robot_project(dataset_ids, bot_id, project_path):
        robot_dir = project_path / "robot" / bot_id
        logger.info(f"Using existing robot project: {robot_dir}")
        # 即使使用现有项目，也要处理 skills（如果提供了）
        if skills:
            _extract_skills_to_robot(robot_dir, skills, project_path)
        return str(robot_dir)

    # 创建机器人目录结构
    robot_dir = _get_robot_dir(project_path, bot_id)
    dataset_dir = robot_dir / "dataset"

    # 清理已存在的目录（如果需要）
    if robot_dir.exists():
            logger.info(f"Robot directory already exists, cleaning up: {robot_dir}")
            shutil.rmtree(robot_dir)

    robot_dir.mkdir(parents=True, exist_ok=True)
    dataset_dir.mkdir(parents=True, exist_ok=True)
    
    copy_results = []
    
    # 遍历每个源项目
    for source_project_id in dataset_ids:
        logger.info(f"\nProcessing source project: {source_project_id}")
        
        source_dataset_dir = project_path / "data" / source_project_id / "dataset"
        
        if not source_dataset_dir.exists():
            logger.warning(f"  Warning: Dataset directory not found for project {source_project_id}")
            continue
        
        # 获取所有子文件夹
        folders = [f for f in source_dataset_dir.iterdir() if f.is_dir()]
        
        if not folders:
            logger.warning(f"  Warning: No folders found in dataset directory for project {source_project_id}")
            continue
        
        # 复制每个文件夹
        for folder in folders:
            result = copy_dataset_folder(source_project_id, dataset_dir, folder.name, project_path)
            copy_results.append(result)
    
    # 保存配置信息
    config_file = robot_dir / "robot_config.json"
    config_data = {
        "dataset_ids": dataset_ids,
        "bot_id": bot_id,
        "created_at": datetime.now().isoformat(),
        "total_folders": len(copy_results),
        "successful_copies": sum(1 for r in copy_results if r["success"])
    }
    
    with open(config_file, 'w', encoding='utf-8') as f:
        json.dump(config_data, f, ensure_ascii=False, indent=2)
    
    # 生成README
    readme_path = generate_robot_readme(bot_id, dataset_ids, copy_results, project_path)
    
    # 统计信息
    successful_copies = sum(1 for r in copy_results if r["success"])
    logger.info(f"\nRobot project creation completed:")
    logger.info(f"  Robot directory: {robot_dir}")
    logger.info(f"  Total folders processed: {len(copy_results)}")
    logger.info(f"  Successful copies: {successful_copies}")
    logger.info(f"  Config saved: {config_file}")
    logger.info(f"  README generated: {readme_path}")

    # 处理 skills 解压
    if skills:
        _extract_skills_to_robot(robot_dir, skills, project_path)

    return str(robot_dir)


if __name__ == "__main__":
    # 测试代码
    test_dataset_ids = ["test-project-1", "test-project-2"]
    test_bot_id = "test-robot-001"

    robot_dir = create_robot_project(test_dataset_ids, test_bot_id, robot_type="catalog_agent")
    logger.info(f"Created robot project at: {robot_dir}")


def _extract_skills_to_robot(robot_dir: Path, skills: List[str], project_path: Path) -> None:
    """
    解压 skills 到 robot 项目的 skills 文件夹

    Args:
        robot_dir: 机器人项目目录
        skills: 技能文件名列表（如 ["rag-retrieve", "device_controller.zip"]）
        project_path: 项目路径
    """
    import zipfile

    # skills 源目录在 projects/skills，需要通过解析软链接获取正确路径
    # project_path 可能是 ~/.deepagents (软链接 -> projects/robot)
    # 所以 skills 源目录是 project_path.resolve().parent / "skills"
    skills_source_dir = project_path / "skills"
    skills_target_dir = robot_dir / "skills"

    # 先清空 skills_target_dir，然后重新解压
    if skills_target_dir.exists():
        logger.info(f"  Removing existing skills directory: {skills_target_dir}")
        shutil.rmtree(skills_target_dir)

    skills_target_dir.mkdir(parents=True, exist_ok=True)
    logger.info(f"Extracting skills to {skills_target_dir}")

    for skill in skills:
        # 规范化文件名（确保有 .zip 后缀）
        if not skill.endswith(".zip"):
            skill_file = skill + ".zip"
        else:
            skill_file = skill

        skill_source_path = skills_source_dir / skill_file

        if not skill_source_path.exists():
            logger.warning(f"  Skill file not found: {skill_source_path}")
            continue

        # 获取解压后的文件夹名称（去掉 .zip 后缀）
        folder_name = skill_file.replace(".zip", "")
        extract_target = skills_target_dir / folder_name

        # 解压文件
        try:
            with zipfile.ZipFile(skill_source_path, 'r') as zip_ref:
                zip_ref.extractall(extract_target)
            logger.info(f"  Extracted: {skill_file} -> {extract_target}")
        except Exception as e:
            logger.error(f"  Failed to extract {skill_file}: {e}")