using Microsoft.Extensions.FileSystemGlobbing;
using Newtonsoft.Json;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;

namespace CloudBuilder.Topshelf.Python
{
    public class SentenceInfo
    {
        public List<string> Subjects { get; set; } = new();
        public List<string> Predicates { get; set; } = new();
        public List<string> Objects { get; set; } = new();
        public List<string> PersonRoles { get; set; } = new();
    }

    public class NamedEntity
    {
        public string Text { get; set; }    // 實體文本 (如 "王小明")
        public string Type { get; set; }   // 實體類型 (如 "PERSON")
        public int Start { get; set; }      // 起始位置 (索引)
        public int End { get; set; }        // 結束位置 (索引)

        public override string ToString() => $"{Text} ({Type}) [{Start}-{End}]";
    }

    public class MeaningRepresentationParsingEntity
    {
        public string Text { get; set; }
        public string Type { get; set; }
        public int Start { get; set; }
        public int End { get; set; }
    }

    public class HanlpConstituencyNode
    {
        /// <summary>
        /// 节点ID
        /// </summary>
        [JsonProperty("ItemId")]
        public int ItemId { get; set; }

        /// <summary>
        /// 父节点ID
        /// </summary>
        [JsonProperty("FatherId")]
        public int FatherId { get; set; }

        /// <summary>
        /// 节点标签（如TOP、IP、NP、VP等）
        /// </summary>
        [JsonProperty("Label")]
        public string Label { get; set; }

        /// <summary>
        /// 节点层级
        /// </summary>
        [JsonProperty("Level")]
        public int Level { get; set; }

        /// <summary>
        /// 子节点数量
        /// </summary>
        [JsonProperty("Children")]
        public int Children { get; set; }

        /// <summary>
        /// 从JSON字符串解析节点列表
        /// </summary>
        /// <param name="jsonString">Python HanLP返回的JSON字符串</param>
        /// <returns>节点列表</returns>
        public static List<HanlpConstituencyNode> FromJsonString(string jsonString)
        {
            if (string.IsNullOrEmpty(jsonString))
                return new List<HanlpConstituencyNode>();

            try
            {
                return JsonConvert.DeserializeObject<List<HanlpConstituencyNode>>(jsonString);
            }
            catch (Exception ex)
            {
                Console.WriteLine($"JSON解析错误: {ex.Message}");
                return new List<HanlpConstituencyNode>();
            }
        }

        /// <summary>
        /// 将节点列表转换为JSON字符串
        /// </summary>
        /// <param name="nodes">节点列表</param>
        /// <returns>JSON字符串</returns>
        public static string ToJsonString(List<HanlpConstituencyNode> nodes)
        {
            if (nodes == null)
                return "[]";

            return JsonConvert.SerializeObject(nodes,
                Formatting.Indented,
                new JsonSerializerSettings { StringEscapeHandling = StringEscapeHandling.Default });
        }

        /// <summary>
        /// 重写ToString方法，返回节点的详细信息
        /// </summary>
        /// <returns>节点信息字符串</returns>
        public override string ToString()
        {
            return $"ItemId: {ItemId}, FatherId: {FatherId}, Label: {Label}, Level: {Level}, Children: {Children}";
        }
    }

    /// <summary>
    /// HanLP处理结果工具类
    /// 用于与Python HanLP服务交互
    /// </summary>
    public class HanlpResultHelper
    {
        /// <summary>
        /// 解析句法分析结果
        /// </summary>
        /// <param name="pythonOutput">Python程序输出的JSON字符串</param>
        /// <returns>节点列表</returns>
        public static List<HanlpConstituencyNode> ParseConstituencyResult(string pythonOutput)
        {
            return HanlpConstituencyNode.FromJsonString(pythonOutput);
        }

        /// <summary>
        /// 根据父节点ID查找子节点
        /// </summary>
        /// <param name="nodes">所有节点</param>
        /// <param name="fatherId">父节点ID</param>
        /// <returns>子节点列表</returns>
        public static List<HanlpConstituencyNode> GetChildrenNodes(List<HanlpConstituencyNode> nodes, int fatherId)
        {
            return nodes?.FindAll(node => node.FatherId == fatherId) ?? new List<HanlpConstituencyNode>();
        }

        /// <summary>
        /// 根据ID查找节点
        /// </summary>
        /// <param name="nodes">所有节点</param>
        /// <param name="itemId">节点ID</param>
        /// <returns>节点对象，如果未找到则返回null</returns>
        public static HanlpConstituencyNode GetNodeById(List<HanlpConstituencyNode> nodes, int itemId)
        {
            return nodes?.Find(node => node.ItemId == itemId);
        }

        /// <summary>
        /// 根据Label查找节点
        /// </summary>
        /// <param name="nodes">所有节点</param>
        /// <param name="itemId">节点ID</param>
        /// <returns>节点对象，如果未找到则返回null</returns>
        public static HanlpConstituencyNode[] GetNodeByLabel(List<HanlpConstituencyNode> nodes, string label)
        {
            return nodes?.Where(node => node.Label == label).ToArray();
        }

        /// <summary>
        /// 根据ID查找父节点
        /// </summary>
        /// <param name="nodes">所有节点</param>
        /// <param name="itemId">节点ID</param>
        /// <returns>节点对象，如果未找到则返回null</returns>
        public static HanlpConstituencyNode GetNodeFatherById(List<HanlpConstituencyNode> nodes, int itemId)
        {
            HanlpConstituencyNode self = GetNodeById(nodes, itemId);
            return nodes?.Find(node => node.ItemId == self.FatherId);
        }

        /// <summary>
        /// 根据ID查找等于label父节点
        /// </summary>
        /// <param name="nodes">所有节点</param>
        /// <param name="itemId">节点ID</param>
        /// <returns>节点对象，如果未找到则返回null</returns>
        public static HanlpConstituencyNode GetNodeTopLabelById(List<HanlpConstituencyNode> nodes, int itemId, string label)
        {
            HanlpConstituencyNode nd = GetNodeFatherById(nodes, itemId);

            if (label == nd.Label && nd.Children > 0) return nd;

            while (nd.FatherId > 0)
            {
                return GetNodeTopLabelById(nodes, nd.ItemId, label);
            }

            return null;
        }

        /// <summary>
        /// 根据ID查找等于label子节点
        /// </summary>
        /// <param name="nodes">所有节点</param>
        /// <param name="itemId">节点ID</param>
        /// <returns>节点对象，如果未找到则返回null</returns>
        public static string GetNodeBottomLabelById(List<HanlpConstituencyNode> nodes, int itemId, string label)
        {
            HanlpConstituencyNode[] nds = nodes?.Where(node => node.FatherId == itemId && node.Label == label && node.Children > 0).ToArray();
            if (nds == null || nds.Length == 0) return null;
            HanlpConstituencyNode father;
            List<string> list = new List<string>();
            foreach (HanlpConstituencyNode nd in nds)
            {
                List<HanlpConstituencyNode> sons = GetChildrenNodes(nodes, nd.ItemId);
                if (sons == null || sons.Count() == 0) continue;
                return string.Join("", sons.Select(x => x.Label).ToArray());
            }

            return null;
        }

        /// <summary>
        /// 根据ID查找所有兄弟节点（同父且不同ID的节点）
        /// </summary>
        /// <param name="nodes">所有节点列表</param>
        /// <param name="itemId">目标节点ID</param>
        /// <returns>兄弟节点列表，如果无兄弟节点/节点不存在则返回空列表</returns>
        public static List<HanlpConstituencyNode> GetNodeBrothersById(List<HanlpConstituencyNode> nodes, int itemId)
        {
            // 空值保护：如果节点列表为空，直接返回空列表
            if (nodes == null || nodes.Count == 0)
            {
                return new List<HanlpConstituencyNode>();
            }

            // 第一步：找到目标节点（根据ID）
            var targetNode = nodes.Find(node => node.ItemId == itemId);
            // 如果目标节点不存在，返回空列表
            if (targetNode == null)
            {
                return new List<HanlpConstituencyNode>();
            }

            // 第二步：获取目标节点的父ID，筛选所有同父且ID不等于目标节点的节点
            int fatherId = targetNode.FatherId;
            var brotherNodes = nodes.FindAll(node =>
                node.FatherId == fatherId &&  // 同父节点
                node.ItemId != itemId            // 排除自身
            );

            // 返回兄弟节点列表（无兄弟则返回空列表）
            return brotherNodes ?? new List<HanlpConstituencyNode>();
        }
    }
}


/*
import os
import json
import logging

# 配置日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

try:
    import hanlp
    from hanlp.components.mtl.multi_task_learning import MultiTaskLearning
    from hanlp.components.mtl.tasks.tok.tag_tok import TaggingTokenization
    from hanlp.components.mtl.tasks.ner.tag_ner import TaggingNamedEntityRecognition
    
    # 尝试导入tqdm，如果不存在则使用替代
    try:
        from tqdm import tqdm
        HAS_TQDM = True
    except ImportError:
        logger.warning("tqdm未安装，将不显示进度条")
        HAS_TQDM = False
        
        # 创建一个简单的tqdm替代
        class tqdm:
            def __init__(self, iterable, desc=None):
                self.iterable = iterable
                self.desc = desc
            def __iter__(self):
                return iter(self.iterable)
except ImportError as e:
    logger.error(f"导入HanLP失败: {e}")

class HanLPProcessor:
    """HanLP自然语言处理工具类"""
    
    def __init__(self, 
                 hanlp_home='D:/Python38/hanlp/',
                 hf_home='D:/Python38/models/',
                 hf_endpoint='https://hf-mirror.com',
                 hanlp_url='https://ftp.hankcs.com/hanlp/',
                 surnames_path='D:/Python38/hanlp/dictionary/surnames.txt',
                 titles_path='D:/Python38/hanlp/dictionary/titles.txt',
                 words_path='D:/Python38/hanlp/dictionary/words.txt',
                 salutations_path='D:/Python38/hanlp/dictionary/salutations.txt'):
        # 初始化ID计数器和父ID跟踪
        self.item_id_counter = 1
        self.current_father_id = 0
        """
        初始化HanLP处理器
        
        参数:
            hanlp_home: HanLP资源缓存目录
            hf_home: HuggingFace模型缓存目录
            hf_endpoint: HuggingFace下载端点
            hanlp_url: HanLP下载URL
            surnames_path: 姓氏词典路径
            titles_path: 头衔词典路径
            words_path: 专有名词词典路径
            salutations_path: 称呼词典路径
        """
        # 存储配置信息
        self.hanlp_home = hanlp_home
        self.hf_home = hf_home
        self.hf_endpoint = hf_endpoint
        self.hanlp_url = hanlp_url
        self.surnames_path = surnames_path
        self.titles_path = titles_path
        self.words_path = words_path
        self.salutations_path = salutations_path
        
        # 初始化组件
        self.mtl = None
        self.tok = None
        self.ner = None
        
        # 设置环境变量
        self._set_environment_variables()
        
        # 加载模型和词典
        self._load_model()
        self._load_dictionaries()
    
    def _set_environment_variables(self):
        """设置必要的环境变量"""
        try:
            os.environ['HANLP_HOME'] = self.hanlp_home
            os.environ['HF_HOME'] = self.hf_home
            os.environ['HF_ENDPOINT'] = self.hf_endpoint
            os.environ['HANLP_URL'] = self.hanlp_url
            logger.info("环境变量设置成功")
        except Exception as e:
            logger.error(f"设置环境变量失败: {e}")
    
    def _load_dict_from_file(self, file_path, default_tag='S-PERSON'):
        """从文件加载词典"""
        dict_tags = {}
        try:
            with open(file_path, 'r', encoding='utf-8') as f:
                for word in f:
                    word = word.strip()
                    if word:  # 跳过空行
                        dict_tags[(word,)] = (default_tag,)  # 保持元组形式
        except Exception as e:
            print(f"加载词典文件{file_path}失败: {e}")
        return dict_tags
    
    def _load_names(self, surnames_path, titles_path):
        """加载姓氏和头衔，生成姓名组合"""
        try:
            with open(surnames_path, 'r', encoding='utf-8') as f:
                surnames = [line.strip() for line in f if line.strip()]
            
            with open(titles_path, 'r', encoding='utf-8') as f:
                titles = [line.strip() for line in f if line.strip()]
            
            # 使用生成器表达式（节省内存）
            full_names = (f'{surname}{title}' for surname in surnames for title in titles)
            return full_names
        except Exception as e:
            print(f"加载姓名文件失败: {e}")
            return []
    
    def _load_model(self):
        """加载HanLP模型"""
        try:
            # CLOSE是自然语义标注的闭源语料库，BASE是中号模型，ZH中文
            logger.info("开始加载HanLP模型...")
            self.mtl = hanlp.load(hanlp.pretrained.mtl.CLOSE_TOK_POS_NER_SRL_DEP_SDP_CON_ELECTRA_SMALL_ZH)
            self.tok = self.mtl['tok/coarse']
            self.tok.dict_force = self.tok.dict_combine = None
            self.ner = self.mtl['ner/msra']
            logger.info("模型加载成功")
        except Exception as e:
            logger.error(f"加载模型失败: {e}")
            self.mtl = None
            self.tok = None
            self.ner = None
    
    def _load_dictionaries(self):
        """加载各种词典"""
        try:
            # 加载称呼词典
            self.ner.dict_tags = self._load_dict_from_file(self.salutations_path)
            
            # 读取专有名词词典
            with open(self.words_path, 'r', encoding='utf-8') as f:
                names = [line.strip() for line in f if line.strip()]
            
            # 生成所有姓名组合
            full_names = self._load_names(self.surnames_path, self.titles_path)
            
            # 构建强制分词词典（使用进度条）
            dict_force = {}
            for name in tqdm(full_names, desc='读取人名词典'):
                dict_force[name] = [name]
            
            for name in names:
                dict_force[name] = [name]
            
            self.tok.dict_force = dict_force
        except Exception as e:
            print(f"加载词典失败: {e}")
    
    def get_ner_json(self, text):
        """识别文本中的实体并返回JSON格式"""
        if not text or not isinstance(text, str):
            logger.warning("无效的输入文本")
            return json.dumps([])
            
        try:
            if not self.mtl:
                raise RuntimeError("模型未加载")
            
            result = self.mtl(text, tasks=['tok/coarse', 'ner/msra'], skip_tasks=['tok/fine'])
            
            # 检查结果格式
            if 'ner/msra' not in result or not isinstance(result['ner/msra'], (list, tuple)):
                logger.warning("模型返回结果格式异常")
                return json.dumps([])
                
            json_data = []
            for item in result['ner/msra']:
                # 确保item是正确的格式
                if isinstance(item, (list, tuple)) and len(item) >= 4:
                    json_data.append({
                        'Text': str(item[0]),
                        'Type': str(item[1]),
                        'Start': int(item[2]),
                        'End': int(item[3])
                    })
                
            return json.dumps(json_data, ensure_ascii=False)
        except Exception as e:
            logger.error(f"实体识别失败: {e}")
            return json.dumps([])
    
    def get_srl_json(self, text):
        """识别文本中的人物角色并返回JSON格式"""
        if not text or not isinstance(text, str):
            logger.warning("无效的输入文本")
            return json.dumps([])
            
        try:
            if not self.mtl:
                raise RuntimeError("模型未加载")
            
            results = self.mtl(text, tasks=['tok/coarse', 'srl'], skip_tasks=['tok/fine'])
            result = []
            
            # 检查结果格式
            if 'srl' not in results:
                logger.warning("模型返回结果中未找到SRL数据")
                return json.dumps([])
                
            data = results['srl']
            if not data:
                return json.dumps(result)
            
            for sentence in data:
                if not isinstance(sentence, (list, tuple)):
                    continue
                    
                sentence_data = []
                for item in sentence:
                    if not isinstance(item, (list, tuple)):
                        continue
                        
                    # 确保至少有2个元素
                    if len(item) < 2:
                        continue
                        
                    entry = {
                        'Text': str(item[0]),
                        'Type': str(item[1]),
                        'Start': int(item[2]) if len(item) > 2 else -1,
                        'End': int(item[3]) if len(item) > 3 else -1
                    }
                    sentence_data.append(entry)
                
                if sentence_data:  # 只添加非空的句子数据
                    result.append(sentence_data)
            
            return json.dumps(result, ensure_ascii=False, indent=2)
        except Exception as e:
            logger.error(f"人物角色识别失败: {e}")
            return json.dumps([])
    
    def get_con(self, text):
        """解析文本的句法结构并返回结果"""
        if not text or not isinstance(text, str):
            logger.warning("无效的输入文本")
            return None
            
        try:
            if not self.mtl:
                raise RuntimeError("模型未加载")
            
            logger.info("开始句法分析...")
            result = self.mtl(text, tasks=['con'])
            logger.info("句法分析完成")
            return result
        except Exception as e:
            logger.error(f"句法分析失败: {e}")
            return None

    def get_all(self,text):
        result = self.mtl(text)
        return result;

    def get_con_json(self, text):
        """解析文本的句法结构并返回JSON格式字符串，便于C#对象处理"""
        # 重置ID计数器和父ID，确保每次调用都从1开始
        self.item_id_counter = 0
        self.current_father_id = 0
        
        # 初始化JSON数组用于存储所有解析结果元素
        result_array = []
        # 保存结果数组的引用，供packLabel方法使用
        self.result_array = result_array
        
        if not text or not isinstance(text, str):
            logger.warning("无效的输入文本")
            return json.dumps([])
            
        if not self.mtl:
            raise RuntimeError("模型未加载")
        
        logger.info("开始句法分析...")
        results = self.mtl(text, tasks=['con'])
        logger.info("句法分析完成")
        
        # 检查结果格式
        if 'con' not in results:
            logger.warning("模型返回结果中未找到CON数据")
            return json.dumps([])
            
        #从results中截取"con": [截取字符串]}
        # 将results转为字符串并提取con字段值
        results_str = str(results)
        # logger.info(results_str)
        # 只取出top的内容
        new_results = self.get_con_top('"con": [',results_str)
        level=0

        self.item_id_counter += 1
        
        # 调用pack_childen处理所有节点，所有生成的元素将通过packLabel方法添加到result_array
        try:
            self.pack_childen(new_results, level)
        except Exception as e:
            logger.warning(f"pack_childen处理时出错: {e}")
            
        # 清理引用，避免内存泄漏
        delattr(self, 'result_array')
            
        # 将结果数组转换为JSON字符串返回，便于C#交互
        return json.dumps(result_array, ensure_ascii=False)

    def pack_childen(self, text, level, father_id=None):
        # 如果没有提供father_id，则使用当前类的current_father_id
        if father_id is None:
            father_id = self.current_father_id
        
        childen_json = []
        json_temp = self.split(text, ',')
        # 去除前后的[]
        childen = self.substring_before_after_one(json_temp[1])
        # 分隔元素
        childens = self.split(childen, ',')
        label = json_temp[0]
        
        if json_temp[0] == '_':
            # "_", ["二愣子"] - 提取列表中的值
            # 当标签为"_"时，提取childen中的值并直接返回值字符串
            label = self.substring_before_after_one(childen)
            # 保存当前的current_father_id
            current_father = self.current_father_id
            # 设置当前元素的父ID
            self.current_father_id = father_id
            # 创建节点
            top_json = self.packLabel(label, level, 0)
            # 恢复原来的current_father_id
            self.current_father_id = current_father
            # logger.info(top_json)
            return
        
        # 保存当前的current_father_id
        current_father = self.current_father_id
        # 设置当前元素的父ID
        self.current_father_id = father_id
        # 创建当前节点
        top_json = self.packLabel(label, level, len(childens))
        # 获取当前节点的ID作为子节点的父ID
        current_node_id = top_json['ItemId']
        # 恢复原来的current_father_id
        self.current_father_id = current_father
        
        # logger.info(top_json)
        
        # 递归处理子节点，传递当前节点的ID作为父ID
        for i, item in enumerate(childens):
            childen_temp = self.substring_before_after_one(item)
            childen_json.append(self.pack_childen(childen_temp, level + 1, current_node_id))
        

    def get_task(self, text, task):
        '''
        执行指定的任务并跳过fine分词
        
        参数:
            text: 输入文本
            task: 要执行的任务名称
            
        返回:
            任务执行结果
        '''
        if not text or not isinstance(text, str):
            logger.warning('无效的输入文本')
            return None
            
        try:
            if not self.mtl:
                raise RuntimeError('模型未加载')
            
            # 跳过tok/fine任务
            result = self.mtl(text, tasks=[task])
            return result
        except Exception as e:
            logger.error(f'执行任务{task}失败: {e}')
            return None

    def get_con_top(self,from_text,text):
        if '"con": [' in text:
            start = text.find(from_text) + len(from_text)
            # 寻找对应的结束括号
            end = start
            bracket_count = 1
            while end < len(text) and bracket_count > 0:
                if text[end] == '[':
                    bracket_count += 1
                elif text[end] == ']':
                    bracket_count -= 1
                end += 1
            new_results = text[start:end-1]  # -1 是为了去掉最后的']'
        else:
            new_results = ''
        return new_results

    def substring_before_after_one(self, text):
        if not text or not isinstance(text, str):
            return text
        if len(text) < 2:
            return text
        return text[1:-1]

    def split(self, text, char):
        if not text:
            return []
            
        result = []
        current = []
        in_quotes = False  # 是否在引号内
        bracket_level = 0  # 括号嵌套级别
        
        i = 0
        while i < len(text):
            c = text[i]
            
            # 处理引号
            if c == '"':
                in_quotes = not in_quotes
                current.append(c)
            
            # 处理括号
            elif c == '[' and not in_quotes:
                bracket_level += 1
                current.append(c)
            elif c == ']' and not in_quotes:
                if bracket_level > 0:
                    bracket_level -= 1
                current.append(c)
            
            # 遇到分隔符且不在括号内和引号内
            elif c == char and bracket_level == 0 and not in_quotes:
                # 添加当前部分到结果
                part = ''.join(current).strip()
                # 清理引号（如果有）
                if part.startswith('"') and part.endswith('"'):
                    part = part[1:-1]
                result.append(part)
                current = []
            
            # 处理转义字符
            elif c == '\\' and i + 1 < len(text):
                current.append(c)
                current.append(text[i + 1])
                i += 1
            
            else:
                current.append(c)
            
            i += 1
        
        # 处理最后一个元素
        if current:
            part = ''.join(current).strip()
            # 清理引号（如果有）
            if part.startswith('"') and part.endswith('"'):
                part = part[1:-1]
            result.append(part)
            
        return result
        
    def packLabel(self, label, level, children):
        # 保存当前ID作为返回值
        current_id = self.item_id_counter
        
        # 创建包含ID和父ID的字典
        result = {
            "ItemId": current_id,
            "FatherId": self.current_father_id,
            "Label": label,
            "Level": level,
            "Children": children
        }
        
        # 将生成的元素添加到result_array（如果存在）
        if hasattr(self, 'result_array'):
            self.result_array.append(result)
        
        # 更新计数器
        self.item_id_counter += 1
        
        return result
    
    def extract_underscore_value(self, text):
        """
        从"_", ["值"]格式的文本中提取值，并直接返回字符串值
        
        Args:
            text: 格式为"_", ["值"]的字符串
            
        Returns:
            提取的字符串值
        """
        # 分割文本以获取值部分
        parts = self.split(text, ',')
        if len(parts) < 2:
            return ""
            
        # 获取第二个部分（包含值的列表）
        value_part = parts[1]
        # 去除前后的[]和可能的引号
        value = self.substring_before_after_one(value_part).strip('"')
        
        # 直接返回提取的值
        return value
        
processor=HanLPProcessor()

# 测试split函数
def test_split_function():
    # 测试用例1: 简单的逗号分隔
    text1 = ' "TOP",[["IP", [["NP", [["_", ["二愣子"]]]], ["VP", [["VP", [["_", ["姓"]], ["NP", [["_", ["韩"]]]]]], ["VP", [["_", ["名"]], ["NP", [["_", ["立"]]]]]]]], ["_", ["。"]]]]]'
    result1 = processor.split(text1, ',')
    print("测试用例1结果:")
    for i, item in enumerate(result1):
        print(f"  {i}: {item}")
    
    text1='二愣子姓韩名立。'
    print(processor.get_con_json(text1))
    # 测试用例2: 括号内包含逗号
    # text2 = '"NP",[["NN",["_", "张三"]],["NN",["_", "李四"]]]'
    # result2 = processor.split(text2, ',')
    # print("\n测试用例2结果:")

def test_extract_underscore_value():
    """
    测试新添加的extract_underscore_value函数
    """
    print("\n测试extract_underscore_value函数:")
    
    # 测试用例1: 基本格式
    test_text1 = '"_", ["二愣子"]'
    result1 = processor.extract_underscore_value(test_text1)
    print(f"输入: {test_text1}")
    print(f"输出: {result1}")
    
    # 测试用例2: 其他值
    test_text2 = '"_", ["张三"]'
    result2 = processor.extract_underscore_value(test_text2)
    print(f"\n输入: {test_text2}")
    print(f"输出: {result2}")
    
    # 测试用例3: 空值情况
    test_text3 = '"_", []'
    result3 = processor.extract_underscore_value(test_text3)
    print(f"\n输入: {test_text3}")
    print(f"输出: {result3}")

# 测试修复后的pack_childen方法对"_", ["二愣子"]格式的处理
def test_pack_childen_with_underscore():
    """
    测试pack_childen方法对"_", ["值"]格式的处理
    """
    print("\n测试pack_childen方法对'_', ['二愣子']格式的处理:")
    # 测试用例：直接调用pack_childen处理"_", ["二愣子"]格式
    test_input = '"_", ["二愣子"]'
    print(f"输入: {test_input}")
    try:
        # 调用pack_childen方法，使用level=0
        result = processor.pack_childen(test_input, 0)
        # 由于pack_childen在处理"_"标签时会直接返回，我们需要查看logger输出
        print("测试完成，请检查日志输出")
    except Exception as e:
        print(f"测试出错: {e}")
    # for i, item in enumerate(result2):
    #     print(f"  {i}: {item}")

# 如果直接运行此文件，则执行测试
def test_id_generation():
    """
    测试ItemId和FatherId的生成逻辑
    """
    print("开始测试ID生成...")
    # 创建处理器实例
    processor = HanLPProcessor()
    
    # 测试基本的packLabel方法
    print("\n测试基本的packLabel方法:")
    result1 = processor.packLabel("TOP", 0, 1)
    print(result1)
    
    # 测试生成第二个节点，应该有正确的父子关系
    print("\n测试生成第二个节点:")
    # 对于第二个节点，我们手动设置它的父ID为第一个节点的ID
    processor.current_father_id = result1['ItemId']
    result2 = processor.packLabel("TOP", 0, 1)
    print(result2)
    
    # 测试pack_childen方法
    print("\n测试pack_childen方法:")
    # 重置处理器的ID计数器
    processor.item_id_counter = 1
    processor.current_father_id = 0
    
    # 使用一个简单的测试字符串来模拟解析结果，确保格式正确
    test_input = "TOP, [IP, [NP, [NN, 你]], [VP, [VV, 好]]]"
    try:
        processor.pack_childen(test_input, 0)
        print("pack_childen测试成功")
    except Exception as e:
        print(f"pack_childen测试失败: {e}")
        # 为了演示，我们可以直接测试packLabel的组合
        print("\n直接测试ID和父子关系:")
        processor.item_id_counter = 1
        processor.current_father_id = 0
        
        # 模拟用户要求的输出格式
        result1 = processor.packLabel("TOP", 0, 1)
        print(f"{{'ItemId':{result1['ItemId']},'FatherId':{result1['FatherId']},'Label': '{result1['Label']}', 'Level': {result1['Level']}, 'Children': {result1['Children']}}}")
        
        # 第二个节点的父ID是第一个节点的ID
        processor.current_father_id = result1['ItemId']
        result2 = processor.packLabel("TOP", 0, 1)
        print(f"{{'ItemId':{result2['ItemId']},'FatherId':{result2['FatherId']},'Label': '{result2['Label']}', 'Level': {result2['Level']}, 'Children': {result2['Children']}}}")
    
    print("\nID生成测试完成")

def test_con_json():
    """
    测试get_con_json方法返回的JSON格式字符串
    """
    print("\n开始测试get_con_json方法...")
    processor = HanLPProcessor()
    
    # 测试空输入
    empty_result = processor.get_con_json("")
    print(f"空输入测试: {empty_result}")
    print(f"空输入结果类型: {type(empty_result)}")
    # 验证可以解析为空数组
    try:
        parsed_empty = json.loads(empty_result)
        print(f"空输入解析结果: {parsed_empty}, 类型: {type(parsed_empty)}")
    except json.JSONDecodeError as e:
        print(f"空输入JSON解析失败: {e}")
    
    # 测试None输入
    none_result = processor.get_con_json(None)
    print(f"None输入测试: {none_result}")
    print(f"None输入结果类型: {type(none_result)}")
    # 验证可以解析为空数组
    try:
        parsed_none = json.loads(none_result)
        print(f"None输入解析结果: {parsed_none}, 类型: {type(parsed_none)}")
    except json.JSONDecodeError as e:
        print(f"None输入JSON解析失败: {e}")
    
    # 尝试测试有效输入（注意：由于可能没有加载模型，这里可能会抛出异常）
    try:
        # 尝试简单的中文文本
        text = "二愣子姓韩名立。"
        print(f"测试有效输入: {text}")
        result = processor.get_con_json(text)
        print(f"返回结果类型: {type(result)}")
        print(f"返回结果字符串长度: {len(result)}")
        
        # 验证返回的是字符串
        assert isinstance(result, str), "返回结果应该是字符串类型"
        print("✓ 验证通过：返回结果是字符串类型")
        
        # 尝试解析JSON字符串
        try:
            parsed_result = json.loads(result)
            print(f"✓ JSON解析成功，解析后类型: {type(parsed_result)}")
            print(f"解析后数组长度: {len(parsed_result)}")
            
            # 如果解析成功且有元素，显示部分内容
            if parsed_result:
                print(f"解析后第一个元素: {parsed_result[0]}")
                if len(parsed_result) > 1:
                    print(f"解析后第二个元素: {parsed_result[1]}")
        except json.JSONDecodeError as e:
            print(f"✗ JSON解析失败: {e}")
            
        print(f"完整返回结果: {result}")
    except Exception as e:
        print(f"有效输入测试出错（可能是模型未加载）: {e}")
    
    print("get_con_json方法测试完成")

if __name__ == "__main__":
    # test_split_function()
    # test_id_generation()
    # test_extract_underscore_value()
    # test_pack_childen_with_underscore()
    test_con_json()
    # processor=HanLPProcessor()
  
*/