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Test.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a2a88bfa",
+   "metadata": {},
+   "source": [
+    "# 测试脚本\n",
+    "\n",
+    "使用此脚本需要打开 RWKV Runner，通过调用 API 接口进行批量测试。\n",
+    "\n",
+    "使用该脚本时，被测试的 jsonl 文件需要是和训练集相同的单论问答对话；\n",
+    "\n",
+    "测试时，会同时生成一个‘测试结果’和‘正确答案’的对比到指定 jsonl 中。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "139fd74a",
+   "metadata": {},
+   "source": [
+    "## 测试指定 jsonl 文件"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11af4de0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %% [markdown]\n",
+    "# # 模型测试脚本 (单单元格, 简化逐行结果, 更新请求参数)\n",
+    "#\n",
+    "# 请按以下步骤操作：\n",
+    "# **1.**  **修改配置部分**: 找到下面的 `--- 配置 ---` 部分，并更新 `JSONL_FILE_PATH`, `OUTPUT_JSONL_PATH`, `API_URL`, `HEADERS`。`REQUEST_PARAMS` 已根据您的要求更新。\n",
+    "# **2.**  **检查 API 响应解析**: 在 `get_model_completion` 函数内部，找到标记为 `!!! 重要 !!!` 的部分，确保代码能正确解析你的模型 API 返回的 JSON 数据以提取文本输出。\n",
+    "# **3.**  **运行此单元格**: 执行这个单元格开始测试。结果将逐行写入指定的 `OUTPUT_JSONL_PATH` 文件。\n",
+    "\n",
+    "# %%\n",
+    "import requests\n",
+    "import json\n",
+    "import sys\n",
+    "import os\n",
+    "from tqdm.notebook import tqdm # 使用 notebook 版本的 tqdm\n",
+    "from datetime import datetime # 用于添加时间戳\n",
+    "\n",
+    "# --- 配置 ---\n",
+    "# vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv\n",
+    "# vvvvvvvvvvvvvvvvv 请在这里修改你的配置 vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv\n",
+    "\n",
+    "JSONL_FILE_PATH = \"./Test/ADD_base_test.jsonl\"  # <--- 替换为你的 **输入** JSONL 文件路径\n",
+    "OUTPUT_JSONL_PATH = \"./Test/result/ADD_base_test-20250526.jsonl\" # <--- 设置 **输出** JSONL 文件的路径\n",
+    "API_URL = \"http://19**2.**168.0.103:8022/v1/completions\"\n",
+    "\n",
+    "# API 请求的 Headers (如果需要身份验证等，在这里添加)\n",
+    "HEADERS = {\n",
+    "    'Content-Type': 'application/json',\n",
+    "    # 'Authorization': 'Bearer YOUR_API_KEY' # 如果需要 API Key\n",
+    "}\n",
+    "\n",
+    "# API 请求的参数 (根据您的要求更新)\n",
+    "# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
+    "# !!!            这里是更新后的请求参数                     !!!\n",
+    "# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
+    "REQUEST_PARAMS = {\n",
+    "    \"max_tokens\": 100,\n",
+    "    \"temperature\": 0.4,\n",
+    "    \"top_p\": 0, # 注意：top_p=0 比较少见，通常接近 1 或等于 1，请确认是否符合预期\n",
+    "    \"presence_penalty\": 0,\n",
+    "    \"frequency_penalty\": 0,\n",
+    "    \"stop\": [\"\\n\", \"User:\"]\n",
+    "}\n",
+    "# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
+    "\n",
+    "# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+    "# ^^^^^^^^^^^^^^^^^^^^^^^^^ 配置结束 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+    "# --- 配置结束 ---\n",
+    "\n",
+    "print(\"--- 配置加载 ---\")\n",
+    "print(f\"输入测试文件路径: {JSONL_FILE_PATH}\")\n",
+    "print(f\"输出结果文件路径: {OUTPUT_JSONL_PATH} (仅包含: expected, actual, is_correct)\")\n",
+    "print(f\"API 地址: {API_URL}\")\n",
+    "print(f\"请求参数 (已更新): {REQUEST_PARAMS}\") # 确认参数已更新\n",
+    "print(\"-\" * 30)\n",
+    "\n",
+    "# --- 辅助函数 (与之前相同) ---\n",
+    "\n",
+    "def process_jsonl_line(line):\n",
+    "    \"\"\"解析 JSONL 行，提取 prompt 和 expected_answer\"\"\"\n",
+    "    try:\n",
+    "        data = json.loads(line)\n",
+    "        full_text = data.get(\"text\")\n",
+    "        if not full_text:\n",
+    "            return None, None\n",
+    "\n",
+    "        parts = full_text.split(\"\\n\\nAssistant:\", 1)\n",
+    "        if len(parts) != 2:\n",
+    "            return None, None\n",
+    "\n",
+    "        prompt = parts[0] + \"\\n\\nAssistant:\"\n",
+    "        expected_answer = parts[1].strip()\n",
+    "\n",
+    "        return prompt, expected_answer\n",
+    "\n",
+    "    except (json.JSONDecodeError, Exception):\n",
+    "        return None, None\n",
+    "\n",
+    "def get_model_completion(prompt):\n",
+    "    \"\"\"向模型 API 发送请求并获取输出\"\"\"\n",
+    "    payload = {\n",
+    "        \"prompt\": prompt,\n",
+    "        **REQUEST_PARAMS # 这里会自动使用上面更新后的参数\n",
+    "    }\n",
+    "    try:\n",
+    "        response = requests.post(API_URL, headers=HEADERS, json=payload, timeout=60)\n",
+    "        response.raise_for_status()\n",
+    "        response_data = response.json()\n",
+    "\n",
+    "        # --- 解析模型输出 ---\n",
+    "        # !!! 重要: 这里需要根据你的 API 返回的具体格式来调整 !!!\n",
+    "        model_output = None\n",
+    "        try:\n",
+    "            model_output = response_data.get('choices', [{}])[0].get('text', '').strip()\n",
+    "            # ===> 如果上面这行无效，你需要根据你的 API 返回调整这里 <===\n",
+    "            if model_output is None: model_output = \"\"\n",
+    "        except (KeyError, IndexError, AttributeError, TypeError):\n",
+    "             return None # 表示解析失败\n",
+    "\n",
+    "        return model_output\n",
+    "\n",
+    "    except (requests.exceptions.RequestException, json.JSONDecodeError, Exception):\n",
+    "        return None\n",
+    "\n",
+    "# --- 主测试逻辑 (与之前相同) ---\n",
+    "\n",
+    "# 检查输入文件是否存在\n",
+    "if not os.path.exists(JSONL_FILE_PATH):\n",
+    "    print(f\"错误：输入文件未找到 '{JSONL_FILE_PATH}'。请确保路径正确并重新运行单元格。\")\n",
+    "else:\n",
+    "    total_count = 0\n",
+    "    correct_count = 0\n",
+    "    lines_processed = 0\n",
+    "    invalid_format_count = 0\n",
+    "    api_errors = 0\n",
+    "\n",
+    "    print(f\"\\n开始使用文件进行模型测试: {JSONL_FILE_PATH}\")\n",
+    "    print(f\"简化结果将逐行写入: {OUTPUT_JSONL_PATH}\")\n",
+    "    print(\"-\" * 30)\n",
+    "\n",
+    "    try:\n",
+    "        with open(OUTPUT_JSONL_PATH, 'a', encoding='utf-8') as outfile:\n",
+    "            try:\n",
+    "                 num_lines = sum(1 for line in open(JSONL_FILE_PATH, 'r', encoding='utf-8'))\n",
+    "            except Exception:\n",
+    "                 num_lines = None\n",
+    "\n",
+    "            with open(JSONL_FILE_PATH, 'r', encoding='utf-8') as infile:\n",
+    "                file_iterator = tqdm(infile, total=num_lines, desc=\"测试进度\", unit=\" 行\", bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]')\n",
+    "\n",
+    "                for line_num, line in enumerate(file_iterator):\n",
+    "                    lines_processed += 1\n",
+    "                    line = line.strip()\n",
+    "                    if not line: continue\n",
+    "\n",
+    "                    prompt, expected_answer = process_jsonl_line(line)\n",
+    "\n",
+    "                    if prompt is None or expected_answer is None:\n",
+    "                        invalid_format_count += 1\n",
+    "                        accuracy = (correct_count / total_count * 100) if total_count > 0 else 0\n",
+    "                        file_iterator.set_postfix_str(f\"正确:{correct_count}/{total_count} Acc:{accuracy:.1f}% APIErr:{api_errors} FormatErr:{invalid_format_count}\")\n",
+    "                        continue\n",
+    "\n",
+    "                    model_output = get_model_completion(prompt)\n",
+    "\n",
+    "                    if model_output is not None:\n",
+    "                        total_count += 1\n",
+    "                        is_correct = (model_output == expected_answer)\n",
+    "                        if is_correct: correct_count += 1\n",
+    "\n",
+    "                        result_data = {\n",
+    "                            \"expected_answer\": expected_answer,\n",
+    "                            \"model_output\": model_output,\n",
+    "                            \"is_correct\": is_correct\n",
+    "                        }\n",
+    "                        json_string = json.dumps(result_data, ensure_ascii=False)\n",
+    "                        outfile.write(json_string + '\\n')\n",
+    "                        outfile.flush()\n",
+    "                    else:\n",
+    "                        api_errors += 1\n",
+    "\n",
+    "                    accuracy = (correct_count / total_count * 100) if total_count > 0 else 0\n",
+    "                    file_iterator.set_postfix_str(f\"正确:{correct_count}/{total_count} Acc:{accuracy:.1f}% APIErr:{api_errors} FormatErr:{invalid_format_count}\")\n",
+    "\n",
+    "    except FileNotFoundError:\n",
+    "         print(f\"错误：处理期间未找到输入文件 '{JSONL_FILE_PATH}'。\")\n",
+    "    except IOError as e:\n",
+    "         print(f\"错误: 读写文件时发生错误: {e}\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"\\n处理文件时发生意外错误: {e}\")\n",
+    "        import traceback\n",
+    "        traceback.print_exc()\n",
+    "\n",
+    "    print(\"\\n测试完成!\")\n",
+    "    print(f\"总共处理了 {lines_processed} 行输入文件。\")\n",
+    "    print(f\"{total_count} 个有效测试结果已写入/追加到 {OUTPUT_JSONL_PATH}\")\n",
+    "\n",
+    "    # --- 显示最终总结 ---\n",
+    "    print(\"\\n\" + \"=\" * 30)\n",
+    "    print(\"测试总结\")\n",
+    "    print(\"=\" * 30)\n",
+    "    print(f\"输入文件中格式无效的行数: {invalid_format_count}\")\n",
+    "    print(f\"API 调用或响应解析错误数: {api_errors}\")\n",
+    "    print(f\"成功获取模型输出的测试用例总数: {total_count}\")\n",
+    "    print(f\"模型正确预测数: {correct_count}\")\n",
+    "\n",
+    "    if total_count > 0:\n",
+    "        accuracy = (correct_count / total_count) * 100\n",
+    "        print(f\"准确率 (AC Rate) [正确数 / 成功获取结果数]: {accuracy:.2f}% ({correct_count}/{total_count})\")\n",
+    "    elif lines_processed > 0:\n",
+    "         print(\"准确率 (AC Rate): N/A (没有成功获取任何模型输出)\")\n",
+    "    else:\n",
+    "        print(\"准确率 (AC Rate): N/A (没有处理任何行)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae91c93a",
+   "metadata": {},
+   "source": [
+    "## 测试整个文件夹中的全部 jsonl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43660c81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %% [markdown]\n",
+    "# # 模型测试脚本 (批量处理文件夹中的jsonl文件)\n",
+    "#\n",
+    "# 请按以下步骤操作：\n",
+    "# **1.**  **修改配置部分**: 找到下面的 `--- 配置 ---` 部分，并更新 `INPUT_FOLDER_PATH`, `OUTPUT_FOLDER_PATH`, `API_URL`, `HEADERS`。`REQUEST_PARAMS` 已根据您的要求更新。\n",
+    "# **2.**  **检查 API 响应解析**: 在 `get_model_completion` 函数内部，找到标记为 `!!! 重要 !!!` 的部分，确保代码能正确解析你的模型 API 返回的 JSON 数据以提取文本输出。\n",
+    "# **3.**  **运行此单元格**: 执行这个单元格开始测试。结果将逐文件写入输出文件夹。\n",
+    "\n",
+    "# %%\n",
+    "import requests\n",
+    "import json\n",
+    "import sys\n",
+    "import os\n",
+    "import csv # 导入csv模块\n",
+    "from tqdm.notebook import tqdm # 使用 notebook 版本的 tqdm\n",
+    "from datetime import datetime # 用于添加时间戳\n",
+    "from IPython import get_ipython # 用于在 Jupyter Notebook 中检测环境\n",
+    "\n",
+    "# --- 配置 ---\n",
+    "# vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv\n",
+    "# vvvvvvvvvvvvvvvvv 请在这里修改你的配置 vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv\n",
+    "\n",
+    "INPUT_FOLDER_PATH = \"./Test\"                            # 输入路径 \n",
+    "OUTPUT_FOLDER_PATH = \"./Test/Results/20250526/15\"       # 输出路径 \n",
+    "API_URL = \"http://19**2.**168.0.103:8022/v1/completions\"    # API 地址\n",
+    "\n",
+    "HEADERS = {\n",
+    "    'Content-Type': 'application/json',\n",
+    "}\n",
+    "\n",
+    "REQUEST_PARAMS = {\n",
+    "    \"max_tokens\": 100,\n",
+    "    \"temperature\": 0.4,\n",
+    "    \"top_p\": 0,\n",
+    "    \"presence_penalty\": 0,\n",
+    "    \"frequency_penalty\": 0,\n",
+    "    \"stop\": [\"\\n\", \"User:\"]\n",
+    "}\n",
+    "# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+    "# ^^^^^^^^^^^^^^^^^^^^^^^^^ 配置结束 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+    "# --- 配置结束 ---\n",
+    "\n",
+    "print(\"--- 配置加载 ---\")\n",
+    "print(f\"输入文件夹路径: {INPUT_FOLDER_PATH}\")\n",
+    "print(f\"输出文件夹路径: {OUTPUT_FOLDER_PATH}\")\n",
+    "print(f\"API 地址: {API_URL}\")\n",
+    "print(f\"请求参数 (已更新): {REQUEST_PARAMS}\")\n",
+    "print(\"-\" * 30)\n",
+    "\n",
+    "# --- 辅助函数 ---\n",
+    "def process_jsonl_line(line):\n",
+    "    try:\n",
+    "        data = json.loads(line)\n",
+    "        full_text = data.get(\"text\")\n",
+    "        if not full_text: return None, None\n",
+    "        parts = full_text.split(\"\\n\\nAssistant:\", 1)\n",
+    "        if len(parts) != 2: return None, None\n",
+    "        prompt = parts[0] + \"\\n\\nAssistant:\"\n",
+    "        expected_answer = parts[1].strip()\n",
+    "        return prompt, expected_answer\n",
+    "    except: return None, None\n",
+    "\n",
+    "def get_model_completion(prompt):\n",
+    "    payload = {\"prompt\": prompt, **REQUEST_PARAMS}\n",
+    "    try:\n",
+    "        response = requests.post(API_URL, headers=HEADERS, json=payload, timeout=60)\n",
+    "        response.raise_for_status()\n",
+    "        response_data = response.json()\n",
+    "        # !!! 重要: 这里需要根据你的 API 返回的具体格式来调整 !!!\n",
+    "        model_output = response_data.get('choices', [{}])[0].get('text', '').strip()\n",
+    "        return model_output if model_output is not None else \"\"\n",
+    "    except (requests.exceptions.RequestException, json.JSONDecodeError, KeyError, IndexError, AttributeError, TypeError) as e:\n",
+    "        # print(f\"API请求或解析错误: {e}\") # 可以取消注释以调试API问题\n",
+    "        return None\n",
+    "\n",
+    "def process_single_file(input_file_path, output_file_path):\n",
+    "    total_count, correct_count, lines_processed, invalid_format_count, api_errors = 0, 0, 0, 0, 0\n",
+    "    print(f\"\\n开始处理文件: {input_file_path}\")\n",
+    "    print(f\"详细结果将写入: {output_file_path}\")\n",
+    "    # print(\"-\" * 30) # 减少重复打印分隔线\n",
+    "    try:\n",
+    "        with open(output_file_path, 'w', encoding='utf-8') as outfile:\n",
+    "            try:\n",
+    "                 with open(input_file_path, 'r', encoding='utf-8') as f_count: num_lines = sum(1 for _ in f_count)\n",
+    "            except: num_lines = None\n",
+    "\n",
+    "            with open(input_file_path, 'r', encoding='utf-8') as infile:\n",
+    "                # tqdm的bar_format可以保持简洁一些\n",
+    "                file_iterator = tqdm(infile, total=num_lines, desc=f\"测试 {os.path.basename(input_file_path)}\", unit=\" 行\", bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]')\n",
+    "                for line in file_iterator:\n",
+    "                    lines_processed += 1\n",
+    "                    line = line.strip()\n",
+    "                    if not line: continue\n",
+    "                    prompt, expected_answer = process_jsonl_line(line)\n",
+    "                    if prompt is None or expected_answer is None:\n",
+    "                        invalid_format_count += 1\n",
+    "                    else:\n",
+    "                        model_output = get_model_completion(prompt)\n",
+    "                        if model_output is not None:\n",
+    "                            total_count += 1 # 有效的API响应和测试对\n",
+    "                            is_correct = (model_output == expected_answer)\n",
+    "                            if is_correct: correct_count += 1\n",
+    "                            result_data = {\"expected_answer\": expected_answer, \"model_output\": model_output, \"is_correct\": is_correct}\n",
+    "                            outfile.write(json.dumps(result_data, ensure_ascii=False) + '\\n')\n",
+    "                        else:\n",
+    "                            api_errors += 1\n",
+    "                    # 更新进度条后缀\n",
+    "                    accuracy = (correct_count / total_count * 100) if total_count > 0 else 0.0\n",
+    "                    file_iterator.set_postfix_str(f\"正确:{correct_count}/{total_count} ({accuracy:.1f}%) APIErr:{api_errors} FormatErr:{invalid_format_count}\")\n",
+    "                outfile.flush()\n",
+    "    except FileNotFoundError:\n",
+    "         print(f\"错误：处理期间未找到输入文件 '{input_file_path}'。\")\n",
+    "         return None\n",
+    "    except IOError as e:\n",
+    "         print(f\"错误: 读写文件 '{output_file_path}' 时发生错误: {e}\")\n",
+    "         return None\n",
+    "    except Exception as e:\n",
+    "        print(f\"\\n处理文件 '{os.path.basename(input_file_path)}' 时发生意外错误: {e}\")\n",
+    "        import traceback; traceback.print_exc()\n",
+    "        return None\n",
+    "    \n",
+    "    final_accuracy = (correct_count / total_count * 100) if total_count > 0 else 0.0\n",
+    "    # 确保返回的字典键名清晰\n",
+    "    return {\n",
+    "        \"filename\": os.path.basename(input_file_path), # 文件全名\n",
+    "        \"lines_processed\": lines_processed,\n",
+    "        \"invalid_format_count\": invalid_format_count,\n",
+    "        \"api_errors\": api_errors,\n",
+    "        \"total_valid_tests\": total_count, # 测试数据条数\n",
+    "        \"correct_predictions\": correct_count, # 正确条数\n",
+    "        \"accuracy_percent\": final_accuracy # 正确率\n",
+    "    }\n",
+    "\n",
+    "# --- 主处理逻辑 ---\n",
+    "def main():\n",
+    "    os.makedirs(OUTPUT_FOLDER_PATH, exist_ok=True)\n",
+    "    today_date = datetime.now().strftime(\"%Y%m%d\")\n",
+    "    \n",
+    "    input_files_paths = [os.path.join(INPUT_FOLDER_PATH, item) for item in os.listdir(INPUT_FOLDER_PATH) if item.endswith('.jsonl') and os.path.isfile(os.path.join(INPUT_FOLDER_PATH, item))]\n",
+    "    \n",
+    "    if not input_files_paths:\n",
+    "        print(f\"错误：在输入文件夹 '{INPUT_FOLDER_PATH}' 中未找到任何jsonl文件。\")\n",
+    "        return\n",
+    "    \n",
+    "    print(f\"\\n找到 {len(input_files_paths)} 个jsonl文件待处理:\")\n",
+    "    for file_path_item in input_files_paths: print(f\"  - {os.path.basename(file_path_item)}\")\n",
+    "    \n",
+    "    all_stats_collected = []\n",
+    "    \n",
+    "    summary_txt_filename = f\"accuracy_summary_{today_date}.txt\"\n",
+    "    summary_txt_filepath = os.path.join(OUTPUT_FOLDER_PATH, summary_txt_filename)\n",
+    "    \n",
+    "    summary_csv_filename = f\"accuracy_summary_{today_date}.csv\"\n",
+    "    summary_csv_filepath = os.path.join(OUTPUT_FOLDER_PATH, summary_csv_filename)\n",
+    "\n",
+    "    try:\n",
+    "        with open(summary_txt_filepath, 'w', encoding='utf-8') as summary_txt_file, \\\n",
+    "             open(summary_csv_filepath, 'w', encoding='utf-8', newline='') as summary_csv_file:\n",
+    "            \n",
+    "            csv_writer = csv.writer(summary_csv_file)\n",
+    "\n",
+    "            # 写入TXT文件头 (保持不变)\n",
+    "            summary_txt_file.write(f\"测试日期: {today_date}\\n\")\n",
+    "            summary_txt_file.write(\"=\"*40 + \"\\n\")\n",
+    "            summary_txt_file.write(f\"{'文件名':<30} | {'正确率':>7}\\n\")\n",
+    "            summary_txt_file.write(\"=\"*40 + \"\\n\")\n",
+    "            summary_txt_file.flush()\n",
+    "\n",
+    "            # --- 修改CSV文件头 ---\n",
+    "            csv_header = ['文件名', '测试数据条数', '正确条数', '正确率 (%)']\n",
+    "            csv_writer.writerow(csv_header)\n",
+    "            summary_csv_file.flush()\n",
+    "\n",
+    "            for current_input_file_path in input_files_paths:\n",
+    "                base_name = os.path.basename(current_input_file_path)\n",
+    "                name_without_ext = os.path.splitext(base_name)[0]\n",
+    "                output_jsonl_file = os.path.join(OUTPUT_FOLDER_PATH, f\"{name_without_ext}_{today_date}.jsonl\")\n",
+    "                \n",
+    "                stats_data = process_single_file(current_input_file_path, output_jsonl_file)\n",
+    "                \n",
+    "                if stats_data:\n",
+    "                    all_stats_collected.append(stats_data)\n",
+    "                    \n",
+    "                    # 写入TXT文件 (文件名缩短逻辑保持)\n",
+    "                    filename_display_txt = stats_data['filename']\n",
+    "                    if len(filename_display_txt) > 28: filename_display_txt = filename_display_txt[:25] + \"...\"\n",
+    "                    summary_txt_file.write(f\"{filename_display_txt:<30} | {stats_data['accuracy_percent']:>6.2f}%\\n\")\n",
+    "                    summary_txt_file.flush()\n",
+    "\n",
+    "                    # --- 修改写入CSV文件的数据行 ---\n",
+    "                    csv_row = [\n",
+    "                        stats_data['filename'],  # 文件全名\n",
+    "                        stats_data['total_valid_tests'],\n",
+    "                        stats_data['correct_predictions'],\n",
+    "                        f\"{stats_data['accuracy_percent']:.2f}%\" # 格式化正确率\n",
+    "                    ]\n",
+    "                    csv_writer.writerow(csv_row)\n",
+    "                    summary_csv_file.flush()\n",
+    "            \n",
+    "            # 所有文件处理完毕后，写入总体统计\n",
+    "            if all_stats_collected:\n",
+    "                # 使用 process_single_file 返回的键名\n",
+    "                grand_total_lines = sum(s[\"lines_processed\"] for s in all_stats_collected)\n",
+    "                grand_total_tests = sum(s[\"total_valid_tests\"] for s in all_stats_collected)\n",
+    "                grand_total_correct = sum(s[\"correct_predictions\"] for s in all_stats_collected)\n",
+    "                overall_accuracy_percent = (grand_total_correct / grand_total_tests * 100) if grand_total_tests > 0 else 0.0\n",
+    "                \n",
+    "                # 写入TXT总体统计 (保持不变)\n",
+    "                summary_txt_file.write(\"=\"*40 + \"\\n\")\n",
+    "                summary_txt_file.write(f\"{'总体准确率':<30} | {overall_accuracy_percent:>6.2f}%\\n\")\n",
+    "                summary_txt_file.write(\"=\"*40 + \"\\n\")\n",
+    "                summary_txt_file.flush()\n",
+    "\n",
+    "                # --- 修改写入CSV的总体统计行 ---\n",
+    "                csv_writer.writerow([]) # 可选：写入一个空行作为分隔\n",
+    "                overall_csv_row = [\n",
+    "                    'TOTAL / OVERALL',\n",
+    "                    grand_total_tests,\n",
+    "                    grand_total_correct,\n",
+    "                    f\"{overall_accuracy_percent:.2f}%\"\n",
+    "                ]\n",
+    "                csv_writer.writerow(overall_csv_row)\n",
+    "                summary_csv_file.flush()\n",
+    "                \n",
+    "                # 控制台打印总结信息\n",
+    "                print(\"\\n\" + \"=\" * 50 + \"\\n所有文件处理完成!\\n\" + \"=\" * 50)\n",
+    "                print(f\"\\n总计处理了 {len(all_stats_collected)} 个文件，{grand_total_lines} 行输入\")\n",
+    "                print(f\"总计 {grand_total_tests} 个有效测试，{grand_total_correct} 个正确预测\")\n",
+    "                print(f\"总体准确率: {overall_accuracy_percent:.2f}%\")\n",
+    "                \n",
+    "                print(\"\\n各文件详细统计 (控制台):\")\n",
+    "                for s_item in all_stats_collected:\n",
+    "                    print(f\"\\n文件: {s_item['filename']}\")\n",
+    "                    print(f\"  处理行数: {s_item['lines_processed']}\")\n",
+    "                    print(f\"  格式错误行数: {s_item['invalid_format_count']}\")\n",
+    "                    print(f\"  API错误数: {s_item['api_errors']}\")\n",
+    "                    print(f\"  有效测试数 (total_valid_tests): {s_item['total_valid_tests']}\")\n",
+    "                    print(f\"  正确预测数 (correct_predictions): {s_item['correct_predictions']}\")\n",
+    "                    print(f\"  准确率 (accuracy_percent): {s_item['accuracy_percent']:.2f}%\")\n",
+    "                \n",
+    "                print(f\"\\n已将准确率摘要写入到 TXT: {summary_txt_filepath}\")\n",
+    "                print(f\"已将准确率摘要写入到 CSV: {summary_csv_filepath}\")\n",
+    "            \n",
+    "            else: \n",
+    "                message = \"所有文件的处理均未成功生成统计数据。\"\n",
+    "                summary_txt_file.write(\"=\"*40 + \"\\n\" + f\"{message}\\n\")\n",
+    "                # CSV中也可以记录此信息\n",
+    "                csv_writer.writerow([message, 'N/A', 'N/A', 'N/A'])\n",
+    "                summary_csv_file.flush()\n",
+    "                print(f\"\\n{message} 摘要文件已更新。\")\n",
+    "                                \n",
+    "    except IOError as e:\n",
+    "        print(f\"\\n错误：处理摘要文件时发生IO错误: {e}\")\n",
+    "        if all_stats_collected: # 尝试打印已收集的数据\n",
+    "            print(\"\\n注意：摘要文件写入可能存在问题，但以下是控制台的统计信息。\")\n",
+    "            # (可以复用上面的控制台打印逻辑，但为了简洁此处省略)\n",
+    "            print(\"请检查控制台输出获取部分结果。\")\n",
+    "\n",
+    "    if not all_stats_collected and input_files_paths:\n",
+    "        print(\"\\n所有文件的处理均未成功生成统计数据（未在摘要文件中记录此信息，若摘要文件创建失败）。\")\n",
+    "\n",
+    "# 执行主函数\n",
+    "if __name__ == \"__main__\":\n",
+    "    try:\n",
+    "        shell = get_ipython().__class__.__name__\n",
+    "        if shell != 'ZMQInteractiveShell': raise NameError\n",
+    "    except NameError:\n",
+    "        try:\n",
+    "            from tqdm import tqdm as std_tqdm\n",
+    "            globals()['tqdm'] = std_tqdm \n",
+    "            print(\"信息：非Jupyter Notebook环境，使用标准tqdm。\")\n",
+    "        except ImportError:\n",
+    "            print(\"警告：标准tqdm库未安装。进度条可能无法正常显示或仅简单打印。\")\n",
+    "            class dummy_tqdm: # 改进的dummy_tqdm\n",
+    "                def __init__(self, iterable=None, desc=\"\", total=None, unit=\"\", bar_format=None, **kwargs):\n",
+    "                    self.iterable, self.desc, self.total, self.current, self.unit = iterable, desc, total, 0, unit\n",
+    "                    self.postfix_text = \"\"\n",
+    "                    if self.total:\n",
+    "                        print(f\"{self.desc}: 开始处理 {self.total} {self.unit}...\")\n",
+    "                    else:\n",
+    "                        print(f\"{self.desc}: 开始处理...\")\n",
+    "\n",
+    "                def __iter__(self):\n",
+    "                    for i, obj in enumerate(self.iterable):\n",
+    "                        yield obj\n",
+    "                        self.update(1)\n",
+    "                        if self.total and (i + 1) % (self.total // 10 if self.total >=10 else 1) == 0: # 每10%或每项打印\n",
+    "                             self.print_status()\n",
+    "                        elif not self.total and (i+1) % 50 == 0: # 如果没有total，每50项打印\n",
+    "                             self.print_status()\n",
+    "\n",
+    "\n",
+    "                def set_postfix_str(self, s):\n",
+    "                    self.postfix_text = s\n",
+    "                    # 不立即打印，由 __iter__ 中的逻辑控制打印频率\n",
+    "\n",
+    "                def print_status(self):\n",
+    "                    total_str = str(self.total) if self.total else \"?\"\n",
+    "                    sys.stdout.write(f\"\\r{self.desc}: {self.current}/{total_str} {self.unit} | {self.postfix_text}      \")\n",
+    "                    sys.stdout.flush()\n",
+    "\n",
+    "                def update(self, n=1):\n",
+    "                    self.current += n\n",
+    "\n",
+    "                def close(self):\n",
+    "                    self.print_status() # 确保最后的状态被打印\n",
+    "                    sys.stdout.write(f\"\\n{self.desc}: 处理完成 {self.current} {self.unit}。\\n\")\n",
+    "                    sys.stdout.flush()\n",
+    "            globals()['tqdm'] = dummy_tqdm\n",
+    "    main()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d12c839",
+   "metadata": {},
+   "source": [
+    "整理一下这个markdown表格，把除了后两列的单独作为一个表格，把后两列重新放到新建的表格里，新建的��格为逐行排列的：\n",
+    "\n",
+    "文件名\n",
+    "数据说明\n",
+    "示例\n",
+    "\n",
+    "表格：\n",
+    "|数据文件名|\t数据条数|\t数据说明|示例|\n",
+    "|--------------|----------|------------------------------------|-----------|   \n",
+    "|   ADD_4M\t                    |3997733\t       |      **1.** 使用‘全角’、‘中文数字’、‘大写中文数字’随机替换整个数字<br>**2.** 运算符附近有 1~2 个随机空格<br>**3.** 含简单自然语言描述/自然语言噪声\t |    {\"text\": \"User: 249476576 减 796580834 还剩多少？\\n\\nAssistant: -547104258\"}    |\n",
+    "|   ADD_2M\t                    |1999673\t       |      **1.** 使用‘全角’、‘中文数字’、‘大写中文数字’随机替换整个数字<br>**2.** 运算符附近有 1~2 个随机空格<br>**3.** 含简单自然语言描述/自然语言噪声  |    {\"text\": \"User: 捌仟玖佰捌拾叁点肆贰陆玖+9334160.73357\\n\\nAssistant: 9343144.16047\"}    |\n",
+    "|   ADD_base_8M\t                |7992002\t       |       -999~999的全部两两组合，仅算术式子，无任何随机更改\t\t\t                                                                     |     {\"text\": \"User: -999 + -991 = ?\\n\\nAssistant: -1990\"}   |\n",
+    "|   ADD_n1m0\t                |270887\t           |      **1.** 使用‘全角’、‘中文数字’、‘大写中文数字’随机替换整个数字；<br>**2.** 运算符附近有 1~2 个随机空格<br>**3.** 额外限制最少有 1 次进/退位        |    {\"text\": \"User: 78041304-805555\\n\\nAssistant: 77235749\"}    |\n",
+    "|   ADD_n1m1\t                |199900\t           |      **1.** 使用‘全角’、‘中文数字’、‘大写中文数字’随机替换整个数字<br>**2.** 运算符附近有 1~2 个随机空格<br>**3.** 含简单自然语言描述/自然语言噪声<br>4. 限制最少 1 次进/退位\t | {\"text\": \"User: ３９２１８５７２１４+玖仟柒佰肆拾壹萬肆仟叁佰玖拾肆 = ?\\n\\nAssistant: 4019271608\"}    | \n",
+    "|   ADD_n2m0\t                |270798\t           |      **1.** 使用‘全角’、‘中文数字’、‘大写中文数字’随机替换整个数字；<br>**2.** 运算符附近有 1~2 个随机空格<br>**3.** 额外限制最少有 2 次进/退位\t\t\t                                                 |    {\"text\": \"User: 9420731235+陆仟柒佰伍拾陆萬肆仟贰佰玖拾玖\\n\\nAssistant: 9488295534\"}    |\n",
+    "|   ADD_n2m1\t                |199847\t           |      **1.** 使用‘全角’、‘中文数字’、‘大写中文数字’随机替换整个数字<br>**2.** 运算符附近有 1~2 个随机空格<br>**3.** 含简单自然语言描述/自然语言噪声<br>4. 限制至少 2 次进/退位\t                          |   {\"text\": \"User: ３０６８５３６２+柒仟壹佰肆拾萬壹仟壹佰贰拾捌 = ?\\n\\nAssistant: 102086490\"}    | \n",
+    "|   ADD_n3m0\t                |270853\t           |      **1.** 使用‘全角’、‘中文数字’、‘大写中文数字’随机替换整个数字；<br>**2.** 运算符附近有 1~2 个随机空格<br>**3.** 额外限制最少有 3 次进/退位\t\t\t                                                 |    {\"text\": \"User: 肆拾叁億肆仟伍佰零肆萬贰仟肆佰捌拾玖加78759970978\\n\\nAssistant: 83105013467\"}    |\n",
+    "|   ADD_n4m3\t                |271345\t           |      **1.** 使用‘全角’、‘中文数字’、‘大写中文数字’随机替换整个数字；<br>**2.** 运算符附近有 1~2 个随机空格<br>**3.** 额外限制最少有 4 次进/退位，且至少有一个连续进/退位长度为 3 的连续段                \t| {\"text\": \"User: 六亿两千三百二十八万一千零二十四点五四五二六减２３２９５５８．３３３１７\\n\\nAssistant: 620951466.21209\"}    |   \n",
+    "|   ADD_random_0.25M\t        |249735\t           |      **1.** 逐位随机进行替换<br>**2.** 替换内容为：大小写中文、全半角数字、（例如：叁2五）<br>**3.** 每个数字和运算符直接随机0~2个空格\t\t\t                                                         |    {\"text\": \"User: ４６４９５５６９．６７８８６-四百６十亿8千六百４十二万贰千陆百8十玖点九３四五伍=？\\n\\nAssistant: -46039927120.25569\"}    |\n",
+    "|   ADD_random_4M\t            |4494484\t       |      **1.** 使用‘全角’、‘中文数字’、‘大写中文数字’随机替换数字的某个位，例如：八4肆<br>**2.** 数字内部和运算符附近有 1~2 个随机空格                                                                   |    {\"text\": \"User: ８千１百陆十六万捌千8百捌十捌-2仟５佰壹拾八 億2仟六佰１拾八萬陆仟 六佰５拾五\\n\\nAssistant: -251744517767\"}    |\n",
+    "|   ADD_many0_50k\t            |50000\t           |      强化数据，如**2.**000+1\t\t\t                                                                                                                                                            |     {\"text\": \"User: -315052474 4.00 和 ８２５８２４１４９１７６．００００ 等于？\\n\\nAssistant: 822673624432\"}   |\n",
+    "|   ADD_en\t                    |976394\t           |      **1.** 使用‘全角’、‘中文数字’、‘大写中文数字’、‘英文数字’随机替换整个数字；<br>例如： 叁3三３three THREE Three\t\t\t                                                                    |     {\"text\": \"User: seven Nine FOUR six six One seven NINE SIX one ZERO zero point Six SIX ONE seven NINE six One NINE+2=?\\n\\nAssistant: 794661796102\"}   |\n",
+    "|   ADD_en_base_1M-v1\t        |1000000\t       |      英语基础数据，从qa_add_base_8M.jsonl下采样直译而来\t\t\t                                                                                                                            |     {\"text\": \"User: 275MINUS six Hundred sixty five= , solve this.\\n\\nAssistant: -390\"}   |\n",
+    "|   ADD_en_base_1M-v2\t        |1000000\t       |      英语基础数据，从qa_add_base_8M.jsonl下采样直译而来\t\t\t                                                                                                                            |     {\"text\": \"User: -684 plusfour Hundred eighty Five=equals WHAT?\\n\\nAssistant: -199\"}   |\n",
+    "|   ADD_en_random_mix\t        |1464727\t       |       **1.** 使用‘全角’、‘中文数字’、‘大写中文数字’、‘英文数字’随机替换整个数字；<br>**2.** 对每个单词逐个字母进行大小写随机<br>例如： 叁3三３three THREE Three tHrEe\t\t\t                          |   {\"text\": \"User: ６９４１+Six MILLION Three HUNDRED FIFTY four THOUSAND TWENTY three=?\\n\\nAssistant: 6360964\"}    | \n",
+    "|   ADD_en_by-desc\t            |489961\t           |      **1.** 使用‘全角’、‘英文数字’随机替换整个数字<br>**2.** 运算符附近有 1~2 个随机空格<br>**3.** 含简单自然语言描述/自然语言噪声\t\t\t                                                             |    {\"text\": \"User: What is Eight three five Eight TWO four zero TWO Point Four added To 10994.06?\\n\\nAssistant: 83593396.46\"}    |\n",
+    "|   ADD_use-connect\t            |1542000\t       |      强制使用标准化的英文连接符\t\t\t                                                                                                                                                    |     {\"text\": \"User: FIVE-BILLION-EIGHT-HUNDRED-NINETY-THREE-MILLION-NINE-HUNDRED-FIFTY-FIVE-THOUSAND-EIGHT-HUNDRED-FORTY-THREE+  NiNE-HUndred-FoRTY-sIx-BiLlIon-sIx-hUnDReD-Eighty-foUr-miLLiON-Eight-HunDRed-seveNty-two-THoUSAND-FOUR-HUndREd-FiFty-nIne-PoInT-EIGHT-nINe-siX-SIX = ?\\n\\nAssistant: 95257882830**2.**8966\"}   |\n",
+    "|   X_ch_mix\t                |2000000\t       |      **1.** 使用‘全角’、‘中文数字’、‘大写中文数字’随机替换整个数字<br>**2.** 运算符附近有 1~2 个随机空格<br>**3.** 随机中英问号<br>4. 解方程格式\t\t\t                                                  |   {\"text\": \"User: x+514=  585.028, 求解 x。\\n\\nAssistant: x = 585.028 - 514 = 7**1.**028\"}    | \n",
+    "|   X_ch_en_mix\t                |250000\t           |      **1.** 使用‘全角’、‘英文数字’、‘中文数字’、‘大写中文数字'随机替换数字的某个位<br>**2.** 运算符附近有 1~2 个随机空格<br>**3.** 随机中英问号<br>4. 解方程格式\t\t\t                                  |   {\"text\": \"User: x-６８５９１９一７１８５=－68伍９1玖1692捌, 求 x 的值。\\n\\nAssistant: x = -68591916928 + 68591917185 = 257\"}     | \n",
+    "|   X_en_random_mix\t            |1000000\t       |      **1.** 使用‘全角’、‘中文数字’、‘大写中文数字’随机替换整个数字<br>**2.** 运算符附近有 1~2 个随机空格<br>**3.** 含简单自然语言描述/自然语言噪声<br>4. 对每个单词逐个字母进行大小写随机<br>5. 解方程格式    |{\"text\": \"User: miNus FouR PoINT TWO fOUr fIve FOUR zERo zerO sIx TiMeS tEN tO the pOWEr OF EigHT+x=－４２４５３９８５４．８  , x = ?\\n\\nAssistant: x = -424539854.8 + 424540060 = 205.2\"}       |    \n",
+    "|   qa_error\t                |248\t           |         噪声数据\t\t\t                                                                                                                                                                    |     {\"text\": \"User: 椅子 + 桌子 =\\n\\nAssistant: 无法相加\"}   |\n",
+    "|   qa_gibberish_unanswerable\t|10000\t           |         乱码，抗干扰，答案为：无法回答\t\t\t                                                                                                                                             |        |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a233fb46",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
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