{
 "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轮问答对话；\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": "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/new\"       # 输出路径 \n",
    "API_URL = \"http://192.168.0.103:8022/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": "code",
   "execution_count": null,
   "id": "55187222",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}