content/generator_new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from generators import *\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All dialogue entries have been successfully archived in generator_verifier_regenerator_prompt_simplified.jsonl\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "\n",
    "\n",
    "system_message = \"\"\"\n",
    "You are provided with three components:\n",
    "1. A verifier\n",
    "2. A generator\n",
    "3. Global variables and auxiliary functions that both the verifier and generator depend on\n",
    "\n",
    "The generator is used to create two matrices, while the verifier is a transformation rule that checks if the transformation from input to output is valid.\n",
    "\n",
    "Note that the two input parameters of the generator, diff_lb and diff_ub, are used to control the generation difficulty, especially the size of the generated grid. These parameters are important, and special attention should be paid to how they are used to adjust the difficulty when designing new generators.\n",
    "\n",
    "Your tasks are:\n",
    "1. Explain the effects of the original verifier and generator, as well as their relationship.\n",
    "2. Referring to the provided verifier and generator, and using only the provided auxiliary functions, create a new pair of verifier and generator that are matched. \n",
    "The new transformation rule from input to output should be different from the original and contain exactly {num_steps} steps.\n",
    "3. Ensure that your new transformation rule is both simple and interesting.\n",
    "\n",
    "(Note: Only use the auxiliary functions provided here, and do not use any other functions or variables. Carefully review all available auxiliary functions to make full use of them.)\n",
    "\n",
    "Your response should be in JSON format. Please ensure it can be parsed by json.loads(). Provide your answer using the following structure:\n",
    "{{\n",
    "  \"original_reasoning\": \"Explanation of the original transformation reasoning\",\n",
    "  \"new_verifier_reasoning\": \"[Your step-by-step reasoning about the new verifier, explaining how each step contributes to the overall transformation]\",\n",
    "  \"new_verifier_code\": \"```python\\\\ndef verify_new(I: Grid) -> Grid:\\\\n    # Your verifier code here\\\\n```\",\n",
    "  \"new_generator_reasoning\": \"[Your step-by-step reasoning about the new generator, including considerations for different difficulty levels]\",\n",
    "  \"new_generator_code\": \"```python\\\\ndef generate_new(diff_lb: float, diff_ub: float) -> dict:\\\\n    # Your generator code here\\\\n```\",\n",
    "  \"transformation_description\": \"A concise description of the new transformation rule\",\n",
    "  \"num_of_rules\": \"{num_steps}\"\n",
    "}}\n",
    "\n",
    "Break down complex problems into smaller parts and reason through them step by step, arriving at sub-conclusions before stating an overall conclusion. This reduces the extent to which you need to do large leaps of reasoning.\n",
    "\n",
    "Reason in substantial detail as necessary to determine the transformation rule. Consider potential errors or edge cases and how to handle them.\n",
    "\n",
    "Be creative and accomplished at solving puzzles. Here are some prompts to inspire creativity:\n",
    "- How could you incorporate multiple colors or shapes?\n",
    "- Can you create a rule that involves rotation or reflection?\n",
    "- Is there a way to make the transformation dependent on the object's position in the grid?\n",
    "\n",
    "Remember to thoroughly test your verifier and generator to ensure they work correctly together for various inputs.\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "# Now you can use formatted_system_message in your code\n",
    "# Read all data and save\n",
    "with open('/mnt/data/zifeng.cao/reasoning/arc-agi/re-arc/origin_code.jsonl', 'r') as f:\n",
    "    data_list = [json.loads(line) for line in f]\n",
    "\n",
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0.0016964001274291176, '508bd3b6': 0.0016645428480407773, 'b8825c91': 0.0016565785281936923, 'd10ecb37': 0.001632685568652437, '6f8cd79b': 0.001624721248805352, 'dbc1a6ce': 0.0015928639694170119, '3af2c5a8': 0.0015610066900286716, '73251a56': 0.0014574705320165658, 'f15e1fac': 0.0014495062121694807, 'ed36ccf7': 0.0014017202930869705, 'a9f96cdd': 0.0013698630136986301, '539a4f51': 0.0013698630136986301, '47c1f68c': 0.00135393437400446, 'a2fd1cf0': 0.0012424338961452691, 'ff805c23': 0.001226505256451099, '88a62173': 0.0011707550175215037, 'd4a91cb9': 0.0011150047785919083, '3c9b0459': 0.0011070404587448233, '5bd6f4ac': 0.0010911118190506531, '31aa019c': 0.0010433258999681427, 'd364b489': 0.000955718381650207, '9ecd008a': 0.0009158967824147818, 'd90796e8': 0.0009079324625676967, 'c9f8e694': 0.0006530742274609749, '7fe24cdd': 0.0006212169480726346, '6150a2bd': 0.0003743230328129978, '46442a0e': 0.0}\n",
    "# Create messages for each data\n",
    "entries = []\n",
    "for data in data_list:\n",
    "    if data[\"name\"] not in name_freq:\n",
    "        name_repeat = 30\n",
    "    else:\n",
    "        name_repeat = int(3000 * name_freq[data[\"name\"]] + 1)\n",
    "    for i in range(name_repeat):\n",
    "        user_message = f\"Here are the components:\\n\\nVerifier:\\n{data['verifier']}\\n\\nGenerator:\\n{data['generator']}\\n\\nGlobal Variables and Auxiliary Functions:\\n{data['global_variable']}\\n{data['additional_functions']}\"\n",
    "        \n",
    "        num_steps_config = {2: 1, 3: 2, 4: 2, 5: 2, 6: 1, 7: 1}\n",
    "        \n",
    "        for num_steps, repeat_times in num_steps_config.items():\n",
    "            for _ in range(repeat_times):\n",
    "                messages = [\n",
    "                    {\n",
    "                        \"role\": \"system\", \n",
    "                        \"content\": system_message.format(num_steps=num_steps)\n",
    "                    },\n",
    "                    {\n",
    "                        \"role\": \"user\",\n",
    "                        \"content\": user_message\n",
    "                    }\n",
    "                ]\n",
    "                \n",
    "                # 准备每个数据作为一行JSON\n",
    "                entry = {\n",
    "                    \"name\": f\"{data['name']}_{num_steps}steps\",\n",
    "                    \"messages\": messages\n",
    "                }\n",
    "                entries.append(entry)\n",
    "\n",
    "# import numpy as np\n",
    "# # 最终对entries进行随机打乱\n",
    "# random.shuffle(entries)\n",
    "\n",
    "# Write all entries at once\n",
    "with open('generator_verifier_regenerator_prompt_simplified_fixed_freq.jsonl', 'w', encoding='utf-8') as file:\n",
    "    for entry in entries:\n",
    "        json.dump(entry, file, ensure_ascii=False)\n",
    "        file.write('\\n')\n",
    "\n",
    "print(\"All dialogue entries have been successfully archived in generator_verifier_regenerator_prompt_simplified.jsonl\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import importlib\n",
    "import random\n",
    "\n",
    "def import_generator_and_verifier(id):\n",
    "    # 从generators.py导入生成器函数\n",
    "    generators_module = importlib.import_module('generators')\n",
    "    generator_function = getattr(generators_module, f'generate_{id}')\n",
    "    \n",
    "    # 从verifiers.py导入验证器函数  \n",
    "    verifiers_module = importlib.import_module('verifiers')\n",
    "    verifier_function = getattr(verifiers_module, f'verify_{id}')\n",
    "    \n",
    "    return generator_function, verifier_function\n",
    "\n",
    "t_id = '3631a71a'\n",
    "generate_func, verify_func = import_generator_and_verifier(t_id)\n",
    "\n",
    "\n",
    "verifier_output = []\n",
    "result_dict_list = []\n",
    "for i in range(5):\n",
    "    result_dict = generate_func(0.5, 0.5)\n",
    "    result_dict[\"output\"] = verify_func(result_dict['input'])\n",
    "    verifier_output.append(result_dict[\"output\"])\n",
    "    result_dict = {k: np.array(v) for k, v in result_dict.items()}\n",
    "    result_dict_list.append(result_dict)\n",
    "\n",
    "plot_task(result_dict_list)\n",
    "\n",
    "# for i in range(5):\n",
    "#     assert np.array_equal(result_dict_list[i]['input'], verifier_output[i])\n",
    "\n",
    "# 获取verify_func的具体实现\n",
    "import inspect\n",
    "verify_func_source = inspect.getsource(verify_func)\n",
    "print(\"verify_func的具体实现:\")\n",
    "print(verify_func_source)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "import astor\n",
    "\n",
    "class FunctionSplitter(ast.NodeTransformer):\n",
    "    def __init__(self):\n",
    "        self.new_functions = []\n",
    "\n",
    "    def visit_FunctionDef(self, node):\n",
    "        # 检查函数是否返回Grid\n",
    "        returns_grid = any(isinstance(child, ast.Return) and \n",
    "                           isinstance(child.value, ast.Name) and \n",
    "                           child.value.id == 'Grid' \n",
    "                           for child in ast.walk(node))\n",
    "        \n",
    "        if not returns_grid:\n",
    "            return node\n",
    "\n",
    "        # 创建步骤函数\n",
    "        step_functions = []\n",
    "        for i, stmt in enumerate(node.body[:-1], start=1):\n",
    "            step_func = ast.FunctionDef(\n",
    "                name=f\"{node.name}_step{i}\",\n",
    "                args=node.args,\n",
    "                body=node.body[:i+1],  # 包括当前语句及之前的所有语句\n",
    "                decorator_list=[],\n",
    "                returns=node.returns\n",
    "            )\n",
    "            step_functions.append(step_func)\n",
    "\n",
    "        # 修改原始函数\n",
    "        node.body = [\n",
    "            ast.Return(\n",
    "                value=ast.Call(\n",
    "                    func=ast.Name(id=f\"{node.name}_step{len(step_functions)}\", ctx=ast.Load()),\n",
    "                    args=[ast.Name(id='I', ctx=ast.Load())],\n",
    "                    keywords=[]\n",
    "                )\n",
    "            )\n",
    "        ]\n",
    "        \n",
    "        self.new_functions.extend(step_functions)\n",
    "        return node\n",
    "\n",
    "def split_functions(source_code):\n",
    "    tree = ast.parse(source_code)\n",
    "    transformer = FunctionSplitter()\n",
    "    transformed_tree = transformer.visit(tree)\n",
    "    \n",
    "    # 将新函数添加到AST\n",
    "    transformed_tree.body.extend(transformer.new_functions)\n",
    "    \n",
    "    return astor.to_source(transformed_tree)\n",
    "\n",
    "# 使用verify_func_source作为输入\n",
    "transformed_code = split_functions(verify_func_source)\n",
    "\n",
    "print(\"转换后的代码:\")\n",
    "print(transformed_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import json\n",
    "\n",
    "from generators import *\n",
    "from utils import *\n",
    "\n",
    "def read_json_files(folder_path):\n",
    "    result = {}\n",
    "    for filename in os.listdir(folder_path):\n",
    "        if filename.endswith('.json'):\n",
    "            file_path = os.path.join(folder_path, filename)\n",
    "            try:\n",
    "                with open(file_path, 'r', encoding='utf-8') as file:\n",
    "                    content = json.load(file)\n",
    "                    result[re.sub(r'\\.json$', '', filename)] = content\n",
    "            except json.JSONDecodeError:\n",
    "                print(f\"警告: 文件 '{filename}' 不是有效的JSON格式。已跳过。\")\n",
    "            except Exception as e:\n",
    "                print(f\"读取文件 '{filename}' 时发生错误: {str(e)}\")\n",
    "    return result\n",
    "\n",
    "\n",
    "raw_training_data = read_json_files('/mnt/data/zifeng.cao/reasoning/arc-agi/re-arc/arc_original/training')\n",
    "original_training_io_data = {k: [format_example(example) for example in v[\"train\"]] for k, v in raw_training_data.items()}\n",
    "\n",
    "raw_generator_data = read_json_files('/mnt/data/zifeng.cao/reasoning/arc-agi/re-arc/re-arc-4-example/tasks')\n",
    "original_generator_io_data = {k: [format_example(example) for example in v] for k, v in raw_generator_data.items()}\n",
    "\n",
    "import pickle\n",
    "\n",
    "# 将数据保存到pickle文件\n",
    "with open('io_data.pkl', 'wb') as f:\n",
    "    pickle.dump({\n",
    "        'original_training_io_data': original_training_io_data,\n",
    "        'original_generator_io_data': original_generator_io_data\n",
    "    }, f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import re\n",
    "import signal\n",
    "import resource\n",
    "import multiprocessing\n",
    "from generators import *\n",
    "from utils import *\n",
    "import hashlib\n",
    "\n",
    "def stable_hash(obj):\n",
    "    return hashlib.md5(json.dumps(obj, sort_keys=True).encode()).hexdigest()\n",
    "\n",
    "\n",
    "def get_jsonl_files(directory):\n",
    "    \"\"\"\n",
    "    获取指定目录下所有的.jsonl文件列表\n",
    "    \"\"\"\n",
    "    jsonl_files = []\n",
    "    for filename in os.listdir(directory):\n",
    "        if filename.endswith('.jsonl'):\n",
    "            file_path = os.path.join(directory, filename)\n",
    "            jsonl_files.append(file_path)\n",
    "            break\n",
    "    return jsonl_files\n",
    "\n",
    "def read_jsonl_file(file_path):\n",
    "    \"\"\"\n",
    "    读取单个.jsonl文件内容\n",
    "    \"\"\"\n",
    "    with open(file_path, 'r') as file:\n",
    "        return file.readlines()\n",
    "\n",
    "def extract_json_from_line(line):\n",
    "    \"\"\"\n",
    "    从单行内容中提取特定的JSON部分\n",
    "    \"\"\"\n",
    "    try:\n",
    "        # 解析整行为JSON\n",
    "        data = json.loads(line)\n",
    "        \n",
    "        # 检查是否存在'response'键\n",
    "        if 'response' in data:\n",
    "            # 使用正则表达式提取JSON部分\n",
    "            match = re.search(r'\\{.*\\}', data['response'])\n",
    "            if match:\n",
    "                json_str = match.group()\n",
    "                # 尝试解析提取的JSON字符串\n",
    "                json_data = json.loads(json_str)\n",
    "                return json_data\n",
    "    except json.JSONDecodeError:\n",
    "        # 如果解析失败,返回None\n",
    "        return None\n",
    "\n",
    "def extract_json_from_content(content_list):\n",
    "    \"\"\"\n",
    "    从内容列表中提取特定的JSON部分\n",
    "    \"\"\"\n",
    "    extracted_data = []\n",
    "    for line in content_list:\n",
    "        json_data = extract_json_from_line(line)\n",
    "        if json_data:\n",
    "            extracted_data.append(json_data)\n",
    "    \n",
    "    return extracted_data\n",
    "\n",
    "\n",
    "def timeout_handler(signum, frame):\n",
    "    raise TimeoutError(\"Execution timeout\")\n",
    "\n",
    "\n",
    "def extract_code_from_line(line):\n",
    "    data = json.loads(line)\n",
    "    json_data = None\n",
    "    \n",
    "    if 'response' in data:\n",
    "        match = re.search(r'\\{(?=.*\"original_reasoning\":)(?=.*\"num_of_rules\":).*\\}', data['response'], re.DOTALL)\n",
    "        if match:\n",
    "            json_str = match.group()\n",
    "            try:\n",
    "                json_data = json.loads(json_str)\n",
    "            except json.JSONDecodeError:\n",
    "                json_data = None\n",
    "\n",
    "    origin_name = data.get(\"name\", \"\").split(\"_\")[0]\n",
    "\n",
    "    if json_data is None or len(origin_name) == 0:\n",
    "        return None\n",
    "\n",
    "    verifier_code = json_data.get('new_verifier_code', '')\n",
    "    generator_code = json_data.get('new_generator_code', '')\n",
    "\n",
    "    verifier_code = re.sub(r'^```(?:python)?\\s*|\\s*```$', '', verifier_code.strip())\n",
    "    verifier_code = verifier_code.replace('\\\\\\\\n', '\\n').replace('\\\\n', '\\n')\n",
    "\n",
    "    generator_code = re.sub(r'^```(?:python)?\\s*|\\s*```$', '', generator_code.strip())\n",
    "    generator_code = generator_code.replace('\\\\\\\\n', '\\n').replace('\\\\n', '\\n')\n",
    "\n",
    "    num_of_rules = json_data.get(\"num_of_rules\", 0)\n",
    "    transformation_description = json_data.get(\"transformation_description\", \"\")\n",
    "\n",
    "\n",
    "    return {\n",
    "        \"origin_name\": origin_name,\n",
    "        \"num_of_rules\": num_of_rules,\n",
    "        \"verifier_code\": verifier_code,\n",
    "        \"generator_code\": generator_code,\n",
    "        \"transformation_description\": transformation_description,\n",
    "    }\n",
    "\n",
    "\n",
    "def execute_code(code, timeout, memory_limit=10*1024*1024):  # 默认100MB内存限制\n",
    "    def isolated_execution():\n",
    "        def limit_memory():\n",
    "            resource.setrlimit(resource.RLIMIT_AS, (memory_limit, memory_limit))\n",
    "\n",
    "        try:\n",
    "            signal.signal(signal.SIGALRM, timeout_handler)\n",
    "            signal.alarm(timeout)\n",
    "            limit_memory()\n",
    "            # 导入模块\n",
    "            import generators\n",
    "            import utils\n",
    "            # 创建全局命名空间，包含模块的命名空间\n",
    "            exec_globals = {}\n",
    "            exec_globals.update(generators.__dict__)\n",
    "            exec_globals.update(utils.__dict__)\n",
    "            # 执行代码\n",
    "            exec(code, exec_globals)\n",
    "            signal.alarm(0)\n",
    "            return True\n",
    "        except TimeoutError:\n",
    "            print(\"代码执行超时\")\n",
    "            return False\n",
    "        except MemoryError:\n",
    "            print(\"内存超出限制\")\n",
    "            return False\n",
    "        except Exception as e:\n",
    "            # print(f\"代码执行错误: {e}\")\n",
    "            return False\n",
    "\n",
    "    process = multiprocessing.Process(target=isolated_execution)\n",
    "    process.start()\n",
    "    process.join(timeout)\n",
    "\n",
    "    if process.is_alive():\n",
    "        process.terminate()\n",
    "        process.join()\n",
    "        print(\"代码执行超时\")\n",
    "        return False\n",
    "\n",
    "    return process.exitcode == 0\n",
    "\n",
    "def execute_function(func_name, args, code, timeout, memory_limit=10*1024*1024):  # 默认100MB内存限制\n",
    "    def isolated_execution(queue):\n",
    "        def limit_memory():\n",
    "            resource.setrlimit(resource.RLIMIT_AS, (memory_limit, memory_limit))\n",
    "\n",
    "        try:\n",
    "            signal.signal(signal.SIGALRM, timeout_handler)\n",
    "            signal.alarm(timeout)\n",
    "            limit_memory()\n",
    "            # 导入模块\n",
    "            import generators\n",
    "            import utils\n",
    "            # 创建全局命名空间，包含模块的命名空间\n",
    "            exec_globals = {}\n",
    "            exec_globals.update(generators.__dict__)\n",
    "            exec_globals.update(utils.__dict__)\n",
    "            # 执行代码\n",
    "            exec(code, exec_globals)\n",
    "            # 调用函数\n",
    "            result = exec_globals[func_name](*args)\n",
    "            signal.alarm(0)\n",
    "            queue.put(result)\n",
    "        except TimeoutError:\n",
    "            print(f\"{func_name} 执行超时\")\n",
    "            queue.put(None)\n",
    "        except MemoryError:\n",
    "            print(f\"{func_name} 内存超出限制\")\n",
    "            queue.put(None)\n",
    "        except Exception as e:\n",
    "            # print(f\"代码执行错误: {e}\")\n",
    "            queue.put(None)\n",
    "\n",
    "    result_queue = multiprocessing.Queue()\n",
    "    process = multiprocessing.Process(target=isolated_execution, args=(result_queue,))\n",
    "    process.start()\n",
    "    process.join(timeout)\n",
    "\n",
    "    if process.is_alive():\n",
    "        process.terminate()\n",
    "        process.join()\n",
    "        print(f\"{func_name} 执行超时\")\n",
    "        return None\n",
    "\n",
    "    return result_queue.get() if not result_queue.empty() else None\n",
    "\n",
    "def check_generator_verifier_match(generator_code, verifier_code, timeout):\n",
    "    generator_func_match = re.search(r'def\\s+(\\w+)', generator_code)\n",
    "    verifier_func_match = re.search(r'def\\s+(\\w+)', verifier_code)\n",
    "    if generator_func_match is None or verifier_func_match is None:\n",
    "        return False, None\n",
    "\n",
    "    generator_func_name = generator_func_match.group(1)\n",
    "    verifier_func_name = verifier_func_match.group(1)\n",
    "\n",
    "    # 执行生成器代码并获取结果\n",
    "    generator_result = execute_function(generator_func_name, (0.5, 0.5), generator_code, timeout)\n",
    "    if generator_result is None or \"input\" not in generator_result or \"output\" not in generator_result:\n",
    "        return False, None\n",
    "\n",
    "    # 合并生成器和验证器代码，确保验证器能访问必要的依赖\n",
    "    combined_code = generator_code + '\\n' + verifier_code\n",
    "\n",
    "    # 调用验证器函数\n",
    "    verifier_output = (verifier_func_name, (generator_result['input'],), combined_code, timeout)\n",
    "    if verifier_output is None:\n",
    "        return False, None\n",
    "\n",
    "    return generator_result['output'] == verifier_output, get_pso_difficulty(generator_result)\n",
    "\n",
    "def check_verifier_on_data(verifier_code, io_data, timeout):\n",
    "    verifier_func_match = re.search(r'def\\s+(\\w+)', verifier_code)\n",
    "    if verifier_func_match is None:\n",
    "        return False, None, None\n",
    "\n",
    "    verifier_func_name = verifier_func_match.group(1)\n",
    "\n",
    "    for io_item in io_data:\n",
    "        # 调用验证器函数\n",
    "        verifier_output = execute_function(verifier_func_name, (io_item['input'],), verifier_code, timeout)\n",
    "        if verifier_output is None or not is_grid(verifier_output):\n",
    "            return False, None, None\n",
    "    return True, stable_hash(verifier_output), get_pso_difficulty(io_item)\n",
    "\n",
    "def execute_and_evaluate(extracted_data, original_training_io_data, original_generator_io_data, timeout):\n",
    "    if not extracted_data:\n",
    "        return None\n",
    "\n",
    "    origin_name = extracted_data[\"origin_name\"]\n",
    "    verifier_code = extracted_data[\"verifier_code\"]\n",
    "    generator_code = extracted_data[\"generator_code\"]\n",
    "\n",
    "    origin_training_io = original_training_io_data[origin_name]\n",
    "    origin_generator_io = original_generator_io_data[origin_name]\n",
    "\n",
    "    generator_verifier_match, generator_difficulty = check_generator_verifier_match(generator_code, verifier_code, timeout)\n",
    "    origin_training_verifier_success, origin_training_verifier_hash, origin_training_verifier_difficulty = check_verifier_on_data(verifier_code, origin_training_io, timeout)\n",
    "    origin_generator_verifier_success, origin_generator_verifier_hash, origin_generator_verifier_difficulty = check_verifier_on_data(verifier_code, origin_generator_io, timeout)\n",
    "\n",
    "    return {\n",
    "        \"new_generator_verifier_match\": generator_verifier_match,\n",
    "        \"new_generator_difficulty\": generator_difficulty,\n",
    "        \"origin_training_verifier_success\": origin_training_verifier_success,\n",
    "        \"origin_generator_verifier_success\": origin_generator_verifier_success,\n",
    "        \"origin_training_verifier_hash\": origin_training_verifier_hash,\n",
    "        \"origin_generator_verifier_hash\": origin_generator_verifier_hash,\n",
    "        \"origin_training_verifier_difficulty\": origin_training_verifier_difficulty,\n",
    "        \"origin_generator_verifier_difficulty\": origin_generator_verifier_difficulty,\n",
    "    }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据已成功加载\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "# 从pickle文件读取数据\n",
    "with open('io_data.pkl', 'rb') as f:\n",
    "    loaded_data = pickle.load(f)\n",
    "\n",
    "original_training_io_data = loaded_data['original_training_io_data']\n",
    "original_generator_io_data = loaded_data['original_generator_io_data']\n",
    "\n",
    "print(\"数据已成功加载\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"name\": \"4258a5f9_4steps\", \"response\": \"```json\\n{\\n  \\\"original_reasoning\\\": \\\"The original verifier and generator work together to create a transformation where the least common color in the input grid is identified, and then the cells of that color and their neighbors are filled with a specific value (1). The generator creates a grid with a background color and places a certain number of dots of a different color. The verifier then checks if the transformation is applied correctly by filling the neighbors of the dots with the value 1.\\\",\\n  \\\"new_verifier_reasoning\\\": \\\"The new verifier will perform a transformation that involves rotating the grid by 90 degrees clockwise, then reflecting it horizontally, and finally filling the cells that were originally on the boundary of the grid with a specific color (2). The steps are as follows:\\\\n1. Rotate the grid 90 degrees clockwise.\\\\n2. Reflect the grid horizontally.\\\\n3. Identify the cells that were originally on the boundary of the grid.\\\\n4. Fill these boundary cells with the value 2.\\\",\\n  \\\"new_verifier_code\\\": \\\"```python\\\\ndef verify_new(I: Grid) -> Grid:\\\\n    # Step 1: Rotate the grid 90 degrees clockwise\\\\n    x1 = rotate(I, RIGHT)\\\\n    # Step 2: Reflect the grid horizontally\\\\n    x2 = reflect(x1, LEFT)\\\\n    # Step 3: Identify the cells that were originally on the boundary\\\\n    h, w = len(I), len(I[0])\\\\n    boundary = frozenset((i, j) for i in range(h) for j in range(w) if i == 0 or i == h-1 or j == 0 or j == w-1)\\\\n    # Step 4: Fill these boundary cells with the value 2\\\\n    x3 = fill(x2, 2, boundary)\\\\n    return x3\\\\n```\\\",\\n  \\\"new_generator_reasoning\\\": \\\"The new generator will create a grid with a background color and place a certain number of dots of a different color. The size of the grid and the number of dots will be controlled by the difficulty parameters `diff_lb` and `diff_ub`. The generator will ensure that the grid is large enough to have a clear boundary and that the dots are placed randomly within the grid. The transformation will be applied to this grid to check if the verifier works correctly.\\\\n1. Choose a background color and a different color for the dots.\\\\n2. Create a grid with the chosen dimensions.\\\\n3. Place a certain number of dots randomly within the grid.\\\\n4. Apply the transformation to the grid to create the output.\\\",\\n  \\\"new_generator_code\\\": \\\"```python\\\\ndef generate_new(diff_lb: float, diff_ub: float) -> dict:\\\\n    colopts = remove(1, interval(0, 10, 1))\\\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\\\n    bgc = choice(colopts)\\\\n    remcols = remove(bgc, colopts)\\\\n    fgc = choice(remcols)\\\\n    gi = canvas(bgc, (h, w))\\\\n    ndots = unifint(diff_lb, diff_ub, (1, (h * w) // 2))\\\\n    inds = totuple(asindices(gi))\\\\n    dots = sample(inds, ndots)\\\\n    gi = fill(gi, fgc, dots)\\\\n    go = verify_new(gi)\\\\n    return {'input': gi, 'output': go}\\\\n```\\\",\\n  \\\"transformation_description\\\": \\\"The new transformation rule involves rotating the grid 90 degrees clockwise, reflecting it horizontally, and filling the cells that were originally on the boundary of the grid with the value 2.\\\",\\n  \\\"num_of_rules\\\": \\\"4\\\"\\n}\\n```\"}\n",
      "\n",
      "{'origin_name': '4258a5f9', 'num_of_rules': '4', 'verifier_code': 'def verify_new(I: Grid) -> Grid:\\n    # Step 1: Rotate the grid 90 degrees clockwise\\n    x1 = rotate(I, RIGHT)\\n    # Step 2: Reflect the grid horizontally\\n    x2 = reflect(x1, LEFT)\\n    # Step 3: Identify the cells that were originally on the boundary\\n    h, w = len(I), len(I[0])\\n    boundary = frozenset((i, j) for i in range(h) for j in range(w) if i == 0 or i == h-1 or j == 0 or j == w-1)\\n    # Step 4: Fill these boundary cells with the value 2\\n    x3 = fill(x2, 2, boundary)\\n    return x3', 'generator_code': \"def generate_new(diff_lb: float, diff_ub: float) -> dict:\\n    colopts = remove(1, interval(0, 10, 1))\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\n    bgc = choice(colopts)\\n    remcols = remove(bgc, colopts)\\n    fgc = choice(remcols)\\n    gi = canvas(bgc, (h, w))\\n    ndots = unifint(diff_lb, diff_ub, (1, (h * w) // 2))\\n    inds = totuple(asindices(gi))\\n    dots = sample(inds, ndots)\\n    gi = fill(gi, fgc, dots)\\n    go = verify_new(gi)\\n    return {'input': gi, 'output': go}\", 'transformation_description': 'The new transformation rule involves rotating the grid 90 degrees clockwise, reflecting it horizontally, and filling the cells that were originally on the boundary of the grid with the value 2.'}\n",
      "{\"name\": \"4258a5f9_4steps\", \"response\": \"{\\n  \\\"original_reasoning\\\": \\\"The original transformation rule involves finding the least common color in the grid, identifying the cells of that color, finding their neighbors, and then filling the neighbors that are not already of that color with the value 1. This transformation is applied to the grid to create the output.\\\",\\n  \\\"new_verifier_reasoning\\\": \\\"The new verifier will perform a transformation that involves rotating the grid 180 degrees, reflecting it along the vertical axis, swapping the colors of the two most common colors, and then filling the boundary cells with a specific color. Each step contributes to a unique and interesting transformation that is different from the original.\\\",\\n  \\\"new_verifier_code\\\": \\\"```python\\\\ndef verify_new(I: Grid) -> Grid:\\\\n    # Step 1: Rotate the grid 180 degrees\\\\n    x1 = rotate(I, 180)\\\\n    # Step 2: Reflect the grid along the vertical axis\\\\n    x2 = reflect(x1, 'vertical')\\\\n    # Step 3: Swap the two most common colors\\\\n    x3 = swap_most_common_colors(x2)\\\\n    # Step 4: Fill the boundary cells with a specific color (e.g., 8)\\\\n    x4 = fill_boundaries(x3, 8)\\\\n    return x4\\\\n```\\\",\\n  \\\"new_generator_reasoning\\\": \\\"The new generator will create a grid with a specified background color, add a few cells of a different color, and ensure that the grid has a reasonable size and complexity. The difficulty level will be controlled by adjusting the number of cells added and the size of the grid. This will ensure that the transformation is both simple and interesting.\\\",\\n  \\\"new_generator_code\\\": \\\"```python\\\\ndef generate_new(diff_lb: float, diff_ub: float) -> dict:\\\\n    colopts = remove(1, interval(0, 10, 1))\\\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\\\n    bgc = choice(colopts)\\\\n    remcols = remove(bgc, colopts)\\\\n    fgc = choice(remcols)\\\\n    gi = canvas(bgc, (h, w))\\\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\\\n    inds = totuple(asindices(gi))\\\\n    dots = sample(inds, ndots)\\\\n    gi = fill(gi, fgc, dots)\\\\n    go = verify_new(gi)\\\\n    return {'input': gi, 'output': go}\\\\n```\\\",\\n  \\\"transformation_description\\\": \\\"The new transformation rule involves rotating the grid 180 degrees, reflecting it along the vertical axis, swapping the two most common colors, and filling the boundary cells with a specific color (e.g., 8).\\\",\\n  \\\"num_of_rules\\\": \\\"4\\\"\\n}\"}\n",
      "\n",
      "{'origin_name': '4258a5f9', 'num_of_rules': '4', 'verifier_code': \"def verify_new(I: Grid) -> Grid:\\n    # Step 1: Rotate the grid 180 degrees\\n    x1 = rotate(I, 180)\\n    # Step 2: Reflect the grid along the vertical axis\\n    x2 = reflect(x1, 'vertical')\\n    # Step 3: Swap the two most common colors\\n    x3 = swap_most_common_colors(x2)\\n    # Step 4: Fill the boundary cells with a specific color (e.g., 8)\\n    x4 = fill_boundaries(x3, 8)\\n    return x4\", 'generator_code': \"def generate_new(diff_lb: float, diff_ub: float) -> dict:\\n    colopts = remove(1, interval(0, 10, 1))\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\n    bgc = choice(colopts)\\n    remcols = remove(bgc, colopts)\\n    fgc = choice(remcols)\\n    gi = canvas(bgc, (h, w))\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\n    inds = totuple(asindices(gi))\\n    dots = sample(inds, ndots)\\n    gi = fill(gi, fgc, dots)\\n    go = verify_new(gi)\\n    return {'input': gi, 'output': go}\", 'transformation_description': 'The new transformation rule involves rotating the grid 180 degrees, reflecting it along the vertical axis, swapping the two most common colors, and filling the boundary cells with a specific color (e.g., 8).'}\n",
      "{\"name\": \"4258a5f9_4steps\", \"response\": \"```json\\n{\\n  \\\"original_reasoning\\\": \\\"The original transformation rule involves finding the least common color in the grid, identifying the cells of that color, finding the neighbors of those cells, and then filling the neighbors with color 1 while keeping the original cells of the least common color unchanged. The generator creates a grid with a background color and a foreground color, places some dots of the foreground color, and then applies the transformation.\\\",\\n  \\\"new_verifier_reasoning\\\": \\\"The new verifier will perform the following steps:\\\\n1. Identify all cells of the background color.\\\\n2. For each cell of the background color, check its neighbors and count the number of neighbors of the foreground color.\\\\n3. If the count of foreground neighbors is exactly 2, change the cell to the foreground color.\\\\n4. Return the transformed grid.\\\",\\n  \\\"new_verifier_code\\\": \\\"```python\\\\ndef verify_new(I: Grid) -> Grid:\\\\n    bgc = leastcolor(I)\\\\n    fgc = 1 if bgc != 1 else 2\\\\n    bg_indices = ofcolor(I, bgc)\\\\n    transformed_grid = fill(I, bgc, asindices(I))\\\\n    for i, j in bg_indices:\\\\n        neighbors = [(i + di, j + dj) for di, dj in [UP, DOWN, LEFT, RIGHT]]\\\\n        count = sum(1 for ni, nj in neighbors if 0 <= ni < len(I) and 0 <= nj < len(I[0]) and I[ni][nj] == fgc)\\\\n        if count == 2:\\\\n            transformed_grid = fill(transformed_grid, fgc, [(i, j)])\\\\n    return transformed_grid\\\\n```\\\",\\n  \\\"new_generator_reasoning\\\": \\\"The new generator will create a grid with a background color and a foreground color. It will place dots of the foreground color in a way that ensures the transformation rule is meaningful and interesting. The difficulty will be controlled by the number of dots and the size of the grid.\\\",\\n  \\\"new_generator_code\\\": \\\"```python\\\\ndef generate_new(diff_lb: float, diff_ub: float) -> dict:\\\\n    colopts = remove(1, interval(0, 10, 1))\\\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\\\n    bgc = choice(colopts)\\\\n    remcols = remove(bgc, colopts)\\\\n    fgc = choice(remcols)\\\\n    gi = canvas(bgc, (h, w))\\\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\\\n    inds = totuple(asindices(gi))\\\\n    dots = sample(inds, ndots)\\\\n    gi = fill(gi, fgc, dots)\\\\n    go = verify_new(gi)\\\\n    return {'input': gi, 'output': go}\\\\n```\\\",\\n  \\\"transformation_description\\\": \\\"The new transformation rule changes cells of the background color to the foreground color if they have exactly 2 neighbors of the foreground color.\\\",\\n  \\\"num_of_rules\\\": \\\"4\\\"\\n}\\n```\"}\n",
      "\n",
      "{'origin_name': '4258a5f9', 'num_of_rules': '4', 'verifier_code': 'def verify_new(I: Grid) -> Grid:\\n    bgc = leastcolor(I)\\n    fgc = 1 if bgc != 1 else 2\\n    bg_indices = ofcolor(I, bgc)\\n    transformed_grid = fill(I, bgc, asindices(I))\\n    for i, j in bg_indices:\\n        neighbors = [(i + di, j + dj) for di, dj in [UP, DOWN, LEFT, RIGHT]]\\n        count = sum(1 for ni, nj in neighbors if 0 <= ni < len(I) and 0 <= nj < len(I[0]) and I[ni][nj] == fgc)\\n        if count == 2:\\n            transformed_grid = fill(transformed_grid, fgc, [(i, j)])\\n    return transformed_grid', 'generator_code': \"def generate_new(diff_lb: float, diff_ub: float) -> dict:\\n    colopts = remove(1, interval(0, 10, 1))\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\n    bgc = choice(colopts)\\n    remcols = remove(bgc, colopts)\\n    fgc = choice(remcols)\\n    gi = canvas(bgc, (h, w))\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\n    inds = totuple(asindices(gi))\\n    dots = sample(inds, ndots)\\n    gi = fill(gi, fgc, dots)\\n    go = verify_new(gi)\\n    return {'input': gi, 'output': go}\", 'transformation_description': 'The new transformation rule changes cells of the background color to the foreground color if they have exactly 2 neighbors of the foreground color.'}\n",
      "{\"name\": \"4258a5f9_4steps\", \"response\": \"```json\\n{\\n  \\\"original_reasoning\\\": \\\"The original transformation rule involves finding the least common color in the grid, identifying the cells with that color, finding their neighbors, and then filling those neighbor cells with a specific value (1). The generator creates a grid with a specified background color and a different foreground color, placing a certain number of foreground cells and their neighbors with a different value.\\\",\\n  \\\"new_verifier_reasoning\\\": \\\"[Step 1: Find the most common color in the grid. Step 2: Identify the cells with that color. Step 3: Rotate the grid 90 degrees clockwise. Step 4: Fill the cells with the most common color with a new value (2).]\\\",\\n  \\\"new_verifier_code\\\": \\\"```python\\\\ndef verify_new(I: Grid) -> Grid:\\\\n    x0 = mostcolor(I)\\\\n    x1 = ofcolor(I, x0)\\\\n    x2 = rotate90(I)\\\\n    x3 = fill(x2, 2, x1)\\\\n    return x3\\\\n```\\\",\\n  \\\"new_generator_reasoning\\\": \\\"[Step 1: Create a grid with a specified background color. Step 2: Choose a different foreground color. Step 3: Place a certain number of foreground cells randomly. Step 4: Ensure the grid has a size that is a multiple of 4 for easier rotation. Step 5: Adjust the difficulty by controlling the number of foreground cells and the size of the grid.]\\\",\\n  \\\"new_generator_code\\\": \\\"```python\\\\ndef generate_new(diff_lb: float, diff_ub: float) -> dict:\\\\n    colopts = remove(1, interval(0, 10, 1))\\\\n    h = unifint(diff_lb, diff_ub, (4, 32))\\\\n    w = unifint(diff_lb, diff_ub, (4, 32))\\\\n    bgc = choice(colopts)\\\\n    remcols = remove(bgc, colopts)\\\\n    fgc = choice(remcols)\\\\n    gi = canvas(bgc, (h, w))\\\\n    mp = ((h * w) // 4)\\\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\\\n    inds = totuple(asindices(gi))\\\\n    dots = sample(inds, ndots)\\\\n    gi = fill(gi, fgc, dots)\\\\n    go = verify_new(gi)\\\\n    return {'input': gi, 'output': go}\\\\n```\\\",\\n  \\\"transformation_description\\\": \\\"The new transformation rule involves finding the most common color in the grid, identifying the cells with that color, rotating the grid 90 degrees clockwise, and then filling the cells with the most common color with a new value (2).\\\",\\n  \\\"num_of_rules\\\": \\\"4\\\"\\n}\\n```\\n\\n### Explanation\\n\\n1. **Original Reasoning**:\\n   - **Verifier**: The original verifier finds the least common color in the grid, identifies the cells with that color, finds their neighbors, and then fills those neighbor cells with a specific value (1).\\n   - **Generator**: The original generator creates a grid with a specified background color and a different foreground color, places a certain number of foreground cells, and then fills the neighbors of those cells with a different value.\\n\\n2. **New Verifier Reasoning**:\\n   - **Step 1**: Find the most common color in the grid.\\n   - **Step 2**: Identify the cells with that color.\\n   - **Step 3**: Rotate the grid 90 degrees clockwise.\\n   - **Step 4**: Fill the cells with the most common color with a new value (2).\\n\\n3. **New Generator Reasoning**:\\n   - **Step 1**: Create a grid with a specified background color.\\n   - **Step 2**: Choose a different foreground color.\\n   - **Step 3**: Place a certain number of foreground cells randomly.\\n   - **Step 4**: Ensure the grid has a size that is a multiple of 4 for easier rotation.\\n   - **Step 5**: Adjust the difficulty by controlling the number of foreground cells and the size of the grid.\\n\\n4. **Transformation Description**:\\n   - The new transformation rule involves finding the most common color in the grid, identifying the cells with that color, rotating the grid 90 degrees clockwise, and then filling the cells with the most common color with a new value (2).\\n\\nBy following these steps, the new verifier and generator ensure a different and interesting transformation rule while maintaining the use of the provided auxiliary functions.\"}\n",
      "\n",
      "{'origin_name': '4258a5f9', 'num_of_rules': '4', 'verifier_code': 'def verify_new(I: Grid) -> Grid:\\n    x0 = mostcolor(I)\\n    x1 = ofcolor(I, x0)\\n    x2 = rotate90(I)\\n    x3 = fill(x2, 2, x1)\\n    return x3', 'generator_code': \"def generate_new(diff_lb: float, diff_ub: float) -> dict:\\n    colopts = remove(1, interval(0, 10, 1))\\n    h = unifint(diff_lb, diff_ub, (4, 32))\\n    w = unifint(diff_lb, diff_ub, (4, 32))\\n    bgc = choice(colopts)\\n    remcols = remove(bgc, colopts)\\n    fgc = choice(remcols)\\n    gi = canvas(bgc, (h, w))\\n    mp = ((h * w) // 4)\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\n    inds = totuple(asindices(gi))\\n    dots = sample(inds, ndots)\\n    gi = fill(gi, fgc, dots)\\n    go = verify_new(gi)\\n    return {'input': gi, 'output': go}\", 'transformation_description': 'The new transformation rule involves finding the most common color in the grid, identifying the cells with that color, rotating the grid 90 degrees clockwise, and then filling the cells with the most common color with a new value (2).'}\n",
      "{\"name\": \"4258a5f9_4steps\", \"response\": \"```json\\n{\\n  \\\"original_reasoning\\\": \\\"The original verifier `verify_4258a5f9` performs the following steps:\\\\n1. Find the least common color in the grid (x0).\\\\n2. Identify all cells of that color (x1).\\\\n3. Find the neighbors of those cells (x2).\\\\n4. Compute the difference between the neighbors and the original cells (x3).\\\\n5. Fill the grid with the value 1 at the positions in x3 (x4).\\\\nThe generator `generate_4258a5f9` creates a grid with a background color, places a certain number of dots of a different color, and then applies a similar transformation to create the output grid. The transformation involves filling the neighbors of the dots with the value 1 and then filling the dots themselves with the foreground color.\\\",\\n  \\\"new_verifier_reasoning\\\": \\\"The new verifier will perform the following steps:\\\\n1. Identify all cells of the least common color (x0).\\\\n2. Find the neighbors of those cells (x1).\\\\n3. Rotate the grid 90 degrees clockwise (x2).\\\\n4. Fill the grid with the value 1 at the positions in x1.\\\\nThis transformation rule is simple yet interesting as it involves both neighbor finding and rotation.\\\",\\n  \\\"new_verifier_code\\\": \\\"```python\\\\ndef verify_new(I: Grid) -> Grid:\\\\n    x0 = leastcolor(I)\\\\n    x1 = mapply(neighbors, ofcolor(I, x0))\\\\n    x2 = rotate90(I)\\\\n    x3 = fill(x2, ONE, x1)\\\\n    return x3\\\\n```\\\",\\n  \\\"new_generator_reasoning\\\": \\\"The new generator will create a grid with a background color, place a certain number of dots of a different color, and then apply the new transformation rule to create the output grid. The transformation involves finding the neighbors of the least common color, rotating the grid 90 degrees clockwise, and filling the neighbors with the value 1.\\\\n- The difficulty parameters `diff_lb` and `diff_ub` will control the size of the grid and the number of dots, ensuring that the problem remains challenging but solvable.\\\",\\n  \\\"new_generator_code\\\": \\\"```python\\\\ndef generate_new(diff_lb: float, diff_ub: float) -> dict:\\\\n    colopts = remove(1, interval(0, 10, 1))\\\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\\\n    bgc = choice(colopts)\\\\n    remcols = remove(bgc, colopts)\\\\n    fgc = choice(remcols)\\\\n    gi = canvas(bgc, (h, w))\\\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\\\n    inds = totuple(asindices(gi))\\\\n    dots = sample(inds, ndots)\\\\n    go = fill(gi, fgc, dots)\\\\n    x0 = leastcolor(go)\\\\n    x1 = mapply(neighbors, ofcolor(go, x0))\\\\n    x2 = rotate90(go)\\\\n    x3 = fill(x2, ONE, x1)\\\\n    return {'input': go, 'output': x3}\\\\n```\\\",\\n  \\\"transformation_description\\\": \\\"The new transformation rule involves finding the neighbors of the least common color in the grid, rotating the grid 90 degrees clockwise, and then filling the grid with the value 1 at the positions of the neighbors.\\\",\\n  \\\"num_of_rules\\\": \\\"4\\\"\\n}\\n```\"}\n",
      "\n",
      "{'origin_name': '4258a5f9', 'num_of_rules': '4', 'verifier_code': 'def verify_new(I: Grid) -> Grid:\\n    x0 = leastcolor(I)\\n    x1 = mapply(neighbors, ofcolor(I, x0))\\n    x2 = rotate90(I)\\n    x3 = fill(x2, ONE, x1)\\n    return x3', 'generator_code': \"def generate_new(diff_lb: float, diff_ub: float) -> dict:\\n    colopts = remove(1, interval(0, 10, 1))\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\n    bgc = choice(colopts)\\n    remcols = remove(bgc, colopts)\\n    fgc = choice(remcols)\\n    gi = canvas(bgc, (h, w))\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\n    inds = totuple(asindices(gi))\\n    dots = sample(inds, ndots)\\n    go = fill(gi, fgc, dots)\\n    x0 = leastcolor(go)\\n    x1 = mapply(neighbors, ofcolor(go, x0))\\n    x2 = rotate90(go)\\n    x3 = fill(x2, ONE, x1)\\n    return {'input': go, 'output': x3}\", 'transformation_description': 'The new transformation rule involves finding the neighbors of the least common color in the grid, rotating the grid 90 degrees clockwise, and then filling the grid with the value 1 at the positions of the neighbors.'}\n",
      "{\"name\": \"4258a5f9_4steps\", \"response\": \"```json\\n{\\n  \\\"original_reasoning\\\": \\\"The original verifier `verify_4258a5f9` performs the following steps:\\\\n1. Finds the least common color in the input grid.\\\\n2. Finds all cells that have this least common color.\\\\n3. Finds the neighbors of these cells.\\\\n4. Removes the original cells from the set of neighbors.\\\\n5. Fills the remaining cells with the value 1.\\\\n\\\\nThe generator `generate_4258a5f9` creates a grid with a background color and a foreground color, places a random number of dots (cells with the foreground color), and then fills the neighbors of these dots with the value 1. The difficulty is controlled by the `diff_lb` and `diff_ub` parameters, which influence the size of the grid and the number of dots.\\\\n\\\\nThe relationship between the verifier and generator is that the generator creates a grid that the verifier can correctly transform. The verifier ensures that the transformation from the input grid to the output grid is valid.\\\",\\n  \\\"new_verifier_reasoning\\\": \\\"The new verifier will perform the following steps:\\\\n1. Find all cells with the least common color.\\\\n2. Rotate the grid 90 degrees clockwise.\\\\n3. Reflect the grid along the vertical axis.\\\\n4. Fill the cells that were originally the least common color with the value 1.\\\\n\\\\nEach step contributes to a unique transformation that is both simple and interesting. The rotation and reflection steps add a spatial transformation, while the final fill step ensures that the transformation is visible and verifiable.\\\",\\n  \\\"new_verifier_code\\\": \\\"```python\\\\ndef verify_new(I: Grid) -> Grid:\\\\n    x0 = leastcolor(I)\\\\n    x1 = ofcolor(I, x0)\\\\n    x2 = rotate90(I)\\\\n    x3 = reflect_vertical(x2)\\\\n    x4 = fill(x3, 1, x1)\\\\n    return x4\\\\n```\\\",\\n  \\\"new_generator_reasoning\\\": \\\"The new generator will create a grid similar to the original but with a different transformation rule:\\\\n1. Choose a random size for the grid within the difficulty bounds.\\\\n2. Choose a background color and a foreground color, ensuring they are different.\\\\n3. Place a random number of dots (cells with the foreground color) on the grid.\\\\n4. Rotate the grid 90 degrees clockwise.\\\\n5. Reflect the grid along the vertical axis.\\\\n6. Fill the cells that were originally the least common color with the value 1.\\\\n\\\\nThe difficulty is controlled by the `diff_lb` and `diff_ub` parameters, which influence the size of the grid and the number of dots. The rotation and reflection steps add complexity and ensure that the transformation is non-trivial.\\\",\\n  \\\"new_generator_code\\\": \\\"```python\\\\ndef generate_new(diff_lb: float, diff_ub: float) -> dict:\\\\n    colopts = remove(1, interval(0, 10, 1))\\\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\\\n    bgc = choice(colopts)\\\\n    remcols = remove(bgc, colopts)\\\\n    fgc = choice(remcols)\\\\n    gi = canvas(bgc, (h, w))\\\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\\\n    inds = totuple(asindices(gi))\\\\n    dots = sample(inds, ndots)\\\\n    gi = fill(gi, fgc, dots)\\\\n    go = rotate90(gi)\\\\n    go = reflect_vertical(go)\\\\n    x0 = leastcolor(gi)\\\\n    x1 = ofcolor(gi, x0)\\\\n    go = fill(go, 1, x1)\\\\n    return {'input': gi, 'output': go}\\\\n```\\\",\\n  \\\"transformation_description\\\": \\\"The new transformation rule involves finding the least common color in the input grid, rotating the grid 90 degrees clockwise, reflecting the grid along the vertical axis, and finally filling the cells that were originally the least common color with the value 1.\\\",\\n  \\\"num_of_rules\\\": \\\"4\\\"\\n}\\n```\\n\\n### Explanation of New Transformation Rule:\\n1. **Find the Least Common Color**: Identify the least common color in the input grid.\\n2. **Rotate 90 Degrees Clockwise**: Rotate the entire grid 90 degrees clockwise.\\n3. **Reflect Vertically**: Reflect the rotated grid along the vertical axis.\\n4. **Fill Original Least Common Color Cells**: Fill the cells that were originally the least common color with the value 1.\\n\\nThis transformation rule is simple yet interesting because it involves both spatial transformations (rotation and reflection) and a color-based operation (filling specific cells). The generator ensures that the input grid is created with these transformations in mind, making the verifier's task straightforward and verifiable.\"}\n",
      "\n",
      "{'origin_name': '4258a5f9', 'num_of_rules': '4', 'verifier_code': 'def verify_new(I: Grid) -> Grid:\\n    x0 = leastcolor(I)\\n    x1 = ofcolor(I, x0)\\n    x2 = rotate90(I)\\n    x3 = reflect_vertical(x2)\\n    x4 = fill(x3, 1, x1)\\n    return x4', 'generator_code': \"def generate_new(diff_lb: float, diff_ub: float) -> dict:\\n    colopts = remove(1, interval(0, 10, 1))\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\n    bgc = choice(colopts)\\n    remcols = remove(bgc, colopts)\\n    fgc = choice(remcols)\\n    gi = canvas(bgc, (h, w))\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\n    inds = totuple(asindices(gi))\\n    dots = sample(inds, ndots)\\n    gi = fill(gi, fgc, dots)\\n    go = rotate90(gi)\\n    go = reflect_vertical(go)\\n    x0 = leastcolor(gi)\\n    x1 = ofcolor(gi, x0)\\n    go = fill(go, 1, x1)\\n    return {'input': gi, 'output': go}\", 'transformation_description': 'The new transformation rule involves finding the least common color in the input grid, rotating the grid 90 degrees clockwise, reflecting the grid along the vertical axis, and finally filling the cells that were originally the least common color with the value 1.'}\n",
      "{\"name\": \"4258a5f9_4steps\", \"response\": \"```json\\n{\\n  \\\"original_reasoning\\\": \\\"The original transformation rule finds the least common color in the grid, identifies the cells with that color, gets the neighbors of those cells, and then fills the grid with the value 1 at the positions that are neighbors but not part of the original cells. This effectively creates a 'border' around the least common color cells.\\\",\\n  \\\"new_verifier_reasoning\\\": \\\"The new transformation rule will involve the following steps:\\\\n1. Identify the most common color in the grid.\\\\n2. Find the cells with that color.\\\\n3. Rotate the grid 90 degrees clockwise.\\\\n4. Fill the grid with the most common color at the positions of the cells found in step 2 after the rotation.\\\\nThis transformation will create a pattern where the most common color cells are rotated and then filled in the new positions.\\\",\\n  \\\"new_verifier_code\\\": \\\"```python\\\\ndef verify_new(I: Grid) -> Grid:\\\\n    x0 = mostcolor(I)\\\\n    x1 = ofcolor(I, x0)\\\\n    x2 = rotate90(I)\\\\n    x3 = fill(x2, x0, x1)\\\\n    return x3\\\\n```\\\",\\n  \\\"new_generator_reasoning\\\": \\\"The new generator will create a grid with a random background color and a random foreground color. It will place a set of cells with the foreground color in random positions. The number of cells and the size of the grid will be controlled by the difficulty parameters diff_lb and diff_ub. The transformation will then be applied to this grid to create the output.\\\",\\n  \\\"new_generator_code\\\": \\\"```python\\\\ndef generate_new(diff_lb: float, diff_ub: float) -> dict:\\\\n    colopts = remove(1, interval(0, 10, 1))\\\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\\\n    bgc = choice(colopts)\\\\n    remcols = remove(bgc, colopts)\\\\n    fgc = choice(remcols)\\\\n    gi = canvas(bgc, (h, w))\\\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\\\n    inds = totuple(asindices(gi))\\\\n    dots = sample(inds, ndots)\\\\n    go = fill(gi, fgc, dots)\\\\n    gi = fill(gi, fgc, dots)\\\\n    return {'input': gi, 'output': verify_new(gi)}\\\\n```\\\",\\n  \\\"transformation_description\\\": \\\"The new transformation rule involves identifying the most common color in the grid, finding the cells with that color, rotating the grid 90 degrees clockwise, and then filling the grid with the most common color at the positions of the cells found in the original grid.\\\",\\n  \\\"num_of_rules\\\": \\\"4\\\"\\n}\\n```\\n\\n### Explanation of the New Transformation Rule\\n\\n1. **Identify the Most Common Color**: The function `mostcolor(I)` is used to find the most common color in the grid.\\n2. **Find the Cells with the Most Common Color**: The function `ofcolor(I, x0)` is used to find the indices of all cells with the most common color.\\n3. **Rotate the Grid 90 Degrees Clockwise**: The function `rotate90(I)` is used to rotate the grid 90 degrees clockwise.\\n4. **Fill the Grid with the Most Common Color**: The function `fill(x2, x0, x1)` is used to fill the rotated grid with the most common color at the positions of the cells found in step 2.\\n\\n### Generator Reasoning\\n\\n- **Grid Size and Color Selection**: The grid size and colors are selected based on the difficulty parameters `diff_lb` and `diff_ub`. The background color and foreground color are chosen randomly, ensuring they are different.\\n- **Placement of Foreground Color**: A random number of cells with the foreground color are placed in the grid.\\n- **Transformation Application**: The transformation rule is applied to the generated input grid to create the output grid.\\n\\nThis new transformation rule is both simple and interesting, as it involves a combination of color identification, cell selection, and rotation, which can create visually appealing and logically challenging patterns.\"}\n",
      "\n",
      "{'origin_name': '4258a5f9', 'num_of_rules': '4', 'verifier_code': 'def verify_new(I: Grid) -> Grid:\\n    x0 = mostcolor(I)\\n    x1 = ofcolor(I, x0)\\n    x2 = rotate90(I)\\n    x3 = fill(x2, x0, x1)\\n    return x3', 'generator_code': \"def generate_new(diff_lb: float, diff_ub: float) -> dict:\\n    colopts = remove(1, interval(0, 10, 1))\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\n    bgc = choice(colopts)\\n    remcols = remove(bgc, colopts)\\n    fgc = choice(remcols)\\n    gi = canvas(bgc, (h, w))\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\n    inds = totuple(asindices(gi))\\n    dots = sample(inds, ndots)\\n    go = fill(gi, fgc, dots)\\n    gi = fill(gi, fgc, dots)\\n    return {'input': gi, 'output': verify_new(gi)}\", 'transformation_description': 'The new transformation rule involves identifying the most common color in the grid, finding the cells with that color, rotating the grid 90 degrees clockwise, and then filling the grid with the most common color at the positions of the cells found in the original grid.'}\n",
      "{\"name\": \"4258a5f9_4steps\", \"response\": \"```json\\n{\\n  \\\"original_reasoning\\\": \\\"The original transformation involves finding the least common color in the grid, identifying the cells of that color, finding their neighbors, and then filling the neighbors with the value 1, while keeping the original cells of the least common color unchanged.\\\",\\n  \\\"new_verifier_reasoning\\\": \\\"The new verifier will perform the following steps:\\\\n1. Find the most common color in the grid.\\\\n2. Identify the cells of that color.\\\\n3. Rotate these cells 90 degrees clockwise.\\\\n4. Fill the rotated cells with the value 2, while keeping the rest of the grid unchanged.\\\",\\n  \\\"new_verifier_code\\\": \\\"```python\\\\ndef verify_new(I: Grid) -> Grid:\\\\n    x0 = mostcolor(I)\\\\n    x1 = ofcolor(I, x0)\\\\n    x2 = rotate(x1, 1)\\\\n    x3 = fill(I, 2, x2)\\\\n    return x3\\\\n```\\\",\\n  \\\"new_generator_reasoning\\\": \\\"The new generator will generate a grid with a specified background color and a few cells of a different color. The number of cells and their positions will be controlled by the difficulty parameters (diff_lb and diff_ub). The transformation will then be applied to these cells as per the new verifier.\\\\n1. Choose a random background color from the available colors (excluding 2).\\\\n2. Generate a grid of random height and width within the specified difficulty bounds.\\\\n3. Choose a different color for the cells to be transformed.\\\\n4. Randomly place a number of cells (controlled by the difficulty bounds) of the chosen color on the grid.\\\\n5. Apply the transformation to these cells as per the new verifier.\\\",\\n  \\\"new_generator_code\\\": \\\"```python\\\\ndef generate_new(diff_lb: float, diff_ub: float) -> dict:\\\\n    colopts = remove(2, interval(0, 10, 1))\\\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\\\n    bgc = choice(colopts)\\\\n    remcols = remove(bgc, colopts)\\\\n    fgc = choice(remcols)\\\\n    gi = canvas(bgc, (h, w))\\\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\\\n    inds = totuple(asindices(gi))\\\\n    dots = sample(inds, ndots)\\\\n    go = fill(gi, 2, rotate(frozenset(dots), 1))\\\\n    gi = fill(gi, fgc, dots)\\\\n    return {'input': gi, 'output': go}\\\\n```\\\",\\n  \\\"transformation_description\\\": \\\"The new transformation rule involves finding the most common color in the grid, identifying the cells of that color, rotating these cells 90 degrees clockwise, and then filling the rotated cells with the value 2, while keeping the rest of the grid unchanged.\\\",\\n  \\\"num_of_rules\\\": \\\"4\\\"\\n}\\n```\\n\\n### Explanation:\\n1. **Original Verifier and Generator:**\\n   - The original verifier finds the least common color in the grid, identifies the cells of that color, finds their neighbors, and fills the neighbors with the value 1.\\n   - The original generator creates a grid with a specified background color, places a few cells of a different color, and applies the transformation to these cells.\\n\\n2. **New Verifier:**\\n   - **Step 1:** Find the most common color in the grid using `mostcolor`.\\n   - **Step 2:** Identify the cells of that color using `ofcolor`.\\n   - **Step 3:** Rotate these cells 90 degrees clockwise using `rotate`.\\n   - **Step 4:** Fill the rotated cells with the value 2 using `fill`.\\n\\n3. **New Generator:**\\n   - **Step 1:** Choose a random background color from the available colors (excluding 2).\\n   - **Step 2:** Generate a grid of random height and width within the specified difficulty bounds.\\n   - **Step 3:** Choose a different color for the cells to be transformed.\\n   - **Step 4:** Randomly place a number of cells (controlled by the difficulty bounds) of the chosen color on the grid.\\n   - **Step 5:** Apply the transformation to these cells as per the new verifier.\\n\\n4. **Transformation Description:**\\n   - The new transformation rule involves finding the most common color in the grid, identifying the cells of that color, rotating these cells 90 degrees clockwise, and then filling the rotated cells with the value 2, while keeping the rest of the grid unchanged.\\n\\nThis transformation is both simple and interesting, as it involves a combination of color identification, cell rotation, and value filling. The difficulty can be adjusted by controlling the number of cells and the size of the grid using the `diff_lb` and `diff_ub` parameters.\"}\n",
      "\n",
      "{'origin_name': '4258a5f9', 'num_of_rules': '4', 'verifier_code': 'def verify_new(I: Grid) -> Grid:\\n    x0 = mostcolor(I)\\n    x1 = ofcolor(I, x0)\\n    x2 = rotate(x1, 1)\\n    x3 = fill(I, 2, x2)\\n    return x3', 'generator_code': \"def generate_new(diff_lb: float, diff_ub: float) -> dict:\\n    colopts = remove(2, interval(0, 10, 1))\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\n    bgc = choice(colopts)\\n    remcols = remove(bgc, colopts)\\n    fgc = choice(remcols)\\n    gi = canvas(bgc, (h, w))\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\n    inds = totuple(asindices(gi))\\n    dots = sample(inds, ndots)\\n    go = fill(gi, 2, rotate(frozenset(dots), 1))\\n    gi = fill(gi, fgc, dots)\\n    return {'input': gi, 'output': go}\", 'transformation_description': 'The new transformation rule involves finding the most common color in the grid, identifying the cells of that color, rotating these cells 90 degrees clockwise, and then filling the rotated cells with the value 2, while keeping the rest of the grid unchanged.'}\n",
      "{\"name\": \"4258a5f9_4steps\", \"response\": \"```json\\n{\\n  \\\"original_reasoning\\\": \\\"The original verifier `verify_4258a5f9` performs the following steps:\\\\n1. Find the least common color in the grid.\\\\n2. Get the indices of all cells with that color.\\\\n3. Get the neighbors of those cells.\\\\n4. Find the difference between the neighbors and the original cells.\\\\n5. Fill the resulting indices with the value 1.\\\\n6. Return the transformed grid.\\\\n\\\\nThe generator `generate_4258a5f9` creates a grid with a background color and a foreground color, places a random number of dots of the foreground color, and then fills the neighbors of these dots with the value 1. The transformation rule is that the neighbors of the foreground dots are filled with 1, while the dots themselves remain unchanged.\\\",\\n  \\\"new_verifier_reasoning\\\": \\\"The new verifier will perform the following steps:\\\\n1. Find the indices of all cells with the most common color.\\\\n2. Rotate these cells 90 degrees clockwise.\\\\n3. Reflect these cells horizontally.\\\\n4. Fill the resulting indices with the value 2.\\\\nThis transformation rule is simple yet interesting, as it involves rotation and reflection, which can create visually appealing patterns.\\\",\\n  \\\"new_verifier_code\\\": \\\"```python\\\\ndef verify_new(I: Grid) -> Grid:\\\\n    x0 = mostcolor(I)\\\\n    x1 = ofcolor(I, x0)\\\\n    x2 = rotate90(x1)\\\\n    x3 = reflect(x2, 'horizontal')\\\\n    x4 = fill(I, 2, x3)\\\\n    return x4\\\\n```\\\",\\n  \\\"new_generator_reasoning\\\": \\\"The new generator will:\\\\n1. Create a grid with a random background color and a random foreground color.\\\\n2. Place a random number of dots of the foreground color on the grid.\\\\n3. Apply the transformation rule to the grid by rotating and reflecting the foreground dots and filling the resulting indices with the value 2.\\\\n4. Ensure the difficulty is controlled by the `diff_lb` and `diff_ub` parameters, which affect the size of the grid and the number of dots.\\\\nThis ensures that the generated grids are varied and challenging.\\\",\\n  \\\"new_generator_code\\\": \\\"```python\\\\ndef generate_new(diff_lb: float, diff_ub: float) -> dict:\\\\n    colopts = remove(2, interval(0, 10, 1))\\\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\\\n    bgc = choice(colopts)\\\\n    remcols = remove(bgc, colopts)\\\\n    fgc = choice(remcols)\\\\n    gi = canvas(bgc, (h, w))\\\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\\\n    inds = totuple(asindices(gi))\\\\n    dots = sample(inds, ndots)\\\\n    gi = fill(gi, fgc, dots)\\\\n    go = verify_new(gi)\\\\n    return {'input': gi, 'output': go}\\\\n```\\\",\\n  \\\"transformation_description\\\": \\\"The new transformation rule involves finding the most common color in the grid, rotating the indices of these cells 90 degrees clockwise, reflecting them horizontally, and filling the resulting indices with the value 2.\\\",\\n  \\\"num_of_rules\\\": \\\"4\\\"\\n}\\n```\\n\\n### Explanation of the New Verifier and Generator\\n\\n1. **Verifier (`verify_new`)**:\\n   - **Step 1**: `x0 = mostcolor(I)`: Find the most common color in the grid.\\n   - **Step 2**: `x1 = ofcolor(I, x0)`: Get the indices of all cells with the most common color.\\n   - **Step 3**: `x2 = rotate90(x1)`: Rotate these cells 90 degrees clockwise.\\n   - **Step 4**: `x3 = reflect(x2, 'horizontal')`: Reflect these cells horizontally.\\n   - **Step 5**: `x4 = fill(I, 2, x3)`: Fill the resulting indices with the value 2.\\n   - **Step 6**: `return x4`: Return the transformed grid.\\n\\n2. **Generator (`generate_new`)**:\\n   - **Step 1**: Create a grid with a random background color and a random foreground color.\\n   - **Step 2**: Place a random number of dots of the foreground color on the grid.\\n   - **Step 3**: Apply the transformation rule to the grid by rotating and reflecting the foreground dots and filling the resulting indices with the value 2.\\n   - **Step 4**: Ensure the difficulty is controlled by the `diff_lb` and `diff_ub` parameters, which affect the size of the grid and the number of dots.\\n\\n### Transformation Description\\nThe new transformation rule involves finding the most common color in the grid, rotating the indices of these cells 90 degrees clockwise, reflecting them horizontally, and filling the resulting indices with the value 2. This rule is simple yet interesting, as it combines rotation and reflection, creating visually appealing patterns.\"}\n",
      "\n",
      "{'origin_name': '4258a5f9', 'num_of_rules': '4', 'verifier_code': \"def verify_new(I: Grid) -> Grid:\\n    x0 = mostcolor(I)\\n    x1 = ofcolor(I, x0)\\n    x2 = rotate90(x1)\\n    x3 = reflect(x2, 'horizontal')\\n    x4 = fill(I, 2, x3)\\n    return x4\", 'generator_code': \"def generate_new(diff_lb: float, diff_ub: float) -> dict:\\n    colopts = remove(2, interval(0, 10, 1))\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\n    bgc = choice(colopts)\\n    remcols = remove(bgc, colopts)\\n    fgc = choice(remcols)\\n    gi = canvas(bgc, (h, w))\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\n    inds = totuple(asindices(gi))\\n    dots = sample(inds, ndots)\\n    gi = fill(gi, fgc, dots)\\n    go = verify_new(gi)\\n    return {'input': gi, 'output': go}\", 'transformation_description': 'The new transformation rule involves finding the most common color in the grid, rotating the indices of these cells 90 degrees clockwise, reflecting them horizontally, and filling the resulting indices with the value 2.'}\n",
      "{\"name\": \"4258a5f9_4steps\", \"response\": \"```json\\n{\\n  \\\"original_reasoning\\\": \\\"The original verifier and generator work together to create a transformation rule. The generator creates a grid with a background color and a foreground color. It places a certain number of foreground color cells and their neighbors in the grid. The verifier then identifies the least common color, finds the indices of that color, gets the neighbors of those indices, and fills the difference between the neighbors and the original indices with the value 1.\\\",\\n  \\\"new_verifier_reasoning\\\": \\\"[Step 1: Identify the most common color in the grid. Step 2: Find the indices of all cells with the most common color. Step 3: Rotate the grid 90 degrees clockwise. Step 4: Fill the cells at the original indices with a new color (e.g., 2).]\\\",\\n  \\\"new_verifier_code\\\": \\\"```python\\\\ndef verify_new(I: Grid) -> Grid:\\\\n    # Step 1: Identify the most common color in the grid\\\\n    x0 = mostcolor(I)\\\\n    # Step 2: Find the indices of all cells with the most common color\\\\n    x1 = ofcolor(I, x0)\\\\n    # Step 3: Rotate the grid 90 degrees clockwise\\\\n    x2 = rotate_90(I)\\\\n    # Step 4: Fill the cells at the original indices with a new color (e.g., 2)\\\\n    x3 = fill(x2, 2, x1)\\\\n    return x3\\\\n```\\\",\\n  \\\"new_generator_reasoning\\\": \\\"[Step 1: Create a grid with a random background color and dimensions based on difficulty. Step 2: Choose a foreground color different from the background color. Step 3: Randomly place a certain number of foreground color cells in the grid. Step 4: Ensure the grid has at least one cell with the most common color.]\\\",\\n  \\\"new_generator_code\\\": \\\"```python\\\\ndef generate_new(diff_lb: float, diff_ub: float) -> dict:\\\\n    colopts = remove(1, interval(0, 10, 1))\\\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\\\n    bgc = choice(colopts)\\\\n    remcols = remove(bgc, colopts)\\\\n    fgc = choice(remcols)\\\\n    gi = canvas(bgc, (h, w))\\\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\\\n    inds = totuple(asindices(gi))\\\\n    dots = sample(inds, ndots)\\\\n    gi = fill(gi, fgc, dots)\\\\n    # Ensure the grid has at least one cell with the most common color\\\\n    if fgc == mostcolor(gi):\\\\n        gi = fill(gi, fgc, [(0, 0)])\\\\n    else:\\\\n        gi = fill(gi, fgc, [dots[0]])\\\\n    return {'input': gi, 'output': verify_new(gi)}\\\\n```\\\",\\n  \\\"transformation_description\\\": \\\"The new transformation rule involves identifying the most common color in the grid, finding the indices of all cells with that color, rotating the grid 90 degrees clockwise, and filling the original indices with a new color (e.g., 2).\\\",\\n  \\\"num_of_rules\\\": \\\"4\\\"\\n}\\n```\\n\\n### Explanation of the New Verifier and Generator\\n\\n#### New Verifier Reasoning\\n1. **Identify the Most Common Color**: The function `mostcolor` is used to find the most common color in the grid.\\n2. **Find Indices of the Most Common Color**: The function `ofcolor` is used to find the indices of all cells that have the most common color.\\n3. **Rotate the Grid 90 Degrees Clockwise**: The function `rotate_90` is used to rotate the grid 90 degrees clockwise.\\n4. **Fill the Original Indices with a New Color**: The function `fill` is used to fill the cells at the original indices with a new color (e.g., 2).\\n\\n#### New Generator Reasoning\\n1. **Create a Grid**: The grid is created with a random background color and dimensions based on the difficulty parameters.\\n2. **Choose a Foreground Color**: A foreground color different from the background color is chosen.\\n3. **Place Foreground Color Cells**: A random number of foreground color cells are placed in the grid.\\n4. **Ensure the Most Common Color**: Ensure that the grid has at least one cell with the most common color to make the transformation meaningful.\\n\\n#### Transformation Description\\nThe new transformation rule involves identifying the most common color in the grid, finding the indices of all cells with that color, rotating the grid 90 degrees clockwise, and filling the original indices with a new color (e.g., 2). This rule is both simple and interesting, as it combines color analysis, rotation, and cell filling.\"}\n",
      "\n",
      "{'origin_name': '4258a5f9', 'num_of_rules': '4', 'verifier_code': 'def verify_new(I: Grid) -> Grid:\\n    # Step 1: Identify the most common color in the grid\\n    x0 = mostcolor(I)\\n    # Step 2: Find the indices of all cells with the most common color\\n    x1 = ofcolor(I, x0)\\n    # Step 3: Rotate the grid 90 degrees clockwise\\n    x2 = rotate_90(I)\\n    # Step 4: Fill the cells at the original indices with a new color (e.g., 2)\\n    x3 = fill(x2, 2, x1)\\n    return x3', 'generator_code': \"def generate_new(diff_lb: float, diff_ub: float) -> dict:\\n    colopts = remove(1, interval(0, 10, 1))\\n    h = unifint(diff_lb, diff_ub, (2, 30))\\n    w = unifint(diff_lb, diff_ub, (2, 30))\\n    bgc = choice(colopts)\\n    remcols = remove(bgc, colopts)\\n    fgc = choice(remcols)\\n    gi = canvas(bgc, (h, w))\\n    mp = ((h * w) // 2) if (h * w) % 2 == 1 else ((h * w) // 2 - 1)\\n    ndots = unifint(diff_lb, diff_ub, (1, mp))\\n    inds = totuple(asindices(gi))\\n    dots = sample(inds, ndots)\\n    gi = fill(gi, fgc, dots)\\n    # Ensure the grid has at least one cell with the most common color\\n    if fgc == mostcolor(gi):\\n        gi = fill(gi, fgc, [(0, 0)])\\n    else:\\n        gi = fill(gi, fgc, [dots[0]])\\n    return {'input': gi, 'output': verify_new(gi)}\", 'transformation_description': 'The new transformation rule involves identifying the most common color in the grid, finding the indices of all cells with that color, rotating the grid 90 degrees clockwise, and filling the original indices with a new color (e.g., 2).'}\n",
      "10 1\n"
     ]
    }
   ],
   "source": [
    "# 使用示例\n",
    "directory = '/mnt/data/zifeng.cao/reasoning/arc-agi/rollout/sampling_code/arc_new_rule_verifier/Qwen2.5-72B-Instruct_ARC_NEW_RULE_1024_SEQ-LEN_8192_temperature_0.7_world-size_8_n-worker-per-node_4'\n",
    "\n",
    "# 获取所有.jsonl文件列表\n",
    "jsonl_files = get_jsonl_files(directory)\n",
    "\n",
    "for file in jsonl_files:\n",
    "    file_contents = read_jsonl_file(file)\n",
    "    file_contents = file_contents[:10]\n",
    "    results = []\n",
    "    from concurrent.futures import ThreadPoolExecutor, as_completed\n",
    "    import multiprocessing\n",
    "\n",
    "    def process_line(line, original_training_io_data, original_generator_io_data):\n",
    "        try:\n",
    "            print(line)\n",
    "            extracted_data = extract_code_from_line(line)\n",
    "            print(extracted_data)\n",
    "            if extracted_data:\n",
    "                result = execute_and_evaluate(extracted_data, original_training_io_data, original_generator_io_data, timeout=1)\n",
    "                if result:\n",
    "                    if not (result[\"new_generator_verifier_match\"] or result[\"origin_training_verifier_success\"] or result[\"origin_generator_verifier_success\"]):\n",
    "                        return None\n",
    "                    return {\n",
    "                        \"name\": extracted_data[\"origin_name\"],\n",
    "                        \"num_of_rules\": extracted_data[\"num_of_rules\"],\n",
    "                        \"verifier_code\": extracted_data[\"verifier_code\"],   \n",
    "                        \"generator_code\": extracted_data[\"generator_code\"],\n",
    "                        \"generator_difficulty\": result[\"new_generator_difficulty\"],\n",
    "                        \"transformation_description\": extracted_data[\"transformation_description\"],\n",
    "                        \"origin_training_verifier_output_hash\": result[\"origin_training_verifier_hash\"],\n",
    "                        \"origin_training_verifier_difficulty\": result[\"origin_training_verifier_difficulty\"],\n",
    "                        \"origin_generator_verifier_output_hash\": result[\"origin_generator_verifier_hash\"],\n",
    "                        \"origin_generator_verifier_difficulty\": result[\"origin_generator_verifier_difficulty\"],\n",
    "                    }\n",
    "        except Exception as e:\n",
    "            print(f\"处理行时发生错误: {str(e)}\")\n",
    "            return None\n",
    "        return None\n",
    "\n",
    "\n",
    "    results = []\n",
    "    max_workers = multiprocessing.cpu_count()\n",
    "    with ThreadPoolExecutor(max_workers=max_workers) as executor:\n",
    "        future_to_line = {executor.submit(process_line, line, original_training_io_data, original_generator_io_data): line for i, line in enumerate(file_contents)}\n",
    "        for future in as_completed(future_to_line):\n",
    "            result = future.result()\n",
    "            if result:\n",
    "                results.append(result)\n",
    "\n",
    "    # if len(results) > 0:\n",
    "        # new_file_name = file.rsplit('.', 1)[0] + '_verify.jsonl'\n",
    "        # with open(new_file_name, \"w\") as f:\n",
    "        #     print(new_file_name)\n",
    "        #     for result in results:\n",
    "        #         f.write(json.dumps(result) + \"\\n\")\n",
    "    print(len(file_contents), len(results))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'new_generator_verifier_match': False, 'origin_training_verifier_success': False, 'origin_generator_verifier_success': False}\n"
     ]
    }
   ],
   "source": [
    "print(result)"
   ]
  }
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