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yaleh commited on Aug 26, 2024

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8f7e2cd

1 Parent(s): fd8d9a5

Add parameter generating_batch_size and updated UI.

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demo/sample_generator.ipynb +112 -132

demo/sample_generator.ipynb CHANGED Viewed

@@ -11,6 +11,10 @@
     "# Load configuration from YAML file\n",
     "config = {\n",
     "    \"model_name\": \"llama3-70b-8192\",\n",
     "}\n"
    ]
   },
@@ -20,11 +24,23 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (4272045825.py, line 148)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  Cell \u001b[0;32mIn[2], line 148\u001b[0;36m\u001b[0m\n\u001b[0;31m    \"additional_examples\": lambda x: (\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -34,15 +50,12 @@
     "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain_core.runnables import RunnablePassthrough, RunnableParallel\n",
     "\n",
-    "class TaskDescriptionGenerator:\n",
-    "    def __init__(self, model_temperature=0.7):\n",
-    "        self.model = ChatOpenAI(model=config[\"model_name\"], temperature=model_temperature)\n",
-    "        self.output_parser = StrOutputParser()\n",
-    "        \n",
-    "        self.description_prompt = ChatPromptTemplate.from_template(\n",
-    "            \"\"\"Given the following JSON example for a task type:\n",
     "{raw_example}\n",
     "\n",
     "Provide a concise description of the task type, including the format and style of the output.\n",
@@ -50,142 +63,103 @@
     "Format your response as follows:\n",
     "Task Description: [Your description here]\n",
     "\"\"\"\n",
-    "        )\n",
     "\n",
-    "        self.briefs_prompt = ChatPromptTemplate.from_template(\n",
-    "            \"\"\"Given the following task type description:\n",
-    "Task Description: {description}\n",
-    "\n",
-    "Generate descriptions for 3 new examples with detailed attributes\n",
-    "based on this task type.\n",
     "\n",
     "Format your response as a valid YAML object with a single key\n",
-    "'brief_descriptions' containing a YAML array of 3 objects, each\n",
-    "with a 'description' field.\n",
-    "\"\"\"\n",
-    "        )\n",
     "\n",
-    "        self.examples_from_briefs_prompt = ChatPromptTemplate.from_template(\n",
-    "            \"\"\"Given the following task type description, brief description for new examples, \n",
-    "and JSON example:\n",
     "\n",
-    "Task Description: {description}\n",
-    "Brief Description: {brief_description}\n",
     "\n",
     "Example:\n",
-    "{raw_example}\n",
     "\n",
-    "Generate 3 more input/output examples for this task type, based on the brief\n",
-    "description, in the same JSON format.\n",
     "\n",
-    "Format your response as a valid JSON object with a single key 'examples' \n",
-    "containing a JSON array of 3 objects, each with 'input' and 'output' fields.\n",
-    "\"\"\"\n",
-    "        )\n",
     "\n",
-    "        self.examples_prompt = ChatPromptTemplate.from_template(\n",
-    "            \"\"\"Given the following task type description, and input/output example:\n",
     "Task Description: {description}\n",
     "Example: {raw_example}\n",
     "\n",
-    "Generate 3 new input/output examples for this task type.\n",
     "\n",
     "Format your response as a valid JSON object with a single key 'examples' \n",
-    "containing a JSON array of 3 objects, each with 'input' and 'output' fields.\n",
     "\"\"\"\n",
-    "        )\n",
     "\n",
-    "        self.description_chain = self.description_prompt | self.model | self.output_parser\n",
-    "        self.briefs_chain = self.briefs_prompt | self.model | self.output_parser\n",
-    "        # bind json_object to the model\n",
-    "        json_model = self.model.bind(response_format={\"type\": \"json_object\"})\n",
-    "        self.examples_from_briefs_chain = self.examples_from_briefs_prompt | json_model | self.output_parser\n",
-    "        self.examples_chain = self.examples_prompt | json_model | self.output_parser\n",
-    "\n",
-    "    def generate_description(self, raw_example_str):\n",
-    "        result = self.description_chain.invoke({\n",
-    "            \"raw_example\": raw_example_str\n",
-    "        })\n",
-    "        return result.split(\"Task Description: \")[1].strip()\n",
-    "\n",
-    "    def generate_briefs(self, description):\n",
-    "        result = self.briefs_chain.invoke({\n",
-    "            \"description\": description\n",
-    "        })\n",
-    "        # return result.split(\"Brief Description: \")[1].strip()\n",
-    "        return result\n",
-    "\n",
-    "    def generate_examples_from_briefs(self, description, brief_description, raw_example_str):\n",
-    "        result = self.examples_from_briefs_chain.invoke({\n",
-    "            \"description\": description,\n",
-    "            \"brief_description\": brief_description,\n",
-    "            \"raw_example\": raw_example_str\n",
-    "        })\n",
     "\n",
     "        try:\n",
-    "            result = result.strip()\n",
-    "            if result.startswith('```') and result.endswith('```'):\n",
-    "                result = result.strip('```').strip()\n",
-    "                if result.startswith('json'):\n",
-    "                    result = result.strip('json').strip()\n",
-    "            return json.loads(result)\n",
-    "        except json.JSONDecodeError as e:\n",
-    "            raise ValueError(f\"The generated examples are not in valid JSON format. Error: {str(e)} Result: {result}\")\n",
-    "        \n",
-    "    def generate_examples(self, description, example):\n",
-    "        result = self.examples_chain.invoke({\n",
-    "            \"description\": description,\n",
-    "            \"example\": example\n",
-    "        })\n",
-    "        \n",
-    "        try:\n",
-    "            result = result.strip()\n",
-    "            if result.startswith('```') and result.endswith('```'):\n",
-    "                result = result.strip('```').strip()\n",
-    "                if result.startswith('json'):\n",
-    "                    result = result.strip('json').strip()\n",
-    "            return json.loads(result)\n",
-    "        except json.JSONDecodeError as e:\n",
-    "            raise ValueError(f\"The generated examples are not in valid JSON format. Error: {str(e)} Result: {result}\")\n",
-    "\n",
-    "    def process(self, input_str):\n",
-    "        try:\n",
-    "            # Parse input\n",
-    "            parse_input = (\n",
-    "                RunnablePassthrough()\n",
-    "                | (lambda x: json.loads(x))\n",
-    "                | (lambda x: x if isinstance(x, dict) and 'input' in x and 'output' in x \n",
-    "                   else (_ for _ in ()).throw(ValueError(\"Invalid input format. Expected an object with 'input' and 'output' fields.\")))\n",
-    "            )\n",
     "\n",
-    "            # Create the chain\n",
-    "            chain = (\n",
-    "                parse_input\n",
-    "                | (lambda x: json.dumps(x, ensure_ascii=False))\n",
-    "                | {\n",
-    "                    \"description\": self.description_chain,\n",
-    "                    \"raw_example\": lambda x: x\n",
-    "                }\n",
-    "                | {\n",
-    "                    \"description\": lambda x: x[\"description\"],\n",
-    "                    \"examples_from_briefs\": {\n",
-    "                        \"description\": lambda x: x[\"description\"],\n",
-    "                        \"brief_description\": lambda x: self.briefs_chain.invoke({\"description\": x[\"description\"]}),\n",
-    "                        \"raw_example\": lambda x: x[\"raw_example\"]\n",
-    "                    } | self.examples_from_briefs_chain,\n",
-    "                    \"examples\": self.examples_chain\n",
-    "                }\n",
-    "                | RunnablePassthrough.assign(\n",
-    "                    additional_examples = lambda x: (\n",
-    "                        list(json.loads(x[\"examples_from_briefs\"])[\"examples\"])\n",
-    "                        + list(json.loads(x[\"examples\"])[\"examples\"])\n",
-    "                    )\n",
-    "                )\n",
-    "                | (lambda x: json.dumps(x, indent=2, ensure_ascii=False))\n",
-    "            )\n",
     "\n",
-    "            # Invoke the chain\n",
-    "            result = chain.invoke(input_str)\n",
     "            return result\n",
     "\n",
     "        except Exception as e:\n",
@@ -193,18 +167,24 @@
     "\n",
     "import gradio as gr\n",
     "\n",
-    "def process_json(input_json):\n",
     "    try:\n",
     "        generator = TaskDescriptionGenerator()\n",
-    "        result = generator.process(input_json)\n",
-    "        return \"Process completed successfully. Result:\\n\" + result\n",
     "    except Exception as e:\n",
-    "        return f\"An error occurred: {str(e)}\"\n",
     "\n",
     "demo = gr.Interface(\n",
     "    fn=process_json,\n",
-    "    inputs=gr.Textbox(label=\"Input JSON\"),\n",
-    "    outputs=gr.Textbox(label=\"Output\"),\n",
     "    title=\"Task Description Generator\",\n",
     "    description=\"Enter a JSON object with 'input' and 'output' fields to generate a task description and additional examples.\"\n",
     ")\n",

     "# Load configuration from YAML file\n",
     "config = {\n",
     "    \"model_name\": \"llama3-70b-8192\",\n",
+    "    # \"model_name\": \"llama3-8b-8192\",\n",
+    "    # \"model_name\": \"llama-3.1-70b-versatile\",\n",
+    "    # \"model_name\": \"llama-3.1-8b-instant\",\n",
+    "    # \"model_name\": \"gemma2-9b-it\",\n",
     "}\n"
    ]
   },
    "metadata": {},
    "outputs": [
     {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yale/work/meta-prompt/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "Dataframe.__init__() got an unexpected keyword argument 'format'",
      "output_type": "error",
      "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 139\u001b[0m\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    132\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m gr\u001b[38;5;241m.\u001b[39mError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn error occurred: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    134\u001b[0m demo \u001b[38;5;241m=\u001b[39m gr\u001b[38;5;241m.\u001b[39mInterface(\n\u001b[1;32m    135\u001b[0m     fn\u001b[38;5;241m=\u001b[39mprocess_json,\n\u001b[1;32m    136\u001b[0m     inputs\u001b[38;5;241m=\u001b[39m[gr\u001b[38;5;241m.\u001b[39mTextbox(label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInput JSON\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m    137\u001b[0m             gr\u001b[38;5;241m.\u001b[39mSlider(label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGenerating Batch Size\u001b[39m\u001b[38;5;124m\"\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, minimum\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, maximum\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m, step\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)],\n\u001b[1;32m    138\u001b[0m     outputs\u001b[38;5;241m=\u001b[39m[gr\u001b[38;5;241m.\u001b[39mTextbox(label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDescription\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m--> 139\u001b[0m              \u001b[43mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataFrame\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mExamples\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m    140\u001b[0m \u001b[43m                           \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mInput\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mOutput\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m                           \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmarkdown\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m],\n\u001b[1;32m    142\u001b[0m     title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTask Description Generator\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    143\u001b[0m     description\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEnter a JSON object with \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minput\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutput\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m fields to generate a task description and additional examples.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    144\u001b[0m )\n\u001b[1;32m    146\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    147\u001b[0m     demo\u001b[38;5;241m.\u001b[39mlaunch()\n",
+      "File \u001b[0;32m~/work/meta-prompt/.venv/lib/python3.10/site-packages/gradio/component_meta.py:163\u001b[0m, in \u001b[0;36mupdateable.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    161\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    162\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 163\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mTypeError\u001b[0m: Dataframe.__init__() got an unexpected keyword argument 'format'"
      ]
     }
    ],
     "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.runnables import RunnablePassthrough, RunnableParallel, RunnableLambda\n",
+    "from langchain_core.output_parsers import JsonOutputParser\n",
+    "from langchain.output_parsers import YamlOutputParser\n",
     "\n",
+    "# Define prompt strings as constants\n",
+    "DESCRIPTION_PROMPT = \"\"\"Given the following JSON example for a task type:\n",
     "{raw_example}\n",
     "\n",
     "Provide a concise description of the task type, including the format and style of the output.\n",
     "Format your response as follows:\n",
     "Task Description: [Your description here]\n",
     "\"\"\"\n",
     "\n",
+    "BRIEFS_PROMPT = [\n",
+    "    (\"system\", \"\"\"Given the task type description, generate descriptions for {generating_batch_size} new \n",
+    "examples with detailed attributes based on this task type. But don't provide any detailed task output.\n",
     "\n",
     "Format your response as a valid YAML object with a single key\n",
+    "'new_example_briefs' containing a YAML array of {generating_batch_size} objects, each\n",
+    "with a 'example_brief' field.\n",
+    "\"\"\"),\n",
+    "    (\"user\", \"\"\"Task Description:\n",
     "\n",
+    "{description}\n",
     "\n",
+    "\"\"\")\n",
+    "]\n",
+    "\n",
+    "EXAMPLES_FROM_BRIEFS_PROMPT = [\n",
+    "    (\"system\", \"\"\"Given the task type description, brief descriptions for new examples, \n",
+    "and JSON example, generate 3 more input/output examples for this task type,\n",
+    "strictly based on the brief descriptions. Ensure that the new examples are\n",
+    "consistent with the brief descriptions and do not introduce any new information\n",
+    "not present in the briefs.\n",
+    "\n",
+    "Format your response as a valid JSON object with a single key 'examples' \n",
+    "containing a JSON array of {generating_batch_size} objects, each with 'input' and 'output' fields.\n",
+    "\"\"\"),\n",
+    "    (\"user\", \"\"\"Task Description:\n",
+    "\n",
+    "{description}\n",
+    "\n",
+    "New Example Briefs: \n",
+    "\n",
+    "{new_example_briefs}\n",
     "\n",
     "Example:\n",
     "\n",
+    "{raw_example}\n",
     "\n",
+    "\"\"\")\n",
+    "]\n",
     "\n",
+    "EXAMPLES_PROMPT = \"\"\"Given the following task type description, and input/output example:\n",
     "Task Description: {description}\n",
     "Example: {raw_example}\n",
     "\n",
+    "Generate {generating_batch_size} new input/output examples for this task type.\n",
     "\n",
     "Format your response as a valid JSON object with a single key 'examples' \n",
+    "containing a JSON array of {generating_batch_size} objects, each with 'input' and 'output' fields.\n",
     "\"\"\"\n",
     "\n",
+    "class TaskDescriptionGenerator:\n",
+    "    def __init__(self, model_temperature=0.7):        \n",
+    "        self.description_prompt = ChatPromptTemplate.from_template(DESCRIPTION_PROMPT)\n",
+    "        self.briefs_prompt = ChatPromptTemplate.from_messages(BRIEFS_PROMPT)\n",
+    "        self.examples_from_briefs_prompt = ChatPromptTemplate.from_messages(EXAMPLES_FROM_BRIEFS_PROMPT)\n",
+    "        self.examples_prompt = ChatPromptTemplate.from_template(EXAMPLES_PROMPT)\n",
+    "\n",
+    "        model = ChatOpenAI(model=config[\"model_name\"], temperature=model_temperature)\n",
+    "        json_model = model.bind(response_format={\"type\": \"json_object\"})\n",
+    "\n",
+    "        output_parser = StrOutputParser()\n",
+    "        json_parse = JsonOutputParser()\n",
+    "\n",
+    "        self.description_chain = self.description_prompt | model | output_parser\n",
+    "        self.briefs_chain = self.briefs_prompt | model | output_parser\n",
+    "        self.examples_from_briefs_chain = self.examples_from_briefs_prompt | json_model | json_parse\n",
+    "        self.examples_chain = self.examples_prompt | json_model | json_parse\n",
+    "\n",
+    "        self.chain = (\n",
+    "            RunnablePassthrough.assign(raw_example = lambda x: json.dumps(x[\"example\"], ensure_ascii=False))\n",
+    "            | RunnablePassthrough.assign(description = self.description_chain)\n",
+    "            | {\n",
+    "                \"description\": lambda x: x[\"description\"],\n",
+    "                \"examples_from_briefs\": RunnablePassthrough.assign(new_example_briefs = lambda x: self.briefs_chain.invoke(x)) | self.examples_from_briefs_chain,\n",
+    "                \"examples\": self.examples_chain\n",
+    "            }\n",
+    "            | RunnablePassthrough.assign(\n",
+    "                additional_examples=lambda x: (\n",
+    "                    list(x[\"examples_from_briefs\"][\"examples\"])\n",
+    "                    + list(x[\"examples\"][\"examples\"])\n",
+    "                )\n",
+    "            )\n",
+    "        )\n",
     "\n",
+    "    def process(self, input_str, generating_batch_size=3):\n",
     "        try:\n",
+    "            # Parse input string to a dictionary\n",
+    "            example_dict = json.loads(input_str) if input_str.startswith('{') else yaml.safe_load(input_str)\n",
+    "            if not isinstance(example_dict, dict) or 'input' not in example_dict or 'output' not in example_dict:\n",
+    "                raise ValueError(\"Invalid input format. Expected an object with 'input' and 'output' fields.\")\n",
     "\n",
+    "            # Move the original content to a key named 'example'\n",
+    "            input_dict = {\"example\": example_dict, \"generating_batch_size\": generating_batch_size}\n",
     "\n",
+    "            # Invoke the chain with the parsed input dictionary\n",
+    "            result = self.chain.invoke(input_dict)\n",
     "            return result\n",
     "\n",
     "        except Exception as e:\n",
     "\n",
     "import gradio as gr\n",
     "\n",
+    "def process_json(input_json, generating_batch_size=3):\n",
     "    try:\n",
     "        generator = TaskDescriptionGenerator()\n",
+    "        result = generator.process(input_json, generating_batch_size)\n",
+    "        description = result[\"description\"]\n",
+    "        examples = [[example[\"input\"], example[\"output\"]] for example in result[\"additional_examples\"]]\n",
+    "        print(examples)\n",
+    "        return description, examples\n",
     "    except Exception as e:\n",
+    "        raise gr.Error(f\"An error occurred: {str(e)}\")\n",
     "\n",
     "demo = gr.Interface(\n",
     "    fn=process_json,\n",
+    "    inputs=[gr.Textbox(label=\"Input JSON\"),\n",
+    "            gr.Slider(label=\"Generating Batch Size\", value=3, minimum=1, maximum=10, step=1)],\n",
+    "    outputs=[gr.Textbox(label=\"Description\"),\n",
+    "             gr.DataFrame(label=\"Examples\", \n",
+    "                           headers=[\"Input\", \"Output\"])],\n",
     "    title=\"Task Description Generator\",\n",
     "    description=\"Enter a JSON object with 'input' and 'output' fields to generate a task description and additional examples.\"\n",
     ")\n",