mirror of
https://github.com/LiteyukiStudio/nonebot-plugin-marshoai.git
synced 2024-11-26 23:05:04 +08:00
218 lines
7.8 KiB
Python
218 lines
7.8 KiB
Python
import base64
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import mimetypes
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import os
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import json
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from typing import Any
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import httpx
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import nonebot_plugin_localstore as store
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from datetime import datetime
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from nonebot.log import logger
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from zhDateTime import DateTime # type: ignore
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from azure.ai.inference.aio import ChatCompletionsClient
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from azure.ai.inference.models import SystemMessage
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from .config import config
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nickname_json = None # 记录昵称
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praises_json = None # 记录夸赞名单
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loaded_target_list = [] # 记录已恢复备份的上下文的列表
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async def get_image_b64(url):
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# noinspection LongLine
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
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}
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async with httpx.AsyncClient() as client:
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response = await client.get(url, headers=headers)
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if response.status_code == 200:
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# 获取图片数据
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image_data = response.content
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content_type = response.headers.get("Content-Type")
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if not content_type:
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content_type = mimetypes.guess_type(url)[0]
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# image_format = content_type.split("/")[1] if content_type else "jpeg"
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base64_image = base64.b64encode(image_data).decode("utf-8")
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data_url = f"data:{content_type};base64,{base64_image}"
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return data_url
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else:
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return None
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async def make_chat(client: ChatCompletionsClient, msg: list, model_name: str):
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"""调用ai获取回复
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参数:
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client: 用于与AI模型进行通信
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msg: 消息内容
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model_name: 指定AI模型名"""
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return await client.complete(
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messages=msg,
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model=model_name,
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temperature=config.marshoai_temperature,
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max_tokens=config.marshoai_max_tokens,
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top_p=config.marshoai_top_p,
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)
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def get_praises():
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global praises_json
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if praises_json is None:
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praises_file = store.get_plugin_data_file("praises.json") # 夸赞名单文件使用localstore存储
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if not os.path.exists(praises_file):
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init_data = {
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"like": [
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{
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"name": "Asankilp",
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"advantages": "赋予了Marsho猫娘人格，使用vim与vscode为Marsho写了许多代码，使Marsho更加可爱",
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}
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]
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}
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with open(praises_file, "w", encoding="utf-8") as f:
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json.dump(init_data, f, ensure_ascii=False, indent=4)
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with open(praises_file, "r", encoding="utf-8") as f:
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data = json.load(f)
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praises_json = data
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return praises_json
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async def refresh_praises_json():
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global praises_json
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praises_file = store.get_plugin_data_file("praises.json")
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if not os.path.exists(praises_file):
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init_data = {
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"like": [
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{
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"name": "Asankilp",
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"advantages": "赋予了Marsho猫娘人格，使用vim与vscode为Marsho写了许多代码，使Marsho更加可爱",
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}
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]
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}
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with open(praises_file, "w", encoding="utf-8") as f:
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json.dump(init_data, f, ensure_ascii=False, indent=4)
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with open(praises_file, "r", encoding="utf-8") as f:
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data = json.load(f)
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praises_json = data
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def build_praises():
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praises = get_praises()
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result = ["你喜欢以下几个人物，他们有各自的优点："]
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for item in praises["like"]:
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result.append(f"名字：{item['name']}，优点：{item['advantages']}")
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return "\n".join(result)
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async def save_context_to_json(name: str, context: Any, path: str):
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context_dir = store.get_plugin_data_dir() / path
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os.makedirs(context_dir, exist_ok=True)
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file_path = os.path.join(context_dir, f"{name}.json")
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with open(file_path, "w", encoding="utf-8") as json_file:
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json.dump(context, json_file, ensure_ascii=False, indent=4)
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async def load_context_from_json(name: str, path: str) -> list:
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"""从指定路径加载历史记录"""
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context_dir = store.get_plugin_data_dir() / path
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os.makedirs(context_dir, exist_ok=True)
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file_path = os.path.join(context_dir, f"{name}.json")
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try:
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with open(file_path, "r", encoding="utf-8") as json_file:
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return json.load(json_file)
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except FileNotFoundError:
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return []
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async def set_nickname(user_id: str, name: str):
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global nickname_json
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filename = store.get_plugin_data_file("nickname.json")
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if not os.path.exists(filename):
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data = {}
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else:
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with open(filename, "r", encoding="utf-8") as f:
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data = json.load(f)
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data[user_id] = name
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if name == "" and user_id in data:
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del data[user_id]
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with open(filename, "w", encoding="utf-8") as f:
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json.dump(data, f, ensure_ascii=False, indent=4)
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nickname_json = data
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# noinspection PyBroadException
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async def get_nicknames():
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"""获取nickname_json, 优先来源于全局变量"""
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global nickname_json
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if nickname_json is None:
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filename = store.get_plugin_data_file("nickname.json")
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try:
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with open(filename, "r", encoding="utf-8") as f:
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nickname_json = json.load(f)
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except Exception:
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nickname_json = {}
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return nickname_json
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async def refresh_nickname_json():
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"""强制刷新nickname_json, 刷新全局变量"""
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global nickname_json
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filename = store.get_plugin_data_file("nickname.json")
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# noinspection PyBroadException
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try:
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with open(filename, "r", encoding="utf-8") as f:
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nickname_json = json.load(f)
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except Exception:
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logger.error("Error loading nickname.json")
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def get_prompt():
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"""获取系统提示词"""
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prompts = ""
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prompts += config.marshoai_additional_prompt
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if config.marshoai_enable_praises:
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praises_prompt = build_praises()
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prompts += praises_prompt
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if config.marshoai_enable_time_prompt:
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current_time = datetime.now().strftime("%Y.%m.%d %H:%M:%S")
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current_lunar_date = (
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DateTime.now().to_lunar().date_hanzify()[5:]
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) # 库更新之前使用切片
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time_prompt = f"现在的时间是{current_time}，农历{current_lunar_date}。"
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prompts += time_prompt
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marsho_prompt = config.marshoai_prompt
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spell = SystemMessage(content=marsho_prompt + prompts).as_dict()
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return spell
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def suggest_solution(errinfo: str) -> str:
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# noinspection LongLine
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suggestions = {
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"content_filter": "消息已被内容过滤器过滤。请调整聊天内容后重试。",
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"RateLimitReached": "模型达到调用速率限制。请稍等一段时间或联系Bot管理员。",
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"tokens_limit_reached": "请求token达到上限。请重置上下文。",
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"content_length_limit": "请求体过大。请重置上下文。",
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"unauthorized": "访问token无效。请联系Bot管理员。",
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"invalid type: parameter messages.content is of type array but should be of type string.": "聊天请求体包含此模型不支持的数据类型。请重置上下文。",
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"At most 1 image(s) may be provided in one request.": "此模型只能在上下文中包含1张图片。如果此前的聊天已经发送过图片，请重置上下文。",
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}
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for key, suggestion in suggestions.items():
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if key in errinfo:
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return f"\n{suggestion}"
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return ""
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async def get_backup_context(target_id: str, target_private: bool) -> list:
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"""获取历史上下文"""
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global loaded_target_list
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if target_private:
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target_uid = f"private_{target_id}"
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else:
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target_uid = f"group_{target_id}"
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if target_uid not in loaded_target_list:
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loaded_target_list.append(target_uid)
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return await load_context_from_json(f"back_up_context_{target_uid}", "contexts/backup")
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return []
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