import ast
import base64
import logging
import math
import re
import xml.etree.ElementTree as ET
from io import BytesIO
from typing import Dict, List
import os
import backoff
import numpy as np
from PIL import Image
from requests.exceptions import SSLError
import openai
from openai import OpenAI
from google.api_core.exceptions import (
    BadRequest,
    InternalServerError,
    InvalidArgument,
    ResourceExhausted,
)

from mm_agents.accessibility_tree_wrap.heuristic_retrieve import (
    filter_nodes,
)
from mm_agents.prompts import (
    UITARS_ACTION_SPACE,
    UITARS_CALL_USR_ACTION_SPACE,
    UITARS_USR_PROMPT_NOTHOUGHT,
    UITARS_USR_PROMPT_THOUGHT,
)


from loguru import logger

FINISH_WORD = "finished"
WAIT_WORD = "wait"
ENV_FAIL_WORD = "error_env"
CALL_USER = "call_user"

pure_text_settings = ["a11y_tree"]

attributes_ns_ubuntu = "https://accessibility.windows.example.org/ns/attributes"
attributes_ns_windows = "https://accessibility.windows.example.org/ns/attributes"
state_ns_ubuntu = "https://accessibility.ubuntu.example.org/ns/state"
state_ns_windows = "https://accessibility.windows.example.org/ns/state"
component_ns_ubuntu = "https://accessibility.ubuntu.example.org/ns/component"
component_ns_windows = "https://accessibility.windows.example.org/ns/component"
value_ns_ubuntu = "https://accessibility.ubuntu.example.org/ns/value"
value_ns_windows = "https://accessibility.windows.example.org/ns/value"
class_ns_windows = "https://accessibility.windows.example.org/ns/class"
# More namespaces defined in OSWorld, please check desktop_env/server/main.py

# 定义一个函数来解析每个 action
def parse_action(action_str):
    try:
        # 解析字符串为 AST 节点
        node = ast.parse(action_str, mode='eval')

        # 确保节点是一个表达式
        if not isinstance(node, ast.Expression):
            raise ValueError("Not an expression")

        # 获取表达式的主体
        call = node.body

        # 确保主体是一个函数调用
        if not isinstance(call, ast.Call):
            raise ValueError("Not a function call")

        # 获取函数名
        if isinstance(call.func, ast.Name):
            func_name = call.func.id
        elif isinstance(call.func, ast.Attribute):
            func_name = call.func.attr
        else:
            func_name = None

        # 获取关键字参数
        kwargs = {}
        for kw in call.keywords:
            key = kw.arg
            # 处理不同类型的值，这里假设都是常量
            if isinstance(kw.value, ast.Constant):
                value = kw.value.value
            elif isinstance(kw.value, ast.Str):  # 兼容旧版本 Python
                value = kw.value.s
            else:
                value = None
            kwargs[key] = value

        return {
            'function': func_name,
            'args': kwargs
        }

    except Exception as e:
        print(f"Failed to parse action '{action_str}': {e}")
        return None
    
def escape_single_quotes(text):
    # 匹配未转义的单引号（不匹配 \\'）
    pattern = r"(?<!\\)'"
    return re.sub(pattern, r"\\'", text)

def parse_action_qwen2vl(text, factor, image_height, image_width):
    text = text.strip()
    # 正则表达式匹配 Action 字符串
    if text.startswith("Thought:"):
        thought_pattern = r"Thought: (.+?)(?=\s*Action:|$)"
        thought_hint = "Thought: "
    elif text.startswith("Reflection:"):
        thought_pattern = r"Reflection: (.+?)Action_Summary: (.+?)(?=\s*Action:|$)"
        thought_hint = "Reflection: "
    elif text.startswith("Action_Summary:"):
        thought_pattern = r"Action_Summary: (.+?)(?=\s*Action:|$)"
        thought_hint = "Action_Summary: "
    else:
        # 修复：当没有明确的"Thought:"标识时，提取Action:之前的所有内容作为思考
        thought_pattern = r"(.+?)(?=\s*Action:|$)"
        thought_hint = ""
        
    reflection, thought = None, None
    thought_match = re.search(thought_pattern, text, re.DOTALL)
    if thought_match:
        if len(thought_match.groups()) == 1:
            thought = thought_match.group(1).strip()
        elif len(thought_match.groups()) == 2:
            thought = thought_match.group(2).strip()
            reflection = thought_match.group(1).strip()
    assert "Action:" in text
    action_str = text.split("Action:")[-1]

    tmp_all_action = action_str.split("\n\n")
    all_action = []
    for action_str in tmp_all_action:
        if "type(content" in action_str:
            # 正则表达式匹配 content 中的字符串并转义单引号
            def escape_quotes(match):
                content = match.group(1)  # 获取 content 的值
                return content

            # 使用正则表达式进行替换
            pattern = r"type\(content='(.*?)'\)"  # 匹配 type(content='...')
            content = re.sub(pattern, escape_quotes, action_str)

            # 处理字符串
            action_str = escape_single_quotes(content)
            action_str = "type(content='" + action_str + "')"
        all_action.append(action_str)

    parsed_actions = [parse_action(action.replace("\n","\\n").lstrip()) for action in all_action]
    actions = []
    for action_instance, raw_str in zip(parsed_actions, all_action):
        if action_instance == None:
            print(f"Action can't parse: {raw_str}")
            continue
        action_type = action_instance["function"]
        params = action_instance["args"]

        # import pdb; pdb.set_trace()
        action_inputs = {}
        for param_name, param in params.items():
            if param == "": continue
            param = param.lstrip()  # 去掉引号和多余的空格
            # 处理start_box或者end_box参数格式 '<bbox>x1 y1 x2 y2</bbox>'
            action_inputs[param_name.strip()] = param
            
            if "start_box" in param_name or "end_box" in param_name:
                ori_box = param
                # Remove parentheses and split the string by commas
                numbers = ori_box.replace("(", "").replace(")", "").split(",")

                # Convert to float and scale by 1000
                float_numbers = [float(num) / factor for num in numbers]
                if len(float_numbers) == 2:
                    float_numbers = [float_numbers[0], float_numbers[1], float_numbers[0], float_numbers[1]]
                action_inputs[param_name.strip()] = str(float_numbers)

        # import pdb; pdb.set_trace()
        actions.append({
            "reflection": reflection,
            "thought": thought,
            "action_type": action_type,
            "action_inputs": action_inputs,
            "text": text
        })
    return actions

def parsing_response_to_pyautogui_code(responses, image_height: int, image_width:int, input_swap:bool=True) -> str:
    '''
    将M模型的输出解析为OSWorld中的action，生成pyautogui代码字符串
    参数:
        response: 包含模型输出的字典，结构类似于：
        {
            "action_type": "hotkey",
            "action_inputs": {
                "hotkey": "v ctrl",
                "start_box": None,
                "end_box": None
            }
        }
    返回:
        生成的pyautogui代码字符串
    '''

    pyautogui_code = f"import pyautogui\nimport time\n"
    if isinstance(responses, dict):
        responses = [responses]
    for response_id, response in enumerate(responses):
        if "observation" in response:
            observation = response["observation"]
        else:
            observation = ""

        if "thought" in response:
            thought = response["thought"]
        else:
            thought = ""
        
        if response_id == 0:
            pyautogui_code += f"'''\nObservation:\n{observation}\n\nThought:\n{thought}\n'''\n"
        else:
            pyautogui_code += f"\ntime.sleep(3)\n"

        action_dict = response
        action_type = action_dict.get("action_type")
        action_inputs = action_dict.get("action_inputs", {})
        
        if action_type == "hotkey":
            # Parsing hotkey action
            if "key" in action_inputs:
                hotkey = action_inputs.get("key", "")
            else:
                hotkey = action_inputs.get("hotkey", "")

            if hotkey:
                # Handle other hotkeys
                keys = hotkey.split()  # Split the keys by space
                pyautogui_code += f"\npyautogui.hotkey({', '.join([repr(k) for k in keys])})"
        
        elif action_type == "type":
            # Parsing typing action using clipboard
            content = action_inputs.get("content", "")
            content = escape_single_quotes(content)
            if content:
                if input_swap:
                    pyautogui_code += f"\nimport pyperclip"
                    pyautogui_code += f"\npyperclip.copy('{content.strip()}')"
                    pyautogui_code += f"\npyautogui.hotkey('ctrl', 'v')"
                    pyautogui_code += f"\ntime.sleep(0.5)\n"
                    if content.endswith("\n") or content.endswith("\\n"):
                        pyautogui_code += f"\npyautogui.press('enter')"
                else:
                    pyautogui_code += f"\npyautogui.write('{content.strip()}', interval=0.1)"
                    pyautogui_code += f"\ntime.sleep(0.5)\n"
                    if content.endswith("\n") or content.endswith("\\n"):
                        pyautogui_code += f"\npyautogui.press('enter')"

        
        elif action_type in ["drag", "select"]:
            # Parsing drag or select action based on start and end_boxes
            start_box = action_inputs.get("start_box")
            end_box = action_inputs.get("end_box")
            if start_box and end_box:
                x1, y1, x2, y2 = eval(start_box)  # Assuming box is in [x1, y1, x2, y2]
                sx = round(float((x1 + x2) / 2) * image_width, 3)
                sy = round(float((y1 + y2) / 2) * image_height, 3)
                x1, y1, x2, y2 = eval(end_box)  # Assuming box is in [x1, y1, x2, y2]
                ex = round(float((x1 + x2) / 2) * image_width, 3)
                ey = round(float((y1 + y2) / 2) * image_height, 3)
                pyautogui_code += (
                    f"\npyautogui.moveTo({sx}, {sy})\n"
                    f"\npyautogui.dragTo({ex}, {ey}, duration=1.0)\n"
                )

        elif action_type == "scroll":
            # Parsing scroll action
            start_box = action_inputs.get("start_box")
            if start_box:
                x1, y1, x2, y2 = eval(start_box)  # Assuming box is in [x1, y1, x2, y2]
                x = round(float((x1 + x2) / 2) * image_width, 3)
                y = round(float((y1 + y2) / 2) * image_height, 3)
                
                # # 先点对应区域，再滚动
                # pyautogui_code += f"\npyautogui.click({x}, {y}, button='left')"
            else:
                x = None
                y = None
            direction = action_inputs.get("direction", "")
            
            if x == None:
                if "up" in direction.lower():
                    pyautogui_code += f"\npyautogui.scroll(5)"
                elif "down" in direction.lower():
                    pyautogui_code += f"\npyautogui.scroll(-5)"
            else:
                if "up" in direction.lower():
                    pyautogui_code += f"\npyautogui.scroll(5, x={x}, y={y})"
                elif "down" in direction.lower():
                    pyautogui_code += f"\npyautogui.scroll(-5, x={x}, y={y})"

        elif action_type in ["click", "left_single", "left_double", "right_single", "hover"]:
            # Parsing mouse click actions
            start_box = action_inputs.get("start_box")
            start_box = str(start_box)
            if start_box:
                start_box = eval(start_box)
                if len(start_box) == 4:
                    x1, y1, x2, y2 = start_box  # Assuming box is in [x1, y1, x2, y2]
                elif len(start_box) == 2:
                    x1, y1 = start_box
                    x2 = x1
                    y2 = y1
                x = round(float((x1 + x2) / 2) * image_width, 3)
                y = round(float((y1 + y2) / 2) * image_height, 3)
                if action_type == "left_single" or action_type == "click":
                    pyautogui_code += f"\npyautogui.click({x}, {y}, button='left')"
                elif action_type == "left_double":
                    pyautogui_code += f"\npyautogui.doubleClick({x}, {y}, button='left')"
                elif action_type == "right_single":
                    pyautogui_code += f"\npyautogui.click({x}, {y}, button='right')"
                elif action_type == "hover":
                    pyautogui_code += f"\npyautogui.moveTo({x}, {y})"
        
        elif action_type in ["finished"]:
            pyautogui_code = f"DONE"
        
        else:
            pyautogui_code += f"\n# Unrecognized action type: {action_type}"

    return pyautogui_code

def pil_to_base64(image):
    buffer = BytesIO()
    image.save(buffer, format="PNG")  # 你可以改成 "JPEG" 等格式
    return base64.b64encode(buffer.getvalue()).decode("utf-8")

def linearize_accessibility_tree(accessibility_tree, platform="ubuntu"):

    if platform == "ubuntu":
        _attributes_ns = attributes_ns_ubuntu
        _state_ns = state_ns_ubuntu
        _component_ns = component_ns_ubuntu
        _value_ns = value_ns_ubuntu
    elif platform == "windows":
        _attributes_ns = attributes_ns_windows
        _state_ns = state_ns_windows
        _component_ns = component_ns_windows
        _value_ns = value_ns_windows
    else:
        raise ValueError("Invalid platform, must be 'ubuntu' or 'windows'")

    filtered_nodes = filter_nodes(ET.fromstring(accessibility_tree), platform)
    linearized_accessibility_tree = [
        "tag\tname\ttext\tclass\tdescription\tposition (top-left x&y)\tsize (w&h)"
    ]

    # Linearize the accessibility tree nodes into a table format
    for node in filtered_nodes:
        if node.text:
            text = (
                node.text
                if '"' not in node.text
                else '"{:}"'.format(node.text.replace('"', '""'))
            )

        elif node.get("{{{:}}}class".format(class_ns_windows), "").endswith(
            "EditWrapper"
        ) and node.get("{{{:}}}value".format(_value_ns)):
            node_text = node.get("{{{:}}}value".format(_value_ns), "")
            text = (
                node_text
                if '"' not in node_text
                else '"{:}"'.format(node_text.replace('"', '""'))
            )
        else:
            text = '""'

        linearized_accessibility_tree.append(
            "{:}\t{:}\t{:}\t{:}\t{:}\t{:}\t{:}".format(
                node.tag,
                node.get("name", ""),
                text,
                (
                    node.get("{{{:}}}class".format(_attributes_ns), "")
                    if platform == "ubuntu"
                    else node.get("{{{:}}}class".format(class_ns_windows), "")
                ),
                node.get("{{{:}}}description".format(_attributes_ns), ""),
                node.get("{{{:}}}screencoord".format(_component_ns), ""),
                node.get("{{{:}}}size".format(_component_ns), ""),
            )
        )

    return "\n".join(linearized_accessibility_tree)

def trim_accessibility_tree(linearized_accessibility_tree, max_tokens):
    # enc = tiktoken.encoding_for_model("gpt-4")
    # tokens = enc.encode(linearized_accessibility_tree)
    # if len(tokens) > max_tokens:
    #     linearized_accessibility_tree = enc.decode(tokens[:max_tokens])
    #     linearized_accessibility_tree += "[...]\n"
    return linearized_accessibility_tree

class UITARSAgent:
    def __init__(
        self,
        model: str,
        platform="ubuntu",
        max_tokens=1000,
        top_p=0.9,
        top_k=1.0,
        temperature=0.0,
        action_space="pyautogui",
        observation_type="screenshot_a11y_tree",
        # observation_type can be in ["screenshot", "a11y_tree", "screenshot_a11y_tree", "som"]
        max_trajectory_length=50,
        a11y_tree_max_tokens=10000,
        runtime_conf: dict = {
            "infer_mode": "qwen2vl_user",
            "prompt_style": "qwen2vl_user",
            "input_swap": True,
            "language": "Chinese",
            "max_steps": 50,
            "history_n": 5,
            "screen_height": 1080,
            "screen_width": 1920
        }
    ):
        self.model = model
        self.platform = platform
        self.max_tokens = max_tokens
        self.top_p = top_p
        self.top_k = top_k
        self.temperature = temperature
        self.action_space = action_space
        self.observation_type = observation_type
        self.max_trajectory_length = max_trajectory_length
        self.a11y_tree_max_tokens = a11y_tree_max_tokens
        self.runtime_conf = runtime_conf
        self.vlm = OpenAI(
            base_url=os.environ['DOUBAO_API_URL'],
            api_key=os.environ['DOUBAO_API_KEY'],
        ) # should replace with your UI-TARS server api
        self.infer_mode = self.runtime_conf["infer_mode"]
        self.prompt_style = self.runtime_conf["prompt_style"]
        self.input_swap = self.runtime_conf["input_swap"]
        self.language = self.runtime_conf["language"]
        self.max_steps = max_trajectory_length

        self.thoughts = []
        self.actions = []
        self.observations = []
        self.history_images = []
        self.history_responses = []
        
        self.prompt_action_space = UITARS_ACTION_SPACE
        self.customize_action_parser = parse_action_qwen2vl
        self.action_parse_res_factor = 1000
        if self.infer_mode == "qwen2vl_user":
            self.prompt_action_space = UITARS_CALL_USR_ACTION_SPACE
    
        self.prompt_template = UITARS_USR_PROMPT_THOUGHT
        
        if self.prompt_style == "qwen2vl_user":
            self.prompt_template = UITARS_USR_PROMPT_THOUGHT

        elif self.prompt_style == "qwen2vl_no_thought":
            self.prompt_template = UITARS_USR_PROMPT_NOTHOUGHT

        
        if "history_n" in self.runtime_conf:
            self.history_n = self.runtime_conf["history_n"]
        else:
            self.history_n = 5

    def predict(
        self, instruction: str, obs: Dict, last_action_after_obs: Dict = None
    ) -> List:
        """
        Predict the next action(s) based on the current observation.
        """

        # Append trajectory
        # print(len(self.observations), len(self.actions), len(self.actions))
        assert len(self.observations) == len(self.actions) and len(self.actions) == len(
            self.thoughts
        ), "The number of observations and actions should be the same."

        if len(self.observations) > self.max_trajectory_length:
            if self.max_trajectory_length == 0:
                _observations = []
                _actions = []
                _thoughts = []
            else:
                _observations = self.observations[-self.max_trajectory_length :]
                _actions = self.actions[-self.max_trajectory_length :]
                _thoughts = self.thoughts[-self.max_trajectory_length :]
        else:
            _observations = self.observations
            _actions = self.actions
            _thoughts = self.thoughts

        
        if last_action_after_obs is not None and self.infer_mode == "double_image":
            self.history_images.append(last_action_after_obs["screenshot"])

        self.history_images.append(obs["screenshot"])

        if self.observation_type in ["screenshot", "screenshot_a11y_tree"]:
            base64_image = obs["screenshot"]
            try:
                linearized_accessibility_tree = (
                    linearize_accessibility_tree(
                        accessibility_tree=obs["accessibility_tree"],
                        platform=self.platform,
                    )
                    if self.observation_type == "screenshot_a11y_tree"
                    else None
                )
            except:
                linearized_accessibility_tree = None
            # logger.debug("LINEAR AT: %s", linearized_accessibility_tree)

            if linearized_accessibility_tree:
                linearized_accessibility_tree = trim_accessibility_tree(
                    linearized_accessibility_tree, self.a11y_tree_max_tokens
                )

            if self.observation_type == "screenshot_a11y_tree":
                self.observations.append(
                    {
                        "screenshot": base64_image,
                        "accessibility_tree": linearized_accessibility_tree,
                    }
                )
            else:
                self.observations.append(
                    {"screenshot": base64_image, "accessibility_tree": None}
                )

        else:
            raise ValueError(
                "Invalid observation_type type: " + self.observation_type
            )  # 1}}}
        
        if self.infer_mode == "qwen2vl_user":
            user_prompt = self.prompt_template.format(
                instruction=instruction,
                action_space=self.prompt_action_space,
                language=self.language
            )
        elif self.infer_mode == "qwen2vl_no_thought":
            user_prompt = self.prompt_template.format(
                instruction=instruction
            )

        if len(self.history_images) > self.history_n:
            self.history_images = self.history_images[-self.history_n:]

        max_pixels = 2116800
        min_pixels = 3136
        messages, images = [], []
        if isinstance(self.history_images, bytes):
            self.history_images = [self.history_images]
        elif isinstance(self.history_images, np.ndarray):
            self.history_images = list(self.history_images)
        elif isinstance(self.history_images, list):
            pass
        else:
            raise TypeError(f"Unidentified images type: {type(self.history_images)}")
        max_image_nums_under_32k = int(32768*0.75/max_pixels*28*28)
        if len(self.history_images) > max_image_nums_under_32k:
            num_of_images = min(5, len(self.history_images))
            max_pixels = int(32768*0.75) // num_of_images

        for turn, image in enumerate(self.history_images):
            if len(images) >= 5:
                break
            try:
                image = Image.open(BytesIO(image))
            except Exception as e:
                raise RuntimeError(f"Error opening image: {e}")

            if image.width * image.height > max_pixels:
                """
                如果图片超过/低于像素限制，则计算一个缩放因子resize_factor，使图片的像素数缩小到等于或小于max_pixels。这个缩放因子是通过开平方根计算的，确保纵横比保持不变,这样原始的相对坐标可以不经转换直接复用
                """
                resize_factor = math.sqrt(max_pixels / (image.width * image.height))
                width, height = int(image.width * resize_factor), int(image.height * resize_factor)
                image = image.resize((width, height))
            if image.width * image.height < min_pixels:
                resize_factor = math.sqrt(min_pixels / (image.width * image.height))
                width, height = math.ceil(image.width * resize_factor), math.ceil(image.height * resize_factor)
                image = image.resize((width, height))

            if image.mode != "RGB":
                image = image.convert("RGB")

            images.append(image)

        messages = [
            {
                "role": "system",
                "content": [{"type": "text", "text": "You are a helpful assistant."}]
            },
            {
                "role": "user",
                "content": [{"type": "text", "text": user_prompt}]
            }
        ]
        
        image_num = 0
        if len(self.history_responses) > 0:
            for history_idx, history_response in enumerate(self.history_responses):
                # send at most history_n images to the model
                if history_idx + self.history_n > len(self.history_responses):

                    cur_image = images[image_num]
                    encoded_string = pil_to_base64(cur_image)
                    messages.append({
                        "role": "user",
                        "content": [{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_string}"}}]
                    })
                    image_num += 1
                    
                messages.append({
                    "role": "assistant",
                    "content": history_response
                })

            cur_image = images[image_num]
            encoded_string = pil_to_base64(cur_image)
            messages.append({
                "role": "user",
                "content": [{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_string}"}}]
            })
            image_num += 1
        
        else:
            cur_image = images[image_num]
            encoded_string = pil_to_base64(cur_image)
            messages.append({
                "role": "user",
                "content": [{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_string}"}}]
            })
            image_num += 1

        try_times = 3
        while True:
            if try_times <= 0:
                print(f"Reach max retry times to fetch response from client, as error flag.")
                return "client error", ["DONE"]
            try:
                
                response = self.vlm.chat.completions.create(
                    model=self.model,
                    messages=messages,
                    frequency_penalty=1,
                    max_tokens=self.max_tokens,
                    temperature=self.temperature,
                    top_p=self.top_p
                )
                print("Response:")
                print(response.choices[0].message.content)

                prediction = response.choices[0].message.content
                parsed_responses = self.customize_action_parser(
                    prediction,
                    self.action_parse_res_factor,
                    self.runtime_conf["screen_height"],
                    self.runtime_conf["screen_width"]
                )
                break
            except Exception as e:
                logger.exception(f"Error when fetching response from client, with response: {e}")
                prediction = None
                try_times -= 1
                
        if prediction is None:
            return "client error", ["DONE"]
        
        self.history_responses.append(prediction)
        self.thoughts.append(prediction)

        try:
            parsed_responses = self.customize_action_parser(
                prediction,
                self.action_parse_res_factor,
                self.runtime_conf["screen_height"],
                self.runtime_conf["screen_width"]
            )
        except Exception as e:
            print(f"Parsing action error: {prediction}, with error:\n{e}")
            return f"Parsing action error: {prediction}, with error:\n{e}", ["DONE"]

        actions = []
        for parsed_response in parsed_responses:
            if "action_type" in parsed_response:

                if parsed_response["action_type"] == FINISH_WORD:
                    self.actions.append(actions)
                    return prediction, ["DONE"]
                
                elif parsed_response["action_type"] == WAIT_WORD:
                    self.actions.append(actions)
                    return prediction, ["WAIT"]
                
                elif parsed_response["action_type"] == ENV_FAIL_WORD:
                    self.actions.append(actions)
                    return prediction, ["FAIL"]

                elif parsed_response["action_type"] == CALL_USER:
                    self.actions.append(actions)
                    return prediction, ["FAIL"]
            
            pyautogui_code = parsing_response_to_pyautogui_code(
                parsed_response,
                self.runtime_conf["screen_height"],
                self.runtime_conf["screen_width"],
                self.input_swap
            )
            actions.append(pyautogui_code)

        self.actions.append(actions)

        if len(self.history_responses) >= self.max_trajectory_length:
            # Default to FAIL if exceed max steps
            actions = ["FAIL"]

        return prediction, actions

    @backoff.on_exception(
        backoff.constant,
        # here you should add more model exceptions as you want,
        # but you are forbidden to add "Exception", that is, a common type of exception
        # because we want to catch this kind of Exception in the outside to ensure each example won't exceed the time limit
        (
            # General exceptions
            SSLError,
            # OpenAI exceptions
            openai.RateLimitError,
            openai.BadRequestError,
            openai.InternalServerError,
            # Google exceptions
            InvalidArgument,
            ResourceExhausted,
            InternalServerError,
            BadRequest,
            # Groq exceptions
            # todo: check
        ),
        interval=30,
        max_tries=10,
    )
    
    def reset(self, runtime_logger):
        self.thoughts = []
        self.actions = []
        self.observations = []
        self.history_images = []
        self.history_responses = []