import ast
import base64
import logging
import math
import re
import xml.etree.ElementTree as ET
from io import BytesIO
from pathlib import Path
from typing import Dict, List

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 llms.llm_utils import call_llm
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,
    UITARS_NORMAL_ACTION_SPACE
)
from verifier.prompts import FEEDBACK_PROMPT, SECOND_PASS_KNOWLEDGE_PROMPT

logger = logging.getLogger("desktopenv.agent")

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

IMAGE_FACTOR = 28
MIN_PIXELS = 100 * 28 * 28
MAX_PIXELS = 16384 * 28 * 28
MAX_RATIO = 200

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 round_by_factor(number: int, factor: int) -> int:
    """Returns the closest integer to 'number' that is divisible by 'factor'."""
    return round(number / factor) * factor


def ceil_by_factor(number: int, factor: int) -> int:
    """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
    return math.ceil(number / factor) * factor


def floor_by_factor(number: int, factor: int) -> int:
    """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
    return math.floor(number / factor) * factor

def linear_resize(
    height: int, width: int, factor: int = IMAGE_FACTOR, min_pixels: int = MIN_PIXELS, max_pixels: int = MAX_PIXELS
) -> tuple[int, int]:
    if width * 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 / (width * height))
        width, height = int(width * resize_factor), int(height * resize_factor)
    if width * height < min_pixels:
        resize_factor = math.sqrt(min_pixels / (width * height))
        width, height = math.ceil(width * resize_factor), math.ceil(height * resize_factor)

    return height, width 

def smart_resize(
    height: int, width: int, factor: int = IMAGE_FACTOR, min_pixels: int = MIN_PIXELS, max_pixels: int = MAX_PIXELS
) -> tuple[int, int]:
    """
    Rescales the image so that the following conditions are met:

    1. Both dimensions (height and width) are divisible by 'factor'.

    2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].

    3. The aspect ratio of the image is maintained as closely as possible.
    """
    if max(height, width) / min(height, width) > MAX_RATIO:
        raise ValueError(
            f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}"
        )
    h_bar = max(factor, round_by_factor(height, factor))
    w_bar = max(factor, round_by_factor(width, factor))
    if h_bar * w_bar > max_pixels:
        beta = math.sqrt((height * width) / max_pixels)
        h_bar = floor_by_factor(height / beta, factor)
        w_bar = floor_by_factor(width / beta, factor)
    elif h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (height * width))
        h_bar = ceil_by_factor(height * beta, factor)
        w_bar = ceil_by_factor(width * beta, factor)
    return h_bar, w_bar

def parse_action_to_structure_output(text, factor, origin_resized_height, origin_resized_width, model_type, max_pixels=16384*28*28, min_pixels=100*28*28):
    text = text.strip()
    if model_type == "qwen25vl":
        smart_resize_height, smart_resize_width = smart_resize(origin_resized_height, origin_resized_width, factor=IMAGE_FACTOR, min_pixels=min_pixels, max_pixels=max_pixels)

    # 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:|$)"
        thought_hint = "Action_Summary: "
    else:
        thought_pattern = r"Thought: (.+?)(?=\s*Action:|$)"
        thought_hint = "Thought: "
    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}")
            raise ValueError(f"Action can't parse: {raw_str}") 
        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
                # Qwen2.5vl output absolute coordinates, qwen2vl output relative coordinates
                if model_type == "qwen25vl":
                    float_numbers = []
                    for num_idx, num in enumerate(numbers):
                        num = float(num)
                        if (num_idx + 1) % 2 == 0:
                            float_numbers.append(float(num/smart_resize_height))
                        else:
                            float_numbers.append(float(num/smart_resize_width))
                else:
                    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(1)\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 == "arrowleft":
                hotkey = "left"

            elif hotkey == "arrowright":
                hotkey = "right"
            
            elif hotkey == "arrowup":
                hotkey = "up"
            
            elif hotkey == "arrowdown":
                hotkey = "down"

            if hotkey:
                # Handle other hotkeys
                keys = hotkey.split()  # Split the keys by space
                convert_keys = []
                for key in keys:
                    if key == "space":
                        key = ' '
                    convert_keys.append(key)
                pyautogui_code += f"\npyautogui.hotkey({', '.join([repr(k) for k in convert_keys])})"
        
        elif action_type == "press":
            # Parsing press action
            if "key" in action_inputs:
                key_to_press = action_inputs.get("key", "")
            else:
                key_to_press = action_inputs.get("press", "")

            if hotkey == "arrowleft":
                hotkey = "left"

            elif hotkey == "arrowright":
                hotkey = "right"
            
            elif hotkey == "arrowup":
                hotkey = "up"
            
            elif hotkey == "arrowdown":
                hotkey = "down"
            
            elif hotkey == "space":
                hotkey = " "
                
            if key_to_press:
                # Simulate pressing a single key
                pyautogui_code += f"\npyautogui.press({repr(key_to_press)})"
            
        elif action_type == "keyup":
            key_to_up = action_inputs.get("key", "")
            pyautogui_code += f"\npyautogui.keyUp({repr(key_to_up)})"
        
        elif action_type == "keydown":
            key_to_down = action_inputs.get("key", "")
            pyautogui_code += f"\npyautogui.keyDown({repr(key_to_down)})"

        elif action_type == "type":
            # Parsing typing action using clipboard
            content = action_inputs.get("content", "")
            content = escape_single_quotes(content)
            stripped_content = content
            if content.endswith("\n") or content.endswith("\\n"):
                stripped_content = stripped_content.rstrip("\\n").rstrip("\n")
            if content:
                if input_swap:
                    pyautogui_code += f"\nimport pyperclip"
                    pyautogui_code += f"\npyperclip.copy('{stripped_content}')"
                    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('{stripped_content}', 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 add_box_token(input_string):
    # Step 1: Split the string into individual actions
    if "Action: " in input_string and "start_box=" in input_string:
        suffix = input_string.split("Action: ")[0] + "Action: "
        actions = input_string.split("Action: ")[1:]
        processed_actions = []
        for action in actions:
            action = action.strip()
            # Step 2: Extract coordinates (start_box or end_box) using regex
            coordinates = re.findall(r"(start_box|end_box)='\((\d+),\s*(\d+)\)'", action)
            
            updated_action = action  # Start with the original action
            for coord_type, x, y in coordinates:
                # Convert x and y to integers
                updated_action = updated_action.replace(f"{coord_type}='({x},{y})'", f"{coord_type}='<|box_start|>({x},{y})<|box_end|>'")
            processed_actions.append(updated_action)
        
        # Step 5: Reconstruct the final string
        final_string = suffix + "\n\n".join(processed_actions)
    else:
        final_string = input_string
    return final_string

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,
        platform="ubuntu",
        action_space="pyautogui",
        observation_type="screenshot",
        # observation_type can be in ["screenshot", "a11y_tree", "screenshot_a11y_tree", "som"]
        max_history_length=15,
        a11y_tree_max_tokens=10000,
        model_type="qwen25vl",
        runtime_conf: dict = {
            "infer_mode": "qwen25vl_normal",
            "prompt_style": "qwen25vl_normal",
            "input_swap": True,
            "language": "English",
            "max_pixels": 16384*28*28,
            "min_pixels": 100*28*28,
            "callusr_tolerance": 3,
            "temperature": 0.0,
            "top_k": -1,
            "top_p": 0.9,
            "max_tokens": 500
        }
    ):
        self.platform = platform
        self.action_space = action_space
        self.observation_type = observation_type
        self.max_history_length = max_history_length
        self.a11y_tree_max_tokens = a11y_tree_max_tokens
        self.model_type = model_type
        self.runtime_conf = runtime_conf
        self.vlm = OpenAI(
            base_url="http://127.0.0.1:8000/v1",
            api_key="empty",
        ) # should replace with your UI-TARS server api
        self.temperature = self.runtime_conf["temperature"]
        self.top_k = self.runtime_conf["top_k"]
        self.top_p = self.runtime_conf["top_p"]
        self.max_tokens = self.runtime_conf["max_tokens"]
        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_pixels = self.runtime_conf["max_pixels"]
        self.min_pixels = self.runtime_conf["min_pixels"]
        self.callusr_tolerance = self.runtime_conf["callusr_tolerance"]

        self.thoughts = []
        self.actions = []
        self.observations = []
        self.history_images = []
        self.history_responses = []
        
        self.prompt_action_space = UITARS_ACTION_SPACE
        self.action_parse_res_factor = 1000
        if self.infer_mode == "qwen2vl_user":
            self.prompt_action_space = UITARS_CALL_USR_ACTION_SPACE
        elif self.infer_mode == "qwen25vl_normal":
            self.prompt_action_space = UITARS_NORMAL_ACTION_SPACE
    
        self.prompt_template = UITARS_USR_PROMPT_THOUGHT
        
        if self.prompt_style == "qwen2vl_user" or self.prompt_style == "qwen25vl_normal":
            self.prompt_template = UITARS_USR_PROMPT_THOUGHT

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

        self.cur_callusr_count = 0

    def predict(self, instruction: str, knowledge: str | None, obs: Dict,
                feedback: str | None, task_results_path: Path) -> list:
        """
        Predict the next action(s) based on the current observation.
        """
        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 feedback is not None:
            previous_response = self.thoughts.pop()
            self.observations.pop()
            self.actions.pop()
            self.history_images.pop()
            self.history_responses.pop()

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

        for previous_obs, previous_action, previous_thought in zip(
            _observations, _actions, _thoughts
        ):
            # {{{1
            if self.observation_type in ["screenshot_a11y_tree", "screenshot"]:
                _screenshot = previous_obs["screenshot"]
                _linearized_accessibility_tree = previous_obs["accessibility_tree"]

            else:
                raise ValueError(
                    "Invalid observation_type type: " + self.observation_type
                )  # 1}}}

        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" or self.infer_mode == "qwen25vl_normal":
            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.max_history_length:
            self.history_images = self.history_images[-self.max_history_length:]

        messages, images = [], []
        for turn, image in enumerate(self.history_images):
            try:
                image = Image.open(BytesIO(image))
            except Exception as e:
                raise RuntimeError(f"Error opening image: {e}")

            if image.width * image.height > self.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(self.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 < self.min_pixels:
                resize_factor = math.sqrt(self.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}]
            }
        ]

        if knowledge:
            messages.append({
                "role": "user",
                "content": [{
                    "type": "text",
                    "text": SECOND_PASS_KNOWLEDGE_PROMPT.format(
                        knowledge=knowledge)
                }]
            })
        
        image_num = 0
        if len(self.history_responses) > 0:
            for history_idx, history_response in enumerate(self.history_responses):
                # send at most max_history_length images to the model
                if history_idx + self.max_history_length > 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": [{"type": "text",
                                 "text": add_box_token(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
        if feedback is not None:
            messages.append(FEEDBACK_PROMPT.strip().format(
                previous_response=previous_response, feedback=feedback))

        try_times = 3
        origin_resized_height = images[-1].height
        origin_resized_width = images[-1].width
        run_results_path = task_results_path.parent.parent
        domain = task_results_path.parent.name
        task_id = task_results_path.name
        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:
                responses, texts = call_llm(
                    gen_kwargs={
                        'provider': 'huggingface',
                        'engine': 'vllm',
                        'endpoint': '127.0.0.1:8000',
                        'model': 'ByteDance-Seed/UI-TARS-1.5-7B',
                        'max_tokens': self.max_tokens,
                        'temperature': self.temperature,
                        'top_p': self.top_p,
                    },
                    prompt=messages,
                    conversation_dir=str(run_results_path / 'conversations'),
                    usage_dir=str(run_results_path / 'usage'),
                    call_id=f'{domain}_{task_id}',
                    dump_txt=False,
                )
                prediction = texts[0].text().strip()
            except Exception as e:
                print(f"Error when fetching response from client: {e}")
                prediction = None
                try_times -= 1
                continue
            try:
                parsed_responses = parse_action_to_structure_output(
                    prediction,
                    self.action_parse_res_factor,
                    origin_resized_height,
                    origin_resized_width,
                    self.model_type,
                    self.max_pixels,
                    self.min_pixels
                )
                break
            except Exception as e:
                print(f"Error when parsing response from client: {e}")
                prediction = None
                try_times -= 1
                continue
        if prediction is None:
            return "client error", ["DONE"]

        self.history_responses.append(prediction)
        self.thoughts.append(prediction)

        try:
            parsed_responses = parse_action_to_structure_output(
                prediction,
                self.action_parse_res_factor,
                origin_resized_height,
                origin_resized_width,
                self.model_type,
                self.max_pixels,
                self.min_pixels
            )
        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 = []
        last_image = Image.open(BytesIO(self.history_images[-1]))
        obs_image_height = last_image.height
        obs_image_width = last_image.width
        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:
                    if self.callusr_tolerance > self.cur_callusr_count:
                        self.actions.append(actions)
                        self.cur_callusr_count += 1
                        return prediction, ["WAIT"]
                    else:
                        self.actions.append(actions)
                        return prediction, ["FAIL"]
            
            pyautogui_code = parsing_response_to_pyautogui_code(
                parsed_response,
                obs_image_height,
                obs_image_width,
                self.input_swap
            )
            actions.append(pyautogui_code)

        self.actions.append(actions)

        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 = []
