#import ctypes
import datetime
import json
import logging
import os
import sys
import func_timeout

from desktop_env.envs.desktop_env import DesktopEnv
from mm_agents.gpt_4v_agent import GPT4v_Agent

#  Logger Configs {{{ # 
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)

datetime_str: str = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")

file_handler = logging.FileHandler(os.path.join("logs", "normal-{:}.log".format(datetime_str)), encoding="utf-8")
debug_handler = logging.FileHandler(os.path.join("logs", "debug-{:}.log".format(datetime_str)), encoding="utf-8")
stdout_handler = logging.StreamHandler(sys.stdout)
sdebug_handler = logging.FileHandler(os.path.join("logs", "sdebug-{:}.log".format(datetime_str)), encoding="utf-8")

file_handler.setLevel(logging.INFO)
debug_handler.setLevel(logging.DEBUG)
stdout_handler.setLevel(logging.INFO)
sdebug_handler.setLevel(logging.DEBUG)

formatter = logging.Formatter(
    fmt="\x1b[1;33m[%(asctime)s \x1b[31m%(levelname)s \x1b[32m%(module)s/%(lineno)d-%(processName)s\x1b[1;33m] \x1b[0m%(message)s")
file_handler.setFormatter(formatter)
debug_handler.setFormatter(formatter)
stdout_handler.setFormatter(formatter)
sdebug_handler.setFormatter(formatter)

stdout_handler.addFilter(logging.Filter("desktopenv"))
sdebug_handler.addFilter(logging.Filter("desktopenv"))

logger.addHandler(file_handler)
logger.addHandler(debug_handler)
logger.addHandler(stdout_handler)
logger.addHandler(sdebug_handler)
#  }}} Logger Configs # 

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

#PATH_TO_VM = r"C:\Users\tianbaox\Documents\Virtual Machines\Ubuntu\Ubuntu.vmx"


PATH_TO_VM = "../../../../大文件/镜像/Ubuntu-1218/Ubuntu/Ubuntu.vmx"

def run_one_example(example, agent, max_steps=10, example_trajectory_dir="exp_trajectory", recording=True):
    trajectory_recording_path = os.path.join(example_trajectory_dir, "trajectory.json")
    env = DesktopEnv(
        path_to_vm=PATH_TO_VM,
        action_space=agent.action_space,
        task_config=example
    )
    # reset the environment to certain snapshot
    observation = env.reset()
    done = False
    step_num = 0

    if recording:
        # send a request to the server to start recording
        env.controller.start_recording()

    while not done and step_num < max_steps:
        actions = agent.predict(observation)
        step_num += 1
        for action in actions:
            # Capture the timestamp before executing the action
            action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
            logger.info("Step %d: %s", step_num, action)

            observation, reward, done, info = env.step(action)

            logger.info("Reward: %.2f", reward)
            logger.info("Done: %s", done)
            logger.info("Info: %s", info)

            # Save screenshot and trajectory information
            with open(os.path.join(example_trajectory_dir, f"step_{step_num}_{action_timestamp}.png"), "wb") as _f:
                with open(observation['screenshot'], "rb") as __f:
                    screenshot = __f.read()
                _f.write(screenshot)

            with open(trajectory_recording_path, "a") as f:
                f.write(json.dumps({
                    "step_num": step_num,
                    "action_timestamp": action_timestamp,
                    "action": action,
                    "reward": reward,
                    "done": done,
                    "info": info,
                    "screenshot_file": f"step_{step_num}_{action_timestamp}.png"
                }))
                f.write("\n")

            if done:
                logger.info("The episode is done.")
                break

    def stop_recording():
        try:
            env.controller.end_recording(os.path.join(example_trajectory_dir, "recording.mp4"))
        except Exception as e:
            print(f"An error occurred while stopping the recording: {e}")

    try:
        func_timeout.func_timeout(30, stop_recording)
    except func_timeout.exceptions.FunctionTimedOut:
        logger.info("Recording timed out.")

    result = env.evaluate()
    logger.info("Result: %.2f", result)

    with open(trajectory_recording_path, "a") as f:
        f.write(json.dumps({
            "result": result
        }))
        f.write("\n")

    # env.close()
    logger.info("Environment closed.")


def main(example_class, example_id):
    action_space = "pyautogui"
    gpt4_model = "gpt-4-vision-preview"
    #gemini_model = "gemini-pro-vision"

    with open(f"evaluation_examples/examples/{example_class}/{example_id}.json", "r", encoding="utf-8") as f:
        example = json.load(f)
    #example["snapshot"] = "exp_v1"
    # example["snapshot"] = "exp_setup4"
    example["snapshot"] = "Snapshot 30"

    api_key = os.environ.get("OPENAI_API_KEY")
    agent = GPT4v_Agent(api_key=api_key, model=gpt4_model, max_tokens=1000, instruction=example['instruction'],
                        action_space=action_space, exp="som")

    # api_key = os.environ.get("GENAI_API_KEY")
    # agent = GeminiPro_Agent(api_key=api_key, model=gemini_model, instruction=example['instruction'], action_space=action_space)

    root_trajectory_dir = "exp_trajectory"

    example_trajectory_dir = os.path.join(root_trajectory_dir, "som", example_class, gpt4_model, example_id)
    # example_trajectory_dir = os.path.join(root_trajectory_dir, "som", example_class, gemini_model, example_id)

    os.makedirs(example_trajectory_dir, exist_ok=True)

    run_one_example(example, agent, 15, example_trajectory_dir)


if __name__ == '__main__':
    xx_list = [ "94d95f96-9699-4208-98ba-3c3119edf9c2"
              , "bedcedc4-4d72-425e-ad62-21960b11fe0d"
              , "43c2d64c-bab5-4dcb-a30c-b888321c319a"
              , "7688b85f-87a4-4e4a-b2f8-f3d6c3f29b82"
              , "ec4e3f68-9ea4-4c18-a5c9-69f89d1178b3"
              , "a462a795-fdc7-4b23-b689-e8b6df786b78"
              , "f9be0997-4b7c-45c5-b05c-4612b44a6118"
              , "ae039631-2b12-4637-84f6-c67d51511be3"
              , "e2eb4bf1-aa93-4192-b55d-03e2fb6dfd15"
              , "28cc3b7e-b194-4bc9-8353-d04c0f4d56d2"
              , "5ea617a3-0e86-4ba6-aab2-dac9aa2e8d57"
              , "e0df059f-28a6-4169-924f-b9623e7184cc"
              , "ddc75b62-7311-4af8-bfb3-859558542b36"
              , "5c433d22-ed9a-4e31-91f5-54cf3e8acd63"
              , "b6781586-6346-41cd-935a-a6b1487918fc"
              , "b3d4a89c-53f2-4d6b-8b6a-541fb5d205fa"
              , "3ce045a0-877b-42aa-8d2c-b4a863336ab8"
              , "fe41f596-a71b-4c2f-9b2f-9dcd40b568c3"
              , "a4d98375-215b-4a4d-aee9-3d4370fccc41"
              , "765d2b74-88a7-4d50-bf51-34e4106fd24a"
              , "13584542-872b-42d8-b299-866967b5c3ef"
              , "23393935-50c7-4a86-aeea-2b78fd089c5c"
              ]
    for example_id in xx_list:
        main("os", example_id)