import datetime
import json
import logging
import os
import sys

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"


def run_one_example(example, agent, max_steps=20, example_trajectory_dir="exp_trajectory"):
    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()
    observation['instruction'] = example['instruction']
    done = False
    step_num = 0

    # todo: save the screenshots and actions to a folder
    while not done and step_num < max_steps:
        actions = agent.predict(observation)
        for action in actions:
            observation, reward, done, info = env.step(action)
            observation['instruction'] = example['instruction']
        step_num += 1
        logger.info("Step %d", step_num)
        logger.info("Action: %s", actions)
        observation.pop("accessibility_tree")
        logger.info("Observation: %s", observation)
        logger.info("Reward: %.2f", reward)
        logger.info("Info: %s", info)

        logger.info("================================\n")

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

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

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


if __name__ == "__main__":
    action_space = "pyautogui"
    example_class = "vlc"
    example_id = "8f080098-ddb1-424c-b438-4e96e5e4786e"

    with open(f"evaluation_examples/examples/{example_class}/{example_id}.json", "r") as f:
        example = json.load(f)
    example["snapshot"] = "chrome_setup"

    api_key = os.environ.get("OPENAI_API_KEY")
    agent = GPT4v_Agent(api_key=api_key, action_space=action_space)

    root_trajectory_dir = "exp_trajectory"

    example_trajectory_dir = os.path.join(root_trajectory_dir, example_class, example_id)
    os.makedirs(example_trajectory_dir, exist_ok=True)

    run_one_example(example, agent, 20, example_trajectory_dir)