from sb3_contrib import MaskablePPO
from sb3_contrib.common.maskable.evaluation import evaluate_policy
from sb3_contrib.common.wrappers import ActionMasker
from stable_baselines3.common.logger import Logger, CSVOutputFormat, TensorBoardOutputFormat

import gymnasium as gym

from minigrid.core.actions import Actions
from minigrid.wrappers import RGBImgObsWrapper, ImgObsWrapper

import time

from utils import MiniGridShieldHandler, create_log_dir, ShieldingConfig, MiniWrapper, expname
from sb3utils import MiniGridSbShieldingWrapper, parse_sb3_arguments, ImageRecorderCallback, InfoCallback
from stable_baselines3.common.callbacks import EvalCallback

import os

GRID_TO_PRISM_BINARY=os.getenv("M2P_BINARY")
def mask_fn(env: gym.Env):
    return env.create_action_mask()

def nomask_fn(env: gym.Env):
    return [1.0] * 7


def main():
    args = parse_sb3_arguments()

    formula = args.formula
    shield_value = args.shield_value
    shield_comparison = args.shield_comparison
    log_dir = create_log_dir(args)
    new_logger = Logger(log_dir, output_formats=[CSVOutputFormat(os.path.join(log_dir, f"progress_{expname(args)}.csv")), TensorBoardOutputFormat(log_dir)])

    env = gym.make(args.env, render_mode="rgb_array")
    env = RGBImgObsWrapper(env)
    env = ImgObsWrapper(env)
    env = MiniWrapper(env)
    if args.shielding == ShieldingConfig.Full or args.shielding == ShieldingConfig.Training:
        shield_handler = MiniGridShieldHandler(GRID_TO_PRISM_BINARY, args.grid_file, args.prism_output_file, formula, shield_value=shield_value, shield_comparison=shield_comparison, nocleanup=args.nocleanup)
        env = MiniGridSbShieldingWrapper(env, shield_handler=shield_handler, create_shield_at_reset=False)
        env = ActionMasker(env, mask_fn)
    else:
        env = ActionMasker(env, nomask_fn)
    model = MaskablePPO("CnnPolicy", env, verbose=1, tensorboard_log=log_dir, device="auto")
    model.set_logger(new_logger)


    evalCallback = EvalCallback(env, best_model_save_path=log_dir,
                                log_path=log_dir, eval_freq=max(500,  int(args.steps/30)),
                                deterministic=True, render=False)
    steps = args.steps

    model.learn(steps,callback=[ImageRecorderCallback(), InfoCallback()])

    #vec_env = model.get_env()
    #obs = vec_env.reset()
    #terminated = truncated = False
    #while not terminated and not truncated:
    #    action_masks = None
    #    action, _states = model.predict(obs, action_masks=action_masks)
    #    print(action)
    #    obs, reward, terminated, truncated, info = env.step(action)
    #    # action, _states = model.predict(obs, deterministic=True)
    #    # obs, rewards, dones, info = vec_env.step(action)
    #    vec_env.render("human")
    #    time.sleep(0.2)



if __name__ == '__main__':
    main()