import os
import random

import retro
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import SubprocVecEnv
from stable_baselines3.common.callbacks import BaseCallback, CheckpointCallback

from rmsprop_optim import RMSpropTF
from custom_cnn import CustomCNN
from street_fighter_custom_wrapper import StreetFighterCustomWrapper

class RandomOpponentChangeCallback(BaseCallback):
    def __init__(self, stages, opponent_interval, verbose=0):
        super(RandomOpponentChangeCallback, self).__init__(verbose)
        self.stages = stages
        self.opponent_interval = opponent_interval

    def _on_step(self) -> bool:
        if self.n_calls % self.opponent_interval == 0:
            new_state = random.choice(self.stages)
            print("\nCurrent state:", new_state)
            self.training_env.env_method("load_state", new_state, indices=None)
        return True
    
def make_env(game, state, seed=0):
    def _init():
        env = retro.make(
            game=game, 
            state=state, 
            use_restricted_actions=retro.Actions.FILTERED, 
            obs_type=retro.Observations.IMAGE    
        )
        env = StreetFighterCustomWrapper(env)
        env.seed(seed)
        return env
    return _init

def main():
    # Set up the environment and model
    game = "StreetFighterIISpecialChampionEdition-Genesis"
    state_stages = [
        "ChampionX.Level1.ChunLiVsKen",
        "ChampionX.Level2.ChunLiVsChunLi",
        "ChampionX.Level3.ChunLiVsZangief",
        "ChampionX.Level4.ChunLiVsDhalsim",
        "ChampionX.Level5.ChunLiVsRyu",
        "ChampionX.Level6.ChunLiVsEHonda",
        "ChampionX.Level7.ChunLiVsBlanka",
        "ChampionX.Level8.ChunLiVsGuile",
        "ChampionX.Level9.ChunLiVsBalrog",
        "ChampionX.Level10.ChunLiVsVega",
        "ChampionX.Level11.ChunLiVsSagat",
        "ChampionX.Level12.ChunLiVsBison"
        # Add other stages as necessary
    ]
    # Champion is at difficulty level 4, ChampionX is at difficulty level 8.

    num_envs = 8

    env = SubprocVecEnv([make_env(game, state_stages[0], seed=i) for i in range(num_envs)])

    # Using CustomCNN as the feature extractor
    policy_kwargs = {
        'features_extractor_class': CustomCNN
    }

    model = PPO(
        "CnnPolicy", 
        env,
        device="cuda", 
        policy_kwargs=policy_kwargs, 
        verbose=1,
        n_steps=5400,
        batch_size=64,
        learning_rate=0.0001,
        ent_coef=0.01,
        clip_range=0.2,
        gamma=0.99,
        gae_lambda=0.95,
        tensorboard_log="logs/"
    )

    # Set the save directory
    save_dir = "trained_models"
    os.makedirs(save_dir, exist_ok=True)

    # Load the model from file
    # model_path = "trained_models/ppo_chunli_1296000_steps.zip"
    
    # Load model and modify the learning rate and entropy coefficient
    # custom_objects = {
    #     "learning_rate": 0.0002
    # }
    # model = PPO.load(model_path, env=env, device="cuda")#, custom_objects=custom_objects)

    # Set up callbacks
    opponent_interval = 5400 # stage_interval * num_envs = total_steps_per_stage
    checkpoint_interval = 54000 # checkpoint_interval * num_envs = total_steps_per_checkpoint (Every 80 rounds)
    checkpoint_callback = CheckpointCallback(save_freq=checkpoint_interval, save_path=save_dir, name_prefix="ppo_chunli")
    stage_increase_callback = RandomOpponentChangeCallback(state_stages, opponent_interval, save_dir)

    # model_params = {
    #     'n_steps': 5, 
    #     'gamma': 0.99, 
    #     'gae_lambda':1, 
    #     'learning_rate': 7e-4, 
    #     'vf_coef': 0.5,
    #     'ent_coef': 0.0,
    #     'max_grad_norm':0.5,
    #     'rms_prop_eps':1e-05 
    # }
    # model = A2C('CnnPolicy', env, tensorboard_log='logs/', verbose=1, **model_params, policy_kwargs=dict(optimizer_class=RMSpropTF))

    model.learn(
        total_timesteps=int(6048000), # total_timesteps = stage_interval * num_envs * num_stages (1120 rounds)
        callback=[checkpoint_callback, stage_increase_callback]
    )
    env.close()

    # Save the final model
    model.save(os.path.join(save_dir, "ppo_sf2_chunli_final.zip"))

if __name__ == "__main__":
    main()