import gymnasium as gym
import minigrid

import ray
from ray.tune import register_env
from ray.tune.experiment.trial import Trial
from ray import tune, air
from ray.rllib.algorithms.ppo import PPOConfig
from ray.tune.logger import UnifiedLogger
from ray.rllib.models import ModelCatalog
from ray.tune.logger import pretty_print, UnifiedLogger, CSVLogger
from ray.rllib.algorithms.algorithm import Algorithm
from ray.air import session

from torch_action_mask_model import TorchActionMaskModel
from wrappers import OneHotShieldingWrapper, MiniGridShieldingWrapper
from helpers import parse_arguments, create_log_dir, ShieldingConfig, test_name
from shieldhandlers import MiniGridShieldHandler, create_shield_query

from torch.utils.tensorboard import SummaryWriter
from callbacks import MyCallbacks
 

def shielding_env_creater(config):
    name = config.get("name", "MiniGrid-LavaCrossingS9N3-v0")
    framestack = config.get("framestack", 4)
    args = config.get("args", None)
    args.grid_path = F"{args.expname}_{args.grid_path}_{config.worker_index}.txt"
    args.prism_path = F"{args.expname}_{args.prism_path}_{config.worker_index}.prism"   
    shielding = config.get("shielding", False)   
    shield_creator = MiniGridShieldHandler(grid_file=args.grid_path, 
                                           grid_to_prism_path=args.grid_to_prism_binary_path,
                                           prism_path=args.prism_path,
                                           formula=args.formula,
                                           shield_value=args.shield_value,
                                           prism_config=args.prism_config)
    
    env = gym.make(name, randomize_start=True)
    env = MiniGridShieldingWrapper(env, shield_creator=shield_creator, shield_query_creator=create_shield_query ,mask_actions=shielding != ShieldingConfig.Disabled)

    env = OneHotShieldingWrapper(env,
                        config.vector_index if hasattr(config, "vector_index") else 0,
                        framestack=framestack
                        )
    
    
    return env


def register_minigrid_shielding_env(args):
    env_name = "mini-grid-shielding"
    register_env(env_name, shielding_env_creater)

    ModelCatalog.register_custom_model(
        "shielding_model", 
        TorchActionMaskModel
    )
    
def trial_name_creator(trial : Trial):
    return "trial"


def ppo(args):
    register_minigrid_shielding_env(args)
    logdir = args.log_dir
    
    config = (PPOConfig()
        .rollouts(num_rollout_workers=args.workers)
        .resources(num_gpus=0)
        .environment( env="mini-grid-shielding",
                      env_config={"name": args.env,
                                  "args": args,                                  
                                  "shielding": args.shielding is ShieldingConfig.Full or args.shielding is ShieldingConfig.Training,
                                  },)
        .framework("torch")
        .callbacks(MyCallbacks)
        .evaluation(evaluation_config={ 
                                       "evaluation_interval": 1,
                                        "evaluation_duration": 10,
                                        "evaluation_num_workers":1,
                                        "env": "mini-grid-shielding", 
                                        "env_config": {"name": args.env, 
                                                       "args": args, 
                                                       "shielding": args.shielding is ShieldingConfig.Full or args.shielding is ShieldingConfig.Evaluation}})        
        .rl_module(_enable_rl_module_api = False)
        .debugging(logger_config={
            "type": UnifiedLogger, 
            "logdir": logdir
        })
        .training(_enable_learner_api=False ,model={
            "custom_model": "shielding_model"      
        }))
    
    tuner = tune.Tuner("PPO",
                       tune_config=tune.TuneConfig(
                           metric="episode_reward_mean",
                           mode="max",
                           num_samples=1,
                           trial_name_creator=trial_name_creator,

                       ),
                        run_config=air.RunConfig(
                                stop = {"episode_reward_mean": 94,
                                        "timesteps_total": args.steps,}, 
                                checkpoint_config=air.CheckpointConfig(checkpoint_at_end=True,
                                                                       num_to_keep=1, 
                                                                       checkpoint_score_attribute="episode_reward_mean",
                                                                       ),
                                
                               storage_path=F"{logdir}",
                               name=test_name(args),
                               
                               
    )
                        ,
    param_space=config,)
    
    results = tuner.fit()
    best_result = results.get_best_result()
    
    import pprint
    
    metrics_to_print = [
    "episode_reward_mean",
    "episode_reward_max",
    "episode_reward_min",
    "episode_len_mean",
]
    pprint.pprint({k: v for k, v in best_result.metrics.items() if k in metrics_to_print})

   # algo = Algorithm.from_checkpoint(best_result.checkpoint)
    
       
    # eval_log_dir = F"{logdir}-eval"
        
    # writer = SummaryWriter(log_dir=eval_log_dir)
    # csv_logger = CSVLogger(config=config, logdir=eval_log_dir)
    
    
    # for i in range(args.evaluations):
    #     eval_result = algo.evaluate()
    #     print(pretty_print(eval_result))
    #     print(eval_result)
    #     # logger.on_result(eval_result)

    #     csv_logger.on_result(eval_result)
        
    #     evaluation = eval_result['evaluation']
    #     epsiode_reward_mean = evaluation['episode_reward_mean']
    #     episode_len_mean = evaluation['episode_len_mean']
    #     print(epsiode_reward_mean)
    #     writer.add_scalar("evaluation/episode_reward_mean", epsiode_reward_mean, i)
    #     writer.add_scalar("evaluation/episode_len_mean", episode_len_mean, i)
        
    
def main():
    ray.init(num_cpus=3)
    import argparse
    args = parse_arguments(argparse)

    ppo(args)
   
    ray.shutdown()

if __name__ == '__main__':
    main()