import datetime
import os
import pprint
import time
import threading
import torch as th
from types import SimpleNamespace as SN
from utils.logging import Logger
from utils.timehelper import time_left, time_str
from os.path import dirname, abspath
import sys

from learners import REGISTRY as le_REGISTRY
from runners import REGISTRY as r_REGISTRY
from controllers import REGISTRY as mac_REGISTRY
from components.episode_buffer import ReplayBuffer
from components.transforms import OneHot


def run(_run, _config, _log):
    # check args sanity
    _config = args_sanity_check(_config, _log)

    args = SN(**_config)

    th.set_num_threads(args.thread_num)
    # th.set_num_interop_threads(8)

    args.device = "cuda" if args.use_cuda else "cpu"

    # setup loggers
    logger = Logger(_log)

    _log.info("Experiment Parameters:")
    experiment_params = pprint.pformat(_config,
                                       indent=4,
                                       width=1)
    _log.info("\n\n" + experiment_params + "\n")

    # configure tensorboard logger
    env_name = args.env
    try:
        map_name = _config["env_args"]["map_name"]
    except:
        map_name = _config["env_args"]["key"]
    unique_token = (
        f"{_config['exp_name']}_{datetime.datetime.now()}_{_config['name']}_seed{_config['seed']}"
    )
    args.unique_token = unique_token
    args.map_name = map_name
    if args.use_tensorboard:
        tb_logs_direc = os.path.join(dirname(dirname(dirname(abspath(__file__)))), args.local_results_path, "tb_logs", map_name, args.name)
        tb_exp_direc = os.path.join(tb_logs_direc, "{}").format(unique_token)
        logger.setup_tb(tb_exp_direc)

    # sacred is on by default
    logger.setup_sacred(_run)

    # Run and train
    run_sequential(args=args, logger=logger)

    # Clean up after finishing
    print("Exiting Main")

    print("Stopping all threads")
    for t in threading.enumerate():
        if t.name != "MainThread":
            print("Thread {} is alive! Is daemon: {}".format(t.name, t.daemon))
            t.join(timeout=1)
            print("Thread joined")

    print("Exiting script")

    # Making sure framework really exits
    os._exit(os.EX_OK)


def evaluate_sequential(args, runner):
    n_test_runs = max(1, args.test_nepisode // runner.batch_size)
    for _ in range(n_test_runs):
        runner.run(test_mode=True)

    if args.save_replay:
        runner.save_replay()

    runner.close_env()


def run_sequential(args, logger):
    # Init runner so we can get env info
    runner = r_REGISTRY[args.runner](args=args, logger=logger)

    # Set up schemes and groups here
    env_info = runner.get_env_info()
    args.n_agents = env_info["n_agents"]
    args.n_actions = env_info["n_actions"]
    args.state_shape = env_info["state_shape"]
    args.obs_shape = env_info["obs_shape"]
    
    args.accumulated_episodes = getattr(args, "accumulated_episodes", None)

    if args.env in ["sc2", "sc2_v2"]:
        args.output_normal_actions = env_info["n_normal_actions"]
        args.n_enemies = env_info["n_enemies"]
        args.n_allies = env_info["n_allies"]
        args.state_ally_feats_size = env_info["state_ally_feats_size"]
        args.state_enemy_feats_size = env_info["state_enemy_feats_size"]
        args.agent_own_state_size = env_info["state_ally_feats_size"]
        args.obs_move_feats_size = env_info["obs_move_feats_size"]
        args.obs_own_feats_size = env_info["obs_own_feats_size"]
        args.obs_ally_feats_size = env_info["obs_ally_feats_size"][1]
        args.obs_enemy_feats_size = env_info["obs_enemy_feats_size"][1]
        args.unit_type_bits = env_info["unit_type_bits"]
        args.obs_component = env_info["obs_component"]
        args.state_component = env_info["state_component"]
        args.map_type = env_info["map_type"]
    elif args.env == "gymma":
        args.n_enemies = env_info["n_enemies"]
        args.n_allies = env_info["n_allies"]
        args.obs_move_feats_size = env_info["obs_move_feats_size"]
        args.obs_enemy_feats_size = env_info["obs_enemy_feats_size"]
        args.obs_ally_feats_size = env_info["obs_ally_feats_size"]
        args.obs_own_feats_size = env_info["obs_own_feats_size"]
        args.obs_component = env_info["obs_component"]
        args.unit_type_bits = env_info["unit_type_bits"]

    # Default/Base scheme
    scheme = {
        "state": {"vshape": env_info["state_shape"]},
        "obs": {"vshape": env_info["obs_shape"], "group": "agents"},
        "actions": {"vshape": (1,), "group": "agents", "dtype": th.long},
        "avail_actions": {"vshape": (env_info["n_actions"],), "group": "agents", "dtype": th.int},
        "probs": {"vshape": (env_info["n_actions"],), "group": "agents", "dtype": th.float},
        "reward": {"vshape": (1,)},
        "terminated": {"vshape": (1,), "dtype": th.uint8},
    }
    if args.delay_type != "":
        scheme["real_obs"] = {"vshape": env_info["obs_shape"], "group": "agents"}
        scheme["enemy_delay_values"] = {"vshape": env_info["n_enemies"], "group": "agents", "dtype": th.int}
        scheme["ally_delay_values"] = {"vshape": env_info["n_allies"], "group": "agents", "dtype": th.int}
    # For individual rewards in gymmai reward is of shape (1, n_agents)
    if args.common_reward:
        scheme["reward"] = {"vshape": (1,)}
    else:
        scheme["reward"] = {"vshape": (args.n_agents,)}
    groups = {
        "agents": args.n_agents
    }
    preprocess = {
        "actions": ("actions_onehot", [OneHot(out_dim=args.n_actions)])
    }
    # [batch, episode_length, n_agents, feature_dim]
    buffer = ReplayBuffer(scheme, groups, args.buffer_size, env_info["episode_limit"] + 1,
                          preprocess=preprocess,
                          device="cpu" if args.buffer_cpu_only else args.device)
    # Setup multiagent controller here
    mac = mac_REGISTRY[args.mac](buffer.scheme, groups, args)

    # Give runner the scheme
    runner.setup(scheme=scheme, groups=groups, preprocess=preprocess, mac=mac)

    # Learner
    learner = le_REGISTRY[args.learner](mac, buffer.scheme, logger, args)

    if args.use_cuda:
        learner.cuda()

    if args.checkpoint_path != "":
        timesteps = []
        timestep_to_load = 0

        if not os.path.isdir(args.checkpoint_path):
            logger.console_logger.info("Checkpoint directiory {} doesn't exist".format(args.checkpoint_path))
            return

        # Go through all files in args.checkpoint_path
        for name in os.listdir(args.checkpoint_path):
            full_name = os.path.join(args.checkpoint_path, name)
            # Check if they are dirs the names of which are numbers
            if os.path.isdir(full_name) and name.isdigit():
                timesteps.append(int(name))

        if args.load_step == 0:
            # choose the max timestep
            timestep_to_load = max(timesteps)
        else:
            # choose the timestep closest to load_step
            timestep_to_load = min(timesteps, key=lambda x: abs(x - args.load_step))

        model_path = os.path.join(args.checkpoint_path, str(timestep_to_load))

        logger.console_logger.info("Loading model from {}".format(model_path))
        learner.load_models(model_path)
        runner.t_env = timestep_to_load

        if args.evaluate or args.save_replay:
            runner.log_train_stats_t = runner.t_env
            evaluate_sequential(args, runner)
            return

    # start training
    episode = 0
    last_test_T = -args.test_interval - 1
    last_log_T = 0
    model_save_time = 0

    start_time = time.time()
    last_time = start_time

    logger.console_logger.info("Beginning training for {} timesteps".format(args.t_max))

    while runner.t_env <= args.t_max:
        # Run for a whole episode at a time
        with th.no_grad():
            t_start = time.time()
            episode_batch = runner.run(test_mode=False)
            time_1 = time.time()
            # print("runner: ", time_1 - t_start)
            if episode_batch.batch_size > 0:  # After clearing the batch data, the batch may be empty.
                buffer.insert_episode_batch(episode_batch)
            episode += args.batch_size_run

        if buffer.can_sample(args.batch_size):
            time_2 = time.time()
            if args.accumulated_episodes and episode % args.accumulated_episodes != 0:
                continue

            episode_sample = buffer.sample(args.batch_size)

            # Truncate batch to only filled timesteps
            max_ep_t = episode_sample.max_t_filled()
            episode_sample = episode_sample[:, :max_ep_t]

            if episode_sample.device != args.device:
                episode_sample.to(args.device)

            time_3 = time.time()
            # print("learner prepare: ", time_3 - time_2)
            learner.train(episode_sample, runner.t_env, episode)
            del episode_sample

        # Execute test runs once in a while
        n_test_runs = max(1, args.test_nepisode // runner.batch_size)
        if (runner.t_env - last_test_T) / args.test_interval >= 1.0:
            logger.console_logger.info("t_env: {} / {}".format(runner.t_env, args.t_max))
            logger.console_logger.info("Estimated time left: {}. Time passed: {}".format(
                time_left(last_time, last_test_T, runner.t_env, args.t_max), time_str(time.time() - start_time)))
            last_time = time.time()
            last_test_T = runner.t_env
            with th.no_grad():
                for _ in range(n_test_runs):
                    runner.run(test_mode=True)

        if args.save_model and (
                runner.t_env - model_save_time >= args.save_model_interval or runner.t_env >= args.t_max):
            model_save_time = runner.t_env
            save_path = os.path.join(args.local_results_path, "models", args.map_name, args.name, args.unique_token,
                                     str(runner.t_env))
            # "results/models/{}".format(unique_token)
            os.makedirs(save_path, exist_ok=True)
            logger.console_logger.info("Saving models to {}".format(save_path))

            # learner should handle saving/loading -- delegate actor save/load to mac,
            # use appropriate filenames to do critics, optimizer states
            learner.save_models(save_path)

        if (runner.t_env - last_log_T) >= args.log_interval:
            logger.log_stat("train/episode", episode, runner.t_env)
            logger.log_stat("train/episode_in_buffer", buffer.episodes_in_buffer, runner.t_env)
            logger.print_recent_stats()
            last_log_T = runner.t_env

    runner.close_env()
    logger.console_logger.info("Finished Training")

    # flush
    sys.stdout.flush()
    time.sleep(10)


def args_sanity_check(config, _log):
    # set CUDA flags
    # config["use_cuda"] = True # Use cuda whenever possible!
    if config["use_cuda"] and not th.cuda.is_available():
        config["use_cuda"] = False
        _log.warning("CUDA flag use_cuda was switched OFF automatically because no CUDA devices are available!")

    if config["test_nepisode"] < config["batch_size_run"]:
        config["test_nepisode"] = config["batch_size_run"]
    else:
        config["test_nepisode"] = (config["test_nepisode"] // config["batch_size_run"]) * config["batch_size_run"]

    return config
