import argparse
import os
import pickle
import pprint

import gym
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter

from tianshou.data import Collector
from tianshou.env import DummyVectorEnv
from tianshou.policy import DiscreteBCQPolicy
from tianshou.trainer import offline_trainer
from tianshou.utils import TensorboardLogger
from tianshou.utils.net.common import ActorCritic, Net
from tianshou.utils.net.discrete import Actor

if __name__ == "__main__":
    from gather_cartpole_data import gather_data
else:  # pytest
    from test.offline.gather_cartpole_data import gather_data


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--task", type=str, default="CartPole-v0")
    parser.add_argument("--seed", type=int, default=1626)
    parser.add_argument("--eps-test", type=float, default=0.001)
    parser.add_argument("--lr", type=float, default=3e-4)
    parser.add_argument("--gamma", type=float, default=0.99)
    parser.add_argument("--n-step", type=int, default=3)
    parser.add_argument("--target-update-freq", type=int, default=320)
    parser.add_argument("--unlikely-action-threshold", type=float, default=0.6)
    parser.add_argument("--imitation-logits-penalty", type=float, default=0.01)
    parser.add_argument("--epoch", type=int, default=5)
    parser.add_argument("--update-per-epoch", type=int, default=2000)
    parser.add_argument("--batch-size", type=int, default=64)
    parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[64, 64])
    parser.add_argument("--test-num", type=int, default=100)
    parser.add_argument("--logdir", type=str, default="log")
    parser.add_argument("--render", type=float, default=0.)
    parser.add_argument(
        "--load-buffer-name",
        type=str,
        default="./expert_QRDQN_CartPole-v0.pkl",
    )
    parser.add_argument(
        "--device",
        type=str,
        default="cuda" if torch.cuda.is_available() else "cpu",
    )
    parser.add_argument("--resume", action="store_true")
    parser.add_argument("--save-interval", type=int, default=4)
    args = parser.parse_known_args()[0]
    return args


def test_discrete_bcq(args=get_args()):
    # envs
    env = gym.make(args.task)
    if args.task == 'CartPole-v0':
        env.spec.reward_threshold = 190  # lower the goal
    args.state_shape = env.observation_space.shape or env.observation_space.n
    args.action_shape = env.action_space.shape or env.action_space.n
    test_envs = DummyVectorEnv(
        [lambda: gym.make(args.task) for _ in range(args.test_num)]
    )
    # seed
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    test_envs.seed(args.seed)
    # model
    net = Net(args.state_shape, args.hidden_sizes[0], device=args.device)
    policy_net = Actor(
        net, args.action_shape, hidden_sizes=args.hidden_sizes, device=args.device
    ).to(args.device)
    imitation_net = Actor(
        net, args.action_shape, hidden_sizes=args.hidden_sizes, device=args.device
    ).to(args.device)
    actor_critic = ActorCritic(policy_net, imitation_net)
    optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr)

    policy = DiscreteBCQPolicy(
        policy_net,
        imitation_net,
        optim,
        args.gamma,
        args.n_step,
        args.target_update_freq,
        args.eps_test,
        args.unlikely_action_threshold,
        args.imitation_logits_penalty,
    )
    # buffer
    if os.path.exists(args.load_buffer_name) and os.path.isfile(args.load_buffer_name):
        buffer = pickle.load(open(args.load_buffer_name, "rb"))
    else:
        buffer = gather_data()

    # collector
    test_collector = Collector(policy, test_envs, exploration_noise=True)

    log_path = os.path.join(args.logdir, args.task, 'discrete_bcq')
    writer = SummaryWriter(log_path)
    logger = TensorboardLogger(writer, save_interval=args.save_interval)

    def save_fn(policy):
        torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))

    def stop_fn(mean_rewards):
        return mean_rewards >= env.spec.reward_threshold

    def save_checkpoint_fn(epoch, env_step, gradient_step):
        # see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
        torch.save(
            {
                'model': policy.state_dict(),
                'optim': optim.state_dict(),
            }, os.path.join(log_path, 'checkpoint.pth')
        )

    if args.resume:
        # load from existing checkpoint
        print(f"Loading agent under {log_path}")
        ckpt_path = os.path.join(log_path, 'checkpoint.pth')
        if os.path.exists(ckpt_path):
            checkpoint = torch.load(ckpt_path, map_location=args.device)
            policy.load_state_dict(checkpoint['model'])
            optim.load_state_dict(checkpoint['optim'])
            print("Successfully restore policy and optim.")
        else:
            print("Fail to restore policy and optim.")

    result = offline_trainer(
        policy,
        buffer,
        test_collector,
        args.epoch,
        args.update_per_epoch,
        args.test_num,
        args.batch_size,
        stop_fn=stop_fn,
        save_fn=save_fn,
        logger=logger,
        resume_from_log=args.resume,
        save_checkpoint_fn=save_checkpoint_fn
    )
    assert stop_fn(result['best_reward'])

    if __name__ == '__main__':
        pprint.pprint(result)
        # Let's watch its performance!
        env = gym.make(args.task)
        policy.eval()
        policy.set_eps(args.eps_test)
        collector = Collector(policy, env)
        result = collector.collect(n_episode=1, render=args.render)
        rews, lens = result["rews"], result["lens"]
        print(f"Final reward: {rews.mean()}, length: {lens.mean()}")


def test_discrete_bcq_resume(args=get_args()):
    args.resume = True
    test_discrete_bcq(args)


if __name__ == "__main__":
    test_discrete_bcq(get_args())