import os
import gym
import torch
import pprint
import argparse
import numpy as np
from torch.utils.tensorboard import SummaryWriter

from tianshou.utils import BasicLogger
from tianshou.env import SubprocVectorEnv
from tianshou.utils.net.common import Net
from tianshou.policy import DiscreteSACPolicy
from tianshou.trainer import offpolicy_trainer
from tianshou.utils.net.discrete import Actor, Critic
from tianshou.data import Collector, VectorReplayBuffer


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('--buffer-size', type=int, default=20000)
    parser.add_argument('--actor-lr', type=float, default=1e-4)
    parser.add_argument('--critic-lr', type=float, default=1e-3)
    parser.add_argument('--alpha-lr', type=float, default=3e-4)
    parser.add_argument('--gamma', type=float, default=0.95)
    parser.add_argument('--tau', type=float, default=0.005)
    parser.add_argument('--alpha', type=float, default=0.05)
    parser.add_argument('--auto-alpha', action="store_true", default=False)
    parser.add_argument('--epoch', type=int, default=5)
    parser.add_argument('--step-per-epoch', type=int, default=10000)
    parser.add_argument('--step-per-collect', type=int, default=10)
    parser.add_argument('--update-per-step', type=float, default=0.1)
    parser.add_argument('--batch-size', type=int, default=128)
    parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[128, 128])
    parser.add_argument('--training-num', type=int, default=10)
    parser.add_argument('--test-num', type=int, default=100)
    parser.add_argument('--logdir', type=str, default='log')
    parser.add_argument('--render', type=float, default=0.0)
    parser.add_argument('--rew-norm', action="store_true", default=False)
    parser.add_argument('--n-step', type=int, default=3)
    parser.add_argument(
        '--device', type=str,
        default='cuda' if torch.cuda.is_available() else 'cpu')
    args = parser.parse_known_args()[0]
    return args


def test_discrete_sac(args=get_args()):
    env = gym.make(args.task)
    args.state_shape = env.observation_space.shape or env.observation_space.n
    args.action_shape = env.action_space.shape or env.action_space.n

    train_envs = SubprocVectorEnv(
        [lambda: gym.make(args.task) for _ in range(args.training_num)])
    test_envs = SubprocVectorEnv(
        [lambda: gym.make(args.task) for _ in range(args.test_num)])
    # seed
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    train_envs.seed(args.seed)
    test_envs.seed(args.seed)
    # model
    net = Net(args.state_shape, hidden_sizes=args.hidden_sizes,
              device=args.device)
    actor = Actor(net, args.action_shape,
                  softmax_output=False, device=args.device).to(args.device)
    actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
    net_c1 = Net(args.state_shape, hidden_sizes=args.hidden_sizes,
                 device=args.device)
    critic1 = Critic(net_c1, last_size=args.action_shape,
                     device=args.device).to(args.device)
    critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
    net_c2 = Net(args.state_shape, hidden_sizes=args.hidden_sizes,
                 device=args.device)
    critic2 = Critic(net_c2, last_size=args.action_shape,
                     device=args.device).to(args.device)
    critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)

    # better not to use auto alpha in CartPole
    if args.auto_alpha:
        target_entropy = 0.98 * np.log(np.prod(args.action_shape))
        log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
        alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
        args.alpha = (target_entropy, log_alpha, alpha_optim)

    policy = DiscreteSACPolicy(
        actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
        args.tau, args.gamma, args.alpha, estimation_step=args.n_step,
        reward_normalization=args.rew_norm)
    # collector
    train_collector = Collector(
        policy, train_envs,
        VectorReplayBuffer(args.buffer_size, len(train_envs)))
    test_collector = Collector(policy, test_envs)
    # train_collector.collect(n_step=args.buffer_size)
    # log
    log_path = os.path.join(args.logdir, args.task, 'discrete_sac')
    writer = SummaryWriter(log_path)
    logger = BasicLogger(writer)

    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

    # trainer
    result = offpolicy_trainer(
        policy, train_collector, test_collector, args.epoch,
        args.step_per_epoch, args.step_per_collect, args.test_num,
        args.batch_size, stop_fn=stop_fn, save_fn=save_fn, logger=logger,
        update_per_step=args.update_per_step, test_in_train=False)
    assert stop_fn(result['best_reward'])
    if __name__ == '__main__':
        pprint.pprint(result)
        # Let's watch its performance!
        env = gym.make(args.task)
        policy.eval()
        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()}")


if __name__ == '__main__':
    test_discrete_sac()