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

from tianshou.policy import PPOPolicy
from tianshou.utils import BasicLogger
from tianshou.env import DummyVectorEnv
from tianshou.utils.net.common import Net
from tianshou.trainer import onpolicy_trainer
from tianshou.data import Collector, VectorReplayBuffer
from tianshou.utils.net.discrete import Actor, Critic


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--task', type=str, default='CartPole-v0')
    parser.add_argument('--seed', type=int, default=0)
    parser.add_argument('--buffer-size', type=int, default=20000)
    parser.add_argument('--lr', type=float, default=3e-4)
    parser.add_argument('--gamma', type=float, default=0.99)
    parser.add_argument('--epoch', type=int, default=10)
    parser.add_argument('--step-per-epoch', type=int, default=50000)
    parser.add_argument('--step-per-collect', type=int, default=2000)
    parser.add_argument('--repeat-per-collect', type=int, default=10)
    parser.add_argument('--batch-size', type=int, default=64)
    parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[64, 64])
    parser.add_argument('--training-num', type=int, default=20)
    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(
        '--device', type=str,
        default='cuda' if torch.cuda.is_available() else 'cpu')
    # ppo special
    parser.add_argument('--vf-coef', type=float, default=0.5)
    parser.add_argument('--ent-coef', type=float, default=0.0)
    parser.add_argument('--eps-clip', type=float, default=0.2)
    parser.add_argument('--max-grad-norm', type=float, default=0.5)
    parser.add_argument('--gae-lambda', type=float, default=0.95)
    parser.add_argument('--rew-norm', type=int, default=0)
    parser.add_argument('--norm-adv', type=int, default=0)
    parser.add_argument('--recompute-adv', type=int, default=0)
    parser.add_argument('--dual-clip', type=float, default=None)
    parser.add_argument('--value-clip', type=int, default=0)
    args = parser.parse_known_args()[0]
    return args


def test_ppo(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 = gym.make(args.task)
    # you can also use tianshou.env.SubprocVectorEnv
    train_envs = DummyVectorEnv(
        [lambda: gym.make(args.task) for _ in range(args.training_num)])
    # test_envs = gym.make(args.task)
    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)
    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, device=args.device).to(args.device)
    critic = Critic(net, device=args.device).to(args.device)
    # orthogonal initialization
    for m in set(actor.modules()).union(critic.modules()):
        if isinstance(m, torch.nn.Linear):
            torch.nn.init.orthogonal_(m.weight)
            torch.nn.init.zeros_(m.bias)
    optim = torch.optim.Adam(
        set(actor.parameters()).union(critic.parameters()), lr=args.lr)
    dist = torch.distributions.Categorical
    policy = PPOPolicy(
        actor, critic, optim, dist,
        discount_factor=args.gamma,
        max_grad_norm=args.max_grad_norm,
        eps_clip=args.eps_clip,
        vf_coef=args.vf_coef,
        ent_coef=args.ent_coef,
        gae_lambda=args.gae_lambda,
        reward_normalization=args.rew_norm,
        dual_clip=args.dual_clip,
        value_clip=args.value_clip,
        action_space=env.action_space,
        deterministic_eval=True,
        advantage_normalization=args.norm_adv,
        recompute_advantage=args.recompute_adv)
    # collector
    train_collector = Collector(
        policy, train_envs,
        VectorReplayBuffer(args.buffer_size, len(train_envs)))
    test_collector = Collector(policy, test_envs)
    # log
    log_path = os.path.join(args.logdir, args.task, 'ppo')
    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 = onpolicy_trainer(
        policy, train_collector, test_collector, args.epoch,
        args.step_per_epoch, args.repeat_per_collect, args.test_num, args.batch_size,
        step_per_collect=args.step_per_collect, stop_fn=stop_fn, save_fn=save_fn,
        logger=logger)
    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_ppo()