import argparse
import pprint

import gym
import numpy as np
import torch

from tianshou.data import Collector, VectorReplayBuffer
from tianshou.env import ContinuousToDiscrete, DummyVectorEnv
from tianshou.policy import BranchingDQNPolicy
from tianshou.trainer import offpolicy_trainer
from tianshou.utils.net.common import BranchingNet


def get_args():
    parser = argparse.ArgumentParser()
    # task
    parser.add_argument("--task", type=str, default="Pendulum-v1")
    parser.add_argument('--reward-threshold', type=float, default=None)
    # network architecture
    parser.add_argument("--common-hidden-sizes", type=int, nargs="*", default=[64, 64])
    parser.add_argument("--action-hidden-sizes", type=int, nargs="*", default=[64])
    parser.add_argument("--value-hidden-sizes", type=int, nargs="*", default=[64])
    parser.add_argument("--action-per-branch", type=int, default=40)
    # training hyperparameters
    parser.add_argument("--seed", type=int, default=1626)
    parser.add_argument("--eps-test", type=float, default=0.01)
    parser.add_argument("--eps-train", type=float, default=0.76)
    parser.add_argument("--eps-decay", type=float, default=1e-4)
    parser.add_argument("--buffer-size", type=int, default=20000)
    parser.add_argument("--lr", type=float, default=1e-3)
    parser.add_argument("--gamma", type=float, default=0.9)
    parser.add_argument("--target-update-freq", type=int, default=200)
    parser.add_argument("--epoch", type=int, default=10)
    parser.add_argument("--step-per-epoch", type=int, default=80000)
    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("--training-num", type=int, default=10)
    parser.add_argument("--test-num", type=int, default=10)
    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"
    )
    args = parser.parse_known_args()[0]
    return args


def test_bdq(args=get_args()):
    env = gym.make(args.task)
    env = ContinuousToDiscrete(env, args.action_per_branch)

    args.state_shape = env.observation_space.shape or env.observation_space.n
    args.num_branches = env.action_space.shape[0]

    if args.reward_threshold is None:
        default_reward_threshold = {"Pendulum-v0": -250, "Pendulum-v1": -250}
        args.reward_threshold = default_reward_threshold.get(
            args.task, env.spec.reward_threshold
        )

    print("Observations shape:", args.state_shape)
    print("Num branches:", args.num_branches)
    print("Actions per branch:", args.action_per_branch)

    train_envs = DummyVectorEnv(
        [
            lambda: ContinuousToDiscrete(gym.make(args.task), args.action_per_branch)
            for _ in range(args.training_num)
        ]
    )
    test_envs = DummyVectorEnv(
        [
            lambda: ContinuousToDiscrete(gym.make(args.task), args.action_per_branch)
            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 = BranchingNet(
        args.state_shape,
        args.num_branches,
        args.action_per_branch,
        args.common_hidden_sizes,
        args.value_hidden_sizes,
        args.action_hidden_sizes,
        device=args.device,
    ).to(args.device)
    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
    policy = BranchingDQNPolicy(
        net, optim, args.gamma, target_update_freq=args.target_update_freq
    )
    # collector
    train_collector = Collector(
        policy,
        train_envs,
        VectorReplayBuffer(args.buffer_size, args.training_num),
        exploration_noise=True
    )
    test_collector = Collector(policy, test_envs, exploration_noise=False)
    # policy.set_eps(1)
    train_collector.collect(n_step=args.batch_size * args.training_num)

    def train_fn(epoch, env_step):  # exp decay
        eps = max(args.eps_train * (1 - args.eps_decay)**env_step, args.eps_test)
        policy.set_eps(eps)

    def test_fn(epoch, env_step):
        policy.set_eps(args.eps_test)

    def stop_fn(mean_rewards):
        return mean_rewards >= args.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,
        update_per_step=args.update_per_step,
        train_fn=train_fn,
        test_fn=test_fn,
        stop_fn=stop_fn,
    )

    # assert stop_fn(result["best_reward"])
    if __name__ == "__main__":
        pprint.pprint(result)
        # Let's watch its performance!
        policy.eval()
        policy.set_eps(args.eps_test)
        test_envs.seed(args.seed)
        test_collector.reset()
        result = test_collector.collect(n_episode=args.test_num, render=args.render)
        rews, lens = result["rews"], result["lens"]
        print(f"Final reward: {rews.mean()}, length: {lens.mean()}")


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