import argparse
import os
import warnings
from typing import Any

import gymnasium as gym
import numpy as np
import torch
from pettingzoo.butterfly import pistonball_v6
from torch import nn
from torch.distributions import Distribution, Independent, Normal
from torch.utils.tensorboard import SummaryWriter

from tianshou.data import Collector, VectorReplayBuffer
from tianshou.env import DummyVectorEnv
from tianshou.env.pettingzoo_env import PettingZooEnv
from tianshou.policy import BasePolicy, MultiAgentPolicyManager, PPOPolicy
from tianshou.trainer import OnpolicyTrainer
from tianshou.utils import TensorboardLogger
from tianshou.utils.net.continuous import ActorProb, Critic


class DQN(nn.Module):
    """Reference: Human-level control through deep reinforcement learning.

    For advanced usage (how to customize the network), please refer to
    :ref:`build_the_network`.
    """

    def __init__(
        self,
        c: int,
        h: int,
        w: int,
        device: str | int | torch.device = "cpu",
    ) -> None:
        super().__init__()
        self.device = device
        self.c = c
        self.h = h
        self.w = w
        self.net = nn.Sequential(
            nn.Conv2d(c, 32, kernel_size=8, stride=4),
            nn.ReLU(inplace=True),
            nn.Conv2d(32, 64, kernel_size=4, stride=2),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, stride=1),
            nn.ReLU(inplace=True),
            nn.Flatten(),
        )
        with torch.no_grad():
            self.output_dim = np.prod(self.net(torch.zeros(1, c, h, w)).shape[1:])

    def forward(
        self,
        x: np.ndarray | torch.Tensor,
        state: Any | None = None,
        info: dict[str, Any] | None = None,
    ) -> tuple[torch.Tensor, Any]:
        r"""Mapping: x -> Q(x, \*)."""
        if info is None:
            info = {}
        x = torch.as_tensor(x, device=self.device, dtype=torch.float32)
        return self.net(x.reshape(-1, self.c, self.w, self.h)), state


def get_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser()
    parser.add_argument("--seed", type=int, default=1626)
    parser.add_argument("--eps-test", type=float, default=0.05)
    parser.add_argument("--eps-train", type=float, default=0.1)
    parser.add_argument("--buffer-size", type=int, default=2000)
    parser.add_argument("--lr", type=float, default=1e-3)
    parser.add_argument(
        "--gamma",
        type=float,
        default=0.9,
        help="a smaller gamma favors earlier win",
    )
    parser.add_argument(
        "--n-pistons",
        type=int,
        default=3,
        help="Number of pistons(agents) in the env",
    )
    parser.add_argument("--n-step", type=int, default=100)
    parser.add_argument("--target-update-freq", type=int, default=320)
    parser.add_argument("--epoch", type=int, default=5)
    parser.add_argument("--step-per-epoch", type=int, default=500)
    parser.add_argument("--step-per-collect", type=int, default=10)
    parser.add_argument("--episode-per-collect", type=int, default=16)
    parser.add_argument("--repeat-per-collect", type=int, default=2)
    parser.add_argument("--update-per-step", type=float, default=0.1)
    parser.add_argument("--batch-size", type=int, default=32)
    parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64])
    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(
        "--watch",
        default=False,
        action="store_true",
        help="no training, watch the play of pre-trained models",
    )
    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=0.25)
    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=1)
    parser.add_argument("--dual-clip", type=float, default=None)
    parser.add_argument("--value-clip", type=int, default=1)
    parser.add_argument("--norm-adv", type=int, default=1)
    parser.add_argument("--recompute-adv", type=int, default=0)
    parser.add_argument("--resume", action="store_true")
    parser.add_argument("--save-interval", type=int, default=4)
    parser.add_argument("--render", type=float, default=0.0)

    return parser


def get_args() -> argparse.Namespace:
    parser = get_parser()
    return parser.parse_known_args()[0]


def get_env(args: argparse.Namespace = get_args()):
    return PettingZooEnv(pistonball_v6.env(continuous=True, n_pistons=args.n_pistons))


def get_agents(
    args: argparse.Namespace = get_args(),
    agents: list[BasePolicy] | None = None,
    optims: list[torch.optim.Optimizer] | None = None,
) -> tuple[BasePolicy, list[torch.optim.Optimizer], list]:
    env = get_env()
    observation_space = (
        env.observation_space["observation"]
        if isinstance(env.observation_space, gym.spaces.Dict)
        else env.observation_space
    )
    args.state_shape = observation_space.shape or observation_space.n
    args.action_shape = env.action_space.shape or env.action_space.n
    args.max_action = env.action_space.high[0]

    if agents is None:
        agents = []
        optims = []
        for _ in range(args.n_pistons):
            # model
            net = DQN(
                observation_space.shape[2],
                observation_space.shape[1],
                observation_space.shape[0],
                device=args.device,
            ).to(args.device)

            actor = ActorProb(
                net,
                args.action_shape,
                max_action=args.max_action,
                device=args.device,
            ).to(args.device)
            net2 = DQN(
                observation_space.shape[2],
                observation_space.shape[1],
                observation_space.shape[0],
                device=args.device,
            ).to(args.device)
            critic = Critic(net2, device=args.device).to(args.device)
            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)

            def dist(*logits: torch.Tensor) -> Distribution:
                return Independent(Normal(*logits), 1)

            agent: PPOPolicy = 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,
                reward_normalization=args.rew_norm,
                advantage_normalization=args.norm_adv,
                recompute_advantage=args.recompute_adv,
                # dual_clip=args.dual_clip,
                # dual clip cause monotonically increasing log_std :)
                value_clip=args.value_clip,
                gae_lambda=args.gae_lambda,
                action_space=env.action_space,
            )

            agents.append(agent)
            optims.append(optim)

    policy = MultiAgentPolicyManager(agents, env, action_scaling=True, action_bound_method="clip")
    return policy, optims, env.agents


def train_agent(
    args: argparse.Namespace = get_args(),
    agents: list[BasePolicy] | None = None,
    optims: list[torch.optim.Optimizer] | None = None,
) -> tuple[dict, BasePolicy]:
    train_envs = DummyVectorEnv([get_env for _ in range(args.training_num)])
    test_envs = DummyVectorEnv([get_env 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)

    policy, optim, agents = get_agents(args, agents=agents, optims=optims)

    # collector
    train_collector = Collector(
        policy,
        train_envs,
        VectorReplayBuffer(args.buffer_size, len(train_envs)),
        exploration_noise=False,  # True
    )
    test_collector = Collector(policy, test_envs)
    # train_collector.collect(n_step=args.batch_size * args.training_num)
    # log
    log_path = os.path.join(args.logdir, "pistonball", "dqn")
    writer = SummaryWriter(log_path)
    writer.add_text("args", str(args))
    logger = TensorboardLogger(writer)

    def save_best_fn(policy: BasePolicy) -> None:
        pass

    def stop_fn(mean_rewards: float) -> bool:
        return False

    def reward_metric(rews):
        return rews[:, 0]

    # trainer
    result = OnpolicyTrainer(
        policy=policy,
        train_collector=train_collector,
        test_collector=test_collector,
        max_epoch=args.epoch,
        step_per_epoch=args.step_per_epoch,
        repeat_per_collect=args.repeat_per_collect,
        episode_per_test=args.test_num,
        batch_size=args.batch_size,
        episode_per_collect=args.episode_per_collect,
        stop_fn=stop_fn,
        save_best_fn=save_best_fn,
        logger=logger,
        resume_from_log=args.resume,
    ).run()

    return result, policy


def watch(args: argparse.Namespace = get_args(), policy: BasePolicy | None = None) -> None:
    env = DummyVectorEnv([get_env])
    if not policy:
        warnings.warn(
            "watching random agents, as loading pre-trained policies is currently not supported",
        )
        policy, _, _ = get_agents(args)
    policy.eval()
    collector = Collector(policy, env)
    collector_result = collector.collect(n_episode=1, render=args.render)
    rews, lens = collector_result["rews"], collector_result["lens"]
    print(f"Final reward: {rews[:, 0].mean()}, length: {lens.mean()}")