import argparse
import os
from datetime import datetime
from typing import Dict, List

import numpy as np

# dm_control
try:
    from dm_control import suite
except Exception as e:
    raise RuntimeError(
        "dm_control is required. Install via: pip install dm-control mujoco"
    ) from e

# gym/gymnasium compatibility
try:
    import gymnasium as gym
except Exception:
    import gym  # type: ignore

# Stable Baselines3
try:
    from stable_baselines3 import SAC, PPO, TD3
    from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv
    from stable_baselines3.common.env_util import make_vec_env
    from stable_baselines3.common.callbacks import BaseCallback
except Exception as e:
    raise RuntimeError(
        "stable-baselines3 is required. Install via: pip install stable-baselines3"
    ) from e

import torch


class DmControlGymWrapper(gym.Env):
    """A minimal Gym/Gymnasium wrapper for dm_control suite tasks with flattened obs."""

    metadata = {"render_modes": ["rgb_array"], "render_fps": 60}

    def __init__(self, domain: str, task: str, seed: int | None = None):
        super().__init__()
        self._domain = domain
        self._task = task
        self._seed = seed if seed is not None else 0
        self._env = suite.load(domain_name=domain, task_name=task, task_kwargs={"random": self._seed})

        # Build observation space by flattening dict in sorted key order
        example_obs = self._env.reset().observation
        self._obs_keys = sorted(example_obs.keys())
        obs_size = int(np.sum([np.asarray(example_obs[k]).size for k in self._obs_keys]))
        # Use unbounded space; algorithms usually normalize internally
        self.observation_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(obs_size,), dtype=np.float32)

        # Action space from spec
        action_spec = self._env.action_spec()
        self._act_low = np.asarray(action_spec.minimum, dtype=np.float32)
        self._act_high = np.asarray(action_spec.maximum, dtype=np.float32)
        self.action_space = gym.spaces.Box(low=self._act_low, high=self._act_high, shape=action_spec.shape, dtype=np.float32)

    def seed(self, seed: int | None = None):
        if seed is not None:
            self._seed = seed
        # dm_control uses task_kwargs random; re-create env to apply new seed
        self._env = suite.load(domain_name=self._domain, task_name=self._task, task_kwargs={"random": self._seed})

    def _flatten_obs(self, obs_dict: Dict[str, np.ndarray]) -> np.ndarray:
        parts: List[np.ndarray] = []
        for k in self._obs_keys:
            v = np.asarray(obs_dict[k], dtype=np.float32).ravel()
            parts.append(v)
        return np.concatenate(parts, axis=0).astype(np.float32)

    def reset(self, *, seed: int | None = None, options: dict | None = None):
        if seed is not None:
            self.seed(seed)
        ts = self._env.reset()
        obs = self._flatten_obs(ts.observation)
        info = {}
        return obs, info

    def step(self, action: np.ndarray):
        action = np.asarray(action, dtype=np.float32)
        action = np.clip(action, self._act_low, self._act_high)
        ts = self._env.step(action)
        obs = self._flatten_obs(ts.observation)
        reward = 0.0 if ts.reward is None else float(ts.reward)
        terminated = bool(ts.last())
        truncated = False  # dm_control provides a single 'last' flag
        info = {}
        if terminated:
            # dm_control envs typically auto-reset; we return terminal step and let VecEnv reset
            pass
        return obs, reward, terminated, truncated, info

    def render(self):
        # Return an RGB array
        return self._env.physics.render(height=480, width=640, camera_id=0)


ALGOS = {
    "sac": SAC,
    "ppo": PPO,
    "td3": TD3,
}


class PeriodicCkptCallback(BaseCallback):
    """Save policy checkpoint every fixed number of timesteps.

    Saves to weights/<domain>/<task>/ckpt-<k>.pt where k starts from 1.
    """

    def __init__(self, save_root: str, domain: str, task: str, interval: int = 10_000, verbose: int = 1):
        super().__init__(verbose)
        self.save_root = save_root
        self.domain = domain
        self.task = task
        self.interval = interval
        self.saved_count = 0
        self.target_dir = os.path.join(save_root, domain, task)
        os.makedirs(self.target_dir, exist_ok=True)

    def _on_step(self) -> bool:
        # num_timesteps is global across envs; trigger exactly on multiples
        if self.num_timesteps > 0 and self.num_timesteps % self.interval == 0:
            self.saved_count += 1
            path = os.path.join(self.target_dir, f"ckpt-{self.saved_count}.pt")
            payload = {
                "algo": self.model.__class__.__name__,
                "domain": self.domain,
                "task": self.task,
                "num_timesteps": int(self.num_timesteps),
                "policy_state_dict": self.model.policy.state_dict(),
            }
            torch.save(payload, path)
            if self.verbose:
                print(f"[CKPT] Saved checkpoint #{self.saved_count} at {self.num_timesteps} steps -> {path}")
        return True


def train(domain: str, task: str, algo: str, total_timesteps: int, n_envs: int, seed: int, device: str, out_dir: str):
    # Build vectorized envs
    def make_env_fn(rank: int):
        def _thunk():
            env = DmControlGymWrapper(domain=domain, task=task, seed=seed + rank)
            return env
        return _thunk

    vec_env = make_vec_env(make_env_fn(0), n_envs=n_envs, seed=seed, vec_env_cls=SubprocVecEnv if n_envs > 1 else DummyVecEnv)

    ALGO_CLS = ALGOS[algo]
    policy = "MlpPolicy"
    model = ALGO_CLS(policy, vec_env, verbose=1, seed=seed, device=device)

    # Periodic checkpoint every 10,000 steps
    ckpt_cb = PeriodicCkptCallback(save_root=out_dir, domain=domain, task=task, interval=10_000, verbose=1)

    print(f"[INFO] Start training {algo.upper()} on {domain}/{task} for {total_timesteps} steps with {n_envs} envs")
    model.learn(total_timesteps=total_timesteps, progress_bar=True, callback=ckpt_cb)

    os.makedirs(out_dir, exist_ok=True)
    timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
    save_stem = f"sb3_{algo}_{domain}-{task}_seed{seed}_{timestamp}"
    save_path = os.path.join(out_dir, save_stem)

    model.save(save_path)
    print(f"[INFO] Saved model to: {save_path}.zip")

    vec_env.close()


def parse_args():
    parser = argparse.ArgumentParser(description="Train dm_control task with Stable Baselines3 and save weights")
    parser.add_argument("--domain", type=str, default="cheetah", help="dm_control domain (e.g., cheetah, quadruped)")
    parser.add_argument("--task", type=str, default="run", help="dm_control task (e.g., run, walk)")
    parser.add_argument("--algo", type=str, choices=list(ALGOS.keys()), default="sac", help="RL algorithm")
    parser.add_argument("--total_timesteps", type=int, default=500_000, help="Total training steps")
    parser.add_argument("--n_envs", type=int, default=1, help="Number of parallel envs")
    parser.add_argument("--seed", type=int, default=0, help="Random seed")
    parser.add_argument("--device", type=str, default="auto", help="Device: cpu, cuda, or auto")
    parser.add_argument(
        "--out_dir",
        type=str,
        default=os.path.join("/home/lau/sim/DynaTraj", "weights"),
        help="Directory to save trained weights",
    )
    return parser.parse_args()


if __name__ == "__main__":
    args = parse_args()

    train(
        domain=args.domain,
        task=args.task,
        algo=args.algo,
        total_timesteps=args.total_timesteps,
        n_envs=args.n_envs,
        seed=args.seed,
        device=args.device,
        out_dir=args.out_dir,
    )