import os
_OBS = os.environ.get("RL_OBS_DIM"); _ACT = os.environ.get("RL_N_ACT")
if _OBS and _ACT:
    _OBS, _ACT = int(_OBS), int(_ACT)
    import numpy as np, gymnasium as gym

    class _Unify(gym.Wrapper):
        def __init__(self, env):
            super().__init__(env)
            self.n_valid = int(env.action_space.n)
            sp = env.observation_space
            self._pad = _OBS - int(sp.shape[0])
            assert self._pad >= 0, f"RL_OBS_DIM={_OBS} is smaller than the observation dimension {sp.shape[0]}"
            z = np.zeros(self._pad, dtype=np.float32)
            self.observation_space = gym.spaces.Box(
                low=np.concatenate([sp.low.astype(np.float32), z - np.inf if False else z]),
                high=np.concatenate([sp.high.astype(np.float32), z]),
                dtype=np.float32)
            self.action_space = gym.spaces.Discrete(_ACT)
        def _o(self, o):
            return np.concatenate([np.asarray(o, dtype=np.float32),
                                   np.zeros(self._pad, dtype=np.float32)])
        def reset(self, **kw):
            o, i = self.env.reset(**kw); return self._o(o), i
        def step(self, a):
            a = int(a)
            if a >= self.n_valid:
                a = self.n_valid - 1
                _Unify.n_clipped += 1
            o, r, te, tr, i = self.env.step(a); return self._o(o), r, te, tr, i
    _Unify.n_clipped = 0

    _orig = gym.make
    def _make(env_id, *a, **kw):
        return _Unify(_orig(env_id, *a, **kw))
    gym.make = _make