"""RSL-RL PPO trainer stub (Isaac Lab ``OnPolicyRunner``).

Hyperparameters come from the Gym task's ``rsl_rl_cfg_entry_point``.
Swap this class when we lock the algorithm (plain PPO vs joint residual, etc.).
"""

from __future__ import annotations

from typing import Any

from .base import Algorithm


class RslRlPpoTrainer(Algorithm):
    def __init__(self, agent_cfg: Any):
        self.agent_cfg = agent_cfg
        self._runner = None

    def train(self, env: Any, log_dir: str) -> None:
        from isaaclab.utils import class_to_dict
        from rsl_rl.runners import OnPolicyRunner

        self._runner = OnPolicyRunner(
            env, class_to_dict(self.agent_cfg), log_dir=log_dir, device=str(env.unwrapped.device)
        )
        self._runner.learn(
            num_learning_iterations=self.agent_cfg.max_iterations,
            init_at_random_ep_len=True,
        )

    def load(self, checkpoint: str) -> None:
        if self._runner is None:
            raise RuntimeError("call train() or construct a runner before load()")
        self._runner.load(checkpoint)

    def act(self, obs: Any) -> Any:
        if self._runner is None:
            raise RuntimeError("no runner")
        return self._runner.alg.act(obs)