import importlib

import gymnasium as gym
from omegaconf import DictConfig


def make_env(cfg: DictConfig, n_envs: int | None = None) -> gym.vector.VectorEnv:
    """Makes a gym vector environment according to the evaluation config.

    n_envs can be used to override eval.batch_size in the configuration. Must be at least 1.
    """
    if n_envs is not None and n_envs < 1:
        raise ValueError("`n_envs must be at least 1")

    kwargs = {
        "obs_type": "pixels_agent_pos",
        "render_mode": "rgb_array",
        "max_episode_steps": cfg.env.episode_length,
        "visualization_width": 384,
        "visualization_height": 384,
    }

    package_name = f"gym_{cfg.env.name}"

    try:
        importlib.import_module(package_name)
    except ModuleNotFoundError as e:
        print(
            f"{package_name} is not installed. Please install it with `pip install 'lerobot[{cfg.env.name}]'`"
        )
        raise e

    gym_handle = f"{package_name}/{cfg.env.task}"

    # batched version of the env that returns an observation of shape (b, c)
    env_cls = gym.vector.AsyncVectorEnv if cfg.eval.use_async_envs else gym.vector.SyncVectorEnv
    env = env_cls(
        [
            lambda: gym.make(gym_handle, disable_env_checker=True, **kwargs)
            for _ in range(n_envs if n_envs is not None else cfg.eval.batch_size)
        ]
    )

    return env