import os
import pathlib
import sys

# Headless rendering: use OSMesa on Linux servers (must be set before mujoco import)
if sys.platform == "linux" and "DISPLAY" not in os.environ:
    os.environ.setdefault("MUJOCO_GL", "osmesa")

import hydra
import hydra.utils as hydra_utils
import structlog
from clearml import Task
from hydra.core.hydra_config import HydraConfig
from omegaconf import DictConfig, OmegaConf

from src.core.env import BaseEnv, BaseEnvConfig, ActuatorConfig
from src.core.runner import BaseRunner
from src.envs.cartpole import CartPoleEnv, CartPoleConfig
from src.envs.rotary_cartpole import RotaryCartPoleEnv, RotaryCartPoleConfig
from src.training.trainer import Trainer, TrainerConfig

logger = structlog.get_logger()

# ── env registry ──────────────────────────────────────────────────────
# Maps Hydra config-group name → (EnvClass, ConfigClass)
ENV_REGISTRY: dict[str, tuple[type[BaseEnv], type[BaseEnvConfig]]] = {
    "cartpole": (CartPoleEnv, CartPoleConfig),
    "rotary_cartpole": (RotaryCartPoleEnv, RotaryCartPoleConfig),
}


def _build_env(env_name: str, cfg: DictConfig) -> BaseEnv:
    """Instantiate the right env + config from the Hydra config-group name."""
    if env_name not in ENV_REGISTRY:
        raise ValueError(f"Unknown env '{env_name}'. Registered: {list(ENV_REGISTRY)}")

    env_cls, config_cls = ENV_REGISTRY[env_name]
    env_dict = OmegaConf.to_container(cfg.env, resolve=True)

    # Convert actuator dicts → ActuatorConfig objects
    if "actuators" in env_dict:
        for a in env_dict["actuators"]:
            if "ctrl_range" in a:
                a["ctrl_range"] = tuple(a["ctrl_range"])
        env_dict["actuators"] = [ActuatorConfig(**a) for a in env_dict["actuators"]]

    return env_cls(config_cls(**env_dict))


# ── runner registry ───────────────────────────────────────────────────
# Maps Hydra config-group name → (RunnerClass, ConfigClass)
# Imports are deferred so JAX is only loaded when runner=mjx is chosen.

RUNNER_REGISTRY: dict[str, tuple[str, str, str]] = {
    "mujoco": ("src.runners.mujoco", "MuJoCoRunner", "MuJoCoRunnerConfig"),
    "mjx":    ("src.runners.mjx",    "MJXRunner",    "MJXRunnerConfig"),
}


def _build_runner(runner_name: str, env: BaseEnv, cfg: DictConfig) -> BaseRunner:
    """Instantiate the right runner from the Hydra config-group name."""
    if runner_name not in RUNNER_REGISTRY:
        raise ValueError(
            f"Unknown runner '{runner_name}'. Registered: {list(RUNNER_REGISTRY)}"
        )
    module_path, cls_name, cfg_cls_name = RUNNER_REGISTRY[runner_name]

    import importlib
    mod = importlib.import_module(module_path)
    runner_cls = getattr(mod, cls_name)
    config_cls = getattr(mod, cfg_cls_name)

    runner_config = config_cls(**OmegaConf.to_container(cfg.runner, resolve=True))
    return runner_cls(env=env, config=runner_config)


def _init_clearml(choices: dict[str, str], remote: bool = False) -> Task:
    """Initialize ClearML task with project structure and tags.

    Project: RL-Trainings/<EnvName>  (e.g. RL-Trainings/Rotary Cartpole)
    Tags:    env, runner, training algo choices from Hydra.
    """
    Task.ignore_requirements("torch")

    env_name = choices.get("env", "cartpole")
    runner_name = choices.get("runner", "mujoco")
    training_name = choices.get("training", "ppo")

    project = "RL-Framework"
    task_name = f"{env_name}-{runner_name}-{training_name}"
    tags = [env_name, runner_name, training_name]

    task = Task.init(project_name=project, task_name=task_name, tags=tags)
    task.set_base_docker(
        "registry.kube.optimize/worker-image:latest",
        docker_setup_bash_script=(
            "apt-get update && apt-get install -y --no-install-recommends "
            "libosmesa6 libgl1-mesa-glx libglfw3 && rm -rf /var/lib/apt/lists/* "
            "&& pip install 'jax[cuda12]' mujoco-mjx"
        ),
    )

    req_file = pathlib.Path(hydra_utils.get_original_cwd()) / "requirements.txt"
    task.set_packages(str(req_file))

    # Execute remotely if requested and running locally
    if remote and task.running_locally():
        logger.info("executing_task_remotely", queue="gpu-queue")
        task.execute_remotely(queue_name="gpu-queue", exit_process=True)

    return task


@hydra.main(version_base=None, config_path="configs", config_name="config")
def main(cfg: DictConfig) -> None:
    choices = HydraConfig.get().runtime.choices

    # ClearML init — must happen before heavy work so remote execution
    # can take over early.  The remote worker re-runs the full script;
    # execute_remotely() is a no-op on the worker side.
    training_dict = OmegaConf.to_container(cfg.training, resolve=True)
    remote = training_dict.pop("remote", False)
    task = _init_clearml(choices, remote=remote)

    env_name = choices.get("env", "cartpole")
    env = _build_env(env_name, cfg)
    runner = _build_runner(choices.get("runner", "mujoco"), env, cfg)
    trainer_config = TrainerConfig(**training_dict)
    trainer = Trainer(runner=runner, config=trainer_config)

    try:
        trainer.train()
    finally:
        trainer.close()
        task.close()


if __name__ == "__main__":
    main()