import tempfile
from pathlib import Path
from typing import TYPE_CHECKING, Optional

import lightgbm

from ray.train._internal.framework_checkpoint import FrameworkCheckpoint
from ray.util.annotations import PublicAPI

if TYPE_CHECKING:
    from ray.data.preprocessor import Preprocessor


@PublicAPI(stability="beta")
class LightGBMCheckpoint(FrameworkCheckpoint):
    """A :py:class:`~ray.train.Checkpoint` with LightGBM-specific functionality."""

    MODEL_FILENAME = "model.txt"

    @classmethod
    def from_model(
        cls,
        booster: lightgbm.Booster,
        *,
        preprocessor: Optional["Preprocessor"] = None,
        path: Optional[str] = None,
    ) -> "LightGBMCheckpoint":
        """Create a :py:class:`~ray.train.Checkpoint` that stores a LightGBM model.

        Args:
            booster: The LightGBM model to store in the checkpoint.
            preprocessor: A fitted preprocessor to be applied before inference.
            path: The path to the directory where the checkpoint file will be saved.
                This should start as an empty directory, since the *entire*
                directory will be treated as the checkpoint when reported.
                By default, a temporary directory will be created.

        Returns:
            An :py:class:`LightGBMCheckpoint` containing the specified ``Estimator``.

        Examples:
            .. testcode::

                import lightgbm
                import numpy as np
                from ray.train.lightgbm import LightGBMCheckpoint

                train_X = np.array([[1, 2], [3, 4]])
                train_y = np.array([0, 1])

                model = lightgbm.LGBMClassifier().fit(train_X, train_y)
                checkpoint = LightGBMCheckpoint.from_model(model.booster_)
        """
        checkpoint_path = Path(path or tempfile.mkdtemp())

        if not checkpoint_path.is_dir():
            raise ValueError(f"`path` must be a directory, but got: {checkpoint_path}")

        booster.save_model(checkpoint_path.joinpath(cls.MODEL_FILENAME).as_posix())

        checkpoint = cls.from_directory(checkpoint_path.as_posix())
        if preprocessor:
            checkpoint.set_preprocessor(preprocessor)

        return checkpoint

    def get_model(self) -> lightgbm.Booster:
        """Retrieve the LightGBM model stored in this checkpoint."""
        with self.as_directory() as checkpoint_path:
            return lightgbm.Booster(
                model_file=Path(checkpoint_path, self.MODEL_FILENAME).as_posix()
            )