"""``MatlabDataset`` loads/saves data from/to a Matlab file using an underlying
filesystem ?(e.g.: local, S3, GCS)?. The underlying functionality is supported by
the specified backend library passed in (defaults to the ``matlab`` library), so it
supports all allowed options for loading and saving matlab files.
"""

from copy import deepcopy
from pathlib import PurePosixPath
from typing import Any

import fsspec
import numpy as np
from kedro.io.core import (
    AbstractVersionedDataset,
    DatasetError,
    Version,
    get_filepath_str,
    get_protocol_and_path,
)
from scipy import io


class MatlabDataset(AbstractVersionedDataset[np.ndarray, np.ndarray]):
    """`MatlabDataSet` loads and saves data from/to a MATLAB file using scipy.io.

    Example usage for the
    `YAML API <https://kedro.readthedocs.io/en/stable/data/\
    data_catalog_yaml_examples.html>`_:

    .. code-block:: yaml

        cars:
          type: matlab.MatlabDataset
          filepath: gcs://your_bucket/cars.mat
          fs_args:
            project: my-project
          credentials: my_gcp_credentials

    Example usage for the
    `Python API <https://kedro.readthedocs.io/en/stable/data/\
    advanced_data_catalog_usage.html>`_:

    .. code-block:: pycon

        >>> from kedro_datasets.matlab import MatlabDataset
        >>> import numpy as np
        >>> data = np.array([1, 2, 3])
        >>> dataset = MatlabDataset(filepath=tmp_path / "test.mat")
        >>> dataset.save(data)
        >>> reloaded = dataset.load()
        >>> assert (data == reloaded["data"]).all()

    """

    DEFAULT_SAVE_ARGS: dict[str, Any] = {"indent": 2}

    def __init__(  # noqa = PLR0913
        self,
        filepath: str,
        save_args: dict[str, Any] = None,
        version: Version = None,
        credentials: dict[str, Any] = None,
        fs_args: dict[str, Any] = None,
        metadata: dict[str, Any] = None,
    ) -> None:
        """Creates a new instance of MatlabDataSet to load and save data from/to a MATLAB file.

        Args:
            filepath: Filepath in POSIX format to a Matlab file prefixed with a protocol like `s3://`.
                If prefix is not provided, `file` protocol (local filesystem) will be used.
                The prefix should be any protocol supported by ``fsspec``.
                Note: `http(s)` doesn't support versioning.
            save_args: .mat options for saving .mat files.
            version: If specified, should be an instance of
                ``kedro.io.core.Version``. If its ``load`` attribute is
                None, the latest version will be loaded. If its ``save``
                attribute is None, save version will be autogenerated.
            credentials: Credentials required to get access to the underlying filesystem.
                E.g. for ``GCSFileSystem`` it should look like `{"token": None}`.
            fs_args: Extra arguments to pass into underlying filesystem class constructor
                (e.g. `{"project": "my-project"}` for ``GCSFileSystem``), as well as
                to pass to the filesystem's `open` method through nested keys
                `open_args_load` and `open_args_save`.
                Here you can find all available arguments for `open`:
                https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.open
                All defaults are preserved, except `mode`, which is set to `r` when loading
                and to `w` when saving.
            metadata: Any arbitrary metadata.
                This is ignored by Kedro, but may be consumed by users or external plugins.
        """
        _fs_args = deepcopy(fs_args) or {}
        _fs_open_args_load = _fs_args.pop("open_args_load", {})
        _fs_open_args_save = _fs_args.pop("open_args_save", {})
        _credentials = deepcopy(credentials) or {}

        protocol, path = get_protocol_and_path(filepath, version)
        self._protocol = protocol
        if protocol == "file":
            _fs_args.setdefault("auto_mkdir", True)
        self._fs = fsspec.filesystem(self._protocol, **_credentials, **_fs_args)
        self.metadata = metadata

        super().__init__(
            filepath=PurePosixPath(path),
            version=version,
            exists_function=self._fs.exists,
            glob_function=self._fs.glob,
        )
        # Handle default save arguments
        self._save_args = deepcopy(self.DEFAULT_SAVE_ARGS)
        if save_args is not None:
            self._save_args.update(save_args)

        _fs_open_args_save.setdefault("mode", "w")
        self._fs_open_args_load = _fs_open_args_load
        self._fs_open_args_save = _fs_open_args_save

    def _describe(self) -> dict[str, Any]:
        return {
            "filepath": self._filepath,
            "protocol": self._protocol,
            "save_args": self._save_args,
            "version": self._version,
        }

    def _load(self) -> np.ndarray:
        """
        Access the specific variable in the .mat file, e.g, data['variable_name']
        """
        load_path = get_filepath_str(self._get_load_path(), self._protocol)
        with self._fs.open(load_path) as f:
            data = io.loadmat(f)
            return data

    def _save(self, data: np.ndarray) -> None:
        save_path = get_filepath_str(self._get_save_path(), self._protocol)
        with self._fs.open(save_path, mode="wb") as f:
            io.savemat(f, {"data": data})
        self._invalidate_cache()

    def _exists(self) -> bool:
        try:
            load_path = get_filepath_str(self._get_load_path(), self._protocol)
        except DatasetError:
            return False

        return self._fs.exists(load_path)

    def _release(self) -> None:
        super()._release()
        self._invalidate_cache()

    def _invalidate_cache(self) -> None:
        """Invalidate underlying filesystem caches."""
        filepath = get_filepath_str(self._filepath, self._protocol)
        self._fs.invalidate_cache(filepath)
