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"""``HDFDataSet`` loads/saves data from/to a hdf file using an underlying
filesystem (e.g.: local, S3, GCS). It uses pandas.HDFStore to handle the hdf file.
"""
from copy import deepcopy
from pathlib import PurePosixPath
from typing import Any, Dict

import fsspec
import pandas as pd

from kedro.io.core import (
    AbstractVersionedDataSet,
    DataSetError,
    Version,
    get_filepath_str,
    get_protocol_and_path,
)

HDFSTORE_DRIVER = "H5FD_CORE"


class HDFDataSet(AbstractVersionedDataSet):
    """``HDFDataSet`` loads/saves data from/to a hdf file using an underlying
    filesystem (e.g. local, S3, GCS). It uses pandas.HDFStore to handle the hdf file.

    Example:
    ::

        >>> from kedro.io.hdf_dataset import HDFDataSet
        >>> import pandas as pd
        >>>
        >>> data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5],
        >>>                      'col3': [5, 6]})
        >>>
        >>> # data_set = HDFDataSet(filepath="gcs://bucket/test.hdf", key='data')
        >>> data_set = HDFDataSet(filepath="test.h5", key='data')
        >>> data_set.save(data)
        >>> reloaded = data_set.load()
        >>> assert data.equals(reloaded)

    """

    DEFAULT_LOAD_ARGS = {}  # type: Dict[str, Any]
    DEFAULT_SAVE_ARGS = {}  # type: Dict[str, Any]

    # pylint: disable=too-many-arguments
    def __init__(
        self,
        filepath: str,
        key: str,
        load_args: Dict[str, Any] = None,
        save_args: Dict[str, Any] = None,
        version: Version = None,
        credentials: Dict[str, Any] = None,
        fs_args: Dict[str, Any] = None,
    ) -> None:
        """Creates a new instance of ``HDFDataSet`` pointing to a concrete hdf file
        on a specific filesystem.

        Args:
            filepath: Filepath to a hdf 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.
            key: Identifier to the group in the HDF store.
            load_args: PyTables options for loading hdf files.
                You can find all available arguments at:
                https://www.pytables.org/usersguide/libref/top_level.html#tables.open_file
                All defaults are preserved.
            save_args: PyTables options for saving hdf files.
                You can find all available arguments at:
                https://www.pytables.org/usersguide/libref/top_level.html#tables.open_file
                All defaults are preserved.
            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.
                E.g. for ``GCSFileSystem`` class: `{"project": "my-project", ...}`
        """
        _fs_args = deepcopy(fs_args) or {}
        _credentials = deepcopy(credentials) or {}
        self._key = key

        protocol, path = get_protocol_and_path(filepath, version)

        self._protocol = protocol
        self._fs = fsspec.filesystem(self._protocol, **_credentials, **_fs_args)

        super().__init__(
            filepath=PurePosixPath(path),
            version=version,
            exists_function=self._fs.exists,
            glob_function=self._fs.glob,
        )

        # Handle default load and save arguments
        self._load_args = deepcopy(self.DEFAULT_LOAD_ARGS)
        if load_args is not None:
            self._load_args.update(load_args)
        self._save_args = deepcopy(self.DEFAULT_SAVE_ARGS)
        if save_args is not None:
            self._save_args.update(save_args)

    def _describe(self) -> Dict[str, Any]:
        return dict(
            filepath=self._filepath,
            key=self._key,
            protocol=self._protocol,
            load_args=self._load_args,
            save_args=self._save_args,
            version=self._version,
        )

    def _load(self) -> pd.DataFrame:
        load_path = get_filepath_str(self._get_load_path(), self._protocol)

        with self._fs.open(load_path, mode="rb") as fs_file:
            binary_data = fs_file.read()

        # Set driver_core_backing_store to False to disable saving
        # contents of the in-memory h5file to disk
        with pd.HDFStore(
            "in-memory-load-file",
            mode="r",
            driver=HDFSTORE_DRIVER,
            driver_core_backing_store=0,
            driver_core_image=binary_data,
            **self._load_args,
        ) as store:
            return store[self._key]

    def _save(self, data: pd.DataFrame) -> None:
        save_path = get_filepath_str(self._get_save_path(), self._protocol)

        with pd.HDFStore(
            "in-memory-save-file",
            mode="w",
            driver=HDFSTORE_DRIVER,
            driver_core_backing_store=0,
            **self._save_args,
        ) as store:
            store.put(self._key, data, format="table")
            # pylint: disable=protected-access
            binary_data = store._handle.get_file_image()

        with self._fs.open(save_path, mode="wb") as fs_file:
            fs_file.write(binary_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:
        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)

pandas.HDFStore