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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 threading import Lock
  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.extras.datasets.pandas 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)
  
      """
  
      # _lock is a class attribute that will be shared across all the instances.
      # It is used to make dataset safe for threads.
      _lock = Lock()
      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 in POSIX format 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 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 `wb` when saving.
          """
          _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)
          if protocol == "file":
              _fs_args.setdefault("auto_mkdir", True)
  
          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,
          )
  
          self._key = key
  
          # 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)
  
          _fs_open_args_save.setdefault("mode", "wb")
          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 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, **self._fs_open_args_load) as fs_file:
              binary_data = fs_file.read()
  
          with HDFDataSet._lock:
              # 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 HDFDataSet._lock:
              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, **self._fs_open_args_save) 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:
          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)
  