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  """``PickleS3DataSet`` loads and saves a Python object to a pickle file on S3.
  The underlying functionality is supported by the ``pickle`` library, so
  it supports all allowed options for loading and saving pickle files.
  """
  import pickle
  from copy import deepcopy
  from functools import partial
  from pathlib import PurePosixPath
  from typing import Any, Dict, Optional
  
  from s3fs.core import S3FileSystem
  
  from kedro.io.core import AbstractVersionedDataSet, DataSetError, Version
  
  
  class PickleS3DataSet(AbstractVersionedDataSet):
      """``PickleS3DataSet`` loads and saves a Python object to a
          pickle file on S3. The underlying functionality is
          supported by the pickle library, so it supports all
          allowed options for loading and saving pickle files.
  
          Example:
          ::
  
              >>> from kedro.io import PickleLocalDataSet
              >>> import pandas as pd
              >>>
              >>> dummy_data =  pd.DataFrame({'col1': [1, 2],
              >>>                             'col2': [4, 5],
              >>>                             'col3': [5, 6]})
              >>> data_set = PickleS3DataSet(filepath="data.pkl",
              >>>                            bucket_name="test_bucket",
              >>>                            load_args=None,
              >>>                            save_args=None)
              >>> data_set.save(dummy_data)
              >>> reloaded = data_set.load()
      """
  
      # pylint: disable=too-many-arguments
      def __init__(
          self,
          filepath: str,
          bucket_name: str,
          credentials: Optional[Dict[str, Any]] = None,
          load_args: Optional[Dict[str, Any]] = None,
          save_args: Optional[Dict[str, Any]] = None,
          version: Version = None,
      ) -> None:
          """Creates a new instance of ``PickleS3DataSet`` pointing to a
          concrete file on S3. ``PickleS3DataSet`` uses pickle backend to
          serialise objects to disk:
  
          pickle.dumps: https://docs.python.org/3/library/pickle.html#pickle.dumps
  
          and to load serialised objects into memory:
  
          pickle.loads: https://docs.python.org/3/library/pickle.html#pickle.loads
  
          Args:
              filepath: path to a pkl file.
              bucket_name: S3 bucket name.
              credentials: Credentials to access the S3 bucket, such as
                  ``aws_access_key_id``, ``aws_secret_access_key``.
              load_args: Options for loading pickle files. Refer to the help
                  file of ``pickle.loads`` for options.
              save_args: Options for saving pickle files. Refer to the help
                  file of ``pickle.dumps`` for options.
              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 = deepcopy(credentials) or {}
          _s3 = S3FileSystem(client_kwargs=_credentials)
          super().__init__(
              PurePosixPath("{}/{}".format(bucket_name, filepath)),
              version,
              exists_function=_s3.exists,
              glob_function=partial(_s3.glob, refresh=True),
          )
  
          default_load_args = {}  # type: Dict[str, Any]
          default_save_args = {}  # type: Dict[str, Any]
  
          self._bucket_name = bucket_name
          self._credentials = _credentials
          self._load_args = (
              {**default_load_args, **load_args}
              if load_args is not None
              else default_load_args
          )
          self._save_args = (
              {**default_save_args, **save_args}
              if save_args is not None
              else default_save_args
          )
          self._s3 = _s3
  
      def _describe(self) -> Dict[str, Any]:
          return dict(
              filepath=self._filepath,
              bucket_name=self._bucket_name,
              load_args=self._load_args,
              save_args=self._save_args,
              version=self._version,
          )
  
      def _load(self) -> Any:
          load_path = PurePosixPath(self._get_load_path())
  
          with self._s3.open(str(load_path), mode="rb") as s3_file:
              return pickle.loads(s3_file.read(), **self._load_args)
  
      def _save(self, data: Any) -> None:
          save_path = PurePosixPath(self._get_save_path())
          bytes_object = pickle.dumps(data, **self._save_args)
  
          with self._s3.open(str(save_path), mode="wb") as s3_file:
              s3_file.write(bytes_object)
  
          load_path = PurePosixPath(self._get_load_path())
          self._check_paths_consistency(load_path, save_path)
  
      def _exists(self) -> bool:
          try:
              load_path = self._get_load_path()
          except DataSetError:
              return False
          return self._s3.isfile(str(PurePosixPath(load_path)))
  