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"""``PickleLocalDataSet`` loads and saves a Python object to a
local pickle file. The underlying functionality is
supported by the ``pickle`` and ``joblib`` libraries, so it supports
all allowed options for loading and saving pickle files.
"""

import copy
import pickle
from pathlib import Path
from typing import Any, Dict

from kedro.io.core import AbstractVersionedDataSet, DataSetError, Version

try:
    import joblib
except ImportError:
    joblib = None


class PickleLocalDataSet(AbstractVersionedDataSet):
    """``PickleLocalDataSet`` loads and saves a Python object to a
    local pickle file. The underlying functionality is
    supported by the pickle and joblib libraries, 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 = PickleLocalDataSet(filepath="data.pkl",
        >>>                               backend='pickle',
        >>>                               load_args=None,
        >>>                               save_args=None)
        >>> data_set.save(dummy_data)
        >>> reloaded = data_set.load()
    """

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

    BACKENDS = {"pickle": pickle, "joblib": joblib}

    # pylint: disable=too-many-arguments
    def __init__(
        self,
        filepath: str,
        backend: str = "pickle",
        load_args: Dict[str, Any] = None,
        save_args: Dict[str, Any] = None,
        version: Version = None,
    ) -> None:
        """Creates a new instance of ``PickleLocalDataSet`` pointing to a
        concrete filepath. ``PickleLocalDataSet`` can use two backends to
        serialise objects to disk:

        pickle.dump: https://docs.python.org/3/library/pickle.html#pickle.dump

        joblib.dump: https://pythonhosted.org/joblib/generated/joblib.dump.html

        and it can use two backends to load serialised objects into memory:

        pickle.load: https://docs.python.org/3/library/pickle.html#pickle.load

        joblib.load: https://pythonhosted.org/joblib/generated/joblib.load.html

        Joblib tends to exhibit better performance in case objects store NumPy
        arrays:
        http://gael-varoquaux.info/programming/new_low-overhead_persistence_in_joblib_for_big_data.html.

        Args:
            filepath: path to a pkl file.
            backend: backend to use, must be one of ['pickle', 'joblib'].
            load_args: Options for loading pickle files. Refer to the help
                file of ``pickle.load`` or ``joblib.load`` for options.
            save_args: Options for saving pickle files. Refer to the help
                file of ``pickle.dump`` or ``joblib.dump`` 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.

        Raises:
            ValueError: If 'backend' is not one of ['pickle', 'joblib'].
            ImportError: If 'backend' could not be imported.

        """
        super().__init__(Path(filepath), version)

        if backend not in ["pickle", "joblib"]:
            raise ValueError(
                "backend should be one of ['pickle', 'joblib'], got %s" % backend
            )
        if backend == "joblib" and joblib is None:
            raise ImportError(
                "selected backend 'joblib' could not be "
                "imported. Make sure it is installed."
            )

        self._backend = backend

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

    def _load(self) -> Any:
        load_path = Path(self._get_load_path())
        with load_path.open("rb") as local_file:
            result = self.BACKENDS[self._backend].load(local_file, **self._load_args)
        return result

    def _save(self, data: Any) -> None:
        save_path = Path(self._get_save_path())
        save_path.parent.mkdir(parents=True, exist_ok=True)

        with save_path.open("wb") as local_file:
            self.BACKENDS[self._backend].dump(data, local_file, **self._save_args)

        load_path = Path(self._get_load_path())
        self._check_paths_consistency(load_path.absolute(), save_path.absolute())

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

    def _exists(self) -> bool:
        try:
            path = self._get_load_path()
        except DataSetError:
            return False
        return Path(path).is_file()
