"""``FeatherDataSet`` is a data set used to load and save data to feather files
using an underlying filesystem (e.g.: local, S3, GCS). The underlying functionality
is supported by pandas, so it supports all operations the pandas supports.
"""
from copy import deepcopy
from io import BytesIO
from pathlib import PurePosixPath
from typing import Any, Dict

import fsspec
import pandas as pd

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


class FeatherDataSet(AbstractVersionedDataSet):
    """``FeatherDataSet`` loads and saves data to a feather file using an
    underlying filesystem (e.g.: local, S3, GCS). The underlying functionality
    is supported by pandas, so it supports all allowed pandas options
    for loading and saving csv files.

    Example:
    ::

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

    """

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

    # pylint: disable=too-many-arguments
    def __init__(
        self,
        filepath: str,
        load_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 ``FeatherDataSet`` pointing to a concrete
        filepath.

        Args:
            filepath: Filepath in POSIX format to a feather 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.
            load_args: Pandas options for loading feather files.
                Here you can find all available arguments:
                https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_feather.html
                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 to `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,
        )

        # Handle default load argument
        self._load_args = deepcopy(self.DEFAULT_LOAD_ARGS)
        if load_args is not None:
            self._load_args.update(load_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,
            protocol=self._protocol,
            load_args=self._load_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:
            return pd.read_feather(fs_file, **self._load_args)

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

        buf = BytesIO()
        data.to_feather(buf)

        with self._fs.open(save_path, **self._fs_open_args_save) as fs_file:
            fs_file.write(buf.getvalue())

        self._invalidate_cache()

    def _exists(self) -> bool:
        load_path = get_filepath_str(self._get_load_path(), self._protocol)

        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)
