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

import fsspec
import pandas as pd

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


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

    Example adding a catalog entry with
    `YAML API <https://kedro.readthedocs.io/en/stable/05_data/\
        01_data_catalog.html#using-the-data-catalog-with-the-yaml-api>`_:

    .. code-block:: yaml

        >>> rockets:
        >>>   type: pandas.ExcelDataSet
        >>>   filepath: gcs://your_bucket/rockets.xlsx
        >>>   fs_args:
        >>>     project: my-project
        >>>   credentials: my_gcp_credentials
        >>>   save_args:
        >>>     sheet_name: Sheet1
        >>>   load_args:
        >>>     sheet_name: Sheet1
        >>>
        >>> shuttles:
        >>>   type: pandas.ExcelDataSet
        >>>   filepath: data/01_raw/shuttles.xlsx

    Example using Python API:
    ::

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

    """

    DEFAULT_LOAD_ARGS = {"engine": "xlrd"}
    DEFAULT_SAVE_ARGS = {"index": False}

    # pylint: disable=too-many-arguments
    def __init__(
        self,
        filepath: str,
        engine: str = "xlsxwriter",
        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 ``ExcelDataSet`` pointing to a concrete Excel file
        on a specific filesystem.

        Args:
            filepath: Filepath in POSIX format to a Excel 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.
            engine: The engine used to write to excel files. The default
                engine is 'xlsxwriter'.
            load_args: Pandas options for loading Excel files.
                Here you can find all available arguments:
                https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_excel.html
                All defaults are preserved, but "engine", which is set to "xlrd".
                Supports multi-sheet Excel files (include `sheet_name = None` in `load_args`).
            save_args: Pandas options for saving Excel files.
                Here you can find all available arguments:
                https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_excel.html
                All defaults are preserved, but "index", which is set to False.
                If you would like to specify options for the `ExcelWriter`,
                you can include them under the "writer" key. Here you can
                find all available arguments:
                https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.ExcelWriter.html
            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 arguments
        self._load_args = deepcopy(self.DEFAULT_LOAD_ARGS)
        if load_args is not None:
            self._load_args.update(load_args)

        # Handle default save arguments
        self._save_args = deepcopy(self.DEFAULT_SAVE_ARGS)
        if save_args is not None:
            self._save_args.update(save_args)
        self._writer_args = self._save_args.pop("writer", {"engine": engine})

        _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,
            save_args=self._save_args,
            writer_args=self._writer_args,
            version=self._version,
        )

    def _load(self) -> Union[pd.DataFrame, Dict[str, 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_excel(fs_file, **self._load_args)

    def _save(self, data: Union[pd.DataFrame, Dict[str, pd.DataFrame]]) -> None:
        output = BytesIO()
        save_path = get_filepath_str(self._get_save_path(), self._protocol)

        # pylint: disable=abstract-class-instantiated
        with pd.ExcelWriter(output, **self._writer_args) as writer:
            if isinstance(data, dict):
                for sheet_name, sheet_data in data.items():
                    sheet_data.to_excel(
                        writer, sheet_name=sheet_name, **self._save_args
                    )
            else:
                data.to_excel(writer, **self._save_args)

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

        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)
