"""``CSVDataSet`` loads/saves data from/to a CSV file using an underlying
filesystem (e.g.: local, S3, GCS). It uses pandas to handle the CSV file.
"""
from copy import deepcopy
from pathlib import PurePosixPath
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,
)


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

        >>> cars:
        >>>   type: pandas.CSVDataSet
        >>>   filepath: data/01_raw/company/cars.csv
        >>>   load_args:
        >>>     sep: ","
        >>>     na_values: ["#NA", NA]
        >>>   save_args:
        >>>     index: False
        >>>     date_format: "%Y-%m-%d %H:%M"
        >>>     decimal: .
        >>>
        >>> motorbikes:
        >>>   type: pandas.CSVDataSet
        >>>   filepath: s3://your_bucket/data/02_intermediate/company/motorbikes.csv
        >>>   credentials: dev_s3
        >>>


    Example using Python API:
    ::

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

    """

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

    # pylint: disable=too-many-arguments
    def __init__(
        self,
        filepath: 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 ``CSVDataSet`` pointing to a concrete CSV file
        on a specific filesystem.

        Args:
            filepath: Filepath in POSIX format to a CSV 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 CSV files.
                Here you can find all available arguments:
                https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
                All defaults are preserved.
            save_args: Pandas options for saving CSV files.
                Here you can find all available arguments:
                https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html
                All defaults are preserved, but "index", which is set to False.
            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 `r` when loading
                and to `w` 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 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", "w")
        _fs_open_args_save.setdefault("newline", "")
        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,
            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_csv(fs_file, **self._load_args)

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

        with self._fs.open(save_path, **self._fs_open_args_save) as fs_file:
            data.to_csv(path_or_buf=fs_file, **self._save_args)

        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)
