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  """``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:
      ::
  
          >>> 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,
          layer: str = None,
      ) -> None:
          """Creates a new instance of ``CSVDataSet`` pointing to a concrete CSV file
          on a specific filesystem.
  
          Args:
              filepath: Filepath 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.
                  E.g. for ``GCSFileSystem`` class: `{"project": "my-project", ...}`.
              layer: The data layer according to the data engineering convention:
                  https://kedro.readthedocs.io/en/stable/06_resources/01_faq.html#what-is-data-engineering-convention
          """
          _fs_args = deepcopy(fs_args) or {}
          _credentials = deepcopy(credentials) or {}
  
          protocol, path = get_protocol_and_path(filepath, version)
  
          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,
          )
  
          self._layer = layer
  
          # 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)
  
      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,
              layer=self._layer,
          )
  
      def _load(self) -> pd.DataFrame:
          load_path = get_filepath_str(self._get_load_path(), self._protocol)
  
          with self._fs.open(load_path, mode="r") 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, mode="w") 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:
          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)
  