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from typing import Optional, Tuple, Union

from pandas import DataFrame

from superset.utils.pandas_postprocessing.utils import validate_column_args


@validate_column_args("groupby_columns")
def resample(  # pylint: disable=too-many-arguments
    df: DataFrame,
    rule: str,
    method: str,
    time_column: str,
    groupby_columns: Optional[Tuple[Optional[str], ...]] = None,
    fill_value: Optional[Union[float, int]] = None,
) -> DataFrame:
    """
    support upsampling in resample

    :param df: DataFrame to resample.
    :param rule: The offset string representing target conversion.
    :param method: How to fill the NaN value after resample.
    :param time_column: existing columns in DataFrame.
    :param groupby_columns: columns except time_column in dataframe
    :param fill_value: What values do fill missing.
    :return: DataFrame after resample
    :raises QueryObjectValidationError: If the request in incorrect
    """

    def _upsampling(_df: DataFrame) -> DataFrame:
        _df = _df.set_index(time_column)
        if method == "asfreq" and fill_value is not None:
            return _df.resample(rule).asfreq(fill_value=fill_value)
        return getattr(_df.resample(rule), method)()

    if groupby_columns:
        df = (
            df.set_index(keys=list(groupby_columns))
            .groupby(by=list(groupby_columns))
            .apply(_upsampling)
        )
        df = df.reset_index().set_index(time_column).sort_index()
    else:
        df = _upsampling(df)
    return df.reset_index()