import polars as pl
import pandas as pd
from typing import Union, List, Optional
import io

def inequality_measures(
    data: Union[str, pd.DataFrame, pl.DataFrame, io.BytesIO],
    value_column: str,
    group_by: Optional[List[str]] = None,
    measures: List[str] = ["gini","theil"]
) -> pl.DataFrame:
    """
    Inequality Measures (Gini, Theil)
    =================================

    What
    ----
    Quantify inequality in distributions (e.g., assistance, income).

    When
    ----
    Equity analysis across provinces, districts, or groups.

    Why
    ---
    Identify disparities to guide targeting and policy.

    Parameters
    ----------
    data : str | pd.DataFrame | pl.DataFrame | io.BytesIO
        Input dataset.
    value_column : str
        Numeric column to measure inequality on.
    group_by : list[str] | None
        Optional grouping (e.g., by sector).
    measures : list[str], default ["gini","theil"]
        Which measures to compute.

    Returns
    -------
    pl.DataFrame
        Placeholder results per measure.

    Example
    -------
    >>> inequality_measures(df, value_column="coverage", group_by=["province"]) 
    """
    if isinstance(data, str):
        df = pl.read_csv(data)
    elif isinstance(data, io.BytesIO):
        df = pl.read_csv(data)
    elif isinstance(data, pd.DataFrame):
        df = pl.from_pandas(data)
    elif isinstance(data, pl.DataFrame):
        df = data
    else:
        raise TypeError("Unsupported data type")

    return pl.DataFrame({"measure": measures, "value": [0.0]*len(measures)})
