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

def livelihood_resilience_analysis(
    data: Union[str, pd.DataFrame, pl.DataFrame, io.BytesIO],
    asset_columns: Optional[List[str]] = None,
    shock_columns: Optional[List[str]] = None
) -> pl.DataFrame:
    """
    Livelihood Resilience Analysis
    ==============================

    What
    ----
    Summarize household resilience based on assets and shocks.

    When
    ----
    Early recovery and resilience programming.

    Why
    ---
    Identify vulnerable households/areas.

    Parameters
    ----------
    data : str | pd.DataFrame | pl.DataFrame | io.BytesIO
        Input dataset.
    asset_columns : list[str] | None
        Asset indicators.
    shock_columns : list[str] | None
        Shock exposure indicators.

    Returns
    -------
    pl.DataFrame
        Placeholder resilience score.
    """
    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 df.with_columns(pl.lit(0.0).alias("resilience_score_placeholder"))
