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


def livelihood_coping_strategies(
    data: Union[str, pd.DataFrame, pl.DataFrame, io.BytesIO],
    strategy_cols: List[str],
    group_by: Optional[List[str]] = None
) -> pl.DataFrame:
    """
    Livelihood Coping Strategies
    ============================

    What
    ----
    Summarize frequency of livelihood coping strategies.

    When
    ----
    Food security and resilience monitoring.

    Why
    ---
    Track reliance on stress/crisis/emergency strategies.

    Parameters
    ----------
    data : str | pd.DataFrame | pl.DataFrame | io.BytesIO
        Input dataset.
    strategy_cols : list[str]
        Columns indicating frequency/occurrence of strategies.
    group_by : list[str] | None
        Optional grouping.

    Returns
    -------
    pl.DataFrame
        Placeholder sum across strategies.
    """
    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")

    exprs = [pl.col(c).cast(pl.Float64) for c in strategy_cols]
    total = exprs[0] if exprs else pl.lit(0.0)
    for e in exprs[1:]:
        total = total + e
    return df.with_columns(total.alias("livelihood_coping_total_placeholder"))
