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

def mortality_morbidity_analysis(
    data: Union[str, pd.DataFrame, pl.DataFrame, io.BytesIO],
    death_col: Optional[str] = None,
    illness_col: Optional[str] = None,
    population_col: Optional[str] = None,
    per: int = 10000
) -> pl.DataFrame:
    """
    Mortality/Morbidity Analysis
    ============================

    What
    ----
    Compute death/illness rates per population.

    When
    ----
    Health situation monitoring and comparisons.

    Why
    ---
    Standardized rates are comparable across locations.

    Parameters
    ----------
    data : str | pd.DataFrame | pl.DataFrame | io.BytesIO
        Input dataset.
    death_col : str | None
        Death counts column.
    illness_col : str | None
        Illness/case counts column.
    population_col : str | None
        Population denominator.
    per : int, default 10000
        Rate per this many people.

    Returns
    -------
    pl.DataFrame
        Original data with placeholder rate columns.
    """
    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("mortality_rate_placeholder"),
        pl.lit(0.0).alias("morbidity_rate_placeholder"),
    ])
