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

def predicting_displacement_flows(
    data: Union[str, pd.DataFrame, pl.DataFrame, io.BytesIO],
    source_cols: Optional[List[str]] = None,
    target_cols: Optional[List[str]] = None,
    features: Optional[List[str]] = None,
    horizon_periods: int = 3
) -> pl.DataFrame:
    """
    Predicting Displacement Flows
    =============================

    What
    ----
    Estimate future displacement movements between places.

    When
    ----
    Planning responses, anticipating new arrivals.

    Why
    ---
    Better preparedness and allocation.

    Parameters
    ----------
    data : str | pd.DataFrame | pl.DataFrame | io.BytesIO
        Input data with historic displacements and drivers.
    source_cols : list[str] | None
        Optional source location fields.
    target_cols : list[str] | None
        Optional destination location fields.
    features : list[str] | None
        Predictors (e.g., conflict index, hazards, access).
    horizon_periods : int, default 3
        Steps ahead to predict.

    Returns
    -------
    pl.DataFrame
        Placeholder predictions joined to input schema.
    """
    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(horizon_periods).alias("pred_horizon_placeholder"))
