"""Illustrative study/content-grouped split, not an official benchmark protocol."""
from __future__ import annotations

import argparse
import hashlib
from pathlib import Path

import pandas as pd

from hesrt_io import RELEASE_TAG, load_catalog


def assign_groups(frame: pd.DataFrame, seed: str) -> pd.DataFrame:
    """Keep each GSE and any exact image/expression duplicates in one component."""
    frame = frame.reset_index(drop=True)
    parent = list(range(len(frame)))
    def root(i: int) -> int:
        while parent[i] != i:
            parent[i] = parent[parent[i]]
            i = parent[i]
        return i
    for column in ["gse_id", "image_sha256", "expression_sha256"]:
        first: dict[str, int] = {}
        for i, value in enumerate(frame[column]):
            if pd.isna(value) or not str(value).strip():
                continue
            value = str(value)
            if value in first:
                parent[root(i)] = root(first[value])
            else:
                first[value] = i
    labels: dict[int, str] = {}
    for i, gsm in enumerate(frame.gsm_id):
        r = root(i)
        labels[r] = min(labels.get(r, str(gsm)), str(gsm))
    result = frame[["gse_id", "gsm_id"]].copy()
    result["leakage_group"] = [labels[root(i)] for i in range(len(frame))]
    def split(group: str) -> str:
        fraction = int(hashlib.sha256((seed + ":" + group).encode()).hexdigest()[:16], 16) / 2**64
        return "train" if fraction < 0.8 else "validation" if fraction < 0.9 else "test"
    result["split"] = result.leakage_group.map(split)
    return result


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--revision", default=RELEASE_TAG)
    parser.add_argument("--seed", default="hesrt-example-0")
    parser.add_argument("--output", type=Path, default=Path("study_split.csv"))
    args = parser.parse_args()
    frame, revision = load_catalog(args.revision)
    result = assign_groups(frame, args.seed)
    result["revision"] = revision
    args.output.parent.mkdir(parents=True, exist_ok=True)
    result.to_csv(args.output, index=False)
    print(result.groupby("split").agg(samples=("gsm_id", "size"), studies=("gse_id", "nunique")))
    print("Approximate 80/10/10 group allocation; not stratified and not donor-disjoint certification.")
    print("Resolve shared donors, near-duplicates, cohort overlap and task eligibility separately.")


if __name__ == "__main__":
    main()