"""Build script for mteb/SMESumRetrieval.

This dataset is the MTEB-ready (retrieval) version of the SMESum dataset, used by
the `SMESumRetrieval` task in https://github.com/embeddings-benchmark/mteb.

Source: NaiveNeuron/SMESum (columns: title, introduction, document)
  https://huggingface.co/datasets/NaiveNeuron/SMESum
Transform: reformulate summarization into retrieval -- take the first `n_sample`
  rows of the "test" split, use `introduction` as the query, `title`+`document` as
  the corpus document, and pair each query 1:1 with its source document as the
  qrel. Produces the three-config (corpus/queries/qrels) layout MTEB's
  AbsTaskRetrieval expects.

If the source dataset publishes a new revision, regenerate this dataset with:
    python build_dataset.py --source-revision <new-sha> --token $HF_TOKEN

then update the `SMESumRetrieval` task's `dataset.path`/`revision` in mteb to the
new revision this script prints on push.
"""

from __future__ import annotations

import argparse

from datasets import Dataset, DatasetDict, load_dataset
from huggingface_hub import HfApi

SOURCE_REPO = "NaiveNeuron/SMESum"
SOURCE_REVISION = "c5a6521a4ddce3450fb04ba218623681a9189c6d"
TARGET_REPO = "mteb/SMESumRetrieval"
N_SAMPLE = 600


def build(
    source_revision: str = SOURCE_REVISION, n_sample: int = N_SAMPLE
) -> dict[str, DatasetDict]:
    corpus, queries, qrels = {}, {}, {}
    for split in ["test"]:
        split_ds = load_dataset(
            SOURCE_REPO, revision=source_revision, split=f"{split}[:{n_sample}]"
        )
        ids = [f"d{e + 1}" for e in range(len(split_ds))]
        query_ids = [f"q{e + 1}" for e in range(len(split_ds))]

        queries[split] = Dataset.from_dict(
            {"id": query_ids, "text": split_ds["introduction"]}
        )
        corpus[split] = Dataset.from_dict(
            {"id": ids, "title": split_ds["title"], "text": split_ds["document"]}
        )
        qrels[split] = Dataset.from_dict(
            {"query-id": query_ids, "corpus-id": ids, "score": [1] * len(split_ds)}
        )

    return {
        "corpus": DatasetDict(corpus),
        "queries": DatasetDict(queries),
        "qrels": DatasetDict(qrels),
    }


def main() -> None:
    p = argparse.ArgumentParser(description=__doc__)
    p.add_argument("--source-revision", default=SOURCE_REVISION)
    p.add_argument("--n-sample", type=int, default=N_SAMPLE)
    p.add_argument("--repo", default=TARGET_REPO)
    p.add_argument("--token", default=None, help="HF token (or use HF_TOKEN env / `huggingface-cli login`).")
    p.add_argument("--private", action="store_true")
    p.add_argument("--dry-run", action="store_true")
    args = p.parse_args()

    result = build(args.source_revision, args.n_sample)
    for config_name, dataset_dict in result.items():
        for split, d in dataset_dict.items():
            print(f"  config={config_name} split={split} rows={len(d)} columns={d.column_names}")

    if args.dry_run:
        print(f"[dry-run] would push configs {list(result)} to {args.repo}")
        return

    for config_name, dataset_dict in result.items():
        dataset_dict.push_to_hub(
            args.repo,
            config_name,
            commit_message=f"Rebuild {config_name} from source revision",
            token=args.token,
            private=args.private,
        )
    revision = HfApi(token=args.token).dataset_info(args.repo).sha
    print(f"-> pushed to {args.repo}, revision={revision}")


if __name__ == "__main__":
    main()