#!/usr/bin/env python3
"""
Create viktoroo/longbench-pro-128k-plus from caskcsg/LongBench-Pro by:
- filtering to token_length in {"128k", "256k"}
- keeping only fields: id, context
- renaming context -> text
- pushing the filtered dataset to the (already-existing) public repo
- uploading this script and a hardcoded README.md into the same dataset repo

Requirements:
  pip install -U datasets huggingface_hub

Auth:
  export HF_TOKEN=...   (must have write access to viktoroo/longbench-pro-128k-plus)
"""

from __future__ import annotations

import os
import sys
import tempfile
from pathlib import Path

from datasets import load_dataset, DatasetDict
from huggingface_hub import HfApi

from dotenv import load_dotenv
load_dotenv()


SOURCE_DATASET = "caskcsg/LongBench-Pro"
TARGET_REPO = "viktoroo/longbench-pro-128k-plus"  # existing, public
ALLOWED_TOKEN_LENGTH = {"128k", "256k"}  # values in token_length field

README_MD = """---
license: other
language:
- en
- zh
tags:
- long-context
- benchmark
- evaluation
- rag
pretty_name: LongBench Pro 128k+
---

# LongBench Pro 128k+

This dataset is a filtered subset of **LongBench Pro** (`caskcsg/LongBench-Pro`).

## What is included

Only examples whose `token_length` field is one of:

- `128k`
- `256k`

## Columns

This repo keeps only:

- `id`: example identifier (copied from source)
- `text`: the original `context` field (renamed from `context` → `text`)

All other fields from the source dataset are dropped.

## Intended use

Use this dataset when you want to benchmark long-context behavior specifically at **≥128k** length buckets, while keeping the input surface minimal (`id`, `text`).

## Provenance / attribution

Source dataset: `caskcsg/LongBench-Pro`.

This repo contains a derived subset. Please consult the source dataset card for:
- full task definitions
- original annotations/fields
- licensing/usage terms

## Reproducibility

The filtering logic and transformation used to build this dataset are contained in `create_dataset.py` in this repo.
"""


def require_token() -> str:
    token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN")
    if not token:
        raise RuntimeError("Missing HF_TOKEN (or HUGGINGFACE_TOKEN) env var.")
    return token


def filter_and_project(ds: DatasetDict) -> DatasetDict:
    out = DatasetDict()
    for split, d in ds.items():
        if "token_length" not in d.column_names:
            raise RuntimeError(f"Split '{split}' has no 'token_length' column.")

        if "context" not in d.column_names:
            raise RuntimeError(f"Split '{split}' has no 'context' column.")

        if "id" not in d.column_names:
            raise RuntimeError(f"Split '{split}' has no 'id' column.")

        d2 = d.filter(lambda ex: ex["token_length"] in ALLOWED_TOKEN_LENGTH)

        # Keep only id + context, then rename context -> text
        d2 = d2.select_columns(["id", "context"]).rename_column("context", "text")
        out[split] = d2

    return out


def main() -> int:
    token = require_token()

    print(f"Loading source dataset: {SOURCE_DATASET}")
    ds = load_dataset(SOURCE_DATASET)  # DatasetDict

    print("Filtering and projecting columns...")
    out = filter_and_project(ds)

    # Quick stats
    for split, d in out.items():
        print(f"Split '{split}': {len(d)} rows; columns={d.column_names}")

    print(f"Pushing dataset to hub: {TARGET_REPO}")
    out.push_to_hub(
        TARGET_REPO,
        token=token,
        private=False,
        commit_message="Create/update filtered LongBench Pro subset (128k, 256k) with id+text",
    )

    # Upload README and script to the dataset repo
    api = HfApi(token=token)

    # Determine the path to this script (works when running as a file)
    script_path = Path(__file__).resolve()

    with tempfile.TemporaryDirectory() as td:
        td_path = Path(td)
        readme_path = td_path / "README.md"
        readme_path.write_text(README_MD, encoding="utf-8")

        print("Uploading README.md...")
        api.upload_file(
            path_or_fileobj=str(readme_path),
            path_in_repo="README.md",
            repo_id=TARGET_REPO,
            repo_type="dataset",
            commit_message="Add dataset README",
        )

    print("Uploading create_dataset.py...")
    api.upload_file(
        path_or_fileobj=str(script_path),
        path_in_repo="create_dataset.py",
        repo_id=TARGET_REPO,
        repo_type="dataset",
        commit_message="Add dataset creation script",
    )

    print("Done.")
    return 0


if __name__ == "__main__":
    try:
        raise SystemExit(main())
    except Exception as e:
        print(f"ERROR: {e}", file=sys.stderr)
        raise