download.py

import json
from pathlib import Path
from multiprocessing import Pool, cpu_count
from datasets import load_dataset
from tqdm import tqdm
import argparse

def process_config(config):
    ds = load_dataset("AI-Sweden-Models/SWEb", name=config, streaming=True, split="train")
    docs = []
    count = 0
    data_path = Path("data")
    data_path.mkdir(parents=True, exist_ok=True)
    config_path = data_path / config
    config_path.mkdir(parents=True, exist_ok=True)

    # Estimate the number of entries based on streaming behavior (no total, so just progress as files are created)
    inner_progress = tqdm(desc=f"Processing {config}", position=1, leave=False)
    for entry in ds:
        if entry["language"] in ("no", "nn"):
            shard_path = config_path / f"{count:04}.json"
            if len(docs) == 1000:
                shard_path.write_text("\n".join(docs))
                docs = []
                count += 1
                inner_progress.update(1)
            else:
                docs.append(json.dumps(entry))
    # Save any remaining documents
    if docs:
        (config_path / f"{count:04}.json").write_text("\n".join(docs))
        inner_progress.update(1)

    inner_progress.close()

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Process multiple dataset configurations in parallel.")
    parser.add_argument(
        "--workers",
        type=int,
        default=cpu_count(),
        help="Number of worker processes to use (default: number of CPU cores)."
    )
    args = parser.parse_args()
    configs = [
    "2013-20",
    "2013-48",
    "2014-10",
    "2014-15",
    "2014-23",
    "2014-35",
    "2014-41",
    "2014-42",
    "2014-49",
    "2014-52",
    "2015-06",
    "2015-11",
    "2015-14",
    "2015-18",
    "2015-22",
    "2015-27",
    "2015-32",
    "2015-35",
    "2015-40",
    "2015-48",
    "2016-07",
    "2016-18",
    "2016-22",
    "2016-26",
    "2016-30",
    "2016-36",
    "2016-40",
    "2016-44",
    "2016-50",
    "2017-04",
    "2017-09",
    "2017-13",
    "2017-17",
    "2017-22",
    "2017-26",
    "2017-30",
    "2017-34",
    "2017-39",
    "2017-43",
    "2017-47",
    "2017-51",
    "2018-05",
    "2018-09",
    "2018-13",
    "2018-17",
    "2018-22",
    "2018-26",
    "2018-30",
    "2018-34",
    "2018-39",
    "2018-43",
    "2018-47",
    "2018-51",
    "2019-04",
    "2019-09",
    "2019-13",
    "2019-18",
    "2019-22",
    "2019-26",
    "2019-30",
    "2019-35",
    "2019-39",
    "2019-43",
    "2019-47",
    "2019-51",
    "2020-05",
    "2020-10",
    "2020-16",
    "2020-24",
    "2020-29",
    "2020-34",
    "2020-40",
    "2020-45",
    "2020-50",
    "2021-04",
    "2021-10",
    "2021-17",
    "2021-21",
    "2021-25",
    "2021-31",
    "2021-39",
    "2021-43",
    "2021-49",
    "2022-05",
    "2022-21",
    "2022-27",
    "2022-33",
    "2022-40",
    "2022-49",
    "2023-06",
    "2023-14",
    "2023-23",
    "2023-40",
    "2023-50",
    "2024-10",
    "2024-18",
    "2024-22",
    "2024-26"
    ]

    # configs = ["config1", "config2", "config3"]  # Replace with actual config names
    num_workers = min(args.workers, len(configs))  # Use at most one process per config

    # Wrap the Pool in tqdm for a progress bar
    with Pool(num_workers) as pool:
        for _ in tqdm(pool.imap(process_config, configs), total=len(configs), desc="Processing configs", position=0):
            pass