import typer
from datasets import (Dataset, DatasetDict, get_dataset_config_names,
                      load_dataset)
from huggingface_hub import list_datasets

app = typer.Typer()


def convert(dataset_id: str):
    dataset_name = dataset_id.split("/")[-1]
    configs = get_dataset_config_names(dataset_id)
    skipped_validation = []

    for config in configs:
        typer.echo(dataset_id)
        raw_datasets = load_dataset(dataset_id, name=config)
        datasets_to_convert = DatasetDict()

        for split, dataset in raw_datasets.items():
            if split not in ["train", "validation"]:
                datasets_to_convert[split] = dataset

        for split, dataset in datasets_to_convert.items():
            columns_to_keep = ["gem_id", "gem_parent_id", "target", "references"]
            remainder_cols = validate_columns(dataset)
            if len(remainder_cols) > 0:
                typer.echo(
                    f"⚠️ Skipping {dataset_name}/{config}/{split} due to missing columns: {', '.join(remainder_cols)}"
                )
                skipped_validation.append(f"{dataset_name}/{config}/{split}")
            else:
                # Add `input` column if it exists
                if "input" in dataset.column_names:
                    columns_to_keep.append("input")
                # The test split doesn't have a parent ID
                if split == "test":
                    columns_to_keep.remove("gem_parent_id")
                # The `datasets` JSON serializer is buggy - use `pandas` for now
                df = dataset.to_pandas()
                # Exclude dummy config names for comparison with GitHub source dataset
                if config in ["default", "xsum", "totto"]:
                    reference_name = f"{dataset_name}_{split}"
                else:
                    reference_name = f"{dataset_name}_{config}_{split}"
                df[columns_to_keep].to_json(f"{reference_name}.json", orient="records")

    typer.echo(f"Skipped validation for {skipped_validation}")


def validate_columns(dataset: Dataset):
    ref_columns = ["gem_id", "gem_parent_id", "target", "references"]
    columns = dataset.column_names
    return set(ref_columns) - set(columns)


@app.command()
def main():
    all_datasets = list_datasets()
    # Filter for GEM datasets
    gem_datasets = [dataset for dataset in all_datasets if dataset.id.startswith("GEM/")]
    # Filter for blocklist - currently exclude all datasets not found on GitHUb release
    blocklist = [
        "ART",
        "mlb_data_to_text",
        "OrangeSum",
        "split_and_rephrase",
        "wiki_cat_sum",
        "viggo",
        "CrossWOZ",
        "RiSAWOZ",
        "indonlg",
        "squad_v2",
        "BiSECT",
        "surface_realisation_st_2020",
        "SciDuet",
        "cochrane-simplification",
        "turku_paraphrase_corpus",
        "turku_hockey_data2text",
        "sportsett_basketball",
        "Taskmaster",
        "wiki_lingua",
        "SIMPITIKI",
        "conversational_weather",
        "RotoWire_English-German",
        "dstc10_track2_task2",
        "opusparcus",
        "xlsum",
        "wiki_auto_asset_turk",  # Can't be loaded
        "references",  # This repo, so exclude!
    ]
    blocklist = ["GEM/" + dataset for dataset in blocklist]
    gem_datasets = [dataset for dataset in gem_datasets if dataset.id not in blocklist]
    for dataset in gem_datasets:
        typer.echo(f"Converting {dataset.id} ...")
        convert(dataset.id)
    typer.echo(f"🥳 All datasets converted!")


if __name__ == "__main__":
    app()