import subprocess
import urllib

import typer
from datasets import (get_dataset_config_names, get_dataset_split_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)

    for config in configs:
        splits = get_dataset_split_names(dataset_id, config)
        splits = [split for split in splits if split not in ["train", "validation"]]
        for split in splits:
            columns_to_keep = ["gem_id", "gem_parent_id", "target"]
            dataset = load_dataset(dataset_id, name=config, split=split)
            # For non-train splits, it seems we use the references column as the target
            if "train" not in split:
                dataset = dataset.map(lambda x: {"target": x["references"]})
            else:
                # Wrap references in list to match GEM schema
                dataset = dataset.map(lambda x: {"target": [x["target"]]})
            # Delete unused columns
            # 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()
            reference_name = f"{dataset_name}_{config}_{split}"
            df[columns_to_keep].to_json(f"{reference_name}.json", orient="records")
            validate(reference_name)


def validate(reference_name: str):
    # Download original references from GitHub repo
    url = f"https://github.com/GEM-benchmark/GEM-metrics/releases/download/data/{reference_name}.json"
    urllib.request.urlretrieve(
        url,
        f"github_references/{reference_name}.json",
    )
    # Run diff - requires `jq`
    process = subprocess.run(
        f"diff <(jq --sort-keys . {reference_name}.json) <(jq --sort-keys . ./github_references/{reference_name}.json)",
        shell=True,
        stdout=subprocess.PIPE,
        executable="/bin/bash",
    )
    if process.stdout:
        raise ValueError(f"❌ Validation failed for {reference_name}! New and original references do not agree 😭")
    else:
        typer.echo(f"✅ Validation successful for {reference_name}!")


@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
    blocklist = ["test", "test-transform"]
    gem_datasets = [dataset for dataset in gem_datasets if dataset.id not in blocklist]
    # Focus on MLSUM for now - later remove this line to evaluate all datasets
    mlsum_datasets = [dataset for dataset in gem_datasets if dataset.id.startswith("GEM/mlsum")]
    for dataset in mlsum_datasets:
        convert(dataset.id)
    typer.echo(f"🥳 All datasets converted!")


if __name__ == "__main__":
    app()