from datasets import load_dataset
from pipeline import export_dataset


def process_func(input_dict: dict):
    conversations = input_dict["conversations"]
    assert len(conversations) % 2 == 0
    n = len(conversations) // 2
    examples = []
    for i in range(n):
        user_msg = conversations[2 * i]["value"]
        assistant_msg = conversations[2 * i + 1]["value"]
        examples.append({"query": user_msg, "response": assistant_msg})
    return examples


if __name__ == "__main__":
    dataset = load_dataset("HuggingFaceTB/Magpie-Pro-300K-Filtered-H4")
    export_dataset(
        dataset=dataset["train_sft"],
        output_dir="./dataset",
        output_prefix="Magpie-Pro-300K-sft",
        process_func=process_func,
    )