lmms_eval/tasks/mmbench/cn_utils.py [61:100]:
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    if model_specific_prompt_kwargs:
        query_prompt = f"{query_prompt}\n{model_specific_prompt_kwargs['post_prompt']}"

    return query_prompt


def mmbench_process_results(doc, results):
    model_response = results[0].strip()
    data = {
        "gpt_eval_score": {
            "index": doc["index"],
            "question": doc["question"],
            "answer": doc["answer"],
            "prediction": model_response,
            "hint": doc["hint"],
            "source": doc["source"],
            "split": doc["split"],
            "category": doc["category"],
            "L2-category": doc["L2-category"],
        },
        "submission": {
            "index": doc["index"],
            "question": doc["question"],
            "answer": doc["answer"],
            "prediction": model_response,
            "hint": doc["hint"],
            "source": doc["source"],
            "split": doc["split"],
            "category": doc["category"],
            "L2-category": doc["L2-category"],
        },
    }
    option_candidate = ["A", "B", "C", "D", "E"]
    for c in option_candidate:
        data["submission"][c] = doc.get(c, "nan")
        data["gpt_eval_score"][c] = doc.get(c, "nan")
    return data


def mmbench_aggregate_dev_results_eval(results, args):
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lmms_eval/tasks/mmbench/en_utils.py [60:99]:
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    if model_specific_prompt_kwargs:
        query_prompt = f"{query_prompt}\n{model_specific_prompt_kwargs['post_prompt']}"

    return query_prompt


def mmbench_process_results(doc, results):
    model_response = results[0].strip()
    data = {
        "gpt_eval_score": {
            "index": doc["index"],
            "question": doc["question"],
            "answer": doc["answer"],
            "prediction": model_response,
            "hint": doc["hint"],
            "source": doc["source"],
            "split": doc["split"],
            "category": doc["category"],
            "L2-category": doc["L2-category"],
        },
        "submission": {
            "index": doc["index"],
            "question": doc["question"],
            "answer": doc["answer"],
            "prediction": model_response,
            "hint": doc["hint"],
            "source": doc["source"],
            "split": doc["split"],
            "category": doc["category"],
            "L2-category": doc["L2-category"],
        },
    }
    option_candidate = ["A", "B", "C", "D", "E"]
    for c in option_candidate:
        data["submission"][c] = doc.get(c, "nan")
        data["gpt_eval_score"][c] = doc.get(c, "nan")
    return data


def mmbench_aggregate_dev_results_eval(results, args):
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