functions.py

import gradio as gr
import numpy as np
import urllib3
from bs4 import BeautifulSoup
from datasets import load_dataset
from huggingface_hub import (
    CommitOperationAdd,
    EvalResult,
    ModelCard,
    RepoUrl,
    create_commit,
)
from huggingface_hub.repocard_data import eval_results_to_model_index
from pytablewriter import MarkdownTableWriter

COMMIT_DESCRIPTION = """This is an automated PR created with [this space](https://huggingface.co/spaces/T145/open-llm-leaderboard-results-to-modelcard)!

The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.

Please report any issues here: https://huggingface.co/spaces/T145/open-llm-leaderboard-results-to-modelcard/discussions"""


def normalize_within_range(value, lower_bound=0, higher_bound=1):
    return (np.clip(value - lower_bound, 0, None)) / (higher_bound - lower_bound) * 100


def calculate_results(repo: str, pool: urllib3.PoolManager):
    try:
        base_url = f"https://huggingface.co/datasets/open-llm-leaderboard/results/tree/main/{repo}"
        html = pool.request("GET", base_url).data
        soup = BeautifulSoup(html, "html.parser")
        dl_link = soup.find_all(title="Download file")[-1]["href"]
        data = pool.request("GET", f"https://huggingface.co{dl_link}").json()

        del base_url
        del html
        del soup
        del dl_link

        model_name = data["model_name"]
        precision = data["config"]["model_dtype"]
        revision = data["config"]["model_revision"]

        # Normalize BBH subtasks scores
        bbh_scores = []
        for subtask_key in data["group_subtasks"]["leaderboard_bbh"]:
            num_choices = len(data["configs"][subtask_key]["doc_to_choice"])
            if subtask_key in data["results"]:
                bbh_raw_score = data["results"][subtask_key]["acc_norm,none"]
                lower_bound = 1 / num_choices
                normalized_score = normalize_within_range(bbh_raw_score, lower_bound, 1.0)
                bbh_scores.append(normalized_score)

        # Average BBH score
        bbh_score = sum(bbh_scores) / len(bbh_scores)

        # Calculate the MATH score
        math_raw_score = data["results"]["leaderboard_math_hard"]["exact_match,none"]
        math_score = normalize_within_range(math_raw_score, 0, 1.0)

        # Normalize GPQA scores
        gpqa_raw_score = data["results"]["leaderboard_gpqa"]["acc_norm,none"]
        gpqa_score = normalize_within_range(gpqa_raw_score, 0.25, 1.0)

        # Normalize MMLU PRO scores
        mmlu_pro_raw_score = data["results"]["leaderboard_mmlu_pro"]["acc,none"]
        mmlu_pro_score = normalize_within_range(mmlu_pro_raw_score, 0.1, 1.0)

        # Compute IFEval
        ifeval_inst_score = (
            data["results"]["leaderboard_ifeval"]["inst_level_strict_acc,none"] * 100
        )
        ifeval_prompt_score = (
            data["results"]["leaderboard_ifeval"]["prompt_level_strict_acc,none"] * 100
        )

        # Average IFEval scores
        ifeval_score = (ifeval_inst_score + ifeval_prompt_score) / 2

        # Normalize MUSR scores
        musr_scores = []
        for subtask_key in data["group_subtasks"]["leaderboard_musr"]:
            subtask_config = data["configs"][subtask_key]
            dataset = load_dataset(subtask_config["dataset_path"], split=subtask_config["test_split"])
            num_choices = max(len(eval(question["choices"])) for question in dataset)
            musr_raw_score = data["results"][subtask_key]["acc_norm,none"]
            lower_bound = 1 / num_choices
            normalized_score = normalize_within_range(musr_raw_score, lower_bound, 1.0)

            musr_scores.append(normalized_score)
            del dataset

        musr_score = sum(musr_scores) / len(musr_scores)

        # Calculate overall score
        overall_score = (
            bbh_score + math_score + gpqa_score + mmlu_pro_score + musr_score + ifeval_score
        ) / 6

        # Round all scores to 2 decimal places
        bbh_score = float(round(bbh_score, 2))
        math_score = float(round(math_score, 2))
        gpqa_score = float(round(gpqa_score, 2))
        mmlu_pro_score = float(round(mmlu_pro_score, 2))
        musr_score = float(round(musr_score, 2))
        ifeval_score = float(round(ifeval_score, 2))
        overall_score = float(round(overall_score, 2))
        results = {
            "Model": model_name,
            "Precision": precision,
            "Revision": revision,
            "Average": overall_score,
            "IFEval": ifeval_score,
            "BBH": bbh_score,
            "MATH Lvl 5": math_score,
            "GPQA": gpqa_score,
            "MUSR": musr_score,
            "MMLU-PRO": mmlu_pro_score,
        }
        # pprint(results, sort_dicts=False)
        return results
    except Exception: # likely will be from no results being available
        return None


def get_details_url(repo):
    author, model = repo.split("/")
    return f"https://huggingface.co/datasets/open-llm-leaderboard/{author}__{model}-details"


def get_contents_url(repo):
    return f"https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q={repo}"


def get_query_url(repo):
    return f"https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query={repo}"


def get_task_summary(results):
    return {
        "IFEval": {
            "dataset_type": "wis-k/instruction-following-eval",
            "dataset_name": "IFEval (0-Shot)",
            "metric_type": "inst_level_strict_acc and prompt_level_strict_acc",
            "metric_value": results["IFEval"],
            "dataset_config": None,
            "dataset_split": "train",
            "dataset_args": {"num_few_shot": 0},
            "metric_name": "averaged accuracy",
        },
        "BBH": {
            "dataset_type": "SaylorTwift/bbh",
            "dataset_name": "BBH (3-Shot)",
            "metric_type": "acc_norm",
            "metric_value": results["BBH"],
            "dataset_config": None,
            "dataset_split": "test",
            "dataset_args": {"num_few_shot": 3},
            "metric_name": "normalized accuracy",
        },
        "MATH Lvl 5": {
            "dataset_type": "lighteval/MATH-Hard",
            "dataset_name": "MATH Lvl 5 (4-Shot)",
            "metric_type": "exact_match",
            "metric_value": results["MATH Lvl 5"],
            "dataset_config": None,
            "dataset_split": "test",
            "dataset_args": {"num_few_shot": 4},
            "metric_name": "exact match",
        },
        "GPQA": {
            "dataset_type": "Idavidrein/gpqa",
            "dataset_name": "GPQA (0-shot)",
            "metric_type": "acc_norm",
            "metric_value": results["GPQA"],
            "dataset_config": None,
            "dataset_split": "train",
            "dataset_args": {"num_few_shot": 0},
            "metric_name": "acc_norm",
        },
        "MuSR": {
            "dataset_type": "TAUR-Lab/MuSR",
            "dataset_name": "MuSR (0-shot)",
            "metric_type": "acc_norm",
            "metric_value": results["MUSR"],
            "dataset_config": None,
            "dataset_split": None,  # three test splits
            "dataset_args": {"num_few_shot": 0},
            "metric_name": "acc_norm",
        },
        "MMLU-PRO": {
            "dataset_type": "TIGER-Lab/MMLU-Pro",
            "dataset_name": "MMLU-PRO (5-shot)",
            "metric_type": "acc",
            "metric_value": results["MMLU-PRO"],
            "dataset_config": "main",
            "dataset_split": "test",
            "dataset_args": {"num_few_shot": 5},
            "metric_name": "accuracy",
        },
    }


def get_eval_results(repo: str, results: dict):
    task_summary = get_task_summary(results)
    table = MarkdownTableWriter()
    table.headers = ["Metric", "Value (%)"]
    table.value_matrix = [["**Average**", f"**{results["Average"]}**"]] + [
        [v["dataset_name"], v["metric_value"]] for v in task_summary.values()
    ]

    text = f"""# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here]({get_details_url(repo)})!
Summarized results can be found [here]({get_contents_url(repo)})!

{table.dumps()}
"""
    return text


def get_edited_yaml_readme(repo: str, results: dict, token: str | None):
    card = ModelCard.load(repo, token=token)

    common = {
        "task_type": "text-generation",
        "task_name": "Text Generation",
        "source_name": "Open LLM Leaderboard",
        "source_url": f"https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search={repo.replace("/", "%2F")}",
    }

    tasks_results = get_task_summary(results)

    if not card.data[
        "eval_results"
    ]:  # No results reported yet, we initialize the metadata
        card.data["model-index"] = eval_results_to_model_index(
            repo.split("/")[1],
            [EvalResult(**task, **common) for task in tasks_results.values()],
        )
    else:  # We add the new evaluations
        for task in tasks_results.values():
            cur_result = EvalResult(**task, **common)
            if any(
                result.is_equal_except_value(cur_result)
                for result in card.data["eval_results"]
            ):
                continue
            card.data["eval_results"].append(cur_result)

    return str(card)


def commit(
    repo,
    pr_number=None, # specify pr number if you want to edit it
    message="Adding Evaluation Results",
    oauth_token: gr.OAuthToken | None = None,
):
    if not oauth_token:
        raise gr.Warning("You are not logged in. Click on 'Sign in with Huggingface' to log in.")
    else:
        token = oauth_token

    with urllib3.PoolManager() as pool:
        results = calculate_results(repo, pool)

    if repo.startswith("https://huggingface.co/"):
        try:
            repo = RepoUrl(repo).repo_id
        except Exception as e:
            raise gr.Error(f"Not a valid repo id: {str(repo)}") from e

    edited = {"revision": f"refs/pr/{pr_number}"} if pr_number else {"create_pr": True}

    try:
        try:  # check if there is a readme already
            readme_text = get_edited_yaml_readme(
                repo, results, token=token
            ) + get_eval_results(repo, results)
        except Exception as e:
            if "Repo card metadata block was not found." in str(e):  # There is no readme
                readme_text = get_edited_yaml_readme(repo, results, token=token)
            else:
                print(f"Something went wrong: {e}")

        ops = [
            CommitOperationAdd(
                path_in_repo="README.md", path_or_fileobj=readme_text.encode()
            )
        ]
        commit = create_commit(
            repo_id=repo,
            token=token,
            operations=ops,
            commit_message=message,
            commit_description=COMMIT_DESCRIPTION,
            repo_type="model",
            **edited,
        ).pr_url

        return commit

    except Exception as e:
        if "Discussions are disabled for this repo" in str(e):
            return "Discussions disabled"
        elif "Cannot access gated repo" in str(e):
            return "Gated repo"
        elif "Repository Not Found" in str(e):
            return "Repository Not Found"
        else:
            return e