yourbench_space/evaluation.py

import asyncio, os
from src.env import INIT_MODELS

ON_SPACES=os.environ.get("system") == "spaces"
OUTPUT_DIR = "/data" if ON_SPACES else "."

def create_eval_file(eval_ds_name):
    # TODO: replace by Nathan's call
    content = """
from aenum import extend_enum

from lighteval.metrics.metrics import Metrics
from lighteval.metrics.utils.metric_utils import (
    CorpusLevelMetricGrouping,
    MetricCategory,
    MetricUseCase,
)
from lighteval.tasks.lighteval_task import LightevalTaskConfig
from lighteval.tasks.extended.hle.main import JudgeLLMHLE
from lighteval.tasks.requests import Doc


def prompt_function(line, task_name: str = None):
    if line["image"] not in [None, ""]:
        return

    return Doc(
        task_name=task_name,
        query="Question: " + line["question"] + "\\nAnswer:",
        choices=[line["answer"]],
        gold_index=0,
        specific={"question": line["question"]},
    )
""" + f"""

hle = LightevalTaskConfig(
    name="{eval_ds_name.replace('/', '_')}",
    suite=["custom"],
    prompt_function=prompt_function,
    hf_repo="{eval_ds_name}",
    hf_subset="default",
    hf_avail_splits=["test"],
    evaluation_splits=["test"],
    few_shots_split=None,
    few_shots_select=None,
    generation_size=8192,
    metric=[Metrics.exact_match],
    stop_sequence=[],
    trust_dataset=True,
    version=0,
)


TASKS_TABLE = [hle]
""" 
    
    with open(f"{OUTPUT_DIR}/custom_task.py", "w") as f:
        f.write(content)

async def run_process(args: list) -> dict:
    process = await asyncio.create_subprocess_exec(
        *args,
        stdout=asyncio.subprocess.PIPE,
        stderr=asyncio.subprocess.PIPE
    )
    await asyncio.wait_for(process.wait(), timeout=180)
    stdout = await process.stdout.read()
    stderr = await process.stderr.read()
    return {
        'pid': process.pid,
        'stdout': stdout.decode(),
        'stderr': stderr.decode()
    }

async def run_evaluations(eval_ds_name: str, org: str) -> list:
    tasks = []
    for model_name, provider in INIT_MODELS:
        args = [
            "lighteval", 
            "endpoint", "inference-providers", f"model={model_name},provider={provider}",
            f"custom|{eval_ds_name.replace('/', '_')}|0|0", "--custom-tasks", f"{OUTPUT_DIR}/custom_task.py", "--max-samples", "10", 
            "--output-dir", f"{OUTPUT_DIR}", "--save-details", "--results-org", org, "--push-to-hub"
        ]
        tasks.append(run_process(args))
    # Will capture the task if failed
    processes = await asyncio.gather(*tasks, return_exceptions=True)
    if all(not isinstance(result, Exception) for result in processes):
        return "✅"
    return "At least one model failed"