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# import lm_eval.base as base
from typing import List
import pytest
# import lm_eval.models as models
import lm_eval.api as api
import lm_eval.evaluator as evaluator
from lm_eval import tasks
# TODO: more fine grained unit tests rather than this big honking integration
# test once we break evaluator into smaller, more manageable pieces
@pytest.mark.parametrize(
"task_name,limit,model,model_args",
[
(
["arc_easy"],
10,
"hf",
"pretrained=EleutherAI/pythia-160m,dtype=float32,device=cpu",
)
],
)
def test_evaluator(task_name: List[str], limit: int, model: str, model_args: str):
task_name = task_name
limit = 10
e1 = evaluator.simple_evaluate(
model=model,
tasks=task_name,
limit=limit,
model_args=model_args,
)
assert e1 is not None
lm = api.registry.get_model(model).create_from_arg_string(
model_args,
{
"batch_size": None,
"max_batch_size": None,
"device": None,
},
)
task_manager = tasks.TaskManager()
task_dict = tasks.get_task_dict(task_name, task_manager)
e2 = evaluator.evaluate(
lm=lm,
task_dict=task_dict,
limit=limit,
)
assert e2 is not None
# check that caching is working
def r(x):
return x["results"]["arc_easy"]
assert all(
x == y
for x, y in zip([y for _, y in r(e1).items()], [y for _, y in r(e2).items()])
)
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