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import logging
import pprint
from huggingface_hub import snapshot_download
logging.getLogger("openai").setLevel(logging.WARNING)
# from src.backend.run_eval_suite import run_evaluation
# from src.backend.manage_requests import check_completed_evals, get_eval_requests, set_eval_request
# from src.backend.sort_queue import sort_models_by_priority
from src.envs import QUEUE_REPO, EVAL_REQUESTS_PATH_BACKEND, RESULTS_REPO, EVAL_RESULTS_PATH_BACKEND, DEVICE, API, LIMIT, TOKEN
from src.about import Tasks, NUM_FEWSHOT
TASKS_HARNESS = [task.value.benchmark for task in Tasks]
logging.basicConfig(level=logging.ERROR)
pp = pprint.PrettyPrinter(width=80)
PENDING_STATUS = "PENDING"
RUNNING_STATUS = "RUNNING"
FINISHED_STATUS = "FINISHED"
FAILED_STATUS = "FAILED"
print('Downloading results and requests.')
snapshot_download(repo_id=RESULTS_REPO, revision="main", local_dir=EVAL_RESULTS_PATH_BACKEND, repo_type="dataset", max_workers=60, token=TOKEN)
snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60, token=TOKEN)
# def run_auto_eval():
# current_pending_status = [PENDING_STATUS]
#
# # pull the eval dataset from the hub and parse any eval requests
# # check completed evals and set them to finished
# check_completed_evals(
# api=API,
# checked_status=RUNNING_STATUS,
# completed_status=FINISHED_STATUS,
# failed_status=FAILED_STATUS,
# hf_repo=QUEUE_REPO,
# local_dir=EVAL_REQUESTS_PATH_BACKEND,
# hf_repo_results=RESULTS_REPO,
# local_dir_results=EVAL_RESULTS_PATH_BACKEND
# )
#
# # Get all eval request that are PENDING, if you want to run other evals, change this parameter
# eval_requests = get_eval_requests(job_status=current_pending_status, hf_repo=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH_BACKEND)
# # Sort the evals by priority (first submitted first run)
# eval_requests = sort_models_by_priority(api=API, models=eval_requests)
#
# print(f"Found {len(eval_requests)} {','.join(current_pending_status)} eval requests")
#
# if len(eval_requests) == 0:
# return
#
# eval_request = eval_requests[0]
# pp.pprint(eval_request)
#
# set_eval_request(
# api=API,
# eval_request=eval_request,
# set_to_status=RUNNING_STATUS,
# hf_repo=QUEUE_REPO,
# local_dir=EVAL_REQUESTS_PATH_BACKEND,
# )
#
# run_evaluation(
# eval_request=eval_request,
# task_names=TASKS_HARNESS,
# num_fewshot=NUM_FEWSHOT,
# local_dir=EVAL_RESULTS_PATH_BACKEND,
# results_repo=RESULTS_REPO,
# batch_size=1,
# device=DEVICE,
# no_cache=True,
# limit=LIMIT
# )
#
#
# if __name__ == "__main__":
# run_auto_eval() |