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()