import logging
import os
import pprint

from huggingface_hub import snapshot_download
import subprocess

subprocess.run(["python", "scripts/fix_harness_import.py"])

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, EvalRequest
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, RUN_MODE
from src.about import NUM_FEWSHOT, HarnessTasks
import asyncio

TASKS_HARNESS = [task.value.benchmark for task in HarnessTasks]

logging.basicConfig(level=logging.ERROR)
pp = pprint.PrettyPrinter(width=80)

PENDING_STATUS = "PENDING"
RUNNING_STATUS = "RUNNING"
FINISHED_STATUS = "FINISHED"
FAILED_STATUS = "FAILED"

# TODO: uncomment
if RUN_MODE != "LOCAL":
    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

    if RUN_MODE != "LOCAL":
        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)

    else:
        local_model_name = os.getenv("LOCAL_MODEL_NAME", "hf-internal-testing/tiny-random-gpt2")
        sample_request = {
            "model": local_model_name, "json_filepath": "", "base_model": "", "revision": "main",
            "private": False,
            "precision": "bfloat16", "weight_type": "Original", "status": "PENDING",
            "submitted_time": "2023-11-21T18:10:08Z", "model_type": "\ud83d\udfe2 : pretrained", "likes": 0,
            "params": 0.1, "license": "custom"
        }
        eval_requests = [EvalRequest(**sample_request)]

    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)

    if RUN_MODE != "LOCAL":
        set_eval_request(
            api=API,
            eval_request=eval_request,
            set_to_status=RUNNING_STATUS,
            hf_repo=QUEUE_REPO,
            local_dir=EVAL_REQUESTS_PATH_BACKEND,
        )

    asyncio.run(
        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
        )
    )
    logging.info("Shopping finished")


if __name__ == "__main__":
    run_auto_eval()