import os
import json

from datetime import datetime

from huggingface_hub import snapshot_download

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.backend.envs import Tasks, EVAL_REQUESTS_PATH_BACKEND,EVAL_RESULTS_PATH_BACKEND, DEVICE, LIMIT

from src.envs import QUEUE_REPO, RESULTS_REPO, API

import logging
import pprint

# TASKS_HARNESS = [task.value.benchmark for task in Tasks]

logging.getLogger("openai").setLevel(logging.WARNING)

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

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

snapshot_download(repo_id=RESULTS_REPO, revision="main", local_dir=EVAL_RESULTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60)


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)

    # results = run_evaluation(eval_request=eval_request, task_names=TASKS_HARNESS, num_fewshot=NUM_FEWSHOT,
    #                          batch_size=1, device=DEVICE, no_cache=True, limit=LIMIT)

    TASKS_HARNESS = [task.value for task in Tasks]

    for task in TASKS_HARNESS:
        results = run_evaluation(eval_request=eval_request, task_names=[task.benchmark], num_fewshot=task.num_fewshot,
                                 batch_size=1, device=DEVICE, no_cache=True, limit=LIMIT)

        dumped = json.dumps(results, indent=2)
        print(dumped)

        output_path = os.path.join(EVAL_RESULTS_PATH_BACKEND, *eval_request.model.split("/"), f"results_{datetime.now()}.json")
        os.makedirs(os.path.dirname(output_path), exist_ok=True)
        with open(output_path, "w") as f:
            f.write(dumped)

        API.upload_file(path_or_fileobj=output_path, path_in_repo=f"{eval_request.model}/results_{datetime.now()}.json",
                        repo_id=RESULTS_REPO, repo_type="dataset")

    set_eval_request(api=API, eval_request=eval_request, set_to_status=FINISHED_STATUS, hf_repo=QUEUE_REPO,
                     local_dir=EVAL_REQUESTS_PATH_BACKEND)

    # breakpoint()


if __name__ == "__main__":
    run_auto_eval()