import logging
import pprint

from huggingface_hub import snapshot_download

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

from src.backend.run_eval_suite_lighteval import run_evaluation
from src.backend.manage_requests import check_completed_evals, get_eval_requests, set_eval_request, set_requests_seen
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, API, LIMIT, TOKEN, ACCELERATOR, VENDOR, REGION
from src.about import TASKS_LIGHTEVAL

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, 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, requests_seen = 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)

    params_size = eval_request.params
    if eval_request.precision == '4bit':
        params_size //= 2
    
    # For GPU
    if not eval_request or params_size < 0:
        raise ValueError("Couldn't detect number of params, please make sure the metadata is available")
    elif params_size < 4:
        instance_size, instance_type, cap = "x1", "nvidia-t4", 40
    elif params_size < 8:
        instance_size, instance_type, cap = "x1", "nvidia-a10g", 40
    elif params_size < 30:
        instance_size, instance_type, cap = "x4", "nvidia-a10g", 20
    elif params_size < 45:
        instance_size, instance_type, cap = "x2", "nvidia-a100", 5
    elif params_size < 80:
        instance_size, instance_type, cap = "x4", "nvidia-a100", 5
    else: 
        set_eval_request(
            api=API,
            eval_request=eval_request,
            set_to_status=FAILED_STATUS,
            hf_repo=QUEUE_REPO,
            local_dir=EVAL_REQUESTS_PATH_BACKEND,
        )
        pp.pprint(dict(message="Number of params too big, can't run this model", params=eval_request.params))
        return
    
    counter_key = f'count_{instance_size}_{instance_type}'
    if not counter_key in requests_seen:
        requests_seen[counter_key] = 0
    if requests_seen[counter_key] >= cap:
        set_eval_request(
            api=API,
            eval_request=eval_request,
            set_to_status=FAILED_STATUS,
            hf_repo=QUEUE_REPO,
            local_dir=EVAL_REQUESTS_PATH_BACKEND,
        )
        pp.pprint(dict(message="Reached maximum cap for requests of this instance type this month", counter=counter_key, instance_type=instance_type, cap=cap))
        return

    # next, check to see who made the last commit to this repo - keep track of that. One person shouldn't commit more 
    # than 4 models in one month. 
    user = eval_request.user_info['name']
    if user in requests_seen and len(requests_seen[user]) >= 4:
        set_eval_request(
            api=API,
            eval_request=eval_request,
            set_to_status=FAILED_STATUS,
            hf_repo=QUEUE_REPO,
            local_dir=EVAL_REQUESTS_PATH_BACKEND,
        )
        pp.pprint(dict(message="Reached maximum cap for requests for this user this month", counter=counter_key, user=user))
        return
    if not user in requests_seen:
        requests_seen[user] = []
        
    requests_seen[user].append(dict(model_id=eval_request.model, revision=eval_request.revision))
    requests_seen[counter_key] += 1
    set_requests_seen(
        api=API,
        requests_seen=requests_seen,
        hf_repo=QUEUE_REPO,
        local_dir=EVAL_REQUESTS_PATH_BACKEND
    )

    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_LIGHTEVAL, 
        local_dir=EVAL_RESULTS_PATH_BACKEND,
        batch_size=25, 
        accelerator=ACCELERATOR, 
        region=REGION, 
        vendor=VENDOR, 
        instance_size=instance_size, 
        instance_type=instance_type,  
        limit=LIMIT
        )


if __name__ == "__main__":
    run_auto_eval()