src/submission/submit.py

import json
import os
from datetime import datetime, timezone

from huggingface_hub import ModelCard, snapshot_download

from src.display.formatting import styled_error, styled_message, styled_warning
from src.envs import API, EVAL_REQUESTS_PATH, DYNAMIC_INFO_PATH, DYNAMIC_INFO_FILE_PATH, DYNAMIC_INFO_REPO, H4_TOKEN, QUEUE_REPO, RATE_LIMIT_PERIOD, RATE_LIMIT_QUOTA
from src.leaderboard.filter_models import DO_NOT_SUBMIT_MODELS
from src.submission.check_validity import (
    already_submitted_models,
    check_model_card,
    get_model_size,
    is_model_on_hub,
    user_submission_permission,
)

REQUESTED_MODELS = None
USERS_TO_SUBMISSION_DATES = None

def add_new_eval(
    model: str,
    base_model: str,
    revision: str,
    precision: str,
    private: bool,
    weight_type: str,
    model_type: str,
):
    global REQUESTED_MODELS
    global USERS_TO_SUBMISSION_DATES
    if not REQUESTED_MODELS:
        REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)

    user_name = ""
    model_path = model
    if "/" in model:
        user_name = model.split("/")[0]
        model_path = model.split("/")[1]

    precision = precision.split(" ")[0]
    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")

    if model_type is None or model_type == "":
        return styled_error("Please select a model type.")

    # Is the user rate limited?
    if user_name != "":
        user_can_submit, error_msg = user_submission_permission(
            user_name, USERS_TO_SUBMISSION_DATES, RATE_LIMIT_PERIOD, RATE_LIMIT_QUOTA
        )
        if not user_can_submit:
            return styled_error(error_msg)

    # Did the model authors forbid its submission to the leaderboard?
    if model in DO_NOT_SUBMIT_MODELS or base_model in DO_NOT_SUBMIT_MODELS:
        return styled_warning("Model authors have requested that their model be not submitted on the leaderboard.")

    # Does the model actually exist?
    if revision == "":
        revision = "main"

    # Is the model on the hub?
    if weight_type in ["Delta", "Adapter"]:
        base_model_on_hub, error, _ = is_model_on_hub(model_name=base_model, revision=revision, token=H4_TOKEN, test_tokenizer=True)
        if not base_model_on_hub:
            return styled_error(f'Base model "{base_model}" {error}')

    architecture = "?"
    downloads = 0
    created_at = ""
    if not weight_type == "Adapter":
        model_on_hub, error, model_config = is_model_on_hub(model_name=model, revision=revision, test_tokenizer=True)
        if not model_on_hub:
            return styled_error(f'Model "{model}" {error}')
        if model_config is not None:
            architectures = getattr(model_config, "architectures", None)
            if architectures:
                architecture = ";".join(architectures)
            downloads = getattr(model_config, 'downloads', 0)
            created_at = getattr(model_config, 'created_at', '')



    # Is the model info correctly filled?
    try:
        model_info = API.model_info(repo_id=model, revision=revision)
    except Exception:
        return styled_error("Could not get your model information. Please fill it up properly.")

    model_size = get_model_size(model_info=model_info, precision=precision)

    # Were the model card and license filled?
    try:
        license = model_info.cardData["license"]
    except Exception:
        license = None
        #return styled_error("Please select a license for your model")

    modelcard_OK, error_msg = check_model_card(model)
    model_card = None
    if modelcard_OK:
        model_card = ModelCard.load(model)
    
    is_merge_from_metadata = False
    is_moe_from_metadata = False
    is_merge_from_model_card = False
    is_moe_from_model_card = False

    # Storing the model tags
    tags = []
    moe_keywords = ["moe", "mixture of experts", "mixtral"]
    if modelcard_OK:
        if model_card.data.tags:
            is_merge_from_metadata = "merge" in model_card.data.tags
            is_moe_from_metadata = "moe" in model_card.data.tags
        merge_keywords = ["mergekit", "merged model", "merge model", "merging"]
        # If the model is a merge but not saying it in the metadata, we flag it
        is_merge_from_model_card = any(keyword in model_card.text.lower() for keyword in merge_keywords)
        if is_merge_from_model_card or is_merge_from_metadata:
            tags.append("merge")
            if not is_merge_from_metadata:
                tags.append("flagged:undisclosed_merge")
        is_moe_from_model_card = any(keyword in model_card.text.lower() for keyword in moe_keywords)    
    is_moe_from_name = "moe" in model.lower().replace("/", "-").replace("_", "-").split("-")
    if is_moe_from_model_card or is_moe_from_name or is_moe_from_metadata:
        tags.append("moe")
        if not is_moe_from_metadata:
            tags.append("flagged:undisclosed_moe")


    # Seems good, creating the eval
    print("Adding new eval")

    eval_entry = {
        "model": model,
        "base_model": base_model,
        "revision": revision,
        "private": private,
        "precision": precision,
        "params": model_size,
        "architectures": architecture,
        "weight_type": weight_type,
        "status": "PENDING",
        "submitted_time": current_time,
        "model_type": model_type,
        "job_id": -1,
        "job_start_time": None,
    }

    supplementary_info = {
        "likes": model_info.likes,
        "license": license,
        "still_on_hub": True,
        "tags": tags,
        "downloads": downloads, 
        "created_at": created_at
    }

    # Check for duplicate submission
    if f"{model}_{revision}_{precision}" in REQUESTED_MODELS:
        return styled_warning("This model has been already submitted.")

    print("Creating eval file")
    OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
    os.makedirs(OUT_DIR, exist_ok=True)
    out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{precision}_{weight_type}.json"

    with open(out_path, "w") as f:
        f.write(json.dumps(eval_entry))

    print("Uploading eval file")
    API.upload_file(
        path_or_fileobj=out_path,
        path_in_repo=out_path.split("eval-queue/")[1],
        repo_id=QUEUE_REPO,
        repo_type="dataset",
        commit_message=f"Add {model} to eval queue",
    )

    # We want to grab the latest version of the submission file to not accidentally overwrite it
    snapshot_download(
        repo_id=DYNAMIC_INFO_REPO, local_dir=DYNAMIC_INFO_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
    )

    with open(DYNAMIC_INFO_FILE_PATH) as f:
        all_supplementary_info = json.load(f)

    all_supplementary_info[model] = supplementary_info
    with open(DYNAMIC_INFO_FILE_PATH, "w") as f:
        json.dump(all_supplementary_info, f, indent=2)

    API.upload_file(
        path_or_fileobj=DYNAMIC_INFO_FILE_PATH,
        path_in_repo=DYNAMIC_INFO_FILE_PATH.split("/")[-1],
        repo_id=DYNAMIC_INFO_REPO,
        repo_type="dataset",
        commit_message=f"Add {model} to dynamic info queue",
    )

    

    # Remove the local file
    os.remove(out_path)

    return styled_message(
        "Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list."
    )