import json import os from datetime import datetime, timezone from huggingface_hub import snapshot_download from src.submission.check_validity import get_model_tags 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, TOKEN, QUEUE_REPO, RATE_LIMIT_PERIOD, RATE_LIMIT_QUOTA from src.submission.check_validity import ( already_submitted_models, check_model_card, get_model_size, is_model_on_hub, ) REQUESTED_MODELS = None USERS_TO_SUBMISSION_DATES = None def add_new_eval( model: str, model_api_url: str, model_api_key: str, model_api_name: str, base_model: str, revision: str, precision: str, private: str, weight_type: str, model_type: str, runsh, adapter ): 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: model_type = "" #return styled_error("Please select a model type.") # Does the model actually exist? if revision == "": revision = "main" architecture = "?" downloads = 0 created_at = "" # Is the model on the hub? if len(model_api_url)==0: #if weight_type in ["Delta", "Adapter"]: # base_model_on_hub, error, _ = is_model_on_hub(model_name=base_model, revision=revision, token=TOKEN, test_tokenizer=True) # if not base_model_on_hub: # return styled_error(f'Base model "{base_model}" {error}') #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', '') #if not weight_type == "Adapter": # model_on_hub, error, _ = is_model_on_hub(model_name=model, revision=revision, token=TOKEN, test_tokenizer=True) # if not model_on_hub: # return styled_error(f'Model "{model}" {error}') # 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: return styled_error("Please select a license for your model") modelcard_OK, error_msg = check_model_card(model) if not modelcard_OK: return styled_error(error_msg) #tags = get_model_tags(model_card, model) # TODO: tags tags = [] likes = model_info.likes else: model_size = 0 license = "" likes = 0 tags = [] downloads = 0 # Seems good, creating the eval print("Adding new eval", runsh) max_size = 5 * 1024 * 1024 # 5MB if (runsh is not None) and (adapter is not None): if os.path.getsize(runsh.name) > max_size: return "错误：文件大小不能超过 5MB！" if os.path.getsize(adapter.name) > max_size: return "错误：文件大小不能超过 5MB！" with open(runsh.name, "r") as f: runsh = f.read() with open(adapter.name, "r") as f: adapter = f.read() else: runsh = "" adapter = "" eval_entry = { "model": model, "model_api_url": model_api_url, "model_api_key": model_api_key, "model_api_name": model_api_name, "base_model": base_model, "revision": revision, "precision": precision, "private": private, "weight_type": weight_type, "status": "PENDING", "submitted_time": current_time, "model_type": model_type, #"likes": model_info.likes, "params": model_size, #"license": license, "private": False, "runsh": runsh, "adapter": adapter, } supplementary_info = { "likes": 0, "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_False_{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." )