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| import glob | |
| import json | |
| import os | |
| from typing import List | |
| from huggingface_hub import HfApi | |
| from tqdm import tqdm | |
| from src.display_models.model_metadata_flags import DO_NOT_SUBMIT_MODELS, FLAGGED_MODELS | |
| from src.display_models.model_metadata_type import MODEL_TYPE_METADATA, ModelType, model_type_from_str | |
| from src.display_models.utils import AutoEvalColumn, model_hyperlink | |
| api = HfApi(token=os.environ.get("H4_TOKEN", None)) | |
| def get_model_metadata(leaderboard_data: List[dict]): | |
| for model_data in tqdm(leaderboard_data): | |
| request_files = os.path.join( | |
| "eval-queue", | |
| model_data["model_name_for_query"] + "_eval_request_*" + ".json", | |
| ) | |
| request_files = glob.glob(request_files) | |
| # Select correct request file (precision) | |
| request_file = "" | |
| if len(request_files) == 1: | |
| request_file = request_files[0] | |
| elif len(request_files) > 1: | |
| request_files = sorted(request_files, reverse=True) | |
| for tmp_request_file in request_files: | |
| with open(tmp_request_file, "r") as f: | |
| req_content = json.load(f) | |
| if ( | |
| req_content["status"] == "FINISHED" | |
| and req_content["precision"] == model_data["Precision"].split(".")[-1] | |
| ): | |
| request_file = tmp_request_file | |
| try: | |
| with open(request_file, "r") as f: | |
| request = json.load(f) | |
| model_type = model_type_from_str(request["model_type"]) | |
| model_data[AutoEvalColumn.model_type.name] = model_type.value.name | |
| model_data[AutoEvalColumn.model_type_symbol.name] = model_type.value.symbol # + ("🔺" if is_delta else "") | |
| model_data[AutoEvalColumn.license.name] = request["license"] | |
| model_data[AutoEvalColumn.likes.name] = request["likes"] | |
| model_data[AutoEvalColumn.params.name] = request["params"] | |
| except Exception: | |
| if model_data["model_name_for_query"] in MODEL_TYPE_METADATA: | |
| model_data[AutoEvalColumn.model_type.name] = MODEL_TYPE_METADATA[ | |
| model_data["model_name_for_query"] | |
| ].value.name | |
| model_data[AutoEvalColumn.model_type_symbol.name] = MODEL_TYPE_METADATA[ | |
| model_data["model_name_for_query"] | |
| ].value.symbol # + ("🔺" if is_delta else "") | |
| else: | |
| model_data[AutoEvalColumn.model_type.name] = ModelType.Unknown.value.name | |
| model_data[AutoEvalColumn.model_type_symbol.name] = ModelType.Unknown.value.symbol | |
| def flag_models(leaderboard_data: List[dict]): | |
| for model_data in leaderboard_data: | |
| if model_data["model_name_for_query"] in FLAGGED_MODELS: | |
| issue_num = FLAGGED_MODELS[model_data["model_name_for_query"]].split("/")[-1] | |
| issue_link = model_hyperlink( | |
| FLAGGED_MODELS[model_data["model_name_for_query"]], | |
| f"See discussion #{issue_num}", | |
| ) | |
| model_data[ | |
| AutoEvalColumn.model.name | |
| ] = f"{model_data[AutoEvalColumn.model.name]} has been flagged! {issue_link}" | |
| def remove_forbidden_models(leaderboard_data: List[dict]): | |
| indices_to_remove = [] | |
| for ix, model in enumerate(leaderboard_data): | |
| if model["model_name_for_query"] in DO_NOT_SUBMIT_MODELS: | |
| indices_to_remove.append(ix) | |
| for ix in reversed(indices_to_remove): | |
| leaderboard_data.pop(ix) | |
| return leaderboard_data | |
| def apply_metadata(leaderboard_data: List[dict]): | |
| leaderboard_data = remove_forbidden_models(leaderboard_data) | |
| get_model_metadata(leaderboard_data) | |
| flag_models(leaderboard_data) | |