import json import os from collections import defaultdict import pandas as pd from huggingface_hub import Repository from transformers import AutoConfig from src.assets.hardcoded_evals import baseline, gpt4_values, gpt35_values from src.display_models.get_model_metadata import apply_metadata from src.display_models.read_results import get_eval_results_dicts, make_clickable_model from src.display_models.utils import AutoEvalColumn, EvalQueueColumn, has_no_nan_values IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True)) def get_all_requested_models(requested_models_dir: str) -> set[str]: depth = 1 file_names = [] users_to_submission_dates = defaultdict(list) for root, _, files in os.walk(requested_models_dir): current_depth = root.count(os.sep) - requested_models_dir.count(os.sep) if current_depth == depth: for file in files: if not file.endswith(".json"): continue with open(os.path.join(root, file), "r") as f: info = json.load(f) file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}") # Select organisation if info["model"].count("/") == 0 or "submitted_time" not in info: continue organisation, _ = info["model"].split("/") users_to_submission_dates[organisation].append(info["submitted_time"]) return set(file_names), users_to_submission_dates def load_all_info_from_hub(QUEUE_REPO: str, RESULTS_REPO: str, QUEUE_PATH: str, RESULTS_PATH: str) -> list[Repository]: eval_queue_repo = None eval_results_repo = None requested_models = None print("Pulling evaluation requests and results.") eval_queue_repo = Repository( local_dir=QUEUE_PATH, clone_from=QUEUE_REPO, repo_type="dataset", ) eval_queue_repo.git_pull() eval_results_repo = Repository( local_dir=RESULTS_PATH, clone_from=RESULTS_REPO, repo_type="dataset", ) eval_results_repo.git_pull() requested_models, users_to_submission_dates = get_all_requested_models("eval-queue") return eval_queue_repo, requested_models, eval_results_repo, users_to_submission_dates def get_leaderboard_df( eval_results: Repository, eval_results_private: Repository, cols: list, benchmark_cols: list ) -> pd.DataFrame: if eval_results: print("Pulling evaluation results for the leaderboard.") eval_results.git_pull() if eval_results_private: print("Pulling evaluation results for the leaderboard.") eval_results_private.git_pull() all_data = get_eval_results_dicts() # if not IS_PUBLIC: all_data.append(gpt4_values) all_data.append(gpt35_values) all_data.append(baseline) apply_metadata(all_data) # Populate model type based on known hardcoded values in `metadata.py` df = pd.DataFrame.from_records(all_data) df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False) df = df[cols].round(decimals=2) # filter out if any of the benchmarks have not been produced df = df[has_no_nan_values(df, benchmark_cols)] return df def get_evaluation_queue_df( eval_queue: Repository, eval_queue_private: Repository, save_path: str, cols: list ) -> list[pd.DataFrame]: if eval_queue: print("Pulling changes for the evaluation queue.") eval_queue.git_pull() if eval_queue_private: print("Pulling changes for the evaluation queue.") eval_queue_private.git_pull() entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")] all_evals = [] for entry in entries: if ".json" in entry: file_path = os.path.join(save_path, entry) with open(file_path) as fp: data = json.load(fp) data[EvalQueueColumn.model.name] = make_clickable_model(data["model"]) data[EvalQueueColumn.revision.name] = data.get("revision", "main") all_evals.append(data) elif ".md" not in entry: # this is a folder sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if not e.startswith(".")] for sub_entry in sub_entries: file_path = os.path.join(save_path, entry, sub_entry) with open(file_path) as fp: data = json.load(fp) data[EvalQueueColumn.model.name] = make_clickable_model(data["model"]) data[EvalQueueColumn.revision.name] = data.get("revision", "main") all_evals.append(data) pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]] running_list = [e for e in all_evals if e["status"] == "RUNNING"] finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"] df_pending = pd.DataFrame.from_records(pending_list, columns=cols) df_running = pd.DataFrame.from_records(running_list, columns=cols) df_finished = pd.DataFrame.from_records(finished_list, columns=cols) return df_finished[cols], df_running[cols], df_pending[cols] def is_model_on_hub(model_name: str, revision: str) -> bool: try: AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=False) return True, None except ValueError: return ( False, "needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.", ) except Exception as e: print(f"Could not get the model config from the hub.: {e}") return False, "was not found on hub!"