import json import os from collections import defaultdict import pandas as pd 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 get_leaderboard_df(results_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame: all_data = get_eval_results_dicts(results_path) 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(save_path: str, cols: list) -> list[pd.DataFrame]: 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: return False, "was not found on hub!"