import json import os import re import numpy as np from collections import defaultdict from datetime import datetime, timedelta, timezone import huggingface_hub from huggingface_hub import ModelCard from huggingface_hub.hf_api import ModelInfo from transformers import AutoConfig from transformers.models.auto.tokenization_auto import AutoTokenizer from src.display.utils import TEXT_TASKS, VISION_TASKS, NUM_EXPECTED_EXAMPLES def check_model_card(repo_id: str) -> tuple[bool, str]: """Checks if the model card and license exist and have been filled""" try: card = ModelCard.load(repo_id) except huggingface_hub.utils.EntryNotFoundError: return False, "Please add a model card to your model to explain how you trained/fine-tuned it." # Enforce license metadata if card.data.license is None: if not ("license_name" in card.data and "license_link" in card.data): return False, ( "License not found. Please add a license to your model card using the `license` metadata or a" " `license_name`/`license_link` pair." ) # Enforce card content if len(card.text) < 200: return False, "Please add a description to your model card, it is too short." return True, "" def is_model_on_hub(model_name: str, revision: str, token: str = None, trust_remote_code=False, test_tokenizer=False) -> tuple[bool, str]: """Checks if the model model_name is on the hub, and whether it (and its tokenizer) can be loaded with AutoClasses.""" try: config = AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token) if test_tokenizer: try: tk = AutoTokenizer.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token) except ValueError as e: return ( False, f"uses a tokenizer which is not in a transformers release: {e}", None ) except Exception as e: return (False, "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?", None) return True, None, config 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.", None ) except Exception as e: return False, "was not found on hub!", None def get_model_size(model_info: ModelInfo, precision: str): """Gets the model size from the configuration, or the model name if the configuration does not contain the information.""" try: model_size = round(model_info.safetensors["total"] / 1e9, 3) except (AttributeError, TypeError): return 0 # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1 model_size = size_factor * model_size return model_size def get_model_arch(model_info: ModelInfo): """Gets the model architecture from the configuration""" return model_info.config.get("architectures", "Unknown") def already_submitted_models(requested_models_dir: str) -> set[str]: """Gather a list of already submitted models to avoid duplicates""" 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['track']}") # 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 is_valid_predictions(predictions: dict) -> tuple[bool, str]: out_msg = "" for task in TEXT_TASKS: if task not in predictions: out_msg = f"Error: {task} not present" break for subtask in TEXT_TASKS[task]: if subtask not in predictions[task]: out_msg = f"Error: {subtask} not present under {task}" break if out_msg != "": break if "vqa" in predictions or "winoground" in predictions or "devbench" in predictions: for task in VISION_TASKS: if task not in predictions: out_msg = f"Error: {task} not present" break for subtask in VISION_TASKS[task]: if subtask not in predictions[task]: out_msg = f"Error: {subtask} not present under {task}" break if out_msg != "": break # Make sure all examples have predictions, and that predictions are the correct type for task in predictions: for subtask in predictions[task]: if task == "devbench": a = np.array(predictions[task][subtask]["predictions"]) if subtask == "sem-things": required_shape = (1854, 1854) elif subtask == "gram-trog": required_shape = (76, 4, 1) elif subtask == "lex-viz_vocab": required_shape = (119, 4, 1) if a.shape[0] != required_shape[0] or a.shape[1] != required_shape[1]: out_msg = f"Error: Wrong shape for results for `{subtask}` in `{task}`." break if not str(a.dtype).startswith("float"): out_msg = f"Error: Results for `{subtask}` ({task}) \ should be floats but aren't." break continue num_expected_examples = NUM_EXPECTED_EXAMPLES[task][subtask] if len(predictions[task][subtask]["predictions"]) != num_expected_examples: out_msg = f"Error: {subtask} has the wrong number of examples." break if task == "glue": if type(predictions[task][subtask]["predictions"][0]["pred"]) != int: out_msg = f"Error: results for `{subtask}` (`{task}`) should be integers but aren't." break else: if type(predictions[task][subtask]["predictions"][0]["pred"]) != str: out_msg = f"Error: results for `{subtask}` (`{task}`) should be strings but aren't." break if out_msg != "": break if out_msg != "": return False, out_msg return True, "Upload successful."