# import json
# import os
# from collections import defaultdict
#
# 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
#
#
# 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 = 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:
#                 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:
#         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)) 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