def list_uniq(l):
    return sorted(set(l), key=l.index)

def get_status(model_name: str):
    from huggingface_hub import InferenceClient
    client = InferenceClient(timeout=10)
    return client.get_model_status(model_name)

def is_loadable(model_name: str, force_gpu: bool = False):
    try:
        status = get_status(model_name)
    except Exception as e:
        print(e)
        print(f"Couldn't load {model_name}.")
        return False
    gpu_state = isinstance(status.compute_type, dict) and "gpu" in status.compute_type.keys()
    if status is None or status.state not in ["Loadable", "Loaded"] or (force_gpu and not gpu_state):
        print(f"Couldn't load {model_name}. Model state:'{status.state}', GPU:{gpu_state}")
    return status is not None and status.state in ["Loadable", "Loaded"] and (not force_gpu or gpu_state)

def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="last_modified", limit: int=30, force_gpu=True):
    from huggingface_hub import HfApi
    api = HfApi()
    #default_tags = ["transformers"]
    default_tags = []
    if not sort: sort = "last_modified"
    models = []
    limit = limit * 20 if force_gpu else limit * 5
    try:
        model_infos = api.list_models(author=author, pipeline_tag="text-generation",
                                       tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit)
    except Exception as e:
        print(f"Error: Failed to list models.")
        print(e)
        return models
    for model in model_infos:
        if not model.private and not model.gated:
           if not_tag and not_tag in model.tags or not is_loadable(model.id, force_gpu): continue
           models.append(model.id)
           if len(models) == limit: break
    return models