import folder_paths
from .model_cache import cache
from .helpers import pull_metadata, clean_keywords

def get_lora_keywords(lora_name):
    lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
    if cache.by_path(lora_path).get("trainedWords") is None:
        pull_metadata(lora_path, timestamp=True)
    return cache.by_path(lora_path).get("trainedWords", [])

def get_lora_stack_keywords(lora_stack=None):
    if not lora_stack:
        return []
    # Collect unique lora names
    lora_names = {lora[0] for lora in lora_stack}
    lora_paths = [folder_paths.get_full_path_or_raise("loras", name) for name in lora_names]
    pull_metadata(lora_paths)
    # Gather all keywords into a set for uniqueness
    all_keywords = set()
    for name in lora_names:
        try:
            keywords = cache.by_path(folder_paths.get_full_path_or_raise("loras", name)).get("trainedWords", [])
            if keywords:
                all_keywords.update(map(str.strip, keywords))
        except Exception as e:
            print(f"Exception getting keywords for {name}: {e}")
            continue
    return clean_keywords(all_keywords)

def add_lora_to_stack(lora_name, model_weight, clip_weight, lora_stack=None):
    lora = (lora_name, model_weight, clip_weight)
    if lora_stack is None:
        return [lora]
    return [*lora_stack, lora]