def clean_output(decoded_list):
    """Remove duplicates and trim whitespace"""
    return list(dict.fromkeys([q.strip() for q in decoded_list if q.strip()]))


def preprocess_context(context):
    return f"generate question: {context.strip()}"



    """
    Compute SHAP token attributions for a given prompt.
    """
    # Tokenize input
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
    input_ids = inputs['input_ids']

    # Define wrapper prediction function
    def f(x):
        with torch.no_grad():
            out = model.generate(
                input_ids=torch.tensor(x).to(model.device),
                max_length=64,
                do_sample=False,
                num_beams=2
            )
        return out[:, 0].detach().cpu().numpy()

    explainer = shap.Explainer(f, tokenizer)
    shap_values = explainer([prompt])

    tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
    return shap_values.values[0], tokens