MAX_CTX_LEN = 50


# -- dummy placeholder untuk model NER/SRL Anda -------------------------------
def predict_ner(tokens):  # ganti sesuai implementasi
    return ["O"] * len(tokens)


def predict_srl(tokens):  # ganti sesuai implementasi
    return ["O"] * len(tokens)


# ------------------------------------------------------------------------------


def greedy_decode(context_tokens):
    """Menghasilkan satu pertanyaan (greedy)."""
    # 6.1  Tagging
    ner_tags = predict_ner(context_tokens)
    srl_tags = predict_srl(context_tokens)

    # 6.2  Encode
    ctx_ids = encode(context_tokens, w2i_ctx, MAX_CTX_LEN)[None]
    ner_ids = encode(ner_tags, t2i_ner, MAX_CTX_LEN)[None]
    srl_ids = encode(srl_tags, t2i_srl, MAX_CTX_LEN)[None]

    dec_seq = [w2i_q["<bos>"]]
    for _ in range(MAX_Q_LEN - 1):
        dec_pad = dec_seq + [w2i_q["<pad>"]] * (MAX_Q_LEN - len(dec_seq))
        pred = model.predict(
            [ctx_ids, ner_ids, srl_ids, np.array([dec_pad])], verbose=0
        )
        next_id = int(pred[0, len(dec_seq) - 1].argmax())
        if i2w_q[next_id] == "<eos>":
            break
        dec_seq.append(next_id)

    tokens_q = [i2w_q[t] for t in dec_seq[1:]]
    return " ".join(tokens_q)


if __name__ == "__main__":
    sample = [
        "Keberagaman",
        "potensi",
        "sumber",
        "daya",
        "alam",
        "Indonesia",
        "tidak",
        "lepas",
        "dari",
        "proses",
        "geografis",
        ".",
    ]
    print("\n[CTX]", " ".join(sample))
    print("[Q]  ", greedy_decode(sample))