import pytest

from aphrodite.endpoints.llm import LLM
from aphrodite.common.sampling_params import SamplingParams


@pytest.mark.skip_v1
@pytest.mark.parametrize("model", ["distilbert/distilgpt2"])
def test_computed_prefix_blocks(model: str):
    # This test checks if the engine generates completions both with and
    # without optional detokenization, that detokenization includes text
    # and no-detokenization doesn't, and that both completions have the same
    # token_ids.
    prompt = (
        "You are a helpful assistant. How do I build a car from cardboard and "
        "paper clips? Is there an easy to follow video tutorial available "
        "online for free?")

    llm = LLM(model=model)
    sampling_params = SamplingParams(max_tokens=10,
                                     temperature=0.0,
                                     detokenize=False)

    outputs_no_detokenization = llm.generate(prompt,
                                             sampling_params)[0].outputs[0]
    sampling_params.detokenize = True
    outputs_with_detokenization = llm.generate(prompt,
                                               sampling_params)[0].outputs[0]

    assert outputs_no_detokenization.text == ''
    assert outputs_with_detokenization.text != ''
    assert outputs_no_detokenization.token_ids == \
        outputs_with_detokenization.token_ids
