# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Compare the outputs of HF and vLLM when using beam search.

Run `pytest tests/samplers/test_beam_search.py`.
"""

import pytest
import os
from transformers import AutoModelForSeq2SeqLM

from vllm.assets.audio import AudioAsset
from ..utils import models_path_prefix


@pytest.fixture(autouse=True)
def v1(run_with_both_engines):
    """We can run both engines for this test."""
    pass


# FIXME(zhuohan): The test can not pass if we:
#   1. Increase max_tokens to 256.
#   2. Increase beam_width to 8.
#   3. Use the model "huggyllama/llama-7b".
MAX_TOKENS = [64]
BEAM_WIDTHS = [4]
MM_BEAM_WIDTHS = [2]
MODELS = [os.path.join(models_path_prefix, "TinyLlama/TinyLlama-1.1B-Chat-v1.0")] 


@pytest.mark.skip_v1  # FIXME: This fails on V1 right now.
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", MAX_TOKENS)
@pytest.mark.parametrize("beam_width", BEAM_WIDTHS)
def test_beam_search_single_input(
    hf_runner,
    vllm_runner,
    example_prompts,
    model: str,
    dtype: str,
    max_tokens: int,
    beam_width: int,
) -> None:
    example_prompts = example_prompts[:1]
    with hf_runner(model, dtype=dtype) as hf_model:
        hf_outputs = hf_model.generate_beam_search(example_prompts, beam_width,
                                                   max_tokens)

    with vllm_runner(model, dtype=dtype) as vllm_model:
        vllm_outputs = vllm_model.generate_beam_search(example_prompts,
                                                       beam_width, max_tokens)

    for i in range(len(example_prompts)):
        hf_output_ids, hf_output_texts = hf_outputs[i]
        vllm_output_ids, vllm_output_texts = vllm_outputs[i]
        for j, (hf_text,
                vllm_text) in enumerate(zip(hf_output_texts,
                                            vllm_output_texts)):
            print(f">>>{j}-th hf output:")
            print(hf_text)
            print(f">>>{j}-th vllm output:")
            print(vllm_text)
        assert len(hf_output_ids) == len(vllm_output_ids)
        for j in range(len(hf_output_ids)):
            assert hf_output_ids[j] == vllm_output_ids[j], (
                f"Test{i} output{j}:\nHF: {hf_output_ids}\n"
                f"vLLM: {vllm_output_ids}")


@pytest.mark.skip_v1  # FIXME: This fails on V1 right now.
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", MAX_TOKENS)
@pytest.mark.parametrize("beam_width", BEAM_WIDTHS)
def test_beam_search_with_concurrency_limit(
    hf_runner,
    vllm_runner,
    example_prompts,
    model: str,
    dtype: str,
    max_tokens: int,
    beam_width: int,
) -> None:
    # example_prompts[1]&[3]&[7] fails due to unknown reason even without
    # concurrency limit. skip them for now.
    example_prompts = (example_prompts[:8])
    concurrency_limit = 2
    assert len(example_prompts) > concurrency_limit
    with vllm_runner(model, dtype=dtype) as vllm_model:
        outputs_with_limit = vllm_model.generate_beam_search(
            example_prompts,
            beam_width,
            max_tokens,
            concurrency_limit=concurrency_limit)
        outputs_without_limit = []

        for i in range(0, len(example_prompts), concurrency_limit):
            outputs_without_limit.extend(
                vllm_model.generate_beam_search(
                    example_prompts[i:i + concurrency_limit], beam_width,
                    max_tokens))

    correct = True
    for i in range(len(example_prompts)):
        output_ids_with_limit, output_texts_with_limit = outputs_with_limit[i]
        output_ids_without_limit, output_texts_without_limit = (
            outputs_without_limit[i])
        for j, (text_with_limit, text_without_limit) in enumerate(
                zip(output_texts_with_limit, output_texts_without_limit)):
            print(f">>>{j}-th with limit output:")
            print(text_with_limit)
            print(f">>>{j}-th without limit output:")
            print(text_without_limit)
        assert len(output_ids_with_limit) == len(output_ids_without_limit)
        for j in range(len(output_ids_with_limit)):
            if output_ids_with_limit[j] != output_ids_without_limit[j]:
                print(f"Test{i} output{j}:\n+limit: {output_ids_with_limit}\n"
                      f"-limit: {output_ids_without_limit}")
                correct = False
    assert correct


@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", MAX_TOKENS)
@pytest.mark.parametrize("beam_width", MM_BEAM_WIDTHS)
def test_beam_search_passes_multimodal_data(
    hf_runner,
    vllm_runner,
    dtype: str,
    max_tokens: int,
    beam_width: int,
) -> None:
    """Ensure that beam search passes multimodal data through correctly."""
    # NOTE - this test is primarily to check that mm data is passed to beams
    # correctly. As such, we just need to check one extra modality to make
    # sure things pass through properly.
    audios = [AudioAsset("mary_had_lamb").audio_and_sample_rate]
    model = os.path.join(models_path_prefix, "Qwen/Qwen2-Audio-7B-Instruct")
    audio_seq = "<|audio_bos|><|AUDIO|><|audio_eos|>"
    prompts = [
        f"<|im_start|>user\n{audio_seq}Can you transcribe this?<|im_end|>\n<|im_start|>assistant\n"  #noqa: E501
    ]

    with hf_runner(model, dtype=dtype,
                   auto_cls=AutoModelForSeq2SeqLM) as hf_model:
        audio_token_id = hf_model.config.audio_token_index
        eos_token_id = hf_model.tokenizer.eos_token_id  # <|im_end|>
        hf_outputs = hf_model.generate_beam_search(
            prompts,
            beam_width=beam_width,
            max_tokens=max_tokens,
            audios=audios,
        )

    with vllm_runner(model, dtype=dtype) as vllm_model:
        vllm_outputs = vllm_model.generate_beam_search(
            prompts,
            beam_width=beam_width,
            max_tokens=max_tokens,
            audios=audios,
        )

    seq_with_no_audio_toks = lambda seq: [
        tok for tok in seq if tok != audio_token_id
    ]

    for i in range(len(prompts)):
        hf_output_ids, hf_output_texts = hf_outputs[i]
        vllm_output_ids, vllm_output_texts = vllm_outputs[i]

        for j, (hf_text,
                vllm_text) in enumerate(zip(hf_output_texts,
                                            vllm_output_texts)):
            print(f">>>{j}-th hf output [NOTE: special tokens are filtered]:")
            print(hf_text)
            print(f">>>{j}-th vllm output:")
            print(vllm_text)
        assert len(hf_output_ids) == len(vllm_output_ids)

        for j in range(len(hf_output_ids)):
            # Compare everything except for the audio tokens; we do this since
            # the IDs returned from the transformers helper expands the audio
            # token to match features, while the vLLM helper maintains the
            # single audio token in the input text
            filtered_hf_output_ids = seq_with_no_audio_toks(hf_output_ids[j])
            filtered_vllm_output_ids = seq_with_no_audio_toks(
                vllm_output_ids[j])

            # HF output IDs may contain the end of sequence
            if len(filtered_hf_output_ids
                   ) == len(filtered_vllm_output_ids) + 1:
                assert filtered_hf_output_ids[-1] == eos_token_id
                filtered_hf_output_ids = filtered_hf_output_ids[:-1]

            assert filtered_hf_output_ids == filtered_vllm_output_ids