from typing import List, Optional, Tuple, Type

import numpy as np
import pytest
from transformers import AutoModel, AutoTokenizer, BatchEncoding

from aphrodite.common.sequence import SampleLogprobs
from aphrodite.common.utils import STR_DTYPE_TO_TORCH_DTYPE

from ..conftest import AphroditeRunner, HfRunner
from .utils import check_logprobs_close

pytestmark = pytest.mark.vlm

MODEL_NAME = "fixie-ai/ultravox-v0_3"

AudioTuple = Tuple[np.ndarray, int]

APHRODITE_PLACEHOLDER = "<|reserved_special_token_0|>"
HF_PLACEHOLDER = "<|audio|>"


@pytest.fixture(scope="session")
def audio_assets():
    from aphrodite.assets.audio import AudioAsset
    return [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]


@pytest.fixture(scope="module", params=("mary_had_lamb", "winning_call"))
def audio(request):
    from aphrodite.assets.audio import AudioAsset
    return AudioAsset(request.param)


def _get_prompt(audio_count, question, placeholder):
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    placeholder = f"{placeholder}\n" * audio_count

    return tokenizer.apply_chat_template([{
        'role': 'user',
        'content': f"{placeholder}{question}"
    }],
                                         tokenize=False,
                                         add_generation_prompt=True)


def aphrodite_to_hf_output(aphrodite_output: Tuple[List[int], str,
                                         Optional[SampleLogprobs]],
                      model: str):
    """Sanitize aphrodite output to be comparable with hf output."""
    output_ids, output_str, out_logprobs = aphrodite_output

    tokenizer = AutoTokenizer.from_pretrained(model)
    eos_token_id = tokenizer.eos_token_id

    hf_output_ids = output_ids[:]
    hf_output_str = output_str
    if hf_output_ids[-1] == eos_token_id:
        hf_output_str = hf_output_str + tokenizer.decode(eos_token_id)

    return hf_output_ids, hf_output_str, out_logprobs


def run_test(
    hf_runner: Type[HfRunner],
    aphrodite_runner: Type[AphroditeRunner],
    prompts_and_audios: List[Tuple[str, str, AudioTuple]],
    model: str,
    *,
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
    tensor_parallel_size: int,
    distributed_executor_backend: Optional[str] = None,
):
    """Inference result should be the same between hf and aphrodite."""
    torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]

    # NOTE: take care of the order. run Aphrodite first, and then run HF.
    # Aphrodite needs a fresh new process without cuda initialization.
    # if we run HF first, the cuda initialization will be done and it
    # will hurt multiprocessing backend with fork method (the default method).

    with aphrodite_runner(model,
                     dtype=dtype,
                     tensor_parallel_size=tensor_parallel_size,
                     distributed_executor_backend=distributed_executor_backend,
                     enforce_eager=True) as aphrodite_model:
        aphrodite_outputs_per_audio = [
            aphrodite_model.generate_greedy_logprobs([aphrodite_prompt],
                                                max_tokens,
                                                num_logprobs=num_logprobs,
                                                audios=[audio])
            for aphrodite_prompt, _, audio in prompts_and_audios
        ]

    def process(hf_inputs: BatchEncoding):
        hf_inputs["audio_values"] = hf_inputs["audio_values"] \
            .to(torch_dtype)  # type: ignore
        return hf_inputs

    with hf_runner(model,
                   dtype=dtype,
                   postprocess_inputs=process,
                   auto_cls=AutoModel) as hf_model:
        import librosa

        hf_outputs_per_audio = [
            hf_model.generate_greedy_logprobs_limit(
                [hf_prompt],
                max_tokens,
                num_logprobs=num_logprobs,
                audios=[(librosa.resample(audio[0],
                                          orig_sr=audio[1],
                                          target_sr=16000), 16000)])
            for _, hf_prompt, audio in prompts_and_audios
        ]

    for hf_outputs, aphrodite_outputs in zip(hf_outputs_per_audio,
                                        aphrodite_outputs_per_audio):
        check_logprobs_close(
            outputs_0_lst=hf_outputs,
            outputs_1_lst=[
                aphrodite_to_hf_output(aphrodite_output, model)
                for aphrodite_output in aphrodite_outputs
            ],
            name_0="hf",
            name_1="aphrodite",
        )


def run_multi_audio_test(
    aphrodite_runner: Type[AphroditeRunner],
    prompts_and_audios: List[Tuple[str, List[AudioTuple]]],
    model: str,
    *,
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
    tensor_parallel_size: int,
    distributed_executor_backend: Optional[str] = None,
):
    with aphrodite_runner(model,
                     dtype=dtype,
                     tensor_parallel_size=tensor_parallel_size,
                     distributed_executor_backend=distributed_executor_backend,
                     enforce_eager=True,
                     limit_mm_per_prompt={
                         "audio":
                         max((len(audio) for _, audio in prompts_and_audios))
                     }) as aphrodite_model:
        aphrodite_outputs = aphrodite_model.generate_greedy_logprobs(
            [prompt for prompt, _ in prompts_and_audios],
            max_tokens,
            num_logprobs=num_logprobs,
            audios=[audios for _, audios in prompts_and_audios])

    # The HuggingFace model doesn't support multiple audios yet, so
    # just assert that some tokens were generated.
    assert all(tokens for tokens, *_ in aphrodite_outputs)


@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(hf_runner, aphrodite_runner, audio, dtype: str, max_tokens: int,
                num_logprobs: int) -> None:

    aphrodite_prompt = _get_prompt(1, "Describe the audio above.",
                                   APHRODITE_PLACEHOLDER)
    hf_prompt = _get_prompt(1, "Describe the audio above.", HF_PLACEHOLDER)
    run_test(
        hf_runner,
        aphrodite_runner,
        [(aphrodite_prompt, hf_prompt, audio.audio_and_sample_rate)],
        MODEL_NAME,
        dtype=dtype,
        max_tokens=max_tokens,
        num_logprobs=num_logprobs,
        tensor_parallel_size=1,
    )


@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models_with_multiple_audios(aphrodite_runner, audio_assets, dtype: str,
                                     max_tokens: int,
                                     num_logprobs: int) -> None:

    aphrodite_prompt = _get_prompt(len(audio_assets),
                              "Describe each of the audios above.",
                              APHRODITE_PLACEHOLDER)
    run_multi_audio_test(
        aphrodite_runner,
        [(aphrodite_prompt, [audio.audio_and_sample_rate
                        for audio in audio_assets])],
        MODEL_NAME,
        dtype=dtype,
        max_tokens=max_tokens,
        num_logprobs=num_logprobs,
        tensor_parallel_size=1,
    )