from typing import List, Optional, Tuple, Type, overload

import pytest
import transformers
from transformers import AutoConfig, AutoModelForVision2Seq, AutoTokenizer

from aphrodite.common.sequence import SampleLogprobs
from aphrodite.multimodal.utils import (rescale_video_size, resize_video,
                                        sample_frames_from_video)

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

pytestmark = pytest.mark.vlm
_PREFACE = (
    "A chat between a curious human and an artificial intelligence assistant. "
    "The assistant gives helpful, detailed, and polite answers to the human's "
    "questions."
)
HF_VIDEO_PROMPTS = VIDEO_ASSETS.prompts(
    {
        "sample_demo_1": f"{_PREFACE}USER: <video>\nWhy is this video funny? "
        "ASSISTANT:"
    }
)
models = ["llava-hf/LLaVA-NeXT-Video-7B-hf"]


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
    config = AutoConfig.from_pretrained(model)
    video_token_id = config.video_token_index
    tokenizer = AutoTokenizer.from_pretrained(model)
    eos_token_id = tokenizer.eos_token_id
    hf_output_ids = [
        token_id
        for idx, token_id in enumerate(output_ids)
        if token_id != video_token_id or output_ids[idx - 1] != video_token_id
    ]
    assert output_str[0] == " "
    hf_output_str = output_str[1:]
    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


@overload
def run_test(
    hf_runner: Type[HfRunner],
    aphrodite_runner: Type[AphroditeRunner],
    video_assets: _VideoAssets,
    model: str,
    *,
    size_factors: List[float],
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
    num_frames: int,
    tensor_parallel_size: int,
    distributed_executor_backend: Optional[str] = None,
):
    ...


@overload
def run_test(
    hf_runner: Type[HfRunner],
    aphrodite_runner: Type[AphroditeRunner],
    video_assets: _VideoAssets,
    model: str,
    *,
    sizes: List[Tuple[int, int]],
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
    num_frames: int,
    tensor_parallel_size: int,
    distributed_executor_backend: Optional[str] = None,
):
    ...


def run_test(
    hf_runner: Type[HfRunner],
    aphrodite_runner: Type[AphroditeRunner],
    video_assets: _VideoAssets,
    model: str,
    *,
    size_factors: Optional[List[float]] = None,
    sizes: Optional[List[Tuple[int, int]]] = None,
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
    num_frames: int,
    tensor_parallel_size: int,
    distributed_executor_backend: Optional[str] = None,
):
    videos = [
        sample_frames_from_video(asset.np_ndarrays, num_frames)
        for asset in video_assets
    ]
    for video in videos:
        print(video.shape)
    if size_factors is not None:
        inputs_per_video = [
            (
                [prompt for _ in size_factors],
                [rescale_video_size(video, factor) for factor in size_factors],
            )
            for video, prompt in zip(videos, HF_VIDEO_PROMPTS)
        ]
    elif sizes is not None:
        inputs_per_video = [
            (
                [prompt for _ in sizes],
                [resize_video(video, size) for size in sizes],
            )
            for video, prompt in zip(videos, HF_VIDEO_PROMPTS)
        ]
    else:
        raise ValueError("You must provide either `size_factors` or `sizes`")
    # max_model_len should be greater than image_feature_size
    with aphrodite_runner(
        model,
        dtype=dtype,
        max_model_len=4096,
        tensor_parallel_size=tensor_parallel_size,
        distributed_executor_backend=distributed_executor_backend,
        enforce_eager=True,
    ) as aphrodite_model:
        aphrodite_outputs_per_video = [
            aphrodite_model.generate_greedy_logprobs(
                prompts, max_tokens, num_logprobs=num_logprobs, videos=videos
            )
            for prompts, videos in inputs_per_video
        ]
    with hf_runner(
        model, dtype=dtype, auto_cls=AutoModelForVision2Seq
    ) as hf_model:
        hf_outputs_per_video = [
            hf_model.generate_greedy_logprobs_limit(
                prompts, max_tokens, num_logprobs=num_logprobs, videos=videos
            )
            for prompts, videos in inputs_per_video
        ]
    for hf_outputs, aphrodite_outputs in zip(
        hf_outputs_per_video, aphrodite_outputs_per_video
    ):
        # TODO: Check whether using original CLIPVisionModel can improve
        # consistency against HF
        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",
        )


@pytest.mark.skipif(
    transformers.__version__ < "4.45",
    reason="Waiting for next transformers release",
)
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize(
    "size_factors",
    [
        # No video
        [],
        # Single-scale
        [1.0],
        # Single-scale, batched
        [1.0, 1.0, 1.0],
        # Multi-scale
        [0.25, 0.5, 1.0],
    ],
)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize("num_frames", [16])
def test_models(
    hf_runner,
    aphrodite_runner,
    video_assets,
    model,
    size_factors,
    dtype,
    max_tokens,
    num_logprobs,
    num_frames,
) -> None:
    """Inference result should be the same between hf and aphrodite.
    All the image fixtures for the test is under tests/videos.
    For huggingface runner, we provide the np.ndarray as input.
    For aphrodite runner, we provide MultiModalDataDict objects
    and corresponding MultiModalConfig as input.
    Note, the text input is also adjusted to abide by aphrodite contract.
    The text output is sanitized to be able to compare with hf.
    """
    run_test(
        hf_runner,
        aphrodite_runner,
        video_assets,
        model,
        size_factors=size_factors,
        dtype=dtype,
        max_tokens=max_tokens,
        num_logprobs=num_logprobs,
        num_frames=num_frames,
        tensor_parallel_size=1,
    )


@pytest.mark.skipif(
    transformers.__version__ < "4.45",
    reason="Waiting for next transformers release",
)
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize(
    "sizes",
    [[(1669, 2560), (2560, 1669), (183, 488), (488, 183)]],
)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
@pytest.mark.parametrize("num_frames", [16])
def test_models_fixed_sizes(
    hf_runner,
    aphrodite_runner,
    video_assets,
    model,
    sizes,
    dtype,
    max_tokens,
    num_logprobs,
    num_frames,
) -> None:
    run_test(
        hf_runner,
        aphrodite_runner,
        video_assets,
        model,
        sizes=sizes,
        dtype=dtype,
        max_tokens=max_tokens,
        num_logprobs=num_logprobs,
        num_frames=num_frames,
        tensor_parallel_size=1,
    )