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

import pytest
import transformers
from transformers import (AutoConfig, AutoModelForVision2Seq, AutoTokenizer,
                          BatchEncoding)

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

from ....conftest import (VIDEO_ASSETS, AphroditeRunner, HfRunner,
                          PromptImageInput, _VideoAssets)
from ...utils import check_logprobs_close

# Video test
HF_VIDEO_PROMPTS = VIDEO_ASSETS.prompts(
    {
        "sample_demo_1": "<|im_start|>user <video>\nwhy is this video funny? \
    <|im_end|><|im_start|>assistant\n"
    }
)
models = ["llava-hf/llava-onevision-qwen2-7b-ov-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
    ]
    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


@overload
def run_video_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_video_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_video_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,
):
    torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]
    videos = [
        sample_frames_from_video(asset.np_ndarrays, num_frames)
        for asset in video_assets
    ]
    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
        ]

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

    with hf_runner(
        model,
        dtype=dtype,
        postprocess_inputs=process,
        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_video_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_video_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,
    )


# Image test
_LIMIT_IMAGE_PER_PROMPT = 4


def run_image_test(
    hf_runner: Type[HfRunner],
    aphrodite_runner: Type[AphroditeRunner],
    inputs: List[Tuple[List[str], PromptImageInput]],
    model: str,
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
    tensor_parallel_size: int,
    distributed_executor_backend: Optional[str] = None,
):
    torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]
    # max_model_len should be greater than image_feature_size
    with aphrodite_runner(
        model,
        dtype=dtype,
        max_model_len=32768,
        tensor_parallel_size=tensor_parallel_size,
        distributed_executor_backend=distributed_executor_backend,
        enforce_eager=True,
        limit_mm_per_prompt={"image": _LIMIT_IMAGE_PER_PROMPT},
    ) as aphrodite_model:
        aphrodite_outputs_per_image = [
            aphrodite_model.generate_greedy_logprobs(
                prompts, max_tokens, num_logprobs=num_logprobs, images=images
            )
            for prompts, images in inputs
        ]

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

    with hf_runner(
        model,
        dtype=dtype,
        postprocess_inputs=process,
        auto_cls=AutoModelForVision2Seq,
    ) as hf_model:
        hf_outputs_per_image = [
            hf_model.generate_greedy_logprobs_limit(
                prompts, max_tokens, num_logprobs=num_logprobs, images=images
            )
            for prompts, images in inputs
        ]
    for hf_outputs, aphrodite_outputs in zip(
        hf_outputs_per_image, aphrodite_outputs_per_image
    ):
        # 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("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models_multiple_image_inputs(
    hf_runner, aphrodite_runner, image_assets, model, dtype, max_tokens, num_logprobs
) -> None:
    stop_sign = image_assets[0].pil_image
    cherry_blossom = image_assets[1].pil_image
    inputs = [
        (
            [
                "<|im_start|>user <image><image>\nDescribe 2 images. \
                <|im_end|><|im_start|>assistant\n",
                "<|im_start|>user <image><image>\nDescribe 2 images. \
                <|im_end|><|im_start|>assistant\n",
                "<|im_start|>user <image><image><image><image>\nDescribe 4 images. \
                <|im_end|><|im_start|>assistant\n",
                "<|im_start|>user <image>\nWhat is the season? \
                <|im_end|><|im_start|>assistant\n",
            ],
            [
                [stop_sign, cherry_blossom],
                # Images with different sizes and aspect-ratios
                [
                    rescale_image_size(stop_sign, 0.1),
                    stop_sign,
                ],
                [
                    stop_sign,
                    rescale_image_size(stop_sign, 0.25),
                    cherry_blossom.resize((183, 488)),
                    cherry_blossom.resize((488, 183)),
                ],
                cherry_blossom,
            ],
        )
    ]
    run_image_test(
        hf_runner,
        aphrodite_runner,
        inputs,
        model,
        dtype=dtype,
        max_tokens=max_tokens,
        num_logprobs=num_logprobs,
        tensor_parallel_size=1,
    )