from typing import List, Optional, Type

import pytest
from transformers import BatchEncoding

from vllm.multimodal.utils import rescale_image_size
from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE

from ..conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets
from .utils import check_outputs_equal

pytestmark = pytest.mark.vlm

HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
    "stop_sign":
    "USER: <image>\nWhat's the content of the image?\nASSISTANT:",
    "cherry_blossom":
    "USER: <image>\nWhat is the season?\nASSISTANT:",
})

models = ["facebook/chameleon-7b"]


def run_test(
    hf_runner: Type[HfRunner],
    vllm_runner: Type[VllmRunner],
    image_assets: _ImageAssets,
    model: str,
    *,
    size_factors: List[float],
    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 vllm.

    All the image fixtures for the test is under tests/images.
    For huggingface runner, we provide the PIL images as input.
    For vllm runner, we provide MultiModalDataDict objects 
    and corresponding vision language config as input.
    Note, the text input is also adjusted to abide by vllm contract.
    The text output is sanitized to be able to compare with hf.
    """
    torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]
    images = [asset.pil_image for asset in image_assets]

    inputs_per_image = [(
        [prompt for _ in size_factors],
        [rescale_image_size(image, factor) for factor in size_factors],
    ) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]

    with vllm_runner(model,
                     max_model_len=4096,
                     dtype=dtype,
                     tensor_parallel_size=tensor_parallel_size,
                     distributed_executor_backend=distributed_executor_backend,
                     enforce_eager=True) as vllm_model:

        vllm_outputs_per_image = [
            vllm_model.generate_greedy_logprobs(prompts,
                                                max_tokens,
                                                num_logprobs=num_logprobs,
                                                images=images)
            for prompts, images in inputs_per_image
        ]

    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,
                   is_vision_model=True) 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_per_image
        ]

    for hf_outputs, vllm_outputs in zip(hf_outputs_per_image,
                                        vllm_outputs_per_image):
        # HF Logprobs include image tokens, unlike vLLM, so we don't directly
        # compare them
        check_outputs_equal(
            outputs_0_lst=[outputs[:2] for outputs in hf_outputs],
            outputs_1_lst=[outputs[:2] for outputs in vllm_outputs],
            name_0="hf",
            name_1="vllm",
        )


@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize(
    "size_factors",
    [
        # No image
        [],
        # 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", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [8])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,
                dtype, max_tokens, num_logprobs) -> None:
    run_test(
        hf_runner,
        vllm_runner,
        image_assets,
        model,
        size_factors=size_factors,
        dtype=dtype,
        max_tokens=max_tokens,
        num_logprobs=num_logprobs,
        tensor_parallel_size=1,
    )
