"""Tests for phi3v's multimodal preprocessing kwargs."""
from typing import Optional

import pytest
from transformers import AutoTokenizer

from vllm.inputs import InputContext, InputProcessingContext
from vllm.model_executor.models.phi3v import _IMAGE_TOKEN_ID

from .....conftest import _ImageAssets
from ....utils import build_model_context

models = ["microsoft/Phi-3.5-vision-instruct"]


# Wrap lazy imports to avoid initializing CUDA during test collection
@pytest.fixture()
def processor_for_phi3v():
    from vllm.model_executor.models.phi3v import Phi3VMultiModalProcessor
    return Phi3VMultiModalProcessor


@pytest.fixture()
def get_max_phi3v_image_tokens():
    from vllm.model_executor.models.phi3v import get_max_phi3v_image_tokens
    return get_max_phi3v_image_tokens


@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize("num_crops,expected_max_tokens", [
    (4, 781),
    (16, 2653),
])
def test_max_tokens_override(get_max_phi3v_image_tokens, model: str,
                             num_crops: int, expected_max_tokens: int):
    """Ensure get_max_phi3v_image_tokens handles num_crops properly."""
    # NOTE: mm_processor_kwargs on the context in this test is unused, since
    # this is testing the mapper directly. In practice, the processor kwargs
    # are wrapped in a closure when calling the max tokens func. We explicitly
    # do NOT use the mm_processor_kwargs in the model context here to ensure
    # that the max image tokens implementation is referencing a mix of the
    # kwargs to the function and the original mm_processor_kwargs in case
    # values are somehow updated and end up in a bad state.
    ctx = build_model_context(
        model_name=model,
        tokenizer_name=model,
        trust_remote_code=True,
        mm_processor_kwargs=None,
    )

    actual_max_tokens = get_max_phi3v_image_tokens(
        InputContext(ctx.model_config),
        num_crops=num_crops,
    )

    assert expected_max_tokens == actual_max_tokens


@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize(
    "num_crops,expected_toks_per_img,num_imgs",
    [
        (4, 757, 1),
        (4, 757, 2),
        (16, 1921, 1),
        (16, 1921, 2),
        # the default num_crops of phi-3.5-vision is 4
        (None, 757, 2),
        (None, 757, 2),
    ])
def test_processor_override(processor_for_phi3v, image_assets: _ImageAssets,
                            model: str, num_crops: Optional[int],
                            expected_toks_per_img: int, num_imgs: int):
    """Ensure input_processor_for_phi3v handles num_crops properly."""
    # Same as the previous test - don't initialize mm_processor_kwargs
    # in this test and assume that the kwargs will be correctly expanded by
    # the partial when calling the custom input processor.
    ctx = build_model_context(
        model_name=model,
        tokenizer_name=model,
        trust_remote_code=True,
    )
    tokenizer = AutoTokenizer.from_pretrained(model, trust_remote_code=True)
    ctx = InputProcessingContext(ctx.model_config, tokenizer)
    # Build the image str / prompt based on the number of images we pass
    img_str = "".join([f"<|image_{idx}|>\n" for idx in range(1, num_imgs + 1)])
    prompt = f"<|user|>\n{img_str}<|end|>\n<|assistant|>\n"
    images = [image_assets[0].pil_image] * num_imgs

    mm_data = {"image": images}
    mm_processor_kwargs = {}
    if num_crops is not None:
        mm_processor_kwargs = {"num_crops": num_crops}

    processor = processor_for_phi3v(ctx)
    processed_inputs = processor.apply(prompt, mm_data, mm_processor_kwargs)

    # Ensure we have the right number of placeholders per num_crops size
    img_tok_count = processed_inputs["prompt_token_ids"].count(_IMAGE_TOKEN_ID)
    assert img_tok_count == expected_toks_per_img * num_imgs
