# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

from collections.abc import Set as AbstractSet
from functools import partial

import numpy as np
import pytest
from PIL import Image

from vllm.config import ModelConfig
from vllm.config.multimodal import (
    AudioDummyOptions,
    BaseDummyOptions,
    ImageDummyOptions,
    VideoDummyOptions,
)
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalDataDict
from vllm.multimodal.cache import MultiModalProcessorOnlyCache
from vllm.multimodal.inputs import MultiModalInputs, batched_tensors_equal
from vllm.multimodal.processing import (
    BaseMultiModalProcessor,
    InputProcessingContext,
)
from vllm.tokenizers import TokenizerLike, cached_tokenizer_from_config
from vllm.utils.mistral import is_mistral_tokenizer

from ....multimodal.utils import random_audio, random_image, random_video
from ...registry import (
    _MULTIMODAL_EXAMPLE_MODELS,
    _TRANSFORMERS_BACKEND_MODELS,
    HF_EXAMPLE_MODELS,
)


def add_video_metadata(mm_data: MultiModalDataDict) -> MultiModalDataDict:
    """
    Add metadata to video mm_data
    """

    def create_metadata(frames: np.ndarray):
        num_frames = len(frames)
        return {
            "total_num_frames": num_frames,
            "fps": 2.0,
            "duration": num_frames / 2.0,
            "video_backend": "opencv",
            "frames_indices": list(range(num_frames)),
            "do_sample_frames": True,
        }

    # Ensure video metadata is included
    if "video" in mm_data:
        video = mm_data["video"]
        if isinstance(video, list):
            # multiple videos
            mm_data["video"] = [(vid, create_metadata(vid)) for vid in video]
        else:
            # single video
            mm_data["video"] = (video, create_metadata(video))
    return mm_data


def glmasr_patch_mm_data(mm_data: MultiModalDataDict) -> MultiModalDataDict:
    """
    Patch the multimodal data for GLM-ASR model.
    GLM-ASR requires text and audio to match 1:1, so we limit audio to 1.
    """
    if "audio" in mm_data:
        audio = mm_data["audio"]
        if isinstance(audio, list) and len(audio) > 1:
            # Limit to single audio to match text requirement
            mm_data["audio"] = [audio[0]]
    return mm_data


_IGNORE_MM_KEYS = {
    # In Ultravox, the audio_features can be different depending on padding
    # The slight difference should not be a problem though, since
    # attention_mask lets us ignore the difference.
    "ultravox": {"audio_features"},
}

MM_DATA_PATCHES = {
    "glmasr": glmasr_patch_mm_data,
}


def _iter_model_ids_to_test(model_arch_list: AbstractSet[str]):
    for model_arch in model_arch_list:
        model_info = HF_EXAMPLE_MODELS.get_hf_info(model_arch)
        yield model_info.default

        for extra_type, extra_model_id in model_info.extras.items():
            if "fp" in extra_type:
                continue  # Redundant to test quantized models

            yield extra_model_id


def _get_model_ids_to_test(model_arch_list: AbstractSet[str]):
    return list(_iter_model_ids_to_test(model_arch_list))


def get_model_ids_to_test():
    transformers_arch_ids = {
        model_id
        for info in _TRANSFORMERS_BACKEND_MODELS.values()
        for model_id in (info.default, *info.extras.values())
    }
    vllm_only_archs = {
        arch
        for arch, info in _MULTIMODAL_EXAMPLE_MODELS.items()
        if not any(
            model_id in transformers_arch_ids
            for model_id in (info.default, *info.extras.values())
        )
    }

    return _get_model_ids_to_test(vllm_only_archs)


def get_text_token_prompts(
    processor: BaseMultiModalProcessor,
    mm_data: MultiModalDataDict,
):
    dummy_inputs = processor.dummy_inputs
    tokenizer: TokenizerLike = processor.info.get_tokenizer()
    model_config = processor.info.ctx.model_config

    if processor.info.data_parser.video_needs_metadata:
        mm_data = add_video_metadata(mm_data)

    model_type = model_config.hf_config.model_type
    if model_type in MM_DATA_PATCHES:
        mm_data = MM_DATA_PATCHES[model_type](mm_data)

    parsed_data = processor.info.parse_mm_data(mm_data)
    mm_counts = {k: len(vs) for k, vs in parsed_data.items()}

    if is_mistral_tokenizer(tokenizer):
        inputs = dummy_inputs.get_dummy_processor_inputs(
            model_config.max_model_len,
            mm_counts,
            mm_options={},
            # Assume all Mistral models define this extra argument
            mm_data=mm_data,  # type: ignore[call-arg]
        )
    else:
        inputs = dummy_inputs.get_dummy_processor_inputs(
            model_config.max_model_len,
            mm_counts,
            mm_options={},
        )

    text_prompt: str | None
    token_prompt: list[int]
    if isinstance(inputs.prompt, list):
        text_prompt = None
        token_prompt = inputs.prompt
    elif isinstance(inputs.prompt, str):
        text_prompt = inputs.prompt
        token_prompt = tokenizer.encode(
            text_prompt,
            **processor.info.get_default_tok_params().get_encode_kwargs(),
        )
    else:
        raise TypeError(type(inputs.prompt))

    return text_prompt, token_prompt


def random_vision_chunk(
    rng: np.random.RandomState,
    min_wh: int,
    max_wh: int,
    min_frames: int,
    max_frames: int,
) -> dict:
    num_frames = rng.randint(min_frames, max_frames + 1)
    if num_frames == 1:
        # Single image chunk
        wh = rng.randint(min_wh, max_wh + 1)
        image = random_image(rng, wh, wh + 1)
        return {"type": "image", "image": image}
    frames = []
    for _ in range(num_frames):
        wh = rng.randint(min_wh, max_wh + 1)
        frame = rng.randint(0, 256, size=(wh, wh, 3), dtype=np.uint8)
        frames.append(frame)
    video_array = np.stack(frames, axis=0)
    return {"type": "video_chunk", "video_chunk": video_array}


def _test_processing_correctness(
    model_id_or_arch: str,
    hit_rate: float,
    num_batches: int,
    simplify_rate: float,
):
    if model_id_or_arch in HF_EXAMPLE_MODELS.get_supported_archs():
        # Use model architecture to get the default model id
        model_info = HF_EXAMPLE_MODELS.get_hf_info(model_id_or_arch)
        model_id = model_info.default
    else:
        model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id_or_arch)
        model_id = model_id_or_arch
    model_info.check_available_online(on_fail="skip")
    model_info.check_transformers_version(
        on_fail="skip",
        check_max_version=False,
        check_version_reason="vllm",
    )

    model_config = ModelConfig(
        model_id,
        tokenizer=model_info.tokenizer or model_id,
        tokenizer_mode=model_info.tokenizer_mode,
        revision=model_info.revision,
        trust_remote_code=model_info.trust_remote_code,
        hf_overrides=model_info.hf_overrides,
        skip_tokenizer_init=model_info.require_embed_inputs,
        enable_prompt_embeds=model_info.require_embed_inputs,
        enable_mm_embeds=model_info.require_embed_inputs,
        enforce_eager=model_info.enforce_eager,
        dtype=model_info.dtype,
    )
    # Ensure that the cache can fit all of the data
    # (set after because ModelConfig would set it to 0 for encoder-decoder models)
    model_config.multimodal_config.mm_processor_cache_gb = 2048

    model_cls = MULTIMODAL_REGISTRY._get_model_cls(model_config)
    factories = model_cls._processor_factory
    ctx = InputProcessingContext(
        model_config,
        tokenizer=cached_tokenizer_from_config(model_config),
    )
    cache = MultiModalProcessorOnlyCache(model_config)

    processing_info = factories.info(ctx)
    supported_mm_limits = processing_info.get_supported_mm_limits()
    # Keep integer limits for local data generation
    limit_mm_per_prompt_ints = {
        modality: 3 if limit is None else limit
        for modality, limit in supported_mm_limits.items()
    }

    def _to_dummy_options(modality: str, count: int) -> BaseDummyOptions:
        if modality == "video":
            return VideoDummyOptions(count=count)
        if modality == "image":
            return ImageDummyOptions(count=count)
        if modality == "audio":
            return AudioDummyOptions(count=count)
        return BaseDummyOptions(count=count)

    # Assign normalized DummyOptions to the model config
    model_config.get_multimodal_config().limit_per_prompt = {
        modality: _to_dummy_options(modality, count)
        for modality, count in limit_mm_per_prompt_ints.items()
    }

    baseline_processor = factories.build_processor(ctx, cache=None)
    cached_processor = factories.build_processor(ctx, cache=cache)

    rng = np.random.RandomState(0)

    # GLM-ASR requires a minimum audio length of 70ms
    min_audio_len = 512 if model_config.hf_config.model_type != "glmasr" else 1120
    input_to_hit = {
        "image": Image.new("RGB", size=(128, 128)),
        "video": np.zeros((4, 128, 128, 3), dtype=np.uint8),
        "audio": (np.zeros((min_audio_len,)), 16000),
        "vision_chunk": {"type": "image", "image": Image.new("RGB", size=(128, 128))},
    }
    input_factory = {
        "image": partial(random_image, rng, min_wh=128, max_wh=256),
        "video": partial(
            random_video, rng, min_frames=2, max_frames=16, min_wh=128, max_wh=256
        ),
        "audio": partial(
            random_audio,
            rng,
            min_len=min_audio_len,
            max_len=min_audio_len + 512,
            sr=16000,
        ),
        "vision_chunk": partial(
            random_vision_chunk, rng, min_wh=128, max_wh=256, min_frames=1, max_frames=1
        ),
    }

    for batch_idx in range(num_batches):
        mm_data = {
            k: [
                (input_to_hit[k] if rng.rand() < hit_rate else input_factory[k]())
                for _ in range(rng.randint(limit + 1))
            ]
            for k, limit in limit_mm_per_prompt_ints.items()
        }

        # Drop unnecessary keys and test single -> multi conversion
        if rng.rand() < simplify_rate:
            for k in list(mm_data.keys()):
                if not mm_data[k]:
                    del mm_data[k]
                elif len(mm_data[k]) == 1:
                    mm_data[k] = mm_data[k][0]

        _test_processing_correctness_one(
            model_config,
            mm_data,
            baseline_processor,
            cached_processor,
            batch_idx,
        )


def _test_processing_correctness_one(
    model_config: ModelConfig,
    mm_data: MultiModalDataDict,
    baseline_processor: BaseMultiModalProcessor,
    cached_processor: BaseMultiModalProcessor,
    batch_idx: int,
):
    model_type = model_config.hf_config.model_type

    text_prompt, token_prompt = get_text_token_prompts(baseline_processor, mm_data)
    mm_items = baseline_processor.info.parse_mm_data(mm_data)
    ignore_mm_keys = _IGNORE_MM_KEYS.get(model_type, set[str]())

    baseline_tokenized_result = baseline_processor(
        token_prompt,
        mm_items=mm_items,
        hf_processor_mm_kwargs={},
    )

    cached_tokenized_result = cached_processor(
        token_prompt,
        mm_items=mm_items,
        hf_processor_mm_kwargs={},
    )

    _assert_inputs_equal(
        baseline_tokenized_result,
        cached_tokenized_result,
        ignore_mm_keys=ignore_mm_keys,
        msg=f"Failed ({batch_idx=}, {token_prompt=}, {mm_data=})",
    )

    if text_prompt is not None:
        baseline_text_result = baseline_processor(
            text_prompt,
            mm_items=mm_items,
            hf_processor_mm_kwargs={},
        )
        cached_text_result = cached_processor(
            text_prompt,
            mm_items=mm_items,
            hf_processor_mm_kwargs={},
        )

        _assert_inputs_equal(
            baseline_text_result,
            cached_text_result,
            ignore_mm_keys=ignore_mm_keys,
            msg=f"Failed ({batch_idx=}, {text_prompt=}, {mm_data=})",
        )

        _assert_inputs_equal(
            baseline_text_result,
            baseline_tokenized_result,
            ignore_mm_keys=ignore_mm_keys,
            msg=f"Failed ({batch_idx=}, {text_prompt=}, {token_prompt=}, {mm_data=})",
        )

        _assert_inputs_equal(
            cached_text_result,
            cached_tokenized_result,
            ignore_mm_keys=ignore_mm_keys,
            msg=f"Failed ({batch_idx=}, {text_prompt=}, {token_prompt=}, {mm_data=})",
        )


@pytest.mark.parametrize("model_id", get_model_ids_to_test())
@pytest.mark.parametrize("hit_rate", [0.3, 0.5, 1.0])
@pytest.mark.parametrize("num_batches", [32])
@pytest.mark.parametrize("simplify_rate", [1.0])
def test_processing_correctness(
    model_id: str,
    hit_rate: float,
    num_batches: int,
    simplify_rate: float,
):
    if model_id == "google/gemma-3n-E2B-it":
        pytest.skip("Fix later")
    if model_id == "OpenGVLab/InternVL2-2B":
        pytest.skip("Fix later")
    if model_id == "jinaai/jina-reranker-m0":
        pytest.skip("Fix later")
    if model_id in {"Qwen/Qwen-VL", "Qwen/Qwen-VL-Chat"}:
        pytest.skip(
            "Qwen-VL tokenizer requires downloading a font file from "
            "servers that often refuse connections in CI"
        )
    if model_id == "mistralai/Voxtral-Mini-4B-Realtime-2602":
        pytest.skip(
            "Voxtral Realtime doesn't make use of any place-holder "
            "tokens and hence cannot pass the processing "
            "correctness test as is. Let's revisit adapting this "
            "test once more realtime models exist."
        )

    _test_processing_correctness(
        model_id,
        hit_rate=hit_rate,
        num_batches=num_batches,
        simplify_rate=simplify_rate,
    )


def _assert_inputs_equal(
    a: MultiModalInputs,
    b: MultiModalInputs,
    *,
    ignore_mm_keys: set[str] | None = None,
    msg: str = "",
):
    if ignore_mm_keys is None:
        ignore_mm_keys = set()

    ignore_prompt_keys = ("prompt", "mm_kwargs")
    a_rest = {k: v for k, v in a.items() if k not in ignore_prompt_keys}
    b_rest = {k: v for k, v in b.items() if k not in ignore_prompt_keys}

    assert a_rest == b_rest, msg

    a_data = a["mm_kwargs"].get_data()
    b_data = b["mm_kwargs"].get_data()

    for key in ignore_mm_keys:
        a_data.pop(key, None)
        b_data.pop(key, None)

    assert batched_tensors_equal(a_data, b_data), msg
