from contextlib import nullcontext
from functools import partial
from typing import cast
from unittest.mock import MagicMock

import numpy as np
import pytest
from PIL import Image

from vllm.config import ModelConfig
from vllm.inputs import InputProcessingContext
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.processing import (ProcessingCache, PromptReplacement,
                                        _PlaceholderInfo, find_mm_placeholders,
                                        find_text_matches, find_token_matches,
                                        iter_token_matches,
                                        replace_text_matches,
                                        replace_token_matches)
from vllm.multimodal.utils import cached_get_tokenizer
from vllm.transformers_utils.tokenizer import AnyTokenizer
from vllm.utils import full_groupby


# yapf: disable
@pytest.mark.parametrize(
    ("token_ids", "match_ids", "expected"),
    [
        ([], [], []),
        ([], [32000], []),
        (
            [32000, 32000, 32000],
            [32000],
            [
                { "start_idx": 0, "end_idx": 1 },
                { "start_idx": 1, "end_idx": 2 },
                { "start_idx": 2, "end_idx": 3 },
            ],
        ),
        (
            [32000, 32000, 32000],
            [32000, 32000],
            [{ "start_idx": 0, "end_idx": 2 }],
        ),
        (
            [32000, 32000, 32000],
            [32000, 32000, 32000],
            [{ "start_idx": 0, "end_idx": 3 }],
        ),
        (
            [9833, 28747, 32000, 32000, 32000, 9833, 28747, 32000, 32000, 918],
            [28747, 32000],
            [
                { "start_idx": 1, "end_idx": 3 },
                { "start_idx": 6, "end_idx": 8 },
            ],
        ),
        (
            [9833, 28747, 32000, 32000, 32000, 9833, 28747, 32000, 32000, 918],
            [28747, 32000, 32000, 32000],
            [
                { "start_idx": 1, "end_idx": 5 },
            ],
        ),
        (
            [9833, 28747, 32000, 32000, 32000, 9833, 28747, 32000, 32000, 918],
            [28747, 0, 32000],
            [],
        ),
    ],
)
# yapf: enable
def test_iter_token_matches(token_ids, match_ids, expected):
    result = list(iter_token_matches(token_ids, match_ids))

    # Manually constructed results
    assert [item._asdict() for item in result] == expected

    # Invariants
    match_lens = [end - start for start, end in result]
    print("match_lens:", match_lens)  # Only displayed on error
    assert all(match_len == len(match_ids) for match_len in match_lens)


# yapf: disable
@pytest.mark.parametrize(
    ("prompt", "target_by_key", "expected_by_key"),
    [
        (
            [],
            {
                "pattern_1": [],
                "pattern_2": [32000],
            },
            {
                "pattern_1": [],
                "pattern_2": [],
            }
        ),
        (
            [32000, 32000, 32000, 32000],
            {
                "pattern_1": [32000],
                "pattern_2": [32000, 32000],
                "pattern_3": [32000, 32000, 32000],
            },
            {
                "pattern_1": [
                    { "start_idx": 0, "end_idx": 1 },
                    { "start_idx": 1, "end_idx": 2 },
                    { "start_idx": 2, "end_idx": 3 },
                    { "start_idx": 3, "end_idx": 4 },
                ],
                "pattern_2": [
                    { "start_idx": 0, "end_idx": 2 },
                    { "start_idx": 2, "end_idx": 4 },
                ],
                "pattern_3": [
                    { "start_idx": 0, "end_idx": 3 },
                ],
            },
        ),
        (
            [9833, 28747, 32000, 32000, 32000, 9833, 28747, 32000, 32000, 918],
            {
                "pattern_1": [28747, 32000],
                "pattern_2": [28747, 32000, 32000, 32000],
                "pattern_3": [28747, 0, 32000],
            },
            {
                "pattern_1": [
                    { "start_idx": 1, "end_idx": 3 },
                    { "start_idx": 6, "end_idx": 8 },
                ],
                "pattern_2": [
                    { "start_idx": 1, "end_idx": 5 },
                ],
                "pattern_3": [],
            },
        ),
    ],
)
# yapf: enable
def test_find_token_matches(prompt, target_by_key, expected_by_key):
    # Should not be used since there is nothing to convert to token IDs
    mock_tokenizer = cast(AnyTokenizer, object())

    prompt_repls = [
        PromptReplacement(key, target, []).bind(mock_tokenizer)
        for key, target in target_by_key.items()
    ]
    result = find_token_matches(prompt, prompt_repls)

    # Only displayed on error
    print("result:", result)

    # Manually constructed results
    result_groups = dict(full_groupby(result, key=lambda x: x.modality))
    assert {
        key: [
            dict(start_idx=item.start_idx, end_idx=item.end_idx)
            for item in result_groups.get(key, [])
        ]
        for key in expected_by_key
    } == expected_by_key


# yapf: disable
@pytest.mark.parametrize(
    ("prompt", "target_by_key", "expected_by_key"),
    [
        # Detokenized test cases of `test_find_token_matches`
        # using the vocab of llava-hf/llava-v1.6-mistral-7b-hf
        (
            "",
            {
                "pattern_1": "",
                "pattern_2": "<image>",
            },
            {
                "pattern_1": [{ "start_idx": 0, "end_idx": 0 }],
                "pattern_2": [],
            }
        ),
        (
            "<image><image><image><image>",
            {
                "pattern_1": "<image>",
                "pattern_2": "<image><image>",
                "pattern_3": "<image><image><image>",
            },
            {
                "pattern_1": [
                    { "start_idx": 0, "end_idx": 7 },
                    { "start_idx": 7, "end_idx": 14 },
                    { "start_idx": 14, "end_idx": 21 },
                    { "start_idx": 21, "end_idx": 28 },
                ],
                "pattern_2": [
                    { "start_idx": 0, "end_idx": 14 },
                    { "start_idx": 14, "end_idx": 28 },
                ],
                "pattern_3": [
                    { "start_idx": 0, "end_idx": 21 },
                ],
            },
        ),
        (
            "Image:<image><image><image>Image:<image><image>!",
            {
                "pattern_1": "Image:<image>",
                "pattern_2": "Image:<image><image><image>",
                "pattern_3": "Image:<unk><image>",
            },
            {
                "pattern_1": [
                    { "start_idx": 0, "end_idx": 13 },
                    { "start_idx": 27, "end_idx": 40 },
                ],
                "pattern_2": [
                    { "start_idx": 0, "end_idx": 27 },
                ],
                "pattern_3": [],
            },
        ),
        # Test regex escape
        (
            "<|image|><image><|image|><image>",
            {
                "pattern_1": "<|image|>",
                "pattern_2": "<|image|><image>",
                "pattern_3": "<|image|><image><|image|>",
            },
            {
                "pattern_1": [
                    { "start_idx": 0, "end_idx": 9 },
                    { "start_idx": 16, "end_idx": 25 },
                ],
                "pattern_2": [
                    { "start_idx": 0, "end_idx": 16 },
                    { "start_idx": 16, "end_idx": 32 },
                ],
                "pattern_3": [
                    { "start_idx": 0, "end_idx": 25 },
                ],
            },
        ),
    ],
)
# yapf: enable
def test_find_text_matches(prompt, target_by_key, expected_by_key):
    # Should not be used since there is nothing to convert to text
    mock_tokenizer = cast(AnyTokenizer, object())

    prompt_repls = [
        PromptReplacement(key, target, []).bind(mock_tokenizer)
        for key, target in target_by_key.items()
    ]
    result = find_text_matches(prompt, prompt_repls)

    # Only displayed on error
    print("result:", result)

    # Manually constructed results
    result_groups = dict(full_groupby(result, key=lambda x: x.modality))
    assert {
        key: [
            dict(start_idx=item.start_idx, end_idx=item.end_idx)
            for item in result_groups.get(key, [])
        ]
        for key in expected_by_key
    } == expected_by_key


# yapf: disable
@pytest.mark.parametrize(
    ("prompt", "target_by_key", "repl_by_key"),
    [
        (
            "Image:<image>Image:<image><image>!",
            {
                # We use `<image>` before `Image:` to test matches that
                # occur out of order
                "pattern_1": "<image>",
                "pattern_2": "Image:",
                "pattern_3": "!",
            },
            {
                # Test whether target is confused with replacement
                "pattern_1": "<image><image>",
                # Test empty replacement
                "pattern_2": "",
                # Test dynamic replacement (beyond the form of `unit * count`)
                "pattern_3": "?!?",
            },
        ),
    ]
)
@pytest.mark.parametrize(
    ("mm_count", "expected"),
    [
        (0, "Image:<image>Image:<image><image>!"),
        (1, "<image><image>Image:<image><image>?!?"),
        (2, "<image><image><image><image><image>?!?"),
    ]
)
# yapf: enable
def test_find_replace_text(
    prompt,
    target_by_key,
    repl_by_key,
    mm_count,
    expected,
):
    # Should not be used since there is nothing to convert to text
    mock_tokenizer = cast(AnyTokenizer, object())

    mm_prompt_repls = {
        key: [
            PromptReplacement(key, target,
                              repl_by_key[key]).bind(mock_tokenizer)
        ]
        for key, target in target_by_key.items()
    }
    mm_matches = {
        key: find_text_matches(prompt, prompt_repls)
        for key, prompt_repls in mm_prompt_repls.items()
    }

    result = replace_text_matches(
        prompt,
        mm_matches,
        {key: mm_count
         for key in repl_by_key},
    )

    # Only displayed on error
    print("mm_matches:", mm_matches)
    print("result:", result)

    # Manually constructed results
    assert result == expected


# yapf: disable
@pytest.mark.parametrize(
    ("prompt", "target_by_key", "repl_by_key"),
    [
        # Tokenized test cases of `test_find_replace_text`
        # using the vocab of llava-hf/llava-v1.6-mistral-7b-hf
        (
            [1, 9833, 28747, 32000, 9833, 28747, 32000, 32000, 918],
            {
                # We use `<image>` before `Image:` to test matches that
                # occur out of order
                "pattern_1": [32000],
                "pattern_2": [9833, 28747],
                "pattern_3": [918],
            },
            {
                # Test whether target is confused with replacement
                "pattern_1": [32000, 32000],
                # Test empty replacement
                "pattern_2": [],
                # Test dynamic replacement (beyond the form of `unit * count`)
                "pattern_3": [1550, 918, 1550],
            },
        ),
    ]
)
@pytest.mark.parametrize(
    ("mm_count", "expected"),
    [
        (0, [1, 9833, 28747, 32000, 9833, 28747, 32000, 32000, 918]),
        (1, [1, 32000, 32000, 9833, 28747, 32000, 32000, 1550, 918, 1550]),
        (2, [1, 32000, 32000, 32000, 32000, 32000, 1550, 918, 1550]),
    ]
)
# yapf: enable
def test_find_replace_tokens(
    prompt,
    target_by_key,
    repl_by_key,
    mm_count,
    expected,
):
    # Should not be used since there is nothing to convert to tokens
    mock_tokenizer = cast(AnyTokenizer, object())

    mm_prompt_repls = {
        key: [
            PromptReplacement(key, target,
                              repl_by_key[key]).bind(mock_tokenizer)
        ]
        for key, target in target_by_key.items()
    }
    mm_matches = {
        key: find_token_matches(prompt, prompt_repls)
        for key, prompt_repls in mm_prompt_repls.items()
    }

    result = replace_token_matches(
        prompt,
        mm_matches,
        {key: mm_count
         for key in repl_by_key},
    )

    # Only displayed on error
    print("mm_matches:", mm_matches)
    print("result:", result)

    # Manually constructed results
    assert result == expected


# yapf: disable
@pytest.mark.parametrize(
    "repl_by_key",
    [
        {
            "pattern_1": [32000, 32000],
            "pattern_2": [],
            "pattern_3": [1550, 918, 1550],
        },
    ],
)
@pytest.mark.parametrize(
    ("prompt", "expected"),
    [
        (
            [1, 9833, 28747, 32000, 9833, 28747, 32000, 32000, 918],
            {
                "pattern_1": [
                    _PlaceholderInfo(
                        modality="pattern_1",
                        item_idx=0,
                        start_idx=6,
                        replacement=[32000, 32000],
                    ),
                ],
            }

        ),
        (
            [1, 32000, 32000, 9833, 28747, 32000, 32000, 1550, 918, 1550],
            {
                "pattern_1": [
                    _PlaceholderInfo(
                        modality="pattern_1",
                        item_idx=0,
                        start_idx=1,
                        replacement=[32000, 32000],
                    ),
                    _PlaceholderInfo(
                        modality="pattern_1",
                        item_idx=1,
                        start_idx=5,
                        replacement=[32000, 32000],
                    ),
                ],
                "pattern_3": [
                    _PlaceholderInfo(
                        modality="pattern_3",
                        item_idx=0,
                        start_idx=7,
                        replacement=[1550, 918, 1550],
                    ),
                ],
            }
        ),
        (
            [1, 32000, 32000, 32000, 32000, 32000, 1550, 918, 1550],
            {
                "pattern_1": [
                    _PlaceholderInfo(
                        modality="pattern_1",
                        item_idx=0,
                        start_idx=1,
                        replacement=[32000, 32000],
                    ),
                    _PlaceholderInfo(
                        modality="pattern_1",
                        item_idx=1,
                        start_idx=3,
                        replacement=[32000, 32000],
                    ),
                ],
                "pattern_3": [
                    _PlaceholderInfo(
                        modality="pattern_3",
                        item_idx=0,
                        start_idx=6,
                        replacement=[1550, 918, 1550],
                    ),
                ],
            }
        ),
    ]
)
# yapf: enable
def test_find_mm_placeholders(
    repl_by_key,
    prompt,
    expected,
):
    # Should not be used since there is nothing to convert to tokens
    mock_tokenizer = cast(AnyTokenizer, object())

    mm_prompt_repls = {
        key: [PromptReplacement(key, [], repl).bind(mock_tokenizer)]
        for key, repl in repl_by_key.items()
    }

    result = find_mm_placeholders(
        mm_prompt_repls,
        prompt,
        # Effectively match all occurrences in the prompt
        {key: 3
         for key in repl_by_key},
    )

    # Only displayed on error
    print("result:", result)

    # Manually constructed results
    assert result == expected


def _rand_img(rng: np.random.RandomState, min_wh: int, max_wh: int):
    w, h = rng.randint(min_wh, max_wh, size=(2, ))
    arr = rng.randint(0, 255, size=(w, h, 3), dtype=np.uint8)
    return Image.fromarray(arr)


def _rand_video(
    rng: np.random.RandomState,
    min_frames: int,
    max_frames: int,
    min_wh: int,
    max_wh: int,
):
    # Temporary workaround for https://github.com/huggingface/transformers/issues/35412
    num_frames = rng.randint(min_frames, max_frames)
    num_frames = (num_frames // 2) * 2

    w, h = rng.randint(min_wh, max_wh, size=(2, ))
    return rng.randint(0, 255, size=(num_frames, w, h, 3), dtype=np.uint8)


def _rand_audio(
    rng: np.random.RandomState,
    min_len: int,
    max_len: int,
    sr: int,
):
    audio_len = rng.randint(min_len, max_len)
    return rng.rand(audio_len), sr


@pytest.mark.parametrize("model_id", ["llava-hf/llava-v1.6-mistral-7b-hf"])
@pytest.mark.parametrize(
    ("limit", "num_supported", "is_valid"),
    [(0, 0, True), (0, 1, True), (1, 0, False), (1, 1, True), (1, 2, True),
     (2, 1, False), (2, 2, True)],
)
def test_limit_mm_per_prompt_dummy(model_id, limit, num_supported, is_valid):
    limit_mm_per_prompt = {"image": limit}

    model_config = ModelConfig(
        model=model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="half",
        revision=None,
        limit_mm_per_prompt=limit_mm_per_prompt,
    )
    model_cls = MULTIMODAL_REGISTRY._get_model_cls(model_config)

    processor_factory = MULTIMODAL_REGISTRY._processor_factories[model_cls]
    ctx = InputProcessingContext(
        model_config,
        tokenizer=cached_get_tokenizer(model_config.tokenizer),
    )

    processor = processor_factory(ctx, cache=None)

    mock_supported_mm_limits = MagicMock(return_value={"image": num_supported})
    processor.get_supported_mm_limits = mock_supported_mm_limits

    if is_valid:
        exc_ctx = nullcontext()
    else:
        exc_ctx = pytest.raises(ValueError, match="this model only supports")

    with exc_ctx:
        processor._get_and_validate_dummy_mm_counts()


@pytest.mark.parametrize("model_id", ["llava-hf/llava-v1.6-mistral-7b-hf"])
@pytest.mark.parametrize(
    ("num_images", "limit", "is_valid"),
    [(0, 0, True), (0, 1, True), (1, 0, False), (1, 1, True), (1, 2, True),
     (2, 1, False), (2, 2, True)],
)
def test_limit_mm_per_prompt_apply(model_id, num_images, limit, is_valid):
    limit_mm_per_prompt = {"image": limit}

    model_config = ModelConfig(
        model=model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=False,
        seed=0,
        dtype="half",
        revision=None,
        limit_mm_per_prompt=limit_mm_per_prompt,
    )
    model_cls = MULTIMODAL_REGISTRY._get_model_cls(model_config)

    processor_factory = MULTIMODAL_REGISTRY._processor_factories[model_cls]
    ctx = InputProcessingContext(
        model_config,
        tokenizer=cached_get_tokenizer(model_config.tokenizer),
    )

    processor = processor_factory(ctx, cache=None)

    rng = np.random.RandomState(0)
    image = _rand_img(rng, min_wh=128, max_wh=256)
    if num_images == 0:
        mm_data = {}
    elif num_images == 1:
        mm_data = {"image": image}
    else:
        mm_data = {"image": [image] * num_images}

    if is_valid:
        exc_ctx = nullcontext()
    else:
        exc_ctx = pytest.raises(ValueError, match=f"passed {num_images} image")

    with exc_ctx:
        processor.apply(
            "<image>" * num_images,
            mm_data=mm_data,
            hf_processor_mm_kwargs={},
        )


def _test_processing_cache_correctness(
    model_id: str,
    modalities: dict[str, bool],
    hit_rate: float,
    num_batches: int,
    simplify_rate: float,
):
    if model_id == "TIGER-Lab/Mantis-8B-siglip-llama3":
        hf_overrides = {"architectures": ["MantisForConditionalGeneration"]}
    else:
        hf_overrides = {}

    limit_mm_per_prompt = {
        modality: 3 if supports_multi else 1
        for modality, supports_multi in modalities.items()
    }

    model_config = ModelConfig(
        model_id,
        task="auto",
        tokenizer=model_id,
        tokenizer_mode="auto",
        trust_remote_code=True,
        seed=0,
        dtype="float16",
        revision=None,
        hf_overrides=hf_overrides,
        limit_mm_per_prompt=limit_mm_per_prompt,
    )
    model_cls = MULTIMODAL_REGISTRY._get_model_cls(model_config)

    processor_factory = MULTIMODAL_REGISTRY._processor_factories[model_cls]
    ctx = InputProcessingContext(
        model_config,
        tokenizer=cached_get_tokenizer(model_config.tokenizer),
    )
    # Ensure that it can fit all of the data
    cache = ProcessingCache(capacity=1 << 30)

    baseline_processor = processor_factory(ctx, cache=None)
    cached_processor = processor_factory(ctx, cache=cache)

    rng = np.random.RandomState(0)

    input_to_hit = {
        "image": Image.new("RGB", size=(128, 128)),
        "video": np.zeros((4, 128, 128, 3), dtype=np.uint8),
        "audio": (np.zeros((512, )), 16000),
    }
    input_factory = {
        "image":
        partial(_rand_img, rng, min_wh=128, max_wh=256),
        "video":
        partial(_rand_video,
                rng,
                min_frames=2,
                max_frames=8,
                min_wh=128,
                max_wh=256),
        "audio":
        partial(_rand_audio, rng, min_len=512, max_len=1024, sr=16000),
    }

    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_mm_per_prompt[k]))]
            for k in modalities
        }

        mm_counts = {k: len(vs) for k, vs in mm_data.items()}
        prompt = baseline_processor._get_dummy_processor_inputs(
            model_config.max_model_len,
            mm_counts,
        ).prompt_text

        # 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]

        baseline_result = baseline_processor.apply(
            prompt,
            mm_data=mm_data,
            hf_processor_mm_kwargs={},
        )
        cached_result = cached_processor.apply(
            prompt,
            mm_data=mm_data,
            hf_processor_mm_kwargs={},
        )

        assert baseline_result == cached_result, (
            f"Failed ({batch_idx=}, {mm_data=})")


# yapf: disable
# True if the model supports multiple data items of the modality per request
@pytest.mark.parametrize(("model_id", "modalities"), [
    ("rhymes-ai/Aria", {"image": True}),
    ("Salesforce/blip2-opt-2.7b", {"image": False}),
    ("facebook/chameleon-7b", {"image": False}),
    ("adept/fuyu-8b", {"image": False}),
    ("llava-hf/llava-1.5-7b-hf", {"image": True}),
    ("llava-hf/llava-v1.6-mistral-7b-hf", {"image": True}),
    ("llava-hf/LLaVA-NeXT-Video-7B-hf", {"video": False}),
    ("llava-hf/llava-onevision-qwen2-0.5b-ov-hf", {"image": True, "video": True}),  # noqa: E501
    ("TIGER-Lab/Mantis-8B-siglip-llama3", {"image": True}),
    ("mistral-community/pixtral-12b", {"image": True}),
    ("Qwen/Qwen2-VL-2B-Instruct", {"image": True, "video": True}),
    ("Qwen/Qwen2-Audio-7B-Instruct", {"audio": True}),
    ("fixie-ai/ultravox-v0_3", {"audio": True}),
])
@pytest.mark.parametrize("hit_rate", [0.3, 0.5, 1.0])
@pytest.mark.parametrize("num_batches", [32])
@pytest.mark.parametrize("simplify_rate", [1.0])
# yapf: enable
def test_processing_cache_correctness(
    model_id: str,
    modalities: dict[str, bool],
    hit_rate: float,
    num_batches: int,
    simplify_rate: float,
):
    _test_processing_cache_correctness(
        model_id,
        modalities,
        hit_rate=hit_rate,
        num_batches=num_batches,
        simplify_rate=simplify_rate,
    )


# yapf: disable
@pytest.mark.parametrize(("model_id", "modalities"), [
    ("microsoft/Phi-3-vision-128k-instruct", {"image": True}),
])
@pytest.mark.parametrize("hit_rate", [0.3, 0.5, 1.0])
@pytest.mark.parametrize("num_batches", [32])
@pytest.mark.parametrize("simplify_rate", [1.0])
# yapf: enable
def test_processing_cache_correctness_phi3v(
    model_id: str,
    modalities: dict[str, bool],
    hit_rate: float,
    num_batches: int,
    simplify_rate: float,
):
    # HACK - this is an attempted workaround for the following bug
    # https://github.com/huggingface/transformers/issues/34307
    from transformers import AutoImageProcessor  # noqa: F401
    from transformers import AutoProcessor  # noqa: F401

    AutoImageProcessor.from_pretrained(model_id, trust_remote_code=True)

    _test_processing_cache_correctness(
        model_id,
        modalities,
        hit_rate=hit_rate,
        num_batches=num_batches,
        simplify_rate=simplify_rate,
    )
