import types
from typing import List, Optional, Tuple, Type, Union

import pytest
import torch
from PIL.Image import Image
from transformers import AutoConfig

from vllm.multimodal.utils import rescale_image_size

from ....conftest import (IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner,
                          _ImageAssets)
from ...utils import check_logprobs_close

HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
    "stop_sign":
    "<|im_start|>User\n<image>\nWhat's the content in the center of the image?<|im_end|>\n<|im_start|>Assistant\n",  # noqa: E501
    "cherry_blossom":
    "<|im_start|>User\n<image>\nWhat is the season?<|im_end|>\n<|im_start|>Assistant\n",  # noqa: E501
})
HF_MULTIIMAGE_IMAGE_PROMPT = "<|im_start|>User\nImage-1: <image>\nImage-2: <image>\nDescribe the two images in short.<|im_end|>\n<|im_start|>Assistant\n"  # noqa: E501

models = [
    "OpenGVLab/InternVL2-1B",
    "OpenGVLab/InternVL2-2B",
    # NOTE: Mono-InternVL-2B doesn't work with fp16,
    # it will result NaN during inference.
    # See: https://huggingface.co/OpenGVLab/Mono-InternVL-2B/discussions/9
    "OpenGVLab/Mono-InternVL-2B",
    # Broken due to outdated implementation of Phi-3
    # See: https://huggingface.co/OpenGVLab/InternVL2-4B/discussions/3
    # "OpenGVLab/InternVL2-4B",
]
target_dtype = "bfloat16"


# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B/blob/main/modeling_internvl_chat.py
def generate(
    self,
    pixel_values: torch.FloatTensor,
    input_ids: torch.FloatTensor,
    attention_mask: Optional[torch.LongTensor] = None,
    **generate_kwargs,
) -> torch.LongTensor:
    """Generate method for InternVL2 model without fixed use_cache."""
    assert self.img_context_token_id is not None
    vit_embeds = self.extract_feature(pixel_values)
    input_embeds = self.language_model.get_input_embeddings()(input_ids)
    B, N, C = input_embeds.shape
    input_embeds = input_embeds.reshape(B * N, C)

    input_ids = input_ids.reshape(B * N)
    selected = (input_ids == self.img_context_token_id)
    assert selected.sum() != 0
    input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)

    input_embeds = input_embeds.reshape(B, N, C)

    forward_kwargs = dict(
        inputs_embeds=input_embeds,
        attention_mask=attention_mask,
    )
    if getattr(self, "use_visual_token_mask", False):
        visual_token_mask = selected.reshape(B, N, 1).to(input_embeds.dtype)
        forward_kwargs["visual_token_mask"] = visual_token_mask
    outputs = self.language_model.generate(
        **forward_kwargs,
        **generate_kwargs,
    )

    return outputs


def run_test(
    hf_runner: Type[HfRunner],
    vllm_runner: Type[VllmRunner],
    inputs: List[Tuple[List[str], PromptImageInput]],
    model: str,
    *,
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
    mm_limit: 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 are from IMAGE_ASSETS.
    For huggingface runner, we provide the PIL images as input.
    For vllm runner, we provide MultiModalDataDict objects
    and corresponding MultiModalConfig 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.
    """

    # NOTE: take care of the order. run vLLM first, and then run HF.
    # vLLM needs a fresh new process without cuda initialization.
    # if we run HF first, the cuda initialization will be done and it
    # will hurt multiprocessing backend with fork method (the default method).

    class InternVLProcessor:
        """A simple processor for InternVL2 which misses a processor."""

        def __init__(self, hf_runner: HfRunner):
            self.num_image_token = hf_runner.model.num_image_token
            self.tokenizer = hf_runner.tokenizer
            self.dtype = hf_runner.model.dtype

            self.config = AutoConfig.from_pretrained(hf_runner.model_name,
                                                     trust_remote_code=True)
            self.vision_config = self.config.vision_config
            self.use_thumbnail = self.config.use_thumbnail
            self.min_num = self.config.min_dynamic_patch
            self.max_num = self.config.max_dynamic_patch
            self.image_size = self.vision_config.image_size

        def __call__(self, text: str, images: Union[Image, List[Image]],
                     **kwargs):
            from vllm.model_executor.models.internvl import (
                IMG_CONTEXT, IMG_END, IMG_START, image_to_pixel_values)
            images = [images] if isinstance(images, Image) else images
            pixel_values = [
                image_to_pixel_values(image, self.image_size, self.min_num,
                                      self.max_num,
                                      self.use_thumbnail).to(self.dtype)
                for image in images
            ]
            num_patches_list = [
                pixel_value.shape[0] for pixel_value in pixel_values
            ]
            pixel_values = torch.cat(pixel_values, dim=0)
            for num_patches in num_patches_list:
                context_tokens = IMG_CONTEXT * self.num_image_token \
                    * num_patches
                image_tokens = IMG_START + context_tokens + IMG_END
                text = text.replace('<image>', image_tokens, 1)
            prompt = self.tokenizer(text, return_tensors="pt")
            prompt.update({"pixel_values": pixel_values})
            return prompt

    # max_model_len should be greater than image_feature_size
    with vllm_runner(model,
                     max_model_len=4096,
                     dtype=dtype,
                     limit_mm_per_prompt={"image": mm_limit},
                     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
        ]

    with hf_runner(model, dtype=dtype) as hf_model:
        img_context_token_id = hf_model.tokenizer.convert_tokens_to_ids(
            "<IMG_CONTEXT>")
        hf_model.model.img_context_token_id = img_context_token_id
        hf_model.processor = InternVLProcessor(hf_model)
        hf_model.model.get_output_embeddings = lambda: \
            hf_model.model.language_model.get_output_embeddings()
        hf_model.model.generate = types.MethodType(generate, hf_model.model)
        eos_token_id = hf_model.tokenizer.eos_token_id
        hf_outputs_per_image = [
            hf_model.generate_greedy_logprobs_limit(prompts,
                                                    max_tokens,
                                                    num_logprobs=num_logprobs,
                                                    images=hf_images,
                                                    eos_token_id=eos_token_id)
            for prompts, hf_images in inputs
        ]

    for hf_outputs, vllm_outputs in zip(hf_outputs_per_image,
                                        vllm_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=vllm_outputs,
            name_0="hf",
            name_1="vllm",
        )


def run_awq_test(
    vllm_runner: Type[VllmRunner],
    image_assets: _ImageAssets,
    models: Tuple[str, str],
    *,
    size_factors: List[float],
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
    tensor_parallel_size: int,
    distributed_executor_backend: Optional[str] = None,
):
    source_model, quant_model = models

    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)]

    # NOTE: take care of the order. run vLLM first, and then run HF.
    # vLLM needs a fresh new process without cuda initialization.
    # if we run HF first, the cuda initialization will be done and it
    # will hurt multiprocessing backend with fork method (the default method).

    # max_model_len should be greater than image_feature_size
    with vllm_runner(source_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:
        source_outputs_per_image = [
            vllm_model.generate_greedy_logprobs(prompts,
                                                max_tokens,
                                                num_logprobs=num_logprobs,
                                                images=images)
            for prompts, images in inputs_per_image
        ]

    with vllm_runner(quant_model,
                     quantization="awq",
                     max_model_len=4096,
                     dtype=dtype,
                     tensor_parallel_size=tensor_parallel_size,
                     distributed_executor_backend=distributed_executor_backend,
                     enforce_eager=True) as vllm_model:
        quant_outputs_per_image = [
            vllm_model.generate_greedy_logprobs(prompts,
                                                max_tokens,
                                                num_logprobs=num_logprobs,
                                                images=images)
            for prompts, images in inputs_per_image
        ]

    for source_outputs, quant_outputs in zip(source_outputs_per_image,
                                             quant_outputs_per_image):
        # TODO: Check whether using original CLIPVisionModel can improve
        # consistency against HF
        check_logprobs_close(
            outputs_0_lst=source_outputs,
            outputs_1_lst=quant_outputs,
            name_0="source",
            name_1="awq",
        )


@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", [target_dtype])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
@torch.inference_mode()
def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,
                dtype: str, max_tokens: int, num_logprobs: int) -> None:
    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)]

    run_test(
        hf_runner,
        vllm_runner,
        inputs_per_image,
        model,
        dtype=dtype,
        max_tokens=max_tokens,
        num_logprobs=num_logprobs,
        mm_limit=1,
        tensor_parallel_size=1,
    )


@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
        [0.5, 0.75, 1.0],
    ],
)
@pytest.mark.parametrize("dtype", [target_dtype])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
@torch.inference_mode()
def test_multi_images_models(hf_runner, vllm_runner, image_assets, model,
                             size_factors, dtype: str, max_tokens: int,
                             num_logprobs: int) -> None:
    images = [asset.pil_image for asset in image_assets]

    inputs_per_case = [
        ([HF_MULTIIMAGE_IMAGE_PROMPT for _ in size_factors],
         [[rescale_image_size(image, factor) for image in images]
          for factor in size_factors])
    ]

    run_test(
        hf_runner,
        vllm_runner,
        inputs_per_case,
        model,
        dtype=dtype,
        max_tokens=max_tokens,
        num_logprobs=num_logprobs,
        mm_limit=2,
        tensor_parallel_size=1,
    )


@pytest.mark.parametrize("model", ["OpenGVLab/InternVL2-2B"])
@pytest.mark.parametrize("size_factors", [[0.5, 1.0]])
@pytest.mark.parametrize("dtype", [target_dtype])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
@torch.inference_mode()
def test_different_num_patches(hf_runner, vllm_runner, image_assets, model,
                               size_factors, dtype: str, max_tokens: int,
                               num_logprobs: int) -> None:
    images = [asset.pil_image.resize((896, 896)) for asset in image_assets]

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

    inputs_multi_images = [
        ([HF_MULTIIMAGE_IMAGE_PROMPT for _ in size_factors],
         [[rescale_image_size(image, factor) for image in images]
          for factor in size_factors])
    ]
    for inputs in [inputs_batching, inputs_multi_images]:
        run_test(
            hf_runner,
            vllm_runner,
            inputs,
            model,
            dtype=dtype,
            max_tokens=max_tokens,
            num_logprobs=num_logprobs,
            mm_limit=2,
            tensor_parallel_size=1,
        )


@pytest.mark.parametrize(
    "models", [("OpenGVLab/InternVL2-2B", "OpenGVLab/InternVL2-2B-AWQ")])
@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", ["half"])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
@torch.inference_mode()
def test_awq_models(vllm_runner, image_assets, models, size_factors,
                    dtype: str, max_tokens: int, num_logprobs: int) -> None:
    run_awq_test(
        vllm_runner,
        image_assets,
        models,
        size_factors=size_factors,
        dtype=dtype,
        max_tokens=max_tokens,
        num_logprobs=num_logprobs,
        tensor_parallel_size=1,
    )
