import math
from functools import cached_property
from typing import (Iterable, List, Literal, Mapping, Optional, Set, Tuple,
                    TypedDict, Union)

import numpy as np
import torch
import torch.nn as nn
from PIL import Image
from transformers import (CLIPVisionConfig, LlavaOnevisionConfig,
                          SiglipVisionConfig)
from transformers.models.llava_onevision.modeling_llava_onevision import (
    get_anyres_image_grid_shape, unpad_image)
from typing_extensions import NotRequired

from vllm.attention import AttentionMetadata
from vllm.config import VllmConfig
from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
                         InputContext, token_inputs)
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import NestedTensors
from vllm.multimodal.utils import (cached_get_tokenizer,
                                   repeat_and_pad_placeholder_tokens)
from vllm.sequence import IntermediateTensors
from vllm.utils import is_list_of

from .clip import (CLIPVisionModel, dummy_seq_data_for_clip,
                   dummy_video_for_clip, get_clip_image_feature_size,
                   get_clip_patch_grid_length, input_processor_for_clip)
from .interfaces import SupportsMultiModal, SupportsPP
from .llava import init_vision_tower_for_llava
from .siglip import (SiglipVisionModel, dummy_seq_data_for_siglip,
                     dummy_video_for_siglip, get_siglip_image_feature_size,
                     get_siglip_patch_grid_length, input_processor_for_siglip)
from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
                    maybe_prefix, merge_multimodal_embeddings)

# Result in the max possible feature size (2x2 grid of 336x336px tiles)
MAX_IMAGE_FEATURE_SIZE_HEIGHT = MAX_IMAGE_FEATURE_SIZE_WIDTH = 448

# For profile run
_MAX_FRAMES_PER_VIDEO = 16


class LlavaOnevisionVideoPixelInputs(TypedDict):
    type: Literal["pixel_values_videos"]
    data: Union[torch.Tensor, List[torch.Tensor]]
    """
    Shape: `(batch_size, num_videos, num_frames, num_channels, height, width)`

    Note that `num_videos` may be different for each batch, and 'num_frames'
    may be different for each video, in which case the data is passed as a
    list instead of a batched tensor.
    """


class LlavaOnevisionImagePixelInputs(TypedDict):
    type: Literal["pixel_values"]
    data: Union[torch.Tensor, List[torch.Tensor]]
    """
    Shape:
    `(batch_size * num_images, 1 + num_patches, num_channels, height, width)`

    Note that `num_patches` may be different per batch and image,
    in which case the data is passed as a list instead of a batched tensor.
    """

    image_sizes: NotRequired[torch.Tensor]
    """
    Shape: `(batch_size * num_images, 2)`

    This should be in `(height, width)` format.
    """


class LlavaOnevisionImageEmbeddingInputs(TypedDict):
    type: Literal["image_embeds"]
    data: torch.Tensor
    """Shape: `(batch_size * num_images, image_feature_size, hidden_size)`

    `hidden_size` must match the hidden size of language model backbone.
    """


LlavaOnevisionImageInputs = Union[LlavaOnevisionImagePixelInputs,
                                  LlavaOnevisionImageEmbeddingInputs]

LlavaOnevisionMultiInputs = Union[LlavaOnevisionImageInputs,
                                  LlavaOnevisionVideoPixelInputs]


def _get_llava_onevision_image_unppaded_feature_size(height, width, patches,
                                                     scale_height,
                                                     scale_width):
    current_height = patches * scale_height
    current_width = patches * scale_width

    original_aspect_ratio = width / height
    current_aspect_ratio = current_width / current_height
    if original_aspect_ratio > current_aspect_ratio:
        new_height = int(height * (current_width / width))
        padding = (current_height - new_height) // 2
        current_height -= padding * 2
    else:
        new_width = int(width * (current_height / height))
        padding = (current_width - new_width) // 2
        current_width -= padding * 2

    unpadded_features = current_height * current_width
    newline_features = current_height

    ratio = math.sqrt(current_height * current_width / (9 * patches**2))
    if ratio > 1.1:
        unpadded_features = int(current_height // ratio) * int(
            current_width // ratio)
        newline_features = int(current_height // ratio)

    return (unpadded_features, newline_features)


def get_llava_onevision_image_feature_size(
    hf_config: LlavaOnevisionConfig,
    *,
    input_height: int,
    input_width: int,
) -> int:
    vision_config = hf_config.vision_config

    if isinstance(vision_config, CLIPVisionConfig):
        num_patches = get_clip_patch_grid_length(
            image_size=vision_config.image_size,
            patch_size=vision_config.patch_size,
        )
        base_feature_size = get_clip_image_feature_size(vision_config)
    elif isinstance(vision_config, SiglipVisionConfig):
        num_patches = get_siglip_patch_grid_length(
            image_size=vision_config.image_size,
            patch_size=vision_config.patch_size,
        )
        base_feature_size = get_siglip_image_feature_size(vision_config)
    else:
        msg = f"Unsupported vision config: {type(vision_config)}"
        raise NotImplementedError(msg)

    strategy = hf_config.vision_feature_select_strategy
    if strategy == "default":
        base_feature_size -= 1
    elif strategy == "full":
        pass
    else:
        raise ValueError(f"Unexpected select feature strategy: {strategy}")

    num_patch_height, num_patch_width = get_anyres_image_grid_shape(
        image_size=(input_height, input_width),
        grid_pinpoints=hf_config.image_grid_pinpoints,
        patch_size=vision_config.image_size,
    )

    (
        unpadded_feature_size,
        newline_feature_size,
    ) = _get_llava_onevision_image_unppaded_feature_size(
        input_height, input_width, num_patches, num_patch_height,
        num_patch_width)

    return unpadded_feature_size + newline_feature_size + base_feature_size


def get_max_llava_onevision_image_tokens(ctx: InputContext):
    return get_llava_onevision_image_feature_size(
        ctx.get_hf_config(LlavaOnevisionConfig),
        input_height=MAX_IMAGE_FEATURE_SIZE_HEIGHT,
        input_width=MAX_IMAGE_FEATURE_SIZE_WIDTH,
    )


def get_llava_onevision_video_frame_feature_size(
        hf_config: LlavaOnevisionConfig) -> int:
    # Support both CLIPVisionConfig and SiglipVisionConfig
    image_size = hf_config.vision_config.image_size
    patch_size = hf_config.vision_config.patch_size
    spatial_pool_stride = hf_config.spatial_pool_stride if hasattr(
        hf_config, "spatial_pool_stride") else 2

    height = width = image_size // patch_size
    return math.ceil(height / spatial_pool_stride) * math.ceil(
        width / spatial_pool_stride)


def get_llava_onevision_video_tokens(ctx: InputContext,
                                     num_frames: int) -> int:
    hf_config = ctx.get_hf_config(LlavaOnevisionConfig)

    # TODO: support configuring (not supported by HF right now)
    num_token_image_newline = 1
    tokens_per_frame = get_llava_onevision_video_frame_feature_size(hf_config)
    video_feature_size = num_frames * tokens_per_frame + num_token_image_newline

    return video_feature_size


def get_max_llava_onevision_video_tokens(ctx: InputContext) -> int:
    return get_llava_onevision_video_tokens(ctx, _MAX_FRAMES_PER_VIDEO)


def dummy_data_for_llava_onevision(ctx: InputContext, seq_len: int,
                                   mm_counts: Mapping[str, int]):
    hf_config = ctx.get_hf_config(LlavaOnevisionConfig)
    vision_config = hf_config.vision_config

    num_videos = mm_counts["video"]

    # TODO: support configuring the number of frames
    num_frames = _MAX_FRAMES_PER_VIDEO
    video_feature_size = get_llava_onevision_video_tokens(ctx, num_frames)

    if isinstance(vision_config, CLIPVisionConfig):
        seq_data, ranges = dummy_seq_data_for_clip(
            vision_config,
            seq_len,
            num_videos,
            image_token_id=hf_config.video_token_index,
            image_feature_size_override=video_feature_size,
            mm_key="video")

        mm_data = dummy_video_for_clip(vision_config,
                                       num_frames=num_frames,
                                       num_videos=num_videos)
        return DummyData(seq_data, mm_data, ranges)
    elif isinstance(vision_config, SiglipVisionConfig):
        seq_data, ranges = dummy_seq_data_for_siglip(
            vision_config,
            seq_len,
            num_videos,
            image_token_id=hf_config.video_token_index,
            image_feature_size_override=video_feature_size,
            mm_key="video")

        mm_data = dummy_video_for_siglip(vision_config,
                                         num_frames=num_frames,
                                         num_videos=num_videos)
        return DummyData(seq_data, mm_data, ranges)

    msg = f"Unsupported vision config: {type(vision_config)}"
    raise NotImplementedError(msg)


def input_processor_when_multimodal_input_image(ctx: InputContext,
                                                inputs: DecoderOnlyInputs):
    multi_modal_data = inputs.get("multi_modal_data")
    if multi_modal_data is None or "image" not in multi_modal_data:
        return inputs

    model_config = ctx.model_config
    hf_config = ctx.get_hf_config(LlavaOnevisionConfig)
    vision_config = hf_config.vision_config

    image_data = multi_modal_data["image"]
    if isinstance(image_data, Image.Image):
        width, height = image_data.size

        image_feature_size = get_llava_onevision_image_feature_size(
            hf_config,
            input_height=height,
            input_width=width,
        )
    elif is_list_of(image_data, Image.Image):
        image_feature_size = [
            get_llava_onevision_image_feature_size(hf_config,
                                                   input_height=img.height,
                                                   input_width=img.width)
            for img in image_data
        ]
    elif isinstance(image_data, torch.Tensor):
        num_images, image_feature_size, hidden_size = image_data.shape
    elif is_list_of(image_data, torch.Tensor):
        image_feature_size = [item.shape[1] for item in image_data]
    else:
        raise TypeError(f"Invalid image type: {type(image_data)}")

    vision_config = hf_config.vision_config

    if isinstance(vision_config, CLIPVisionConfig):
        return input_processor_for_clip(
            model_config,
            vision_config,
            inputs,
            image_token_id=hf_config.image_token_index,
            image_feature_size_override=image_feature_size,
        )
    elif isinstance(vision_config, SiglipVisionConfig):
        return input_processor_for_siglip(
            model_config,
            vision_config,
            inputs,
            image_token_id=hf_config.image_token_index,
            image_feature_size_override=image_feature_size,
        )

    msg = f"Unsupported vision config: {type(vision_config)}"
    raise NotImplementedError(msg)


def input_processor_when_multimodal_input_video(ctx: InputContext,
                                                inputs: DecoderOnlyInputs):
    multi_modal_data = inputs.get("multi_modal_data")
    if multi_modal_data is None or "video" not in multi_modal_data:
        return inputs
    video_data = multi_modal_data["video"]

    model_config = ctx.model_config
    hf_config = ctx.get_hf_config(LlavaOnevisionConfig)

    if isinstance(video_data, np.ndarray):
        # Supports both CLIP and Siglip
        num_frames = video_data.shape[0]
        video_feature_size = get_llava_onevision_video_tokens(ctx, num_frames)
        tokenizer = cached_get_tokenizer(model_config.tokenizer)

        new_prompt, new_token_ids, ranges = repeat_and_pad_placeholder_tokens(
            tokenizer,
            inputs.get("prompt"),
            inputs["prompt_token_ids"],
            placeholder_token_id=hf_config.video_token_index,
            repeat_count=video_feature_size,
        )

        return token_inputs(prompt_token_ids=new_token_ids,
                            prompt=new_prompt,
                            multi_modal_data=multi_modal_data,
                            multi_modal_placeholders={"video": ranges})

    elif is_list_of(video_data, np.ndarray):
        video_feature_size = []
        for video in video_data:
            num_frames = video.shape[0]
            video_feature_size.append(
                get_llava_onevision_video_tokens(ctx, num_frames))

        tokenizer = cached_get_tokenizer(model_config.tokenizer)
        new_prompt, new_token_ids, ranges = repeat_and_pad_placeholder_tokens(
            tokenizer,
            inputs.get("prompt"),
            inputs["prompt_token_ids"],
            placeholder_token_id=hf_config.video_token_index,
            repeat_count=video_feature_size,
        )
        return token_inputs(prompt_token_ids=new_token_ids,
                            prompt=new_prompt,
                            multi_modal_data=multi_modal_data,
                            multi_modal_placeholders={"video": ranges})
    else:
        raise TypeError(f"Invalid video type: {type(video_data)}")

    msg = f"Unsupported video type: {type(video_data)}"
    raise NotImplementedError(msg)


def input_processor_for_llava_onevision(ctx: InputContext,
                                        inputs: DecoderOnlyInputs):
    multi_modal_data = inputs.get("multi_modal_data")
    if multi_modal_data is None or ("video" not in multi_modal_data
                                    and "image" not in multi_modal_data):
        return inputs
    if "image" in multi_modal_data:
        return input_processor_when_multimodal_input_image(ctx, inputs)
    if "video" in multi_modal_data:
        return input_processor_when_multimodal_input_video(ctx, inputs)

    msg = "Unsupported multi data type"
    raise NotImplementedError(msg)


class LlavaOnevisionMultiModalProjector(nn.Module):

    def __init__(self, config: LlavaOnevisionConfig):
        super().__init__()

        self.linear_1 = nn.Linear(config.vision_config.hidden_size,
                                  config.text_config.hidden_size,
                                  bias=True)
        self.act = get_act_fn(config.projector_hidden_act)
        self.linear_2 = nn.Linear(config.text_config.hidden_size,
                                  config.text_config.hidden_size,
                                  bias=True)

    def forward(self, image_features: torch.Tensor) -> torch.Tensor:
        hidden_states = self.linear_1(image_features)
        hidden_states = self.act(hidden_states)
        hidden_states = self.linear_2(hidden_states)
        return hidden_states


@MULTIMODAL_REGISTRY.register_image_input_mapper()
@MULTIMODAL_REGISTRY.register_input_mapper("video")
@MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
    "image", get_max_llava_onevision_image_tokens)
@MULTIMODAL_REGISTRY.register_max_multimodal_tokens(
    "video", get_max_llava_onevision_video_tokens)
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava_onevision)
@INPUT_REGISTRY.register_input_processor(input_processor_for_llava_onevision)
class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
                                             SupportsPP):

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config

        self.config = config
        self.multimodal_config = multimodal_config

        # Initialize the vision tower only up to the required feature layer
        self.vision_tower = init_vision_tower_for_llava(
            config,
            quant_config,
            require_post_norm=False,
            prefix=maybe_prefix(prefix, "vision_tower"))
        self.multi_modal_projector = LlavaOnevisionMultiModalProjector(config)
        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            hf_config=config.text_config,
            prefix=maybe_prefix(prefix, "language_model"),
        )
        self.image_newline = nn.Parameter(
            torch.empty(config.text_config.hidden_size))

        self.make_empty_intermediate_tensors = (
            self.language_model.model.make_empty_intermediate_tensors)

    @cached_property
    def sampler(self):
        if hasattr(self.language_model, "sampler"):
            return self.language_model.sampler

        return get_sampler()

    def _validate_image_sizes(self, data: torch.Tensor) -> torch.Tensor:
        expected_dims = (2, )

        def _validate_shape(d: torch.Tensor):
            actual_dims = tuple(d.shape)

            if actual_dims != expected_dims:
                expected_expr = str(expected_dims)
                raise ValueError(
                    f"The expected shape of image sizes per image per batch "
                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")

        for d in data:
            _validate_shape(d)

        return data

    def _validate_image_pixel_values(
        self, data: Union[torch.Tensor, List[torch.Tensor]]
    ) -> Union[torch.Tensor, List[torch.Tensor]]:

        h = w = self.config.vision_config.image_size
        expected_dims = (3, h, w)

        def _validate_shape(d: torch.Tensor):
            actual_dims = tuple(d.shape[1:])

            if actual_dims != expected_dims:
                expected_expr = ("num_patches", *map(str, expected_dims))
                raise ValueError(
                    "The expected shape of pixel values per image per batch "
                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")

        for d in data:
            _validate_shape(d)

        return data

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[LlavaOnevisionImageInputs]:
        pixel_values = kwargs.pop("pixel_values", None)
        image_sizes = kwargs.pop("image_sizes", None)
        image_embeds = kwargs.pop("image_embeds", None)

        if pixel_values is None and image_embeds is None:
            return None

        if pixel_values is not None:
            if not isinstance(pixel_values, (torch.Tensor, list)):
                raise ValueError("Incorrect type of pixel values. "
                                 f"Got type: {type(pixel_values)}")

            if not isinstance(image_sizes, (torch.Tensor, list)):
                raise ValueError("Incorrect type of image sizes. "
                                 f"Got type: {type(image_sizes)}")

            return LlavaOnevisionImagePixelInputs(
                type="pixel_values",
                data=self._validate_image_pixel_values(
                    flatten_bn(pixel_values)),
                image_sizes=self._validate_image_sizes(
                    flatten_bn(image_sizes, concat=True)),
            )

        if image_embeds is not None:
            if not isinstance(image_embeds, torch.Tensor):
                raise ValueError("Incorrect type of image embeds. "
                                 f"Got type: {type(image_embeds)}")

            return LlavaOnevisionImageEmbeddingInputs(
                type="image_embeds",
                data=flatten_bn(image_embeds),
            )

        raise AssertionError("This line should be unreachable.")

    def _validate_video_pixel_values(
        self, data: Union[torch.Tensor, List[torch.Tensor]]
    ) -> Union[torch.Tensor, List[torch.Tensor]]:

        h = w = self.config.vision_config.image_size
        expected_dims = (3, h, w)

        def _validate_shape(d: torch.Tensor):
            actual_dims = tuple(d.shape[2:])

            if actual_dims != expected_dims:
                expected_expr = ("num_frames", *map(str, expected_dims))
                raise ValueError(
                    "The expected shape of pixel values in each video frame "
                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")

        for d in data:
            _validate_shape(d)

        return data

    def _parse_and_validate_video_input(
            self,
            **kwargs: object) -> Optional[LlavaOnevisionVideoPixelInputs]:
        """
        A legal video input should have the following dimensions:
        {
            "pixel_values_videos" : 
                List[b, Tensor(nb_frames, nb_channels, height, width)]
        }
        """
        pixel_values = kwargs.pop("pixel_values_videos", None)

        if pixel_values is None:
            return None

        if not (is_list_of(pixel_values,
                           (torch.Tensor))  # different shape videos 
                or isinstance(pixel_values,
                              torch.Tensor)):  # same shape videos
            raise ValueError("Incorrect type of pixel values. "
                             f"Got type: {type(pixel_values)}")

        return LlavaOnevisionVideoPixelInputs(
            type="pixel_values_videos",
            data=pixel_values,
        )

    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
        modalities = {}

        if "pixel_values" in kwargs:
            modalities["images"] = self._parse_and_validate_image_input(
                **kwargs)

        if "pixel_values_videos" in kwargs:
            modalities["videos"] = self._parse_and_validate_video_input(
                **kwargs)

        return modalities

    def _select_image_features(self, image_features: torch.Tensor, *,
                               strategy: str) -> torch.Tensor:
        if strategy == "default":
            return image_features[:, 1:]
        elif strategy == "full":
            return image_features

        raise ValueError(f"Unexpected select feature strategy: {strategy}")

    def _image_pixels_to_features(
        self,
        vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
        pixel_values: torch.Tensor,
    ) -> torch.Tensor:

        # NOTE: we skip the step to select the vision feature layer since
        # this is already done inside the vision tower
        image_features = vision_tower(pixel_values)
        return self._select_image_features(
            image_features,
            strategy=self.config.vision_feature_select_strategy,
        )

    # Based on: https://github.com/haotian-liu/LLaVA/blob/main/llava/model/llava_arch.py
    def _merge_image_patch_embeddings(self,
                                      image_size: torch.Tensor,
                                      patch_embeddings: torch.Tensor,
                                      *,
                                      image_newline=None,
                                      vision_aspect_ratio="anyres_max_9",
                                      strategy: str) -> torch.Tensor:
        if strategy == "flat":
            return patch_embeddings.flatten(0, 1)

        if strategy.startswith("spatial"):
            height = width = self.config.vision_config.image_size \
                // self.config.vision_config.patch_size

            base_patch_embeds = patch_embeddings[0]
            if height * width != base_patch_embeds.shape[0]:
                raise ValueError(
                    "The number of patches is not consistent with the "
                    "image size.")

            if patch_embeddings.shape[0] > 1:
                other_patch_embeds = patch_embeddings[1:]

                # Move to CPU to avoid floating-point errors
                orig_height, orig_width = image_size.tolist()

                # image_aspect_ratio == "anyres"
                num_patch_height, num_patch_width = get_anyres_image_grid_shape(
                    (orig_height, orig_width),
                    self.config.image_grid_pinpoints,
                    self.config.vision_config.image_size,
                )
                num_patches = num_patch_height * num_patch_width

                # Image patches might be padded for batch processing
                other_patch_embeds = other_patch_embeds[:num_patches] \
                    .view(num_patch_height, num_patch_width, height, width, -1)

                if "unpad" in strategy:
                    other_patch_embeds = other_patch_embeds \
                        .permute(4, 0, 2, 1, 3).contiguous() \
                        .flatten(1, 2).flatten(2, 3)
                    other_patch_embeds = unpad_image(other_patch_embeds,
                                                     (orig_height, orig_width))
                    max_num_patches = int(
                        vision_aspect_ratio.removeprefix("anyres_max_"))
                    channels, curr_height, curr_width = other_patch_embeds.shape
                    ratio = math.sqrt(curr_height * curr_width /
                                      (max_num_patches * height**2))
                    if ratio > 1.1:
                        other_patch_embeds = other_patch_embeds[None]
                        other_patch_embeds = nn.functional.interpolate(
                            other_patch_embeds, [
                                int(curr_height // ratio),
                                int(curr_width // ratio)
                            ],
                            mode="bilinear")[0]
                    if image_newline is not None:
                        other_patch_embeds = torch.cat(
                            (
                                other_patch_embeds,
                                image_newline[:, None, None] \
                                .expand(*other_patch_embeds.shape[:-1], 1) \
                                .to(other_patch_embeds.device),
                            ),
                        dim=-1)
                    other_patch_embeds = other_patch_embeds \
                        .flatten(1, 2).transpose(0, 1)
                else:
                    other_patch_embeds = other_patch_embeds \
                        .permute(0, 2, 1, 3, 4).contiguous() \
                        .flatten(0, 3)

                merged_patch_embeddings = torch.cat(
                    (base_patch_embeds, other_patch_embeds), dim=0)
            else:
                if "unpad" in strategy:
                    merged_patch_embeddings = torch.cat(
                        (base_patch_embeds,
                         self.image_newline[None] \
                            .to(base_patch_embeds.device)
                    ), dim=0)
                else:
                    merged_patch_embeddings = base_patch_embeds

            return merged_patch_embeddings

        raise ValueError(f"Unexpected patch merge strategy: {strategy}")

    def _process_image_pixels(
        self,
        inputs: LlavaOnevisionImagePixelInputs,
    ) -> Union[torch.Tensor, List[torch.Tensor]]:
        assert self.vision_tower is not None

        pixel_values = inputs["data"]

        if isinstance(pixel_values, torch.Tensor):
            b, num_patches, c, h, w = pixel_values.shape
            stacked_pixel_values = pixel_values.view(b * num_patches, c, h, w)
            stacked_image_features = self._image_pixels_to_features(
                self.vision_tower, stacked_pixel_values)
            stacked_patch_embeddings = self.multi_modal_projector(
                stacked_image_features)

            return stacked_patch_embeddings.view(
                b, num_patches, *stacked_patch_embeddings.shape[1:])

        num_patches_per_batch = [v.shape[0] for v in pixel_values]
        stacked_pixel_values = torch.cat(pixel_values)
        stacked_image_features = self._image_pixels_to_features(
            self.vision_tower, stacked_pixel_values)

        return [
            self.multi_modal_projector(image_features) for image_features in
            torch.split(stacked_image_features, num_patches_per_batch)
        ]

    def _process_image_input(
        self,
        image_input: LlavaOnevisionImageInputs,
    ) -> Union[torch.Tensor, List[torch.Tensor]]:
        if image_input["type"] == "image_embeds":
            return [image_input["data"]]

        patch_embeddings = self._process_image_pixels(image_input)

        image_sizes = image_input.get("image_sizes")
        if image_sizes is None:
            batch_size = len(image_input["data"])
            vision_config = self.config.vision_config
            default_height = default_width = vision_config.image_size
            image_sizes = torch.as_tensor([[default_height, default_width]
                                           for _ in range(batch_size)])

        return [
            self._merge_image_patch_embeddings(
                image_sizes[i],
                patch_features_batch,
                image_newline=self.image_newline,
                strategy="spatial_unpad")
            for i, patch_features_batch in enumerate(patch_embeddings)
        ]

    def _add_image_newline(
        self,
        video_features: torch.Tensor,
        videos: int = 1,
        frames: int = 1,
        strategy: str = "one_token",
    ) -> torch.Tensor:
        if strategy == "one_token":
            video_features = video_features.reshape(
                videos, frames * video_features.shape[1], -1)
            image_newline = self.image_newline[None, None, :].repeat(
                videos, 1, 1).to(video_features.device)
            video_features = torch.cat((video_features, image_newline), dim=1)
            return video_features
        raise ValueError(f"Unexpected video newline strategy: {strategy}")

    def _video_pixels_to_features(
        self,
        vision_tower: Union[CLIPVisionModel, SiglipVisionModel],
        pixel_values: torch.Tensor,
    ) -> torch.Tensor:

        # NOTE: we skip the step to select the vision feature layer since
        # this is already done inside the vision tower
        video_features = vision_tower(pixel_values)
        video_features = self._select_image_features(
            video_features,
            strategy=self.config.vision_feature_select_strategy,
        )
        video_features = self.multi_modal_projector(video_features)
        video_features = self.apply_pooling(video_features)
        return video_features

    def _process_video_pixels(self, inputs: LlavaOnevisionVideoPixelInputs):
        assert self.vision_tower is not None

        video_pixels = inputs["data"]

        if isinstance(video_pixels, torch.Tensor):
            b, num_videos, frames, c, h, w = video_pixels.shape
            pixel_values = video_pixels.view(b * num_videos * frames, c, h, w)
            stacked_embeddings = self._video_pixels_to_features(
                self.vision_tower, pixel_values)
            stacked_embeddings = self._add_image_newline(stacked_embeddings,
                                                         videos=b * num_videos,
                                                         frames=frames,
                                                         strategy="one_token")
            return stacked_embeddings
        elif is_list_of(video_pixels, torch.Tensor):
            stacked_embeddings = []
            for video_pixel in video_pixels:
                num_videos, frames, c, h, w = video_pixel.shape
                pixel_values = video_pixel.view(num_videos * frames, c, h, w)
                embeddings = self._video_pixels_to_features(
                    self.vision_tower, pixel_values)
                embeddings = self._add_image_newline(embeddings,
                                                     videos=num_videos,
                                                     frames=frames,
                                                     strategy="one_token")
                stacked_embeddings.append(embeddings)
            return stacked_embeddings
        else:
            raise ValueError(
                f"Unsupported type of video input {type(video_pixels)}")

    def apply_pooling(self, image_features, stride=2):
        vision_config = self.config.vision_config
        height = width = vision_config.image_size // vision_config.patch_size
        batch_frames, _, dim = image_features.shape
        image_features = image_features.view(batch_frames, height, width, -1)
        image_features = image_features.permute(0, 3, 1, 2)

        # TODO support other pooling types config
        height, width = image_features.shape[2:]
        scaled_shape = [math.ceil(height / stride), math.ceil(width / stride)]
        image_feature = nn.functional.interpolate(image_features,
                                                  size=scaled_shape,
                                                  mode='bilinear')
        image_feature = image_feature.permute(0, 2, 3, 1)
        image_feature = image_feature.view(batch_frames, -1, dim)
        return image_feature

    def get_multimodal_embeddings(
            self, **kwargs) -> Optional[List[Tuple[NestedTensors, str]]]:
        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
        if not modalities:
            return None

        # We make a tuple of each embedding with its modality string. This is a
        # temporary workaround for models to handle mixed modalities when
        # get_multimodal_embeddings and get_input_embeddings are called
        # separately.
        # TODO(ywang96): Add support for mixed-modality inference for v1.
        multimodal_embeddings: List[Tuple[NestedTensors, str]] = []

        if "images" in modalities:
            image_input = modalities["images"]
            vision_embeddings = self._process_image_input(image_input)
            multimodal_embeddings.append((vision_embeddings, "image"))
        if "videos" in modalities:
            video_input = modalities["videos"]
            video_embeddings = self._process_video_pixels(video_input)
            multimodal_embeddings.append((video_embeddings, "video"))

        return multimodal_embeddings

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: Optional[List[Tuple[NestedTensors,
                                                   str]]] = None,
    ) -> torch.Tensor:
        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
        if multimodal_embeddings is not None:
            for embeddings, modality in multimodal_embeddings:
                if modality == "image":
                    inputs_embeds = merge_multimodal_embeddings(
                        input_ids, inputs_embeds, embeddings,
                        self.config.image_token_index)
                if modality == "video":
                    inputs_embeds = merge_multimodal_embeddings(
                        input_ids, inputs_embeds, embeddings,
                        self.config.video_token_index)
        return inputs_embeds

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs: object,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        """Run forward pass for LlaVA-Onevision.
        Args:
            input_ids: Flattened (concatenated) input_ids corresponding to a
                batch.
            pixel_values_videos: Pixels in each frames for each input videos.
        """
        if intermediate_tensors is not None:
            inputs_embeds = None

        # NOTE: In v1, inputs_embeds is always generated at model runner, this
        # condition is for v0 compatibility.
        elif inputs_embeds is None:
            multimodal_embeddings = self.get_multimodal_embeddings(**kwargs)
            inputs_embeds = self.get_input_embeddings(input_ids,
                                                      multimodal_embeddings)
            input_ids = None

        hidden_states = self.language_model.model(input_ids,
                                                  positions,
                                                  kv_caches,
                                                  attn_metadata,
                                                  intermediate_tensors,
                                                  inputs_embeds=inputs_embeds)

        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
        return self.language_model.compute_logits(hidden_states,
                                                  sampling_metadata)

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        return self.language_model.sample(logits, sampling_metadata)

    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights)
