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

# adapted from https://huggingface.co/OpenGVLab/InternVL2-4B/blob/main/modeling_internvl_chat.py
# --------------------------------------------------------
# InternVL
# Copyright (c) 2023 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from abc import abstractmethod
from collections.abc import Iterable, Mapping, Sequence
from functools import cached_property
from typing import Annotated, Any, Literal, TypeAlias, TypeVar

import torch
import torch.nn as nn
from transformers import BatchFeature, PretrainedConfig

from vllm.config import VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.inputs import MultiModalDataDict
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.quantization.auto_awq import AutoAWQConfig
from vllm.model_executor.models.intern_vit import (
    InternVisionModel,
)
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (
    BatchedTensorInputs,
    MultiModalFieldConfig,
    MultiModalKwargsItems,
)
from vllm.multimodal.parse import (
    ImageEmbeddingItems,
    ImageProcessorItems,
    ImageSize,
    MultiModalDataItems,
)
from vllm.multimodal.processing import (
    BaseDummyInputsBuilder,
    BaseMultiModalProcessor,
    BaseProcessingInfo,
    PromptReplacement,
    PromptUpdate,
)
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.processors.internvl import (
    InternVLImageProcessor,
    InternVLProcessor,
    InternVLVideoProcessor,
)
from vllm.utils.tensor_schema import TensorSchema, TensorShape

from .interfaces import (
    MultiModalEmbeddings,
    SupportsEncoderCudaGraph,
    SupportsLoRA,
    SupportsMultiModal,
    SupportsPP,
)
from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix


class InternVLImagePixelInputs(TensorSchema):
    """
    Dimensions:
        - bn: Batch size * number of images
        - bnp: Batch size * number of images * (1 + num_patches)
        - c: Number of channels (3)
        - h: Height of each image patch
        - w: Width of each image patch
    """

    type: Literal["pixel_values"]
    pixel_values_flat: Annotated[torch.Tensor, TensorShape("bnp", 3, "h", "w")]
    num_patches: Annotated[torch.Tensor, TensorShape("bn")]


class InternVLImageEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - n: Number of images
        - f: Total image feature size
        - h: Hidden size (must match the hidden size of language model backbone)
    """

    type: Literal["image_embeds"]
    data: Annotated[torch.Tensor | list[torch.Tensor], TensorShape("n", "f", "h")]


InternVLImageInputs: TypeAlias = InternVLImagePixelInputs | InternVLImageEmbeddingInputs


class InternVLVideoPixelInputs(TensorSchema):
    """
    Dimensions:
        - bvf: Batch size * number of videos * num_frames
        - bn: Batch size * number of images
        - c: Number of channels (3)
        - h: Height of each video frame
        - w: Width of each video frame
    """

    type: Literal["pixel_values_videos"]
    pixel_values_flat: Annotated[torch.Tensor, TensorShape("bvf", 3, "h", "w")]
    num_patches: Annotated[torch.Tensor, TensorShape("bn")]


class InternVLVideoEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - n: Number of videos
        - f: Total video feature size
        - h: Hidden size (must match the hidden size of language model backbone)
    """

    type: Literal["video_embeds"]
    data: Annotated[torch.Tensor | list[torch.Tensor], TensorShape("n", "f", "h")]


InternVLVideoInputs: TypeAlias = InternVLVideoPixelInputs | InternVLVideoEmbeddingInputs


class BaseInternVLProcessingInfo(BaseProcessingInfo):
    """Basic image-only ProcessingInfo for InternVL-style models."""

    @abstractmethod
    def get_hf_processor(self, **kwargs: object) -> InternVLProcessor:
        raise NotImplementedError

    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
        return {"image": None}

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        processor: InternVLProcessor,
    ) -> int:
        return processor.get_num_image_tokens(
            image_width=image_width,
            image_height=image_height,
        )

    def get_image_size_with_most_features(self) -> ImageSize:
        processor = self.get_hf_processor()
        image_processor = processor.image_processor

        base_size = image_processor.image_size
        target_ratios = processor.resolve_target_ratios()

        largest_feature_size, largest_feature_pinpoint = 0, None
        for wr, hr in target_ratios:
            width, height = base_size * wr, base_size * hr

            feat_size = self.get_num_image_tokens(
                image_width=width,
                image_height=height,
                processor=processor,
            )
            if feat_size > largest_feature_size:
                largest_feature_size = feat_size
                largest_feature_pinpoint = ImageSize(width=width, height=height)

        if largest_feature_size == 0 or largest_feature_pinpoint is None:
            raise ValueError("Cannot have a largest feature size of 0!")

        return largest_feature_pinpoint

    def get_max_image_tokens(self) -> int:
        processor = self.get_hf_processor()
        target_width, target_height = self.get_image_size_with_most_features()

        return self.get_num_image_tokens(
            image_width=target_width,
            image_height=target_height,
            processor=processor,
        )


_I = TypeVar("_I", bound=BaseInternVLProcessingInfo)


class BaseInternVLDummyInputsBuilder(BaseDummyInputsBuilder[_I]):
    """Basic image-only DummyInputsBuilder for InternVL-style models."""

    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_images = mm_counts.get("image", 0)

        return "<image>" * num_images

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
        mm_options: Mapping[str, BaseDummyOptions],
    ) -> MultiModalDataDict:
        target_width, target_height = self.info.get_image_size_with_most_features()
        num_images = mm_counts.get("image", 0)

        image_overrides = mm_options.get("image")

        return {
            "image": self._get_dummy_images(
                width=target_width,
                height=target_height,
                num_images=num_images,
                overrides=image_overrides,
            )
        }


class BaseInternVLMultiModalProcessor(BaseMultiModalProcessor[_I]):
    """Basic image-only MultiModalProcessor for InternVL-style models."""

    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        processed_outputs = super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )

        hf_processor = self.info.get_hf_processor(**mm_kwargs)
        image_token_id = hf_processor.ctx_image_token_id

        # Since there may be extra tokens in the feature placeholders,
        # we need to pass the image token ID to the model to select the
        # tokens to merge from the vision encoder outputs
        processed_outputs["image_token_id"] = torch.tensor(image_token_id)

        return processed_outputs

    def _get_image_fields_config(self, hf_inputs: BatchFeature):
        image_num_patches = hf_inputs.get("image_num_patches", torch.empty(0))
        num_images = len(image_num_patches)

        return dict(
            pixel_values_flat=MultiModalFieldConfig.flat_from_sizes(
                "image", image_num_patches
            ),
            image_num_patches=MultiModalFieldConfig.batched("image", keep_on_cpu=True),
            image_embeds=MultiModalFieldConfig.batched("image"),
            image_token_id=MultiModalFieldConfig.shared(
                "image", num_images, keep_on_cpu=True
            ),
        )

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return self._get_image_fields_config(hf_inputs)

    def _get_prompt_repl_image(
        self,
        mm_items: MultiModalDataItems,
        hf_processor: InternVLProcessor,
        out_mm_data: BatchedTensorInputs,
    ):
        if "image_num_patches" in out_mm_data:
            image_num_patches = out_mm_data["image_num_patches"]
            assert isinstance(image_num_patches, torch.Tensor)
            image_num_patches = image_num_patches.tolist()
        elif "image_embeds" in out_mm_data:
            # TODO: Use image size information in dictionary embedding inputs
            # to compute num_patches (similar to Qwen2-VL)
            image_num_patches = [None] * len(out_mm_data["image_embeds"])
        else:
            image_num_patches = []

        def get_replacement_internvl(item_idx: int):
            images = mm_items.get_items(
                "image", (ImageEmbeddingItems, ImageProcessorItems)
            )

            if isinstance(images, ImageEmbeddingItems):
                feature_size = images.get_feature_size(item_idx)
            else:
                image_size = images.get_image_size(item_idx)
                feature_size = self.info.get_num_image_tokens(
                    image_width=image_size.width,
                    image_height=image_size.height,
                    processor=hf_processor,
                )

            num_patches = image_num_patches[item_idx]
            if num_patches is not None:
                assert isinstance(num_patches, int)

            return hf_processor.get_image_repl(num_patches, num_features=feature_size)

        return PromptReplacement(
            modality="image",
            target="<image>",
            replacement=get_replacement_internvl,
        )

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> Sequence[PromptUpdate]:
        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
        out_mm_data = out_mm_kwargs.get_data()

        return [
            self._get_prompt_repl_image(mm_items, hf_processor, out_mm_data),
        ]


class InternVLProcessingInfo(BaseInternVLProcessingInfo):
    """InternVL ProcessingInfo extended for video processing"""

    def get_image_processor(self, **kwargs):
        config = self.get_hf_config()
        vision_config = config.vision_config

        kwargs = self.ctx.get_merged_mm_kwargs(kwargs)
        kwargs.setdefault("image_size", vision_config.image_size)
        kwargs.setdefault("min_dynamic_patch", config.min_dynamic_patch)
        kwargs.setdefault("max_dynamic_patch", config.max_dynamic_patch)
        kwargs.setdefault("dynamic_image_size", config.dynamic_image_size)
        kwargs.setdefault("use_thumbnail", config.use_thumbnail)

        return InternVLImageProcessor(**kwargs)

    def get_video_processor(self, **kwargs):
        config = self.get_hf_config()
        vision_config = config.vision_config

        kwargs = self.ctx.get_merged_mm_kwargs(kwargs)
        kwargs.setdefault("image_size", vision_config.image_size)

        return InternVLVideoProcessor(**kwargs)

    @cached_property
    def ctx_video_token(self):
        text_model_type = self.get_hf_config().get_text_config().model_type
        ctx_video_token_map = {
            "qwen2": "<|video_pad|>",
            "qwen3": "<|video_pad|>",
            "qwen3_moe": "<|video_pad|>",
            "gpt_oss": "<|reserved_200000|>",
        }

        if text_model_type not in ctx_video_token_map:
            return None

        ctx_video_token = ctx_video_token_map[text_model_type]
        if ctx_video_token not in self.get_tokenizer().get_vocab():
            return None

        return ctx_video_token

    def get_hf_processor(self, **kwargs: object) -> InternVLProcessor:
        config = self.get_hf_config()
        vision_config = config.vision_config

        image_processor = self.get_image_processor(**kwargs)
        image_size = image_processor.image_size
        patch_size = vision_config.patch_size
        downsample_ratio = config.downsample_ratio
        image_seq_length = int((image_size // patch_size) ** 2 * (downsample_ratio**2))

        ctx_video_token = self.ctx_video_token
        video_processor = (
            self.get_video_processor(**kwargs) if ctx_video_token else None
        )

        return InternVLProcessor(
            tokenizer=self.get_tokenizer(),
            image_processor=image_processor,
            video_processor=video_processor,
            image_seq_length=image_seq_length,
            ctx_video_token=ctx_video_token,
        )

    def get_supported_mm_limits(self):
        video_limit = {"video": None} if self.ctx_video_token else {}
        return {**super().get_supported_mm_limits(), **video_limit}

    def get_num_frames_with_most_features(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> int:
        max_images = mm_counts.get("image", 0)
        max_videos = mm_counts.get("video", 0)

        processor = self.get_hf_processor()
        num_image_token = processor.image_seq_length

        max_image_tokens = self.get_max_image_tokens() * max_images
        max_total_frames = (seq_len - max_image_tokens) // num_image_token
        max_frames_per_video = max_total_frames // max(max_videos, 1)

        return max(max_frames_per_video, 1)


class InternVLDummyInputsBuilder(
    BaseInternVLDummyInputsBuilder[InternVLProcessingInfo]
):
    """InternVL DummyInputsBuilder extended for video support"""

    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_videos = mm_counts.get("video", 0)

        return super().get_dummy_text(mm_counts) + "<video>" * num_videos

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
        mm_options: Mapping[str, BaseDummyOptions],
    ) -> MultiModalDataDict:
        dummy_image = super().get_dummy_mm_data(seq_len, mm_counts, mm_options)
        if self.info.ctx_video_token:
            config = self.info.get_hf_config()
            image_size: int = config.vision_config.image_size
            target_num_frames = self.info.get_num_frames_with_most_features(
                seq_len, mm_counts
            )
            num_videos = mm_counts.get("video", 0)
            video_overrides = mm_options.get("video")
            dummy_video = {
                "video": self._get_dummy_videos(
                    width=image_size,
                    height=image_size,
                    num_frames=target_num_frames,
                    num_videos=num_videos,
                    overrides=video_overrides,
                )
            }
        else:
            dummy_video = {}
        return {**dummy_image, **dummy_video}


class InternVLMultiModalProcessor(
    BaseInternVLMultiModalProcessor[InternVLProcessingInfo]
):
    """InternVL MultiModalProcessor extended for video support"""

    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        processed_outputs = super()._call_hf_processor(
            prompt, mm_data, mm_kwargs, tok_kwargs
        )

        hf_processor = self.info.get_hf_processor(**mm_kwargs)
        if (video_token_id := hf_processor.ctx_video_token_id) is not None:
            processed_outputs["video_token_id"] = torch.tensor(video_token_id)

        return processed_outputs

    def _get_video_fields_config(self, hf_inputs: BatchFeature):
        video_num_patches = hf_inputs.get("video_num_patches", torch.empty(0))
        num_videos = len(video_num_patches)

        return dict(
            pixel_values_flat_video=MultiModalFieldConfig.flat_from_sizes(
                "video", video_num_patches
            ),
            video_num_patches=MultiModalFieldConfig.batched("video", keep_on_cpu=True),
            video_token_id=MultiModalFieldConfig.shared("video", num_videos),
        )

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        fields = self._get_image_fields_config(hf_inputs)
        if self.info.ctx_video_token:
            fields |= self._get_video_fields_config(hf_inputs)

        return fields

    def _get_prompt_repl_video(
        self,
        mm_items: MultiModalDataItems,
        hf_processor: InternVLProcessor,
        out_mm_data: BatchedTensorInputs,
    ):
        if "video_num_patches" in out_mm_data:
            video_num_patches = out_mm_data["video_num_patches"]
            assert isinstance(video_num_patches, torch.Tensor)
            video_num_patches = video_num_patches.tolist()
        else:
            video_num_patches = []

        def get_video_replacement_internvl(item_idx: int):
            num_patches = video_num_patches[item_idx]
            if num_patches is not None:
                assert isinstance(num_patches, int)

            return hf_processor.get_video_repl(num_patches)

        return PromptReplacement(
            modality="video",
            target="<video>",
            replacement=get_video_replacement_internvl,
        )

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> Sequence[PromptUpdate]:
        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
        out_mm_data = out_mm_kwargs.get_data()

        prompt_repls = [
            self._get_prompt_repl_image(mm_items, hf_processor, out_mm_data),
        ]
        if self.info.ctx_video_token is not None:
            prompt_repls.append(
                self._get_prompt_repl_video(mm_items, hf_processor, out_mm_data)
            )

        return prompt_repls


@MULTIMODAL_REGISTRY.register_processor(
    InternVLMultiModalProcessor,
    info=InternVLProcessingInfo,
    dummy_inputs=InternVLDummyInputsBuilder,
)
class InternVLChatModel(
    nn.Module,
    SupportsMultiModal,
    SupportsPP,
    SupportsLoRA,
    SupportsEncoderCudaGraph,
):
    supports_encoder_tp_data = True

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
        if modality.startswith("image"):
            return "<image>"
        if modality.startswith("video"):
            return "<video>"

        raise ValueError("Only image or video modality is supported")

    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
        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
        self._patch_quant_config(config, quant_config)

        image_size = config.force_image_size or config.vision_config.image_size
        patch_size = config.vision_config.patch_size
        self.patch_size = patch_size
        self.patch_tokens = (image_size // patch_size) ** 2
        self.num_image_token = int(self.patch_tokens * (config.downsample_ratio**2))
        self.downsample_ratio = config.downsample_ratio
        self.ps_version = config.ps_version

        with self._mark_tower_model(vllm_config, {"image", "video"}):
            self.vision_model = self._init_vision_model(
                config,
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "vision_model"),
            )
            self.mlp1 = self._init_mlp1(config)

        with self._mark_language_model(vllm_config):
            self.language_model = init_vllm_registered_model(
                vllm_config=vllm_config,
                hf_config=config.text_config,
                prefix=maybe_prefix(prefix, "language_model"),
            )

        self.img_context_token_id = None
        self.video_context_token_id = None

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors
        )

    def _patch_quant_config(
        self, config: PretrainedConfig, quant_config: QuantizationConfig
    ):
        # the awq models from OpenGVLab missing `modules_to_not_convert`
        # patch the quant_config to add `modules_to_not_convert` back
        if isinstance(quant_config, AutoAWQConfig):
            text_config = config.text_config
            llm_quant_config = getattr(text_config, "quantization_config", None)
            if (not quant_config.modules_to_not_convert) and (
                llm_quant_config is not None
            ):
                quant_config.modules_to_not_convert.append("vision_model")

    def _init_vision_model(
        self,
        config: PretrainedConfig,
        quant_config: QuantizationConfig | None,
        *,
        prefix: str,
    ):
        vision_feature_layer = config.select_layer
        if vision_feature_layer < 0:
            num_hidden_layers = (
                config.vision_config.num_hidden_layers + vision_feature_layer + 1
            )
        else:
            num_hidden_layers = vision_feature_layer + 1

        return InternVisionModel(
            config.vision_config,
            quant_config=quant_config,
            num_hidden_layers_override=num_hidden_layers,
            prefix=prefix,
        )

    def _init_mlp1(self, config: PretrainedConfig) -> nn.Module:
        vit_hidden_size = config.vision_config.hidden_size
        llm_hidden_size = config.text_config.hidden_size

        return nn.Sequential(
            nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
            nn.Linear(
                vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size
            ),
            nn.GELU(),
            nn.Linear(llm_hidden_size, llm_hidden_size),
        )

    def pixel_shuffle(self, x, scale_factor=0.5):
        n, w, h, c = x.size()
        # N, W, H, C --> N, W, H * scale, C // scale
        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
        x = x.permute(0, 2, 1, 3).contiguous()
        x = x.view(
            n,
            int(h * scale_factor),
            int(w * scale_factor),
            int(c / (scale_factor * scale_factor)),
        )
        if self.ps_version == "v1":
            pass
        else:
            x = x.permute(0, 2, 1, 3).contiguous()
        return x

    def extract_feature(self, pixel_values: torch.Tensor) -> torch.Tensor:
        vit_embeds = self.vision_model(pixel_values=pixel_values)
        vit_embeds = vit_embeds[:, 1:, :]

        h = w = int(vit_embeds.shape[1] ** 0.5)
        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
        vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
        vit_embeds = self.mlp1(vit_embeds)
        return vit_embeds

    def _parse_and_validate_image_input(
        self, **kwargs: object
    ) -> InternVLImageInputs | None:
        pixel_values_flat = kwargs.pop("pixel_values_flat", None)
        image_num_patches = kwargs.pop("image_num_patches", None)
        image_embeds = kwargs.pop("image_embeds", None)

        if pixel_values_flat is None and image_embeds is None:
            return None

        if image_embeds is not None:
            return InternVLImageEmbeddingInputs(
                type="image_embeds",
                data=image_embeds,
            )

        image_token_id = kwargs["image_token_id"]
        if isinstance(image_token_id, torch.Tensor):
            image_token_id = image_token_id.flatten().unique().item()

        assert isinstance(image_token_id, int)
        self.img_context_token_id = image_token_id

        if pixel_values_flat is not None:
            expected_h = expected_w = self.config.vision_config.image_size
            resolve_bindings = {"h": expected_h, "w": expected_w}

            return InternVLImagePixelInputs(
                type="pixel_values",
                pixel_values_flat=pixel_values_flat,
                num_patches=image_num_patches,
                resolve_bindings=resolve_bindings,
            )

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

    def _parse_and_validate_video_input(
        self, **kwargs: object
    ) -> InternVLVideoPixelInputs | None:
        pixel_values_flat_video = kwargs.pop("pixel_values_flat_video", None)
        video_num_patches = kwargs.pop("video_num_patches", None)
        video_embeds = kwargs.pop("image_embeds", None)

        if pixel_values_flat_video is None and video_embeds is None:
            return None

        if video_embeds is not None:
            return InternVLVideoEmbeddingInputs(
                type="video_embeds",
                data=video_embeds,
            )

        video_token_id = kwargs["video_token_id"]
        if isinstance(video_token_id, torch.Tensor):
            video_token_id = video_token_id.flatten().unique().item()

        assert isinstance(video_token_id, int)
        self.video_context_token_id = video_token_id

        if pixel_values_flat_video is not None:
            expected_h = expected_w = self.config.vision_config.image_size
            resolve_bindings = {"h": expected_h, "w": expected_w}

            return InternVLVideoPixelInputs(
                type="pixel_values_videos",
                pixel_values_flat=pixel_values_flat_video,
                num_patches=video_num_patches,
                resolve_bindings=resolve_bindings,
            )

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

    def _process_vision_input(
        self,
        image_input: InternVLImageInputs | InternVLVideoInputs,
    ) -> tuple[torch.Tensor, ...]:
        if (
            image_input["type"] == "image_embeds"
            or image_input["type"] == "video_embeds"
        ):
            return image_input["data"]

        image_embeds = self.extract_feature(image_input["pixel_values_flat"])

        num_patches = image_input["num_patches"]

        # Only one image in the current batch
        if len(num_patches) == 1:
            return (image_embeds.view(-1, self.config.text_config.hidden_size),)

        # NOTE: Image embeddings are split into separate tensors for each image
        # by the size of each embedding.
        feature_size = image_embeds.shape[1]
        image_embeds = image_embeds.view(-1, self.config.text_config.hidden_size)
        image_feature_sizes = [
            num_patches * feature_size for num_patches in num_patches
        ]
        return image_embeds.split(image_feature_sizes)

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

        # Preserve the order of modalities if there are multiple of them
        # from the order of kwargs.
        for input_key in kwargs:
            if (
                input_key in ("pixel_values_flat", "image_embeds")
                and "images" not in modalities
            ):
                modalities["images"] = self._parse_and_validate_image_input(**kwargs)
            if input_key in ("pixel_values_flat_video",) and "videos" not in modalities:
                modalities["videos"] = self._parse_and_validate_video_input(**kwargs)

        return modalities

    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
        if not modalities:
            return []

        # The result multimodal_embeddings is tuple of tensors, with each
        # tensor correspoending to a multimodal data item (image or video).
        multimodal_embeddings: tuple[torch.Tensor, ...] = ()

        # NOTE: It is important to iterate over the keys in this dictionary
        # to preserve the order of the modalities.
        for modality in modalities:
            if modality == "images":
                image_input = modalities["images"]
                image_embeddings = self._process_vision_input(image_input)
                multimodal_embeddings += tuple(image_embeddings)
            if modality == "videos":
                video_input = modalities["videos"]
                video_embeddings = self._process_vision_input(video_input)
                multimodal_embeddings += tuple(video_embeddings)

        return multimodal_embeddings

    def embed_input_ids(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: MultiModalEmbeddings | None = None,
        *,
        is_multimodal: torch.Tensor | None = None,
    ) -> torch.Tensor:
        # This is to satisfy the type checker for each overload
        if multimodal_embeddings is None or is_multimodal is None:
            return super().embed_input_ids(input_ids)

        return super().embed_input_ids(
            input_ids,
            multimodal_embeddings=multimodal_embeddings,
            is_multimodal=is_multimodal,
        )

    def forward(
        self,
        input_ids: torch.Tensor | None,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs: object,
    ) -> IntermediateTensors:
        if intermediate_tensors is not None:
            inputs_embeds = None

        forward_kwargs = {
            "input_ids": input_ids,
            "positions": positions,
            "intermediate_tensors": intermediate_tensors,
            "inputs_embeds": inputs_embeds,
        }

        hidden_states = self.language_model.model(**forward_kwargs)
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor | None:
        return self.language_model.compute_logits(hidden_states)

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        # unused modules appear in OpenGVLab/InternVideo2_5_Chat_8B
        skip_prefixes = [
            "action_embed",
            "temporal_embed",
            "track_embed",
            "track_embed_decoder",
            "box_token",
            "cg_criterion",
            "cg_model",
            "loc_encoder",
            "loc_decoder",
            "sam",
            "temporal_token",
            "track_token",
        ]
        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
        return loader.load_weights(weights)

    def get_mm_mapping(self) -> MultiModelKeys:
        """
        Get the module prefix in multimodal models
        """
        return MultiModelKeys.from_string_field(
            language_model="language_model",
            connector="mlp1",
            tower_model="vision_model",
        )

    def get_num_mm_encoder_tokens(self, num_image_tokens: int) -> int:
        if num_image_tokens <= 0 or self.num_image_token <= 0:
            return 0

        num_patches = num_image_tokens // self.num_image_token
        return num_patches * (self.patch_tokens + 1)

    def get_num_mm_connector_tokens(self, num_vision_tokens: int) -> int:
        if num_vision_tokens <= 0 or self.num_image_token <= 0:
            return 0

        num_patches = num_vision_tokens // (self.patch_tokens + 1)
        return num_patches * self.num_image_token

    # -- SupportsEncoderCudaGraph protocol methods --

    def get_encoder_cudagraph_config(self):
        from vllm.v1.worker.encoder_cudagraph_defs import EncoderCudaGraphConfig

        return EncoderCudaGraphConfig(
            modalities=["image", "video"],
            # InternVision uses standard ViT attention (no rotary embeddings,
            # no variable-length sequence metadata), so the only graph-recorded
            # buffer is pixel_values_flat itself.
            buffer_keys=["pixel_values_flat"],
            out_hidden_size=self.config.text_config.hidden_size,
        )

    def get_input_modality(
        self,
        mm_kwargs: dict[str, Any],
    ) -> str:
        if "pixel_values_flat" in mm_kwargs:
            return "image"
        return "video"

    def get_encoder_cudagraph_budget_range(
        self,
        vllm_config: "VllmConfig",
    ) -> tuple[int, int]:
        # Min: 1 tile → num_image_token output tokens.
        min_budget = self.num_image_token
        max_budget = min(
            vllm_config.scheduler_config.max_num_batched_tokens,
            vllm_config.model_config.max_model_len,
        )
        return (min_budget, max_budget)

    def _get_internvl_patches_list(
        self,
        mm_kwargs: dict[str, Any],
    ) -> list[int]:
        """Return per-item tile counts as a plain list of ints."""
        if self.get_input_modality(mm_kwargs) == "image":
            patches = mm_kwargs.get("image_num_patches", [])
        else:
            patches = mm_kwargs.get("video_num_patches", [])
        if isinstance(patches, torch.Tensor):
            return patches.tolist()
        return [int(n) for n in patches]

    def get_encoder_cudagraph_item_specs(
        self,
        mm_kwargs: dict[str, Any],
    ):
        from vllm.v1.worker.encoder_cudagraph_defs import EncoderItemSpec

        return [
            EncoderItemSpec(
                input_size=n,
                output_tokens=n * self.num_image_token,
            )
            for n in self._get_internvl_patches_list(mm_kwargs)
        ]

    def select_encoder_cudagraph_items(
        self,
        mm_kwargs: dict[str, Any],
        indices: list[int],
    ) -> dict[str, Any]:
        modality = self.get_input_modality(mm_kwargs)
        pv_key = (
            "pixel_values_flat" if modality == "image" else "pixel_values_flat_video"
        )
        patches_key = (
            "image_num_patches" if modality == "image" else "video_num_patches"
        )

        pixel_values = mm_kwargs[pv_key]
        patches_list = self._get_internvl_patches_list(mm_kwargs)

        if len(indices) == 0:
            return {pv_key: pixel_values[:0], patches_key: []}

        # Compute cumulative tile offsets for slicing pixel_values.
        cum_patches = [0]
        for n in patches_list:
            cum_patches.append(cum_patches[-1] + n)

        selected_pv = torch.cat(
            [pixel_values[cum_patches[i] : cum_patches[i + 1]] for i in indices]
        )
        selected_patches = [patches_list[i] for i in indices]

        return {pv_key: selected_pv, patches_key: selected_patches}

    def prepare_encoder_cudagraph_capture_inputs(
        self,
        token_budget: int,
        max_batch_size: int,
        max_frames_per_batch: int,
        device: torch.device,
        dtype: torch.dtype,
        path: str = "default",
    ):
        from vllm.v1.worker.encoder_cudagraph_defs import (
            EncoderCudaGraphCaptureInputs,
        )

        # Size the buffer to hold the maximum possible tiles for this budget.
        total_tiles = max(token_budget // self.num_image_token, 1)
        image_size = self.config.vision_config.image_size

        dummy_pixel_values = torch.randn(
            total_tiles, 3, image_size, image_size, device=device, dtype=dtype
        )

        return EncoderCudaGraphCaptureInputs(
            values={"pixel_values_flat": dummy_pixel_values},
        )

    def prepare_encoder_cudagraph_replay_buffers(
        self,
        mm_kwargs: dict[str, Any],
        max_batch_size: int,
        max_frames_per_batch: int,
        path: str = "default",
    ):
        from vllm.v1.worker.encoder_cudagraph_defs import (
            EncoderCudaGraphReplayBuffers,
        )

        modality = self.get_input_modality(mm_kwargs)
        pv_key = (
            "pixel_values_flat" if modality == "image" else "pixel_values_flat_video"
        )
        return EncoderCudaGraphReplayBuffers(
            values={"pixel_values_flat": mm_kwargs[pv_key]},
        )

    def encoder_cudagraph_forward(
        self,
        values: dict[str, torch.Tensor],
        path: str = "default",
    ) -> torch.Tensor:
        # The graph is always captured with pixel_values_flat as the input
        # buffer. During video replay the manager copies video tiles into
        # this same buffer before calling graph.replay(), so we always read
        # from pixel_values_flat here.
        pixel_values = values["pixel_values_flat"]
        out = self.extract_feature(pixel_values)  # [N, num_image_token, H]
        return out.view(-1, self.config.text_config.hidden_size)

    def encoder_eager_forward(
        self,
        mm_kwargs: dict[str, Any],
        path: str = "default",
    ) -> torch.Tensor:
        if self.get_input_modality(mm_kwargs) == "image":
            pixel_values = mm_kwargs["pixel_values_flat"]
        else:
            pixel_values = mm_kwargs["pixel_values_flat_video"]
        out = self.extract_feature(pixel_values)  # [N, num_image_token, H]
        return out.view(-1, self.config.text_config.hidden_size)
