# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# --------------------------------------------------------
# Adapted from
# https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/internvl.py
# under Apache-2.0 License
#     LICENSE is in root directory.
# --------------------------------------------------------

import copy
import math
import warnings
from abc import ABC, abstractmethod
from collections.abc import Iterable, Mapping, Sequence
from dataclasses import dataclass
from functools import cached_property
from typing import Annotated, Any, Literal, TypeAlias, TypeVar

import einops
import numpy.typing as npt
import regex as re
import torch
import torch.nn as nn
import torchvision.transforms as T
from PIL import Image
from transformers import BatchFeature, PretrainedConfig, TensorType

from vllm.config import VllmConfig
from vllm.config.multimodal import BaseDummyOptions, VideoDummyOptions
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import ReLUSquaredActivation
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.interfaces import (
    HasInnerState,
    IsHybrid,
    MultiModalEmbeddings,
    SupportsMultiModal,
    SupportsMultiModalPruning,
)
from vllm.model_executor.models.internvl import (
    calculate_internvl_targets,
    get_internvl_target_ratios,
)
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.model_executor.models.nemotron_h import NemotronHForCausalLM
from vllm.model_executor.models.radio import RadioModel, calc_seq_lens
from vllm.model_executor.models.utils import (
    init_vllm_registered_model,
    maybe_prefix,
)
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.evs import (
    compute_retained_tokens_count,
    compute_retention_mask,
)
from vllm.multimodal.inputs import (
    MultiModalDataDict,
    MultiModalFieldConfig,
    MultiModalKwargsItems,
    VideoItem,
)
from vllm.multimodal.parse import (
    ImageEmbeddingItems,
    ImageProcessorItems,
    ImageSize,
    MultiModalDataItems,
    MultiModalDataParser,
)
from vllm.multimodal.processing import BaseDummyInputsBuilder
from vllm.multimodal.processing.processor import (
    BaseMultiModalProcessor,
    BaseProcessingInfo,
    PromptReplacement,
    PromptUpdate,
    PromptUpdateDetails,
    _seq2tokens,
)
from vllm.renderers import TokenizeParams
from vllm.sequence import IntermediateTensors
from vllm.tokenizers import TokenizerLike, cached_tokenizer_from_config
from vllm.transformers_utils.configs.radio import RadioConfig
from vllm.utils.tensor_schema import TensorSchema, TensorShape

from .utils import _merge_multimodal_embeddings

logger = init_logger(__name__)
# Configure PIL to handle large images without warnings
# This prevents DecompressionBombWarning for legitimate large images
Image.MAX_IMAGE_PIXELS = None  # Disable the limit entirely
# Alternative: Set a specific higher limit
# Image.MAX_IMAGE_PIXELS = 300000000  # ~300M pixels

IMG_START = "<img>"
IMG_END = "</img>"
IMG_CONTEXT = "<image>"

# Profiling
# MAX_FRAMES = 16
DEFAULT_NUM_TILES = 12


class NanoNemotronVLImagePixelInputs(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"
    pixel_values_flat: Annotated[torch.Tensor, TensorShape("bnp", 3, "h", "w")]
    num_patches: Annotated[torch.Tensor, TensorShape("bn")]


class NanoNemotronVLImagePixelInputsDynamic(TensorSchema):
    """
    Dynamic-resolution image inputs.

    imgs_sizes: per-image (height, width) in pixels.
    num_tokens_per_image: per-image number of embedding tokens (post downsample).
    """

    type: Literal["pixel_values_dynamic"] = "pixel_values_dynamic"
    pixel_values_flat: Annotated[torch.Tensor, TensorShape("bn", "h", "w")]
    imgs_sizes: list[tuple[int, int]]
    num_tokens_per_image: list[int]


class NanoNemotronVLImageEmbeddingInputs(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")]


NanoNemotronVLImageInputs: TypeAlias = (
    NanoNemotronVLImagePixelInputs
    | NanoNemotronVLImagePixelInputsDynamic
    | NanoNemotronVLImageEmbeddingInputs
)


class NanoNemotronVLVideoPixelInputs(TensorSchema):
    """
    Dimensions:
        - bvf: Batch size * number of videos * num_frames
        - bn: Batch size * number of videos
        - f: Number of frames
        - 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")]
    frames_indices: Annotated[torch.Tensor, TensorShape("bvf")]
    frame_duration_ms: Annotated[torch.Tensor, TensorShape("bn")]


class NanoNemotronVLVideoEmbeddingInputs(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")]


NanoNemotronVLVideoInputs: TypeAlias = (
    NanoNemotronVLVideoPixelInputs | NanoNemotronVLVideoEmbeddingInputs
)


def dynamic_preprocess(
    image, *, image_size=512, max_num_tiles=12, use_thumbnail=True, idx=0
):
    orig_width, orig_height = image.size

    target_ratios = get_internvl_target_ratios(1, max_num_tiles)

    blocks, target_width, target_height = calculate_internvl_targets(
        orig_width=orig_width,
        orig_height=orig_height,
        target_ratios=target_ratios,
        image_size=image_size,
        use_thumbnail=False,
    )
    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size,
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)

    processed_images = [
        img.convert("RGB") if img.mode != "RGB" else img for img in processed_images
    ]
    processed_images = [
        T.Resize((image_size, image_size), interpolation=T.InterpolationMode.BICUBIC)(
            img
        )
        for img in processed_images
    ]
    processed_images = [T.ToTensor()(img) for img in processed_images]
    return processed_images


def image_to_pixel_values(
    image: Image.Image,
    *,
    input_size: int,
    max_num: int,
    use_thumbnail: bool,
    idx: int,
) -> torch.Tensor:
    images = dynamic_preprocess(
        image,
        image_size=input_size,
        max_num_tiles=max_num,
        use_thumbnail=use_thumbnail,
        idx=idx,
    )

    pixel_values = torch.stack(images)
    return pixel_values


def video_to_pixel_values(
    video: npt.NDArray,
    *,
    input_size: int,
    max_num_tiles: int = 1,
    use_thumbnail: bool,
) -> torch.Tensor:
    assert max_num_tiles == 1, "Video modality always uses one tile"

    # Convert each frame to a single resized tile tensor consistent
    # with image path
    frames_tensors: list[torch.Tensor] = []
    for frame in video:
        pil_frame = dynamic_preprocess(
            Image.fromarray(frame, mode="RGB"),
            image_size=input_size,
            max_num_tiles=max_num_tiles,
            use_thumbnail=use_thumbnail,
            idx=0,
        )
        # dynamic_preprocess returns tensors already; take the single tile
        assert len(pil_frame) >= 1
        frames_tensors.append(pil_frame[-1])

    return torch.stack(frames_tensors)


def input_conditioner(x, norm_mean, norm_std):
    return (x - norm_mean) / norm_std


def calculate_timestamps(
    indices: list[int] | torch.Tensor,
    frame_duration_ms: int,
):
    if not isinstance(indices, list):
        indices = indices.tolist()

    timestamps = [int(i) * frame_duration_ms / 1000.0 for i in indices]
    return timestamps


class DynamicResolutionImageTiler:
    CONV_MERGING = False
    PIXEL_SHUFFLE = True
    USE_THUMBNAIL = False

    def __init__(
        self,
        *,
        max_model_len: int,
        patch_size: int,
        min_num_patches: int,
        max_num_patches: int,
        downsample_ratio: int,
        norm_mean: Sequence[float],
        norm_std: Sequence[float],
        factor_max: float = 1.0,
        use_thumbnail: bool = False,
    ) -> None:
        assert use_thumbnail is False, "use_thumbnail is not supported"
        self._patch_size: int = patch_size
        self._max_model_len = max_model_len
        self._min_num_patches = min_num_patches
        self._max_num_patches = max_num_patches if max_num_patches > 0 else float("inf")
        self._factor_max = factor_max
        self.norm_mean = torch.tensor(norm_mean).reshape(3, 1, 1)
        self.norm_std = torch.tensor(norm_std).reshape(3, 1, 1)
        self._transform = T.Compose(
            [
                T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
                T.ToTensor(),
            ]
        )
        assert downsample_ratio < 1
        reduction_factor = 1 / downsample_ratio
        assert reduction_factor == 2.0
        self._downsample_ratio = int(reduction_factor) ** (
            self.PIXEL_SHUFFLE + self.CONV_MERGING
        )
        assert self._downsample_ratio == 2

    def _get_num_embeddings(self, width: int, height: int) -> int:
        num_patches = (width // self._patch_size) * (height // self._patch_size)
        num_tokens = num_patches // (self._downsample_ratio**2)
        return num_tokens

    def width_and_height_for_max_num_tokens_available(
        self,
        target_num_tokens_post_shuffle: int,
    ) -> tuple[int, int]:
        """
        TODO: optimize this so it squeezes closer to target number of tokens.
        Calculate image dimensions that produce approximately `target` tokens after
        pixel_shuffle.

        With pixel_shuffle enabled, each 2x2 patch grid becomes 1 token, so we
        need 4*B patches to get B tokens.

        Examples:
        >>> PATCH_SIZE = 16
        >>> DOWNSAMPLE_RATIO = 0.5
        >>> tiler = DynamicResolutionImageTiler(
        ...     max_model_len=16384,
        ...     patch_size=PATCH_SIZE,
        ...     downsample_ratio=DOWNSAMPLE_RATIO,
        ...     min_num_patches=4,
        ...     max_num_patches=0,
        ... )
        >>> width, height = tiler.width_and_height_for_max_num_tokens_available(
        ...     target_num_tokens_post_shuffle=8192,
        ... )
        >>> assert width, height == (2880, 2880)
        >>> assert (width // PATCH_SIZE) * (
        ...     height // PATCH_SIZE
        ... ) // 2**2 == 8100  # tokens post-shuffle
        >>> assert tiler._get_num_embeddings(width=width, height=height) == 8100
        """
        side_pixels = (
            math.isqrt(target_num_tokens_post_shuffle)
            * self._downsample_ratio
            * self._patch_size
        )
        assert isinstance(side_pixels, int) and side_pixels % self._patch_size == 0
        return side_pixels, side_pixels

    def max_num_tokens_available(self, text_prompt_length: int) -> int:
        return self._max_model_len - text_prompt_length - 4

    def _images_to_pixel_values_lst(
        self,
        text_prompt_length: int,
        images: list[Image.Image],
    ) -> tuple[list[torch.Tensor], list[int]]:
        num_tokens_available = self.max_num_tokens_available(text_prompt_length)
        params_per_image = self.compute_params(images, num_tokens_available)

        feature_sizes = []
        images = []
        for param in params_per_image:
            for t in self.apply_params(param):
                assert t.ndim == 3, f"{t.ndim=}: expected 3 dim tensor"
                images.append(t)
                feature_sizes.append(param.num_embeddings)
        return images, feature_sizes

    feature_size_cache: dict[Image.Image, int] = {}

    @classmethod
    def get_cached_feature_size(cls, image: Image.Image) -> int:
        feature_size = cls.feature_size_cache[id(image)]
        # hard assert that we only use the feature size once
        del cls.feature_size_cache[id(image)]
        return feature_size

    @dataclass
    class DynamicResolutionParams:
        media: Image.Image
        num_tiles: int
        num_embeddings: int
        patch_size: tuple[int, int]

    def apply_params(self, params: DynamicResolutionParams) -> list[torch.Tensor]:
        resized_img = params.media.resize(
            (
                params.patch_size[0] * self._patch_size,
                params.patch_size[1] * self._patch_size,
            )
        )
        processed_images = [resized_img]

        return [self._transform(img) for img in processed_images]

    def process_media(
        self,
        media: Image.Image,
        num_tokens_available: int,
    ) -> tuple[DynamicResolutionParams, int]:
        """Process a single media item and return its parameters.

        Args:
            media: The media item to process
            num_tokens_available: Number of tokens available for this media
        Returns:
            DynamicResolutionParams for the media
        """
        current_num_tokens_available = num_tokens_available
        assert isinstance(media, Image.Image), (
            "Dynamic resolution is only supported for image media"
        )
        orig_width, orig_height = media.width, media.height
        closest_patch_height = round(orig_height / self._patch_size + 0.5)
        closest_patch_width = round(orig_width / self._patch_size + 0.5)
        patches = closest_patch_height * closest_patch_width

        factor = min(
            math.sqrt(current_num_tokens_available / patches), self._factor_max
        )
        target_patch_height = math.floor(factor * closest_patch_height)
        target_patch_width = math.floor(factor * closest_patch_width)

        # Consider self._min_num_patches if > current_num_tokens_available.
        if (
            current_num_tokens_available > self._min_num_patches
            and target_patch_height * target_patch_width < self._min_num_patches
        ):
            up_factor = math.sqrt(
                self._min_num_patches / (target_patch_height * target_patch_width)
            )
            target_patch_height = math.ceil(up_factor * target_patch_height)
            target_patch_width = math.ceil(up_factor * target_patch_width)

        # Round patch grid to be divisible by 2 (pixel-shuffle OR conv-merging)
        # or by 4 when BOTH are enabled (two successive 2x reductions)
        if self.PIXEL_SHUFFLE or self.CONV_MERGING:
            required_divisor = 4 if (self.PIXEL_SHUFFLE and self.CONV_MERGING) else 2

            rem_h = target_patch_height % required_divisor
            if rem_h != 0:
                inc_h = required_divisor - rem_h
                if (
                    target_patch_height + inc_h
                ) * target_patch_width <= current_num_tokens_available:
                    target_patch_height += inc_h
                else:
                    target_patch_height = max(
                        required_divisor, target_patch_height - rem_h
                    )

            rem_w = target_patch_width % required_divisor
            if rem_w != 0:
                inc_w = required_divisor - rem_w
                if (
                    target_patch_height * (target_patch_width + inc_w)
                    <= current_num_tokens_available
                ):
                    target_patch_width += inc_w
                else:
                    target_patch_width = max(
                        required_divisor, target_patch_width - rem_w
                    )

        # Calculate embeddings for the main dynamic resolution image
        num_embeddings = self._get_num_embeddings(
            target_patch_width * self._patch_size,
            target_patch_height * self._patch_size,
        )

        token_count = target_patch_width * target_patch_height

        # Add thumbnail embeddings if enabled and image area is below threshold
        num_tiles = 1  # Base dynamic resolution image

        return self.DynamicResolutionParams(
            media=media,
            num_tiles=num_tiles,
            num_embeddings=num_embeddings,
            patch_size=(target_patch_width, target_patch_height),
        ), token_count

    def compute_params(
        self,
        media_list: list[Image.Image],
        num_tokens_available: int | None = None,
    ) -> list[DynamicResolutionParams]:
        """Compute parameters for all media with iterative token budgeting.

        Args:
            media_list: List of media items to process
            num_tokens_available: Total number of tokens available across all media
        Returns:
            List of ImageTilingParams for each media item
        """
        num_tokens_available = (
            num_tokens_available
            * (4 if self.PIXEL_SHUFFLE else 1)
            * (4 if self.CONV_MERGING else 1)
        )
        # When the number of available token is too small,
        # allow self._min_num_patches per media and let the sample be truncated.
        num_tokens_available = max(
            num_tokens_available, self._min_num_patches * len(media_list)
        )

        # Clip the number of tokens available per media to >min and <max patches.
        num_tokens_available_per_media = [
            max(min(num_tokens_available, self._max_num_patches), self._min_num_patches)
            for _ in range(len(media_list))
        ]

        # prevent infinite loop in any case
        for _ in range(10):
            # Step 1: Process each media with current token budget
            params = []
            token_counts = []

            for media, tokens_for_media in zip(
                media_list, num_tokens_available_per_media
            ):
                param, token_count = self.process_media(media, tokens_for_media)
                params.append(param)
                token_counts.append(token_count)
                self.feature_size_cache[id(param.media)] = param.num_embeddings

            # Step 2: Check if total tokens is within budget
            total_tokens = sum(token_counts)

            if total_tokens <= num_tokens_available:
                # We're within budget, return the params
                return params

            # Step 3: We're over budget, need to scale down
            # Calculate scaling factor to get under budget
            scaling_factor = num_tokens_available / total_tokens

            # Recalculate token budgets for each media based on scaling
            # Each media gets a proportional share of the total budget
            scaled_down_num_tokens_available_per_media = [
                max(self._min_num_patches, int(token_count * scaling_factor))
                for token_count in token_counts
            ]
            scaled_down = any(
                [
                    scaled_down_num_tokens_available_per_media[i]
                    < num_tokens_available_per_media[i]
                    for i in range(len(num_tokens_available_per_media))
                ]
            )
            # If there wasn't scaling down, we're stuck with min_num_patches per media,
            # else try with the scaled down num_tokens_available_per_media.
            if not scaled_down:
                num_tokens_available_per_media = [self._min_num_patches] * len(
                    media_list
                )
            else:
                num_tokens_available_per_media = (
                    scaled_down_num_tokens_available_per_media
                )
        ctx = f"{params=} {total_tokens=} {num_tokens_available=}"
        raise ValueError(
            f"Should be unreachable - `return params` above must be reached: {ctx}"
        )

    @staticmethod
    def stack(images: list[torch.Tensor], patch_size: int) -> torch.Tensor:
        assert len(images) > 0, "No images to stack"

        def rearrange_img(x):
            py = x.shape[-2] // patch_size
            px = x.shape[-1] // patch_size
            x = einops.rearrange(
                x,
                "c (py yy) (px xx) -> (py px) (c yy xx)",
                py=py,
                yy=patch_size,
                px=px,
                xx=patch_size,
            )
            return x

        imgs = [rearrange_img(img) for img in images]
        pixel_values_flat = torch.cat(imgs, dim=0).unsqueeze(0)
        return pixel_values_flat


class BaseNanoNemotronVLProcessor(ABC):
    """
    This model doesn't define its own HF processor,
    so we implement our own one here.

    The code to insert image tokens is based on:
    https://huggingface.co/OpenGVLab/InternVL2-1B/blob/main/modeling_internvl_chat.py#L252
    """

    def __init__(
        self,
        config: PretrainedConfig,
        tokenizer: TokenizerLike,
        *args,
        max_model_len: int,
        max_num_tiles: int | None = None,
        **kwargs,
    ) -> None:
        super().__init__()

        self.config = config
        self.tokenizer = tokenizer

        self.max_num_tiles = max_num_tiles or DEFAULT_NUM_TILES
        image_size: int = config.force_image_size
        patch_size: int = config.patch_size
        downsample_ratio: int = config.downsample_ratio

        self.num_image_token = int(
            (image_size // patch_size) ** 2 * (downsample_ratio**2)
        )
        self.image_size = image_size
        self.use_thumbnail: bool = config.use_thumbnail
        self.norm_mean = torch.Tensor(config.norm_mean).reshape(1, 3, 1, 1)
        self.norm_std = torch.Tensor(config.norm_std).reshape(1, 3, 1, 1)

        self.dynamic_tiler: DynamicResolutionImageTiler | None = None
        if self.use_dynamic_resolution(config):
            self.dynamic_tiler = DynamicResolutionImageTiler(
                max_model_len=max_model_len,
                patch_size=patch_size,
                downsample_ratio=downsample_ratio,
                min_num_patches=config.vision_config.args["min_num_patches"],
                max_num_patches=config.vision_config.args["max_num_patches"],
                norm_mean=config.norm_mean,
                norm_std=config.norm_std,
            )

    @staticmethod
    def use_dynamic_resolution(config: PretrainedConfig) -> bool:
        return "min_num_patches" in config.vision_config.args

    @property
    @abstractmethod
    def image_token_id(self) -> int:
        raise NotImplementedError

    @abstractmethod
    def get_image_repl(
        self,
        feature_size: int,
        num_patches: int | None,
    ) -> PromptUpdateDetails[str]:
        raise NotImplementedError

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        max_num_tiles: int,
    ) -> int:
        target_ratios = get_internvl_target_ratios(1, max_num_tiles)

        num_patches, _, _ = calculate_internvl_targets(
            orig_width=image_width,
            orig_height=image_height,
            target_ratios=target_ratios,
            image_size=self.image_size,
            use_thumbnail=self.use_thumbnail,
        )

        return num_patches * self.num_image_token

    def _images_to_pixel_values_lst(
        self,
        images: list[Image.Image],
        max_num_tiles: int,
    ) -> list[torch.Tensor]:
        return [
            image_to_pixel_values(
                image,
                input_size=self.image_size,
                max_num=max_num_tiles,
                use_thumbnail=self.use_thumbnail,
                idx=idx,
            )
            for idx, image in enumerate(images)
        ]

    def _preprocess_image(
        self,
        text: list[str],
        images: list[Image.Image],
        max_num_tiles: int,
    ) -> tuple[list[str], dict[str, Any]]:
        if len(images) == 0:
            image_inputs = {}
            return text, image_inputs

        if tiler := self.dynamic_tiler:
            sans_images = text[0].replace("<image>", "")
            text_prompt_length = len(
                self.tokenizer(sans_images, add_special_tokens=False).input_ids
            )
            pixel_values_lst, num_tokens_per_image = tiler._images_to_pixel_values_lst(
                text_prompt_length=text_prompt_length,
                images=images,
            )
            imgs_sizes = [(pv.shape[-2], pv.shape[-1]) for pv in pixel_values_lst]
            normalized = [
                input_conditioner(img, tiler.norm_mean, tiler.norm_std)
                for img in pixel_values_lst
            ]
            image_num_patches = torch.tensor([1] * len(num_tokens_per_image))
            image_inputs = {
                "pixel_values_flat": normalized,
                "imgs_sizes": imgs_sizes,
                "num_tokens_per_image": num_tokens_per_image,
            }
        else:
            pixel_values_lst = self._images_to_pixel_values_lst(images, max_num_tiles)
            image_num_patches = torch.tensor([len(item) for item in pixel_values_lst])
            pixel_values_flat = input_conditioner(
                torch.cat(pixel_values_lst), self.norm_mean, self.norm_std
            )
            image_inputs = {
                "pixel_values_flat": pixel_values_flat,
                "image_num_patches": image_num_patches,
            }
            num_tokens_per_image = [
                self.num_image_token * len(item) for item in pixel_values_lst
            ]

        assert len(text) == 1, (
            "hf_processor is called on the output of get_dummy_text, "
            "which should be a single string"
        )
        parts = [x for x in re.split(r"(<image>)", text[0]) if x]
        assert parts.count("<image>") == len(pixel_values_lst), (
            "the number of <image> tokens in the text should be the "
            "same as the number of images"
        )

        for i, (feature_size, num_patches) in enumerate(
            zip(num_tokens_per_image, image_num_patches, strict=True)
        ):
            image_repl = self.get_image_repl(feature_size, num_patches)
            parts[i] = parts[i].replace("<image>", image_repl.full)
        text = ["".join(parts)]
        return text, image_inputs

    def _make_batch_input(self, input_item: Any | list[Any] | None = None):
        if input_item is None:
            input_item = []
        if not isinstance(input_item, list):
            input_item = [input_item]
        return input_item

    @abstractmethod
    def __call__(
        self,
        text: str | list[str] | None = None,
        images: Image.Image | list[Image.Image] | None = None,
        return_tensors: str | TensorType | None = None,
        max_num_tiles: int | None = None,
    ) -> BatchFeature:
        raise NotImplementedError


class NanoNemotronVLProcessor(BaseNanoNemotronVLProcessor):
    """
    HF Processor  with extended video processing logic.
    Code for video processing is adapted from video example:
    https://huggingface.co/OpenGVLab/InternVL3-1B#inference-with-transformers
    """

    def __init__(
        self,
        config: PretrainedConfig,
        tokenizer: TokenizerLike,
        *,
        max_model_len: int,
        max_num_tiles: int | None = None,
        video_token: str | None = None,
        video_pruning_rate: float | None = None,
    ) -> None:
        super().__init__(
            config=config,
            tokenizer=tokenizer,
            max_model_len=max_model_len,
            max_num_tiles=max_num_tiles,
        )
        # add extra video token for video processing
        self.video_token = video_token
        self.video_pruning_rate = video_pruning_rate

        # Pre-tokenize special tokens for video processing
        # to avoid repeated tokenization
        self._img_start_token_ids = tokenizer.encode(
            IMG_START, add_special_tokens=False
        )
        self._img_end_token_ids = tokenizer.encode(IMG_END, add_special_tokens=False)
        self._img_context_token_ids = tokenizer.encode(
            IMG_CONTEXT, add_special_tokens=False
        )

    @property
    def supports_video(self) -> bool:
        return self.video_token_id is not None

    @property
    def video_token_id(self) -> int | None:
        if self.video_token is None:
            return None
        return self.tokenizer.get_vocab().get(self.video_token, None)

    @property
    def image_token_id(self) -> int:
        return self.tokenizer.convert_tokens_to_ids(IMG_CONTEXT)

    def _videos_to_pixel_values_lst(
        self,
        videos: list[npt.NDArray],
        max_num_tiles: int,
    ) -> list[torch.Tensor]:
        return [
            video_to_pixel_values(
                video,
                input_size=self.image_size,
                max_num_tiles=max_num_tiles,
                use_thumbnail=self.use_thumbnail,
            )
            for video in videos
        ]

    def _preprocess_video(
        self,
        text: list[str],
        videos: list[tuple[npt.NDArray, dict[str, Any]]],
        max_num_tiles: int,
    ):
        if len(videos) == 0 or not self.supports_video:
            video_inputs = {}
        else:
            videos_lst = [v[0] for v in videos]
            video_metadata_lst = [v[1] for v in videos]
            pixel_values_lst_video = self._videos_to_pixel_values_lst(
                videos_lst,
                max_num_tiles=max_num_tiles,
            )

            # We use frame duration in milliseconds (as integer) to ensure
            # we have consistent timestamps calculation. At preprocessing
            # fps parameter is given in fp32, while at inference it is bf16
            # which leads to inaccurate timestamp calculation and causes
            # timestamp values to differ.In rare cases this causes
            # mismatching number of output tokens for tokenized  frame prefixes
            frame_duration_ms_lst = [
                int(1000.0 / metadata["fps"]) for metadata in video_metadata_lst
            ]
            frames_indices_lst = [
                metadata["frames_indices"] for metadata in video_metadata_lst
            ]

            video_inputs = {
                "pixel_values_flat_video": input_conditioner(
                    torch.cat(pixel_values_lst_video), self.norm_mean, self.norm_std
                ),
                "video_num_patches": torch.tensor(
                    [len(item) for item in pixel_values_lst_video]
                ),
                "frames_indices": frames_indices_lst,
                "frame_duration_ms": torch.tensor(frame_duration_ms_lst),
            }

            image_size: int = self.config.force_image_size
            patch_size: int = self.config.patch_size
            downsample_ratio = self.config.downsample_ratio
            tokens_in_single_frame = int(
                (image_size * image_size // patch_size**2) * (downsample_ratio**2)
            )

            for pixel_values, video_metadata, frames_indices, frame_duration_ms in zip(
                pixel_values_lst_video,
                video_metadata_lst,
                frames_indices_lst,
                frame_duration_ms_lst,
            ):
                num_frames = pixel_values.shape[0]

                if (
                    self.video_pruning_rate is not None
                    and self.video_pruning_rate > 0.0
                ):
                    # Start of EVS-specific code
                    num_tokens = compute_retained_tokens_count(
                        tokens_per_frame=tokens_in_single_frame,
                        num_frames=num_frames,
                        q=self.video_pruning_rate,
                    )

                    # Here we just need placeholders that won't actually be replaced -
                    # we just need to make sure the total number of tokens is correct
                    # assign all tokens to the first frame
                    tokens_per_frame = [num_tokens] + [0] * (num_frames - 1)

                    # End of EVS-specific code
                else:
                    tokens_per_frame = [tokens_in_single_frame] * num_frames

                video_repl = self.get_video_repl(
                    tokens_per_frame=tokens_per_frame,
                    frames_indices=frames_indices,
                    frame_duration_ms=frame_duration_ms,
                    tokenizer=self.tokenizer,
                    img_start_token_ids=self._img_start_token_ids,
                    img_end_token_ids=self._img_end_token_ids,
                    img_context_token_ids=self._img_context_token_ids,
                )

                # video_repl.full is a list of token IDs
                # Convert token IDs back to text for the HF processor flow
                video_repl_text = self.tokenizer.decode(
                    video_repl.full, skip_special_tokens=False
                )
                text = [t.replace("<video>", video_repl_text, 1) for t in text]
        return text, video_inputs

    def __call__(
        self,
        text: str | list[str] | None = None,
        images: Image.Image | list[Image.Image] | None = None,
        videos: list[tuple[npt.NDArray, dict[str, Any]]] | None = None,
        return_tensors: str | TensorType | None = None,
        max_num_tiles: int | None = None,
    ) -> BatchFeature:
        # Use default if not provided
        if max_num_tiles is None:
            max_num_tiles = self.max_num_tiles

        text, images, videos = [
            self._make_batch_input(x) for x in (text, images, videos)
        ]

        text, image_inputs = self._preprocess_image(
            text=text,
            images=images,
            max_num_tiles=max_num_tiles,
        )

        text, video_inputs = self._preprocess_video(
            text=text,
            videos=videos,
            max_num_tiles=1,
        )

        text_inputs = self.tokenizer(text, add_special_tokens=False)

        if self.dynamic_tiler is None:
            batch = BatchFeature(
                {**text_inputs, **video_inputs, **image_inputs},
                tensor_type=return_tensors,
            )
        else:
            batch = BatchFeature(
                {**text_inputs, **video_inputs}, tensor_type=return_tensors
            )
            # allow images to be exempt from the BatchFeature validation:
            # We will .stack() them in _parse_and_validate_image_input
            batch.update(image_inputs)
        return batch

    def get_image_repl(
        self,
        feature_size: int,
        num_patches: int | None,
    ) -> PromptUpdateDetails[str]:
        repl_features = IMG_CONTEXT * feature_size
        repl_full = IMG_START + repl_features + IMG_END

        return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)

    @classmethod
    def get_video_repl(
        cls,
        *,
        tokens_per_frame: list[int],
        frames_indices: list[int],
        frame_duration_ms: int,
        tokenizer: TokenizerLike,
        img_start_token_ids: list[int],
        img_end_token_ids: list[int],
        img_context_token_ids: list[int],
    ) -> PromptUpdateDetails[list[int]]:
        """
        Build prompt replacement for a video.
        The replacement returned is not actually used to replace the placeholder
        tokens - it's just used to make sure we allocate the correct number
        of tokens.
        Actual replacement is done in embed_multimodal of
        NemotronH_Nano_VL_V2
        (specifically in _process_video_input -> _create_final_video_embeddings).
        There, we create the final embeddings with text embeddings for indicator tokens
        and video embeddings for video tokens.
        This is a single function that handles all cases - non EVS, EVS dummy, EVS real.
        The differentiation is done via tokens_per_frame parameter.
        - non EVS case - constant value same value across all frames
        - EVS dummy - Doesn't matter how tokens are distributed between frames - just
                        make sure the total number of tokens is correct.
        - EVS real (called from get_real_video_repl_for_evs) - different value per frame
        Args:
            tokens_per_frame (list[int]): number of tokens per frame
            frames_indices (list[int]): frame indices
            frame_duration_ms (int): duration of each frame in milliseconds
            tokenizer (TokenizerLike): tokenizer to use for tokenizing frame separators
            img_start_token_ids (list[int]): pre-tokenized IMG_START tokens
            img_end_token_ids (list[int]): pre-tokenized IMG_END tokens
            img_context_token_ids (list[int]): pre-tokenized IMG_CONTEXT tokens
        """
        # TODO: Add support of frame_duration_ms to be None
        # At preprocessing step we should allow absent / metadata without
        # frames_indices field.
        timestamps_enabled = frame_duration_ms is not None

        if timestamps_enabled:
            timestamps = calculate_timestamps(frames_indices, frame_duration_ms)

            assert len(timestamps) == len(tokens_per_frame), (
                "timestamps and tokens_per_frame must have the same length"
            )
            frame_separators = [
                f"Frame {i + 1} sampled at {timestamp:.2f} seconds: "
                for i, timestamp in enumerate(timestamps)
            ]
        else:
            frame_separators = [
                f"Frame {i + 1}: " for i, _ in enumerate(tokens_per_frame)
            ]

        # Tokenize frame separator independently
        frame_separators_tokenized = [
            _seq2tokens(tokenizer, sep) for sep in frame_separators
        ]

        # Tokenize each component independently to avoid tokenizer merging tokens
        # across boundaries. This ensures consistent tokenization regardless of
        # num_tokens_per_frame values.
        all_token_ids = []
        for i, num_tokens in enumerate(tokens_per_frame):
            frame_sep_token_ids = frame_separators_tokenized[i]
            all_token_ids.extend(frame_sep_token_ids)

            # Add pre-tokenized special tokens
            all_token_ids.extend(img_start_token_ids)
            all_token_ids.extend(img_context_token_ids * num_tokens)
            all_token_ids.extend(img_end_token_ids)

        return PromptUpdateDetails.from_seq(all_token_ids)


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

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

    def get_default_tok_params(self) -> TokenizeParams:
        return super().get_default_tok_params().with_kwargs(add_special_tokens=False)

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

    def get_image_size_with_most_features(self, max_num_tiles: int) -> ImageSize:
        processor = self.get_hf_processor()

        base_size = processor.image_size
        target_ratios = get_internvl_target_ratios(1, max_num_tiles)

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

            feat_size = processor.get_num_image_tokens(
                image_width=width, image_height=height, max_num_tiles=max_num_tiles
            )
            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()
        # Use default max_num_tiles for max tokens calculation
        max_num_tiles = processor.max_num_tiles
        target_width, target_height = self.get_image_size_with_most_features(
            max_num_tiles
        )

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


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


class NanoNemotronVLProcessingInfo(BaseNanoNemotronVLProcessingInfo):
    """ProcessingInfo extended for video processing"""

    @property
    def supports_video(self):
        return self.get_hf_processor().supports_video

    def get_data_parser(self):
        return MultiModalDataParser(
            video_needs_metadata=True,
            expected_hidden_size=self._get_expected_hidden_size(),
        )

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

    def get_video_token(self) -> str | None:
        return IMG_CONTEXT

    def get_video_pruning_rate(self) -> float | None:
        return self.ctx.get_mm_config().video_pruning_rate

    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()  # we get the CustomProcessor here

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

    def get_hf_processor(self, **kwargs: object) -> NanoNemotronVLProcessor:
        return self.ctx.init_processor(
            NanoNemotronVLProcessor,
            config=self.get_hf_config(),
            tokenizer=self.get_tokenizer(),
            video_token=self.get_video_token(),
            video_pruning_rate=self.get_video_pruning_rate(),
            max_model_len=self.ctx.model_config.max_model_len,
            **kwargs,
        )


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

    @cached_property
    def is_dynamic_tiler(self) -> bool:
        return self.info.get_hf_processor().dynamic_tiler is not None

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        if self.is_dynamic_tiler:
            pixel_values_flat = MultiModalFieldConfig.batched("image")
        else:
            image_num_patches = hf_inputs.get("image_num_patches", torch.empty(0))
            pixel_values_flat = MultiModalFieldConfig.flat_from_sizes(
                "image", image_num_patches
            )

        return dict(
            pixel_values_flat=pixel_values_flat,
            image_num_patches=MultiModalFieldConfig.batched("image"),
            image_embeds=MultiModalFieldConfig.batched("image"),
            num_tokens_per_image=MultiModalFieldConfig.batched("image"),
            imgs_sizes=MultiModalFieldConfig.batched("image"),
        )

    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()
        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:
            # 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_custom(item_idx: int):
            images = mm_items.get_items(
                "image", (ImageEmbeddingItems, ImageProcessorItems)
            )

            if isinstance(images, ImageEmbeddingItems):
                feature_size = images.get_feature_size(item_idx)
            elif tiler := hf_processor.dynamic_tiler:
                image = images.get(item_idx)
                feature_size = tiler.get_cached_feature_size(image)
            else:
                image_size = images.get_image_size(item_idx)
                # Extract max_num_tiles from kwargs, default to 12
                max_num_tiles = hf_processor_mm_kwargs.get(
                    "max_num_tiles", hf_processor.max_num_tiles
                )
                feature_size = hf_processor.get_num_image_tokens(
                    image_width=image_size.width,
                    image_height=image_size.height,
                    max_num_tiles=max_num_tiles,
                )

            num_patches = None
            local_image_num_patches = image_num_patches
            if isinstance(local_image_num_patches, torch.Tensor):
                local_image_num_patches = local_image_num_patches.tolist()
            if isinstance(local_image_num_patches, (list, tuple)) and item_idx < len(
                local_image_num_patches
            ):
                num_patches = int(local_image_num_patches[item_idx])

            return hf_processor.get_image_repl(feature_size, num_patches)

        return [
            PromptReplacement(
                modality="image",
                target="<image>",
                replacement=get_replacement_custom,
            )
        ]


class NanoNemotronVLMultiModalProcessor(
    NanoNemotronBaseVLMultiModalProcessor[NanoNemotronVLProcessingInfo]
):
    """MultiModalProcessor extended for video support"""

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        image_fields = super()._get_mm_fields_config(hf_inputs, hf_processor_mm_kwargs)
        if self.info.supports_video:
            video_num_patches = hf_inputs.get("video_num_patches", torch.empty(0))

            video_fields = dict(
                pixel_values_flat_video=MultiModalFieldConfig.flat_from_sizes(
                    "video", video_num_patches
                ),
                video_num_patches=MultiModalFieldConfig.batched("video"),
                frames_indices=MultiModalFieldConfig.batched("video"),
                frame_duration_ms=MultiModalFieldConfig.batched("video"),
            )
        else:
            video_fields = {}

        return image_fields | video_fields

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> Sequence[PromptUpdate]:
        prompt_repl = super()._get_prompt_updates(
            mm_items=mm_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
            out_mm_kwargs=out_mm_kwargs,
        )

        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)

        out_mm_data = out_mm_kwargs.get_data()
        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):
            feature_size = hf_processor.num_image_token
            video, metadata = mm_items["video"][item_idx]
            num_patches = video_num_patches[item_idx]
            if num_patches is not None:
                assert isinstance(num_patches, int)

            video_pruning_rate = self.info.ctx.get_mm_config().video_pruning_rate
            if video_pruning_rate is not None and video_pruning_rate > 0.0:
                # Start of EVS-specific code
                num_tokens = compute_retained_tokens_count(
                    tokens_per_frame=feature_size,
                    num_frames=num_patches,
                    q=video_pruning_rate,
                )
                # Here we just need placeholders that won't actually be replaced -
                # we just need to make sure the total number of tokens is correct
                # assign all tokens to the first frame
                tokens_per_frame = [num_tokens] + [0] * (num_patches - 1)

                # End of EVS-specific code
            else:
                tokens_per_frame = [feature_size] * num_patches

            frame_duration_ms = int(1000 / metadata["fps"])
            return hf_processor.get_video_repl(
                tokens_per_frame=tokens_per_frame,
                frames_indices=metadata["frames_indices"],
                frame_duration_ms=frame_duration_ms,
                tokenizer=hf_processor.tokenizer,
                img_start_token_ids=hf_processor._img_start_token_ids,
                img_end_token_ids=hf_processor._img_end_token_ids,
                img_context_token_ids=hf_processor._img_context_token_ids,
            )

        if self.info.supports_video:
            prompt_repl = [
                *prompt_repl,
                PromptReplacement(
                    modality="video",
                    target="<video>",
                    replacement=get_video_replacement_internvl,
                ),
            ]

        return prompt_repl


class NanoNemotronVLDummyInputsBuilder(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:
        num_images = mm_counts.get("image", 0)
        processor = self.info.get_hf_processor()
        if tiler := processor.dynamic_tiler:
            budget = tiler.max_num_tokens_available(text_prompt_length=num_images)
            target_width, target_height = (
                tiler.width_and_height_for_max_num_tokens_available(budget)
            )
        else:
            max_num_tiles = 12
            target_width, target_height = self.info.get_image_size_with_most_features(
                max_num_tiles
            )

        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 NanoNemotronVLDummyInputsBuilder(
    NanoNemotronVLDummyInputsBuilder[NanoNemotronVLProcessingInfo]
):
    """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_videos(
        self,
        *,
        width: int,
        height: int,
        num_frames: int,
        num_videos: int,
        overrides: VideoDummyOptions | None = None,
    ) -> list[VideoItem]:
        video = super()._get_dummy_videos(
            width=width,
            height=height,
            num_frames=num_frames,
            num_videos=1,
            overrides=overrides,
        )[0]
        video_items = []
        for _ in range(num_videos):
            video_metadata = {
                "total_num_frames": num_frames,
                "fps": 2,
                "duration": num_frames / 2.0,
                "video_backend": "opencv_dynamic",
                "frames_indices": [i for i in range(num_frames)],
                "do_sample_frames": False,
            }
            video_item = (video.copy(), video_metadata)
            video_items.append(video_item)

        return video_items

    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.supports_video:
            config = self.info.get_hf_config()
            image_size: int = config.force_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}


@MULTIMODAL_REGISTRY.register_processor(
    NanoNemotronVLMultiModalProcessor,
    info=NanoNemotronVLProcessingInfo,
    dummy_inputs=NanoNemotronVLDummyInputsBuilder,
)
class NemotronH_Nano_VL_V2(
    nn.Module, HasInnerState, IsHybrid, SupportsMultiModal, SupportsMultiModalPruning
):
    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
        if modality.startswith("image"):
            return "<image>"
        if modality.startswith("video"):
            return "<video>"
        return None

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        multimodal_config = vllm_config.model_config.multimodal_config
        image_size = config.force_image_size
        patch_size = config.patch_size
        self.patch_size = patch_size
        self.template = config.template
        self.num_image_token = int(
            (image_size // patch_size) ** 2 * (config.downsample_ratio**2)
        )
        self.downsample_ratio = config.downsample_ratio
        self.ps_version = config.ps_version
        self.image_tag_type = config.image_tag_type
        self.video_pruning_rate = multimodal_config.video_pruning_rate

        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"),
            )

        with self._mark_tower_model(vllm_config, {"image", "video"}):
            self.vision_model = self.get_vit_model_from_radio_config(config).to(
                self.language_model.config.dtype
            )

            # Construct the vision projection.
            vit_hidden_size = config.vit_hidden_size
            vision_projection_hidden_size = config.projector_hidden_size
            llm_hidden_size = config.text_config.hidden_size

            mlp1 = nn.Sequential(
                RMSNorm(
                    hidden_size=vit_hidden_size * int(1 / self.downsample_ratio) ** 2,
                    eps=1e-5,
                ),
                nn.Linear(
                    vit_hidden_size * int(1 / self.downsample_ratio) ** 2,
                    vision_projection_hidden_size,
                    bias=False,
                ),
                ReLUSquaredActivation(),
                nn.Linear(vision_projection_hidden_size, llm_hidden_size, bias=False),
            )
            self.mlp1 = mlp1.to(self.language_model.config.dtype)

        self.config = config
        self.model_config = vllm_config.model_config

        # Pre-tokenize special tokens for video processing
        # to avoid repeated tokenization
        tokenizer = cached_tokenizer_from_config(vllm_config.model_config)
        self._img_start_token_ids = tokenizer.encode(
            IMG_START, add_special_tokens=False
        )
        self._img_end_token_ids = tokenizer.encode(IMG_END, add_special_tokens=False)
        self._img_context_token_ids = tokenizer.encode(
            IMG_CONTEXT, add_special_tokens=False
        )
        self.dynamic_resolution = BaseNanoNemotronVLProcessor.use_dynamic_resolution(
            config
        )
        if self.dynamic_resolution:
            logger.info("Dynamic resolution is enabled for NanoNemotronVLProcessor")

    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()
        # N, H * scale, W, C // scale -->
        # N, H * scale, W * scale, C // (scale ** 2)
        x = x.view(
            n,
            int(h * scale_factor),
            int(w * scale_factor),
            int(c / (scale_factor * scale_factor)),
        )
        if self.ps_version == "v1":
            warnings.warn(
                "In ps_version 'v1', the height and width have not "
                "been swapped back, which results in a transposed image.",
                stacklevel=2,
            )
        else:
            x = x.permute(0, 2, 1, 3).contiguous()
        return x

    def pixel_shuffle_dynamic_res(
        self, x: torch.Tensor, *, imgs_sizes: list[tuple[int, int]]
    ) -> torch.Tensor:
        scale_factor = self.downsample_ratio
        patch_dim = self.patch_size
        seq_lens = calc_seq_lens(imgs_sizes, patch_dim)
        splits = torch.split(x, seq_lens, dim=-2)
        out = []
        for i, sv in enumerate(splits):
            h = imgs_sizes[i][0] // patch_dim
            w = imgs_sizes[i][1] // patch_dim
            sv = sv.reshape(sv.shape[0], h, w, -1)

            n, h, w, c = sv.size()

            sv = sv.view(n, h, int(w * scale_factor), int(c / scale_factor))
            sv = sv.permute(0, 2, 1, 3).contiguous()
            sv = sv.view(
                n,
                int(w * scale_factor),
                int(h * scale_factor),
                int(c / (scale_factor * scale_factor)),
            )

            if self.ps_version == "v2":
                sv = sv.permute(0, 2, 1, 3).contiguous()

            sv = sv.reshape(sv.shape[0], -1, sv.shape[-1])
            out.append(sv)

        x = torch.cat(out, dim=-2)

        return x

    def extract_feature_dynamic(
        self, pixel_values: torch.Tensor, imgs_sizes: list[tuple[int, int]]
    ):
        """Dynamic resolution extract_feature for images."""
        _, vit_embeds = self.vision_model(pixel_values, imgs_sizes=imgs_sizes)
        vit_embeds = vit_embeds.to(dtype=torch.bfloat16)
        vit_embeds = self.pixel_shuffle_dynamic_res(vit_embeds, imgs_sizes=imgs_sizes)
        vit_embeds = self.mlp1(vit_embeds)
        return vit_embeds

    def extract_feature(self, pixel_values: torch.Tensor):
        # Process images in a micro-batch of at most 128 frames per call
        # This is done on purpose to ensure peak GPU ram usage of huge batch
        # (namely for really long videos with EVS ON) won't cause any problems
        # as we don't support chunked prefill for video media
        micro_batch_size = 128
        n = pixel_values.shape[0]
        vit_embeds_list = []
        for i in range(0, n, micro_batch_size):
            _, vit_embeds = self.vision_model(pixel_values[i : i + micro_batch_size])
            vit_embeds = vit_embeds.to(dtype=torch.bfloat16)
            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)
            vit_embeds_list.append(vit_embeds)

        vit_embeds = torch.cat(vit_embeds_list, dim=0)
        return vit_embeds

    def _parse_and_validate_image_input(
        self, **kwargs: object
    ) -> NanoNemotronVLImageInputs | None:
        if image_embeds := kwargs.pop("image_embeds", None):
            return NanoNemotronVLImageEmbeddingInputs(
                type="image_embeds",
                data=image_embeds,
            )

        if self.dynamic_resolution:
            pixel_values_flat = DynamicResolutionImageTiler.stack(
                kwargs.pop("pixel_values_flat"), self.patch_size
            )
            return NanoNemotronVLImagePixelInputsDynamic(
                pixel_values_flat=pixel_values_flat, **kwargs
            )
        else:
            return NanoNemotronVLImagePixelInputs(
                num_patches=kwargs.pop("image_num_patches"), **kwargs
            )

    def _process_image_input_dynamic(
        self, image_input: NanoNemotronVLImagePixelInputsDynamic
    ) -> tuple[torch.Tensor, ...]:
        image_embeds = self.extract_feature_dynamic(
            image_input.pixel_values_flat, image_input.imgs_sizes
        )
        num_tokens_per_image = image_input.num_tokens_per_image

        if len(num_tokens_per_image) == 1:
            return (image_embeds.view(-1, self.config.text_config.hidden_size),)

        image_embeds = image_embeds.view(-1, self.config.text_config.hidden_size)
        return image_embeds.split(num_tokens_per_image)

    def _process_image_input(
        self, image_input: NanoNemotronVLImagePixelInputs
    ) -> tuple[torch.Tensor, ...]:
        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 _process_video_input(
        self, video_input: NanoNemotronVLVideoPixelInputs
    ) -> tuple[torch.Tensor, ...]:
        """Process video input and create final embeddings with video content
        and indicator tokens."""
        # Get video embeddings using the same processing as images
        video_embeddings = self._process_image_input(video_input)

        final_video_embeddings: tuple[torch.Tensor, ...] = ()

        image_rows = image_cols = self.config.force_image_size
        downsample_ratio = self.config.downsample_ratio
        patch_size = self.config.patch_size
        rows = int(image_rows * downsample_ratio // patch_size)
        cols = int(image_cols * downsample_ratio // patch_size)
        video_pruning_rate = self.video_pruning_rate
        video_num_frames = video_input["num_patches"].tolist()
        video_frames_indices = video_input["frames_indices"].split(video_num_frames)
        # Calculate video feature dimensions (number of frames and
        # their feature size (AKA tokens per frame))
        # TODO: Maybe this can be optimized to avoid the loop?
        for i, single_video_embeddings in enumerate(video_embeddings):
            num_frames = video_num_frames[i]
            frames_indices = video_frames_indices[i].tolist()
            frame_duration_ms = video_input["frame_duration_ms"][i].item()
            assert single_video_embeddings.shape[0] % num_frames == 0

            if video_pruning_rate is not None and video_pruning_rate > 0.0:
                # Start of EVS-specific code
                retention_mask = compute_retention_mask(
                    single_video_embeddings,
                    video_size_thw=(num_frames, rows, cols),
                    spatial_merge_size=1,
                    q=video_pruning_rate,
                )

                # apply retention mask
                single_video_embeddings = single_video_embeddings[retention_mask]

                # calculate the actual number of retained tokens per frame
                retention_mask_thw = retention_mask.reshape(num_frames, rows, cols)
                num_tokens_per_frame = (
                    retention_mask_thw.sum(dim=(1, 2)).long().tolist()
                )
                # End of EVS-specific code
            else:
                feature_size = single_video_embeddings.shape[0] // num_frames
                num_tokens_per_frame = [feature_size] * num_frames

            final_video_embeddings += (
                self._create_final_video_embeddings(
                    single_video_embeddings,
                    num_tokens_per_frame,
                    frames_indices,
                    frame_duration_ms,
                ),
            )

        return final_video_embeddings

    def _create_final_video_embeddings(
        self,
        video_embeddings: torch.Tensor,
        num_tokens_per_frame: list[int],
        frames_indices: list[int],
        frame_duration_ms: int,
    ) -> torch.Tensor:
        """Create final embeddings that combine video embeddings with
        text embeddings of indicator tokens.

        These final embeddings contain:
        - Actual video embeddings in positions corresponding to video content
        - Text embeddings for indicator tokens (<img>, </img>, and
          frame separation text) in their respective positions

        These embeddings will replace the placeholder embeddings to create
        input_embeds for the LLM.
        """
        device = video_embeddings.device
        tokenizer = cached_tokenizer_from_config(self.model_config)

        # Generate video replacement token IDs using get_video_repl
        # This tokenizes each frame separator independently, then uses pre-tokenized
        # special tokens to ensure consistent tokenization regardless of
        # num_tokens_per_frame values.
        video_repl = NanoNemotronVLProcessor.get_video_repl(
            tokens_per_frame=num_tokens_per_frame,
            frames_indices=frames_indices,
            frame_duration_ms=frame_duration_ms,
            tokenizer=tokenizer,
            img_start_token_ids=self._img_start_token_ids,
            img_end_token_ids=self._img_end_token_ids,
            img_context_token_ids=self._img_context_token_ids,
        )

        # video_repl.full is a list of token IDs
        repl_token_ids = torch.tensor(video_repl.full, device=device)

        # Get embedding token IDs for image context (use pre-tokenized version)
        embed_token_ids = torch.tensor(self._img_context_token_ids, device=device)

        # Create mask for video embedding positions
        is_video_embed = torch.isin(repl_token_ids, embed_token_ids)

        # Create final video embeddings, merging text embeddings for indicator
        # tokens with video embeddings
        text_embeddings = self.get_language_model().embed_input_ids(repl_token_ids)
        final_video_embeddings = _merge_multimodal_embeddings(
            inputs_embeds=text_embeddings,
            multimodal_embeddings=video_embeddings,
            is_multimodal=is_video_embed,
        )

        return final_video_embeddings

    def _parse_and_validate_video_input(
        self, **kwargs: object
    ) -> NanoNemotronVLVideoPixelInputs | 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("video_embeds", None)
        frames_indices = kwargs.pop("frames_indices", None)
        frame_duration_ms = kwargs.pop("frame_duration_ms", None)

        if pixel_values_flat_video is None and video_embeds is None:
            return None

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

        if pixel_values_flat_video is not None:
            if torch.is_tensor(frames_indices):
                frames_indices = frames_indices.flatten()
            else:
                frames_indices = torch.cat([f.flatten() for f in frames_indices], dim=0)

            frame_duration_ms = frame_duration_ms.flatten()
            expected_h = expected_w = self.config.force_image_size
            num_frames = video_num_patches[0].item()
            resolve_bindings = {"h": expected_h, "w": expected_w, "f": num_frames}

            return NanoNemotronVLVideoPixelInputs(
                type="pixel_values_videos",
                pixel_values_flat=pixel_values_flat_video,
                num_patches=video_num_patches,
                frames_indices=frames_indices,
                frame_duration_ms=frame_duration_ms,
                resolve_bindings=resolve_bindings,
            )

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

    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:
        # Validate the multimodal input keyword arguments
        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
        if modalities is None:
            return []

        # # The result multimodal_embeddings is tuple of tensors, with each
        # tensor corresponding 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"]
                if image_input["type"] == "image_embeds":
                    image_embeddings = image_input["data"]
                elif self.dynamic_resolution:
                    assert image_input["type"] == "pixel_values_dynamic"
                    image_embeddings = self._process_image_input_dynamic(image_input)
                else:
                    image_embeddings = self._process_image_input(image_input)
                multimodal_embeddings += tuple(image_embeddings)
            if modality == "videos":
                video_input = modalities["videos"]
                video_embeddings = self._process_video_input(video_input)
                multimodal_embeddings += tuple(video_embeddings)

        return multimodal_embeddings

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

        hidden_states = self.language_model(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
            **kwargs,
        )

        return hidden_states

    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 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]]):
        adapter_dict = dict(self.mlp1.named_parameters())

        def is_llm(name: str) -> bool:
            return name.startswith("language_model")

        def is_adapter_weights(weight: tuple[str, torch.Tensor]):
            return weight[0].startswith("mlp1")

        def is_vision_weights(name: str) -> bool:
            return name.startswith("vision_model.radio_model.")

        # Separate weights by component
        llm_weights = []
        vision_weights = []

        for name, w in weights:
            if is_llm(name):
                # Strip 'language_model.' prefix for LLM weights
                llm_weights.append((".".join(name.split(".")[1:]), w))
            elif is_adapter_weights((name, w)):
                # Load vision-language adapter weights directly
                trimmed_name = ".".join(name.split(".")[1:])
                param = adapter_dict[trimmed_name]
                with torch.no_grad():
                    default_weight_loader(param, w)
            elif is_vision_weights(name):
                # Convert: vision_model.radio_model.* → radio_model.*
                hf_key = name[len("vision_model.") :]  # Remove "vision_model." prefix
                vision_weights.append((hf_key, w))

        self.language_model.load_weights(llm_weights)
        self.vision_model.load_weights(vision_weights)

    def print_architecture(self, detailed: bool = True, save_to_file: str = None):
        """
        Print model architecture with parameter names, shapes, and sizes.

        Args:
            detailed: If True, show detailed parameter breakdown
            save_to_file: If provided, save output to this file path
        """
        import sys
        from io import StringIO

        # Capture output if saving to file
        original_stdout = sys.stdout
        if save_to_file:
            sys.stdout = StringIO()

        try:
            print("=" * 100)
            print("NemotronH_Nano_VL_V2 Model Architecture")
            print("=" * 100)

            total_params = 0
            param_groups = {
                "language_model": [],
                "vision_model": [],
                "mlp1": [],
                "other": [],
            }

            for name, param in self.named_parameters():
                param_size = param.numel()
                total_params += param_size

                # Group parameters by main component
                if name.startswith("language_model"):
                    param_groups["language_model"].append(
                        (name, param.shape, param_size, param.dtype)
                    )
                elif name.startswith("vision_model"):
                    param_groups["vision_model"].append(
                        (name, param.shape, param_size, param.dtype)
                    )
                elif name.startswith("mlp1"):
                    param_groups["mlp1"].append(
                        (name, param.shape, param_size, param.dtype)
                    )
                else:
                    param_groups["other"].append(
                        (name, param.shape, param_size, param.dtype)
                    )

                if detailed:
                    print(
                        f"{name:<70} | Shape: {str(param.shape):<25} | "
                        f"Size: {param_size:>12,} | Dtype: {param.dtype}"
                    )

            print("=" * 100)
            print("Summary by Component:")
            print("-" * 60)

            for component, params in param_groups.items():
                if params:  # Only show components that have parameters
                    component_total = sum(size for _, _, size, _ in params)
                    percentage = (
                        (component_total / total_params) * 100
                        if total_params > 0
                        else 0
                    )
                    print(
                        f"{component:<20} | Parameters: {len(params):>4} | "
                        f"Total Size: {component_total:>15,} | "
                        f"{percentage:>6.2f}%"
                    )

            print("-" * 60)
            print(f"{'Total Parameters':<20} | {total_params:>15,}")

            # Estimate memory usage (assuming bfloat16 = 2 bytes per parameter)
            memory_mb = total_params * 2 / (1024**2)
            memory_gb = memory_mb / 1024
            print(f"{'Est. Memory (MB)':<20} | {memory_mb:>15.2f}")
            print(f"{'Est. Memory (GB)':<20} | {memory_gb:>15.2f}")
            print("=" * 100)

            # Save to file if requested
            if save_to_file:
                output = sys.stdout.getvalue()
                sys.stdout = original_stdout
                with open(save_to_file, "w") as f:
                    f.write(output)
                print(f"Architecture saved to: {save_to_file}")
                print(output)  # Also print to console

        finally:
            if save_to_file and sys.stdout != original_stdout:
                sys.stdout = original_stdout

    def get_vit_model_from_radio_config(self, hf_config):
        hf_config_vision = hf_config.vision_config
        model_name = hf_config_vision.args.get("model")
        if model_name is None:
            raise ValueError(f"Unsupported vit model type: {model_name}")

        preferred_resolution = getattr(hf_config_vision, "preferred_resolution", None)
        image_size = preferred_resolution[0] if preferred_resolution else 224
        patch_size = getattr(hf_config_vision, "patch_size", 16)

        radio_config = RadioConfig(
            model_name=model_name,
            image_size=image_size,
            patch_size=patch_size,
            norm_mean=hf_config.norm_mean,
            norm_std=hf_config.norm_std,
            **hf_config_vision.args,
        )

        return RadioModel(config=radio_config)

    def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
        return self.language_model.mamba_cache.copy_inputs_before_cuda_graphs(
            input_buffers, **kwargs
        )

    def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
        return self.language_model.mamba_cache.get_seqlen_agnostic_capture_inputs(
            batch_size
        )

    @classmethod
    def get_mamba_state_shape_from_config(cls, vllm_config: "VllmConfig"):
        text_config = vllm_config.model_config.hf_config.text_config
        temp_vllm_config = copy.deepcopy(vllm_config)
        temp_vllm_config.model_config.hf_config = text_config
        return NemotronHForCausalLM.get_mamba_state_shape_from_config(temp_vllm_config)

    @classmethod
    def get_mamba_state_dtype_from_config(cls, vllm_config: "VllmConfig"):
        text_config = vllm_config.model_config.hf_config.text_config
        temp_vllm_config = copy.deepcopy(vllm_config)
        temp_vllm_config.model_config.hf_config = text_config
        return NemotronHForCausalLM.get_mamba_state_dtype_from_config(temp_vllm_config)

    @classmethod
    def get_mamba_state_copy_func(cls):
        return NemotronHForCausalLM.get_mamba_state_copy_func()