# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import math
from collections.abc import Iterable, Mapping, Sequence
from functools import partial
from typing import Annotated, Any, Literal, Optional, Union

import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from transformers import PretrainedConfig
from transformers.activations import GELUActivation
from transformers.feature_extraction_utils import BatchFeature
from transformers.modeling_outputs import (BaseModelOutput,
                                           BaseModelOutputWithPooling)
from transformers.utils import torch_int

from vllm.config import VllmConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.logger import init_logger
from vllm.model_executor import SamplingMetadata
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               QKVParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.quantization.gptq import GPTQConfig
from vllm.model_executor.layers.quantization.gptq_marlin import (
    GPTQMarlinConfig)
from vllm.model_executor.model_loader.weight_utils import (
    default_weight_loader, maybe_remap_kv_scale_name)
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (ImageItem, ModalityData,
                                    MultiModalDataDict, MultiModalFieldConfig,
                                    MultiModalKwargs, VideoItem)
from vllm.multimodal.parse import (DictEmbeddingItems, ImageSize,
                                   ModalityDataItems, MultiModalDataItems,
                                   MultiModalDataParser)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                        BaseProcessingInfo, PromptReplacement,
                                        PromptUpdate)
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.platforms import _Backend
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.config import uses_mrope
from vllm.utils.tensor_schema import TensorSchema, TensorShape

from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
                         SupportsMultiModal, SupportsPP)
from .siglip import SiglipMLP
from .utils import (AutoWeightsLoader, WeightsMapper,
                    init_vllm_registered_model, is_pp_missing_parameter,
                    maybe_prefix, merge_multimodal_embeddings)
from .vision import get_vit_attn_backend

logger = init_logger(__name__)

_MAX_FRAMES_PER_VIDEO = 16
_MAX_IMAGE_SIZE = 9999999


def smart_resize(
    height: int,
    width: int,
    factor: int = 28,
    min_pixels: int = 28 * 28 * 130,
    max_pixels: int = 28 * 28 * 1280,
):
    if height < factor:
        logger.warning(
            "smart_resize: height=%s < factor=%s, reset height=factor",
            height,
            factor,
        )
        width = round((width * factor) / height)
        height = factor

    if width < factor:
        logger.warning(
            "smart_resize: width=%s < factor=%s, reset width=factor",
            width,
            factor,
        )
        height = round((height * factor) / width)
        width = factor

    if max(height, width) / min(height, width) > 200:
        raise ValueError("absolute aspect ratio must be smaller than 200, got "
                         "{max(height, width) / min(height, width)}")
    h_bar = round(height / factor) * factor
    w_bar = round(width / factor) * factor
    if h_bar * w_bar > max_pixels:
        beta = math.sqrt((height * width) / max_pixels)
        h_bar = math.floor(height / beta / factor) * factor
        w_bar = math.floor(width / beta / factor) * factor
    elif h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (height * width))
        h_bar = math.ceil(height * beta / factor) * factor
        w_bar = math.ceil(width * beta / factor) * factor
    return h_bar, w_bar


class KeyeImagePixelInputs(TensorSchema):
    """
    Dimensions:
        - b: Batch size
        - np: Number of patches
        - c: Number of channels
        - ps: Patch size
        - ni: Number of images
        - g: Grid dimensions (3 for t, h, w)
    """
    type: Literal["pixel_values"]
    pixel_values: Annotated[torch.Tensor,
                            TensorShape("b", "np", 3, "ps", "ps")]
    image_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)]


class KeyeImageEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - nf: Number of image features
        - hs: Hidden size (must match the hidden size of language model 
          backbone)
        - ni: Number of images
        - g: Grid dimensions (3 for t, h, w)
    """
    type: Literal["image_embeds"]
    image_embeds: Annotated[torch.Tensor, TensorShape("nf", "hs")]
    image_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)]


KeyeImageInputs = Union[KeyeImagePixelInputs, KeyeImageEmbeddingInputs]


class KeyeVideoPixelInputs(TensorSchema):
    """
    Dimensions:
        - b: Batch size
        - np: Number of patches
        - c: Number of channels
        - ps: Patch size
        - ni: Number of images
        - g: Grid dimensions (3 for t, h, w)
    """
    type: Literal["pixel_values_videos"]
    pixel_values_videos: Annotated[torch.Tensor,
                                   TensorShape("b", "np", 3, "ps", "ps")]
    video_grid_thw: Annotated[torch.Tensor, TensorShape("nv", 3)]


class KeyeVideoEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - nf: Number of video features
        - hs: Hidden size (must match the hidden size of language model 
          backbone)
        - nv: Number of videos
        - g: Grid dimensions (3 for t, h, w)
    """
    type: Literal["video_embeds"]
    video_embeds: Annotated[torch.Tensor, TensorShape("nf", "hs")]
    video_grid_thw: Annotated[torch.Tensor, TensorShape("nv", 3)]


KeyeVideoInputs = Union[KeyeVideoPixelInputs, KeyeVideoEmbeddingInputs]


class KeyeVisionEmbeddings(nn.Module):

    def __init__(self, config: PretrainedConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.image_size = config.image_size
        self.patch_size = config.patch_size

        self.patch_embedding = nn.Conv2d(
            in_channels=config.num_channels,
            out_channels=self.embed_dim,
            kernel_size=self.patch_size,
            stride=self.patch_size,
            padding="valid",
        )

        self.num_patches = (self.image_size // self.patch_size)**2
        self.num_positions = self.num_patches
        self.cache_position_embedding = dict()
        self.cache_position_count = dict()
        self.position_embedding = nn.Embedding(self.num_positions,
                                               self.embed_dim)
        self.packing_position_embedding = nn.Embedding(32768, self.embed_dim)

        self.register_buffer(
            "position_ids",
            torch.arange(self.num_positions).expand((1, -1)),
            persistent=False,
        )

    def interpolate_pos_encoding(
        self,
        embeddings: torch.Tensor,
        height: int,
        width: int,
        is_after_patchify: bool = False,
    ) -> torch.Tensor:

        num_positions = self.position_embedding.weight.shape[0]

        patch_pos_embed = self.position_embedding.weight.unsqueeze(0)

        dim = embeddings.shape[-1]

        if is_after_patchify:
            new_height = height
            new_width = width
        else:
            new_height = height // self.patch_size
            new_width = width // self.patch_size

        sqrt_num_positions = torch_int(num_positions**0.5)
        patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions,
                                                  sqrt_num_positions, dim)
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)

        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            size=(new_height, new_width),
            mode="bilinear",
            align_corners=False,
        )

        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        return patch_pos_embed

    def fetch_position_embedding_lfu_cache(self,
                                           embeddings,
                                           h,
                                           w,
                                           max_cache: int = 20):
        grid = (h, w)
        if grid in self.cache_position_embedding:
            self.cache_position_count[grid] += 1
            return self.cache_position_embedding[grid]

        if len(self.cache_position_embedding) >= max_cache:
            min_hit_grid = min(
                self.cache_position_count,
                key=self.cache_position_count.get,
            )
            self.cache_position_count.pop(min_hit_grid)
            self.cache_position_embedding.pop(min_hit_grid)

        position_embedding = self.interpolate_pos_encoding(
            embeddings, h, w, True)
        self.cache_position_count[grid] = 1
        self.cache_position_embedding[grid] = position_embedding
        return position_embedding

    def forward(
        self,
        pixel_values: torch.FloatTensor,
        position_ids: Optional[torch.Tensor] = None,
        image_grid_thw: Optional[list[Union[
            tuple[int, int, int],
            list[tuple[int, int, int]],
        ]]] = None,
        interpolate_pos_encoding=False,
    ) -> torch.Tensor:
        if pixel_values.dim() == 4:
            pixel_values = pixel_values.unsqueeze(0)
        if pixel_values.dim() == 5:
            if position_ids is None:
                raise ValueError(
                    "position_ids cannot be None when pixel_values.dim() is 5."
                )
            (
                batch_size,
                squence_len,
                channel,
                height,
                width,
            ) = pixel_values.shape
            target_dtype = self.patch_embedding.weight.dtype
            pixel_values = rearrange(pixel_values, "b l c h w -> (b l) c h w")
            patch_embeds = self.patch_embedding(
                pixel_values.to(dtype=target_dtype))
            embeddings = patch_embeds.flatten(-2).squeeze(-1)

            if interpolate_pos_encoding and image_grid_thw is not None:
                start = 0
                tmp_embeddings = list()
                for image_grid in image_grid_thw:
                    t, h, w = image_grid
                    end = start + t * h * w
                    image_embeddings = embeddings[start:end, :]
                    position_embedding = (self.interpolate_pos_encoding(
                        image_embeddings, h, w, True).squeeze(0).repeat(t, 1))
                    image_embeddings = image_embeddings + position_embedding
                    tmp_embeddings.append(image_embeddings)
                    start = end
                embeddings = torch.concat(tmp_embeddings, dim=0).unsqueeze(0)
            else:
                embeddings = embeddings + self.packing_position_embedding(
                    position_ids)
            return embeddings
        else:
            raise ValueError("Unsupported pixel_values dimension:"
                             f" {pixel_values.dim()}. Expected 4 or 5.")


def apply_rotary_pos_emb_flashatt(
    q: torch.Tensor,
    k: torch.Tensor,
    cos: torch.Tensor,
    sin: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    cos = cos.chunk(2, dim=-1)[0].contiguous()
    sin = sin.chunk(2, dim=-1)[0].contiguous()

    from vllm.vllm_flash_attn.layers.rotary import apply_rotary_emb

    q_embed = apply_rotary_emb(q.float(), cos.float(), sin.float()).type_as(q)
    k_embed = apply_rotary_emb(k.float(), cos.float(), sin.float()).type_as(k)
    return q_embed, k_embed


class KeyeSiglipAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You
    Need' paper."""

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.config = config

        hidden_size = config.hidden_size
        self.hidden_size = config.hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = config.num_attention_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = config.num_attention_heads
        if self.total_num_kv_heads >= tp_size:
            assert self.total_num_kv_heads % tp_size == 0
        else:
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = config.hidden_size // self.total_num_heads
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scale = self.head_dim**-0.5

        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=True,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )
        self.out_proj = RowParallelLinear(
            input_size=hidden_size,
            output_size=hidden_size,
            quant_config=quant_config,
            prefix=f"{prefix}.out_proj",
        )

        # Detect attention implementation.
        self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
        if self.attn_backend not in {_Backend.FLASH_ATTN, _Backend.XFORMERS}:
            raise RuntimeError(
                f"Keye-VL does not support {self.attn_backend} backend now.")

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = False,
        cu_seqlens: Optional[list[torch.Tensor]] = None,
        rope_emb: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split(
            [self.q_size, self.kv_size, self.kv_size],
            dim=-1,
        )

        max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
        seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
        batch_size = q.shape[0]

        if rope_emb is None:
            q = q.view(*q.shape[:-1], self.num_heads, self.head_dim)
            k = k.view(
                *k.shape[:-1],
                self.num_kv_heads,
                self.head_dim,
            )
            v = v.view(
                *v.shape[:-1],
                self.num_kv_heads,
                self.head_dim,
            )
        else:
            if cu_seqlens is None:
                raise ValueError(
                    "cu_seqlens cannot be None when rope_emb is not None.")
            cos, sin = rope_emb
            q = q.view(*q.shape[:-1], self.num_heads, self.head_dim)
            k = k.view(
                *k.shape[:-1],
                self.num_kv_heads,
                self.head_dim,
            )
            q, k = apply_rotary_pos_emb_flashatt(q, k, cos, sin)
            v = v.view(
                *v.shape[:-1],
                self.num_kv_heads,
                self.head_dim,
            )

        if self.attn_backend == _Backend.FLASH_ATTN:
            from flash_attn import flash_attn_varlen_func

            q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])

            output = flash_attn_varlen_func(
                q,
                k,
                v,
                cu_seqlens_q=cu_seqlens,
                cu_seqlens_k=cu_seqlens,
                max_seqlen_q=max_seqlen,
                max_seqlen_k=max_seqlen,
                causal=False,
                softmax_scale=self.scale,
            )
            context_layer = rearrange(output,
                                      "(b s) ... -> b s ...",
                                      b=batch_size)
        elif self.attn_backend == _Backend.XFORMERS:
            from xformers import ops as xops
            from xformers.ops.fmha.attn_bias import BlockDiagonalMask

            attn_bias = BlockDiagonalMask.from_seqlens(q_seqlen=seqlens,
                                                       kv_seqlen=None,
                                                       device=q.device)

            context_layer = xops.memory_efficient_attention_forward(
                q, k, v, attn_bias=attn_bias, p=0, scale=None)

        context_layer = rearrange(context_layer,
                                  "b s h d -> b s (h d)").contiguous()

        output, _ = self.out_proj(context_layer)
        return output


class SigLIPRotaryEmbedding(nn.Module):

    def __init__(self, dim: int, theta: float = 10000.0) -> None:
        super().__init__()
        self.dim = dim
        self.theta = theta
        self.rope_init()

    def rope_init(self):
        inv_freq = 1.0 / (self.theta**(
            torch.arange(0, self.dim, 2, dtype=torch.float) / self.dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

    def forward(self, seqlen: int) -> torch.Tensor:
        seq = torch.arange(
            seqlen,
            device=self.inv_freq.device,
            dtype=self.inv_freq.dtype,
        )
        freqs = torch.outer(seq, self.inv_freq)
        return freqs


class KeyeSiglipEncoderLayer(nn.Module):

    def __init__(
        self,
        config: Union[PretrainedConfig],
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.embed_dim = config.hidden_size
        self.layer_norm1 = nn.LayerNorm(self.embed_dim,
                                        eps=config.layer_norm_eps)
        self.self_attn = KeyeSiglipAttention(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn",
        )
        self.layer_norm2 = nn.LayerNorm(self.embed_dim,
                                        eps=config.layer_norm_eps)
        self.mlp = SiglipMLP(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.mlp",
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        output_attentions: Optional[bool] = False,
        cu_seqlens: Optional[list[torch.Tensor]] = None,
        rope_emb: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
    ) -> tuple[torch.FloatTensor]:

        residual = hidden_states

        hidden_states = self.layer_norm1(hidden_states)
        hidden_states = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            cu_seqlens=cu_seqlens,
            rope_emb=rope_emb,
        )

        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.layer_norm2(hidden_states)
        hidden_states = self.mlp(hidden_states)

        hidden_states = residual + hidden_states

        return hidden_states


class KeyeSiglipEncoder(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        embed_dim = config.hidden_size
        num_heads = config.num_attention_heads
        head_dim = embed_dim // num_heads
        self.layers = nn.ModuleList([
            KeyeSiglipEncoderLayer(
                config,
                quant_config=quant_config,
                prefix=f"{prefix}.layers.{layer_idx}",
            ) for layer_idx in range(config.num_hidden_layers)
        ])
        self.rotary_pos_emb = SigLIPRotaryEmbedding(head_dim // 2)

    @staticmethod
    def flatten_list(image_grid_thw):
        tmp_image_grid_thw = list()
        for image_grid in image_grid_thw:
            if isinstance(image_grid, list):
                tmp_image_grid_thw.extend(image_grid)
            else:
                tmp_image_grid_thw.append(image_grid)
        return tmp_image_grid_thw

    def forward(
        self,
        inputs_embeds,
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        cu_seqlens: Optional[list[torch.Tensor]] = None,
        image_grid_thw: Optional[list[Union[
            tuple[int, int, int],
            list[tuple[int, int, int]],
        ]]] = None,
        height_position_ids: Optional[torch.Tensor] = None,
        width_position_ids: Optional[torch.Tensor] = None,
        use_rope: Optional[bool] = False,
        window_size: Optional[bool] = -1,
        vision_or_text: str = "vision",
    ) -> BaseModelOutput:
        device = inputs_embeds.device
        hidden_states = inputs_embeds
        if use_rope is True:
            flatten_image_grid_thw = self.flatten_list(image_grid_thw)

            if width_position_ids is None or height_position_ids is None:
                split_hids = list()
                split_wids = list()
                for t, h, w in flatten_image_grid_thw:
                    image_pids = torch.arange(t * h * w,
                                              device=device) % (h * w)
                    sample_hids = image_pids // w
                    sample_wids = image_pids % w
                    split_hids.append(sample_hids)
                    split_wids.append(sample_wids)
                width_position_ids = torch.concat(split_wids, dim=0)
                height_position_ids = torch.concat(split_hids, dim=0)

            pids = torch.stack(
                [height_position_ids, width_position_ids],
                dim=-1,
            )
            max_grid_size = pids.max() + 1
            rope_emb_max_grid = self.rotary_pos_emb(max_grid_size)
            rope_emb = rope_emb_max_grid[pids].flatten(1)
            rope_emb = rope_emb.repeat(1, 2)
            rope_emb = (rope_emb.cos(), rope_emb.sin())
        else:
            rope_emb = None

        attn_cu_seqlens = cu_seqlens
        hidden_states = inputs_embeds
        assert attention_mask is None

        for encoder_layer in self.layers:
            hidden_states = encoder_layer(
                hidden_states,
                attention_mask,
                output_attentions=output_attentions,
                cu_seqlens=attn_cu_seqlens,
                rope_emb=rope_emb,
            )
        return hidden_states


class KeyeSiglipVisionTransformer(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        embed_dim = config.hidden_size

        self.embeddings = KeyeVisionEmbeddings(config)
        self.encoder = KeyeSiglipEncoder(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.encoder",
        )
        self.post_layernorm = nn.LayerNorm(embed_dim,
                                           eps=config.layer_norm_eps)

    def forward(
        self,
        pixel_values,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        interpolate_pos_encoding: Optional[bool] = False,
        attention_mask: Optional[torch.Tensor] = None,
        sample_indices: Optional[torch.Tensor] = None,
        image_indices: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        height_position_ids: Optional[torch.Tensor] = None,
        width_position_ids: Optional[torch.Tensor] = None,
        cu_seqlens: Optional[list[torch.Tensor]] = None,
        padding_mask: Optional[torch.Tensor] = None,
        vision_return_embed_list: Optional[bool] = False,
        image_grid_thw: Optional[list[Union[
            tuple[int, int, int],
            list[tuple[int, int, int]],
        ]]] = None,
        return_pooler_output: Optional[bool] = True,
        use_rope: Optional[bool] = False,
        window_size: Optional[bool] = -1,
    ) -> BaseModelOutputWithPooling:

        hidden_states = self.embeddings(
            pixel_values,
            interpolate_pos_encoding=interpolate_pos_encoding,
            position_ids=position_ids,
            image_grid_thw=image_grid_thw,
        )

        last_hidden_state = self.encoder(
            inputs_embeds=hidden_states,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            attention_mask=attention_mask,
            cu_seqlens=cu_seqlens,
            image_grid_thw=image_grid_thw,
            use_rope=use_rope,
            height_position_ids=height_position_ids,
            width_position_ids=width_position_ids,
            window_size=window_size,
            vision_or_text="vision",
        )

        last_hidden_state = self.post_layernorm(last_hidden_state)

        sample_hidden_state = list()
        if cu_seqlens is None:
            raise ValueError("cu_seqlens cannot be None for "
                             "SiglipVisionTransformer output processing.")
        for i in range(cu_seqlens.shape[0] - 1):
            start = cu_seqlens[i]
            end = cu_seqlens[i + 1]
            tensor = last_hidden_state[:, start:end, :].squeeze(0)
            sample_hidden_state.append(tensor)

        return sample_hidden_state


class KeyeSiglipVisionModel(nn.Module):
    config_class = PretrainedConfig
    main_input_name = "pixel_values"

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()

        self.vision_model = KeyeSiglipVisionTransformer(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.vision_model",
        )
        self.quant_config = quant_config

    @property
    def dtype(self) -> torch.dtype:
        return self.vision_model.embeddings.patch_embedding.weight.dtype

    @property
    def device(self) -> torch.device:
        return self.vision_model.embeddings.patch_embedding.weight.device

    def get_input_embeddings(self) -> nn.Module:
        return self.vision_model.embeddings.patch_embedding

    def forward(
        self,
        pixel_values,
        sample_indices: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        interpolate_pos_encoding: bool = False,
        position_ids: Optional[torch.Tensor] = None,
        vision_return_embed_list: Optional[bool] = False,
        image_grid_thw: Optional[list[Union[
            tuple[int, int, int],
            list[tuple[int, int, int]],
        ]]] = None,
        cu_seqlens: Optional[list[torch.Tensor]] = None,
        return_pooler_output: Optional[bool] = True,
        use_rope: Optional[bool] = False,
        window_size: Optional[bool] = -1,
    ) -> BaseModelOutputWithPooling:

        return self.vision_model(
            pixel_values=pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            interpolate_pos_encoding=interpolate_pos_encoding,
            position_ids=position_ids,
            vision_return_embed_list=vision_return_embed_list,
            image_grid_thw=image_grid_thw,
            sample_indices=sample_indices,
            cu_seqlens=cu_seqlens,
            return_pooler_output=return_pooler_output,
            use_rope=use_rope,
            window_size=window_size,
        )

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]
        params_dict = dict(self.named_parameters(remove_duplicate=False))
        loaded_params: set[str] = set()
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
            if "head.attention" in name or "head.layernorm" in name:
                continue
            if "head.mlp" in name or "head.probe" in name:
                continue
            if self.quant_config is not None and (
                    scale_name := self.quant_config.get_cache_scale(name)):
                param = params_dict[scale_name]
                weight_loader = getattr(
                    param,
                    "weight_loader",
                    default_weight_loader,
                )
                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
                                 loaded_weight[0])
                weight_loader(param, loaded_weight)
                loaded_params.add(scale_name)
                continue
            for (
                    param_name,
                    weight_name,
                    shard_id,
            ) in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                if name.endswith(".bias") and name not in params_dict:
                    continue
                if is_pp_missing_parameter(name, self):
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                if name.endswith(".bias") and name not in params_dict:
                    continue
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    continue
                if is_pp_missing_parameter(name, self):
                    continue
                param = params_dict[name]
                weight_loader = getattr(
                    param,
                    "weight_loader",
                    default_weight_loader,
                )
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


class Projector(nn.Module):

    def __init__(
        self,
        text_config: PretrainedConfig,
        vision_config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.text_config = text_config
        self.vision_config = vision_config
        self.merge_kernel_size = (2, 2)

        self.hidden_size = (self.vision_config.hidden_size *
                            self.merge_kernel_size[0] *
                            self.merge_kernel_size[1])

        self.pre_norm = torch.nn.LayerNorm(self.vision_config.hidden_size,
                                           eps=1e-05)
        self.act = GELUActivation()

        self.linear_1 = ColumnParallelLinear(
            self.hidden_size,
            self.hidden_size,
            bias=True,
            quant_config=quant_config,
            prefix=f"{prefix}.linear_1",
        )
        self.linear_2 = RowParallelLinear(
            self.hidden_size,
            self.text_config.hidden_size,
            bias=True,
            quant_config=quant_config,
            prefix=f"{prefix}.linear_2",
        )

    def forward(
        self,
        image_features: torch.Tensor,
        image_grid_thw: list[tuple[int, int, int]],
    ) -> torch.Tensor:
        m1, m2 = self.merge_kernel_size
        if isinstance(image_features, (list, tuple)):
            processed_features = list()
            for image_feature, image_grid in zip(image_features,
                                                 image_grid_thw):
                image_feature = self.pre_norm(image_feature)
                t, h, w = image_grid

                image_feature = rearrange(
                    image_feature,
                    "(t h p1 w p2) d -> (t h w) (p1 p2 d)",
                    t=t,
                    h=h // m1,
                    p1=m1,
                    w=w // m2,
                    p2=m2,
                )
                hidden_states, _ = self.linear_1(image_feature)
                hidden_states = self.act(hidden_states)
                hidden_states, _ = self.linear_2(hidden_states)
                processed_features.append(hidden_states)

            return processed_features

        dims = image_features.shape[:-1]
        dim = image_features.shape[-1]
        image_features = image_features.view(np.prod(dims), dim)
        hidden_states = self.pre_norm(image_features).view(
            -1, self.hidden_size)
        hidden_states = self.linear_1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.linear_2(hidden_states)

        return hidden_states.view(*dims, -1)


def _keye_field_config(hf_inputs: Mapping[str, torch.Tensor], ):
    image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3)))
    image_grid_sizes = image_grid_thw.prod(-1)

    video_grid_thw = hf_inputs.get("video_grid_thw", torch.empty((0, 3)))
    video_grid_sizes = video_grid_thw.prod(-1)

    return dict(
        pixel_values=MultiModalFieldConfig.flat_from_sizes(
            "image", image_grid_sizes),
        image_embeds=MultiModalFieldConfig.flat_from_sizes(
            "image", image_grid_sizes),
        image_grid_thw=MultiModalFieldConfig.batched("image"),
        pixel_values_videos=MultiModalFieldConfig.flat_from_sizes(
            "video", video_grid_sizes),
        video_embeds=MultiModalFieldConfig.flat_from_sizes(
            "video", video_grid_sizes),
        video_grid_thw=MultiModalFieldConfig.batched("video"),
    )


class KeyeMultiModalDataParser(MultiModalDataParser):

    def _parse_image_data(
        self,
        data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
    ) -> ModalityDataItems[Any, Any]:
        if isinstance(data, dict):
            return DictEmbeddingItems(
                data,
                modality="image",
                required_fields={
                    "image_embeds",
                    "image_grid_thw",
                },
                fields_factory=_keye_field_config,
            )

        return super()._parse_image_data(data)

    def _parse_video_data(
        self,
        data: Union[dict[str, torch.Tensor], ModalityData[VideoItem]],
    ) -> ModalityDataItems[Any, Any]:
        if isinstance(data, dict):
            return DictEmbeddingItems(
                data,
                modality="video",
                required_fields={
                    "video_embeds",
                    "video_grid_thw",
                },
                fields_factory=_keye_field_config,
            )

        return super()._parse_video_data(data)


class KeyeProcessingInfo(BaseProcessingInfo):

    def get_image_processor(self, **kwargs: object):
        return self.get_hf_processor(**kwargs).image_processor

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

    def get_mm_max_tokens_per_item(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> Mapping[str, int]:
        return {
            "image": self.get_max_image_tokens(),
            "video": self.get_max_video_tokens(seq_len),
        }

    def _get_vision_info(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int = 1,
        do_resize: bool = True,
        image_processor,
    ) -> tuple[ImageSize, int]:
        if image_processor is None:
            image_processor = self.get_image_processor()

        hf_config = self.get_hf_config()
        vision_config = hf_config.vision_config
        patch_size = vision_config.patch_size
        merge_size = vision_config.spatial_merge_size
        temporal_patch_size = 1

        if do_resize:
            resized_height, resized_width = smart_resize(
                height=image_height,
                width=image_width,
                factor=patch_size * merge_size,
                min_pixels=image_processor.min_pixels,
                max_pixels=image_processor.max_pixels,
            )
            preprocessed_size = ImageSize(width=resized_width,
                                          height=resized_height)
        else:
            preprocessed_size = ImageSize(width=image_width,
                                          height=image_height)

        padded_num_frames = num_frames + num_frames % temporal_patch_size

        grid_t = max(padded_num_frames // temporal_patch_size, 1)
        grid_h = preprocessed_size.height // patch_size
        grid_w = preprocessed_size.width // patch_size

        num_patches = grid_t * grid_h * grid_w
        num_vision_tokens = num_patches // (merge_size**2)

        return preprocessed_size, num_vision_tokens

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        image_processor,
    ) -> int:
        _, num_image_tokens = self._get_vision_info(
            image_width=image_width,
            image_height=image_height,
            image_processor=image_processor,
        )
        return num_image_tokens

    def get_num_video_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int,
        image_processor,
    ) -> int:
        _, num_video_tokens = self._get_vision_info(
            image_width=image_width,
            image_height=image_height,
            num_frames=num_frames,
            image_processor=image_processor,
        )
        return num_video_tokens

    def get_image_size_with_most_features(self, ) -> ImageSize:
        max_image_size, _ = self._get_vision_info(
            image_width=_MAX_IMAGE_SIZE,
            image_height=_MAX_IMAGE_SIZE,
            image_processor=None,
        )
        return max_image_size

    def get_max_image_tokens(self) -> int:
        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,
            image_processor=None,
        )

    def _get_max_video_frames(self, max_tokens: int) -> int:
        target_width, target_height = self.get_image_size_with_most_features()

        num_frames = 0

        while True:
            next_num_frames = num_frames + 1
            next_max_tokens = self.get_num_video_tokens(
                image_width=target_width,
                image_height=target_height,
                num_frames=next_num_frames,
                image_processor=None,
            )

            if next_max_tokens > max_tokens:
                break

            num_frames = next_num_frames

        return num_frames

    def get_num_frames_with_most_features(self, seq_len: int) -> int:
        mm_config = self.ctx.get_mm_config()
        max_images = mm_config.get_limit_per_prompt("image")
        max_videos = mm_config.get_limit_per_prompt("video")

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

        return max(max_frames_per_video, 1)

    def get_max_video_tokens(self, seq_len: int) -> int:
        target_width, target_height = self.get_image_size_with_most_features()

        return self.get_num_video_tokens(
            image_width=target_width,
            image_height=target_height,
            num_frames=self.get_num_frames_with_most_features(seq_len),
            image_processor=None,
        )


class KeyeDummyInputsBuilder(BaseDummyInputsBuilder[KeyeProcessingInfo]):

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

        hf_processor = self.info.get_hf_processor()
        image_token: str = hf_processor.image_token
        video_token: str = hf_processor.video_token

        return image_token * num_images + video_token * num_videos

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> MultiModalDataDict:
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)

        target_width, target_height = (
            self.info.get_image_size_with_most_features())
        target_num_frames = self.info.get_num_frames_with_most_features(
            seq_len)

        mm_data = {
            "image":
            self._get_dummy_images(
                width=target_width,
                height=target_height,
                num_images=num_images,
            ),
            "video":
            self._get_dummy_videos(
                width=target_width,
                height=target_height,
                num_frames=target_num_frames,
                num_videos=num_videos,
            ),
        }

        return mm_data


class KeyeMultiModalProcessor(BaseMultiModalProcessor[KeyeProcessingInfo]):

    def _get_data_parser(self) -> MultiModalDataParser:
        return KeyeMultiModalDataParser()

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, Any],
        out_mm_kwargs: MultiModalKwargs,
    ) -> Sequence[PromptUpdate]:
        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
        image_processor = self.info.get_image_processor(
            **hf_processor_mm_kwargs)
        tokenizer = self.info.get_tokenizer()
        vocab = tokenizer.get_vocab()

        placeholder = {
            "image": vocab[hf_processor.image_token],
            "video": vocab[hf_processor.video_token],
        }

        merge_length = image_processor.merge_size**2

        def get_replacement_keye(item_idx: int, modality: str):
            grid_thw = out_mm_kwargs[f"{modality}_grid_thw"][item_idx]
            assert isinstance(grid_thw, torch.Tensor)

            num_tokens = int(grid_thw.prod()) // merge_length
            return [placeholder[modality]] * num_tokens

        return [
            PromptReplacement(
                modality=modality,
                target=[placeholder[modality]],
                replacement=partial(get_replacement_keye, modality=modality),
            ) for modality in ("image", "video")
        ]

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


@MULTIMODAL_REGISTRY.register_processor(
    KeyeMultiModalProcessor,
    info=KeyeProcessingInfo,
    dummy_inputs=KeyeDummyInputsBuilder,
)
class KeyeForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsLoRA,
                                   SupportsPP):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
        "lm_head.": "language_model.lm_head.",
        "model.": "language_model.model.",
    })

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("image"):
            return "<|vision_start|><|image_pad|><|vision_end|>"
        if modality.startswith("video"):
            return "<|vision_start|><|video_pad|><|vision_end|>"

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

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config: PretrainedConfig = 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.visual = KeyeSiglipVisionModel(
            config.vision_config,
            quant_config=self._maybe_ignore_quant_config(quant_config),
            prefix=maybe_prefix(prefix, "visual"),
        )
        self.mlp_AR = Projector(
            config,
            config.vision_config,
            quant_config=self._maybe_ignore_quant_config(quant_config),
            prefix=maybe_prefix(prefix, "mlp_AR"),
        )

        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            prefix=maybe_prefix(prefix, "language_model"),
            architectures=["Qwen3ForCausalLM"],
        )

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors)

    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
        if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
            return None
        return quant_config

    def _validate_and_reshape_mm_tensor(self, mm_input: object,
                                        name: str) -> torch.Tensor:
        if not isinstance(mm_input, (torch.Tensor, list)):
            raise ValueError(f"Incorrect type of {name}. "
                             f"Got type: {type(mm_input)}")
        if isinstance(mm_input, torch.Tensor):
            if mm_input.ndim == 2:
                return mm_input
            if mm_input.ndim == 5:
                return mm_input
            if mm_input.ndim != 3:
                raise ValueError(f"{name} should be 2D or batched 3D tensor. "
                                 f"Got ndim: {mm_input.ndim} "
                                 f"(shape={mm_input.shape})")
            return torch.concat(list(mm_input))
        else:
            return torch.concat(mm_input)

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

        if pixel_values is None and image_embeds is None:
            return None

        if pixel_values is not None:
            pixel_values = self._validate_and_reshape_mm_tensor(
                pixel_values, "image pixel values")
            image_grid_thw = self._validate_and_reshape_mm_tensor(
                image_grid_thw, "image grid_thw")

            return KeyeImagePixelInputs(
                type="pixel_values",
                pixel_values=pixel_values,
                image_grid_thw=image_grid_thw,
            )

        if image_embeds is not None:
            image_embeds = self._validate_and_reshape_mm_tensor(
                image_embeds, "image embeds")
            image_grid_thw = self._validate_and_reshape_mm_tensor(
                image_grid_thw, "image grid_thw")

            return KeyeImageEmbeddingInputs(
                type="image_embeds",
                image_embeds=image_embeds,
                image_grid_thw=image_grid_thw,
            )

    def _parse_and_validate_video_input(
            self, **kwargs: object) -> Optional[KeyeVideoInputs]:
        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
        video_embeds = kwargs.pop("video_embeds", None)
        video_grid_thw = kwargs.pop("video_grid_thw", None)

        if pixel_values_videos is None and video_embeds is None:
            return None

        if pixel_values_videos is not None:
            pixel_values_videos = self._validate_and_reshape_mm_tensor(
                pixel_values_videos,
                "video pixel values",
            )
            video_grid_thw = self._validate_and_reshape_mm_tensor(
                video_grid_thw, "video grid_thw")

            return KeyeVideoPixelInputs(
                type="pixel_values_videos",
                pixel_values_videos=pixel_values_videos,
                video_grid_thw=video_grid_thw,
            )

        if video_embeds is not None:
            video_embeds = self._validate_and_reshape_mm_tensor(
                video_embeds, "video embeds")
            video_grid_thw = self._validate_and_reshape_mm_tensor(
                video_grid_thw, "video grid_thw")

            return KeyeVideoEmbeddingInputs(
                type="video_embeds",
                video_embeds=video_embeds,
                video_grid_thw=video_grid_thw,
            )

    def _process_image_input(
            self, image_input: KeyeImageInputs) -> tuple[torch.Tensor, ...]:
        siglip_position_ids = list()
        image_grid_hws = list()
        sample_indices = list()
        cu_seqlens = [0]

        image_grid_thw = image_input["image_grid_thw"]
        assert image_grid_thw.ndim == 2

        for idx, thaw in enumerate(image_grid_thw):
            thw_tuple = tuple(thaw.detach().cpu().numpy().tolist())
            numel = np.prod(thw_tuple)
            image_grid_hws.append(thw_tuple)
            image_position_ids = torch.arange(numel) % np.prod(thw_tuple[1:])
            siglip_position_ids.append(image_position_ids)
            sample_indices.append(torch.full((numel, ), idx,
                                             dtype=torch.int64))
            cu_seqlens.append(cu_seqlens[-1] + numel)

        if image_input["type"] == "image_embeds":
            raise ValueError(
                "Image embeddings are not supported for this processing path.")
        else:
            pixel_values = image_input["pixel_values"].type(self.visual.dtype)
            siglip_position_ids = torch.concat(siglip_position_ids,
                                               dim=0).to(pixel_values.device)
            cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32).to(
                pixel_values.device)
            sample_indices = torch.concat(sample_indices,
                                          dim=0).to(pixel_values.device)

            image_embeds = self.visual(
                pixel_values=pixel_values,
                image_grid_thw=image_grid_hws,
                position_ids=siglip_position_ids,
                vision_return_embed_list=False,
                interpolate_pos_encoding=True,
                sample_indices=sample_indices,
                cu_seqlens=cu_seqlens,
                use_rope=True,
                window_size=-1,
            )
            image_embeds = tuple(self.mlp_AR(image_embeds, image_grid_thw))
            return image_embeds

    def _process_video_input(
            self, video_input: KeyeVideoInputs) -> tuple[torch.Tensor, ...]:
        siglip_position_ids = list()
        video_grid_hws = list()
        sample_indices = list()
        cu_seqlens = [0]

        video_grid_thw = video_input["video_grid_thw"]
        assert video_grid_thw.ndim == 2

        for idx, thaw in enumerate(video_grid_thw):
            thw_tuple = tuple(thaw.detach().cpu().numpy().tolist())
            numel = np.prod(thw_tuple)

            video_grid_hws.append(thw_tuple)
            video_position_ids = torch.arange(numel) % np.prod(thw_tuple[1:])
            siglip_position_ids.append(video_position_ids)
            sample_indices.append(torch.full((numel, ), idx,
                                             dtype=torch.int64))
            cu_seqlens.append(cu_seqlens[-1] + numel)

        if video_input["type"] == "video_embeds":
            raise ValueError(
                "Video embeddings are not supported for this processing path.")
        else:
            pixel_values_videos = video_input["pixel_values_videos"].type(
                self.visual.dtype)
            siglip_position_ids = torch.concat(siglip_position_ids, dim=0).to(
                pixel_values_videos.device)
            cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32).to(
                pixel_values_videos.device)
            sample_indices = torch.concat(sample_indices,
                                          dim=0).to(pixel_values_videos.device)

            video_embeds = self.visual(
                pixel_values=pixel_values_videos,
                image_grid_thw=video_grid_hws,
                position_ids=siglip_position_ids,
                vision_return_embed_list=True,
                interpolate_pos_encoding=True,
                sample_indices=sample_indices,
                cu_seqlens=cu_seqlens,
                use_rope=True,
                window_size=-1,
            )
            video_embeds = tuple(self.mlp_AR(video_embeds, video_grid_thw))
            return video_embeds

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

        for input_key in kwargs:
            if (input_key in ("pixel_values", "image_embeds")
                    and "images" not in modalities):
                modalities["images"] = self._parse_and_validate_image_input(
                    **kwargs)
            if (input_key in ("pixel_values_videos", "video_embeds")
                    and "videos" not in modalities):
                modalities["videos"] = self._parse_and_validate_video_input(
                    **kwargs)

        return modalities

    def get_language_model(self) -> torch.nn.Module:
        return self.language_model

    def get_multimodal_embeddings(
            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:

        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
        if not modalities:
            return None

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

        for modality in modalities:
            if modality == "images":
                image_input = modalities["images"]
                vision_embeddings = self._process_image_input(image_input)
                multimodal_embeddings += vision_embeddings
            if modality == "videos":
                video_input = modalities["videos"]
                video_embeddings = self._process_video_input(video_input)
                multimodal_embeddings += video_embeddings
        return multimodal_embeddings

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
    ) -> torch.Tensor:
        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
        if multimodal_embeddings is not None:
            inputs_embeds = merge_multimodal_embeddings(
                input_ids,
                inputs_embeds,
                multimodal_embeddings,
                [
                    self.config.image_token_id,
                    self.config.video_token_id,
                ],
            )
        return inputs_embeds

    def get_input_embeddings_v0(
        self,
        input_ids: torch.Tensor,
        image_input: Optional[KeyeImagePixelInputs] = None,
        video_input: Optional[KeyeVideoPixelInputs] = None,
    ) -> torch.Tensor:
        inputs_embeds = self.get_input_embeddings(input_ids)
        if image_input is not None:
            image_embeds = self._process_image_input(image_input)
            inputs_embeds = merge_multimodal_embeddings(
                input_ids,
                inputs_embeds,
                image_embeds,
                placeholder_token_id=self.config.image_token_id,
            )

        if video_input is not None:
            video_embeds = self._process_video_input(video_input)
            inputs_embeds = merge_multimodal_embeddings(
                input_ids,
                inputs_embeds,
                video_embeds,
                placeholder_token_id=self.config.video_token_id,
            )
        return inputs_embeds

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs: object,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        """Run forward pass for Qwen2-VL.

        Args:
            input_ids: Flattened (concatenated) input_ids corresponding to a
                batch.
            positions: Flattened (concatenated) position ids corresponding to a
                batch.
                **NOTE**: If mrope is enabled (default setting for Qwen2-VL
                opensource models), the shape will be `(3, seq_len)`,
                otherwise it will be `(seq_len,).
            pixel_values: Pixel values to be fed to a model.
                `None` if no images are passed.
            image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in LLM.
                `None` if no images are passed.
            pixel_values_videos: Pixel values of videos to be fed to a model.
                `None` if no videos are passed.
            video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in LLM.
                `None` if no videos are passed.
        """

        if intermediate_tensors is not None:
            inputs_embeds = None

        elif inputs_embeds is None:
            image_input = self._parse_and_validate_image_input(**kwargs)
            video_input = self._parse_and_validate_video_input(**kwargs)

            if image_input is None and video_input is None:
                inputs_embeds = None
            else:
                if uses_mrope(self.config):
                    assert positions.ndim == 2 and positions.size(0) == 3, (
                        "multimodal section rotary embedding requires "
                        f"(3, seq_len) positions, but got {positions.size()}")
                inputs_embeds = self.get_input_embeddings_v0(
                    input_ids,
                    image_input=image_input,
                    video_input=video_input,
                )
                input_ids = None

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

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

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:

        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

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