# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2024 The Qwen team.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only Qwen2.5-Omni model (thinker part)."""

from collections.abc import Callable, Iterable, Iterator, Mapping, Sequence
from functools import partial
from typing import Annotated, Any, Literal

import numpy as np
import torch
import torch.nn as nn
from transformers import PretrainedConfig
from transformers.feature_extraction_utils import BatchFeature
from transformers.models.qwen2_5_omni.configuration_qwen2_5_omni import (
    Qwen2_5OmniConfig,
    Qwen2_5OmniThinkerConfig,
)
from transformers.models.qwen2_5_omni.modeling_qwen2_5_omni import (
    Qwen2_5OmniAudioEncoder,
)
from transformers.models.qwen2_5_omni.processing_qwen2_5_omni import (
    Qwen2_5OmniProcessor,
)
from transformers.models.whisper import WhisperFeatureExtractor

from vllm.config import VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.forward_context import set_forward_context
from vllm.logger import init_logger
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.model_executor.models.qwen2_5_vl import (
    Qwen2_5_VisionTransformer,
    Qwen2_5_VLImageEmbeddingInputs,
    Qwen2_5_VLImageInputs,
    Qwen2_5_VLImagePixelInputs,
    Qwen2_5_VLProcessingInfo,
    Qwen2_5_VLVideoEmbeddingInputs,
    Qwen2_5_VLVideoInputs,
    Qwen2_5_VLVideoPixelInputs,
)
from vllm.model_executor.models.qwen2_audio import (
    Qwen2AudioProcessingInfo,
    _get_feat_extract_output_lengths,
)
from vllm.model_executor.models.qwen2_vl import Qwen2VLMultiModalDataParser
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (
    ImageItem,
    ModalityData,
    MultiModalDataDict,
    MultiModalFeatureSpec,
    MultiModalFieldConfig,
    MultiModalKwargsItems,
)
from vllm.multimodal.parse import (
    AudioProcessorItems,
    DictEmbeddingItems,
    ModalityDataItems,
    MultiModalDataItems,
)
from vllm.multimodal.processing import BaseDummyInputsBuilder
from vllm.multimodal.processing.processor import (
    BaseMultiModalProcessor,
    MultiModalPromptUpdates,
    PlaceholderFeaturesInfo,
    PromptReplacement,
    PromptUpdate,
    PromptUpdateDetails,
)
from vllm.sequence import IntermediateTensors
from vllm.utils.tensor_schema import TensorSchema, TensorShape

from .interfaces import (
    MultiModalEmbeddings,
    SupportsLoRA,
    SupportsMRoPE,
    SupportsMultiModal,
    SupportsPP,
)
from .utils import (
    AutoWeightsLoader,
    WeightsMapper,
    init_vllm_registered_model,
    maybe_prefix,
    split_list_into_ranges,
)

try:
    import flash_attn
except (ImportError, ModuleNotFoundError):
    flash_attn = None

logger = init_logger(__name__)


def check_interleaved_audio_video(
    is_video: torch.Tensor,
    is_audio: torch.Tensor,
    num_video: int,
    num_audio: int,
) -> bool:
    """
    Check if video and audio positions are interleaved in the multimodal region.

    Returns:
        True if video and audio tokens are interleaved, False otherwise.
    """
    if num_video == 0 or num_audio == 0:
        return False

    video_pos = is_video.nonzero(as_tuple=True)[0]
    audio_pos = is_audio.nonzero(as_tuple=True)[0]

    return (
        video_pos[0].item() < audio_pos[-1].item()
        and audio_pos[0].item() < video_pos[-1].item()
    )


def merge_interleaved_embeddings(
    inputs_embeds: torch.Tensor,
    multimodal_embeddings: "MultiModalEmbeddings",
    is_video: torch.Tensor,
    is_audio: torch.Tensor,
    is_multimodal: torch.Tensor,
    num_video: int,
    num_audio: int,
) -> torch.Tensor:
    """
    Merge embeddings for interleaved audio-in-video sequences.

    When use_audio_in_video=True, video and audio tokens are interleaved in
    the token sequence, but embeddings are provided as separate contiguous
    tensors (video first, then audio). This function reorders video and audio
    embeddings to match sequence position order and scatters them efficiently.

    Args:
        inputs_embeds: The input embeddings tensor to merge into.
        multimodal_embeddings: List of embedding tensors (video, audio, other).
        is_video: Boolean mask for video token positions.
        is_audio: Boolean mask for audio token positions.
        is_multimodal: Boolean mask for all multimodal token positions.
        num_video: Total count of video tokens.
        num_audio: Total count of audio tokens.

    Returns:
        The merged inputs_embeds tensor with multimodal embeddings scattered
        to their correct positions.
    """
    # Categorize embeddings by modality based on token counts.
    # Embeddings come grouped by modality but order varies (e.g., image, video, audio
    # or video, audio depending on input kwargs order).
    video_embeds: list[torch.Tensor] = []
    audio_embeds: list[torch.Tensor] = []
    other_embeds: list[torch.Tensor] = []
    video_remaining = num_video
    audio_remaining = num_audio

    for emb in multimodal_embeddings:
        n = emb.shape[0]
        if video_remaining > 0 and n <= video_remaining:
            video_embeds.append(emb)
            video_remaining -= n
        elif audio_remaining > 0 and n <= audio_remaining:
            audio_embeds.append(emb)
            audio_remaining -= n
        else:
            other_embeds.append(emb)

    # Scatter each modality to its positions
    if video_embeds:
        video_positions = is_video.nonzero(as_tuple=True)[0]
        inputs_embeds[video_positions] = torch.cat(video_embeds, dim=0)
    if audio_embeds:
        audio_positions = is_audio.nonzero(as_tuple=True)[0]
        inputs_embeds[audio_positions] = torch.cat(audio_embeds, dim=0)
    if other_embeds:
        other_mask = is_multimodal & ~is_video & ~is_audio
        other_positions = other_mask.nonzero(as_tuple=True)[0]
        inputs_embeds[other_positions] = torch.cat(other_embeds, dim=0)

    return inputs_embeds


class Qwen2_5OmniAudioFeatureInputs(TensorSchema):
    """
    Dimensions:
        - na: Number of audios
        - nmb: Number of mel bins
        - msl: Maximum sequence length
        - tsl: Total sequence length
    """

    type: Literal["audio_features"]
    input_features: Annotated[
        torch.Tensor | list[torch.Tensor],
        TensorShape("nmb", "tsl", dynamic_dims={"tsl"}),
    ]

    audio_feature_lengths: Annotated[torch.Tensor, TensorShape("na")]

    feature_attention_mask: Annotated[
        torch.Tensor | list[torch.Tensor],
        TensorShape("na", "msl", dynamic_dims={"msl"}),
    ]


def create_qwen2_5_omni_thinker_field_factory(
    spatial_merge_size: int,
) -> Callable[[Mapping[str, torch.Tensor]], Mapping[str, MultiModalFieldConfig]]:
    def _qwen2_5_omni_thinker_field_config(hf_inputs: Mapping[str, torch.Tensor]):
        audio_feature_lengths = hf_inputs.get(
            "audio_feature_lengths", torch.empty((0,))
        )

        image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3)))
        image_pixel_grid_sizes = image_grid_thw.prod(-1)
        image_embed_grid_sizes = (
            image_pixel_grid_sizes // spatial_merge_size // spatial_merge_size
        )

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

        num_videos = len(video_grid_sizes)

        return dict(
            input_audio_features=MultiModalFieldConfig.flat_from_sizes(
                "audio", audio_feature_lengths, dim=1
            ),
            feature_attention_mask=MultiModalFieldConfig.batched("audio"),
            audio_feature_lengths=MultiModalFieldConfig.batched("audio"),
            pixel_values=MultiModalFieldConfig.flat_from_sizes(
                "image", image_pixel_grid_sizes
            ),
            image_embeds=MultiModalFieldConfig.flat_from_sizes(
                "image", image_embed_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_embed_grid_sizes
            ),
            video_grid_thw=MultiModalFieldConfig.batched("video"),
            second_per_grid_ts=MultiModalFieldConfig.batched("video"),
            use_audio_in_video=MultiModalFieldConfig.shared("video", num_videos),
        )

    return _qwen2_5_omni_thinker_field_config


class Qwen2_5OmniThinkerMultiModalDataParser(Qwen2VLMultiModalDataParser):
    def __init__(self, spatial_merge_size: int, *args, **kwargs):
        self._spatial_merge_size = spatial_merge_size
        super().__init__(self._spatial_merge_size, *args, **kwargs)

    def _parse_audio_data(
        self,
        data: dict[str, torch.Tensor] | ModalityData[ImageItem],
    ) -> ModalityDataItems[Any, Any]:
        if isinstance(data, dict):
            return DictEmbeddingItems(
                data,
                modality="audio",
                required_fields={"input_audio_features", "audio_feature_lengths"},
                fields_factory=create_qwen2_5_omni_thinker_field_factory(
                    self._spatial_merge_size
                ),
            )

        return super()._parse_audio_data(data)


class Qwen2_5OmniThinkerProcessingInfo(
    Qwen2AudioProcessingInfo, Qwen2_5_VLProcessingInfo
):
    def get_hf_config(self):
        return self.ctx.get_hf_config(Qwen2_5OmniConfig).thinker_config

    def get_hf_processor(self, **kwargs: object) -> Qwen2_5OmniProcessor:
        return self.ctx.get_hf_processor(
            Qwen2_5OmniProcessor,
            use_fast=kwargs.pop("use_fast", True),
            **kwargs,
        )

    def get_feature_extractor(self, **kwargs: object):
        hf_processor = self.get_hf_processor(**kwargs)
        feature_extractor = hf_processor.feature_extractor  # type: ignore
        assert isinstance(feature_extractor, WhisperFeatureExtractor)
        return feature_extractor

    def get_data_parser(self):
        feature_extractor = self.get_feature_extractor()

        return Qwen2_5OmniThinkerMultiModalDataParser(
            spatial_merge_size=self.get_hf_config().vision_config.spatial_merge_size,
            target_sr=feature_extractor.sampling_rate,
            target_channels=self.get_target_channels(),
            expected_hidden_size=self._get_expected_hidden_size(),
        )

    def get_target_channels(self) -> int:
        """Return target audio channels for Qwen2.5 Omni models (mono)."""
        return 1

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


class Qwen2_5OmniThinkerDummyInputsBuilder(
    BaseDummyInputsBuilder[Qwen2_5OmniThinkerProcessingInfo]
):
    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_audios = mm_counts.get("audio", 0)
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)

        hf_processor = self.info.get_hf_processor()

        audio_token: str = hf_processor.audio_token
        image_token: str = hf_processor.image_token
        video_token: str = hf_processor.video_token

        return (
            audio_token * num_audios
            + image_token * num_images
            + video_token * num_videos
        )

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

        feature_extractor = self.info.get_feature_extractor()

        target_audio_length = (
            min(
                feature_extractor.chunk_length,
                30,
            )
            * feature_extractor.sampling_rate
        )

        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_counts
        )

        image_overrides = mm_options.get("image")
        video_overrides = mm_options.get("video")
        audio_overrides = mm_options.get("audio")

        mm_data = {
            "audio": self._get_dummy_audios(
                length=target_audio_length,
                num_audios=num_audios,
                overrides=audio_overrides,
            ),
            "image": self._get_dummy_images(
                width=target_width,
                height=target_height,
                num_images=num_images,
                overrides=image_overrides,
            ),
            "video": self._get_dummy_videos(
                width=target_width,
                height=target_height,
                num_frames=target_num_frames,
                num_videos=num_videos,
                overrides=video_overrides,
            ),
        }

        return mm_data


class Qwen2_5OmniThinkerMultiModalProcessor(
    BaseMultiModalProcessor[Qwen2_5OmniThinkerProcessingInfo]
):
    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        mm_data = dict(mm_data)
        audios = mm_data.pop("audios", [])

        # NOTE: WhisperFeatureExtractor cannot handle empty list of audios
        if audios:
            # NOTE: Qwen2.5-Omni processor accept "audio"
            mm_data["audio"] = audios
            mm_kwargs = dict(
                **mm_kwargs,
            )

        hf_inputs = super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )

        input_features = hf_inputs.pop("input_features", None)
        feature_attention_mask = hf_inputs.get("feature_attention_mask", None)
        if "input_audio_features" not in hf_inputs and input_features is not None:
            if feature_attention_mask is not None:
                input_features = input_features.permute(0, 2, 1)[
                    feature_attention_mask.bool()
                ].permute(1, 0)
            hf_inputs["input_audio_features"] = input_features
        if (
            "audio_feature_lengths" not in hf_inputs
            and feature_attention_mask is not None
        ):
            hf_inputs["audio_feature_lengths"] = feature_attention_mask.sum(-1)

        video_second_per_grid = hf_inputs.get("video_second_per_grid", None)
        if video_second_per_grid is not None:
            hf_inputs["second_per_grid_ts"] = video_second_per_grid

        use_audio_in_video = mm_kwargs.get("use_audio_in_video", False)
        hf_inputs["use_audio_in_video"] = torch.tensor(use_audio_in_video)

        return hf_inputs

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return create_qwen2_5_omni_thinker_field_factory(
            self.info.get_hf_config().vision_config.spatial_merge_size
        )(hf_inputs)

    def _derive_audio_from_video_placeholders(
        self,
        placeholders: Mapping[str, list[PlaceholderFeaturesInfo]],
        mm_prompt_updates: MultiModalPromptUpdates,
    ) -> Mapping[str, list[PlaceholderFeaturesInfo]]:
        """
        Helper to derive audio placeholders from video placeholders when
        use_audio_in_video=True.
        """
        if "video" not in placeholders:
            return placeholders

        # Validate audio and video counts match
        num_videos = len(placeholders["video"])
        num_audios = len(mm_prompt_updates.get("audio", []))
        if num_audios != num_videos:
            raise ValueError(
                f"use_audio_in_video requires equal number of audio and video "
                f"items, got {num_audios=}, {num_videos=}"
            )

        tokenizer = self.info.get_tokenizer()
        processor = self.info.get_hf_processor()
        audio_token_id = tokenizer.get_vocab()[processor.audio_token]
        video_token_id = tokenizer.get_vocab()[processor.video_token]

        result_placeholders = dict(placeholders)
        audio_placeholders = []
        video_placeholders = []

        # Each video is paired with one audio
        for video_idx, video_placeholder in enumerate(placeholders["video"]):
            # Create is_embed mask selecting only audio tokens
            audio_is_embed = torch.tensor(video_placeholder.tokens) == audio_token_id

            # Create is_embed mask selecting only video tokens
            video_is_embed = torch.tensor(video_placeholder.tokens) == video_token_id

            audio_placeholder = PlaceholderFeaturesInfo(
                modality="audio",
                item_idx=video_idx,
                start_idx=video_placeholder.start_idx,
                tokens=video_placeholder.tokens,
                is_embed=audio_is_embed,
            )
            audio_placeholders.append(audio_placeholder)

            # Update video placeholder with is_embed mask
            video_placeholder_with_mask = PlaceholderFeaturesInfo(
                modality="video",
                item_idx=video_idx,
                start_idx=video_placeholder.start_idx,
                tokens=video_placeholder.tokens,
                is_embed=video_is_embed,
            )
            video_placeholders.append(video_placeholder_with_mask)

        result_placeholders["audio"] = audio_placeholders
        result_placeholders["video"] = video_placeholders
        return result_placeholders

    def _maybe_apply_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        prompt_ids: list[int],
        mm_kwargs: MultiModalKwargsItems,
        mm_prompt_updates: MultiModalPromptUpdates,
        is_update_applied: bool,
    ) -> tuple[list[int], Mapping[str, list[PlaceholderFeaturesInfo]]]:
        """
        Qwen2.5-Omni reimplements this function to handle `use_audio_in_video`.
        """
        mm_item_counts = mm_items.get_all_counts()
        self._validate_mm_kwargs(mm_kwargs, mm_item_counts)
        self._validate_mm_updates(mm_prompt_updates, mm_item_counts)

        # Detect use_audio_in_video from mm_kwargs
        use_audio_in_video = False
        if "video" in mm_kwargs:
            for item in mm_kwargs["video"]:
                if item and item.get("use_audio_in_video"):
                    use_audio_in_video_tensor = item["use_audio_in_video"].data
                    if use_audio_in_video_tensor.numel() > 0:
                        use_audio_in_video = bool(use_audio_in_video_tensor.item())
                        break

        if is_update_applied:
            mm_placeholders = self._find_mm_placeholders(
                prompt_ids,
                mm_prompt_updates,
            )
            self._validate_mm_placeholders(
                mm_placeholders,
                mm_item_counts,
            )
        else:
            if use_audio_in_video and "audio" in mm_prompt_updates:
                # Filter out audio updates - they are embedded in video
                filtered_updates = {
                    k: v for k, v in mm_prompt_updates.items() if k != "audio"
                }
                prompt_ids, mm_placeholders = self._apply_prompt_updates(
                    prompt_ids,
                    filtered_updates,
                )
                # Derive audio placeholders from video placeholders
                mm_placeholders = self._derive_audio_from_video_placeholders(
                    mm_placeholders, mm_prompt_updates
                )
            else:
                prompt_ids, mm_placeholders = self._apply_prompt_updates(
                    prompt_ids,
                    mm_prompt_updates,
                )

            self._validate_mm_placeholders(
                mm_placeholders,
                mm_item_counts,
            )

        return prompt_ids, mm_placeholders

    @classmethod
    def omni_get_updates_use_audio_in_video(
        cls,
        thinker_config: PretrainedConfig,
        audio_len: int,
        video_grid_thw: list[int] | torch.Tensor,
        video_second_per_grid_t: float,
    ) -> list[int]:
        """Get video prompt updates when `use_audio_in_video` is True.

        In this case, audio and vision update ids will be split into
        chunks and interleaved (details in `_omni_get_input_positions_tensor`).

        <|video_bos|><|VIDEO|><|video_eos|> =>
        <|video_bos|><|audio_bos|>(... chunks ...)<|audio_eos|><|video_eos|>
        """

        audio_token_id = thinker_config.audio_token_index
        video_token_id = thinker_config.video_token_index
        audio_start_token_id = thinker_config.audio_start_token_id
        audio_end_token_id = thinker_config.audio_end_token_id
        seconds_per_chunk = thinker_config.seconds_per_chunk
        spatial_merge_size = thinker_config.vision_config.spatial_merge_size
        tokens_per_second = getattr(
            thinker_config.vision_config, "tokens_per_second", 25
        )

        grid_t = video_grid_thw[0]
        grid_h = video_grid_thw[1]
        grid_w = video_grid_thw[2]
        t_ntoken_per_chunk = int(tokens_per_second * seconds_per_chunk)
        t_index = (
            torch.arange(grid_t) * video_second_per_grid_t * tokens_per_second
        ).long()
        t_index_split_chunk = split_list_into_ranges(t_index, t_ntoken_per_chunk)

        updates = [audio_start_token_id]
        added_audio_len = 0
        for t_chunk in t_index_split_chunk:
            vision_ntoken_per_chunk = (
                len(t_chunk) * grid_h * grid_w // (spatial_merge_size**2)
            )
            updates.extend([video_token_id] * vision_ntoken_per_chunk)

            audio_chunk_size = min(t_ntoken_per_chunk, audio_len - added_audio_len)
            updates.extend(audio_chunk_size * [audio_token_id])
            added_audio_len += audio_chunk_size
        if added_audio_len < audio_len:
            updates.extend((audio_len - added_audio_len) * [audio_token_id])
        updates.extend([audio_end_token_id])

        return updates

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

        audio_token = processor.audio_token
        image_token = processor.image_token
        video_token = processor.video_token
        audio_token_id = vocab[audio_token]
        image_token_id = vocab[image_token]
        video_token_id = vocab[video_token]

        out_mm_data = out_mm_kwargs.get_data()
        audio_feature_lengths = out_mm_data.get("audio_feature_lengths")
        feature_attention_mask = out_mm_data.get("feature_attention_mask")
        if audio_feature_lengths is None and feature_attention_mask is None:
            audio_output_lengths = []
        elif audio_feature_lengths is not None:
            _, audio_output_lens = _get_feat_extract_output_lengths(
                audio_feature_lengths
            )
            audio_output_lengths = audio_output_lens.tolist()
        elif feature_attention_mask is not None:
            assert isinstance(feature_attention_mask, torch.Tensor)
            _, audio_output_lens = _get_feat_extract_output_lengths(
                feature_attention_mask.sum(-1)
            )
            audio_output_lengths = audio_output_lens.tolist()

        # number of audios read from video.
        audio_in_video_item_idx = 0

        def get_replacement_qwen2_audio(item_idx: int):
            item_idx += audio_in_video_item_idx

            num_features = audio_output_lengths[item_idx]
            if num_features == 0:
                audios = mm_items.get_items("audio", AudioProcessorItems)
                audio = audios.get(item_idx)
                raise ValueError(
                    f"The audio {audio} (len={len(audio)}) is too short "
                    "to be represented inside the model"
                )

            return [audio_token_id] * num_features

        def get_replacement_qwen2_vision(item_idx: int, modality: str):
            grid_thw = out_mm_data[f"{modality}_grid_thw"][item_idx]
            assert isinstance(grid_thw, torch.Tensor)
            merge_length = image_processor.merge_size**2

            token_id = image_token_id if modality == "image" else video_token_id
            return [token_id] * (int(grid_thw.prod()) // merge_length)

        use_audio_in_video = hf_processor_mm_kwargs.get("use_audio_in_video", False)
        thinker_config = self.info.get_hf_config()

        def get_replacement_qwen2_use_audio_in_video(item_idx: int):
            nonlocal audio_in_video_item_idx

            audio_num_features = audio_output_lengths[
                audio_in_video_item_idx + item_idx
            ]
            video_grid_thw = out_mm_data["video_grid_thw"][item_idx]

            audio_in_video_item_idx += 1

            second_per_grid_ts = hf_processor_mm_kwargs.get("second_per_grid_ts", None)
            if second_per_grid_ts:
                video_second_per_grid_t = second_per_grid_ts[item_idx]
            else:
                video_second_per_grid_t = 1.0

            updates = self.omni_get_updates_use_audio_in_video(
                thinker_config=thinker_config,
                audio_len=audio_num_features,
                video_grid_thw=video_grid_thw,
                video_second_per_grid_t=video_second_per_grid_t,
            )

            # Only video tokens should receive video embeddings
            return PromptUpdateDetails.select_token_id(
                seq=updates,
                embed_token_id=video_token_id,
            )

        video_replacement_fn = (
            get_replacement_qwen2_use_audio_in_video
            if use_audio_in_video
            else partial(get_replacement_qwen2_vision, modality="video")
        )

        return [
            PromptReplacement(
                modality="audio",
                target=audio_token,
                replacement=get_replacement_qwen2_audio,
            ),
            PromptReplacement(
                modality="image",
                target=image_token,
                replacement=partial(get_replacement_qwen2_vision, modality="image"),
            ),
            PromptReplacement(
                modality="video",
                target=video_token,
                replacement=video_replacement_fn,
            ),
        ]

    def _apply_hf_processor_main(
        self,
        prompt: str | list[int],
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        tokenization_kwargs: Mapping[str, object],
        *,
        enable_hf_prompt_update: bool,
    ) -> tuple[list[int], BatchFeature, bool]:
        """
        Qwen2.5-Omni reimplements this function to handle text only.
        """
        if isinstance(prompt, str):
            if enable_hf_prompt_update:
                return self._apply_hf_processor_text_mm(
                    prompt_text=prompt,
                    mm_items=mm_items,
                    hf_processor_mm_kwargs=hf_processor_mm_kwargs,
                    tokenization_kwargs=tokenization_kwargs,
                )
            tokenizer = self.info.get_tokenizer()
            prompt_ids = tokenizer.encode(prompt)
        else:
            prompt_ids = self._apply_hf_processor_tokens_only(prompt)

        mm_processed_data = self._apply_hf_processor_mm_only(
            mm_items=mm_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
            tokenization_kwargs=tokenization_kwargs,
        )

        return prompt_ids, mm_processed_data, False

    def _apply_hf_processor_mm_only(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        tokenization_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        """
        Qwen2.5-Omni reimplements this function to handle `use_audio_in_video`.
        """
        mm_counts = mm_items.get_all_counts()

        use_audio_in_video = hf_processor_mm_kwargs.get("use_audio_in_video", False)
        if use_audio_in_video and "video" in mm_counts:
            assert "audio" in mm_counts
            mm_counts["audio"] -= mm_counts["video"]

        _, mm_processed_data, _ = self._apply_hf_processor_text_mm(
            prompt_text=self.dummy_inputs.get_dummy_text(mm_counts),
            mm_items=mm_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
            tokenization_kwargs=tokenization_kwargs,
        )

        return mm_processed_data


class Qwen2_5OmniConditionalGenerationMixin:
    def _parse_and_validate_audio_input(
        self, **kwargs: object
    ) -> Qwen2_5OmniAudioFeatureInputs | None:
        input_audio_features = kwargs.pop("input_audio_features", None)
        audio_feature_lengths = kwargs.pop("audio_feature_lengths", None)
        feature_attention_mask = kwargs.pop("feature_attention_mask", None)
        if input_audio_features is None:
            return None

        return Qwen2_5OmniAudioFeatureInputs(
            type="audio_features",
            input_features=input_audio_features,
            audio_feature_lengths=audio_feature_lengths,
            feature_attention_mask=feature_attention_mask,
        )

    def _parse_and_validate_image_input(
        self,
        **kwargs: dict[str, Any],
    ) -> Qwen2_5_VLImageInputs | None:
        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:
            return Qwen2_5_VLImagePixelInputs(
                type="pixel_values",
                pixel_values=pixel_values,
                image_grid_thw=image_grid_thw,
            )

        if image_embeds is not None:
            return Qwen2_5_VLImageEmbeddingInputs(
                type="image_embeds",
                image_embeds=image_embeds,
                image_grid_thw=image_grid_thw,
            )

    def _parse_and_validate_video_input(
        self,
        **kwargs: dict[str, Any],
    ) -> Qwen2_5_VLVideoInputs | None:
        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:
            return Qwen2_5_VLVideoPixelInputs(
                type="pixel_values_videos",
                pixel_values_videos=pixel_values_videos,
                video_grid_thw=video_grid_thw,
            )

        if video_embeds is not None:
            if not isinstance(video_embeds, torch.Tensor):
                raise ValueError(
                    "Incorrect type of video embeddings. "
                    f"Got type: {type(video_embeds)}"
                )
            return Qwen2_5_VLVideoEmbeddingInputs(
                type="video_embeds",
                video_embeds=video_embeds,
                video_grid_thw=video_grid_thw,
            )

    def _process_audio_input(
        self,
        audio_input: Qwen2_5OmniAudioFeatureInputs,
        audio_hashes: list[str] | None = None,
        cached_audio_features: torch.Tensor | None = None,
    ) -> torch.Tensor:
        input_features = audio_input["input_features"]
        audio_feature_lengths = audio_input["audio_feature_lengths"]

        audio_feat_lengths, audio_output_lengths = (
            self.audio_tower._get_feat_extract_output_lengths(audio_feature_lengths)
        )

        audio_outputs = self.audio_tower(
            input_features.to(self.audio_tower.dtype),
            feature_lens=audio_feature_lengths,
            aftercnn_lens=audio_feat_lengths,
        )
        return audio_outputs.last_hidden_state.split(audio_output_lengths.tolist())

    def _process_image_input(
        self, image_input: Qwen2_5_VLImageInputs
    ) -> tuple[torch.Tensor, ...]:
        if image_input["type"] == "image_embeds":
            return image_input["image_embeds"].type(self.visual.dtype)

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

        pixel_values = image_input["pixel_values"].type(self.visual.dtype)
        with set_forward_context(None, self.vllm_config):
            image_embeds = self.visual(pixel_values, grid_thw=grid_thw)
        # Split concatenated embeddings for each image item.
        merge_size = self.visual.spatial_merge_size
        sizes = grid_thw.prod(-1) // merge_size // merge_size

        return image_embeds.split(sizes.tolist())

    def _process_video_input(
        self,
        video_input: Qwen2_5_VLVideoInputs,
        video_hashes: list[str] = None,
        cached_video_embeds: torch.Tensor = None,
    ) -> torch.Tensor:
        if video_input["type"] == "video_embeds":
            return video_input["video_embeds"].type(self.visual.dtype)

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

        pixel_values_videos = video_input["pixel_values_videos"].type(self.visual.dtype)
        with set_forward_context(None, self.vllm_config):
            video_embeds = self.visual(pixel_values_videos, grid_thw=grid_thw)
        # Split concatenated embeddings for each video item.
        merge_size = self.visual.spatial_merge_size
        sizes = grid_thw.prod(-1) // merge_size // merge_size

        return video_embeds.split(sizes.tolist())


@MULTIMODAL_REGISTRY.register_processor(
    Qwen2_5OmniThinkerMultiModalProcessor,
    info=Qwen2_5OmniThinkerProcessingInfo,
    dummy_inputs=Qwen2_5OmniThinkerDummyInputsBuilder,
)
class Qwen2_5OmniThinkerForConditionalGeneration(
    nn.Module,
    SupportsMultiModal,
    SupportsPP,
    SupportsLoRA,
    SupportsMRoPE,
    Qwen2_5OmniConditionalGenerationMixin,
):
    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            "thinker.lm_head.": "language_model.lm_head.",
            "thinker.model.": "language_model.model.",
            "thinker.": "",
        }
    )
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "attn.qkv": [
            "attn.q",
            "attn.k",
            "attn.v",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
        if modality.startswith("image"):
            return "<|vision_start|><|IMAGE|><|vision_end|>"
        if modality.startswith("video"):
            return "<|vision_start|><|VIDEO|><|vision_end|>"
        if modality.startswith("audio"):
            return f"Audio {i}: <|audio_bos|><|AUDIO|><|audio_eos|>"

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

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        self.vllm_config = vllm_config
        thinker_config: Qwen2_5OmniThinkerConfig = (
            vllm_config.model_config.hf_config.thinker_config
        )
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config
        self.config = thinker_config
        self.multimodal_config = multimodal_config
        self.quant_config = quant_config

        # force "use_flash_attention_2=True" to audio tower to align
        # the results.
        if flash_attn is not None:
            audio_config = thinker_config.audio_config
            audio_config._attn_implementation_autoset = True
            audio_config._attn_implementation = "flash_attention_2"
        else:
            logger.warning(
                "flash_attn is not available, the model may not yield the "
                "exactly same result as the transformers implementation "
                "in the audio tower part."
            )

        with self._mark_tower_model(vllm_config, "audio"):
            self.audio_tower = Qwen2_5OmniAudioEncoder(thinker_config.audio_config)

        with self._mark_tower_model(vllm_config, {"image", "video"}):
            self.visual = Qwen2_5_VisionTransformer(
                vision_config=thinker_config.vision_config,
                norm_eps=getattr(thinker_config.text_config, "rms_norm_eps", 1e-6),
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "visual"),
            )

        with self._mark_language_model(vllm_config):
            self.language_model = init_vllm_registered_model(
                vllm_config=vllm_config,
                prefix=maybe_prefix(prefix, "language_model"),
                hf_config=thinker_config.text_config,
                architectures=["Qwen2ForCausalLM"],
            )

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors
        )

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

        # 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", "image_embeds")
                and "image" not in mm_input_by_modality
            ):
                mm_input_by_modality["image"] = self._parse_and_validate_image_input(
                    **kwargs
                )
            if (
                input_key in ("pixel_values_videos", "video_embeds")
                and "video" not in mm_input_by_modality
            ):
                mm_input_by_modality["video"] = self._parse_and_validate_video_input(
                    **kwargs
                )
            if (
                input_key in ("input_audio_features")
                and "audio" not in mm_input_by_modality
            ):
                mm_input_by_modality["audio"] = self._parse_and_validate_audio_input(
                    **kwargs
                )
        return mm_input_by_modality

    def _get_audio_for_video_mapping(
        self, mm_features: list[MultiModalFeatureSpec]
    ) -> tuple[dict[int, int], set[int]]:
        """
        Map video offset -> paired audio_feature_length for use_audio_in_video.

        When use_audio_in_video=True, audio is interleaved within video chunks.
        The pairing is based on feature order in mm_features.

        Returns:
            Tuple of (video_offset -> audio_feature_length mapping,
                      set of paired audio offsets to skip)
        """
        videos_with_audio = [
            f
            for f in mm_features
            if f.modality == "video"
            and f.data.get("use_audio_in_video")
            and f.data["use_audio_in_video"].data.item()
        ]
        audios = [f for f in mm_features if f.modality == "audio"]

        # Pair videos with audio features (assumes matching order)
        mapping: dict[int, int] = {}
        paired_audio_offsets: set[int] = set()
        for i, video_f in enumerate(videos_with_audio):
            if i < len(audios):
                audio_len = audios[i].data["audio_feature_lengths"].data.item()
                mapping[video_f.mm_position.offset] = audio_len
                paired_audio_offsets.add(audios[i].mm_position.offset)
        return mapping, paired_audio_offsets

    def _compute_audio_token_count(self, audio_feature_length: int) -> int:
        """Compute audio tokens from feature length."""
        return ((audio_feature_length - 1) // 2 + 1 - 2) // 2 + 1

    def iter_mm_features(
        self, mm_features: list[MultiModalFeatureSpec]
    ) -> Iterator[tuple[int, str, dict[str, Any]]]:
        """
        Iterate over multimodal features sorted by position offset.

        Yields: (offset, modality, feature_data) where feature_data contains:
        - image: {"grid_t", "grid_h", "grid_w", "t_factor"}
        - video: {"grid_t", "grid_h", "grid_w", "t_factor",
                  "use_audio_in_video", "audio_feature_length"}
        - audio: {"audio_feature_length"}
        """
        thinker_config = self.config
        spatial_merge_size = thinker_config.vision_config.spatial_merge_size
        tokens_per_second = getattr(
            thinker_config.vision_config, "tokens_per_second", 25
        )

        # Sort features by offset first, then pair audio with video
        sorted_features = sorted(mm_features, key=lambda f: f.mm_position.offset)
        audio_for_video, paired_audio_offsets = self._get_audio_for_video_mapping(
            sorted_features
        )

        for mm_feature in sorted_features:
            offset = mm_feature.mm_position.offset
            modality = mm_feature.modality

            if modality == "image":
                t, h, w = mm_feature.data["image_grid_thw"].data.tolist()
                yield (
                    offset,
                    "image",
                    {
                        "grid_t": t,
                        "grid_h": h // spatial_merge_size,
                        "grid_w": w // spatial_merge_size,
                        "t_factor": 1.0 * tokens_per_second,
                    },
                )
            elif modality == "video":
                t, h, w = mm_feature.data["video_grid_thw"].data.tolist()
                second_per_grid_ts = 1.0
                if mm_feature.data.get("second_per_grid_ts"):
                    second_per_grid_ts = mm_feature.data[
                        "second_per_grid_ts"
                    ].data.item()
                use_audio_in_video = False
                if mm_feature.data.get("use_audio_in_video"):
                    use_audio_in_video = bool(
                        mm_feature.data["use_audio_in_video"].data.item()
                    )

                yield (
                    offset,
                    "video",
                    {
                        "grid_t": t,
                        "grid_h": h // spatial_merge_size,
                        "grid_w": w // spatial_merge_size,
                        "t_factor": second_per_grid_ts * tokens_per_second,
                        "use_audio_in_video": use_audio_in_video,
                        "audio_feature_length": audio_for_video.get(offset),
                    },
                )
            elif modality == "audio":
                # Skip audio that's paired with video (handled in video case)
                if offset not in paired_audio_offsets:
                    audio_len = mm_feature.data["audio_feature_lengths"].data.item()
                    yield offset, "audio", {"audio_feature_length": audio_len}

    def _compute_interleaved_positions(
        self, start_idx: int, data: dict[str, Any]
    ) -> tuple[np.ndarray, int]:
        """
        Compute positions for interleaved video+audio chunks.

        Returns: (position_ids, total_token_count)
        """
        grid_t = data["grid_t"]
        grid_h = data["grid_h"]
        grid_w = data["grid_w"]
        t_factor = data["t_factor"]
        audio_len = data["audio_feature_length"]

        thinker_config = self.config
        tokens_per_second = getattr(
            thinker_config.vision_config, "tokens_per_second", 25
        )
        seconds_per_chunk = thinker_config.seconds_per_chunk
        t_ntoken_per_chunk = int(tokens_per_second * seconds_per_chunk)

        # Temporal indices with scaling
        t_index = (np.arange(grid_t) * t_factor).astype(np.int64)

        # Split temporal indices into chunks
        t_index_split_chunk: list[list[int]] = [
            [] for _ in range((int(t_index.max()) // t_ntoken_per_chunk) + 1)
        ]
        for t_val in t_index:
            idx = int(t_val) // t_ntoken_per_chunk
            t_index_split_chunk[idx].append(int(t_val))

        pure_audio_len = self._compute_audio_token_count(audio_len)
        added_audio_len = 0
        pos_ids_list: list[np.ndarray] = []
        audio_start_idx = start_idx

        for t_chunk in t_index_split_chunk:
            if not t_chunk:
                continue

            chunk_t = len(t_chunk)

            # Build vision positions for this chunk
            h_indices = np.tile(
                np.arange(grid_h).reshape(1, -1, 1), (chunk_t, 1, grid_w)
            ).flatten()
            w_indices = np.tile(
                np.arange(grid_w).reshape(1, 1, -1), (chunk_t, grid_h, 1)
            ).flatten()
            t_indices = np.repeat(np.array(t_chunk), grid_h * grid_w)

            vision_pos = np.stack([t_indices, h_indices, w_indices]) + start_idx
            pos_ids_list.append(vision_pos)

            # Audio tokens for this chunk
            audio_chunk_size = min(t_ntoken_per_chunk, pure_audio_len - added_audio_len)
            if audio_chunk_size > 0:
                audio_pos = (
                    np.broadcast_to(np.arange(audio_chunk_size), (3, audio_chunk_size))
                    + audio_start_idx
                )
                pos_ids_list.append(audio_pos)
                audio_start_idx = audio_start_idx + audio_chunk_size
                added_audio_len += audio_chunk_size

        # Handle remaining audio that doesn't fit in chunks
        if added_audio_len < pure_audio_len:
            remaining = pure_audio_len - added_audio_len
            remaining_audio_pos = (
                np.broadcast_to(np.arange(remaining), (3, remaining)) + audio_start_idx
            )
            pos_ids_list.append(remaining_audio_pos)

        # Calculate total token count
        vision_tokens = grid_t * grid_h * grid_w
        total_tokens = vision_tokens + pure_audio_len

        return np.concatenate(pos_ids_list, axis=1), total_tokens

    def get_mrope_input_positions(
        self,
        input_tokens: list[int],
        mm_features: list[MultiModalFeatureSpec],
    ) -> tuple[torch.Tensor, int]:
        """
        Compute M-RoPE input positions using mm_features directly.

        Example for use_audio_in_video case:
            (V_i are vision position ids, A_i are audio position ids)

            |V_1 ...    V_n|A_1 ...   A_n|V_n+1 ... V_2n|A_n+1 ... A_2n|...
            |vision chunk 1|audio chunk 1|vision chunk 2|audio chunk 2 |...
        """
        llm_pos_ids_list: list[np.ndarray] = []
        st = 0

        for offset, modality, data in self.iter_mm_features(mm_features):
            # Add text segment before this feature
            text_len = offset - st
            st_idx = int(llm_pos_ids_list[-1].max()) + 1 if llm_pos_ids_list else 0
            if text_len > 0:
                llm_pos_ids_list.append(
                    np.broadcast_to(np.arange(text_len), (3, text_len)) + st_idx
                )
                st_idx += text_len

            if modality == "audio":
                # Standalone audio positions
                audio_tokens = self._compute_audio_token_count(
                    data["audio_feature_length"]
                )
                llm_pos_ids_list.append(
                    np.broadcast_to(np.arange(audio_tokens), (3, audio_tokens)) + st_idx
                )
                st = offset + audio_tokens

            elif modality == "image":
                # Image uses np.indices like Qwen2-VL
                grid_t = data["grid_t"]
                grid_h = data["grid_h"]
                grid_w = data["grid_w"]
                t_factor = data["t_factor"]

                grid_indices = np.indices((grid_t, grid_h, grid_w))
                if t_factor != 1.0:
                    grid_indices[0] = (grid_indices[0] * t_factor).astype(np.int64)
                llm_pos_ids_list.append(grid_indices.reshape(3, -1) + st_idx)
                st = offset + grid_t * grid_h * grid_w

            elif modality == "video":
                grid_t = data["grid_t"]
                grid_h = data["grid_h"]
                grid_w = data["grid_w"]
                t_factor = data["t_factor"]

                if not data["use_audio_in_video"]:
                    # Simple video (same as Qwen2-VL)
                    grid_indices = np.indices((grid_t, grid_h, grid_w))
                    if t_factor != 1.0:
                        grid_indices[0] = (grid_indices[0] * t_factor).astype(np.int64)
                    llm_pos_ids_list.append(grid_indices.reshape(3, -1) + st_idx)
                    st = offset + grid_t * grid_h * grid_w
                else:
                    # Interleaved video+audio
                    pos_ids, token_count = self._compute_interleaved_positions(
                        st_idx, data
                    )
                    llm_pos_ids_list.append(pos_ids)
                    st = offset + token_count

        # Add trailing text
        if st < len(input_tokens):
            st_idx = int(llm_pos_ids_list[-1].max()) + 1 if llm_pos_ids_list else 0
            text_len = len(input_tokens) - st
            llm_pos_ids_list.append(
                np.broadcast_to(np.arange(text_len), (3, text_len)) + st_idx
            )

        llm_positions = np.concatenate(llm_pos_ids_list, axis=1).reshape(3, -1)
        mrope_position_delta = int(llm_positions.max()) + 1 - len(input_tokens)

        return torch.from_numpy(llm_positions), mrope_position_delta

    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
        mm_input_by_modality = self._parse_and_validate_multimodal_inputs(**kwargs)
        if not mm_input_by_modality:
            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 mm_input_by_modality:
            multimodal_input = mm_input_by_modality[modality]
            if modality == "image":
                image_embeddings = self._process_image_input(multimodal_input)
                multimodal_embeddings += tuple(image_embeddings)
            if modality == "video":
                video_embeddings = self._process_video_input(multimodal_input)
                multimodal_embeddings += tuple(video_embeddings)
            if modality == "audio":
                audio_embeddings = self._process_audio_input(multimodal_input)
                multimodal_embeddings += tuple(audio_embeddings)
        return multimodal_embeddings

    def embed_input_ids(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: MultiModalEmbeddings | None = None,
        *,
        is_multimodal: torch.Tensor | None = None,
        handle_oov_mm_token: bool = False,
    ) -> torch.Tensor:
        if multimodal_embeddings is None or is_multimodal is None:
            return super().embed_input_ids(input_ids)

        # Check for audio-in-video: interleaved video and audio tokens
        # in the multimodal region. Only use the interleaved path when
        # needed; otherwise fall back to the default parent implementation.
        video_token_id = self.config.video_token_index
        audio_token_id = self.config.audio_token_index

        is_video = is_multimodal & (input_ids == video_token_id)
        is_audio = is_multimodal & (input_ids == audio_token_id)

        num_video = is_video.sum().item()
        num_audio = is_audio.sum().item()

        if check_interleaved_audio_video(is_video, is_audio, num_video, num_audio):
            inputs_embeds = self._embed_text_input_ids(
                input_ids,
                self.get_language_model().embed_input_ids,
                is_multimodal=is_multimodal,
                handle_oov_mm_token=handle_oov_mm_token,
            )
            return merge_interleaved_embeddings(
                inputs_embeds,
                multimodal_embeddings,
                is_video,
                is_audio,
                is_multimodal,
                num_video,
                num_audio,
            )

        # Default: standard merge (no interleaving), same as parent class
        return super().embed_input_ids(
            input_ids,
            multimodal_embeddings=multimodal_embeddings,
            is_multimodal=is_multimodal,
            handle_oov_mm_token=handle_oov_mm_token,
        )

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

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

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(self, skip_prefixes=["talker.", "token2wav."])
        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="merger.",
            tower_model=["visual.", "audio_tower."],
        )
