# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2026 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 Qwen3-ASR model."""

from collections.abc import Iterable, Mapping, Sequence
from typing import Any, Literal, cast

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.feature_extraction_utils import BatchFeature
from transformers.models.whisper import WhisperFeatureExtractor

from vllm.config import MultiModalConfig, ModelConfig, SpeechToTextConfig, VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.inputs.data import PromptType
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import _ACTIVATION_REGISTRY
from vllm.model_executor.layers.attention.mm_encoder_attention import (
    MMEncoderAttention,
)
from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.interfaces import (
    MultiModalEmbeddings,
    SupportsMRoPE,
    SupportsMultiModal,
    SupportsPP,
    SupportsTranscription,
)
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.model_executor.models.qwen3 import Qwen3ForCausalLM
from vllm.model_executor.models.qwen2_5_omni_thinker import (
    Qwen2_5OmniAudioFeatureInputs,
    Qwen2_5OmniThinkerMultiModalProcessor,
)
from vllm.model_executor.models.utils import (
    AutoWeightsLoader,
    WeightsMapper,
    _merge_multimodal_embeddings,
    maybe_prefix,
)
from vllm.model_executor.models.whisper import ISO639_1_SUPPORTED_LANGS
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (
    AudioItem,
    ModalityData,
    MultiModalDataDict,
    MultiModalFeatureSpec,
    MultiModalFieldConfig,
    MultiModalKwargsItems,
)
from vllm.multimodal.parse import (
    AudioProcessorItems,
    DictEmbeddingItems,
    ModalityDataItems,
    MultiModalDataItems,
    MultiModalDataParser,
)
from vllm.multimodal.processing import (
    BaseDummyInputsBuilder,
    BaseProcessingInfo,
    PromptReplacement,
    PromptUpdate,
)
from vllm.sequence import IntermediateTensors
from vllm.v1.attention.backends.registry import AttentionBackendEnum
from vllm.tokenizers import cached_tokenizer_from_config
from vllm.transformers_utils.processor import cached_processor_from_config
from vllm.transformers_utils.processors.qwen3_asr import Qwen3ASRProcessor
from vllm.model_executor.models.vision import (
    get_vit_attn_backend,
)

# NOTE:
# Qwen3-ASR config/processor classes are loaded from model repository
# (trust_remote_code) in production. Keep annotations generic to avoid
# compile-time dependency on external modules.
Qwen3ASRThinkerConfig = Any
Qwen3ASRAudioEncoderConfig = Any

logger = init_logger(__name__)


def _get_feat_extract_output_lengths(input_lengths: torch.Tensor):
    input_lengths_leave = input_lengths % 100
    feat_lengths = (input_lengths_leave - 1) // 2 + 1
    output_lengths = (
        ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // 100) * 13
    )
    return output_lengths


# ============= Audio Encoder Components =============


class SinusoidsPositionEmbedding(nn.Module):
    """Sinusoidal position embedding for audio encoder."""

    def __init__(self, length: int, channels: int, max_timescale: int = 10000):
        super().__init__()
        self.length = length
        self.channels = channels
        self.max_timescale = max_timescale

        if channels % 2 != 0:
            raise ValueError("SinusoidsPositionEmbedding needs even channels input")

        log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
        inv_timescales = torch.exp(
            -log_timescale_increment * torch.arange(channels // 2).float()
        )
        scaled_time = (
            torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
        )
        positional_embedding = torch.cat(
            [torch.sin(scaled_time), torch.cos(scaled_time)], dim=1
        )
        self.register_buffer(
            "positional_embedding", positional_embedding, persistent=False
        )

    def forward(self, seqlen: int) -> torch.Tensor:
        return self.positional_embedding[:seqlen, :]


class Qwen3ASRAudioAttention(nn.Module):
    """Multi-headed attention for Qwen3-Omni Audio Encoder using MMEncoderAttention."""

    def __init__(
        self,
        config: Qwen3ASRAudioEncoderConfig,
        multimodal_config: MultiModalConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()
        self.embed_dim = config.d_model
        self.num_heads = config.encoder_attention_heads
        self.head_dim = self.embed_dim // self.num_heads
        tp_size = get_tensor_model_parallel_world_size()
        self.num_local_heads = self.num_heads // tp_size

        if (self.head_dim * self.num_heads) != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: "
                f"{self.embed_dim} and `num_heads`: {self.num_heads})."
            )

        self.scaling = self.head_dim**-0.5

        self.qkv = QKVParallelLinear(
            hidden_size=self.embed_dim,
            head_size=self.head_dim,
            total_num_heads=self.num_heads,
            total_num_kv_heads=self.num_heads,
            bias=True,
            prefix=f"{prefix}.qkv",
        )

        self.out_proj = RowParallelLinear(
            input_size=self.embed_dim,
            output_size=self.embed_dim,
            bias=True,
            prefix=f"{prefix}.out_proj",
        )

        self.attn = MMEncoderAttention(
            num_heads=self.num_local_heads,
            head_size=self.head_dim,
            scale=self.scaling,
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        cu_seqlens: torch.Tensor,
        max_seqlen: torch.Tensor | None,
    ) -> torch.Tensor:
        seq_length, _ = hidden_states.size()
        qkv, _ = self.qkv(hidden_states)
        q, k, v = qkv.chunk(3, dim=-1)
        q = q.view(1, seq_length, -1, self.head_dim)
        k = k.view(1, seq_length, -1, self.head_dim)
        v = v.view(1, seq_length, -1, self.head_dim)

        attn_output = self.attn(
            query=q,
            key=k,
            value=v,
            cu_seqlens=cu_seqlens,
            max_seqlen=max_seqlen,
        )

        attn_output = attn_output.view(seq_length, -1)
        output, _ = self.out_proj(attn_output)
        return output


class Qwen3ASRAudioEncoderLayer(nn.Module):
    """Transformer encoder layer for Qwen3-Omni Audio Encoder."""

    def __init__(
        self,
        config: Qwen3ASRAudioEncoderConfig,
        multimodal_config: MultiModalConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()
        self.embed_dim = config.d_model
        self.self_attn = Qwen3ASRAudioAttention(
            config, multimodal_config=multimodal_config, prefix=f"{prefix}.self_attn"
        )
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.activation_fn = _ACTIVATION_REGISTRY[config.activation_function]
        self.fc1 = ColumnParallelLinear(
            self.embed_dim,
            config.encoder_ffn_dim,
            bias=True,
            prefix=f"{prefix}.fc1",
        )
        self.fc2 = RowParallelLinear(
            config.encoder_ffn_dim,
            self.embed_dim,
            bias=True,
            prefix=f"{prefix}.fc2",
        )
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        cu_seqlens: torch.Tensor,
        max_seqlen: torch.Tensor | None,
    ) -> torch.Tensor:
        """
        Args:
            hidden_states: Input tensor of shape (seq_len, hidden_size)
            cu_seqlens: Cumulative sequence lengths
            max_seqlen: Maximum sequence length in the batch
        """
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states = self.self_attn(
            hidden_states=hidden_states,
            cu_seqlens=cu_seqlens,
            max_seqlen=max_seqlen,
        )
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states, _ = self.fc1(hidden_states)
        hidden_states = self.activation_fn(hidden_states)
        hidden_states, _ = self.fc2(hidden_states)
        hidden_states = residual + hidden_states

        # Clamp for numerical stability with fp16
        if hidden_states.dtype == torch.float16:
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(
                hidden_states, min=-clamp_value, max=clamp_value
            )

        return hidden_states


class Qwen3ASRAudioEncoder(nn.Module):
    """vLLM-native Qwen3-ASR Audio Encoder."""

    def __init__(
        self,
        config: Qwen3ASRAudioEncoderConfig,
        multimodal_config: MultiModalConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()

        embed_dim = config.d_model
        self.num_mel_bins = config.num_mel_bins
        self.max_source_positions = config.max_source_positions
        self.n_window = config.n_window
        self.n_window_infer = config.n_window_infer
        self.conv_chunksize = config.conv_chunksize

        # Position embedding
        self.positional_embedding = SinusoidsPositionEmbedding(
            self.max_source_positions, embed_dim
        )

        # Convolutional layers for mel-spectrogram processing
        self.conv2d1 = nn.Conv2d(1, config.downsample_hidden_size, 3, 2, padding=1)
        self.conv2d2 = nn.Conv2d(
            config.downsample_hidden_size,
            config.downsample_hidden_size,
            3,
            2,
            padding=1,
        )
        self.conv2d3 = nn.Conv2d(
            config.downsample_hidden_size,
            config.downsample_hidden_size,
            3,
            2,
            padding=1,
        )

        conv_out_dim = config.downsample_hidden_size * (
            (((config.num_mel_bins + 1) // 2 + 1) // 2 + 1) // 2
        )
        self.conv_out = nn.Linear(conv_out_dim, config.d_model, bias=False)

        # Transformer encoder layers
        self.layers = nn.ModuleList(
            [
                Qwen3ASRAudioEncoderLayer(
                    config,
                    multimodal_config=multimodal_config,
                    prefix=f"{prefix}.layers.{i}",
                )
                for i in range(config.encoder_layers)
            ]
        )

        # Output layers
        self.ln_post = nn.LayerNorm(config.d_model)
        self.proj1 = nn.Linear(config.d_model, config.d_model)
        self.act = _ACTIVATION_REGISTRY[config.activation_function]
        self.proj2 = nn.Linear(config.d_model, config.output_dim)

        # Get attention backend
        self.attn_backend = get_vit_attn_backend(
            head_size=config.d_model // config.encoder_attention_heads,
            dtype=torch.get_default_dtype(),
        )

    def compute_attn_mask_seqlen(self, cu_seqlens: torch.Tensor) -> torch.Tensor | None:
        """Compute max_seqlen only for flash attention backends."""
        max_seqlen = None
        if self.attn_backend in {
            AttentionBackendEnum.FLASH_ATTN,
            AttentionBackendEnum.ROCM_AITER_FA,
        }:
            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
        return max_seqlen

    @property
    def dtype(self) -> torch.dtype:
        return self.conv2d1.weight.dtype

    @property
    def device(self) -> torch.device:
        return self.conv2d1.weight.device

    def forward(
        self,
        input_features: torch.Tensor,
        feature_lens: torch.Tensor,
        aftercnn_lens: torch.Tensor,
    ):
        # Compute chunk information
        chunk_num = torch.ceil(feature_lens / (self.n_window * 2)).long()

        chunk_lengths = torch.tensor(
            [self.n_window * 2] * chunk_num.sum(),
            dtype=torch.long,
            device=feature_lens.device,
        )
        tail_chunk_index = F.pad(chunk_num, (1, 0), value=-1).cumsum(0)[1:]
        chunk_lengths[tail_chunk_index] = feature_lens % (self.n_window * 2)
        chunk_lengths[chunk_lengths == 0] = self.n_window * 2

        # Split input features into chunks and pad
        chunk_list = input_features.T.split(chunk_lengths.tolist(), dim=0)
        padded_feature = nn.utils.rnn.pad_sequence(
            chunk_list, batch_first=True
        ).transpose(1, 2)

        # Compute feature lengths after CNN
        feature_lens_after_cnn = self._get_cnn_output_lengths(chunk_lengths)
        # Vectorized mask creation: avoid creating many small tensors
        max_len_after_cnn = feature_lens_after_cnn.max().item()
        indices = torch.arange(max_len_after_cnn, device=padded_feature.device)
        padded_mask_after_cnn = indices.unsqueeze(0) < feature_lens_after_cnn.unsqueeze(
            1
        )

        # Add channel dimension for conv2d
        padded_feature = padded_feature.unsqueeze(1)

        # Apply convolutional layers (chunk if needed to avoid OOM)
        if padded_feature.size(0) <= self.conv_chunksize:
            # Fast path: no chunking needed
            padded_embed = F.gelu(self.conv2d1(padded_feature))
            padded_embed = F.gelu(self.conv2d2(padded_embed))
            padded_embed = F.gelu(self.conv2d3(padded_embed))
        else:
            # Chunked processing to avoid OOM
            padded_embeds = []
            for chunk in padded_feature.split(self.conv_chunksize, dim=0):
                padded_embed = F.gelu(self.conv2d1(chunk))
                padded_embed = F.gelu(self.conv2d2(padded_embed))
                padded_embed = F.gelu(self.conv2d3(padded_embed))
                padded_embeds.append(padded_embed)
            padded_embed = torch.cat(padded_embeds, dim=0)

        # (batch, channels, freq, time) -> (batch, time, channels*freq)
        b, c, f, t = padded_embed.size()
        padded_embed = self.conv_out(
            padded_embed.permute(0, 3, 1, 2).contiguous().view(b, t, c * f)
        )

        # Add positional embedding
        positional_embedding = (
            self.positional_embedding.positional_embedding[: padded_embed.shape[1], :]
            .unsqueeze(0)
            .to(padded_embed.dtype)
        )
        padded_embed = padded_embed + positional_embedding

        # Extract valid hidden states and compute cu_seqlens
        hidden_states = padded_embed[padded_mask_after_cnn]

        # Compute cumulative sequence lengths for chunked attention
        cu_chunk_lens = [0]
        window_aftercnn = padded_mask_after_cnn.shape[-1] * (
            self.n_window_infer // (self.n_window * 2)
        )
        # Use tolist() for efficient batch conversion from tensor to Python
        for cnn_len in aftercnn_lens.tolist():
            num_full_chunks = cnn_len // window_aftercnn
            remainder = cnn_len % window_aftercnn
            cu_chunk_lens.extend([window_aftercnn] * num_full_chunks)
            if remainder:
                cu_chunk_lens.append(remainder)
        cu_seqlens = torch.tensor(cu_chunk_lens, device=aftercnn_lens.device).cumsum(
            -1, dtype=torch.int32
        )

        max_seqlen = self.compute_attn_mask_seqlen(cu_seqlens)

        # Apply transformer layers
        for encoder_layer in self.layers:
            hidden_states = encoder_layer(
                hidden_states,
                cu_seqlens,
                max_seqlen,
            )

        # Apply output layers
        hidden_states = self.ln_post(hidden_states)
        hidden_states = self.proj1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.proj2(hidden_states)

        return hidden_states

    def _get_cnn_output_lengths(self, input_lengths: torch.Tensor) -> torch.Tensor:
        """Compute output lengths after the three conv2d layers."""
        lengths = input_lengths
        for _ in range(3):
            lengths = (lengths - 1) // 2 + 1
        return lengths

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        """Load weights with mapping from HuggingFace format."""
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("self_attn.qkv.", "self_attn.q_proj.", "q"),
            ("self_attn.qkv.", "self_attn.k_proj.", "k"),
            ("self_attn.qkv.", "self_attn.v_proj.", "v"),
        ]
        params_dict = dict(self.named_parameters(remove_duplicate=False))
        loaded_params: set[str] = set()

        for name, loaded_weight in weights:
            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)

                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict.get(name)
                if param is not None:
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


class Qwen3ASRProcessingInfo(BaseProcessingInfo):
    def get_hf_config(self):
        hf_config = self.ctx.get_hf_config()
        return getattr(hf_config, "thinker_config", hf_config)

    def get_hf_processor(self, **kwargs: object):
        processor = self.ctx.get_hf_processor(
            Qwen3ASRProcessor,
            use_fast=kwargs.pop("use_fast", True),
            **kwargs,
        )
        if not hasattr(processor, "audio_token"):
            processor.audio_token = "<|audio_pad|>"
        return processor

    def get_feature_extractor(self, **kwargs: object) -> WhisperFeatureExtractor:
        hf_processor = self.get_hf_processor(**kwargs)
        feature_extractor = hf_processor.feature_extractor
        assert isinstance(feature_extractor, WhisperFeatureExtractor)
        return feature_extractor

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


class Qwen3ASRDummyInputsBuilder(BaseDummyInputsBuilder[Qwen3ASRProcessingInfo]):
    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_audios = mm_counts.get("audio", 0)

        hf_processor = self.info.get_hf_processor()
        audio_token = hf_processor.audio_token

        return audio_token * num_audios

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
        mm_options: Mapping[str, BaseDummyOptions] | None = None,
    ) -> MultiModalDataDict:
        num_audios = mm_counts.get("audio", 0)

        feature_extractor = self.info.get_feature_extractor()

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

        audio_overrides = mm_options.get("audio") if mm_options else None

        return {
            "audio": self._get_dummy_audios(
                length=target_audio_length,
                num_audios=num_audios,
                overrides=audio_overrides,
            ),
        }


def _qwen3asr_field_config(hf_inputs: Mapping[str, torch.Tensor]):
    audio_feature_lengths = hf_inputs.get("audio_feature_lengths", torch.empty((0,)))
    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"),
    )


class Qwen3ASRMultiModalDataParser(MultiModalDataParser):
    def _parse_audio_data(
        self,
        data: dict[str, torch.Tensor] | ModalityData[AudioItem],
    ) -> ModalityDataItems[Any, Any] | None:
        if isinstance(data, dict):
            return DictEmbeddingItems(
                data,
                modality="audio",
                required_fields={"input_audio_features", "audio_feature_lengths"},
                fields_factory=_qwen3asr_field_config,
            )

        return super()._parse_audio_data(data)


class Qwen3ASRMultiModalProcessor(
    Qwen2_5OmniThinkerMultiModalProcessor,
):
    def _get_data_parser(self) -> MultiModalDataParser:
        feature_extractor = self.info.get_feature_extractor()
        return Qwen3ASRMultiModalDataParser(
            target_sr=feature_extractor.sampling_rate,
        )

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return _qwen3asr_field_config(hf_inputs)

    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()
        vocab = tokenizer.get_vocab()

        audio_token = processor.audio_token
        audio_token_id = vocab[audio_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()

        def get_replacement_qwen2_audio(item_idx: int):
            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

        return [
            PromptReplacement(
                modality="audio",
                target=audio_token,
                replacement=get_replacement_qwen2_audio,
            ),
        ]


@MULTIMODAL_REGISTRY.register_processor(
    Qwen3ASRMultiModalProcessor,
    info=Qwen3ASRProcessingInfo,
    dummy_inputs=Qwen3ASRDummyInputsBuilder,
)
class Qwen3ASRForConditionalGeneration(
    nn.Module,
    SupportsMultiModal,
    SupportsPP,
    SupportsMRoPE,
    SupportsTranscription,
):
    supported_languages = ISO639_1_SUPPORTED_LANGS

    #map部分权重名
    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            "thinker.lm_head.": "language_model.lm_head.",
            "thinker.model.": "language_model.model.",
            "thinker.": "",
        }
    )

    #根据输入模式填充string token
    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
        if modality.startswith("audio"):
            return "<|audio_start|><|audio_pad|><|audio_end|>"

        raise ValueError("Only audio modality is supported")

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        self.vllm_config = vllm_config  # needed for torch compile forward context
        thinker_config = (
            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.audio_tower = Qwen3ASRAudioEncoder(
            thinker_config.audio_config,
            multimodal_config=multimodal_config,
            prefix=maybe_prefix(prefix, "audio_tower"),
        )
        self.quant_config = quant_config

        self.language_model = Qwen3ForCausalLM(
            vllm_config=vllm_config.with_hf_config(
                thinker_config.text_config, architectures=["Qwen3ForCausalLM"]
            ),
            prefix=maybe_prefix(prefix, "language_model"),
        )

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors
        )

    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_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 ("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 _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_output_lengths = _get_feat_extract_output_lengths(audio_feature_lengths)

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

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

    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings | None:
        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 correspoending to a multimodal data item (image or video).
        multimodal_embeddings: tuple[torch.Tensor, ...] = ()

        # NOTE: It is important to iterate over the keys in this dictionary
        # to preserve the order of the modalities.
        for modality in mm_input_by_modality:
            multimodal_input = mm_input_by_modality[modality]
            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:
        inputs_embeds = self._embed_text_input_ids(
            input_ids,
            self.language_model.embed_input_ids,
            is_multimodal=is_multimodal,
            handle_oov_mm_token=handle_oov_mm_token,
        )

        if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
            return inputs_embeds

        inputs_embeds = _merge_multimodal_embeddings(
            inputs_embeds=inputs_embeds,
            multimodal_embeddings=multimodal_embeddings,
            is_multimodal=is_multimodal,
        )

        return inputs_embeds

    def forward(
        self,
        input_ids: torch.Tensor,
        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.", "code2wav."],
        )
        loaded_weights = loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

        return loaded_weights

    def get_mrope_input_positions(
        self,
        input_tokens: list[int],
        mm_features: list[MultiModalFeatureSpec],
    ) -> tuple[torch.Tensor, int]:
        seq_len = len(input_tokens)

        if not mm_features:
            # No audio features, just return linear positions
            llm_positions = (
                torch.arange(seq_len, dtype=torch.long).view(1, -1).expand(3, -1)
            )
            return llm_positions.clone(), 0

        llm_pos_ids_list: list[torch.Tensor] = []
        st = 0

        for mm_feature in sorted(mm_features, key=lambda f: f.mm_position.offset):
            offset = mm_feature.mm_position.offset

            # Get audio feature length from mm_feature data
            audio_feature_length = mm_feature.data["audio_feature_lengths"].data
            if isinstance(audio_feature_length, torch.Tensor):
                audio_feature_length = audio_feature_length.item()
            audio_len = _get_feat_extract_output_lengths(
                torch.tensor(audio_feature_length)
            ).item()

            # Text segment before audio (includes audio_start token)
            text_len = offset - st
            st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
            text_positions = (
                torch.arange(text_len, dtype=torch.long).view(1, -1).expand(3, -1)
                + st_idx
            )
            llm_pos_ids_list.append(text_positions)
            st_idx = st_idx + text_len

            # Audio token segment
            audio_positions = (
                torch.arange(audio_len, dtype=torch.long).view(1, -1).expand(3, -1)
                + st_idx
            )
            llm_pos_ids_list.append(audio_positions)

            st = offset + audio_len

        # Handle remaining text (includes audio_end and any trailing text)
        if st < seq_len:
            st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
            text_len = seq_len - st
            final_text_positions = (
                torch.arange(text_len, dtype=torch.long).view(1, -1).expand(3, -1)
                + st_idx
            )
            llm_pos_ids_list.append(final_text_positions)

        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
        if llm_positions.shape[1] != seq_len:
            raise RuntimeError("Position ids length mismatch with input ids length")

        mrope_position_delta = (llm_positions.max() + 1 - seq_len).item()
        return llm_positions, mrope_position_delta

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

    @classmethod
    def get_speech_to_text_config(
        cls, model_config: ModelConfig, task_type: str
    ) -> SpeechToTextConfig:
        processor = cached_processor_from_config(model_config)
        feature_extractor: WhisperFeatureExtractor = processor.feature_extractor
        return SpeechToTextConfig(
            max_audio_clip_s=feature_extractor.chunk_length,
            sample_rate=feature_extractor.sampling_rate,
        )

    @classmethod
    def get_generation_prompt(
        cls,
        audio: np.ndarray,
        model_config: ModelConfig,
        stt_config: SpeechToTextConfig,
        language: str | None,
        task_type: Literal["transcribe", "translate"],
        request_prompt: str,
        to_language: str | None,
    ) -> PromptType:
        """Get the generation prompt to be used for transcription requests."""
        tokenizer = cached_tokenizer_from_config(model_config)
        audio_placeholder = cls.get_placeholder_str("audio", 0)

        if task_type not in ("transcribe", "translate"):
            raise ValueError(
                f"Unsupported task_type '{task_type}'. "
                "Supported task types are 'transcribe' and 'translate'."
            )
        full_lang_name_to = cls.supported_languages.get(to_language, to_language)
        if to_language is None:
            prompt = (
                f"<|im_start|>user\n{audio_placeholder}<|im_end|>\n"
                f"<|im_start|>assistant\n"
            )
        else:
            prompt = (
                f"<|im_start|>user\n{audio_placeholder}<|im_end|>\n"
                f"<|im_start|>assistant\nlanguage {full_lang_name_to}<asr_text>"
            )

        prompt_token_ids = tokenizer.encode(prompt)
        prompt_dict = {
            "prompt_token_ids": prompt_token_ids,
            "multi_modal_data": {"audio": audio},
        }
        return cast(PromptType, prompt_dict)
