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

# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
# Copyright 2025 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 IBM Granite speech model."""
import math
from collections.abc import Iterable, Mapping
from typing import Optional, TypedDict, Union

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

from vllm.config import CacheConfig, VllmConfig
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import get_sampler
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                    MultiModalKwargs)
from vllm.multimodal.parse import (AudioProcessorItems, MultiModalDataItems,
                                   MultiModalDataParser)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                        BaseProcessingInfo, PromptReplacement,
                                        PromptUpdate)
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors

from .blip2 import Blip2QFormerModel
from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
                         SupportsMultiModal, SupportsPP)
from .utils import (AutoWeightsLoader, embed_multimodal,
                    init_vllm_registered_model, maybe_prefix)


### Audio Input
class GraniteSpeechAudioInputs(TypedDict):

    input_features: torch.Tensor
    """Shape: `(bsz, num_features, 160)`"""

    input_features_mask: torch.Tensor
    """Shape: `(bsz, num_features)`"""

    audio_embed_sizes: list[int]
    """List of length `bsz`"""


class GraniteSpeechMultiModalProcessingInfo(BaseProcessingInfo):

    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {"audio": 1}

    # There is no limit to the maximum number of audio tokens that can be
    # encoded as features; we pick ~5000 as a number that is probably higher
    # than we would expect to encounter. The sequence of length
    # get_max_audio_len() produces get_max_audio_tokens().
    def get_max_audio_tokens(self):
        return 5001

    def get_max_audio_len(self):
        return 8000000


### Input Processing  & Multimodal utils
class GraniteSpeechMultiModalProcessor(
        BaseMultiModalProcessor[GraniteSpeechMultiModalProcessingInfo]):

    def _get_data_parser(self) -> MultiModalDataParser:
        feature_extractor = self.info.get_hf_processor().audio_processor
        sampling_rate = feature_extractor.melspec_kwargs["sample_rate"]
        return MultiModalDataParser(target_sr=sampling_rate)

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return dict(
            input_features=MultiModalFieldConfig.batched("audio"),
            audio_embed_sizes=MultiModalFieldConfig.batched("audio"),
        )

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargs,
    ) -> list[PromptUpdate]:
        processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
        tokenizer = self.info.get_tokenizer()
        feature_extractor = processor.audio_processor
        vocab = tokenizer.get_vocab()

        # Use getattr with default to be compatible with transformers<4.48
        audio_token = getattr(processor, "audio_token", "<|audio|>")
        audio_token_id = vocab[audio_token]

        def get_replacement(item_idx: int):
            audios = mm_items.get_items("audio", AudioProcessorItems)
            audio = audios.get(item_idx)
            audio_length = audio.shape[-1]
            num_projector_features = feature_extractor._get_num_audio_features(
                [audio_length])[0]
            return [audio_token_id] * num_projector_features

        return [
            PromptReplacement(
                modality="audio",
                target=[audio_token_id],
                replacement=get_replacement,
            )
        ]

    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", [])

        if audios:
            # GraniteSpeechFeatureExtractor accepts "audio"
            mm_data["audio"] = audios

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

        if "audio" in mm_data:
            # Calculate the number of audio tokens per entry in the batch;
            # This is used to split the batch back out after padding.
            audio_token_index = self.info.get_hf_config().audio_token_index
            processed_outputs["audio_embed_sizes"] = [
                torch.sum(indices == audio_token_index).item()
                for indices in processed_outputs["input_ids"]
            ]

        return processed_outputs


class GraniteSpeechDummyInputsBuilder(
        BaseDummyInputsBuilder[GraniteSpeechMultiModalProcessingInfo]):

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> MultiModalDataDict:
        num_audios = mm_counts.get("audio", 0)
        return {
            "audio":
            self._get_dummy_audios(
                length=self.info.get_max_audio_len(),
                num_audios=num_audios,
            )
        }

    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 = getattr(hf_processor, "audio_token", "<|audio|>")
        return audio_token * num_audios


### QFormer Projector
class GraniteSpeechEncoderProjector(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        cache_config: CacheConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.hidden_size = config.projector_config.hidden_size
        self.downsample_rate = config.downsample_rate
        self.window_size = config.window_size
        self.num_queries = config.window_size // config.downsample_rate

        self.query = nn.Parameter(
            torch.zeros(1, self.num_queries,
                        config.projector_config.hidden_size))

        # NOTE - this is implemented generically in transformers,
        # but for now we create the QFormer model directly since
        # all existing models use this for the projector.
        self.qformer = Blip2QFormerModel(
            config.projector_config,
            quant_config=quant_config,
            cache_config=cache_config,
            prefix=f"{prefix}.qformer",
        )
        self.linear = nn.Linear(config.projector_config.hidden_size,
                                config.text_config.hidden_size)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        batch_size, seq_len, dim = hidden_states.size()
        nblocks = math.ceil(seq_len / self.window_size)
        pad = nblocks * self.window_size - seq_len
        hidden_states = nn.functional.pad(hidden_states, (0, 0, 0, pad),
                                          "constant", 0)
        hidden_states = hidden_states.view(batch_size * nblocks,
                                           self.window_size, dim)

        last_hidden_state = self.qformer(
            query_embeds=self.query.data,
            encoder_hidden_states=hidden_states,
        )

        query_proj = self.linear(
            last_hidden_state.view(
                batch_size,
                nblocks * self.window_size // self.downsample_rate,
                -1,
            ))
        return query_proj


# Encoder - conformer is adapted from: https://github.com/lucidrains/conformer.git
# NOTE - it would be nice to see if we can align this with other models using
# conformer in vLLM, e.g., phi4mm audio.
class GraniteSpeechConformerFeedForward(nn.Module):
    """Feedforward module for conformer encoder blocks."""

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

        self.up_proj = ColumnParallelLinear(
            input_size=config.hidden_dim,
            output_size=config.hidden_dim * config.feedforward_mult,
            quant_config=quant_config,
            prefix=f"{prefix}.up_proj",
        )
        self.silu = nn.SiLU()

        self.down_proj = RowParallelLinear(
            input_size=config.hidden_dim * config.feedforward_mult,
            output_size=config.hidden_dim,
            quant_config=quant_config,
            prefix=f"{prefix}.down_proj",
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.pre_norm(hidden_states)
        hidden_states, _ = self.up_proj(hidden_states)
        hidden_states = self.silu(hidden_states)
        hidden_states, _ = self.down_proj(hidden_states)
        return hidden_states


class GraniteSpeechConformerAttention(nn.Module):
    """Attention for conformer blocks using Shaw's relative positional
    embeddings. See the following [paper](https://arxiv.org/pdf/1803.02155)
    for more details.
    """

    def __init__(self, config: PretrainedConfig, prefix: str = ""):
        super().__init__()

        inner_dim = config.dim_head * config.num_heads
        self.max_pos_emb = config.max_pos_emb
        self.context_size = config.context_size
        self.num_heads = config.num_heads
        self.dim_head = config.dim_head
        self.scale = self.dim_head**-0.5
        self.pre_norm = nn.LayerNorm(config.hidden_dim)
        self.to_q = nn.Linear(config.hidden_dim, inner_dim, bias=False)
        self.to_kv = nn.Linear(config.hidden_dim, inner_dim * 2, bias=False)
        self.to_out = nn.Linear(inner_dim, config.hidden_dim)
        self.rel_pos_emb = nn.Embedding(2 * self.max_pos_emb + 1,
                                        self.dim_head)

        if self.context_size <= 0 or self.context_size > self.max_pos_emb:
            raise ValueError(
                "Context size is either less than 0 or exceeds the max_pos_emb"
            )

    def forward(self, hidden_states: torch.Tensor,
                attention_dists: torch.Tensor) -> torch.Tensor:
        hidden_states = self.pre_norm(hidden_states)
        bsz, num_features, _ = hidden_states.shape

        num_blocks = math.ceil(num_features / self.context_size)
        remainder = num_features % self.context_size
        if remainder > 0:
            # right padding to reach block size
            hidden_states = torch.nn.functional.pad(
                hidden_states, (0, 0, 0, self.context_size - remainder))

        # NOTE: would be nice to try to use qkvparallellinear
        # here for this block attention implementation if possible
        query_states = self.to_q(hidden_states)
        key_states, value_states = self.to_kv(hidden_states).chunk(2, dim=-1)

        query_states = query_states.reshape(bsz, num_blocks, self.context_size,
                                            self.num_heads,
                                            -1).transpose(2, 3)
        key_states = key_states.reshape(bsz, num_blocks, self.context_size,
                                        self.num_heads, -1).transpose(2, 3)
        value_states = value_states.reshape(bsz, num_blocks, self.context_size,
                                            self.num_heads,
                                            -1).transpose(2, 3)

        # shaw's relative positional embedding
        dist = attention_dists.to(hidden_states.device)
        rel_pos_emb = self.rel_pos_emb(dist)
        rel_pos_emb_expanded = rel_pos_emb.view([1, 1, 1] +
                                                list(rel_pos_emb.shape))
        pos_attn = torch.sum(query_states.unsqueeze(-2) * rel_pos_emb_expanded,
                             dim=-1) * self.scale

        if remainder > 0:
            # masked attention in the extended block
            mask = torch.ones(self.context_size,
                              self.context_size,
                              dtype=bool,
                              device=hidden_states.device)
            mask[:remainder, :remainder] = 0
            mask_value = -torch.finfo(pos_attn.dtype).max
            pos_attn[:, -1, :].masked_fill_(mask, mask_value)

        with torch.nn.attention.sdpa_kernel(
                torch.nn.attention.SDPBackend.MATH):
            out = F.scaled_dot_product_attention(query_states,
                                                 key_states,
                                                 value_states,
                                                 attn_mask=pos_attn,
                                                 scale=self.scale)
        out = out.transpose(2, 3).reshape(bsz, hidden_states.shape[1], -1)
        return self.to_out(out[:, :num_features, :])


class GraniteSpeechConformerDepthWiseConv1d(nn.Module):
    """Wrapper for padded 1D pointwise convolution."""

    def __init__(self,
                 chan_in: int,
                 chan_out: int,
                 kernel_size: int,
                 prefix: str = ""):
        super().__init__()
        # Padding for the 1D conv is symmetric or close (i.e., offset by one).
        pad = kernel_size // 2
        pad_offset = (kernel_size + 1) % 2
        self.padding = (pad, pad - pad_offset)

        self.conv = nn.Conv1d(chan_in,
                              chan_out,
                              kernel_size,
                              groups=chan_in,
                              bias=False)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = F.pad(hidden_states, self.padding)
        return self.conv(hidden_states)


class GraniteSpeechConformerConvModule(nn.Module):
    """Conformer conv module consisting of several 1D/depthwise 1D
    convolutional layers.
    """

    def __init__(self, config: PretrainedConfig, prefix: str = ""):
        super().__init__()
        inner_dim = config.hidden_dim * config.conv_expansion_factor

        self.norm = nn.LayerNorm(config.hidden_dim)
        self.up_conv = nn.Conv1d(config.hidden_dim, inner_dim * 2, 1)
        self.glu = nn.GLU(dim=1)
        self.depth_conv = GraniteSpeechConformerDepthWiseConv1d(
            inner_dim,
            inner_dim,
            kernel_size=config.conv_kernel_size,
            prefix=f"{prefix}.depth_conv",
        )
        self.silu = nn.SiLU()
        self.batch_norm = nn.BatchNorm1d(inner_dim)
        self.down_conv = nn.Conv1d(inner_dim, config.hidden_dim, 1)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.norm(hidden_states)
        hidden_states = self.up_conv(hidden_states.permute(0, 2, 1))
        hidden_states = self.glu(hidden_states)
        hidden_states = self.depth_conv(hidden_states)
        hidden_states = self.silu(self.batch_norm(hidden_states))
        hidden_states = self.down_conv(hidden_states).permute(0, 2, 1)
        return hidden_states


class GraniteSpeechConformerBlock(nn.Module):
    """Conformer block, consisting largely of linear layers,
    attention, and convolutional layers."""

    def __init__(self, config: PretrainedConfig, prefix: str = ""):
        super().__init__()
        self.ff1 = GraniteSpeechConformerFeedForward(config,
                                                     prefix=f"{prefix}.ff1")
        self.attn = GraniteSpeechConformerAttention(config,
                                                    prefix=f"{prefix}.attn")
        self.conv = GraniteSpeechConformerConvModule(config,
                                                     prefix=f"{prefix}.conv")
        self.ff2 = GraniteSpeechConformerFeedForward(config,
                                                     prefix=f"{prefix}.ff2")
        self.post_norm = nn.LayerNorm(config.hidden_dim)

    def forward(self, hidden_states: torch.Tensor,
                attention_dists: torch.Tensor) -> torch.Tensor:
        hidden_states = 0.5 * self.ff1(hidden_states) + hidden_states
        hidden_states = self.attn(
            hidden_states, attention_dists=attention_dists) + hidden_states
        hidden_states = self.conv(hidden_states) + hidden_states
        hidden_states = 0.5 * self.ff2(hidden_states) + hidden_states
        hidden_states = self.post_norm(hidden_states)
        return hidden_states


class GraniteSpeechCTCEncoder(nn.Module):
    """CTC Encoder comprising conformer blocks and additional linear layers."""

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

        # Precompute clamped relative positional encoding distances
        seq = torch.arange(config.context_size)
        relpos_dist = seq.view(-1, 1) - seq.view(1, -1)
        self.attention_dists = torch.clamp(
            relpos_dist, -config.context_size,
            config.context_size) + config.max_pos_emb

        self.input_linear = nn.Linear(config.input_dim,
                                      config.hidden_dim,
                                      bias=True)
        self.layers = nn.ModuleList([
            GraniteSpeechConformerBlock(
                config,
                prefix=f"{prefix}.layers.{idx}",
            ) for idx in range(config.num_layers)
        ])

        self.out = ColumnParallelLinear(
            input_size=config.hidden_dim,
            output_size=config.output_dim,
            bias=True,
            quant_config=quant_config,
            prefix=f"{prefix}.out",
        )

        self.out_mid = RowParallelLinear(
            input_size=config.output_dim,
            output_size=config.hidden_dim,
            bias=True,
            quant_config=quant_config,
            prefix=f"{prefix}.out_mid",
        )
        self.softmax = nn.Softmax(dim=-1)
        self.num_layers = config.num_layers

    def forward(self, hidden_states: torch.Tensor):
        hidden_states = self.input_linear(hidden_states)
        for idx, layer in enumerate(self.layers, start=1):
            hidden_states = layer(hidden_states,
                                  attention_dists=self.attention_dists)

            if idx == self.num_layers // 2:
                hidden_states_mid = hidden_states.clone()
                hidden_states_mid, _ = self.out(hidden_states_mid)
                hidden_states_mid = self.softmax(hidden_states_mid)
                hidden_states_mid, _ = self.out_mid(hidden_states_mid)
                hidden_states += hidden_states_mid
        return hidden_states


@MULTIMODAL_REGISTRY.register_processor(
    GraniteSpeechMultiModalProcessor,
    info=GraniteSpeechMultiModalProcessingInfo,
    dummy_inputs=GraniteSpeechDummyInputsBuilder)
class GraniteSpeechForConditionalGeneration(
        nn.Module,
        SupportsMultiModal,
        SupportsPP,
        SupportsLoRA,
):

    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str):
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        cache_config = vllm_config.cache_config

        self.config = config
        self.quant_config = quant_config
        self.cache_config = cache_config
        self.sampler = get_sampler()

        # The language model is typically a Granite LLM
        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            hf_config=config.text_config,
            prefix=maybe_prefix(prefix, "language_model"),
        )

        # Conformer encoder
        self.encoder = GraniteSpeechCTCEncoder(
            config=config.encoder_config,
            quant_config=quant_config,
            prefix=f"{prefix}.encoder",
        )

        # Blip2 QFormer
        self.projector = GraniteSpeechEncoderProjector(
            config=config,
            quant_config=quant_config,
            cache_config=cache_config,
            prefix=f"{prefix}.projector",
        )

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors)

    def _parse_and_validate_audio_input(
        self,
        **kwargs: object,
    ) -> Optional[GraniteSpeechAudioInputs]:
        input_features = kwargs.pop("input_features", None)
        input_features_mask = kwargs.pop("input_features_mask", None)
        audio_embed_sizes = kwargs.pop("audio_embed_sizes", None)
        if input_features is None:
            return None

        # If we have a batch of variable feature length audio clips, we need
        # to mask the features; usually we would get an input_features_mask
        # from the processor, but we handle rebuilding it here since
        # vLLM generally processes everything independently + batches.
        if input_features_mask is None:
            input_features_mask = self._build_input_features_mask(
                audio_embed_sizes)

        if not isinstance(input_features, (torch.Tensor, list)):
            raise ValueError("Incorrect type of audio input features. "
                             f"Got type: {type(input_features)}")

        if input_features_mask is not None and not isinstance(
                input_features_mask, torch.Tensor):
            raise ValueError("Incorrect type of audio input features mask. "
                             f"Got type: {type(input_features_mask)}")

        if isinstance(input_features, torch.Tensor):
            # Granite speech currently only allows one audio token per instance
            # and features are already unsqueezed in the processor, so one
            # instance will have shape [1, {num_features}, 160]. As such,
            # input features will usually be of shape
            # [bsz, 1, num_features, 160], which we squeeze to be 3D here.
            if len(input_features.shape) == 4:
                input_features = input_features.squeeze(1)
            if len(input_features.shape) != 3:
                raise ValueError(
                    "Squeezed input features should be 3D but are of shape "
                    f"{input_features.shape}")
            input_features = input_features.to(
                self.encoder.input_linear.weight.dtype)

        else:
            # Otherwise we have a list of tensors, which are almost certainly
            # differing in their respective numbers of audio features;
            # stack them into a 3D tensor of size [bsz, most_num_features, 160].
            input_features = self._pad_and_stack_input_features(
                input_features, ).to(self.encoder.input_linear.weight.dtype)

        return GraniteSpeechAudioInputs(
            input_features=input_features,
            input_features_mask=input_features_mask,
            audio_embed_sizes=audio_embed_sizes.flatten().tolist(),
        )

    def _build_input_features_mask(
        self,
        audio_embed_sizes: torch.Tensor,
    ) -> torch.Tensor:
        """Calculate the input features mask, which will generally be used
        to mask the padded features for all entries in the batch except
        for those with the most audio features.

        Args:
            audio_embed_sizes: torch.Tensor
                Tensor of num features in each seq in the batch.
        Returns:
            torch.Tensor: Mask of shape (bsz, num_features) to be applied to
            the audio features prior to splitting the audio embeddings.
        """
        most_audio_features = torch.max(audio_embed_sizes).item()
        mask_indices = torch.arange(
            most_audio_features,
            device=audio_embed_sizes.device,
        ).view(1, -1)
        input_features_mask = mask_indices < audio_embed_sizes.view(-1, 1)
        return input_features_mask

    def _pad_and_stack_input_features(
        self,
        input_features: list[torch.Tensor],
    ) -> torch.Tensor:
        """Given a list of input features of varying length, pad them to the
        same length and stack them into a torch.Tensor.

        NOTE: Usually, padding is done in the input processor/feature extractor
        and zero padded prior to the computation of the Mel features; the
        resulting values are only constant within a batch and generally nonzero
        (i.e., slightly negative nums); we should validate that this is okay
        since we don't use a feature attention mask, but the more important
        thing is that we apply the input_features_mask with variable len
        batches.

        Args:
            input_features: list[torch.Tensor]
                Input features to be coerced into a tensor.
        Returns:
            torch.Tensor: Tensor of shape [bsz, num_features, 160], where
            num_features is the max number of features of any entry in the
            batch.
        """
        # Input features are of shape [bsz, num_features, 160]
        feat_lens = [feats.shape[1] for feats in input_features]
        padding = [max(feat_lens) - length for length in feat_lens]
        # TODO (Alex) - Validate that it's okay to zero pad like this;
        # in transformers we zero pad prior to calculating the speech features,
        # so the value is not zero and is dependent on the batched features.
        padded = [
            torch.nn.functional.pad(feats, (0, 0, 0, pad, 0, 0))
            for feats, pad in zip(input_features, padding)
        ]
        stacked_features = torch.cat(padded, dim=0).to(input_features[0])
        return stacked_features

    def _process_audio_input(
        self,
        audio_input: GraniteSpeechAudioInputs,
    ) -> tuple[torch.Tensor]:
        """Compute the audio features to be merged into the LLM embeddings.
        
        Args:
            audio_input: GraniteSpeechAudioInputs
                Audio inputs object containing Mel features, an input features
                mask, and the (flattened) number of audio tokens per instance.
        Returns:
            tuple[torch.Tensor]: List of length bsz.
        """
        # TODO (Alex) - support embedding inputs
        encoder_embeds = self.encoder(audio_input["input_features"])
        # [bsz, <max feature size>, 4096]
        projected_embeds = self.projector(encoder_embeds)
        # Apply mask on variable length audio features
        masked_embeds = projected_embeds[audio_input["input_features_mask"]]
        # Split variable length features into a tuple
        return torch.split(masked_embeds, audio_input["audio_embed_sizes"])

    def get_multimodal_embeddings(
        self,
        **kwargs: object,
    ) -> MultiModalEmbeddings:
        """Compute the audio embeddings if audio inputs are present."""
        audio_input = self._parse_and_validate_audio_input(**kwargs)
        if audio_input is None:
            return []
            return None
        audio_features = self._process_audio_input(audio_input)
        return audio_features

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
    ) -> torch.Tensor:
        """Compute the merged LLM / audio embeddings."""
        if multimodal_embeddings is None \
            or len(multimodal_embeddings) == 0:
            return self.language_model.get_input_embeddings(input_ids)

        inputs_embeds = embed_multimodal(
            input_ids,
            self.config.audio_token_index,
            self.language_model.model.get_input_embeddings,
            multimodal_embeddings,
        )
        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]:
        if intermediate_tensors is not None:
            inputs_embeds = None

        # NOTE: In v1, inputs_embeds is always generated at model runner, this
        # condition is for v0 compatibility.
        elif inputs_embeds is None:
            audio_embeds = self.get_multimodal_embeddings(**kwargs)
            inputs_embeds = self.get_input_embeddings(input_ids, audio_embeds)
            input_ids = None

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

    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)

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