modeling_youtu_vl.py

# coding=utf-8
# Copyright 2026 Tencent Youtu lab, DeepSeek-AI 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.
import math
import os
from functools import partial
from typing import Callable, Optional, Tuple, Union

import torch
import torch.nn.functional as F
from torch import nn
import numpy as np
import pydensecrf.densecrf as dcrf
from pydensecrf.utils import unary_from_softmax
from PIL import Image
import requests
from io import BytesIO
import base64
import cv2

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache, StaticCache
from transformers.generation import GenerationMixin
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from transformers.processing_utils import Unpack
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    can_return_tuple,
    is_torch_flex_attn_available,
    logging,
    replace_return_docstrings,
    is_flash_attn_2_available,
)
from transformers.utils.deprecation import deprecate_kwarg
from .configuration_youtu_vl import YoutuVLConfig

from .modeling_siglip2 import Siglip2VisionModel, Siglip2VisionEmbeddings
from .configuration_siglip2 import Siglip2VisionConfig



if is_torch_flex_attn_available():
    from torch.nn.attention.flex_attention import BlockMask
    from transformers.integrations.flex_attention import make_flex_block_causal_mask

is_aiter_available = False

if is_flash_attn_2_available():
    try:
        from aiter import flash_attn_varlen_func
        is_aiter_available = True
    except ImportError:
        from flash_attn import flash_attn_varlen_func
else:
    flash_attn_varlen_func = None
    
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "YoutuVLConfig"

class YoutuRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype

        hidden_states = hidden_states.to(torch.float32)

        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)

    def extra_repr(self):
        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"


class YoutuRotaryEmbedding(nn.Module):
    def __init__(self, config: YoutuVLConfig, device=None):
        super().__init__()
        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
        else:
            self.rope_type = "default"
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config
        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]

        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.original_inv_freq = self.inv_freq

    @torch.no_grad()
    @dynamic_rope_update  
    def forward(self, x, position_ids):
        """
        Compute rotary positional embeddings.
        Args:
            x (torch.Tensor): Input tensor, shape (batch_size, seq_len, feature_dim)
            position_ids (torch.LongTensor): Position indices, shape (batch_size, seq_len)

        """
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
        position_ids_expanded = position_ids[:, None, :].float()

        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos() * self.attention_scaling
            sin = emb.sin() * self.attention_scaling

        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


class YoutuMLP(nn.Module):
    def __init__(self, config, hidden_size=None, intermediate_size=None):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
        self.intermediate_size = config.intermediate_size if intermediate_size is None else intermediate_size

        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
        return down_proj


def rotate_half(x):
    """
    Rotates half the hidden dims of the input.
    """
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        position_ids (`torch.Tensor`, *optional*):
            Deprecated and unused.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    key_states = repeat_kv(key, module.num_key_value_groups)
    value_states = repeat_kv(value, module.num_key_value_groups)

    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
    if attention_mask is not None:
        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        attn_weights = attn_weights + causal_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
    attn_output = torch.matmul(attn_weights, value_states)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


def apply_rotary_pos_emb_interleave(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    r"""
    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        position_ids (`torch.Tensor`):
            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
            used to pass offsetted position ids when working with a KV-cache.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)

    b, h, s, d = q.shape
    q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)

    b, h, s, d = k.shape
    k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)

    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


def yarn_get_mscale(scale=1, mscale=1):
    if scale <= 1:
        return 1.0
    return 0.1 * mscale * math.log(scale) + 1.0


class YoutuMLAttention(nn.Module):
    """Multi-latent attention from 'DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model' paper"""
    
    def __init__(self, config: YoutuVLConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.num_key_value_groups = 1 # needed for eager attentions
        self.attention_dropout = config.attention_dropout
        self.num_heads = config.num_attention_heads
        self.rope_theta = config.rope_theta
        self.q_lora_rank = config.q_lora_rank
        self.qk_rope_head_dim = config.qk_rope_head_dim
        self.kv_lora_rank = config.kv_lora_rank
        self.v_head_dim = config.v_head_dim
        self.qk_nope_head_dim = config.qk_nope_head_dim
        self.qk_head_dim = config.qk_head_dim
        self.flash_att_sliding_window = config.flash_att_sliding_window
        self.is_causal = True


        if self.q_lora_rank is None:
            self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.qk_head_dim, bias=False)
        else:
            self.q_a_proj = nn.Linear(config.hidden_size, config.q_lora_rank, bias=config.attention_bias)
            self.q_a_layernorm = YoutuRMSNorm(config.q_lora_rank)
            self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False)

        self.kv_a_proj_with_mqa = nn.Linear(
            config.hidden_size,
            self.kv_lora_rank + self.qk_rope_head_dim,
            bias=config.attention_bias,
        )
        self.kv_a_layernorm = YoutuRMSNorm(self.kv_lora_rank)
        self.kv_b_proj = nn.Linear(
            self.kv_lora_rank,
            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
            bias=False,
        )

        self.o_proj = nn.Linear(
            self.num_heads * self.v_head_dim,
            config.hidden_size,
            bias=config.attention_bias,
        )

        self.scaling = self.qk_head_dim ** (-0.5)
        if self.config.rope_scaling is not None:
            mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
            scaling_factor = self.config.rope_scaling["factor"]
            if mscale_all_dim:
                mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
                self.scaling = self.scaling * mscale * mscale

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor],
        instance_length: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        batch_size, seq_length = hidden_states.shape[:-1]
        query_shape = (batch_size, seq_length, -1, self.qk_head_dim)
        key_shape = (batch_size, seq_length, -1, self.qk_nope_head_dim + self.v_head_dim)

        if self.q_lora_rank is None:
            q_states = self.q_proj(hidden_states).view(query_shape).transpose(1, 2)
        else:
            q_states = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states))).view(query_shape).transpose(1, 2)
        q_pass, q_rot = torch.split(q_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)

        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
        k_pass, k_rot = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)

        k_pass = self.kv_b_proj(self.kv_a_layernorm(k_pass)).view(key_shape).transpose(1, 2)
        k_pass, value_states = torch.split(k_pass, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)

        k_rot = k_rot.view(batch_size, 1, seq_length, self.qk_rope_head_dim)

        cos, sin = position_embeddings
        if self.config.rope_interleave:  # support using interleaved weights for efficiency
            q_rot, k_rot = apply_rotary_pos_emb_interleave(q_rot, k_rot, cos, sin)
        else:
            q_rot, k_rot = apply_rotary_pos_emb(q_rot, k_rot, cos, sin)
        k_rot = k_rot.expand(*k_pass.shape[:-1], -1)

        query_states = torch.cat((q_pass, q_rot), dim=-1)
        key_states = torch.cat((k_pass, k_rot), dim=-1)

        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        if self.config._attn_implementation == "flash_attention_2" and self.qk_head_dim != self.v_head_dim:
            value_states = F.pad(value_states, [0, self.qk_head_dim - self.v_head_dim])

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
                logger.warning_once(
                    "`torch.nn.functional.scaled_dot_product_attention` does not support"
                    "`output_attentions=True`. Falling back to 'eager attention. This warning"
                    'can be removed using the argument `attn_implementation="eager"` when loading the model.'
                )
            else:
                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

        if instance_length is None or flash_attn_varlen_func is None:
            attn_output, attn_weights = attention_interface(
                self,
                query_states,
                key_states,
                value_states,
                attention_mask,
                dropout=0.0 if not self.training else self.attention_dropout,
                scaling=self.scaling,
                **kwargs,
            )
            if self.config._attn_implementation == "flash_attention_2" and self.qk_head_dim != self.v_head_dim:
                attn_output = attn_output[:, :, :, : self.v_head_dim]
        else:
            instance_length = instance_length.view(-1)
            query_states = query_states.squeeze(0).transpose(0,1)
            key_states = key_states.squeeze(0).transpose(0,1)
            value_states = value_states.squeeze(0).transpose(0,1)
            max_seqlen_in_batch = instance_length.max().item()
            cu_seqlens = F.pad(torch.cumsum(instance_length, dim=0, dtype=torch.int32), (1, 0))
            if is_aiter_available:
                attn_output = flash_attn_varlen_func(query_states, key_states, value_states, cu_seqlens,
                    cu_seqlens, max_seqlen_in_batch, max_seqlen_in_batch,
                    dropout_p=0.0 if not self.training else self.attention_dropout, 
                    softmax_scale=self.scaling, 
                    causal=self.is_causal, return_lse=True)[0]
            else:
                attn_output = flash_attn_varlen_func(query_states, key_states, value_states, cu_seqlens,
                    cu_seqlens, max_seqlen_in_batch, max_seqlen_in_batch,
                    dropout_p=0.0 if not self.training else self.attention_dropout, 
                    softmax_scale=self.scaling, 
                    causal=self.is_causal)

            attn_output = attn_output.unsqueeze(0)
            attn_output = attn_output[:, :, :, : self.v_head_dim]
            attn_weights = None

        attn_output = attn_output.reshape(batch_size, seq_length, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights


class YoutuDecoderLayer(nn.Module):
    def __init__(self, config: YoutuVLConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = YoutuMLAttention(config=config, layer_idx=layer_idx)
        self.mlp = YoutuMLP(config)

        self.input_layernorm = YoutuRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = YoutuRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: Optional[bool] = False,
        instance_length: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            instance_length=instance_length,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)
        if output_attentions:
            outputs += (self_attn_weights,)

        return outputs


YOUTU_VL_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`YoutuVLConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""


@add_start_docstrings(
    "The bare Youtu Model outputting raw hidden-states without any specific head on top.",
    YOUTU_VL_START_DOCSTRING,
)
class YoutuPreTrainedModel(PreTrainedModel):
    config_class = YoutuVLConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["YoutuDecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_flex_attn = True
    _supports_cache_class = True
    _supports_quantized_cache = True
    _supports_static_cache = True
    _supports_attention_backend = True

    def init_weights(self):
        if self.config.pruned_heads:
            self.prune_heads(self.config.pruned_heads)

        if "-init" in self.name_or_path:
            self.apply(self._initialize_weights)

            for name, module in self.named_modules():
                if "o_proj" in name or "down_proj" in name:
                    scaled_std = self.config.initializer_range * (1.0 / self.config.num_hidden_layers) ** 0.5
                    module.weight.data.normal_(mean=0.0, std=scaled_std)

            self.tie_weights()

    def _init_weights(self, module):
        std = self.config.initializer_range
        embedding_std = self.config.embedding_initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=embedding_std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.Parameter):
            module.weight.data.normal_(mean=0.0, std=std)
        elif isinstance(module, YoutuRMSNorm):
            module.weight.data.fill_(1.0)


YOUTU_VL_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
            `past_key_values`).

            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`.

            [What are position IDs?](../glossary#position-ids)
        past_key_values (`Cache`, *optional*):
            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

            It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
            of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
            the complete sequence length.
"""


@add_start_docstrings(
    "The bare Youtu Model outputting raw hidden-states without any specific head on top.",
    YOUTU_VL_START_DOCSTRING,
)
class YoutuModel(YoutuPreTrainedModel):
    _keys_to_ignore_on_load_unexpected = [r"model\.layers\.61.*"]

    def __init__(self, config: YoutuVLConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList(
            [YoutuDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = YoutuRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = YoutuRotaryEmbedding(config=config)
        self.gradient_checkpointing = False

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    @can_return_tuple
    @add_start_docstrings_to_model_forward(YOUTU_VL_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        instance_length: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
    ) -> BaseModelOutputWithPast:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)
            
        if use_cache and past_key_values is None:
            past_key_values = DynamicCache()

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(
                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
            )

        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        causal_mask = self._update_causal_mask(
            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
        )

        hidden_states = inputs_embeds
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None

        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)
            layer_outputs = decoder_layer(
                hidden_states,
                attention_mask=causal_mask,
                position_ids=position_ids,
                past_key_value=past_key_values,
                output_attentions=output_attentions,
                instance_length=instance_length,
                use_cache=use_cache,
                cache_position=cache_position,
                position_embeddings=position_embeddings,
                **flash_attn_kwargs,
            )
            hidden_states = layer_outputs[0]
            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values if use_cache else None,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )

    def _update_causal_mask(
        self,
        attention_mask: torch.Tensor,
        input_tensor: torch.Tensor,
        cache_position: torch.Tensor,
        past_key_values: Cache,
        output_attentions: bool = False,
    ):
        if self.config._attn_implementation == "flash_attention_2":
            if attention_mask is not None and (attention_mask == 0.0).any():
                return attention_mask
            return None

        if self.config._attn_implementation == "flex_attention":
            if isinstance(attention_mask, torch.Tensor):
                attention_mask = make_flex_block_causal_mask(attention_mask)
            if isinstance(attention_mask, BlockMask):
                return attention_mask

        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        using_static_cache = isinstance(past_key_values, StaticCache)

        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
            if AttentionMaskConverter._ignore_causal_mask_sdpa(
                attention_mask,
                inputs_embeds=input_tensor,
                past_key_values_length=past_seen_tokens,
                is_training=self.training,
            ):
                return None

        dtype, device = input_tensor.dtype, input_tensor.device
        sequence_length = input_tensor.shape[1]
        if using_static_cache:
            target_length = past_key_values.get_max_cache_shape()
        else:
            target_length = (
                attention_mask.shape[-1]
                if isinstance(attention_mask, torch.Tensor)
                else past_seen_tokens + sequence_length + 1
            )

        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
            attention_mask,
            sequence_length=sequence_length,
            target_length=target_length,
            dtype=dtype,
            device=device,
            cache_position=cache_position,
            batch_size=input_tensor.shape[0],
        )

        if (
            self.config._attn_implementation == "sdpa"
            and attention_mask is not None
            and attention_mask.device.type in ["cuda", "xpu"]
            and not output_attentions
        ):
            min_dtype = torch.finfo(dtype).min
            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)

        return causal_mask

    @staticmethod
    def _prepare_4d_causal_attention_mask_with_cache_position(
        attention_mask: torch.Tensor,
        sequence_length: int,
        target_length: int,
        dtype: torch.dtype,
        device: torch.device,
        cache_position: torch.Tensor,
        batch_size: int,
        **kwargs,
    ):
        """
        Args:
            attention_mask (`torch.Tensor`):
                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
                `(batch_size, 1, query_length, key_value_length)`.
            sequence_length (`int`):
                The sequence length being processed.
            target_length (`int`):
                The target length: when generating with static cache, the mask should be as long as the static cache,
                to account for the 0 padding, the part of the cache that is not filled yet.
            dtype (`torch.dtype`):
                The dtype to use for the 4D attention mask.
            device (`torch.device`):
                The device to place the 4D attention mask on.
            cache_position (`torch.Tensor`):
                Indices depicting the position of the input sequence tokens in the sequence.
            batch_size (`torch.Tensor`):
                Batch size.
        """
        if attention_mask is not None and attention_mask.dim() == 4:
            causal_mask = attention_mask
        else:
            min_dtype = torch.finfo(dtype).min
            causal_mask = torch.full(
                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
            )
            if sequence_length != 1:
                causal_mask = torch.triu(causal_mask, diagonal=1)
            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
            if attention_mask is not None:
                causal_mask = causal_mask.clone()  
                mask_length = attention_mask.shape[-1]
                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
                    causal_mask.device
                )
                padding_mask = padding_mask == 0
                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
                    padding_mask, min_dtype
                )

        return causal_mask


class KwargsForCausalLM(FlashAttentionKwargs): ...


class YoutuForCausalLM(YoutuPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    _tp_plan = {"lm_head": "colwise_rep"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}

    def __init__(self, config):
        super().__init__(config)

        self.model = YoutuModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    @can_return_tuple
    @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
    @add_start_docstrings_to_model_forward(YOUTU_VL_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        **kwargs: Unpack[KwargsForCausalLM],
    ) -> CausalLMOutputWithPast:
        r"""
        Returns:

        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

        outputs: BaseModelOutputWithPast = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            cache_position=cache_position,
            **kwargs,
        )

        hidden_states = outputs.last_hidden_state
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.lm_head(hidden_states[:, slice_indices, :])
        
        loss = None

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

class VLPatchMerger(nn.Module):
    def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None:
        super().__init__()
        self.hidden_size = context_dim * (spatial_merge_size**2)
        self.ln_q = YoutuRMSNorm(context_dim, eps=1e-06) 
        self.mlp = nn.Sequential(
            nn.Linear(self.hidden_size, self.hidden_size),
            nn.GELU(),
            nn.Linear(self.hidden_size, dim),
        )
        
    def forward(self, x: torch.Tensor, spatial_shapes: torch.Tensor) -> torch.Tensor:
        x = self.ln_q(x).view(-1, self.hidden_size)
        x = self.mlp(x)
        return x


class YoutuDensePrediction(object):
    def __init__(self, custom_tokens):
        self.custom_tokens = custom_tokens
        self.custom_ids = list(range(self.custom_tokens["<custom_1>"][0], self.custom_tokens["<custom_1>"][0] + 1000))
    
    def dense_crf(self, probs, img, iters=10, kernel='both'):
        C, H, W = probs.shape
        img = np.array(img)
        d = dcrf.DenseCRF2D(W, H, C)
        U = unary_from_softmax(probs)
        d.setUnaryEnergy(U)
        d.addPairwiseGaussian(sxy=(3, 3), compat=3, kernel=dcrf.DIAG_KERNEL, normalization=dcrf.NORMALIZE_SYMMETRIC)
        if kernel in ['bilateral', 'both']:
            d.addPairwiseBilateral(sxy=(80, 80), srgb=(13, 13, 13), rgbim=img, compat=10,
                                kernel=dcrf.DIAG_KERNEL, normalization=dcrf.NORMALIZE_SYMMETRIC)
        Q = d.inference(iters)
        pred = np.argmax(Q, 0)
        return pred
    
    def contains_subsequence(self, seq, sub):
        if not seq or not sub:
            return False
        if isinstance(sub[0], int):
            subs = [sub]
        else:
            subs = sub
        n = len(seq)
        for s in subs:
            m = len(s)
            if m == 0 or m > n:
                continue
            for i in range(n - m + 1):
                if seq[i: i + m] == s:
                    return True
        return False


    def extract_ref_spans(self, token_list):
        spans = []
        i = 0
        while i < len(token_list):
            if token_list[i] != self.custom_tokens["<ref>"][0]:
                i += 1
                continue
            j = i + 1
            while j < len(token_list) and token_list[j] != self.custom_tokens["</ref>"][0]:
                j += 1
            if j < len(token_list):
                spans.append(token_list[i + 1 : j])
                i = j + 1
            else:
                break
        return spans
    
    def dense_decoding(self, inp_ids, output, inp_shape=None, dense_logits=None, raw_img=None, use_crf=False):
        img_token_id = self.custom_tokens["<|image_pad|>"][0]
        img_token_mask = inp_ids[0] == img_token_id

        logits = dense_logits[0]
        img_logits = logits[img_token_mask]
        target_logits = []
        w, h = inp_shape
        raw_w, raw_h = raw_img.size

        if self.contains_subsequence(output, self.custom_tokens["<depth>"]):
            target_logits = img_logits[:, self.custom_ids]
            pred = target_logits.reshape(1, h, w, -1).permute(0, 3, 1, 2)
            pred = F.interpolate(pred, size=(h*2, w*2), mode='bilinear', align_corners=False)
            pred = pred[0].argmax(0).cpu().numpy().astype('uint16')
            pred = pred.reshape(-1)
        else:
            labels = self.extract_ref_spans(output)
            for tokens in labels:
                if tokens:
                    target_logits.append(img_logits[:, tokens].mean(-1))
            if target_logits != []:
                pred = torch.stack(target_logits, 0)
                if inp_shape != None:
                    if self.custom_tokens["<OTHERS>"] in labels:
                        pred = torch.sigmoid(pred)
                        others_idx = labels.index(self.custom_tokens["<OTHERS>"])
                        pred[others_idx] = 0.5
                    else:
                        pred = pred / 0.2
                        pred = (torch.exp(pred) / torch.sum(torch.exp(pred), dim=0, keepdims=True))

                    pred_reshape = pred.reshape((-1, h, w))
                    pred_resize = F.interpolate(pred_reshape.unsqueeze(0), size=(raw_h, raw_w), mode='bilinear', align_corners=False)
                    pred_resize = pred_resize.float().cpu().numpy()
                    if use_crf:
                        pred = self.dense_crf(pred_resize[0], raw_img)
                    else:
                        pred = pred_resize[0].argmax(0).reshape(-1)
                else:
                    pred = pred.argmax(0)
        
        def encode_int_as_digit_tokens(x: int):
            s = str(int(x))
            return [self.custom_tokens["digit_start"][0] + (ord(ch) - ord("0")) for ch in s]
        
        def encode_int_as_digit_tokens(x: int):
            s = str(int(x))
            return [self.custom_tokens["digit_start"][0] + (ord(ch) - ord("0")) for ch in s]

        def rle_value_run(arr):
            if isinstance(arr, torch.Tensor):
                arr = arr.detach().cpu().numpy()
            runs = []
            n = len(arr)
            if n == 0:
                return runs
            prev = int(arr[0])
            cnt = 1
            for i in range(1, n):
                v = int(arr[i])
                if v == prev:
                    cnt += 1
                else:
                    runs.append((prev, cnt))
                    prev = v
                    cnt = 1
            runs.append((prev, cnt))
            return runs

        def build_mask_rle_token_ids_from_runs(runs):
            body = []
            m = len(runs)
            for i, (v, c) in enumerate(runs):
                body.append(self.custom_tokens["<mask_rle>"][0])
                body.extend(encode_int_as_digit_tokens(v))
                body.append(self.custom_tokens["comma"][0])
                body.extend(encode_int_as_digit_tokens(c))
                body.append(self.custom_tokens["</mask_rle>"][0])
                if i != m - 1:
                    body.append(self.custom_tokens["comma"][0])
            return self.custom_tokens["<mask>"] + body + self.custom_tokens["</mask>"]

        runs = rle_value_run(pred if isinstance(pred, torch.Tensor) else torch.as_tensor(pred))
        mask_token_ids = build_mask_rle_token_ids_from_runs(runs)
        return  mask_token_ids

    def convert_coord_ids(self, ids, scale_x, scale_y, max_coord=2047):
        x0_id = self.custom_tokens["<x_0>"][0]
        y_max_id = self.custom_tokens[f"<y_2047>"][0]
        out = []
        for tid in ids:
            if x0_id <= tid <= y_max_id:
                offset = tid - x0_id
                is_y = (offset & 1) == 1
                i = offset >> 1
                if 0 <= i <= max_coord:
                    if not is_y:
                        new_i = int(round(i * scale_x))
                        new_i = 0 if new_i < 0 else (max_coord if new_i > max_coord else new_i)
                        new_tid = x0_id + (new_i << 1)
                    else:
                        new_i = int(round(i * scale_y))
                        new_i = 0 if new_i < 0 else (max_coord if new_i > max_coord else new_i)
                        new_tid = x0_id + (new_i << 1) + 1
                    out.append(new_tid)
                    continue
            out.append(tid)
        return out

    def _is_url(self, s):
        return s.startswith("http://") or s.startswith("https://")

    def load_image(self, img_input):
        if img_input is None:
            raise ValueError("img_input is None")
        if not isinstance(img_input, str):
            raise TypeError(
                f"Unsupported img_input type (only str supported): {type(img_input)}"
            )
        s = img_input.strip()
        if not s:
            raise ValueError("img_input is empty string")
        if self._is_url(s):
            resp = requests.get(s)
            resp.raise_for_status()
            img = Image.open(BytesIO(resp.content))
            return img.convert("RGB")
        if os.path.isfile(s):
            with open(s, "rb") as f:
                img = Image.open(f)
                return img.convert("RGB")
        try:
            b64 = "".join(s.split())
            img_bytes = base64.b64decode(b64, validate=True)
        except Exception as e:
            raise ValueError(
                "img_input is not a valid URL, file path, or pure base64 string"
            ) from e
        try:
            img = Image.open(BytesIO(img_bytes))
            return img.convert("RGB")
        except Exception as e:
            raise ValueError(
                "Base64 decoded successfully, but content is not a valid image"
            ) from e

    def __call__(self, input_ids, spatial_shapes, dense_logits, output, img_input, use_crf):
        output_ids = output[0, input_ids.shape[1]:].tolist()
        if any(self.custom_tokens["<x_0>"][0] <= tid <= self.custom_tokens["<y_2047>"][0] for tid in output_ids):
            img = self.load_image(img_input)
            raw_w, raw_h = img.size
            inp_w, inp_h = spatial_shapes[0][1].item() * 16, spatial_shapes[0][0].item() * 16
            scale_w, scale_h = float(raw_w) / inp_w, float(raw_h) / inp_h
            coord_ids = self.convert_coord_ids(output_ids, scale_w, scale_h)
            coord_tensor = torch.tensor(coord_ids, dtype=output.dtype, device=output.device).unsqueeze(0)
            output = torch.cat([output[:, :input_ids.shape[1]], coord_tensor], dim=1)
        elif ((self.custom_tokens["<ref>"][0] in output_ids and self.custom_tokens["<ins>"][0] not in output_ids) or self.contains_subsequence(output_ids, self.custom_tokens["<depth>"])):
            img = self.load_image(img_input)
            inp_w, inp_h = spatial_shapes[0][1].item() // 2, spatial_shapes[0][0].item() // 2
            mask_ids = self.dense_decoding(input_ids, output_ids, (inp_w, inp_h), dense_logits, img, use_crf)
            mask_tensor = torch.tensor(mask_ids, dtype=output.dtype, device=output.device).unsqueeze(0)
            output = torch.cat([output, mask_tensor], dim=1)
        return output

class YoutuVLForConditionalGeneration(YoutuPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    _tp_plan = {"lm_head": "colwise_rep"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}

    def __init__(self, config):
        super().__init__(config)

        config.vision_config.out_hidden_size = config.hidden_size
        config.vision_config.vision_use_head = False
        self.siglip2 = Siglip2VisionModel._from_config(config.vision_config)
        self.merger = VLPatchMerger(
            dim=config.hidden_size,
            context_dim=config.vision_config.hidden_size,
            spatial_merge_size=2,
        )
        self.rope_deltas = None

        self.model = YoutuModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.dense_logits = None
        self.dense_prediction = YoutuDensePrediction(config.custom_tokens)
        self.post_init()


    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model
    

    def generate(self, *args, img_input=None, use_crf=False, **kwargs):
        kwargs.pop("img_input", None)
        kwargs.pop("use_crf", None)
        output = super().generate(*args, **kwargs)
        if img_input == None:
            return output
        if isinstance(output, torch.Tensor):
            sequences = output
            generate_output = None
        else:
            sequences = output.sequences
            generate_output = output

        input_ids = kwargs.get("input_ids", None)
        spatial_shapes = kwargs.get("spatial_shapes", None)
        sequences_with_mask = self.dense_prediction(
            input_ids,
            spatial_shapes,
            self.dense_logits,
            sequences,
            img_input,
            use_crf
        )
        if generate_output is None:
            return sequences_with_mask
        else:
            generate_output.sequences = sequences_with_mask
            return generate_output


    @can_return_tuple
    @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
    @add_start_docstrings_to_model_forward(YOUTU_VL_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        pixel_values: Optional[torch.Tensor] = None,
        pixel_attention_mask: Optional[torch.LongTensor] = None,
        spatial_shapes: Optional[torch.LongTensor] = None,
        instance_length: Optional[torch.LongTensor] = None,
        coefficients: Optional[torch.FloatTensor] = None,
        rope_deltas: Optional[torch.LongTensor] = None,
        cache_position: Optional[torch.LongTensor] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        **kwargs: Unpack[KwargsForCausalLM],
    ) -> CausalLMOutputWithPast:
        r"""
        Example:
            TODO: Add example

        Returns:
        """

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

        if inputs_embeds is None:
            inputs_embeds = self.model.embed_tokens(input_ids)
            
            if pixel_values is not None:
                bs, length, dim_size = inputs_embeds.shape
                pixel_values = pixel_values.type(self.siglip2.dtype)
            
                image_embeds = self.siglip2(pixel_values, pixel_attention_mask, spatial_shapes).last_hidden_state
                image_embeds = self.merger(image_embeds, spatial_shapes)

                n_image_tokens = (input_ids == self.config.image_token_id).sum().item()
                n_image_features = image_embeds.shape[0]

                if n_image_tokens > n_image_features:
                    raise ValueError(
                        "Image features and image tokens do not match: tokens: {}, features {}".format(
                            n_image_tokens, n_image_features
                        )
                    )

                mask = input_ids == self.config.image_token_id
                mask_unsqueezed = mask.unsqueeze(-1)
                mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
                image_mask = mask_expanded.to(inputs_embeds.device)
                image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)

                if bs != 1:
                    raise ValueError("Only support batch size = 1")

                image_embeds = image_embeds.unsqueeze(0)
                inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
                                 
            if attention_mask is not None:
                attention_mask = attention_mask.to(inputs_embeds.device)

        outputs: BaseModelOutputWithPast = self.model(
            input_ids=None,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            cache_position=cache_position,
            instance_length=instance_length,
            **kwargs,
        )

        hidden_states = outputs.last_hidden_state
        logits = self.lm_head(hidden_states)
        if logits.shape[1] != 1:
            self.dense_logits = logits
        loss = None

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

__all__ = ["YoutuPreTrainedModel", "YoutuModel", "YoutuVLForConditionalGeneration"]