# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/19e6e80e10118f855137b90740936c0b11ac397f/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
# Copyright 2024 The Qwen team.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only Qwen2-VL model compatible with HuggingFace weights."""
import logging
from functools import lru_cache, partial
from typing import Iterable, List, Optional, Tuple, Type

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from transformers import AutoModel, Qwen2VLConfig
from transformers.activations import ACT2FN
from transformers.models.qwen2.modeling_qwen2 import Qwen2RMSNorm

from sglang.srt.configs import Qwen2_5_VLConfig, Qwen2_5_VLVisionConfig
from sglang.srt.distributed import (
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
)
from sglang.srt.hf_transformers_utils import get_processor
from sglang.srt.layers.attention.vision import VisionAttention
from sglang.srt.layers.linear import ColumnParallelLinear, RowParallelLinear
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.pooler import Pooler, PoolingType
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
from sglang.srt.managers.schedule_batch import ImageInputs
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.model_loader.weight_utils import default_weight_loader
from sglang.srt.models.qwen2 import Qwen2Model
from sglang.srt.models.qwen2_vl import Qwen2VLImageInputs, Qwen2VLVideoInputs

logger = logging.getLogger(__name__)


class Qwen2_5_VLMLP(nn.Module):

    def __init__(
        self,
        in_features: int,
        hidden_features: int = None,
        bias: bool = True,
        hidden_act="silu",
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.gate_proj = ColumnParallelLinear(
            in_features, hidden_features, bias=bias, quant_config=quant_config
        )
        self.up_proj = ColumnParallelLinear(
            in_features, hidden_features, bias=bias, quant_config=quant_config
        )
        self.down_proj = RowParallelLinear(
            hidden_features, in_features, bias=bias, quant_config=quant_config
        )
        self.act = ACT2FN[hidden_act]

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x_parallel_gate, _ = self.gate_proj(x)
        x_parallel_gate = self.act(x_parallel_gate)
        x_parallel_up, _ = self.up_proj(x)
        x_parallel = x_parallel_gate * x_parallel_up
        x, _ = self.down_proj(x_parallel)
        return x


class Qwen2_5_VisionBlock(nn.Module):

    def __init__(
        self,
        dim: int,
        intermediate_dim: int,
        num_heads: int,
        hidden_act="silu",
        norm_layer: Type[nn.Module] = None,
        attn_implementation: Optional[str] = "sdpa",
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        if norm_layer is None:
            norm_layer = partial(nn.LayerNorm, eps=1e-6)
        self.norm1 = Qwen2RMSNorm(dim, eps=1e-6)
        self.norm2 = Qwen2RMSNorm(dim, eps=1e-6)
        if attn_implementation == "sdpa":
            use_context_forward = False
            use_full_precision_softmax = False
        elif attn_implementation == "flash_attention_2":
            use_full_precision_softmax = False
            use_context_forward = True
        elif attn_implementation == "eager":
            use_full_precision_softmax = True
            use_context_forward = False

        self.attn = VisionAttention(
            embed_dim=dim,
            num_heads=num_heads,
            projection_size=dim,
            use_qkv_parallel=False,
            use_context_forward=use_context_forward,
            use_full_precision_softmax=use_full_precision_softmax,
            flatten_batch=True,
            quant_config=quant_config,
        )
        self.mlp = Qwen2_5_VLMLP(
            dim, intermediate_dim, hidden_act=hidden_act, quant_config=quant_config
        )

    def forward(
        self, x: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor
    ) -> torch.Tensor:
        hidden_states = self.norm1(x)
        hidden_states = rearrange(hidden_states, "s b ... -> b s ...")
        attn = self.attn(
            hidden_states, cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb
        )
        attn = rearrange(attn, "b s ... -> s b ...")
        x = x + attn
        norm2 = self.norm2(x)
        mlp = self.mlp(norm2)
        x = x + mlp
        return x


class Qwen2_5_VisionPatchEmbed(nn.Module):

    def __init__(
        self,
        patch_size: int = 14,
        temporal_patch_size: int = 2,
        in_chans: int = 3,
        embed_dim: int = 1152,
    ) -> None:
        super().__init__()
        self.patch_size = patch_size
        self.temporal_patch_size = temporal_patch_size
        self.embed_dim = embed_dim

        kernel_size = [temporal_patch_size, patch_size, patch_size]
        self.proj = nn.Conv3d(
            in_chans, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        L, C = x.shape
        x = x.view(L, -1, self.temporal_patch_size, self.patch_size, self.patch_size)
        x = self.proj(x).view(L, self.embed_dim)
        return x


class Qwen2_5_VisionPatchMerger(nn.Module):

    def __init__(
        self,
        dim: int,
        context_dim: int,
        spatial_merge_size: int = 2,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.hidden_size = context_dim * (spatial_merge_size**2)
        self.ln_q = Qwen2RMSNorm(context_dim, eps=1e-6)
        self.mlp = nn.ModuleList(
            [
                ColumnParallelLinear(
                    self.hidden_size,
                    self.hidden_size,
                    bias=True,
                    quant_config=quant_config,
                ),
                nn.GELU(),
                RowParallelLinear(
                    self.hidden_size, dim, bias=True, quant_config=quant_config
                ),
            ]
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.ln_q(x)
        x = x.view(-1, self.hidden_size)

        mlp_fc1, mlp_act, mlp_fc2 = self.mlp
        x_parallel, _ = mlp_fc1(x)
        x_parallel = mlp_act(x_parallel)
        out, _ = mlp_fc2(x_parallel)
        return out


class Qwen2_5_VisionRotaryEmbedding(nn.Module):

    def __init__(self, dim: int, theta: float = 10000.0) -> None:
        super().__init__()
        self.dim = dim
        self.theta = theta
        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self._seq_len_cached = 0
        self._freqs_cached = None

    def update_freqs_cache(self, seqlen: int) -> None:
        if seqlen > self._seq_len_cached:
            seqlen *= 2
            self._seq_len_cached = seqlen
            self.inv_freq = 1.0 / (
                self.theta
                ** (
                    torch.arange(
                        0, self.dim, 2, dtype=torch.float, device=self.inv_freq.device
                    )
                    / self.dim
                )
            )
            seq = torch.arange(
                seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
            )
            freqs = torch.outer(seq, self.inv_freq)
            self._freqs_cached = freqs

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


class Qwen2_5_VisionTransformer(nn.Module):

    def __init__(
        self,
        vision_config: Qwen2_5_VLVisionConfig,
        norm_eps: float = 1e-6,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()

        patch_size: int = vision_config.patch_size
        temporal_patch_size: int = vision_config.temporal_patch_size
        spatial_merge_size: int = vision_config.spatial_merge_size
        self.spatial_merge_size = spatial_merge_size
        self.spatial_merge_unit: int = spatial_merge_size * spatial_merge_size
        in_chans: int = vision_config.in_chans
        hidden_size: int = vision_config.hidden_size
        depth: int = vision_config.depth
        num_heads: int = vision_config.num_heads
        self.fullatt_block_indexes = vision_config.fullatt_block_indexes
        self.window_size = vision_config.window_size
        self.patch_size = vision_config.patch_size
        mlp_hidden_size: int = vision_config.intermediate_size
        self.patch_embed = Qwen2_5_VisionPatchEmbed(
            patch_size=patch_size,
            temporal_patch_size=temporal_patch_size,
            in_chans=in_chans,
            embed_dim=hidden_size,
        )

        norm_layer = partial(nn.LayerNorm, eps=norm_eps)
        head_dim = hidden_size // num_heads
        self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
        self.blocks = nn.ModuleList(
            [
                Qwen2_5_VisionBlock(
                    dim=hidden_size,
                    intermediate_dim=mlp_hidden_size,
                    num_heads=num_heads,
                    hidden_act=vision_config.hidden_act,
                    norm_layer=norm_layer,
                    attn_implementation="sdpa",
                    quant_config=quant_config,
                )
                for _ in range(depth)
            ]
        )
        self.merger = Qwen2_5_VisionPatchMerger(
            dim=vision_config.out_hidden_size,
            context_dim=hidden_size,
            spatial_merge_size=spatial_merge_size,
            quant_config=quant_config,
        )

    def get_window_index(self, grid_thw):
        window_index: list = []
        cu_window_seqlens: list = [0]
        window_index_id = 0
        vit_merger_window_size = (
            self.window_size // self.spatial_merge_size // self.patch_size
        )

        for grid_t, grid_h, grid_w in grid_thw:
            llm_grid_h, llm_grid_w = (
                grid_h // self.spatial_merge_size,
                grid_w // self.spatial_merge_size,
            )
            index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(
                grid_t, llm_grid_h, llm_grid_w
            )
            pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size
            pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size
            num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size
            num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size
            index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100)
            index_padded = index_padded.reshape(
                grid_t,
                num_windows_h,
                vit_merger_window_size,
                num_windows_w,
                vit_merger_window_size,
            )
            index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape(
                grid_t,
                num_windows_h * num_windows_w,
                vit_merger_window_size,
                vit_merger_window_size,
            )
            seqlens = (index_padded != -100).sum([2, 3]).reshape(-1)
            index_padded = index_padded.reshape(-1)
            index_new = index_padded[index_padded != -100]
            window_index.append(index_new + window_index_id)
            cu_seqlens_tmp = (
                seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1]
            )
            cu_window_seqlens.extend(cu_seqlens_tmp.tolist())
            window_index_id += (grid_t * llm_grid_h * llm_grid_w).item()
        window_index = torch.cat(window_index, dim=0)

        return window_index, cu_window_seqlens

    @property
    def dtype(self) -> torch.dtype:
        return self.blocks[0].mlp.gate_proj.weight.dtype

    @property
    def device(self) -> torch.device:
        return self.blocks[0].mlp.gate_proj.weight.device

    def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
        pos_ids = []
        for t, h, w in grid_thw:
            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
            hpos_ids = (
                hpos_ids.reshape(
                    h // self.spatial_merge_size,
                    self.spatial_merge_size,
                    w // self.spatial_merge_size,
                    self.spatial_merge_size,
                )
                .permute(0, 2, 1, 3)
                .flatten()
            )
            wpos_ids = (
                wpos_ids.reshape(
                    h // self.spatial_merge_size,
                    self.spatial_merge_size,
                    w // self.spatial_merge_size,
                    self.spatial_merge_size,
                )
                .permute(0, 2, 1, 3)
                .flatten()
            )
            pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
        pos_ids = torch.cat(pos_ids, dim=0)
        max_grid_size = grid_thw[:, 1:].max()
        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
        return rotary_pos_emb

    def forward(
        self,
        x: torch.Tensor,
        grid_thw: torch.Tensor,
    ) -> torch.Tensor:
        # patchify
        x = x.to(device=self.device, dtype=self.dtype)
        x = self.patch_embed(x)

        # compute position embedding
        rotary_pos_emb = self.rot_pos_emb(grid_thw)

        window_index, cu_window_seqlens = self.get_window_index(grid_thw)
        cu_window_seqlens = torch.tensor(
            cu_window_seqlens,
            device=x.device,
            dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
        )
        cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens)

        seq_len, _ = x.size()

        x = x.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
        x = x[window_index, :, :]
        x = x.reshape(seq_len, -1)
        rotary_pos_emb = rotary_pos_emb.reshape(
            seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1
        )
        rotary_pos_emb = rotary_pos_emb[window_index, :, :]
        rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)

        # compute cu_seqlens
        cu_seqlens = torch.repeat_interleave(
            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
        ).cumsum(dim=0, dtype=torch.int32)
        cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)

        # transformers
        x = x.unsqueeze(1)
        for layer_num, blk in enumerate(self.blocks):
            if layer_num in self.fullatt_block_indexes:
                cu_seqlens_now = cu_seqlens
            else:
                cu_seqlens_now = cu_window_seqlens
            x = blk(x, cu_seqlens=cu_seqlens_now, rotary_pos_emb=rotary_pos_emb)

        # adapter
        x = self.merger(x)

        reverse_indices = torch.argsort(window_index)
        x = x[reverse_indices, :]

        return x


cached_get_processor = lru_cache(get_processor)


class Qwen2_5_VLForConditionalGeneration(nn.Module):
    def __init__(
        self,
        config: Qwen2VLConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()

        self.config = config
        self.visual = Qwen2_5_VisionTransformer(
            config.vision_config,
            norm_eps=getattr(config, "rms_norm_eps", 1e-6),
            # NOTE: Qwen2-VL vision encoder does not support any
            # quantization method now.
            quant_config=None,
        )

        self.model = Qwen2Model(config, quant_config)

        if config.tie_word_embeddings:
            self.lm_head = self.model.embed_tokens
        else:
            self.lm_head = ParallelLMHead(
                config.vocab_size, config.hidden_size, quant_config=quant_config
            )

        self.logits_processor = LogitsProcessor(config)
        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)

    def calculate_num_image_tokens(self, image_grid_thw: Tuple[int, int, int]):
        processor = cached_get_processor(self.config._name_or_path)
        grid_t, grid_h, grid_w = image_grid_thw
        num_image_tokens = (
            grid_t
            * grid_h
            * grid_w
            // processor.image_processor.merge_size
            // processor.image_processor.merge_size
        )
        return num_image_tokens

    def pad_input_ids(self, input_ids: List[int], image_inputs: ImageInputs):
        new_input_ids = []
        last_idx = 0
        image_idx = -1
        image_inputs.image_offsets = []

        # Get all special token IDs
        im_start_id = image_inputs.im_start_id
        im_end_id = image_inputs.im_end_id

        # Find all start and end positions for both types
        start_indices = [i for i, x in enumerate(input_ids) if x == im_start_id]
        end_indices = [i for i, x in enumerate(input_ids) if x == im_end_id]

        if len(start_indices) != len(end_indices):
            return input_ids
        # Process each region (both image and slice)
        for start_idx, end_idx in zip(start_indices, end_indices):
            # Add non-image tokens before this region
            new_input_ids.extend(input_ids[last_idx : start_idx + 1])

            is_image_start = input_ids[start_idx] == im_start_id

            if is_image_start:
                image_inputs.image_offsets += [start_idx]
                image_idx += 1

            num_tokens = end_idx - start_idx - 1  # exclude start and end tokens

            # Generate pad_ids
            pad_values = [image_inputs.pad_values[image_idx]]

            pad_ids = pad_values * ((num_tokens + len(pad_values)) // len(pad_values))
            pad_ids = pad_ids[:num_tokens]

            # Add pad_ids
            new_input_ids.extend(pad_ids)

            # Update last_idx to after end token
            last_idx = end_idx

        # Add remaining tokens after last region
        new_input_ids.extend(input_ids[last_idx:])
        assert len(input_ids) == len(new_input_ids)
        return new_input_ids

    def _process_image_input(self, image_input: Qwen2VLImageInputs) -> torch.Tensor:
        pixel_values = image_input["pixel_values"].type(self.visual.dtype)
        image_embeds = self.visual(pixel_values, grid_thw=image_input["image_grid_thw"])
        return image_embeds

    def _process_video_input(self, video_input: Qwen2VLVideoInputs) -> torch.Tensor:
        pixel_values_videos = video_input["pixel_values_videos"].type(self.visual.dtype)
        video_embeds = self.visual(
            pixel_values_videos, grid_thw=video_input["video_grid_thw"]
        )
        return video_embeds

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        get_embedding: bool = False,
    ):
        """Run forward pass for Qwen2_5-VL.

        Args:
            input_ids: Flattened (concatenated) input_ids corresponding to a
                batch.
            positions: Flattened (concatenated) position ids corresponding to a
                batch.
                **NOTE**: If mrope is enabled (default setting for Qwen2-VL
                opensource models), the shape will be `(3, seq_len)`,
                otherwise it will be `(seq_len,).
                (Use input_metadata.mrope_positions to replace it)
        """
        if getattr(self.config, "rope_scaling", {}).get("type", None) == "mrope":
            positions = forward_batch.mrope_positions

        image_inputs = None
        if forward_batch.image_inputs is not None:
            image_inputs = [
                img for img in forward_batch.image_inputs if img is not None
            ]

        if (
            forward_batch.forward_mode.is_decode()
            or image_inputs is None
            or len(image_inputs) == 0
        ):
            inputs_embeds = self.model.embed_tokens(input_ids)
        else:
            if getattr(self.config, "rope_scaling", {}).get("type", None) == "mrope":
                assert positions.ndim == 2 and positions.size(0) == 3, (
                    "multimodal section rotary embedding requires "
                    f"(3, seq_len) positions, but got {positions.size()}"
                )

            # Clamp input ids. This is because the input_ids for the image tokens are
            # filled with the hash values of the image for the prefix matching in the radix attention.
            # There values are useless because their embeddings will be replaced by vision embeddings anyway.
            input_ids.clamp_(min=0, max=self.config.vocab_size - 1)
            # [B, s, hidden_size]
            inputs_embeds = self.model.embed_tokens(input_ids)
            extend_start_loc_cpu = forward_batch.extend_start_loc.cpu().numpy()
            prefix_lens_cpu = forward_batch.extend_prefix_lens_cpu
            for i, image in enumerate(forward_batch.image_inputs):
                if image is None:
                    continue
                start_idx = extend_start_loc_cpu[i]
                prefix_len = prefix_lens_cpu[i]

                pixel_values = image.pixel_values.clone().detach().requires_grad_(False)
                image_grid_thws = torch.tensor(
                    np.array(image.image_grid_thws), device="cuda"
                )
                image_offsets = image.image_offsets
                image_input = Qwen2VLImageInputs(
                    pixel_values=pixel_values, image_grid_thw=image_grid_thws
                )
                image_embeds = self._process_image_input(image_input)

                image_embeds_offset = 0
                for idx, image_offset in enumerate(image_offsets):
                    if image_offset < prefix_len:
                        continue
                    num_image_tokens = self.calculate_num_image_tokens(
                        image_grid_thws[idx]
                    )

                    left_idx = start_idx + (image_offset - prefix_len)
                    right_idx = left_idx + num_image_tokens

                    tp_size = get_tensor_model_parallel_world_size()

                    hidden_size = image_embeds.shape[-1]

                    if hidden_size % tp_size != 0:
                        padding_size = tp_size - (hidden_size % tp_size)
                        image_embeds = F.pad(image_embeds, (0, padding_size))
                        inputs_embeds = F.pad(inputs_embeds, (0, padding_size))

                    hidden_chunk_size = image_embeds.shape[-1] // tp_size
                    rank = get_tensor_model_parallel_rank()
                    start_dim = rank * hidden_chunk_size
                    end_dim = (rank + 1) * hidden_chunk_size
                    inputs_embeds[left_idx:right_idx, ..., start_dim:end_dim] = (
                        image_embeds[
                            image_embeds_offset : image_embeds_offset
                            + num_image_tokens,
                            ...,
                            start_dim:end_dim,
                        ]
                    )
                    image_embeds_offset += num_image_tokens

        input_ids = None
        hidden_states = self.model(
            input_ids=input_ids,
            positions=positions,
            forward_batch=forward_batch,
            input_embeds=inputs_embeds,
        )

        if not get_embedding:
            return self.logits_processor(
                input_ids, hidden_states, self.lm_head, forward_batch
            )
        else:
            return self.pooler(hidden_states, forward_batch)

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("gate_up_proj", "up_proj", 1),
            ("gate_up_proj", "gate_proj", 0),
        ]
        params_dict = dict(self.named_parameters(remove_duplicate=False))
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue

            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                if "visual" in name:
                    continue
                name = name.replace(weight_name, param_name)

                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                if "visual" in name and "qkv.weight" in name:
                    visual_num_heads = self.config.vision_config.num_heads
                    visual_embed_dim = self.config.vision_config.hidden_size
                    head_size = visual_embed_dim // visual_num_heads
                    loaded_weight = loaded_weight.view(
                        3, visual_num_heads, head_size, visual_embed_dim
                    )
                    loaded_weight = loaded_weight.transpose(0, 1)
                    loaded_weight = loaded_weight.reshape(-1, visual_embed_dim)
                elif "visual" in name and "qkv.bias" in name:
                    visual_num_heads = self.config.vision_config.num_heads
                    visual_embed_dim = self.config.vision_config.hidden_size
                    head_size = visual_embed_dim // visual_num_heads
                    loaded_weight = loaded_weight.view(3, visual_num_heads, head_size)
                    loaded_weight = loaded_weight.transpose(0, 1)
                    loaded_weight = loaded_weight.reshape(-1)

                if "visual" in name:
                    # adapt to VisionAttention
                    name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")

                try:
                    # Skip loading extra bias for GPTQ models.
                    if name.endswith(".bias") and name not in params_dict:
                        continue
                    param = params_dict[name]
                except KeyError:
                    print(params_dict.keys())
                    raise

                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)


EntryClass = [Qwen2_5_VLForConditionalGeneration]
AutoModel.register(Qwen2_5_VLConfig, Qwen2_5_VLForConditionalGeneration)
