webapp/seed/Lib/site-packages/transformers/models/internvl/modeling_internvl.py

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# Copyright 2025 HuggingFace Inc. team. All rights reserved.
#
# 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 collections.abc
from collections.abc import Callable
from dataclasses import dataclass

import torch
import torch.nn as nn

from ... import initialization as init
from ...activations import ACT2FN
from ...cache_utils import Cache
from ...generation import GenerationMixin
from ...integrations import use_kernel_forward_from_hub
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, BaseModelOutputWithPooling
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, torch_compilable_check, torch_int
from ...utils.generic import check_model_inputs
from ..auto import AutoModel
from .configuration_internvl import InternVLConfig, InternVLVisionConfig


@use_kernel_forward_from_hub("RMSNorm")
class InternVLVisionRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        InternVLVisionRMSNorm is equivalent to T5LayerNorm
        """
        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}"


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: torch.Tensor | None,
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    key_states = key
    value_states = value

    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

    # No upcasting of the attention weights to float32 in this implementation
    attn_weights = nn.functional.softmax(attn_weights, dim=-1)
    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


class InternVLVisionAttention(nn.Module):
    """Attention Class for InternVL Vision Encoder"""

    def __init__(self, config: InternVLVisionConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_heads
        if self.head_dim * self.num_heads != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
                f" {self.num_heads})."
            )
        self.scale = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        proj_dropout = config.projection_dropout
        qk_norm = config.use_qk_norm

        # Needed for flash attention
        self.is_causal = False

        self.q_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias)
        self.k_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias)
        self.v_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias)
        self.projection_layer = nn.Linear(self.embed_dim, self.embed_dim)
        self.projection_dropout = nn.Dropout(proj_dropout) if proj_dropout > 0 else nn.Identity()

        self.q_norm = InternVLVisionRMSNorm(self.embed_dim) if qk_norm else nn.Identity()
        self.k_norm = InternVLVisionRMSNorm(self.embed_dim) if qk_norm else nn.Identity()

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ):
        batch_size, seq_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = self.q_norm(query_states)
        key_states = self.k_norm(key_states)

        query_states = query_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)

        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
            self.config._attn_implementation, eager_attention_forward
        )

        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.scale,
            is_causal=False,
            **kwargs,
        )
        attn_output = attn_output.reshape(batch_size, seq_len, self.embed_dim)

        output = self.projection_layer(attn_output)
        output = self.projection_dropout(output)

        return output, attn_weights


@dataclass
@auto_docstring(
    custom_intro="""
    Class for outputs of [`InternVLVisionModel`].
    """
)
class InternVLVisionModelOutputWithPooling(BaseModelOutputWithPooling):
    r"""
    pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
        Average of the last layer hidden states of the patch tokens (excluding the *[CLS]* token) if
        *config.use_mean_pooling* is set to True. If set to False, then the final hidden state of the *[CLS]* token
        will be returned.
    """


class InternVLVisionPatchEmbeddings(nn.Module):
    """
    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
    Transformer.
    """

    def __init__(self, config):
        super().__init__()
        image_size, patch_size = config.image_size, config.patch_size
        num_channels, hidden_size = config.num_channels, config.hidden_size

        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.num_patches = num_patches
        self.patch_shape = patch_shape

        self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)

    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        batch_size, num_channels, height, width = pixel_values.shape
        if num_channels != self.num_channels:
            raise ValueError(
                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
            )

        embeddings = self.projection(pixel_values.to(self.projection.weight.dtype))
        embeddings = embeddings.flatten(2).transpose(1, 2)

        return embeddings


# Based on timm implementation, which can be found here:
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
class InternVLVisionEmbeddings(nn.Module):
    """
    Construct the CLS token, position and patch embeddings. Optionally, also the mask token.

    """

    def __init__(self, config: InternVLVisionConfig) -> None:
        super().__init__()

        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        if config.use_mask_token:
            self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        else:
            self.mask_token = None
        self.patch_embeddings = InternVLVisionPatchEmbeddings(config)
        self.patch_size = config.patch_size
        self.image_size = (
            config.image_size
            if isinstance(config.image_size, collections.abc.Iterable)
            else (config.image_size, config.image_size)
        )
        num_patches = self.patch_embeddings.num_patches
        if config.use_absolute_position_embeddings:
            self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size))
        else:
            self.position_embeddings = None
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
        """
        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
        images. This method is also adapted to support torch.jit tracing.

        Adapted from:
        - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
        - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
        """

        num_patches = embeddings.shape[1] - 1
        num_positions = self.position_embeddings.shape[1] - 1

        # always interpolate when tracing to ensure the exported model works for dynamic input shapes
        if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
            return self.position_embeddings

        class_pos_embed = self.position_embeddings[:, :1]
        patch_pos_embed = self.position_embeddings[:, 1:]

        dim = embeddings.shape[-1]

        new_height = height // self.patch_size[0]
        new_width = width // self.patch_size[1]

        sqrt_num_positions = torch_int(num_positions**0.5)
        patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)

        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            size=(new_height, new_width),
            mode="bicubic",
            align_corners=False,
        )

        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)

        return torch.cat((class_pos_embed, patch_pos_embed), dim=1)

    def forward(
        self,
        pixel_values: torch.Tensor,
        bool_masked_pos: torch.BoolTensor | None = None,
    ) -> torch.Tensor:
        _, _, height, width = pixel_values.shape
        embeddings = self.patch_embeddings(pixel_values)
        batch_size, seq_len, _ = embeddings.size()

        if bool_masked_pos is not None:
            mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
            # replace the masked visual tokens by mask_tokens
            w = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
            embeddings = embeddings * (1 - w) + mask_tokens * w

        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
        embeddings = torch.cat((cls_tokens, embeddings), dim=1)

        if self.position_embeddings is not None:
            embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)

        embeddings = self.dropout(embeddings)

        return embeddings


class InternVLVisionMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.activation_fn = ACT2FN[config.hidden_act]
        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.fc1(hidden_states)
        hidden_states = self.activation_fn(hidden_states)
        hidden_states = self.fc2(hidden_states)
        return hidden_states


NORM2FN = {"layer_norm": nn.LayerNorm, "rms_norm": InternVLVisionRMSNorm}


class InternVLVisionLayer(GradientCheckpointingLayer):
    """This corresponds to the Block class in the timm implementation."""

    def __init__(self, config: InternVLVisionConfig) -> None:
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = InternVLVisionAttention(config)
        self.mlp = InternVLVisionMLP(config)
        # InternVL uses different layernorm implementations for different models
        self.layernorm_before = NORM2FN[config.norm_type](config.hidden_size, eps=config.layer_norm_eps)
        self.layernorm_after = NORM2FN[config.norm_type](config.hidden_size, eps=config.layer_norm_eps)

        init_values = config.layer_scale_init_value
        self.lambda_1 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True)
        self.lambda_2 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(
        self,
        hidden_states: torch.Tensor,
    ) -> tuple[torch.Tensor] | tuple[torch.Tensor, torch.Tensor]:
        attention_output, _ = self.attention(
            self.layernorm_before(hidden_states),  # in InternVLVision, layernorm is applied before self-attention
        )

        attention_output = self.lambda_1 * attention_output

        # first residual connection
        hidden_states = attention_output + hidden_states

        # in InternVLVision, layernorm is also applied after self-attention
        layer_output = self.layernorm_after(hidden_states)

        layer_output = self.mlp(layer_output)
        layer_output = self.dropout(layer_output)

        if self.lambda_2 is not None:
            layer_output = self.lambda_2 * layer_output

        # second residual connection
        layer_output = layer_output + hidden_states

        return layer_output


class InternVLVisionEncoder(nn.Module):
    def __init__(self, config: InternVLVisionConfig) -> None:
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([InternVLVisionLayer(config) for i in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
    ) -> tuple | BaseModelOutput:
        for layer_module in self.layer:
            hidden_states = layer_module(hidden_states)

        return BaseModelOutput(
            last_hidden_state=hidden_states,
        )


@auto_docstring
class InternVLVisionPreTrainedModel(PreTrainedModel):
    config: InternVLVisionConfig
    base_model_prefix = "internvl_vision"
    main_input_name = "pixel_values"
    input_modalities = ("image", "video")
    supports_gradient_checkpointing = True
    _no_split_modules = ["InternVLVisionLayer"]
    _supports_sdpa = True
    _supports_flash_attn = True
    _supports_flex_attn = True
    _supports_attention_backend = True

    _can_record_outputs = {
        "hidden_states": InternVLVisionLayer,
        "attentions": InternVLVisionAttention,
    }

    @torch.no_grad()
    def _init_weights(self, module):
        """Initialize the weights"""
        super()._init_weights(module)
        if isinstance(module, InternVLVisionEmbeddings):
            init.zeros_(module.cls_token)
            if module.mask_token is not None:
                init.zeros_(module.mask_token)
            if module.position_embeddings is not None:
                init.zeros_(module.position_embeddings)
        elif isinstance(module, InternVLVisionLayer):
            init.constant_(module.lambda_1, self.config.layer_scale_init_value)
            init.constant_(module.lambda_2, self.config.layer_scale_init_value)


@auto_docstring
class InternVLVisionModel(InternVLVisionPreTrainedModel):
    def __init__(self, config: InternVLVisionConfig) -> None:
        super().__init__(config)
        self.config = config

        self.embeddings = InternVLVisionEmbeddings(config)
        self.encoder = InternVLVisionEncoder(config)

        self.layernorm = (
            nn.Identity() if config.use_mean_pooling else nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        )

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

    def get_input_embeddings(self):
        return self.embeddings.patch_embeddings

    @check_model_inputs(tie_last_hidden_states=False)
    @auto_docstring
    def forward(
        self, pixel_values: torch.Tensor, bool_masked_pos: torch.BoolTensor | None = None, **kwargs
    ) -> tuple | InternVLVisionModelOutputWithPooling:
        r"""
        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
        """
        embedding_output = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)

        encoder_outputs = self.encoder(embedding_output)
        sequence_output = encoder_outputs[0]
        sequence_output = self.layernorm(sequence_output)

        return InternVLVisionModelOutputWithPooling(
            last_hidden_state=sequence_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


@auto_docstring
class InternVLPreTrainedModel(PreTrainedModel):
    config: InternVLConfig
    base_model_prefix = "model"
    input_modalities = ("image", "text", "video")
    supports_gradient_checkpointing = True
    _skip_keys_device_placement = "past_key_values"

    _supports_flash_attn = True
    _supports_sdpa = True

    _can_compile_fullgraph = True
    _supports_flex_attn = True
    _supports_attention_backend = True


class InternVLMultiModalProjector(nn.Module):
    def __init__(self, config: InternVLConfig):
        super().__init__()
        self.layer_norm = nn.LayerNorm(config.vision_config.hidden_size * int(1 / config.downsample_ratio) ** 2)
        self.linear_1 = nn.Linear(
            config.vision_config.hidden_size * int(1 / config.downsample_ratio) ** 2, config.text_config.hidden_size
        )
        self.act = ACT2FN[config.projector_hidden_act]
        self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size)

    def forward(self, image_features):
        hidden_states = self.layer_norm(image_features)
        hidden_states = self.linear_1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.linear_2(hidden_states)
        return hidden_states


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for InternVL outputs, with hidden states and attentions.
    """
)
class InternVLModelOutputWithPast(BaseModelOutputWithPast):
    r"""
    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `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).

        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
        `past_key_values` input) to speed up sequential decoding.
    image_hidden_states (`torch.FloatTensor`, *optional*):
        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
    """

    image_hidden_states: torch.FloatTensor | None = None


@auto_docstring(
    custom_intro="""
    The InternVL model which consists of a vision backbone and a language model, without a language modeling head.
    """
)
class InternVLModel(InternVLPreTrainedModel):
    _checkpoint_conversion_mapping = {
        r"^language_model.model": "language_model",
    }

    def __init__(self, config: InternVLConfig):
        super().__init__(config)
        self.vision_tower = AutoModel.from_config(config.vision_config)

        self.multi_modal_projector = InternVLMultiModalProjector(config)
        self.language_model = AutoModel.from_config(config.text_config)
        self.post_init()

    def get_input_embeddings(self):
        return self.language_model.get_input_embeddings()

    def set_input_embeddings(self, value):
        self.language_model.set_input_embeddings(value)

    @check_model_inputs(tie_last_hidden_states=False)
    @auto_docstring(
        custom_intro="Obtains image last hidden states from the vision tower and apply multimodal projection."
    )
    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
        vision_feature_layer: int | list[int] | None = None,
        vision_feature_select_strategy: str | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutputWithPooling:
        r"""
        pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`)
            The tensors corresponding to the input images.
        vision_feature_layer (`int` or `list[int]`):
            Layer index or list of layer indices to extract features from.
        """
        pixel_values = pixel_values.to(dtype=self.dtype)  # fp16 compatibility

        downsample_ratio = self.config.downsample_ratio
        if vision_feature_layer != -1:
            kwargs["output_hidden_states"] = True
        vision_outputs = self.vision_tower(pixel_values=pixel_values, return_dict=True, **kwargs)
        if vision_feature_layer == -1:
            vision_features = vision_outputs.last_hidden_state
        else:
            vision_features = vision_outputs.hidden_states[vision_feature_layer]
        if vision_feature_select_strategy == "default":
            vision_features = vision_features[:, 1:, :]

        # Calculate dimensions based on vision features
        channels = vision_features.shape[1]
        feature_size = int(channels**0.5)
        batch_size = vision_features.shape[0]

        # Reshape tensor to spatial dimensions
        vision_features = vision_features.reshape(batch_size, feature_size, feature_size, -1)

        # Apply downsampling using pixel shuffle
        vision_features = self.pixel_shuffle(vision_features, scale_factor=downsample_ratio)

        # Reshape tensor to prepare for projection
        vision_features = vision_features.reshape(batch_size, -1, vision_features.shape[-1])

        # Project features through multi-modal projector
        vision_features = self.multi_modal_projector(vision_features)
        vision_outputs.pooler_output = vision_features

        return vision_outputs

    def get_placeholder_mask(
        self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor
    ):
        """
        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
        equal to the length of multimodal features. If the lengths are different, an error is raised.
        """
        if input_ids is None:
            special_image_mask = inputs_embeds == self.get_input_embeddings()(
                torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
            )
            special_image_mask = special_image_mask.all(-1)
        else:
            special_image_mask = input_ids == self.config.image_token_id

        n_image_tokens = special_image_mask.sum()
        n_image_features = image_features.shape[0] * image_features.shape[1]
        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
        torch_compilable_check(
            inputs_embeds[special_image_mask].numel() == image_features.numel(),
            f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
        )
        return special_image_mask

    @check_model_inputs(tie_last_hidden_states=False)
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        pixel_values: torch.FloatTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        vision_feature_layer: int | list[int] | None = None,
        vision_feature_select_strategy: str | None = None,
        cache_position: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | InternVLModelOutputWithPast:
        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.get_input_embeddings()(input_ids)

        if pixel_values is not None:
            image_features = self.get_image_features(
                pixel_values=pixel_values,
                vision_feature_layer=vision_feature_layer,
                vision_feature_select_strategy=vision_feature_select_strategy,
                return_dict=True,
            ).pooler_output
            image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
            special_image_mask = self.get_placeholder_mask(
                input_ids, inputs_embeds=inputs_embeds, image_features=image_features
            )
            inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)

        outputs = self.language_model(
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            cache_position=cache_position,
            **kwargs,
        )

        return InternVLModelOutputWithPast(
            last_hidden_state=outputs.last_hidden_state,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            image_hidden_states=image_features if pixel_values is not None else None,
        )

    def pixel_shuffle(self, vision_features: torch.Tensor, scale_factor: float = 0.5):
        """Perform pixel shuffle downsampling on vision features.

        Args:
            vision_features (`torch.Tensor`):
                Input tensor of shape (batch_size, width, height, channels).
            scale_factor (`float`, *optional*, defaults to `0.5`):
                Factor by which to downsample. Default is 0.5, which halves the dimensions.

        Returns:
            vision_features (`torch.Tensor`):
                Downsampled tensor of shape (batch_size, height*scale_factor, width*scale_factor, channels/(scale_factor^2)).
        """
        batch_size, width, height, channels = vision_features.size()

        if height % scale_factor != 0 or width % scale_factor != 0:
            raise ValueError("Height and width must be divisible by scale_factor for proper downsampling.")

        # Reshape to allow downsampling
        vision_features = vision_features.view(
            batch_size, width, int(height * scale_factor), int(channels / scale_factor)
        )
        # Permute dimensions to align downsampled axis correctly
        vision_features = vision_features.permute(0, 2, 1, 3).contiguous()

        # Reshape to achieve final downsampled dimensions
        vision_features = vision_features.view(
            batch_size, int(height * scale_factor), int(width * scale_factor), int(channels / (scale_factor**2))
        )

        # Swap height and width back for proper orientation
        vision_features = vision_features.permute(0, 2, 1, 3).contiguous()

        return vision_features


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for InternVL causal language model (or autoregressive) outputs.
    """
)
class InternVLCausalLMOutputWithPast(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
        Language modeling loss (for next-token prediction).
    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
        Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `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).

        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
        `past_key_values` input) to speed up sequential decoding.
    image_hidden_states (`torch.FloatTensor`, *optional*):
        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
    """

    loss: torch.FloatTensor | None = None
    logits: torch.FloatTensor | None = None
    past_key_values: Cache | None = None
    hidden_states: tuple[torch.FloatTensor] | None = None
    attentions: tuple[torch.FloatTensor] | None = None
    image_hidden_states: torch.FloatTensor | None = None


@auto_docstring(
    custom_intro="""
    The INTERNVL model which consists of a vision backbone and a language model.
    """
)
class InternVLForConditionalGeneration(InternVLPreTrainedModel, GenerationMixin):
    _checkpoint_conversion_mapping = {
        r"^language_model.model": "model.language_model",
        r"^vision_tower": "model.vision_tower",
        r"^multi_modal_projector": "model.multi_modal_projector",
        r"^language_model.lm_head": "lm_head",
    }
    _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}

    def __init__(self, config: InternVLConfig):
        super().__init__(config)
        self.model = InternVLModel(config)
        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
        self.post_init()

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

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

    def get_output_embeddings(self) -> nn.Module:
        return self.lm_head

    @auto_docstring
    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
        vision_feature_layer: int | list[int] | None = None,
        vision_feature_select_strategy: str | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutputWithPooling:
        return self.model.get_image_features(
            pixel_values=pixel_values,
            vision_feature_layer=vision_feature_layer,
            vision_feature_select_strategy=vision_feature_select_strategy,
            **kwargs,
        )

    @check_model_inputs(tie_last_hidden_states=False)
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        pixel_values: torch.FloatTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        vision_feature_layer: int | list[int] | None = None,
        vision_feature_select_strategy: str | None = None,
        labels: torch.LongTensor | None = None,
        cache_position: torch.LongTensor | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        image_sizes: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | InternVLCausalLMOutputWithPast:
        r"""
        Example:

        ```python
        >>> import torch
        >>> from transformers import AutoProcessor, AutoModelForImageTextToText

        >>> torch_device = "cuda"
        >>> processor = AutoProcessor.from_pretrained("OpenGVLab/InternVL3-1B-hf")
        >>> model = AutoModelForImageTextToText.from_pretrained(
        ...     "OpenGVLab/InternVL3-1B-hf", dtype=torch.bfloat16, device_map=torch_device
        ... )

        >>> messages = [
        ...     {
        ...         "role": "user",
        ...         "content": [
        ...             {
        ...                 "type": "image",
        ...                 "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
        ...             },
        ...             {
        ...                 "type": "image",
        ...                 "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
        ...             },
        ...             {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
        ...         ],
        ...     },
        ... ]

        >>> inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(torch_device)
        >>> generate_ids = model.generate(**inputs, max_new_tokens=200)
        >>> print(processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True))
        The images depict the Statue of Liberty and the Golden Gate Bridge.
        ```"""
        outputs = self.model(
            input_ids=input_ids,
            pixel_values=pixel_values,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            vision_feature_layer=vision_feature_layer,
            vision_feature_select_strategy=vision_feature_select_strategy,
            cache_position=cache_position,
            image_sizes=image_sizes,
            **kwargs,
        )

        hidden_states = outputs[0]
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        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
        if labels is not None:
            loss = self.loss_function(
                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
            )

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

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        inputs_embeds=None,
        pixel_values=None,
        attention_mask=None,
        cache_position=None,
        logits_to_keep=None,
        is_first_iteration=False,
        **kwargs,
    ):
        # Overwritten -- in specific circumstances we don't want to forward image inputs to the model

        model_inputs = super().prepare_inputs_for_generation(
            input_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            logits_to_keep=logits_to_keep,
            is_first_iteration=is_first_iteration,
            **kwargs,
        )

        if is_first_iteration or not kwargs.get("use_cache", True):
            # Pixel values are used only in the first iteration if available
            # In subsquent iterations, they are already merged with text and cached
            # NOTE: first iteration doesn't have to be prefill, it can be the first
            # iteration with a question and cached system prompt (continue generate from cache)
            model_inputs["pixel_values"] = pixel_values

        return model_inputs


__all__ = [
    "InternVLVisionPreTrainedModel",
    "InternVLVisionModel",
    "InternVLPreTrainedModel",
    "InternVLModel",
    "InternVLForConditionalGeneration",
]