content: # coding=utf-8
  # Copyright 2023 Meta AI and The 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.
  """PyTorch DINOv2 model."""
  
  import collections.abc
  import math
  from dataclasses import dataclass
  from typing import Dict, List, Optional, Set, Tuple, Union
  
  import torch
  import torch.nn.functional as F
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import (
      BackboneOutput,
      BaseModelOutput,
      BaseModelOutputWithPooling,
      ImageClassifierOutput,
  )
  from transformers.modeling_utils import PreTrainedModel
  from transformers.models.dinov2.configuration_dinov2 import Dinov2Config
  from transformers.pytorch_utils import (
      find_pruneable_heads_and_indices,
      prune_linear_layer,
  )
  from transformers.utils import (
      add_code_sample_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      logging,
      replace_return_docstrings,
  )
  from transformers.utils.backbone_utils import BackboneMixin
  
  logger = logging.get_logger(__name__)
  
  # General docstring
  _CONFIG_FOR_DOC = "Dinov2Config"
  
  # Base docstring
  _CHECKPOINT_FOR_DOC = "facebook/dinov2-base"
  _EXPECTED_OUTPUT_SHAPE = [1, 257, 768]
  
  # Image classification docstring
  _IMAGE_CLASS_CHECKPOINT = "facebook/dinov2-base"
  
  
  DINOV2_PRETRAINED_MODEL_ARCHIVE_LIST = [
      "facebook/dinov2-base",
      # See all DINOv2 models at https://huggingface.co/models?filter=dinov2
  ]
  
  
  class Dinov2Embeddings(nn.Module):
      """
      Construct the CLS token, mask token, position and patch embeddings.
      """
  
      def __init__(self, config: Dinov2Config) -> None:
          super().__init__()
  
          self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size))
          # register as mask token as it's not used in optimization
          # to avoid the use of find_unused_parameters_true
          # self.mask_token = nn.Parameter(torch.zeros(1, config.hidden_size))
          self.register_buffer("mask_token", torch.zeros(1, config.hidden_size))
          self.patch_embeddings = Dinov2PatchEmbeddings(config)
          num_patches = self.patch_embeddings.num_patches
          self.position_embeddings = nn.Parameter(
              torch.randn(1, num_patches + 1, config.hidden_size)
          )
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.config = config
  
      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.
  
          Source:
          https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
          """
  
          num_patches = embeddings.shape[1] - 1
          num_positions = self.position_embeddings.shape[1] - 1
          if num_patches == num_positions and height == width:
              return self.position_embeddings
          class_pos_embed = self.position_embeddings[:, 0]
          patch_pos_embed = self.position_embeddings[:, 1:]
          dim = embeddings.shape[-1]
          height = height // self.config.patch_size
          width = width // self.config.patch_size
          # we add a small number to avoid floating point error in the interpolation
          # see discussion at https://github.com/facebookresearch/dino/issues/8
          height, width = height + 0.1, width + 0.1
          patch_pos_embed = patch_pos_embed.reshape(
              1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim
          )
          patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
          patch_pos_embed = nn.functional.interpolate(
              patch_pos_embed,
              scale_factor=(
                  height / math.sqrt(num_positions),
                  width / math.sqrt(num_positions),
              ),
              mode="bicubic",
              align_corners=False,
          )
          if (
              int(height) != patch_pos_embed.shape[-2]
              or int(width) != patch_pos_embed.shape[-1]
          ):
              raise ValueError(
                  "Width or height does not match with the interpolated position embeddings"
              )
          patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
          return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
  
      def forward(
          self,
          pixel_values: torch.Tensor,
          bool_masked_pos: Optional[torch.Tensor] = None,
      ) -> torch.Tensor:
          batch_size, _, height, width = pixel_values.shape
          patch_embeddings = self.patch_embeddings(pixel_values)
          embeddings = patch_embeddings
  
          if bool_masked_pos is not None:
              embeddings = torch.where(
                  bool_masked_pos.unsqueeze(-1),
                  self.mask_token.to(embeddings.dtype).unsqueeze(0),
                  embeddings,
              )
  
          # add the [CLS] token to the embedded patch tokens
          cls_tokens = self.cls_token.expand(batch_size, -1, -1)
          embeddings = torch.cat((cls_tokens, embeddings), dim=1)
  
          # add positional encoding to each token
          embeddings = embeddings + self.interpolate_pos_encoding(
              embeddings, height, width
          )
  
          embeddings = self.dropout(embeddings)
  
          return embeddings
  
  
  class Dinov2PatchEmbeddings(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
  
          image_size = (
              image_size
              if isinstance(image_size, collections.abc.Iterable)
              else (image_size, image_size)
          )
          patch_size = (
              patch_size
              if isinstance(patch_size, collections.abc.Iterable)
              else (patch_size, patch_size)
          )
          num_patches = (image_size[1] // patch_size[1]) * (
              image_size[0] // patch_size[0]
          )
          self.image_size = image_size
          self.patch_size = patch_size
          self.num_channels = num_channels
          self.num_patches = num_patches
  
          self.projection = nn.Conv2d(
              num_channels, hidden_size, kernel_size=patch_size, stride=patch_size
          )
  
      def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
          """
          num_channels = pixel_values.shape[1]
          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."
                  f" Expected {self.num_channels} but got {num_channels}."
              )
          """
          embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
          return embeddings
  
  
  # Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->Dinov2
  class Dinov2SelfAttention(nn.Module):
      def __init__(self, config: Dinov2Config) -> None:
          super().__init__()
          if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
              config, "embedding_size"
          ):
              raise ValueError(
                  f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
                  f"heads {config.num_attention_heads}."
              )
  
          self.num_attention_heads = config.num_attention_heads
          self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
          self.all_head_size = self.num_attention_heads * self.attention_head_size
          self.attention_probs_dropout_prob = config.attention_probs_dropout_prob
  
          self.query = nn.Linear(
              config.hidden_size, self.all_head_size, bias=config.qkv_bias
          )
          self.key = nn.Linear(
              config.hidden_size, self.all_head_size, bias=config.qkv_bias
          )
          self.value = nn.Linear(
              config.hidden_size, self.all_head_size, bias=config.qkv_bias
          )
  
          self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
  
      def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
          new_x_shape = x.size()[:-1] + (
              self.num_attention_heads,
              self.attention_head_size,
          )
          x = x.view(new_x_shape)
          return x.permute(0, 2, 1, 3)
  
      def forward(
          self,
          hidden_states,
          head_mask: Optional[torch.Tensor] = None,
          output_attentions: bool = False,
      ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
          mixed_query_layer = self.query(hidden_states)
  
          if hasattr(F, "scaled_dot_product_attention"):
              assert head_mask is None and not output_attentions
              new_size = hidden_states.size()[:-1] + (
                  self.num_attention_heads,
                  self.attention_head_size,
              )
              key_layer = self.key(hidden_states).reshape(new_size).transpose(1, 2)
              value_layer = self.value(hidden_states).reshape(new_size).transpose(1, 2)
              query_layer = mixed_query_layer.reshape(new_size).transpose(1, 2)
              context_layer = F.scaled_dot_product_attention(
                  query_layer,
                  key_layer,
                  value_layer,
                  dropout_p=self.attention_probs_dropout_prob,
                  is_causal=False,
              )
              context_layer = context_layer.transpose(1, 2).reshape(
                  *hidden_states.size()[:-1], -1
              )
          else:
              key_layer = self.transpose_for_scores(self.key(hidden_states))
              value_layer = self.transpose_for_scores(self.value(hidden_states))
              query_layer = self.transpose_for_scores(mixed_query_layer)
  
              # Take the dot product between "query" and "key" to get the raw attention scores.
              attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
  
              attention_scores = attention_scores / math.sqrt(self.attention_head_size)
  
              # Normalize the attention scores to probabilities.
              attention_probs = nn.functional.softmax(attention_scores, dim=-1)
  
              # This is actually dropping out entire tokens to attend to, which might
              # seem a bit unusual, but is taken from the original Transformer paper.
              attention_probs = self.dropout(attention_probs)
  
              # Mask heads if we want to
              if head_mask is not None:
                  attention_probs = attention_probs * head_mask
  
              context_layer = torch.matmul(attention_probs, value_layer)
  
              context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
              new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
              context_layer = context_layer.view(new_context_layer_shape)
  
          outputs = (
              (context_layer, attention_probs) if output_attentions else (context_layer,)
          )
  
          return outputs
  
  
  # Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->Dinov2
  class Dinov2SelfOutput(nn.Module):
      """
      The residual connection is defined in Dinov2Layer instead of here (as is the case with other models), due to the
      layernorm applied before each block.
      """
  
      def __init__(self, config: Dinov2Config) -> None:
          super().__init__()
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
      def forward(
          self, hidden_states: torch.Tensor, input_tensor: torch.Tensor
      ) -> torch.Tensor:
          hidden_states = self.dense(hidden_states)
          hidden_states = self.dropout(hidden_states)
  
          return hidden_states
  
  
  # Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->Dinov2
  class Dinov2Attention(nn.Module):
      def __init__(self, config: Dinov2Config) -> None:
          super().__init__()
          self.attention = Dinov2SelfAttention(config)
          self.output = Dinov2SelfOutput(config)
          self.pruned_heads = set()
  
      def prune_heads(self, heads: Set[int]) -> None:
          if len(heads) == 0:
              return
          heads, index = find_pruneable_heads_and_indices(
              heads,
              self.attention.num_attention_heads,
              self.attention.attention_head_size,
              self.pruned_heads,
          )
  
          # Prune linear layers
          self.attention.query = prune_linear_layer(self.attention.query, index)
          self.attention.key = prune_linear_layer(self.attention.key, index)
          self.attention.value = prune_linear_layer(self.attention.value, index)
          self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
  
          # Update hyper params and store pruned heads
          self.attention.num_attention_heads = self.attention.num_attention_heads - len(
              heads
          )
          self.attention.all_head_size = (
              self.attention.attention_head_size * self.attention.num_attention_heads
          )
          self.pruned_heads = self.pruned_heads.union(heads)
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          head_mask: Optional[torch.Tensor] = None,
          output_attentions: bool = False,
      ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
          self_outputs = self.attention(hidden_states, head_mask, output_attentions)
  
          attention_output = self.output(self_outputs[0], hidden_states)
  
          outputs = (attention_output,) + self_outputs[
              1:
          ]  # add attentions if we output them
          return outputs
  
  
  class Dinov2LayerScale(nn.Module):
      def __init__(self, config) -> None:
          super().__init__()
          self.lambda1 = nn.Parameter(
              config.layerscale_value * torch.ones(config.hidden_size)
          )
  
      def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
          return hidden_state * self.lambda1
  
  
  # Copied from transformers.models.beit.modeling_beit.drop_path
  def drop_path(
      input: torch.Tensor, drop_prob: float = 0.0, training: bool = False
  ) -> torch.Tensor:
      """
      Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
  
      Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
      however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
      See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
      layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
      argument.
      """
      if drop_prob == 0.0 or not training:
          return input
      keep_prob = 1 - drop_prob
      shape = (input.shape[0],) + (1,) * (
          input.ndim - 1
      )  # work with diff dim tensors, not just 2D ConvNets
      random_tensor = keep_prob + torch.rand(
          shape, dtype=input.dtype, device=input.device
      )
      random_tensor.floor_()  # binarize
      output = input.div(keep_prob) * random_tensor
      return output
  
  
  # Copied from transformers.models.beit.modeling_beit.BeitDropPath
  class Dinov2DropPath(nn.Module):
      """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
  
      def __init__(self, drop_prob: Optional[float] = None) -> None:
          super().__init__()
          self.drop_prob = drop_prob
  
      def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
          return drop_path(hidden_states, self.drop_prob, self.training)
  
      def extra_repr(self) -> str:
          return "p={}".format(self.drop_prob)
  
  
  class Dinov2MLP(nn.Module):
      def __init__(self, config) -> None:
          super().__init__()
          in_features = out_features = config.hidden_size
          hidden_features = int(config.hidden_size * config.mlp_ratio)
          self.fc1 = nn.Linear(in_features, hidden_features, bias=True)
          if isinstance(config.hidden_act, str):
              self.activation = ACT2FN[config.hidden_act]
          else:
              self.activation = config.hidden_act
          self.fc2 = nn.Linear(hidden_features, out_features, bias=True)
  
      def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
          hidden_state = self.fc1(hidden_state)
          hidden_state = self.activation(hidden_state)
          hidden_state = self.fc2(hidden_state)
          return hidden_state
  
  
  class Dinov2SwiGLUFFN(nn.Module):
      def __init__(self, config) -> None:
          super().__init__()
          in_features = out_features = config.hidden_size
          hidden_features = int(config.hidden_size * config.mlp_ratio)
          hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
  
          self.weights_in = nn.Linear(in_features, 2 * hidden_features, bias=True)
          self.weights_out = nn.Linear(hidden_features, out_features, bias=True)
  
      def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
          hidden_state = self.weights_in(hidden_state)
          x1, x2 = hidden_state.chunk(2, dim=-1)
          hidden = nn.functional.silu(x1) * x2
          return self.weights_out(hidden)
  
  
  class Dinov2Layer(nn.Module):
      """This corresponds to the Block class in the original implementation."""
  
      def __init__(self, config: Dinov2Config) -> None:
          super().__init__()
  
          self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.norm1_modulation = None
          self.attention = Dinov2Attention(config)
          self.layer_scale1 = Dinov2LayerScale(config)
          self.drop_path1 = (
              Dinov2DropPath(config.drop_path_rate)
              if config.drop_path_rate > 0.0
              else nn.Identity()
          )
  
          self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
          self.norm2_modulation = None
  
          if config.use_swiglu_ffn:
              self.mlp = Dinov2SwiGLUFFN(config)
          else:
              self.mlp = Dinov2MLP(config)
          self.layer_scale2 = Dinov2LayerScale(config)
          self.drop_path2 = (
              Dinov2DropPath(config.drop_path_rate)
              if config.drop_path_rate > 0.0
              else nn.Identity()
          )
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          head_mask: Optional[torch.Tensor] = None,
          modulation_cond: Optional[torch.Tensor] = None,
          output_attentions: bool = False,
      ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
          hidden_states_norm = self.norm1(hidden_states)
          if self.norm1_modulation is not None:
              assert modulation_cond is not None
              hidden_states_norm = self.norm1_modulation(
                  hidden_states_norm, modulation_cond
              )
          self_attention_outputs = self.attention(
              hidden_states_norm,  # in Dinov2, layernorm is applied before self-attention
              head_mask,
              output_attentions=output_attentions,
          )
          attention_output = self_attention_outputs[0]
  
          attention_output = self.layer_scale1(attention_output)
          outputs = self_attention_outputs[
              1:
          ]  # add self attentions if we output attention weights
  
          # first residual connection
          hidden_states = attention_output + hidden_states
  
          # in Dinov2, layernorm is also applied after self-attention
          layer_output = self.norm2(hidden_states)
          if self.norm2_modulation is not None:
              assert modulation_cond is not None
              layer_output = self.norm2_modulation(layer_output, modulation_cond)
          layer_output = self.mlp(layer_output)
          layer_output = self.layer_scale2(layer_output)
  
          # second residual connection
          layer_output = layer_output + hidden_states
  
          outputs = (layer_output,) + outputs
  
          return outputs
  
      def register_ada_norm_modulation(self, norm1_mod: nn.Module, norm2_mod: nn.Module):
          self.norm1_modulation = norm1_mod
          self.norm2_modulation = norm2_mod
  
  
  # Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->Dinov2
  class Dinov2Encoder(nn.Module):
      def __init__(self, config: Dinov2Config) -> None:
          super().__init__()
          self.config = config
          self.layer = nn.ModuleList(
              [Dinov2Layer(config) for _ in range(config.num_hidden_layers)]
          )
          self.gradient_checkpointing = False
  
      def forward(
          self,
          hidden_states: torch.Tensor,
          head_mask: Optional[torch.Tensor] = None,
          modulation_cond: Optional[torch.Tensor] = None,
          output_attentions: bool = False,
          output_hidden_states: bool = False,
          return_dict: bool = True,
      ) -> Union[tuple, BaseModelOutput]:
          all_hidden_states = () if output_hidden_states else None
          all_self_attentions = () if output_attentions else None
  
          for i, layer_module in enumerate(self.layer):
              if output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_states,)
  
              layer_head_mask = head_mask[i] if head_mask is not None else None
  
              if self.gradient_checkpointing and self.training:
  
                  def create_custom_forward(module):
                      def custom_forward(*inputs):
                          return module(*inputs, output_attentions)
  
                      return custom_forward
  
                  layer_outputs = torch.utils.checkpoint.checkpoint(
                      create_custom_forward(layer_module),
                      hidden_states,
                      layer_head_mask,
                      modulation_cond,
                      use_reentrant=False,
                  )
              else:
                  layer_outputs = layer_module(
                      hidden_states, layer_head_mask, modulation_cond, output_attentions
                  )
  
              hidden_states = layer_outputs[0]
  
              if output_attentions:
                  all_self_attentions = all_self_attentions + (layer_outputs[1],)
  
          if output_hidden_states:
              all_hidden_states = all_hidden_states + (hidden_states,)
  
          if not return_dict:
              return tuple(
                  v
                  for v in [hidden_states, all_hidden_states, all_self_attentions]
                  if v is not None
              )
          return BaseModelOutput(
              last_hidden_state=hidden_states,
              hidden_states=all_hidden_states,
              attentions=all_self_attentions,
          )
  
  
  class Dinov2PreTrainedModel(PreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = Dinov2Config
      base_model_prefix = "dinov2"
      main_input_name = "pixel_values"
      supports_gradient_checkpointing = True
  
      def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
          """Initialize the weights"""
          if isinstance(module, (nn.Linear, nn.Conv2d)):
              # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
              # `trunc_normal_cpu` not implemented in `half` issues
              module.weight.data = nn.init.trunc_normal_(
                  module.weight.data.to(torch.float32),
                  mean=0.0,
                  std=self.config.initializer_range,
              ).to(module.weight.dtype)
              if module.bias is not None:
                  module.bias.data.zero_()
          elif isinstance(module, nn.LayerNorm):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0)
          elif isinstance(module, Dinov2Embeddings):
              module.position_embeddings.data = nn.init.trunc_normal_(
                  module.position_embeddings.data.to(torch.float32),
                  mean=0.0,
                  std=self.config.initializer_range,
              ).to(module.position_embeddings.dtype)
  
              module.cls_token.data = nn.init.trunc_normal_(
                  module.cls_token.data.to(torch.float32),
                  mean=0.0,
                  std=self.config.initializer_range,
              ).to(module.cls_token.dtype)
  
      def _set_gradient_checkpointing(
          self, module: Dinov2Encoder, value: bool = False
      ) -> None:
          if isinstance(module, Dinov2Encoder):
              module.gradient_checkpointing = value
  
  
  DINOV2_START_DOCSTRING = r"""
      This model is 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 ([`Dinov2Config`]): 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.
  """
  
  DINOV2_BASE_INPUTS_DOCSTRING = r"""
      Args:
          pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
              Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
              [`BitImageProcessor.preprocess`] for details.
  
          bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, sequence_length)`):
              Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Only relevant for
              pre-training.
  
          head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
              Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          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.
  """
  
  DINOV2_INPUTS_DOCSTRING = r"""
      Args:
          pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
              Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
              [`BitImageProcessor.preprocess`] for details.
  
          head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
              Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          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.
  """
  
  
  @dataclass
  class CustomBaseModelOutputWithPooling(BaseModelOutputWithPooling):
      patch_embeddings: Optional[torch.FloatTensor] = None
  
  
  @add_start_docstrings(
      "The bare DINOv2 Model transformer outputting raw hidden-states without any specific head on top.",
      DINOV2_START_DOCSTRING,
  )
  class Dinov2Model(Dinov2PreTrainedModel):
      def __init__(self, config: Dinov2Config):
          super().__init__(config)
          self.config = config
  
          self.embeddings = Dinov2Embeddings(config)
          self.encoder = Dinov2Encoder(config)
  
          self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
  
          # Initialize weights and apply final processing
          self.post_init()
  
      def get_input_embeddings(self) -> Dinov2PatchEmbeddings:
          return self.embeddings.patch_embeddings
  
      def expand_input_channels(self, extra_input_channels: int) -> None:
          if extra_input_channels == 0:
              return
          conv_old = self.embeddings.patch_embeddings.projection
          conv_new = nn.Conv2d(
              self.config.num_channels + extra_input_channels,
              self.config.hidden_size,
              kernel_size=self.config.patch_size,
              stride=self.config.patch_size,
          ).to(self.device)
          with torch.no_grad():
              conv_new.weight[:, :3] = conv_old.weight
              conv_new.bias = conv_old.bias
          self.embeddings.patch_embeddings.projection = conv_new
          del conv_old
  
      def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
          """
          Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
          class PreTrainedModel
          """
          for layer, heads in heads_to_prune.items():
              self.encoder.layer[layer].attention.prune_heads(heads)
  
      @add_start_docstrings_to_model_forward(DINOV2_BASE_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=BaseModelOutputWithPooling,
          config_class=_CONFIG_FOR_DOC,
          modality="vision",
          expected_output=_EXPECTED_OUTPUT_SHAPE,
      )
      def forward(
          self,
          pixel_values: Optional[torch.Tensor] = None,
          bool_masked_pos: Optional[torch.Tensor] = None,
          head_mask: Optional[torch.Tensor] = None,
          modulation_cond: Optional[torch.Tensor] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
      ) -> Union[Tuple, BaseModelOutputWithPooling]:
          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
          )
          return_dict = (
              return_dict if return_dict is not None else self.config.use_return_dict
          )
  
          if pixel_values is None:
              raise ValueError("You have to specify pixel_values")
  
          # Prepare head mask if needed
          # 1.0 in head_mask indicate we keep the head
          # attention_probs has shape bsz x n_heads x N x N
          # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
          # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
          head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
  
          embedding_output = self.embeddings(
              pixel_values, bool_masked_pos=bool_masked_pos
          )
  
          encoder_outputs = self.encoder(
              embedding_output,
              head_mask=head_mask,
              modulation_cond=modulation_cond,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
          sequence_output = encoder_outputs[0]
          sequence_output = self.layernorm(sequence_output)
          pooled_output = sequence_output[:, 0, :]
  
          if not return_dict:
              head_outputs = (sequence_output, pooled_output)
              return head_outputs + encoder_outputs[1:]
  
          return CustomBaseModelOutputWithPooling(
              last_hidden_state=sequence_output,
  