code: def convert_vit_checkpoint(
      model_name, pytorch_dump_folder_path, base_model=True
  ):
      """
      Copy/paste/tweak model's weights to our ViT structure.
      """
  
      # define default ViT configuration
      config = ViTConfig()
      # patch_size
      if model_name[-1] == "8":
          config.patch_size = 8
      # set labels if required
      if not base_model:
          config.num_labels = 1000
          repo_id = "huggingface/label-files"
          filename = "imagenet-1k-id2label.json"
          id2label = json.load(
              open(
                  hf_hub_download(
                      repo_id, filename, repo_type="dataset"
                  ),
                  "r",
              )
          )
          id2label = {int(k): v for k, v in id2label.items()}
          config.id2label = id2label
          config.label2id = {v: k for k, v in id2label.items()}
      # size of the architecture
      if model_name in ["dino_vits8", "dino_vits16"]:
          config.hidden_size = 384
          config.intermediate_size = 1536
          config.num_hidden_layers = 12
          config.num_attention_heads = 6
  
      # load original model from torch hub
      original_model = torch.hub.load(
          "facebookresearch/dino:main", model_name
      )
      original_model.eval()
  
      # load state_dict of original model, remove and rename some keys
      state_dict = original_model.state_dict()
      if base_model:
          remove_classification_head_(state_dict)
      rename_keys = create_rename_keys(config, base_model=base_model)
      for src, dest in rename_keys:
          rename_key(state_dict, src, dest)
      read_in_q_k_v(state_dict, config, base_model)
  
      # load HuggingFace model
      if base_model:
          model = ViTModel(config, add_pooling_layer=False).eval()
      else:
          model = ViTForImageClassification(config).eval()
      model.load_state_dict(state_dict)
  
      # Check outputs on an image, prepared by ViTImageProcessor
      image_processor = ViTImageProcessor()
      encoding = image_processor(
          images=prepare_img(), return_tensors="pt"
      )
      pixel_values = encoding["pixel_values"]
      outputs = model(pixel_values)
  
      if base_model:
          final_hidden_state_cls_token = original_model(pixel_values)
          assert torch.allclose(
              final_hidden_state_cls_token,
              outputs.last_hidden_state[:, 0, :],
              atol=1e-1,
          )
      else:
          logits = original_model(pixel_values)
          assert logits.shape == outputs.logits.shape
          assert torch.allclose(logits, outputs.logits, atol=1e-3)
  
      Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
      print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
      model.save_pretrained(pytorch_dump_folder_path)
      print(f"Saving image processor to {pytorch_dump_folder_path}")
      image_processor.save_pretrained(pytorch_dump_folder_path)
  
file_path: transformers/src/transformers/models/vit/convert_dino_to_pytorch.py
imports: import argparse
  import json
  from pathlib import Path
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
  from transformers.utils import logging
label_desc: Use image node_id 6 for calling a global function with example usage: convert_vit_checkpoint(model_name, pytorch_dump_folder_path, base_model) without return types