code: def convert_groupvit_checkpoint(
      checkpoint_path,
      pytorch_dump_folder_path,
      model_name="groupvit-gcc-yfcc",
      push_to_hub=False,
  ):
      """
      Copy/paste/tweak model's weights to the Transformers design.
      """
      config = GroupViTConfig()
      model = GroupViTModel(config).eval()
  
      state_dict = torch.load(checkpoint_path, map_location="cpu")[
          "model"
      ]
      new_state_dict = convert_state_dict(state_dict, config)
      missing_keys, unexpected_keys = model.load_state_dict(
          new_state_dict, strict=False
      )
      assert missing_keys == ["text_model.embeddings.position_ids"]
      assert (unexpected_keys == ["multi_label_logit_scale"]) or (
          len(unexpected_keys) == 0
      )
  
      # verify result
      processor = CLIPProcessor.from_pretrained(
          "openai/clip-vit-base-patch32"
      )
      image = prepare_img()
      inputs = processor(
          text=["a photo of a cat", "a photo of a dog"],
          images=image,
          padding=True,
          return_tensors="pt",
      )
  
      with torch.no_grad():
          outputs = model(**inputs)
  
      if model_name == "groupvit-gcc-yfcc":
          expected_logits = torch.tensor([[13.3523, 6.3629]])
      elif model_name == "groupvit-gcc-redcaps":
          expected_logits = torch.tensor([[16.1873, 8.6230]])
      else:
          raise ValueError(f"Model name {model_name} not supported.")
      assert torch.allclose(
          outputs.logits_per_image, expected_logits, atol=1e-3
      )
  
      processor.save_pretrained(pytorch_dump_folder_path)
      model.save_pretrained(pytorch_dump_folder_path)
      print(
          "Successfully saved processor and model to",
          pytorch_dump_folder_path,
      )
  
      if push_to_hub:
          print("Pushing to the hub...")
          processor.push_to_hub(model_name, organization="nielsr")
          model.push_to_hub(model_name, organization="nielsr")
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/groupvit/convert_groupvit_nvlab_to_hf.py
imports: import argparse
  import torch
  from PIL import Image
  import requests
  from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
label_desc: Use image node_id 4 for calling a global function with example usage: convert_groupvit_checkpoint(checkpoint_path, pytorch_dump_folder_path, model_name, push_to_hub) without return types