code: def convert_vitmatte_checkpoint(
      model_name, pytorch_dump_folder_path, push_to_hub
  ):
      config = get_config(model_name)
  
      # load original state dict
      model_name_to_filename = {
          "vitmatte-small-composition-1k": "ViTMatte_S_Com.pth",
          "vitmatte-base-composition-1k": "ViTMatte_B_Com.pth",
          "vitmatte-small-distinctions-646": "ViTMatte_S_DIS.pth",
          "vitmatte-base-distinctions-646": "ViTMatte_B_DIS.pth",
      }
  
      filename = model_name_to_filename[model_name]
      filepath = hf_hub_download(
          repo_id="nielsr/vitmatte-checkpoints",
          filename=filename,
          repo_type="model",
      )
      state_dict = torch.load(filepath, map_location="cpu")
  
      # rename keys
      for key in state_dict.copy().keys():
          val = state_dict.pop(key)
          if "backbone.blocks" in key:
              key = key.replace(
                  "backbone.blocks", "backbone.encoder.layer"
              )
          if "attn" in key:
              key = key.replace("attn", "attention")
          if "fusion_blks" in key:
              key = key.replace("fusion_blks", "fusion_blocks")
          if "bn" in key:
              key = key.replace("bn", "batch_norm")
          state_dict[key] = val
  
      # rename keys
      rename_keys = create_rename_keys(config)
      for src, dest in rename_keys:
          rename_key(state_dict, src, dest)
  
      # create model
      processor = VitMatteImageProcessor()
      model = VitMatteForImageMatting(config)
      model.eval()
  
      # load state dict
      model.load_state_dict(state_dict)
  
      # verify on dummy image + trimap
      url = "https://github.com/hustvl/ViTMatte/blob/main/demo/bulb_rgb.png?raw=true"
      image = Image.open(requests.get(url, stream=True).raw).convert(
          "RGB"
      )
      url = "https://github.com/hustvl/ViTMatte/blob/main/demo/bulb_trimap.png?raw=true"
      trimap = Image.open(requests.get(url, stream=True).raw)
  
      pixel_values = processor(
          images=image, trimaps=trimap.convert("L"), return_tensors="pt"
      ).pixel_values
  
      with torch.no_grad():
          alphas = model(pixel_values).alphas
  
      if model_name == "vitmatte-small-composition-1k":
          expected_slice = torch.tensor(
              [
                  [0.9977, 0.9987, 0.9990],
                  [0.9980, 0.9998, 0.9998],
                  [0.9983, 0.9998, 0.9998],
              ]
          )
      elif model_name == "vitmatte-base-composition-1k":
          expected_slice = torch.tensor(
              [
                  [0.9972, 0.9971, 0.9981],
                  [0.9948, 0.9987, 0.9994],
                  [0.9963, 0.9992, 0.9995],
              ]
          )
      elif model_name == "vitmatte-small-distinctions-646":
          expected_slice = torch.tensor(
              [
                  [0.9880, 0.9970, 0.9972],
                  [0.9960, 0.9996, 0.9997],
                  [0.9963, 0.9996, 0.9997],
              ]
          )
      elif model_name == "vitmatte-base-distinctions-646":
          expected_slice = torch.tensor(
              [
                  [0.9963, 0.9998, 0.9999],
                  [0.9995, 1.0000, 1.0000],
                  [0.9992, 0.9999, 1.0000],
              ]
          )
  
      assert torch.allclose(
          alphas[0, 0, :3, :3], expected_slice, atol=1e-4
      )
      print("Looks ok!")
  
      if pytorch_dump_folder_path is not None:
          print(
              f"Saving model and processor of {model_name} to {pytorch_dump_folder_path}"
          )
          model.save_pretrained(pytorch_dump_folder_path)
          processor.save_pretrained(pytorch_dump_folder_path)
  
      if push_to_hub:
          print(f"Pushing model and processor for {model_name} to hub")
          model.push_to_hub(f"hustvl/{model_name}")
          processor.push_to_hub(f"hustvl/{model_name}")
  
file_path: transformers/src/transformers/models/vitmatte/convert_vitmatte_to_hf.py
imports: import argparse
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from transformers import VitDetConfig, VitMatteConfig, VitMatteForImageMatting, VitMatteImageProcessor
label_desc: Use image node_id 4 for calling a global function with example usage: convert_vitmatte_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub) without return types