code: def convert_deformable_detr_checkpoint(
      checkpoint_path,
      single_scale,
      dilation,
      with_box_refine,
      two_stage,
      pytorch_dump_folder_path,
      push_to_hub,
  ):
      """
      Copy/paste/tweak model's weights to our Deformable DETR structure.
      """
  
      # load default config
      config = DeformableDetrConfig()
      # set config attributes
      if single_scale:
          config.num_feature_levels = 1
      config.dilation = dilation
      config.with_box_refine = with_box_refine
      config.two_stage = two_stage
      # set labels
      config.num_labels = 91
      repo_id = "huggingface/label-files"
      filename = "coco-detection-id2label.json"
      id2label = json.load(
          open(
              cached_download(
                  hf_hub_url(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()}
  
      # load image processor
      image_processor = DeformableDetrImageProcessor(
          format="coco_detection"
      )
  
      # prepare image
      img = prepare_img()
      encoding = image_processor(images=img, return_tensors="pt")
      pixel_values = encoding["pixel_values"]
  
      logger.info("Converting model...")
  
      # load original state dict
      state_dict = torch.load(checkpoint_path, map_location="cpu")[
          "model"
      ]
      # rename keys
      for key in state_dict.copy().keys():
          val = state_dict.pop(key)
          state_dict[rename_key(key)] = val
      # query, key and value matrices need special treatment
      read_in_q_k_v(state_dict)
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      prefix = "model."
      for key in state_dict.copy().keys():
          if not key.startswith("class_embed") and not key.startswith(
              "bbox_embed"
          ):
              val = state_dict.pop(key)
              state_dict[prefix + key] = val
      # finally, create HuggingFace model and load state dict
      model = DeformableDetrForObjectDetection(config)
      model.load_state_dict(state_dict)
      model.eval()
  
      device = "cuda" if torch.cuda.is_available() else "cpu"
      model.to(device)
      # verify our conversion
      outputs = model(pixel_values.to(device))
  
      expected_logits = torch.tensor(
          [
              [-9.6645, -4.3449, -5.8705],
              [-9.7035, -3.8504, -5.0724],
              [-10.5634, -5.3379, -7.5116],
          ]
      )
      expected_boxes = torch.tensor(
          [
              [0.8693, 0.2289, 0.2492],
              [0.3150, 0.5489, 0.5845],
              [0.5563, 0.7580, 0.8518],
          ]
      )
  
      if single_scale:
          expected_logits = torch.tensor(
              [
                  [-9.9051, -4.2541, -6.4852],
                  [-9.6947, -4.0854, -6.8033],
                  [-10.0665, -5.8470, -7.7003],
              ]
          )
          expected_boxes = torch.tensor(
              [
                  [0.7292, 0.4991, 0.5532],
                  [0.7959, 0.2426, 0.4236],
                  [0.7582, 0.3518, 0.4451],
              ]
          )
  
      if single_scale and dilation:
          expected_logits = torch.tensor(
              [
                  [-8.9652, -4.1074, -5.6635],
                  [-9.0596, -4.9447, -6.6075],
                  [-10.1178, -4.5275, -6.2671],
              ]
          )
          expected_boxes = torch.tensor(
              [
                  [0.7665, 0.4130, 0.4769],
                  [0.8364, 0.1841, 0.3391],
                  [0.6261, 0.3895, 0.7978],
              ]
          )
  
      if with_box_refine:
          expected_logits = torch.tensor(
              [
                  [-8.8895, -5.4187, -6.8153],
                  [-8.4706, -6.1668, -7.6184],
                  [-9.0042, -5.5359, -6.9141],
              ]
          )
          expected_boxes = torch.tensor(
              [
                  [0.7828, 0.2208, 0.4323],
                  [0.0892, 0.5996, 0.1319],
                  [0.5524, 0.6389, 0.8914],
              ]
          )
  
      if with_box_refine and two_stage:
          expected_logits = torch.tensor(
              [
                  [-6.7108, -4.3213, -6.3777],
                  [-8.9014, -6.1799, -6.7240],
                  [-6.9315, -4.4735, -6.2298],
              ]
          )
          expected_boxes = torch.tensor(
              [
                  [0.2583, 0.5499, 0.4683],
                  [0.7652, 0.9068, 0.4882],
                  [0.5490, 0.2763, 0.0564],
              ]
          )
  
      print("Logits:", outputs.logits[0, :3, :3])
  
      assert torch.allclose(
          outputs.logits[0, :3, :3],
          expected_logits.to(device),
          atol=1e-4,
      )
      assert torch.allclose(
          outputs.pred_boxes[0, :3, :3],
          expected_boxes.to(device),
          atol=1e-4,
      )
  
      print("Everything ok!")
  
      # Save model and image processor
      logger.info(
          f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."
      )
      Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
      model.save_pretrained(pytorch_dump_folder_path)
      image_processor.save_pretrained(pytorch_dump_folder_path)
  
      # Push to hub
      if push_to_hub:
          model_name = "deformable-detr"
          model_name += "-single-scale" if single_scale else ""
          model_name += "-dc5" if dilation else ""
          model_name += "-with-box-refine" if with_box_refine else ""
          model_name += "-two-stage" if two_stage else ""
          print("Pushing model to hub...")
          model.push_to_hub(
              repo_path_or_name=model_name,
              organization="nielsr",
              commit_message="Add model",
          )
  
file_path: transformers/src/transformers/models/deformable_detr/convert_deformable_detr_to_pytorch.py
imports: import argparse
  import json
  from pathlib import Path
  import requests
  import torch
  from huggingface_hub import cached_download, hf_hub_url
  from PIL import Image
  from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
  from transformers.utils import logging
label_desc: Use image node_id 4 for calling a global function with example usage: convert_deformable_detr_checkpoint(checkpoint_path, single_scale, dilation, with_box_refine, two_stage, pytorch_dump_folder_path, push_to_hub) without return types