code: 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
  
  
  logging.set_verbosity_info()
  a     :			Any					    =						logging.get_logger(__name__)
  def 		snake_case__  (		lowercase				):
     if "backbone.0.body" in orig_key:
        lowerCAmelCase_:       str         =  orig_key.replace("backbone.0.body"       ,							"backbone.conv_encoder.model"				)
     if "transformer" in orig_key:
        lowerCAmelCase_:       Tuple         =  orig_key.replace("transformer."       ,							""				)
     if "norm1" in orig_key:
        if "encoder" in orig_key:
           lowerCAmelCase_:       Any         =  orig_key.replace("norm1"       ,							"self_attn_layer_norm"				)
        else:
           lowerCAmelCase_:       Optional[Any]         =  orig_key.replace("norm1"       ,							"encoder_attn_layer_norm"				)
     if "norm2" in orig_key:
        if "encoder" in orig_key:
           lowerCAmelCase_:       Any         =  orig_key.replace("norm2"       ,							"final_layer_norm"				)
        else:
           lowerCAmelCase_:       Union[str, Any]         =  orig_key.replace("norm2"       ,							"self_attn_layer_norm"				)
     if "norm3" in orig_key:
        lowerCAmelCase_:       Dict         =  orig_key.replace("norm3"       ,							"final_layer_norm"				)
     if "linear1" in orig_key:
        lowerCAmelCase_:       str         =  orig_key.replace("linear1"       ,							"fc1"				)
     if "linear2" in orig_key:
        lowerCAmelCase_:       str         =  orig_key.replace("linear2"       ,							"fc2"				)
     if "query_embed" in orig_key:
        lowerCAmelCase_:       Union[str, Any]         =  orig_key.replace("query_embed"       ,							"query_position_embeddings"				)
     if "cross_attn" in orig_key:
        lowerCAmelCase_:       Optional[Any]         =  orig_key.replace("cross_attn"       ,							"encoder_attn"				)
  
     return orig_key
  def 		snake_case__  (		lowercase				):
     # transformer decoder self-attention layers
     for i in range(6				):
        # read in weights + bias of input projection layer of self-attention
        lowerCAmelCase_:       List[str]         =  state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''				)
        lowerCAmelCase_:       Tuple         =  state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_bias'''				)
        # next, add query, keys and values (in that order) to the state dict
        lowerCAmelCase_:       Optional[Any]         =  in_proj_weight[:256, :]
        lowerCAmelCase_:       Union[str, Any]         =  in_proj_bias[:256]
        lowerCAmelCase_:       Union[str, Any]         =  in_proj_weight[256:512, :]
        lowerCAmelCase_:       str         =  in_proj_bias[256:512]
        lowerCAmelCase_:       Union[str, Any]         =  in_proj_weight[-256:, :]
        lowerCAmelCase_:       Optional[int]         =  in_proj_bias[-256:]
  def 		snake_case__  (		):
     lowerCAmelCase_:       Union[str, Any]         =  "http://images.cocodataset.org/val2017/000000039769.jpg"
     lowerCAmelCase_:       Any         =  Image.open(requests.get(lowercase       ,							stream=lowercase				).raw				)
  
     return im
  @torch.no_grad()
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase       ,							lowercase       ,							lowercase       ,							lowercase       ,							lowercase       ,							):
     lowerCAmelCase_:       Tuple         =  DeformableDetrConfig()
     # set config attributes
     if single_scale:
        lowerCAmelCase_:       Tuple         =  1
     lowerCAmelCase_:       Tuple         =  dilation
     lowerCAmelCase_:       List[str]         =  with_box_refine
     lowerCAmelCase_:       Tuple         =  two_stage
     # set labels
     lowerCAmelCase_:       Union[str, Any]         =  91
     lowerCAmelCase_:       List[str]         =  "huggingface/label-files"
     lowerCAmelCase_:       Tuple         =  "coco-detection-id2label.json"
     lowerCAmelCase_:       int         =  json.load(open(cached_download(hf_hub_url(lowercase       ,							lowercase       ,							repo_type="dataset"				)				)       ,							"r"				)				)
     lowerCAmelCase_:       List[Any]         =  {int(lowercase				): v for k, v in idalabel.items()}
     lowerCAmelCase_:       Dict         =  idalabel
     lowerCAmelCase_:       Union[str, Any]         =  {v: k for k, v in idalabel.items()}
  
     # load image processor
     lowerCAmelCase_:       Optional[int]         =  DeformableDetrImageProcessor(format="coco_detection"				)
  
     # prepare image
     lowerCAmelCase_:       List[str]         =  prepare_img()
     lowerCAmelCase_:       Tuple         =  image_processor(images=lowercase       ,							return_tensors="pt"				)
     lowerCAmelCase_:       int         =  encoding["pixel_values"]
  
     logger.info("Converting model..."				)
  
     # load original state dict
     lowerCAmelCase_:       Optional[int]         =  torch.load(lowercase       ,							map_location="cpu"				)["model"]
     # rename keys
     for key in state_dict.copy().keys():
        lowerCAmelCase_:       Dict         =  state_dict.pop(lowercase				)
        lowerCAmelCase_:       Union[str, Any]         =  val
     # query, key and value matrices need special treatment
     read_in_q_k_v(lowercase				)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     lowerCAmelCase_:       List[str]         =  "model."
     for key in state_dict.copy().keys():
        if not key.startswith("class_embed"				) and not key.startswith("bbox_embed"				):
           lowerCAmelCase_:       Optional[int]         =  state_dict.pop(lowercase				)
           lowerCAmelCase_:       Any         =  val
      # finally, create HuggingFace model and load state dict
     lowerCAmelCase_:       Optional[int]         =  DeformableDetrForObjectDetection(lowercase				)
     model.load_state_dict(lowercase				)
     model.eval()
  
     lowerCAmelCase_:       Dict         =  "cuda" if torch.cuda.is_available() else "cpu"
     model.to(lowercase				)
     # verify our conversion
     lowerCAmelCase_:       int         =  model(pixel_values.to(lowercase				)				)
  
     lowerCAmelCase_:       List[Any]         =  torch.tensor(
         [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]				)
     lowerCAmelCase_:       List[str]         =  torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]				)
  
     if single_scale:
        lowerCAmelCase_:       Optional[Any]         =  torch.tensor(
            [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]				)
        lowerCAmelCase_:       Tuple         =  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:
        lowerCAmelCase_:       Optional[Any]         =  torch.tensor(
            [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]				)
        lowerCAmelCase_:       Dict         =  torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]				)
  
     if with_box_refine:
        lowerCAmelCase_:       Any         =  torch.tensor(
            [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]				)
        lowerCAmelCase_:       Tuple         =  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:
        lowerCAmelCase_:       str         =  torch.tensor(
            [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]				)
        lowerCAmelCase_:       int         =  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(lowercase				)       ,							atol=1E-4				)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,							expected_boxes.to(lowercase				)       ,							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(lowercase				).mkdir(exist_ok=lowercase				)
     model.save_pretrained(lowercase				)
     image_processor.save_pretrained(lowercase				)
  
     # Push to hub
     if push_to_hub:
        lowerCAmelCase_:       Union[str, Any]         =  "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=lowercase       ,							organization="nielsr"       ,							commit_message="Add model"				)
  
  
  if __name__ == "__main__":
   a     :			Dict					    =						argparse.ArgumentParser()
  
   parser.add_argument(
       """--checkpoint_path""",
       type=str,
       default="""/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth""",
       help="""Path to Pytorch checkpoint (.pth file) you'd like to convert.""",
   )
   parser.add_argument("""--single_scale""", action="""store_true""", help="""Whether to set config.num_features_levels = 1.""")
   parser.add_argument("""--dilation""", action="""store_true""", help="""Whether to set config.dilation=True.""")
   parser.add_argument("""--with_box_refine""", action="""store_true""", help="""Whether to set config.with_box_refine=True.""")
   parser.add_argument("""--two_stage""", action="""store_true""", help="""Whether to set config.two_stage=True.""")
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=str,
       required=True,
       help="""Path to the folder to output PyTorch model.""",
   )
   parser.add_argument(
       """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
   )
   a     :			List[Any]					    =						parser.parse_args()
   convert_deformable_detr_checkpoint(
       args.checkpoint_path,
       args.single_scale,
       args.dilation,
       args.with_box_refine,
       args.two_stage,
       args.pytorch_dump_folder_path,
       args.push_to_hub,
   )
style_context: import itertools
  def 		snake_case__  (		lowercase				):
     return (
         int("".join(combination[0:2]				)				) * int("".join(combination[2:5]				)				)
         == int("".join(combination[5:9]				)				)
     ) or (
         int("".join(combination[0]				)				) * int("".join(combination[1:5]				)				)
         == int("".join(combination[5:9]				)				)
     )
  def 		snake_case__  (		):
     return sum(
         {
             int("".join(pandigital[5:9]				)				)
             for pandigital in itertools.permutations("123456789"				)
             if is_combination_valid(lowercase				)
         }				)
  
  
  if __name__ == "__main__":
   print(solution())