code: 
  
  import argparse
  import json
  from pathlib import Path
  
  import requests
  import torch
  from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
  from PIL import Image
  
  from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  snake_case_    						=				logging.get_logger(__name__)
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Union[str, Any]   = DetaConfig(
           num_queries=900			,  encoder_ffn_dim=2_048			,  decoder_ffn_dim=2_048			,  num_feature_levels=5			,  assign_first_stage=SCREAMING_SNAKE_CASE_			,  with_box_refine=SCREAMING_SNAKE_CASE_			,  two_stage=SCREAMING_SNAKE_CASE_			,  )
  
       # set labels
       lowercase__					:     int   = 91
       lowercase__					:     Tuple   = 'huggingface/label-files'
       lowercase__					:     List[Any]   = 'coco-detection-id2label.json'
       lowercase__					:     Optional[int]   = json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  repo_type='dataset'    )    )			,  'r'    )    )
       lowercase__					:     Dict   = {int(SCREAMING_SNAKE_CASE_    ): v for k, v in idalabel.items()}
       lowercase__					:     Optional[int]   = idalabel
       lowercase__					:     Dict   = {v: k for k, v in idalabel.items()}
  
       return config
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					str    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     str   = []
  
       # stem
       # fmt: off
       rename_keys.append(('backbone.0.body.conv1.weight', 'model.backbone.model.embedder.embedder.convolution.weight')    )
       rename_keys.append(('backbone.0.body.bn1.weight', 'model.backbone.model.embedder.embedder.normalization.weight')    )
       rename_keys.append(('backbone.0.body.bn1.bias', 'model.backbone.model.embedder.embedder.normalization.bias')    )
       rename_keys.append(('backbone.0.body.bn1.running_mean', 'model.backbone.model.embedder.embedder.normalization.running_mean')    )
       rename_keys.append(('backbone.0.body.bn1.running_var', 'model.backbone.model.embedder.embedder.normalization.running_var')    )
       # stages
       for stage_idx in range(len(config.backbone_config.depths    )    ):
            for layer_idx in range(config.backbone_config.depths[stage_idx]    ):
                 # shortcut
                 if layer_idx == 0:
                      rename_keys.append(
                          (
                              f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                              f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                          )    )
                      rename_keys.append(
                          (
                              f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                              f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                          )    )
                      rename_keys.append(
                          (
                              f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                              f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                          )    )
                      rename_keys.append(
                          (
                              f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                              f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                          )    )
                      rename_keys.append(
                          (
                              f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                              f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                          )    )
                 # 3 convs
                 for i in range(3    ):
                      rename_keys.append(
                          (
                              f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                              f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                          )    )
                      rename_keys.append(
                          (
                              f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                              f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                          )    )
                      rename_keys.append(
                          (
                              f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                              f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                          )    )
                      rename_keys.append(
                          (
                              f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                              f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                          )    )
                      rename_keys.append(
                          (
                              f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                              f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                          )    )
      # transformer encoder
       for i in range(config.encoder_layers    ):
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")    )
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")    )
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")    )
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")    )
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""")    )
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""")    )
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""")    )
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""")    )
            rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")    )
            rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")    )
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""")    )
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""")    )
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""")    )
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""")    )
            rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""")    )
            rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""")    )
  
       # transformer decoder
       for i in range(config.decoder_layers    ):
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""")    )
            rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""")    )
  
       # fmt: on
  
       return rename_keys
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					List[str]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Union[str, Any]   = dct.pop(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     List[Any]   = val
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					str    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Dict   = config.d_model
       for i in range(config.decoder_layers    ):
            # read in weights + bias of input projection layer of self-attention
            lowercase__					:     List[str]   = state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""    )
            lowercase__					:     Union[str, Any]   = state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""    )
            # next, add query, keys and values (in that order) to the state dict
            lowercase__					:     Optional[int]   = in_proj_weight[:hidden_size, :]
            lowercase__					:     Union[str, Any]   = in_proj_bias[:hidden_size]
            lowercase__					:     Optional[Any]   = in_proj_weight[
                hidden_size : hidden_size * 2, :
            ]
            lowercase__					:     Optional[Any]   = in_proj_bias[hidden_size : hidden_size * 2]
            lowercase__					:     Dict   = in_proj_weight[-hidden_size:, :]
            lowercase__					:     Any   = in_proj_bias[-hidden_size:]
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Dict   = 'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowercase__					:     Optional[Any]   = Image.open(requests.get(SCREAMING_SNAKE_CASE_			,  stream=SCREAMING_SNAKE_CASE_    ).raw    )
  
       return im
  
  @torch.no_grad()
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					int    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Any   = get_deta_config()
  
       # load original state dict
       if model_name == "deta-resnet-50":
            lowercase__					:     int   = 'adet_checkpoint0011.pth'
       elif model_name == "deta-resnet-50-24-epochs":
            lowercase__					:     Dict   = 'adet_2x_checkpoint0023.pth'
       else:
            raise ValueError(f"""Model name {model_name} not supported"""    )
       lowercase__					:     List[str]   = hf_hub_download(repo_id='nielsr/deta-checkpoints'			,  filename=SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Optional[int]   = torch.load(SCREAMING_SNAKE_CASE_			,  map_location='cpu'    )['model']
  
       # rename keys
       lowercase__					:     Optional[Any]   = create_rename_keys(SCREAMING_SNAKE_CASE_    )
       for src, dest in rename_keys:
            rename_key(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
       read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
       # fix some prefixes
       for key in state_dict.copy().keys():
            if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                 lowercase__					:     Union[str, Any]   = state_dict.pop(SCREAMING_SNAKE_CASE_    )
                 lowercase__					:     List[Any]   = val
            if "input_proj" in key:
                 lowercase__					:     Optional[Any]   = state_dict.pop(SCREAMING_SNAKE_CASE_    )
                 lowercase__					:     List[Any]   = val
            if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                 lowercase__					:     Dict   = state_dict.pop(SCREAMING_SNAKE_CASE_    )
                 lowercase__					:     List[str]   = val
  
      # finally, create HuggingFace model and load state dict
       lowercase__					:     List[Any]   = DetaForObjectDetection(SCREAMING_SNAKE_CASE_    )
       model.load_state_dict(SCREAMING_SNAKE_CASE_    )
       model.eval()
  
       lowercase__					:     Union[str, Any]   = 'cuda' if torch.cuda.is_available() else 'cpu'
       model.to(SCREAMING_SNAKE_CASE_    )
  
       # load image processor
       lowercase__					:     Tuple   = DetaImageProcessor(format='coco_detection'    )
  
       # verify our conversion on image
       lowercase__					:     Tuple   = prepare_img()
       lowercase__					:     Dict   = processor(images=SCREAMING_SNAKE_CASE_			,  return_tensors='pt'    )
       lowercase__					:     List[str]   = encoding['pixel_values']
       lowercase__					:     Tuple   = model(pixel_values.to(SCREAMING_SNAKE_CASE_    )    )
  
       # verify logits
       if model_name == "deta-resnet-50":
            lowercase__					:     Tuple   = torch.tensor(
                [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]    )
            lowercase__					:     Optional[int]   = torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]    )
       elif model_name == "deta-resnet-50-24-epochs":
            lowercase__					:     Tuple   = torch.tensor(
                [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]    )
            lowercase__					:     Optional[int]   = torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]    )
  
       assert torch.allclose(outputs.logits[0, :3, :3]			,  expected_logits.to(SCREAMING_SNAKE_CASE_    )			,  atol=1E-4    )
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,  expected_boxes.to(SCREAMING_SNAKE_CASE_    )			,  atol=1E-4    )
       print('Everything ok!'    )
  
       if pytorch_dump_folder_path:
            # Save model and processor
            logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""    )
            Path(SCREAMING_SNAKE_CASE_    ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_    )
            model.save_pretrained(SCREAMING_SNAKE_CASE_    )
            processor.save_pretrained(SCREAMING_SNAKE_CASE_    )
  
       # Push to hub
       if push_to_hub:
            print('Pushing model and processor to hub...'    )
            model.push_to_hub(f"""jozhang97/{model_name}"""    )
            processor.push_to_hub(f"""jozhang97/{model_name}"""    )
  
  
  if __name__ == "__main__":
         snake_case_    						=				argparse.ArgumentParser()
  
         parser.add_argument(
             '''--model_name''',
             type=str,
             default='''deta-resnet-50''',
             choices=['''deta-resnet-50''', '''deta-resnet-50-24-epochs'''],
             help='''Name of the model you\'d like to convert.''',
         )
         parser.add_argument(
             '''--pytorch_dump_folder_path''',
             default=None,
             type=str,
             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.'''
         )
         snake_case_    						=				parser.parse_args()
         convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
style_context: 
  
  # as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.
  
  import os
  import sys
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow
  
  from .utils import load_json
  
  
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      @classmethod
      def    snake_case_       (   cls):
           return cls
  
  
  
  
  
      @slow
      @require_torch_gpu
      def    snake_case_       (   self):
           lowercase__					:     List[Any]   = self.get_auto_remove_tmp_dir()
           lowercase__					:     List[str]   = f"""
              --model_name Helsinki-NLP/opus-mt-en-ro
              --save_dir {output_dir}
              --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
              --num_beams 2
              --task translation
          """.split()
  
           # we want this test to run even if there is only one GPU, but if there are more we use them all
           lowercase__					:     int   = get_gpu_count()
           lowercase__					:     Tuple   = f"""
              -m torch.distributed.launch
              --nproc_per_node={n_gpu}
              {self.test_file_dir}/run_distributed_eval.py
          """.split()
           lowercase__					:     Optional[Any]   = [sys.executable] + distributed_args + args
           execute_subprocess_async(a		,     env=self.get_env())
  
           lowercase__					:     str   = os.path.join(a		,     'test_bleu.json')
           lowercase__					:     List[str]   = load_json(a)
           # print(metrics)
           self.assertGreaterEqual(metrics['bleu']		,     25)
  
  