code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  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()
  lowerCamelCase_     :     int    		=						logging.get_logger(__name__)
  def _A		(		):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=DetaConfig(
          num_queries=9_00 ,					encoder_ffn_dim=20_48 ,					decoder_ffn_dim=20_48 ,					num_feature_levels=5 ,					assign_first_stage=__a ,					with_box_refine=__a ,					two_stage=__a ,					)
  
      # set labels
      a					=91
      a					='''huggingface/label-files'''
      a					='''coco-detection-id2label.json'''
      a					=json.load(open(cached_download(hf_hub_url(__a ,					__a ,					repo_type='''dataset'''	)	) ,					'''r'''	)	)
      a					={int(__a	): v for k, v in idalabel.items()}
      a					=idalabel
      a					={v: k for k, v in idalabel.items()}
  
      return config
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=[]
  
      # 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 _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=dct.pop(__a	)
      a					=val
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=config.d_model
      for i in range(config.decoder_layers	):
          # read in weights + bias of input projection layer of self-attention
          a					=state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''	)
          a					=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
          a					=in_proj_weight[:hidden_size, :]
          a					=in_proj_bias[:hidden_size]
          a					=in_proj_weight[
              hidden_size : hidden_size * 2, :
          ]
          a					=in_proj_bias[hidden_size : hidden_size * 2]
          a					=in_proj_weight[-hidden_size:, :]
          a					=in_proj_bias[-hidden_size:]
  def _A		(		):
  
  
  
  
  
  
  
      """simple docstring"""
      a					='''http://images.cocodataset.org/val2017/000000039769.jpg'''
      a					=Image.open(requests.get(__a ,					stream=__a	).raw	)
  
      return im
  @torch.no_grad()
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=get_deta_config()
  
      # load original state dict
      if model_name == "deta-resnet-50":
          a					='''adet_checkpoint0011.pth'''
      elif model_name == "deta-resnet-50-24-epochs":
          a					='''adet_2x_checkpoint0023.pth'''
      else:
          raise ValueError(f'''Model name {model_name} not supported'''	)
      a					=hf_hub_download(repo_id='''nielsr/deta-checkpoints''' ,					filename=__a	)
      a					=torch.load(__a ,					map_location='''cpu'''	)['''model''']
  
      # rename keys
      a					=create_rename_keys(__a	)
      for src, dest in rename_keys:
          rename_key(__a ,					__a ,					__a	)
      read_in_decoder_q_k_v(__a ,					__a	)
  
      # fix some prefixes
      for key in state_dict.copy().keys():
          if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
              a					=state_dict.pop(__a	)
              a					=val
          if "input_proj" in key:
              a					=state_dict.pop(__a	)
              a					=val
          if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
              a					=state_dict.pop(__a	)
              a					=val
  
      # finally, create HuggingFace model and load state dict
      a					=DetaForObjectDetection(__a	)
      model.load_state_dict(__a	)
      model.eval()
  
      a					='''cuda''' if torch.cuda.is_available() else '''cpu'''
      model.to(__a	)
  
      # load image processor
      a					=DetaImageProcessor(format='''coco_detection'''	)
  
      # verify our conversion on image
      a					=prepare_img()
      a					=processor(images=__a ,					return_tensors='''pt'''	)
      a					=encoding['''pixel_values''']
      a					=model(pixel_values.to(__a	)	)
  
      # verify logits
      if model_name == "deta-resnet-50":
          a					=torch.tensor(
              [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]	)
          a					=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":
          a					=torch.tensor(
              [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]	)
          a					=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(__a	) ,					atol=1E-4	)
      assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,					expected_boxes.to(__a	) ,					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(__a	).mkdir(exist_ok=__a	)
          model.save_pretrained(__a	)
          processor.save_pretrained(__a	)
  
      # 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__":
        lowerCamelCase_     :     Union[str, Any]    		=						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."""
        )
        lowerCamelCase_     :     Optional[Any]    		=						parser.parse_args()
        convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from math import gcd
  def _A		(		lowercase = 1_20_00	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=0
      for d in range(max_d + 1	):
          for n in range(d // 3 + 1 ,					(d + 1) // 2	):
              if gcd(lowercase ,					lowercase	) == 1:
                  fractions_number += 1
      return fractions_number
  
  
  if __name__ == "__main__":
         print(F'{solution() = }')