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				:	List[Any]							   =						logging.get_logger(__name__)
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      )       ->	List[str]:
  
  
  
  
  
  
  
   '''simple docstring'''
  
  
   __magic_name__	:	Optional[Any]			     =  DetaConfig(
       num_queries=900   ,     encoder_ffn_dim=2048   ,     decoder_ffn_dim=2048   ,     num_feature_levels=5   ,     assign_first_stage=snake_case__   ,     with_box_refine=snake_case__   ,     two_stage=snake_case__   ,     )
  
   # set labels
   __magic_name__	:	int			     =  91
   __magic_name__	:	Tuple			     =  'huggingface/label-files'
   __magic_name__	:	List[str]			     =  'coco-detection-id2label.json'
   __magic_name__	:	int			     =  json.load(open(cached_download(hf_hub_url(snake_case__   ,     snake_case__   ,     repo_type="dataset"     )     )   ,     "r"     )     )
   __magic_name__	:	List[Any]			     =  {int(snake_case__     ): v for k, v in idalabel.items()}
   __magic_name__	:	Tuple			     =  idalabel
   __magic_name__	:	str			     =  {v: k for k, v in idalabel.items()}
  
   return config
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Dict     )       ->	Dict:
  
  
  
  
  
  
  
   '''simple docstring'''
  
  
   __magic_name__	:	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 						lowerCAmelCase_		(      _snake_case   :						Any   ,     _snake_case   :						List[Any]   ,     _snake_case   :						Optional[int]     )       ->	List[Any]:
  
  
  
  
  
  
  
   '''simple docstring'''
  
  
   __magic_name__	:	int			     =  dct.pop(snake_case__     )
   __magic_name__	:	List[Any]			     =  val
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Dict   ,     _snake_case   :						Tuple     )       ->	Any:
  
  
  
  
  
  
  
   '''simple docstring'''
  
  
   __magic_name__	:	Union[str, Any]			     =  config.d_model
   for i in range(config.decoder_layers     ):
    # read in weights + bias of input projection layer of self-attention
    __magic_name__	:	List[str]			     =  state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''     )
    __magic_name__	:	Tuple			     =  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
    __magic_name__	:	Optional[Any]			     =  in_proj_weight[:hidden_size, :]
    __magic_name__	:	int			     =  in_proj_bias[:hidden_size]
    __magic_name__	:	Optional[Any]			     =  in_proj_weight[
        hidden_size : hidden_size * 2, :
    ]
    __magic_name__	:	str			     =  in_proj_bias[hidden_size : hidden_size * 2]
    __magic_name__	:	Optional[int]			     =  in_proj_weight[-hidden_size:, :]
    __magic_name__	:	Union[str, Any]			     =  in_proj_bias[-hidden_size:]
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      )       ->	List[Any]:
  
  
  
  
  
  
  
   '''simple docstring'''
  
  
   __magic_name__	:	Optional[int]			     =  'http://images.cocodataset.org/val2017/000000039769.jpg'
   __magic_name__	:	Union[str, Any]			     =  Image.open(requests.get(snake_case__   ,     stream=snake_case__     ).raw     )
  
   return im
  
  
  
  
  
  
  @torch.no_grad()
  def 						lowerCAmelCase_		(      _snake_case   :						List[Any]   ,     _snake_case   :						Union[str, Any]   ,     _snake_case   :						List[Any]     )       ->	Tuple:
  
  
  
  
  
  
  
   '''simple docstring'''
  
  
   __magic_name__	:	Union[str, Any]			     =  get_deta_config()
  
   # load original state dict
   if model_name == "deta-resnet-50":
    __magic_name__	:	Optional[int]			     =  'adet_checkpoint0011.pth'
   elif model_name == "deta-resnet-50-24-epochs":
    __magic_name__	:	Optional[Any]			     =  'adet_2x_checkpoint0023.pth'
   else:
    raise ValueError(F'''Model name {model_name} not supported'''     )
   __magic_name__	:	Optional[int]			     =  hf_hub_download(repo_id="nielsr/deta-checkpoints"   ,     filename=snake_case__     )
   __magic_name__	:	Any			     =  torch.load(snake_case__   ,     map_location="cpu"     )['model']
  
   # rename keys
   __magic_name__	:	Optional[int]			     =  create_rename_keys(snake_case__     )
   for src, dest in rename_keys:
    rename_key(snake_case__   ,     snake_case__   ,     snake_case__     )
   read_in_decoder_q_k_v(snake_case__   ,     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:
     __magic_name__	:	List[str]			     =  state_dict.pop(snake_case__     )
     __magic_name__	:	Optional[Any]			     =  val
    if "input_proj" in key:
     __magic_name__	:	str			     =  state_dict.pop(snake_case__     )
     __magic_name__	:	Tuple			     =  val
    if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
     __magic_name__	:	List[Any]			     =  state_dict.pop(snake_case__     )
     __magic_name__	:	Dict			     =  val
  
      # finally, create HuggingFace model and load state dict
   __magic_name__	:	List[Any]			     =  DetaForObjectDetection(snake_case__     )
   model.load_state_dict(snake_case__     )
   model.eval()
  
   __magic_name__	:	int			     =  'cuda' if torch.cuda.is_available() else 'cpu'
   model.to(snake_case__     )
  
   # load image processor
   __magic_name__	:	Optional[Any]			     =  DetaImageProcessor(format="coco_detection"     )
  
   # verify our conversion on image
   __magic_name__	:	Dict			     =  prepare_img()
   __magic_name__	:	List[Any]			     =  processor(images=snake_case__   ,     return_tensors="pt"     )
   __magic_name__	:	List[Any]			     =  encoding['pixel_values']
   __magic_name__	:	Dict			     =  model(pixel_values.to(snake_case__     )     )
  
   # verify logits
   if model_name == "deta-resnet-50":
    __magic_name__	:	Dict			     =  torch.tensor(
        [[-7.3_978, -2.5_406, -4.1_668], [-8.2_684, -3.9_933, -3.8_096], [-7.0_515, -3.7_973, -5.8_516]]     )
    __magic_name__	:	Tuple			     =  torch.tensor([[0.5_043, 0.4_973, 0.9_998], [0.2_542, 0.5_489, 0.4_748], [0.5_490, 0.2_765, 0.0_570]]     )
   elif model_name == "deta-resnet-50-24-epochs":
    __magic_name__	:	Any			     =  torch.tensor(
        [[-7.1_688, -2.4_857, -4.8_669], [-7.8_630, -3.8_154, -4.2_674], [-7.2_730, -4.1_865, -5.5_323]]     )
    __magic_name__	:	List[Any]			     =  torch.tensor([[0.5_021, 0.4_971, 0.9_994], [0.2_546, 0.5_486, 0.4_731], [0.1_686, 0.1_986, 0.2_142]]     )
  
   assert torch.allclose(outputs.logits[0, :3, :3]   ,     expected_logits.to(snake_case__     )   ,     atol=1E-4     )
   assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,     expected_boxes.to(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(snake_case__     ).mkdir(exist_ok=snake_case__     )
    model.save_pretrained(snake_case__     )
    processor.save_pretrained(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				:	str							   =						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				:	Dict							   =						parser.parse_args()
      convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
  
style_context: 
  def 						lowerCAmelCase_		(      _snake_case   :						int   ,     _snake_case   :						list[tuple[int, int, int]]     )       ->	list[tuple[int, int, int]]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Union[str, Any]			     =  sorted(_snake_case   ,     key=lambda _snake_case     : edge[2]     )
  
  	__magic_name__	:	Union[str, Any]			     =  list(range(_snake_case     )     )
  
  	def find_parent(_snake_case   :						Dict     ):
  		if i != parent[i]:
  			__magic_name__	:	Any			     =  find_parent(parent[i]     )
  		return parent[i]
  
  	__magic_name__	:	List[str]			     =  0
  	__magic_name__	:	int			     =  []
  
  	for edge in edges:
  		__magic_name__	:	Any			     =  find_parent(edge[0]     )
  		__magic_name__	:	int			     =  find_parent(edge[1]     )
  		if parent_a != parent_b:
  			minimum_spanning_tree_cost += edge[2]
  			minimum_spanning_tree.append(_snake_case     )
  			__magic_name__	:	Optional[int]			     =  parent_b
  
  	return minimum_spanning_tree
  
  
  if __name__ == "__main__":  # pragma: no cover
  				snake_case   ,snake_case				:	int							   =						list(map(int, input().strip().split()))
  				snake_case				:	int							   =						[]
  
  				for _ in range(num_edges):
  								snake_case   ,snake_case   ,snake_case				:	Dict							   =						(int(x) for x in input().strip().split())
  								edges.append((nodea, nodea, cost))
  
  				kruskal(num_nodes, edges)
  