code: 
  
  
  
  
  
  import heapq
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    Union[str, Any]   ,     lowercase       :    Dict   ,     lowercase       :    Optional[Any]				) ->    Optional[int]:
    __snake_case   :				Union[str, Any]        =       [(0, start)]  # cost from start node,end node
    __snake_case   :				Dict        =       set()
    while heap:
      ((__snake_case)    ,   (__snake_case))   :				List[str]        =       heapq.heappop(lowercase				)
      if u in visited:
        continue
      visited.add(lowercase				)
      if u == end:
        return cost
      for v, c in graph[u]:
        if v in visited:
          continue
        __snake_case   :				List[str]        =       cost + c
        heapq.heappush(lowercase   ,     (next_item, v)				)
    return -1
  
  
  _UpperCamelCase       = {
      '''A''': [['''B''', 2], ['''C''', 5]],
      '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
      '''C''': [['''A''', 5], ['''F''', 3]],
      '''D''': [['''B''', 3]],
      '''E''': [['''B''', 4], ['''F''', 3]],
      '''F''': [['''C''', 3], ['''E''', 3]],
  }
  _UpperCamelCase       = {
      '''B''': [['''C''', 1]],
      '''C''': [['''D''', 1]],
      '''D''': [['''F''', 1]],
      '''E''': [['''B''', 1], ['''F''', 3]],
      '''F''': [],
  }
  _UpperCamelCase       = {
      '''B''': [['''C''', 1]],
      '''C''': [['''D''', 1]],
      '''D''': [['''F''', 1]],
      '''E''': [['''B''', 1], ['''G''', 2]],
      '''F''': [],
      '''G''': [['''F''', 1]],
  }
  
  _UpperCamelCase       = dijkstra(G, '''E''', '''C''')
  print(short_distance)  # E -- 3 --> F -- 3 --> C == 6
  
  _UpperCamelCase       = dijkstra(Ga, '''E''', '''F''')
  print(short_distance)  # E -- 3 --> F == 3
  
  _UpperCamelCase       = dijkstra(Ga, '''E''', '''F''')
  print(short_distance)  # E -- 2 --> G -- 1 --> F == 3
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
  
style_context: 
  
  
  
  
  
  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()
  _UpperCamelCase       = logging.get_logger(__name__)
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    Tuple				) ->    int:
    if "backbone.0.body" in orig_key:
      __snake_case   :				str        =       orig_key.replace("backbone.0.body"   ,     "backbone.conv_encoder.model"				)
    if "transformer" in orig_key:
      __snake_case   :				List[Any]        =       orig_key.replace("transformer."   ,     ""				)
    if "norm1" in orig_key:
      if "encoder" in orig_key:
        __snake_case   :				List[str]        =       orig_key.replace("norm1"   ,     "self_attn_layer_norm"				)
      else:
        __snake_case   :				Optional[int]        =       orig_key.replace("norm1"   ,     "encoder_attn_layer_norm"				)
    if "norm2" in orig_key:
      if "encoder" in orig_key:
        __snake_case   :				Optional[Any]        =       orig_key.replace("norm2"   ,     "final_layer_norm"				)
      else:
        __snake_case   :				List[str]        =       orig_key.replace("norm2"   ,     "self_attn_layer_norm"				)
    if "norm3" in orig_key:
      __snake_case   :				Dict        =       orig_key.replace("norm3"   ,     "final_layer_norm"				)
    if "linear1" in orig_key:
      __snake_case   :				Optional[Any]        =       orig_key.replace("linear1"   ,     "fc1"				)
    if "linear2" in orig_key:
      __snake_case   :				Dict        =       orig_key.replace("linear2"   ,     "fc2"				)
    if "query_embed" in orig_key:
      __snake_case   :				Optional[int]        =       orig_key.replace("query_embed"   ,     "query_position_embeddings"				)
    if "cross_attn" in orig_key:
      __snake_case   :				str        =       orig_key.replace("cross_attn"   ,     "encoder_attn"				)
  
    return orig_key
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    Optional[int]				) ->    Tuple:
    # transformer decoder self-attention layers
    for i in range(6				):
      # read in weights + bias of input projection layer of self-attention
      __snake_case   :				str        =       state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""				)
      __snake_case   :				Optional[Any]        =       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
      __snake_case   :				Tuple        =       in_proj_weight[:256, :]
      __snake_case   :				Dict        =       in_proj_bias[:256]
      __snake_case   :				List[str]        =       in_proj_weight[256:512, :]
      __snake_case   :				Optional[Any]        =       in_proj_bias[256:512]
      __snake_case   :				Optional[Any]        =       in_proj_weight[-256:, :]
      __snake_case   :				List[Any]        =       in_proj_bias[-256:]
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	) ->    str:
    __snake_case   :				Union[str, Any]        =       "http://images.cocodataset.org/val2017/000000039769.jpg"
    __snake_case   :				Optional[Any]        =       Image.open(requests.get(lowercase   ,     stream=lowercase				).raw				)
  
    return im
  
  
  
  
  
  
  
  @torch.no_grad()
  def       lowerCAmelCase__(	lowercase       :    List[str]   ,     lowercase       :    int   ,     lowercase       :    Any   ,     lowercase       :    Optional[int]   ,     lowercase       :    List[Any]   ,     lowercase       :    str   ,     lowercase       :    int   ,     ) ->    Optional[Any]:
    __snake_case   :				Optional[int]        =       DeformableDetrConfig()
    # set config attributes
    if single_scale:
      __snake_case   :				List[str]        =       1
    __snake_case   :				Any        =       dilation
    __snake_case   :				List[str]        =       with_box_refine
    __snake_case   :				List[str]        =       two_stage
    # set labels
    __snake_case   :				int        =       91
    __snake_case   :				Any        =       "huggingface/label-files"
    __snake_case   :				List[Any]        =       "coco-detection-id2label.json"
    __snake_case   :				Tuple        =       json.load(open(cached_download(hf_hub_url(lowercase   ,     lowercase   ,     repo_type="dataset"				)				)   ,     "r"				)				)
    __snake_case   :				List[str]        =       {int(lowercase				): v for k, v in idalabel.items()}
    __snake_case   :				Optional[int]        =       idalabel
    __snake_case   :				str        =       {v: k for k, v in idalabel.items()}
  
    # load image processor
    __snake_case   :				Union[str, Any]        =       DeformableDetrImageProcessor(format="coco_detection"				)
  
    # prepare image
    __snake_case   :				List[Any]        =       prepare_img()
    __snake_case   :				Optional[int]        =       image_processor(images=lowercase   ,     return_tensors="pt"				)
    __snake_case   :				List[Any]        =       encoding["pixel_values"]
  
    logger.info("Converting model..."				)
  
    # load original state dict
    __snake_case   :				Optional[Any]        =       torch.load(lowercase   ,     map_location="cpu"				)["model"]
    # rename keys
    for key in state_dict.copy().keys():
      __snake_case   :				List[Any]        =       state_dict.pop(lowercase				)
      __snake_case   :				Dict        =       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
    __snake_case   :				Union[str, Any]        =       "model."
    for key in state_dict.copy().keys():
      if not key.startswith("class_embed"				) and not key.startswith("bbox_embed"				):
        __snake_case   :				List[str]        =       state_dict.pop(lowercase				)
        __snake_case   :				int        =       val
      # finally, create HuggingFace model and load state dict
    __snake_case   :				int        =       DeformableDetrForObjectDetection(lowercase				)
    model.load_state_dict(lowercase				)
    model.eval()
  
    __snake_case   :				str        =       "cuda" if torch.cuda.is_available() else "cpu"
    model.to(lowercase				)
    # verify our conversion
    __snake_case   :				Any        =       model(pixel_values.to(lowercase				)				)
  
    __snake_case   :				Union[str, Any]        =       torch.tensor(
        [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]				)
    __snake_case   :				Any        =       torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]				)
  
    if single_scale:
      __snake_case   :				Union[str, Any]        =       torch.tensor(
          [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]				)
      __snake_case   :				Any        =       torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]				)
  
    if single_scale and dilation:
      __snake_case   :				Dict        =       torch.tensor(
          [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]				)
      __snake_case   :				List[str]        =       torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]				)
  
    if with_box_refine:
      __snake_case   :				List[str]        =       torch.tensor(
          [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]				)
      __snake_case   :				List[str]        =       torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]				)
  
    if with_box_refine and two_stage:
      __snake_case   :				Tuple        =       torch.tensor(
          [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]				)
      __snake_case   :				Any        =       torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]				)
  
    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:
      __snake_case   :				int        =       "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__":
       _UpperCamelCase       = 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.'''
       )
       _UpperCamelCase       = 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,
       )
  
  