



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	List[str]				)  ->     Dict:
 return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]=0				)  ->     Union[str, Any]:
 return sorted(SCREAMING_SNAKE_CASE__				,       key=lambda SCREAMING_SNAKE_CASE__				: x[column]				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[str]				,       SCREAMING_SNAKE_CASE__       :	List[str]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]=float("""inf"""				)				)  ->     Dict:

 for i in range(points_counts - 1				):
  for j in range(i + 1				,       SCREAMING_SNAKE_CASE__				):
   _snake_case    :  int					=					euclidean_distance_sqr(points[i]				,       points[j]				)
   if current_dis < min_dis:
    _snake_case    :  int					=					current_dis
 return min_dis



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	Optional[int]=float("""inf"""				)				)  ->     List[Any]:

 for i in range(min(6				,       points_counts - 1				)				,       SCREAMING_SNAKE_CASE__				):
  for j in range(max(0				,       i - 6				)				,       SCREAMING_SNAKE_CASE__				):
   _snake_case    :  Dict					=					euclidean_distance_sqr(points[i]				,       points[j]				)
   if current_dis < min_dis:
    _snake_case    :  Optional[int]					=					current_dis
 return min_dis



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	List[str]				)  ->     Optional[Any]:

 # base case
 if points_counts <= 3:
  return dis_between_closest_pair(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 # recursion
 _snake_case    :  Optional[Any]					=					points_counts // 2
 _snake_case    :  Any					=					closest_pair_of_points_sqr(
     SCREAMING_SNAKE_CASE__				,       points_sorted_on_y[:mid]				,       SCREAMING_SNAKE_CASE__				)
 _snake_case    :  str					=					closest_pair_of_points_sqr(
     SCREAMING_SNAKE_CASE__				,       points_sorted_on_y[mid:]				,       points_counts - mid				)
 _snake_case    :  Dict					=					min(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 _snake_case    :  List[Any]					=					[]
 for point in points_sorted_on_x:
  if abs(point[0] - points_sorted_on_x[mid][0]				) < closest_pair_dis:
   cross_strip.append(SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Dict					=					dis_between_closest_in_strip(
     SCREAMING_SNAKE_CASE__				,       len(SCREAMING_SNAKE_CASE__				)				,       SCREAMING_SNAKE_CASE__				)
 return min(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				)  ->     Any:
 _snake_case    :  int					=					column_based_sort(SCREAMING_SNAKE_CASE__				,       column=0				)
 _snake_case    :  Any					=					column_based_sort(SCREAMING_SNAKE_CASE__				,       column=1				)
 return (
     closest_pair_of_points_sqr(
         SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 ) ** 0.5


if __name__ == "__main__":
  a__    =	[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
  print("""Distance:""", closest_pair_of_points(points, len(points)))





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
a__    =	logging.get_logger(__name__)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[Any]				)  ->     Optional[Any]:
 # initialize config
 if "resnet-50" in model_name:
  _snake_case    :  List[Any]					=					ResNetConfig.from_pretrained("""microsoft/resnet-50"""				)
 elif "resnet-101" in model_name:
  _snake_case    :  Any					=					ResNetConfig.from_pretrained("""microsoft/resnet-101"""				)
 else:
  raise ValueError("""Model name should include either resnet50 or resnet101"""				)

 _snake_case    :  Union[str, Any]					=					DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE__				,       backbone_config=SCREAMING_SNAKE_CASE__				)

 # set label attributes
 _snake_case    :  List[str]					=					"""panoptic""" in model_name
 if is_panoptic:
  _snake_case    :  Optional[int]					=					250
 else:
  _snake_case    :  Optional[int]					=					91
  _snake_case    :  Optional[Any]					=					"""huggingface/label-files"""
  _snake_case    :  Optional[int]					=					"""coco-detection-id2label.json"""
  _snake_case    :  str					=					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       repo_type="""dataset"""				)				,       """r"""				)				)
  _snake_case    :  Dict					=					{int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}
  _snake_case    :  List[str]					=					idalabel
  _snake_case    :  str					=					{v: k for k, v in idalabel.items()}

 return config, is_panoptic



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Optional[int]:
 # here we list all keys to be renamed (original name on the left, our name on the right)
 _snake_case    :  Optional[Any]					=					[]

 # stem
 # fmt: off
 rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")				)
 rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")				)
 rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")				)
 rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")				)
 rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
        )				)
    # fmt: on

 for i in range(config.encoder_layers				):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (
          F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
          F'''encoder.layers.{i}.self_attn.out_proj.weight''',
      )				)
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')				)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight''')				)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias''')				)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight''')				)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias''')				)
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')				)
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias''')				)
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight''')				)
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias''')				)
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
          F'''decoder.layers.{i}.self_attn.out_proj.weight''',
      )				)
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')				)
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )				)
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
      )				)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight''')				)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias''')				)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight''')				)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias''')				)
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')				)
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias''')				)
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')				)
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')				)
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight''')				)
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias''')				)

 # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
 rename_keys.extend(
     [
         ("""input_proj.weight""", """input_projection.weight"""),
         ("""input_proj.bias""", """input_projection.bias"""),
         ("""query_embed.weight""", """query_position_embeddings.weight"""),
         ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
         ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
         ("""class_embed.weight""", """class_labels_classifier.weight"""),
         ("""class_embed.bias""", """class_labels_classifier.bias"""),
         ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
         ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
         ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
         ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
         ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
         ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
     ]				)

 return rename_keys



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	str				)  ->     Optional[Any]:
 _snake_case    :  str					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  str					=					val



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	str=False				)  ->     Union[str, Any]:
 _snake_case    :  Optional[Any]					=					""""""
 if is_panoptic:
  _snake_case    :  Optional[Any]					=					"""detr."""

 # first: transformer encoder
 for i in range(6				):
  # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
  _snake_case    :  Any					=					state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
  _snake_case    :  List[str]					=					state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''				)
  # next, add query, keys and values (in that order) to the state dict
  _snake_case    :  Optional[Any]					=					in_proj_weight[:256, :]
  _snake_case    :  Any					=					in_proj_bias[:256]
  _snake_case    :  List[Any]					=					in_proj_weight[256:512, :]
  _snake_case    :  Optional[int]					=					in_proj_bias[256:512]
  _snake_case    :  int					=					in_proj_weight[-256:, :]
  _snake_case    :  Union[str, Any]					=					in_proj_bias[-256:]
 # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
 for i in range(6				):
  # read in weights + bias of input projection layer of self-attention
  _snake_case    :  Tuple					=					state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''				)
  _snake_case    :  Dict					=					state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''				)
  # next, add query, keys and values (in that order) to the state dict
  _snake_case    :  Optional[int]					=					in_proj_weight[:256, :]
  _snake_case    :  Optional[int]					=					in_proj_bias[:256]
  _snake_case    :  Any					=					in_proj_weight[256:512, :]
  _snake_case    :  int					=					in_proj_bias[256:512]
  _snake_case    :  int					=					in_proj_weight[-256:, :]
  _snake_case    :  Tuple					=					in_proj_bias[-256:]
  # read in weights + bias of input projection layer of cross-attention
  _snake_case    :  List[str]					=					state_dict.pop(
      F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''				)
  _snake_case    :  Optional[Any]					=					state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''				)
  # next, add query, keys and values (in that order) of cross-attention to the state dict
  _snake_case    :  int					=					in_proj_weight_cross_attn[:256, :]
  _snake_case    :  Tuple					=					in_proj_bias_cross_attn[:256]
  _snake_case    :  int					=					in_proj_weight_cross_attn[256:512, :]
  _snake_case    :  Tuple					=					in_proj_bias_cross_attn[256:512]
  _snake_case    :  Dict					=					in_proj_weight_cross_attn[-256:, :]
  _snake_case    :  Union[str, Any]					=					in_proj_bias_cross_attn[-256:]



def       lowercase       (   )  ->     Optional[Any]:
 _snake_case    :  Dict					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 _snake_case    :  Dict					=					Image.open(requests.get(SCREAMING_SNAKE_CASE__				,       stream=SCREAMING_SNAKE_CASE__				).raw				)

 return im



@torch.no_grad()
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	Dict=None				,       SCREAMING_SNAKE_CASE__       :	Any=False				)  ->     List[Any]:
 _snake_case	,    _snake_case    :  Union[str, Any]					=					get_detr_config(SCREAMING_SNAKE_CASE__				)

 # load original model from torch hub
 _snake_case    :  Dict					=					{
     """detr-resnet-50""": """detr_resnet50""",
     """detr-resnet-101""": """detr_resnet101""",
 }
 logger.info(F'''Converting model {model_name}...'''				)
 _snake_case    :  List[Any]					=					torch.hub.load("""facebookresearch/detr"""				,       model_name_to_original_name[model_name]				,       pretrained=SCREAMING_SNAKE_CASE__				).eval()
 _snake_case    :  Optional[int]					=					detr.state_dict()
 # rename keys
 for src, dest in create_rename_keys(SCREAMING_SNAKE_CASE__				):
  if is_panoptic:
   _snake_case    :  int					=					"""detr.""" + src
  rename_key(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 # query, key and value matrices need special treatment
 read_in_q_k_v(SCREAMING_SNAKE_CASE__				,       is_panoptic=SCREAMING_SNAKE_CASE__				)
 # 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    :  List[Any]					=					"""detr.model.""" if is_panoptic else """model."""
 for key in state_dict.copy().keys():
  if is_panoptic:
   if (
       key.startswith("""detr"""				)
       and not key.startswith("""class_labels_classifier"""				)
       and not key.startswith("""bbox_predictor"""				)
   ):
    _snake_case    :  List[str]					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
    _snake_case    :  Optional[int]					=					val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    _snake_case    :  Tuple					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
    _snake_case    :  List[str]					=					val
   elif key.startswith("""bbox_attention"""				) or key.startswith("""mask_head"""				):
    continue
   else:
    _snake_case    :  Optional[int]					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
    _snake_case    :  Dict					=					val
  else:
   if not key.startswith("""class_labels_classifier"""				) and not key.startswith("""bbox_predictor"""				):
    _snake_case    :  Union[str, Any]					=					state_dict.pop(SCREAMING_SNAKE_CASE__				)
    _snake_case    :  Tuple					=					val

    # finally, create HuggingFace model and load state dict
 _snake_case    :  int					=					DetrForSegmentation(SCREAMING_SNAKE_CASE__				) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__				)
 model.load_state_dict(SCREAMING_SNAKE_CASE__				)
 model.eval()

 # verify our conversion on an image
 _snake_case    :  List[Any]					=					"""coco_panoptic""" if is_panoptic else """coco_detection"""
 _snake_case    :  int					=					DetrImageProcessor(format=SCREAMING_SNAKE_CASE__				)

 _snake_case    :  int					=					processor(images=prepare_img()				,       return_tensors="""pt"""				)
 _snake_case    :  str					=					encoding["""pixel_values"""]

 _snake_case    :  Tuple					=					detr(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[int]					=					model(SCREAMING_SNAKE_CASE__				)

 assert torch.allclose(outputs.logits				,       original_outputs["""pred_logits"""]				,       atol=1e-3				)
 assert torch.allclose(outputs.pred_boxes				,       original_outputs["""pred_boxes"""]				,       atol=1e-3				)
 if is_panoptic:
  assert torch.allclose(outputs.pred_masks				,       original_outputs["""pred_masks"""]				,       atol=1e-4				)
 print("""Looks ok!"""				)

 if pytorch_dump_folder_path is not None:
  # Save model and image processor
  logger.info(F'''Saving PyTorch model and image 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__				)

 if push_to_hub:
  # Upload model and image processor to the hub
  logger.info("""Uploading PyTorch model and image processor to the hub..."""				)
  model.push_to_hub(F'''nielsr/{model_name}'''				)
  processor.push_to_hub(F'''nielsr/{model_name}'''				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""",
      default="""detr-resnet-50""",
      type=str,
      choices=["""detr-resnet-50""", """detr-resnet-101"""],
      help="""Name of the DETR 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 to push the model to the hub or not.""")
  a__    =	parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
