code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  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()
  _a      :				str											=					logging.get_logger(__name__)
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase			)      ->      Optional[Any]:
  			if "backbone.0.body" in orig_key:
  						__lowerCAmelCase      		=       orig_key.replace("""backbone.0.body"""      ,			"""backbone.conv_encoder.model"""			)
  			if "transformer" in orig_key:
  						__lowerCAmelCase      		=       orig_key.replace("""transformer."""      ,			""""""			)
  			if "norm1" in orig_key:
  						if "encoder" in orig_key:
  									__lowerCAmelCase      		=       orig_key.replace("""norm1"""      ,			"""self_attn_layer_norm"""			)
  						else:
  									__lowerCAmelCase      		=       orig_key.replace("""norm1"""      ,			"""encoder_attn_layer_norm"""			)
  			if "norm2" in orig_key:
  						if "encoder" in orig_key:
  									__lowerCAmelCase      		=       orig_key.replace("""norm2"""      ,			"""final_layer_norm"""			)
  						else:
  									__lowerCAmelCase      		=       orig_key.replace("""norm2"""      ,			"""self_attn_layer_norm"""			)
  			if "norm3" in orig_key:
  						__lowerCAmelCase      		=       orig_key.replace("""norm3"""      ,			"""final_layer_norm"""			)
  			if "linear1" in orig_key:
  						__lowerCAmelCase      		=       orig_key.replace("""linear1"""      ,			"""fc1"""			)
  			if "linear2" in orig_key:
  						__lowerCAmelCase      		=       orig_key.replace("""linear2"""      ,			"""fc2"""			)
  			if "query_embed" in orig_key:
  						__lowerCAmelCase      		=       orig_key.replace("""query_embed"""      ,			"""query_position_embeddings"""			)
  			if "cross_attn" in orig_key:
  						__lowerCAmelCase      		=       orig_key.replace("""cross_attn"""      ,			"""encoder_attn"""			)
  
  			return orig_key
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase			)      ->      Optional[Any]:
  			# transformer decoder self-attention layers
  			for i in range(6			):
  						# read in weights + bias of input projection layer of self-attention
  						__lowerCAmelCase      		=       state_dict.pop(f'decoder.layers.{i}.self_attn.in_proj_weight'			)
  						__lowerCAmelCase      		=       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
  						__lowerCAmelCase      		=       in_proj_weight[:256, :]
  						__lowerCAmelCase      		=       in_proj_bias[:256]
  						__lowerCAmelCase      		=       in_proj_weight[256:512, :]
  						__lowerCAmelCase      		=       in_proj_bias[256:512]
  						__lowerCAmelCase      		=       in_proj_weight[-256:, :]
  						__lowerCAmelCase      		=       in_proj_bias[-256:]
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      Any:
  			__lowerCAmelCase      		=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  			__lowerCAmelCase      		=       Image.open(requests.get(a_      ,			stream=a_			).raw			)
  
  			return im
  
  
  
  
  
  @torch.no_grad()
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase      ,			lowercase      ,			lowercase      ,			lowercase      ,			)      ->      Tuple:
  			__lowerCAmelCase      		=       DeformableDetrConfig()
  			# set config attributes
  			if single_scale:
  						__lowerCAmelCase      		=       1
  			__lowerCAmelCase      		=       dilation
  			__lowerCAmelCase      		=       with_box_refine
  			__lowerCAmelCase      		=       two_stage
  			# set labels
  			__lowerCAmelCase      		=       91
  			__lowerCAmelCase      		=       """huggingface/label-files"""
  			__lowerCAmelCase      		=       """coco-detection-id2label.json"""
  			__lowerCAmelCase      		=       json.load(open(cached_download(hf_hub_url(a_      ,			a_      ,			repo_type="""dataset"""			)			)      ,			"""r"""			)			)
  			__lowerCAmelCase      		=       {int(a_			): v for k, v in idalabel.items()}
  			__lowerCAmelCase      		=       idalabel
  			__lowerCAmelCase      		=       {v: k for k, v in idalabel.items()}
  
  			# load image processor
  			__lowerCAmelCase      		=       DeformableDetrImageProcessor(format="""coco_detection"""			)
  
  			# prepare image
  			__lowerCAmelCase      		=       prepare_img()
  			__lowerCAmelCase      		=       image_processor(images=a_      ,			return_tensors="""pt"""			)
  			__lowerCAmelCase      		=       encoding["""pixel_values"""]
  
  			logger.info("""Converting model..."""			)
  
  			# load original state dict
  			__lowerCAmelCase      		=       torch.load(a_      ,			map_location="""cpu"""			)["""model"""]
  			# rename keys
  			for key in state_dict.copy().keys():
  						__lowerCAmelCase      		=       state_dict.pop(a_			)
  						__lowerCAmelCase      		=       val
  			# query, key and value matrices need special treatment
  			read_in_q_k_v(a_			)
  			# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  			__lowerCAmelCase      		=       """model."""
  			for key in state_dict.copy().keys():
  						if not key.startswith("""class_embed"""			) and not key.startswith("""bbox_embed"""			):
  									__lowerCAmelCase      		=       state_dict.pop(a_			)
  									__lowerCAmelCase      		=       val
      # finally, create HuggingFace model and load state dict
  			__lowerCAmelCase      		=       DeformableDetrForObjectDetection(a_			)
  			model.load_state_dict(a_			)
  			model.eval()
  
  			__lowerCAmelCase      		=       """cuda""" if torch.cuda.is_available() else """cpu"""
  			model.to(a_			)
  			# verify our conversion
  			__lowerCAmelCase      		=       model(pixel_values.to(a_			)			)
  
  			__lowerCAmelCase      		=       torch.tensor(
  			    [[-9.66_45, -4.34_49, -5.87_05], [-9.70_35, -3.85_04, -5.07_24], [-10.56_34, -5.33_79, -7.51_16]]			)
  			__lowerCAmelCase      		=       torch.tensor([[0.86_93, 0.22_89, 0.24_92], [0.31_50, 0.54_89, 0.58_45], [0.55_63, 0.75_80, 0.85_18]]			)
  
  			if single_scale:
  						__lowerCAmelCase      		=       torch.tensor(
  						    [[-9.90_51, -4.25_41, -6.48_52], [-9.69_47, -4.08_54, -6.80_33], [-10.06_65, -5.84_70, -7.70_03]]			)
  						__lowerCAmelCase      		=       torch.tensor([[0.72_92, 0.49_91, 0.55_32], [0.79_59, 0.24_26, 0.42_36], [0.75_82, 0.35_18, 0.44_51]]			)
  
  			if single_scale and dilation:
  						__lowerCAmelCase      		=       torch.tensor(
  						    [[-8.96_52, -4.10_74, -5.66_35], [-9.05_96, -4.94_47, -6.60_75], [-10.11_78, -4.52_75, -6.26_71]]			)
  						__lowerCAmelCase      		=       torch.tensor([[0.76_65, 0.41_30, 0.47_69], [0.83_64, 0.18_41, 0.33_91], [0.62_61, 0.38_95, 0.79_78]]			)
  
  			if with_box_refine:
  						__lowerCAmelCase      		=       torch.tensor(
  						    [[-8.88_95, -5.41_87, -6.81_53], [-8.47_06, -6.16_68, -7.61_84], [-9.00_42, -5.53_59, -6.91_41]]			)
  						__lowerCAmelCase      		=       torch.tensor([[0.78_28, 0.22_08, 0.43_23], [0.08_92, 0.59_96, 0.13_19], [0.55_24, 0.63_89, 0.89_14]]			)
  
  			if with_box_refine and two_stage:
  						__lowerCAmelCase      		=       torch.tensor(
  						    [[-6.71_08, -4.32_13, -6.37_77], [-8.90_14, -6.17_99, -6.72_40], [-6.93_15, -4.47_35, -6.22_98]]			)
  						__lowerCAmelCase      		=       torch.tensor([[0.25_83, 0.54_99, 0.46_83], [0.76_52, 0.90_68, 0.48_82], [0.54_90, 0.27_63, 0.05_64]]			)
  
  			print("""Logits:"""      ,			outputs.logits[0, :3, :3]			)
  
  			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!"""			)
  
  			# Save model and image processor
  			logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'			)
  			Path(a_			).mkdir(exist_ok=a_			)
  			model.save_pretrained(a_			)
  			image_processor.save_pretrained(a_			)
  
  			# Push to hub
  			if push_to_hub:
  						__lowerCAmelCase      		=       """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=a_      ,			organization="""nielsr"""      ,			commit_message="""Add model"""			)
  
  
  if __name__ == "__main__":
  	_a      :				Tuple											=					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."""
  	)
  	_a      :				List[Any]											=					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,
  	)
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase = 1000			)      ->      int:
  			__lowerCAmelCase      		=       []
  			__lowerCAmelCase      		=       []
  			__lowerCAmelCase      		=       3
  			__lowerCAmelCase      		=       5
  			__lowerCAmelCase      		=       1
  			while True:
  						__lowerCAmelCase      		=       z * temp
  						if result < n:
  									zmulti.append(lowercase			)
  									temp += 1
  						else:
  									__lowerCAmelCase      		=       1
  									break
  			while True:
  						__lowerCAmelCase      		=       x * temp
  						if result < n:
  									xmulti.append(lowercase			)
  									temp += 1
  						else:
  									break
  			__lowerCAmelCase      		=       list(set(xmulti + zmulti			)			)
  			return sum(lowercase			)
  
  
  if __name__ == "__main__":
  	print(f'{solution() = }')
  
  