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()
  SCREAMING_SNAKE_CASE_     				=   logging.get_logger(__name__)
  def 			__UpperCAmelCase		(		a_			:		Dict				)     ->       Optional[Any]:
     if "backbone.0.body" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("backbone.0.body"    ,  "backbone.conv_encoder.model"				)
     if "transformer" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("transformer."    ,  ""				)
     if "norm1" in orig_key:
        if "encoder" in orig_key:
           _UpperCAmelCase   	=					orig_key.replace("norm1"    ,  "self_attn_layer_norm"				)
        else:
           _UpperCAmelCase   	=					orig_key.replace("norm1"    ,  "encoder_attn_layer_norm"				)
     if "norm2" in orig_key:
        if "encoder" in orig_key:
           _UpperCAmelCase   	=					orig_key.replace("norm2"    ,  "final_layer_norm"				)
        else:
           _UpperCAmelCase   	=					orig_key.replace("norm2"    ,  "self_attn_layer_norm"				)
     if "norm3" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm3"    ,  "final_layer_norm"				)
     if "linear1" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("linear1"    ,  "fc1"				)
     if "linear2" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("linear2"    ,  "fc2"				)
     if "query_embed" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("query_embed"    ,  "query_position_embeddings"				)
     if "cross_attn" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("cross_attn"    ,  "encoder_attn"				)
  
     return orig_key
  def 			__UpperCAmelCase		(		a_			:		Dict				)     ->       Optional[Any]:
     # transformer decoder self-attention layers
     for i in range(6				):
        # read in weights + bias of input projection layer of self-attention
        _UpperCAmelCase   	=					state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''				)
        _UpperCAmelCase   	=					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
        _UpperCAmelCase   	=					in_proj_weight[:2_56, :]
        _UpperCAmelCase   	=					in_proj_bias[:2_56]
        _UpperCAmelCase   	=					in_proj_weight[2_56:5_12, :]
        _UpperCAmelCase   	=					in_proj_bias[2_56:5_12]
        _UpperCAmelCase   	=					in_proj_weight[-2_56:, :]
        _UpperCAmelCase   	=					in_proj_bias[-2_56:]
  def 			__UpperCAmelCase		(		)     ->       Any:
     _UpperCAmelCase   	=					"http://images.cocodataset.org/val2017/000000039769.jpg"
     _UpperCAmelCase   	=					Image.open(requests.get(a_    ,  stream=a_				).raw				)
  
     return im
  @torch.no_grad()
  def 			__UpperCAmelCase		(		a_			:		int    ,  a_			:		List[str]    ,  a_			:		List[Any]    ,  a_			:		int    ,  a_			:		Union[str, Any]    ,  a_			:		int    ,  a_			:		List[str]    ,  )     ->       Tuple:
     _UpperCAmelCase   	=					DeformableDetrConfig()
     # set config attributes
     if single_scale:
        _UpperCAmelCase   	=					1
     _UpperCAmelCase   	=					dilation
     _UpperCAmelCase   	=					with_box_refine
     _UpperCAmelCase   	=					two_stage
     # set labels
     _UpperCAmelCase   	=					91
     _UpperCAmelCase   	=					"huggingface/label-files"
     _UpperCAmelCase   	=					"coco-detection-id2label.json"
     _UpperCAmelCase   	=					json.load(open(cached_download(hf_hub_url(a_    ,  a_    ,  repo_type="dataset"				)				)    ,  "r"				)				)
     _UpperCAmelCase   	=					{int(a_				): v for k, v in idalabel.items()}
     _UpperCAmelCase   	=					idalabel
     _UpperCAmelCase   	=					{v: k for k, v in idalabel.items()}
  
     # load image processor
     _UpperCAmelCase   	=					DeformableDetrImageProcessor(format="coco_detection"				)
  
     # prepare image
     _UpperCAmelCase   	=					prepare_img()
     _UpperCAmelCase   	=					image_processor(images=a_    ,  return_tensors="pt"				)
     _UpperCAmelCase   	=					encoding["pixel_values"]
  
     logger.info("Converting model..."				)
  
     # load original state dict
     _UpperCAmelCase   	=					torch.load(a_    ,  map_location="cpu"				)["model"]
     # rename keys
     for key in state_dict.copy().keys():
        _UpperCAmelCase   	=					state_dict.pop(a_				)
        _UpperCAmelCase   	=					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
     _UpperCAmelCase   	=					"model."
     for key in state_dict.copy().keys():
        if not key.startswith("class_embed"				) and not key.startswith("bbox_embed"				):
           _UpperCAmelCase   	=					state_dict.pop(a_				)
           _UpperCAmelCase   	=					val
      # finally, create HuggingFace model and load state dict
     _UpperCAmelCase   	=					DeformableDetrForObjectDetection(a_				)
     model.load_state_dict(a_				)
     model.eval()
  
     _UpperCAmelCase   	=					"cuda" if torch.cuda.is_available() else "cpu"
     model.to(a_				)
     # verify our conversion
     _UpperCAmelCase   	=					model(pixel_values.to(a_				)				)
  
     _UpperCAmelCase   	=					torch.tensor(
         [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]				)
     _UpperCAmelCase   	=					torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]				)
  
     if single_scale:
        _UpperCAmelCase   	=					torch.tensor(
            [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]				)
        _UpperCAmelCase   	=					torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]				)
  
     if single_scale and dilation:
        _UpperCAmelCase   	=					torch.tensor(
            [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]				)
        _UpperCAmelCase   	=					torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]				)
  
     if with_box_refine:
        _UpperCAmelCase   	=					torch.tensor(
            [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]				)
        _UpperCAmelCase   	=					torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]				)
  
     if with_box_refine and two_stage:
        _UpperCAmelCase   	=					torch.tensor(
            [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]				)
        _UpperCAmelCase   	=					torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]				)
  
     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:
        _UpperCAmelCase   	=					"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__":
    SCREAMING_SNAKE_CASE_     				=   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.'
    )
    SCREAMING_SNAKE_CASE_     				=   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"""
  
  
  
  
  
  
  
  import numpy as np
  
  
  
  
  
  
  class     __a			:
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_=5							,lowerCamelCase_=5		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					depth
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					None
            _UpperCAmelCase   	=					None
            _UpperCAmelCase   	=					min_leaf_size
            _UpperCAmelCase   	=					None
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if labels.ndim != 1:
               print("Error: Input labels must be one dimensional"		)
  
            return np.mean((labels - prediction) ** 2		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if x.ndim != 1:
               print("Error: Input data set must be one dimensional"		)
               return
            if len(lowerCamelCase_		) != len(lowerCamelCase_		):
               print("Error: X and y have different lengths"		)
               return
            if y.ndim != 1:
               print("Error: Data set labels must be one dimensional"		)
               return
  
            if len(lowerCamelCase_		) < 2 * self.min_leaf_size:
               _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
               return
  
            if self.depth == 1:
               _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
               return
  
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					self.mean_squared_error(lowerCamelCase_							,np.mean(lowerCamelCase_		)		) * 2
            for i in range(len(lowerCamelCase_		)		):
               if len(x[:i]		) < self.min_leaf_size:
                  continue
               elif len(x[i:]		) < self.min_leaf_size:
                  continue
               else:
                  _UpperCAmelCase   	=					self.mean_squared_error(x[:i]							,np.mean(y[:i]		)		)
                  _UpperCAmelCase   	=					self.mean_squared_error(x[i:]							,np.mean(y[i:]		)		)
                  _UpperCAmelCase   	=					error_left + error_right
                  if error < min_error:
                     _UpperCAmelCase   	=					i
                     _UpperCAmelCase   	=					error
  
            if best_split != 0:
               _UpperCAmelCase   	=					x[:best_split]
               _UpperCAmelCase   	=					y[:best_split]
               _UpperCAmelCase   	=					x[best_split:]
               _UpperCAmelCase   	=					y[best_split:]
  
               _UpperCAmelCase   	=					x[best_split]
               _UpperCAmelCase   	=					DecisionTree(
                   depth=self.depth - 1							,min_leaf_size=self.min_leaf_size		)
               _UpperCAmelCase   	=					DecisionTree(
                   depth=self.depth - 1							,min_leaf_size=self.min_leaf_size		)
               self.left.train(lowerCamelCase_							,lowerCamelCase_		)
               self.right.train(lowerCamelCase_							,lowerCamelCase_		)
            else:
               _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
  
            return
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if self.prediction is not None:
               return self.prediction
            elif self.left or self.right is not None:
               if x >= self.decision_boundary:
                  return self.right.predict(lowerCamelCase_		)
               else:
                  return self.left.predict(lowerCamelCase_		)
            else:
               print("Error: Decision tree not yet trained"		)
               return None
  
  
  
  
  
  
  class     __a			:
  
  
  
  
  
  
  
         @staticmethod
         def    _snake_case			(					lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					float(0		)
            for label in labels:
               squared_error_sum += (label - prediction) ** 2
  
            return float(squared_error_sum / labels.size		)
  
  
  
  def 			__UpperCAmelCase		(		)     ->       Dict:
     _UpperCAmelCase   	=					np.arange(-1.0    ,  1.0    ,  0.005				)
     _UpperCAmelCase   	=					np.sin(a_				)
  
     _UpperCAmelCase   	=					DecisionTree(depth=10    ,  min_leaf_size=10				)
     tree.train(a_    ,  a_				)
  
     _UpperCAmelCase   	=					(np.random.rand(10				) * 2) - 1
     _UpperCAmelCase   	=					np.array([tree.predict(a_				) for x in test_cases]				)
     _UpperCAmelCase   	=					np.mean((predictions - test_cases) ** 2				)
  
     print("Test values: " + str(a_				)				)
     print("Predictions: " + str(a_				)				)
     print("Average error: " + str(a_				)				)
  
  
  if __name__ == "__main__":
    main()
    import doctest
  
    doctest.testmod(name='mean_squarred_error', verbose=True)
  
  