code: 
  
  
  
  
  from sklearn.metrics import mean_absolute_error
  
  import datasets
  
  
  _A							    =	"""\
  @article{scikit-learn,
   title={Scikit-learn: Machine Learning in {P}ython},
   author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
   journal={Journal of Machine Learning Research},
   volume={12},
   pages={2825--2830},
   year={2011}
  }
  """
  
  _A							    =	"""\
  Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
  values.
  """
  
  
  _A							    =	"""
  Args:
      predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
          Estimated target values.
      references: array-like of shape (n_samples,) or (n_samples, n_outputs)
          Ground truth (correct) target values.
      sample_weight: array-like of shape (n_samples,), default=None
          Sample weights.
      multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
          Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
  
                   \"raw_values\" : Returns a full set of errors in case of multioutput input.
  
                   \"uniform_average\" : Errors of all outputs are averaged with uniform weight.
  
  Returns:
      mae : mean absolute error.
          If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
          MAE output is non-negative floating point. The best value is 0.0.
  Examples:
  
      >>> mae_metric = datasets.load_metric(\"mae\")
      >>> predictions = [2.5, 0.0, 2, 8]
      >>> references = [3, -0.5, 2, 7]
      >>> results = mae_metric.compute(predictions=predictions, references=references)
      >>> print(results)
      {'mae': 0.5}
  
      If you're using multi-dimensional lists, then set the config as follows :
  
      >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
      >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
      >>> references = [[0, 2], [-1, 2], [8, -5]]
      >>> results = mae_metric.compute(predictions=predictions, references=references)
      >>> print(results)
      {'mae': 0.75}
      >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
      >>> print(results)
      {'mae': array([0.5, 1. ])}
  """
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,   _KWARGS_DESCRIPTION	)
  class 							_lowerCAmelCase		(     datasets.Metric	):
  
  
  
  
  				def     __a		( self							)    ->      List[str]:
  											return datasets.MetricInfo(
  											    description=_DESCRIPTION		,		citation=_CITATION		,		inputs_description=_KWARGS_DESCRIPTION		,		features=datasets.Features(self._get_feature_types()							)		,		reference_urls=[
  											        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'''
  											    ]		,		)
  
  
  
  
  				def     __a		( self							)    ->      Optional[Any]:
  											if self.config_name == "multilist":
  																		return {
  																		    "predictions": datasets.Sequence(datasets.Value('''float'''							)							),
  																		    "references": datasets.Sequence(datasets.Value('''float'''							)							),
  																		}
  											else:
  																		return {
  																		    "predictions": datasets.Value('''float'''							),
  																		    "references": datasets.Value('''float'''							),
  																		}
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_=None		,		snake_case_="uniform_average"							)    ->      str:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =mean_absolute_error(snake_case_		,		snake_case_		,		sample_weight=snake_case_		,		multioutput=snake_case_							)
  
  											return {"mae": mae_score}
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  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()
  _A							    =	logging.get_logger(__name__)
  def 						lowerCAmelCase_       (	)       ->			Any:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Dict	     =DetaConfig(
  							    num_queries=900	,	encoder_ffn_dim=2048	,	decoder_ffn_dim=2048	,	num_feature_levels=5	,	assign_first_stage=__a	,	with_box_refine=__a	,	two_stage=__a	,	)
  
  							# set labels
  							SCREAMING_SNAKE_CASE			: Dict	     =91
  							SCREAMING_SNAKE_CASE			: Dict	     ='''huggingface/label-files'''
  							SCREAMING_SNAKE_CASE			: List[str]	     ='''coco-detection-id2label.json'''
  							SCREAMING_SNAKE_CASE			: Tuple	     =json.load(open(cached_download(hf_hub_url(__a	,	__a	,	repo_type='''dataset''' ) )	,	'''r''' ) )
  							SCREAMING_SNAKE_CASE			: int	     ={int(__a ): v for k, v in idalabel.items()}
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =idalabel
  							SCREAMING_SNAKE_CASE			: Any	     ={v: k for k, v in idalabel.items()}
  
  							return config
  def 						lowerCAmelCase_       (	__a )       ->			Union[str, Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: int	     =[]
  
  							# 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_       (	__a	,	__a	,	__a )       ->			Union[str, Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =dct.pop(__a )
  							SCREAMING_SNAKE_CASE			: List[Any]	     =val
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			Dict:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =config.d_model
  							for i in range(config.decoder_layers ):
  														# read in weights + bias of input projection layer of self-attention
  														SCREAMING_SNAKE_CASE			: List[str]	     =state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =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
  														SCREAMING_SNAKE_CASE			: Dict	     =in_proj_weight[:hidden_size, :]
  														SCREAMING_SNAKE_CASE			: List[Any]	     =in_proj_bias[:hidden_size]
  														SCREAMING_SNAKE_CASE			: int	     =in_proj_weight[
  														    hidden_size : hidden_size * 2, :
  														]
  														SCREAMING_SNAKE_CASE			: Any	     =in_proj_bias[hidden_size : hidden_size * 2]
  														SCREAMING_SNAKE_CASE			: List[str]	     =in_proj_weight[-hidden_size:, :]
  														SCREAMING_SNAKE_CASE			: Dict	     =in_proj_bias[-hidden_size:]
  def 						lowerCAmelCase_       (	)       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =Image.open(requests.get(__a	,	stream=__a ).raw )
  
  							return im
  @torch.no_grad()
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Optional[Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: str	     =get_deta_config()
  
  							# load original state dict
  							if model_name == "deta-resnet-50":
  														SCREAMING_SNAKE_CASE			: int	     ='''adet_checkpoint0011.pth'''
  							elif model_name == "deta-resnet-50-24-epochs":
  														SCREAMING_SNAKE_CASE			: str	     ='''adet_2x_checkpoint0023.pth'''
  							else:
  														raise ValueError(f'Model name {model_name} not supported' )
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =hf_hub_download(repo_id='''nielsr/deta-checkpoints'''	,	filename=__a )
  							SCREAMING_SNAKE_CASE			: str	     =torch.load(__a	,	map_location='''cpu''' )['''model''']
  
  							# rename keys
  							SCREAMING_SNAKE_CASE			: int	     =create_rename_keys(__a )
  							for src, dest in rename_keys:
  														rename_key(__a	,	__a	,	__a )
  							read_in_decoder_q_k_v(__a	,	__a )
  
  							# fix some prefixes
  							for key in state_dict.copy().keys():
  														if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
  																					SCREAMING_SNAKE_CASE			: str	     =state_dict.pop(__a )
  																					SCREAMING_SNAKE_CASE			: List[str]	     =val
  														if "input_proj" in key:
  																					SCREAMING_SNAKE_CASE			: Tuple	     =state_dict.pop(__a )
  																					SCREAMING_SNAKE_CASE			: Tuple	     =val
  														if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
  																					SCREAMING_SNAKE_CASE			: Dict	     =state_dict.pop(__a )
  																					SCREAMING_SNAKE_CASE			: List[str]	     =val
  
      # finally, create HuggingFace model and load state dict
  							SCREAMING_SNAKE_CASE			: str	     =DetaForObjectDetection(__a )
  							model.load_state_dict(__a )
  							model.eval()
  
  							SCREAMING_SNAKE_CASE			: int	     ='''cuda''' if torch.cuda.is_available() else '''cpu'''
  							model.to(__a )
  
  							# load image processor
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =DetaImageProcessor(format='''coco_detection''' )
  
  							# verify our conversion on image
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =prepare_img()
  							SCREAMING_SNAKE_CASE			: Any	     =processor(images=__a	,	return_tensors='''pt''' )
  							SCREAMING_SNAKE_CASE			: List[Any]	     =encoding['''pixel_values''']
  							SCREAMING_SNAKE_CASE			: Dict	     =model(pixel_values.to(__a ) )
  
  							# verify logits
  							if model_name == "deta-resnet-50":
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =torch.tensor(
  														    [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]] )
  														SCREAMING_SNAKE_CASE			: Tuple	     =torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]] )
  							elif model_name == "deta-resnet-50-24-epochs":
  														SCREAMING_SNAKE_CASE			: Any	     =torch.tensor(
  														    [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]] )
  														SCREAMING_SNAKE_CASE			: List[Any]	     =torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]] )
  
  							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!''' )
  
  							if pytorch_dump_folder_path:
  														# Save model and processor
  														logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...' )
  														Path(__a ).mkdir(exist_ok=__a )
  														model.save_pretrained(__a )
  														processor.save_pretrained(__a )
  
  							# 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__":
  							_A							    =	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."""
  							)
  							_A							    =	parser.parse_args()
  							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
  
  
  
  
  