




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}












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)






