code: 
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  _A							    =	logging.get_logger(__name__)
  
  _A							    =	{
      "mnaylor/mega-base-wikitext": "https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json",
  }
  
  
  class 							_lowerCAmelCase		(     snake_case__	):
  				lowerCamelCase__									=    'mega'
  
  
  
  
  				def __init__( self		,		snake_case_=30_522		,		snake_case_=128		,		snake_case_=4		,		snake_case_=256		,		snake_case_=16		,		snake_case_=True		,		snake_case_=64		,		snake_case_=False		,		snake_case_=-1		,		snake_case_=None		,		snake_case_=True		,		snake_case_="scalenorm"		,		snake_case_=True		,		snake_case_="silu"		,		snake_case_="softmax"		,		snake_case_=0.1		,		snake_case_=0.1		,		snake_case_=0.1		,		snake_case_=False		,		snake_case_=True		,		snake_case_=256		,		snake_case_=True		,		snake_case_=0.1		,		snake_case_=2_048		,		snake_case_=False		,		snake_case_=2		,		snake_case_=0.02		,		snake_case_=0.2		,		snake_case_=0.02		,		snake_case_=1.0		,		snake_case_=1		,		snake_case_=0		,		snake_case_=2		,		snake_case_="rotary"		,		snake_case_=None		,		snake_case_=True		,		snake_case_=True		,		**snake_case_		,		)    ->      Any:
  											super().__init__(pad_token_id=__snake_case		,		bos_token_id=__snake_case		,		eos_token_id=__snake_case		,		**__snake_case							)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =vocab_size
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =hidden_size
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =num_hidden_layers
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =activation
  											SCREAMING_SNAKE_CASE			: List[str]	     =attention_activation
  											SCREAMING_SNAKE_CASE			: Tuple	     =intermediate_size
  											SCREAMING_SNAKE_CASE			: Tuple	     =ema_projection_size
  											SCREAMING_SNAKE_CASE			: Any	     =bidirectional
  											SCREAMING_SNAKE_CASE			: Tuple	     =shared_representation_size
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =use_chunking
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =chunk_size
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =truncation
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =normalize_before_mega
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =normalization_type
  											SCREAMING_SNAKE_CASE			: int	     =norm_affine
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =dropout_prob
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =hidden_dropout_prob
  											SCREAMING_SNAKE_CASE			: Tuple	     =attention_probs_dropout_prob
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =use_feature_dropout
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =use_normalized_ffn
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =nffn_hidden_size
  											SCREAMING_SNAKE_CASE			: Dict	     =normalize_before_ffn
  											SCREAMING_SNAKE_CASE			: int	     =nffn_activation_dropout_prob
  											SCREAMING_SNAKE_CASE			: List[str]	     =max_positions
  											SCREAMING_SNAKE_CASE			: List[Any]	     =add_token_type_embeddings
  											SCREAMING_SNAKE_CASE			: Dict	     =type_vocab_size
  											SCREAMING_SNAKE_CASE			: str	     =initializer_range
  											SCREAMING_SNAKE_CASE			: List[Any]	     =ema_delta_alpha_range
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =ema_beta_range
  											SCREAMING_SNAKE_CASE			: Tuple	     =ema_gamma_omega_range
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =relative_positional_bias
  											SCREAMING_SNAKE_CASE			: Tuple	     =use_cache
  											SCREAMING_SNAKE_CASE			: str	     =classifier_dropout
  											SCREAMING_SNAKE_CASE			: List[str]	     =add_lm_hidden_dense_layer
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =1  # not used but required by Hugging Face
  
  
  
  
  
  
  
  class 							_lowerCAmelCase		(     snake_case__	):
  
  
  
  
  				@property
  				def     __a		( self							)    ->      Optional[Any]:
  											if self.task == "multiple-choice":
  																		SCREAMING_SNAKE_CASE			: Dict	     ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
  											else:
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     ={0: '''batch''', 1: '''sequence'''}
  											return OrderedDict(
  											    [
  											        ('''input_ids''', dynamic_axis),
  											        ('''attention_mask''', dynamic_axis),
  											    ]							)
  
  
  
  
  
  
  
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)
  
  
  
  
  
  
  