

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, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase  :      Union[str, Any]			 =logging.get_logger(__name__)



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Dict									= SwinConfig(
	    embed_dim=1_9_2	,depths=(2, 2, 1_8, 2)	,num_heads=(6, 1_2, 2_4, 4_8)	,window_size=1_2	,out_features=["stage2", "stage3", "stage4"]	,)

	UpperCAmelCase__:	List[str]									= DetaConfig(
	    backbone_config=__lowerCamelCase	,num_queries=9_0_0	,encoder_ffn_dim=2_0_4_8	,decoder_ffn_dim=2_0_4_8	,num_feature_levels=5	,assign_first_stage=__lowerCamelCase	,with_box_refine=__lowerCamelCase	,two_stage=__lowerCamelCase	,)

	# set labels
	UpperCAmelCase__:	Optional[Any]									= "huggingface/label-files"
	if "o365" in model_name:
		UpperCAmelCase__:	int									= 3_6_6
		UpperCAmelCase__:	str									= "object365-id2label.json"
	else:
		UpperCAmelCase__:	Optional[Any]									= 9_1
		UpperCAmelCase__:	List[Any]									= "coco-detection-id2label.json"

	UpperCAmelCase__:	Union[str, Any]									= num_labels
	UpperCAmelCase__:	Any									= json.load(open(cached_download(hf_hub_url(__lowerCamelCase	,__lowerCamelCase	,repo_type="dataset"      )      )	,"r"      )      )
	UpperCAmelCase__:	Dict									= {int(__lowerCamelCase      ): v for k, v in idalabel.items()}
	UpperCAmelCase__:	Optional[Any]									= idalabel
	UpperCAmelCase__:	int									= {v: k for k, v in idalabel.items()}

	return config



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= []

	# stem
	# fmt: off
	rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")      )
	rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")      )
	rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")      )
	rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias")      )
	# stages
	for i in range(len(config.backbone_config.depths      )      ):
		for j in range(config.backbone_config.depths[i]      ):
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")      )
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")      )
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")      )
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")      )
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")      )
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")      )
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")      )
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")      )
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")      )
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")      )
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")      )
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")      )

		if i < 3:
			rename_keys.append((f"backbone.0.body.layers.{i}.downsample.reduction.weight", f"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight")      )
			rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.weight", f"model.backbone.model.encoder.layers.{i}.downsample.norm.weight")      )
			rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.bias", f"model.backbone.model.encoder.layers.{i}.downsample.norm.bias")      )

	rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")      )
	rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")      )
	rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")      )
	rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")      )
	rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")      )
	rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")      )

	# 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 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	str									= dct.pop(__lowerCamelCase      )
	UpperCAmelCase__:	int									= val



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[Any]									= [int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
	for i in range(len(backbone_config.depths      )      ):
		UpperCAmelCase__:	Union[str, Any]									= num_features[i]
		for j in range(backbone_config.depths[i]      ):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			UpperCAmelCase__:	List[Any]									= state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"      )
			UpperCAmelCase__:	int									= state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"      )
			# next, add query, keys and values (in that order) to the state dict
			UpperCAmelCase__:	Tuple									= in_proj_weight[:dim, :]
			UpperCAmelCase__:	Optional[Any]									= in_proj_bias[: dim]
			UpperCAmelCase__:	Optional[Any]									= in_proj_weight[
			    dim : dim * 2, :
			]
			UpperCAmelCase__:	Union[str, Any]									= in_proj_bias[
			    dim : dim * 2
			]
			UpperCAmelCase__:	List[Any]									= in_proj_weight[
			    -dim :, :
			]
			UpperCAmelCase__:	Optional[Any]									= in_proj_bias[-dim :]
			# fmt: on



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	# transformer decoder self-attention layers
	UpperCAmelCase__:	Tuple									= config.d_model
	for i in range(config.decoder_layers      ):
		# read in weights + bias of input projection layer of self-attention
		UpperCAmelCase__:	Dict									= state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight"      )
		UpperCAmelCase__:	Tuple									= 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
		UpperCAmelCase__:	str									= in_proj_weight[:hidden_size, :]
		UpperCAmelCase__:	Any									= in_proj_bias[:hidden_size]
		UpperCAmelCase__:	Optional[int]									= in_proj_weight[
		    hidden_size : hidden_size * 2, :
		]
		UpperCAmelCase__:	Any									= in_proj_bias[hidden_size : hidden_size * 2]
		UpperCAmelCase__:	Tuple									= in_proj_weight[-hidden_size:, :]
		UpperCAmelCase__:	Optional[int]									= in_proj_bias[-hidden_size:]



def 			_A	(      ):
	UpperCAmelCase__:	List[Any]									= "http://images.cocodataset.org/val2017/000000039769.jpg"
	UpperCAmelCase__:	Dict									= Image.open(requests.get(__lowerCamelCase	,stream=__lowerCamelCase      ).raw      )

	return im



@torch.no_grad()
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[Any]									= get_deta_config(__lowerCamelCase      )

	# load original state dict
	if model_name == "deta-swin-large":
		UpperCAmelCase__:	Tuple									= hf_hub_download(repo_id="nielsr/deta-checkpoints"	,filename="adet_swin_ft.pth"      )
	elif model_name == "deta-swin-large-o365":
		UpperCAmelCase__:	List[str]									= hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"	,filename="deta_swin_pt_o365.pth"      )
	else:
		raise ValueError(f"Model name {model_name} not supported"      )

	UpperCAmelCase__:	List[Any]									= torch.load(__lowerCamelCase	,map_location="cpu"      )["model"]

	# original state dict
	for name, param in state_dict.items():
		print(__lowerCamelCase	,param.shape      )

	# rename keys
	UpperCAmelCase__:	str									= create_rename_keys(__lowerCamelCase      )
	for src, dest in rename_keys:
		rename_key(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase      )
	read_in_swin_q_k_v(__lowerCamelCase	,config.backbone_config      )
	read_in_decoder_q_k_v(__lowerCamelCase	,__lowerCamelCase      )

	# fix some prefixes
	for key in state_dict.copy().keys():
		if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
			UpperCAmelCase__:	Optional[Any]									= state_dict.pop(__lowerCamelCase      )
			UpperCAmelCase__:	Optional[Any]									= val
		if "input_proj" in key:
			UpperCAmelCase__:	Optional[int]									= state_dict.pop(__lowerCamelCase      )
			UpperCAmelCase__:	Tuple									= val
		if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
			UpperCAmelCase__:	List[Any]									= state_dict.pop(__lowerCamelCase      )
			UpperCAmelCase__:	Any									= val

    # finally, create HuggingFace model and load state dict
	UpperCAmelCase__:	List[str]									= DetaForObjectDetection(__lowerCamelCase      )
	model.load_state_dict(__lowerCamelCase      )
	model.eval()

	UpperCAmelCase__:	List[str]									= "cuda" if torch.cuda.is_available() else "cpu"
	model.to(__lowerCamelCase      )

	# load image processor
	UpperCAmelCase__:	List[Any]									= DetaImageProcessor(format="coco_detection"      )

	# verify our conversion on image
	UpperCAmelCase__:	Tuple									= prepare_img()
	UpperCAmelCase__:	Any									= processor(images=__lowerCamelCase	,return_tensors="pt"      )
	UpperCAmelCase__:	List[str]									= encoding["pixel_values"]
	UpperCAmelCase__:	str									= model(pixel_values.to(__lowerCamelCase      )      )

	# verify logits
	print("Logits:"	,outputs.logits[0, :3, :3]      )
	print("Boxes:"	,outputs.pred_boxes[0, :3, :3]      )
	if model_name == "deta-swin-large":
		UpperCAmelCase__:	int									= torch.tensor(
		    [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]      )
		UpperCAmelCase__:	List[Any]									= torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]      )
	elif model_name == "deta-swin-large-o365":
		UpperCAmelCase__:	Union[str, Any]									= torch.tensor(
		    [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]      )
		UpperCAmelCase__:	str									= torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]      )
	assert torch.allclose(outputs.logits[0, :3, :3]	,expected_logits.to(__lowerCamelCase      )	,atol=1e-4      )
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,expected_boxes.to(__lowerCamelCase      )	,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(__lowerCamelCase      ).mkdir(exist_ok=__lowerCamelCase      )
		model.save_pretrained(__lowerCamelCase      )
		processor.save_pretrained(__lowerCamelCase      )

	# 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__":
					_lowerCAmelCase  :      str			 =argparse.ArgumentParser()

					parser.add_argument(
					    """--model_name""",
					    type=str,
					    default="""deta-swin-large""",
					    choices=["""deta-swin-large""", """deta-swin-large-o365"""],
					    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."""
					)
					_lowerCAmelCase  :      Optional[Any]			 =parser.parse_args()
					convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__UpperCamelCase   :			Any          = logging.get_logger(__name__)

__UpperCamelCase   :			Tuple          = {
    "SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''deformable_detr'''
							UpperCamelCase__   	=    {
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							}
							def __init__(		self		:List[str]						, __magic_name__		:Tuple=True						, __magic_name__		:str=None						, __magic_name__		:Tuple=3						, __magic_name__		:List[str]=300						, __magic_name__		:Dict=1024						, __magic_name__		:Tuple=6						, __magic_name__		:Tuple=1024						, __magic_name__		:int=8						, __magic_name__		:List[Any]=6						, __magic_name__		:Tuple=1024						, __magic_name__		:List[Any]=8						, __magic_name__		:Union[str, Any]=0.0						, __magic_name__		:Union[str, Any]=True						, __magic_name__		:str="relu"						, __magic_name__		:Optional[Any]=256						, __magic_name__		:int=0.1						, __magic_name__		:List[str]=0.0						, __magic_name__		:str=0.0						, __magic_name__		:str=0.02						, __magic_name__		:int=1.0						, __magic_name__		:List[str]=True						, __magic_name__		:Union[str, Any]=False						, __magic_name__		:List[Any]="sine"						, __magic_name__		:Union[str, Any]="resnet50"						, __magic_name__		:int=True						, __magic_name__		:Any=False						, __magic_name__		:List[str]=4						, __magic_name__		:Tuple=4						, __magic_name__		:Union[str, Any]=4						, __magic_name__		:Optional[int]=False						, __magic_name__		:Tuple=300						, __magic_name__		:Tuple=False						, __magic_name__		:List[Any]=1						, __magic_name__		:List[str]=5						, __magic_name__		:List[Any]=2						, __magic_name__		:Optional[int]=1						, __magic_name__		:Union[str, Any]=1						, __magic_name__		:List[Any]=5						, __magic_name__		:Optional[Any]=2						, __magic_name__		:Optional[int]=0.1						, __magic_name__		:List[str]=0.25						, __magic_name__		:str=False						, **__magic_name__		:List[str]						, ):







								'''simple docstring'''







								if backbone_config is not None and use_timm_backbone:
									raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""							)

								if not use_timm_backbone:
									if backbone_config is None:
										logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
										a									=				CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]							)
									elif isinstance(__magic_name__						, __magic_name__							):
										a									=				backbone_config.get("""model_type"""							)
										a									=				CONFIG_MAPPING[backbone_model_type]
										a									=				config_class.from_dict(__magic_name__							)
								a									=				use_timm_backbone
								a									=				backbone_config
								a									=				num_channels
								a									=				num_queries
								a									=				max_position_embeddings
								a									=				d_model
								a									=				encoder_ffn_dim
								a									=				encoder_layers
								a									=				encoder_attention_heads
								a									=				decoder_ffn_dim
								a									=				decoder_layers
								a									=				decoder_attention_heads
								a									=				dropout
								a									=				attention_dropout
								a									=				activation_dropout
								a									=				activation_function
								a									=				init_std
								a									=				init_xavier_std
								a									=				encoder_layerdrop
								a									=				auxiliary_loss
								a									=				position_embedding_type
								a									=				backbone
								a									=				use_pretrained_backbone
								a									=				dilation
								# deformable attributes
								a									=				num_feature_levels
								a									=				encoder_n_points
								a									=				decoder_n_points
								a									=				two_stage
								a									=				two_stage_num_proposals
								a									=				with_box_refine
								if two_stage is True and with_box_refine is False:
									raise ValueError("""If two_stage is True, with_box_refine must be True."""							)
								# Hungarian matcher
								a									=				class_cost
								a									=				bbox_cost
								a									=				giou_cost
								# Loss coefficients
								a									=				mask_loss_coefficient
								a									=				dice_loss_coefficient
								a									=				bbox_loss_coefficient
								a									=				giou_loss_coefficient
								a									=				eos_coefficient
								a									=				focal_alpha
								a									=				disable_custom_kernels
								super().__init__(is_encoder_decoder=__magic_name__						, **__magic_name__							)
							@property
							def  lowerCamelCase__				(		self		:List[Any]							):







								'''simple docstring'''







								return self.encoder_attention_heads
							@property
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								return self.d_model





							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				copy.deepcopy(self.__dict__							)
								if self.backbone_config is not None:
									a									=				self.backbone_config.to_dict()
								a									=				self.__class__.model_type
								return output

