




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()
UpperCAmelCase_		:					Optional[Any]          =  logging.get_logger(__name__)




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Tuple										=SwinConfig(
				    embed_dim=192						,  depths=(2, 2, 18, 2)						,  num_heads=(6, 12, 24, 48)						,  window_size=12						,  out_features=["""stage2""", """stage3""", """stage4"""]						,  )

				__magic_name__						:						Tuple										=DetaConfig(
				    backbone_config=lowerCamelCase						,  num_queries=900						,  encoder_ffn_dim=2048						,  decoder_ffn_dim=2048						,  num_feature_levels=5						,  assign_first_stage=lowerCamelCase						,  with_box_refine=lowerCamelCase						,  two_stage=lowerCamelCase						,  )

				# set labels
				__magic_name__						:						Optional[Any]										="""huggingface/label-files"""
				if "o365" in model_name:
								__magic_name__						:						int										=366
								__magic_name__						:						Any										="""object365-id2label.json"""
				else:
								__magic_name__						:						Optional[int]										=91
								__magic_name__						:						Any										="""coco-detection-id2label.json"""

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

				return config




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						str										=[]

				# 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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						str										=dct.pop(lowerCamelCase )
				__magic_name__						:						List[str]										=val




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Tuple										=[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
				for i in range(len(backbone_config.depths ) ):
								__magic_name__						:						Optional[int]										=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)
												__magic_name__						:						List[str]										=state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight" )
												__magic_name__						:						Any										=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
												__magic_name__						:						List[Any]										=in_proj_weight[:dim, :]
												__magic_name__						:						Tuple										=in_proj_bias[: dim]
												__magic_name__						:						str										=in_proj_weight[
												    dim : dim * 2, :
												]
												__magic_name__						:						Optional[int]										=in_proj_bias[
												    dim : dim * 2
												]
												__magic_name__						:						Union[str, Any]										=in_proj_weight[
												    -dim :, :
												]
												__magic_name__						:						Tuple										=in_proj_bias[-dim :]
												# fmt: on




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




def 		lowerCAmelCase_ (						):
				__magic_name__						:						int										="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				__magic_name__						:						str										=Image.open(requests.get(lowerCamelCase						,  stream=lowerCamelCase ).raw )

				return im




@torch.no_grad()
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Dict										=get_deta_config(lowerCamelCase )

				# load original state dict
				if model_name == "deta-swin-large":
								__magic_name__						:						List[str]										=hf_hub_download(repo_id="""nielsr/deta-checkpoints"""						,  filename="""adet_swin_ft.pth""" )
				elif model_name == "deta-swin-large-o365":
								__magic_name__						:						Dict										=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" )

				__magic_name__						:						int										=torch.load(lowerCamelCase						,  map_location="""cpu""" )["""model"""]

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

				# rename keys
				__magic_name__						:						Any										=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:
												__magic_name__						:						Any										=state_dict.pop(lowerCamelCase )
												__magic_name__						:						Any										=val
								if "input_proj" in key:
												__magic_name__						:						List[str]										=state_dict.pop(lowerCamelCase )
												__magic_name__						:						Optional[Any]										=val
								if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
												__magic_name__						:						int										=state_dict.pop(lowerCamelCase )
												__magic_name__						:						Any										=val

    # finally, create HuggingFace model and load state dict
				__magic_name__						:						str										=DetaForObjectDetection(lowerCamelCase )
				model.load_state_dict(lowerCamelCase )
				model.eval()

				__magic_name__						:						Optional[Any]										="""cuda""" if torch.cuda.is_available() else """cpu"""
				model.to(lowerCamelCase )

				# load image processor
				__magic_name__						:						Any										=DetaImageProcessor(format="""coco_detection""" )

				# verify our conversion on image
				__magic_name__						:						Tuple										=prepare_img()
				__magic_name__						:						Optional[Any]										=processor(images=lowerCamelCase						,  return_tensors="""pt""" )
				__magic_name__						:						int										=encoding["""pixel_values"""]
				__magic_name__						:						List[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":
								__magic_name__						:						str										=torch.tensor(
								    [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]] )
								__magic_name__						:						Dict										=torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]] )
				elif model_name == "deta-swin-large-o365":
								__magic_name__						:						List[Any]										=torch.tensor(
								    [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]] )
								__magic_name__						:						Optional[Any]										=torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]] )
				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__":
							UpperCAmelCase_		:					Optional[Any]          =  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."
							)
							UpperCAmelCase_		:					str          =  parser.parse_args()
							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







import copy
from typing import Dict, List, Optional

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


SCREAMING_SNAKE_CASE		:Optional[Any]						=     {
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

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






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Optional[int]										=      """mask2former"""
     UpperCamelCase_	:Dict										=      ["""swin"""]
     UpperCamelCase_	:List[Any]										=      {"""hidden_size""": """hidden_dim"""}






     def __init__(				self		,						_lowercase = None		,						_lowercase = 256		,						_lowercase = 256		,						_lowercase = 256		,						_lowercase = 1_024		,						_lowercase = "relu"		,						_lowercase = 6		,						_lowercase = 10		,						_lowercase = 8		,						_lowercase = 0.0		,						_lowercase = 2_048		,						_lowercase = False		,						_lowercase = False		,						_lowercase = 4		,						_lowercase = 255		,						_lowercase = 100		,						_lowercase = 0.1		,						_lowercase = 2.0		,						_lowercase = 5.0		,						_lowercase = 5.0		,						_lowercase = 12_544		,						_lowercase = 3.0		,						_lowercase = 0.75		,						_lowercase = 0.02		,						_lowercase = 1.0		,						_lowercase = True		,						_lowercase = [4, 8, 16, 32]		,						_lowercase = None		,						**_lowercase		,						)->     Union[str, Any]:
         if backbone_config is None:
             logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."	)
             UpperCamelCase_			  =						CONFIG_MAPPING["swin"](
                 image_size=224		,						in_channels=3		,						patch_size=4		,						embed_dim=96		,						depths=[2, 2, 18, 2]		,						num_heads=[3, 6, 12, 24]		,						window_size=7		,						drop_path_rate=0.3		,						use_absolute_embeddings=_lowercase		,						out_features=["stage1", "stage2", "stage3", "stage4"]		,						)

         if isinstance(_lowercase		,						_lowercase	):
             UpperCamelCase_			  =						backbone_config.pop("model_type"	)
             UpperCamelCase_			  =						CONFIG_MAPPING[backbone_model_type]
             UpperCamelCase_			  =						config_class.from_dict(_lowercase	)

         # verify that the backbone is supported
         if backbone_config.model_type not in self.backbones_supported:
             logger.warning_once(
                 F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
                 F"Supported model types: {','.join(self.backbones_supported	)}"	)

         UpperCamelCase_			  =						backbone_config
         UpperCamelCase_			  =						feature_size
         UpperCamelCase_			  =						mask_feature_size
         UpperCamelCase_			  =						hidden_dim
         UpperCamelCase_			  =						encoder_feedforward_dim
         UpperCamelCase_			  =						activation_function
         UpperCamelCase_			  =						encoder_layers
         UpperCamelCase_			  =						decoder_layers
         UpperCamelCase_			  =						num_attention_heads
         UpperCamelCase_			  =						dropout
         UpperCamelCase_			  =						dim_feedforward
         UpperCamelCase_			  =						pre_norm
         UpperCamelCase_			  =						enforce_input_projection
         UpperCamelCase_			  =						common_stride
         UpperCamelCase_			  =						ignore_value
         UpperCamelCase_			  =						num_queries
         UpperCamelCase_			  =						no_object_weight
         UpperCamelCase_			  =						class_weight
         UpperCamelCase_			  =						mask_weight
         UpperCamelCase_			  =						dice_weight
         UpperCamelCase_			  =						train_num_points
         UpperCamelCase_			  =						oversample_ratio
         UpperCamelCase_			  =						importance_sample_ratio
         UpperCamelCase_			  =						init_std
         UpperCamelCase_			  =						init_xavier_std
         UpperCamelCase_			  =						use_auxiliary_loss
         UpperCamelCase_			  =						feature_strides
         UpperCamelCase_			  =						output_auxiliary_logits
         UpperCamelCase_			  =						decoder_layers

         super().__init__(**_lowercase	)






     @classmethod
     def 	UpperCAmelCase_				(				cls		,						_lowercase		,						**_lowercase	)->     Optional[int]:
         return cls(
             backbone_config=_lowercase		,						**_lowercase		,						)






     def 	UpperCAmelCase_				(				self	)->     Dict[str, any]:
         UpperCamelCase_			  =						copy.deepcopy(self.__dict__	)
         UpperCamelCase_			  =						self.backbone_config.to_dict()
         UpperCamelCase_			  =						self.__class__.model_type
         return output
