

import copy

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


UpperCAmelCase_							:					List[str]		 =   logging.get_logger(__name__)

UpperCAmelCase_							:					List[Any]		 =   {
    '''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_      (	_lowercase	):
	_lowercase		:   List[Any]     					=				'''deformable_detr'''
	_lowercase		:   int     					=				{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	}




	def __init__(     self						:     Union[str, Any]					,      __A						:     int=True					,      __A						:     Any=None					,      __A						:     int=3					,      __A						:     Dict=300					,      __A						:     Optional[int]=1024					,      __A						:     Any=6					,      __A						:     Optional[int]=1024					,      __A						:     Optional[int]=8					,      __A						:     Dict=6					,      __A						:     Optional[Any]=1024					,      __A						:     Optional[Any]=8					,      __A						:     str=0.0					,      __A						:     List[Any]=True					,      __A						:     Optional[int]="relu"					,      __A						:     Any=256					,      __A						:     Union[str, Any]=0.1					,      __A						:     List[str]=0.0					,      __A						:     Union[str, Any]=0.0					,      __A						:     Optional[Any]=0.0_2					,      __A						:     Union[str, Any]=1.0					,      __A						:     str=True					,      __A						:     List[str]=False					,      __A						:     Tuple="sine"					,      __A						:     List[str]="resnet50"					,      __A						:     Optional[int]=True					,      __A						:     Union[str, Any]=False					,      __A						:     str=4					,      __A						:     Optional[Any]=4					,      __A						:     List[Any]=4					,      __A						:     str=False					,      __A						:     List[str]=300					,      __A						:     List[Any]=False					,      __A						:     Optional[Any]=1					,      __A						:     Union[str, Any]=5					,      __A						:     Dict=2					,      __A						:     Tuple=1					,      __A						:     int=1					,      __A						:     List[Any]=5					,      __A						:     List[Any]=2					,      __A						:     Any=0.1					,      __A						:     Union[str, Any]=0.2_5					,      __A						:     Optional[Any]=False					,      **__A						:     Any					,      ):
		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 :			int		    =			CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]							)
			elif isinstance(__A					,      __A							):
				__A :			Any		    =			backbone_config.get("""model_type"""							)
				__A :			str		    =			CONFIG_MAPPING[backbone_model_type]
				__A :			List[str]		    =			config_class.from_dict(__A							)
		__A :			List[Any]		    =			use_timm_backbone
		__A :			List[Any]		    =			backbone_config
		__A :			Tuple		    =			num_channels
		__A :			Union[str, Any]		    =			num_queries
		__A :			str		    =			max_position_embeddings
		__A :			Any		    =			d_model
		__A :			Any		    =			encoder_ffn_dim
		__A :			Dict		    =			encoder_layers
		__A :			Optional[Any]		    =			encoder_attention_heads
		__A :			Tuple		    =			decoder_ffn_dim
		__A :			Dict		    =			decoder_layers
		__A :			Optional[Any]		    =			decoder_attention_heads
		__A :			List[Any]		    =			dropout
		__A :			int		    =			attention_dropout
		__A :			Union[str, Any]		    =			activation_dropout
		__A :			Optional[Any]		    =			activation_function
		__A :			List[str]		    =			init_std
		__A :			int		    =			init_xavier_std
		__A :			Dict		    =			encoder_layerdrop
		__A :			List[Any]		    =			auxiliary_loss
		__A :			Tuple		    =			position_embedding_type
		__A :			Dict		    =			backbone
		__A :			Any		    =			use_pretrained_backbone
		__A :			List[Any]		    =			dilation
		# deformable attributes
		__A :			Optional[Any]		    =			num_feature_levels
		__A :			str		    =			encoder_n_points
		__A :			Any		    =			decoder_n_points
		__A :			Tuple		    =			two_stage
		__A :			Any		    =			two_stage_num_proposals
		__A :			Optional[int]		    =			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 :			Tuple		    =			class_cost
		__A :			Dict		    =			bbox_cost
		__A :			List[str]		    =			giou_cost
		# Loss coefficients
		__A :			Any		    =			mask_loss_coefficient
		__A :			Any		    =			dice_loss_coefficient
		__A :			Optional[Any]		    =			bbox_loss_coefficient
		__A :			Tuple		    =			giou_loss_coefficient
		__A :			str		    =			eos_coefficient
		__A :			Optional[Any]		    =			focal_alpha
		__A :			Optional[int]		    =			disable_custom_kernels
		super().__init__(is_encoder_decoder=__A					,      **__A							)




	@property
	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		return self.encoder_attention_heads




	@property
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		return self.d_model




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			int		    =			copy.deepcopy(self.__dict__							)
		if self.backbone_config is not None:
			__A :			Optional[Any]		    =			self.backbone_config.to_dict()
		__A :			List[Any]		    =			self.__class__.model_type
		return output








import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp
def 							__SCREAMING_SNAKE_CASE     (   )					->				Tuple:
	__A :			List[Any]		    =			ArgumentParser(
	    description=(
	        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
	    )       )

	# Optional arguments for the launch helper
	parser.add_argument("""--num_cores"""			,type=a__			,default=1			,help="""Number of TPU cores to use (1 or 8)."""       )

	# positional
	parser.add_argument(
	    """training_script"""			,type=a__			,help=(
	        """The full path to the single TPU training """
	        """program/script to be launched in parallel, """
	        """followed by all the arguments for the """
	        """training script"""
	    )			,)

	# rest from the training program
	parser.add_argument("""training_script_args"""			,nargs=a__       )

	return parser.parse_args()
def 							__SCREAMING_SNAKE_CASE     (   )					->				str:
	__A :			Union[str, Any]		    =			parse_args()

	# Import training_script as a module.
	__A :			List[Any]		    =			Path(args.training_script       )
	sys.path.append(str(script_fpath.parent.resolve()       )       )
	__A :			str		    =			script_fpath.stem
	__A :			int		    =			importlib.import_module(a__       )

	# Patch sys.argv
	__A :			List[str]		    =			[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores       )]

	xmp.spawn(mod._mp_fn			,args=()			,nprocs=args.num_cores       )


if __name__ == "__main__":
			main()





