





"""simple docstring"""






import copy

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


__UpperCAmelCase						=			logging.get_logger(__name__)

__UpperCAmelCase						=			{
    '''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 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	Union[str, Any]				     =				"deformable_detr"
				__lowerCamelCase				:	Optional[int]				     =				{
				    "hidden_size": "d_model",
				    "num_attention_heads": "encoder_attention_heads",
				}
				def __init__(		self    :			Dict	, a_    :			List[str]=True	, a_    :			Optional[int]=None	, a_    :			Union[str, Any]=3	, a_    :			Optional[int]=3_00	, a_    :			int=10_24	, a_    :			Dict=6	, a_    :			Tuple=10_24	, a_    :			str=8	, a_    :			Tuple=6	, a_    :			Any=10_24	, a_    :			int=8	, a_    :			Optional[int]=0.0	, a_    :			int=True	, a_    :			int="relu"	, a_    :			Union[str, Any]=2_56	, a_    :			Tuple=0.1	, a_    :			Tuple=0.0	, a_    :			Optional[Any]=0.0	, a_    :			Any=0.02	, a_    :			Optional[Any]=1.0	, a_    :			Any=True	, a_    :			Union[str, Any]=False	, a_    :			List[str]="sine"	, a_    :			List[Any]="resnet50"	, a_    :			Union[str, Any]=True	, a_    :			Dict=False	, a_    :			int=4	, a_    :			Optional[Any]=4	, a_    :			Any=4	, a_    :			List[Any]=False	, a_    :			Tuple=3_00	, a_    :			List[str]=False	, a_    :			int=1	, a_    :			Optional[Any]=5	, a_    :			Any=2	, a_    :			List[str]=1	, a_    :			str=1	, a_    :			Any=5	, a_    :			Tuple=2	, a_    :			Optional[int]=0.1	, a_    :			Optional[int]=0.25	, a_    :			List[str]=False	, **a_    :			Tuple	, )	->	List[Any]:



							'''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__  :	Optional[Any]		 =			CONFIG_MAPPING["resnet"](out_features=["stage4"]    )
										elif isinstance(a_	, a_    ):
													a__  :	Optional[int]		 =			backbone_config.get("model_type"    )
													a__  :	str		 =			CONFIG_MAPPING[backbone_model_type]
													a__  :	int		 =			config_class.from_dict(a_    )
							a__  :	Optional[Any]		 =			use_timm_backbone
							a__  :	List[str]		 =			backbone_config
							a__  :	Tuple		 =			num_channels
							a__  :	Dict		 =			num_queries
							a__  :	Any		 =			max_position_embeddings
							a__  :	Any		 =			d_model
							a__  :	Optional[Any]		 =			encoder_ffn_dim
							a__  :	str		 =			encoder_layers
							a__  :	str		 =			encoder_attention_heads
							a__  :	Tuple		 =			decoder_ffn_dim
							a__  :	List[Any]		 =			decoder_layers
							a__  :	List[str]		 =			decoder_attention_heads
							a__  :	List[str]		 =			dropout
							a__  :	int		 =			attention_dropout
							a__  :	Dict		 =			activation_dropout
							a__  :	Any		 =			activation_function
							a__  :	str		 =			init_std
							a__  :	Any		 =			init_xavier_std
							a__  :	Tuple		 =			encoder_layerdrop
							a__  :	int		 =			auxiliary_loss
							a__  :	List[str]		 =			position_embedding_type
							a__  :	Optional[Any]		 =			backbone
							a__  :	int		 =			use_pretrained_backbone
							a__  :	str		 =			dilation
							# deformable attributes
							a__  :	Union[str, Any]		 =			num_feature_levels
							a__  :	int		 =			encoder_n_points
							a__  :	Dict		 =			decoder_n_points
							a__  :	Any		 =			two_stage
							a__  :	Optional[Any]		 =			two_stage_num_proposals
							a__  :	Optional[Any]		 =			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__  :	int		 =			bbox_cost
							a__  :	Dict		 =			giou_cost
							# Loss coefficients
							a__  :	List[Any]		 =			mask_loss_coefficient
							a__  :	Tuple		 =			dice_loss_coefficient
							a__  :	Any		 =			bbox_loss_coefficient
							a__  :	Optional[int]		 =			giou_loss_coefficient
							a__  :	Union[str, Any]		 =			eos_coefficient
							a__  :	Union[str, Any]		 =			focal_alpha
							a__  :	str		 =			disable_custom_kernels
							super().__init__(is_encoder_decoder=a_	, **a_    )
				@property
				def       UpperCAmelCase      (		self    :			str    )	->	int:



							'''simple docstring'''



							return self.encoder_attention_heads
				@property
				def       UpperCAmelCase      (		self    :			Any    )	->	int:



							'''simple docstring'''



							return self.d_model






				def       UpperCAmelCase      (		self    :			str    )	->	int:



							'''simple docstring'''



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






"""simple docstring"""






def      lowercase__ (					lowerCAmelCase__   :			str			)    ->			list[int]:



			'''simple docstring'''
			a__  :	List[str]		 =			[0 for i in range(len(lowerCAmelCase__			)			)]

			# initialize interval's left pointer and right pointer
			a__ ,	a__  :	int		 =			0, 0

			for i in range(1							,			len(lowerCAmelCase__			)			):
						# case when current index is inside the interval
						if i <= right_pointer:
									a__  :	List[Any]		 =			min(right_pointer - i + 1							,			z_result[i - left_pointer]			)
									a__  :	List[str]		 =			min_edge

						while go_next(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			):
									z_result[i] += 1

						# if new index's result gives us more right interval,
						# we've to update left_pointer and right_pointer
						if i + z_result[i] - 1 > right_pointer:
									a__ ,	a__  :	str		 =			i, i + z_result[i] - 1

			return z_result






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			list[int]							,			lowerCAmelCase__   :			str			)    ->			bool:



			'''simple docstring'''
			return i + z_result[i] < len(lowerCAmelCase__			) and s[z_result[i]] == s[i + z_result[i]]






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			str			)    ->			int:



			'''simple docstring'''
			a__  :	List[Any]		 =			0
			# concatenate 'pattern' and 'input_str' and call z_function
			# with concatenated string
			a__  :	List[str]		 =			z_function(pattern + input_str			)

			for val in z_result:
						# if value is greater then length of the pattern string
						# that means this index is starting position of substring
						# which is equal to pattern string
						if val >= len(lowerCAmelCase__			):
									answer += 1

			return answer


if __name__ == "__main__":
		import doctest

		doctest.testmod()