
import copy

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


SCREAMING_SNAKE_CASE				:int									=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:List[str]									=							{
    '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 (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	"deformable_detr"
	snake_case_          =	{
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	}






	def __init__(			self   :					Dict				,A   :					str=True				,A   :					Dict=None				,A   :					Tuple=3				,A   :					Optional[Any]=3_00				,A   :					Optional[Any]=10_24				,A   :					int=6				,A   :					Tuple=10_24				,A   :					List[Any]=8				,A   :					Any=6				,A   :					int=10_24				,A   :					int=8				,A   :					Tuple=0.0				,A   :					int=True				,A   :					Any="relu"				,A   :					Dict=2_56				,A   :					List[str]=0.1				,A   :					Optional[int]=0.0				,A   :					List[str]=0.0				,A   :					List[Any]=0.02				,A   :					Optional[Any]=1.0				,A   :					Optional[Any]=True				,A   :					Optional[Any]=False				,A   :					Optional[Any]="sine"				,A   :					int="resnet50"				,A   :					Dict=True				,A   :					List[str]=False				,A   :					Tuple=4				,A   :					int=4				,A   :					str=4				,A   :					Optional[int]=False				,A   :					Optional[Any]=3_00				,A   :					Optional[int]=False				,A   :					Union[str, Any]=1				,A   :					Optional[Any]=5				,A   :					List[str]=2				,A   :					List[str]=1				,A   :					Dict=1				,A   :					Union[str, Any]=5				,A   :					Optional[int]=2				,A   :					Optional[int]=0.1				,A   :					Tuple=0.25				,A   :					int=False				,**A   :					Optional[int]				,):
					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(A				,A					):
													__A       =					backbone_config.get("model_type"					)
													__A       =					CONFIG_MAPPING[backbone_model_type]
													__A       =					config_class.from_dict(A					)
					__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=A				,**A					)






	@property
	def 							UpperCamelCase_    (			self   :					List[Any]					):
					return self.encoder_attention_heads






	@property
	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					return self.d_model







	def 							UpperCamelCase_    (			self   :					List[str]					):
					__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






from __future__ import annotations

from dataclasses import dataclass
@dataclass
class 						UpperCAmelCase  :







						'''simple docstring'''





						lowerCAmelCase_							=  42
						lowerCAmelCase_							=  None
						lowerCAmelCase_							=  None







def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	def is_valid_tree(_A				) -> bool:
		if node is None:
			return True

		if not isinstance(_A	,      _A				):
			return False

		try:
			float(node.data				)
		except (TypeError, ValueError):
			return False

		return is_valid_tree(node.left				) and is_valid_tree(node.right				)

	if not is_valid_tree(_A				):
		raise ValueError(
		    "Each node should be type of TreeNode and data should be float."				)

	def is_binary_search_tree_recursive_check(
	    _A	,      _A	,      _A				) -> bool:

		if node is None:
			return True

		return (
		    left_bound < node.data < right_bound
		    and is_binary_search_tree_recursive_check(node.left	,      _A	,      node.data				)
		    and is_binary_search_tree_recursive_check(
		        node.right	,      node.data	,      _A				)
		)

	return is_binary_search_tree_recursive_check(_A	,      -float("inf"				)	,      float("inf"				)				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
