'''simple docstring'''




import copy

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


_a   :	Dict 							=   logging.get_logger(__name__)

_a   :	Union[str, Any] 							=   {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}






class 		__A       (__magic_name__   ):
       snake_case       :Dict				    =					"deta"
       snake_case       :List[Any]				    =					{
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
       }






       def __init__(      self				,     UpperCamelCase_=None				,     UpperCamelCase_=9_00				,     UpperCamelCase_=20_48				,     UpperCamelCase_=6				,     UpperCamelCase_=20_48				,     UpperCamelCase_=8				,     UpperCamelCase_=6				,     UpperCamelCase_=10_24				,     UpperCamelCase_=8				,     UpperCamelCase_=0.0				,     UpperCamelCase_=True				,     UpperCamelCase_="relu"				,     UpperCamelCase_=2_56				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=1.0				,     UpperCamelCase_=True				,     UpperCamelCase_=False				,     UpperCamelCase_="sine"				,     UpperCamelCase_=5				,     UpperCamelCase_=4				,     UpperCamelCase_=4				,     UpperCamelCase_=True				,     UpperCamelCase_=3_00				,     UpperCamelCase_=True				,     UpperCamelCase_=True				,     UpperCamelCase_=1				,     UpperCamelCase_=5				,     UpperCamelCase_=2				,     UpperCamelCase_=1				,     UpperCamelCase_=1				,     UpperCamelCase_=5				,     UpperCamelCase_=2				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.2_5				,     **UpperCamelCase_				,     ):
              if backbone_config is None:
                     logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."      )
                     __UpperCAmelCase  :				Tuple       				=		CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]      )
              else:
                     if isinstance(UpperCamelCase_				,     UpperCamelCase_      ):
                            __UpperCAmelCase  :				List[str]       				=		backbone_config.pop("model_type"      )
                            __UpperCAmelCase  :				List[str]       				=		CONFIG_MAPPING[backbone_model_type]
                            __UpperCAmelCase  :				Dict       				=		config_class.from_dict(UpperCamelCase_      )

              __UpperCAmelCase  :				List[str]       				=		backbone_config
              __UpperCAmelCase  :				List[str]       				=		num_queries
              __UpperCAmelCase  :				Optional[Any]       				=		max_position_embeddings
              __UpperCAmelCase  :				Optional[Any]       				=		d_model
              __UpperCAmelCase  :				int       				=		encoder_ffn_dim
              __UpperCAmelCase  :				List[Any]       				=		encoder_layers
              __UpperCAmelCase  :				Dict       				=		encoder_attention_heads
              __UpperCAmelCase  :				Union[str, Any]       				=		decoder_ffn_dim
              __UpperCAmelCase  :				str       				=		decoder_layers
              __UpperCAmelCase  :				str       				=		decoder_attention_heads
              __UpperCAmelCase  :				Tuple       				=		dropout
              __UpperCAmelCase  :				Union[str, Any]       				=		attention_dropout
              __UpperCAmelCase  :				List[Any]       				=		activation_dropout
              __UpperCAmelCase  :				List[str]       				=		activation_function
              __UpperCAmelCase  :				List[Any]       				=		init_std
              __UpperCAmelCase  :				Any       				=		init_xavier_std
              __UpperCAmelCase  :				Optional[int]       				=		encoder_layerdrop
              __UpperCAmelCase  :				Union[str, Any]       				=		auxiliary_loss
              __UpperCAmelCase  :				Optional[Any]       				=		position_embedding_type
              # deformable attributes
              __UpperCAmelCase  :				Any       				=		num_feature_levels
              __UpperCAmelCase  :				str       				=		encoder_n_points
              __UpperCAmelCase  :				str       				=		decoder_n_points
              __UpperCAmelCase  :				Tuple       				=		two_stage
              __UpperCAmelCase  :				Optional[int]       				=		two_stage_num_proposals
              __UpperCAmelCase  :				int       				=		with_box_refine
              __UpperCAmelCase  :				int       				=		assign_first_stage
              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
              __UpperCAmelCase  :				int       				=		class_cost
              __UpperCAmelCase  :				Optional[Any]       				=		bbox_cost
              __UpperCAmelCase  :				Any       				=		giou_cost
              # Loss coefficients
              __UpperCAmelCase  :				int       				=		mask_loss_coefficient
              __UpperCAmelCase  :				Union[str, Any]       				=		dice_loss_coefficient
              __UpperCAmelCase  :				int       				=		bbox_loss_coefficient
              __UpperCAmelCase  :				str       				=		giou_loss_coefficient
              __UpperCAmelCase  :				Optional[Any]       				=		eos_coefficient
              __UpperCAmelCase  :				int       				=		focal_alpha
              super().__init__(is_encoder_decoder=UpperCamelCase_				,     **UpperCamelCase_      )






       @property
       def 			_snake_case						(      self      ):
              return self.encoder_attention_heads






       @property
       def 			_snake_case						(      self      ):
              return self.d_model







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		copy.deepcopy(self.__dict__      )
              __UpperCAmelCase  :				str       				=		self.backbone_config.to_dict()
              __UpperCAmelCase  :				Optional[int]       				=		self.__class__.model_type
              return output







'''simple docstring'''




class 		__A       :






       def __init__(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Any       				=		set_counts
              __UpperCAmelCase  :				int       				=		max(UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		len(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		[1] * num_sets
              __UpperCAmelCase  :				Any       				=		list(range(UpperCamelCase_      )      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Optional[int]       				=		self.get_parent(UpperCamelCase_      )
              __UpperCAmelCase  :				List[Any]       				=		self.get_parent(UpperCamelCase_      )

              if src_parent == dst_parent:
                     return False

              if self.ranks[dst_parent] >= self.ranks[src_parent]:
                     self.set_counts[dst_parent] += self.set_counts[src_parent]
                     __UpperCAmelCase  :				Optional[Any]       				=		0
                     __UpperCAmelCase  :				List[Any]       				=		dst_parent
                     if self.ranks[dst_parent] == self.ranks[src_parent]:
                            self.ranks[dst_parent] += 1
                     __UpperCAmelCase  :				Union[str, Any]       				=		self.set_counts[dst_parent]
              else:
                     self.set_counts[src_parent] += self.set_counts[dst_parent]
                     __UpperCAmelCase  :				Union[str, Any]       				=		0
                     __UpperCAmelCase  :				Dict       				=		src_parent
                     __UpperCAmelCase  :				Dict       				=		self.set_counts[src_parent]

              __UpperCAmelCase  :				Dict       				=		max(self.max_set				,     UpperCamelCase_      )
              return True







       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if self.parents[disj_set] == disj_set:
                     return disj_set
              __UpperCAmelCase  :				str       				=		self.get_parent(self.parents[disj_set]      )
              return self.parents[disj_set]






