
'''simple docstring'''


import copy

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


__lowercase							:   List[Any]					=     logging.get_logger(__name__)

__lowercase							:   int					=     {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}

class 	__lowercase				(  lowercase_    ):
			lowerCamelCase			:		Optional[int]	      =			'''deta'''
			lowerCamelCase			:		str	      =			{
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			}

			def __init__(self			,   A=None			,   A=9_0_0			,   A=2_0_4_8			,   A=6			,   A=2_0_4_8			,   A=8			,   A=6			,   A=1_0_2_4			,   A=8			,   A=0.0			,   A=True			,   A="relu"			,   A=2_5_6			,   A=0.1			,   A=0.0			,   A=0.0			,   A=0.02			,   A=1.0			,   A=True			,   A=False			,   A="sine"			,   A=5			,   A=4			,   A=4			,   A=True			,   A=3_0_0			,   A=True			,   A=True			,   A=1			,   A=5			,   A=2			,   A=1			,   A=1			,   A=5			,   A=2			,   A=0.1			,   A=0.25			,   **A			,   ):
								if backbone_config is None:
													logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''      )
													lowerCamelCase_					:      int     =		CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']      )
								else:
													if isinstance(A			,   A      ):
																		lowerCamelCase_					:      List[Any]     =		backbone_config.pop('''model_type'''      )
																		lowerCamelCase_					:      List[Any]     =		CONFIG_MAPPING[backbone_model_type]
																		lowerCamelCase_					:      Optional[Any]     =		config_class.from_dict(A      )

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

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

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






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		copy.deepcopy(self.__dict__      )
								lowerCamelCase_					:      Any     =		self.backbone_config.to_dict()
								lowerCamelCase_					:      str     =		self.__class__.model_type
								return output




'''simple docstring'''


def lowercase_	(   _lowercase			,       _lowercase				)     ->							Dict:
					'''simple docstring'''

					lowerCamelCase_					:      List[Any]     =		0
					while b > 0:
										if b & 1:
															res += a

										a += a
										b >>= 1

					return res




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							Optional[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      str     =		0
					while b > 0:
										if b & 1:
															lowerCamelCase_					:      Optional[int]     =		((res % c) + (a % c)) % c

										a += a
										b >>= 1

					return res


