





"""simple docstring"""
import copy

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


_snake_case     :		Dict       						= logging.get_logger(__name__)

_snake_case     :		str       						= {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							'''deta'''
					UpperCamelCase      =							{
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''encoder_attention_heads''',
					}


					def __init__(					self			:Union[str, Any]     ,       __UpperCamelCase			:List[Any]=None     ,       __UpperCamelCase			:Dict=9_00     ,       __UpperCamelCase			:Optional[Any]=20_48     ,       __UpperCamelCase			:List[Any]=6     ,       __UpperCamelCase			:Tuple=20_48     ,       __UpperCamelCase			:str=8     ,       __UpperCamelCase			:List[Any]=6     ,       __UpperCamelCase			:Dict=10_24     ,       __UpperCamelCase			:str=8     ,       __UpperCamelCase			:Optional[int]=0.0     ,       __UpperCamelCase			:Any=True     ,       __UpperCamelCase			:Optional[int]="relu"     ,       __UpperCamelCase			:Union[str, Any]=2_56     ,       __UpperCamelCase			:Dict=0.1     ,       __UpperCamelCase			:Any=0.0     ,       __UpperCamelCase			:List[str]=0.0     ,       __UpperCamelCase			:Union[str, Any]=0.02     ,       __UpperCamelCase			:List[Any]=1.0     ,       __UpperCamelCase			:Tuple=True     ,       __UpperCamelCase			:Union[str, Any]=False     ,       __UpperCamelCase			:Union[str, Any]="sine"     ,       __UpperCamelCase			:Union[str, Any]=5     ,       __UpperCamelCase			:List[str]=4     ,       __UpperCamelCase			:Optional[Any]=4     ,       __UpperCamelCase			:Dict=True     ,       __UpperCamelCase			:Optional[Any]=3_00     ,       __UpperCamelCase			:str=True     ,       __UpperCamelCase			:Tuple=True     ,       __UpperCamelCase			:str=1     ,       __UpperCamelCase			:List[str]=5     ,       __UpperCamelCase			:Tuple=2     ,       __UpperCamelCase			:Optional[Any]=1     ,       __UpperCamelCase			:int=1     ,       __UpperCamelCase			:Optional[Any]=5     ,       __UpperCamelCase			:str=2     ,       __UpperCamelCase			:int=0.1     ,       __UpperCamelCase			:int=0.25     ,       **__UpperCamelCase			:List[str]     ,       ):
												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=["stage2", "stage3", "stage4"]		)
												else:
																			if isinstance(__UpperCamelCase     ,       __UpperCamelCase		):
																										A		=  backbone_config.pop("model_type"		)
																										A		=  CONFIG_MAPPING[backbone_model_type]
																										A		=  config_class.from_dict(__UpperCamelCase		)

												A		=  backbone_config
												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
												# 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
												A		=  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
												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
												super().__init__(is_encoder_decoder=__UpperCamelCase     ,       **__UpperCamelCase		)


					@property
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												return self.encoder_attention_heads


					@property
					def 	lowerCamelCase       (					self			:str		):
												return self.d_model


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  copy.deepcopy(self.__dict__		)
												A		=  self.backbone_config.to_dict()
												A		=  self.__class__.model_type
												return output









"""simple docstring"""
_snake_case     :		Optional[int]       						= [
    'DownloadConfig',
    'DownloadManager',
    'DownloadMode',
    'StreamingDownloadManager',
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager


