






'''simple docstring'''





import copy

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


_UpperCamelCase						:						str  =					logging.get_logger(__name__)

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


class  snake_case__      (					lowerCamelCase_):
					a_        =					"deta"
					a_        =					{
					    "hidden_size": "d_model",
					    "num_attention_heads": "encoder_attention_heads",
					}




					def __init__(  self  :		Tuple				, _A  :		int=None				, _A  :		Dict=9_00				, _A  :		int=20_48				, _A  :		List[str]=6				, _A  :		Optional[Any]=20_48				, _A  :		Dict=8				, _A  :		List[Any]=6				, _A  :		List[str]=10_24				, _A  :		Union[str, Any]=8				, _A  :		int=0.0				, _A  :		Optional[Any]=True				, _A  :		Any="relu"				, _A  :		Dict=2_56				, _A  :		Dict=0.1				, _A  :		int=0.0				, _A  :		int=0.0				, _A  :		List[Any]=0.02				, _A  :		Optional[Any]=1.0				, _A  :		Optional[Any]=True				, _A  :		Union[str, Any]=False				, _A  :		Optional[int]="sine"				, _A  :		List[Any]=5				, _A  :		Optional[int]=4				, _A  :		str=4				, _A  :		Dict=True				, _A  :		Optional[int]=3_00				, _A  :		List[Any]=True				, _A  :		List[str]=True				, _A  :		Union[str, Any]=1				, _A  :		Optional[Any]=5				, _A  :		Dict=2				, _A  :		Optional[Any]=1				, _A  :		Optional[Any]=1				, _A  :		List[Any]=5				, _A  :		str=2				, _A  :		int=0.1				, _A  :		Tuple=0.25				, **_A  :		Optional[int]				, )    ->							Tuple:
								if backbone_config is None:
											logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''      )
											UpperCAmelCase_      :       List[Any]          =       CONFIG_MAPPING['resnet'](out_features=['''stage2''', '''stage3''', '''stage4''']      )
								else:
											if isinstance(snake_case__				, snake_case__      ):
														UpperCAmelCase_      :       List[str]          =       backbone_config.pop('''model_type'''      )
														UpperCAmelCase_      :       Union[str, Any]          =       CONFIG_MAPPING[backbone_model_type]
														UpperCAmelCase_      :       Optional[Any]          =       config_class.from_dict(snake_case__      )

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




					@property
					def 		A      (  self  :		int      )    ->							Union[str, Any]:
								return self.encoder_attention_heads




					@property
					def 		A      (  self  :		Tuple      )    ->							Union[str, Any]:
								return self.d_model





					def 		A      (  self  :		Optional[Any]      )    ->							str:
								UpperCAmelCase_      :       Optional[Any]          =       copy.deepcopy(self.__dict__      )
								UpperCAmelCase_      :       Optional[Any]          =       self.backbone_config.to_dict()
								UpperCAmelCase_      :       Any          =       self.__class__.model_type
								return output








import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


UpperCamelCase__ :   Dict						=							logging.get_logger(__name__)







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,*snake_case__      ,**snake_case__ ):
							warnings.warn(
							    'The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use ChineseCLIPImageProcessor instead.'      ,snake_case__      ,)
							super().__init__(*snake_case__      ,**snake_case__ )



