




import copy

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


_snake_case										=       logging.get_logger(__name__)

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







class 						lowercase							(   _a		):
     _a							 =						"deta"
     _a							 =						{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }

     def __init__(  self		,     _a=None		,     _a=900		,     _a=2048		,     _a=6		,     _a=2048		,     _a=8		,     _a=6		,     _a=1024		,     _a=8		,     _a=0.0		,     _a=True		,     _a="relu"		,     _a=256		,     _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=300		,     _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		,     )						->							Dict:







           if backbone_config is None:
                 logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""				)
                 _A							:     Dict			=						CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]				)
           else:
                 if isinstance(UpperCamelCase__		,     UpperCamelCase__				):
                       _A							:     Optional[Any]			=						backbone_config.pop("""model_type"""				)
                       _A							:     str			=						CONFIG_MAPPING[backbone_model_type]
                       _A							:     Optional[int]			=						config_class.from_dict(UpperCamelCase__				)

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

     @property
     def 					a__	(  self				)						->							int:
           return self.encoder_attention_heads

     @property
     def 					a__	(  self				)						->							Optional[Any]:
           return self.d_model



     def 					a__	(  self				)						->							Dict:
           _A							:     str			=						copy.deepcopy(self.__dict__				)
           _A							:     int			=						self.backbone_config.to_dict()
           _A							:     Optional[int]			=						self.__class__.model_type
           return output






'''simple docstring'''


import warnings

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


_lowerCamelCase	:       str      	=	logging.get_logger(__name__)

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					Dict  ,						*UpperCamelCase__      :					List[Any]  ,						**UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







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

