



import copy

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


__snake_case		:int   	=  logging.get_logger(__name__)

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

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					Optional[int]		      =		'''deta'''
       UpperCamelCase__						:					Any		      =		{
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''encoder_attention_heads''',
       }



       def __init__(	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  str=None  ,	__SCREAMING_SNAKE_CASE							:  str=900  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=2_048  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=6  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=2_048  ,	__SCREAMING_SNAKE_CASE							:  Any=8  ,	__SCREAMING_SNAKE_CASE							:  Dict=6  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=1_024  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=8  ,	__SCREAMING_SNAKE_CASE							:  List[str]=0.0  ,	__SCREAMING_SNAKE_CASE							:  Dict=True  ,	__SCREAMING_SNAKE_CASE							:  int="relu"  ,	__SCREAMING_SNAKE_CASE							:  Dict=256  ,	__SCREAMING_SNAKE_CASE							:  int=0.1  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=0.0  ,	__SCREAMING_SNAKE_CASE							:  Any=0.0  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=0.02  ,	__SCREAMING_SNAKE_CASE							:  List[str]=1.0  ,	__SCREAMING_SNAKE_CASE							:  str=True  ,	__SCREAMING_SNAKE_CASE							:  List[str]=False  ,	__SCREAMING_SNAKE_CASE							:  Tuple="sine"  ,	__SCREAMING_SNAKE_CASE							:  List[str]=5  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=4  ,	__SCREAMING_SNAKE_CASE							:  List[str]=4  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=True  ,	__SCREAMING_SNAKE_CASE							:  List[str]=300  ,	__SCREAMING_SNAKE_CASE							:  str=True  ,	__SCREAMING_SNAKE_CASE							:  List[str]=True  ,	__SCREAMING_SNAKE_CASE							:  Any=1  ,	__SCREAMING_SNAKE_CASE							:  Any=5  ,	__SCREAMING_SNAKE_CASE							:  List[str]=2  ,	__SCREAMING_SNAKE_CASE							:  Dict=1  ,	__SCREAMING_SNAKE_CASE							:  str=1  ,	__SCREAMING_SNAKE_CASE							:  Dict=5  ,	__SCREAMING_SNAKE_CASE							:  int=2  ,	__SCREAMING_SNAKE_CASE							:  List[str]=0.1  ,	__SCREAMING_SNAKE_CASE							:  int=0.25  ,	**__SCREAMING_SNAKE_CASE							:  Dict  ,	):


           '''simple docstring'''

           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(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
                   __a		 =       backbone_config.pop('''model_type''')
                   __a		 =       CONFIG_MAPPING[backbone_model_type]
                   __a		 =       config_class.from_dict(__SCREAMING_SNAKE_CASE)

           __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=__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)



       @property
       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           return self.encoder_attention_heads



       @property
       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           return self.d_model





       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       copy.deepcopy(self.__dict__)
           __a		 =       self.backbone_config.to_dict()
           __a		 =       self.__class__.model_type
           return output










def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase = False       ):

    if not isinstance(_UpperCAmelCase     ,				_UpperCAmelCase       ):
        __a		 =       f'Expected string as input, found {type(_UpperCAmelCase       )}'
        raise ValueError(_UpperCAmelCase       )
    if not isinstance(_UpperCAmelCase     ,				_UpperCAmelCase       ):
        __a		 =       f'Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase       )}'
        raise ValueError(_UpperCAmelCase       )

    __a		 =       input_str.split('''_'''       )

    __a		 =       0 if use_pascal else 1

    __a		 =       words[start_index:]

    __a		 =       [word[0].upper() + word[1:] for word in words_to_capitalize]

    __a		 =       '''''' if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]       )


if __name__ == "__main__":
 from doctest import testmod

 testmod()





