




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase__ :   str					=							logging.get_logger(__name__)

lowerCamelCase__ :   int					=							{
    'shi-labs/dinat-mini-in1k-224': 'https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}





class        lowerCamelCase_							(      __UpperCamelCase       ,   __UpperCamelCase					):


 '''simple docstring'''







 lowercase_				    =    'dinat'

 lowercase_				    =    {
     'num_attention_heads': 'num_heads',
     'num_hidden_layers': 'num_layers',
 }



 def __init__(							self  :		Tuple				,				_lowerCAmelCase  :		Tuple=4				,				_lowerCAmelCase  :		Optional[Any]=3				,				_lowerCAmelCase  :		Any=64				,				_lowerCAmelCase  :		Optional[Any]=[3, 4, 6, 5]				,				_lowerCAmelCase  :		int=[2, 4, 8, 16]				,				_lowerCAmelCase  :		Optional[Any]=7				,				_lowerCAmelCase  :		Any=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]				,				_lowerCAmelCase  :		int=3.0				,				_lowerCAmelCase  :		Dict=True				,				_lowerCAmelCase  :		Optional[Any]=0.0				,				_lowerCAmelCase  :		Dict=0.0				,				_lowerCAmelCase  :		Optional[int]=0.1				,				_lowerCAmelCase  :		Optional[Any]="gelu"				,				_lowerCAmelCase  :		List[str]=0.02				,				_lowerCAmelCase  :		Dict=1E-5				,				_lowerCAmelCase  :		Dict=0.0				,				_lowerCAmelCase  :		int=None				,				_lowerCAmelCase  :		Dict=None				,				**_lowerCAmelCase  :		Dict				,				):
       super().__init__(**lowerCAmelCase__						)

       SCREAMING_SNAKE_CASE_							=			patch_size
       SCREAMING_SNAKE_CASE_							=			num_channels
       SCREAMING_SNAKE_CASE_							=			embed_dim
       SCREAMING_SNAKE_CASE_							=			depths
       SCREAMING_SNAKE_CASE_							=			len(lowerCAmelCase__						)
       SCREAMING_SNAKE_CASE_							=			num_heads
       SCREAMING_SNAKE_CASE_							=			kernel_size
       SCREAMING_SNAKE_CASE_							=			dilations
       SCREAMING_SNAKE_CASE_							=			mlp_ratio
       SCREAMING_SNAKE_CASE_							=			qkv_bias
       SCREAMING_SNAKE_CASE_							=			hidden_dropout_prob
       SCREAMING_SNAKE_CASE_							=			attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_							=			drop_path_rate
       SCREAMING_SNAKE_CASE_							=			hidden_act
       SCREAMING_SNAKE_CASE_							=			layer_norm_eps
       SCREAMING_SNAKE_CASE_							=			initializer_range
       # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       SCREAMING_SNAKE_CASE_							=			int(embed_dim * 2 ** (len(lowerCAmelCase__						) - 1)						)
       SCREAMING_SNAKE_CASE_							=			layer_scale_init_value
       SCREAMING_SNAKE_CASE_							=			['stem'] + [F"stage{idx}" for idx in range(1				,				len(lowerCAmelCase__						) + 1						)]
       SCREAMING_SNAKE_CASE_							=			get_aligned_output_features_output_indices(
           out_features=lowerCAmelCase__				,				out_indices=lowerCAmelCase__				,				stage_names=self.stage_names						)



def    snake_case_     (  snake_case			,							snake_case     )  ->				bool:
       return numa ^ numa < 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()


