


'''simple docstring'''







from __future__ import annotations







class A__			:






      def __init__(					self     :List[str]	,		SCREAMING_SNAKE_CASE     :Any      )			->   None:




             '''simple docstring'''
             _a       :					str	   =data
             _a       :					Any	   =None
             _a       :					List[Any]	   =None


def       SCREAMING_SNAKE_CASE_   (    _UpperCAmelCase      :      Node | None    )    ->     Dict:  # In Order traversal of the tree





       if tree:
              display(tree.left    )
              print(tree.data    )
              display(tree.right    )


def       SCREAMING_SNAKE_CASE_   (    _UpperCAmelCase      :      Node | None    )    ->     Dict:
       return 1 + max(depth_of_tree(tree.left    )       ,depth_of_tree(tree.right    )    ) if tree else 0


def       SCREAMING_SNAKE_CASE_   (    _UpperCAmelCase      :      Node    )    ->     int:





       if not tree:
              return True
       if tree.left and tree.right:
              return is_full_binary_tree(tree.left    ) and is_full_binary_tree(tree.right    )
       else:
              return not tree.left and not tree.right


def       SCREAMING_SNAKE_CASE_   (    )    ->     Tuple:  # Main function for testing.
       _a       :					List[str]	   =Node(1    )
       _a       :					List[Any]	   =Node(2    )
       _a       :					Optional[Any]	   =Node(3    )
       _a       :					str	   =Node(4    )
       _a       :					Union[str, Any]	   =Node(5    )
       _a       :					str	   =Node(6    )
       _a       :					Dict	   =Node(7    )
       _a       :					int	   =Node(8    )
       _a       :					Dict	   =Node(9    )

       print(is_full_binary_tree(SCREAMING_SNAKE_CASE_    )    )
       print(depth_of_tree(SCREAMING_SNAKE_CASE_    )    )
       print("""Tree is: """    )
       display(SCREAMING_SNAKE_CASE_    )


if __name__ == "__main__":
   main()









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "swinv2"

 __lowerCamelCase    :				int								=       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }




 def __init__(						self      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=96      ,  _lowerCAmelCase=[2, 2, 6, 2]      ,  _lowerCAmelCase=[3, 6, 12, 24]      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=4.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=32      ,  **_lowerCAmelCase      ,  )     ->				Tuple:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   embed_dim
     _lowerCAmelCase						      =   depths
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   window_size
     _lowerCAmelCase						      =   mlp_ratio
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   drop_path_rate
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   use_absolute_embeddings
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   encoder_stride
     # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     _lowerCAmelCase						      =   int(embed_dim * 2 ** (len(_lowerCAmelCase		) - 1)		)
     _lowerCAmelCase						      =   (0, 0, 0, 0)


