"""simple docstring"""


from __future__ import annotations


class        a				:


    def __init__(					self	:  Optional[int]   ,				__lowerCAmelCase	:  int					):
      _UpperCAmelCase						 =      data
      _UpperCAmelCase						 =      None
      _UpperCAmelCase						 =      None






def     __UpperCAmelCase					(			lowercase		):  # In Order traversal of the tree







  """simple docstring"""
  if tree:
    display(tree.left		)
    print(tree.data		)
    display(tree.right		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return 1 + max(depth_of_tree(tree.left		)   ,depth_of_tree(tree.right		)		) if tree else 0






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  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     __UpperCAmelCase					(			):  # Main function for testing.







  """simple docstring"""
  _UpperCAmelCase						 =      Node(1		)
  _UpperCAmelCase						 =      Node(2		)
  _UpperCAmelCase						 =      Node(3		)
  _UpperCAmelCase						 =      Node(4		)
  _UpperCAmelCase						 =      Node(5		)
  _UpperCAmelCase						 =      Node(6		)
  _UpperCAmelCase						 =      Node(7		)
  _UpperCAmelCase						 =      Node(8		)
  _UpperCAmelCase						 =      Node(9		)

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


if __name__ == "__main__":
    main()

"""simple docstring"""


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


UpperCAmelCase__  		=						logging.get_logger(__name__)

UpperCAmelCase__  		=						{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class        a				(		lowerCAmelCase_	,							lowerCAmelCase_   ):
    _snake_case     :  Union[str, Any]     							=							'nat'

    _snake_case     :  List[str]     							=							{
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }


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

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