


"""simple docstring"""





from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any



@dataclass
class 						snake_case_:
     __UpperCamelCase     =    42
     __UpperCamelCase     =    None
     __UpperCamelCase     =    None



def    _snake_case  (  ):
      lowerCAmelCase    :		Optional[int]				=      Node(1	)
      lowerCAmelCase    :		Optional[Any]				=      Node(2	)
      lowerCAmelCase    :		Optional[int]				=      Node(3	)
      lowerCAmelCase    :		Optional[Any]				=      Node(4	)
      lowerCAmelCase    :		Dict				=      Node(5	)
      return tree


def    _snake_case  (  _snake_case :	Node | None	):
      return [root.data, *preorder(root.left	), *preorder(root.right	)] if root else []


def    _snake_case  (  _snake_case :	Node | None	):
      return postorder(root.left	) + postorder(root.right	) + [root.data] if root else []


def    _snake_case  (  _snake_case :	Node | None	):
      return [*inorder(root.left	), root.data, *inorder(root.right	)] if root else []


def    _snake_case  (  _snake_case :	Node | None	):
      return (max(height(root.left	)       ,      height(root.right	)	) + 1) if root else 0


def    _snake_case  (  _snake_case :	Node | None	):
      lowerCAmelCase    :		int				=      []

      if root is None:
            return output

      lowerCAmelCase    :		Dict				=      deque([root]	)

      while process_queue:
            lowerCAmelCase    :		List[Any]				=      process_queue.popleft()
            output.append(node.data	)

            if node.left:
                  process_queue.append(node.left	)
            if node.right:
                  process_queue.append(node.right	)
      return output


def    _snake_case  (  _snake_case :	Node | None       ,      _snake_case :	int	):
      lowerCAmelCase    :		List[str]				=      []

      def populate_output(_snake_case :	Node | None       ,      _snake_case :	int	) -> None:
            if not root:
                  return
            if level == 1:
                  output.append(root.data	)
            elif level > 1:
                  populate_output(root.left       ,      level - 1	)
                  populate_output(root.right       ,      level - 1	)

      populate_output(_UpperCamelCase       ,      _UpperCamelCase	)
      return output


def    _snake_case  (  _snake_case :	Node | None       ,      _snake_case :	int	):
      lowerCAmelCase    :		List[str]				=      []

      def populate_output(_snake_case :	Node | None       ,      _snake_case :	int	) -> None:
            if root is None:
                  return
            if level == 1:
                  output.append(root.data	)
            elif level > 1:
                  populate_output(root.right       ,      level - 1	)
                  populate_output(root.left       ,      level - 1	)

      populate_output(_UpperCamelCase       ,      _UpperCamelCase	)
      return output


def    _snake_case  (  _snake_case :	Node | None	):




      if root is None:
            return []

      lowerCAmelCase    :		Optional[int]				=      []

      lowerCAmelCase    :		List[str]				=      0
      lowerCAmelCase    :		str				=      height(_UpperCamelCase	)

      for h in range(1       ,      height_tree + 1	):
            if not flag:
                  output.append(get_nodes_from_left_to_right(_UpperCamelCase       ,      _UpperCamelCase	)	)
                  lowerCAmelCase    :		Any				=      1
            else:
                  output.append(get_nodes_from_right_to_left(_UpperCamelCase       ,      _UpperCamelCase	)	)
                  lowerCAmelCase    :		int				=      0

      return output


def    _snake_case  (  ):  # Main function for testing.
      lowerCAmelCase    :		Any				=      make_tree()
      print(f'''In-order Traversal: {inorder(_UpperCamelCase	)}'''	)
      print(f'''Pre-order Traversal: {preorder(_UpperCamelCase	)}'''	)
      print(f'''Post-order Traversal: {postorder(_UpperCamelCase	)}'''       ,      '''\n'''	)

      print(f'''Height of Tree: {height(_UpperCamelCase	)}'''       ,      '''\n'''	)

      print('''Complete Level Order Traversal: '''	)
      print(level_order(_UpperCamelCase	)       ,      '''\n'''	)

      print('''Level-wise order Traversal: '''	)

      for level in range(1       ,      height(_UpperCamelCase	) + 1	):
            print(f'''Level {level}:'''       ,      get_nodes_from_left_to_right(_UpperCamelCase       ,      level=_UpperCamelCase	)	)

      print('''\nZigZag order Traversal: '''	)
      print(zigzag(_UpperCamelCase	)	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()





'''simple docstring'''
import copy

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


lowerCamelCase :      Dict					 =     logging.get_logger(__name__)

lowerCamelCase :      List[Any]					 =     {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class 							A__    (       A__  ):
  A__		 =				'deta'
  A__		 =				{
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
  }





  def __init__(		self      :	Tuple							,					_a      :	Optional[int]=None							,					_a      :	int=900							,					_a      :	Optional[Any]=2048							,					_a      :	int=6							,					_a      :	Tuple=2048							,					_a      :	Optional[int]=8							,					_a      :	Any=6							,					_a      :	str=1024							,					_a      :	int=8							,					_a      :	int=0.0							,					_a      :	Optional[Any]=True							,					_a      :	Tuple="relu"							,					_a      :	Union[str, Any]=256							,					_a      :	Tuple=0.1							,					_a      :	str=0.0							,					_a      :	Dict=0.0							,					_a      :	Tuple=0.02							,					_a      :	Union[str, Any]=1.0							,					_a      :	Any=True							,					_a      :	Tuple=False							,					_a      :	List[Any]="sine"							,					_a      :	str=5							,					_a      :	List[Any]=4							,					_a      :	str=4							,					_a      :	Union[str, Any]=True							,					_a      :	Optional[int]=300							,					_a      :	Dict=True							,					_a      :	List[Any]=True							,					_a      :	List[Any]=1							,					_a      :	List[str]=5							,					_a      :	int=2							,					_a      :	Dict=1							,					_a      :	str=1							,					_a      :	Optional[Any]=5							,					_a      :	Union[str, Any]=2							,					_a      :	List[str]=0.1							,					_a      :	List[Any]=0.25							,					**_a      :	Union[str, Any]							,					)	->    List[str]:





       '''simple docstring'''






       if backbone_config is None:
            logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
            _SCREAMING_SNAKE_CASE							    =CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'] )
       else:
            if isinstance(_a							,					_a ):
                 _SCREAMING_SNAKE_CASE							    =backbone_config.pop('model_type' )
                 _SCREAMING_SNAKE_CASE							    =CONFIG_MAPPING[backbone_model_type]
                 _SCREAMING_SNAKE_CASE							    =config_class.from_dict(_a )

       _SCREAMING_SNAKE_CASE							    =backbone_config
       _SCREAMING_SNAKE_CASE							    =num_queries
       _SCREAMING_SNAKE_CASE							    =max_position_embeddings
       _SCREAMING_SNAKE_CASE							    =d_model
       _SCREAMING_SNAKE_CASE							    =encoder_ffn_dim
       _SCREAMING_SNAKE_CASE							    =encoder_layers
       _SCREAMING_SNAKE_CASE							    =encoder_attention_heads
       _SCREAMING_SNAKE_CASE							    =decoder_ffn_dim
       _SCREAMING_SNAKE_CASE							    =decoder_layers
       _SCREAMING_SNAKE_CASE							    =decoder_attention_heads
       _SCREAMING_SNAKE_CASE							    =dropout
       _SCREAMING_SNAKE_CASE							    =attention_dropout
       _SCREAMING_SNAKE_CASE							    =activation_dropout
       _SCREAMING_SNAKE_CASE							    =activation_function
       _SCREAMING_SNAKE_CASE							    =init_std
       _SCREAMING_SNAKE_CASE							    =init_xavier_std
       _SCREAMING_SNAKE_CASE							    =encoder_layerdrop
       _SCREAMING_SNAKE_CASE							    =auxiliary_loss
       _SCREAMING_SNAKE_CASE							    =position_embedding_type
       # deformable attributes
       _SCREAMING_SNAKE_CASE							    =num_feature_levels
       _SCREAMING_SNAKE_CASE							    =encoder_n_points
       _SCREAMING_SNAKE_CASE							    =decoder_n_points
       _SCREAMING_SNAKE_CASE							    =two_stage
       _SCREAMING_SNAKE_CASE							    =two_stage_num_proposals
       _SCREAMING_SNAKE_CASE							    =with_box_refine
       _SCREAMING_SNAKE_CASE							    =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
       _SCREAMING_SNAKE_CASE							    =class_cost
       _SCREAMING_SNAKE_CASE							    =bbox_cost
       _SCREAMING_SNAKE_CASE							    =giou_cost
       # Loss coefficients
       _SCREAMING_SNAKE_CASE							    =mask_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =dice_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =bbox_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =giou_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =eos_coefficient
       _SCREAMING_SNAKE_CASE							    =focal_alpha
       super().__init__(is_encoder_decoder=_a							,					**_a )





  @property
  def 						A  (		self      :	Dict )	->    int:





       '''simple docstring'''






       return self.encoder_attention_heads





  @property
  def 						A  (		self      :	List[Any] )	->    int:





       '''simple docstring'''






       return self.d_model





  def 						A  (		self      :	Optional[int] )	->    List[str]:





       '''simple docstring'''






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