from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				42
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				None





def 			lowercase(	)		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      Node(1       )
 UpperCamelCase			  =      Node(2       )
 UpperCamelCase			  =      Node(3       )
 UpperCamelCase			  =      Node(4       )
 UpperCamelCase			  =      Node(5       )
 return tree





def 			lowercase(	UpperCamelCase_       )		->	Tuple:







 '''simple docstring'''
 return [root.data, *preorder(root.left       ), *preorder(root.right       )] if root else []





def 			lowercase(	UpperCamelCase_       )		->	Tuple:







 '''simple docstring'''
 return postorder(root.left       ) + postorder(root.right       ) + [root.data] if root else []





def 			lowercase(	UpperCamelCase_       )		->	str:







 '''simple docstring'''
 return [*inorder(root.left       ), root.data, *inorder(root.right       )] if root else []





def 			lowercase(	UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 return (max(height(root.left       )							,					height(root.right       )       ) + 1) if root else 0





def 			lowercase(	UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      []

 if root is None:
  return output

 UpperCamelCase			  =      deque([root]       )

 while process_queue:
  UpperCamelCase			  =      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 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	int:







 '''simple docstring'''
 UpperCamelCase			  =      []

 def populate_output(UpperCamelCase_							,					UpperCamelCase_       ) -> 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(A__							,					A__       )
 return output





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	int:







 '''simple docstring'''
 UpperCamelCase			  =      []

 def populate_output(UpperCamelCase_							,					UpperCamelCase_       ) -> 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(A__							,					A__       )
 return output





def 			lowercase(	UpperCamelCase_       )		->	List[Any]:







 '''simple docstring'''
 if root is None:
  return []

 UpperCamelCase			  =      []

 UpperCamelCase			  =      0
 UpperCamelCase			  =      height(A__       )

 for h in range(1							,					height_tree + 1       ):
  if not flag:
   output.append(get_nodes_from_left_to_right(A__							,					A__       )       )
   UpperCamelCase			  =      1
  else:
   output.append(get_nodes_from_right_to_left(A__							,					A__       )       )
   UpperCamelCase			  =      0

 return output





def 			lowercase(	)		->	List[Any]:  # Main function for testing.







 '''simple docstring'''
 UpperCamelCase			  =      make_tree()
 print(f"""In-order Traversal: {inorder(A__       )}"""       )
 print(f"""Pre-order Traversal: {preorder(A__       )}"""       )
 print(f"""Post-order Traversal: {postorder(A__       )}"""							,					"""\n"""       )

 print(f"""Height of Tree: {height(A__       )}"""							,					"""\n"""       )

 print("""Complete Level Order Traversal: """       )
 print(level_order(A__       )							,					"""\n"""       )

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

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

 print("""\nZigZag order Traversal: """       )
 print(zigzag(A__       )       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
lowerCAmelCase__			:      Optional[int]								=0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
lowerCAmelCase__			:      str								=[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]







class 	__lowercase    :





							"""simple docstring"""







							def __init__(      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					WATERMARK_BITS
										SCREAMING_SNAKE_CASE_		:     int					=					WatermarkEncoder()

										self.encoder.set_watermark('bits'			,						self.watermark       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if images.shape[-1] < 2_5_6:
													return images

										SCREAMING_SNAKE_CASE_		:     Tuple					=					(2_5_5 * (images / 2 + 0.5)).cpu().permute(0			,						2			,						3			,						1       ).float().numpy()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[self.encoder.encode(lowerCAmelCase__			,						'dwtDct'       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.from_numpy(np.array(lowerCAmelCase__       )       ).permute(0			,						3			,						1			,						2       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.clamp(2 * (images / 2_5_5 - 0.5)			,						min=-1.0			,						max=1.0       )
										return images





