




'''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      A       :
							lowercase_							     =							42
							lowercase_							     =							None
							lowercase_							     =							None



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							Node(1 )
							_a      	=							Node(2 )
							_a      	=							Node(3 )
							_a      	=							Node(4 )
							_a      	=							Node(5 )
							return tree
def 						snake_case_       (UpperCamelCase			:    Node | None ):





							'''simple docstring'''


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





							'''simple docstring'''


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





							'''simple docstring'''


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





							'''simple docstring'''


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





							'''simple docstring'''


							_a      	=							[]

							if root is None:
														return output

							_a      	=							deque([root] )

							while process_queue:
														_a      	=							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_       (UpperCamelCase			:    Node | None					,		UpperCamelCase			:    int ):





							'''simple docstring'''


							_a      	=							[]

							def populate_output(UpperCamelCase			:    Node | None					,		UpperCamelCase			:    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_       (UpperCamelCase			:    Node | None					,		UpperCamelCase			:    int ):





							'''simple docstring'''


							_a      	=							[]

							def populate_output(UpperCamelCase			:    Node | None					,		UpperCamelCase			:    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_       (UpperCamelCase			:    Node | None ):





							'''simple docstring'''


							if root is None:
														return []

							_a      	=							[]

							_a      	=							0
							_a      	=							height(UpperCamelCase )

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

							return output
def 						snake_case_       ():  # Main function for testing.





							'''simple docstring'''


							_a      	=							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'''

from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCamelCase				:  Optional[int]    		=      logging.get_logger(__name__)
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      lowerCAmelCase__   :	Optional[int]       =				["""pixel_values"""]



      def __init__(self       :       str  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       Union[int, float] = 1 / 255  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       int = 8  ,		**UpperCamelCase       :       Dict  ,		):

             '''simple docstring'''







             super().__init__(**UpperCamelCase					)

             lowercase__      =					do_rescale
             lowercase__      =					rescale_factor
             lowercase__      =					do_pad
             lowercase__      =					pad_size



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       np.ndarray  ,		UpperCamelCase       :       float  ,		UpperCamelCase       :       Optional[Union[str, ChannelDimension]] = None  ,		**UpperCamelCase       :       Tuple					):

             '''simple docstring'''







             return rescale(UpperCamelCase  ,		scale=UpperCamelCase  ,		data_format=UpperCamelCase  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       np.ndarray  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Optional[Union[str, ChannelDimension]] = None					):

             '''simple docstring'''







             lowercase__     ,lowercase__      =					get_image_size(UpperCamelCase					)
             lowercase__      =					(old_height // size + 1) * size - old_height
             lowercase__      =					(old_width // size + 1) * size - old_width

             return pad(UpperCamelCase  ,		((0, pad_height), (0, pad_width))  ,		mode='''symmetric'''  ,		data_format=UpperCamelCase					)






      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       ImageInput  ,		UpperCamelCase       :       Optional[bool] = None  ,		UpperCamelCase       :       Optional[float] = None  ,		UpperCamelCase       :       Optional[bool] = None  ,		UpperCamelCase       :       Optional[int] = None  ,		UpperCamelCase       :       Optional[Union[str, TensorType]] = None  ,		UpperCamelCase       :       Union[str, ChannelDimension] = ChannelDimension.FIRST  ,		**UpperCamelCase       :       int  ,		):

             '''simple docstring'''







             lowercase__      =					do_rescale if do_rescale is not None else self.do_rescale
             lowercase__      =					rescale_factor if rescale_factor is not None else self.rescale_factor
             lowercase__      =					do_pad if do_pad is not None else self.do_pad
             lowercase__      =					pad_size if pad_size is not None else self.pad_size

             lowercase__      =					make_list_of_images(UpperCamelCase					)

             if not valid_images(UpperCamelCase					):
                    raise ValueError(
                        '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                        '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

             if do_rescale and rescale_factor is None:
                    raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

             # All transformations expect numpy arrays.
             lowercase__      =					[to_numpy_array(UpperCamelCase					) for image in images]

             if do_rescale:
                    lowercase__      =					[self.rescale(image=UpperCamelCase  ,		scale=UpperCamelCase					) for image in images]

             if do_pad:
                    lowercase__      =					[self.pad(UpperCamelCase  ,		size=UpperCamelCase					) for image in images]

             lowercase__      =					[to_channel_dimension_format(UpperCamelCase  ,		UpperCamelCase					) for image in images]

             lowercase__      =					{'''pixel_values''': images}
             return BatchFeature(data=UpperCamelCase  ,		tensor_type=UpperCamelCase					)




