'''simple docstring'''

from __future__ import annotations

import queue





class 							SCREAMING_SNAKE_CASE__						:


   def __init__(   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	str)      ->					Optional[int]:
         lowercase_     =      data
         lowercase_     =      None
         lowercase_     =      None




def __a (	)	->							int:
      '''simple docstring'''


      print("\n********Press N to stop entering at any point of time********\n"				)
      lowercase_     =      input("Enter the value of the root node: "				).strip().lower()
      lowercase_     =      queue.Queue()
      lowercase_     =      TreeNode(int(lowerCAmelCase__				)				)
      q.put(lowerCAmelCase__				)
      while not q.empty():
            lowercase_     =      q.get()
            lowercase_     =      f'Enter the left node of {node_found.data}: '
            lowercase_     =      input(lowerCAmelCase__				).strip().lower() or "n"
            if check == "n":
                  return tree_node
            lowercase_     =      TreeNode(int(lowerCAmelCase__				)				)
            lowercase_     =      left_node
            q.put(lowerCAmelCase__				)
            lowercase_     =      f'Enter the right node of {node_found.data}: '
            lowercase_     =      input(lowerCAmelCase__				).strip().lower() or "n"
            if check == "n":
                  return tree_node
            lowercase_     =      TreeNode(int(lowerCAmelCase__				)				)
            lowercase_     =      right_node
            q.put(lowerCAmelCase__				)
      raise


def __a (	__lowerCamelCase			:						Dict				)	->							Optional[int]:
      '''simple docstring'''


      if not isinstance(lowerCAmelCase__ ,		lowerCAmelCase__				) or not node:
            return
      print(node.data ,		end=","				)
      pre_order(node.left				)
      pre_order(node.right				)


def __a (	__lowerCamelCase			:						Union[str, Any]				)	->							Optional[int]:
      '''simple docstring'''


      if not isinstance(lowerCAmelCase__ ,		lowerCAmelCase__				) or not node:
            return
      in_order(node.left				)
      print(node.data ,		end=","				)
      in_order(node.right				)


def __a (	__lowerCamelCase			:						Any				)	->							Optional[int]:
      '''simple docstring'''


      if not isinstance(lowerCAmelCase__ ,		lowerCAmelCase__				) or not node:
            return
      post_order(node.left				)
      post_order(node.right				)
      print(node.data ,		end=","				)


def __a (	__lowerCamelCase			:						List[str]				)	->							str:
      '''simple docstring'''


      if not isinstance(lowerCAmelCase__ ,		lowerCAmelCase__				) or not node:
            return
      lowercase_     =      queue.Queue()
      q.put(lowerCAmelCase__				)
      while not q.empty():
            lowercase_     =      q.get()
            print(node_dequeued.data ,		end=","				)
            if node_dequeued.left:
                  q.put(node_dequeued.left				)
            if node_dequeued.right:
                  q.put(node_dequeued.right				)


def __a (	__lowerCamelCase			:						str				)	->							List[str]:
      '''simple docstring'''


      if not isinstance(lowerCAmelCase__ ,		lowerCAmelCase__				) or not node:
            return
      lowercase_     =      queue.Queue()
      q.put(lowerCAmelCase__				)
      while not q.empty():
            lowercase_     =      []
            while not q.empty():
                  lowercase_     =      q.get()
                  print(node_dequeued.data ,		end=","				)
                  if node_dequeued.left:
                        list_.append(node_dequeued.left				)
                  if node_dequeued.right:
                        list_.append(node_dequeued.right				)
            print()
            for node in list_:
                  q.put(lowerCAmelCase__				)


def __a (	__lowerCamelCase			:						Tuple				)	->							Optional[int]:
      '''simple docstring'''


      if not isinstance(lowerCAmelCase__ ,		lowerCAmelCase__				) or not node:
            return
      lowercase_     =      []
      lowercase_     =      node
      while n or stack:
            while n:  # start from root node, find its left child
                  print(n.data ,		end=","				)
                  stack.append(lowerCAmelCase__				)
                  lowercase_     =      n.left
            # end of while means current node doesn't have left child
            lowercase_     =      stack.pop()
            # start to traverse its right child
            lowercase_     =      n.right


def __a (	__lowerCamelCase			:						int				)	->							int:
      '''simple docstring'''


      if not isinstance(lowerCAmelCase__ ,		lowerCAmelCase__				) or not node:
            return
      lowercase_     =      []
      lowercase_     =      node
      while n or stack:
            while n:
                  stack.append(lowerCAmelCase__				)
                  lowercase_     =      n.left
            lowercase_     =      stack.pop()
            print(n.data ,		end=","				)
            lowercase_     =      n.right


def __a (	__lowerCamelCase			:						Dict				)	->							Dict:
      '''simple docstring'''


      if not isinstance(lowerCAmelCase__ ,		lowerCAmelCase__				) or not node:
            return
      lowercase_ ,							lowercase_     =      [], []
      lowercase_     =      node
      stacka.append(lowerCAmelCase__				)
      while stacka:  # to find the reversed order of post order, store it in stack2
            lowercase_     =      stacka.pop()
            if n.left:
                  stacka.append(n.left				)
            if n.right:
                  stacka.append(n.right				)
            stacka.append(lowerCAmelCase__				)
      while stacka:  # pop up from stack2 will be the post order
            print(stacka.pop().data ,		end=","				)


def __a (	__lowerCamelCase			:						Any = "" ,		__lowerCamelCase			:						str=50 ,		__lowerCamelCase			:						Any="*"				)	->							Optional[int]:
      '''simple docstring'''


      if not s:
            return "\n" + width * char
      lowercase_ ,							lowercase_     =      divmod(width - len(lowerCAmelCase__				) - 2 ,		2				)
      return f'{left * char} {s} {(left + extra) * char}'


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(prompt("Binary Tree Traversals"))

       lowerCAmelCase_ : Optional[Any]						  =      build_tree()
       print(prompt("Pre Order Traversal"))
       pre_order(node)
       print(prompt() + "\n")

       print(prompt("In Order Traversal"))
       in_order(node)
       print(prompt() + "\n")

       print(prompt("Post Order Traversal"))
       post_order(node)
       print(prompt() + "\n")

       print(prompt("Level Order Traversal"))
       level_order(node)
       print(prompt() + "\n")

       print(prompt("Actual Level Order Traversal"))
       level_order_actual(node)
       print("*" * 50 + "\n")

       print(prompt("Pre Order Traversal - Iteration Version"))
       pre_order_iter(node)
       print(prompt() + "\n")

       print(prompt("In Order Traversal - Iteration Version"))
       in_order_iter(node)
       print(prompt() + "\n")

       print(prompt("Post Order Traversal - Iteration Version"))
       post_order_iter(node)
       print(prompt())







'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase_ : Any						  =      logging.get_logger(__name__)





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=['pixel_values']


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	int = 32   ,		__lowerCAmelCase				:	List[str]=PILImageResampling.BILINEAR   ,		__lowerCAmelCase				:	bool = True   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					None:
         lowercase_     =      do_resize
         lowercase_     =      do_rescale
         lowercase_     =      size_divisor
         lowercase_     =      resample
         super().__init__(**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Optional[ChannelDimension] = None   ,		**__lowerCAmelCase				:	Any)      ->					np.ndarray:
         lowercase_ ,							lowercase_     =      get_image_size(__lowerCAmelCase)
         # Rounds the height and width down to the closest multiple of size_divisor
         lowercase_     =      height // size_divisor * size_divisor
         lowercase_     =      width // size_divisor * size_divisor
         lowercase_     =      resize(__lowerCAmelCase   ,		(new_h, new_w)   ,		resample=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)
         return image


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	float   ,		__lowerCAmelCase				:	Optional[ChannelDimension] = None   ,		**__lowerCAmelCase				:	int)      ->					np.ndarray:
         return rescale(image=__lowerCAmelCase   ,		scale=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[int] = None   ,		__lowerCAmelCase				:	str=None   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[Union[TensorType, str]] = None   ,		__lowerCAmelCase				:	ChannelDimension = ChannelDimension.FIRST   ,		**__lowerCAmelCase				:	List[str]   ,		)      ->					BatchFeature:
         lowercase_     =      do_resize if do_resize is not None else self.do_resize
         lowercase_     =      do_rescale if do_rescale is not None else self.do_rescale
         lowercase_     =      size_divisor if size_divisor is not None else self.size_divisor
         lowercase_     =      resample if resample is not None else self.resample

         if do_resize and size_divisor is None:
               raise ValueError("size_divisor is required for resizing")

         lowercase_     =      make_list_of_images(__lowerCAmelCase)

         if not valid_images(__lowerCAmelCase):
               raise ValueError("Invalid image(s)")

         # All transformations expect numpy arrays.
         lowercase_     =      [to_numpy_array(__lowerCAmelCase) for img in images]

         if do_resize:
               lowercase_     =      [self.resize(__lowerCAmelCase   ,		size_divisor=__lowerCAmelCase   ,		resample=__lowerCAmelCase) for image in images]

         if do_rescale:
               lowercase_     =      [self.rescale(__lowerCAmelCase   ,		scale=1 / 255) for image in images]

         lowercase_     =      [to_channel_dimension_format(__lowerCAmelCase   ,		__lowerCAmelCase) for image in images]

         lowercase_     =      {"pixel_values": images}
         return BatchFeature(data=__lowerCAmelCase   ,		tensor_type=__lowerCAmelCase)






