'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import LevitImageProcessor




class        __A      ( unittest.TestCase	):



    def __init__(self    :    str   ,			__a    :    str   ,			__a    :    Optional[int]=7   ,			__a    :    List[str]=3   ,			__a    :    Any=18   ,			__a    :    List[str]=30   ,			__a    :    Dict=400   ,			__a    :    Optional[Any]=True   ,			__a    :    Optional[Any]=None   ,			__a    :    int=True   ,			__a    :    int=None   ,			__a    :    List[str]=True   ,			__a    :    Union[str, Any]=[0.5, 0.5, 0.5]   ,			__a    :    Any=[0.5, 0.5, 0.5]   ,			):
         UpperCAmelCase_           =   size if size is not None else {"shortest_edge": 18}
         UpperCAmelCase_           =   crop_size if crop_size is not None else {"height": 18, "width": 18}
         UpperCAmelCase_           =   parent
         UpperCAmelCase_           =   batch_size
         UpperCAmelCase_           =   num_channels
         UpperCAmelCase_           =   image_size
         UpperCAmelCase_           =   min_resolution
         UpperCAmelCase_           =   max_resolution
         UpperCAmelCase_           =   do_resize
         UpperCAmelCase_           =   size
         UpperCAmelCase_           =   do_center_crop
         UpperCAmelCase_           =   crop_size
         UpperCAmelCase_           =   do_normalize
         UpperCAmelCase_           =   image_mean
         UpperCAmelCase_           =   image_std






    def 					_lowercase						(self    :    int ):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "do_center_crop": self.do_center_crop,
             "size": self.size,
             "crop_size": self.crop_size,
         }




@require_torch
@require_vision
class        __A      ( UpperCamelCase__		,	unittest.TestCase	):
    a__       :						Any          =  LevitImageProcessor if is_vision_available() else None



    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   LevitImageProcessingTester(self )



    @property
    def 					_lowercase						(self    :    Any ):
         return self.image_processor_tester.prepare_image_processor_dict()



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(__a   ,			"image_mean" ) )
         self.assertTrue(hasattr(__a   ,			"image_std" ) )
         self.assertTrue(hasattr(__a   ,			"do_normalize" ) )
         self.assertTrue(hasattr(__a   ,			"do_resize" ) )
         self.assertTrue(hasattr(__a   ,			"do_center_crop" ) )
         self.assertTrue(hasattr(__a   ,			"size" ) )



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   self.image_processing_class.from_dict(self.image_processor_dict )
         self.assertEqual(image_processor.size   ,			{"shortest_edge": 18} )
         self.assertEqual(image_processor.crop_size   ,			{"height": 18, "width": 18} )

         UpperCAmelCase_           =   self.image_processing_class.from_dict(self.image_processor_dict   ,			size=42   ,			crop_size=84 )
         self.assertEqual(image_processor.size   ,			{"shortest_edge": 42} )
         self.assertEqual(image_processor.crop_size   ,			{"height": 84, "width": 84} )



    def 					_lowercase						(self    :    int ):
         pass



    def 					_lowercase						(self    :    Optional[Any] ):
         # Initialize image_processing
         UpperCAmelCase_           =   self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         UpperCAmelCase_           =   prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=__a )
         for image in image_inputs:
              self.assertIsInstance(__a   ,			Image.Image )

         # Test not batched input
         UpperCAmelCase_           =   image_processing(image_inputs[0]   ,			return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,			(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)

         # Test batched
         UpperCAmelCase_           =   image_processing(__a   ,			return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,			(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)



    def 					_lowercase						(self    :    Dict ):
         # Initialize image_processing
         UpperCAmelCase_           =   self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         UpperCAmelCase_           =   prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=__a   ,			numpify=__a )
         for image in image_inputs:
              self.assertIsInstance(__a   ,			np.ndarray )

         # Test not batched input
         UpperCAmelCase_           =   image_processing(image_inputs[0]   ,			return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,			(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)

         # Test batched
         UpperCAmelCase_           =   image_processing(__a   ,			return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,			(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)






    def 					_lowercase						(self    :    int ):
         # Initialize image_processing
         UpperCAmelCase_           =   self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         UpperCAmelCase_           =   prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=__a   ,			torchify=__a )
         for image in image_inputs:
              self.assertIsInstance(__a   ,			torch.Tensor )

         # Test not batched input
         UpperCAmelCase_           =   image_processing(image_inputs[0]   ,			return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,			(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)

         # Test batched
         UpperCAmelCase_           =   image_processing(__a   ,			return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,			(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)





'''simple docstring'''





from __future__ import annotations

import queue




class        __A      :



    def __init__(self    :    Optional[Any]   ,			__a    :    str ):
         UpperCAmelCase_           =   data
         UpperCAmelCase_           =   None
         UpperCAmelCase_           =   None





def 	lowerCAmelCase_				(					)    ->      TreeNode:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




     if not isinstance(snake_case_					,    snake_case_			) or not node:
          return
     UpperCAmelCase_           =   queue.Queue()
     q.put(snake_case_			)
     while not q.empty():
          UpperCAmelCase_           =   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 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




     if not isinstance(snake_case_					,    snake_case_			) or not node:
          return
     UpperCAmelCase_           =   queue.Queue()
     q.put(snake_case_			)
     while not q.empty():
          UpperCAmelCase_           =   []
          while not q.empty():
               UpperCAmelCase_           =   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(snake_case_			)





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : str = ""					,    snake_case_       : Any=50					,    snake_case_       : Union[str, Any]="*"			)    ->      str:


     '''simple docstring'''




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


if __name__ == "__main__":
    import doctest

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

    SCREAMING_SNAKE_CASE_:     TreeNode  =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())




