


"""simple docstring"""
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor




class 	lowercase__				(					unittest.TestCase				):





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=7      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=30      ,   SCREAMING_SNAKE_CASE=400      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=1 / 255      ,   SCREAMING_SNAKE_CASE=True      ,   )  ->  Dict:
      # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
      _lowerCamelCase	:					Union[str, Any]   =		size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
      _lowerCamelCase	:					Union[str, Any]   =		parent
      _lowerCamelCase	:					Optional[Any]   =		batch_size
      _lowerCamelCase	:					Optional[int]   =		num_channels
      _lowerCamelCase	:					Union[str, Any]   =		min_resolution
      _lowerCamelCase	:					Optional[int]   =		max_resolution
      _lowerCamelCase	:					List[Any]   =		do_resize
      _lowerCamelCase	:					str   =		size
      _lowerCamelCase	:					Union[str, Any]   =		do_normalize
      _lowerCamelCase	:					Union[str, Any]   =		image_mean
      _lowerCamelCase	:					Tuple   =		image_std
      _lowerCamelCase	:					List[Any]   =		do_rescale
      _lowerCamelCase	:					Dict   =		rescale_factor
      _lowerCamelCase	:					Any   =		do_pad





   def 		UpperCamelCase_  (							self)  ->  int:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_pad": self.do_pad,
      }





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=False)  ->  Any:
      if not batched:
         _lowerCamelCase	:					Tuple   =		image_inputs[0]
         if isinstance(SCREAMING_SNAKE_CASE      ,   Image.Image):
            _lowerCamelCase   ,    _lowerCamelCase	:					Any   =		image.size
         else:
            _lowerCamelCase   ,    _lowerCamelCase	:					int   =		image.shape[1], image.shape[2]
         if w < h:
            _lowerCamelCase	:					Optional[int]   =		int(self.size["""shortest_edge"""] * h / w)
            _lowerCamelCase	:					Optional[int]   =		self.size["""shortest_edge"""]
         elif w > h:
            _lowerCamelCase	:					Any   =		self.size["""shortest_edge"""]
            _lowerCamelCase	:					Union[str, Any]   =		int(self.size["""shortest_edge"""] * w / h)
         else:
            _lowerCamelCase	:					Optional[Any]   =		self.size["""shortest_edge"""]
            _lowerCamelCase	:					Optional[Any]   =		self.size["""shortest_edge"""]

      else:
         _lowerCamelCase	:					Any   =		[]
         for image in image_inputs:
            _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		self.get_expected_values([image])
            expected_values.append((expected_height, expected_width))
         _lowerCamelCase	:					List[Any]   =		max(SCREAMING_SNAKE_CASE      ,   key=lambda SCREAMING_SNAKE_CASE: item[0])[0]
         _lowerCamelCase	:					int   =		max(SCREAMING_SNAKE_CASE      ,   key=lambda SCREAMING_SNAKE_CASE: item[1])[1]

      return expected_height, expected_width







@require_torch
@require_vision
class 	lowercase__				(					A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					ConditionalDetrImageProcessor if is_vision_available() else None





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Dict   =		ConditionalDetrImageProcessingTester(self)





   @property
   def 		UpperCamelCase_  (							self)  ->  str:
      return self.image_processor_tester.prepare_image_processor_dict()





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Any   =		self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """image_mean"""))
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """image_std"""))
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """do_normalize"""))
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """do_resize"""))
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,   """size"""))





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					int   =		self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size      ,   {"""shortest_edge""": 18, """longest_edge""": 1333})
      self.assertEqual(image_processor.do_pad      ,   SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[int]   =		self.image_processing_class.from_dict(
          self.image_processor_dict      ,   size=42      ,   max_size=84      ,   pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE)
      self.assertEqual(image_processor.size      ,   {"""shortest_edge""": 42, """longest_edge""": 84})
      self.assertEqual(image_processor.do_pad      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      pass





   def 		UpperCamelCase_  (							self)  ->  Any:
      # Initialize image_processing
      _lowerCamelCase	:					Tuple   =		self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      _lowerCamelCase	:					Optional[Any]   =		prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=SCREAMING_SNAKE_CASE)
      for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE      ,   Image.Image)

      # Test not batched input
      _lowerCamelCase	:					Optional[Any]   =		image_processing(image_inputs[0]      ,   return_tensors="""pt""").pixel_values

      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE)

      self.assertEqual(
          encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

      # Test batched
      _lowerCamelCase   ,    _lowerCamelCase	:					Union[str, Any]   =		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE      ,   batched=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[Any]   =		image_processing(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").pixel_values
      self.assertEqual(
          encoded_images.shape      ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )      ,   )





   def 		UpperCamelCase_  (							self)  ->  int:
      # Initialize image_processing
      _lowerCamelCase	:					Dict   =		self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      _lowerCamelCase	:					Dict   =		prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=SCREAMING_SNAKE_CASE      ,   numpify=SCREAMING_SNAKE_CASE)
      for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE      ,   np.ndarray)

      # Test not batched input
      _lowerCamelCase	:					Optional[int]   =		image_processing(image_inputs[0]      ,   return_tensors="""pt""").pixel_values

      _lowerCamelCase   ,    _lowerCamelCase	:					int   =		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE)

      self.assertEqual(
          encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

      # Test batched
      _lowerCamelCase	:					int   =		image_processing(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").pixel_values

      _lowerCamelCase   ,    _lowerCamelCase	:					Any   =		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE      ,   batched=SCREAMING_SNAKE_CASE)

      self.assertEqual(
          encoded_images.shape      ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )      ,   )





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      # Initialize image_processing
      _lowerCamelCase	:					Tuple   =		self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      _lowerCamelCase	:					Union[str, Any]   =		prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=SCREAMING_SNAKE_CASE      ,   torchify=SCREAMING_SNAKE_CASE)
      for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE      ,   torch.Tensor)

      # Test not batched input
      _lowerCamelCase	:					Optional[Any]   =		image_processing(image_inputs[0]      ,   return_tensors="""pt""").pixel_values

      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE)

      self.assertEqual(
          encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

      # Test batched
      _lowerCamelCase	:					Optional[Any]   =		image_processing(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").pixel_values

      _lowerCamelCase   ,    _lowerCamelCase	:					Tuple   =		self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE      ,   batched=SCREAMING_SNAKE_CASE)

      self.assertEqual(
          encoded_images.shape      ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )      ,   )





   @slow
   def 		UpperCamelCase_  (							self)  ->  str:
      # prepare image and target
      _lowerCamelCase	:					Union[str, Any]   =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
      with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""      ,   """r""") as f:
         _lowerCamelCase	:					List[Any]   =		json.loads(f.read())

      _lowerCamelCase	:					Union[str, Any]   =		{"""image_id""": 3_9769, """annotations""": target}

      # encode them
      _lowerCamelCase	:					Any   =		ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50""")
      _lowerCamelCase	:					Optional[Any]   =		image_processing(images=SCREAMING_SNAKE_CASE      ,   annotations=SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""")

      # verify pixel values
      _lowerCamelCase	:					Dict   =		torch.Size([1, 3, 800, 1066])
      self.assertEqual(encoding["""pixel_values"""].shape      ,   SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Tuple   =		torch.tensor([0.27_96, 0.31_38, 0.34_81])
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=1e-4))

      # verify area
      _lowerCamelCase	:					List[str]   =		torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,   SCREAMING_SNAKE_CASE))
      # verify boxes
      _lowerCamelCase	:					Union[str, Any]   =		torch.Size([6, 4])
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,   SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[str]   =		torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,   SCREAMING_SNAKE_CASE      ,   atol=1e-3))
      # verify image_id
      _lowerCamelCase	:					Union[str, Any]   =		torch.tensor([3_9769])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,   SCREAMING_SNAKE_CASE))
      # verify is_crowd
      _lowerCamelCase	:					Any   =		torch.tensor([0, 0, 0, 0, 0, 0])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,   SCREAMING_SNAKE_CASE))
      # verify class_labels
      _lowerCamelCase	:					Any   =		torch.tensor([75, 75, 63, 65, 17, 17])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,   SCREAMING_SNAKE_CASE))
      # verify orig_size
      _lowerCamelCase	:					List[str]   =		torch.tensor([480, 640])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,   SCREAMING_SNAKE_CASE))
      # verify size
      _lowerCamelCase	:					Tuple   =		torch.tensor([800, 1066])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,   SCREAMING_SNAKE_CASE))





   @slow
   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      # prepare image, target and masks_path
      _lowerCamelCase	:					Union[str, Any]   =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
      with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""      ,   """r""") as f:
         _lowerCamelCase	:					Optional[int]   =		json.loads(f.read())

      _lowerCamelCase	:					Optional[Any]   =		{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

      _lowerCamelCase	:					List[str]   =		pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""")

      # encode them
      _lowerCamelCase	:					List[str]   =		ConditionalDetrImageProcessor(format="""coco_panoptic""")
      _lowerCamelCase	:					Dict   =		image_processing(images=SCREAMING_SNAKE_CASE      ,   annotations=SCREAMING_SNAKE_CASE      ,   masks_path=SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""")

      # verify pixel values
      _lowerCamelCase	:					int   =		torch.Size([1, 3, 800, 1066])
      self.assertEqual(encoding["""pixel_values"""].shape      ,   SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		torch.tensor([0.27_96, 0.31_38, 0.34_81])
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=1e-4))

      # verify area
      _lowerCamelCase	:					Union[str, Any]   =		torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,   SCREAMING_SNAKE_CASE))
      # verify boxes
      _lowerCamelCase	:					int   =		torch.Size([6, 4])
      self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,   SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,   SCREAMING_SNAKE_CASE      ,   atol=1e-3))
      # verify image_id
      _lowerCamelCase	:					List[str]   =		torch.tensor([3_9769])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,   SCREAMING_SNAKE_CASE))
      # verify is_crowd
      _lowerCamelCase	:					Dict   =		torch.tensor([0, 0, 0, 0, 0, 0])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,   SCREAMING_SNAKE_CASE))
      # verify class_labels
      _lowerCamelCase	:					str   =		torch.tensor([17, 17, 63, 75, 75, 93])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,   SCREAMING_SNAKE_CASE))
      # verify masks
      _lowerCamelCase	:					Any   =		82_2873
      self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,   SCREAMING_SNAKE_CASE)
      # verify orig_size
      _lowerCamelCase	:					List[Any]   =		torch.tensor([480, 640])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,   SCREAMING_SNAKE_CASE))
      # verify size
      _lowerCamelCase	:					Optional[Any]   =		torch.tensor([800, 1066])
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,   SCREAMING_SNAKE_CASE))





"""simple docstring"""
from __future__ import annotations

import queue




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					int   =		data
      _lowerCamelCase	:					List[str]   =		None
      _lowerCamelCase	:					Any   =		None







def 				_snake_case      (   ):




   """simple docstring"""







   print("""\n********Press N to stop entering at any point of time********\n"""   )
   _lowerCamelCase	:					Optional[int]   =		input("""Enter the value of the root node: """   ).strip().lower()
   _lowerCamelCase	:					queue.Queue   =		queue.Queue()
   _lowerCamelCase	:					Optional[int]   =		TreeNode(int(__snake_case   )   )
   q.put(__snake_case   )
   while not q.empty():
      _lowerCamelCase	:					Tuple   =		q.get()
      _lowerCamelCase	:					Any   =		F'Enter the left node of {node_found.data}: '
      _lowerCamelCase	:					Union[str, Any]   =		input(__snake_case   ).strip().lower() or """n"""
      if check == "n":
         return tree_node
      _lowerCamelCase	:					Dict   =		TreeNode(int(__snake_case   )   )
      _lowerCamelCase	:					List[str]   =		left_node
      q.put(__snake_case   )
      _lowerCamelCase	:					Optional[int]   =		F'Enter the right node of {node_found.data}: '
      _lowerCamelCase	:					Optional[Any]   =		input(__snake_case   ).strip().lower() or """n"""
      if check == "n":
         return tree_node
      _lowerCamelCase	:					List[Any]   =		TreeNode(int(__snake_case   )   )
      _lowerCamelCase	:					List[Any]   =		right_node
      q.put(__snake_case   )
   raise

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """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 				_snake_case      (   __snake_case							:      TreeNode   ):




   """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 				_snake_case      (   __snake_case							:      TreeNode   ):




   """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 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					queue.Queue   =		queue.Queue()
   q.put(__snake_case   )
   while not q.empty():
      _lowerCamelCase	:					Any   =		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 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					queue.Queue   =		queue.Queue()
   q.put(__snake_case   )
   while not q.empty():
      _lowerCamelCase	:					Optional[Any]   =		[]
      while not q.empty():
         _lowerCamelCase	:					Dict   =		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 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					list[TreeNode]   =		[]
   _lowerCamelCase	:					Optional[int]   =		node
   while n or stack:
      while n:  # start from root node, find its left child
         print(n.data						, end=""","""   )
         stack.append(__snake_case   )
         _lowerCamelCase	:					Tuple   =		n.left
      # end of while means current node doesn't have left child
      _lowerCamelCase	:					Optional[Any]   =		stack.pop()
      # start to traverse its right child
      _lowerCamelCase	:					Dict   =		n.right

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					list[TreeNode]   =		[]
   _lowerCamelCase	:					int   =		node
   while n or stack:
      while n:
         stack.append(__snake_case   )
         _lowerCamelCase	:					Any   =		n.left
      _lowerCamelCase	:					Optional[Any]   =		stack.pop()
      print(n.data						, end=""","""   )
      _lowerCamelCase	:					List[Any]   =		n.right

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase   ,    _lowerCamelCase	:					Union[str, Any]   =		[], []
   _lowerCamelCase	:					Optional[Any]   =		node
   stacka.append(__snake_case   )
   while stacka:  # to find the reversed order of post order, store it in stack2
      _lowerCamelCase	:					Union[str, Any]   =		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 				_snake_case      (   __snake_case							:      str = ""						, __snake_case							:      Any=50						, __snake_case							:      List[str]="*"   ):




   """simple docstring"""







   if not s:
      return "\n" + width * char
   _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		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"""))

 UpperCAmelCase				=						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())

