






"""simple docstring"""






from __future__ import annotations

import queue





class       snake_case_       :



	"""simple docstring"""

	def __init__(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       data
					A__							 =       None
					A__							 =       None





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



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



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



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



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



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



def  __lowerCamelCase					(					lowerCAmelCase__				):
				if not isinstance(lowerCAmelCase__       ,lowerCAmelCase__				) or not node:
								return
				A__ ,			A__							 =       [], []
				A__							 =       node
				stacka.append(lowerCAmelCase__				)
				while stacka:  # to find the reversed order of post order, store it in stack2
								A__							 =       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  __lowerCamelCase					(					lowerCAmelCase__ = ""       ,lowerCAmelCase__=50       ,lowerCAmelCase__="*"				):
				if not s:
								return "\n" + width * char
				A__ ,			A__							 =       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'''))

					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())










"""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 PoolFormerImageProcessor





class       snake_case_       (    unittest.TestCase       ):



	"""simple docstring"""

	def __init__(      self  ,					__a  ,					__a=7  ,					__a=3  ,					__a=30  ,					__a=400  ,					__a=True  ,					__a=None  ,					__a=0.9  ,					__a=None  ,					__a=True  ,					__a=[0.5, 0.5, 0.5]  ,					__a=[0.5, 0.5, 0.5]  ,					):


					"""simple docstring"""

					A__							 =       size if size is not None else {'shortest_edge': 30}
					A__							 =       crop_size if crop_size is not None else {'height': 30, 'width': 30}
					A__							 =       parent
					A__							 =       batch_size
					A__							 =       num_channels
					A__							 =       min_resolution
					A__							 =       max_resolution
					A__							 =       do_resize_and_center_crop
					A__							 =       size
					A__							 =       crop_pct
					A__							 =       crop_size
					A__							 =       do_normalize
					A__							 =       image_mean
					A__							 =       image_std






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return {
					    "size": self.size,
					    "do_resize_and_center_crop": self.do_resize_and_center_crop,
					    "crop_pct": self.crop_pct,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					}





@require_torch
@require_vision
class       snake_case_       (    _lowerCamelCase    ,							unittest.TestCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							List[Any]   			=					PoolFormerImageProcessor if is_vision_available() else None


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       PoolFormerImageProcessingTester(self						)


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return self.image_processor_tester.prepare_image_processor_dict()


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.image_processing_class(**self.image_processor_dict						)
					self.assertTrue(hasattr(__a  ,					'do_resize_and_center_crop'						)						)
					self.assertTrue(hasattr(__a  ,					'size'						)						)
					self.assertTrue(hasattr(__a  ,					'crop_pct'						)						)
					self.assertTrue(hasattr(__a  ,					'do_normalize'						)						)
					self.assertTrue(hasattr(__a  ,					'image_mean'						)						)
					self.assertTrue(hasattr(__a  ,					'image_std'						)						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.image_processing_class.from_dict(self.image_processor_dict						)
					self.assertEqual(image_processor.size  ,					{'shortest_edge': 30}						)
					self.assertEqual(image_processor.crop_size  ,					{'height': 30, 'width': 30}						)

					A__							 =       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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					pass


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.image_processing_class(**self.image_processor_dict						)
					# create random PIL images
					A__							 =       prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=__a						)
					for image in image_inputs:
									self.assertIsInstance(__a  ,					Image.Image						)

					# Test not batched input
					A__							 =       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
					A__							 =       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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.image_processing_class(**self.image_processor_dict						)
					# create random numpy tensors
					A__							 =       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
					A__							 =       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
					A__							 =       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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.image_processing_class(**self.image_processor_dict						)
					# create random PyTorch tensors
					A__							 =       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
					A__							 =       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
					A__							 =       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'],
					    )  ,					)


