





from __future__ import annotations

import queue

class   lowerCAmelCase				:
 def __init__( self    : Dict     ,	UpperCAmelCase    : List[str]      )     ->      List[Any]:
     lowerCamelCase__       : Union[str, Any]   					=						data
     lowerCamelCase__       : str   					=						None
     lowerCamelCase__       : List[str]   					=						None


def 			SCREAMING_SNAKE_CASE (			)     ->	TreeNode:
    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__       : Union[str, Any]   					=						TreeNode(int(_UpperCAmelCase					)					)
    q.put(_UpperCAmelCase					)
    while not q.empty():
        lowerCamelCase__       : Dict   					=						q.get()
        lowerCamelCase__       : Optional[Any]   					=						F"""Enter the left node of {node_found.data}: """
        lowerCamelCase__       : Union[str, Any]   					=						input(_UpperCAmelCase					).strip().lower() or 'n'
        if check == "n":
            return tree_node
        lowerCamelCase__       : Any   					=						TreeNode(int(_UpperCAmelCase					)					)
        lowerCamelCase__       : Optional[Any]   					=						left_node
        q.put(_UpperCAmelCase					)
        lowerCamelCase__       : Optional[Any]   					=						F"""Enter the right node of {node_found.data}: """
        lowerCamelCase__       : Optional[Any]   					=						input(_UpperCAmelCase					).strip().lower() or 'n'
        if check == "n":
            return tree_node
        lowerCamelCase__       : Optional[int]   					=						TreeNode(int(_UpperCAmelCase					)					)
        lowerCamelCase__       : Dict   					=						right_node
        q.put(_UpperCAmelCase					)
    raise


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	None:
    if not isinstance(_UpperCAmelCase		,      _UpperCAmelCase					) or not node:
        return
    print(node.data		,      end=','					)
    pre_order(node.left					)
    pre_order(node.right					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	None:
    if not isinstance(_UpperCAmelCase		,      _UpperCAmelCase					) or not node:
        return
    in_order(node.left					)
    print(node.data		,      end=','					)
    in_order(node.right					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	None:
    if not isinstance(_UpperCAmelCase		,      _UpperCAmelCase					) or not node:
        return
    post_order(node.left					)
    post_order(node.right					)
    print(node.data		,      end=','					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	None:
    if not isinstance(_UpperCAmelCase		,      _UpperCAmelCase					) or not node:
        return
    lowerCamelCase__       : queue.Queue   					=						queue.Queue()
    q.put(_UpperCAmelCase					)
    while not q.empty():
        lowerCamelCase__       : Optional[int]   					=						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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	None:
    if not isinstance(_UpperCAmelCase		,      _UpperCAmelCase					) or not node:
        return
    lowerCamelCase__       : queue.Queue   					=						queue.Queue()
    q.put(_UpperCAmelCase					)
    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(_UpperCAmelCase					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	None:
    if not isinstance(_UpperCAmelCase		,      _UpperCAmelCase					) or not node:
        return
    lowerCamelCase__       : list[TreeNode]   					=						[]
    lowerCamelCase__       : str   					=						node
    while n or stack:
        while n:  # start from root node, find its left child
            print(n.data		,      end=','					)
            stack.append(_UpperCAmelCase					)
            lowerCamelCase__       : Tuple   					=						n.left
        # end of while means current node doesn't have left child
        lowerCamelCase__       : Optional[int]   					=						stack.pop()
        # start to traverse its right child
        lowerCamelCase__       : Dict   					=						n.right


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	None:
    if not isinstance(_UpperCAmelCase		,      _UpperCAmelCase					) or not node:
        return
    lowerCamelCase__       : list[TreeNode]   					=						[]
    lowerCamelCase__       : List[str]   					=						node
    while n or stack:
        while n:
            stack.append(_UpperCAmelCase					)
            lowerCamelCase__       : List[Any]   					=						n.left
        lowerCamelCase__       : Union[str, Any]   					=						stack.pop()
        print(n.data		,      end=','					)
        lowerCamelCase__       : Dict   					=						n.right


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	None:
    if not isinstance(_UpperCAmelCase		,      _UpperCAmelCase					) or not node:
        return
    lowerCamelCase__					,				lowerCamelCase__       : List[str]   					=						[], []
    lowerCamelCase__       : Dict   					=						node
    stacka.append(_UpperCAmelCase					)
    while stacka:  # to find the reversed order of post order, store it in stack2
        lowerCamelCase__       : List[str]   					=						stacka.pop()
        if n.left:
            stacka.append(n.left					)
        if n.right:
            stacka.append(n.right					)
        stacka.append(_UpperCAmelCase					)
    while stacka:  # pop up from stack2 will be the post order
        print(stacka.pop().data		,      end=','					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase = ""		,      _UpperCAmelCase=50		,      _UpperCAmelCase="*"					)     ->	str:
    if not s:
        return "\n" + width * char
    lowerCamelCase__					,				lowerCamelCase__       : Optional[int]   					=						divmod(width - len(_UpperCAmelCase					) - 2		,      2					)
    return F"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
    import doctest

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

    _UpperCAmelCase							:			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())












import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor

class   lowerCAmelCase				(   unittest.TestCase     ):
 def __init__( self    : Any     ,	UpperCAmelCase    : Optional[int]     ,	UpperCAmelCase    : Dict=7     ,	UpperCAmelCase    : Union[str, Any]=3     ,	UpperCAmelCase    : List[str]=18     ,	UpperCAmelCase    : Dict=30     ,	UpperCAmelCase    : List[Any]=400     ,	UpperCAmelCase    : Optional[Any]=True     ,	UpperCAmelCase    : Tuple=None     ,	UpperCAmelCase    : str=True     ,	UpperCAmelCase    : Tuple=False     ,	UpperCAmelCase    : List[str]=True     ,	UpperCAmelCase    : Optional[int]=True     ,	UpperCAmelCase    : Optional[Any]=[0.5, 0.5, 0.5]     ,	UpperCAmelCase    : List[str]=[0.5, 0.5, 0.5]     ,	)     ->      List[str]:
     lowerCamelCase__       : List[Any]   					=						parent
     lowerCamelCase__       : str   					=						batch_size
     lowerCamelCase__       : List[str]   					=						num_channels
     lowerCamelCase__       : str   					=						image_size
     lowerCamelCase__       : List[Any]   					=						min_resolution
     lowerCamelCase__       : int   					=						max_resolution
     lowerCamelCase__       : int   					=						do_resize
     lowerCamelCase__       : int   					=						size if size is not None else {'height': 18, 'width': 20}
     lowerCamelCase__       : Tuple   					=						do_thumbnail
     lowerCamelCase__       : str   					=						do_align_axis
     lowerCamelCase__       : str   					=						do_pad
     lowerCamelCase__       : Optional[Any]   					=						do_normalize
     lowerCamelCase__       : List[str]   					=						image_mean
     lowerCamelCase__       : Dict   					=						image_std







 def 	A_      ( self    : str      )     ->      Any:
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_thumbnail": self.do_thumbnail,
         "do_align_long_axis": self.do_align_axis,
         "do_pad": self.do_pad,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }


@require_torch
@require_vision
class   lowerCAmelCase				(   __UpperCamelCase,    unittest.TestCase     ):
 UpperCAmelCase__						=						DonutImageProcessor if is_vision_available() else None
 def 	A_      ( self    : List[Any]      )     ->      int:
     lowerCamelCase__       : Union[str, Any]   					=						DonutImageProcessingTester(self      )
 @property
 def 	A_      ( self    : Dict      )     ->      Union[str, Any]:
     return self.image_processor_tester.prepare_image_processor_dict()
 def 	A_      ( self    : Dict      )     ->      Any:
     lowerCamelCase__       : Tuple   					=						self.image_processing_class(**self.image_processor_dict      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_resize'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'size'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_thumbnail'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_align_long_axis'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_pad'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'do_normalize'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'image_mean'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'image_std'      )      )
 def 	A_      ( self    : Tuple      )     ->      Union[str, Any]:
     lowerCamelCase__       : List[Any]   					=						self.image_processing_class.from_dict(self.image_processor_dict      )
     self.assertEqual(image_processor.size     ,	{'height': 18, 'width': 20}      )

     lowerCamelCase__       : int   					=						self.image_processing_class.from_dict(self.image_processor_dict     ,	size=42      )
     self.assertEqual(image_processor.size     ,	{'height': 42, 'width': 42}      )

     # Previous config had dimensions in (width, height) order
     lowerCamelCase__       : Dict   					=						self.image_processing_class.from_dict(self.image_processor_dict     ,	size=(42, 84)      )
     self.assertEqual(image_processor.size     ,	{'height': 84, 'width': 42}      )
 def 	A_      ( self    : Optional[Any]      )     ->      List[str]:
     pass
 @is_flaky()
 def 	A_      ( self    : List[str]      )     ->      Any:
     # Initialize image_processing
     lowerCamelCase__       : str   					=						self.image_processing_class(**self.image_processor_dict      )
     # create random PIL images
     lowerCamelCase__       : List[str]   					=						prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=UpperCAmelCase      )
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase     ,	Image.Image      )

     # Test not batched input
     lowerCamelCase__       : List[Any]   					=						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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)

     # Test batched
     lowerCamelCase__       : Tuple   					=						image_processing(UpperCAmelCase     ,	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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)
 @is_flaky()
 def 	A_      ( self    : int      )     ->      Tuple:
     # Initialize image_processing
     lowerCamelCase__       : Tuple   					=						self.image_processing_class(**self.image_processor_dict      )
     # create random numpy tensors
     lowerCamelCase__       : List[str]   					=						prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=UpperCAmelCase     ,	numpify=UpperCAmelCase      )
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase     ,	np.ndarray      )

     # Test not batched input
     lowerCamelCase__       : Optional[Any]   					=						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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)

     # Test batched
     lowerCamelCase__       : Tuple   					=						image_processing(UpperCAmelCase     ,	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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)







 @is_flaky()
 def 	A_      ( self    : Any      )     ->      Tuple:
     # Initialize image_processing
     lowerCamelCase__       : int   					=						self.image_processing_class(**self.image_processor_dict      )
     # create random PyTorch tensors
     lowerCamelCase__       : Tuple   					=						prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=UpperCAmelCase     ,	torchify=UpperCAmelCase      )
     for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase     ,	torch.Tensor      )

     # Test not batched input
     lowerCamelCase__       : List[str]   					=						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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)

     # Test batched
     lowerCamelCase__       : Dict   					=						image_processing(UpperCAmelCase     ,	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.size['height'],
             self.image_processor_tester.size['width'],
         )     ,	)





