






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 MobileNetVaImageProcessor
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def __init__(  self		:					str,						lowerCamelCase		:					Any,						lowerCamelCase		:					Tuple=7,						lowerCamelCase		:					str=3,						lowerCamelCase		:					Tuple=18,						lowerCamelCase		:					int=30,						lowerCamelCase		:					Tuple=400,						lowerCamelCase		:					Any=True,						lowerCamelCase		:					Any=None,						lowerCamelCase		:					List[str]=True,						lowerCamelCase		:					Union[str, Any]=None,						):

								'''simple docstring'''
								lowercase__     				=    size if size is not None else {'''shortest_edge''': 20}
								lowercase__     				=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    num_channels
								lowercase__     				=    image_size
								lowercase__     				=    min_resolution
								lowercase__     				=    max_resolution
								lowercase__     				=    do_resize
								lowercase__     				=    size
								lowercase__     				=    do_center_crop
								lowercase__     				=    crop_size




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}



@require_torch
@require_vision
class    _UpperCAmelCase      ( A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= MobileNetVaImageProcessor if is_vision_available() else None




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    MobileNetVaImageProcessingTester(self						)




				@property
				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_resize'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''size'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_center_crop'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''crop_size'''						)						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class.from_dict(self.image_processor_dict						)
								self.assertEqual(image_processor.size,						{'''shortest_edge''': 20}						)
								self.assertEqual(image_processor.crop_size,						{'''height''': 18, '''width''': 18}						)

								lowercase__     				=    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		:					Optional[int]						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								lowercase__     				=    prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						Image.Image						)

								# Test not batched input
								lowercase__     				=    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
								lowercase__     				=    image_processing(lowerCamelCase,						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		:					str						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								lowercase__     				=    prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase,						numpify=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						np.ndarray						)

								# Test not batched input
								lowercase__     				=    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
								lowercase__     				=    image_processing(lowerCamelCase,						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		:					str						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								lowercase__     				=    prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase,						torchify=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						torch.Tensor						)

								# Test not batched input
								lowercase__     				=    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
								lowercase__     				=    image_processing(lowerCamelCase,						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'''],
								    ),						)












from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= None
				lowercase__       							= None






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    Node(1       )
				lowercase__     				=    Node(2       )
				lowercase__     				=    Node(3       )
				lowercase__     				=    Node(4       )
				lowercase__     				=    Node(5       )
				return tree






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return [root.data, *preorder(root.left       ), *preorder(root.right       )] if root else []






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return postorder(root.left       ) + postorder(root.right       ) + [root.data] if root else []






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return [*inorder(root.left       ), root.data, *inorder(root.right       )] if root else []






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return (max(height(root.left       )	,     height(root.right       )       ) + 1) if root else 0






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    []

				if root is None:
								return output

				lowercase__     				=    deque([root]       )

				while process_queue:
								lowercase__     				=    process_queue.popleft()
								output.append(node.data       )

								if node.left:
												process_queue.append(node.left       )
								if node.right:
												process_queue.append(node.right       )
				return output






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    []

				def populate_output(lowerCamelCase_	,     lowerCamelCase_       ) -> None:
								if not root:
												return
								if level == 1:
												output.append(root.data       )
								elif level > 1:
												populate_output(root.left	,     level - 1       )
												populate_output(root.right	,     level - 1       )

				populate_output(lowerCamelCase_	,     lowerCamelCase_       )
				return output






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    []

				def populate_output(lowerCamelCase_	,     lowerCamelCase_       ) -> None:
								if root is None:
												return
								if level == 1:
												output.append(root.data       )
								elif level > 1:
												populate_output(root.right	,     level - 1       )
												populate_output(root.left	,     level - 1       )

				populate_output(lowerCamelCase_	,     lowerCamelCase_       )
				return output






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				if root is None:
								return []

				lowercase__     				=    []

				lowercase__     				=    0
				lowercase__     				=    height(lowerCamelCase_       )

				for h in range(1	,     height_tree + 1       ):
								if not flag:
												output.append(get_nodes_from_left_to_right(lowerCamelCase_	,     lowerCamelCase_       )       )
												lowercase__     				=    1
								else:
												output.append(get_nodes_from_right_to_left(lowerCamelCase_	,     lowerCamelCase_       )       )
												lowercase__     				=    0

				return output






def 						a    (		):  # Main function for testing.






				'''simple docstring'''
				lowercase__     				=    make_tree()
				print(F"""In-order Traversal: {inorder(lowerCamelCase_       )}"""       )
				print(F"""Pre-order Traversal: {preorder(lowerCamelCase_       )}"""       )
				print(F"""Post-order Traversal: {postorder(lowerCamelCase_       )}"""	,     '''\n'''       )

				print(F"""Height of Tree: {height(lowerCamelCase_       )}"""	,     '''\n'''       )

				print('''Complete Level Order Traversal: '''       )
				print(level_order(lowerCamelCase_       )	,     '''\n'''       )

				print('''Level-wise order Traversal: '''       )

				for level in range(1	,     height(lowerCamelCase_       ) + 1       ):
								print(F"""Level {level}:"""	,     get_nodes_from_left_to_right(lowerCamelCase_	,     level=lowerCamelCase_       )       )

				print('''\nZigZag order Traversal: '''       )
				print(zigzag(lowerCamelCase_       )       )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()




