





from __future__ import annotations

import math
import random
from typing import Any



class    _a     :






							"""simple docstring"""

							def __init__(      self				:     Any    )	->None:
													SCREAMING_SNAKE_CASE__     :				list[Any]   =					[]
													SCREAMING_SNAKE_CASE__     :				int   =					0
													SCREAMING_SNAKE_CASE__     :				int   =					0

							def      A_  (      self				:     List[str]    )	->bool:
													return self.head == self.tail

							def      A_  (      self				:     Optional[int]							,	a				:     Any    )	->None:
													self.data.append(a    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.tail + 1

							def      A_  (      self				:     Union[str, Any]    )	->Any:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.data[self.head]
													SCREAMING_SNAKE_CASE__     :				int   =					self.head + 1
													return ret

							def      A_  (      self				:     Tuple    )	->int:
													return self.tail - self.head






							def      A_  (      self				:     Tuple    )	->None:
													print(self.data    )
													print("**************"    )
													print(self.data[self.head : self.tail]    )







class    _a     :






							"""simple docstring"""

							def __init__(      self				:     List[Any]							,	a				:     Any    )	->None:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					data
													SCREAMING_SNAKE_CASE__     :				MyNode | None   =					None
													SCREAMING_SNAKE_CASE__     :				MyNode | None   =					None
													SCREAMING_SNAKE_CASE__     :				int   =					1

							def      A_  (      self				:     int    )	->Any:
													return self.data

							def      A_  (      self				:     Union[str, Any]    )	->MyNode | None:
													return self.left

							def      A_  (      self				:     List[Any]    )	->MyNode | None:
													return self.right

							def      A_  (      self				:     Dict    )	->int:
													return self.height

							def      A_  (      self				:     Optional[Any]							,	a				:     Any    )	->None:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					data

							def      A_  (      self				:     Any							,	a				:     MyNode | None    )	->None:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					node

							def      A_  (      self				:     Any							,	a				:     MyNode | None    )	->None:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					node






							def      A_  (      self				:     Tuple							,	a				:     int    )	->None:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					height







def       UpperCAmelCase							(     _lowerCamelCase      :  MyNode | None     ):





						'''simple docstring'''


						if node is None:
												return 0
						return node.get_height()







def       UpperCAmelCase							(     _lowerCamelCase      :  int			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						if a > b:
												return a
						return b







def       UpperCAmelCase							(     _lowerCamelCase      :  MyNode     ):





						'''simple docstring'''


						print("left rotation node:"			,   node.get_data()     )
						SCREAMING_SNAKE_CASE__     :				str   =					node.get_left()
						assert ret is not None
						node.set_left(ret.get_right()     )
						ret.set_right(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				str   =					my_max(get_height(node.get_right()     )			,   get_height(node.get_left()     )     ) + 1
						node.set_height(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Tuple   =					my_max(get_height(ret.get_right()     )			,   get_height(ret.get_left()     )     ) + 1
						ret.set_height(_lowerCamelCase     )
						return ret







def       UpperCAmelCase							(     _lowerCamelCase      :  MyNode     ):





						'''simple docstring'''


						print("right rotation node:"			,   node.get_data()     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					node.get_right()
						assert ret is not None
						node.set_right(ret.get_left()     )
						ret.set_left(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					my_max(get_height(node.get_right()     )			,   get_height(node.get_left()     )     ) + 1
						node.set_height(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Tuple   =					my_max(get_height(ret.get_right()     )			,   get_height(ret.get_left()     )     ) + 1
						ret.set_height(_lowerCamelCase     )
						return ret







def       UpperCAmelCase							(     _lowerCamelCase      :  MyNode     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Tuple   =					node.get_left()
						assert left_child is not None
						node.set_left(left_rotation(_lowerCamelCase     )     )
						return right_rotation(_lowerCamelCase     )







def       UpperCAmelCase							(     _lowerCamelCase      :  MyNode     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[Any]   =					node.get_right()
						assert right_child is not None
						node.set_right(right_rotation(_lowerCamelCase     )     )
						return left_rotation(_lowerCamelCase     )







def       UpperCAmelCase							(     _lowerCamelCase      :  MyNode | None			,   _lowerCamelCase      :  Any     ):





						'''simple docstring'''


						if node is None:
												return MyNode(_lowerCamelCase     )
						if data < node.get_data():
												node.set_left(insert_node(node.get_left()			,   _lowerCamelCase     )     )
												if (
												    get_height(node.get_left()     ) - get_height(node.get_right()     ) == 2
												):  # an unbalance detected
																		SCREAMING_SNAKE_CASE__     :				str   =					node.get_left()
																		assert left_child is not None
																		if (
																		    data < left_child.get_data()
																		):  # new node is the left child of the left child
																								SCREAMING_SNAKE_CASE__     :				List[Any]   =					right_rotation(_lowerCamelCase     )
																		else:
																								SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					lr_rotation(_lowerCamelCase     )
						else:
												node.set_right(insert_node(node.get_right()			,   _lowerCamelCase     )     )
												if get_height(node.get_right()     ) - get_height(node.get_left()     ) == 2:
																		SCREAMING_SNAKE_CASE__     :				Optional[int]   =					node.get_right()
																		assert right_child is not None
																		if data < right_child.get_data():
																								SCREAMING_SNAKE_CASE__     :				Any   =					rl_rotation(_lowerCamelCase     )
																		else:
																								SCREAMING_SNAKE_CASE__     :				List[Any]   =					left_rotation(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					my_max(get_height(node.get_right()     )			,   get_height(node.get_left()     )     ) + 1
						node.set_height(_lowerCamelCase     )
						return node







def       UpperCAmelCase							(     _lowerCamelCase      :  MyNode     ):





						'''simple docstring'''


						while True:
												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					root.get_right()
												if right_child is None:
																		break
												SCREAMING_SNAKE_CASE__     :				int   =					right_child
						return root.get_data()







def       UpperCAmelCase							(     _lowerCamelCase      :  MyNode     ):





						'''simple docstring'''


						while True:
												SCREAMING_SNAKE_CASE__     :				List[Any]   =					root.get_left()
												if left_child is None:
																		break
												SCREAMING_SNAKE_CASE__     :				str   =					left_child
						return root.get_data()







def       UpperCAmelCase							(     _lowerCamelCase      :  MyNode			,   _lowerCamelCase      :  Any     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[Any]   =					root.get_left()
						SCREAMING_SNAKE_CASE__     :				int   =					root.get_right()
						if root.get_data() == data:
												if left_child is not None and right_child is not None:
																		SCREAMING_SNAKE_CASE__     :				Dict   =					get_left_most(_lowerCamelCase     )
																		root.set_data(_lowerCamelCase     )
																		root.set_right(del_node(_lowerCamelCase			,   _lowerCamelCase     )     )
												elif left_child is not None:
																		SCREAMING_SNAKE_CASE__     :				List[str]   =					left_child
												elif right_child is not None:
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					right_child
												else:
																		return None
						elif root.get_data() > data:
												if left_child is None:
																		print("No such data"     )
																		return root
												else:
																		root.set_left(del_node(_lowerCamelCase			,   _lowerCamelCase     )     )
						else:  # root.get_data() < data
												if right_child is None:
																		return root
												else:
																		root.set_right(del_node(_lowerCamelCase			,   _lowerCamelCase     )     )

						if get_height(_lowerCamelCase     ) - get_height(_lowerCamelCase     ) == 2:
												assert right_child is not None
												if get_height(right_child.get_right()     ) > get_height(right_child.get_left()     ):
																		SCREAMING_SNAKE_CASE__     :				str   =					left_rotation(_lowerCamelCase     )
												else:
																		SCREAMING_SNAKE_CASE__     :				List[str]   =					rl_rotation(_lowerCamelCase     )
						elif get_height(_lowerCamelCase     ) - get_height(_lowerCamelCase     ) == -2:
												assert left_child is not None
												if get_height(left_child.get_left()     ) > get_height(left_child.get_right()     ):
																		SCREAMING_SNAKE_CASE__     :				str   =					right_rotation(_lowerCamelCase     )
												else:
																		SCREAMING_SNAKE_CASE__     :				Tuple   =					lr_rotation(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Dict   =					my_max(get_height(root.get_right()     )			,   get_height(root.get_left()     )     ) + 1
						root.set_height(_lowerCamelCase     )
						return root



class    _a     :






							"""simple docstring"""

							def __init__(      self				:     List[Any]    )	->None:
													SCREAMING_SNAKE_CASE__     :				MyNode | None   =					None

							def      A_  (      self				:     Optional[Any]    )	->int:
													return get_height(self.root    )

							def      A_  (      self				:     Optional[Any]							,	a				:     Any    )	->None:
													print("insert:" + str(a    )    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					insert_node(self.root							,	a    )

							def      A_  (      self				:     Any							,	a				:     Any    )	->None:
													print("delete:" + str(a    )    )
													if self.root is None:
																			print("Tree is empty!"    )
																			return
													SCREAMING_SNAKE_CASE__     :				str   =					del_node(self.root							,	a    )






							def __str__(      self				:     str							,	)	->str:  # a level traversale, gives a more intuitive look on the tree
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					""
													SCREAMING_SNAKE_CASE__     :				List[str]   =					MyQueue()
													q.push(self.root    )
													SCREAMING_SNAKE_CASE__     :				str   =					self.get_height()
													if layer == 0:
																			return output
													SCREAMING_SNAKE_CASE__     :				int   =					0
													while not q.is_empty():
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					q.pop()
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					" " * int(math.pow(2							,	layer - 1    )    )
																			output += space
																			if node is None:
																									output += "*"
																									q.push(a    )
																									q.push(a    )
																			else:
																									output += str(node.get_data()    )
																									q.push(node.get_left()    )
																									q.push(node.get_right()    )
																			output += space
																			SCREAMING_SNAKE_CASE__     :				Dict   =					cnt + 1
																			for i in range(1_00    ):
																									if cnt == math.pow(2							,	a    ) - 1:
																															SCREAMING_SNAKE_CASE__     :				List[Any]   =					layer - 1
																															if layer == 0:
																																					output += "\n*************************************"
																																					return output
																															output += "\n"
																															break
													output += "\n*************************************"
													return output







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						import doctest

						doctest.testmod()


if __name__ == "__main__":
				_test()
				__lowercase       :int       =  AVLtree()
				__lowercase       :str       =  list(range(10))
				random.shuffle(lst)
				for i in lst:
								t.insert(i)
								print(str(t))
				random.shuffle(lst)
				for i in lst:
								t.del_node(i)
								print(str(t))






import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor



class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def __init__(      self				:     Optional[int]							,	a				:     Any							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     int = 32							,	a				:     bool = True							,	a				:     Union[int, float] = 1 / 2_55							,	a				:     bool = True							,	a				:     bool = True							,	a				:     Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]							,	a				:     Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]							,	a				:     bool = True							,	a				:     Any=7							,	a				:     str=30							,	a				:     Dict=4_00							,	a				:     Optional[int]=3							,	)	->int:
													SCREAMING_SNAKE_CASE__     :				int   =					parent
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_resize
													SCREAMING_SNAKE_CASE__     :				List[str]   =					size if size is not None else {"shortest_edge": 2_88}
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					size_divisor
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				Tuple   =					rescale_factor
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					do_center_crop
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					image_mean
													SCREAMING_SNAKE_CASE__     :				Dict   =					image_std
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_pad
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					batch_size
													SCREAMING_SNAKE_CASE__     :				int   =					num_channels
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					min_resolution
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					max_resolution

							def      A_  (      self				:     List[str]    )	->Tuple:
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "size_divisor": self.size_divisor,
													}






							def      A_  (      self				:     int							,	a				:     Optional[int]							,	a				:     Union[str, Any]=False    )	->Optional[Any]:
													if not batched:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.size["shortest_edge"]
																			SCREAMING_SNAKE_CASE__     :				Dict   =					image_inputs[0]
																			if isinstance(a							,	Image.Image    ):
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					image.size
																			else:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[str]   =					image.shape[1], image.shape[2]
																			SCREAMING_SNAKE_CASE__     :				Any   =					size / min(a							,	a    )
																			if h < w:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					size, scale * w
																			else:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[str]   =					scale * h, size

																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					int((13_33 / 8_00) * size    )
																			if max(a							,	a    ) > max_size:
																									SCREAMING_SNAKE_CASE__     :				List[Any]   =					max_size / max(a							,	a    )
																									SCREAMING_SNAKE_CASE__     :				int   =					newh * scale
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					neww * scale

																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					int(newh + 0.5    ), int(neww + 0.5    )
																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					(
																			    newh // self.size_divisor * self.size_divisor,
																			    neww // self.size_divisor * self.size_divisor,
																			)

													else:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					[]
																			for image in image_inputs:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.get_expected_values([image]    )
																									expected_values.append((expected_height, expected_width)    )
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					max(a							,	key=lambda a    : item[0]    )[0]
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					max(a							,	key=lambda a    : item[1]    )[1]

													return expected_height, expected_width







@require_torch
@require_vision
class    _a     (				lowercase__					,							unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						BridgeTowerImageProcessor if is_vision_available() else None

							def      A_  (      self				:     List[Any]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Any   =					BridgeTowerImageProcessingTester(self    )

							@property
							def      A_  (      self				:     Optional[int]    )	->Optional[Any]:
													return self.image_processor_tester.prepare_image_processor_dict()

							def      A_  (      self				:     Tuple    )	->str:
													SCREAMING_SNAKE_CASE__     :				int   =					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							,	"size"    )    )
													self.assertTrue(hasattr(a							,	"size_divisor"    )    )

							def      A_  (      self				:     List[Any]    )	->List[Any]:
													pass

							def      A_  (      self				:     Tuple    )	->Optional[Any]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					prepare_image_inputs(self.image_processor_tester							,	equal_resolution=a    )
													for image in image_inputs:
																			self.assertIsInstance(a							,	Image.Image    )

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

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.image_processor_tester.get_expected_values(a    )
													self.assertEqual(
													    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

													# Test batched
													SCREAMING_SNAKE_CASE__     :				int   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					self.image_processor_tester.get_expected_values(a							,	batched=a    )
													self.assertEqual(
													    encoded_images.shape							,	(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,	)

							def      A_  (      self				:     Optional[int]    )	->Any:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				str   =					self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													SCREAMING_SNAKE_CASE__     :				str   =					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
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.image_processor_tester.get_expected_values(a    )
													self.assertEqual(
													    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

													# Test batched
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.image_processor_tester.get_expected_values(a							,	batched=a    )
													self.assertEqual(
													    encoded_images.shape							,	(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,	)






							def      A_  (      self				:     str    )	->Optional[int]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					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
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Tuple   =					self.image_processor_tester.get_expected_values(a    )
													self.assertEqual(
													    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

													# Test batched
													SCREAMING_SNAKE_CASE__     :				Any   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.image_processor_tester.get_expected_values(a							,	batched=a    )
													self.assertEqual(
													    encoded_images.shape							,	(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,	)