






from __future__ import annotations

from random import random

class 		_snake_case  :






			def __init__(       self		,					a = None)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= value
						SCREAMING_SNAKE_CASE						= random()
						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= None






			def __repr__(       self)      ->       str:
						from pprint import pformat

						if self.left is None and self.right is None:
									return f'''\'{self.value}: {self.prior:.5}\''''
						else:
									return pformat(
									    {f'''{self.value}: {self.prior:.5}''': (self.left, self.right)}		,					indent=1)







			def __str__(       self)      ->       str:
						SCREAMING_SNAKE_CASE						= str(self.value) + ' '
						SCREAMING_SNAKE_CASE						= str(self.left or '')
						SCREAMING_SNAKE_CASE						= str(self.right or '')
						return value + left + right






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			if root is None:  # None tree is split into 2 Nones
						return None, None
			elif root.value is None:
						return None, None
			else:
						if value < root.value:
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= split(root.left							,       _UpperCAmelCase)
									return left, root
						else:
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= split(root.right							,       _UpperCAmelCase)
									return root, right






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			if (not left) or (not right):  # If one node is None, return the other
						return left or right
			elif left.prior < right.prior:
						SCREAMING_SNAKE_CASE						= merge(left.right							,       _UpperCAmelCase)
						return left
			else:
						SCREAMING_SNAKE_CASE						= merge(_UpperCAmelCase							,       right.left)
						return right






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= Node(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= split(_UpperCAmelCase							,       _UpperCAmelCase)
			return merge(merge(_UpperCAmelCase							,       _UpperCAmelCase)							,       _UpperCAmelCase)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= split(_UpperCAmelCase							,       value - 1)
			SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= split(_UpperCAmelCase							,       _UpperCAmelCase)
			return merge(_UpperCAmelCase							,       _UpperCAmelCase)






def 				lowerCamelCase__				(_UpperCAmelCase):
			if not root:  # None
						return
			else:
						inorder(root.left)
						print(root.value							,       end=',')
						inorder(root.right)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			for arg in args.split():
						if arg[0] == "+":
									SCREAMING_SNAKE_CASE						= insert(_UpperCAmelCase							,       int(arg[1:]))

						elif arg[0] == "-":
									SCREAMING_SNAKE_CASE						= erase(_UpperCAmelCase							,       int(arg[1:]))

						else:
									print('Unknown command')

			return root






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= None
			print(
			    'enter numbers to create a tree, + value to add value into treap, '
			    '- value to erase all nodes with value. \'q\' to quit. ')

			SCREAMING_SNAKE_CASE						= input()
			while args != "q":
						SCREAMING_SNAKE_CASE						= interact_treap(_UpperCAmelCase							,       _UpperCAmelCase)
						print(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= input()

			print('good by!')


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()












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 MobileViTImageProcessor

class 		_snake_case  (  unittest.TestCase					):






			def __init__(       self		,					a		,					a=7		,					a=3		,					a=18		,					a=30		,					a=400		,					a=True		,					a=None		,					a=True		,					a=None		,					a=True		,					)      ->       Any:
						SCREAMING_SNAKE_CASE						= size if size is not None else {'shortest_edge': 20}
						SCREAMING_SNAKE_CASE						= crop_size if crop_size is not None else {'height': 18, 'width': 18}
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= num_channels
						SCREAMING_SNAKE_CASE						= image_size
						SCREAMING_SNAKE_CASE						= min_resolution
						SCREAMING_SNAKE_CASE						= max_resolution
						SCREAMING_SNAKE_CASE						= do_resize
						SCREAMING_SNAKE_CASE						= size
						SCREAMING_SNAKE_CASE						= do_center_crop
						SCREAMING_SNAKE_CASE						= crop_size
						SCREAMING_SNAKE_CASE						= do_flip_channel_order







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_flip_channel_order": self.do_flip_channel_order,
						}



@require_torch
@require_vision
class 		_snake_case  (  A__   ,    unittest.TestCase					):
			_lowercase		:    Optional[int]			     =							MobileViTImageProcessor if is_vision_available() else None






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= MobileViTImageProcessingTester(self)






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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(a		,					'do_resize'))
						self.assertTrue(hasattr(a		,					'size'))
						self.assertTrue(hasattr(a		,					'do_center_crop'))
						self.assertTrue(hasattr(a		,					'center_crop'))
						self.assertTrue(hasattr(a		,					'do_flip_channel_order'))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= 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})

						SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						pass






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						SCREAMING_SNAKE_CASE						= 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						= 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
						SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						SCREAMING_SNAKE_CASE						= 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						= 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
						SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE						= 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						= 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
						SCREAMING_SNAKE_CASE						= 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'],
						    )		,					)




