class 						lowercase    :




							def __init__(						self   :					Optional[Any]	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					Optional[int]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       name
												SCREAMING_SNAKE_CASE    			=       val




							def __str__(						self   :					Union[str, Any]      )					->				Optional[Any]:



												'''simple docstring'''

												return F"{self.__class__.__name__}({self.name}, {self.val})"





							def __lt__(						self   :					Optional[Any]	,							_UpperCamelCase   :					List[str]      )					->				Any:



												'''simple docstring'''

												return self.val < other.val



class 						lowercase    :




							def __init__(						self   :					str	,							_UpperCamelCase   :					str      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {}
												SCREAMING_SNAKE_CASE    			=       {}
												SCREAMING_SNAKE_CASE    			=       self.build_heap(__UpperCamelCase      )




							def __getitem__(						self   :					List[str]	,							_UpperCamelCase   :					str      )					->				Optional[int]:



												'''simple docstring'''

												return self.get_value(__UpperCamelCase      )




							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					int      )					->				Union[str, Any]:



												'''simple docstring'''

												return (idx - 1) // 2




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					Dict      )					->				List[Any]:



												'''simple docstring'''

												return idx * 2 + 1




							def 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					Optional[int]      )					->				Tuple:



												'''simple docstring'''

												return idx * 2 + 2




							def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					str      )					->				List[Any]:



												'''simple docstring'''

												return self.heap_dict[key]




							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					int      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       len(__UpperCamelCase      ) - 1
												SCREAMING_SNAKE_CASE    			=       self.get_parent_idx(__UpperCamelCase      )

												for idx, i in enumerate(__UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       idx
																	SCREAMING_SNAKE_CASE    			=       i.val

												for i in range(__UpperCamelCase	,							-1	,							-1      ):
																	self.sift_down(__UpperCamelCase	,							__UpperCamelCase      )
												return array




							def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					Tuple      )					->				Optional[Any]:



												'''simple docstring'''

												while True:
																	SCREAMING_SNAKE_CASE    			=       self.get_left_child_idx(__UpperCamelCase      )  # noqa: E741
																	SCREAMING_SNAKE_CASE    			=       self.get_right_child_idx(__UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       idx
																	if l < len(__UpperCamelCase      ) and array[l] < array[idx]:
																						SCREAMING_SNAKE_CASE    			=       l
																	if r < len(__UpperCamelCase      ) and array[r] < array[smallest]:
																						SCREAMING_SNAKE_CASE    			=       r

																	if smallest != idx:
																						SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       array[smallest], array[idx]
																						(
																						    (
																						    SCREAMING_SNAKE_CASE
																						)			,   (
																						    SCREAMING_SNAKE_CASE
																						)			,   
																						)    			=       (
																						    self.idx_of_element[array[smallest]],
																						    self.idx_of_element[array[idx]],
																						)
																						SCREAMING_SNAKE_CASE    			=       smallest
																	else:
																						break




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					Any      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_parent_idx(__UpperCamelCase      )
												while p >= 0 and self.heap[p] > self.heap[idx]:
																	SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.heap[idx], self.heap[p]
																	SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       (
																	    self.idx_of_element[self.heap[idx]],
																	    self.idx_of_element[self.heap[p]],
																	)
																	SCREAMING_SNAKE_CASE    			=       p
																	SCREAMING_SNAKE_CASE    			=       self.get_parent_idx(__UpperCamelCase      )




							def 	__snake_case(						self   :					List[Any]      )					->				Union[str, Any]:



												'''simple docstring'''

												return self.heap[0]




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.heap[-1], self.heap[0]
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       (
												    self.idx_of_element[self.heap[-1]],
												    self.idx_of_element[self.heap[0]],
												)

												SCREAMING_SNAKE_CASE    			=       self.heap.pop()
												del self.idx_of_element[x]
												self.sift_down(0	,							self.heap      )
												return x




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					Tuple      )					->				Tuple:



												'''simple docstring'''

												self.heap.append(__UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       len(self.heap      ) - 1
												SCREAMING_SNAKE_CASE    			=       node.val
												self.sift_up(len(self.heap      ) - 1      )




							def 	__snake_case(						self   :					List[Any]      )					->				str:



												'''simple docstring'''

												return len(self.heap      ) == 0





							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					Optional[Any]	,							_UpperCamelCase   :					List[str]      )					->				Optional[Any]:



												'''simple docstring'''

												assert (
												    self.heap[self.idx_of_element[node]].val > new_value
												), "newValue must be less that current value"
												SCREAMING_SNAKE_CASE    			=       new_value
												SCREAMING_SNAKE_CASE    			=       new_value
												self.sift_up(self.idx_of_element[node]      )



_lowerCamelCase					:   int							=		Node('''R''', -1)
_lowerCamelCase					:   Tuple							=		Node('''B''', 6)
_lowerCamelCase					:   Union[str, Any]							=		Node('''A''', 3)
_lowerCamelCase					:   List[str]							=		Node('''X''', 1)
_lowerCamelCase					:   Dict							=		Node('''E''', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
_lowerCamelCase					:   Tuple							=		MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
							print(i)

print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
							print(i)

if __name__ == "__main__":
							import doctest

							doctest.testmod()





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor


class 						lowercase    (     unittest.TestCase     ):




							def __init__(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					List[Any]=7	,							_UpperCamelCase   :					Any=3	,							_UpperCamelCase   :					str=18	,							_UpperCamelCase   :					Tuple=30	,							_UpperCamelCase   :					Optional[int]=400	,							_UpperCamelCase   :					Optional[int]=True	,							_UpperCamelCase   :					Optional[Any]=None	,							_UpperCamelCase   :					int=True	,							_UpperCamelCase   :					Optional[int]=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					List[str]=[0.5, 0.5, 0.5]	,							)					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       size if 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_normalize
												SCREAMING_SNAKE_CASE    			=       image_mean
												SCREAMING_SNAKE_CASE    			=       image_std





							def 	__snake_case(						self   :					List[Any]      )					->				Any:



												'''simple docstring'''

												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												}



@require_torch
@require_vision
class 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       Any								=							DPTImageProcessor if is_vision_available() else None




							def 	__snake_case(						self   :					List[str]      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       DPTImageProcessingTester(self      )




							@property
							def 	__snake_case(						self   :					List[Any]      )					->				List[str]:



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def 	__snake_case(						self   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_mean"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_std"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_normalize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_resize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"size"      )      )




							def 	__snake_case(						self   :					Dict      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class.from_dict(self.image_processor_dict      )
												self.assertEqual(image_processor.size	,							{"height": 18, "width": 18}      )

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class.from_dict(self.image_processor_dict	,							size=42      )
												self.assertEqual(image_processor.size	,							{"height": 42, "width": 42}      )




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Tuple:



												'''simple docstring'''

												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=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							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.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       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"],
												    )	,							)




							def 	__snake_case(						self   :					Optional[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												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=_UpperCamelCase	,							numpify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							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.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       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"],
												    )	,							)





							def 	__snake_case(						self   :					Optional[Any]      )					->				str:



												'''simple docstring'''

												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=_UpperCamelCase	,							torchify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							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.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       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"],
												    )	,							)




