






class       __a  :







 def __init__(		self :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       Tuple							,       SCREAMING_SNAKE_CASE :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  name
      UpperCamelCase__      :       str	    =  val







 def __str__(		self :       List[Any]						):


      '''simple docstring'''






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







 def __lt__(		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       Optional[Any]						):


      '''simple docstring'''






      return self.val < other.val







class       __a  :







 def __init__(		self :       int							,       SCREAMING_SNAKE_CASE :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  {}
      UpperCamelCase__      :       Tuple	    =  {}
      UpperCamelCase__      :       str	    =  self.build_heap(SCREAMING_SNAKE_CASE						)







 def __getitem__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       Optional[int]						):


      '''simple docstring'''






      return self.get_value(SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      return (idx - 1) // 2







 def  __lowercase (		self :       Optional[int]							,       SCREAMING_SNAKE_CASE :       Optional[Any]						):


      '''simple docstring'''






      return idx * 2 + 1







 def  __lowercase (		self :       Optional[int]							,       SCREAMING_SNAKE_CASE :       str						):


      '''simple docstring'''






      return idx * 2 + 2







 def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       Dict						):


      '''simple docstring'''






      return self.heap_dict[key]







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       Tuple						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  len(SCREAMING_SNAKE_CASE						) - 1
      UpperCamelCase__      :       int	    =  self.get_parent_idx(SCREAMING_SNAKE_CASE						)

      for idx, i in enumerate(SCREAMING_SNAKE_CASE						):
           UpperCamelCase__      :       Dict	    =  idx
           UpperCamelCase__      :       List[Any]	    =  i.val

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







 def  __lowercase (		self :       List[str]							,       SCREAMING_SNAKE_CASE :       Tuple							,       SCREAMING_SNAKE_CASE :       List[Any]						):


      '''simple docstring'''






      while True:
           UpperCamelCase__      :       List[Any]	    =  self.get_left_child_idx(SCREAMING_SNAKE_CASE						)  # noqa: E741
           UpperCamelCase__      :       Optional[int]	    =  self.get_right_child_idx(SCREAMING_SNAKE_CASE						)

           UpperCamelCase__      :       str	    =  idx
           if l < len(SCREAMING_SNAKE_CASE						) and array[l] < array[idx]:
                UpperCamelCase__      :       Optional[int]	    =  l
           if r < len(SCREAMING_SNAKE_CASE						) and array[r] < array[smallest]:
                UpperCamelCase__      :       Any	    =  r

           if smallest != idx:
                UpperCamelCase__    ,					UpperCamelCase__      :       Dict	    =  array[smallest], array[idx]
                (
                    (
                    UpperCamelCase__
                )    ,					(
                    UpperCamelCase__
                )    ,					
                )      :       Union[str, Any]	    =  (
                    self.idx_of_element[array[smallest]],
                    self.idx_of_element[array[idx]],
                )
                UpperCamelCase__      :       Optional[int]	    =  smallest
           else:
                break







 def  __lowercase (		self :       List[str]							,       SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  self.get_parent_idx(SCREAMING_SNAKE_CASE						)
      while p >= 0 and self.heap[p] > self.heap[idx]:
           UpperCamelCase__    ,					UpperCamelCase__      :       Optional[Any]	    =  self.heap[idx], self.heap[p]
           UpperCamelCase__    ,					UpperCamelCase__      :       List[str]	    =  (
               self.idx_of_element[self.heap[idx]],
               self.idx_of_element[self.heap[p]],
           )
           UpperCamelCase__      :       List[Any]	    =  p
           UpperCamelCase__      :       Any	    =  self.get_parent_idx(SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      return self.heap[0]







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__    ,					UpperCamelCase__      :       int	    =  self.heap[-1], self.heap[0]
      UpperCamelCase__    ,					UpperCamelCase__      :       List[str]	    =  (
          self.idx_of_element[self.heap[-1]],
          self.idx_of_element[self.heap[0]],
      )

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







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       Any						):


      '''simple docstring'''






      self.heap.append(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[str]	    =  len(self.heap						) - 1
      UpperCamelCase__      :       Dict	    =  node.val
      self.sift_up(len(self.heap						) - 1						)







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      return len(self.heap						) == 0







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






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







lowerCamelCase     :							Any    =Node('''R''', -1)
lowerCamelCase     :							Dict    =Node('''B''', 6)
lowerCamelCase     :							Tuple    =Node('''A''', 3)
lowerCamelCase     :							Optional[int]    =Node('''X''', 1)
lowerCamelCase     :							Dict    =Node('''E''', 4)

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

# Generating Min-Heap from array
lowerCamelCase     :							List[Any]    =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.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 GLPNImageProcessor





class       __a  (   unittest.TestCase	):







 def __init__(		self :       Tuple							,       SCREAMING_SNAKE_CASE :       Optional[int]							,       SCREAMING_SNAKE_CASE :       List[Any]=7							,       SCREAMING_SNAKE_CASE :       str=3							,       SCREAMING_SNAKE_CASE :       Dict=18							,       SCREAMING_SNAKE_CASE :       str=30							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=4_00							,       SCREAMING_SNAKE_CASE :       int=True							,       SCREAMING_SNAKE_CASE :       Any=32							,       SCREAMING_SNAKE_CASE :       int=True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  parent
      UpperCamelCase__      :       Dict	    =  batch_size
      UpperCamelCase__      :       Optional[Any]	    =  num_channels
      UpperCamelCase__      :       int	    =  image_size
      UpperCamelCase__      :       Dict	    =  min_resolution
      UpperCamelCase__      :       List[str]	    =  max_resolution
      UpperCamelCase__      :       Union[str, Any]	    =  do_resize
      UpperCamelCase__      :       Optional[Any]	    =  size_divisor
      UpperCamelCase__      :       int	    =  do_rescale







 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      return {
          "do_resize": self.do_resize,
          "size_divisor": self.size_divisor,
          "do_rescale": self.do_rescale,
      }







@require_torch
@require_vision
class       __a  (   A__	,       unittest.TestCase	):
 _lowerCAmelCase	:	List[Any]				=			GLPNImageProcessor if is_vision_available() else None







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  GLPNImageProcessingTester(self						)







 @property
 def  __lowercase (		self :       int						):


      '''simple docstring'''






      return self.image_processor_tester.prepare_image_processor_dict()







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_resize"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "size_divisor"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "resample"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_rescale"						)						)







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      pass







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Any	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      UpperCamelCase__      :       Tuple	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       Image.Image						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       List[Any]	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      UpperCamelCase__      :       Optional[Any]	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE							,       numpify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       np.ndarray						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       str	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      UpperCamelCase__      :       Tuple	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE							,       torchify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       torch.Tensor						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       Dict	    =  image_processing(image_inputs[0]							,       return_tensors="pt"						).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)