
'''simple docstring'''





import sys
from random import randint


class  UpperCamelCase__  :






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







							'''simple docstring'''



							__A       	=			key
							__A       	=			freq






	def __str__(			self  :List[str]			)				->			str:







							'''simple docstring'''



							return F'Node(key={self.key}, freq={self.freq})'





def        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)->			Dict:







						"""simple docstring"""
						if i > j or i < 0 or j > len(UpperCAmelCase	) - 1:
												return

						__A       	=			root[i][j]
						if parent == -1:  # root does not have a parent
												print(f'{key[node]} is the root of the binary search tree.'	)
						elif is_left:
												print(f'{key[node]} is the left child of key {parent}.'	)
						else:
												print(f'{key[node]} is the right child of key {parent}.'	)

						print_binary_search_tree(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  node - 1      ,  key[node]      ,  UpperCAmelCase	)
						print_binary_search_tree(UpperCAmelCase      ,  UpperCAmelCase      ,  node + 1      ,  UpperCAmelCase      ,  key[node]      ,  UpperCAmelCase	)





def        snake_case							(		UpperCAmelCase	)->			Dict:







						"""simple docstring"""
						nodes.sort(key=lambda UpperCAmelCase	: node.key	)

						__A       	=			len(UpperCAmelCase	)

						__A       	=			[nodes[i].key for i in range(UpperCAmelCase	)]
						__A       	=			[nodes[i].freq for i in range(UpperCAmelCase	)]

						# This 2D array stores the overall tree cost (which's as minimized as possible);
						# for a single key, cost is equal to frequency of the key.
						__A       	=			[[freqs[i] if i == j else 0 for j in range(UpperCAmelCase	)] for i in range(UpperCAmelCase	)]
						# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
						# array
						__A       	=			[[freqs[i] if i == j else 0 for j in range(UpperCAmelCase	)] for i in range(UpperCAmelCase	)]
						# stores tree roots that will be used later for constructing binary search tree
						__A       	=			[[i if i == j else 0 for j in range(UpperCAmelCase	)] for i in range(UpperCAmelCase	)]

						for interval_length in range(2      ,  n + 1	):
												for i in range(n - interval_length + 1	):
																		__A       	=			i + interval_length - 1

																		__A       	=			sys.maxsize  # set the value to "infinity"
																		__A       	=			total[i][j - 1] + freqs[j]

																		# Apply Knuth's optimization
																		# Loop without optimization: for r in range(i, j + 1):
																		for r in range(root[i][j - 1]      ,  root[i + 1][j] + 1	):  # r is a temporal root
																								__A       	=			dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
																								__A       	=			dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
																								__A       	=			left + total[i][j] + right

																								if dp[i][j] > cost:
																														__A       	=			cost
																														__A       	=			r

						print('Binary search tree nodes:'	)
						for node in nodes:
												print(UpperCAmelCase	)

						print(f'\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.'	)
						print_binary_search_tree(UpperCAmelCase      ,  UpperCAmelCase      ,  0      ,  n - 1      ,  -1      ,  UpperCAmelCase	)





def        snake_case							(		)->			str:







						"""simple docstring"""
						__A       	=			[Node(UpperCAmelCase      ,  randint(1      ,  5_0	)	) for i in range(1_0      ,  0      ,  -1	)]
						find_optimal_binary_search_tree(UpperCAmelCase	)


if __name__ == "__main__":
	main()








'''simple docstring'''





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_video_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import VideoMAEImageProcessor


class  UpperCamelCase__  (    unittest.TestCase):






 def __init__(			self  :Dict	,				_A  :Optional[Any]	,				_A  :Any=7	,				_A  :Dict=3	,				_A  :Optional[Any]=10	,				_A  :List[Any]=18	,				_A  :int=30	,				_A  :List[Any]=400	,				_A  :Any=True	,				_A  :Any=None	,				_A  :Optional[int]=True	,				_A  :Tuple=[0.5, 0.5, 0.5]	,				_A  :int=[0.5, 0.5, 0.5]	,				_A  :List[Any]=None	,				)				->			int:







       '''simple docstring'''



       __A       	=			size if size is not None else {'shortest_edge': 18}
       __A       	=			crop_size if crop_size is not None else {'height': 18, 'width': 18}

       __A       	=			parent
       __A       	=			batch_size
       __A       	=			num_channels
       __A       	=			num_frames
       __A       	=			image_size
       __A       	=			min_resolution
       __A       	=			max_resolution
       __A       	=			do_resize
       __A       	=			size
       __A       	=			do_normalize
       __A       	=			image_mean
       __A       	=			image_std
       __A       	=			crop_size






 def 	lowercase_						(			self  :Any			)				->			Union[str, 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,
           "crop_size": self.crop_size,
       }


@require_torch
@require_vision
class  UpperCamelCase__  (    SCREAMING_SNAKE_CASE      ,     unittest.TestCase):
 UpperCAmelCase__    :    Tuple										=					VideoMAEImageProcessor if is_vision_available() else None






 def 	lowercase_						(			self  :Dict			)				->			Dict:







       '''simple docstring'''



       __A       	=			VideoMAEImageProcessingTester(self			)






 @property
 def 	lowercase_						(			self  :Dict			)				->			List[str]:







       '''simple docstring'''



       return self.image_processor_tester.prepare_image_processor_dict()






 def 	lowercase_						(			self  :Dict			)				->			Optional[int]:







       '''simple docstring'''



       __A       	=			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	,				'do_center_crop'			)			)
       self.assertTrue(hasattr(_A	,				'size'			)			)






 def 	lowercase_						(			self  :Optional[int]			)				->			List[Any]:







       '''simple docstring'''



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

       __A       	=			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  :Any			)				->			Dict:







       '''simple docstring'''



       pass






 def 	lowercase_						(			self  :Optional[int]			)				->			Tuple:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random PIL videos
       __A       	=			prepare_video_inputs(self.image_processor_tester	,				equal_resolution=_A			)
       for video in video_inputs:
             self.assertIsInstance(_A	,				_A			)
             self.assertIsInstance(video[0]	,				Image.Image			)

       # Test not batched input
       __A       	=			image_processing(video_inputs[0]	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_videos.shape	,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)

       # Test batched
       __A       	=			image_processing(_A	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_videos.shape	,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)






 def 	lowercase_						(			self  :Dict			)				->			List[Any]:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       __A       	=			prepare_video_inputs(self.image_processor_tester	,				equal_resolution=_A	,				numpify=_A			)
       for video in video_inputs:
             self.assertIsInstance(_A	,				_A			)
             self.assertIsInstance(video[0]	,				np.ndarray			)

       # Test not batched input
       __A       	=			image_processing(video_inputs[0]	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_videos.shape	,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)

       # Test batched
       __A       	=			image_processing(_A	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_videos.shape	,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)






 def 	lowercase_						(			self  :int			)				->			List[str]:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       __A       	=			prepare_video_inputs(self.image_processor_tester	,				equal_resolution=_A	,				torchify=_A			)
       for video in video_inputs:
             self.assertIsInstance(_A	,				_A			)
             self.assertIsInstance(video[0]	,				torch.Tensor			)

       # Test not batched input
       __A       	=			image_processing(video_inputs[0]	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_videos.shape	,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)

       # Test batched
       __A       	=			image_processing(_A	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_videos.shape	,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)






