




'''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_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor





class     lowerCAmelCase   ( unittest.TestCase):


       def __init__(   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE=7    ,  __SCREAMING_SNAKE_CASE=3    ,  __SCREAMING_SNAKE_CASE=18    ,  __SCREAMING_SNAKE_CASE=30    ,  __SCREAMING_SNAKE_CASE=400    ,  __SCREAMING_SNAKE_CASE=True    ,  __SCREAMING_SNAKE_CASE=None    ,  __SCREAMING_SNAKE_CASE=True    ,  __SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,  __SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,  )      ->  int:



            '''simple docstring'''




            __snake_case    					=  size if size is not None else {'''height''': 18, '''width''': 18}
            __snake_case    					=  parent
            __snake_case    					=  batch_size
            __snake_case    					=  num_channels
            __snake_case    					=  image_size
            __snake_case    					=  min_resolution
            __snake_case    					=  max_resolution
            __snake_case    					=  do_resize
            __snake_case    					=  size
            __snake_case    					=  do_normalize
            __snake_case    					=  image_mean
            __snake_case    					=  image_std




       def       lowerCAmelCase      (   self  )      ->  Dict:



            '''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     lowerCAmelCase   ( __lowerCAmelCase				,   unittest.TestCase):
       __lowercase  :      Optional[Any]        =			ViTImageProcessor if is_vision_available() else None


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



            '''simple docstring'''




            __snake_case    					=  ViTImageProcessingTester(self  )


       @property
       def       lowerCAmelCase      (   self  )      ->  Dict:



            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()


       def       lowerCAmelCase      (   self  )      ->  Optional[int]:



            '''simple docstring'''




            __snake_case    					=  self.image_processing_class(**self.image_processor_dict  )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,  '''image_mean'''  )  )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,  '''image_std'''  )  )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,  '''do_normalize'''  )  )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,  '''do_resize'''  )  )
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,  '''size'''  )  )


       def       lowerCAmelCase      (   self  )      ->  int:



            '''simple docstring'''




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

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


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



            '''simple docstring'''




            pass


       def       lowerCAmelCase      (   self  )      ->  Dict:



            '''simple docstring'''




            __snake_case    					=  self.image_processing_class(**self.image_processor_dict  )
            # create random PIL images
            __snake_case    					=  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
            __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
            __snake_case    					=  image_processing(__SCREAMING_SNAKE_CASE    ,  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       lowerCAmelCase      (   self  )      ->  Optional[Any]:



            '''simple docstring'''




            __snake_case    					=  self.image_processing_class(**self.image_processor_dict  )
            # create random numpy tensors
            __snake_case    					=  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
            __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
            __snake_case    					=  image_processing(__SCREAMING_SNAKE_CASE    ,  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       lowerCAmelCase      (   self  )      ->  List[str]:



            '''simple docstring'''




            __snake_case    					=  self.image_processing_class(**self.image_processor_dict  )
            # create random PyTorch tensors
            __snake_case    					=  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
            __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
            __snake_case    					=  image_processing(__SCREAMING_SNAKE_CASE    ,  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'''],
                )    ,  )












'''simple docstring'''



# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint





class     lowerCAmelCase   :


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



            '''simple docstring'''




            __snake_case    					=  key
            __snake_case    					=  freq




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



            '''simple docstring'''




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







def 			_UpperCamelCase (_lowerCamelCase							:     str   ,	_lowerCamelCase							:     List[str]   ,	_lowerCamelCase							:     Union[str, Any]   ,	_lowerCamelCase							:     int   ,	_lowerCamelCase							:     List[Any]   ,	_lowerCamelCase							:     Tuple	)->   Any:




     '''simple docstring'''





     if i > j or i < 0 or j > len(_lowerCamelCase	) - 1:
          return

     __snake_case    					=  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(_lowerCamelCase   ,	_lowerCamelCase   ,	_lowerCamelCase   ,	node - 1   ,	key[node]   ,	_lowerCamelCase	)
     print_binary_search_tree(_lowerCamelCase   ,	_lowerCamelCase   ,	node + 1   ,	_lowerCamelCase   ,	key[node]   ,	_lowerCamelCase	)







def 			_UpperCamelCase (_lowerCamelCase							:     List[Any]	)->   Union[str, Any]:




     '''simple docstring'''





     nodes.sort(key=lambda _lowerCamelCase	: node.key	)

     __snake_case    					=  len(_lowerCamelCase	)

     __snake_case    					=  [nodes[i].key for i in range(_lowerCamelCase	)]
     __snake_case    					=  [nodes[i].freq for i in range(_lowerCamelCase	)]

     # 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.
     __snake_case    					=  [[freqs[i] if i == j else 0 for j in range(_lowerCamelCase	)] for i in range(_lowerCamelCase	)]
     # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
     # array
     __snake_case    					=  [[freqs[i] if i == j else 0 for j in range(_lowerCamelCase	)] for i in range(_lowerCamelCase	)]
     # stores tree roots that will be used later for constructing binary search tree
     __snake_case    					=  [[i if i == j else 0 for j in range(_lowerCamelCase	)] for i in range(_lowerCamelCase	)]

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

               __snake_case    					=  sys.maxsize  # set the value to "infinity"
               __snake_case    					=  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
                    __snake_case    					=  dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
                    __snake_case    					=  dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
                    __snake_case    					=  left + total[i][j] + right

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

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

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







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




     '''simple docstring'''





     __snake_case    					=  [Node(_lowerCamelCase   ,	randint(1   ,	50	)	) for i in range(10   ,	0   ,	-1	)]
     find_optimal_binary_search_tree(_lowerCamelCase	)


if __name__ == "__main__":
     main()






