code: 
  
  
  '''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  UpperCAmelCase :
  
   def __init__( self :	Tuple,					a_ :	Any,					a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						key
      UpperCamelCase__ 						=						freq
  
   def __str__( self :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return f'Node(key={self.key}, freq={self.freq})'
  
  
  
  
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				List[str]   ):
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     if i > j or i < 0 or j > len(_A   ) - 1:
        return
  
     UpperCamelCase__ 						=						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(_A    ,		_A    ,		_A    ,		node - 1    ,		key[node]    ,		_A   )
     print_binary_search_tree(_A    ,		_A    ,		node + 1    ,		_A    ,		key[node]    ,		_A   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[Any]   ):
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     nodes.sort(key=lambda _UpperCamelCase   : node.key   )
  
     UpperCamelCase__ 						=						len(_A   )
  
     UpperCamelCase__ 						=						[nodes[i].key for i in range(_A   )]
     UpperCamelCase__ 						=						[nodes[i].freq for i in range(_A   )]
  
     # 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.
     UpperCamelCase__ 						=						[[freqs[i] if i == j else 0 for j in range(_A   )] for i in range(_A   )]
     # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
     # array
     UpperCamelCase__ 						=						[[freqs[i] if i == j else 0 for j in range(_A   )] for i in range(_A   )]
     # stores tree roots that will be used later for constructing binary search tree
     UpperCamelCase__ 						=						[[i if i == j else 0 for j in range(_A   )] for i in range(_A   )]
  
     for interval_length in range(2    ,		n + 1   ):
        for i in range(n - interval_length + 1   ):
           UpperCamelCase__ 						=						i + interval_length - 1
  
           UpperCamelCase__ 						=						sys.maxsize  # set the value to "infinity"
           UpperCamelCase__ 						=						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
              UpperCamelCase__ 						=						dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
              UpperCamelCase__ 						=						dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
              UpperCamelCase__ 						=						left + total[i][j] + right
  
              if dp[i][j] > cost:
                 UpperCamelCase__ 						=						cost
                 UpperCamelCase__ 						=						r
  
     print("Binary search tree nodes:"   )
     for node in nodes:
        print(_A   )
  
     print(F'\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.'   )
     print_binary_search_tree(_A    ,		_A    ,		0    ,		n - 1    ,		-1    ,		_A   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		):
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						[Node(_A    ,		randint(1    ,		50   )   ) for i in range(10    ,		0    ,		-1   )]
     find_optimal_binary_search_tree(_A   )
  
  
  if __name__ == "__main__":
         main()
style_context: 
  
  
  '''simple docstring'''
  
  from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
  from transformers.testing_utils import require_tokenizers, slow
  
  from ..bert.test_tokenization_bert import BertTokenizationTest
  
  
  
  
  
  
  
  @require_tokenizers
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
   _lowerCamelCase :					Optional[int]    					=					SqueezeBertTokenizer
   _lowerCamelCase :					List[Any]    					=					SqueezeBertTokenizerFast
   _lowerCamelCase :					int    					=					True
  
   def 		lowercase_						( self :	List[str],					**a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname,					**a_					)
  
   @slow
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						SqueezeBertTokenizer.from_pretrained("squeezebert/squeezebert-mnli-headless"					)
  
      UpperCamelCase__ 						=						tokenizer.encode("sequence builders",					add_special_tokens=a_					)
      UpperCamelCase__ 						=						tokenizer.encode("multi-sequence build",					add_special_tokens=a_					)
  
      UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_					)
      UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_,					a_					)
  
      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]