



# 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 		_lowercase		:

   def __init__(   self      :							Optional[Any]				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							Union[str, Any]				)							->					Optional[int]:


    """simple docstring"""






    a							=		key
    a							=		freq




   def __str__(   self      :							Optional[Any]				)							->					Dict:


    """simple docstring"""






    return f"""Node(key={self.key}, freq={self.freq})"""


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[int]			,       UpperCAmelCase__			:List[str]			,       UpperCAmelCase__			:int			,       UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:List[Any]			,       UpperCAmelCase__			:Optional[int]       ):






 '''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 						UpperCAmelCase__   (   UpperCAmelCase__			:str       ):






 '''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 						UpperCAmelCase__   (   ):






 '''simple docstring'''

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


if __name__ == "__main__":
     main()




"""simple docstring"""
import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					List[str]   )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		int        =     TaConfig.from_json_file(lowerCamelCase   )
       print(f"""Building PyTorch model from configuration: {config}"""   )
       __magic_name__			:		Union[str, Any]        =     TaForConditionalGeneration(lowerCamelCase   )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
       model.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
 A									=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 A									=						parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
