






"""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   _lowercase			:


      def __init__(     self					:						Tuple				, a					:						str				, a					:						str    ):





            """simple docstring"""







            __snake_case   :     Tuple	      =key
            __snake_case   :     Dict	      =freq







      def __str__(     self					:						Any    ):





            """simple docstring"""







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



def      __lowercase       (     a			:					Optional[Any]							,		a			:					Union[str, Any]							,		a			:					List[Any]							,		a			:					Dict							,		a			:					List[str]							,		a			:					Optional[Any]			)       ->       Optional[int]:






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

      __snake_case   :     str	      =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      __lowercase       (     a			:					Any			)       ->       Union[str, Any]:
      nodes.sort(key=lambda a			: node.key			)

      __snake_case   :     Optional[Any]	      =len(_UpperCAmelCase			)

      __snake_case   :     str	      =[nodes[i].key for i in range(_UpperCAmelCase			)]
      __snake_case   :     int	      =[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.
      __snake_case   :     Tuple	      =[[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
      __snake_case   :     str	      =[[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
      __snake_case   :     List[Any]	      =[[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			):
                  __snake_case   :     Any	      =i + interval_length - 1

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

                        if dp[i][j] > cost:
                              __snake_case   :     Any	      =cost
                              __snake_case   :     Dict	      =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      __lowercase       (     )       ->       Optional[Any]:
      __snake_case   :     List[str]	      =[Node(_UpperCAmelCase							,		randint(1							,		50			)			) for i in range(10							,		0							,		-1			)]
      find_optimal_binary_search_tree(_UpperCAmelCase			)


if __name__ == "__main__":
    main()







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       RoFormerConfig.from_json_file(_UpperCAmelCase				)
					print(f"""Building PyTorch model from configuration: {config}"""				)
					lowercase						   =       RoFormerForMaskedLM(_UpperCAmelCase				)

					# Load weights from tf checkpoint
					load_tf_weights_in_roformer(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				)

					# Save pytorch-model
					print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
					torch.save(model.state_dict()       ,			_UpperCAmelCase       ,			_use_new_zipfile_serialization=_UpperCAmelCase				)


if __name__ == "__main__":
		__magic_name__       =     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(
		    '''--bert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained BERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__magic_name__       =     parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






