

# 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							,       _UpperCamelCase							,       _UpperCamelCase				):
        _UpperCAmelCase											=      key
        _UpperCAmelCase											=      freq





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




def     A__					(						SCREAMING_SNAKE_CASE_      :			Optional[int]							,  SCREAMING_SNAKE_CASE_      :			Tuple							,  SCREAMING_SNAKE_CASE_      :			Dict							,  SCREAMING_SNAKE_CASE_      :			List[Any]							,  SCREAMING_SNAKE_CASE_      :			Union[str, Any]							,  SCREAMING_SNAKE_CASE_      :			Union[str, Any]	)     ->     List[str]:






   """simple docstring"""







   if i > j or i < 0 or j > len(SCREAMING_SNAKE_CASE_	) - 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(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  node - 1							,  key[node]							,  SCREAMING_SNAKE_CASE_	)
   print_binary_search_tree(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  node + 1							,  SCREAMING_SNAKE_CASE_							,  key[node]							,  SCREAMING_SNAKE_CASE_	)
def     A__					(						SCREAMING_SNAKE_CASE_      :			Optional[Any]	)     ->     int:






   """simple docstring"""







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

   _UpperCAmelCase											=      len(SCREAMING_SNAKE_CASE_	)

   _UpperCAmelCase											=      [nodes[i].key for i in range(SCREAMING_SNAKE_CASE_	)]
   _UpperCAmelCase											=      [nodes[i].freq for i in range(SCREAMING_SNAKE_CASE_	)]

   # 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(SCREAMING_SNAKE_CASE_	)] for i in range(SCREAMING_SNAKE_CASE_	)]
   # 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(SCREAMING_SNAKE_CASE_	)] for i in range(SCREAMING_SNAKE_CASE_	)]
   # stores tree roots that will be used later for constructing binary search tree
   _UpperCAmelCase											=      [[i if i == j else 0 for j in range(SCREAMING_SNAKE_CASE_	)] for i in range(SCREAMING_SNAKE_CASE_	)]

   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(SCREAMING_SNAKE_CASE_	)

   print(F'''\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.'''	)
   print_binary_search_tree(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  0							,  n - 1							,  -1							,  SCREAMING_SNAKE_CASE_	)
def     A__					(						)     ->     Optional[Any]:






   """simple docstring"""







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


if __name__ == "__main__":
   main()






import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_											=  logging.get_logger(__name__)
def     A__					(						SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			Tuple							,  SCREAMING_SNAKE_CASE_      :			Dict	)     ->     str:






   """simple docstring"""







   _UpperCAmelCase											=      WavLMForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_							,  config=SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      downstream_dict['''projector.weight''']
   _UpperCAmelCase											=      downstream_dict['''projector.bias''']
   _UpperCAmelCase											=      downstream_dict['''model.post_net.linear.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.post_net.linear.bias''']
   return model
def     A__					(						SCREAMING_SNAKE_CASE_      :			Optional[Any]							,  SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			List[Any]	)     ->     Optional[int]:






   """simple docstring"""







   _UpperCAmelCase											=      WavLMForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE_							,  config=SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      downstream_dict['''model.linear.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.linear.bias''']
   return model
def     A__					(						SCREAMING_SNAKE_CASE_      :			Any							,  SCREAMING_SNAKE_CASE_      :			Tuple							,  SCREAMING_SNAKE_CASE_      :			int	)     ->     str:






   """simple docstring"""







   _UpperCAmelCase											=      WavLMForXVector.from_pretrained(SCREAMING_SNAKE_CASE_							,  config=SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      downstream_dict['''connector.weight''']
   _UpperCAmelCase											=      downstream_dict['''connector.bias''']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
      _UpperCAmelCase											=      downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      _UpperCAmelCase											=      downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
   _UpperCAmelCase											=      downstream_dict['''objective.W''']
   return model
@torch.no_grad()
def     A__					(						SCREAMING_SNAKE_CASE_      :			Dict							,  SCREAMING_SNAKE_CASE_      :			List[str]							,  SCREAMING_SNAKE_CASE_      :			int							,  SCREAMING_SNAKE_CASE_      :			List[str]	)     ->     Optional[Any]:






   """simple docstring"""







   _UpperCAmelCase											=      torch.load(SCREAMING_SNAKE_CASE_							,  map_location='''cpu'''	)

   _UpperCAmelCase											=      checkpoint['''Downstream''']

   _UpperCAmelCase											=      WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      WavaVecaFeatureExtractor.from_pretrained(
       SCREAMING_SNAKE_CASE_							,  return_attention_mask=SCREAMING_SNAKE_CASE_							,  do_normalize=SCREAMING_SNAKE_CASE_	)

   _UpperCAmelCase											=      hf_config.architectures[0]
   if arch.endswith('''ForSequenceClassification'''	):
      _UpperCAmelCase											=      convert_classification(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
   elif arch.endswith('''ForAudioFrameClassification'''	):
      _UpperCAmelCase											=      convert_diarization(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
   elif arch.endswith('''ForXVector'''	):
      _UpperCAmelCase											=      convert_xvector(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
   else:
      raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''	)

   if hf_config.use_weighted_layer_sum:
      _UpperCAmelCase											=      checkpoint['''Featurizer''']['''weights''']

   hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_	)
   hf_model.save_pretrained(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
   UpperCAmelCase_											=  argparse.ArgumentParser()
   parser.add_argument(
       "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
   )
   parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
   parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
   parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
   UpperCAmelCase_											=  parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)