


'''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 					snake_case   :





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  key
   lowerCamelCase_								=  freq







  def __str__(   self					):







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





def __snake_case (				UpperCAmelCase_						:						List[Any] ,    UpperCAmelCase_						:						str ,    UpperCAmelCase_						:						Optional[Any] ,    UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						int ,    UpperCAmelCase_						:						Tuple		):
 if i > j or i < 0 or j > len(UpperCAmelCase_		) - 1:
  return

 lowerCamelCase_								=  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 __snake_case (				UpperCAmelCase_						:						List[Any]		):
 nodes.sort(key=lambda UpperCAmelCase_		: node.key		)

 lowerCamelCase_								=  len(UpperCAmelCase_		)

 lowerCamelCase_								=  [nodes[i].key for i in range(UpperCAmelCase_		)]
 lowerCamelCase_								=  [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.
 lowerCamelCase_								=  [[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
 lowerCamelCase_								=  [[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
 lowerCamelCase_								=  [[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		):
   lowerCamelCase_								=  i + interval_length - 1

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

    if dp[i][j] > cost:
     lowerCamelCase_								=  cost
     lowerCamelCase_								=  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 __snake_case (				):
 # A sample binary search tree
 lowerCamelCase_								=  [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 csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
a_  :    Any      =			"""\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
a_  :    Optional[Any]      =			"""\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
a_  :    List[str]      =			""""""

# TODO: Add the licence for the dataset here if you can find it
a_  :    Dict      =			""""""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
a_  :    Optional[int]      =			{
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}





class 					snake_case   (   datasets.GeneratorBasedBuilder      ):





  """simple docstring"""




  _lowerCamelCase		   =  datasets.Version("1.1.0"      )

  # This is an example of a dataset with multiple configurations.
  # If you don't want/need to define several sub-sets in your dataset,
  # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

  # If you need to make complex sub-parts in the datasets with configurable options
  # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
  # BUILDER_CONFIG_CLASS = MyBuilderConfig

  # You will be able to load one or the other configurations in the following list with
  # data = datasets.load_dataset('my_dataset', 'first_domain')
  # data = datasets.load_dataset('my_dataset', 'second_domain')
  _lowerCamelCase		   =  [
      datasets.BuilderConfig(name="first_domain"      ,						version=lowercase      ,						description="This part of my dataset covers a first domain"      ),
      datasets.BuilderConfig(name="second_domain"      ,						version=lowercase      ,						description="This part of my dataset covers a second domain"      ),
  ]

  _lowerCamelCase		   =  "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.






  def   snake_case		(   self					):







   """simple docstring"""
   # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
   if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
    lowerCamelCase_								=  datasets.Features(
        {
            "sentence": datasets.Value("string"					),
            "option1": datasets.Value("string"					),
            "answer": datasets.Value("string"					)
            # These are the features of your dataset like images, labels ...
        }					)
   else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
    lowerCamelCase_								=  datasets.Features(
        {
            "sentence": datasets.Value("string"					),
            "option2": datasets.Value("string"					),
            "second_domain_answer": datasets.Value("string"					)
            # These are the features of your dataset like images, labels ...
        }					)
   return datasets.DatasetInfo(
       # This is the description that will appear on the datasets page.
       description=_DESCRIPTION      ,  features=UpperCamelCase      ,  homepage=_HOMEPAGE      ,  license=_LICENSE      ,  citation=_CITATION      ,  )






  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
   # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

   # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
   # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
   # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
   lowerCamelCase_								=  _URLS[self.config.name]
   lowerCamelCase_								=  dl_manager.download_and_extract(UpperCamelCase					)
   return [
       datasets.SplitGenerator(
           name=datasets.Split.TRAIN      ,  gen_kwargs={
               "filepath": os.path.join(UpperCamelCase      ,  "train.jsonl"					),
               "split": "train",
           }      ,  ),
       datasets.SplitGenerator(
           name=datasets.Split.VALIDATION      ,  gen_kwargs={
               "filepath": os.path.join(UpperCamelCase      ,  "dev.jsonl"					),
               "split": "dev",
           }      ,  ),
       datasets.SplitGenerator(
           name=datasets.Split.TEST      ,  gen_kwargs={
               "filepath": os.path.join(UpperCamelCase      ,  "test.jsonl"					),
               "split": "test"
           }      ,  ),
   ]







  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
   # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
   with open(UpperCamelCase      ,  encoding="utf-8"					) as f:
    for key, row in enumerate(UpperCamelCase					):
     lowerCamelCase_								=  json.loads(UpperCamelCase					)
     if self.config.name == "first_domain":
      # Yields examples as (key, example) tuples
      yield key, {
          "sentence": data["sentence"],
          "option1": data["option1"],
          "answer": "" if split == "test" else data["answer"],
      }
     else:
      yield key, {
          "sentence": data["sentence"],
          "option2": data["option2"],
          "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
      }



