



from collections.abc import Iterable
from typing import Any






class 		lowercase						:






 """simple docstring"""


 def __init__(     self     :						List[Any]						,  __UpperCAmelCase     :						int | None = None					)      ->			Optional[Any]:
       UpperCAmelCase_=       value
       UpperCAmelCase_=       None  # Added in order to delete a node easier
       UpperCAmelCase_=       None
       UpperCAmelCase_=       None




 def __repr__(     self     :						int					)      ->			str:
       from pprint import pformat

       if self.left is None and self.right is None:
             return str(self.value					)
       return pformat({F"""{self.value}""": (self.left, self.right)}						,  indent=1					)






class 		lowercase						:






 """simple docstring"""


 def __init__(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Node | None = None					)      ->			str:
       UpperCAmelCase_=       root




 def __str__(     self     :						Dict					)      ->			str:
       return str(self.root					)




 def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						Node						,  __UpperCAmelCase     :						Node | None					)      ->			None:


       if new_children is not None:  # reset its kids
             UpperCAmelCase_=       node.parent
       if node.parent is not None:  # reset its parent
             if self.is_right(__UpperCAmelCase					):  # If it is the right children
                   UpperCAmelCase_=       new_children
             else:
                   UpperCAmelCase_=       new_children
       else:
             UpperCAmelCase_=       new_children




 def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						Node					)      ->			bool:


       if node.parent and node.parent.right:
             return node == node.parent.right
       return False




 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			bool:
       return self.root is None




 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						Dict					)      ->			None:
       UpperCAmelCase_=       Node(__UpperCAmelCase					)  # create a new Node
       if self.empty():  # if Tree is empty
             UpperCAmelCase_=       new_node  # set its root
       else:  # Tree is not empty
             UpperCAmelCase_=       self.root  # from root
             if parent_node is None:
                   return
             while True:  # While we don't get to a leaf
                   if value < parent_node.value:  # We go left
                         if parent_node.left is None:
                               UpperCAmelCase_=       new_node  # We insert the new node in a leaf
                               break
                         else:
                               UpperCAmelCase_=       parent_node.left
                   else:
                         if parent_node.right is None:
                               UpperCAmelCase_=       new_node
                               break
                         else:
                               UpperCAmelCase_=       parent_node.right
             UpperCAmelCase_=       parent_node




 def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  *__UpperCAmelCase     :						str					)      ->			None:


       for value in values:
             self.__insert(__UpperCAmelCase					)




 def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						List[str]					)      ->			Node | None:


       if self.empty():
             raise IndexError("""Warning: Tree is empty! please use another."""					)
       else:
             UpperCAmelCase_=       self.root
             # use lazy evaluation here to avoid NoneType Attribute error
             while node is not None and node.value is not value:
                   UpperCAmelCase_=       node.left if value < node.value else node.right
             return node




 def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						Node | None = None					)      ->			Node | None:


       if node is None:
             if self.root is None:
                   return None
             UpperCAmelCase_=       self.root

       if not self.empty():
             while node.right is not None:
                   UpperCAmelCase_=       node.right
       return node




 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Node | None = None					)      ->			Node | None:


       if node is None:
             UpperCAmelCase_=       self.root
       if self.root is None:
             return None
       if not self.empty():
             UpperCAmelCase_=       self.root
             while node.left is not None:
                   UpperCAmelCase_=       node.left
       return node




 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						int					)      ->			None:
       UpperCAmelCase_=       self.search(__UpperCAmelCase					)  # Look for the node with that label
       if node is not None:
             if node.left is None and node.right is None:  # If it has no children
                   self.__reassign_nodes(__UpperCAmelCase						,  __UpperCAmelCase					)
             elif node.left is None:  # Has only right children
                   self.__reassign_nodes(__UpperCAmelCase						,  node.right					)
             elif node.right is None:  # Has only left children
                   self.__reassign_nodes(__UpperCAmelCase						,  node.left					)
             else:
                   UpperCAmelCase_=       self.get_max(
                       node.left					)  # Gets the max value of the left branch
                   self.remove(tmp_node.value					)  # type: ignore
                   UpperCAmelCase_=       (
                       tmp_node.value  # type: ignore
                   )  # Assigns the value to the node to delete and keep tree structure




 def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						Node | None					)      ->			Iterable:


       if node is not None:
             yield node  # Preorder Traversal
             yield from self.preorder_traverse(node.left					)
             yield from self.preorder_traverse(node.right					)




 def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						str=None					)      ->			Any:


       if traversal_function is None:
             return self.preorder_traverse(self.root					)
       else:
             return traversal_function(self.root					)




 def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						list						,  __UpperCAmelCase     :						Node | None					)      ->			None:


       if node:
             self.inorder(__UpperCAmelCase						,  node.left					)
             arr.append(node.value					)
             self.inorder(__UpperCAmelCase						,  node.right					)




 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						Node					)      ->			int:
       UpperCAmelCase_=       []
       self.inorder(__UpperCAmelCase						,  __UpperCAmelCase					)  # append all values to list using inorder traversal
       return arr[k - 1]



def 	__a					(   lowerCAmelCase_       : Node | None    )				->			list[Node]:






      '''simple docstring'''



      UpperCAmelCase_=       []
      if curr_node is not None:
            UpperCAmelCase_=       postorder(curr_node.left    ) + postorder(curr_node.right    ) + [curr_node]
      return node_list



def 	__a					(   )				->			None:






      '''simple docstring'''



      UpperCAmelCase_=       (8, 3, 6, 1, 10, 14, 13, 4, 7)
      UpperCAmelCase_=       BinarySearchTree()
      for i in testlist:
            t.insert(lowerCAmelCase_    )

      # Prints all the elements of the list in order traversal
      print(lowerCAmelCase_    )

      if t.search(6    ) is not None:
            print("""The value 6 exists"""    )
      else:
            print("""The value 6 doesn\'t exist"""    )

      if t.search(-1    ) is not None:
            print("""The value -1 exists"""    )
      else:
            print("""The value -1 doesn\'t exist"""    )

      if not t.empty():
            print("""Max Value: """      ,t.get_max().value    )  # type: ignore
            print("""Min Value: """      ,t.get_min().value    )  # type: ignore

      for i in testlist:
            t.remove(lowerCAmelCase_    )
            print(lowerCAmelCase_    )


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)









from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__A       =						'''\
@misc{wu2016googles,
      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
'''

__A       =						'''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''

__A       =						'''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    \'google_bleu\': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.44

    Example 2:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.61

    Example 3:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results["google_bleu"], 2))
        0.53

    Example 4:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results["google_bleu"], 2))
        0.4
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,   _KWARGS_DESCRIPTION)
class 		lowercase						(			datasets.Metric):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			MetricInfo:
							return datasets.MetricInfo(
							    description=_DESCRIPTION						,  citation=_CITATION						,  inputs_description=_KWARGS_DESCRIPTION						,  features=datasets.Features(
							        {
							            """predictions""": datasets.Sequence(datasets.Value("""string"""						,  id="""token"""					)						,  id="""sequence"""					),
							            """references""": datasets.Sequence(
							                datasets.Sequence(datasets.Value("""string"""						,  id="""token"""					)						,  id="""sequence"""					)						,  id="""references"""					),
							        }					)						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						List[List[List[str]]]						,  __UpperCAmelCase     :						List[List[str]]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						int = 4						,  )      ->			Dict[str, float]:
							return {
							    "google_bleu": gleu_score.corpus_gleu(
							        list_of_references=__UpperCAmelCase						,  hypotheses=__UpperCAmelCase						,  min_len=__UpperCAmelCase						,  max_len=__UpperCAmelCase					)
							}




