






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


__A							:							Tuple  							=		version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
 from nltk import word_tokenize


__A							:							Dict  							=		'\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n'

__A							:							Optional[int]  							=		'\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'

__A							:							Optional[Any]  							=		'\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results["meteor"], 4))\n    0.6944\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,       _KWARGS_DESCRIPTION      )
class 				lowercase       (							datasets.Metric      ):


    '''simple docstring'''

    def    a__				(					self     :     Optional[int]     )							->							Dict:

     '''simple docstring'''




     return datasets.MetricInfo(
         description=_DESCRIPTION				,     citation=_CITATION				,     inputs_description=_KWARGS_DESCRIPTION				,     features=datasets.Features(
             {
                 "predictions": datasets.Value("string"				,     id="sequence"     ),
                 "references": datasets.Value("string"				,     id="sequence"     ),
             }     )				,     codebase_urls=["https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"]				,     reference_urls=[
             "https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score",
             "https://en.wikipedia.org/wiki/METEOR",
         ]				,     )

    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     Any     )							->							List[str]:

     '''simple docstring'''




     import nltk

     nltk.download("wordnet"     )
     if NLTK_VERSION >= version.Version("3.6.5"     ):
      nltk.download("punkt"     )
     if NLTK_VERSION >= version.Version("3.6.6"     ):
      nltk.download("omw-1.4"     )




    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     Any				,     __lowerCamelCase     :     List[str]=0.9				,     __lowerCamelCase     :     Any=3				,     __lowerCamelCase     :     int=0.5     )							->							int:

     '''simple docstring'''




     if NLTK_VERSION >= version.Version("3.6.5"     ):
      lowerCamelCase__  =     [
          meteor_score.single_meteor_score(
              word_tokenize(__lowerCamelCase     )				,     word_tokenize(__lowerCamelCase     )				,     alpha=__lowerCamelCase				,     beta=__lowerCamelCase				,     gamma=__lowerCamelCase     )
          for ref, pred in zip(__lowerCamelCase				,     __lowerCamelCase     )
      ]
     else:
      lowerCamelCase__  =     [
          meteor_score.single_meteor_score(__lowerCamelCase				,     __lowerCamelCase				,     alpha=__lowerCamelCase				,     beta=__lowerCamelCase				,     gamma=__lowerCamelCase     )
          for ref, pred in zip(__lowerCamelCase				,     __lowerCamelCase     )
      ]

     return {"meteor": np.mean(__lowerCamelCase     )}









'''simple docstring'''





class 				lowercase       :


    '''simple docstring'''

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

     '''simple docstring'''




     lowerCamelCase__  =     name
     lowerCamelCase__  =     val

    def __str__(					self     :     Tuple     )							->							Union[str, Any]:

     '''simple docstring'''




     return f'''{self.__class__.__name__}({self.name}, {self.val})'''




    def __lt__(					self     :     List[str]				,     __lowerCamelCase     :     List[str]     )							->							str:

     '''simple docstring'''




     return self.val < other.val
class 				lowercase       :


    '''simple docstring'''

    def __init__(					self     :     int				,     __lowerCamelCase     :     Optional[Any]     )							->							List[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     {}
     lowerCamelCase__  =     {}
     lowerCamelCase__  =     self.build_heap(__lowerCamelCase     )

    def __getitem__(					self     :     Tuple				,     __lowerCamelCase     :     Optional[int]     )							->							Tuple:

     '''simple docstring'''




     return self.get_value(__lowerCamelCase     )

    def    a__				(					self     :     str				,     __lowerCamelCase     :     int     )							->							Optional[Any]:

     '''simple docstring'''




     return (idx - 1) // 2

    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     List[Any]     )							->							Optional[int]:

     '''simple docstring'''




     return idx * 2 + 1

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     str     )							->							Tuple:

     '''simple docstring'''




     return idx * 2 + 2

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     int     )							->							Dict:

     '''simple docstring'''




     return self.heap_dict[key]

    def    a__				(					self     :     str				,     __lowerCamelCase     :     List[str]     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     len(__lowerCamelCase     ) - 1
     lowerCamelCase__  =     self.get_parent_idx(__lowerCamelCase     )

     for idx, i in enumerate(__lowerCamelCase     ):
      lowerCamelCase__  =     idx
      lowerCamelCase__  =     i.val

     for i in range(__lowerCamelCase				,     -1				,     -1     ):
      self.sift_down(__lowerCamelCase				,     __lowerCamelCase     )
     return array

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     Optional[int]     )							->							int:

     '''simple docstring'''




     while True:
      lowerCamelCase__  =     self.get_left_child_idx(__lowerCamelCase     )  # noqa: E741
      lowerCamelCase__  =     self.get_right_child_idx(__lowerCamelCase     )

      lowerCamelCase__  =     idx
      if l < len(__lowerCamelCase     ) and array[l] < array[idx]:
       lowerCamelCase__  =     l
      if r < len(__lowerCamelCase     ) and array[r] < array[smallest]:
       lowerCamelCase__  =     r

      if smallest != idx:
       lowerCamelCase__  ,	lowerCamelCase__  =     array[smallest], array[idx]
       (
           (
           lowerCamelCase__
       )  ,	(
           lowerCamelCase__
       )  ,	
       )  =     (
           self.idx_of_element[array[smallest]],
           self.idx_of_element[array[idx]],
       )
       lowerCamelCase__  =     smallest
      else:
       break

    def    a__				(					self     :     str				,     __lowerCamelCase     :     Optional[int]     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     self.get_parent_idx(__lowerCamelCase     )
     while p >= 0 and self.heap[p] > self.heap[idx]:
      lowerCamelCase__  ,	lowerCamelCase__  =     self.heap[idx], self.heap[p]
      lowerCamelCase__  ,	lowerCamelCase__  =     (
          self.idx_of_element[self.heap[idx]],
          self.idx_of_element[self.heap[p]],
      )
      lowerCamelCase__  =     p
      lowerCamelCase__  =     self.get_parent_idx(__lowerCamelCase     )

    def    a__				(					self     :     Union[str, Any]     )							->							List[Any]:

     '''simple docstring'''




     return self.heap[0]

    def    a__				(					self     :     Tuple     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  =     self.heap[-1], self.heap[0]
     lowerCamelCase__  ,	lowerCamelCase__  =     (
         self.idx_of_element[self.heap[-1]],
         self.idx_of_element[self.heap[0]],
     )

     lowerCamelCase__  =     self.heap.pop()
     del self.idx_of_element[x]
     self.sift_down(0				,     self.heap     )
     return x

    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     Tuple     )							->							List[Any]:

     '''simple docstring'''




     self.heap.append(__lowerCamelCase     )
     lowerCamelCase__  =     len(self.heap     ) - 1
     lowerCamelCase__  =     node.val
     self.sift_up(len(self.heap     ) - 1     )

    def    a__				(					self     :     Dict     )							->							Any:

     '''simple docstring'''




     return len(self.heap     ) == 0




    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     Union[str, Any]     )							->							Tuple:

     '''simple docstring'''




     assert (
         self.heap[self.idx_of_element[node]].val > new_value
     ), "newValue must be less that current value"
     lowerCamelCase__  =     new_value
     lowerCamelCase__  =     new_value
     self.sift_up(self.idx_of_element[node]     )
__A							:							str  							=		Node("""R""", -1)
__A							:							Dict  							=		Node("""B""", 6)
__A							:							Tuple  							=		Node("""A""", 3)
__A							:							str  							=		Node("""X""", 1)
__A							:							Optional[Any]  							=		Node("""E""", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
__A							:							Tuple  							=		MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("""Min Heap - before decrease key""")
for i in my_min_heap.heap:
 print(i)

print("""Min Heap - After decrease key of node [B -> -17]""")
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
 print(i)

if __name__ == "__main__":
 import doctest

 doctest.testmod()

