


"""simple docstring"""







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


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

__lowercase    =				"""\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.

METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
"""

__lowercase    =				"""
Computes METEOR score of translated segments against one or more references.
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
    gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
    'meteor': meteor score.
Examples:

    >>> meteor = datasets.load_metric('meteor')
    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]
    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]
    >>> results = meteor.compute(predictions=predictions, references=references)
    >>> print(round(results[\"meteor\"], 4))
    0.6944
"""






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







    """simple docstring"""




    def      __snake_case							( self    :   List[str]):
      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      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   Optional[Any]):
      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      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   Union[str, Any]    ,			__UpperCAmelCase    :   Optional[Any]    ,			__UpperCAmelCase    :   List[Any]=0.9    ,			__UpperCAmelCase    :   List[str]=3    ,			__UpperCAmelCase    :   Union[str, Any]=0.5):
      if NLTK_VERSION >= version.Version("3.6.5"):
        a						:		Optional[Any]											=      [
            meteor_score.single_meteor_score(
                word_tokenize(__UpperCAmelCase)    ,			word_tokenize(__UpperCAmelCase)    ,			alpha=__UpperCAmelCase    ,			beta=__UpperCAmelCase    ,			gamma=__UpperCAmelCase)
            for ref, pred in zip(__UpperCAmelCase    ,			__UpperCAmelCase)
        ]
      else:
        a						:		Tuple											=      [
            meteor_score.single_meteor_score(__UpperCAmelCase    ,			__UpperCAmelCase    ,			alpha=__UpperCAmelCase    ,			beta=__UpperCAmelCase    ,			gamma=__UpperCAmelCase)
            for ref, pred in zip(__UpperCAmelCase    ,			__UpperCAmelCase)
        ]

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










"""simple docstring"""







from __future__ import annotations






class 						_A  :







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   List[Any]=None):
      a						:		int											=      data
      a						:		Dict											=      None






    def __repr__( self    :   Dict):
      a						:		List[Any]											=      []
      a						:		str											=      self
      while temp:
        string_rep.append(f'''{temp.data}''')
        a						:		Tuple											=      temp.next
      return "->".join(__UpperCAmelCase)






def 					lowercase      (    A_	)->				Any:
  '''simple docstring'''







  if not elements_list:
    raise Exception("The Elements List is empty"	)

  a						:		Any											=      Node(elements_list[0]	)
  for i in range(1  ,       len(A_	)	):
    a						:		int											=      Node(elements_list[i]	)
    a						:		Optional[Any]											=      current.next
  return head


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if head_node is not None and isinstance(A_  ,       A_	):
    print_reverse(head_node.next	)
    print(head_node.data	)


def 					lowercase      (    )->				List[Any]:
  '''simple docstring'''







  from doctest import testmod

  testmod()

  a						:		Union[str, Any]											=      make_linked_list([14, 52, 14, 12, 43]	)
  print("Linked List:"	)
  print(A_	)
  print("Elements in Reverse:"	)
  print_reverse(A_	)


if __name__ == "__main__":
 main()






