





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


__UpperCAmelCase	:       Optional[Any]    	=							version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
      from nltk import word_tokenize


__UpperCAmelCase	:       List[Any]    	=							"\\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"

__UpperCAmelCase	:       Tuple    	=							"\\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"

__UpperCAmelCase	:       Dict    	=							"\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    __snake_case	(							datasets.Metric      ):





       '''simple docstring'''







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

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













from __future__ import annotations

import numpy as np







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   List[str]:
  return np.maximum(0 , SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]






