import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


_SCREAMING_SNAKE_CASE										=						'\\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'

_SCREAMING_SNAKE_CASE										=						'\\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'

_SCREAMING_SNAKE_CASE										=						'\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 				a							(			datasets.Metric       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        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		,				lowerCAmelCase_							)  ->					Tuple:
        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		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_=0.9		,				lowerCAmelCase_=3		,				lowerCAmelCase_=0.5							)  ->					Any:
        if NLTK_VERSION >= version.Version("""3.6.5"""							):
               _A        =  [
                   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:
               _A        =  [
                   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_							)}





import random
from typing import Any






def  snake_case  (       snake_case__			:list)						->   list[Any]:
       for _ in range(len(snake_case__)):
              _A        =  random.randint(0   ,	len(snake_case__) - 1)
              _A        =  random.randint(0   ,	len(snake_case__) - 1)
              _A   ,  _A        =  data[b], data[a]
       return data


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						[0, 1, 2, 3, 4, 5, 6, 7]
    _SCREAMING_SNAKE_CASE										=						['python', 'says', 'hello', '!']
    print('Fisher-Yates Shuffle:')
    print('List', integers, strings)
    print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))




