





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


_snake_case  :    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'

_snake_case  :    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'

_snake_case  :    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 				_UpperCAmelCase    (    datasets.Metric    ):

  """simple docstring"""


  def 				lowercase		(				self :		Optional[Any]     )							->						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 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		Optional[Any]     )							->						List[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 				lowercase		(				self :		int					,  lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		Union[str, Any]=0.9					,  lowerCAmelCase_ :		List[Any]=3					,  lowerCAmelCase_ :		Tuple=0.5     )							->						List[Any]:
     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_     )}








import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_snake_case  :    Tuple						    =  importlib.util.find_spec('s3fs') is not None

if _has_safs:
     from .safilesystem import SaFileSystem  # noqa: F401

_snake_case  :    List[compression.BaseCompressedFileFileSystem]						    =  [
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
     if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
          warnings.warn(F"""A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.""")
     fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)

def   a_    ( lowerCAmelCase_      :			str	):
   if "://" in dataset_path:
      __lowerCAmelCase								=	dataset_path.split('://'	)[1]
   return dataset_path

def   a_    ( lowerCAmelCase_      :			fsspec.AbstractFileSystem	):
   if fs is not None and fs.protocol != "file":
      return True
   else:
      return False

def   a_    ( lowerCAmelCase_      :			fsspec.AbstractFileSystem,	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			str	):
   __lowerCAmelCase								=	not is_remote_filesystem(lowerCAmelCase_	)
   if is_local:
      # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
      shutil.move(fs._strip_protocol(lowerCAmelCase_	),	fs._strip_protocol(lowerCAmelCase_	)	)
   else:
      fs.mv(lowerCAmelCase_,	lowerCAmelCase_,	recursive=lowerCAmelCase_	)




def   a_    ( ):
   if hasattr(fsspec.asyn,	'reset_lock'	):
      # for future fsspec>2022.05.0
      fsspec.asyn.reset_lock()
   else:
      __lowerCAmelCase								=	None
      __lowerCAmelCase								=	None
      __lowerCAmelCase								=	threading.Lock()

