

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_       		=  "\\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"

lowercase_       		=  "\\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"

lowercase_       		=  "\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 						snake_case__     (      self :       str		)->    str:





        '''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 						snake_case__     (      self :       Union[str, Any],lowercase_ :       Optional[int]		)->    Any:





        '''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 						snake_case__     (      self :       int,lowercase_ :       str,lowercase_ :       Tuple,lowercase_ :       Any=0.9,lowercase_ :       Union[str, Any]=3,lowercase_ :       Tuple=0.5		)->    Union[str, Any]:





        '''simple docstring'''







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

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








'''simple docstring'''


import pytest

from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str] ):
   monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"	,set() )
@pytest.fixture
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Any ):
   class       a_   :





        def __init__( self		:					int	,      lowercase		:					int						):





           """simple docstring"""




           lowercase_			:Optional[Any]    			= metric_id







   class       a_   :
        __A      =			[MetricMock(_lowerCAmelCase							) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]





        def 							lowercase__      ( self		:					Union[str, Any]						):





           """simple docstring"""




           return self._metrics







   monkeypatch.setattr("datasets.inspect.huggingface_hub"	,HfhMock() )


@pytest.mark.parametrize(
    "func, args"	,[(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					int	,__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					Tuple ):
   if "tmp_path" in args:
      lowercase_			:Union[str, Any]    			= tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
   with pytest.warns(__lowerCamelCase	,match="https://huggingface.co/docs/evaluate" ):
      func(*__lowerCamelCase )


