





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


_lowerCamelCase   				=			'\\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'

_lowerCamelCase   				=			'\\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'

_lowerCamelCase   				=			'\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    ):


							def      _snake_case  (							self    :Union[str, Any]		)   ->   Dict:



									"""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    :Dict							,				__A    :Optional[Any]		)   ->   Union[str, 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							,				__A    :Any							,				__A    :Optional[Any]							,				__A    :Optional[int]=0.9							,				__A    :List[str]=3							,				__A    :Any=0.5		)   ->   Optional[Any]:



									"""simple docstring"""



									if NLTK_VERSION >= version.Version("""3.6.5"""		):
											SCREAMING_SNAKE_CASE__			=      [
											    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:
											SCREAMING_SNAKE_CASE__			=      [
											    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		)}






import warnings
from functools import wraps
from typing import Callable






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Callable    ):

		@wraps(UpperCamelCase__    )
		def _inner_fn(*UpperCamelCase__:							Dict					,   **UpperCamelCase__:							Any    ):
				warnings.warn(
				    (f'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')					,   UpperCamelCase__					,   )
				return fn(*UpperCamelCase__					,   **UpperCamelCase__    )

		return _inner_fn