





'''simple docstring'''







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


lowerCAmelCase_					:    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'

lowerCAmelCase_					:    Optional[Any]      	=			'\\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_					:    Tuple      	=			'\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 				__SCREAMING_SNAKE_CASE  (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     :					Union[str, Any]					,				__a     :					int					):
								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     :					Optional[Any]					,				__a     :					int					,				__a     :					Any=0.9					,				__a     :					int=3					,				__a     :					Union[str, Any]=0.5					):
								if NLTK_VERSION >= version.Version("3.6.5"					):
												_a								= [
												    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:
												_a								= [
												    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					)}







'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      bytes						)	->	str:
				return "".join([hex(lowercase						)[2:].zfill(2						).upper() for byte in list(lowercase						)]						)






def  _lowerCamelCase				( lowercase						:      str						)	->	bytes:
				# Check data validity, following RFC3548
				# https://www.ietf.org/rfc/rfc3548.txt
				if (len(lowercase						) % 2) != 0:
								raise ValueError(
								    "Base16 encoded data is invalid:\nData does not have an even number of hex digits."						)
				# Check the character set - the standard base16 alphabet
				# is uppercase according to RFC3548 section 6
				if not set(lowercase						) <= set("0123456789ABCDEF"						):
								raise ValueError(
								    "Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters."						)
				# For every two hexadecimal digits (= a byte), turn it into an integer.
				# Then, string the result together into bytes, and return it.
				return bytes(int(data[i] + data[i + 1]    ,							16						) for i in range(0    ,							len(lowercase						)    ,							2						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
