






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 					UpperCamelCase_ (     datasets.Metric							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       str:
								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			,     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			,     a			,     a			,     a=0.9			,     a=3			,     a=0.5   )			->       List[Any]:
								if NLTK_VERSION >= version.Version('3.6.5'   ):
															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:
															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   )}













from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=snake_case_							)
class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					field(default='''language-modeling'''			,   metadata={'''include_in_asdict_even_if_is_default''': True}							)

	lowerCAmelCase						=					Features({'''text''': Value('''string'''							)}							)
	lowerCAmelCase						=					Features({}							)
	lowerCAmelCase						=					"text"




	@property
	def 				_UpperCamelCase     (			self   )			->       Dict[str, str]:
								return {self.text_column: "text"}





