


'''simple docstring'''



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 ):




	def 				__UpperCAmelCase							(       self							):

						'''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 				__UpperCAmelCase							(       self						,    __a							):

						'''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 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a=0.9						,    __a=3						,    __a=0.5							):

						'''simple docstring'''





						if NLTK_VERSION >= version.Version('3.6.5'							):
											__a      :	str    =  [
											    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      :	Tuple    =  [
											    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'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowercase     :						Union[str, Any]	=     {
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	pass

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						List[str]	=     [
	    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RoCBertForCausalLM',
	    'RoCBertForMaskedLM',
	    'RoCBertForMultipleChoice',
	    'RoCBertForPreTraining',
	    'RoCBertForQuestionAnswering',
	    'RoCBertForSequenceClassification',
	    'RoCBertForTokenClassification',
	    'RoCBertLayer',
	    'RoCBertModel',
	    'RoCBertPreTrainedModel',
	    'load_tf_weights_in_roc_bert',
	]

if TYPE_CHECKING:
	from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
	from .tokenization_roc_bert import RoCBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		raise OptionalDependencyNotAvailable()

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roc_bert import (
		    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoCBertForCausalLM,
		    RoCBertForMaskedLM,
		    RoCBertForMultipleChoice,
		    RoCBertForPreTraining,
		    RoCBertForQuestionAnswering,
		    RoCBertForSequenceClassification,
		    RoCBertForTokenClassification,
		    RoCBertLayer,
		    RoCBertModel,
		    RoCBertPreTrainedModel,
		    load_tf_weights_in_roc_bert,
		)


else:
	import sys

	__lowercase     :						Any	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



