


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase	=							{'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase	=							['ReformerTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase	=							['ReformerTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase	=							[
			    'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'ReformerAttention',
			    'ReformerForMaskedLM',
			    'ReformerForQuestionAnswering',
			    'ReformerForSequenceClassification',
			    'ReformerLayer',
			    'ReformerModel',
			    'ReformerModelWithLMHead',
			    'ReformerPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_reformer import ReformerTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_reformer_fast import ReformerTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_reformer import (
						    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ReformerAttention,
						    ReformerForMaskedLM,
						    ReformerForQuestionAnswering,
						    ReformerForSequenceClassification,
						    ReformerLayer,
						    ReformerModel,
						    ReformerModelWithLMHead,
						    ReformerPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase	=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





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 	_a							(      datasets.Metric      ):

							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Union[str, Any]:





									"""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 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     List[str]							)      ->       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 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Dict=0.9   ,						UpperCamelCase_:     Union[str, Any]=3   ,						UpperCamelCase_:     Optional[int]=0.5							)      ->       Dict:





									"""simple docstring"""







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

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


