"""simple docstring"""




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_a   :	Optional[Any] 					=     version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
     from nltk import word_tokenize


_a   :	Optional[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'

_a   :	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'

_a   :	Optional[Any] 					=     '\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 					__A						(	self						):
       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 					__A						(	self       ,  a__						):
       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 					__A						(	self       ,  a__       ,  a__       ,  a__=0.9       ,  a__=3       ,  a__=0.5						):
       if NLTK_VERSION >= version.Version("""3.6.5"""						):
         _lowerCAmelCase		: List[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:
         _lowerCAmelCase		: 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"""




import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


_a   :	Tuple 					=     logging.get_logger(__name__)
class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def __init__(	self       ,  *a__       ,  **a__						):
       warnings.warn(
           """The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
           """ Please use SegformerImageProcessor instead."""       ,  a__       ,  )
       super().__init__(*a__       ,  **a__						)




