





"""simple docstring"""





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


__A											=		version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
  from nltk import word_tokenize


__A											=		"""\
@inproceedings{banarjee2005,
  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
  author    = {Banerjee, Satanjeev  and Lavie, Alon},
  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
  month     = jun,
  year      = {2005},
  address   = {Ann Arbor, Michigan},
  publisher = {Association for Computational Linguistics},
  url       = {https://www.aclweb.org/anthology/W05-0909},
  pages     = {65--72},
}
"""

__A											=		"""\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.

METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
"""

__A											=		"""
Computes METEOR score of translated segments against one or more references.
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
    gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
    'meteor': meteor score.
Examples:

    >>> meteor = datasets.load_metric('meteor')
    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]
    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]
    >>> results = meteor.compute(predictions=predictions, references=references)
    >>> print(round(results[\"meteor\"], 4))
    0.6944
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,   _KWARGS_DESCRIPTION       )
class 					_lowerCAmelCase   (     datasets.Metric       ):
       """simple docstring"""




       def      snake_case						(    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      snake_case						(    self    , __UpperCAmelCase      ):



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



         '''simple docstring'''

         if NLTK_VERSION >= version.Version('3.6.5'      ):
           lowerCAmelCase__			:List[str]											=							[
               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:
           lowerCAmelCase__			:Tuple											=							[
               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      )}







"""simple docstring"""





import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
  import torch






@require_torch
@require_optimum
@slow
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:List[Any]											=							AutoTokenizer.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:str											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:Any											=							tokenizer('This is me'    , return_tensors='pt'      )

         lowerCAmelCase__			:Dict											=							model.to_bettertransformer()

         self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

         lowerCAmelCase__			:Optional[Any]											=							model.generate(**__UpperCAmelCase      )

         lowerCAmelCase__			:List[Any]											=							model.reverse_bettertransformer()

         self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

         with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(__UpperCAmelCase      )

           lowerCAmelCase__			:Any											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

           self.assertFalse(
               any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()      )      )

           lowerCAmelCase__			:Union[str, Any]											=							model_reloaded.generate(**__UpperCAmelCase      )
           self.assertTrue(torch.allclose(__UpperCAmelCase    , __UpperCAmelCase      )      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:Union[str, Any]											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							model.to_bettertransformer()

         with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(__UpperCAmelCase      ):
             model.save_pretrained(__UpperCAmelCase      )

           lowerCAmelCase__			:Optional[int]											=							model.reverse_bettertransformer()
           model.save_pretrained(__UpperCAmelCase      )
