





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


snake_case								=							"""\
@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},
}
"""

snake_case								=							"""\
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.
"""

snake_case								=							"""
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 					A_					(   datasets.Metric						):







       """simple docstring"""





       def  __UpperCAmelCase			(     self						:						List[str]	)    ->       Optional[Any]:
         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						:						int    ,__A						:						str	)    ->       Union[str, Any]:
         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						:						str    ,__A						:						Any    ,__A						:						Optional[Any]    ,__A						:						Any=0.9    ,__A						:						int=3    ,__A						:						Optional[Any]=0.5	)    ->       List[str]:




         if NLTK_VERSION >= version.Version('3.6.5'	):
           _lowercase						    =			[
               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:
           _lowercase						    =			[
               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_	)}



import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


__snake_case   							=						get_tests_dir('''fixtures''')



class 	__lowerCamelCase			(unittest.TestCase						):







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            __UpperCamelCase =			mock.Mock()
            __UpperCamelCase =			500
            __UpperCamelCase =			{}
            __UpperCamelCase =			HTTPError
            __UpperCamelCase =			{}

            # Download this model to make sure it's in the cache.
            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'					)
            # Under the mock environment we get a 500 error when trying to reach the model.
            with mock.patch('requests.Session.request',return_value=A_					) as mock_head:
                 __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'					)
                 # This check we did call the fake head request
                 mock_head.assert_called()







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(
                'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'					)



@is_staging_test
class 	__lowerCamelCase			(unittest.TestCase						):







       @classmethod
       def 		snake_case_	(			cls: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			TOKEN
            HfFolder.save_token(A_					)







       @classmethod
       def 		snake_case_	(			cls: Tuple					):






            '''simple docstring'''




            try:
                 delete_repo(token=cls._token,repo_id='test-feature-extractor'					)
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token,repo_id='valid_org/test-feature-extractor-org'					)
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token,repo_id='test-dynamic-feature-extractor'					)
            except HTTPError:
                 pass







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(A_					)
            feature_extractor.push_to_hub('test-feature-extractor',use_auth_token=self._token					)

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)

            # Reset repo
            delete_repo(token=self._token,repo_id='test-feature-extractor'					)

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     A_,repo_id='test-feature-extractor',push_to_hub=A_,use_auth_token=self._token					)

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(A_					)
            feature_extractor.push_to_hub('valid_org/test-feature-extractor',use_auth_token=self._token					)

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)

            # Reset repo
            delete_repo(token=self._token,repo_id='valid_org/test-feature-extractor'					)

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     A_,repo_id='valid_org/test-feature-extractor-org',push_to_hub=A_,use_auth_token=self._token					)

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            CustomFeatureExtractor.register_for_auto_class()
            __UpperCamelCase =			CustomFeatureExtractor.from_pretrained(A_					)

            feature_extractor.push_to_hub('test-dynamic-feature-extractor',use_auth_token=self._token					)

            # This has added the proper auto_map field to the config
            self.assertDictEqual(
                feature_extractor.auto_map,{'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'},)

            __UpperCamelCase =			AutoFeatureExtractor.from_pretrained(
                F'''{USER}/test-dynamic-feature-extractor''',trust_remote_code=A_					)
            # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
            self.assertEqual(new_feature_extractor.__class__.__name__,'CustomFeatureExtractor'					)


