





from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


a__			:    Tuple			      =		'\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n'

a__			:    Optional[Any]			      =		'\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n'


a__			:    Dict			      =		'\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\nExamples:\n    >>> sources=["About 95 species are currently accepted ."]\n    >>> predictions=["About 95 you now get in ."]\n    >>> references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]\n    >>> sari = datasets.load_metric("sari")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 26.953601953601954}\n'

def     UpperCAmelCase_						(						_UpperCAmelCase       :Dict     ,	_UpperCAmelCase       :Any     ,	_UpperCAmelCase       :Optional[int]     ,	_UpperCAmelCase       :Union[str, Any]							)  ->							List[Any]:
     '''simple docstring'''


     A_					      =					[rgram for rgrams in rgramslist for rgram in rgrams]
     A_					      =					Counter(_UpperCAmelCase							)

     A_					      =					Counter(_UpperCAmelCase							)
     A_					      =					Counter()
     for sgram, scount in sgramcounter.items():
          A_					      =					scount * numref

     A_					      =					Counter(_UpperCAmelCase							)
     A_					      =					Counter()
     for cgram, ccount in cgramcounter.items():
          A_					      =					ccount * numref

     # KEEP
     A_					      =					sgramcounter_rep & cgramcounter_rep
     A_					      =					keepgramcounter_rep & rgramcounter
     A_					      =					sgramcounter_rep & rgramcounter

     A_					      =					0
     A_					      =					0
     for keepgram in keepgramcountergood_rep:
          keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
          # Fix an alleged bug [2] in the keep score computation.
          # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
          keeptmpscorea += keepgramcountergood_rep[keepgram]
     # Define 0/0=1 instead of 0 to give higher scores for predictions that match
     #      a target exactly.
     A_					      =					1
     A_					      =					1
     if len(_UpperCAmelCase							) > 0:
          A_					      =					keeptmpscorea / len(_UpperCAmelCase							)
     if len(_UpperCAmelCase							) > 0:
          # Fix an alleged bug [2] in the keep score computation.
          # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
          A_					      =					keeptmpscorea / sum(keepgramcounterall_rep.values()							)
     A_					      =					0
     if keepscore_precision > 0 or keepscore_recall > 0:
          A_					      =					2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

     # DELETION
     A_					      =					sgramcounter_rep - cgramcounter_rep
     A_					      =					delgramcounter_rep - rgramcounter
     A_					      =					sgramcounter_rep - rgramcounter
     A_					      =					0
     A_					      =					0
     for delgram in delgramcountergood_rep:
          deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
          deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
     # Define 0/0=1 instead of 0 to give higher scores for predictions that match
     # a target exactly.
     A_					      =					1
     if len(_UpperCAmelCase							) > 0:
          A_					      =					deltmpscorea / len(_UpperCAmelCase							)

     # ADDITION
     A_					      =					set(_UpperCAmelCase							) - set(_UpperCAmelCase							)
     A_					      =					set(_UpperCAmelCase							) & set(_UpperCAmelCase							)
     A_					      =					set(_UpperCAmelCase							) - set(_UpperCAmelCase							)

     A_					      =					0
     for addgram in addgramcountergood:
          addtmpscore += 1

     # Define 0/0=1 instead of 0 to give higher scores for predictions that match
     # a target exactly.
     A_					      =					1
     A_					      =					1
     if len(_UpperCAmelCase							) > 0:
          A_					      =					addtmpscore / len(_UpperCAmelCase							)
     if len(_UpperCAmelCase							) > 0:
          A_					      =					addtmpscore / len(_UpperCAmelCase							)
     A_					      =					0
     if addscore_precision > 0 or addscore_recall > 0:
          A_					      =					2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

     return (keepscore, delscore_precision, addscore)

def     UpperCAmelCase_						(						_UpperCAmelCase       :List[str]     ,	_UpperCAmelCase       :List[str]     ,	_UpperCAmelCase       :Optional[Any]							)  ->							Union[str, Any]:
     '''simple docstring'''


     A_					      =					len(_UpperCAmelCase							)

     A_					      =					ssent.split(''' '''							)
     A_					      =					csent.split(''' '''							)
     A_					      =					[]
     A_					      =					[]
     A_					      =					[]
     A_					      =					[]
     A_					      =					[]
     A_					      =					[]

     A_					      =					[]
     A_					      =					[]
     A_					      =					[]
     A_					      =					[]
     for rsent in rsents:
          A_					      =					rsent.split(''' '''							)
          A_					      =					[]
          A_					      =					[]
          A_					      =					[]
          ragramslist.append(_UpperCAmelCase							)
          for i in range(0     ,	len(_UpperCAmelCase							) - 1							):
               if i < len(_UpperCAmelCase							) - 1:
                    A_					      =					ragrams[i] + ''' ''' + ragrams[i + 1]
                    ragrams.append(_UpperCAmelCase							)
               if i < len(_UpperCAmelCase							) - 2:
                    A_					      =					ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
                    ragrams.append(_UpperCAmelCase							)
               if i < len(_UpperCAmelCase							) - 3:
                    A_					      =					ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
                    ragrams.append(_UpperCAmelCase							)
          ragramslist.append(_UpperCAmelCase							)
          ragramslist.append(_UpperCAmelCase							)
          ragramslist.append(_UpperCAmelCase							)

     for i in range(0     ,	len(_UpperCAmelCase							) - 1							):
          if i < len(_UpperCAmelCase							) - 1:
               A_					      =					sagrams[i] + ''' ''' + sagrams[i + 1]
               sagrams.append(_UpperCAmelCase							)
          if i < len(_UpperCAmelCase							) - 2:
               A_					      =					sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
               sagrams.append(_UpperCAmelCase							)
          if i < len(_UpperCAmelCase							) - 3:
               A_					      =					sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
               sagrams.append(_UpperCAmelCase							)

     for i in range(0     ,	len(_UpperCAmelCase							) - 1							):
          if i < len(_UpperCAmelCase							) - 1:
               A_					      =					cagrams[i] + ''' ''' + cagrams[i + 1]
               cagrams.append(_UpperCAmelCase							)
          if i < len(_UpperCAmelCase							) - 2:
               A_					      =					cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
               cagrams.append(_UpperCAmelCase							)
          if i < len(_UpperCAmelCase							) - 3:
               A_					      =					cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
               cagrams.append(_UpperCAmelCase							)

     ((A_)      ,						(A_)      ,						(A_))					      =					SARIngram(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     ((A_)      ,						(A_)      ,						(A_))					      =					SARIngram(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     ((A_)      ,						(A_)      ,						(A_))					      =					SARIngram(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     ((A_)      ,						(A_)      ,						(A_))					      =					SARIngram(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     A_					      =					sum([keepascore, keepascore, keepascore, keepascore]							) / 4
     A_					      =					sum([delascore, delascore, delascore, delascore]							) / 4
     A_					      =					sum([addascore, addascore, addascore, addascore]							) / 4
     A_					      =					(avgkeepscore + avgdelscore + avgaddscore) / 3
     return finalscore

def     UpperCAmelCase_						(						_UpperCAmelCase       :str     ,	_UpperCAmelCase       :bool = True     ,	_UpperCAmelCase       :str = "13a"     ,	_UpperCAmelCase       :bool = True							)  ->							Any:
     '''simple docstring'''


     if lowercase:
          A_					      =					sentence.lower()

     if tokenizer in ["13a", "intl"]:
          if version.parse(sacrebleu.__version__							).major >= 2:
               A_					      =					sacrebleu.metrics.bleu._get_tokenizer(_UpperCAmelCase							)()(_UpperCAmelCase							)
          else:
               A_					      =					sacrebleu.TOKENIZERS[tokenizer]()(_UpperCAmelCase							)
     elif tokenizer == "moses":
          A_					      =					sacremoses.MosesTokenizer().tokenize(_UpperCAmelCase     ,	return_str=_UpperCAmelCase     ,	escape=_UpperCAmelCase							)
     elif tokenizer == "penn":
          A_					      =					sacremoses.MosesTokenizer().penn_tokenize(_UpperCAmelCase     ,	return_str=_UpperCAmelCase							)
     else:
          A_					      =					sentence

     if not return_str:
          A_					      =					normalized_sent.split()

     return normalized_sent





@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(
               {
                   '''sources''': datasets.Value('''string'''   ,id='''sequence'''   ),
                   '''predictions''': datasets.Value('''string'''   ,id='''sequence'''   ),
                   '''references''': datasets.Sequence(datasets.Value('''string'''   ,id='''sequence'''   )   ,id='''references'''   ),
               }   )   ,codebase_urls=[
               '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
               '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
           ]   ,reference_urls=['''https://www.aclweb.org/anthology/Q16-1029.pdf''']   ,)




  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case   ,__snake_case   ):



       """simple docstring"""





       if not (len(__snake_case   ) == len(__snake_case   ) == len(__snake_case   )):
            raise ValueError('''Sources length must match predictions and references lengths.'''   )
       A_					      =					0
       for src, pred, refs in zip(__snake_case   ,__snake_case   ,__snake_case   ):
            sari_score += SARIsent(normalize(__snake_case   )   ,normalize(__snake_case   )   ,[normalize(__snake_case   ) for sent in refs]   )
       A_					      =					sari_score / len(__snake_case   )
       return {"sari": 1_0_0 * sari_score}










import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
      from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


a__			:    Optional[Any]			      =		get_tests_dir('fixtures/test_sentencepiece.model')





@require_torch
@require_torchaudio
@require_sentencepiece
class UpperCAmelCase_							(   unittest.TestCase   ):


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					tempfile.mkdtemp()

       A_					      =					['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
       A_					      =					dict(zip(__snake_case   ,range(len(__snake_case   )   )   )   )
       A_					      =					Path(self.tmpdirname   )
       save_json(__snake_case   ,save_dir / VOCAB_FILES_NAMES['''vocab_file''']   )
       if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
            copyfile(__snake_case   ,save_dir / VOCAB_FILES_NAMES['''spm_file''']   )

       A_					      =					SpeechaTextTokenizer.from_pretrained(self.tmpdirname   )
       tokenizer.save_pretrained(self.tmpdirname   )

       A_					      =					{
           '''feature_size''': 2_4,
           '''num_mel_bins''': 2_4,
           '''padding_value''': 0.0,
           '''sampling_rate''': 1_6_0_0_0,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }
       save_json(__snake_case   ,save_dir / FEATURE_EXTRACTOR_NAME   )


  def 	__UpperCAmelCase    (       self   ,**__snake_case   ):



       """simple docstring"""





       return SpeechaTextTokenizer.from_pretrained(self.tmpdirname   ,**__snake_case   )


  def 	__UpperCAmelCase    (       self   ,**__snake_case   ):



       """simple docstring"""





       return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname   ,**__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       shutil.rmtree(self.tmpdirname   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.get_tokenizer()
       A_					      =					self.get_feature_extractor()

       A_					      =					SpeechaTextProcessor(tokenizer=__snake_case   ,feature_extractor=__snake_case   )

       processor.save_pretrained(self.tmpdirname   )
       A_					      =					SpeechaTextProcessor.from_pretrained(self.tmpdirname   )

       self.assertEqual(processor.tokenizer.get_vocab()   ,tokenizer.get_vocab()   )
       self.assertIsInstance(processor.tokenizer   ,__snake_case   )

       self.assertEqual(processor.feature_extractor.to_json_string()   ,feature_extractor.to_json_string()   )
       self.assertIsInstance(processor.feature_extractor   ,__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					SpeechaTextProcessor(
           tokenizer=self.get_tokenizer()   ,feature_extractor=self.get_feature_extractor()   )
       processor.save_pretrained(self.tmpdirname   )

       A_					      =					self.get_tokenizer(bos_token='''(BOS)'''   ,eos_token='''(EOS)'''   )
       A_					      =					self.get_feature_extractor(do_normalize=__snake_case   ,padding_value=1.0   )

       A_					      =					SpeechaTextProcessor.from_pretrained(
           self.tmpdirname   ,bos_token='''(BOS)'''   ,eos_token='''(EOS)'''   ,do_normalize=__snake_case   ,padding_value=1.0   )

       self.assertEqual(processor.tokenizer.get_vocab()   ,tokenizer_add_kwargs.get_vocab()   )
       self.assertIsInstance(processor.tokenizer   ,__snake_case   )

       self.assertEqual(processor.feature_extractor.to_json_string()   ,feature_extractor_add_kwargs.to_json_string()   )
       self.assertIsInstance(processor.feature_extractor   ,__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.get_feature_extractor()
       A_					      =					self.get_tokenizer()

       A_					      =					SpeechaTextProcessor(tokenizer=__snake_case   ,feature_extractor=__snake_case   )

       A_					      =					floats_list((3, 1_0_0_0)   )

       A_					      =					feature_extractor(__snake_case   ,return_tensors='''np'''   )
       A_					      =					processor(__snake_case   ,return_tensors='''np'''   )

       for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum()   ,input_processor[key].sum()   ,delta=1E-2   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.get_feature_extractor()
       A_					      =					self.get_tokenizer()

       A_					      =					SpeechaTextProcessor(tokenizer=__snake_case   ,feature_extractor=__snake_case   )

       A_					      =					'''This is a test string'''

       A_					      =					processor(text=__snake_case   )

       A_					      =					tokenizer(__snake_case   )

       for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]   ,encoded_processor[key]   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.get_feature_extractor()
       A_					      =					self.get_tokenizer()

       A_					      =					SpeechaTextProcessor(tokenizer=__snake_case   ,feature_extractor=__snake_case   )

       A_					      =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       A_					      =					processor.batch_decode(__snake_case   )
       A_					      =					tokenizer.batch_decode(__snake_case   )

       self.assertListEqual(__snake_case   ,__snake_case   )




  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.get_feature_extractor()
       A_					      =					self.get_tokenizer()

       A_					      =					SpeechaTextProcessor(tokenizer=__snake_case   ,feature_extractor=__snake_case   )

       self.assertListEqual(
           processor.model_input_names   ,feature_extractor.model_input_names   ,msg='''`processor` and `feature_extractor` model input names do not match'''   ,)



