



import shutil
import tempfile
import unittest

import pytest

from transformers import WhisperTokenizer, is_speech_available
from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio

from .test_feature_extraction_whisper import floats_list


if is_speech_available():
							from transformers import WhisperFeatureExtractor, WhisperProcessor


UpperCamelCase_	       =   50_358
UpperCamelCase_	       =   50_362







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

						def 	__a       (    self	:int)		->					Optional[Any]:
												UpperCAmelCase_     							=     '''openai/whisper-small.en'''
												UpperCAmelCase_     							=     tempfile.mkdtemp()

						def 	__a       (    self	:Tuple   ,						**_lowercase	:int)		->					Union[str, Any]:
												return WhisperTokenizer.from_pretrained(self.checkpoint   ,						**_lowercase)

						def 	__a       (    self	:Any   ,						**_lowercase	:Union[str, Any])		->					Dict:
												return WhisperFeatureExtractor.from_pretrained(self.checkpoint   ,						**_lowercase)

						def 	__a       (    self	:Optional[int])		->					List[Any]:
												shutil.rmtree(self.tmpdirname)

						def 	__a       (    self	:Optional[int])		->					Any:
												UpperCAmelCase_     							=     self.get_tokenizer()
												UpperCAmelCase_     							=     self.get_feature_extractor()

												UpperCAmelCase_     							=     WhisperProcessor(tokenizer=_lowercase   ,						feature_extractor=_lowercase)

												processor.save_pretrained(self.tmpdirname)
												UpperCAmelCase_     							=     WhisperProcessor.from_pretrained(self.tmpdirname)

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

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

						def 	__a       (    self	:str)		->					Optional[int]:
												UpperCAmelCase_     							=     WhisperProcessor(tokenizer=self.get_tokenizer()   ,						feature_extractor=self.get_feature_extractor())
												processor.save_pretrained(self.tmpdirname)

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

												UpperCAmelCase_     							=     WhisperProcessor.from_pretrained(
												    self.tmpdirname   ,						bos_token='''(BOS)'''   ,						eos_token='''(EOS)'''   ,						do_normalize=_lowercase   ,						padding_value=1.0)

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

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

						def 	__a       (    self	:Any)		->					Dict:
												UpperCAmelCase_     							=     self.get_feature_extractor()
												UpperCAmelCase_     							=     self.get_tokenizer()

												UpperCAmelCase_     							=     WhisperProcessor(tokenizer=_lowercase   ,						feature_extractor=_lowercase)

												UpperCAmelCase_     							=     floats_list((3, 1000))

												UpperCAmelCase_     							=     feature_extractor(_lowercase   ,						return_tensors='''np''')
												UpperCAmelCase_     							=     processor(_lowercase   ,						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 	__a       (    self	:Optional[Any])		->					Optional[int]:
												UpperCAmelCase_     							=     self.get_feature_extractor()
												UpperCAmelCase_     							=     self.get_tokenizer()

												UpperCAmelCase_     							=     WhisperProcessor(tokenizer=_lowercase   ,						feature_extractor=_lowercase)

												UpperCAmelCase_     							=     '''This is a test string'''

												UpperCAmelCase_     							=     processor(text=_lowercase)

												UpperCAmelCase_     							=     tokenizer(_lowercase)

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

						def 	__a       (    self	:Optional[Any])		->					List[str]:
												UpperCAmelCase_     							=     self.get_feature_extractor()
												UpperCAmelCase_     							=     self.get_tokenizer()

												UpperCAmelCase_     							=     WhisperProcessor(tokenizer=_lowercase   ,						feature_extractor=_lowercase)

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

												UpperCAmelCase_     							=     processor.batch_decode(_lowercase)
												UpperCAmelCase_     							=     tokenizer.batch_decode(_lowercase)

												self.assertListEqual(_lowercase   ,						_lowercase)

						def 	__a       (    self	:Optional[Any])		->					Union[str, Any]:
												UpperCAmelCase_     							=     self.get_feature_extractor()
												UpperCAmelCase_     							=     self.get_tokenizer()

												UpperCAmelCase_     							=     WhisperProcessor(tokenizer=_lowercase   ,						feature_extractor=_lowercase)

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

						def 	__a       (    self	:List[str])		->					Dict:
												UpperCAmelCase_     							=     self.get_feature_extractor()
												UpperCAmelCase_     							=     self.get_tokenizer()

												UpperCAmelCase_     							=     WhisperProcessor(tokenizer=_lowercase   ,						feature_extractor=_lowercase)
												UpperCAmelCase_     							=     processor.get_decoder_prompt_ids(task='''transcribe'''   ,						no_timestamps=_lowercase)

												self.assertIsInstance(_lowercase   ,						_lowercase)
												for ids in forced_decoder_ids:
																		self.assertIsInstance(_lowercase   ,						(list, tuple))

												UpperCAmelCase_     							=     [TRANSCRIBE, NOTIMESTAMPS]
												self.assertListEqual([ids[-1] for ids in forced_decoder_ids]   ,						_lowercase)

						def 	__a       (    self	:Optional[Any])		->					Optional[int]:
												UpperCAmelCase_     							=     WhisperProcessor(tokenizer=self.get_tokenizer()   ,						feature_extractor=self.get_feature_extractor())
												UpperCAmelCase_     							=     processor.get_prompt_ids('''Mr. Quilter''')
												UpperCAmelCase_     							=     processor.tokenizer.decode(_lowercase)

												self.assertListEqual(prompt_ids.tolist()   ,						[50360, 1770, 13, 2264, 346, 353])
												self.assertEqual(_lowercase   ,						'''<|startofprev|> Mr. Quilter''')

						def 	__a       (    self	:Dict)		->					str:
												UpperCAmelCase_     							=     WhisperProcessor(tokenizer=self.get_tokenizer()   ,						feature_extractor=self.get_feature_extractor())
												UpperCAmelCase_     							=     processor.get_prompt_ids('''''')
												UpperCAmelCase_     							=     processor.tokenizer.decode(_lowercase)

												self.assertListEqual(prompt_ids.tolist()   ,						[50360, 220])
												self.assertEqual(_lowercase   ,						'''<|startofprev|> ''')




						def 	__a       (    self	:str)		->					Union[str, Any]:
												UpperCAmelCase_     							=     WhisperProcessor(tokenizer=self.get_tokenizer()   ,						feature_extractor=self.get_feature_extractor())

												def _test_prompt_error_raised_helper(_lowercase	:int   ,						_lowercase	:Optional[Any]):
																		with pytest.raises(_lowercase) as excinfo:
																								processor.get_prompt_ids(_lowercase)
																		UpperCAmelCase_     							=     f"Encountered text in the prompt corresponding to disallowed special token: {special_token}."
																		self.assertEqual(_lowercase   ,						str(excinfo.value))

												_test_prompt_error_raised_helper('''<|startofprev|> test'''   ,						'''<|startofprev|>''')
												_test_prompt_error_raised_helper('''test <|notimestamps|>'''   ,						'''<|notimestamps|>''')
												_test_prompt_error_raised_helper('''test <|zh|> test <|transcribe|>'''   ,						'''<|zh|>''')













from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case			 =						"""\
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415},
}
"""

snake_case			 =						"""\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


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


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Dict		  =			[rgram for rgrams in rgramslist for rgram in rgrams]
  SCREAMING_SNAKE_CASE				:					Dict		  =			Counter(lowercase )

  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			Counter(lowercase )
  SCREAMING_SNAKE_CASE				:					Dict		  =			Counter()
  for sgram, scount in sgramcounter.items():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			scount * numref

  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			Counter(lowercase )
  SCREAMING_SNAKE_CASE				:					Dict		  =			Counter()
  for cgram, ccount in cgramcounter.items():
    SCREAMING_SNAKE_CASE				:					Tuple		  =			ccount * numref

  # KEEP
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			sgramcounter_rep & cgramcounter_rep
  SCREAMING_SNAKE_CASE				:					Dict		  =			keepgramcounter_rep & rgramcounter
  SCREAMING_SNAKE_CASE				:					Tuple		  =			sgramcounter_rep & rgramcounter

  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			0
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			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.
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			1
  SCREAMING_SNAKE_CASE				:					int		  =			1
  if len(lowercase ) > 0:
    SCREAMING_SNAKE_CASE				:					Optional[int]		  =			keeptmpscorea / len(lowercase )
  if len(lowercase ) > 0:
    # Fix an alleged bug [2] in the keep score computation.
    # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			keeptmpscorea / sum(keepgramcounterall_rep.values() )
  SCREAMING_SNAKE_CASE				:					Any		  =			0
  if keepscore_precision > 0 or keepscore_recall > 0:
    SCREAMING_SNAKE_CASE				:					int		  =			2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

  # DELETION
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			sgramcounter_rep - cgramcounter_rep
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			delgramcounter_rep - rgramcounter
  SCREAMING_SNAKE_CASE				:					Dict		  =			sgramcounter_rep - rgramcounter
  SCREAMING_SNAKE_CASE				:					List[str]		  =			0
  SCREAMING_SNAKE_CASE				:					int		  =			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.
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			1
  if len(lowercase ) > 0:
    SCREAMING_SNAKE_CASE				:					str		  =			deltmpscorea / len(lowercase )

  # ADDITION
  SCREAMING_SNAKE_CASE				:					Dict		  =			set(lowercase ) - set(lowercase )
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			set(lowercase ) & set(lowercase )
  SCREAMING_SNAKE_CASE				:					int		  =			set(lowercase ) - set(lowercase )

  SCREAMING_SNAKE_CASE				:					Any		  =			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.
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			1
  SCREAMING_SNAKE_CASE				:					Tuple		  =			1
  if len(lowercase ) > 0:
    SCREAMING_SNAKE_CASE				:					str		  =			addtmpscore / len(lowercase )
  if len(lowercase ) > 0:
    SCREAMING_SNAKE_CASE				:					List[str]		  =			addtmpscore / len(lowercase )
  SCREAMING_SNAKE_CASE				:					Any		  =			0
  if addscore_precision > 0 or addscore_recall > 0:
    SCREAMING_SNAKE_CASE				:					str		  =			2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

  return (keepscore, delscore_precision, addscore)


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[Any]		  =			len(lowercase )

  SCREAMING_SNAKE_CASE				:					Dict		  =			ssent.split(" " )
  SCREAMING_SNAKE_CASE				:					str		  =			csent.split(" " )
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[]
  SCREAMING_SNAKE_CASE				:					Any		  =			[]
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[]
  SCREAMING_SNAKE_CASE				:					Any		  =			[]
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[]
  SCREAMING_SNAKE_CASE				:					Any		  =			[]

  SCREAMING_SNAKE_CASE				:					List[str]		  =			[]
  SCREAMING_SNAKE_CASE				:					int		  =			[]
  SCREAMING_SNAKE_CASE				:					Dict		  =			[]
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[]
  for rsent in rsents:
    SCREAMING_SNAKE_CASE				:					Any		  =			rsent.split(" " )
    SCREAMING_SNAKE_CASE				:					int		  =			[]
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[]
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			[]
    ragramslist.append(lowercase )
    for i in range(0  ,   len(lowercase ) - 1 ):
      if i < len(lowercase ) - 1:
        SCREAMING_SNAKE_CASE				:					Tuple		  =			ragrams[i] + " " + ragrams[i + 1]
        ragrams.append(lowercase )
      if i < len(lowercase ) - 2:
        SCREAMING_SNAKE_CASE				:					Optional[int]		  =			ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
        ragrams.append(lowercase )
      if i < len(lowercase ) - 3:
        SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
        ragrams.append(lowercase )
    ragramslist.append(lowercase )
    ragramslist.append(lowercase )
    ragramslist.append(lowercase )

  for i in range(0  ,   len(lowercase ) - 1 ):
    if i < len(lowercase ) - 1:
      SCREAMING_SNAKE_CASE				:					List[Any]		  =			sagrams[i] + " " + sagrams[i + 1]
      sagrams.append(lowercase )
    if i < len(lowercase ) - 2:
      SCREAMING_SNAKE_CASE				:					int		  =			sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
      sagrams.append(lowercase )
    if i < len(lowercase ) - 3:
      SCREAMING_SNAKE_CASE				:					Dict		  =			sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
      sagrams.append(lowercase )

  for i in range(0  ,   len(lowercase ) - 1 ):
    if i < len(lowercase ) - 1:
      SCREAMING_SNAKE_CASE				:					Optional[int]		  =			cagrams[i] + " " + cagrams[i + 1]
      cagrams.append(lowercase )
    if i < len(lowercase ) - 2:
      SCREAMING_SNAKE_CASE				:					str		  =			cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
      cagrams.append(lowercase )
    if i < len(lowercase ) - 3:
      SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
      cagrams.append(lowercase )

  ((SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE))				:					Any		  =			SARIngram(lowercase  ,   lowercase  ,   lowercase  ,   lowercase )
  ((SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE))				:					List[Any]		  =			SARIngram(lowercase  ,   lowercase  ,   lowercase  ,   lowercase )
  ((SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE))				:					List[str]		  =			SARIngram(lowercase  ,   lowercase  ,   lowercase  ,   lowercase )
  ((SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE))				:					Optional[Any]		  =			SARIngram(lowercase  ,   lowercase  ,   lowercase  ,   lowercase )
  SCREAMING_SNAKE_CASE				:					int		  =			sum([keepascore, keepascore, keepascore, keepascore] ) / 4
  SCREAMING_SNAKE_CASE				:					List[str]		  =			sum([delascore, delascore, delascore, delascore] ) / 4
  SCREAMING_SNAKE_CASE				:					Any		  =			sum([addascore, addascore, addascore, addascore] ) / 4
  SCREAMING_SNAKE_CASE				:					int		  =			(avgkeepscore + avgdelscore + avgaddscore) / 3
  return finalscore


def      lowerCamelCase__	(   lowercase  ,   lowercase = True  ,   lowercase = "13a"  ,   lowercase = True ):

  """simple docstring"""






  if lowercase:
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			sentence.lower()

  if tokenizer in ["13a", "intl"]:
    if version.parse(sacrebleu.__version__ ).major >= 2:
      SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			sacrebleu.metrics.bleu._get_tokenizer(lowercase )()(lowercase )
    else:
      SCREAMING_SNAKE_CASE				:					Tuple		  =			sacrebleu.TOKENIZERS[tokenizer]()(lowercase )
  elif tokenizer == "moses":
    SCREAMING_SNAKE_CASE				:					Dict		  =			sacremoses.MosesTokenizer().tokenize(lowercase  ,   return_str=lowercase  ,   escape=lowercase )
  elif tokenizer == "penn":
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			sacremoses.MosesTokenizer().penn_tokenize(lowercase  ,   return_str=lowercase )
  else:
    SCREAMING_SNAKE_CASE				:					str		  =			sentence

  if not return_str:
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			normalized_sent.split()

  return normalized_sent


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


     def  _A				( self				:		Optional[int]					):
       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  _A				( self				:		str    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		Optional[int]					):
       if not (len(UpperCAmelCase_					) == len(UpperCAmelCase_					) == len(UpperCAmelCase_					)):
         raise ValueError("Sources length must match predictions and references lengths."					)
       SCREAMING_SNAKE_CASE				:					Any		  =			0
       for src, pred, refs in zip(UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					):
         sari_score += SARIsent(normalize(UpperCAmelCase_					)    ,       normalize(UpperCAmelCase_					)    ,       [normalize(UpperCAmelCase_					) for sent in refs]					)
       SCREAMING_SNAKE_CASE				:					List[str]		  =			sari_score / len(UpperCAmelCase_					)
       return {"sari": 100 * sari_score}






