code: 
  
  
  
  
  from collections import Counter
  
  import sacrebleu
  import sacremoses
  from packaging import version
  
  import datasets
  
  
  _A							    =	"""\
  @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},
  }
  """
  
  _A							    =	"""\
  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
  """
  
  
  _A							    =	"""
  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_       (	__a	,	__a	,	__a	,	__a )       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: int	     =[rgram for rgrams in rgramslist for rgram in rgrams]
  							SCREAMING_SNAKE_CASE			: str	     =Counter(__a )
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =Counter(__a )
  							SCREAMING_SNAKE_CASE			: int	     =Counter()
  							for sgram, scount in sgramcounter.items():
  														SCREAMING_SNAKE_CASE			: List[Any]	     =scount * numref
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =Counter(__a )
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =Counter()
  							for cgram, ccount in cgramcounter.items():
  														SCREAMING_SNAKE_CASE			: int	     =ccount * numref
  
  							# KEEP
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =sgramcounter_rep & cgramcounter_rep
  							SCREAMING_SNAKE_CASE			: str	     =keepgramcounter_rep & rgramcounter
  							SCREAMING_SNAKE_CASE			: int	     =sgramcounter_rep & rgramcounter
  
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =0
  							SCREAMING_SNAKE_CASE			: Dict	     =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[Any]	     =1
  							SCREAMING_SNAKE_CASE			: Dict	     =1
  							if len(__a ) > 0:
  														SCREAMING_SNAKE_CASE			: Tuple	     =keeptmpscorea / len(__a )
  							if len(__a ) > 0:
  														# Fix an alleged bug [2] in the keep score computation.
  														# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
  														SCREAMING_SNAKE_CASE			: str	     =keeptmpscorea / sum(keepgramcounterall_rep.values() )
  							SCREAMING_SNAKE_CASE			: str	     =0
  							if keepscore_precision > 0 or keepscore_recall > 0:
  														SCREAMING_SNAKE_CASE			: Any	     =2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
  
  							# DELETION
  							SCREAMING_SNAKE_CASE			: Any	     =sgramcounter_rep - cgramcounter_rep
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =delgramcounter_rep - rgramcounter
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =sgramcounter_rep - rgramcounter
  							SCREAMING_SNAKE_CASE			: Dict	     =0
  							SCREAMING_SNAKE_CASE			: Dict	     =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			: Optional[int]	     =1
  							if len(__a ) > 0:
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =deltmpscorea / len(__a )
  
  							# ADDITION
  							SCREAMING_SNAKE_CASE			: List[str]	     =set(__a ) - set(__a )
  							SCREAMING_SNAKE_CASE			: List[str]	     =set(__a ) & set(__a )
  							SCREAMING_SNAKE_CASE			: Any	     =set(__a ) - set(__a )
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =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			: str	     =1
  							SCREAMING_SNAKE_CASE			: List[str]	     =1
  							if len(__a ) > 0:
  														SCREAMING_SNAKE_CASE			: Any	     =addtmpscore / len(__a )
  							if len(__a ) > 0:
  														SCREAMING_SNAKE_CASE			: str	     =addtmpscore / len(__a )
  							SCREAMING_SNAKE_CASE			: str	     =0
  							if addscore_precision > 0 or addscore_recall > 0:
  														SCREAMING_SNAKE_CASE			: List[Any]	     =2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
  
  							return (keepscore, delscore_precision, addscore)
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Union[str, Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =len(__a )
  
  							SCREAMING_SNAKE_CASE			: List[str]	     =ssent.split(''' ''' )
  							SCREAMING_SNAKE_CASE			: Any	     =csent.split(''' ''' )
  							SCREAMING_SNAKE_CASE			: List[Any]	     =[]
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =[]
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =[]
  							SCREAMING_SNAKE_CASE			: str	     =[]
  							SCREAMING_SNAKE_CASE			: Dict	     =[]
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =[]
  
  							SCREAMING_SNAKE_CASE			: int	     =[]
  							SCREAMING_SNAKE_CASE			: Dict	     =[]
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =[]
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[]
  							for rsent in rsents:
  														SCREAMING_SNAKE_CASE			: int	     =rsent.split(''' ''' )
  														SCREAMING_SNAKE_CASE			: Dict	     =[]
  														SCREAMING_SNAKE_CASE			: str	     =[]
  														SCREAMING_SNAKE_CASE			: List[str]	     =[]
  														ragramslist.append(__a )
  														for i in range(0	,	len(__a ) - 1 ):
  																					if i < len(__a ) - 1:
  																												SCREAMING_SNAKE_CASE			: Dict	     =ragrams[i] + ''' ''' + ragrams[i + 1]
  																												ragrams.append(__a )
  																					if i < len(__a ) - 2:
  																												SCREAMING_SNAKE_CASE			: str	     =ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
  																												ragrams.append(__a )
  																					if i < len(__a ) - 3:
  																												SCREAMING_SNAKE_CASE			: Optional[int]	     =ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
  																												ragrams.append(__a )
  														ragramslist.append(__a )
  														ragramslist.append(__a )
  														ragramslist.append(__a )
  
  							for i in range(0	,	len(__a ) - 1 ):
  														if i < len(__a ) - 1:
  																					SCREAMING_SNAKE_CASE			: Union[str, Any]	     =sagrams[i] + ''' ''' + sagrams[i + 1]
  																					sagrams.append(__a )
  														if i < len(__a ) - 2:
  																					SCREAMING_SNAKE_CASE			: Optional[Any]	     =sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
  																					sagrams.append(__a )
  														if i < len(__a ) - 3:
  																					SCREAMING_SNAKE_CASE			: Tuple	     =sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
  																					sagrams.append(__a )
  
  							for i in range(0	,	len(__a ) - 1 ):
  														if i < len(__a ) - 1:
  																					SCREAMING_SNAKE_CASE			: Optional[Any]	     =cagrams[i] + ''' ''' + cagrams[i + 1]
  																					cagrams.append(__a )
  														if i < len(__a ) - 2:
  																					SCREAMING_SNAKE_CASE			: Any	     =cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
  																					cagrams.append(__a )
  														if i < len(__a ) - 3:
  																					SCREAMING_SNAKE_CASE			: Optional[Any]	     =cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
  																					cagrams.append(__a )
  
  							((SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE))			: Any	     =SARIngram(__a	,	__a	,	__a	,	__a )
  							((SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE))			: Dict	     =SARIngram(__a	,	__a	,	__a	,	__a )
  							((SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE))			: List[Any]	     =SARIngram(__a	,	__a	,	__a	,	__a )
  							((SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE))			: Any	     =SARIngram(__a	,	__a	,	__a	,	__a )
  							SCREAMING_SNAKE_CASE			: int	     =sum([keepascore, keepascore, keepascore, keepascore] ) / 4
  							SCREAMING_SNAKE_CASE			: Any	     =sum([delascore, delascore, delascore, delascore] ) / 4
  							SCREAMING_SNAKE_CASE			: Dict	     =sum([addascore, addascore, addascore, addascore] ) / 4
  							SCREAMING_SNAKE_CASE			: Dict	     =(avgkeepscore + avgdelscore + avgaddscore) / 3
  							return finalscore
  def 						lowerCAmelCase_       (	__a	,	__a = True	,	__a = "13a"	,	__a = True )       ->			Any:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							if lowercase:
  														SCREAMING_SNAKE_CASE			: str	     =sentence.lower()
  
  							if tokenizer in ["13a", "intl"]:
  														if version.parse(sacrebleu.__version__ ).major >= 2:
  																					SCREAMING_SNAKE_CASE			: Any	     =sacrebleu.metrics.bleu._get_tokenizer(__a )()(__a )
  														else:
  																					SCREAMING_SNAKE_CASE			: Tuple	     =sacrebleu.TOKENIZERS[tokenizer]()(__a )
  							elif tokenizer == "moses":
  														SCREAMING_SNAKE_CASE			: Tuple	     =sacremoses.MosesTokenizer().tokenize(__a	,	return_str=__a	,	escape=__a )
  							elif tokenizer == "penn":
  														SCREAMING_SNAKE_CASE			: List[Any]	     =sacremoses.MosesTokenizer().penn_tokenize(__a	,	return_str=__a )
  							else:
  														SCREAMING_SNAKE_CASE			: Dict	     =sentence
  
  							if not return_str:
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =normalized_sent.split()
  
  							return normalized_sent
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,   _KWARGS_DESCRIPTION	)
  class 							_lowerCAmelCase		(     datasets.Metric	):
  
  
  
  
  				def     __a		( self							)    ->      Any:
  											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		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      List[str]:
  											if not (len(snake_case_							) == len(snake_case_							) == len(snake_case_							)):
  																		raise ValueError('''Sources length must match predictions and references lengths.'''							)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =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]							)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =sari_score / len(snake_case_							)
  											return {"sari": 100 * sari_score}
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
  from ...utils import logging
  
  
  _A							    =	logging.get_logger(__name__)
  
  _A							    =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
  
  _A							    =	{
      """vocab_file""": {
          """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
      },
      """tokenizer_file""": {
          """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
      },
  }
  
  
  class 							_lowerCAmelCase		(     UpperCamelCase__	):
  
  
  
  
  				def __init__( self		,		snake_case_=None		,		snake_case_=None		,		snake_case_=False		,		snake_case_=True		,		snake_case_=False		,		snake_case_="<s>"		,		snake_case_="</s>"		,		snake_case_="<unk>"		,		snake_case_="<sep>"		,		snake_case_="<pad>"		,		snake_case_="<cls>"		,		snake_case_="<mask>"		,		snake_case_=["<eop>", "<eod>"]		,		**snake_case_		,		)    ->      str:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =AddedToken(snake_case_		,		lstrip=snake_case_		,		rstrip=snake_case_							) if isinstance(snake_case_		,		snake_case_							) else mask_token
  
  											super().__init__(
  											    vocab_file=snake_case_		,		tokenizer_file=snake_case_		,		do_lower_case=snake_case_		,		remove_space=snake_case_		,		keep_accents=snake_case_		,		bos_token=snake_case_		,		eos_token=snake_case_		,		unk_token=snake_case_		,		sep_token=snake_case_		,		pad_token=snake_case_		,		cls_token=snake_case_		,		mask_token=snake_case_		,		additional_special_tokens=snake_case_		,		**snake_case_		,		)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =3
  											SCREAMING_SNAKE_CASE			: Dict	     =do_lower_case
  											SCREAMING_SNAKE_CASE			: int	     =remove_space
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =keep_accents
  											SCREAMING_SNAKE_CASE			: Tuple	     =vocab_file
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =False if not self.vocab_file else True
  
  											try:
  																		import jieba
  											except ModuleNotFoundError as error:
  																		raise error.__class__(
  																		    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
  																		    '''See https://pypi.org/project/jieba/ for installation.'''							)
  											SCREAMING_SNAKE_CASE			: str	     =jieba
  											SCREAMING_SNAKE_CASE			: Dict	     =str.maketrans(''' \n'''		,		'''\u2582\u2583'''							)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
  											SCREAMING_SNAKE_CASE			: Dict	     =[self.sep_token_id]
  											SCREAMING_SNAKE_CASE			: Any	     =[self.cls_token_id]
  											if token_ids_a is None:
  																		return token_ids_a + sep + cls
  											return token_ids_a + sep + token_ids_a + sep + cls
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[self.sep_token_id]
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[2]
  
  											if token_ids_a is None:
  																		return len(token_ids_a + sep							) * [0] + cls_segment_id
  											return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      Tuple[str]:
  											if not self.can_save_slow_tokenizer:
  																		raise ValueError(
  																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
  																		    '''tokenizer.'''							)
  
  											if not os.path.isdir(snake_case_							):
  																		logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
  																		return
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =os.path.join(
  											    snake_case_		,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
  
  											if os.path.abspath(self.vocab_file							) != os.path.abspath(snake_case_							):
  																		copyfile(self.vocab_file		,		snake_case_							)
  
  											return (out_vocab_file,)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		*snake_case_		,		**snake_case_							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: int	     =[
  											    ''' '''.join([x.translate(self.translator							) for x in self.jieba.cut(snake_case_		,		cut_all=snake_case_							)]							)
  											    for text in batch_text_or_text_pairs
  											]
  											return super()._batch_encode_plus(snake_case_		,		*snake_case_		,		**snake_case_							)
  
  
  
  
  
  
  
  				def     __a		( self		,		*snake_case_		,		**snake_case_							)    ->      str:
  											SCREAMING_SNAKE_CASE			: List[Any]	     =super()._decode(*snake_case_		,		**snake_case_							)
  											SCREAMING_SNAKE_CASE			: List[Any]	     =text.replace(''' '''		,		''''''							).replace('''\u2582'''		,		''' '''							).replace('''\u2583'''		,		'''\n'''							)
  											return text
  
  
  
  
  
  
  