code: 
  
  
  
  
  
  
  from collections import Counter
  
  import sacrebleu
  import sacremoses
  from packaging import version
  
  import datasets
  
  
  lowerCamelCase										=  '''\
  @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},
  }
  '''
  
  lowerCamelCase										=  '''\
  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
  '''
  
  
  lowerCamelCase										=  '''
  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        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	[rgram for rgrams in rgramslist for rgram in rgrams]
      _lowercase     =	Counter(_lowercase      )
  
      _lowercase     =	Counter(_lowercase      )
      _lowercase     =	Counter()
      for sgram, scount in sgramcounter.items():
          _lowercase     =	scount * numref
  
      _lowercase     =	Counter(_lowercase      )
      _lowercase     =	Counter()
      for cgram, ccount in cgramcounter.items():
          _lowercase     =	ccount * numref
  
      # KEEP
      _lowercase     =	sgramcounter_rep & cgramcounter_rep
      _lowercase     =	keepgramcounter_rep & rgramcounter
      _lowercase     =	sgramcounter_rep & rgramcounter
  
      _lowercase     =	0
      _lowercase     =	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.
      _lowercase     =	1
      _lowercase     =	1
      if len(_lowercase      ) > 0:
          _lowercase     =	keeptmpscorea / len(_lowercase      )
      if len(_lowercase      ) > 0:
          # Fix an alleged bug [2] in the keep score computation.
          # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
          _lowercase     =	keeptmpscorea / sum(keepgramcounterall_rep.values()      )
      _lowercase     =	0
      if keepscore_precision > 0 or keepscore_recall > 0:
          _lowercase     =	2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
  
      # DELETION
      _lowercase     =	sgramcounter_rep - cgramcounter_rep
      _lowercase     =	delgramcounter_rep - rgramcounter
      _lowercase     =	sgramcounter_rep - rgramcounter
      _lowercase     =	0
      _lowercase     =	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.
      _lowercase     =	1
      if len(_lowercase      ) > 0:
          _lowercase     =	deltmpscorea / len(_lowercase      )
  
      # ADDITION
      _lowercase     =	set(_lowercase      ) - set(_lowercase      )
      _lowercase     =	set(_lowercase      ) & set(_lowercase      )
      _lowercase     =	set(_lowercase      ) - set(_lowercase      )
  
      _lowercase     =	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.
      _lowercase     =	1
      _lowercase     =	1
      if len(_lowercase      ) > 0:
          _lowercase     =	addtmpscore / len(_lowercase      )
      if len(_lowercase      ) > 0:
          _lowercase     =	addtmpscore / len(_lowercase      )
      _lowercase     =	0
      if addscore_precision > 0 or addscore_recall > 0:
          _lowercase     =	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
  
      return (keepscore, delscore_precision, addscore)
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	len(_lowercase      )
  
      _lowercase     =	ssent.split(' '      )
      _lowercase     =	csent.split(' '      )
      _lowercase     =	[]
      _lowercase     =	[]
      _lowercase     =	[]
      _lowercase     =	[]
      _lowercase     =	[]
      _lowercase     =	[]
  
      _lowercase     =	[]
      _lowercase     =	[]
      _lowercase     =	[]
      _lowercase     =	[]
      for rsent in rsents:
          _lowercase     =	rsent.split(' '      )
          _lowercase     =	[]
          _lowercase     =	[]
          _lowercase     =	[]
          ragramslist.append(_lowercase      )
          for i in range(0,      len(_lowercase      ) - 1      ):
              if i < len(_lowercase      ) - 1:
                  _lowercase     =	ragrams[i] + ' ' + ragrams[i + 1]
                  ragrams.append(_lowercase      )
              if i < len(_lowercase      ) - 2:
                  _lowercase     =	ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
                  ragrams.append(_lowercase      )
              if i < len(_lowercase      ) - 3:
                  _lowercase     =	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:
              _lowercase     =	sagrams[i] + ' ' + sagrams[i + 1]
              sagrams.append(_lowercase      )
          if i < len(_lowercase      ) - 2:
              _lowercase     =	sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
              sagrams.append(_lowercase      )
          if i < len(_lowercase      ) - 3:
              _lowercase     =	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:
              _lowercase     =	cagrams[i] + ' ' + cagrams[i + 1]
              cagrams.append(_lowercase      )
          if i < len(_lowercase      ) - 2:
              _lowercase     =	cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
              cagrams.append(_lowercase      )
          if i < len(_lowercase      ) - 3:
              _lowercase     =	cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
              cagrams.append(_lowercase      )
  
      ((_lowercase) ,    (_lowercase) ,    (_lowercase))     =	SARIngram(_lowercase,      _lowercase,      _lowercase,      _lowercase      )
      ((_lowercase) ,    (_lowercase) ,    (_lowercase))     =	SARIngram(_lowercase,      _lowercase,      _lowercase,      _lowercase      )
      ((_lowercase) ,    (_lowercase) ,    (_lowercase))     =	SARIngram(_lowercase,      _lowercase,      _lowercase,      _lowercase      )
      ((_lowercase) ,    (_lowercase) ,    (_lowercase))     =	SARIngram(_lowercase,      _lowercase,      _lowercase,      _lowercase      )
      _lowercase     =	sum([keepascore, keepascore, keepascore, keepascore]      ) / 4
      _lowercase     =	sum([delascore, delascore, delascore, delascore]      ) / 4
      _lowercase     =	sum([addascore, addascore, addascore, addascore]      ) / 4
      _lowercase     =	(avgkeepscore + avgdelscore + avgaddscore) / 3
      return finalscore
  def        UpperCAmelCase      (   _lowercase,      _lowercase = True,      _lowercase = "13a",      _lowercase = True      ):
  
  
      '''simple docstring'''
  
  
  
      if lowercase:
          _lowercase     =	sentence.lower()
  
      if tokenizer in ["13a", "intl"]:
          if version.parse(sacrebleu.__version__      ).major >= 2:
              _lowercase     =	sacrebleu.metrics.bleu._get_tokenizer(_lowercase      )()(_lowercase      )
          else:
              _lowercase     =	sacrebleu.TOKENIZERS[tokenizer]()(_lowercase      )
      elif tokenizer == "moses":
          _lowercase     =	sacremoses.MosesTokenizer().tokenize(_lowercase,      return_str=_lowercase,      escape=_lowercase      )
      elif tokenizer == "penn":
          _lowercase     =	sacremoses.MosesTokenizer().penn_tokenize(_lowercase,      return_str=_lowercase      )
      else:
          _lowercase     =	sentence
  
      if not return_str:
          _lowercase     =	normalized_sent.split()
  
      return normalized_sent
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,			_KWARGS_DESCRIPTION      )
  class 					snake_case_							(       datasets.Metric      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		Any:
  
  
         """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 			SCREAMING_SNAKE_CASE__				(    self:	Any   ,       snake_case__:	int   ,       snake_case__:	Optional[Any]   ,       snake_case__:	Optional[int]	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         if not (len(snake_case__	) == len(snake_case__	) == len(snake_case__	)):
             raise ValueError('Sources length must match predictions and references lengths.'	)
         _lowercase     =	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]	)
         _lowercase     =	sari_score / len(snake_case__	)
         return {"sari": 100 * sari_score}
  
style_context: 
  
  
  
  
  
  
  import functools
  import operator
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase										=  logging.get_logger(__name__)
  
  lowerCamelCase										=  {
      '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
      # See all Hubert models at https://huggingface.co/models?filter=hubert
  }
  
  class 					snake_case_							(       lowercase_      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						"""hubert"""
  
     def __init__(    self:	Any   ,       snake_case__:	str=32   ,       snake_case__:	int=768   ,       snake_case__:	Dict=12   ,       snake_case__:	Optional[int]=12   ,       snake_case__:	List[str]=3_072   ,       snake_case__:	Optional[Any]="gelu"   ,       snake_case__:	Optional[int]=0.1   ,       snake_case__:	List[str]=0.1   ,       snake_case__:	Optional[int]=0.1   ,       snake_case__:	List[str]=True   ,       snake_case__:	Union[str, Any]=0.0   ,       snake_case__:	Union[str, Any]=0.1   ,       snake_case__:	List[str]=0.1   ,       snake_case__:	List[Any]=0.02   ,       snake_case__:	Dict=1E-5   ,       snake_case__:	Any="group"   ,       snake_case__:	Union[str, Any]="gelu"   ,       snake_case__:	List[str]=(512, 512, 512, 512, 512, 512, 512)   ,       snake_case__:	Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)   ,       snake_case__:	Any=(10, 3, 3, 3, 3, 2, 2)   ,       snake_case__:	int=False   ,       snake_case__:	Any=128   ,       snake_case__:	Dict=16   ,       snake_case__:	Optional[Any]=False   ,       snake_case__:	Tuple=True   ,       snake_case__:	int=0.05   ,       snake_case__:	List[Any]=10   ,       snake_case__:	Tuple=2   ,       snake_case__:	List[Any]=0.0   ,       snake_case__:	Optional[int]=10   ,       snake_case__:	Optional[int]=0   ,       snake_case__:	Union[str, Any]="sum"   ,       snake_case__:	Union[str, Any]=False   ,       snake_case__:	int=False   ,       snake_case__:	Any=256   ,       snake_case__:	Union[str, Any]=0   ,       snake_case__:	Optional[int]=1   ,       snake_case__:	List[str]=2   ,       **snake_case__:	Optional[int]   ,       )  ->		Any:
  
  
         """simple docstring"""
  
  
  
         super().__init__(**snake_case__   ,       pad_token_id=snake_case__   ,       bos_token_id=snake_case__   ,       eos_token_id=snake_case__	)
         _lowercase     =	hidden_size
         _lowercase     =	feat_extract_norm
         _lowercase     =	feat_extract_activation
         _lowercase     =	list(snake_case__	)
         _lowercase     =	list(snake_case__	)
         _lowercase     =	list(snake_case__	)
         _lowercase     =	conv_bias
         _lowercase     =	num_conv_pos_embeddings
         _lowercase     =	num_conv_pos_embedding_groups
         _lowercase     =	len(self.conv_dim	)
         _lowercase     =	num_hidden_layers
         _lowercase     =	intermediate_size
         _lowercase     =	hidden_act
         _lowercase     =	num_attention_heads
         _lowercase     =	hidden_dropout
         _lowercase     =	attention_dropout
         _lowercase     =	activation_dropout
         _lowercase     =	feat_proj_layer_norm
         _lowercase     =	feat_proj_dropout
         _lowercase     =	final_dropout
         _lowercase     =	layerdrop
         _lowercase     =	layer_norm_eps
         _lowercase     =	initializer_range
         _lowercase     =	vocab_size
         _lowercase     =	do_stable_layer_norm
         _lowercase     =	use_weighted_layer_sum
         _lowercase     =	classifier_proj_size
  
         if (
             (len(self.conv_stride	) != self.num_feat_extract_layers)
             or (len(self.conv_kernel	) != self.num_feat_extract_layers)
             or (len(self.conv_dim	) != self.num_feat_extract_layers)
         ):
             raise ValueError(
                 'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
                 ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
                 f''' {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,'''
                 f''' `len(config.conv_kernel) = {len(self.conv_kernel	)}`.'''	)
  
         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         _lowercase     =	apply_spec_augment
         _lowercase     =	mask_time_prob
         _lowercase     =	mask_time_length
         _lowercase     =	mask_time_min_masks
         _lowercase     =	mask_feature_prob
         _lowercase     =	mask_feature_length
         _lowercase     =	mask_feature_min_masks
  
         # ctc loss
         _lowercase     =	ctc_loss_reduction
         _lowercase     =	ctc_zero_infinity
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         return functools.reduce(operator.mul   ,       self.conv_stride   ,       1	)
  