code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  from collections import Counter
  
  import sacrebleu
  import sacremoses
  from packaging import version
  
  import datasets
  
  
  __A :		List[Any]      							=    '''\
  @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 :		str      							=    '''\
  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 :		str      							=    '''
  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 					A_			(		snake_case_     :       int						,snake_case_     :       List[str]						,snake_case_     :       str						,snake_case_     :       List[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   List[str]          =							[rgram for rgrams in rgramslist for rgram in rgrams]
      UpperCamelCase				:   List[Any]          =							Counter(snake_case_    )
  
      UpperCamelCase				:   Any          =							Counter(snake_case_    )
      UpperCamelCase				:   List[Any]          =							Counter()
      for sgram, scount in sgramcounter.items():
          UpperCamelCase				:   str          =							scount * numref
  
      UpperCamelCase				:   Tuple          =							Counter(snake_case_    )
      UpperCamelCase				:   Tuple          =							Counter()
      for cgram, ccount in cgramcounter.items():
          UpperCamelCase				:   Any          =							ccount * numref
  
      # KEEP
      UpperCamelCase				:   Any          =							sgramcounter_rep & cgramcounter_rep
      UpperCamelCase				:   str          =							keepgramcounter_rep & rgramcounter
      UpperCamelCase				:   Optional[int]          =							sgramcounter_rep & rgramcounter
  
      UpperCamelCase				:   Dict          =							0
      UpperCamelCase				:   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.
      UpperCamelCase				:   int          =							1
      UpperCamelCase				:   Optional[int]          =							1
      if len(snake_case_    ) > 0:
          UpperCamelCase				:   Optional[int]          =							keeptmpscorea / len(snake_case_    )
      if len(snake_case_    ) > 0:
          # Fix an alleged bug [2] in the keep score computation.
          # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
          UpperCamelCase				:   Tuple          =							keeptmpscorea / sum(keepgramcounterall_rep.values()    )
      UpperCamelCase				:   Optional[int]          =							0
      if keepscore_precision > 0 or keepscore_recall > 0:
          UpperCamelCase				:   List[Any]          =							2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
  
      # DELETION
      UpperCamelCase				:   List[Any]          =							sgramcounter_rep - cgramcounter_rep
      UpperCamelCase				:   Any          =							delgramcounter_rep - rgramcounter
      UpperCamelCase				:   Optional[int]          =							sgramcounter_rep - rgramcounter
      UpperCamelCase				:   Union[str, Any]          =							0
      UpperCamelCase				:   Union[str, Any]          =							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.
      UpperCamelCase				:   Optional[Any]          =							1
      if len(snake_case_    ) > 0:
          UpperCamelCase				:   List[str]          =							deltmpscorea / len(snake_case_    )
  
      # ADDITION
      UpperCamelCase				:   Union[str, Any]          =							set(snake_case_    ) - set(snake_case_    )
      UpperCamelCase				:   str          =							set(snake_case_    ) & set(snake_case_    )
      UpperCamelCase				:   Dict          =							set(snake_case_    ) - set(snake_case_    )
  
      UpperCamelCase				:   List[str]          =							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.
      UpperCamelCase				:   Union[str, Any]          =							1
      UpperCamelCase				:   Union[str, Any]          =							1
      if len(snake_case_    ) > 0:
          UpperCamelCase				:   Optional[Any]          =							addtmpscore / len(snake_case_    )
      if len(snake_case_    ) > 0:
          UpperCamelCase				:   int          =							addtmpscore / len(snake_case_    )
      UpperCamelCase				:   List[Any]          =							0
      if addscore_precision > 0 or addscore_recall > 0:
          UpperCamelCase				:   Union[str, Any]          =							2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
  
      return (keepscore, delscore_precision, addscore)
  def 					A_			(		snake_case_     :       Optional[int]						,snake_case_     :       Tuple						,snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   List[Any]          =							len(snake_case_    )
  
      UpperCamelCase				:   Union[str, Any]          =							ssent.split(""" """    )
      UpperCamelCase				:   Optional[Any]          =							csent.split(""" """    )
      UpperCamelCase				:   Any          =							[]
      UpperCamelCase				:   Optional[Any]          =							[]
      UpperCamelCase				:   Tuple          =							[]
      UpperCamelCase				:   Union[str, Any]          =							[]
      UpperCamelCase				:   Tuple          =							[]
      UpperCamelCase				:   List[Any]          =							[]
  
      UpperCamelCase				:   Tuple          =							[]
      UpperCamelCase				:   Dict          =							[]
      UpperCamelCase				:   List[str]          =							[]
      UpperCamelCase				:   Optional[int]          =							[]
      for rsent in rsents:
          UpperCamelCase				:   Dict          =							rsent.split(""" """    )
          UpperCamelCase				:   int          =							[]
          UpperCamelCase				:   Union[str, Any]          =							[]
          UpperCamelCase				:   List[str]          =							[]
          ragramslist.append(snake_case_    )
          for i in range(0						,len(snake_case_    ) - 1    ):
              if i < len(snake_case_    ) - 1:
                  UpperCamelCase				:   List[str]          =							ragrams[i] + """ """ + ragrams[i + 1]
                  ragrams.append(snake_case_    )
              if i < len(snake_case_    ) - 2:
                  UpperCamelCase				:   Optional[int]          =							ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
                  ragrams.append(snake_case_    )
              if i < len(snake_case_    ) - 3:
                  UpperCamelCase				:   int          =							ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
                  ragrams.append(snake_case_    )
          ragramslist.append(snake_case_    )
          ragramslist.append(snake_case_    )
          ragramslist.append(snake_case_    )
  
      for i in range(0						,len(snake_case_    ) - 1    ):
          if i < len(snake_case_    ) - 1:
              UpperCamelCase				:   List[Any]          =							sagrams[i] + """ """ + sagrams[i + 1]
              sagrams.append(snake_case_    )
          if i < len(snake_case_    ) - 2:
              UpperCamelCase				:   Optional[Any]          =							sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
              sagrams.append(snake_case_    )
          if i < len(snake_case_    ) - 3:
              UpperCamelCase				:   Optional[int]          =							sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
              sagrams.append(snake_case_    )
  
      for i in range(0						,len(snake_case_    ) - 1    ):
          if i < len(snake_case_    ) - 1:
              UpperCamelCase				:   Optional[Any]          =							cagrams[i] + """ """ + cagrams[i + 1]
              cagrams.append(snake_case_    )
          if i < len(snake_case_    ) - 2:
              UpperCamelCase				:   Any          =							cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
              cagrams.append(snake_case_    )
          if i < len(snake_case_    ) - 3:
              UpperCamelCase				:   Optional[Any]          =							cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
              cagrams.append(snake_case_    )
  
      ((UpperCamelCase)       ,							(UpperCamelCase)       ,							(UpperCamelCase))				:   Union[str, Any]          =							SARIngram(snake_case_						,snake_case_						,snake_case_						,snake_case_    )
      ((UpperCamelCase)       ,							(UpperCamelCase)       ,							(UpperCamelCase))				:   Dict          =							SARIngram(snake_case_						,snake_case_						,snake_case_						,snake_case_    )
      ((UpperCamelCase)       ,							(UpperCamelCase)       ,							(UpperCamelCase))				:   Tuple          =							SARIngram(snake_case_						,snake_case_						,snake_case_						,snake_case_    )
      ((UpperCamelCase)       ,							(UpperCamelCase)       ,							(UpperCamelCase))				:   Any          =							SARIngram(snake_case_						,snake_case_						,snake_case_						,snake_case_    )
      UpperCamelCase				:   List[str]          =							sum([keepascore, keepascore, keepascore, keepascore]    ) / 4
      UpperCamelCase				:   Dict          =							sum([delascore, delascore, delascore, delascore]    ) / 4
      UpperCamelCase				:   Optional[Any]          =							sum([addascore, addascore, addascore, addascore]    ) / 4
      UpperCamelCase				:   List[Any]          =							(avgkeepscore + avgdelscore + avgaddscore) / 3
      return finalscore
  def 					A_			(		snake_case_     :       int						,snake_case_     :       bool = True						,snake_case_     :       str = "13a"						,snake_case_     :       bool = True    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      # Normalization is requried for the ASSET dataset (one of the primary
      # datasets in sentence simplification) to allow using space
      # to split the sentence. Even though Wiki-Auto and TURK datasets,
      # do not require normalization, we do it for consistency.
      # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
      # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7
  
      if lowercase:
          UpperCamelCase				:   str          =							sentence.lower()
  
      if tokenizer in ["13a", "intl"]:
          if version.parse(sacrebleu.__version__    ).major >= 2:
              UpperCamelCase				:   List[str]          =							sacrebleu.metrics.bleu._get_tokenizer(snake_case_    )()(snake_case_    )
          else:
              UpperCamelCase				:   int          =							sacrebleu.TOKENIZERS[tokenizer]()(snake_case_    )
      elif tokenizer == "moses":
          UpperCamelCase				:   List[str]          =							sacremoses.MosesTokenizer().tokenize(snake_case_						,return_str=snake_case_						,escape=snake_case_    )
      elif tokenizer == "penn":
          UpperCamelCase				:   Optional[Any]          =							sacremoses.MosesTokenizer().penn_tokenize(snake_case_						,return_str=snake_case_    )
      else:
          UpperCamelCase				:   int          =							sentence
  
      if not return_str:
          UpperCamelCase				:   List[str]          =							normalized_sent.split()
  
      return normalized_sent
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,  _KWARGS_DESCRIPTION				)
  class 							lowerCamelCase		( datasets.Metric				):
  
  
  
  
  
  
  
     def       a_							(							self ):
         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    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         if not (len(SCREAMING_SNAKE_CASE_ ) == len(SCREAMING_SNAKE_CASE_ ) == len(SCREAMING_SNAKE_CASE_ )):
             raise ValueError("""Sources length must match predictions and references lengths.""" )
         UpperCamelCase				:   Tuple          =							0
         for src, pred, refs in zip(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             sari_score += SARIsent(normalize(SCREAMING_SNAKE_CASE_ )    ,      normalize(SCREAMING_SNAKE_CASE_ )    ,      [normalize(SCREAMING_SNAKE_CASE_ ) for sent in refs] )
         UpperCamelCase				:   Tuple          =							sari_score / len(SCREAMING_SNAKE_CASE_ )
         return {"sari": 100 * sari_score}
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  from ...utils import is_transformers_available
  from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline
  
  
  if is_transformers_available():
    from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
  
  
  
  
  