
"""simple docstring"""

from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCamelCase      :							Dict          =					"\\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"

UpperCamelCase      :							List[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"


UpperCamelCase      :							Optional[int]          =					"\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 				A		(							snake_case :List[Any]							,   snake_case :str							,   snake_case :Union[str, Any]							,   snake_case :Optional[int]						) ->      Optional[Any]:
 __UpperCamelCase							    =			[rgram for rgrams in rgramslist for rgram in rgrams]
 __UpperCamelCase							    =			Counter(__UpperCamelCase						)

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

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

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

 __UpperCamelCase							    =			0
 __UpperCamelCase							    =			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							    =			1
 __UpperCamelCase							    =			1
 if len(__UpperCamelCase						) > 0:
  __UpperCamelCase							    =			keeptmpscorea / len(__UpperCamelCase						)
 if len(__UpperCamelCase						) > 0:
  # Fix an alleged bug [2] in the keep score computation.
  # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
  __UpperCamelCase							    =			keeptmpscorea / sum(keepgramcounterall_rep.values()						)
 __UpperCamelCase							    =			0
 if keepscore_precision > 0 or keepscore_recall > 0:
  __UpperCamelCase							    =			2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

 # DELETION
 __UpperCamelCase							    =			sgramcounter_rep - cgramcounter_rep
 __UpperCamelCase							    =			delgramcounter_rep - rgramcounter
 __UpperCamelCase							    =			sgramcounter_rep - rgramcounter
 __UpperCamelCase							    =			0
 __UpperCamelCase							    =			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							    =			1
 if len(__UpperCamelCase						) > 0:
  __UpperCamelCase							    =			deltmpscorea / len(__UpperCamelCase						)

 # ADDITION
 __UpperCamelCase							    =			set(__UpperCamelCase						) - set(__UpperCamelCase						)
 __UpperCamelCase							    =			set(__UpperCamelCase						) & set(__UpperCamelCase						)
 __UpperCamelCase							    =			set(__UpperCamelCase						) - set(__UpperCamelCase						)

 __UpperCamelCase							    =			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							    =			1
 __UpperCamelCase							    =			1
 if len(__UpperCamelCase						) > 0:
  __UpperCamelCase							    =			addtmpscore / len(__UpperCamelCase						)
 if len(__UpperCamelCase						) > 0:
  __UpperCamelCase							    =			addtmpscore / len(__UpperCamelCase						)
 __UpperCamelCase							    =			0
 if addscore_precision > 0 or addscore_recall > 0:
  __UpperCamelCase							    =			2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

 return (keepscore, delscore_precision, addscore)






def 				A		(							snake_case :Optional[Any]							,   snake_case :Dict							,   snake_case :Optional[Any]						) ->      Dict:
 __UpperCamelCase							    =			len(__UpperCamelCase						)

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

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

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






def 				A		(							snake_case :List[str]							,   snake_case :str = True							,   snake_case :Optional[int] = "13a"							,   snake_case :Optional[Any] = True						) ->      List[Any]:
 # 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							    =			sentence.lower()

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

 if not return_str:
  __UpperCamelCase							    =			normalized_sent.split()

 return normalized_sent





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,    _KWARGS_DESCRIPTION     )
class __lowerCAmelCase						( 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      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      if not (len(lowerCamelCase_ ) == len(lowerCamelCase_ ) == len(lowerCamelCase_ )):
       raise ValueError('Sources length must match predictions and references lengths.' )
      __UpperCamelCase							    =			0
      for src, pred, refs in zip(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_ ):
       sari_score += SARIsent(normalize(lowerCamelCase_ )      ,	normalize(lowerCamelCase_ )      ,	[normalize(lowerCamelCase_ ) for sent in refs] )
      __UpperCamelCase							    =			sari_score / len(lowerCamelCase_ )
      return {"sari": 100 * sari_score}







class 	a__						:

       def __init__( self   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  Dict				      =     data
            a_     :  int
            a_     :  Node



def 			_a      (    __UpperCamelCase			):
     a_     :  int				      =     0
     a_     :  Optional[Any]				      =     x



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_      ,      a_     :  Dict				      =     find_set(__UpperCamelCase			), find_set(__UpperCamelCase			)
     if x == y:
          return

     elif x.rank > y.rank:
          a_     :  Tuple				      =     x
     else:
          a_     :  Optional[Any]				      =     y
          if x.rank == y.rank:
               y.rank += 1



def 			_a      (    __UpperCamelCase			):
     if x != x.parent:
          a_     :  Tuple				      =     find_set(x.parent			)
     return x.parent



def 			_a      (    __UpperCamelCase			):
     a_     :  List[Any]				      =     ({0, 1, 2}, {3, 4, 5})
     for s in sets:
          if node.data in s:
               return s
     a_     :  Union[str, Any]				      =     F'''{node.data} is not in {sets}'''
     raise ValueError(__UpperCamelCase			)



def 			_a      (    ):
     a_     :  int				      =     [Node(__UpperCamelCase			) for i in range(6			)]
     for v in vertex:
          make_set(__UpperCamelCase			)

     union_set(vertex[0]	,	vertex[1]			)
     union_set(vertex[1]	,	vertex[2]			)
     union_set(vertex[3]	,	vertex[4]			)
     union_set(vertex[3]	,	vertex[5]			)

     for nodea in vertex:
          for nodea in vertex:
               if find_python_set(__UpperCamelCase			).isdisjoint(find_python_set(__UpperCamelCase			)			):
                    assert find_set(__UpperCamelCase			) != find_set(__UpperCamelCase			)
               else:
                    assert find_set(__UpperCamelCase			) == find_set(__UpperCamelCase			)


if __name__ == "__main__":
       test_disjoint_set()



