





import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_A :   str  			=  '\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

_A :   Tuple  			=  '\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n'


_A :   Union[str, Any]  			=  '\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\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\n    sacrebleu: sacrebleu score\n    exact: exact score\n\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 ."]]\n    >>> wiki_split = datasets.load_metric("wiki_split")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n'



def 	_a						(   UpperCAmelCase					)    ->    Union[str, Any]:







   """simple docstring"""

   def remove_articles(UpperCAmelCase					):
      lowerCamelCase__       :						List[Any]        =							re.compile(R'''\b(a|an|the)\b'''      ,  re.UNICODE					)
      return re.sub(UpperCAmelCase      ,  ''' '''      ,  UpperCAmelCase					)

   def white_space_fix(UpperCAmelCase					):
      return " ".join(text.split()					)

   def remove_punc(UpperCAmelCase					):
      lowerCamelCase__       :						Optional[Any]        =							set(string.punctuation					)
      return "".join(ch for ch in text if ch not in exclude					)

   def lower(UpperCAmelCase					):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase					)					)					)					)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Tuple:







   """simple docstring"""
   return int(normalize_answer(UpperCAmelCase					) == normalize_answer(UpperCAmelCase					)					)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[int]:







   """simple docstring"""
   lowerCamelCase__       :						Any        =							[any(compute_exact(UpperCAmelCase      ,  UpperCAmelCase					) for ref in refs					) for pred, refs in zip(UpperCAmelCase      ,  UpperCAmelCase					)]
   return (sum(UpperCAmelCase					) / len(UpperCAmelCase					)) * 100



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[int]:







   """simple docstring"""
   lowerCamelCase__       :						Optional[Any]        =							[rgram for rgrams in rgramslist for rgram in rgrams]
   lowerCamelCase__       :						int        =							Counter(UpperCAmelCase					)

   lowerCamelCase__       :						Any        =							Counter(UpperCAmelCase					)
   lowerCamelCase__       :						str        =							Counter()
   for sgram, scount in sgramcounter.items():
      lowerCamelCase__       :						Tuple        =							scount * numref

   lowerCamelCase__       :						str        =							Counter(UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							Counter()
   for cgram, ccount in cgramcounter.items():
      lowerCamelCase__       :						int        =							ccount * numref

   # KEEP
   lowerCamelCase__       :						List[str]        =							sgramcounter_rep & cgramcounter_rep
   lowerCamelCase__       :						str        =							keepgramcounter_rep & rgramcounter
   lowerCamelCase__       :						List[Any]        =							sgramcounter_rep & rgramcounter

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

   # DELETION
   lowerCamelCase__       :						str        =							sgramcounter_rep - cgramcounter_rep
   lowerCamelCase__       :						Tuple        =							delgramcounter_rep - rgramcounter
   lowerCamelCase__       :						Optional[int]        =							sgramcounter_rep - rgramcounter
   lowerCamelCase__       :						Optional[Any]        =							0
   lowerCamelCase__       :						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.
   lowerCamelCase__       :						Dict        =							1
   if len(UpperCAmelCase					) > 0:
      lowerCamelCase__       :						str        =							deltmpscorea / len(UpperCAmelCase					)

   # ADDITION
   lowerCamelCase__       :						str        =							set(UpperCAmelCase					) - set(UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							set(UpperCAmelCase					) & set(UpperCAmelCase					)
   lowerCamelCase__       :						int        =							set(UpperCAmelCase					) - set(UpperCAmelCase					)

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

   return (keepscore, delscore_precision, addscore)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    str:







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							len(UpperCAmelCase					)

   lowerCamelCase__       :						Optional[Any]        =							ssent.split(''' '''					)
   lowerCamelCase__       :						Dict        =							csent.split(''' '''					)
   lowerCamelCase__       :						int        =							[]
   lowerCamelCase__       :						Dict        =							[]
   lowerCamelCase__       :						Optional[Any]        =							[]
   lowerCamelCase__       :						Dict        =							[]
   lowerCamelCase__       :						Any        =							[]
   lowerCamelCase__       :						Tuple        =							[]

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

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



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase = True      ,  UpperCAmelCase = "13a"      ,  UpperCAmelCase = True					)    ->    Any:







   """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:
      lowerCamelCase__       :						List[str]        =							sentence.lower()

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

   if not return_str:
      lowerCamelCase__       :						Dict        =							normalized_sent.split()

   return normalized_sent



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Tuple:







   """simple docstring"""
   if not (len(UpperCAmelCase					) == len(UpperCAmelCase					) == len(UpperCAmelCase					)):
      raise ValueError('''Sources length must match predictions and references lengths.'''					)
   lowerCamelCase__       :						Optional[int]        =							0
   for src, pred, refs in zip(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					):
      sari_score += SARIsent(normalize(UpperCAmelCase					)      ,  normalize(UpperCAmelCase					)      ,  [normalize(UpperCAmelCase					) for sent in refs]					)
   lowerCamelCase__       :						Optional[Any]        =							sari_score / len(UpperCAmelCase					)
   return 100 * sari_score



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase="exp"      ,  UpperCAmelCase=None      ,  UpperCAmelCase=False      ,  UpperCAmelCase=False      ,  UpperCAmelCase=False      ,  )    ->    str:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							len(references[0]					)
   if any(len(UpperCAmelCase					) != references_per_prediction for refs in references					):
      raise ValueError('''Sacrebleu requires the same number of references for each prediction'''					)
   lowerCamelCase__       :						int        =							[[refs[i] for refs in references] for i in range(UpperCAmelCase					)]
   lowerCamelCase__       :						int        =							sacrebleu.corpus_bleu(
       UpperCAmelCase      ,  UpperCAmelCase      ,  smooth_method=UpperCAmelCase      ,  smooth_value=UpperCAmelCase      ,  force=UpperCAmelCase      ,  lowercase=UpperCAmelCase      ,  use_effective_order=UpperCAmelCase      ,  )
   return output.score




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,_KWARGS_DESCRIPTION )
class        __SCREAMING_SNAKE_CASE      (			datasets.Metric ):







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->List[Any]:
          return datasets.MetricInfo(
              description=_DESCRIPTION		, citation=_CITATION		, inputs_description=_KWARGS_DESCRIPTION		, features=datasets.Features(
                  {
                      '''predictions''': datasets.Value('''string'''		, id='''sequence'''      ),
                      '''references''': datasets.Sequence(datasets.Value('''string'''		, id='''sequence'''      )		, id='''references'''      ),
                  }      )		, codebase_urls=[
                  '''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
                  '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
                  '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
                  '''https://github.com/mjpost/sacreBLEU''',
              ]		, reference_urls=[
                  '''https://www.aclweb.org/anthology/Q16-1029.pdf''',
                  '''https://github.com/mjpost/sacreBLEU''',
                  '''https://en.wikipedia.org/wiki/BLEU''',
                  '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
              ]		, )







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     Any		, A					:     Tuple		, A					:     Union[str, Any]      )       ->Optional[int]:
          lowerCamelCase__       :						str        =							{}
          result.update({'''sari''': compute_sari(sources=A		, predictions=A		, references=A      )}      )
          result.update({'''sacrebleu''': compute_sacrebleu(predictions=A		, references=A      )}      )
          result.update({'''exact''': compute_em(predictions=A		, references=A      )}      )
          return result










import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
       # Create universe of discourse in Python using linspace ()
       _A :   Optional[int]  			=  np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

       # Create two fuzzy sets by defining any membership function
       # (trapmf(), gbellmf(), gaussmf(), etc).
       _A :   Any  			=  [0, 25, 50]
       _A :   Dict  			=  [25, 50, 75]
       _A :   Any  			=  fuzz.membership.trimf(X, abca)
       _A :   List[Any]  			=  fuzz.membership.trimf(X, abca)

       # Compute the different operations using inbuilt functions.
       _A :   List[str]  			=  np.ones(75)
       _A :   Optional[int]  			=  np.zeros((75,))
       # 1. Union = max(µA(x), µB(x))
       _A :   Optional[Any]  			=  fuzz.fuzzy_or(X, young, X, middle_aged)[1]
       # 2. Intersection = min(µA(x), µB(x))
       _A :   Union[str, Any]  			=  fuzz.fuzzy_and(X, young, X, middle_aged)[1]
       # 3. Complement (A) = (1- min(µA(x))
       _A :   List[Any]  			=  fuzz.fuzzy_not(young)
       # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
       _A :   Tuple  			=  fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
       # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
       _A :   List[Any]  			=  young + middle_aged - (young * middle_aged)
       # 6. Algebraic Product = (µA(x) * µB(x))
       _A :   Optional[Any]  			=  young * middle_aged
       # 7. Bounded Sum = min[1,(µA(x), µB(x))]
       _A :   Dict  			=  fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
       # 8. Bounded difference = min[0,(µA(x), µB(x))]
       _A :   List[str]  			=  fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

       # max-min composition
       # max-product composition

       # Plot each set A, set B and each operation result using plot() and subplot().
       from matplotlib import pyplot as plt

       plt.figure()

       plt.subplot(4, 3, 1)
       plt.plot(X, young)
       plt.title('Young')
       plt.grid(True)

       plt.subplot(4, 3, 2)
       plt.plot(X, middle_aged)
       plt.title('Middle aged')
       plt.grid(True)

       plt.subplot(4, 3, 3)
       plt.plot(X, union)
       plt.title('union')
       plt.grid(True)

       plt.subplot(4, 3, 4)
       plt.plot(X, intersection)
       plt.title('intersection')
       plt.grid(True)

       plt.subplot(4, 3, 5)
       plt.plot(X, complement_a)
       plt.title('complement_a')
       plt.grid(True)

       plt.subplot(4, 3, 6)
       plt.plot(X, difference)
       plt.title('difference a/b')
       plt.grid(True)

       plt.subplot(4, 3, 7)
       plt.plot(X, alg_sum)
       plt.title('alg_sum')
       plt.grid(True)

       plt.subplot(4, 3, 8)
       plt.plot(X, alg_product)
       plt.title('alg_product')
       plt.grid(True)

       plt.subplot(4, 3, 9)
       plt.plot(X, bdd_sum)
       plt.title('bdd_sum')
       plt.grid(True)

       plt.subplot(4, 3, 10)
       plt.plot(X, bdd_difference)
       plt.title('bdd_difference')
       plt.grid(True)

       plt.subplots_adjust(hspace=0.5)
       plt.show()



