




'''simple docstring'''







import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowercase      :   Union[str, Any]      =       """\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"""

lowercase      :   Optional[int]      =       """\\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"""


lowercase      :   Tuple      =       """\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        lowerCamelCase__				(      __lowercase				):

     def remove_articles(__lowercase				):
          snake_case			:      List[str]        =			re.compile(r"""\b(a|an|the)\b"""     ,   re.UNICODE				)
          return re.sub(lowerCamelCase__     ,   """ """     ,   lowerCamelCase__				)

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

     def remove_punc(__lowercase				):
          snake_case			:      Optional[int]        =			set(string.punctuation				)
          return "".join(ch for ch in text if ch not in exclude				)

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

     return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase__				)				)				)				)



def        lowerCamelCase__				(      __lowercase     ,   __lowercase				):
     return int(normalize_answer(lowerCamelCase__				) == normalize_answer(lowerCamelCase__				)				)



def        lowerCamelCase__				(      __lowercase     ,   __lowercase				):
     snake_case			:      Tuple        =			[any(compute_exact(lowerCamelCase__     ,   lowerCamelCase__				) for ref in refs				) for pred, refs in zip(lowerCamelCase__     ,   lowerCamelCase__				)]
     return (sum(lowerCamelCase__				) / len(lowerCamelCase__				)) * 100



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      Any        =			[rgram for rgrams in rgramslist for rgram in rgrams]
     snake_case			:      Optional[Any]        =			Counter(lowerCamelCase__				)

     snake_case			:      Union[str, Any]        =			Counter(lowerCamelCase__				)
     snake_case			:      Optional[int]        =			Counter()
     for sgram, scount in sgramcounter.items():
          snake_case			:      int        =			scount * numref

     snake_case			:      Tuple        =			Counter(lowerCamelCase__				)
     snake_case			:      List[Any]        =			Counter()
     for cgram, ccount in cgramcounter.items():
          snake_case			:      Tuple        =			ccount * numref

     # KEEP
     snake_case			:      Optional[Any]        =			sgramcounter_rep & cgramcounter_rep
     snake_case			:      str        =			keepgramcounter_rep & rgramcounter
     snake_case			:      Union[str, Any]        =			sgramcounter_rep & rgramcounter

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

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

     # ADDITION
     snake_case			:      Dict        =			set(lowerCamelCase__				) - set(lowerCamelCase__				)
     snake_case			:      Any        =			set(lowerCamelCase__				) & set(lowerCamelCase__				)
     snake_case			:      List[Any]        =			set(lowerCamelCase__				) - set(lowerCamelCase__				)

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

     return (keepscore, delscore_precision, addscore)



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      Optional[Any]        =			len(lowerCamelCase__				)

     snake_case			:      List[Any]        =			ssent.split(""" """				)
     snake_case			:      Union[str, Any]        =			csent.split(""" """				)
     snake_case			:      Any        =			[]
     snake_case			:      Optional[int]        =			[]
     snake_case			:      Union[str, Any]        =			[]
     snake_case			:      Optional[Any]        =			[]
     snake_case			:      Tuple        =			[]
     snake_case			:      Any        =			[]

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

     for i in range(0     ,   len(lowerCamelCase__				) - 1				):
          if i < len(lowerCamelCase__				) - 1:
               snake_case			:      Union[str, Any]        =			sagrams[i] + """ """ + sagrams[i + 1]
               sagrams.append(lowerCamelCase__				)
          if i < len(lowerCamelCase__				) - 2:
               snake_case			:      Union[str, Any]        =			sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
               sagrams.append(lowerCamelCase__				)
          if i < len(lowerCamelCase__				) - 3:
               snake_case			:      int        =			sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
               sagrams.append(lowerCamelCase__				)

     for i in range(0     ,   len(lowerCamelCase__				) - 1				):
          if i < len(lowerCamelCase__				) - 1:
               snake_case			:      List[str]        =			cagrams[i] + """ """ + cagrams[i + 1]
               cagrams.append(lowerCamelCase__				)
          if i < len(lowerCamelCase__				) - 2:
               snake_case			:      Optional[int]        =			cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
               cagrams.append(lowerCamelCase__				)
          if i < len(lowerCamelCase__				) - 3:
               snake_case			:      List[str]        =			cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
               cagrams.append(lowerCamelCase__				)

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



def        lowerCamelCase__				(      __lowercase     ,   __lowercase = True     ,   __lowercase = "13a"     ,   __lowercase = True				):
     # 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:
          snake_case			:      Tuple        =			sentence.lower()

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

     if not return_str:
          snake_case			:      Tuple        =			normalized_sent.split()

     return normalized_sent



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



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase="exp"     ,   __lowercase=None     ,   __lowercase=False     ,   __lowercase=False     ,   __lowercase=False     ,   ):
     snake_case			:      int        =			len(references[0]				)
     if any(len(lowerCamelCase__				) != references_per_prediction for refs in references				):
          raise ValueError("""Sacrebleu requires the same number of references for each prediction"""				)
     snake_case			:      Optional[int]        =			[[refs[i] for refs in references] for i in range(lowerCamelCase__				)]
     snake_case			:      Optional[Any]        =			sacrebleu.corpus_bleu(
         lowerCamelCase__     ,   lowerCamelCase__     ,   smooth_method=lowerCamelCase__     ,   smooth_value=lowerCamelCase__     ,   force=lowerCamelCase__     ,   lowercase=lowerCamelCase__     ,   use_effective_order=lowerCamelCase__     ,   )
     return output.score







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





     '''simple docstring'''



     def 			snake_case_      (				self							)      ->				Optional[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 			snake_case_      (				self							,__a							,__a							,__a							)      ->				str:
          snake_case			:      Union[str, Any]        =			{}
          result.update({"""sari""": compute_sari(sources=lowerCamelCase__							,predictions=lowerCamelCase__							,references=lowerCamelCase__							)}							)
          result.update({"""sacrebleu""": compute_sacrebleu(predictions=lowerCamelCase__							,references=lowerCamelCase__							)}							)
          result.update({"""exact""": compute_em(predictions=lowerCamelCase__							,references=lowerCamelCase__							)}							)
          return result







"""simple docstring"""


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[Any]  ):
 lowercase__       :       List[str]				=						len(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						sum(lowerCamelCase__  )

 lowercase__       :       Optional[int]				=						[[False for x in range(s + 1  )] for y in range(n + 1  )]

 for i in range(1       ,		n + 1  ):
  lowercase__       :       int				=						True

 for i in range(1       ,		s + 1  ):
  lowercase__       :       int				=						False

 for i in range(1       ,		n + 1  ):
  for j in range(1       ,		s + 1  ):
   lowercase__       :       Optional[Any]				=						dp[i][j - 1]

   if arr[i - 1] <= j:
    lowercase__       :       int				=						dp[i][j] or dp[i - 1][j - arr[i - 1]]

 for j in range(int(s / 2  )       ,		-1       ,		-1  ):
  if dp[n][j] is True:
   lowercase__       :       List[Any]				=						s - 2 * j
   break

 return diff