


"""simple docstring"""


import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__snake_case    					=					'\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'

__snake_case    					=					'\\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'


__snake_case    					=					'\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   (     lowerCamelCase__	:							Tuple  ):

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

 def white_space_fix(lowerCamelCase__	:							Optional[int]  ):
  return " ".join(text.split()  )

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

 def lower(lowerCamelCase__	:							Union[str, Any]  ):
  return text.lower()

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


def    _lowerCamelCase   (     lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							int  ):
 return int(normalize_answer(lowerCamelCase__  ) == normalize_answer(lowerCamelCase__  )  )


def    _lowerCamelCase   (     lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							Optional[int]  ):
 lowercase__       :       Tuple				=						[any(compute_exact(lowerCamelCase__       ,		lowerCamelCase__  ) for ref in refs  ) for pred, refs in zip(lowerCamelCase__       ,		lowerCamelCase__  )]
 return (sum(lowerCamelCase__  ) / len(lowerCamelCase__  )) * 1_00


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							List[str]       ,		lowerCamelCase__	:							List[str]  ):
 lowercase__       :       Any				=						[rgram for rgrams in rgramslist for rgram in rgrams]
 lowercase__       :       Optional[Any]				=						Counter(lowerCamelCase__  )

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

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

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

 lowercase__       :       Optional[int]				=						0
 lowercase__       :       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.
 lowercase__       :       Dict				=						1
 lowercase__       :       Any				=						1
 if len(lowerCamelCase__  ) > 0:
  lowercase__       :       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)
  lowercase__       :       List[str]				=						keeptmpscorea / sum(keepgramcounterall_rep.values()  )
 lowercase__       :       Union[str, Any]				=						0
 if keepscore_precision > 0 or keepscore_recall > 0:
  lowercase__       :       Union[str, Any]				=						2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

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

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

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

 return (keepscore, delscore_precision, addscore)


def    _lowerCamelCase   (     lowerCamelCase__	:							List[str]       ,		lowerCamelCase__	:							Dict       ,		lowerCamelCase__	:							str  ):
 lowercase__       :       Optional[Any]				=						len(lowerCamelCase__  )

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

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

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


def    _lowerCamelCase   (     lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							bool = True       ,		lowerCamelCase__	:							str = "13a"       ,		lowerCamelCase__	:							bool = 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:
  lowercase__       :       Tuple				=						sentence.lower()

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

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

 return normalized_sent


def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							str  ):
 if not (len(lowerCamelCase__  ) == len(lowerCamelCase__  ) == len(lowerCamelCase__  )):
  raise ValueError("""Sources length must match predictions and references lengths."""  )
 lowercase__       :       int				=						0
 for src, pred, refs in zip(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  ):
  sari_score += SARIsent(normalize(lowerCamelCase__  )       ,		normalize(lowerCamelCase__  )       ,		[normalize(lowerCamelCase__  ) for sent in refs]  )
 lowercase__       :       Dict				=						sari_score / len(lowerCamelCase__  )
 return 1_00 * sari_score


def    _lowerCamelCase   (     lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							Optional[int]       ,		lowerCamelCase__	:							Dict="exp"       ,		lowerCamelCase__	:							Union[str, Any]=None       ,		lowerCamelCase__	:							Any=False       ,		lowerCamelCase__	:							Optional[int]=False       ,		lowerCamelCase__	:							List[Any]=False       ,		):
 lowercase__       :       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"""  )
 lowercase__       :       Optional[int]				=						[[refs[i] for refs in references] for i in range(lowerCamelCase__  )]
 lowercase__       :       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 				_SCREAMING_SNAKE_CASE				(							datasets.Metric  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						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 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							str:
    lowercase__       :       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