
import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowercase__          ='\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__          ='\\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__          ='\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        __UpperCamelCase					(    lowerCAmelCase__							:    Any  ):

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

 def white_space_fix(lowerCAmelCase__							:    List[Any]  ):
  return " ".join(text.split()  )

 def remove_punc(lowerCAmelCase__							:    str  ):
  __a			:  Dict		    =  set(string.punctuation  )
  return "".join(ch for ch in text if ch not in exclude  )

 def lower(lowerCAmelCase__							:    Optional[int]  ):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase__  )  )  )  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Any  ):
 return int(normalize_answer(lowerCAmelCase__  ) == normalize_answer(lowerCAmelCase__  )  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    List[Any]  ):
 __a			:  Tuple		    =  [any(compute_exact(lowerCAmelCase__  ,       lowerCAmelCase__  ) for ref in refs  ) for pred, refs in zip(lowerCAmelCase__  ,       lowerCAmelCase__  )]
 return (sum(lowerCAmelCase__  ) / len(lowerCAmelCase__  )) * 1_0_0




def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Optional[Any]  ):
 __a			:  int		    =  [rgram for rgrams in rgramslist for rgram in rgrams]
 __a			:  Union[str, Any]		    =  Counter(lowerCAmelCase__  )

 __a			:  int		    =  Counter(lowerCAmelCase__  )
 __a			:  Dict		    =  Counter()
 for sgram, scount in sgramcounter.items():
  __a			:  Optional[Any]		    =  scount * numref

 __a			:  Optional[int]		    =  Counter(lowerCAmelCase__  )
 __a			:  Union[str, Any]		    =  Counter()
 for cgram, ccount in cgramcounter.items():
  __a			:  int		    =  ccount * numref

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

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

 # DELETION
 __a			:  Any		    =  sgramcounter_rep - cgramcounter_rep
 __a			:  Any		    =  delgramcounter_rep - rgramcounter
 __a			:  List[str]		    =  sgramcounter_rep - rgramcounter
 __a			:  Any		    =  0
 __a			:  Dict		    =  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.
 __a			:  Any		    =  1
 if len(lowerCAmelCase__  ) > 0:
  __a			:  List[str]		    =  deltmpscorea / len(lowerCAmelCase__  )

 # ADDITION
 __a			:  Optional[Any]		    =  set(lowerCAmelCase__  ) - set(lowerCAmelCase__  )
 __a			:  Tuple		    =  set(lowerCAmelCase__  ) & set(lowerCAmelCase__  )
 __a			:  Optional[int]		    =  set(lowerCAmelCase__  ) - set(lowerCAmelCase__  )

 __a			:  Union[str, 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.
 __a			:  List[str]		    =  1
 __a			:  List[str]		    =  1
 if len(lowerCAmelCase__  ) > 0:
  __a			:  Any		    =  addtmpscore / len(lowerCAmelCase__  )
 if len(lowerCAmelCase__  ) > 0:
  __a			:  Optional[Any]		    =  addtmpscore / len(lowerCAmelCase__  )
 __a			:  List[Any]		    =  0
 if addscore_precision > 0 or addscore_recall > 0:
  __a			:  Optional[Any]		    =  2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

 return (keepscore, delscore_precision, addscore)




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    str  ):
 __a			:  List[Any]		    =  len(lowerCAmelCase__  )

 __a			:  List[Any]		    =  ssent.split(''' '''  )
 __a			:  Union[str, Any]		    =  csent.split(''' '''  )
 __a			:  List[Any]		    =  []
 __a			:  Any		    =  []
 __a			:  Union[str, Any]		    =  []
 __a			:  str		    =  []
 __a			:  List[Any]		    =  []
 __a			:  Optional[int]		    =  []

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

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




def        __UpperCamelCase					(    lowerCAmelCase__							:    Dict  ,       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:
  __a			:  Optional[Any]		    =  sentence.lower()

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

 if not return_str:
  __a			:  Optional[int]		    =  normalized_sent.split()

 return normalized_sent




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    Dict  ):
 if not (len(lowerCAmelCase__  ) == len(lowerCAmelCase__  ) == len(lowerCAmelCase__  )):
  raise ValueError('''Sources length must match predictions and references lengths.'''  )
 __a			:  Any		    =  0
 for src, pred, refs in zip(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ):
  sari_score += SARIsent(normalize(lowerCAmelCase__  )  ,       normalize(lowerCAmelCase__  )  ,       [normalize(lowerCAmelCase__  ) for sent in refs]  )
 __a			:  int		    =  sari_score / len(lowerCAmelCase__  )
 return 1_0_0 * sari_score




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    List[Any]="exp"  ,       lowerCAmelCase__							:    Optional[Any]=None  ,       lowerCAmelCase__							:    str=False  ,       lowerCAmelCase__							:    Tuple=False  ,       lowerCAmelCase__							:    Tuple=False  ,       ):
 __a			:  Dict		    =  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'''  )
 __a			:  Optional[Any]		    =  [[refs[i] for refs in references] for i in range(lowerCAmelCase__  )]
 __a			:  Union[str, 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 		UpperCamelCase__  (	datasets.Metric   ):
   def 				lowerCAmelCase      (self			: List[str]       ):
    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] ,    snake_case_			: Union[str, Any] ,    snake_case_			: int ,    snake_case_			: Union[str, Any]       ):
    __a			:  int		    =  {}
    result.update({'''sari''': compute_sari(sources=snake_case_ ,    predictions=snake_case_ ,    references=snake_case_       )}       )
    result.update({'''sacrebleu''': compute_sacrebleu(predictions=snake_case_ ,    references=snake_case_       )}       )
    result.update({'''exact''': compute_em(predictions=snake_case_ ,    references=snake_case_       )}       )
    return result


def        __UpperCamelCase					(    lowerCAmelCase__							:    list  ):

 def merge(lowerCAmelCase__							:    list  ,       lowerCAmelCase__							:    list  ) -> list:

  def _merge():
   while left and right:
    yield (left if left[0] <= right[0] else right).pop(0  )
   yield from left
   yield from right

  return list(_merge()  )

 if len(lowerCAmelCase__  ) <= 1:
  return collection
 __a			:  str		    =  len(lowerCAmelCase__  ) // 2
 return merge(merge_sort(collection[:mid]  )  ,       merge_sort(collection[mid:]  )  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      lowercase__          =input('Enter numbers separated by a comma:\n').strip()
      lowercase__          =[int(item) for item in user_input.split(',')]
      print(*merge_sort(unsorted), sep=',')
