


import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


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

_snake_case             =     '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''


_snake_case             =     '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known ."]]
    >>> wiki_split = datasets.load_metric("wiki_split")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  def remove_articles(SCREAMING_SNAKE_CASE_	):
    lowerCamelCase :					int    =				re.compile(r"\b(a|an|the)\b"      ,						re.UNICODE	)
    return re.sub(SCREAMING_SNAKE_CASE_      ,						" "      ,						SCREAMING_SNAKE_CASE_	)

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

  def remove_punc(SCREAMING_SNAKE_CASE_	):
    lowerCamelCase :					Union[str, Any]    =				set(string.punctuation	)
    return "".join(ch for ch in text if ch not in exclude	)

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

  return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_	)	)	)	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return int(normalize_answer(SCREAMING_SNAKE_CASE_	) == normalize_answer(SCREAMING_SNAKE_CASE_	)	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					str    =				[any(compute_exact(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	) for ref in refs	) for pred, refs in zip(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)]
  return (sum(SCREAMING_SNAKE_CASE_	) / len(SCREAMING_SNAKE_CASE_	)) * 100






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					str    =				[rgram for rgrams in rgramslist for rgram in rgrams]
  lowerCamelCase :					Union[str, Any]    =				Counter(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					Tuple    =				Counter(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Optional[int]    =				Counter()
  for sgram, scount in sgramcounter.items():
    lowerCamelCase :					Dict    =				scount * numref

  lowerCamelCase :					str    =				Counter(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Union[str, Any]    =				Counter()
  for cgram, ccount in cgramcounter.items():
    lowerCamelCase :					Dict    =				ccount * numref

  # KEEP
  lowerCamelCase :					Tuple    =				sgramcounter_rep & cgramcounter_rep
  lowerCamelCase :					Dict    =				keepgramcounter_rep & rgramcounter
  lowerCamelCase :					int    =				sgramcounter_rep & rgramcounter

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

  # DELETION
  lowerCamelCase :					List[Any]    =				sgramcounter_rep - cgramcounter_rep
  lowerCamelCase :					Union[str, Any]    =				delgramcounter_rep - rgramcounter
  lowerCamelCase :					Optional[int]    =				sgramcounter_rep - rgramcounter
  lowerCamelCase :					int    =				0
  lowerCamelCase :					str    =				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 :					List[Any]    =				1
  if len(SCREAMING_SNAKE_CASE_	) > 0:
    lowerCamelCase :					Optional[int]    =				deltmpscorea / len(SCREAMING_SNAKE_CASE_	)

  # ADDITION
  lowerCamelCase :					Tuple    =				set(SCREAMING_SNAKE_CASE_	) - set(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Optional[int]    =				set(SCREAMING_SNAKE_CASE_	) & set(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					List[Any]    =				set(SCREAMING_SNAKE_CASE_	) - set(SCREAMING_SNAKE_CASE_	)

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

  return (keepscore, delscore_precision, addscore)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Any    =				len(SCREAMING_SNAKE_CASE_	)

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

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

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

  for i in range(0      ,						len(SCREAMING_SNAKE_CASE_	) - 1	):
    if i < len(SCREAMING_SNAKE_CASE_	) - 1:
      lowerCamelCase :					Optional[Any]    =				cagrams[i] + " " + cagrams[i + 1]
      cagrams.append(SCREAMING_SNAKE_CASE_	)
    if i < len(SCREAMING_SNAKE_CASE_	) - 2:
      lowerCamelCase :					Optional[int]    =				cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
      cagrams.append(SCREAMING_SNAKE_CASE_	)
    if i < len(SCREAMING_SNAKE_CASE_	) - 3:
      lowerCamelCase :					Tuple    =				cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
      cagrams.append(SCREAMING_SNAKE_CASE_	)

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_ = True      ,						SCREAMING_SNAKE_CASE_ = "13a"      ,						SCREAMING_SNAKE_CASE_ = True	):


  '''simple docstring'''

  if lowercase:
    lowerCamelCase :					Union[str, Any]    =				sentence.lower()

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

  if not return_str:
    lowerCamelCase :					Optional[Any]    =				normalized_sent.split()

  return normalized_sent






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_="exp"      ,						SCREAMING_SNAKE_CASE_=None      ,						SCREAMING_SNAKE_CASE_=False      ,						SCREAMING_SNAKE_CASE_=False      ,						SCREAMING_SNAKE_CASE_=False      ,						):


  '''simple docstring'''

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







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

   '''simple docstring'''




   def _snake_case    (		self ):






     """simple docstring"""





     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 ):






     """simple docstring"""





     lowerCamelCase :					Any    =				{}
     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







def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for i in range(len(SCREAMING_SNAKE_CASE_	) - 1      ,						0      ,						-1	):
    lowerCamelCase :					Tuple    =				False

    for j in range(SCREAMING_SNAKE_CASE_      ,						0      ,						-1	):
      if unsorted[j] < unsorted[j - 1]:
        lowerCamelCase    ,	lowerCamelCase :					int    =				unsorted[j - 1], unsorted[j]
        lowerCamelCase :					Optional[int]    =				True

    for j in range(SCREAMING_SNAKE_CASE_	):
      if unsorted[j] > unsorted[j + 1]:
        lowerCamelCase    ,	lowerCamelCase :					Union[str, Any]    =				unsorted[j + 1], unsorted[j]
        lowerCamelCase :					Optional[Any]    =				True

    if not swapped:
      break
  return unsorted


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _snake_case             =     input('''Enter numbers separated by a comma:\n''').strip()
   _snake_case             =     [int(item) for item in user_input.split(''',''')]
   print(f'''{cocktail_shaker_sort(unsorted) = }''')



