import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


SCREAMING_SNAKE_CASE__		:					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"

SCREAMING_SNAKE_CASE__		:					Dict						=					"\\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"


SCREAMING_SNAKE_CASE__		:					int						=					"\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				(      lowercase__  :			str ):







     '''simple docstring'''


     def remove_articles(lowercase__  :			str ):
          SCREAMING_SNAKE_CASE__				:				str			=		re.compile(r'\b(a|an|the)\b'     ,  re.UNICODE )
          return re.sub(lowercase__     ,  ' '     ,  lowercase__ )

     def white_space_fix(lowercase__  :			Dict ):
          return " ".join(text.split() )

     def remove_punc(lowercase__  :			Any ):
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		set(string.punctuation )
          return "".join(ch for ch in text if ch not in exclude )

     def lower(lowercase__  :			List[Any] ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(lowercase__ ) ) ) )







def     _a				(      lowercase__  :			Dict     ,  lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     return int(normalize_answer(lowercase__ ) == normalize_answer(lowercase__ ) )







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			Optional[int] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		[any(compute_exact(lowercase__     ,  lowercase__ ) for ref in refs ) for pred, refs in zip(lowercase__     ,  lowercase__ )]
     return (sum(lowercase__ ) / len(lowercase__ )) * 1_00







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			int     ,  lowercase__  :			Any     ,  lowercase__  :			int ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Dict			=		[rgram for rgrams in rgramslist for rgram in rgrams]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		Counter(lowercase__ )

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

     SCREAMING_SNAKE_CASE__				:				int			=		Counter(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		Counter()
     for cgram, ccount in cgramcounter.items():
          SCREAMING_SNAKE_CASE__				:				Dict			=		ccount * numref

     # KEEP
     SCREAMING_SNAKE_CASE__				:				str			=		sgramcounter_rep & cgramcounter_rep
     SCREAMING_SNAKE_CASE__				:				Any			=		keepgramcounter_rep & rgramcounter
     SCREAMING_SNAKE_CASE__				:				Dict			=		sgramcounter_rep & rgramcounter

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

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

     # ADDITION
     SCREAMING_SNAKE_CASE__				:				Tuple			=		set(lowercase__ ) - set(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				int			=		set(lowercase__ ) & set(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		set(lowercase__ ) - set(lowercase__ )

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

     return (keepscore, delscore_precision, addscore)







def     _a				(      lowercase__  :			Dict     ,  lowercase__  :			Any     ,  lowercase__  :			Any ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		len(lowercase__ )

     SCREAMING_SNAKE_CASE__				:				int			=		ssent.split(' ' )
     SCREAMING_SNAKE_CASE__				:				List[str]			=		csent.split(' ' )
     SCREAMING_SNAKE_CASE__				:				int			=		[]
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[]
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     SCREAMING_SNAKE_CASE__				:				List[str]			=		[]
     SCREAMING_SNAKE_CASE__				:				Dict			=		[]
     SCREAMING_SNAKE_CASE__				:				int			=		[]

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

     for i in range(0     ,  len(lowercase__ ) - 1 ):
          if i < len(lowercase__ ) - 1:
               SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		sagrams[i] + ' ' + sagrams[i + 1]
               sagrams.append(lowercase__ )
          if i < len(lowercase__ ) - 2:
               SCREAMING_SNAKE_CASE__				:				Dict			=		sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
               sagrams.append(lowercase__ )
          if i < len(lowercase__ ) - 3:
               SCREAMING_SNAKE_CASE__				:				str			=		sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
               sagrams.append(lowercase__ )

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

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







def     _a				(      lowercase__  :			Tuple     ,  lowercase__  :			bool = True     ,  lowercase__  :			str = "13a"     ,  lowercase__  :			bool = True ):







     '''simple docstring'''


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

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

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

     return normalized_sent







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			Optional[Any]     ,  lowercase__  :			Optional[Any] ):







     '''simple docstring'''


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







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str     ,  lowercase__  :			Any="exp"     ,  lowercase__  :			Any=None     ,  lowercase__  :			List[str]=False     ,  lowercase__  :			Tuple=False     ,  lowercase__  :			List[str]=False     ,  ):







     '''simple docstring'''


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







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




  def 	__lowercase(      self     :	int				)->       List[str]:




       """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 	__lowercase(      self     :	int     , a_     :	Dict     , a_     :	Dict     , a_     :	List[Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		{}
       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     _a				(      lowercase__  :			Optional[int] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		set({'(', '[', '{'} )
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		set({')', ']', '}'} )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{'{': '}', '[': ']', '(': ')'}

     for i in range(len(lowercase__ ) ):
          if s[i] in open_brackets:
               stack.append(s[i] )

          elif s[i] in closed_brackets and (
              len(lowercase__ ) == 0 or (len(lowercase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(lowercase__ ) == 0







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		input('Enter sequence of brackets: ' )
     if is_balanced(lowercase__ ):
          print(lowercase__     ,  'is balanced' )
     else:
          print(lowercase__     ,  'is not balanced' )


if __name__ == "__main__":
      main()



