



"""simple docstring"""





import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCamelCase      =		"""
@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\",
}
"""

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


lowerCamelCase      =		"""
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      a__			(   lowerCAmelCase__   ):

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

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

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

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

      return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase__   )   )   )   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      return int(normalize_answer(lowerCAmelCase__   ) == normalize_answer(lowerCAmelCase__   )   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[any(compute_exact(lowerCAmelCase__			,    lowerCAmelCase__   ) for ref in refs   ) for pred, refs in zip(lowerCAmelCase__			,    lowerCAmelCase__   )]
      return (sum(lowerCAmelCase__   ) / len(lowerCAmelCase__   )) * 100
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[rgram for rgrams in rgramslist for rgram in rgrams]
      UpperCAmelCase_    				=							Counter(lowerCAmelCase__   )

      UpperCAmelCase_    				=							Counter(lowerCAmelCase__   )
      UpperCAmelCase_    				=							Counter()
      for sgram, scount in sgramcounter.items():
            UpperCAmelCase_    				=							scount * numref

      UpperCAmelCase_    				=							Counter(lowerCAmelCase__   )
      UpperCAmelCase_    				=							Counter()
      for cgram, ccount in cgramcounter.items():
            UpperCAmelCase_    				=							ccount * numref

      # KEEP
      UpperCAmelCase_    				=							sgramcounter_rep & cgramcounter_rep
      UpperCAmelCase_    				=							keepgramcounter_rep & rgramcounter
      UpperCAmelCase_    				=							sgramcounter_rep & rgramcounter

      UpperCAmelCase_    				=							0
      UpperCAmelCase_    				=							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.
      UpperCAmelCase_    				=							1
      UpperCAmelCase_    				=							1
      if len(lowerCAmelCase__   ) > 0:
            UpperCAmelCase_    				=							keeptmpscorea / len(lowerCAmelCase__   )
      if len(lowerCAmelCase__   ) > 0:
            # Fix an alleged bug [2] in the keep score computation.
            # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
            UpperCAmelCase_    				=							keeptmpscorea / sum(keepgramcounterall_rep.values()   )
      UpperCAmelCase_    				=							0
      if keepscore_precision > 0 or keepscore_recall > 0:
            UpperCAmelCase_    				=							2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

      # DELETION
      UpperCAmelCase_    				=							sgramcounter_rep - cgramcounter_rep
      UpperCAmelCase_    				=							delgramcounter_rep - rgramcounter
      UpperCAmelCase_    				=							sgramcounter_rep - rgramcounter
      UpperCAmelCase_    				=							0
      UpperCAmelCase_    				=							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.
      UpperCAmelCase_    				=							1
      if len(lowerCAmelCase__   ) > 0:
            UpperCAmelCase_    				=							deltmpscorea / len(lowerCAmelCase__   )

      # ADDITION
      UpperCAmelCase_    				=							set(lowerCAmelCase__   ) - set(lowerCAmelCase__   )
      UpperCAmelCase_    				=							set(lowerCAmelCase__   ) & set(lowerCAmelCase__   )
      UpperCAmelCase_    				=							set(lowerCAmelCase__   ) - set(lowerCAmelCase__   )

      UpperCAmelCase_    				=							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.
      UpperCAmelCase_    				=							1
      UpperCAmelCase_    				=							1
      if len(lowerCAmelCase__   ) > 0:
            UpperCAmelCase_    				=							addtmpscore / len(lowerCAmelCase__   )
      if len(lowerCAmelCase__   ) > 0:
            UpperCAmelCase_    				=							addtmpscore / len(lowerCAmelCase__   )
      UpperCAmelCase_    				=							0
      if addscore_precision > 0 or addscore_recall > 0:
            UpperCAmelCase_    				=							2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

      return (keepscore, delscore_precision, addscore)
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							len(lowerCAmelCase__   )

      UpperCAmelCase_    				=							ssent.split(" "   )
      UpperCAmelCase_    				=							csent.split(" "   )
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_    				=							[]

      UpperCAmelCase_    				=							[]
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_    				=							[]
      for rsent in rsents:
            UpperCAmelCase_    				=							rsent.split(" "   )
            UpperCAmelCase_    				=							[]
            UpperCAmelCase_    				=							[]
            UpperCAmelCase_    				=							[]
            ragramslist.append(lowerCAmelCase__   )
            for i in range(0			,    len(lowerCAmelCase__   ) - 1   ):
                  if i < len(lowerCAmelCase__   ) - 1:
                        UpperCAmelCase_    				=							ragrams[i] + " " + ragrams[i + 1]
                        ragrams.append(lowerCAmelCase__   )
                  if i < len(lowerCAmelCase__   ) - 2:
                        UpperCAmelCase_    				=							ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
                        ragrams.append(lowerCAmelCase__   )
                  if i < len(lowerCAmelCase__   ) - 3:
                        UpperCAmelCase_    				=							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:
                  UpperCAmelCase_    				=							sagrams[i] + " " + sagrams[i + 1]
                  sagrams.append(lowerCAmelCase__   )
            if i < len(lowerCAmelCase__   ) - 2:
                  UpperCAmelCase_    				=							sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
                  sagrams.append(lowerCAmelCase__   )
            if i < len(lowerCAmelCase__   ) - 3:
                  UpperCAmelCase_    				=							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:
                  UpperCAmelCase_    				=							cagrams[i] + " " + cagrams[i + 1]
                  cagrams.append(lowerCAmelCase__   )
            if i < len(lowerCAmelCase__   ) - 2:
                  UpperCAmelCase_    				=							cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
                  cagrams.append(lowerCAmelCase__   )
            if i < len(lowerCAmelCase__   ) - 3:
                  UpperCAmelCase_    				=							cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
                  cagrams.append(lowerCAmelCase__   )

      ((UpperCAmelCase_)				,					(UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							SARIngram(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
      ((UpperCAmelCase_)				,					(UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							SARIngram(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
      ((UpperCAmelCase_)				,					(UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							SARIngram(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
      ((UpperCAmelCase_)				,					(UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							SARIngram(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
      UpperCAmelCase_    				=							sum([keepascore, keepascore, keepascore, keepascore]   ) / 4
      UpperCAmelCase_    				=							sum([delascore, delascore, delascore, delascore]   ) / 4
      UpperCAmelCase_    				=							sum([addascore, addascore, addascore, addascore]   ) / 4
      UpperCAmelCase_    				=							(avgkeepscore + avgdelscore + avgaddscore) / 3
      return finalscore
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__ = True			,    lowerCAmelCase__ = "13a"			,    lowerCAmelCase__ = 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:
            UpperCAmelCase_    				=							sentence.lower()

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

      if not return_str:
            UpperCAmelCase_    				=							normalized_sent.split()

      return normalized_sent
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      if not (len(lowerCAmelCase__   ) == len(lowerCAmelCase__   ) == len(lowerCAmelCase__   )):
            raise ValueError("Sources length must match predictions and references lengths."   )
      UpperCAmelCase_    				=							0
      for src, pred, refs in zip(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
            sari_score += SARIsent(normalize(lowerCAmelCase__   )			,    normalize(lowerCAmelCase__   )			,    [normalize(lowerCAmelCase__   ) for sent in refs]   )
      UpperCAmelCase_    				=							sari_score / len(lowerCAmelCase__   )
      return 100 * sari_score
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__="exp"			,    lowerCAmelCase__=None			,    lowerCAmelCase__=False			,    lowerCAmelCase__=False			,    lowerCAmelCase__=False			,    ):
      UpperCAmelCase_    				=							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"   )
      UpperCAmelCase_    				=							[[refs[i] for refs in references] for i in range(lowerCAmelCase__   )]
      UpperCAmelCase_    				=							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    lowercase__     (					datasets.Metric   ):







       '''simple docstring'''


       def   lowercase__       (							self    :						List[Any]					)				->		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    :						Optional[Any]	,       _UpperCAmelCase    :						Optional[Any]	,       _UpperCAmelCase    :						List[Any]	,       _UpperCAmelCase    :						int					)				->		Dict:


             '''simple docstring'''




             UpperCAmelCase_    				=							{}
             result.update({"sari": compute_sari(sources=_UpperCAmelCase	,       predictions=_UpperCAmelCase	,       references=_UpperCAmelCase					)}					)
             result.update({"sacrebleu": compute_sacrebleu(predictions=_UpperCAmelCase	,       references=_UpperCAmelCase					)}					)
             result.update({"exact": compute_em(predictions=_UpperCAmelCase	,       references=_UpperCAmelCase					)}					)
             return result









"""simple docstring"""





import string
def      a__			(   lowerCAmelCase__   ):
      for key in range(len(string.ascii_uppercase   )   ):
            UpperCAmelCase_    				=							""
            for symbol in message:
                  if symbol in string.ascii_uppercase:
                        UpperCAmelCase_    				=							string.ascii_uppercase.find(lowerCAmelCase__   )
                        UpperCAmelCase_    				=							num - key
                        if num < 0:
                              UpperCAmelCase_    				=							num + len(string.ascii_uppercase   )
                        UpperCAmelCase_    				=							translated + string.ascii_uppercase[num]
                  else:
                        UpperCAmelCase_    				=							translated + symbol
            print(f"""Decryption using Key #{key}: {translated}"""   )
def      a__			(   ):
      UpperCAmelCase_    				=							input("Encrypted message: "   )
      UpperCAmelCase_    				=							message.upper()
      decrypt(lowerCAmelCase__   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




