

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCAmelCase   :      int     =     """
@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   :      Any     =     """\
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   :      Tuple     =     """
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_     (							a     ):

      """simple docstring"""



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

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

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

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

      return white_space_fix(remove_articles(remove_punc(lower(a     )     )     )     )

def       A_     (							a    ,      a     ):

      """simple docstring"""



      return int(normalize_answer(a     ) == normalize_answer(a     )     )

def       A_     (							a    ,      a     ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     Tuple							=   [any(compute_exact(a    ,      a     ) for ref in refs     ) for pred, refs in zip(a    ,      a     )]
      return (sum(a     ) / len(a     )) * 1_0_0

def       A_     (							a    ,      a    ,      a    ,      a     ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     Tuple							=   [rgram for rgrams in rgramslist for rgram in rgrams]
      SCREAMING_SNAKE_CASE_ :     List[Any]							=   Counter(a     )

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

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

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

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

      # DELETION
      SCREAMING_SNAKE_CASE_ :     str							=   sgramcounter_rep - cgramcounter_rep
      SCREAMING_SNAKE_CASE_ :     Optional[int]							=   delgramcounter_rep - rgramcounter
      SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   sgramcounter_rep - rgramcounter
      SCREAMING_SNAKE_CASE_ :     int							=   0
      SCREAMING_SNAKE_CASE_ :     Optional[int]							=   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_ :     Tuple							=   1
      if len(a     ) > 0:
            SCREAMING_SNAKE_CASE_ :     Any							=   deltmpscorea / len(a     )

      # ADDITION
      SCREAMING_SNAKE_CASE_ :     str							=   set(a     ) - set(a     )
      SCREAMING_SNAKE_CASE_ :     Dict							=   set(a     ) & set(a     )
      SCREAMING_SNAKE_CASE_ :     List[str]							=   set(a     ) - set(a     )

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

      return (keepscore, delscore_precision, addscore)

def       A_     (							a    ,      a    ,      a     ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     Any							=   len(a     )

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

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

      for i in range(0    ,      len(a     ) - 1     ):
            if i < len(a     ) - 1:
                  SCREAMING_SNAKE_CASE_ :     Tuple							=   sagrams[i] + ''' ''' + sagrams[i + 1]
                  sagrams.append(a     )
            if i < len(a     ) - 2:
                  SCREAMING_SNAKE_CASE_ :     List[str]							=   sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
                  sagrams.append(a     )
            if i < len(a     ) - 3:
                  SCREAMING_SNAKE_CASE_ :     Any							=   sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
                  sagrams.append(a     )

      for i in range(0    ,      len(a     ) - 1     ):
            if i < len(a     ) - 1:
                  SCREAMING_SNAKE_CASE_ :     str							=   cagrams[i] + ''' ''' + cagrams[i + 1]
                  cagrams.append(a     )
            if i < len(a     ) - 2:
                  SCREAMING_SNAKE_CASE_ :     Any							=   cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
                  cagrams.append(a     )
            if i < len(a     ) - 3:
                  SCREAMING_SNAKE_CASE_ :     str							=   cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
                  cagrams.append(a     )

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

def       A_     (							a    ,      a = True    ,      a = "13a"    ,      a = True     ):

      """simple docstring"""



      if lowercase:
            SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   sentence.lower()

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

      if not return_str:
            SCREAMING_SNAKE_CASE_ :     str							=   normalized_sent.split()

      return normalized_sent

def       A_     (							a    ,      a    ,      a     ):

      """simple docstring"""



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

def       A_     (							a    ,      a    ,      a="exp"    ,      a=None    ,      a=False    ,      a=False    ,      a=False    ,      ):

      """simple docstring"""



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



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

  def        UpperCAmelCase			( 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        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     int							=   {}
        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'''

import doctest
from collections import deque

import numpy as np



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self )  ->  None:
        lowercase__				:    str  =					[2, 1, 2, -1]
        lowercase__				:    str  =					[1, 2, 3, 4]


       def _lowerCAmelCase(						self )  ->  list[float]:
        lowercase__				:    Optional[Any]  =					len(self.first_signal )
        lowercase__				:    Union[str, Any]  =					len(self.second_signal )

        lowercase__				:    int  =					max(__lowerCAmelCase	,   __lowerCAmelCase )

        # create a zero matrix of max_length x max_length
        lowercase__				:    List[str]  =					[[0] * max_length for i in range(__lowerCAmelCase )]

        # fills the smaller signal with zeros to make both signals of same length
        if length_first_signal < length_second_signal:
         self.first_signal += [0] * (max_length - length_first_signal)
        elif length_first_signal > length_second_signal:
         self.second_signal += [0] * (max_length - length_second_signal)
        for i in range(__lowerCAmelCase ):
         lowercase__				:    int  =					deque(self.second_signal )
         rotated_signal.rotate(__lowerCAmelCase )
         for j, item in enumerate(__lowerCAmelCase ):
          matrix[i][j] += item

        # multiply the matrix with the first signal
        lowercase__				:    Optional[int]  =					np.matmul(np.transpose(__lowerCAmelCase )	,   np.transpose(self.first_signal ) )

        # rounding-off to two decimal places
        return [round(__lowerCAmelCase	,   2 ) for i in final_signal]


if __name__ == "__main__":
  doctest.testmod()

