'''simple docstring'''


from __future__ import annotations


class 						lowercase__	:






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  TypeError(
          '''Matrices must be formed from a list of zero or more lists containing at '''
          '''least one and the same number of values, each of which must be of type '''
          '''int or float.'''   )
      if len(__A   ) != 0:
         UpperCamelCase       =  len(rows[0]   )
         if cols == 0:
            raise error
         for row in rows:
            if len(__A   ) != cols:
               raise error
            for value in row:
               if not isinstance(__A ,   (int, float)   ):
                  raise error
         UpperCamelCase       =  rows
      else:
         UpperCamelCase       =  []







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return [[row[i] for row in self.rows] for i in range(len(self.rows[0]   )   )]







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return len(self.rows   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return len(self.rows[0]   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return (self.num_rows, self.num_columns)







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.order[0] == self.order[1]







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  [
          [0 if column_num != row_num else 1 for column_num in range(self.num_rows   )]
          for row_num in range(self.num_rows   )
      ]
      return Matrix(__A   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      if not self.is_square:
         return 0
      if self.order == (0, 0):
         return 1
      if self.order == (1, 1):
         return int(self.rows[0][0]   )
      if self.order == (2, 2):
         return int(
             (self.rows[0][0] * self.rows[1][1])
             - (self.rows[0][1] * self.rows[1][0])   )
      else:
         return sum(
             self.rows[0][column] * self.cofactors().rows[0][column]
             for column in range(self.num_columns   )   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return bool(self.determinant()   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  [
          [
              self.rows[other_row][other_column]
              for other_column in range(self.num_columns   )
              if other_column != column
          ]
          for other_row in range(self.num_rows   )
          if other_row != row
      ]
      return Matrix(__A   ).determinant()







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      if (row + column) % 2 == 0:
         return self.get_minor(__A ,   __A   )
      return -1 * self.get_minor(__A ,   __A   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return Matrix(
          [
              [self.get_minor(__A ,   __A   ) for column in range(self.num_columns   )]
              for row in range(self.num_rows   )
          ]   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return Matrix(
          [
              [
                  self.minors().rows[row][column]
                  if (row + column) % 2 == 0
                  else self.minors().rows[row][column] * -1
                  for column in range(self.minors().num_columns   )
              ]
              for row in range(self.minors().num_rows   )
          ]   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  [
          [self.cofactors().rows[column][row] for column in range(self.num_columns   )]
          for row in range(self.num_rows   )
      ]
      return Matrix(__A   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.determinant()
      if not determinant:
         raise TypeError('''Only matrices with a non-zero determinant have an inverse'''   )
      return self.adjugate() * (1 / determinant)







   def __repr__(			self   ):

      '''simple docstring'''







      return str(self.rows   )







   def __str__(			self   ):

      '''simple docstring'''







      if self.num_rows == 0:
         return "[]"
      if self.num_rows == 1:
         return "[[" + ". ".join(str(self.rows[0]   )   ) + "]]"
      return (
          "["
          + "\n ".join(
              [
                  '''[''' + '''. '''.join([str(__A   ) for value in row]   ) + '''.]'''
                  for row in self.rows
              ]   )
          + "]"
      )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      UpperCamelCase       =  TypeError('''Row must be a list containing all ints and/or floats'''   )
      if not isinstance(__A ,   __A   ):
         raise type_error
      for value in row:
         if not isinstance(__A ,   (int, float)   ):
            raise type_error
      if len(__A   ) != self.num_columns:
         raise ValueError(
             '''Row must be equal in length to the other rows in the matrix'''   )
      if position is None:
         self.rows.append(__A   )
      else:
         UpperCamelCase       =  self.rows[0:position] + [row] + self.rows[position:]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      UpperCamelCase       =  TypeError(
          '''Column must be a list containing all ints and/or floats'''   )
      if not isinstance(__A ,   __A   ):
         raise type_error
      for value in column:
         if not isinstance(__A ,   (int, float)   ):
            raise type_error
      if len(__A   ) != self.num_rows:
         raise ValueError(
             '''Column must be equal in length to the other columns in the matrix'''   )
      if position is None:
         UpperCamelCase       =  [self.rows[i] + [column[i]] for i in range(self.num_rows   )]
      else:
         UpperCamelCase       =  [
             self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
             for i in range(self.num_rows   )
         ]







   def __eq__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if not isinstance(__A ,   __A   ):
         return NotImplemented
      return self.rows == other.rows







   def __ne__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return not self == other







   def __neg__(			self   ):

      '''simple docstring'''







      return self * -1







   def __add__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if self.order != other.order:
         raise ValueError('''Addition requires matrices of the same order'''   )
      return Matrix(
          [
              [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns   )]
              for i in range(self.num_rows   )
          ]   )







   def __sub__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if self.order != other.order:
         raise ValueError('''Subtraction requires matrices of the same order'''   )
      return Matrix(
          [
              [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns   )]
              for i in range(self.num_rows   )
          ]   )







   def __mul__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if isinstance(__A ,   (int, float)   ):
         return Matrix(
             [[int(element * other   ) for element in row] for row in self.rows]   )
      elif isinstance(__A ,   __A   ):
         if self.num_columns != other.num_rows:
            raise ValueError(
                '''The number of columns in the first matrix must '''
                '''be equal to the number of rows in the second'''   )
         return Matrix(
             [
                 [Matrix.dot_product(__A ,   __A   ) for column in other.columns()]
                 for row in self.rows
             ]   )
      else:
         raise TypeError(
             '''A Matrix can only be multiplied by an int, float, or another matrix'''   )







   def __pow__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if not isinstance(__A ,   __A   ):
         raise TypeError('''A Matrix can only be raised to the power of an int'''   )
      if not self.is_square:
         raise ValueError('''Only square matrices can be raised to a power'''   )
      if other == 0:
         return self.identity()
      if other < 0:
         if self.is_invertable():
            return self.inverse() ** (-other)
         raise ValueError(
             '''Only invertable matrices can be raised to a negative power'''   )
      UpperCamelCase       =  self
      for _ in range(other - 1   ):
         result *= self
      return result







   @classmethod
   def       UpperCAmelCase				(			cls ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      return sum(row[i] * column[i] for i in range(len(__A   )   )   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()







'''simple docstring'''


import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case_ :  int     =					'\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'

snake_case_ :  Optional[int]     =					'\\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'


snake_case_ :  Optional[Any]     =					'\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  __snake_case     (    _UpperCAmelCase					:       Optional[Any]):

   def remove_articles(_UpperCAmelCase					:       str):
      UpperCamelCase       =  re.compile(R'''\b(a|an|the)\b''',      re.UNICODE)
      return re.sub(_UpperCAmelCase,      ''' ''',      _UpperCAmelCase)

   def white_space_fix(_UpperCAmelCase					:       Union[str, Any]):
      return " ".join(text.split())

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

   def lower(_UpperCAmelCase					:       List[str]):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase))))



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       List[str]):
   return int(normalize_answer(_UpperCAmelCase) == normalize_answer(_UpperCAmelCase))



def  __snake_case     (    _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Optional[int]):
   UpperCamelCase       =  [any(compute_exact(_UpperCAmelCase,      _UpperCAmelCase) for ref in refs) for pred, refs in zip(_UpperCAmelCase,      _UpperCAmelCase)]
   return (sum(_UpperCAmelCase) / len(_UpperCAmelCase)) * 100



def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       List[str]):
   UpperCamelCase       =  [rgram for rgrams in rgramslist for rgram in rgrams]
   UpperCamelCase       =  Counter(_UpperCAmelCase)

   UpperCamelCase       =  Counter(_UpperCAmelCase)
   UpperCamelCase       =  Counter()
   for sgram, scount in sgramcounter.items():
      UpperCamelCase       =  scount * numref

   UpperCamelCase       =  Counter(_UpperCAmelCase)
   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(_UpperCAmelCase) > 0:
      UpperCamelCase       =  keeptmpscorea / len(_UpperCAmelCase)
   if len(_UpperCAmelCase) > 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(_UpperCAmelCase) > 0:
      UpperCamelCase       =  deltmpscorea / len(_UpperCAmelCase)

   # ADDITION
   UpperCamelCase       =  set(_UpperCAmelCase) - set(_UpperCAmelCase)
   UpperCamelCase       =  set(_UpperCAmelCase) & set(_UpperCAmelCase)
   UpperCamelCase       =  set(_UpperCAmelCase) - set(_UpperCAmelCase)

   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(_UpperCAmelCase) > 0:
      UpperCamelCase       =  addtmpscore / len(_UpperCAmelCase)
   if len(_UpperCAmelCase) > 0:
      UpperCamelCase       =  addtmpscore / len(_UpperCAmelCase)
   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  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       str):
   UpperCamelCase       =  len(_UpperCAmelCase)

   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(_UpperCAmelCase)
      for i in range(0,      len(_UpperCAmelCase) - 1):
         if i < len(_UpperCAmelCase) - 1:
            UpperCamelCase       =  ragrams[i] + ''' ''' + ragrams[i + 1]
            ragrams.append(_UpperCAmelCase)
         if i < len(_UpperCAmelCase) - 2:
            UpperCamelCase       =  ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
            ragrams.append(_UpperCAmelCase)
         if i < len(_UpperCAmelCase) - 3:
            UpperCamelCase       =  ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
            ragrams.append(_UpperCAmelCase)
      ragramslist.append(_UpperCAmelCase)
      ragramslist.append(_UpperCAmelCase)
      ragramslist.append(_UpperCAmelCase)

   for i in range(0,      len(_UpperCAmelCase) - 1):
      if i < len(_UpperCAmelCase) - 1:
         UpperCamelCase       =  sagrams[i] + ''' ''' + sagrams[i + 1]
         sagrams.append(_UpperCAmelCase)
      if i < len(_UpperCAmelCase) - 2:
         UpperCamelCase       =  sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
         sagrams.append(_UpperCAmelCase)
      if i < len(_UpperCAmelCase) - 3:
         UpperCamelCase       =  sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
         sagrams.append(_UpperCAmelCase)

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

   ((UpperCamelCase)     , (UpperCamelCase)     , (UpperCamelCase))       =  SARIngram(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)
   ((UpperCamelCase)     , (UpperCamelCase)     , (UpperCamelCase))       =  SARIngram(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)
   ((UpperCamelCase)     , (UpperCamelCase)     , (UpperCamelCase))       =  SARIngram(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)
   ((UpperCamelCase)     , (UpperCamelCase)     , (UpperCamelCase))       =  SARIngram(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)
   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  __snake_case     (    _UpperCAmelCase					:       List[str],      _UpperCAmelCase					:       bool = True,      _UpperCAmelCase					:       str = "13a",      _UpperCAmelCase					:       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:
      UpperCamelCase       =  sentence.lower()

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

   if not return_str:
      UpperCamelCase       =  normalized_sent.split()

   return normalized_sent



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       Optional[Any]):
   if not (len(_UpperCAmelCase) == len(_UpperCAmelCase) == len(_UpperCAmelCase)):
      raise ValueError('''Sources length must match predictions and references lengths.''')
   UpperCamelCase       =  0
   for src, pred, refs in zip(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase):
      sari_score += SARIsent(normalize(_UpperCAmelCase),      normalize(_UpperCAmelCase),      [normalize(_UpperCAmelCase) for sent in refs])
   UpperCamelCase       =  sari_score / len(_UpperCAmelCase)
   return 100 * sari_score



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Any="exp",      _UpperCAmelCase					:       str=None,      _UpperCAmelCase					:       Optional[Any]=False,      _UpperCAmelCase					:       Union[str, Any]=False,      _UpperCAmelCase					:       List[Any]=False,      ):
   UpperCamelCase       =  len(references[0])
   if any(len(_UpperCAmelCase) != 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(_UpperCAmelCase)]
   UpperCamelCase       =  sacrebleu.corpus_bleu(
       _UpperCAmelCase,      _UpperCAmelCase,      smooth_method=_UpperCAmelCase,      smooth_value=_UpperCAmelCase,      force=_UpperCAmelCase,      lowercase=_UpperCAmelCase,      use_effective_order=_UpperCAmelCase,      )
   return output.score


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






   '''simple docstring'''







   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 ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  {}
      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





