


'''simple docstring'''




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCAmelCase_      :		Union[str, 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"

UpperCAmelCase_      :		List[str]          =		"\\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"


UpperCAmelCase_      :		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    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   def remove_articles(A      ):
      _a					:  Optional[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      ):
      _a					:  Dict						=  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    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   return int(normalize_answer(A      ) == normalize_answer(A      )      )


def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   _a					:  List[Any]						=  [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    UpperCAmelCase_  (       A		,	A		,	A		,	A      ):
   '''simple docstring'''





   _a					:  Optional[int]						=  [rgram for rgrams in rgramslist for rgram in rgrams]
   _a					:  List[str]						=  Counter(A      )

   _a					:  Dict						=  Counter(A      )
   _a					:  List[Any]						=  Counter()
   for sgram, scount in sgramcounter.items():
      _a					:  str						=  scount * numref

   _a					:  Union[str, Any]						=  Counter(A      )
   _a					:  List[str]						=  Counter()
   for cgram, ccount in cgramcounter.items():
      _a					:  Tuple						=  ccount * numref

   # KEEP
   _a					:  Any						=  sgramcounter_rep & cgramcounter_rep
   _a					:  Optional[int]						=  keepgramcounter_rep & rgramcounter
   _a					:  Optional[int]						=  sgramcounter_rep & rgramcounter

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

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

   # ADDITION
   _a					:  List[Any]						=  set(A      ) - set(A      )
   _a					:  List[Any]						=  set(A      ) & set(A      )
   _a					:  List[Any]						=  set(A      ) - set(A      )

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

   return (keepscore, delscore_precision, addscore)


def    UpperCAmelCase_  (       A		,	A		,	A      ):
   '''simple docstring'''





   _a					:  int						=  len(A      )

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

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

   ((_a)		,						(_a)		,						(_a))					:  Any						=  SARIngram(A		,	A		,	A		,	A      )
   ((_a)		,						(_a)		,						(_a))					:  str						=  SARIngram(A		,	A		,	A		,	A      )
   ((_a)		,						(_a)		,						(_a))					:  Dict						=  SARIngram(A		,	A		,	A		,	A      )
   ((_a)		,						(_a)		,						(_a))					:  Tuple						=  SARIngram(A		,	A		,	A		,	A      )
   _a					:  List[str]						=  sum([keepascore, keepascore, keepascore, keepascore]      ) / 4
   _a					:  str						=  sum([delascore, delascore, delascore, delascore]      ) / 4
   _a					:  Dict						=  sum([addascore, addascore, addascore, addascore]      ) / 4
   _a					:  List[str]						=  (avgkeepscore + avgdelscore + avgaddscore) / 3
   return finalscore


def    UpperCAmelCase_  (       A		,	A = True		,	A = "13a"		,	A = True      ):
   '''simple docstring'''





   if lowercase:
      _a					:  Dict						=  sentence.lower()

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

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

   return normalized_sent


def    UpperCAmelCase_  (       A		,	A		,	A      ):
   '''simple docstring'''





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


def    UpperCAmelCase_  (       A		,	A		,	A="exp"		,	A=None		,	A=False		,	A=False		,	A=False		,	):
   '''simple docstring'''





   _a					:  List[str]						=  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'      )
   _a					:  int						=  [[refs[i] for refs in references] for i in range(A      )]
   _a					:  Optional[int]						=  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  ):
 '''simple docstring'''




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    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_      )      ->    List[Any]:
    _a					:  Optional[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 warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_      :		Tuple          =		logging.get_logger(__name__)







class 					a			(				snake_case__  ):
 '''simple docstring'''




 def __init__(    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    None:
    warnings.warn(
        'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
        ' Please use PerceiverImageProcessor instead.'		,      lowerCamelCase_		,      )
    super().__init__(*lowerCamelCase_		,      **lowerCamelCase_      )


