
'''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







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case_ :  List[Any]     =					{
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Union[str, Any]     =					[
       'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Data2VecAudioForAudioFrameClassification',
       'Data2VecAudioForCTC',
       'Data2VecAudioForSequenceClassification',
       'Data2VecAudioForXVector',
       'Data2VecAudioModel',
       'Data2VecAudioPreTrainedModel',
   ]
   snake_case_ :  str     =					[
       'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Data2VecTextForCausalLM',
       'Data2VecTextForMaskedLM',
       'Data2VecTextForMultipleChoice',
       'Data2VecTextForQuestionAnswering',
       'Data2VecTextForSequenceClassification',
       'Data2VecTextForTokenClassification',
       'Data2VecTextModel',
       'Data2VecTextPreTrainedModel',
   ]
   snake_case_ :  Optional[int]     =					[
       'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Data2VecVisionForImageClassification',
       'Data2VecVisionForMaskedImageModeling',
       'Data2VecVisionForSemanticSegmentation',
       'Data2VecVisionModel',
       'Data2VecVisionPreTrainedModel',
   ]

if is_tf_available():
   snake_case_ :  List[str]     =					[
       'TFData2VecVisionForImageClassification',
       'TFData2VecVisionForSemanticSegmentation',
       'TFData2VecVisionModel',
       'TFData2VecVisionPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
   from .configuration_dataavec_text import (
       DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DataaVecTextConfig,
       DataaVecTextOnnxConfig,
   )
   from .configuration_dataavec_vision import (
       DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DataaVecVisionConfig,
       DataaVecVisionOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dataavec_audio import (
          DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecAudioForAudioFrameClassification,
          DataaVecAudioForCTC,
          DataaVecAudioForSequenceClassification,
          DataaVecAudioForXVector,
          DataaVecAudioModel,
          DataaVecAudioPreTrainedModel,
      )
      from .modeling_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecTextForCausalLM,
          DataaVecTextForMaskedLM,
          DataaVecTextForMultipleChoice,
          DataaVecTextForQuestionAnswering,
          DataaVecTextForSequenceClassification,
          DataaVecTextForTokenClassification,
          DataaVecTextModel,
          DataaVecTextPreTrainedModel,
      )
      from .modeling_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
          DataaVecVisionForImageClassification,
          DataaVecVisionForMaskedImageModeling,
          DataaVecVisionForSemanticSegmentation,
          DataaVecVisionModel,
          DataaVecVisionPreTrainedModel,
      )
   if is_tf_available():
      from .modeling_tf_dataavec_vision import (
          TFDataaVecVisionForImageClassification,
          TFDataaVecVisionForSemanticSegmentation,
          TFDataaVecVisionModel,
          TFDataaVecVisionPreTrainedModel,
      )

else:
   import sys

   snake_case_ :  int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





