



'''simple docstring'''





import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__a          =		"\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"

__a          =		"\\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"


__a          =		"\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(				_lowerCAmelCase		)		-> List[str]:

      def remove_articles(_lowerCAmelCase		):
            snake_case__  :    int    						=      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		):
            snake_case__  :    Union[str, Any]    						=      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 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Dict:
      return int(normalize_answer(_lowerCAmelCase		) == normalize_answer(_lowerCAmelCase		)		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Any:
      snake_case__  :    Union[str, Any]    						=      [any(compute_exact(_lowerCAmelCase     ,   _lowerCAmelCase		) for ref in refs		) for pred, refs in zip(_lowerCAmelCase     ,   _lowerCAmelCase		)]
      return (sum(_lowerCAmelCase		) / len(_lowerCAmelCase		)) * 100





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Any:
      snake_case__  :    Dict    						=      [rgram for rgrams in rgramslist for rgram in rgrams]
      snake_case__  :    Optional[int]    						=      Counter(_lowerCAmelCase		)

      snake_case__  :    Tuple    						=      Counter(_lowerCAmelCase		)
      snake_case__  :    Any    						=      Counter()
      for sgram, scount in sgramcounter.items():
            snake_case__  :    str    						=      scount * numref

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

      # KEEP
      snake_case__  :    str    						=      sgramcounter_rep & cgramcounter_rep
      snake_case__  :    Any    						=      keepgramcounter_rep & rgramcounter
      snake_case__  :    List[str]    						=      sgramcounter_rep & rgramcounter

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

      # DELETION
      snake_case__  :    Any    						=      sgramcounter_rep - cgramcounter_rep
      snake_case__  :    Any    						=      delgramcounter_rep - rgramcounter
      snake_case__  :    Optional[int]    						=      sgramcounter_rep - rgramcounter
      snake_case__  :    Tuple    						=      0
      snake_case__  :    Dict    						=      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.
      snake_case__  :    Any    						=      1
      if len(_lowerCAmelCase		) > 0:
            snake_case__  :    Tuple    						=      deltmpscorea / len(_lowerCAmelCase		)

      # ADDITION
      snake_case__  :    List[str]    						=      set(_lowerCAmelCase		) - set(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      set(_lowerCAmelCase		) & set(_lowerCAmelCase		)
      snake_case__  :    Tuple    						=      set(_lowerCAmelCase		) - set(_lowerCAmelCase		)

      snake_case__  :    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.
      snake_case__  :    Optional[Any]    						=      1
      snake_case__  :    int    						=      1
      if len(_lowerCAmelCase		) > 0:
            snake_case__  :    Optional[Any]    						=      addtmpscore / len(_lowerCAmelCase		)
      if len(_lowerCAmelCase		) > 0:
            snake_case__  :    Union[str, Any]    						=      addtmpscore / len(_lowerCAmelCase		)
      snake_case__  :    Any    						=      0
      if addscore_precision > 0 or addscore_recall > 0:
            snake_case__  :    Dict    						=      2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

      return (keepscore, delscore_precision, addscore)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[Any]:
      snake_case__  :    Tuple    						=      len(_lowerCAmelCase		)

      snake_case__  :    Union[str, Any]    						=      ssent.split(""" """		)
      snake_case__  :    Tuple    						=      csent.split(""" """		)
      snake_case__  :    Dict    						=      []
      snake_case__  :    int    						=      []
      snake_case__  :    Union[str, Any]    						=      []
      snake_case__  :    Dict    						=      []
      snake_case__  :    Union[str, Any]    						=      []
      snake_case__  :    Any    						=      []

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

      ((snake_case__)						,				(snake_case__)						,				(snake_case__))  :    Any    						=      SARIngram(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
      ((snake_case__)						,				(snake_case__)						,				(snake_case__))  :    Dict    						=      SARIngram(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
      ((snake_case__)						,				(snake_case__)						,				(snake_case__))  :    Optional[Any]    						=      SARIngram(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
      ((snake_case__)						,				(snake_case__)						,				(snake_case__))  :    Optional[Any]    						=      SARIngram(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
      snake_case__  :    List[Any]    						=      sum([keepascore, keepascore, keepascore, keepascore]		) / 4
      snake_case__  :    Optional[Any]    						=      sum([delascore, delascore, delascore, delascore]		) / 4
      snake_case__  :    Dict    						=      sum([addascore, addascore, addascore, addascore]		) / 4
      snake_case__  :    Any    						=      (avgkeepscore + avgdelscore + avgaddscore) / 3
      return finalscore





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase = True     ,   _lowerCAmelCase = "13a"     ,   _lowerCAmelCase = True		)		-> Dict:
      # 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:
            snake_case__  :    int    						=      sentence.lower()

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

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

      return normalized_sent





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[int]:
      if not (len(_lowerCAmelCase		) == len(_lowerCAmelCase		) == len(_lowerCAmelCase		)):
            raise ValueError("""Sources length must match predictions and references lengths."""		)
      snake_case__  :    Optional[Any]    						=      0
      for src, pred, refs in zip(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		):
            sari_score += SARIsent(normalize(_lowerCAmelCase		)     ,   normalize(_lowerCAmelCase		)     ,   [normalize(_lowerCAmelCase		) for sent in refs]		)
      snake_case__  :    int    						=      sari_score / len(_lowerCAmelCase		)
      return 100 * sari_score





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase="exp"     ,   _lowerCAmelCase=None     ,   _lowerCAmelCase=False     ,   _lowerCAmelCase=False     ,   _lowerCAmelCase=False     ,   )		-> Any:
      snake_case__  :    Optional[int]    						=      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"""		)
      snake_case__  :    Tuple    						=      [[refs[i] for refs in references] for i in range(_lowerCAmelCase		)]
      snake_case__  :    List[str]    						=      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     UpperCAmelCase_       (       datasets.Metric	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     List[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 lowerCamelCase       (    self					:     Optional[Any] ,     snake_case_					:     Optional[Any] ,     snake_case_					:     Dict ,     snake_case_					:     str						):
          snake_case__  :    List[str]    						=      {}
          result.update({"""sari""": compute_sari(sources=snake_case_ ,     predictions=snake_case_ ,     references=snake_case_						)}						)
          result.update({"""sacrebleu""": compute_sacrebleu(predictions=snake_case_ ,     references=snake_case_						)}						)
          result.update({"""exact""": compute_em(predictions=snake_case_ ,     references=snake_case_						)}						)
          return result





'''simple docstring'''





import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    int    						=      FileLock(str(tmpdir / """foo.lock"""		)		)
      snake_case__  :    Dict    						=      FileLock(str(tmpdir / """foo.lock"""		)		)
      snake_case__  :    List[str]    						=      0.01
      with locka.acquire():
            with pytest.raises(_lowerCAmelCase		):
                  snake_case__  :    str    						=      time.time()
                  locka.acquire(_lowerCAmelCase		)
                  assert time.time() - _start > timeout





def 						__snake_case(				_lowerCAmelCase		)		-> Tuple:
      snake_case__  :    Dict    						=      """a""" * 1_000 + """.lock"""
      snake_case__  :    int    						=      FileLock(str(tmpdir / filename		)		)
      assert locka._lock_file.endswith(""".lock"""		)
      assert not locka._lock_file.endswith(_lowerCAmelCase		)
      assert len(os.path.basename(locka._lock_file		)		) <= 255
      snake_case__  :    Dict    						=      FileLock(tmpdir / filename		)
      with locka.acquire():
            with pytest.raises(_lowerCAmelCase		):
                  locka.acquire(0		)
