
import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCAmelCase_					:   List[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_					:   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_					:   str 							=						"\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 							_lowerCAmelCase (  _a   :					Any			)	->			str:



     def remove_articles(_a   :					Union[str, Any]			):
          lowerCAmelCase_					:       Tuple    =		re.compile(R"""\b(a|an|the)\b"""					,   re.UNICODE			)
          return re.sub(_a					,   """ """					,   _a			)

     def white_space_fix(_a   :					Tuple			):
          return " ".join(text.split()			)

     def remove_punc(_a   :					Union[str, Any]			):
          lowerCAmelCase_					:       Optional[Any]    =		set(string.punctuation			)
          return "".join(ch for ch in text if ch not in exclude			)

     def lower(_a   :					Optional[Any]			):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(_a			)			)			)			)






def 							_lowerCAmelCase (  _a   :					str					,   _a   :					List[Any]			)	->			Tuple:
     return int(normalize_answer(_a			) == normalize_answer(_a			)			)






def 							_lowerCAmelCase (  _a   :					str					,   _a   :					Tuple			)	->			List[Any]:
     lowerCAmelCase_					:       List[Any]    =		[any(compute_exact(_a					,   _a			) for ref in refs			) for pred, refs in zip(_a					,   _a			)]
     return (sum(_a			) / len(_a			)) * 1_00






def 							_lowerCAmelCase (  _a   :					Tuple					,   _a   :					Optional[Any]					,   _a   :					Any					,   _a   :					List[Any]			)	->			Optional[Any]:
     lowerCAmelCase_					:       int    =		[rgram for rgrams in rgramslist for rgram in rgrams]
     lowerCAmelCase_					:       int    =		Counter(_a			)

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

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

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

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

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

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

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

     return (keepscore, delscore_precision, addscore)






def 							_lowerCAmelCase (  _a   :					List[Any]					,   _a   :					Union[str, Any]					,   _a   :					Optional[Any]			)	->			List[str]:
     lowerCAmelCase_					:       Optional[Any]    =		len(_a			)

     lowerCAmelCase_					:       List[str]    =		ssent.split(""" """			)
     lowerCAmelCase_					:       List[Any]    =		csent.split(""" """			)
     lowerCAmelCase_					:       int    =		[]
     lowerCAmelCase_					:       Optional[int]    =		[]
     lowerCAmelCase_					:       str    =		[]
     lowerCAmelCase_					:       Dict    =		[]
     lowerCAmelCase_					:       Optional[Any]    =		[]
     lowerCAmelCase_					:       List[str]    =		[]

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

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






def 							_lowerCAmelCase (  _a   :					Dict					,   _a   :					bool = True					,   _a   :					str = "13a"					,   _a   :					bool = True			)	->			str:



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

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

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

     return normalized_sent






def 							_lowerCAmelCase (  _a   :					Optional[Any]					,   _a   :					str					,   _a   :					List[Any]			)	->			str:



     if not (len(_a			) == len(_a			) == len(_a			)):
          raise ValueError("""Sources length must match predictions and references lengths."""			)
     lowerCAmelCase_					:       List[Any]    =		0
     for src, pred, refs in zip(_a					,   _a					,   _a			):
          sari_score += SARIsent(normalize(_a			)					,   normalize(_a			)					,   [normalize(_a			) for sent in refs]			)
     lowerCAmelCase_					:       List[Any]    =		sari_score / len(_a			)
     return 1_00 * sari_score






def 							_lowerCAmelCase (  _a   :					Tuple					,   _a   :					Union[str, Any]					,   _a   :					Tuple="exp"					,   _a   :					int=None					,   _a   :					Dict=False					,   _a   :					str=False					,   _a   :					int=False					,   )	->			Optional[int]:
     lowerCAmelCase_					:       List[Any]    =		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"""			)
     lowerCAmelCase_					:       str    =		[[refs[i] for refs in references] for i in range(_a			)]
     lowerCAmelCase_					:       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      lowercase__    (							datasets.Metric  ):







  def 	UpperCAmelCase__		(	self						):
       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    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       List[Any]    =		{}
       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







import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


UpperCAmelCase_					:   int 							=						get_logger(__name__)





class      lowercase__    (							enum.Enum  ):
  __UpperCamelCase	     =  """all_checks"""
  __UpperCamelCase	     =  """basic_checks"""
  __UpperCamelCase	     =  """no_checks"""





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass






def 							_lowerCAmelCase (  _a   :					Optional[dict]					,   _a   :					dict					,   _a   :					str=None			)	->			Optional[Any]:
     if expected_checksums is None:
          logger.info("""Unable to verify checksums."""			)
          return
     if len(set(_a			) - set(_a			)			) > 0:
          raise ExpectedMoreDownloadedFiles(str(set(_a			) - set(_a			)			)			)
     if len(set(_a			) - set(_a			)			) > 0:
          raise UnexpectedDownloadedFile(str(set(_a			) - set(_a			)			)			)
     lowerCAmelCase_					:       Union[str, Any]    =		[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
     lowerCAmelCase_					:       Optional[Any]    =		""" for """ + verification_name if verification_name is not None else """"""
     if len(_a			) > 0:
          raise NonMatchingChecksumError(
              F'Checksums didn\'t match{for_verification_name}:\n'
              F'{bad_urls}\n'
              """Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"""			)
     logger.info("""All the checksums matched successfully""" + for_verification_name			)





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass





class      lowercase__    (							__A  ):
  pass






def 							_lowerCAmelCase (  _a   :					Optional[dict]					,   _a   :					dict			)	->			str:
     if expected_splits is None:
          logger.info("""Unable to verify splits sizes."""			)
          return
     if len(set(_a			) - set(_a			)			) > 0:
          raise ExpectedMoreSplits(str(set(_a			) - set(_a			)			)			)
     if len(set(_a			) - set(_a			)			) > 0:
          raise UnexpectedSplits(str(set(_a			) - set(_a			)			)			)
     lowerCAmelCase_					:       Optional[int]    =		[
         {"""expected""": expected_splits[name], """recorded""": recorded_splits[name]}
         for name in expected_splits
         if expected_splits[name].num_examples != recorded_splits[name].num_examples
     ]
     if len(_a			) > 0:
          raise NonMatchingSplitsSizesError(str(_a			)			)
     logger.info("""All the splits matched successfully."""			)






def 							_lowerCAmelCase (  _a   :					str					,   _a   :					bool = True			)	->			dict:
     if record_checksum:
          lowerCAmelCase_					:       int    =		shaaaa()
          with open(_a					,   """rb"""			) as f:
               for chunk in iter(lambda: f.read(1 << 20			)					,   B""""""			):
                    m.update(_a			)
               lowerCAmelCase_					:       List[str]    =		m.hexdigest()
     else:
          lowerCAmelCase_					:       Union[str, Any]    =		None
     return {"num_bytes": os.path.getsize(_a			), "checksum": checksum}







def 							_lowerCAmelCase (  _a   :					Tuple			)	->			Optional[int]:
     if dataset_size and config.IN_MEMORY_MAX_SIZE:
          return dataset_size < config.IN_MEMORY_MAX_SIZE
     else:
          return False
