"""simple docstring"""







import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__SCREAMING_SNAKE_CASE    	="\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"

__SCREAMING_SNAKE_CASE    	="\\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"


__SCREAMING_SNAKE_CASE    	="\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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[str]      ):

     def remove_articles(__SCREAMING_SNAKE_CASE							:     List[str]      ):
          lowercase_	:				Any													=	re.compile(R'\b(a|an|the)\b'     ,			re.UNICODE      )
          return re.sub(__SCREAMING_SNAKE_CASE     ,			' '     ,			__SCREAMING_SNAKE_CASE      )

     def white_space_fix(__SCREAMING_SNAKE_CASE							:     List[Any]      ):
          return " ".join(text.split()      )

     def remove_punc(__SCREAMING_SNAKE_CASE							:     Optional[int]      ):
          lowercase_	:				Any													=	set(string.punctuation      )
          return "".join(ch for ch in text if ch not in exclude      )

     def lower(__SCREAMING_SNAKE_CASE							:     Any      ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(__SCREAMING_SNAKE_CASE      )      )      )      )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     return int(normalize_answer(__SCREAMING_SNAKE_CASE      ) == normalize_answer(__SCREAMING_SNAKE_CASE      )      )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     lowercase_	:				List[Any]													=	[any(compute_exact(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      ) for ref in refs      ) for pred, refs in zip(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )]
     return (sum(__SCREAMING_SNAKE_CASE      ) / len(__SCREAMING_SNAKE_CASE      )) * 1_00



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     Any     ,			__SCREAMING_SNAKE_CASE							:     List[Any]      ):
     lowercase_	:				int													=	[rgram for rgrams in rgramslist for rgram in rgrams]
     lowercase_	:				List[Any]													=	Counter(__SCREAMING_SNAKE_CASE      )

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

     lowercase_	:				int													=	Counter(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				str													=	Counter()
     for cgram, ccount in cgramcounter.items():
          lowercase_	:				int													=	ccount * numref

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

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

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

     # ADDITION
     lowercase_	:				Tuple													=	set(__SCREAMING_SNAKE_CASE      ) - set(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	set(__SCREAMING_SNAKE_CASE      ) & set(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				int													=	set(__SCREAMING_SNAKE_CASE      ) - set(__SCREAMING_SNAKE_CASE      )

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

     return (keepscore, delscore_precision, addscore)



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any     ,			__SCREAMING_SNAKE_CASE							:     Any     ,			__SCREAMING_SNAKE_CASE							:     List[Any]      ):
     lowercase_	:				Optional[Any]													=	len(__SCREAMING_SNAKE_CASE      )

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

     lowercase_	:				Any													=	[]
     lowercase_	:				Dict													=	[]
     lowercase_	:				List[str]													=	[]
     lowercase_	:				Dict													=	[]
     for rsent in rsents:
          lowercase_	:				Any													=	rsent.split(' '      )
          lowercase_	:				Dict													=	[]
          lowercase_	:				Dict													=	[]
          lowercase_	:				List[str]													=	[]
          ragramslist.append(__SCREAMING_SNAKE_CASE      )
          for i in range(0     ,			len(__SCREAMING_SNAKE_CASE      ) - 1      ):
               if i < len(__SCREAMING_SNAKE_CASE      ) - 1:
                    lowercase_	:				Union[str, Any]													=	ragrams[i] + ' ' + ragrams[i + 1]
                    ragrams.append(__SCREAMING_SNAKE_CASE      )
               if i < len(__SCREAMING_SNAKE_CASE      ) - 2:
                    lowercase_	:				int													=	ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
                    ragrams.append(__SCREAMING_SNAKE_CASE      )
               if i < len(__SCREAMING_SNAKE_CASE      ) - 3:
                    lowercase_	:				int													=	ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
                    ragrams.append(__SCREAMING_SNAKE_CASE      )
          ragramslist.append(__SCREAMING_SNAKE_CASE      )
          ragramslist.append(__SCREAMING_SNAKE_CASE      )
          ragramslist.append(__SCREAMING_SNAKE_CASE      )

     for i in range(0     ,			len(__SCREAMING_SNAKE_CASE      ) - 1      ):
          if i < len(__SCREAMING_SNAKE_CASE      ) - 1:
               lowercase_	:				Tuple													=	sagrams[i] + ' ' + sagrams[i + 1]
               sagrams.append(__SCREAMING_SNAKE_CASE      )
          if i < len(__SCREAMING_SNAKE_CASE      ) - 2:
               lowercase_	:				Optional[int]													=	sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
               sagrams.append(__SCREAMING_SNAKE_CASE      )
          if i < len(__SCREAMING_SNAKE_CASE      ) - 3:
               lowercase_	:				Optional[int]													=	sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
               sagrams.append(__SCREAMING_SNAKE_CASE      )

     for i in range(0     ,			len(__SCREAMING_SNAKE_CASE      ) - 1      ):
          if i < len(__SCREAMING_SNAKE_CASE      ) - 1:
               lowercase_	:				Any													=	cagrams[i] + ' ' + cagrams[i + 1]
               cagrams.append(__SCREAMING_SNAKE_CASE      )
          if i < len(__SCREAMING_SNAKE_CASE      ) - 2:
               lowercase_	:				Union[str, Any]													=	cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
               cagrams.append(__SCREAMING_SNAKE_CASE      )
          if i < len(__SCREAMING_SNAKE_CASE      ) - 3:
               lowercase_	:				int													=	cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
               cagrams.append(__SCREAMING_SNAKE_CASE      )

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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     bool = True     ,			__SCREAMING_SNAKE_CASE							:     str = "13a"     ,			__SCREAMING_SNAKE_CASE							:     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:
          lowercase_	:				Tuple													=	sentence.lower()

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

     if not return_str:
          lowercase_	:				int													=	normalized_sent.split()

     return normalized_sent



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]      ):
     if not (len(__SCREAMING_SNAKE_CASE      ) == len(__SCREAMING_SNAKE_CASE      ) == len(__SCREAMING_SNAKE_CASE      )):
          raise ValueError('Sources length must match predictions and references lengths.'      )
     lowercase_	:				str													=	0
     for src, pred, refs in zip(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      ):
          sari_score += SARIsent(normalize(__SCREAMING_SNAKE_CASE      )     ,			normalize(__SCREAMING_SNAKE_CASE      )     ,			[normalize(__SCREAMING_SNAKE_CASE      ) for sent in refs]      )
     lowercase_	:				Optional[int]													=	sari_score / len(__SCREAMING_SNAKE_CASE      )
     return 1_00 * sari_score



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     List[Any]="exp"     ,			__SCREAMING_SNAKE_CASE							:     str=None     ,			__SCREAMING_SNAKE_CASE							:     int=False     ,			__SCREAMING_SNAKE_CASE							:     str=False     ,			__SCREAMING_SNAKE_CASE							:     List[str]=False     ,			):
     lowercase_	:				List[str]													=	len(references[0]      )
     if any(len(__SCREAMING_SNAKE_CASE      ) != references_per_prediction for refs in references      ):
          raise ValueError('Sacrebleu requires the same number of references for each prediction'      )
     lowercase_	:				Any													=	[[refs[i] for refs in references] for i in range(__SCREAMING_SNAKE_CASE      )]
     lowercase_	:				Union[str, Any]													=	sacrebleu.corpus_bleu(
         __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			smooth_method=__SCREAMING_SNAKE_CASE     ,			smooth_value=__SCREAMING_SNAKE_CASE     ,			force=__SCREAMING_SNAKE_CASE     ,			lowercase=__SCREAMING_SNAKE_CASE     ,			use_effective_order=__SCREAMING_SNAKE_CASE     ,			)
     return output.score







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,      _KWARGS_DESCRIPTION  )
class        UpperCamelCase		(			datasets.Metric  ):
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''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     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Tuple													=	{}
        result.update({'sari': compute_sari(sources=__UpperCamelCase     ,predictions=__UpperCamelCase     ,references=__UpperCamelCase			)}			)
        result.update({'sacrebleu': compute_sacrebleu(predictions=__UpperCamelCase     ,references=__UpperCamelCase			)}			)
        result.update({'exact': compute_em(predictions=__UpperCamelCase     ,references=__UpperCamelCase			)}			)
        return result


"""simple docstring"""







__SCREAMING_SNAKE_CASE    	=[
    "DownloadConfig",
    "DownloadManager",
    "DownloadMode",
    "StreamingDownloadManager",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager

