

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case_        =			'''
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

snake_case_        =			'''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''


snake_case_        =			'''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known ."]]
    >>> wiki_split = datasets.load_metric("wiki_split")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     def remove_articles(SCREAMING_SNAKE_CASE_		:   Dict      ):
          lowercase__   :       str				=	re.compile(R'\b(a|an|the)\b'     ,					re.UNICODE      )
          return re.sub(snake_case_     ,					' '     ,					snake_case_      )

     def white_space_fix(SCREAMING_SNAKE_CASE_		:   Optional[int]      ):
          return " ".join(text.split()      )

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

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

     return white_space_fix(remove_articles(remove_punc(lower(snake_case_      )      )      )      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]      ):



     '''simple docstring'''





     return int(normalize_answer(snake_case_      ) == normalize_answer(snake_case_      )      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	[any(compute_exact(snake_case_     ,					snake_case_      ) for ref in refs      ) for pred, refs in zip(snake_case_     ,					snake_case_      )]
     return (sum(snake_case_      ) / len(snake_case_      )) * 100

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     lowercase__   :       Union[str, Any]				=	[rgram for rgrams in rgramslist for rgram in rgrams]
     lowercase__   :       Dict				=	Counter(snake_case_      )

     lowercase__   :       Optional[int]				=	Counter(snake_case_      )
     lowercase__   :       Optional[int]				=	Counter()
     for sgram, scount in sgramcounter.items():
          lowercase__   :       List[str]				=	scount * numref

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

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

     lowercase__   :       Union[str, Any]				=	0
     lowercase__   :       Tuple				=	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__   :       List[Any]				=	1
     lowercase__   :       List[str]				=	1
     if len(snake_case_      ) > 0:
          lowercase__   :       List[Any]				=	keeptmpscorea / len(snake_case_      )
     if len(snake_case_      ) > 0:
          # Fix an alleged bug [2] in the keep score computation.
          # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
          lowercase__   :       Tuple				=	keeptmpscorea / sum(keepgramcounterall_rep.values()      )
     lowercase__   :       List[Any]				=	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__   :       Optional[int]				=	delgramcounter_rep - rgramcounter
     lowercase__   :       List[Any]				=	sgramcounter_rep - rgramcounter
     lowercase__   :       List[Any]				=	0
     lowercase__   :       Optional[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.
     lowercase__   :       Tuple				=	1
     if len(snake_case_      ) > 0:
          lowercase__   :       str				=	deltmpscorea / len(snake_case_      )

     # ADDITION
     lowercase__   :       str				=	set(snake_case_      ) - set(snake_case_      )
     lowercase__   :       Any				=	set(snake_case_      ) & set(snake_case_      )
     lowercase__   :       Union[str, Any]				=	set(snake_case_      ) - set(snake_case_      )

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

     return (keepscore, delscore_precision, addscore)

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Any      ):



     '''simple docstring'''





     lowercase__   :       Optional[Any]				=	len(snake_case_      )

     lowercase__   :       Dict				=	ssent.split(' '      )
     lowercase__   :       Optional[Any]				=	csent.split(' '      )
     lowercase__   :       Union[str, Any]				=	[]
     lowercase__   :       List[str]				=	[]
     lowercase__   :       str				=	[]
     lowercase__   :       Any				=	[]
     lowercase__   :       Optional[Any]				=	[]
     lowercase__   :       str				=	[]

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

     for i in range(0     ,					len(snake_case_      ) - 1      ):
          if i < len(snake_case_      ) - 1:
               lowercase__   :       Dict				=	sagrams[i] + """ """ + sagrams[i + 1]
               sagrams.append(snake_case_      )
          if i < len(snake_case_      ) - 2:
               lowercase__   :       Optional[Any]				=	sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
               sagrams.append(snake_case_      )
          if i < len(snake_case_      ) - 3:
               lowercase__   :       List[str]				=	sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
               sagrams.append(snake_case_      )

     for i in range(0     ,					len(snake_case_      ) - 1      ):
          if i < len(snake_case_      ) - 1:
               lowercase__   :       Optional[Any]				=	cagrams[i] + """ """ + cagrams[i + 1]
               cagrams.append(snake_case_      )
          if i < len(snake_case_      ) - 2:
               lowercase__   :       Optional[Any]				=	cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
               cagrams.append(snake_case_      )
          if i < len(snake_case_      ) - 3:
               lowercase__   :       Optional[int]				=	cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
               cagrams.append(snake_case_      )

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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   bool = True     ,					SCREAMING_SNAKE_CASE_		:   str = "13a"     ,					SCREAMING_SNAKE_CASE_		:   bool = True      ):



     '''simple docstring'''





     if lowercase:
          lowercase__   :       int				=	sentence.lower()

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

     if not return_str:
          lowercase__   :       Union[str, Any]				=	normalized_sent.split()

     return normalized_sent

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     if not (len(snake_case_      ) == len(snake_case_      ) == len(snake_case_      )):
          raise ValueError('Sources length must match predictions and references lengths.'      )
     lowercase__   :       Tuple				=	0
     for src, pred, refs in zip(snake_case_     ,					snake_case_     ,					snake_case_      ):
          sari_score += SARIsent(normalize(snake_case_      )     ,					normalize(snake_case_      )     ,					[normalize(snake_case_      ) for sent in refs]      )
     lowercase__   :       List[Any]				=	sari_score / len(snake_case_      )
     return 100 * sari_score

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   List[Any]="exp"     ,					SCREAMING_SNAKE_CASE_		:   Dict=None     ,					SCREAMING_SNAKE_CASE_		:   int=False     ,					SCREAMING_SNAKE_CASE_		:   List[str]=False     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]=False     ,					):



     '''simple docstring'''





     lowercase__   :       Dict				=	len(references[0]      )
     if any(len(snake_case_      ) != references_per_prediction for refs in references      ):
          raise ValueError('Sacrebleu requires the same number of references for each prediction'      )
     lowercase__   :       Dict				=	[[refs[i] for refs in references] for i in range(snake_case_      )]
     lowercase__   :       Optional[int]				=	sacrebleu.corpus_bleu(
         snake_case_     ,					snake_case_     ,					smooth_method=snake_case_     ,					smooth_value=snake_case_     ,					force=snake_case_     ,					lowercase=snake_case_     ,					use_effective_order=snake_case_     ,					)
     return output.score


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,	_KWARGS_DESCRIPTION					)
class 				SCREAMING_SNAKE_CASE__ (datasets.Metric					):





    def 				snake_case_    (				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 				snake_case_    (				self						,       a						,       a						,       a):
         lowercase__   :       Optional[int]				=	{}
         result.update({'sari': compute_sari(sources=SCREAMING_SNAKE_CASE_						,       predictions=SCREAMING_SNAKE_CASE_						,       references=SCREAMING_SNAKE_CASE_)})
         result.update({'sacrebleu': compute_sacrebleu(predictions=SCREAMING_SNAKE_CASE_						,       references=SCREAMING_SNAKE_CASE_)})
         result.update({'exact': compute_em(predictions=SCREAMING_SNAKE_CASE_						,       references=SCREAMING_SNAKE_CASE_)})
         return result









import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)

