




'''simple docstring'''




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowercase__   :				Tuple      =   '''
@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",
}
'''

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


lowercase__   :				Optional[Any]      =   '''
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 	_lowerCAmelCase      (  __snake_case  :			List[str]      )					->					List[str]:



     def remove_articles(__snake_case  :			str      ):
          __A						:			Union[str, Any]								=	re.compile(r'\b(a|an|the)\b'			,							re.UNICODE      )
          return re.sub(A__			,							' '			,							A__      )

     def white_space_fix(__snake_case  :			int      ):
          return " ".join(text.split()      )

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

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

     return white_space_fix(remove_articles(remove_punc(lower(A__      )      )      )      )






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






def 	_lowerCAmelCase      (  __snake_case  :			Dict			,							__snake_case  :			Any      )					->					Tuple:
     __A						:			Optional[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      (  __snake_case  :			Tuple			,							__snake_case  :			Tuple			,							__snake_case  :			Union[str, Any]			,							__snake_case  :			int      )					->					Union[str, Any]:
     __A						:			Any								=	[rgram for rgrams in rgramslist for rgram in rgrams]
     __A						:			Tuple								=	Counter(A__      )

     __A						:			str								=	Counter(A__      )
     __A						:			Tuple								=	Counter()
     for sgram, scount in sgramcounter.items():
          __A						:			Tuple								=	scount * numref

     __A						:			str								=	Counter(A__      )
     __A						:			Any								=	Counter()
     for cgram, ccount in cgramcounter.items():
          __A						:			Optional[Any]								=	ccount * numref

     # KEEP
     __A						:			Optional[Any]								=	sgramcounter_rep & cgramcounter_rep
     __A						:			Tuple								=	keepgramcounter_rep & rgramcounter
     __A						:			Dict								=	sgramcounter_rep & rgramcounter

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

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

     # ADDITION
     __A						:			Optional[int]								=	set(A__      ) - set(A__      )
     __A						:			Tuple								=	set(A__      ) & set(A__      )
     __A						:			Tuple								=	set(A__      ) - set(A__      )

     __A						:			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.
     __A						:			Union[str, Any]								=	1
     __A						:			List[Any]								=	1
     if len(A__      ) > 0:
          __A						:			Union[str, Any]								=	addtmpscore / len(A__      )
     if len(A__      ) > 0:
          __A						:			List[str]								=	addtmpscore / len(A__      )
     __A						:			str								=	0
     if addscore_precision > 0 or addscore_recall > 0:
          __A						:			List[Any]								=	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

     return (keepscore, delscore_precision, addscore)






def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			List[str]			,							__snake_case  :			Tuple      )					->					Optional[int]:
     __A						:			Optional[Any]								=	len(A__      )

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

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

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






def 	_lowerCAmelCase      (  __snake_case  :			Optional[Any]			,							__snake_case  :			Optional[int] = True			,							__snake_case  :			Optional[Any] = "13a"			,							__snake_case  :			List[Any] = True      )					->					List[str]:



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

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

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

     return normalized_sent






def 	_lowerCAmelCase      (  __snake_case  :			List[Any]			,							__snake_case  :			str			,							__snake_case  :			Union[str, Any]      )					->					Dict:



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






def 	_lowerCAmelCase      (  __snake_case  :			int			,							__snake_case  :			Optional[int]			,							__snake_case  :			List[Any]="exp"			,							__snake_case  :			List[str]=None			,							__snake_case  :			str=False			,							__snake_case  :			int=False			,							__snake_case  :			int=False			,							)					->					Tuple:
     __A						:			str								=	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'      )
     __A						:			Tuple								=	[[refs[i] for refs in references] for i in range(A__      )]
     __A						:			Tuple								=	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 			SCREAMING_SNAKE_CASE   (datasets.Metric							):


     def 							SCREAMING_SNAKE_CASE  (						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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Dict								=	{}
          result.update({'sari': compute_sari(sources=lowercase__						,     predictions=lowercase__						,     references=lowercase__)})
          result.update({'sacrebleu': compute_sacrebleu(predictions=lowercase__						,     references=lowercase__)})
          result.update({'exact': compute_em(predictions=lowercase__						,     references=lowercase__)})
          return result






'''simple docstring'''




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
  from .camera import create_pan_cameras
  from .pipeline_shap_e import ShapEPipeline
  from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
  from .renderer import (
      BoundingBoxVolume,
      ImportanceRaySampler,
      MLPNeRFModelOutput,
      MLPNeRSTFModel,
      ShapEParamsProjModel,
      ShapERenderer,
      StratifiedRaySampler,
      VoidNeRFModel,
  )