






import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCAmelCase_           = '\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'

lowerCAmelCase_           = '\\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'


lowerCAmelCase_           = '\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		(							UpperCAmelCase	:       Any   ):

				def remove_articles(UpperCAmelCase	:       str   ):
								A	 =      re.compile(r'\b(a|an|the)\b',						re.UNICODE   )
								return re.sub(UpperCAmelCase,						' ',						UpperCAmelCase   )

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

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

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

				return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase   )   )   )   )







def      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       str   ):
				return int(normalize_answer(UpperCAmelCase   ) == normalize_answer(UpperCAmelCase   )   )







def      snake_case		(							UpperCAmelCase	:       Dict,						UpperCAmelCase	:       Dict   ):
				A	 =      [any(compute_exact(UpperCAmelCase,						UpperCAmelCase   ) for ref in refs   ) for pred, refs in zip(UpperCAmelCase,						UpperCAmelCase   )]
				return (sum(UpperCAmelCase   ) / len(UpperCAmelCase   )) * 1_00







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       Dict   ):
				A	 =      [rgram for rgrams in rgramslist for rgram in rgrams]
				A	 =      Counter(UpperCAmelCase   )

				A	 =      Counter(UpperCAmelCase   )
				A	 =      Counter()
				for sgram, scount in sgramcounter.items():
								A	 =      scount * numref

				A	 =      Counter(UpperCAmelCase   )
				A	 =      Counter()
				for cgram, ccount in cgramcounter.items():
								A	 =      ccount * numref

				# KEEP
				A	 =      sgramcounter_rep & cgramcounter_rep
				A	 =      keepgramcounter_rep & rgramcounter
				A	 =      sgramcounter_rep & rgramcounter

				A	 =      0
				A	 =      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	 =      1
				A	 =      1
				if len(UpperCAmelCase   ) > 0:
								A	 =      keeptmpscorea / len(UpperCAmelCase   )
				if len(UpperCAmelCase   ) > 0:
								# Fix an alleged bug [2] in the keep score computation.
								# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
								A	 =      keeptmpscorea / sum(keepgramcounterall_rep.values()   )
				A	 =      0
				if keepscore_precision > 0 or keepscore_recall > 0:
								A	 =      2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

				# DELETION
				A	 =      sgramcounter_rep - cgramcounter_rep
				A	 =      delgramcounter_rep - rgramcounter
				A	 =      sgramcounter_rep - rgramcounter
				A	 =      0
				A	 =      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	 =      1
				if len(UpperCAmelCase   ) > 0:
								A	 =      deltmpscorea / len(UpperCAmelCase   )

				# ADDITION
				A	 =      set(UpperCAmelCase   ) - set(UpperCAmelCase   )
				A	 =      set(UpperCAmelCase   ) & set(UpperCAmelCase   )
				A	 =      set(UpperCAmelCase   ) - set(UpperCAmelCase   )

				A	 =      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	 =      1
				A	 =      1
				if len(UpperCAmelCase   ) > 0:
								A	 =      addtmpscore / len(UpperCAmelCase   )
				if len(UpperCAmelCase   ) > 0:
								A	 =      addtmpscore / len(UpperCAmelCase   )
				A	 =      0
				if addscore_precision > 0 or addscore_recall > 0:
								A	 =      2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

				return (keepscore, delscore_precision, addscore)







def      snake_case		(							UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       List[Any],						UpperCAmelCase	:       Any   ):
				A	 =      len(UpperCAmelCase   )

				A	 =      ssent.split(' '   )
				A	 =      csent.split(' '   )
				A	 =      []
				A	 =      []
				A	 =      []
				A	 =      []
				A	 =      []
				A	 =      []

				A	 =      []
				A	 =      []
				A	 =      []
				A	 =      []
				for rsent in rsents:
								A	 =      rsent.split(' '   )
								A	 =      []
								A	 =      []
								A	 =      []
								ragramslist.append(UpperCAmelCase   )
								for i in range(0,						len(UpperCAmelCase   ) - 1   ):
												if i < len(UpperCAmelCase   ) - 1:
																A	 =      ragrams[i] + ' ' + ragrams[i + 1]
																ragrams.append(UpperCAmelCase   )
												if i < len(UpperCAmelCase   ) - 2:
																A	 =      ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
																ragrams.append(UpperCAmelCase   )
												if i < len(UpperCAmelCase   ) - 3:
																A	 =      ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
																ragrams.append(UpperCAmelCase   )
								ragramslist.append(UpperCAmelCase   )
								ragramslist.append(UpperCAmelCase   )
								ragramslist.append(UpperCAmelCase   )

				for i in range(0,						len(UpperCAmelCase   ) - 1   ):
								if i < len(UpperCAmelCase   ) - 1:
												A	 =      sagrams[i] + ' ' + sagrams[i + 1]
												sagrams.append(UpperCAmelCase   )
								if i < len(UpperCAmelCase   ) - 2:
												A	 =      sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
												sagrams.append(UpperCAmelCase   )
								if i < len(UpperCAmelCase   ) - 3:
												A	 =      sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
												sagrams.append(UpperCAmelCase   )

				for i in range(0,						len(UpperCAmelCase   ) - 1   ):
								if i < len(UpperCAmelCase   ) - 1:
												A	 =      cagrams[i] + ' ' + cagrams[i + 1]
												cagrams.append(UpperCAmelCase   )
								if i < len(UpperCAmelCase   ) - 2:
												A	 =      cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
												cagrams.append(UpperCAmelCase   )
								if i < len(UpperCAmelCase   ) - 3:
												A	 =      cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
												cagrams.append(UpperCAmelCase   )

				((A) ,		(A) ,		(A))	 =      SARIngram(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase   )
				((A) ,		(A) ,		(A))	 =      SARIngram(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase   )
				((A) ,		(A) ,		(A))	 =      SARIngram(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase   )
				((A) ,		(A) ,		(A))	 =      SARIngram(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase   )
				A	 =      sum([keepascore, keepascore, keepascore, keepascore]   ) / 4
				A	 =      sum([delascore, delascore, delascore, delascore]   ) / 4
				A	 =      sum([addascore, addascore, addascore, addascore]   ) / 4
				A	 =      (avgkeepscore + avgdelscore + avgaddscore) / 3
				return finalscore







def      snake_case		(							UpperCAmelCase	:       Any,						UpperCAmelCase	:       bool = True,						UpperCAmelCase	:       str = "13a",						UpperCAmelCase	:       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:
								A	 =      sentence.lower()

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

				if not return_str:
								A	 =      normalized_sent.split()

				return normalized_sent







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







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



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







		"""simple docstring"""



		def       A(  self		:       Dict		)    ->   str:


						'''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       A(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      {}
						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













from collections import deque

from .hash_table import HashTable
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def __init__(  self		:       Any						,*_SCREAMING_SNAKE_CASE		:       Optional[int]						,**_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   int:


						'''simple docstring'''
						super().__init__(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   List[str]:


						'''simple docstring'''
						A	 =      deque([]		) if self.values[key] is None else self.values[key]
						self.values[key].appendleft(_SCREAMING_SNAKE_CASE		)
						A	 =      self.values[key]
		def       A(  self		:       Optional[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						return (
						    sum(self.charge_factor - len(_SCREAMING_SNAKE_CASE		) for slot in self.values		)
						    / self.size_table
						    * self.charge_factor
						)







		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       str=None		)    ->   Union[str, Any]:


						'''simple docstring'''
						if not (
						    len(self.values[key]		) == self.charge_factor and self.values.count(_SCREAMING_SNAKE_CASE		) == 0
						):
										return key
						return super()._collision_resolution(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)





