


"""simple docstring"""


import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


A	:  List[str]            =     "\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"

A	:  Any            =     "\\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"


A	:  Union[str, Any]            =     "\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      (						_UpperCamelCase ):

			'''simple docstring'''




			def remove_articles(_UpperCamelCase ):
						__lowerCAmelCase							       =     re.compile(R"\b(a|an|the)\b"       ,					re.UNICODE )
						return re.sub(_UpperCamelCase       ,					" "       ,					_UpperCamelCase )

			def white_space_fix(_UpperCamelCase ):
						return " ".join(text.split() )

			def remove_punc(_UpperCamelCase ):
						__lowerCAmelCase							       =     set(string.punctuation )
						return "".join(ch for ch in text if ch not in exclude )

			def lower(_UpperCamelCase ):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase ) ) ) )


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			return int(normalize_answer(_UpperCamelCase ) == normalize_answer(_UpperCamelCase ) )


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     [any(compute_exact(_UpperCamelCase       ,					_UpperCamelCase ) for ref in refs ) for pred, refs in zip(_UpperCamelCase       ,					_UpperCamelCase )]
			return (sum(_UpperCamelCase ) / len(_UpperCamelCase )) * 100


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     [rgram for rgrams in rgramslist for rgram in rgrams]
			__lowerCAmelCase							       =     Counter(_UpperCamelCase )

			__lowerCAmelCase							       =     Counter(_UpperCamelCase )
			__lowerCAmelCase							       =     Counter()
			for sgram, scount in sgramcounter.items():
						__lowerCAmelCase							       =     scount * numref

			__lowerCAmelCase							       =     Counter(_UpperCamelCase )
			__lowerCAmelCase							       =     Counter()
			for cgram, ccount in cgramcounter.items():
						__lowerCAmelCase							       =     ccount * numref

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

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

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

			# ADDITION
			__lowerCAmelCase							       =     set(_UpperCamelCase ) - set(_UpperCamelCase )
			__lowerCAmelCase							       =     set(_UpperCamelCase ) & set(_UpperCamelCase )
			__lowerCAmelCase							       =     set(_UpperCamelCase ) - set(_UpperCamelCase )

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

			return (keepscore, delscore_precision, addscore)


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     len(_UpperCamelCase )

			__lowerCAmelCase							       =     ssent.split(" " )
			__lowerCAmelCase							       =     csent.split(" " )
			__lowerCAmelCase							       =     []
			__lowerCAmelCase							       =     []
			__lowerCAmelCase							       =     []
			__lowerCAmelCase							       =     []
			__lowerCAmelCase							       =     []
			__lowerCAmelCase							       =     []

			__lowerCAmelCase							       =     []
			__lowerCAmelCase							       =     []
			__lowerCAmelCase							       =     []
			__lowerCAmelCase							       =     []
			for rsent in rsents:
						__lowerCAmelCase							       =     rsent.split(" " )
						__lowerCAmelCase							       =     []
						__lowerCAmelCase							       =     []
						__lowerCAmelCase							       =     []
						ragramslist.append(_UpperCamelCase )
						for i in range(0       ,					len(_UpperCamelCase ) - 1 ):
									if i < len(_UpperCamelCase ) - 1:
												__lowerCAmelCase							       =     ragrams[i] + " " + ragrams[i + 1]
												ragrams.append(_UpperCamelCase )
									if i < len(_UpperCamelCase ) - 2:
												__lowerCAmelCase							       =     ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
												ragrams.append(_UpperCamelCase )
									if i < len(_UpperCamelCase ) - 3:
												__lowerCAmelCase							       =     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:
									__lowerCAmelCase							       =     sagrams[i] + " " + sagrams[i + 1]
									sagrams.append(_UpperCamelCase )
						if i < len(_UpperCamelCase ) - 2:
									__lowerCAmelCase							       =     sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
									sagrams.append(_UpperCamelCase )
						if i < len(_UpperCamelCase ) - 3:
									__lowerCAmelCase							       =     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:
									__lowerCAmelCase							       =     cagrams[i] + " " + cagrams[i + 1]
									cagrams.append(_UpperCamelCase )
						if i < len(_UpperCamelCase ) - 2:
									__lowerCAmelCase							       =     cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
									cagrams.append(_UpperCamelCase )
						if i < len(_UpperCamelCase ) - 3:
									__lowerCAmelCase							       =     cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
									cagrams.append(_UpperCamelCase )

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


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase = True       ,					_UpperCamelCase = "13a"       ,					_UpperCamelCase = True ):

			'''simple docstring'''




			if lowercase:
						__lowerCAmelCase							       =     sentence.lower()

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

			if not return_str:
						__lowerCAmelCase							       =     normalized_sent.split()

			return normalized_sent


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




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


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase="exp"       ,					_UpperCamelCase=None       ,					_UpperCamelCase=False       ,					_UpperCamelCase=False       ,					_UpperCamelCase=False       ,					):

			'''simple docstring'''




			__lowerCAmelCase							       =     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" )
			__lowerCAmelCase							       =     [[refs[i] for refs in references] for i in range(_UpperCamelCase )]
			__lowerCAmelCase							       =     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      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      ):
										__lowerCAmelCase							       =     {}
										result.update({"sari": compute_sari(sources=__a       ,  predictions=__a       ,  references=__a      )}      )
										result.update({"sacrebleu": compute_sacrebleu(predictions=__a       ,  references=__a      )}      )
										result.update({"exact": compute_em(predictions=__a       ,  references=__a      )}      )
										return result








"""simple docstring"""


import heapq as hq
import math
from collections.abc import Iterator
class     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     str(id_      )
										__lowerCAmelCase							       =     None
										__lowerCAmelCase							       =     None
										__lowerCAmelCase							       =     []
										__lowerCAmelCase							       =     {}  # {vertex:distance}

							def __lt__(	self       ,  __a      ):
										return self.key < other.key

							def __repr__(	self      ):
										return self.id

							def      snake_case							(	self       ,  __a      ):
										self.neighbors.append(__a      )



							def      snake_case							(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     weight


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			graph[a - 1].add_neighbor(graph[b - 1] )
			graph[b - 1].add_neighbor(graph[a - 1] )
			# add the edges:
			graph[a - 1].add_edge(graph[b - 1]       ,					_UpperCamelCase )
			graph[b - 1].add_edge(graph[a - 1]       ,					_UpperCamelCase )


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     []
			for u in graph:
						__lowerCAmelCase							       =     math.inf
						__lowerCAmelCase							       =     None
			__lowerCAmelCase							       =     0
			__lowerCAmelCase							       =     graph[:]
			while q:
						__lowerCAmelCase							       =     min(_UpperCamelCase )
						q.remove(_UpperCamelCase )
						for v in u.neighbors:
									if (v in q) and (u.edges[v.id] < v.key):
												__lowerCAmelCase							       =     u
												__lowerCAmelCase							       =     u.edges[v.id]
			for i in range(1       ,					len(_UpperCamelCase ) ):
						a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
			return a


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			for u in graph:
						__lowerCAmelCase							       =     math.inf
						__lowerCAmelCase							       =     None
			__lowerCAmelCase							       =     0

			__lowerCAmelCase							       =     list(_UpperCamelCase )
			hq.heapify(_UpperCamelCase )

			while h:
						__lowerCAmelCase							       =     hq.heappop(_UpperCamelCase )
						for v in u.neighbors:
									if (v in h) and (u.edges[v.id] < v.key):
												__lowerCAmelCase							       =     u
												__lowerCAmelCase							       =     u.edges[v.id]
												hq.heapify(_UpperCamelCase )

			for i in range(1       ,					len(_UpperCamelCase ) ):
						yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)


def _lowerCamelCase      (						):

			'''simple docstring'''






if __name__ == "__main__":
	import doctest

	doctest.testmod()




