



import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_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"

_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"


_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        A       (      _lowerCamelCase						):





							'''simple docstring'''




							def remove_articles(_lowerCamelCase						):
														_lowerCAmelCase  :							Tuple				     =					re.compile(r"\b(a|an|the)\b"       ,				re.UNICODE						)
														return re.sub(_lowerCamelCase       ,				" "       ,				_lowerCamelCase						)

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

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

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

							return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase						)						)						)						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							return int(normalize_answer(_lowerCamelCase						) == normalize_answer(_lowerCamelCase						)						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Union[str, Any]				     =					[any(compute_exact(_lowerCamelCase       ,				_lowerCamelCase						) for ref in refs						) for pred, refs in zip(_lowerCamelCase       ,				_lowerCamelCase						)]
							return (sum(_lowerCamelCase						) / len(_lowerCamelCase						)) * 100






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							str				     =					[rgram for rgrams in rgramslist for rgram in rgrams]
							_lowerCAmelCase  :							Optional[Any]				     =					Counter(_lowerCamelCase						)

							_lowerCAmelCase  :							List[Any]				     =					Counter(_lowerCamelCase						)
							_lowerCAmelCase  :							Optional[Any]				     =					Counter()
							for sgram, scount in sgramcounter.items():
														_lowerCAmelCase  :							str				     =					scount * numref

							_lowerCAmelCase  :							str				     =					Counter(_lowerCamelCase						)
							_lowerCAmelCase  :							str				     =					Counter()
							for cgram, ccount in cgramcounter.items():
														_lowerCAmelCase  :							Dict				     =					ccount * numref

							# KEEP
							_lowerCAmelCase  :							Dict				     =					sgramcounter_rep & cgramcounter_rep
							_lowerCAmelCase  :							str				     =					keepgramcounter_rep & rgramcounter
							_lowerCAmelCase  :							Tuple				     =					sgramcounter_rep & rgramcounter

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

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

							# ADDITION
							_lowerCAmelCase  :							List[Any]				     =					set(_lowerCamelCase						) - set(_lowerCamelCase						)
							_lowerCAmelCase  :							Dict				     =					set(_lowerCamelCase						) & set(_lowerCamelCase						)
							_lowerCAmelCase  :							str				     =					set(_lowerCamelCase						) - set(_lowerCamelCase						)

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

							return (keepscore, delscore_precision, addscore)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[str]				     =					len(_lowerCamelCase						)

							_lowerCAmelCase  :							Tuple				     =					ssent.split(" "						)
							_lowerCAmelCase  :							List[Any]				     =					csent.split(" "						)
							_lowerCAmelCase  :							Optional[Any]				     =					[]
							_lowerCAmelCase  :							Union[str, Any]				     =					[]
							_lowerCAmelCase  :							Dict				     =					[]
							_lowerCAmelCase  :							str				     =					[]
							_lowerCAmelCase  :							Dict				     =					[]
							_lowerCAmelCase  :							Tuple				     =					[]

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

							for i in range(0       ,				len(_lowerCamelCase						) - 1						):
														if i < len(_lowerCamelCase						) - 1:
																					_lowerCAmelCase  :							List[str]				     =					sagrams[i] + " " + sagrams[i + 1]
																					sagrams.append(_lowerCamelCase						)
														if i < len(_lowerCamelCase						) - 2:
																					_lowerCAmelCase  :							Union[str, Any]				     =					sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
																					sagrams.append(_lowerCamelCase						)
														if i < len(_lowerCamelCase						) - 3:
																					_lowerCAmelCase  :							Union[str, Any]				     =					sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
																					sagrams.append(_lowerCamelCase						)

							for i in range(0       ,				len(_lowerCamelCase						) - 1						):
														if i < len(_lowerCamelCase						) - 1:
																					_lowerCAmelCase  :							Union[str, Any]				     =					cagrams[i] + " " + cagrams[i + 1]
																					cagrams.append(_lowerCamelCase						)
														if i < len(_lowerCamelCase						) - 2:
																					_lowerCAmelCase  :							List[Any]				     =					cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
																					cagrams.append(_lowerCamelCase						)
														if i < len(_lowerCamelCase						) - 3:
																					_lowerCAmelCase  :							Optional[int]				     =					cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
																					cagrams.append(_lowerCamelCase						)

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






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





							'''simple docstring'''




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

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

							if not return_str:
														_lowerCAmelCase  :							Tuple				     =					normalized_sent.split()

							return normalized_sent






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




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






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





							'''simple docstring'''




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



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







							def      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      snake_case__   (     self,    __a,    __a,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					{}
														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





import string






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							for key in range(len(string.ascii_uppercase						)						):
														_lowerCAmelCase  :							str				     =					""
														for symbol in message:
																					if symbol in string.ascii_uppercase:
																												_lowerCAmelCase  :							List[str]				     =					string.ascii_uppercase.find(_lowerCamelCase						)
																												_lowerCAmelCase  :							Dict				     =					num - key
																												if num < 0:
																																			_lowerCAmelCase  :							Dict				     =					num + len(string.ascii_uppercase						)
																												_lowerCAmelCase  :							Optional[Any]				     =					translated + string.ascii_uppercase[num]
																					else:
																												_lowerCAmelCase  :							int				     =					translated + symbol
														print(F"Decryption using Key #{key}: {translated}"						)






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Tuple				     =					input("Encrypted message: "						)
							_lowerCAmelCase  :							Dict				     =					message.upper()
							decrypt(_lowerCamelCase						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()
