

"""simple docstring"""


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 			_lowerCAmelCase				(       lowercase_						):

						def remove_articles(lowercase_						):
												UpperCAmelCase					=			re.compile(R'\b(a|an|the)\b'		,     re.UNICODE						)
												return re.sub(_A		,     ' '		,     _A						)

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

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

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

						return white_space_fix(remove_articles(remove_punc(lower(_A						)						)						)						)

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						return int(normalize_answer(_A						) == normalize_answer(_A						)						)

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						UpperCAmelCase					=			[any(compute_exact(_A		,     _A						) for ref in refs						) for pred, refs in zip(_A		,     _A						)]
						return (sum(_A						) / len(_A						)) * 100

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			[rgram for rgrams in rgramslist for rgram in rgrams]
						UpperCAmelCase					=			Counter(_A						)

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

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

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

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

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

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

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

						return (keepscore, delscore_precision, addscore)

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			len(_A						)

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

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

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

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_ = True		,     lowercase_ = "13a"		,     lowercase_ = 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:
												UpperCAmelCase					=			sentence.lower()

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

						if not return_str:
												UpperCAmelCase					=			normalized_sent.split()

						return normalized_sent

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

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_="exp"		,     lowercase_=None		,     lowercase_=False		,     lowercase_=False		,     lowercase_=False		,     ):
						UpperCAmelCase					=			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'						)
						UpperCAmelCase					=			[[refs[i] for refs in references] for i in range(_A						)]
						UpperCAmelCase					=			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 				A_      (					datasets.Metric    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Optional[int]:
												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      UpperCAmelCase__					( self			:str       ,  lowercase_			:Optional[Any]       ,  lowercase_			:int       ,  lowercase_			:str )    ->      Optional[int]:
												UpperCAmelCase					=			{}
												result.update({'sari': compute_sari(sources=__snake_case       ,  predictions=__snake_case       ,  references=__snake_case )} )
												result.update({'sacrebleu': compute_sacrebleu(predictions=__snake_case       ,  references=__snake_case )} )
												result.update({'exact': compute_em(predictions=__snake_case       ,  references=__snake_case )} )
												return result










"""simple docstring"""


import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

snake_case_    					=	3

def 			_lowerCAmelCase				(       lowercase_						):
						print('Generating primitive root of p'						)
						while True:
												UpperCAmelCase					=			random.randrange(3		,     lowercase_						)
												if pow(lowercase_		,     2		,     lowercase_						) == 1:
																		continue
												if pow(lowercase_		,     lowercase_		,     lowercase_						) == 1:
																		continue
												return g

def 			_lowerCAmelCase				(       lowercase_						):
						print('Generating prime p...'						)
						UpperCAmelCase					=			rabin_miller.generate_large_prime(lowercase_						)  # select large prime number.
						UpperCAmelCase					=			primitive_root(lowercase_						)  # one primitive root on modulo p.
						UpperCAmelCase					=			random.randrange(3		,     lowercase_						)  # private_key -> have to be greater than 2 for safety.
						UpperCAmelCase					=			cryptomath.find_mod_inverse(pow(lowercase_		,     lowercase_		,     lowercase_						)		,     lowercase_						)

						UpperCAmelCase					=			(key_size, e_a, e_a, p)
						UpperCAmelCase					=			(key_size, d)

						return public_key, private_key

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						if os.path.exists(F"""{name}_pubkey.txt"""						) or os.path.exists(F"""{name}_privkey.txt"""						):
												print('\nWARNING:'						)
												print(
												    F"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
												    'Use a different name or delete these files and re-run this program.'						)
												sys.exit()

						UpperCAmelCase				,   UpperCAmelCase					=			generate_key(lowercase_						)
						print(F"""\nWriting public key to file {name}_pubkey.txt..."""						)
						with open(F"""{name}_pubkey.txt"""		,     'w'						) as fo:
												fo.write(F"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""						)

						print(F"""Writing private key to file {name}_privkey.txt..."""						)
						with open(F"""{name}_privkey.txt"""		,     'w'						) as fo:
												fo.write(F"""{private_key[0]},{private_key[1]}"""						)

def 			_lowerCAmelCase				(       ):
						print('Making key files...'						)
						make_key_files('elgamal'		,     2048						)
						print('Key files generation successful'						)


if __name__ == "__main__":
				main()





