

from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


SCREAMING_SNAKE_CASE						:Dict      =			"""\
@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},
}
"""

SCREAMING_SNAKE_CASE						:Optional[Any]      =			"""\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


SCREAMING_SNAKE_CASE						:int      =			"""
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
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
Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]
    >>> sari = datasets.load_metric(\"sari\")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 26.953601953601954}
"""




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		List[str]:



    """simple docstring"""






    UpperCamelCase_          =		[rgram for rgrams in rgramslist for rgram in rgrams]
    UpperCamelCase_          =		Counter(SCREAMING_SNAKE_CASE_       )

    UpperCamelCase_          =		Counter(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		Counter()
    for sgram, scount in sgramcounter.items():
        UpperCamelCase_          =		scount * numref

    UpperCamelCase_          =		Counter(SCREAMING_SNAKE_CASE_       )
    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(SCREAMING_SNAKE_CASE_       ) > 0:
        UpperCamelCase_          =		keeptmpscorea / len(SCREAMING_SNAKE_CASE_       )
    if len(SCREAMING_SNAKE_CASE_       ) > 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(SCREAMING_SNAKE_CASE_       ) > 0:
        UpperCamelCase_          =		deltmpscorea / len(SCREAMING_SNAKE_CASE_       )

    # ADDITION
    UpperCamelCase_          =		set(SCREAMING_SNAKE_CASE_       ) - set(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		set(SCREAMING_SNAKE_CASE_       ) & set(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		set(SCREAMING_SNAKE_CASE_       ) - set(SCREAMING_SNAKE_CASE_       )

    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(SCREAMING_SNAKE_CASE_       ) > 0:
        UpperCamelCase_          =		addtmpscore / len(SCREAMING_SNAKE_CASE_       )
    if len(SCREAMING_SNAKE_CASE_       ) > 0:
        UpperCamelCase_          =		addtmpscore / len(SCREAMING_SNAKE_CASE_       )
    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(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		int:



    """simple docstring"""






    UpperCamelCase_          =		len(SCREAMING_SNAKE_CASE_       )

    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(SCREAMING_SNAKE_CASE_       )
        for i in range(0       ,		len(SCREAMING_SNAKE_CASE_       ) - 1       ):
            if i < len(SCREAMING_SNAKE_CASE_       ) - 1:
                UpperCamelCase_          =		ragrams[i] + " " + ragrams[i + 1]
                ragrams.append(SCREAMING_SNAKE_CASE_       )
            if i < len(SCREAMING_SNAKE_CASE_       ) - 2:
                UpperCamelCase_          =		ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
                ragrams.append(SCREAMING_SNAKE_CASE_       )
            if i < len(SCREAMING_SNAKE_CASE_       ) - 3:
                UpperCamelCase_          =		ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
                ragrams.append(SCREAMING_SNAKE_CASE_       )
        ragramslist.append(SCREAMING_SNAKE_CASE_       )
        ragramslist.append(SCREAMING_SNAKE_CASE_       )
        ragramslist.append(SCREAMING_SNAKE_CASE_       )

    for i in range(0       ,		len(SCREAMING_SNAKE_CASE_       ) - 1       ):
        if i < len(SCREAMING_SNAKE_CASE_       ) - 1:
            UpperCamelCase_          =		sagrams[i] + " " + sagrams[i + 1]
            sagrams.append(SCREAMING_SNAKE_CASE_       )
        if i < len(SCREAMING_SNAKE_CASE_       ) - 2:
            UpperCamelCase_          =		sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
            sagrams.append(SCREAMING_SNAKE_CASE_       )
        if i < len(SCREAMING_SNAKE_CASE_       ) - 3:
            UpperCamelCase_          =		sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
            sagrams.append(SCREAMING_SNAKE_CASE_       )

    for i in range(0       ,		len(SCREAMING_SNAKE_CASE_       ) - 1       ):
        if i < len(SCREAMING_SNAKE_CASE_       ) - 1:
            UpperCamelCase_          =		cagrams[i] + " " + cagrams[i + 1]
            cagrams.append(SCREAMING_SNAKE_CASE_       )
        if i < len(SCREAMING_SNAKE_CASE_       ) - 2:
            UpperCamelCase_          =		cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
            cagrams.append(SCREAMING_SNAKE_CASE_       )
        if i < len(SCREAMING_SNAKE_CASE_       ) - 3:
            UpperCamelCase_          =		cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
            cagrams.append(SCREAMING_SNAKE_CASE_       )

    ((UpperCamelCase_)   ,    (UpperCamelCase_)   ,    (UpperCamelCase_))          =		SARIngram(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    ((UpperCamelCase_)   ,    (UpperCamelCase_)   ,    (UpperCamelCase_))          =		SARIngram(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    ((UpperCamelCase_)   ,    (UpperCamelCase_)   ,    (UpperCamelCase_))          =		SARIngram(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    ((UpperCamelCase_)   ,    (UpperCamelCase_)   ,    (UpperCamelCase_))          =		SARIngram(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    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(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = True       ,		SCREAMING_SNAKE_CASE_ = "13a"       ,		SCREAMING_SNAKE_CASE_ = True       )->		Union[str, Any]:



    """simple docstring"""






    if lowercase:
        UpperCamelCase_          =		sentence.lower()

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

    if not return_str:
        UpperCamelCase_          =		normalized_sent.split()

    return normalized_sent






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






     def      UpperCAmelCase_							(							self     )->			List[str]:
         return datasets.MetricInfo(
             description=_DESCRIPTION						,		citation=_CITATION						,		inputs_description=_KWARGS_DESCRIPTION						,		features=datasets.Features(
                 {
                     "sources": datasets.Value("string"						,		id="sequence"     ),
                     "predictions": datasets.Value("string"						,		id="sequence"     ),
                     "references": datasets.Sequence(datasets.Value("string"						,		id="sequence"     )						,		id="references"     ),
                 }     )						,		codebase_urls=[
                 "https://github.com/cocoxu/simplification/blob/master/SARI.py",
                 "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
             ]						,		reference_urls=["https://www.aclweb.org/anthology/Q16-1029.pdf"]						,		)






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase     )->			List[Any]:
         if not (len(_lowercase     ) == len(_lowercase     ) == len(_lowercase     )):
             raise ValueError("Sources length must match predictions and references lengths."     )
         UpperCamelCase_          =		0
         for src, pred, refs in zip(_lowercase						,		_lowercase						,		_lowercase     ):
             sari_score += SARIsent(normalize(_lowercase     )						,		normalize(_lowercase     )						,		[normalize(_lowercase     ) for sent in refs]     )
         UpperCamelCase_          =		sari_score / len(_lowercase     )
         return {"sari": 100 * sari_score}






from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->list:






					"""simple docstring"""






					seed(1       )
					return [gauss(UpperCAmelCase,	UpperCAmelCase       ) for _ in range(UpperCAmelCase       )]







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->list:






					"""simple docstring"""






					return [k for k in range(UpperCAmelCase       ) for _ in range(instance_count[k]       )]







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					return sum(UpperCAmelCase       ) / instance_count







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					return instance_count / total_count







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					__magic_name__					:							Any		   =							[]  # An empty list to store all squared differences
					# iterate over number of elements in items
					for i in range(len(UpperCAmelCase       )       ):
										# for loop iterates over number of elements in inner layer of items
										for j in range(len(items[i]       )       ):
															# appending squared differences to 'squared_diff' list
															squared_diff.append((items[i][j] - means[i]) ** 2       )

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
					__magic_name__					:							Optional[int]		   =							len(UpperCAmelCase       )  # Number of classes in dataset
					return 1 / (total_count - n_classes) * sum(UpperCAmelCase       )







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->list:






					"""simple docstring"""






					__magic_name__					:							int		   =							[]
					# for loop iterates over number of elements in list
					for i in range(len(UpperCAmelCase       )       ):
										# for loop iterates over number of inner items of each element
										for j in range(len(x_items[i]       )       ):
															__magic_name__					:							Dict		   =							[]  # to store all discriminant values of each item as a list
															# for loop iterates over number of classes we have in our dataset
															for k in range(len(UpperCAmelCase       )       ):
																				# appending values of discriminants for each class to 'temp' list
																				temp.append(
																				    x_items[i][j] * (means[k] / variance)
																				    - (means[k] ** 2 / (2 * variance))
																				    + log(probabilities[k]       )       )
															# appending discriminant values of each item to 'results' list
															results.append(UpperCAmelCase       )

					return [result.index(max(UpperCAmelCase       )       ) for result in results]







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					__magic_name__					:							Optional[Any]		   =							sum(1 for i, j in zip(UpperCAmelCase,	UpperCAmelCase       ) if i == j       )
					# percentage of accuracy equals to number of correct predictions divided by number
					# of all data and multiplied by 100
					return (correct / len(UpperCAmelCase       )) * 100


lowercase_		=							TypeVar('''num''')







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase = lambda UpperCAmelCase       : True,	UpperCAmelCase = None,	)  ->num:






					"""simple docstring"""






					while True:
										try:
															__magic_name__					:							List[Any]		   =							input_type(input(UpperCAmelCase       ).strip() or default       )
															if condition(UpperCAmelCase       ):
																				return user_input
															else:
																				print(F'''{user_input}: {err_msg}'''       )
																				continue
										except ValueError:
															print(
															    F'''{user_input}: Incorrect input type, expected {input_type.__name__!r}'''       )







def  lowerCAmelCase  (	)  ->str:






					"""simple docstring"""






					while True:
										print(''' Linear Discriminant Analysis '''.center(50,	'''*'''       )       )
										print('''*''' * 50,	'''\n'''       )
										print('''First of all we should specify the number of classes that'''       )
										print('''we want to generate as training dataset'''       )
										# Trying to get number of classes
										__magic_name__					:							List[Any]		   =							valid_input(
										    input_type=UpperCAmelCase,	condition=lambda UpperCAmelCase       : x > 0,	input_msg='''Enter the number of classes (Data Groupings): ''',	err_msg='''Number of classes should be positive!''',	)

										print('''-''' * 100       )

										# Trying to get the value of standard deviation
										__magic_name__					:							Any		   =							valid_input(
										    input_type=UpperCAmelCase,	condition=lambda UpperCAmelCase       : x >= 0,	input_msg=(
										        '''Enter the value of standard deviation'''
										        '''(Default value is 1.0 for all classes): '''
										    ),	err_msg='''Standard deviation should not be negative!''',	default='''1.0''',	)

										print('''-''' * 100       )

										# Trying to get number of instances in classes and theirs means to generate
										# dataset
										__magic_name__					:							Optional[Any]		   =							[]  # An empty list to store instance counts of classes in dataset
										for i in range(UpperCAmelCase       ):
															__magic_name__					:							Dict		   =							valid_input(
															    input_type=UpperCAmelCase,	condition=lambda UpperCAmelCase       : x > 0,	input_msg=(F'''Enter The number of instances for class_{i+1}: '''),	err_msg='''Number of instances should be positive!''',	)
															counts.append(UpperCAmelCase       )
										print('''-''' * 100       )

										# An empty list to store values of user-entered means of classes
										__magic_name__					:							Dict		   =							[]
										for a in range(UpperCAmelCase       ):
															__magic_name__					:							Dict		   =							valid_input(
															    input_type=UpperCAmelCase,	input_msg=(F'''Enter the value of mean for class_{a+1}: '''),	err_msg='''This is an invalid value.''',	)
															user_means.append(UpperCAmelCase       )
										print('''-''' * 100       )

										print('''Standard deviation: ''',	UpperCAmelCase       )
										# print out the number of instances in classes in separated line
										for i, count in enumerate(UpperCAmelCase,	1       ):
															print(F'''Number of instances in class_{i} is: {count}'''       )
										print('''-''' * 100       )

										# print out mean values of classes separated line
										for i, user_mean in enumerate(UpperCAmelCase,	1       ):
															print(F'''Mean of class_{i} is: {user_mean}'''       )
										print('''-''' * 100       )

										# Generating training dataset drawn from gaussian distribution
										__magic_name__					:							Optional[int]		   =							[
										    gaussian_distribution(user_means[j],	UpperCAmelCase,	counts[j]       )
										    for j in range(UpperCAmelCase       )
										]
										print('''Generated Normal Distribution: \n''',	UpperCAmelCase       )
										print('''-''' * 100       )

										# Generating Ys to detecting corresponding classes
										__magic_name__					:							List[str]		   =							y_generator(UpperCAmelCase,	UpperCAmelCase       )
										print('''Generated Corresponding Ys: \n''',	UpperCAmelCase       )
										print('''-''' * 100       )

										# Calculating the value of actual mean for each class
										__magic_name__					:							Dict		   =							[calculate_mean(counts[k],	x[k]       ) for k in range(UpperCAmelCase       )]
										# for loop iterates over number of elements in 'actual_means' list and print
										# out them in separated line
										for i, actual_mean in enumerate(UpperCAmelCase,	1       ):
															print(F'''Actual(Real) mean of class_{i} is: {actual_mean}'''       )
										print('''-''' * 100       )

										# Calculating the value of probabilities for each class
										__magic_name__					:							Union[str, Any]		   =							[
										    calculate_probabilities(counts[i],	sum(UpperCAmelCase       )       ) for i in range(UpperCAmelCase       )
										]

										# for loop iterates over number of elements in 'probabilities' list and print
										# out them in separated line
										for i, probability in enumerate(UpperCAmelCase,	1       ):
															print(F'''Probability of class_{i} is: {probability}'''       )
										print('''-''' * 100       )

										# Calculating the values of variance for each class
										__magic_name__					:							List[Any]		   =							calculate_variance(UpperCAmelCase,	UpperCAmelCase,	sum(UpperCAmelCase       )       )
										print('''Variance: ''',	UpperCAmelCase       )
										print('''-''' * 100       )

										# Predicting Y values
										# storing predicted Y values in 'pre_indexes' variable
										__magic_name__					:							List[Any]		   =							predict_y_values(UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )
										print('''-''' * 100       )

										# Calculating Accuracy of the model
										print(F'''Accuracy: {accuracy(UpperCAmelCase,	UpperCAmelCase       )}'''       )
										print('''-''' * 100       )
										print(''' DONE '''.center(100,	'''+'''       )       )

										if input('''Press any key to restart or \'q\' for quit: '''       ).strip().lower() == "q":
															print('''\n''' + '''GoodBye!'''.center(100,	'''-'''       ) + '''\n'''       )
															break
										system('''cls''' if name == '''nt''' else '''clear'''       )  # noqa: S605


if __name__ == "__main__":
				main()



