209 lines
8.1 KiB
Python
209 lines
8.1 KiB
Python
import random
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import time
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import sys
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import tools
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import pyviennacl as vcl
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import numpy
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from deap import algorithms
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from collections import OrderedDict as odict
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def closest_divisor(N, x):
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x_low=x_high=max(1,min(round(x),N))
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while N % x_low > 0 and x_low>0:
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x_low = x_low - 1
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while N % x_high > 0 and x_high < N:
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x_high = x_high + 1
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return x_low if x - x_low < x_high - x else x_high
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class GeneticOperators(object):
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def __init__(self, device, statement, parameters, parameter_names, TemplateType, build_template):
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self.device = device
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self.statement = statement
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self.parameters = parameters
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self.parameter_names = parameter_names
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self.TemplateType = TemplateType
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self.ParameterType = TemplateType.Parameters
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self.build_template = build_template
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self.cache = {}
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def init(self):
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while True:
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result = [random.choice(L) for L in self.parameters]
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template = self.build_template(self.TemplateType.Parameters(*result))
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registers_usage = template.registers_usage(vcl.atidlas.StatementsTuple(self.statement))/4
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lmem_usage = template.lmem_usage(vcl.atidlas.StatementsTuple(self.statement))
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local_size = template.parameters.local_size_0*template.parameters.local_size_1
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occupancy_record = tools.OccupancyRecord(self.device, local_size, lmem_usage, registers_usage)
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if template.check(self.statement)==0 and occupancy_record.occupancy >= 10 :
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return result
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@staticmethod
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def min_to_hyperbol(a, tup):
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x = 1
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for i in range(100):
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dx = 2*(-a**2/x**3 + a*tup[1]/x**2 - tup[0] + x);
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ddx = 6*a**2/x**4 - 4*a*tup[1]/x**3 + 2;
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if abs(dx) < 1e-7 or abs(ddx) < 1e-7:
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break
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x-=dx/ddx;
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if x<1 or x>a:
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x = max(1, min(x, a))
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break
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new_x = int(closest_divisor(a, x))
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new_y = int(a / new_x)
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return (new_x, new_y)
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def repair(self,func):
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def repair_impl(child):
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D = odict(zip(self.parameter_names, child))
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dummy_template = self.build_template(self.ParameterType(*D.values()))
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FetchingPolicy = vcl.atidlas.FetchingPolicy;
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D['local-size-0'] = max(1, D['local-size-0'])
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D['local-size-1'] = max(1, D['local-size-1'])
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if 'local-size-1' not in D:
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D['local-size-0'] = min(D['local-size-0'], self.device.max_work_group_size)
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elif D['local-size-0']*D['local-size-1'] > self.device.max_work_group_size:
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res = GeneticOperators.min_to_hyperbol(self.device.max_work_group_size, (D['local-size-0'], D['local-size-1']))
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D['local-size-0'] = res[0]
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D['local-size-1'] = res[1]
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if self.ParameterType is vcl.atidlas.MatrixProductTemplate.Parameters:
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if dummy_template.A_trans != 'N' and dummy_template.B_trans != 'T':
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D['simd-width'] = 1
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D['kL'] = max(1, D['kL'])
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D['kS'] = max(1, D['kS'])
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D['mS'] = max(D['mS'], D['simd-width'])
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D['nS'] = max(D['nS'], D['simd-width'])
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D['mS'] = D['mS'] - D['mS']%D['simd-width']
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D['nS'] = D['nS'] - D['nS']%D['simd-width']
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if D['A-fetch-policy']!=FetchingPolicy.FETCH_FROM_LOCAL and D['B-fetch-policy']!=FetchingPolicy.FETCH_FROM_LOCAL:
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D['local-fetch-size-0']=D['local-fetch-size-1']=0
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else:
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res = GeneticOperators.min_to_hyperbol(D['local-size-0']*D['local-size-1'], (D['local-fetch-size-0'], D['local-fetch-size-1']))
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D['local-fetch-size-0'] = res[0]
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D['local-fetch-size-1'] = res[1]
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if D['A-fetch-policy']==FetchingPolicy.FETCH_FROM_LOCAL and dummy_template.A_trans=='N' and D['kL'] % D['local-fetch-size-1'] > 0:
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D['kL'] = max(1,round(D['kL']/D['local-fetch-size-1']))*D['local-fetch-size-1']
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if D['B-fetch-policy']==FetchingPolicy.FETCH_FROM_LOCAL and dummy_template.B_trans=='T' and D['kL'] % D['local-fetch-size-1'] > 0:
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D['kL'] = max(1,round(D['kL']/D['local-fetch-size-1']))*D['local-fetch-size-1']
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D['kS'] = min(D['kL'], D['kS'])
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return D.values()
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def wrappper(*args, **kargs):
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offspring = func(*args, **kargs)
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for child in offspring:
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new_child = repair_impl(child)
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for i in range(len(child)):
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if child[i] != new_child[i]:
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child[i] = new_child[i]
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return offspring
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return wrappper
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def mutate(self, individual, indpb = 0.15):
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for i in individual:
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if random.random() < indpb:
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coef = 2**(1 + numpy.random.poisson())
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funs = [lambda x:x/coef, lambda x:x*coef]
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F = random.choice(funs)
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nF = funs[1] if F==funs[0] else funs[0]
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#swapping-based mutations
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def m0():
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individual[1], individual[3] = individual[3], individual[1]
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def m1():
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individual[4], individual[6] = individual[6], individual[4]
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def m2():
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individual[9], individual[10] = individual[10], individual[9]
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#value modification mutations
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def m3():
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individual[0] = random.choice(self.parameters[0])
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def m4():
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individual[1] = F(individual[1])
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individual[9] = F(individual[9])
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def m5():
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individual[2] = F(individual[2])
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def m6():
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individual[3] = F(individual[3])
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individual[10] = F(individual[10])
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def m7():
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individual[4] = F(individual[4])
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def m8():
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individual[5] = F(individual[5])
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def m9():
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individual[6] = F(individual[6])
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def m10():
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individual[7] = random.choice([x for x in self.parameters[7] if x!=individual[7]])
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def m11():
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individual[8] = random.choice([x for x in self.parameters[8] if x!=individual[8]])
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def m12():
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individual[9] = F(individual[9])
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individual[10] = nF(individual[10])
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def m13():
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individual[10] = F(individual[10])
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individual[9] = nF(individual[9])
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random.choice([m0, m1, m2, m3, m4, m5, m6, m7, m8, m9, m10, m11, m12, m13])()
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return individual,
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def evaluate(self, individual):
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if tuple(individual) not in self.cache:
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template = self.build_template(self.TemplateType.Parameters(*individual))
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try:
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self.cache[tuple(individual)] = tools.benchmark(template, self.statement, self.device)
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except:
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self.cache[tuple(individual)] = 10
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return self.cache[tuple(individual)],
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def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, maxtime, maxgen, halloffame, compute_perf, perf_metric):
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# Evaluate the individuals with an invalid fitness
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invalid_ind = [ind for ind in population if not ind.fitness.valid]
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fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
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for ind, fit in zip(invalid_ind, fitnesses):
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ind.fitness.values = fit
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if halloffame is not None:
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halloffame.update(population)
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# Begin the generational process
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gen = 0
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maxtime = time.strptime(maxtime, '%Mm%Ss')
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maxtime = maxtime.tm_min*60 + maxtime.tm_sec
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start_time = time.time()
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while time.time() - start_time < maxtime and gen < maxgen:
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# Vary the population
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offspring = algorithms.varOr(population, toolbox, lambda_, cxpb, mutpb)
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# Evaluate the individuals with an invalid fitness
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invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
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fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
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for ind, fit in zip(invalid_ind, fitnesses):
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ind.fitness.values = fit
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# Update the hall of fame with the generated individuals
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if halloffame is not None:
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halloffame.update(offspring)
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# Select the next generation population
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population[:] = toolbox.select(population + offspring, mu)
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# Update the statistics with the new population
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gen = gen + 1
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best_profile = '(%s)'%','.join(map(str,halloffame[0]));
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best_performance = compute_perf(halloffame[0].fitness.values[0])
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sys.stdout.write('Generation %d | Time %d | Best %d %s [ for %s ]\r'%(gen, time.time() - start_time, best_performance, perf_metric, best_profile))
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sys.stdout.flush()
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sys.stdout.write('\n')
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return population
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