BnP-v1.0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Branch-and-Price Framework\n",
    "\n",
    "- Features:\n",
    "    - Python + Gurobi/etc.\n",
    "    - Self-defined PricingProblem/BranchingRules\n",
    "    - A demo of GraphColoringProblem\n",
    "    \n",
    "- TODOs: \n",
    "    - Add Cut\n",
    "    - Parallel\n",
    "    - Node Visiting Priority"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "from gurobipy import *\n",
    "import queue\n",
    "import time\n",
    "from threading import Lock\n",
    "from threading import Thread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from abc import ABC, abstractmethod\n",
    "from timeit import default_timer as timer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# debug utils\n",
    "from importlib import reload\n",
    "import logging\n",
    "import inspect\n",
    "\n",
    "\n",
    "def log(message, logger=logging.error):\n",
    "    func = inspect.currentframe().f_back.f_code\n",
    "    logging.debug(\"{}\\t[{}, {}, {}]\".format(message,\n",
    "        func.co_name, \n",
    "        func.co_filename, \n",
    "        func.co_firstlineno))\n",
    "    \n",
    "    \n",
    "def _printLP(m):\n",
    "    m.write('tmp.lp')\n",
    "    f = open('tmp.lp', 'r')\n",
    "    for l in f.readlines():\n",
    "        print(l)\n",
    "    print('====================')\n",
    "    \n",
    "\n",
    "def isInt(num):\n",
    "    return (float(num)).is_integer()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display flags\n",
    "DEBUG = True\n",
    "OUTPUT_LP = True\n",
    "GRB_OUTPUT = False\n",
    "\n",
    "reload(logging)\n",
    "logging.basicConfig(level=logging.DEBUG if DEBUG else logging.INFO, format='%(asctime)s %(levelname)s: %(message)s', datefmt='%I:%M:%S')\n",
    "printGRBLP = lambda m: _printLP(m) if OUTPUT_LP else False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_IT = 100000\n",
    "MAX = 1e6\n",
    "EPS = 1e-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# constants\n",
    "class pybnp:\n",
    "    REAL = 0\n",
    "    BINARY = 1\n",
    "    INTEGER = 2\n",
    "    INFINITY = 3\n",
    "        \n",
    "\n",
    "# abstract model\n",
    "# min obj\n",
    "# s.t. Ax <= b; x >= 0\n",
    "class AbstractModel(ABC):\n",
    "    @abstractmethod\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "        pass\n",
    "    \n",
    "    @abstractmethod\n",
    "    def addRHS(self, bs):\n",
    "        pass\n",
    "    \n",
    "    @abstractmethod\n",
    "    def addVar(self, vtype, name, ub, obj, col):\n",
    "        pass\n",
    "    \n",
    "    @abstractmethod\n",
    "    def addConstrsByVarId(self, lhCoes, lhVarIds, rhs, name):\n",
    "         pass\n",
    "        \n",
    "    @abstractmethod\n",
    "    def addConstrsByVarName(self, lhCoes, lhVarNames, rhs, name):\n",
    "         pass\n",
    "        \n",
    "    @abstractmethod\n",
    "    def removeConstrById(self, constrId):\n",
    "        pass\n",
    "    \n",
    "    @abstractmethod\n",
    "    def removeConstrsByName(self, constrName):\n",
    "        pass\n",
    "    \n",
    "    @abstractmethod\n",
    "    def removeVarById(self, varId):\n",
    "        pass\n",
    "\n",
    "    @abstractmethod\n",
    "    def removeVarByName(self, varName):\n",
    "        pass\n",
    "\n",
    "    @abstractmethod\n",
    "    def optimize(self):\n",
    "        pass\n",
    "    \n",
    "    @abstractmethod\n",
    "    def updateObjById(self, objCoes, varIds):\n",
    "        pass\n",
    "    \n",
    "    @abstractmethod\n",
    "    def updateObjByName(self, objCoes, varNames):\n",
    "        pass\n",
    "    \n",
    "    @abstractmethod\n",
    "    def getIsOptimal(self):\n",
    "        pass\n",
    "        \n",
    "    @abstractmethod\n",
    "    def getObjVal(self):\n",
    "        pass\n",
    "    \n",
    "    @abstractmethod\n",
    "    def getSol(self):\n",
    "        pass\n",
    "    \n",
    "    @abstractmethod\n",
    "    def getSolVals(self):\n",
    "        pass\n",
    "\n",
    "    @abstractmethod\n",
    "    def getDualPis(self):\n",
    "        pass\n",
    "    \n",
    "    @abstractmethod\n",
    "    def printProgram(self):\n",
    "        pass\n",
    "\n",
    "    @abstractmethod\n",
    "    def printSolution(self):\n",
    "        pass\n",
    "    \n",
    "    @abstractmethod\n",
    "    def getBs(self):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def getNonZeroColsSol(self):\n",
    "        pass\n",
    "\n",
    "    \n",
    "    \n",
    "# GurobiSolver\n",
    "class GurobiModel(AbstractModel):\n",
    "    def __init__(self, name='untitled_model'):\n",
    "        self.model = Model(name)\n",
    "        self.model.setParam(\"OutputFlag\", GRB_OUTPUT)\n",
    "        self.model.setAttr('ModelSense', GRB.MINIMIZE)\n",
    "        self.vars = []\n",
    "        self.constrs = {}\n",
    "        self.bs = []\n",
    "        self.name = name\n",
    "    \n",
    "    \n",
    "    def addRHS(self, bs):\n",
    "        self.bs = bs\n",
    "        self.model.addConstrs((0 <= b for b in bs))\n",
    "        self.model.update()\n",
    "        for b, c in zip(bs, self.model.getConstrs()):\n",
    "            c.setAttr('RHS', b)\n",
    "        self.model.update()\n",
    "\n",
    "    \n",
    "    # col= ([RowIds], [Coes])\n",
    "    def addVar(self, name, vtype=pybnp.REAL, ub=pybnp.INFINITY, obj=0, col=None):\n",
    "        newCols = None\n",
    "        if col != None:\n",
    "            constrs = self.model.getConstrs()\n",
    "            newCols = Column(col[1], [constrs[cid] for cid in col[0]])\n",
    "        ub = GRB.INFINITY if ub == pybnp.INFINITY else ub\n",
    "        GRBvtypeMap = dict({pybnp.INTEGER: GRB.INTEGER, pybnp.REAL: GRB.CONTINUOUS, pybnp.BINARY: GRB.BINARY})\n",
    "        v = self.model.addVar(obj=obj, vtype=GRBvtypeMap[vtype], name=name, ub=ub, column=newCols)\n",
    "        self.vars.append(v)\n",
    "    \n",
    "    \n",
    "    def addConstrsByVarId(self, lhCoes, lhVarIds, rhs, name):\n",
    "        #print('add constr', name)\n",
    "        newConstrs = self.model.addConstrs(( quicksum( lhc * self.vars[lhVid] for lhc, lhVid in zip(lhCoes[i], lhVarIds[i])) <= rhs[i] \n",
    "                               for i in range(len(rhs))), name=name)\n",
    "        self.constrs[name] = newConstrs\n",
    "        self.model.update()\n",
    "        \n",
    "        \n",
    "    def addConstrsByVarName(self, lhCoes, lhVarNames, rhs, name):\n",
    "        raise NotImplemented('TBD') \n",
    "        \n",
    "        \n",
    "    def removeConstrById(self, constrId):\n",
    "        constrs = self.model.getConstrs()\n",
    "        self.model.remove(constrs[constrId])\n",
    "        self.model.update()\n",
    "    \n",
    "    \n",
    "    def removeConstrsByName(self, constrName):\n",
    "        constrs = self.constrs[constrName]\n",
    "        self.model.remove(constrs)\n",
    "        self.model.update()\n",
    "    \n",
    "    \n",
    "    def removeVarById(self, varId):\n",
    "        vs = self.model.getVars()\n",
    "        self.model.remove(vs[varId])\n",
    "\n",
    "\n",
    "    def removeVarByName(self, varName):\n",
    "        raise NotImplemented('TBD') \n",
    "\n",
    "\n",
    "    def optimize(self):\n",
    "        self.model.optimize()\n",
    "        return self.model.Runtime\n",
    "        \n",
    "    \n",
    "    def updateObjById(self, objCoes, varIds):\n",
    "        self.model.setObjective( quicksum( c * self.vars[i] for c,i in zip(objCoes, varIds) ))\n",
    "        \n",
    "        \n",
    "    def updateObjByName(self, objCoes, varNames):\n",
    "        raise NotImplemented('TBD') \n",
    "    \n",
    "\n",
    "    def getIsOptimal(self):\n",
    "        return self.model.status == GRB.OPTIMAL\n",
    "        \n",
    "        \n",
    "    def getObjVal(self):\n",
    "        return self.model.objVal\n",
    "    \n",
    "\n",
    "    def getSol(self):\n",
    "        return self.model.getVars()\n",
    "    \n",
    "    \n",
    "    def getSolVals(self):\n",
    "        return [v.x for v in self.model.getVars()]\n",
    "\n",
    "\n",
    "    def getDualPis(self):\n",
    "        return self.model.getAttr('Pi', self.model.getConstrs())\n",
    "        \n",
    "    \n",
    "    def printProgram(self):\n",
    "        printGRBLP(self.model)\n",
    "\n",
    "        \n",
    "    def printSolution(self):\n",
    "        if not OUTPUT_LP:\n",
    "            return \n",
    "        if self.getIsOptimal():\n",
    "            for v in self.getSol():\n",
    "                print('{} = {}'.format(v.varName, v.x))\n",
    "        else:\n",
    "            print('not optimal')\n",
    "    \n",
    "    \n",
    "    def getBs(self):\n",
    "        return self.bs\n",
    "    \n",
    "    \n",
    "    def getNonZeroColsSol(self):\n",
    "        m = self.model\n",
    "        cs = m.getConstrs()\n",
    "        c2i = dict(zip(cs, range(len(cs))))\n",
    "        cols = []\n",
    "        sol = []\n",
    "        for v in m.getVars():\n",
    "            if v.x == 0:\n",
    "                continue\n",
    "            rawCol = m.getCol(v)\n",
    "            col = ([], [])\n",
    "            for i in range(rawCol.size()):\n",
    "                col[0].append(c2i[m.getCol(v).getConstr(i)])\n",
    "                col[1].append(m.getCol(v).getCoeff(i))\n",
    "            cols.append(col)\n",
    "            sol.append(v.x)\n",
    "        return (cols, sol)\n",
    "\n",
    "        \n",
    "\n",
    "\n",
    "class abstractBnP(ABC):\n",
    "    def __init__(self, name, data, modelClass, pq=True):\n",
    "        self.name = name\n",
    "        self.data = data\n",
    "        self.modelClass = modelClass\n",
    "        \n",
    "        self.muteColGened = False\n",
    "        self.muteBranchBegin = False\n",
    "        self.muteBranchSolved = False\n",
    "        self.muteNewBranchAdded = False\n",
    "        self.runTime = 0\n",
    "        self.MuteOutputFlags()\n",
    "        self.pq = pq\n",
    "        \n",
    "        \n",
    "    @abstractmethod\n",
    "    def ProvideMasterRHS(self):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def BuildPricingProblem(self):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def SolvePricingProblem(self):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def GetNewColFromPrcing(self):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def UpdatePricingProblem(self, dualPrices):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def onBranchingPerformed(self, branchDecision):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def onBranchingReverted(self, branchDecision):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def CreateBranches(self, cols, sol):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    #([RowIds], [Coes])\n",
    "    @abstractmethod\n",
    "    def EvaluateCol(self, col):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    #([RowIds], [Coes])\n",
    "    @abstractmethod\n",
    "    def NameCol(self, col):\n",
    "        pass\n",
    "        \n",
    "    \n",
    "    @abstractmethod\n",
    "    def UBCol(self, col):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def CheckIntegralSolution(self, cols, sol):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def dispPricing(self):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def dispASolution(self, cols, sol):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def MuteOutputFlags(self):\n",
    "        pass\n",
    "\n",
    "    \n",
    "    @abstractmethod\n",
    "    def UniqueIDofBranchDecision(self, branchDecision):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    def dispMasterSolution(self):\n",
    "        cols, sol = self.master.getNonZeroColsSol()\n",
    "        self.dispASolution(cols, sol)\n",
    "        \n",
    "    \n",
    "    def logColGened(self):\n",
    "        if self.muteColGened:\n",
    "            return\n",
    "        #self.master.printProgram()\n",
    "        print(\"pis: \", self.master.getDualPis())\n",
    "        #self.master.printSolution()\n",
    "        if self.master.getIsOptimal():  \n",
    "            col = self.GetNewColFromPrcing()\n",
    "            if col:\n",
    "                print('new col: ', col)\n",
    "            else:\n",
    "                print('no new col')\n",
    "        else:\n",
    "            print('bad pricing')\n",
    "            #self.pricing.printProgram()\n",
    "            #self.pricing.printSolution()\n",
    "    \n",
    "    \n",
    "    def logBranchBegin(self):\n",
    "        if self.muteBranchBegin:\n",
    "            return\n",
    "        print('\\n', ''.join(['-']*64), '\\n')\n",
    "        print('Current Branch: ', self.currentBranch, '\\n\\n')\n",
    "        self.dispPricing()\n",
    "        print('\\n\\n')\n",
    "        \n",
    "        \n",
    "    def logBranchSolved(self):\n",
    "        if self.muteBranchSolved:\n",
    "            return\n",
    "        print('\\n\\n\\n')\n",
    "        self.master.printProgram()\n",
    "        print('\\n\\n')\n",
    "        self.dispMasterSolution()\n",
    "        print('current incumbent: ', self.incumbent)\n",
    "        print('current bound: ', self.master.getObjVal(), '\\n\\n')\n",
    "        \n",
    "    \n",
    "    def logNewBranchAdded(self, newBranch):\n",
    "        if self.muteNewBranchAdded:\n",
    "            return\n",
    "        print('new branch: {}'.format(newBranch))\n",
    "\n",
    "        \n",
    "    def initMaster(self):\n",
    "        self.master = self.modelClass(self.name+'[Master]')\n",
    "        bs = self.ProvideMasterRHS()\n",
    "        self.master.addRHS(bs)\n",
    "        self.master.model.setParam(GRB.Param.Threads,  4)\n",
    "        \n",
    "        \n",
    "    \n",
    "    def addMasterCol(self, col, tag=''):\n",
    "        self.master.addVar(vtype=pybnp.REAL, name=self.NameCol(col)+tag, \n",
    "                           ub=self.UBCol(col), obj=self.EvaluateCol(col), col=col)\n",
    "        \n",
    "        \n",
    "    def addMasterArtificialCol(self, nodeName, rowId, rowCoe):\n",
    "        col = ([rowId], [rowCoe])\n",
    "        colName = '[{}at{}]'.format(nodeName, rowId)\n",
    "        colObj = MAX\n",
    "        colUb = self.UBCol(col)\n",
    "        self.master.addVar(vtype=pybnp.REAL, name=colName, ub=colUb, obj=colObj, col=col)\n",
    "    \n",
    "    \n",
    "    \n",
    "    def _pricingWholeSol2Col(sol):\n",
    "        return (list(range(len(sol))), [-s for s in sol])\n",
    "    \n",
    "    \n",
    "    def genACol(self, i):\n",
    "        self.master.model.params.presolve = 0\n",
    "        te = self.master.optimize()\n",
    "        \n",
    "        self.runTime += te\n",
    "        \n",
    "        colAddedFlag = False\n",
    "        \n",
    "        if self.master.getIsOptimal():\n",
    "            pis = self.master.getDualPis()\n",
    "            self.UpdatePricingProblem(pis)\n",
    "            te = self.SolvePricingProblem()\n",
    "            \n",
    "            self.runTime += te\n",
    "            \n",
    "            newCol = self.GetNewColFromPrcing()\n",
    "            if newCol != False:\n",
    "                self.addMasterCol(newCol, tag='[{}]'.format(i))\n",
    "                colAddedFlag = True\n",
    "        \n",
    "        self.logColGened()\n",
    "        \n",
    "        return colAddedFlag\n",
    "        \n",
    "        \n",
    "    def addMaterArtificialCols(self, nodeName=''):\n",
    "        # add Artificial cols\n",
    "        masterBs = self.master.getBs()\n",
    "        for i in range(len(masterBs)):\n",
    "            b = masterBs[i]\n",
    "            rowId = i\n",
    "            rowCoe = 1 if self.UBCol(None) ==pybnp.INFINITY else self.UBCol(None)\n",
    "            rowCoe = b / rowCoe\n",
    "\n",
    "            self.addMasterArtificialCol(nodeName, rowId, rowCoe)\n",
    "    \n",
    "    \n",
    "    def isStillArtificial(self):\n",
    "        # check if still has artificial\n",
    "        sol = self.master.getSolVals()\n",
    "        masterBs = self.master.getBs()\n",
    "        return abs(sum(sol[:len(masterBs)])) > EPS\n",
    "    \n",
    "    \n",
    "    def gcSolve(self, nodeName=''):\n",
    "        self.addMaterArtificialCols(nodeName)\n",
    "        \n",
    "        # gen col and solve\n",
    "        for it in range(MAX_IT):\n",
    "            if not self.genACol(it):\n",
    "                break\n",
    "            \n",
    "            if not self.master.getIsOptimal():\n",
    "                log('master not good')\n",
    "                break\n",
    "\n",
    "        return not self.isStillArtificial()\n",
    "    \n",
    "    \n",
    "    def processBranch(self, branchDecisions):\n",
    "        A = branchDecisions\n",
    "        B = self.currentBranch\n",
    "\n",
    "#         print('two branches')\n",
    "#         print(A)\n",
    "#         print(B)\n",
    "#         print('before', [c.ConstrName for c in self.pricing.model.getConstrs()])\n",
    "        \n",
    "        count = 0\n",
    "        for i in range(min(len(A),len(B))):\n",
    "            if B[i] != A[i]:\n",
    "                break\n",
    "            count += 1\n",
    "        for i in range(1, 1+len(B) - count):\n",
    "#             print('ready to remove', B[-i])\n",
    "            bd = self.id2branchDecision[B[-i]]\n",
    "            self.onBranchingReverted(bd)\n",
    "        for i in range(count, len(A)):\n",
    "#             print('ready to add', A[i])\n",
    "            bd = self.id2branchDecision[A[i]]\n",
    "            self.onBranchingPerformed(bd)\n",
    "        self.currentBranch = A\n",
    "#         print('after', [c.ConstrName for c in self.pricing.model.getConstrs()])\n",
    "                \n",
    "        \n",
    "    def prepareGC(self):\n",
    "        self.initMaster()\n",
    "        self.BuildPricingProblem()\n",
    "        \n",
    "        \n",
    "    def prepareBP(self):    \n",
    "        self.Q = queue.PriorityQueue(maxsize=0)\n",
    "        self.currentBranch = []\n",
    "        \n",
    "        self.incumbent = MAX\n",
    "        self.intSol = []\n",
    "        \n",
    "        self.Q.put( (0, []) )\n",
    "        \n",
    "        self.runTime = 0\n",
    "        self.nodeCount = 0\n",
    "        \n",
    "        self.branch2Node = {}\n",
    "        self.nodeStatus = []\n",
    "        self.nodeCreator = []\n",
    "        self.nodeBound = []\n",
    "        \n",
    "        self.bd2id = {}\n",
    "        self.id2branchDecision = {}\n",
    "        self.bd2id[()] = 0\n",
    "        self.id2branchDecision[0] = ()\n",
    "        \n",
    "        self._addNode([],[])\n",
    "        \n",
    "        \n",
    "       \n",
    "    \n",
    "    def branchNamer(self, branch):\n",
    "        return tuple(branch)\n",
    "    \n",
    "    \n",
    "    def UniqueIDofBranchDecision(self, _branchDecision):\n",
    "        branchDecision = tuple(_branchDecision)\n",
    "        if not branchDecision in self.bd2id.keys():\n",
    "            self.bd2id[branchDecision] = len(self.bd2id)\n",
    "        return self.bd2id[branchDecision]\n",
    "    \n",
    "    \n",
    "    def _addNode(self, _branch, _currentBranch):\n",
    "        nid = len(self.branch2Node)\n",
    "        branch = self.branchNamer(_branch)\n",
    "        currentBranch = self.branchNamer(_currentBranch)\n",
    "        self.branch2Node[branch] = nid\n",
    "        self.nodeCreator.append( self.branch2Node[currentBranch] )\n",
    "        self.nodeBound.append(MAX)\n",
    "        self.nodeStatus.append(abstractBnP.NOT_SOLVED)\n",
    "        \n",
    "        \n",
    "    def _updateNode(self, _branch, bound, status):\n",
    "        branch = self.branchNamer(_branch)\n",
    "        nid = self.branch2Node[branch]\n",
    "        #print(nid, branch, _branch)\n",
    "        self.nodeStatus[nid] = status\n",
    "        self.nodeBound[nid] = bound\n",
    "        \n",
    "    \n",
    "    def _updateGlobal(self):\n",
    "        for nid in range(len(self.branch2Node)):\n",
    "            if self.nodeBound[nid] <= self.incumbent and self.nodeStatus[nid] == abstractBnP.LOCAL_OPT:\n",
    "                self.nodeStatus[nid] = abstractBnP.GLOBAL_OPT\n",
    "\n",
    "        \n",
    "    INFEASIBLE = 'inf'\n",
    "    WORSE_BOUND = 'boundWorse' \n",
    "    WORSE_INT = 'intWorse'\n",
    "    LOCAL_OPT = 'localOpt'\n",
    "    GLOBAL_OPT = 'globalOpt'\n",
    "    NOT_SOLVED = 'unsolved'\n",
    "    FRAC = 'frac'\n",
    "    \n",
    "    \n",
    "    # seq bnp\n",
    "    def branchAndPrice(self):\n",
    "        ta = timer()\n",
    "        \n",
    "        self.prepareGC()\n",
    "        self.prepareBP()\n",
    "        \n",
    "        while not self.Q.empty():\n",
    "            _, branchDecisions = self.Q.get()\n",
    "            self.initMaster()\n",
    "            self.processBranch(branchDecisions)\n",
    "            \n",
    "            self.logBranchBegin()\n",
    "            \n",
    "            isOptimial = self.gcSolve()\n",
    "            \n",
    "            self.nodeCount += 1\n",
    "            \n",
    "            self.logBranchSolved()\n",
    "            \n",
    "            # prune if infeasible\n",
    "            if not isOptimial:\n",
    "                self._updateNode(branchDecisions, MAX, abstractBnP.INFEASIBLE)\n",
    "                continue\n",
    "            \n",
    "            bound = self.master.getObjVal()\n",
    "            \n",
    "            # prune if lower bound is larger than best interal obj\n",
    "            if bound > self.incumbent:\n",
    "                self._updateNode(branchDecisions, bound, abstractBnP.WORSE_BOUND)\n",
    "                continue\n",
    "            \n",
    "            cols, sol = self.master.getNonZeroColsSol()    \n",
    "            \n",
    "            # integral\n",
    "            if self.CheckIntegralSolution(cols, sol):\n",
    "                if bound < self.incumbent:\n",
    "                    self.incumbent = bound\n",
    "                    self.intSol = [(cols, sol)]\n",
    "                    self._updateNode(branchDecisions, bound, abstractBnP.LOCAL_OPT)\n",
    "                elif bound == self.incumbent:\n",
    "                    #print(branchDecisions, '==')\n",
    "                    self._updateNode(branchDecisions, bound, abstractBnP.LOCAL_OPT)\n",
    "                    self.intSol.append((cols, sol))\n",
    "            else: # branch\n",
    "                self._updateNode(branchDecisions, bound, abstractBnP.FRAC)\n",
    "                \n",
    "                branches = self.CreateBranches(cols, sol)\n",
    "                for b in branches:\n",
    "                    bdid = self.UniqueIDofBranchDecision(b)\n",
    "                    self.id2branchDecision[bdid] = b\n",
    "                    \n",
    "                    newBranch = self.currentBranch + [bdid]\n",
    "                    self.Q.put( (bound if self.pq else 0, newBranch) )\n",
    "                    \n",
    "                    self._addNode(newBranch, branchDecisions)\n",
    "                    \n",
    "                    self.logNewBranchAdded(newBranch)\n",
    "                    \n",
    "        self._updateGlobal()\n",
    "        tb = timer()\n",
    "        return tb - ta\n",
    "    \n",
    "    \n",
    "    def drawBnPTree(self):\n",
    "        import networkx as nx\n",
    "        g = nx.DiGraph()\n",
    "        for t in range(len(self.nodeCreator)):\n",
    "            s = self.nodeCreator[t]\n",
    "            g.add_edge(s,t)\n",
    "        \n",
    "        cm = {}  \n",
    "        cm[abstractBnP.INFEASIBLE] = 'red'\n",
    "        cm[abstractBnP.WORSE_BOUND] = 'purple'\n",
    "        cm[abstractBnP.WORSE_INT] = 'yellow'\n",
    "        cm[abstractBnP.LOCAL_OPT] = 'blue'\n",
    "        cm[abstractBnP.GLOBAL_OPT] = 'green'\n",
    "        cm[abstractBnP.FRAC] = 'orange'\n",
    "        cm[abstractBnP.NOT_SOLVED] = 'gray'\n",
    "    \n",
    "        colorMap = ['black'] * len(g.nodes())\n",
    "        #print(g.nodes(), self.nodeStatus)\n",
    "        for n in g.nodes():\n",
    "            colorMap[n] = cm[self.nodeStatus[n]]\n",
    "\n",
    "        nx.draw(g, pos=nx.spring_layout(g), node_color = colorMap)\n",
    "        \n",
    "#         from networkx.drawing.nx_agraph import write_dot, graphviz_layout\n",
    "#         write_dot(g,'test.dot')\n",
    "\n",
    "#         pos =graphviz_layout(g, prog='dot')\n",
    "#         nx.draw(g, pos, with_labels=False, arrows=True,  node_color = colorMap)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "class GCPBnP(abstractBnP):\n",
    "    \n",
    "    def dispPricing(self):\n",
    "        self.pricing.printProgram()\n",
    "        \n",
    "        \n",
    "    def dispASolution(self, cols, sol):\n",
    "        print('solution: ')\n",
    "        for col, s in zip(cols, sol):\n",
    "            print(self.col2Nodes(col), s)\n",
    "        \n",
    "        \n",
    "    def MuteOutputFlags(self):\n",
    "        pass\n",
    "#         self.muteColGened = True\n",
    "#         self.muteBranchBegin = True\n",
    "#         self.muteBranchSolved = True\n",
    "#         self.muteNewBranchAdded = True\n",
    "    \n",
    "    \n",
    "    # new method\n",
    "    def decisionNamer(self, branchDecision):\n",
    "        return str(branchDecision)\n",
    "    \n",
    "    \n",
    "    def onBranchingPerformed(self, branchDecision):\n",
    "        a, b, isSame, tag = branchDecision\n",
    "        branchName = self.decisionNamer(branchDecision)\n",
    "        cs = []\n",
    "        \n",
    "        if not isSame:\n",
    "            self.pricing.addConstrsByVarId(lhCoes=[[1, 1]], lhVarIds=[[a,b]], rhs=[1], name=branchName)            \n",
    "        else:\n",
    "            self.pricing.addConstrsByVarId(lhCoes=[[1, -1], [-1, 1]], lhVarIds=[[a,b], [a,b]],\n",
    "                                           rhs=[0, 0], name=branchName)\n",
    "        \n",
    "    \n",
    "    def onBranchingReverted(self, branchDecision):\n",
    "        decisionName = self.decisionNamer(branchDecision)\n",
    "#         print(decisionName)\n",
    "#         print(self.pricing.constrs.keys())\n",
    "        self.pricing.removeConstrsByName(decisionName)\n",
    "    \n",
    "    \n",
    "    def CreateBranches(self, cols, sol):\n",
    "        N, edges = data\n",
    "        v, u = -1, -1\n",
    "        tag = random.randint(1,1e5)\n",
    "        for col1, s1, i in zip(cols, sol, range(len(sol))):\n",
    "            for col2, s2, j in zip(cols, sol, range(len(sol))):\n",
    "                nodeSet1 = self.col2Nodes(col1)\n",
    "                nodeSet2 = self.col2Nodes(col2)\n",
    "                if i != j and not isInt(s1) and not isInt(s2):\n",
    "                    for x in range(N):\n",
    "                        for y in range(N):\n",
    "                            if x == y:\n",
    "                                continue\n",
    "                            if x in nodeSet1 and x in nodeSet2 and y in nodeSet1 and y not in nodeSet2:\n",
    "                                v = min(x, y)\n",
    "                                u = max(x, y)\n",
    "                                return [(v, u, True, tag), (v, u, False, tag)]\n",
    "        return [(v, u, True, tag), (v, u, False, tag)]\n",
    "\n",
    "    \n",
    "    def CheckIntegralSolution(self, cols, sol):\n",
    "        for s in sol:\n",
    "            if not isInt(s):\n",
    "                return False\n",
    "        return True\n",
    "    \n",
    "    \n",
    "    def ProvideMasterRHS(self):\n",
    "        N, edges = self.data\n",
    "        return [-1] * N\n",
    "    \n",
    "    \n",
    "    def BuildPricingProblem(self):\n",
    "        N, edges = self.data\n",
    "        pricing = self.modelClass(self.name + '_Pricing')\n",
    "        vs = [pricing.addVar(name='v{}'.format(i), ub=1, obj=0, vtype=pybnp.BINARY) for i in range(N)]\n",
    "        for s,t in edges:\n",
    "            pricing.addConstrsByVarId( [[1,1]], [[s,t]], [1], 'v_{}_{}'.format(s,t) )\n",
    "        self.pricing = pricing\n",
    "    \n",
    "    \n",
    "    def SolvePricingProblem(self):\n",
    "        return self.pricing.optimize()\n",
    "    \n",
    "    \n",
    "    def GetNewColFromPrcing(self):\n",
    "        newCol = abstractBnP._pricingWholeSol2Col( self.pricing.getSolVals() )\n",
    "        if self.EvaluateCol(newCol) + self.pricing.getObjVal() <= -EPS:\n",
    "            return newCol\n",
    "        else:\n",
    "            return False\n",
    "        \n",
    "    \n",
    "    def UpdatePricingProblem(self, dualPrices):\n",
    "        self.pricing.updateObjById(dualPrices, list(range(len(dualPrices))))\n",
    "      \n",
    "    \n",
    "    #([RowIds], [Coes])\n",
    "    def EvaluateCol(self, col):\n",
    "        return 1\n",
    "    \n",
    "    \n",
    "    #([RowIds], [Coes])\n",
    "    def NameCol(self, col):\n",
    "        return 'g' + ''.join(['{}'.format(node) for node in self.col2Nodes(col)])\n",
    "        \n",
    "        \n",
    "    def UBCol(self, col):\n",
    "        return 1\n",
    "    \n",
    "    \n",
    "    # new methods\n",
    "    def col2Nodes(self, col):\n",
    "        return [rid for rid, rCoe in zip(col[0], col[1]) if rCoe != 0]\n",
    "    \n",
    "    \n",
    "    def plot(self,colored=False):\n",
    "        cols, sol = self.intSol[0]\n",
    "        self._plot(cols, sol, colored)\n",
    "            \n",
    "    \n",
    "    def _plot(self, cols, sol, colored=False):\n",
    "        import networkx as nx\n",
    "        N, edges = self.data\n",
    "        g = nx.Graph()\n",
    "        g.add_edges_from(edges)\n",
    "        node2id = dict(zip(g.nodes(), range(len(g.nodes()))))\n",
    "        if not colored:\n",
    "            nx.draw(g, pos=nx.spring_layout(g), with_labels = True)\n",
    "        else:\n",
    "            colorMap = list(range(N))\n",
    "            colors = ['blue', 'red', 'yellow', 'green', 'purple', 'orange', 'black', 'gray', 'gold', 'roralblue', 'coral', 'pink' ]\n",
    "            count = 0\n",
    "            for col in cols:\n",
    "                for node in self.col2Nodes(col):\n",
    "                    colorMap[node2id[node]] = colors[count]\n",
    "                count += 1\n",
    "            nx.draw(g, pos=nx.spring_layout(g), with_labels = True, node_color = colorMap)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# demo\n",
    "N = 5\n",
    "edges = [(0, 1), (1, 4), (2, 3), (0, 3), (2, 4)]\n",
    "data = (N, edges)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/SunJc/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:579: MatplotlibDeprecationWarning: \n",
      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
      "  if not cb.iterable(width):\n",
      "/Users/SunJc/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:676: MatplotlibDeprecationWarning: \n",
      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
      "  if cb.iterable(node_size):  # many node sizes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.014871817000084775, 0.006102085113525391, 3, None)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bnp = GCPBnP('demo', data, GurobiModel)\n",
    "OUTPUT_LP = False\n",
    "bnp.muteColGened = True\n",
    "bnp.muteBranchBegin = True\n",
    "bnp.muteBranchSolved = True\n",
    "bnp.muteNewBranchAdded = True\n",
    "bnp.branchAndPrice(), bnp.runTime, bnp.nodeCount, bnp.drawBnPTree()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0\n",
      "solution: \n",
      "[0, 2] 1.0\n",
      "[3, 4] 1.0\n",
      "[1, 3] 1.0\n",
      "solution: \n",
      "[1, 3] 1.0\n",
      "[0, 4] 1.0\n",
      "[1, 2] 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bnp.plot(colored=True)\n",
    "print(bnp.incumbent)\n",
    "for cols, sol in bnp.intSol:\n",
    "    bnp.dispASolution(cols, sol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# par\n",
    "class abstractParBnP(ABC):\n",
    "    def __init__(self, name, data, modelClass, par=1):\n",
    "        self.name = name\n",
    "        self.data = data\n",
    "        self.modelClass = modelClass\n",
    "        \n",
    "        self.par = par\n",
    "        \n",
    "        self.muteColGened = False\n",
    "        self.muteBranchBegin = False\n",
    "        self.muteBranchSolved = False\n",
    "        self.muteNewBranchAdded = False\n",
    "        self.runTime = 0\n",
    "        self.MuteOutputFlags()\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "    @abstractmethod\n",
    "    def ProvideMasterRHS(self):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def PricingProblemBuilder(self, tag):\n",
    "        pass # return pricing\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def PricingProblemSolver(self, pricingProblem):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def NewColFromPrcingGetter(self, pricingProblem):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def PricingProblemUpdater(self, dualPrices, pricingProblem):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def onBranchingPerformed(self, branchDecision, masterProblem, pricingProblem):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def onBranchingReverted(self, branchDecision, masterProblem, pricingProblem):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def CreateBranches(self, cols, sol):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    #([RowIds], [Coes])\n",
    "    @abstractmethod\n",
    "    def EvaluateCol(self, col):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    #([RowIds], [Coes])\n",
    "    @abstractmethod\n",
    "    def NameCol(self, col):\n",
    "        pass\n",
    "        \n",
    "    \n",
    "    @abstractmethod\n",
    "    def UBCol(self, col):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def CheckIntegralSolution(self, cols, sol):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def dispPricing(self, pricingProblem):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def dispASolution(self, cols, sol):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    @abstractmethod\n",
    "    def MuteOutputFlags(self):\n",
    "        pass\n",
    "\n",
    "    \n",
    "    @abstractmethod\n",
    "    def UniqueIDofBranchDecision(self, branchDecision):\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    def dispMasterSolution(self, masterProblem):\n",
    "        cols, sol = masterProblem.getNonZeroColsSol()\n",
    "        self.dispASolution(cols, sol)\n",
    "        \n",
    "    \n",
    "    def logColGened(self, masterProblem, pricingProblem):\n",
    "        if self.muteColGened:\n",
    "            return\n",
    "        #self.master.printProgram()\n",
    "        print(\"pis: \", masterProblem.getDualPis())\n",
    "        #self.master.printSolution()\n",
    "        if masterProblem.getIsOptimal():  \n",
    "            col = self.NewColFromPrcingGetter(pricingProblem)\n",
    "            if col:\n",
    "                print('[{}] new col: '.format(masterProblem.name), col)\n",
    "            else:\n",
    "                print('[{}] no new col'.format(masterProblem.name))\n",
    "        else:\n",
    "            print('bad pricing')\n",
    "            #self.pricing.printProgram()\n",
    "            #self.pricing.printSolution()\n",
    "    \n",
    "    \n",
    "    def logBranchBegin(self, currentBranch, masterProblem, pricingProblem):\n",
    "        if self.muteBranchBegin:\n",
    "            return\n",
    "        print('\\n', ''.join(['-']*64), '\\n')\n",
    "        print('Current Branch: ', currentBranch, '\\n\\n')\n",
    "        self.dispPricing(pricingProblem)\n",
    "        print('\\n\\n')\n",
    "        \n",
    "        \n",
    "    def logBranchSolved(self, currentBranch, masterProblem, pricingProblem):\n",
    "        if self.muteBranchSolved:\n",
    "            return\n",
    "        print('\\n\\n\\n')\n",
    "        masterProblem.printProgram()\n",
    "        print('\\n\\n')\n",
    "        self.dispMasterSolution(masterProblem)\n",
    "        print('current incumbent: ', self.incumbent)\n",
    "        print('current bound: ', masterProblem.getObjVal(), '\\n\\n')\n",
    "        \n",
    "    \n",
    "    def logNewBranchAdded(self, newBranch, masterProblem, pricingProblem):\n",
    "        if self.muteNewBranchAdded:\n",
    "            return\n",
    "        print('new branch: {}'.format(newBranch))\n",
    "\n",
    "        \n",
    "    def MasterBuilder(self, tag):\n",
    "        master = self.modelClass(self.name+'[Master]'+tag)\n",
    "        bs = self.ProvideMasterRHS()\n",
    "        master.addRHS(bs)\n",
    "        return master\n",
    "        \n",
    "    \n",
    "    def MasterColAdder(self, masterProblem, col, tag=''):\n",
    "        masterProblem.addVar(vtype=pybnp.REAL, name=self.NameCol(col)+tag, \n",
    "                           ub=self.UBCol(col), obj=self.EvaluateCol(col), col=col)\n",
    "        \n",
    "        \n",
    "    def MasterArtificialColAdder(self, masterProblem, nodeName, rowId, rowCoe):\n",
    "        col = ([rowId], [rowCoe])\n",
    "        colName = '[{}at{}]'.format(nodeName, rowId)\n",
    "        colObj = MAX\n",
    "        colUb = self.UBCol(col)\n",
    "        masterProblem.addVar(vtype=pybnp.REAL, name=colName, ub=colUb, obj=colObj, col=col)\n",
    "    \n",
    "    \n",
    "    \n",
    "    def _pricingWholeSol2Col(sol):\n",
    "        return (list(range(len(sol))), [-s for s in sol])\n",
    "    \n",
    "    \n",
    "    def AColGener(self, masterProblem, pricingProblem, i):\n",
    "        #masterProblem.model.params.presolve = 0\n",
    "        te = masterProblem.optimize()\n",
    "        \n",
    "        with self.lock:\n",
    "            self.runTime += te\n",
    "        \n",
    "        colAddedFlag = False\n",
    "        \n",
    "        if masterProblem.getIsOptimal():\n",
    "            pis = masterProblem.getDualPis()\n",
    "            self.PricingProblemUpdater(pis, pricingProblem)\n",
    "            te = self.PricingProblemSolver(pricingProblem)\n",
    "            \n",
    "            with self.lock:\n",
    "                self.runTime += te\n",
    "            \n",
    "            newCol = self.NewColFromPrcingGetter(pricingProblem)\n",
    "            if newCol != False:\n",
    "                self.MasterColAdder(masterProblem, newCol, tag='[{}]'.format(i))\n",
    "                colAddedFlag = True\n",
    "        \n",
    "        self.logColGened(masterProblem, pricingProblem)\n",
    "        \n",
    "        return colAddedFlag\n",
    "        \n",
    "        \n",
    "    def MaterArtificialColsAdder(self, masterProblem, nodeName=''):\n",
    "        # add Artificial cols\n",
    "        masterBs = masterProblem.getBs()\n",
    "        for i in range(len(masterBs)):\n",
    "            b = masterBs[i]\n",
    "            rowId = i\n",
    "            rowCoe = 1 if self.UBCol(None) ==pybnp.INFINITY else self.UBCol(None)\n",
    "            rowCoe = b / rowCoe\n",
    "\n",
    "            self.MasterArtificialColAdder(masterProblem, nodeName, rowId, rowCoe)\n",
    "    \n",
    "    \n",
    "    def isStillArtificial(self, masterProblem):\n",
    "        # check if still has artificial\n",
    "        sol = masterProblem.getSolVals()\n",
    "        masterBs = masterProblem.getBs()\n",
    "        return abs(sum(sol[:len(masterBs)])) > EPS\n",
    "    \n",
    "    \n",
    "    def gcSolve(self, masterProblem, pricingProblem, nodeName=''):\n",
    "        self.MaterArtificialColsAdder(masterProblem, nodeName)\n",
    "        \n",
    "        # gen col and solve\n",
    "        for it in range(MAX_IT):\n",
    "            if not self.AColGener(masterProblem, pricingProblem, it):\n",
    "                break\n",
    "            \n",
    "            if not masterProblem.getIsOptimal():\n",
    "                log('master not good')\n",
    "                break\n",
    "\n",
    "        return not self.isStillArtificial(masterProblem)\n",
    "    \n",
    "    \n",
    "    def processBranch(self, currentBranch, branchDecisions, masterProblem, pricingProblem):\n",
    "        A = branchDecisions\n",
    "        B = currentBranch\n",
    "\n",
    "#         print('two branches')\n",
    "#         print(A)\n",
    "#         print(B)\n",
    "#         print('before', [c.ConstrName for c in self.pricing.model.getConstrs()])\n",
    "        \n",
    "        count = 0\n",
    "        for i in range(min(len(A),len(B))):\n",
    "            if B[i] != A[i]:\n",
    "                break\n",
    "            count += 1\n",
    "        for i in range(1, 1+len(B) - count):\n",
    "#             print('ready to remove', B[-i])\n",
    "            bd = self.id2branchDecision[B[-i]]\n",
    "            self.onBranchingReverted(bd, masterProblem, pricingProblem)\n",
    "        for i in range(count, len(A)):\n",
    "#             print('ready to add', A[i])\n",
    "            bd = self.id2branchDecision[A[i]]\n",
    "            self.onBranchingPerformed(bd, masterProblem, pricingProblem)\n",
    "        return A\n",
    "#         print('after', [c.ConstrName for c in self.pricing.model.getConstrs()])\n",
    "                \n",
    "            \n",
    "        \n",
    "    def prepareBP(self):    \n",
    "        self.Q = queue.PriorityQueue()\n",
    "        self.currentBranch = [[] for i in range(self.par)]\n",
    "        \n",
    "        self.incumbent = MAX\n",
    "        self.intSol = []\n",
    "        \n",
    "        self.Q.put((0,[]))\n",
    "        self.threadStatus = [abstractParBnP.IDLE for i in range(self.par)]\n",
    "        \n",
    "        self.runTime = 0\n",
    "        self.nodeCount = 0\n",
    "        \n",
    "        self.branch2Node = {}\n",
    "        self.nodeStatus = []\n",
    "        self.nodeCreator = []\n",
    "        self.nodeBound = []\n",
    "        \n",
    "        self.bd2id = {}\n",
    "        self.id2branchDecision = {}\n",
    "        self.bd2id[()] = 0\n",
    "        self.id2branchDecision[0] = ()\n",
    "        \n",
    "        self.lock = Lock()\n",
    "        self.qlock = Lock()\n",
    "        \n",
    "        self.mtlog = []\n",
    "        \n",
    "        self._addNode([],[])\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "       \n",
    "    \n",
    "    def branchNamer(self, branch):\n",
    "        return tuple(branch)\n",
    "    \n",
    "    \n",
    "    def UniqueIDofBranchDecision(self, _branchDecision):\n",
    "        branchDecision = tuple(_branchDecision)\n",
    "        if not branchDecision in self.bd2id.keys():\n",
    "            with self.lock:\n",
    "                self.bd2id[branchDecision] = len(self.bd2id)\n",
    "        return self.bd2id[branchDecision]\n",
    "    \n",
    "    \n",
    "    def _addNode(self, _branch, _currentBranch):\n",
    "        with self.lock:\n",
    "            nid = len(self.branch2Node)\n",
    "            branch = self.branchNamer(_branch)\n",
    "            #print(branch)\n",
    "            currentBranch = self.branchNamer(_currentBranch)\n",
    "            self.branch2Node[branch] = nid\n",
    "            self.nodeCreator.append( self.branch2Node[currentBranch] )\n",
    "            self.nodeBound.append(MAX)\n",
    "            self.nodeStatus.append(abstractParBnP.NOT_SOLVED)\n",
    "        \n",
    "        \n",
    "    def _updateNode(self, _branch, bound, status):\n",
    "        with self.lock:\n",
    "            branch = self.branchNamer(_branch)\n",
    "            nid = self.branch2Node[branch]\n",
    "            #print(nid, branch, _branch)\n",
    "            self.nodeStatus[nid] = status\n",
    "            self.nodeBound[nid] = bound\n",
    "        \n",
    "    \n",
    "    def _updateGlobal(self):\n",
    "        for nid in range(len(self.branch2Node)):\n",
    "            if self.nodeBound[nid] <= self.incumbent and self.nodeStatus[nid] == abstractParBnP.LOCAL_OPT:\n",
    "                self.nodeStatus[nid] = abstractParBnP.GLOBAL_OPT\n",
    "        \n",
    "        \n",
    "    INFEASIBLE = 'inf'\n",
    "    WORSE_BOUND = 'boundWorse' \n",
    "    WORSE_INT = 'intWorse'\n",
    "    LOCAL_OPT = 'localOpt'\n",
    "    GLOBAL_OPT = 'globalOpt'\n",
    "    NOT_SOLVED = 'unsolved'\n",
    "    FRAC = 'frac'\n",
    "    BUSY = 1\n",
    "    IDLE = 0\n",
    "    SUICIDE = 'kill'\n",
    "    \n",
    "    \n",
    "    def criticalQueueGet(self, threadId):\n",
    "        res = None\n",
    "        res = self.Q.get()\n",
    "        with self.qlock:\n",
    "            #print(threadId, 'get', res)\n",
    "            self.mtlog.append((threadId, res))\n",
    "            self.threadStatus[threadId] = abstractParBnP.BUSY\n",
    "        return res\n",
    "    \n",
    "    \n",
    "    def criticalRelease(self, threadId):\n",
    "        res = False\n",
    "        if self.Q.empty() and sum(self.threadStatus) == 1:\n",
    "            return True\n",
    "        with self.qlock:\n",
    "            self.threadStatus[threadId] = abstractParBnP.IDLE\n",
    "            #print(threadId, 'release')\n",
    "            #print(self.Q.empty(), self.threadStatus, 'finish')\n",
    "            res = self.Q.empty() and sum(self.threadStatus) == 0\n",
    "        return res\n",
    "        \n",
    "    \n",
    "    # main bnp\n",
    "    def branchAndPriceMain(self):\n",
    "        ta = timer()\n",
    "        \n",
    "        self.masters = []\n",
    "        self.pricings = []\n",
    "        for i in range(self.par):\n",
    "            self.masters.append( self.MasterBuilder('<{}>'.format(i)) )\n",
    "            self.pricings.append( self.PricingProblemBuilder('<{}>'.format(i)) )\n",
    "        self.prepareBP()\n",
    "        \n",
    "        ths = [Thread(target=self.branchAndPriceDo, args=(i,)) for i in range(self.par)]\n",
    "        #print(ths)\n",
    "        for i in range(self.par):\n",
    "            ths[i].start()\n",
    "        for i in range(self.par):\n",
    "            ths[i].join()\n",
    "        \n",
    "        tb = timer()\n",
    "        \n",
    "        self._updateGlobal()\n",
    "        \n",
    "        return tb - ta\n",
    "    \n",
    "    \n",
    "    def branchAndPriceDo(self, i):\n",
    "        masterProblem = self.masters[i]\n",
    "        pricingProblem = self.pricings[i]\n",
    "        \n",
    "        while True:\n",
    "            res = self.criticalQueueGet(i)\n",
    "            if res == abstractParBnP.SUICIDE:\n",
    "                # suicide\n",
    "                return\n",
    "            _, branchDecisions = res\n",
    "            with self.lock:\n",
    "                self.masters[i] = self.MasterBuilder('<{}>'.format(i)) \n",
    "                masterProblem = self.masters[i]\n",
    "\n",
    "#             log(\"{}x\".format(self.currentBranch, i))\n",
    "            res = self.processBranch(self.currentBranch[i], branchDecisions, masterProblem, pricingProblem)\n",
    "            with self.lock:\n",
    "                self.currentBranch[i] = res\n",
    "\n",
    "\n",
    "            self.logBranchBegin(branchDecisions, masterProblem, pricingProblem)\n",
    "\n",
    "            isOptimial = self.gcSolve(masterProblem, pricingProblem)\n",
    "            \n",
    "            #print('lcocked', self.qlock.locked())\n",
    "            #time.sleep(2)\n",
    "\n",
    "            self.nodeCount += 1\n",
    "\n",
    "            self.logBranchSolved(branchDecisions, masterProblem, pricingProblem)\n",
    "\n",
    "            # prune if infeasible\n",
    "            if not isOptimial:\n",
    "                self._updateNode(branchDecisions, MAX, abstractParBnP.INFEASIBLE)\n",
    "                if self.criticalRelease(i):\n",
    "                    break\n",
    "\n",
    "            bound = masterProblem.getObjVal()\n",
    "\n",
    "            # prune if lower bound is larger than best interal obj\n",
    "            if bound > self.incumbent:\n",
    "                self._updateNode(branchDecisions, bound, abstractParBnP.WORSE_BOUND)\n",
    "                if self.criticalRelease(i):\n",
    "                    break\n",
    "\n",
    "            cols, sol = masterProblem.getNonZeroColsSol()    \n",
    "\n",
    "            # integral\n",
    "            if self.CheckIntegralSolution(cols, sol):\n",
    "                if bound < self.incumbent:\n",
    "                    with self.lock:\n",
    "                        self.incumbent = bound\n",
    "                        self.intSol = [(cols, sol)]\n",
    "                    self._updateNode(branchDecisions, bound, abstractParBnP.LOCAL_OPT)\n",
    "                elif bound == self.incumbent:\n",
    "                    #print(branchDecisions, '==')\n",
    "                    self._updateNode(branchDecisions, bound, abstractParBnP.LOCAL_OPT)\n",
    "                    with self.lock:\n",
    "                        self.intSol.append((cols, sol))\n",
    "\n",
    "                if self.criticalRelease(i):\n",
    "                    break\n",
    "\n",
    "            else: # branch\n",
    "                self._updateNode(branchDecisions, bound, abstractParBnP.FRAC)\n",
    "\n",
    "                branches = self.CreateBranches(cols, sol)\n",
    "                for b in branches:\n",
    "                    bdid = self.UniqueIDofBranchDecision(b)\n",
    "                    with self.lock:\n",
    "                        self.id2branchDecision[bdid] = b\n",
    "\n",
    "                    newBranch = branchDecisions + [bdid]                    \n",
    "                    self.Q.put( (bound, newBranch) )\n",
    "\n",
    "                    self._addNode(newBranch, branchDecisions)\n",
    "\n",
    "                    self.logNewBranchAdded(newBranch, masterProblem, pricingProblem)\n",
    "                \n",
    "                #time.sleep(2)\n",
    "                if self.criticalRelease(i):\n",
    "                    break\n",
    "        \n",
    "        for i in range(self.par-1):\n",
    "            self.Q.put(abstractParBnP.SUICIDE)\n",
    "        \n",
    "    \n",
    "        \n",
    "    \n",
    "    def drawBnPTree(self):\n",
    "        import networkx as nx\n",
    "        g = nx.DiGraph()\n",
    "        for t in range(len(self.nodeCreator)):\n",
    "            s = self.nodeCreator[t]\n",
    "            g.add_edge(s,t)\n",
    "        \n",
    "        cm = {}  \n",
    "        cm[abstractParBnP.INFEASIBLE] = 'red'\n",
    "        cm[abstractParBnP.WORSE_BOUND] = 'purple'\n",
    "        cm[abstractParBnP.WORSE_INT] = 'yellow'\n",
    "        cm[abstractParBnP.LOCAL_OPT] = 'blue'\n",
    "        cm[abstractParBnP.GLOBAL_OPT] = 'green'\n",
    "        cm[abstractParBnP.FRAC] = 'orange'\n",
    "        cm[abstractParBnP.NOT_SOLVED] = 'gray'\n",
    "    \n",
    "        colorMap = ['black'] * len(g.nodes())\n",
    "        #print(g.nodes(), self.nodeStatus)\n",
    "        for n in g.nodes():\n",
    "            colorMap[n] = cm[self.nodeStatus[n]]\n",
    "\n",
    "        nx.draw(g, pos=nx.spring_layout(g), with_labels = True, node_color = colorMap)\n",
    "        \n",
    "#         from networkx.drawing.nx_agraph import write_dot, graphviz_layout\n",
    "#         write_dot(g,'test.dot')\n",
    "\n",
    "#         pos =graphviz_layout(g, prog='dot')\n",
    "#         nx.draw(g, pos, with_labels=False, arrows=True,  node_color = colorMap)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# par imp\n",
    "class GCPParBnP(abstractParBnP):\n",
    "    \n",
    "    def dispPricing(self, pricingProblem):\n",
    "        pricingProblem.printProgram()\n",
    "        \n",
    "        \n",
    "    def dispASolution(self, cols, sol):\n",
    "        print('solution: ')\n",
    "        for col, s in zip(cols, sol):\n",
    "            print(self.col2Nodes(col), s)\n",
    "        \n",
    "        \n",
    "    def MuteOutputFlags(self):\n",
    "        pass\n",
    "#         self.muteColGened = True\n",
    "#         self.muteBranchBegin = True\n",
    "#         self.muteBranchSolved = True\n",
    "#         self.muteNewBranchAdded = True\n",
    "    \n",
    "    \n",
    "    # new method\n",
    "    def decisionNamer(self, branchDecision):\n",
    "        return str(branchDecision)\n",
    "    \n",
    "    \n",
    "    def onBranchingPerformed(self, branchDecision, masterProblem, pricingProblem):\n",
    "        a, b, isSame, tag = branchDecision\n",
    "        branchName = self.decisionNamer(branchDecision)\n",
    "        cs = []\n",
    "        \n",
    "        if not isSame:\n",
    "            pricingProblem.addConstrsByVarId(lhCoes=[[1, 1]], lhVarIds=[[a,b]], rhs=[1], name=branchName)            \n",
    "        else:\n",
    "            pricingProblem.addConstrsByVarId(lhCoes=[[1, -1], [-1, 1]], lhVarIds=[[a,b], [a,b]],\n",
    "                                           rhs=[0, 0], name=branchName)\n",
    "        \n",
    "    \n",
    "    def onBranchingReverted(self, branchDecision, masterProblem, pricingProblem):\n",
    "        decisionName = self.decisionNamer(branchDecision)\n",
    "#         print(decisionName)\n",
    "#         print(self.pricing.constrs.keys())\n",
    "        pricingProblem.removeConstrsByName(decisionName)\n",
    "    \n",
    "    \n",
    "    def CreateBranches(self, cols, sol):\n",
    "        N, edges = data\n",
    "        v, u = -1, -1\n",
    "        tag = random.randint(1,1e5)\n",
    "        for col1, s1, i in zip(cols, sol, range(len(sol))):\n",
    "            for col2, s2, j in zip(cols, sol, range(len(sol))):\n",
    "                nodeSet1 = self.col2Nodes(col1)\n",
    "                nodeSet2 = self.col2Nodes(col2)\n",
    "                if i != j and not isInt(s1) and not isInt(s2):\n",
    "                    for x in range(N):\n",
    "                        for y in range(N):\n",
    "                            if x == y:\n",
    "                                continue\n",
    "                            if x in nodeSet1 and x in nodeSet2 and y in nodeSet1 and y not in nodeSet2:\n",
    "                                v = min(x, y)\n",
    "                                u = max(x, y)\n",
    "                                return [(v, u, True, tag), (v, u, False, tag)]\n",
    "        return [(v, u, True, tag), (v, u, False, tag)]\n",
    "\n",
    "    \n",
    "    def CheckIntegralSolution(self, cols, sol):\n",
    "        for s in sol:\n",
    "            if not isInt(s):\n",
    "                return False\n",
    "        return True\n",
    "    \n",
    "    \n",
    "    def ProvideMasterRHS(self):\n",
    "        N, edges = self.data\n",
    "        return [-1] * N\n",
    "    \n",
    "    \n",
    "    def PricingProblemBuilder(self, tag):\n",
    "        N, edges = self.data\n",
    "        pricing = self.modelClass(self.name + '_Pricing'+tag)\n",
    "        vs = [pricing.addVar(name='v{}'.format(i), ub=1, obj=0, vtype=pybnp.BINARY) for i in range(N)]\n",
    "        for s,t in edges:\n",
    "            pricing.addConstrsByVarId( [[1,1]], [[s,t]], [1], 'v_{}_{}'.format(s,t) )\n",
    "        return pricing\n",
    "    \n",
    "    \n",
    "    def PricingProblemSolver(self, pricingProblem):\n",
    "        return pricingProblem.optimize()\n",
    "    \n",
    "    \n",
    "    def NewColFromPrcingGetter(self, pricingProblem):\n",
    "        newCol = abstractParBnP._pricingWholeSol2Col( pricingProblem.getSolVals() )\n",
    "        if self.EvaluateCol(newCol) + pricingProblem.getObjVal() <= -EPS:\n",
    "            return newCol\n",
    "        else:\n",
    "            return False\n",
    "        \n",
    "    \n",
    "    def PricingProblemUpdater(self, dualPrices, pricingProblem):\n",
    "        pricingProblem.updateObjById(dualPrices, list(range(len(dualPrices))))\n",
    "      \n",
    "    \n",
    "    #([RowIds], [Coes])\n",
    "    def EvaluateCol(self, col):\n",
    "        return 1\n",
    "    \n",
    "    \n",
    "    #([RowIds], [Coes])\n",
    "    def NameCol(self, col):\n",
    "        return 'g' + ''.join(['{}'.format(node) for node in self.col2Nodes(col)])\n",
    "        \n",
    "        \n",
    "    def UBCol(self, col):\n",
    "        return 1\n",
    "    \n",
    "    \n",
    "    # new methods\n",
    "    def col2Nodes(self, col):\n",
    "        return [rid for rid, rCoe in zip(col[0], col[1]) if rCoe != 0]\n",
    "    \n",
    "    \n",
    "    def plot(self,colored=False):\n",
    "        cols, sol = self.intSol[0]\n",
    "        self._plot(cols, sol, colored)\n",
    "            \n",
    "    \n",
    "    def _plot(self, cols, sol, colored=False):\n",
    "        import networkx as nx\n",
    "        N, edges = self.data\n",
    "        g = nx.Graph()\n",
    "        g.add_edges_from(edges)\n",
    "        node2id = dict(zip(g.nodes(), range(len(g.nodes()))))\n",
    "        if not colored:\n",
    "            nx.draw(g, pos=nx.spring_layout(g), with_labels = True)\n",
    "        else:\n",
    "            colorMap = list(range(N))\n",
    "            colors = ['blue', 'red', 'yellow', 'green', 'purple', 'orange', 'black', 'gray', 'gold', 'roralblue', 'coral', 'pink' ]\n",
    "            count = 0\n",
    "            for col in cols:\n",
    "                for node in self.col2Nodes(col):\n",
    "                    colorMap[node2id[node]] = colors[count]\n",
    "                count += 1\n",
    "            nx.draw(g, pos=nx.spring_layout(g), with_labels = True, node_color = colorMap)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.177772538 5.66511344909668 275\n",
      "6.0\n",
      "solution: \n",
      "[2, 8, 9, 11, 13] 1.0\n",
      "[0, 6, 10, 15, 18] 1.0\n",
      "[1, 4, 5, 16] 1.0\n",
      "[3, 12, 17] 1.0\n",
      "[14, 19] 1.0\n",
      "[7, 8, 9] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 13, 18] 1.0\n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[11, 14, 19] 1.0\n",
      "[0, 4, 7] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[3, 12] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 13, 18] 1.0\n",
      "[0, 4, 7] 1.0\n",
      "[14, 19] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "[1, 6, 10, 15] 1.0\n",
      "[3, 6, 12] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[2, 8, 9, 18] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[11, 14, 19] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "solution: \n",
      "[11, 14, 19] 1.0\n",
      "[8, 9, 10, 14] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[1, 2, 4, 13] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[5, 15, 18] 1.0\n",
      "solution: \n",
      "[4, 8, 12, 13, 18] 1.0\n",
      "[1, 5, 16, 17] 1.0\n",
      "[9, 11, 14, 19] 1.0\n",
      "[0, 6, 10, 15] 1.0\n",
      "[2, 3, 4] 1.0\n",
      "[7, 9] 1.0\n",
      "solution: \n",
      "[1, 2, 4, 8, 13] 1.0\n",
      "[0, 9, 10, 15, 18] 1.0\n",
      "[14] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "[1, 7, 16] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[4, 8, 12, 13, 18] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[0, 10, 15] 1.0\n",
      "[1, 7, 8] 1.0\n",
      "[2, 11] 1.0\n",
      "solution: \n",
      "[2, 4, 8, 13, 18] 1.0\n",
      "[9, 11, 14, 19] 1.0\n",
      "[3, 5, 16, 17] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[0, 6, 12] 1.0\n",
      "[7, 9] 1.0\n",
      "solution: \n",
      "[9, 10, 14] 1.0\n",
      "[0, 10, 15] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[2, 3, 6] 1.0\n",
      "[4, 8, 12, 13, 18] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[4, 12, 13, 18] 1.0\n",
      "[2, 8, 9, 11] 1.0\n",
      "[14, 19] 1.0\n",
      "[1, 6, 10, 15] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "solution: \n",
      "[8, 9, 12, 13, 18] 1.0\n",
      "[11, 14, 19] 1.0\n",
      "[2, 11] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[3, 6, 10, 17] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "solution: \n",
      "[8, 9, 11, 13, 19] 1.0\n",
      "[1, 5, 15] 1.0\n",
      "[10, 14] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[6, 12, 17, 18] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "solution: \n",
      "[1, 2, 4, 8, 13] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[9, 11, 14, 19] 1.0\n",
      "[3, 5, 16, 17] 1.0\n",
      "[10, 15] 1.0\n",
      "[1, 7] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[0, 7] 1.0\n",
      "[14, 19] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[9, 10, 15, 18] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "solution: \n",
      "[9, 11, 19] 1.0\n",
      "[10, 14] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[2, 4, 8, 13, 18] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[2, 8, 9, 11] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[10, 15, 18] 1.0\n",
      "[14, 19] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[1, 4, 7] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[0, 4, 12] 1.0\n",
      "[2, 4, 13, 18] 1.0\n",
      "solution: \n",
      "[8, 9, 12, 13, 18] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[2, 3, 6] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "[14, 19] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "solution: \n",
      "[8, 9, 12, 13, 18] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[0, 7] 1.0\n",
      "[3, 5, 16, 17] 1.0\n",
      "[1, 2, 4, 13] 1.0\n",
      "solution: \n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[1, 2, 4] 1.0\n",
      "[3, 10, 13] 1.0\n",
      "[5, 6, 15, 18] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[3, 12, 13, 17] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 11, 13] 1.0\n",
      "[10, 14] 1.0\n",
      "[5, 15, 18] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[1, 5, 19] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[2, 8, 9, 11, 13] 1.0\n",
      "[0, 4, 7] 1.0\n",
      "[10, 15, 18] 1.0\n",
      "[3, 12] 1.0\n",
      "[14, 19] 1.0\n",
      "solution: \n",
      "[3, 4, 5, 16] 1.0\n",
      "[6, 12, 17, 18] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[0, 7] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[1, 2, 13] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[7, 16, 17] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[2, 4, 13] 1.0\n",
      "[3, 5, 17] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[2, 3, 13] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "[4, 7] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "solution: \n",
      "[2, 4, 8, 13, 18] 1.0\n",
      "[9, 10, 14] 1.0\n",
      "[3, 12, 17] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[0, 6, 15] 1.0\n",
      "[1, 4, 7, 16] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[1, 5, 16, 17] 1.0\n",
      "[7, 8, 9] 1.0\n",
      "[11, 14, 19] 1.0\n",
      "[10, 15, 18] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "solution: \n",
      "[3, 5, 16, 17] 1.0\n",
      "[2, 6, 18] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[0, 4, 7] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[12, 13, 18] 1.0\n",
      "solution: \n",
      "[0, 4, 12, 18] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[5, 15] 1.0\n",
      "[3, 10, 13, 17] 1.0\n",
      "[1, 2, 6] 1.0\n",
      "solution: \n",
      "[3, 5, 16, 17] 1.0\n",
      "[9, 10, 15, 18] 1.0\n",
      "[1, 2, 4, 13] 1.0\n",
      "[8, 11, 14, 19] 1.0\n",
      "[0, 6, 12] 1.0\n",
      "[1, 7] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[10, 14] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "[9, 15, 18] 1.0\n",
      "solution: \n",
      "[8, 12, 13, 17, 18] 1.0\n",
      "[1, 6, 10, 15] 1.0\n",
      "[2, 3, 4] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[9, 14] 1.0\n",
      "solution: \n",
      "[3, 6, 10, 17] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[5, 15, 18] 1.0\n",
      "[1, 2] 1.0\n",
      "[12, 13] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "solution: \n",
      "[0, 4, 12, 18] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[5, 15] 1.0\n",
      "[2, 3, 6] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[1, 10, 13] 1.0\n",
      "solution: \n",
      "[8, 10, 13, 17, 18] 1.0\n",
      "[9, 11, 14, 19] 1.0\n",
      "[2, 3, 4] 1.0\n",
      "[1, 5, 15] 1.0\n",
      "[0, 6, 12] 1.0\n",
      "[7, 16] 1.0\n",
      "solution: \n",
      "[1, 5, 15] 1.0\n",
      "[2, 9, 11, 13] 1.0\n",
      "[3, 10, 13, 17] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[4, 7, 16] 1.0\n",
      "solution: \n",
      "[2, 3, 4, 13] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[7, 16, 17] 1.0\n",
      "[9, 10, 14] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "solution: \n",
      "[8, 11, 14, 19] 1.0\n",
      "[1, 5, 15] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[4, 7, 16] 1.0\n",
      "[2, 9, 13] 1.0\n",
      "[3, 10, 13, 17] 1.0\n",
      "solution: \n",
      "[9, 10, 14] 1.0\n",
      "[2, 3, 4] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "[12, 13, 17, 18] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[9, 10, 15, 18] 1.0\n",
      "[2, 11] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "solution: \n",
      "[1, 10, 15] 1.0\n",
      "[2, 3, 6] 1.0\n",
      "[5, 11, 17] 1.0\n",
      "[4, 12, 13, 18] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "solution: \n",
      "[4, 8, 12, 13, 18] 1.0\n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[0, 10, 15] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[14, 19] 1.0\n",
      "[1, 7, 8] 1.0\n",
      "solution: \n",
      "[4, 8, 12, 13, 18] 1.0\n",
      "[1, 6, 10, 15] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[3, 5, 17] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[2, 8, 9, 11] 1.0\n",
      "[0, 4, 7] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[14, 19] 1.0\n",
      "[12, 13, 18] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[0, 4, 12, 18] 1.0\n",
      "[0, 7] 1.0\n",
      "[2, 3, 13] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[10, 15, 18] 1.0\n",
      "solution: \n",
      "[3, 4, 5, 16] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[10, 15] 1.0\n",
      "[1, 7, 8, 17] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[2, 8, 9, 11, 13] 1.0\n",
      "[0, 4, 12, 18] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[11, 14, 19] 1.0\n",
      "[7] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 13, 18] 1.0\n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[11, 14, 19] 1.0\n",
      "[0, 4, 12] 1.0\n",
      "[1, 4, 7] 1.0\n",
      "[10, 15] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[2, 8, 9, 11] 1.0\n",
      "[14, 19] 1.0\n",
      "[3, 12, 13] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 11] 1.0\n",
      "[14, 19] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[1, 4, 5, 16] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[3, 12, 13, 17] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 11] 1.0\n",
      "[5, 6, 15, 18] 1.0\n",
      "[10, 14] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[1, 13, 19] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 11] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "[10, 14] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[3, 13, 19] 1.0\n",
      "[12, 13, 17, 18] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 11] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[10, 14] 1.0\n",
      "[1, 5, 19] 1.0\n",
      "[3, 12, 13, 17] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 18] 1.0\n",
      "[14, 19] 1.0\n",
      "[0, 4, 7] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "[1, 6, 10, 15] 1.0\n",
      "[3, 12, 13, 17] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 18] 1.0\n",
      "[11, 13, 19] 1.0\n",
      "[10, 14] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "solution: \n",
      "[8, 9, 11, 13, 19] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "[3, 12, 17] 1.0\n",
      "[10, 14] 1.0\n",
      "[2, 18] 1.0\n",
      "solution: \n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "[10, 13, 17, 18] 1.0\n",
      "[3, 12] 1.0\n",
      "[2, 11] 1.0\n",
      "solution: \n",
      "[3, 6, 10, 17] 1.0\n",
      "[0, 4, 12] 1.0\n",
      "[1, 5, 15] 1.0\n",
      "[7, 16] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[2, 13, 18] 1.0\n",
      "solution: \n",
      "[1, 5, 15] 1.0\n",
      "[2, 3, 13] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[7, 16] 1.0\n",
      "[0, 4, 12, 18] 1.0\n",
      "[3, 6, 10, 17] 1.0\n",
      "solution: \n",
      "[0, 4, 12, 18] 1.0\n",
      "[1, 5, 16, 17] 1.0\n",
      "[7] 1.0\n",
      "[2, 3, 13] 1.0\n",
      "[6, 10, 15] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "solution: \n",
      "[0, 4, 12] 1.0\n",
      "[7, 16] 1.0\n",
      "[1, 5, 17] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[2, 3, 13] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[2, 4, 13] 1.0\n",
      "[1, 5, 15] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[3, 10, 13, 17] 1.0\n",
      "[7, 16, 17] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[4, 7, 16] 1.0\n",
      "[1, 5, 15] 1.0\n",
      "[2, 3, 13] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[6, 10, 17, 18] 1.0\n",
      "solution: \n",
      "[0, 4, 7, 16] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[5, 11] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[2, 13, 18] 1.0\n",
      "solution: \n",
      "[8, 9, 11, 13, 19] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[5, 15, 18] 1.0\n",
      "[1, 2, 13] 1.0\n",
      "[10, 14] 1.0\n",
      "solution: \n",
      "[0, 4, 7, 16] 1.0\n",
      "[6, 12, 17, 18] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[3, 5] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "solution: \n",
      "[0, 4, 7, 16] 1.0\n",
      "[6, 12, 17, 18] 1.0\n",
      "[10, 15] 1.0\n",
      "[8, 9, 11, 14] 1.0\n",
      "[3, 5, 19] 1.0\n",
      "[1, 2, 13] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[3, 4, 5] 1.0\n",
      "[10, 15] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "solution: \n",
      "[8, 9, 11, 13, 19] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[2, 3, 4] 1.0\n",
      "[10, 14] 1.0\n",
      "[5, 15] 1.0\n",
      "solution: \n",
      "[8, 9, 12, 13, 18] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[3, 5, 17] 1.0\n",
      "[1, 2, 6] 1.0\n",
      "[11, 14, 19] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "solution: \n",
      "[8, 9, 12, 13, 18] 1.0\n",
      "[2, 3, 4] 1.0\n",
      "[0, 6, 15] 1.0\n",
      "[10, 14] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "solution: \n",
      "[0, 6, 15, 18] 1.0\n",
      "[3, 12, 13, 17] 1.0\n",
      "[1, 4, 7, 16] 1.0\n",
      "[3, 5, 19] 1.0\n",
      "[2, 9, 11, 13] 1.0\n",
      "[8, 10, 14] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[5, 15] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[9, 11, 13, 19] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "[8, 10, 14] 1.0\n",
      "solution: \n",
      "[0, 6, 15, 18] 1.0\n",
      "[3, 4, 12] 1.0\n",
      "[2, 9, 13] 1.0\n",
      "[8, 10, 14] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "solution: \n",
      "[9, 10, 15, 18] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "[0, 6, 12] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[1, 7] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "solution: \n",
      "[3, 5, 16, 17] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[1, 4, 7] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[9, 10, 15, 18] 1.0\n",
      "solution: \n",
      "[9, 10, 15, 18] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[1, 4, 7, 16] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[3, 5, 17] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "solution: \n",
      "[1, 4, 7, 16] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[0, 6, 12] 1.0\n",
      "[5, 15, 18] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[3, 10, 13, 17] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "[1, 4, 7, 8] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "[10, 15, 18] 1.0\n",
      "solution: \n",
      "[0, 4, 12, 18] 1.0\n",
      "[3, 5, 6, 16] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[1, 7, 8, 17] 1.0\n",
      "[9, 10, 15] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[0, 9, 12, 18] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[14, 19] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[1, 4, 7, 8] 1.0\n",
      "solution: \n",
      "[0, 9, 12, 18] 1.0\n",
      "[6, 10, 15] 1.0\n",
      "[5, 8, 11, 17] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "solution: \n",
      "[3, 4, 5, 16] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "[0, 9, 12] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "solution: \n",
      "[8, 14, 19] 1.0\n",
      "[0, 9, 12] 1.0\n",
      "[1, 4, 7] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "solution: \n",
      "[1, 7, 16] 1.0\n",
      "[8, 10, 14] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[0, 9, 15, 18] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "solution: \n",
      "[8, 12, 13, 17, 18] 1.0\n",
      "[2, 3, 6] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "[0, 9, 15] 1.0\n",
      "[1, 4, 7, 16] 1.0\n",
      "[10, 14] 1.0\n",
      "solution: \n",
      "[1, 2, 4, 8, 13] 1.0\n",
      "[9, 10, 14] 1.0\n",
      "[5, 6, 15, 18] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[3, 12, 17] 1.0\n",
      "solution: \n",
      "[2, 4, 8, 13, 18] 1.0\n",
      "[9, 10, 14] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[0, 15, 18] 1.0\n",
      "solution: \n",
      "[7, 9] 1.0\n",
      "[14, 19] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[1, 2, 4, 8, 13] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[7, 9] 1.0\n",
      "[14, 19] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "solution: \n",
      "[2, 4, 8, 13, 18] 1.0\n",
      "[0, 10, 15] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[3, 6, 12] 1.0\n",
      "[9, 14] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[3, 4, 12] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[7, 17] 1.0\n",
      "solution: \n",
      "[6, 10, 15, 18] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[1, 4, 5, 16] 1.0\n",
      "[3, 12, 17] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[0, 7] 1.0\n",
      "solution: \n",
      "[1, 5, 15] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[6, 10, 17, 18] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "solution: \n",
      "[0, 10, 15, 18] 1.0\n",
      "[3, 6, 12] 1.0\n",
      "[4, 7, 16] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[1, 5, 17] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "solution: \n",
      "[1, 4, 5] 1.0\n",
      "[7, 16, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[3, 6, 12] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[9, 12, 13, 18] 1.0\n",
      "[0, 10, 15] 1.0\n",
      "[1, 4, 7] 1.0\n",
      "[2, 8, 11] 1.0\n",
      "[14, 19] 1.0\n",
      "solution: \n",
      "[1, 4, 7, 16] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[5, 6, 17, 18] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[3, 4, 12] 1.0\n",
      "solution: \n",
      "[1, 4, 7, 16] 1.0\n",
      "[3, 6, 10, 17] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[5, 15] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[0, 12, 18] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[2, 9, 11, 13] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[7, 8, 17] 1.0\n",
      "[3, 4, 12] 1.0\n",
      "solution: \n",
      "[6, 10, 15, 18] 1.0\n",
      "[2, 9, 11, 13] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[3, 12, 17] 1.0\n",
      "[1, 4, 5, 8] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "solution: \n",
      "[1, 4, 7, 16] 1.0\n",
      "[2, 9, 11, 13] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[3, 12] 1.0\n",
      "[5, 6, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[9, 10, 15, 18] 1.0\n",
      "[0, 7] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "[3, 4, 12] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[0, 12] 1.0\n",
      "[1, 4, 7] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[9, 10, 15, 18] 1.0\n",
      "solution: \n",
      "[1, 2, 4, 13] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[8, 14] 1.0\n",
      "[9, 10, 15, 18] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "[7, 9] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[3, 4, 12] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[1, 4, 7, 8] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "[9, 12] 1.0\n",
      "[14, 19] 1.0\n",
      "solution: \n",
      "[9, 14] 1.0\n",
      "[1, 4, 7, 16] 1.0\n",
      "[6, 12, 17, 18] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "solution: \n",
      "[9, 10, 14] 1.0\n",
      "[2, 3, 13] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[1, 4, 7, 16] 1.0\n",
      "[0, 15, 18] 1.0\n",
      "[6, 12, 17, 18] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[7, 8] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "solution: \n",
      "[4, 8, 12, 13, 18] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[14, 19] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "[3, 6, 10, 17] 1.0\n",
      "solution: \n",
      "[2, 9, 11] 1.0\n",
      "[1, 5, 15] 1.0\n",
      "[3, 6, 10, 17] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[4, 12, 13, 18] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 15] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[7, 16, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "solution: \n",
      "[2, 9, 11] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[1, 5, 8, 17] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "solution: \n",
      "[1, 7, 16, 17] 1.0\n",
      "[0, 10, 15] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[5, 6, 15, 18] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "solution: \n",
      "[5, 6, 16, 17] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[1, 7, 8] 1.0\n",
      "solution: \n",
      "[0, 7, 16] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[14, 19] 1.0\n",
      "[5, 6, 15, 18] 1.0\n",
      "[1, 8, 10, 17] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[3, 12, 13] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[1, 4, 7, 16] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[1, 4, 7, 8] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[14, 19] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[3, 12, 13] 1.0\n",
      "solution: \n",
      "[9, 10, 14] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "[2, 4, 18] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[3, 12, 13, 17] 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.742749331999903 5.997005939483643 223\n",
      "root bound as queue weight, enhance: ['5.798946218848581%', '5.534303179555291%', '-23.318385650224215%']\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb4AAAEuCAYAAADx63eqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOydd1hUx/fGX3oVkCKLFBU0Khawo2LvFWvsICii0YRoDGpiL1F/kmiwawIae0EFrFgRNaLYe4tdMVYEpe3u+/tjlS/IAruwKMb5PM8+C/fOnJlZlvvec+6ZGS2ShEAgEAgEXwjan7oDAoFAIBB8TITwCQQCgeCLQgifQCAQCL4ohPAJBAKB4ItCCJ9AIBAIviiE8AkEAoHgi0IIn0AgEAi+KITwCQQCgeCLQgifQCAQCL4ohPAJBAKB4ItCCJ9AIBAIviiE8AkEAoHgi0IIn0AgEAi+KITwCQQCgeCLQvdTd0AgEPyXeQUgEsBjAKkASgLwAFAHgNYn7JfgS0YIn0AgKALOAPgVQDgUl5kUADIABgB0ADgAGAOgDwCjT9RHwZeKltiIViAQaA4CmAxgDoB0KMQuN0wA2ACIAeBU5D0TCN4jhE8gEGiQkQCWAXirYnkdKMKfZ6DwAgWCokcIn0Ag0BB/ARgG1UXvPToAygG4AvH0RfAxEFmdAoFAAxDAT1AmegsWALVrAwYGwMCByurKADwBsKMoOygQZCKETyAQaIADABKVnildGhg/HvDzy6t+EoDZRdAvgSAnIq4gEAg0wBwAyUrPdOumeI+PBx48yMvGGQA3AZTXbNcEgg8QHp9AINAAZzVgQx/ABQ3YEQjyRgifQCDQAG80YEOG3MKlAoEmEcInEAg0gIEGbGgDMNaAHYEgb4TwCQSCQkESKSmWmrAEwFEDdgSCvBHCJxAI1CYjIwMHDhxAYGAgHBwc8OOP95GsPLcFUimQmgrIZIpXaqriWE7MoVjHUyAoWkRWp0AgUImkpCTs2bMHERER2L59O8zMzCCVSiGTyWBj8x2MjedDsSZndqZPB6ZM+d/vq1cDkyYBkydnLWUMYDTEwtWCj4FYuUUgEOTKo0ePEBUVhYiICMTGxqJixYrQ0tLC9evX0a5dO/j5+aFFixbQ0dEBEAjFcmWpBWjJBMADABaa7L5AoBTh8QkEgkxI4vLly4iIiEBERARu3LiBRo0awdjYGBYWFiAJX19f9OnTB5aWHz7Xmw3gMMhL0NLKUKNVIyh2cRCiJ/g4CI9PIPjCkUqlOHbsWKbYZWRkoH379jA3N8fx48dx6dIl9OvXD76+vnBzc8vH1lPcvOkMF5dU6OkpfZCXCQmkpAAJCXPg7Dxak0MSCPJECJ9A8AXy5s0bREdHIyIiAjt27ICjoyM6d+6McuXK4ciRIwgPD0f9+vXh5+eHjh07wsBAtekKQUFBuHz5DCIjG0Jbez5kslTo6Hy4fqcBpFIZzp2zwPPnP2Do0GWIj49X4kEKBEWDED6B4AvhyZMnmc/rYmJiUK9ePXh5ecHDwwMHDx5EaGgoZDIZ/Pz84O3tjdKlS6tlPzw8HKNHj0Z8fDysrKwASLFlyyBUrHgIVaqY4cWLBCQn68LJyRdpaYNQoUIzbN68GevWrcONGzcQGRkJbW2RaC74CFAgEPxnuXLlCmfNmsX69evT3NycX3/9NdesWcMnT55wy5Yt7NSpEy0sLOjn58cjR45QLpcXuB0bGxvGx8dnO96hQwdu2rSJJLlixQr2798/89zChQvZoUMHpqens2HDhpw+fXrBByoQqIHw+ASC/xAymQzHjx/PfF735s0bdO7cGV5eXmjatClu3LiBsLAwrF69GpUqVYKvry969OgBU1PTAreZlJSEevXqYfTo0fDLsgWDTCaDlZUVrl27BltbW+zZswe//voroqOjAQBpaWkoX748tmzZgtKlS6NOnTpYuXIlWrVqVejPQSDICxFXEAg+c1JSUhAZGYlBgwahdOnSGDZsGAwMDLBmzRrcv38fv/zyC27fvo1GjRqhbdu2MDQ0xJEjRxATE4OBAwcWSvRIYtCgQfD09MwmegBw7tw52NnZwdbWFgBga2uLhISEzPMGBgYYN24cpkyZAnt7e6xZswYDBgzA/fv3C9wfgUAVxHQGgeAz5OnTp9i+fTsiIiJw8OBB1KxZE15eXhg/fjzKlSsHuVyOAwcOoH///tixYwfatGmDqVOnolWrVu/m3GmGuXPn4vbt24iNjc1xLiYmBk2aNMn83dbWFk+ePMlWZtCgQZg5cyZOnjyJZs2aYeTIkejZsycOHz4MfX19jfVTIMiKCHUKBJ8JN27cyAxhnj9/Hq1atYKXlxc6dOiQmRF5584drFixAitWrIClpSV8fX3Rt2/fd8kmmiUmJga9evVCXFwcypQpk+N8ly5d0KtXL/Tp0weAYtqEkZERUlJSoKv7v3vuhQsXYvfu3YiKigJJdO3aFY6Ojpg/f77G+ywQABDJLQJBcUUmk/H48eMcN24cXV1dKZFIOGTIEO7YsYMpKSmZ5d68ecNVq1axefPmtLa25nfffcczZ84Uad8ePHhAOzs7RkdH59p3S0tLPnz4MNtxGxsbPn78ONuxlJQU2tvb8+TJkyTJly9f0sXFhWvWrCmazgu+eESoUyAoRqSmpuLAgQOIiIhAVFQUSpYsCS8vL4SGhqJOnTqZ6f4kERcXh9DQUGzatAkeHh4YOnQoOnfurPKcu4KSnp6Or7/+GiNGjMg1EeXixYuwtLTMMSVCIpEgISEBEokk85ihoWHms76oqChYWFggPDwcLVu2hJubG6pUqVKk4xF8eQjhEwg+MS9evMCOHTsQERGBvXv3onr16ujSpQtiYmJQoUKFbGWfPHmCVatWISwsDOnp6fDz88OFCxdgb2//0fo7evRoWFtbY+zYsbmWOXz4cLbne+9R9pwP+N+zvvj4eNSuXRtubm4IDg5Gt27dcPLkSZiZmWl0DIIvGyF8AsEn4Pbt25nP606fPo3mzZujc+fOWLx4MWxsbLKVzcjIwM6dOxEaGorDhw+ja9euWLJkCTw9PaGl9XF3M1izZg127dqFkydP5jnZPCYmBp06dcpxXCKRKBU+Q0NDjB07FlOnTkVkZCQAwMfHB8eOHcOgQYOwcePGjz5WwX8XkdwiEHwESOLUqVOZYvfkyRN07NgRXbp0QcuWLWFkZJSjzqVLlzLn3FWoUAG+vr7o2bMnSpQo8QlGAJw/fx4tWrTAgQMHUK1atVzLkYREIsGJEydyJL2MHj0apUqVQlBQUI56qampKF++PCIiIlCrVq3MY56enujXrx9Gjhyp2QEJvliExycQFBHp6ek4ePAgIiIiEBkZCRMTE3h5eWHx4sXw8PBQOq0gMTER69evR2hoKB48eAAfHx8cPnwYX3311ScYwf949eoVunXrhnnz5uUpegBw9epVGBkZKc30lEgkePz4sdJ6772+KVOmZHp9hoaG2Lx5M+rVq4c6derA09Oz8IMRfPEI4RMINMirV6+wc+dOREREYM+ePXB1dYWXlxf27duHSpUqKa0jl8tx6NAhhIaGYvv27WjVqhUmTZqE1q1bZ0v7/1TI5XJ4e3ujffv26NevX77lP5y/lxVbW1ucOXMm17qDBw/GrFmzcOrUqUyvr2zZsggLC0Pv3r1x6tSpzAnxAkFB+fT/VQLBZ869e/cyQ5gnTpxAkyZN4OXlhZCQkDwv0nfv3s2cc2dmZoZBgwZh3rx5sLa2/oi9z5+ZM2fi+fPn2Lx5s0rlY2Jics32zC255T2GhoYYM2YMpk6dioiIiMzj7du3h5+fH3r37o29e/cWixsCwWfMJ51MIRB8hsjlcp4+fZqTJk2iu7s7rays6OPjwy1btjA5OTnPum/fvuWaNWvYokULWllZccSIETx16lSBF4cuavbs2UM7O7sc8/FyQy6X087Ojjdv3lR6/ty5c6xSpUqeNlJSUli6dGmeOnUq23GpVMpWrVpxzJgxqnVeIMgFIXwCgQqkp6dz7969HDFiBJ2cnOji4sJRo0YxJiaGGRkZedaVy+U8ceIEhw4dypIlS7JNmzbcsGFDtknoxZHbt2/T1taWhw4dUrnOjRs3aG9vn6uQJyQk0NraOl87ISEh7Ny5c47jT58+pZOTE7du3apynwSCDxFZnYLPg/SXwO3VwMuzip/1zAGLqkC5AYBhqSJp8vXr19i1axciIiKwe/duVKhQAV5eXvDy8oKrq2u+6fX//vsvVq9ejdDQUKSmpsLX1xfe3t5wdHQskv5qkvfZlH379sWoUaNUrvfnn39i//79WLt2rdLzMpkMhoaGePv2LfT09PJs38XFBVFRUahZs2a2c3FxcejUqROOHTuG8uXLq9w3geA9QvgExZtXF4BLs4AHWwBoA7Isu3nrGAIEULod4DoWsK5b6OYePnyIyMhIRERE4NixY/D09ISXlxc6deqk0sasUqkUu3btQmhoKA4ePIguXbrAz88PjRo1+qzmofn7+yMxMREbNmxQq9/e3t5o2LAhAgICci0jkUhw+vTpfD/PkJAQHDhwANu2bctxbtGiRVi6dCn+/vtvGBsbq9w/gQAQwicoztxeBZwYCshTAcrzKKgF6BgB7jOBit+p1QRJXLx4MTM55datW2jfvj28vLzQtm1blefMXblyBWFhYVi1ahWcnZ3h5+eHr7/++pPNuSsMf/zxB3777TfExcWp3f8yZcpgz549uWawAoC7uzvCwsJQo0aNPG2lpKSgfPny2L59e46yJDFgwADo6OhgxYoVn9VNheDTI1KjBMWT26uBEwGALEWFwlR4gmfHKQSy0vd5lpZKpThy5Eim2Mnlcnh5eWH27Nlo1KhRniG4rLx+/RobNmxAaGgo7t69C29vbxw6dAgVK1ZUqX5xJD4+HuPGjUNsbKzaonfnzh2kpaXlO/4P9+XLDSMjI4wZMwZTpkzJ4fVpaWlh6dKlqFevHpYvX44hQ4ao1VfBl40QPkHx49UlpaJ35SEwfAVw6jZgYwbM6QN0rZOlgOwtcO5nwKoOYNMwW93k5GTs2bMHERER2LlzJ8qUKQMvLy9s3boV1atXV9ljkMvliImJQVhYGCIjI9GiRQuMHz8ebdq0+exT7J89e4YePXpgyZIleXpsuRETE4PGjRvn+1nmtmyZMvz9/TF79mycOXMmh9dnYmKC8PBweHp6ombNmqhdu7bafRZ8mXze/6mC/yaXZwPytGyHpDLA6zdgaAtg7zgg5grQ6VfgjAPwlV2WgrK3wMXpQLNdePz4MaKiohAREYHY2Fh4eHjAy8sLM2bMUDvB5N69e1i5ciXCwsJgamoKPz8//PrrrznW1fxckclk6NevH3r16oXu3bsXyEZuC1N/iKoeH6Dw+oKCgjB16lRs3bo1x/mKFStiyZIl6NGjB06dOlUk+w4K/nvkvsqsQPApSE8E7m8CKMt2+Ooj4NFLYGQ7QEcbaF4FaFgBWHUkpwnpw33o1KImXF1dcfDgQQwYMAD3799HdHQ0hg8frrLopaSkYN26dWjdujVq1KiBhIQEbNq0CefOncP333//nxE9AJg8eTIyMjIwY8aMAtvIa8WWrOQ3if1DhgwZgri4uFxXfOnevTt69OiB/v37Qy7P61mwQKBACJ+geHF3HZR9LZWlYBHAxQc5j8tJzPu2Kp48eYJ169ahd+/eMDc3V6l5koiPj8fw4cPh4OCAFStWwM/PDw8fPsTChQtRq1at/1wiRVRUFFauXIn169cXOFz78OFDvHr1Cq6urvmWfb8nn6pk9fpyY+bMmXjz5g2mT5+usl3Bl4sQPkHx4tWF7FMW3lGpNFDKDJizHciQAtHnFeHOt2k5TejryOBimQx9fX2Vm3369Cnmzp0LNzc39OrVCxKJBGfOnMGePXvQu3dvGBoaFmZUxZabN29mbvtTqlTB50PGxMSgUaNGeW5V9B51PT4ACAgIQFxcHM6ePav0vJ6eHjZs2IClS5diz549atkWfHkI4RMUL9JfKD2spwtsGwXsOAtIhgO/7gS+9gAcLHOz8yrfpqRSKbZv347u3bujQoUKOHv2LEJCQnDjxg1MmDABTk5OhRhI8eft27fo1q0bpkyZAg8Pj0LZUjXMCajv8QGqeX12dnZYt24dfHx8cPfuXbXsC74sxDw+QfHi5HDgxiKVijaYDPg0AgJaKDnp0AVonDMZAlBsm/N+zl3ZsmXh6+uLXr16fVG7fJOEt7c3tLW1NTIPrlKlSli3bl2+c/MAhXddqVIlPH/+XK02UlJS4OLigp07d8Ld3T3XcsHBwdi4cSNiY2NhYGCgVhuCLwPh8QmKF+ZVAB3lK3GcvwekpivCm8E7gMevgIGNlRTUNgAssu8Z9/r1a/zxxx9o0KABmjVrBgDYv38/jh07Bn9//y9K9ADFyicXLlzA4sWLCy16CQkJePLkCapXr65SeSsrKyQlJSE9PV2tdoyMjPDjjz/m6fUBwA8//ABHR0exca0gV4TwCYoXZfvkukrLqiOA3XCg1DBg/0Vg71jAQMlc8/SMDBy8Xx4ymQwxMTHw8fGBk5MTdu7ciXHjxuHevXuYPXs2KleuXMSDKZ4cO3YMU6dORXh4uEaW+4qNjYWnp6fSjXWVoa2tDWtra/z7779qtxUQEIC///4b586dy7WMlpYWQkNDsW/fPqxatUrtNgT/fcQ8PkHxQr8knhk1hsXraOh+cB2d01fxygsSeJThjH7+Y/H06SBYWVnh22+/xfXr1wuVvPFfISEhAb169UJoaChcXFw0YvP9xHV1eD+J3cHBQa16xsbGmc/6wsPDcy1nbm6OzZs3o0WLFnB3d89313jBl4Xw+ATFhufPn8Pf3x+9pp4GtFXPyMzK23TA77fbaN26NZYuXYqWLVsiODgY48aNw+nTpzXc48+LjIwM9OrVC35+fujQoYPG7KqT2PIedSaxf0hAQACOHTuWp9cHANWrV8dvv/2G7t27IzExsUBtCf6bCOETfHLkcjmWL18OV1dXGBsbY8uBm9CttzjXZ325kZKhjau6PVGz7ffYvXs3zM3NsXr1aly7dg3ly5dH165dUb9+faxevRppaUrmQfzHGTduHIyNjTFx4kSN2Xz27Bnu3r2bY+ug/FBn2bIPMTY2VulZHwAMGDAALVq0gK+vL0Qen+A9IqtT8Ek5ffo0vvnmG2hra2PRokXZs/Vu/gmc+u7dmp25f03lcgC6htCuNhlwDQK0tPD333/Dx8cHderUwfz582FpaQmZTIYdO3Zg4cKFOHv2LPz8/DB06FCUKVOmqIf5ydm0aROCgoJw6tQpWFrmNgdEfbZu3YqlS5di9+7datUbO3YszMzM8NNPPxWo3bdv38LFxQV79uzJN6kmLS0Nnp6e6NWrF0aPHv2/E2fOAAcPAs+fA3p6QKlSgJcXYG9foD4JPh+Exyf4JLx69QojRoxA+/btMWTIEBw5ciRninr5QUCrw3hj2Rop6UCGPPsjaRn0QG1DnHlSCuuf+AJVxgDvMhTr16+Ps2fPwtraGtWrV8fOnTuho6ODzp07Y8+ePYiNjUVqaipq1qwJLy8vREdH/2eXu7py5Qq++eYbhIeHa1T0gIKFOYGCTWLPijpen4GBATZv3ozg4GDE7tsHrF4NVK0KeHoC48YBv/wCTJ0KjB4NuLgA7doBBw4oXy5I8N/gE+z6LviCkcvlXLlyJSUSCQMCAvj8+fM8y1+7do3Vq1enjRkYMsyR11fUYXp0S/JIb/L8VPLNA16+fJnW1ta52jpw4ADLlCnDwYMHMzExMdu55ORkLlu2jG5ubqxQoQLnzp3Lly9famy8n5rExERWrFiRoaGhRWLf3d2dR48eVbve2rVr+fXXXxeq7Tdv3lAikfDcuXMqld+/bh2v6OpSZmxMKmRN+UtLizQxIQcMIDMyCtVHQfFECJ/go3H+/Hl6enqyVq1aPHHiRK7lXr9+zT///JMNGzakra0t9fT02KdPnzxtDxkyhKNHj871fGJiIgcPHsyyZcvywIEDOc7L5XIeOXKEffr0oYWFBf39/Xn27FnVB1cMkcvl7N69O4cMGVIk9l++fElTU1OmpaWpXXf//v1s0qRJofsQHBzM7t2751/w2TPSyYlSbe28RS/ry9iY7NyZlMkK3U9B8UIIn6DISUxM5MiRI2ljY8NFixZRKpXmKCOXy3n48GEOHDiQ5ubm9PLyYkREBLdu3UpdXV3+888/ebbx6NEjWlpa8s6dO3mW27FjB+3t7fndd9/xzZs3Sss8fvyY06ZNo4ODAxs2bMi1a9cW6OL+qZkzZw7r1KnD1NTUIrEfFRXF5s2bF6jupUuXWKlSpUL3ITk5mba2tjx//nzuheRy0sOD1NdXXfSyit+ECYXup6B4IYRPkCfJacm89+oe7726x+S0ZLXqyuVyrlu3jvb29hw4cCCfPHmSo8z9+/c5Y8YMli9fnq6urgwODmZCQkLm+YYNG9LFxUWl9saPH88BAwbkW+758+fs27cvK1SowGPHjuVaLiMjg+Hh4WzevDklEgnHjx/P+/fvq9SXT82BAwdoa2vLu3fvFlkbo0eP5pQpUwpU99mzZ7SwsNBIP4KDg9mjR4/cCxw/rghdApwPsBZAfYA+H4jcBoCVAJoCrAxw6/tzpqbk27ca6augeCCET5CDdGk6N17cyNrLalN3qi6NZxjTeIYxdafqsubSmtxwcQPTpHl7QFeuXGHz5s1ZvXp1HjlyJNu51NRUbty4kW3btmXJkiUZEBDAuLg4yuXybOX+/fdf6unpce7cuSr1OzExkba2tiqHKDdv3kxbW1uOGTMmX6/o8uXLHDFiBEuWLMlu3bpx3759OfpbXLh//z4lEgn37t1bpO3UqVOHhw4dKlBdmUxGPT09jXij+Xp9PXqQ70Kc4e8EbegHwvcAoB7AnYp1g7gdoBHAJ++FLyys0P0UFB+E8AmysfnSZlrMsmCJX0oQk6H0VeKXEjSfac6NFzfmqJ+cnMyxY8fS2tqa8+bNY0aW5IDTp0/z22+/pbW1NZs3b87Vq1fnGm4kyRkzZlBPTy/fBJiszJ8/n23atFG5/JMnT9ilSxdWrVqVp06dyrd8UlISFy9ezKpVq7JSpUoMCQnJkTDzKUlLS6OHhwd/+eWXIm3n9evXNDExYUpKSoFt2Nvba8wjnTNnDnv27JnzxIsXpIFBjhDmzx8I33GANh+UsQZ47P3vlStrpJ+C4oEQPkEmIXEhNJpulKvgffgynm7MuX8rvDG5XM4tW7bQycmJffv25aNHj0gqQlohISF0d3enk5MTJ02alO/zuvf2SpcuTU9PT7XGkJaWRhcXF7W8HblczlWrVtHGxoZTpkxhenq6SnViYmL49ddfs2TJkhw6dCgvXLigVl+LguHDh9PLy4uyIk7I2LVrFxs3blwoG/klOanDe68vx98gLo40N89X+KQAGwOMePfzVoD2AJPfl9HT00g/BcUDsVanAAAQfjkcY/aOQYo0ReU6b6Vv8fP+n6H9Rhu7f92NO3fuYMWKFWjcuDGio6MRGBiI6OhodOjQAcHBwWjWrJlKG5UCwPHjx/Hy5UsEBgaqNQ59fX388ssvCAoKQnx8vErtaWlpoX///mjatCkGDx6M+vXr46+//spzN3EtLS00btwYjRs3xqNHj7B8+XK0adMG5cuXx/Dhw9G1a1fo6SlZQbsIWbVqFaKjo3Hy5EmVP+eCcvjw4QLN38uKsmXLpFIpUlNTkZaWpvZ77dq10bt3b3Tp0iXzuPOtWwh4+xb5rQGkA8AbQF8AqQD0AWwCYPK/jgEZGYqJ7l8gJBAbC8yZo3h/8wbQ0QFKlgQGDABGjAA+p+0rxcotAmTIMlAquBRepSrZvDUcwG0A6QBMATQEUCt7Ea00LUw3nw6vjl5Yu3YtVq5cCXt7e/j6+qJ3796wsLBQu099+vRBREQEXrx4ofbu5yRRr149fP/99+jbN59VrZXUXb58OX7++WeMGTMGI0eOVHnXgYyMDGzbtg0LFy7EjRs34O/vjyFDhqB06dJq9aEgnDt3Di1btsTBgwdRtWpVteqSVFtwJkyYgI4dO6JcuXIFFqoHDx5AR0cH2tramccBwNDQEIaGhjAwMFDrXVtbG3/++ScGDhyIMmXKwMDAAGUePEC7BQugn5L9hm48gAcAVrz7fR+AXgD2AKgJ4BSAzgB2AXAHAG1thfgVcgunz5Ht24FvvwWePgXevs05r19fX/HxNGwIhIZ+HgIohE+ATZc2YVDkICSlJ+U8+S8ASyj28XgKxZWiH4As13IDGKDsxbJ4efgl+vfvD19fX7UvvllJSkqCra0tunXrhtWrVxfIRkxMDAYOHIirV68WaDPSf/75B76+vpDJZFixYgXKly+vVv2LFy9i0aJFWL9+PVq2bInhw4ejcePG2fa+I4mMjIwCC8f795cvXyIsLAx169aFk5NTgezo6OioLDA6OjqIjIxE//79UaJECbUF6v17SEgISpQogTFjxmQe19UtXBBqzpw5iI+Px4YNGxQH7t8HvvoKeCeq7/lQ+IIBHAWQdeviLgA8AYwGAFtboICLan/OhIQAY8cCKSoEgnR0ADMz4NAhQMWtGT8ZItQpwOyjs5WLHgBk3clH693rBbIJXxrSkFY7DQ/WPtBIeG/Dhg3Q09PDkCFDCmyjSZMmqFKlChYtWpRjQ1KSSE9Pz1cMfvzxR2zZsgU1atSAl5cXGjRooFK9rO/lypXD0aNHERERAZIwMjKClpYW0tLSkJaWBh0dnQKJxvt3fX197Nq1C9WqVUPnzp0LZEddwdm/fz8eP36MsLCwAv99AMWu7Tdv3oSVlVWh7GTlm2++gYuLCy5evKi4+XJ0BNzcgLg4AID03Uv27pUKxUWwDoBZAM5C4eGdARAL4BsAMDQEvvlGY338XFi9WrGimyqiBwAyGfDyJdC0KXD2bPH2/ITH94WTlJYEq/+zQoY8I/dC26G4IkgBSAD4AvjAidLT1sO/P/4LC8Pcw5okMy/4eQmGv78/Xrx4gYULF6otNFnfExMT8c8//8DOzi6bnfT0dOjq6qosDOnp6Thx4gQMDAzQunVr2NjY5Fne9uFDOMXGwuTZM7Y/wQQAACAASURBVOjKZJBbWSG1USPE2thg1caNOHr0KHr16oVvv/22UJ4xAEyfPh27d+/GwYMHP9ozxYkTJyIjIwMzZ84slJ0NGzYgPDwcGzdu1FDPFOTw+rZsAQYOBJKSMBnAlA/KTwIwGcACAPMAPAFgA2A4gB8AhfDdvg1IJBrtZ3Hm5UvFWt0K0VsAhW98AUAf/M9PvgzFk9Fb736vBSAEOjquaNIE2L//o3ZZLYTH94XzPOU59HX08xa+jgDaA7gP4A6UfmtkGTK07NQSeIlchSg9PR36+vp5ioZMJsP9+/fh4uKCHTt2KH3eY2ZmBhsbG5WEa+rUqShVqhQmTpyYzcNRN/lDKpXi//7v/zB37lzMmTMHPj4+2cKWkMuBNWuAKVMUF8nU1HfbRrzjwAH01tZG74AAPJo2DUu2bUOrVq1QuXJlDB8+HF5eXmqH+fbs2YPFixcjPj7+oybSxMTEYNy4cYW2U5g9+fLim2++gbOzMy5duoQqVaoAnTsDRkbgO+GbnEu9Ee9e2TAwANq3/6JEDwDCwrI+ziwNRXB4D4Cs7l9pAJsBlAEgB7AQQG/IZOdx7Bhw9y5QXDc+ER7fF86dV3dQdVFVvMl4o1qFKChuhz2yHzbUNsSKeivgXNI5z7BcfoITGBiIsLAwHD9+PM+sSlV58OAB3NzccPbsWTg6Ohba3rlz5+Dj4wMnJycsW7YMEolEcVvcvTtw+LAi3S0v9PUBU1Ng3z6kV6mCLVu2YOHChbh9+zYCAgLg7++vsJkPd+7cgYeHBzZt2oRGjRoVelyqkpqaCmtrazx+/BglSpQolK2rV6/Cy8sL165d01Dv/sf//d//4fTp01i/fj0AQBofD6mHBwxlMtWN6OoqQqVnzgDm5hrvY3FFLgccHIDHjz888+GT0axIASwF8COAt9DXB777TpEFWhwR2xJ94VgaWSJdlq56BTmAlzkPyyBDm8ZtUKdOHVSvXh1fffUVypQpA1tbW1hYWGRm3eVFWloa/vrrLzg6OmpE9ADAwcEBQ4YMwaRJkzRiz83NDSdOnICbmxvc3Nywce1aoEMHxb5u+YkeAKSnAy9eAE2aQP/mTfTu3RuxsbHYsWMHHjx4gMqVK6NPnz44cuRIrhunpqamonv37hg7duxHFT0AOHHiBFxdXQstekDReXyAwus7ePAgLl26BKlUin5z5uCn2rXBEiUgVcWAoSHg7AwcPfpFiR6geD6XlMsjf+VYADAE8C0Axf6K6enAX39pvm+aQgjfF46ZgRkq21RWfjIZirB+GhSCdxPARQDlcha11bWFAdXPnsxKREQEjIyMMGjQoELZ+ZCxY8dix44duHDhgkbs6evrY9q0aYiKisKrb79F6uHDObIGAWA9gMpQzAVzgSJZIpPkZKBFC8XcMCgEdenSpbh9+zY8PDwwaNAguLu7Y9myZXiTRVBJYvjw4ahQoYLacxw1QUxMDBo3bqwRWxYWFkhNTUWKqtkTamBqaooffvgBU6ZMQf/+/fH69Wv8cugQLq9Zg41QBOxonHN2H01NFUL3/fdAfDxgZ6fxvhU3ZDIZUlJSkJiYiKdPn+LSpWfQ0lLDM8YrAIlQPAuskXk0MVHDHdUgItT5BZOYmIgVK1bgl6hf8Kz+M8h1P9iI9Q2AjQASoNgA3QJAPeSYx4c0wOSQCbQvaqNPnz4YOHAgPDw8sj8DU4GWLVvi77//xo0bNzQ+923evHnYu3cvduzYoTmjKSmgjQ20lHh6ewEMBrABQF0A76NG2fb2LlEC+PNPoGfPHPXlcjn279+PhQsXIjY2FgMGDMA333yDmJgYzJs3D3FxcTA1NdXcWFSkZcuWCAwMRKdOnTRiz8nJCYcPH0bZsmU1Yi8rr169gkQiQe3atbFv3z4YGBigatWquHfvHiYHBuKHkiWByEiFB66nhwNXrsB19mxIhg0r9ET193Mj09PT1X6lpaUVqF5B7ZGEgYEB9PX1oa+vD7m8A16+DAFp9sGo8gp1Aoq7YxsAVwCUgo6OYupjcUQkt3yBXL9+HfPnz8eaNWvQpk0bbJyyEZ1jO+N12uvsBU2gyODMB109XWhd0YKenh62b9+OqKgomJqawtfXFwMGDICDg0O+Nu7cuYMTJ07Aw8OjSCZ8Dxs2DCEhITh06BCaNm2qGaObNuUq7pMATMT/HoXaKyuUlATMnq1U+LS1tdGqVSu0atUK9+7dw5IlS+Dh4YHk5GTMmzdP7Un9miA9PR1xcXEaDa++34ldVeGTy+XIyMjI92L/9u1bTJs2LXOqRFRUFI4ePYqrV69CR0cH6SYmCCaR3qZNZp1NSUkov2sXyly+rBFh0dXVzRSTwryyipKhoSHMzMw0Zk9fXz/HAg2HDwMdO8rVDHcCCuF7C+AhgFJQ4lAXG4TwfSHI5XJER0cjJCQEp06dgr+/Py5cuAB7e8UleYnZEgyKHKTWkmUAYKRrhCUdl6B2r9oYN24c/v77b1haWuLVq1eIiorCnDlzULt2bQwcOBBdunSBcS7/DWFhYbC2toaPj0+hx6oMAwMDzJgxA0FBQYiLi1PbG1XKr78qQpYfIAMQD8XKH+WhmCvWBcAcAEYfFr58GbhxA6hQIddmnJycMGrUKKxevRre3t5YtWoVZs+ejYCAAAwePBilSpXKta6qyGSyfC/wp06dgkQiwcmTJzXmsdy9exf+/v4wMzNTyZ5UKs33wq6np4c7d+5AKpXC3d0dR48exbJlyxAbGwsDAwOUK1cOjx49yiYEpqamcHZ2RkZGBtzd3QstLHp6ekW+bJwmSUtLw7FjxxAdHY1du44gKWkPkLnQW26zHw8CsAZQHYrw0HgAJaEI8AOenh91CGohQp3/cZKSkrBy5UrMnz8fxsbGCAwMRO/evZV6DMHHgjHp4CS8lb5VybYudWF3zQ6Xll3KTHY4evQoxowZg4SEBJQuXRoXL16Ep6cnkpKScO7cOXTv3h0DBw5EgwYNMsVHJpOhTJkySExMxKNHjzSSOKEMuVyOunXrIigoCF9//XXhDZqZKc0CeASFh1cLiiRYPQBeAJoCmPFBWampKa5NmoR/a9XK9cKfmpqKBQsWwM7ODi1atEBaWhru37+P+Ph43Lx5E46OjqhQoQLMzMxU8oaUCQvJXD2C96+nT59CLpejcuXKhfIwsr4WLVoEV1dXdOvWTSVbenp6ed60yGQy+Pj44MmTJ4iMjISRkRFmzZqF9evX48qVKzAyMkJcXBwqVqyYo+7OnTsxd+5c7N27t6DfiM8Gkrhy5Qqio6MRHR2NI0eOoHLlymjdujVat26N3393w5YtxlAs5zwZymc/VgEwAYrwpxH+twxAdZiaKqLIzZp9xEGpgRC+/yi3bt3CggUL8Ndff6F58+b47rvv4Onpma+ns+bCGgzbPgwEkZye05sBAK10LRgZGWFJpyWIXRiLmzdvYseOHTAyUvgzJLF9+3aMHTsWJiYmKFeuHPbu3YtmzZrB0dER0dHRyMjIgI+PD7y9vXH58mUEBASgYcOGWLt2rcY/i6zs378fQ4YMwZUrV6Cvr184Y/r6mckpWXkJxSpvKwC891/DAUyHYkWQrCRra2N2+fI4Urp0rhf706dP4+nTp+jVq1fmtJD3r4yMDMTHxyMmJgbGxsZo164dmjdvrnY4TEdHJ9/vRtu2bREQEICuXbsW7nPLwvjx42FgYIAJEyYU2pZMJsPAgQPx+PFjREZGZkYX7t69i7Jly8LZ2Rmurq6IiopSWj8hIQGurq54/vy5ZiICxYynT59i3759mWKnp6eXKXTNmzeHpaUlnj9/Dl9fX2zffhfAcZA5YhQq4eAA3LtXjJc2/TibQAg+BnK5nHv37mWnTp1obW3NMWPGFGi/s5SMFK46t4pVF1al/lR9mv5iStNfTKk/TZ9VFlZh31l92bVnV5KkVCplnz592KFDB6alZd+cViqVMiwsjI6OjuzQoQNHjRpFe3t7tmjRgr///juHDRtGKysr2tjY0NramuHh4Rr5HPKjTZs2DAkJKZSN1NRUZrzb1VvZywHgyiy/bwborqysuTm5a1eu7Wzbto2Ojo5Kd6/Pikwm465du9ixY0daWVlx1KhRvHHjRqHGmJWMjAyWKFGCT58+1ZhNkgwJCeE333xTaDtSqZQDBgxg8+bNc+zxGBAQQF1dXZqYmPDAgQN52ildujRv375d6P4UB1JTU7l//36OGTOGNWvWpJmZGTt37swFCxbw+vXr2TZSTk5Opre3N3V0dGhra8vw8HB260YaGeX6Fc/1ZWREbtnyCQeuAkL4/gMkJydz8eLFdHV1ZdWqVbls2bI8N3hVh2dvnvH6s+u8/uw6n715ltleqVKleOnSJZJkeno6O3XqxK+//ppSqTSHjZSUFAYHB9PGxoa+vr6cN28e3dzcWKVKFQYHB9PQ0JB6eno0Nzenr68vY2JiinR387Nnz9LW1lblDWSlUikvXrzI0NBQDhs2jLVr16axsTGvGBrm+t8/AWDtdzt4vwDoCXB8bleJXATq+vXrtLGx4fHjx9Ua3z///MOgoCDa2Niwbdu2jIqKUvp3UYcTJ06wSpUqhbKhjI0bN7J79+6FsiGVSunt7c1mzZrl+N5fvXqVRkZGbNiwIXV1dTO/s7nRsWNHbt68uVD9+VTI5XJevHiRv/32G9u1a8cSJUqwXr16nDBhAmNjY5XuM5mWlsaRI0dST0+PZmZmXLx4ceb/XmoqWa8eaWQkV1n0jI3J4OCPPXL1EcL3GXP79m2OHj2aVlZW7NKlCw8cOFCkgpGVX375hf3798/8PSUlhS1atKCfn1+um6C+fPmSY8eOpaWlJYOCgrhlyxZWrFiRurq69PDw4KVLlzhnzhxWqVKFzs7OnDx5skqb1hYEb29v/vzzzzmOy+Vy/vPPP9ywYQNHjx7Nxo0bs0SJEqxQoQL79u3LefPm8ejRo4oL7KpVpKmp0itAOsBhAM0B2gL8FmCKsitFnTpK+5ecnMyqVaty8eLFBR5jSkoKV6xYwTp16rBs2bKcPXt2gT22OXPmaMQz+5CYmBg2bNiwwPWlUil9fHzYtGlTJicn5zjfqlUrGhkZ0dPTkz169GCfPn3ytDdp0iT+9NNPBe7Px+bff//l2rVrOXDgQJYuXZplypThkCFDuHnzZr548SLXelKplNOnT6eRkRENDQ05ZcoUpTdHqalk+fJnqaOTSh2dvL08Q0MyLKwIB6tBhPB9Zsjlch48eJBdu3allZUVf/jhhyITh7xITEyktbU1b968mXksKSmJ9evXZ2BgYJ4C/ODBA/r7+9Pa2po2Nja0sbFhhw4dWLJkSY4YMYI3btxgfHw8R4wYQWtrazZt2pQrVqxgUlKSxvp/9+5dWlpa8ty5c9y+fTsnTpzIdu3a0dramqVLl2aXLl04Y8YMRkdH534BSUnJVfhUeaUaGJBKvAu5XM6+ffvSx8dHYzcyJ06coI+PDy0sLOjj48O4uDi16nfs2JEbNmzQSF+ycu3aNZYvX75AdWUyGQcOHJir6EVHR9Pc3Jy9e/emnZ0dnz17RhsbG165ciVXmxEREWzTpk2B+vMxSElJ4b59+zhmzBjWqFGD5ubm9PLy4sKFC3OEL5Uhl8u5ePFimpubU09Pj8OHD2dKSkqu5Y8fP06JRMJDh15w4ECFwJmbk2Zminctrde0ts7g7Nnks2eaHm3RIYTvM+Ht27dcvnw5q1WrxsqVK3Px4sUaFYKCMHHiRA4ePDjbsZcvX9Ld3Z0TJ07Mt/7atWtpbGxMXV1d/vHHH7x79y7HjRtHa2trdu/enceOHWNqairDw8PZqVMnmpub08fHhwcPHszVq8yLxMREHjhwgLNnz2b37t1ZokQJ6uvrs1WrVvzpp5+4bds2Pnz4UD2jP/+siO+oKXoygA8AVq1Ykffu3ctmMiQkhO7u7nz79q3aY8yPp0+fcvbs2Sxbtixr167NsLCwfNuRSqW0sLDg48ePNd6fV69e0dTUVO16MpmMvr6+bNKkiVLRy8jIYMWKFWlqasrevXtzxowZJBWRir59++Zq98GDB7SxsflokZP8yBq+bNu2LU1NTenh4cGJEyfmGr7Mzc6GDRtoa2tLHR0d9u7dmy9fvsyzTmpqKl1dXbl+/frMY4mJ5MGDimd427eTTZv+yNWr1xZmiJ8EIXzFnHv37nHs2LG0sbFhx44dGR0dXWz+KZ89e0ZLS8scCTRPnjxhxYoVOWfOnDzr+/r6skGDBhw8eDAbN25MV1dXbtu2ja9fv2ZISAjLlSvHBg0aMDw8nFKplAkJCfztt99YrVo1li1blhMnTszmcWYlNTWVcXFxnD9/Pr29vVm5cmWamJiwQYMG/P7777lmzRqePHmSNjY2vHz5csE/BKmUbxs14ls1RE8OkCVK8P6+fXRxcaGuri6nT59OuVzOI0eOsFSpUrx161bB+6RSt6WMiorK9HJ//PHHXCMHp0+fZsWKFYukH3K5nAYGBkrFKzdkMhn9/PzYuHHjXOstWbKEtra2DAwMpIWFBZ+9c0dev36dp9cnl8tpa2ub42bkY/LkyROuWbOGPj4+LF26NMuWLatS+DI39u7dy7Jly1JHR4etWrXigwcPVKo3YcIEenl55Xm9mTVrFgMDA9Xu06dGCF8xRC6XMzY2lj169GDJkiUZGBio0Qw9TRIUFMQRI0bkOH7//n2WLVuWS5YsUVovMTGRFhYWtLKy4q1btyiXy7ljxw5Wq1aNDRo0YGxsLKVSKTdt2sR69erRxcWFCxYsYHJyMuVyOU+fPs3AwEDa2NjQ09OTU6ZM4aJFizhs2DDWqlWLRkZGdHd3p7+/P5ctW8azZ88yIyMjRz/mzJnDzp07F3j8z549o1vFirzh6krmkeWZ+exPW5uv9fXJ8+dJKv7WkydPpo6ODp2dnSmRSLhz584C96cg3Lx5kz/88AOtrKzYoUMH7ty5M5tHPW/ePA4ZMqTI2i9TpozKQi+TyTho0CA2atQo14jHq1evaGVlRQsLC/74448cOnRotvMzZszI0+tr164dt27dqvoACsn78GVQUBDd3d2zhS9v3LhR4BvduLg4VqlShTo6OqxTp06+iT1ZOXv2LG1sbPKNgBw8eJD169cvUP8+JUL4ihEpKSkMCwtjjRo1WKFCBc6fP5+vX7/+1N3Kk4SEBJYsWVJpGOzmzZu0t7fn6tWrc5xbtmwZGzRowAYNGmQ7LpVK+ddff7FMmTLs1KkTL168mOkJde3aldbW1hw+fDiXLl3KH374gY0aNaKRkRFNTEyop6fH2rVr8/fff1c5DJySkkInJyfGxsaqPfakpCTWq1ePQUFBpExGrltHursrHoR8mAlgakpaWDA9KIj1XVy4bt26bLauXr1KQ0NDamtrc/LkyZ/Eq3/z5g3//PNP1qxZky4uLgwODubz58/ZtWtXrlmzpsjarVu3Lo8dO5ZvOZlMxsGDB9PT0zPPv29QUBCdnJw4Y8YM2tjY8OrVq9nOJyYm5un1jR8/nhMmTFBvEGogl8t54cIF/vrrr2zTpg1LlCiRGb48cuSIyuHL3Lh8+TLr169PHR0dfvXVV2p/tzMyMlizZk2GhobmWzYpKYkmJiY5pjIVd4TwFQMePnzI8ePHs1SpUmzbtm2OO+7iznfffcfRo0crPXfx4kXa2tpy27Zt2Y7XrVuXjRs35qJFi5TWS0lJ4W+//UZra2u2aNGCgYGBbNeuHUuWLEljY2Pq6emxVq1aXL58eWb4599//+W8efNYo0YNOjk5cfz48Sp5yn/99Rc9PDzUEpu0tDS2adOGvr6+OeudP0+OGUP27ct7deow2tmZ3LiRfHdBO3HiBG1sbLKFnEaNGsX27dtz4sSJ1NXVpYuLC69fv65yfzSJXC7n33//zf79+9Pc3JwGBgZF6oV27tw5Xw9LJpPR39+fDRs2zPNm8NatWzQzM6OtrS3nz5/Pjh07Ki03Y8YM9uvXT+m5LVu2sH379qoPQAUSEhK4evVqent7087OjuXKlWNAQADDw8MLFL5Uxu3bt9mmTRvq6urSzs6O27ZtK9AN1MyZM9m6dWuV61arVo0nT55Uu51PiRC+T4RcLuexY8fYu3fvzGzGD+9MPxfu37/PkiVL5poqHx8fTxsbG+7du5ckef78edrb27NEiRKZz17I/yWfzJo1i927d6eTkxMtLCxYrlw5GhkZsUuXLrx48SJJRZLGtGnTaGtry/bt23P//v3Z/lHPnj3LkSNHslSpUmzYsCGXL1/OV69eKe2fTCajm5ubyvO3ZDIZ+/Tpw86dOysNn2YlJiYmh1dLkpMnT868uGzYsIHlypXj8+fPSSo85fLly1NXV5c//fTTJ70JOnToEC0tLenk5EQPDw+uWrWKqampGm3D398/z2kbMpmMAQEB+YoeSfbo0YPOzs5cuHAhK1eunOuE9by8vrt371Iikag3iA9ISUnh3r17+eOPP2aGL7t06cJFixbl+ly6oDx+/Ji9e/emnp4eLSwsuGzZsgJ/Z65cuUIrKyu1JvEPGjSICxYsKFB7nwohfB+Z1NRUrlq1inXq1KGzszPnzp2b6wX5c2LIkCEcP358rucPHz5MGxsbHj16lIGBgezQoQMbNWqUmXxSqVIlmpiYsGHDhpnJJ1mfbzx69IhDhw6ltbU1Z86cmTlROSUlhX/88QcrV67MGjVqcPXq1dlCRenp6YyMjGS3bt1obm7Ovn37Mjo6Osecpd27d7NChQr5hpnkcjm//fZbNmrUSKWsy1u3brFMmTI5jqenp7NOnTr8+eefaW1tzdOnT2c7L5PJMr2/smXLqvV8RpMsWLCAvr6+lEql3LZtG1u1asVSpUpx7NixvHPnjkbamDBhAidPnqz0nEwm49ChQ9mgQYN8Re/w4cMsVaoUy5Urx8jISLq7u+fptUyfPl2p1yeXy2llZaVWhq9cLuf58+cZHBzMNm3a0NTUlPXr1+ekSZN49OjRfG+QCsLz5885dOhQ6uvr09jYmDNmzChUyFEqlbJBgwacP3++WvWWLVvGAQMGFLjdT4EQvo/E48ePOXnyZEokErZs2ZKRkZGFXk2jOHHr1i1aWVkpTZGWSqW8cOECR40alfkcS0tLi2XLlqW/vz+XL1+ea/LJh1y7do09e/akvb09ly1blllHJpNx+/btbNasGR0dHTlnzpwcNxTPnj3j/PnzWatWLTo4OHDcuHGZXrZcLmfLli1zDb2+Z9q0aXRzc8s3Ffw9KSkp1NPTU3oHHh8fT21tbc6cOTPX+tevX8/M/Bw9enSRXEDzomfPnlyxYkW2Y9euXWNgYCAtLS3ZuXNn7tmzp1Be6YIFC3IkoJCKv8mwYcNYv379fFfZkclkrFWrFl1cXLhmzRq2bNmSK1euzLPO+7moyiItrVu3ZlRUVJ71ExISuGrVqmzhy6FDh3LLli0qfz8KQlJSEseNG0dDQ0MaGBjw+++/10guwO+//05PT0+1/5bnzp3jV199Vej2PyZC+IqYEydOsH///rSwsODQoUM/2Z37x8Db25vTpk3jrVu3uH79+szkE1NTU1aoUIH9+vVjkyZNqKuryxIlShRqnlpcXBybNWvGihUrcsuWLdnu7E+dOsW+ffvS0tKSo0aNUrpe6YULFzh69GhKJBJ6eHhwyZIlPHToECUSSa4XkSVLltDZ2Vnt+WzW1tZMSEjIdkwul7Nbt2709PRk3bp18xQ0mUzGCRMmUE9Pj46Ojjxz5oxa7RcUuVzOUqVK5Rr2Sk5O5rJly+jm5sYKFSpw7ty5Bbrgb968mV27ds3R9rBhw+jh4aHS0nIrV67kV199xapVq/Ls2bO0s7NTyfuZPn16thWI3jN27NgcXmjW8KWbmxvNzc3ZtWtXLl68WOPhS2WkpKRw1qxZNDExob6+Pvv375/je1VQ/vnnH1pZWfHatWtq15VKpTQ1NdXYs8qPgRC+IiA9PZ3r1q1j/fr1WaZMGc6ZM+ez+lKoQ0JCAqOiojhhwgR6enpSS0uLdnZ2mSuf7N27N9vYW7Zsyfr169PExKTQK87I5XLu3r2bbm5u9PDwYExMTLbzd+/e5Q8//EBLS0v26dOHp06dymEjIyODO3bsYM+ePWlubk4nJyf269cvhze+adMm2tnZFegC5+bmxvj4+GzHZs+ezbp16zIlJYWtW7fmlClT8rVz5coVOjs7U09Pj999912RZ9JdvXqVTk5O+ZZ7n3Xbp08fWlhY0N/fn2fPnlW5ndjY2Gwp8XK5nMOHD2e9evVUegyQnJxMe3t7Ojs7MzIykr6+vpkT1vMjN69v06ZN7NixI8+dO8fg4GC2bt2apqambNCgASdPnlxk4UtlZGRkcPHixbSwsKCBgQHbtWunUaF9H+2YPXt2gW00adKEu3fv1lifihohfBrk33//5fTp02lvb8+mTZty69at/6lwZmJiIvfv35+ZfOLo6MiSJUuyVatW/Pnnn7lt2zZ26tSJwbmsUvvPP//Q2tqa1atXZ2BgIJ2dnVWeTJsXMpmMq1evZrly5di+fXueO3cu2/lXr14xODiYDg4ObNq0Kbdv3640nPP8+XNOmTIlc4X6oKAgXrp0ifv27aONjU2BPa0OHTowIiIi8/f9+/dTIpFkTpJ+v1rIiRMn8rUllUr5888/U19fn6VLl1Z76TF1WLp0qVJvKC8eP37MadOm0cHBgQ0bNuTatWvzFejr16/T2dmZpOIiPGLECJVFj1SsIOTh4cH69evz0aNH2Sasq8K0adMyx/n48WOuWrWKXbt2pba2Nl1cXD5K+FIZMpmMa9asoUQioZGREevVq6f05q2w/PHHH6xVq1ahhDwoKEilm7fighA+DXD69GkOHDiQFhYWHDx4cI4L7+dISkoKjx8/zvnz53PAgAH5Jp+859y5c7Szs1Maxhw/fjz79+9PBwcHymQyzpo1i5UrV9bYVjepqan82zQB/AAAIABJREFU/fffWapUKXp7e+dIvkhPT+eaNWtYo0YNVqpUicuXL1e6TuHIkSPZq1cvBgUF0dramrq6uvz+++8zsy7VZciQIVy4cCFJxUo8EomE+/bty1Zm3bp1rFixosq7aly8eJHOzs7U19dnQEBAkSxv1rdvXy5fvrxAdTMyMhgeHs7mzZtTIpFw/PjxvH//vtKyr1+/prGxcWbiUN26dVUWvfv379PS0pIODg48ePAgJ0yYoPR5YW68ffuWW7dupZGREStWrEgLCwt27dqVixYtopmZmcZCieogl8sZGRnJcuXK0djYmF999VVmRrSmefDgAa2trXn+3YIKBSU8PFzjU0CKEiF8BSQjI4ObNm1io0aN6ODgwJkzZ2p8r7KPxfvkkz///JNDhw7NsfKJOsknJDP3/PqwDXt7e/r5+SkmfL/jp59+Ys2aNTWa2ZqYmMiJEyfS0tKSI0eOzPF3kcvl3L9/P9u3b0+JRMKpU6dmK/Ps2TNaW1tz165dlEgknDBhAnv16kVzc3P27NmT27dvV+vueOrUqfzpp5+YmprKevXqcdasWUrL9enTh99++63KdjMyMjh27FgaGBjQ1tY2R6i3MMjlctrb22tkLuHly5c5YsQIlixZkt26deO+ffuy3TDJ5XIaGhpy2LBhrFOnjlqeVf/+/dmmTRu2bt2ab9++VTphPStyuZznzp3jnDlz2KpVq8zwZbNmzdi2bdtsf9cWLVp89FV0Dhw4wGrVqtHExIR2dnZcv359kU1nkcvl7NSpEydNmlRoW+8FtLgsp5gfQvjU5NmzZ5w5cyYdHR3ZqFEjbtq06aNn2hUGuVyemXwyatSoHMkn2bbdKSBxcXF0dHTMFuLasWMH69atS0dHx2x3l+9DWw0bNlRrvUZVSEhI4PDhw2llZcXp06crtX/p0iUOGjSIJUuW5LBhwzIv9GPHjqWxsTH/+OOPzLIvXrzgkiVL6OHhQYlEwtGjR/PChQv59iM0NJTe3t4cNmwYu3btmuvF4cWLF3RwcGB0dLRa4zx79izLli1LAwMDDhw4UCOLl9+8eZN2dnYavZC9fv2aixYtYpUqVVipUiWGhIQwMTGRcrmcZmZmrFatmlqiFxcXR4lEQltbW8bHx3PZsmVKJ6w/fvyYf/31F/v370+JREIXFxcOGzaMW7duzbzhevXqFa2trbMld/z444+cNm1a4Qeu4lgaNGhAU1NTWlhYcMGCBYVewSU/1q5dy6pVq/4/e9cZFtXVdRcgSFHaDB1BQUCaiGhsYAdUFBUF7CK2IDassSOWWGIXRbF3sPeIMdiiEktU7BK7WFBA6WVmfT8I84q0AQY1X1w+POC95+xz7p07Z929zy4y2ys2MjKq9ByzssJ34pMSN2/e5KBBg6ipqUk/P79KsbVXBl69esVDhw5x2rRpbNeuHQUCAY2MjNilSxfOnTu3kPOJrODm5laANLp27cpx48axbt26hdrml5dxdXWVeXA0ST58+JA9evSgoaEhw8LCilxQXr16xalTp1IoFLJDhw40NTWlurp6sam07t27x0mTJtHIyIhOTk5csWJFsftKJ06coLW1NS0tLUvVbKOiomhsbFzmzyQrK4vjx4+nsrIyhUIhT5w4Uab+n2P9+vXs0aNHhWQUB7FYzNOnT0sciuzt7amiolIm7UosFrNZs2bs1q0bu3fvTpFIJAlYT09P54kTJzh27FjWrVuXmpqa9PLyYlhYWIkLc0hISIF4tF27dhXyNpU1YmNj2a5dO6qpqVFNTY3BwcFfpOrK27dvqaenJ9W+srTo2rUrd+z4d1Rq+E58JSA3N5f79+9nq1ataGhoyFmzZvHNmzdfe1rFIjk5WeJ84uXlJXE+cXNzkziflLnsTjlx9uxZmpubMycnh69fv6ampib79u3LBQsWFNk+JyeH3bt3Z5cuXSpNg75y5Qrbtm1LCwsLRkZGFqnNvH37lrVq1aKGhgbNzMxYp06dEueTm5vLEydOsFevXtTQ0KCXlxcPHTpUgFz37NlDBQUFSdaZ0jB8+PBSC6YWhytXrtDU1JQqKirs2bNnuR0y+vXrV6EiuNJALBZzyJAhNDAwYNWqVWljY8OIiAipNJ2IiAja29tTIBDw9u3bXLlyJQ0MDCTmy2bNmnHmzJm8cOGC1M9TvtaXr/U/ePBAKq/W8iAuLo4+Pj5UVVWlqqoqhw8fzrdv31bKWEXB19eX48ePl6nMf1Olhu/EVwQSExO5cOFC1qxZk02aNOHOnTu/uSSs+c4ny5cvL9L5ZMeOHRXK7C4LNG/enNu2beOCBQvYr18/amlpFevgQOZpLe3atWOfPn0qNU1XVFQU69evz4YNGxZIaZWdnc0OHTqwb9++zM7O5p49e6iqqko9PT0uX7681Dfx5ORkrl27ls2aNaOuri6DgoJ47tw5iRlS2s8iLS2NVlZWBeqglQWZmZkcM2YMVVRUqK2tXcCjVFrUrFmzYuWaSoFYLObYsWNZv359JiYmctCgQRw4cCBbtGhBQ0NDzpgxo9iXtIyMDBobG7Np06Y0Nzennp4eVVRU2KZNGx44cKBC+8UhISHs168fyTxLhLq6ukz37l+8eMFBgwZRVVVVUiuwLOnBZIEDBw7QwsJC5g5R0dHRbNy4sUxlVha+E98nuH37Nn/88UdqamqyT58+MjUDVAS5ubm8efOmxPmkfv36EueTIUOGlNn55EshKiqKNjY2tLS05OzZs9m6detS+6SlpbF58+YMCAioVNIWiUTcuXMnzczM6O7uzqtXr7JPnz708PAooHEcOXKEpqam9PLyolAo5KRJkxgfH1+q/AcPHnDy5MlUVlamjo4Oq1atWqbYqz///JO6uroVCve4dOkSa9SowWrVqtHLy0vqBfzp06eVWoxVLBZz3LhxdHR0lHjKzpgxQ1K8ODY2lgEBAdTS0qK3tzdPnz7NtLQ0iflSX1+fVapUoaKiIufMmcNjx45JHbBeGj7X+lq0aFFhszGZl1t2zJgxVFNTo4aGBtu2bftVvL+TkpJoZGQkU0eofKSkpFBVVfWbUxKKwn+e+EQiEQ8fPkxXV1fq6+tzxowZlVJpWloU53xiaWkpcT65cOFCpbivyxpisZjW1tY0MjKip6enVGVOyDyvzAYNGnDChAmVrrFmZWVxxYoVVFVVpVAoLJRZRywWs2XLlly7di3j4uIk3ol+fn6lOrbMnDmTzs7O/PXXX6murs5q1aqxS5cu3L9/v1SLQ3BwMN3d3St0D9LT0zly5EiqqalRU1OTERERpcrbsmULu3XrVu4xS4JYLJYkbv40PGTVqlUFav6JRCKeP3+enTt3pqqqqiSmbuTIkVRXV2fv3r05cuRIkixTwLo0mDlzJvv3708yr2rG3Llzyy0rOTmZ06ZNY7Vq1SgQCOjo6Mjo6GjZTLQc8Pf357BhwypNvr29/TejMJSE/yzxJScnc8mSJTQ3N2eDBg0qJeu8NPjU+cTd3b1I55MvHTgrS7Ru3ZoGBgasXr16mUxQ7969o52dHWfPnl2Js8vDzz//TGtra/70008UCAQcOXJkgf2WP//8k4aGhhKv0Pfv33POnDnU19enu7s7T548WYhMjh07RkNDQ4l22KZNG+7bt4/r16+ni4sLdXR0OGrUqBKD4vMTWefHAFYE586do7GxMdXV1dmhQ4cStdaBAwdy+fLlFR7zc4jFYkmx1c8dgfbu3Ut3d3du3ryZvXv3pq6uLi0sLDhs2DDu37+fBw8epJeXF5WUlGhtbU0NDQ2+efOGr169KnPAemlISkqiUCjkw4cPuX37dnbv3r3MMtLS0rhgwQJqampSX1+ftWrV4t69e7/q1sOJEydoYmJSqTU+Bw0aVOYk118D/zniu3fvnuStvUePHrxw4cIXexjznU9+/vnnr+588iWQnJxMdXV1GhgY0MXFpcz94+PjWbt2bS5btqwSZpeH8PBw1qxZU2JSfPPmDUeOHEmBQMCQkBDJvp6Pj08h1/bMzExu2LCBNjY2rFu3Lrds2cKsrCw+evSIurq6BQqA9uvXj+vXr5f8Py4ujtOnT6epqSnr1q3LxYsXF+k4de/ePQoEApmUrEpNTeWwYcNYvXp1qqurc+PGjUU++7Vr15a5GU4sFvOnn36ig4ODhKTS0tL466+/csyYMTQzM6OCggK7devGNWvWFJnO7vr16xQKhbS1taWamhpbtWrF7t27V0p1+Hyt7+7du6xVq5bU/bKysrhq1Srq6urSyMiIurq6BZKpfy2kpKTQ1NS00tOKhYeHlznbz9fAf4L4RCIRjx8/znbt2lFXV5dTpkyRSaqskpCRkcGLFy9KnE+srKy+OeeTykZYWBi7detGKysrWllZletanzx5QhMTE6nNpGXB/v37qa+vX2Ri3r///pu9evWivr4+V65cyTt37lAgEBTpeScWi3n8+HG2adOGhoaGNDQ0LFRxYfLkyQwJCSnUVyQS8ffff2f//v2poaHBTp06ce/evQVMoStXriw1kXVZEB0dTUNDQ2pra7N169YFkni/fPmS2traMnUuEovFnDRpEu3t7fn7779z/vz5bNOmDatVq0ZnZ2eGhIRwz549RZZv+lRG69atOWXKFOro6PDt27fcvHkzFRUVqaenx5CQEJluUSQlJUmSNkuTgDk3N5ebN29mjRo1aGxsTA0NjQLls742hg8fTj8/v0of5+bNm7SwsKj0cSqK/9fE9/HjR65YsYKWlpasV68eN27cWGSKqooi3/lk3bp1HDp0qMT5xNHRUeJ8cuPGja/+1vel0aBBA27YsIECgYCWlpY8depUueTcv3+fBgYGjIyMlNncoqOjqaOjUyh59Oe4du0a3d3daW5uTnd3dw4fPrzYtmKxmJ6enjQ1NaWmpiZHjx4t8dgLDQ0tVTP5+PEjN27cyJYtW1IoFHL48OG8cuUKRSKR1ImspcXHjx85ZMgQampqUl1dnatWraJIJOKOHTvYuXNnmY3z4sULduzYkRoaGtTR0aGFhQUDAwN58ODBAlUXUlJSqKysXOzL0cGDB2ltbc0uXbpIkinnB6xfv35dci09evTguXPnZPJCGRwcTD8/Pzo7OxdKMZcPsVjMvXv30srKSmJKHjdunExNrxXF2bNnaWho+EUS5edXaihver8vhf+XxBcXF8fRo0dTW1ub3bt359mzZ2WmWYnFYsbFxXHnzp0cM2YMnZ2dCzifLFu27F/jfFKZuH79OmvUqMGZM2cyMDCQmzdvZqtWrSokT1dXl0ePHq3w3K5du0YdHZ0yEfGpU6dYr149KigoFKpPl481a9bQzs6OqampfP78OcePH09tbW36+vryl19+oYeHh9TjPXr0iMHBwaxVqxbt7OwkwfWXL1+WWoY0iIqKor6+vqRSfc+ePbl48eJyy0tLS+Px48cZFBREOzs7KisrU11dnYsWLSrVbV9NTa3IEkRZWVm0sLDg0qVLaWhoyLS0tAIB6/lISkri0qVLaWlpybp163LNmjUVygaUr/X179+/UOUCsVjMEydOsH79+jQ0NKSGhgb9/PyKLIH1NZGenk4LCwvu27fvi435b6jU8HWILzeTTI8nU5+QWcmkDEhJLBYzKiqKHTt2pFAo5E8//SSThzDf+WTq1Kl0d3entrY2jYyM2LVr1/8XzieVhREjRnDatGm0srLixYsXmZ2dzVq1avH8+fPllnnx4kXq6Ojw9OnT5Zbx8OFDGhgYcM+ePWXuKxaL6evry2rVqtHV1bVA9p6YmBjq6OgUMpt++PCBixcvpr6+PtXU1Hjo0KEymRFFIhHPnDnDAQMGUFVVlWpqaty2bZtMLRfJyckcMGAAtbW1KS8vz9GjR0tdVUQkEvHatWucN2+exHzp4uLCkJAQDhgwgLa2tlIHZpubmxeZG3TJkiVs164dXV1dJUH1x44do4ODQ5EvtCKRiFFRUezcuTO1tbU5atSoctWZI/PCLJydnenr6ys5dv78eTZv3pz6+voUCoXs1KmT1MkJvjQmTJhAHx+fLz7mt16p4csRn1hMvj5NRnckdyiSu1TIXWp5f+83Je+vJLNLLzj5OVJTU7l69WpaW1vTzs6O4eHh5barJycn87fffpM4nxgbGxdwPjl48KBUMVz/dWRkZFAgEPDgwYM0NzeXLE5r1qxh+/btKyT7999/p46OTrnK8cTHx9PMzIxhYWHlHj81NZUGBgacOHEiDQwM2KNHD8bExNDExIQHDhwott+LFy9YvXp1Ojk50crKimvWrCmzVSA1NZWNGzemsbExBQIBhw0bxpiYGJlZM7Zt20Y5OTkaGhqyQYMGxS7mL1++5KZNm9irVy/q6OjQ0tKSw4cPL2C+nD59Om1tbcuU6ahp06Y8e/ZsgWP5CcM3bNhAMzMzyd6nNBXWybw94kmTJlFXV5eurq48cOBAmbYckpKSqKmpSVNTU167do3t27enjo4OjYyM2KRJkwIOTN8aLl++TD09vS+eberfUKnhyxDf+2vkgVpkhBq5XY7cjsI/u9TyyPBmsFQa4OPHjzlu3DgKBAJ26dKFv//+e5kWgHznk2XLlrFPnz4FnE+CgoL+E84nlYUdO3bQ1dWVo0aNKpD5PTMzk8bGxqXuq5WGw4cPU1dXt0ylVJKSkli3bl2ZJB1eu3YtW7RowZSUFIaEhFBRUZH169cvsYSNSCSioqIi09PTefr0aXbs2JG6uroMDg4uU6qq/ETWW7du5axZs2hubk5ra2vOnz+/wh7Bu3fvpqurK3v37k0dHR1qaGhw1qxZTEpK4rFjxxgUFERbW1tqa2vT29ub4eHhhUo/kXlako2NTZkXXC8vL+7evbvAseHDhzMgIICNGjXitm3bSOY5UJQ1YD0zM5Nbt25l48aNaWJiwjlz5kg9v4CAAMrJyVFLS0tyvw8ePPhNrw1ZWVm0t7eX3LMviRcvXlAgEHzT96fyie91dB6pFUV2xRHgH31IcWFzkFgs5u+//84uXbpQIBBw3LhxUlXxzsnJ4Y0bNyTOJ46Ojt+dTyoRrVu35o4dO6inp1fIdLVs2TKZJP7dtWsXDQ0NpSqbk56eThcXF44cOVImX8acnBzWqVOHR44c4aRJk9iiRQuOHDmS2tranD59erFxUqampgWSJN+9e5eDBw+mpqYmhw4dKnXIQlRUFGvUqMHExERJ9fP8BOrt2rXjrl27ymUKHT58OOfPn0+RSMRFixZRTU2NysrKlJeXZ/369Tlr1izGxMSUaAYNDg6mjY1NuerYBQQEFIgBu3PnDoVCIbdu3Up7e3uJibiiAetXr16lv78/NTU12bt372JDmp48eUJ/f39qaGgQALW0tLhhw4Z/RXHp4OBgenh4fDXyMTIykmmVeFmjcokv6RYZUU160vuU/K6Ok4hJS0tjeHg47e3taW1tzdWrVxe7af2p80lQUNB355MvjLi4OAqFQh48eJCNGjUqdD4tLY16enpSlfMpDevWraOpqWmJe7k5OTn09PRkz549Zeqif/DgQZqYmNDExESisT1+/Jh9+/aV5Pb8XCNp1qxZkami3rx5w+nTp1NHR4eenp48c+ZMqQvW8OHD2atXrwLH0tLSuG3bNrq6ulJbW5tDhw7lxYsXpVr8Xrx4QWNjY7q5uVFHR4dWVlYcOHAgnZ2dqaenR01NTU6ePLlEQp05cyatra3LXbx15syZnDp1quT/HTp04IIFC2hnZ8dDhw6RpEwD1t+/f89FixbR3Nycjo6OXLduHdPS0vj69WuOGDGCmpqadHBwoEAgoLGxMZ2cnCo85pdAbGwshUJhiXlxKxteXl7cvn37Vxu/NFQu8Z1oWiSxPV4KtncANVVBPQ0w0BXM2fI5+Snz5f2znDhxIoVCITt27Fhkhoz4+HgePHjwu/PJN4IpU6Zw9OjR7N27d7EZHObPn1/u6gOfY8mSJbSwsChysRWLxfTz86O7u7vM8wfeu3ePioqKBRbqfNy4cYMdOnRgrVq1uH37dgnh+vj4lLgYpKWlcfXq1bSwsGDDhg0ZERFRrBUiP5F1REREkeefPXvGOXPm0MLCgpaWlpw7d26BhTA1NZXHjh3j6NGjaWNjQy0tLVapUoVhYWGFzJcRERGSvbx8Z6XPERISwjp16lQoli4sLIyDBg0imZdlxNzcnBs2bGCTJk0k3/uyVliXBvlxvu7u7lRRUWHVqlVpZ2dHTU1N/vTTT0xKSuKKFSvKnG/1ayAnJ4cNGzbk2rVrv+o85s+fL0kp9y2i8ojv40Nyl3KRxNfeAezvAmZsBF+FgnbG4LK+Bdtkb5Fj2OCqHD16NB8+fEiyoPNJ165dCzifTJ069bvzyVdGTk4ODQ0NGRMTQw0NjWL3rj5+/Fio6GdFMHPmTNrb2xeKHZowYQIbNWok8wK3KSkptLW15YQJE2hsbFysM9Xp06fZqFEj1qtXj7/++iuDgoKKLcv0KUQiEQ8cOEAXFxfWrFmTS5YsKdJ8Kk0ia7FYzAsXLnDw4MGsXr06a9euTRsbG1arVo3Nmzfn7Nmz+eeff3Lv3r10dXUtVs7r16/ZpUsX1qhRgwKBgEFBQZLrnjVrVoVJj8xLKNCpUyfm5OTQ1taWkZGRNDMzk+S2TE9Pp66urkyy2HyKlJQUzpkzhwKBgHXr1qWysjKVlZXZsmVLHj58mLm5ubxx4wYFAgH9/f1lOrassXDhQrZu3fqr76/lP/vfKiqP+C4Pz/PYLIL46hiCR8f/7//jPMAhrQu3y9mhzJXLFhZwPnF2dpY4n8TFxX31D/g7/ofDhw+zcePG3Lp1a6kxazNnzuSAAQNkMm5+tv8ffvhBQhALFy6ktbW1zAOJ80MaBgwYQLFYzG7duhXK0vJ5+71799LS0pK1a9cu4BYvDWJiYujj40OBQMAJEyYUIrmSElk/f/6cGzZsYM+ePSkUCmlpaUl3d3c6OjpSU1OTgwYN4vnz5ykWixkUFFRqXlSxWMxt27ZRKBTS3t6etWrV4sCBA2llZSWTF86LFy+yYcOGXL16NVu0aMHQ0FC6ublJzhdXYb28yMzM5LJly6inp8eGDRtSX1+fXl5evHv3LtPT07lp0yY2bNiQNWvW5Ny5cyVlnr7VKuP379+nQCD4JuaXkpJCFRWVr5L/WBpUHvHtNSh2D2/1ALCvM5i2AXyxArQ1BveNLtzu4wZ5LprgwXXr1n13PvkXoEuXLgwPD6e7uzt37txZYtvExERqa2vLrBZZflHT/EoKJiYmfPbsmUxkf4qlS5fS0dFRskd8//59CoXCUgk2JyeHQ4YMobKyMr29vaVyyvkUjx494siRI6mlpcV+/fpJcml+msg6NTWVR48elZgvtbW16ePjw3Xr1hXaB33x4gXnzZvHOnXqsHbt2jQ0NJQ6tvHly5f08PCglpYWFRQU2Ldv3yIDz8uKR48e0djYmHp6evzjjz9oaGgoCdgvKmC9vMjJyeG6detobGzMBg0asFatWmzevHmRJlwyT7Pu378/FRQUaG5uzk6dOlV4DrKGSCSii4sLly5d+rWnIsG3XKmh8ohvl2qxxHdnAVi/JqggDwJ5Zk/xtiLaRqiTT4rew/iObwv5TgcPHjygpqamVLGUkyZNYkBAgMzmkJubyxYtWlBJSalSap2dPXuWurq6hTyJAwICGBQUVGr/8+fPs0GDBpw7dy6FQiF//PHHMmtKiYmJ/Pnnn2loaMi2bdtyxYoVHDNmDKtUqUJVVVW2aNGCc+bM4eXLl6XyPhSLxfztt99YpUoVamlpsU2bNtyyZUup5uG5c+dST0+PWlpadHJyorGxMY8dO1ama/kcaWlpVFBQ4IABAzhv3rwCpZFKCliXFiKRiLt27aKlpSUdHBxoZ2dHe3t7Hj16VCq5+fvF8vLytLe358aNG78ZJ7nQ0FA2adLkm/I4/ZYrNXxx4hNtBWsIwNneYOYm8F0Y6FkfHO9RuG3KBnnOHGhLHx8fBgYGcsaMGVy5ciV37drFU6dO8caNG4yPj/9XFD78/4558+bR39+fixcvltQyKw1v376llpaWzCpSnDt3jkKhkC4uLvTx8ZHpIhAfH09DQ8MiF/fXr19TW1u71NCaJ0+e0NjYmGReYPbYsWOpra3NqVOnSq0x5ZsvfXx8WL16dVatWpUCgYAtW7ako6NjuawiR44cYatWrZiRkcGIiAh26NCBmpqa9Pf3LzLd37x582hhYcGXL1/y2bNndHNzo6WlJQ0NDdm/f/9y52mMi4ujnJwc//zzTwqFwgIV4F1dXaUKWC8KYrGYR44coYODA21tbdmoUSOamppyy5YtZXpGwsPD2bdvX06ZMoWurq5s3749hUIhx48fL1VYVWXhyZMnhe7Xt4BvuVJDJZo69YskvoSwPC0vOfx/x/YH5Zk7P2+bu1ONV4/O586dO7l8+XJOnz6dAQEB9Pb2ZsuWLWlnZ0c9PT1WqVKFGhoaNDc3Z+PGjdmxY0cOGDCA48eP54IFC7hhwwYePnyYFy9eZFxcHJOTk7/vDcoQYrGYFhYWvHDhAuvXr8+TJ09K3TcoKEgqbak03Lhxg7q6uoyKimJGRgbbtGlDf39/mYQwZGdn09nZucQ0TMHBwYXCCz5HVlYWFRUVCyy2T58+pZ+fH3V1dblkyZJCeyL55stRo0bR2tqaAoGAvr6+EvNlfs5INzc3Vq1alW3bti1zMuLx48czODi4wLH4+HguWLCANjY2NDMz48yZM/n48WPOnz+ftWvXLrDXKBaLuWbNGgoEAjZt2pQGBgblyg3p5eVFgUDAH3/8scD+b3kC1vMRHR3NJk2a0MLCgq1atSr2PkuDq1ev0tbWlu/fv5fspcXFxXHs2LEUCAT08PDgsWPHZBo2UxrEYjHd3NxkWohXVviWKzXIkSQqA38GAH+vA5hb6JTZaGBIa2CcB5CaCQxYC6gqAdsDC7bLzJHHXrkwdOzsAw0NjWKHEovF+PC6kKsZAAAgAElEQVThA969e4eEhASpfmdmZkIoFEJHR0eq3wKBAEpKSrK+S/8vcPbsWQQEBCAyMhKurq54/vw5FBQUpOobHx8POzs73L9/Hzo6OuUa//Hjx3BxccGiRYvg6+sLAEhLS4ObmxsaNmyIJUuWQE5OrlyyASAoKAgPHjzA4cOHIS8vX2Sb1NRUWFpa4siRI6hfv36xsvT19XHt2jUYGhoWOH7r1i1MnjwZN2/ehL+/PxQUFPDbb7/h8uXLaNiwIVxdXeHm5gZHR8di7+3Jkyfh6ekJJSUl9O/fH6NHj4aZmVmp19e4cWPMmzcPLVu2LHSOJK5evYpNmzZh48aNIIk5c+Zg8ODBqFatWoG2T548gb+/P96+fYu0tDQ0bNgQK1asgJ6eXqlzOHPmDPr16wddXV08ePAAN2/ehKmpKQDA398ftWvXxuTJk0uVk4/Lly9jypQpePDgAWxsbBATE4Nhw4Zh3LhxJa4lJSE7OxuamppISEjAvHnz8OrVK6xbtw4AkJ6ejl27diE0NBQfPnxAQEAABgwYAG1t7XKNJS02bdqE5cuXIyYmBoqKipU6VlkhEomgpaWFJ0+eVPp9KCsqj/g+3geOOwKijEKnrj8BRm8DbjwFFOSBVjZAqB+g+8nzmCuWw4Yz8pj1qwGSkpLQpk0b+Pr6olOnTqhevXqFp5eZmYn3799LTZTv37+HmppamchSXV29QgvuvwX9+/eHg4MD3r9/j6ysLPzyyy9l6j9s2DBoampi7ty5ZR77zZs3cHZ2RlBQEIYNG1bgXHJyMlq1aoVOnTohJCSkaAHp6UByMqCoCGhq5v3+BLt27cKUKVNw5coVaGlplTiX1atXY+/evTh58mSxn7uTkxNWr16NH374QXLs+fPnOHnyJKKiovDrr78iOzsbqqqqGD58OMaOHVum533Xrl2YOnUqunbtio0bN6J169YYO3YsGjVqVGT71NRU6OvrIyEhASoqKsXKXbRoEVatWoWJEyfi8OHDOH/+PLp06YL+/fujefPmkhcCsViMVatWITg4GA4ODoiNjcWSJUvQq1evYu9JbnYuutTpAhcFF3x89BFVWAVVVauiukF11OlfB31+6YM7f9+BQCAo9fpv376NadOm4dKlS2jYsCH++OMP+Pj4YNq0aTAwMJDiDpaMBg0aYPny5ahTpw4sLCxw5coV1KpVS3KeJGJiYhAaGoojR47Ay8sLgYGBJb4MlRevXr2Cg4MDoqKiUK9ePZnLlwVatWqFiRMnol27dl97KgVQecQHACcaAe//LF9fBRUkN4nG+t3nsXLlSpCEhoYGnjx5grZt28LX1xceHh5QU1OT7ZyLQb5WmU+ElaFVCoXCb+6trTQkJyejZs2auH//Pho1aoQDBw6U+Uv45MkTODk5IS4urlRy+RQfPnxAq1at4OnpieDg4CLbJCQkoHnz5vD398f48ePzDqanAxERwPz5QFwcoKQEkEBuLuDmBowfD7Rogdt37qBly5Y4efKkVNeUk5MDOzs7LF++HO7u7kW26dy5M3x9faGhoYGoqChERUUhISEBbdu2hZubG1xdXWFsbIxDhw5h0qRJ0NXVxfz584slrqLQs2dP6OrqYs6cOdiwYQOWLFkCIyMjjBs3Dp06dSqgMUZFRWHWrFk4d+5csfIWL16MVatWITo6GjVq1ACQ98Kxfft2bNy4Eampqejfvz/69esn0TDj4uIwYMAApKWlIS0tDZaWlggLC4ORkZFELknELI3ByWknkZ2RjSriKoXGpiIBMeDYzxHtV7SHklrRVpdHjx4hODgYx48fR4sWLXDhwgW4uLhg9uzZsLCwkPrelYYhQ4agbt26GD58OKZNm4bXr18jPDy8yLZv377F+vXrERYWBkNDQwQGBsLb2xtVq1at8DxIwsvLC7a2tpg9e3aF5VUWfvrpJ6ioqGDGjBlfeyoFULnEl/gXcNIFEKWVrZ+CKmDmBzQMBZBHOidOnEBoaCguXryIBg0aICMjAzdv3oS7uzt8fX3Rvn37Et9YvwbKq1WWhSi/tla5evVqREdHY8SIERg2bBhu3rxZrvn4+/ujZs2amD59ulTtMzMz0b59e9jY2GDlypUljvnixQs0b94cEydOxNCMDGDqVEBODkhNLdxYTg5QU4NISwu+cnLwnDUL/fr1k/o69u3bh5CQEFy7dk2iBYlEIvz111+IiopCaGgo3r17h6ZNm8LNzU1ivizKhJqbm4vNmzcjODgYP/zwA+bOnQsrK6tS55CYmAgHBwds3LgRbdu2RW5uLvbv34+FCxciKSkJY8aMQf/+/aGqqoopU6ZATk6u2MVzyZIlWLlyJU6fPi0hvU9BEn/99Rc2b96MHTt2wMbGBn5+fujevTtUVVWxbNkyzJ07F40aNUJMTAzmzZuHgQMHAgQODDiAO3vuIDe98HbI51BQVoC2mTb8zvhBVagqOf7y5UvMnj0bkZGRaNOmDa5duwZTU1PMnz8fDRo0KFVuWbFmzRpcunQJGzduRGJiYpFa3+cQiUQ4cuQIQkNDcePGDfj7++PHH3+UmHLLg8jISAQHB+Ovv/6SCZFWFvbv34/w8HAcO3bsa0+lACqX+AAg/gRwzgsQpUvXXkEVMGgHuOwG5AovBo8ePUJYWBg2btwIBwcHWFtb4+7du7hy5Qo8PDzg4+ODdu3afdMPQ3H4XKuU5ndWVtZX1SobNGiAOXPmYN++fTAzM8PEiRPLJefBgwdo1qwZHj16VKppTyQSwdvbG4qKitixY4dU+4l/x8XhTL166JubC8WsLKnmlFWlCqoePZqnBUoJkmjWrBl8fHxQvXp1REVF4dSpU9DV1YWbmxuSkpIgEAiwePFiqWVmZGRgxYoVWLhwIby8vDBjxoxCe4Sf4+TJkxg4cCBu3Lgh0aJJ4vz581i0aBEuXLiAH3/8ESdOnMDs2bPh6upaSMbSpUuxYsUKREdHw8TEpNR5Zmdn4+jRo9i8eTNOnz4NT09P+Pn5QV9fH/7+/iCJjIwM6OrqYpDRIDza+wg5aTlS3wd5RXno2Ohg4MWB+JD2AfPnz8eGDRvQtm1bPHz4ECQxf/58uLq6VtrL4OXLlzFo0CDcuHEDADB16lS8efOmWK3vczx48ACrVq3C1q1b4ezsjMDAQLRt27bYveOi8O7dO9jb22P//v1o3Lhxua7jS+Hly5dwcHBAQkLCN7XtU/nEBwDvYoCzXYHcVCA3peg2Cip55iarEUC9eUWS3qfIyMiQbCYnJSWhT58+qF69Oo4cOYKbN2+iU6dO8PHxgaur6/9rp5TMzMxCpldZapU6OjqoXr16kQ/tX3/9hS5duuDu3buoUaMGrl+/XqRWIC169eqFevXqYcKECcW2IYkhQ4bgyZMnOHLkiPQvOHPnQjxrFuQzM8s2KTU14Nw5wNGxxGapqak4ffo0oqKicOjQITx//hzdunVD+/btJeZLANi8eTNOnjyJbdu2lW0eAJKSkjBv3jysW7cOQ4cOxYQJE6CpqVls+xEjRiAxMRHbt28HADx9CmzbBjx+DLx+/RGPH1/GnTvr0KePGiZPHgtra2tJ32XLlmHZsmWIjo4ul2by9u1b7NixA5s3b0ZiYiL69OmD7OxsbNq0Ce1s28H0jCmu4Rqu4zre4i3sYIeu6FpIzul//vVFX5jDHArKChA1EmF57HK0adMGCQkJePr0KebMmQNfX98yEUh5kJmZCW1tbbx//x4qKip4//49LC0tcfXqVdSsWVNqOWlpadixYwdCQ0ORkZGBgIAA+Pn5lfh55qNPnz7Q1dUt08vT14SxsTFOnz6N2rVrf+2pSPBliA8AKAZeRQF3FwIJ5wF5JQByAHMARU2gzhjA3B+oWvoGdgGxJP7880+Ehobi8OHD6Nq1K3x9ffHgwQNERETg7t27kn2V1q1b/+v20GSN8miV2dnZEm3xU0KMiYmBjo4OrK2t8dtvv2Hr1q0V0ipv3bqFtm3b4tGjR1BVVS2yzZQpU3Dy5EmcOnVKeqePV68AMzOgCNJLBDAQQBQAIYCfAfT6vJGDA3D9eoFDIpEI165dkzilXLlyBT/88INkny4kJAQuLi4YN25cgX6nTp3C7NmzER0dLd3ci8CLFy8QHByMQ4cOYeLEiQgMDISysnKhdunp6ahfvz66dVuDixdb4OJFQCwGsrP/10ZePg2KigpQUFiHJk0uY+rUAbhx4waWLl2K06dPV8gcl4/r169LTKFGRkaod68eamTUwD3cgxzk8Df+Rg5yChFfIhKxC7uQjnR0RVeYwxwAkKuYi9h2sbj05yVMnToVQ4YM+aIvt/Xq1cPatWslDkpTp07F27dvsXbt2jLLIokLFy4gNDQUx48fh7e3NwIDA+Hg4FBk+yNHjmDUqFGIjY0t9jvyraFbt27o1q0bevUq9M36avhyxPcpcj4CmQmAOBtQ0gSU9UrV8KRBQkIC1q9fj9WrV8PAwACBgYFo0qQJDh8+jIiICPz9998SYmzRogWqVCm8mf4dhVGUVhkfH4+pU6eie/fuiI6OhpqaGhQVFZGQkIDExMRya5VeXl5o2bIlRo4cWWgeS5cuRVhYGM6dO1e20IcZM4AFC4okvp4AxADWA7gOwAPABQC2nzZSUQFiYvBMQ0NCdKdOnYKenp5kn6558+YFHK3u3r2L5s2b4/79+wVcue/fv4+OHTvi4cOH0s+/GNy5cweTJ0/GtWvXEBISgr59+xYw+4rFQI8eb7B7dzUAJTuBKSkRCgrZUFbuirS0U1i4cCECAgJk+qKYk5ODQzsP4eaAm5AX/+/7fgqn8BEfCxHfNmzDD/gBR3EUnvCUEF82sqHkq4Qx4WNk4uFdVgwcOBANGjRAQEAAAJRb6/scr1+/xrp167BmzRqYmpoiMDAQ3bp1k5D6hw8fYGdnhy1btqBVq1ayuJQvggULFuDly5dYtmzZ156KBF+H+CoZIpEIR48eRWhoKK5fvy7ZTCaJ3bt3IzIyEs+ePUO3bt3g6+sLZ2dnqePOviMP27dvx9atW7Fz507UrFkTT58+lZhpxGIxkpOTS9Qki9Mq1dTU8OzZM3h6ekJPT09CjHfv3sWuXbuwbds2ODg4QCAQSLco5+YCurpAUlKhU2kAtADcAmD5z7G+AIwAzPuknUhODgeqV8ePSkpwdXWV/OSbL4vDkCFDoKGhgYULF0qOpaamQldXF2lpaTLb87hw4QImTpwoMYV6eHgAkMPgwcDOnXlOrNJCTi4dU6acxLlzS/Do0SOMHj0agwYNgrq6ukzmenXtVZwIOoGc9P/t7RVFfLdxGzdxEz3RE0uwpADxAYCFhwV6Hfk6GkT+uvLpvt6UKVOQkJBQLq3vc+Tm5uLQoUMIDQ3FnTt3MGjQIAwdOhQhISGQl5dHWFhYhcf4kjhz5gwmTpyIS5cufe2pSPD/kvg+xYMHD7B69Wps2bKlwGby48ePERkZicjISLx58wbdu3eHj48PmjZtWun7BP8f0KpVKwwbNgzJycmIiorC7t27KyTvU60y32W8bt26ePfuHa5evYpTp07B3t4eGRkZEq2yWrVqpWqSpm/ewGbUKMgX4cH5F4CmAD6NNP0FwBkAhz9rm1u9OuSTk8v0bMTHx8Pe3h5//fVXAecQTU1NPHr0SKZBvSRx5MgRTJo0CVpaWnB23ojly2sjPX0lgE0AYpGn3276p0c28oy6VwA8BRANoOU/8yNiY1Pw8OFVrFy5EtHR0ejZsycGDBgAAwMDiMXiAj8ikUiqY2KxGH9v+BuPNjyCHP5H+p8TXxaysAZr0Bd9oQWtIolP31EfQ68Nldn9KwsuXbqEYcOG4dq1a5Jj+VpfvleprHD37l2sWrUKmzZtgkgkwq5du9CpU6dvylGkNKSmpkJPTw+JiYnfjNPh/3viy8enm8np6ekYNmyYZDP5wYMHiIyMREREBJKTk+Ht7Q0fHx80atToX/WAfSnExcWhadOmeP78Odzc3DBmzBh07txZZvIvXLiAPn364P79+7hy5Qo8PT1x6NAhNGnSRNJGGq0yISEBFnFxmBUXB/UiHvNzALwBvP7kWDiA7QBOf9ZWJCeHCUFBZV70b9y4gfT0dDg5OUmOR0dHw8HBAdWqVSsTaUjTViQSIS0tHSkptwDUALAPgDyAE8ij+E3/XFE2gFUAGvxzF3Yin/iAVCgpTYOKykbIy8uDJLKyspCZmQllZWVUr14dysrKkJeXl/woKCgU+H9xx8yfmsP8pTk+xefEdwInUBVV0fKf+RRFfDq2Ohh2q2DCgi+FjIwMCAQCJCUlFVjIp0yZgnfv3mHNmjUyHS8tLQ12dnbo0KEDzpw5A5FIhGHDhqF///4y08QrGw4ODggPDy+QuOFr4j9DfPkobTP5zp07EhLMyMiAj48PfHx84OTk9J0E/8HkyZORlZWFkSNHwsnJCfHx8TJ3LmjdujXatm2LZcuWYdOmTWjfvn35BB09CvTuDXz4UOjUXwCaAfjUErgIeaRXSOMD0KJJE2hoakJdXR3q6urQ1NSEhoYGNDU1UbVq1SIX/czMTAQFBWHatGkwMzODgoICgoOD0aVLF/zwww/FEkRZyOTzYxcvKqNfP02kp3+qnU4F8AL/I75PYQxgG/5HfICpaZ7356ePfHJyMsLDw7Fs2TJYWVlh3LhxaNeuXZm+F7M6zELur7mQZ/F7fKuxGh/xEfLIa5OOdFRFVTj/8w8AkqonIaNnBuzs7GBvbw97e3upMrvICvb29ti0aROcnJwkxypL6xs9ejTev3+PrVu3giTOnj2L0NBQnDx5Ej169EBgYCDs7OxkNl5lYMiQIbC3t8eIESO+9lQA/AeJ71PkZ11Ys2YNatWqhcDAQHh5eUFJSQkkcevWLURERCAiIgJisRg+Pj7w9fWFg4PDf5YEc3NzYWJigt9++w0HDx7Es2fPsHr1apmPs3PnTvTr1w8bNmxA3759yy/o0iXA3R34+LHQqfw9vtsA8nN79ANgiIJ7fACQraKCsHnz8Pr1a7x586bA77dv36JatWrQ09ODvr4+9PX1JX/r6enhzz//xI0bN7Bnzx7o6upiyJAhaNKkCQYPHlz+6yoBnTsDhw/nRQf9D2UjvmrVgBMngKZNC7fOzs5GZGQkfvnlF+Tk5GDs2LHo3bt3iWasrKws7Nu3D5P7T0bv3N5QpCJEEEEMMc7gDD7iIzqhE+QhjyxkQQSRpG84wuEOd9RGbVRFVVRRroIafWogpV4KYmNjERsbi1u3bkFNTQ329vYFyNDGxqZSElv4+fmhadOmGDJkSIHjkydPxvv372Wm9V24cAHdu3dHbGxsIWKPj4/H2rVrsXbtWlhYWCAwMBBdu3b9Jj3X169fj+jo6HKF8VQG/tPEl4/c3FwcPHgQoaGhuHv3LgYPHoyhQ4dK0iuRxPXr1xEREYHIyEgoKipKSNDW1vY/RYKHDx/Gzz//jD/++AO2trYIDw9Hs2bNZDpGQkICmjVrhpycHMyfPx8+Pj7lF5aVlefcUgTxAUAPAHIA1iHPq7MDivDqlJcHvL2BXbuKlCEWi5GUlFSADD/9Oz4+HmfPnoWamhpSUlKgpKSEatWqoW7dusWSpb6+PgQCQbmcrszNgUePPj9aNuJTUwOWLwf8/YsfhyR+//13/PLLL7h+/TqGDx+OH3/8UbJAv3v3DseOHcOhQ4fw22+/oWrVqrCytELHpx2R8TwD0YjGGZwpILMFWqAVCnosfm7qrKJcBaOfjYaazv88VUni+fPnEiLM/3n48CFMTEwKkKG9vT3Mzc0r5NC2fPly3Llzp5Cjybt372BlZSUTrS8zMxOOjo6YNWsWunfvXmy7nJwc7N+/H6tWrcLDhw8xePBgDBkypNQkB18SsbGx8PLykok3syzwnfg+w507d7Bq1Srs2LEDrVu3RmBgIFq2bCkhN5K4cuWKhASrVasGX19f+Pr6ok6dOl959pWPzp07o3PnznB0dISXlxcePXokU+JPSUlB69at4e7ujqZNm+Knn37C9evXK+ZwNGECsGxZwQC2f5AIwB/ASQAC5Gl6hXwFVVWBM2eACqTAioyMxPz583Hp0iUsXboUFy5cQEBAQLFk+fr1a3z48AFCobAAGRb3t5aWluRz0NcH3rz5fAZlI76qVYF584DRo6W7vlu3bmHRokXYv38/bGxskJ2djYcPH6JNmzbw9PSEiYkJevXqBU9PT1xddxXt2A5KKLt5nCAsPS3R66B0Hp05OTl48OBBATK8desW3rx5A2tr60KEqK+vL9Xz/Mcff2D06NG4fPlyoXOTJ09GYmJihb0vp0yZgnv37mHv3r1S97l16xZWrVqFXbt2oW3btggMDETz5s2/+st5fqWGx48ff1GTdHH4TnzFICUlBVu3bkVoaF6+0GHDhqFfv34F4obEYjFiYmIQERGB3bt3QyAQwNfXFz4+PjJNjPut4NWrV7CxscHz588xY8YMqKqqYtasWTKTn5WVBQ8PD5iZmUlMRU5OTggODoanp2f5BT99CtSpU2Qcn1SoUwe4e7f84yPvhalRo0YYPXo0NDQ0sHLlShw/frzEPjk5OXj79m2xxPipyTUtLU1CgrdvH0ZGhv5n0squ8S1bBgwcWPJ15ebm4vz58zh8+DAOHTqE1NRUGBoaIi4uDq1atcKECRPQpEkTWFlZ4enTp3l7kmIFDM4dDA1qQI5lW5Bz5HJwstZJhO0Jg2Mp2XRKQkpKCm7fvl2ADGNjY0GyEBna2dkVihfMD0v58OFDIdNivtb3uTdvWXDt2jW0a9cON2/ehL7+559l6fj48SO2bNmC0NBQKCgoIDAwUJLd6mvhW6rU8J34SgFJnDlzBqGhoTh16hR69uyJwMBA2NjYFGgnFovxxx9/IDIyEnv27IGBgQF8fX3h7e0tVU20fwPmzZuHv//+G6tXr0aNGjUQHR0tMy1XJBKhR48eEIvFiIyMlJih9u3bh3nz5iEmJqZib63jxwOrVpUtqA3IC16PigKcncs/9j84ffo0BgwYgIiICAwcOBCxsbEVlpmPzMxMvHnzBm/evMGPP5rir7/ya+Dl/vMzE3nEFw6gyj8/WQAIoDaADQCaA6gKQA7KyjnYsycbHh6FA9+Tk5Px66+/4vDhwzh+/DjMzMzQqVMneHp6ol69epCTk0NaWho2btyIOXPmICEhASKRCE2aNEFsbCy0tbVxPPI4jnkeQ9r7NHyynVci5JTksDV3K0SmIiQnJ+Onn37C2LFjZRaDSxJv3rwpRIZ37tyBjo5OATK0t7eHt7c3du7cWWTljkmTJiE5Oblc+985OTn44YcfEBQUVKYE6cVdU3R0NEJDQxEdHY1evXohMDCwQHq6L4VvqVLDd+IrA168eIG1a9ciPDwc1tbWCAwMROfOnQtlgBGJRDh37hwiIiKwd+9e1KxZU+IdWt43wK8NkrC0tMS2bdvw4cMHTJkypUgzT3llDxs2DPfv38exY8cKpN8Si8Wwt7fHkiVL4FaGZNGFIBYjvk0baJw+XUoOk0/mpaICuc2b8/b3ZISOHTuicePGWLx4MRITE2UmF8gr77Rjxw6EhT3BixeLQVYDEIw80vsUM/45XhN5MXyf4vE/x+MBGENRsQr09PSgp6eH3NxcSZiIlZUVnJ2d0aFDB8le5afOLbdv30aPHj1w69YtyfcjXzM6d+4cnJyccP/qfYQ2CoVuVV2I0otnP6VqSpBTkEPvY72RLkhH06ZNkZWVhVq1akEoFGLz5s2V+r0SiUR49OhRIUJ8+PAh9PX10aJFiwLaoampKd6/f19urW/OnDk4f/48jh07JlMT5YsXL7BmzRqsW7euxPWrsvBNVWqoYAX3/ySysrK4c+dOOjs708jIiCEhIXz16lWRbXNycnjy5EkOGjSIAoGAjRs35pIlS/jixYsvPOuK4fTp07S1taVYLGbfvn25dOlSmcmePn06HR0d+eHDhyLPb9++nS4uLhUa4/bt29QRCvm0Xz9SWZlUVSXzHB8L/eSqqjJVWZnTmjalWCyu0LifIzY2lrq6ulRSUmJaWlqF5SUmJnLNmjV0cXGhQCBgQEAAz537g3p64uIur9QfObk09ujxJ7du3comTZpQUVGRcnJyNDY2ZqdOnThu3DgOHDiQHh4edHJyorGxMRUVFampqcnatWtTS0uLAKiurk41NTWqqKhw/vz57NixIw0NDSkQCDhp0iQ6OztzVsgsxkXFcUvbLZytPJshqiGcVmUaf9b4mXNU53BFnRX8a+NfzE7PllxzcnIy69atSxUVFTZs2JBCoZA7d+6s8L0sK+bPn09vb29u2rSJ48aNo7u7Ow0NDVm9enU2adKEDg4OdHZ2ZnR0NN+9eyeVzNu3b1MoFPLp06eVNu+srCzu2LGDzZo1o7GxcYnrlyzx8uVLCgQCmX+nyoPvxFdBXL9+nUOGDKGmpiZ79OjBc+fOFfvBZmdn8/jx4/Tz86OWlhadnZ25YsWKL/LQVRR9+vThkiVLmJqaSg0NDb5+/VomclesWMHatWuXKC8nJ4e1a9fmmTNnyjXG27dvWatWLW7evDnvwPv35MKFpL4+qaJCqquT1auTSkp8amjITZ07MzM1lQ4ODly3bl25xiwJAwYMoKamJh88eFCu/pmZmdy7dy+7du1KdXV1ent78+DBg8zKypK0WbpUTEXFrHIRX5UqaVRQ0KGKigr79evHS5cu8datW5w5cyZtbW1pbGzMoKAgXrhwgSKRiGTeYtq3b1/Ky8tTQ0ODJiYmFAqFVFJSYufOndmqVSsqKChQR0eHVapUYZUqVQiABgYG9PDw4JgxYzhvyjwObz+cPnY+PLHyBO+cvsPc3Nxi74GXlxf19PRoYGDAGjVqsHfv3kxOTi7XPS0Pzpw5w0aNGhU6/v79e54+fZo///wzq1atSicnJ6qrq9PAwIBubm4cO3YsN23axCtXrjA9PV3SLzc3l5SaVOoAACAASURBVI0aNeKqVau+2DVcv36dgwcPlmr9kgWMjY358OHDSpMvLb4Tn4yQlJTEpUuX0tLSknXr1uWaNWuYmppabPvMzEwePnyYffv2paamJlu2bMnVq1fzzZs3X3DW0iEpKYkaGhpMSEjg9u3b2a5dO5nI3blzJ42MjPjo0aNS265fv56urq5lHiMzM5PNmjXj5MmTC58Uicjnz8mbN8l798h373jv3j3q6+szJyeHsbGxFAqFjIuLK/O4JeH58+esUqUKIyIipO4jEol49uxZDh48mNra2mzZsiXXrVtX5EKflZVFf/+B1NA4QWVlUZlIT14+gyNH7uHff//NLVu20MjIiD169Ciggdy6dYszZsxgnTp1WKNGDbq4uFBJSYkqKip0dHSkkZERw8PDGRAQwMDAQJLkyJEjOXLkSJJ5Wq+2tja3b9/O/v37U0NDg1ZWVvT29maDBg1oZGREOzs7CoVCVqlShXp6enRwcKC7uzv79evHCRMmcPHixdy6dSu7d+9OfX19CoVC2tra0sTEhGfPnq3gJyQdPnz4QFVVVebk5BTbZuLEiQwICKBYLOaTJ0945MgR/vzzz+zVqxft7e2prKxMS0tLduvWje7u7rSxseHdu3eLJfzKQlnXr/KiW7du3LZtm8zllhXfiU/GEIlEjIqKYufOnamtrc1Ro0bx/v37JfbJyMjg/v372bNnT2poaLBt27Zcu3at1OaRykZoaCh9fHxIku3bt5fJg3vixAnq6ury5s2bUrXPysqiiYkJL126JPUY+WbZbt26STQTadC4cWMeOXKEJLlkyRI2bty4xMWtPLC2tmbz5s1LbXfnzh1OnjyZpqamtLW15bx580o0gyUkJLB58+b09PRkYmIKe/Ui1dSkMW/mUk4ulZGRiQXkpaamcvr06dTW1ubUqVOZkpIiObdv3z6JWVNJSYny8vJ0cXHh2bNneevWLQqFQiYkJPDJkyfU1tbm69evmZ2dTScnJ4aFhUnkZGRkcN26dbS2tqaBgQE7duzI7Ow802Z2djZfvnzJq1ev8tixY9ywYQPnzp3LUaNG0dfXly1btqSuri7l5OQoJydHeXl5KigosHbt2vTz8+PkyZO5bNkyRkRE8MyZM7x37x6Tk5NlptVYWlqW+Ay/ffuW2trafPbsWZHns7OzeevWLS5dupQqKips3bo1a9asSVVVVTo5OdHPz4+//PILT5w4wfj4+Eo3E5Zn/SoLFixYwBEjRshMXnnxnfgqEU+ePOGkSZOoq6tLV1dXHjhwoNQ3ubS0NO7evZve3t5UV1dnu3btuGHDBiYmJpbYrzLh6OjIqKgovn79mhoaGhV+E7x06RKFQiHPnTtXpn4rV65kp06dpG4/Z84cOjk5lXkvLSwsjN27dyeZtxC0bduWISEhZZJRGoYPH041NTXGxsYWOhcfH8/Fixezfv36NDQ05Lhx43j9+vVSF73bt2/TzMyMEydOlBC9WExu3ZpFM7NkKihkEcguQHhqamKqqJD+/uTQob+wXbt2Rb4kPH36lL169aKhoSFnzpxJS0tLAqClpSW1tLQ4btw4nj17llOmTKGFhQVVVFTYqlUrXrlyhX5+fpwyZQrJvP3cDh06FHktIpGI3t7eNDMzo7GxMRcsWCC16XLnzp0UCoUcM2YMtbS0qKenxxo1anDUqFEMDAxkt27d6OzszNq1a7N69epUVlamqakpGzVqRE9PTw4ZMoTTp09naGgo9+7dy/PnzzMuLq7UZ71Hjx7ctGlTiW0mTJjAgICAYs+LRCK2atWKv/zyi+TYx48fefHiRa5du5YjRoxgy5YtKRAIKBAI2LJlS44YMYJr1qzhhQsX+PHjR6nuUVlR1PpVsRdAMa9dC+WePTok25NsQ9KH5HqSFd/vLgu+E98XQGZmJrdu3crGjRvTxMSEc+fO5du3b0vtl5KSwl27dkn2cjw8PLhly5ZinUAqA1evXqWpqSlFIhGXLVvGvn37VkjenTt3qKenx8OHD5e5b3p6Og0MDHj9+vVS2+7evZvGxsZ8+fJlmcdJSkqiurq6RON+8eIFdXR0GBMTU2ZZxWHp0qV0dnamh4cHybzPesuWLXRzc6Ompib79+/P3377TWqT1/Hjx6mjoyNZhF+/fs3169ezc+fOVFdXZ/PmzTlmzEb26/eenp5k27akry8ZGkrmr5vZ2dls3LgxFy1aVOQYr1+/Zr169QiAioqKVFdXp7+/fyFt5vjx4zQxMeHEiRNZo0YNysvLc/To0dy0aRN1dXUZHx9f7HUEBgZy+fLlvHr1Knv16kVtbW0GBQXxyZMnpd6D33//nTo6Oly7di27d+9OPT09qqurc/Xq1YWINjU1lX///TcvXLjAffv2cfXq1ZwxYwaHDh3KLl26sHHjxqxZsyaVlZWppqZGc3NzNmvWjF5eXhw2bBhnzpzJ/2PvrMOiSvsG/KCEdEzQrSKoKCiK7SoGKBZ2rq2gYGC3a8eqa7euitidr2u32GususYaWIiCgAJz7u8PlnlFhhR399vX+7rOpXPyOYeZ83t+vXjxYr7//ntatGjBw4cPSUpK0jiuly9fYm5unqXWt2jRIipUqJDj31qSJKKjozl48CAzZ87k+++/p1y5chgYGODk5ETDhg0ZNmwYERERXL9+Xa01fymfv78mTpyYR5dMCrAEcEWSDElNFaSJnvTFCDAEQgDNz6ig+Sb4/mKioqLUwQ0dOnTg7NmzuTJfvHv3jrVr1xIYGIiJiQmNGzcmIiIig+npa5D+Iwfw8fHhwIED+T7XH3/8gYODw3+DTPLBjBkz1GbXrLhw4QJyuZyLFy/m+zqtW7dm3rx56s8bN26kWLFiBeb32Lx5Mw0aNMDS0hI/Pz9MTU1p0KAB69evz5OGKkkSc+bMwcrKitWrVzNx4kR8fX0xNTWlRYsWrFmzJk8m8/v376NQKIiKilKv+/DhAx06dEBLSwuZTIZSqaRcuXJYWlrSsmVLHjx4oN43JSUFDw8Ptm/fDqT5dEJDQxk4cCA6OjpYWloybNgwLl++rPF737FjR1auXKn+/OjRIwYOHIiFhQVt2rTJMC5NXL16FTs7O2bNmkVkZCQymQxLS0sCAgLy5T+XJIl3795x584djh8/zsaNG5k7dy4jRoyga9eu+Pr6YmhoiL29Pbq6ump/ZfXq1WnZsiWhoaFMnDiR+vXrExAQQFRUFE+ePFELpT/++AO5XM6vv/6a57Glk5qayp07d9iyZQtjx44lKCgINzc3ihQpQqlSpWjTpg2TJk1i165dPHz48IvMpRcvXqRLly6YmZnRrl07Tp8+ncP53pOm2RmQUdhpWnQAU6DgJphZ8U3w/U28fv2a6dOn4+LiQrly5VixYkWGCK/siI2NZdWqVfj7+2NiYkJQUBAbN24scGd0QkKC2j/xadBHfnj9+jUlSpTIYM7JD/Hx8SgUCm7duqVx++PHj7G1tWXbtm1fdJ39+/fj4+OTYV2HDh3o2bPnF51XkiQuXLhAy5Yt0dbWxtXVFQcHh3xFyb5//54GDRogk8mwtbXF0dGRvn37cvDgwQwRnnklMjKSokWL8u7dO8aOHYuuri4GBgbY2tpStWpVTp48qb7+2LFjsbCwYPjw4cTHx7NgwQK+++479X3a2NiQkJBAcHAw7dq1IyoqisGDB+Pk5ESxYsUYMWIEV69eVb88mzRpwpYtWzKN6e3bt8yYMQN7e3tq1qzJrl27svTbPnr0CHd3dwYOHMjTp08JDAxEoVAgl8vZs2dPvp+LJmJjYzE0NCQ1NRVJkoiJieHmzZscPnyYiIgIZs2axZAhQ2jdujU6OjqULFkSa2trtLW1kclkGBkZ4ezsTNu2benfvz9Tp05l9erV7N+/nytXrvD8+fN8B7okJiZy8eJFVq1axcCBA9XpFiYmJlSqVIkePXowd+5cjh49SkxMTJ7OHRMTw4wZM3B1dcXLy4tly5ZpmLAlA1WBIuQs9D7XAHO26nwJ3wTf34xKpWLPnj0EBAQgl8sJDw/n999/z/XxMTExLFu2jLp162JqakqrVq3YsmVLroVodvz888/qCM5Ro0bRv3//fJ0nPj6eihUrMmTIkC8eE8CECRPo2LGjxuuULVuWqVOnfvE1UlNTsbGxyTATf/v2LU5OTvky096/f58ffvgBNzc3XF1d6d+/PwqFApVKhbe3d64jPGNiYlizZg2NGzdGW1sbMzMzRo4cybVr1wo08KFmzZro6uqira2NnZ0dJUuWZOfOnRqv8fjxY9q3b4+VlRUmJiZqTbtu3bosWLBAbfqMjY1VHyNJEufPnyc8PBwHBwfc3NwYNWoUFStWzNaqkJyczLp16/D29qZEiRIsWbJEo4kxJiaGKlWq0KZNG5KSklizZg1mZmaYmprSs2fPAsmhTMfV1ZUbN27kuN+gQYMIDg4G0n738+fPp1ixYuzdu5c1a9Ywffp0Bg4cSPv27fHz86N06dLq9A9LS0s8PT2pW7cuHTt2ZNCgQcycOZO1a9dy6NAhfv31V169epWrIK7Xr19z9OhR5s6dS8+ePalcubLGdIuLFy/m+B5RqVTs3btXPQEbMGDAJ+kK4YA+nwu2GjUEenoCQ8O0pXhxTcJPAXzI8V7yyzfB9w/i3r17hIeHI5fLCQgIYM+ePXmKRnz58iWLFi2iVq1aalPEjh07+PAhf1+gGjVqsHnzZiRJwtnZOV+mw48fP1K3bl26dOlSYC/mt2/fIpPJMkwQVCoVjRs3pnPnzgV2naFDhzJo0KAM644fP46VlVWuzGYxMTEsWrSIqlWrIpfLCQ4OVpuGUlJS0NbWJjk5mUOHDuHq6pqllvbbb78xY8YMqlevjrGxMbVq1cLS0pJevXoVeNj72bNncXFxQQiBtrY2FhYWrFy5MlfXadu2LQqFAm9vb+bMmYOLiwvPnj3D1taWX375JcvjJEni7NmzDBw4EF1dXZycnBgzZky25j9Jkjh8+LDaXDxu3DhevXqVYZ/ExESaNWtGrVq1ePv2LU+ePMHPzw9zc3OcnJy4dOlS7h9MNqSblHPixYsXmJub8/jxY54/f45SqczRdAtp5uNnz55x6dIl9u3bx8qVK5k8eTL9+vWjdevW1KxZE3d3d8zNzdHR0cHW1hZvb28CAgLo3Lkzw4YNY86cOURGRnL06FFu3bpFbGxsht9JerrFrl27sky3GDt2LFu2bOG3337T+H24f/8+gwcPRqFQ0KhRHVJSNGt6NWoIli7NjdYXkeOzyS/fBN8/kMTERFasWIG3tzcuLi5Mnz49z6aI6Oho5s+fT/Xq1TE3N6dTp07s2bMn1yaw3377DaVSycePHzl16hTu7u55FigqlYrWrVvTuHHjAk8HGDlyJD169FB/Hjx4MNWrV/8iE9/n3Lp1S6N5d+jQoQQGBmp8HklJSWzevJkmTZpgYmJCy5Yt2blzp8Zx2djYqAMe6tWrx9y5c4G0F92xY8cIDw/Hzc0Na2trevTowe7du9m1axdKpbLAE+v/+OMPKlWqhBACpVKpnr3LZLJcJdrfvXsXCwsLnj17xrp169DV1aVChQoEBATQr1+/XI+jePHiRERE0L9/f+zs7PDw8GDs2LHcvHkzy2Nu3LhBt27dMDMzo1evXhnC71NTUwkJCcHT05OnT58iSRLLly/H2NgYAwMDJk6c+MWThylTpuT6HgcNGkRISAjNmzdn6NChX3RdTXz48IE//viD8+fPs2vXLpYuXcoPP/xAnz59aN68OdWqVaNYsWIYGxujp6eHg4MDFSpUoFGjRnTv3p1Ro0Yxb948Nm/ezMmTJ7lx4wbnzp0jIiKC4cOHExgYmCndYubMmRw8eFCdbpGYmMiJE115/74Q+Rd8AihT4M8nnW+C7x+MJEmcOXOG9u3bY2ZmRufOnXM1Q/ycp0+fMmfOHCpXroyFhQVdunThwIED2UZ9DRkyhIEDBwLQu3dvJk6cmOex9+nTh+rVqxeI2fVzXr16pZ49r1ixAldX10wz/oKgYsWKmfxCHz9+xMvLi8WLFwNpAv7o0aN069YNc3NzatWqxYoVK3IMxffx8eH06dMAnDx5ElNTU1q2bIlMJqNs2bKMHj2aCxcuqLX++fPnY2lpydGjRwvs/hISEmjRogVaWlqYmZlhYmLCsGHD1GbJuXPnUq5cuRwnFE2bNlV/R3bu3ImHhwdBQUEULlyY8PDwXIfc29jY8PjxYyDtuZ46dYqwsDBsbGwoVaoU48eP5/bt2xqPjY6OZuTIkSgUCho3bqyuQiJJEpMnT8bR0VEtQB89ekTVqlUxNjamfPnyX1Qi7ODBg7nKyYQ0rc/IyAgXF5cso0D/KhISErh//z6nT59m27ZtLFy4kLFjx9KrVy+aNGlCpUqVcHZ2Rl9fH0NDQ1xcXKhcuTJNmzala9eudOvWjXbt2tGgQQO8vb0xMzNTF1d48kRGVgKtRg2BXC6QyQSVKwuOHMlK8BkAmn35X8o3wff/hBcvXjBp0iQcHByoWLEiP//8c75MmI8ePWLmzJlUqFABuVxOjx49+OWXXzLMepOTk7GysuLmzZt8/PgRmUyWIXIvN4wfP54yZcp81RJS4eHhBAUFZRvs8qUsXLiQFi1aZFp/48YNtb/IwcGBUqVKMXXq1CxD1jVRr149OnfuTJ06dTA2NsbGxgZ/f/9M50hJSSEkJAR3d/cCqyKjUqkYMmQI2tra6OvrY2xsTI8ePTKlf0iSRKNGjdSTIE0cOXIER0dHEhMTUalUlC5dmuXLl6NQKNi3bx8dO3bE2tqaZcuW5ahdGRkZafzOqFQqTpw4Qd++fbG2tsbT05MJEyZoTK5+//498+fPp2jRolSoUIGNGzeSkpLC6tWrUSqV6vxRSZJYsGABhoaGGBoa5rsww+vXrzE2Ns6VWyImJgZDQ0OaNWuWr2v9HUiSRFxcHHfu3OHEiRNs2rSJuXPnMnLkSLp160ZgYCA+Pj7qyFYDAwPevdMiK8F39qwgLk7w4YNg1SqBkZHg3j1N+5oCBRuMlM43wff/jNTUVLZv306dOnVQKpUMGzYs37PVBw8eMHXqVHVoenBwMMeOHWPLli1UqVIFgB07duS5QPTChQtxcXH56jVIT58+jZaWFhs3bvxq13jz5g2mpqbqAgLPnj1j5syZeHl5YWpqipWVVa61cJVKxdmzZxkxYoTaf+Lj48OWLVuIi4vj0aNHanNhOrGxsdSpU4d69eoV2CRixYoVGBsbo62trY4KzkqLgrQXu52dHXv37s20LTU1FS8vLyIjIwFYu3Ytvr6+1KpViwkTJqj3O3/+PJUrV8bLyytLjVWlUlGoUKEchWN6+bY+ffpgZWVF2bJlmTRpUqYakKmpqWzdupUqVarg5OTEnDlz2LZtG3K5nK1bt6r3u3//PuXKlaNIkSIEBgbm6zk7Ojpm+wzT6dSpE127dlVbK/5tSJLEmzdvUKl0yG0UZ716gp9+0rTNGIj8KuP8Jvj+H3P79m3CwsKwsLCgcePGHDx4ME/BMJ9y9+5dJk6cSJkyZdDT08PPz4+TJ0/SvHlztUkvN2zatAkbG5sCr2/5OW/evMHNzY2aNWsyePDgr3qtZs2a0bFjR+rUqaM2Of/yyy8kJydTt25dxowZk+Wx79+/Z/v27XTt2hVLS0s8PDwYMmQIp06dYtKkSYSHh2fYPzw8XO27vHv3Lm5uboSFhRWIj/To0aPY2dmhpaWFqakp1atXz3UJuKNHj2JlZZVpMrNixQoq/9nF4uPHj7i4uNCnTx+NZd4kSSIyMhIHBweCgoIyRS+/e/cOQ0PDPN1TamoqR48eJTg4GEtLS7y8vJg8eXKm79+ZM2do3rw5MpmM77//HqVSyfz589XbVSoVM2bMoEiRIpibm3PkyJE8jaNZs2ZERGQfjLF3716cnJyIj48nPDycPn365Oka/2RUKhX3799n9+7dTJs2jYSE3Au++vUFc+ZkpfFlnmwVBN8E37+A9+/fs3jxYjw9PSlevDizZ8/OEDqeF548eYKxsTEjRozA3d0dLS0tgoODc5Vof+jQIRQKBZcvX87XtXNLcnIytWvXJiwsTK0lFXRd05SUFPbu3Uvbtm0xNDTEzMyMyMjITP7Kp0+folQqOXPmTIZ1ixcvpkGDBuoozFmzZmV6Ga9Zs4bWrVtnWBcTE4NcLmflypVYWlpmqGmZX37//XfKlSunbhXk4eHB3r178xysNGrUKPz8/NSTq/j4eGxsbNQVbRYuXEjlypWRy+XZVuBPTExkwoQJWFhYMGTIEHUloidPnmBtbZ3Pu0wTgocPH6ZXr14oFArKlSvH1KlTMxRB//333+nbty+mpqYYGxvTtWvXDM/hzp07uLu7o6OjQ69evXIdLDVhwoRszcHv3r3DwcGB//znP8B/Izz/v7UnS0+W3759O5MmTaJ9+/Z4e3tjYGCAnZ0ddevWpV+/frx86YgmIRcbK9i/X5CUJEhJEaxdKzAwENy+rUnw6QNfZwL9TfD9i5AkiRMnTtC6dWvMzMzo0aMHV69ezdM5Jk6cqNY4li1bRu3atRk9ejRubm44OjoyaNAgoqKiMr00L1y4gEKhKNDAC01IkkTPnj0JCAhQm8S6devG6NGjC+Tc58+fJzQ0FKVSia+vL/PmzSM6Ohpra+ssc7U2bdqEvb09w4cPp3z58pibm9OmTRvWr1+f7QTkyJEjGs3IQUFB6OnpZZsCkBvevXtHo0aNEEJgaGiIjY0Na9asybdVICUlhapVqzJlyhQgLbK2Xbt2QFqghI2NDSVKlGDhwoW5Ot/Tp0/5/vvvsbKyYunSpVy/fp3ixYvna2yaxnro0CF69OiBQqHAx8eH6dOnq0ufxcTEMHz4cHR0dLC2ts4wEUhNTWXMmDHo6uri4OCQbURpOkd2r+bH3sXh+g9wbTz8Ng/i/iv8e/fuTdeuXTMcM3DgwH+s1pecnMytW7fYsmULP/zwA23atKFMmTLo6+vj5OREQEAA4eHhrFixgrNnz2ooo7ieNFNlRmH28qWgfPk0v56pqaBiRcHBg1lpg5lbPhUU3wTfv5To6GjGjx+vrraxfv36HGevKpUKFxcX9Qy+Zs2a6ioakiRx9epVhg8fjqurK66urgwbNowrV66oq7p8abWU3DB79mxKlSqV4Yd27949ZDJZvn1gv//+O+PHj6d48eK4uroyduzYTBrL4MGDM5hUP3z4wL59+wgODsbe3h5jY2NKlSrF4cOHc10j8c6dOzg7O6s/p6SkEBYWRrFixbC2ts5zEe90UlNTCQsLo3Dhwujp6WFqasrs2bPznc/5KY8ePUKhULB9+/YMXQemTp1KiRIl8Pf3z7MmeeHCBapWrUqxYsUKTPB9Snoz6O7duyOXy6lQoQIzZszg0aNHvHnzhjJlymBkZESpUqVYvXq1+ndy8+ZNnJyc0NHRYeLEiZnvS5Lg2QE4VBtpvR4JKwXSukKwTgsii8D6InCgCtf3TcTezibTJOj58+d/u9b34cMHrl+/zoYNGxgzZgwtWrSgZMmSFClSBFdXVwIDAxkyZAirV68mKioqDyUSPwIm5K1iy+f+va2ZzlpQfBN8/3KSk5PZtGkTNWvWxNramtGjR2f5Qzt8+DClS5dGkiQePXqEubm5xpBrSZK4ePEigwcPxt7eHm1tbQIDAzV2GihIdu/ejbW1tcYI0/bt2zNp0qRcn+v169csXLiQKlWqIJfLCQkJ4cyZM1m+tG/cuIGlpSXLli2jWbNmmJiYUKVKFaZMmcLNmzd5+/Ytzs7O7NixI9djSEhIQE9PD0mSePv2Lf7+/vj5+fHmzRtWr16Nr69vnoXIvHnzMDAwoHDhwhgYGDBy5MgCL2q+ZcsWjIyM1Mn9b9++xdzcHJlMlm0B6uyQJImRI0dSpEgRmjZt+tV8xMnJyRw4cICuXbtiYWGBr68v06dPp3Xr1hQtWpTq1atjY2PDlClTiI2NJSUlhQEDBqCtrY2np+d/S8ulfoCjjWGDEawT2S7vV2jxan1JSM4sNAYOHPiXtOlJSkriypUrREREMHLkSJo2bYqbmxt6enq4ubnRtGlTRowYwbp167h8+XIBpSCNJzlZl7wLvUKAPWnFrb8O3wTf/xC//vorwcHBmJub07x5c44cOZLhxdq2bVtmz54NpCXldu/ePdvzxcTE4OHhQUhICAMGDFAnHI8bN67A0wuuXbuGQqFQ5719zo0bN1AqldnWK01KSmLTpk3qjgWtWrVi165dWWpokiRx48YNpkyZQuXKlSlUqBCVK1dm1apVGrtrnDhxQmMASHaYm5tz/vx5PDw8CA4OVo8lNTWVMmXKaKxbqYn9+/djaWmJlpYWRYoUoVevXl8tqvbMmTMYGBjQvHlzJEli8ODBGBsbs2nTpi8679atW2nYsCGTJk3CwsKCQYMGfdV0mOTkZPbt20fnzp2xsLDA3t4emUzG8uXL6dChA+bm5oSFhfHgwQMuXbqEpaUlenp6/Lx6ORysDpH6OQo99bK+COzxhJSMAqWgtb73798TFRXFzz//zNChQ2nUqBFFixZFT08PDw8PmjdvzujRo9mwYQPXr18vECuAJtImMsM5dMgAlSovtTq1SAtq+bpd2r8Jvv9B3r17x7x583B3d6dkyZLMnz+fR48eYWpqyuvXr5EkiZIlS3Ls2LEsz5GQkEClSpUYMGCAWnimJxyHhoZmyLXKLtAhNzx//hxHR0fWrVuX7X7Nmzfnxx9/zLBOpVJx5MgRdQh57dq1WblyZZZaUHJyMr/88gv9+vXD1dUVe3t7goOD2b9/Pz/99BOtWrXKdgzDhw/Pst+cJlxcXJDJZBk6QaSzf/9+ihcvnq3p9ObNm5QuXRohBHp6ejRt2vSLn3d2SJKEr68vixcvpmTJksyaNQs9PT2aNGnyxedetWoV7du3B9LSRrp06YKlpSWLFy/+6h3JP378yJ49vF4d5wAAIABJREFUe6hUqRJaWlqUKVOG8ePH07t3bywsLGjZsiWnTp2ic+fOLOwiSFpVKPdCL32JLALHMufvDRgwIM9aX1xcHOfOnWPlypWEh4cTEBCgbqPk6elJ69atGT9+PJs3b+bmzZsF1qIoN3z48IF27dpRsWJFXrx4zPXrJUlI0FzFJeNSBFACOftUv5Rvgu9/mPR6h0FBQRgYGFCsWDFu3rzJlStXcHBwyDIIIjk5mYCAADp27JjlPiqVimPHjhESEoKlpSXe3t5MmTIlQ4RdbkhKSsLX1zdXwSuXL1/GxsaGpKQkfv31V3U/OE9PT6ZNm5blrPrNmzdERETQunVrzM3NKV++POPHj8/UOufznD5NfPz4kXLlyuUqwGPFihXo6uqq2z59jiRJ1K5dW+O5YmJiqFu3rrr7eaVKlfJV1SevRERE4O3tjUql4vr16+qE5fxGEX/K3Llz1UWc07l48SLVqlXD09Pzi4N9csvWrVsxMTFR17xN78Nna2tLwzq+JK/RziTUDPUyLoW0BH3qZqH5xWf8DURHR2Nubq6xd2RsbCynTp1i6dKl9O/fn3r16mFvb4++vj5eXl5qE//27du5c+dOgZcGzCtv3ryhZs2aNG3alISEhD+tIJaMG+fF06cOpAk3bTL78+TAD0DBRmdnxTfB9w0kScLd3Z127dphZWWFvb09TZs21fgjUqlUtG/fngYNGuR6Fpmamsovv/xCz549kcvl+Pj4MGPGjByrnEiSRJs2bWjVqlWuNKinT5/i7u6OnZ0dtra2DB48OMuo1nv37vHjjz/y3XffYWxsTMOGDVmyZEmOjWtbtGiRo1C7desWMpksy4Tm1NRUwsPDKVq0KM2bN882ZSEqKgorKyt1UEFycjI9evSgUKFC6OjoULRoUQ4ePJjteAqKxMREHBwcOH78OJA20RBC4OjoWCDltyZOnKixg4ckSWzevBlnZ2caN26cq9qhX8rZs2exsrJiwYIF7Ny5U102cEYXBYkrtbL36S1PE37HRmoSfLpwcUCm6/Xu3ZugoCAWLlxI3759qV27NtbW1hgZGeHj40OnTp2YOnUqu3bt4vfff//qGnB+ePDgAe7u7vTv35/U1FRiY2NxdHRk+vTp2Nvb//kd+Q0YB3QD2gEDgJ3AX3s/3wTfN4iKisLJyQmVSkViYiJmZmZ4eXlha2vL+PHj1b4iSZLo168fVapUyXdbl5SUFA4ePKgOLqhUqRKzZ8/WKHDGjRtHhQoVsnW0x8XFsWrVKvz8/DAzM6Nhw4ZYWlpmehGnpqZy8uRJhgwZgru7O5aWlnTr1o0dO3bk6V727NlDxYo5h1nPnz+f8uXLZ5ocxMXF0bBhQ7777jtiYmIYO3YsI0eOzPZcbdu2ZcyYMUyfPh09PT0KFy6MXC5n/fr1+U5NyA8TJkwgKCgISPsuODk54ePjo242+6UMHTo025qwSUlJTJkyBZlMxsCBAwtEy8yO3377DRcXF8aOHYskSSQlJpK0zixHk+aqngJnhUBaq3l7SoQ+C+bNJjg4mJo1a6JUKjEyMqJw4cK0bt2amTNnsm/fPh49evSX/n2/hPTeiz/99BOQ9v1o2bIlISEhVK9eneXLl//NI8zIN8H3DXr16sUPP/wApCWhe3t7A3DlyhW6d++OmZkZrVu3pmfPnpQsWTJbU19e+PjxI3v37qVTp06Ym5tTrVo1dd7c+vXrcXBw0BigkZyczJ49e2jdujUmJiYEBgayceNGtYD08/NjxYoVxMfHs2XLFjp16oRCocDT05MRI0Zw7ty5L8plS69jmh2SJOHv759BqD148IBSpUrRvXt3tUBcunQp33//fbbnWrx4MUIItLS0MDAw4KeffirQLhS54dmzZ1hYWKirrUybNo3ChQvz7Nkz3rx5g4ODAzt37vyiawQHB6tfnNkRHR1N165dUSqVLFy48Kua954/f463tzfdunUjJfENRGQ2c36+fOchGNMs6+2JqwoxtG8bZs+ezcGDB3ny5AmSJNG/f/8CmUD81ezYsQO5XM727dvV61asWEGpUqXYtm0b7u7uf7sJ9nO+Cb7/cRISEjLUDfz+++8zBYjExsbSqlUrtLW18fDwYMmSJQXe7f3Dhw/s3LmTdu3aYWRkhI6ODiNGjFBHT0qSxLlz5+jbty9KpZJKlSoxf/78TB0Z/vjjD/r164eBgQFGRkbUqVOHuXPnqhOXC4JBgwblqqnus2fPsLS05NSpU5w8eRIrKytmz56dwWy7b98+6tSpo/H4K1euULRoUYQQFCpUCG9v7zzkURUsnTt3VqcvPH78GF1d3QwBGSdOnMDS0vKLohM7dOjAqlWrcr3/pUuXqFGjBqVKlVJXRPkaxMXFUa9ePTq1qI2UQyTnozlp/r37s7IRjhtM4E3mDuPpvr78poT8HcydOxdra2t17i+kacpyuZxr167h6emZQSD+U/gm+P7HWb16NQEBAUCaEDQzM8v0w9u6dSvW1tbcvn2bAwcO0KhRIywsLOjXr1+B+1sePXqElZUVw4YNo1WrVhgZGeHq6opSqcTFxYVx48ZlyPFSqVRcuHCBUaNGUbZsWWQyGe3bt6dEiRJfzbzy66+/YmNjkys/y9atW1EoFMjlco1Fnq9du4a7u3uGdc+fP6datWoIIShcuDCdOnXi1q1byOVyjd0IvjYXL17E0tKSt2/folKpqFChAsbGxplMxOPHj6dmzZr59j81btw41+kb6UiSxJYtW3BxcSEwMLDAn09KSgp3795lx44d1KpSlg+rstf2fmguqF4ih+jODUYZqrp8Sv/+/QkLCyvQe/gaqFQqBgwYgJubW4aAtY8fP+Lt7c2CBQtYs2aNuo7rP41vgu9/nGrVqqkr1UdGRlK3bt0M248cOYJCocjUff3hw4cMHToUhUJB3bp12bFjxxc73OPi4vD09GT8+PHMnz+fSpUqIZfLqV+/PrVq1cLExAR/f38WL17M+vXr6dGjB9bW1ri5uTFo0CCOHz+uNqns37+fkiVLfjUfiY+PD/v37892H5VKxbBhwzA2NqZp06Ya93nz5g0mJiZAmtbbvn17tLS0KFSoEH5+fhkKOU+ePFntY/urkCSJGjVqqAN6fvrpJ0xNTdVNcz8lNTWVGjVqZOjKkBdq1aqVb83tw4cPTJ06FZlMRv/+/fNkjpckiWfPnnH06FGWLFlCeHg4jRo1Uid4Ozk5UbduXfqE9ObD6uzTGIpZCZZ3z0HwrdeFZM39Cf8/aH2JiYkEBQVRo0aNTA2yw8PDady4MUlJSTg5OakDof5pfBN8/8Pcvn0bS0tLtb+pYcOG/Pzzz+rtly5dQqFQZBtGnpSUxM8//0zFihVxdHRk8uTJGpO7cyI+Ph5vb28cHR0xMTGhdevW7N69Wz226OhodVNUbW1tChcujLu7O9OmTdOYkydJEuXLl8/QfqYgmTdvXqYC05/fT5MmTahWrRoPHjzAxcVF41gkSUJfX58RI0agra2NlpYWpUqV0ljoOzExETs7uwwFsb82W7dupWTJkqSkpHDz5k1MTEyws7PL0sf4+PFjtXk3r5QvXz7X3SKy4vnz5/To0UPdfeFT39K7d++Iiopi3bp1jBkzhjZt2lCuXDmMjY1RKBRUqVKFzp07M3nyZLZs2cKvv/6aOVr1TFdS12oWfqfGCAz0BHHLshN8WnC0Ubb30K9fv3+s1vfy5UsqVapE27ZtMyW/Hzx4EDs7O169esXs2bNp0KDB3zTKnPkm+P6HGTx4sNpv8/LlS0xNTdU+pLt372Jtbc3mzZtzfb6oqCg6d+6MmZkZHTp0yGD314RKpeLw4cN06dIFPT09zM3NWbZsGe/evVPXBp0wYQIVKlTAzMyMli1bsnbtWmJiYnj37h1r1qyhYcOGmJiY0KRJE9avX5/BB7Z9+3a8vb2/iqnl9evXmJiYaIwsfPToEWXKlKFz585qAXH69GksLS0zzeQjIiLQ0tJCCIG1tTWHDh3K9rrLly+natWqf4n56MOHD7i6unLw4EGSk5Px9vbGxcWFNWvWZHvcjh07cHR0zHPUpZubW64KQmfHx48fuXXrFrNnz8bV1RUzMzNKlSqFlZUVBgYGlClThpYtWzJy5Eh+/vlnzp49m0lryZbYX5HWa/bz9aglaF8lJzOnIbzIXgt69uzZP1Lr++2333B1dWXEiBGZvn8vX77ExsaGX375hXfv3qFUKrl27drfNNKc+Sb4/kdJTk7G0tJSXVps3rx5tG3bFkj74Tk7O+epD9+nvH79mmnTpuHs7Ez58uVZuXJlhpSEa9euMXjwYOzs7ChTpgxBQUE4Ozvz/PlzDh48SJ8+fXB0dMTJyYnQ0FD+85//ZBvF+ObNG1auXEn9+vUxMTGhefPmbNq0ifj4eEqXLq3Rt1YQaMrBO3PmDDY2NsyYMSPTy2HUqFHUr19fHahja2ur9uMNHTo0V8IsNTWVkiVL5qkmaH6ZMWOGetY+evRovL29KVWqVK5M2n369FGXNMst1tbWuWrOqlKp+OOPPzh06BALFiwgLCwMf39/XF1d0dPTo2jRogQEBBAWFkbPnj2xsbGhdu3aXyxUIc2EfnGyLilrss/ly1Lb21Esrbh1DvTr149+/fp98XgLipMnT2JpacnSpUszbZMkiQYNGjB06FAg7bvSoUOHv3qIeeKb4PsfZevWrVStWlX92dfXl7179xIbG6suNfalpKamsnv3bvz9/bGwsKBGjRqUKFECOzs7hgwZwrVr19RVMurVq4epqSm+vr5MmjSJ69ev50uref36NUuXLqVOnTqYmppSuXJlihcvXkBFdzOye/dufH191Z8jIiJQKBTs2rVL4/7JycmUKVMGOzs7hBDo6OgwadIk2rZtm6doxt27d3/1EPGXL18il8u5desWZ8+eRaFQ4O7unusIvaSkJDw9PfM0eTIyMspgto6JieHMmTOsXr2aESNG0KJFCzw9PTEwMMDa2poaNWrQvXt3pk+fzs6dO7l9+7bGCdKHDx+YPn06MpmMsLCwvGl4f/L27Vu6du2Ko6Mjx/evh83yNEGWS6GnWiuQNpjAu9wFg/2TtL4NGzagUCiy9Gn/9NNP+Pj4kJyczPPnz7GwsNBYSP6fxDfB9z9KgwYN1C/bu3fvolQqiYuLo2rVqoSGhhaIKe3du3esXLmS2rVrY2JiQqlSpTAxMaFGjRp06dIFb29vtLS0qFKlCsuXL/9v5fsC4uXLl8yfPx99fX2MjIxo3749O3fuLLDCvJ/m9I0aNQonJ6cszTsJCQnq3nhCCFq1aqVOCRkyZEieJhrpASdLliwpkPvQRHBwMH379uX9+/cUK1aM0NDQPHeLuHnzJjKZjF9//TXLfRITE7l27RobN25ES0uLTp06qZvZGhsbU758edq2bcvYsWOJiIggKiqKuDjNgSE58eLFC3r27IlCoWDu3Lm5rjy0b98+7O3t6dmz538F87s7sMUa1bqc8/qkiMLEr9IluE3lPNXMDAsL+1u1PkmSmDp1KnZ2dly5kjn9AuDq1asZGg/36dPnH+uf/JRvgu9/kMePH2Nubq5+8Y4dO5Y+ffoQGBhI27ZtvygSMjk5mV27dtGqVStMTExo3LgxkZGRHDhwgIEDB6r9LjKZjMKFC9OsWbN8zcDzwqpVq6hSpQpz586lWrVqmJub06lTJ/bu3fvFieBhYWGUKFGCypUr8+LFi0zb08O+CxUqhJaWFgEBAUyfPh1vb2/1tefOnUvv3r3zdN1z585hY2NT4PmUkJauoVAoeP36NcHBwbRp0wZXV1cOHz6c53MtW7aMUqVKcfPmTfbv38+cOXMICQmhTp06ODo6oqenR4kSJQgICEBHR4elS5dy7NgxoqOjv5of8+rVq9SuXRt3d3f27duX5X6xsbF06dIFR0dHjdGmqe+j2RAuI2WdHlKkYWahF2mYVpj69PekvLuPv79/po7v2ZGu9X2tLhvZkZKSQq9evfD09MzS/JyQkICHhwerV68G0vpaymSyfAW3/dV8E3z/ej4AT4E7wEsglR9++IGePXsCabM6V1dXGjZsSP369fMlCCRJ4uzZs/Tp0weFQkHlypWZOXMmy5Yto127dlhYWODt7c2YMWO4ePEiHz58oEaNGrRt25Z27dphZmZGly5dMqVMFBTJyck4OTmpIw2fPHnC7NmzqVSpEjKZjK5du3Lw4ME8mw6fPHmCh4cH+vr6GsueLV68mCJFiiCEoEyZMmrzT7pPZPjw4UCa2TkwMDDP99WyZcsCMUl/Tr169Zg1axb79+/H3t6emTNnZplkn44kSbx48YITJ06wfPlyBg8eTJMmTXB3d6dQoUIYGRlRu3ZtevfuzezZs9m7dy/37t1TP/PHjx9jY2NT4PeS3Xh37NhB0aJF8ff3z+T/27t3L3Z2dvTq1StLDXP16tVUqlQJKTke7i1DdaAafyw04c6P2jxYYge350Lyf0236ZHL48ePz/U4Q0ND6d+/f/5uMp/Ex8cTEBBA3bp1s+3l2Lt3b9q2basW5G3bts3Tvf2dfBN8/1qigLaAHqAPGAF6SJI5c+aYceVK2kz3zJkzmJubU7FixTxrD3fv3mXs2LEULVqU4sWL079/f0aNGoWfnx/Gxsb4+/uzcOHCDDNGSZLo0qULjRo1UgdJvHjxgokTJ2Jvb4+vry9r1qwp8D5hixYtUifqf8qjR4+YMWMGPj4+KBQKevbsyS+//JJjAMeFCxewtbVlypQpeHt7c+DAAfW2w4cPI5PJEEJga2vLhQsXMh3//PlzrKysOHHiBOfPn1eXicsLd+/eLfAZ9t69eylWrBjR0dHY2tqye/dubGxsOH/+PJD2Urx06RKRkZGMHz+e9u3b4+Pjg6mpKebm5vj6+tKxY0cmTJjAxo0buXr1qjpYKrvUkps3b+Lm5lZg95FbPn78yMyZM5HJZPTt25f79+/TuXNnnJycso2wTUpKwsHBgZMnT2ZYr1KpqF27NmZmZjx69CjTcdHR0Tg5ObFy5cpcje/p06d/qdb39OlTvLy86Nq1a7Zm2W3btuHs7Kzul3j58uUMxdT/6XwTfP86HgKegAFpnYxFpiUpSQtJ0gM6UL16RbVZKze8evWKefPm4evri1KppGXLlnTq1ImSJUuiVCrp3LkzW7duzfIHMH36dMqUKaNxe0pKCtu2bcPPzw+lUsmwYcM0vjzyw4cPH7C1tc1Wq7x//z5TpkzBy8sLS0tLQkJCOHbsWCbT74YNG5DL5Wzbtg1IM1W2adOGe/fuqUuMmZiYqLdnxY4dO3BycuL27dsolcp83Vffvn0LrIN3cnIy7u7ubN68GX9/fwIDA2nYsCHOzs5899132NjYoK+vT+nSpQkKCmLYsGGsXLmSU6dOZSod9zlnzpxBqVRm+fc8e/YsPj4+BXIf+eHly5cEBARQqFAhqlSpkqP5fdq0aVkWJdi+fTseHh7Y2dlp7A5y69YtlEplhslSdoSGhjJgQOaODgXN9evXcXBwYMKECdmaY588eYJSqczQFLp+/foae0r+U/km+P5V3ADMgcLkpttxcrIuZ88KTp/OPncsMTGRDRs2EBgYiKmpKdWqVcPPzw+FQkHJkiUZNmwYp0+fzlFL2rFjBzY2Njm2I4K05PrQ0FAsLCxo0qQJ//nPf77Y5zN79myaNcvcCFQTd+7cYcKECXh6emJjY0NoaCgnT55kzJgxODg4ZEgwf/DgAYULF1b3xps5c2aux9q9e3c6duyIjo5OvszML1++RCaTZSjjlhskSeLp06ccOXKERYsWMWDAAEqVKoW+vj7a2tro6OhQs2ZNdXL9wYMHefjw4Rf5fydNmkS1atU0mpT/85//UKtWrXyf+0uIjY3l+++/x8nJiaVLl+Ln50eJEiWyTIN5/fo1crk8y7ZTFy5cwMvLi8jISBQKhUbf6PHjx1EoFFkGjXzKX6H1HTp0CIVCwdq1a7PdLzU1le+++05d1B7SLBwuLi5/eeH0L+Gb4PvXEA0oAC1yI/Q+1f6gHpDxhZbeQ69z586Ympri7u6Op6cnhoaG1K5dm9mzZ2cop5UTly9fRi6X55jU/jnx8fEsWrSI0qVL4+bmxpw5c9TmlbySkJCApaVlWpRhbCxs3AgLFsC8eRARAVmEjt+8eZMRI0ZgYmKCrq4uPXv25Ny5cyQnJ9OpUyd1AnqNGjXy7CeMj4+naNGiyGSyfIeA//DDD1l2hn/79i3nz59n7dq1jBo1ilatWuHl5YWRkRFKpZKqVavSpUsXxowZg6mpKUuWLEEulxMVFcXIkSPp1KlTvsakiXQz4JgxYzJt27JlS4F0cc8re/bswc7OjuDgYLUvT5Ikdu3aRfHixalXrx43btzIcMyAAQPo1atXlueMjo5Wa/CHDx9GoVCwfv36TPtt2LABOzu7XE0E+/bt+9W0vlWrVqFUKjl69GiO+06ePJnq1aurJ7mSJFGhQgUiIiK+yti+Ft8E37+G7mTubPzf5c4dgZ6eoF07TduNgD1AWsRbeHg4SqUSKysrbG1tMTMzo23btkRGRuarB9qzZ8+wt7dnw4YN+b47SZI4fvw4rVq1wszMjJ49e+arMsTyvn057uQERYqAsTHo66ctxsagpwcNG8KxYxmSjJ89e4aPjw9t27YlKiqKUaNGqX14QgiqVKnCxo0bqVy5cr7u7cyZM+jo6ORoGs2KmJgYFAoF06ZNY+rUqXTt2pWqVauiVCoxNDTEy8uLVq1aMWrUKNasWcP58+cz/R0HDBhAt27d8PPz44cffuDFixdfJR/r2bNnWFlZcezYsQzrV61a9ZcmPX+q5WVVku/jx4/MmjULuVxOSEgIr1694sGDB1hYWGSrfalUKnR0dNR+6mvXrmFnZ8fMmTMz7TtjxgxKliyZ4+/qyZMnmJubF2jKjyRJjB07Fmdn51wl9587dw6lUplBUG/evBkvL6//N30D0/km+P4VxJMWwJK1ZlenjqBq1awEn+DhQ2ecnZ0xMjLC2NgYe3t7BgwYwJEjR/KUe/Q5CQkJ+Pj4ZDCNfCnPnj1j3Lhx2NjYUK1aNSIjI3MeY2oq9O6NpK9PihCQ1aKlBYaG4O8PiYlcunQJe3t7td9jy5YtGBkZIYSgePHihIaG4uLiQtGiRTE0NGTHjh35Msmma9RZvUBUKhUPHz7k4MGDzJs3j9DQUOrXr4+zszO6uroolUp1geZFixZx+PBhdZ+3nLhz5w4ymYwJEybg6+tLSkoKYWFhBeY7/Jw9e/Zgb2+fwa/8008/ERIS8lWu9zm7d+9Wa3m5CcZ49eoVISEhyOVyypUrx6hRo3I8xt7ePsOk4dGjR3h4eNC/f/8Mf2NJkujbty/fffddjgFdffv2ZeDAgTleOzd8/PiRTp06Ub58+VwJ07i4OFxdXTOUMExJScHNzS3HYu3/RL4Jvn8FCwBDshJ669cLWrQQjBmTteBLTBTUr+/GlClTuHXrVoHkUKlUKlq0aEG7du2+Sk5WcnIymzZtokaNGlhbWzN69GiNndyRJGjRAgwMshZ4ny/6+sQUK4aNTMamTZu4cuUKVlZWCCGwsbHJ4JuRJIkLFy5Qvnx5TE1NcXNzY/To0dkmbn9Onz59cHBwYPLkyZw6dYqVK1cybNgwgoKCKF26NPr6+tjY2FCzZk169OjBzJkz2bVrF7/99hvJycmkpKRQokQJ9uzZk+fn2KRJE/r3749MJuPOnTs8fPgQCwuLAi8o8Cn9+/encePG6u/FxIkT1SWvvhZv3ryhU6dOODs75ysncePGjejq6lKsWDF2796d7Xfa19c3U8RnTEwMVatWpVWrVhmEXGpqKk2bNs3xd1JQWl9sbCy1atWiUaNGuY7k7tixI927d8+wbsmSJXz33Xf/yLZDOfFN8P0rqE5WQu/dO0GxYoI//she8KlURYD5BTqqUaNGUalSpcwV7r8C169fp3fv3pibm9OiRQuOHj363x/kmDFpWlxuhd6fS6IQRNesSenSpRFCYGxsnG2NzKtXr2Jra8upU6fo378/tra2lCxZknHjxmUIhEhISODq1ats2rSJCRMm0LFjRxwcHNDV1VV3Z2jXrh3jxo0jMjKSS5cu5apayfbt23NdSzOdw4cP4+TkhJeXFwsWLADSms6m5xh+LT58+ICXlxfz56d954YMGcKkSZO+2vV2796Nra0tISEh+Qq5lySJWrVqsWDBAvbs2YObmxt169bNcnLTvHlzjab9pKQkmjVrRs2aNTP4qhMTE/H19WXYsGHZjqNPnz5fpPU9evSIkiVL0rdv31x/T9atW0eJEiUyCMmEhARsbW3z7LP/p/BN8P0r8CArwRcaKpgyJe3/2Qk+SdIiNXVsgY1o7dq1ODk5aaxm8jV59+4dc+fOpUSJEpQsWZIls2Yh6euDEMwVgnJCoCsEnT4RcA/+9NUZfrKM/0T4FdPWZvr06bm6vre3tzoZ/s6dO0yfPp1q1aqpO8Kbmpqiq6uLh4cHTZo0YfDgwSxbtozRo0cTGBjI4sWLKVu2bL4LCVStWpUVK1bkav/U1FR1kfD04tnpDW/z48vNK+mduq9evUpwcLDGHn9fyps3b+jYsSMuLi4cOXIk3+fZt28fbm5uapN6cnIyc+bMQaFQEBwcnCmdIywsTKNPD9Kee0hICKVLl87Qsf7ly5cULVo0U+HzT/kSre/ixYvY2toya9asXGtpv//+OwqFIlObrClTpvzlvSELkm+C71+BZsF3+bLAw0Pw8WPOgi81VTBqlBZ6enooFApcXFwoW7Ys1apVo0GDBrRu3Zru3bszcOBAxo4dy48//sjSpUvZsGEDe/fu5eTJk1y9epX79++zZ88e5HI5169f/5ueR5oQ+OWXX1hQtizxfwqxLUKwTQh6ZSH4NPn+kgsVQsqiXqIkSURHR3Ps2DGWLl3KoEGDKF26NCYmJujp6eHg4ICfnx8hISEAQlB/AAAgAElEQVTMmjWLqVOn0q5dO5RKJeXKlWPq1KlqP9Dx48fV3aobNWrEkCFD8nXfp0+fxs7OLldFuZctW4anpycKhUJtIm7evDlTpkzJ17Xzw+rVq3F3d6d169bq0lcFxc6dO7G1taVPnz5flFidmpqKp6enxuCj169f07dvX+RyOT/++KN6wjJt2rRsozAlSWLKlCk4ODhkiBq9e/cuVlZWWRY6hzStLzw8PE/3sHv3bhQKRZ463CcnJ+Pr68usWbMyrH/z5k226Rz/H9ACEN/4f04NIcTxTGtnzxZixAghjI3TPr9/L4RKJYS7uxCXLn2+t76A6SIpqbOIj48X8fHxIi4uLtt/Na178+aNiI6OFlpaWkJHR0cYGxsLExMTjf9mt+3zfXR1dfP3aIoVE+LevQyrRgohngghVv35+aEQwlkIkSKE0NZwCoyMxOWDB8VvDx+KO3fuZFh0dHRE8eLFRfHixYWbm5uwsrISYWFh4s6dO8LKykrjkFJTU8WxY8fExo0bxdatW4WLi4vw8/MTq1atEk+fPhUvX74UZcuWFZGRkaJ69ep5vuWgoCBRoYKPGDKkmhBiphAiSgjxXghRRAhhJ4ToK+Lj64vixT2Fjo6OmDlzpmjRooW4ePGiCAwMFPfu3RMGBgZ5vm5+AESHDh3EyZMnxaxZs0TTpk2/+JyxsbEiLCxMnDp1SqxYsULUqFHji863evVqsXTpUnHixAmhpaWlcZ9bt26JgQMHinv37omZM2eK+Ph4sXPnThEZGZntudesWSPCw8PFli1bRNWqVYUQQpw7d040bNhQ7N27V/j4+GQ65unTp6J06dLi9u3bQqlU5jj+hQsXivHjx4tt27YJX1/fXNxxGqNGjRJRUVFiz549olChQur1Q4YMEW/fvhWLFy/O9bn+aXwTfP8KFgghBgshEjKsTUwUIi7uv59nzBDi4UMhFi4UQqHIeIbUVG0RG3tSKBQV8z2KuLg4UblyZdGjRw/Rt29f8eHDh2yFZG4Eafq/hQoVyrWQTP/XRF9f1G/SRGh99hXPSvDZCCG0hBB1hBDThRDyP7fHCyHaFS8u9Dw9Mwi5YsWKCZlMluk5BAUFCX9/f9GtW7ccn1lKSoo4cuSIiIiIEKtXrxaVK1cWrVq1EmZmZmL06NHi6tWrwtTUNE9/h6dPZ4uUlIHC0bGI0NJKEmkK7acYiY8fk8XatYbixIl6YtWq9UIIIerXry8aNWokgoOD83S9LyUuLk5YWVmJ8PBwMX78+C86165du0SvXr1EUFCQmDx5sjA0NPyi8yUlJQk3NzcRGRkpKleunOP++/btEwMGDBCGhoZCpVKJy5cv53jMwYMHRbt27cTixYtFs2bNhBBC7Ny5U/Tq1UucPHlSuLi4ZDqmT58+Ql9fX0yfPj3L80qSJIYOHSp27Ngh9u7dK1xdXXMcSzrHjh0Tbdq0EZcvXxaWlpbq9U+ePBFlypQR169fFzY2Nrk+3z+Ov1Xf/EYBEUdO6Qw5mTrPndPH2NiYhg0bsmXLljz7mFJSUvD396d3794FHuUlSRKJiYm8ePGCe/fucenSJY4dO8auXbuIiIhg0aJFTJ8+nVGjRtGvXz+6dOlCixYtaFGrFh+1tDKZL0d8ZuqMF4ILf5o6nwtBkBDU/WS7ZGoKZ8/merw7duygSpUqeb5PmUzG2rVr6dixI2ZmZlhbW1OhQoU8+HMkYARp5eqy/y6kR/KmpJQBYjl69CjOzs5/W/UNd3d3zM3N8503GBMTQ/v27XF1dc1VInZumTJlSq6r/aSTnJzM6NGjKVSoEL169cpVLdWLFy9iY2OToezX/PnzKV68uMZygukdVrLyoSclJdGyZUuqVKmS63KE6cTExGBvb6+xc0XXrl2/evTtX8E3wfevoSvZJbBnt0iSIWvXtkEmk+Hr60v58uWRy+WEhoZmcmpnRWhoKHXq1PminL8C5/170NbOUfB9vkT/6fN79+fnt0JQUV8fOzs7vLy8aNiwIX379mXevHkcP36cJ0+eZKjYkpycjFKp5M6d3DUdTads2bJERUUBaS+uDRs2YGxsjIGBAbVq1WLRokU51MScSm6F3n8XXSTJh+rVK7JmzZo8jbcgKV68OIMGDaJSpUp5/g6ll8ILDQ0t0DZN6aXJfvvttzwfm5iYiI6ODqGhocjlcmbMmJHjpOL333+nWLFiDBs2TD15HDx4MJUrV9bosw0JCWHQoEEax12lShVatWqV54hqSZJo1qyZxo4QN2/eRKFQ/CWBT1+bb4LvX8MzQE5eS5ZBEaAOoOLdu3dMnjwZpVJJvXr16NatG/b29pQtW5Y5c+Zk+dKdP38+JUqU+Of9ICQprSpLHgXf8z8F39s/P6t0dFg6cSIhISEEBARQpkwZbGxsMDAwQEtLS12yTE9PD7lcru4y7+npyaRJk9i+fTuXL1/mxYsX2Va4aNCgQaYO5+fOnUMul7NkyRJatmyJiYkJdevWZdmyZZ8VUr4B6DN3rqBcOYGurqBTp4x/64QEQe/eAplMYGIiqFYtPbBJl7lzFXlKgyhorK2t+eOPP6hXrx4jRozI1TExMTG0a9cOV1fXTJVgCoL+/fsTHByc7+NlMhkvXrzg1q1bNGjQgKJFi7J9+/ZsLSKvXr2iYsWKdOzYkeTkZFQqFa1btyYoKCjT30eT1nf37l2KFSvGkCFD8lVNZcmSJZQtW1ZjMn3Tpk2ZNm1ans/5T+Sb4PtX8StpRao1d2XIvOgD5YGMveTev3/PrFmzsLGxISAggDlz5tC2bVtMTExo1qwZu3btUms4Bw4cwNLSMs9Fkv8yunVTa30pQpAkBEOFoP2f/08RgrNCcFsIVELwWghaCkHNT4Vh1apZnj69D93Zs2dZvnw54eHhNG3aFA8PDwoXLoyhoSFaWlpoa2ujra2NlpYWxsbGODo6UqFCBZo3b86QIUNYsWIFDRo0YOTIkcTGxmZ4OY4fPx4/Pz9UKhXv379nw4YNBAUFYWJigr+/P6tWreLDhw6ANlu2CLZtE/TqlVnwtWsnaNVK8PJlWhRvVNR/t338aATkrc5oQWJoaEhcXBzPnz/H2to6yzJi6Wzfvh1ra+sC1/LSuX///hcn8Xt6enLp0iX15/379+Ph4UGtWrU0dm1IJyEhgYYNG1K3bl3i4uLU/Ss1dWMPDg5Wa32nT5/Gysoq23SI7Lh58yZyuZxbt25l2nbmzJlcRwv/f+Cb4PvX8QAoDRiiUmUlAPVI0/TakdaoVjNJSUksXLgQR0dHateuza5du1i4cCEVK1bEysqKzp07Y25uzvHjx7/yPeWfm5s3k/inn2/Mn5rcp8sYIYgQAichMBACKyHo8Ke5EyHSanhmE1qeHV5eXhw6dIjU1FQeP37M6dOnWbt2LcOHD6dFixZUrFgRBwcHDA0NKVSoEIULF1YLSG1tbeRyOe7u7uo2TYGBgURERHD06FHu3LlDdHQ069ato1WrABISMv6NR4zIKPhu3xYYG6cVNNBs7jYGsu6X9zVJTU2lUKFCag3lwIED2NraarQwvH79+qtqeem0adOGcePGfdE5/P39M6UlpKSkMH/+fJRKJT169MjSR5eSkkL37t3x9vYmOjqaN2/e4PF/7J15XM3Z/8ffrSrt3fZ90Z5MiSwptNnKFoNEadAgWbPLvjOMXUplEE1MaMhumEQMUmSNbCVNi9Z77+f1+6Ppjqvt3hvzpV9Pj/tQn88553M+t3s/7/N+n/dibV0ntODFixdQUVHB3r17wWKxRMrcA9QkFLC3t8fu3bvrnGMYBq6uroiMjBRp7K+RVsHXYrmBGzfagc2WACCH6moZVFaKA1AGsADAy8a7f0R1dTWio6PRrl07dOvWDb///juuXLkCJSUlKCsro3Pnzti5c+dXZep88+YNXF1dISYmhoy2bcFISDRo2mzsxWhq1uT5FIHNmzfD399foLZVVVVYs2YNvLy8EBcXh8WLF2PkyJHo0qULDA0NeZXc5eTkoKSkBHl5eUhISEBGRgaTJ6viwwf+Rc6ngi8mhmBrSwgLqzF12toSEhI+FYCeIt1ncykqKoKCggLfsVmzZqF///58mu/Ro0ehra2NsLCweivefy5u3LgBbW3tZhdV/eGHHxrUvgoLCxEWFgY1NTWsXbu2XtMiwzBYsmQJjI2NkZ2djZycHOjq6vLly2QYBl27doW8vHyjtSabIiwsDEOGDKnXDJucnAxLS0uhK498zbQKvhaMmZkZbt++BiAXEyf2wsGDPwEQfR+Hw+Hg4MGDsLGxgby8PAYPHoyqqiqcOHGCZ3obMWIEUlJS/mf7RZWVlQgICIC4uDgMDAxqtIKcHEBFRWihVyEhgfEODkJ7xdWSn58PJSUlFBcXC9Q+JSWl0Zp0tV5+J0+eRFRUFCIiIjB69Ghs3WrES1LQkOBbseIfDXdxTUKDixcJbdsSsrI+1vqsRLrP5pKbmwsdHR2+Y1VVVXBycsJPP/2EgoICjBw5EmZmZl/cusAwDHr27Ildu3Y1e6yIiAgsWLCg0TYPHjxA//79YWJigsTExHoFT2RkJLS0tJCamopbt26BxWLhypUr4HA4mDx5MiwsLKCkpCSQ92h9JCcnw8DAoN7iu1wuF/b29iJXDvlaaRV8LZTaDBAMw+Ddu3dQVFQU+AHcGAzDICAgAJ07d0bHjh1hY2ODAwcOgMPhoKCgAFu2bMF3330HfX19LFiwAI8ePfoMdyPYvJYuXYo2bdpAUVERe/fu5W+QkQGoqQECaH7MPyEO2Tt2YPbs2TAxMalTk01QBg4cKLCJKDMzExYWFo3e48CBA+vx5FuOT/d1PxV8GzcSpKQIbPa/x/r3J/z007+/P35MUFVVhbW1NTw9PTFhwgQsXboUkZGRSE5Oxp07d/Du3bvPHq6SmZkJS0vLOscfP34MRUVFsFisL67l1fI5tZs9e/YgMDBQoLYpKSmwsbGBm5tbvZ7UJ06cAIvFQlJSEk6dOgV1dXX07NkT7u7uKCoqQkhICGbPni30HGv3VBsyG+/fvx9dunT5JhNRN0ar4GuhbN26lVdEdPv27Q0WKhWWVatWwcHBAR8+fADDMDh16hS6deuGdu3aISoqiueKfvv2bUydOhXq6upwcXFBVFRUs01HDXHo0CGoqqpCSkoKc+bMadib7eVLYNiwmrp79Xh7ok2bmpenJ06uXQtjY2MUFBRg3759UFdXF2n/5NixY+jeiHPMxxQXF6Nt27aNPmTy8/Ohra39Sd7Jn1GzZ9uw4Dt7tmnB9+yZKtzc3NCuXTsoKSlBXFwcbdu2haqqKlgsFlRUVHgV2tXV1WFrawtvb2+EhIRg9erViI2NxdmzZ5GVlYWioiKBH5bXrl2Dk5MT37F3795hxIgR0NTUhL6+/hf77HwMh8OBra1tHc9aUUlOToanp+DmYzabjR07dkBDQwPBwcF1HGvS0tKgpaWFdevWwcjICPLy8rzaeC9evICqqqpQWh+Xy4W3t3eDWmllZSWMjY2/6F7q/4pWwddC6devHw4dOgQA6Nq1a6O5/wTl119/hZ6eHl9iXaBGE7l48SLc3d1haGiI7du38+KHqqqqkJiYiAEDBkBZWRljx47lr5zQDNLS0mBmZgZxcXH4+fkJ7t1XUACsXQvY2QE6OoCWFmBpCcyZA+Tm8prNnDkT7u7uYLPZuHr1KrS1tbF+/Xqh5l5dXQ11dXWBNV95efkm90prTVP/truB2rJUbDahooIwZw7B37/mZzabUF1NMDUlLF1a8/uVKwR5ecL9+7WCTxoAf27J6upqvHz5Eunp6Th+/Dh2796NJUuW8ArWWltbQ01NDRISEjxNW1lZGYqKipCSkoKUlBS0tLRgb2+Pfv36YfLkyVi/fj0OHjyIS5cu4fHjxygrK0NKSgp69+7Nu25iYiK0tbUxbdo0lJWVYezYsQJrTs0hKioK3bt3/2zazd27d2FtbS10v7///hszZsyAmpoaVq9ezReLl5ycDElJSbi5uWHhwoXo2LEj73MfEhIiVI7XjRs3okuXLg1qt5s3b0bfvn2Fnv+3QKvga4FUVFRAQUEB79+/x5MnT8BisZodWJ6eng4Wi8ULsG6I1NRU9O/fHzo6Oti4cSOfMHrz5g3WrVsHa2trmJiYYOnSpXj+/LnQc3n58iW6desGMTExODs7IycnR+gxBIHNZsPd3Z2XEDgnJwf29vYIDAxssmjox4SFhQlUvBQALC0tBarj9+OPP2LkyJEfHbEAULOHV8dzdXGNcLt3j+DsTJCTI1hZERITP94XlAHwVOB7+hiGYVBQUICMjAycOXMGsbGxWLNmDX788Uf06dMH7du3h5aWFtq0acML8agNzJeQkICUlBRkZGRga2sLHR0dKCkpYerUqfj111+RmpqKrKwstGvXDgcOHBBpfoJQXl4OPT09pKamfrYx379/DyUlJZH7P3z4ED4+PjA2NkZCQgLOnz8PDQ0NbNmyBY6OjggKCsKYMWPQr18/sNlsPH/+vKZyQ95bpDxOwaSTkzAkfgj8DvshNDkUl3Iu8YT6rVu3oK6ujqdP6/+bl5SUQFNTs9Gwi2+ZVsHXAklJSUGXLl0A1MSANbey9cuXL6GnpydUZvdbt25h6NCh0NDQwMqVK/n2FxmGwfXr1xESEgJVVVW4u7tj//79TcYIlZeXY+TIkRAXF4exsTGuXr0q8j0JSkFBAYyNjXkP3dLSUgwaNAjdu3cXuOTS7du3YWBgIFBAce/evQWqaF1WVgYLC4uPhEEUGitG3PTLVaB7aS4fPnzAo0ePcPnyZcTHx+Onn36Cu7s71NTUICkpCXl5eV5igDZt2kBOTg4yMjK8JAFaWlpwcHCAj48PQkND8fPPPyMpKQnp6el48+aNyE5Vq1atwtChQz/rvTIMAxkZmWabac+cOQM9PT1ISUnxvERLS0vh5eWFPn36oHfv3pgwYQJKKkvgPN0ZbRe1hfxKeVAE8V5iEWKQXykPw02G2PjHRrSzanwhsXjxYoE9kr9FWgVfC2T69OlYsmQJGIaBubl5s1axHz58gIODA1atWiVS/8zMTPj7+4PFYmHRokV1PMcqKipw8OBBeHl5QUVFBePHj0dqaiqfuYlhGCxatAjS0tJQVlZGbGysyPcjCrdv3waLxeI5HXC5XMyfPx9GRka4e/euQGPY29s3GZQNAGPGjKnrmNMA6enpUFdX/2efpxyAIQAJCCv0yssJ+/dP+p84MLx79w4ODg5QVlbmW8hUV1cjNzcX169fx2+//Ybt27ejZ8+eUFFRQceOHWFoaAgFBQVecgAZGRlISUlBXFwc8vLy0NfXh6OjI3x8fBAWFoatW7fi999/x927d1FQUMB3r+/eveNVn//cmJmZNat8D8MwWLZsGQwNDbFo0SJoamoiKCgIb968QXV1NcaMGQNHR0eYO5pDLUINMstk+ARefS+JRRJQmauCgrL6vZXz8vKgqqraoDbYEmgVfC0QKysrXL9+HdevX4eZmZnIDzQul4tBgwZhzJgxzX4oPnr0CMHBwVBVVcXs2bPrzYiRm5uLFStWwMzMDJaWlli9ejW2bNkCZWVlSEtLY8GCBf8z77KDBw/CyMiIL7Thl19+AYvFarQqey2bNm3C6NGjm2w3b948oQKnly9fjp49e/6jTeYAUINwwk8W795thrOzMwYMGFCvS/uXIiEhAVpaWujWrZtA9eUYhkG/fv34vBe5XC7evXuHu3fv4vTp04iMjER4eDiGDx+Orl27wtTUFCoqKpCUlIS4uDikpKQgISEBcXFxKCoqwtDQEBoaGjA2NsbUqVOxbds2nDlzBvfv3xeo6n1TuLq6CrTgqY/q6moEBQXBwcEBr1+/BlAT8zhz5kyoqqpi5cqVKC8vR9i8MEjMkgAtbFzgffySWiqFdlvaoaSy7j1OmTIFoaGhzbrvr51WwdfCyMnJAYvFApfLRWhoKCIiIkQea86cOXBxcRFqP6spnj9/jsmTJ0NFRQWhoaHI/ciZpBaGYbBz507Iy8uDiKCjo4Nffvnls85DFD52dqklLS0Nurq6WLVqVaNCOS8vT6CYvu3bt2P8+PECz4nD4aBbt25Yv379P0eeAzAGw8ijcS1PDAwjB6DGg7GqqgrTpk2DoaEhrglRiUIU8vPzMWzYMJibm+Pq1asIDw/HypUrBe6rq6uL06dPC33dkpISPHz4EJcuXUJMTAzmz5+PAQMGQFJSEqamplBTU4O0tDSIiCccJSQkoKSkBENDQzg5OcHX15cnIM+fP48nT540aqIfOXKkSBaK4uJieHh4oF+/fvWaSh89eoSBAwfCyMgIFmssIBEhAepDIG0CSRDI/iNBN45AJgSSIZAcgawJNIPQZlkb+Bzw4Rv3yZMnUFVVFdiM/63SKvhaGDt37sSoUaN4FQJEjaOLjo6GiYlJE9UAROf169e8lev48ePx5MkTADWC0dnZGWJiYujevTsePnyImJgYuLm5gcViYcqUKXz5D/9LPnV2qSU3NxcODg4YPXp0o9nwfX19mzRj/vbbb0J70j19+hQsFusjR4Rq3LsXgYwMGTCMLAAFcLkyKC2lfwSiOvbsMcH+/ZvqjHX06FGoq6tj06ZNX0S7PnLkCLS0tDBz5kyewAgJCeErx9MU586dg46OTrPyaNby/fffY9myZXzHqqqq8Pz5c1y7dg0HDhzg7Xe5ubnBysoKGhoavH1HcXFxnrlVSUkJRkZG6NSpEwYOHIipU6fC3d0dwcHBePHihcAOZrm5ubCzs0NISEiT8YSRxyMhtkCsRsANI9BwAnX8RPCNIpAfgeYQaB6BOhDItOaczHIZPC/618Fs1KhRzU7V9i3QKvhaGAMHDkRcXBySk5Ph7Ows0hiXL1+Guro6srKyPvPs6vLu3TssWLAAqqqqMDQ0hJiYGMzMzJCWllan7ZMnT7Bo0SIYGhrC3t4emzZtEjlbhah86uxSS1lZGfz8/ODs7Iw3b97U2/fo0aPo0aNHo+PfvHkT9vb2Qs8rOjoadnZ2PME7fPhwbNmyBcB9AAexc6cDkpL8AFwAwMGVK1dgaGhYb6mcJ0+ewNHREYMGDfpsaejy8/Ph5+cHCwsL/Pnnn3zn/P39ERMTI9R48+bNg5eXl0gVCGq5fv06dHR0RE5yzeVykZeXh7/++gvx8fFYunQpxo4di969e8Pa2hpaWlo8DbLWOUdKSgrKysowMjLiaZC1Tjrnzp1DSkoK9PT0sG7dOoEWHoHHAiG5VJLflOnyieD79DWeQNI1P0svk8bslBrT8e3bt6GlpfWfxEz+r2kVfC2IqqoqXuqikSNHCrWKruXx48fQ1NQUyZQkCgzDYO7cuZCUlISsrCwUFRUxbNiwRt2ouVwuzp07B39/fygpKWHQoEFISkr6z2oBfursUgvDMIiIiICBgUG9WmlVVRXU1dUbrWTx9u1bqKmpCT2n2jpq06dPx9u3b6GsrMwTWunp6dDW1q6T+cTDw6PB1FyVlZWYPHkyTExMmgxhaYrDhw9DU1OTT8v7GB8fH6FTYlVXV6NLly4fmXiFozbxcn1JmT8nCQkJ8PX1RXFxMTIzM5GQkIDly5cjKCgIHh4esLOzg46ODuTl5SEuLs4LQWlIQG7evBlnzpzB27dvUVZVBtnlsnUFW1OCz4tAuv/+rrhKEVyGiz59+uDnn3/+ou/H10Kr4GtBXLhwAR07dkRJSYlIufv+/vtvWFpaYvv27V9ohvxER0dDSUkJbdq04XmhlpaWYv369dDW1oaPj0+9mt/HFBUVYffu3ejSpQs0NTUxY8YMgeLgmkt9zi61HD58GCwWiy+ZcC2hoaFYtGhRg+NyuVxIS0sLXUAUqNGedXR0EBQUhHHjxvGOe3l5Ydu2bXXa//nnnzAwMGh07/Tw4cNQV1fH1q1bhTZ95uXlNajlfYybmxvOnj0r1NgA8OzZM6irq+PGjRtC9z1x4gSsrKy+eOLl1NTUOllp6mP37t3Q1NTE+fPnkZ2djcTERKxatQo//PADvLy80L59e+jq6kJBQQGSkpI1AlL5H9OlMIJv4j97fYH/HpNeJo3jZ47DxMSkyWK5LYVWwdeCCA8Px8KFCxEbG4v+/fsL1be6uhoeHh6YMmXKF5rdv1y+fBmGhoYQFxdHQEBAvQ/58vJybN26FQYGBvD09BQobdKDBw8wZ84c6OjowMnJCdu3b0dhYeGXuAUANRUEevfuXe/DMz09HXp6eli6dCmfwLh16xYMDQ0bNdEZGhqKXN/w5MmTkJCQ4HkSXrx4EcbGxg0+0Ly9vZtc6Dx69AgdOnTAsGHDBM73Gh8fD01NTcyePbvJ+ExHR0dcv35doHE/5fDhwzA1NRXKA5PD4cDGxgZJSUkiXVMYcnNzoa2t3eB5LpeLuXPnwszMTKhwCg6Hg5PpJyG7VAiNbwqBFAg0iP+43Ao52Pewxy+//PI5bvmboFXwtSDat2+Pq1evwtPTk5euTBAYhkFISAi8vb2/6Ar42bNncHJygpiYGNzc3BrcC/uYqqoq7N27F6ampnBxccHp06eb1DzYbDaSk5Ph5+cHRUVFDB8+HKdOnfrsFSM4HA48PDwwY8aMes+/fv0aTk5O+P7773kPf4Zh0L59e5w/f77Bcbt16yZyfsSTJ09CQ0MDw4cPB5fLRdeuXRv1KkxLS4Oenl6TGmZFRQUmTJiAdu3a4fbt2w22y8vLw9ChQ2FpaSlw/Gi7du3qLX4qKMHBwUIFW+/duxcuLi7/SWgMm82GlJRUvd+ryspKjBgxAl26dBFprzo7PxuyywQUfGEEUiJQv7oCUXKJJGydbJu1X/qt0Sr4WgivXr2CiooKXrx4AWVlZaEy2W/ZsgU2NjYoKir6InMrLS3F4MGDIS4uDgsLC5HqhrHZbPzyyy+wtraGk5MTfvvtN4EeXO/fv8fWrVvh6OgIPT09zJs377MGKr9//x4mJl255vgAACAASURBVCYNrpbLy8sxYsQIdOzYEa9evQJQkyMxICCgwTGHDx8u8up7wIAB2L59O6ysrDBjxgzY2Ng0KfD79esn8N7OgQMHwGKxsGvXrjpJBj7W8oQx1WppafHeG1EoKyuDlZWVQA4yZWVl0NXV/eIhGx+jra1dJ2zn/fv36NGjB4YOHSpwVfOXL1/i119/xaxZs9CjRw+0VW77r0dnBNXE8c0nUHcCtf/n54UEmk4gFQJ51G/+FJ8vjpPJohWw/VZpFXwthL1792LYsGHYuHEjxo4dK3C/5ORkaGlpfZEsDRwOBzNnzoSUlBRYLJZQKc8agsvl4tdff8V3332H9u3bIz4+XmBN7u7du5g2bRo0NDTQrVs3REZGfpYg5Tt37tTr7FILwzBYsWIF9PT0cOPGDV5MX0PXnj59OtauXSv0PJ4/fw5VVVV8+PABN27cgISEhEDOGzdu3ICurq7Awur+/fuwtbXFyJEjUVpairdv32LIkCGwsrISSaDIyck1++9Q+zfIzs5utN2KFSvg5+fXrGsJi5OTE5/2++TJE1hYWGDmzJkNalkfPnzApUuXsGbNGgwePBi6urpQU1ND3759sWTJEpw+fRqFhYUYc3QMJJZI1AgxV6qTp5VcCeT2z89Sn7wiCBIREtAP0m9xZYeaolXwtRD8/PwQFRUFBwcHgR0FMjIyoK6u/kVyXu7evRsKCgqQkZFpMrhbFBiGwcmTJ+Hs7AwLCwvExMQI7NVZXV2NY8eOwdfXF0pKSggICMCFCxeaZeqJj4+HkZFRo3GPiYmJYLFYOHToEHx8fBAdHV1vu40bN4qUOWP+/Pm8PdpffvkF+vr6cHV1FWhh4OPjg82bNwt8rbKyMgQGBkJHRwdqamoIDw8XySGHw+FAXFz8s5jZtm7dCgcHhwaddfLz86Gmpvaf1YisZeDAgTxHp7S0NGhra/M5G3G5XGRmZiIqKgrjx4+Hvb09ZGVl0alTJ0yZMgX79+/H48eP6/0O3X5zG22WthE4Y0ud1wLC0Qstq8isILQKvhYAm82GiooKLl68CB0dHYEedHl5eTAyMsL+/fs/61wuXLgAPT09SEhIICgo6It7iTEMg3PnzqFnz54wNjbGrl27hMrwkpeXhw0bNsDW1hbGxsaIiIjAs2fPRJrL7Nmz0atXr0b3Sf/66y8YGBhg6NChDcb0xcfHY8iQIUJdu7q6GlpaWsjMzER1dTVMTU2RkpICFxcXgbTHW7duQVtbW2Cz29u3bzF48GBoa2tDWVkZUVFRQs23lr///hsKCgoi9f0UhmHg6+uL6dOn13s+NDQUkydP/izXEoZJkyZh8+bNOHr0KFgsFmJjY5GUlIT58+ejd+/eUFRUhImJCUaMGIGffvoJqampAi0iSkpK8OOPP0JqolRN5hYhhZ5khCS0pmv9B+/A10er4GsBXLlyBfb29pg7d65AOQ8rKirQpUuXBgtQisLjx4/h4OAAMTEx9O7d+z8PLAdq3oc+ffpAT08PmzdvFmqfk2EYpKenY9KkSVBTU0OvXr0QFxcn1BgcDgeenp4NPnhrefv2LZydnSEtLY2MjIw6569evYrOnTsLfF2gxruxVpDu3LkT7u7uAGocihozw37MoEGDsHHjxkbbMAyDgwcPQlNTE3PmzEFFRQXu3bsHKysrjB07Vugq6S9evICurq5QfRqjoKAA+vr6SE5O5jv+6NEjqKmp/eepuCoqKjBx4kS0a9cOsrKy0NbWhpKSEjw8PLBgwQIcP35cpO/KqVOnYGBggMDAQDx8+RDa67X/NXkKmKtTPEwc6RnNi9H8VmkVfC2ABQsWIDw8HAYGBo163AE1D65Ro0bBz8/vs5iXiouL4ePjAzExMVhZWQlcreBLkp6ejkGDBkFLSwtr1qwRev+ooqIC8fHx8Pb2hrKyMoKDg3H16lWBnWkac3appbKyEpaWltDS0uJV0a4lJydHaGHQq1cvHDx4EOXl5dDV1eWLf4yJiYGNjU2T2tydO3egpaXVYCaTt2/fYtCgQbC2tq4TflBaWorRo0fDxsZGqIw/9+7dg6WlpcDtBeHSpUvQ0tLiJXYGgGHDhmH58uWf9TqfwjAMHj58iLi4OEyePBkdO3aEjIwMFBQUICUlhXXr1uH+/fvN+t69f/8eY8aMgZGREVJSUnjHc4tzYbLZpP6A9k9esstlob5IHf4TWm7ZoaZoFXwtAEdHR2zevBm2trZNPpyXLVsGJycnoVfmn8LhcDB16lRISkpCQ0NDoAoF/zUZGRkYMWIEWCwWlixZIlJM38uXL7Fq1SqYm5vDwsICq1atatID8e7du2CxWE3mFE1PT4eKigp0dHT4nEKqqqoadIGvjwcPHkBDQwNVVVVYu3YtBg0axHeeYRj4+flh6tSpTY41ZMgQrFu3rk7/AwcOQENDA3Pnzm3QDMcwDCIjI8FisRAXFyfQ3FNTU9GpUyeB2grD4sWL0bt3b3C5XF4i8eZ+5j/l/fv3+P333xEREYE+ffpAVVUV+vr6GDp0KNatW4czZ87Ax8cH9vb26Nq1a7Ovl5CQAG1tbUyZMqXetGIllSVYc2UNZBbIoE0E/75fbT0+vY16iDgVARV1Fbx8+bLZc/pWaRV83zi1HoLjxo3D6tWrG20bHx8PfX19vpWwKGzfvh3y8vKQlZXF+vXrv3qPsOzsbAQGBkJVVRVz584VybTEMAyuXr2K4OBgKCsro0+fPoiPj29wPzE+Ph6GhoaNOrswDAM7OzusWLGijrDQ1NQU+ME0bdo0zJkzB0VFRVBXV0dmZmadNu/fv4euri6fllAfGRkZ0NTU5D1Y37x5g4EDB9ar5TXEnTt3YG5ujuDg4Ca1zNOnT6N3794CjSsMbDYbLi4uWLlyJVxdXREZGdms8aqqqnDjxg1s3boVo0ePRrt27aCgoAA3NzeEh4cjMTGRb0GUl5eHTp06YfTo0bh79y7MzMxEvvabN28wePBgWFhY4MqVK422LSoqgqKSIg6lH8KE4xMw8OBADDo0CD+e+BHnn54HwzAIDg5GeHi4yPNpCbQKvm+c2NhY+Pj48GL4GiItLU3gvZ6GSElJgY6ODiQkJDB+/Pgvnu7pc/Ps2TOEhIRARUUF06ZNEzl2rKysDHFxcejVqxfU1NQwadIkpKen11kAhIeHN+nssmHDBowdOxYZGRkwNjbGnDlzwOVy4ejo2GS6NqAmTlBNTQ1Pnz7FwoULMWbMmAbb1iZAbqrm3rBhw7Bq1Sr88ssv0NDQwLx584QuCVVSUoLvv/8e7du3bzTEICEhoY6G+rl4/vw5L9+lMJ9VhmHw7NkzHDp0CNOmTUOXLl0gJycHW1tbjBs3Drt378bdu3cbdCJ78OABTExMsHDhQl4aPhkZGaEXiAzDYN++fVBXV8e8efMEcnjZuXNno5Xk79+/DxaL9UUzGn0LtAq+b5yRI0di4sSJcHNza7DNixcvoKOjI7I5Mjs7G/b29hATE4Onp+d/Wqz0S/Dq1StMmzYNKioqCAkJEdmLE6gRphERETAyMoKdnR02bNjAc6CodXaZNm1ag/3fvn0LJSUllJaWIj8/Hy4uLvD19UXfvn0Finvct28f+vTpw6ua3dS9TJ06FX5+fo0+hC9evIg2bdrA0tJSpDyYtTAMgx07dvBCOOojKiqq0WD+5sBms6GnpwdNTc1GkzMUFxfj7NmzWLFiBXx8fKCpqQktLS34+vpi5cqVOHfunMCp2i5fvgwNDY065aeUlJSE+t7k5OTAy8sLHTp0EKoMV6dOneo49nzM4MGDsWbNGoHHa6m0Cr5vGA6HAxaLBW9v7wbrvJWWlsLe3r7Ovo0g/P333+jbty/ExMRga2v7nyR//i/Jz8/H3LlzoaamhsDAwCaDnxuDy+XiwoULCAgIgJKSEnx9fXHs2DG8ffsWJiYmjYaN9O/fH/v27QNQY1IbN24c1NTUBCoi3LlzZyQlJSEsLEwgV/3y8nLY2NjUm8aMYRjs378fGhoasLa2/mx12W7evAlTU1OEhITU0Vo2b978xUIM9uzZA1dXV0ycOBHDhw8HwzBgs9m4ffs2du3ahaCgINjY2KBt27bo1q0bpk+fjvj4eOTk5Ihkvj948CDU1dXrNSdbW1sL5PjF5XKxdetWqKmpYcWKFUJVHMnIyICurm6Dmui1a9egp6cncMhKS6ZV8H3DpKWlwcLCAoqKivWuaDkcDnx8fDBu3DihvsgcDgeTJk2CpKQktLS0Gl1BtgQKCwuxdOlSqKur4/vvv2+2Z2pJSQkiIyPRrVs3aGhoYPTo0VBWVm5w5Z6QkMCnsTMMg759+6Jt27aN7uncunUL+vr6ePr0KVRVVQXKfQr8W1bpY+3w9evX8PX1ha2tLW7cuIH79+9DXV1dYE2nKYqKijBkyBA4ODjwJeBetmwZ5s6d+1mu8TEfPnyAjo4Ojh8/zkuzZm5uDnl5eVhYWCAgIADbtm1Denp6s8tZMQyDVatWwcDAoMHPjoeHR5Pfo+zsbHTv3h1du3YVKXfp9OnTMW/evAbn6Obmhj179gg9bkukVfB9wyxZsgQeHh4NpmCaOXMm3NzchAoi37JlC9q2bQs5OTmhMnm0BEpKSrB27VpoaWlh4MCBza5DB9Q8zObNmwdVVVVIS0tj9erVdUxelZWVvH26WmqrzqurqzeY4WX8+PFYtmwZgoKChBYea9euhYuLC9hsNuLi4qChoYEFCxbw7eX5+/tj6dKlQo3bGAzDYMuWLVBXV+dlMpk9ezZWrVr1WcavTfO1du1a2NjYQFZWlpfma9KkSVBSUhI4cbagsNlsjB8/Hh06dGh0zzgwMLBBocNms7F69Wqoqalh8+bNIiVTr6qqgoaGRoNZaX7//XdYWlp+c/vyX4pWwfcN4+zsDDs7u3r37vbs2YN27doJvK9Qm7NTQkICkyZN+uyVDL4lysrKsGXLFujp6cHb27tJTzpB4HA48PPzg4aGBhQVFeHn54fk5GTe+zx58mQ+0+bZs2fh6uqKrKwsmJqaYsaMGXx/k+LiYigrK+PSpUsiOStwOBx06dIFVlZWsLW1rVfIZ2dng8VifbYq7LVcv34dRkZGCA0NxQ8//CBSweSP03xNmDABHTp04KX5GjduHOTl5XHu3Dk+S8euXbtgb28vUmq1+igpKYG3tze8vb2bjBVdsGBBvabr27dvw9HREe7u7s3Kl5uYmNhgJiAulwt7e3skJiaKPH5Lo1XwfaMUFBRAXl4eampqdTS68+fPQ0NDQ6A9q6ysLNja2kJMTAx9+/b97A+5b5nKykrs3r0bxsbGvGKpzQnd4HA48PLyQkhICLZv3w4nJyfo6Ohgzpw5SEhIgLGxMS+4+cGDBzwX+IKCAvTs2RP9+vXjmR63bduGoUOHws/PT2iNiWEYxMXFQU1NDXJyco1qQWPGjBFor1FYCgsL4evrCzU1NWzYsKHJ9nl5ebw0X+7u7lBSUmowzdfkyZPrzXXKMAyGDBnyWWpOvnz5Eh06dBDYu3nHjh344YcfeL9XVlZiwYIFUFdXx969e5sdEvTxPvGn/PLLL3B2dv7qw47+S1oF3zfKwYMHYWlpiZCQEL7jDx8+hIaGRpOJqt+/fw8vLy+IiYmhQ4cOzXLsaOmw2WzExsbC0tISzs7OOHHihMgPkffv38PU1JQXs5eRkYEZM2ZAU1MTsrKymDlzJoqLi+u4wFdXV2PixImwtrbG48ePYWtrix07dkBbW7vBTCv18erVKwwYMAB2dna4efMm9u/fDysrqwYdHh4/fgw1NbUv4v7OMAysra2hpKTEZ7WoqKjAn3/+iU2bNuH777+HsbGxwGm+Hj58CDU1tQbPFxYWwtDQsFkJF+7evQt9fX2hkq8fP34cffr0AVATtG9tbQ1fX99mlWOq5dWrV1BWVq73c1BVVQVjY2NcvHix2ddpSbQKvm+UgIAAaGpq8lVWeP/+PczNzRstRVNdXY3x48dDQkICurq6OHPmzH8x3RYBh8PB4cOH0b59e3To0AEJCQkipZ+qzezycV3C6upqBAYGwsDAAEpKSvD390fbtm35AuAZhsHWrVuhqqoKPT09eHp6CmwmZBgGsbGxUFdXx8KFC3lWAoZhMHz48Ea1oKCgICxcuFDo+xQEV1dXLFy4EGpqaujQoQMcHR0hJyeH7777DhMnTkR0dDSysrIEfp/9/PywYsWKRttcvXoVGhoaImUuSUlJgbq6Og4ePChUv1u3bsHW1hbTpk2DlpYWDh069Nk0sNWrVyM4OLjec1u2bOEJ3Fb+pVXwfQMwDINnfz/Dzdc38debv/D87+dQUVWBvr4+n0bQs2fPRhMkr1+/HrKyspCXl8f27dv/q+m3OBiGQVJSEjp16gRra2vs379faKeBw4cPw9DQkE8zef36NZSVlfH06VNs2rQJbdq0gY6ODhYtWsS3/9OrVy+e44YgjkuvXr1C//790b59+3qLABcWFkJfXx+nTp2qt3+t1+jniN8sLCzEqVOneGm+JCQkoKmpiQEDBsDS0hI2NjYiWx+uXbsmcGqyZcuWCVyyqZa9e/dCU1MTly9fFnpuCQkJEBcXh7+/f6PZfISFYRiYm5vXa64uKSmBpqZmk/l7/z/SKvi+Yv6u+BubUjdBZ4MOZJfLQnGVIhRXKaLN0jYQnyqO/ov6o6y6jJeGqH///vV+kZOSkqChoQFJSUmEhYX9v3Zc+ZwwDIOUlBT06NEDpqamiIyMFMqDds6cOejZsyef0OzXrx+vkriHhwd+/vlnTJkyBSwWC25ubti6dSsUFRVhZ2cHLS0thIaGNih0GYZBTEwM1NXVsWjRokbndvbsWejo6KCgoKDe88HBwQ26yjdEdXU10tPTeWm+zM3N66T5MjY2xoMHDwDUOGGsXr0ampqaOHlSuIrgDMPAxcWlwXjWT+FwOHBzc8OyZcsEGnvBggUwMTHhzVVQioqKEBwcDH19fUhKSn72GLo//vgDVlZW9WqPERERGDVq1Ge9XkuhVfB9hTAMg7VX1kJmuQzkVsg1mGVdbrkc2q5oi5GrRqJ9+/Z1PMsyMjJgbW0NMTEx+Pr6fraYrFbqcunSJXh6ekJfXx9bt24V6AHH4XDg7e2NsLAw3rEjR46gZ8+eAGpMjLVm68rKShw5cgSWlpaQlJSEsrIyjh8/Dnd3d3h5edVxSnr16hX69euH9u3bC5z5Y/r06Rg8eHC9D9Fnz55BVVW1QW2FYRjk5OTw0nx17dq1TpqvO3fu1Fl0aWpq1skde/nyZejp6WHOnDkCa9K//fYbbGxshFrUvXz5Epqamo167VZVVcHf3x+dOnUSuqRRUlIS9PT0MGHCBBQXF8PIyIgvhvFzEBgYWG9yitpMPs3xFG3JtAq+rwyGYTAleUqjAq/Oaz5h/on5vDHy8/PRu3dviImJwdHREU+ePPkf3tH/L9LS0uDj4wNtbW2sX7++3iz6H1NYWAhTU1NeJpXamL7039KxqMciLHZcjOMTj+Pikot4dfMVTE1NYWhoiICAAFhaWqJdu3ZwdnaGiYkJHj58yJffcfHixUJpoBUVFbCzs2swbnDChAm85MYlJSU4d+4cX5ovTU1NodN8ycnJ1fse5efnw9PTEy4uLk3uxbHZbFhZWeHEiRNN3+QnJCUlwcDAoF7nncLCQri5uWHgwIFCVXbIz8/HiBEjYGpqigsXLvCOd+vWDZcuXRJ6jg1RUlICZWVlvH37ts650NDQz+K92lIRAwBq5ath3dV1FHEpgsrZ5UL1k5WUpagBUXRm8xmKiYkhHR0diomJoZ49e36hmbbSGHfv3qWVK1fShQsXKDQ0lCZNmkTKysr1tr137x717NmTko8nk+R9SUqcnkiSZZJEHCL659spJiFGYpJiVMAU0Av9F3Tk/hGSkJKgtLQ0io6Opv379xObzSZDQ0OSlZWl2NhY6tChg0jz7t27N6WlpZGJiQkREXG5XMrMzKTk5GSKiIggIyMjys3Npe+++446d+7MexkYGJCYmJjA1+JwONSmTRvicDj19mMYhlauXEnbtm2jmJgY8vT0rHecPXv20IEDB+j8+fNCXb+WqVOn0qtXr+jIkSO8/jk5OdS3b1/y8vKi9evXk4SERJPjAKBDhw7RtGnTyN/fn5YuXUpycnK888OHDydfX18aOXKk0HOsj6ioKEpKSqJjx47xHX/27Bl17NiR7t+/TxoaGp/lWi2NVsH3FVFUWUTaG7SpklNJlEZEt4kon4hsiWjQP43uEtHxjzqBah6Q44lIhUh+hzxt2rCJgoOD/8upt9IADx48oFWrVtHJkycpJCSEpk6dSiwWq067Q9GH6M8Jf5KmlCZxyjmNjikuI066jro0KnkUtVFsQwBoz549FBYWRlVVVSQjI0NjxoyhwMBA6tixo9DCYNGiRXTkyBHq378/3bhxg27evEm6urrUuXNnysnJIUNDQ9q7dy9JSUkJNe6nFBUVkaGhIRUXFzfa7sKFC+Tv70/jxo2jxYsX8wmhsrIyMjc3p2PHjpGTk5NI86isrCRnZ2cKCQmhCRMmUHp6Ovn6+lJ4eDiFhoYKNMarV68oJCSEnj59SlFRUdSpU6c6bWbMmEFaWlo0a9Yskeb5Kd27d6fZs2eTj48P3/HRo0eTmZkZLV68+LNcpyUi+b+eQCv/su/2PhIXE6/5RYGIehDREyJif9So/T+vWv4iostEpE0kRVIUkxpDg60H/zcTbqVJLC0tKSYmhp4+fUpr164lCwsLCgoK4j0EiYiqSqro3cp3pMqoNin0iIiYSoZep7+mqG5R1PdoX/px6o/06tUr+vPPP0lBQYG8vb3p5s2bdPr0aZKVlaXAwEDy9/cnTU3NOmOVl5fTzZs3KS0tja5du0ZpaWlUXl5OYmJi9Ndff9HcuXOpU6dOpKKiQkREL1++JHt7eyosLKx3PGEoKSkhRUXFJtv17NmTbt68SSNHjiQPDw86cOAA773buHEj9ejRQ2ShR0QkIyNDhw4dIhcXF+JwOBQREUF79uyhgQMHNtkXAEVGRtK8efNo0qRJlJCQQNLS0vW21dXVpRcvXog8z4/Jzs6mx48fU58+ffiO3717l86ePUvbt2//LNdpqbRqfF8JAEh/kz69Kn3Ff+IcEZXQvxrfp+wjIiMicqv5tYteF/pz3J9fZpKtNJuXL1/S+vXrKTY2lkaOHEmzZ8+myxMuU86FHPqz6k+6Tbcpn/LJlmxp0Ed/9Jt0k67QFfpAH8iADMiXfElJSokeij2kdvPa0bx583gaWHFxMY0YMYKqqqpo2rRp9Ouvv9LRo0fJxcWFPD09SVZWlm7evEnXrl2jhw8fkq2tLZ/J0tTUlF6+fEmOjo6UnJxMHTt25LuH0NBQkpKSog0bNjTrvcjMzCQ/Pz/KysoSqD2Xy6WlS5dSZGQkxcXFka2tLVlZWdGNGzd4Ztnm4O/vT/Hx8XTu3Dnq0aNHk+2fPn1KP/zwA5WUlFBUVBTZ2dk12j4+Pp6OHDlCCQkJzZ7rnDlziGEYWrt2Ld/xfv36kbe3N02ZMqXZ12jJtAq+r4TnRc/JapsVVXAq+E80JviKiGgzEYUSUc2CnMTFxKlqQRVJircq818zeXl5tGnTJjq88zCN/jCaxLnilEVZJEZi9ISeEJvYPMGXQzl0mA7TWBpLqqRKp+gUvaN3FEiBJC4tTlOfTiVFXX7Nicvl0uTJk+m3334jX19fevDgAaWlpRHDMERE5OzsTOPGjSM/Pz+SkZGpd46HDh2iiIgIunXrFt9e1evXr8nW1pYyMzNJW1tb5PcgNTWVpk2bRteuXROq35kzZyggIIAMDAyoc+fOtGXLFpHnQFSzlzh79mw6fvw4mZubk56eHu3YsaPB9lwul37++Wdavnw5zZkzh8LCwkhSsunv29WrV2nmzJmUmprarPlyOBzS19enCxcukKWlJe/4pUuXKDAwkB48eNCg1tlKDa1Px6+E9xXvSUpCqq7ga4w7RGRAPKFHRCQOcdoTt4dUZVRJSkqKpKWlSUpKqsGfGzovJSVF4uLin/s2W/kHTU1NWr16NXUu6Ex3ou8QEZE1WRMR0Wt6TeyP7NvZlE02ZEMaVOOo0IN60EbaSIVUSOpi6pS+I526LexGf/31F6WlpfHMloWFhaSrq0uxsbEUHh5O8fHxpKGhQY8fP6aYmBiaP38+bdq0iQIDA2nkyJGkpqbGN8fvv/+ejh8/TrNmzaJt27bxjuvo6NCYMWNozZo19NNPP4n8HpSWlpKCgoLQ/Tw8POjw4cPUq1cvkpOTo/z8fJGdOCoqKiggIIDy8/MpNTWVJCQk6LvvvqPExEQaPLjulkFWVhaNGzeOpKWlKTU1ldq1ayfwtXR1denVq1dNN2yC33//nUxMTPiEHgAKDw+nZcuWtQo9AWgVfF8JEmJNe43V4Q4RufAfYhiGLpy/QGKVYsRms4nNZlN1dTXf/00dq/1ZQkJCZMH5vz4vKSkpkofffwm3mkv3D90nMabpeYLqGmbyKZ9Uq1Tp/OrzNGDjADK3MCdnZ2fy9PSkhQsXkoWFBYmLi9Mff/xBw4YNIyUlJQoNDSUzMzNatmwZRURE0Pnz5yk6OpoWLlxIHh4eNHbsWPLy8uJpMNu2bSN7e3vq168f9e3bl3ft8PBwsra2plmzZpGurq5I9y/oHl99bN68mSIiIqisrIwcHBzowIEDApknP+bdu3fk6+tLxsbGlJKSQm3atCEiooMHD5KPjw917NiRDAwMiIiIzWbTmjVraPPmzbRs2TIaP3680AtDHR0devv2LXG5XIG8RBsiKiqKgoKC+I4dO3aMKisracSIESKP+/+JVsH3lcCSY1E1t1rwDi+IqJToHyWBB0MMnTp2imxtbMnGxoZsbW3J1rbmZ01NTYGFAQDicrmNCkZRhOnH/5eXl1NRUZFQfQQ9z+Vyv2rBLC0tTdXvqglM0zsN7agdI7SmAAAAIABJREFUHaEj1JE6khqp0SW6RETE0wqlxaQp93Euqeqo1tvfxcWFUlNTacCAAZSZmUlbt24laWlpkpCQIA8PD/Lw8KCioiKKj4+nZcuW0Q8//ECjR4+mwMBAnnPOyJEj6c6dO6Surk5ERFpaWhQUFESrV6+mn3/+WaDP1KeIqvGlpqZSWloaxcXFkaysLLm4uNCwYcMoLCyMZs+eLZBAevToEfXt25eGDRtGy5Yt4+vTuXNnmj59Oo0aNYouXLhAd+7coaCgINLV1aVbt26Rvr6+0HMmIpKWliZlZWXKz88X2UScl5dHFy9epNjYWN4xDodD8+bNo40bN7ZaaQSkdY/vKwEAWW6zpIfvH9Yc4BIRQ0SXqGaPbwARiRNR7UIxiWrCGD6xxniZetF+7/2UmZlJmZmZdO/ePd7/YmJidYShjY1NHRNXS4BhGJ4g/BJCu7l92Gw2yX2Qoz6v+5A0+E1T5+gclVAJn3PLdbpO1+gaVVIldaEudIWu0EgaSYZkSFJyUjTp/iRSMlBq9D0pLS2lUaNGUUlJCSUkJNQbVkFUY87bt28fxcXFkZGREQUGBtK9e/coNzeXEhMTeYun/Px8srKyojt37pCenp7Qf6PNmzfTkydPhNqjA0A9evSgoKAgCgwM5B3Pzc2l77//npSVlSk2NrbRz/TVq1dpyJAhPCFfHwzDkIeHB1VWVtLjx49pw4YNNGrUqGZbERwcHGj37t11HIYEZcOGDXTv3j2Kjo7mHdu7dy/FxcXRhQsXvnorx9dCq8b3lSAmJkbh3cJp6u9T6QP7Q02IwqWPGtwlIlci6kk14Q2ZRDScfwx5aXma1XUWsVgscnV1JVdXV945AJSXl0f37t2je/fu0a1btyg2NpYyMzOpbdu2PEFYKxStra1FNkN9DYiLi1ObNm145quvkb+f/U077HYQu4zdZNtO//wjIiqgArpMl3l7fgyXIam2TcfUKSgo0NGjR2n+/PnUuXNnSkpKIhsbmzrtrK2tae3atbRy5Uo6deoU7du3j86ePUtiYmI0c+ZMWrduHYmLi5OGhgaNGzeOVq5cKZL7vCgaX1JSEhUVFVFAQADfcX19fbp48SLNmzePvvvuOzp06BB17dq1Tv8jR47QpEmTKDY2lry9vRu8ztWrVyknJ4dev35NBw4coEGDGnKrFo7afT5RBB8AioqKop07d/KOVVRUUEREBF/wfStN06rxfUWUs8tJY50GlbHLROqvq6BLudNyhfoCAKDc3FyeVlirId6/f59YLFYdDdHKyorPw68V0WFXsGmt6lriVNbE7nGJSwwxdIkuUQmV0AAaQOIkTgwxVEiFpEEaVEzFdJSOkj7pkzu5ExERJEFOiU7Us3dPgf82cXFxNGPGDIqOjqZ+/fo12b6goIA2bNhA69atI01NTRo3bhyNHTuWFBQUyNLSkv766y/efpigzJ49m9TU1Cg8PFyg9hwOh2xtbWnTpk114tc+5vjx4xQcHEyzZs2iGTNmkJiYGAGg9evX05YtW+jEiRNkb29fb9/S0lKaO3cuHT16lLZu3UoyMjI0fvx4un379mexjEycOJHs7Oxo0qRJQvdNS0sjf39/evjwIe87vm7dOkpNTaXExMRmz+3/E62C7yvjYMZBGpc0TjjvTqpJWfb7qN/J1ci16cYCwOVyKScnh08Y3rt3jx49ekS6urp8wtDW1pbMzc2/au3qayXRP5HuHbpH4IIu0AXe/l0truRKzuRM0RRNf9PfJE3S9B19R72oF4mTOJEEkXhHcTrb5izdunWLunTpQl5eXuTt7U3W1taNLoJSU1NpyJAhNH36dJ6AaIqffvqJoqOjydXVlQ4dOkRWVlYkLy9PmpqaFBUVJdS9h4SEkJ2dHf34448Ctd+1axcdPnyYp302Rk5ODg0fPpy0tLQoMjKSFi9eTFevXqWTJ082aJY9ffo0TZgwgXr16kUbNmzgBe3PmDGDHj9+TMeOHWu2VrV8+XIqLy+nlStXCt13woQJZGRkRHPnziWimsw35ubmdPnyZT4Pz1aaplXwfYVsTN1IC88vpHKOYPk6ZSVlKWZgDPnZ+H3hmdV4tz1+/LiOhvjs2TMyMTGpoyGamZkJFOP0/5U3t95QtEs0scubNnfWB5vYVDKyhOasm0Py8vJ0/vx5OnXqFJ06dYq4XC5PCPbu3Zv3IP+YFy9ekK+vL3Xo0IF27tzZ5OKFYRjy9PQkV1dXCg8PpxMnTtCuXbvozJkzNHToUJoyZQp1795dIAExatQo6tOnD/n7+zfZ9sOHD2Rubk7Hjx8nR0fHJtsTEVVXV9O0adMoOjqa7Ozs6MyZM/Wa7wsLC2n69Ol08eJF2r17d52coNXV1dS1a1cKDAwUSVP7mOjoaLp48SLFxMQI1a+8vJz09PQoIyOD50U7d+5cKigooD179jRrTv8faRV8XylHMo9QyMkQquZWU2l1ab1t5KXlSbmNMsUNjiM3I7f/doKfUFVVRdnZ2XU0xNevX5O5uXkdDdHIyKjVA+0fdjvupry7ecRwGKH6iUuJk5aTFmV3yabo6GgKCgqi8PBwYrFYBIAePnxIp06dotOnT9Mff/xB7du3J29vb/Ly8iJHR0eeS31ZWRmNHj2a8vPzKTExscmYuNqsLsePH+flpAwLC6PU1FT68OEDVVVV0dixY2nMmDGNekD6+PjQuHHjyNfXt8l7Xbp0KT148IAOHDgg8Pvz5s0b6t+/PykrK1NGRgYtWLCApkyZwieUExMTafLkyTR06FBauXIlycvL1zvWo0ePqGvXrnT27NkGzaSCkJKSQmvWrKFz584J1S82NpYOHz5MJ06cIKKa3KDt27enu3fvihxO8v+ZVsH3FcNhOHTy4Ulac3UNpb1MIykJKQKBuAyX3E3caVbXWdTLuNdXvaldVlZG9+/fr6MhFhYWkpWVVR0NUU9P76u+ny9B6etS2mm/kyoKKwQKbyCqqdYgryVPE29PJDmWHL169YpWrFhB8fHxNGnSJJoxYwYpKf3r5VlRUUF//PEHnT59mk6dOkV5eXnk4eFB3t7e5OnpSZqamrR48WKKi4ujpKQkat++fSNXJzp8+DAtWLCA/vrrL2rbti0VFhaSubk5paWl0fv37yk6OpoOHz5Mjo6OFBgYSAMHDiRZWVm+Mdzc3Gjx4sVNVhDJy8sja2trSk9PJ2NjY4Hen8zMTOrXrx8FBwfT/Pnz6dmzZ+Tn50fGxsa0d+9eqqiooMmTJ9O9e/coMjKSunfv3uSYcXFxtHLlSkpPT6e2bdsKNI9PycrKosGDB9ODBw+E6ufm5kahoaG8oPrx48eTiooKrVmzRqR5/H+nVfB9I3AZLhVXFZO4mDgptlH8N5n1N0pxcTFlZWXV0RArKirqCENbW1vS0NBo0QKx8Ekh7XPdR+XvyolbzW20rYSMBCnqKNKYi2NISZ8/hOHZs2e0dOlSOnHiBE2fPp1CQ0PrfUjn5ubyhOC5c+fI0NCQvL29SUpKinbu3EmRkZFNamIBAQHUtm1bXnqvxYsXU25uLm+vr6Kign777TeKjo6m9PR08vPzo8DAQOrUqROJiYmRo6Mj7dq1q0kPxx9//JFkZGRo48aNjbar5fz58zRixAjasGEDnxm1srKSpk+fTgkJCcThcGjChAm0ePHiBlO21cfo0aNJRkZGZPNicXEx6enpUWlp/Vac+nj8+DF17dqVXr58SdLS0pSdnU3du3enhw8f1mu+bqVpWgVfK18VBQUFfDGItS9xcfE6wtDGxoZUVesP2v4WqSisoKvrrlL6jnQCF1T9gT+hgbSCNElISZDTZCfqOqMrtVFseD/uwYMHFBERQZcuXaI5c+bQhAkTGnzAczgcSktL45lFs7KyiMvlkqenJ23cuJFMTU3r7VdcXEz29va0detW6t+/PxUVFZGZmRldu3aNzMzM+Nrm5uZSbGws7du3j6SkpCgwMJC2b99Op0+fJnNz8wbvIzs7m7p160bZ2dkCeVXGxsbSrFmzKD4+ntzc3PjOvXjxgiZMmED379+n4uJiWrFiBYWEhAi1oCotLSUHBwdavnw5DR8+vOkOnwCAFBQU6PXr1wKHCy1YsIDKy8t5gn/o0KHk5OQksDdsK/XwJavcttLK54BhGLx+/RpnzpzBpk2bEBwcDGdnZygoKEBbWxvu7u4ICwtDZGQkUlNTBar8/TXDqeLgXvw9xA+JR5RLFKJ7ROPwsMPISswCl80Vaqzbt29jwIAB0NPTw65du1BdXd1kn/z8fPz8889QU1ODjIwMTE1NMWXKFJw4cQIfPnzga3vp0iVoa2sjLy8PALBkyRIEBAQ0ODbDMPjjjz8QFBQEMTExuLu7IyEhocFK8YMHD8bq1aubnDPDMIiIiICRkRGysrL4znG5XGzbtg0sFgsrVqxAdXU1srOz0b59ewwfPlzoz0t6ejrU1dXx9OlTofrVYm5ujszMTIHacjgc6OrqIiMjAwCQlpYGXV1doSrCt1KXVsHXyjcLwzB4/vw5Tp48ibVr1yIgIACOjo6Qk5ODgYEB+vbti9mzZyMmJgY3b95EeXn5/3rK/zOuXbsGd3d3mJiYIDY2FhwOp8k+ZWVl8PPzg62tLebNmwdXV1fIy8ujd+/eWLduHTIyMsAwDMLDwzFgwAAwDIOioiKwWCw8ePCgyfFlZGSwa9cuuLq6gsViITQ0FLdu3eKdv3r1KvT19Zv8u1VVVWHs2LHo2LEj3rx5w3cuOzsbLi4u6NKlSx2BWF5ejh9++AHt2rXD7du3m5zvx6xfvx7Ozs4CLSQ+pVevXkhJSRGo7e+//w4nJycANZ/3nj17Yvfu3UJfsxV+WgVfKy0ODoeDR48e4dixY1i+fDlGjBgBOzs7yMjIwMzMDL6+vpg/fz4OHjyIu3fvNqhttEQuXLiArl27wtraGgkJCeByG9cgGYbBkiVLoK+vj5s3b6K4uBjHjh3DxIkTYWRkBF1dXYwZMwZGRkbYtGkTAGD58uUYNWpUo+Oy2WyIi4uDYRgAwJMnT7Bo0SIYGBjA3t4emzZtgpOTE/bt29foOEVFRejduzcGDBjAp42y2WysWbMGampq+OmnnxoV9HFxcWCxWNi9ezdvPk3B5XLh7e2NuXPnCtT+Y0aPHo3o6GiB2vr5+WHHjh0AgFOnTsHCwgJsNlvoa7bCT6vga+X/DdXV1cjKysLhw4exePFiDBkyBBYWFpCRkYGVlRX8/PywZMkSJCQk4MGDBy32AcMwDJKTk+Hg4IDvvvsOJ0+ebPKBf+TIkf9r796DqizzOIB/AUEBidsBjgJKaOQi3lJx1zInyZY0ibyghMbB0ggdHRkv6x3M0ZHUoqxWFqNVF2W9jdoWKaBuooxoqOStMEHDuIggIAJyznf/OHLyyAEOBLbF7zNzZjjv+7zv+5yXGX48z/v7nYcKhYK7d+/WO8+VK1cYGxvLESNGEAAHDhzIxYsX097eXjc9Z8jt27dpa2vbYLtarWZKSgqff/55mpqa8rXXXuPBgwcN/i7y8vLYt29fzp49Wy+wnTt3joMHD6afn5/R05EXL15k3759OXXqVFZUVBh1TEFBAbt168aUlBSj2tf729/+xnfffbfZdsXFxbS1tWVpaSnVajUHDhzIPXv2tOhawjAJfKLDu3fvHs+ePct//etfXLx4MQMCAujp6ckuXbpwwIABDAkJ4Zo1a3jgwAFevXq12VHS74VGo+GePXvo7e3N4cOHMy0trcn2Z86cobu7O6Ojow0GyvXr17NPnz6cN28enZ2daWFhwSlTpjAhIYE3b97Ua5uXl0c3NzeD16mtraWXlxd3797NuLg4/uUvf6FSqeT8+fN1z8bOnDlDV1dXbty4UdeX6upqLl++nAqFgvHx8UaP3upVVlZSpVKxT58+TQbthx06dIiurq4sKioy+jqbNm1ieHh4s+1iY2N1I+fExEQOGzasxZ9JGCaBT4hGVFZWMjMzkwkJCZw/fz79/f3p7u5Oa2trDhkyhCqViuvXr+dXX33FGzdu/G7/KNXV1XHbtm3s1asX/fz8ePLkyUbb3rx5k76+vpw8eXKDBAu1Ws2XXnqJUVFRLC8vp0KhYHR0NCdOnEh7e3v279+fCxcuZFpaGr/99lt6e3sbvMann35KPz8/vft56dIlLlq0iN26daOXlxetra35z3/+U7c/IyOD3t7eDAgIYH5+/q+6HwkJCVQoFEZPRy5atIhjxowx+ve/b98+jhs3rsk2Go2G/fv3Z1paGmtqaujp6ckjR44YdX7RPAl8QrRQWVkZ09PTGRcXxzlz5tDPz48uLi60tbXl8OHDOXPmTMbGxjI1NZUFBQW/m4BYW1vLuLg4uru785VXXmFWVpbBdlVVVQwJCeGQIUP4008/6e3Lz8+ns7MzMzIyuG7dOk6aNImk9plbeno6ly9fTl9fX1pbW9POzo4ff/wxc3JydMeXl5dTqVTyzJkzBq+9adMm2tvbc9SoUbS1teXEiRM5fvx4Ojs7c+fOnW12r7Ozs9mnTx+qVKpmMyhra2vp6+ure8bZnFOnTnHQoEFNtjl9+jSffPJJqtVqfvTRR/T39ze676J5EviEaCPFxcU8evQoN23axHfeeYcjRoygvb09FQoFR44cyVmzZvHTTz/lf//7X5aUlPzW3W3UvXv3GBsbS6VSyaCgIF66dKlBG41GwzVr1tDV1ZWnTp3S27dr1y727t2bBQUFdHFx4fnz5xscn5SURB8fH4aGhtLFxYW9e/fmrFmzGBwczKCgoAbt1Wo1Fy5cyKeeeoo//PADSe3IydHRkQ4ODnR1deXSpUt1+9pCRUUFQ0JC6OPjY/AePOzq1atUKBSNBuyH1f9z0JSIiAiuWrWKFRUVVCqVjf4TIlpHAp8Q7ai+BvHQoUN8//33+eabb+rVII4ePZrz5s1jfHw8MzIyWF5e/lt3WaeyspJr166lQqFgaGiowUSRffv2UaFQcMeOHXrbQ0NDOWPGDK5fv54TJkxocNyuXbs4fvx4ktqglpWVxaVLl7JTp060trbmqFGjGBMTw/Pnz7OqqoqTJ0/ms88+y+LiYpaVlXHmzJl0c3PjwYMHSWoTWuqfLT733HPcsmVLm9xLjUbDuLg4KhQKbt++vcm2iYmJ9PLyajY5pq6ujubm5o1mE1dVVdHBwYF5eXmMjo7m66+/3ur+C8Mk8AnxG9BoNMzNzeV//vMfrlu3jm+88QafeeYZWllZsWfPnhw7diwXLlzIrVu3/uY1iGVlZVyxYgUdHBwYHh7eYHrz7Nmz7NmzJ5ctW6ZL/Llz5w6ffPJJ/vvf/zY4YtmyZQtVKpXetvDwcEZGRrK8vJz79+/nO++8wx49etDCwoIeHh7ctm0bExMT6ebmxrfffptlZWUN+lpTU8N9+/YxICCAtra2DA0N5dGjR391QlJWVhZ79+7NmTNnNvm7CAsLY2hoaLPnc3NzY25ursF9iYmJfOmll1hUVERHR0devXq1td0WjZDAJ8T/kfoaxH379nH16tWcMmWKXg1iYGAgly1bxh07djA7O/ux1iAWFxdzwYIFdHBwYGRkpF4mY0FBAYcPH87x48fr6um++eYbKpVKrlq1ioGBgbxwgQwPJwcPJp2cblOhuMFJk8hjx8iLFy9RoVDoTQHn5OTQy8uLM2bM4OrVq9m9e3eamJjQ29ubUVFRPHnyZJP1eQUFBdywYQN9fHzo6enJ6OjoRoONMe7cucOgoCAOGDCA33//vcE2lZWVfPrpp5sdHQ4bNozHjx83uO/FF1/kzp07OXfuXM6ePbvV/RWNk8AnxO/AwzWIK1as0KtB9Pb2ZlBQEKOjo7lnz552r0G8efMmZ82aRQcHBy5dupSlpaUkteUEoaGhHDhwIK9fv06SXLJkCQcPXkJz87Ps3FnNTp1I4JeXiQlpbU1aWhZywoSvWJ+bcvLkSSqVSn7yySfcsWMHXVxcGBkZyZKSEh4+fJjz58+nj48PHRwcGBQUxM8++6zRbE6NRsPMzExGRETQwcGBfn5+3L59e6u+9kuj0ei+/iwpKclgm6ysLCoUCr2knUdNmDDB4PHXrl2jo6MjL1++TAcHBxYUFLS4j6J5EviE+B2rr0Hcvn07Fy9ezHHjxtHT05OWlpa6GsS1a9fywIED/PHHH9u0BjE3N5fTp0+nQqHg6tWrWVFRQY1Gw/fee4/dunXjiRMnuHr1fZqYVOkFu8ZeVlYaTppEJiXtpUKh4NatWxkQEMC+ffsyIyPDYB9u3LjBLVu2cNKkSbS3t2e/fv24YMECpqamsrq62uD9SkpKor+/P+3t7TljxgyeOHGixdmgp0+fpqenJyMiIgxeJzY2lkOHDm10RD5nzhxu3LixwfaoqCjOnj2b06ZN48qVK1vUJ2E8WZ1BiD+g+nUQH132qbS0FN7e3g1WuXB1dW31sk/ff/89oqKikJaWhkWLFiE8PBypqamYMiUD9+9Hoba2k9HnMje/j06dvkRk5Gls3vx3REREYMmSJc2uDA9oV5nIzMzUW2Vi5MiRulXoH10xIj8/H1u3bkVCQgLMzMygUqkwbdo0dO/e3ai+lpWVYfr06cjLy8OuXbvg6emp20cSAQEB+NOf/oSYmJgGx8bExKCwsBAbNmzQbdNoNPD09MS6deswd+5c/PDDD7CxsTGqL6KFfuPAK4R4jEpLS5mens7Nmzdzzpw5HDVqlK4G8dlnn+XMmTP54YcfMjU1VbfigrHOnz/PV1999UFpQRK7dFEbGNlVE5hOoAeBrgQGEvhSr42paRU9PFbw3Llzv+qz3rp1izt27KBKpaJSqWSvXr0YERHBAwcO6GVeajQaHj9+nG+99Rbt7e05ZswY7tq1y+BI7lEajYYffPABnZycuHfvXr19xcXFdHNzY3JycoPjtm/fzsmTJ+ttO3z4MAcNGsSxY8cyNja2lZ9aGENGfEII3TqIj44QzczMDK6D2NQCqJmZmXj11TL8/PMLAB4d7d0F8B4AFYAeAL4EEAwgG4CHrtVTTxFXrpigrdYeJons7GwkJycjOTkZmZmZGDp0KPz9/eHv749+/frBxMQEd+/exd69e5GQkIDs7GxMmTIFYWFhGDRoUJMj4lOnTiEoKAiBgYGIiYmBhYUFAODIkSMICQlBVlYWXFxcdO3TDx7E36OisC0+HrC1Bdzd8XpoKJydnbF//35cvnzZqFGuaB0JfEIIg0iioKCgQTC8ePEibGxsGgRDb29v2NjY4M4dQKkEqquNvVJ/ACsBTNBtsbICjhwBfH3b4YMBqKysxJEjR3SB8N69e7op0RdffBGOjo64du2abvHcJ554AmFhYQgJCYGTk5PBc96+fRsqlQqFhYVISkqCh4cHAO1CspmZmfjqiy9gevgwEBMDnjiBu3V16GpjA6jV0JiZ4f3qaqT16YPgBQv0Vo4XbU8CnxCiRUji+vXrDUaIly9fhrOzM2xs5uHy5bdx/74xI5ZCAD0BnAXQR7fV1BSYNg34/PP2+QyPysnJ0T0bPHbsGLy9vXWBcMiQITh+/DgSEhJw4MABvPDCCwgLC8PLL78Mc3NzvfOQxIYNGxATE4P4+HgEBASgrq4OM555Bh/l5qIrCVRWGuxDzYMRpcUbb8DkH/8AHjm3aDsS+IQQbUKtVuPatWtYvLgOu3c/DaC5ecr7AF4G0AvA5gZ7n3sO+Oabtu9nc2pqanD8+HF8/fXXSE5ORn5+Pvz8/ODv74/hw4cjPT0dCQkJyMnJwdSpU6FSqeDj46N3jvT0dAQHB2Py5MlYO3o0TAMDYXrvnnEdsLICBg8GDh8GZLqzXUjgE0K0qblzgQ8/bK6VBsDrAMoB7AfQcHTj7l6CTZvS4ezsrHtZW1u3Ovu0tfLz83Ho0CEkJycjJSUF3bt3h7+/P3x8fHDhwgUkJiaiW7duCAsLQ3BwsO75561bt7A0MBDvnzwJK42mZRe1tARefhnYvRtt9qBT6EjgE0K0qXffBVau1OZoGkYA0wHkQpvcYmmwlVJ5CUOGLERRURGKiopQWFgIAHByctILhs7Ozo1ua+sEEbVarVcyceHCBYwYMQIeHh7Izc1Feno6/P39oVKpMHr0aJj+9a9gaio+AfA5tCk8wQ9+rpcKYBaA6wCGPdjXEwCsrYGUFODPf27TzyAk8Akh2tjBg0BICFBR0ViLcGif6aUA6GqwRefOQGQksGaN/va7d++iuLhYFwwffj26vbi4GJaWlkYHSUdHR3TqZHzNIaBNaElJSdElyVhYWKBHjx74+eefYVdejhMlJTBXq7EXgCmArwHcwy+B7xa0E73xAMYBWA7gGwAZgPZB52uvaUd9ok1J4BNCtKm6Om1WZ0mJob150JYtdIZ+qcNmACG6d507Azk5gJtb6/tBEnfu3Gk2QNZvKy0thZ2dXbMBsv5nOzs7vWnX+pKJ+meDY44dw2y1Gg+POZcB+Am/BL64Bz+fePD+LgAFgCw8SPXp0gW4cQNQKFp/I0QDLfv3RgghmtGpEzBnDrB2raGShp7QTnU27fnnf13QAwATExPY2dnBzs4OXl5ezbZXq9UoKSkxGCCzsrIabKuqqoKTk5PBABkSEoJJFy+ic0FBk9e8AGDAQ++toR0BXsCDwGduDhw9Ckyc2NrbIAyQwCeEaHPh4cDGjS2p5fuFpSWwalXb96k5ZmZmusBljJqaGl0gfHQUeeXKFYy/fbvZc1QCeLQq0BaAbpZYrQaMOI9oGQl8Qog25+wMHDoEvPACUFVl/HFWVsBHH/0+8jk6d+4MNzc3uDU2NP3yS6CoqMlzdIU2r/Vh5QD0vqHT1LT1nRQGyR0VQrQLX1/tLJ2dnXYU1xQLC23Qi48Hpk9/LN1rf018rVu9vgDOPfT+LoCrD7YD0M4bOzq2edc6Ogl8Qoh2M3SoNkklKgpwcQFsbLRBDtD+TbexAbp2BSIigO++A4KDf9Putq2QEG0feRNDAAABiElEQVRyCoA6ANUA1A9e1Q+2vQbgOwB7HmxbBe0XuOm+w6a2Fhg16rF2uyOQrE4hxGOh0WjL0r77Digv15apeXgA48bp4sMfS2Eh0LMnUFODKADRj+xeCSAK2qKO2dDmu9bX8XkAgJmZ9nvbEhIeU4c7Dgl8QgjRXgIDgS++0CaptJSlJZCRAfTv3/b96uBkqlMIIdpLbCzwxBMtP87KClCpJOi1ExnxCSFEe8rK0qa3VlRo53ubY2UFjB0L7NwpGZ3tRO6qEEK0p0GDgDNngH79tEHNzMxwO2tr7fRmZCSQlCRBrx3JiE8IIR6Xs2e1lf1JSdr3pqbazM0ePYCFC4GpU7WprqJdSeATQojHTaPRprZWV2sLHf+Qaa3/vyTwCSGE6FBkElkIIUSHIoFPCCFEhyKBTwghRIcigU8IIUSHIoFPCCFEhyKBTwghRIcigU8IIUSHIoFPCCFEhyKBTwghRIcigU8IIUSHIoFPCCFEhyKBTwghRIcigU8IIUSHIoFPCCFEh/I/oQUUa/BMMzAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test\n",
    "N = 20\n",
    "M = int(N*N * 0.3)\n",
    "edges = set() \n",
    "x = None\n",
    "data = (N, edges)\n",
    "\n",
    "class tmpGCPBnP(GCPBnP):\n",
    "    def MuteOutputFlags(self):\n",
    "        self.muteColGened = True\n",
    "\n",
    "for i in range(1000):\n",
    "    edges = set() \n",
    "    nodes = set()\n",
    "    for i in range(M):\n",
    "        a = random.randint(0, N-1)\n",
    "        b = random.randint(0, N-1)\n",
    "        if a == b:\n",
    "            continue\n",
    "        nodes.add(a)\n",
    "        nodes.add(b)\n",
    "        edges.add((min(a, b), max(a, b)))\n",
    "    \n",
    "    for n in set(range(N)) - nodes:\n",
    "        edges.add((0, n))\n",
    "    edges = list(edges)\n",
    "    data = (N, edges)\n",
    "\n",
    "\n",
    "    x = tmpGCPBnP('demo', data, GurobiModel)\n",
    "    \n",
    "    x.prepareGC()\n",
    "    x.gcSolve()\n",
    "\n",
    "    cols, sol = x.master.getNonZeroColsSol()\n",
    "    if not x.CheckIntegralSolution(cols, sol):\n",
    "        bnp = GCPBnP('demo', data, GurobiModel)\n",
    "        OUTPUT_LP = False\n",
    "        bnp.muteColGened = True\n",
    "        bnp.muteBranchBegin = True\n",
    "        bnp.muteBranchSolved = True\n",
    "        bnp.muteNewBranchAdded = True\n",
    "        ts = bnp.branchAndPrice()\n",
    "        if ts < 10:\n",
    "            continue\n",
    "        break\n",
    "#x.plot()\n",
    "\n",
    "\n",
    "# x.master.printSolution()\n",
    "data = (N, edges)\n",
    "\n",
    "\n",
    "bnp = GCPBnP('demo', data, GurobiModel)\n",
    "OUTPUT_LP = False\n",
    "bnp.muteColGened = True\n",
    "bnp.muteBranchBegin = True\n",
    "bnp.muteBranchSolved = True\n",
    "bnp.muteNewBranchAdded = True\n",
    "ts = bnp.branchAndPrice()\n",
    "\n",
    "print(ts, bnp.runTime, bnp.nodeCount)\n",
    "print(bnp.incumbent)\n",
    "for cols, sol in bnp.intSol:\n",
    "    bnp.dispASolution(cols, sol)\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "# print(edges)\n",
    "# bnp.plot(colored=False)\n",
    "# plt.figure()\n",
    "bnp.plot(colored=True)\n",
    "plt.figure()\n",
    "bnp.drawBnPTree()\n",
    "\n",
    "\n",
    "base_bnp = GCPBnP('demo', data, GurobiModel, pq=False)\n",
    "OUTPUT_LP = False\n",
    "base_bnp.muteColGened = True\n",
    "base_bnp.muteBranchBegin = True\n",
    "base_bnp.muteBranchSolved = True\n",
    "base_bnp.muteNewBranchAdded = True\n",
    "base_ts = base_bnp.branchAndPrice()\n",
    "\n",
    "print(base_ts, base_bnp.runTime, base_bnp.nodeCount)\n",
    "print('root bound as queue weight, enhance:', ['{}%'.format((b-a)/b*100) for a,b in zip([ts, bnp.runTime, bnp.nodeCount], [base_ts, base_bnp.runTime, base_bnp.nodeCount])])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.0\n",
      "solution: \n",
      "[2, 8, 9, 11, 13] 1.0\n",
      "[0, 6, 10, 15, 18] 1.0\n",
      "[1, 4, 5, 16] 1.0\n",
      "[3, 12, 17] 1.0\n",
      "[14, 19] 1.0\n",
      "[7, 8, 9] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 13, 18] 1.0\n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[11, 14, 19] 1.0\n",
      "[0, 4, 7] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[3, 12] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 13, 18] 1.0\n",
      "[0, 4, 7] 1.0\n",
      "[14, 19] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "[1, 6, 10, 15] 1.0\n",
      "[3, 6, 12] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[2, 8, 9, 18] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[11, 14, 19] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "solution: \n",
      "[11, 14, 19] 1.0\n",
      "[8, 9, 10, 14] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[1, 2, 4, 13] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[5, 15, 18] 1.0\n",
      "solution: \n",
      "[4, 8, 12, 13, 18] 1.0\n",
      "[1, 5, 16, 17] 1.0\n",
      "[9, 11, 14, 19] 1.0\n",
      "[0, 6, 10, 15] 1.0\n",
      "[2, 3, 4] 1.0\n",
      "[7, 9] 1.0\n",
      "solution: \n",
      "[1, 2, 4, 8, 13] 1.0\n",
      "[0, 9, 10, 15, 18] 1.0\n",
      "[14] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "[1, 7, 16] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[4, 8, 12, 13, 18] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[0, 10, 15] 1.0\n",
      "[1, 7, 8] 1.0\n",
      "[2, 11] 1.0\n",
      "solution: \n",
      "[2, 4, 8, 13, 18] 1.0\n",
      "[9, 11, 14, 19] 1.0\n",
      "[3, 5, 16, 17] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[0, 6, 12] 1.0\n",
      "[7, 9] 1.0\n",
      "solution: \n",
      "[9, 10, 14] 1.0\n",
      "[0, 10, 15] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[2, 3, 6] 1.0\n",
      "[4, 8, 12, 13, 18] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[4, 12, 13, 18] 1.0\n",
      "[2, 8, 9, 11] 1.0\n",
      "[14, 19] 1.0\n",
      "[1, 6, 10, 15] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "solution: \n",
      "[8, 9, 12, 13, 18] 1.0\n",
      "[11, 14, 19] 1.0\n",
      "[2, 11] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[3, 6, 10, 17] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "solution: \n",
      "[8, 9, 11, 13, 19] 1.0\n",
      "[1, 5, 15] 1.0\n",
      "[10, 14] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[6, 12, 17, 18] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "solution: \n",
      "[1, 2, 4, 8, 13] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[9, 11, 14, 19] 1.0\n",
      "[3, 5, 16, 17] 1.0\n",
      "[10, 15] 1.0\n",
      "[1, 7] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[0, 7] 1.0\n",
      "[14, 19] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[9, 10, 15, 18] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "solution: \n",
      "[9, 11, 19] 1.0\n",
      "[10, 14] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[2, 4, 8, 13, 18] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[2, 8, 9, 11] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[10, 15, 18] 1.0\n",
      "[14, 19] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[1, 4, 7] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[0, 4, 12] 1.0\n",
      "[2, 4, 13, 18] 1.0\n",
      "solution: \n",
      "[8, 9, 12, 13, 18] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[2, 3, 6] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "[14, 19] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "solution: \n",
      "[8, 9, 12, 13, 18] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[0, 7] 1.0\n",
      "[3, 5, 16, 17] 1.0\n",
      "[1, 2, 4, 13] 1.0\n",
      "solution: \n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[1, 2, 4] 1.0\n",
      "[3, 10, 13] 1.0\n",
      "[5, 6, 15, 18] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[3, 12, 13, 17] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 11, 13] 1.0\n",
      "[10, 14] 1.0\n",
      "[5, 15, 18] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[1, 5, 19] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[2, 8, 9, 11, 13] 1.0\n",
      "[0, 4, 7] 1.0\n",
      "[10, 15, 18] 1.0\n",
      "[3, 12] 1.0\n",
      "[14, 19] 1.0\n",
      "solution: \n",
      "[3, 4, 5, 16] 1.0\n",
      "[6, 12, 17, 18] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[0, 7] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[1, 2, 13] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[7, 16, 17] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[2, 4, 13] 1.0\n",
      "[3, 5, 17] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[2, 3, 13] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "[4, 7] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "solution: \n",
      "[2, 4, 8, 13, 18] 1.0\n",
      "[9, 10, 14] 1.0\n",
      "[3, 12, 17] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[0, 6, 15] 1.0\n",
      "[1, 4, 7, 16] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[1, 5, 16, 17] 1.0\n",
      "[7, 8, 9] 1.0\n",
      "[11, 14, 19] 1.0\n",
      "[10, 15, 18] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "solution: \n",
      "[3, 5, 16, 17] 1.0\n",
      "[2, 6, 18] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[0, 4, 7] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[12, 13, 18] 1.0\n",
      "solution: \n",
      "[0, 4, 12, 18] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[5, 15] 1.0\n",
      "[3, 10, 13, 17] 1.0\n",
      "[1, 2, 6] 1.0\n",
      "solution: \n",
      "[3, 5, 16, 17] 1.0\n",
      "[9, 10, 15, 18] 1.0\n",
      "[1, 2, 4, 13] 1.0\n",
      "[8, 11, 14, 19] 1.0\n",
      "[0, 6, 12] 1.0\n",
      "[1, 7] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[10, 14] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "[9, 15, 18] 1.0\n",
      "solution: \n",
      "[8, 12, 13, 17, 18] 1.0\n",
      "[1, 6, 10, 15] 1.0\n",
      "[2, 3, 4] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[9, 14] 1.0\n",
      "solution: \n",
      "[3, 6, 10, 17] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[5, 15, 18] 1.0\n",
      "[1, 2] 1.0\n",
      "[12, 13] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "solution: \n",
      "[0, 4, 12, 18] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[5, 15] 1.0\n",
      "[2, 3, 6] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[1, 10, 13] 1.0\n",
      "solution: \n",
      "[8, 10, 13, 17, 18] 1.0\n",
      "[9, 11, 14, 19] 1.0\n",
      "[2, 3, 4] 1.0\n",
      "[1, 5, 15] 1.0\n",
      "[0, 6, 12] 1.0\n",
      "[7, 16] 1.0\n",
      "solution: \n",
      "[1, 5, 15] 1.0\n",
      "[2, 9, 11, 13] 1.0\n",
      "[3, 10, 13, 17] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[4, 7, 16] 1.0\n",
      "solution: \n",
      "[2, 3, 4, 13] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[7, 16, 17] 1.0\n",
      "[9, 10, 14] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "solution: \n",
      "[8, 11, 14, 19] 1.0\n",
      "[1, 5, 15] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[4, 7, 16] 1.0\n",
      "[2, 9, 13] 1.0\n",
      "[3, 10, 13, 17] 1.0\n",
      "solution: \n",
      "[9, 10, 14] 1.0\n",
      "[2, 3, 4] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "[12, 13, 17, 18] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[9, 10, 15, 18] 1.0\n",
      "[2, 11] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "solution: \n",
      "[1, 10, 15] 1.0\n",
      "[2, 3, 6] 1.0\n",
      "[5, 11, 17] 1.0\n",
      "[4, 12, 13, 18] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "solution: \n",
      "[4, 8, 12, 13, 18] 1.0\n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[0, 10, 15] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[14, 19] 1.0\n",
      "[1, 7, 8] 1.0\n",
      "solution: \n",
      "[4, 8, 12, 13, 18] 1.0\n",
      "[1, 6, 10, 15] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[3, 5, 17] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[2, 8, 9, 11] 1.0\n",
      "[0, 4, 7] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[14, 19] 1.0\n",
      "[12, 13, 18] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[0, 4, 12, 18] 1.0\n",
      "[0, 7] 1.0\n",
      "[2, 3, 13] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[10, 15, 18] 1.0\n",
      "solution: \n",
      "[3, 4, 5, 16] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[10, 15] 1.0\n",
      "[1, 7, 8, 17] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[2, 8, 9, 11, 13] 1.0\n",
      "[0, 4, 12, 18] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[11, 14, 19] 1.0\n",
      "[7] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 13, 18] 1.0\n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[11, 14, 19] 1.0\n",
      "[0, 4, 12] 1.0\n",
      "[1, 4, 7] 1.0\n",
      "[10, 15] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[2, 8, 9, 11] 1.0\n",
      "[14, 19] 1.0\n",
      "[3, 12, 13] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 11] 1.0\n",
      "[14, 19] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[1, 4, 5, 16] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[3, 12, 13, 17] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 11] 1.0\n",
      "[5, 6, 15, 18] 1.0\n",
      "[10, 14] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[1, 13, 19] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 11] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "[10, 14] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[3, 13, 19] 1.0\n",
      "[12, 13, 17, 18] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 11] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[10, 14] 1.0\n",
      "[1, 5, 19] 1.0\n",
      "[3, 12, 13, 17] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 18] 1.0\n",
      "[14, 19] 1.0\n",
      "[0, 4, 7] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "[1, 6, 10, 15] 1.0\n",
      "[3, 12, 13, 17] 1.0\n",
      "solution: \n",
      "[2, 8, 9, 18] 1.0\n",
      "[11, 13, 19] 1.0\n",
      "[10, 14] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "solution: \n",
      "[8, 9, 11, 13, 19] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "[3, 12, 17] 1.0\n",
      "[10, 14] 1.0\n",
      "[2, 18] 1.0\n",
      "solution: \n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "[10, 13, 17, 18] 1.0\n",
      "[3, 12] 1.0\n",
      "[2, 11] 1.0\n",
      "solution: \n",
      "[3, 6, 10, 17] 1.0\n",
      "[0, 4, 12] 1.0\n",
      "[1, 5, 15] 1.0\n",
      "[7, 16] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[2, 13, 18] 1.0\n",
      "solution: \n",
      "[1, 5, 15] 1.0\n",
      "[2, 3, 13] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[7, 16] 1.0\n",
      "[0, 4, 12, 18] 1.0\n",
      "[3, 6, 10, 17] 1.0\n",
      "solution: \n",
      "[0, 4, 12, 18] 1.0\n",
      "[1, 5, 16, 17] 1.0\n",
      "[7] 1.0\n",
      "[2, 3, 13] 1.0\n",
      "[6, 10, 15] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "solution: \n",
      "[0, 4, 12] 1.0\n",
      "[7, 16] 1.0\n",
      "[1, 5, 17] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[2, 3, 13] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[2, 4, 13] 1.0\n",
      "[1, 5, 15] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[3, 10, 13, 17] 1.0\n",
      "[7, 16, 17] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[4, 7, 16] 1.0\n",
      "[1, 5, 15] 1.0\n",
      "[2, 3, 13] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[6, 10, 17, 18] 1.0\n",
      "solution: \n",
      "[0, 4, 7, 16] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[5, 11] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[2, 13, 18] 1.0\n",
      "solution: \n",
      "[8, 9, 11, 13, 19] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[5, 15, 18] 1.0\n",
      "[1, 2, 13] 1.0\n",
      "[10, 14] 1.0\n",
      "solution: \n",
      "[0, 4, 7, 16] 1.0\n",
      "[6, 12, 17, 18] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[3, 5] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "solution: \n",
      "[0, 4, 7, 16] 1.0\n",
      "[6, 12, 17, 18] 1.0\n",
      "[10, 15] 1.0\n",
      "[8, 9, 11, 14] 1.0\n",
      "[3, 5, 19] 1.0\n",
      "[1, 2, 13] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[3, 4, 5] 1.0\n",
      "[10, 15] 1.0\n",
      "[8, 9, 11, 14, 19] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "solution: \n",
      "[8, 9, 11, 13, 19] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[2, 3, 4] 1.0\n",
      "[10, 14] 1.0\n",
      "[5, 15] 1.0\n",
      "solution: \n",
      "[8, 9, 12, 13, 18] 1.0\n",
      "[0, 4, 7, 16] 1.0\n",
      "[3, 5, 17] 1.0\n",
      "[1, 2, 6] 1.0\n",
      "[11, 14, 19] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "solution: \n",
      "[8, 9, 12, 13, 18] 1.0\n",
      "[2, 3, 4] 1.0\n",
      "[0, 6, 15] 1.0\n",
      "[10, 14] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "solution: \n",
      "[0, 6, 15, 18] 1.0\n",
      "[3, 12, 13, 17] 1.0\n",
      "[1, 4, 7, 16] 1.0\n",
      "[3, 5, 19] 1.0\n",
      "[2, 9, 11, 13] 1.0\n",
      "[8, 10, 14] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[5, 15] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[9, 11, 13, 19] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "[8, 10, 14] 1.0\n",
      "solution: \n",
      "[0, 6, 15, 18] 1.0\n",
      "[3, 4, 12] 1.0\n",
      "[2, 9, 13] 1.0\n",
      "[8, 10, 14] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "solution: \n",
      "[9, 10, 15, 18] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "[0, 6, 12] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[1, 7] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "solution: \n",
      "[3, 5, 16, 17] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[1, 4, 7] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[9, 10, 15, 18] 1.0\n",
      "solution: \n",
      "[9, 10, 15, 18] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[1, 4, 7, 16] 1.0\n",
      "[0, 6, 12, 18] 1.0\n",
      "[3, 5, 17] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "solution: \n",
      "[1, 4, 7, 16] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[0, 6, 12] 1.0\n",
      "[5, 15, 18] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[3, 10, 13, 17] 1.0\n",
      "solution: \n",
      "[0, 6, 12, 18] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "[1, 4, 7, 8] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "[10, 15, 18] 1.0\n",
      "solution: \n",
      "[0, 4, 12, 18] 1.0\n",
      "[3, 5, 6, 16] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[1, 7, 8, 17] 1.0\n",
      "[9, 10, 15] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[0, 9, 12, 18] 1.0\n",
      "[1, 10, 15] 1.0\n",
      "[14, 19] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[1, 4, 7, 8] 1.0\n",
      "solution: \n",
      "[0, 9, 12, 18] 1.0\n",
      "[6, 10, 15] 1.0\n",
      "[5, 8, 11, 17] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "solution: \n",
      "[3, 4, 5, 16] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "[0, 9, 12] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "solution: \n",
      "[8, 14, 19] 1.0\n",
      "[0, 9, 12] 1.0\n",
      "[1, 4, 7] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "solution: \n",
      "[1, 7, 16] 1.0\n",
      "[8, 10, 14] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[0, 9, 15, 18] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "solution: \n",
      "[8, 12, 13, 17, 18] 1.0\n",
      "[2, 3, 6] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "[0, 9, 15] 1.0\n",
      "[1, 4, 7, 16] 1.0\n",
      "[10, 14] 1.0\n",
      "solution: \n",
      "[1, 2, 4, 8, 13] 1.0\n",
      "[9, 10, 14] 1.0\n",
      "[5, 6, 15, 18] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[3, 12, 17] 1.0\n",
      "solution: \n",
      "[2, 4, 8, 13, 18] 1.0\n",
      "[9, 10, 14] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[0, 15, 18] 1.0\n",
      "solution: \n",
      "[7, 9] 1.0\n",
      "[14, 19] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[1, 2, 4, 8, 13] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "[5, 11, 16, 17] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[7, 9] 1.0\n",
      "[14, 19] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "solution: \n",
      "[2, 4, 8, 13, 18] 1.0\n",
      "[0, 10, 15] 1.0\n",
      "[5, 11, 19] 1.0\n",
      "[1, 7, 16, 17] 1.0\n",
      "[3, 6, 12] 1.0\n",
      "[9, 14] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[3, 4, 12] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[7, 17] 1.0\n",
      "solution: \n",
      "[6, 10, 15, 18] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[1, 4, 5, 16] 1.0\n",
      "[3, 12, 17] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[0, 7] 1.0\n",
      "solution: \n",
      "[1, 5, 15] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[6, 10, 17, 18] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "solution: \n",
      "[0, 10, 15, 18] 1.0\n",
      "[3, 6, 12] 1.0\n",
      "[4, 7, 16] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[1, 5, 17] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "solution: \n",
      "[1, 4, 5] 1.0\n",
      "[7, 16, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[3, 6, 12] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[9, 12, 13, 18] 1.0\n",
      "[0, 10, 15] 1.0\n",
      "[1, 4, 7] 1.0\n",
      "[2, 8, 11] 1.0\n",
      "[14, 19] 1.0\n",
      "solution: \n",
      "[1, 4, 7, 16] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[5, 6, 17, 18] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[3, 4, 12] 1.0\n",
      "solution: \n",
      "[1, 4, 7, 16] 1.0\n",
      "[3, 6, 10, 17] 1.0\n",
      "[9, 14, 19] 1.0\n",
      "[5, 15] 1.0\n",
      "[2, 8, 11, 13] 1.0\n",
      "[0, 12, 18] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[2, 9, 11, 13] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[7, 8, 17] 1.0\n",
      "[3, 4, 12] 1.0\n",
      "solution: \n",
      "[6, 10, 15, 18] 1.0\n",
      "[2, 9, 11, 13] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[3, 12, 17] 1.0\n",
      "[1, 4, 5, 8] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "solution: \n",
      "[1, 4, 7, 16] 1.0\n",
      "[2, 9, 11, 13] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[3, 12] 1.0\n",
      "[5, 6, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[9, 10, 15, 18] 1.0\n",
      "[0, 7] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "[3, 4, 12] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[0, 12] 1.0\n",
      "[1, 4, 7] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[9, 10, 15, 18] 1.0\n",
      "solution: \n",
      "[1, 2, 4, 13] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[8, 14] 1.0\n",
      "[9, 10, 15, 18] 1.0\n",
      "[3, 6, 12, 17] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "[7, 9] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[3, 4, 12] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[1, 4, 7, 8] 1.0\n",
      "[2, 11, 13] 1.0\n",
      "[9, 12] 1.0\n",
      "[14, 19] 1.0\n",
      "solution: \n",
      "[9, 14] 1.0\n",
      "[1, 4, 7, 16] 1.0\n",
      "[6, 12, 17, 18] 1.0\n",
      "[2, 3, 4, 13] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "solution: \n",
      "[9, 10, 14] 1.0\n",
      "[2, 3, 13] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[1, 4, 7, 16] 1.0\n",
      "[0, 15, 18] 1.0\n",
      "[6, 12, 17, 18] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[7, 8] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "solution: \n",
      "[4, 8, 12, 13, 18] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[14, 19] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "[3, 6, 10, 17] 1.0\n",
      "solution: \n",
      "[2, 9, 11] 1.0\n",
      "[1, 5, 15] 1.0\n",
      "[3, 6, 10, 17] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[4, 12, 13, 18] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 15] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[7, 16, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "solution: \n",
      "[2, 9, 11] 1.0\n",
      "[6, 10, 15, 18] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[1, 5, 8, 17] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "solution: \n",
      "[1, 7, 16, 17] 1.0\n",
      "[0, 10, 15] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[5, 6, 15, 18] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "solution: \n",
      "[5, 6, 16, 17] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[1, 7, 8] 1.0\n",
      "solution: \n",
      "[0, 7, 16] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[3, 4, 12, 13] 1.0\n",
      "[14, 19] 1.0\n",
      "[5, 6, 15, 18] 1.0\n",
      "[1, 8, 10, 17] 1.0\n",
      "solution: \n",
      "[1, 5, 6, 16, 17] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[3, 12, 13] 1.0\n",
      "[8, 14, 19] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[1, 4, 7, 16] 1.0\n",
      "solution: \n",
      "[3, 5, 6, 16, 17] 1.0\n",
      "[1, 4, 7, 8] 1.0\n",
      "[2, 9, 11] 1.0\n",
      "[14, 19] 1.0\n",
      "[0, 10, 15, 18] 1.0\n",
      "[3, 12, 13] 1.0\n",
      "solution: \n",
      "[9, 10, 14] 1.0\n",
      "[1, 5, 6, 15] 1.0\n",
      "[2, 4, 18] 1.0\n",
      "[0, 7, 16] 1.0\n",
      "[5, 8, 11, 19] 1.0\n",
      "[3, 12, 13, 17] 1.0\n",
      "12.040301991999968 6.8194568157196045 263\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "p_bnp = GCPParBnP('demo', data, GurobiModel, par=4)\n",
    "OUTPUT_LP = False\n",
    "p_bnp.muteColGened = True\n",
    "p_bnp.muteBranchBegin = True\n",
    "p_bnp.muteBranchSolved = True\n",
    "p_bnp.muteNewBranchAdded = True\n",
    "\n",
    "ts = p_bnp.branchAndPriceMain()\n",
    "\n",
    "print(p_bnp.incumbent)\n",
    "for cols, sol in bnp.intSol:\n",
    "    p_bnp.dispASolution(cols, sol)\n",
    "    \n",
    "print(ts, p_bnp.runTime, p_bnp.nodeCount)\n",
    "p_bnp.drawBnPTree()\n",
    "plt.figure()\n",
    "p_bnp.plot(colored=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.558046521000051 5.259777069091797 223\n",
      "root bound as queue weight, enhance: ['-104.27376510635409%', '-7.706341441479991%', '-23.318385650224215%']\n"
     ]
    }
   ],
   "source": [
    "base_bnp = GCPBnP('demo', data, GurobiModel, pq=False)\n",
    "OUTPUT_LP = False\n",
    "base_bnp.muteColGened = True\n",
    "base_bnp.muteBranchBegin = True\n",
    "base_bnp.muteBranchSolved = True\n",
    "base_bnp.muteNewBranchAdded = True\n",
    "base_ts = base_bnp.branchAndPrice()\n",
    "\n",
    "print(base_ts, base_bnp.runTime, base_bnp.nodeCount)\n",
    "print('root bound as queue weight, enhance:', ['{}%'.format((b-a)/b*100) for a,b in zip([ts, bnp.runTime, bnp.nodeCount], [base_ts, base_bnp.runTime, base_bnp.nodeCount])])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}