Problem:
 Score: 500 points    Problem Statement  Takahashi will take part in an eating contest. Teams of N members will compete in this contest, and Takahashi's team consists of N players numbered 1 through N from youngest to oldest. The consumption coefficient of Member i is A_i .  In the contest, N foods numbered 1 through N will be presented, and the difficulty of Food i is F_i . The details of the contest are as follows:   A team should assign one member to each food, and should not assign the same member to multiple foods.  It will take x \times y seconds for a member to finish the food, where x is the consumption coefficient of the member and y is the difficulty of the dish.  The score of a team is the longest time it takes for an individual member to finish the food.   Before the contest, Takahashi's team decided to do some training. In one set of training, a member can reduce his/her consumption coefficient by 1 , as long as it does not go below 0 . However, for financial reasons, the N members can do at most K sets of training in total.  What is the minimum possible score of the team, achieved by choosing the amounts of members' training and allocating the dishes optimally?      Constraints   All values in input are integers.  1 \leq N \leq 2 \times 10^5  0 \leq K \leq 10^{18}  1 \leq A_i \leq 10^6\ (1 \leq i \leq N)  1 \leq F_i \leq 10^6\ (1 \leq i \leq N)         Input  Input is given from Standard Input in the following format:  N  K  A_1  A_2  ...  A_N  F_1  F_2  ...  F_N       Output  Print the minimum possible score of the team.        Sample Input 1 3 5
4 2 1
2 3 1      Sample Output 1 2  They can achieve the score of 2 , as follows:   Member 1 does 4 sets of training and eats Food 2 in (4-4) \times 3 = 0 seconds.  Member 2 does 1 set of training and eats Food 3 in (2-1) \times 1 = 1 second.  Member 3 does 0 sets of training and eats Food 1 in (1-0) \times 2 = 2 seconds.   They cannot achieve a score of less than 2 , so the answer is 2 .       Sample Input 2 3 8
4 2 1
2 3 1      Sample Output 2 0  They can choose not to do exactly K sets of training.       Sample Input 3 11 14
3 1 4 1 5 9 2 6 5 3 5
8 9 7 9 3 2 3 8 4 6 2      Sample Output 3 12

Solution (Python):
import subprocess
import gzip, base64, os
import stat

gz = b'H4sIAB/stl0C/+29DXgUVZYwXN1JSCOYbpDRCKiNEzWohDQESAAlgTRUSQcyJCKjMEWn0yGtne5Md3UICoommbFso8ys6/i6zrzsjs7n7s7u6MyoyIyQgBD840/lR1SCilaMaMQRogj9nnPvrU5VdVeAmX3f5/mex1Zy65w695x7zj333N+qusftmWu1WDj1Z+Vu5BCyjC0lcCnDr2tJkgCumBsKf6/gxnJDAM7S0JVypbq0n7FWUxujy4B/mfCv2ErhYmupLh3L6NTUokmzOO2vVJeu/SmnSznOmcyHZT2whmIPrFmuS3MzKb4wW5/PyvL1s3z9jF5NOSunS1X9Mtm/aoavZnqpaTmjK9fQ46/yqFSL10/fTuGnby/VpY8wukcM+X4E+YZw5/5zsHQRk2dml3xWfjVV62FiMFAztWhisHZCMBCKNU9oLp46YWpRQTRcMImUycFo5y24WWfHXFbmUcwH8P69M2+54Te//s2i17qbHmq++fYNU7g/fon3prN8WCVtIUd95rihVgtXxK0F1njvBvg3G/798d3Jb19w7zrx176Xtj3UkNhxf5vv4req1rc8+OPSLyd8Kf4kx9lUmO9+29Px6dUdox4bPuc3Yxs3rBLueH4w+3wEBRiZBv+mNT1+oiU9/o9cerwj6WH63xUm+F1Z6fmsMcFfOCQ9/jWT8rSb8FlswifbBO83wf88Mz0+YCK3yIRPsYn9nzPBX2PCZ7KJnVeY8Flpwue4Cf0EE/qFJnL/yaReskz8qtpE7kYT+jEm5VluQr/MhP6ESbsYZ8Lndyb1/qIJfqMJ/7tN7BMwsWeZSflXmdjtShP6/zbRq8GknAtN+HxmUv4SEz7vm9hHMsG/alLOz03a1zUmdthrwn+nSTl5E31fMpH7Q+AzjhvNPXMf63cZfiTD97Us1/VP12RRPNdK8UsYflgmwzM+axn+KSa3+9JSndx5zD7PjNbj54PcEcCnm/FXfenOTIrn2pbr+r2DFip37X16ejuz51qD3Mosymf9fXo+NzD+3QzvYDfmMb2Wtxr0YvYvHaPnz0E/HJVqfdddh33vVE4UVzSEQ2JU8kYkUeTEm5rERf4Vgajkj8wJeqNRf5QTheoKsdYfYejqijnBcMhf7a0J+um99HfERqk+4vfWinf4V4k+uJIQ6Wv2inURv1/0N/v8jVIgHOLEWxdURYtAbKOrUERJUSkSXuVeNL/KK/jc5Ha4BG4EQlF/RBKCbveiqnC1iDfmV0lTfNKqRj8QuiYDzcpArT8ElAHJ3QQEVZJrmihCOcIrxRooijcYDPuaaM6oa3JdIFQr1gUiUUkM17kr5/saGqi4KXXBWLReZTFVy8LnjUqEA9yYHImFpEADKBOJhCPlLk2GMCjh9zaohfYB1uWr90ZEKeINSFEoL2gBHGq80YBPpRaqxepC0V01VaycXzVZDDJ7EeGEcwlYuSEQAlPSIkTnuNBOUVLu6mpgWDLAMBBawdimkU5sS40rFQfCUdAt6i8SwHBMDcBPBWN5g/4BhEFhJjxZEjCgttIQ1aDDSKx0IC+tRab4gn5vxA13JokClAn+ScRtQPVJhWothGNYXWLEG1rhB5FqbU4ltQk+K4mhsL5C8fY0TWUb7lcLUDKtPyLCoCurihpw9RD4gOSr1/gtlK1K9Dc0SqugLTRCYwpHvCv8buahrilUa1olNbE68/qYDDRobHFFc7PY6I9EwyFvMABsmwqZAZPFLJ/EaiUKxqoQwXB3iMCxVsVO9TY2+kO1REnA1AYrm6AqpBLQnVhRQj0Xn1fpSD0U+cD+qXnBzUzyYrbQygaSeRqTrrFdCZiORgd3Q4POWZJuSVUl9icNGGgN4SM8uTEmuX3MS8+vDYCXYm0Th1sIJmsI1/qJfgsgHIHQZm9NoMmF7hcNiD6MiSJGHGjZdWG3Nni5QBPWPoNM6RSLA/G0iB9CQhOom671zWGNbb62rotW1nslY8WypocVi0G9gcZzuFrhgxpByMWJN4dWotsv8kdjDX686aNkdWHgATbFCA+Bv84bCHJgKCkc5Or84TquboVfiiImSMiI6SV/c0DiGvwNvoZGkjSu4up8wXDUzzWQekFkQ7gJwzxKYp1KgzcQ4jDkc/PmzBEnFxRy8zzC7DnipAJXUfJyUsEUbs6SJWWzBdFVMJmilywB6qICl0sHlmghOlPE/zI49ToT5vv4F/+jMP3LsevMZB68zmD3rTDbtJIZJ6UZwqiHsLvZLLVpqFP/GwL9tYXrzhqYr1VevWoYzkhXD1Hnw1buNLtfiPPyXzw6BFcBWodQXGx0YCiW4eEkfSYZA6jjmzf/cEcWzoz/hdF7+39lxRWO3zFYvme8FWfKzzFYGv9LG+q7lfHrv4eOE4YY10XYuCKLzZPV31oNfrYGv16DL9fgn9Hgp2vw3Rq8W4NXx2+In6EtT+sAfqZ2/qHBz9Xy1+B1I562AXyZBp27luKzNWMsMj7T4K0afL4Gn6HBF2rwmdp5pwavtXOpBp+tHRdr8DbteFCDH6rBL9HgL9DaR4MfpsHXa/DDNfhGDf5CDb5Zg8/R+oMGb9fg79fgHRr8Og1+hAb/mAavHfev1+Av0uCf1uBHaf1Ng5+jwW/Q4C/W4Ds0+Es0+B0afK4Gv1uDv1SDP6DBj9H6oQZ/owavaPBjtf6vwV+mwfdr8Jdr/fneAfwVGrRNg9fObx0a/Dit/2vwV2r9X4P/odb/Nfg8rf9r8Fdp/V+Dv1rr/xr8NVr/1+Bnaf1fgx+v9X8N/lqt/2vw12n9X4O/Xuv/GvwErf9r8AVa/9fgJ2r9X4Mv1Pq/Bj9P6/8avEvr/xr8JK3/a/CTtf6vwRdp/V+Dn6L1fw1+qtb/NfhpWv/X4Iu1/q/Bl2jrseUzGx/PemOFk+PbOqQs5TaCfNm2ld5PTNkCtxJXvQx/7VeUwhXC9XirpzsBv6s2IowhuWc3gf+IMIbing4C/wfCGIJ7niHwvyGMobdnPYEfRxhDbs86Av8SYezuetYS+AGEsdg9jQS+D2EMuT3LCXwnwhhqeyoJHEEYQ2xPKYFvRxhDa08hgWsQxpDa4yTwrQhjKO1xEHgRwhhCezgC34Qwhs6evjMIz0bYQfQn8HSERxD9CTwJ4ZFEfwJfi/BFRH8CX4nwKKI/gUcj/AOiP4FHInwx0Z/AFyB8CdGfwFaEc4n+BP62DuBLif4EPo7waKI/gT9FeAzRn8AfIDyW6E/gdxC+jOhP4L0IX070J/CrCF9B9D+N8BaEnUR/Am9EeBzRn8B/RPhKoj+B/wPhHxL9CfxvCOcR/Qn8OMJXEf0J/EuEryb6E/gBhK8h+hP4PoTzif4EvhPh8UR/AkcQvpboT+DbEb6O6E/gGoSvJ/oT+FaEJxD9CbwI4QKiP4FvQngi0f87Uv8IFxL9CTwdYRfRn8CTEJ5E9CfwtQhPJvoT+EqEi4j+BB6N8BSiP4FHIjyV6E/gCxCeRvQnsBXhYqI/wJWbM2HEpHwNl5txbWoTghsVaO1KGRR/yVbXMUHe8xNe/oBv+aivslro6ij1lXJ8V+fuGky6+saWcsrHkP1v9itgYLjhJGS9mZ/QBwnf0p/Dy0dXWpdt3UBiBQSIvy3bjLcS3fYryNLVhj4NfWd/BtDz8ha+89NZvGU3v6c/Bpk30sx7ITMvb6f5P2D5195wKzpTbMTNkFO5GxRctj1rHqAsX23FeMO33JDvBQLp8mQRRt28uW8NSJOPJnYv67kLstiv4HqiNL257JayxWU3l1VX8fd+tgTCzmZsKptwWKk4QUte3up2neDbDkn5fPsjeRsgcG2qRoPIhxWsP75rjg2DGaRkWCTI72s4vH8KOWzj245JczB7B2avJdm/UH5BsrtJNr6rNW8dG6fy8u/z1lsQ57ZhXOwqzyOrnL1OHRVcr2fXz1hkoHEd88h/znsacBVtJ6RMITHX5pGlvHpNeRacYhp9hBpN4NsfzNuBRUI+Gy8gsg8r8ql0ar3rkZ/MewZwnngMyIJ5y90PFGhYf/UtUVWDOQIYQe5yHapoS0iTyuzPb2vpvzmWISS2oyl2Iys54ZFPKPeDQKFrHhEIKRHokU954lB4IX4jH5+s+Al3N4fFvoBPzLfZW/uzqYloMZkZwXTrSBlX5y3nW9bAGLaVh9i4vTXvMUBb4mCm1o7YD4XEQlvsMiGezbc3Ky/i3IIv2WVv844kZVJ+9U0igTYEugWEboie7t4RSHdSCQMd3/7XvLVYW+1/JqKV0dci8Ne8+xkSbyrPgUtuzk+aZjtkJG7VIY2z39JhHwEalDJHKM2lGpU6qeG3YyU6+Hgr5RjP9cSboWJX59V7Sk7ZW38IyLq6AvsVrcx5DivFpZhuE+KLbcKMxQ5pDLYlaGnX8i1bbIK82NazGyjrSPutiE/l5d2e8X3YGAV524so3GMv7xEs2+y/7PDIPXxnzw32f+60dfIbEvR3hcfuVirajpVtJjsQkwZuCCXb7D+/D5A3tc88RqoR6tChZJ8inuCR+zzxQj5eafOMPy7Ibwmd30GpdvMt3f0eSz+WML+i7SNpGHOLZ8FI89uDn9iwKmxo78TJRIJwrJBvFOSQbX575heCPNeB1V3Pt2y1CSWH7W3fQDfUa1cWgcv0SFCqnidGDNSIMuNGNOITtM7AmyaiN8XG8/EMfR2/ZqfGjJ/E1tBK6EmFgDPE8zc7k3X57UnSplwdyO0animu/JagM7bPsZFGxBpTLq2jA7zcmrecVCeHlzgeQt9ttNCG3Wyhs1Ysdb7WuV66hiILGRJvKksuR0atFAnc+AzKpTAZUFrzii00GGbyidJcbL71oAUSlWqiDk+aTr0N8ZVwjXXqAfwSC5WKjJVvZw4UQTk2k1ozn1lzFrFmSYo1/zOHWvOGE9Sa+arUuCqVSiG+7ik5Zm/9gHjPn0n5PO1P5mGrKNuMDHvn8y0d/S/S8P6KdOmLWMw+e6dNfpCUME6T3uy4rf1HCdnR9so9zWirSiz9Z1CA3oCmvcSXQcGeIOxRAbDPHBvGUScpzSdK70wSTz3yl6uG8cAaq8hj+RJo+fgjrOIyX5WuJYrnMcU9JQl72+IEesBhRfia6pyrehAEwJZt4KvHYveiIbFqlCdnQIAYcKqnvh5wKj4OtaXxLMJvUM/q55KexTHPsjHPcmg8q4PTeJbvKorcwZB4UzkzhnrWDk7vWTu4Ac/azWk9izrVAW7AqbpJRS4nTqVwqlM9mdfHaZzqqekD0pXHplOnIuUbxKlah1OnUr6iBu7gBpyKSqVSPCVH7a0fcpQpFs0Dkp7BBaNNf6c/rUr60xN/A3+6fcCfPPEGDpsMsocQid2w24YOtoGjfcu/TCdK8fJB6k+kYiwHgRb9iVP96TohUWGLXSXEoc8jDnXS3nYzsdwXSv9xqu8zqr6JrdShPoutRSNijShVJdSi61h9YrRRLi9EIzxIkTiYYBywoT2GKz4OGFQwOzTy8TJHHW+/e5trLwxgeLvwNi9X5y3hEx28XAYd0tI8R+w6Pl7CQ/c+Yu0qGxcbJsi7lFu/wlJ+w8u7EPIcpxDwEtqrP+9HFjY+sQNZ2Ft/S3xkeF7PZnaB8jpcr4A/gTwg8uQtEdrLt0O2csiGInseIRWpcwXS3dvb/jqU+sOBL8E+UNXrLOYjBKgCe+s7GKNxMNG2G6/iM1CVLMX2N9pw1wGfnufJHa2SvPyGEsMqgL4RigyKKnUoUYbS8e2r8xrR3NgElT8U0KJC4Z38XzjHU28pHB2cNGIHXuo6tg5DcSHfXn4Z9vRC55lZggU6/C+h052FUWbPNzB48sj90jS0BY8yijF/M/r2WuJjHxHPFezC63xLqcPaW1hXpxsPnD0bkFTyODTiSzpBOd7uUTShaDTRbSsYjIyoNYGoo2/wQFSel++JQ5cO0fwUVg04EXY89lY8VMNDP14plPxNGgplsbeNIp0hlqGtw/7IFj7ugR6qZIf9ka18yU77IxAHT9gf6bS9aW/9FFfDdqgdBJBVtL1SRgf7ECj5u4NMle08mNHu2YGCIKJ/Qcc7QvwKoeWMdXX2Hy2gUbMdgIT9CQyz9v+9xbllA1Fi/IHWvYK9vIO3KPz/4k+e4uMX8+O38PIlfEuXTYB/fAu4ZOKV2LML2oveQNEV8kmhM5FRUfKNvV1OqhJHw5Z0CvLrHrune3770qOc0PlRBgnobD4DfuaRP/fIx5UtaM0MuOOxfOgpOb36aA92fTe1Zx6ldJ185zcZYJ2yjbTiFryKFZfLtN0BfZUgvwxyjoA9PdBSePtDT3C0JPUwjeJnSHlL0JDxaizUfrBmuf35Tr5kX4XdvUXo7M4AcIfQeSRDOHmQB18Bm30Iw7+6Or0/xacJ8YkDJjymMeExrQkVYsItHssBNOFpfjzUh4tv6bRVWLo8ll184rV7/kTK5mmfuY1oROz33ZqKAZXLYRCvGjIXnROqU2hf+gbMHKGY8k6PfBQHjA5qRNRsuXLXFxBwLEc9lq/QiN1o61L0PGCRL/2Ej7sdJPq4HbGrefk1Pr6Gw6Y9bO0aaNpZyq8/R5f+QvnnY9jqgVY+qJJfmYY8xMjrkBxIIWxCNwihTn6d2R3nfmSYDPHsNN/5SYbywDHWno69RA0K7cD+0G841WkGHLftFerWdh6vpYt4eQeMlS0kG22Llwk4xyGIu4fzXWTj4iW0P6kEiNst31gjc1F34LsTmMK45QTwO16n1qfQshqsf3c2dDq9fGIbgtCa20stSRTG+5LtsX2akJCNKoAnbiKFGM3LudtLbWQKw6YuuWzqUiF/UNG2tyJ+IzZUHjvD2Q4czYMD3e3gYvkV8lse+TWcH9rL1t4NJrVhF5l7DPuMk4rjM5w2HAQmYKy/kFM1D10CEjYmDWBv/f+IIEVo6T4zgN5E760lTvQp3/LBGb79zjP8ni/4RJcEo99+NOOmJDlhLV0M00EhvtImxMu3QZiQvub+YiVrmwlrZKrQvvpwLj/+nXV8XFT4+MI+IaP8PU7IkMAX4wv7eYgqGeVvIOI9yNHlhJnWd7E3PfIW3tLHl0DHuVVazoOLjt+Hvcb4PRKEko8tsRGuQ71jde1LiBftB1ZfQ6e+Bb0EK/BqfvzbQrtnvw0R8TKQNkLIWAqFzFj6NkjrdEDvfyYGTeEEepATB1tSAUxdHWjgtz3yGziJBhsPpzbOUl7opQb+w6do4J4K+VOoJXvrM0T+bGwgggwJNJ3C2KQKeacQn475R5atvRM7QGCqSMAC8nnk/QjVfUogzFwBzkvzm2Ut0WW9TpdVaBcsnpIPefvCHuztiyHMHWTspCLswUp7q/X2ai8jGbCskEtJ5qLE11D5BNAUYp/yZyL2OBRC+fce8OaS9+wPNvYnEptxIxNqSokiNp71epUTargU+D62u2U5HTbckc1xa+8q5qSsB+bM6b0Uhwr/TKghspcWbmLrKInd9k19hJ0y7zNs8l1KAoWCNWBwt9CBg7tZrFruIdXi/BSrBfwfmXWV5zXS+JCZh8MhUNABfW+Cd8TG8fFiOh5ppuORrcpRzIJ9bpfyjkJHP/1s9DwBRjiO2DVsdDNs7V1E1p976Bjn9wodS/Ynx85P0Impac47Wc4Iy7lcncrBaHcsn6jIjV2shskspUKhAdKt0JUSMtUoHg+xqYO/92Vcb7utbGnZsrKflInLtmqH4fZWK5CaDMV7jpGGqaX+mjOn7k6hVgahfjOF+tAg1NtTqHcOQv1iCnXnINS/h3vJOYf94XE471PnHXlg0B47YDQEgWMaggwkuOWYjuD6LzQEH3wCBKO/0BH8uE9D0IkE8/p0BO3HNQTrkeCu4zqClz7TENyDBP8JmM2ir5TbVAd/NuKByRefgSvlRx+ju8N8HzoKvmV1ngPHlhDAIP7wubGx9JJ6epaS+TF6NRAp3x6lrkRWcxqu163m2FvdOLQkQ/oZ1pR5gr31REoFvD5IBbxAezHCGJ+JoYxPcmkY70xh/OQgjB9m3SMOjT8kSwIVwPggib90wjfA+Q8pnNvMOfdGByapFybr4eRR2mQ3f6Rfy+rB3jRdhu0sw0Mf6ZcukssVX19rJit+lE5Sqz4yTFLVienGa2mVDR6fRrESDP/ofOPTOx/RnHs/NMSns7nYv36outivPtREqwfy6GJvDXFXmGhudpD59VHsO18mqx1A1g/VRZYGGxWYHIzC3IhWljJWCGwaWw8NALf2lKMfDKBxBFivVAF7zeL5kx+qOwf2tklWugqDxK5jPZVW4jw9tyawBNt7vvtuoB31vHtG06gGFubsrVvPqHPdF8/QvkrnZR6r0cuutJr774WsCEVnSBE0izX2VmdS0MXpBI1KEfTpIIH+bbThgSMQRuL9RBSZDp8iy20OXG7TVf3hD2jV7z+i7UBbZubBYFA+jYWBvhR6UHvbXSpEK9+O16Q3zVIeRybxmXnKL1DsotNMQ2p+vS6bMvS6SLebNcslwINULFmvAgk9k210tbN+4M4Xyk6880o2MS5B4nDgRUAmKX6PFIszdB3DtAy6xEPa3zAkuCpjwGPItonGS40rIhsIUqfXvxpCjnSXmV6BdCHg/iO0Giq79Yu9PbO+0/QVmgy3sAxXdOsX8nreOpU+w9VHcMcoM6/w9lISWBRsXaRZPkhqbxm6YmwCmkAhLQwGcO1/zcTr+J8zcVetZadF6QYuvT9EP8620JWjTd20WZJM8vu8vEUZc4R2aH1sDbwbPXLfEV1jPXmY7G+Ab9rbRluo6ZHO7Ur0TLXQPiSeNU5wkgVTWqILrxXYyFJ+kBQMFUchDhyAAgN8sklbLdKs816mHDDY14epZVFAI6nvv+bhI3zKE0eoxqUIhLsxeHyi/BzSP+Hk3t66n6wvYu/0Bom+/2DvpFpGkLOxON1q5DTs/qXxyf7zHmils8Ox9+l49Ln3EonWQ7ir9Iny74dxV4lubuJaVOufMbolPHmg9L/jJcaJ/6mKCL5PTVz0Husek23Dmr7EM94n85CP5uqc59jc/4fOs+E9E+cRD2ucZxIUVLPF+eG7mm2x+WSTM0PdTd6Mx3GUL0ldQPfAw8R2vgP3EIZ75GKcIGUKiXmItOF6IltXdLJ1RQGXgrGptb5HRk4LoZreJCMn/W5u6/MpHvPgIK55J2mkbAX40UPJkHtYKXmPzNrsrUcgfFF5+/HKKO+5lJ4tPkgX2mRS3/PfpZPAiw7RELccQtwmDv5sdNSUkh0pAasDN+C1fcoj79JZ39NsbHQ72TOpNXS//5AbPHuIRulV71B3eNoyMCRb/39P7HQm1srErteIZSv5OC8GseOS9Tf3XZpn50GaRz00wfb/b8eB33IYCdhiIr2M/8+1lgfeocPI5UolSodRZz0n8XQ5ECXeQC8NEufpJI4zk2jHkeiBQ+l7RQeTXK/sO0AXeP77EBm9uA71TPw2fZ53D1J3exJy9HBAZFjv1ZyPafkMxionePm0puc7fYC28lewFq7i26vzlm/KrKfHY549mHqOBBdJZU9evobFxgPaMymER2mSR8PBtGdRyKmQbL6lWeHoRAvbg0OIF8GAboHi2qscpUz5llPQbh8j0eFGT3yWYDmsEvFdW9DGyh370UTacyvjD1CjHatoO2Fv3Uz27Y7x7W5L2UukWyrZam/9J3WNlR9/gLdsw0LzrhPYsxzaTyR72u/I5VtOW2KVZQ9kl93v5npvJmV0HUMPbeBJfDvdW6uR+yzkxNsd0gK+iw3VSpnm7HCIvK13EkyuPPIZXv6U1PeGblakNnsWbkMUCF1zWVc6lx2k+ZpsCMC8RrqK9Gqxy409mr0tnEl3u3+9TxfEL8ci4W6cIH/mkWvzbGc7rsLLnYJsdx3CUxr5eB5LiE9FawNvXn5HeYgYZxuRX7JEsd8XwoEqrogexJ2zSo/8MZJ9ckA3vHpsn7oDJRVi46nEXgOG95XURnrvIEOHXvvAfgXQuQ6h7Yv1KsesePplYkU8ZquYEQPj4MJ0liBf7joktGy3VcgxW4/Diodr9PtpS/MKBRhgeuQ+rHtyaqZLPTXzjWDpIqdmvkl/aqYf+hGyWi9dpz0002X/eYuVHJo5TTbW9qtmWsZh8ZgouZNvOdLPW3pwulKdV8jLI6BuoDUVekq+srf+knV3fKeSwSc6gIhksJBtynfwENAxKYePD+PlCo5tFfDtVTa+vcKG1nHyLf3WyGyMPhfycSsvW/guKyWaDQQ7YVZrk2CyfCcAq6Bfnm2jk6sO3AyzCXTXQt4S2yS0zyXbtbm8/F5vpafdb+t5wjifuGggvnnylsbhnzaW3sC3S3lLlLK3YXjuMsQjT9yPgzRpPHXlK42uHPsVuvG+N8nROjbvx63xz2Pb0BVy0ZAt3f24nOr0tNfmO8DAHFk5h3qlp4z+15vY/hugMfltN7WXn4aA4yZHjOAPNAF2IqOYuLh0Bel5LjV0eLFbyfwHS9FyGoZIyyz2tuPf4WLYeVnC/vBtOE8l1pj8FmT3IHge2ZsSava/QVl6VpADBLV55PiYA2ef+ZuzIdIqdXhXggIqY8HoPVEQ01NFh8PSOGrp0SmWFtHSwb30qNwV5KjcpYajcrGlaFB+L4rt51terjdZ7ZVCqXrU6AjuSCUQdQT1qQRLk53OdQ9kbZ3h5O4vvy6358C3ugXLIadUE+2BYvac1N+9NGnAP+HdCxK6u1ck7/4z3nUk6DJ/8xgMpjozY+T+Kf4J4qIOjAkKOcmLDllIxgSL6aVhTDD1PNrMBbQcF+6lQ4BC5c3d6mqWcAld/LIxkbYBkbb/AZGP76GCbMpyIhJ6d3p8JCHYcKRlwX3Tf1DG1D10PHdmF52+7dtLAmRy9PvoHorfZMDfDfieAKuWI5eiK3QpB0m6ja2vnFR+vAddGOZ9eP6fzfum4KVx3vd3FHzNbjrdc0PByfl/3XljG1np+ZqXZ+aV4Y6yvfVgJg5V1jg29awo5Vx7X8QIrCxBu7b0w0hmWpbugPI9u1j3LI02HTF0kmUu2eZ2JXqH1q0T7u3i8Nyx2vvcI7RnZvM3JDKQJwgvs3tOkjXoODnL4lBu20X2eu2teKxTIztjl9op0w4NXPww6uzAw73KEJwv7Eo3/ntPXXaTP1dErDbolq7FFQZczTOsLrS9TtZhvlCG7ISBKTu/gWUrVorAJlCqthIr3SLzxAvJ8mu9ou4V8/YF77U093PNw7CS39qNFToEWyR0eZ7LEg+MtWm0mbJTXW4t+yvtn+2b3iTHokE8HpPuQ43CO0m3ols+g5FRhfyFhtWnbxgMsymLDmbRMP3I5rGdaQ0jxGeCCW5R8IB1RXtIEWYsVmJ2PAl7zS6i7CNbe60wlAGD5RKDjSSDP6FkW2wJGunI68SPpWziw9IPWg9VwAhzQp1hf1nv1EASm40e+tvXcUUEuzmbvXUfdgDvk+MB2/uTB+nridP0ldk7h8iteZngvHGa9N6iaxl12tkLIaNJb7WObLkZ2YKy+JB2PlEm54DSD4+jp95wxoJPIynXgn17husD8YwEXaQjBEORYLye4IXTGgIFVO15Sl0BvvdlbIaajons+Byzt+5OLn1tT7v0NStlvv8Dw3xfo1QPR5fMe9do1nt779BO4few+ruNdrjVKaP01uMWo8QdFnOJf7IMrCMj/5++rq555+JQOkBGLr7zmKpr68g49SWGffg1GqZveZVJ6qJjNmh/eVApdONk9Ou0fXRlqKu3zkx19Xbn63RBiWBehxC4ER88Un71Gq29/Ey6BosvZ1MWva6bJERfHViDvY1tmCAdrsGuBJgK//VrRuH3Z6nCf8SEE8xnKPwSUl4mfF0WFb4WUuXt13TCT7wyIHweE450KNyXutBDjFXzKu2zroW8PbVnUqbnhOiGV2n/kYVEs/BgO9l3qUkuKr8+ia4LosX4+IVvTxpYFyQoXH7PHFgXbMw07mE8m7JK9nPO1Kd6Y1TqYiYVTQVSRY1UgsIll6wBqQeyDKuR9+ic7Mdm0ualMwnaS1VLXY1cjg6x51Vaf80IPAlUvTemy//pDrYmlDWQfwfW6S9Yfny8X6l9hbryapa2vkJXVJYS16auxRax8fIcF7HPvlquVT+quhEThJfnKOj1lEjxb4NEinZLehfd3EXWnusLdT7208Lz87F/KKTM7DKp7Q93aGr7+S7dssWK7aQ3x7XnY1IFWbFwJztsuvgs7WAPFJHV54p0q88wL+5ys9UNN1vdAKXn0MXnWqu6+HyrNc3i8w0pncNFg3QOZ0BKz70mtVCyndTCBwW6NtdbcJ5t7h+phee3mbSZZV2aNlO4XVcLR14+ay18vv0frIXhFrUW8LhQSi0cTWlvfzFvbz2/Y8c9kqeXHt6q2XmdtI1tAxw9pW4DHDqVZhvgP1Jkrh2kjYfSmXvey3RR1r4VzZ6xDrcAKgOlXO/16agvZdRHthBq/YYBxq7cbek7l+6tNOPTW/As0Snj85Atn+XC5ESQExVxv0Nz+nPoVs3OztV4oBl7qD9tOdthcJsn7hTImVo8dYFTijVWemgb5gxjj9HRKa52kaXCP4GUivbrP8PM+Jyo/aXdfMvxWbxlLyR3A5EHJhAPYai599S9eNzzwd+QJ3Zm5nnkD/mW3rUVMBmxVcifGMe/BcKM6/PWTPPIxz3jP8DHz4C7NIrMtPjxnUL7zF0e+WOlaCv26Nfn9U435s/mS7Z55C898rdrxtStw3WyeFkfXSDrg1u8vIeXj68GHcaTNY8OB19yAI+Hy9tj95EzKA4PDPTBK9gxcVA+c1szDmDZwt1QtnAnlfPxqn5csSsBlu+tmcC339XPy+8b1iNP8/LFfHwxFOEk39LVR4g/R/kVHD4Sh8t69vsuAmFk/sTH5/fjkK9kBxh5zbV8exRYHtbyO87LdjzhystnqEpAuR/5Wfn4RZAdpk9zbETK9jWj16UR/j4vn0L6HFwc9JRAY90au7/nF6RZnLbQ8YXQvhQr5+N1fPw6Wh9QHJP62In18WSnWX0MIUY/CPV59vrow/qAIglYyK2x+zRePblD/8SVQ/Ogw/7Ng/p22zHsoOPFgnyPTZDDDuiXacCKg5KSne88mkEWtj9V3kch0GrGkeMrYwzHV5wddKyRu1ldL8vVTEvo/LdsMWuYZ4T4T5zI6zoyjr8q5emo/2BPg766CTcS59KnSeM/yR0sz69YnkdpnlyWxzFYnlaWp4bmoZPi+OWY5UqSZWxyW+4PTMFphHQIXZ1FJX+iXw28Wbu0khmHf72CjuBHWoLhQDC8160jWKAlGAUEo3pna6aDH+HJpPaiPOU9KEnPvjO6ueKpM3RVz6Zsxruf6e+OYGt+DuVf8a41QbbMkv5Iwmd1VaUg74PIyct9nvH9/A00QD2bwcHEfoVNS0/2mM6Qjeyd6jMenQqEuR38nn4hfotNiM/s4uML+vmSb9eMBIOvuUyI2wV5liAP7+r1ruNLXl0zA4JS79Xq+kg2osBB7Lw8t793lmH9XJAvEeR5tt5r2PMnQvwSZDqU0C/u76WsR3UNxIM+j3ykN2cAnsHLjop4dvL5ldcwLLTPh5a+ZfWX0J6bMej1CyX9q7/iS7ZGdpKHM44rH54hJ5SJOejB/PftD58BZM/jZKjU77A/2GIZeNCl/Udn8K0AnvbqEWeEPd8CnCBvCQA4we/59kQnRJKnOLY9M6Dfwnjmfk/JXo/8pkfec/dP1/Htw8cI8dW7HMKM1btsa3BRa4ylNyf5/APCViE+HILL8MN8yX6MXa95MP3m7snQH/LtCxP4MBbf7rGD3G+E+NhdnpKta3IEeWzCg+Fu1z2HeiFkf8Oep5Yzd+EjD+2LYCoYxqeP/xtL1V4+0lIhv1m3bmF8+GFPyTse+fDdJeR9Be3DZwnxpVi+pbR8nln68nlmWVEoyiPhdRcpX2zY/Pjwrvny8P0euctTchjLO9W0vK+tuYSW9yAt7/AB/vI3FfJwKPKhivbboMhLE2Stcaj9wQx8sBzqcy64IfgGeGB7U78g91WM76+QOwWoI6FdSOADCqtvRYkQ0oeCxFOuvbr+qX34SL39PSMN+o0E/WaCfjP381i+V1e/1jssed/1ShlUdSxfYP4goD9A/YMpYuB2o4YK8er9EHVH7QJOQy3roPEDi7tnDPBfBW73rdZ1wCQgaeuaUWCSXXxLwgJdxD37e220f5RfFeTM/UJLv83+cAJ9tuVlG0ZhiMFbdeMjaOJnKuI3kiHh0/QkSq697TccfcYsHoY+sldqKvsrecS02HzpmMY3zbD7GhKZ5myECezYdZ74dfgY6ZXkcdCxA4+DfhL7Lzxle2wDGfPRSAqdfcl7sTdAPAZfG0baNzbQId62DdqIa9N2K8b9/sW4Mr2WLDCcIUuquThgxAedk2+IOPkiPjHxBEFKw/l7X0ZyiOLLtvLYr5PlFKRLgA6bmnElZTzDvPEiWYalS1D0AK6Nns5bnWmLS5n5eC6PR9VzkbwABbXjki09jPcQwNtb81aCuGsoHoqGEL7mmlPCL6qLTM+9SI9t0gXpmXlKHvAsfc6CpsVpvH8D7rVvwrfZbcRXACPuyxfp2lA8a+U4J8eeOL2wZZw6z/Jk2nDX2UFnWPio1jlOr1ZbyHgfk5TpFT7pr9yygZ5XddC51eo8XsncSDVfrryzgXaaH9Di9S4jZTz3I2lnlX7wBTBN6zF7qzVB1zgeA4k9X58h84EqCIjJ1bK7bMp35J0oh2/r8W1Ie/9del8p38Do5j+PHWTSv4z0v2f0o1T6MYPTr2b0H73A6HufA/ok+bpK5XbgpLhfwNKVk/5YTpQtFuQTZTcL8ttl1Z72q/pGO7mb+fYJ/ZBWecYfdR3iO09n4JutlMueozsSlyf3m/FBpRPlwCMx6l1oYhZo1bFPSRtapjairZryfrWVvSOLvRIL3wc5sdbfNDEq1QZCXE2srs4f4SbGopGJ5O27+N2EiY2rpPpwaHJB0cTaQFSa0Oj13eFd4Y8SfMQbYvcDvon4YtLoxFCtNxLxriqob2zkmsKBWme+NxQOrWoIx6LOkLfBHwUG/vHTp6vZpk8n+aZPZxlnBsOhFdefc67GaL230U8y3ei8cfp0fPtywBsM3OkX6yLhBtHf3BjJ94VDUcnpdl493nlbtfMGJxXRWAWXf4+c64HVuecMxRoaV5FyzDxrlnCjP+KVwhFx+vS6WMgHV9OnR2M115+nsBXnJu0fMjzJQ14dyzK6zj2vFGsM+sUmfyQaCIeg2s5Dv3CkgdRurRgNBnz+G51XX/9/pSYIraYW6oJhqJbaQFOg1n/uEmsiYW8tvrSb2Yi5z/9/agb/W8YFQk1g9FpnMCCBfwaddeEIUSV/vHNlQKp3oiynq3D6NZz2dc7Tp+PblbHpRWI+yRmKBYPOUFhyEl6cMHGhk7o7SHLC/+RlvrXOukDQz0Wu4+ZC6hyHJCEiD9/mDkw55wUGIdA+4JKbE44Fa0PXSCwH5uYWuCaJ8zwLZ5d5RHGB6CpW1Z1CtHUVLvYGY343vmGbnsY5C/3kKsmLVVKrZhmcvsStvkJZfefs2fjPBjtqMznYe2U76kt176tNvl+T3V+n3q8vTXt/OcM7AunvF7L7hYb7g5d3mrBQY7tz0G+qO9QUiIRDDf6QZMipL8/6QPryqPcbGb7bcD/5XlNV3xrGh6XG7ywkf7fr6Zefhb4yoKdfZ6C3jMmY8Qh75+xj/5VI4DOexc8mEvkwFv4xpPjYGf9HgGGEU/92IoFPgW14P5HA14At6YURAj558Hki4YTJ52N9MACCtPlviQS+iXLJ14nEekjrTyYSOyC9H9JuK77rLpHox6PukDpATn4/5IP0MUjvZy8NVt9da7lzEWdpdljGDM+2rbNkO0ax98L+6zOJhPZ9punp6TtYkf6Z30NZkHeOY25O7k32YStta7lZo2dcOznvSu33sDLurPQQDtkZ+EmIDJVPEXufay7YZMQPOS7TEyrlynIcs3NyZ+c4Z+fkz84pLMspnpezLrsl+8Eh7VkPZT6c8TMrybcUGb+VSFTiG6ufDqfLV56z3pKSD+sl971E4tfDIV91Y3p5HTm6fKOSeujtUMR0rO9JJB5HO6QrQ6WOVfI7YdX4ftPPEolHcHIwO8dRlpNbnlPYkvGQldpEQj8DH3gvg96nfMty8suAKuvBzIcyHraifDy+aDueSOzh0sovyylNlY/8nwC5z0A+fFou8/qfqnYoy3HOy1mf0QL8f2ZFvb9DHwbfu4zxx7OgiMd3oqwH/FUG/PXoy2nw5YDfnQa/FPC2/04kxjG8+p2x1XC/G+j9HNX/Yas7J/ehDHeOsz3TnZP/YBafU/izIXxOcUt2RU5pEDUFffOJ7rnEXjbC/2XgY4M2YzW04UTG2/etXctl7LXi6zTxlaXkPaO3M2ANAnjgFdMXcH0yZqEvhf4VYsqslOwFXIVczACy577bSuvkHsQEofIaVIkHM+jbuD/EGv1NJs3ThXn+xIC3ELhsCH0J9zh8bSoeycE70/GdqBdmU2A2Ag02SnYnksXg6l/UN3Q7RnKcF9L/QrKfAvCx+g7wW3Lpe3fzUIXfXkqLQ8i2jKFvwraia54aQ1+D7EdgOvyZob50PHYFfeH3BWiQp5w0zwdXMhPdak3a9ql70bZPWCawN2w/jkX9KQP+iMCf2bvDuwCwHGQv9N6FwNXs6zf7kQwf6LWr70jH0ws57Lt3loCVvuy7GF+nK2VQa/wIgT1Z9HXZHgS+yKJqfoN//gZ/lmbovtKXyHjzXi5jNwHeUavqHeYFePKcG2qhvH+LGSUGnEbg3y30fd1WJPvKQvPYEcCn+GyqIAf7zE8pLnFcmUXfAX0TAguyNC+cVr9qNhvv/G9G1oHAawC8oHK7byg13Gj8YxtGgfX4TuCR8CduSdbBbi5jAfnanioAL/Cp0wQCndp3n7/JXvCOu1TcYfYW799aGG2Ohfv+9/3v+9/3v+9/3/++/33/+/73/e/73/e//5lfB/t+ezf7Xuj6sfp1N4tJui9LD3cb4NMMVr855cylfNVvTZX7KKx+W0n9jrj6TSX1W1KjWaqw++q6lvqtrK/PJMKYPmal/NT5dsdQCie/dZ5BYfWbWbvYDfVbWeq3n35gsE/ym1/su0zqPHtJln6dTv2ml/ptqR0jS3X4jhGlunLbmKGGGuSfSVB9nIw+wWC1HH0MXuco/X/qJ8nv2Bt+i1m91rG0iaU/Y+mjLP0dS59n6XaW7mPpxyw9wdIhbD33YpZezdKpLJ3L0sUsrWNpE0t/xtJHWfo7lj7P0u0s3cfSj1l6gqVD2Pd/L2bp1SydytK5LF3M0jqWNrH0Zyx9lKW/Y+nzLN3O0n0s/ZilJ1g6hLXHi1l6NUunsnQuSxeztI6lTSz9GUsfZenvWPo8S7ezdN+Y9H40b86c6c78m2tiISnmLCooLiiaMClGINfdrqKCwqKCovEUzzEiX9AbWuFk+1fOyQVTCgonFNEsk+6WIrGotGqSM1/yrohOXOT2uMuq3OLkKYUT6/CzmeOd+biRVYubUR7P4gqafTzHFURXNUjeGkilCE3r1atASPJHGrmCUFjyF5TNFiYAawatCMUKamKBYO2EQC1HoHpvtJ4rqF0VAn40lSL0DiuwDhDhXsQf9CIhu2oMSigyAH8lfzP8hXIH4F641it5uQJ/vVgX8Tb4xfrayAAETH0+9slSUSKfRyY8RLJPSHmo17f7IqRg3oaAD/KFJfKHyqUyaqJRrsAXbsA9nH+4PY9hsVqNZ8UsfhZr1snT9QPq74ec/luJ1Sx/NUM4DfSZBthlyC+x/JJmnXyw/LhOfQJioZpfjf+PMUQtw2cZ+gv1t4DFfquhf1ADbQfrH4Yw3W2GOL2YkVoN/U3HUH3/Yma/n7DYruZX4/k61vGtM5TfakjvYH2FCqv9hZN9bHE9N1D+jDT638lsajX0Tx0j9P2T0X6q/q2G/Gp/t2Okvn/MYmMAY/6HmE2GGMYHzlx9v29W/7IhfynLX8ryr8/Q0zsM6SOG/Or4p5sNQGrmpc+v/h435FfHM+U+fb9uVv71hvZXyfJXsvxLsgeX/7Qh/9NsH/Xp2wf0G0z+84b8pXcw+93Bxm0G+xn9p8OQH7cPaUrhoozB5e/l6H5ChmH8d2BNen2N8vEhZLsmvzo+VNakpzfCn7LyJ8dVLH/fOeb/QtO2tOOj/jX6hjrEkF/Vq98gX/0+b/89g8tX09OG/Or4lFvL/NkyeP4hFn3+bvZdx+57z83+w9lmTFI++85vH8tvM9S/sT8YweQXGvBq/p+Z9D9qOoLTf49X/TXdx+KHZfD4q/Vd7a8yTtNm9pFb/IDhwjTxa6iJ/B2P0/Q/Rw4uf7BfBlCr/bEeb032s3p8RrL/1OMzk/2iHp810N/p8EOS/Zgen512vpEBVlH7HT1+aLI/0eMvSPYTevywZPzX44cn47oef2EyXuvxOck4rMfbk/FVj3ck46YePyIZD/X4kck4p8dflIxfevyoZFzS43+QjDd6/MVp51kZMKtU44MenzvQ7nX4S5PtWY8fnWynevyYFJyFxOwvE0Y8/VZuqh1mMTweB9H+fsTweNxD+/MxPB5/0P5ipF0O1O843XhhNJfFvuuttqeXGL16Dkj9FvHnDF9owF9poXj1/JD67eXFDL/+xxT/CBOwluGXG/j8F8M7RYrfo9rNyviXsv6cDQSeYPjlAsX/mi2MlGVQPLeY4h9nQeU9Fb+crZuwYGXLpPjS20qTdGT+l0ntk2mwT4zg7clzU2r8eorh1fNaKv4Awxca6IdlUXy3AV/G8Oo5LhXfwPAOA/2/MLx67k39JvNrDK+et1O/sXyG4Zcb8K4hFK+eV1O/mbyC4dVzavkM/08Mr57XU89adTL82tv19F8yvNOAz8tm9jTgK7PTt5fbCH1qe8cvERP/NKy3rWH45Qb8OoY3rs/9LpvWu8VQ788RfGr8eZfx6TDwUQh9ahwz0ysbj4tAnOQNceAHtvT6cibxZByhT41j00iHe1FK+XmGN5bzNsInNX7W2nDskRoPwzb99+fV30Mm+I0ED+2O9SPq2OFVhrcY7LnHhrFrIN6uVcd/Q9Pzn2KCXzY0deyEv6AJfq0Jn0dM8M+a4F83wZ8m+NR+NuOC9PT5JviZJviFJviQCf5xE/xfTPBvm+DPmODHD4M+wwrthcV/db24dBit37WsftW6aBmWns+jDL/WcK71L8PS1+P+YTjGHRi3qPOnj034Zw1Pj7/EBF9ugl8yPH15IsNxTpDaju4x4fOfDG88x7vbhP6b4ajvaG79ffo4dsWF1M7PMLzaj8wG/AjrpVyfoTwLLjTxnwsp/27Gx8EEtJnQ/xvjrxj4/8GEfuuFNA7sZn7iZ/g3TegLc9Lj3TlUX/W8uToP85nQt+UgzegUO/zChP73jD/HznMvYfgjJvQWe3r81Sb4eSZ4/PJ9Or9aw+ifGa33k9+a8HnBBI+GSocfaYK/1kHtZmvT222iCb1kgr/fQe25vFUf57eY0CsO5oet+rhxyQiTfmEEbXfG+p3J6EsN86BlJnz+ZILfboL/wAT/rQk+dyTVizOMQyaNTE8/3wQfMsHfa4JfP5K2uz7W/+JZdjK+Jfg088qJkXBYmti4yiv5wrX+yMTGSLgm6G+ITvTW+FxFRRPdExu8gdDkAl9jI26C6FbvC1MwrhTMpBRMMZd8MiMaqxEFcUCAeKvZcxsulyjiFoYUCImip2hBOOR3D0Ke+lhNeaG76ewZ9M+9/F15Jp1DniJ8KOicKNUnW85WlJgUCIK+0XpvxF8rRvx1QpU0tcmPT4wJC6oKRcptclSKuKu8QtUU0e12qwWYfy42x6zT2DNoU6KNwYDkCwo3GVgj02pfMBox5+GmmaONSAaIWve0unBkpTdSK1SL7rpGUrDGhVXF4jkXjVhTLVsxPp0VDIT8WLxFA+UjRGcrICVKsjhrIUsGLeQU8uSeWrCiaDgiGQo1jT1WJwQXQJVMpU92CjcF3exnKCtl6Caczlq22YOXLVlpvqYgcYMqyTVVFAMhKRJG/mIwHG4UKoPBBYNqp785RfSFGxoFLH9TtVg1TawuFKtdIuHuKQbuYbJlKN5aLZzPA3HnQG98YO1cshieoTuHLGkeGzt7roGH085OW6JXoeo8rVR1/laqOn8rVf1dVqo6DytVnZeVFp+nlRafv5UWn2vhfdAypVhdXYGPE8Wb5iwSPUJVtShy0LX6VwSikj8iSg2iLwj9V5RLgxLF2rC4Ihiu8QbFWogaUdEba+awVQX9kr+2YGpJ8aT0ROLA7rkIho+s4ugOfG2soWEVZNFA4sCmOyMVxbmLyircontBORSWlpxdM60Xzp1b5a4Wq8tme9xIoWNRq0dEJW8EWnn5jxeUVQhzONyuV3G3LqiKTvU2NkIed+V8X0Np6TyPMHvOkiXi5IIiErCqwIFAlVox6I1KYigsieE6QmqkFYOBGp/oi8aI5lxSQvViCGVTtE/m1sTqBB8gXT7oHkUp4oX+R/BhR+hzu40loHFQqo+EV4o13loRn5luSilmVbhErACdo/6IhPFuUVW4WtRRFZRAEVc0hEO0YGjKm5rERazK54B2UazvW2uDlWnYR6dF/MC7ye9OURszAr86byDIbomTCiYVTKHWk6b40CVBM9dkKOHKQK0/ROrGrZdS4HJxzHAgTioOhKPkQe0iAWjnuAzUXIM3GAz79PLq8PlmQxFopVCNcVyXUsKqaBEYobEEH8iGflYCBRsWzSdVYbRCaCXekUrAB0htSEZL+Jq9InBoDkipYiTX5EgsJAXA5/3YNEElYBZNre1JhWpth2Poa2LEG1rhB4dLkYYHesAdgkERKrAhEALR6G7VIKuEuluY+hv89XsbztXl6lb4pWgaBfRVUp5SJaJQXSEOxJXqijkYQqrJsRpRWCiSN2mIsai/ltodLNDgS2vquojfn8aVoq5ppCHWBSKDtkRQN5jiCFi49EUTjfGAOISrUERtgFd4ldvEIdC9S/xqYC5aWe+V3E1pI8hkTcFNwseAZBf4IqvRYIrMaiHFlYyOAeqpwZLZGeSZqFAlFfnAz1LRJUmfMmok1kbDYj10wmA8POwVDqZtcxgI2eiKhrAwdUM1SKXxRohaoNuA66LTVuOYzV01VaycXzVZDBpDWoO/oSHclOItaKWBmjFqhyFu8JZcE41qPCI8pS4Yi9an1i32w4XY7pu9NYEmFzbdaAAbJmTHqAe61oXB5pCjbLYgugomG5oSTnnSRhGMbmB7UaODkcmAfmZcsB2ZcRAbQXWYV4h3+FexyJc2TqW2czC5r6ExTYBgQQd0S1u5U3xBvxemexIMXQTQH/5JINXQ9P3hOqM7oSqknvQakG6etMHz6F3JlM+gUB15v0aKC+m7bfBFE8bpeuxp2h6bVGaTWXMHp6kS/Q2N0iqIT40gKhzxrvC709VnDcSvEER8yVdvMAU5gQjde3Oz2OiPRMMQOQLAsKkw1W3Os2+A6IE+QWpsIfSvDeFaf5qOMY12rIdLCXQ3h1ZiMFzkj8YakNWcOYAvpIWbSl6u5E/TuYBVp7HmmmbwM7kxJrl9aQoBvlYhgt/dAREL5gxpXLlx1UDNu4r0I8LUHhrHEByNa/8H/s/c9XOtAAA='
bin = gzip.decompress(base64.b64decode(gz))
p = os.path.dirname(__file__)
pymain = os.path.join(p, "a.out")
with open(pymain, "wb") as f:
    f.write(bin)
os.chmod(pymain, stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR)
if p == "":
    pymain = "./" + pymain
subprocess.call(pymain)



'''

import numpy as np
i8 = np.int64
i4 = np.int32


def solve(A, F, K):
    min_ = 0
    max_ = 10 ** 12
    while min_ < max_:
        mid = (min_ + max_) // 2
        count = A - mid // F
        sum_ = count[count > 0].sum()
        if sum_ <= K:
            max_ = mid
        else:
            min_ = mid + 1
    return min_


def main2():
    stdin = open('/dev/stdin')
    N, K = map(int, stdin.readline().split())
    A = np.empty(N, i8)
    for i, c in enumerate(stdin.readline().split()):
        A[i] = int(c)
    F = np.empty(N, i8)
    for i, c in enumerate(stdin.readline().split()):
        F[i] = int(c)
    A = np.sort(A)[::-1]
    F = np.sort(F)
    ans = solve(A, F, K)
    print(ans)
'''