Problem:


Score : 500 points

Problem Statement

Takahashi is at an all-you-can-eat restaurant.

The restaurant offers N kinds of dishes. It takes A_i minutes to eat the i-th dish, whose deliciousness is B_i.

The restaurant has the following rules:

You can only order one dish at a time. The dish ordered will be immediately served and ready to eat.

You cannot order the same kind of dish more than once.

Until you finish eating the dish already served, you cannot order a new dish.

After T-0.5 minutes from the first order, you can no longer place a new order, but you can continue eating the dish already served.

Let Takahashi's happiness be the sum of the deliciousness of the dishes he eats in this restaurant.

What is the maximum possible happiness achieved by making optimal choices?

Constraints

2 \leq N \leq 3000

1 \leq T \leq 3000

1 \leq A_i \leq 3000

1 \leq B_i \leq 3000

All values in input are integers.

Input

Input is given from Standard Input in the following format:

N T
A_1 B_1
:
A_N B_N

Output

Print the maximum possible happiness Takahashi can achieve.

Sample Input 1

2 60
10 10
100 100

Sample Output 1

110

By ordering the first and second dishes in this order, Takahashi's happiness will be 110.

Note that, if we manage to order a dish in time, we can spend any amount of time to eat it.

Sample Input 2

3 60
10 10
10 20
10 30

Sample Output 2

60

Takahashi can eat all the dishes within 60 minutes.

Sample Input 3

3 60
30 10
30 20
30 30

Sample Output 3

50

By ordering the second and third dishes in this order, Takahashi's happiness will be 50.

We cannot order three dishes, in whatever order we place them.

Sample Input 4

10 100
15 23
20 18
13 17
24 12
18 29
19 27
23 21
18 20
27 15
22 25

Sample Output 4

145



Sample input:
2 60
10 10
100 100


Sample output:
110

Submission status: Wrong Answer

Solution code:
import subprocess
import gzip, base64, os
import stat

gz = b'H4sIABXC0F0C/+19e2ATVfbwJG0hKDRF0S2IGtiIrUhpaIFWQVtoYAZSqLQgq7JDmiY0kjbZZFLBRa223TWGKrs/P9fffrv7sS8/d9dd3Ycuugotj5aHyEsRQbCiYkJFUATqA/Kdc++ddGaSQdjd3+/3/WGU3jlnzj33nHPPPfc5Mw/YHTOMBgMn/4zcLRxCt40sI3AZwz/ZkiQBXAk3CP5ey43kBgCcpaAr48pUaR9jLacmRpcB/zLhX4mRwiXGMlU6ktHJqUGRZnHKX5kqfTzIqVKOsyTzoaz77qPYffctVqWWTIovHKjOZ2T5+li+PkYvp5yRU6WyfpnsXw3D1zC95LSC0VUo6PFX9YFUh9f7ljI5l5ap0pWMbqUm322QbwB34b8cls6Ty9OxSx6TX07lehjv89ZOKh7vqxvn8zaGl41bVjJp3KTigpC/YAKRKYfRzpwzX2XHXCbzMOYDeP83y0u/uPvK7/x6Smzj6r+sfOK3V//2zbF472aWD6ukrTGnPnPUIKOBK+aagTXeuwX+TYd/f3m76I1LHlwl/tz1yqZHGxKbH25zXfl69eqWld8p+2TcJ+J3sy1NhXn2NxwdR8d0DHty8PRfjAysWS4sfeF89nkfBLgsDX6PMT2+fEB6/HhDevxfuPT4nKTnqX/X6uB3ZKXnc58OfoiOnNt05GnX4bNIh89AHbxPB//DzPR4r065xTp8SnTq5Xkd/PU6fIp07LxEh8/9Onw+1aEfp0M/V6fc/9Cplywdv6rRKfclHfqrdORZrENfp0N/Wqe9jNLh85ROvb+og39Jh//9Ovbx6tiT15F/uY7dRuvQ/0lHrwYdORfo8PlIR/5SHT6HdOwj6eC36sj5sU77ul7HDrt1+L+mI+dcHX1f0Sn328BnFDeCe+4h2u/kMvxlDH+iZbGq37o+i+K5VopfyPCXZjI849PM8L9l5RaOKFOVO1O2z1Vq/Gwodyjw6WH8ZV+6N5PiubbFqv7wLQMtt/khNb2Z2XPncDX/qizKZ/VDaj5TGf8ehs9hN2YyvRa3avRi9l+lkZ+D/jkk1bnGjsU+eRIniksa/I1iSHIGJVHkxFlN4jz3Em9Icgen+5yhkDvEiUJNpVjnDjJ0TeV0n7/RXeOs9bnpvfR3xIBUH3Q768Sl7uWiC64kRLqWOUVP0O0W3ctc7oDk9Tdy4h1zqkPFUGzAVihiSSEp6F9unze72im47OS2vxRueBtD7qAk+Oz2edX+GhFvzK6WJrqk5QE3ENqKgOYeb527ESi9kr0JCKol22RRBDn894i1IIrT5/O7mmjOkK3I422sEz3eYEgS/R571WxXQwMtbqLHFw7VyywmKVm4nCGJcIAbRcFwo+RtAGWCQX+wwqbI4Acl3M4GWWgXYG2uemdQlIJOrxQCeUEL4FDrDHldMrVQI9YUivbqSWLV7Ooi0cfsRQonnEvByg3eRjAlFSE03YZ2ChG5a2qAYWk/Q2/jEsY2TenEttS4UonXHwLdQu5iAQzH1AD8JDCW0+fuR2gUZoUnJQEDKisNUQ0qjMSkg/LSWmSiy+d2Bu1wZ4IogEzwTyJuA6pPKJRrwR/G6hKDzsYlbihSrs1JpDbBZyWx0a+uULw9WVHZmvs1Akim9EdEaHRlVVELrt4IPiC56hV+C7JVi+6GgLQc2kIAGpM/6FzitjMPtU2kWtMqqQ179OujCGjQ2OKSZcvEgDsY8jc6fV5g21TIDJgUs2ICq5UQGKtSBMMtFYFjnYyd5AwE3I11REnA1PmqmqAqpFLQnVhRQj0XXJR0pB6KXWD/1LzgZjp5MVvjPQ0k82RWusJ2pWA6Gh3sDQ0qZ0m6JVWV2J80YKDVhA9/USAs2V3MSy+uDYCXYm0Th5sLJmvw17mJfnMgHEGhy5y13iYbul/IK7owJooYcaBle/x2ZfCygSasffqY0ikWB+LJQTeEhCZQN13rm84a22xlXRffU++UtBXLmh5WLAb1BhrP4WqJC2oEIRsnzm+8B91+njsUbnDjTRcl8/iBB9gUIzwEfo/T6+PAUJLfx3ncfg/nWeKWQojxETJiesm9zCtxDe6GkJskroYASQLLOY/L5w+5uQZSPYhs8DdhtMcCWd/S4PQ2chj5uZnTp4tFBYXcTIcwbbo4ocBWnLycUDCRm75wYfk0QbQVFFH0woVAXVxgs6nAUiVE5oNGmDca2byXQlnkbwbBG5N/OXadwegoNpPdN8Js1EhmpJRmAKMewO4OZKlJQZ363wDotw1cT1b/vK1qzPJLccbaMkCeLxu5s+x+Ia5L/OiJAbhKEB1AceER3kEowxNJ+kwyFpDHOXueXZqFM+dfMXpn30+MuALyRwZHHsg34oz6ZQZL+T82ob5bGb++B+h4YQD7l/yx8UUWm0fLv2YFfroCv1qBtyvwzynwNyvwPQr8DAVeHschfopSntZ+/FTlPESBn6nkr8CXK/m09eOnKdC5zRQ/UDHWIuM0Bd6owOcp8BkKfKECn6mcfyrwyvWpMgVeaX9egTcpx4UK/CAFfqECf4nSPgr8pQp8vQI/WIEPKPBDFPhlCny20h8UeLMC/7ACn6PAr1LghyrwTyrwyvH/agX+cgX+aQV+mNLfFPgrFPg1CnyFAt+hwH9Lgd+swOcq8DsV+OEK/D4FfoTSDxX4kQp8TIG/Ven/CvzVCnyfAn+N0p8f7Mdfq0CbFHjlPDdHgR+l9H8FfrTS/xX4byv9X4G3Kv1fgb9O6f8K/Bil/yvw1yv9X4HPU/q/Aq+c0SxU4G9Q+r8CP1bp/wr8jUr/V+DHKf1fgS9Q+r8CP17p/wp8odL/FXib0v8VeF7p/wr8BKX/K/BFSv9X4IuV/q/AT1T6vwI/Sen/Cvxkpf8r8CVK/1fgS5X+r8DfpKzflo9MfDTrfy2xcHxbh5QVCxHkRtMG9TQ1MfEhIElc1wp/zdeWwRXC9Xgr3pOA33X3IowhO76TwEGEMVTHOwh8N8IYouPPEbgWYQzN8dUEvgNhDMnxVQSehzCG4ngzgWchjOLHAwSehjCG5PhiAt+EMIbieBWBJyCMITheRuAbEMbQGy8k8GiEMeTGLQQegTCG2ngOgS9DGENsnCPwJQhjaI2fOIewEeEcoj+Bv/AAPJToT+BPEb6M6E/gowhfTvQn8GGEhxH9Cbwf4SuI/gTejfCVRH8Cb0X4W0R/Aq9HOJfoT+CXEB5O9CfwXxAeQfQn8O8RvoroT+BfITyS6E/gnyJ8NdGfwD9G+BqiP4EfQfhaov9ZhB9C2EL0J/C9CI8i+hM4iPBooj+B70b420R/AtcibCX6E/gOhK8j+hN4HsJjiP4EnoXw9UR/Ak9DOI/oT+CbEM4n+hN4AsI3EP0JfAPCY4n+BB6N8I1EfwKPQHgc0Z/AlyFcQPQn8CUIjyf6f0XqH+FCoj+Bv3ADbCP6E/hThCcQ/Ql8FOEioj+BDyNcTPQn8H6EJxL9Cbwb4UlEfwJvRXgy0Z/A6xEuIfoT+CWES4n+AFety4QRVuwUXK7DNa21CL50AqJArBzUWbjBdkyI7PouHznMt7x/oqpG6O7IqSvj+O7O1S5MusuuLuNiRyD7Z+ZroaNek4Cs8/lxfZDwLX3ZfOSDe4yLNqwhMQQCx2eL1uGtRI/5WrLktaZPQd/ZlwH0fGQ933n0Vt6wk9/VF4bML9HMuyEzH+mi+Q+z/M1TL0HnCg+dDzlj94OCi7qyzkCTM5zcgPGHb5m6bzEQSNckRRg2f92J+6C0yAeJnYvi34cs5mu5eIim88tvL19QPr+8ppp/8KPNEIbWYdNZi8PQmAW05CMb7LbTfNsBKY9vf9yaB4FsbRUaJPJODOuT755uwuAGKRlGCZFDCg6HvkQOm/i2Y9IMzF6I2ReT7MdjPyLZ7SQb391qlce1fOQZKy4bwj0Txkm49TSLtr3fVhHCtby++JwhQslsxxyRv1lzAV3ZdlrKFBIzTIhAaygEs3/JVEugauP49pXWEpQNub10CRHinVjzl+n0e8cRedlqAZwjGiayPodTikfGKLh/+AVRW4F5AzBCZJPtQCWYYkK5+YWNLX3zwxlCogvNUobcIscdkdOxFVCmAIpjmQIzjiPS64hSFYToLXy0KLaAFGDnUPhL+MRsk7kVN5x4ljFpVZCOI5K+TKTkW+6DUXDr7yBadrVakdQQpVZr7Qh/W0jMNYWvFqID+fZlsRdxgsKX7jC3fXUFSpCIPfR5IoEmBbo5hG6Amu5+QncmdgfQ8e0vW2NYYPvfiACxK8Yj8LL1BEPizdhz11FkH0PizdgfRvVTxq6BKRAf/RnlBbqOR13D+Xw0Q132uCtojR3qwxprJfTEclHKHZWK5q6z9Nf/56SGbB3omuDZUw6SQe/HfdSuXXYTMSIzZi41ZoyPVFgL+ShcthJ7Khh+1peW4c+/nmEeY7iG6mh+dAoQ2cGNwQg7iWWese5D36/gWzr6XqSteos0/EXsfU+YO02RldZmaLhRmvQOjJrab0tEctq2PBBCq5YBMnYK5Oitg/YONZmslOZxKAS43bnYddDhVEa+QsP3ENE+jo22kLsCXP6kjJoSxalsS0jjiavkp7jKnSARusrtZ2g17JQHU9FnKNvoffAPqmIlMd+sSGacj840IbxGhtuOVUY+lKx8ojI3fA0PhWXFTMgOAgbH2PYwtkJik1B60BE5ZV55HZbMKtsRWWHNQdlYRHBEl5kUNTWLcNtkt72PVTUW3EYwP5vgOz/MiB0/rV9bQuQjZLxQiGZasdqqHO3fN/BRuYh6ZREfnlY4w1iwCJRwlpTQfvq8/oChZTVRwWct4aMrrFVgAXNr1+VYRB4Wa+GjPutC5HsF3z7sGOaznRbA5r8+RWyfDBSgVmfiVr59egKzlVVGPhdGdVZGeiqhR4CoWJZj28JH9gmRtx35m3mzsBlKByk7gekmIozhCx7UrTK31l5OHM7cegcEY9sWR+SwI38rb+Z3QQ9lbn0eBQBOe/mWI1+ZW39OogyYyWBu+xFH6T9w5G8H+jjf8sFXfMumr/jOY0Y+sUkaBHXKt3xokIZ7zMc7+KjDWnXS/EBmJ/yp6MzBjVRgddYYFKA1GVDtKr59xdlCDPDvOfK3gdC9hGX+dszP+kfIPHMn/unBPyfgz0C+pbsMyu0Lv8dHPuFLt5pbV4HZbbsdkSOO/NeAzV4UH65ajmTw+VtBrrbT5taPSfHnQI8HieeCTkSG+PMEVAUgOb6a22bn0EA0/zOoZwhcnEE/KmNjb/ViJMcA3ibiVfRm6vJtxE/eiQ0BPnE73mk5ZyRChAcSMXqv4iMg+7NneEMXtU13xSna2BKdilsqLJKhD+vcb/kiM7jM1iHXsLCez98stA8bJLRLgzL46F2Q0fzsSRALXDlLaPcNMgLcK7Q7BhngAirk2T1IVeXxeArM17ayPjpzD0u3sbSXpSepn30JYe2r8Jvol8VCgs8JFzgi24VoCRrisvLmZSYufKkjsiFm/Ay70H2OSDdCn31KICESd0QcVujiK6y5GGaw9QijIKa9Kxh28NEKa4nD/GyfAG2v0nDc3Loih46JcmU/h7FNZBvGLNenaPG3MCh39t3Kj9rsAIe0VEKkhdZSniNEdth285E4n98JltmF3loCCm/m24u3k57G8LG5dTLhDq1iBwnhHRBG+fwuwcyfIC71CsewZ8DtobH8hjWWEnCyJ+V7vULkKJ+/ATLt41vegwazARpMLzSYDdhgDoLHG6RcdPiT5dBOtnM0ySlfQ9vLl8bgLKH9RhNUz7FC2wFHZDefvx4k3o/MHPkbMKeyfqCB2LG92LG92LG9wKiiZRM0mA194cNCpM8RecNR2mVu/YoZ6zif3w38zoAWDrhq+TDDkd8lZWFt+XgQvSVhoC5a0nsLAEYZuAoqjlzxGAsNO4TuirMsinfgrVQs+muJ3q03oEFkBu+G0JO/CcTZ7sjvnNVezM1qX8GBq9aQYr5Ass4Psma138Whq+6f1V7BoauCQZ791BE5Cv0h+OGnzB/Xs3Q/S7+g/gkqdRL/3CIVCAkhJ5wnREuZcy6nzrkt5v4E3fEtR2QHQrefYJDPyqN7WirRjhXWZUJLnxFdpxBFzDOv/BYO2lgfwkfLTOg8j7cSJ+YhkJMxG7QoE3jZIR5GsQ+j+uy6mdyUWFji232nTGixeqA9RcOMZF1MBrR4FWDxEfvPjgy6hgzzFaN55VEMRdBQAkIp2BW8dc4JZP8kyXiGz3+HTiTMfB8PvdMq0tvVWevLCetV0DPwbAAiZfLQUPjIdtAClSLSQx/8cAZ2aK3WVRm0+Cfl4iM/s65GHJT2NElftq7JoAZ5mOV9juAfJ4OY8ucN0mA+MS2n4pFnmInYqAkIeVZIFStkoaKQxayQelbIMlZIM8sbyKBDzuYMOg7FwoTImzgyR1PFnpyAvL8QIplWe+ux8BQ+MSsnPImP3oReMLT5XnQCoI8tPo5dMHjdmwjN+5hCIEwZW4FHf4MQAp50K4+eVCqAg1JnGkp9CawXG34cI1Gcj+xA6NKPKQRsmhkb8FyYOOCspiND7T9Cy0aTUNrlgOpchg5EKjFKa828Etc/0GMCfOQUdMDoLQGEF/MGyVrvKN1kbjt4lpKUoFPxX+tU8mhDGe9brSUq/8oxqvyLNzska5nsYdi888+keFjSfdozTxAXIn6feRqvm1mjqH/FQIfC6H99Rtn/hBT/45hrmJhr5CRdA+aJ7J6FuUahxv/ymP+tAf7lf0/6nzxKgxowUgabjZT5TmM/833sXo8RB9ddyP+EUe16MSObAhmp62E5QuR1dD20YuzqQmRxmI9k2FsT4WnE86aket6Rj5Set6836Xlr2J6QR64fx/n9D7q52H8SZp+D/yEU7aUQMOszJv2PuM8zREZV/GrZYOJLN1VGTsiekeqFs77eC/9+FstkDCIQKXkIooUwYodBAunTzY+S6W6ks38AzoPzf24wP/p3nPm3L08gHUTF+hy8WOhg3b0D6hLFsCWElh0GPrJZiHTZtgBnB3gUT9rnSqpNu2DAa3QiZFxOvO0fpBW3dnHUW54kNxOGlyjr06TV0tiEfT0lpzmFyHoJKgHSgzjSbr9rKEw5vrxVaF8x9BwPA3XHqD2OUW86DNt4w0kHjFwMZ4So70CfEL3ryxN8hrePzxjOl8Z4c2UMpjyCuRIGAp05fEvXOaG94lvn+Jb18K/PEF5dGTnLlx4EuoOVpXESBxyRnfLAyBF9nDUvKrs02xHZ4siPC4az2DLRYrPah8GsFBrdIQ9Z34LREcrJj9rgMJzkDduE6MgDfMYIvvQQlHEIyjXxCWiBh8J7WW3QSn+cGjFj5E60cenbDrPjPYfhDQFGnBlkJE0t6yjtNLdFwCHi/5mBkwWo3zXUxGvZZKODb+n5igxazMdhDmIkTarTEdnEt7wL09XbYHB01AjjksR6HG5jZmkoNAOcV2AOzClJJ83+y+HfnTkes3/IF0XYHPzr4VYnpD0es+ddjznw0WqPuQkQgROnPOYD8C+wEf70dgHJe0AKJP6PgDQON04hKeZ5H0mPwMV2JH0VAOB6YNtaOhT7wmh+yIGda3vNyUI+/xNPhdmftQJLV4y/gG3MU24+HgP55sFAzH9vz0mgm3cC/14Cdz/FuyfgAirEcxDKeg9LR/lOY+mfwUUvlv4xkLyFtPvg4kO8AM38cNtzEgoOHMJcoEjg7VNw78ABlr/3JPB4w2N+JwbXR+H6HFx/AtdnIPMm5NIBf/xb8WozoHYBv52Qdz+yA0RgCwqxDS7WIzc06GuUthyJ4Qqo/fsh11uy6MAisBtzvQ4X2zDXVhDwANC9gwX2rkcIKu4dZNS7BVwMBqOdfea2XRg4Ip1CBGT1X1E0gRgP2B7fT6vx+GFWR71yHTF9zQfAdoEPsCjAHNgIvKH2etE+cN3Q4eHN9x+m+Ym4UJGerZDjLeTxuizuHqYtiGt+YKW1AysPMnacbiXXMK60rxdKIaBsNLduI/Ggi00qMbxE1uPUEobJp6UFOK0MX4KNEv2190Y6t6OQZIZYuB0mlSego+zCWZkGjDzDmpgKDaWS6dt8AWbz0PoISbtjay7c8KjmY9HyGB+97YRQuhHaZI9Q+jokhwHbxzB81CBftXRaBJybbcPIYMFp0/U8TtBG8XR2NpROzniYjrmPYN3s4yPdsTs+SJDgm3veDMX9GfIhwzo8bwET8thX72PurKy5FuxQQNSnd7bAHLvtGMxAIEQ031fCSVmP2Kf3jsAVoWtIWXYkLVxLl22PJ3aa154g/GI7PsRCumM/PEJFyuOx//BZc/Tl+tkHSbl+9D7NVXheRZb2Z1iMGcjyHy5ejSarXWQ2P/N9uoB16/t0AatPXheDnp+sxD0xBqJdB//gRlyBv7P8rvJF5d8tFxdtUK74mVv/E8+Xp1/1i7eSkKOkXqlP3XuvmvSh85AGVKuOK/TI7k6uOpof+xKHrPLK4/dB53gcMAqCN+IKgjuQYENcRVB4VEFQigTXHFURLFVyyEWC78RxztUVf8+AnW1XLwwEu9C+uHQa2zuWLkKS9VT0JeyToZZM5ra/kukPuJBiPQeGRxe71DokKc0j79HKnndYvQgKvSzHrbuzroxbe9xTxr2Ep3tenLqkjIvxQElv1MKflx4Huhefg6vY+MM4DqJrS+tw7Sh28D3Ushv58kDWB/LiQnw0EIOxwDDUF9GxI+/SNXgE1lZ4gSlusMZEBZpnA7TYfrCeYqdi7GGyM4KLU21j2GgV6W0H4tPJ4LQrnqMxMVmqD92gWqo3tx4lq0q4stWTumJmbv2zQeuwPzTou3cQtdrZA/X8xZeknuXmjCvF0DCvwYaJUiekS5u/D20zK7b7XbqEtrVH0Yx1qX/DqH/WQxfu+tiOA7T72bnY7tnC3IoeSif16DTlbaPpNkjRe0xOaj618u0ZauWlBj0XuxN4kIrBQp99F1uCic486CqvfPN47Md48+mB/UgHIO8DZJIigBSjkXl/Q8pQzHljO9DAnxgVBAq//gvT/N531HsscUMCTcbUjGQofVO7e7aGIP9dLc3Sg3tlAxcvLSPmX4jOTlrJD7Eyo2OhpYfHoWQLib9Db9L+ciZeR/+WuQySltcMsbeBR++30asHEvGPx156hzYSkinyMR9ZH/tWD402iw20oquwonf3qNrNyUNY3xsr2943t40w0HZTRZYB34+X0gYD/dr2mRYy76MSDdk7k3VzkZVEMLQdFpIDvReZ2mg8518y1yeHaMVhAQFSDzALQE2e7KEaP4fA0nfoHk4LpGXPG0hTfivZlHeka8p/Sel7Hj5Ph9IkWwbXU1CeKjmQdVdY2b5nhTW9u5hbz6SUtf08vSK2kXSmOHKQ+vKf3gYlW49JJtT4l4foHpKF7B0nzK3/wHiXmAtq/9lI9xD/XVXhOUiNXPA2rRIL28WO8zqNb+JBMix61K5yn5/Y/xvd5y9v67jPdw4p3OdGEHRdXrJVHDyg2AmbY769wzzUntwIWYenlGJHD7K97HbexEcqczBMD3FELNCSpCwhAT4BWBNukrFNFAvbLBMiM03Y3FoPk0VhhxUq6k2y2Qt+pXKaF1Oc5rHzOOh9pKnKY7fofkUExfoiRe7/Clo7LXIHXqYUucOoLfK3Rn0/XaVT7bccQD/5MDZgP4l0qyDSrS2EPy/11JaRTfmpWCnsDIKyp2g7QLuxXLrPJN1NNs7rNE33X/KHX+2nbejut6hf5MqnMqDYnP+6YseyYk/uo8XmKIplu2qk0zYpOu3JB2iedSyPfICk//BDKLlgmOBNYXcSiv772k8Q7cQ2CmMTUZKWl0knzklzkgsnWHpZ/zKKuvSZqtJH6ZVuxuHH3/YrxssKKT7aR6UgQ79n30T3OhP74X4yVrEdiw/5In225/dRR7wPcsQPfo5Hh+h8UnV+6EkS1U/zkbOKELAfstgOYKseRdo/NOEyduanjDXpMtakN/CRRRwuCJpgUOuIFuLOPdnNryw9Z26txrWf9t9Ymw2K9Uwy+s1jR3aO4wzyjn3kqA1f+gyhNLdmkGBZbeJvrs6RrsVzVRmOyA1k6SpSbYrvVq9fR4v4yE4+/wQezoKR9osoocNc8blg6Db/uANXQjvjU83/q9PUya9J0N+1DrO9D3oMsuYn3dCPF0q7zT98DU82tE8he1exs/vogSgoBSXK/1SIvC50fpWBZbb09PGGGAqXh7Evmxy+OBX7Pamkj+e0131gwuUDkPpM7NQbyGesI+IyzWqvOAu6T89xRMFSsoGEli5TZekZc9sBXF0jYaB9hdVCx0dLUAoCjsC5AR4OjBW9iWEMjAnz9gPhMXTKT8ZMKzItUSmzDEdLfwKi3tGxM+gFeQnaqB7Zy5jxkUM4WDoKcFer9TEo8nqKBydCCB/hhvEx3KZzmaFv0plGuwQjeSDMjT3wJh1zACaXtFfQ0nZsLT6j8RI+4Iq42Vj2uHN0GkSWGVRxd0rKtOIyzbQCp1dRmsTPcnRvwgRTb3RexdS7t5YUkHXVLdjlEmMMuf4Wub91ZFp4GKXk0Z62whpQNVT1SZ1rdErvHQwl9E7VNDc8mxCbgdWL/GlHu8K6OHaSmXlZbMsbdEJjonWVMkV5aw+tmF17ZLputmyfiP18LzFc76h0pe58nYyGzK1ZrGofg5LifWDr/vahaO8tH9Wzxt4/Cj5Gyt5g24KCXce311gXrzV6ac398vXU43Rkx91hzVOw+P0edjTv/SSPsiSPRa+nO5L3NjkWN5BvWRbj2LY6BJEcIVps5aNzYrbdsTcoU77lSxjXPUkP0jmitwqGd2Qivns9VlNs9m4SPhQCDd/DomMlbuKvI5Y8xrfbDeWvkPFp6QZzK77mYA0RKn8fb9iEQvNktzy2bTcp2dG+NJdvOWsIV5U/MrD8YTvXO5/IaDuGldbAJ2ZCuz7bW6co95e70fsxcM6BGqTTqTKmOYugkU29E8jBwXN85GiMg+pa08NEajNn4a5TgdA9gw2qZ7DDhKcgupBYIV1HhjDha1KGL23+TKT8MNa+SzWcy0aRoG47yGmoOqvp62N6pxAx2w44otDI8XyqEJ2E1gbefGR/7H5inE2k/NKFMfNDjWS/Lc7nv4W+XgXxHMne3KOaaj28S16ikArpNg/OOiHVnXL2mvvjO9BBd0QOWKlUDuMuXXR8ZTRsqrw5DMbBw45ZQuQa2wESTCNhUxz3Mj3q9dTIXdZCIboIrHUC6/6f7TXGanqNFqOi1+iWzbSIQ/FYUZFOvuVd6DLiuGZcYy3kI0PxJJbDWugoPWlu/TEb9PKdsQw+0QFEJIOBbFLtl67CEXk2H70URtdgrAy6iAF9Unsl2fK14O5tcBoGsCF81MhHDHy3kRJNM+EBIrCJlMNH7wVgOYSYaSb62ESHCe8IpV142CqyPrxWaJ/Rx5PjOZGDvVWOdrcp/jPNeFy6vD9EOqx3ReGfcgw1FTuEhTHbTuh8bB61/R1RN4e9ZT515dFaVw7/BN246zVy1Jg92bEJxPs4vIn2HmBI7HijoLGjvS4vBwyMJ4dxh7kQ54VnYpHXsP03QGNy085WiNhZZxuFJgBuSM61EBeH8QUOPYdrRrzhOzB4laEULWchjC8ymNs+hXlD/A8XZQnzY3fi/jyxxrd3QHYHgheRvSkhZ38fZIkvIec166x0kOWIgALrsiDSxhbgXQkEjA0Go8dDUEy8mkyL35dGUUuPSLG0iJa+azs9JXwtOSU8XHNKOHwXGrRkOxbbx7dsrNdZ65YaU/WoVREsTSUQVQT1qQR3JTudsY9kuSZZuIcrxubG932hWlke8KVsok4QM35GfXd40oC/xruXJFR3r03ebcO7OQm6/RC7EoOpyswYub+Hf3w4GmxZYS3kJCddpcQpwAJ6qRn/T7qINnMJlePsqzgGAPaxDdvoVU5syzB67MzEijT1F2n6NxT5CCvSFKsiRTpwnXaLFOATgil8Nx814Jbyv1jGmFfpAOXjrXStvWs7CZDJ6e8PXqX4P2nwjYCPe1m13EYOjXfHBJJuYmumZ2LCq2ySj89HsUn+xETqJP+fEbxhG51HTQTByfNRqvmTiazenuIjU6zlzfeZYCzzViYOVe7LWftKfRln2/0iRuAYj3Zt6YORzOQs1VMN/q2se5ZG6I4YOnlygNlktyV6B3lWCQ92c/gchtz7PCC0Zw7kpyYykCcUXm52nBHIDlsO9sg5sdlo8rYOummkXP/cInfKtEPLw3OioHMOnmWJDQCxf7k13fjvIJEH1+M/js19lW6g3YBbM2GrdlvG3PYqW6M9vRnGpOR8ApWtJGYFm4BUbaVkyyLTCrNHMpupjwHDf9DzRXMOtizr45ZdipW8cRuZ7mCLhC7PcXXikZEmhTbXbZEfXCl/mfbP5rV72Lmn4/i0yAnUSNxCuhXV6V4YGVVGjitY7d+sMczaDDqYRcP0IZuHt6Q1jBCdAia4PYbPmVS2N8aEmxfEwmZ8SORbW4myj2/oNeLM+piUSwx2GRn8CaWbwgvRSLu6iR9LA4kPS1e0HqiEEeY4df+pdWogCU9DD/0PyN16ALs5k7l1L3YAh8hpl66+5INF9cRpTpSbOwdEWq2ZA8mCCia9t6tahkc5ASJkNOmtUZEt1iObUx4d0M4nyiPZoPRjo0indZw8xQB3YyPAvvHB6kB8c4Iu2BOCz0GTeL6a4O9nFQT7kOC35OQbGOPBjdgMFR0TLgfiniHZPCR7huT8j9Y5W29NWfK7QrPkp1AqztHNNLLaKK8v9i5VruF1svq7k3a4NamLjJ+mTHY3G/RL/KtBvUbo7GYa02mkl4xcXBexVqesI+2SFzHsA100TM/cxEqSJ6JnYsM2y8sAl3TT9tGdIW/k4Lss6ULF2m66tEwwqyEEvjSS7O900drLy6T7MbmQxqZ1qyYJrk1kposL+W13sn1MpIO4F7/HKO+ntndpC384Sy68nBVOMB1Y+LeIvKzwVVns1CiksU1dqsKPbOwvfKZ85C+LFu5KXeslxpq3ifZZIyBvvO5cyjocIRq3ifYfpzYA0a1A1Et2ZGuTG0y+cXSHAC3GR4eEx/XvEBBUdyuxmrxDEMjUbk3+OWWt/Iecrk/1hmmpp2+kpT5MzjMPSdzYXypBQaloLrnUfVmafYkHVE72Hb3SZqYzCdpLVkvel1iMDtG5idbfMgQeB6reW9Ll37+B5kcB5fybsU4fZPnx9Sixmo3UlX0slTbSpVPHJrr5ia4lRAaShfBMeT9rJpt5z9TbzXo2xdht5zF2SHYjVhBeXmBBr6ZEil+dJ1K0G9K76LPryS6UaazKx4aOvTgf+5dCyo3rdWp7zwZFbT+1XrVscXunYheqMu0uVN2G1F2owY5ICT7ulimQ1Zm0m1B8ZLqJbBbWJTcL70jdLDS3Tk3pHC4/T+dwDhcnH9SphfxOUgsP5avaXDT/Itvcv1ILT3XotJnK9Yo2Y+lU1cKudV9bC4c6/8VaGGyQa8FoSFMLH6S0t3/ot7f4UxyLqvJe4ANrFXuBozuwe4YyP8ATKaTMA3ilLfP3KWU2n6eNN6Yz9+R1dLs48QqaPaMDtwGb7y7jem9MRz2IUe+i1OpNQ4xdpo70ncvOtTTjk5Axbv6SPB9evgAXenOFyDkh+l0LjhPGknHCdSnP4P6ePTV9xyu4VznDQkeX3809X56fsDxjaJ5clifnfHlaWZ5PXyZ56KA7eg1mGU2yjEyuhN+6jgbrbkI6gK7+bMy9s/y76tWG+cqpWyZM3TJ7BRXBbUqCwUAwuNeuIpijJBgGBMN6pymGm+/j0dX2YmusAU2795xqLPrlObpqYIrNxbsfqe8OZWsKOTEb3jUCqBy/q9bjoZr28pGTQiRRGR2jeB719pfJmrYdD1gfk/JntU/5gKwrJv6BeDtXjo+jCuxxVIE9jloZiQvRUluH0HILd3+u4nm+blSVT3QINw9YcYLvJi8cF1oeKOHuDwvRmYWO6PRC2+7++cUkPjpyJ1966P45fOI1Pv+QZv00OvIMVN2yPD56406+fXAB3/n5rY5R7/KGOG/4RMAD5NEbd7Aj7Xxk5A6+7TTf3pR4oItPrBduvr1kxc/5NfK7bmC0fP8cVBIm71+wA+Q9eIBcKnNE3qUnxr3k+VM8Mb5Jupk8sHbYEJ7gMR8/4DH7s45l4d4inhv376RPekY2xCszyFHjITuz6JHjuMGID875r4d/M/FM+dVP4R083v1HvCjHM93m4730KPHxbZB+4jF78Mz2OTxSvB0uduGR4jfh4jV2pJic294DZG8A8lMkO8iOdh/4GA9cIxlgDrwL7N/ZzI6bb0AAMvbuI2fOgQOewN6Dj82GIFZ2xm+n58/PJM+ff5Wpf/58Vrrz559cxPnz/f/c+fPt7Px5nJ0///S//Pz5VswFBgzswVy7Us+fQ4W8sxehXSnnz4HfZsrTn/Un6hl72MHxdz1UazyEflQ+hH4YLl7H8vbKh9AP08LfAXP0vkudpGGLfAi9hz2X8LpS041w0XGKVDg9jd4Ltx4YZsVMm09nWtnh81fBW82tvyBTrCPs8Dk+A3o4efa8FM+eQ5xsOWfExf/iHcqD5ueBcOvsi8zgDCH/Vb7dsSMXEOz5EHaenC/dALT7+NI9kOzH8+QyJmqQr8h58o1fhbfhs5TXkWcpr1U/SLk9NnINhqS4I7IjNvTv9GQ1idjJ2bhm/b8//lVBH1UZvYV0yU+TE0Fzc81tvyAx6Foh6jfxpb1SU/nL2JH0lugv3dH4rxj2XE8i919Bmt6RqxzRsRhDRwu44DJSYIsAjtIPw3/E85aLXiB9Lu1p8Fmog+HtUDx2Tibsiaa+QLvYiS8oeySTvn7VfOTL5Oz0+6bYYLo1e2f8vb+T/ll7/6NztMvb+HdGt+157DB0+b3M6B+X6f/3+elXMvo6mX7p88rt4VVVsaNwJ7bpBZQO/AOqJ5IoXyBETpfPFyJvlNc42q+zjLBw8/n2cXmQVjvyP7Ad4DvPZuAbt2Jtf6MrgNf0P6921BE5XQE8EsPeBpOCM/WGjxKbLZKNtkH5PPMG9u4u9qoug+qtiKpXTzenw42vczeND0l13kauNuzxuIPc+HAoOJ68YRi/GTE+sFyq9zcWFRSPr/OGpHEBp2upc4k7RPBBZyO773WNx5evhsY3hhsCy8Ul7mWBYEF9IMAhttHZ4LZAGTfdBJe1Prfo9UwZ1eBcLvqb3EGfM0DJp9hvuemmJqcv7L7RouAypfxGS3VBQcEtljFwH9lZ8pyN/sblDf5wyIKsQyCTO/+mm2RJKFXoppuUXFz+xpB04TnrnMGgc/kUn79xyY0XnCsQqncG3CwT/r3FAlJfeH6QUfIuCQOd2OgPNjh93nvddWLI53W5Gb+bbhJd/sDyPKqN3TIm33JneXCJZarlf1K/aih/yr9PyRtBsankctG/xRm9SWdUwFPKQfSLcyTv/7AjQe37A+6gU/IHp/5/5gH9FSaMn2uhUnr9jRb4n7yBuM7i8frcXHAsNwNSyygkabR4/EELvone27iEU77sGry8WiTqBMMuCYKBz2dp9EsWCA7eOs5yiYY4FK6FS87bSO5bfF4JivcR7ihTXr7lHq9Ub8FXOVtshTddz033h311jddLTAqUKBkY59gmiDMdc6eVO0RxjmgrkU0wkVjAVrgAA5Qd3zxuvxD6omrJiaauk7Ocn77ULr9a2n5h8hRNA62UmXLYe3ZPeMtU7+9Nvm+U3e9g9y13l6W9v4rdL9O5v5jdX6y5f355JwtzFba7AP0m2RubvEF/Y4O7UdLkVMuz8+708sj3VzN8ztKytN8Pku2Aawbk56Kp9vsTyfeqLlXTF34NfbOG/2INveGqjJtXsnfzPv1MIoGPcS18LpHAh/iXQ4ojvPo/JxJ4cv7pNxIJPP7+5FuJBDnIdjiRwI2Qh48nErnQ/ed9kkjg93XyTicSAUjxfVJPIwwpPr+/EFJ8Vv9hSPEB/c2Q4gsBSvoSiXr2zgX5Hb6Ge+dxhmU5hqsGDzStMgzMGcbem7vl2URC+V7X9PT0XbRIv/kPiQS+3ILLzpmRnTvLfOk9pmbu1hE331BkHa38bljGvVUOwmFgBn4iI0PmU8zea1sGtiiBi8y7/GVceXbOtOzcadmWadl507ILy7NLZmY3m1oGrhzQnvVo5mMZPzCSfHfhqPP1ROIIlJ/ZGkifb2dGSr7HyYtUE4mqQZAv53vp8lVkrzak5HsV39/7biLx88GQ7+XvpS+vI1uVb1hSf7X9RjLb1B9LJPAQJTctO6c8O7ciu7Al41EjtcsU8iq3ROITnBOnk7FKVRT9LhNuzeJ7BE8mEru4tPnKs8tS86Gcz6BvfpZIXM3y4dktxG9Evwb8dRr82/i+3jT4zzDOpMHjoib/x0RiFMPL313DJefVQO/mqB0eM9qzcx/NsGdb2jPt2Xkrs/jswh8M4LNLWgZWZpf5UAPQI4/oBJqBfiYqP/o88NGOlBMZv3youZnL+IVxLHtb9S/xPal3M+AFBAYY6PukN6BwuGKNDxruwTsLjJSsEG8vZUAJAs0MmIrAiQzayLfnss/B4ato38hln8JBbvsRuAYAr/zi7R9eyXG1+HEs8hpXAN6XRd8+nL7edxFK8IcR9F3mhOw5+PMtmax7JH2L9wCsssRI+oprLwK3wJ9b5Bemr7DQl5UPQ+X+PIrmGWRl6oaNSTu1PYh2eshQYKBvQ5+KHCYZqEuR3+8N9P3jb2EmPB6IUh5A4DMDlZLcGWCkr2kmdyYqv2WE24goy18z2FeyUKZ7kQzl+J4hKctviSw/M4xjbyr/KQr0PQb8BYG/sXewdwNgeIu9GH0HAmPY14TeRDJ8IabZoCg+m31f0OA10jorwbU/KYPqfBsCu7Loa8cdCBzPopp9jn8+gz93Zai+hpjI2PMgl7GTAPtlPfcz78ITqNwgZs9fY0aJAWcR+J2BvvfciGQnDTSPGQF8Z6hJLiiHfTapDKfao7Pou7RnITAnS/Fic/krcdPwzv9hZB0IbAPg7zK3hwZRw43AP6ZLKbAaHfYy+BM1cN/8vvl98/vm983vm983v29+3/y++X3z+2d+OVez9SD2HfcTI9XrSAaddG+WGu7RwGcZLH9TamEu5St/S2pYHYXlbyfJ3xG/SpaLpfI3lGLsvrxeI38L69S5hB/TJ42Un/xNrX2DKJz8llYGheVvYu1lN+RvYcnfdrpCY5/kN73Yd5fkuWggS73+JH8OXv52VOyyMhW+Z2iZSm4TM9QgTfnnElSfMkafYLAsxwkG/ymn7L/VT5Lfsdf8nmD1+hRLX2BpF0v3svQIS0+zdAD7zu2VLB3D0kksncHSBSz1sLSJpT9g6RMsfYqlL7C0i6V7WXqEpadZOoCtf17J0jEsncTSGSxdwFIPS5tY+gOWPsHSp1j6Aku7WLqXpUdYepqlA9h3eK9k6RiWTmLpDJYuYKmHpU0s/QFLn2DpUyx9gaVdLN3L0iMsPc3SAaydX8nSMSPT+9PM6dNvsuTNrw03SmFLcUFJQfG4CWEC2e63FRcUFhcU51M8x4hcPmfjEkuTOxjCnZiigokFheOKaZYJ90vBcEhaPsGSJzmXhMbPszvs5dV2sWhi4XgPfiYz35KH2yV1uIHjcCyopNnzOa4gtLxBctZCKgVpWi9feRsldzDAFTT6JXdB+TRhHLBm0JLGcEFt2OurG+et4whU7wzVcwV1yxuBH02lIL3DBFYBItwLun1OJGRXAZ+ERXrhr+ReBn9Bbi/c89c5JSdX4K4XPUFng1usrwv2Q8DU5WKfKBUl8jlkwkMke2GUh3x9tytIBHM2eF2Qzy+RP7RcWkZtKMQVuPwNuDfxL7frq1jMluNaCYujJQwxUkOvXXv5Nqf+JmINy1/DEBYNfaYGtmnySyy/xBDWr8mP67OnISbK+eV+4EmGqGP4LE2/If/msD7AqOkn5IC7j/UTA5juJk28XsBIjZp+Z98gdT+jZ7/vshgv55fj+p9YB/hjjfxGTbqU9RkyLPcbZeyjiqu5fvkz0uh/L7OpUdNP9QxV91Na+8n6t2ryy/1e7DJ1P5nFxgLa/I8ymwzQjBMW5qr7f736j2jyN7P8zSz/cxma8Y4mfVyTXx4HFTLHPzwzfX7591NNfnlcM6xO3b/ryb9a0/4sLL+F5V8z8PzlP63Jv4/tD+5bSuGVX1P+C5r89b4yllL4M81mhdZ/OjT5cXuMpiyeZJy/fHwrTLZifCOPA/fdl15fbfn4sJxZkV8eJ8buS0+vhY8y+ZPjK5b/xAXmP65oW8pxUt996oY6QJNf1qtPU778Hd6+B85fvpye1eSXx6nySauY4fz5cV9JmT/GvtMYe/DC7D+YbRYky2ff8+1j+U2a+tf2B0NZ+YXa8SbL/wOd/kdOh3Lq7+7Kv/sfYvHDcP74q/Rd5W9hlKbL2Mdsr2F9hTZ+DdIpf+dPaTrk8vOXf75fBlDL/bEab0z2s2p8RrL/VOMzk/2iGp/V39+p8AOS/ZgaPzDtvCMDrCL3O2r8oGR/osZfkuwn1PhLk/FfjR+cjOtq/JBkvFbjs5NxWI03J+OrGp+TjJtq/NBkPFTjL0vGOTX+8mT8UuOHJeOSGn9FMt6o8VemnW9lwOxSjg9qfG5/u1fhhyfbsxo/ItlO1firUnAGErM/SWjx9Ju4qXa4leHxuIPydxvD43EG5c/F8C9r8GHSLvvrd5RqvDCCy2Lf75bb0yuMXj7fIn9z+GOG5zT40QaKl8/FyN9YXsDwi++k+MdZAc0MX6jh80eG5xifXbLdjBQfsFN8FRsI/IzhO+ZQ/M/ZAkl5BsU3l7P5GgsqRxieW0Txn7AO9G+Z1A6ZGjtsIHhz8tyPHKdOMrxFgx+TRfGLNfjvMLx8bknGP8bwhRr8Pxi+TMMnLvNn57bkbywPH0Dx8nkx+ZvJDoZfpcE/wPDyeSv5G8jPM7x8zkr+RvS7DC+fN5PPDJkHUvxzS9X0tzJ8lQYfZPhmDf4XA9O3i98R+tR2/cJAVr+aeLWN4Z/T4A8xvHY97rOBtN4NmnrPwGMFaeLMDSbKR17nk3+TCH1qvNLTaz6hH8ot1LR3lym9vpxO3PAT+tR41UY61stT5H+C4bVy/o7wSY2TePDCkibuvWJSf09e/r2tgzcNSo8fSfBgT9aPyGOHGxn+Nk19FQ3C2NUfb5tlv9Lh/xMd/LpBqWMn/L2mgz+sw+e0Dv7yS9LjC3TwIsGn9rNuHfpHdPC/0MH/WQe/Uwf/lQ7+6kvT4yfr4Bfr4KOAH20cwq1i8V9eN/7VpbR+m1n9ynXxgQ6fPobfOVxtt6sHp6/H0sE4xu0ft8jzJ35wev5LdPDLdfBP6eBf0pHn9cE4J0htX+/q8BkyhOK151MnDElPf+cQ1HcEt/ohdXx7cAi183MML/cvv0F643CuTyPPczr8dzL+PYyP/FrbD3XoM7Mp/xMa/kOz09Nfl03jwGrmJ26Gn6hD/2Md/P/NpvrK56jledgmHfoPs5FmRIodTurQZ5tZXGLnlBcy/HRzenqXDv5hHfzvdPBbzen96h2Z/iq1n2TlpOczXAdfq4OXdPArc6jdTG1qu/1Ih36vDv5oDrXn4lZ1nLcOTU8/ayjzw1Z13FiuQ/+TobTdaev3F4x+lcZu63T4XHFZenyeDr5CB3+XDv7ey6henGZ88rgO/R918Dt18O/p4I2X03ZXxsY5JWzheCDBp5lXjg/6/dL4wHKn5PLXuYPjA0F/rc/dEBrvrHXZiieOt49vcHobJxS4AgHcBFGt3hemYGwpmAkpmCIu+cRBKFwrCmJ/AeIdes8j2GyiiFsYkrdRFB3Fc/yNbvt5yFMfF6kotDd9fQb18xz/VJ4JF5CnGB96uSBK+YmNrxMlLHl9oG+o3hl014lBt0eoliY1uV2SPyjMqS4UKbeikBS0VzuF6omi3W6XBZh9ITbHrJM94UbkODEU8Hkll0+YpWGNTGtcvlBQn4edZg4FkAwQdfbJHn/wHmewTqgR7Z4AESwwt7pEvGDRiDVl2UrwiSaft9GN4s3rl48QfZ2AlCjJ4muFLL1wIWX5ihqcAWq5CWKxt1ES7QoZfd6QBNU1SZSteX5x7cjsa4UsZ0JOFEX2nB5tceAtfl+TW3T5g2isr/Fym+JpTmHe7DnVxeJk9hCb4ENoEn04TZjlI3LPQtyEyed53BCpqivEeUCPJijGckDymkKxxibabbagOxT2QcwAtH1u9UxxbjUP/wTUplpylIii1082IMU7aoSLeWzsAui1j3VdSBbNk2YXkCXNw1Vfn6v/Ea6vpy1Vq1B9kVaqvngrVV+8lar/KStVX4SVqi/KSgsu0koLLt5KCy5UeFdQCklhj6fAxYnirOnzRIdQXSOKHHTU7iUQK9xBUWoQXT7oDUNcGpQo1vnFJT5/rdMn1kHwCYnO8DLO5W8I+NySu65gUmnJhPREYv9evAiGDy7n6H5+XbihYTlkUUBi/xY+IxXFGfPKK+0Q2ipAWCo5u2Zaz50xo9oObb18msOOFCoWdWpESHIGoZVXfGdOeaUwncPNfxl3x5zq0CRnIAB57FWzXQ1lZTMdwrTpCxeKRQXFJOpVgwOBKnWizxmSIARJot9DSDW0DTDkcUsMJ04omFAwEaTweWtdoisUJtbgkqXWLKiWbBOVT8HWhj2CC5A2F3TAohR0Qg8nuLCrddntWqmAbpIoQiT23yPWOutEF4jWlCJ6tb9UrAQ7hNxBSYAYOa/aXyOqqApKQcQlDf5GKhiad1aTOI+5wXTQOIQ+cEedryoN+9BkCLDuYJPbrjWbiBmBn8fp9WksghaVJrpIqHbZbUUg4T3eOncjqS+7upQCm41jhoPipBKvPyTiqZZiAWin2zTUHPQNPr9LXZ4HnxBOWylUY+zHUiSsDhWDEQKl+BAz9OQSKNgwbzapCq0VGu/BO1Ip+AWpDUlrCdcypwgclnml1GIkW1EwDL0btAM3NldQCZiFUmt7QqFc2/4w+p8IXfASNzhhSml4ZAjcwecToQIbvI1QNLpbDZRVSt3NT/0N/rqdDRfqcp4lbimURgF1lVSkVIko1FSK/bGmpnI6hpUacnBHFOaK5L0VYjjkrqN2Bws0uNKa2hN0u9O4Usg2mTROjzd43tYJ6vpSHAGFSy+aqI0RxCFshSJqA7z8y+06DoHuXeqWg3XxPfVOyd6UNqoUKQRPL7SiZBv4IqtRX0qZNUKKK2kdA9STAyizM5Sno0K1VOwCP0tFlyZ9SquRWBfyi/XQMYPx8DiZ36cbCNmIi4YwP3VDOUil8UaIWqBbv+ui05Ihnh0GuVWzq4tEnzakQSRu8DeleAtaqb9mtNphiDt/S64NhRQe4Z/o8YVD9al1i31zIbb7Zc5ab5MNm27Iiw0TsmPUA109frA55CifJoi2giJNU8JJVdoogtENbC8qdNAy6ddPjwu2Iz0OYgBUh5mLuNS9nEW+tHEqtZ2DyV0NgTQBggUd0C1t5U50+dxOmFBKMJwRQH/4J0Gpmqbv9nu07oSqkHpSa0C6ftIGL6J3JVMljUIe8taLFBdSd9vgizqM0/XYk5U9NqnMJr3mDk5TLbobAtJyiE8BKMofdC5x29PVZy3Er0aI+JKrXmMKcsYRuvdly8SAOxjyQ+TwAsOmwlS3uci+AaIH+gSpsbnQvzb469xpOsY02rEeLiXQzW+8B4PhPJiwNSCr6dMBX0iFm0TeHuNO07mAVSez5ppm8FMUCEt2VxohwNcqRfC7pRCxYB6RxpUDy/tr3lasHiWm9tA4huBoXPt/ap79Vt2tAAA='
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)


# compiled by pythran
'''
#pythran export main2()
import numpy as np
i8 = np.int64
i4 = np.int32


def solve_core(a, b, t):
    d = np.empty(t, i8)
    for i in range(t):
        d[i] = -1
    d[0] = 0
    for i in range(a.shape[0]):
        for j in range(t - a[i] - 1, -1, -1):
            if d[j] >= 0:
                d[j + a[i]] = max(d[j + a[i]], d[j] + b[i])
    return np.max(d)


def solve(A, B, N, T):
    dp1 = np.empty((N, T), i8)
    dp2 = np.empty((N + 1, T), i8)
    dp1[0] = 0
    dp1[:, 0] = 0
    dp2[N] = 0
    dp2[:, 0] = 0
    for i in range(1, N):
        for t in range(1, T):
            dp1[i, t] = solve_core(A[:i], B[:i], t)
    for i in range(1, N):
        for t in range(1, T):
            dp2[i, t] = solve_core(A[i:], B[i:], t)
    ret = 0
    for i in range(N):
        m = 0
        for t in range(1, T):
            m = max(m, dp1[i, t] + dp2[i + 1, T - t])
        ret = max(ret, m + B[i])
    return ret

def main2():
    stdin = open('/dev/stdin')
    N, T = map(int, stdin.readline().split())
    A = np.empty(N, i8)
    B = np.empty(N, i8)
    for i in range(N):
        A[i], B[i] = map(int, stdin.readline().split())
    ans = solve(A, B, N, T)
    print(ans)

'''