Problem:


Score : 300 points

Problem Statement

We have two desks: A and B. Desk A has a vertical stack of N books on it, and Desk B similarly has M books on it.

It takes us A_i minutes to read the i-th book from the top on Desk A (1 \leq i \leq N), and B_i minutes to read the i-th book from the top on Desk B (1 \leq i \leq M).

Consider the following action:

Choose a desk with a book remaining, read the topmost book on that desk, and remove it from the desk.

How many books can we read at most by repeating this action so that it takes us at most K minutes in total? We ignore the time it takes to do anything other than reading.

Constraints

1 \leq N, M \leq 200000

1 \leq K \leq 10^9

1 \leq A_i, B_i \leq 10^9

All values in input are integers.

Input

Input is given from Standard Input in the following format:

N M K
A_1 A_2 \ldots A_N
B_1 B_2 \ldots B_M

Output

Print an integer representing the maximum number of books that can be read.

Sample Input 1

3 4 240
60 90 120
80 150 80 150

Sample Output 1

3

In this case, it takes us 60, 90, 120 minutes to read the 1-st, 2-nd, 3-rd books from the top on Desk A, and 80, 150, 80, 150 minutes to read the 1-st, 2-nd, 3-rd, 4-th books from the top on Desk B, respectively.

We can read three books in 230 minutes, as shown below, and this is the maximum number of books we can read within 240 minutes.

Read the topmost book on Desk A in 60 minutes, and remove that book from the desk.

Read the topmost book on Desk B in 80 minutes, and remove that book from the desk.

Read the topmost book on Desk A in 90 minutes, and remove that book from the desk.

Sample Input 2

3 4 730
60 90 120
80 150 80 150

Sample Output 2

7

Sample Input 3

5 4 1
1000000000 1000000000 1000000000 1000000000 1000000000
1000000000 1000000000 1000000000 1000000000

Sample Output 3

0

Watch out for integer overflows.



Sample input:
3 4 240
60 90 120
80 150 80 150


Sample output:
3

Submission status: Accepted

Solution code:
# compiled by pythran
"""
#pythran export main2()
import numpy as np
i4 = np.int32
i8 = np.int64


def main(in_file):
    stdin = open(in_file)
    N, M, K = [int(x) for x in stdin.readline().split()]
    a = np.array([int(x) for x in stdin.readline().split()])
    b = np.array([int(x) for x in stdin.readline().split()])

    asum = 0
    for i in range(N):
        asum += a[i]
        if asum > K:
            i0 = i
            asum -= a[i]
            break
    else:
        i0 = N

    b_total = K - asum
    j = 0
    bsum = 0
    ans = 0
    for i in range(i0 - 1, -1, -1):
        while True:
            if bsum > b_total:
                ans = max(ans, i + j)
                break
            else:
                if j == M:
                    ans = max(ans, i + j + 1)
                    break
                bsum += b[j]
                j += 1
        if j == M:
            break
        b_total += a[i]
    else:
        while True:
            if bsum > b_total:
                ans = max(ans, j - 1)
                break
            else:
                if j == M:
                    ans = max(ans, j)
                    break
                bsum += b[j]
                j += 1

    print(ans)
    

def main2():
    main('/dev/stdin')
"""
import os, sys
p = os.path.dirname(__file__)
pymain = os.path.join(p, "a.out")
if len(sys.argv) == 1:
    import subprocess
    if p == "":
        pymain = "./" + pymain
    subprocess.call(pymain)
else:
    import gzip, base64, stat
    gz = b'H4sIAPGfVF8C/+29C3xTRdowfpImbcqlCVfLRQkYsBUoSS/QcrOFBk4gYKXFxQumoU1pJG267Qm0eKHagmRLXFTeXd3Vd9ld13Vd3WV3eX1dl9VyR9QVr6CooCKcUBRUQFwu+T/PzJzk5DQHShf9f7/v90XLnHnOMzPPbZ55Zs6cOSvszhlajYaTfkncVA5z60fRfCGDl2yKogAsn0uFf4dx13DJkNfJ8JTpGU18aoi2Q8vla2lemQ7l4lONLNVz6r9rGuNTjjNHyyGtGU0UmtFkjktPJFO4yRBfTsvKme5m9+82x6eMXimViuvYXxmDK9NiLj7VSXL+XKjE615emlem2zTxqVTuJiiXzHX9Z2LpPNaemlz2sUqlVNLDOJ930fjccb7KsT5vbaBxbGP++LHjc7Ma/FnZhCYTw505d35U31oZr/0ZDO8Pe3rqM5//4cyGSbNOPTV0/9m392iuewLvTZXp7aZ8a2HS02kEhrY1Hf6eeWjylr99mPNu/abkiWO+ct2x5L82jv3voXUv+NLHFfd/tNf0XuajF14bcvRC21VHL6jJ4VlQWN8E8H1cYvji5MTwDfrE8DuTEsOH6RLD+6Vy3IgE8M9U6PxWBV6uTQz/TpMYHlCBW1Tqv1MFvlUFvlxFbq0pieEXVOo5qgKvVaF/kApcn5oYfkpFjzeo6PHnKnz9VYXOvip6OaMCf0qlnskq9P8Z/vokgPdSwV+iwu+bKu3+UoXOmSryeVYFP1/F/keo0LNPhZ5XVdr9SAX/OxV9nVVpd4SK3H6h4h+Wq/CVrWKHs1ToGRYdgeJ/IRW+HlPpR1Uq9FyvwteLSYntx6RCzyQVeA8VefZUoedmFTnMV5HzbqhnODeQy19ulo3WMH4yeDmDpzP42xoKL2yKx9+QjPwCfpM5bnz7ZzLFb2T1NDN4DUfh1U3x8EdY/SKDl0h2mIL1D+bMzSxeYA0cSUE/n8aVLKD5XIZ/T1Ji+EyGv5bBiyRBwDjcIFRWjB6NY+94zuVaXOOvdTUI7nrB5eJcjrI5rkpPvWext0Hw1JfNme7z13rK3It8Hnov8Z1b55YK+V5/g2uRu8GT66j1CsU2+1KAl91cKtjyAOqtgDbqPe6aRYEqRwUAbRXV7nqXUO/2Cg2OCrsdKWlsdNV56hv8tW6fV2hyLbWSGubarC5XRWOje5F3qS0brhu8rgqfu6HBJTTVeVze2iq/nZIwgeDZbLYC2qIfmvTWLqYNJ2q11I1NM1rnlvoLXHOgvgZPveDw2e3zSv1lLlozqxBYTEh8XoXP4663w51slwPEAH+CW0DBVPpKsO5SAQkXquv9y1w+T+1iodrlqa/315fMrqA8dpt4O5PyJSjksHa3y+3z+SuAMpenscJTJ3j9tYQ423gXaQ9akvhPVMk8gOXEqEO6ylxlVpe9dLyrZHZpjstHasv11Fb6LrsCKujxSJ/PI6lktkwu2VEzArGoC8SWU+WtrXRVeesbBJe/yg4irqlR2EdXq8oGe6jw1wJeoEKw11QwhdZQjeZKGvUHsCVXvbt2scdVVSNAk8sVnaI4m9lYjIg8OREq3SLOQMscoGa54rrB0oR6D6h3qceOPNQuq5kHEi6o9VM+BGoMEyS+FrkrqcUsTdTFp8eosuXUB2oFb42HWjXrM1FSJea7o80JMm0CnXKNlpX9Z90G6YkRmbus2i10UlJXybS60DfW+dwVINoapLBLjiPXC4K0lyR2kjaXvXvUoBtz19VBN7QzSkDT3VXAeKIA8LdK+ROHQgwlrutOl7xpzFymU/1DEwVRJ+hfDI3LfODcTlbErB5IZvbZyVN2vQfZE7YApM6b3Q352rvfk63IE5FcVb0n3g13U9EVYDCCxz6vhnql3ArwRt2nL7u7PYDYXEODd3EtyLQ018VcpcK/QPSxuAIqw+DDxrnm1y5D45rnaQjUePBmBY1KqvxgvFyV4PHBiILBScUSV0X1EleV24sAaAeGKJoDugS/j6vy+Ku4qsUeoQEhMMRKA57gafQKXI2npqKmjiR1TVxVhc/f4OFqiG+T1OHFqGO5B3Fq/Eth7HYhPSwyqnF7gR7QGDdz+nRXTpaVm+l0TJu+YAFc52ZlWzm4LJrmcNmycrIK4u4VyG7FF7LJs/TalS27ssUus7PyyHqIjksif0lwjf8lcXoGodfJJE0h/xrgLxWudQRTT2D414PVomV/OnJXR8pDvB2tja7C0P90shT/NOQ/Gv9quBWGWLxfMqqpJ9ZSlCqt/yQTTLxfh39nfg4zvB6cI5XCmt78c09cBVvA8sEVmXA/hfOw/Nt/xhmnnmtg+R+T8jruXpYXMh82IJ2haHtJ3OOG2HpVf6+3NwdyeIzhr3voZ8m4QvRblg8M9qaiZP+UGr+eJa3LNV9HU6sCXsfWO/MV8EKGz90VD1+/mKbpCvhBBjcr4GfYPMKqgJurWbsKuInleQW8nOGXKOAZLF+upJPhV6vgNyrpZ/jNCnghy69V4etRBdyaw9pXwNfm0fQFBXwBy7cr4BtsNN2lgJ9g9R9UwOtYXlTCs1k5BbydTbAMzfFwKc/dHZsXFsvmkwYZfJpsPVWSh4bN4U0KfSrhGSrwQhX4AhV4nQrcIINPkM2fn5bBJ8rge1TgZ1Tgu1ZQOK7RpsjrkcEHyddxZPBsGfygDD5eBhdl8HwZ/IQMXiCnUwafIl+/aI7RnyWDS+sBetkzC9KPZPAiGbxaBp8up1MGL5avcy2PwafK65fBb5DBG2XwaTK4tH6AcLu8/nspPEW2dkH4ksG1MjgvgyfJ4CUyuE4GXyCDy5+7lMvg8ucO1TK4QQavk8FT5fzK4D1k8GYZvKcMvloG7yWDr5XBe8vgj8rgaTL4ehncKO8XMrhJvr4vg8vXx16QweXrVO0yeD95f5HB+8v7iww+QN5fZPCB8v4ig18lt0MZPF3eX2TwwfL+IoMPkS+wrYjBh8rABhn8ann/ksGvkcHTZXD5OqFZBh8ug2fI4PLnIFYZ/Fq5/cvgFrn9y+Aj5fYvg4+S278Mfp3c/mXwDLn9y+CZcvuXwa+X278MPlpu/zL4GLn9y+Bj5fYvg4+T278MbpXbvwxuk9u/DJ4jt38ZPFdu/zJ4ntz+ZfBJcvuXwSfL5d9yzMC36WvcZo5f2S5oI3v4lm2GrdH7kbyHXWYuMnId/GscVghXmCdBSfhgBH4jf4J5dHXhPSR/P+bRxYXbSX455tG1hTeQfD3m0aWF15P8nZhHVxZeS/KLMI8uLNxM8rdiHskO15H8PMyjKwuXk/wszKMLC5eQ/DTMo+sKF5L8RMyjywpbST4b8+iqwmaSvx7z6KLCJpIfgXl0TWGO5AdjHl1S+MQFzPfFvInwT/I9MN+H8E/yWsz3JfyT/L/vgHw/wj/Jf435/oR/kj+K+QGEf5L/FPMDCf8k/wHmryL8k/xbmE8n/JP8bswPIvyT/BbMDyb8k/yLmB9C+Cf5v2J+KOGf5J/B/NWEf5L/DeavIfyT/C8wP4zwfx7zD2PeTPgn+Z9gfjjhn+Tvx/wIwj/JL8f8tYR/kq/HvIXwT/J3Yn4k4Z/kF2F+FOGf5G/F/HWEf5Kfh/kMwj/Jz8J8JuGf5Kdh/nrCP8lPxPxowj/JZ2N+DOGf5K/H/FjCP8mPwHwW4Z/kB2N+HOH/HNE/5q2Ef5LvgXkb4Z/ktZCvWhvtX22T+JURQS9+BpXxwQN8KK/pR2ZOFEj2uHh3RAIvQvBCBvYguO1ui1kckoqV3G0xiF+BUfKhuy1WMQVhwQPiDYgVysuAkphFfhDVJB42IMJx8RpWjYlv2W7iC44DHbcBkjN4xBn8guJDufxztFG0b75tgGMhVBfKewtqBURxN0KD2zve5UN3GcRnwdk42ootojNYabE6g4LOCvA6Z/BZy3oc7oayup7EUljgLnkBM9/WexGh1qkz03IbLSgr8dBZWs5HaJhsiRH8zDlK5cNnCeHirE4YqxlGHcPIkNqO5CraPnxzorarWdsniPyYvnLOSYp5ZT4oZhvDWQOeCNs4RMR4XNyEZUJ6/QKU2YAvexPF8MEkKBdEha4h9++2FIrzSMlvxVkpVHn3Rw2iej5S1SGWQxvNd1uu44S9VOPDALXjVWxuACtUw1TmYeRMIdXnjZqPugJ9A7jjF4hQzBCGUnWL7yRTixhD8hFxG8sPBTxH6N5C8W8AgIt88Rl6YRV/TS/M4hk9uTCJP03mSDMPJFNqXkYZtW2ytIO7d7RsMzkLjgjPiN/9G4j4LR/c6gitMIm3AW7HE8hPungDchHaZNkAw4U4ohc17AwxvZdk4lnJ9MosvqPHq8kWsScTWwNrc/Y5IvB1NxOB39OLsMEH7WiyVwFQHMwscBqQEW5iHSBd3JpM2z6IbT/VU2r78Z5S23+FFjvWoKwWwFU4EKFmJv6EMB0R6xmmScTRBMXQrGeDEBZqQIJDeY+Xmolc130Hrf+WGGLeagZrRtijF6gGp+upAu4CBVRlGYe10rBhfimMyBmVfJtuJPo4PlhsSbe186HboQ+H9BkTqX2FBIsBbxnEocwwyiw0PQYjK+C3lVnSK/mc/qSSQAqM6Rm3Ldwq/uk70pfDdwHRVcZhxWz8D015/laQXOG/keUpP74FyIUgAMS79BbiCh6Fu+G+ULZqre0LR/DNO/jgp3zLoRMlZc6QfncuMt67lCRTJuOYOWAglDtJ6w9NKSKV6CdiUnBGGADhxpcLaLiRGjloHEYe2W5lKeD3oY32wCTzAr/5QhIfPMFvFm/gNTv4Ny8I/aGC7awCQ+Qg5UMq3zwlDOW4wLj5fMuUpYAFUvlc6AVsPbMA/SuwKFYDiTv0mwFPs3Cron0qmpg+SoGSXTeh/s6+jE5fPIsihJav5UMlJn5nIZlU8UHTjkITB1kzRkKQZlBw0m1bMWqKVYj1NbL6EEf8I6tvGBQiE5wq4/F2uE5nFZGwzdiH1lW1luqr9ymYu75YTuSV9+McIrZ5E6C2sd9GImur1sbaY/h2xL+F4hdR/BGIf/50Yvz/mhLD70Pxvx4P+K+r4P9ucoyeT7IJ/hbEf0IF/9SkGP5fKP7DiF+jgm+fFKPnPoq/CPGnKPGL5heVkT6Uzrclg1yNrc8RRZx3BE+9jLJ0tpVDhXknbwSPOdosjjmDPWIrwVzNUd++7zTCbrcYvjHeW8w6Gr9zejpTqXEg5HdMN4B2ppPtA8Y+dhhm5kPeTjTIr3xLmMi3zYLAOpsP7uYzv+aDe/nNEbDje2DI+pfQF7pXOh/cedvCotuLFobzQWkIodmt4j50ETvpqgLwnloQk9VKG+G9CgZVsegU8M7kszIfcAZQnFcozvUgJvFqwDEO4zrLH5T/4kCK/xCV59eI/8VJhTyJLIGRYfzK/cIYvmWJQRMYCeKMjpOjoYWX0jnqfaaeklEOLgR8ztp4+4+rbyzUZ9IERsXqS26+y8AJQ18yswqRHkWFYiY00vHRWua/2q5CcfN8y51mTaAIqg+epS2M41d+Ab2q7R5gzg5i3wnE3gHVvWTlqOd9iNSdJKsaJKWkl9QXWlEoPkJirbvyxZ/QC6t4H70wU32F7jLdFq6NoLgLlfJuS8EePolvM/FthQY+cw+/+UwSX7CjIZUP6b4mEy7Qya9RJ+c0RCenqA7vh64n3vIN+Kxt52jNnfSRrtDHXYD9UgYT35++uZQ+im52BC8QT0R6Dh88Ba3vdZq5UrHPqVjX+DnpOTUg7hoDc1AmyTFt/hwMW5wTqhzLIV/Ccv7FCP0N4wv2LPOjrAcgIZrtGBgFl5jmFBwTcvngDmcwzLf14YMDUYV6vmWaWcO3bDbwLTsMjoLXAp/NyTzmCJVZzvCbj0JAVbwHuuJ00hOpb5xOfKMjeA3tOEV3bLW1dzwm/uErOsjdh7aMXDmCJ4EpLTAF/J0nbpx4iLhR1mBrF4edlDgWFsxps1j4UKEJSJ6z8pCx9TfMOdwGtTuCwIkOItAzxmV3oORbzmgC9iLj8zrL6hSo1IIwIHWHLoWQychlY8JCkCK0B8MzbT1ws6zzY9vbOixVUfsJHra9haqsQAkGd6IEsYHgdJMzeNQZPCzeiezSNjt+AjYzGPgT8wAotjNBPP4N2g7T94/44Feo9NPMUwa/wYCDb/MbnNBlghBXB0VnZtiZKaIyja2jtWiJuZxj57YzaPAFJ4yrjmiILPggRBkQTkMYkkeIgCsSeTiCO2eFIMgGsZrmhBYTP7hTS5WVa3QWdBhbnyC+p/SMI3QjMAItHuWD7/Gbv0xyFLzuCM0xFRof2ekoeMP4yOZCwxvG1n9it2j5t8m46m+kg9yrsb1F7DB4hn8TKD2oY+P3N0VGf9kArsjYp/9V8E/xACi2BXr/lsDnfPDffMunF/hQr3cJKaHi3pxT8x1fsFfojV0z2IffSWQEpvwtFOkQAnzwKJ/5Ad9y1MCoA9PUgLEaW+cjGcGvGBVvhvnNn+iwZ31j9PfqZ+yj6weN9+pP294dCPPBr7FtZyiXtD071CvXofmYL3hP6O1oGwhNO3Ym06YtJ53BDkfBF8J05t968itfQU/pBf82ytmWM2flaeEaR9tUR+hmE9IO85/jzKthnPyP4ziYbDUA/YEtc8AFGjBU9RyXjAQCWGfwbmlsw4kR8NZoIoMa9mYSKdLuVOTaupZ/IdaXdxgfSEZXt/J04OqiTRj42G3gXkDJJHIaCPfWvoS27gxZThHQ3vNSnOkM/pvxA927Hf62QLOzzoAfOUPMvGBr4FC45QLG0p+Ew1gTLVd0M7NT6s8hkq1J1wip0R4PTgnMayEY1jlHwUfCVL6tHzqTsXQ8iJfXDDYKLPhSJq+2L6m8oHRgCxktiBvN/TLqRo+LiyHj2Kkj65kdrfHxxs0Q2h5wmDnqaM4hpX5gLUVGbb/boLMwgsN/PE5G5CjdQLQd8l2he/0XMrrf/iKObnAqMCAY0FM0fUEmQIR68VnIyJsfcxyjeAX9wy6D/o4vu03/wWMy+vup0v/3YzL6jx2Lp9/3ZQL6XXzX6R/dffrNcvpLj6nR/12HjP4xCvpRH53o/9VMszT8ni91ti1G+mHQEQyOFp8FGBiBRliPmsR6sbdJ1b3egdxAP5nT5rMYHG1DLciQUxZ/dIEvvkPG110dMr6wwihfwztYl8BhsAwyYVyl6hRPAD9vzOgCP785loCf80evAD+NR2X8/PGoGj83HZXx03r0IvxE7F3g562OBPyMvRL8PB2W8bM/rMbPfWEZPxvDjB/SuoyfH0U9Koz89x1bTVwcjAe4psDHRUSNuO6gH5yJOcFCRnMeojBLIQlDwOuLvzotLZjBCK3DEXqFgTOuHJ2ElzMhmIOKrXTNpOwkWQq5Z5qZo/Pevx3t4rwXowrbfmco75EMpESDJOSTdRG8KsGrDGeo3BR+HufrfMtZjXHlBagEwjE7t7rYUu4MHsTZ3AJo/kIRNI+S3KFL50QrJcHY+nsSU8B0PmjC+KXEWfCpsfUtMsITQDkfLCEXCzrP8EsMVDAY0vFt+UQWlSiAtmUGftIyk7H1MayoDXEgruWDM87wwWUw8MI05TzgvqKhy0pWUrJnEqrv9N91pO4PxCdElLAAvNKBW0DWt4ujTtFlLVroPS1toJAwoyON80CMFcIXsQNqcILxFUJQhuHAKEdLDVjaMAcMdY7oxMh+JGpix8XGI1h7scXKwh+stV7Lai1433h/UIuIlSCotgw+832H5iO+4E3eOONNiOR4zR4++LaQCm3BtAFLgeyB9bNG4yrXBcqNmay92d4SHyTslcH9IFElEfUHzuCXYGLiQ4fx5u1UtiXh/AvoOTHc4GnHuRsVdcTYeh/cEFsBObzsAto7iQNR6Sv3G1u1AOr4Y8z+q4x+p6Xc2Ic3hUdjlIGtm8XDJ6WmsAw+V4GZlRkMaf4FauGh6UxQQw+T5Tsmq7OiE/OoyJUTLtCVXqtY/RWVnwFttxHi/F6kTxlX/jfq+r5t2OdkMVZMdYImqjoQju0ICd9hcgE6/lP0WYKxdZ5Gru8bYoX4lt0R8YPDdILSlkRum+mEUncDs3xnm1mccCQ6z7nR0bJdg/Mch+aYI/MLmF44gz4L72g5b1xWic4puAeuNYFCOrvRIojYMpneaBNMb263WMOGc5IsNoGXD393NhJB29tOpyyo3I2HUEb9Ov5K7IFOcZoPR60Bpomlpo49MCvuOSa2+vGAmcyK8emROB0qEO8loc4HcOsuC7k151pc/hRv/hzb7/3G9bFVo9m07Bgsm4KNh6acGAHXPbGeZKhH/DWQKq45REQTXnGYpvcdpnOo/zpM599R73nfMVzFljnMVsvTGrr2mkIEv4D0mHScm+Ny7Ys92Jz8F9hCwe0WgzCab1tDCslM5DotMRGsOxrjYru4Amx1hsaOmYKLo3fViY98iv16ZK+pZmjTdhq9ZxXQ0IxTs7a884Bm7NNqWa3hSPqoBp+E3I1PQuiDGl5zgDzB4Xe2WhrZRhVncIJ47DOyvB6A8sQhh+4qxL4JptWegTySsiH965PNZIzIN2aCHeI/JfhPuXiMiFb/T0JmgwF6a6OJjSf5SDf0pHKsp5nYDBDInpPtaCXPYjhxCRDwYibV2f8OpyJLQaLa7ByRK0fsPYNH/7KDBeg7PkEtfUkevWwR932KNAiWlBc1VOY7ii0p14mbAfwyISbkgxFpC5SBG0AnEJlOJ6fbyFIRjm1o5OWkvftJp0H3BL15gbOgfekkPnOX7S3baWjZEWwXB31GGsdRd+5n8c/bjK3PonkdgabCvzmE/ssRLSp+AvR07JDWvYm/bWsE/3mgbZm55V8asQgqC+eyFSUQZgl5fgNVdTwknoLhXTz4Cenp4cGHaNr3ELXVJZCGHSwz4xDOpeLGfxhLP5+IEY0jeJ6u+aA5P60050epOYPhkcdl4vCDxOJ+MSne4uqIPvRtk6jFNTKLWx2zOPqYjVmcGbVezSwO4ihx+SfEYpImxVuc2RlqzAB4xURqaRnO4CZSD9qZNWZ24nKibf3sSVGLq5YsLiNmccWWOtRzI7M1InMxclBmV/tkdtWmoXZVe0BuV82IHvx8B054sTK69E8sBmfWaCJYUZOGGkwhGgwPBmN8AF/ol2n+yEESDNUVMJ8sPvUJi0SOcywSQfvB4KO/hS84amwdGAtGTFIwYkgUjDiCdg6m9vqNKA4QP2E/KrqB+idRTSA3Mx3O5hwgwruQT7wKEXyGaADyyGNTIr623hsKzBxVHT46jT42tX2Ms3URndij8U6MBJX3bXtaEz/OxfWaz6Dl8LeyZ7kOaZ0Su00BmL24ARoIj4vQwdQc3zksQGPYjyEBDugmMqAfISMw7Se6j9mzNvFFuBK3MzfuJV1mW/ixT2i+keV/+QmLlFn/2D7h8vuH5iPSP5bnJ+wfS/K72T9u/Zio6LMJifvH9AmX6B+3UhWPzr/8/nHgI/oodfGH8m7Q9FGXusEtnbvBeS6uGxz8iHQD73ipG/zqY9YNOlS6wWnuMrvBc/mJu8ET+XHdYMaHREbf5sm7AepT3g3+MD5hN7h+f+JuEPBdqgschFbDZ9W6wBTsAs9B5eGcSJyZp0RiZn413n8Bzfzt/dSSv/mYWvZZlvY9QOGvfEye7BTNB4nfOwQMna2WJFgHB0n0Jlb1Oa6joL8i4Vxee640eTv9YezRwM+o6KNzuJcNdOImDt+PnI2xgCINLKLjg+hfcanfjrPD0gSL+RAYSUs3Zo0wRLFUni1fKo97tNPygezRzvOQ6diGjXc8J/79Azo8Dv+YplM+JrP8TvPh+4416hRdvJo8TTc+PyMDJpLXtQK3I3RI5AW+7V4rPivBcPxTmHDZ3vq7lsYsNwwG+Rz+AGcFGLf30dFp+8ZPyFBjJiJbg1M0CFAKnaFKnPEI2BMFnG8J+JifTPIgZAyj14/GB3irjUzU9Q8MorqyilcfQRtN41e+Ymy9lUz8bsKnmwNzWJfiQ9MivGYLtMob+S18W59i4/NjtOL4/TH94XwRfImpyPjSuy/RgGmnM5gv/uZ96njzO4X4WOpfJLoFBbb1tNu+4CeVGIytuzQxRToLvsNViTfEjn2xCZN4zft0npWPixYjyRwrcE3c/GrTvqgaD4jHIWNv/cK4chLxoUx6c0nTppc4HV3qeOV95jXOUA0Mypb8yY1U8+RmEXmOUQhz6M/QOcA0pmWrLnR/5MKFC9++NXzPyGb4CeHgls1HTX9oBpEbi7fwoVItbyz+ILiZ28y37E9yZn6G05tCMpl9YARx4BA8kBUHHarqeZi/rjYhg4V4xxFcaMCFBujWRICQEgE62qbiJKuQWM9KUs18g6MtwIn/eo/Kp1B6umfGp3ty+Ty+VyafN/ZG57tR6ViJqPLFR0B2mN9KNjVtJKYM/Sh3JF0hOmFc+WM6G0Wjl7koVh/Yic9yHXnUkUk8FBjnG/isJ130vkef9aSvbC8yrtseTiWjsLSehRPoJPBQHcPZer+AM31cVvj1Pjq9K0T5O4LzTcH3W84aGq5u+UwX8K18JXBjVHDhLRek5wYOdP2hRgNOG1uOnAHnH/zA0ebsD1dO0xk+80yoVxKfuWNzOOkb/nexCnBNAGas/S2rTac3a4XfFRn/t6zXhapgmeFCx6NFUGfQVNRW1uscAM51PCC+R8bY3r8aGpsvDulL5ovnBoAd7X0XHOz9FOcuGc63fTgyAhaynStvIPIz77Jphlj+Lntkt1VciZuR3gFDFUe/x25j318gTn6XuNZeA9kTta3Qvdfa0PkmiXXf0nlMIW5mw5GnkKx+oCmJxZ/SbUfnSCipf85GtkNliXRPGdl/NhxgYq+9dB57//t0OPjz+zS/k6XlDF4Pacd7ZKbLIM+/j/MV+XrhWXCReC6KM7TRUkjSZy1rlVGRqEWX6QyeBqq+608WlBCM+KL/U7LZzIQhTqE2OvHj8VJ8GMcU6WnTCkQp10pRUKuFzqZbSXuYr9MSR4RDCYz+uNgBwMZ4YCgvLUtyBcXv0cUUuHs9ugEyHWfujmwfE//6DnlWzlwd7amxFp+1NGuZ69P3IKsoOE7mPTcWGsDJY1HzZMt847p2h2abuO/daFuVzuCTlvUYCYWeJNveeJDdevr4VCwnY0IrhWPNfyR7Pa3S0s3jqbQrZyBlQCKW6XWAUGlgVJokKsuJPjZZTFpKLckHIygVBIm5H9O2yiUu8nE7W0s+Z2ytJs1sIiIFbeDebQ1xTUNT5QNbVT+opvc7bGBbWZNCFwvvIrOA03i5AffShFoZf19Gl4lXh2nb65nuTBINu3GbakthugbXSXC5iD5/ftySrqUbdVB6GUj+9g/kC5546Ao6hMyPcXeY+DTZmefDmzB3W0JuPklj6ja6rEDGVAih1tAcOPE6/KccHUYdkrSHLg2WQS5zH5ZupusBxsw1lnYSVK8jSx+OzeeSkMANHF0Vh1ilfYW5wyl7Xp9MlnnxCWwwYHC0HNMIWeirt5KlKhxO2wIGflLAFHiHRq8guJ1EF1sJduAt3GyolxYSxLOf0o2m/TBYbhtqYTZ7QPzNR3RgJeppyxAf/ogogoiSzTqoAgziVd9SxTRrqEM2sDsZ4hdHqGqamWoOSqY4LoWpxtmWj3GzqJG2TsUeAKSjoPAgOXHU+5RcOqR9SXZMHnbSzXtiKukvMN6RJeHXkql6TBqqHpLiswcQ/+Mkh82doPp4klwgOkfap3kkdANDPIj3Mw8aMx+3FOJIGtxpzHzSYtbQUjgYOsjGK4yeQFVXdzjYviGqJ0fLNpgroJ6Og56wgw05RvXEw4BscET1ZEIdDOtB9rkSbNAT7isl9q6NyhqmBRfExvfpHtsSMqtgW05zPqEiyhAz2ZVBvGE/1Qvl+nRUX4tPUbi1k76mHKb6smpiYiD6WqGP19cLMn2VyPWFQZq4dC8loYToa6o+Tl+3GKi+Soi+huqpKJuZvpqZvhaAep6ly43QXLmG6GsjuUD0OqavclmfR8QSDdPXs0QsRF8biY+VPOVl6mvJUaqvBQn05Td00hf64XZCyxFU1et7qap+9wFVFdmZvPqApKp7D0iqWvc+nabZTouZH9B4aOVp44MYyTvRhUOVc0APD77BUWf6NBmvjHzBq8Y1eIaQrb1jKK7j4Ty49RmC1H8nr9nNF7x9dy8++Cbf0q7hC3YEDmAov/ppPPmJdPTNn95AsYxrVmN8n/kq3yJqHMHNxtXXJ+EGjHZca3BIO65WOAu2Gtf8lMwhtxtbRxGlrbHs0VKqdkHaMYysJxa8ee8oZ5JuLzbsKNi34g5S4BTpXjhfaPoIty5qtjS9C0HP4d5AzwX6JGFeLxLr/AMS8aXXQBTRthmhPz2gJYQ6Q3wkfJu0frmyvdHoDL7FZ25xFuw2rlmHW8ZD/YHn93ij82vS+EYyb1hDhHlvkG9bR6Q4K2nobkdLWONo+Q6GnaG43Rp5/CgRa5a1wJfxwVmIFOPN+NMtGiaQgCbK37tAbtOe8DWEQELfQBkrjMwvED8mXM0uvmCPsXUv2Y1EFAbEnwBpeMZArGV6jTxWuAWvy16l8VmvvdKLHNq9bEN5wT689a34awDg44KNJGaKxubGlS/QwBxDLVlgjrvpcdHBRIfpdBI0LXw7EmneRMZsGMlbNbFggHiF0iQaQczYHSGhQAaLosgIzDwTytmspavxZlnEhQGa8Xna0GoapGHREuLs1rGUjhji0veoW8ogWl1hkiI2cSHcCL+Lww8OltJ+Wp/FQPcbZcLwjPUUnLp3Ejq8R0kj1vAzEL7H7B9F3kZ2MIoaHpddWNMEu+DEiu1YFIEdW1+ghQ5CoR3CrXzwRPj1cyDl0x0nYWLSdB3yuFbikRVCGNaEGu64X/wjRjjg+FYO1bJn0gxPIpaYtLgR0ET/W/DPraACcST+cwKzs3dL4b/7FRb+bxdXfQlimPFGNPKnPki8ATBAlXu07GHIrqj4tztC+BACmGwnIsGAUPgp31ZnwFlCy47+4tbdceHyaFoaYrO+xlVTQQov9mO7sVNpsNmwC1VEA8tBRO0l+MiQvO+wi8xBPhkhBctXvUqfzXtxDc4RclroItw6Sgz0i1ZRWohbRzsgWYtbR0nA5bcQWaBjBWScxbhVrNmxt0ikoYKyT2a8zxDq9HdZcPXtApkkQbSei4ELxNLEgkP3kEiRXENjGKuQSdKwd2kn3Ps2qWI1qYKE0uI2Ahpwnvj9vNPX0jc0PgaFiN/tId34ERg8xOd2kqW3TYRqY59GU3gAav+pPWhUtnZwv+FlZ+lM6vnXaNrB0lWyNPwlywx4naa3sXQQSxew9McsDbD0OCu3iaVfs3SRAm+5Il31OlvPBrZtoFcyewum8G0reD40fQGVbMlOIhbNcLLqmY9vGIR77ZK//lC1Fu5/NVylvImW32yOlhdf30mDUnxB9LbwuJ3yyig90f2Espkl7tbH3YW/J5Ywjm+bacL26BZU6OH1g2LLfsQXxip1tq0wOEO5ZLvhnOB5fIS98rRwC/GvO9Die0IU7yg4LkDwc5Q+paM7VrfulHasrnyFrlQV7UBruXaO5vM5mpPO4BKwqQqTouG1fDBstx3qeA4M5CmIZMU12+PlVfQjR3AvH/waeSOPDxzBk/SdHr7tRrIrPbiLzzzKS9trz5FeOplz7NxBt9eeMa56gW71we0QJkrse9vp0omJUhzc6tDscLSlOIJvOEPOfoY5mcCUrt/skK6ABJCOgmPG1l+QLoqL9k5ctsGKTOLrOyjXJicu4DgKXhOmOoP/xuAPBGDeTlxfYDTOjUzEWZvmBAN0b2gRvuWg2B2K4yb0GgOU63i1SqFfsBHjA3lwZYeesp8Emg50sX3puk7Ba4GVHQ+tfZnuF809T/aLJkfiDaaQrM+CRNFQnKGRydfgHu7Q2DRIS4vwpY7TzszPyY74zeeTxHFbCQOC2bY/ah/I3eni4PGiSP8PYY4FY/ZHgaN0L3xMq7E2v9nK9uzHvQUd+2nY6Q3jqv01nnG13ju942q8td4Kf22lO2ecp3Zpwzi3UOGv9NTjiczj6pqEan9tTlb+uAav4Blb565Y4l7saSDwenctu++tGBcQvL6GcQ3V7npPpaveU5VVXVf3fbTjr/PUuwV/vWucu7JSagTfpYffIvPUKWZrpvkGc4bbPHmKuUGonDixNlDjqfdWuHzeGq/QMLnSU+HDYyEzFmVOnTixxt2YkWkea16UaZ5ozugCvreW4mP17kz4mUeNMo/wL/XUV/n8y8wV1Z6KJSO+X74bAos68T25O2yPvky2R3eB7SvONxLRAEUaBMq0/LCxiRPlxy+aawM+n7nWL5iXun3eSiafkQ0TzS5Xnb/BnLGs2ltRbfY2mEcuD2Sap5qFam/D2KkN3uUeYC7+Lsd5a0ktZh+QV+/2mav89Wafv3YxoC7zCtVmPLbObLNOlM52uNJ8e2srfIFKD+MfKquvdzfJ9G5FVXhRDV7QPlHhYo8w2To1wwWdsM4jnSFR46mhuLWIWwu4lW7BLbHNmXvA/71TFWIFE4NLBVCe4xzjbjRTk/T6a83wPzkxrdJc5fV5uPrR3AxIiS7woDYivHqPuxJLxldKTyHkpvsDvsra6yR0LMyNq/QsHQeMeWu5pZ4KMP2JE9nZlBc7h14yXDP0C2Ia5rvctf7aphp/oOGeiRMl+U6cSAQLdk8lO7lsjLmudOr1mWgUmWOnXk6hiROr3A1Chhd6xeUVk/r0bQsz0LTM3loh00yJHmW+jZhZmXmKWbo3CUpB9uJtkBbIoYqTpXJjzLYxlyglBOp8Hhd05wZQ59RJ5v9/pRhjedRCpX6lO2oERf0kseKMmAyiEr74dwwuo34YgC6//rLr1WomIyhQHR1CJ5fJbGTs1AzJNmKWcXH51ruXuaiEJdqmLuQuXgT9LGkWmvfUe2orPOYuFkhoy4ls+LIJiLeGslHdk9/1CcRHvCb1LbLOQqDsHGTZjanmqQuvtP66QcD31v7F9QL+uhNhlyzBRBZv/1daf+wY0vjxaTKeQNq58Svc/sUF4MJhbOH32v+7IYDr59qyXTOdN04rcrpcc122fImAPEK2LWcaDB126dRZ++WWL7hI0a61Xyq4cWCotOMBvJfdvm28vXapt95fW+OpFRJVcany1pvdvoBHpfFLl5/guDFWVjMkaVK7hp6T2Po/kUgvmHpthPllLqTbIBV1MAGHFE+PikCKO9Fm/D0SeQ3SdkjxwebgFyORdyAVIB3Tg+OehnQXpPw/IhFrL457f1Mk8jikJyHlIa38ZySyEdJ1kH4Ibbe+FIk4ob13IMVV1v4vRyKtkBZDiquGAqT4TY0nIcUFrHcgfQdPUG0H+iDNhfQcpJbNuCQN9gtpOaStkK6D9B1I2yG1bIlEjiEc0nTgaxukuZD2hyktfkOhElLcqVG8LRLBg//KYM6N54/odgG/kD4JqSEZ8F6JRMogteyORNZDegjSY5BufDUSyUiB+l+LRCohLXsd7kO67q1IZAHIq+zdSOQQpMI+qBfkVbYfykFa+WEkUgzyav0E+OwJ7R+PRCYDXiukBwH+DqTnIN14AviC+7qvgR6QnwBp/96xM/U0y+dxmkaTZkivFMNaTYqpPzvHbgPoQX7+WmJ8epYc4tf9DfSG7+ynmWakpc8y9lxmaOZuGDzp+hzLCKk8nvfY+DzIQ37uHfzdjnE/6Jmc41iUZmrRagvTDPQebnyshHtTo/fuxlv0TFs85w3ukXPuitNMCMMnNE8qYPhJrnYFDB8sf6iAHcJz/hQw3LvU/58xmBPPUwS+rQDDTcHctDRTSDstLX1NUlGaeZWuKC2jRV+cVqhd3yMtozjNXJSWDhjT0gz2nsUAKZJBinoyPm6H+vb8M75dfE53TAaLft9reYmTaCDll/f0M3NDJD1ksHP9RCiD53DqHkgl8lqlhfZakoCSxrR0yBeB9BB3D5TpBX3HweQaw2vqISEigYiLZx9YAfcmcgBgFHdmmrleQi3euoPqC580VAJudlRfPqIvvFeMTyyw3zKeKnrQNizsO0ybXqK0q9GN23lwg8UhwDuAPP7eQOT/oHZWWnooaVqaeQ3Kf5V+dpq1JVn7LJX3tDh5Z3SWY17fqByx3Cr9Gl0o6UEttVlcd38HbBtp1NFDZcg5fSjzyeBTkB6d1SC3g9loBw7JDkKdtW7pTMNf+sRoKGzRr9KtSQpp+7OzDhuhfTxoTvcWPTkW37NaC33tNWj/D1pqgz/V2tPSH0yyp5lDumlpGWv0RWnWVclFafktKTPTypN6a3uk5QMIxDNNJpJpPfGs2A+hLiv4tUeYPT+IfMjlWYRyKekkTqQDfeI6KLsxWtYRLVscLVtGO0O0LN9TW6OAYD+Hugxbr0xdG6Cuu69AXSifc1DXCahrc4K6iHyKsa6FUJdDVnIWkW0+jAGt27pXVoCy6dsvvyx+U8cKND8LZSfjWPQbPbORaWgj0xLYyKy0dk2SkJTQSEi/GdPZZs+nxWy2XdOSsip5jT6kezDpp1rm7/FczLc3RCIz0XZv0Ef9vROPVDgSiZBzM3n0EYuJj8DxAWOHXBgb/wfH6n76i9jjzLSDSdrbexBy5XZp75mgj98ko/VgkqyTS328HuhcjnS+rYt+lwjlXw3j8hDoH7pXdXIZOlCG9qgMi2k/O5iUNE97WTL8e+84uhLLsARouxVpq4vRZgIZHvtXJPIRyqm8a7TlXYQ2bAs3Uk2V2srURX3d3dBW+ZuRSAr6ukE65usc6OuK48a8DZ19HdZ7AooUbqD61h1JivKAZ9+lQ5xzDdro3qRL2ejMtA2pSacS88CryPeWXjH5bkhVke++P0PMhbStSYra6AkwyMcPsfGI2KhAbLScfSNuD9x7hzzIuCjNxWklFQkJnt1TW6nikol/r8PHw59DTKSR/DsfbYPotljWRrl2V8K6insSOWNcuAfiyH9A3Kf7i/bSvmBDctKS1Muy4696yOScrCJnE8j5IbShDfRZzRj2vbonwTbuRPn/Ths/9hdqy+TBiLlzu0095ONm0ioyZmL/z4M6MY7SLdRG49dtOM8AeBaLP34EGkWf8yHArSBrD8fJZRMbS3mUDY+ymZNWWJ9ILiSOs4BwFkA98nPEIxEN/umrfryXXOiG6QkweZcmp1lLIH0p5GonpNuTXtc824P7r2T9kz24X6bqN/bgPk+1AgQRh48iiNYDGv16PXdQq39Oz53Uzn5azz2s0z+q557Q6dfqud3k+qhOD/AIuYYyvTKh8hv0xVyANjbyIY2+LZlbo9Wf1XP/0OofSeZak4LalmRupx4zR/R6uMZGc64jBQZBgdf1WGC7nvuNVv9PPdeh3aV9l9Suu3oKQeqRqwd279R/S1kdR9vKe0mjP5TKPabVt/bgtiTp1/TgQnr9uVTuaLL+ZCp32KD/GeH1wR7c/6bqYXJzIJUW1A/BTfhwsQJFE9bo/zaE26/V/3EI15Kk/+0Q7o86fdsQbqtO/8sh3G/1OzSPDOG2pej3D+Y+TtF3DOa2GN7QfDaYO9lDf3Yw92BP/auDua97vqFpgYJ9+3wzmNveT78D/h2of3sw9+hVJX8azP18kP6lwdymQfrnB3NfDNI/NZi7MEgPcCAhdUETx32h0QcwY0DOJugXES576rn/94udx31d/HdCCq/7fzL5v+EnfcdF+ZO+4yJ9z4NTfK9F+p4Hp/guS4YCvl4FLn1npVABj34nJif+eynKn/QdEOn7KCdyEuNJ3wWRvn/SnntxeWhU0hWG+PzjLC99g6GOHfovfXuh9TaaSt/+kL7DM0T2PVFO9i0C6bs70nqAtJZy6kLET86xZ4OP5JTa2UckpG9PiOy+9A2JF9n9nrL1GE7xbQVO9g0MM/sOgjTGbdDFrw9J3zaRvrXQPigefrB/PN0HWflURfvXKPg7G6H8aRjoAsub+kvjLM1LdJ5g+eXsoxLfsXz699Q/pO+cK39PMUaeZ+kOlr7H0sMsPc3S5GE0HcjSUSwdz9IZLL2ZpVUsXcrSVSz9GUufYunzLN3B0vdYepilp1mazNgYyNJRLB3P0hksvZmlVSxdytJVLP0ZS59i6fMs3cHS91h6mKWnWZrMPnIxkKWjWDqepTNYejNLq1i6lKWrWPozlj7F0ueHXxk9R793xH4zp0+faM6YvyhQKwTM+Vm5WdaxtgDJ2e615WdZczMptCt14/fF8rWJ4FquLCE8iRMSwnVRPxAP10f7fzw8Oeon4uEp0f4TDzdE+108PDXav+PhPaJ+IB7eM+oP4+G9uLUJ4b259uGJ4GncnoRwY9S/xsNN3PqE8D5cL28ieF9uz52J4P24/KWJ4P2jfjwePiDqv+Phse8tx8OvSuhXksCLSX44Hg5Cbk4Ej31XOR4+JOG4puO+inSye+Lz07i1LJ6Tvrm0TAV+mMEPMrj0TSf8vnci+L0MbmaLN9K3a06TPcMmzsziDD+DB7QUv0SBv1tL8ddX0fyPGXxUEsVvVuD/dxKrn+HXMvjVOoq/QYF/v47il7PdOn9h8C8Z/kEFPh4nj/gH2catFQz+Kz3FN2XG459k+OUM/17puzvJFL+Q4R9n8JeTGf0VLP5m8BEpifEfYnDr9fHtbk+h9bQvYnRLejdQ/HIFPq4pEDoV+CsY/lqGL30T7E0GP8jgDum5Yyqjhz1sukmKBxl8LYNL30DbrQI/yeB7GNzJ4MN7MHtg8nmZwT09mL2NiafzBQavY/ADDG7vyepnfkZaw9/P4CXs40MGFvjN7UXhJ4fHx0O7eyXGn9KbyZl9dO0PzE+/zOAmFr8+Iuk3jdkng29k8NI05Hcgt5rFs9Ja6CoV/P9lcCnuleCHVOB9jExfOfFwuwq8icGluHszgz/D4FKcLcH3MrgUz0vwniYmH/bRu8kswBUYvCf7CNZMyf8w+Hr2saX/YfLs04fC5zH85RL9DF7OHNIQFkiuZfDXGP6tknwYvJkZzkesfltfCh91bTz+A30T47/D4OXsY3gpzB4s/Rg+qyf63XsGr2NP8a9hctjUj+p9n0LvIoNL8xsp/u3fn9azkdW/nsFv70/xNyjqWcPgCxT1bGL1rJ1F8/9gExvLANrvmstZ/2d8PTaA4pexDvQQg/cYSOGfj4yf96QzuLSeIH2TcMxArH8wZ7vPHDffmjSQ0inNDyU6awcyP8DGESkg+zXDNyn4eoPhS/Nb6Vn4eYafocC3XsX8cHV8/RVXUfxCBf5jDF+aD0vT0jcYXJoPS980izC4NB+WtuONTk88Xo9PZ/5qVDw8n8E5xbrMbAZXrte406mcrQo51xF453jm56yeZkU9vyX4neMiNfq3EPy+XG6C+CoR/h4i2M7x22ekns5x1LF0OmtV0n+ewZV0mgZhPZ3jrlGD4r9nKP0KBuH3+mJxnVTKp4L/z0Fo84O4dEWctm0QcjUw+p1NSf4fq9RzYRB+DxDG2QXMv0jxzODE+C4z0tY5bqxXwX94MNITi5+lb2D+WgV/5+D47xNG9aKC/60K/LqBCE+LfoxQCsd7DkmMX6oCb1SB/1YF/h3A+2gHdZr3JQ9NjJ8xlOpd+v6pFI8tUsG/B/GhfpPiu7qPqeBvVYH/24LwzvOdQyr436rA+1ydGD5SBT7nasqv9F3XEsnvmam+1o+K959PqNTzFxX4NhX4BypwXDhCeqTvyUpaK7kmMX7aCIR3nof6rqF6V/bHRpV63lWBjxmWGH6DCvymYdi/YvM1E+vw1cPiv6cq/Z5Qqecvw6gfaL+FzctYPT3NifEXmqn/UfqBM+kIH8zl3xcPv9NM6y9ZEB+f/F6l/n+owN830/qVcv5cBT99eGK4oAJ/TAX+tQpcN4LarYn1I+mljP4jEuNXqsCXqcDfVYHnXpsY7rg2sZxXqeD/XgWeYkkML1GB41m+iVY0W1XwH1eBn7BQeTYzeUaXn0Ymxh+uAi8cmdj+7xiZmM4aFfgDKvU/O5L6jXJF3PgGg0vfm5b8+ZBRieuZPIrqa+2C+Hllkwr+z1XgXIUtq6KujnPdqroxl2ystlljG6sdc0uFCWzbtC1bvm3aUVEq2Gy4d9ol1Lu9QoOjwl7qhn/gNz3bMQuS4roby1w/RHO27jZXKoyne/odPqjM9x+RnqAuBV2zv2c51An2pVnehnp3Vt74rDp3vZCVNwHa/WEattsKXK6SefaysltcM+bPnV7muHGu6/LZtrro7vA88k6A3V7pifKUz3gquHyeEtV6hQjuhrhiPI23Up7G234QPV0xtmO2HpNtDrSPLZXmueJscXwu4zHvcnnsWitXmCGp88YYmJBHGZgwvvsMyGv9ngiOWdWECYzg/P+c4IubzMVf8GBvLTp8c0ttLps19r6lw+essdkJNCfufUo2eMzDW7n4ghvSsNRupw60m52+IPp2XxdU8L1wZFPjSK25gui7kznuykq7z0cVmk+7UFcLXTaXshoaAotizY7vYrO00OULtysue0Klt6HO3xA18oLs7taosIdOFef8ACGLslFbTs4lrS8XBVGcbV/aRUxbVzCZSXob7b6LKbkz9pXQcmd300kuxOTnXskxAyO8eaCZ8d5ar+B1+7zLPfUuwhi9T+M11rwVQmZ8k7Cu3l+XlZ2d838UMZDzVcJF7g8U0s8rmV3xQ4b1iiYldvN+oCmFkl2ZNi/aS6IazgEN2ia45gAZ5FVgl7e2wVMvOGaRO/alc6EHLa4NENqAxFp/fY3b5yKnaGArJYBGrOA/moyoWV5nD5R3SW8hf+WTOaKymy/zLdnLaqMrLkx6j7RLnjGK3JWaFbR3qQFlmS61I2fZejlcWLtB0cXL5C8KeH0CGCpaS15Dnc8r2OfNVhgRAnJcBbX+Wo8LoXaf5K2tcg9ly/7BGpJ8w/iLhtHRiiuW+qgUuogqVT+hKwwVRF9ax/LKDmi3Q/+bN/uiHfpyq5LIy+9GnUXZBS5wc50E3y0C1SuTSCzoVq3jL5/EhMaPQwqjEbwqoys2omZf6foY1znWroVs+Xjyj89bi3YuN29rFxxt7HV6wHV0Hfc/IUtiz9aVgSDuuIBL06jEJ13VxQ5/clXYcmvcePBTV8Ziajq5CHCUuew2W72nIeATqEe5sXQCi6+yrWy1bN73MQm8eMz8QzAnaSubw9qlHIS0UgswfXM5XHgTSCoVnPkul9ePgWlX1NV5nC8rvfwijq6fmXHpBgouq+5O52l0hYPORRyXf+pGFxrqXilllyvtqluoqBcahEBVVVYFV+mp9yyGCQnMS4QaV4UPhuIGzuWq9LsW+/yLIF6thCizweUONHIV/howesFTmTWhoGBCYiSYzdd66ezbBbzUN3FV9e4aj6syUFPTBEVkOReaXhyqyzVjXtEcu8s+txi7jWvm3PkuO8+AfPE8zlV8y9yiOY7pcC+ucGU8oEFw1wtRu79xxoxSe5mrrGia0+7iqgSPz1dYONPpmDbdlZ2VnZVHnFupUEDnDl5/Q6KJg50VWbDAlZOVS6cL0WkHK+qn8w741+OuUZ99YMwYVxu4Ws7lwcPoSL+cUOsHP+FflqDFfKCOuKLp2Yo64icwbLo9gZ0SZ69RENzFeVIBzG3osXR2mCvVdCY6jqbizjQ5bnSRY+tcgQZPJWEOJ0KEOyhS6SLTJmUhoNGNZkRmy/GaAoJ9ntp4WI2npqKmTqnRUiEXyPYlVCVOyHNiKkNlgXlYXfbS8a5EtLBznjwuj+RvCgsBo2iaw2XLyiGNZVsltoC+xUK1y4MdDYR2MbO5HC00NHgX10JMVJrr6qwGV2WD31UNnsrngSYqfSVLAVEoYHbkEpREoLnb8qKtgwAWBaq6YvVIB8xwXVVur6IPFf8oC486hQlvY6OrDoZFP+pPaHIttVK9F0QF5F8MzarIh+q+3qMma1R2XVOsaVtUR4Kn0SsorQBF0VkDfpQom6v70Bz8ZQqhFtCAp9td3N5JR1XkVEglfbXLapTkIck1cp4JMXPRZOsDtYIXvKdHmuF20mvZlSTZhX60YomronqJQuEo9LI50yU/jeod75L6kiTZy+15JbNhKujrrIdu9BgYBWWnodprKjqrA0/XjNcGWl0CL1LhDyToP3NtVkKTe5F3qQ07f4MXxk7oHSQuAxFU+e3xhkuciLL+zi474XqSryurST6IS+NWsy4xoOXigGkvSewJoCplz8TxSRpZ45SSJ1eKiiNhIx+usACe4PclkgR6WbqurWx7EQhWahqcba7kS0A3Ln+VC4LqxR5XVY0ADmW5Qtdkaja7GzaUA0NQJYxD9Q3YBhkALzUC5jpApNNtCYfmy27fSgyhzueugPG7JuH4W+XxVykE6fJ5F1W4KhoCLhbYd6PpWNjQucXFHqEhge4UzglkgEs7l78wm0jA3VHeeKI86JACdJSL6A/Gkxr/Uk8Cjrpj3mQxMMEIqAgXusXRBJk5XpSlrvT9vAqfx11vhzvZLgcYL/wJMIImGoxZKCpnwFE2xxWbNsBAgJOGMvcijD7m1y5DOufBJLUG65s+Heqydj/wqQCfJHjs8xIxGrN1nHQwxj3y5VwlQ6QAcSRkGtxpGuCQV6AsvLjGX8sKu6gQEouA6DdWTe6yareQYKx2dOo1CYaZbtlhol4EkcbFY8JumjxbbY56bxXvF+8iO7t4LquhqUZwL4IUhgeSVktX3loQcR2XBaR7ssAIxwruxSwH42EWLipWjvVWciRX7W6o5rIqm2qhPpoK9fQOW6uJy7jgXr3H50ZEdlXnE7BJ8J14mbXYDxcQW8K/xMiy6v1kopblqWZz2erK+lgOaq+oYLGrSyD2kBWbmNI6pGusB9uEKgml7hpvBYVyWTDgcVkw38b1gCv1vvkQ9i6AtJ9Sem9YSoeqnFcg/a5l7+RL5aX3i6XU3Omdi/ifTVFeeg9ZSi2XKI+vO52ORPxSeel9ZSmV9pHrFecASL+57MwCreJcAyndlxw790AjKy/t67yZnRWgVZyTIKUvpl5cfnewMwek8tJ70lL6sIJ+rSJdws4wkPLS+9RSuoeL0Z+UgP/lTKZaxbkK0VSXWH4S/62s/DTFOQ1SukFWPj1B+QeZTJIV51pI6VWX0H9QUV5631tKrUmK8xUU6TpFeem9cCk1JHh/WZ4+oSgvvdcppX8tSNx+9H0RRXnpvQspTb0E/39Q9F/pfRop9faOx1f2x78oykvvPUnpNs3F239ZUV56v11Kq3smlp/0e0Xpf5bGp4c1F+8/+/HdC9l5HdL7VhlNieWt1N8R+DPKykvvceU3dU3/3zCapPLSe1qFrPwe7cX1j4WTZeWl98tMd7O8NtZ/5e1K9TyqaF96r8zMXvg+qLk4/ama+PLR9zaa4/upmv6M7CCVaPusfLpUXnNx++vP2lcebCGVz1QZf+RpgqMoOJ6Vz2B+7Gr2DrzS/6TKeZf96tax91JHXdz++qiUH/MP1u4l7Pf/A6KL8t4QxAAA'
    bin = gzip.decompress(base64.b64decode(gz))
    with open(pymain, "wb") as f:
        f.write(bin)
    os.chmod(pymain, 0o775)

Submission status: Accepted

Solution code:
N,M,K = map(int,input().split())
A = list(map(int,input().split()))
B = list(map(int,input().split()))

A_dp = [0] * (N + 1)
B_dp = [0] * (M + 1)

for i in range(1,N+1):
  if i == 1:
    A_dp[i] += A[i-1]
    continue
  A_dp[i] += A_dp[i-1] + A[i-1]
  
for j in range(1,M+1):
  if j == 1:
    B_dp[j] += B[j-1]
    continue
  B_dp[j] += B_dp[j-1] + B[j-1]
  
cnt = 0
j = M

for i in range(N+1):
  if A_dp[i] > K:
    continue
  while A_dp[i] + B_dp[j] > K:
    j -= 1
  cnt = max(cnt, i+j)
  
print(cnt)

Submission status: Accepted

Solution code:
n, m, k = map(int, input().split())

A = list(map(int, input().split()))
B = list(map(int, input().split()))

a, b=[0],[0]
for i in range(n):
    a.append(a[i]+A[i])
for i in range(m):
    b.append(b[i]+B[i])

ans, j=0,m

for i in range(n+1):
    if a[i]>k:
        break
    while b[j]>k-a[i]:
        j-=1
    ans=max(ans, i+j)
print(ans)

Submission status: Accepted

Solution code:
N, M, K = map(int,input().split())
A = list(map(int,input().split()))
B = list(map(int,input().split()))
Asum = [0]
Bsum = [0]
a = 0
b = 0
for i in range(N):
    a += A[i]
    Asum.append(a)
for i in range(M):
    b += B[i]
    Bsum.append(b)
Asum.append(0)
Bsum.append(0)
res, j = 0, M
for i in range(N+1):
    if Asum[i] > K:
        break
    while Asum[i] + Bsum[j] > K:
        j -= 1
    res = max(res,i+j)
print(res)