Buggy python submission (verdict: Time Limit Exceeded):
# compiled by pythran
"""
#pythran export main2()
import numpy as np
i4 = np.int32
i8 = np.int64

INF = 1000000000


def solve(a, ch, cw):
    sh1 = []
    sw1 = []
    sh2 = []
    sw2 = []

    dis = 0
    a[ch, cw] = dis
    sh1.append(ch)
    sw1.append(cw)

    while True:
        while len(sh1) > 0:
            h = sh1.pop()
            w = sw1.pop()
            for i in range(-2, 3):
                for j in range(-2, 3):
                    if ((i == 1 or i == -1) and j == 0) or (i == 0 and (j == 1 or j == -1)):
                        if a[h + i, w + j] > dis:
                            a[h + i, w + j] = dis
                            sh1.append(h + i)
                            sw1.append(w + j)
                    else:
                        if a[h + i, w + j] > dis + 1:
                            a[h + i, w + j] = dis + 1
                            sh2.append(h + i)
                            sw2.append(w + j)

        dis += 1
        for h, w in zip(sh2, sw2):
            if a[h, w] == dis:
                sh1.append(h)
                sw1.append(w)
        del sh2[:]
        del sw2[:]

        if len(sh1) == 0:
            break


def main(in_file):
    stdin = open(in_file)
    H, W = [int(x) for x in stdin.readline().split()]
    ch, cw = [int(x) for x in stdin.readline().split()]
    dh, dw = [int(x) for x in stdin.readline().split()]
    a = np.empty((H + 4, W + 4), i8)
    for i in range(H + 4):
        for j in range(W + 4):
            a[i, j] = -1
    t = stdin.read()
    m = 0
    for i in range(2, H + 2):
        for j in range(2, W + 3):
            if ord(t[m]) == 46:
                a[i, j] = INF
            m += 1
    solve(a, ch + 1, cw + 1)
    ans = a[dh + 1, dw + 1]
    if ans == INF:
        print(-1)
    else:
        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'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'
    bin = gzip.decompress(base64.b64decode(gz))
    with open(pymain, "wb") as f:
        f.write(bin)
    os.chmod(pymain, 0o775)


Fixed submission:
import numpy as np
from numba import njit
i4 = np.int32
i8 = np.int64

INF = 1000000000


@njit
def solve(s, ch, cw):
    H = s.shape[0]
    W = s.shape[1] - 1
    ah = H + 4
    aw = W + 4

    a = np.full((ah, aw), -1, i4)
    for i in range(H):
        for j in range(W):
            if s[i, j] == 46:
                a[i + 2, j + 2] = INF

    sh1 = np.empty(1000000, i4)
    sw1 = np.empty(1000000, i4)
    p1 = 0
    sh2 = np.empty(1000000, i4)
    sw2 = np.empty(1000000, i4)
    p2 = 0

    def push2(h2, w2, dis2):
        nonlocal p2, sh2, sw2

    dis = 0
    a[ch + 1, cw + 1] = dis
    sh1[p1] = ch + 1
    sw1[p1] = cw + 1
    p1 += 1

    while True:
        while p1 > 0:
            p1 -= 1
            h = sh1[p1]
            w = sw1[p1]
            for i in range(-2, 3):
                for j in range(-2, 3):
                    if ((i == 1 or i == -1) and j == 0) or (i == 0 and (j == 1 or j == -1)):
                        if a[h + i, w + j] > dis:
                            a[h + i, w + j] = dis
                            sh1[p1] = h + i
                            sw1[p1] = w + j
                            p1 += 1
                    else:
                        if a[h + i, w + j] > dis + 1:
                            push2(h + i, w + j, dis + 1)
                            a[h + i, w + j] = dis + 1
                            sh2[p2] = h + i
                            sw2[p2] = w + j
                            p2 += 1

        dis += 1
        for i in range(p2):
            if a[sh2[i], sw2[i]] == dis:
                sh1[p1] = sh2[i]
                sw1[p1] = sw2[i]
                p1 += 1
        p2 = 0

        if p1 == 0:
            break

    return a


def main(in_file):
    stdin = open(in_file)
    H, W = [int(x) for x in stdin.readline().split()]
    ch, cw = [int(x) for x in stdin.readline().split()]
    dh, dw = [int(x) for x in stdin.readline().split()]
    s = np.frombuffer(np.array(stdin.read()), i4).reshape((H, W + 1))
    a = solve(s, ch, cw)
    ans = a[dh + 1, dw + 1]
    if ans == INF:
        print(-1)
    else:
        print(ans)


main('/dev/stdin')

Buggy python submission (verdict: Time Limit Exceeded):
# compiled by pythran
"""
#pythran export main2()
import numpy as np
i4 = np.int32
i8 = np.int64

L1 = 1000000001
L = 1000000000


def push(h, w, dis, s, stack_h, stack_w, p):
    if s[h, w] > dis:
        s[h, w] = dis
        stack_h[p] = h
        stack_w[p] = w
        p += 1
    return p


def solve(s, ih, iw):
    sh1 = np.empty(1000000, i8)
    sw1 = np.empty(1000000, i8)
    p1 = 0
    sh2 = np.empty(1000000, i8)
    sw2 = np.empty(1000000, i8)
    p2 = 0

    sh1[p1] = ih
    sw1[p1] = iw
    dis = 0
    p1 = push(ih, iw, dis, s, sh1, sw1, p1)

    while True:
        while p1 > 0:
            p1 -= 1
            h = sh1[p1]
            w = sw1[p1]
            p1 = push(h, w + 1, dis, s, sh1, sw1, p1)
            p1 = push(h, w - 1, dis, s, sh1, sw1, p1)
            p1 = push(h + 1, w, dis, s, sh1, sw1, p1)
            p1 = push(h - 1, w, dis, s, sh1, sw1, p1)
            for i in range(-2, 3):
                for j in range(-2, 3):
                    p2 = push(h + i, w + j, dis + 1, s, sh2, sw2, p2)

        dis += 1
        for i in range(p2):
            if s[sh2[i], sw2[i]] == dis:
                sh1[p1] = sh2[i]
                sw1[p1] = sw2[i]
                p1 += 1

        if p1 == 0:
            break


def main(in_file):
    stdin = open(in_file)
    H, W = [int(x) for x in stdin.readline().split()]
    ch, cw = [int(x) for x in stdin.readline().split()]
    dh, dw = [int(x) for x in stdin.readline().split()]
    s = np.empty((H + 4, W + 4), i8)
    for i in range(H + 4):
        for j in range(W + 4):
            s[i, j] = -1
    t = stdin.read()
    m = 0
    for i in range(2, H + 2):
        for j in range(2, W + 3):
            if ord(t[m]) == 46:
                s[i, j] = L1
            m += 1
    solve(s, ch + 1, cw + 1)
    if s[dh + 1, dw + 1] > L:
        print(-1)
    else:
        print(s[dh + 1, dw + 1])


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 = 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/HavUHPfmthWWN6IQpSK45hg6ft+ksOZ+LV/G+gcj4CRok/PIg8bb/yE+BQOsOighQo/3r0Aze+4l6dTOmhKN6ZwAZDZG/Jkh+SmsR9A9Y/NGf2kl/dhT6Uxn9ST+pghcTCQQC4qVXKry050/US7uSWhNSZ4b73ekxar8rGo69x1oCya8/ra974yr07Ykp0HFk5PvK5G535jRzu1evQoMWUzOjuV1d6mKaJ55nST+W7z1CjHS9ZJGWnGb9/V9dxbqLuzSI8Ij/vh+ZBTIEkguO/RGlv4QAmRdS28xNwvpLRxPjYJVUjmjZcizGUrtd05oV6tDLGq3elpA/+sEPUQsGzELZgKX2LDjjN8i79x0MMAZUR7GZeWIlF/TEf4d4JeGmmE/fEc0juyk8cjd45F7qkXvBc4grisngjPuIM971AxGFwg9TmB9afQvBexPBGa0wCgFPfPfHoBvt0OWDyHQYOXQ/ILSOSS3ogSaZB7a0aAK0aqbSAxsVHtgFPdCo8sBUWg1cYHLrn4j9f99e+/9esv9Mpf1/z+z/+6D/VUXyv0yl/6W0l/6RU5Hpv3eK+f+p9vjf305R/3vwVFT/m5Oj9r8Pjap+7zpD9H5v7g/MAY2Gi/Z7BRH6vfUh69uhv99lYL3fXaz3050K9n4FQdfbdpL2fgWRer9c1vsVRO39hur71dBB9SX3f7lR+r+CP7z/O9nO/u/Uxfq/E7T/K2hj/4d+XRB0wScULtgLXXCsygWNwV6ouPU2xXwFzVd3op3+8NF3kfujzd9Rf/j3d0F/LGlDf7j4u3bSvzEK/XGM/sjv2uOPHb6j/qj5TuaPFl+CxTdVx6dvgTF6HKDY8gsY6i8a/X1TMUeGeT4Qq0VnTT+lrxsFwHxfBr8xOAtruP2sJee4flluirxyMilXBm08pl82AB/5dDhRayQKM+mwS3wUvm82BJ35hRPMmU/1oQZeuyMW+p5YK9iz/pWz/JYjMf4BpAkxO/N0ZMLCJi4pHPOSmWic5VZvaWqVFaLBPJz+1XB09pTj1y97ogdK/sy5hfNh1hBjfLM1uUwyET5nC99g0SwSLL7pOj7nqH5ZFSn867mFX/M+MbWcr90aYwzIyltT5+mX7sJCOQtTq/RL3+hBZxA1xONe+ZjX/AYz6M9RSnzOR3zD3ZqFu1sfrXmdzHxgHjQIm6mY//RWz3+OS/Ofa/rQ/pxOp0BGib3JaMD/MBSBidAgBaL5iOij2hCicUFEPaFMiIWfe6vQ/rsXRbvpKMp5G4ZmjNMbCZ7T/vvHy2sfUteez2rXkNo7pNq893PgYTLU5dM/4xsWafyuo8FhTbDyGFJZR+baJKoc9ZcfI3hkE7/7jtOJ359IWG1KgqjIezU4XyP9Re25pOpU7FxWkPHTVzCEAsr9j9NpMYyXSCnv5CR0uZlQXOOx5j6QuFjXWmg83jpfRuiK48wYSy7HGm2bZZp4b7PVexpZrzxKhxqUkW8sYJc6i69z6s5YDWdpxnUINEv4kiatsIxuRS5XpSZpmNvxPg26HfHMbQSYInO72q3nabDO+UC/bEc32nMt/Y1IYZuWOuLrBBlo6gkC/kWrv3cBfCNWgZ2TAWbFoeWU2i3nWI91a2pxo9VXrrOOdqRm65fN7oaj0uP6xToyzd2hzd2EGFqvkI8fId5r8nO+y2+Yo9E3fMFRy6Bt4Sbo141Jkwm3qZUJd08KDii3k5WWbSDflFz9po/Yakuzv6UFreSLcHFZahem6jT6+tJu6NpjUvXr+pFCRJQ52En3baGz7u6Q1vyWQfoRfX1xgHY/Bj5nAjpvp25YXkTnxT07fFSOVsI8GARk1ZwCH96Fa3MtvQLyJTCKp7ZJgzG3GOMOYD2waJFKdcGIeP+LuBJ6890mbpMH/tnwIfyz8SyOgbT+QIA4GL8ppIud51Tro6i+6s28bx4Ep5/1yz5Ipr7uPYyxtXarhs/5ZOFBaUoQoi8usfrSQmZjydmnr/trsjxSlwPc6qvSBQdDUGinvq46WRGcuxxlKpvRg/jDZi55Aef/qx9t1gzegPHYzOKxmcVj5pQQ6VNYupIt062G1DReLyewqoURONudRH9KYIT/AvEeQi1bjzrJJlkGRucMR+lw4XQmSHSWUzrI5mLsiAi9nUcuSG8Po3OA4T/EheH/2s/wZ1H8loZKTX5DmYbiL7sw/tUM/3qGv4nhR1+iMkzDrwYSVAzMXqEfgrGApfbc+bs78973eM0J/eJAEnZL7+kbTifRUQH0h4cttedJkU+smsP6xT+RIp/oG46RImslqz/Paz7ia8ec4f5cHoZ2pYT2ySQygpUwfmzVfKlf/Ax5+rG+4bEkCdMv6D0ciwbhLSCj01at8c31pJXQ8jQNWa0Gd8rEpC41EwCtSRBfoJ0wnHpTX5fUmUYvklu6vSP1xUzii8QNsWQjf88vuYBT37BVL6Pum7DrDGIls7nRSwg5/bInWRFDqAhEGsvrkkNYcw7p62xYqHZLQKIDQ5jO0Nc8TnF5v7DCxEaH008ye/IeteJ0xuo9ZW2gfpDv9VsblhBT9Q8ZR2N8mqWhKFWDcTybz9mrrxsi0YDmAf7YVMRLZrSASI7gu7GIYAmhbc35EjqIRFJTE6wJMwW+4dbUNOh8w6ufGBeqzo+GmK+vW5uoIo0IItVdM47Op1JQYgSBvAuJ4XN26ZcMJ8iazlM5gXbSEG0xedZT9iwpFHQwJGfq637rQlWRSWwFBIBSxZlhEs5CDHyDFYTaUEc8EVlCj/HPNCmbU6xfsqYLac552hwUNWvnY11C7Uwi7WwoRb0dYUTIJOwTvsGBg8kWVB6SIgJ4nVDL9/6ikJ8VBofF1pxT+iXjwmkGiUj4AbW3BbAhluflWBDB+c5hCEIdPtrHZ51DWk5qs3hyx4WJZ3nnSOIx6ZfdRR9o20fhi7Fh9jS0s9yeJApAuqeMdNspLJIbvK8UJoto9e90UhMh4HWdVDomJNAhk3B41jYdd1K72MwwYir8OPKTqXf3dSoE/cIRKNV7vuOlqHfWdWHCf61jJOGDeh/vqFKvZP3tFM53Y2Q01Roe2VGlYcSvEs5LUREA+6c7qH00HIEjMoIL1BgVuYZSBXd1UKkgAqJTo6PxDhIe1UEt4XAEq+UIlPR/08njuEyoh3WX4jYTo1N6MCIlkP48XZj3OLBLOEGIWXFZ2Crv3l5nBnLG2vAPZiBf+t8ZJSP8nxDhj/V1KeGEg5QkIqzXOYOo7pWjQgQfJYQhQHBTwqV4T6oa+/xw7OATJny2bEbCpUTHLTlKEsu6y9AolHwu/lKUbMtRBgCwwrXxkSiAcv9X/IWVW4rKPRxRuauYclv8J7NlBIPCMYXTDNfrYUmvLf4XslViCcSFIVCa7Odxl6Lh8QpuyZR5r37ZQ+HEEHxX3CX1fyPpuD2Tzd3J0HTZ/J9wZkXcBYaEZA6Kc3IsxN2d2nLtT4r5ZC2MfzTB5zA5JbXpXMPqW07mBFbAXY7DozPyCY+PTnSCw+fTrd1wPr4T5oRrCTZ93SbcR/J1O2nxxX7PZgwf8fXvz6cj+4/0i9+NZSP7HbG0CVUwiZnfgdfs0i9+izzbBUPpWDKuf51OFTTNZNxtyYn9xfOg1Op5fWHgbSIDb0usfNjfbRfv3dWaAU83v3EyoF86UPF0+S6cHkEJPoY2Qr+4IxYYzXINZ/GEia8zML+czfI6b0WRpWhakgP0qAGZPNUfxM07XFnEIyB0tZFO8MQMvjYvKYALs6NxJb9TzV06zhPnf+ZjuiraeoCupa6WNuh9bL5Xf1BMIFUBg4/NOUn2Br7WDMlC+cx5u77+H3gEalgMYlAQGsUI3csI7ZERCiL1vKiqFctqWQ+ozg8gbU+ddPKiU80iUnzfAboyfMUB1cqwgse65VoQJ07zffgF5gkaK51MimgaH1tqz5J53Sle87F+8ata1P8pfcPzpPBEDQ+mXMV7t0rTv195zVb94n+QYr/qG/6XlkqdTSjf4TV7LfeMIYsoC8dvxgPyfPNOsizqo8td/rs/wkxcsWDAxWE/73v0JJbCvZiPIVbW3JXNiXc9kJfX4iWrP8AcWOYP+sUphOQP+gY9cobcAPyMfnFPAj+jb0jSEhY+4zUHLL6sJkpcotqBUj18WzjVconqUKDamocKmExKx8DXTXgMKFCQqd90kiD0r96HQm/28/sDgdaBWLhv9MK1rHDv/bhQcFzsT1f+eymP68zYT/V4A6bSqj4zwzRL7aSUgGgN7rTWmiBro0cg8Lu+finOqz0qU9qwn63/75ev/yMuz1qLbxzd3vp6H66cBfyf7gvtlRKknieDW2BN5Nnr5Jl/zT52poGW6hEs9Rh5sDCV9zfiN7YY7P+gPyC8ZzseGbsl99bcGbm35QozthmPtwSOBgKbtfeYuE3L4J8Np+CfjXjc1P/lh/Aguw4e3Az/bNgO/2w8j6h24YOn6+FBxn3woBH+2YgHUv2v4IO9kN00+V54EMAHE7DGI/jAslh6MAe+bRTwwSJ8MPRe6cFL8M/GG8B4/KX4ADcT/R0/JIvW+T6c00JrsD/ZhNeZN+Lqz4YsYMvY5O82jO5o4tMNv9WauNz1RrJ4bnzff4DIYQlZ1sHNEBKxfEnS4bec3Z4kfZKWb1hOSui7alu7QWhkxQqS8HsT/a7D72SVSHViLjSfHsiWkWrPxM3tuPmLu0ycv/gDqgdEuGkwipG88UTzAWXrEGNrT1S2DsnZ2iNj65CMrQPhbFma44t9puBO7yYiz5uRLARriofVk868vUN4WsU6tlV0ha+ZdnzsMCG5RoM95B5yVKMJTK9hbepJDcVC1irmvg8OOR7NuElDBwG7cAk7p2lOd0v6LqkY0c0Y0G5rD14JtXib/FehAYTWrmP3kfU9T3d0AMJVzgkw9peAkvFg6+zQoUGkRp+3eDrok8wgOK5Vz3s7hfrrvKRQX56nu1Azw84b+uJ+KDKQLp+ssfq6nC1iUcy7JHYFW2bF5dZ5T8wku0xULoCOhEYMMb/upV0EliqGpmHDD6Hsv3yfmgkGT/+Pe6D5oR33uA9p82ONx1sn894dyv32zahx/15EDMaD2yW1TdAuCGrenhjA4nBbEoZvZh2uErPVYnokxuvURT1uV7CXBi4eUjCl7CXgpknwz8Yq0UT2TvqBtWy+cgGAB+D22TAw9o03ISvP7lE82LAIHmDY8zcoHxxcKD0Q90j+3msP+nszLvGtZuvMcsEy9azWcMHTmfW4bS7D+uQCCWtfxHo3UPd/+J70bXfwW9N7soAsq2+qkep//p7EVd17dGcOepDn+4JWVviYHFBAPd6Xis0IFXv/Kiimkxc7uVcqNiJU7JYwbG8Gi+lDxbLCij2N2sENhNqd3YL+2wWBimBE1v5rzyTP7UaD0VvvBgL+ESAXv3EPtTcSGnwoM7yF4L/9XRyzomA9PWVC+TGo3DffRcKZJO5IdIe/q/DW6XupufZAFyDay/kCDCoApcwwTi7DTUiyjRgf3B3M+dij0yfFEG+1eBPRQ1HHFu9E4q0r6XfdBc2heSLbsJpI96uaE8LD3y3AAzlg9tY5bGdfPCqQJm0ZEk9uGBPL39OMWd/aWAxgte9q/KkgrNbeKPfb3wmJjazUbvX/FSUCo7kquhkOJkUuwOyckFqliJTdQpFyYepMfwFgah0TKUxeRsIklCExMgXMAGK/DIQBUvueQuRfvUdFfhn2/TOpvJcBAQgY0+UbqTBhmCkXdhfem09W8qtIwCDDUQwU4S1R77tCMPx2qiIYnpja3mB4/O0owfDDd2TB8MhbimB45l0pGB5svR78I1Iw3P42C4YxNBZCTPOm47gvDsd9GhL4m/NSpCBPY6Ggiz4yzHubjgxHv43BMCETDCvk3ThyS0a/8e4wHmxpJch2sGFgnF8HVQjnaD53vHWh8ednb1Eq+96iw8HjIIY1mpa/SRiZajv610KB1hgCi5s5RaGFiint1cLst6JoYcrbMi3YdjOSyFgRYQwcSbzDUjvREBB71R30zMQ51Tx29Lz+IFRq6UYmkGNh+sJa+NtubNnP/h8grTsovtM6sDW2VdtKmgfieRzALX/vwJorDx5bd1ORLNhN551BOm6ks0rLqkgn4v+yOyj0E/7Tb6LQYYJBxnHY1enrX+PoVDHY3WH7kIWbkIW/UonD+PhRLsiMNO7o6B++OyT+L60K8R+1tlf8/jejiP+93TLxH94VFP/PxD5Mr5Fz/GQc10A3RQGf5yYErmbAJga8C4GNDLiCAvX1eLLLVHdcX/8cJx2k19e9Ij9EgL1rK0zKWjqBV/nHvUknICrs4tOtvYEvxo/6Yb1fx6qp6It1rf1C1dTM1eHJLv870GjksO62gKz1cwHK2K3/UwDD+JhUegDhiV30AMLDu+g0KtiIAdiIiQlElUFf/KKZ2uJIYLAF34TVcgWxJbDZlglxyrJrmhVmJ46lI6aRChPqu4uaUPddVKPzWA/VmiRHVc5QVTFUgyiqqxWoPmimqN5qpqiqNKFli/ILVXyMVXyIVSyXVWTXQsgdJB27g0S8pZ5VmsEqFcsq8ReiNohVTGUVeVaRHKe+Lz5oVvUL4umhEdkdjZM7qbL8O5VrLFbw2A93kWgaOnf4DZ47/JQqpeXDOOaT0tNSVNkR9vQL9dNPYuHpMfb0a+mpzDp0AGtJi/DgBNbUs5oJUgE880km/0Pwewr9Pjn0XV+3VnFmMh15N8RJZ1SlmfrJHXSmXuD/dgedzNPqntfod18KHhCx5vwg5rLjimm00+iv7DRW7qCdxood6nOOnmYJU/AM412MaIr/TgVRfb01IOXbSngII3xNWwhrg4TPbI98fFJfh8uNLDyfilGF563bqaVt3C4btbfsjgkqLbQmM4SdTMDvfwp919dtVKhlCKrl1RjZkX1JM9O3y1ZaLNupXCgSzyv0e1tl1Gk7lVHM9sgyypTL6PNtVEaZ/g+3yYnq6ycGpHxbCT+0jRJ+YFsbCNuDhG/ZFk05i7VB5SzSqpRz9TaqnCu2yZUzXRt0GL79DjNaK11pktSyeau0tPXa1t/jMHO3UtG4trbBbq/fKtmtaevvdZjzWyjh01vaQHjvFolw85ZoOumhCeqks0alkwe3UJ14t8h1Qi+T0M4Ig+/BJkVPiXcLTQZytzCbzBkIqolbqD+Q9ducLfSseEFwgXQgXUNPVS18NjEGdHIGyLltXMaRBmpHm6hMDjepVz57BRW/s0nmjxuaoqx8rmiSzOORJtnK58edaZdyD6ivMXR9tvYYdMo/W7znZZOov2+l51xvwxNk3h2W2h1JVm9aPh7u9ZGOKeC/YzN2TNvpEZvaM/o5fXGXChUGM7WMrbTXwmUoclQpD+8Fif3xHo+nS+4DCbmLzVxrMsxbMoAjq/e81XvYv2EzbY+OTEp8nfCsqnrhyeLty9duTwqu3G7brMG7vrcRZnYGRxaJm+mA5hGQeBk5v3iDvMEws/vB4j3Le49s+uXPUP3EedzrGoiHTOsmkL56kY7T1/2HmNigVHIPxIzbiz4P/OXrgGdoZIct0qnmf3LSbou+7gqs791p9Y2TSvPNW3EVwv88UfA2mZhHbmGH9R6ixpivOW/V/EZXBMycNf0HvLKBM1CQ8G9J1X2IhCnxt5uwxXQeq7N48/H+h1iOlzY8RfRI8S0WMsGj/HtGtDykvMP6nyaq4l4WOilGZBZ2ujgXjxe3DjQbj1Pvvbl1hHS0+BN/8iamJqihs/jyOXqm2MyWHNicmNS7Uun1uk3UwrlNocvdsulsehOTxqpYlEZzlLs00jUab2mqKd87JpVobSlUyW+Yq7M0TEpCk8w2G7/Gbc80q3chXvcQU/nWDMV54eDll5Povpl488X7KV8LE0tQ4q8QkjadBdPYEEvHYWif8wn7OwicbMv6/32W7o7oSAy6imwpDuN9k1FHeAr7CKlBOq/w6xC/vk6jwvev06igo9chwF68u6zph/T3no6hl4pmWtO/wX3fFLJXbMGLGDl+Xj/Rj/G1AyG9hRQFTTeQqHwuqfoKunHs/RZsZQVjA5yqcyo7unhcHGjVfEWOoHd7oBOYSx/l+wFA3/T4nf9K5M+Hm+g4YEVzQ4XLLOnOTdSSBskXn3FXu6EIKOXsFYeRVWfkDOJA60B6nthH9rURSKRx7D9kAXtUkIckLSrfhzTJ4nQRFAcpJbFlmJSw0ECi037saX0Z0sUkfX03cO188EWv35p+BvzSgu9aqG8CtcsvAmZGuAh4UnYRsFicG7wIWP0fEvgme/v8iAeU0n8Rb+drt5zx7trij8GzmbWHEsQq4/vG04r7PzDk/8jiHfOhpTn2I+ogO8/Qc9N7PF+BdfHp31sbqs9YvWf49JN87S/Z7jEtWBDsOETJ80g+ibFosNjcerxO0dKHHGY9jFWhnv5eMnOs3Qqt3S8/Lw7+OgVEyHI8ZpOkTRLIQwCYkhJ0Nh+fAnkDOh3LGyCfJjkf5MFdpmRKb0aAfCbksyGfyfLZkDdBPpvu8u00gWmY8BZM/Z0g5nzvudarlf74hd+/AZeUzPQ96OibOcfFayzs4tF31IeOyHzoLf86qNG6He0VKJCzQWmScOiVy4c3BOgtrnzvNfneucEbX9vpjS8fxAWF59fHn8UpNVmRskDwsgSD12TEBMErbwNG/e3lss1DszGgiEb11b8pVvdD6/CPrFc8CK0lL8IHpCv7C367fyEUGXg3LqgdNjb5T2yIso0wEQq33in1fMhet/XkShgOhJYRRcgD8P3raQC+e73y7RqujfQ+DIj5GipmgyJUTV1PQ9UkJBeHa1Q70X9N/vh4OjRNo4MlA/0uHzERfqBsmr/jetona4FYWaPy/R0QOWv+nAZd7nAyjfmZ9/7G+/6cSTrUDjGqa0mn1kv97lUxii4N7QcD0bXRujR1L4WXCo+T/tXqHWjxTTKQ7gd/SgHPJSYp7NMHI6Ajm/Cdd/6m9ayjOqjsUss3MPh1WnJJAcMNmqU3EweYo9axTQw8k3eSdOg98WASCdHn1tObDDRC0978WtKbd9evs6YmQYDurbpPCP3xt9gf37tOis9mMG/aHauXqEkf+ZxGGsHcgB2U3/saMxU6CrXm/Az6Kl1HTeTWdbicxCQyXkMvVj33b7ztpa+frGEoK3Gd3mdm3dZufsuv4/j+u3nNbv26W5Pkq/L5VGRitmwJXr/pA3bX5oR/EZJV32fqEmpxo/H4pl+x3wVB5v5EVo4h2JMN2KxUso3xJrH2G3SW0dN1MN2of18cavGNwIb5D71G2F6+jazQerq0XlnWaDxIl2t7EAmI2XUHxWmtc9ZoaJGjjYq12qmvUdOd9FrIb6jkdikdbMBrVHp9X8NFXc8ASYL1b5yLskNYtxZq3LMdPUAWUSLGiJvWRokqprXSRtm3a4IbZZ1i2EZZay/6bHfo2TidektuBsgf/boZM4a1dIW5eB3ZgQyuHq9fg1tGxz1FrXfLwU8BuNWEdYlIRoSMqcdaKg49pK19sQSMO7rTYnqZ5Dz7Wg/QoDtAGXT3r6FB99010tSgma2MBfwbX5OfX8P3FeErgHyCSTqqAmodZvHdlo35IhrW8hVhbd4aGtaq1mC/M5H0VK3XKorcwIrk0yImOkYjOD2r4EumjNgQyBswX0BXFS3BVcUhDEsCxWKIQOjYq7TI16+SIpmMEOLzrIYvSTJCOultSgoMLzAMT1MMtB9nBcsaCzGksn3A2mad30pvjfiXExV9cUvLHa/KxRlWvg8rXyqVH60sHwznMPHB3vcuerBHBJ1YfP3yfS7ozVs9/VsnBe+fH/Gf+Zdq9v+ZeLXVdxXOFE+w19kcUSyV7vbvgCqt2wBjcHVgzb+osb74L7I5T97pH5wdyuwD+SOdzaMa+rIoaVN0YaqB7ohakB2S3YTb8dhiQg93NCFg9UkNjtJ34jgUT6wV5ENMI72eiVy8wz1LA+5Zeo+QHcsyEFJrf5y2VWHkmr0assl88DluX+a9qti+TF1DA2Uqzq1JHwaRHTuFnI/Fqyx45QeHxHifM6m1Jw/CoZtCL63GHeWvW7Pobiabn5G5tJrv8PuM+F7DYQaO3V7qYhwmbdxYYw1YN41u2eChu4Y6eqyOWFz2ajpzSaObNbiVe/W/6IID7mL4s19RbFdaXqVNmx5lq7LnavVWpdXbX/bmrMh7lbfp8KVXXVozIKLTDMiJGk9v1Tr74lfoOnvNK8RUdqH679mO9oAWg/aCIUpy2ptfke1WvvEyhr6v9XUrQjtE9cu5sKX8qxmJKyBtnUvQURV19Hd8he7cAChunTEk7q3GNor7zZfDxL3uFZm433yJEcCu6vXV1C2WQ4rj4zD/Z69pW75a8v+XFf5MxmfoLuTchC8BFXcTCbApaM5pwdeRXYUs5NyamiYmgjBXEmGSPgzdJhNPIBLnOgbMbVqGA9d0Fklufzni+YpQt1bxEgZ98g6Dfmyhlp1KqG3O9ImxBXgeYTlKNQXxfbKKum8mO4zQEfCDweuuoUCcOG1E0/GfeknqLv+9inSJeHUuBRcKVDNM9PCZwfMKNeztITr/CqjWOoLtYVhhPiE7qaAjfu98WXZSQSe5evHLdLOSrZP5F7+MxxI24SueN+LvjyDsw5fZ1XS80ZMTEPv630dqPeSwOP+mVWQ7UOyCVe5cRfUOBeJmDKGGlYn730NChpWJLcuWtl5r2B5jY9C2KldR28qWbKvKf/NL7HwHSqryRToCMr+M5jShTfH0lxeleGrCy8b46k2/Z9XviqfdX1LE0x4vhsfTH1Yp4umHL11SPJ3x4u+Lp/sGhxz8y8FtdHD/C2EOvu9FmYP7n1fE019WXTievvnC/3A8zX6BBrvMF0g8zbxwPO34giyeVj7ftni673lK4p3nw+LpxudD8bRsUEjc1YPaKO75z4eJu+wFmbjnr5TFU8eLNJ5OeJHGUxxv1tydxuHbYX6NcWfxdKQvTiOzXzLqapn3vBRopqwMjr3dP4MY5OPyoaFnkzj1uHzHC1HmDdaVdMC+E0u5VlLvfO4FZK6mjeOdm1ZK/vlJLTtF7N+38nf555znFf4597lw/yx8XuGfWS9ckn++8dzv88/h14YMJvfaNhoM/1yYwQxfKTMY/lmFf970/IX9M/25/2H/fPRZ6jyNz7bFP29/Vuaf7/yzbf45nJEY/GyYf/Z6NuSfO9NC4n4/rY3i/vifYeLe+axM3B//Q+afbz9H/fMfz0Ue79z6AxvnPEMKhD0fQ5/7655j5W75Z7T5TT+rr7vVd6fO6rPBqOFgfv3B+Z3xZS+ag/rFm8ll26/0DevI2sT8pHzvvny86JTvPZev2adf/G9cjck5p294mZNeHOCzGPB9PVZNqyWn2+d/9iDCuHzNV/M8+TlfLarC6pD9dd7d+Tm/LlpghaL5mk8me7vt5316i28euOpWvCyloxdh5t3JQzGHBd/U4e38Ee9LtPjulJX5Hsvk8znfL+LhAZbZj68UkO2oRbq48NkzUS4ubH8myuWIF5/5r/xC8nvi6Sjyq3s6ivzuePq/8gvJb8ZTUeQ34ako8hv4lHI+w3u/kaY09IbID9Ap1jWJg/Tp9fgCfH16QwFJHp5JkieqSGKv0adXNurTPSv06YtWW731+HJ8q7chG4/b4VsVxuv52kMnLekQhc7yDfX4bnzea95DVra85l38egP7BQPINQG0ibzYYj+ulnq3BD7gSw7gOiOeAPfOOEQXhRs8hyCkQXd65GkkEs/78g+R3Zr6g2JvsiickPuAfrGuNZGsAp/HVeCFK3CyZD6EZ8MtvkRcBMZV5ga7X5qOwfji/qcV4wOL9yM0KWvD1fddaeBu4BsGL4W0MNd7zuI9jZuO+JsHW87F+AN/p2d1DcaDkkCt3l+t3tMTvCdyA90+JdvUOZ95jpKRz4ygKhtDCvhhG/sNBfarCfh7PUNK7XOGuMXSCidXPRB/bokbUu6qtA9xVtxeMaSywllR4nKW2oYNsTvnuIfYxBJXqb0afw94SNV8sdzlHJaRPcRdIdoHV9lK7rDNtrsJvNrmZM8rSoZ4xAqHe4i73FZtLxWq7WUZ5VVVXJ7L4yh1XiMa8KekDGUVDruBKzJcm2ZzupzzK10et8Fpq7S7Aas9fdQoCdeoUQTZqFEhbGOA91Gj2K9kjRol/5WsMfgDWWMNY0eNAiLVNtFVPXhsWrqB/E6e4ZYiw3WGi9WdATwNuASeLloFf5MRqgjYcBl/1yrZaxeaGZyc/1Gj6G+7XWID2iPUa9sr06vdowyCUOVyG9LmlleUlBsq3IarF3jSDWMNYnmFe/BYd8UCOyBVPuUsQ6YaKM0Kl9MA/5OfCyslxvN7bGeOvQRaMcbhcs4eRLlnP0nJgNDYi9pQG3DM4GihUaPY7yJyFc45NkdFqcEB/lNtcxjKXNUGLAz451aI5Qb8/UODMXPUNX9A69pqSqCpsAa0o247ZPV/jqMZnKEj/N+lg8pD5L+ZaXB6HA6D0yUaiE5UJd2eWfCVm4hhCsvQoAXqwt+ogxJcpmHMdQanYcAA+GeModQm2iQr/sPDKWkaVHGLJJBiFmVgQMiYorFoXmX2aruzxN7m+EGqEjMNqe6WGWkISaeKw68zLtEM28pFtW2uYKuuts2XPCa6HV0iyhlcGnxmGcZeZ8hMN4wDNKg2YltOT6W9GjTuqKisEN1jSu0lDsSRNisd2Ki0zQP6gw2z0g2jDGltKF/hpOURvS0dPmgZV7rm2KvLHK65hpJye8kdV/7hliFJSxhiKy0lxoGtvriwgvXAxUpL02gQI8qHEDXL5XGWChXO0ooSuzuNpf9TVg1IUFtKwybyhq+zHHahgkW1CjewJNpnQ9gcUzQfRA5e67EPMkBvN5Zqv802wmiy2N3WWlVg4FX2MSFZEXsts7nFNGqnFGnR/EEGwtpYwwCZKw0yVBW2w4zDiUFL5wfdstrGYR+Ygb+qmYYmarCwnsVWPRvs1ClmcK7qUnhkn1cFwRf6TJsB+2IbfIfgOssjGmikM7jKDPS3mg0cxDfQPHe1o5RGtwq04QqIbkQBs+3imMyxaQLh7I83hwpnicNTao9gFuBqOK6BRhnw4dj/c3pWRKRgeKTaHvBH6DYYn8ZcSnga2M7wNPD/XniC7vTSwhNUVIQnaphgDfMMNmcp+yaz0HwIwtaxacxI20ZOMhdXWZkbUAyVKT0tnTl3oWHAIIPaz/PHhqBgAgOCPZYVzGHoIEN7rOKPC0qySAEsUk5+Z/CZIZc7BoVIYjf+PrEb/xixG/+/EvsU41BhknXq+FyrIEwRjNlSpeGkzkjLVDP+qLX5IuWMI8zOORXVLif2DG2rMaxQtKFsSttUPMcs/SL2xRGPhzmOrDj7HVL2+7zuB0yK3+WV4BYGX8lSCd6L5ZtUcL+X5g+o4E0MfvIB+a/0chzu/pDf6RRpWjOb8dFHWU6iL5VbrSp3kcZnTseRAROpnK8qn5Lf4O90qvm6oz30corgXxm1EL1DUejh+zXk9BpFJT1N75jRP7Lffm1cEgjgT2AZGgOBW2M4rgBSvKFTA+kYfAXzg4EA3kAtgHQJpDMhXY7Hjh4KBLZDanonEMADeJnvBgIzIcX3C62FNOmTQACvGmUeDAQ+xHqQ4q3lRkg7awH+aSCANyYzvwgEeMjPhNQK+ZovgQ6mXwcCeyA1fBMI4I8fG/yBQCmUM0FaA2kVpI9DurolEPgU0pNHA4E+wE/msUAAr6GYjgPfkK6GtIn9eLL0276aBdM4zbwkTe/OCbpGTUJSEvt93OxlgYD0+62Ry9LfH8ayY6Cs9JvPqWyVMtsXCDSibBKTJiamXK/vNFdXw43rNfraYank526xbjHaL9SVfs8ZYQ74e10GQ1y40Fh+IBDoiztY4xOTchNTchMNtTFLtdrj+DVpfKIOAHbpOx41x/tvRR8HAuQ3cQFcqxUSdey3hfGGd3mEZ3hw7xj8LYRnj3CU1vjElPGJhvGJabmJmbXxS+IaYpfGLNNq6xLTCDiFFNFRvPiKt0OfRqaJq89rQX9XBJ/dGHyGv0Ow/pvwevh7ygvh2dvwbLjmgvwcUPIDD1n917H+t5deH1/t/vaRS6uPdjMGDON18Kv+rG3Ysj5BeyqwEotKiNnT28TFSHY1iNnR9oZA4C/4++BpfzGp9D5VpneuAHWNv7O+NBAYNxrKZ0P5XBmv4xMzcxOzJyXuia9NWBLfELc0dlnMfdqYYQMSs4HT8TLecxHjHjC+CRgb8bec3wwEjiIPK5eb1O2fkGiSieBfSEQlBRPhDe9h7wI8SxMAT9PySLxNSFyhUfCWFx+ZN4wjBcyeYvcGAhMhvsSeXR6lvToFTm1MlPbq6G+8446u4aNA4CzoLHbMw7L2QsMmJTbF1MYtIW2NSdKiAOQtbYqh9vIp4OgM8a434rj1YRlfBMcewEG5ccowUC5ikIc+UO/YQcZDXft5QNuZCXVXfh4I4Fs+Yv/xsMp2bpVMxwymQ9rdCOVTvmA0m9pPE239Q6j7NfhpB9TLIdru+7R5XeKttMHak2DcuZ3jpd9sR/sqhpjeBL5pZ7FmmRYQL40BvhpiQUlL4vjEzPvi+cTs2oT8RFM1UZ7KxEjMXBGHR7ZDMZj8jnRA0xkdsJvuRq7PXJJv0XLHgLtjmuT1s7nD2i7Hy7im+ORNt3PvxyevvJ07nKDzl3GtukX/upPzd9gD7VrfJXnF7dzuLskPwNNE3Vdl3Bn9ouVO7qekbfi0W/K98LRb8rEK7tDlus+hbvdFNXdw/h7nwM6/6Xfrmdu5x/pfvqWMe6X/c1B+f2rXHSK3nu/5xe3ca5bknbdzxy0D4ftj1w+E7zuu3wBlzuf3PW/nHp/S5z079/LU/o/aubUFyTWl3P6CLg0l3ON/+tPpWdzDRR0fn8UFipKPzeIevOEpaNGr0zsC569P7wgc7pzeETjZM30INHm67mMbd/+NycDI327swnHP3qj7yMa9TABNCHjnRt0+G7ePAI4g4McbdR/YuLME8EjxkBYbt6M4DnjcXRwHPL5XbAXImpuT77FxP9985dKZ3M5b+F8F7unbOq4UuCVC8t8F7u/CUz2Y/LsV6t/XcLPKwPk1HeHb+5oUAh/O7QCmd2j0n3bhPtScBq3tjtW/k8iditO/lcjVxL8JkDd1cU8lcs906ftJF25Ll3c0kk5H3aftsjKB+4sW+smAVrc3gauNTdqcwL0a2+WHBG5z7N+h7mfxuocTuG/iQfu/xOv+ksDdk7BDw+r3TtG3xHHXdFscxzmH/BTLPRHT8dlY7rmYfVr6vD8G0iKf5l8x3BeakRz3iHY8eMVBbX/oy89qnwzycTE83fHH0Ifr7tNw1wEbf9LVabibTG2sm8S9CHRe1CTeo+U2aAq4/37++/nv57+f/37++2nzp+YKNu9n8/yCK0z/Fcr/Qx9NlPSjeGX+S1X+PMt3ZvmmFKr3Liwfu4Dme7J8Gls/6i1bgyDrcCzNZs+lOauOpT+dD7gwfVRL8cUxuKkDzUvrF2fY845SfYagE8unsPRyVftjggtmlL40tjfFKddkEljaQ7L7y0wKeGOyScG39E6wDir65wO0PXu6mtgYj+YlPk6yPN/1/64f+dk6qvpzmuk5vidNu7N0AEtHsHQiS6eztIylc1h6H0sfYemzLF3H0p0s3c/Sb1l6mqXxvRh9lg5g6QiWTmTpdJaWsXQOS+9j6SMsfZal61i6k6X7WfotS0+zNL43o8/SASwdwdKJLJ3O0jKWzmHpfSx9hKXPsnQdS3eydD9Lv2XpaZbGs3jbnaUDWDqCpRNZOp2lZX0i29OkvLxRhrQbZnmcoseQnZGVkTnY6CE545+N2RmZWekUypU4bM7Zhjn2ajceqTJmZsB/g7MURTOGGi5oUzEQPbK1pghwLVcUER7DiRHhscF4oITHBeOAEh4fjBdKeEJEP4sBL94TEd4h6OdKeMdgPFDCOwXjohLemTsUEd4l2I8q4YnBeKqE67mkiPAkrmBJJHhXjm+IBE/mcOkvHH5ZMG4r4d2C8VoJv5wzRYR3jxhPYiCa+p+MBE8JxmMlvCd34KlIcOhFno4E7x0G+xT7Ju5UIHI/mMzhcpz8043Bb1XBr2HwOhV8FIP/QwXPZ/AmFXwmgx9Swd0MrtZLPYNnq+B/ZXBcPpV/npfoquD/YfCzKvg7pA8M2WF/Bj/M4DXlFH6WdZT9NQzO9peuZuWnMbi0zyXB72NwaZ/oCgbfyOCNdPmCO8rg3zD4nioK/xuDX6al8JllFN6XDUyuY/A9lUo+5zF41Z1K+BoGr1HxeYjBpX0sCa6PofBDrPwjDF4SgzbUi0t+ZqZinNTAyhf0peVx3Zy+yoTCmxj+4awCvksL4dwcJbwyjsJNc5Xwa+MZ/zdT+FI2IHmDwZNmsf4glsJvTmB6YfrqzeTwzwTKf1cV/2sJXB/cT5TGMV8zuLSvKcF76Cj8gAp+PYOfVMFrGFynwr+KwZtU5T9hcGm/UYLrOlC4tB86SBpXMri0PzyEwe9g8F4q+N8ZXNoHHsrgHzK4tA+cJjlMRwqX9pmvZeAMBpf2SyX4rQy+3qfEs4TBeRV8M4MbVPBWBk9booT37BQ5vqV2Ynar6l8GMHiSat6Ww+Dq+dzUTtROklR2chuBh/cL9QxPjQrPg6R8eP8Sjf/VpHxXLjVCPxWp/KZOdJah7gf3Ejzh/dEnpPxlYfy3Mriaz/MET3g/1a0zRtXwzzVR4KOiwAs7I/c9Ob+qv7sZ4F1l/awkfzEKnrEANEToN9+MUr6F4A+NK2Kl+VWU8p27ILwLt+IK5XzsagZfzeB/YfBru0TGMzUKfEEU+B6Ad9X25A6p2vVZlPIBBm/sqbSH7onYx13O8UyeNQxekBgZzx1R4PdFga+OAn+vO8LDx3V7sbwW+i/mp9J8tCUKnrNR4DP0keFzo8B9UeBr9FSPjSr9jk9memd8SmcY3oqC54so8O8ZvKCXUg4pSZHLX5tE9bWL6UuacSyIUn7bZQgPH4c3JFH7UfvX36LgOR0Ffk3XyPA/R4E/GAX+TBT4rq7oj6HxbRJz+C+ilP+hK5WPn8mnWFp/SY5cfkwyjTPq+PBtJ4T34lKeUcItUfDMiAK/Kwp8aTLFr5b/Y1HKvxMFnn5ZZPiUKPDnosA3XMbsfD61E7sUJ6OU79ktMnxQFPgDUeAtUeDnGXxPb6Xd9rs8cvm7o8B3AfxK7H/nUTwdmP3kdo9c/qYo8DujwHd0p3LjFijj1b4o5U9HgV/eg9qtdH5PWh8c1iPk4/LP+B4Uj/qc3WyGp4DhkcaHDzB4+RPKOL+/R2R+fmbwmer5+KS8PMFOTiEKIt4zMBrDQcPCQVnhoOHhoBHhoOww0NAwSDi9cHLh1EZwwROAbs8swSKUZpRUVXHCzW0/i5g31HJ97tAcocRsNk+omja5SGhX9QmZ5jkXr6A8+HlJdYa2oc4wtwjtMRdMLrlo0Sy8Q5GFdyjMDkfbSreJBelYLhXriPaIVVF12LBLqdomyYZOhl6sOLndZcwM3e6yFIoj6IU+y5TCTCEkdXOhzVI4XECOLyyl7FmeCodY4XRnTXE57WYsOdloFAR2f0Aozaq04XUQy8U1DcSQiSwEWIoEs9FYbXd7HOAa2L6phSOFCNirPO5ywD7N4SgcKky7iFTZAXGLYwq0bQQ9o2253uEw42falMIRAj4aKQCMgAonCIBVyVZRplBkFIqGCkXDhKIsoWh4OKMThamFk+CPhz8L/F0Pf5Phz3qBBjim/b/eAIejHdxnReJ+OOF+RIj7PGGa4w9n/iJ2jJY4sszjRKcY7q5yVIjQuOtV3oF+UVTicFdHrm+mFd1VWAQApeaRZa7qubbqUrTrsiriV1VTC7PbxhKJVhJP2Xhp1VHhtCNb00J8kUIXYowWCFa/KHM5bWNO4iurwikK6IlhonJMJRGvULRmC0KFq8JZIUK2yNKe8/VtKK++jNCWKqqLEW2oEuH2xcVrhe52XLxsTruaIL8VAKUL2ynTwvbLtLD9Mi28JJkWtkOmhe2SaWG7ZDq9nTKd3n6ZTm9HU6e3nfmSatEtesrKMkq4Unu1fXaFW7RXC2IluCn01W5OEEpdwmyHa5bNIZSCC7sFm2ceV+KqrHLYRXtpxsicnJGRCwll4MX0brYASquez5VV2yrtQqmnsnI+VJHlBPR3RVFBmDgtN98smKdMECAwCJOm3CCYeQbkJ0zjhAk3TcnNt+TBM0XlUiXALdqqxeCAeerEiYVm6CByx1vNAlcm2h0Ok2mS1TI+TxiaMTRjOMjOaatkoOJiYVhGFsa3QtewKo9oLlE9sONLAEjQGul0QU/nmhteUxzJ3pNhHCp/34ClpFA0GvGCriBW26AXsJTgaApGfzlCvkDf64FDWiUvGUONFGl2hcst4LsjcOClImqZKpB3vQget72UA3IOu1PdSFX3PNRqLHHACA5HYRzyaxPYixjsbOYBRmgyAYnc8RbBmDGMNHpopiCQVgv0PrFgR4sCpv8oKbjdFbOd5mmTYSAQLgah1O0SyiGKOOxAotRRMAcKijlMEYKoZGKO21kFFMUytSSKpgMLw4NM2W2Vszxlkfgyq1qF7NmrRaHMVhFmQ2Aww8scMOQJUw6IzZgTFJtrNlCNIjWihbJqe1QNlDpgcM6UBFYuAqqqCju+3qekan6II6NaAzm0sS6qAtrm6IrIM5rh0TDBAgY3Fayy0lVqj6AMYjKifV6FqJYFaiaCQ6GCYWAAErTA6G1aoatIpeOccDM1WomVlpGXwoT57dwIfgucV3ucYgXEGSLmCUa1RmaVVFaF2UTRpcvLHCacMpxvqkgIRfl5UmhDkxghEPyAWRJKJAuchu0JcYN8kMGuGYbDBZMLhwmOSCIsFIeXkIFxiRn0mC/MrSi1O0mIVMkiw2i8NGeFvkb2mhVzZUm4DPBtKkoZoGmrBU8lQZ1jlq1UKLG5xQgelFXi8ohqcKkt3M6wH8wkjbHNqphjxIDlroCODVyXTARACmUus8KtKkngU6sLAz3rSqCRoiuCx4sj0Bcd9nATK8PRtBrjLGCB9U0YSrOkZkPLBFeZAPY+2y6UVYoQGBaoxJnJ1NpuNQ2DDrkUeuVqN9Ig3cvF+pcsC9hJntE85w+I6aCKfAhZVQ5bid1cWRmxdyuzu9QhWnBUzCoRStwegU0KLoH0SPY+JnMEirPtojuCOlWRA2QwbfIlUDZHEvClKG8EUR6YrihAHxddfxD/K11z1K4lzJ43T6iyV7tdThtMOucLczJV/fklMTVSZlEX5IqIlEVSsK2I4W14icNuq8buZijpbuAPujV7mOVB/znb6SGcDgW3cQnzGJ+Xoh74KxIKJlYUQBwFk4TZdgFESWGOTcA3Jgmibba5snC8sID1cWyUJ5fcDc65KIRpdrenEpnNywNGMy992FMC4UK0m6dFkmLIF3BszaRqly+XqkcRpAIJNAKO7sK6Ooscgbry7EqXk1UWmImECmfNLbeJ4YEBMap8J0JYlolmuFw0UQZf0X0Juv0LjfsiRLPwAM1luOdXirZZkEJwJ2m59A1fQVRdxWUAfnsG6HwwWATLgRFm4AJH6eCKUo7kym3uci6jdL4T8NFUrKZP2PlLRUaAZzCCs2FB9q3KISJJCHMZMJiCf4maM6pdZKaRYS9nk6by0upQDpCWlCh2CCgOOgOiOKTviAdJAUrCoK2yogTqu0TyD6VPaUEHxWXANA/n23/AueTe7MyDtM8knSfNZoA+Uc63S5+r2Jltqb507rSIAQxhZ0uUH6OqvnQ+VWSA1IvUx/uopwMBl1RfOsf6qFZ+HjB0zl2n3tdkZ9q1qnPvZxiAZxv08aztOtX58+nsrLlWdY7e1EF5bj6a/G5jZ9al+tL5Wb4rOweh4l+rSu9gZ+ClvHTOdg+r38SF+I+J0P4FTKZa1bn7xmTluXu1/KT217H641Xn+GsuU577j2N11PWXMpnEq+49NKUo7wdE079XVV86B3yI1W+MUZZPUqXLVfWlc5pVzPAfmhG5fnB/XVVfOn8Su0B5XyEa/ytU/iedE0pi9U2qzVS1/laq6kvnswqW0PyPF6G/TlVfOs/MNzB5xl2YfpOqvnS+No0dUHpbe2H678Nfouy+hnQuLO3xyPLWqdLP8QyprL503iz78cj8qusfZfxL9aVzYCZWf6XKYdXx7GeZb8nvefifUDpqvIpukiwgyOlL57f9T7at/XGq+sHzcytockBz4fqJqvrSuZUDT12YfvB8nobCgvTZuRo/q59yEfn1ZPQz1fdlWP2CKP2PPNVG6Neyn2b2yxpwBesL1PGng5x3+XmpNaz/S7lw/O4apX7WWzS9UnPh+v8bvKJIARjUAAA='
    bin = gzip.decompress(base64.b64decode(gz))
    with open(pymain, "wb") as f:
        f.write(bin)
    os.chmod(pymain, 0o775)


Fixed submission:
import numpy as np
from numba import njit
i4 = np.int32
i8 = np.int64

INF = 1000000000


@njit
def solve(s, ch, cw):
    H = s.shape[0]
    W = s.shape[1] - 1
    ah = H + 4
    aw = W + 4

    a = np.full((ah, aw), -1, i4)
    for i in range(H):
        for j in range(W):
            if s[i, j] == 46:
                a[i + 2, j + 2] = INF

    sh1 = np.empty(1000000, i4)
    sw1 = np.empty(1000000, i4)
    p1 = 0
    sh2 = np.empty(1000000, i4)
    sw2 = np.empty(1000000, i4)
    p2 = 0

    def push2(h2, w2, dis2):
        nonlocal p2, sh2, sw2

    dis = 0
    a[ch + 1, cw + 1] = dis
    sh1[p1] = ch + 1
    sw1[p1] = cw + 1
    p1 += 1

    while True:
        while p1 > 0:
            p1 -= 1
            h = sh1[p1]
            w = sw1[p1]
            for i in range(-2, 3):
                for j in range(-2, 3):
                    if ((i == 1 or i == -1) and j == 0) or (i == 0 and (j == 1 or j == -1)):
                        if a[h + i, w + j] > dis:
                            a[h + i, w + j] = dis
                            sh1[p1] = h + i
                            sw1[p1] = w + j
                            p1 += 1
                    else:
                        if a[h + i, w + j] > dis + 1:
                            push2(h + i, w + j, dis + 1)
                            a[h + i, w + j] = dis + 1
                            sh2[p2] = h + i
                            sw2[p2] = w + j
                            p2 += 1

        dis += 1
        for i in range(p2):
            if a[sh2[i], sw2[i]] == dis:
                sh1[p1] = sh2[i]
                sw1[p1] = sw2[i]
                p1 += 1
        p2 = 0

        if p1 == 0:
            break

    return a


def main(in_file):
    stdin = open(in_file)
    H, W = [int(x) for x in stdin.readline().split()]
    ch, cw = [int(x) for x in stdin.readline().split()]
    dh, dw = [int(x) for x in stdin.readline().split()]
    s = np.frombuffer(np.array(stdin.read()), i4).reshape((H, W + 1))
    a = solve(s, ch, cw)
    ans = a[dh + 1, dw + 1]
    if ans == INF:
        print(-1)
    else:
        print(ans)


main('/dev/stdin')

Buggy python submission (verdict: Time Limit Exceeded):
# compiled by pythran
"""
#pythran export main2()
import numpy as np
i4 = np.int32
i8 = np.int64

L1 = 1000000001
L = 1000000000


def push(h, w, dis, s, stack_h, stack_w, p):
    s[h, w] = dis
    stack_h[p] = h
    stack_w[p] = w
    p += 1
    return p


def pop(stack_h, stack_w, p):
    p -= 1
    h = stack_h[p]
    w = stack_w[p]
    return h, w, p


def solve(s, ih, iw):
    sh1 = np.empty(1000000, i8)
    sw1 = np.empty(1000000, i8)
    p1 = 0
    sh2 = np.empty(1000000, i8)
    sw2 = np.empty(1000000, i8)
    p2 = 0

    sh1[p1] = ih
    sw1[p1] = iw
    dis = 0
    p1 = push(ih, iw, dis, s, sh1, sw1, p1)

    while True:
        while p1 > 0:
            h, w, p1 = pop(sh1, sw1, p1)
            if s[h, w + 1] > dis:
                p1 = push(h, w + 1, dis, s, sh1, sw1, p1)
            if s[h, w - 1] > dis:
                p1 = push(h, w - 1, dis, s, sh1, sw1, p1)
            if s[h + 1, w] > dis:
                p1 = push(h + 1, w, dis, s, sh1, sw1, p1)
            if s[h - 1, w] > dis:
                p1 = push(h - 1, w, dis, s, sh1, sw1, p1)
            for i in range(-2, 3):
                for j in range(-2, 3):
                    if s[h + i, w + j] > L:
                        p2 = push(h + i, w + j, dis + 1, s, sh2, sw2, p2)

        dis += 1
        for i in range(p2):
            if s[sh2[i], sw2[i]] == dis:
                sh1[p1] = sh2[i]
                sw1[p1] = sw2[i]
                p1 += 1

        if p1 == 0:
            break


def main(in_file):
    stdin = open(in_file)
    H, W = [int(x) for x in stdin.readline().split()]
    ch, cw = [int(x) for x in stdin.readline().split()]
    dh, dw = [int(x) for x in stdin.readline().split()]
    s = np.empty((H + 4, W + 4), i8)
    t = stdin.read()
    m = 0
    for i in range(2, H + 2):
        for j in range(2, W + 3):
            if ord(t[m]) == 46:
                s[i, j] = L1
            else:
                s[i, j] = -1
            m += 1
    s[0] = -1
    s[1] = -1
    s[-2] = -1
    s[-1] = -1
    s[:, 0] = -1
    s[:, 1] = -1
    s[:, -1] = -1
    solve(s, ch + 1, cw + 1)
    if s[dh + 1, dw + 1] > L:
        print(-1)
    else:
        print(s[dh + 1, dw + 1])


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'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'
    bin = gzip.decompress(base64.b64decode(gz))
    with open(pymain, "wb") as f:
        f.write(bin)
    os.chmod(pymain, 0o775)


Fixed submission:
import numpy as np
from numba import njit
i4 = np.int32
i8 = np.int64

INF = 1000000000


@njit
def solve(s, ch, cw):
    H = s.shape[0]
    W = s.shape[1] - 1
    ah = H + 4
    aw = W + 4

    a = np.full((ah, aw), -1, i4)
    for i in range(H):
        for j in range(W):
            if s[i, j] == 46:
                a[i + 2, j + 2] = INF

    sh1 = np.empty(1000000, i4)
    sw1 = np.empty(1000000, i4)
    p1 = 0
    sh2 = np.empty(1000000, i4)
    sw2 = np.empty(1000000, i4)
    p2 = 0

    def push2(h2, w2, dis2):
        nonlocal p2, sh2, sw2

    dis = 0
    a[ch + 1, cw + 1] = dis
    sh1[p1] = ch + 1
    sw1[p1] = cw + 1
    p1 += 1

    while True:
        while p1 > 0:
            p1 -= 1
            h = sh1[p1]
            w = sw1[p1]
            for i in range(-2, 3):
                for j in range(-2, 3):
                    if ((i == 1 or i == -1) and j == 0) or (i == 0 and (j == 1 or j == -1)):
                        if a[h + i, w + j] > dis:
                            a[h + i, w + j] = dis
                            sh1[p1] = h + i
                            sw1[p1] = w + j
                            p1 += 1
                    else:
                        if a[h + i, w + j] > dis + 1:
                            push2(h + i, w + j, dis + 1)
                            a[h + i, w + j] = dis + 1
                            sh2[p2] = h + i
                            sw2[p2] = w + j
                            p2 += 1

        dis += 1
        for i in range(p2):
            if a[sh2[i], sw2[i]] == dis:
                sh1[p1] = sh2[i]
                sw1[p1] = sw2[i]
                p1 += 1
        p2 = 0

        if p1 == 0:
            break

    return a


def main(in_file):
    stdin = open(in_file)
    H, W = [int(x) for x in stdin.readline().split()]
    ch, cw = [int(x) for x in stdin.readline().split()]
    dh, dw = [int(x) for x in stdin.readline().split()]
    s = np.frombuffer(np.array(stdin.read()), i4).reshape((H, W + 1))
    a = solve(s, ch, cw)
    ans = a[dh + 1, dw + 1]
    if ans == INF:
        print(-1)
    else:
        print(ans)


main('/dev/stdin')