From 57dc12b305babee3e2150cdc82b36ea6dcae6e9d Mon Sep 17 00:00:00 2001
From: vd <>
Date: Fri, 2 Sep 2022 03:48:26 +0300
Subject: [PATCH] First commit

---
 classifier_config.json |   6 +
 download-dataset       |   4 +
 embedding_config.json  |  15 +++
 embedding_loss.pdf     | Bin 0 -> 13139 bytes
 requirements.txt       |   3 +
 train-classifier.py    | 163 +++++++++++++++++++++++++
 train-embedding.py     | 265 +++++++++++++++++++++++++++++++++++++++++
 visualized_simple.pdf  | Bin 0 -> 23115 bytes
 8 files changed, 456 insertions(+)
 create mode 100644 classifier_config.json
 create mode 100755 download-dataset
 create mode 100644 embedding_config.json
 create mode 100644 embedding_loss.pdf
 create mode 100644 requirements.txt
 create mode 100644 train-classifier.py
 create mode 100644 train-embedding.py
 create mode 100644 visualized_simple.pdf

diff --git a/classifier_config.json b/classifier_config.json
new file mode 100644
index 00000000..4d8c8652
--- /dev/null
+++ b/classifier_config.json
@@ -0,0 +1,6 @@
+{
+	"n_neighbors": 10,
+	"classes" : ["black_red", "green_orange", "yellow_grey"],
+	"path_to_dataset": "./triplet_dataset",
+	"embedding_model": "./model_embedding.pt"
+}
diff --git a/download-dataset b/download-dataset
new file mode 100755
index 00000000..73b17505
--- /dev/null
+++ b/download-dataset
@@ -0,0 +1,4 @@
+#!/bin/bash
+gdown 1rP7GHDqx6BKTGTh9I6ecEmRgn5-HG1N0
+unzip -q "triplet_dataset.zip"
+rm ./"triplet_dataset.zip"
diff --git a/embedding_config.json b/embedding_config.json
new file mode 100644
index 00000000..7d75d770
--- /dev/null
+++ b/embedding_config.json
@@ -0,0 +1,15 @@
+{
+	"epochs": 4,
+	"embedding_dims": 128,
+	"batch_size": 32,
+	"classes" : ["black_red", "green_orange", "yellow_grey"],
+	"lr": 0.001,
+	"triplet-loss-margin": 1.0,
+	"triplet-loss-p": 2,
+	"train_size": 0.9,
+	"augmentations": true,	
+	"env": "",
+	"path_to_dataset": "./triplet_dataset",
+	"visualize": true,
+	"pca_components": 16
+}
diff --git a/embedding_loss.pdf b/embedding_loss.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..5518c41ba943c46283f316e71d7bd89407f4f81d
GIT binary patch
literal 13139
zcmb_@2{@I{7jR|EwG=|KbXCZD_sh+aeT}#z6teHvc1g4#L_%3pN<?K(iiqq>vPNW0
zc3CPZA$;>vzf!*c)AK#weV#G%o_A)>GH1>?Gw;Ett*9)D62rpyGJD|}MKBZ`33oQP
zhe=7n5!z-pR_<^#fDqvby)!OWaD;*x+04<|1}-B5vm#kS9VLDnpyWkX(IuOat>D=8
zLCVe~G90ts);1%Pt=vd(9P|i7XuCOE=vtBCCjcHrWiY>$7a5LFa{@T!f7A*;YBjhq
z9HDD#W@$&Vf#cVQ8yq#ax3VC^#n&ePMGoqB2INR2XR<pSg<PjV0Z?N_0szALv!<1$
zoteC|7oY*^fyE=mB;Xh<P7Dt>1_YY9fzChKCtA5Xd$?Huc|Z*RCl5$OzX=1H_^0%g
z?HmEWaD=iW5Qw6cg|j7)-w`X44cQisS{EuH+nwxYW#$C)N{ETqjN&R_2>P%rylA`k
z)gb%!SzEsF^s{+c>0CrY3jUE}oMo-P(Bje=*+++MJd3+tskcRVt6rrv+0vJ@GCNax
zSh{3srg3ti`R&o1uNn7`MkQVtonM|BO<Y)(T3TQz{UA{Lx>nGtV3*9f-HGOreC{Lp
zM-OYY?P$x$`BpD5<AVwDw=hg-{(3HvjbZZ3rRna}>Py?k3!LLD9=E9YMDQ6@7biM7
z?vJWTt-2#Ouf}*#^wns*N#29zHs|ltqlXDKIt7*?V){=w6;2Yj%Z=WTXqznfoKd>i
z^=x5!hf|mHDsK7QO2s+Cs>S)G#<?#&6Fq5N$TNu0P&s0+z~M(@HuGAsdKN-W>}Oi%
z`ui}lv&`nfL`G_^Hxv{^6SuIW%k4xHbJ?d3vak872XNf<5)WypcNwdP_4|x1htdbn
z({Dd=^KMS+3^iBRXX*fL_Tvf+guuCd6t9da9uGP_e_F}N)kQ(!B6qKISfibt13piG
z>b|Q)Vx`RLR_VjiCnD&mnHUC5u;-pjo@0D^^QaEv=e+>+fdPMB`#Qq&x-BI(vTkQl
zi|`dVhUROT?`7@w0S(t1rk1jWHnLG7uA%dOX;c$NIWxD5Ow;R+d{aWK<PM6=bS)rD
z_ys5zuDj9fu+DlDV=jg&Oum53lfvXh6xn9?PagW>Cw09?MWlVF%ju|~OHoT>v5lwM
z({6A$-#RN0!GFBHhOiPKYan&|dtuBFr<W=tDl2Ml-NhFatE1XF!(6)Oq|SJeMtC~|
zGm|~sA5omao~co6egE2Z_5yOjZltuawwJrGEH&iyXm@Gp%~kv=`qIM9_~nAL4Xcsf
z>WoL;$41viJlgI-gmDPAFFX_fT-YyR>7b`ja=9SywW4>^o>vVo7NXrA@7C{-e;8DF
zfGy5GKUFwsIMRH-Am8ic)V}o41NUzSK4P)W)t`O}t3OeGq})$JQc_f-LG6}*UZG%G
zu0{2egflrJs!!CPJwu<;VJ}bF!@hJSCPM2LU#I`AH2xi8qr}Xn`$bi<6*=6I9&V!C
z!k$-rG#UyA$1C!+RlWr+%U`a?HOBTT1t;QF9~z?VnucIr{`Xp3(+DgBw@bcEWhqu(
za}p{vK%TNoYqWhpeQDog+4Nv*dx<V~_uOyh&6)=GwZ|lMOHP^1tLujzt%n_b|ApJZ
zHU88*PUNZV^w)QCmd0AG$M3znf<2vJTu*N-Ja{WP@AIJQr{|8dQxDz<)!sa$l~jg!
zx|1nZtIY1*MfPb48>v_R+e|-JM3}+0G{*B$)@%0!oW~zARnLDk<)7gm?@WC(a75kC
zj!LasBQJo?nn&vu$(l+c@taoQ#m8>@p9T)@U+{DAG<cJ;FU5X^mNAsCHu^@>9eApf
zumQpIE1FV|q4#VI<pAO9u(QkP8w)3@9q;4pIVBClV(<12n3va34|Xv-c*JT8H8mHi
z)M#bKwb14cIWjfB@hV>M6z+B~(e7@rVWdc$w}|O_AI9ZYIjDR3xXVay$Q)esl1}U%
zeP+uhzhlXsksVq4Ub}p}r7t4dIOVVW)Y0R>K7YjGsMJ7xwzsaF{!rEdRPR2qC9jJy
z?(-$+{i-J=G0u!ZLP&G%(p}N%N{k-Dm(@6{#?=p)YiRh^=op>lXcA-p(%5r_o$+m8
zO?5{Hp_=*rVH$I$u8`ceI)47N(-K7jwsHsW)g@t<#hVOieNMj%VR0dz8)EX{&b#~~
z{SLGE$pds~rtO@Qj5lQW<OHjP91mY)o4fUimP#(+$%j3Kl`M)2^=blsw*_z{ic$2_
z3EFg*cV0GHk)KdpZ*htFi|Ct*RV<dZ0ncAH<ro=hDN!RX&s}#Pz&uHw3v2T(x_)CQ
z*P<|aC@hbn{A}CFD`}tJ27P{IY2-a+?TN!TomQh8)eJZ^@uDfy-Ltbewzy7oN{@q!
zlbI=eM(lyN>hgJf*-$mtb{n;sJ#B2}@gjlwMhEFk3^{E~lMTM2T}4fbM)wF@<73|=
zaAxHEVX^532i22wm3D@L@faf>`K;8Sma{U0;|COAmXTDs>d8)ld~N|}JA%ax>yzB+
z)M(<yEi{y>w)I@2=1*1BP>Q#{wC%_t0rn8Zmr4h)l7a2_HQ7$*2Al||ahD2UW(mBl
zxPQdgz4eB`%jDo9PW{N6s&`@|TKH;tg0Nn+dqjEpzSg`J!}Zpgbl7qTG8G@$GdsX>
zM*G4@0!Hn%8*8ew&aecdmZ2|2xhB`uSEGK>Oy#}dee`ecMlKl|o_VLnvQnRh5b4iS
z<(=Bz6iJ<<c6XTWzT<#(agr-FIZFnaogh~D!XEzJU@?tdIQYm>soRc*g^PZv>IWpx
z5aX{q&i7ojdRV76l=#4Hwl6AcrHsR1h(W-H`L+v}@3_mkv!6{vQj+NkL^2YF9EE~D
zWvj)xruSIewCP0_GMap(zD#l$P~F<?d>>ZMxs!tEQ65!mo+j@SWE-uA<LbJ+^W;{Y
zhd2Ck2;A3GRLmb6MY<T)!d2dF_qt{$+!ypAXn(@6x*M;+-TKBxhTW}0)Q&voZwnSF
z(y$f#9&oJ@4L9h`)Th2W{=STlS$A5EwVR-~-RZz^i$th9y`ZahMFOu)dtgx^x%6)K
zoubROmqM=1<_%VT+Rt6lO_=OO#Yb5@_q%*mn``G8#{TzPClgQvZK5e^8&`URsz8|9
z9+pAwb|0@UR^j80s1Q@w^Y-M>^orx#<eiZ*_`(Zp-D!A}O520_v2@2^Y>ZFMUTMw<
zZac!tf;D{@GkM^_Q}LPkH*?E#)8AJ;l=_W$iF|B>B~f-R=?~cXx+?YS7^BDOx=$os
zjihFIY2I@?-RW&VuVI!(AM=g6cz)v2i9SX3)B|oK?Sw$z&nOK^6Bb4O+@gtqg7Eru
z*?s(W6T9#A!*1H9QK=-H(6G91Mf=fTg~GG??p6m6%G8sjb6p+2hKmK%BF(iclZpjj
zC*@U^JW?FyJ<RSd6t%Sa+#2ujHa<wjleToERqg#3@4&li&stjV0&ueNxNWNzd9p=J
z?}6eIlL^}%@X#$r#ySZUeQ{*(K9SGTE18cm=FG>4M&lPA4U~jRwmk`vd{uG@)PbN}
zl+>0<wlo}!$#&|yMl$Z>$kyzO&zA1HHX{mZZcsCTdb#%+zYjV3uE|9A@U4mNA?}I(
zK~TQ~^#G{fg3=G#eYYlTXTn`ROgHI$WXd^Sgg>iNH1A_ml<z}h-^b%&@A-u~M{|Jo
zgY*DZjwmSH1GG5|160f;H>Na;6JF&G$3fBcs_;4LRj#K<%+`1-Sxslc3CdCxOMj1I
z?(IsUC*@CGTk@zq9>mOxMSq`;G_cw$*hIlyS+8<D<Xe|QP4c<xSs8O30lc!uMs<&$
zsib4bIhJrR_sQiFLf?%>N5*u$EfgpA2DaRKC{VEL^7g{=L9Qp&BIS`6oYBgkPNn+w
zn3&)Gyt;5~Vnp-c+ry<&%U(;f(LOw-q(0Ja)560+4*0KeS<g%C&t33-=or!Q;OW$e
z(p>hr4<8K(344VvEPpUfJfCp*`{G2~r>@zIoaB;MeJ0pE&Sl)^VNK-sgu$<3jW-Iv
zEK+oO(M$8qU<@95X7<S?#))z=z^6+S@45EfXzEsPjyd2%^S<HuyiR=1TfB9DcA|a{
zzQSz(Rj<2^MLIs|h7lKY;5^j!mImL<p7qDN>^_}2TZ}mR)%yFFMeE|%H$4p^604e<
z4~Ha9HI}B`7dNe;>^XC~*t+fe!s4*t65T?ZaEG;f{_Ka=j7tTk`SYz*KBeE56Av%D
z4T&|gwtpV-_s*=ljqk9WDH63;=_v{yuh!7`%1DqtA7+#Ba@Qk~nZEn0y_wTJ6Bmw^
zJf4A$C_m!sABUB-xEl)4(F7#w=Lu%*cmM%UfUy5(N9bJfFOj?i3JU-Ki{dpKpD~od
z=_y-o3rcMpSe4O<-q%$c#=w-6n@AS8b?Op~<Z3|wSduI2Tf6S{bK^YBk=pKkiUt_I
z?tm22#h1v+>jriCIpW8&-JY}ca)oZf!SCm-|8>N`<2Mk7!C>`}+AX+k%+^8tuu<h$
zm|)OJS()INmsBh-x8^k*Y48=X(A7N<yY(s_jl0;kljF`-7iz@|)W`R1Av)|T+k}#T
zJ?vnR;u}bjj?^Ggpy(CmWdg-^mMwPO{+x2K=z9N!9s$0I%UoS-O7^iuM+|j&u41DC
zE=+uM$-Olz>DX*-Jz$g^7cvq(*1~E&lYF+TLD|?)p!$7)^`U}s9RfQIUNb#0LnO6N
z%!{#;|MrQmOoxkG?(nqZ#AwCI$M0GRVlC2_F1+&jeg!>Xf5h04nBlWm%0%(l_&AM9
z$UciW_OT&3+6navTchBZ(@GR_GqaEDeQ0NT`t9o;(WG5(k`%OlI7udok=QTs^3=^f
z_&9R+je~siw}--fbE<EGFm3`fwl$;eb%R$|`okzSBC>6HKM&-PI>b&D>mE`vx72dW
zv<p3^HLty^t+29$e&-iQB?UqEYp>s%RyNT&#5%HhsV`FT-0kDnVASvGd7$+nORB47
zey>p{dGFt{M?>e;e^E~~2E9S{I2|=o6_Wnh66^P}Egdt(0@ZgeeO>J&*6-Rb%a{L@
z&)j1FHU#P$A-MNjB<?Q%c^kLm-$jlFs#IhRsO|ELduQV5|B|!D>M$c_Z*iP{Q%X$~
zeVr0sT8at%hHvK~<TGlIH`d)D&w1bF?I$)W$F_Ac&%F|&tk=zdJK@jll(Fc(lu75b
z)Xk#h>GNJ~ge7<^HnYrXh&8I(fLdCoPy==D>U+l|fu!Pti5DGZ?1L5K4%!Tal$RfD
zVVo+Qwi!IeYirzh-^t>EvU+V>DTAhF<n{$cTAO_A4BN_u=91@~OFOi9^_Vsh*zeZ@
z|7+Wzv4jl*lh&3DSLCIiBm^Qp9+=(l!lRraYW?ADHpLA=?IY*&F$Y?h{8Z>s(K$nW
zS$DGiN<-y8kp?F(R=_h9i0~%w9l_+Od-!WChqx0kht0$-!poFU)Cm*hd(t%ggry2t
zfIFwvl)kD!Ncz6;_n2_O+d@8xUTh-Mb@&qkk<!EPcSK^yi`2yE`om83(Xd0-t&}3n
zg#(r>ni4OcsS&SmM5EvH-1FRbE>o)R?xAr@&8E!#@m+04d*Z)wxfGaoTWZDBl6e}w
zB|L7rNMJ3R9}2<KXohu<PtCK~2_cBjxQ%X|_h63c+da0MInL$+$6L$Q1)7yhM*B9A
zAarH%FDi#2;5JH-s6nch-QF{{>*SqRA8$6FAs*{1UUGI&Af^cwhoEYZ=jyK(j%XM&
zy9T}}Fv&h4b?>a$38VNk!Oud&_+;{X<7RmCjmcuU65J}Ybm&_`iVqsoTa&)Y#|F||
z)%uu32d)%Wh7i8xoevqFzLQ4FJv=ZfC|^U;;kdyp$7D8kjrBo_cA{a$Tb-H7c{G*1
z^OaOO3$8iZ$5ir8s#V+G%WhGeiq^}z8g6rdrIfYL#+>WaSnH#$*&KR`u|89^!D&qo
zt`sX)$sE;OJQ#aKRKJF#%VGLG`bzs*GL=vob~bvp<yc+|ok7SM=C6eBs^7*s9C?Ed
zkve8WM)qQ=(acS!Gzh1X;)YZPdJH|7vbhVNHj(}I#&ozGc|3W(?#s~?a*t7?N3q-X
zWZ4l-xsT5nvz2qJs?ajG!UVmOMIP?CQLKz9H`~e7F<)6nv1i$>cqjJ0!@kD``))ol
zR}8!C=q_DC*>U7UHKC)DrNZ&Ut&XHe7>p3Fe|J%k{iLjC-t*5c`H6=y<H=5&tSNLg
z_b+Su=dRGyQM*h5%*Yt)qAagTRPlX<c!jam&U(f`0wrxuWK`^R1G*ikfW0Ep->&hx
zi!XDYPwskV=pFf@YxVlrK3$z}#0O&L^2WiO%}GojrIQ?(4RcFA5k&6ByFaJd8~BtG
zleE?1oEp0D!DtLGix#~H7o$;1#Zw3K%0Z)6sjyzslCrfGe*i;-rF`#h-(dLUwnw`~
zqVaeZv|$s)G{Xx9qf?z}oI0WY!qFYH<YfJ-^oD`F(GE{>1<~1o2j@O5+Um!h3T)N*
zV&G*OD04k6-g72W-us=V<h}+TSPEKFRQQQc)q9fP;^CLXkIOISzj5d6X#9RMVG{{L
z7oGniK>~KerceqiXDWklr(D&OqlLk`kMc(WGaR-PefHEC<4gb*UH%#6y;L#$CaeZD
z>K47)5wa!k+(cjWQtfaNxX|6Amk`@_+U}a#F~Qyx<}<5|HvCr~oN2YVHBV=;v|nly
zDxo{qf1whE|AWk{wXRgaRewm|#)tHUeaWo-ykl}DCupus&U?2!z4_6KhbND_nvp9b
z3Pdps7gRWAY8KHJ#jg$E0^=0)*FrK{Qx-j|Inw8bUz=y&O+JY@w2!$VawtP~|Jg&2
zYNPV=G0S9=y;Hm&SXPCE#z<yc#nNYF7l?a5wMTU6yuXnYAtda;W8Zx|z?ezBM@H;E
zTC1;w!suN|Oonv#hr>Lchh{fn7P=w+7iPsbx<(ATKpUz35WAH*hz&$Y*3@ee((|;C
zLU}QERr;#>t$FmmkNFb(gKd1T!<}|k!`T#b<3l(Uxf8ZtdpV#^v4yXP?Zqba{A(zQ
z-54fqN>A)geKGbUA~X%#T4P;c`)-%VP;V);VG!5^?Y{<!C_HAPYF?FOrr@Pl@X<=z
z|7|~oOtUpw5I^s1@y3x@)@A^|_>9HBz1u%z-2YNOUmw+nEJ_=T?-O_uj(M2U!&g6L
z$TpgIuCUrJhWyDE?=DQ2#lmgP=pB8R<Ic`Qto_r;!*2IxnidpXzL<U~HSZ(b4DowW
z&%M3zy>9OUUNX6;=*3D(ZEt4hdEu#JW_E@eFKG*IJ+qdj?bRi8XGkC33p@066JGx{
z6h)&XHt;H~T^L4*qJL=|xW}N(dpgaAEmY&)>YVDr*L|&QN<<-Ce54@4Bbv`pxBt-D
z*eb>|K_etfJfDyfBI+UEd<*Q;xscIa^L?*l^AxagwuJL^7@>4d?lukl4HqGnqC35n
zm_-IBGKKynp4=1gmXuSS`Ea+|9lPnv<5@!Y(W`4xuV%YNB$|YB%2a+**mW(EaTK>|
zusrL|kfR$RaE;{re9$Bzjgf+hEAmG1eq)1NF=i$^oSbHJnUSdPvjUS9&d2+U=w-i#
zRJq<Dh9{LexTH2CMg(-<{4FBPzebD_e`v!MaG*hMSM<4eit;@FS8$-I=r3j1$wzuB
z&?kM8n>JpMgsD`ZzD+L@WbEoU7mK7jsiGZzYwE7q>*wz&!@k@je*Ul{`u0@jV9?~&
zZSs4pHzDnxV>8Ug*i2LVg%eo4rDrf4vTU5|84pdU)T8AK3iY&+akd(RNwd)>V(YEu
z_Y<676pbic(u%R0CABo><gVPM-{!fht+z9aFIT<c<SSAxRt?crDG?Uu@FaXJ%_qR{
zuGhz*H*JD`6Vm8%>%0;IGDEtF@v7F-nw<A-k}(G;LW-__mQRIE^0f0MM+o;^C*IN$
z9oZe1IPP;ce2a8-e^_s8?MwD6%1=ZewA#l=>_D>Uw#K+-T=R=&p;oX_IgX`Et*iHz
z$5n(8lHyA$T~1NF%nL|K%_0`>u-ZDl&A9u}5LVqkd`rdKgIB&RQm2165#K~^{~D3u
zHl9&2_{YG|A+boBNaY~e*lQv3mwDM$w^TKZ_|uO18dA&dNtU3==D#F-QJ7)NyT=UI
zH$nZcfddMU+_2%4t`e9jP}?cpOZ){?Px{YizI;-?xSB@Op^`kY4a-%;rdjX3%T{J0
zS2?Mu#bI1TJyNlpEs|UNzD2O)LDeIw7Lls?JQw%hFvv5$9<L{qM|}6#uDYVM%jk8T
zVwg+V_mBx{z3)fgpN#&fc`+vvwYXcaSn}c(wQnX$y+)|%6-8t&$5&csC4Q=akleJc
z=gp^r8s=f061AThj`)jDZo=rl`gd@?-oU6RQNyhoPT%7WGroiQMl&omK`!ztS<R1(
zQc`9-HHR7r3Q<362rszXQB@GuARG8DC!-;-S+%F`Q1HvEp-&@41AP=lhEY{gRsJ6`
zKJoA@WH!9Wqp3l3o!%k*wLQR!cc_}9+l;l=jCI)Hp$BiekC$#rW!ix^L!qlO)$XE{
zqGy#`d`*WFbHWD}BH8TbuRFD6dp|rPnV#~H%kikY3BRe4P2nM)W||jYv)?cgpKaR}
zScqwIe|NA=U->bcCtlV5q@#2AvVnR_AnBy?<^5L^)AmW-NqN!04;zd~;Bgb8FnfO^
zo9xDU%=P~52Hefi0*spV<a5>B_=+>e5_v21y4Fw52sFDTx2J9YkZYsS(n83-T@q_(
zOTNicoD$Gnqjgm4`Ekr7FM*Xd&D`jCx!taKbJ`weQ(Yb18{Ql%OS3bdB+svG6>&f7
zzR9xuYd|BuQT;F&ye{zZ$ZgE`?gedf3nJ3sC$gXJYAlzS0-XiY$=osJl(jjvr6C*z
zFG4?kwdMK^7K%-%`PYa81J3Qg-xlG((TEDQy{B35sch=UqfMXF(?g$wqY+C9>k;Is
zAwh)UD~;#vd_Ad^!zcF6^X1C-M@-*nAXd+_<?U(~%~5G)DiDQH>aa)~#3NYuwKva=
zA9~=Tp6GXcd%-Tw;)`lqF4}*i;Yr27wZ~*@o|&VG6#fU^xZAmI4{_rUxsXzP`>jA$
zO46LPVf@RtQ5vZ&+?rbBTdV7<)$SOpE!>H}uC5pvH+e2selDmiQc6Yz^}=@wAL5iu
z#XrWA%GXe}>(Im6ZN=6%ULPAjRl##f@XB$zyazmvvnN<$Qn<5*IJ%JiFOj<DN$(y~
zwU3%x`Ol0D;c_a1r*)!?D_v&5XU^@dwkw=$^#c~Hk-jSTbD0_JB+7!zZktF{n|x*=
zFqQ>d+`1t`Z(+-S-#a{{tmSrIs##4fkI|RUJM<>bUfN_K{x#J4BMexxh`yD(6tb~f
zxJN07o6P*r(G=zn{~4jdNXffhaXukAysUQbm@9fB(0z5<_ZEr8LOM{-uh2h9TFims
z7VBPKj{HvrX|dr-hBA8IMExBv1}<gRyyWe@Bl6K-O(=+0JM{XVYob=90^c4y!=pzr
zx~bt0L+>IZzCLzj<uOe@vCkv&@Wm=2MDI`$cU@}7v;O4J=ND48qG&ha=3nC_4E9fj
zC6Iu}om_^UgZq+tK6JrVuYE6NNbA}~;Yub(OB(5UzPuEu_hgE^@HnQMS$z1kRbUnE
ziwx#zVvy>CVtwA%AL7wD1vneZI$~<YaLTaj3;)BFsrTt>7xHs+M(VgDcnnzgCn=Vp
ztE<NM#5T=5v_5e@)~+nVD<DC*L$ua_ipbd~F@1SH>5S+j>fn<2*vl|({SdbEa-Z&~
zgq{1kTh9RAI?bi#awSCjny;r{w<<lsN5oTS?;PeJie<c;Q>j<Zk1z5%p}L4X_x4*$
z?wyr4SHw#X2~C_ZGPHOl!#N#YBZ8jrHkc!S?x?*;hy9TJee5+oGuA3BvW6zBn-G*7
zucPl(pb*8-h^poN%y_$!gI0Y}YFB9l9}9PM-CIV6qzk4zcdsPi`?rlIA9iA;VI0h>
zG%56BRcCjIoZp&uWZZ9JHRscl=bfE<xu<<={$3m)@_#$Op#SjeqB?4B6mr|2H4D!L
zSl@qZ5hr|Uc(rqTJq<NOs4UrN@XnP)xXAJ1k!u;Ym2WX9|F=|^&I@-s84Szn!PP9Z
zaY>$^yytk{xL&7ii0XJqZ=Jd0`xU|7@#=Rd4x55btM;8!#yQgQaxXGZR`Id0A9-!I
z--hvUJX-9ryR<lbOxYmL>?%or4~97$8>q?~n0_;*iBgXFO15$s-fh=XDk6d$bS&%v
zYRX;I$+THh!l7l#Aaiy&n#b5xOn@Dc$u;){Pr<HZmpLn|TDDBu@x}d7)LSo&u_m^*
zA)m$<vlV)$s&96ZWbv;D-xdcCEnUeO8(a8JVS4IB@Fp_+=a3S$@v;i1Ltq4lpQ(A)
z71;w<5MD=<4>qopHePAf=P5rK^f;O~ewG;ZfOkY!#yegtp{TdB!5%vn`&iW7_i27H
zA}g{i#5(w5x!v`rTW?5hRp@MeprgRf)6$-*d+=c|rOf35(vCihCw_7HykjA7^Mf2B
za*b(9ENo8A0x7ssa17{=o9?UAO|6OD?MJ;;UxKT1yz^s>^ilp+GAp`01NU+_EsN4V
zsfF-+uURIIu&>a}?KMo9_7ugE%Sf#)TUcCQZnI2OkIoyea?YL0qdva4vNcgfFZOTw
z0Iq-On7o<0)mow!LS9oxM^Rr`(aPRT-$U1o<PH+PY|Y%DR4tH2^&|62i3C!apiC?&
zDHuZ0&f3}v<Vit^Q77QwvKis-VrBvIzwmH`i<O(5vn3oW0Y`XSxjDlT&LoflMLrE$
zWLq~Y(6$CCRp76kCuqCdd4WGxo>n9n!phFZmJG)e;0PtS1Qz6m!3pAUgc+OwAQm8<
z3k645feO5_g(K`h3!rd>qrit81PP!4sKEnx>jpyr7RXRG*LwQef3yQHj8L|7b0<Um
z0a+j*0m!fCf{CJ#XcSx&3(mzTad2;gLIax)GSHwdidOCxZgwtYXE!)Nu$IUMfi=y@
zZh(G}M<#{@)ImG=R|^b1V)g?Q8i#`;w9K3U!+!}IhS2peC$FX1LDXV?=G?8X&2c+R
zvMpGJ^=vvo|IaT~zelKY?iNta8UYOirC{cwx-Lk-9z-|vMih+!D+5x}Y}~<W0U7)o
z+y7UJ)j&F+orN69#?cCrqNbVG8l8YlEYJmAvXzrQ1Yei_9|A-e{72t!?EE}goPgtz
z$p0S&_WwE$21E&1u$mw*5H5j{fa5WE7y*Zbi%THkXmK1ILqM(-9H0|=5B103QR_8E
z9P0nS3hd{54Co6LXpWx+hd}|gh6DigA1?tWB1pi+K{Sg8dkhDVi$ev6hNB5M@G%ny
z$K&vD91ctZv{oE!IdKU<01~vJ8hVcb1tdp`gYnRu1PKDD*I`h-#tcRr2GtM|5K~aW
z1BHXe;sE38bAY}8?^+w2dC=k#YXv7xSgQ%*fL;K{qtJlCb-Y-B7YDW!h5(Jj!f~LM
z0K$UmwStEF;n#!!)e!!*MS}zgXaItRK{#<>%$iVs5(m|5ng(IQLJvqtL=f2!Yfuf!
z+PXmK)(Z>=SX>(k$@f<S5V$@C8gLA~fv5tM|5SsY%k{GcO9t*8ewzU+j)K@;BOlU(
z4WvRmttk^wC}`Awvp_^4k+8LQKPDgm%lETDim<K&kREI-YqAD<0qMkVWlbL-o%mVS
zbmfOmK>F}|f#_O$26I5V@v}gj<B>S9hJaQ`2R11L39u%RUI2IUd-<soKWj)RVBKMB
zHPrsCtgZF>d#KNU1=0&>RiXOVSNY!$T1vn=1KUD`+yfBeTFVTMf<jvWvw*LCBLyuh
zI9S;=*t#`DL)RuC0eQ{sfVEzGwdRS?YZe`HDq!i?z;<x-n%9He3;G8(5*+;ln=_E`
z`dpA_Tf1Qc_*{U-tdIWT0D)B3CUXM{zTN`1_XnyqhX*#)`ZSOa1UkRgd)<!$v#|CA
zvZ#L`U3Z<}CTtxJyy;rN2oSG1S?n5a*e`?n)1E>J-O4~I*E#$3euMi!0jCZ;ha8yv
zr^oy+r5j-ZqzfHi5pw9C-GC$h;aE58hWXhI%B$YABlJQ>2AcGTH~TFv`33xK)qeqh
zJL>%c{(d8-;Oqe$9-vUe&JqevfI>qe1KxO@<n`?KUt@v8`V|u?fUIn?oij-hN@hQ(
zcnFO|qmcw8sIU@Pf-n*zfJ6!i!VqiyKrnC+L;x<1&SXbBbGRo)3@3&bf!mVFF7AgA
z2&eyEi8;I3fI%Rhvh=V3x#7Q$a<R0Ao10lU073kiLGU-Zc)3|w!$68U1_q4Qj~}r4
zIG{P!@c&>?hz9-Oq#rO83fiN8!o;ya%zyL~7Y73U14e*0)1NR%+x~<BW493op74L-
zKq8?#vOoG^Fwka!e*e*o1X2dNz7Ynx{e>5a0rqfXKM)%J#Rrlg4w;7y^MU9P+F2W5
zfIM)Z`V)o&QRbg8Jftcc$6|qg`~|~6CgYEOc#vQ42MmXT4B5thI4rco|9}B|{1;5(
zZ(48!5HI{O7LWRSEHJl!@r#H2$e&{&|L_M)90_9aKVj&<WdKy|FPg<A@PFZuK>jUf
z2@Ht(|G*)E{hJmEJOLOv==V#92q@%VG9X|f6TP7y3XS|-eq=W@J4Y+GwSRt5BUuB}
q3J8U~3DMaZG9_zmT{~}ZzWH&>W9CL)+nOjeia>zz@hKfuhW#HOQ3DD9

literal 0
HcmV?d00001

diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 00000000..128c5b39
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,3 @@
+torch
+torchvision
+sklearn
diff --git a/train-classifier.py b/train-classifier.py
new file mode 100644
index 00000000..baa5286c
--- /dev/null
+++ b/train-classifier.py
@@ -0,0 +1,163 @@
+import torch
+import random
+import numpy as np
+import pandas as pd
+import torch.nn as nn
+import torch.optim as optim
+from tqdm.notebook import tqdm
+import matplotlib.pyplot as plt
+from pathlib import Path
+from torch.utils.data import Dataset, DataLoader
+from torchvision.io import read_image
+from torchvision import transforms
+from sklearn.neighbors import KNeighborsClassifier as kNN
+import pickle
+import json
+
+with open('classifier_config.json') as config_file:
+    config = json.load(config_file)
+
+path_to_dataset = Path(config["path_to_dataset"])
+
+if len(config["classes"]) == 0:
+    classes = sorted([x.name for x in path_to_dataset.iterdir() if x.is_dir()])
+else:
+    classes = config["classes"]
+
+class SquarePad:
+	def __call__(self, image):
+		_, w, h = image.size()
+		max_wh = max(w, h)
+		hp = int((max_wh - w) / 2)
+		vp = int((max_wh - h) / 2)
+		padding = (vp, hp, vp, hp)
+		return transforms.functional.pad(image, padding, 0, 'constant')
+
+class Normalize01:
+  def __call__(self, image):
+    image -= image.min()
+    image /= image.max()
+    return image
+
+class CustomDataset(Dataset):
+    def __init__(self, path, classes = None, augmentations=None, target_transform=None, size = (64, 64)):
+        self.path = path # path to directories
+
+        self.transform_aug = augmentations
+        self.target_transform = target_transform
+        self.size = size
+
+        self.classes = classes
+
+        self.paths_to_images = [] 
+        self.labels = []
+
+        for c in self.classes:
+          paths_to_class = list(Path(self.path, c).glob('*.jpg'))
+          self.labels += [c]*len(paths_to_class)
+          self.paths_to_images += paths_to_class
+        self.labels = np.array(self.labels)
+
+    def __len__(self):
+      return len(self.labels)
+
+    def __getitem__(self, idx):
+        anchor_label = self.labels[idx]
+
+        positive_index_list = [i for i, x in enumerate(self.labels) if x == anchor_label]
+        negative_index_list = [i for i, x in enumerate(self.labels) if x != anchor_label]
+        
+        positive_idx = np.random.choice(positive_index_list)
+        negative_idx = np.random.choice(negative_index_list)
+
+        images = []
+        for i in [idx, positive_idx, negative_idx]:
+          images += [read_image(str(self.paths_to_images[i])).float()]
+
+        transform = transforms.Compose([
+                                        SquarePad(),
+                                        transforms.Resize(self.size),
+                                        Normalize01()
+                                        ])
+        
+        for i, image in enumerate(images):
+          images[i] = transform(image)
+        
+        if self.transform_aug is not None:
+          for i, image in enumerate(images):
+            images[i] = self.transform_aug(image)
+
+        return images, anchor_label
+
+batch_size = 1
+dataset = CustomDataset(path_to_dataset, classes=classes, size=(64, 64))
+loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
+
+class EmbeddingModel(nn.Module):
+    def __init__(self, emb_dim=128):
+        super(EmbeddingModel, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(3, 16, 3),
+            nn.BatchNorm2d(16),
+            nn.PReLU(),
+            nn.MaxPool2d(2),
+
+            nn.Conv2d(16, 32, 3),
+            nn.BatchNorm2d(32),
+            nn.PReLU(32),
+            nn.MaxPool2d(2),
+
+            nn.Conv2d(32, 64, 3),
+            nn.PReLU(),
+            nn.BatchNorm2d(64),
+            nn.MaxPool2d(2)
+        )
+        
+        self.fc = nn.Sequential(
+            nn.Linear(64*6*6, 256),
+            nn.PReLU(),
+            nn.Linear(256, emb_dim)
+        )
+        
+    def forward(self, x):
+        x = self.conv(x)
+        x = x.view(-1, 64*6*6)
+        x = self.fc(x)
+        return x
+
+
+if torch.cuda.is_available(): 
+    print('Using GPU.')
+    device = 'cuda'
+else:
+    print("CUDA not detected, using CPU.")
+    device = 'cpu'
+
+model_embedding = EmbeddingModel()
+
+model_embedding.load_state_dict(torch.load(config["embedding_model"]))
+model_embedding.to(device)
+model_embedding.eval()
+
+X = []
+labels = []
+#images = []
+for step, ((batch, _, _), label) in enumerate(loader):
+        X += [*model_embedding(batch.to(device)).cpu().detach().numpy()]
+        labels += [*label]
+        for x in batch:
+          x -= x.min()
+          x /= x.max()
+          #images += [transforms.functional.to_pil_image(x)]
+          
+X = np.array(X)
+labels = np.array(labels)
+
+model = kNN(config["n_neighbors"])
+model.fit(X, labels)
+
+with open(b'./model_classifier.obj', 'wb') as file:
+  pickle.dump(model, file)
+
+score = model.score(X, labels)
+print(f'Score: {score:.4f}')
diff --git a/train-embedding.py b/train-embedding.py
new file mode 100644
index 00000000..976b2a23
--- /dev/null
+++ b/train-embedding.py
@@ -0,0 +1,265 @@
+import time
+import torch
+import random
+import numpy as np
+import pandas as pd
+import torch.nn as nn
+import torch.optim as optim
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+from pathlib import Path
+import json
+from torch.utils.data import Dataset, DataLoader
+from torchvision.io import read_image
+from torchvision import transforms
+from PIL import Image, ImageOps
+
+from sklearn.manifold import TSNE
+from sklearn.decomposition import PCA
+
+with open('embedding_config.json') as config_file:
+    config = json.load(config_file)
+
+if config["env"] == "colab":
+    from tqdm.notebook import tqdm
+elif config["env"] == "":
+    from tqdm import tqdm
+
+path_to_dataset = Path(config["path_to_dataset"])
+
+if len(config["classes"]) == 0:
+    classes = sorted([x.name for x in path_to_dataset.iterdir() if x.is_dir()])
+else:
+    classes = config["classes"]
+
+
+class SquarePad:
+	def __call__(self, image):
+		_, w, h = image.size()
+		max_wh = max(w, h)
+		hp = int((max_wh - w) / 2)
+		vp = int((max_wh - h) / 2)
+		padding = (vp, hp, vp, hp)
+		return transforms.functional.pad(image, padding, 0, 'constant')
+
+class Normalize01:
+  def __call__(self, image):
+    image -= image.min()
+    image /= image.max()
+    return image
+
+class CustomDataset(Dataset):
+    def __init__(self, path, classes = None, augmentations=None, target_transform=None, size = (64, 64)):
+        self.path = path # path to directories
+
+        self.transform_aug = augmentations
+        self.target_transform = target_transform
+        self.size = size
+
+        self.classes = classes
+
+        self.paths_to_images = [] 
+        self.labels = []
+
+        for c in self.classes:
+          paths_to_class = list(Path(self.path, c).glob('*.jpg'))
+          self.labels += [c]*len(paths_to_class)
+          self.paths_to_images += paths_to_class
+        self.labels = np.array(self.labels)
+
+    def __len__(self):
+      return len(self.labels)
+
+    def __getitem__(self, idx):
+        anchor_label = self.labels[idx]
+
+        positive_index_list = [i for i, x in enumerate(self.labels) if x == anchor_label]
+        negative_index_list = [i for i, x in enumerate(self.labels) if x != anchor_label]
+        
+        positive_idx = np.random.choice(positive_index_list)
+        negative_idx = np.random.choice(negative_index_list)
+
+        images = []
+        for i in [idx, positive_idx, negative_idx]:
+          images += [read_image(str(self.paths_to_images[i])).float()]
+
+        transform = transforms.Compose([
+                                        SquarePad(),
+                                        transforms.Resize(self.size),
+                                        Normalize01()
+                                        ])
+        
+        for i, image in enumerate(images):
+          images[i] = transform(image)
+        
+        if self.transform_aug is not None:
+          for i, image in enumerate(images):
+            images[i] = self.transform_aug(image)
+
+        return images, anchor_label
+
+class EmbeddingModel(nn.Module):
+    def __init__(self, emb_dim=128):
+        super(EmbeddingModel, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(3, 16, 3),
+            nn.BatchNorm2d(16),
+            nn.PReLU(),
+            nn.MaxPool2d(2),
+
+            nn.Conv2d(16, 32, 3),
+            nn.BatchNorm2d(32),
+            nn.PReLU(32),
+            nn.MaxPool2d(2),
+
+            nn.Conv2d(32, 64, 3),
+            nn.PReLU(),
+            nn.BatchNorm2d(64),
+            nn.MaxPool2d(2)
+        )
+        
+        self.fc = nn.Sequential(
+            nn.Linear(64*6*6, 256),
+            nn.PReLU(),
+            nn.Linear(256, emb_dim)
+        )
+        
+    def forward(self, x):
+        x = self.conv(x)
+        x = x.view(-1, 64*6*6)
+        x = self.fc(x)
+        return x
+
+augmentations = None
+if config["augmentations"]:
+    augmentations = transforms.Compose(
+        [
+         transforms.RandomHorizontalFlip(),
+         transforms.RandomAdjustSharpness(sharpness_factor=2),
+         transforms.RandomAutocontrast(),
+         transforms.ColorJitter(brightness=0.3)
+        ]
+    )
+
+batch_size = config["batch_size"]
+
+dataset = CustomDataset(path_to_dataset, augmentations=augmentations, 
+                              classes=classes, size=(64, 64))
+
+train_size = int(config["train_size"] * len(dataset))
+test_size = len(dataset) - train_size
+
+train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])
+
+train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
+
+
+if torch.cuda.is_available(): 
+    print('Using GPU.')
+    device = 'cuda'
+else:
+    print("CUDA not detected, using CPU.")
+    device = 'cpu'
+
+embedding_dims = config["embedding_dims"]
+epochs = config["epochs"]
+
+model = EmbeddingModel(embedding_dims).to(device)
+
+optimizer = optim.Adam(model.parameters(), lr=config["lr"])
+triplet_loss = nn.TripletMarginLoss(margin=config["triplet-loss-margin"], p=config["triplet-loss-p"])
+
+model.train()
+
+train_loss = []
+test_loss = []
+epoch_all_loss = []
+
+for epoch in range(epochs):
+    
+    train_one_epoch_loss = []
+    test_one_epoch_loss = []
+    
+    for step, ((anchor_image, positive_image, negative_image), anchor_label) in enumerate(train_loader):
+        
+        anchor_image = anchor_image.to(device)
+        positive_image = positive_image.to(device)
+        negative_image = negative_image.to(device)
+        
+        optimizer.zero_grad()
+        
+        anchor_pred = model(anchor_image)
+        positive_pred = model(positive_image)
+        negative_pred = model(negative_image)
+        
+        loss = triplet_loss(anchor_pred, positive_pred, negative_pred)
+        loss.backward()
+        optimizer.step()
+        loss = loss.cpu().detach().numpy()
+        
+        train_one_epoch_loss += [loss]
+        train_loss += [loss]
+    
+    for step, ((anchor_image, positive_image, negative_image), anchor_label) in enumerate(test_loader):
+        
+        anchor_image = anchor_image.to(device)
+        positive_image = positive_image.to(device)
+        negative_image = negative_image.to(device)
+
+        anchor_pred = model(anchor_image)
+        positive_pred = model(positive_image)
+        negative_pred = model(negative_image)
+
+        loss = triplet_loss(anchor_pred, positive_pred, negative_pred)
+        loss = loss.cpu().detach().numpy()
+        
+        test_one_epoch_loss += [loss]
+        test_loss += [loss]
+
+    print(f"Epoch: {epoch+1}/{epochs} - Training loss: {np.mean(train_one_epoch_loss):.4f} - Test loss: {np.mean(test_one_epoch_loss):.4f}")
+
+train_loss = np.array(train_loss)
+test_loss = np.array(test_loss)
+q = 10
+plt.plot(np.convolve(train_loss, np.ones(len(train_loss) - len(test_loss) + q)/(len(train_loss) - len(test_loss) + q), mode = 'valid'), legend = 'Train loss')
+plt.plot(np.convolve(test_loss, np.ones(q)/q, mode = 'valid'), legend = 'Test loss')
+plt.legend()
+plt.title("Epoch loss")
+plt.savefig("embedding_loss.pdf")
+
+if config["visualize"]:
+
+    X_train = []
+    labels_train = []
+    images_train = []
+    for step, ((batch, _, _), label) in enumerate(train_loader):
+            X_train += [*model(batch.to(device)).cpu().detach().numpy()]
+            labels_train += [*label]
+            for x in batch:
+              x -= x.min()
+              x /= x.max()
+              images_train += [transforms.functional.to_pil_image(x)]
+    X_train = np.array(X_train)
+    labels_train = np.array(labels_train)
+
+    pca = PCA(n_components=config["pca_components"])
+    X_train_pca = pca.fit_transform(X_train)
+    
+    tsne = TSNE(n_components=2, learning_rate = 'auto', init = 'pca')
+    X_train_tsne = tsne.fit_transform(X_train_pca)
+
+    cmap=plt.get_cmap('tab20')
+    colors = cmap(np.linspace(0, 1, len(classes)))
+    classes_to_colors = dict(zip(classes, colors))
+    
+    plt.figure(figsize=(15, 10))
+    for cls in classes:
+      plt.scatter(*X_train_tsne[labels_train == cls].T, color = classes_to_colors[cls], label = cls, s = 1);
+    
+    legend = plt.legend(fontsize=10)
+    for i in range(len(classes)):
+      legend.legendHandles[i]._sizes = [30]
+    
+    plt.savefig('visualized_simple.pdf')
+
diff --git a/visualized_simple.pdf b/visualized_simple.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..6b2750628e5970bc1c9f679ebe3c6aaaf0c68f04
GIT binary patch
literal 23115
zcma&OW0YmfwyvGFot3t2+eW2r+pe^2+s>@CZQHi({A!(j&i(ejceT^juQ5loz}w$w
zF{6($=MyCI!lE<`w9HT>l^ggKZBPvO^!T<07Es*W_;m96rpAu=jDJcL@#&P^?Tqp1
zg!G;Ct!z#4d3m9XZH)e^$o_8)MBJRjl$`XPjPaTO)kxIV#tEP4U!Udmot%svZ17qB
zx`m>Xcd#{7GIqk({4<WQ=%4<L-JJ00B&`38Dfr)4q5r;0;A`X4DVgaTncJA+v;M2O
znw)`!v7r+_+kbWb|I1<c_W%TJY;2tz@fqm<r9kM98e^M31swmnmoYXn*B7*P`=jBn
z8VvO8^!RLy^nZ;eukY}u=D%WxqEj?>v~_kc{A0#n(f%JZ{_^MFocOEP|FKTg-0DxT
z_;jLHf7}o@HncVRW1h6Jjj59vKEuBp{iE5@$-!9P8p<uRGfO*$delDq`dIa$$cdp-
z{q<*BO1?P_eh+#+#Cab*gvalEVLFP?K;e_^clZ3GiHFRnVpYH0B*>ikk~U5%Cf==3
zH=?tQ9N+Kj?~l#r?w2Ri=I=XKx^1tI%XYpl?B?ye?(L1S%jS=3x#s;Rn(y~Xsa@M{
zPmZxWcs?y(_6pyRyYKJ$@o~N`uX|Ok4>8);r$e)Smn+Xh%lFD<Jl{~hsimv=47<S;
zzTxKY$EUBN#i@0S&n;Wum#?Si=I!mS*NPvmU3iZej*}j6UvtOnzGS`}<7yR63eV$s
zKK<ps<KJHo$ldnugUvbBUhY1Jcs}=cH5YMM_8U_U8_!43?<ZB;Z|ToEL)Bu(qc+>0
zS5K3^RLwcx<L{5}EzS2QAKOQey6aszUd>5Y(;qK)%Z^(yy`9m&hc7Xnf4_Dwx=dG_
zP)%<R**CS>{N7$>ALbmcQ|XEygiG<RKfc^jp&KqOz+GiTTUR(&@XWe@(okoC?;JBw
zA3Zc}$GhiVu6EuyYfIUZ+H7yU*jwIDeYQtg+K3t5el06d$#%ch<m>t}JN~pX-JS=%
zeZ1dt&9YAR8rF@=Pw?hw<w3d`tC?f~8$UOFt339mMRUwP-e?|jk8+-v<@h`}bFlro
zQjomg3P+G~^yX=It#BnSz&^CX9<#)8#dX4Ul*1+Am~qw>GtC%3TAlLbp!#l28yy6u
z_{n<28I9#CIs~^+TH<Q5d_9;^b)PCVidi+?^)CB7<9y9B#xb7V?4e3^l=>(xmxxpS
zyDr303%v1&^Blv|D5YUx$wM<B$>yoh!JR|>eRYlp`@U;TFNbGNvs*Q>q*^HL`*v{?
zw#G7--`c&&u%2A_cH(MtW-9>J7CNfqx4%|f#)wTD6?lE8%C$GkSNo(V566_k`$(dJ
zp~4d<bjn@oiGx1orkKPa!egdq;*=_<@A@-?+ZeLB__J1)Qyyr<f^E|8%AlR5+4{kW
zKEy|#DV1k;A+$A}&Mb^%Wi^Xl6*G@mcv)sRt4PN4ktZ!wB_oa*d*;EshHvlO`m(6`
ziECDcQU6MVCi=e4qBdR1Hy1lc;saqgIcY{;oHCm>^U%S{sz`e*R~KIbjfTQ$H$tok
zSj_v2$9R1ahvYcz=%*X_#qFB#pTlR>?s}^65p~(WoeORBk!??a8HW_IuDngGX6X*#
z3s_>yRY@>hxfr}wl?ZpCA-9Q)o=_2a)qmtW7iz9tac?oysh<yhnQ7hVsI^=W#N}($
zWt9S&bZ>9ESDMHepY3cfGAHwVW^qP$DZFirxjY@Lgh2g@v}4&8VlgF^cSC4AO8pJf
zIW35KlUqW{$MZCQo&p99?T<H5pJeB}TCZE7>7U&=YM<*e+$$T?X<<S@ucF2v07Qrr
z{!5yc=PvC^0uU#2cvKym>Vr*iOR|@uMC!CfkUI7Y3cs*j%rCC~!;Q5Xse}id^4D`3
zyXXRvh-RyEKaH!8`_60da9AA#2baU|%;!!!%Mo0%frN5cZ@kmR)SR%^KAv2wEv!~`
zLmOO{gw-O~vz7j;pF<k3nV0J5CY-3r9@~TrE||Z?W>(|@#=`(5I~PrsU!DCHtt4-I
z>a&0^;wcbV?|5VBqRUW8a<`Ibe8O}kREc!bOd<ILF11RXFJaZEscG^%<Fq$`;3QWw
zcZ@;Y4^4b!J3iJff`{xj?T<@d=pSur5_E^dCXnCH7K64q*y|Qxeoxs#lLEF3gR1mY
z6dd=hV0q#~9}`~SoGAbR;PN|sutUrwS!QGV@(8qo1`R<U$gPUt9m7mdw7sjL-ovnZ
zw7l8semy-U2m*0Saj??rhYkOb4}hoit>#LEOnBrVe1X($0rHQ5*3H&=;lO2QMsWP)
zwMl`J0>1fDabIbR<?2PUb{H+fA2cgf`T5KF+ycA2sew-U@d>~g#@7q~tR)H7?_)25
zPkRf9oq1<1EaLbH!c)kA-$A!y^mskM=$d^Sxt)9QfMfw&c&~l_Fp&RxEPhp*8qJ|I
zru<y4`p95rmLnXF!vL%5IL~o>c`nHe2(~aZW6PAn@5S}}>GUjmhTs<^))TyI3>-n%
z%JjwAaxn33`zi~H!gDtZL;h}ou@ra;VW1l}g8uS@Yn63b`QVs}FN_PA-M0Fzutyyw
zh%~6@CZoWKnkfiOgqD3X#GCg|FVLs%l9UFm4LfwoQ)GM1oy?$VZMkU3B%|g>*b8qr
zAE2csK>O2qGzKl;o4n;q*n-Cw2btz$X3jKzH=v3i?elUupt0GbB+#52b_GR(6RjX3
zlRd8|tDk^<?5ROo$^Pjl5_N8X5p$2g-0JkS5-EznX!e#1h|}4ZWQZ)J5%Xd+0I*(o
zjZ?q~LD;Kr3_8>pzN*qNxliP<zc6w2k2JU{zK~;tv7H2zcm<;}z8K?hwGOD&&PHW8
z^sLz8mhr!IS}`erRBn=y!n~_oGkZwIj~D7Mh12}tXfZ*8k%fF_c67v~QsCL!ESY=S
z9fKJ+7&Z#rwOT)RM8arR`$k#ov|Zs-U<Y%IVk&-WZdgMaEqYOd*2#$gJx@I4+z)2u
z3;`~F41y)!ip8o?T36$32ktZVX$$dhO(qo1sk2Rklk-j-Uf5-VfPGjmKRoS7HF~;h
ztxcF*@RPeWURc1Q8d$Q!0<`JhutggqI~S8hZ~)9UM6@sE2Ve*;^c_}})T5<CoM?&Y
zdS$k_$ApKnc`w;O87>Z<KCQg=>xmM5W<$SN2&6JDsMzcOY<5NnLM{9Okf_bqsvo5X
zX`Gp-uGRJ_Do1mVy@w$PSzYF}OWZ>dF}UDjls^N4w`0K{gGnTu!za^spo}7nE6y;5
z73zkCvFWddBya>D5x#A222w&q&&qnhdvlzHN40v#&}W9m6=N>wkkK0iDMC2D05^1N
zuo|u5%x%MK&zegV1U<a*0&3)DQ4~tGsbr-fby!b9j(@W#U5V`qz<`#&xZsaBBrpQ=
zs}kKW&y@T?-4VJ10~tXtJ}3ujN|5?Sg<KYjW<@m72ssE4Vdc#yjKxI12zQVkL(TCA
z`h?{%ygWK0m0axRwlGnMe*k}M3{@4%3`z4&3%Qjo=r3VfxKa;3r<OPqiEG=Dwg-L_
zffVynqK|uIp3{D2a<<`#{A1y8zo2TYv8yeW!fUmwR6=Pj1x!*JrnKSQuycr=mUhXJ
zvB=!npbv-S{I|?vPof>5_XY1G#iLm}k}2#%{`~9y4T`STBB+QU0!GcfgR8FksK9of
zXqp73NOM#TY#_y=>iU2W{xc7Hx-a%T0!jM}K~WdHaVEuPfD5j+=^;>mnEV<R!W|w5
ztp!3@S*sx*A{LChy)vY>=nrW2BcIgYJC;^O2K@M;5*ZYsgN*?T9^8JIzJWmQ<RQ3(
zNsL;DDl)P-iS{fyD3F^(!y`Rp7a^<Wze=$R`5~kf6F1a&6;b0b$(Kels!V;^FE>``
z?!!Y-HQ9;m&dGkTvdoO4*P7g`Ma>&4@xVEOpmK>`trN1sA1o*!GD#1#Z(^H!IMTxr
z>~WxnSaoH{i0DrfEvkB<cs4_32^6mJ2~`l5SGV4Dz$xU!gb=Cw_1HMU@}Ct?)a$0E
zV&5REB&~~@1?#<>h*{*>^`tTv;t1*4bzh6zyC8iQkv0)TVJJ!e=*7oJjtpbKc3WS_
zo$NmeUm4$LbjSdaJxcemK?dL;H6e;VG!NP-jeafEdWeEtfw-UpMA#DZj3LZv8Fu=%
zujcop)*)KhVV`O@j{Y?{iQ~)0P27jI!72^xasnWH>zSLSoZ4JlL8r&AKMf?5-XP%V
zC?EcdE?95Sic~SYU?_RHPK@&XWeqCcz!wY`O4U_0h-SMe^+$z=#XewID&<e_?O*LL
z7O|o>Kd5zbeMuy7UJA^cqR7M<c-sLxtguj8V*Fk>HE_9`$3p1m+du^U08%g0QXM<^
zX+J{3j95)Y1q4OJAi*L|SC_D0BVbJ~rhfVWd_&z!Mmx%C#WGNO0*msdV-i7!hblJt
z1g5Qk+hZ*fv+rV(Epy$)6{rErK8S}RkB%ud2y$n>3$eKF%tGi*5!I)V-9o<m6!MRq
zRIxRpH5MQa$2$Qr?bR`-yNmF#t0zE~w1zH=gMxr$QkV_Mz+NH^$E^41bpcvA?S`fF
zw`r7urC6-6`p0mA?jN6*sd-nlTdS|_m~WB^t*3ALc<@!iRNG`MAeO?S^rBc;u(J7g
zbtIFUi&6q4vW7X$?p##%ETB@2R__J6X}RD@MMqS~38!6}?oZO-X|S?|?(_!U+zv<q
zyRj?4u-Q+vU5<DG%ezE-N2xG8WFQ!y9Jcj^C1X{W+y$uYmWR^QH?P?IoM#qIliM5Z
z19qay4G#6nva>f!8%}q30dXs7yq~Gm_|=B>UO>qrbN`cPnKfzYM|P<@zF%k3RkSm(
zm%)OjDf;&A^yI+w0-OfFIx3sTN#I0bZG8fLKA-&=`rVR}ri#a4SemTqwN$!?`=T5^
zOk8B!Zep#%017p)T*W5C3U){6!VU|go+x4&tXP3t+Y*8_U>0j<7|ZI!!m-6+N*r-$
z^k4=JYQHKQTXQ;W&+i*cE1yDhP8V=!w&@EGxDU9S!Rt&?Q209iu#oA5&SKjtz$kr;
zK+MKU6`2R_2<S0-cvV4G+Z7z*g4D4|o(KBX$dFRgGKIq!WrjT8vs(TD$*CsXA`KDq
zZp|U?KV4D}Hx1M>V3)8Q7c1(Q-4Q3`xf|-zr*l%*A-(aqP~H}+5+#Br2}8PAIst@S
z7&W2Ydy<MkO!OwdC!Q;*O-ds#)>y+TCt6NoIEP_iNlV=<&pRpG7E7^dT1zrP;dAwM
zy%CSjJ~rviE6R`vV+0<m17oF_D1`o)yjCZ*r=wA<x=|zP&PE+E?#V-j8(~Yu7@$$p
zWcs<z3KN1T?X?%#;Va|`$W8N`;NqRfpMEm+F>O9_SRai@JE{;TI>8k5W}|IX68Z~U
zy!^llyeH<oSzMkFY|0za=<t-ykvuts<BMLQr!Qcn+rVc2ptT2j+!D%C7)%-@*mY`?
z<_cGzo>yDImtiN0v3xqyLs;uv+bpmjDRpmZlDS@~%sXiz_hM}5R4cOn2^i%Aq%~F2
znqP$q)x;UXZ)Vr0m_P}2z`=g@w@6Y`z>~Vy4@LqO2#*}Xuuhi+R2Vz3JMV)f3}w~A
z+O+0SEA$az&)@e^=IJH*GU<<Frx@4>ow(*dK7I}jGlCfK#I^Q>-hemWNelTG9XCC-
zh;~B2?XaH5bI@}V(r9Th8R!uD&~Id|ZowPcLlOhf5n4y9twWBPtu_RYAUEH%3<PS#
zQk|0%kVL7ZusA+-x(fVYR5`~Qr=6}H9~H!w8QC992^+dE7cZ1F;Dl{ila9}^<&LAh
zJ{q}CWjDz6J-|)R%rO{5<L42jG+O}%QJmPU1J|kqk3<nYx>evH7JeMc-K_N%v6;H1
z_FOEa8f^ve7o&_#0P;^Am!9V&d%^l7hO;Ha#*wlO2KVNxdjgPb+!*P=R=~wbk53>Q
zyLF1OkZnk{k;N*b{h>19UQXIK3q=ZcfH3J4A$4cuLdoIAR|)=B*`Z|)u~VOvPBchu
z&@Gy&|0$W3#1rV>kc*9kn4)qIXVPUKG+^(yd@0J)00Hbox89rhLr#YofwO%f<^jQ}
z@0a2>C5Oqv*x9u5#vY~s<yKXy|GvHOQ_*PoB<?e9K{4dCs=UzI)sE7Qu?9{ox0713
zas@kEEB|`BJ1zuvDJXhCRv3|t4-FHDX?7zl&~f}B^WKwyjBqyuvqLXOZEUK0fmIW^
zS}OGUrow2WhW;C@p~gX<&LT)zoLX5jaHEOLr<IjXEo*U+mrz~22@12mOpNV{?aT((
ziYiDv_|FZjS{9>vkzaahjs$iiop&f&Hp@R48WsTT$;qjIv)LGP)tO0^KmnBExbq$q
z9my{!LdKr-oScByaR|qTJaKT9n^>%9;l!1r$_1d%2rjHU8tMWmp@874@w&Yu4o0K+
zFL<zJ*t`ui(p$_$Gf`_;09EOZaU|$kz-;(e<Ac*AF)0PD1L3eb2BU8r@r#`pC7*KA
z-A%E6V;VrQ_p_@Dc^;)ygfRqMJO+U}jBIl%QW>m<BLFaPv$)6k$AV}_C#Qqv_nGvW
zftluOM`w|H<WiBq%hK9-acwT3fdCf4<|ZDgKtc(CxdjCjtOSRJ>X-`%O)4>uK(pmc
zVk2{z1c4|8g3BH%t?45^{ET%-D2i@nqxDpzk9q4N(UBro)QeDtcAQNc6hrPc-3k2V
zT<gC+;Ah<BIM&6Qb`G}%l?xUN{IH`hTl;L$3e18L`z2b+N^75vl|UtQ2Pr#$MIDF7
z7$VkZami3%k{$dzT&f>C(tvq>4^V#7;b<1pG+K)HH>m*aL1SeEg_V2$CqiVd#!;>R
z6POvwRZZrV0W1_V9Zvg?afnA6{9{aLrK0{4CU2i~%EKKntc8XQ!=6TNWOxRKx02Kp
zvyODGX+yVr{k8I9njU5=WDD5LEK@S$yd0n)lIw)nZ71<<vFbl*&WL_?&OY0wL6NOl
zsbdJQt8x!y3DnW*1ssIDZRcEJE5e)brEC_V1z}t2T&~I);$c~&aoCi98#6i(Fp#;l
zT(-ppQX72OJ(VPsu(6HJxPTS>)uGB9A)QHU*&d@bO;HaOH+y`jwO|!IvS%@_K#4nT
zdD&4Y7KB{;H&mrD3piI&il2vog4#_zI5`S9SEvHM%0ykIJs)ji1aar7A|lkiOWAI7
zY1c2XAn+sh!%)cHOL;dzxPc-uCSkVvBm>pQ>G&hX+>2{fXeVKuW}HZQpn*k4l~k9&
zc@n!E0U5sE^{ILFIUtDR?}n_?<ghuc%4#v?RMR?n^M2v;C$Z}pAgOajCZnwh9u%4-
zwF%kPuT+8!`XXl%#cLafwbi^p-lz?<NNHAAV@)LpFXwU)yB3A>ZI~rCrZP55=|-|U
zBq?l`7}`DsIS0hWRdTBeP7y6&))WT!%BY|<eFlZgRY?k{QIf9>xOXP>7TL?p6#iKT
z)@SPBrRgFNdkoZ3%0J(gjQ+fiP!!y%2)g97%iFMB$wOk!4Be}3cUSD%HXc(2e8)`f
zg+Py*Ze{S-9e5&^B+2YHU#TPoC!va{W9E6Wd(!M^H5vli80Lz2w}g*MAS`Vx$P=9a
zN10V#U=E^V%3U8?h|Xnj=b5UD0G!*?nJpPTFBgYXQ-jv2v_6&vV#lh|mQ-c3rJ*{h
zt)XXuNkyEcC^u@KR=A#RH!O`cjZ%Xdi6gfO{>gPU#8%9jGeD7B#t#kh(?TT)$8xiE
ziu;>`Ou--y#Hvk@O8~@-cuuJuyCZ@|W`KSc24Kr<oKqHF%0my5@-+O(-*^)T=lG3@
zjo27##!#ciDx4arcBszPJ6>n-EQd8O$?TZHy_V&*ta#;|o><c#t)N6&_J;Sqs+ZKV
zsC=+OzxxL{z$8tXN}8BWyKog^z^Zj&1Z%+u18AqXUng{Ya3n1U1GZmOW?3^MWMt&E
z`!2RQsbEuMu63w!bdR7Zhku(t8Mta=ylT}=pF?V(I?LH2l~w>Lc?=o4fLIi9!>H(n
z+`R%InfU|(&PQl`S|TKq&UqtaP`8`nd)@MBBAT~WSSU?#GW5fQI-)!Q{nLzai<Fap
zl(t-LUuxYe05*c`P9yoH+2&ng?Nw6gM4(^_bj*;(G}nA(!v{!&mf@-<+A9}7n16s(
z@e}-Z{~FOK3L@t1BCBW|Xx31uN?LQ`jlSTGd{EdhHRBN)6NtOywSsw7p0cA=@T0lB
zAt?B@g^*H|3OScUIOHm}2W$hDXsp_*cilH4PLIMU=98*=r#J*S-G=|(cqbsSIpTJq
z*KmC1T~<DO(Ofcx#)vh(=z0?`lXnx9+VetajOhfK=BZkk6Ew_H523i-1k+_Ud~fQh
zNtwBp?I7M}4TU`G77;OmI@tS~FcEya!*<6d<b#g0whZ1#x)lU^Dyi+I;+#%KQltZ1
z@%G(x0tz7n$%sgwB${nGhML2gL|jDICP=a11!T7F$)*?9;dG}p_;cGDIkD8^8KQ8i
zwuHiCZ63r=0?EzKsL1dKq6!kRVv{ccTo<XNpeFHZ!SsNdxVH+WLY+%}RL*q0-WxNm
zxzu_M(&lzJ=lX8|c9!B5o2d<gA@oQ!bwNmWR0<m@3J$DA1oUsC{^2|t5VABvN{Y0m
zC_OJIM9Q&Q!?s?9n=5yJTq+Y8D<UA3$;E^D2S5SaA9bQ@%5nzN4)pvJ`K<e)-0Dg)
zdaJ&K?HGe(29X#9u-^qRw0Z6K-_mcicwg9x%J&!J9dq}&TrgFI3b;bxnN3g96*mxH
zc{JT`-xu(B$I`FwXk=7A(CP_G7!h@$joGtPa1lpLeMrevmT(c~S;WW_{j{?DmEH~Y
z!GNpcK2BV`kke#++n{tTeR5$%J3HA_;e~9OTCu+j$wQXFQO)JjCkvw_%$P2i@2h3~
z9q&-omCpU-=R2}oO2jm!XU~ew>-8c0Vyy+<LApbuWtHrxs(LyJb)V)j?`)a4+9Lbk
zwx%Fcj&ec6E(mt0+nk;@4B8)_bo5m7NxJY|Pl#^FjSv0HQ1uMgKmzfacev2p>6pAV
zxcsc{PpF(M)}9p#XO3|c-NxH7>*hA|%*ud>_cz5X@+W|Q@%40wOA@PH@1X5b!SpA{
z@+onVT!2Z2I=73#Zif;%+r?Km)<S%vA_3?W!|OVbMDikz(kybTOG~k111k_=#;PNB
z04ap&pEaq_{1U%NbCt!eU#VC~8c`T~RYFrUguBKDHqb1>7AYGD++4a>g~Jt?b+vx1
zM_Qp}#uu7q)RIj|Z}X}}Q^7b5FIQ<mR$VfFgT8YfUv=h~buY*QYD)`{l^`MteGz63
z{gy#rt82f<RA19dN#nnVk8`1}2g9+ESLCf_20`BO$f5&5V6Iz3Pqy`qLO`7+|9&>R
zLz?o-XuTTRK*J`tY3PdTinBzb8K=<7=n>%Rsg9lAN1eixTM^pyc!X2oMQkX`>(>-}
zK5IA$nB*IbnVmC1bpX94v@T5IH$+4}>_iBw2ph$dGsv4Kq>FhM)lw7i6Tnbh<4eA@
zS#nWNOh+=Zx?42oft<O7Ph^LmYEMNWYyqxe@l1b)U6;Hd;424d9}~`m&k0F(U>@HM
zOl4xc+W8fBnDr<jp#`FsgaS&fV6RFUo}Xz#C#(5us7xSj7N;eoJk2pAW(3+5C0V;u
zTd!%YqE(4&(P%ooJBcGWuC^yUV04i+|A$hj1pygdlc;qVluP<&b3_>j>F`mhyTH21
z`*$Uc?auiW`b-d9iJ}(D0LAFRNCKTUWBCM$r*xD0jPnY$QZ_>8Ec?ERcIkos=U9Bn
zmP5DTi(3ru>|#KMm5m6S9-G0La0Zi;i8ofgBeaDDq>h%2cz&*2E;#4B+9o}W=djNq
zg_8iZ!dCnp0!#qO;(+pEruzl4B)(!~U!Tk<*%eV@6-kMcMOx-WeWi(pWqF8;M>MvT
zL-by6kfFM$CXgc&Y4o0W%6?{aZ?|kldltr^2`<AoIbA`FS=%bBR1BL#OH@6`Pjee6
zX#CQICW}JK=GbvVB>A>)OYj9(J8@|1GM=#~Dw=t!Zcu^RtL<t`#82f7i|l+dFYr%X
z#^R+maX^Mh<C+dU2MTQlY5X4{H1B%~G;Eb9Aa=|fcIi)cXYs8qdUF{;qe1~l*4R53
znHfHxPA~qQx#ss(Yvr2y&2%}9S#eF-%%eNi#0BCH9EaPi8H8CVig%nm`l1N&!<8nG
zg}(@yKYkZsvNp7glZNWUA6N>@2&-djhsRK3H_fwqF3{pa6WfTMj2ckuB=z5EL^3l{
zSXNki#wYMA1o|FByk#e9L{H*gV<Xg4AT?Lq@FMKiHyTUH;Ii);^v(yKGR0%R1ivT8
znCxULv4)?==@^2+3v4Yqy^k3tNyRg8hUAeEa0bg$ZhOoz`D%s89T-k+?<HC{Dyk_p
z6N)sa@AUia1#xRA;Tv9M`s=?RGqRZ9-`Fz@7QvUAnfu6y4V*>6)UnjJv6|Se+`k=w
z`>{`b?1|&>4)0H3EE^GUz54g+U>-q8KSOQ9e723AFdOTogiQ*f4(h}?vZX-}tjpbW
zp~jpB`W<Oy<5B>^?U$Ajk@(p;<k0gs>jv;66}2yfYr>>Q^Dq=F5s~QAjDYMSyHWE0
zV1iP-Hz`<gfsnU(fE28I9Vr5hv)oahwcyDUTWMJ~@DRc<daq<5`W!h$kGG>CwK_%j
zVCS-fQK=j$G#LgN3#I<mX<oek@Xer3p8`tZJ&UA7SBQsmzchD`rd6hH1>H-=y0o{@
zRAkGikBrkR9!u$xZbeARn}r6sHuc<vBWQUnD_Isz?!7uV1I$TgV&^za0BE=GQN2sR
z4eNF$aU#s`h*u2Ao_AuM0DDc1xanepm0lI0wF0fvrxHM8iex`xZTj0#!`<;D_~fY;
z&~F`&Ms64uGjC8uoI9!0Yuu)pwt&xb&-n7NRz^tNb1Vd0TX(P<CJMUEDFQ@DwZS8X
z!w4fJ+rXqsZj(Xe1R}i!#^pVXn&1Fq{Q)H1MyCQAOV*zwk<|9e3ay~Ze3hePD|CCq
z>)KR!w91jcG>k3<|5Tn^pj<APpSfTigHd0^X1Ma#?!iLlVQW@~p6HA@%M&T3`x76}
zDOZy#2x{0arKd;{8V<XXb1#xAK}%eYc@)NOSVbuXI#g0;##KEdm7}~&wZ|(N2iZBH
zOb&a|KKqR$NEz49hiX{Y+@k2!D`H|Rbz131v<RaB^If%w&k3m0MA9R(Y!ny`W(U!H
z^K#USgoIQ&k0i#l7<qCxO@U~;zVI#PqVy3}HrJn(23fsnSB&*X36)kcVDsK_iPnC=
z5B2+|I=%QhpdCBl@a~gT^1rPBCpL84$Bmo2A)I-Luc)Ooo65O*X;CrP@!@}|PK-Ao
zL-(xmtZNNf{n!OR=otsaWQ>W_7;BIqYRPSo`NZQgaxdpX0eMaWJ3*H{YHmiRs!5M!
zk{`M?;tURyAxn!gIbAKIoQ+v4@TO#Wn?zKWMMe0E3T-P1aGHMy|L!ky888+q#?W77
zmmW#Vly(B?p0DmfLdHKN#Df#(arj;T`PK-!Fq^O6>pVUG6+{*pj=MqX515q+3Cq;G
z4x@-03}l+}`xvb%2=2nc12IobBL{SUgYN6b%<tKncDmQp9jWJ4(|^+)l(CnT2Pc<E
zpG8b*fCa-2rdF$H{$LO7eA(+woeHe#n3Cv0Bp5vVY=a4p&BZxYLzNv?L)Va$!j5i;
z)o8C<dU=pKWF*-cvDc&~v|-N5sb_r8BxPx<K49^Xd$y(*8Esk@s;!^;a5pa}R8Q7;
zhG@OJlX|wiTUJreH~U~e61hf0th4-mPiS?4KfMEbpSY_p*S_!N*0QIlDAshLJ|DpL
z6j8R1>tL5a0l<$LdVa!2Ly;=_+TJRJM_K$V!9^h9el907v)*7X5R}#Y%e-lhGG1G0
zytp2N1|ND3XYBNRFT*%Q+dg!B(Vhmy#GM1=s&_<RqSrRUsG*=!nxM$OxmwUd$6oo`
zG9^xG0v5n+uE3!E2tjM5^`;Gm8oMFu`QANwrOC#*T&<YX7^<0LE;2ApMr<24<I)Aj
zvVNWz{>@{iPZOGQnS^DHZ9m_uJa&L6K}yzzt44GPW5YN@t@@ZUd^1!K0A-f<r~0fM
zdt~t-hhvo9#B^A%m&!(FZPpNFv~JTmkY-nYHo1^LJ5>#5otLK_P2WN)PY0XXI;Ss`
z;pFak#@bZ1#*!`ZiJoUpVjzf4OWftnRYU=K%3Ul|rtEj?I8Qa4+=;DWDg3*IY8xAd
zO~oAmR|1kG4-{d*LzoP`f`l?*_FJFI5xnDX<v_#Ojt`7uD46xMS+!b5$s|*;<T9$=
z1<WY1=uV{LLy37DSUN+4#sWynVt-tPJvrAc^B-!Sfg7QtPy1IJvpjcu(8J>5qBABC
z8g%}6>t?otaZgg+qC)z0=wpJ9^fbJ33Ge1;YlTglgA7RYGve#AwQC&R{9S|bm|B^b
zNFBD2&Wv$NwaAx?a1}MbdIOfUq*)J^SV+;wwTMMK2KD5yGpNs%4Pzq4_c9xCnddl4
zst8_FnJdzZ@x1_hLl0^WwAH2L&Y<BMw`BvPaBD0HQI`NulEByJF6hB!L6}8a4dRQv
z1`3Y2X<G%Ra?rdZ=A}VaqGA~V<mI)8Yf%o!q4ZqJQi6FTXw+VS1fpm)FHEiv*JOuz
z9kVkYgT&jy;Eu4wHin&d#Iv0TjzA{Y`%?^~%yFveFT3nM@;V5H%dafaMov%k+`;4`
zWhcBara0>F4$<GD1)MoFB}~-f7DGsvEO%@okVm)%E4$gHyJIQ<`(bL`S04+HR|vL4
z;ng}dMmoU=&tTRQ804F@L~+?nF1pqzkBnZj`_!y+i4MY9o6_~c8t)4aS4)4KubIWI
zf}X3Z9F+^{PHW*ta;ePd;&k9ScbBo}S(_tJR!n2(Oq}fmvac-Zyxm2PISY%C6^m(-
zO!ExcsymD`fr`mz6!pbKsGICp_8&W_7=%5|U0J2$gL9zwrobO0KoFW*@8<|qFwfD#
zTZt?^NU%tmxFsCSNWXDm*Kp>(Vwb(06-q*0DZ#dTt;E;Dpx%AL;Dwq2RaqjmUtSKU
zPGt65uDCy{=S8w6zrI1EkdxtwB>0h7iuG?8h3a8Z!yXa+pi#<U&97n#u7F^Q2uvai
zKq*&vUB#iq#r8WrtF**4HMzpWvz*xg=BZ*jbNf9KKAr7+L0*!3-UUN^(jBc*S3<y&
zsbXsP<?`{HDqmQVXEo7c5rw89yYFB>{T9*I+b!8iSjs*fA9~@`S~)jB<+5bDfo`f5
zVAXswd{UB!RC<hl!qEi{<KV<K!!y6e>U{}b0%IDg>Z%im$Xj?!G5wVhGC8@;8p|0Y
z5`dl3n?K&&-*@ha`sZkz?8Ov|ZDT5FO!L*mLWL!y)h2Ju2^a-j*RW{gXQ0BaIAc|<
z{@$xf3JrI3)^iy<I}^<5#^V@4It}n$8uO?UH>+v$)q>IXkzwc+0zjB&%=&%8@Z7Ye
zL&tpI&$VWSJK1>MF123yyAJPksqX`<X1VQ`!Dc$X+cCL>Q_TU+^*EbN9a64+f3nbz
z?l9X=;;Z>LLZ7X)d9m92>66i6$H@Z^!i!)Ujo?nddhd-aGV6j(bn{oIRwr{SyVH0b
zLZb6h`;w!Wi@R9TKR@FIQ|bpjP?xNFM_PVcu*?E(3Ma;MV#z|Y9kg^<-NHdRd!!Tn
zx*7fR$UNEgYEkUOGX6{dM1xH2M0KIis`@sF@aFVQ&t{ddg%-lN(%RDTn{n-AesNwE
zf*KNiXzNY{WsiN)i7abGuXO-yY85D&6TQpmIa<YT9j+FQW$Qz!O@_-a_2LdgwRSn#
zZpOc1(Fl;PB)g=2>-tIHjWp9z4LY@m!}&(sb+%2-nb?nqZZ;Aln^(g>UdY1ap;pD`
zI5IzF&m7&mjX}Y|c`tY}`%;pyL82=^KT6uHy7;zevTp|ph7}(aJ3H&N;%&zZtt1<R
zV*l0XsMA%?`!`yMTWL~POpjGG{)g4|HOHIN@76ng>@HwrQ8WXU`x_Ee-G<iwt7j9M
zErC4Qs*?)i@Tx7%%Cso~yP<la<|R*)!~3X7|DlLjyOlmFO^-k(rp37(q$musE3Y%c
zx{<BJbSeeuOqe!$(#=h=mV@@1<V|k3rO>ASIe(BwZ)9OtEfW{l`BRn9NBXhOPJSwJ
zq16D^QJ{G?meeVa#rX!BdoNqCkB>~&bAphr+oMGFvqF<-pTcD2VJ#9-<$}Oe+EA2}
zmgy|oPYc!_qIw)g2}8346He{O%0moCKdtsC0VcC(O9|ughZLT|E+berJ>QA|`tA1z
z-VDNH+1mc1SD^IeQaqYgflj_zv*<xp9rpc8r=AhzqL?(@Yw_!TS``HGNK3SdB_h#>
z;vuQ0Hba)G-(o-N#(pKjWJ}V4335IWl!-Q+9cYyF`-BnmQe|nwO<kmS-I6E5+3qw{
zr?+9$c{uAq-kx;;#DB^<bHMSv1D@Zp#<4$9>m0L9W^i!ftHxYP<cUX@wQqT%f*ibU
z!9D&Cvo8+4x87rkf?L~C0e<3I`F8453J#vnF~sjEa1y!d5+QJoO+lfh5?#hP-@@rI
zTV>qz*l%J9=&jv1%a}03>QU6hIXnB9A?2+5z#<Zlc~&`@PKm*d4FBDOr4fDNar09o
zt;k+$NDB#@-YJE~1v`Aor-Tc05oGC{3a>gPCsRPl#ZT-kHTYg4gbuOC2^OoSC@1;Z
z^}@@$-qm`mssHS)Vz!o-GDuQj)@do+_PJsiLN-l)snGN(2qRB|+frw#Pov`iSzT&y
z=Vu?GY;K#5cL?*r5p|i<K8de57UTDZ&8^Ke>1&-4*7Xd!;Vp&h-WivRWRgnzF>M~T
zBVEaeskWwJDWwz<i;2J+Cr>Hn^)~0>9cx45b>i=u%QQy(=|1lKdmxVv$U-B{2Jc~U
zks+Ew;j;}=GoU@9-*{1m8y|+X_t>v{k=Z8o1j|dIB~JI~Omkl#$AC#%p;>AZ)dkKT
zX{i}>ucL<J*$?47)6VAIKx;)pg)Jr8wmaN@aRfU34B(TAB64in4q@D09F0U<37D^y
zFI68Nn<TjAg9{LPsmTMqZe?$nE83HW3S~BHqp@Gus7T50oI+yndk$(J$tAQs5(H4k
zb5*AtDDr4IOzxMVk|g+Abp*dHn_&gMj~Wv@#oAI)9j%*`zPlb)X&m3g+O8Q1J)|M*
za`CsF1n0ob&#4kz2QKQbB1AMM?r9m04@Zl+{5iKI)c2F%3GXo)Yqw2>eRU$XiqcY!
zo^5Ycf>9TDu%{#^GiNQ08g16qBN8v;G+dcFaEL97=?UFA80J)_QRqdeV7f$zO!@NS
zws)~COxl=CRthAYPfiOW4Y%{JV<&&c+<a~AX%9VlYDKTFE-t0lAF`R&s2pnz95`nX
zMCIY|WnFbV2yC}3$w<h0-r?x~R2(iBSrZBi`<3xy1Oxl&OR;Mj!wouh?S@BxIT2zu
za-K<PAn!)SU8HM*ol#}+S?k9|5#xMT6}ol)@Zqq*)9n|&j1R0|hYu)D*-(VcC1RKY
z*C_`o5A2$uj6E1$8XvpUlbeHY<a;fdwI$|nzwuv)igk{46hkHb>hnuUx^m!B<8tMu
zO<V#~OQM{kb}uRly2J9%vJ3<E%TVG6(LfxcB_Ja9Ib~#y>Dj0N{`OQ`aT@grc6`6c
zZ>z)R4Y0o$Md#kukTTEXLLiPdaa8&o)MBcf!8!eVC+hSkVh`ktBC19fen6SODsEgW
z)R|uuSe2JvAuDPacmV}vTnZvJW^Qt4p*e7}69zRcqX>J^o|6>T5iPOtvR7WNPQt5b
zRyRjUd{*5u>et$3Pt^7HJzF`A#>YJ}LvE>@@$XF<V$5}JbDCv=5u1NV%=I+}bUxpT
z>NJ3a6fD)jlu2e0tzo8@0V*MM?Ft5~PDeZA$IE&rWnj$rwl`P>Wv8jn#iRp1TGIi_
zc}-u`PJfk+m?34!<IpkKZ3H2PG9fTN5E(U_9IX#kN5$P15KO@Rgmg6Ec_M;Qk79$v
z=|X2|_mY&a&sv9!Y3?uUB*-!!1G092Q|YX~ED7z;73ql&uR*@bs42Ogq7Nl3(OO-+
z*cpEw$_$esnCgI4EZ)GO`S~Lsk5r5Z{ZL#Br&Y_S%&e>+hOpL=C8g#OGB*GI_NmN2
zFo5$iCgK?_?Ua!lL$H2h6&-x<#pBdbRKMpeaUN6CrFHERrfzP^3^#Mx4#*VlXsLg>
zcwKx=CVItW!|U7J-5+lHM-5nSBd3yzOBM6P3BP1ORX_WJLVJoDCrppUE-l4?g{(g7
zZF`ixv3AU9Y|a7-h%=jx8uJ3LSosT~Qr|@Uk77Ohb6~G;(Vd1vu92}-rMkFcI^Ws1
z8;8(XVi?a&m8%?oYP-(ISrs9ro&B|R<pL{qOc#Z8<P>^O@#qcCpoO7rp@zp5RfMya
zSPezU2*A;<B=2erO?0;kzNZ;)<2vutTtn~i$1Ahb)vl+-W>F(c`;~4~nGk7-q}wd3
z#i_61B;BM$y2tqSpO6&K#xw^{RXH6m*)mp4;SN7FeV$K|Bd;$*Qc`km+kHK#oI7;N
zGTn{8!>af>i#5j|t4~sLKJT6;b+K4Bd|qSd6eX9VefO@vZ|?YPd=<uZx4w=qQ+)65
zbbVj%YS^%1_u{s`ch0`w+&*8QyFVX&zni<?AFsb(-r9Y?KD$5ThFfph>FVB6nlk(C
zei(LDz1=35R=5CV*fKlIkG)J+y}kI>S!BRW@jvLQe7=k}{ER`vm&vcOZSiS4yX#sB
z`R((1YR)mTw)@V&=W};aQkUC8&!ylkb0YV8O7wbZT(XGi|M9dQlf(UP&7H!7#nA5X
zRk&R}YCBAYXA0w@X0>&#queNmXWUIg+cmZARZ)Y1aVc5*^^It&dihR*U8!*PUXI5r
zsT$>-Xrt4~NW3XIClc`?7aGoYwa&pW?>795=leqbX}^tS=zV*n2Bqt(mtjDE{@rkV
zxK7H<=iUAN(l6%wHQX-L3<DB4|33ZU=6kn4uWE3;^Wyt@8xr&N5yR!AY7(`%1-P62
z`SS6w|M<lAF^=B<F*)7+{p|a7^{(sn-mvcK>*ZeK|9z#A1w5ubxZOFD=k|{N{c=MG
zzwO?A@`+b{?(>jk`&F*ZyJzc0_&Jbcz_I<-oRVTpX1;$A61FZi&@%Y(&<pQHe@*k{
z`LQwAYnSTA5kQ0CTc!K;*pAMZ>O3S_&4<M{q>XFCUX-x?F?uey?M;35wgb*vYqS~D
z5`tHiOFM{q-R$*Z)wOx_a%c6puYk+zt^3;liHA$Ko^h@7B_ZHWMv2{pU_a{LHK<p5
z-hFQpG(TD@b2f+9)p8tDp_{Gy8DZ=D=FPz`;eWdjQZxU+f|0JjzIQ>t5|dMd&yX~d
zs!mUcwob&Wtw;3KD|dNVFF47QX=otEl*461uY-GDGS8;e{Nj{i)3q~SGS$s(P=m|&
z^hl&~^?Z3dBpZXxvxb11te>zNxeVtP6C<)-wlA%Q{(0-iXyTn#!(2j_aeKS!S|@S~
zcE5lB8$jj#?d<sd3>7=$`H}KqZ+E6!qoI_&c+c0PD@KmbdZ>RGp6l6ue^~oVZRnWk
zRqbIP(uaFz-~3yRace-e=LM9Mm-FaT<a$EvQ`hV6`)zl5`}11&bC(Xd{XVPv<qMB@
zeUok?UCBbmvLq~I@7x{M&&!_F4{g%TWp}&;T8P4D4F}ITU-<fz{Y;hf_V%^FjYBVQ
zE$@pwLS)@uSo4c4iM&frnsb+ImyY#lnEF*NM`v&w?=+ApUJ54u)5%m9&HkmnMDDz0
zYsaGeAVC&on0sXU>%nXGfx>ZLpKgoqy%H&A*++c2-<nVO_WlPi$g}+*p5WJu6MT-(
zT13b+p3Bwl6ny(fZM2-}UF}H>u$Nm`NW)j1)`xNw`7Dw9hkDY>WC5$!_q*=rAI|0T
zecS6>oyv#s{i4KSN082kvfxsP0UN9YRZExSukMVjDtEFu%O3>@KOYhU?~)o~ahD|r
z8+gzBa^8^Q_Fq2AK*MVm46L?qPc$#^77hHq2`9b|C_kKJG1YSx4XhkJgz_Ku_g}Yk
z-f{7?2K!BO9`@#cUlTr^c3lI#E>dm{KF_Cbi+s-CdwadrqQc|xUaSpU<GH>VBIZo*
z8;-`5Jm0#-kbN4Ky@XlpcKTv-dz%gTdVTl9f8UkpdMieLyD5iud%yDC9{@l9#d!TU
zqx9dj*MG4~e?d+ROw6n-|HFX&7hv^QsV2Vm|CIgVsQwua#YE5c7ew`cSSX%&DH~u0
z7@=q0AX<c;x7F<XLh`oME#GxAlGAYPRWuQc<Thz_CEQr%gh1cZ8@sx!J1(nf6O$c{
z{FJc6#N!!MgR6Y6)d^8;b+W;;Koid969o=*a8{Y}oC@lqO<Fg&WzuZTCq%xEnOxij
z7FtL)C)EOD3TDIdci%bBk4VNH3u$dD#R^Y6ZXMyDfBq087Di~8f_}U&0C_6eXQmOF
z<SGIzaCO%><_USVzGX2|4_=l!#YJIKd+wx2Yl9v_fID(mdUIH?VktQNQcFehAifgJ
z<Un?1HgvQwvKRZc6$K&{Q*B0Yzf)tgM61`K#3^E6B<oOV9`RH5SsrD+wZ9Vv;n7M&
zh{7>$@l3aW3feN+3e8RO4HUOvlT->$Wp%w&_PUCDb><mQW7!Gs|D7eqe}~}yHx!ML
znfX7w7m1T`040D3e9q3NyD2|V<(V#2RF}T$hMnvnrqT#H0-gs#7P~<bx(G``#$)uN
zJ7M%{X5_lzTurl+Yq8PlH|}TB*WBTv9Ou#za&1!N%gQUi{=u<DtH|PHY?R}XmkvD<
z8(NL1?nIU1%s-!arMUaUO4A?ZQL>UOE0_5MaqPx5(bCHqS+1t;KzWitW3meQM|iE3
zo4~>-G2(C72(n~H0)^LdtO`zAy#$iI2fvNnBoi+WMF~Xu0I$kXvk4&9nS^#z^r3Vq
zT#;y1=G0m)36>kb=e?$`XIA@|d-Rte*X?gAIPYa3US9yIFKw{DG4C%t@1Jmdj7*IG
zVIGTugv|gwO#3_PM-RZ#RR`H%ZphR3vf>yD1V2er3yFat5fB~23rFb2OFT;fsgJ3H
z>Ib!4kXT>Ujs(iDlwBPc|8cA#V?H=0yp9x=sluTIm=O_H9&SX&6%qta`gSnq6_d5F
zE`r?#BE?D3<oRW!hdD}+F{P&MQ-37uiZ{piN@(l%HDp;A&ohZb<j~{f${yo=)PzAb
zFdl_gDF*M@GpjtZybki504rXLP~jAE)19#1Uh)~Zi`Gliy`KbT+M6ZThNYsC!}Hy+
zGBWWHufmX~P0UwlU%u0wUCZzAvINSAe`DBRfagCmjNv~GljDd7A%Gco58|c&iLuV#
zT~gA-y7KDV7M~^?dqXffkBpP%mPSNWYcjm9VT5n8qa;1p{nQALT>iX+OPCk|tsfj`
zw^X@rlO2uz?UFRD27b-@60WX;u+k^Jq>5%ryAEn-o;koVY7?oATo>hBRidOOMxO^0
z=+xAoi0`Oe_%IsHcyz)ZN0uFN{j^T)!r=pV4$T@xfO`Bw>EX{BkbmKR4fMl7;9Gn1
z1y`i~gajW85^YJr;41SlP`A^?3HLWSlA~a{04*}X<SAt_Lb`lqLqV>hf?XMJ$w_67
z@1d?1&ENR-7aaSK{9@+#55IWixuS&$V9q&$=x&Jbi0p7hD`-rvx2u8EDdeSnnwW@Z
z5PylmFeKLOlT_ta|LTqqytUao59q_M5K_dS@_-L@x+rFiL*~TEWa86jGmP#LVF1fK
zbt>ioC*^qWg9>!SGQLm|Ckrblj6P$ErpTuB%yC1bz8qoIB#Y-cz~5C=3>z)VNgU&|
z9!rGcG?@jVMrz$LLY844Z<kPv#7ty7!!33p^seL{Dd0RYl9{R`N?V<mTTgqzwrkd1
zGm=djcEX)_$!weo;6QDA-Vb91mx)?Cxp+o4r=(MC$I;00aYjno#5l%4N-_1t+&21t
z1^)`sAp9E#|6+*$iGv*f*VkKAFJccq1jx6t03;ODnjC4up9LC)!04rS40ja>3f<%`
ziU*oRs)MQqE@`+SAH(0d>p(NQ0Sa$N=DRkdoS8iDY91%?lVYO~$^9G7lr*-~ebzAR
z8QSoji2H9;{)H<46O~NN|G6$i6s2s086eiXmF#|r@214wH<tgk0cvJQFHY`2>RGvs
zD-|1!)wYrl5icjoc@qj%7Jo215EB-^NI-(RB*hF{N>xT(#Fe7ttljR{J%|>sqG~)|
z%@$4OD@=}!aEu`2Wuvy$doIAO-gsz<w_PVr5@3&i%m1P_-gJN>5zw4*y+GWC$Eo(G
z^4G7mqz$+<W?cAw3qjMm_I@@Pru9RsJnrg+11K#)ZUh~{T)SeWFa!A679r7BaOYv;
zl5bV;^Yj%PfC-M?p(m6?6Q^r-3n1BB`$Qxv{F91bL7o3&WYMK}i(ypU4($jmy3>w(
zFwIVieF%s@ymJr3<A!AAh)%USH2ntBxy^{Kqc3~y=vXiT!nFzjPRFRfs-mDY`SO(8
z|H)LuK{Rq9f}N8fUSp2Ke{U`qS7Tp`t!~ensIFCY`wDM2lTPdD99`-naNuud=Pz>n
zpUe(B(|<0)2@WJbdI->&Y^4vPz!Ab{d+Z3eZx#AkFx(1{Tmtrhw%)2;y|FeY<ZGq<
z#i2|j$Wk{+ndCNTkA=prg)Xz5_VXjD#W23fY4j}kb{x#jzmfLuO#T11eKY@O=ETI<
zEKe_A%M6GVj7AD|WC8|75v-MrphAl-O$|N)G@EP(MVQ^21P0J*3QP7k!~Q$KF8D7l
zo`!+`PclKn%*uq%!1iY~F)%VQ{l&-sZ>pF$py-sG4V?b#zyMKO2kZYiJm7zr{eRE)
z_8FP!mFQVnevTY6vatd+IP_2xITGPR6G!L?7%{cX0ifPPdHugynf_^Z$?!+{zmF#p
z%Y_6?03+lnn@99Q1k5{a!brjTY-_k;rPwpCh9A(5?7y()A9ms&(nPWedR+x#YWQ)=
zYQutb0OIVYQn+uHsOa4~XrAPBYsTtG1zm-VV*=-qSb&*}z=^tbnSbC=d^PndWcR51
z*ln=MkrDQ5bPNY#@=R&tm6a>Mt!?zHaCoC~*@x=lr@pzm)Hq~wR=Wv5<c1Q*S_Kau
z9u()(-^BWlvpEL#|A>`GzBLMj0cKn~=!aU5$7Pu*T7*>b_k;NB6X7hHh$1CRT08}v
zb0Uel(iW#z@&KHB@S%+nD+#3tT|zC%^9<Cjci0ii^X6i5gAj9y8HW!v6J<FT&b$<B
zx*a8QTkb|b(;KX{6R=7MZk;CnOrhR#6TZVRJO)f}8gc|7jHC?MceO)Ij!py?Z~v_j
zN?bhL5zDvQ=ba;LjZzF*oQ-YQo=#>N955nwe0m3wwptx65~4YafXsA{28~~Pv(6V*
zBT*X+|5Mn2eY#?FUbm%P(KOv5nbOMNSoBYGw*PA}i$u1=_RvEJdlu_~_>eyFDkS!8
zb;BZ%*tC#s@@U~ercu~1^$USz!%zon+y8n<ria!NlaJ22D9~T*Is=J%ELOa~hEL4C
zsN4%a2LuxQVe&W9{%Ll^z)JrgvnLYEfdtF|0pi%{PbRMA|6Do#yZ7z8417r}{}hNB
zyA4fd%mc-Y_q0wluWiQigjzCQxEC!RN4~@`l#5(kTHG*Rya_jeC|#{VJ2g$2vO#gT
z(R{G4dsSm`L^#SW>LctFO!-6ZOe^t5CZHys;SEE%gDW6X;zdVfLxbV+OPIb6^9j;c
zgcLL|tgdXeYg#XO;u-4Cu?qLF(*A7ce-r3GO>3C`JR1J{l4SX_-GTnu?xrbP_=|4j
zrtZtjBf8#gEs;A>rRnwdDd^Paq`DSJ){FWNH1VEE>IAo9F4HFz2cOXzP^M{W#HJCO
zX`nz9ka^V7=uin4rXNl?OYJ0ceyKt<qhNIeNB{&_yny2tG2zP}^AEKfFe(E36aRnZ
z9BounS5j0grUEMB$EuvmS_D?%edWF6S#&`_MMa7rx}ww&5{N<qNvIZeSKBJEMQlMu
z&=qw-wBoAOwtnD>B3eH{w7BRwDiw606<ycjvDPX(_l7_|*y`D{{~FJ5U*^u8J2Q9g
zoq0E&xo*&;Ic4j|X8)<AVq2s4zLFj76aP?nu`z!}Y2E1XS*;G$7plXyFARIOy>RpN
zki3HXD-TX?Ut5_MFlj1tepQ>0owCz$Y>P*!*QGN<CLB1|yFy)bZBFZ=(;k_=Idk7U
zwA&;3aje_MlF|E`N8F%qU!<ercicJPSpUy>)dvsnHS_yVXFZ6_Us#p;a8u1vrz=S>
zM-IL4t<pVj)zq?sgPk?<%B;$5aq{Z8AKXNj$}HtyipCF6CXFr2$jz>-+jgi_arT_Y
z{3k!SMt`>=vxkLv-Ry|vt@94Q$S{4MAgAM($F+Z?E^Aa4jLW>+(cpC9jegD_2O8%$
zZO<u2<L6e~`*d$oRU^lI(CC=?$8ilQ^X>*lqcG(={Eqkjozd=bJ#BOUOZopeFhE`I
z`g4x&&DQEW%!tIV8dUWI+ZOJhSQvD^e8Cwnw_!7{DaIta{JxO&KWvyNLoHM06(~N@
z#{7mGoGV>3&2!CXTS{vC1`W>n+mv;Je#rAuYQ1sooOQdIHiKWvyO+b|OX}L@l|R0_
z!DC^XzxObzd{p}rq0g{LP5I-%X_YUuu1^e~=Ql2!(Na6~o95*;=O3SrUR1rMK^rJs
zYig{>YJQ%xzoq5b&wbuq6r0t94PQ5R<E=AHj;A36*L@LP$Fss5n(zC$bInZm*#nM#
z+qS^T?XBV}$Ef~_)g^|3zcU<RA-WkNUDx+8`)lVW)-MP=9A~TO0U^sH?hI-=dE*UF
z$@P=fVX4Z-TlsObn*#4m7?itfwRg~y!Z>u}JBOIirpT~M9pNYTghpN7^<Zy!UvDn=
zN&2YWMc-u9&G$*kA9_1=Wv{KHAM_yL>t-m7O=>%F{VA6!+9~*7&m<jzCD(m-tcWOE
zocXY0|Ckxa-^}Q(%vn=cH}bQhs<n%@G*@5sieB>Lkm^7$+1G`OTuMu>j-L8yM!bCZ
z0*}KZbDIvf{xs-#o_b~7*~!Pel2$D*kJ8-SmF9M4H1GJm@Aq5(S~2iw@t(x%(!5*J
zLwSZH&L@{&j%w&A+gow*nd6D2{x5sb$}$1^r3{B<c<U1^Qgnf&qV1{s%fPpCsAUm5
zeQI8wtjW0&<8f-i+QS<>3m?zU-|cxXYEnj_|JGwS8!j!Dwro1=XIORQ%L-~=US+mA
z>%&u;%|{%H0vv)HuI!Es9_CS3UmE3e;AY=RfBs7AdQ17m>VhvlTe4BS&xr9sHG4wc
zhNfKhF5yo?c)eZl;MUhsrDrz{U)|3kMn0;cwc*jmi4kM37~R?Wz5K=D{oO);8}KZ5
z*EP5M^QBkW!S!bcl`rm7@%9UPR<B-eX%~AZ6i?r9=<XTa!S+M_=Kl23p?GTaCVTrp
zdeNOSPgWRIq`VU~IXp5lBxYQQYOx|FJxZZ9K$)l{g&r4pg4#=@){=w@)j|y<T)!zG
zKthFR)M^zJSi%*RVv$sis0@`}hbWyEsw5eg>JZN&N)08K5Nt2Xpjn1Q9#QGq1eM;P
z)ag}7lp#uisCYyv5tV=_6%=pcBvc|a(6a<l$%uhVNlFVm;R%@Np%D?a6f#%_QSZY&
zWoJdJF2NuqL0YRf7_so6D226qRT>N=Au5AXuSqrPU<y)=3fKTTTw&CM;80r1pQdpJ
z2miXBAcLN%=$wh=LAP0o6j0sHk`{?HuD`{ZGij6I=V}s+Nnm1pR?Wr^X}+YjJU)U9
zN?gfGWI_92Md~z_CNar~)x&b*9zT{7NTI4k1L9bd@UNlvf0Z~4Duij2LE6M*6`m$s
zv5d%x;KmqMHOi<;iNSbNJu$3^g#oE~UGiC|V?K*xkwDY`50Lz?d^p$wQdmtX<Z5zG
zjs#ASh&)scgUVs7j7OYE6W~Ek+>hxwfibn54AcJ-NX-2l&|<(m%)oODxHmR{TnYe;
zL^+bl;5*zy@-Vp!1MD1D<Rt(|;046<zyw}_WU$F(au9%qXWZg`4gl(<$zVL@Dau7?
zO*q^VWjL7xw^#_Q6b6Ce$XFf}H}L>kn3p_rQZR&^0520sE6PA#zzYlu3YzAX!n{1}
zL{7xxq=<)B4#vVQ0T$B<#0a>>^OHrx1_T+vuo65c4`YZ?%);T8cp9Ebioej<L|AOB
z8g2o|y5Q+dAmKqpG7{Uj`vC+rv0y=C+=Hb8$<1zHUM_PBOC~W3LZp()7_2rCANzro
zs8}cBOyE#>R2LU46irJ=Unc`m5Rn<MBba;u`++qOTZ3O<pRfSp57;NnKzyasC$K+Q
z0+x$>2Oiir%z!l)Xdc!OWW_$v1Bh~16YLidAuYk|6Xq5h1=d|cTKsGQWUWp8n5GM`
zU*J{6ZP(9CB)*uhaTJLB>I4P);t{MJDb=S$EPgozTmph+Bu^^Db`~fkK?YV8&^Uas
zrlS-h2uqG~97x#CntxhEE**f84jjP52nQ^%5MinZXEpJ`0Sn|NSRBnjX7X$iz`%_O
zYAJ$UPM+RJV0Q9EVj*XWo870N)oUbpl8Lyx-}3D%h=DK*$9)hmG>5sa(u|Z+fk@s@
z1+iu_2#KcixVwU*1sP{1qx~UN_e6+0CQY&kK^Ctd-6ERLagePWX&I}gFn--xgkOqA
zn-1kCi6J14;C*c}P&laHY2I)afu~`!b#o+SW|uHWGTxs@e)x|WUU7*9XNX1cebp^i
z$|pfCAJI;sPq*$I_M@{OyDqjMu4)}q_*^%&K95chzL41=*<c)4wz0NXZPDM)9G&(e
z^@O2zPKyxBhKEmo+dVSUJ%3xnl(<mY<`K2W-kDqMxzL#KG@vr@A+K|p`SHl=VtVtV
zwP)#Gr&qK_jq(35Ew&=gvH8iPh;t_@XC1C{-Fknza}W8NrBD2-4PoJq)UVkPme*v#
zx^&3?VCT=!B;Yh0PItVIU|*PaxT*SU_qLK+UbzNC;ZmbUrwzfyUVTC)ur$lkA`OjH
zq^09%&YPyaeI*p3gUr|m(&5x(oiSMxkKW__d4G01N-`Q#4HGCTrK`(dr%!}I@aB+^
zu7vWbmZMS=)F@t|Tmm_6CkJ1PJ-<w^QcGwgWh4;6J3o*B@o@C2Q5Ozp%J_q{oj8^e
zAz0erKo9U2YdTuQ=d?8rG7C7HY;X|a?Qu93S<%54!FCRs=5ScCqGLF@2#1~(js`2+
z$_#XnC|J|63=7GgElw)fj%67c4qR4qFrt71s|`*HLD>c;7v*;RXhCL2$BFn<w&o{g
z?Q~<L0{*`wHgqCB;;nIzJKD*|^KjW<ODBUDLK__98g_I7{P<u;$3enmJC+yh`N{3<
z%*bf?vBYMq4EBZ{P6`Q=Eu9<^c^jM@9MulT;lu@hy8M}uOX0=97ALd!NtWg<*3GC_
zXp&WWQuH}YtJXnUYKpY8bvk@RkhCO9lL2*|J8ujXdL!9#EDLub5-+dNh$)i)0B(HQ
AVgLXD

literal 0
HcmV?d00001