python3-nltk-3.7-bp155.3.3.1<>,$fkII%z HO>Wp_us֦j|AirB+K4 Ch3T)F2_BgJb8~ UL7ͱƅg ֋bc"TVҤDXUr(ɩtF>wa{/J(80\8;Qx!wVɮd8݋- v>gC|9V9L8b,nn#?9sh$7( 0)د׊di?@bÕĎolef@JarL y’SbڇDvM^V5nO?-F,jX4!Qg7q{uǽHe]LC!k; oZ)~Gy>E?ظd  8  6BSY`` h ' :p Z _rP,   (&809|:>@FG,HIX8Y<\L]^~@bkcdefluvwx4y*z׌לנ > H lrشCpython3-nltk3.7bp155.3.3.1Natural Language ToolkitNLTK -- the Natural Language Toolkit -- is a suite of Python modules, data sets and tutorials supporting research and development in Natural Language Processing.fkIi03-ch2bFSUSE Linux Enterprise 15openSUSEApache-2.0http://bugs.opensuse.orgUnspecifiedhttp://nltk.org/linuxnoarch# python3_install_alternative: update-alternatives --quiet --install /usr/bin/nltk nltk /usr/bin/nltk-3.6 36# python3_uninstall_alternative: if [ ! -e "/usr/bin/nltk-3.6" ]; then update-alternatives --quiet --remove "nltk" "/usr/bin/nltk-3.6" fiki%   Z[::mmV%0%2211oo--XYmjmx==TTa*;99XhXh//eeUUyyX9Ha20aCH--22%y%yMM(x7C)&OO__pp 88%7 -WW$0$0EE+vS?**%%r/ ++'9'93) 02i  D D <%&*r !!'7\y:t;$$GsvK|K|$$&d&K#K#$7$7rr'l'lC*CSSj--h(@@;;66ss###a#a <<./#_#== XX+x+xA)AO Y[SS*#*Y)j)jL%% [e$ &/W+2  3$81??w |D@ 0 ! WEp/-s /lH)I }ba ?)!Nt*PP3pp:W:xJkc.//SASAQQ))@R@|11QRHILhX/J?hfzs@@gg'}@@0J0<<//9@9@,,pp@25@2D95ll?? T T n ni 44  JY`YtRtt@h@II&'TTCCILIL\\;;D0D0S)S)dd=P=P 2*`|GbzX M@s PnSeBJ}!Gm  pFRR4G4GTzUMmM*+CE4/4/:grϣJe}v.7$CRj>#f~SS%%XX33(z S ? ?'P'P$$STK %%2@[:1+n*fwy?%?%8:8IqI> J ,,hh[[22 ! Z9Amĭ4qo H 11(1(''&&(v(v0u0u;%,2\))%%$$``((3^MJyZ"$t,wn-. $O 6FNlpJ*-PM1 k GEoo, (&xZQ$&%6Jbf/ g 6N(S $<h''YY&&S+ ++OBB jw G GSSnn B 4 c e 31BUzUzFIK*8'&1,003Nj[]9j $@m2 T {pm `$,"[$ ? ? < <))EE&;&;5Z$P$P> m 1]Yi%m'"q 9 p!! ((aarCC11//)) 5W5WWW0c0c! ! 5:%X\U( 5=&,BmA=(G32E2ghN2ZZ> ,,2B2BCCYfYf@@:u:ujGG*a*aCC+ev#"f"%1 7CQm.PC6P$AOOEG'ffPQ F F$t$tuvRZ#d 5y3MM@@88T' uM3-(AA큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤A큤fk?fk?fk?fk=fk>fk>fk>fk>fk>bfk>fk>baXfk?fk?fk?fk>fk>fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk>fk>fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk>fk>fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk=aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aXaXaXaXa6aXaXaXfk=aXfk=aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aXaXaXaXaXfk=aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aXaXaXaXaXaXfk=aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aXaXaXaXfk=aXfk?fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>aXaXaXaXaXa6aXa6aXaXaXaXaXaXaXfk=aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aXaXaXaXaXaXaXaXfk=aXfk?fk?fk?fk?fk?fk?fk?aXfk=aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aXaXaXaXaXaXaXaXaXaXaXaXaXaXaXaXaXaXaXa6aXaXaXaXaXaXaXaXaXaXaXaXaXaXaXaXaXaXaXaXa6aXaXaaXaXaXaXaXa6aXfk=a6aXfk=aXfk?fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>aXaXaXaXaXaXaXaXfk=aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?a6a6a6aXaXaXaXaXaXa6fk=aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aXaXaXaXaXaXaXfk=aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aXaXaXaXaXaXaXaXaXfk=aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aDZa6aXaXaXfk=aXfk?fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>aXaXaXaXaXaXaXaXaXaXaXaXaXaXaXaXaXaXaXaXfk=aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aXaXaXaXaXaXaXaXaXaXaXaXaXaXfk=aXfk?fk?fk?fk?fk?fk?fk?fk?fk?aXaXaXfk=aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aXaXaXaXaXaXa6aXaXaXaXaXfk>aXfk?fk?fk?fk?fk?fk>fk>fk?fk?fk>fk>fk?fk?fk?fk?fk?fk?fk>fk>fk?fk?fk>fk>fk>fk>fk>fk>fk>fk>aXaXa6aXaXaXaXa6aXaXaXaXaXfk>aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aDZaXaXaXaXaXfk>aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aa6aXaXaXaXaaaXaXaaXaXaaaXaXaXaXaXaaXaXaXaXaXaXaaXaXa6aXaXaXaXaaXaXaXaXaXaXaXaXaaaXaXaaXaaXaXaXaXaXaXaXaXaXaXaXaXaXaXaXfk>afk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk>aDZfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aXaXaXaXaaXaaaa6aaa6aaaaaa6a6aa6aXaaaaaaaaaaa6aaXaaaafk>aDZfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?a6aaaaaaaaaaXaXaXaXaXaXaXfk>aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aXaXaXaXaXa6aXaXa6aXaXaXaXaXaXa6aXaXaXfk>aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aXaXaXaXaXaXa6a6a6aXaXaXaXaXaXaXaXaXfk>aXfk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?fk?aXaXaXaXaXa6aXaXa6fk>aXfk?fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>fk>aXaXaXaXaXaXaXfk@aXfk@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@rootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootrootpython-nltk-3.7-bp155.3.3.1.src.rpmpython3-nltk@@     /bin/sh/bin/sh/usr/bin/python3.6python(abi)python3-regexpython3-sixrpmlib(CompressedFileNames)rpmlib(FileDigests)rpmlib(PartialHardlinkSets)rpmlib(PayloadFilesHavePrefix)rpmlib(PayloadIsXz)update-alternatives3.63.0.4-14.6.0-14.0.4-14.0-15.2-14.14.3fb9@^@^l@]c@]x]6\@\`@\^\Z@ZZ1@Zp^@Z]@Z8@X+XU]Daniel Garcia Matej Cepl John Vandenberg Tomáš Chvátal Matej Cepl Tomáš Chvátal Tomáš Chvátal pgajdos@suse.comJohn Vandenberg John Vandenberg John Vandenberg jengelh@inai.debadshah400@gmail.comguigo.lourenco@gmail.comguigo.lourenco@gmail.combadshah400@gmail.comstephan.barth@suse.comtoddrme2178@gmail.comtoddrme2178@gmail.com- Add CVE-2024-39705.patch and restrict-wordnet-app-pickle.patch upstream patches to fix unsafe pickle usage. (CVE-2024-39705, gh#nltk/nltk#3266, bsc#1227174).- Update to 3.7 - Improve and update the NLTK team page on nltk.org (#2855, [#2941]) - Drop support for Python 3.6, support Python 3.10 (#2920) - Update to 3.6.7 - Resolve IndexError in `sent_tokenize` and `word_tokenize` (#2922) - Update to 3.6.6 - Refactor `gensim.doctest` to work for gensim 4.0.0 and up (#2914) - Add Precision, Recall, F-measure, Confusion Matrix to Taggers (#2862) - Added warnings if .zip files exist without any corresponding .csv files. (#2908) - Fix `FileNotFoundError` when the `download_dir` is a non-existing nested folder (#2910) - Rename omw to omw-1.4 (#2907) - Resolve ReDoS opportunity by fixing incorrectly specified regex (#2906, bsc#1191030, CVE-2021-3828). - Support OMW 1.4 (#2899) - Deprecate Tree get and set node methods (#2900) - Fix broken inaugural test case (#2903) - Use Multilingual Wordnet Data from OMW with newer Wordnet versions (#2889) - Keep NLTKs "tokenize" module working with pathlib (#2896) - Make prettyprinter to be more readable (#2893) - Update links to the nltk book (#2895) - Add `CITATION.cff` to nltk (#2880) - Resolve serious ReDoS in PunktSentenceTokenizer (#2869) - Delete old CI config files (#2881) - Improve Tokenize documentation + add TokenizerI as superclass for TweetTokenizer (#2878) - Fix expected value for BLEU score doctest after changes from [#2572] - Add multi Bleu functionality and tests (#2793) - Deprecate 'return_str' parameter in NLTKWordTokenizer and TreebankWordTokenizer (#2883) - Allow empty string in CFG's + more (#2888) - Partition `tree.py` module into `tree` package + pickle fix (#2863) - Fix several TreebankWordTokenizer and NLTKWordTokenizer bugs (#2877) - Rewind Wordnet data file after each lookup (#2868) - Correct __init__ call for SyntaxCorpusReader subclasses (#2872) - Documentation fixes (#2873) - Fix levenstein distance for duplicated letters (#2849) - Support alternative Wordnet versions (#2860) - Remove hundreds of formatting warnings for nltk.org (#2859) - Modernize `nltk.org/howto` pages (#2856) - Fix Bleu Score smoothing function from taking log(0) (#2839) - Update third party tools to newer versions and removing MaltParser fixed version (#2832) - Fix TypeError: _pretty() takes 1 positional argument but 2 were given in sem/drt.py (#2854) - Replace `http` with `https` in most URLs (#2852) - Update to 3.6.5 - modernised nltk.org website - addressed LGTM.com issues - support ZWJ sequences emoji and skin tone modifer emoji in TweetTokenizer - METEOR evaluation now requires pre-tokenized input - Code linting and type hinting - implement get_refs function for DrtLambdaExpression - Enable automated CoreNLP, Senna, Prover9/Mace4, Megam, MaltParser CI tests - specify minimum regex version that supports regex.Pattern - avoid re.Pattern and regex.Pattern which fail for Python 3.6, 3.7 - Update to 3.6.4 - deprecate `nltk.usage(obj)` in favor of `help(obj)` - resolve ReDoS vulnerability in Corpus Reader - solidify performance tests - improve phone number recognition in tweet tokenizer - refactored CISTEM stemmer for German - identify NLTK Team as the author - replace travis badge with github actions badge - add SECURITY.md - Update to 3.6.3 - Dropped support for Python 3.5 - Run CI tests on Windows, too - Moved from Travis CI to GitHub Actions - Code and comment cleanups - Visualize WordNet relation graphs using Graphviz - Fixed large error in METEOR score - Apply isort, pyupgrade, black, added as pre-commit hooks - Prevent debug_decisions in Punkt from throwing IndexError - Resolved ZeroDivisionError in RIBES with dissimilar sentences - Initialize WordNet IC total counts with smoothing value - Fixed AttributeError for Arabic ARLSTem2 stemmer - Many fixes and improvements to lm language model package - Fix bug in nltk.metrics.aline, C_skip = -10 - Improvements to TweetTokenizer - Optional show arg for FreqDist.plot, ConditionalFreqDist.plot - edit_distance now computes Damerau-Levenshtein edit-distance - Update to 3.6.2 - move test code to nltk/test - fix bug in NgramAssocMeasures (order preserving fix) - Update to 3.6 - add support for Python 3.9 - add Tree.fromlist - compute Minimum Spanning Tree of unweighted graph using BFS - fix bug with infinite loop in Wordnet closure and tree - fix bug in calculating BLEU using smoothing method 4 - Wordnet synset similarities work for all pos - new Arabic light stemmer (ARLSTem2) - new syllable tokenizer (LegalitySyllableTokenizer) - remove nose in favor of pytest- Update to v3.5 * add support for Python 3.8 * drop support for Python 2 * create NLTK's own Tokenizer class distinct from the Treebank reference tokeniser * update Vader sentiment analyser * fix JSON serialization of some PoS taggers * minor improvements in grammar.CFG, Vader, pl196x corpus reader, StringTokenizer * change implementation <= and >= for FreqDist so they are partial orders * make FreqDist iterable * correctly handle Penn Treebank trees with a unlabeled branching top node- Fix build without python2- Replace %fdupes -s with plain %fdupes; hardlinks are better.- Update to 3.4.5 (bsc#1146427, CVE-2019-14751): * Fixed security bug in downloader: Zip slip vulnerability - for the unlikely situation where a user configures their downloader to use a compromised server CVE-2019-14751- Update to 3.4.4: * fix bug in plot function (probability.py) * add improved PanLex Swadesh corpus reader * add Text.generate() * add QuadgramAssocMeasures * add SSP to tokenizers * return confidence of best tag from AveragedPerceptron * make plot methods return Axes objects * don't require list arguments to PositiveNaiveBayesClassifier.train * fix Tree classes to work with native Python copy library * fix inconsistency for NomBank * fix random seeding in LanguageModel.generate * fix ConditionalFreqDist mutation on tabulate/plot call * fix broken links in documentation * fix misc Wordnet issues * update installation instructions- version update to 3.4.1 * add chomsky_normal_form for CFGs * add meteor score * add minimum edit/Levenshtein distance based alignment function * allow access to collocation list via text.collocation_list() * support corenlp server options * drop support for Python 3.4 * other minor fixes- Remove Python 3 dependency on singledispatch- Update to v3.4 + Support Python 3.7 + New Language Modeling package + Cistem Stemmer for German + Support Russian National Corpus incl POS tag model + Krippendorf Alpha inter-rater reliability test + Comprehensive code clean-ups + Switch continuous integration from Jenkins to Travis - from v3.3 + Support Python 3.6 + New interface to CoreNLP + Support synset retrieval by sense key + Minor fixes to CoNLL Corpus Reader + AlignedSent + Fixed minor inconsistencies in APIs and API documentation + Better conformance to PEP8 + Drop Moses Tokenizer (incompatible license)- Add missing dependency six - Remove unnecessary build dependency six - Recommend all optional dependencies- Trim redundant wording from description.- Use \%license instead of \%doc to install License.txt.- Depend on the full python interpreter to fix sqlite3 import during %check- Depend on python-rpm-macros - Build for both Python2 and Python3- Update to version 3.2.5: * Arabic stemmers (ARLSTem, Snowball) * NIST MT evaluation metric and added NIST international_tokenize * Moses tokenizer * Document Russian tagger * Fix to Stanford segmenter * Improve treebank detokenizer, VerbNet, Vader * Misc code and documentation cleanups * Implement fixes suggested by LGTM - Convert specfile to python single-spec style. - Drop unneeded BuildRequires: python-PyYAML, python-xml, python-devel; not required for building. - Change existing Requires to Recommends: these are really needed for additional features, and not required for basic nltk usage. - Add new Recommends: python-scipy, python-matplotlib, python-pyparsing, and python-gensim; enables other optional features. - Run fdupes to link-up duplicate files. - Remove exec permissions for a file not intended to be executed (not in exec path, no hashbang, etc.) - Remove hashbangs from non-executable files. - Run tests following the suggestion from http://www.nltk.org/install.html.- update to version 3.2.2 Upstream changelog: Support for Aline, ChrF and GLEU MT evaluation metrics, Russian POS tagger model, Moses detokenizer, rewrite Porter Stemmer and FrameNet corpus reader, update FrameNet Corpus to version 1.7, fixes: stanford_segmenter.py, SentiText, CoNLL Corpus Reader, BLEU, naivebayes, Krippendorff’s alpha, Punkt, Moses tokenizer, TweetTokenizer, ToktokTokenizer; improvements to testing framework- Update to version 3.2.1 + No changelog available- Remove upstreamed nltk-2.0.4-dont-use-python-distribute.patch - Update to version 3.0.2 + No changelog available/bin/sh/bin/shi03-ch2b 1721985865  !"#$$&&()**,,./0123446688:;<<>?@AACCEFGGIIKKMMOOQQSTUVWXYZ[\]^_`abbddfghhjjlmnopqrstuvvxxzz||~~      !!##%%''))++--/01134567799;;==??ABCCEEGGIJKLMMOPQRSSUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~       ""$%&&((**,,..0022446689:;<=>?@ABCDEFGHIJKLMNOOQRSTUUWXYY[\]^_`aacdefggiiklmnopqrstuvwxyz{|}~~     !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUUWXYZ[[]]__abcdefggiiklmmopqqstuvwxyy{|}}      !!##%%'')*+,-./0123456789:;<=>??ABCDEFGGIIKLMMOOQQSSUUWWYY[\]^__aaccefghijklmnopqrstuvwxyzz||~3.7-bp155.3.3.1    !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ""##############################""""""""""""""$$%%%%%%%%$$$&&''''''''''''''''''''''''''&&&&&&&&&&&&(())))))))))))))))))))))))))))(((((((((((((**++++++++++++++******,,------------------------,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,..//////////////////////////////////////////////////////////////////////////.0011111111110000.....................................2233333333333333333333333322222222222,,,,445555555555555555555555555555555555555544444444444444444466777777777777777777777777777777777777776666666666666666668899999999999999998888888::;;;;;;;;;;;;:::::<=>?nltknltknltk-3.6nltknltk-3.7-py3.6.egg-infoPKG-INFOSOURCES.txtdependency_links.txtentry_points.txtnot-zip-saferequires.txttop_level.txtVERSION__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycbook.cpython-36.opt-1.pycbook.cpython-36.pyccli.cpython-36.opt-1.pyccli.cpython-36.pyccollections.cpython-36.opt-1.pyccollections.cpython-36.pyccollocations.cpython-36.opt-1.pyccollocations.cpython-36.pyccompat.cpython-36.opt-1.pyccompat.cpython-36.pycdata.cpython-36.opt-1.pycdata.cpython-36.pycdecorators.cpython-36.opt-1.pycdecorators.cpython-36.pycdownloader.cpython-36.opt-1.pycdownloader.cpython-36.pycfeatstruct.cpython-36.opt-1.pycfeatstruct.cpython-36.pycgrammar.cpython-36.opt-1.pycgrammar.cpython-36.pychelp.cpython-36.opt-1.pychelp.cpython-36.pycinternals.cpython-36.opt-1.pycinternals.cpython-36.pycjsontags.cpython-36.opt-1.pycjsontags.cpython-36.pyclazyimport.cpython-36.opt-1.pyclazyimport.cpython-36.pycprobability.cpython-36.opt-1.pycprobability.cpython-36.pyctext.cpython-36.opt-1.pyctext.cpython-36.pyctgrep.cpython-36.opt-1.pyctgrep.cpython-36.pyctoolbox.cpython-36.opt-1.pyctoolbox.cpython-36.pyctreeprettyprinter.cpython-36.opt-1.pyctreeprettyprinter.cpython-36.pyctreetransforms.cpython-36.opt-1.pyctreetransforms.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pycwsd.cpython-36.opt-1.pycwsd.cpython-36.pycapp__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycchartparser_app.cpython-36.opt-1.pycchartparser_app.cpython-36.pycchunkparser_app.cpython-36.opt-1.pycchunkparser_app.cpython-36.pyccollocations_app.cpython-36.opt-1.pyccollocations_app.cpython-36.pycconcordance_app.cpython-36.opt-1.pycconcordance_app.cpython-36.pycnemo_app.cpython-36.opt-1.pycnemo_app.cpython-36.pycrdparser_app.cpython-36.opt-1.pycrdparser_app.cpython-36.pycsrparser_app.cpython-36.opt-1.pycsrparser_app.cpython-36.pycwordfreq_app.cpython-36.opt-1.pycwordfreq_app.cpython-36.pycwordnet_app.cpython-36.opt-1.pycwordnet_app.cpython-36.pycchartparser_app.pychunkparser_app.pycollocations_app.pyconcordance_app.pynemo_app.pyrdparser_app.pysrparser_app.pywordfreq_app.pywordnet_app.pybook.pyccg__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycapi.cpython-36.opt-1.pycapi.cpython-36.pycchart.cpython-36.opt-1.pycchart.cpython-36.pyccombinator.cpython-36.opt-1.pyccombinator.cpython-36.pyclexicon.cpython-36.opt-1.pyclexicon.cpython-36.pyclogic.cpython-36.opt-1.pyclogic.cpython-36.pycapi.pychart.pycombinator.pylexicon.pylogic.pychat__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pyceliza.cpython-36.opt-1.pyceliza.cpython-36.pyciesha.cpython-36.opt-1.pyciesha.cpython-36.pycrude.cpython-36.opt-1.pycrude.cpython-36.pycsuntsu.cpython-36.opt-1.pycsuntsu.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pyczen.cpython-36.opt-1.pyczen.cpython-36.pyceliza.pyiesha.pyrude.pysuntsu.pyutil.pyzen.pychunk__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycapi.cpython-36.opt-1.pycapi.cpython-36.pycnamed_entity.cpython-36.opt-1.pycnamed_entity.cpython-36.pycregexp.cpython-36.opt-1.pycregexp.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pycapi.pynamed_entity.pyregexp.pyutil.pyclassify__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycapi.cpython-36.opt-1.pycapi.cpython-36.pycdecisiontree.cpython-36.opt-1.pycdecisiontree.cpython-36.pycmaxent.cpython-36.opt-1.pycmaxent.cpython-36.pycmegam.cpython-36.opt-1.pycmegam.cpython-36.pycnaivebayes.cpython-36.opt-1.pycnaivebayes.cpython-36.pycpositivenaivebayes.cpython-36.opt-1.pycpositivenaivebayes.cpython-36.pycrte_classify.cpython-36.opt-1.pycrte_classify.cpython-36.pycscikitlearn.cpython-36.opt-1.pycscikitlearn.cpython-36.pycsenna.cpython-36.opt-1.pycsenna.cpython-36.pycsvm.cpython-36.opt-1.pycsvm.cpython-36.pyctadm.cpython-36.opt-1.pyctadm.cpython-36.pyctextcat.cpython-36.opt-1.pyctextcat.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pycweka.cpython-36.opt-1.pycweka.cpython-36.pycapi.pydecisiontree.pymaxent.pymegam.pynaivebayes.pypositivenaivebayes.pyrte_classify.pyscikitlearn.pysenna.pysvm.pytadm.pytextcat.pyutil.pyweka.pycli.pycluster__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycapi.cpython-36.opt-1.pycapi.cpython-36.pycem.cpython-36.opt-1.pycem.cpython-36.pycgaac.cpython-36.opt-1.pycgaac.cpython-36.pyckmeans.cpython-36.opt-1.pyckmeans.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pycapi.pyem.pygaac.pykmeans.pyutil.pycollections.pycollocations.pycompat.pycorpus__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pyceuroparl_raw.cpython-36.opt-1.pyceuroparl_raw.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pyceuroparl_raw.pyreader__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycaligned.cpython-36.opt-1.pycaligned.cpython-36.pycapi.cpython-36.opt-1.pycapi.cpython-36.pycbnc.cpython-36.opt-1.pycbnc.cpython-36.pycbracket_parse.cpython-36.opt-1.pycbracket_parse.cpython-36.pyccategorized_sents.cpython-36.opt-1.pyccategorized_sents.cpython-36.pycchasen.cpython-36.opt-1.pycchasen.cpython-36.pycchildes.cpython-36.opt-1.pycchildes.cpython-36.pycchunked.cpython-36.opt-1.pycchunked.cpython-36.pyccmudict.cpython-36.opt-1.pyccmudict.cpython-36.pyccomparative_sents.cpython-36.opt-1.pyccomparative_sents.cpython-36.pycconll.cpython-36.opt-1.pycconll.cpython-36.pyccrubadan.cpython-36.opt-1.pyccrubadan.cpython-36.pycdependency.cpython-36.opt-1.pycdependency.cpython-36.pycframenet.cpython-36.opt-1.pycframenet.cpython-36.pycieer.cpython-36.opt-1.pycieer.cpython-36.pycindian.cpython-36.opt-1.pycindian.cpython-36.pycipipan.cpython-36.opt-1.pycipipan.cpython-36.pycknbc.cpython-36.opt-1.pycknbc.cpython-36.pyclin.cpython-36.opt-1.pyclin.cpython-36.pycmte.cpython-36.opt-1.pycmte.cpython-36.pycnkjp.cpython-36.opt-1.pycnkjp.cpython-36.pycnombank.cpython-36.opt-1.pycnombank.cpython-36.pycnps_chat.cpython-36.opt-1.pycnps_chat.cpython-36.pycopinion_lexicon.cpython-36.opt-1.pycopinion_lexicon.cpython-36.pycpanlex_lite.cpython-36.opt-1.pycpanlex_lite.cpython-36.pycpanlex_swadesh.cpython-36.opt-1.pycpanlex_swadesh.cpython-36.pycpl196x.cpython-36.opt-1.pycpl196x.cpython-36.pycplaintext.cpython-36.opt-1.pycplaintext.cpython-36.pycppattach.cpython-36.opt-1.pycppattach.cpython-36.pycpropbank.cpython-36.opt-1.pycpropbank.cpython-36.pycpros_cons.cpython-36.opt-1.pycpros_cons.cpython-36.pycreviews.cpython-36.opt-1.pycreviews.cpython-36.pycrte.cpython-36.opt-1.pycrte.cpython-36.pycsemcor.cpython-36.opt-1.pycsemcor.cpython-36.pycsenseval.cpython-36.opt-1.pycsenseval.cpython-36.pycsentiwordnet.cpython-36.opt-1.pycsentiwordnet.cpython-36.pycsinica_treebank.cpython-36.opt-1.pycsinica_treebank.cpython-36.pycstring_category.cpython-36.opt-1.pycstring_category.cpython-36.pycswitchboard.cpython-36.opt-1.pycswitchboard.cpython-36.pyctagged.cpython-36.opt-1.pyctagged.cpython-36.pyctimit.cpython-36.opt-1.pyctimit.cpython-36.pyctoolbox.cpython-36.opt-1.pyctoolbox.cpython-36.pyctwitter.cpython-36.opt-1.pyctwitter.cpython-36.pycudhr.cpython-36.opt-1.pycudhr.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pycverbnet.cpython-36.opt-1.pycverbnet.cpython-36.pycwordlist.cpython-36.opt-1.pycwordlist.cpython-36.pycwordnet.cpython-36.opt-1.pycwordnet.cpython-36.pycxmldocs.cpython-36.opt-1.pycxmldocs.cpython-36.pycycoe.cpython-36.opt-1.pycycoe.cpython-36.pycaligned.pyapi.pybnc.pybracket_parse.pycategorized_sents.pychasen.pychildes.pychunked.pycmudict.pycomparative_sents.pyconll.pycrubadan.pydependency.pyframenet.pyieer.pyindian.pyipipan.pyknbc.pylin.pymte.pynkjp.pynombank.pynps_chat.pyopinion_lexicon.pypanlex_lite.pypanlex_swadesh.pypl196x.pyplaintext.pyppattach.pypropbank.pypros_cons.pyreviews.pyrte.pysemcor.pysenseval.pysentiwordnet.pysinica_treebank.pystring_category.pyswitchboard.pytagged.pytimit.pytoolbox.pytwitter.pyudhr.pyutil.pyverbnet.pywordlist.pywordnet.pyxmldocs.pyycoe.pyutil.pydata.pydecorators.pydownloader.pydraw__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pyccfg.cpython-36.opt-1.pyccfg.cpython-36.pycdispersion.cpython-36.opt-1.pycdispersion.cpython-36.pyctable.cpython-36.opt-1.pyctable.cpython-36.pyctree.cpython-36.opt-1.pyctree.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pyccfg.pydispersion.pytable.pytree.pyutil.pyfeatstruct.pygrammar.pyhelp.pyinference__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycapi.cpython-36.opt-1.pycapi.cpython-36.pycdiscourse.cpython-36.opt-1.pycdiscourse.cpython-36.pycmace.cpython-36.opt-1.pycmace.cpython-36.pycnonmonotonic.cpython-36.opt-1.pycnonmonotonic.cpython-36.pycprover9.cpython-36.opt-1.pycprover9.cpython-36.pycresolution.cpython-36.opt-1.pycresolution.cpython-36.pyctableau.cpython-36.opt-1.pyctableau.cpython-36.pycapi.pydiscourse.pymace.pynonmonotonic.pyprover9.pyresolution.pytableau.pyinternals.pyjsontags.pylazyimport.pylm__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycapi.cpython-36.opt-1.pycapi.cpython-36.pyccounter.cpython-36.opt-1.pyccounter.cpython-36.pycmodels.cpython-36.opt-1.pycmodels.cpython-36.pycpreprocessing.cpython-36.opt-1.pycpreprocessing.cpython-36.pycsmoothing.cpython-36.opt-1.pycsmoothing.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pycvocabulary.cpython-36.opt-1.pycvocabulary.cpython-36.pycapi.pycounter.pymodels.pypreprocessing.pysmoothing.pyutil.pyvocabulary.pymetrics__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycagreement.cpython-36.opt-1.pycagreement.cpython-36.pycaline.cpython-36.opt-1.pycaline.cpython-36.pycassociation.cpython-36.opt-1.pycassociation.cpython-36.pycconfusionmatrix.cpython-36.opt-1.pycconfusionmatrix.cpython-36.pycdistance.cpython-36.opt-1.pycdistance.cpython-36.pycpaice.cpython-36.opt-1.pycpaice.cpython-36.pycscores.cpython-36.opt-1.pycscores.cpython-36.pycsegmentation.cpython-36.opt-1.pycsegmentation.cpython-36.pycspearman.cpython-36.opt-1.pycspearman.cpython-36.pycagreement.pyaline.pyassociation.pyconfusionmatrix.pydistance.pypaice.pyscores.pysegmentation.pyspearman.pymisc__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycbabelfish.cpython-36.opt-1.pycbabelfish.cpython-36.pycchomsky.cpython-36.opt-1.pycchomsky.cpython-36.pycminimalset.cpython-36.opt-1.pycminimalset.cpython-36.pycsort.cpython-36.opt-1.pycsort.cpython-36.pycwordfinder.cpython-36.opt-1.pycwordfinder.cpython-36.pycbabelfish.pychomsky.pyminimalset.pysort.pywordfinder.pyparse__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycapi.cpython-36.opt-1.pycapi.cpython-36.pycbllip.cpython-36.opt-1.pycbllip.cpython-36.pycchart.cpython-36.opt-1.pycchart.cpython-36.pyccorenlp.cpython-36.opt-1.pyccorenlp.cpython-36.pycdependencygraph.cpython-36.opt-1.pycdependencygraph.cpython-36.pycearleychart.cpython-36.opt-1.pycearleychart.cpython-36.pycevaluate.cpython-36.opt-1.pycevaluate.cpython-36.pycfeaturechart.cpython-36.opt-1.pycfeaturechart.cpython-36.pycgenerate.cpython-36.opt-1.pycgenerate.cpython-36.pycmalt.cpython-36.opt-1.pycmalt.cpython-36.pycnonprojectivedependencyparser.cpython-36.opt-1.pycnonprojectivedependencyparser.cpython-36.pycpchart.cpython-36.opt-1.pycpchart.cpython-36.pycprojectivedependencyparser.cpython-36.opt-1.pycprojectivedependencyparser.cpython-36.pycrecursivedescent.cpython-36.opt-1.pycrecursivedescent.cpython-36.pycshiftreduce.cpython-36.opt-1.pycshiftreduce.cpython-36.pycstanford.cpython-36.opt-1.pycstanford.cpython-36.pyctransitionparser.cpython-36.opt-1.pyctransitionparser.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pycviterbi.cpython-36.opt-1.pycviterbi.cpython-36.pycapi.pybllip.pychart.pycorenlp.pydependencygraph.pyearleychart.pyevaluate.pyfeaturechart.pygenerate.pymalt.pynonprojectivedependencyparser.pypchart.pyprojectivedependencyparser.pyrecursivedescent.pyshiftreduce.pystanford.pytransitionparser.pyutil.pyviterbi.pyprobability.pysem__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycboxer.cpython-36.opt-1.pycboxer.cpython-36.pycchat80.cpython-36.opt-1.pycchat80.cpython-36.pyccooper_storage.cpython-36.opt-1.pyccooper_storage.cpython-36.pycdrt.cpython-36.opt-1.pycdrt.cpython-36.pycdrt_glue_demo.cpython-36.opt-1.pycdrt_glue_demo.cpython-36.pycevaluate.cpython-36.opt-1.pycevaluate.cpython-36.pycglue.cpython-36.opt-1.pycglue.cpython-36.pychole.cpython-36.opt-1.pychole.cpython-36.pyclfg.cpython-36.opt-1.pyclfg.cpython-36.pyclinearlogic.cpython-36.opt-1.pyclinearlogic.cpython-36.pyclogic.cpython-36.opt-1.pyclogic.cpython-36.pycrelextract.cpython-36.opt-1.pycrelextract.cpython-36.pycskolemize.cpython-36.opt-1.pycskolemize.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pycboxer.pychat80.pycooper_storage.pydrt.pydrt_glue_demo.pyevaluate.pyglue.pyhole.pylfg.pylinearlogic.pylogic.pyrelextract.pyskolemize.pyutil.pysentiment__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycsentiment_analyzer.cpython-36.opt-1.pycsentiment_analyzer.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pycvader.cpython-36.opt-1.pycvader.cpython-36.pycsentiment_analyzer.pyutil.pyvader.pystem__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycapi.cpython-36.opt-1.pycapi.cpython-36.pycarlstem.cpython-36.opt-1.pycarlstem.cpython-36.pycarlstem2.cpython-36.opt-1.pycarlstem2.cpython-36.pyccistem.cpython-36.opt-1.pyccistem.cpython-36.pycisri.cpython-36.opt-1.pycisri.cpython-36.pyclancaster.cpython-36.opt-1.pyclancaster.cpython-36.pycporter.cpython-36.opt-1.pycporter.cpython-36.pycregexp.cpython-36.opt-1.pycregexp.cpython-36.pycrslp.cpython-36.opt-1.pycrslp.cpython-36.pycsnowball.cpython-36.opt-1.pycsnowball.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pycwordnet.cpython-36.opt-1.pycwordnet.cpython-36.pycapi.pyarlstem.pyarlstem2.pycistem.pyisri.pylancaster.pyporter.pyregexp.pyrslp.pysnowball.pyutil.pywordnet.pytag__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycapi.cpython-36.opt-1.pycapi.cpython-36.pycbrill.cpython-36.opt-1.pycbrill.cpython-36.pycbrill_trainer.cpython-36.opt-1.pycbrill_trainer.cpython-36.pyccrf.cpython-36.opt-1.pyccrf.cpython-36.pychmm.cpython-36.opt-1.pychmm.cpython-36.pychunpos.cpython-36.opt-1.pychunpos.cpython-36.pycmapping.cpython-36.opt-1.pycmapping.cpython-36.pycperceptron.cpython-36.opt-1.pycperceptron.cpython-36.pycsenna.cpython-36.opt-1.pycsenna.cpython-36.pycsequential.cpython-36.opt-1.pycsequential.cpython-36.pycstanford.cpython-36.opt-1.pycstanford.cpython-36.pyctnt.cpython-36.opt-1.pyctnt.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pycapi.pybrill.pybrill_trainer.pycrf.pyhmm.pyhunpos.pymapping.pyperceptron.pysenna.pysequential.pystanford.pytnt.pyutil.pytbl__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycapi.cpython-36.opt-1.pycapi.cpython-36.pycdemo.cpython-36.opt-1.pycdemo.cpython-36.pycerroranalysis.cpython-36.opt-1.pycerroranalysis.cpython-36.pycfeature.cpython-36.opt-1.pycfeature.cpython-36.pycrule.cpython-36.opt-1.pycrule.cpython-36.pyctemplate.cpython-36.opt-1.pyctemplate.cpython-36.pycapi.pydemo.pyerroranalysis.pyfeature.pyrule.pytemplate.pytest__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycall.cpython-36.opt-1.pycall.cpython-36.pycchildes_fixt.cpython-36.opt-1.pycchildes_fixt.cpython-36.pycclassify_fixt.cpython-36.opt-1.pycclassify_fixt.cpython-36.pycconftest.cpython-36.opt-1.pycconftest.cpython-36.pycdiscourse_fixt.cpython-36.opt-1.pycdiscourse_fixt.cpython-36.pycgensim_fixt.cpython-36.opt-1.pycgensim_fixt.cpython-36.pycgluesemantics_malt_fixt.cpython-36.opt-1.pycgluesemantics_malt_fixt.cpython-36.pycinference_fixt.cpython-36.opt-1.pycinference_fixt.cpython-36.pycnonmonotonic_fixt.cpython-36.opt-1.pycnonmonotonic_fixt.cpython-36.pycportuguese_en_fixt.cpython-36.opt-1.pycportuguese_en_fixt.cpython-36.pycprobability_fixt.cpython-36.opt-1.pycprobability_fixt.cpython-36.pycall.pybleu.doctestbnc.doctestccg.doctestccg_semantics.doctestchat80.doctestchildes.doctestchildes_fixt.pychunk.doctestclassify.doctestclassify_fixt.pycollections.doctestcollocations.doctestconcordance.doctestconftest.pycorpus.doctestcrubadan.doctestdata.doctestdependency.doctestdiscourse.doctestdiscourse_fixt.pydrt.doctestfeatgram.doctestfeatstruct.doctestframenet.doctestgenerate.doctestgensim.doctestgensim_fixt.pygluesemantics.doctestgluesemantics_malt.doctestgluesemantics_malt_fixt.pygrammar.doctestgrammartestsuites.doctestindex.doctestinference.doctestinference_fixt.pyinternals.doctestjapanese.doctestlm.doctestlogic.doctestmeteor.doctestmetrics.doctestmisc.doctestnonmonotonic.doctestnonmonotonic_fixt.pypaice.doctestparse.doctestportuguese_en.doctestportuguese_en_fixt.pyprobability.doctestprobability_fixt.pypropbank.doctestrelextract.doctestresolution.doctestsemantics.doctestsentiment.doctestsentiwordnet.doctestsimple.docteststem.doctesttag.doctesttokenize.doctesttoolbox.doctesttranslate.doctesttree.doctesttreeprettyprinter.doctesttreetransforms.doctestunit__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pyctest_aline.cpython-36.opt-1.pyctest_aline.cpython-36.pyctest_bllip.cpython-36.opt-1.pyctest_bllip.cpython-36.pyctest_brill.cpython-36.opt-1.pyctest_brill.cpython-36.pyctest_cfd_mutation.cpython-36.opt-1.pyctest_cfd_mutation.cpython-36.pyctest_cfg2chomsky.cpython-36.opt-1.pyctest_cfg2chomsky.cpython-36.pyctest_chunk.cpython-36.opt-1.pyctest_chunk.cpython-36.pyctest_classify.cpython-36.opt-1.pyctest_classify.cpython-36.pyctest_collocations.cpython-36.opt-1.pyctest_collocations.cpython-36.pyctest_concordance.cpython-36.opt-1.pyctest_concordance.cpython-36.pyctest_corenlp.cpython-36.opt-1.pyctest_corenlp.cpython-36.pyctest_corpora.cpython-36.opt-1.pyctest_corpora.cpython-36.pyctest_corpus_views.cpython-36.opt-1.pyctest_corpus_views.cpython-36.pyctest_data.cpython-36.opt-1.pyctest_data.cpython-36.pyctest_disagreement.cpython-36.opt-1.pyctest_disagreement.cpython-36.pyctest_distance.cpython-36.opt-1.pyctest_distance.cpython-36.pyctest_downloader.cpython-36.opt-1.pyctest_downloader.cpython-36.pyctest_freqdist.cpython-36.opt-1.pyctest_freqdist.cpython-36.pyctest_hmm.cpython-36.opt-1.pyctest_hmm.cpython-36.pyctest_json2csv_corpus.cpython-36.opt-1.pyctest_json2csv_corpus.cpython-36.pyctest_json_serialization.cpython-36.opt-1.pyctest_json_serialization.cpython-36.pyctest_metrics.cpython-36.opt-1.pyctest_metrics.cpython-36.pyctest_naivebayes.cpython-36.opt-1.pyctest_naivebayes.cpython-36.pyctest_nombank.cpython-36.opt-1.pyctest_nombank.cpython-36.pyctest_pl196x.cpython-36.opt-1.pyctest_pl196x.cpython-36.pyctest_pos_tag.cpython-36.opt-1.pyctest_pos_tag.cpython-36.pyctest_ribes.cpython-36.opt-1.pyctest_ribes.cpython-36.pyctest_rte_classify.cpython-36.opt-1.pyctest_rte_classify.cpython-36.pyctest_seekable_unicode_stream_reader.cpython-36.opt-1.pyctest_seekable_unicode_stream_reader.cpython-36.pyctest_senna.cpython-36.opt-1.pyctest_senna.cpython-36.pyctest_stem.cpython-36.opt-1.pyctest_stem.cpython-36.pyctest_tag.cpython-36.opt-1.pyctest_tag.cpython-36.pyctest_tgrep.cpython-36.opt-1.pyctest_tgrep.cpython-36.pyctest_tokenize.cpython-36.opt-1.pyctest_tokenize.cpython-36.pyctest_twitter_auth.cpython-36.opt-1.pyctest_twitter_auth.cpython-36.pyctest_util.cpython-36.opt-1.pyctest_util.cpython-36.pyctest_wordnet.cpython-36.opt-1.pyctest_wordnet.cpython-36.pyclm__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pyctest_counter.cpython-36.opt-1.pyctest_counter.cpython-36.pyctest_models.cpython-36.opt-1.pyctest_models.cpython-36.pyctest_preprocessing.cpython-36.opt-1.pyctest_preprocessing.cpython-36.pyctest_vocabulary.cpython-36.opt-1.pyctest_vocabulary.cpython-36.pyctest_counter.pytest_models.pytest_preprocessing.pytest_vocabulary.pytest_aline.pytest_bllip.pytest_brill.pytest_cfd_mutation.pytest_cfg2chomsky.pytest_chunk.pytest_classify.pytest_collocations.pytest_concordance.pytest_corenlp.pytest_corpora.pytest_corpus_views.pytest_data.pytest_disagreement.pytest_distance.pytest_downloader.pytest_freqdist.pytest_hmm.pytest_json2csv_corpus.pytest_json_serialization.pytest_metrics.pytest_naivebayes.pytest_nombank.pytest_pl196x.pytest_pos_tag.pytest_ribes.pytest_rte_classify.pytest_seekable_unicode_stream_reader.pytest_senna.pytest_stem.pytest_tag.pytest_tgrep.pytest_tokenize.pytest_twitter_auth.pytest_util.pytest_wordnet.pytranslate__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pyctest_bleu.cpython-36.opt-1.pyctest_bleu.cpython-36.pyctest_gdfa.cpython-36.opt-1.pyctest_gdfa.cpython-36.pyctest_ibm1.cpython-36.opt-1.pyctest_ibm1.cpython-36.pyctest_ibm2.cpython-36.opt-1.pyctest_ibm2.cpython-36.pyctest_ibm3.cpython-36.opt-1.pyctest_ibm3.cpython-36.pyctest_ibm4.cpython-36.opt-1.pyctest_ibm4.cpython-36.pyctest_ibm5.cpython-36.opt-1.pyctest_ibm5.cpython-36.pyctest_ibm_model.cpython-36.opt-1.pyctest_ibm_model.cpython-36.pyctest_meteor.cpython-36.opt-1.pyctest_meteor.cpython-36.pyctest_nist.cpython-36.opt-1.pyctest_nist.cpython-36.pyctest_stack_decoder.cpython-36.opt-1.pyctest_stack_decoder.cpython-36.pyctest_bleu.pytest_gdfa.pytest_ibm1.pytest_ibm2.pytest_ibm3.pytest_ibm4.pytest_ibm5.pytest_ibm_model.pytest_meteor.pytest_nist.pytest_stack_decoder.pyutil.doctestwordnet.doctestwordnet_lch.doctestwsd.doctesttext.pytgrep.pytokenize__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycapi.cpython-36.opt-1.pycapi.cpython-36.pyccasual.cpython-36.opt-1.pyccasual.cpython-36.pycdestructive.cpython-36.opt-1.pycdestructive.cpython-36.pyclegality_principle.cpython-36.opt-1.pyclegality_principle.cpython-36.pycmwe.cpython-36.opt-1.pycmwe.cpython-36.pycnist.cpython-36.opt-1.pycnist.cpython-36.pycpunkt.cpython-36.opt-1.pycpunkt.cpython-36.pycregexp.cpython-36.opt-1.pycregexp.cpython-36.pycrepp.cpython-36.opt-1.pycrepp.cpython-36.pycsexpr.cpython-36.opt-1.pycsexpr.cpython-36.pycsimple.cpython-36.opt-1.pycsimple.cpython-36.pycsonority_sequencing.cpython-36.opt-1.pycsonority_sequencing.cpython-36.pycstanford.cpython-36.opt-1.pycstanford.cpython-36.pycstanford_segmenter.cpython-36.opt-1.pycstanford_segmenter.cpython-36.pyctexttiling.cpython-36.opt-1.pyctexttiling.cpython-36.pyctoktok.cpython-36.opt-1.pyctoktok.cpython-36.pyctreebank.cpython-36.opt-1.pyctreebank.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pycapi.pycasual.pydestructive.pylegality_principle.pymwe.pynist.pypunkt.pyregexp.pyrepp.pysexpr.pysimple.pysonority_sequencing.pystanford.pystanford_segmenter.pytexttiling.pytoktok.pytreebank.pyutil.pytoolbox.pytranslate__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycapi.cpython-36.opt-1.pycapi.cpython-36.pycbleu_score.cpython-36.opt-1.pycbleu_score.cpython-36.pycchrf_score.cpython-36.opt-1.pycchrf_score.cpython-36.pycgale_church.cpython-36.opt-1.pycgale_church.cpython-36.pycgdfa.cpython-36.opt-1.pycgdfa.cpython-36.pycgleu_score.cpython-36.opt-1.pycgleu_score.cpython-36.pycibm1.cpython-36.opt-1.pycibm1.cpython-36.pycibm2.cpython-36.opt-1.pycibm2.cpython-36.pycibm3.cpython-36.opt-1.pycibm3.cpython-36.pycibm4.cpython-36.opt-1.pycibm4.cpython-36.pycibm5.cpython-36.opt-1.pycibm5.cpython-36.pycibm_model.cpython-36.opt-1.pycibm_model.cpython-36.pycmeteor_score.cpython-36.opt-1.pycmeteor_score.cpython-36.pycmetrics.cpython-36.opt-1.pycmetrics.cpython-36.pycnist_score.cpython-36.opt-1.pycnist_score.cpython-36.pycphrase_based.cpython-36.opt-1.pycphrase_based.cpython-36.pycribes_score.cpython-36.opt-1.pycribes_score.cpython-36.pycstack_decoder.cpython-36.opt-1.pycstack_decoder.cpython-36.pycapi.pybleu_score.pychrf_score.pygale_church.pygdfa.pygleu_score.pyibm1.pyibm2.pyibm3.pyibm4.pyibm5.pyibm_model.pymeteor_score.pymetrics.pynist_score.pyphrase_based.pyribes_score.pystack_decoder.pytree__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycimmutable.cpython-36.opt-1.pycimmutable.cpython-36.pycparented.cpython-36.opt-1.pycparented.cpython-36.pycparsing.cpython-36.opt-1.pycparsing.cpython-36.pycprettyprinter.cpython-36.opt-1.pycprettyprinter.cpython-36.pycprobabilistic.cpython-36.opt-1.pycprobabilistic.cpython-36.pyctransforms.cpython-36.opt-1.pyctransforms.cpython-36.pyctree.cpython-36.opt-1.pyctree.cpython-36.pycimmutable.pyparented.pyparsing.pyprettyprinter.pyprobabilistic.pytransforms.pytree.pytreeprettyprinter.pytreetransforms.pytwitter__init__.py__pycache____init__.cpython-36.opt-1.pyc__init__.cpython-36.pycapi.cpython-36.opt-1.pycapi.cpython-36.pyccommon.cpython-36.opt-1.pyccommon.cpython-36.pyctwitter_demo.cpython-36.opt-1.pyctwitter_demo.cpython-36.pyctwitterclient.cpython-36.opt-1.pyctwitterclient.cpython-36.pycutil.cpython-36.opt-1.pycutil.cpython-36.pycapi.pycommon.pytwitter_demo.pytwitterclient.pyutil.pyutil.pywsd.pypython3-nltkREADME.mdpython3-nltkLICENSE.txt/etc/alternatives//usr/bin//usr/lib/python3.6/site-packages//usr/lib/python3.6/site-packages/nltk-3.7-py3.6.egg-info//usr/lib/python3.6/site-packages/nltk//usr/lib/python3.6/site-packages/nltk/__pycache__//usr/lib/python3.6/site-packages/nltk/app//usr/lib/python3.6/site-packages/nltk/app/__pycache__//usr/lib/python3.6/site-packages/nltk/ccg//usr/lib/python3.6/site-packages/nltk/ccg/__pycache__//usr/lib/python3.6/site-packages/nltk/chat//usr/lib/python3.6/site-packages/nltk/chat/__pycache__//usr/lib/python3.6/site-packages/nltk/chunk//usr/lib/python3.6/site-packages/nltk/chunk/__pycache__//usr/lib/python3.6/site-packages/nltk/classify//usr/lib/python3.6/site-packages/nltk/classify/__pycache__//usr/lib/python3.6/site-packages/nltk/cluster//usr/lib/python3.6/site-packages/nltk/cluster/__pycache__//usr/lib/python3.6/site-packages/nltk/corpus//usr/lib/python3.6/site-packages/nltk/corpus/__pycache__//usr/lib/python3.6/site-packages/nltk/corpus/reader//usr/lib/python3.6/site-packages/nltk/corpus/reader/__pycache__//usr/lib/python3.6/site-packages/nltk/draw//usr/lib/python3.6/site-packages/nltk/draw/__pycache__//usr/lib/python3.6/site-packages/nltk/inference//usr/lib/python3.6/site-packages/nltk/inference/__pycache__//usr/lib/python3.6/site-packages/nltk/lm//usr/lib/python3.6/site-packages/nltk/lm/__pycache__//usr/lib/python3.6/site-packages/nltk/metrics//usr/lib/python3.6/site-packages/nltk/metrics/__pycache__//usr/lib/python3.6/site-packages/nltk/misc//usr/lib/python3.6/site-packages/nltk/misc/__pycache__//usr/lib/python3.6/site-packages/nltk/parse//usr/lib/python3.6/site-packages/nltk/parse/__pycache__//usr/lib/python3.6/site-packages/nltk/sem//usr/lib/python3.6/site-packages/nltk/sem/__pycache__//usr/lib/python3.6/site-packages/nltk/sentiment//usr/lib/python3.6/site-packages/nltk/sentiment/__pycache__//usr/lib/python3.6/site-packages/nltk/stem//usr/lib/python3.6/site-packages/nltk/stem/__pycache__//usr/lib/python3.6/site-packages/nltk/tag//usr/lib/python3.6/site-packages/nltk/tag/__pycache__//usr/lib/python3.6/site-packages/nltk/tbl//usr/lib/python3.6/site-packages/nltk/tbl/__pycache__//usr/lib/python3.6/site-packages/nltk/test//usr/lib/python3.6/site-packages/nltk/test/__pycache__//usr/lib/python3.6/site-packages/nltk/test/unit//usr/lib/python3.6/site-packages/nltk/test/unit/__pycache__//usr/lib/python3.6/site-packages/nltk/test/unit/lm//usr/lib/python3.6/site-packages/nltk/test/unit/lm/__pycache__//usr/lib/python3.6/site-packages/nltk/test/unit/translate//usr/lib/python3.6/site-packages/nltk/test/unit/translate/__pycache__//usr/lib/python3.6/site-packages/nltk/tokenize//usr/lib/python3.6/site-packages/nltk/tokenize/__pycache__//usr/lib/python3.6/site-packages/nltk/translate//usr/lib/python3.6/site-packages/nltk/translate/__pycache__//usr/lib/python3.6/site-packages/nltk/tree//usr/lib/python3.6/site-packages/nltk/tree/__pycache__//usr/lib/python3.6/site-packages/nltk/twitter//usr/lib/python3.6/site-packages/nltk/twitter/__pycache__//usr/share/doc/packages//usr/share/doc/packages/python3-nltk//usr/share/licenses//usr/share/licenses/python3-nltk/-fmessage-length=0 -grecord-gcc-switches -O2 -Wall -D_FORTIFY_SOURCE=2 -fstack-protector-strong -funwind-tables -fasynchronous-unwind-tables -fstack-clash-protectionobs://build.opensuse.org/openSUSE:Maintenance:18495/openSUSE_Backports_SLE-15-SP5_Update/5461e92b3a85d263a4689c3d3cc69dd0-python-nltk.openSUSE_Backports_SLE-15-SP5_Updatedrpmxz5noarch-suse-linux                                        emptyPython script, ASCII text executabledirectoryASCII textASCII text, with CRLF line terminatorsPython script, ASCII text executable, with CRLF line terminatorspython 3.6 byte-compiledPython script, ASCII text executable, with very long lines, with CRLF line terminatorsa /usr/bin/env python script, ASCII text executable, with CRLF line terminatorsPython script, UTF-8 Unicode text executable, with CRLF line terminatorsUTF-8 Unicode text, with CRLF line terminatorsASCII text, with very long lines, with CRLF line terminatorsUTF-8 Unicode text, with very long lines, with CRLF line terminatorsassembler source, ASCII text, with CRLF line terminatorsPython script, UTF-8 Unicode text executable, with very long lines, with CRLF line terminators  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR6S;<JSְpython3-gensimpython3-matplotlibpython3-numpypython3-pyparsingpython3-python-crfsuitepython3-requestspython3-scikit-learnpython3-scipypython3-twythonutf-87a0c32968b795d53e43a84f62e5961dd68f7c374a03695abec1f6c3434b15914?7zXZ !t/5b]"k%{^E) `>GulcMеiҕJP& f\+ <~@UuT(["^i"tPwes0[Yp1uƟ،3+znq`9U\ax&|wкAH%K{d&0L}d<5t2W ʦJ׈%hgVi<9L^jZ8@{@c F}6п*v۸%1ɡ'29J%{| Ei_=n ШMz:u}:$>s}^Q80ک |^`_:y4H6rmL[q]RJ7]ĿIsG!*x $9muރ;l7@w-^T<pj`DL` JeŲSo|B \A74aeRHa(M7f"_9l &Xڻr;p{ k*oanFP#xEIV`m> G*U8>K8)7@v-{-q X6+cg6&Mbzlm :Z6LsRIio>Jf}*faa g]vq ^T c8𥐎|we%c6oqօ1~?͹w`?,Ɠf9*KXP7T`r=TԨy/6RS2Tpt{D{U7UP^魁Ikjw7R nv~¨I!9Ψ|jM#abu &lh1mBħi䡃ʕ 6sˆ 1kh8 /MZᇼ)7:IBד5L,{pe]ǢےSs+ajI9v1:yӅab^{>MӐj>IpdXwQ%PRow?0n8 n5@ae{ELgx *#1Ͻ~AbA9}<"<~:ݬ1xoA9EG-ߺmaGgޛTڢAeLV1]KVc%n .kmxѰK@&eBoLL"\ZN?ldgtb'O^.bc=jVd+—=k#5}к1+ sPF1ޙٜ^<Y%6¢Jͥ_ 7̤R(0Ғ^Za;)RbE$7zHҿ2A\VB?X{P%զ:7~}7h9=Ajv].BS)sBo:\\1u]'=}}6قU? 9by6A-&B#TUB4:"KMq59Vȗ C?,eS_?P,bqȗ~ }igFi‘H4J*xu7>۫5'iHGv̼2-ǩ7N3*LOdmP-%})faJ /̳kN6?F;o[PQL?H_f=Ily5X;`ّnGc"vD!.O 1Lm:4*W3ྎY#xg:vPJn F2Xצ_[thlHXt"FH9nß_4b~;3$ݜs!U &;([6Ml|w0Ja6:X"_~E2J8 G:n>m{aϟóTwsf+{p b$4^efȋ iⷨQ&Z ͠ 2?Ѽl'4[$oiv1I3rm 6S}VL9i'|JilBif Q~2U*ESo@A(#LM5(n9 ɚOvݜG[349}͆ D+G86lpnyn^>{F- +:m#7ڼZqR6z#1izEvrSjC8 h,4gpȍ4Xh_uvdHhkH"لXLyr%lE'F$Sbg(i- i1Ide0LrRm_"Ў%%wץSH@7 SBIeyXE-&Гӥ-5eG&[,hpPY&KL?RkNHw)ai|WK\vŲOHV"CLRP[Z OjSr{Lw>S u*g =Ucpcѷ}}Q\?Z$ǎ]S+?-׎YإIS4JA)d84$:="/Vz|zI0=FB'Rm($/hgvX\B Ʈ})dS4ۈ\,Tǔv'u ^zo1"x )BzA'W5.*].Qra>6b(iG9v,%[ wŧz\ǿ$<&(㐹Yt6F}Y3Е~* -&snlPG˿/:p#?̳!P`u4685.=C9ϕɗ6Rę}Jض| 6H k.h|]3YZ8,Ks%P#ʖp2*F. 5Gu|KBJi;`?uhypGT>kԕ"( q -1 gڻz]7ېZqT5(!zSU _\רoVWL7 `Y z $Aċd84bo:ht3k J!곀w1Qx 9()#^$L O0rS2qi;(UͶȨCiPDSWj s)?Bg9{qb|٪eBSCTJ![U:]/ VA?@k[r)膌4L({`[x KFK[cͶ #0r3L~ץpX߾uQtz a-Ȫ˅ B-%b_ByCw=G1s)fW2ݛ2k@1ȩkWF5MA w,Ai*m1RDBG=A^Չk=.8yHNd8|YK+XL:nᅷEb" ii´64mcsgNܢ=xj!iv 6yT )X7]U>~NVMZi+beQMO|q;&w^.ɓUNDэi0,fM7*j $gvvOS s\);S[W|_3H7_WEW=dϺ2A񜞆AG:O8 |xo+H0j+6SM+\.Ʉ)ٹd/ B)鐱) 8(ڍw%Raĝs>OȺany\W Q ť~>e0^8k{"鷵abt'5EPAJ9y"0׹I;_ f9ۉe/7x 1MkS-a^B&p',;DFp-D&[{P+dH`>ӊ/5CN`{0lRk 3)%C"*!9{8E1#Nhq>afO&e|5!nhs|GX|hOnCm#|DFP(F3)%|BK7 }ΈeR^ c${2RjT_$&xmYnTgX?rT?#¡3^ƃW~gzuIXʚQ_ش(m-]߲pDqZ. t5G2ʠ9z.'zzrYv fѷ2#T:mgʖpTNLC৩ ,e:$~ M)/v: {l6"fZ>t3V@ lϏ.ۋp 5qF31N\g>aj1(w՜^}P&T ·#(M' otob#>'r.ԓQRTTŹb_]@ت"]#ݲ ɭF(9GW_I% }mʽP`>J;tZa%Qz$pwV t A'aevNp3S^";jhc]6HO2*nZ(t$ =nݶmDj7"ub.8x%k\XW7}a ;$&ӣeLj^h %fPE[p >UQH NJ?ZB_eV &B~eSxv\Bi^g\Q  6,誏Zjgh8DX PO:Z^JZ=fHss:x LH]Y(^}Nzh9fÖv[hGi_C˝(PWIj0zT%[xs |&~{J]l\ [>}e'zJW,luoWg_qp9cJ:cl#Z r+Bֹ[3j7bGzyeh]oh1Y7szW1CzhPʞ0r_saڒ@Q5}]G=#B]d#3x|qEbrPDcפ>yswJ,Hʛȿb.=;Vr%VOl~ZF&M,Y@َۨ+*e2y^!z})-a֠lv5b5^Сe{ i aEfיuZ(k>^5 մ9_M8Mg[gPISkfN)@C& 8v.ZlOg3DM'Y"˥^۹Gpcnu6_s\٥UhTSXIܼFfY~ts*\jꉏ|NOKXpӄ!fp 0U_j3ЊA/{.G&<$y|G&or GUo4*pPX,n%W$T .d]^NP'XO(8Woa%d=k@j9Ir=˰(S;9AWfIY6L77ǓdBt#|m:;qZaUظOZ.Kd`oe!NT556kz0aK٬ՃY{?H-qF|mM+5, dI<6*Y0ڀ {Ez7u{˙=dTsJV#"\O3Vm VD{ )R5ZdZ8)7!XS}1Stz5!%DM w4l]zoG piRvZM )Fӎbe~U;?PWF֭;v"JZ lS6x:ZTucPX]Y+BRBl},Xܼ\lr|m?O>3;@6k*e)̒ ~ZZOB#݋⟇*/q%տ IZf{9FNyPb'p ұtKMkV#)  W}Ya3p-#FI)U0~ c̋R X*r[2?v4~͉+'9$ ;BTQ1},6Ur^scg:eRT ¤"7 㡴Tb_1'oEZXFI֯9oW$L.}E,.S1&Hs^)|rgGS$zT O'$ȗΎJ&xaVwW߾ju Pp]`19(FϼeZN#,_~ \$&,}!ksƟ+Sd!j]mPfF.lL/"R\xݒݼK7.B:ЃX=Kxeruu_U99_qyù\*W<V3*3LRQ7յ>dgV*;0kÖh Abja;H lĬi > qE Vaw&~)5/Gzw\LD '3C']\ Fw$ <g5V(z4S+50+G㵼;jZ !'Aca%}{NDF{ bpk40Lc\Å=rSm"l ª)cuW7䀱,=@~Ju!"kG%h3GJw|Yj*C l \qHKgFoG/b]F)4b5 ᥲd6v,ELP׶bcZJі۳(i7$f7V+Ȇ x돿ݫ}RV+wJ}BzAfC-8![(#'OnL0/g~NϠ^4m8Ѐ'\`D2_FO@v5 GC=l5/:Eu] %O_8gْZog 'kIvueppLn"+|3|b5FTT{)=+fNҘNi'WRaM0h>f( 4aݰ,uAw*@?o)6-t6@+Qw/'?|ZOKBYcBQM$[˟ĿGЁ77^I Fد҅>S|XgiKyaNV X GUԋ'' Jc㗢DhaFXkvԑ Kͧ"4 ęԃz̰bX\>uJM+,RIDp sHmwO7|8IaGHγ]^W5 &? C`Yp\? `&͔tKwY71' qxDrWJ>g95 ?yN{SV_dwKi눛= F3k)Z2\pt>1aF|s6uwXi 尯l\;x ίb3١{r [XBGOk< ^aL0*)Lw"o2WwþmK+#>%UL!:'~Emx2  (m1TOf5['_Hii\z5.U&XQF4+zẗ́"}QcM\LF,DbѸLWgW-iƷ"*=IV*Jt'Ч8ck3VC^.01?/#2A5|I<)~@:3IB]iRLHd t_0.mK?6=΄YbyuI ] \r0#rOۙ垵LT.p#DBh P`:Pt{<>s?&bգ**7@jͮ@gW`F2 q\LpVQ4+uY@k;oGm6#cp0&/wf0ތ9L >,iB.adӠd2ײfL|ɧye;&%$BHzsoe3gƘ $~Yy&|}^.J~gN1ڰlݻ#)j]RF yoC{n<竖Rz(8J.^dƇ6*D4(Ƴ@?7<sQFN9jzR"t ($ hx{r0UF-|t8m̬ )'t>wە̬ Jp 숮*שo>%Tdn9Ykݖl/pwiE \k/NuDו("v~JV1~$)o)BJ)ܰmװ]xG~Xj֨s\NZXh\' bp"vMHً(nK*&X/nXcc2KҍWha";oMt#Pɔ|}Y)Wz}G0 <ͤu C7͌l<R~!!Tx6(֏ipi2}X2JkU5F?ڭHKFiBK@LiikRxɓAzi9mO޼|a ZsDC?f\uSxG)6;)6k5X]}ˊʶ֧}Mzj%z` iʯ/6jKi.*X|蒕̈́ cMw)+ v"c[U0TN{`qL|hBh{K>12_"$o(PŽ3w\R5.pL1C"%N-Y"HLN9ىה?h >t{No቎[uc#yrpт*?7FMu徭ͣ ˱@zۋ,X m oBGlm&ޓhC=ݠ[; 7ɏ֗/GQP:SD`!{.Z@@ǠoS*3a_v^Djru3= T_Is2WB$E^s;}x6 <3N0TZTSq](Xts\sJ&=aujo&:_M=7y$X"砰/׬Fc`⌍LC4J{DƁᐴ $DK)ɨ3xzI?d=笚t8*6XEJMJ)ՂIsQ}QGZǕmB#y#=κ)Hn]iYUKr?Ñ+Ü/̧>ċW4r?HCA!8$.|Qy(vV `+Vx.(=zsnaDBk]yyɫ uU1We6 ;Uh^/r V̈́NC9m_Ԝ]zt^0} :5q,)>9@6O#zy7VjX-n֫n6Vϐ.{_:V)#.~&`6[jpjX`ˇId/ް)o/ycy/bӼǖ➔ c s#L[=*1,7NgиZOo)SZʾu:y&E'h..퀰6fb X!#CFfw;\pJJt |[GQ @d̈sFUeR& Y^U/I)^5Ϛ"kT$&3 [-fdUwO*?9/P+ĨwӳMwluHp?vYaj( )(] !㓘Tust_`3xJV"ε\*xs*{ &bGESytc;F1$Lj8YҴ28L=')7V޸FBӖ`忣1?A3 RLF-ik=j2)R5yؘXHrX 9T [FvP؞:e7WȂtV%H 0u5:Nl*"7CL<iNkbkjKbt[.c"!ki9ZoQUw.t-r<ͺWٽ(CEJɾu j((V%"}W.*q{_ZnJi&I`׏ Um8 0J} %oȆ@Qtgd_ike3\h䙗Tӎ|@CYunZ}.߭L`| QKi9gSvz%d|#Bw)ӏ0 ^105FG/^, Ay<7!rvvVdC-CB ҟCwVJ`z(}4u!zkr'mGcRwgT3.g2qX ؓh)EHvE9,wB|\q.LgGFn#fJ&"YMzPjcԠ>BUw*x8fy!vG}糨R薋AD E+@o9'E cR(.US8cum&7/J=pź^T@34DS|~FyMe'A Uloa{d|3pokMzqXyW=r^HQ j?0HL.Sl?OוsEmtqLsR/iCY{jV4VSÞ\Z䖍)DC>jL'ڛP{d-(.scxH@T- ]d1 r0ŷnuhDԨg ūBi- 75;~ w!<t-ٓDq & %NWR7O/R+:,ˊN#ck`MG٪s p?0ʉMMjA3 u%~RB Ê^;p]7O1)m+U~66G@{Pe7v!WH% -' v1&xp2}[9A"ʥTxe$O+ n÷/hN_FPJM,Nb$qj/7yFoE`]ic55w1|(2b1wA'sje^u+;/=yYHq ]WrV\LLb%ม삾ֆX[1cTBrT3|eĢ+\뤬lMȀm Ħ"TZP{P{ 0m hB.ڎyjXIrml6Ctns\#:4{M{u4~IӒ1.,ہ*bvȓJz8>?.שG#T M;]dUP>`_e杀.<Tˍ(#H>*3W3)cT~`~y8RS-xQ$"3 Y d>Q^ 멠CXLN/MȕFfVNfـv& 8,% .:246aA";Gu%|ƒ+aV.:7`Z-e gy Wr`pXެ0"ꃸq_@2h?):v gm(UT5Ex9M8){|: f^j"dVXg7v%CWavn);$Ct'΍-6DO@Cw8x!bzKlX 4$Ũf]uAQLݚXbl[+Ctp]!آ{- 2>\ldo8Ӣ<Z A 7AZNO j/cSzp4i3]`TrmFFk IRƝ4em6fV&IDoxhPA59} `7ev1pd^J@T/d {}ws-YRDDSew5b{{O Rkpw]"Km 1ŕX=2u\%TB  /&1ئp?%']٬Usƺg)R)><-75Lnd|.^ ڱmsUa!?>uX҉? 939Q٧눧b-w,&9G0ugT1͘ cD֧3C|a#)ctZ2ה5}YP^ό%EdZjAd#ULb 3[ȫ *tέJef#%,_-t뮝:(D>]c-a5S1cT'qOp8,.SqyzJ"[Y!#oiEUIQxoS}cFUboC<r5`aj|H?7MU93MNz'å-Maw8o^ 5hO˶ғ'-ۘ'E _: 5ՃukLY+=]EE?;*Xf 7gwan,EX,pܱm@4$yI+J'6iK+#q?IJE| C&ϔHzdN %uۨ mq{ 9rŤ}(RsALkz#2?_$wL* w Ih քA:Ns^%zϑ6SbG\&\hC_6C"\bd~qSFEU ytB2^ב0zQ^\f6CKXfwXqhW>q %Vq}ߐ`}mFA'%zooIw/_$T& *cx4.uv vbW;Zٰ.M˂4*3TbNW2Ir2)6~'{9(>`w8J9t8!5NFhƿQ0ڈ4m(&Q:a )iM Ӫ}d|yG:sV垥o%n^<aLZ$xg?_׆Á*QU \ "SZ%st2*>ǔ=≞XF6 yUF49R8I%2!Ucko[_(zC޿NH1FƵ> ?;`, Giz t14J ~ )d^4CPVY2*Q%ؼܨ#/CБ8ߙ.UzmfV> iMxy]4Ĝd`|ȖYZv$ p!ϤWZtԠ~9e1y4hݞ|_#z{ӌ}%&T?|Yaֱo,|u,?/*<26*l!5dKa:{s|guEFv+a 3G@ p*:0m;1aaZ`#%?F{ږ}\mGi SF)|Ϡ] F:FfT!Q):7 #$vhZl;QlN-XH[x5&ECrߛn_D V,Q'co#`3ys~PR$ri:}Ts䃝!}7Yh4:Xl#A) $xJ~ '"Kt d6~9Wd%NXz*RDƖ&nҧ|+\|47S"OQviH7jpĠ^rΎ>7X~j)yѡcaM:vv vTzϴvIiZV.l9,~DdX{`D>$A2 K  9{it`I]XW&R[uU$$Gf@C[Mm֟8URR?h.c '.pif5:5N >?|{#X+ylz NQ^Cxd@ؑRbTGwɈVN 8 d>JzOÓC;P~5p׵K ^vKtTuFՙ.ČZk Ր.Rb~SD #vos'0&%"~ *ymEH͍#^Ѷ1 q ,#1YΩԸ(fL-2Vw7T_gqO[O⊛G&/ϕF 5~ݾ> {Si[^ (+DTHXlۨ?xҲ}ޤb`xw -t@2z"QF-WhiGfZ cp9k/`Ho5ÞL_͗>R>,V8 ZvNQK-9`c_ @sBxX=NIrI{d=x d؜˝Q1JR|sf7}Y10NY¶OՃ$Gr )aȨ>7*uIgY:RTGne$yUV뺪@ǸXXC/D״67=T:׀B~p"xs)~FX^oihpvKλ9ˎsBĩfQl*mSȏ^uh3B{| yf$![O wn&kC¸/g5 .c_vO6wm2z6 i>dA{g:î#EFQGy!a֌U)c##Qf[xv> 1`'Bh9S"te"qiAu>7‡[g ~'JrW)Ѫ/{[D>]/ ;.%݂ K9z>췠5QY3TcQRB&Dm`2D0}~d{ v%T 3+zs AU c&Nh;3qPֳ^(c im\;DͩK8N-&G$*DLHCq!8MO z;JшմMmQzj1z}-q"j,DJ9]<R،%1T.GRgE ͅs;-^TO J̭3#Sh8|y1/w-D@|W+Hi" ( {`)6e|բ=| Va 扄 (⡟4CpfV/eeRND捡ަΩ`fK=bb49b,OiY*`yh Ivsʻ +[*#4ڡ k &Z8kϋD^5 iF}]=ޔb*H]F? s*Intp켧~;s2Ͱ[WrKϬ} rv M._uxJ֚{J-X+&2y#**lq_ie=Pek ~+ȗn-f ˿ ar|Gx\l5|eM땊+Ӗժ,a2" E s>_ϣDMbgzjq51{^y ѪĶۣ~y]/wh%U^އpi[9DYVT8Ic՟ $/rU Ӌ{תkGzɟkC=W#7$z Pkcx3 R )[5Lȴ7 \`\0 pjud_l-sXF0IF 2sj65.2+LQ$}~v?@ZDT*3 wc%w)H5v:37t>7gZ{5-s47\!6v>@.wEb&ڞf߱t06]@-dk y=U+BuB-Mekka\  `[(=әѲ*br.=?Ǔ09n| -t.`YȬaY=Xf;2n=ۇjQ 8Jvɥ{TFpT&8؁Z~<WTrVoCiRR凲ܣ 5F %ڡ͔ >po ]}Hڟ,K0ziF,_yߛe8&I4_f^qqm`(eDobm)P3aC4 bVa8۲B1 5UUX/ 1ubbnS9\bcO-ώX5Y)>V-4MpXMj]QW\R|}F:ʸ P97>=-δxA(;e0< %͢|BX_֖(ī`l&5W3RzC=N ^rl/uG*7@0 _6fQ@oImtz=@H~F0+R 9A} 6I:$I},)>CrŒF ĝfJpjGyҝe]dL읱*qGcn5caK4`9j/`˟Ι(t:CN+`9S1r!jh4TbV墫>4.z.)&N K3SB4+᥈5xEXm9#ϖ$ڝ`SL?м(Z-*IWq[U6B9OyOP/23ߛ;Jl5J~H (\1t,RSKwm= *o$إ.jL&$}9N=lFڽ`"nKgheH/Jɭm&)a{}~>͠BM"Ƣn* ZU?'JoF/&Og]&8d^gddD&Y-nSW?w]ѥU#-'0{Smn1~w 5^|(UN! L6QY3KaCTjw:W"өd!J :V@_CM8?J@dԲrIV/C6-* ˆ 1hL 7g*ظQ;:D1: X\I<3%ϋ0s[hي2/ԍe`T^(|Q0U9}^zc;tM:i[W"BhʄoÀ7K.p'.s*D퀉sݼ~ Vt`m褹 L3Q44#hzDO$#]wC;jTWb=o7! E7u !Gq` w|,տ|KV~+9CrE8 ;OOmۣ:59i@8hECv ]SyT` Lo3SlZA1]d-.e$Uv{Bf0Ay5)΀<ܩlhB!=7ҧhxBi^gՔ UڤD򌤙Ĵ~Ց3^7;VrR8l %vR5Zrfõ0q& NE0#DЭf ]&Kc{VU4TD2X 1ÃLX4Lb6Gl%r@TĆ)b <%6}x2MDl'aWu`Jdk\zN@r 쾍DO/ P8RD:5cNÂi:쫃Ļ}ڲnX@hP| ،s,J0ۓ,< jS |)x׋?T:Gyx˅P,IQmBP%]r@v5mU!hgK 7;2ݗMJ`5i+ LƎfx$!>0 {Փ)WX(nt @Rt_s |.xG`@\oh3;L9fk¯c]2(9M<*zJE8 co7Hif}$NR=cI'cHOm-g2dcބ atv )׆f />=ol]w:@`5pȣU)fN{VYNW 1( Ec+@ /n^q,Xk e;1t$~7X";/ӽ#I7Ũ&fgȹC{q9iR*PQ|l= 4kAw GWsFOWI<->?jB   eb}g^(H['H݊xv2^ՈhH<gz 2uYVhQ3& 48‡-~UMdGANcq䛠Nv:$"DWq +HA{C+5⪑Cb\?0Tٶ8 :e:laX(G<ؑ:N7q+ؽ9?/H&# k`J$Js& i=I}l8-XhORmܮS4TgP#>u P//*@Z.W4D~^1 {yA}Dl7$̪sb.75w7T^qՕV EC)HpTPvuԌ_X~ OmpQ2/qOJM%LLEyAXQoW!T5ߺ"J0HnBl-,O*OEoyHe=&]9 C$Ru__^5nxLQ Mqee[pAl4|:b/@~MC {$);j9iF9vËym(cAM:h/B/Agda5D"Rk{YxyF%_B`! ?eCG"fsu@8+X!^_ӽӠwo4ߩک֍VqHwt?^Ij@027:! uEp>HmvhVkb3cJ;lԙi%m^nKD6 >g_CXGZI9g_)_Y|3xStzG(V5t"::m-̲s1{eQ~ɔ6e|vWh'6ZJL3R@^YuHQԱy~+h_rEV8,nvX;D56T@F""(;7]LznJV5.*pژ6|֊!˯:W[TE_# *.V48x'v>b9ji% ݤogTSEY9,+CA@p^}:@!x/LЊ^$Q]oDm%81J0+d2k4rA~ +dž`>nQNTєHLMZszE )7DLّ֝FȁcyFO)VH.ŬV7n^3Kuo.חTʶh*{ @Ks^SѭmP'UT8]|[4 VQ"%s-/, _ =딸+:m$.OW$;^yF7A+qSd օ<ǜa~G1h3 Ek_v_!I jfMCGOlG|re5,:k3ۙ̏S+>;cXf~.H5I8}pƘG}m>>{ҕA;ݭ/W H,nP k)^R=3ㇽvR-,϶ ZjL1p@N*sI 45V);6al }R/ptx|]K**K+w!wcC&2Jx*tDpxn!Fަ*IW}䲣R/ RM 'Ce9iqM&30gD(&| ȡ+.OoDI|*-t3pAEfe0pa2V4X(RW:y} K~3| ;VJRLk5J̢Mo `m k1oRpAoZ+30JyJZ,~gpZ<d6oiuhh 0~iUB(V1t`x]%p(]boN-Մľ,6y` Er&z.v艖{vb%iMY=LݞoJg3yTt85Z3gn@g\6 a?awH"TU[}(Y\|rT;Z>ETf 2(csb?I$e"bZOF'Mp`!z?8J 36iWWU%~>D4ԢM2C&媰=-`Ľ~eV0;<6 k]tZ./릺wQ 2>$ :`;i]TmZ\Jw~6%FQ4{.#*pO⪏ "MpTPѷNCw޾w}aK &ʏ`Mkf; X%FtPŠd厄 `{>KNсecHpF Lyl;gN}0mAeWyxzFZu%#Ԫ:]XǦ O~'43\b8jZ!XkhV`'-szN~W$#g'$_K.ngjm.q׮,E1AJMd+Zo'Cs5`wѤBl0U#(3}S il^eL:3yso2Z\_tzY-ZuG.7ދG=5Lttefۉ@y/WOWiBf9ߪ2 2 !BM'֓\L%HZ)V;rIŪ7lt|&M[Hy%G7p7liS}J@(g_v^y>t3d!A3" ,MH`8+੄fJ<^NʧisEIZXD ;~bh mR-R8Q5)S}[wKk'n_g#EZ&OȟҷFEmƇ_,Ktl&%r Hi zo`~Ɔ^aT$M.IG1 gYUEL vZ$L[h?J!mfd܄Il#Oz8qnۇVQXclN܀}Ҏ]wOU]BB1 ldIH|AI/̮>\\h_*K} pّ/CS<7rijx N-HCa4iW!CIҵn6le tmFuOԇ]\ĹQZvc;#XC-+*&'#5g0eK P-=xG"Oӡƒ<9Cض4(gD8=-w1.jg!*cܯǛdžqZ hp8[bs9/,G$s 1E9w W˰ÕRhɿAsH lD펊}l%^߂yu7>jąuӡDۤZΫ=(o+G+0QIr).fjbij wXNK$|x]fK }2%d';p# Ax .'IMlWVB:P[5oDxGڔS,f4hT*w+V#-S]25Ou7ڷnwՂka֔>^&BLQB$v(EtaڍK\ $bEOOMpRַ!E=Kb1Ayh͈}ux&dr9g'-9VKaî-H gCd6ۓ_|K=oP߆#ZX53E]OW3b|Jx2p#ɲdmT:" t[߸Dq"~+W$]2T1RBEސNMN`]APl"G%?g?պv[7I7׃(9m'yeL7uT nS Eׇ`!A  -FHj7hQIaF*}Mچ'NC)'3<,6 L[ Y w\ HiS WbpT/ 1ʼnx L1vgݕ׌ҥW0^mgnBվg 'i-q1h8]9i-VI1yɣ] =j<$I{Τ@pQ?{}'G|ɕGrUN,d/Fb#ZO z$-L."vcwnqs{gjoQ}*H!;af{UTNp*Eh̺ b łSTfI%Yʼn)"{ ogqlz&@| ؂keE#Qv`D__e] f1bIxiXJas~ŽCa`lUt| Q_VN˂vyaS" \64V8k3aoFtKYtqb1l1at ry 7jZ6Ytn>+T962&e5? m:P458с T#iE$J_ kE,]'P<\7g \.v4(u G[[;5ѮymMZ.[yn3+bMQQT^o͇I DyY^@{:A%>sycքvaȚZ5M@1V`*eΝ~WXa?9#09g[I^ʜ]i)3Ԋ%(ovZv0nVn?*-.K_1i7{4Dگ^>٘F]Jz`;w?ZCfݩn3ѽ%R'9-V[1i}c;rd=?I)-E]k|KRR_pGv'9\t]o>3za[) `W&0p2cbs6YE=F-#rsѬ @(P(''G ?_EK1xn qqkkxt? $RA/ ೽<(՚Q' u|X4RT2d9*tbv ʅ-skQ],UN^w`ZyAO`Лn D">|5.c^e[h n'9<49f񙡊sFrM'^h@Mi7;%͡M "N2cOPÂ=Q:W+v } QM6O _1o3KW TڏQEb6R9FEj3j Aii=5a8gI)rO  2vn)a}B AP\vrvrT s|ľ;6QO_i=~Zهya4 ' k]W*t(IHo!'a@"М)75S ލ2&aCAI`dNS.BFRڃLйX̝rT OG[iVKn 6=2+BC2XOU\y kĆ\֤d?&lYNtÃVr݈1,:J#qC5YNMc`i3׎:F?f_,h̃靺lf:h ~5xK5z=z09gҳ+`Y)j z}>-?+K (:8.tgn*NbZw1pJv),ͅTd,^Ef͵~3όl;W<la\V}[:| O5c"D S H'65FG{!i~LCtQTjqe9| 7` [ŵ񤮛朞ḺY%K`h\‘:A;Xc Su?YK^z$0'7T BYݐ;>w2["x`)F`: mc (_=X`^av+{ y9볆$[ ~!/Pw$V jC$kkW!JpEw㺘F}xh?dݮy=U i26!a$^0+">ɺ#_K@v q)}8\yb٪h_v\z*]N]FC9{CsAvq'jt}Eߝ7(OFZ4' 2£t)X-{T1:F.Pp?d7qvX?Bt:8R_QbmL `P@6U54*}?3=L\Pt2ux4>1gz"(v0>jH`mVhغ9Ɨ5l s cIC"(%7TTgѕ֝"|ݯl뜑ȴS~B۱>I1=I'WC62*ý;nf Hkp$ 6<{[vz@ۥ5=Ynu8BnE= iƋpJ́L9ZO5jMq!1mshp#иuMuP6bKȺ9Ŀo#QA4WljfKdJ %@$w %W6Nh?%"R:n} ڰT_G#^'ocue5$AN:wo/ZQm|՛ d=ڷ_K篦'}ڬhQ 1.Uad+BVC _8+[1|=z;"tP*Jۂ_/b9}.al0)sy- ZiVfQ=K/ lZz 0v~CӍ&GFinVz&"9Z!l_lJ][J;DRDD^8umG KK.cuAc"n<܄iFGK xc\ba{4?Y LyvzyÌPgo4j-G ?+\i5n9 vKeJ%sp44^cB㽏~f"ðw^huTz$c=lNք$3PXWG[ڿ ?fϽvNsywQD7rc.G >1Rs9 et \NRqWh޶F}PSMaK@k[|N{vzFNddLF1,'{^}~9勩J<%cD^;J"! '@̮z~{@wOt RY!LoB>=`49z9}ΆʝTmt7?.~R!2⠠v.ք Oz_u4Lh ; lHJxjg |/cqAzz#$ q1XUXZ.r|Vj;q˙+ۼE@ӭs3 ~۷%MMPoO^pN:%; ({yv)#UV/zcieÿci mIĽҰ9_01A=d:i2)E Buz{ª n?~|\$ÿWchUkmdvs`?c.#Tw!0Pr.H$4xZf )b&v{C;O8;TXd-1.BQ2F%wh&Lyw˳ZV#NpAV a={|:sJۚVX5gJ,OZ3MN6bƼɯὦf U[;9vu7ڬm,_on3Z?Kvwd < u9Ȧ` ՌV%{ '$ϓY6v+Ўd ~ <;wR1"zrv6ϰڦHso)סgr #%MC&jM;-~}ǃrΆݪA㲎QnyT+NcWt:0 ` G l, b.leK&#ZJ^RSy8WTdTOKXE@\,8ݟL_'\.UX;e*97FF VCcY_AUL&AswغՁ8=,?W]oBE#Ԥ֦kj{(l^y3]X?tе]0%h6.e S xH@˹{r51@RSxGF*^CbU@:l)~XdnUVZ;RF ;AE<x׎+u8GTφfh:$ɸaG)M2+?n1Be|;tr]n@jzYؽf|\,xgY,pw [8l]FGid}&3m{B,XFVepWdVc-Pj)/ lwb Qovܓ._^e=kVс`~,@G=Yw럗32`,VppLa_ʠ'{ˢӔ e+7ځQdҰ- @hj/|d:F!FI& L Fv7B>˜T&$CNE #`;1[6^gwljZCmN%9WGIw$RkɩFפe;(2WVZI= U >axt&rjy(:M߽{X-Tj$_d[.I4_L.9\(f[Ԗ,Bk>r.cCKޜ-ZcxDy߁@(',J>&dȐ'; zЗjg`Vn& MZ#ZS[ɏUFDq}Nj[IjfPrWy^a?}ot[sψ/pm|Iӓ2dmiIFB("O ezL&y:(p,핈lNN{'psXRy[񌷴:!iNK ;0yLV/%4JϺ.V;Uz;j|.r -^؜cW\EeS#'d8<+LgØ+w)GŽhS ܾ+ $N6 |cVe?.HEؕY'oJ4P|G=PN{Rqn &WXtXт>83?cޥKh`IWL3]TwP=bqm+}pT[$k! #*tRfPb-oj~i mEfl!cdO3>V|䙠k{qDNfCQDa,qCHFNK]eSdt'Fi*iq9w?bn=?$YrytʽӲG =9.iWẽA;=B΢24X`݂ljaDG*RMUu(nƋvTjr *="X\?gk,, y%}CJb3 [gO &>Gqm w7O(3!_#9/H)bˬ,`Kdk˛hy.wZ!si/ElہdsLdH6zKA#bN>Y/Ol<֐wa:FI ~#K XahX'iFzk)6NIh\Qܧyy|jnY&suǒmSgHPLȭZ?.S^tleٕ`X³ ʗE!%X\lPb2|jN}oVrfo[GdPE?͟ h)_׃d`4C¸M /8l2U00{K4.8-B)9.N ,J>l7A:ٝ^vD̨Aפ?Kܟzv_2;䟫_1Sq-rJ&JX{ֻՋH6RIt Ikx&M󒓋my\=tYYK0 ?zYW"BT? K&U׋}L7hUtҲrz|2X0UxEQbK\$E,ms_0T4j* Q.܌۶7ݯ֩\h1S-H$(1Է "2$/sc2x 3WP?4nx Uʉb6\pv&)2½/(y#r .Pd?J-S_o0(0A\#9`v GEJS-nj/&cF݌CV*2nqVRt9 q"R:8~2Pզ8ZlM:Lz@H@rUvi7i-O|1 7djė(hF-oZYXgG@rpCiE4xt`[lrN'XoN{ؖe #+ Q9̞VKMr'*atߛ#AT(.!%ΰ"X|4wT @)/ O]JQԃ~}8QѫenQyU!t+-Ye.}Ag@] '&j+$b949d-PTT2aѐuu @jTql#sWsqR3ZFIxהC, @ێ۝%}y B A P&6h,Ա YeZV>5K3}唲5xMŰV: 6%\6;칫j7++ž ~׻ ]v;٢%| ڋ+88N?Ђ2U؁'NuLw3Rml 9ejFD\f1fѠ?KaP18x+m qh9m]JdgyWG#UvPQ0ZOQc~#ֻneN ;tZ9Y}D}6qpU [5(>A~ܷ ;!-FD8T~ipa q/<=D=/ H/+ytc\ctʚGM(<'JGՋԲ;*EX^=ͼ|:u::8y<;jn}^\-M7.V:G$ ahkc `5DOdq}.‘CE-ڻg䝜;ʛ/-ۼln^CU~qr;a]kj--.ɄpEXri:>{ ]m_By(p˘dg^f%'~psEoM̰ $1jTOځ<L,d17t D2죭&5MYaˢ*c\I(&aO*L ~їGW#A ,~FT̶|pֲҫYElmO޻N>|D2otOq/GxUg=-b pJ7P'\vI)+JYF`&pk+Z;T>"[<b1?-t5E_(z]/.h0 2sT#Y~En-raG(V$滦DF-e:cL?"&G)BCsKA+6KB.gꗛC-WR"ʽp7G=opU]1E#M.8yRߒ[SdhSr)A妍'K# \3,a[1O qB.Z5?TU5'l4E{xKRsD"`T܅#s.HaԥL WYQ| Yw|4D5w"k%6 I΅,dL1|$wq+hLinjN}<.̬ϟ!!SKJ8ݒp%WzŊ6"Y: Bgu_̲ \%8Q Xf%5ugѫ64/ǀ*[u!EuɨBr\cϔiA6̠9|t\}9R keQo/s$xBRZf> N2rx5scY̗/v(Y)BOy*x):D烷Zر_qfྦD$ 9+J@>W hFPoR"@{/S"!KI5%OHzD3T3LR{MKKz;C/@}0{N)=9e4 n+ ?7nϸL˩:um!S I(Hc ffsC}ONślVlZ:] Xdg>ne*dq茵xG3FddsN<<=7@VpC[`w*Q. I_Qܫ@-(hwM5>" -/V:!ȳTWSc t+-`3[h-pai)E(΢Y t^nI{>hђ줗v'!r+!c$pbf6Q~c 'z9/h`5^?CGN9tf S}pEH:tskya~7' r)h)dQ/r}3|)j#ƞ{IC/Zc-)\T/ νA}ˬC9l;p.W~`Gg1e&2|+osS/qa6: ܫ?Ǹw V|avs֓|N"K}qQwBk*َ=JHW@IOPNf Kw+iiNnNmF{$F6z.c4a"Y%/r 2q>`>oSV-;A} 7Pfwb`/ z[Ay)yO63M:je4:Pc ה]bڟCVÜ!.ΑZu6ybhN>=y(`Z]qa|k5և|;{c@ .L 8O(UStn85Bl |yDJ7eJ r!/A Ð drpNLc?h/%\cNUg@p  #D1r -?>qMϻNMʶYY3ikng h+ F ~+xsda |1{/\ i-j6xX[8! TypAhv+1" 7|+xN/URbEl2< MHU@ND*B.|*\ȥww"Hjd?CiatP1O챊-+2n1w+Omv44qC%2$:ht#Nn2j1oQa܁-4Ym9)G4ժ$1r'ȗ2*ܠlBskų/]60~:yGw(E|-1zOAd@⒙WX˛flcShntZp R9_ȣrDmDW#P7fER aB*(z-kܧJh4Tߪ`m[830260P% ՗gp+0.l4QDoFzd~⫫\w=tD=,+݃B$`v/yRO#ƞ,-{L- I䀳8o?!&1<>.Qc0|:RDĄ`ǏSH=-yUDRGe$"7 c?:)ѦSuZJ2x(bKWd _ͫi!yL(ؼyFZ~rws};vGNXהAeR8>i)Jw_f[:cfR;1u4)e&mBAۧY&腠$`_ĵ{$nU'* k~vKq(vJ vnBN EЎweiƂ6,F$Y[@6zyk]TGC+V|2j n]~O?xټxVfԋF"ǒ:]D M*=PT6Z1 YwFy))4YE?P~QQL 7;$/"oBK5loO?_{2._wm|T2,=P0o/J<A,(hGy;TB&j pW`G4ݝ"D3̌8yZBox׺_fȆZL~Q|;M८h򍼀Z%$Πsmɓ*ڊ wXE%Tܐ4Ӱ ;Yaͱ6Y8.+Չ~b|&e?2b_5pE9 Q+@ 69kn!5e} ;yh! g>)o->DX" ? &˅c[5sQo۩`H_?sCFECc=p*W"<ȉ(O {զPʻtFNG? +a-R C8b`˞UZ xO\=d$ kħOdTCJOIȼoF*3,3JGb7 Y~K;dƮ }}>KmNI~IvP_%XGah>ق@)΅=I^ez8]٫&LDpުP(=J-FH(h7+FjI6M|, sj8EnVgT21aTU)k !l85s km,b$+duC1pAS?m:)(n`C5qZ3h59 U? pcH;ձxΛ% #sg9#G)kMJҿO>1)I! 0YJ}lC~&98@U9i6u$c+iR/bne 0.ys2 Xt=t<[nmO?(q<Ot6ʝ.t`:X5 BF;x(FZ؏3!jkWK@>vD? $mр~Zi%q)H^/XEע^h@X{ާ}`~}Rj0\v!SycvS(疨y dh`һ0]V`..,\*.16֗\L%IM$:lRT7C %mք`}S!v1`iޫ!Xx85NeD4A*@Ӿn߰{QDe2|q9/}BRpNNmg}d]wZ̪_+PW~>ny&KdjG8ޚ]1'^;n+[Gg]@ LgwBh>cN+jk|+ij)"XfYQeJN5!+ezh˫u}TeqebJ39&{<\iLn13 ^籭u #m8\A0Q^ "zJ{Wz.觑<Xq< KCn@Q9}PSTUhCɷZM"{Qwj_JYu 89||u;LS,B1LuJb \V)/obtQ/΍ķ Ok[?_e@hvBe "9YЄLEgW:BDQ@Б .@Q|1"o P"}ӆݕ?!Y@zN(ڲAsuEVrG'۱Fx ԍFpy~Lj~ _YZkpƖ(۲?JF!{oơv!;1~-MA0zF]FOP.5N`fY4dWV-'|#p\swUg0^ ֎MKk2<*.dVhDؔxird*o^JX~oѸb}[AaivKdE}@^P&WUX5c4NB54ҠBQ0eKiLa܉Gm] ?x4Zvql`ܶ&86I@@ŲaKjKŲ[6ȼ} 5BAr9vΆRS@u2Vbهs< ֹ6bۣ{[im;&zNٟm&V$LoDRl8iwZ^c/ZK&5#$ 9V9P8!ICx*#cdbPrn Uov.爳8LD=0/+绤RF) ۑ媸y+SxXj}"3\)yYsmEtWĝER X,whZ]" K`3}t>jV,tCkڟe>\X:"C *4D|Qָ0./Ɣ].ےkئ2'1'LUI83,TgxfhGE܊Eu.£^g,%zua3` #s\vqԓDYx.9Ur_/M@hH ;<;hp=;uTiv׻N܏I"ěDLʘg&rsm{د62Nzˣ_9Z邆X-J&BIyNW1m3>CkĜ6Q6@ >n2z]zw`ل;%m+D3(?fǥd]ZaÎkMk{_&,V@@2}m~:ck"GfY|a{O^-g[$$mOq nN1zL.:+B/;`c"4!h_ p+$q4j^X b? f)ef3/i lb+ڱ@ˣg䜑W Yz%Qs2 OkmwKM':k$[f%5>:nP՞ߡ$- 1ب¶33 r5}0M'+9%a0bQ\]NH|?nG"3ֵ)Zm10&R BB#=xh,(ceAQV[BQϮ& #@ -0T#_m!\4C8DLխ~i^PL렗XlM=,tنF\o8ch$VAUeQ:J(>~gW:3O7@w ^" ub{3( 6I)(C[4qp*Bļ;Kִ4McJ z`TSΨ<]A 1^aDf VQ˧$N#D`;b>nC YAثNfC9N'-r&K,zSwVtWzlBR%vZ)@sƙO*$gʎu"-W|R,Jd3\rWjRW5?-PriF1%sNPA]yE|'2T7$v`f|D80tVl-m832GJ ĚzcaWNmZ#YWLp^O 0CM,봗 D d??h|F1"ΝNq6euE^[Kǣm®vD":}iY`etM` c^ʁ%qͫҍ$xHX"TkEʊƽ!Nmem C 6> vD8۰px_a !'+}l\D:( 1\;:dsY|AMm02%vAAV CV̫P{ o+_Vr>q["4qgiKt爆sSzf:ЖuI[W,s(ƣ: 6HYm)sLٿz h][}l3n`q;k$[ha75jZtcmmxҿ\e]`: EA97Vv1BюSGaMY̖nózĘ^X=ђJ@ +ߋ$h=SfI!Y+cY.͂ y'/Yh*2h^ͪ?JS@Q F?!a)}KQIKVpC[JDJ(@GevR҆]O oҠr? D4aZlNΓ؄괢{!|M}!DcjD3& wU9#h7DI24b%pUC2U[' [|sf TOs7+50m^9uI,ڣ'+#ɐ@V1]6ZuV>1cL7WM,M[#^;RbE5* $#=Y"Dt/)2TuW,4L겤9 B7mzՆ_X؃yNĦ6H:r|0Xoe"EcHļ.BxTl(|E*+pCC앑U5i mHpà KG^/ymq7fܞ(`ÓN q-O+Hy?3Mu@_Z,Y5Ӎ2(YOXƅ:ϧWmͦGAgOVv{zt黄ڤ`'vmGviM Rѕ(gu(|[/PHˎkFya[R:7X'=|dY[F/I:BtB8FWUƷ zE'J7+ֆ%a֞u-tj;AI0nD}_0K9#5O >N1XBF,Fi~;Ɓ8ڏ\Yjbn}z6ebWhMFr1M] DNMn4Ȟij,x&h ௵< ?X1Jv ACB ^-=Y׭WDSi!Ф"eoj[4OoBtj+f ٮ\c8eDŽ4F]3"\M d\pT+w^ r:3L v2?iP+#?e!xm.Ͻl=-nM#Viy ߦޢHd%\L.!;RO:H1]0 j(!1@`+gArxY/y,HdpKzR. 2Xm4#Wj֪>Xy/WxkZt~-1}N䮜BIb6BЁu_ (-4YT*dRMd5wfsrKKò8 ^`C#^SZ󖀽aܜwFCҺ6sY@R(F aYǢ?)8b1{0"{nOe(Y bD9ٔ%*:\*z \HBX*8 N?X;-@3CDAN%Tiuɤ.A׶zT`-f #k0r~!PEbJ D 3#lMhtz5x\\P z`TʣM]|3jdpizx}QN9d;BڈJ?g[ ꀘH&~5On݌K^_WبF rE瞴\+{NCRa75f1MQ۫ǂ x iэKxC/93j&'zלE7*u.\nI?1VRS7p.2fKIj[?SIS 1S L8x>4 .jGh4b]iJ:paz/leB$!DjSs>PP Z*"syN.nBuqif?z˼;c$5SƴLD _$fla3Uu[Brw{zGk¼:6K_O/o`2w=&i~: 0ux+;9H3 ɠ R*[c;jō.z>$XExx C`;A|V͞_PtatܳI,Bx-r&0l)0Tc1q}|}Ez~SZuY(~31g!0? O՞kZ'܌&2{mBe1urNP,q "ZЁ[ǘź]~kuG`S ucF(+ Nw;kyrgbz݌\i#?zJ8v~4Vw<~]{%Ǚ_1὿6+`:Wh"P706'd1j|:੒,vxd0gbl3}F̧Csd)`*:״@mb2ӳ˫$i( HcXYۥ8zV8n'߿l%33Nn3ڼu^R׷oNk7fִ]T )b#> ̚ 7C`֙jGʚ- cmiHtqo4uvF9mENncױE>ďmu2ua$/E[\60wRMuq}=(w9Dhؠ׫v =X\ Ft@pJ5J7/ˣ- "|a$.*, d5fO+I«&mk^R$d.g|mX[ (O7NdAWn #Mse4]8\i-ljT~P玡ȴ r0= !v;-q .t mhK&3^-7ӫ&`^oނ\xA:ICx=s"DcMT5f1-Y8JJxI G \)`893Nߟ0㡬a',97՞C $/tkZ>V+M`]gP.xJ5XUt)#[&Nʣcd`A"]~(J *ϲD! v5ﺩTaxF9T0Sm#z0*Mk&J@WE%3,ܑ؅ii"$OXtcD",R20h (*<[z) ›Ӹv 㱭#!Z  `M`n}|&A27- n_X ܝm%4?h҃r%ԅ(SKȹ߂}H&{{Hw^D&!R.G/ -!7T@_Th@C"R8ODy6 G>Hl髻]cXU( aђr`y:<D̙|g!3ڨ:F3RwI†TE:pAF? Mʤ>\vL7BAF{4~uCS%?'_F"kQ,e'0TuQT9|䒜vT q5U" )T`.=(8fT6F2-C 5@[>#X=E^guc8BQwOjaY{&ZIRк|bu/IةZWØBՎ *>;e*v@7bW/s 8ΔǛdˏ3@AOx-ICF)*qH i%{.nO3A,ް9'vEDI4Ǘ:Ls8M˫g1+-gT")|د[:cWeN>b!K>5[@+fVCߧ]&{3 kVLkIv&.yF{?N!'+7*ɢ\߄uxf50;%vSY#@lƱGxT^w QOuG_\T{g`w1Z J |L0Zp]2~ g2tUlY\(vRϙ5s?uz⡤ֱ9~\0Pxܬ8UR}5^֤SzTZFPbU~\C9` F%NGxy;qMc8W+OU}:!'ǹxA?؃M/ӮTJPX0<ЯN^"=th՟?`j&Ы;ΒCDd㧢2'~`eyo!C)@oHFwM-O=RT_X2kWIPԾ@Xrb9l?|2X}O}5MѱG s+ Ֆ :z G0@x,`Y۴ 3Wp<U5}vzl D&'.")}( 2WՍ/P_jMA"x2fQ' o>uTful 7pQ?hlibh5b WM% pt&w`EX_@*-|n7l̂{:?yϪK`R w-qM xFgpH2JKjZ3[S _<.w1"_uk }$s)sMӍ.` AJ.L)tQM),F2 G~@wOL ?FtQ V7L_tl%&ǔl;o,Cfv1q JX[?꽑(="ԇbs^ai9L^ejQ$_A~,&{\ez׭"pXb uՓQG_0JEHduҘz큠6NJb[Ai,sG52`U}tP'J^!躡wK>QO]AB*ԾEkQd ͟9/^mEưPZNX^d|bP:?՞ek@Ҹ{*YV0(F& y6Zھ48)ͺRL g\T#Z5s$Ds+'YF7B`%Zw#b9ı3Fh .`Ϩ5K`wB샡C⟫ɌRHiŊAIIVs%XՑK6͘Z իœn& -42I 3skw|H*(4TNwZ{^2!N{`}2l;@(RHDT>V5oQ`5PͲP] ,F.VYBg|?헬`Γܱ | KnLuK}]`I0h`k7,M\]7%\sW`AG'\":Q"}Qw Hx!Kbs }!q#VPfVa5|N]D ( .ƭQtQpVnP?W.ˮa%e3Sgn<# Iz%\ٌ3`| W7MQA=9 qgJi^)v->1ׯA@ՉtT^WB1 QlO@,wBOE1mńr|X DS4mADKӊbS. HB,7q+%0h ZWL~+,#@Ybu֬ZꢳJtc x-n8^i@㭕h3'9HdiKJuX? =޼r*9ό%\C).d 97F5jbaoEG^p{M:[x ~0V4Ebe;?ZIo PIh'WW|DdiRj2yK3:Ebzo:,AHeo:  F3pN"ړ܏e|4#N eR ug乿7f봹_i 7HNc-D͸KMZ7j~¼g@hٝj͕BIt$u?~(L6x\vKf/͏%G}_ [1)}{(U~y#gP;uRS-l>lG~54l\pS|7 Vh"ڿh40$ ;?E)-TJqA1VfVS@GLgl`3v5@/n'^렬כ,yO\Y ^Z Uuy6~T~IQ-j ̌]l(@cFL)M3;tCaF.DrZ͘p . #"!^:wcqjs1$F:oO3ǃ<@7ɿ*ywD*K\3 AS*F!h6kD%Ɯd#bAc5)~f}tcj7;h.D{pqދR3J(wr>9 h;##Lwv+?@o}gCH㸓s׿L=#g6USRCo0'{׌&4`qژOE>$ښ|$g3Z֗,X߯)AA1 *Fݠ;b,]3Ye>yhߊJ ifx<\$< hhǖkm}C&5z=HDrFkHjc#8H#OqZވnc\|'SR~>/wT&Ha-âwZwb YT"qҶA/=.$y9[#;haګMl,6zr>nι74ۂ0CWoxxʮ}BtD49(*'EaBr^PYN+{7wU|4@Ml#N@j'ז}w {X}W8mN6n/नEL"d[n}2+^W%yU\4k?,FSq4_ŀaR9PL ,Ҵ3g}`d%j܁נ ˀ|nK<83;ީlo]xcP=Rv9qg'evuk(xh[`FIpoP֬ -U+NV`h|eN8h1wNU²F)KbHَhP^xÑ"|bqIZ=tk t:DImoqgy(0Kt P*VMUƺ[VcM i\L:>]ԥP X΢ӼSي`7NYM. <4=fΌ+{^Ǡ KvYTgkB٭m*V_t)V!:Zw3tp y{y:\DE83!{Ŏe ^1v&c_xM9JC D&qΝB ʙ=xa<^/I}15篭D,n؍bwy/[^ ; A͌2D<]%~172tc"٠)"y$qKr?9?;t[o/0OgCR3%ϑ&-y|P> V]`VX1>I7~mNX8E0VxҞLr >=cצivqqx{Fzh;.sGQ`+R5RvkB hJĸp ~?l<]דO YZ