From e32220ccc1e46b0a22f4dfb20239824aa8893bf1 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 5 Feb 2024 10:38:42 -0500
Subject: [PATCH] experimental segmoe support

---
 CHANGELOG.md                                  |   26 +-
 extensions-builtin/Lora/network_lora.py       |    2 +-
 html/reference.json                           |   15 +
 models/Reference/segmind--SegMoE-2x1-v0.jpg   |  Bin 0 -> 21996 bytes
 models/Reference/segmind--SegMoE-4x2-v0.jpg   |  Bin 0 -> 21996 bytes
 .../Reference/segmind--SegMoE-SD-4x2-v0.jpg   |  Bin 0 -> 21996 bytes
 .../control/proc/marigold/util/ensemble.py    |    4 +-
 modules/face/instantid_model.py               |    2 +-
 modules/processing_diffusers.py               |   11 +-
 modules/sd_models.py                          |   12 +
 modules/segmoe/segmoe_model.py                | 1315 +++++++++++++++++
 modules/shared_items.py                       |    3 +-
 pyproject.toml                                |    1 +
 13 files changed, 1376 insertions(+), 15 deletions(-)
 create mode 100644 models/Reference/segmind--SegMoE-2x1-v0.jpg
 create mode 100644 models/Reference/segmind--SegMoE-4x2-v0.jpg
 create mode 100644 models/Reference/segmind--SegMoE-SD-4x2-v0.jpg
 create mode 100644 modules/segmoe/segmoe_model.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5590b2ee6..c31c4daa6 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,14 +1,14 @@
 # Change Log for SD.Next
 
-## Future
+## TODO Future
 
 - ipadapter multi image  
 - control second pass  
 - diffusers public callbacks  
 - image2video: pia and vgen pipelines  
+- video2video
 - wuerstchen v3 [pr](https://github.com/huggingface/diffusers/pull/6487)  
 - more pipelines: <https://github.com/huggingface/diffusers/blob/main/examples/community/README.md>  
-- segmoe: <https://github.com/segmind/segmoe>
 - control api  
 - masking api  
 - preprocess api  
@@ -21,12 +21,12 @@
 - update docs  
 - diffusers 0.26.2
 
-## Update for 2023-02-04
+## TODO Release notes
 
 Another big release, highlights being:  
 - A lot more functionality in the **Control** module:
   - Inpaint and outpaint support, flexible resizing options, optional hires  
-  - Built-in support for many new processors and models which are auto-downloaded on first use  
+  - Built-in support for many new processors and models, all auto-downloaded on first use  
   - Full support for scripts and extensions  
 - Complete **Face** module  
   implements all variations of **FaceID**, **FaceSwap** and latest **PhotoMaker** and **InstantID**  
@@ -34,14 +34,21 @@ Another big release, highlights being:
 - Brand new **Intelligent masking**, manual or automatic  
   Using ML models (*LAMA* object removal, *REMBG* background removal, *SAM* segmentation, etc.) and with live previews  
   With granular blur, erode and dilate controls  
+- New models and pipelines:  
+  **Segmind SegMoE**, **Mixture Tiling**, **InstaFlow**, **SAG**, **BlipDiffusion**  
 - Massive work integrating latest advances with [OpenVINO](https://github.com/vladmandic/automatic/wiki/OpenVINO), [IPEX](https://github.com/vladmandic/automatic/wiki/Intel-ARC) and [ONNX Olive](https://github.com/vladmandic/automatic/wiki/ONNX-Runtime-&-Olive)
-- **New models** and pipelines: *Mixture Tiling*, *SAG*, *InstaFlow*, *BlipDiffusion*  
 - Full control over brightness, sharpness and color during generate process directly in latent space  
 
 Plus welcome additions to **UI performance, usability and accessibility** and flexibility of deployment  
 And it also includes fixes for all reported issues so far  
 
-As of this release, default backend is set to **diffusers** as its more feature rich than **original** and supports many additional models  
+As of this release, default backend is set to **diffusers** as its more feature rich than **original** and supports many additional models (original backend does remain as fully supported)  
+
+- For basic instructions, see [README](https://github.com/vladmandic/automatic/blob/master/README.md)  
+- For more details on all new features see full [CHANGELOG](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md)  
+- For documentation, see [WIKI](https://github.com/vladmandic/automatic/wiki)
+
+## Update for 2023-02-04
 
 - **Control**:  
   - add **inpaint** support  
@@ -129,6 +136,13 @@ As of this release, default backend is set to **diffusers** as its more feature
     **SD15**: Base, Base ViT-G, Light, Plus, Plus Face, Full Face  
     **SDXL**: Base SXDL, Base ViT-H SXDL, Plus ViT-H SXDL, Plus Face ViT-H SXDL  
   - enable use via api, thanks @trojaner  
+- [Segmind SegMoE](https://github.com/segmind/segmoe)  
+  - initial support for reference models  
+    download&load via network -> models -> reference -> **SegMoE SD 4x2** (3.7GB), **SegMoE XL 2x1** (10GB), **SegMoE XL 4x2**  
+  - note: since segmoe is basically sequential mix of unets from multiple models, it can get large  
+    SD 4x2 is ~4GB, XL 2x1 is ~10GB and XL 4x2 is 18GB  
+  - support for create and load custom mixes will be added in the future  
+  - support for lora and other advanced features will be added in the future  
 - [Mixture Tiling](https://arxiv.org/abs/2302.02412)  
   - uses multiple prompts to guide different parts of the grid during diffusion process  
   - can be used ot create complex scenes with multiple subjects  
diff --git a/extensions-builtin/Lora/network_lora.py b/extensions-builtin/Lora/network_lora.py
index 8f31388d4..1405a9d13 100644
--- a/extensions-builtin/Lora/network_lora.py
+++ b/extensions-builtin/Lora/network_lora.py
@@ -42,7 +42,7 @@ class NetworkModuleLora(network.NetworkModule):
         elif is_conv and key == "lora_up.weight" or key == "dyn_down":
             module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
         else:
-            raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}')
+            raise AssertionError(f'Lora unsupported: layer={self.network_key} type={type(self.sd_module).__name__}')
         with torch.no_grad():
             if weight.shape != module.weight.shape:
                 weight = weight.reshape(module.weight.shape)
diff --git a/html/reference.json b/html/reference.json
index eadcec5be..0d48447fd 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -88,6 +88,21 @@
     "desc": "Segmind's Tiny-SD offers a compact, efficient, and distilled version of Realistic Vision 4.0 and is up to 80% faster than SD1.5",
     "preview": "segmind--tiny-sd.jpg"
   },
+  "Segmind SegMoE SD 4x2": {
+    "path": "segmind/SegMoE-SD-4x2-v0",
+    "desc": "SegMoE-SD-4x2-v0 is an untrained Segmind Mixture of Diffusion Experts Model generated using segmoe from 4 Expert SD1.5 models. SegMoE is a powerful framework for dynamically combining Stable Diffusion Models into a Mixture of Experts within minutes without training",
+    "preview": "segmind--SegMoE-SD-4x2-v0.jpg"
+  },
+  "Segmind SegMoE XL 2x1": {
+    "path": "segmind/SegMoE-2x1-v0",
+    "desc": "SegMoE-2x1-v0 is an untrained Segmind Mixture of Diffusion Experts Model generated using segmoe from 2 Expert SDXL models. SegMoE is a powerful framework for dynamically combining Stable Diffusion Models into a Mixture of Experts within minutes without training",
+    "preview": "segmind--SegMoE-2x1-v0.jpg"
+  },
+  "Segmind SegMoE XL 4x2": {
+    "path": "segmind/SegMoE-4x2-v0",
+    "desc": "SegMoE-4x2-v0 is an untrained Segmind Mixture of Diffusion Experts Model generated using segmoe from 4 Expert SDXL models. SegMoE is a powerful framework for dynamically combining Stable Diffusion Models into a Mixture of Experts within minutes without training",
+    "preview": "segmind--SegMoE-4x2-v0.jpg"
+  },
   "LCM SD-1.5 Dreamshaper 7": {
     "path": "SimianLuo/LCM_Dreamshaper_v7",
     "desc": "Latent Consistencey Models enable swift inference with minimal steps on any pre-trained LDMs, including Stable Diffusion. By distilling classifier-free guidance into the model's input, LCM can generate high-quality images in very short inference time. LCM can generate quality images in as few as 3-4 steps, making it blazingly fast.",
diff --git a/models/Reference/segmind--SegMoE-2x1-v0.jpg b/models/Reference/segmind--SegMoE-2x1-v0.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7d7989ec2a1c0825b092263fe09324ed9a995119
GIT binary patch
literal 21996
zcmbTdc|4Tg|37@qFvw0r_MOI>Wyo&qL)Njx&|+tjvQ|nBvNYDQ#Uu^JFt#YW6xr7-
zLy}6eE4>RzO25nd^Z9=7$K&_cecu=7T<2VKjO+Ehp68s`cKx0Gy8v)oTUc2D5C{ag
z2>t+nKLWny*Ms~4z}6N}004jmV1x((Ft7yyiy=h#f7=!i1i<j`btw3u8UWS+Cs^#j
za&%k-%hBKbd%o%w6cV8l;uj&IsiF$}eG8ZZ+-&Te?5y0J9Gtv7+{XnFLIV8!0&3E-
zq6l3L1AQG0?b9f<rya`dtodnehs%y<FL(z81{m6g-ni;}-P13?=jclyJiNRDd;+J0
zgiiUGXq))_e?I<p0z52G@ZK39ya1F3!oUOh+XIM$cMbzFIfCWC2L#H%2xDSqImXHc
z4rt^Cpb!QIC?f+5#>fbcjsZUh7<pj4l4{0G#~r<xr9$EAvBY8)X_NXcKFq{FG8*1t
zamQHsPY4JK$;zEX$SWW<wN7j6=$e|DTUes4teu=)T+h0>V|{%6`~w1maN!Y=QP-~D
zh>pLTkeHNwkC2s}lbd%xzkpOyN-3k3SI{b-JZ)%fYHn$5d)eL7+t)uZI5as$pPqR=
z`)2Or;-{tMmDRQNjc?z#cXog5{oMa`Bo_pf^M8*22=;%;#RJL(Wn^SvWImD$0=)(n
z1|CM3q#6^iu_Lor=y56aSQfYmvADkLn6w7wA3pD}308g?<VV?WN22{J+5bDi;{Jb0
z_P>JtKXT0h><kd_;W6+4M!>Q;^of>?6Ivc3MKUKJbDm^nvZfMkf&W${4#{kJDvf+B
zXUh1fa-I~_C3%y8hqh|AfSZ#ejm*YM??vJtVoRM+DUeE4I=)V*gab7Usg$6z)CrYx
zppGY2bUR30o#w_}wEMTuR1xpM__or3&T?Oqe?x__wW8Vas_Z+*kdg&7wnQ1#pI4FM
zz>wnXM*|k8d2mefxYR~KQ7E-hGG5bS6vZHvM#3{`3Tvvx3wa@d)S%Qrh`^M<I8=>#
z+{Q$WnvLS{LEEYd^^y8w!4@6pqaZtj$m0wgOtI`pApmSb%imM%J5c=h%h)C<#dHU?
z8e=lcTq0HKL>bOE(?D^WC4q*`Pac)0N@*CAnI=%I2zVm8L0K}ri~_}s1M)Cc8F(CC
zhAGp55kJXlD(i&i01+c`05((-6))>#>xB00JVMO*Kd`~a)g!^1fczIb3l0e-2FFD5
zv2JIt3h32o0UYW-&?JJ;!0;ecK%G!A#}S|sbci<Yt|nk7RPqkRyH(MA1f4P+BDawe
z3H%Eem$D;2nHj_k16i(=qO*tMl0mpMEwFfaqXjm<6RhxXQYr~xb2>%_NFZc)r>v;Z
z<F<0KEVf7zU=l=`f*d=7O)itfh@2F<C`myA;C-9gN|G6oiFm%-yKH+E(8-80yIfh$
zGKyMa&J<Lx4CgJ06Ut9MpHwN6JWlVi2(kq%g0T6C4uKGJXyzb)0!`qfH9{b#f#o9E
ziTfKGAt-^wkArwIpavlnI6g-xkbKOT0*4@w<3a3T4|&uIggOL)qdKP`WZw;(Y7j&l
z*%=7EI4yt#aY)sa%m&$nIqKtpd`$}>{7DW+rhjpyK~!<cM>rL~voSlU6s6-IMVhwZ
z<=#1Lk&iVy>s3H8)5mZ?W}^u<HjDp=VZ+X{H2imvVZgtTX*Zf-@gheA<WzR7msl8=
zw4(A;q1iTacxcewao~Tj0^~@xP;OEX5&$90X9?l}K)hH?(SZ<-BfNwFmjikVh}U97
zSw@*vV5G8y5D1MKy~bIJsm#mv*b%>YP3Sc?X8t+jjF+wHEUDu`_>x$(&rB}PLK{P1
zl2w+Fg0q%bvKpfXVG`tH|0&2sG=~Hd@*fbVj46!dBW@W}<3TvViZ|YdDK;zKmI*J*
zMB<Q;Wy%LBDOAdYm*a3uq^KXwKM*l9p-gZ-g7fd=f0&gN3D~z4ze640s<?K<EKxed
za3ekjux%^;a6~uoVp%0G9U_Ul+X&LFSQ*93UvcILJHHA@FG#n}BkZ6jTRsh6`ARmL
z`i1cn-#O#@BXs}tU*P-$6Ekygp0kG{Uj{;~sK-&PM<5<?4gX*KKxmIqp+cspyCBiH
z$B!@syBvVRpO5GcbBR-ylgYoF^X?%iYSf%@DXo7==UU0bFxgTmgP&|v;~_brcr+Cq
z7(py0;3cu?SeGN($+IKKEHG#_@g)dr0u(PM4+GHhFv%<sQZD#^S;)&^%ar*a?7$W{
zo5A^u<^UgHj?hIUNY9H%h+I$s`51%>GS;|a&w;U>=AFmx9hm<YXh@6S7efFXXMLth
z+dXIJt6u#6p1pkKA7tFJDm{ymiJllrP46%k8E4o7sVAtlk<8l<_>MEarPZ0rJWa++
zJi-D|8!yDt^hWD%Z)vo{v5RepkeaEFi{pr~*P=1&-|NFm+5OtsUpp}+u)FE#^dt8L
zS|^5hEwP&?%$hluQnz+XiAlOTN~HC~SRq9qJiAxLRb*}D^jW*-Vt0p9-!VIG+UJ4;
zCa%akytO-4JEJGy<4|k?GqgArm~OuF<_yAIf46q9#9XkmG*f5n^nuEid_Emf#Ttd!
z`dI6(k*gVuRVVWtw#F2j4)m4YHl-4j41EY~4O#E_9&|*h&vt1rNX0SROk8a3_k=11
zcZvL7{R@cIb~L=bA!ud%J>~-r7M|A76tp^P46u_vPuV7_j}T4^DT(y+&U{@NfZwV>
zk4Ao9TbJ<yUhW$1xZUdKwT;-m9;lb~EQ;Y5__f3SZHV8>O3ktipYKJt9-5>utLsd=
zMzNCX4FJA1eynYJ{)={-R+08FTfJ*1AJo6J^b1$aNk1l-<jI-4W)XNl!I6NyCf1Rm
zd($H!v(GE0yMjS;&G@6Y#kht=ENx}1%q@|TXZCgIO|tv>5MN;#qn?J(C$ycgLR6x`
z-CmOh;wQbA<9Y>snUfF>c&Diq3MLCE?2;oX>YvXclFH7=A9s*vH?<Dr5q6;Wn7z!c
zyo=hYl`rPj)Jag|5puFIyKMR|FQKfCXl7-Q!Y^|$0*)xYg#Yl8nH9!x#7lh#gupkn
zJOHkcki|>~hAwbz%ycp)`^NnbAFm%Ri>RY5tcyxO86goe|7f7tNX?uDH+8qt-wo{c
zA=q-m&;AAKHQDd<KfRsfra};;QJ=QriAub32F~M*lJ)PaJ=})%D&|b;`#w7`<n2FX
zye2(nJf6LMYWy!?{jTnx**T#r&p7X}-m1wSNp<_|OEUldJi_3Y&*r%vEr*v(J&>g6
zTdAHavPE&Ofy*M0*ZQx`bVOVbQa9dU6<MS&E;AU>s+5L0?Bw4_o~r{m<Kf+ic@*O}
zyERuav?wn1{>jm~uH2Z{1kdhe6(pfF+dbS~4ElAyzf8sJJ4rlHU_*DtX7bC%b^WEN
zvXTCw;Oe1E$7+ZAOdfVljEIz@SPV85`gVrz8d%n7N`{f%R8F2#h#fWba=Vqq@zFLw
zb1}J~?WqXsgY@p&Et|kCrmI(h4O~tbQuSG?DSE^@{<jp-bjoqGn@Q5;toB~DnH5Lp
ztt^ikMfUk~b_Rs-v1nIJXIM-N8F@Nk@sQ~$(LUd5z0iTFC%g(U-gJ)b19qs=j(&{O
z@Gzb7P`8Qq<g|Xihl&k_QSdFr6Vz6>*UM7%WlEJjSvL{1Y+@+kBxyBt)17MJ6hBjU
z?X=Yi_JkHbc3V=`XFE>4$;w#zxRhHVZJ^9~6w|8Gn_BZyV4j`m=qtHb18*<TK3U&;
zDcXz_{ARf{#MGy}F>-FY{>9{yv$&zI3o)u8pvY-Ct%OMI6SQL?m*|=v5fibA+C=-!
z>awDBou3eW|HDk^(Sj+3bER4_Om<*+*f8LNc|=TGYdS=bJPv?bjXpT?IvE3$;1vkQ
zuIP^_3PS=kHUrddKyFI@_YCM`Sb^&xFRvrP>l{c2wS`IW0&)Vy%SsxXyfk>Lg}e38
z8KRkxy0^{p(eqdRnqt$GlGl_{CtH&Aiosxc$kKL{)D%+hCgTUm50V@9J2VxQGsm+r
zt9*5InLTBza1kF?n9muVFQfjOhKYpOsvi?y+~k&X=m$Y3KJ!c{ztrwVNY(Kkh}vqp
zXRV1cUVY0nPaywtmAa6pXS~wja%DWpytdrZUG+TrnRbnHt@lSC+a;Gx_opN7*O6Ms
z`2E_Pg`f7{33bc3I`Bbl|Bk-%e65p-$J%V|V2=2YSA4NITQA6P)VvbqY9U9R_Uqs|
z{q@9EqrN1*;kfQ{DSZ92wk+#wHy-nAOQf#Uk7xIV5>Y2=Wdkx*vrcyn_zC>p$YOug
zv5$dnj!jlD!VF70%<d7pe#_%4YsPd5MaVAkF}&p2ywmf~qFr7n2?6*s(%hYoq8sW0
zGPRMg0cT!?*}D)_?b+|GGT#+uGnn_M*m9l`#a5-Wr{-$&pAI{Ec~(dbpqOuw8u5@H
zaEM6g(y146<;!QOs)#xcm(AGs6bF;oH3YC`uqIM?=nVYxLPg$OjoZ2~fKLxFhlH#9
zvh8Yy1jY&?Ny_MAZpA@b0c)JSbrKtimeqq2^l^XUY51qK%zJU7l0I{M=_4-HN#XHH
z(pjHni}a7vl@;02GE=Sk)P$<9mV6iNv-i>=U$9;I(`o4^JqYMXdHs<PpLT+6v}KH|
z_m`IVj52qYSCml(R%^g<CKl~C@LyU6V_1oVKqSy@jk^mfL^fv1*7OM(4(n+XX{3<1
zG|rXGk|s}PgtMB8{g;{uB*ex+VL$}zBFg&`#$W*SvB&IJX-)lsD0#x%;TY-}XHdB0
zoBYKl-hqHEf=;H}U`^i?z1C!M)p*F8wcViLe9l%}FDl60x=_d)?qm+=3mK#O<89#(
z2?`e2d?p<r;o7>bn%4IL+xpQBYoK!mI31?5X(xl8Y|+b4-Kp^PS`lfE9<%#AsrE#Y
zC~R0liJH@(y9Q5dIcKS*rgLa5IymQQYi4VnMVwzaRa|+tS&&h!7#DPj=SB}t`_?Zy
zdgz7|aqUv%@nbeq$-vM^Sb57AN9+Lf%lhX_X2g_i%}LCO+K<l9zLcgbqz<QP>CO0@
z*T0;LGxole_-?Y~z&9-8eNXux%D(+i2MGLAP~2@F1Fi_elKerDq&@YJzkpw&@L%Au
z4a;);U}jwCJkNNBqm#w8Q^ZrlfBKAG-gz0zU5f7}_Sa~cUtdzNm=QGx+&CJxn>Q4=
zG|CM+0tGMoN$iMwy7&&CX2PG6Nu5t*o^=!w(?~7a2Q}-uF6s}7KL}_<PW<clRaYR1
zfzV$@(Q!KJrA)CWT%H(I@`dE|xP5Wlk_aDn;$GR2e#|FiQ-kn4AOZQc))`KZh6<~>
zMs&1U4LgF8g9NpAHZhmy4D7c{y2A7?`jVo$r`!Gl3Q8xqeGf2`0uXamo<8$aSd?q7
zdby^KBYj*ie@`trv+!15&3u2mk|i|Tyw5^mVCaJmb)I20M4xrAXCt*1d%TIc48=8^
zJz1VZ9ZeH6S+V#Gv!tj$(0LTagj=i*ceysc-1sAT1H;l5zbzj9xKjUAR3B%-(ywW0
z^)gwt6ssc>58oVeozlFH88WX&Q5-g`K~r&xyRVp*e6Ipmf+m6Xp@uQ_NC^d!CrN-Y
zilVj%s+Yac%+5&?$`Q^w9=pcI4fCwSw}(g-5B181jg~b5i;b5T)_Z-I0!wt~*x45w
zE+(bto)%4Z75PyEkDp74MR^>!=Eu)v3N9)|)iA)+1YYy+zv3%b(vK($E$n#rm@(D^
z#70aZC&4FAg4R`af2LRXDqm&pAg)(6NIp4I7Ai)~6au#N<^BR|*SGgFwjO<~zQT5{
z7xu=AA>{Vmh~hxG^tjzgJu2g3@oRiqR`Av9nmIGn9TKi{JH(Tz`kvhWHTh}}UHCDT
z;XZX?O^cxOW=5Kd{JE(I?eWr|B!p;2yoHC6&!kc}tV&H_Dg~mJp)QvT#^W-G*uomp
zgiUCZ^U@veikVMWyEy|To=h!7E>@g539g!QSq}X*4(j1uNS2nzhvStK{66C`fza=&
zuNZ}bgHf9{Y~PsgFH2veMrZVq(ib@f_NAX*qgwHOP6`>+CK|3A4Lyw%ex<{C+x2{Z
z1=%NSL652{5@)cHnAhi#$z}WI)r&HIhVrIl##f8Cb0#F&64vpdKey3rLwY`h%cb{N
z2A3<Ko_^tT<!re>`T`kr;sv!gV73Z;66x(UxGk`3S=?*Ia#C#G>9~@`Gx~t`k_)%o
z2u*BP;jyEQg-Gw`m8g&;?UOSNjlzB;UY0U%`s3CMw(ah~;481KJ7Y!){Zleo@4rk8
zGVAlqIwVd^Zvnf4yOmkz`r&6D-ItCn6!MQ!4!hGct#|XMoVFD)gL;F?ulz|=Ro<8}
z01iyb&r;MH8t^QOdHKn5DCgBO>u#yo!kH2Z?~<3L^*FtzyI6+*2Q6?$y*_rOVZfx&
z$tTY5VZm$fD0p??a@dEYt!bf4LFraHf?u6^PwEz4;r=?xbea7dqoK=tSA&XZ9@pP?
zZu_R<2+BR3U-I~d9Ck+(na%Z&yz(zlMF<Vzrxfqx0EwC7l4+(cENL{M0-4KT1GGky
zeH{K(5TL9uIkm;yAW%baPQ@aig@|*VRdW%77t!VNZdO83{0GjVSpNEptX)r?tKFNE
z%FZ=sl%WT`QlK>!&z#VST|TsFT(xZ<BOd}=1skw-?!nY~mdixy)yO3l+()|LoN4kJ
z(C_o=MT#bi`tc@yAz>$Y1m=Iami=imUTnljmh$Jux=KuE3Q|S^&#5ia<g(xSe&QDg
zV_{o`zDGCGVL-I-4Q$avyhiT856ufEQ?<%otdofstd+yA1VmiqcaBPQncKK(9(28J
zj^(0z7n{}{-M)yiw#dh&I-6a~%Q@d%<cRMLbt+OLPlu0J%e<4+9+g6z%GJiLSxM)G
z2R92r!;+XMA973F>hv!lp4ML7re^nBGg?Ygtt}_>xu1&c{SKlmER9RiWp>AIH}cl^
z!2ny$DTdhX+kB5=!n|J=uaM62je2-cT`m_Mr-v*Y^1YIsuY9v@|H;KW#FJjGt|Kk6
z$K2?g_vzL0^S$UOwmf7?Jo^~eJFkHqgEXf&+pIr<fjrZeH#^km>0B>r{IT^Xuhmx|
z)<4hQH!dRR>{PnmSCV06+0VJ7fQqQvmW%dva@;ddPZA%>PaJ9W?+>xHIiX;u^A#O*
zFGTgMew=*W)e*|hx(U+-<34$5ZINjycXa~ftFsm6cW!dgyEXc&UpfK@odg?7Sk%+T
z-ftlf>=YD%@ppqQVaD2fl)xX7uOscXd>8}Dnd>xPT^Kn3rBk1QKuK=(R(vCQKdf2e
z8{W7Q?Z%$|sEnVji1@MN!hs1#`km{g+qD5NCxbh<gfIH6U_D^9(GcygE=&0W-Tv9$
z=t9l2iKx{Y#k7*KKv9KztTqed4CfeFBLN>=)CbAnLq3|4aUTm)E&)^jYOU$9ayQzN
zA=abLOyWw8H?>#Cv%F&M+erHg`_IpcQS$NZs^z}6Z&q{*)p~W@N_`pV-ryc{oaOdd
zulM`aam;Mc`~q!6wV%KwQ4*VX`4`3*T=IJ<SYif+?0p;!Zc`NGpa4D1SxO6B{Jkj1
z9!(ktpgJB1z~eFpVN#QV5=Zj^3(%k31+=T8>Ob+N_}@G6CA-Y6*%N|MUs8;<&maWm
z*~KMjnjL2i)HIQyuH>@@Q6?cZ=)TMWU$_p<PpWX3lx2*;sE3pcXnIGEA~g!0H_yGr
z*C@8cG7~1%WlJB?0+Oq~VFm9U>!}PoOC0!ujSk7UA>FT<<`PbPw)-2wGas}iJ(c_2
z?t~7~<MFRkc}o#py_ngea^^)2>oafT+qY<o*>3e(uhWzCtOhi54<3eE&k1_HyKy1k
z<-tv#!iDKTQy*{RQ`%is!<=&}s9qb^8v=01@s&qUZ!Ap7l*`57MrKB6?Sk+g|ECvs
zE4G;WUg0jfr#)#j^9){0Vg01SSN5=eJkl_(aMF7^<A%WMKkI*R6G|1RQ90Z_I6L=E
z|G3XXm2SD`<Mmk?wNr(%=8UvZ<u9Ti66U9$`~_SoyS=u&u(X&Y>(M-WhRX%^WvaZ7
ziKwxX$;(SIidth~?G`t;QEuzfS9`e5H*!>kw(KGY;VN(JYjlS8+baaUHF{Yrbf=EX
zaS>9#Yf~kg*;eH~V9oTu$YlnHwG}1`rYJo8c@l7KsxROFbanswetsl0ZmLaxx@%jY
znXz!@{o~+06W(OT3EEljJ2T;3zZPqkkOK{(7DGLr%DdzickTqS#HY3m1HGQGzd$ri
z9orgiRoHvo{kp)YF;44DgUD)?4<Qeud*+?R^+Jo$?=c^LPIF*g0`6|HN8wSMlO?_L
zYdf}PHgKJ#vzk5{-Rj%vLD;U=wY9DsaqO$fudtW5I^dB88`+XoCk9q>e79{(9Bwz@
zk4$|bVwxQC%rU{G33m41tMEjXV$B?_p7|%R{%@}0_mKOb2dI$cFVId9kj|f8d78KM
z0QvG)tg~p8j^_@Nz#EGiR{dXIr<cU0ArtcxNfuPeH1sr@2v#Nnfk+mR=)e>j<n@0v
zZqP-B?w>%38a&x4@9GUF-CtZJr4blCEGlx>crIhfXDeH(v0N@aIa`YS&C^%ZQxZ~O
z8?9L>3HQ#pgv%<X_oPlAi#Dy(sVG<zgg=||#W7(XhLa-b<ANs~>D#H()HCMSAAg-y
zIu<JT9?xKPMWiN-#=4w8T`;@ZKyOWdqu<AluHjJc?1l>3d5EM>TPtTSInpCCnqOYy
zdQ@L?2zUAs2%ppDn+j<1ej+LG^_)tT+!ln7!>v(n>^!>O$um+@7A_+|$s<9L!A3@h
zZC(PuTqK3uO*yxU8qFzx0ZZ*nMW6WD0~cPujR&R=pScLr-18H~bCLIgYq>^kt0Z#Y
z3THQk$=_YAVGs&3`86&Hj2h88OBT-6#}*k&tZ73ZuYbD{3boXH;D3*Iq3Vlt#lqA>
znJB16{I5wtDGQIsWu8sR&$@&Y8Hkga98JQWm}7jW1mXX1I0%pH%$7b^D7=O%uVJV!
zpQ;Jae1Bt9F(YUC?A!4f)U)7Kq^FMUPhXC%>WV9|(uJCOS&Nq%u3vsxl6Xh^$tvRx
z)k>O64Q-(7L^60b#WTD~y4iVqB1A3S&4;l5UbaYG7@k<r^`K%)`VI2Zqpy(B3o|?Z
z%{imN8O^kp-)#IndU-`Y74?fa``f0xy=M1}74Yd@(Uw^iWx6hojc!}uA#R{R6JEHA
zs@_>|!0ct03AALKJm|UOgc2t4&Go$%K4}1KT{5!CL%-$W>1Y{TgQ55IS{_Bm|8nu*
zH^ij99n&$OQBBReCo>PGc~vLXC~92TAY}e<eW7_*S4HS^=xOdyx&yP`qP&WOxZ&mT
zqI`i~%Nq2|1dq;k42KnZDL<}XRNn9EmiyU2vN!0d9afE`;I}b-p67E?(8W6#2sLvG
zl4j}zI^3qsh>aXJrl7Z267+q8PFqH3CPq+dQd#OAj18j({u`&`E`dwcO1QwPY4iK<
z@kqv8n=@ZgC+_At`n`%~JjZ+7#jF8x=|<)AYaM|5Hm=mFFAzGBM-z-$6AT=59(le4
z@uO5GVu6C9DnWVtx-jxx`er-+#e1Jv8*}%{E;QZOQfBq-vi&O>HR0++8TXjGl)7~^
zApfKJ^r!^km}H~fM|i+*+rZSNDAy|*Jwkpg_Bg*}HfUV$1ChLqOU}N->Z&d>Q~t}g
z9QOot@5au45unh|GG;3BUp_8$n|iNp5>bi}TzPo%Jj{Wx8e2qPL_pKtdRbX88-%_u
zh~{2p4%(|UQTLOnAC)1PM$aUVrhc{1upx$K;Oxu+c_jO48H3Ur;}!HV5!PnkUZ-%g
zPR_;RCzHq$7Z<_KiY&i_k<g;Jr-(XRlke;cJR~5?V=BSbhu9gi`1GsDmr4_{J=r@c
zD)!hRch%ACoyt172!}K6;G(Hqvu_1$ws!R=pf?<R{mBjOO!{I$aBKp5I{aE?{k&h>
zUa?xk=&_66^J)4Srta5H|7<~<`m{UA%3WE{QY(bs5Zw?}-6{|f<B*Y8<|O9BPfiwy
zK_7*?dU-cwXe*3Yh<|m7>x|P~%p6dWYHhh&UR1%d%9j0$o+Xndq0nXjINDpg#?qWL
zIJ}?GWnldm*xf9i4qnZhkH6XzeR<)~B(^lutuD;Uu${KT`-CrGO!v>%<z5&^whU}g
zf9GVGwwc>UPsrh_H9#6tTY;*LV(S`5;`e%f`J_76>D~mSQ<<*{r03+-=?7}*-T5H>
zTfW3Sf|$;UW9ChNB0g{@K`+~D#2j_)&MS-G35x06+oNhO_YPLS!|}JiTfx1ucPsa%
zl1tx6GK&2{2x&xCJ#bg<9x}Xr*F|``>D|E_S#twU2Cj9qzrs94X{cUNa=opl=>cQ^
z-dQ5g;f!;}tKWfw`F3uFJ_eN|bC>5CLX+F>#29-0a!{))3X<H!^rfUs5_K_%tZXE>
zQA|M+GpBjgDsZ6t1YF${<88pO9aRzxMZx}~tk83g0+$&S3RB5&?99`I{tip|vzlh_
zck@*Y)|S~wFRw(8oDkayOJ8ZT?Z=L$F9g&m!bUIXy{8=uf1jIpZaZe<QGq$W#s5={
zLsl@me+q0^*{H+m-uXpGAwI@E|4#U4#XOtCUG^t)Sdowe=hCUW;-BnZw)(42a=e2_
z`gHOULo3ec-CB*Eumo<y_f7L>F13^_s&%}0_Ra|6cKfE#t&$Z@+4dWp=Z3|jhqkfl
z4lAb`yTz*QKRKVS|7xG(+4raEF-KWjS%XgDpOq`vc$=rq3Fb58?kg^)f9|{_$v&*j
z*3UMn?a|;FdmsO;5*t$Sx?1{4itm}OD1qMzSjh*U)f<RjXJO4kB{mODG+yb{unn*i
zLVE)c_xVHj*b=3xKXm$pzkjN!?h9Xssc~zmy@r2P%FcXokIHm;MXN~kFTmOn9<nL6
z`Zj+2xUH(rJquyH`MI{_NO5wN&1k7^+4rAgR=-%=v%M8EE$ofWU-snH3HmZHRnK-y
z8}j}5qaZVcrYBDDs_GayVQz5JMku7Qm3Y|%E5S8ACz$}Q*T~MtQit2u$}|h&MW%Sz
z3v`$}B$dK#STQ}OE>zo3?<*JU9VPW*tJsbwF8HU!5s*s53JX&g{T;IulrDvxu?kx~
z+2PuBHpt@&1JbS5hfElnS-785ApvR@ih*vitv`QIZGVr(sKG%mjLU*^UJ}k*lbko|
zHs+DtYb?}i+cTx%H&P*2%2R7$7nFQKTZ{)3=URb?&tyK~e%e(jrKAvM9h<)Z)}#IB
z1nYv<^_N9=q^;t#50Xe$ZY{SCC2p5AEq<T(xjlIkt%_3#^f6^mi}MM8WFxd8(crkv
zt+CO-x)Pb=XgOScZ`X<NhOHqqV<^8?T@L?GR_ZV2E=u+#c3t1w5iB?JE-cF2oHmKQ
zGhKh7_%O%wm`zbhRlLgAk5|SgMN{hh(r>b&pQ8sQs%}p4+^<o~lZR`=qA?ddWO|<J
zNg@-&)XzwBq%XzJlA3RndG@xUUyr36tdAK!&tWGE&=!cc$j(OC&Unm>WV@=irfY8E
zrQXUL{U@Tn*%_ecKIB)g*(38Oo=7%6Iy6{OJH02j{uhY3ljdaFiymBU&U@E1ln_4t
z!^qS=am#ILSVljf(nu<Eh*~`ece*uxtLB}+UqIT%T&uVXV=SPFHoJ_b_o&f?NZj3^
zO<{UejL857RJ9sdiCM~QXrFbMWX`Q_o0{N&Fnd*cM73VFQj!5qbJNr~Y$+rrh)7Dr
zsL0&CRxwVWE$Pqi@37!dWWRx&yC@9?Op}X1f0XnLv2B_c?gR;h1IsepmX2rykGIz8
z;}u1o^7{3kK^vL!?!w1Xhxz~}%a3=sF1*YXPO0^n?aU(u`&+EHi`v=;Q0LasvhSYT
z?{7-ZF;D#kYDkR1>r9&$Z}wf#Y@IDFb7-LVJiQUMFmSU;)kvlMTggYCvRmpt$k{v7
zY5j7K!`L4jTz3<F`vT$`DZ?Q+f7PH$D@|(5dwC(PE}r<4M67=A>ce|CHD+C8fvb<w
z!9M64-YFoqQa$jaX_fo&obO+tD2pp6=ERFnHtgHSwdcbu9~<w}vu$NS)A)+L@lOYs
zl`<HjV)4N=;BS%nuNb4??`KatI;jtye6MFo-n214Ch4qy826aP<WWxe{8Wou+?xWo
z%jaI4FaM`XAy#Mi_o&><-d?5QESM$yPcpcvEPEBXQm@u(*syY*Fu=J>IgYBzH%$=<
z`4!ASBvp!L7+0?5`ws%jz2seV=#0wKkuG##m<s=+=DhrHdFb)b!>jVKI9cD5jAA!k
z;P1;7Y#B~_wFCp^{-%*&*t(FF!Ku2wH4KcUJ@m%n%pgVDWSh-AcMkx7`E}85k&nOL
zpOTq(2~TbGD?1Q_?+P-d5JqMck~NAh$%|b|>=w`2h1c>c&nwt1@G+(@MNs18QoUOD
z4A$A58(6~!UX|MqmCpl<u^ivyQj7Z!%HjRNVcMX}ADLBtwfjT-K3!RU9mV=u!A=+v
zBm5i3boKfl4#})M#_tLO^d;CrbQGDb=^JlX@<3JI#G+a+pffF#5>(loK9P}^5@(@B
zQTY<~R)6+p`isNLy0$jy&yOVT+!R#^aY;364Lb~Zq@h$bqkiKrusQw5(z^K>ba8r8
z&|YDEPk8gsl-(LTEt2i#6Z755yu)7{sYZQO@4IyC&$NtOWBc$l0~-bLow`<>*Js~g
zp<AoExhwwB**g#I%bq^eUCe|RKf%->-P-(o@$-%FsE5oK_L6pKf<h5Anb?J3EfxcT
zFb>JYAcR1H#5Z&x)Pm?I?7R7<5*n1H<u-IP1A9rWPoo#Za~z}TC=F)%fGXidSuiX{
z`A>)|)1gsX+zRa<$w$j?aDYC>GGl?Kg4E&)L_;Deky7FXCh!<OATe>E{|)Ct*dmL;
zi3P23ck6iLKVSc{a8U6NU7UX4!02GC+*1WtYsgaTbP<$mT4+g}d_($hi~mK$=wCp&
zmWzD2c6ffL7M3E)`F=wGkNKp@qZ_AhZyD`ngnw4#{c(7y4^l@wIiGfW<1cV;kIK~h
zHJNC22C?U})t;*G=fv+z56R{QS5CI%3?kE{i$3Gvuw=lO#1s-p%53^GXL8=v0QWxI
zUzuA&ZhtmFH0S04hJp5kWSNSj`2KRaq$L}x-12MZcLK(@A@G$zgiI<Ozh%SfTS%=I
zX%c<zxaSbTN2{1qIg`t7Y5R`(_3L$aB(7ix>5K4R5O3f3VBM^dEuG8g^L1pt`Q!Qy
zoP9Jfe`z4<6nWD=b7__QSw8lJz{}d{ovqu{oUoV*$cr+4dH>GN!#KQ9rP~*V@^$Mi
zmf1&FuO04n0LY4yY(KN@K3W**0)aNH*WPQBnRw537$gS89@ixfY?}5GZOnLWPB3yd
zWI(=-sw$2m(<z)VYwvy>P+r%%wC#GUe%)??zo~e;Wr4Lm7_RoNSMnZ5$&G5tjy9iC
zSbS*VRa=JE%gCQtwSKt<<)w_lPjJ>{=gATop!{1+3v2Eg;)Gg0vVrlUm`=?#Ul9T<
ztfrBikqI$S2zbcswC^CfR?Ot_7npV~Ya6>!^3nUire)LJVwLWAwf?qMqB`{{`+>(y
zrd#`u)Hn_I&!20)p!i29(~-l4<LaGy8P>PhMgfne3`T!y><u`}WSV6z;WR8gpKx0C
z8vOR3jj|m|>!%ECBG$s9#($tjA#O<bz@@iMN6lJ;*neO`wA8#3qgi;j=Y#ygzzvRF
zJ|CAl9&tl4?<;oGSe6dH&!_8h)b2@djMwjHzq1Xm{34uWq0pUaiBmeEvVjfAdKA>?
zqgj8bvUW7f-zAcLy>Gi@rlKjgY7Ihv_X?5KO7y1>Hj!x~GYiSv+b7Q@{mL*!aWy}}
z-3U2taWmSPr%1uVJ<p(!HdK_DrK76VtPsSWOp2g2d>fwvr;!llU+)*_7Oo&BW=?S@
z5`d!|Cor|EpA}4`QI?<y%1Thw^X490J~vp;IhRwTmynEFp}aNk^%^t*(*%XQNhmj_
zQV#3xRxO<V#Ko4yAPShINApT^6O@(pI!}o-e2Hhf<QJ(i)~6S#t^6*d<Y3ZD>HgCa
zfX|Ypw1kz>eY<yn$^3ip#dRUE@~L+jHBPO$Zq<%=Dl-!H3|@UQPgI^Bo*!<lt7PIi
zQNm^H;Dk0Am&bp^Mw^$2@6hP*3oSJh8MGg8d@E&rlhJ!?3Frwi4<Q&md;h+u@%+ZO
zt>w;>!_4~sc>a2{riWoTf9(90jfHh@Vb<;KgGc{RCVc`V81^v?VQ)+Tn2euUEPQ9v
zmSrY>%Ri5+MbXSux-AW>L9~CgHR)q1Pn*lt7U68E!WLP{d~??@3r~~_=)3)#e$Wb)
zPmR>C2d$Fa*;G89I37*%v6e!8==-$%L+nMG8drEjFtgEnLuyquevj<iZD5J>u?AQT
zsd(`Z@AJj*GNL#Q3FaEWhQoNZc76q~%q~RxfsYnLKqpYR$8()J?`)2dl%gu~cKA*T
zF5=<6Q|S#e&lxhAYg9!f40DgsWehlBfSZT^Sg4Eb=bGOZcw`BOfHd7T%yU4{>Nc|+
zUMp{Xnps<#@N#>}TfHw<_~p)%!j|>X@Ryb<H^@qt8|UhmpEnZN#3(DVq|vM708LWG
zNGS;wsM+~tK=V98Ac1`B_hPqHweb~V9D<D_t;je|H=>5+q6^t9>YLV$8Wy4@4v27E
zh@jmsQ{%NzHJ8dJF$VJec_$an^2<S5+<~NRKWg)$(Ou-e4$VKgzbLplzd%~&mvc7K
z0onp!1dn@GoEtf9e2U$u4F_ywHP|0ko;OUI!D~FOd3Mk4y~B5EnbI}QhIi?Z9ZeI^
z$)@e6EwLmNQQ=q@lsxma<-i)Iy$vkfxqj`!Kh?>;f%AOYNRsHNU@?z2B3wAE{Fe(w
zYJ;O&{PYEXnxF$ljFW>wL|8g#-9|2!Jv5%+x}VhHkc=hLig_BPT|mc%kT)3DmPbI$
zB`6HNP@-8}CPv^J7!1^!`i5pnF6Xgf87K#%*>Gxp5(A2C+ABf!d1^xQ1~!6#pV=cJ
zcJQ$NaYv1W3-VkHU&WnJ$MoNOv6cp3dYQ55Sn~Di^M+tX{J!hbUqGag%ddhOvxQ#M
z7Pkg2!#lAs0Q;or!0d&NB5&B2FYCL=pXJwsZ?f0VGKbGUDm&9MeD?mu!)4XVQbtZZ
z*T=trc=VXr4Q=SdgfK@n<%Fs^|NK$iXDzcYG-43robh&S0T&+(@S-)o62^Qx+FDP&
zJZbK5d+oD=rO)m0;$ZUcptj4^$xNSYk3ATV>9R}qf(53&5gnA|ly7#Q3sNg3(J5WB
zU7afrs#k~mEUX5r3o3Q?n1bBPw9WS)h!s6@nk<Owl16t8UU~R}{QJd2Bzw6YGE1XL
z=d<2TCxHyCH?QiM?Y-zn<T4S9RfDT9R=<PZN$D*6a4Q?waaDt8r_k5ysJ;TXy;3nc
z(0<P*phE!&LqN*LRCO_5$c%%EgsYzMC@8NseAfx(=CvL>S<bVy+QCrI;^b=P4DnR<
z;JN7ly9la*CJHDNv*M!~oh}X%vp4$V=owrXsw{Li`mOIyjhh!cc`={h`1Yo2MUp5D
zVyAb@<)b(f7%1s=L$5(Vr@#>2LDJC@7k$9WsLyHxCK<0z;?+<o1gj)M1RzksLyQrr
zDb$*9eM}sXG?$tfI`07setLXY(|_BprQo|S$lwAEMf0xZ>*ZFy5=zxQ*(mOTl8=~k
zvlq3RUzC}YFGuVi<^aQ4BEBNTt_u1#2#r*kSG*0|ui%3VUiz9&v2}Ek5OV8maKUbg
z|F+~=8lpB3*`s>o&P@#hb8zQ~mG=aKIMj}rLwN~Ipw0t)L(ve?<ML`5a8{Haa+FRG
z0aI8dkdwH(k`$`s%UCcAk`*Y-kTd!*d)uU#a+Gp;YCg3&YQZ_!>XQKiA}^0GApo}m
z&rP4G5^-8I#Ogo%)(~S4nt+-8_6iLT&TdJ}y|9jp<zFapj78>!3d)G9m;({~_Z=y$
zo?o_g8P<$`{RM^+p7IVc_^?9hsnc<byi6R*5ARx~BN#BRRE!x2PXixVLTjXil`A^Q
zJ5>_2XuEj^Le-t^(;s>R<UAc?t2Bu5vvh2mLVZs`KVLzKs+@G_;=SnIO6XJX+{2t6
zwfg1QuOu!7ZK{-hv`~}*E%nNXhCLK7{KydlW5NrR7Ag1{qXxcxadnZ0ux8*|h+Ni8
zCiUBAF(TuP@_ZN4#c@Ej<SX3sx^I(s`Q(@?lxaF0Qs~`T^_U;QAQsV8b=nFw)U<kO
zJe~tHWbr2@#L;Fd1A133P0QhgRH6N#voUjY6ihPx0?tLkfq@=9&KbxfA%S6K)RUR?
z{XsTa$z){=eDix^vGnOJ5hoL$txVXcTLDA4^<`+l>6>BP+{~I$mt<t}937^Vk)h2`
ztd!n#{Z@?rg($^9W6`BKI+m}Bt4=#^WfE;zIl3JsB$L5N)UOp1y+>DL8*}nKnM2?~
zgr5<QquHzkJx9Lyrw<CG0uA3s!BMP^k`gUImdMJg!Qgn<QBGG128l+%3F77wQXG!)
zE?%DZ!JKqApL-x9%ypy#v>j{#HdoiN-~g|(2!3CbGxdc1GYY1KXYnC{NtF;vm6C&l
zPDp*sVm^yJilT10D09KHzQq1Te+U9Vr-uc7dm1H3R`P7TKTJen>?{H2re&gW;OuR9
zg7>@93${86TD0J8EEdynmoM!(#eu2uE-%2ImW|~f&twXs$PGKxPM1SsU2~It{4Q6y
zoC<LJ>0?z-@x$3LtqjW1a6<UG0kwE=Z}mC%e6fF$T;%K>7HG~V)AiM~kL#VLiKLF`
zTHwGJ8<Q||IkneFpar=L>`|dAF$x#K%|fWEXj;9PqQ-B8dnmm}zDNvcbSG$_8Pk;_
z`JxcpsAF(cdM*wz0FwV6_fD0Gez*;j()npG7*ta2o70mi=-t8IGrHf<{s!aa=dO|c
zVB!Yg^8tC$fnRX?oCtTgJIb&ppFZy-f#M7T^9!t7z_j6evCw=DsO(q}eiG_v!(sQ)
z+h*#tgCf6VObsakn_nH=^Kpju_DsBU0l!>p@{>a6r6g-1#xeizv;4zi28t#>#-HD)
z2%ON!f996?Ep>YMp#f7kx~i~akja$L9&-gAz^WQlEND?U{EE*+s3TBB2zZi2z=_GT
z7o<Cjzbqi3q%lGl)heV<Li3YJ4K+IjRLBFC@|pCA_}r%m!h2mIpFdZOtj|v{gWK&?
z`=ngL897nQa%>rLFf?$uT%9;q;VOh6GZT!_zEx-iYjN;35l;t5mCW@rhw#bF;V9R{
zkKeC5(q|eehAwTjI(@c&@=?lC=o0jO90FPfr4*YZm@<12wW>ob<q(wM*&pW}wxtg`
z`pa<7ywPvCOyp^9dFj4WW#?)wi)+wQM>d-Z0>X@SKtTPHqL+l+WM{8NV+49e><8Ad
z+)80aBamMcSCxYHLfc{b;?~nsAJYW5D-BfTS>~qt9B#E#Y2iGYU>LI}pU=eF_njcL
zb2G-y(B{XajWoQoebA39ykc1kPImZz*to00P1j1X%HNyW+A>ynfqfhrC3?>%8{>XI
zG~{E3&M%i&y8POsYJxJ>NvSimG!HV^J`=X^F#*?48I52w=SOLuM0S=7Q=VO``qX{@
z#sJD_!6d(yP=qp2#ESBMvk<T_d~7|UlG<p}Q56~Ce(>y0K?&H+*^z2WO#QQDKQQ61
zSE|V^mRn1*67{>#>F6&qK)%<Hoj#{?wi=9`dyNnrYdM10;D_6eL)``3mf5Z|bDCQp
z9vZre4BRuWl9Cb4ST#==Np^O@6ZD+w!ljr*T+*u65OuAo!?C?V0-p?goPIibZ5NhJ
zEWLJGGW`qa`}_r-o2xjiYR@Kmo(-(;>yS3yakNDIDNeyr5R1)$%O`ZQ<rY=phb}cF
z9rH0CnK1E%D??4;mJ<6TFI1c+wSHG&(v8ClULJd~`oJ|YaLIUV$mH0I)?d@}69e%N
zb9$dWkKeA!36l(eQrDc_D*n)Y)X-UE1dr<enP6|Ja6&_x#b*_{caWHv6!z{}fk0)Q
zRl{V9TXO`ZFVr3*V-2i)EiQ<i>vZiMeZf>*GNqj~6_97z(Xw^P19Sf~`HOHz1)`^;
zIdM*j>o0K5sc<-4-hWu!crKsNI_xy3Wjd<HbrdS{EugO88LV~3nToRgh4;R*$L^U2
z&5_Rp((gpLVseMkd4#OEEIccMLHKOFgZ|Igf(M<hHABqJMEl6fRFZ7ps>C2hyG!$l
zzpG@Qe2)_t9tC1#B}qJQPnCHjvodneQ9pj)s|tmX@*FGjHe)+!mpyg4938#pVPKv?
zgi9G#>4XIT;GYRse7Tort+LZWNlmPI+_Gbk_?|g7j4x)b&hzC1bkj8@9}&`YAFf&&
zz82O0(+K-^4#5CvP`urF7}@6Rz-$<SXy^aEc{7mAd27w;4XF(x7RZ*b7HD~mb9%D-
z1oGvQZWJjKGQW4S=8_3Mib_<@lR#B<mREFEp<`vTz3JhZ%yNlc(uxivm2T`@PL3PL
zOKMNZJ@HTOY!g=#AzkhrPzpsq$!Xd;oIW+jawtOb)_7|h7Iv)0NNr$HfGVwadCN=3
zRVLas<(#QF`x`?#`{X|d=*tw|RPMjP<fD?IC)MpYd0cp9a1IK|tk{ci2Z%YK3<p>d
zkU%nwLWY&#`Lq7#mjED+u0fB^|5wF$XZP|>@!_W!d4t4cmwHF0>J(&6*%wkdJRdp5
zmu7W#<h%mHpIxDu=l%_<Qqt-=wps1IeIB(}F6`Qm%()(GJo$EG$ghdQtR<3Z>ww*v
zR_)%)263xf;~I>t^9RtsKvYU>ipPbE_FLmjh)oYud$0j}Yq_oL<F!s8z2v*6^@%WA
zGR5xlEjU$O-c2Q7vB41B3F@{2yc=vQW3v!-o<nU{ROT!U60Gj94z&aOK@`Qp?C-Xh
z2B~BNMUR5~%UI0%LKKzBt*UPr&Qk7bTZ<69eY$d8Vpsg3-tlmZJj}@|^nGqpxc@N%
zw{i8_7ITdv=c+l!B5|9wKYby3vQ88_Z*hlEKN-4dj(>An@waCF)0^W``<eBFnvQ;A
z4V_IwWZn$WYekA5As>6{u%81_R`WnY)JeD0w)9B=iThaRO07p9LDxm*jBmXh%$lXw
z<z7qbRRzHnXOZz)8P~S^g+7`lKeivWq4~h@HQ(crr_hSiT;_Za?o``3_Y3U@tu-_Y
zT3;vBGsnBwOtxQrHsO_Bl4<Cey%BWpS=j17R4I!%E9Q~#FD9?#^;P)sLQMiXZ=EPv
zs>33zrl$f!kr*4UtMYSaR;~#i3V|uZu6Yd_tOzi*oD~aLBLFamOBZBAHVKG4ZP);8
z*o-q&Y7zwrRW#`eMqUKeI!IgaWGsra^4t5lfsec^az5kCj3l7mCd-VVm1V(7P`CPm
z;;MAmXu5U+%0hi7xu*4Kl>oFm4kNFcO%8SOSXdcUK&?ko&ei9kihNY;7<3o>@yK-q
z<P!WRfg8;~6}qMKCy)VGPkudlDY=AyOTUjTt2-kacmq=PU$!zGr0tOZOzlvZoNP|e
zYR`YgTv4r!7IF2YzSfOw_uQ>TT^O+UWW7}~{#E(P32D+&@UB$t>1H^NAIv30<X*3i
z(QveK1_E0OZG|iNatfV4n^J?4UuG$Z<@&FF<%UVsI;atD@}PY^U`A4SA#SH92q-#{
z1E~pMF=vZ=&7MHzAwHv5u=qxP$fVwBC&#>!PK!k%2`@>6$)wB2x|$1?5oxjRbWAJR
zJOJy?p(Zl!tw*|y^GC~uPP|%moVa+>qu@RzZJ(|~fi=Epxv(FIs|~Ch-mIK|{EgwK
z3p@B?O-0-Ez~OrJ2-vJ^+wDAr8KwF9=VqRJ;M1eQS<f|AuVD^$x_iQ|CEt!rASuOt
z{valOXaCb*Aj9=?TKqdz*(TcWbz~B)<5IP~2G={-1q|*>;(bQMX^}^+n!4~bwll3F
zKq09$;EE8pGlnT}PC6I%O4D2N`!vPW=O?r@6VGX@#y#Sa4h&WgA3Q##9qg=yH96OS
z(%Q~mQ+(7$M-OoPq<dDuCY&TCEetm0ZZ6n=pk{8JQ;uJEdvYQ3I<D$?xG%bt{Ckt@
z!0Z!A>E)`~6Ge7*SJn&RnRK^ny0Wa@E1HD+J?RbAdJk1d6Dc)SuY;;b`M=oywkq<F
z-YMs!sn2dy>d2+@y;CB@Pp2!Is#{mL<=*@B#qrJuXZA+3kgVFZcb+$eP_puH$b(v(
za=F_+5$_i@S0Kpn{^{@YHA{1Kj1^I@#9oq)tGmVo_FTEU2NnOCo%_4Ph;;+m&9Rce
zD=92=O1UFlHL!&#H#$;TSx#RE<{?VDZ#896!1nS<1L0MRuaAfWvt$S<u~?Gu-s;@A
zUlQr6T7UTF?@i0@J1UL3MP=fD0TU|2#c@sLwXq7;y5NGRA2eqsMN==pKQuO08uLi5
zo|F+0E&YwlAQ)e^m=$lYyInJRSI((*GNj?B)N|g;A%<Vsi*l!N<H%pZ-z*-?J|22r
zj`W00h1eCB_3DTtlTEp9CzAj9eEe<DdzT@&mozt5F`3hD@OPTYyE>D8g;k>(nR|Kd
zoq}oc?Cd@q8Jjko-RJcpE)^D$x#%wEqV8yP69;tEKO0f+l*up|#v^nao7ir>*-(2+
zIK%Y4@v`1k33#e6my6LS8;%QnHp7=06)Z$kME3gs0=49C{_J_res{UWzN(-=aYDMr
zl>MQeflBtGg<mm>xZPW$Qht5m5HPqg%CubD(BeLJ?zV-I{b3{KpX`;f$Uf+BreO&l
zvCf})9(RJr)3Vipmnn4Ya}*6pTWo$Ek~*n$X}i{K;dqM!6CLZ4YFC>#AZTa2DnTEA
z=P|bIRUlelDE|r$(S!qzC^j47H1~aNu?FC~g^uRnC87Kv#;8e~7^@I@&sry`jXv{d
z_oJX+wO5;P9bK8<m$uP9%jpt;tHi<dN$;4T*|R5??ZRDVEX1v&*dDZ<+b5VaE<W+e
zJYJNmA}cTUz$*%j;>3dQoY2`rq?H|}lUiSU_OOg9)R9y=Ko5cuN9@eQTUCNG4(iqE
zPPFQCCcp2`rH08<<VNr4N54M(<4sJ^_muo~{coF|t8e{wLE|tz`wMWb_R5zh(Gx==
z{Whn7jY|aw!9@*WNotc1>Y{~Kp1+ki8kSHS{=W4u&}?N1db@0mI=o{V{~(q*eO$`+
zs<W^6H}bQQxMck`2a%soC_l?JE|*IIW3{sG&`Afiqjhmq7SKb^0Z~B+$Q$Z7!&6%_
zbG)mPigcXKfc9=*kXl|a>=1}kXRcz0xZ1T;cAE1Te!+a5jTY8&-AhR%eY$y*6J})o
z>?RE&9l9e5GqyCpn3bt+bo}fWX+2|H5bqnDTEfc1ahEJiM@)siCR2Hsv<cj=Qf8Q-
zlji@!gyn4l-@~f+ozila+x}PDd3P>E8BJ!{=0>F6*oqIC?-A>Ckr${}x;aBYB)$<I
zu-ddH-oFSeP4il2U9$Yl3zS}wnQRkR)WB=b1l<;QC1Smb7%u+8teP&VHN2C%R3ex@
z@W(!SeV6ZmJ;cjc&cKT8P_*7@<c%wOd6(s|Ux%Q-6ghgfjhLDw0V({496GxYQH4&D
zlxokD=lc|vuFFGr){Eg#RqB`;d0Q40wd-w*s*I6`)Rdp=7WTi;G5gCk+PJ+i)T(5v
ze%52&!>0Y(my^Xztu0cysP7Ll><Vc0mz<v0NtVp{U-P2tFn|A1dk6jeW>EJ#i$0Zv
zZx4$S@Ae`d%snLF`0YbhgY>KJL?)TKHZwyq6P*U6GjXZuiZ&gO%${FA>H2-Jhxiw`
z_NQ#O=F1(+a~2;)QoR>dqri7n4LJ0Y>1mVBR?-&-I=L-P6bt@&c(9-0BPomiAa*l4
z15`MMPlSPKC?j>b^MbEAIdwFn-oUTdOpmLow=7>@3yQsBF|&205gz9x*5_)Z@F`Qn
zV}qcUz#tZvDe{3pk7=8}C-~+s@YL}WOzO;8!M+)0u^yp7L0a|37b;TH%UyqdH`u%E
zMPuAod-Y)fe>W?Wn|?tMv3pg9)G6i!@9*Cxo))e=`7IPGJKksLjD%Q8spEtS-Bx{D
zK8>%`&MN!`Ub=@1voi;s5Nm})us_02-u;|-!-{Z-`U|85%<IXfxbJ?i6*$a4{6Ae>
zX*gS3+un(JESi{VrjD2@#7u=!Gck|tAxI@fjn(651*N3atVIz+3^g?lik|k+iWnL-
zrskSk9XN(M)jIlaec$W*@m<&2YwvaKpU+y)TI;^==gx=Js@`&-*6^;bB_FzW)3@3)
zjaIXBZlw~>?%-INGb1)S8WHoZWh$)J!Eayg)$6^c09W-FsyhsOe*mo$GU3tDVQ;Ve
z0A}#Kul`QXlkCy^P@7dz^VRbBvV-BdgeD|heDv+d({oUP!+X71#>xxnb1T0kuBs_2
zK@W<mo~4Wx)d~ga<UEloU-mAd$&&gYd9LTS;_C((8qzU!mSNR-z7OwTTv>jiJA}~8
z55vzdTK_I%Hi!ArsM5d$((W05h{2Ht#;2r)76o9*GR^Tm^LD9N{ueg_7E9A(1De&3
zm;)bOEU*Jb{#kK>)0MH(&ko(v3tYB91oMtDqHk?&LIBbG(Rz}95^`%ON@B>d#XE`r
zqA>!6dTl!KPAyFTvnxv9)<@lu(EFa@Ue$i~H?6%DN|#`6tcG+Zig!)DDlk7OYTGAj
zW>(M7@t5Zx#;h1srHo6Rt*NTZb31aSgH&5>F+V;H1%_oe@qeAg^OLXVi9)y&lLA~7
zNJ(fpg^@Y(oVLiENLvzzKhl8)@$am~CQgSM!q{%pz%CVpm7p&p9Dn|Rpgqwlw)W%I
z&m#(_%btOX8xP4;d`EhOW~xzabI02Y?H#u{hp%n*?O~?JFBazE`5L*M%qBjo*`&;`
z{YyYkcZ4n=Mgfv?j%uOHZKJ8HT~?wn9WK$hzJ9$2h_jg<OwUGD>&8q6skJ|BJ}pGq
zbtWuI+cDn{W?NhTQ&yLE1x*zZvGF0VG0Rg#-d*{8j{qhh)LE)DA+dA!pQvq*E4@BZ
zlZMd@-0+2&8Lr(bJ&B-;;lWRx`0`-9j*GoZW##1rA)bfL4ZkVe`))o!S0?Mp983Q7
zubZVC;EN2Bo$5yU*v2K9{s8ovoHaujSg}MF$Q`{e+sCNP*Cv@x@&%d(F0pNa65+p*
z1LwFinz$mX{fWn}ysRyPBb*KH8~UE-B*qbPTjepQYorQq%B1Xt_V?2lzSQB;iwp0w
zGh=e}Ig$)D_8XtZ7TofJ417?wZ4I7hE}pKXOyy?@GiN`Zf%o4;Z!#<>M*O>`;a%4E
z{S<cgAKle_7)q_Ln7`YkF1+Y98WQQ?VA-<lsVgP%SE{>@rAJyBS7fc!?brq_x$zvw
zyIvK&=-tQi6UDQm``12=j=x&ok_fm*GG>D!*JF}tE%J}ePw&!+n$H-QCyh+h*7qmU
zS6vH|LzS&1ute>IwvanMohI8<&wv;AI8ClPWm&!cI?B3;@Z4&9(Ci<SBcFFBf6Z^_
zh=!lV*RvNLi1{!sWIG@yN7?kgD3!c8zVhI?-<{!EM?>np0=pa5@yjJ0$Zw&CRJ7m?
zOXS;;3}m-}W#?=7GvN|mkKvdzngwYVkE46YyjGU?S9SFz1`^zw{-Dg2qA#x>Et!6w
zkEYuI>+LseKDPE$r3bl7XMu;3?yk0BuWjKE=88f6{3{%idSqV5(?{Kzf1KJnHP#Tf
z(a-!`&@Ai4odE^e1rs6fk|vFA^^KH2dw>sy%vw_rLLOA?05gb5p!u1kkW=N$&FO8!
z!uMb4P{T%|I*5p`gc<T<{H5zfCE-#F4Zl)=NRL<{?~l4deGXr8y45YUnG$i_!1@|f
z8(PgWS9h;ltej8`&?YmopliFXxCX)--bgmt2d%`W#K{;`|I)W&Zb@;K&b{;7do`)t
zH}SBdrg#0zvCCM~7fgxQs<qf-R|TE#!tIh)^R}&mDU!f(OqAugfZ*A|o)7r>lJhkh
zB+F~%`c0DF8#7@(1N|HTcd)(KwwMEgvi5Mlr^c#>ebt4|<(tQlkp@^4A9A`$wLwPT
zkFfnkac{e(KF|LpzhI{Ny84Lom6u3bz{p&+es<kKO}36@{miGEo3j4fuFbX_%4c!E
zHhF6WMfT^x%|v1pm4Jnd;A2e!E=|s3WMiGNM3ER>81S^d4fs4c>!<!96tL}}&@32-
zRvnX_mCHjGr28;meXAX3?A<#EDP=p1Aur7YOe5G*5}UtBaxo$$^Gv9_Z8%g7gR$<R
z_?N0Z@A^}@?l7xK>tZVjzE{(ma4V@|`no};fprktC#y-<F?;Zz^V=;=a^gCb1DEk4
z>o$R~F9@58UYhG<d3``neNdJ7Zi1%pqTWB-UiaUekAFqXPOkq_Q>pVxrCoMdu-C|S
zw#a$umj!sGRuG#W!E@5+QRAFzna`-3K=iRxK0Qc9tI;Vr3;FHJXk0nP@AdVd{E#7<
z=$v>$bVB+Ef_Ino^j7AYiv?H4X;C8)UzX|pW*pu5-&yl_DVtw^01uDh{AX)^ea^gp
zRc2Kl&w527You%V^VocAS*ddF>42~rAqU28^24Jl7HywrUaZ8VNE7Z1331j-n9kWa
z{sJh-wTSu6r0ZWceuj`OstO@SSW*02z2<!cmaTtv@+pjc?-EE7yUC}LUp1Zd(%lcL
ztG8O*{%<L~Z^br1To)r<aZs;*FrP0NAs5nv4d>eQzFzD-fQ$+o)BUbiaE&6&AMKsg
z4@DZ+S<+u|ED)$rdrsuj!!(YeciT5lwJ;y`+vfprvy3HP-iPBD8O8|}0Uot~c0AVT
z|KEzuKqsFi7l|D-Ts^Qy8$PNa4!oH3QJ8$9A&lbf3)yJ4WuNG&W@A@4YIyTzyh;11
z@*0WV0^*sbr!bYKCUs%_E}x;m<;%V(L)YxuW`n8Wk2-f8bDfU#Dr6$HLK@@Wy=qE)
ztL-{(`|qlwlVaxg2cMVQXQbxewZy$UdSkrlDRGw}(+_!+lN!K43NNv(OVaFvANV1V
z)xiSpMkK{hYD}GKMWkEzq?V2YpYz#l%{XyQ(?<&azAl3ds603udvLS2lprPeuVl`H
zi5;}y=Xy>uC)H7oC{HsY0a+k@BJj$Eiaw1i>aL+%3?fSE9&cIO@u7Wh>Z15p>$R-d
zK#&C1)`N$&0n`pUAE|7FtuScs)L_}oO&wuW+wj$f1CL1Oh2OtTeJsTp-e46bi8~~-
zq}0!A8}N9LnA*<azq{jdt|^+oaTdwndrdIcqGS#$;&ZIT!vCC*VNAJdBCl{(HAw$m
z^ARbMu{vH|9Wk5Pr!=QlKs|CcaX&KTZmCnG(}L53^h+Y@<|t0`mO$|-G$Ji{X>5@w
zB~d?MtZV>KRm-q-VdR)PuAwYYjw0DnEKZaXh^^*B?@Ixapl?8_=KG)m&sec$(rZ3E
z*B6UpS0SuLg@7p|Islrzb;V~BS-UuRbk#~+RYX{n37ajVcQM4s9JoAGpE@4&wy+1G
zqLO03+iny(jw4F`Zy2MC0>HFjP;&D3VT>M`twu~BCPq4q%psoM>?euKNaz9eY=?E;
z2ct1vw@(I|R(p0gZF=Jt%}kcCi3RIVUK2c~+IpG0=Xyrju2I+4NIC5cM8f)}<ES{0
zF~k1Z_6NYlUijDURMr?D;F<JFY18em<zFT|YKw2BqlII9{Gxxa$U4N?J3Zae&OgX~
zk;AxeRwWY)<6M2QL2=u7z)9V6QQol%sB&`5jKuS{haed!$*LwrXX(gKmh25SbO~`G
z_JM~RMP-C^Za$E%1-xxeniAR6+76lvR>hz>gMZox*(gD@tW7C2##u-Nc=IyMWwB$(
z&gX$%AUs(N8C0qvs)!sbSsV(9G*w~iS*aoi=5$%Z6W1grnbZou*8`IE8V2^7USWdP
zL;Z1ilg!@<6qse$pZg5-r|Saqmf|TK<u-leRrxI?S`tqJm`UO~%_i};bL_2?kI3VK
z16RXCvzOdAT5t+x=#66ku9skym~Ob}{EoLGZp`W){*^vh4$aIbVb9p2Q^C@>N+JxS
zBqldux{+AgRQSvvp24H~H3lsj`ip3@RxTbgCw+SJC2{=8m`a9Q4_Wm(2TkW@<%44&
z!FvtQPxp*>yEZ%P@|CoH#|BDwh<7i48T|Hc*5A#3;!yfYOq`6>I97iF`R6Vw^KQz$
zw68TT<4hA;rk}H9PG`Jhbet*Y5d-TK-m2r6ww+}G20!83muwE0uWIIn6+ZBuAbzTn
z`xTVfdag9q$z*pYtCIAZ2Eoo<^-ee0Iau9Hbftn~E(6N<n5QMj-yV@MtQ+4xcQ4o_
zEa58V&~)v-&rH1I5$|{i**s*+QJww)Pg8<wI^frppu)8VJ7_-9eeiZ1WH&*VXD_8)
zE#SBC3XU2t9b8wzRcqupEaNo9V+$AaeOXWyXU2&Vr(kR8gYWUa*!qn|3qs$1<+6Vr
zSHoRS(*aPHkO_lW%Yqbsztx&l8(nN0oqD<$KTrc|uRt{QzfdKJqvt+IA^|ohGl%tL
z1kxFhHVg)=^7RHG*F><Q-8USS&%e5NGHY((yT}21;4-pljKdYO@h*wmRr|Edhc}g;
zUo<(lRd=MKL;syea*_f)$MWWx0~YgEO{N=#+U{1i<;;Sm=bb}A&HD|e1~BkP)Y1`K
zkMZQd%C&EAJR(AxYe_mhWnNK)F(j*pf8D0?1r69?5}k9v;_3o^!|0(~6i+@)9v-wL
zGv!+XR}K)YT%q#W$)bE!&9vML1uf`Un3=sdH_W;7(?#k^wXqUZ%V7@93oR2V2G|Ux
zwOP(AmOm**1FobtVBWO?ln_E(SXUA=cxaxUV3vhW<w(k?z#F7~ML~S7Xa`|ySa~FU
zJ?kMp!wr?H_wpgG79Q@(K&5{}iwFvWrg_-8LW&e(;(~9CD6z<9CwoXq9Ms6Av5L2^
zN!zfSKb*Qv{N@a)7YLh*6y=X|hARBQH``9WBU#r8mzjWP$#pDcK$SWLX5Xh>jPN-i
zhLU+zO0{LhXknLq(qS7!y{j(-)8LePFS#&Td<}=LJQN=WV_&JuRqJ!H2lP7d`F(0<
zf&Xi;{61I_n8m;>hk^X`7lP%3`y-dNsoB`cE_o76FgqxTw&eqD-0VgOc^OygW16+O
zU%~lQin8da<_+~)QSNuJ@4<<3lq?tQ3y(-$iOJb`f2;b_20ZWqxOVmu2FKt`Wi!@;
zn`h?LDCH1g4`8)f%2DIFE0=l_ot^a;>_xKKOq>9yqql{`r}q$)ukI^Cc9CZtrB~ZL
zryOL<S&D%)1kFLj7?J3SP-A%R9+EkAf~fA!M&iJo;f5e3=;JJSV2v;laeLUUZla#d
z>26fqE_AdH5u)#58{2NgU62>taqs&L7-fZ?D{M|X_68br+@`C@+|S+lF756HL+|H#
zutr0iunngA<UqM|_q9V^R7k8Ip2yy;>9g4azxm>tln=<<3~4ylyob!4iX^qVtdqGj
zg)buAI|4CU`b~$M9wZFgsXz9{TMna}*|eXCZF(?G2<oiJ7aW9+dJ<rs1=l12uA-=A
zbSBTml_J74inB{&NIZcJpEz_CX}6RzcmQv|CGU1Nty(Lwc-(D0RmH`r^R`e`aa-TW
z%{Tb^vwrrPRtQhhiLh8r5f4_>1GwBo@Dv03?|{@7_=W+_ClnYABMUA?5L2{9(|+;_
z-2V+B25CxhXiK#hxNgK<#^E$M9wfI=tr-~Mt`;UBh;d_Np65`&HRAUga-I~McZ7Lc
zwp-YSaXHyB3OS(nC0qnq=_K2fLxSg6d6WjmY?pxW3c!MzoJTnq&yy8o3Rfzp)bFk-
zz4!qb0R8##{lzoCDAt)l7`laeus)-pdu+BstYRpLoFWJ63tJ|Dvdk-{X7FNIp+-^8
zPv&WW*<V^Kr9#Vppjq#ZC_8s;5M|~PoeSQ<01({NR$$=`7X8OJ$Kk_1L{+6L!d75*
z5~AdoTjNwfW;fGvNWcCZ!Ry>fD7b@y2(_RNL}f-<bfR274{Z=-GvcRfmHP4l%Jt&-
zWMZ(Ol*UZ$0U2x6<vUsGoG8dPG?AA~x**3hO9|>_0A}uGmT>?M>G;tK`zRT`LX=<=
zBVVZR0)a=+rcDh!O(5y?*2ue^QE@Umg!MU1VPh|}P0ba|-yvNv`i@B)OIPe@w++a>
zzEO`vMTVTB_;y|D64mxlq;b11?bpbH+NQ3bJfzmoBk0edoh-nUxcv+w=Z~5H0Qn&t
A1ONa4

literal 0
HcmV?d00001

diff --git a/models/Reference/segmind--SegMoE-4x2-v0.jpg b/models/Reference/segmind--SegMoE-4x2-v0.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7d7989ec2a1c0825b092263fe09324ed9a995119
GIT binary patch
literal 21996
zcmbTdc|4Tg|37@qFvw0r_MOI>Wyo&qL)Njx&|+tjvQ|nBvNYDQ#Uu^JFt#YW6xr7-
zLy}6eE4>RzO25nd^Z9=7$K&_cecu=7T<2VKjO+Ehp68s`cKx0Gy8v)oTUc2D5C{ag
z2>t+nKLWny*Ms~4z}6N}004jmV1x((Ft7yyiy=h#f7=!i1i<j`btw3u8UWS+Cs^#j
za&%k-%hBKbd%o%w6cV8l;uj&IsiF$}eG8ZZ+-&Te?5y0J9Gtv7+{XnFLIV8!0&3E-
zq6l3L1AQG0?b9f<rya`dtodnehs%y<FL(z81{m6g-ni;}-P13?=jclyJiNRDd;+J0
zgiiUGXq))_e?I<p0z52G@ZK39ya1F3!oUOh+XIM$cMbzFIfCWC2L#H%2xDSqImXHc
z4rt^Cpb!QIC?f+5#>fbcjsZUh7<pj4l4{0G#~r<xr9$EAvBY8)X_NXcKFq{FG8*1t
zamQHsPY4JK$;zEX$SWW<wN7j6=$e|DTUes4teu=)T+h0>V|{%6`~w1maN!Y=QP-~D
zh>pLTkeHNwkC2s}lbd%xzkpOyN-3k3SI{b-JZ)%fYHn$5d)eL7+t)uZI5as$pPqR=
z`)2Or;-{tMmDRQNjc?z#cXog5{oMa`Bo_pf^M8*22=;%;#RJL(Wn^SvWImD$0=)(n
z1|CM3q#6^iu_Lor=y56aSQfYmvADkLn6w7wA3pD}308g?<VV?WN22{J+5bDi;{Jb0
z_P>JtKXT0h><kd_;W6+4M!>Q;^of>?6Ivc3MKUKJbDm^nvZfMkf&W${4#{kJDvf+B
zXUh1fa-I~_C3%y8hqh|AfSZ#ejm*YM??vJtVoRM+DUeE4I=)V*gab7Usg$6z)CrYx
zppGY2bUR30o#w_}wEMTuR1xpM__or3&T?Oqe?x__wW8Vas_Z+*kdg&7wnQ1#pI4FM
zz>wnXM*|k8d2mefxYR~KQ7E-hGG5bS6vZHvM#3{`3Tvvx3wa@d)S%Qrh`^M<I8=>#
z+{Q$WnvLS{LEEYd^^y8w!4@6pqaZtj$m0wgOtI`pApmSb%imM%J5c=h%h)C<#dHU?
z8e=lcTq0HKL>bOE(?D^WC4q*`Pac)0N@*CAnI=%I2zVm8L0K}ri~_}s1M)Cc8F(CC
zhAGp55kJXlD(i&i01+c`05((-6))>#>xB00JVMO*Kd`~a)g!^1fczIb3l0e-2FFD5
zv2JIt3h32o0UYW-&?JJ;!0;ecK%G!A#}S|sbci<Yt|nk7RPqkRyH(MA1f4P+BDawe
z3H%Eem$D;2nHj_k16i(=qO*tMl0mpMEwFfaqXjm<6RhxXQYr~xb2>%_NFZc)r>v;Z
z<F<0KEVf7zU=l=`f*d=7O)itfh@2F<C`myA;C-9gN|G6oiFm%-yKH+E(8-80yIfh$
zGKyMa&J<Lx4CgJ06Ut9MpHwN6JWlVi2(kq%g0T6C4uKGJXyzb)0!`qfH9{b#f#o9E
ziTfKGAt-^wkArwIpavlnI6g-xkbKOT0*4@w<3a3T4|&uIggOL)qdKP`WZw;(Y7j&l
z*%=7EI4yt#aY)sa%m&$nIqKtpd`$}>{7DW+rhjpyK~!<cM>rL~voSlU6s6-IMVhwZ
z<=#1Lk&iVy>s3H8)5mZ?W}^u<HjDp=VZ+X{H2imvVZgtTX*Zf-@gheA<WzR7msl8=
zw4(A;q1iTacxcewao~Tj0^~@xP;OEX5&$90X9?l}K)hH?(SZ<-BfNwFmjikVh}U97
zSw@*vV5G8y5D1MKy~bIJsm#mv*b%>YP3Sc?X8t+jjF+wHEUDu`_>x$(&rB}PLK{P1
zl2w+Fg0q%bvKpfXVG`tH|0&2sG=~Hd@*fbVj46!dBW@W}<3TvViZ|YdDK;zKmI*J*
zMB<Q;Wy%LBDOAdYm*a3uq^KXwKM*l9p-gZ-g7fd=f0&gN3D~z4ze640s<?K<EKxed
za3ekjux%^;a6~uoVp%0G9U_Ul+X&LFSQ*93UvcILJHHA@FG#n}BkZ6jTRsh6`ARmL
z`i1cn-#O#@BXs}tU*P-$6Ekygp0kG{Uj{;~sK-&PM<5<?4gX*KKxmIqp+cspyCBiH
z$B!@syBvVRpO5GcbBR-ylgYoF^X?%iYSf%@DXo7==UU0bFxgTmgP&|v;~_brcr+Cq
z7(py0;3cu?SeGN($+IKKEHG#_@g)dr0u(PM4+GHhFv%<sQZD#^S;)&^%ar*a?7$W{
zo5A^u<^UgHj?hIUNY9H%h+I$s`51%>GS;|a&w;U>=AFmx9hm<YXh@6S7efFXXMLth
z+dXIJt6u#6p1pkKA7tFJDm{ymiJllrP46%k8E4o7sVAtlk<8l<_>MEarPZ0rJWa++
zJi-D|8!yDt^hWD%Z)vo{v5RepkeaEFi{pr~*P=1&-|NFm+5OtsUpp}+u)FE#^dt8L
zS|^5hEwP&?%$hluQnz+XiAlOTN~HC~SRq9qJiAxLRb*}D^jW*-Vt0p9-!VIG+UJ4;
zCa%akytO-4JEJGy<4|k?GqgArm~OuF<_yAIf46q9#9XkmG*f5n^nuEid_Emf#Ttd!
z`dI6(k*gVuRVVWtw#F2j4)m4YHl-4j41EY~4O#E_9&|*h&vt1rNX0SROk8a3_k=11
zcZvL7{R@cIb~L=bA!ud%J>~-r7M|A76tp^P46u_vPuV7_j}T4^DT(y+&U{@NfZwV>
zk4Ao9TbJ<yUhW$1xZUdKwT;-m9;lb~EQ;Y5__f3SZHV8>O3ktipYKJt9-5>utLsd=
zMzNCX4FJA1eynYJ{)={-R+08FTfJ*1AJo6J^b1$aNk1l-<jI-4W)XNl!I6NyCf1Rm
zd($H!v(GE0yMjS;&G@6Y#kht=ENx}1%q@|TXZCgIO|tv>5MN;#qn?J(C$ycgLR6x`
z-CmOh;wQbA<9Y>snUfF>c&Diq3MLCE?2;oX>YvXclFH7=A9s*vH?<Dr5q6;Wn7z!c
zyo=hYl`rPj)Jag|5puFIyKMR|FQKfCXl7-Q!Y^|$0*)xYg#Yl8nH9!x#7lh#gupkn
zJOHkcki|>~hAwbz%ycp)`^NnbAFm%Ri>RY5tcyxO86goe|7f7tNX?uDH+8qt-wo{c
zA=q-m&;AAKHQDd<KfRsfra};;QJ=QriAub32F~M*lJ)PaJ=})%D&|b;`#w7`<n2FX
zye2(nJf6LMYWy!?{jTnx**T#r&p7X}-m1wSNp<_|OEUldJi_3Y&*r%vEr*v(J&>g6
zTdAHavPE&Ofy*M0*ZQx`bVOVbQa9dU6<MS&E;AU>s+5L0?Bw4_o~r{m<Kf+ic@*O}
zyERuav?wn1{>jm~uH2Z{1kdhe6(pfF+dbS~4ElAyzf8sJJ4rlHU_*DtX7bC%b^WEN
zvXTCw;Oe1E$7+ZAOdfVljEIz@SPV85`gVrz8d%n7N`{f%R8F2#h#fWba=Vqq@zFLw
zb1}J~?WqXsgY@p&Et|kCrmI(h4O~tbQuSG?DSE^@{<jp-bjoqGn@Q5;toB~DnH5Lp
ztt^ikMfUk~b_Rs-v1nIJXIM-N8F@Nk@sQ~$(LUd5z0iTFC%g(U-gJ)b19qs=j(&{O
z@Gzb7P`8Qq<g|Xihl&k_QSdFr6Vz6>*UM7%WlEJjSvL{1Y+@+kBxyBt)17MJ6hBjU
z?X=Yi_JkHbc3V=`XFE>4$;w#zxRhHVZJ^9~6w|8Gn_BZyV4j`m=qtHb18*<TK3U&;
zDcXz_{ARf{#MGy}F>-FY{>9{yv$&zI3o)u8pvY-Ct%OMI6SQL?m*|=v5fibA+C=-!
z>awDBou3eW|HDk^(Sj+3bER4_Om<*+*f8LNc|=TGYdS=bJPv?bjXpT?IvE3$;1vkQ
zuIP^_3PS=kHUrddKyFI@_YCM`Sb^&xFRvrP>l{c2wS`IW0&)Vy%SsxXyfk>Lg}e38
z8KRkxy0^{p(eqdRnqt$GlGl_{CtH&Aiosxc$kKL{)D%+hCgTUm50V@9J2VxQGsm+r
zt9*5InLTBza1kF?n9muVFQfjOhKYpOsvi?y+~k&X=m$Y3KJ!c{ztrwVNY(Kkh}vqp
zXRV1cUVY0nPaywtmAa6pXS~wja%DWpytdrZUG+TrnRbnHt@lSC+a;Gx_opN7*O6Ms
z`2E_Pg`f7{33bc3I`Bbl|Bk-%e65p-$J%V|V2=2YSA4NITQA6P)VvbqY9U9R_Uqs|
z{q@9EqrN1*;kfQ{DSZ92wk+#wHy-nAOQf#Uk7xIV5>Y2=Wdkx*vrcyn_zC>p$YOug
zv5$dnj!jlD!VF70%<d7pe#_%4YsPd5MaVAkF}&p2ywmf~qFr7n2?6*s(%hYoq8sW0
zGPRMg0cT!?*}D)_?b+|GGT#+uGnn_M*m9l`#a5-Wr{-$&pAI{Ec~(dbpqOuw8u5@H
zaEM6g(y146<;!QOs)#xcm(AGs6bF;oH3YC`uqIM?=nVYxLPg$OjoZ2~fKLxFhlH#9
zvh8Yy1jY&?Ny_MAZpA@b0c)JSbrKtimeqq2^l^XUY51qK%zJU7l0I{M=_4-HN#XHH
z(pjHni}a7vl@;02GE=Sk)P$<9mV6iNv-i>=U$9;I(`o4^JqYMXdHs<PpLT+6v}KH|
z_m`IVj52qYSCml(R%^g<CKl~C@LyU6V_1oVKqSy@jk^mfL^fv1*7OM(4(n+XX{3<1
zG|rXGk|s}PgtMB8{g;{uB*ex+VL$}zBFg&`#$W*SvB&IJX-)lsD0#x%;TY-}XHdB0
zoBYKl-hqHEf=;H}U`^i?z1C!M)p*F8wcViLe9l%}FDl60x=_d)?qm+=3mK#O<89#(
z2?`e2d?p<r;o7>bn%4IL+xpQBYoK!mI31?5X(xl8Y|+b4-Kp^PS`lfE9<%#AsrE#Y
zC~R0liJH@(y9Q5dIcKS*rgLa5IymQQYi4VnMVwzaRa|+tS&&h!7#DPj=SB}t`_?Zy
zdgz7|aqUv%@nbeq$-vM^Sb57AN9+Lf%lhX_X2g_i%}LCO+K<l9zLcgbqz<QP>CO0@
z*T0;LGxole_-?Y~z&9-8eNXux%D(+i2MGLAP~2@F1Fi_elKerDq&@YJzkpw&@L%Au
z4a;);U}jwCJkNNBqm#w8Q^ZrlfBKAG-gz0zU5f7}_Sa~cUtdzNm=QGx+&CJxn>Q4=
zG|CM+0tGMoN$iMwy7&&CX2PG6Nu5t*o^=!w(?~7a2Q}-uF6s}7KL}_<PW<clRaYR1
zfzV$@(Q!KJrA)CWT%H(I@`dE|xP5Wlk_aDn;$GR2e#|FiQ-kn4AOZQc))`KZh6<~>
zMs&1U4LgF8g9NpAHZhmy4D7c{y2A7?`jVo$r`!Gl3Q8xqeGf2`0uXamo<8$aSd?q7
zdby^KBYj*ie@`trv+!15&3u2mk|i|Tyw5^mVCaJmb)I20M4xrAXCt*1d%TIc48=8^
zJz1VZ9ZeH6S+V#Gv!tj$(0LTagj=i*ceysc-1sAT1H;l5zbzj9xKjUAR3B%-(ywW0
z^)gwt6ssc>58oVeozlFH88WX&Q5-g`K~r&xyRVp*e6Ipmf+m6Xp@uQ_NC^d!CrN-Y
zilVj%s+Yac%+5&?$`Q^w9=pcI4fCwSw}(g-5B181jg~b5i;b5T)_Z-I0!wt~*x45w
zE+(bto)%4Z75PyEkDp74MR^>!=Eu)v3N9)|)iA)+1YYy+zv3%b(vK($E$n#rm@(D^
z#70aZC&4FAg4R`af2LRXDqm&pAg)(6NIp4I7Ai)~6au#N<^BR|*SGgFwjO<~zQT5{
z7xu=AA>{Vmh~hxG^tjzgJu2g3@oRiqR`Av9nmIGn9TKi{JH(Tz`kvhWHTh}}UHCDT
z;XZX?O^cxOW=5Kd{JE(I?eWr|B!p;2yoHC6&!kc}tV&H_Dg~mJp)QvT#^W-G*uomp
zgiUCZ^U@veikVMWyEy|To=h!7E>@g539g!QSq}X*4(j1uNS2nzhvStK{66C`fza=&
zuNZ}bgHf9{Y~PsgFH2veMrZVq(ib@f_NAX*qgwHOP6`>+CK|3A4Lyw%ex<{C+x2{Z
z1=%NSL652{5@)cHnAhi#$z}WI)r&HIhVrIl##f8Cb0#F&64vpdKey3rLwY`h%cb{N
z2A3<Ko_^tT<!re>`T`kr;sv!gV73Z;66x(UxGk`3S=?*Ia#C#G>9~@`Gx~t`k_)%o
z2u*BP;jyEQg-Gw`m8g&;?UOSNjlzB;UY0U%`s3CMw(ah~;481KJ7Y!){Zleo@4rk8
zGVAlqIwVd^Zvnf4yOmkz`r&6D-ItCn6!MQ!4!hGct#|XMoVFD)gL;F?ulz|=Ro<8}
z01iyb&r;MH8t^QOdHKn5DCgBO>u#yo!kH2Z?~<3L^*FtzyI6+*2Q6?$y*_rOVZfx&
z$tTY5VZm$fD0p??a@dEYt!bf4LFraHf?u6^PwEz4;r=?xbea7dqoK=tSA&XZ9@pP?
zZu_R<2+BR3U-I~d9Ck+(na%Z&yz(zlMF<Vzrxfqx0EwC7l4+(cENL{M0-4KT1GGky
zeH{K(5TL9uIkm;yAW%baPQ@aig@|*VRdW%77t!VNZdO83{0GjVSpNEptX)r?tKFNE
z%FZ=sl%WT`QlK>!&z#VST|TsFT(xZ<BOd}=1skw-?!nY~mdixy)yO3l+()|LoN4kJ
z(C_o=MT#bi`tc@yAz>$Y1m=Iami=imUTnljmh$Jux=KuE3Q|S^&#5ia<g(xSe&QDg
zV_{o`zDGCGVL-I-4Q$avyhiT856ufEQ?<%otdofstd+yA1VmiqcaBPQncKK(9(28J
zj^(0z7n{}{-M)yiw#dh&I-6a~%Q@d%<cRMLbt+OLPlu0J%e<4+9+g6z%GJiLSxM)G
z2R92r!;+XMA973F>hv!lp4ML7re^nBGg?Ygtt}_>xu1&c{SKlmER9RiWp>AIH}cl^
z!2ny$DTdhX+kB5=!n|J=uaM62je2-cT`m_Mr-v*Y^1YIsuY9v@|H;KW#FJjGt|Kk6
z$K2?g_vzL0^S$UOwmf7?Jo^~eJFkHqgEXf&+pIr<fjrZeH#^km>0B>r{IT^Xuhmx|
z)<4hQH!dRR>{PnmSCV06+0VJ7fQqQvmW%dva@;ddPZA%>PaJ9W?+>xHIiX;u^A#O*
zFGTgMew=*W)e*|hx(U+-<34$5ZINjycXa~ftFsm6cW!dgyEXc&UpfK@odg?7Sk%+T
z-ftlf>=YD%@ppqQVaD2fl)xX7uOscXd>8}Dnd>xPT^Kn3rBk1QKuK=(R(vCQKdf2e
z8{W7Q?Z%$|sEnVji1@MN!hs1#`km{g+qD5NCxbh<gfIH6U_D^9(GcygE=&0W-Tv9$
z=t9l2iKx{Y#k7*KKv9KztTqed4CfeFBLN>=)CbAnLq3|4aUTm)E&)^jYOU$9ayQzN
zA=abLOyWw8H?>#Cv%F&M+erHg`_IpcQS$NZs^z}6Z&q{*)p~W@N_`pV-ryc{oaOdd
zulM`aam;Mc`~q!6wV%KwQ4*VX`4`3*T=IJ<SYif+?0p;!Zc`NGpa4D1SxO6B{Jkj1
z9!(ktpgJB1z~eFpVN#QV5=Zj^3(%k31+=T8>Ob+N_}@G6CA-Y6*%N|MUs8;<&maWm
z*~KMjnjL2i)HIQyuH>@@Q6?cZ=)TMWU$_p<PpWX3lx2*;sE3pcXnIGEA~g!0H_yGr
z*C@8cG7~1%WlJB?0+Oq~VFm9U>!}PoOC0!ujSk7UA>FT<<`PbPw)-2wGas}iJ(c_2
z?t~7~<MFRkc}o#py_ngea^^)2>oafT+qY<o*>3e(uhWzCtOhi54<3eE&k1_HyKy1k
z<-tv#!iDKTQy*{RQ`%is!<=&}s9qb^8v=01@s&qUZ!Ap7l*`57MrKB6?Sk+g|ECvs
zE4G;WUg0jfr#)#j^9){0Vg01SSN5=eJkl_(aMF7^<A%WMKkI*R6G|1RQ90Z_I6L=E
z|G3XXm2SD`<Mmk?wNr(%=8UvZ<u9Ti66U9$`~_SoyS=u&u(X&Y>(M-WhRX%^WvaZ7
ziKwxX$;(SIidth~?G`t;QEuzfS9`e5H*!>kw(KGY;VN(JYjlS8+baaUHF{Yrbf=EX
zaS>9#Yf~kg*;eH~V9oTu$YlnHwG}1`rYJo8c@l7KsxROFbanswetsl0ZmLaxx@%jY
znXz!@{o~+06W(OT3EEljJ2T;3zZPqkkOK{(7DGLr%DdzickTqS#HY3m1HGQGzd$ri
z9orgiRoHvo{kp)YF;44DgUD)?4<Qeud*+?R^+Jo$?=c^LPIF*g0`6|HN8wSMlO?_L
zYdf}PHgKJ#vzk5{-Rj%vLD;U=wY9DsaqO$fudtW5I^dB88`+XoCk9q>e79{(9Bwz@
zk4$|bVwxQC%rU{G33m41tMEjXV$B?_p7|%R{%@}0_mKOb2dI$cFVId9kj|f8d78KM
z0QvG)tg~p8j^_@Nz#EGiR{dXIr<cU0ArtcxNfuPeH1sr@2v#Nnfk+mR=)e>j<n@0v
zZqP-B?w>%38a&x4@9GUF-CtZJr4blCEGlx>crIhfXDeH(v0N@aIa`YS&C^%ZQxZ~O
z8?9L>3HQ#pgv%<X_oPlAi#Dy(sVG<zgg=||#W7(XhLa-b<ANs~>D#H()HCMSAAg-y
zIu<JT9?xKPMWiN-#=4w8T`;@ZKyOWdqu<AluHjJc?1l>3d5EM>TPtTSInpCCnqOYy
zdQ@L?2zUAs2%ppDn+j<1ej+LG^_)tT+!ln7!>v(n>^!>O$um+@7A_+|$s<9L!A3@h
zZC(PuTqK3uO*yxU8qFzx0ZZ*nMW6WD0~cPujR&R=pScLr-18H~bCLIgYq>^kt0Z#Y
z3THQk$=_YAVGs&3`86&Hj2h88OBT-6#}*k&tZ73ZuYbD{3boXH;D3*Iq3Vlt#lqA>
znJB16{I5wtDGQIsWu8sR&$@&Y8Hkga98JQWm}7jW1mXX1I0%pH%$7b^D7=O%uVJV!
zpQ;Jae1Bt9F(YUC?A!4f)U)7Kq^FMUPhXC%>WV9|(uJCOS&Nq%u3vsxl6Xh^$tvRx
z)k>O64Q-(7L^60b#WTD~y4iVqB1A3S&4;l5UbaYG7@k<r^`K%)`VI2Zqpy(B3o|?Z
z%{imN8O^kp-)#IndU-`Y74?fa``f0xy=M1}74Yd@(Uw^iWx6hojc!}uA#R{R6JEHA
zs@_>|!0ct03AALKJm|UOgc2t4&Go$%K4}1KT{5!CL%-$W>1Y{TgQ55IS{_Bm|8nu*
zH^ij99n&$OQBBReCo>PGc~vLXC~92TAY}e<eW7_*S4HS^=xOdyx&yP`qP&WOxZ&mT
zqI`i~%Nq2|1dq;k42KnZDL<}XRNn9EmiyU2vN!0d9afE`;I}b-p67E?(8W6#2sLvG
zl4j}zI^3qsh>aXJrl7Z267+q8PFqH3CPq+dQd#OAj18j({u`&`E`dwcO1QwPY4iK<
z@kqv8n=@ZgC+_At`n`%~JjZ+7#jF8x=|<)AYaM|5Hm=mFFAzGBM-z-$6AT=59(le4
z@uO5GVu6C9DnWVtx-jxx`er-+#e1Jv8*}%{E;QZOQfBq-vi&O>HR0++8TXjGl)7~^
zApfKJ^r!^km}H~fM|i+*+rZSNDAy|*Jwkpg_Bg*}HfUV$1ChLqOU}N->Z&d>Q~t}g
z9QOot@5au45unh|GG;3BUp_8$n|iNp5>bi}TzPo%Jj{Wx8e2qPL_pKtdRbX88-%_u
zh~{2p4%(|UQTLOnAC)1PM$aUVrhc{1upx$K;Oxu+c_jO48H3Ur;}!HV5!PnkUZ-%g
zPR_;RCzHq$7Z<_KiY&i_k<g;Jr-(XRlke;cJR~5?V=BSbhu9gi`1GsDmr4_{J=r@c
zD)!hRch%ACoyt172!}K6;G(Hqvu_1$ws!R=pf?<R{mBjOO!{I$aBKp5I{aE?{k&h>
zUa?xk=&_66^J)4Srta5H|7<~<`m{UA%3WE{QY(bs5Zw?}-6{|f<B*Y8<|O9BPfiwy
zK_7*?dU-cwXe*3Yh<|m7>x|P~%p6dWYHhh&UR1%d%9j0$o+Xndq0nXjINDpg#?qWL
zIJ}?GWnldm*xf9i4qnZhkH6XzeR<)~B(^lutuD;Uu${KT`-CrGO!v>%<z5&^whU}g
zf9GVGwwc>UPsrh_H9#6tTY;*LV(S`5;`e%f`J_76>D~mSQ<<*{r03+-=?7}*-T5H>
zTfW3Sf|$;UW9ChNB0g{@K`+~D#2j_)&MS-G35x06+oNhO_YPLS!|}JiTfx1ucPsa%
zl1tx6GK&2{2x&xCJ#bg<9x}Xr*F|``>D|E_S#twU2Cj9qzrs94X{cUNa=opl=>cQ^
z-dQ5g;f!;}tKWfw`F3uFJ_eN|bC>5CLX+F>#29-0a!{))3X<H!^rfUs5_K_%tZXE>
zQA|M+GpBjgDsZ6t1YF${<88pO9aRzxMZx}~tk83g0+$&S3RB5&?99`I{tip|vzlh_
zck@*Y)|S~wFRw(8oDkayOJ8ZT?Z=L$F9g&m!bUIXy{8=uf1jIpZaZe<QGq$W#s5={
zLsl@me+q0^*{H+m-uXpGAwI@E|4#U4#XOtCUG^t)Sdowe=hCUW;-BnZw)(42a=e2_
z`gHOULo3ec-CB*Eumo<y_f7L>F13^_s&%}0_Ra|6cKfE#t&$Z@+4dWp=Z3|jhqkfl
z4lAb`yTz*QKRKVS|7xG(+4raEF-KWjS%XgDpOq`vc$=rq3Fb58?kg^)f9|{_$v&*j
z*3UMn?a|;FdmsO;5*t$Sx?1{4itm}OD1qMzSjh*U)f<RjXJO4kB{mODG+yb{unn*i
zLVE)c_xVHj*b=3xKXm$pzkjN!?h9Xssc~zmy@r2P%FcXokIHm;MXN~kFTmOn9<nL6
z`Zj+2xUH(rJquyH`MI{_NO5wN&1k7^+4rAgR=-%=v%M8EE$ofWU-snH3HmZHRnK-y
z8}j}5qaZVcrYBDDs_GayVQz5JMku7Qm3Y|%E5S8ACz$}Q*T~MtQit2u$}|h&MW%Sz
z3v`$}B$dK#STQ}OE>zo3?<*JU9VPW*tJsbwF8HU!5s*s53JX&g{T;IulrDvxu?kx~
z+2PuBHpt@&1JbS5hfElnS-785ApvR@ih*vitv`QIZGVr(sKG%mjLU*^UJ}k*lbko|
zHs+DtYb?}i+cTx%H&P*2%2R7$7nFQKTZ{)3=URb?&tyK~e%e(jrKAvM9h<)Z)}#IB
z1nYv<^_N9=q^;t#50Xe$ZY{SCC2p5AEq<T(xjlIkt%_3#^f6^mi}MM8WFxd8(crkv
zt+CO-x)Pb=XgOScZ`X<NhOHqqV<^8?T@L?GR_ZV2E=u+#c3t1w5iB?JE-cF2oHmKQ
zGhKh7_%O%wm`zbhRlLgAk5|SgMN{hh(r>b&pQ8sQs%}p4+^<o~lZR`=qA?ddWO|<J
zNg@-&)XzwBq%XzJlA3RndG@xUUyr36tdAK!&tWGE&=!cc$j(OC&Unm>WV@=irfY8E
zrQXUL{U@Tn*%_ecKIB)g*(38Oo=7%6Iy6{OJH02j{uhY3ljdaFiymBU&U@E1ln_4t
z!^qS=am#ILSVljf(nu<Eh*~`ece*uxtLB}+UqIT%T&uVXV=SPFHoJ_b_o&f?NZj3^
zO<{UejL857RJ9sdiCM~QXrFbMWX`Q_o0{N&Fnd*cM73VFQj!5qbJNr~Y$+rrh)7Dr
zsL0&CRxwVWE$Pqi@37!dWWRx&yC@9?Op}X1f0XnLv2B_c?gR;h1IsepmX2rykGIz8
z;}u1o^7{3kK^vL!?!w1Xhxz~}%a3=sF1*YXPO0^n?aU(u`&+EHi`v=;Q0LasvhSYT
z?{7-ZF;D#kYDkR1>r9&$Z}wf#Y@IDFb7-LVJiQUMFmSU;)kvlMTggYCvRmpt$k{v7
zY5j7K!`L4jTz3<F`vT$`DZ?Q+f7PH$D@|(5dwC(PE}r<4M67=A>ce|CHD+C8fvb<w
z!9M64-YFoqQa$jaX_fo&obO+tD2pp6=ERFnHtgHSwdcbu9~<w}vu$NS)A)+L@lOYs
zl`<HjV)4N=;BS%nuNb4??`KatI;jtye6MFo-n214Ch4qy826aP<WWxe{8Wou+?xWo
z%jaI4FaM`XAy#Mi_o&><-d?5QESM$yPcpcvEPEBXQm@u(*syY*Fu=J>IgYBzH%$=<
z`4!ASBvp!L7+0?5`ws%jz2seV=#0wKkuG##m<s=+=DhrHdFb)b!>jVKI9cD5jAA!k
z;P1;7Y#B~_wFCp^{-%*&*t(FF!Ku2wH4KcUJ@m%n%pgVDWSh-AcMkx7`E}85k&nOL
zpOTq(2~TbGD?1Q_?+P-d5JqMck~NAh$%|b|>=w`2h1c>c&nwt1@G+(@MNs18QoUOD
z4A$A58(6~!UX|MqmCpl<u^ivyQj7Z!%HjRNVcMX}ADLBtwfjT-K3!RU9mV=u!A=+v
zBm5i3boKfl4#})M#_tLO^d;CrbQGDb=^JlX@<3JI#G+a+pffF#5>(loK9P}^5@(@B
zQTY<~R)6+p`isNLy0$jy&yOVT+!R#^aY;364Lb~Zq@h$bqkiKrusQw5(z^K>ba8r8
z&|YDEPk8gsl-(LTEt2i#6Z755yu)7{sYZQO@4IyC&$NtOWBc$l0~-bLow`<>*Js~g
zp<AoExhwwB**g#I%bq^eUCe|RKf%->-P-(o@$-%FsE5oK_L6pKf<h5Anb?J3EfxcT
zFb>JYAcR1H#5Z&x)Pm?I?7R7<5*n1H<u-IP1A9rWPoo#Za~z}TC=F)%fGXidSuiX{
z`A>)|)1gsX+zRa<$w$j?aDYC>GGl?Kg4E&)L_;Deky7FXCh!<OATe>E{|)Ct*dmL;
zi3P23ck6iLKVSc{a8U6NU7UX4!02GC+*1WtYsgaTbP<$mT4+g}d_($hi~mK$=wCp&
zmWzD2c6ffL7M3E)`F=wGkNKp@qZ_AhZyD`ngnw4#{c(7y4^l@wIiGfW<1cV;kIK~h
zHJNC22C?U})t;*G=fv+z56R{QS5CI%3?kE{i$3Gvuw=lO#1s-p%53^GXL8=v0QWxI
zUzuA&ZhtmFH0S04hJp5kWSNSj`2KRaq$L}x-12MZcLK(@A@G$zgiI<Ozh%SfTS%=I
zX%c<zxaSbTN2{1qIg`t7Y5R`(_3L$aB(7ix>5K4R5O3f3VBM^dEuG8g^L1pt`Q!Qy
zoP9Jfe`z4<6nWD=b7__QSw8lJz{}d{ovqu{oUoV*$cr+4dH>GN!#KQ9rP~*V@^$Mi
zmf1&FuO04n0LY4yY(KN@K3W**0)aNH*WPQBnRw537$gS89@ixfY?}5GZOnLWPB3yd
zWI(=-sw$2m(<z)VYwvy>P+r%%wC#GUe%)??zo~e;Wr4Lm7_RoNSMnZ5$&G5tjy9iC
zSbS*VRa=JE%gCQtwSKt<<)w_lPjJ>{=gATop!{1+3v2Eg;)Gg0vVrlUm`=?#Ul9T<
ztfrBikqI$S2zbcswC^CfR?Ot_7npV~Ya6>!^3nUire)LJVwLWAwf?qMqB`{{`+>(y
zrd#`u)Hn_I&!20)p!i29(~-l4<LaGy8P>PhMgfne3`T!y><u`}WSV6z;WR8gpKx0C
z8vOR3jj|m|>!%ECBG$s9#($tjA#O<bz@@iMN6lJ;*neO`wA8#3qgi;j=Y#ygzzvRF
zJ|CAl9&tl4?<;oGSe6dH&!_8h)b2@djMwjHzq1Xm{34uWq0pUaiBmeEvVjfAdKA>?
zqgj8bvUW7f-zAcLy>Gi@rlKjgY7Ihv_X?5KO7y1>Hj!x~GYiSv+b7Q@{mL*!aWy}}
z-3U2taWmSPr%1uVJ<p(!HdK_DrK76VtPsSWOp2g2d>fwvr;!llU+)*_7Oo&BW=?S@
z5`d!|Cor|EpA}4`QI?<y%1Thw^X490J~vp;IhRwTmynEFp}aNk^%^t*(*%XQNhmj_
zQV#3xRxO<V#Ko4yAPShINApT^6O@(pI!}o-e2Hhf<QJ(i)~6S#t^6*d<Y3ZD>HgCa
zfX|Ypw1kz>eY<yn$^3ip#dRUE@~L+jHBPO$Zq<%=Dl-!H3|@UQPgI^Bo*!<lt7PIi
zQNm^H;Dk0Am&bp^Mw^$2@6hP*3oSJh8MGg8d@E&rlhJ!?3Frwi4<Q&md;h+u@%+ZO
zt>w;>!_4~sc>a2{riWoTf9(90jfHh@Vb<;KgGc{RCVc`V81^v?VQ)+Tn2euUEPQ9v
zmSrY>%Ri5+MbXSux-AW>L9~CgHR)q1Pn*lt7U68E!WLP{d~??@3r~~_=)3)#e$Wb)
zPmR>C2d$Fa*;G89I37*%v6e!8==-$%L+nMG8drEjFtgEnLuyquevj<iZD5J>u?AQT
zsd(`Z@AJj*GNL#Q3FaEWhQoNZc76q~%q~RxfsYnLKqpYR$8()J?`)2dl%gu~cKA*T
zF5=<6Q|S#e&lxhAYg9!f40DgsWehlBfSZT^Sg4Eb=bGOZcw`BOfHd7T%yU4{>Nc|+
zUMp{Xnps<#@N#>}TfHw<_~p)%!j|>X@Ryb<H^@qt8|UhmpEnZN#3(DVq|vM708LWG
zNGS;wsM+~tK=V98Ac1`B_hPqHweb~V9D<D_t;je|H=>5+q6^t9>YLV$8Wy4@4v27E
zh@jmsQ{%NzHJ8dJF$VJec_$an^2<S5+<~NRKWg)$(Ou-e4$VKgzbLplzd%~&mvc7K
z0onp!1dn@GoEtf9e2U$u4F_ywHP|0ko;OUI!D~FOd3Mk4y~B5EnbI}QhIi?Z9ZeI^
z$)@e6EwLmNQQ=q@lsxma<-i)Iy$vkfxqj`!Kh?>;f%AOYNRsHNU@?z2B3wAE{Fe(w
zYJ;O&{PYEXnxF$ljFW>wL|8g#-9|2!Jv5%+x}VhHkc=hLig_BPT|mc%kT)3DmPbI$
zB`6HNP@-8}CPv^J7!1^!`i5pnF6Xgf87K#%*>Gxp5(A2C+ABf!d1^xQ1~!6#pV=cJ
zcJQ$NaYv1W3-VkHU&WnJ$MoNOv6cp3dYQ55Sn~Di^M+tX{J!hbUqGag%ddhOvxQ#M
z7Pkg2!#lAs0Q;or!0d&NB5&B2FYCL=pXJwsZ?f0VGKbGUDm&9MeD?mu!)4XVQbtZZ
z*T=trc=VXr4Q=SdgfK@n<%Fs^|NK$iXDzcYG-43robh&S0T&+(@S-)o62^Qx+FDP&
zJZbK5d+oD=rO)m0;$ZUcptj4^$xNSYk3ATV>9R}qf(53&5gnA|ly7#Q3sNg3(J5WB
zU7afrs#k~mEUX5r3o3Q?n1bBPw9WS)h!s6@nk<Owl16t8UU~R}{QJd2Bzw6YGE1XL
z=d<2TCxHyCH?QiM?Y-zn<T4S9RfDT9R=<PZN$D*6a4Q?waaDt8r_k5ysJ;TXy;3nc
z(0<P*phE!&LqN*LRCO_5$c%%EgsYzMC@8NseAfx(=CvL>S<bVy+QCrI;^b=P4DnR<
z;JN7ly9la*CJHDNv*M!~oh}X%vp4$V=owrXsw{Li`mOIyjhh!cc`={h`1Yo2MUp5D
zVyAb@<)b(f7%1s=L$5(Vr@#>2LDJC@7k$9WsLyHxCK<0z;?+<o1gj)M1RzksLyQrr
zDb$*9eM}sXG?$tfI`07setLXY(|_BprQo|S$lwAEMf0xZ>*ZFy5=zxQ*(mOTl8=~k
zvlq3RUzC}YFGuVi<^aQ4BEBNTt_u1#2#r*kSG*0|ui%3VUiz9&v2}Ek5OV8maKUbg
z|F+~=8lpB3*`s>o&P@#hb8zQ~mG=aKIMj}rLwN~Ipw0t)L(ve?<ML`5a8{Haa+FRG
z0aI8dkdwH(k`$`s%UCcAk`*Y-kTd!*d)uU#a+Gp;YCg3&YQZ_!>XQKiA}^0GApo}m
z&rP4G5^-8I#Ogo%)(~S4nt+-8_6iLT&TdJ}y|9jp<zFapj78>!3d)G9m;({~_Z=y$
zo?o_g8P<$`{RM^+p7IVc_^?9hsnc<byi6R*5ARx~BN#BRRE!x2PXixVLTjXil`A^Q
zJ5>_2XuEj^Le-t^(;s>R<UAc?t2Bu5vvh2mLVZs`KVLzKs+@G_;=SnIO6XJX+{2t6
zwfg1QuOu!7ZK{-hv`~}*E%nNXhCLK7{KydlW5NrR7Ag1{qXxcxadnZ0ux8*|h+Ni8
zCiUBAF(TuP@_ZN4#c@Ej<SX3sx^I(s`Q(@?lxaF0Qs~`T^_U;QAQsV8b=nFw)U<kO
zJe~tHWbr2@#L;Fd1A133P0QhgRH6N#voUjY6ihPx0?tLkfq@=9&KbxfA%S6K)RUR?
z{XsTa$z){=eDix^vGnOJ5hoL$txVXcTLDA4^<`+l>6>BP+{~I$mt<t}937^Vk)h2`
ztd!n#{Z@?rg($^9W6`BKI+m}Bt4=#^WfE;zIl3JsB$L5N)UOp1y+>DL8*}nKnM2?~
zgr5<QquHzkJx9Lyrw<CG0uA3s!BMP^k`gUImdMJg!Qgn<QBGG128l+%3F77wQXG!)
zE?%DZ!JKqApL-x9%ypy#v>j{#HdoiN-~g|(2!3CbGxdc1GYY1KXYnC{NtF;vm6C&l
zPDp*sVm^yJilT10D09KHzQq1Te+U9Vr-uc7dm1H3R`P7TKTJen>?{H2re&gW;OuR9
zg7>@93${86TD0J8EEdynmoM!(#eu2uE-%2ImW|~f&twXs$PGKxPM1SsU2~It{4Q6y
zoC<LJ>0?z-@x$3LtqjW1a6<UG0kwE=Z}mC%e6fF$T;%K>7HG~V)AiM~kL#VLiKLF`
zTHwGJ8<Q||IkneFpar=L>`|dAF$x#K%|fWEXj;9PqQ-B8dnmm}zDNvcbSG$_8Pk;_
z`JxcpsAF(cdM*wz0FwV6_fD0Gez*;j()npG7*ta2o70mi=-t8IGrHf<{s!aa=dO|c
zVB!Yg^8tC$fnRX?oCtTgJIb&ppFZy-f#M7T^9!t7z_j6evCw=DsO(q}eiG_v!(sQ)
z+h*#tgCf6VObsakn_nH=^Kpju_DsBU0l!>p@{>a6r6g-1#xeizv;4zi28t#>#-HD)
z2%ON!f996?Ep>YMp#f7kx~i~akja$L9&-gAz^WQlEND?U{EE*+s3TBB2zZi2z=_GT
z7o<Cjzbqi3q%lGl)heV<Li3YJ4K+IjRLBFC@|pCA_}r%m!h2mIpFdZOtj|v{gWK&?
z`=ngL897nQa%>rLFf?$uT%9;q;VOh6GZT!_zEx-iYjN;35l;t5mCW@rhw#bF;V9R{
zkKeC5(q|eehAwTjI(@c&@=?lC=o0jO90FPfr4*YZm@<12wW>ob<q(wM*&pW}wxtg`
z`pa<7ywPvCOyp^9dFj4WW#?)wi)+wQM>d-Z0>X@SKtTPHqL+l+WM{8NV+49e><8Ad
z+)80aBamMcSCxYHLfc{b;?~nsAJYW5D-BfTS>~qt9B#E#Y2iGYU>LI}pU=eF_njcL
zb2G-y(B{XajWoQoebA39ykc1kPImZz*to00P1j1X%HNyW+A>ynfqfhrC3?>%8{>XI
zG~{E3&M%i&y8POsYJxJ>NvSimG!HV^J`=X^F#*?48I52w=SOLuM0S=7Q=VO``qX{@
z#sJD_!6d(yP=qp2#ESBMvk<T_d~7|UlG<p}Q56~Ce(>y0K?&H+*^z2WO#QQDKQQ61
zSE|V^mRn1*67{>#>F6&qK)%<Hoj#{?wi=9`dyNnrYdM10;D_6eL)``3mf5Z|bDCQp
z9vZre4BRuWl9Cb4ST#==Np^O@6ZD+w!ljr*T+*u65OuAo!?C?V0-p?goPIibZ5NhJ
zEWLJGGW`qa`}_r-o2xjiYR@Kmo(-(;>yS3yakNDIDNeyr5R1)$%O`ZQ<rY=phb}cF
z9rH0CnK1E%D??4;mJ<6TFI1c+wSHG&(v8ClULJd~`oJ|YaLIUV$mH0I)?d@}69e%N
zb9$dWkKeA!36l(eQrDc_D*n)Y)X-UE1dr<enP6|Ja6&_x#b*_{caWHv6!z{}fk0)Q
zRl{V9TXO`ZFVr3*V-2i)EiQ<i>vZiMeZf>*GNqj~6_97z(Xw^P19Sf~`HOHz1)`^;
zIdM*j>o0K5sc<-4-hWu!crKsNI_xy3Wjd<HbrdS{EugO88LV~3nToRgh4;R*$L^U2
z&5_Rp((gpLVseMkd4#OEEIccMLHKOFgZ|Igf(M<hHABqJMEl6fRFZ7ps>C2hyG!$l
zzpG@Qe2)_t9tC1#B}qJQPnCHjvodneQ9pj)s|tmX@*FGjHe)+!mpyg4938#pVPKv?
zgi9G#>4XIT;GYRse7Tort+LZWNlmPI+_Gbk_?|g7j4x)b&hzC1bkj8@9}&`YAFf&&
zz82O0(+K-^4#5CvP`urF7}@6Rz-$<SXy^aEc{7mAd27w;4XF(x7RZ*b7HD~mb9%D-
z1oGvQZWJjKGQW4S=8_3Mib_<@lR#B<mREFEp<`vTz3JhZ%yNlc(uxivm2T`@PL3PL
zOKMNZJ@HTOY!g=#AzkhrPzpsq$!Xd;oIW+jawtOb)_7|h7Iv)0NNr$HfGVwadCN=3
zRVLas<(#QF`x`?#`{X|d=*tw|RPMjP<fD?IC)MpYd0cp9a1IK|tk{ci2Z%YK3<p>d
zkU%nwLWY&#`Lq7#mjED+u0fB^|5wF$XZP|>@!_W!d4t4cmwHF0>J(&6*%wkdJRdp5
zmu7W#<h%mHpIxDu=l%_<Qqt-=wps1IeIB(}F6`Qm%()(GJo$EG$ghdQtR<3Z>ww*v
zR_)%)263xf;~I>t^9RtsKvYU>ipPbE_FLmjh)oYud$0j}Yq_oL<F!s8z2v*6^@%WA
zGR5xlEjU$O-c2Q7vB41B3F@{2yc=vQW3v!-o<nU{ROT!U60Gj94z&aOK@`Qp?C-Xh
z2B~BNMUR5~%UI0%LKKzBt*UPr&Qk7bTZ<69eY$d8Vpsg3-tlmZJj}@|^nGqpxc@N%
zw{i8_7ITdv=c+l!B5|9wKYby3vQ88_Z*hlEKN-4dj(>An@waCF)0^W``<eBFnvQ;A
z4V_IwWZn$WYekA5As>6{u%81_R`WnY)JeD0w)9B=iThaRO07p9LDxm*jBmXh%$lXw
z<z7qbRRzHnXOZz)8P~S^g+7`lKeivWq4~h@HQ(crr_hSiT;_Za?o``3_Y3U@tu-_Y
zT3;vBGsnBwOtxQrHsO_Bl4<Cey%BWpS=j17R4I!%E9Q~#FD9?#^;P)sLQMiXZ=EPv
zs>33zrl$f!kr*4UtMYSaR;~#i3V|uZu6Yd_tOzi*oD~aLBLFamOBZBAHVKG4ZP);8
z*o-q&Y7zwrRW#`eMqUKeI!IgaWGsra^4t5lfsec^az5kCj3l7mCd-VVm1V(7P`CPm
z;;MAmXu5U+%0hi7xu*4Kl>oFm4kNFcO%8SOSXdcUK&?ko&ei9kihNY;7<3o>@yK-q
z<P!WRfg8;~6}qMKCy)VGPkudlDY=AyOTUjTt2-kacmq=PU$!zGr0tOZOzlvZoNP|e
zYR`YgTv4r!7IF2YzSfOw_uQ>TT^O+UWW7}~{#E(P32D+&@UB$t>1H^NAIv30<X*3i
z(QveK1_E0OZG|iNatfV4n^J?4UuG$Z<@&FF<%UVsI;atD@}PY^U`A4SA#SH92q-#{
z1E~pMF=vZ=&7MHzAwHv5u=qxP$fVwBC&#>!PK!k%2`@>6$)wB2x|$1?5oxjRbWAJR
zJOJy?p(Zl!tw*|y^GC~uPP|%moVa+>qu@RzZJ(|~fi=Epxv(FIs|~Ch-mIK|{EgwK
z3p@B?O-0-Ez~OrJ2-vJ^+wDAr8KwF9=VqRJ;M1eQS<f|AuVD^$x_iQ|CEt!rASuOt
z{valOXaCb*Aj9=?TKqdz*(TcWbz~B)<5IP~2G={-1q|*>;(bQMX^}^+n!4~bwll3F
zKq09$;EE8pGlnT}PC6I%O4D2N`!vPW=O?r@6VGX@#y#Sa4h&WgA3Q##9qg=yH96OS
z(%Q~mQ+(7$M-OoPq<dDuCY&TCEetm0ZZ6n=pk{8JQ;uJEdvYQ3I<D$?xG%bt{Ckt@
z!0Z!A>E)`~6Ge7*SJn&RnRK^ny0Wa@E1HD+J?RbAdJk1d6Dc)SuY;;b`M=oywkq<F
z-YMs!sn2dy>d2+@y;CB@Pp2!Is#{mL<=*@B#qrJuXZA+3kgVFZcb+$eP_puH$b(v(
za=F_+5$_i@S0Kpn{^{@YHA{1Kj1^I@#9oq)tGmVo_FTEU2NnOCo%_4Ph;;+m&9Rce
zD=92=O1UFlHL!&#H#$;TSx#RE<{?VDZ#896!1nS<1L0MRuaAfWvt$S<u~?Gu-s;@A
zUlQr6T7UTF?@i0@J1UL3MP=fD0TU|2#c@sLwXq7;y5NGRA2eqsMN==pKQuO08uLi5
zo|F+0E&YwlAQ)e^m=$lYyInJRSI((*GNj?B)N|g;A%<Vsi*l!N<H%pZ-z*-?J|22r
zj`W00h1eCB_3DTtlTEp9CzAj9eEe<DdzT@&mozt5F`3hD@OPTYyE>D8g;k>(nR|Kd
zoq}oc?Cd@q8Jjko-RJcpE)^D$x#%wEqV8yP69;tEKO0f+l*up|#v^nao7ir>*-(2+
zIK%Y4@v`1k33#e6my6LS8;%QnHp7=06)Z$kME3gs0=49C{_J_res{UWzN(-=aYDMr
zl>MQeflBtGg<mm>xZPW$Qht5m5HPqg%CubD(BeLJ?zV-I{b3{KpX`;f$Uf+BreO&l
zvCf})9(RJr)3Vipmnn4Ya}*6pTWo$Ek~*n$X}i{K;dqM!6CLZ4YFC>#AZTa2DnTEA
z=P|bIRUlelDE|r$(S!qzC^j47H1~aNu?FC~g^uRnC87Kv#;8e~7^@I@&sry`jXv{d
z_oJX+wO5;P9bK8<m$uP9%jpt;tHi<dN$;4T*|R5??ZRDVEX1v&*dDZ<+b5VaE<W+e
zJYJNmA}cTUz$*%j;>3dQoY2`rq?H|}lUiSU_OOg9)R9y=Ko5cuN9@eQTUCNG4(iqE
zPPFQCCcp2`rH08<<VNr4N54M(<4sJ^_muo~{coF|t8e{wLE|tz`wMWb_R5zh(Gx==
z{Whn7jY|aw!9@*WNotc1>Y{~Kp1+ki8kSHS{=W4u&}?N1db@0mI=o{V{~(q*eO$`+
zs<W^6H}bQQxMck`2a%soC_l?JE|*IIW3{sG&`Afiqjhmq7SKb^0Z~B+$Q$Z7!&6%_
zbG)mPigcXKfc9=*kXl|a>=1}kXRcz0xZ1T;cAE1Te!+a5jTY8&-AhR%eY$y*6J})o
z>?RE&9l9e5GqyCpn3bt+bo}fWX+2|H5bqnDTEfc1ahEJiM@)siCR2Hsv<cj=Qf8Q-
zlji@!gyn4l-@~f+ozila+x}PDd3P>E8BJ!{=0>F6*oqIC?-A>Ckr${}x;aBYB)$<I
zu-ddH-oFSeP4il2U9$Yl3zS}wnQRkR)WB=b1l<;QC1Smb7%u+8teP&VHN2C%R3ex@
z@W(!SeV6ZmJ;cjc&cKT8P_*7@<c%wOd6(s|Ux%Q-6ghgfjhLDw0V({496GxYQH4&D
zlxokD=lc|vuFFGr){Eg#RqB`;d0Q40wd-w*s*I6`)Rdp=7WTi;G5gCk+PJ+i)T(5v
ze%52&!>0Y(my^Xztu0cysP7Ll><Vc0mz<v0NtVp{U-P2tFn|A1dk6jeW>EJ#i$0Zv
zZx4$S@Ae`d%snLF`0YbhgY>KJL?)TKHZwyq6P*U6GjXZuiZ&gO%${FA>H2-Jhxiw`
z_NQ#O=F1(+a~2;)QoR>dqri7n4LJ0Y>1mVBR?-&-I=L-P6bt@&c(9-0BPomiAa*l4
z15`MMPlSPKC?j>b^MbEAIdwFn-oUTdOpmLow=7>@3yQsBF|&205gz9x*5_)Z@F`Qn
zV}qcUz#tZvDe{3pk7=8}C-~+s@YL}WOzO;8!M+)0u^yp7L0a|37b;TH%UyqdH`u%E
zMPuAod-Y)fe>W?Wn|?tMv3pg9)G6i!@9*Cxo))e=`7IPGJKksLjD%Q8spEtS-Bx{D
zK8>%`&MN!`Ub=@1voi;s5Nm})us_02-u;|-!-{Z-`U|85%<IXfxbJ?i6*$a4{6Ae>
zX*gS3+un(JESi{VrjD2@#7u=!Gck|tAxI@fjn(651*N3atVIz+3^g?lik|k+iWnL-
zrskSk9XN(M)jIlaec$W*@m<&2YwvaKpU+y)TI;^==gx=Js@`&-*6^;bB_FzW)3@3)
zjaIXBZlw~>?%-INGb1)S8WHoZWh$)J!Eayg)$6^c09W-FsyhsOe*mo$GU3tDVQ;Ve
z0A}#Kul`QXlkCy^P@7dz^VRbBvV-BdgeD|heDv+d({oUP!+X71#>xxnb1T0kuBs_2
zK@W<mo~4Wx)d~ga<UEloU-mAd$&&gYd9LTS;_C((8qzU!mSNR-z7OwTTv>jiJA}~8
z55vzdTK_I%Hi!ArsM5d$((W05h{2Ht#;2r)76o9*GR^Tm^LD9N{ueg_7E9A(1De&3
zm;)bOEU*Jb{#kK>)0MH(&ko(v3tYB91oMtDqHk?&LIBbG(Rz}95^`%ON@B>d#XE`r
zqA>!6dTl!KPAyFTvnxv9)<@lu(EFa@Ue$i~H?6%DN|#`6tcG+Zig!)DDlk7OYTGAj
zW>(M7@t5Zx#;h1srHo6Rt*NTZb31aSgH&5>F+V;H1%_oe@qeAg^OLXVi9)y&lLA~7
zNJ(fpg^@Y(oVLiENLvzzKhl8)@$am~CQgSM!q{%pz%CVpm7p&p9Dn|Rpgqwlw)W%I
z&m#(_%btOX8xP4;d`EhOW~xzabI02Y?H#u{hp%n*?O~?JFBazE`5L*M%qBjo*`&;`
z{YyYkcZ4n=Mgfv?j%uOHZKJ8HT~?wn9WK$hzJ9$2h_jg<OwUGD>&8q6skJ|BJ}pGq
zbtWuI+cDn{W?NhTQ&yLE1x*zZvGF0VG0Rg#-d*{8j{qhh)LE)DA+dA!pQvq*E4@BZ
zlZMd@-0+2&8Lr(bJ&B-;;lWRx`0`-9j*GoZW##1rA)bfL4ZkVe`))o!S0?Mp983Q7
zubZVC;EN2Bo$5yU*v2K9{s8ovoHaujSg}MF$Q`{e+sCNP*Cv@x@&%d(F0pNa65+p*
z1LwFinz$mX{fWn}ysRyPBb*KH8~UE-B*qbPTjepQYorQq%B1Xt_V?2lzSQB;iwp0w
zGh=e}Ig$)D_8XtZ7TofJ417?wZ4I7hE}pKXOyy?@GiN`Zf%o4;Z!#<>M*O>`;a%4E
z{S<cgAKle_7)q_Ln7`YkF1+Y98WQQ?VA-<lsVgP%SE{>@rAJyBS7fc!?brq_x$zvw
zyIvK&=-tQi6UDQm``12=j=x&ok_fm*GG>D!*JF}tE%J}ePw&!+n$H-QCyh+h*7qmU
zS6vH|LzS&1ute>IwvanMohI8<&wv;AI8ClPWm&!cI?B3;@Z4&9(Ci<SBcFFBf6Z^_
zh=!lV*RvNLi1{!sWIG@yN7?kgD3!c8zVhI?-<{!EM?>np0=pa5@yjJ0$Zw&CRJ7m?
zOXS;;3}m-}W#?=7GvN|mkKvdzngwYVkE46YyjGU?S9SFz1`^zw{-Dg2qA#x>Et!6w
zkEYuI>+LseKDPE$r3bl7XMu;3?yk0BuWjKE=88f6{3{%idSqV5(?{Kzf1KJnHP#Tf
z(a-!`&@Ai4odE^e1rs6fk|vFA^^KH2dw>sy%vw_rLLOA?05gb5p!u1kkW=N$&FO8!
z!uMb4P{T%|I*5p`gc<T<{H5zfCE-#F4Zl)=NRL<{?~l4deGXr8y45YUnG$i_!1@|f
z8(PgWS9h;ltej8`&?YmopliFXxCX)--bgmt2d%`W#K{;`|I)W&Zb@;K&b{;7do`)t
zH}SBdrg#0zvCCM~7fgxQs<qf-R|TE#!tIh)^R}&mDU!f(OqAugfZ*A|o)7r>lJhkh
zB+F~%`c0DF8#7@(1N|HTcd)(KwwMEgvi5Mlr^c#>ebt4|<(tQlkp@^4A9A`$wLwPT
zkFfnkac{e(KF|LpzhI{Ny84Lom6u3bz{p&+es<kKO}36@{miGEo3j4fuFbX_%4c!E
zHhF6WMfT^x%|v1pm4Jnd;A2e!E=|s3WMiGNM3ER>81S^d4fs4c>!<!96tL}}&@32-
zRvnX_mCHjGr28;meXAX3?A<#EDP=p1Aur7YOe5G*5}UtBaxo$$^Gv9_Z8%g7gR$<R
z_?N0Z@A^}@?l7xK>tZVjzE{(ma4V@|`no};fprktC#y-<F?;Zz^V=;=a^gCb1DEk4
z>o$R~F9@58UYhG<d3``neNdJ7Zi1%pqTWB-UiaUekAFqXPOkq_Q>pVxrCoMdu-C|S
zw#a$umj!sGRuG#W!E@5+QRAFzna`-3K=iRxK0Qc9tI;Vr3;FHJXk0nP@AdVd{E#7<
z=$v>$bVB+Ef_Ino^j7AYiv?H4X;C8)UzX|pW*pu5-&yl_DVtw^01uDh{AX)^ea^gp
zRc2Kl&w527You%V^VocAS*ddF>42~rAqU28^24Jl7HywrUaZ8VNE7Z1331j-n9kWa
z{sJh-wTSu6r0ZWceuj`OstO@SSW*02z2<!cmaTtv@+pjc?-EE7yUC}LUp1Zd(%lcL
ztG8O*{%<L~Z^br1To)r<aZs;*FrP0NAs5nv4d>eQzFzD-fQ$+o)BUbiaE&6&AMKsg
z4@DZ+S<+u|ED)$rdrsuj!!(YeciT5lwJ;y`+vfprvy3HP-iPBD8O8|}0Uot~c0AVT
z|KEzuKqsFi7l|D-Ts^Qy8$PNa4!oH3QJ8$9A&lbf3)yJ4WuNG&W@A@4YIyTzyh;11
z@*0WV0^*sbr!bYKCUs%_E}x;m<;%V(L)YxuW`n8Wk2-f8bDfU#Dr6$HLK@@Wy=qE)
ztL-{(`|qlwlVaxg2cMVQXQbxewZy$UdSkrlDRGw}(+_!+lN!K43NNv(OVaFvANV1V
z)xiSpMkK{hYD}GKMWkEzq?V2YpYz#l%{XyQ(?<&azAl3ds603udvLS2lprPeuVl`H
zi5;}y=Xy>uC)H7oC{HsY0a+k@BJj$Eiaw1i>aL+%3?fSE9&cIO@u7Wh>Z15p>$R-d
zK#&C1)`N$&0n`pUAE|7FtuScs)L_}oO&wuW+wj$f1CL1Oh2OtTeJsTp-e46bi8~~-
zq}0!A8}N9LnA*<azq{jdt|^+oaTdwndrdIcqGS#$;&ZIT!vCC*VNAJdBCl{(HAw$m
z^ARbMu{vH|9Wk5Pr!=QlKs|CcaX&KTZmCnG(}L53^h+Y@<|t0`mO$|-G$Ji{X>5@w
zB~d?MtZV>KRm-q-VdR)PuAwYYjw0DnEKZaXh^^*B?@Ixapl?8_=KG)m&sec$(rZ3E
z*B6UpS0SuLg@7p|Islrzb;V~BS-UuRbk#~+RYX{n37ajVcQM4s9JoAGpE@4&wy+1G
zqLO03+iny(jw4F`Zy2MC0>HFjP;&D3VT>M`twu~BCPq4q%psoM>?euKNaz9eY=?E;
z2ct1vw@(I|R(p0gZF=Jt%}kcCi3RIVUK2c~+IpG0=Xyrju2I+4NIC5cM8f)}<ES{0
zF~k1Z_6NYlUijDURMr?D;F<JFY18em<zFT|YKw2BqlII9{Gxxa$U4N?J3Zae&OgX~
zk;AxeRwWY)<6M2QL2=u7z)9V6QQol%sB&`5jKuS{haed!$*LwrXX(gKmh25SbO~`G
z_JM~RMP-C^Za$E%1-xxeniAR6+76lvR>hz>gMZox*(gD@tW7C2##u-Nc=IyMWwB$(
z&gX$%AUs(N8C0qvs)!sbSsV(9G*w~iS*aoi=5$%Z6W1grnbZou*8`IE8V2^7USWdP
zL;Z1ilg!@<6qse$pZg5-r|Saqmf|TK<u-leRrxI?S`tqJm`UO~%_i};bL_2?kI3VK
z16RXCvzOdAT5t+x=#66ku9skym~Ob}{EoLGZp`W){*^vh4$aIbVb9p2Q^C@>N+JxS
zBqldux{+AgRQSvvp24H~H3lsj`ip3@RxTbgCw+SJC2{=8m`a9Q4_Wm(2TkW@<%44&
z!FvtQPxp*>yEZ%P@|CoH#|BDwh<7i48T|Hc*5A#3;!yfYOq`6>I97iF`R6Vw^KQz$
zw68TT<4hA;rk}H9PG`Jhbet*Y5d-TK-m2r6ww+}G20!83muwE0uWIIn6+ZBuAbzTn
z`xTVfdag9q$z*pYtCIAZ2Eoo<^-ee0Iau9Hbftn~E(6N<n5QMj-yV@MtQ+4xcQ4o_
zEa58V&~)v-&rH1I5$|{i**s*+QJww)Pg8<wI^frppu)8VJ7_-9eeiZ1WH&*VXD_8)
zE#SBC3XU2t9b8wzRcqupEaNo9V+$AaeOXWyXU2&Vr(kR8gYWUa*!qn|3qs$1<+6Vr
zSHoRS(*aPHkO_lW%Yqbsztx&l8(nN0oqD<$KTrc|uRt{QzfdKJqvt+IA^|ohGl%tL
z1kxFhHVg)=^7RHG*F><Q-8USS&%e5NGHY((yT}21;4-pljKdYO@h*wmRr|Edhc}g;
zUo<(lRd=MKL;syea*_f)$MWWx0~YgEO{N=#+U{1i<;;Sm=bb}A&HD|e1~BkP)Y1`K
zkMZQd%C&EAJR(AxYe_mhWnNK)F(j*pf8D0?1r69?5}k9v;_3o^!|0(~6i+@)9v-wL
zGv!+XR}K)YT%q#W$)bE!&9vML1uf`Un3=sdH_W;7(?#k^wXqUZ%V7@93oR2V2G|Ux
zwOP(AmOm**1FobtVBWO?ln_E(SXUA=cxaxUV3vhW<w(k?z#F7~ML~S7Xa`|ySa~FU
zJ?kMp!wr?H_wpgG79Q@(K&5{}iwFvWrg_-8LW&e(;(~9CD6z<9CwoXq9Ms6Av5L2^
zN!zfSKb*Qv{N@a)7YLh*6y=X|hARBQH``9WBU#r8mzjWP$#pDcK$SWLX5Xh>jPN-i
zhLU+zO0{LhXknLq(qS7!y{j(-)8LePFS#&Td<}=LJQN=WV_&JuRqJ!H2lP7d`F(0<
zf&Xi;{61I_n8m;>hk^X`7lP%3`y-dNsoB`cE_o76FgqxTw&eqD-0VgOc^OygW16+O
zU%~lQin8da<_+~)QSNuJ@4<<3lq?tQ3y(-$iOJb`f2;b_20ZWqxOVmu2FKt`Wi!@;
zn`h?LDCH1g4`8)f%2DIFE0=l_ot^a;>_xKKOq>9yqql{`r}q$)ukI^Cc9CZtrB~ZL
zryOL<S&D%)1kFLj7?J3SP-A%R9+EkAf~fA!M&iJo;f5e3=;JJSV2v;laeLUUZla#d
z>26fqE_AdH5u)#58{2NgU62>taqs&L7-fZ?D{M|X_68br+@`C@+|S+lF756HL+|H#
zutr0iunngA<UqM|_q9V^R7k8Ip2yy;>9g4azxm>tln=<<3~4ylyob!4iX^qVtdqGj
zg)buAI|4CU`b~$M9wZFgsXz9{TMna}*|eXCZF(?G2<oiJ7aW9+dJ<rs1=l12uA-=A
zbSBTml_J74inB{&NIZcJpEz_CX}6RzcmQv|CGU1Nty(Lwc-(D0RmH`r^R`e`aa-TW
z%{Tb^vwrrPRtQhhiLh8r5f4_>1GwBo@Dv03?|{@7_=W+_ClnYABMUA?5L2{9(|+;_
z-2V+B25CxhXiK#hxNgK<#^E$M9wfI=tr-~Mt`;UBh;d_Np65`&HRAUga-I~McZ7Lc
zwp-YSaXHyB3OS(nC0qnq=_K2fLxSg6d6WjmY?pxW3c!MzoJTnq&yy8o3Rfzp)bFk-
zz4!qb0R8##{lzoCDAt)l7`laeus)-pdu+BstYRpLoFWJ63tJ|Dvdk-{X7FNIp+-^8
zPv&WW*<V^Kr9#Vppjq#ZC_8s;5M|~PoeSQ<01({NR$$=`7X8OJ$Kk_1L{+6L!d75*
z5~AdoTjNwfW;fGvNWcCZ!Ry>fD7b@y2(_RNL}f-<bfR274{Z=-GvcRfmHP4l%Jt&-
zWMZ(Ol*UZ$0U2x6<vUsGoG8dPG?AA~x**3hO9|>_0A}uGmT>?M>G;tK`zRT`LX=<=
zBVVZR0)a=+rcDh!O(5y?*2ue^QE@Umg!MU1VPh|}P0ba|-yvNv`i@B)OIPe@w++a>
zzEO`vMTVTB_;y|D64mxlq;b11?bpbH+NQ3bJfzmoBk0edoh-nUxcv+w=Z~5H0Qn&t
A1ONa4

literal 0
HcmV?d00001

diff --git a/models/Reference/segmind--SegMoE-SD-4x2-v0.jpg b/models/Reference/segmind--SegMoE-SD-4x2-v0.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7d7989ec2a1c0825b092263fe09324ed9a995119
GIT binary patch
literal 21996
zcmbTdc|4Tg|37@qFvw0r_MOI>Wyo&qL)Njx&|+tjvQ|nBvNYDQ#Uu^JFt#YW6xr7-
zLy}6eE4>RzO25nd^Z9=7$K&_cecu=7T<2VKjO+Ehp68s`cKx0Gy8v)oTUc2D5C{ag
z2>t+nKLWny*Ms~4z}6N}004jmV1x((Ft7yyiy=h#f7=!i1i<j`btw3u8UWS+Cs^#j
za&%k-%hBKbd%o%w6cV8l;uj&IsiF$}eG8ZZ+-&Te?5y0J9Gtv7+{XnFLIV8!0&3E-
zq6l3L1AQG0?b9f<rya`dtodnehs%y<FL(z81{m6g-ni;}-P13?=jclyJiNRDd;+J0
zgiiUGXq))_e?I<p0z52G@ZK39ya1F3!oUOh+XIM$cMbzFIfCWC2L#H%2xDSqImXHc
z4rt^Cpb!QIC?f+5#>fbcjsZUh7<pj4l4{0G#~r<xr9$EAvBY8)X_NXcKFq{FG8*1t
zamQHsPY4JK$;zEX$SWW<wN7j6=$e|DTUes4teu=)T+h0>V|{%6`~w1maN!Y=QP-~D
zh>pLTkeHNwkC2s}lbd%xzkpOyN-3k3SI{b-JZ)%fYHn$5d)eL7+t)uZI5as$pPqR=
z`)2Or;-{tMmDRQNjc?z#cXog5{oMa`Bo_pf^M8*22=;%;#RJL(Wn^SvWImD$0=)(n
z1|CM3q#6^iu_Lor=y56aSQfYmvADkLn6w7wA3pD}308g?<VV?WN22{J+5bDi;{Jb0
z_P>JtKXT0h><kd_;W6+4M!>Q;^of>?6Ivc3MKUKJbDm^nvZfMkf&W${4#{kJDvf+B
zXUh1fa-I~_C3%y8hqh|AfSZ#ejm*YM??vJtVoRM+DUeE4I=)V*gab7Usg$6z)CrYx
zppGY2bUR30o#w_}wEMTuR1xpM__or3&T?Oqe?x__wW8Vas_Z+*kdg&7wnQ1#pI4FM
zz>wnXM*|k8d2mefxYR~KQ7E-hGG5bS6vZHvM#3{`3Tvvx3wa@d)S%Qrh`^M<I8=>#
z+{Q$WnvLS{LEEYd^^y8w!4@6pqaZtj$m0wgOtI`pApmSb%imM%J5c=h%h)C<#dHU?
z8e=lcTq0HKL>bOE(?D^WC4q*`Pac)0N@*CAnI=%I2zVm8L0K}ri~_}s1M)Cc8F(CC
zhAGp55kJXlD(i&i01+c`05((-6))>#>xB00JVMO*Kd`~a)g!^1fczIb3l0e-2FFD5
zv2JIt3h32o0UYW-&?JJ;!0;ecK%G!A#}S|sbci<Yt|nk7RPqkRyH(MA1f4P+BDawe
z3H%Eem$D;2nHj_k16i(=qO*tMl0mpMEwFfaqXjm<6RhxXQYr~xb2>%_NFZc)r>v;Z
z<F<0KEVf7zU=l=`f*d=7O)itfh@2F<C`myA;C-9gN|G6oiFm%-yKH+E(8-80yIfh$
zGKyMa&J<Lx4CgJ06Ut9MpHwN6JWlVi2(kq%g0T6C4uKGJXyzb)0!`qfH9{b#f#o9E
ziTfKGAt-^wkArwIpavlnI6g-xkbKOT0*4@w<3a3T4|&uIggOL)qdKP`WZw;(Y7j&l
z*%=7EI4yt#aY)sa%m&$nIqKtpd`$}>{7DW+rhjpyK~!<cM>rL~voSlU6s6-IMVhwZ
z<=#1Lk&iVy>s3H8)5mZ?W}^u<HjDp=VZ+X{H2imvVZgtTX*Zf-@gheA<WzR7msl8=
zw4(A;q1iTacxcewao~Tj0^~@xP;OEX5&$90X9?l}K)hH?(SZ<-BfNwFmjikVh}U97
zSw@*vV5G8y5D1MKy~bIJsm#mv*b%>YP3Sc?X8t+jjF+wHEUDu`_>x$(&rB}PLK{P1
zl2w+Fg0q%bvKpfXVG`tH|0&2sG=~Hd@*fbVj46!dBW@W}<3TvViZ|YdDK;zKmI*J*
zMB<Q;Wy%LBDOAdYm*a3uq^KXwKM*l9p-gZ-g7fd=f0&gN3D~z4ze640s<?K<EKxed
za3ekjux%^;a6~uoVp%0G9U_Ul+X&LFSQ*93UvcILJHHA@FG#n}BkZ6jTRsh6`ARmL
z`i1cn-#O#@BXs}tU*P-$6Ekygp0kG{Uj{;~sK-&PM<5<?4gX*KKxmIqp+cspyCBiH
z$B!@syBvVRpO5GcbBR-ylgYoF^X?%iYSf%@DXo7==UU0bFxgTmgP&|v;~_brcr+Cq
z7(py0;3cu?SeGN($+IKKEHG#_@g)dr0u(PM4+GHhFv%<sQZD#^S;)&^%ar*a?7$W{
zo5A^u<^UgHj?hIUNY9H%h+I$s`51%>GS;|a&w;U>=AFmx9hm<YXh@6S7efFXXMLth
z+dXIJt6u#6p1pkKA7tFJDm{ymiJllrP46%k8E4o7sVAtlk<8l<_>MEarPZ0rJWa++
zJi-D|8!yDt^hWD%Z)vo{v5RepkeaEFi{pr~*P=1&-|NFm+5OtsUpp}+u)FE#^dt8L
zS|^5hEwP&?%$hluQnz+XiAlOTN~HC~SRq9qJiAxLRb*}D^jW*-Vt0p9-!VIG+UJ4;
zCa%akytO-4JEJGy<4|k?GqgArm~OuF<_yAIf46q9#9XkmG*f5n^nuEid_Emf#Ttd!
z`dI6(k*gVuRVVWtw#F2j4)m4YHl-4j41EY~4O#E_9&|*h&vt1rNX0SROk8a3_k=11
zcZvL7{R@cIb~L=bA!ud%J>~-r7M|A76tp^P46u_vPuV7_j}T4^DT(y+&U{@NfZwV>
zk4Ao9TbJ<yUhW$1xZUdKwT;-m9;lb~EQ;Y5__f3SZHV8>O3ktipYKJt9-5>utLsd=
zMzNCX4FJA1eynYJ{)={-R+08FTfJ*1AJo6J^b1$aNk1l-<jI-4W)XNl!I6NyCf1Rm
zd($H!v(GE0yMjS;&G@6Y#kht=ENx}1%q@|TXZCgIO|tv>5MN;#qn?J(C$ycgLR6x`
z-CmOh;wQbA<9Y>snUfF>c&Diq3MLCE?2;oX>YvXclFH7=A9s*vH?<Dr5q6;Wn7z!c
zyo=hYl`rPj)Jag|5puFIyKMR|FQKfCXl7-Q!Y^|$0*)xYg#Yl8nH9!x#7lh#gupkn
zJOHkcki|>~hAwbz%ycp)`^NnbAFm%Ri>RY5tcyxO86goe|7f7tNX?uDH+8qt-wo{c
zA=q-m&;AAKHQDd<KfRsfra};;QJ=QriAub32F~M*lJ)PaJ=})%D&|b;`#w7`<n2FX
zye2(nJf6LMYWy!?{jTnx**T#r&p7X}-m1wSNp<_|OEUldJi_3Y&*r%vEr*v(J&>g6
zTdAHavPE&Ofy*M0*ZQx`bVOVbQa9dU6<MS&E;AU>s+5L0?Bw4_o~r{m<Kf+ic@*O}
zyERuav?wn1{>jm~uH2Z{1kdhe6(pfF+dbS~4ElAyzf8sJJ4rlHU_*DtX7bC%b^WEN
zvXTCw;Oe1E$7+ZAOdfVljEIz@SPV85`gVrz8d%n7N`{f%R8F2#h#fWba=Vqq@zFLw
zb1}J~?WqXsgY@p&Et|kCrmI(h4O~tbQuSG?DSE^@{<jp-bjoqGn@Q5;toB~DnH5Lp
ztt^ikMfUk~b_Rs-v1nIJXIM-N8F@Nk@sQ~$(LUd5z0iTFC%g(U-gJ)b19qs=j(&{O
z@Gzb7P`8Qq<g|Xihl&k_QSdFr6Vz6>*UM7%WlEJjSvL{1Y+@+kBxyBt)17MJ6hBjU
z?X=Yi_JkHbc3V=`XFE>4$;w#zxRhHVZJ^9~6w|8Gn_BZyV4j`m=qtHb18*<TK3U&;
zDcXz_{ARf{#MGy}F>-FY{>9{yv$&zI3o)u8pvY-Ct%OMI6SQL?m*|=v5fibA+C=-!
z>awDBou3eW|HDk^(Sj+3bER4_Om<*+*f8LNc|=TGYdS=bJPv?bjXpT?IvE3$;1vkQ
zuIP^_3PS=kHUrddKyFI@_YCM`Sb^&xFRvrP>l{c2wS`IW0&)Vy%SsxXyfk>Lg}e38
z8KRkxy0^{p(eqdRnqt$GlGl_{CtH&Aiosxc$kKL{)D%+hCgTUm50V@9J2VxQGsm+r
zt9*5InLTBza1kF?n9muVFQfjOhKYpOsvi?y+~k&X=m$Y3KJ!c{ztrwVNY(Kkh}vqp
zXRV1cUVY0nPaywtmAa6pXS~wja%DWpytdrZUG+TrnRbnHt@lSC+a;Gx_opN7*O6Ms
z`2E_Pg`f7{33bc3I`Bbl|Bk-%e65p-$J%V|V2=2YSA4NITQA6P)VvbqY9U9R_Uqs|
z{q@9EqrN1*;kfQ{DSZ92wk+#wHy-nAOQf#Uk7xIV5>Y2=Wdkx*vrcyn_zC>p$YOug
zv5$dnj!jlD!VF70%<d7pe#_%4YsPd5MaVAkF}&p2ywmf~qFr7n2?6*s(%hYoq8sW0
zGPRMg0cT!?*}D)_?b+|GGT#+uGnn_M*m9l`#a5-Wr{-$&pAI{Ec~(dbpqOuw8u5@H
zaEM6g(y146<;!QOs)#xcm(AGs6bF;oH3YC`uqIM?=nVYxLPg$OjoZ2~fKLxFhlH#9
zvh8Yy1jY&?Ny_MAZpA@b0c)JSbrKtimeqq2^l^XUY51qK%zJU7l0I{M=_4-HN#XHH
z(pjHni}a7vl@;02GE=Sk)P$<9mV6iNv-i>=U$9;I(`o4^JqYMXdHs<PpLT+6v}KH|
z_m`IVj52qYSCml(R%^g<CKl~C@LyU6V_1oVKqSy@jk^mfL^fv1*7OM(4(n+XX{3<1
zG|rXGk|s}PgtMB8{g;{uB*ex+VL$}zBFg&`#$W*SvB&IJX-)lsD0#x%;TY-}XHdB0
zoBYKl-hqHEf=;H}U`^i?z1C!M)p*F8wcViLe9l%}FDl60x=_d)?qm+=3mK#O<89#(
z2?`e2d?p<r;o7>bn%4IL+xpQBYoK!mI31?5X(xl8Y|+b4-Kp^PS`lfE9<%#AsrE#Y
zC~R0liJH@(y9Q5dIcKS*rgLa5IymQQYi4VnMVwzaRa|+tS&&h!7#DPj=SB}t`_?Zy
zdgz7|aqUv%@nbeq$-vM^Sb57AN9+Lf%lhX_X2g_i%}LCO+K<l9zLcgbqz<QP>CO0@
z*T0;LGxole_-?Y~z&9-8eNXux%D(+i2MGLAP~2@F1Fi_elKerDq&@YJzkpw&@L%Au
z4a;);U}jwCJkNNBqm#w8Q^ZrlfBKAG-gz0zU5f7}_Sa~cUtdzNm=QGx+&CJxn>Q4=
zG|CM+0tGMoN$iMwy7&&CX2PG6Nu5t*o^=!w(?~7a2Q}-uF6s}7KL}_<PW<clRaYR1
zfzV$@(Q!KJrA)CWT%H(I@`dE|xP5Wlk_aDn;$GR2e#|FiQ-kn4AOZQc))`KZh6<~>
zMs&1U4LgF8g9NpAHZhmy4D7c{y2A7?`jVo$r`!Gl3Q8xqeGf2`0uXamo<8$aSd?q7
zdby^KBYj*ie@`trv+!15&3u2mk|i|Tyw5^mVCaJmb)I20M4xrAXCt*1d%TIc48=8^
zJz1VZ9ZeH6S+V#Gv!tj$(0LTagj=i*ceysc-1sAT1H;l5zbzj9xKjUAR3B%-(ywW0
z^)gwt6ssc>58oVeozlFH88WX&Q5-g`K~r&xyRVp*e6Ipmf+m6Xp@uQ_NC^d!CrN-Y
zilVj%s+Yac%+5&?$`Q^w9=pcI4fCwSw}(g-5B181jg~b5i;b5T)_Z-I0!wt~*x45w
zE+(bto)%4Z75PyEkDp74MR^>!=Eu)v3N9)|)iA)+1YYy+zv3%b(vK($E$n#rm@(D^
z#70aZC&4FAg4R`af2LRXDqm&pAg)(6NIp4I7Ai)~6au#N<^BR|*SGgFwjO<~zQT5{
z7xu=AA>{Vmh~hxG^tjzgJu2g3@oRiqR`Av9nmIGn9TKi{JH(Tz`kvhWHTh}}UHCDT
z;XZX?O^cxOW=5Kd{JE(I?eWr|B!p;2yoHC6&!kc}tV&H_Dg~mJp)QvT#^W-G*uomp
zgiUCZ^U@veikVMWyEy|To=h!7E>@g539g!QSq}X*4(j1uNS2nzhvStK{66C`fza=&
zuNZ}bgHf9{Y~PsgFH2veMrZVq(ib@f_NAX*qgwHOP6`>+CK|3A4Lyw%ex<{C+x2{Z
z1=%NSL652{5@)cHnAhi#$z}WI)r&HIhVrIl##f8Cb0#F&64vpdKey3rLwY`h%cb{N
z2A3<Ko_^tT<!re>`T`kr;sv!gV73Z;66x(UxGk`3S=?*Ia#C#G>9~@`Gx~t`k_)%o
z2u*BP;jyEQg-Gw`m8g&;?UOSNjlzB;UY0U%`s3CMw(ah~;481KJ7Y!){Zleo@4rk8
zGVAlqIwVd^Zvnf4yOmkz`r&6D-ItCn6!MQ!4!hGct#|XMoVFD)gL;F?ulz|=Ro<8}
z01iyb&r;MH8t^QOdHKn5DCgBO>u#yo!kH2Z?~<3L^*FtzyI6+*2Q6?$y*_rOVZfx&
z$tTY5VZm$fD0p??a@dEYt!bf4LFraHf?u6^PwEz4;r=?xbea7dqoK=tSA&XZ9@pP?
zZu_R<2+BR3U-I~d9Ck+(na%Z&yz(zlMF<Vzrxfqx0EwC7l4+(cENL{M0-4KT1GGky
zeH{K(5TL9uIkm;yAW%baPQ@aig@|*VRdW%77t!VNZdO83{0GjVSpNEptX)r?tKFNE
z%FZ=sl%WT`QlK>!&z#VST|TsFT(xZ<BOd}=1skw-?!nY~mdixy)yO3l+()|LoN4kJ
z(C_o=MT#bi`tc@yAz>$Y1m=Iami=imUTnljmh$Jux=KuE3Q|S^&#5ia<g(xSe&QDg
zV_{o`zDGCGVL-I-4Q$avyhiT856ufEQ?<%otdofstd+yA1VmiqcaBPQncKK(9(28J
zj^(0z7n{}{-M)yiw#dh&I-6a~%Q@d%<cRMLbt+OLPlu0J%e<4+9+g6z%GJiLSxM)G
z2R92r!;+XMA973F>hv!lp4ML7re^nBGg?Ygtt}_>xu1&c{SKlmER9RiWp>AIH}cl^
z!2ny$DTdhX+kB5=!n|J=uaM62je2-cT`m_Mr-v*Y^1YIsuY9v@|H;KW#FJjGt|Kk6
z$K2?g_vzL0^S$UOwmf7?Jo^~eJFkHqgEXf&+pIr<fjrZeH#^km>0B>r{IT^Xuhmx|
z)<4hQH!dRR>{PnmSCV06+0VJ7fQqQvmW%dva@;ddPZA%>PaJ9W?+>xHIiX;u^A#O*
zFGTgMew=*W)e*|hx(U+-<34$5ZINjycXa~ftFsm6cW!dgyEXc&UpfK@odg?7Sk%+T
z-ftlf>=YD%@ppqQVaD2fl)xX7uOscXd>8}Dnd>xPT^Kn3rBk1QKuK=(R(vCQKdf2e
z8{W7Q?Z%$|sEnVji1@MN!hs1#`km{g+qD5NCxbh<gfIH6U_D^9(GcygE=&0W-Tv9$
z=t9l2iKx{Y#k7*KKv9KztTqed4CfeFBLN>=)CbAnLq3|4aUTm)E&)^jYOU$9ayQzN
zA=abLOyWw8H?>#Cv%F&M+erHg`_IpcQS$NZs^z}6Z&q{*)p~W@N_`pV-ryc{oaOdd
zulM`aam;Mc`~q!6wV%KwQ4*VX`4`3*T=IJ<SYif+?0p;!Zc`NGpa4D1SxO6B{Jkj1
z9!(ktpgJB1z~eFpVN#QV5=Zj^3(%k31+=T8>Ob+N_}@G6CA-Y6*%N|MUs8;<&maWm
z*~KMjnjL2i)HIQyuH>@@Q6?cZ=)TMWU$_p<PpWX3lx2*;sE3pcXnIGEA~g!0H_yGr
z*C@8cG7~1%WlJB?0+Oq~VFm9U>!}PoOC0!ujSk7UA>FT<<`PbPw)-2wGas}iJ(c_2
z?t~7~<MFRkc}o#py_ngea^^)2>oafT+qY<o*>3e(uhWzCtOhi54<3eE&k1_HyKy1k
z<-tv#!iDKTQy*{RQ`%is!<=&}s9qb^8v=01@s&qUZ!Ap7l*`57MrKB6?Sk+g|ECvs
zE4G;WUg0jfr#)#j^9){0Vg01SSN5=eJkl_(aMF7^<A%WMKkI*R6G|1RQ90Z_I6L=E
z|G3XXm2SD`<Mmk?wNr(%=8UvZ<u9Ti66U9$`~_SoyS=u&u(X&Y>(M-WhRX%^WvaZ7
ziKwxX$;(SIidth~?G`t;QEuzfS9`e5H*!>kw(KGY;VN(JYjlS8+baaUHF{Yrbf=EX
zaS>9#Yf~kg*;eH~V9oTu$YlnHwG}1`rYJo8c@l7KsxROFbanswetsl0ZmLaxx@%jY
znXz!@{o~+06W(OT3EEljJ2T;3zZPqkkOK{(7DGLr%DdzickTqS#HY3m1HGQGzd$ri
z9orgiRoHvo{kp)YF;44DgUD)?4<Qeud*+?R^+Jo$?=c^LPIF*g0`6|HN8wSMlO?_L
zYdf}PHgKJ#vzk5{-Rj%vLD;U=wY9DsaqO$fudtW5I^dB88`+XoCk9q>e79{(9Bwz@
zk4$|bVwxQC%rU{G33m41tMEjXV$B?_p7|%R{%@}0_mKOb2dI$cFVId9kj|f8d78KM
z0QvG)tg~p8j^_@Nz#EGiR{dXIr<cU0ArtcxNfuPeH1sr@2v#Nnfk+mR=)e>j<n@0v
zZqP-B?w>%38a&x4@9GUF-CtZJr4blCEGlx>crIhfXDeH(v0N@aIa`YS&C^%ZQxZ~O
z8?9L>3HQ#pgv%<X_oPlAi#Dy(sVG<zgg=||#W7(XhLa-b<ANs~>D#H()HCMSAAg-y
zIu<JT9?xKPMWiN-#=4w8T`;@ZKyOWdqu<AluHjJc?1l>3d5EM>TPtTSInpCCnqOYy
zdQ@L?2zUAs2%ppDn+j<1ej+LG^_)tT+!ln7!>v(n>^!>O$um+@7A_+|$s<9L!A3@h
zZC(PuTqK3uO*yxU8qFzx0ZZ*nMW6WD0~cPujR&R=pScLr-18H~bCLIgYq>^kt0Z#Y
z3THQk$=_YAVGs&3`86&Hj2h88OBT-6#}*k&tZ73ZuYbD{3boXH;D3*Iq3Vlt#lqA>
znJB16{I5wtDGQIsWu8sR&$@&Y8Hkga98JQWm}7jW1mXX1I0%pH%$7b^D7=O%uVJV!
zpQ;Jae1Bt9F(YUC?A!4f)U)7Kq^FMUPhXC%>WV9|(uJCOS&Nq%u3vsxl6Xh^$tvRx
z)k>O64Q-(7L^60b#WTD~y4iVqB1A3S&4;l5UbaYG7@k<r^`K%)`VI2Zqpy(B3o|?Z
z%{imN8O^kp-)#IndU-`Y74?fa``f0xy=M1}74Yd@(Uw^iWx6hojc!}uA#R{R6JEHA
zs@_>|!0ct03AALKJm|UOgc2t4&Go$%K4}1KT{5!CL%-$W>1Y{TgQ55IS{_Bm|8nu*
zH^ij99n&$OQBBReCo>PGc~vLXC~92TAY}e<eW7_*S4HS^=xOdyx&yP`qP&WOxZ&mT
zqI`i~%Nq2|1dq;k42KnZDL<}XRNn9EmiyU2vN!0d9afE`;I}b-p67E?(8W6#2sLvG
zl4j}zI^3qsh>aXJrl7Z267+q8PFqH3CPq+dQd#OAj18j({u`&`E`dwcO1QwPY4iK<
z@kqv8n=@ZgC+_At`n`%~JjZ+7#jF8x=|<)AYaM|5Hm=mFFAzGBM-z-$6AT=59(le4
z@uO5GVu6C9DnWVtx-jxx`er-+#e1Jv8*}%{E;QZOQfBq-vi&O>HR0++8TXjGl)7~^
zApfKJ^r!^km}H~fM|i+*+rZSNDAy|*Jwkpg_Bg*}HfUV$1ChLqOU}N->Z&d>Q~t}g
z9QOot@5au45unh|GG;3BUp_8$n|iNp5>bi}TzPo%Jj{Wx8e2qPL_pKtdRbX88-%_u
zh~{2p4%(|UQTLOnAC)1PM$aUVrhc{1upx$K;Oxu+c_jO48H3Ur;}!HV5!PnkUZ-%g
zPR_;RCzHq$7Z<_KiY&i_k<g;Jr-(XRlke;cJR~5?V=BSbhu9gi`1GsDmr4_{J=r@c
zD)!hRch%ACoyt172!}K6;G(Hqvu_1$ws!R=pf?<R{mBjOO!{I$aBKp5I{aE?{k&h>
zUa?xk=&_66^J)4Srta5H|7<~<`m{UA%3WE{QY(bs5Zw?}-6{|f<B*Y8<|O9BPfiwy
zK_7*?dU-cwXe*3Yh<|m7>x|P~%p6dWYHhh&UR1%d%9j0$o+Xndq0nXjINDpg#?qWL
zIJ}?GWnldm*xf9i4qnZhkH6XzeR<)~B(^lutuD;Uu${KT`-CrGO!v>%<z5&^whU}g
zf9GVGwwc>UPsrh_H9#6tTY;*LV(S`5;`e%f`J_76>D~mSQ<<*{r03+-=?7}*-T5H>
zTfW3Sf|$;UW9ChNB0g{@K`+~D#2j_)&MS-G35x06+oNhO_YPLS!|}JiTfx1ucPsa%
zl1tx6GK&2{2x&xCJ#bg<9x}Xr*F|``>D|E_S#twU2Cj9qzrs94X{cUNa=opl=>cQ^
z-dQ5g;f!;}tKWfw`F3uFJ_eN|bC>5CLX+F>#29-0a!{))3X<H!^rfUs5_K_%tZXE>
zQA|M+GpBjgDsZ6t1YF${<88pO9aRzxMZx}~tk83g0+$&S3RB5&?99`I{tip|vzlh_
zck@*Y)|S~wFRw(8oDkayOJ8ZT?Z=L$F9g&m!bUIXy{8=uf1jIpZaZe<QGq$W#s5={
zLsl@me+q0^*{H+m-uXpGAwI@E|4#U4#XOtCUG^t)Sdowe=hCUW;-BnZw)(42a=e2_
z`gHOULo3ec-CB*Eumo<y_f7L>F13^_s&%}0_Ra|6cKfE#t&$Z@+4dWp=Z3|jhqkfl
z4lAb`yTz*QKRKVS|7xG(+4raEF-KWjS%XgDpOq`vc$=rq3Fb58?kg^)f9|{_$v&*j
z*3UMn?a|;FdmsO;5*t$Sx?1{4itm}OD1qMzSjh*U)f<RjXJO4kB{mODG+yb{unn*i
zLVE)c_xVHj*b=3xKXm$pzkjN!?h9Xssc~zmy@r2P%FcXokIHm;MXN~kFTmOn9<nL6
z`Zj+2xUH(rJquyH`MI{_NO5wN&1k7^+4rAgR=-%=v%M8EE$ofWU-snH3HmZHRnK-y
z8}j}5qaZVcrYBDDs_GayVQz5JMku7Qm3Y|%E5S8ACz$}Q*T~MtQit2u$}|h&MW%Sz
z3v`$}B$dK#STQ}OE>zo3?<*JU9VPW*tJsbwF8HU!5s*s53JX&g{T;IulrDvxu?kx~
z+2PuBHpt@&1JbS5hfElnS-785ApvR@ih*vitv`QIZGVr(sKG%mjLU*^UJ}k*lbko|
zHs+DtYb?}i+cTx%H&P*2%2R7$7nFQKTZ{)3=URb?&tyK~e%e(jrKAvM9h<)Z)}#IB
z1nYv<^_N9=q^;t#50Xe$ZY{SCC2p5AEq<T(xjlIkt%_3#^f6^mi}MM8WFxd8(crkv
zt+CO-x)Pb=XgOScZ`X<NhOHqqV<^8?T@L?GR_ZV2E=u+#c3t1w5iB?JE-cF2oHmKQ
zGhKh7_%O%wm`zbhRlLgAk5|SgMN{hh(r>b&pQ8sQs%}p4+^<o~lZR`=qA?ddWO|<J
zNg@-&)XzwBq%XzJlA3RndG@xUUyr36tdAK!&tWGE&=!cc$j(OC&Unm>WV@=irfY8E
zrQXUL{U@Tn*%_ecKIB)g*(38Oo=7%6Iy6{OJH02j{uhY3ljdaFiymBU&U@E1ln_4t
z!^qS=am#ILSVljf(nu<Eh*~`ece*uxtLB}+UqIT%T&uVXV=SPFHoJ_b_o&f?NZj3^
zO<{UejL857RJ9sdiCM~QXrFbMWX`Q_o0{N&Fnd*cM73VFQj!5qbJNr~Y$+rrh)7Dr
zsL0&CRxwVWE$Pqi@37!dWWRx&yC@9?Op}X1f0XnLv2B_c?gR;h1IsepmX2rykGIz8
z;}u1o^7{3kK^vL!?!w1Xhxz~}%a3=sF1*YXPO0^n?aU(u`&+EHi`v=;Q0LasvhSYT
z?{7-ZF;D#kYDkR1>r9&$Z}wf#Y@IDFb7-LVJiQUMFmSU;)kvlMTggYCvRmpt$k{v7
zY5j7K!`L4jTz3<F`vT$`DZ?Q+f7PH$D@|(5dwC(PE}r<4M67=A>ce|CHD+C8fvb<w
z!9M64-YFoqQa$jaX_fo&obO+tD2pp6=ERFnHtgHSwdcbu9~<w}vu$NS)A)+L@lOYs
zl`<HjV)4N=;BS%nuNb4??`KatI;jtye6MFo-n214Ch4qy826aP<WWxe{8Wou+?xWo
z%jaI4FaM`XAy#Mi_o&><-d?5QESM$yPcpcvEPEBXQm@u(*syY*Fu=J>IgYBzH%$=<
z`4!ASBvp!L7+0?5`ws%jz2seV=#0wKkuG##m<s=+=DhrHdFb)b!>jVKI9cD5jAA!k
z;P1;7Y#B~_wFCp^{-%*&*t(FF!Ku2wH4KcUJ@m%n%pgVDWSh-AcMkx7`E}85k&nOL
zpOTq(2~TbGD?1Q_?+P-d5JqMck~NAh$%|b|>=w`2h1c>c&nwt1@G+(@MNs18QoUOD
z4A$A58(6~!UX|MqmCpl<u^ivyQj7Z!%HjRNVcMX}ADLBtwfjT-K3!RU9mV=u!A=+v
zBm5i3boKfl4#})M#_tLO^d;CrbQGDb=^JlX@<3JI#G+a+pffF#5>(loK9P}^5@(@B
zQTY<~R)6+p`isNLy0$jy&yOVT+!R#^aY;364Lb~Zq@h$bqkiKrusQw5(z^K>ba8r8
z&|YDEPk8gsl-(LTEt2i#6Z755yu)7{sYZQO@4IyC&$NtOWBc$l0~-bLow`<>*Js~g
zp<AoExhwwB**g#I%bq^eUCe|RKf%->-P-(o@$-%FsE5oK_L6pKf<h5Anb?J3EfxcT
zFb>JYAcR1H#5Z&x)Pm?I?7R7<5*n1H<u-IP1A9rWPoo#Za~z}TC=F)%fGXidSuiX{
z`A>)|)1gsX+zRa<$w$j?aDYC>GGl?Kg4E&)L_;Deky7FXCh!<OATe>E{|)Ct*dmL;
zi3P23ck6iLKVSc{a8U6NU7UX4!02GC+*1WtYsgaTbP<$mT4+g}d_($hi~mK$=wCp&
zmWzD2c6ffL7M3E)`F=wGkNKp@qZ_AhZyD`ngnw4#{c(7y4^l@wIiGfW<1cV;kIK~h
zHJNC22C?U})t;*G=fv+z56R{QS5CI%3?kE{i$3Gvuw=lO#1s-p%53^GXL8=v0QWxI
zUzuA&ZhtmFH0S04hJp5kWSNSj`2KRaq$L}x-12MZcLK(@A@G$zgiI<Ozh%SfTS%=I
zX%c<zxaSbTN2{1qIg`t7Y5R`(_3L$aB(7ix>5K4R5O3f3VBM^dEuG8g^L1pt`Q!Qy
zoP9Jfe`z4<6nWD=b7__QSw8lJz{}d{ovqu{oUoV*$cr+4dH>GN!#KQ9rP~*V@^$Mi
zmf1&FuO04n0LY4yY(KN@K3W**0)aNH*WPQBnRw537$gS89@ixfY?}5GZOnLWPB3yd
zWI(=-sw$2m(<z)VYwvy>P+r%%wC#GUe%)??zo~e;Wr4Lm7_RoNSMnZ5$&G5tjy9iC
zSbS*VRa=JE%gCQtwSKt<<)w_lPjJ>{=gATop!{1+3v2Eg;)Gg0vVrlUm`=?#Ul9T<
ztfrBikqI$S2zbcswC^CfR?Ot_7npV~Ya6>!^3nUire)LJVwLWAwf?qMqB`{{`+>(y
zrd#`u)Hn_I&!20)p!i29(~-l4<LaGy8P>PhMgfne3`T!y><u`}WSV6z;WR8gpKx0C
z8vOR3jj|m|>!%ECBG$s9#($tjA#O<bz@@iMN6lJ;*neO`wA8#3qgi;j=Y#ygzzvRF
zJ|CAl9&tl4?<;oGSe6dH&!_8h)b2@djMwjHzq1Xm{34uWq0pUaiBmeEvVjfAdKA>?
zqgj8bvUW7f-zAcLy>Gi@rlKjgY7Ihv_X?5KO7y1>Hj!x~GYiSv+b7Q@{mL*!aWy}}
z-3U2taWmSPr%1uVJ<p(!HdK_DrK76VtPsSWOp2g2d>fwvr;!llU+)*_7Oo&BW=?S@
z5`d!|Cor|EpA}4`QI?<y%1Thw^X490J~vp;IhRwTmynEFp}aNk^%^t*(*%XQNhmj_
zQV#3xRxO<V#Ko4yAPShINApT^6O@(pI!}o-e2Hhf<QJ(i)~6S#t^6*d<Y3ZD>HgCa
zfX|Ypw1kz>eY<yn$^3ip#dRUE@~L+jHBPO$Zq<%=Dl-!H3|@UQPgI^Bo*!<lt7PIi
zQNm^H;Dk0Am&bp^Mw^$2@6hP*3oSJh8MGg8d@E&rlhJ!?3Frwi4<Q&md;h+u@%+ZO
zt>w;>!_4~sc>a2{riWoTf9(90jfHh@Vb<;KgGc{RCVc`V81^v?VQ)+Tn2euUEPQ9v
zmSrY>%Ri5+MbXSux-AW>L9~CgHR)q1Pn*lt7U68E!WLP{d~??@3r~~_=)3)#e$Wb)
zPmR>C2d$Fa*;G89I37*%v6e!8==-$%L+nMG8drEjFtgEnLuyquevj<iZD5J>u?AQT
zsd(`Z@AJj*GNL#Q3FaEWhQoNZc76q~%q~RxfsYnLKqpYR$8()J?`)2dl%gu~cKA*T
zF5=<6Q|S#e&lxhAYg9!f40DgsWehlBfSZT^Sg4Eb=bGOZcw`BOfHd7T%yU4{>Nc|+
zUMp{Xnps<#@N#>}TfHw<_~p)%!j|>X@Ryb<H^@qt8|UhmpEnZN#3(DVq|vM708LWG
zNGS;wsM+~tK=V98Ac1`B_hPqHweb~V9D<D_t;je|H=>5+q6^t9>YLV$8Wy4@4v27E
zh@jmsQ{%NzHJ8dJF$VJec_$an^2<S5+<~NRKWg)$(Ou-e4$VKgzbLplzd%~&mvc7K
z0onp!1dn@GoEtf9e2U$u4F_ywHP|0ko;OUI!D~FOd3Mk4y~B5EnbI}QhIi?Z9ZeI^
z$)@e6EwLmNQQ=q@lsxma<-i)Iy$vkfxqj`!Kh?>;f%AOYNRsHNU@?z2B3wAE{Fe(w
zYJ;O&{PYEXnxF$ljFW>wL|8g#-9|2!Jv5%+x}VhHkc=hLig_BPT|mc%kT)3DmPbI$
zB`6HNP@-8}CPv^J7!1^!`i5pnF6Xgf87K#%*>Gxp5(A2C+ABf!d1^xQ1~!6#pV=cJ
zcJQ$NaYv1W3-VkHU&WnJ$MoNOv6cp3dYQ55Sn~Di^M+tX{J!hbUqGag%ddhOvxQ#M
z7Pkg2!#lAs0Q;or!0d&NB5&B2FYCL=pXJwsZ?f0VGKbGUDm&9MeD?mu!)4XVQbtZZ
z*T=trc=VXr4Q=SdgfK@n<%Fs^|NK$iXDzcYG-43robh&S0T&+(@S-)o62^Qx+FDP&
zJZbK5d+oD=rO)m0;$ZUcptj4^$xNSYk3ATV>9R}qf(53&5gnA|ly7#Q3sNg3(J5WB
zU7afrs#k~mEUX5r3o3Q?n1bBPw9WS)h!s6@nk<Owl16t8UU~R}{QJd2Bzw6YGE1XL
z=d<2TCxHyCH?QiM?Y-zn<T4S9RfDT9R=<PZN$D*6a4Q?waaDt8r_k5ysJ;TXy;3nc
z(0<P*phE!&LqN*LRCO_5$c%%EgsYzMC@8NseAfx(=CvL>S<bVy+QCrI;^b=P4DnR<
z;JN7ly9la*CJHDNv*M!~oh}X%vp4$V=owrXsw{Li`mOIyjhh!cc`={h`1Yo2MUp5D
zVyAb@<)b(f7%1s=L$5(Vr@#>2LDJC@7k$9WsLyHxCK<0z;?+<o1gj)M1RzksLyQrr
zDb$*9eM}sXG?$tfI`07setLXY(|_BprQo|S$lwAEMf0xZ>*ZFy5=zxQ*(mOTl8=~k
zvlq3RUzC}YFGuVi<^aQ4BEBNTt_u1#2#r*kSG*0|ui%3VUiz9&v2}Ek5OV8maKUbg
z|F+~=8lpB3*`s>o&P@#hb8zQ~mG=aKIMj}rLwN~Ipw0t)L(ve?<ML`5a8{Haa+FRG
z0aI8dkdwH(k`$`s%UCcAk`*Y-kTd!*d)uU#a+Gp;YCg3&YQZ_!>XQKiA}^0GApo}m
z&rP4G5^-8I#Ogo%)(~S4nt+-8_6iLT&TdJ}y|9jp<zFapj78>!3d)G9m;({~_Z=y$
zo?o_g8P<$`{RM^+p7IVc_^?9hsnc<byi6R*5ARx~BN#BRRE!x2PXixVLTjXil`A^Q
zJ5>_2XuEj^Le-t^(;s>R<UAc?t2Bu5vvh2mLVZs`KVLzKs+@G_;=SnIO6XJX+{2t6
zwfg1QuOu!7ZK{-hv`~}*E%nNXhCLK7{KydlW5NrR7Ag1{qXxcxadnZ0ux8*|h+Ni8
zCiUBAF(TuP@_ZN4#c@Ej<SX3sx^I(s`Q(@?lxaF0Qs~`T^_U;QAQsV8b=nFw)U<kO
zJe~tHWbr2@#L;Fd1A133P0QhgRH6N#voUjY6ihPx0?tLkfq@=9&KbxfA%S6K)RUR?
z{XsTa$z){=eDix^vGnOJ5hoL$txVXcTLDA4^<`+l>6>BP+{~I$mt<t}937^Vk)h2`
ztd!n#{Z@?rg($^9W6`BKI+m}Bt4=#^WfE;zIl3JsB$L5N)UOp1y+>DL8*}nKnM2?~
zgr5<QquHzkJx9Lyrw<CG0uA3s!BMP^k`gUImdMJg!Qgn<QBGG128l+%3F77wQXG!)
zE?%DZ!JKqApL-x9%ypy#v>j{#HdoiN-~g|(2!3CbGxdc1GYY1KXYnC{NtF;vm6C&l
zPDp*sVm^yJilT10D09KHzQq1Te+U9Vr-uc7dm1H3R`P7TKTJen>?{H2re&gW;OuR9
zg7>@93${86TD0J8EEdynmoM!(#eu2uE-%2ImW|~f&twXs$PGKxPM1SsU2~It{4Q6y
zoC<LJ>0?z-@x$3LtqjW1a6<UG0kwE=Z}mC%e6fF$T;%K>7HG~V)AiM~kL#VLiKLF`
zTHwGJ8<Q||IkneFpar=L>`|dAF$x#K%|fWEXj;9PqQ-B8dnmm}zDNvcbSG$_8Pk;_
z`JxcpsAF(cdM*wz0FwV6_fD0Gez*;j()npG7*ta2o70mi=-t8IGrHf<{s!aa=dO|c
zVB!Yg^8tC$fnRX?oCtTgJIb&ppFZy-f#M7T^9!t7z_j6evCw=DsO(q}eiG_v!(sQ)
z+h*#tgCf6VObsakn_nH=^Kpju_DsBU0l!>p@{>a6r6g-1#xeizv;4zi28t#>#-HD)
z2%ON!f996?Ep>YMp#f7kx~i~akja$L9&-gAz^WQlEND?U{EE*+s3TBB2zZi2z=_GT
z7o<Cjzbqi3q%lGl)heV<Li3YJ4K+IjRLBFC@|pCA_}r%m!h2mIpFdZOtj|v{gWK&?
z`=ngL897nQa%>rLFf?$uT%9;q;VOh6GZT!_zEx-iYjN;35l;t5mCW@rhw#bF;V9R{
zkKeC5(q|eehAwTjI(@c&@=?lC=o0jO90FPfr4*YZm@<12wW>ob<q(wM*&pW}wxtg`
z`pa<7ywPvCOyp^9dFj4WW#?)wi)+wQM>d-Z0>X@SKtTPHqL+l+WM{8NV+49e><8Ad
z+)80aBamMcSCxYHLfc{b;?~nsAJYW5D-BfTS>~qt9B#E#Y2iGYU>LI}pU=eF_njcL
zb2G-y(B{XajWoQoebA39ykc1kPImZz*to00P1j1X%HNyW+A>ynfqfhrC3?>%8{>XI
zG~{E3&M%i&y8POsYJxJ>NvSimG!HV^J`=X^F#*?48I52w=SOLuM0S=7Q=VO``qX{@
z#sJD_!6d(yP=qp2#ESBMvk<T_d~7|UlG<p}Q56~Ce(>y0K?&H+*^z2WO#QQDKQQ61
zSE|V^mRn1*67{>#>F6&qK)%<Hoj#{?wi=9`dyNnrYdM10;D_6eL)``3mf5Z|bDCQp
z9vZre4BRuWl9Cb4ST#==Np^O@6ZD+w!ljr*T+*u65OuAo!?C?V0-p?goPIibZ5NhJ
zEWLJGGW`qa`}_r-o2xjiYR@Kmo(-(;>yS3yakNDIDNeyr5R1)$%O`ZQ<rY=phb}cF
z9rH0CnK1E%D??4;mJ<6TFI1c+wSHG&(v8ClULJd~`oJ|YaLIUV$mH0I)?d@}69e%N
zb9$dWkKeA!36l(eQrDc_D*n)Y)X-UE1dr<enP6|Ja6&_x#b*_{caWHv6!z{}fk0)Q
zRl{V9TXO`ZFVr3*V-2i)EiQ<i>vZiMeZf>*GNqj~6_97z(Xw^P19Sf~`HOHz1)`^;
zIdM*j>o0K5sc<-4-hWu!crKsNI_xy3Wjd<HbrdS{EugO88LV~3nToRgh4;R*$L^U2
z&5_Rp((gpLVseMkd4#OEEIccMLHKOFgZ|Igf(M<hHABqJMEl6fRFZ7ps>C2hyG!$l
zzpG@Qe2)_t9tC1#B}qJQPnCHjvodneQ9pj)s|tmX@*FGjHe)+!mpyg4938#pVPKv?
zgi9G#>4XIT;GYRse7Tort+LZWNlmPI+_Gbk_?|g7j4x)b&hzC1bkj8@9}&`YAFf&&
zz82O0(+K-^4#5CvP`urF7}@6Rz-$<SXy^aEc{7mAd27w;4XF(x7RZ*b7HD~mb9%D-
z1oGvQZWJjKGQW4S=8_3Mib_<@lR#B<mREFEp<`vTz3JhZ%yNlc(uxivm2T`@PL3PL
zOKMNZJ@HTOY!g=#AzkhrPzpsq$!Xd;oIW+jawtOb)_7|h7Iv)0NNr$HfGVwadCN=3
zRVLas<(#QF`x`?#`{X|d=*tw|RPMjP<fD?IC)MpYd0cp9a1IK|tk{ci2Z%YK3<p>d
zkU%nwLWY&#`Lq7#mjED+u0fB^|5wF$XZP|>@!_W!d4t4cmwHF0>J(&6*%wkdJRdp5
zmu7W#<h%mHpIxDu=l%_<Qqt-=wps1IeIB(}F6`Qm%()(GJo$EG$ghdQtR<3Z>ww*v
zR_)%)263xf;~I>t^9RtsKvYU>ipPbE_FLmjh)oYud$0j}Yq_oL<F!s8z2v*6^@%WA
zGR5xlEjU$O-c2Q7vB41B3F@{2yc=vQW3v!-o<nU{ROT!U60Gj94z&aOK@`Qp?C-Xh
z2B~BNMUR5~%UI0%LKKzBt*UPr&Qk7bTZ<69eY$d8Vpsg3-tlmZJj}@|^nGqpxc@N%
zw{i8_7ITdv=c+l!B5|9wKYby3vQ88_Z*hlEKN-4dj(>An@waCF)0^W``<eBFnvQ;A
z4V_IwWZn$WYekA5As>6{u%81_R`WnY)JeD0w)9B=iThaRO07p9LDxm*jBmXh%$lXw
z<z7qbRRzHnXOZz)8P~S^g+7`lKeivWq4~h@HQ(crr_hSiT;_Za?o``3_Y3U@tu-_Y
zT3;vBGsnBwOtxQrHsO_Bl4<Cey%BWpS=j17R4I!%E9Q~#FD9?#^;P)sLQMiXZ=EPv
zs>33zrl$f!kr*4UtMYSaR;~#i3V|uZu6Yd_tOzi*oD~aLBLFamOBZBAHVKG4ZP);8
z*o-q&Y7zwrRW#`eMqUKeI!IgaWGsra^4t5lfsec^az5kCj3l7mCd-VVm1V(7P`CPm
z;;MAmXu5U+%0hi7xu*4Kl>oFm4kNFcO%8SOSXdcUK&?ko&ei9kihNY;7<3o>@yK-q
z<P!WRfg8;~6}qMKCy)VGPkudlDY=AyOTUjTt2-kacmq=PU$!zGr0tOZOzlvZoNP|e
zYR`YgTv4r!7IF2YzSfOw_uQ>TT^O+UWW7}~{#E(P32D+&@UB$t>1H^NAIv30<X*3i
z(QveK1_E0OZG|iNatfV4n^J?4UuG$Z<@&FF<%UVsI;atD@}PY^U`A4SA#SH92q-#{
z1E~pMF=vZ=&7MHzAwHv5u=qxP$fVwBC&#>!PK!k%2`@>6$)wB2x|$1?5oxjRbWAJR
zJOJy?p(Zl!tw*|y^GC~uPP|%moVa+>qu@RzZJ(|~fi=Epxv(FIs|~Ch-mIK|{EgwK
z3p@B?O-0-Ez~OrJ2-vJ^+wDAr8KwF9=VqRJ;M1eQS<f|AuVD^$x_iQ|CEt!rASuOt
z{valOXaCb*Aj9=?TKqdz*(TcWbz~B)<5IP~2G={-1q|*>;(bQMX^}^+n!4~bwll3F
zKq09$;EE8pGlnT}PC6I%O4D2N`!vPW=O?r@6VGX@#y#Sa4h&WgA3Q##9qg=yH96OS
z(%Q~mQ+(7$M-OoPq<dDuCY&TCEetm0ZZ6n=pk{8JQ;uJEdvYQ3I<D$?xG%bt{Ckt@
z!0Z!A>E)`~6Ge7*SJn&RnRK^ny0Wa@E1HD+J?RbAdJk1d6Dc)SuY;;b`M=oywkq<F
z-YMs!sn2dy>d2+@y;CB@Pp2!Is#{mL<=*@B#qrJuXZA+3kgVFZcb+$eP_puH$b(v(
za=F_+5$_i@S0Kpn{^{@YHA{1Kj1^I@#9oq)tGmVo_FTEU2NnOCo%_4Ph;;+m&9Rce
zD=92=O1UFlHL!&#H#$;TSx#RE<{?VDZ#896!1nS<1L0MRuaAfWvt$S<u~?Gu-s;@A
zUlQr6T7UTF?@i0@J1UL3MP=fD0TU|2#c@sLwXq7;y5NGRA2eqsMN==pKQuO08uLi5
zo|F+0E&YwlAQ)e^m=$lYyInJRSI((*GNj?B)N|g;A%<Vsi*l!N<H%pZ-z*-?J|22r
zj`W00h1eCB_3DTtlTEp9CzAj9eEe<DdzT@&mozt5F`3hD@OPTYyE>D8g;k>(nR|Kd
zoq}oc?Cd@q8Jjko-RJcpE)^D$x#%wEqV8yP69;tEKO0f+l*up|#v^nao7ir>*-(2+
zIK%Y4@v`1k33#e6my6LS8;%QnHp7=06)Z$kME3gs0=49C{_J_res{UWzN(-=aYDMr
zl>MQeflBtGg<mm>xZPW$Qht5m5HPqg%CubD(BeLJ?zV-I{b3{KpX`;f$Uf+BreO&l
zvCf})9(RJr)3Vipmnn4Ya}*6pTWo$Ek~*n$X}i{K;dqM!6CLZ4YFC>#AZTa2DnTEA
z=P|bIRUlelDE|r$(S!qzC^j47H1~aNu?FC~g^uRnC87Kv#;8e~7^@I@&sry`jXv{d
z_oJX+wO5;P9bK8<m$uP9%jpt;tHi<dN$;4T*|R5??ZRDVEX1v&*dDZ<+b5VaE<W+e
zJYJNmA}cTUz$*%j;>3dQoY2`rq?H|}lUiSU_OOg9)R9y=Ko5cuN9@eQTUCNG4(iqE
zPPFQCCcp2`rH08<<VNr4N54M(<4sJ^_muo~{coF|t8e{wLE|tz`wMWb_R5zh(Gx==
z{Whn7jY|aw!9@*WNotc1>Y{~Kp1+ki8kSHS{=W4u&}?N1db@0mI=o{V{~(q*eO$`+
zs<W^6H}bQQxMck`2a%soC_l?JE|*IIW3{sG&`Afiqjhmq7SKb^0Z~B+$Q$Z7!&6%_
zbG)mPigcXKfc9=*kXl|a>=1}kXRcz0xZ1T;cAE1Te!+a5jTY8&-AhR%eY$y*6J})o
z>?RE&9l9e5GqyCpn3bt+bo}fWX+2|H5bqnDTEfc1ahEJiM@)siCR2Hsv<cj=Qf8Q-
zlji@!gyn4l-@~f+ozila+x}PDd3P>E8BJ!{=0>F6*oqIC?-A>Ckr${}x;aBYB)$<I
zu-ddH-oFSeP4il2U9$Yl3zS}wnQRkR)WB=b1l<;QC1Smb7%u+8teP&VHN2C%R3ex@
z@W(!SeV6ZmJ;cjc&cKT8P_*7@<c%wOd6(s|Ux%Q-6ghgfjhLDw0V({496GxYQH4&D
zlxokD=lc|vuFFGr){Eg#RqB`;d0Q40wd-w*s*I6`)Rdp=7WTi;G5gCk+PJ+i)T(5v
ze%52&!>0Y(my^Xztu0cysP7Ll><Vc0mz<v0NtVp{U-P2tFn|A1dk6jeW>EJ#i$0Zv
zZx4$S@Ae`d%snLF`0YbhgY>KJL?)TKHZwyq6P*U6GjXZuiZ&gO%${FA>H2-Jhxiw`
z_NQ#O=F1(+a~2;)QoR>dqri7n4LJ0Y>1mVBR?-&-I=L-P6bt@&c(9-0BPomiAa*l4
z15`MMPlSPKC?j>b^MbEAIdwFn-oUTdOpmLow=7>@3yQsBF|&205gz9x*5_)Z@F`Qn
zV}qcUz#tZvDe{3pk7=8}C-~+s@YL}WOzO;8!M+)0u^yp7L0a|37b;TH%UyqdH`u%E
zMPuAod-Y)fe>W?Wn|?tMv3pg9)G6i!@9*Cxo))e=`7IPGJKksLjD%Q8spEtS-Bx{D
zK8>%`&MN!`Ub=@1voi;s5Nm})us_02-u;|-!-{Z-`U|85%<IXfxbJ?i6*$a4{6Ae>
zX*gS3+un(JESi{VrjD2@#7u=!Gck|tAxI@fjn(651*N3atVIz+3^g?lik|k+iWnL-
zrskSk9XN(M)jIlaec$W*@m<&2YwvaKpU+y)TI;^==gx=Js@`&-*6^;bB_FzW)3@3)
zjaIXBZlw~>?%-INGb1)S8WHoZWh$)J!Eayg)$6^c09W-FsyhsOe*mo$GU3tDVQ;Ve
z0A}#Kul`QXlkCy^P@7dz^VRbBvV-BdgeD|heDv+d({oUP!+X71#>xxnb1T0kuBs_2
zK@W<mo~4Wx)d~ga<UEloU-mAd$&&gYd9LTS;_C((8qzU!mSNR-z7OwTTv>jiJA}~8
z55vzdTK_I%Hi!ArsM5d$((W05h{2Ht#;2r)76o9*GR^Tm^LD9N{ueg_7E9A(1De&3
zm;)bOEU*Jb{#kK>)0MH(&ko(v3tYB91oMtDqHk?&LIBbG(Rz}95^`%ON@B>d#XE`r
zqA>!6dTl!KPAyFTvnxv9)<@lu(EFa@Ue$i~H?6%DN|#`6tcG+Zig!)DDlk7OYTGAj
zW>(M7@t5Zx#;h1srHo6Rt*NTZb31aSgH&5>F+V;H1%_oe@qeAg^OLXVi9)y&lLA~7
zNJ(fpg^@Y(oVLiENLvzzKhl8)@$am~CQgSM!q{%pz%CVpm7p&p9Dn|Rpgqwlw)W%I
z&m#(_%btOX8xP4;d`EhOW~xzabI02Y?H#u{hp%n*?O~?JFBazE`5L*M%qBjo*`&;`
z{YyYkcZ4n=Mgfv?j%uOHZKJ8HT~?wn9WK$hzJ9$2h_jg<OwUGD>&8q6skJ|BJ}pGq
zbtWuI+cDn{W?NhTQ&yLE1x*zZvGF0VG0Rg#-d*{8j{qhh)LE)DA+dA!pQvq*E4@BZ
zlZMd@-0+2&8Lr(bJ&B-;;lWRx`0`-9j*GoZW##1rA)bfL4ZkVe`))o!S0?Mp983Q7
zubZVC;EN2Bo$5yU*v2K9{s8ovoHaujSg}MF$Q`{e+sCNP*Cv@x@&%d(F0pNa65+p*
z1LwFinz$mX{fWn}ysRyPBb*KH8~UE-B*qbPTjepQYorQq%B1Xt_V?2lzSQB;iwp0w
zGh=e}Ig$)D_8XtZ7TofJ417?wZ4I7hE}pKXOyy?@GiN`Zf%o4;Z!#<>M*O>`;a%4E
z{S<cgAKle_7)q_Ln7`YkF1+Y98WQQ?VA-<lsVgP%SE{>@rAJyBS7fc!?brq_x$zvw
zyIvK&=-tQi6UDQm``12=j=x&ok_fm*GG>D!*JF}tE%J}ePw&!+n$H-QCyh+h*7qmU
zS6vH|LzS&1ute>IwvanMohI8<&wv;AI8ClPWm&!cI?B3;@Z4&9(Ci<SBcFFBf6Z^_
zh=!lV*RvNLi1{!sWIG@yN7?kgD3!c8zVhI?-<{!EM?>np0=pa5@yjJ0$Zw&CRJ7m?
zOXS;;3}m-}W#?=7GvN|mkKvdzngwYVkE46YyjGU?S9SFz1`^zw{-Dg2qA#x>Et!6w
zkEYuI>+LseKDPE$r3bl7XMu;3?yk0BuWjKE=88f6{3{%idSqV5(?{Kzf1KJnHP#Tf
z(a-!`&@Ai4odE^e1rs6fk|vFA^^KH2dw>sy%vw_rLLOA?05gb5p!u1kkW=N$&FO8!
z!uMb4P{T%|I*5p`gc<T<{H5zfCE-#F4Zl)=NRL<{?~l4deGXr8y45YUnG$i_!1@|f
z8(PgWS9h;ltej8`&?YmopliFXxCX)--bgmt2d%`W#K{;`|I)W&Zb@;K&b{;7do`)t
zH}SBdrg#0zvCCM~7fgxQs<qf-R|TE#!tIh)^R}&mDU!f(OqAugfZ*A|o)7r>lJhkh
zB+F~%`c0DF8#7@(1N|HTcd)(KwwMEgvi5Mlr^c#>ebt4|<(tQlkp@^4A9A`$wLwPT
zkFfnkac{e(KF|LpzhI{Ny84Lom6u3bz{p&+es<kKO}36@{miGEo3j4fuFbX_%4c!E
zHhF6WMfT^x%|v1pm4Jnd;A2e!E=|s3WMiGNM3ER>81S^d4fs4c>!<!96tL}}&@32-
zRvnX_mCHjGr28;meXAX3?A<#EDP=p1Aur7YOe5G*5}UtBaxo$$^Gv9_Z8%g7gR$<R
z_?N0Z@A^}@?l7xK>tZVjzE{(ma4V@|`no};fprktC#y-<F?;Zz^V=;=a^gCb1DEk4
z>o$R~F9@58UYhG<d3``neNdJ7Zi1%pqTWB-UiaUekAFqXPOkq_Q>pVxrCoMdu-C|S
zw#a$umj!sGRuG#W!E@5+QRAFzna`-3K=iRxK0Qc9tI;Vr3;FHJXk0nP@AdVd{E#7<
z=$v>$bVB+Ef_Ino^j7AYiv?H4X;C8)UzX|pW*pu5-&yl_DVtw^01uDh{AX)^ea^gp
zRc2Kl&w527You%V^VocAS*ddF>42~rAqU28^24Jl7HywrUaZ8VNE7Z1331j-n9kWa
z{sJh-wTSu6r0ZWceuj`OstO@SSW*02z2<!cmaTtv@+pjc?-EE7yUC}LUp1Zd(%lcL
ztG8O*{%<L~Z^br1To)r<aZs;*FrP0NAs5nv4d>eQzFzD-fQ$+o)BUbiaE&6&AMKsg
z4@DZ+S<+u|ED)$rdrsuj!!(YeciT5lwJ;y`+vfprvy3HP-iPBD8O8|}0Uot~c0AVT
z|KEzuKqsFi7l|D-Ts^Qy8$PNa4!oH3QJ8$9A&lbf3)yJ4WuNG&W@A@4YIyTzyh;11
z@*0WV0^*sbr!bYKCUs%_E}x;m<;%V(L)YxuW`n8Wk2-f8bDfU#Dr6$HLK@@Wy=qE)
ztL-{(`|qlwlVaxg2cMVQXQbxewZy$UdSkrlDRGw}(+_!+lN!K43NNv(OVaFvANV1V
z)xiSpMkK{hYD}GKMWkEzq?V2YpYz#l%{XyQ(?<&azAl3ds603udvLS2lprPeuVl`H
zi5;}y=Xy>uC)H7oC{HsY0a+k@BJj$Eiaw1i>aL+%3?fSE9&cIO@u7Wh>Z15p>$R-d
zK#&C1)`N$&0n`pUAE|7FtuScs)L_}oO&wuW+wj$f1CL1Oh2OtTeJsTp-e46bi8~~-
zq}0!A8}N9LnA*<azq{jdt|^+oaTdwndrdIcqGS#$;&ZIT!vCC*VNAJdBCl{(HAw$m
z^ARbMu{vH|9Wk5Pr!=QlKs|CcaX&KTZmCnG(}L53^h+Y@<|t0`mO$|-G$Ji{X>5@w
zB~d?MtZV>KRm-q-VdR)PuAwYYjw0DnEKZaXh^^*B?@Ixapl?8_=KG)m&sec$(rZ3E
z*B6UpS0SuLg@7p|Islrzb;V~BS-UuRbk#~+RYX{n37ajVcQM4s9JoAGpE@4&wy+1G
zqLO03+iny(jw4F`Zy2MC0>HFjP;&D3VT>M`twu~BCPq4q%psoM>?euKNaz9eY=?E;
z2ct1vw@(I|R(p0gZF=Jt%}kcCi3RIVUK2c~+IpG0=Xyrju2I+4NIC5cM8f)}<ES{0
zF~k1Z_6NYlUijDURMr?D;F<JFY18em<zFT|YKw2BqlII9{Gxxa$U4N?J3Zae&OgX~
zk;AxeRwWY)<6M2QL2=u7z)9V6QQol%sB&`5jKuS{haed!$*LwrXX(gKmh25SbO~`G
z_JM~RMP-C^Za$E%1-xxeniAR6+76lvR>hz>gMZox*(gD@tW7C2##u-Nc=IyMWwB$(
z&gX$%AUs(N8C0qvs)!sbSsV(9G*w~iS*aoi=5$%Z6W1grnbZou*8`IE8V2^7USWdP
zL;Z1ilg!@<6qse$pZg5-r|Saqmf|TK<u-leRrxI?S`tqJm`UO~%_i};bL_2?kI3VK
z16RXCvzOdAT5t+x=#66ku9skym~Ob}{EoLGZp`W){*^vh4$aIbVb9p2Q^C@>N+JxS
zBqldux{+AgRQSvvp24H~H3lsj`ip3@RxTbgCw+SJC2{=8m`a9Q4_Wm(2TkW@<%44&
z!FvtQPxp*>yEZ%P@|CoH#|BDwh<7i48T|Hc*5A#3;!yfYOq`6>I97iF`R6Vw^KQz$
zw68TT<4hA;rk}H9PG`Jhbet*Y5d-TK-m2r6ww+}G20!83muwE0uWIIn6+ZBuAbzTn
z`xTVfdag9q$z*pYtCIAZ2Eoo<^-ee0Iau9Hbftn~E(6N<n5QMj-yV@MtQ+4xcQ4o_
zEa58V&~)v-&rH1I5$|{i**s*+QJww)Pg8<wI^frppu)8VJ7_-9eeiZ1WH&*VXD_8)
zE#SBC3XU2t9b8wzRcqupEaNo9V+$AaeOXWyXU2&Vr(kR8gYWUa*!qn|3qs$1<+6Vr
zSHoRS(*aPHkO_lW%Yqbsztx&l8(nN0oqD<$KTrc|uRt{QzfdKJqvt+IA^|ohGl%tL
z1kxFhHVg)=^7RHG*F><Q-8USS&%e5NGHY((yT}21;4-pljKdYO@h*wmRr|Edhc}g;
zUo<(lRd=MKL;syea*_f)$MWWx0~YgEO{N=#+U{1i<;;Sm=bb}A&HD|e1~BkP)Y1`K
zkMZQd%C&EAJR(AxYe_mhWnNK)F(j*pf8D0?1r69?5}k9v;_3o^!|0(~6i+@)9v-wL
zGv!+XR}K)YT%q#W$)bE!&9vML1uf`Un3=sdH_W;7(?#k^wXqUZ%V7@93oR2V2G|Ux
zwOP(AmOm**1FobtVBWO?ln_E(SXUA=cxaxUV3vhW<w(k?z#F7~ML~S7Xa`|ySa~FU
zJ?kMp!wr?H_wpgG79Q@(K&5{}iwFvWrg_-8LW&e(;(~9CD6z<9CwoXq9Ms6Av5L2^
zN!zfSKb*Qv{N@a)7YLh*6y=X|hARBQH``9WBU#r8mzjWP$#pDcK$SWLX5Xh>jPN-i
zhLU+zO0{LhXknLq(qS7!y{j(-)8LePFS#&Td<}=LJQN=WV_&JuRqJ!H2lP7d`F(0<
zf&Xi;{61I_n8m;>hk^X`7lP%3`y-dNsoB`cE_o76FgqxTw&eqD-0VgOc^OygW16+O
zU%~lQin8da<_+~)QSNuJ@4<<3lq?tQ3y(-$iOJb`f2;b_20ZWqxOVmu2FKt`Wi!@;
zn`h?LDCH1g4`8)f%2DIFE0=l_ot^a;>_xKKOq>9yqql{`r}q$)ukI^Cc9CZtrB~ZL
zryOL<S&D%)1kFLj7?J3SP-A%R9+EkAf~fA!M&iJo;f5e3=;JJSV2v;laeLUUZla#d
z>26fqE_AdH5u)#58{2NgU62>taqs&L7-fZ?D{M|X_68br+@`C@+|S+lF756HL+|H#
zutr0iunngA<UqM|_q9V^R7k8Ip2yy;>9g4azxm>tln=<<3~4ylyob!4iX^qVtdqGj
zg)buAI|4CU`b~$M9wZFgsXz9{TMna}*|eXCZF(?G2<oiJ7aW9+dJ<rs1=l12uA-=A
zbSBTml_J74inB{&NIZcJpEz_CX}6RzcmQv|CGU1Nty(Lwc-(D0RmH`r^R`e`aa-TW
z%{Tb^vwrrPRtQhhiLh8r5f4_>1GwBo@Dv03?|{@7_=W+_ClnYABMUA?5L2{9(|+;_
z-2V+B25CxhXiK#hxNgK<#^E$M9wfI=tr-~Mt`;UBh;d_Np65`&HRAUga-I~McZ7Lc
zwp-YSaXHyB3OS(nC0qnq=_K2fLxSg6d6WjmY?pxW3c!MzoJTnq&yy8o3Rfzp)bFk-
zz4!qb0R8##{lzoCDAt)l7`laeus)-pdu+BstYRpLoFWJ63tJ|Dvdk-{X7FNIp+-^8
zPv&WW*<V^Kr9#Vppjq#ZC_8s;5M|~PoeSQ<01({NR$$=`7X8OJ$Kk_1L{+6L!d75*
z5~AdoTjNwfW;fGvNWcCZ!Ry>fD7b@y2(_RNL}f-<bfR274{Z=-GvcRfmHP4l%Jt&-
zWMZ(Ol*UZ$0U2x6<vUsGoG8dPG?AA~x**3hO9|>_0A}uGmT>?M>G;tK`zRT`LX=<=
zBVVZR0)a=+rcDh!O(5y?*2ue^QE@Umg!MU1VPh|}P0ba|-yvNv`i@B)OIPe@w++a>
zzEO`vMTVTB_;y|D64mxlq;b11?bpbH+NQ3bJfzmoBk0edoh-nUxcv+w=Z~5H0Qn&t
A1ONa4

literal 0
HcmV?d00001

diff --git a/modules/control/proc/marigold/util/ensemble.py b/modules/control/proc/marigold/util/ensemble.py
index ae254329d..d7a9023ca 100644
--- a/modules/control/proc/marigold/util/ensemble.py
+++ b/modules/control/proc/marigold/util/ensemble.py
@@ -74,7 +74,7 @@ def ensemble_depths(
 
     # objective function
     def closure(x):
-        l = len(x) # noqa
+        l = len(x)
         s = x[: int(l / 2)]
         t = x[int(l / 2) :]
         s = torch.from_numpy(s).to(dtype=dtype).to(device)
@@ -102,7 +102,7 @@ def ensemble_depths(
         closure, x, method="BFGS", tol=tol, options={"maxiter": max_iter, "disp": False}
     )
     x = res.x
-    l = len(x) # noqa
+    l = len(x)
     s = x[: int(l / 2)]
     t = x[int(l / 2) :]
 
diff --git a/modules/face/instantid_model.py b/modules/face/instantid_model.py
index 5ae70b1fa..7f70aff98 100644
--- a/modules/face/instantid_model.py
+++ b/modules/face/instantid_model.py
@@ -94,7 +94,7 @@ class PerceiverAttention(nn.Module):
         x = self.norm1(x)
         latents = self.norm2(latents)
 
-        b, l, _ = latents.shape # noqa:E741
+        b, l, _ = latents.shape
 
         q = self.to_q(latents)
         kv_input = torch.cat((x, latents), dim=-2)
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index b7ab23a2a..59fb9be9a 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -158,7 +158,9 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
         if hasattr(model, "set_progress_bar_config"):
             model.set_progress_bar_config(bar_format='Progress {rate_fmt}{postfix} {bar} {percentage:3.0f}% {n_fmt}/{total_fmt} {elapsed} {remaining} ' + '\x1b[38;5;71m' + desc, ncols=80, colour='#327fba')
         args = {}
-        signature = inspect.signature(type(model).__call__)
+        if hasattr(model, 'pipe'): # recurse
+            model = model.pipe
+        signature = inspect.signature(type(model).__call__, follow_wrapped=True)
         possible = signature.parameters.keys()
         debug(f'Diffusers pipeline possible: {possible}')
         if shared.opts.diffusers_generator_device == "Unset":
@@ -201,14 +203,15 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
         if 'generator' in possible and generator is not None:
             args['generator'] = generator
         if 'output_type' in possible:
-            args['output_type'] = 'np'
+            if hasattr(model, 'vae'):
+                args['output_type'] = 'np' # only set latent if model has vae
         if 'callback_steps' in possible:
             args['callback_steps'] = 1
         if 'callback' in possible:
             args['callback'] = diffusers_callback_legacy
         elif 'callback_on_step_end_tensor_inputs' in possible:
             args['callback_on_step_end'] = diffusers_callback
-            if 'prompt_embeds' in possible and 'negative_prompt_embeds' in possible:
+            if 'prompt_embeds' in possible and 'negative_prompt_embeds' in possible and hasattr(model, '_callback_tensor_inputs'):
                 args['callback_on_step_end_tensor_inputs'] = model._callback_tensor_inputs # pylint: disable=protected-access
             else:
                 args['callback_on_step_end_tensor_inputs'] = ['latents']
@@ -405,7 +408,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
         desc='Base',
     )
     update_sampler(shared.sd_model)
-    shared.state.sampling_steps = base_args['num_inference_steps']
+    shared.state.sampling_steps = base_args.get('num_inference_steps', p.steps)
     p.extra_generation_params['Pipeline'] = shared.sd_model.__class__.__name__
     if shared.opts.scheduler_eta is not None and shared.opts.scheduler_eta > 0 and shared.opts.scheduler_eta < 1:
         p.extra_generation_params["Sampler Eta"] = shared.opts.scheduler_eta
diff --git a/modules/sd_models.py b/modules/sd_models.py
index fc1abca30..64679973e 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -586,6 +586,10 @@ def detect_pipeline(f: str, op: str = 'model', warning=True):
                 if shared.backend == shared.Backend.ORIGINAL:
                     warn(f'Model detected as InstaFlow model, but attempting to load using backend=original: {op}={f} size={size} MB')
                 guess = 'InstaFlow'
+            if 'SegMoE' in f:
+                if shared.backend == shared.Backend.ORIGINAL:
+                    warn(f'Model detected as SegMoE model, but attempting to load using backend=original: {op}={f} size={size} MB')
+                guess = 'SegMoE'
             if 'PixArt' in f:
                 if shared.backend == shared.Backend.ORIGINAL:
                     warn(f'Model detected as PixArt Alpha model, but attempting to load using backend=original: {op}={f} size={size} MB')
@@ -794,6 +798,14 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
                 except Exception as e:
                     shared.log.error(f'Diffusers Failed loading {op}: {checkpoint_info.path} {e}')
                     return
+            if model_type in ['SegMoE']: # forced pipeline
+                try:
+                    from modules.segmoe.segmoe_model import SegMoEPipeline
+                    sd_model = SegMoEPipeline(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
+                    sd_model = sd_model.pipe # segmoe pipe does its stuff in __init__ and __call__ is the original pipeline
+                except Exception as e:
+                    shared.log.error(f'Diffusers Failed loading {op}: {checkpoint_info.path} {e}')
+                    return
             elif 'ONNX' in model_type: # forced pipeline
                 sd_model = pipeline.from_pretrained(checkpoint_info.path)
             else:
diff --git a/modules/segmoe/segmoe_model.py b/modules/segmoe/segmoe_model.py
new file mode 100644
index 000000000..3dd5a8cb1
--- /dev/null
+++ b/modules/segmoe/segmoe_model.py
@@ -0,0 +1,1315 @@
+import gc
+from collections import OrderedDict
+from typing import Any, Dict, Callable
+import os
+from copy import deepcopy
+from math import ceil
+import json
+import safetensors
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from diffusers import (
+    DiffusionPipeline,
+    StableDiffusionPipeline,
+    StableDiffusionXLPipeline,
+    DDPMScheduler,
+    UNet2DConditionModel,
+)
+import tqdm
+import yaml
+
+
+def remove_all_forward_hooks(model: torch.nn.Module) -> None:
+    for _name, child in model._modules.items(): # pylint: disable=protected-access
+        if child is not None:
+            if hasattr(child, "_forward_hooks"):
+                child._forward_hooks: Dict[int, Callable] = OrderedDict()
+            remove_all_forward_hooks(child)
+
+
+# Inspired from transformers.models.mixtral.modeling_mixtral.MixtralSparseMoeBlock
+class SparseMoeBlock(nn.Module):
+    def __init__(self, config, experts):
+        super().__init__()
+        self.hidden_dim = config["hidden_size"]
+        self.num_experts = config["num_local_experts"]
+        self.top_k = config["num_experts_per_tok"]
+        self.out_dim = config.get("out_dim", self.hidden_dim)
+
+        # gating
+        self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
+        self.experts = nn.ModuleList([deepcopy(exp) for exp in experts])
+
+    def forward(self, hidden_states: torch.Tensor, scale=None) -> torch.Tensor: # pylint: disable=unused-argument
+        batch_size, sequence_length, f_map_sz = hidden_states.shape
+        hidden_states = hidden_states.view(-1, f_map_sz)
+        # router_logits: (batch * sequence_length, n_experts)
+        router_logits = self.gate(hidden_states)
+        _, selected_experts = torch.topk(
+            router_logits.sum(dim=0, keepdim=True), self.top_k, dim=1
+        )
+        routing_weights = F.softmax(
+            router_logits[:, selected_experts[0]], dim=1, dtype=torch.float
+        )
+
+        # we cast back to the input dtype
+        routing_weights = routing_weights.to(hidden_states.dtype)
+
+        final_hidden_states = torch.zeros(
+            (batch_size * sequence_length, self.out_dim),
+            dtype=hidden_states.dtype,
+            device=hidden_states.device,
+        )
+
+        # Loop over all available experts in the model and perform the computation on each expert
+        for i, expert_idx in enumerate(selected_experts[0].tolist()):
+            expert_layer = self.experts[expert_idx]
+
+            current_hidden_states = routing_weights[:, i].view(
+                batch_size * sequence_length, -1
+            ) * expert_layer(hidden_states)
+
+            # However `index_add_` only support torch tensors for indexing so we'll use
+            # the `top_x` tensor here.
+            final_hidden_states = final_hidden_states + current_hidden_states
+        final_hidden_states = final_hidden_states.reshape(
+            batch_size, sequence_length, self.out_dim
+        )
+        return final_hidden_states
+
+
+def getActivation(activation, name):
+    def hook(model, inp, output): # pylint: disable=unused-argument
+        activation[name] = inp
+
+    return hook
+
+
+class SegMoEPipeline:
+    def __init__(self, config_or_path, **kwargs) -> Any:
+        """
+        Instantiates the SegMoEPipeline. SegMoEPipeline implements the Segmind Mixture of Diffusion Experts, efficiently combining Stable Diffusion and Stable Diffusion Xl models.
+
+        Usage:
+
+        from segmoe import SegMoEPipeline
+        pipeline = SegMoEPipeline(config_or_path, **kwargs)
+
+        config_or_path: Path to Config or Directory containing SegMoE checkpoint or HF Card of SegMoE Checkpoint.
+
+        Other Keyword Arguments:
+        torch_dtype: Data Type to load the pipeline in. (Default: torch.float16)
+        variant: Variant of the Model. (Default: fp16)
+        device: Device to load the model on. (Default: cuda)
+        Other args supported by diffusers.DiffusionPipeline are also supported.
+
+        For more details visit https://github.com/segmind/segmoe.
+        """
+        self.torch_dtype = kwargs.pop("torch_dtype", torch.float16)
+        self.use_safetensors = kwargs.pop("use_safetensors", True)
+        self.variant = kwargs.pop("variant", "fp16")
+        self.device = kwargs.pop("device", "cuda")
+        if os.path.isfile(config_or_path):
+            self.load_from_scratch(config_or_path, **kwargs)
+        else:
+            if not os.path.isdir(config_or_path):
+                cached_folder = DiffusionPipeline.download(config_or_path)
+            else:
+                cached_folder = config_or_path
+            unet = self.create_empty(cached_folder)
+            unet.load_state_dict(
+                safetensors.torch.load_file(
+                    f"{cached_folder}/unet/diffusion_pytorch_model.safetensors"
+                )
+            )
+            self.pipe = DiffusionPipeline.from_pretrained(
+                cached_folder,
+                unet=unet,
+                torch_dtype=self.torch_dtype,
+                use_safetensors=self.use_safetensors,
+            )
+            self.pipe.to(self.device)
+            self.pipe.unet.to(
+                device=self.device,
+                dtype=self.torch_dtype,
+                memory_format=torch.channels_last,
+            )
+
+    def to(self, *args, **kwargs): # TODO added no-op to avoid error
+        self.pipe.to(*args, **kwargs)
+
+    def load_from_scratch(self, config: str, **kwargs) -> None:
+        # Load Config
+        with open(config, "r", encoding='utf8') as f:
+            config = yaml.load(f, Loader=yaml.SafeLoader)
+        self.config = config
+        if self.config.get("num_experts", None):
+            self.num_experts = self.config["num_experts"]
+        else:
+            if self.config.get("experts", None):
+                self.num_experts = len(self.config["experts"])
+            else:
+                if self.config.get("loras", None):
+                    self.num_experts = len(self.config["loras"])
+                else:
+                    self.num_experts = 1
+        num_experts_per_tok = self.config.get("num_experts_per_tok", 1)
+        self.config["num_experts_per_tok"] = num_experts_per_tok
+        moe_layers = self.config.get("moe_layers", "attn")
+        self.config["moe_layers"] = moe_layers
+        # Load Base Model
+        if self.config["base_model"].startswith(
+            "https://civitai.com/api/download/models/"
+        ):
+            os.makedirs("base", exist_ok=True)
+            if not os.path.isfile("base/model.safetensors"):
+                os.system(
+                    "wget -O "
+                    + "base/model.safetensors"
+                    + self.config["base_model"]
+                    + " --content-disposition"
+                )
+            self.config["base_model"] = "base/model.safetensors"
+            self.pipe = DiffusionPipeline.from_single_file(
+                self.config["base_model"], torch_dtype=self.torch_dtype
+            )
+        else:
+            try:
+                self.pipe = DiffusionPipeline.from_pretrained(
+                    self.config["base_model"],
+                    torch_dtype=self.torch_dtype,
+                    use_safetensors=self.use_safetensors,
+                    variant=self.variant,
+                    **kwargs,
+                )
+            except Exception:
+                self.pipe = DiffusionPipeline.from_pretrained(
+                    self.config["base_model"], torch_dtype=self.torch_dtype, **kwargs
+                )
+        if self.pipe.__class__ == StableDiffusionPipeline:
+            self.up_idx_start = 1
+            self.up_idx_end = len(self.pipe.unet.up_blocks)
+            self.down_idx_start = 0
+            self.down_idx_end = len(self.pipe.unet.down_blocks) - 1
+        elif self.pipe.__class__ == StableDiffusionXLPipeline:
+            self.up_idx_start = 0
+            self.up_idx_end = len(self.pipe.unet.up_blocks) - 1
+            self.down_idx_start = 1
+            self.down_idx_end = len(self.pipe.unet.down_blocks)
+        self.config["up_idx_start"] = self.up_idx_start
+        self.config["up_idx_end"] = self.up_idx_end
+        self.config["down_idx_start"] = self.down_idx_start
+        self.config["down_idx_end"] = self.down_idx_end
+
+        # TODO: Add Support for Scheduler Selection
+        self.pipe.scheduler = DDPMScheduler.from_config(self.pipe.scheduler.config)
+
+        # Load Experts
+        experts = []
+        positive = []
+        negative = []
+        if self.config.get("experts", None):
+            for i, exp in enumerate(self.config["experts"]):
+                positive.append(exp["positive_prompt"])
+                negative.append(exp["negative_prompt"])
+                if exp["source_model"].startswith(
+                    "https://civitai.com/api/download/models/"
+                ):
+                    try:
+                        if not os.path.isfile(f"expert_{i}/model.safetensors"):
+                            os.makedirs(f"expert_{i}", exist_ok=True)
+                            if not os.path.isfile(f"expert_{i}/model.safetensors"):
+                                os.system(
+                                    f"wget {exp['source_model']} -O "
+                                    + f"expert_{i}/model.safetensors"
+                                    + " --content-disposition"
+                                )
+                        exp["source_model"] = f"expert_{i}/model.safetensors"
+                        expert = DiffusionPipeline.from_single_file(
+                            exp["source_model"],
+                        ).to(self.device, self.torch_dtype)
+                    except Exception as e:
+                        print(f"Expert {i} {exp['source_model']} failed to load")
+                        print("Error:", e)
+                else:
+                    try:
+                        expert = DiffusionPipeline.from_pretrained(
+                            exp["source_model"],
+                            torch_dtype=self.torch_dtype,
+                            use_safetensors=self.use_safetensors,
+                            variant=self.variant,
+                            **kwargs,
+                        )
+
+                        # TODO: Add Support for Scheduler Selection
+                        expert.scheduler = DDPMScheduler.from_config(
+                            expert.scheduler.config
+                        )
+                    except Exception:
+                        expert = DiffusionPipeline.from_pretrained(
+                            exp["source_model"], torch_dtype=self.torch_dtype, **kwargs
+                        )
+                        expert.scheduler = DDPMScheduler.from_config(
+                            expert.scheduler.config
+                        )
+                if exp.get("loras", None):
+                    for j, lora in enumerate(exp["loras"]):
+                        if lora.get("positive_prompt", None):
+                            positive[-1] += " " + lora["positive_prompt"]
+                        if lora.get("negative_prompt", None):
+                            negative[-1] += " " + lora["negative_prompt"]
+                        if lora["source_model"].startswith(
+                            "https://civitai.com/api/download/models/"
+                        ):
+                            try:
+                                os.makedirs(f"expert_{i}/lora_{i}", exist_ok=True)
+                                if not os.path.isfile(
+                                    f"expert_{i}/lora_{i}/pytorch_lora_weights.safetensors"
+                                ):
+                                    os.system(
+                                        f"wget {lora['source_model']} -O "
+                                        + f"expert_{i}/lora_{j}/pytorch_lora_weights.safetensors"
+                                        + " --content-disposition"
+                                    )
+                                lora["source_model"] = f"expert_{j}/lora_{j}"
+                                expert.load_lora_weights(lora["source_model"])
+                                if len(exp["loras"]) == 1:
+                                    expert.fuse_lora()
+                            except Exception as e:
+                                print(
+                                    f"Expert{i} LoRA {j} {lora['source_model']} failed to load"
+                                )
+                                print("Error:", e)
+                        else:
+                            expert.load_lora_weights(lora["source_model"])
+                            if len(exp["loras"]) == 1:
+                                expert.fuse_lora()
+                experts.append(expert)
+        else:
+            experts = [deepcopy(self.pipe) for _ in range(self.num_experts)]
+        if self.config.get("experts", None):
+            if self.config.get("loras", None):
+                for i, lora in enumerate(self.config["loras"]):
+                    if lora["source_model"].startswith(
+                        "https://civitai.com/api/download/models/"
+                    ):
+                        try:
+                            os.makedirs(f"lora_{i}", exist_ok=True)
+                            if not os.path.isfile(
+                                f"lora_{i}/pytorch_lora_weights.safetensors"
+                            ):
+                                os.system(
+                                    f"wget {lora['source_model']} -O "
+                                    + f"lora_{i}/pytorch_lora_weights.safetensors"
+                                    + " --content-disposition"
+                                )
+                            lora["source_model"] = f"lora_{i}"
+                            self.pipe.load_lora_weights(lora["source_model"])
+                            if len(self.config["loras"]) == 1:
+                                self.pipe.fuse_lora()
+                        except Exception as e:
+                            print(f"LoRA {i} {lora['source_model']} failed to load")
+                            print("Error:", e)
+                    else:
+                        self.pipe.load_lora_weights(lora["source_model"])
+                        if len(self.config["loras"]) == 1:
+                            self.pipe.fuse_lora()
+        else:
+            if self.config.get("loras", None):
+                j = []
+                n_loras = len(self.config["loras"])
+                i = 0
+                positive = [""] * len(experts)
+                negative = [""] * len(experts)
+                while n_loras:
+                    n = ceil(n_loras / len(experts))
+                    j += [i] * n
+                    n_loras -= n
+                    i += 1
+                for i, lora in enumerate(self.config["loras"]):
+                    positive[j[i]] += lora["positive_prompt"] + " "
+                    negative[j[i]] += lora["negative_prompt"] + " "
+                    if lora["source_model"].startswith(
+                        "https://civitai.com/api/download/models/"
+                    ):
+                        try:
+                            os.makedirs(f"lora_{i}", exist_ok=True)
+                            if not os.path.isfile(
+                                f"lora_{i}/pytorch_lora_weights.safetensors"
+                            ):
+                                os.system(
+                                    f"wget {lora['source_model']} -O "
+                                    + f"lora_{i}/pytorch_lora_weights.safetensors"
+                                    + " --content-disposition"
+                                )
+                            lora["source_model"] = f"lora_{i}"
+                            experts[j[i]].load_lora_weights(lora["source_model"])
+                            experts[j[i]].fuse_lora()
+                        except Exception:
+                            print(f"LoRA {i} {lora['source_model']} failed to load")
+                    else:
+                        experts[j[i]].load_lora_weights(lora["source_model"])
+                        experts[j[i]].fuse_lora()
+
+        # Replace FF and Attention Layers with Sparse MoE Layers
+        for i in range(self.down_idx_start, self.down_idx_end):
+            for j in range(len(self.pipe.unet.down_blocks[i].attentions)):
+                for k in range(
+                    len(self.pipe.unet.down_blocks[i].attentions[j].transformer_blocks)
+                ):
+                    if not moe_layers == "attn":
+                        config = {
+                            "hidden_size": next(
+                                self.pipe.unet.down_blocks[i]
+                                .attentions[j]
+                                .transformer_blocks[k]
+                                .ff.parameters()
+                            ).size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": len(experts),
+                        }
+                        # FF Layers
+                        layers = []
+                        for l in range(len(experts)):
+                            layers.append(
+                                deepcopy(
+                                    experts[l]
+                                    .unet.down_blocks[i]
+                                    .attentions[j]
+                                    .transformer_blocks[k]
+                                    .ff
+                                )
+                            )
+                        self.pipe.unet.down_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].ff = SparseMoeBlock(config, layers)
+                    if not moe_layers == "ff":
+                        ## Attns
+                        config = {
+                            "hidden_size": self.pipe.unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn1.to_q.weight.size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": self.num_experts,
+                        }
+                        layers = []
+                        for l in range(len(experts)):
+                            layers.append(
+                                deepcopy(
+                                    experts[l]
+                                    .unet.down_blocks[i]
+                                    .attentions[j]
+                                    .transformer_blocks[k]
+                                    .attn1.to_q
+                                )
+                            )
+                        self.pipe.unet.down_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn1.to_q = SparseMoeBlock(config, layers)
+
+                        layers = []
+                        for l in range(len(experts)):
+                            layers.append(
+                                deepcopy(
+                                    experts[l]
+                                    .unet.down_blocks[i]
+                                    .attentions[j]
+                                    .transformer_blocks[k]
+                                    .attn1.to_k
+                                )
+                            )
+                        self.pipe.unet.down_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn1.to_k = SparseMoeBlock(config, layers)
+
+                        layers = []
+                        for l in range(len(experts)):
+                            layers.append(
+                                deepcopy(
+                                    experts[l]
+                                    .unet.down_blocks[i]
+                                    .attentions[j]
+                                    .transformer_blocks[k]
+                                    .attn1.to_v
+                                )
+                            )
+                        self.pipe.unet.down_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn1.to_v = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": self.pipe.unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_q.weight.size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": len(experts),
+                        }
+
+                        layers = []
+                        for l in range(len(experts)):
+                            layers.append(
+                                deepcopy(
+                                    experts[l]
+                                    .unet.down_blocks[i]
+                                    .attentions[j]
+                                    .transformer_blocks[k]
+                                    .attn2.to_q
+                                )
+                            )
+                        self.pipe.unet.down_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn2.to_q = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": self.pipe.unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_k.weight.size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": len(experts),
+                            "out_dim": self.pipe.unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_k.weight.size()[0],
+                        }
+                        layers = []
+                        for l in range(len(experts)):
+                            layers.append(
+                                deepcopy(
+                                    experts[l]
+                                    .unet.down_blocks[i]
+                                    .attentions[j]
+                                    .transformer_blocks[k]
+                                    .attn2.to_k
+                                )
+                            )
+                        self.pipe.unet.down_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn2.to_k = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": self.pipe.unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_v.weight.size()[-1],
+                            "out_dim": self.pipe.unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_v.weight.size()[0],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": len(experts),
+                        }
+                        layers = []
+                        for l in range(len(experts)):
+                            layers.append(
+                                deepcopy(
+                                    experts[l]
+                                    .unet.down_blocks[i]
+                                    .attentions[j]
+                                    .transformer_blocks[k]
+                                    .attn2.to_v
+                                )
+                            )
+                        self.pipe.unet.down_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn2.to_v = SparseMoeBlock(config, layers)
+
+        for i in range(self.up_idx_start, self.up_idx_end):
+            for j in range(len(self.pipe.unet.up_blocks[i].attentions)):
+                for k in range(
+                    len(self.pipe.unet.up_blocks[i].attentions[j].transformer_blocks)
+                ):
+                    if not moe_layers == "attn":
+                        config = {
+                            "hidden_size": next(
+                                self.pipe.unet.up_blocks[i]
+                                .attentions[j]
+                                .transformer_blocks[k]
+                                .ff.parameters()
+                            ).size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": len(experts),
+                        }
+                        # FF Layers
+                        layers = []
+                        for l in range(len(experts)):
+                            layers.append(
+                                deepcopy(
+                                    experts[l]
+                                    .unet.up_blocks[i]
+                                    .attentions[j]
+                                    .transformer_blocks[k]
+                                    .ff
+                                )
+                            )
+                        self.pipe.unet.up_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].ff = SparseMoeBlock(config, layers)
+
+                    if not moe_layers == "ff":
+                        # Attns
+                        config = {
+                            "hidden_size": self.pipe.unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn1.to_q.weight.size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": len(experts),
+                        }
+
+                        layers = []
+                        for l in range(len(experts)):
+                            layers.append(
+                                deepcopy(
+                                    experts[l]
+                                    .unet.up_blocks[i]
+                                    .attentions[j]
+                                    .transformer_blocks[k]
+                                    .attn1.to_q
+                                )
+                            )
+
+                        self.pipe.unet.up_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn1.to_q = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": self.pipe.unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn1.to_k.weight.size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": len(experts),
+                        }
+                        layers = []
+
+                        for l in range(len(experts)):
+                            layers.append(
+                                deepcopy(
+                                    experts[l]
+                                    .unet.up_blocks[i]
+                                    .attentions[j]
+                                    .transformer_blocks[k]
+                                    .attn1.to_k
+                                )
+                            )
+
+                        self.pipe.unet.up_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn1.to_k = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": self.pipe.unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn1.to_v.weight.size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": len(experts),
+                        }
+                        layers = []
+
+                        for l in range(len(experts)):
+                            layers.append(
+                                deepcopy(
+                                    experts[l]
+                                    .unet.up_blocks[i]
+                                    .attentions[j]
+                                    .transformer_blocks[k]
+                                    .attn1.to_v
+                                )
+                            )
+
+                        self.pipe.unet.up_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn1.to_v = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": self.pipe.unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_q.weight.size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": len(experts),
+                        }
+                        layers = []
+
+                        for l in range(len(experts)):
+                            layers.append(
+                                deepcopy(
+                                    experts[l]
+                                    .unet.up_blocks[i]
+                                    .attentions[j]
+                                    .transformer_blocks[k]
+                                    .attn2.to_q
+                                )
+                            )
+
+                        self.pipe.unet.up_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn2.to_q = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": self.pipe.unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_k.weight.size()[-1],
+                            "out_dim": self.pipe.unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_k.weight.size()[0],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": len(experts),
+                        }
+
+                        layers = []
+
+                        for l in range(len(experts)):
+                            layers.append(
+                                deepcopy(
+                                    experts[l]
+                                    .unet.up_blocks[i]
+                                    .attentions[j]
+                                    .transformer_blocks[k]
+                                    .attn2.to_k
+                                )
+                            )
+
+                        self.pipe.unet.up_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn2.to_k = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": self.pipe.unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_v.weight.size()[-1],
+                            "out_dim": self.pipe.unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_v.weight.size()[0],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": len(experts),
+                        }
+                        layers = []
+
+                        for l in range(len(experts)):
+                            layers.append(
+                                deepcopy(
+                                    experts[l]
+                                    .unet.up_blocks[i]
+                                    .attentions[j]
+                                    .transformer_blocks[k]
+                                    .attn2.to_v
+                                )
+                            )
+
+                        self.pipe.unet.up_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn2.to_v = SparseMoeBlock(config, layers)
+
+        # Routing Weight Initialization
+        if self.config.get("init", "hidden") == "hidden":
+            gate_params = self.get_gate_params(experts, positive, negative)
+            for i in range(self.down_idx_start, self.down_idx_end):
+                for j in range(len(self.pipe.unet.down_blocks[i].attentions)):
+                    for k in range(
+                        len(
+                            self.pipe.unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks
+                        )
+                    ):
+                        # FF Layers
+                        if not moe_layers == "attn":
+                            self.pipe.unet.down_blocks[i].attentions[
+                                j
+                            ].transformer_blocks[k].ff.gate.weight = nn.Parameter(
+                                gate_params[f"d{i}a{j}t{k}"]
+                            )
+
+                        # Attns
+                        if not moe_layers == "ff":
+                            self.pipe.unet.down_blocks[i].attentions[
+                                j
+                            ].transformer_blocks[
+                                k
+                            ].attn1.to_q.gate.weight = nn.Parameter(
+                                gate_params[f"sattnqd{i}a{j}t{k}"]
+                            )
+
+                            self.pipe.unet.down_blocks[i].attentions[
+                                j
+                            ].transformer_blocks[
+                                k
+                            ].attn1.to_k.gate.weight = nn.Parameter(
+                                gate_params[f"sattnkd{i}a{j}t{k}"]
+                            )
+
+                            self.pipe.unet.down_blocks[i].attentions[
+                                j
+                            ].transformer_blocks[
+                                k
+                            ].attn1.to_v.gate.weight = nn.Parameter(
+                                gate_params[f"sattnvd{i}a{j}t{k}"]
+                            )
+
+                            self.pipe.unet.down_blocks[i].attentions[
+                                j
+                            ].transformer_blocks[
+                                k
+                            ].attn2.to_q.gate.weight = nn.Parameter(
+                                gate_params[f"cattnqd{i}a{j}t{k}"]
+                            )
+
+                            self.pipe.unet.down_blocks[i].attentions[
+                                j
+                            ].transformer_blocks[
+                                k
+                            ].attn2.to_k.gate.weight = nn.Parameter(
+                                gate_params[f"cattnkd{i}a{j}t{k}"]
+                            )
+
+                            self.pipe.unet.down_blocks[i].attentions[
+                                j
+                            ].transformer_blocks[
+                                k
+                            ].attn2.to_v.gate.weight = nn.Parameter(
+                                gate_params[f"cattnvd{i}a{j}t{k}"]
+                            )
+
+            for i in range(self.up_idx_start, self.up_idx_end):
+                for j in range(len(self.pipe.unet.up_blocks[i].attentions)):
+                    for k in range(
+                        len(
+                            self.pipe.unet.up_blocks[i].attentions[j].transformer_blocks
+                        )
+                    ):
+                        # FF Layers
+                        if not moe_layers == "attn":
+                            self.pipe.unet.up_blocks[i].attentions[
+                                j
+                            ].transformer_blocks[k].ff.gate.weight = nn.Parameter(
+                                gate_params[f"u{i}a{j}t{k}"]
+                            )
+                        if not moe_layers == "ff":
+                            self.pipe.unet.up_blocks[i].attentions[
+                                j
+                            ].transformer_blocks[
+                                k
+                            ].attn1.to_q.gate.weight = nn.Parameter(
+                                gate_params[f"sattnqu{i}a{j}t{k}"]
+                            )
+
+                            self.pipe.unet.up_blocks[i].attentions[
+                                j
+                            ].transformer_blocks[
+                                k
+                            ].attn1.to_k.gate.weight = nn.Parameter(
+                                gate_params[f"sattnku{i}a{j}t{k}"]
+                            )
+
+                            self.pipe.unet.up_blocks[i].attentions[
+                                j
+                            ].transformer_blocks[
+                                k
+                            ].attn1.to_v.gate.weight = nn.Parameter(
+                                gate_params[f"sattnvu{i}a{j}t{k}"]
+                            )
+
+                            self.pipe.unet.up_blocks[i].attentions[
+                                j
+                            ].transformer_blocks[
+                                k
+                            ].attn2.to_q.gate.weight = nn.Parameter(
+                                gate_params[f"cattnqu{i}a{j}t{k}"]
+                            )
+
+                            self.pipe.unet.up_blocks[i].attentions[
+                                j
+                            ].transformer_blocks[
+                                k
+                            ].attn2.to_k.gate.weight = nn.Parameter(
+                                gate_params[f"cattnku{i}a{j}t{k}"]
+                            )
+
+                            self.pipe.unet.up_blocks[i].attentions[
+                                j
+                            ].transformer_blocks[
+                                k
+                            ].attn2.to_v.gate.weight = nn.Parameter(
+                                gate_params[f"cattnvu{i}a{j}t{k}"]
+                            )
+        self.config["num_experts"] = len(experts)
+        remove_all_forward_hooks(self.pipe.unet)
+        try:
+            del experts
+            del expert
+        except Exception:
+            pass
+        # Move Model to Device
+        self.pipe.to(self.device)
+        self.pipe.unet.to(
+            device=self.device,
+            dtype=self.torch_dtype,
+            memory_format=torch.channels_last,
+        )
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def __call__(self, *args: Any, **kwds: Any) -> Any:
+        """
+        Inference the SegMoEPipeline.
+
+        Calls diffusers.DiffusionPipeline forward with the keyword arguments. See https://github.com/segmind/segmoe#usage for detailed usage.
+        """
+        return self.pipe(*args, **kwds)
+
+    def create_empty(self, path):
+        with open(f"{path}/unet/config.json", encoding='utf8') as f:
+            config = json.load(f)
+        self.config = config["segmoe_config"]
+        unet = UNet2DConditionModel.from_config(config)
+        num_experts_per_tok = self.config["num_experts_per_tok"]
+        num_experts = self.config["num_experts"]
+        moe_layers = self.config["moe_layers"]
+        self.up_idx_start = self.config["up_idx_start"]
+        self.up_idx_end = self.config["up_idx_end"]
+        self.down_idx_start = self.config["down_idx_start"]
+        self.down_idx_end = self.config["down_idx_end"]
+        for i in range(self.down_idx_start, self.down_idx_end):
+            for j in range(len(unet.down_blocks[i].attentions)):
+                for k in range(
+                    len(unet.down_blocks[i].attentions[j].transformer_blocks)
+                ):
+                    if not moe_layers == "attn":
+                        config = {
+                            "hidden_size": next(
+                                unet.down_blocks[i]
+                                .attentions[j]
+                                .transformer_blocks[k]
+                                .ff.parameters()
+                            ).size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": num_experts,
+                        }
+                        # FF Layers
+                        layers = [
+                            unet.down_blocks[i].attentions[j].transformer_blocks[k].ff
+                        ] * num_experts
+                        unet.down_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].ff = SparseMoeBlock(config, layers)
+                    if not moe_layers == "ff":
+                        ## Attns
+                        config = {
+                            "hidden_size": unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn1.to_q.weight.size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": num_experts,
+                        }
+                        layers = [
+                            unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn1.to_q
+                        ] * num_experts
+                        unet.down_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn1.to_q = SparseMoeBlock(config, layers)
+
+                        layers = [
+                            unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn1.to_k
+                        ] * num_experts
+                        unet.down_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn1.to_k = SparseMoeBlock(config, layers)
+
+                        layers = [
+                            unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn1.to_v
+                        ] * num_experts
+                        unet.down_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn1.to_v = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_q.weight.size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": num_experts,
+                        }
+
+                        layers = [
+                            unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_q
+                        ] * num_experts
+                        unet.down_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn2.to_q = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_k.weight.size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": num_experts,
+                            "out_dim": unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_k.weight.size()[0],
+                        }
+                        layers = [
+                            unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_k
+                        ] * num_experts
+                        unet.down_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn2.to_k = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_v.weight.size()[-1],
+                            "out_dim": unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_v.weight.size()[0],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": num_experts,
+                        }
+                        layers = [
+                            unet.down_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_v
+                        ] * num_experts
+                        unet.down_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn2.to_v = SparseMoeBlock(config, layers)
+        for i in range(self.up_idx_start, self.up_idx_end):
+            for j in range(len(unet.up_blocks[i].attentions)):
+                for k in range(len(unet.up_blocks[i].attentions[j].transformer_blocks)):
+                    if not moe_layers == "attn":
+                        config = {
+                            "hidden_size": next(
+                                unet.up_blocks[i]
+                                .attentions[j]
+                                .transformer_blocks[k]
+                                .ff.parameters()
+                            ).size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": num_experts,
+                        }
+                        # FF Layers
+                        layers = [
+                            unet.up_blocks[i].attentions[j].transformer_blocks[k].ff
+                        ] * num_experts
+                        unet.up_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].ff = SparseMoeBlock(config, layers)
+
+                    if not moe_layers == "ff":
+                        # Attns
+                        config = {
+                            "hidden_size": unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn1.to_q.weight.size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": num_experts,
+                        }
+
+                        layers = [
+                            unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn1.to_q
+                        ] * num_experts
+
+                        unet.up_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn1.to_q = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn1.to_k.weight.size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": num_experts,
+                        }
+                        layers = [
+                            unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn1.to_k
+                        ] * num_experts
+
+                        unet.up_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn1.to_k = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn1.to_v.weight.size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": num_experts,
+                        }
+                        layers = [
+                            unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn1.to_v
+                        ] * num_experts
+
+                        unet.up_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn1.to_v = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_q.weight.size()[-1],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": num_experts,
+                        }
+                        layers = [
+                            unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_q
+                        ] * num_experts
+
+                        unet.up_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn2.to_q = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_k.weight.size()[-1],
+                            "out_dim": unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_k.weight.size()[0],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": num_experts,
+                        }
+
+                        layers = [
+                            unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_k
+                        ] * num_experts
+
+                        unet.up_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn2.to_k = SparseMoeBlock(config, layers)
+
+                        config = {
+                            "hidden_size": unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_v.weight.size()[-1],
+                            "out_dim": unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_v.weight.size()[0],
+                            "num_experts_per_tok": num_experts_per_tok,
+                            "num_local_experts": num_experts,
+                        }
+                        layers = [
+                            unet.up_blocks[i]
+                            .attentions[j]
+                            .transformer_blocks[k]
+                            .attn2.to_v
+                        ] * num_experts
+
+                        unet.up_blocks[i].attentions[j].transformer_blocks[
+                            k
+                        ].attn2.to_v = SparseMoeBlock(config, layers)
+        return unet
+
+    def save_pretrained(self, path):
+        """
+        Save SegMoEPipeline to Disk.
+
+        Usage:
+        pipeline.save_pretrained(path)
+
+        Parameters:
+        path: Path to Directory to save the model in.
+        """
+        for param in self.pipe.unet.parameters():
+            param.data = param.data.contiguous()
+        self.pipe.unet.config["segmoe_config"] = self.config
+        self.pipe.save_pretrained(path)
+        safetensors.torch.save_file(
+            self.pipe.unet.state_dict(),
+            f"{path}/unet/diffusion_pytorch_model.safetensors",
+        )
+
+    def cast_hook(self, pipe, dicts):
+        for i in range(self.down_idx_start, self.down_idx_end):
+            for j in range(len(pipe.unet.down_blocks[i].attentions)):
+                for k in range(
+                    len(pipe.unet.down_blocks[i].attentions[j].transformer_blocks)
+                ):
+                    pipe.unet.down_blocks[i].attentions[j].transformer_blocks[
+                        k
+                    ].ff.register_forward_hook(getActivation(dicts, f"d{i}a{j}t{k}"))
+
+                    ## Down Self Attns
+                    pipe.unet.down_blocks[i].attentions[j].transformer_blocks[
+                        k
+                    ].attn1.to_q.register_forward_hook(
+                        getActivation(dicts, f"sattnqd{i}a{j}t{k}")
+                    )
+                    pipe.unet.down_blocks[i].attentions[j].transformer_blocks[
+                        k
+                    ].attn1.to_k.register_forward_hook(
+                        getActivation(dicts, f"sattnkd{i}a{j}t{k}")
+                    )
+                    pipe.unet.down_blocks[i].attentions[j].transformer_blocks[
+                        k
+                    ].attn1.to_v.register_forward_hook(
+                        getActivation(dicts, f"sattnvd{i}a{j}t{k}")
+                    )
+
+                    ## Down Cross Attns
+
+                    pipe.unet.down_blocks[i].attentions[j].transformer_blocks[
+                        k
+                    ].attn2.to_q.register_forward_hook(
+                        getActivation(dicts, f"cattnqd{i}a{j}t{k}")
+                    )
+                    pipe.unet.down_blocks[i].attentions[j].transformer_blocks[
+                        k
+                    ].attn2.to_k.register_forward_hook(
+                        getActivation(dicts, f"cattnkd{i}a{j}t{k}")
+                    )
+                    pipe.unet.down_blocks[i].attentions[j].transformer_blocks[
+                        k
+                    ].attn2.to_v.register_forward_hook(
+                        getActivation(dicts, f"cattnvd{i}a{j}t{k}")
+                    )
+
+        for i in range(self.up_idx_start, self.up_idx_end):
+            for j in range(len(pipe.unet.up_blocks[i].attentions)):
+                for k in range(
+                    len(pipe.unet.up_blocks[i].attentions[j].transformer_blocks)
+                ):
+                    pipe.unet.up_blocks[i].attentions[j].transformer_blocks[
+                        k
+                    ].ff.register_forward_hook(getActivation(dicts, f"u{i}a{j}t{k}"))
+                    ## Up Self Attns
+                    pipe.unet.up_blocks[i].attentions[j].transformer_blocks[
+                        k
+                    ].attn1.to_q.register_forward_hook(
+                        getActivation(dicts, f"sattnqu{i}a{j}t{k}")
+                    )
+                    pipe.unet.up_blocks[i].attentions[j].transformer_blocks[
+                        k
+                    ].attn1.to_k.register_forward_hook(
+                        getActivation(dicts, f"sattnku{i}a{j}t{k}")
+                    )
+                    pipe.unet.up_blocks[i].attentions[j].transformer_blocks[
+                        k
+                    ].attn1.to_v.register_forward_hook(
+                        getActivation(dicts, f"sattnvu{i}a{j}t{k}")
+                    )
+
+                    ## Up Cross Attns
+                    pipe.unet.up_blocks[i].attentions[j].transformer_blocks[
+                        k
+                    ].attn2.to_q.register_forward_hook(
+                        getActivation(dicts, f"cattnqu{i}a{j}t{k}")
+                    )
+                    pipe.unet.up_blocks[i].attentions[j].transformer_blocks[
+                        k
+                    ].attn2.to_k.register_forward_hook(
+                        getActivation(dicts, f"cattnku{i}a{j}t{k}")
+                    )
+                    pipe.unet.up_blocks[i].attentions[j].transformer_blocks[
+                        k
+                    ].attn2.to_v.register_forward_hook(
+                        getActivation(dicts, f"cattnvu{i}a{j}t{k}")
+                    )
+
+    @torch.no_grad
+    def get_hidden_states(self, model, positive, negative, average: bool = True):
+        intermediate = {}
+        self.cast_hook(model, intermediate)
+        with torch.no_grad():
+            _ = model(positive, negative_prompt=negative, num_inference_steps=25)
+        hidden = {}
+        for key in intermediate:
+            hidden_states = intermediate[key][0][-1]
+            if average:
+                # use average over sequence
+                hidden_states = hidden_states.sum(dim=0) / hidden_states.shape[0]
+            else:
+                # take last value
+                hidden_states = hidden_states[:-1]
+            hidden[key] = hidden_states.to(self.device)
+        del intermediate
+        gc.collect()
+        torch.cuda.empty_cache()
+        return hidden
+
+    @torch.no_grad
+    def get_gate_params(
+        self,
+        experts,
+        positive,
+        negative,
+    ):
+        gate_vects = {}
+        for i, expert in enumerate(tqdm.tqdm(experts, desc="Expert Prompts")):
+            expert.to(self.device)
+            expert.unet.to(
+                device=self.device,
+                dtype=self.torch_dtype,
+                memory_format=torch.channels_last,
+            )
+            hidden_states = self.get_hidden_states(expert, positive[i], negative[i])
+            del expert
+            gc.collect()
+            torch.cuda.empty_cache()
+            for h in hidden_states:
+                if i == 0:
+                    gate_vects[h] = []
+                hidden_states[h] /= (
+                    hidden_states[h].norm(p=2, dim=-1, keepdim=True).clamp(min=1e-8)
+                )
+                gate_vects[h].append(hidden_states[h])
+        for h in hidden_states:
+            gate_vects[h] = torch.stack(
+                gate_vects[h], dim=0
+            )  # (num_expert, num_layer, hidden_size)
+            gate_vects[h].permute(1, 0)
+
+        return gate_vects
diff --git a/modules/shared_items.py b/modules/shared_items.py
index a785aeab2..74c4de8ff 100644
--- a/modules/shared_items.py
+++ b/modules/shared_items.py
@@ -54,7 +54,8 @@ def get_pipelines():
         'ONNX Stable Diffusion XL': getattr(diffusers, 'OnnxStableDiffusionXLPipeline', None),
         'ONNX Stable Diffusion XL Img2Img': getattr(diffusers, 'OnnxStableDiffusionXLImg2ImgPipeline', None),
         'Custom Diffusers Pipeline': getattr(diffusers, 'DiffusionPipeline', None),
-        'InstaFlow': getattr(diffusers, 'StableDiffusionPipeline', None) # dynamically redefined and loaded in sd_models.load_diffuser
+        'InstaFlow': getattr(diffusers, 'StableDiffusionPipeline', None), # dynamically redefined and loaded in sd_models.load_diffuser
+        'SegMoE': getattr(diffusers, 'StableDiffusionPipeline', None), # dynamically redefined and loaded in sd_models.load_diffuser
         # Segmind SSD-1B, Segmind Tiny
     }
 
diff --git a/pyproject.toml b/pyproject.toml
index a1491bd98..dc5c9c4b2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -57,6 +57,7 @@ ignore = [
   "C408", # Rewrite as a literal
   "E402", # Module level import not at top of file
   "E721", # Do not compare types, use `isinstance()`
+  "E741", # Do not use variables named `l`, `O`, or `I`
   "EXE001", # Shebang present
   "F401", # Imported but unused
   "ISC003", # Implicit string concatenation