From 055fbf890adb30efc3308531df67aa98dac99d0d Mon Sep 17 00:00:00 2001 From: K Pranit Abhinav Date: Mon, 31 Oct 2022 15:27:26 +0530 Subject: [PATCH 1/2] nst training script added --- Requester/create_distributed_nst.py | 79 ++++++++++++++++ Requester/examples/nst.py | 142 ++++++++++++++++++++++++++++ 2 files changed, 221 insertions(+) create mode 100644 Requester/create_distributed_nst.py create mode 100644 Requester/examples/nst.py diff --git a/Requester/create_distributed_nst.py b/Requester/create_distributed_nst.py new file mode 100644 index 00000000..d739f202 --- /dev/null +++ b/Requester/create_distributed_nst.py @@ -0,0 +1,79 @@ +from ctypes.wintypes import POINT +from dotenv import load_dotenv +load_dotenv() + +import os +import ravop as R + +from examples import nst +import numpy as np + + +content_layers = ['Conv2D_9'] +style_layers = ['Conv2D_1', + 'Conv2D_3', + 'Conv2D_5', + 'Conv2D_8', + 'Conv2D_11'] + + + +# Initialize and create graph +R.initialize(ravenverse_token=os.environ.get("TOKEN")) +# R.initialize(ravenverse_token="eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ0b2tlbl90eXBlIjoiYWNjZXNzIiwiZXhwIjoxNjY1NzgzOTgwLCJpYXQiOjE2NjMwNjI5MzMsImp0aSI6ImVhMDBiM2Q3NTE0NjRlNDZhMGYzYzg3ZTUzMmRkZjNhIiwidXNlcl9pZCI6Ijc4MjA4MjgwNDUifQ.xAY2HFgzFYASKVBubDV0fgQOfWeNQuZai6eb_vJl89M") + +R.flush() +R.Graph(name='nst', algorithm='neural_style_transfer', approach='distributed') + +# dataset +X_train, X_test, y_train, y_test = nst.get_dataset() +# X_train, X_test, y_train, y_test = np.random.random((224,224,3)),np.random.random((224,224,3)),list([1]),list([1]) + + +# create cnn model +model = nst.create_model() +po=[] +for _ in range(30): + layer_vals=nst.forward_p(model,X_train) + content_layers = ['Conv2D_9'] + style_layers = ['Conv2D_1', + 'Conv2D_3', + 'Conv2D_5', + 'Conv2D_8', + 'Conv2D_11'] + content_layer_out=[] + style_layer_out=[] + for con in content_layers: + content_layer_out.append(layer_vals[con]) + layer_vals[con].persist_op(name =con+"_iter"+str(_)) + for sty in style_layers: + style_layer_out.append(layer_vals[sty]) + layer_vals[sty].persist_op(name = sty+"_iter"+str(_)) + # conv2d_1=layer_vals['Conv2D_1'] + # conv2d_1.persist_op(name = "val_{}_batch_{}".format('conv1',_)) + # Activation_10_relu=layer_vals['Activation_10_relu'] + # Activation_10_relu.persist_op(name = "val_{}_batch_{}".format('activ_10_',_)) + print(layer_vals) + + + +# compile it and start the execution +nst.compile() +nst.execute() + +# cnn.get_score() +# print("Loss: ", loss) +# print("Accuracy: ", acc) + + + +cont,st=[],[] +for _ in range(3): + for con in content_layers: + cont.append( R.fetch_persisting_op(op_name=con+"_iter"+str(_))) + for sty in style_layers: + st .append(R.fetch_persisting_op(op_name=sty+"_iter"+str(_))) +# print(conv,act) +print( np.shape(cont[0]['result']), np.shape(st[0]['result']),np.shape(cont[1]['result']), np.shape(st[1]['result']))#,np.shape(conv[2]['result']), np.shape(act[2]['result'])) +print("____________________________________________________________________") +print(cont[0].keys()) \ No newline at end of file diff --git a/Requester/examples/nst.py b/Requester/examples/nst.py new file mode 100644 index 00000000..bec5ad88 --- /dev/null +++ b/Requester/examples/nst.py @@ -0,0 +1,142 @@ +# from __future__ import print_function + +import pickle as pkl + +import numpy as np +import ravop as R + + +from ravdl.v2 import NeuralNetwork +from ravdl.v2.layers import Activation, Dense, BatchNormalization, Dropout, Conv2D, Flatten, MaxPooling2D +from ravdl.v2.optimizers import Adam, RMSprop +from ravdl.v2.loss_functions import CrossEntropy,SquareLoss + +from sklearn import datasets +from sklearn.model_selection import train_test_split + + +def to_categorical(x, n_col=None): + if not n_col: + n_col = np.amax(x) + 1 + one_hot = np.zeros((x.shape[0], n_col)) + one_hot[np.arange(x.shape[0]), x] = 1 + return one_hot + + +def get_dataset(): + + from keras_preprocessing import image + from keras.applications.vgg16 import preprocess_input + img_path="/Users/pranitkandarpa/Desktop/style_image.jpeg" + img = image.load_img(img_path, target_size=(224, 224)) + img = image.img_to_array(img) + print(img) + + print(np.shape(img)) + img = np.expand_dims(img, axis=0) + img = preprocess_input(img) + from numpy import moveaxis + print(np.shape(img)) + img= moveaxis(img, 3, 1) + # img = img.reshape((-1, 224, 224,3)) + print("=================\n\n\n") + print(img,np.shape(img)) + return img, img ,[[1]] ,[[1]] + pass + + + +def create_model():#n_hidden, n_features): + optimizer = Adam() + model = NeuralNetwork(optimizer=optimizer, loss='SquareLoss') + + + + + + model.add(Conv2D(input_shape=(3,224,224),n_filters=64,filter_shape=(3,3),padding="same"))#, activation="relu")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=64,filter_shape=(3,3),padding="same"))#, activation="relu")) + model.add(Activation('relu')) + model.add(MaxPooling2D(pool_shape=(2,2),stride=2)) + + model.add(Conv2D(n_filters=128, filter_shape=(3,3), padding="same"))#, activation="relu")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=128, filter_shape=(3,3), padding="same"))#, activation="relu")) + model.add(Activation('relu')) + model.add(MaxPooling2D(pool_shape=(2,2),stride=2)) + + model.add(Conv2D(n_filters=256, filter_shape=(3,3), padding="same"))#, activation="relu")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=256, filter_shape=(3,3), padding="same"))#, activation="relu")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=256, filter_shape=(3,3), padding="same"))#, activation="relu")) + model.add(Activation('relu')) + model.add(MaxPooling2D(pool_shape=(2,2),stride=2)) + + model.add(Conv2D(n_filters=512, filter_shape=(3,3), padding="same"))#, activation="relu")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=512, filter_shape=(3,3), padding="same"))#, activation="relu")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=512, filter_shape=(3,3), padding="same"))#, activation="relu")) + model.add(Activation('relu')) + model.add(MaxPooling2D(pool_shape=(2,2),stride=2)) + + model.add(Conv2D(n_filters=512, filter_shape=(3,3), padding="same"))#, activation="relu")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=512, filter_shape=(3,3), padding="same"))#, activation="relu")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=512, filter_shape=(3,3), padding="same"))#, activation="relu")) + model.add(Activation('relu')) + model.add(MaxPooling2D(pool_shape=(2,2),stride=2)) + + model.add(Flatten()) + model.add(Dense(4096))#,activation="relu")) + model.add(Activation('relu')) + model.add(Dense(4096))#,activation="relu")) + model.add(Activation('relu')) + model.add(Dense(1000))#, activation="softmax")) + model.add(Activation('softmax')) + model.summary() + + return model + + + + +def train(model, X_train, y_train, n_epochs=5): + model.fit(X_train, y_train, n_epochs=n_epochs, batch_size=1, persist_weights=True) #v2 + # model.fit(X_train, y_train, n_epochs=n_epochs, batch_size=256, save_model=True) #v1 + # pkl.dump(model, open("cnn_model.pkl", "wb")) + return model + +def forward_p(model,X_train): + out=model._forward_pass(R.t(X_train),training=False,return_all_layer_output=True) + return out + + +def test(model, X_test, y_test): + print('\nTesting...') + loss, acc = model.test_on_batch(R.t(X_test), R.t(y_test)) + # loss.persist_op(name='cnn_test_loss') + # acc.persist_op(name='cnn_test_acc') + +def compile(): + R.activate() + + +def execute(): + R.execute() + R.track_progress() + +def get_score(): + conv,act=[],[] + for _ in range(3): + conv.append( R.fetch_persisting_op("val_{}_batch_{}".format('conv1',_))) + print(np.shape( R.fetch_persisting_op("val_{}_batch_{}".format('conv1',_)))) + act .append(R.fetch_persisting_op("val_{}_batch_{}".format('activ_10_',_))) + print(np.shape(R.fetch_persisting_op("val_{}_batch_{}".format('activ_10_',_)))) + # print(conv,act) + # print( np.shape(conv[0]['result']), np.shape(act[0]['result']),np.shape(conv[1]['result']), np.shape(act[1]['result']),np.shape(conv[2]['result']), np.shape(act[2]['result'])) + # print("____________________________________________________________________") + # print(conv[0].keys()) \ No newline at end of file From 93cba8db5f3a23c56e87186cdff032ed12de01d4 Mon Sep 17 00:00:00 2001 From: K Pranit Abhinav Date: Mon, 31 Oct 2022 15:35:36 +0530 Subject: [PATCH 2/2] added one training script --- Requester/create_distributed_nst.py | 139 ++++++++++++++++++- Requester/examples/datasets/content.jpeg | Bin 0 -> 28655 bytes Requester/examples/datasets/style_image.jpeg | Bin 0 -> 58384 bytes 3 files changed, 135 insertions(+), 4 deletions(-) create mode 100644 Requester/examples/datasets/content.jpeg create mode 100644 Requester/examples/datasets/style_image.jpeg diff --git a/Requester/create_distributed_nst.py b/Requester/create_distributed_nst.py index d739f202..e8453738 100644 --- a/Requester/create_distributed_nst.py +++ b/Requester/create_distributed_nst.py @@ -18,6 +18,140 @@ +import pickle as pkl + +import numpy as np +import ravop as R + + +from ravdl.v2 import NeuralNetwork +from ravdl.v2.layers import Activation, Dense, BatchNormalization, Dropout, Conv2D, Flatten, MaxPooling2D +from ravdl.v2.optimizers import Adam, RMSprop +from ravdl.v2.loss_functions import CrossEntropy,SquareLoss + +from sklearn import datasets +from sklearn.model_selection import train_test_split + + +def to_categorical(x, n_col=None): + if not n_col: + n_col = np.amax(x) + 1 + one_hot = np.zeros((x.shape[0], n_col)) + one_hot[np.arange(x.shape[0]), x] = 1 + return one_hot + + +def get_dataset(): + + from keras_preprocessing import image + from keras.applications.vgg16 import preprocess_input + img_path="/Users/pranitkandarpa/Desktop/style_image.jpeg" + img = image.load_img(img_path, target_size=(224, 224)) + img = image.img_to_array(img) + print(img) + + print(np.shape(img)) + img = np.expand_dims(img, axis=0) + img = preprocess_input(img) + from numpy import moveaxis + print(np.shape(img)) + img= moveaxis(img, 3, 1) + # img = img.reshape((-1, 224, 224,3)) + print("=================\n\n\n") + print(img,np.shape(img)) + return img, img ,[[1]] ,[[1]] + pass + + + +def create_model():#n_hidden, n_features): + optimizer = Adam() + model = NeuralNetwork(optimizer=optimizer, loss='SquareLoss') + + + + + + model.add(Conv2D(input_shape=(3,224,224),n_filters=64,filter_shape=(3,3),padding="same")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=64,filter_shape=(3,3),padding="same")) + model.add(Activation('relu')) + model.add(MaxPooling2D(pool_shape=(2,2),stride=2)) + + model.add(Conv2D(n_filters=128, filter_shape=(3,3), padding="same")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=128, filter_shape=(3,3), padding="same")) + model.add(Activation('relu')) + model.add(MaxPooling2D(pool_shape=(2,2),stride=2)) + + model.add(Conv2D(n_filters=256, filter_shape=(3,3), padding="same")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=256, filter_shape=(3,3), padding="same")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=256, filter_shape=(3,3), padding="same")) + model.add(Activation('relu')) + model.add(MaxPooling2D(pool_shape=(2,2),stride=2)) + + model.add(Conv2D(n_filters=512, filter_shape=(3,3), padding="same")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=512, filter_shape=(3,3), padding="same")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=512, filter_shape=(3,3), padding="same")) + model.add(Activation('relu')) + model.add(MaxPooling2D(pool_shape=(2,2),stride=2)) + + model.add(Conv2D(n_filters=512, filter_shape=(3,3), padding="same")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=512, filter_shape=(3,3), padding="same")) + model.add(Activation('relu')) + model.add(Conv2D(n_filters=512, filter_shape=(3,3), padding="same")) + model.add(Activation('relu')) + model.add(MaxPooling2D(pool_shape=(2,2),stride=2)) + + model.add(Flatten()) + model.add(Dense(4096)) + model.add(Activation('relu')) + model.add(Dense(4096)) + model.add(Activation('relu')) + model.add(Dense(1000)) + model.add(Activation('softmax')) + model.summary() + + return model + + + + +def train(model, X_train, y_train, n_epochs=5): + model.fit(X_train, y_train, n_epochs=n_epochs, batch_size=1, persist_weights=True) #v2 + # model.fit(X_train, y_train, n_epochs=n_epochs, batch_size=256, save_model=True) #v1 + # pkl.dump(model, open("cnn_model.pkl", "wb")) + return model + +def forward_p(model,X_train): + out=model._forward_pass(R.t(X_train),training=False,return_all_layer_output=True) + return out + + +def test(model, X_test, y_test): + print('\nTesting...') + loss, acc = model.test_on_batch(R.t(X_test), R.t(y_test)) + # loss.persist_op(name='cnn_test_loss') + # acc.persist_op(name='cnn_test_acc') + +def compile(): + R.activate() + + +def execute(): + R.execute() + R.track_progress() + + + + + + # Initialize and create graph R.initialize(ravenverse_token=os.environ.get("TOKEN")) # R.initialize(ravenverse_token="eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ0b2tlbl90eXBlIjoiYWNjZXNzIiwiZXhwIjoxNjY1NzgzOTgwLCJpYXQiOjE2NjMwNjI5MzMsImp0aSI6ImVhMDBiM2Q3NTE0NjRlNDZhMGYzYzg3ZTUzMmRkZjNhIiwidXNlcl9pZCI6Ijc4MjA4MjgwNDUifQ.xAY2HFgzFYASKVBubDV0fgQOfWeNQuZai6eb_vJl89M") @@ -49,10 +183,7 @@ for sty in style_layers: style_layer_out.append(layer_vals[sty]) layer_vals[sty].persist_op(name = sty+"_iter"+str(_)) - # conv2d_1=layer_vals['Conv2D_1'] - # conv2d_1.persist_op(name = "val_{}_batch_{}".format('conv1',_)) - # Activation_10_relu=layer_vals['Activation_10_relu'] - # Activation_10_relu.persist_op(name = "val_{}_batch_{}".format('activ_10_',_)) + print(layer_vals) diff --git a/Requester/examples/datasets/content.jpeg b/Requester/examples/datasets/content.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..b845337e854039abc5926f1fdac186ed48703ed9 GIT binary patch literal 28655 zcmbrlcQjnl_Xm0z3?f8Jh*4sQC`ojq_Zq#1h=>|BjNV%a!Z4#m9YiOhM2!*zqZ3_} zU@$?5F8Z5%zw5on0U-Jh27vqc8UP4$3IER)g2Y_V|KNWI zueSl1?yJ|HuRUJ9c4HHI#1Fs})U}BIjet-8!JPkrw9#w&sp-vwsv2KU5cIXu?(@pSu#6Y-eN z`qfJtJTAavJ~)1Ycs%;A-Ri%1^&htXFaG-v!wvNn@oJ>NV>bK$V!nUa>c99O7dGox za2I?ZXFTR`arVN`;Xmmg6AD{bBLjR5`OnV-&<9ij1%M5(0=xhRz!~ra9^z|Pyw3i2 zIl=$PX#=nEGS>Lg4e$ftcnN#p6<(GP-|7W;0Jiwr4&QEz&(8QB`1DWr|7rmEADw#G z3H=)nPnkjk0En^I*9Z7D1kMD2%eU9p=O3=GFY^HaGy?$b$^Rq&IvKCd9ejJ@|Fm&r z0{~S70MxhrPn%UC05srr%&_S868`c(=KTcn0C=iH{2t-W$??xa9!XF2SsEKLr3CNSs>b(TByVD7V zA@fL~3e_F-`rmgsgskA zMMy*hA_D*8ML_8Dk2p0E@jU?&8hJhNOLtm!!7x%f1!P`z2N_gIf0y10{+;|bhww7z z-al&pVfO!wSor@ZX8#wl|KT+UC<4SFBK)lJb0#7t#$P51QZPPI-MB$UK}|(NLrq0Z zO?w-1hnDUxJvBAMJ%+o?%&e@ew0GD!*jYFrEUYZo_(2HpqF@p*B`GN-3mr8b%m3?i zy?}pkGG6}xC_n`G#RQ@TWP#K4gT}a%qNF)yhe4Htu&@k=P5(W8Qw~1DM0u%IkFi#v zP9*E^{qJ$MHSJl!GDqv`6P6~Wf6Zk7rn1M7JRz8{*&AyT2qBn$Gx^z6&h05}lPXuv z!H*BwpdK~N>Kzh6o?q`Q*}kN5$!b&xy2NMtF#JI{4XNfeCQ<;O=q#=C=yoG~ZB$%l zp%JmMjL+C?EGg{0ob-ppIXVzQ!#+Bi6Mc{Jpn%eEK z2-rxWfe9_YTy&F6RAgf&rf5h4I8n_#vGRu*2W4hAOwcY56 zQh|RI=BP|h!NXcL&4iDv1G~18MGsjnAoo869hMSS%HHFuTKJJe&K<0qDAW_wd^d3F z_A2-5lV@-&GAk5z$s>T0akgiGc$cq;SU%&)gY$E&d}xaCwkh|*u8(XPxX(B%uoDbW zrgIS{t90)c!@Y)=6Nb^b4@QQP&I0bfscts`yL#-$npm&Nb(3#XO#=}b$x0wl3$YQF z-sTT%N`?eJwB}wKUJfeRtVS6ewi=l@J7M+sCE!lYDE;90Nu0`~;Y4(ya z_7CM0InK+GP<%@G%CVdrH{sIQq`r+qJ~=HbNIGzF&nXfzsaX4=?f|+kPEiaxT(27Z zjDhls4WQlQ3{U}YECVkH$%aETvO*EH4^x*7@uF_2{aNsVtJu(aeDa$G1xm8#=!|7ZCv1aT$EOmuusnDtY^#~_m z7%5bz8K%4K+b%_escg{5Oh#3M7>VL4XbEO4SI00pyaWOb43;E16BCQ3R-7nNFXL0n zYv8I?1f?=xMnD0ltGXyHy)TLgz|?3x3f*GII+YS#I*n{&2DL+Y4j(gezowioCq7jW zV{Uu7*m68i;+)gdD#d#_Cv~Av``wMACU>aD05XTd4^PDi)D$``*Xcw_%o^ ze?5Ad14c`ZK|mP2DYQI=oV3KAChV|;&s%7$^+`wCIUy)q9$YN4*6+t~RB5tHsN!rg zr0{e#Y>s{)&O2O^{{^iCX(G&RC@v0JyU#-sSwJY#H^TNt%H7OODQ`dL(J23HN6C!! zOO7~*wU2CypZVD@e^EQr&%BJhZ1WP~1r)(X+|kM`VxLyDNqc1Vddtsmg^H34Kwf^i zqU$pR?T3QP5(UspR_{eR^7(%o8~^a3O$8D#4H`D(APqSl2HN;G^ikgGP8u!BJ#G)P zH6-{%F!q@v3a-L)7CCrO^;R!j?R*wk&n5TODu{!-kJXMX1lBBrRy{@t%hhl2s?iSz z*x02Lzr3-QRq(dR_kPaZH_3@Q3mkzdQhE}P2tNso462Io5ZpysOI~K42GdEa)bg}# z|7F1avHrUJBkBMG`3%7#c=`-Inj)g4Jy!BWfC*{;Rf>m(H`; zMY5Y&iEi$HViQkE^!c*|B_cGMBi_H^#V;P&o;1ON)P0pXWGEI=DVw52C#3q`_b2#o z_?4HR-l{G8ta(Cfn6@WVhU|kb*lE^9?)kZ46QS~L^>>d4b6L&4OBj|KbEc@Dw4Y#o^mAaM zQ!5W`nzePLRy&ThP^tYar!(bXbX0v zi@<=PmC_GVt^iLW`@KTM>glhilr7=F$cARBS`DA0X+xVF5JwZDk-+k-?;gi?Ur(_0 z6<0=vpLa8VXy{)qkjzy!tYpgeaRhD2T&>TAzb=~+$(*Z=k(IVK?0!K5w9n`#8x~lC z2d49d~*1==?Ep7p~UJ3_6EH$&^1u0JJ)1v{Lnlf6ds3#-13OH60ZN=NRX-$hoD@J zyJ9TXTw@<7>jA6S64^y_QYDV9+CYeU9F^WQM;RnXmyQheM?@F~HgN=vK?Lj7muM4u z>U%ngP;Gld#;MDkr&jhLaa-dnFFj+qg$2YjjA-NT!t%n2AwTQxJ&WfqIhtA1AO9}e zvfOTacM{>&^Ymn~Zp}E?)ir&{e@5)Q-fj9X(jnM9U{8N8u6nK!smGPi&306}-Cwf0 zN;}u;YKOf3ep(m^mS<^@wGsI<%&gvl&pq{&-Ck0S zOWLuZ3-wg4?CJ5g@tP{+9tU+VG$YEA(1par+2)o6Yfno_{%8|8V-G^y3kmmQ4}-Sh zkP2Ly#bB8_7RG{+KToX18CKp!JRmlm%Ytl^l-qHYQdDmz_M&!f#91C)IzC>k{mEO! zfRnlgG|>D#x1K<}Pt`6Rw&=<`C85*{9@oI;1@AM|cT#X4#r|uKeNsqpZFfwAwXd$k zt#5>%pze?kA(tw7TH>IP z<+X~9t@CnTF`1!%&jD&UVvlxNWs9S*KhH0&kc^I{Yfxb_Wd$?ujU1JV=JP^}AiKq% zC>_iez6p`2a)3tTLNrigO7e-bFFQ^XHhG>$MQc*?!R|Gno3{SCYd;Hc%f&=p1HRPy z^T61ywXHLu4ukk{(IxUZL$~M)n9(rEzACT5DOzp&B3Y0mK8lLz9G9^g=(3`2wNPx@ zTPXnbMCLT*ESrbi=C$uK%Q7>5&uj15Kbs_d{{Cc-;eO75o(HA zi+9lK$znFkeB5<6d9@Vd%|iayp=2w-wT^(ZEM$T<>09GKJYrFrd=16W!ND0wYC|CeU&{$MiCkzp$@K z^wpgWR9pe+r_*2E!D%Zk@4M0D&0`!cvEA}gUfn^jOXQl!yWO`ITTU={olW=~eMhUo z5UW-S7JFlw+U)Ns=FAf1E;^}e@yO%GZq;$f-gEnRhaolQ;<|FfZcZs~@*S)iXJ5SN z)k>kbay`%D{@#(2Yv9P}9dD^ivAUJFs$$bqmZ;nzvBKw{Y_hYz|CWJFM3ol4OY1eI zTQM@|ZwUF;`l`T%U5FXwaz1O$&#-k^DZI<_>5F%9jrDnYPY*a({8VRL>h586d}fNu z(W=cV^?Y!vQ*WitZ2tn4MZAs^-C4YncxRTF1^C5qHr+#1kD%`%Cd7;$%jJLtIS>Wc z3EPSR2GBTY))a5bR`1$ zu8}MBSm90zQfxJaZl)In|G=<1EB96M z4SjWlcx!#4J?xg`{O!}4JC@hLmcb7Wx8|ojS=e>R`MyKZ{dW$5EZ)`g z?W~2egB}to?^0o~RsLhlsP3O2@?ZDod>Z67p9`T4EiZ#^@^FN>(-VvUQNxn@o0>ym zG-=~fB<5Jzx58aE0yle^)lU)a!P#a0@$7>YOo~y_;c^{|s)uE|ZyeI{UVTB44u{+` z?&@z(X)v^9s zcHhpBBbiSc#bzj5e*D-~ufrobEuS%ZJ$9N)=mypmFACKG?0;mzz%byrU}P|)uUp=x z-u@2HLfCp+^k_)Pa=B@~b?3!g^TAY`cW5FO9boc(k;3>n

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