diff --git "a/app.ipynb" "b/app.ipynb" new file mode 100644--- /dev/null +++ "b/app.ipynb" @@ -0,0 +1,729 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "44eb0ad3", + "metadata": {}, + "outputs": [], + "source": [ + "#export\n", + "from fastai.vision.all import *\n", + "import gradio as gr\n", + "import timm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "37155c47", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['beit_base_patch16_224',\n", + " 'beit_base_patch16_224_in22k',\n", + " 'beit_base_patch16_384',\n", + " 'beit_large_patch16_224',\n", + " 'beit_large_patch16_224_in22k',\n", + " 'beit_large_patch16_384',\n", + " 'beit_large_patch16_512']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "timm.list_models('beit*')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d838c0b3", + "metadata": {}, + "outputs": [], + "source": [ + "path = untar_data(URLs.PETS)/'images'\n", + "\n", + "dls = ImageDataLoaders.from_name_func('.',\n", + " get_image_files(path), valid_pct=0.2, seed=42,\n", + " label_func=RegexLabeller(pat = r'^([^/]+)_\\d+'),\n", + " item_tfms=Resize(224, method='squish'), bs=32)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b41bd4df", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jhoward/mambaforge/lib/python3.9/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755888534/work/aten/src/ATen/native/TensorShape.cpp:2228.)\n", + " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n" + ] + } + ], + "source": [ + "learn = vision_learner(dls, 'beit_base_patch16_224_in22k', metrics=error_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8ba3d47b", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
epochtrain_lossvalid_losserror_ratetime
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See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mlearn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfine_tune\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/git/fastai/fastai/callback/schedule.py:164\u001b[0m, in \u001b[0;36mfine_tune\u001b[0;34m(self, epochs, base_lr, freeze_epochs, lr_mult, pct_start, div, **kwargs)\u001b[0m\n\u001b[1;32m 162\u001b[0m base_lr \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m 163\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munfreeze()\n\u001b[0;32m--> 164\u001b[0m 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pct_start, wd, moms, cbs, reset_opt)\u001b[0m\n\u001b[1;32m 113\u001b[0m lr_max \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([h[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlr\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m h \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mopt\u001b[38;5;241m.\u001b[39mhypers])\n\u001b[1;32m 114\u001b[0m scheds \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlr\u001b[39m\u001b[38;5;124m'\u001b[39m: combined_cos(pct_start, lr_max\u001b[38;5;241m/\u001b[39mdiv, lr_max, lr_max\u001b[38;5;241m/\u001b[39mdiv_final),\n\u001b[1;32m 115\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmom\u001b[39m\u001b[38;5;124m'\u001b[39m: combined_cos(pct_start, \u001b[38;5;241m*\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmoms \u001b[38;5;28;01mif\u001b[39;00m moms \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m moms))}\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_epoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcbs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mParamScheduler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscheds\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mL\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcbs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset_opt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset_opt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwd\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/git/fastai/fastai/learner.py:221\u001b[0m, in \u001b[0;36mLearner.fit\u001b[0;34m(self, n_epoch, lr, wd, cbs, reset_opt)\u001b[0m\n\u001b[1;32m 219\u001b[0m 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\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_end_cleanup\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/git/fastai/fastai/learner.py:163\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_with_events\u001b[39m(\u001b[38;5;28mself\u001b[39m, f, event_type, ex, final\u001b[38;5;241m=\u001b[39mnoop):\n\u001b[0;32m--> 163\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbefore_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m); \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 164\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ex: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_cancel_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m); final()\n", + "File \u001b[0;32m~/git/fastai/fastai/learner.py:212\u001b[0m, in \u001b[0;36mLearner._do_fit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_epoch):\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mepoch\u001b[38;5;241m=\u001b[39mepoch\n\u001b[0;32m--> 212\u001b[0m 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\u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbefore_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m); \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 164\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ex: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_cancel_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m); final()\n", + "File \u001b[0;32m~/git/fastai/fastai/learner.py:206\u001b[0m, in \u001b[0;36mLearner._do_epoch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 205\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_do_epoch\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 206\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_epoch_train\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_do_epoch_validate()\n", + "File \u001b[0;32m~/git/fastai/fastai/learner.py:198\u001b[0m, in \u001b[0;36mLearner._do_epoch_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 196\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_do_epoch_train\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 197\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdl \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdls\u001b[38;5;241m.\u001b[39mtrain\n\u001b[0;32m--> 198\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_with_events\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mall_batches\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrain\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mCancelTrainException\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/git/fastai/fastai/learner.py:163\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_with_events\u001b[39m(\u001b[38;5;28mself\u001b[39m, f, event_type, ex, final\u001b[38;5;241m=\u001b[39mnoop):\n\u001b[0;32m--> 163\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbefore_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m); \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 164\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ex: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_cancel_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m); final()\n", + "File \u001b[0;32m~/git/fastai/fastai/learner.py:169\u001b[0m, in \u001b[0;36mLearner.all_batches\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mall_batches\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 168\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_iter \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdl)\n\u001b[0;32m--> 169\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdl): \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mone_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mo\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/git/fastai/fastai/learner.py:194\u001b[0m, in \u001b[0;36mLearner.one_batch\u001b[0;34m(self, i, b)\u001b[0m\n\u001b[1;32m 192\u001b[0m b \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_device(b)\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_split(b)\n\u001b[0;32m--> 194\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_with_events\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_one_batch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbatch\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mCancelBatchException\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/git/fastai/fastai/learner.py:163\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_with_events\u001b[39m(\u001b[38;5;28mself\u001b[39m, f, event_type, ex, final\u001b[38;5;241m=\u001b[39mnoop):\n\u001b[0;32m--> 163\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbefore_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m); \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 164\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ex: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_cancel_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m); final()\n", + "File \u001b[0;32m~/git/fastai/fastai/learner.py:172\u001b[0m, in \u001b[0;36mLearner._do_one_batch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_do_one_batch\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 172\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_pred\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 174\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39myb):\n", + "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/torch/nn/modules/module.py:1110\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1106\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1109\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1110\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1111\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1112\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/torch/nn/modules/container.py:141\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 141\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n", + "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/torch/nn/modules/module.py:1110\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1106\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1109\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1110\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1111\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1112\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/git/fastai/fastai/vision/learner.py:174\u001b[0m, in \u001b[0;36mTimmBody.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m,x):\n\u001b[0;32m--> 174\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mforward_features(x) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mneeds_pool \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/torch/nn/modules/module.py:1110\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1106\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1109\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1110\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1111\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1112\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/timm/models/beit.py:337\u001b[0m, in \u001b[0;36mBeit.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 336\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m--> 337\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward_features\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 338\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhead(x)\n\u001b[1;32m 339\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n", + "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/timm/models/beit.py:327\u001b[0m, in \u001b[0;36mBeit.forward_features\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 325\u001b[0m rel_pos_bias \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrel_pos_bias() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrel_pos_bias \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 326\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks:\n\u001b[0;32m--> 327\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mblk\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrel_pos_bias\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrel_pos_bias\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 329\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnorm(x)\n\u001b[1;32m 330\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc_norm \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/torch/nn/modules/module.py:1110\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1106\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1109\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1110\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1111\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1112\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/timm/models/beit.py:188\u001b[0m, in \u001b[0;36mBlock.forward\u001b[0;34m(self, x, rel_pos_bias)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 187\u001b[0m x \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdrop_path(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgamma_1 \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattn(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnorm1(x), rel_pos_bias\u001b[38;5;241m=\u001b[39mrel_pos_bias))\n\u001b[0;32m--> 188\u001b[0m x \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdrop_path(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgamma_2 \u001b[38;5;241m*\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmlp\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnorm2\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n", + "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/torch/nn/modules/module.py:1110\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1106\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1109\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1110\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1111\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1112\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/timm/models/layers/mlp.py:27\u001b[0m, in \u001b[0;36mMlp.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m 26\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc1(x)\n\u001b[0;32m---> 27\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mact\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 28\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdrop1(x)\n\u001b[1;32m 29\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc2(x)\n", + "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/torch/nn/modules/module.py:1110\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1106\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1109\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1110\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1111\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1112\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/torch/nn/modules/activation.py:670\u001b[0m, in \u001b[0;36mGELU.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 670\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgelu\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/git/fastai/fastai/torch_core.py:341\u001b[0m, in \u001b[0;36mTensorBase.__torch_function__\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 339\u001b[0m convert\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 340\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _torch_handled(args, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_opt, func): convert,types \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m),(torch\u001b[38;5;241m.\u001b[39mTensor,)\n\u001b[0;32m--> 341\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__torch_function__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convert: res \u001b[38;5;241m=\u001b[39m convert(res)\n\u001b[1;32m 343\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(res, TensorBase): res\u001b[38;5;241m.\u001b[39mset_meta(\u001b[38;5;28mself\u001b[39m, as_copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/torch/_tensor.py:1142\u001b[0m, in \u001b[0;36mTensor.__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 1139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[1;32m 1141\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _C\u001b[38;5;241m.\u001b[39mDisableTorchFunction():\n\u001b[0;32m-> 1142\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1143\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01min\u001b[39;00m get_default_nowrap_functions():\n\u001b[1;32m 1144\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n", + "\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 74.00 MiB (GPU 0; 4.00 GiB total capacity; 3.31 GiB already allocated; 0 bytes free; 3.47 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF" + ] + } + ], + "source": [ + "learn.fine_tune(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c107f724", + "metadata": {}, + "outputs": [], + "source": [ + "learn = vision_learner(dls, resnet18, metrics=error_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "552c2f57", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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jrhwtgeugzydmbGn64sPloNueXakbV7dRu8viEb/H9/na/paXfvB3t2YJRGBtPc9G1Me84fxxyVe82CqrEMPFBusPUCQBzQADQYsE1eZ+YaNXics56m/vrqGlWS6oY22nhQSyO6e40MajPB4sgUL4MHzxA+RBEDCSgQ9y47uuLwCCEBURseX7Pi/dVy894L0FWn+/TrRO9CNL9OXMNjXhkX2lIcTVnL1eetnVD4b6NzawLO+RpwIB0Cm1achnTvZI1D5BQEs/ZEHNQCMbWTKr+73rT9LvAABYw9ZQaZmAFFCFyouoYzTr8mNpeDWuznkWM/TZOXDFoxXCJVK5m4tXDIUPZ3n5QqHpwyjiJsoXuaMfPNaF6SKAO9bnpezrb/iB6NVzo7KkQl3EHT6650woCxa8hpN/q0uuiVs+eKGsZClY46GWfc/VYHYZRNV22optFqHRcWR7qV47FhYn7znwbQ2j9iUolAgPXCibT2Rfrhh/jWCRFu/V8cQ57uPymaYEsv/dNPFGcESPh7ma8hbHSVK6CLvgF43lCdbmF9y0gpU+SQsXl0XQXs3HFTB8Mhz+6v5HX6wdXbbRmY1d2yHW+TpbyZYd8IOEtTDT1so68qewUgcMy6rGQhdLp7moqFdM+FwBYHkFRYx9JfRMVcvNsnCGxeG9zLMIV6KRZtA7S5nLSlkXQ26TtjAzUaG2vjxVj0YgZM9jxKX/a9WzP2T1sjSXLjz9svRa0KEo5AkCWuak+9oItEilJ+e9TdRjeXbN8UMu7r94jQDaGPvym+KKWFf2SjSAcNXuD3MmAVSvJZH0TfsYHqAjY15ICz5Gme3nzpJX/t0XYllOkbYpmjn+mlj7SLWFc/P6aywRzaoLrkfnHJdDolwQgfQ8llWLfFS2hqCuYqqvYv++AyWuGsEy8AubRWeW6VKSnyfsgmmsNzzBRBqIA3oG1fnluCCoRnvLjrkW2V1V+ZB4lgevPp57wet/H5Hl5SN86mODO21LnMmldfOxAJPLsKcz23jK59XWb4F3F2jkov99JU2u0OeaX6Zcw53WSSGgLfEwelDDmQxkVb+vIFQjGpU1F0qa/ni+ApeQnoRexGz0ISxBkGf7z1JMT9C0AC7lXX6/jPSrSwSgntN+PowWuO7xpaYmTy/A+d6LVgToUHrdDesuR2tFCFAveSD12vd0sfHO3FSvIry47NDvnYm2/NS2VT64jVg3LPEENZ/HpfKETUsXkXj5k64Rx2jRWE2tjwvJuOyBfiMABFS54kV7RPoAmlkIssTnAFfbevnzKfx7pdMIkJpiurLtNYjpKSR5PWtsfV3B3qUaJQZdC00Krpq9ZDddK3l8XW7Z6NTTrQnX0q/3pP0U11vFF5azdlzO2EceIx5ZnFnBx/5RLBus031TSz8UYWdxt+QKXuRexSX1PEVA6+fLMh/dlrMEPQcC0l7Vkcr1e/oAFSGdwUp3aEBEzglazbO94KcGRHRNGnzE2PvA+3otsn6xQ6QF2pwpXxahfbVKi/p+MV+y5Ewtt1xYOfpmvrD/LaTJK5/RxaCWdnprS+OLfVLWl8jy6KMFaBOyZCbLakhdEckyrNUQ0VfgKiLoB8opcH0fLmlvwYvLYAmAKyxeDH8rmBN57Nh8xLCut9RqQOnIhtAe9NlAl/SlWF+19vDMa1vrPDM3yupfBOTC4tB/hcQajd1W6FKsdqoE2TAQl82+jnc1S8r1Hr2+PtxDK3Np4Wc9pkiuyLkPXeTCB/DBay+scIvlUxrNnO9/BDQej/HDF3IdzxPY6tHHJyWQXOapfEBAH45iedO6H/t6yvf4lS+7sD6/hi1zmdj10R6g3X8EVjPkctM6XQstX11nuHsmgfWXDxjbsggXXwtwrs7B5aYFjMvlS9lxOi56THmiU0uLF+UvuoBB3/UrZzuzmuteXdHB1S1XE3uNua6oB08SgABcloOLzelvGml1GaW7NtM52hUZLVCg/buwn8un1hs/mIEnxgNcCLb22OMhniWBNAdUryvZ30Q+oTn+3lfvyIehdGn5cdWlO4zvIUrLx4u/wXNkwtlJ9ESbLUG7z1nbPOeArPNW+Q8f1VVry7985KVapVVjvVwG9uHK4WLBvo8JAb8fvXX9OFbqwcrb2MM3Lm/8sIknW+Hyf83st1j/Lv8nDW2LWLd4nGF4B4nf1+j3XZTzvx9OWlolel/vS7+WQMSvAhYuhtJkJOXDlKLlURHR5hOUNbFk5aixciG9CjFbfIByprzL1z4STWeOIx8wgP6eM010C8KS8QfgrMC0u+J858L2n4z+4VN/X3187JlZE5i4GFnjkZZ67nozaa89/MDC9tS1DkHQYsd61tST9yx5p4/aXcGYXHCLdvcK6Rch2oEIkAjvkZM9dcll4fNLMFI0jf088VwAbQumFzxWiZvBDN1gcN3PntpnLQGyq69E9w/pNZP8AUTbfIQXja7wGXbOnL+YpWtfE3p0HPWMqzreJKBCLlokl4dVmm+wR9ej736FBgTROIIAFKVAFz95CwBt7/HW08aaNdBA1VNFFtbeeg82a/9rpRR5gQv7C1tkKi4McApZpQMBIGKx7LTeohkD0aL2Gk9oUXlylZx+xti8Ts8GgISIZYaa2qmrnQgrqXyAOpdXEIt3qNP/38Se9Ncy0rOBf+nw95mXn8Q+j/yMy8cOWC6eaJTRvlNAhSYiSaLtriAjQkQhJro0ItFQyWUk5ApxlpjD9eqOsojgakxvM3ze+OuoLxj9B9P5/XN2yePlUdblQrULlOlbAEscZEeUl1bvNZW626YXllwX00R7NJ1vaxphz2m8aJtPsIHvhwhxZTE5j5kfPnHeRudhP3m1257OOvgQbHLBPeujwKK+r+qAaHdaCloSetINKTltd+PtZri52Tw3tebrIOM07ytPUzkejvtTOTkKESLBzt8b2kDn7kuxHgG4VNkK9gIHTXQJ0dMQzq+4HNT3TMU6qGUyrgOK27T3cS2qtAANqsqS2HbeWJ2IevcRjbYEIkumnxDCZhtbK1R3MgOFkhrqWRMp+Ggt+VhS/HXXEsvUvaCywCVepAkvPe+ToM1f9iFxLHdyKf4tF1zlTHd4BM7W1y+E3me0gw/VEIukkgCq6pjTmG6e7T4bh9vtuNtttknUxAgYLEQFSmWgiMQ0l+Px8Gb/zd3hd6fpXYmJoqFBEa5z3oJr2pglHA1HtmJ5RHcR6Op7ancuXoI+jqd4OddChTxzrlXQ9Ke4LGDI0hf08TeGGYxeHX4BQos1iV1aXrIHMkBKUDpvWOysDd+cayReBo6d1/KHCOdpqmoy74cl2SO8+yTpXIqYNddOzmFJ1yx6sYY/JcKW6RDShQPGF9vXW9sONnrEkIdnNy92m5dj3knbiiTDgULSKaIJotDm17Wd2u5m++r2o5k//+79b799+5uH+e7Eo2MKhi4UK9fydoHFa6qeRA85PI/tkpP0/j8yNy6Oeq5FsX5ointwz/mF5zRPAj0Ff9nD1yoIe7AG0ConsPt3RXnRcvsrARENGwJCua5K2qefEVQKlRQuvtHGarAoO90PIEJEMLThstWU3+Fp61Osplsu9oPWb2XDr1wlaZ+y7h8Ocgn7Wh0Xy5TEmey4HBKwSGYxsqk9+mL7+kfPfnKz2ZoNqqPqkG1QKMIZBfRokIeoUTzqUgqOKikPY3jAkqRsZp+/+snHzz9+e//dr779qzfTb0VCwporomrbuWzFy9Cy586qhohILH7yKyuf4AJVc62kEfRoR301MuzOeV74sAViWGzu5EJDZ8JonM7ZKof2qjeNTbKVH1/d740PLWYC76ZPci1y38hK6Sn6wQ9NfOrSg0csQUCJc/3EFY6cb1soutXmgEsIoaEXXV9KpsWiRPQwEK42wc5tiCV8vXHkxld5mU8oot3bfIGwFlDFWICtQJaa8Kpir3effHbz+UCLOfJowzBCEiOcrnSheynVSxudMDTco9YyR8wGKWKiKQ9jqIpJSskkf7r9fPfFs3/9VXy3/w06Fm9cPD6w3J0X20Hl2Z3Ay4W+WPZYCQgMOEAHKJcF4FZ+IFexAIvwWahylU/RKthfGXQetb1oeWSQvmzPtltjba+pDwkL7uk9kCvVe5UJAawxMCqgLNVdu4Tt/DbO5SVWYb+0tkRcdbIWCUjTlhtr6tbaaA+2TdGrg8aC/diyZgER0xbPcjbyStd1lxIpjQpEAMYm7T5/+aOPN59tMBoAWE4bodQ6w+t0egCLUNxrh2+6LkYkIyB1OhUPQuopqZlZRs5mIxJvNjef3Px4P925lGDh6kyKbuiLTkxcFrN5OblM+xn0C1sR+dXMTnZCCCIigu1EhLMdkqunVagijS4lViG0zA/JiGix1bwgrEtG8IEtLRbg1SUOuRSqXjxxiY14L+TtZSms80pg2ZYrSS3QvZHRI/pfAjyjk4+co02aE6QlJmo3AEan0ZCFtlYSp5COhaNyNSMF1QLW/cNsBppGiBd2XqAdivZifPHZzae39sIkq6iqRdR5fvAyR5lPp/tp3pskNVtqjLp7UZWc8jiOhESIe5QyR/hm3ORhE15diwynzfD6o2effnf65ru7r1wre7nytn2DiICvw5S2R6m9lFyDAYuRWEQ7v+4WlAAZ6HQT9IYQFsaw3EaEQGEaQVERibbY61qxIxGcWfrF9qe2eIwFj2FhQ7GaoARcBuIr4AY0BTtVNPCjXc4sJoGFkpa4Cz3Xc70m1gvquZS6Kyvu3Vq4RBs0CEH0CIqmi7SNSul9bSTflQWSiMVFGxYNSHfg33zU7cZzc6CE7Ybdx+OLPPPo7yBipgJ6KeGFtdTi7vM87SNku92KZXdn1NO0n6cppeHjjz9Jmqr78XgEPLx6rdug5ZQ3Y5QZ07DZvXo2vHwvbyhTICKccIa35PNAxdJ5QpTaZBwW9n82CzU/TJvhZWqDDX14sPGPSjrhbWs2pMTFkCNKWTlQBMSB0M73CXZc2YBKi+UBVWhL6wub7Gymi8umU/ZdsVRvFWGKoCouUe1CEp1u2uXdmNhgXGOSsQLAuHB5dQWzZSO2olerTF3eKX2L6flAj/7/TrXok1XJZoatC+Rst3etPNgifrRhaVK75XxpDghCt+nmsxc/udFdOZ4criKEHPZ7uJvpdNo/3B9KLZBqNuwfHjSl7XbH8IeHh/v3d1OpJvn1q1e1luN08HkOLyKWh1GyQVQtnw7HNGxf716/efjdm+N71xrSaIiAgx50LDJAuoq/cvw2BFkZQDO4rL6nJrkCTgRRQaJtLTDQiyhGlyMkKK6iGvC+EFyQ03nHn60fXU4w5LogmHJReHvZ4bWMaKDrGACgQIrzmykXxu8lNGARbavoErQYb3I1rbDbxmSlg26QajPSNtAFu1oYqJLxqOoZGE5GiAecUQMRrN2yHgGKinWdr9vOTboC0jST1sBS54rj7e7T57uP5VTnaX+a7llP5TTtH/YmabPbufvxeJymIwByLyaq6f79nfu8v3/37t37N9++Pdzt/+RP/vTZi1sRi6ZbeZ3mKY+jQmr1Usrh7t3z1x8/2z17c4J7OCtRyQAqJJrpBS1KsDGAqICJSMuaOqNDLLyHWJaua9HR2YqT3sRENErkYiZouqqKUNhuxzIlywxfK0dn7Z1cCKvHqPXox9XI0GEcnYxVRwmRtFgvGreRWBMTiWXDdKJdkNLSZNv23S3WGPTSr8UPGT0V7pG8W2yhBOHtAKu+AwGBBIIsQCX6Ju6wsYMpCKLFVouIqopAeh0/CWmPVJDg+Gr3xc8+/sUYinCvMe9Px/vvTvNxt71NKR0O93f3D6pqqqAG/XR6EBlEs9cyTdP9N998+cuvvvrNN0L5xZ/8YhyHYRwEQ4R7KV4qw8Pj4f3b6bh//fKj2zQqNcIDM1mJZj0IRoQsAhtKULnunCB7cFTb2E3KYV35MxCJQBA1EAFvTDyApplLt2GFhEr7o2n9l2bgi2KuV0tBXFpoBUpRUtsWbeTbzRIMF6eQDIFoaOqqfa9VL9EiQZvC3EKesShaS33PZpwKQKBLrdyLTvWdYAsnaB091xDhoqlD1zNtzqyLQkGQNeBkRUejER3XNDekAFXQrNghUAMIMahJVlUJZNl89OLHP/n45y/SjvvjCVPOmxcvPmPIfv7y6+++VWKz2ZpKKfNhLqoyl1Kpn3z0+vb2xTDkr3776391+NXXd1VV/u0vf/Xy9fPPPvs8W765uYFgLrV6eA0wxmH45S//6p/9k//+P/kv/rNX29f7u72jtnVldw+wEwcgLYFPRFeQEwvubGR0FdyNbvaRaP91ASkRKxQmFogpawstjZ9cUfECHs6L1V7efm/FNJdvZTnYhNLhR7ArAU34RjSrApRgIr2pZ+ia8PKStjnWsP7GHFTA5QiVpi9I9zNcx5NLN7p3a6NcBDO3g9WC0gAVtGXX9AeDLstWC7Lbjtg3QiMh9k3WMTKhESJKjmn3xfOffXz7aU7ZUtoON0qL0x5+iHr//u13QB5uX+dTueEGESnhND28e/dmmkrOBkmvP/vDmxefCOL/8Q//3//H//P/Le9ez4fTzS7fTdMnLz+5vXn+8evnm3EYNpsSnEuB6DAMaRz+6Bd/XMu/Vtrr7evfvv/NhBBtUpctczeWGRbRhg5AikCCDMeiJSw79GKZexKfYkECXTmSc/R225NLAGtHDgtsXl+2qPXnYnsLNl1W/SL0u8mOVUfm0kgnUnaDg6QGdNn9s4s5tJu4Vhdks/O0s3uWaNnml2a0h0NWxoSFV66U1xQnrttt4YpNcofIGm7WgvTZvUaLjko29k6gW2i4+BsDYdA2toRxi81OhgHGKerhHYImfjp88/7N13ff/O7h7vT80z/60Rc/Lvt3b9++ATDdvyHi2bObcpqrq9rw3Tdf/X/+0T/+P/1f/++edv/pn/5invyf/JN/WKeH3/7mtz/+6euPX92AoWabcRi3EBGzJIqb2+evPvp0dnf1L3af/Xo/zTw0yRzrxHd+Q5HzsWCrdoQFZ0pc8oPFqYglFJoaUSlc0jPO+7Zvr6US40KO11Lrwud4pdxEZ+zNzSA9L2B9sm/vCzTSVzM1I8vKDBc2IQC0FZg7h3EGLjKLZXUXXAx+JR05d331+SysBFwSvNuwY0G/fYjoSkjjw0GuQ73IKW9srkN4gjLYZrB8PL1/YM1EzCVKVVJjunv7m6+//Ktvvvn27dvT67cPOfP9u98dDsdkwzRNm3F8uNuX0zRsnr397pt//o//xX/9j/789qOfAuUv/uLfjGn87NPXf/Z3Pv/pT16+erUDfT4dJaXxJqkayTJPpBxOx2/e3R3nOk1z2tx+cfPH7/x3D/O3lVUAvwKybQNGP0prkWMAeD5z4oxcFgMLnQ3BC6BYF36ZXHa4+mF9gZVcLqmhC4dzp5b3YREry8fVbNglMjsjbDvgIqT1Mh5vcVV2frB0ZnHsdsS7EsF5WpatoxdlYthFJ9n1EXpDSQFfzZbS2B5VBKIqkOiGg+iKemdRj6dHEQxRtSHvNnk7pgyW0+FYHvbT6aBe5vs33/zq35b5NPv85s3hGF/+6A//8N3bOzMNP5TpOM/luD8lTV7LL//8n/+//r9/LjL+3Z998buvf/Py+bO/87MvfvLTl1nml69vo/i7t+/mbXmu0ixrtZZwpLT7d7/+8r/9Z/9mf+TN8xc3L168ePmqyBfDs42MX3q8t14IpwevtfVWVGoLp+sBWMvulnPNqF6cBCHh0uZtMfSKgLqCnB4/18mifamXPKH/2jdeDxSVRZttyH7ZjCvVxsp+uiZI75ahXnFRU+prG2sCti7KQlPyBY2lPg5+DlydDkk21e9sm1f2BDqS0ZwTaHsLAQeDKFg0LwISZqoiqfE8bftUAIQ2ww+i+QFx2ZFmsQia2HYYs9p0OOk8n/aHMh0N5c0333z71XelxCFpUD38q6++mufp5fNnd3dvvMxlKuV0OtV4d7f/6nff/tFPb19/8tmr1y9/8r/5j25vdyoyDLeH+2+fP7+J8OK+Jwsr3nx3OJ5SHk7TzPzs//mP/uU//uf/8rA/UNQhQx5P5fizP/zov/rf/j3s0Gxmj6ifGmu0Sywme+2OiSX0j20nS0g06LnGmnb+21nyymY+4EAXIYSNyFTQqm9eMqEzVrl6zeplIUmHN6v4hdGBaaHcxfx5ZdGKHt8PqHRXa3+9xKITsu8tiSVlpS3qRQKdQCnRUB8cCCEDTnjLlANURQUCGkRXy5B0Dz4DIhCDdhTaFMDGlUQAmhnoh+Odu44UTKfTYY+op+P777753d1pPgbrvHFqRHgtr16+OO4fDvv9/bs3+4fDcX8IJzX/6IvPP/78s+evX6c8SkrHqaQ0jmnzxU8+j3L35s2323GMkM282948yylPp+mvfvnLf/fl3T/87/7V12/v3r979/DwEMDu5hZS/uk/vf/5Lz77T/6XP347fSkai7wnBEJhkNosN4xeppVOLKUXVi2kpZaGhBCMLt26haU5cFpI1QUylL5Xz4hHFlNPw5XavCeL41xlOQB4iUnnGdMTALxheHIllk5AtgiYVQR2KgHRNfY1JFHWwKNF9KwgvsXWdoguq3LeLJEL/Edgqb7Z+9EfbcWB23ahrtHmvUjXmc9Fq/ur0nzsvfaUQIPBqDlLptaHw+Hb7/bv3gv9/t1v37397jDFQ9XpVAvnn/7iFy+eP5+m49u3b3739Vfv374X0e32drPdhqmm8Viqv70/zXelxvNnz//4j378xacv6/ztr756s3/Yz5uNO58HhzwW56/+/b//N//jX/y3//Qv/vK3709VT6dpngvBOZ3MJAp+8voXf/jyDw6/fVvw0AeP5ohvoSuxLFDXqhbBgQvnlKzLuBydKaB0p/KCnVvEukgzd0hHIpf2bhHhGr1AbXV/G+wQESQseuJFCE6P41uPiFphLxbkm3roI5cs036A4dImVdr+73WHCCypu2dE0t5sq2hdWWd3bDXa7Sedt+fbfHV9QRZ3xNLvrlk0nsTuLiIfZ2W1hhrVuoijlthPp2+/u/vd7+7u3m/GNB0eDtPp/oR3D3p3v//RH3xy++L5b7/68uHu7u79u1rrq48/3t2+2OxuxDT6sVpCyUrcbrc//slP/s6f/mlM9//+q7t3b94xgsHnz1+kPLx99/7d3X2t/uMf/eg/11ff/Tf/3cM372cPSTkZSNZS/8Hf//s//dHPfvTRz3/9zV+8rXNIBBYHskQPBe3jaJr5eiLAOrdrjvayJD1+WS/dj73GUlfC11nFqms0zrIqbGf6OF+Xwm5FUSuUXtb2ygMDQFIXMwuY6o7UDj9akoYs5LL8eWGdknY3lyaXRprqqUu4AYJkBVZex4A3jiiLqDv/uwjjHsoFCar0gDKeIycXhiziBCOO4jvuT4d3b/fv3pymSZBPh/lw8G/eHL96c8jD8MVP/5P3D3f7u/cCjuO4vdnePH++2d1sdjc5mZfKFrCh44/+4Ec/+9HPP/r4o/n47s3vfv31b78sU005CbQ6fvftm7lEjUpGdf7xn/zxl++Od//dP7dhqGWuUcM9wn/72y/fP7zfPXv56as/KO9OM6eZk2NqhnIy4iKLebFrsaXINHZugosjdQSLiGlExoVALhCMdnlHBxTSwoU7SxDpM7hq0gCXOb7yVQFyPuVosbg0tCzARUKSJOVFJxZ3BDt0iuakRX9TyMqolmC5czzhGdxRJJoZHC24O1YXLtuvsgxVBNqrkKyGdK5kv5IRpHOpxRvfXEgiS7PSwVWdDvv79+/2x8NxnrPheCi/+2b/5Tf3TOPPf/azFy9fnqb9uN2W6ThsxmGzGba7cbMdclJRTQbjsBlfvP78489/tN0MDw+//d2Xv/z2N//u4eHdZrt7/vy5Uu4eDu+Pk0NOp6N7efbyo6+//mo75o8/evn23cP7uwqPiDBLz5/dqPh8Om3zGFOpmG0Yg8I4NbvXMp848+VujVtU3TPIXcHK4k7iUlQeFbJIwmu9S9ruW1eIK6ni3CjOgBy4fvqRFtcxBpbuEdTUS271DIMA4IuFuNn3RINAQHsKYXhfsK4edNKUAHtSV+PRVAi7/T2E0eFwt0O2oiypqXYibIfiWaubLWxokVDAu0mr0V+Xd6rLWVsNDyXJCeZT2d/d7fcP7+7uLW/GNL558/Y4FbE0bLeff/6FB+fpNE+nlNI4bsfdNueR5DyVPAzjMG7G7Xa7HcZ89/7rr3/z/u1337x/9/bhzRuD3Dx/cXd/v78/zCU8DXeHqcy1lGk/fXcoOBwO5p4Qt7vtZvfq7Xdvc0p/9nf/zvG4v7+7++jlZ1/MP//u9Pb9Yb+EnM4iJLxnki0gtkFG6RhUAi4dVKhQdcmqkx6B1WZSQYb4iqYAsB9CLFgiTrtlGsthBouG0qHthd8cHRY3LTu67QdCdsvcWpOfl0cddLEkncG1Ua500fzAWFE6Hl+Lfan/2QUem909ZLEvCBuLkmZZbWNcA4UXQtELL06Lg1rDqD9ouG8NATGfjnfv371/d/fm3fv/6M9+enh4f3+/n0vZ7J6Nt8+3u93pdJzL5OFj3mi2IGqtIml3uzNNaimPmyDu797vj3fv33572j9Mp1lt2O12h2ki1Ifkwrv9cS5xmiZADnf7++P8/v6QkyiixcX++Ec/rvPs03T/7s13v/vdZ198dH/3ZQnf7Z4VP9WIWp3wVkywy2YGGlu+sOpGiAIq+QKULKAGDRoAXIOL1qDRy1VdsUdgpdEzsmm0IU8tKR4Z3hoyXwqqSAuUTAESdHFvDZxtO7HA2jVxrpG29N8gAnRg2OpOrxblULKy1dFB41exaGvobSzhJgu3bmpotJ1EcomBC8JB51J1S5fwxUZ4zcDtnOYa5duHN9+9udvvx3EYhvSbb74u7g+neXyZ1PRwfNiITqdpHIcg3T0NNEOtZb/fv3790e3tDcDDYX86Ht/fvT2e9gJ4hKYhUq7F87DZDNt6mIbQEtPu2bC/fzhNBYJaKuDbTdKkQSQFVKPUKDOmOkR+sfnszds/n+PtMNwAkSzVqFwC6dg8Qs2Bfgl2RUBdzEJdHV6rw5CASDT9pgcsL6Y/8TVqFliAlBBYKmjKot6s+LcHVqz0cnVKaw/0XzSu/igkVUYwHO50NkWRZ9G4pKfJYvXpwni1NwTD0fRBSq/KK81o1IxjTcS0TRagMxYVj4sesdI32IMxdIkyoKMiuFZCRc+uDfRwiLZjHaLYl3e/+d3bt3f3+8OPPv3E6+lwOsKy2HA4Ta8++aTGXF2r141sUk4qqGU+nQ7b7e12ux2GNE3H9+/fPzw8nE4n91p9LnOt5KnE/ansbp6d9qfTaTpO0/3+dDxNN9vdu/s7Uqe5BOI0H3K2NA6H/enrf/+rjz75AuS333z97u3Xr15ut3n37Ob12/LtNN2lNBIQsSYm+s4GucSuLwuNpdzUeYpwVb9ttZh0rrJoXtEpZMk8v2A5za7LJYQ8FkS9srKVF12dIbw22IVKWydBqhHBqAhvdTqpZ77YDVTR5Q5WA0wIu9YXpGO1PXiziuuFtA4R1Wbqjoj1cKFlJ1xdga61xaq6BZ3wDgTl7LhfUELXIDTI94f7N+8Pp4mBm5vdw/0dYMm2lMPN7e1mt1VLWKoWlXn2XpeeHB3A/f394XCYp+lhv69lLvNUSpnmkjbboI6bfDiezDKhtXIYh1JrkGnc3L97OE6TR93ebrbZHu73v/7Lv4oJ797eT8e3H7+Ww/Hruzt94H1QDVvKKbygV8/Qxs07O76eDunKTTf3yRrsf3lPx6JNk18lUbN3rCGzQGcZnYAWvap/SfaA2FYpYAGavZjJKpFkQdvrGwikGgyiCitaCG3nqkGALgu8EEBFdVlClaSgiHgLl8SikC/6ppDrVokwUFoYVDS6Jpa9E4tyAcjicqeu1NPEY3QFkQLRVhV7OUaujcoC9TgXRimx2+y24/Du8KCaDseHlPL2ZhPCPA7gnCw3xl7qXL3mNNRavvnm69aheZoOp8PDw+Hu3cP+eLp59izuyuZm56G1ulk2aKl+OB4guj+epmkSVVN79/bN19+9/eKjZ3/y00/+8//9f/VP/vt/8/a7/etn+bPXu9i/nd4ab3iKE1UVuuKE7icCezxWLCyEUFKATKiESQBFRBWJtHVJ2409LG3RMHq6WRcSVACivsQyrBTTtyDRo1U7C3MwqaiJKkWhBV57Ux2zC3vt/CbMkoOBqOGFdQGqpIajNn9XYxgqolClGRRUlVBRESUkBBEeERCqmqoRGoDC0N8HgQQrGdE9Ih3WXyLyltACLuoAw1ocFRiANzQJsGHwHmHTFEUwWEqdSokaz16/VEUp8/44hWbIPJXpueI4TRpVyVqqRwSru9e5Ethsd6o2lflu/3B3uP9X//KrX/7qu1evbkXe3mxv/uBnu+oz4OOoUykka61qOs3zaZ4e7o7ffP3my2+/O3mU282L59v/1f/67/8v/sv/+b/+F/+yzm9v9fTLf/E/fPSf/f0YNiXmaEkfrdMiSqUoyGDbuLJgwjDQEEk9QUyqQqiJQEQ4kktqi6VNADbNLJqqES0HdEEZHYIskIeLXtyoSxp4JWqLkU8RZpKVmTD2/MOCJQugm6NJ1qY2ppZI0+LlnCCcUiM8UIi6ShpVafWqTZJAFFWZFNqsw978+4AyltoqulCjClyAoFOatrkYyxvrXAIk0TGQphAjM6vSBRGMECnI0echorljF+e2itL1tD95nTdD2m23UymEFLqOw/Hde9RSS9nf74esZqxzmIpIeLhQIoKkR+wPh8Pp9Je/evhH//Tfeuje7duvv/no5YuHA0z8xYvtbjs+u9nu9w/V/XA4vn2/f//+dDqepuN0c3Pzo2fPvvhs94e/+IPb55/faP4Xb//h2ze//Y7ls+cv5oe3+dNPIL6e0i0iaz335nNoIqNh3eZzGCR2oqlnT0ogRKprOrGxLGVgSW9FrxYjNCggiVXh0o7yBpy1Mgq1OZOkOSvbwgWXswxVoYnYog6BDBEwMSDisJDGDqK7xHpZHCSLSkamKx2sFb2IjEvPzV1jMV1EoKouokaKuKJ7zgOiygs9rFsSINTGUVpkT/eEBRaCiR7AK2z5BRRjZE5DePZi4UAEokInkVm0eXEXk0nPNDJQD7U8nEyE9PuH+8325jRNNBMkR0yl1FKLyTCMIqomqtqC881sGEZRbRj/cJz+x3/1V9Pkm9vd/jiFpGF3+6tf/zaBb77N2+2w24zzNKnY3f3+cJydMgyb29txTPnjF69+/gefffzJq93t7S///H/4+jdfus95ULiTpdaTWkAlRBUmi2/ciCayL2wfFNDoG9iNpExRaDTjGHSGuCAgAV2qWLW5bBFhYYCQicwoIlVCDBJeThJHMYlcuwYiPfCC0kSnQQfEjfhN+NAJEUUB5AidkCo7/haIIUwoZEoxka500lPUGShIVPFe2robYJq4aKQg0toXNDYga/BQE8seMGmZIVCILjaxVZlfo+jbfAmooLSA/o3LNk5jlLEyeYhERZxMIEmgsyipzZRFgQKJvqPKwxxzEPAIM2sR1KopaRKBqphpzjnlDLqIqqmQapaHTKCUApWUjIGHu7uUkqZ02B9evHwx5FxEVMQr6VYneXbzEUJMdptxhlm4q/DZbucRz25fPb/97PRw/OrL36Yhl1Mpxb99945kSIiKQY0wiLbcyM7CA5AKCVGwpUhqlrxJNlAGtjyvpkdbNA8Fu/ynWFNaDVAJAQxM8EE8gRqe2dBiJNKEo6hbLhAPqUDABGLiiRg5b8Sfi+/AHAqREJyoQCrQeQ2DgIp4QgzChJrGqI09gOGUBJkUBQDNwxd3fdO5W/rZ4tBTMei5Ym0oKDAqVUVNkQiFK6qQQXe4R0RPRRK4qoiJmCIHlISEQW4rtl6GqKMze0A4mUuISkmAmU0YGhk6mYgtuKv25rv3h8OpzDXnZGbhbUAiJjmllEwVwzA0BUVNQXEGJA7zMYfnPPTQANebm+276RREysOY8+uXrz59/dFvf/3rj15/FKUyAtSPPnrtHofjaX86QTSbZNPb29vDg435J9us+/u3wsOnL283u83LT3Y1ThmemYaAKbJFFktqQDgkwp0RAqGERI0gkNVG0RyNWTUEa4Codpdr079EW14pCNBEiIzIoaOkzGGgZMwqpNgY3IlU8SoyY5xEJ3CCqMhApOCWsfNyE7EzUdEgHS1OIiblHnCKiJrAaBvUbdQBNW2955pSxLsLnQogLLApi++TAKgCE0IRBk2hWdppP02TUFcjTQUDMDJG8Y3VDEcjIIaLuVgRmb2ZIjQTIyRrKCCMDNlFbMNz+EBmYShG4ZYxs06QE3kSLWIupEoGdkSa6uHN3fFhgtjxcLJ0v90+jwhVYfUhZS9RSpWWEACBKCMivNYSCJ/9yOO4G8ORVX/+0x/v/Xd3p9hst4fj6Zu3b7749NM//KNf/N0//jt/8a//9a/+6i/HlG+2OyeHYXO7rZKGYZum4/z89sXrl194HcPldot5G198fPN3/uM/++gPPnr79ksaX4mpOpRZMIikFoRMDYR3UBquEqoOBWITMixmxAacAi2LVLquDEr/veX0qCAyORIjZaANgkHEpJ015hV0RQWPLCexLJIChI+IAXUT0w38RmPX6rUBlUlDBXHAnANOCUiCjOAWccs60tOtVyHCGugyFUmqRdQc4U0J6khNkUwkaUnUEZKADCZGy4esikIpUCi3Es8htxpbzCYh0kxACEgRPZkUaAkRkRuzrWVBUGiU7DGGj5Qkmi0SlElCNBhV9aRyEJyAAp0VIhxDdpBjmU/TqThLKc64fXY7+ewa7nPULIHZj2U+HU+HzTiIIbwVrpRaa0S935+2my0jNKfNTfr5H36uOv7bX3/zbipp3L159/54PPzsix8/HA+f//hH83x6cfvsu++++9GPfjxrcRsoOg7jy2ev6+wP9/tSOFf+2Z/+fPy7n+RhfP3pR0WK3mySplGoFEEY3SQSxCiCEInQqAxngEpVQiCaBcmSiohoBWYnkQwg1MBAJaRnRi0Z4SliGzEYR4HSTDUjJ3XhHEYPOqMilDRB4pCBEAyCbfhGfCOxM2x6IAaq00TcuQU3cKdBoMoN44a+Ew7QdOMt6YQVVtQy0qQippGMgbG4MoSeSRPNWkfxkchUE0uAUptuVxSz6pxUzDeCm8CN+IbsSbIioeJMk1oSLRJUzSI3wpGxuvtSuMAHYVJmSCKgUDVKnt1G6kbSrGkWmTTCY0fZqlY/jeYtH9hSJuV0mpY8NyY1C6vu9/f31Te7zdYlAjGXU63Fa0lmIMtpyiKD2Ytn6Y9+/vG4tb/41W/3e9eU5mn+i7/8d3/5l3/5/Nmzn//BT0/K+4f7z5zjsAnj89tnBMZxs3v9rEb8qz//1//5P/iPf/KT/2I3HilfTXVyr+NuBwoYWTBQBTCKNbDD5hGSQYWuICRCVU3NLJmmVsimgrNELVHDM30Obfa0HvfAXo8pgxuJJEzafdEGilBRA94OaS+ECEwiSWypImrCTcLGkchMz6RKCDVDhFpoB48NPBBJVMAtsKGMlJGS0uImSbAkedZEU5qGmISIeOZsiA0wIkbGQCYiESowiIhVYVWpEFdSQokEJOMGGJmNVCFEqliFJREVKaIq2IrcqAzSPe2mYUloagxlKN2IZi4HVWxIYllTVXXFLKzASCTBoLi9sWGYpxmsePf2/e3zUSVHzGSMY75/ON6/fz8fh+l4Krdlsx2TiQgo2mIHSp1VVessMg7JcMMfffbs9sZ+/eW3X369358yodM8f/Pu3Td3b0T0ddoNNvzxH/2C4c+fPxuG/O6bN5D00SefSkplf/rq19tXr8rt80OznjcHXgsQT6oJoi3lBSFwChIki4kArN30H5JElKKiIWqgEkcQ4CaodW46joiIqiQTQE1z0gEmKoZQklE9HIwUjPAI1koJZDVNkjqupYonRBYxoDPFcJEAYdSslh1bQBBFAMQY3FC3SBswZem1mpKaaUbKopZME1IVyeYjJANbcENmtxymUQQQCRNraHg2qw3X0ZshPSlHIosYXRlkLmIVWVsgh4nSN8INPYdDQkCRUGlqomtEd54ACBVVhRpVgUIwaJBCNUgye3azefZifPZi2h9Ox2OB8OZmq5IZUFFVMbNaqopsxk0ttWZLaZOyAomWEF7mOUivrupJ02bI2dKYZczy8avn33x7+uqbe1CdKURUdRL8y1/+u7/67W+y2Z+9f/MP/uTvDof59dZSqbevXm3GzZC3Xr5FOYVmVegStAshFAbYUqyMQWWkwBhIDKAKAzChSLiJibAoW5nC5gy34FCLMkBqU2UiiVDDklpSE0BEIXSo90hiVRq9oBRlqKVRDCpN+giooorBICrO0GgRo5C2/YeQrYhBSgiJIbiF3prdKJJJalZrmpllk2FWKyJZMgVZdIPYBLb0AZ4EKQzhwhCBIlTFTWdjaTo/KAEFlDGwJgmDK7QFLiRRE8ndWBaZdYiaAAWlq42gkOEQyhpwBhOYQRSi0RI7m5O6RfzrkPDJ693xYd6/P9y9Pz0c8Gx+pqbhcIaYfvTy5VQnAME4HA6BMDORlJKZmYiGMaKWWnIeRPq0Ddle3I670V6/uPni05ffvrl/d79/d5rvHqbZTDbjg8dNHn7z/rvpz/+Z7suf3N9/+vEnh+k4ffSgNuTxV3/2rDhANFEsSzqNB7Ga0ISijhyeI7K4RhG4UFuRTKpQWJkBYVA8zInqqNVYwVCFQRUmFEs2MCWIkMEU0o9YaQopXOBgdWEZDNaOfOrmaWnJsuyxJbKalxihjByxkSRL2a0NdAOMsEQmVVMNNcCMyVQTRUQVSDBuwK3ooLaJSOHaxm3JWlkhEYEUE+/unLbulKD26LBo8QYkQgMSKpa7p5bZ6+BVlL08giwFEhtrb8EFVETq1WvaSV0hkJ43FQArRx0+enY7vy6cWOb5m7en6XjY3Y4p2zR5jeo1gtUjpik02RBDeHcAq5oINqNN5YQqYJQyAUgpDYOqDZZMtDAwps2PPrmZavqLL9/+1ZffiOh2szmW8uU333073tda/sVXf/X5Rx9tt9ubm9s8bn7xi90f/b2fBaeWakKJACk6RzC8RIglVc0Qg4kQVgFXhoYDULYglrmAASuB2txZ0SKzo8V3NddFaIirISS5IURcOBBDBGo4qzNIjwjUkKRDEgiqtagMiDs9SCiVjoSlZAaAVj5Cg0nCe65VbMBBSbDCkkk2cVVClSpqRrEsqkiKGMRHaoaOzbEHKMRExASQRhEUUKn9JPnuem2x6R4aLbSDVlXF0P184RpVq5s7FGIKAsZeeEksdHFxRCaTtMBDItZUAm1W+hYSa+M4fPbJMzjBl59/cnp3f6ohgihlmudJk+UxK6mqqsqIucx5SCJKWDOPJYyEHI6nFpi93W4sJQazKbNxxG67LaWOzlcv8tdvMmke7u4hqIeTmshg//7uO7wPNf3TP/uz//K/+t/l28Pd/b81ab7kFj9mIRpSBIFwFTjU+tkj0nO+PUiECswdqKEVXkQmcg463VlDKOHKMKpHmCOLkOGTR4QJXKpDi8dUileXAAIRNEkJiOKJoRIi9FaqKlghlRaiS7aMSKuMTqXQxIdeL5obi0GpwmBJYWrdvtCAnY6qFEvwjJpYBy9JIhEGE4YtAQakQDRCqN3lLwARCtfOGC1CK8QlARbUcDZPrAXMW3xHKxJEoSjUO6dXp1ZYBAgJiEdI4xYQ7wGW2t08CksypjHdJv+IY/JNevmXXz/81VcVCPcKlZTzbrtNQwpimqfitdQ6V08RCQqVHsMgKqrhUetUazGzcRiQsjBEXGFjTnmUH3/+8v39fXAQNa+hNlDgXgRIScdsr1+/+J/9g7//sy++8PiNDFtGtGixIARSkSA0FFURMQhDWr0ooRiELWWsUKrXmnROcZJ6dBwjCsMJUgnT5lNoODdsVJqXwkIPFa2CIpwDJbwuJRkAZNXECEYrKBUMr7M7Zmq1VFQozSAu2kKzxCBCMkkYqghVbUySLCSIiDSZAJJbzhpcMRsywQFlZM3hibOgGZAD0WIz2sGe5qpu6oR3kwRZKyM0ADGGOlFVvC0MBSpOIJBDWrZOU0W9px9ZlRTWuLnV0FaIxls8mkd0WxtBRvNlCE0xZozZDPZ8e7tV2aR6iPS++te/OzT5//FHr9R09orgdrPJbV8FIghrwThqWTWlnFOdS5k16BExTyetRUQU4VGTDkJ5vk1//09/vD+V43FKNuZhc5rnAIchm8ntbvzRF5/+2S8+Pt79UtMDYJDuEWL0BKkW1S1QpalSJEhBJGmn2DbpIDiJzKIn4EieJA7gJEJL2jxRFrLEDGVIqcViNtRZakrmJi4skCPDl+whQkJFg83BFFT3mCacaqk6hKK2kx5ExAMezfnZGIaqJ6WRgsgWSZxK0lNpKXqkRJUoMDFCWTI9oRpdpBBVAAYRFiFwVapDnDIHp4i5hXyESIjRmtO5yZpQhAYaOlaRloIqUBWhii4ID+pMJYaQJGIOc1EHAi2fFhHVW/hjiHdeLyKSTEhouGlkCc0qdvvRZz/98aD/7t9+O5X9cHP70Sev7u8fpjoFFxAOycnMkqZFHxJK0EQs5XG7iailzmU6ebiJtNDLoCtMOD/b2C5hb1BxyLTJTMnGjYrw9lY/+yg/f+bOdxHzcqaL9FiclrAiYpIHWBYdGYNUVWmKASUKUAWucJGinFVmyARMohMQlEZyrUaQEsKgV6EYkzI5QlgR4hpFdWrCpWVYtRAxJd1a7cnjXE9znT3cKsRDmQirTF41KtQoRohpy6VxQ5hpEhdWBLxGmlt7DHUakCEmbGkCKk4pgQLWWpwuwkGRWvB7ICqkBif3QiVco9GGoRezinaOjRDqFRpwaqtx2f1yJuoBhLLCCsfqI5BUk/ZTlRkkoqK6eyvyxkJWXyIEiEpBVashVpNJTTf68o8//vwf7H/56+32n4ry53/y892zmxLV4afTCaSZBSTcTSRZUhWSqpK6dShUJLxWn6c8zNMpShHIkFRExmE0TaISjJvbUUXKXJoR5OZmO4zD608++9O/9x8/e/nqbv92mt71aFMsmEJFtSaJLW0LGQQjPLFxbIhIEHPU5l3WDIUYkMiM2AFKqZ2HrYFWzRwUVQOBoFWohEO8gdSkKkJp1V0dKqwR7jwFInhyniBhmWoiAvdckSPEJ4lJJHHMkRJhBgijhwIwEBVF/BjpyKgek1dVSYGNUzQnUQXG7NCJnBDdVxqOkCX7LxA1StRKmUE2axK1RaQ1ft1MaEZPjIQQwijaYqpaWJ0qGBB1yc6BshUkLKlvRsJdKlnDI2ayAjNQWzK5IILClhbV0KjF+PzmxU/q+Gq3e7fdbX72Bz/9w1/8SS2l1gqBmZVSIzjkISgerfS9iTKn1CC2IFTUTeEiojlnnyaGlzKZaNLs5HbcjpvN7ubmZvds3GwsjznlZze3w2b70Sefv/7kM48Y8u3dfvtw/G3wKG0GlQYfWTbkRrghB2JgTSAh4kCIew+2RNBKDBLqYiIZUiEbbYyZjnZKVwso7HA4BCFL1gQpggQMHiYUOKIxbpx8PhEzUCCTJocF0cocGb3WyupWJqklpYAI1arQPCQqpYYgCzQqK31imgMVSaFKWqh7hE5bSSIYJJK26pCtaru5N2kDoVpw9pglqqSqGkjdLtP06xY3opJABJVVGKZJW3p2UzxEQhCgUwMigkE1KArph2TRw0t4AYKCIF3gphUaATCgFMZEihhFwc128+mw+2g+HqdaXnz6sd6M283thNP2pgYEYiNBolSnJDWrUSUEZK2TiYybMVsCIZoUDqpqSiZey1yK2tAClFSz2iblZ+P2o1cff3pz+zwPg6mMm83u2XPTnSV59XoQs/30hn6yaDXhkAw5kCQZROFKb/YvIcJB+hLIbsrQUjNPrsyailqRXCiFrCIzEWylzFvRBbSUaRELKmAmNOEQsXEmhAKsHjXm6vSo1CnLe2VJCa7BEIUpB4lNlDTVOlV1llGmhFqKldnDRy8ZFHKCKEOB+aRpwigtlESYoER41MrqcKGaoNUKYmg0tEI3V2OLd2eIVEGVVqpEe06ZApBoQatBhGuUqg6jaA7AXSjUBCAK/AQ4Yb0CkKFlsIW711Ln6s6e1IEeRiImwlYwokX/OjiJjJvXzz/+M+f43buv3u7vbj/5abp5SHkotQ7Dxt29RjP+DKGH0xxBVjajqFcPMzUXqghCvMWYkhq11hoRyMNYa6UIoZYHJ07zHIRqYvA0TSL0m12NSKoRMMsGsRrZCQAqRhgyugXHKUGKO72ydopAN3t4Ud+b7wf6oLnYUCSXNLhZUZsUFfQetaIIRg0GHZLCVGHqg/vWy1gj00EUR5npc1GnanKmMshRGO3EAFDIsYWVBuDmNHc51QiVkUEvGjVC1AnXoAQ0ak4zkvQs6mDPXDWiIAI1soQxhCm81WyGQk0orBKs4Ik8CIskmmgPs2N3voMWpNfkpempVd20GSxFEDo7JGriST2kmrhGVZGMZgWKKKWWuSJcUWBVezlhU1kS8CQgAamqmm+2209oN/uH4/7hcJrr5ua16EgWWKXOallTiqBagkoeUGoFEM7mnXf3aeLd3X1EhfD2Zjfm5BGlxP3DfjpNz569GjaDakrDOAybnMdafZ7n6XSCsJTJkoSXWo9BqVFEmcThxaq30jTGZvNiQQjh1JlE7YepeEPzzuRliEMuD3LayzybGCyntB2225rzbJZUi3Dy5unKEaLR0xhI1QirMcq8cR+qIyodUgJTSCVdZ3AOqyo19UCRFjFdiAMEZp6ZqGJSVCFqHrnSi9OjOdmI5DK45qQOaSHPMAVJqQiGIhSoo5QhTDx5qAdJaccEtODCGZgDBVGkElBLAjVpVB0qEb20lhBjaaH9DgmYhy3FKdx0NrjVZC6oib4BEuBepul4Yp2MTik6RHM39koOQW3RmOqiaqPl5+OzT2r42/dvT8eJlSJqeSglWtgeRNVSRK3Vi7N6uAcDqmpmZsKIMpfTNEW4qZ6s0KmCcbM7nqaaUMltGrfbjVqapnm7e57zJtyn+dRS+iO8zJMIxCyljcku686ZJIqxFYmLKghR9Fq6EHRNlSRVTDVZjKz0GjXsSDsxoVIDo4CUcUjZLFkSgrWEMMI993Iwsfgk3EmP8CiMKtVZS5xO87HG7GO0MzWyZjVIO4QpECEUBqroKUNETNWSJrQwrrDiNkdIUEwkSQpaTSlgItZQuEAEzlZQJGuoiHokVg8nIW0lFEZEEc7kLFHJo1cIjRTRQcyCJj1SFYqgFmjtoajtKAdqEJUGVFPPBkkJYvTBvYUtlDKV8DmhqM1iVaJ5WHr+T0u1h1EQSK4p2WCb3f50LPPJawEDCFmKzLbnch6GvCHggbn48XSqNUwlpdTCgwYzCryWcRhNFQGPapJudrftDaXM47jZbAaRNM8l5627n44nSAzD0Gv3EgLNts2b3eYP/4vj/Td3X/4z3v9OatTqLjKrVm3nty1pPN4SC3QQbMlArQEvKUVOPRY44O7TATFrZHFNKgOC0NrCXcMuqpr07Ilp9vkU7qgulXaMfPJ6CoRAK0dXYao9fo0SBR50dREmgYgNuoNtqTvIENMmVFqNcQHULWDqKQdUkEW01RJhCCwAgQvFmaJ6eLAKoZGDoqoS1FOdS42ZcSImFRMOqgY1hkEyMLLXq60Ws/Dg5sgGMYG4s5TwkBBmCVNWJJRNAz71JJzJgIpEC01RlSZeW7ZlK8cuAA1iIhjGFy9emw1v998dDgdGO0KqnfskqqJJrSppwlaoMRSyGccYmvbYNXn32Aw719lrsaQ52eS1lGKWxnEj0JwHVVNN47AhNTwYdEZEmHtOOedBbbA0akp5GDbpWS1p0P+x1upelXAO0BSCAinoVvHwKoKtDC0RQisYVjUnm3OKIqJJkAhDMms0JyFGhbZwvyKoS01BqkRAK+xIddGi4pQQqbY9mk+1OsxyHpGUaRILwByDe1C1+fFJUR3ENrAd862lcWDWXGXiVNBq4DBGWBorVZAYFqKqEHNEhSvQDmloerKrkB0GKySEE3QK94hKOrXlBFgWgSTKCN3CkwlhE2JmuMqdOEgLGkNYggHC3aI2nhqbWkb3TZRBmZQqVIucIyWxTG3Jdj172nqcrcCFrjHAfI4yTaXMACK81uruQVdVlaRiIh4etdSG81u1apIwM7NSSq01JTPNkkzNCAJWihMch+24GXLOpE5TJSspmrzn5nmIipgB0uoA53FMwygwMzWowQsmR/KW0BMIwJFmj1MJj8iG7E6kSgUGFyiY07baYC2jwJiShmVaq1MBbyWkJUSKBrTX8RcKK3SSfFA5ZU7RwnaMkDnVucwiamqq2o4bHkQG+ja1kj2CYKlVmHYcbpB3lm805TTMh71XcQRYMj2LDBrpWd2LiIRJSsoENSoqHQyVUKlEhMqs2cXCFGYwMuApl+ru7gBNqeJogdNi7egAMTNXFWE6uFNkJoo74AMjS7iSpiEkIlxN2mk0SGqqmoXZKLluxpDsKRUztlkQJIH1NENwYlSTZDadTvN0inDvNWFajrSomGpbWgSZhiyWMpFKnUrNaVTVUgtEUsoCoYiaiaK6i+VeVa+ZqDejIJ2OZT6chmE8nU45J6kIcBiTqeakaRw0D5QUTknMu+3N81f1sOHpEKWZR3uYeUBLqLPXaqnVq8BEIeIUarJBEjiCTk9KmLlItKI4Ko7wKN3VIFWjgmCVoFZLNZunzHZ0hiQgkSIyN7M6RFSplA1kSLFN81BP5uERIV5NCGy9PpPxmQy7YYRvSx3dtZZJ6yxKUapFenb8CmKwjLSTPCAZaQ4HakhQoyKoqcJOYq4qZlASbKZfdw+h9PCaJNnQzp0QqWghPl6EhVLYc4NC5WSsESKInuEiriAsibHFf4OhboYByCyGSRGGZDKYKahOivbkpgItNkwiD8d9mecIlyUXSUA19WblVFM1VVpKaxW4m91WNB2PEyDb3Y41avWUTBTT6QhtFYaRctJkKW/VBpGUBnt/954EIsacbp7thiFb8PjubqOy1WwbE+0+kjRsfPcRbaf6VlRaZROFJpUakVDFQiHCsKYLSVSvHlVUBGYSCGk1Skokwp2EikJ6pQEwEaiOUsRVqFQRowpvhnQDJRVMpBaPQ+QUNnulUtWyIMFHzKMcRjkp3BGEe4jHMEhs1TaySdCwLKOFH0tRbb7VlhiTvvudIEUeuLmx7c62Q2Rrx0I54IgQEU2hMgPe82jbaQeEahqSADZktZxUU1JVBuBAbWpWSIHMFpUBmqhCEeFuCuuhITSjCihhUoLde6nCmBNjdA7wvNRxVXdAixhaKocmYQaGOYaIVjbPF2gawlAVb3GSJpzDTHsGX8A0RYmp7FMezLJ7lWQtTte9QtRUPVxVkuWcc68HQvFwVbx58+bTjz4ehsFExXk8PNwFdsOAtBmfSYuHD1BFIu/mtOk1C4LCmpBMkBM3QjIBAddkKYkwvAqKigIWFLTgD1OKIktEFVZAPAAxqNJjLpjmNE+oIppiHNJmSOOQzAYxIdStOA5ENhuYTpZcRbKa1cFOCfsR8wbVWN1bhT56LYrJDNANuKUKsk3GySSreMsQdaT5ywdQOab0vOrLahwlo6qDNIqoMOeakjKHWjstjAjQEczgYJIM2Zp805Yur5IcOqm2eg9VpBJ0iFJVCTEMyWKRwamK1AhTAaKoTkQSZXUQEaHGFJK7Ek9FAQLMzlypIaPjWdWPQ26rv3Mvtcxea3PnqqjXmKdT9AxpzKWEt3P9TGC1+u3NLcHTaRqG4TRNlnMttYZDzN3NNCKGYdhud2aZgcNxP5fTfJxePn8eUY/Hw5CSDUaz4+nwcP+g21t6ZThFJQSK5x//iPXvHn8z6f49o6hPCUmpIhEpwhEegWiJ7g5S1NUSOYLmYkJSSkhLICuCyUgNSIwmA9WaxZMCURIekSGmNqa8FaYIdRaaiLRiXaLmppCazAedEvc56ujQUn32OocFPJx0ptOMQ2DjJhNirsWBDIFLiHqVVL+ZK92GlOaQCCkzB7o6ERCxISkHQJCMHKrkiB5GZnDTyOAgnulKGiEuhJawghb9Hg4nxVWLSoiIWAJMJTmsHc6gaqqZUJG2zO2ErO7+DwlXupYJbh1rG53gBJsk+5AwjpFvQlKppXrxOnupbBmZKhERdW5wP6KFltM0JRtKqc+ePZtLLV7HcTwcD83IUN3XKs0ppVJrR+8qp1N58+ZN1LIdNzfbzTzP3/7um/3d/Reff/bixfOcBhtG09ZBp6NZHijDzef/aeDm+Mv/Rut3o3o2Ty25nyRiqlMEQmuoBSWTI5CgQ8tJgUBkijjRZ3JSmQUlIhQHyk5kp5aTBaBhlZhNXDkCW4ghhqgaVMfsafDosRJRxYrKlDFZTOYzJo8j/QifKKKazFWKV9Y55uI2FWmgRjWURWZXTUOaCibRFIZqdUJKUQtnuDSQMXqNeuA0SQ7RQpsDIpG1CptlUSOEEImaUBWsHETC4fuUjtoLYFHCW7y0tLx0NV2rk4U2vbyhFMCiCLyCE2C0wmyRDdSg6qzhc42ZfkDluAtm5WbcvjxNKdxbOT1BP6LLq9fqHgFAVYdxgx7yotOp5DxM03yappevX715+8ZSTmaN8vI4TtNEE5JDztJgtWgp5bg/kPHqxXMzu93tDkL3MpVps9ttd7fjZmc5AYHwELY9QjFwkHxb0mBDzgGLGFhyzKwMd86zV4HmYkZRpYhYSpaSiar3syFIi6mglZkLpFloGq1EXCQVDVBrpIOAzm0JkUp4nTlWLwVTiTlYg6Ak0yRuUiyKFZeo1YsHUBNmVngQTFpJ4TzI5M4ZToYGNEQgxQWS0rfjdjKkMW+223HYGsxBD+/FgcKmqseih2wnYYHXFkngkegRmEhRoYezgJMFIlBFitkx+TG14wQBCaEkTdkitfAStpMcIY16RINgC64iS7S6HDoDEZkxDKKC0qy3RJo5hCVXwJJtPt08+8nb6Xe1tFo2oi12pPGbWmtxMU1pqNNklgDZ749eY9xs3OuLFy9SStvN9vbZs/u7u3HcHA/HiHh4eHj58lVEzXnMOQ05j+Pm4WFvZgil4/7u/tmz23Ecd7tdzrlGWEp5HDa7DenzdMx5EDGBQlXBbDlvbjh/y2mOckJMKSbM9XSaZXJWrZrqZtTNhpqTiSksq6qlJCW8kKTaKOohCEbNImPUTa2ZMFGaBHBy3YeeJibxI2UbdTtzcATrPvQUCNJo2VVqSZxyFDjCszu8FQ5QIaWGzkWmcOq8wUEzQ0NYs2JMzIpCzFHT+92zKREb3dxuxt0oOVVjoQNIopKsJBQZ9gXuQYsWXmuMgDulBmcy1TqGe3gmQZ2ppxyHgVM4oGIC4eiyzfnFdpcsFfcHPx7nU0TjSZaGgYJwBCKqF/pEJiAHS2AmNwkqplKVamKqG01DGm5j9/Hmkz/RcTPPkwcVqmIVThGVJECQKRBEmSYGk0mpnrNtxs04DOEhCBNsNsN2M0zHZAoT3d/fm1gtVVSS5a6zASRVFaCHR/jheLq52RKy290OeUjJSplLmfO4Oewf8rgdNhtFyiQEeftiN+4OMc+nhzTPpcxaJp+meZpOs0+0mjLoQ7I8KDWzB4kHqSHiGiXC2RODsighiZIixhpJhKJHwQPx3rF3Csq+cuuxDWZ6qMywqi0tGmPEzVTUfENapJnCSGQ1kZRMRcMwVdRQD8bhNO6USaB1zNwNmolKtcJ0+Oj5pCw5ym6ct5tINmmcEBBJkkR1VhQBXZWiDJeiZGGVKKWdpSbMJTauFWlLT62HTqvUdvJ7YCP6wobXw+5ZHg16ou79eJiKOzQlNUmASTC8khLRTrOLMluNXdh+ltFcoc2VO4642drOxu3waX7+p7F9PU37Ms/NENqiHpNlRIRQAzDVaKe6OclmWc7J3KuqHI9HETRL8TgMtfrtze50OEk/nAIpD6oUIBilFIiYaam+2Qwe4cFpnt1D1Qh4mafTiao5ZRARreSsUBAxYT75fKy1HI/1eKraK7bpbDiKRdKkanSNIqEKmYMRrI6JPAkPEZUQqAJDK/3HCA8vEs6iuE94A3nveqJqpHnCFPmUJOlcJWYVipqCYSkwuXm10fPYIjFgIaoWaokmFNdJo3AmS9R5PoGaN3UzeBo4qOSZZEl3H99M8ABn0zFlpFQ0nG4hAQNsEpnF1cMYRtSorVpU7QHNFKcVn0uFwkQDTTDJLjhWhEDVbvP4IqVnkrc94kxyBYtUaIQqNBOJLlFAuogrWr0Bg54ijl4yBDRAzPQmiYMDx6kmExXW0/EwTUeyBZ/AVJ10Rm01OERD2HQdr+61DnkId0cxzc0om1SODw+qBkbO9uz5Llmei6uZWU4JAYr2lw8pkTHkHOEM32x27mWeJ92r5XQ47ievz569SDlaAdYW9eNxnOYDy1Qq9y4MMR3MzFAiWM2qqZlQ5uozy0ydqcMsMkMONU4RJQiKCVVF2/kQ4dVl7+auB8qd6UPaPsAKYSkF6c6jUGVwrZ6VomYSYVbhdRhqnArFqEZLTJaHQVJKsdEo0/zeY8KRRGqWfGyU2+Q1ldEoXrLUdNgN7lXgUA0TkXaOMgXR4PEAI1jDK6v3M2eUKqG65AGGuVvlTKmmDaRqyABUoagYUq7MxhRhjohyOh7m45HhoeqtohVdouY6K7QqPEkIIQqliRuUFTW8qNioAxRUx/6U6rh7aVEf7t8f9g+1FjJaLrBISKikxocqkFJYiJgqIsI9vJ0biyhlEuaci5dSJ4Fayje3GwZ3u91cXQSWkyVNOe9ub+/fv1fFkPOQc7hsxiGppGQPD3cQSE1pGHbDaKpLgaUQUOGb3Sc3L/+wfP2vGKfCWiQ2acgqSeCCrDqYjlHSXA2UcmQZatoWSycdZleWGMKFoVJFI0QTIJSKNBmOKnsZD5qPOpys1YDSluIxNwuKWdOfUjviWzwp7zSNKaWsu4GjliQwtZSTJ6nJp0JFLpGLZIopgsGbiLkgzZSJMSPVVjosghaUFgapAI1VgsZIrgY8CKtAIwQiKnR1tnKtZAlOmGccCWRRDUvRUz7FYNYsEzXK6cRSioff7Q+neRJTRqsSaUGi1lSLKqBJNVdRgLdenys3GSHYzyVEVIecU7as48vx9U8939bj/v79+1pKDydoxl4qBMkSwi0NIqCHWT7tj2Hh7qLSDjkKxt37u812SMMQpIq4FxVLKQ1DtmFoyewQY8hmu3n+/PkwJFNRU3eklI7H4812eyqVIqJpd/vs5rmt5bnakcFNHRvyR1lfpHTYZsvGTY7bHDkJzY4eMblNsx0OyUmhj0PaaGS4WK3MHuY1SWSbRGoL76uS55QfEt9FPnFwy265VaBqSXzZVASkU1GkH0OgERRMabzTaomW5iRlSxkMkgxCRoio7AYi15KmMA8RcoCobExynQ9xAA43SYoPMaeYgwKpxM6RKMlZhHWISGFhZs07jx7Q3IsNA/TQEig8lXCVWWAGUx+SDiln27hICAoraj3iJEBQ97U4XWEq1oKsASGZWDOwS+MkOmkSkxvVG6lbR8sKaikxo4kK0/bz56//9Fjl7u7u/v4+vKacop14J0KRbAaVFAnQoBcvHhBLltpBC2IpTdNEQs0Oh1MqnlLKWb1GHlMNJtXmYJ/mWShwbvJ4+/z5bjtOp4PXAuE0Henx9t27PAyEbG5um51CoLVWY1iP2RKBlEpSdnk7WkZMKU/bwZNBRLeBk8Y0ocxzmd1hEjLIJJLQQgCdyf0ml5sNLYFCJ2f4EYkYjj5OkUJSI9WWKSzN5GZJJNNE6FOUYAmlQl1kMu5DssoY2ADuhESEuKKIztQqFqouVoQDdat5EBsMmuxEV96mHaatlK1WgnPYrGliamkELpybo7OV09WWVKBJzZuC7JUMJVQlklbVGLJmFeWUbMy7rFsXFokUOoEmFQqSdWNzhYvSTGxQNWUk1VF0JJWSaIlphhSpRbGjJMF2sBa4lwOWXg3P/xDY8PT+uH+YmwsMWdUsDaqJCFWt7r06bHSnuw5SAaSEaIeiUFU1ZwjC41RO4ZGHITxuXzwvHuI+DNLrJqsMaSO4ayWeNZnXcjqdxjwcD8ecx5yGzbhtBUbdY9yMtVbLQwscULP8/DbvPh45Gx4shBZZS5JQ+AiK0bd6v9uchMFseTCYlDoLZklCmvluK892YgolvNa51I1KjhyQSHgP0Vatp+WFt8QFwTCOQXrQoXPLk9EWucuT6F3AmMJpcHqEtDB9PUZ22aAVCzCxiI1yazYkKK1mkbRNr7NvBFuqkTXJfead1MnhgoBWswBKRCskIpCkyVIKdwq9BsFIIjSaAiJ5Y5uB4tUSho1LqmAVschFsiYXa0IjmvEdMpgOpsZaLVlyyWLWkkggEDuJPgAbyM7yjQpZ9rVOsak3P8svPp/LNE+HCA86IWIGUsyavzoAdwckorq3wxIoCs0pqosCxYdxKNNpmqaISCmllEjW6qIM93HY1FLLPJuZqAybjXvN41g9SNYSp2neDMNUijRQa5IGG8bsXslwr2oDulkTquDwbMq7HSIzjEFGrmWIAtFk5pbus0w3u3tJc5jlbKamUciJAQu14sYkGOj0GbMPVZtF5rmkPdOcVERLzC1SSlUcnqRZNpEaV1KpkAQINNQmGVqA+yGQ0OPSVcQkUTfFNsZIEqaaw4mCSmgSTwDDkJ6PTeaokcm0ZjuApdmH1aoKBKXUQLPXaMqmyapSXMTFWrnTLC1f2/KQ82hJzVrKZyRVtaSi5DYkxIpISQahzLMnJmsBUMowm4ZxlNTCYhVMok49CN5L1TSMlhLSOEJ0O2y2EdWnUtxb4f0gUs41WoX82k7XhmqdZ48CIsKbi7XVPwGRJEnxMmEYhmmetSUbp9Rgy+k0DYFhGMK9llmSbXajZWvQzZ21uqVBzUop4yYP2+2wGQOhpqXM03xKQ1ZttSeNwQhR2IMf5ig3YSlSniVPkFLC6Jss2wFiTENN44zcah8GY0JUE2gZTPaU3cFjquU0TSVEtpLIjWjKo2xeIL+EiI0aUQWzwN0tSioEJFhbcG1RQFpUEYO5iEaKo20CjZFjG7pDFuQgE1pFaxhlqnh7qgXYyuBVXTxth7F6P81LGqVZRlQmE8sAagWspeNIzjllNXNRphqDWa/jwYxKoaTNqNnMlCGt+HNLtlJLqgOhHkc12W1NiWzuc6/35lJJ9fR80nYEkgfgaEU05EFC1J/lgSoh+eb2s82LT49TidP88LDfPzxM0zQMG1JItWRQo1e6mxlyrqdSS5F+1lYrLqMCVgaE292Wwe1uV8rclPyUhhqRSIHUWi2lNbrIVJ4/f/Fwf+8eOWUgt5yvnDeb7baVXI6AJSmllFKps7tzCLNRRE6nu7v5QMp9yFBlO+V6GjZzlcRArmY5bV/sNjqkWbMzjrN/N5eDpKoqpnu1O1Y9nYZDOhzzhMBgwzCY7ibZmm5uXbdgVoVgAg+II6vVSX2OaIk3SBBVdZMQKU4SIckJSMxUVxdgEnXaJjQjNkq1xsxwmnlyvJnLM7NRMpDSRnVPCUmzajFUk1CkARpMedMibxC1VTIWRuI0wHISGTJoTkwYC0apoIemJMliPc9FVdSah9KSAbn9/xY1wyu8SDGCgpoA3SQOBWj2u8qojHBxGtUMSSSJWUTayKa6RJQ6T6fpsD/u3V1VPQJqZpmU4iUiKBpkVJqZAFMpLSa/nWUWtZI8Hk/7/SHnvBlHTTYdqxhElO6mOpdS3cV0M2xaqqypPjw8NJtQS8wahnG3vS3VN8MQkBpVmTzieNxnL+NmJ8bMSCKRN5Pt9jLQMMr2NrTMfKEcLZLmjPGZ7Z4NA0fzIZ1K+eaBp3vsS4XkUTdU7MPBATwcLJ0GLaPd3jzb5Rtwo8iqSPQUtVXaKdBJU0SBMyCtdlULium5M9W9FSLq5THUREJIsxI6hAgjSWxTFuhReSeYEgQ6mW4gSTSNKA8mkw4Th1nhUHN/aUiJMLSzxRKpLAHM05QCW25GbCylgM/UA4dJMpIFo59LSBGF9kN6QimgNPwtEgN0U32HgvDiTkgBJli1xBgcCNGWPxKgSm4hPgV24GAyjONu3NyyOCFznadpcq/NzxBOVQ20sh8SlHmeaymaNLyWuXh4BJOZ16qCYMxze4OfTqfD4TCOo4qpiIgBOB4Pxd1SMiQhDw8P4zgIkc3UUi3zZrMJ91YOJudUa93sbpqXh9Iiah1iadgIAuBgw+34rB5396H3Uk6JmjYbzrtcB2ienXKXc0p5I9s0DbFTBFD2+paW1BLVOe7zMG30tPUyZqbEPBbbaGzgljQYkSMYMQuOJrMBagKv1BNZ6dIiuiq8xUzVYISQqppaPI7EKLKhmhqip5RDrahMyScJMaPaLJZE0qRxhB9MakTFEBwAJp5ehrSY1kHqyLnGPNOPcrphDHMxEDZ4HkqYgckLVc0yWoplQASh/QCqiCICE7VkpGpwE/6sTOrhwVA7EmSE+iyluopGV/ogmkYF3L06CMtMW9vlvIvwWuf9/v3ptI/wdoZeRBXLEd6iPCPcvQBeozKCLdvGWqUTunt1D9DMhmFQVXefjkdAhZKHHAWb7XaeSyJP03G325RSSjndbHdq4rVsNnnIaZ7D3Y+nveZnp3kicHv7TAUUg8hcIg9jLVUIJWT/9jMetsqN21fldHSfRKpZqHslHw6Ih+GVGreoeZP0WeRXKb3YbI5zGgp2wY2i5uT5dtYIS6pWkEIyGR5u8Cw1ePLq0BRpkM1GgFqD1UH1KhHVojSN2lQgGhTSldCwjEjwHBxtSKYESq0n5gidmEVUrVBQAZpU0fSdbO9Q7msl50ENAoexSp7JQtnEaDG4T2TlvKuHYZ7Bcgqvclsi3XtUqGm2FqohClXQ+wEgPYDRE5GDSglV9RjmeTwehYWSCQ3VeUjBOjkr1R2SUvNeiog27xgREYBCaz1NB0Q5TdPpSDpJd48INUQ4RBqi6xUeGB4OQpONOc+lhDtUPKApjcOInRwOx+P+EAyVVKb67t3bV69eETjuqSmdDgdLmlQeTsfT8Xgnb3PO4f7+zT685jyYKSWgstntVKWUSRBZhmHcjCkrSK+lzHnYDml4LjpK5bzfz/tTcUbMVR/qMJfqR+5OlLnWwzGGSbZiN7ut3jzP6VDzUPncy86q55QMbnJILTomB7VIrXAVKWrQgRamGTqYbVXVLDAwR6Q6jZif1aLkbDqZnCaejtVJwACo1xxz4mgmUCs1jmV6cFjKIR0Xh7QDB0FB2tvtiWXyA8MVp6yJzHvPb9wV3ERQYmYcPQ6uw7z1SuVUif003Mc00eDYbvLYTk8zU22J7EWdLVXJNJuIMuA1otZ59ruHerijTJLHkKHmAclExTTBW90ocfc6zwCTqomYaAZuILdJs8hxfzjs99Npql5LrYS2rOdaq5lF1BbLHBGlVkByMjUTEVNDMKDDMCYzAUqtOQ/YYYxxOs1RMeShlHKzuyk1BGQEKWUum2HMag8PD9/+7hsB5un08tULoiU+RSlz5qbZO1vJxf8fUf/RNN26pOdhaR6zTJnXfm7749qgG2QDAkgqxInEqf6rJpooQhESEQKpICAQjo3T3efss+1nXldmmcdlpga1W6ofUDVYqx6Teed1EdWhG7oYiaG1qgbIvVVz6xzO526dhbh692L+CHxClRg+i7to2eacZsGT7O7B7f3WD8lGSCWU2kH12LatjWSfun7tkEJH1GOuamsTRXBAjA6RGMgZeEQkEjIEtB7tLdONOoVWGFemo9dnLamAGohVwCQmzjo1yiLnVjKoA+suDBX8BVt5mcs3Agd2QbP6guqk7UgauUzuidWBjQo9gihOokqxD2NHA7YlMyTiBFSMWSE1QzajhmaIpAAmCrUJGBL5LhKhoYikVSZq85RXrEU8NG3C0MwJcOWo2DEpGigZNmuttlYdIBMF5uDQtRwHEoVWcim55BUQVQWIwKzVVmulvgNTFam1XCb98AKONGy1pZSkCZg551wMtTVmX6HhLyH4ys6DmjYTMUSQpt67Uso0LeM4xD4u85pzBbDdbhe8LzWPm7HrfG0lLwuZUozOe2gNEUXqmlYXlWNviGutj8cTvJymwwHN+k0fYsip5GqFBqH+iSWYFwhzbQSYrPd45ei+97HEU2pLcK632pe104VtPPMofdTgE0QVlDTr5Y5JxOyNWMTALlcuMsCAPDAObJdL8gagc9pt3VqpNclV12IKHtkrUdW6gFXyo4uCKNZqK6r//yOtATknKRJUuqQpm5fMDoVscbQiVeJKlFVm0BBG5MgKWvMCdUYv0CGwsSpQUSIDwksQAxBJrUnL7FiAgo8CVtpaZNaWXpytgxPHGhwAI0QVViH1DN6jGV9YkoByOZECKhB7PKuNG8EqtZaWV1NpTRoAB6emJo0dE2BpLef1Ep4xhdqaZ7DLVLOKmjJS7GKtpYk4doBYWis5l2VtzboQEOB8PO73+yKNgFW1SZvnOfowzzMAOM8xhJwzEtRSZ5laqcXPcLV3V3swIGc15/Px5GPqt9uuG0wboImI1OSD3I2Dv9qGbkhrPk75sLbs4pmbIgmElb1nn6K79t4TjnPjVBuyhK5p9WXdSOJSdrnVzDN1n2w7V6vNsFUDcOjAE6imXFFL8+bRI7CBPTcyaYOujGZs3tnO+dE7MJFiLzlM2jcbFHyrYuQdO+9HMijaStML5sY7BgQzdZmLIjM6alxRF2vWUgJs5BoQCjFRRRCgpq75bmEE73PNTc2QmBkcG7hm4EEZmDg2EQVtF/6xMyFtRApkZCDaWjt7WjGyc8REQGROEZpU4gu+GQFNtKJDZ84hkoI1zdWC6xWdyoXsV2tuVUSJLtBsAGPi2orUgmaOmZHMAphd6mMhBiTSJsx0Wa5UtUC5aMcPL4eyrn0/qvouhNba4XTqhkFVfBfHYUCAl+cXMyOClJbJIRNfvF/ckbSmREyg2loDAgG4cKvISm55bZLH7WZz96akD1Fh3HbxeoghTL12geHkjuDPaE9qqozABtTAGMu2fRwONh8TbKP3sQfyFR3YyMXJVFdRDdAkp5ZLZTMEEGgX0nOVKpJas5E7QlcBzHhqthccABilBYMBERVrClZGjsmoGjXixuxgcEzeRwTDmkyqVEBCcL+MIbrMiMwNUJlWcwVUtSo68lzFmBApOuBAgI3QkxIpgJkgXEgLSMSqYNIIhIiYRbU1XQ0TsqJziq20Bd1gTAwBqkuXzpcPjPAL0+RCo0JyxEiXEFiMccedhWbYWsnr+bTc3N27uJOmUmpel5QaeW8AaFJrRXZsJrWaVtPWqsYY+uA9oQoAwAVqJ05KyZc5VEQsOccQCTGE4ImcCymnPkYRETNuhb3rY9ztdk+PTznneZ5KSWq6phKC62IEoloViKq0dV2qSew7r9HRxXSBYKZSpRbajJvP/iIT8fE7Co37PbBFT9t+5Nev3mzvD+eHaX6a5k/nw8de5drgXvI+zzlV19Tz9SZ03rL1pHGsddFWoWDAlyvVAviCLXMwxJKT1sxIAEAOBCVBA1MFE+jMO2MvyGxl1dQKaa1yXgdq4uKp1RUWCVAJkR07DxwQJGCQ1sQ175wjIlECcAmMzAStIQgRAzhpjHqhajRQ8OwMAcy54EMUVRVQcEDmvffsjKiWilo9qeNCapjnYTmZLaXvKwc1rXUhMwoxxE5srLM2YiIvSKANAB2Sd0wMRErsGFjYxs4jkheBlpjxfMo+bNkN63QqObdSWmuOkTmIqJpeOLkEANq0VjSoJRtYbQJAhFS1SZN5nrvY1VpLzkR06RfX1mKM55RqVRN9enoSkdAFlUsRGHNKT4+PrbUQQpNWUyq1EqGGy2QJE1NrdZqXDgyJHPtL9fIXW5aaiQL6+6/+0t58vR4eAZr5UMxi19+44S5eW+i+qKmt58fnb//wx39rhx+v8GmrmSxVnIuX3rkdeLSm3htva0WbE1HchD5217cUPoketWuGJedLJ8AMioiRmKnohTvvlYJYnwmaLFOuucFptvML3Y43/XZ7gmWtGdD5EBy7S7KAkBWU2cdoDmAw7J0fus5Na3GsTI5EUHFQuMnC1A6IlahhKxRYURH7rt+Mm5SLphVBHVMfY8deRBerhugYgzYriY/nuyUZpAO2Y3DNbTvzVYo2jT5eOpT/SHQVRo4uoovqvKEINAJg6hBEtKn+Y3kj+BijiFz0lK01U2VGMFVRDiTaLr0KNWmllpxMVFSm89xUQ9eBwYXjEmMAtZLy6XRqrfZ9X5xHRGmt1mZqoNDMvPfeu0tRPS3z7NzV1dXFC15yXdK6cXxp1v5iSWbXec9MKiqlQQ/MTP/4wV+acWbAHLebt1cApARGTEAXkDQAYBi6OMbNze761w8//Ad+/B/z+nFueHJylMWpKoCLAyMqttB11FNxWze84asvM488l6tmRk5EL0NpTWpOq0BLaV3yKtBCZA+cs1UppdWpiqBR9N3tK+12JUC1RS66AWQiAxOwSuzIwECJcVR6E4ebfuz6zpGxCZgZC7PSVuT+XDcpb7bNXfULmoMWCBFggNC76yqGlAIFI+hi17Mvy7KUDHbp11VaFpSmzKCOq2hLNUQPzkzXtKjFX6ZGDUsuKth3A1BQdAqmlmuV1sS5KrJYqRFxdJ2RByRybrPZs/MiVVptUqvC5W9urRmRGYFqzcVEWs0llZTWUuXq6sYHD+QuCk9QW9Z0GfQR0XmemVw/9BfLeSlFauu6aGAhBGQQbWqeiYrWC6tMwYh9ac0FX0VJ9ILQ7GJ0jDmnkos2sSbcc4gds6vSpLV/5LnIRRlC8Iux1exirr5oqNC58Wp/3Y3j8nQ/f/uvHn7+fev9i/FSW19146wZM/VD2FA/Ot6F4TM3vDVzw/y0taLYTaANSAzYnHdetXmO3nWzJDRMtU6nF3CpWm4CCNgFd3N75UKf6sIaovdMvfeBodSylNSGoYvBd+jWuQSDDXDPwQO5GKOpqRqa6/sdWtaXl/jj6Zqp+/W7885xXq5kGXuzcVjx/hmsqlyyWqbaqFUprSUAbeyXpqxZmCEEApecNuTSmumi1USbVAEk+4Vbar9QkaSZiI8BkUTVrGkTsbOhbbm7C87C/tQKOrfb30AzkybamgqRJ0NpIlpEresdADhmc24YekfOsfM+dMNIxLnWZV2lNVVrtSHA1X5PxKfTUdDaSS7PUlX6ITpmBV1TCp2vtYbQEVGtlYjWtBphM01LJqbgomPvPBPxMi+OaV3nru+klloz01UIcdxuRQyZLuha+kcrjbYLZ80uzBMkUnQIDqABydBt+9f/VI1TpcPhD8m7l3nFRfsyVcA7t7/v+tD1Pmx5Rmonqqk8/RxaKwbPTZ9MGiESilmM0bmwtjznhI2Ph4Pm89WVjKFwDPMMg994cqLVE4zdRk1EjCx31rzVlzrNObPbOx8xtKXmp7Y29ZjRefDmsLQa3ECuX8TO4rywJUgPE3Afg+5Eb4EbycdywkomzdAAXc6lZsvrCuSQqJLPZMZ+abKADH3MHiqgVFARaADgpF303uacZ7rkIlorDQHG0AX2TVFVHGEVJvMmWGqNHpYlEYahG/PL0kpuKkjO+0iXhJFqq82N7IiVCVBBlRHGvmPn0ExaLSnlNa0ppVRSSldXN30/Xu09AC7LPM9LWlYmjl1noLmW7W6/2W7WtBBha+39+/ciVkoxs1Kb8661pgKtac7VuVBKTct0gWUOXSeiOZdccl7X3Xa/u94bBQRSs6KNEcGqmIgaIDGzgjcwvhiLEZAuXHC4uv+zd62e/yjt/FOB8b2y1SVZPogcpG7TesMJXanYWs2fDu9LTU3aM/lHBHEYu07NqPJud7W2XEVQ4HR6//Y2fn3jr2KfEjwqV6NaijhjR8EFkdZq2nG44q7UVnR9zLXCHAJUgmxFBU7lQk1oau4XbwEo5GIH7NvV9XC/z5JPOSXfdZvbvqeay6zPrUQoF2qd2gVb2YS4Q2IBbI6r4bEdV8Q7xmpYlFQNRVEQQCtoA1WBnsboO7XlYgtFQ21CLnQ+GItpNfEiYZbUzoet+FzLzfbGg5tqbirIDrGWUth5IGrSnOPtdmtg0trlRmeErdS8JiAWsFovimi0Jp69d65U6YdhqNXM0lrgsv4BgEHshmHYILnWFMAQWy1NVJB43G5draU1kUUNm9pw0fmC9sNmnacQLtsWE/2CjWxSpEk3dAweodRaLj6KZhWQiL0Zggpe1mRUIDXjS1caqL+9/+3u8OPD8nMLYYVO2a1lfg94Ruu07OsjSWqYK9UnOr+QdN04OuqBK2nofVWodT2XY2kFwOc1e9feXm1uO6ACaamRQtWM0rAWQGi57jg2795cX91weDpPu7B5pFzMWsmlFUfIjKtkAHTRh6VlaTqvq7/qNpue376hYVfuRmoS1uVM8i3KQcELJNB1mtbU+uvrC69T0dSLZw/KRDi4sZBvWgzaZABqETDWilINsIJVbMYK5ktK0Y/j5lWtq4kwkoiVXJABVFCbwUXc61NeaF0O6/JPf/NPyCyl9XKSKbWBWm1K3iGHYTMiUVoXlQYmrZRlWtZ5IWLnIwfn2XtHAOxdLKVcBPQXQnLwIYS4LKuaXpohznsxXXMGAxElbCH61ljNYt9xC+vzIad2tfPjuItdZwbEPqVFDdh5BSByMUQCSut6ODwBQohDgB4Rl1Jya855cgRIYKACRGpggv8/Lrld8C6E6jlGd1WyMyxwSRCzP7cytdQHPKNUnVco2QoysxvfbtrXg6ZGz6U9wPKkUKVBBgZa6rrk6d3VyLU+/5zKS43cYKgRObRGa8lNjtPsd7f++ooYuqHnEF2TDlnwkh6yX2K7AKLqHDmUZE01N2ADhLaJc8BTWaxpF0NzLhk9zC/Bw9XYAWfnQUWMGYAC2tatt50F0j74bZelLNn8TxP86XlqFq/IR0Xm0IiqLz54AZizjZ0vkrmFcbw9t2JgRMYI0qqWDC0bs3ex872anGtuQAhuPp9bzZeYGSE1awBGSM77LsZSSitN5XJAqszUd10uBcDAwHsXuiF2sKbEKyuSquaUiB0552NQMDElDjEGYrp0e7qxb1VEmiGRIxNZ1rTbXc0xEdMlAltLdV1cUi6ljf3QDX0XO0DKtW0JibDWmtKS0tmHPvgAQKUWRET0F+q/ooogMyMDXTBYLGR8UaoEz+9ef/3dT5//9Px3DBMjcegMca05mDZ0hWMDtIZ3LO/G89c03VZXMt1g2PKuJ/pQxJiR3HlJDjUgLMaPszx9/2kPabPvhz5EdlQx1bwcX16mAjnRpn+zvbYQjy9P4qmwEaGnEEMntSGh98GVmk2RyQ9jXyXNJYewm1puUkGlkUYiBwHjPtf5uFbvuJEAaMsJnRsI/tmVvao/iRoV7HE0AQX+ZnB/1bnvFjc9Jg89dbQf6/1ud8POkr0/nIDh1aurh+fHTd99r+FBSC5AnTqvdV3aVBU71eqDBixijsbAMc2TaLnsMxcPoXN8KWupGQI4hwyuGvsQHVtzzXWdKbJ3PkT2vpn0w4BEqZSSilUNXb+W0lTIUYiuGzr6RVpljETMnY96STUjOAyayprTuBn7vl/XOcbQdXtVfH45Xu23PnboHThPzosoOXd1e9v1mzj0IfZNqzPH7EUURJtkdB6JEMEu9hDCi5JFGyqKOiVWIrq9uX91+9UPT9/XOkf05JjBkc5Baq82nJdtSirLzc4+65s/P885rlML2/6Lke63d+e5Puf6fDweU369H0Gd4pD7mt4N81Olx+OOvZ/qdeiGnXsdfJpO5SzL46CN/vi3f/f9fOi//gLJO7XOBzUgYCZn1tyH6f0Q7is4H9y0Hs/z+fY6qFmTutvvarYGrdRHj2HsxiJVkAopoK5rUoQk5WN9ftcDrsdW20S9GqeSYwy7q80/Hbu/nfRjWl8HftfnTZ2WR12ezs7qn+DqPsbXN1n8h3cfB2hOIzqwXR+m0H5Y1zNiri2b5y6o81G2aNjkF2gmEqkBEbfWWNUhwQWwB1Za1db+8T7sCIDQITly3jkHF9Asc1VBhEuUsbU2LwsghhCc41YrOKi1eh8vJUAAY+YmCgTe+3VJAmAAORdV8z6cjpM07brRB8/O+dgB0/7qCoGJXDd0PkZERDTVhuRqkbXNaMA+XOQ97IM4JTVERnLEjghBGuClLh9e3315/f5tylNkOucj5fk1wO680Jp8mm/Pi5OyC4NvxRCqmhbAdFadtMx5Hb7/lBspRlpawhq3iQACbXbNj7nKNNelng4V6h8fX382Xr0bJpK5lfr0+HGdFlSQ2qHPtRJaS6WjLrqwpuw+TB/f3d3GfpfPy5xXRZvzogBEIqVGfyvyQlRKLrUqcVRHq7WmaFJTywL4Pz3Q8Hrz21TDy7n03PbjUisBnJ4VlsN1bIGWr+7eDj6Wc/34/PGKmG1TU/nDh59vXtcatzAt3U8/ftltFwIv+BX4P4/0tE3/kcpLCzCv6vx++zUqahPViyCTkZgcmNovEHsg1dZqynkFFTNT0VZrCIGYkDA4RiZiQmJsFEusl9yy1K7rTqeTmW0v921yhlxbE8sRAjsAAyQzU6kqClUlpTLPKxMhcS7lNJ32u40PzN51fdd1XfB+u91f314jQl4Tk1MyNSXyapbXlOoSfCDfLsPToqYFmAN77wM6pkuB8RddKtj97Zt3b379p+//dkvLl9SAeW20kD34IwXZj9f1p6eOpMcUO5tzE/TOAmqpL4d91dvt7sVZcPZ61f7DuVjZXEVI8rDOaR9PA/Ov36nrT3/yLdppM66MRGFB2Hz+BlueTZd1bXWR6kJl531jcLhxRaeTfLrVu9AHMCJ1ik3EPHZtatbNI5QxhgOqEJY8p3NtVKoUUEVsoxuupD89nT5EcqGHw/pMVxo+f8zTx2f1t/brt/vXTvcw12Tff5hOp+kqi+5/tfXyw5q2T/YJT/d8/dnGb5flaTl9JPtChutt/43X1/dvnqxLx/O3irHf1Qt0VX8BC13YYYAgqimlYRDQkqdTrenCJiMA551znpmQ+QJKybX2sUd2wScEIuJWJXax6+I8L6qmKjEGVUPES9rVsSMm7z0im1la8+l0ErXTNI19z46XdSHGru+JiJgQ2Yd4f38XQwzehxiI3aUo05owJSIHaCUtUiu56kNwGkkBHZlV9h4BRUTNAAH10rPGPvaB4+n8zPNPnSz+cY33t6e7ffW7kMvD0/pQ29fohqKhntJjPdgb85+vqery9L///IqT/1/nx2va4Un/4YcPp2WKN8Hd7OPtKxlIy3K9v5O1DF+9U63PqQ6xc96Dcy7iGNya5jmvYHXLrEtdt4XSicC5EeqcTrG8ON9oaOksHQXP3Tbup/MkrnyRePfp1N9u0t4d6vqyHtg7F7sktLPhHttnS9nHku6679fDrdTj6QO8+/PvDOeNbYKOM4cz7fuSy/Ihrzejjzt3uLtZCn/8+OM/WX3qTg+28Xjzw/rw84f5447+sKRX8/hXsBmmg318Cq5+/hd/VXjI9ZJBveDyBBB8CFqriDIpooIIksbgTBUUVYSJEVGkMYFzPRDEGJkZkHwIyHwpHtZSQoy5VL2gPy/KBkRDrNIMLm4citET45KOa8455el8vr7aex9KLV3fExN7BwYA4JwjxGGIjhGtXcwxlwnynJcubrRJSis5icQO8Je5JMMYgnfeTNUQVQ0FgREcoBHRGIdh2D9OP5+e5/7HH283rlu2t5PSw/RxLafPX39H5+7s9s9ueaT/HKrD8xXFf3qzLc/nLZT/agw/n+TD9+9jH4eba3e7WUkUMqTcYwyKk6GO/elctthH3iM1RKxtTfV03W8t2XFaPJBnWk0qZrbifnX75/9wPiif5+RzeUQZuRvHOEprSrrA+adhfHws9vFFaumR7lbqmq9Dr167hF9BjG7z6fq12fJJjmvK+WZ/4jY7ikMsaf6ptJfjfbq+O57+/i70YwQIeeJ2sF1F/oc6r9vwox5OfFW1ILkA1Ebc7vfTuv708IxJbu7iNvYHA5VLZ6yKVFUR/SWIeNGbAYJoNRVpxQTArBZh55CIiJx3gHbRb5vZhbUXuk6RcsmneV5TNoC0ruQYmAjJe29YAejChKtNxHLXxVTyPK/LNNfaHDsFa6oxBue9qYauf3X/6uZqh2DLMjOK8xxidJ5/0TNZU63seUl5t+85hKJSUmZnEanVhlQAyXu++JaJ2F32PnW7bnB6TlJOWT/7s/uXEb55WvRl/RDs/HbzVNPLpLD73HMpm/Shd7uufLPVVvHHc+n4MY6v/tDy3Vt++/rWD6+Nxp/t448PfzRyu2HAZpEpaS7LNF6/i912rc9SisKa0svr288AeUi4sXhwCZwTrc47F9xvBvf7agvijZWOEFMuAQsZzutZ8DENnD57d3p84pSvtjsLAyijwOiktQMXXMbwHwqmXGTYff7Faxh8Pa6ftaBcsrc+1yX3/6XeHOjqt2At4xCmVOX0+CCg33aC1T/SYzO6JcKvrj/r/U6sQPkw12m7H8vUcxyQQLRV/WVE+YJrEDWzy9GYibRJS7mmVWoh+EXcwMxMxN5daGUClX38JUrHHEMHSAa4LinnQkS1qamQc0xmBqJgpo4u3bZmVS5Fv3XN5/NyKSKJaoix6zokqrXFGLu+EzA0c0TLPIfO1Vq01X5zfQETgwoSEXkgnqfZDIDcuGEVKSkjkiGiOiRTUAE0FfYeCPdD3xFs93c/zyD3eF340/pgv/vzOOyn97/fSptp6N78dWnLp6dvQ9+/7g+f+5d2ame33/xU767h7dg+W8vVpw/pZvt3g306f9yEN36zaTKzNcwJdG5p6fpNP+7mwxOonI5ndHg6PWmMtBvOajnpnWGpZTFxK22YNiF261RBDF3NuYiXQPF6u/04f1DMXPzx6aXf86vu5kU3BN6X+Q7147icXgAFxthdqV/FHj3cOvpnoXVUP4L7NMP9q18/Hh4eDj/1t/xjbhPfnRf/6Q/fPhfWtxGwI7/P5eEBs726fb0bXIPjp/efHL29/mKFFxzXQ6jrdFj9nIXXklqrBhft2MW7Dhfkei251XppuV+6ewAQUMG0teoBGzZijxflJpFzLoTwj8xPLjmHEKsaEszTFEJg5/DiaiUlZkQDoiWl1tTUqrRLSNQA+27wjsyAnDOyaZn6bm9NtHE/hFpzKxlNQz+ijwaoJtfXN9PheJ6mUmsIXT8EuAwOmK7rQsy1GCMwoRhU57q+J8QQx6v9r9X9uIlXsPypAMavf/18ePLDTqIHmL94/aYb4Ha88Tt7/3K4Hf1Ns1zmDYwfX9r4H37A39z5/V6/e67TD99/fpMJXw27ZlWlFde6zaY9nLq4QQitATn38vigVWpaHB9n7Ux9HwIel/F0iF/vZ4qu8YTI17tfOXpwRQgMrGc3tCTOxfSpnsNU8sF7V6XldS3NrdGn2qiSxuFhXca+03D94enjXRzBTSW0H9ey3d7/wznPGKann2xbkRs03iT0G//p5fTzDz9v/nd/0+KAMfkN4UtXhB6q5Vw1bnF387EWp80tH/NtW8b9uWazslQ005QTiCACMbcmoHrB5YqIY6rVSskq5p2PIZpZLgnZeR8uq9El4EUX65Bz6MAMuhibWF1WZvaeVbU1dd4H75s0MI3xMveMOa211ksZ0znnfHDOee8B1ACc913XAVhKa2S35iXEIYSQ09pKqTk5DoikaoYQ+i42PZ+fdtthu9kBQs4pdBEQvPeEoFKbKDCioZZcCBRJ1Ah5v42pxfl4LNJJriGdN9S/pMg6p9O/XdfBbd4N2/zyfGqbiG6jy/kHErrZvUjc3t797tdvHz48daKAQ01rQykJC7i7uyv39AlU13I+508vpydsbb/Z5NZaEw1WS3EEVfJDW/5s2q2RXZGfFKfT+SGEyO4VNQh+cLGnoJZbb/dc48eff97e9+/efn5+eU6kEF9B7MBHJf12h2bWl/qskrU5VefqH2iDj1Y0uNEmbXXJ7fAeH+D8D2kOP+z/8svur//6gRqnqWcEO6d1vYJbM/90XLZv3tr17vHTT/ejnjjejbfZ8ULb0RSk5XnqYzxNp5zz2I9am0pz5J1jZoKmAAqAPrjOd8ycW1WVSIzM49AbufILyRWIyAUvBiTSjWNTvQwWAngAULMikmsOwYG6aVq2u10tVaSpNtXGRNH7IcbgAzGbASCEGDebDV40D86btpxbCH6z3ZmKqTIBEiM6JkIO5/lxd3V9dXurl7QkWggeiZCQHRKRiphdmMMkpRRAIDQzKNU12GZYW95udjEdur6f6u1xhupQcakv3ynpzxn/X9n+j3/99d+8+mkO24PrY+Ofnp5HvJI3N1/ELgG5Pua17Grpx7hMH6+vrgyfIsLpcGC0L998hh0cs137+5dFH58+ssLuOhYHv3/++MWa3XxupVqw1ZFbS9u4aK2+pBTDBksqmaYyS7Mi1Fr0bkCcpmV9s3E/fPfvbt6+mRihKudmGVOrEblOttvtkg7Hjx8iZHbjUmBtYTfnl99/d3//pvubPw+b4fDd7/dXm3UhjLWklrhq61wLz4dD8sC1ybDH3/yzpaVS5QW6N5JZtAuuSvXRL2Vd8moAIq2JeO+l1ZbXVusl0n+ZZmfiruv7oXdEJefQoZQSvScCJnIOL1vesN2IaVXh1qrDC1sTnUMEH8cqBcQdjseu66bzOeUEqM7DZjM455HQTJGQiYeuR4AQPDM1aah68QBdAijcBFSIL7ZXLEWA/O2rN+R8a80Rd0NkJmZO65xSjZFbKSUnRgZVDJ0xO+e9D48PP/jl8V0DLLMG/eoK+zdXEnYzDafD0XJG0ZeXp+ub7fPu3R8+TH+1wX9663Td/m/D9gep3xG93V3vxnED/dX2Vk2fnh+X/LHrdsf8YfD0tn/rVwCYfvv5Vz8+/TRovJ5hx+HtN1//9PTdKjpj7mpbHl/cfv/m/Xd/enz+9M2vr4sr5CWVqgSP08d32/Av/mr4d//xub/f7W8bnf/AyK+6+i8+t1fB/tDf/JtD6f3Vmsp0XksG9Gxrd1gnup9VKHbd+XwcN/1hTnG8brvCf/bm/ZI//vB3Ol69vbsrbX16Onx8nghdGYrAtA27MeDH46e1PtLh8Z+9+psvwtX/8v7nZ5ChrkNSKbWp/PjxfRi6UitUFVUi6rrOd50CtqZMZAopZe+dj8EzmYoarKWINu97MEUAJkKzyymGAMdxXJYFAHIu0gQQqImIRE8mqOLUlJDO5zmllRk3m3HYDOxYREk1EDvmCzyP0TmmGLy16hyZNiTn2AFCSaszdB5rUzW8vr0bxi0SsBdE7LoeCRFgs9mIlFZXbY3ApJWWHQJCF7sYU41xF3vslqfH0zDc3d80redju96+k9YO9gjSbnZ7aUs61/pq8+358CFNb8+H3YJu75p3P6a0VVjnaWlPj8vD0N99qA/r8dM3wz/P7Y8c8fePfxcc/Pr6+vnj03k+jUjz8cjXdz9+9/etw0UY9+Nr34XngzOz++39FD52EdfGcz4YcCotdr2sn/7rL/lf/p/+eoYXT8+n95Yx/3Bov02kc/3vePtTtB8LgOT9fuhdhBaicsHxuz89RcrX+xHXwBw3Qc/nQ/rsZv56Z59OuMyLEIdwPD1R2JXFB+KrMRY/YNdDk+vh6qff/0Nd7T8/4/H6mz/mY4PyCodOuJWiqteb3b/749+Nsd+HwbF37GouFoPzAbWvNa9p7Xx0zjOytIZgQPYL/xjssi8QIQnKpSlbS/T+4skCQFFrtSKSiKyRPbtWm4jOBoyupDKO3Wa7cZ4VVER65kCsrS3reo9XaV0DxH4zFjDPiADMTOiQyFRVKgRfWmXntrsrM3MXrwazKBAKGqCZSq0l5zRP58mRczvquqioRJLreXAhwq/WLz8fd0hrezquI77l8Vr0p37bMfrRb+xeU2PRMnzxV9/Paf0w/csinepu3D3Aw2M7h9glLz+8vN/VBNiS8x/S027/RmUZQ7/ZdGfL8+EMyp8I7L67C0MALs26urpum6L70Z9omk+1SWD/cvp7cmVdK2qp59kta+de/7//uO7bt/9kWOmhvDyUr9G9TPTHafd4lHJ8+QtPPk3Xu9vRhzc3uCFhyYw4HeCH7z+t5bjZDiW3f/6rL76+77reD90oJEteW71knIer/e12GJC4D/GL/e2OwkvLJZX2Cbfd2xff/tX3P74/1sPLk9SqTVSbSuuI//LrXx9Ox59PzwuIGpRcSm5IfFHhxNB1XWytzuczqKGBSQVTlQoqqGK1RseOiUBFWmtSa2Nmaa2UIiJmILWa2DItYr94MNd1JbQu+th5vrTi1USbiuRSWq0mLa8ropWa1/ngSVKaS1pbLY7pF/I8qGkjhO12x84106ZykUMbGJELwXvHjMiInh0DgLTpdK45rfM5tSO6RTp+/Zu/oaFf13MFk7j5VJ6/+/ifzscPzrVu7Ob1sN/esSdCenx6Znj349VXP474KJZL3RA80kMNGcENw77IUZVuru/Py/tUyjDesPcA7rzMV79+c/Wrb7q7fQJ9Ws7bL96h5F+9CndhQ8bjX/3aYclGMsbrtZwbHaUQhOl2lHKasMp3Of1f/sPhd7tBijWx3x9HaOnnsOvagtNH5/rPh6vfv5Suu395+vd73g/+6qT1t7/9ap6mzbCPgwQZnbj99fjh9PGrz/+53f3NP/zt/1zv41rlZrwz70+m3oduxOvd+uHnCty+++EntUYUT6T+etOXFqtyEUWvRBeK3951//Xv/vJ//P/828O8/lff/BYVTMSccz4E76yJ895MvSPvWU0ucXs0qXUFBO8dojEqoNSaL8igy7FJ5AJkNFF16KVJRclSOwzO+2H0iJ3vfXCRIWorNa8rYRe89y7XtJS0vxlZ0/PTh83YMxEzOyL1kRyV1i6XQMKutbKmBZm6btdtBu+catNW8ppBNee1pLXrenfjptOpllZrS6qnwwkdn05rusmVTleb24obvuXp5Tttddt163o+rmdo6pew34xtBRtHlXT32V99gg+P+SXtV1cDQPnh5WFHN52S210N3a4kHLfh6enn03x499mvX6YpdruPD5/u7/4M4MnWtpbTfbfZhD5RzDQs02MevZOSyQdV8N6v0qLfei6pnI+puWXavnm3ufvvbP77b+dP2vcLBul5Xmfef/PjaVpxP61wOh9ytrs3Xz0dfnjVbbZbnuDcDz74zX7f18P0XOan0vBxRTr9+V/9D59J+w4ePp5OuzC6LuUhPkwa9vj7h/fTGm5eu6ub66vta4L4anu33Y2hSj5OL8spwbyNW0ByhNqsU/oXf/HX//rf/7u///nHP3v7VTRzl8K0qPfeO0dEYNqkEBJ5VhUy0tbM1thFlUpIoOIdscOx714Qm4ia9iGWnNCgaTFF39CwtYrDOHz+9u15Pp3TsizrfjeknA3UMTnQLobtbtsPvbRmNXmysi7jMAR0DkxrxuBD8E1aLSV2PYCotqur6+12RESp+TISKSUTmOSSU0qLjn0XYzCD1tpue/XP//y/Oc3vP/L7rtkXb754Ok4UNynNIW4bnFr1u3A304t30FJZq90OV4+H591u+PaHn3/75jev7ekv3tRi8P/8949JiMpjOs0yjY/8/d3tbz7/4rebzf777396//NPn735fE3T1bh9+vSQmnehAznn009Py3ze3Hd9twjchJ1DEM8hBp601gasOZ/P/bV/ONlhxjt33cLmD39Yf1iqM+yoO56dY3W7+LL/dW99bS+//c2XJXkKq7u93mx2DeZY+er+y+PhmM6t6wc9zwGjbK9PWlef4tffhP/07e+u36Y2f8nzhHryfj6Vxw/H7fbG6nD76gtGOh1+XF++e/5U7q9uqXWsKlpntW3oAzAT5Vqcwj/5zZ/93U8/fJgnvxmCokeutGirjgbvUWqDptQ7+2V9UUBKefEpRIzOD0zFTEtOqaxmJqK11rt3+/Yha4FaSm2oUYce16f56v5m/+XbT//52Xk/H6d5nrrOg0qJQWrux6HruqHvzWopbRhCSWsrBU1BTVQ6dsE573ypIiKbcbPduRDDPJ1ba6CS1qWVZLXG4PveB+8PL095naL3TL7lmk/H3Xb37u1f/vr+Vykt0xx2fH6Y15ryelp1ndQb7G+Eu45y3799Sp9+/Pm7z1+/62z8/PXu6fCH/8Ob8lebd+WTjof1w3fz33159d7T9bY7lXZ6+aFzwn745vPf/vThx2k5msi5zD//8cP1/etUHyPU3I+467QfHh+f37z+bDo+OTIDI2Z/1d1uLYg1Xuh66+b94+b69rkUd35anZuju7lyoBji/dPDue8X3t88//TztnclVAJ9fj62aqj56u628x+Fv6MAy4m6GPtx26o28iqbDz/8+3D9uR+vljxTLacyN3UD+/lpefX2VR/vatXD+mNZVktlt9uktR5fDiy+q/y7r36VtH374ac156+Gu1bycVmii19cXT2+fGAqt1/9GqxZ11qjQmZaGJC8UwKRarm2xhy70lKuqw9b9hCil1aIoLUq0lRM1bprPz9hbSJNAKmJbq73b17t3v3qa74bq8e32/vPXn35xz9+Wys4iirSmuSSidl5hlqA+XiaoJVlmoLzQ9d1rTSgYLa73nddL4J931WF4/FUUiLELoYuBiN9WY7LlMrY92OX0zyfp+v9ljGeT8fdVQz6qsNbrdXVHBe5aY7ZOQ67bjve3TzNx++fXrI733/5jaTeT7SwHZ8OV19db2JPJ0fPP3348YfDaYz/6TR8XD//za/r7tVxPd/fvHs+vLx///27V29cGN69eTPnpNhGHl35aX162t7exM1+WT91PU/L6e2rL5dPz8eaXfC+i/08Lw7SfnzF159vNunx4Y9cUrLzT8cV3MIsOHSMMnASxTd/8Y0IzIdPXWfDzZjakX3pOsI4lipPD099N4ZtGHudckWZkwylYHRjDOW0pq4vJe68ouzyqRvxeL5a45V3eduT+aY1lxcRJXDEUOtKTQ/H9SruDsfnIfRf71//3cMPf/vzn26H3dh5yXkb6LNvvj6eX5oeEEgDL1p5IGcmcxa1EMZaipUKARyK96BSrTXURgDMVMoybrqnJ4uds9SYubRaakOGt5/dfHqZXdd99bsvUy7jLvoh/Okf/rTbXXsfh6Ebh7C/2k3Tue/6cRjTOkOZrBWQJrWW5dzFUMu6lnWH+DIdDOTq+pUnqq3lKgCw220JLYTLmGRQTe9/eDjXY8mb6P2CrZR8+vTh5dNP+vUdQ27pRVNt8zmtL4iwWsOifqke5C6M3X3/0/nBsDuv86vPf11+/tuPf/rgfWfj3ZjmdR5+/5+/tz/75+uN2t39R8E3u89HS4tiayeNVtcXXaf+1e8cZnGdVQwUGV0u5bpzounl5XRKm6/u/qLwabvbOQPXWs3l0nm0h48/8O5aXB+38PDp0328HjmcVaK4mHvu8l3ff/Pmsx9//jEbI/enkrabYVlsYOvD5qUu6/rQw7jkcdyRqeRKzYp6e3V701Ixvx23d8fn76b88VX31tPY7059F7V0VvlwnudpHeFeOI+b8POHP5rKOJIN+Th/+PZD2/vdV5//5vV2zEWe5HBz83ao19M6726vt6/G44ePt5vbtdW55jUd7jebIage15KxIURiBVzXGamxc2s9MBD5vtvFtZxDsC+/vq2lfPoIStVfd9MPeXPVf/mXd/Rj/rO/+PLjdy9Qc/vr1N/1P/6n51JaN46bfki0/PFPT8Ow8SGWMpf04lHm43HsOtNmqIItq0lSm4/NrHuJqHx98xbRfHBdH0EVLEk9G2LN9e7+Zgj6/R/+fj4fgKmW9WXNP/3wczo/dntC1Emgljodnuf1zA7B4ct0fnl/vr/ut19/PdzBZ1v60/E7Nvzu4bvP3/xldNuXCQbNyyJd99m8B/vPz8MAnzb64cOH6yZf/8u/6Q0Oy7eyVDn2K4rApyI5m+alDWKk9JKnOf3QasvLut28nuZj1VVRHIPXImRda3R4mJu2R+BXr9+Wx9PX77akwXuG87Lz3eC6nA+hH87H6fnx0XfDeHPz8OmDadvv3kw1L/PJDxx2NzfD9uVs05rTNEO3b3juuv755QFYaJ1ZbOufj9PDdIo2RXez9yES7E/nD6B6FWOaa+zGkpcRvGG/u/38y193f/sf/0tZK20M/XPsS+wbue65zZ8epuvtPo6DBF+fKTudJM+WWs1UHTnfj757fbOqNjtVMmgaFYrLq29iqrqDLg1D/NM/fPjqN6+74GNPfNXdbMenH3Kj9e1fvHr3l6/WYz48HV7dbJj56vrq8y/vrsfrlWDzelhLWR+Pu9gDLtP0PqcTVFnOZxm3TdoYOERGMHbu43IkdFelrtPM/NLtb0OIrUw1rcfnD1rP0No0z9dX169evb1788Xf/+HvRNq0zNNSfn765Kg+zqeqBU5rY0y5qNl0Oq9pXQ7L8yTtpm+dPebzCFef1uktj9GO//nf/l+//Oxfdre3Fup6xn9o5er13e5v/93xrTve39Ia6zz/6cd/6DZ9LW4j/frxcP32c1lPOpfQd6f3H3itDx9+2v327cPHT2CSpnK1459+/C6qfHx5dCrmPd1eXQX088vHEIofw3maN/0XMhcuFUrrqYv9EGOPzbSWD+//uL26M8SH55/O51ynxH4s2E10GIDfvPq8rMv2dvvzp5+nOb+NvtbcRS1rclyGDRzPfwp0+uzVX85nbCEBfgZNRI67vQcf4pSP2vx+k1KO/euSGciTc19+/fbHPx32d7sYME5pt+UXlXG7SXniTk7zy+m8bF7dLqU5itBW7/GsdRuHenjp4sCo+2+Gl4cnzz3kvLokWlrqaactTQCujzEM/Pbzt+9+9Tr7aTfE//KfPv3ZP/snw/UrWHVZvvvVb64hd9H6d7d3/MXNb3/72z98/LSI3N3d4HTY70jw8JSfctMb7aoUsTrut5HZ9c6xTyY/Ph/+2W/+sh9Hcm5eZ3Vc5jw9PtU1LXm5v72KcTwcH+cTfGzgunG7v/r+T39SNNd32Pu0rg/zYXZx7EcMmzS0hm2e/Jx0aSfuw2nXdQ4QxvefTlnXKfAWx91Wf/+//eurr//6t3/zV3a6fqqazsv6T78ssqTV91+8fuOGx2mdtUm167gNX4/vH6dIbrfbPTw/kXfHl5dh02+G+LyuaYar63s1CQadC9+8/YL6fmQmhXO35bvPX1tXoM0OMjve7DeV8ioFGIvqlOawu+LI7PTx+GHKzwDz/rYnR4+fHnNZbq5edTQ8PBw+vZy9G4fhjsPYd13JoladwfJ0Pp+ej+l0mOXlcFzz2Xc7MJnSA7vmkLVpAoHtEDu3G+Po+68/++LNbr+cTv2we/XuBsh86G0Tu82wHfoY6LOvbu/ebnM7n+cjORIHpRYUqcvapLJjP0Yti+TSXDjlJYFUsLysfdjfvPqV98G03t28fvPZq7tX4/39Ju777XB9/+qb//7//N9+/ldfyZo7H66/eP27v/mLq/v9FdOXN5svfvNqeHv79le/VVHH7ur21ggTLvHK1FKCGm53tNn4bSh9O8KpdnrmJmPnQh9Cn0puklOeP3366fnhPWqLXdzsb6/v3/pxuH77uqE9PH2Ksfv8iy+fDscs9frm+tW7t/3nb/Tdlw8y/P7ffPt//7/9P1406x33X27c28696lofSirQyESC7x6eHkulde3imy9KP73/43+ZTjIG8OvpB86Tg3OtrXfzlR/6jaMudiAj0dWt731q9eeHTz+//9Bf7Q+lQD+GYZ9zY+6222siSGl9yUtjcmjBeyi4vD991/vtccLzy7Lb8/7GH3NeMFsTE66izJjOfmCtZWmtaS5D5MPp+Xhab67upvOJmC3XtaxK8vjw3TnPiu3n50/TuupT6bw7zyVh6+6uvI7L6Thsr5hdrifuCvKYlrzUE5G4bliPDzc+dnEgkKt+M6efzPfmraT15WwpEBNfRXYOW60XJIZrtfOh68bH5xmotlZ1PdXNdtgETUeDUPoRu7DK+uOf/jDebep1k7xyPnHTz37zpUDe72uqBwu7LlwtLbodGhRXK4RqO1+wyWDJz8f0ctbzYHW8ud3f7p2nV59/g+xO9uR72Y5xTeI3g3bdc32EroBSrpibc2EEioj2p4/fbq9uR6pLLrvrOzP9+PD+/vr6jPrx+Nxvxvu337QP70/zgXu+e/fqv3z3bZryN7/+vO5zVx4/fvjTZ//tb09/LORvqcOqYfdW6ywSsOt8T/Hq9ZcvLj2bV+H9xiV5mQ7JbfaKKZsb7vttjXVeMpIgPk7TF9dv25pi0My2vMxZ57u3nz0dX7ZY+qvdu1//rnl3XFuzen/7Wl2Q5XBzc/XHn3/+9scf3f769eH0MQnN8/PBPnT9l1PVn56fWogpHSkfduCveNz3I3XdIRWqmqZSoKWao7tF0M1u32+6ltvx5WE7bGpZx7Gr5dBkQedKq1Xk0+N0f7dxMTRRP/hQA3gkpjWfT9OxC1r6fYTWFFOVDmTsxrxkGFJpk2MYNv1jfZlEbpQBfJ2SC7HvY5OaGziAYmW/7yPTUtulRhdqeZjOz+sTuaDWzOkyvQgAQB1ebSz45/nFrPPHlyGGNZzefnM9eviwvohRxvp8evHobT7Ny3mSBXpOde3Hu8P0pDQb0Mvjj1dfDBbLOdWb7XB3d/fx/cclyWazM99Wqu+nn3YjEkkzOGfZDDex8SmfvS0f5STSp6zn5an0cdPvPiwv7+aXef7wrDN9/GHYv/nmd7/7X/+3/2mmNN4Nr+FN4egHxHXyTr/63W/TuLv6/D7bmfQmF+29v70ZFtGh87RoEzU5uttrSOXLTdtz+OOzHLl5hrRMK/nd9tqu7/PDUdLJGS/lOKVz7Dqvob9xbnt3KLJ5c33z9SY92t1nX+1ev8tpOacXijze3LR1udr0m+tfGYDDPnS0e/k4O7d5/Pi83Z3Z9ZTt+//y/YjpL19vu9z+/J5eW3WHCsj95urbK/evlvd/nE65ttBTk2JkirXvqUlyDg2KIw0BqHHNqg6GXWTO4+0gbNN0UB2vtxvswrGsvut8qQ1ARZA9ioiY7/vEXJcXZ+nndQn9nZkNsdTT+SSNx+1utz3PT6kWpjHXooqM/vnlwY99yhLDDdEppFO2XNBve0/dYK77dHoMPvfXozSYS1nhqfMah/h8/FmmdN3dcOcKlJLauiR2qbq0u96dUxm60cWua7thF84pxzFKhcPz+zU/hTie9RSfys2mFz2j96+ub75//rTM1XKHNRTN+81VsmFKn8JpepZQMLycXuIaptOnKem+3BXS42Cfnl4WVNj3/8sf/vVf4r9YGA55AZH9/S43fXn+NnCZCr1++6tt3yHreS0prRet81ySEtcmd5vtp8ePza1WsIXNWrpPh+fZIYZxCN399T7G7sPxx7XWq37j6QwGx/lnM6hl/+arP1/lBy3h/PJxM7j7zeuPHz+d27Ksx3Vuv/rmv/n09LcpTzd372pNV1d32tSdzp9SWX3HDnwXr80KQNuNm+nhfMv43/MNH55213iu67Rsbotd16TOWn9zk/17CrMtZr6Jned1v9vV0opVT9D1nRSuCzjD63HruDFX9eS6oBXztGQfyLxmQUJwHokWFETqYy8qiogCfeirJkCZaykC27DHja7rer9/Ne4GIVmfP1Vdx3DF4Fs7l1qsOXYcuu3YDNJRXIccDBA0efauk1SK1Xq1uXKus6oQQzYLwR0dPMzP3Ay3w5oqu4iI4F0c9mbl7e7mvB6j4UV8S+aI6fD00akCMXp8fvo+BwtjzJ11gCU3QlcaAXdDtwPGOc1OMc2HxHslOpXTpr8h559PHx7XuiL8z3//b9D41f6zj1M5n2f67vdzngRgDOGnD9/e3Lzbbt4eTx/EMJtEcG/vvwzH51Ls49P3Njg1YXZrKWeZFbQUTOUkvU/7r16wVpvpVLmLWSaFQ4wNMvBuczgeoJWhH0tel2XOVVaJ4KAjPn8649RDt6nrp3R88f5+GG+HdD2fjm53xdvdD5+ebsetS8vzknNDt7l6/evf3P70/vt5WsNma0ocaDofxnn58HF+eXf7GPnrk6yHlxnp1XbnKlTXz9h6P+RcmtjheN7v96kt5gjcYIcyPc+16LanLkK43RTQ0e+G8WZ187omXxohmQIPOzVsrY1dxFqT1aWUAdm5OB1VSF2knIrrPGy77a7zHYtQ393sdnA8nkC7rvMFSrZ2PuZ+25mq0+5+uH8SqNVj7FtZzulH1YRkTAwGJZd+GFYEqeKAK5HrQCkjTJ6iA7/kUsyw+Te7gcux1ccIY0pFxBj7Ja91mrZdp8gNlLZ9kxlbA1vnlUttkWMYxuOSvetqfZkXveLrvruZWiFe2BE777w9zm3SubS0bepoc5iOVzf3Pm9fzp+STlfbd0DeGIIPwLs11Hl5OZ1+vOJEvMFGN6PXEiadDTCom14m2NAQr3SieX5ZDt9tzW5fffFxfp/XWWvexQG0Oh+tWYNammO0m9evPz188LEXtLS0uRz1JQ/+9oDW4GhOvAtq9Yf3f6e4dNutgruL+8oncugAtBu1gOSisev2u/s+pOlwaKUct/vv77bXnT3VZXqZpiE4ph/8nDLtfjiVzVhwkDSjp1xqN/TewJGO265JygUePh7nY1nWDNfk3RZpQG2ehkCOhs70SAjgtVWpF58dAbEhkFU5LyfsOyyqjbY9iVbqO8TWSMdNj4FyldbMh10IWIo6Io93nT/X/IgQp3k5lbrv3e12x+qX88uwHfMyc3TeMbQWeQ+upXYCZrEOckpldexMC04l2qBSpmVym9ERRLLzcd5u7gAbWFqn/LKuzgUkd3t9pxyOS820F0VXJsmnsNt9+dnX1uRwPpqtn54O5M27nr0maVUnllRS8EEP6VydC2Rvzf7FV2+/ncvfHj7pqb7avj6fX5IdprkPtJ3O6yN8vL76ati9XuYUSPP6gKFuhu18ftz72OZWiFrDq/HGNhuEYTDv5jVaBS8Y+hFft/ozmTss87XvTfRweNzub652V8jpw+MnDuH2+g4lj313ro/b6yjrbKZ+3FkLKKpoLmAzrXreDFsuy0jueTq74Hyhdb+L9ayttc1mKwEiRyxXYXf1n6QM3rbbtx7c6uzT1pdRttk/utk+v+/GcbfwbK3ru+ur6yiqUieD6flYz89Ndbwe+31/c3Pf951ag6KWNQSPVp15QwFQ0mxldp13TADajaFWLpILNnZud/Wmw9ZcP1y/PhwOucE6F+8ImXKurbQmQpjWnKMPb1691Ye51bbthn6/BV1VS7Uy24uujWkYI+d1BokBQ9HU2grOh7gFMFsVmAColYbtpFX6cQi9W9eXZ/E9Xw10u+DxfDoi2LBFM3DdqMTT8cwcKqJhDNRytscff9pursfowGbSWWoehm3oQNuhIaXyDLmpds/zU1Px3L2xepdK+e4HvHkzdHfPHz/m4+nm7sbxNs+t1nmg/uXlI3B0/c57/3r/eskHxGBC0EykBAMKQarqlNSF1Q9i6tjtdm/ZdU1sG6/dTqDa03xs1uVlZhfWNW3Gmxi7lA7FWit5xLa2bMC5imeIzqbl7GxA9H4TERxV+fDx+xyRb9/NNSujCzGU0qmq2EkNHG/6Tb8dbdN3edW1tMW2Av6rL78qZT7NL4Zhv79eN33rHUJTHwnZ+w6ZDEAqEY3My+Hw1O1udrttcEwUdtc3aiW2OnQdA5WyOCBjMlkMJM+HWgwcKcC6zlLyhRXoQlALpRJhvx7r+SkLEHddmgt69aETLUSZSZA1t7WUsY+70toYYvAdsRerh+mkZF3n+3BrNAkZ4yByUjyhSDOtbr1EO7ab69qmnCq3ktPsehcZOt/bZK3pvByP6Zhr2+w9eluWBEpLyqmUoY9ExOjG/vW6nJb1sdn5fFbnewcVoVo6zw19C0MYtAmRKuRaXkxdmc8U/YzhRftFbqpUQlnXl5JH77ebzXWdk7cBqKR86jrzgaK/MoFPhw+lrle7jeQYEJU1MMM5uyrb6/54Ol/tb7zjlmvjVHN6eT7sNkOVdjgcSCoFZXRSUSh6GmY9LsvUu6GWSsatmmJrINi8Y4TgBXC/2T49LSH0YHZcZw7uqh8c0MCMoKfdzuVTTosO13uEYaW63fV+4lwLATwdH1aQ+TSNIdSdLyppXdCFGoKltrQFI+5316vgnBJvNlYS9xEccexr1dyScz63IvPZOa61eLdVqIig6NZlpnKOw6DIfezRSy655KoNmGqACFXneUnLHIZYK8NiHCL6AdEPwx7bwlF9oDWdam0IXNOa02oEm+2VFYl4vRnfraaVatiOWnux0o/XXScv56d1fokxenabblcar8tTCOHm5nUFaUse716vaztPZ+fLUo4cWqskNYum5ZyZkve9kAbnDXsgIsfec9cBo5Ws1lhqrblk0NGDU0QwJMYGAbXI2m/DxGERa/5G1UeaqSdt7vz4893N12Hj1/JYpsl1naLHebrbvHJi83k6nZ6LnAjT3fVbSHVZj9aZf70LXVfqAQmHYSu1hECMMs1zSctBylyWsQtgpaY2DqGVts6lwlxpyQUz5iTFMRPxlMpcly+uvzTlU1nntUY3EHtValLbmt9+8Sb46FqFmhqBdYM30aYlLZmZEaOPA2PwZRUryzqvoMzematzhty4iaAKeGvNsCnaUlKpWaSh434cEaDWFr04tvP5eb/db4ZxTacpLwpaWiYooJBKlSr7sUNzZarIwxivtR6SJHSOUKsskptoZXcxkrEVJ40sNCCIPoSO1CXvPDlZpoyGTQugmcdpeRFtMexXsxk+mk274TeOtmk9t5p9x/eum9ZDseZj7MLQWk5ihuiid+Zay9KmteRUVq+OQB3T6XjKdeo2LvjBRypaC9S9H5m4pOUCUyYERFNFVfLkYh8O5xOIMQGDAwEUiN6Rb8gEFKQmAOV6dvUcEV0Y/a4Tteenh5bm08tL6WIfeq/16elb/MyEzUzR4KKsCqFvx6dV5zDEgdGkFbRaS0RTawb4+vXVfts9Ho+Sz601VkEyA8tlSmkFTBCtaH06vwjiuNsOu+20HKJ6SsJkJJPUktccQh9jJ0vtx66i/fjTDy7lRaSR+Om41FWC92taox9CHFRBgI0prTWjgffeYWDn0bN3fYQZjGpmJmKoNZ+rUNVAPITotS1zQkCPFLwXbcy+769zLioTO0dWLz7oUio2C4qmtaVsXQH2KC7guB12BlJLKi1DsIvqS0xA1FFtUIFdNwTv/VryfFz7MG6GXSuy2+6XdWqYgLPFknS18mThUYzm3IbO+bAhs2V9GTr/anx1zqUIA6DUDLkpccXmo2cRzRM77keKxucKpZUQCjkMzgW3qYAppZ3bYJqXaYoxFmilNZxQq8xVgfz99nobu5YKefGBAViqDNsrJErTKqAKRWCuBSA7besyzRx8v9uSt7RWTXn/7vWpCFQxwLWkD9Oji0MIXQeOgKZ1kaZlTWHjtOrxNO2Ga2ilzVPXdQK6tNI5rFJaTb7z1MwrYHAKUCQjVbVCEEPcoFmbDud6oGHX+26a22xpHLuIFMlAARRiQEAfo395fHg4PDgCMgHJ1McYRyy1VKlI6sMGgFI7GBjwIK1BI2QzNSJyzosplORNOlRBKKIezANFHzyH3gloAq1UkneI0PJyGuPtprvWegawQMzEU8ke2TsihdqqJ2WoohmsEAQEYHLKnkMAU6yKDcwsMA9Ep5oJ0ROptbLWdU0Q3GbcdIF9iANBaq1icSHVNisxYivJn9PHzufbzd7VlI8fylnu79/cjPdFxpLO63xkQQ/oDXZmofetpladae4jFzM0DV2gMBiSihfBGF3ve0uT5KWKFi8+xN7CVDJZZXbzMlORkXtz8ouDBqzb7FttSyoUJDdlcmvKvfcAsVgidqVWLpOp+SFMrRCHzW7nW+5k04DqNA3kr/Z352VeSl2mSZtcDfdrLcd1meeXOmfnmD2AC45jmlYrbefCsN+2JemyNBHvR0RdlyTQtCqX5B2Pe78upznh1e6+zHo4HcWBBOuHUXCZzqtJG+NuWUvOqQd0jj1a0bUpYb8dm8maZuRuu3vDSHM6mGHX7/KyqjVtJVsj9ITtYqPY+Y4V5rIygffRk3NCnDUYv7m+Ma1rXaZpUgNCni3strcb1zWpjhBVvcHYxQgudoM3AatIqpaQiqnmFbvYIeJF2U1EgEa/iJuqNvSxI7BWpVVjYBBzHPrNRswYla0rDRwzhey7ataLBNG6lsO8wAato11a0vSpmn9CfwYS07bdjlBbB/DGmvf1QyvaYEPcYS4oDr1p9kgNQq5amkU/Enkw7GJv7NQaIXMcHLqx1KEP8zSleXbKIAAOq0gTnZfFTJtqpNgM61pAXDf2iuZVILILoS4rGAKx5to0HQG2Qxi92wEvqVotUpoBlXJBZuM6J3PY1+Tm08YhDqNzXpVobcjakXNDaIxHqrLZgKGhY7rMZKJBTetJw8bYsR88s6EIFGLMpWSTzbADbMflECDuhn5a1mWeOwFHyMF37KCUWUs2l8ElF6jrt2DNwHLJAkRYGZAUi0hLqyE200i839xoHNLxRbRN6wrmOvQ344bZixRUIqSUJxUb3Qh2aqDUmklrgRxCTy50hOrMAJGNzFCI+q7vpGFLTVmNSVTBlNBTYFQA0WbgoyeyUlMuiYGHoTeB4CNxaFXNoPM9uZTai0IBWQE2rEDSxNqsCOBGt7m9vculiMpyPqd6ptBixybk17Zbl+tOOtRthThT9KVzfHRhtdiUVoMkpRkHAimigs5FRfNCTC72Q24ATgkBVFsrgI4slGpiCBjneVGwEDbOcVPJIpB1Op8gkJhipSKFyXsOhBjHUKoic20tEzkzNbGqnw4HYTIFMIgxiJpUgecj/fwP46sA+3fGQyk+F3FMDSx4t55y0dZd36DSup4uw9QFmFRRVWuqGgIFVX05PueWb29uypwkiSsoilDZHDat5JXZ1nl2OVdEouBUp0WP0Wkcoo+hSlXJhlCt1XyE1thCM6vYmpgCoxqhTdMJ0EIIbO7pdCzQrGNhCsy1JGkNCJgYxMpSo8+iZKK1ZvROCHzEEL0CqtgvQDckpuh89A4qJSK3trWKIKEjJHJoBGyEzOSQuLWiqsToQ2ipPj8+xm02IAPruti5INKXttcWaxVuRkUMoNZ1VSDuPNHQd0W1WcsCzOjIAROA2FqHpsQGa3aFOgfBQi4eu1crOGmnagmVMItKIvKNAbV16MkcVPGITa2sCdXIO3RB2IEAWFAwRQEm7wekomgZyRHUUlRJ6aI3tD52zgGB8z6oFTFZU17VChNJRTVRFCI1BUBGh0aoUBvOk57qebNbh7ua8pqWFmLcjhtp6jl04zYVbWvC2igCB2cVkDE6AjUoxqGTVkttQAyEHmlQ1xk5Dra5Pi3Tupxi5xyXMKJLKZOX5q1IUoEAvY9XJjzND6WkJkKEJkYNWKoymcNmJgIkiIFSmtO6EgVgVjX1KKTH+bSPHTEbAmhjYmRG7xpSKtWaIpO2pgAi6MQce0dERqAsVmtbs2mMnRtHbTUlaaJkVFAVJBDxhZV5sfgCmEBTncpCTUsqS1uZyNBycv0woCgLgjqtIq2xAZmp5dYstcpN+mFDfQRUYgvsHTpwmn341HZS03w4Wsufv7u/eXXjUz0cuPm9C4SarckFW47EhigXFic6Q6pSnUeoVFd15AVAiMQMBB2w1Sok3DkjR6ai0vVjFz0VzaYmrbVGSFKboDeotbUmLZdipkSwqPgmbN4QGkqDZggGDGIpp0JdunmHVuUcik0tmwi7PnbjQA1M6bQsH96/J3YxegFTZ2IVvYGXtrbAQ4y7RqWUomrF2t3dVcpTwckhbmJclymncy1KAt3QOXK16jmlcxUiG8Ht0XbW9Hx4v6Qi0BARFdkYQS+MXzaMfiB1DE0kExMhNTHHXphEWmo1gPbdiEyAyIrMjv0QALmpomJgNRORVmouqffqIyKaAihZRWng2pphnkQlS2JAjxfhjVUzZCIjBTNsCNhaW1MCsjF0fR8VAQBUreWyqDI7REQLDJC1mRiCOTMgqCCLrjU1J6FJIhFDMGVEahCeXDxWTHUaKe5ctGZLo7VBXs+NgiGBMhky4sXyyogGWAwB0AH0vgO+WGXUCARQTOEXIawDE6mAQEJeDTrX9cClrCYFgBkRAERNmsJl9NVUSlVT7+lC62iEpZaLqyqyIyAEFIXqQnj9GYO2nKeDOEc4ROs4t0rFXp5OP/zw82k6j5tNN3Y7N7oGDFFUSpIx9J/ffwa6PRwPwUcbCNlPUipKtqXlEvzV0IdUS6uNjEHJYVAUEW2ITEjOjd4N1qb5fFiW4jqO0SExksIFDqdKgEMIDLEtZ2nG5GM3bmNfTI5pLiUhkYKlWhRRCX5hEjof2TuRVpoyKRgYAIkWaKDKhggN7WLIdaHDonXJU1rUY98HdylcmAGSAACogVlrBlpKadIY2bELPiCSiZVamooq2sV7AGQAhgVAGBsAqpohNlSEqtKkNm2CAuibY8eGhlqCq9vdUfO3mfBDktVyYRjnSk1AQQAAkBkMvfM+uLmmXKo0GTiAOqAOnEhdDYCYnXIzqSDOObwIxMTIHFNAY5HWpIkYMl4EILWIrtPQdc6zqCIiGhIwIhmpXERg1RgRHDVGaZbVxIfQ9Qhmbk1pYULHQHmdBDDrmmZlQWepJapYitfs0EUCk7oCWkuZLAZ2u81VLjnldVoW8opMqaxrghAGJhJAZofEri6JHEQLznVMju1ktaR1mk6n1sDA934TQ0SHNdfSUoOScqUWx35n1sQATEvO3gWPGMVMjNDM1BQMnYEiALJ3PoYQKJd1zbk1RWutqQoQsvPORSYSU7jYJQzR0BExkoqwgENUgKoApGJ2kY+aNjWttRoAIYmaqDKC1Iu7ngUvpbaKQAoASkzERqIVxAgRmc1QBIlD8AMBEsHF+IzEwGg9pEJLIRLujdhzQyqqaialiYEDjtGbWk5lLanU4thTQyUzxWKWa3Oe/UWpXhUMkAGRyNBUFbxjj6q11aKiCIjEiCpScqEivfccPBkqoHfe+6BSW1MFBTQEBKIiCiiplKXVEDpg1lLVAACqmdbWB8eBRRV6G+8G7C5EGpjmRbMNm7GL0ep6mI6y/rTfg4+9CJScl3kmM8eslealIa4uBiRCduSDILnzx6ftPg6bLpBXqG19Sgi1thhcFyIigxgaAhE4kmpoEAilzubYs5ioCpjWVhZG9qJ4sYUSgnMEXg0BEMAhcGsipebWKoISCFgzC0zk2NRExdAEsVQgbZ0RkGNmaUKCSIRgoCYmaCSqquIYGRGcuzi3ilQ1RQMQJCIlbiK11Qtzo5kCyOWwJYLQTNHMFBSZnQud98H04lczAzFHxoQNALipEGJBIqJK2pqpmTUlIlMAwNraus5LSYbgPSmm3FSalpyRyIBEpKpdvgcUEBGQFC6mAxETM2uECngBTKGKcxwubL2qYMDIptCKtKatqSEoCKARMzguYqW0VCqiI0ham9R6QbdptaUUSoukytJuiHbDmA0KQEMSM8iADB3EVev5NC3tQ7+5QtN5mqW04DinKlC0sVhaZmQO6II4Lmqu4+isAw25tNZEtHXRhn7gfsPYXaSBcHmFnAsxKlCvSgaElQCjD+CRAFJdoSoBMTKhI3SIZGgmJmqMrZVUTEvKioCOkIAQApMDMNMsGQ2aQUWtRC5yUWPirh+wOUMU01ZFDMUUoBA5JiJEAHSM2aqi5pISmCkyxT52AKBVTVVMRZuoEIERiQIagYECoCCAgYk6Fb2IeVDAAAHIAICQ2ZxWALPqEBCbNa1qhoR0oX2WUrTVtKZSEjHXZtha9V6bqZr3joC0NbmQ8YxIlZiE2YCwtZpXVVERUTEgdkBAZuKYiKkqIMiFB3kxLqhekPxNWvtlPYbLhqhqVktFQJILJwkBDIhU8TzN2CQW4aIkwJ7IOwMDo1Yhp8IIIK40QUo5P4ACmhGRqqlWhRrRlcZlquzRj75ZrU3d1f6avGsIqYFUAuVGSt6zC8jeI5CJgakZE/l+MLgImaHmVFIhdr4PiKiiay1E7JlI1JkEEHBAqgAMaLVmA7iob40RQIjRGbGZKVQVATNAUTOkBiagrOoQu9g1qbW13Johml22oYsnUkVUTIENGQGglVqbhYAcvDNAEAZQEZViqsBQjRkIiI0IARARRBRVmhgUZCQAAkP65biLQIAsKIrARGAmTVqtxA6JpUrW1ATRVKSZqKoVM0PAVlUU0YFdSqCAZIzICChAyMheFZqsUi6AcwMARDVVYiJgZAazYqJmIIiiF2mngimSGRIxIAJRM6tVVC8aZPLeO4+MWgsCMCCSIYCJQQYrtThAgqCqRRWMHXHOwGCmCMZaBVFRkZxDADNDME/MyNp8UwNj8GQmVptTaegYyTkKQA1AtUnNxnQ5sQICgV0qfcDOqTVEDwq15ZQESHokHyOy5x5ENItiU4KqgrGLiBCCR6YmTVTBIQKKStNChg5YTa0pIQqzIKoaqBGQEVQyNeuI0UxQERkQmZmRGC/aclWV1ioaMwIhCRJaNdPWKqKSISI0018kiQqIBkwGgB7ILu24f1RGA6G1y4pkZmKKiqLaRAURyASMDETsgqUCQEBorbWmhGCmxIiISBdGOSKgmRbJpEjMSI6RwNAQDS+/09QaXphqaKqmlxUUwXsmRhXVpg1/ebYOzeBi6VBEQgZA1Au108zUHGEIYew6z1xqVlOigAisKgLCVrSBRwP2zIDkkAAQTcHMgC4vMaghETMDKOgvrwEzUhUnpEpKqA1ETau4VoogOkeBHV30eWAX7bCxVwSVJmagLXAgAwR/+UGmgNSalLQmQPY+uKFrraUlCSoApFZB2MVARAjIFBBRCNRUdW21QW1oTGom4pwPMRiSVFMRbMUIxURBWREAL6UyRHSIbBcxlcFFmByCETGTioEZAphpaw1NHCCgMRoQil4o8sCI3kdEUtVSkjT95X+GgAiIAoq1Scm1FQMzZTZ34QepiIkqMzM7AyMGMGuttdYQwXuPjMhMTI4YkUSrSjNDJCRUREMwJSBStAp6Qa8SBjbR1i5nG0O4HObpH3n5AITeMapqk6YKpgj4ywkKEC4mPVAiJqZfOCSX7ZCJEQNYE9JmVTI5RnDAjEje8UVhfLn1GNLlrocGIk3aRTOvg7MeCFHBSMCpUantAuZy6B3SL6o8x4ysjBEQFJC9R3Y5pyoCql7VxJBILwuc8+QJzMTU4OJtjGaBnBErAohK1orkkQGQHQRPDpCqNU2FtIEJGTCQGIIhe4/OAVqb17SuQmBIHig1MWQEJGLHziOQCIhcXofggusCEANQLimnTACXRyoCDsk5IgZHxOQaqhkYARASkaiYqZmpWWuZSJkAARTMREBBm6ipipqi86wATYSZQxeD82oKtTVoUrOKIXL4/xZ1RruxJLkRjQgyq7qlmYUNexf+/8+zvR7PzL1SV2WS4YfsgQGhnySggcpKBoMhnsFgWJE5UgJK2SBc0UveTwD76C+h0900yFDstdR7QxG2X+R9rCl60dj7x98G2oKbBCRu2hWLIqiu+vnza5CZGZFNgFxuRNJSZjpotWIBNghYtLHxIJsKDLRrQ2zWo+d/UH9HVMTvB+v27b7mq1fBTA5BtNtGHhnavWUvLKGJ3uUiDL5NMZdtoEUNHUPl7rwrAjwa2K/q+/WoqrqP8zGOz+C5H5aW2VZZ7ZGROoqdx2AEBYldq+57ET4yY+SRyjFiCDpziFqv73Vfa1WtHsOHxnE8DQP+trphVDe6XIKQhBiwSMUmts66q9Wu/UP7nqt8Z2lEsOmywIzsrlndqztkkhnHeJznGZZRVhKcN19v77hAKUiqy1BJLbE7rFiQ7YSHEdMZEGRyoaUWJCK2EIrA/0sw967QNMGBwJqgaaBhUREZ6dp/iXXNHVgIs2qZ5HmYsRW87CaWBHq1TShg0KbdJjb5iKDpILL9r+h/GP92425V88/q3+/7lkN7Md+ItTYlEvmgOBwNzPa8rz+Zh5FBBOVlEmVOeK0Z5POXjwpPXPYskA7FcRy5poGW6PZad9E4ExqurrU8C1Vs0IzIiJFB5mizG9UdimM8Aa7Qx8fzOT6O8fg4PwRxu4F8vvr1PV/z++vHb6/7d//tF8SRd8++F7pphJihCBp2N4gWzdjbV+3i5qq43FXV4KY565YGDy+jIUe3WRZQDY44juMYD1HR6FohlhhSRBIbCNW0u3tvtAJ45Ad03pfvmgWr29XqYEHhhBvl2i64CY0IU9h78EmAAQOmdieGjEgjQFKOLGUDMgykpMBpPKhYq6v20mkdamPe5VcBUeGWl9tsQQq4yGCXqwowRBABfy78a/FX+1i1lrHWKD6EO40RsXfT3ndXtdRcMjCOEcrudde3e1JHgECuOYGsVIPXfZ9jHOMYTw/G1/d3r25WCAoE6CZalmb5+3WN5w2pUN1Xzy/XTGUEGly2FNaWxKDi119+PZhEbCdIyoMxOjLSxJzVk570siCAf/z2x//+9+95JD9c41YgxVCcY5gul70aMtjk5iZ3F7yrSRtec3XfkR0xGGo7Wq5yq+G/NgELysjDe/Vzdb9uYHUTHUfKJqDdttZspUSJIVLKFeXutwT2nsqATbGFTZnakKpQJrZ3RmCZbrjBTTREA7sqJxQRDk17dh0RjsyMwTgYUcvXS72IGN7rh2d9XZrucKcBituINNtgbxirpC09AWTjl4nnvS7cc3YXo5+fOdZJBRbc7DSsne4Bqkx22iEZQl+rrrqvwQOVnpv9UYuea9Za+eBoNg/XNkdW+4ZRvdAWCNLo+fpa19meq9vX1a+ftHOcDLXRYOzre8PVEBmZGopcNWuu1bVqXrilsPH6uq7rdm366yMotH/8+fXb//x2+jz//eEBRxR9eQl7s53b3S1086/SgPeT3IKjqwvVoGL3aLaNRjdQcJtoq+2GiVqFu/yqvqsFWiFuJQgQ1XAFdKRGOe8ibvuybsCASG0Bt6EL0aja3DyJAsmgJNgtrrvplQihyaBNSSltAC5Nt4hIKfI8TzHU0OyaVvLIERmX+75nl2kJMsiMjYWl0bNpHGNEEuz79eqyjI/2edf8ff3eDU4WBsbH8+AzHkMdnKxssI3ykkVnikEK6Pfp7PZ91ap5HHpW91pz9rzXJPzju4ZMBUs0XV59dXteM8CQ+JcS6nW31uqGZ/seCsThCG00jWGw7XmvOedCnNkZNdf6+vr6+fPH9fOuq9csQjbaJfH5ePzj7/9QikN5cLR0KB9PH2w2vHrOsAls17i63YjNhAuw32fLbMg5gsQmsbwVXmgLqSZBEpZdNeGKJu2unnetru++KIHMMUSFGBFpnebZFNr68RnXoYaVOjIVJqBqfHs1b5CwCEBuORQSu3flglqBSMtSw5v0Yb87ANpJcgxmIGl63Xf2jENjRCp7I2NWYROvBscQkzmiu93GRhrFPpX3YJw1zonznr7nj+/aDsFoP2imzsf5GA32qs7VWj2rFyCgJVOATdAtdwJlGmIcR4Ps1TUFMNlYbYQRSBDmdg22O9Md+TbQyr265a7FXkgjNOI0sH+xZ8OY1X/++fX99VWzI0JkVTU8r9ePP37Oq+c99+3RfUP++Hhex+vv//b3+HRS5/OBjzM/nkquvrBsTO8CRHVVrakkz9jngQFgD/NgAZYpM+AAowULCORAMiGSIsxa6N1PtWXTa83uVu4GHSPyOA90D44D+Ij4eFaMRULWdsBB722yP2+v7/lyGbkb92YVCUMcUIMdubM6CRBkw033dqO6AQYUQgzx0F6j3r6BmWSI615X18tlgMcwiUCOkAhgZHADQSNplZfan8Cvt89y1/rntb6APtMjHnfdI/0Zv4z4NPLuKucsr/bq7YV0d3eVhTJcW6DrbZIIAXmPKLf8QNsNcRNljN7kWwLVb7NrY1D8niYsdo0Q/Rd9rb3udX3fNL6+r//8r39+fX2v7jzPj89HkAo6yY/MJ57jc7tbqy6oH8+nPvSH/xgSTvCRPNOERVJABI6ECQFsrzWv7o5tGxLkFrkAdvdjILhNakah/dfVE6GtF/ai+307t4sZeR4DLQyG9gQ2wNRu1XmGfj3966MfGVJGO2BHQ21wrjVyXd3fP3t2GYHgksvV1WCLJrd5TYlkiNHsQr/WqqogQnFkZoZGKIj2jQn6HOPR9qxV1R5xpB/DXe6QOmK7oNvzaVKb6DHhJ3Q6jr6i++52Zv/ynMM3/ZLzmTwl9rgbV83XlSaabLDfXkDtwORatQwjtkHa7q7XGKnuPeeobV8uILjN3H6LwSW6aKMyjzEyR1pYq6orugumC/u0FWrW98+vOdfX12utSek8xvOXRw5SzBjG6KEQf/n8HOPozY7zxfA4RpdXlVIhHkn1LezwicNMikzIhCK5Xq9tJRABeLvk25JbqzfGUAySjfc7Q2p/isDbtN4XFjubiuf5aTTgWt31BrpqZBpHzAfmk/xEBzvkoD1IgcIMyusa/c0F+wUVuah2udu9RmZINGx3V4RIBriAququkXmMcY7MEJnAns/uEsio7tZxHNBYR9zyfb0oBxWQKHfXXIVaqIjkkfv++q6FXup1Zc9/eSbVWFzz6rzPPI74ChnNWRnIe9WyeSTERSZRru5evYcKrB3M6HnhVgwxM1QgTEPoxqy36HzXbB1HjgEDkaEIRRa3Eiq421VGApJCUNY43F3nIxiP6oXA41ORYozIYfOoQePj+TgfTxhrra5BT4Il1wZfRKVe6vuoA4S6s53KTQkza3V9E9fykhEkISFCzDFizLnKu94RIh3B3Ti/7eCASL5zSIATkCmNkd1Vq+oqFJzykY5QT+GlvvK1zqPHvNMO2R0cB2IMQFHOxSc/Lvyx6kerVxV6tSNTTW1B5tWFXa32v9TNmugO8hQPxb5PBZGk0t0Ga2SM8cAjpRfWWveISOAAw3B7LazvZUBDOsDCANv8QX1phOBP+vMhpte91lWuI4eej878eV93raDyni9CMVIRyCzyLgMo/HUzmQs9XN21lmOQ0u6fQYCbs1fcsAAic/vhstHCEig2TDdR4F7nDQPJpKAD52cwkq87h8BE8PFxKFMxAP3lcXhrR0Ay0iEQ9nIxTSIEuQ6vc65hBjDM0zzD3PmMiN8zf/NygQCjMxSG3OMxMsfsumvaFsF3/vo95NgZoaBtL3RtE0aKEGXY5eXDdOQYSJoQoXasi9dPqpIruh3oZJ4PjBEjDsxfH27wIB/L55yc/L1ckPd8TgG02ggCtfq7que6G+tUnBrJDKuqdvncnaUYDfY4pVPOkDyNRoSSzqbqDQFGOcjn+OAxltpdjWqrkJWxczgwW2MMyisjYoSlylGfn3NUdtdO2mxyFqVuG3iHBLakcFAtF0wDDIoKBlWG622+b9Pr3TUDDaopwMvvTJAENkBaIHaEHgzkyK61qiMh5aZRRh6KYQLmbpmA9zeNUHSmAEBegdVsGqrO9ln1aIrcReTMPXSXZZzZxM/GDSMsMdAyXSD3dFJb/cTGhEN294Lbpm1ws3TezMN3l2k36EgRVDQwaWavo1d2jQbXtNeOkmGi54tZdYinz4y/PSMo3ZDaY/QdL8ei9jBDVKRE7NakerYniKBkskA0yuXZAOgWLZqhGFK6sLqqqntH3LqteN+ifhwjIh/no1KNBTTKMkkptMN7be8BorZ5aXeVQeVIHf8H9/hUNm4GZ1kAAAAASUVORK5CYII=\n", + "text/plain": [ + "PILImage mode=RGB size=192x191" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "im = PILImage.create('dog.jpg')\n", + "im.thumbnail((192,192))\n", + "im" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ae2bc6ac", + "metadata": {}, + "outputs": [], + "source": [ + "#export\n", + "learn = load_learner('model.pkl')" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "id": "6e0bf9da", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "('False', TensorBase(0), TensorBase([9.9995e-01, 4.8196e-05]))" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "learn.predict(im)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "0419ed3a", + "metadata": {}, + "outputs": [], + "source": [ + "#export\n", + "categories = ('Dog', 'Cat')\n", + "\n", + "def classify_image(img):\n", + " pred,idx,probs = learn.predict(img)\n", + " return dict(zip(categories, map(float,probs)))" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "id": "762dec00", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "{'Dog': 0.9999518394470215, 'Cat': 4.8195579438470304e-05}" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classify_image(im)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "id": "0518a30a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on local URL: http://127.0.0.1:7866/\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(,\n", + " 'http://127.0.0.1:7866/',\n", + " None)" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#export\n", + "image = gr.inputs.Image(shape=(192, 192))\n", + "label = gr.outputs.Label()\n", + "examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']\n", + "\n", + "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n", + "intf.launch()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "103be39f", + "metadata": {}, + "outputs": [], + "source": [ + "import requests,base64\n", + "from PIL import Image\n", + "from io import BytesIO" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "fd962acc", + "metadata": {}, + "outputs": [], + "source": [ + "def data_url(filename, size=(192,192)):\n", + " image = PILImage.create(filename)\n", + " image.thumbnail(size)\n", + " buff = BytesIO()\n", + " image.save(buff, format=\"JPEG\")\n", + " prefix = f'data:image/{Path(filename).suffix[1:]};base64,'\n", + " return prefix + base64.b64encode(buff.getvalue()).decode('utf-8')" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "a55f921b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': [{'label': 'Cat',\n", + " 'confidences': [{'label': 'Cat', 'confidence': 1.0},\n", + " {'label': 'Dog', 'confidence': 3.827755170118796e-13}]}],\n", + " 'flag_index': None,\n", + " 'updated_state': None,\n", + " 'durations': [1.636493444442749],\n", + " 'avg_durations': [1.2002585530281067]}" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = {\"data\": [data_url('cat.jpg')]}\n", + "res = requests.post(url='https://hf.space/embed/jph00/testing/+/api/predict/', json=data).json()\n", + "res" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82774c08", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}