From c366d148d8942ad5cdd984319fe662f99f63284c Mon Sep 17 00:00:00 2001 From: Javier Campos Date: Fri, 20 Sep 2024 13:59:55 -0500 Subject: [PATCH] Checkpoint workflow notebook --- notebooks/workflow_h8.ipynb | 613 +++++++++++++++++++++--------------- 1 file changed, 361 insertions(+), 252 deletions(-) diff --git a/notebooks/workflow_h8.ipynb b/notebooks/workflow_h8.ipynb index f611594..14d7428 100644 --- a/notebooks/workflow_h8.ipynb +++ b/notebooks/workflow_h8.ipynb @@ -4,18 +4,39 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vivado v2020.1 (64-bit)\n", + "SW Build 2902540 on Wed May 27 19:54:35 MDT 2020\n", + "IP Build 2902112 on Wed May 27 22:43:36 MDT 2020\n", + "Copyright 1986-2020 Xilinx, Inc. All Rights Reserved.\n" + ] + } + ], + "source": [ + "#!source /data/Xilinx_no_Vitis/Vivado/2020.1/settings64.sh\n", + "!vivado -version" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2024-09-18 12:46:34.779013: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", + "2024-09-20 13:23:54.993607: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2024-09-18 12:46:34.876555: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n", - "2024-09-18 12:46:34.876577: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n", - "2024-09-18 12:46:35.381115: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n", - "2024-09-18 12:46:35.381174: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n", - "2024-09-18 12:46:35.381181: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" + "2024-09-20 13:23:55.089726: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n", + "2024-09-20 13:23:55.089746: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n", + "2024-09-20 13:23:55.558854: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n", + "2024-09-20 13:23:55.558912: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n", + "2024-09-20 13:23:55.558919: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" ] }, { @@ -34,7 +55,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/data/jcampos/projects/ml-quantum-readout/environment/hls4ml/hls4ml/converters/__init__.py:27: UserWarning: WARNING: Pytorch converter is not enabled!\n", + "/data/jcampos/projects/ml-quantum-readout/environment/hls4ml-jcampos/hls4ml/converters/__init__.py:27: UserWarning: WARNING: Pytorch converter is not enabled!\n", " warnings.warn(\"WARNING: Pytorch converter is not enabled!\", stacklevel=1)\n" ] } @@ -65,12 +86,14 @@ "from qkeras.quantizers import quantized_bits, quantized_relu\n", "from qkeras.utils import _add_supported_quantized_objects\n", "from tensorflow.keras.models import load_model\n", - "from qkeras.utils import _add_supported_quantized_objects" + "from qkeras.utils import _add_supported_quantized_objects\n", + "# need updated tensorflow-model-optimization==0.8.0\n", + "os.environ['PATH'] = os.environ['XILINX_VIVADO'] + '/bin:' + os.environ['PATH']" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -90,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -106,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -119,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -164,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -217,38 +240,77 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "hidden_neurons = 8\n", + "input_shape = int((end_window-start_location)*2)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-09-20 13:24:13.635506: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n", + "2024-09-20 13:24:13.635541: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n", + "2024-09-20 13:24:13.635567: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (correlator4.fnal.gov): /proc/driver/nvidia/version does not exist\n", + "2024-09-20 13:24:13.635845: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/jcampos/miniforge3/envs/ml4qick-env/lib/python3.8/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n", + "Instructions for updating:\n", + "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/jcampos/miniforge3/envs/ml4qick-env/lib/python3.8/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n", + "Instructions for updating:\n", + "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Model: \"sequential_8\"\n", + "Model: \"sequential\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", - " prune_low_magnitude_fc1 (Pr (None, 8) 12810 \n", - " uneLowMagnitude) \n", + " fc1 (QDense) (None, 8) 6408 \n", " \n", - " prune_low_magnitude_batchno (None, 8) 33 \n", - " rm1 (PruneLowMagnitude) \n", + " batchnorm1 (QBatchNormaliza (None, 8) 32 \n", + " tion) \n", " \n", - " prune_low_magnitude_fc2 (Pr (None, 2) 36 \n", - " uneLowMagnitude) \n", + " fc2 (QDense) (None, 2) 18 \n", " \n", "=================================================================\n", - "Total params: 12,879\n", + "Total params: 6,458\n", "Trainable params: 6,442\n", - "Non-trainable params: 6,437\n", + "Non-trainable params: 16\n", "_________________________________________________________________\n", - "None\n" + "None\n", + "Input shape: 800\n", + "Number of hidden neurons: 8\n" ] } ], "source": [ "def get_model(input_shape, hidden=8, is_pruned=True):\n", - " model = Sequential()\n", + " model = keras.models.Sequential()\n", " model.add(QDense(\n", " hidden, \n", " activation='relu', \n", @@ -256,6 +318,7 @@ " input_shape=(input_shape,), \n", " kernel_quantizer=quantized_bits(6,0,alpha=1), bias_quantizer=quantized_bits(6,0,alpha=1)\n", " ))\n", + " # model.add(BatchNormalization(name='batchnorm1'))\n", " model.add(QBatchNormalization(\n", " name='batchnorm1',\n", " gamma_quantizer=quantized_bits(6, 0, 1),\n", @@ -274,9 +337,10 @@ " return model\n", "\n", "\n", - "input_shape = int((end_window-start_location)*2)\n", - "model = get_model(input_shape=input_shape)\n", - "print(model.summary())" + "model = get_model(input_shape=input_shape, hidden=hidden_neurons, is_pruned=False)\n", + "print(model.summary())\n", + "print('Input shape:', input_shape)\n", + "print('Number of hidden neurons:', hidden_neurons)" ] }, { @@ -288,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -297,22 +361,23 @@ "batch_size = 1024*8\n", "epochs = 150\n", "early_stopping_patience = 20\n", - "checkpoint_dir = '../checkpoints/scan_window_location_and_size_h8'\n", + "checkpoint_dir = f'../checkpoints/scan_window_location_and_size_h{hidden_neurons}'\n", "checkpoint_filename = 'qkeras_model_best.h5'\n", + "# checkpoint_filename = 'qkeras_model_best.weights.h5'\n", "\n", "assert os.path.exists(checkpoint_dir), f'ERROR: Checkpoint directory {checkpoint_dir} does not exist.'" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Saving to ../checkpoints/scan_window_location_and_size_h14/sl100_ws400\n" + "Saving to ../checkpoints/scan_window_location_and_size_h8/sl100_ws400\n" ] } ], @@ -346,7 +411,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -354,173 +419,130 @@ "output_type": "stream", "text": [ "Epoch 1/150\n", - "105/105 [==============================] - 3s 16ms/step - loss: 0.2003 - accuracy: 0.9514 - val_loss: 0.1911 - val_accuracy: 0.9575\n", + "105/105 [==============================] - 3s 16ms/step - loss: 0.2064 - accuracy: 0.9488 - val_loss: 0.1943 - val_accuracy: 0.9584\n", "Epoch 2/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1727 - accuracy: 0.9604 - val_loss: 0.1773 - val_accuracy: 0.9582\n", + "105/105 [==============================] - 1s 11ms/step - loss: 0.1733 - accuracy: 0.9606 - val_loss: 0.1743 - val_accuracy: 0.9584\n", "Epoch 3/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1720 - accuracy: 0.9608 - val_loss: 0.1743 - val_accuracy: 0.9583\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1725 - accuracy: 0.9609 - val_loss: 0.1749 - val_accuracy: 0.9580\n", "Epoch 4/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1721 - accuracy: 0.9608 - val_loss: 0.1744 - val_accuracy: 0.9579\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1725 - accuracy: 0.9607 - val_loss: 0.1732 - val_accuracy: 0.9581\n", "Epoch 5/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1721 - accuracy: 0.9608 - val_loss: 0.1743 - val_accuracy: 0.9581\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1724 - accuracy: 0.9608 - val_loss: 0.1732 - val_accuracy: 0.9583\n", "Epoch 6/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1721 - accuracy: 0.9608 - val_loss: 0.1748 - val_accuracy: 0.9582\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1723 - accuracy: 0.9609 - val_loss: 0.1724 - val_accuracy: 0.9588\n", "Epoch 7/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1724 - accuracy: 0.9608 - val_loss: 0.1760 - val_accuracy: 0.9574\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1721 - accuracy: 0.9612 - val_loss: 0.1740 - val_accuracy: 0.9586\n", "Epoch 8/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1721 - accuracy: 0.9608 - val_loss: 0.1748 - val_accuracy: 0.9580\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1718 - accuracy: 0.9613 - val_loss: 0.1730 - val_accuracy: 0.9590\n", "Epoch 9/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1719 - accuracy: 0.9609 - val_loss: 0.1731 - val_accuracy: 0.9582\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1716 - accuracy: 0.9614 - val_loss: 0.1727 - val_accuracy: 0.9588\n", "Epoch 10/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1719 - accuracy: 0.9609 - val_loss: 0.1737 - val_accuracy: 0.9582\n", + "105/105 [==============================] - 1s 11ms/step - loss: 0.1716 - accuracy: 0.9614 - val_loss: 0.1737 - val_accuracy: 0.9590\n", "Epoch 11/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1717 - accuracy: 0.9610 - val_loss: 0.1755 - val_accuracy: 0.9581\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1714 - accuracy: 0.9615 - val_loss: 0.1730 - val_accuracy: 0.9592\n", "Epoch 12/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1717 - accuracy: 0.9610 - val_loss: 0.1730 - val_accuracy: 0.9586\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1714 - accuracy: 0.9615 - val_loss: 0.1723 - val_accuracy: 0.9593\n", "Epoch 13/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1716 - accuracy: 0.9611 - val_loss: 0.1736 - val_accuracy: 0.9583\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1714 - accuracy: 0.9615 - val_loss: 0.1728 - val_accuracy: 0.9590\n", "Epoch 14/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1716 - accuracy: 0.9611 - val_loss: 0.1732 - val_accuracy: 0.9587\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1713 - accuracy: 0.9615 - val_loss: 0.1718 - val_accuracy: 0.9592\n", "Epoch 15/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1715 - accuracy: 0.9612 - val_loss: 0.1729 - val_accuracy: 0.9590\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1713 - accuracy: 0.9615 - val_loss: 0.1723 - val_accuracy: 0.9592\n", "Epoch 16/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1715 - accuracy: 0.9612 - val_loss: 0.1729 - val_accuracy: 0.9590\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1713 - accuracy: 0.9615 - val_loss: 0.1727 - val_accuracy: 0.9591\n", "Epoch 17/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1715 - accuracy: 0.9612 - val_loss: 0.1747 - val_accuracy: 0.9585\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1713 - accuracy: 0.9615 - val_loss: 0.1727 - val_accuracy: 0.9593\n", "Epoch 18/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1714 - accuracy: 0.9613 - val_loss: 0.1721 - val_accuracy: 0.9587\n", + "105/105 [==============================] - 1s 11ms/step - loss: 0.1713 - accuracy: 0.9615 - val_loss: 0.1716 - val_accuracy: 0.9591\n", "Epoch 19/150\n", - "105/105 [==============================] - 1s 11ms/step - loss: 0.1714 - accuracy: 0.9613 - val_loss: 0.1724 - val_accuracy: 0.9588\n", + "105/105 [==============================] - 1s 11ms/step - loss: 0.1712 - accuracy: 0.9616 - val_loss: 0.1731 - val_accuracy: 0.9591\n", "Epoch 20/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1713 - accuracy: 0.9614 - val_loss: 0.1721 - val_accuracy: 0.9590\n", + "105/105 [==============================] - 1s 11ms/step - loss: 0.1713 - accuracy: 0.9615 - val_loss: 0.1727 - val_accuracy: 0.9591\n", "Epoch 21/150\n", - "105/105 [==============================] - 1s 11ms/step - loss: 0.1713 - accuracy: 0.9613 - val_loss: 0.1737 - val_accuracy: 0.9587\n", + "105/105 [==============================] - 1s 11ms/step - loss: 0.1712 - accuracy: 0.9615 - val_loss: 0.1731 - val_accuracy: 0.9591\n", "Epoch 22/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1713 - accuracy: 0.9613 - val_loss: 0.1735 - val_accuracy: 0.9589\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1712 - accuracy: 0.9615 - val_loss: 0.1734 - val_accuracy: 0.9590\n", "Epoch 23/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1712 - accuracy: 0.9614 - val_loss: 0.1747 - val_accuracy: 0.9587\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1711 - accuracy: 0.9615 - val_loss: 0.1726 - val_accuracy: 0.9591\n", "Epoch 24/150\n", - "105/105 [==============================] - 1s 11ms/step - loss: 0.1712 - accuracy: 0.9614 - val_loss: 0.1725 - val_accuracy: 0.9589\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1712 - accuracy: 0.9615 - val_loss: 0.1731 - val_accuracy: 0.9589\n", "Epoch 25/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1711 - accuracy: 0.9614 - val_loss: 0.1731 - val_accuracy: 0.9589\n", + "105/105 [==============================] - 1s 11ms/step - loss: 0.1711 - accuracy: 0.9616 - val_loss: 0.1722 - val_accuracy: 0.9593\n", "Epoch 26/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1711 - accuracy: 0.9615 - val_loss: 0.1733 - val_accuracy: 0.9589\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1712 - accuracy: 0.9616 - val_loss: 0.1742 - val_accuracy: 0.9592\n", "Epoch 27/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1711 - accuracy: 0.9614 - val_loss: 0.1725 - val_accuracy: 0.9589\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1711 - accuracy: 0.9616 - val_loss: 0.1719 - val_accuracy: 0.9593\n", "Epoch 28/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1712 - accuracy: 0.9614 - val_loss: 0.1726 - val_accuracy: 0.9590\n", + "105/105 [==============================] - 1s 11ms/step - loss: 0.1711 - accuracy: 0.9615 - val_loss: 0.1720 - val_accuracy: 0.9591\n", "Epoch 29/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1711 - accuracy: 0.9615 - val_loss: 0.1731 - val_accuracy: 0.9589\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1710 - accuracy: 0.9616 - val_loss: 0.1733 - val_accuracy: 0.9590\n", "Epoch 30/150\n", - "105/105 [==============================] - 1s 11ms/step - loss: 0.1711 - accuracy: 0.9615 - val_loss: 0.1739 - val_accuracy: 0.9589\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1710 - accuracy: 0.9616 - val_loss: 0.1733 - val_accuracy: 0.9592\n", "Epoch 31/150\n", - "105/105 [==============================] - 1s 11ms/step - loss: 0.1710 - accuracy: 0.9615 - val_loss: 0.1742 - val_accuracy: 0.9585\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1710 - accuracy: 0.9616 - val_loss: 0.1740 - val_accuracy: 0.9592\n", "Epoch 32/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1710 - accuracy: 0.9615 - val_loss: 0.1739 - val_accuracy: 0.9588\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1710 - accuracy: 0.9616 - val_loss: 0.1734 - val_accuracy: 0.9589\n", "Epoch 33/150\n", - "105/105 [==============================] - 1s 11ms/step - loss: 0.1710 - accuracy: 0.9615 - val_loss: 0.1727 - val_accuracy: 0.9592\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1710 - accuracy: 0.9616 - val_loss: 0.1739 - val_accuracy: 0.9590\n", "Epoch 34/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1710 - accuracy: 0.9615 - val_loss: 0.1752 - val_accuracy: 0.9588\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1710 - accuracy: 0.9616 - val_loss: 0.1739 - val_accuracy: 0.9590\n", "Epoch 35/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1710 - accuracy: 0.9616 - val_loss: 0.1726 - val_accuracy: 0.9588\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1710 - accuracy: 0.9615 - val_loss: 0.1728 - val_accuracy: 0.9592\n", "Epoch 36/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1710 - accuracy: 0.9615 - val_loss: 0.1737 - val_accuracy: 0.9590\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1710 - accuracy: 0.9616 - val_loss: 0.1749 - val_accuracy: 0.9590\n", "Epoch 37/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1710 - accuracy: 0.9615 - val_loss: 0.1725 - val_accuracy: 0.9591\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1710 - accuracy: 0.9615 - val_loss: 0.1732 - val_accuracy: 0.9592\n", "Epoch 38/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1710 - accuracy: 0.9615 - val_loss: 0.1739 - val_accuracy: 0.9590\n", - "Epoch 39/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1709 - accuracy: 0.9615 - val_loss: 0.1728 - val_accuracy: 0.9589\n", - "Epoch 40/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1708 - accuracy: 0.9615 - val_loss: 0.1719 - val_accuracy: 0.9594\n", - "Epoch 41/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1708 - accuracy: 0.9615 - val_loss: 0.1731 - val_accuracy: 0.9591\n", - "Epoch 42/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1707 - accuracy: 0.9615 - val_loss: 0.1730 - val_accuracy: 0.9588\n", - "Epoch 43/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1707 - accuracy: 0.9615 - val_loss: 0.1739 - val_accuracy: 0.9588\n", - "Epoch 44/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1706 - accuracy: 0.9614 - val_loss: 0.1722 - val_accuracy: 0.9588\n", - "Epoch 45/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1706 - accuracy: 0.9615 - val_loss: 0.1723 - val_accuracy: 0.9588\n", - "Epoch 46/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1706 - accuracy: 0.9615 - val_loss: 0.1736 - val_accuracy: 0.9591\n", - "Epoch 47/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1706 - accuracy: 0.9614 - val_loss: 0.1736 - val_accuracy: 0.9590\n", - "Epoch 48/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1705 - accuracy: 0.9615 - val_loss: 0.1720 - val_accuracy: 0.9590\n", - "Epoch 49/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1705 - accuracy: 0.9614 - val_loss: 0.1729 - val_accuracy: 0.9589\n", - "Epoch 50/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1705 - accuracy: 0.9615 - val_loss: 0.1722 - val_accuracy: 0.9591\n", - "Epoch 51/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1705 - accuracy: 0.9614 - val_loss: 0.1724 - val_accuracy: 0.9590\n", - "Epoch 52/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1705 - accuracy: 0.9614 - val_loss: 0.1735 - val_accuracy: 0.9590\n", - "Epoch 53/150\n", - "105/105 [==============================] - 1s 11ms/step - loss: 0.1705 - accuracy: 0.9614 - val_loss: 0.1734 - val_accuracy: 0.9590\n", - "Epoch 54/150\n", - "105/105 [==============================] - 1s 11ms/step - loss: 0.1705 - accuracy: 0.9614 - val_loss: 0.1723 - val_accuracy: 0.9588\n", - "Epoch 55/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1705 - accuracy: 0.9615 - val_loss: 0.1725 - val_accuracy: 0.9589\n", - "Epoch 56/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1705 - accuracy: 0.9614 - val_loss: 0.1726 - val_accuracy: 0.9587\n", - "Epoch 57/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1704 - accuracy: 0.9614 - val_loss: 0.1743 - val_accuracy: 0.9589\n", - "Epoch 58/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1705 - accuracy: 0.9614 - val_loss: 0.1725 - val_accuracy: 0.9591\n", - "Epoch 59/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1705 - accuracy: 0.9614 - val_loss: 0.1728 - val_accuracy: 0.9589\n", - "Epoch 60/150\n", - "105/105 [==============================] - 1s 12ms/step - loss: 0.1704 - accuracy: 0.9614 - val_loss: 0.1724 - val_accuracy: 0.9589\n", - "3125/3125 [==============================] - 7s 2ms/step\n", + "105/105 [==============================] - 1s 12ms/step - loss: 0.1709 - accuracy: 0.9616 - val_loss: 0.1736 - val_accuracy: 0.9590\n", + "3125/3125 [==============================] - 6s 2ms/step\n", "\n", "===================================\n", "Start location = 100, Window size = 400\n", - " Accuracy 0.9606\n", - " Fidelity 0.9212\n" + " Accuracy 0.96077\n", + " Fidelity 0.92154\n" ] } ], "source": [ - "#########################\n", - "# 1. declare model \n", - "#########################\n", - "opt = Adam(learning_rate=init_learning_rate)\n", - "model = get_model(input_shape=window_size*2)\n", - "model.compile(\n", - " optimizer=opt, \n", - " loss=CategoricalCrossentropy(from_logits=True), \n", - " metrics=['accuracy']\n", - ")\n", + "if True:\n", + " #########################\n", + " # 1. declare model \n", + " #########################\n", + " opt = Adam(learning_rate=init_learning_rate)\n", + " model = get_model(input_shape=window_size*2, is_pruned=True)\n", + " model.compile(\n", + " optimizer=opt, \n", + " loss=CategoricalCrossentropy(from_logits=True), \n", + " metrics=['accuracy']\n", + " )\n", "\n", - "#########################\n", - "# 3. train \n", - "#########################\n", - "history = model.fit(\n", - " X_train_val, \n", - " y_train_val, \n", - " batch_size=batch_size,\n", - " epochs=epochs, \n", - " validation_split=validation_split, \n", - " shuffle=True, \n", - " callbacks=callbacks,\n", - ")\n", + " #########################\n", + " # 3. train \n", + " #########################\n", + " history = model.fit(\n", + " X_train_val, \n", + " y_train_val, \n", + " batch_size=batch_size,\n", + " epochs=epochs, \n", + " validation_split=validation_split, \n", + " shuffle=True, \n", + " callbacks=callbacks,\n", + " )\n", "\n", - "# Save the history dictionary\n", - "with open(os.path.join(ckp_dir, 'qkeras_training_history.pkl'), 'wb') as f:\n", - " pickle.dump(history.history, f)\n", + " # Save the history dictionary\n", + " with open(os.path.join(ckp_dir, 'qkeras_training_history.pkl'), 'wb') as f:\n", + " pickle.dump(history.history, f)\n", "\n", - "#########################\n", - "# 3. compute fidelity \n", - "#########################\n", - "y_pred = model.predict(X_test)\n", - "test_acc = accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_pred, axis=1))\n", + " #########################\n", + " # 3. compute fidelity \n", + " #########################\n", + " y_pred = model.predict(X_test)\n", + " test_acc = accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_pred, axis=1))\n", "\n", - "print('\\n===================================')\n", - "print(f'Start location = {start_location}, Window size = {window_size}')\n", - "print(' Accuracy', test_acc)\n", - "print(' Fidelity', test_acc*2-1)\n" + " print('\\n===================================')\n", + " print(f'Start location = {start_location}, Window size = {window_size}')\n", + " print(' Accuracy', test_acc)\n", + " print(' Fidelity', test_acc*2-1)\n" ] }, { @@ -532,65 +554,49 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'../checkpoints/scan_window_location_and_size_h14/sl100_ws400/qkeras_model_best.h5'" + "'../checkpoints/scan_window_location_and_size_h8/sl100_ws400/qkeras_model_best.h5'" ] }, - "execution_count": 55, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "checkpoint_filename = os.path.join(ckp_dir, checkpoint_filename)\n", - "checkpoint_filename" + "ckp_filename = os.path.join(ckp_dir, checkpoint_filename)\n", + "ckp_filename" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "3125/3125 [==============================] - 7s 2ms/step\n", - "Keras Accuracy: 0.96069\n", - "Keras Fidelity: 0.9213800000000001\n", - "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n" + "3125/3125 [==============================] - 6s 2ms/step\n", + "Keras Accuracy: 0.96083\n", + "Keras Fidelity: 0.9216599999999999\n" ] } ], "source": [ - "# co = {}\n", - "# custom_objects={'PruneLowMagnitude':tfmot.sparsity.PruneLowMagnitude}\n", - "# _add_supported_quantized_objects(co)\n", - "# checkpoint_model = load_model(checkpoint_filename, custom_objects=co, compile=False)\n", - "\n", - "checkpoint_model = get_model(input_shape=input_shape)\n", - "checkpoint_model.load_weights(checkpoint_filename)\n", + "checkpoint_model = get_model(input_shape=input_shape, hidden=hidden_neurons, is_pruned=True)\n", + "checkpoint_model.load_weights(ckp_filename)\n", "\n", "y_pred = checkpoint_model.predict(X_test)\n", "test_acc = accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_pred, axis=1))\n", "\n", "print(f\"Keras Accuracy: {test_acc}\")\n", - "print(f\"Keras Fidelity: {test_acc*2-1}\")\n", - "\n", - "checkpoint_model = strip_pruning(checkpoint_model)\n", - "checkpoint_model.save(checkpoint_filename)" + "print(f\"Keras Fidelity: {test_acc*2-1}\")" ] }, { @@ -602,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -617,7 +623,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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AaAMASIBoAwBIgGgDAEiAaAMASIBoAwBIgGgDAEiAaAMASIBoAwBIgGgDAEiAaAMASIBoAwBIgGgDAEiAaAMASIBoAwBIgGgDAEiAaAMASIBoAwBIgGgDAEiAaAMASIBoAwBIgGgDAEiAaAMASIBoAwBIgGgDAEiAaAMASIBoAwBIgGgDAEiAaAMASIBoAwBIgGgDAEiAaAMASIBoAwBIgGgDAEiAaAMASMAJHW2PPvpojBo1KkaOHBn33ntvd48DANBped09QFc5ePBg1NTUxOrVq6OwsDDGjh0bs2bNioEDB3b3aAAAHXbCftK2du3aOO+886K4uDj69esXlZWVsWrVqu4eCwCgUzocbXfddVdccMEFUVBQEAUFBTFx4sRYsWLFMR3q2WefjRkzZkRRUVHk5OTEww8/3O662traGD58ePTp0ycmTJgQa9eubblv586dUVxc3HK7uLg46uvrj+mcAADHS4ej7cwzz4xFixbFhg0bYv369VFeXh6XXXZZvPTSS+2uf/755+Odd95ps33r1q3R0NDQ7mMaGxujtLQ0amtrDznH8uXLo6amJhYuXBgbN26M0tLSmDZtWuzevbujLwkAoMfrcLTNmDEjPvGJT8TIkSPjQx/6UHz961+Pfv36xQsvvNBmbXNzc1RXV8fs2bOjqampZfu2bduivLw87r///nafo7KyMm6++eaYNWvWIee44447Yu7cuTFnzpwoKSmJu+++O/r27RtLliyJiIiioqJWn6zV19dHUVFRR18uAECPcFQ/09bU1BTLli2LxsbGmDhxYtud5+bG448/Hps2bYqrrroqmpub45VXXony8vK4/PLLY968eZ163rfffjs2bNgQFRUVrZ6roqIi1qxZExERZWVlsWXLlqivr4/9+/fHihUrYtq0aYfcZ21tbZSUlMT48eM7NRMAQFfq1NWjL774YkycODF+85vfRL9+/eKhhx6KkpKSdtcWFRXF008/HZMmTYrZs2fHmjVroqKiIu66665OD71nz55oamqKwYMHt9o+ePDgePnllyMiIi8vL26//faYMmVKNDc3x7x58w575Wh1dXVUV1fHvn37orCwsNOzAQB0hU5F26hRo2Lz5s2xd+/e+P73vx9VVVXxwx/+8JDhNnTo0HjggQdi8uTJMWLEiFi8eHHk5OQc1eBHYubMmTFz5swufx4AgK7WqW+P5ufnxwc/+MEYO3Zs3HrrrVFaWhr//M//fMj1DQ0Ncc0118SMGTPiwIEDcd1113V64IiIQYMGRa9evdpcyNDQ0BBDhgw5qn0DAPREx+T3tDU3N8dbb73V7n179uyJqVOnxrnnnhsPPvhg1NXVxfLly+OGG27o9PPl5+fH2LFjo66urtUMdXV17f5sHQBA6jr87dH58+dHZWVlDB06NN588834zne+E88880w88cQTbdY2NzdHZWVlDBs2LJYvXx55eXlRUlISTz75ZJSXl0dxcXG7n7rt378/tm/f3nJ7x44dsXnz5hgwYEAMHTo0IiJqamqiqqoqxo0bF2VlZXHnnXdGY2NjzJkzp6MvCQCgx+twtO3evTuuuuqq+OUvfxmFhYVxwQUXxBNPPBEf//jH26zNzc2NW265JSZNmhT5+fkt20tLS+Opp56K0047rd3nWL9+fUyZMqXldk1NTUREVFVVxdKlSyMi4oorrojXX389FixYELt27YoxY8bEypUr21ycAABwIsjJsizr7iF6knevHt27d28UFBR09zhA4obf+Fibba8umt4NkwA9UUe644T9t0cBAE4kog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABog0AIAGiDQAgAaINACABJ3S0PfroozFq1KgYOXJk3Hvvvd09DgBAp+V19wBd5eDBg1FTUxOrV6+OwsLCGDt2bMyaNSsGDhzY3aMBAHTYCftJ29q1a+O8886L4uLi6NevX1RWVsaqVau6eywAgE7pcLTdeuutMX78+Ojfv3+cfvrpcfnll8e2bduO6VDPPvtszJgxI4qKiiInJycefvjhdtfV1tbG8OHDo0+fPjFhwoRYu3Zty307d+6M4uLiltvFxcVRX19/TOcEADheOhxtP/zhD6O6ujpeeOGFePLJJ+Odd96JSy65JBobG9td//zzz8c777zTZvvWrVujoaGh3cc0NjZGaWlp1NbWHnKO5cuXR01NTSxcuDA2btwYpaWlMW3atNi9e3dHXxIAQI/X4Z9pW7lyZavbS5cujdNPPz02bNgQH/vYx1rd19zcHNXV1TFy5MhYtmxZ9OrVKyIitm3bFuXl5VFTUxPz5s1r8xyVlZVRWVl52DnuuOOOmDt3bsyZMyciIu6+++547LHHYsmSJXHjjTdGUVFRq0/W6uvro6ys7JD7q62tjdra2mhqajr8ASAJw298rM22VxdN74ZJAODYOOqfadu7d29ERAwYMKDtznNz4/HHH49NmzbFVVddFc3NzfHKK69EeXl5XH755e0G25F4++23Y8OGDVFRUdHquSoqKmLNmjUREVFWVhZbtmyJ+vr62L9/f6xYsSKmTZt2yH1WV1fH1q1bY926dZ2aCQCgKx3V1aPNzc3xxS9+MT760Y/G+eef3+6aoqKiePrpp2PSpEkxe/bsWLNmTVRUVMRdd93V6efds2dPNDU1xeDBg1ttHzx4cLz88ssREZGXlxe33357TJkyJZqbm2PevHmuHAUAknVU0VZdXR1btmyJ55577rDrhg4dGg888EBMnjw5RowYEYsXL46cnJyjeeojMnPmzJg5c2aXPw8AQFfr9LdHr7322nj00Udj9erVceaZZx52bUNDQ1xzzTUxY8aMOHDgQFx33XWdfdqIiBg0aFD06tWrzYUMDQ0NMWTIkKPaNwBAT9ThaMuyLK699tp46KGH4umnn46zzz77sOv37NkTU6dOjXPPPTcefPDBqKuri+XLl8cNN9zQ6aHz8/Nj7NixUVdX17Ktubk56urqYuLEiZ3eLwBAT9Xhb49WV1fHd77znXjkkUeif//+sWvXroiIKCwsjJNPPrnV2ubm5qisrIxhw4bF8uXLIy8vL0pKSuLJJ5+M8vLyKC4ubvdTt/3798f27dtbbu/YsSM2b94cAwYMiKFDh0ZERE1NTVRVVcW4ceOirKws7rzzzmhsbGy5mhQA4ETS4Wh79wKCiy++uNX2++67L66++upW23Jzc+OWW26JSZMmRX5+fsv20tLSeOqpp+K0005r9znWr18fU6ZMabldU1MTERFVVVWxdOnSiIi44oor4vXXX48FCxbErl27YsyYMbFy5co2FycAAJwIOhxtWZZ1aP3HP/7xdrdfeOGFh3zMxRdffETPc+2118a1117boXkAAFJ0wv7bowAAJxLRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQgBMy2h599NEYNWpUjBw5Mu69997uHgcA4KjldfcAx9rBgwejpqYmVq9eHYWFhTF27NiYNWtWDBw4sLtHAwDotBPuk7a1a9fGeeedF8XFxdGvX7+orKyMVatWdfdYAABHpcdF27PPPhszZsyIoqKiyMnJiYcffrjNmtra2hg+fHj06dMnJkyYEGvXrm25b+fOnVFcXNxyu7i4OOrr64/H6AAAXabHRVtjY2OUlpZGbW1tu/cvX748ampqYuHChbFx48YoLS2NadOmxe7du4/zpAAAx0+Pi7bKysq4+eabY9asWe3ef8cdd8TcuXNjzpw5UVJSEnfffXf07ds3lixZEhERRUVFrT5Zq6+vj6KiokM+31tvvRX79u1r9QcAoKdJ6kKEt99+OzZs2BDz589v2ZabmxsVFRWxZs2aiIgoKyuLLVu2RH19fRQWFsaKFSviK1/5yiH3eeutt8ZXv/rVLp/99w2/8bE2215dNP24znA4PX0+gPernvL+fDRz9JTXcCjvna8nzdbjPmk7nD179kRTU1MMHjy41fbBgwfHrl27IiIiLy8vbr/99pgyZUqMGTMmrr/++sNeOTp//vzYu3dvy5/XXnutS18DAEBnJPVJ25GaOXNmzJw584jW9u7dO3r37t3FEwEAHJ2kPmkbNGhQ9OrVKxoaGlptb2hoiCFDhnTTVAAAXS+paMvPz4+xY8dGXV1dy7bm5uaoq6uLiRMnduNkAABdq8d9e3T//v2xffv2lts7duyIzZs3x4ABA2Lo0KFRU1MTVVVVMW7cuCgrK4s777wzGhsbY86cOd04NQBA1+px0bZ+/fqYMmVKy+2ampqIiKiqqoqlS5fGFVdcEa+//nosWLAgdu3aFWPGjImVK1e2uTgBAOBE0uOi7eKLL44syw675tprr41rr732OE0EAND9kvqZNgCA9yvRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJAA0QYAkADRBgCQANEGAJCAvO4eoKfJsiwiIvbt29dlz9H81oE227ry+Tqqp893JE6E18CJwbnIsdRTzqejmaOnvIZDee98XT3bu/t/tz8OJyc7klXvI7/4xS/irLPO6u4xAID3kddeey3OPPPMw64Rbe/R3NwcO3fujP79+0dOTk5E/LaCzzrrrHjttdeioKCgmyfsORyXthyT9jkubTkm7XNc2nJM2jqRjkmWZfHmm29GUVFR5OYe/qfWfHv0PXJzcw9ZugUFBcmfHF3BcWnLMWmf49KWY9I+x6Utx6StE+WYFBYWHtE6FyIAACRAtAEAJEC0HYHevXvHwoULo3fv3t09So/iuLTlmLTPcWnLMWmf49KWY9LW+/WYuBABACABPmkDAEiAaAMASIBoAwBIgGgDAEiAaAMASIBoi4ivf/3rcdFFF0Xfvn3jlFNOOaLHZFkWCxYsiDPOOCNOPvnkqKioiJ/97Get1rzxxhtx5ZVXRkFBQZxyyinx2c9+Nvbv398Fr6BrdHT+V199NXJyctr9873vfa9lXXv3L1u27Hi8pKPWma/pxRdf3Ob1/vVf/3WrNT//+c9j+vTp0bdv3zj99NPjS1/6Uhw8eLArX8ox1dHj8sYbb8Tf/u3fxqhRo+Lkk0+OoUOHxuc///nYu3dvq3WpnSu1tbUxfPjw6NOnT0yYMCHWrl172PXf+9734sMf/nD06dMnRo8eHY8//nir+4/kfaan68gxueeee2LSpElx6qmnxqmnnhoVFRVt1l999dVtzolLL720q1/GMdeR47J06dI2r7lPnz6t1rzfzpX23ldzcnJi+vTpLWtOlHOllYxswYIF2R133JHV1NRkhYWFR/SYRYsWZYWFhdnDDz+c/fjHP85mzpyZnX322dmvf/3rljWXXnppVlpamr3wwgvZf/7nf2Yf/OAHs8985jNd9CqOvY7Of/DgweyXv/xlqz9f/epXs379+mVvvvlmy7qIyO67775W637/uPVknfmaTp48OZs7d26r17t3796W+w8ePJidf/75WUVFRbZp06bs8ccfzwYNGpTNnz+/q1/OMdPR4/Liiy9mn/zkJ7Mf/OAH2fbt27O6urps5MiR2Z/+6Z+2WpfSubJs2bIsPz8/W7JkSfbSSy9lc+fOzU455ZSsoaGh3fXPP/981qtXr+wf//Efs61bt2Z///d/n5100knZiy++2LLmSN5nerKOHpPZs2dntbW12aZNm7Kf/vSn2dVXX50VFhZmv/jFL1rWVFVVZZdeemmrc+KNN944Xi/pmOjocbnvvvuygoKCVq95165drda8386VX/3qV62Ox5YtW7JevXpl9913X8uaE+FceS/R9nvuu+++I4q25ubmbMiQIdk3v/nNlm3/93//l/Xu3Tv7j//4jyzLsmzr1q1ZRGTr1q1rWbNixYosJycnq6+vP+azH2vHav4xY8Zkf/mXf9lqW0RkDz300LEa9bjp7DGZPHly9oUvfOGQ9z/++ONZbm5uqzfhu+66KysoKMjeeuutYzJ7VzpW58p3v/vdLD8/P3vnnXdatqV0rpSVlWXV1dUtt5uamrKioqLs1ltvbXf9pz71qWz69Omttk2YMCH7q7/6qyzLjux9pqfr6DF5r4MHD2b9+/fP7r///pZtVVVV2WWXXXasRz2uOnpc/tDfTc6VLPunf/qnrH///tn+/ftbtp0I58p7+fZoJ+zYsSN27doVFRUVLdsKCwtjwoQJsWbNmoiIWLNmTZxyyikxbty4ljUVFRWRm5sbP/rRj477zB11LObfsGFDbN68OT772c+2ua+6ujoGDRoUZWVlsWTJksgS+B3PR3NM/v3f/z0GDRoU559/fsyfPz8OHDjQar+jR4+OwYMHt2ybNm1a7Nu3L1566aVj/0KOsWN1ru/duzcKCgoiLy+v1fYUzpW33347NmzY0Oo9ITc3NyoqKlreE95rzZo1rdZH/Pbr/u76I3mf6ck6c0ze68CBA/HOO+/EgAEDWm1/5pln4vTTT49Ro0bF5z73ufjVr351TGfvSp09Lvv3749hw4bFWWedFZdddlmr9wbnSsTixYvj05/+dHzgAx9otT3lc6U9eX94Ce+1a9euiIhWf8m+e/vd+3bt2hWnn356q/vz8vJiwIABLWt6smMx/+LFi+Pcc8+Niy66qNX2m266KcrLy6Nv376xatWq+Ju/+ZvYv39/fP7znz9m83eFzh6T2bNnx7Bhw6KoqCh+8pOfxJe//OXYtm1bPPjggy37be9ceve+nu5YnCt79uyJr33ta3HNNde02p7KubJnz55oampq9+v48ssvt/uYQ33df/895N1th1rTk3XmmLzXl7/85SgqKmr1l/mll14an/zkJ+Pss8+OV155Jf7u7/4uKisrY82aNdGrV69j+hq6QmeOy6hRo2LJkiVxwQUXxN69e+O2226Liy66KF566aU488wz3/fnytq1a2PLli2xePHiVttTP1fac8JG24033hjf+MY3Drvmpz/9aXz4wx8+ThP1DEd6XI7Wr3/96/jOd74TX/nKV9rc9/vbLrzwwmhsbIxvfvOb3fYXcVcfk98PkdGjR8cZZ5wRU6dOjVdeeSXOOeecTu+3qx2vc2Xfvn0xffr0KCkpiX/4h39odV9PO1c4fhYtWhTLli2LZ555ptUP3X/6059u+d+jR4+OCy64IM4555x45plnYurUqd0xapebOHFiTJw4seX2RRddFOeee25861vfiq997WvdOFnPsHjx4hg9enSUlZW12n4inisnbLRdf/31cfXVVx92zYgRIzq17yFDhkRERENDQ5xxxhkt2xsaGmLMmDEta3bv3t3qcQcPHow33nij5fHd4UiPy9HO//3vfz8OHDgQV1111R9cO2HChPja174Wb731Vrf847/H65i8a8KECRERsX379jjnnHNiyJAhba6SamhoiIg44c+VN998My699NLo379/PPTQQ3HSSScddn13nyuHMmjQoOjVq1fL1+1dDQ0NhzwGQ4YMOez6I3mf6ck6c0zeddttt8WiRYviqaeeigsuuOCwa0eMGBGDBg2K7du3J/EX8dEcl3eddNJJceGFF8b27dsj4v19rjQ2NsayZcvipptu+oPPk9q50q7u/qG6nqSjFyLcdtttLdv27t3b7oUI69evb1nzxBNPJHchQmfnnzx5cpsrAQ/l5ptvzk499dROz3q8HKuv6XPPPZdFRPbjH/84y7L/fyHC718l9a1vfSsrKCjIfvOb3xy7F9BFOntc9u7dm/3RH/1RNnny5KyxsfGInqsnnytlZWXZtdde23K7qakpKy4uPuyFCH/yJ3/SatvEiRPbXIhwuPeZnq6jxyTLsuwb3/hGVlBQkK1Zs+aInuO1117LcnJyskceeeSo5z1eOnNcft/BgwezUaNGZdddd12WZe/fcyXLfvv3du/evbM9e/b8wedI8Vx5L9GWZdn//M//ZJs2bWr59RSbNm3KNm3a1OrXVIwaNSp78MEHW24vWrQoO+WUU7JHHnkk+8lPfpJddtll7f7KjwsvvDD70Y9+lD333HPZyJEjk/uVH4eb/xe/+EU2atSo7Ec/+lGrx/3sZz/LcnJyshUrVrTZ5w9+8IPsnnvuyV588cXsZz/7WfZv//ZvWd++fbMFCxZ0+es5Fjp6TLZv357ddNNN2fr167MdO3ZkjzzySDZixIjsYx/7WMtj3v2VH5dcckm2efPmbOXKldlpp52W3K/86Mhx2bt3bzZhwoRs9OjR2fbt21tdkn/w4MEsy9I7V5YtW5b17t07W7p0abZ169bsmmuuyU455ZSWq4L/4i/+Irvxxhtb1j///PNZXl5edtttt2U//elPs4ULF7b7Kz/+0PtMT9bRY7Jo0aIsPz8/+/73v9/qnHj3vfjNN9/MbrjhhmzNmjXZjh07sqeeeir7yEc+ko0cOTKJ/4Pzro4el69+9avZE088kb3yyivZhg0bsk9/+tNZnz59spdeeqllzfvtXHnXH//xH2dXXHFFm+0nyrnyXqIt++1lwRHR5s/q1atb1sTvfl/Uu5qbm7OvfOUr2eDBg7PevXtnU6dOzbZt29Zqv7/61a+yz3zmM1m/fv2ygoKCbM6cOa1CsKf7Q/Pv2LG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" ] @@ -647,9 +653,6 @@ } ], "source": [ - "checkpoint_model = get_model(input_shape=input_shape, is_pruned=False)\n", - "checkpoint_model.load_weights(checkpoint_filename)\n", - "\n", "num_layers = len(checkpoint_model.layers)\n", "print(f'Number of layers: {num_layers}')\n", "\n", @@ -664,7 +667,13 @@ " plt.semilogy()\n", " plt.savefig(f'model-dist-idx{idx}.png')\n", "\n", - " print('% of zeros = {}'.format(np.sum(w == 0) / np.size(w)))" + " print('% of zeros = {}'.format(np.sum(w == 0) / np.size(w)))\n", + "\n", + "\n", + "checkpoint_model = strip_pruning(checkpoint_model) # remove prune layers for hls4ml parsing \n", + "checkpoint_model.save_weights(ckp_filename) # save as weights only for keras tracing (cannot directly pass strip_pruned model)\n", + "checkpoint_model = get_model(input_shape=input_shape, hidden=hidden_neurons, is_pruned=False)\n", + "checkpoint_model.load_weights(ckp_filename)" ] }, { @@ -676,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -690,7 +699,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -699,7 +708,8 @@ "text": [ "Model\n", " Precision: ap_fixed<16,6>\n", - " ReuseFactor: 1\n", + " ReuseFactor: 64\n", + " Strategy: Resource\n", "LayerName\n", " fc1\n", " Precision\n", @@ -728,7 +738,7 @@ " Trace: True\n", " fc1_input\n", " Precision: ap_fixed<14,14>\n", - " Trace: True\n" + " Trace: False\n" ] } ], @@ -737,7 +747,8 @@ "hls_config = {}\n", "hls_config['Model'] = {}\n", "hls_config['Model']['Precision'] = 'ap_fixed<16,6>' # Default precision\n", - "hls_config['Model']['ReuseFactor'] = 1 # fully parallelized \n", + "hls_config['Model']['ReuseFactor'] = 64 # fully parallelized \n", + "hls_config['Model']['Strategy'] = 'Resource'\n", "\n", "hls_config['LayerName'] = {}\n", "keras_layers = ['fc1', 'fc1_relu', 'batchnorm1', 'fc2', 'fc2_linear']\n", @@ -749,7 +760,7 @@ "# Input - ZCU216 uses 14-bit ADCS \n", "hls_config['LayerName']['fc1_input'] = {}\n", "hls_config['LayerName']['fc1_input']['Precision'] = {}\n", - "hls_config['LayerName']['fc1_input']['Trace'] = True\n", + "hls_config['LayerName']['fc1_input']['Trace'] = False\n", "hls_config['LayerName']['fc1_input']['Precision'] = 'ap_fixed<14,14>' \n", "# Fc1\n", "hls_config['LayerName']['fc1']['Precision']['result'] = 'ap_fixed<17,17>'\n", @@ -782,7 +793,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -791,15 +802,15 @@ "io_type = 'io_parallel'\n", "clock_period = 3.225 # 3.225ns (307.2 MHz)\n", "hls_fig = os.path.join(output_dir, 'model.png')\n", - "backend = 'VivadoAccelerator'\n", - "interface = 'axi_master'\n", - "driver = 'c'\n", + "backend = 'VivadoAccelerator' \n", + "interface = 'axi_stream'\n", + "#driver = 'c'\n", "board = 'zcu216'" ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -813,8 +824,11 @@ "Layer name: batchnorm1, layer type: QBatchNormalization, input shapes: [[None, 8]], output shape: [None, 8]\n", "Layer name: fc2, layer type: QDense, input shapes: [[None, 8]], output shape: [None, 2]\n", "Creating HLS model\n", + "WARNING: Changing pipeline style to \"dataflow\".\n", "WARNING: Config parameter \"accum_t\" overwrites an existing attribute in layer \"fc1\" (Dense)\n", "WARNING: Config parameter \"accum_t\" overwrites an existing attribute in layer \"fc2\" (Dense)\n", + "WARNING: Invalid ReuseFactor=64 in layer \"fc1\".Using ReuseFactor=50 instead. Valid ReuseFactor(s): 1,2,4,5,8,10,16,20,25,32,40,50,80,100,160,200,400,800,1600,3200,6400.\n", + "WARNING: Invalid ReuseFactor=64 in layer \"fc2\".Using ReuseFactor=16 instead. Valid ReuseFactor(s): 1,2,4,8,16.\n", "Creating hls4ml project directory ../hls4ml_projects/sl-100_ws-400\n", "Writing HLS project\n", "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n" @@ -846,7 +860,8 @@ " backend=backend,\n", " board=board,\n", " interface=interface,\n", - " driver=driver,\n", + " #driver=driver,\n", + " project_name='NN'\n", ")\n", "\n", "print(f\"Creating hls4ml project directory {output_dir}\")\n", @@ -855,7 +870,7 @@ "# Visualize model\n", "hls4ml.utils.plot_model(\n", " hls_model, show_shapes=True, show_precision=True, to_file=hls_fig \n", - ")\n" + ")" ] }, { @@ -867,17 +882,17 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Keras Acc: 96.069%\n", - "Keras Fidelity: 92.138%\n", - "HLS Acc: 95.322:%\n", - "HLS Fidelity: 90.644:%\n" + "Keras Acc: 96.083%\n", + "Keras Fidelity: 92.166%\n", + "HLS Acc: 95.685:%\n", + "HLS Fidelity: 91.37:%\n" ] } ], @@ -899,20 +914,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Correlation plots (Keras vs HLS)\n", - "Let's compare the output of the Qkeras and HLS model. If properly configured, the HLS activations will be aligned with the Qkeras model. " + "### Collect traces and compare" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Recompiling myproject with tracing\n", + "Recompiling NN with tracing\n", "Writing HLS project\n", "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n" ] @@ -929,7 +943,7 @@ "output_type": "stream", "text": [ "Done\n", - "3125/3125 [==============================] - 5s 2ms/step\n", + "3125/3125 [==============================] - 6s 2ms/step\n", "3125/3125 [==============================] - 7s 2ms/step\n", "Done taking outputs for Keras model.\n", "HLS Keys: dict_keys(['fc1', 'fc1_relu', 'batchnorm1', 'fc2'])\n", @@ -947,36 +961,30 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Layer(s): fc1 \t\t 180.77695\n", - "hls/keras min: -58119.0/-57952.375\n", - "hls/keras max: 60864.0/61038.59375\n", - "Layer(s): fc1_relu \t\t 92.82321\n", + "Layer(s): fc1 \t\t 196.51971\n", + "hls/keras min: -65522.0/-59665.96875\n", + "hls/keras max: 65435.0/86210.1875\n", + "Layer(s): fc1_relu \t\t 100.62073\n", "hls/keras min: 0.0/0.0\n", - "hls/keras max: 60864.0/61038.59375\n", - "Layer(s): batchnorm1 \t\t 0.8381822\n", - "hls/keras min: -1.0823593139648438/-0.25010451674461365\n", - "hls/keras max: 4.688243865966797/5.808126926422119\n", - "Layer(s): fc2 \t\t 0.47903883\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hls/keras min: -6.9375/-7.593348503112793\n", - "hls/keras max: 5.625/6.295644760131836\n" + "hls/keras max: 65435.0/86210.1875\n", + "Layer(s): batchnorm1 \t\t 0.7777888\n", + "hls/keras min: -1.4557085037231445/-0.5938081741333008\n", + "hls/keras max: 4.380687713623047/5.0974931716918945\n", + "Layer(s): fc2 \t\t 0.6880743\n", + "hls/keras min: -6.625/-8.308148384094238\n", + "hls/keras max: 7.75/7.848916053771973\n" ] }, { "data": { - "image/png": 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20ze4eusunnmcqe2TDzvbf7+TSVZROBQRERF5TFYfuMyYkENcjr5rPubt7syo1hVpXsn7sYxJp5VFREREHoPVBy7T86vdKYIhQET0XXp+tZvVBy4/lnEpHIqIiIg8YskmgzEhhzDSaLt/bEzIIZJNafXIWtnqtPLFixcZNGgQq1at4s6dO5QpU4ZFixZRs2ZNAAzDYNSoUXz++edERUVRr1495s+fT9myZc01bty4QZ8+fQgJCcHW1pa2bdsya9YsXF1dzX32799Pr1692LFjBwULFqRPnz4MHDgwxViWL1/OiBEjOHPmDGXLlmXSpEm0bNky4x/q9m2ws3uwL0RERESyjWSTwc4zN4iMjef6rXiiIqNwAWxNyby1dyUrn6lHpGs+c/+oyLvsPHieOqXyP/yb376d/r5GNnHjxg2jRIkSRufOnY1t27YZp06dMtasWWOcOHHC3GfixImGu7u78fPPPxv79u0zXnzxRcPHx8eIi4sz92nevLlRtWpV488//zR+//13o0yZMkaHDh3M7dHR0UahQoWMjh07GgcOHDC++eYbw8XFxfj000/NfbZs2WLY2dkZkydPNg4dOmQMHz7ccHBwMP766690f57o6GgDMKLBMPTQQw899NBDDz2y8BENBmBER0f/a0Yh3WnmMRs0aJBRv359i+0mk8nw8vIypkyZYj4WFRVlODk5Gd98841hGIZx6NAhAzB27Nhh7rNq1SrDxsbGuHjxomEYhjFv3jwjb968Rnx8fIr3LleunPn5a6+9ZgQGBqZ4/zp16hjvvfdeuj+PwqEeeuihhx566PGoHhkJh9nmtPKvv/5KQEAA7dq1Y9OmTRQpUoT333+fbt26AXD69GkiIiLw9/c3v8bd3Z06deoQHh5O+/btCQ8Px8PDw3waGsDf3x9bW1u2bdvGyy+/THh4OA0aNMDR0dHcJyAggEmTJnHz5k3y5s1LeHg4wcHBKcYXEBDAzz//bHH88fHxxMfHm5/HxMTc+z+XLoGb28N8NSIiIvIECjsYwbgVh7h6KyHFcdeEOwzb+B/aHNoEwP5CpRnYoi9n8xY297EBCrk7sS64UeZsaxMTA4UL/3s/stE1h6dOnWL+/PkEBwczdOhQduzYwQcffICjoyOdOnUiIiICgEKFCqV4XaFChcxtEREReHp6pmi3t7cnX758Kfr4+PikqnG/LW/evERERFh9n7RMmDCBMWPGpG7InfveQ0RERHKM1Qcu0+PHI4AtODqbj9e4cJiZoVMpHn2FZBtbPqnbjtn1OpBk979Idj8KDmpbA7s8rmSK5OR0d8024dBkMlGzZk3Gjx8PQPXq1Tlw4AALFiygU6dOj3l0/27IkCEpZhtjYmIoVqzYYxyRiIiIZIVkk8HgH/9KcczOlEyfrcvos/Vb7AwTF9w8CWrdn51Fn031eq/HvM9htgmH3t7eVKxYMcWxChUq8MMPPwDg5eUFwJUrV/D2/t+XeeXKFapVq2buc/Xq1RQ1kpKSuHHjhvn1Xl5eXLlyJUWf+8//rc/99rQ4OTnh5OSUrs8qIiIi2defp64TdSfR/Lz4zcvMDJ1KjUtHAfipYiNGNuvJLaf/nTkcEViBAnmcnog7pGSbfQ7r1avH0aNHUxw7duwYJUqUAMDHxwcvLy/Wr19vbo+JiWHbtm34+fkB4OfnR1RUFLt27TL32bBhAyaTiTp16pj7bN68mcTE//1Qw8LCKFeuHHnz5jX3+fv73O9z/31ERETk6RV+8vq9/2MYvPrXOlYu/oAal44S45SbD1p/SL/WA8zB0IZ7d0TpXM+Hl6oVwa90/scaDCEbzRz269eP5557jvHjx/Paa6+xfft2PvvsMz777DMAbGxsCAoKYty4cZQtWxYfHx9GjBhB4cKFadOmDXBvprF58+Z069aNBQsWkJiYSO/evWnfvj2F//8izTfeeIMxY8bQtWtXBg0axIEDB5g1axYzZswwj6Vv3740bNiQadOmERgYyLJly9i5c6d5LCIiIvJ0iEtIZvzKQ5y5foeS+XMxtGVFwMA97hbj18wl8OgWALYVq0RwYDAX3T1T1RjVuuJjD4R/Z2MYhvG4B5FeoaGhDBkyhOPHj+Pj40NwcLB5tTKAYdzbBPuzzz4jKiqK+vXrM2/ePJ555hlznxs3btC7d+8Um2DPnj3b4ibYBQoUoE+fPgwaNCjFWJYvX87w4cPNm2BPnjw5Q5tgx8TE4O7uTnR0NG5arSwiIpLtdPvvDsIOXU11vFPcSXosHIt37HUSbe2YUb8jC+q0xWSb8qYX+XI7Mv7lSo/k2sKM5I5sFQ5zEoVDERGR7CutYOiYlEj/37+k2/afsMXgVN7C9G39IX95l031+txOduwZ0QxH+0dzhV9Gcke2Oa0sIiIi8jglmwz+PHmdTceupAqGpa+dZ3bIFJ69egqApVWb81Hjd4n72zY2fzetXdVHFgwzSuFQRERE5F+s3H+ZgT/sJzY+KWWDYfDmnpUM3/gfnJMSuOHixqAWHxBWti4Nyhbg2JVbRMT87yYYXm5OjH7x2ce2TU16KByKiIiIWDFh5SE+3Xw61fECt28yadVsmpzcAcDmktXpH9iPSNd8wL371W0Z3ITtp29w9dbdJ2KbmvRQOBQRERFJQ7LJYNa6Y2kGwxdO7mDKypkUuBNNvJ0DExt1ZrFvawyb/50qLpk/F3a2NviVzv8oh/3QFA5FRERE/mH1gcsM+mE/0XEpTyM7J95l6MZFvL1nBQBHCpSg74sfcrRgyVQ17m1rk/0oHIqIiIj8zeoDl+nx1e5UxyteOcWskCmUvX4egP/UfInJDTsRb++Yqm/Tip64ONqlOp4dKByKiIiI8P+rkU9dJ/i7fSmO2xgmum3/iQGbv8TRlMTV3HnpH9iP331qpFmnaUVPPn+71qMYcpZQOBQREZGnWkKSiaE//sXKvy5zJzE5RZtXzDWmrZxOvbP7AVhTti6Dm/fhZi73VHVc7G3YMbwZrs7ZO15l79GLiIiIPIRxIQf5YsuZNNtaHvmD8Wvm4nE3ljsOToxp0p1vqzQDm7RXG89oXz3bB0NQOBQREZGn1Itzfmf/xZhUx3PH32H0us9od2AdAPu8yhLUegCn8xVJs45HLgcmvlL5id67MCMUDkVEROSpkpBkosWsTZyMvJOqrcbFw8wInUaJqAhM2DDPrx0z671Bkl3qyBRQsRBv+5Wkbun8T/zehRmhcCgiIiJPjVG//sWSredSHbczJdN767f02boMe8PEBbeCBLfqz/ZildKsM7d9NVpVS3smMbtTOBQREZEcLyHJRLUxa7iTaErVViwqgpkhU/G9dASAnys2ZGTTnsQ4u6ZZ670GPjk2GILCoYiIiORwH684xOe/p77LCYbBKwc3MCZsAXkS4ohxzMWIZj355dkX0qyTL5cD49pUpmWVnHFtoSUKhyIiIpIjJZsM2s3fwu7z0ana3O7GMn7NJ7Q68jsA24tWJLhVfy64F0rV9516JWla0Stb3Bc5MygcioiISI6zcv9lPvhmN0lG6ja/s/uZtmI6hW9dI9HWjpn13mB+3Vcx2aa+o8mcDtVpXbXwIxjxk0PhUERERHKU0b/8xeLw1ItOHJIT6f/7V3Tf9iO2GJzKW5ig1gPY7/1MmnW6PV/yqQuGoHAoIiIiOUSyyaDOuLVcu5OUqq309fPMDJlK5SsnAfimSjM+atKNO44uadZ6r4EPQ1pWzNLxPqkUDkVERCTb+2HXBfov35e6wTB4c+8qhm34Dy5J8dx0zsPgFn1Y88xzadapXcKDr7r54Whvm8UjfnIpHIqIiEi2lWwyqD52LTF3U88W5r8dxaRVs/A/uQOAzSWrM6BlEFfz5E+zVrfnfRgW+HTOFv6dwqGIiIhkSyv3X+L9pXvSbGt0cidTVs6k4J0o4u3smdSwC4tqtsawSXtGcN4b1WlZ5em7vjAtCociIiKSrSSbDHos2UbY0eup2pwS4xny2yI67w4F4GiB4vRt/SFHPH3SrOVkC4fGtXwqtqhJL4VDERERyTZWH7hMj692p9lW8copZoZM5Znr91YqL/JtzcSGnYl3cEqzf6XCeQj9oEGWjTW7UjgUERGRbOHnnRcI+j71ohMbw0TXHT/z4eb/4pScRGRuDwa07MemUr5p1imQ24HfPmyMq7NiUFr0rYiIiMgTLSHJxHPjw9LcoqbQrWtMWzGD+mfvhcawMnUY1OIDbuRyT7NW1/olGdHq2Swdb3ancCgiIiJPrME/7mPZ9gtptjU/uoUJq+eS9+4t4uyd+KjJuyyt2hxs0r5+UItO0kfhUERERJ5IJQevSPN4roQ4Rq37jNf/CgPgr0KlCWo9gJP5i6XZ394WjmrRSbopHIqIiMgT5dy1OzSYujHNtmqXjjIzZColoy5jwoYFddsyo35HEu0c0uw/6ZUqvF477dAoaVM4FBERkSeGpdlCO1My74d/R98t32BvmLiYpyDBrYLZVrxymv3tbeDox5otfBAKhyIiIvLYRUTdpe7E9Wm2FY2KYEbodGpdPARASPnnGRbQixhn1zT753G246/RzbNsrDmdwqGIiIg8VqUGr8CUVoNh8PLBjYwNm0+ehDhuObowsmlPfnr2BYuLTqa8WoV2NXUa+WEoHIqIiMhjEZeQTIWRq9Nsc7sby7i183jx8GYAdhSpSL9WwVzw8Eqzvw1wYrxOI2cGhUMRERF55N74bAtbT0Wl2Vbn3F9MD51OkVuRJNnYMqteB+b5vUayrV2a/Z8v5c6X3etn4WifLgqHIiIi8khZWnTikJxIvz++psefP2CLwRkPb4JaD2Bv4XIWa81tX51W1bR3YWZSOBQREZFH4tCFGFrO/T3NtlLXLzAzdCpVIk4A8G3lpoxt0o3bTrks1jup08hZQuFQREREspyl2UIMgzf2rWbE+i9wSYonytmVwc37sLpcPYu1HG3h2PjALBqpKByKiIhIlom9m0Sl0WvSbMt3J5pJq2bT9MQ2AP4oUZX+gf24kqeAxXp/Dm6Cl4dzloxV7lE4FBERkSzRYPxqzsUkp9nW8NQupqycieftm8Tb2TOlwdv8p1YbDBvbNPvbAKcnarbwUVA4FBERkUxn6TSyU2I8gzctpsuuEACO5S9O3xcHcNizlMVaxTyc+X1wkywZp6SmcCgiIiKZZuuRa7yxeFuabeWvnmZWyBTKXTsHwOIarZjQqAvxDk4W6+0b2Qz3XGnfN1myhsKhiIiIZApLs4U2hol3dv7KwE2LcUpOIjKXBx+27MtvpWtZrPVKdS+mv+6bVUMVKxQORURE5KFExsRTa/y6NNs8b11n6sqZNDizB4B1pWsxqEVfruf2sFjv2LgWONqnfe2hZD2FQxEREXlgFreoAQKObWXiqjnkvXuLOHsnPm7cla+qtbB4X2SAM1p08tgpHIqIiEiGRUTdpe7E9Wm25UqIY8T6z+mwfy0ABwqVpm/rAZzMX8xivQ61izLhlapZMlbJGIVDERERyRBrs4VVLh9jVsgUfG5exoQNn9V5hWnPv0mineVFJTqN/GRROBQREZF0sxQMbU3J9Pzze4K2LMXBlMylPAXoHxhMeIkqFmt5utqzfXhAVg1VHpDCoYiIiPyrPw5F8uZ/t6fZVjT6CtNDp1H7wiEAQss/z9CAXsQ4u1qspy1qnlwKhyIiImKVtdPILx3cyEdr5+OWcIdYRxdGNu3Bj8821qKTbCzbnuCfOHEiNjY2BAUFmY/dvXuXXr16kT9/flxdXWnbti1XrlxJ8bpz584RGBhIrly58PT05MMPPyQpKSlFn99++40aNWrg5OREmTJlWLx4car3/+STTyhZsiTOzs7UqVOH7dvT/teUiIhIdrX3TJTFYOh2N5aZIVOYFToNt4Q77CpcnhZd5vBjpSYWg+GOof4KhtlAtgyHO3bs4NNPP6VKlZTXMfTr14+QkBCWL1/Opk2buHTpEq+88oq5PTk5mcDAQBISEti6dStLlixh8eLFjBw50tzn9OnTBAYG8sILL7B3716CgoJ49913WbPmfzcN//bbbwkODmbUqFHs3r2bqlWrEhAQwNWrV7P+w4uIiDwCJQevoM2CLWm21T5/gJWL+tDm0CaSbGyZUe8NXus4ifMeXhbrnZkYSEE3y3dCkSeHjWEYxuMeREbExsZSo0YN5s2bx7hx46hWrRozZ84kOjqaggULsnTpUl599VUAjhw5QoUKFQgPD6du3bqsWrWKVq1acenSJQoVKgTAggULGDRoEJGRkTg6OjJo0CBWrFjBgQMHzO/Zvn17oqKiWL16NQB16tShVq1azJ07FwCTyUSxYsXo06cPgwcPTtfniImJwd3dnejoaNzc3DLzKxIREXkolmYL7ZOTCNqylPfDl2OLwVkPL/q16s/uIhUs1rIHTmi28LHLSO7IdjOHvXr1IjAwEH9//xTHd+3aRWJiYorj5cuXp3jx4oSHhwMQHh5O5cqVzcEQICAggJiYGA4ePGju88/aAQEB5hoJCQns2rUrRR9bW1v8/f3NfdISHx9PTExMioeIiMiTZPHGoxaDoc+Ni/zw1Yf0Dv8OWwyWV/KnZefZVoPh7uFNFQyzoWy1IGXZsmXs3r2bHTt2pGqLiIjA0dERDw+PFMcLFSpERESEuc/fg+H99vtt1vrExMQQFxfHzZs3SU5OTrPPkSNHLI59woQJjBkzJn0fVERE5BGzuOjEMGi/bw0jN3xOrsR4opxdGRrQm5Xl61utp2sLs690hcPZs2dnuHCXLl3IkydPhl9nyfnz5+nbty9hYWE4OztnWt1HZciQIQQHB5ufx8TEUKyY5Z3iRUREHoVDF2JoOff3NNvy3olm4uo5BBz/E4CtxasQHBhMhFsBi/UWtvelcTXL1x7Kky9d4TAoKIiiRYtiZ2eXrqLnz5+nVatWmRoOd+3axdWrV6lRo4b5WHJyMps3b2bu3LmsWbOGhIQEoqKiUsweXrlyBS+ve39Ivby8Uq0qvr+a+e99/rnC+cqVK7i5ueHi4oKdnR12dnZp9rlfIy1OTk44OelCXBEReXJY26Lm+dO7mbZiBp63b5Jga8/UBm/xee2XMWwsX5Gm2cKcId2nlXfu3Imnp2e6+mZmKLyvSZMm/PXXXymOdenShfLlyzNo0CCKFSuGg4MD69evp23btgAcPXqUc+fO4efnB4Cfnx8ff/wxV69eNX+WsLAw3NzcqFixornPypUrU7xPWFiYuYajoyO+vr6sX7+eNm3aAPcWpKxfv57evXtn+ucWERHJbLF3k6g0ek2abU5JCQzctISuO38B4Hj+YgS1HsDBQqUt1rMFTikY5hjpCoejRo3C1dXyLuf/NHToUPLly/fAg0pLnjx5qFSpUopjuXPnJn/+/ObjXbt2JTg4mHz58uHm5kafPn3w8/Ojbt26ADRr1oyKFSvy1ltvMXnyZCIiIhg+fDi9evUyz+r16NGDuXPnMnDgQN555x02bNjAd999x4oV//vXVXBwMJ06daJmzZrUrl2bmTNncvv2bbp06ZKpn1lERCSzVRy8gjsW2spFnmFmyFQqRJ4B4L/VAxn/QhfuOli+nGvHUH9tUZPDpDscZsSQIUMeaDAPa8aMGdja2tK2bVvi4+MJCAhg3rx55nY7OztCQ0Pp2bMnfn5+5M6dm06dOjF27FhzHx8fH1asWEG/fv2YNWsWRYsW5YsvviAg4H/3fnz99deJjIxk5MiRREREUK1aNVavXp1qkYqIiMiTxNJpZBvDROddIQz+bTFOyYlcy+XOwBZ92VCmttV6Oo2cMz30PoeLFy/m5Zdfxt3dPbPG9FTQPociIvKoTAzZxYItEWm2FYy9wbQVM2hwZg8AG0rVZGDLvlzLnddivWXv1KXuM/mzZKySNTKSOx56K5vu3btTp04dhUMREZEnkLVFJ02P/8mkVbPJFxfDXXtHxr3Qla+qt9R9kZ9y6Q6Hlq4hTEpKws/PD1vbe6uXbty4kTkjExERkQe2+cBV3v4q9b7AAC4Jdxmx4Qve2Hfvzl8HPUvRt/UAThQobrFeJW8I7atg+DRIdzhMTEykYcOGtGvXznzMMAzeffddBg4cSJEiRbJkgCIiIpIx1mYLK18+zszQqZS+cRGAT2u/wrTn3yLB3sHia46Na4Gjfba7qZo8oHRfc3jixAneeOMNKlSowCeffGJevezg4MC+ffvMW8FI+uiaQxERyQqWgqGtKZke236g3x9f42BK5rJrfoJbBRNeoqrVejqNnDNkyb2Vy5Qpw9atW/Hy8qJatWps2bLloQcqIiIimWP4D+EWg2GR6Kt8s2wYAzf/FwdTMivK1aP5O3OtBsOF7X0VDJ9SGVqQYm9vz6RJkwgICOCNN96gY8eO2Fi5aFVERESynrXTyC8e2sS4tfNwi79NrKMLo/3f4/tKTbToRCx6oNXKjRs3Zvfu3XTr1o3cuXOn+7Z6IiIiknmW/X6SwSuOpNmWJ/42Y9fO5+VDvwGwx7scfVsP4Fxeb4v12vt6MLFdvawYqmQjD7yVTf78+fnxxx8zcywiIiKSTtZmC2teOMjMkGkUjblKso0tc/1eZ85zr5NkZ/k/+5otlPsyHA5v377Nrl27uHz5Mra2tpQuXZrq1avr9LKIiMgjkJBk4pnhq9Jss09O4oMt39Drz+XYGSbOuRciqNUAdhetYLWmgqH8XbrDoclkYvDgwXzyySfcvXsXuLeVDUDx4sWZM2cOrVu3zppRioiIiNXZwpI3LjIzdBrVLh8D4IdKjRnl34NYp1wWXzO+ZTneaFAm08cp2Vu6w+HQoUMJDQ3l22+/xdnZmY8++ojAwEBefPFFli5dSrt27fj1119p1qxZVo5XRETkqWQxGBoGr+0PY9T6z8ideJdop9wMDejNigrPW62n2UKxJN37HBYuXJhvv/2W55+/94ft4sWLlC9fnmvXruHk5MRHH33EqlWr2Lp1a5YOOKfQPociIpIenT9ZwW/n027ziIth4uo5ND8WDkB48coEBwZz2a2gxXrPusKK4QqGT5ssubdybGxsirugeHt7c/fuXW7evImXlxdt27Zl4sSJDz5qERERScHaaeR6Z/YybcV0vGJvkGBrz7QGb/J5rZcx2VreQeTk+JbY2WqNgFiX7k2wK1euzDfffGN+/t133+Hq6oqXlxdw75pEJyenzB+hiIjIUyb2bpLFYOiUlMDw9Z/z9bfD8Yq9wcl8RXn5ral8WudVq8HwzMRABUNJl3TPHI4dO5bAwEB+/fVXnJ2d2bp1K1OmTDG3r169murVq2fJIEVERJ4W1mYLy0aeZXbIFCpEngHgq2otGNe4K3cdnC2+Zu7LVWhVp1hmD1NysHRfcwiwb98+vvvuO+Lj4wkICKBp06ZZObYcTdcciojIP1lbdNJpdyhDNy7EKTmRa7ncGdTiA9aXqWO1nhadyH1Zcs0hQNWqVala1foNukVERCRjAoav4GhS2m0FY28yZeVMGp3eBcBvPr582DKISNe8FuuVcYJ1YxQM5cE88B1SAE6fPs2JEyfw9vamUqVKmTUmERGRp4a108hNTmxj8spZ5I+LId7OgY9feIf/1mhl9b7IWnQiDyvdC1Lef/99YmNjAYiLi+PVV1+lTJkyBAQEULVqVRo3bmxuFxEREet+Dj9rMRi6JNzl4zVz+c8PH5E/LobDBUvSqtNM/uvb2mow1KITyQzpDoeffvopd+7cAeCjjz5i27ZtrFu3jtjYWDZv3sy5c+f4+OOPs2ygIiIiOUXJwSsI+uVAmm2VIk4QuiSIjntXA/BZrZd56e0ZHC9YwmK9r96uresLJdOkOxz+fd1KSEgIkydP5oUXXiBXrlzUq1eP6dOn8+OPP2bJIEVERHICa1vU2JqS6fHn9/z0ZX9K37hAhGs+Or4+jvGNu5Jg72Cx5pmJgdSvaHnTa5GMytA1hzb/P5UdERFBlSpVUrRVrVqV8+ctbOEuIiLylLN2bWHhmKtMD51O3fP3ZhNXPfMcQ5r3JsrF8qrSik6wUotOJAtkKByOGDGCXLlyYWtry6VLl3j22WfNbdevXyd37tyZPkAREZHszlowbHV4M+PXfIJb/G1uOzgz2r87yys3/ddrC0WySrrDYYMGDTh69CgAFStW5OzZsynaV65cmSIsioiIPO26fbaCsFNpt7nG32FM2HzaHtwIwF7vZ+jbegBn8xa2WlPBULJahjbBtubUqVM4OjpStGjRzCiX42kTbBGRnM3abGGNC4eZGTqV4tFXSLaxZa7fa8x5rj1JdpbnbLYMbEyRfC5ZMVR5CmTZJtjWlCpVKrNKiYiIZFu7Tt2k7Wdb02yzT06iz9Zl9A7/DjvDxHn3QvRrFczOotbPvGm2UB6lTAuHO3fu5M6dOzRo0CCzSoqIiGQr1mYLS9y8xMyQaVS/fO8SrR+ffYFRTXtwy8ny9frNSsNn3RQM5dHKtHD41ltvcezYMZKTkzOrpIiISLaQbDIoPXRl2o2GQbu/1jF63afkTrxLjFNuhjV7n5CKDa3W1GyhPC6ZFg7Xr19PYmJiZpUTERHJFqzNFrrH3WLC6jm0PHbvNPO2YpXo1yqYS26eVmsqGMrjlGnhsHBh66urREREchprwfC5M3uZtmIG3rHXSbS1Y/rzb/Jp7Vcw2dpZfM3K3s9TsagWKcrjlaFwmJyczNmzZylZsiS2trbEx8fzyy+/YDKZeOGFFyhUqFBWjVNEROSJEbrtPL1/2p9mm2NSIgM2/5fuO34C4GS+IgS1GsBf3mWt1tRsoTwp0h0O9+/fT/Pmzbly5QoVK1Zk5cqVtGzZktOnT2NjY4ODgwNr1qyhVq1aWTleERGRx8rabGGZa+eYHTKFildPA/B1teaMe+Fd4hydLb6mSUn4Tw8FQ3lypPveygMHDqRevXrs27ePJk2aEBAQQIUKFbh58yY3b94kMDCQoUOHZuVYRUREHpuLN+IsB0PD4K3doYQuCaLi1dNcd3Gj2yvDGRbQ22owPDMxUMFQnjjp3gQ7X758bNmyhQoVKhAXF0eePHnYunUrtWvXBuDgwYM0bNiQa9euZemAcwptgi0ikn1Ymy0scPsmk1fOovGpnQBs8qnBgJZBRLrms1pTp5HlUcqSTbANw8De/l73f/4vgJ2dHSaT6UHGKyIi8sSyFgwbn9jO5FWzKHAnmng7ByY06sIS31YYNpZPzB0YHYCrc6atBxXJdOk+rezr68ukSZO4ePEiEyZMwMfHh7lz55rb58yZQ6VKlbJkkCIiIo9ag8ErLAZD58S7fLR2Hgt/GEuBO9EcLliS1p1msLjmi1aD4ZmJgQqG8sRL95/QCRMm0KJFCxYtWkT+/PnZuHEjXbt2xdvbG1tbW27evElISEhWjlVEROSRsDZb+OyVk8z6dQplblwA4IuaLzGlYSfi7R0tvuaNmnkZ/+pzmT5OkayQ7msOAW7fvs2RI0coV64crq6u3L17l6+//pq4uDiaNm1KuXLlsnKsOYquORQRefIs+/0kg1ccSbPNxjDRffuP9N/8FY6mJK645mNAyyB+96lhtaauLZQnQUZyR4bCoWQehUMRkSeLtdlC75hIpq2YwXPn7u1tuKZsXQY378PNXO5WayoYypMiSxakwL1FKWfOnKFYsWLY29uTkJDATz/9RHx8PC1btqRAgQIPNXAREZHHwVowbHnkDyasnoN7/G3uODgxpkl3vq3SDGxsLL5Gi04kO0v3n9yjR48SEBDA+fPnKVWqFGvXrqVdu3YcOXIEwzDIlSsXW7dupWxZ6zvAi4iIPCmshULX+DuMXvcprx5YD8A+r7IEtR7A6XxFrNbUbKFkd+lerTxo0CCqVq3K3r17adWqFYGBgRQtWpSbN29y48YN/Pz8GDt2bFaOVUREJNNYC4Y1Lh5m5aI+vHpgPck2tszxe522b06xGgyH+vsoGEqOkO5rDj09PVm7di3VqlXj9u3b5MmTh82bN1O/fn0Atm7dSocOHTh79myWDjin0DWHIiKPxzvzV7DBwn+q7EzJ9Nm6jN5bv8XeMHHBzZN+rYLZUcz6Vm0KhfKky5JrDmNjY8mX795u77lz5yZ37tx4e3ub24sVK8aVK1cecMgiIiJZz9psYbGoCGaGTMX30r3Vyj9VbMTIZj255ZTbak0FQ8lp0h0OCxcuzLlz5yhevDgAkydPxtPT09weGRlJ3rx5M3+EIiIiDynZZFB66Mq0Gw2DVw+sZ/S6T3FNiCPGMRfDA97n14qNrNZUKJScKt3h0N/fnyNHjphPI/fs2TNF+9q1a6lRw/peTyIiIo+atdlC97hbfLzmE1od/QOAbUWfpX+rYC64F7JaU8FQcrJM2+fw9OnTODs7pzjVLJbpmkMRkaxnLRj6nd3P9NBpeMdeJ9HWjhn1O7KgTltMtnYWXxPUsChBLapmxVBFslSW7XNojY+PT2aVEhEReSjWQqFDciL9N39J9+0/YYvBqbyFCWo9gP3ez1itqdlCeVqkeysbgGvXrjF58mRefvll/Pz88PPz4+WXX2bKlClERkZm1RiBe/d2rlWrFnny5MHT05M2bdpw9OjRFH3u3r1Lr169yJ8/P66urrRt2zbVIplz584RGBhIrly58PT05MMPPyQpKSlFn99++40aNWrg5OREmTJlWLx4carxfPLJJ5QsWRJnZ2fq1KnD9u3bM/0zi4hIxlkLhqWvneenLwfQY/uP2GKwtGoArTrPUjAU+Zt0h8MdO3bwzDPPMHv2bNzd3WnQoAENGjTA3d2d2bNnU758eXbu3JllA920aRO9evXizz//JCwsjMTERJo1a8bt27fNffr160dISAjLly9n06ZNXLp0iVdeecXcnpycTGBgIAkJCWzdupUlS5awePFiRo4cae5z+vRpAgMDeeGFF9i7dy9BQUG8++67rFmzxtzn22+/JTg4mFGjRrF7926qVq1KQEAAV69ezbLPLyIi1i0IO2g5GBoGb+5ewYolfal05SQ3XNx47+WhDG3ehzuOLhZrnpkYqGAoT510X3NYt25dqlatyoIFC7D5xy2DDMOgR48e7N+/n/Dw8CwZ6D9FRkbi6enJpk2baNCgAdHR0RQsWJClS5fy6quvAnDkyBEqVKhAeHg4devWZdWqVbRq1YpLly5RqNC9i40XLFjAoEGDiIyMxNHRkUGDBrFixQoOHDhgfq/27dsTFRXF6tWrAahTpw61atVi7ty5AJhMJooVK0afPn0YPHhwusavaw5FRDKPtdnC/LejmLxqFk1O7gBgc8nqDGgZxNU8+a3WVCiUnCQjuSPdM4f79u2jX79+qYIhgI2NDf369WPv3r0ZHuyDio6OBjDvvbhr1y4SExPx9/c39ylfvjzFixc3B9bw8HAqV65sDoYAAQEBxMTEcPDgQXOfv9e43+d+jYSEBHbt2pWij62tLf7+/laDcXx8PDExMSkeIiLy8KwFw0Ynd7B6YW+anNxBvJ09Yxt3o9NrY6wGw6/erq1gKE+1dC9I8fLyYvv27ZQvXz7N9u3bt6cIXVnJZDIRFBREvXr1qFTp3q71ERERODo64uHhkaJvoUKFiIiIMPf55xjvP/+3PjExMcTFxXHz5k2Sk5PT7HPkyBGLY54wYQJjxozJ+IcVEZE0WQuFTonxDP1tIZ123+tzpEAJgloP4Iin9cWTCoUiGQiHAwYMoHv37uzatYsmTZqYw9GVK1dYv349n3/+OVOnTs2ygf5dr169OHDgAH/88ccjeb/MMGTIEIKDg83PY2JiKFas2GMckYhI9mUtGFa8copZIVMoe/08AAt9X2RSo87E2ztaralgKHJPusNhr169KFCgADNmzGDevHkkJycDYGdnh6+vL4sXL+a1117LsoHe17t3b0JDQ9m8eTNFixY1H/fy8iIhIYGoqKgUs4dXrlzBy8vL3Oefq4rvr2b+e59/rnC+cuUKbm5uuLi4YGdnh52dXZp97tdIi5OTE05OThn/wCIiYtZ8xAqOJKbdZmOYeHf7z3y4+b84mpK4mjsvA1oGsbmUr9WaCoUiKWVoK5vXX3+dP//8kzt37nDx4kUuXrzInTt3+PPPP7M8GBqGQe/evfnpp5/YsGFDqn0VfX19cXBwYP369eZjR48e5dy5c/j5+QHg5+fHX3/9lWJVcVhYGG5ublSsWNHc5+817ve5X8PR0RFfX98UfUwmE+vXrzf3ERGRzFdysOVgWOjWNb78dgTDfluIoymJtWXr0vyduQqGIg/ggTbBdnBweOR3QunVqxdLly7ll19+IU+ePOZrBN3d3XFxccHd3Z2uXbsSHBxMvnz5cHNzo0+fPvj5+VG3bl0AmjVrRsWKFXnrrbeYPHkyERERDB8+nF69epln9Xr06MHcuXMZOHAg77zzDhs2bOC7775jxYr/ncIIDg6mU6dO1KxZk9q1azNz5kxu375Nly5dHul3IiLytLB2GrnFkT+YsGYuHndjuePgxEeNu/FN1QBIYwHlfbuHNyWfq/XTzCJPq0y7fd7Jkyfp1q0bGzZsyIxyqaS1Shpg0aJFdO7cGbi3CXb//v355ptviI+PJyAggHnz5qU43Xv27Fl69uzJb7/9Ru7cuenUqRMTJ07E3v5/Ofm3336jX79+HDp0iKJFizJixAjze9w3d+5cpkyZQkREBNWqVWP27NnUqVMn3Z9HW9mIiPw7a6Ewd/wdRq3/jNf+WgfAfq8yBLUawKn8RS2+BjRbKE+njOSOTAuH+/bto0aNGuZrEcU6hUMREeusBcPqF48wI3QaJaMuY8KG+XVfZWb9N0i0c7D4mtLAegVDeUplyb2VZ8+ebbX94sWL6S0lIiJikbVQaGdKplf4d3yw5RvsDRMX8xQkuFUw24pXtlpTs4Ui6ZfucBgUFIS3tzeOjmlfo5GQkJBpgxIRkaeTtWBYNCqCmaHTqHnxMAC/VmjA8GbvE+PsarWmgqFIxqQ7HJYoUYJJkyZZXJW8d+9efH2trwoTERFJS+zdJCqNXpN2o2HwysENjAlbQJ6EOGIcczGyWU9+rtjI6qKTk+NbYmdruV1E0pbucOjr68uuXbsshkMbGxsy6fJFERF5ilibLXS7G8vHaz6h9ZHfAdhRpCL9Wvfngrv1O3JptlDkwaU7HI4dO5Y7d+5YbK9YsSKnT5/OlEGJiMjTwVowrHtuP9NCZ1DkViRJNrbMrP8G8+u2I9nWzuJrGpeAhT0VDEUeRrrD4f1Noi1xcHCgRIkSDz0gERHJ+ayFQofkRIJ//5r3tv2ALQZnPLwJaj2AvYXLWa2p2UKRzPFAm2DfFx8fz4ULFyhatKhuDSciIuliLRiWvn6emSFTqXzlJADLqjRjbJNu3HF0sVpTwVAk86T79nmLFy8mPDwcuLfZdNeuXcmdOzfPPPMMrq6u9OjRg/j4+CwbqIiIZG8/bj1jORgaBh33rCR0cRCVr5zkpnMe3mszlMEtPrAaDM9MDFQwFMlk6Q6HY8eOxdb2XvcRI0awYcMGli9fzsGDB/n+++/ZuHEjI0aMyLKBiohI9lVy8AqCfz2YZlu+O9F8/uNHfLx2Hi5J8fxeohrN35nDmnLPWa2pUCiSNdJ9WvnSpUvm+yn/+uuvzJ8/n+bNmwNQvnx58ubNa75nsYiIyH3WTiM3OrmTKatmUvB2FPF29kxu0ImFtV7CsLE8dzG4SUl6NH02K4YqImQgHHp5eXHy5EmKFy/O7du3KVCgQIr2ggULcv369UwfoIiIZE/WQqFTYjyDNy2my64QAI7lL07fFwdw2LOU1ZqaLRTJeuk+rdyxY0eGDRtGVFQUb731FmPHjiU2NhaAO3fuMHr0aOrVq5dlAxURkezDWjCscPUUv/63nzkYLvJtTetOMxQMRZ4Q6Z45HDVqFAcOHKBUqVLUrFmT33//nUKFClGkSBEuXbpE/vz5CQsLy8qxiojIE67i4BVY2hHXxjDxzo5fGLh5CU7JSUTm9uDDFkH8Vrqm1ZoKhSKPVrrDoaOjI7/88gurV68mJCQEOzs7TCYT3t7e1KtXjzfeeIPcuXNn5VhFROQJZm22sNCta0xdMZPnz+4FIKxMbQY3/4DruT2s1lQwFHn0bAzd8+6xiImJwd3dnejoaNzc3B73cEREHoq1YBhwdCsTV88h791bxNk7Ma5xV76u1sLqfZH/HNwELw/nrBiqyFMpI7njoTbBFhGRp5u1UJgrIY6R6z+n/f61APxVqDRBrQdwMn8xqzU1WyjyeCkciojIA7EWDKteOsrM0Kn43LyMCRs+rdOW6c93JNHOwWpNBUORx0/hUEREMsRaKLQ1JfP+n8sJ+mMp9oaJS3kKENwqmD+LV7FaU6FQ5MmhcCgiIulmLRgWjb7CjJBp1Lp4CICQ8s8zLKAXMc6uVmsqGIo8WRQORUTkXyWbDEoPXWmx/aWDG/lo7XzcEu5wy9GFkU178tOzL1hddHJyfEvsbC23i8jjoXAoIiJWWZstdLsby0dr5/PS4U0A7CxSgaBW/bng4WW1pmYLRZ5cCociImKRtWBY+/wBpodOo2hMJEk2tsyu14FP/F4j2dbO4mvKO8DqjxQMRZ5kCociIpKKtVDokJxI0B9L6fnn99hicMbDm36t+rOnSHmrNTVbKJI9KByKiEgK1oJhqesXmBk6lSoRJwD4rrI/Y5p057ZTLqs1FQxFso9MDYfnzp2jSJEi2NlZPqUgIiJPpg17I3hn2a60Gw2DDvvWMGLD5+RKjCfK2ZUhAb1ZVb6+1ZoKhSLZT6aGw5IlS1K2bFkmTJjAK6+8kpmlRUQkC1mbLcx3J5qJq+fQ7PifAGwpUYX+LYOJcCtgtaaCoUj2lKnhcOPGjZw6dYpvv/1W4VBEJJuwFgwbnNrF1JUz8bx9kwRbeyY3fJv/1GqDYWNr8TVtq+VhWvsGWTFUEXkEbAzDMB73IJ5GGbkBtohIVrAWCp2SEhj022Le2fUrAMfzF6Nv6w85VKiU1ZqaLRR5MmUkd2hBiojIU8haMCwXeYZZv06h/LWzACypEciERl246+BstaaCoUjOkO5wuH37dnx9fc2LTUJDQ5kyZQonTpzA29ubDz74gLfffjvLBioiIg+v6dAVHDel3WZjmOiyM4RBmxbhlJxEZC4PBrbsy8bStazWVCgUyVnSHQ79/Py4fPkynp6ehISE0KZNG958801ef/119uzZQ9euXcmTJw8vv/xyVo5XREQekLXZQs9b15m6ciYNzuwBYH3pWgxs0ZfruT2s1lQwFMl50h0O/35p4uTJkxk4cCATJkwwH/Px8WHy5MkKhyIiTyBrwbDZsXAmrp5DvrgY7to7Mq7xu3xVrYXV+yIv7VyH58pbX60sItnTA11zeOzYMWbOnJniWNu2bZkyZUpmjElERDKJtVDoknCXERs+5419awA46FmKD1p/yMkCxazW1GyhSM6WoXB46NAhIiIicHFxwWRKfdFKUlJSpg1MREQejrVgWOXyMWaGTKXUzUuYsOGz2i8z/fm3SLB3sFpTwVAk58tQOGzSpIn59PKWLVuoVet/Fynv2bOH4sWLZ+7oREQkw6yFQltTMj22/UC/P77GwZTMZdf8BLfqT3iJKlZrKhSKPD3SHQ5Pnz6d4rmrq2uK5wkJCQwaNChzRiUiIg/EWjAsEn2V6aHTqHPhIACh5eozLKAX0S55rNZUMBR5umgT7MdEm2CLSGazFgxfPPQb49bOxy3+NrGOLozy78EPlRpbXXSiUCiSc2TJJti7du3C19f3oQcnIiKZy1oozBN/m4/WzqPNoU0A7C5cjqBWAziX19tqTQVDkaeX5Ztj/kOtWrUoU6YM48eP59KlS1k5JhERSSdrwbDW+QOsWtiHNoc2kWxjy4x6b9Cu42QFQxGxKkMLUho3bsysWbMYNWoUAQEBvPvuu7Ru3dp81xQREXk0rIVC++Qk+m75hvf/XI6dYeKceyGCWg9gd5EKVmsqFIoIZGDmEGDcuHFcvHiRZcuWYRgGr776KkWKFGHQoEEcO3Ysq8YoIiJ/Yy0Ylrxxke+//pA+4d9iZ5j4vlITWnaZo2AoIumW7gUptra2RERE4OnpaT528eJFFi5cyOLFizlz5gz16tVj8+bNWTbYnEQLUkQko2LvJlFp9Jq0Gw2D1/evZdT6z8iVGE+0U26GBvRmRYXnrdZUKBR5OmQkd6Q7HNrZ2ZnvrZyW9evXs3DhQr7++uuMj/gppHAoIhlhbbbQIy6Giavn0PxYOADhxSsTHBjMZbeCVmsqGIo8PbJktfK/ZcgmTZrQpEmT9JYTEZF0shYM65/ew7SVMygUe4MEW3umNniLz2u/jGFj+aqh9r4eTGxXLyuGKiI5QLrD4caNG8mXL19WjkVERP7GWih0Skrgw01LeHfnLwCczFeUD1oP4KBXGas1NVsoIv8m3eGwYcOGWTkOERH5G2vB8JnIM8wKmUqFyDMAfFm9JR+/8A53HZyt1lQwFJH0yNBWNtevX2f//v1UrVqVfPnyce3aNf7zn/8QHx9Pu3btqFDB+mo4ERGxbnLobub9cTntRsOg864Qhvy2CKfkRK7lcmdgi75sKFPbak2FQhHJiHSHw+3bt9OsWTNiYmLw8PAgLCyMdu3aYW9vj8lkYuLEifzxxx/UqFEjK8crIpJjWZstLBh7g6krZ9Lw9G4ANpby5cOWQVzLnddqTQVDEcmodO9zOGzYMNq1a0d0dDRDhw6lTZs2NGnShGPHjnHixAnat2/PRx99lJVjfeJ88sknlCxZEmdnZ+rUqcP27dsf95BEJJuyFgybHv+T1Qt70/D0bu7aOzKiaQ+6vDraajAc37KcgqGIPJB0b2WTL18+tmzZQoUKFUhMTMTZ2Znw8HBq1753OmP37t28+OKLXLhwIUsH/KT49ttvefvtt1mwYAF16tRh5syZLF++nKNHj1rc7ufvtJWNiID1UOiScJfhG7+g497VABzy9OGD1h9yokBxqzUVCkXknzKSO9I9c5iQkICLiwsADg4O5MqViwIFCpjbCxQowPXr1x9wyNnP9OnT6datG126dKFixYosWLCAXLlysXDhwsc9NBHJJqwFw0oRJwhd0tccDD+t/Qpt3pquYCgiWS7d1xwWK1aMU6dOUbJkSQCWLVuGt/f/bt5++fLlFGExJ0tISGDXrl0MGTLEfMzW1hZ/f3/Cw8PTfE18fDzx8fHm5zExMVk+ThF5MlkLhbamZN7b/iPBv3+FgymZCNd89A8MZkvJalZrKhSKSGZJdzhs3749V69eNT8PDEz5i+jXX381n2LO6a5du0ZycjKFChVKcbxQoUIcOXIkzddMmDCBMWPGPIrhicgTzFowLBxzlRmh06lz/gAAK595jqHNexPlYv0UkIKhiGSmdIfDUaNGWW0fNmwYdnZ2Dz2gnGrIkCEEBwebn8fExFCsWLHHOCIRedSsBcPWhzbx8dp5uMXf5raDM6P932N5ZX+wsbH4mmPjWuBon+6rg0RE0iVD+xxakytXrswq9cQrUKAAdnZ2XLlyJcXxK1eu4OXlleZrnJyccHJyehTDE5EnjLVQ6Bp/hzFh82l7cCMAe7zLEdS6P2fzFrZaU7OFIpJVMu2fnL/88gv//e9/M6vcE83R0RFfX1/Wr19vPmYymVi/fj1+fn6PcWQi8qSxFgx9Lxxi1aI+tD24kWQbW2Y91552HSdZDYYFUDAUkayVaTOHgwYN4vjx47z99tuZVfKJFhwcTKdOnahZsya1a9dm5syZ3L59my5dujzuoYnIE8BaKLRPTuKDLd/Q68/l2BkmzrsXIqhVf3YVrWi1pkKhiDwKmRYOLS3EyKlef/11IiMjGTlyJBEREVSrVo3Vq1enWqQiIk8fa8GwxM1LzAqZSrXLxwD4oVJjRvn3INbJ+qU5CoYi8qikexNsyVzaBFsk5/nz2HXaL/wz7UbDoN1fYYxe9xm5E+8S45SboQG9CK3QwGpNhUIRyQwZyR0Znjk0mUzY2qa+VNFkMnHhwgWKF7e+QauISE5kbbbQIy6GCavn0uLYVgD+LFaJ4FbBXHKzfjclBUMReRzSvSAlJiaG1157jdy5c1OoUCFGjhxJcnKyuT0yMhIfH58sGaSIyJPMWjCsd2Yvqxf2psWxrSTa2jGxYWfeaP+x1WDYspy9gqGIPDbpnjkcMWIE+/bt48svvyQqKopx48axe/dufvzxRxwdHQHQGWoReZpYC4WOSYkM2Pxfuu/4CYCT+YrSt/UADniVsVpToVBEHrd0X3NYokQJlixZQqNGjYB7dwkJDAzEw8ODX3/9laioKAoXLpxiNlEs0zWHItmbtWBYNvIss0KnUvHqaQC+qtaCj1/oSpyjs9WaCoYiklWy5JrDyMhISpQoYX5eoEAB1q1bR0BAAC1btuSLL7548BGLiGQTdQevIMJSo2Hw9u5Qhv62COekBK67uDGoRV/Wla1jtaZCoYg8SdJ9zWHx4sU5fPhwimN58uRh7dq1xMXF8fLLL2f64EREniQlrQTDgrE3WfT9aMau+xTnpAR+8/Gl+TufKBiKSLaT7nDYrFkzFi1alOq4q6sra9aswdnZ+ukSEZHszNpp5MYntrNqUW9eOLWLeDsHRvm/R+d2o4l0zWvxNUP9fRQMReSJlO7TymPGjOHSpUtptuXJk4ewsDB2796daQMTEXkSWAuFzol3GbZxIW/tWQnA4YIl6dt6AMcKlrRaU6FQRJ5k6Q6HefPmJW9ey/8KzpMnDw0bNsyUQYmIPAmsBcNnI04wK2QqZW5cAOCLmi8xpWEn4u0drdZUMBSRJ90D3z7v9u3bfPfdd5w4cQJvb286dOhA/vz5M3NsIiKPhbVQaGtKptuOn+i/+SscTUlccc1H/5b9+MOnutWaCoUikl2keyubihUr8scff5AvXz7Onz9PgwYNuHnzJs888wwnT57E3t6eP//8Uxthp5O2shF5MlkLht4xkUxfMR2/c38BsPoZPwY370OUi/W/wwqGIvK4ZclWNkeOHCEpKQmAIUOGULhwYfbu3Yu7uzuxsbG8/PLLDBs2jKVLlz7c6EVEHhNrwTDw8O+MXzMX9/jb3HFwYnST9/iuSlOwsbH4msNjm+PiaJcVQxURyTIPdFo5PDycBQsW4O7uDtxbsTxmzBjat2+fqYMTEXkUrIVC1/g7jFm3gLYHNgCw17ssQa0GcCZfEas1NVsoItlVhsKhzf//C/nu3bt4e3unaCtSpAiRkZGZNzIRkUfAWjCsceEwM0OnUjz6Csk2tsyr245Z9TqQZGf9V6eCoYhkZxkKh02aNMHe3p6YmBiOHj1KpUqVzG1nz57VghQRyTashUI7UzJ9ti6jz9ZvsTNMXHDzJKh1f3YWfdZqTYVCEckJ0h0OR40aleK5q6triuchISE8//zzmTMqEZEsZC0YFr95mZmhU6lx6SgAP1VsxMhmPbnllNtqTQVDEckp0r1aWTKXViuLPHoRUXepO3F92o2GwasH1jN63ae4JsQR45iL4QHv82vFRlZrKhSKSHaQJauVRUSyM2uzhe5xtxi/Zi6BR7cAsK3oswS36s9Fd0+rNRUMRSQnSve9lQH27dvH22+/TalSpXBxcSF37txUrlyZESNGEBMTk1VjFBF5KNaCod/Zfaxe2JvAo1tItLVjcoO36dBhvNVg+HxhBUMRybnSfVp5zZo1vPzyy7Rs2RIXFxd+/PFH3nnnHXLnzs0PP/yAYRj88ccfeHl5ZfWYcwSdVhbJetZCoWNSIv1//5Ju23/CFoNTeQvTt/WH/OVd1mpNhUIRyY4ykjvSHQ6rV6/Oe++9R48ePQAICwvjgw8+4PDhwyQmJtKiRQuKFSvGokWLHv4TPAUUDkWylrVgWPraeWaHTOHZq6cAWFq1OR81fpc4R2erNRUMRSS7ypJw6OLiwuHDhylZsiQAhmHg5OTE2bNn8fb25vfff6dt27ZcvXr1oT/A00DhUCRr9P1qA78ciEu70TB4c89Khm/8D85JCdxwcWNQiw8IK1vXak2FQhHJ7rJkQUqRIkU4evSoORyePHkSk8lk3tuwaNGixMbGPvioRUQekrXZwgK3bzJp1WyanNwBwOaS1ekf2I9I13xWayoYisjTJt3h8O233+bdd99l2LBhODk5MX36dF588UUcHR0B2Lt3Lz4+Plk2UBERa6wFwxdO7mDyylkUvBNFvJ0DExt1ZrFvawwby2vyvnitBv41vC22i4jkVOkOh0OHDuX27dt89NFHxMfHExAQwKxZs8ztRYoUYf78+VkySBERS6yFQufEuwzduIi399zrc6RACfq++CFHC5a0WlOzhSLyNNMm2I+JrjkUeXjWgmHFK6eYFTKFstfPA/Cfmi8xuWEn4u0drdZUMBSRnEibYItIjtZw8ArOWmizMUy8u/1nPtz8XxxNSVzNnZf+gf343aeG1ZoKhSIi96Q7HObJk4fXXnuNrl278txzz2XlmERELLI2W+gVc41pK6dT7+x+ANaWrcug5n24mcvdak0FQxGR/0l3OLx9+zbbtm1j0aJFlCtXjnfffZe3336bggULZuX4RETMrAXDlkf+YPyauXjcjeWOgxNjG3djWdUAsLGx+JrDY5vj4miXFUMVEcm20n3Noa2tLREREVy+fJkvvviCpUuXEhsbS6tWrXj33Xdp3rw5NlZ+CUtKuuZQJP2shcLc8XcYve4z2h1YB8A+r7IEtR7A6XxFrNbUbKGIPE0ykjsydG9lgKpVqzJnzhwuXbrE4sWLiY6OplWrVhQvXpyRI0c+8KBFRNJiLRhWv3iElYs/oN2BdZiwYa7fa7R9c4rVYJgLBUMREWvSPXNoZ2fH5cuX8fRMfTP6M2fO8J///IclS5Zw7ty5TB9kTqSZQxHr6g5eQYSFNjtTMr23fkufrcuwN0xccCtIcKv+bC9WyWpNhUIReVplye3z7p9WTisc3mcYhk4tp5PCoYhl1mYLi0VFMDNkKr6XjgDwc8WGjGzakxhnV6s1FQxF5GmWJVvZjBo1CldX6798FQxF5GFE30mk6ti1aTcaBq8c3MCYsAXkSYgjxjEXI5r15JdnX7BaU6FQRCRjtAn2Y6KZQ5GUrM0Wut2NZfyaT2h15HcAthetSHCr/lxwL2S1poKhiMg9j2wT7MTERM6cOYOnpyfu7tb3ERMRscRaMPQ7u59pK6ZT+NY1kmxsmVG/I/PrvorJ1vIWNJ3rFGD0y3WyYqgiIjleulcrT548mbi4OACSk5MZMGAArq6ulC9fngIFCvDOO++QmJiYZQMVkZzHf8gKi8HQITmRwb8t4utlwyh86xqn83rT9s0pfPLc61aD4ZmJgQqGIiIPId3hcMiQIdy6dQuAGTNmsHDhQhYsWMBff/3F4sWLWbFiBTNmzMiygYpIzlJy8ApOWLiopfT18/z45QB6bPsBWwy+qdKMwM6z2Ve4nNWaOo0sIvLw0n1a+e+XJi5dupSJEyfSpUsXACpWrAjAhAkTGDhwYCYPUURyknPX7tBg6sa0Gw2DjntXMXzDf3BJiuemcx4Gt+jDmmes37JToVBEJPNk6JrD+6uRz507l+r+ys899xynT5/OvJGJSI5j7drC/LejmLh6Nk1PbAfg9xLV6B/Yj6t58lutqWAoIpK5MhQOP//8c1xdXXF0dOTGjRsp2m7duoWTk1OmDk5Ecg5rwbDRyZ1MWTmTgneiiLezZ3LDziys+SKGjeUrX1b2fp6KRbXSX0Qks6U7HBYvXpzPP/8cACcnJ3bv3k2DBg3M7Rs3bqRcOevXA4nI06fZsBUcS067zSkxniG/LaLz7lAAjhYoTt/WH3LE08dqTc0WiohknXSHwzNnzlhtr1OnToqwKCJibbawwtVTzPp1Ks9cv3fLzUW+rZnYsDPxDtbPQCgYiohkrYfa5/Dv6tatm1mlRCSb23smijYLtqTZZmOY6LrjZz7c/F+ckpOIzO3BgJb92FTK12pNhUIRkUfjocKhm5sbe/fupVSpUpk1HhHJ5qzNFha6dY1pK2ZQ/+w+AMLK1GFQiw+4kcv6JvoKhiIij85DhUPdeU9E/s5aMGx+dAsTVs8l791bxNk78VGTd1latTlYuSf7gdEBuDpn2gkOERFJB/3WFZGHVn3wCm5aaMuVEMeodZ/x+l9hAPxVqDRBrQdwMn8xqzU1Wygi8nik+w4paXnzzTf/9ebNmeHMmTN07doVHx8fXFxcKF26NKNGjSIhISFFv/379/P888/j7OxMsWLFmDx5cqpay5cvp3z58jg7O1O5cmVWrlyZot0wDEaOHIm3tzcuLi74+/tz/PjxFH1u3LhBx44dcXNzw8PDg65duxIbG5v5H1wkGyhpJRhWu3SUlYs+4PW/wjBhw7y6r/LKW1OtBsOmpRQMRUQep4cKh/Pnz6dAgQKZNRaLjhw5gslk4tNPP+XgwYPMmDGDBQsWMHToUHOfmJgYmjVrRokSJdi1axdTpkxh9OjRfPbZZ+Y+W7dupUOHDnTt2pU9e/bQpk0b2rRpw4EDB8x9Jk+ezOzZs1mwYAHbtm0jd+7cBAQEcPfuXXOfjh07cvDgQcLCwggNDWXz5s107949y78HkSfJxRtxFk8j25mS6bPlG77/6kNKRl3mYp6CvNHhYyY37EyinYPFmmcmBvJ5dwVDEZHHycZIx4WDs2fPpnv37jg7O6er6IIFC+jYsSN58uR56AFaMmXKFObPn8+pU6eAe0F12LBhRERE4OjoCMDgwYP5+eefOXLkCACvv/46t2/fJjQ01Fynbt26VKtWjQULFmAYBoULF6Z///4MGDAAgOjoaAoVKsTixYtp3749hw8fpmLFiuzYsYOaNWsCsHr1alq2bMmFCxcoXLhwusYfExODu7s70dHRj2T2VSQzWbu2sGhUBDNCp1Pr4iEAQso/z7CAXsQ4u1qtqdlCEZGsk5Hcka6Zw379+nHr1q10D2DgwIFERkamu/+DiI6OJl++fObn4eHhNGjQwBwMAQICAjh69Cg3b9409/H3909RJyAggPDwcABOnz5NREREij7u7u7UqVPH3Cc8PBwPDw9zMATw9/fH1taWbdu2WRxvfHw8MTExKR4i2ZHFYGgYvHxgA6sW9aHWxUPccnShX2AwfV4caDUYnpkYqGAoIvIESdeCFMMwaNKkCfb26Vu/EhcX91CD+jcnTpxgzpw5TJ061XwsIiICH5+Ud1UoVKiQuS1v3rxERESYj/29T0REhLnf319nqY+np2eKdnt7e/Lly2fuk5YJEyYwZsyYjHxMkSdKy1ErOBSfdpvb3VjGrZ3Hi4c3A7CzSAWCWvXngoeX1ZoKhSIiT550pb1Ro0ZlqOhLL72UYlbPksGDBzNp0iSrfQ4fPkz58uXNzy9evEjz5s1p164d3bp1y9C4HqchQ4YQHBxsfh4TE0OxYtZXa4o8KaydRq5z7i+mh06nyK1IkmxsmVWvA/P8XiPZ1s7iaz57tTrNaqbvEgwREXm0siQcplf//v3p3Lmz1T5/32D70qVLvPDCCzz33HMpFpoAeHl5ceXKlRTH7j/38vKy2ufv7fePeXt7p+hTrVo1c5+rV6+mqJGUlMSNGzfMr0+Lk5MTTk7Wbwsm8qSJvpNI1bFr02xzSE6k3x9f0+PPH7DF4IyHN0GtB7C3sPV7rGu2UETkyfZY9zksWLAgBQsWTFffixcv8sILL+Dr68uiRYuwtU15uaSfnx/Dhg0jMTERB4d7qyHDwsIoV64cefPmNfdZv349QUFB5teFhYXh5+cHgI+PD15eXqxfv94cBmNiYti2bRs9e/Y014iKimLXrl34+t673deGDRswmUzUqVPngb8LkSeNtdnCUtcvMDN0KlUiTgDwbeWmjG3SjdtOuazWVDAUEXnypWu18uN28eJFGjVqRIkSJViyZAl2dv87XXV/ti46Oppy5crRrFkzBg0axIEDB3jnnXeYMWOGeZuZrVu30rBhQyZOnEhgYCDLli1j/Pjx7N69m0qVKgEwadIkJk6cyJIlS/Dx8WHEiBHs37+fQ4cOmVdrt2jRgitXrrBgwQISExPp0qULNWvWZOnSpen+TFqtLE+qhCQTzwxflXajYfDGvtWMWP8FLknxRDm7Mrh5H1aXq2e1pkKhiMjjlZHckS3ukBIWFsaJEyc4ceIERYsWTdF2P9u6u7uzdu1aevXqha+vLwUKFGDkyJEp9h987rnnWLp0KcOHD2fo0KGULVuWn3/+2RwM4d5K69u3b9O9e3eioqKoX78+q1evTrGNz9dff03v3r1p0qQJtra2tG3bltmzZ2fxtyCS9azNFua7E82kVbNpeuLeqvw/SlSlf2A/ruSxvtepgqGISPaSLWYOcyLNHMqTxlowbHhqF1NWzsTz9k3i7eyZ0uBt/lOrDYaN5d2w9o1shnsuyxtei4jIo/PIZg63bNlCzZo1tdBCJBvbeuQabyxOe49Op8R4Bm9aTJddIQAcz1+Mvq0/5FChUmn2v0+zhSIi2ddDhcMWLVqwd+/eFCuKRST7sDZbWP7qaWaFTKHctXMALK7RigmNuhDvYPkfg1Vyw68jFAxFRLKzhwqHOiMtkn1ZCoY2hol3dv7KwE2LcUpOIjKXBx+27MtvpWtZrafZQhGRnCFbLEgRkcxTcfAK7lho87x1nakrZ9LgzB4A1pWuxaAWfbme28NqTQVDEZGc46HC4aeffprqVnMi8uSydho54NhWJqyeS764GOLsnfi4cVe+qtYCbGwsvkahUEQk53mocPjGG29k1jhEJAuFbjtP75/2p9mWKyGOEes/p8P+e3dCOVCoNH1bDeBkAeu3d1QwFBHJmXRaWSSHszZbWOXyMWaFTMHn5mVM2PBZnVeY9vybJNpZ3oJmQdtqNK9VJCuGKiIiTwCFQ5EczFIwtDUl0/PP7wnashQHUzKX8hSgf2Aw4SWqWK2n2UIRkZxP4VAkB3p+8ArOW2grGn2F6aHTqH3hEACh5eoztHlvYpxdLdazAU4rGIqIPBUUDkVyGGunkV86uJGP1s7HLeEOsY4ujPLvwQ+VGmvRiYiImFm+95UFS5YsYcWK//3HZ+DAgXh4ePDcc89x9uzZTB2ciKTf+F93WgyGbndjmRkyhVmh03BLuMOuwuVp0WUOP1RuomAoIiIpZPjeyuXKlWP+/Pk0btyY8PBw/P39mTFjBqGhodjb2/Pjjz9m1VhzFN1bWTKTtdnCWucPMCN0GkVjIkmysWXOc+2Z+9zrJNvaWXzNgdEBuDrrxIKISE6RpfdWPn/+PGXKlAHg559/pm3btnTv3p169erRqFGjBxqwiDw4S8HQPjmJoC1L6fnn99gZJs56eNGvVX92F6lgtZ5mC0VEnm4ZDoeurq5cv36d4sWLs3btWoKDgwFwdnYmLi4u0wcoImlrNHgFZyy0+dy4yMyQqVSNOA7A8kr+jPbvzm2nXBbr+fvAF+8pGIqIPO0yHA6bNm3Ku+++S/Xq1Tl27BgtW7YE4ODBg5QsWTKzxyciabB4GtkwaL9vDSM3fE6uxHiinF0ZGtCbleXrW62n2UIREbkvw+Hwk08+Yfjw4Zw/f54ffviB/PnzA7Br1y46dOiQ6QMUkf+ZumIPc3+/lGZb3jvRTFw9h4DjfwKwtXgVggODiXArYLWmgqGIiPxdhhekSObQghTJKGuLTp4/vZupK2dSKPYGCbb2TG3wFp/XfhnDxvKGBMfGtcDRPsMbFoiISDaUpQtS7rtz5w7nzp0jISEhxfEqVazfYUFEMib6TiJVx65Ns80pKYGBm5bQdecvABzPX4yg1gM4WKi01ZqaLRQREUsyHA4jIyPp3Lkzq1evTrM9OTn5oQclIvdYmy0sF3mGmSFTqRB5BoD/Vg9k/AtduOvgbPE17/p5MvylWpk9TBERyUEyHA6DgoKIjo5m27ZtNGrUiJ9++okrV64wbtw4pk2blhVjFHkqWQqGNoaJzrtCGPzbYpySE7mWy52BLfqyoUxtq/U0WygiIumR4XC4YcMGfvnlF2rWrImtrS0lSpSgadOmuLm5MWHCBAID9R8gkYcx7pcdfBF+Nc22grE3mLpyJg1P7wZgQ6maDGzZl2u581qslws4pGAoIiLplOFwePv2bTw9PQHImzcvkZGRPPPMM1SuXJndu3dn+gBFnibWTiM3Pf4nk1bNJl9cDHftHfn4hXf4snqg1dvfnRzfEjtby+0iIiL/lOFwWK5cOY4ePUrJkiWpWrUqn376KSVLlmTBggV4e3tnxRhFcry1Oy/R/fs9aba5JNxlxIYveGPfvet8D3qWom/rAZwoUNxqTZ1GFhGRB5HhcNi3b18uX74MwKhRo2jevDlff/01jo6OLF68OLPHJ5LjWZstrHz5ODNDp1L6xkUAPq39CtOef4sEeweLr9kysDFF8rlk+jhFROTp8ND7HN65c4cjR45QvHhxChSwvtmu/I/2ORSwHAxtTcn02PYD/f74GgdTMpdd89M/sB9bS1azWk+zhSIikpYs2+cwMTGR8uXLExoaSoUKFQDIlSsXNWrUePDRijyFrN3ppEj0VaavmE6d8wcAWFGuHkMDehPtksdiveZlbVnQtUWWjFVERJ4uGQqHDg4O3L17N6vGIvJUsHYa+cVDmxi3dh5u8beJdXRhtP97fF+pidVFJ5otFBGRzJThe2f16tWLSZMmkZSUlBXjEcmxth65ZjEY5om/zYyQqcwOmYJb/G32eJejZefZfF/ZX8FQREQeqQwvSNmxYwfr169n7dq1VK5cmdy5c6do//HHHzNtcCI5hbXZwpoXDjIzZBpFY66SbGPLXL/XmfPc6yTZWf7ruW9kM9xzWV6UIiIi8qAyHA49PDxo27ZtVoxFJEeyFAztk5P4YMs39PpzOXaGiXPuhQhqNYDdRStYrafZQhERyUoZDoeLFi3KinGI5DgzV+1j5qYLabaVvHGRmaHTqHb5GAA/VGrMKP8exDrlslhvcqsKvFa/VJaMVURE5L4Mh0OApKQkfvvtN06ePMkbb7xBnjx5uHTpEm5ubri6umb2GEWyHYunkQ2D1/aHMWr9Z+ROvEu0U26GBfQitEIDq/U0WygiIo9KhsPh2bNnad68OefOnSM+Pp6mTZuSJ08eJk2aRHx8PAsWLMiKcYpkC9tP3OC1L8LTbPOIi2Hi6jk0P3avPbx4ZYIDg7nsVtBivZbl7JnXJSBLxioiIpKWB7pDSs2aNdm3bx/58+c3H3/55Zfp1q1bpg5OJDuxtuik3pm9TFsxHa/YGyTY2jOtwZt8XutlTLZ2Fl+j2UIREXkcMhwOf//9d7Zu3Yqjo2OK4yVLluTixYuZNjCR7CLZZFB66Mo02xyTEvlw8xK67fgZgJP5ivJB6wEc9CpjtaaCoYiIPC4ZDocmk4nk5ORUxy9cuECePJbv4CCSEw38KZzvtt1Is61s5Flmh0yhQuQZAL6q1oJxjbty18HZYr2Q9+tTubh7VgxVREQkXTIcDps1a8bMmTP57LPPALCxsSE2NpZRo0bRsmXLTB+gyJPK2qKTTrtDGbpxIU7JiVzL5c6gFh+wvkwdq/U0WygiIk8CG8MwjIy84MKFCwQEBGAYBsePH6dmzZocP36cAgUKsHnzZjw9PbNqrDlKRm6ALU+Wdbsv8+53u9NsKxh7kykrZ9Lo9C4AfvPx5cOWQUS65rVYb2DjErzfrFKWjFVERAQyljsyHA7h3lY23377Lfv27SM2NpYaNWrQsWNHXFxcHnjQTxuFw+zJ2qKTJie2MXnlLPLHxRBv58DHL7zDf2u00u3vRETkscvScPjNN9/QoUOHNNs+/PBDpkyZkpFyTy2Fw+wlMiaeWuPXpdnmknCX4Ru/oOPe1QAcLliSD1p/yPGCJSzWcwaOKBiKiMgjkpHckeFrDnv27ImHhwctWrRIcbxfv34sW7ZM4VByHJ/BK7D0L6hKESeYFTKV0jfu3Qnl81ptmNKgEwn2lu97fGB0AK7OD7T/vIiISJbL8H+hvv76azp06EBoaCj169cHoE+fPvz4449s3Lgx0wco8jhZOo1sa0qm+/af6P/7lziYkolwzUf/wGC2lKxmtZ5OI4uIyJMuw+EwMDCQefPm8eKLLxIWFsZ//vMffvnlFzZu3MgzzzyTFWMUeeT+OBTJm//dnmabd0wkM0KnUff8AQBWPfMcQ5r3JsrF8jT9zJcq0cbP8mlmERGRJ8UDndt64403iIqKol69ehQsWJBNmzZRpoz1TX1Fsgtri05aHd7Mx2s+wT3+NrcdnBnt353llZtq0YmIiOQY6QqHwcHBaR4vWLAgNWrUYN68eeZj06dPz5yRiTxiEVF3qTtxfZptrvF3GBM2n7YH7106sdf7GYJa9edMviIW673pW4Rx7aplxVBFRESyTLrC4Z49e9I8XqZMGWJiYsztNlZmT0SeZNZmC2tcOMzM0KkUj75Cso0tc/1eY85z7Umys/zX5+T4ltjZ6u+DiIhkP+kKh1poIjmZpWBoZ0rmgy3L6B3+LXaGifPuhejXKpidRZ+1Wk+nkUVEJDvTfhry1Lp4I456kzek2Vbi5iVmhkyj+uWjAPz47AuMatqDW065Ldb7oftz+JayfCcUERGR7MD2cQ8go+Lj46lWrRo2Njbs3bs3Rdv+/ft5/vnncXZ2plixYkyePDnV65cvX0758uVxdnamcuXKrFy5MkW7YRiMHDkSb29vXFxc8Pf35/jx4yn63Lhxg44dO+Lm5oaHhwddu3YlNjY20z+rZJ2Sg1ekHQwNg3b7w1i56AOqXz5KjFNu+rT+kOBW/a0GwzMTAxUMRUQkR8h24XDgwIEULlw41fGYmBiaNWtGiRIl2LVrF1OmTGH06NF89tln5j5bt26lQ4cOdO3alT179tCmTRvatGnDgQMHzH0mT57M7NmzWbBgAdu2bSN37twEBARw9+5dc5+OHTty8OBBwsLCCA0NZfPmzXTv3j1rP7hkioiouxZPI7vH3WLezxOYsmoWuRPvsq1YJZq/M4eQig0t1lvZ+3mdRhYRkRzlge6t/LisWrWK4OBgfvjhB5599ln27NlDtWrVAJg/fz7Dhg0jIiICR0dHAAYPHszPP//MkSNHAHj99de5ffs2oaGh5pp169alWrVqLFiwAMMwKFy4MP3792fAgAEAREdHU6hQIRYvXkz79u05fPgwFStWZMeOHdSsWROA1atX07JlSy5cuJBmcE2Lbp/36JUavAKThTa/s/uYHjod79jrJNraMf35N/m09iuYbO0s1lMoFBGR7CIjuSPbzBxeuXKFbt268eWXX5IrV65U7eHh4TRo0MAcDAECAgI4evQoN2/eNPfx9/dP8bqAgADCw8MBOH36NBERESn6uLu7U6dOHXOf8PBwPDw8zMEQwN/fH1tbW7Zt22Zx/PHx8cTExKR4yKNT0kIwdExKZOiG//DNsmF4x17nZL4ivPLmVObXbWcxGNqiYCgiIjlXtliQYhgGnTt3pkePHtSsWZMzZ86k6hMREYGPj0+KY4UKFTK35c2bl4iICPOxv/eJiIgw9/v76yz18fT0TNFub29Pvnz5zH3SMmHCBMaMGZOOTyuZ6dy1OzSYmvZq+zLXzjE7ZAoVr54GYGnV5nzU+F3iHJ0t1ts9vCn5XB0ttouIiGR3jzUcDh48mEmTJlntc/jwYdauXcutW7cYMmTIIxpZ5hsyZEiKzcRjYmIoVqzYYxxRzmdx70LD4K09Kxi2cSHOSQlcd3FjcIsPCCtb12o9zRaKiMjT4LGGw/79+9O5c2erfUqVKsWGDRsIDw/HyckpRVvNmjXp2LEjS5YswcvLiytXrqRov//cy8vL/L9p9fl7+/1j3t7eKfrcv7bRy8uLq1evpqiRlJTEjRs3zK9Pi5OTU6rxS9aIS0imwsjVabYVuH2TyStn0fjUTgA2+dRgQMsgIl3zWaz35+AmeHlYnk0UERHJSR5rOCxYsCAFCxb8136zZ89m3Lhx5ueXLl0iICCAb7/9ljp16gDg5+fHsGHDSExMxMHBAYCwsDDKlStH3rx5zX3Wr19PUFCQuVZYWBh+fn4A+Pj44OXlxfr1681hMCYmhm3bttGzZ09zjaioKHbt2oWvry8AGzZswGQymccij0/7eZv481za2wo1PrGdyatmUeBONPF2Dkxo1IUlvq0wbCxfeqvZQhERedpki2sOixcvnuK5q6srAKVLl6Zo0aIAvPHGG4wZM4auXbsyaNAgDhw4wKxZs5gxY4b5dX379qVhw4ZMmzaNwMBAli1bxs6dO83b3djY2BAUFMS4ceMoW7YsPj4+jBgxgsKFC9OmTRsAKlSoQPPmzenWrRsLFiwgMTGR3r170759+3SvVJasYek0snPiXYZtXMhbe+7taXm4YEmCWg/gaMGSFmsFPuvFJ2/5ZsUwRUREnmjZIhymh7u7O2vXrqVXr174+vpSoEABRo4cmWL/weeee46lS5cyfPhwhg4dStmyZfn555+pVKmSuc/AgQO5ffs23bt3Jyoqivr167N69Wqcnf93WvHrr7+md+/eNGnSBFtbW9q2bcvs2bMf6eeV/4m9m0Sl0WvSbHv2yklm/TqFMjcuAPBFzZeY0rAT8faWF5UcG9cCR/tss5BfREQkU2WrfQ5zEu1zmDmaTdvAsci4VMdtDBPdt/9I/81f4WhK4oprPga0DOJ3nxoWaxXMbcuOES2ycrgiIiKPRUZyR46ZOZSni7XZQu+YSKatmMFz5/YDsKZsXQY378PNXO4W6+0b2Qz3XA5ZMlYREZHsROFQsp0WMzdxOCLtRSctj/zBhNVzcI+/zR0HJ8Y06c63VZqBjU2a/fM42vDX2JZZOVwREZFsReFQshVLi05yx99hzLpPefXAegD2eZUlqPUATucrYrGWNrQWERFJTeFQsoUbsQnUGBeWZluNi4eZETqNElERmLDhE7/XmFWvA0l2lv94a4saERGRtCkcyhOv+ujV3LybnOq4nSmZPluX0Xvrt9gbJi64edKvVTA7ilVKo8o9P/eoR7WSHlk4WhERkexN4VCeWAlJJp4ZvirNtmJREcwMmYrvpSMA/FSxESOb9eSWU26L9TRbKCIi8u8UDuWJNOqXAywJP5u6wTBoe2ADY9YtwDUhjhjHXAwPeJ9fKzayWMsOOKlgKCIiki4Kh/LEeXbESm4npt5+0z3uFh+v+YRWR/8AYHvRigS36s8F90IWa+0Y6k9BN93TWkREJL0UDuWJEZeQTIWRq9Ns8zu7n+mh0/COvU6irR0z6ndkQZ22mGzt0uxvA5zWbKGIiEiGKRzKE+HtL8LZfOJGquMOyYn03/wl3bf/hC0Gp/IWJqj1APZ7P2Ox1pu1izLulapZOVwREZEcS+FQHqtkk0HZYSsxpXETx9LXzjMrdCqVrpwEYGnVAMY1fpc7ji5p1nK0hQNjdV9kERGRh6FwKI/NzzsvEPT9vtQNhsGbe1YybONCXJLiueHixpDmvVnzzHMWa1Uq4kZon+ezcLQiIiJPB4VDeeQSkkzU/GgNMfGmVG35b0cxedUsmpzcAcDmktUZ0DKIq3nyW6x3YHQArs76oywiIpIZ9F9UeaTGhRzkiy1n0mxrdHIHU1bOouCdKOLt7JnUsAuLarbGsEn7NLFnblu2j2iRhaMVERF5+igcyiPz4pzf2X8xJtVxp8R4hv62kE677903+UiBEgS1HsARTx+LtWa0q8rLvkWzbKwiIiJPK4VDeSQ6/yc8zWBY8copZoZM5Znr5wBY6Psikxp1Jt7eMc06lbxd+aVPA+xsbbJ0vCIiIk8rhUPJcq3n/M5f/wiGNoaJd7f/zIeb/4ujKYmrufMyoGUQm0v5Wqwzt311WlUrnNXDFREReaopHEqWSDYZbD99g49XHOLApZTBsNCta0xbMYP6Z++tVF5bti6Dm/fhRi53i/VOjm+p2UIREZFHQOFQMt3qA5cZ/eshImLupmprfnQLE1fPweNuLHccnPiocTe+qRoANmkHvzGtn6VTvZJZPGIRERG5T+FQMtXK/Zd5f+nuVMdzx99h1PrPeO2vdQDs9ypDUKsBnMqf9qKSgrkc+HN4U80WioiIPGIKh5JpVu6/RK9v9qQ6Xv3iEWaETqNk1GVM2DC/7qvMrP8GiXYOadYp6OrIjuFNs3q4IiIikgaFQ3loySaDuRuOM2Pd8RTH7UzJ9Ar/jg+2fIO9YeJinoIEtwpmW/HKFmtNb1uFV2oVy+ohi4iIiAUKh/LA7oXCEyzccprouMQUbUWjIpgZOo2aFw8D8GuFBgxv9j4xzq5p1srjZMfeUQE6jSwiIvKYKRzKA1m5/xIffr+f2wnJKRsMg1cObmBM2ALyJMRxy9GFEc3e5+eKjSwuOtFsoYiIyJND4VAy7KPQg/znjzOpjrvdjeXjNZ/Q+sjvAOwoUpF+rYK54OGVZh3f4h581+M5zRaKiIg8QRQOJUPeXbKDdYevpjpe99x+poXOoMitSJJsbJlZ/w3m121Hsq1din52NjZ0qF2MYYEVcXG0S1VHREREHi+FQ0m3cSEHUwVDh+RE+v3xNT3+/AFbDM54eBPUegB7C5dLs8YnHavTvJL3oxiuiIiIPACFQ/lXySaDWeuO8cWWMymOl75+npkhU6l85SQAy6o0Y2yTbtxxdElVw93FnkltqygYioiIPOEUDsWq1QcuM/jHv4i687fVyIZBx72rGL7hP7gkxXPTOQ+Dm/dhTbnnUr3e3cWed+qVonfjMrq2UEREJBtQOJQ0JSSZGPrjX3y/+0KK4/nuRDNp1SyantgOwO8lqjEgMIgreQqkqtHP/xmFQhERkWxG4VBSmbDyEJ//fhqTkfJ4o5M7mbJqJgVvRxFvZ8/kBp1YWOslDBvbVDXmvVGDllV0CllERCS7UTgUs2STQd9lewjdfznFcafEeAZvWkyXXSEAHMtfnL4vDuCwZ6k068x7o7qCoYiISDalcCjAvWsLR/1ykCu34lMcr3D1FDNDplLu2jkAFvm2ZmLDzsQ7OKWq4Whvw+z2Wo0sIiKSnSkcCiv3X+L9pXtSHLMxTLyz81cGblqMU3ISkbk9+LBFEL+VrplmjRaVCjH3DV9dXygiIpLNKRw+5Vbuv0zvb1IGQ89b15m2YgbPn90LQFiZ2gxu/gHXc3ukWWNu++q0qlY4i0cqIiIij4LC4VMo2WSw/fQNwg5FsPAfexcGHN3KxNVzyHv3FnH2Toxr3JWvq7VI877IeXM5MOGVyjqNLCIikoMoHD5lVh+4zJiQQ1yOvpvieK6EOEau/5z2+9cC8Feh0gS1HsDJ/MVS1XB3tued+j70blxWp5FFRERyGIXDp8jqA5fp+dVu/rFDDVUvHWVm6FR8bl7GhA2f1mnL9Oc7kmjnkKpGqyrezGpfXaFQREQkh1I4fAokJJlYsvUMM8KOpQiGtqZk3v9zOUF/LMXeMHEpTwGCWwXzZ/EqqWrY2kC3530Y0rLioxu4iIiIPHIKhzmcpQ2ti0ZfYUbINGpdPARASPnnGRbQixhn11Q1Xq1RlPGvVMbRPvVm1yIiIpKzKBzmYBNWHuLTzadTHX/p4EY+Wjsft4Q73HJ0YWTTnvz07AupFp3Y2sDcDtVpWUUrkUVERJ4WCoc5ULLJYOuJa3z2j2DodjeWj9bO56XDmwDYWaQCQa36c8HDK806czvoFngiIiJPG4XDHMbSauTa5w8wPXQaRWMiSbKxZXa9Dnzi9xrJtnapani7OzOqdUVtUSMiIvIUUjjMQdJajeyQnEjQH0vp+ef32GJw1sOLoFYD2FOkfKrXd61XEv+KXtT2yafVyCIiIk8phcPH7fZtsEs9e5dRySaDST/swjnhf/dG9rlxkUmrZlH56ikAfnj2BSY06sJtx1y4JPxvZtHL3YmhLSrQ9Nn/P70cd+ehxyMiIiJPkNu3093VxjCMf257J49ATEwM7u7uRANuj3swIiIikqPFAO5AdHQ0bm7Wk4f2JhERERERM51WftwuXYJ/SfDpse3UdTov2mF+7hYfS+CR31lWJQDDJuW/AWxtoEu9EgwIqPDQ7ysiIiLZQEwMFE7f1nTZKhyuWLGCsWPHsn//fpydnWnYsCE///yzuf3cuXP07NmTjRs34urqSqdOnZgwYQL29v/7mL/99hvBwcEcPHiQYsWKMXz4cDp37pzifT755BOmTJlCREQEVatWZc6cOdSuXdvcfvfuXfr378+yZcuIj48nICCAefPmUahQoYx/qNy57z0eUs1nc+FR8DgR0XcxgDhHZxbWejlFn1yOdvTzL0un53y0obWIiMjTJDk53V2zTUL44YcfeOutt+jSpQv79u1jy5YtvPHGG+b25ORkAgMDSUhIYOvWrSxZsoTFixczcuRIc5/Tp08TGBjICy+8wN69ewkKCuLdd99lzZo15j7ffvstwcHBjBo1it27d1O1alUCAgK4evWquU+/fv0ICQlh+fLlbNq0iUuXLvHKK688mi/CAjtbG0a1vndru3+uM7b5/8f016rSrUFpBUMRERGxKFssSElKSqJkyZKMGTOGrl27ptln1apVtGrVikuXLpln8BYsWMCgQYOIjIzE0dGRQYMGsWLFCg4cOGB+Xfv27YmKimL16tUA1KlTh1q1ajF37lwATCYTxYoVo0+fPgwePJjo6GgKFizI0qVLefXVVwE4cuQIFSpUIDw8nLp166brM5kXpKTjwtCMSGufQ+1bKCIi8nTLSO7IFlNIu3fv5uLFi9ja2lK9enW8vb1p0aJFipAXHh5O5cqVU5zaDQgIICYmhoMHD5r7+Pv7p6gdEBBAeHg4AAkJCezatStFH1tbW/z9/c19du3aRWJiYoo+5cuXp3jx4uY+aYmPjycmJibFIys0r+TNH4Ma8023usxqX41vutXlj0GNFQxFREQkXbJFODx16t4+faNHj2b48OGEhoaSN29eGjVqxI0bNwCIiIhIdc3f/ecRERFW+8TExBAXF8e1a9dITk5Os8/fazg6OuLh4WGxT1omTJiAu7u7+VGsWLEMfgvpZ2drg1/p/LxUrQh+pfNrQ2sRERFJt8caDgcPHoyNjY3Vx5EjRzCZTAAMGzaMtm3b4uvry6JFi7CxsWH58uWP8yOk25AhQ4iOjjY/zp8//7iHJCIiIpLKY12t3L9//1Qrhf+pVKlSXL58GYCKFSuajzs5OVGqVCnOnTsHgJeXF9u3b0/x2itXrpjb7v/v/WN/7+Pm5oaLiwt2dnbY2dml2efvNRISEoiKikoxe/j3PmlxcnLCycnJ6mcVERERedwe68xhwYIFKV++vNWHo6Mjvr6+ODk5cfToUfNrExMTOXPmDCVKlADAz8+Pv/76K8Wq4rCwMNzc3Myh0s/Pj/Xr16cYQ1hYGH5+fgDm9/p7H5PJxPr16819fH19cXBwSNHn6NGjnDt3ztxHREREJLvKFvscurm50aNHD0aNGkWxYsUoUaIEU6ZMAaBdu3YANGvWjIoVK/LWW28xefJkIiIiGD58OL169TLP2PXo0YO5c+cycOBA3nnnHTZs2MB3333HihUrzO8VHBxMp06dqFmzJrVr12bmzJncvn2bLl26AODu7k7Xrl0JDg4mX758uLm50adPH/z8/NK9UllERETkSZUtwiHAlClTsLe356233iIuLo46deqwYcMG8ubNC4CdnR2hoaH07NkTPz8/cufOTadOnRg7dqy5ho+PDytWrKBfv37MmjWLokWL8sUXXxAQEGDu8/rrrxMZGcnIkSOJiIigWrVqrF69OsUilRkzZmBra0vbtm1TbIItIiIikt1li30Oc6Ks2udQRERE5J9y3D6HIiIiIvJoKByKiIiIiJnCoYiIiIiYKRyKiIiIiFm2Wa2c09xfB5RV91gWERERue9+3kjPOmSFw8fk1q1bAFl6j2URERGRv7t16xbu7u5W+2grm8fEZDJx6dIl8uTJg42NzQPViImJoVixYpw/f17b4TwkfZeZQ99j5tF3mXn0XWYefZeZ51F/l4ZhcOvWLQoXLoytrfWrCjVz+JjY2tpStGjRTKnl5uamv6SZRN9l5tD3mHn0XWYefZeZR99l5nmU3+W/zRjepwUpIiIiImKmcCgiIiIiZgqH2ZiTkxOjRo3CycnpcQ8l29N3mTn0PWYefZeZR99l5tF3mXme5O9SC1JERERExEwzhyIiIiJipnAoIiIiImYKhyIiIiJipnAoIiIiImYKh0+AFStWUKdOHVxcXMibNy9t2rRJ0X7u3DkCAwPJlSsXnp6efPjhhyQlJaXo89tvv1GjRg2cnJwoU6YMixcvTvU+n3zyCSVLlsTZ2Zk6deqwffv2FO13796lV69e5M+fH1dXV9q2bcuVK1cy++Nmufj4eKpVq4aNjQ179+5N0bZ//36ef/55nJ2dKVasGJMnT071+uXLl1O+fHmcnZ2pXLkyK1euTNFuGAYjR47E29sbFxcX/P39OX78eIo+N27coGPHjri5ueHh4UHXrl2JjY3N9M+aFc6cOUPXrl3x8fHBxcWF0qVLM2rUKBISElL003eZtf7t72tONmHCBGrVqkWePHnw9PSkTZs2HD16NEWf9Py+elS/O7OTiRMnYmNjQ1BQkPmYvsv0u3jxIm+++Sb58+fHxcWFypUrs3PnTnN7Zv1Oy4zfrw/FkMfq+++/N/LmzWvMnz/fOHr0qHHw4EHj22+/NbcnJSUZlSpVMvz9/Y09e/YYK1euNAoUKGAMGTLE3OfUqVNGrly5jODgYOPQoUPGnDlzDDs7O2P16tXmPsuWLTMcHR2NhQsXGgcPHjS6detmeHh4GFeuXDH36dGjh1GsWDFj/fr1xs6dO426desazz333KP5IjLRBx98YLRo0cIAjD179piPR0dHG4UKFTI6duxoHDhwwPjmm28MFxcX49NPPzX32bJli2FnZ2dMnjzZOHTokDF8+HDDwcHB+Ouvv8x9Jk6caLi7uxs///yzsW/fPuPFF180fHx8jLi4OHOf5s2bG1WrVjX+/PNP4/fffzfKlCljdOjQ4ZF8/oe1atUqo3PnzsaaNWuMkydPGr/88ovh6elp9O/f39xH32XWSs/f15wsICDAWLRokXHgwAFj7969RsuWLY3ixYsbsbGx5j7/9vvqUf7uzC62b99ulCxZ0qhSpYrRt29f83F9l+lz48YNo0SJEkbnzp2Nbdu2GadOnTLWrFljnDhxwtwnM36nZdbv14ehcPgYJSYmGkWKFDG++OILi31Wrlxp2NraGhEREeZj8+fPN9zc3Iz4+HjDMAxj4MCBxrPPPpvida+//roREBBgfl67dm2jV69e5ufJyclG4cKFjQkTJhiGYRhRUVGGg4ODsXz5cnOfw4cPG4ARHh7+cB/0EVq5cqVRvnx54+DBg6nC4bx584y8efOavzfDMIxBgwYZ5cqVMz9/7bXXjMDAwBQ169SpY7z33nuGYRiGyWQyvLy8jClTppjbo6KiDCcnJ+Obb74xDMMwDh06ZADGjh07zH1WrVpl2NjYGBcvXszUz/uoTJ482fDx8TE/13eZtf7t7+vT5urVqwZgbNq0yTCM9P2+elS/O7OLW7duGWXLljXCwsKMhg0bmsOhvsv0GzRokFG/fn2L7Zn1Oy0zfr8+LJ1Wfox2797NxYsXsbW1pXr16nh7e9OiRQsOHDhg7hMeHk7lypUpVKiQ+VhAQAAxMTEcPHjQ3Mff3z9F7YCAAMLDwwFISEhg165dKfrY2tri7+9v7rNr1y4SExNT9ClfvjzFixc393nSXblyhW7duvHll1+SK1euVO3h4eE0aNAAR0dH87GAgACOHj3KzZs3zX2sfZenT58mIiIiRR93d3fq1Klj7hMeHo6Hhwc1a9Y09/H398fW1pZt27Zl3gd+hKKjo8mXL5/5ub7LrJOev69Pm+joaADzn8H0/L56VL87s4tevXoRGBiY6vPqu0y/X3/9lZo1a9KuXTs8PT2pXr06n3/+ubk9s36nZcbv14elcPgYnTp1CoDRo0czfPhwQkNDyZs3L40aNeLGjRsAREREpPgLCZifR0REWO0TExNDXFwc165dIzk5Oc0+f6/h6OiIh4eHxT5PMsMw6Ny5Mz169Ejxl+7vHua7/Hv7319nqY+np2eKdnt7e/Lly5ctvst/OnHiBHPmzOG9994zH9N3mXXS8/f1aWIymQgKCqJevXpUqlQJSN/vq0f1uzM7WLZsGbt372bChAmp2vRdpt+pU6eYP38+ZcuWZc2aNfTs2ZMPPviAJUuWAJn3Oy0zfr8+LIXDLDB48GBsbGysPo4cOYLJZAJg2LBhtG3bFl9fXxYtWoSNjQ3Lly9/zJ/iyZDe73LOnDncunWLIUOGPO4hP7HS+13+3cWLF/+vvXuPiuK+4gD+RfbBIiyguCxiQTnyihoRH7gBowTLwgFdNKmE1ERaotFgfURt9ZwaTNJYa2jrCSEq1YJWDdEkjQ8sSETUKKIgCAiuVDFoRG1UggmLwHL7h4cp4y6yRhSh93POHs7M7878fvNjGS6zcweEh4fjF7/4BWbPnt1NI2f/zxISElBeXo6MjIzuHkqPdPnyZSxcuBDbt2+HjY1Ndw+nR2ttbUVAQABWr16NUaNGYc6cOZg9ezY2bNjQ3UPrcpLuHkBvtGTJEsTFxT0wxtPTE7W1tQCAZ555Rlgvl8vh6emJmpoaAIBarTap5mqrIlOr1cLX+yvLrl+/DqVSCYVCAWtra1hbW5uNab+PpqYm1NXVif6CbB/THSydy9zcXOTn55v8j8oxY8bgl7/8JbZs2dLhPAGdz2X79rZ1rq6uohh/f38h5saNG6J9tLS04NatWz1iLttcvXoVISEheO6555CamiqK+3+fy8fJ2dm505/X/xfz58/Hvn37cOTIEQwaNEhYb8n56kmdO592RUVFuHHjBgICAoR1RqMRR44cwUcffYTs7GyeSwu5urqKfl8DgJ+fHz7//HMAXXdO64rz6yPrkjsX2U/y/fffk1wuFxWkNDU1kUqlEqqS2m4Ebl/NtXHjRlIqldTY2EhE924EHj58uGjfsbGxJjcCz58/X1g2Go3k5uZmUpDy2WefCTHnzp3rMQUp33zzDZWVlQmv7OxsAkCfffYZXb58mYj+d5NvU1OTsN2KFStMbvKNiooS7Vuj0ZgUUSQlJQntbd/H+284LiwsFGKys7N7VBHFlStXyMvLi15++WVqaWkxaee5fLw6+3nt7VpbWykhIYEGDhxI58+fN2m35Hz1pM6dT7v6+nrRubGsrIzGjBlDM2fOpLKyMp7LhxAbG2tSkLJo0SLSaDRE1HXntK44vz4qTg672cKFC8nNzY2ys7Pp3LlzFB8fTyqVim7dukVE/3uEQFhYGJWUlFBWVhYNGDDA7CMEli1bRpWVlZSSkmL2EQJyuZzS09OpoqKC5syZQ46OjqLqs7lz55K7uzvl5uZSYWEhaTQa4U3f01RXV5tUK9fV1ZGLiwu9+uqrVF5eThkZGWRra2vyeACJREJJSUlUWVlJiYmJZh+/4ujoSLt376bS0lLS6XRmH1UwatQoKigooK+//pq8vLx6zONXrly5QkOHDqXQ0FC6cuUK1dbWCq82PJePlyU/r73ZvHnzyMHBgfLy8kTvv4aGBiGms/PVkzx39jTtq5WJeC4tdfLkSZJIJPT+++9TVVUVbd++nWxtbWnbtm1CTFec07rq/PooODnsZk1NTbRkyRJSqVRkb29PkydPpvLyclHMpUuXKCIighQKBTk7O9OSJUuoublZFHPo0CHy9/cnmUxGnp6elJaWZtJXcnIyubu7k0wmo3HjxtGJEydE7QaDgd58801ycnIiW1tbmjZtmigh6EnMJYdERGfOnKHg4GCSy+Xk5uZGa9asMdl2586d5O3tTTKZjIYNG0aZmZmi9tbWVlq5ciW5uLiQXC6n0NBQ0uv1opibN29SbGws2dnZkVKppF/96ld0586dLj/OxyEtLY0AmH21x3P5eHX289qbdfT+a39es+R89aTOnT3N/ckhz6Xl9u7dS8OHDye5XE6+vr6Umpoqau+qc1pXnF8fhRURUdd8QM0YY4wxxno6rlZmjDHGGGMCTg4ZY4wxxpiAk0PGGGOMMSbg5JAxxhhjjAk4OWSMMcYYYwJODhljjDHGmICTQ8YYY4wxJuDkkDHGGGOMCTg5ZKwXmzRpEhYtWvTAGCsrK3z55ZdPZDwdsWScncnLy4OVlRWsrKwQHR3dJeNi7HFre886Ojp291AYE3ByyBh7aESEiIiIpyKxvJ9er0d6evoDY+Li4oRfym2v8PBwUcz58+eh0+ng7OwMpVKJ4OBgHDp0SBSzYMECjB49GnK5HP7+/ib9NDY2Ii4uDiNGjIBEIukwaU1JSYGfnx8UCgV8fHywdevWTo+zpqYGkZGRsLW1hUqlwrJly9DS0iKKycvLQ0BAAORyOYYOHWp2XlJSUjB48GDY2NggMDAQJ0+e7LRvS1RWVmLq1KlwcHBA3759MXbsWNTU1AjtjY2NSEhIQP/+/WFnZ4cXX3wR169fF+3j1KlTCA0NhaOjI5ycnKDVanHmzBlRDBEhKSkJ3t7ekMvlcHNzw/vvv292TMeOHYNEIjH7vbpfaWkpJkyYABsbG/zsZz/D2rVrTWJ27doFX19f2NjYYMSIEdi/f7/J2N5++224urpCoVBg8uTJqKqqEsXU1tZi3bp1nY6HsSeJk0PG2ENbt24drKysunsYZqlUKouuwoSHh6O2tlZ4ffLJJ6L2qKgotLS0IDc3F0VFRRg5ciSioqJw7do1Udyvf/1rxMTEmO3DaDRCoVBgwYIFmDx5stmY9evXY8WKFVi1ahXOnj2Ld955BwkJCdi7d2+HYzcajYiMjERTUxOOHz+OLVu2ID09HW+//bYQU11djcjISISEhKCkpASLFi3C66+/juzsbCHm008/xVtvvYXExEScPn0aI0eOhFarxY0bNzqdvwe5cOECgoOD4evri7y8PJSWlmLlypWwsbERYhYvXoy9e/di165dOHz4MK5evYrp06cL7T/88APCw8Ph7u6OgoICfP3117C3t4dWq0Vzc7MQt3DhQmzatAlJSUk4d+4c9uzZg3HjxpmMqa6uDq+99hpCQ0M7HX99fT3CwsLg4eGBoqIifPDBB1i1ahVSU1OFmOPHjyM2Nhbx8fEoLi5GdHQ0oqOjUV5eLsSsXbsWH374ITZs2ICCggL07dsXWq0WjY2NQoxarYaDg4Plk8vYk9Bl/6WZMfbUmThxIv3mN7+hZcuWkZOTE7m4uFBiYqIoBgD985//JCKiu3fvUkJCAqnVapLL5eTu7k6rV68WxRcXF5ObmxvV1taKtiUiOnToEAGgrKws8vf3JxsbGwoJCaHr16/T/v37ydfXl+zt7Sk2NpZ+/PFH0TgXLlz4SMfa1vft27c7jZ01axbpdLoO2//zn/8QADpy5Iiwrr6+ngBQTk6OSXxiYiKNHDnyJ/Wp0Who6dKlonVvvfUWBQUFdbiv/fv3U58+fejatWvCuvXr15NSqaS7d+8SEdFvf/tbGjZsmGi7mJgY0mq1wvK4ceMoISFBWDYajTRw4ED64x//+MBj6UxMTAzNnDmzw/a6ujqSSqW0a9cuYV1lZSUBoPz8fCIiOnXqFAGgmpoaIaa0tJQAUFVVFRERVVRUkEQioXPnzlk0pt///vcWfa8+/vhjcnJyEuaSiOh3v/sd+fj4CMszZsygyMhI0XaBgYH0xhtvEBFRa2srqdVq+uCDD0THLZfL6ZNPPhFtl5aWRg4ODp0eA2NPCl85ZKyX27JlC/r27YuCggKsXbsW7777LnJycszGfvjhh9izZw927twJvV6P7du3Y/DgwUJ7Q0MDXnnlFaSkpECtVnfY56pVq/DRRx/h+PHjuHz5MmbMmIF169Zhx44dyMzMxIEDB5CcnNzVh/pQ8vLyoFKp4OPjg3nz5uHmzZtCW//+/YWPd3/88Ue0tLRg48aNUKlUGD16dJeO4+7du6IragCgUChw8uRJ0RWy9vLz8zFixAi4uLgI67RaLerr63H27Fkh5v6rlVqtFvn5+QCApqYmFBUViWL69OmDyZMnCzE/RWtrKzIzM+Ht7Q2tVguVSoXAwEDR7QdFRUVobm4W9e3r6wt3d3ehbx8fH/Tv3x+bN29GU1MTDAYDNm/eDD8/P+E9uXfvXnh6emLfvn0YMmQIBg8ejNdffx23bt0SjSktLQ0XL15EYmKiRceQn5+P559/HjKZTFin1Wqh1+tx+/ZtIeZB81tdXY1r166JYhwcHBAYGPhI88vYk8DJIWO93LPPPovExER4eXnhtddew5gxY3Dw4EGzsTU1NfDy8kJwcDA8PDwQHByM2NhYoX3x4sV47rnnoNPpHtjnH/7wBwQFBWHUqFGIj4/H4cOHsX79eowaNQoTJkzASy+9ZHL/3pMUHh6OrVu34uDBg/jTn/6Ew4cPIyIiAkajEcC9IoGvvvoKxcXFsLe3h42NDf7yl78gKysLTk5OXToWrVaLTZs2oaioCESEwsJCbNq0Cc3Nzfjuu+/MbnPt2jVRYghAWG772LujmPr6ehgMBnz33XcwGo1mY+7/6Pxh3LhxAz/88APWrFmD8PBwHDhwANOmTcP06dNx+PBhYWwymczk4//2fdvb2yMvLw/btm2DQqGAnZ0dsrKy8K9//QsSiQQAcPHiRXzzzTfYtWsXtm7divT0dBQVFeGll14S9llVVYXly5dj27ZtwnadeZT5bd/efjtzMYw9rTg5ZKyXe/bZZ0XLrq6uHd5TFhcXh5KSEvj4+GDBggU4cOCA0LZnzx7k5uZadPN8+z5dXFxga2sLT09P0TpL72urqamBnZ2d8Fq9erVF2wHA9u3bRdsePXoUAPDyyy9j6tSpGDFiBKKjo7Fv3z6cOnUKeXl5AO4VEiQkJEClUuHo0aM4efIkoqOjMWXKFNTW1lrcvyVWrlyJiIgIjB8/HlKpFDqdDrNmzQJw70peT9Pa2goA0Ol0WLx4Mfz9/bF8+XJERUVhw4YNFu/HYDAgPj4eQUFBOHHiBI4dO4bhw4cjMjISBoNB6Ovu3bvYunUrJkyYgEmTJmHz5s04dOgQ9Ho9jEYjXnnlFbzzzjvw9vZ+LMfLWG/U8848jLGHIpVKRctWVlbCL/D7BQQEoLq6Gu+99x4MBgNmzJghXIXJzc3FhQsX4OjoCIlEIlyFefHFFzFp0qQO+7SysnqoMdxv4MCBKCkpEV5z5861aDsAmDp1qmjbMWPGmI3z9PSEs7Mz/v3vfwO4d6z79u1DRkYGgoKCEBAQgI8//hgKhQJbtmyxuH9LKBQK/P3vf0dDQwMuXbqEmpoaDB48GPb29hgwYIDZbdRqtUllb9ty28f9HcUolUooFAo4OzvD2trabMyDbhnojLOzMyQSCZ555hnRej8/P6FaWa1Wo6mpCXV1dR32vWPHDly6dAlpaWkYO3Ysxo8fjx07dqC6uhq7d+8GcO8PHYlEIkr8/Pz8ANz7o+LOnTsoLCzE/Pnzhffsu+++izNnzkAikSA3N9fsMTzK/LZvb7+duRjGnlacHDLGRJRKJWJiYvC3v/0Nn376KT7//HPcunULy5cvR2lpqSjZAoC//vWvSEtLe2zjkUgkGDp0qPDq16+fxdva29uLtlUoFGbjrly5gps3b8LV1RXAvXsrAdMrd3369LE4qX1YUqkUgwYNgrW1NTIyMhAVFdXhlUONRoOysjLR1decnBwolUohKdNoNCa3D+Tk5ECj0QAAZDIZRo8eLYppbW3FwYMHhZifQiaTYezYsdDr9aL158+fh4eHBwBg9OjRkEqlor71ej1qamqEvhsaGtCnTx9RVXzbctv3ICgoCC0tLbhw4YKoHwDw8PCAUqlEWVmZyR8XPj4+KCkpQWBgoNlj0Gg0OHLkiOiez5ycHPj4+Ai3FXQ2v0OGDIFarRbF1NfXo6Cg4JHml7EnorsrYhhjj4+5KmCdTkezZs0SltGu4vjPf/4z7dixgyorK0mv11N8fDyp1WoyGo1m948OqpXbVwybq8S8v2L0SVYr37lzh5YuXUr5+flUXV1NX331FQUEBJCXlxc1NjYS0b1q5f79+9P06dOppKSE9Ho9LV26lKRSKZWUlAj7qqqqouLiYnrjjTfI29ubiouLqbi4WFTlevbsWSouLqYpU6bQpEmThJg2er2e/vGPf9D58+epoKCAYmJiqF+/flRdXS3EfPHFF6JK2ZaWFho+fDiFhYVRSUkJZWVl0YABA2jFihVCzMWLF8nW1paWLVtGlZWVlJKSQtbW1pSVlSXEZGRkkFwup/T0dKqoqKA5c+aQo6OjqAr6p/jiiy9IKpVSamoqVVVVUXJyMllbW9PRo0eFmLlz55K7uzvl5uZSYWEhaTQa0mg0QntlZSXJ5XKaN28eVVRUUHl5Oc2cOZMcHBzo6tWrRHSvujogIICef/55On36NBUWFlJgYCD9/Oc/73Bs5qqVk5OT6YUXXhCW6+rqyMXFhV599VUqLy+njIwMsrW1pY0bNwoxx44dI4lEQklJSVRZWUmJiYkklUqprKxMiFmzZg05OjrS7t27qbS0lHQ6HQ0ZMoQMBoOof65WZk8bTg4Z68UeNjlMTU0lf39/6tu3LymVSgoNDaXTp093uP+emBw2NDRQWFgYDRgwgKRSKXl4eNDs2bNNEqJTp05RWFgY9evXj+zt7Wn8+PG0f/9+UczEiRMJgMmrfWLn4eFhNqZNRUUF+fv7k0KhIKVSSTqdzuTRLGlpaXT/3/KXLl2iiIgIUigU5OzsTEuWLKHm5maTOfH39yeZTEaenp6UlpZmMh/Jycnk7u5OMpmMxo0bRydOnBC1z5o1iyZOnPjAOTVn8+bNNHToULKxsaGRI0fSl19+KWo3GAz05ptvkpOTE9na2tK0adOotrZWFHPgwAEKCgoiBwcHcnJyohdeeEF41E2bb7/9lqZPn052dnbk4uJCcXFxdPPmzQ7HZS45TExMJA8PD9G6M2fOUHBwMMnlcnJzc6M1a9aY7Gvnzp3k7e1NMpmMhg0bRpmZmaL21tZWWrlyJbm4uJBcLqfQ0FDS6/Um++HkkD1trIiInvDFSsYY63J5eXkICQnB7du3+V+RdaGJEyciJCQEq1at6u6h9Frp6elYtGiRyT2YjHUXy+r6GWOshxg0aBCmTJli8h9P2MP7/vvvceHCBWRmZnb3UHotOzs7tLS0mDzrkrHuxFcOGWO9gsFgwLfffgvg3i9crghlPUFbhby1tTWGDBnSzaNh7B5ODhljjDHGmIAfZcMYY4wxxgScHDLGGGOMMQEnh4wxxhhjTMDJIWOMMcYYE3ByyBhjjDHGBJwcMsYYY4wxASeHjDHGGGNMwMkhY4wxxhgT/BfoHLAbxWMoNgAAAABJRU5ErkJggg==", + "image/png": 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", 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", 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", 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", + "image/png": 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", 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", + "image/png": 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", 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" ] @@ -1062,7 +1070,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1078,21 +1086,19 @@ "source /data/Xilinx/Vivado/2020.1/scripts/vivado_hls/hls.tcl -notrace\n", "INFO: Applying HLS Y2K22 patch v1.2 for IP revision\n", "INFO: [HLS 200-10] Running '/data/Xilinx/Vivado/2020.1/bin/unwrapped/lnx64.o/vivado_hls'\n", - "INFO: [HLS 200-10] For user 'jcampos' on host 'correlator4.fnal.gov' (Linux_x86_64 version 5.14.0-427.33.1.el9_4.x86_64) on Mon Sep 16 23:00:57 CDT 2024\n", + "INFO: [HLS 200-10] For user 'jcampos' on host 'correlator4.fnal.gov' (Linux_x86_64 version 5.14.0-427.33.1.el9_4.x86_64) on Fri Sep 20 13:30:01 CDT 2024\n", "INFO: [HLS 200-10] On os \"AlmaLinux release 9.4 (Shamrock Pampas Cat)\"\n", - "INFO: [HLS 200-10] In directory '/data/jcampos/projects/ml-quantum-readout/hls4ml_projects/sl-100_ws-450'\n", + "INFO: [HLS 200-10] In directory '/data/jcampos/projects/ml-quantum-readout/hls4ml_projects/sl-100_ws-400'\n", "Sourcing Tcl script 'build_prj.tcl'\n", - "INFO: [HLS 200-10] Opening project '/data/jcampos/projects/ml-quantum-readout/hls4ml_projects/sl-100_ws-450/myproject_prj'.\n", - "INFO: [HLS 200-10] Adding design file 'firmware/myproject_axi.cpp' to the project\n", - "INFO: [HLS 200-10] Adding design file 'firmware/myproject.cpp' to the project\n", - "INFO: [HLS 200-10] Adding test bench file 'myproject_test.cpp' to the project\n", + "INFO: [HLS 200-10] Opening project '/data/jcampos/projects/ml-quantum-readout/hls4ml_projects/sl-100_ws-400/NN_prj'.\n", + "INFO: [HLS 200-10] Adding design file 'firmware/NN_axi.cpp' to the project\n", + "INFO: [HLS 200-10] Adding design file 'firmware/NN.cpp' to the project\n", + "INFO: [HLS 200-10] Adding test bench file 'NN_test.cpp' to the project\n", "INFO: [HLS 200-10] Adding test bench file 'firmware/weights' to the project\n", "INFO: [HLS 200-10] Adding test bench file 'tb_data' to the project\n", - "INFO: [HLS 200-10] Opening solution '/data/jcampos/projects/ml-quantum-readout/hls4ml_projects/sl-100_ws-450/myproject_prj/solution1'.\n", - "INFO: [XFORM 203-101] Allowed max sub elements number after partition is 4096.\n", - "INFO: [XFORM 203-1161] The maximum of name length is set into 80.\n", - "INFO: [XFORM 203-101] Allowed max sub elements number after partition is 4096.\n", - "INFO: [XFORM 203-1161] The maximum of name length is set into 80.\n", + "INFO: [HLS 200-10] Opening solution '/data/jcampos/projects/ml-quantum-readout/hls4ml_projects/sl-100_ws-400/NN_prj/solution1'.\n", + "INFO: [SYN 201-201] Setting up clock 'default' with a period of 3.225ns.\n", + "INFO: [SYN 201-201] Setting up clock 'default' with an uncertainty of 0.403ns.\n", "ERROR: [HLS 200-70] Part 'xczu49dr-ffvf1760-2-e' is not installed.\n", "command 'ap_source' returned error code\n", " while executing\n", @@ -1101,12 +1107,10 @@ " invoked from within\n", "\"uplevel \\#0 [list source $arg] \"\n", "\n", - "INFO: [Common 17-206] Exiting vivado_hls at Mon Sep 16 23:00:58 2024...\n", - "CSynthesis report not found.\n", + "INFO: [Common 17-206] Exiting vivado_hls at Fri Sep 20 13:30:03 2024...\n", "Vivado synthesis report not found.\n", "Cosim report not found.\n", "Timing report not found.\n", - "CSynthesis report not found.\n", "Vivado synthesis report not found.\n", "Cosim report not found.\n", "Timing report not found.\n" @@ -1115,10 +1119,25 @@ { "data": { "text/plain": [ - "{}" + "{'CSynthesisReport': {'TargetClockPeriod': '3.22',\n", + " 'EstimatedClockPeriod': '2.768',\n", + " 'BestLatency': '896',\n", + " 'WorstLatency': '898',\n", + " 'IntervalMin': '897',\n", + " 'IntervalMax': '899',\n", + " 'BRAM_18K': '22',\n", + " 'DSP': '129',\n", + " 'FF': '175582',\n", + " 'LUT': '195887',\n", + " 'URAM': '0',\n", + " 'AvailableBRAM_18K': '2160',\n", + " 'AvailableDSP': '4272',\n", + " 'AvailableFF': '850560',\n", + " 'AvailableLUT': '425280',\n", + " 'AvailableURAM': '80'}}" ] }, - "execution_count": 19, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1142,14 +1161,104 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Path hls4ml_prj//hls4ml_prj does not exist. Exiting.\n" + "Found 1 solution(s) in ../hls4ml_projects/sl-100_ws-400/NN_prj.\n", + "Reports for solution \"solution1\":\n", + "\n", + "C simulation report not found.\n", + "SYNTHESIS REPORT:\n", + "================================================================\n", + "== Vivado HLS Report for 'NN_axi'\n", + "================================================================\n", + "* Date: Fri Sep 20 12:47:18 2024\n", + "\n", + "* Version: 2020.1.1 (Build 2951007 on Wed Aug 05 23:24:06 MDT 2020)\n", + "* Project: NN_prj\n", + "* Solution: solution1\n", + "* Product family: zynquplus\n", + "* Target device: xczu49dr-ffvf1760-2-e\n", + "\n", + "\n", + "================================================================\n", + "== Performance Estimates\n", + "================================================================\n", + "+ Timing: \n", + " * Summary: \n", + " +--------+---------+----------+------------+\n", + " | Clock | Target | Estimated| Uncertainty|\n", + " +--------+---------+----------+------------+\n", + " |ap_clk | 3.22 ns | 2.768 ns | 0.40 ns |\n", + " +--------+---------+----------+------------+\n", + "\n", + "+ Latency: \n", + " * Summary: \n", + " +---------+---------+----------+----------+-----+-----+---------+\n", + " | Latency (cycles) | Latency (absolute) | Interval | Pipeline|\n", + " | min | max | min | max | min | max | Type |\n", + " +---------+---------+----------+----------+-----+-----+---------+\n", + " | 896| 898| 2.890 us | 2.896 us | 896| 898| none |\n", + " +---------+---------+----------+----------+-----+-----+---------+\n", + "\n", + " + Detail: \n", + " * Instance: \n", + " +---------------+-------+---------+---------+----------+----------+-----+-----+----------+\n", + " | | | Latency (cycles) | Latency (absolute) | Interval | Pipeline |\n", + " | Instance | Module| min | max | min | max | min | max | Type |\n", + " +---------------+-------+---------+---------+----------+----------+-----+-----+----------+\n", + " |grp_NN_fu_237 |NN | 83| 85| 0.268 us | 0.274 us | 50| 50| dataflow |\n", + " +---------------+-------+---------+---------+----------+----------+-----+-----+----------+\n", + "\n", + " * Loop: \n", + " +----------+---------+---------+----------+-----------+-----------+------+----------+\n", + " | | Latency (cycles) | Iteration| Initiation Interval | Trip | |\n", + " | Loop Name| min | max | Latency | achieved | target | Count| Pipelined|\n", + " +----------+---------+---------+----------+-----------+-----------+------+----------+\n", + " |- Loop 1 | 803| 803| 5| 1| 1| 800| yes |\n", + " |- Loop 2 | 5| 5| 5| 1| 1| 2| yes |\n", + " +----------+---------+---------+----------+-----------+-----------+------+----------+\n", + "\n", + "\n", + "\n", + "================================================================\n", + "== Utilization Estimates\n", + "================================================================\n", + "* Summary: \n", + "+-----------------+---------+-------+--------+--------+-----+\n", + "| Name | BRAM_18K| DSP48E| FF | LUT | URAM|\n", + "+-----------------+---------+-------+--------+--------+-----+\n", + "|DSP | -| -| -| -| -|\n", + "|Expression | -| -| 40| 18092| -|\n", + "|FIFO | -| -| -| -| -|\n", + "|Instance | 22| 129| 163257| 177402| -|\n", + "|Memory | -| -| -| -| -|\n", + "|Multiplexer | -| -| -| 137| -|\n", + "|Register | 0| -| 12285| 256| -|\n", + "+-----------------+---------+-------+--------+--------+-----+\n", + "|Total | 22| 129| 175582| 195887| 0|\n", + "+-----------------+---------+-------+--------+--------+-----+\n", + "|Available | 2160| 4272| 850560| 425280| 80|\n", + "+-----------------+---------+-------+--------+--------+-----+\n", + "|Utilization (%) | 1| 3| 20| 46| 0|\n", + "+-----------------+---------+-------+--------+--------+-----+\n", + "\n", + "+ Detail: \n", + " * Instance: \n", + " +-------------------------------+--------------------------+---------+-------+--------+--------+-----+\n", + " | Instance | Module | BRAM_18K| DSP48E| FF | LUT | URAM|\n", + " +-------------------------------+--------------------------+---------+-------+--------+--------+-----+\n", + " |grp_NN_fu_237 |NN | 22| 129| 163157| 177264| 0|\n", + " |NN_axi_fpext_32ns_64_2_1_U190 |NN_axi_fpext_32ns_64_2_1 | 0| 0| 100| 138| 0|\n", + " +-------------------------------+--------------------------+---------+-------+--------+--------+-----+\n", + " |Total | | 22| 129| 163257| 177402| 0|\n", + " +-------------------------------+--------------------------+---------+-------+--------+--------+-----+\n", + "\n", + "Co-simulation report not found.\n" ] } ],