From 43b06411a611051539e89216c4cb65a02f7dbab8 Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Mon, 16 Oct 2023 11:45:48 +1000 Subject: [PATCH 01/14] Created a google Colab Version --- Colab version.ipynb | 5303 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 5303 insertions(+) create mode 100644 Colab version.ipynb diff --git a/Colab version.ipynb b/Colab version.ipynb new file mode 100644 index 0000000000..a914e70c9c --- /dev/null +++ b/Colab version.ipynb @@ -0,0 +1,5303 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyOHMcV3sgfpuOe9f8kAGUP9", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yzCmT9TUJY10", + "outputId": "e25f99c9-66c9-41f5-df60-391e55ddcea4" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-Lb7CGdEJCQg", + "outputId": "a4c11d2b-0675-4e0b-e73a-86a07cacc29b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[Errno 2] No such file or directory: '/content/drive/MyDrive/Root'\n", + "/content/drive/MyDrive\n" + ] + } + ], + "source": [ + "%cd /content/drive/MyDrive" + ] + }, + { + "cell_type": "code", + "source": [ + "!unzip ADNI_AD_NC_2D.zip" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_eAax1mMJpyw", + "outputId": "5e752810-d2dd-41bf-fbee-c95550ec0384" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", + " inflating: AD_NC/test/AD/413796_78.jpeg \n", + " inflating: AD_NC/test/AD/413796_79.jpeg \n", + " inflating: AD_NC/test/AD/413796_80.jpeg \n", + " inflating: AD_NC/test/AD/413796_81.jpeg \n", + " inflating: AD_NC/test/AD/413796_82.jpeg \n", + " inflating: AD_NC/test/AD/413796_83.jpeg \n", + " inflating: AD_NC/test/AD/413796_84.jpeg \n", + " inflating: AD_NC/test/AD/413796_85.jpeg \n", + " inflating: AD_NC/test/AD/413796_86.jpeg \n", + " inflating: AD_NC/test/AD/413796_87.jpeg \n", + " inflating: AD_NC/test/AD/413796_88.jpeg \n", + " inflating: AD_NC/test/AD/413796_89.jpeg \n", + " inflating: AD_NC/test/AD/413796_90.jpeg \n", + " inflating: AD_NC/test/AD/413796_91.jpeg \n", + " inflating: AD_NC/test/AD/413796_92.jpeg \n", + " inflating: AD_NC/test/AD/413796_93.jpeg \n", + " inflating: AD_NC/test/AD/413796_94.jpeg \n", + " inflating: AD_NC/test/AD/413796_95.jpeg \n", + " inflating: AD_NC/test/AD/413796_96.jpeg \n", + " inflating: AD_NC/test/AD/413796_97.jpeg \n", + " inflating: AD_NC/test/AD/414389_78.jpeg \n", + " inflating: AD_NC/test/AD/414389_79.jpeg \n", + " inflating: AD_NC/test/AD/414389_80.jpeg \n", + " inflating: AD_NC/test/AD/414389_81.jpeg \n", + " inflating: AD_NC/test/AD/414389_82.jpeg \n", + " inflating: AD_NC/test/AD/414389_83.jpeg \n", + " inflating: AD_NC/test/AD/414389_84.jpeg \n", + " inflating: AD_NC/test/AD/414389_85.jpeg \n", + " inflating: AD_NC/test/AD/414389_86.jpeg \n", + " inflating: AD_NC/test/AD/414389_87.jpeg \n", + " inflating: AD_NC/test/AD/414389_88.jpeg \n", + " inflating: AD_NC/test/AD/414389_89.jpeg \n", + " inflating: AD_NC/test/AD/414389_90.jpeg \n", + " inflating: AD_NC/test/AD/414389_91.jpeg \n", + " inflating: AD_NC/test/AD/414389_92.jpeg \n", + " inflating: AD_NC/test/AD/414389_93.jpeg \n", + " inflating: AD_NC/test/AD/414389_94.jpeg \n", + " inflating: AD_NC/test/AD/414389_95.jpeg \n", + " inflating: AD_NC/test/AD/414389_96.jpeg \n", + " inflating: AD_NC/test/AD/414389_97.jpeg \n", + " inflating: AD_NC/test/AD/414697_100.jpeg \n", + " inflating: AD_NC/test/AD/414697_101.jpeg \n", + " inflating: AD_NC/test/AD/414697_102.jpeg \n", + " inflating: AD_NC/test/AD/414697_103.jpeg \n", + " inflating: AD_NC/test/AD/414697_104.jpeg \n", + " inflating: AD_NC/test/AD/414697_105.jpeg \n", + " inflating: AD_NC/test/AD/414697_106.jpeg \n", + " inflating: AD_NC/test/AD/414697_107.jpeg \n", + " inflating: AD_NC/test/AD/414697_88.jpeg \n", + " inflating: AD_NC/test/AD/414697_89.jpeg \n", + " inflating: AD_NC/test/AD/414697_90.jpeg \n", + " inflating: AD_NC/test/AD/414697_91.jpeg \n", + " inflating: AD_NC/test/AD/414697_92.jpeg \n", + " inflating: AD_NC/test/AD/414697_93.jpeg \n", + " inflating: AD_NC/test/AD/414697_94.jpeg \n", + " inflating: AD_NC/test/AD/414697_95.jpeg \n", + " inflating: AD_NC/test/AD/414697_96.jpeg \n", + " inflating: AD_NC/test/AD/414697_97.jpeg \n", + " inflating: AD_NC/test/AD/414697_98.jpeg \n", + " inflating: AD_NC/test/AD/414697_99.jpeg \n", + " inflating: AD_NC/test/AD/415180_75.jpeg \n", + " inflating: AD_NC/test/AD/415180_76.jpeg \n", + " inflating: AD_NC/test/AD/415180_77.jpeg \n", + " inflating: AD_NC/test/AD/415180_78.jpeg \n", + " inflating: AD_NC/test/AD/415180_79.jpeg \n", + " inflating: AD_NC/test/AD/415180_80.jpeg \n", + " inflating: AD_NC/test/AD/415180_81.jpeg \n", + " inflating: AD_NC/test/AD/415180_82.jpeg \n", + " inflating: AD_NC/test/AD/415180_83.jpeg \n", + " inflating: AD_NC/test/AD/415180_84.jpeg \n", + " inflating: AD_NC/test/AD/415180_85.jpeg \n", + " inflating: AD_NC/test/AD/415180_86.jpeg \n", + " inflating: AD_NC/test/AD/415180_87.jpeg \n", + " inflating: AD_NC/test/AD/415180_88.jpeg \n", + " inflating: AD_NC/test/AD/415180_89.jpeg \n", + " inflating: AD_NC/test/AD/415180_90.jpeg \n", + " inflating: AD_NC/test/AD/415180_91.jpeg \n", + " inflating: AD_NC/test/AD/415180_92.jpeg \n", + " inflating: AD_NC/test/AD/415180_93.jpeg \n", + " inflating: AD_NC/test/AD/415180_94.jpeg \n", + " inflating: AD_NC/test/AD/415186_75.jpeg \n", + " inflating: AD_NC/test/AD/415186_76.jpeg \n", + " inflating: AD_NC/test/AD/415186_77.jpeg \n", + " inflating: AD_NC/test/AD/415186_78.jpeg \n", + " inflating: AD_NC/test/AD/415186_79.jpeg \n", + " inflating: AD_NC/test/AD/415186_80.jpeg \n", + " inflating: AD_NC/test/AD/415186_81.jpeg \n", + " inflating: AD_NC/test/AD/415186_82.jpeg \n", + " inflating: AD_NC/test/AD/415186_83.jpeg \n", + " inflating: AD_NC/test/AD/415186_84.jpeg \n", + " inflating: AD_NC/test/AD/415186_85.jpeg \n", + " inflating: AD_NC/test/AD/415186_86.jpeg \n", + " inflating: AD_NC/test/AD/415186_87.jpeg \n", + " inflating: AD_NC/test/AD/415186_88.jpeg \n", + " inflating: AD_NC/test/AD/415186_89.jpeg \n", + " inflating: AD_NC/test/AD/415186_90.jpeg \n", + " inflating: AD_NC/test/AD/415186_91.jpeg \n", + " inflating: AD_NC/test/AD/415186_92.jpeg \n", + " inflating: AD_NC/test/AD/415186_93.jpeg \n", + " inflating: AD_NC/test/AD/415186_94.jpeg \n", + " inflating: AD_NC/test/AD/415211_75.jpeg \n", + " inflating: AD_NC/test/AD/415211_76.jpeg \n", + " inflating: AD_NC/test/AD/415211_77.jpeg \n", + " inflating: AD_NC/test/AD/415211_78.jpeg \n", + " inflating: AD_NC/test/AD/415211_79.jpeg \n", + " inflating: AD_NC/test/AD/415211_80.jpeg \n", + " inflating: AD_NC/test/AD/415211_81.jpeg \n", + " inflating: AD_NC/test/AD/415211_82.jpeg \n", + " inflating: AD_NC/test/AD/415211_83.jpeg \n", + " inflating: AD_NC/test/AD/415211_84.jpeg \n", + " inflating: AD_NC/test/AD/415211_85.jpeg \n", + " inflating: AD_NC/test/AD/415211_86.jpeg \n", + " inflating: AD_NC/test/AD/415211_87.jpeg \n", + " inflating: AD_NC/test/AD/415211_88.jpeg \n", + " inflating: AD_NC/test/AD/415211_89.jpeg \n", + " inflating: AD_NC/test/AD/415211_90.jpeg \n", + " inflating: AD_NC/test/AD/415211_91.jpeg \n", + " inflating: AD_NC/test/AD/415211_92.jpeg \n", + " inflating: AD_NC/test/AD/415211_93.jpeg \n", + " inflating: AD_NC/test/AD/415211_94.jpeg \n", + " inflating: AD_NC/test/AD/415587_78.jpeg \n", + " inflating: AD_NC/test/AD/415587_79.jpeg \n", + " inflating: AD_NC/test/AD/415587_80.jpeg \n", + " inflating: AD_NC/test/AD/415587_81.jpeg \n", + " inflating: AD_NC/test/AD/415587_82.jpeg \n", + " inflating: AD_NC/test/AD/415587_83.jpeg \n", + " inflating: AD_NC/test/AD/415587_84.jpeg \n", + " inflating: AD_NC/test/AD/415587_85.jpeg \n", + " inflating: AD_NC/test/AD/415587_86.jpeg \n", + " inflating: AD_NC/test/AD/415587_87.jpeg \n", + " inflating: AD_NC/test/AD/415587_88.jpeg \n", + " inflating: AD_NC/test/AD/415587_89.jpeg \n", + " inflating: AD_NC/test/AD/415587_90.jpeg \n", + " inflating: AD_NC/test/AD/415587_91.jpeg \n", + " inflating: AD_NC/test/AD/415587_92.jpeg \n", + " inflating: AD_NC/test/AD/415587_93.jpeg \n", + " inflating: AD_NC/test/AD/415587_94.jpeg \n", + " inflating: AD_NC/test/AD/415587_95.jpeg \n", + " inflating: AD_NC/test/AD/415587_96.jpeg \n", + " inflating: AD_NC/test/AD/415587_97.jpeg \n", + " inflating: AD_NC/test/AD/415594_78.jpeg \n", + " inflating: AD_NC/test/AD/415594_79.jpeg \n", + " inflating: AD_NC/test/AD/415594_80.jpeg \n", + " inflating: AD_NC/test/AD/415594_81.jpeg \n", + " inflating: AD_NC/test/AD/415594_82.jpeg \n", + " inflating: AD_NC/test/AD/415594_83.jpeg \n", + " inflating: AD_NC/test/AD/415594_84.jpeg \n", + " inflating: AD_NC/test/AD/415594_85.jpeg \n", + " inflating: AD_NC/test/AD/415594_86.jpeg \n", + " inflating: AD_NC/test/AD/415594_87.jpeg \n", + " inflating: AD_NC/test/AD/415594_88.jpeg \n", + " inflating: AD_NC/test/AD/415594_89.jpeg \n", + " inflating: AD_NC/test/AD/415594_90.jpeg \n", + " inflating: AD_NC/test/AD/415594_91.jpeg \n", + " inflating: AD_NC/test/AD/415594_92.jpeg \n", + " inflating: AD_NC/test/AD/415594_93.jpeg \n", + " inflating: AD_NC/test/AD/415594_94.jpeg \n", + " inflating: AD_NC/test/AD/415594_95.jpeg \n", + " inflating: AD_NC/test/AD/415594_96.jpeg \n", + " inflating: AD_NC/test/AD/415594_97.jpeg \n", + " inflating: AD_NC/test/AD/416324_78.jpeg \n", + " inflating: AD_NC/test/AD/416324_79.jpeg \n", + " inflating: AD_NC/test/AD/416324_80.jpeg \n", + " inflating: AD_NC/test/AD/416324_81.jpeg \n", + " inflating: AD_NC/test/AD/416324_82.jpeg \n", + " inflating: AD_NC/test/AD/416324_83.jpeg \n", + " inflating: AD_NC/test/AD/416324_84.jpeg \n", + " inflating: AD_NC/test/AD/416324_85.jpeg \n", + " inflating: AD_NC/test/AD/416324_86.jpeg \n", + " inflating: AD_NC/test/AD/416324_87.jpeg \n", + " inflating: AD_NC/test/AD/416324_88.jpeg \n", + " inflating: AD_NC/test/AD/416324_89.jpeg \n", + " inflating: AD_NC/test/AD/416324_90.jpeg \n", + " inflating: AD_NC/test/AD/416324_91.jpeg \n", + " inflating: AD_NC/test/AD/416324_92.jpeg \n", + " inflating: AD_NC/test/AD/416324_93.jpeg \n", + " inflating: AD_NC/test/AD/416324_94.jpeg \n", + " inflating: AD_NC/test/AD/416324_95.jpeg \n", + " inflating: AD_NC/test/AD/416324_96.jpeg \n", + " inflating: AD_NC/test/AD/416324_97.jpeg \n", + " inflating: AD_NC/test/AD/416335_78.jpeg \n", + " inflating: AD_NC/test/AD/416335_79.jpeg \n", + " inflating: AD_NC/test/AD/416335_80.jpeg \n", + " inflating: AD_NC/test/AD/416335_81.jpeg \n", + " inflating: AD_NC/test/AD/416335_82.jpeg \n", + " inflating: AD_NC/test/AD/416335_83.jpeg \n", + " inflating: AD_NC/test/AD/416335_84.jpeg \n", + " inflating: AD_NC/test/AD/416335_85.jpeg \n", + " inflating: AD_NC/test/AD/416335_86.jpeg \n", + " inflating: AD_NC/test/AD/416335_87.jpeg \n", + " inflating: AD_NC/test/AD/416335_88.jpeg \n", + " inflating: AD_NC/test/AD/416335_89.jpeg \n", + " inflating: AD_NC/test/AD/416335_90.jpeg \n", + " inflating: AD_NC/test/AD/416335_91.jpeg \n", + " inflating: AD_NC/test/AD/416335_92.jpeg \n", + " inflating: AD_NC/test/AD/416335_93.jpeg \n", + " inflating: AD_NC/test/AD/416335_94.jpeg \n", + " inflating: AD_NC/test/AD/416335_95.jpeg \n", + " inflating: AD_NC/test/AD/416335_96.jpeg \n", + " inflating: AD_NC/test/AD/416335_97.jpeg \n", + " inflating: AD_NC/test/AD/416701_78.jpeg \n", + " inflating: AD_NC/test/AD/416701_79.jpeg \n", + " inflating: AD_NC/test/AD/416701_80.jpeg \n", + " inflating: AD_NC/test/AD/416701_81.jpeg \n", + " inflating: AD_NC/test/AD/416701_82.jpeg \n", + " inflating: AD_NC/test/AD/416701_83.jpeg \n", + " inflating: AD_NC/test/AD/416701_84.jpeg \n", + " inflating: AD_NC/test/AD/416701_85.jpeg \n", + " inflating: AD_NC/test/AD/416701_86.jpeg \n", + " inflating: AD_NC/test/AD/416701_87.jpeg \n", + " inflating: AD_NC/test/AD/416701_88.jpeg \n", + " inflating: AD_NC/test/AD/416701_89.jpeg \n", + " inflating: AD_NC/test/AD/416701_90.jpeg \n", + " inflating: AD_NC/test/AD/416701_91.jpeg \n", + " inflating: AD_NC/test/AD/416701_92.jpeg \n", + " inflating: AD_NC/test/AD/416701_93.jpeg \n", + " inflating: AD_NC/test/AD/416701_94.jpeg \n", + " inflating: AD_NC/test/AD/416701_95.jpeg \n", + " inflating: AD_NC/test/AD/416701_96.jpeg \n", + " inflating: AD_NC/test/AD/416701_97.jpeg \n", + " inflating: AD_NC/test/AD/416936_100.jpeg \n", + " inflating: AD_NC/test/AD/416936_101.jpeg \n", + " inflating: AD_NC/test/AD/416936_102.jpeg \n", + " inflating: AD_NC/test/AD/416936_103.jpeg \n", + " inflating: AD_NC/test/AD/416936_104.jpeg \n", + " inflating: AD_NC/test/AD/416936_105.jpeg \n", + " inflating: AD_NC/test/AD/416936_106.jpeg \n", + " inflating: AD_NC/test/AD/416936_107.jpeg \n", + " inflating: AD_NC/test/AD/416936_88.jpeg \n", + " inflating: AD_NC/test/AD/416936_89.jpeg \n", + " inflating: AD_NC/test/AD/416936_90.jpeg \n", + " inflating: AD_NC/test/AD/416936_91.jpeg \n", + " inflating: AD_NC/test/AD/416936_92.jpeg \n", + " inflating: AD_NC/test/AD/416936_93.jpeg \n", + " inflating: AD_NC/test/AD/416936_94.jpeg \n", + " inflating: AD_NC/test/AD/416936_95.jpeg \n", + " inflating: AD_NC/test/AD/416936_96.jpeg \n", + " inflating: AD_NC/test/AD/416936_97.jpeg \n", + " inflating: AD_NC/test/AD/416936_98.jpeg \n", + " inflating: AD_NC/test/AD/416936_99.jpeg \n", + " inflating: AD_NC/test/AD/417901_100.jpeg \n", + " inflating: AD_NC/test/AD/417901_101.jpeg \n", + " inflating: AD_NC/test/AD/417901_102.jpeg \n", + " inflating: AD_NC/test/AD/417901_103.jpeg \n", + " inflating: AD_NC/test/AD/417901_104.jpeg \n", + " inflating: AD_NC/test/AD/417901_105.jpeg \n", + " inflating: AD_NC/test/AD/417901_106.jpeg \n", + " inflating: AD_NC/test/AD/417901_107.jpeg \n", + " inflating: AD_NC/test/AD/417901_88.jpeg \n", + " inflating: AD_NC/test/AD/417901_89.jpeg \n", + " inflating: AD_NC/test/AD/417901_90.jpeg \n", + " inflating: AD_NC/test/AD/417901_91.jpeg \n", + " inflating: AD_NC/test/AD/417901_92.jpeg \n", + " inflating: AD_NC/test/AD/417901_93.jpeg \n", + " inflating: AD_NC/test/AD/417901_94.jpeg \n", + " inflating: AD_NC/test/AD/417901_95.jpeg \n", + " inflating: AD_NC/test/AD/417901_96.jpeg \n", + " inflating: AD_NC/test/AD/417901_97.jpeg \n", + " inflating: AD_NC/test/AD/417901_98.jpeg \n", + " inflating: AD_NC/test/AD/417901_99.jpeg \n", + " inflating: AD_NC/test/AD/417905_100.jpeg \n", + " inflating: AD_NC/test/AD/417905_101.jpeg \n", + " inflating: AD_NC/test/AD/417905_102.jpeg \n", + " inflating: AD_NC/test/AD/417905_103.jpeg \n", + " inflating: AD_NC/test/AD/417905_104.jpeg \n", + " inflating: AD_NC/test/AD/417905_105.jpeg \n", + " inflating: AD_NC/test/AD/417905_106.jpeg \n", + " inflating: AD_NC/test/AD/417905_107.jpeg \n", + " inflating: AD_NC/test/AD/417905_88.jpeg \n", + " inflating: AD_NC/test/AD/417905_89.jpeg \n", + " inflating: AD_NC/test/AD/417905_90.jpeg \n", + " inflating: AD_NC/test/AD/417905_91.jpeg \n", + " inflating: AD_NC/test/AD/417905_92.jpeg \n", + " inflating: AD_NC/test/AD/417905_93.jpeg \n", + " inflating: AD_NC/test/AD/417905_94.jpeg \n", + " inflating: AD_NC/test/AD/417905_95.jpeg \n", + " inflating: AD_NC/test/AD/417905_96.jpeg \n", + " inflating: AD_NC/test/AD/417905_97.jpeg \n", + " inflating: AD_NC/test/AD/417905_98.jpeg \n", + " inflating: AD_NC/test/AD/417905_99.jpeg \n", + " inflating: AD_NC/test/AD/420239_100.jpeg \n", + " inflating: AD_NC/test/AD/420239_101.jpeg \n", + " inflating: AD_NC/test/AD/420239_102.jpeg \n", + " inflating: AD_NC/test/AD/420239_103.jpeg \n", + " inflating: AD_NC/test/AD/420239_104.jpeg \n", + " inflating: AD_NC/test/AD/420239_105.jpeg \n", + " inflating: AD_NC/test/AD/420239_106.jpeg \n", + " inflating: AD_NC/test/AD/420239_107.jpeg \n", + " inflating: AD_NC/test/AD/420239_88.jpeg \n", + " inflating: AD_NC/test/AD/420239_89.jpeg \n", + " inflating: AD_NC/test/AD/420239_90.jpeg \n", + " inflating: AD_NC/test/AD/420239_91.jpeg \n", + " inflating: AD_NC/test/AD/420239_92.jpeg \n", + " inflating: AD_NC/test/AD/420239_93.jpeg \n", + " inflating: AD_NC/test/AD/420239_94.jpeg \n", + " inflating: AD_NC/test/AD/420239_95.jpeg \n", + " inflating: AD_NC/test/AD/420239_96.jpeg \n", + " inflating: AD_NC/test/AD/420239_97.jpeg \n", + " inflating: AD_NC/test/AD/420239_98.jpeg \n", + " inflating: AD_NC/test/AD/420239_99.jpeg \n", + " inflating: AD_NC/test/AD/420398_78.jpeg \n", + " inflating: AD_NC/test/AD/420398_79.jpeg \n", + " inflating: AD_NC/test/AD/420398_80.jpeg \n", + " inflating: AD_NC/test/AD/420398_81.jpeg \n", + " inflating: AD_NC/test/AD/420398_82.jpeg \n", + " inflating: AD_NC/test/AD/420398_83.jpeg \n", + " inflating: AD_NC/test/AD/420398_84.jpeg \n", + " inflating: AD_NC/test/AD/420398_85.jpeg \n", + " inflating: AD_NC/test/AD/420398_86.jpeg \n", + " inflating: AD_NC/test/AD/420398_87.jpeg \n", + " inflating: AD_NC/test/AD/420398_88.jpeg \n", + " inflating: AD_NC/test/AD/420398_89.jpeg \n", + " inflating: AD_NC/test/AD/420398_90.jpeg \n", + " inflating: AD_NC/test/AD/420398_91.jpeg \n", + " inflating: AD_NC/test/AD/420398_92.jpeg \n", + " inflating: AD_NC/test/AD/420398_93.jpeg \n", + " inflating: AD_NC/test/AD/420398_94.jpeg \n", + " inflating: AD_NC/test/AD/420398_95.jpeg \n", + " inflating: AD_NC/test/AD/420398_96.jpeg \n", + " inflating: AD_NC/test/AD/420398_97.jpeg \n", + " inflating: AD_NC/test/AD/420404_78.jpeg \n", + " inflating: AD_NC/test/AD/420404_79.jpeg \n", + " inflating: AD_NC/test/AD/420404_80.jpeg \n", + " inflating: AD_NC/test/AD/420404_81.jpeg \n", + " inflating: AD_NC/test/AD/420404_82.jpeg \n", + " inflating: AD_NC/test/AD/420404_83.jpeg \n", + " inflating: AD_NC/test/AD/420404_84.jpeg \n", + " inflating: AD_NC/test/AD/420404_85.jpeg \n", + " inflating: AD_NC/test/AD/420404_86.jpeg \n", + " inflating: AD_NC/test/AD/420404_87.jpeg \n", + " inflating: AD_NC/test/AD/420404_88.jpeg \n", + " inflating: AD_NC/test/AD/420404_89.jpeg \n", + " inflating: AD_NC/test/AD/420404_90.jpeg \n", + " inflating: AD_NC/test/AD/420404_91.jpeg \n", + " inflating: AD_NC/test/AD/420404_92.jpeg \n", + " inflating: AD_NC/test/AD/420404_93.jpeg \n", + " inflating: AD_NC/test/AD/420404_94.jpeg \n", + " inflating: AD_NC/test/AD/420404_95.jpeg \n", + " inflating: AD_NC/test/AD/420404_96.jpeg \n", + " inflating: AD_NC/test/AD/420404_97.jpeg \n", + " inflating: AD_NC/test/AD/421209_100.jpeg \n", + " inflating: AD_NC/test/AD/421209_101.jpeg \n", + " inflating: AD_NC/test/AD/421209_102.jpeg \n", + " inflating: AD_NC/test/AD/421209_103.jpeg \n", + " inflating: AD_NC/test/AD/421209_104.jpeg \n", + " inflating: AD_NC/test/AD/421209_105.jpeg \n", + " inflating: AD_NC/test/AD/421209_106.jpeg \n", + " inflating: AD_NC/test/AD/421209_107.jpeg \n", + " inflating: AD_NC/test/AD/421209_88.jpeg \n", + " inflating: AD_NC/test/AD/421209_89.jpeg \n", + " inflating: AD_NC/test/AD/421209_90.jpeg \n", + " inflating: AD_NC/test/AD/421209_91.jpeg \n", + " inflating: AD_NC/test/AD/421209_92.jpeg \n", + " inflating: AD_NC/test/AD/421209_93.jpeg \n", + " inflating: AD_NC/test/AD/421209_94.jpeg \n", + " inflating: AD_NC/test/AD/421209_95.jpeg \n", + " inflating: AD_NC/test/AD/421209_96.jpeg \n", + " inflating: AD_NC/test/AD/421209_97.jpeg \n", + " inflating: AD_NC/test/AD/421209_98.jpeg \n", + " inflating: AD_NC/test/AD/421209_99.jpeg \n", + " inflating: AD_NC/test/AD/421402_100.jpeg \n", + " inflating: AD_NC/test/AD/421402_101.jpeg \n", + " inflating: AD_NC/test/AD/421402_102.jpeg \n", + " inflating: AD_NC/test/AD/421402_103.jpeg \n", + " inflating: AD_NC/test/AD/421402_104.jpeg \n", + " inflating: AD_NC/test/AD/421402_105.jpeg \n", + " inflating: AD_NC/test/AD/421402_106.jpeg \n", + " inflating: AD_NC/test/AD/421402_107.jpeg \n", + " inflating: AD_NC/test/AD/421402_88.jpeg \n", + " inflating: AD_NC/test/AD/421402_89.jpeg \n", + " inflating: AD_NC/test/AD/421402_90.jpeg \n", + " inflating: AD_NC/test/AD/421402_91.jpeg \n", + " inflating: AD_NC/test/AD/421402_92.jpeg \n", + " inflating: AD_NC/test/AD/421402_93.jpeg \n", + " inflating: AD_NC/test/AD/421402_94.jpeg \n", + " inflating: AD_NC/test/AD/421402_95.jpeg \n", + " inflating: AD_NC/test/AD/421402_96.jpeg \n", + " inflating: AD_NC/test/AD/421402_97.jpeg \n", + " inflating: AD_NC/test/AD/421402_98.jpeg \n", + " inflating: AD_NC/test/AD/421402_99.jpeg \n", + " inflating: AD_NC/test/AD/422625_78.jpeg \n", + " inflating: AD_NC/test/AD/422625_79.jpeg \n", + " inflating: AD_NC/test/AD/422625_80.jpeg \n", + " inflating: AD_NC/test/AD/422625_81.jpeg \n", + " inflating: AD_NC/test/AD/422625_82.jpeg \n", + " inflating: AD_NC/test/AD/422625_83.jpeg \n", + " inflating: AD_NC/test/AD/422625_84.jpeg \n", + " inflating: AD_NC/test/AD/422625_85.jpeg \n", + " inflating: AD_NC/test/AD/422625_86.jpeg \n", + " inflating: AD_NC/test/AD/422625_87.jpeg \n", + " inflating: AD_NC/test/AD/422625_88.jpeg \n", + " inflating: AD_NC/test/AD/422625_89.jpeg \n", + " inflating: AD_NC/test/AD/422625_90.jpeg \n", + " inflating: AD_NC/test/AD/422625_91.jpeg \n", + " inflating: AD_NC/test/AD/422625_92.jpeg \n", + " inflating: AD_NC/test/AD/422625_93.jpeg \n", + " inflating: AD_NC/test/AD/422625_94.jpeg \n", + " inflating: AD_NC/test/AD/422625_95.jpeg \n", + " inflating: AD_NC/test/AD/422625_96.jpeg \n", + " inflating: AD_NC/test/AD/422625_97.jpeg \n", + " inflating: AD_NC/test/AD/422626_78.jpeg \n", + " inflating: AD_NC/test/AD/422626_79.jpeg \n", + " inflating: AD_NC/test/AD/422626_80.jpeg \n", + " inflating: AD_NC/test/AD/422626_81.jpeg \n", + " inflating: AD_NC/test/AD/422626_82.jpeg \n", + " inflating: AD_NC/test/AD/422626_83.jpeg \n", + " inflating: AD_NC/test/AD/422626_84.jpeg \n", + " inflating: AD_NC/test/AD/422626_85.jpeg \n", + " inflating: AD_NC/test/AD/422626_86.jpeg \n", + " inflating: AD_NC/test/AD/422626_87.jpeg \n", + " inflating: AD_NC/test/AD/422626_88.jpeg \n", + " inflating: AD_NC/test/AD/422626_89.jpeg \n", + " inflating: AD_NC/test/AD/422626_90.jpeg \n", + " inflating: AD_NC/test/AD/422626_91.jpeg \n", + " inflating: AD_NC/test/AD/422626_92.jpeg \n", + " inflating: AD_NC/test/AD/422626_93.jpeg \n", + " inflating: AD_NC/test/AD/422626_94.jpeg \n", + " inflating: AD_NC/test/AD/422626_95.jpeg \n", + " inflating: AD_NC/test/AD/422626_96.jpeg \n", + " inflating: AD_NC/test/AD/422626_97.jpeg \n", + " inflating: AD_NC/test/AD/422736_78.jpeg \n", + " inflating: AD_NC/test/AD/422736_79.jpeg \n", + " inflating: AD_NC/test/AD/422736_80.jpeg \n", + " inflating: AD_NC/test/AD/422736_81.jpeg \n", + " inflating: AD_NC/test/AD/422736_82.jpeg \n", + " inflating: AD_NC/test/AD/422736_83.jpeg \n", + " inflating: AD_NC/test/AD/422736_84.jpeg \n", + " inflating: AD_NC/test/AD/422736_85.jpeg \n", + " inflating: AD_NC/test/AD/422736_86.jpeg \n", + " inflating: AD_NC/test/AD/422736_87.jpeg \n", + " inflating: AD_NC/test/AD/422736_88.jpeg \n", + " inflating: AD_NC/test/AD/422736_89.jpeg \n", + " inflating: AD_NC/test/AD/422736_90.jpeg \n", + " inflating: AD_NC/test/AD/422736_91.jpeg \n", + " inflating: AD_NC/test/AD/422736_92.jpeg \n", + " inflating: AD_NC/test/AD/422736_93.jpeg \n", + " inflating: AD_NC/test/AD/422736_94.jpeg \n", + " inflating: AD_NC/test/AD/422736_95.jpeg \n", + " inflating: AD_NC/test/AD/422736_96.jpeg \n", + " inflating: AD_NC/test/AD/422736_97.jpeg \n", + " inflating: AD_NC/test/AD/422891_75.jpeg \n", + " inflating: AD_NC/test/AD/422891_76.jpeg \n", + " inflating: AD_NC/test/AD/422891_77.jpeg \n", + " inflating: AD_NC/test/AD/422891_78.jpeg \n", + " inflating: AD_NC/test/AD/422891_79.jpeg \n", + " inflating: AD_NC/test/AD/422891_80.jpeg \n", + " inflating: AD_NC/test/AD/422891_81.jpeg \n", + " inflating: AD_NC/test/AD/422891_82.jpeg \n", + " inflating: AD_NC/test/AD/422891_83.jpeg \n", + " inflating: AD_NC/test/AD/422891_84.jpeg \n", + " inflating: AD_NC/test/AD/422891_85.jpeg \n", + " inflating: AD_NC/test/AD/422891_86.jpeg \n", + " inflating: AD_NC/test/AD/422891_87.jpeg \n", + " inflating: AD_NC/test/AD/422891_88.jpeg \n", + " inflating: AD_NC/test/AD/422891_89.jpeg \n", + " inflating: AD_NC/test/AD/422891_90.jpeg \n", + " inflating: AD_NC/test/AD/422891_91.jpeg \n", + " inflating: AD_NC/test/AD/422891_92.jpeg \n", + " inflating: AD_NC/test/AD/422891_93.jpeg \n", + " inflating: AD_NC/test/AD/422891_94.jpeg \n", + " inflating: AD_NC/test/AD/423655_75.jpeg \n", + " inflating: AD_NC/test/AD/423655_76.jpeg \n", + " inflating: AD_NC/test/AD/423655_77.jpeg \n", + " inflating: AD_NC/test/AD/423655_78.jpeg \n", + " inflating: AD_NC/test/AD/423655_79.jpeg \n", + " inflating: AD_NC/test/AD/423655_80.jpeg \n", + " inflating: AD_NC/test/AD/423655_81.jpeg \n", + " inflating: AD_NC/test/AD/423655_82.jpeg \n", + " inflating: AD_NC/test/AD/423655_83.jpeg \n", + " inflating: AD_NC/test/AD/423655_84.jpeg \n", + " inflating: AD_NC/test/AD/423655_85.jpeg \n", + " inflating: AD_NC/test/AD/423655_86.jpeg \n", + " inflating: AD_NC/test/AD/423655_87.jpeg \n", + " inflating: AD_NC/test/AD/423655_88.jpeg \n", + " inflating: AD_NC/test/AD/423655_89.jpeg \n", + " inflating: AD_NC/test/AD/423655_90.jpeg \n", + " inflating: AD_NC/test/AD/423655_91.jpeg \n", + " inflating: AD_NC/test/AD/423655_92.jpeg \n", + " inflating: AD_NC/test/AD/423655_93.jpeg \n", + " inflating: AD_NC/test/AD/423655_94.jpeg \n", + " inflating: AD_NC/test/AD/423659_75.jpeg \n", + " inflating: AD_NC/test/AD/423659_76.jpeg \n", + " inflating: AD_NC/test/AD/423659_77.jpeg \n", + " inflating: AD_NC/test/AD/423659_78.jpeg \n", + " inflating: AD_NC/test/AD/423659_79.jpeg \n", + " inflating: AD_NC/test/AD/423659_80.jpeg \n", + " inflating: AD_NC/test/AD/423659_81.jpeg \n", + " inflating: AD_NC/test/AD/423659_82.jpeg \n", + " inflating: AD_NC/test/AD/423659_83.jpeg \n", + " inflating: AD_NC/test/AD/423659_84.jpeg \n", + " inflating: AD_NC/test/AD/423659_85.jpeg \n", + " inflating: AD_NC/test/AD/423659_86.jpeg \n", + " inflating: AD_NC/test/AD/423659_87.jpeg \n", + " inflating: AD_NC/test/AD/423659_88.jpeg \n", + " inflating: AD_NC/test/AD/423659_89.jpeg \n", + " inflating: AD_NC/test/AD/423659_90.jpeg \n", + " inflating: AD_NC/test/AD/423659_91.jpeg \n", + " inflating: AD_NC/test/AD/423659_92.jpeg \n", + " inflating: AD_NC/test/AD/423659_93.jpeg \n", + " inflating: AD_NC/test/AD/423659_94.jpeg \n", + " inflating: AD_NC/test/AD/423923_75.jpeg \n", + " inflating: AD_NC/test/AD/423923_76.jpeg \n", + " inflating: AD_NC/test/AD/423923_77.jpeg \n", + " inflating: AD_NC/test/AD/423923_78.jpeg \n", + " inflating: AD_NC/test/AD/423923_79.jpeg \n", + " inflating: AD_NC/test/AD/423923_80.jpeg \n", + " inflating: AD_NC/test/AD/423923_81.jpeg \n", + " inflating: AD_NC/test/AD/423923_82.jpeg \n", + " inflating: AD_NC/test/AD/423923_83.jpeg \n", + " inflating: AD_NC/test/AD/423923_84.jpeg \n", + " inflating: AD_NC/test/AD/423923_85.jpeg \n", + " inflating: AD_NC/test/AD/423923_86.jpeg \n", + " inflating: AD_NC/test/AD/423923_87.jpeg \n", + " inflating: AD_NC/test/AD/423923_88.jpeg \n", + " inflating: AD_NC/test/AD/423923_89.jpeg \n", + " inflating: AD_NC/test/AD/423923_90.jpeg \n", + " inflating: AD_NC/test/AD/423923_91.jpeg \n", + " inflating: AD_NC/test/AD/423923_92.jpeg \n", + " inflating: AD_NC/test/AD/423923_93.jpeg \n", + " inflating: AD_NC/test/AD/423923_94.jpeg \n", + " inflating: AD_NC/test/AD/424228_78.jpeg \n", + " inflating: AD_NC/test/AD/424228_79.jpeg \n", + " inflating: AD_NC/test/AD/424228_80.jpeg \n", + " inflating: AD_NC/test/AD/424228_81.jpeg \n", + " inflating: AD_NC/test/AD/424228_82.jpeg \n", + " inflating: AD_NC/test/AD/424228_83.jpeg \n", + " inflating: AD_NC/test/AD/424228_84.jpeg \n", + " inflating: AD_NC/test/AD/424228_85.jpeg \n", + " inflating: AD_NC/test/AD/424228_86.jpeg \n", + " inflating: AD_NC/test/AD/424228_87.jpeg \n", + " inflating: AD_NC/test/AD/424228_88.jpeg \n", + " inflating: AD_NC/test/AD/424228_89.jpeg \n", + " inflating: AD_NC/test/AD/424228_90.jpeg \n", + " inflating: AD_NC/test/AD/424228_91.jpeg \n", + " inflating: AD_NC/test/AD/424228_92.jpeg \n", + " inflating: AD_NC/test/AD/424228_93.jpeg \n", + " inflating: AD_NC/test/AD/424228_94.jpeg \n", + " inflating: AD_NC/test/AD/424228_95.jpeg \n", + " inflating: AD_NC/test/AD/424228_96.jpeg \n", + " inflating: AD_NC/test/AD/424228_97.jpeg \n", + " inflating: AD_NC/test/AD/424234_78.jpeg \n", + " inflating: AD_NC/test/AD/424234_79.jpeg \n", + " inflating: AD_NC/test/AD/424234_80.jpeg \n", + " inflating: AD_NC/test/AD/424234_81.jpeg \n", + " inflating: AD_NC/test/AD/424234_82.jpeg \n", + " inflating: AD_NC/test/AD/424234_83.jpeg \n", + " inflating: AD_NC/test/AD/424234_84.jpeg \n", + " inflating: AD_NC/test/AD/424234_85.jpeg \n", + " inflating: AD_NC/test/AD/424234_86.jpeg \n", + " inflating: AD_NC/test/AD/424234_87.jpeg \n", + " inflating: AD_NC/test/AD/424234_88.jpeg \n", + " inflating: AD_NC/test/AD/424234_89.jpeg \n", + " inflating: AD_NC/test/AD/424234_90.jpeg \n", + " inflating: AD_NC/test/AD/424234_91.jpeg \n", + " inflating: AD_NC/test/AD/424234_92.jpeg \n", + " inflating: AD_NC/test/AD/424234_93.jpeg \n", + " inflating: AD_NC/test/AD/424234_94.jpeg \n", + " inflating: AD_NC/test/AD/424234_95.jpeg \n", + " inflating: AD_NC/test/AD/424234_96.jpeg \n", + " inflating: AD_NC/test/AD/424234_97.jpeg \n", + " inflating: AD_NC/test/AD/424525_78.jpeg \n", + " inflating: AD_NC/test/AD/424525_79.jpeg \n", + " inflating: AD_NC/test/AD/424525_80.jpeg \n", + " inflating: AD_NC/test/AD/424525_81.jpeg \n", + " inflating: AD_NC/test/AD/424525_82.jpeg \n", + " inflating: AD_NC/test/AD/424525_83.jpeg \n", + " inflating: AD_NC/test/AD/424525_84.jpeg \n", + " inflating: AD_NC/test/AD/424525_85.jpeg \n", + " inflating: AD_NC/test/AD/424525_86.jpeg \n", + " inflating: AD_NC/test/AD/424525_87.jpeg \n", + " inflating: AD_NC/test/AD/424525_88.jpeg \n", + " inflating: AD_NC/test/AD/424525_89.jpeg \n", + " inflating: AD_NC/test/AD/424525_90.jpeg \n", + " inflating: AD_NC/test/AD/424525_91.jpeg \n", + " inflating: AD_NC/test/AD/424525_92.jpeg \n", + " inflating: AD_NC/test/AD/424525_93.jpeg \n", + " inflating: AD_NC/test/AD/424525_94.jpeg \n", + " inflating: AD_NC/test/AD/424525_95.jpeg \n", + " inflating: AD_NC/test/AD/424525_96.jpeg \n", + " inflating: AD_NC/test/AD/424525_97.jpeg \n", + " inflating: AD_NC/test/AD/424528_78.jpeg \n", + " inflating: AD_NC/test/AD/424528_79.jpeg \n", + " inflating: AD_NC/test/AD/424528_80.jpeg \n", + " inflating: AD_NC/test/AD/424528_81.jpeg \n", + " inflating: AD_NC/test/AD/424528_82.jpeg \n", + " inflating: AD_NC/test/AD/424528_83.jpeg \n", + " inflating: AD_NC/test/AD/424528_84.jpeg \n", + " inflating: AD_NC/test/AD/424528_85.jpeg \n", + " inflating: AD_NC/test/AD/424528_86.jpeg \n", + " inflating: AD_NC/test/AD/424528_87.jpeg \n", + " inflating: AD_NC/test/AD/424528_88.jpeg \n", + " inflating: AD_NC/test/AD/424528_89.jpeg \n", + " inflating: AD_NC/test/AD/424528_90.jpeg \n", + " inflating: AD_NC/test/AD/424528_91.jpeg \n", + " inflating: AD_NC/test/AD/424528_92.jpeg \n", + " inflating: AD_NC/test/AD/424528_93.jpeg \n", + " inflating: AD_NC/test/AD/424528_94.jpeg \n", + " inflating: AD_NC/test/AD/424528_95.jpeg \n", + " inflating: AD_NC/test/AD/424528_96.jpeg \n", + " inflating: AD_NC/test/AD/424528_97.jpeg \n", + " inflating: AD_NC/test/AD/498565_78.jpeg \n", + " inflating: AD_NC/test/AD/498565_79.jpeg \n", + " inflating: AD_NC/test/AD/498565_80.jpeg \n", + " inflating: AD_NC/test/AD/498565_81.jpeg \n", + " inflating: AD_NC/test/AD/498565_82.jpeg \n", + " inflating: AD_NC/test/AD/498565_83.jpeg \n", + " inflating: AD_NC/test/AD/498565_84.jpeg \n", + " inflating: AD_NC/test/AD/498565_85.jpeg \n", + " inflating: AD_NC/test/AD/498565_86.jpeg \n", + " inflating: AD_NC/test/AD/498565_87.jpeg \n", + " inflating: AD_NC/test/AD/498565_88.jpeg \n", + " inflating: AD_NC/test/AD/498565_89.jpeg \n", + " inflating: AD_NC/test/AD/498565_90.jpeg \n", + " inflating: AD_NC/test/AD/498565_91.jpeg \n", + " inflating: AD_NC/test/AD/498565_92.jpeg \n", + " inflating: AD_NC/test/AD/498565_93.jpeg \n", + " inflating: AD_NC/test/AD/498565_94.jpeg \n", + " inflating: AD_NC/test/AD/498565_95.jpeg \n", + " inflating: AD_NC/test/AD/498565_96.jpeg \n", + " inflating: AD_NC/test/AD/498565_97.jpeg \n", + " inflating: AD_NC/test/AD/505732_78.jpeg \n", + " inflating: AD_NC/test/AD/505732_79.jpeg \n", + " inflating: AD_NC/test/AD/505732_80.jpeg \n", + " inflating: AD_NC/test/AD/505732_81.jpeg \n", + " inflating: AD_NC/test/AD/505732_82.jpeg \n", + " inflating: AD_NC/test/AD/505732_83.jpeg \n", + " inflating: AD_NC/test/AD/505732_84.jpeg \n", + " inflating: AD_NC/test/AD/505732_85.jpeg \n", + " inflating: AD_NC/test/AD/505732_86.jpeg \n", + " inflating: AD_NC/test/AD/505732_87.jpeg \n", + " inflating: AD_NC/test/AD/505732_88.jpeg \n", + " inflating: AD_NC/test/AD/505732_89.jpeg \n", + " inflating: AD_NC/test/AD/505732_90.jpeg \n", + " inflating: AD_NC/test/AD/505732_91.jpeg \n", + " inflating: AD_NC/test/AD/505732_92.jpeg \n", + " inflating: AD_NC/test/AD/505732_93.jpeg \n", + " inflating: AD_NC/test/AD/505732_94.jpeg \n", + " inflating: AD_NC/test/AD/505732_95.jpeg \n", + " inflating: AD_NC/test/AD/505732_96.jpeg \n", + " inflating: AD_NC/test/AD/505732_97.jpeg \n", + " inflating: AD_NC/test/AD/505735_78.jpeg \n", + " inflating: AD_NC/test/AD/505735_79.jpeg \n", + " inflating: AD_NC/test/AD/505735_80.jpeg \n", + " inflating: AD_NC/test/AD/505735_81.jpeg \n", + " inflating: AD_NC/test/AD/505735_82.jpeg \n", + " inflating: AD_NC/test/AD/505735_83.jpeg \n", + " inflating: AD_NC/test/AD/505735_84.jpeg \n", + " inflating: AD_NC/test/AD/505735_85.jpeg \n", + " inflating: AD_NC/test/AD/505735_86.jpeg \n", + " inflating: AD_NC/test/AD/505735_87.jpeg \n", + " inflating: AD_NC/test/AD/505735_88.jpeg \n", + " inflating: AD_NC/test/AD/505735_89.jpeg \n", + " inflating: AD_NC/test/AD/505735_90.jpeg \n", + " inflating: AD_NC/test/AD/505735_91.jpeg \n", + " inflating: AD_NC/test/AD/505735_92.jpeg \n", + " inflating: AD_NC/test/AD/505735_93.jpeg \n", + " inflating: AD_NC/test/AD/505735_94.jpeg \n", + " inflating: AD_NC/test/AD/505735_95.jpeg \n", + " inflating: AD_NC/test/AD/505735_96.jpeg \n", + " inflating: AD_NC/test/AD/505735_97.jpeg \n", + " inflating: AD_NC/test/AD/525734_100.jpeg \n", + " inflating: AD_NC/test/AD/525734_101.jpeg \n", + " inflating: AD_NC/test/AD/525734_102.jpeg \n", + " inflating: AD_NC/test/AD/525734_103.jpeg \n", + " inflating: AD_NC/test/AD/525734_104.jpeg \n", + " inflating: AD_NC/test/AD/525734_105.jpeg \n", + " inflating: AD_NC/test/AD/525734_106.jpeg \n", + " inflating: AD_NC/test/AD/525734_107.jpeg \n", + " inflating: AD_NC/test/AD/525734_88.jpeg \n", + " inflating: AD_NC/test/AD/525734_89.jpeg \n", + " inflating: AD_NC/test/AD/525734_90.jpeg \n", + " inflating: AD_NC/test/AD/525734_91.jpeg \n", + " inflating: AD_NC/test/AD/525734_92.jpeg \n", + " inflating: AD_NC/test/AD/525734_93.jpeg \n", + " inflating: AD_NC/test/AD/525734_94.jpeg \n", + " inflating: AD_NC/test/AD/525734_95.jpeg \n", + " inflating: AD_NC/test/AD/525734_96.jpeg \n", + " inflating: AD_NC/test/AD/525734_97.jpeg \n", + " inflating: AD_NC/test/AD/525734_98.jpeg \n", + " inflating: AD_NC/test/AD/525734_99.jpeg \n", + " inflating: AD_NC/test/AD/844180_78.jpeg \n", + " inflating: AD_NC/test/AD/844180_79.jpeg \n", + " inflating: AD_NC/test/AD/844180_80.jpeg \n", + " inflating: AD_NC/test/AD/844180_81.jpeg \n", + " inflating: AD_NC/test/AD/844180_82.jpeg \n", + " inflating: AD_NC/test/AD/844180_83.jpeg \n", + " inflating: AD_NC/test/AD/844180_84.jpeg \n", + " inflating: AD_NC/test/AD/844180_85.jpeg \n", + " inflating: AD_NC/test/AD/844180_86.jpeg \n", + " inflating: AD_NC/test/AD/844180_87.jpeg \n", + " inflating: AD_NC/test/AD/844180_88.jpeg \n", + " inflating: AD_NC/test/AD/844180_89.jpeg \n", + " inflating: AD_NC/test/AD/844180_90.jpeg \n", + " inflating: AD_NC/test/AD/844180_91.jpeg \n", + " inflating: AD_NC/test/AD/844180_92.jpeg \n", + " inflating: AD_NC/test/AD/844180_93.jpeg \n", + " inflating: AD_NC/test/AD/844180_94.jpeg \n", + " inflating: AD_NC/test/AD/844180_95.jpeg \n", + " inflating: AD_NC/test/AD/844180_96.jpeg \n", + " inflating: AD_NC/test/AD/844180_97.jpeg \n", + " inflating: AD_NC/test/AD/844181_78.jpeg \n", + " inflating: AD_NC/test/AD/844181_79.jpeg \n", + " inflating: AD_NC/test/AD/844181_80.jpeg \n", + " inflating: AD_NC/test/AD/844181_81.jpeg \n", + " inflating: AD_NC/test/AD/844181_82.jpeg \n", + " inflating: AD_NC/test/AD/844181_83.jpeg \n", + " inflating: AD_NC/test/AD/844181_84.jpeg \n", + " inflating: AD_NC/test/AD/844181_85.jpeg \n", + " inflating: AD_NC/test/AD/844181_86.jpeg \n", + " inflating: AD_NC/test/AD/844181_87.jpeg \n", + " inflating: AD_NC/test/AD/844181_88.jpeg \n", + " inflating: AD_NC/test/AD/844181_89.jpeg \n", + " inflating: AD_NC/test/AD/844181_90.jpeg \n", + " inflating: AD_NC/test/AD/844181_91.jpeg \n", + " inflating: AD_NC/test/AD/844181_92.jpeg \n", + " inflating: AD_NC/test/AD/844181_93.jpeg \n", + " inflating: AD_NC/test/AD/844181_94.jpeg \n", + " inflating: AD_NC/test/AD/844181_95.jpeg \n", + " inflating: AD_NC/test/AD/844181_96.jpeg \n", + " inflating: AD_NC/test/AD/844181_97.jpeg \n", + " inflating: AD_NC/test/AD/879209_78.jpeg \n", + " inflating: AD_NC/test/AD/879209_79.jpeg \n", + " inflating: AD_NC/test/AD/879209_80.jpeg \n", + " inflating: AD_NC/test/AD/879209_81.jpeg \n", + " inflating: AD_NC/test/AD/879209_82.jpeg \n", + " inflating: AD_NC/test/AD/879209_83.jpeg \n", + " inflating: AD_NC/test/AD/879209_84.jpeg \n", + " inflating: AD_NC/test/AD/879209_85.jpeg \n", + " inflating: AD_NC/test/AD/879209_86.jpeg \n", + " inflating: AD_NC/test/AD/879209_87.jpeg \n", + " inflating: AD_NC/test/AD/879209_88.jpeg \n", + " inflating: AD_NC/test/AD/879209_89.jpeg \n", + " inflating: AD_NC/test/AD/879209_90.jpeg \n", + " inflating: AD_NC/test/AD/879209_91.jpeg \n", + " inflating: AD_NC/test/AD/879209_92.jpeg \n", + " inflating: AD_NC/test/AD/879209_93.jpeg \n", + " inflating: AD_NC/test/AD/879209_94.jpeg \n", + " inflating: AD_NC/test/AD/879209_95.jpeg \n", + " inflating: AD_NC/test/AD/879209_96.jpeg \n", + " inflating: AD_NC/test/AD/879209_97.jpeg \n", + " inflating: AD_NC/test/AD/879215_78.jpeg \n", + " inflating: AD_NC/test/AD/879215_79.jpeg \n", + " inflating: AD_NC/test/AD/879215_80.jpeg \n", + " inflating: AD_NC/test/AD/879215_81.jpeg \n", + " inflating: AD_NC/test/AD/879215_82.jpeg \n", + " inflating: AD_NC/test/AD/879215_83.jpeg \n", + " inflating: AD_NC/test/AD/879215_84.jpeg \n", + " inflating: AD_NC/test/AD/879215_85.jpeg \n", + " inflating: AD_NC/test/AD/879215_86.jpeg \n", + " inflating: AD_NC/test/AD/879215_87.jpeg \n", + " inflating: AD_NC/test/AD/879215_88.jpeg \n", + " inflating: AD_NC/test/AD/879215_89.jpeg \n", + " inflating: AD_NC/test/AD/879215_90.jpeg \n", + " inflating: AD_NC/test/AD/879215_91.jpeg \n", + " inflating: AD_NC/test/AD/879215_92.jpeg \n", + " inflating: AD_NC/test/AD/879215_93.jpeg \n", + " inflating: AD_NC/test/AD/879215_94.jpeg \n", + " inflating: AD_NC/test/AD/879215_95.jpeg \n", + " inflating: AD_NC/test/AD/879215_96.jpeg \n", + " inflating: AD_NC/test/AD/879215_97.jpeg \n", + " inflating: AD_NC/test/AD/895578_100.jpeg \n", + " inflating: AD_NC/test/AD/895578_101.jpeg \n", + " inflating: AD_NC/test/AD/895578_102.jpeg \n", + " inflating: AD_NC/test/AD/895578_103.jpeg \n", + " inflating: AD_NC/test/AD/895578_104.jpeg \n", + " inflating: AD_NC/test/AD/895578_105.jpeg \n", + " inflating: AD_NC/test/AD/895578_106.jpeg \n", + " inflating: AD_NC/test/AD/895578_107.jpeg \n", + " inflating: AD_NC/test/AD/895578_108.jpeg \n", + " inflating: AD_NC/test/AD/895578_109.jpeg \n", + " inflating: AD_NC/test/AD/895578_110.jpeg \n", + " inflating: AD_NC/test/AD/895578_111.jpeg \n", + " inflating: AD_NC/test/AD/895578_112.jpeg \n", + " inflating: AD_NC/test/AD/895578_113.jpeg \n", + " inflating: AD_NC/test/AD/895578_114.jpeg \n", + " inflating: AD_NC/test/AD/895578_95.jpeg \n", + " inflating: AD_NC/test/AD/895578_96.jpeg \n", + " inflating: AD_NC/test/AD/895578_97.jpeg \n", + " inflating: AD_NC/test/AD/895578_98.jpeg \n", + " inflating: AD_NC/test/AD/895578_99.jpeg \n", + " inflating: AD_NC/test/AD/895579_100.jpeg \n", + " inflating: AD_NC/test/AD/895579_101.jpeg \n", + " inflating: AD_NC/test/AD/895579_102.jpeg \n", + " inflating: AD_NC/test/AD/895579_103.jpeg \n", + " inflating: AD_NC/test/AD/895579_104.jpeg \n", + " inflating: AD_NC/test/AD/895579_105.jpeg \n", + " inflating: AD_NC/test/AD/895579_106.jpeg \n", + " inflating: AD_NC/test/AD/895579_107.jpeg \n", + " inflating: AD_NC/test/AD/895579_108.jpeg \n", + " inflating: AD_NC/test/AD/895579_109.jpeg \n", + " inflating: AD_NC/test/AD/895579_110.jpeg \n", + " inflating: AD_NC/test/AD/895579_111.jpeg \n", + " inflating: AD_NC/test/AD/895579_112.jpeg \n", + " inflating: AD_NC/test/AD/895579_113.jpeg \n", + " inflating: AD_NC/test/AD/895579_114.jpeg \n", + " inflating: AD_NC/test/AD/895579_95.jpeg \n", + " inflating: AD_NC/test/AD/895579_96.jpeg \n", + " inflating: AD_NC/test/AD/895579_97.jpeg \n", + " inflating: AD_NC/test/AD/895579_98.jpeg \n", + " inflating: AD_NC/test/AD/895579_99.jpeg \n", + " inflating: AD_NC/test/AD/898881_100.jpeg \n", + " inflating: AD_NC/test/AD/898881_101.jpeg \n", + " inflating: AD_NC/test/AD/898881_102.jpeg \n", + " inflating: AD_NC/test/AD/898881_103.jpeg \n", + " inflating: AD_NC/test/AD/898881_104.jpeg \n", + " inflating: AD_NC/test/AD/898881_105.jpeg \n", + " inflating: AD_NC/test/AD/898881_106.jpeg \n", + " inflating: AD_NC/test/AD/898881_107.jpeg \n", + " inflating: AD_NC/test/AD/898881_108.jpeg \n", + " inflating: AD_NC/test/AD/898881_109.jpeg \n", + " inflating: AD_NC/test/AD/898881_110.jpeg \n", + " inflating: AD_NC/test/AD/898881_111.jpeg \n", + " inflating: AD_NC/test/AD/898881_112.jpeg \n", + " inflating: AD_NC/test/AD/898881_113.jpeg \n", + " inflating: AD_NC/test/AD/898881_94.jpeg \n", + " inflating: AD_NC/test/AD/898881_95.jpeg \n", + " inflating: AD_NC/test/AD/898881_96.jpeg \n", + " inflating: AD_NC/test/AD/898881_97.jpeg \n", + " inflating: AD_NC/test/AD/898881_98.jpeg \n", + " inflating: AD_NC/test/AD/898881_99.jpeg \n", + " inflating: AD_NC/test/AD/973656_78.jpeg \n", + " inflating: AD_NC/test/AD/973656_79.jpeg \n", + " inflating: AD_NC/test/AD/973656_80.jpeg \n", + " inflating: AD_NC/test/AD/973656_81.jpeg \n", + " inflating: AD_NC/test/AD/973656_82.jpeg \n", + " inflating: AD_NC/test/AD/973656_83.jpeg \n", + " inflating: AD_NC/test/AD/973656_84.jpeg \n", + " inflating: AD_NC/test/AD/973656_85.jpeg \n", + " inflating: AD_NC/test/AD/973656_86.jpeg \n", + " inflating: AD_NC/test/AD/973656_87.jpeg \n", + " inflating: AD_NC/test/AD/973656_88.jpeg \n", + " inflating: AD_NC/test/AD/973656_89.jpeg \n", + " inflating: AD_NC/test/AD/973656_90.jpeg \n", + " inflating: AD_NC/test/AD/973656_91.jpeg \n", + " inflating: AD_NC/test/AD/973656_92.jpeg \n", + " inflating: AD_NC/test/AD/973656_93.jpeg \n", + " inflating: AD_NC/test/AD/973656_94.jpeg \n", + " inflating: AD_NC/test/AD/973656_95.jpeg \n", + " inflating: AD_NC/test/AD/973656_96.jpeg \n", + " inflating: AD_NC/test/AD/973656_97.jpeg \n", + " inflating: AD_NC/test/AD/985197_100.jpeg \n", + " inflating: AD_NC/test/AD/985197_101.jpeg \n", + " inflating: AD_NC/test/AD/985197_102.jpeg \n", + " inflating: AD_NC/test/AD/985197_103.jpeg \n", + " inflating: AD_NC/test/AD/985197_104.jpeg \n", + " inflating: AD_NC/test/AD/985197_105.jpeg \n", + " inflating: AD_NC/test/AD/985197_106.jpeg \n", + " inflating: AD_NC/test/AD/985197_107.jpeg \n", + " inflating: AD_NC/test/AD/985197_108.jpeg \n", + " inflating: AD_NC/test/AD/985197_109.jpeg \n", + " inflating: AD_NC/test/AD/985197_110.jpeg \n", + " inflating: AD_NC/test/AD/985197_111.jpeg \n", + " inflating: AD_NC/test/AD/985197_112.jpeg \n", + " inflating: AD_NC/test/AD/985197_113.jpeg \n", + " inflating: AD_NC/test/AD/985197_94.jpeg \n", + " inflating: AD_NC/test/AD/985197_95.jpeg \n", + " inflating: AD_NC/test/AD/985197_96.jpeg \n", + " inflating: AD_NC/test/AD/985197_97.jpeg \n", + " inflating: AD_NC/test/AD/985197_98.jpeg \n", + " inflating: AD_NC/test/AD/985197_99.jpeg \n", + " inflating: AD_NC/test/AD/991768_100.jpeg \n", + " inflating: AD_NC/test/AD/991768_101.jpeg \n", + " inflating: AD_NC/test/AD/991768_102.jpeg \n", + " inflating: AD_NC/test/AD/991768_103.jpeg \n", + " inflating: AD_NC/test/AD/991768_104.jpeg \n", + " inflating: AD_NC/test/AD/991768_105.jpeg \n", + " inflating: AD_NC/test/AD/991768_106.jpeg \n", + " inflating: AD_NC/test/AD/991768_107.jpeg \n", + " inflating: AD_NC/test/AD/991768_88.jpeg \n", + " inflating: AD_NC/test/AD/991768_89.jpeg \n", + " inflating: AD_NC/test/AD/991768_90.jpeg \n", + " inflating: AD_NC/test/AD/991768_91.jpeg \n", + " inflating: AD_NC/test/AD/991768_92.jpeg \n", + " inflating: AD_NC/test/AD/991768_93.jpeg \n", + " inflating: AD_NC/test/AD/991768_94.jpeg \n", + " inflating: AD_NC/test/AD/991768_95.jpeg \n", + " inflating: AD_NC/test/AD/991768_96.jpeg \n", + " inflating: AD_NC/test/AD/991768_97.jpeg \n", + " inflating: AD_NC/test/AD/991768_98.jpeg \n", + " inflating: AD_NC/test/AD/991768_99.jpeg \n", + " inflating: AD_NC/test/AD/992628_100.jpeg \n", + " inflating: AD_NC/test/AD/992628_101.jpeg \n", + " inflating: AD_NC/test/AD/992628_102.jpeg \n", + " inflating: AD_NC/test/AD/992628_103.jpeg \n", + " inflating: AD_NC/test/AD/992628_104.jpeg \n", + " inflating: AD_NC/test/AD/992628_105.jpeg \n", + " inflating: AD_NC/test/AD/992628_106.jpeg \n", + " inflating: AD_NC/test/AD/992628_107.jpeg \n", + " inflating: AD_NC/test/AD/992628_108.jpeg \n", + " inflating: AD_NC/test/AD/992628_109.jpeg \n", + " inflating: AD_NC/test/AD/992628_110.jpeg \n", + " inflating: AD_NC/test/AD/992628_111.jpeg \n", + " inflating: AD_NC/test/AD/992628_112.jpeg \n", + " inflating: AD_NC/test/AD/992628_113.jpeg \n", + " inflating: AD_NC/test/AD/992628_94.jpeg \n", + " inflating: AD_NC/test/AD/992628_95.jpeg \n", + " inflating: AD_NC/test/AD/992628_96.jpeg \n", + " inflating: AD_NC/test/AD/992628_97.jpeg \n", + " inflating: AD_NC/test/AD/992628_98.jpeg \n", + " inflating: AD_NC/test/AD/992628_99.jpeg \n", + " inflating: AD_NC/test/AD/992839_100.jpeg \n", + " inflating: AD_NC/test/AD/992839_101.jpeg \n", + " inflating: AD_NC/test/AD/992839_102.jpeg \n", + " inflating: AD_NC/test/AD/992839_103.jpeg \n", + " inflating: AD_NC/test/AD/992839_104.jpeg \n", + " inflating: AD_NC/test/AD/992839_105.jpeg \n", + " inflating: AD_NC/test/AD/992839_106.jpeg \n", + " inflating: AD_NC/test/AD/992839_107.jpeg \n", + " inflating: AD_NC/test/AD/992839_108.jpeg \n", + " inflating: AD_NC/test/AD/992839_109.jpeg \n", + " inflating: AD_NC/test/AD/992839_110.jpeg \n", + " inflating: AD_NC/test/AD/992839_111.jpeg \n", + " inflating: AD_NC/test/AD/992839_112.jpeg \n", + " inflating: AD_NC/test/AD/992839_113.jpeg \n", + " inflating: AD_NC/test/AD/992839_94.jpeg \n", + " inflating: AD_NC/test/AD/992839_95.jpeg \n", + " inflating: AD_NC/test/AD/992839_96.jpeg \n", + " inflating: AD_NC/test/AD/992839_97.jpeg \n", + " inflating: AD_NC/test/AD/992839_98.jpeg \n", + " inflating: AD_NC/test/AD/992839_99.jpeg \n", + " creating: AD_NC/test/NC/\n", + " inflating: AD_NC/test/NC/1182968_100.jpeg \n", + " inflating: AD_NC/test/NC/1182968_101.jpeg \n", + " inflating: AD_NC/test/NC/1182968_102.jpeg \n", + " inflating: AD_NC/test/NC/1182968_103.jpeg \n", + " inflating: AD_NC/test/NC/1182968_104.jpeg \n", + " inflating: AD_NC/test/NC/1182968_105.jpeg \n", + " inflating: AD_NC/test/NC/1182968_106.jpeg \n", + " inflating: AD_NC/test/NC/1182968_107.jpeg \n", + " inflating: AD_NC/test/NC/1182968_108.jpeg \n", + " inflating: AD_NC/test/NC/1182968_109.jpeg \n", + " inflating: AD_NC/test/NC/1182968_110.jpeg \n", + " inflating: AD_NC/test/NC/1182968_111.jpeg \n", + " inflating: AD_NC/test/NC/1182968_112.jpeg \n", + " inflating: AD_NC/test/NC/1182968_113.jpeg \n", + " inflating: AD_NC/test/NC/1182968_94.jpeg \n", + " inflating: AD_NC/test/NC/1182968_95.jpeg \n", + " inflating: AD_NC/test/NC/1182968_96.jpeg \n", + " inflating: AD_NC/test/NC/1182968_97.jpeg \n", + " inflating: AD_NC/test/NC/1182968_98.jpeg \n", + " inflating: AD_NC/test/NC/1182968_99.jpeg \n", + " inflating: AD_NC/test/NC/1185628_100.jpeg \n", + " inflating: AD_NC/test/NC/1185628_101.jpeg \n", + " inflating: AD_NC/test/NC/1185628_102.jpeg \n", + " inflating: AD_NC/test/NC/1185628_103.jpeg \n", + " inflating: AD_NC/test/NC/1185628_104.jpeg \n", + " inflating: AD_NC/test/NC/1185628_105.jpeg \n", + " inflating: AD_NC/test/NC/1185628_106.jpeg \n", + " inflating: AD_NC/test/NC/1185628_107.jpeg \n", + " inflating: AD_NC/test/NC/1185628_88.jpeg \n", + " inflating: AD_NC/test/NC/1185628_89.jpeg \n", + " inflating: AD_NC/test/NC/1185628_90.jpeg \n", + " inflating: AD_NC/test/NC/1185628_91.jpeg \n", + " inflating: AD_NC/test/NC/1185628_92.jpeg \n", + " inflating: AD_NC/test/NC/1185628_93.jpeg \n", + " inflating: AD_NC/test/NC/1185628_94.jpeg \n", + " inflating: AD_NC/test/NC/1185628_95.jpeg \n", + " inflating: AD_NC/test/NC/1185628_96.jpeg \n", + " inflating: AD_NC/test/NC/1185628_97.jpeg \n", + " inflating: AD_NC/test/NC/1185628_98.jpeg \n", + " inflating: AD_NC/test/NC/1185628_99.jpeg \n", + " inflating: AD_NC/test/NC/1185714_100.jpeg \n", + " inflating: AD_NC/test/NC/1185714_101.jpeg \n", + " inflating: AD_NC/test/NC/1185714_102.jpeg \n", + " inflating: AD_NC/test/NC/1185714_103.jpeg \n", + " inflating: AD_NC/test/NC/1185714_104.jpeg \n", + " inflating: AD_NC/test/NC/1185714_105.jpeg \n", + " inflating: AD_NC/test/NC/1185714_106.jpeg \n", + " inflating: AD_NC/test/NC/1185714_107.jpeg \n", + " inflating: AD_NC/test/NC/1185714_88.jpeg \n", + " inflating: AD_NC/test/NC/1185714_89.jpeg \n", + " inflating: AD_NC/test/NC/1185714_90.jpeg \n", + " inflating: AD_NC/test/NC/1185714_91.jpeg \n", + " inflating: AD_NC/test/NC/1185714_92.jpeg \n", + " inflating: AD_NC/test/NC/1185714_93.jpeg \n", + " inflating: AD_NC/test/NC/1185714_94.jpeg \n", + " inflating: AD_NC/test/NC/1185714_95.jpeg \n", + " inflating: AD_NC/test/NC/1185714_96.jpeg \n", + " inflating: AD_NC/test/NC/1185714_97.jpeg \n", + " inflating: AD_NC/test/NC/1185714_98.jpeg \n", + " inflating: AD_NC/test/NC/1185714_99.jpeg \n", + " inflating: AD_NC/test/NC/1186516_100.jpeg \n", + " inflating: AD_NC/test/NC/1186516_101.jpeg \n", + " inflating: AD_NC/test/NC/1186516_102.jpeg \n", + " inflating: AD_NC/test/NC/1186516_103.jpeg \n", + " inflating: AD_NC/test/NC/1186516_104.jpeg \n", + " inflating: AD_NC/test/NC/1186516_105.jpeg \n", + " inflating: AD_NC/test/NC/1186516_106.jpeg \n", + " inflating: AD_NC/test/NC/1186516_107.jpeg \n", + " inflating: AD_NC/test/NC/1186516_88.jpeg \n", + " inflating: AD_NC/test/NC/1186516_89.jpeg \n", + " inflating: AD_NC/test/NC/1186516_90.jpeg \n", + " inflating: AD_NC/test/NC/1186516_91.jpeg \n", + " inflating: AD_NC/test/NC/1186516_92.jpeg \n", + " inflating: AD_NC/test/NC/1186516_93.jpeg \n", + " inflating: AD_NC/test/NC/1186516_94.jpeg \n", + " inflating: AD_NC/test/NC/1186516_95.jpeg \n", + " inflating: AD_NC/test/NC/1186516_96.jpeg \n", + " inflating: AD_NC/test/NC/1186516_97.jpeg \n", + " inflating: AD_NC/test/NC/1186516_98.jpeg \n", + " inflating: AD_NC/test/NC/1186516_99.jpeg \n", + " inflating: AD_NC/test/NC/1186714_100.jpeg \n", + " inflating: AD_NC/test/NC/1186714_101.jpeg \n", + " inflating: AD_NC/test/NC/1186714_102.jpeg \n", + " inflating: AD_NC/test/NC/1186714_103.jpeg \n", + " inflating: AD_NC/test/NC/1186714_104.jpeg \n", + " inflating: AD_NC/test/NC/1186714_105.jpeg \n", + " inflating: AD_NC/test/NC/1186714_106.jpeg \n", + " inflating: AD_NC/test/NC/1186714_107.jpeg \n", + " inflating: AD_NC/test/NC/1186714_108.jpeg \n", + " inflating: AD_NC/test/NC/1186714_109.jpeg \n", + " inflating: AD_NC/test/NC/1186714_110.jpeg \n", + " inflating: AD_NC/test/NC/1186714_111.jpeg \n", + " inflating: AD_NC/test/NC/1186714_112.jpeg \n", + " inflating: AD_NC/test/NC/1186714_113.jpeg \n", + " inflating: AD_NC/test/NC/1186714_94.jpeg \n", + " inflating: AD_NC/test/NC/1186714_95.jpeg \n", + " inflating: AD_NC/test/NC/1186714_96.jpeg \n", + " inflating: AD_NC/test/NC/1186714_97.jpeg \n", + " inflating: AD_NC/test/NC/1186714_98.jpeg \n", + " inflating: AD_NC/test/NC/1186714_99.jpeg \n", + " inflating: AD_NC/test/NC/1186737_100.jpeg \n", + " inflating: AD_NC/test/NC/1186737_101.jpeg \n", + " inflating: AD_NC/test/NC/1186737_102.jpeg \n", + " inflating: AD_NC/test/NC/1186737_103.jpeg \n", + " inflating: AD_NC/test/NC/1186737_104.jpeg \n", + " inflating: AD_NC/test/NC/1186737_105.jpeg \n", + " inflating: AD_NC/test/NC/1186737_106.jpeg \n", + " inflating: AD_NC/test/NC/1186737_107.jpeg \n", + " inflating: AD_NC/test/NC/1186737_108.jpeg \n", + " inflating: AD_NC/test/NC/1186737_109.jpeg \n", + " inflating: AD_NC/test/NC/1186737_110.jpeg \n", + " inflating: AD_NC/test/NC/1186737_111.jpeg \n", + " inflating: AD_NC/test/NC/1186737_112.jpeg \n", + " inflating: AD_NC/test/NC/1186737_113.jpeg \n", + " inflating: AD_NC/test/NC/1186737_94.jpeg \n", + " inflating: AD_NC/test/NC/1186737_95.jpeg \n", + " inflating: AD_NC/test/NC/1186737_96.jpeg \n", + " inflating: AD_NC/test/NC/1186737_97.jpeg \n", + " inflating: AD_NC/test/NC/1186737_98.jpeg \n", + " inflating: AD_NC/test/NC/1186737_99.jpeg \n", + " inflating: AD_NC/test/NC/1188738_100.jpeg \n", + " inflating: AD_NC/test/NC/1188738_101.jpeg \n", + " inflating: AD_NC/test/NC/1188738_102.jpeg \n", + " inflating: AD_NC/test/NC/1188738_103.jpeg \n", + " inflating: AD_NC/test/NC/1188738_104.jpeg \n", + " inflating: AD_NC/test/NC/1188738_105.jpeg \n", + " inflating: AD_NC/test/NC/1188738_106.jpeg \n", + " inflating: AD_NC/test/NC/1188738_107.jpeg \n", + " inflating: AD_NC/test/NC/1188738_108.jpeg \n", + " inflating: AD_NC/test/NC/1188738_109.jpeg \n", + " inflating: AD_NC/test/NC/1188738_110.jpeg \n", + " inflating: AD_NC/test/NC/1188738_111.jpeg \n", + " inflating: AD_NC/test/NC/1188738_112.jpeg \n", + " inflating: AD_NC/test/NC/1188738_113.jpeg \n", + " inflating: AD_NC/test/NC/1188738_94.jpeg \n", + " inflating: AD_NC/test/NC/1188738_95.jpeg \n", + " inflating: AD_NC/test/NC/1188738_96.jpeg \n", + " inflating: AD_NC/test/NC/1188738_97.jpeg \n", + " inflating: AD_NC/test/NC/1188738_98.jpeg \n", + " inflating: AD_NC/test/NC/1188738_99.jpeg \n", + " inflating: AD_NC/test/NC/1189614_100.jpeg \n", + " inflating: AD_NC/test/NC/1189614_101.jpeg \n", + " inflating: AD_NC/test/NC/1189614_102.jpeg \n", + " inflating: AD_NC/test/NC/1189614_103.jpeg \n", + " inflating: AD_NC/test/NC/1189614_104.jpeg \n", + " inflating: AD_NC/test/NC/1189614_105.jpeg \n", + " inflating: AD_NC/test/NC/1189614_106.jpeg \n", + " inflating: AD_NC/test/NC/1189614_107.jpeg \n", + " inflating: AD_NC/test/NC/1189614_108.jpeg \n", + " inflating: AD_NC/test/NC/1189614_109.jpeg \n", + " inflating: AD_NC/test/NC/1189614_110.jpeg \n", + " inflating: AD_NC/test/NC/1189614_111.jpeg \n", + " inflating: AD_NC/test/NC/1189614_112.jpeg \n", + " inflating: AD_NC/test/NC/1189614_113.jpeg \n", + " inflating: AD_NC/test/NC/1189614_114.jpeg \n", + " inflating: AD_NC/test/NC/1189614_95.jpeg \n", + " inflating: AD_NC/test/NC/1189614_96.jpeg \n", + " inflating: AD_NC/test/NC/1189614_97.jpeg \n", + " inflating: AD_NC/test/NC/1189614_98.jpeg \n", + " inflating: AD_NC/test/NC/1189614_99.jpeg \n", + " inflating: AD_NC/test/NC/1189640_100.jpeg \n", + " inflating: AD_NC/test/NC/1189640_101.jpeg \n", + " inflating: AD_NC/test/NC/1189640_102.jpeg \n", + " inflating: AD_NC/test/NC/1189640_103.jpeg \n", + " inflating: AD_NC/test/NC/1189640_104.jpeg \n", + " inflating: AD_NC/test/NC/1189640_105.jpeg \n", + " inflating: AD_NC/test/NC/1189640_106.jpeg \n", + " inflating: AD_NC/test/NC/1189640_107.jpeg \n", + " inflating: AD_NC/test/NC/1189640_88.jpeg \n", + " inflating: AD_NC/test/NC/1189640_89.jpeg \n", + " inflating: AD_NC/test/NC/1189640_90.jpeg \n", + " inflating: AD_NC/test/NC/1189640_91.jpeg \n", + " inflating: AD_NC/test/NC/1189640_92.jpeg \n", + " inflating: AD_NC/test/NC/1189640_93.jpeg \n", + " inflating: AD_NC/test/NC/1189640_94.jpeg \n", + " inflating: AD_NC/test/NC/1189640_95.jpeg \n", + " inflating: AD_NC/test/NC/1189640_96.jpeg \n", + " inflating: AD_NC/test/NC/1189640_97.jpeg \n", + " inflating: AD_NC/test/NC/1189640_98.jpeg \n", + " inflating: AD_NC/test/NC/1189640_99.jpeg \n", + " inflating: AD_NC/test/NC/1190195_100.jpeg \n", + " inflating: AD_NC/test/NC/1190195_101.jpeg \n", + " inflating: AD_NC/test/NC/1190195_102.jpeg \n", + " inflating: AD_NC/test/NC/1190195_103.jpeg \n", + " inflating: AD_NC/test/NC/1190195_104.jpeg \n", + " inflating: AD_NC/test/NC/1190195_105.jpeg \n", + " inflating: AD_NC/test/NC/1190195_106.jpeg \n", + " inflating: AD_NC/test/NC/1190195_107.jpeg \n", + " inflating: AD_NC/test/NC/1190195_88.jpeg \n", + " inflating: AD_NC/test/NC/1190195_89.jpeg \n", + " inflating: AD_NC/test/NC/1190195_90.jpeg \n", + " inflating: AD_NC/test/NC/1190195_91.jpeg \n", + " inflating: AD_NC/test/NC/1190195_92.jpeg \n", + " inflating: AD_NC/test/NC/1190195_93.jpeg \n", + " inflating: AD_NC/test/NC/1190195_94.jpeg \n", + " inflating: AD_NC/test/NC/1190195_95.jpeg \n", + " inflating: AD_NC/test/NC/1190195_96.jpeg \n", + " inflating: AD_NC/test/NC/1190195_97.jpeg \n", + " inflating: AD_NC/test/NC/1190195_98.jpeg \n", + " inflating: AD_NC/test/NC/1190195_99.jpeg \n", + " inflating: AD_NC/test/NC/1190397_100.jpeg \n", + " inflating: AD_NC/test/NC/1190397_101.jpeg \n", + " inflating: AD_NC/test/NC/1190397_102.jpeg \n", + " inflating: AD_NC/test/NC/1190397_103.jpeg \n", + " inflating: AD_NC/test/NC/1190397_104.jpeg \n", + " inflating: AD_NC/test/NC/1190397_105.jpeg \n", + " inflating: AD_NC/test/NC/1190397_106.jpeg \n", + " inflating: AD_NC/test/NC/1190397_107.jpeg \n", + " inflating: AD_NC/test/NC/1190397_108.jpeg \n", + " inflating: AD_NC/test/NC/1190397_109.jpeg \n", + " inflating: AD_NC/test/NC/1190397_110.jpeg \n", + " inflating: AD_NC/test/NC/1190397_111.jpeg \n", + " inflating: AD_NC/test/NC/1190397_112.jpeg \n", + " inflating: AD_NC/test/NC/1190397_113.jpeg \n", + " inflating: AD_NC/test/NC/1190397_114.jpeg \n", + " inflating: AD_NC/test/NC/1190397_95.jpeg \n", + " inflating: AD_NC/test/NC/1190397_96.jpeg \n", + " inflating: AD_NC/test/NC/1190397_97.jpeg \n", + " inflating: AD_NC/test/NC/1190397_98.jpeg \n", + " inflating: AD_NC/test/NC/1190397_99.jpeg \n", + " inflating: AD_NC/test/NC/1190623_100.jpeg \n", + " inflating: AD_NC/test/NC/1190623_101.jpeg \n", + " inflating: AD_NC/test/NC/1190623_102.jpeg \n", + " inflating: AD_NC/test/NC/1190623_103.jpeg \n", + " inflating: AD_NC/test/NC/1190623_104.jpeg \n", + " inflating: AD_NC/test/NC/1190623_105.jpeg \n", + " inflating: AD_NC/test/NC/1190623_106.jpeg \n", + " inflating: AD_NC/test/NC/1190623_107.jpeg \n", + " inflating: AD_NC/test/NC/1190623_108.jpeg \n", + " inflating: AD_NC/test/NC/1190623_109.jpeg \n", + " inflating: AD_NC/test/NC/1190623_110.jpeg \n", + " inflating: AD_NC/test/NC/1190623_111.jpeg \n", + " inflating: AD_NC/test/NC/1190623_112.jpeg \n", + " inflating: AD_NC/test/NC/1190623_113.jpeg \n", + " inflating: AD_NC/test/NC/1190623_94.jpeg \n", + " inflating: AD_NC/test/NC/1190623_95.jpeg \n", + " inflating: AD_NC/test/NC/1190623_96.jpeg \n", + " inflating: AD_NC/test/NC/1190623_97.jpeg \n", + " inflating: AD_NC/test/NC/1190623_98.jpeg \n", + " inflating: AD_NC/test/NC/1190623_99.jpeg \n", + " inflating: AD_NC/test/NC/1191372_100.jpeg \n", + " inflating: AD_NC/test/NC/1191372_101.jpeg \n", + " inflating: AD_NC/test/NC/1191372_102.jpeg \n", + " inflating: AD_NC/test/NC/1191372_103.jpeg \n", + " inflating: AD_NC/test/NC/1191372_104.jpeg \n", + " inflating: AD_NC/test/NC/1191372_105.jpeg \n", + " inflating: AD_NC/test/NC/1191372_106.jpeg \n", + " inflating: AD_NC/test/NC/1191372_107.jpeg \n", + " inflating: AD_NC/test/NC/1191372_108.jpeg \n", + " inflating: AD_NC/test/NC/1191372_109.jpeg \n", + " inflating: AD_NC/test/NC/1191372_110.jpeg \n", + " inflating: AD_NC/test/NC/1191372_111.jpeg \n", + " inflating: AD_NC/test/NC/1191372_112.jpeg \n", + " inflating: AD_NC/test/NC/1191372_113.jpeg \n", + " inflating: AD_NC/test/NC/1191372_94.jpeg \n", + " inflating: AD_NC/test/NC/1191372_95.jpeg \n", + " inflating: AD_NC/test/NC/1191372_96.jpeg \n", + " inflating: AD_NC/test/NC/1191372_97.jpeg \n", + " inflating: AD_NC/test/NC/1191372_98.jpeg \n", + " inflating: AD_NC/test/NC/1191372_99.jpeg \n", + " inflating: AD_NC/test/NC/1192854_100.jpeg \n", + " inflating: AD_NC/test/NC/1192854_101.jpeg \n", + " inflating: AD_NC/test/NC/1192854_102.jpeg \n", + " inflating: AD_NC/test/NC/1192854_103.jpeg \n", + " inflating: AD_NC/test/NC/1192854_104.jpeg \n", + " inflating: AD_NC/test/NC/1192854_105.jpeg \n", + " inflating: AD_NC/test/NC/1192854_106.jpeg \n", + " inflating: AD_NC/test/NC/1192854_107.jpeg \n", + " inflating: AD_NC/test/NC/1192854_108.jpeg \n", + " inflating: AD_NC/test/NC/1192854_109.jpeg \n", + " inflating: AD_NC/test/NC/1192854_110.jpeg \n", + " inflating: AD_NC/test/NC/1192854_111.jpeg \n", + " inflating: AD_NC/test/NC/1192854_112.jpeg \n", + " inflating: AD_NC/test/NC/1192854_113.jpeg \n", + " inflating: AD_NC/test/NC/1192854_94.jpeg \n", + " inflating: AD_NC/test/NC/1192854_95.jpeg \n", + " inflating: AD_NC/test/NC/1192854_96.jpeg \n", + " inflating: AD_NC/test/NC/1192854_97.jpeg \n", + " inflating: AD_NC/test/NC/1192854_98.jpeg \n", + " inflating: AD_NC/test/NC/1192854_99.jpeg \n", + " inflating: AD_NC/test/NC/1193331_100.jpeg \n", + " inflating: AD_NC/test/NC/1193331_101.jpeg \n", + " inflating: AD_NC/test/NC/1193331_102.jpeg \n", + " inflating: AD_NC/test/NC/1193331_103.jpeg \n", + " inflating: AD_NC/test/NC/1193331_104.jpeg \n", + " inflating: AD_NC/test/NC/1193331_105.jpeg \n", + " inflating: AD_NC/test/NC/1193331_106.jpeg \n", + " inflating: AD_NC/test/NC/1193331_107.jpeg \n", + " inflating: AD_NC/test/NC/1193331_108.jpeg \n", + " inflating: AD_NC/test/NC/1193331_109.jpeg \n", + " inflating: AD_NC/test/NC/1193331_110.jpeg \n", + " inflating: AD_NC/test/NC/1193331_111.jpeg \n", + " inflating: AD_NC/test/NC/1193331_112.jpeg \n", + " inflating: AD_NC/test/NC/1193331_113.jpeg \n", + " inflating: AD_NC/test/NC/1193331_94.jpeg \n", + " inflating: AD_NC/test/NC/1193331_95.jpeg \n", + " inflating: AD_NC/test/NC/1193331_96.jpeg \n", + " inflating: AD_NC/test/NC/1193331_97.jpeg \n", + " inflating: AD_NC/test/NC/1193331_98.jpeg \n", + " inflating: AD_NC/test/NC/1193331_99.jpeg \n", + " inflating: AD_NC/test/NC/1193762_100.jpeg \n", + " inflating: AD_NC/test/NC/1193762_101.jpeg \n", + " inflating: AD_NC/test/NC/1193762_102.jpeg \n", + " inflating: AD_NC/test/NC/1193762_103.jpeg \n", + " inflating: AD_NC/test/NC/1193762_104.jpeg \n", + " inflating: AD_NC/test/NC/1193762_105.jpeg \n", + " inflating: AD_NC/test/NC/1193762_106.jpeg \n", + " inflating: AD_NC/test/NC/1193762_107.jpeg \n", + " inflating: AD_NC/test/NC/1193762_108.jpeg \n", + " inflating: AD_NC/test/NC/1193762_109.jpeg \n", + " inflating: AD_NC/test/NC/1193762_110.jpeg \n", + " inflating: AD_NC/test/NC/1193762_111.jpeg \n", + " inflating: AD_NC/test/NC/1193762_112.jpeg \n", + " inflating: AD_NC/test/NC/1193762_113.jpeg \n", + " inflating: AD_NC/test/NC/1193762_94.jpeg \n", + " inflating: AD_NC/test/NC/1193762_95.jpeg \n", + " inflating: AD_NC/test/NC/1193762_96.jpeg \n", + " inflating: AD_NC/test/NC/1193762_97.jpeg \n", + " inflating: AD_NC/test/NC/1193762_98.jpeg \n", + " inflating: AD_NC/test/NC/1193762_99.jpeg \n", + " inflating: AD_NC/test/NC/1193770_100.jpeg \n", + " inflating: AD_NC/test/NC/1193770_101.jpeg \n", + " inflating: AD_NC/test/NC/1193770_102.jpeg \n", + " inflating: AD_NC/test/NC/1193770_103.jpeg \n", + " inflating: AD_NC/test/NC/1193770_104.jpeg \n", + " inflating: AD_NC/test/NC/1193770_105.jpeg \n", + " inflating: AD_NC/test/NC/1193770_106.jpeg \n", + " inflating: AD_NC/test/NC/1193770_107.jpeg \n", + " inflating: AD_NC/test/NC/1193770_108.jpeg \n", + " inflating: AD_NC/test/NC/1193770_109.jpeg \n", + " inflating: AD_NC/test/NC/1193770_110.jpeg \n", + " inflating: AD_NC/test/NC/1193770_111.jpeg \n", + " inflating: AD_NC/test/NC/1193770_112.jpeg \n", + " inflating: AD_NC/test/NC/1193770_113.jpeg \n", + " inflating: AD_NC/test/NC/1193770_94.jpeg \n", + " inflating: AD_NC/test/NC/1193770_95.jpeg \n", + " inflating: AD_NC/test/NC/1193770_96.jpeg \n", + " inflating: AD_NC/test/NC/1193770_97.jpeg \n", + " inflating: AD_NC/test/NC/1193770_98.jpeg \n", + " inflating: AD_NC/test/NC/1193770_99.jpeg \n", + " inflating: AD_NC/test/NC/1194377_78.jpeg \n", + " inflating: AD_NC/test/NC/1194377_79.jpeg \n", + " inflating: AD_NC/test/NC/1194377_80.jpeg \n", + " inflating: AD_NC/test/NC/1194377_81.jpeg \n", + " inflating: AD_NC/test/NC/1194377_82.jpeg \n", + " inflating: AD_NC/test/NC/1194377_83.jpeg \n", + " inflating: AD_NC/test/NC/1194377_84.jpeg \n", + " inflating: AD_NC/test/NC/1194377_85.jpeg \n", + " inflating: AD_NC/test/NC/1194377_86.jpeg \n", + " inflating: AD_NC/test/NC/1194377_87.jpeg \n", + " inflating: AD_NC/test/NC/1194377_88.jpeg \n", + " inflating: AD_NC/test/NC/1194377_89.jpeg \n", + " inflating: AD_NC/test/NC/1194377_90.jpeg \n", + " inflating: AD_NC/test/NC/1194377_91.jpeg \n", + " inflating: AD_NC/test/NC/1194377_92.jpeg \n", + " inflating: AD_NC/test/NC/1194377_93.jpeg \n", + " inflating: AD_NC/test/NC/1194377_94.jpeg \n", + " inflating: AD_NC/test/NC/1194377_95.jpeg \n", + " inflating: AD_NC/test/NC/1194377_96.jpeg \n", + " inflating: AD_NC/test/NC/1194377_97.jpeg \n", + " inflating: AD_NC/test/NC/1195471_100.jpeg \n", + " inflating: AD_NC/test/NC/1195471_101.jpeg \n", + " inflating: AD_NC/test/NC/1195471_102.jpeg \n", + " inflating: AD_NC/test/NC/1195471_103.jpeg \n", + " inflating: AD_NC/test/NC/1195471_104.jpeg \n", + " inflating: AD_NC/test/NC/1195471_105.jpeg \n", + " inflating: AD_NC/test/NC/1195471_106.jpeg \n", + " inflating: AD_NC/test/NC/1195471_107.jpeg \n", + " inflating: AD_NC/test/NC/1195471_108.jpeg \n", + " inflating: AD_NC/test/NC/1195471_109.jpeg \n", + " inflating: AD_NC/test/NC/1195471_110.jpeg \n", + " inflating: AD_NC/test/NC/1195471_111.jpeg \n", + " inflating: AD_NC/test/NC/1195471_112.jpeg \n", + " inflating: AD_NC/test/NC/1195471_113.jpeg \n", + " inflating: AD_NC/test/NC/1195471_114.jpeg \n", + " inflating: AD_NC/test/NC/1195471_95.jpeg \n", + " inflating: AD_NC/test/NC/1195471_96.jpeg \n", + " inflating: AD_NC/test/NC/1195471_97.jpeg \n", + " inflating: AD_NC/test/NC/1195471_98.jpeg \n", + " inflating: AD_NC/test/NC/1195471_99.jpeg \n", + " inflating: AD_NC/test/NC/1195531_100.jpeg \n", + " inflating: AD_NC/test/NC/1195531_101.jpeg \n", + " inflating: AD_NC/test/NC/1195531_102.jpeg \n", + " inflating: AD_NC/test/NC/1195531_103.jpeg \n", + " inflating: AD_NC/test/NC/1195531_104.jpeg \n", + " inflating: AD_NC/test/NC/1195531_105.jpeg \n", + " inflating: AD_NC/test/NC/1195531_106.jpeg \n", + " inflating: AD_NC/test/NC/1195531_107.jpeg \n", + " inflating: AD_NC/test/NC/1195531_88.jpeg \n", + " inflating: AD_NC/test/NC/1195531_89.jpeg \n", + " inflating: AD_NC/test/NC/1195531_90.jpeg \n", + " inflating: AD_NC/test/NC/1195531_91.jpeg \n", + " inflating: AD_NC/test/NC/1195531_92.jpeg \n", + " inflating: AD_NC/test/NC/1195531_93.jpeg \n", + " inflating: AD_NC/test/NC/1195531_94.jpeg \n", + " inflating: AD_NC/test/NC/1195531_95.jpeg \n", + " inflating: AD_NC/test/NC/1195531_96.jpeg \n", + " inflating: AD_NC/test/NC/1195531_97.jpeg \n", + " inflating: AD_NC/test/NC/1195531_98.jpeg \n", + " inflating: AD_NC/test/NC/1195531_99.jpeg \n", + " inflating: AD_NC/test/NC/1195981_100.jpeg \n", + " inflating: AD_NC/test/NC/1195981_101.jpeg \n", + " inflating: AD_NC/test/NC/1195981_102.jpeg \n", + " inflating: AD_NC/test/NC/1195981_103.jpeg \n", + " inflating: AD_NC/test/NC/1195981_104.jpeg \n", + " inflating: AD_NC/test/NC/1195981_105.jpeg \n", + " inflating: AD_NC/test/NC/1195981_106.jpeg \n", + " inflating: AD_NC/test/NC/1195981_107.jpeg \n", + " inflating: AD_NC/test/NC/1195981_108.jpeg \n", + " inflating: AD_NC/test/NC/1195981_109.jpeg \n", + " inflating: AD_NC/test/NC/1195981_110.jpeg \n", + " inflating: AD_NC/test/NC/1195981_111.jpeg \n", + " inflating: AD_NC/test/NC/1195981_112.jpeg \n", + " inflating: AD_NC/test/NC/1195981_113.jpeg \n", + " inflating: AD_NC/test/NC/1195981_94.jpeg \n", + " inflating: AD_NC/test/NC/1195981_95.jpeg \n", + " inflating: AD_NC/test/NC/1195981_96.jpeg \n", + " inflating: AD_NC/test/NC/1195981_97.jpeg \n", + " inflating: AD_NC/test/NC/1195981_98.jpeg \n", + " inflating: AD_NC/test/NC/1195981_99.jpeg \n", + " inflating: AD_NC/test/NC/1196891_100.jpeg \n", + " inflating: AD_NC/test/NC/1196891_101.jpeg \n", + " inflating: AD_NC/test/NC/1196891_102.jpeg \n", + " inflating: AD_NC/test/NC/1196891_103.jpeg \n", + " inflating: AD_NC/test/NC/1196891_104.jpeg \n", + " inflating: AD_NC/test/NC/1196891_105.jpeg \n", + " inflating: AD_NC/test/NC/1196891_106.jpeg \n", + " inflating: AD_NC/test/NC/1196891_107.jpeg \n", + " inflating: AD_NC/test/NC/1196891_108.jpeg \n", + " inflating: AD_NC/test/NC/1196891_109.jpeg \n", + " inflating: AD_NC/test/NC/1196891_110.jpeg \n", + " inflating: AD_NC/test/NC/1196891_111.jpeg \n", + " inflating: AD_NC/test/NC/1196891_112.jpeg \n", + " inflating: AD_NC/test/NC/1196891_113.jpeg \n", + " inflating: AD_NC/test/NC/1196891_94.jpeg \n", + " inflating: AD_NC/test/NC/1196891_95.jpeg \n", + " inflating: AD_NC/test/NC/1196891_96.jpeg \n", + " inflating: AD_NC/test/NC/1196891_97.jpeg \n", + " inflating: AD_NC/test/NC/1196891_98.jpeg \n", + " inflating: AD_NC/test/NC/1196891_99.jpeg \n", + " inflating: AD_NC/test/NC/1199310_100.jpeg \n", + " inflating: AD_NC/test/NC/1199310_101.jpeg \n", + " inflating: AD_NC/test/NC/1199310_102.jpeg \n", + " inflating: AD_NC/test/NC/1199310_103.jpeg \n", + " inflating: AD_NC/test/NC/1199310_104.jpeg \n", + " inflating: AD_NC/test/NC/1199310_105.jpeg \n", + " inflating: AD_NC/test/NC/1199310_106.jpeg \n", + " inflating: AD_NC/test/NC/1199310_107.jpeg \n", + " inflating: AD_NC/test/NC/1199310_88.jpeg \n", + " inflating: AD_NC/test/NC/1199310_89.jpeg \n", + " inflating: AD_NC/test/NC/1199310_90.jpeg \n", + " inflating: AD_NC/test/NC/1199310_91.jpeg \n", + " inflating: AD_NC/test/NC/1199310_92.jpeg \n", + " inflating: AD_NC/test/NC/1199310_93.jpeg \n", + " inflating: AD_NC/test/NC/1199310_94.jpeg \n", + " inflating: AD_NC/test/NC/1199310_95.jpeg \n", + " inflating: AD_NC/test/NC/1199310_96.jpeg \n", + " inflating: AD_NC/test/NC/1199310_97.jpeg \n", + " inflating: AD_NC/test/NC/1199310_98.jpeg \n", + " inflating: AD_NC/test/NC/1199310_99.jpeg \n", + " inflating: AD_NC/test/NC/1199414_100.jpeg \n", + " inflating: AD_NC/test/NC/1199414_101.jpeg \n", + " inflating: AD_NC/test/NC/1199414_102.jpeg \n", + " inflating: AD_NC/test/NC/1199414_103.jpeg \n", + " inflating: AD_NC/test/NC/1199414_104.jpeg \n", + " inflating: AD_NC/test/NC/1199414_105.jpeg \n", + " inflating: AD_NC/test/NC/1199414_106.jpeg \n", + " inflating: AD_NC/test/NC/1199414_107.jpeg \n", + " inflating: AD_NC/test/NC/1199414_88.jpeg \n", + " inflating: AD_NC/test/NC/1199414_89.jpeg \n", + " inflating: AD_NC/test/NC/1199414_90.jpeg \n", + " inflating: AD_NC/test/NC/1199414_91.jpeg \n", + " inflating: AD_NC/test/NC/1199414_92.jpeg \n", + " inflating: AD_NC/test/NC/1199414_93.jpeg \n", + " inflating: AD_NC/test/NC/1199414_94.jpeg \n", + " inflating: AD_NC/test/NC/1199414_95.jpeg \n", + " inflating: AD_NC/test/NC/1199414_96.jpeg \n", + " inflating: AD_NC/test/NC/1199414_97.jpeg \n", + " inflating: AD_NC/test/NC/1199414_98.jpeg \n", + " inflating: AD_NC/test/NC/1199414_99.jpeg \n", + " inflating: AD_NC/test/NC/1209877_100.jpeg \n", + " inflating: AD_NC/test/NC/1209877_101.jpeg \n", + " inflating: AD_NC/test/NC/1209877_102.jpeg \n", + " inflating: AD_NC/test/NC/1209877_103.jpeg \n", + " inflating: AD_NC/test/NC/1209877_104.jpeg \n", + " inflating: AD_NC/test/NC/1209877_105.jpeg \n", + " inflating: AD_NC/test/NC/1209877_106.jpeg \n", + " inflating: AD_NC/test/NC/1209877_107.jpeg \n", + " inflating: AD_NC/test/NC/1209877_108.jpeg \n", + " inflating: AD_NC/test/NC/1209877_109.jpeg \n", + " inflating: AD_NC/test/NC/1209877_110.jpeg \n", + " inflating: AD_NC/test/NC/1209877_111.jpeg \n", + " inflating: AD_NC/test/NC/1209877_112.jpeg \n", + " inflating: AD_NC/test/NC/1209877_113.jpeg \n", + " inflating: AD_NC/test/NC/1209877_94.jpeg \n", + " inflating: AD_NC/test/NC/1209877_95.jpeg \n", + " inflating: AD_NC/test/NC/1209877_96.jpeg \n", + " inflating: AD_NC/test/NC/1209877_97.jpeg \n", + " inflating: AD_NC/test/NC/1209877_98.jpeg \n", + " inflating: AD_NC/test/NC/1209877_99.jpeg \n", + " inflating: AD_NC/test/NC/1211451_100.jpeg \n", + " inflating: AD_NC/test/NC/1211451_101.jpeg \n", + " inflating: AD_NC/test/NC/1211451_102.jpeg \n", + " inflating: AD_NC/test/NC/1211451_103.jpeg \n", + " inflating: AD_NC/test/NC/1211451_104.jpeg \n", + " inflating: AD_NC/test/NC/1211451_105.jpeg \n", + " inflating: AD_NC/test/NC/1211451_106.jpeg \n", + " inflating: AD_NC/test/NC/1211451_107.jpeg \n", + " inflating: AD_NC/test/NC/1211451_108.jpeg \n", + " inflating: AD_NC/test/NC/1211451_109.jpeg \n", + " inflating: AD_NC/test/NC/1211451_110.jpeg \n", + " inflating: AD_NC/test/NC/1211451_111.jpeg \n", + " inflating: AD_NC/test/NC/1211451_112.jpeg \n", + " inflating: AD_NC/test/NC/1211451_113.jpeg \n", + " inflating: AD_NC/test/NC/1211451_94.jpeg \n", + " inflating: AD_NC/test/NC/1211451_95.jpeg \n", + " inflating: AD_NC/test/NC/1211451_96.jpeg \n", + " inflating: AD_NC/test/NC/1211451_97.jpeg \n", + " inflating: AD_NC/test/NC/1211451_98.jpeg \n", + " inflating: AD_NC/test/NC/1211451_99.jpeg \n", + " inflating: AD_NC/test/NC/1212969_100.jpeg \n", + " inflating: AD_NC/test/NC/1212969_101.jpeg \n", + " inflating: AD_NC/test/NC/1212969_102.jpeg \n", + " inflating: AD_NC/test/NC/1212969_103.jpeg \n", + " inflating: AD_NC/test/NC/1212969_104.jpeg \n", + " inflating: AD_NC/test/NC/1212969_105.jpeg \n", + " inflating: AD_NC/test/NC/1212969_106.jpeg \n", + " inflating: AD_NC/test/NC/1212969_107.jpeg \n", + " inflating: AD_NC/test/NC/1212969_88.jpeg \n", + " inflating: AD_NC/test/NC/1212969_89.jpeg \n", + " inflating: AD_NC/test/NC/1212969_90.jpeg \n", + " inflating: AD_NC/test/NC/1212969_91.jpeg \n", + " inflating: AD_NC/test/NC/1212969_92.jpeg \n", + " inflating: AD_NC/test/NC/1212969_93.jpeg \n", + " inflating: AD_NC/test/NC/1212969_94.jpeg \n", + " inflating: AD_NC/test/NC/1212969_95.jpeg \n", + " inflating: AD_NC/test/NC/1212969_96.jpeg \n", + " inflating: AD_NC/test/NC/1212969_97.jpeg \n", + " inflating: AD_NC/test/NC/1212969_98.jpeg \n", + " inflating: AD_NC/test/NC/1212969_99.jpeg \n", + " inflating: AD_NC/test/NC/1214021_78.jpeg \n", + " inflating: AD_NC/test/NC/1214021_79.jpeg \n", + " inflating: AD_NC/test/NC/1214021_80.jpeg \n", + " inflating: AD_NC/test/NC/1214021_81.jpeg \n", + " inflating: AD_NC/test/NC/1214021_82.jpeg \n", + " inflating: AD_NC/test/NC/1214021_83.jpeg \n", + " inflating: AD_NC/test/NC/1214021_84.jpeg \n", + " inflating: AD_NC/test/NC/1214021_85.jpeg \n", + " inflating: AD_NC/test/NC/1214021_86.jpeg \n", + " inflating: AD_NC/test/NC/1214021_87.jpeg \n", + " inflating: AD_NC/test/NC/1214021_88.jpeg \n", + " inflating: AD_NC/test/NC/1214021_89.jpeg \n", + " inflating: AD_NC/test/NC/1214021_90.jpeg \n", + " inflating: AD_NC/test/NC/1214021_91.jpeg \n", + " inflating: AD_NC/test/NC/1214021_92.jpeg \n", + " inflating: AD_NC/test/NC/1214021_93.jpeg \n", + " inflating: AD_NC/test/NC/1214021_94.jpeg \n", + " inflating: AD_NC/test/NC/1214021_95.jpeg \n", + " inflating: AD_NC/test/NC/1214021_96.jpeg \n", + " inflating: AD_NC/test/NC/1214021_97.jpeg \n", + " inflating: AD_NC/test/NC/1214909_100.jpeg \n", + " inflating: AD_NC/test/NC/1214909_101.jpeg \n", + " inflating: AD_NC/test/NC/1214909_102.jpeg \n", + " inflating: AD_NC/test/NC/1214909_103.jpeg \n", + " inflating: AD_NC/test/NC/1214909_104.jpeg \n", + " inflating: AD_NC/test/NC/1214909_105.jpeg \n", + " inflating: AD_NC/test/NC/1214909_106.jpeg \n", + " inflating: AD_NC/test/NC/1214909_107.jpeg \n", + " inflating: AD_NC/test/NC/1214909_108.jpeg \n", + " inflating: AD_NC/test/NC/1214909_109.jpeg \n", + " inflating: AD_NC/test/NC/1214909_110.jpeg \n", + " inflating: AD_NC/test/NC/1214909_111.jpeg \n", + " inflating: AD_NC/test/NC/1214909_112.jpeg \n", + " inflating: AD_NC/test/NC/1214909_113.jpeg \n", + " inflating: AD_NC/test/NC/1214909_94.jpeg \n", + " inflating: AD_NC/test/NC/1214909_95.jpeg \n", + " inflating: AD_NC/test/NC/1214909_96.jpeg \n", + " inflating: AD_NC/test/NC/1214909_97.jpeg \n", + " inflating: AD_NC/test/NC/1214909_98.jpeg \n", + " inflating: AD_NC/test/NC/1214909_99.jpeg \n", + " inflating: AD_NC/test/NC/1215566_100.jpeg \n", + " inflating: AD_NC/test/NC/1215566_101.jpeg \n", + " inflating: AD_NC/test/NC/1215566_102.jpeg \n", + " inflating: AD_NC/test/NC/1215566_103.jpeg \n", + " inflating: AD_NC/test/NC/1215566_104.jpeg \n", + " inflating: AD_NC/test/NC/1215566_105.jpeg \n", + " inflating: AD_NC/test/NC/1215566_106.jpeg \n", + " inflating: AD_NC/test/NC/1215566_107.jpeg \n", + " inflating: AD_NC/test/NC/1215566_88.jpeg \n", + " inflating: AD_NC/test/NC/1215566_89.jpeg \n", + " inflating: AD_NC/test/NC/1215566_90.jpeg \n", + " inflating: AD_NC/test/NC/1215566_91.jpeg \n", + " inflating: AD_NC/test/NC/1215566_92.jpeg \n", + " inflating: AD_NC/test/NC/1215566_93.jpeg \n", + " inflating: AD_NC/test/NC/1215566_94.jpeg \n", + " inflating: AD_NC/test/NC/1215566_95.jpeg \n", + " inflating: AD_NC/test/NC/1215566_96.jpeg \n", + " inflating: AD_NC/test/NC/1215566_97.jpeg \n", + " inflating: AD_NC/test/NC/1215566_98.jpeg \n", + " inflating: AD_NC/test/NC/1215566_99.jpeg \n", + " inflating: AD_NC/test/NC/1215774_100.jpeg \n", + " inflating: AD_NC/test/NC/1215774_101.jpeg \n", + " inflating: AD_NC/test/NC/1215774_102.jpeg \n", + " inflating: AD_NC/test/NC/1215774_103.jpeg \n", + " inflating: AD_NC/test/NC/1215774_104.jpeg \n", + " inflating: AD_NC/test/NC/1215774_105.jpeg \n", + " inflating: AD_NC/test/NC/1215774_106.jpeg \n", + " inflating: AD_NC/test/NC/1215774_107.jpeg \n", + " inflating: AD_NC/test/NC/1215774_88.jpeg \n", + " inflating: AD_NC/test/NC/1215774_89.jpeg \n", + " inflating: AD_NC/test/NC/1215774_90.jpeg \n", + " inflating: AD_NC/test/NC/1215774_91.jpeg \n", + " inflating: AD_NC/test/NC/1215774_92.jpeg \n", + " inflating: AD_NC/test/NC/1215774_93.jpeg \n", + " inflating: AD_NC/test/NC/1215774_94.jpeg \n", + " inflating: AD_NC/test/NC/1215774_95.jpeg \n", + " inflating: AD_NC/test/NC/1215774_96.jpeg \n", + " inflating: AD_NC/test/NC/1215774_97.jpeg \n", + " inflating: AD_NC/test/NC/1215774_98.jpeg \n", + " inflating: AD_NC/test/NC/1215774_99.jpeg \n", + " inflating: AD_NC/test/NC/1219059_100.jpeg \n", + " inflating: AD_NC/test/NC/1219059_101.jpeg \n", + " inflating: AD_NC/test/NC/1219059_102.jpeg \n", + " inflating: AD_NC/test/NC/1219059_103.jpeg \n", + " inflating: AD_NC/test/NC/1219059_104.jpeg \n", + " inflating: AD_NC/test/NC/1219059_105.jpeg \n", + " inflating: AD_NC/test/NC/1219059_106.jpeg \n", + " inflating: AD_NC/test/NC/1219059_107.jpeg \n", + " inflating: AD_NC/test/NC/1219059_108.jpeg \n", + " inflating: AD_NC/test/NC/1219059_109.jpeg \n", + " inflating: AD_NC/test/NC/1219059_110.jpeg \n", + " inflating: AD_NC/test/NC/1219059_111.jpeg \n", + " inflating: AD_NC/test/NC/1219059_112.jpeg \n", + " inflating: AD_NC/test/NC/1219059_113.jpeg \n", + " inflating: AD_NC/test/NC/1219059_94.jpeg \n", + " inflating: AD_NC/test/NC/1219059_95.jpeg \n", + " inflating: AD_NC/test/NC/1219059_96.jpeg \n", + " inflating: AD_NC/test/NC/1219059_97.jpeg \n", + " inflating: AD_NC/test/NC/1219059_98.jpeg \n", + " inflating: AD_NC/test/NC/1219059_99.jpeg \n", + " inflating: AD_NC/test/NC/1219675_78.jpeg \n", + " inflating: AD_NC/test/NC/1219675_79.jpeg \n", + " inflating: AD_NC/test/NC/1219675_80.jpeg \n", + " inflating: AD_NC/test/NC/1219675_81.jpeg \n", + " inflating: AD_NC/test/NC/1219675_82.jpeg \n", + " inflating: AD_NC/test/NC/1219675_83.jpeg \n", + " inflating: AD_NC/test/NC/1219675_84.jpeg \n", + " inflating: AD_NC/test/NC/1219675_85.jpeg \n", + " inflating: AD_NC/test/NC/1219675_86.jpeg \n", + " inflating: AD_NC/test/NC/1219675_87.jpeg \n", + " inflating: AD_NC/test/NC/1219675_88.jpeg \n", + " inflating: AD_NC/test/NC/1219675_89.jpeg \n", + " inflating: AD_NC/test/NC/1219675_90.jpeg \n", + " inflating: AD_NC/test/NC/1219675_91.jpeg \n", + " inflating: AD_NC/test/NC/1219675_92.jpeg \n", + " inflating: AD_NC/test/NC/1219675_93.jpeg \n", + " inflating: AD_NC/test/NC/1219675_94.jpeg \n", + " inflating: AD_NC/test/NC/1219675_95.jpeg \n", + " inflating: AD_NC/test/NC/1219675_96.jpeg \n", + " inflating: AD_NC/test/NC/1219675_97.jpeg \n", + " inflating: AD_NC/test/NC/1220921_100.jpeg \n", + " inflating: AD_NC/test/NC/1220921_101.jpeg \n", + " inflating: AD_NC/test/NC/1220921_102.jpeg \n", + " inflating: AD_NC/test/NC/1220921_103.jpeg \n", + " inflating: AD_NC/test/NC/1220921_104.jpeg \n", + " inflating: AD_NC/test/NC/1220921_105.jpeg \n", + " inflating: AD_NC/test/NC/1220921_106.jpeg \n", + " inflating: AD_NC/test/NC/1220921_107.jpeg \n", + " inflating: AD_NC/test/NC/1220921_108.jpeg \n", + " inflating: AD_NC/test/NC/1220921_109.jpeg \n", + " inflating: AD_NC/test/NC/1220921_110.jpeg \n", + " inflating: AD_NC/test/NC/1220921_111.jpeg \n", + " inflating: AD_NC/test/NC/1220921_112.jpeg \n", + " inflating: AD_NC/test/NC/1220921_113.jpeg \n", + " inflating: AD_NC/test/NC/1220921_94.jpeg \n", + " inflating: AD_NC/test/NC/1220921_95.jpeg \n", + " inflating: AD_NC/test/NC/1220921_96.jpeg \n", + " inflating: AD_NC/test/NC/1220921_97.jpeg \n", + " inflating: AD_NC/test/NC/1220921_98.jpeg \n", + " inflating: AD_NC/test/NC/1220921_99.jpeg \n", + " inflating: AD_NC/test/NC/1221051_100.jpeg \n", + " inflating: AD_NC/test/NC/1221051_101.jpeg \n", + " inflating: AD_NC/test/NC/1221051_102.jpeg \n", + " inflating: AD_NC/test/NC/1221051_103.jpeg \n", + " inflating: AD_NC/test/NC/1221051_104.jpeg \n", + " inflating: AD_NC/test/NC/1221051_105.jpeg \n", + " inflating: AD_NC/test/NC/1221051_106.jpeg \n", + " inflating: AD_NC/test/NC/1221051_107.jpeg \n", + " inflating: AD_NC/test/NC/1221051_108.jpeg \n", + " inflating: AD_NC/test/NC/1221051_109.jpeg \n", + " inflating: AD_NC/test/NC/1221051_110.jpeg \n", + " inflating: AD_NC/test/NC/1221051_111.jpeg \n", + " inflating: AD_NC/test/NC/1221051_112.jpeg \n", + " inflating: AD_NC/test/NC/1221051_113.jpeg \n", + " inflating: AD_NC/test/NC/1221051_94.jpeg \n", + " inflating: AD_NC/test/NC/1221051_95.jpeg \n", + " inflating: AD_NC/test/NC/1221051_96.jpeg \n", + " inflating: AD_NC/test/NC/1221051_97.jpeg \n", + " inflating: AD_NC/test/NC/1221051_98.jpeg \n", + " inflating: AD_NC/test/NC/1221051_99.jpeg \n", + " inflating: AD_NC/test/NC/1221363_100.jpeg \n", + " inflating: AD_NC/test/NC/1221363_101.jpeg \n", + " inflating: AD_NC/test/NC/1221363_102.jpeg \n", + " inflating: AD_NC/test/NC/1221363_103.jpeg \n", + " inflating: AD_NC/test/NC/1221363_104.jpeg \n", + " inflating: AD_NC/test/NC/1221363_105.jpeg \n", + " inflating: AD_NC/test/NC/1221363_106.jpeg \n", + " inflating: AD_NC/test/NC/1221363_107.jpeg \n", + " inflating: AD_NC/test/NC/1221363_108.jpeg \n", + " inflating: AD_NC/test/NC/1221363_109.jpeg \n", + " inflating: AD_NC/test/NC/1221363_110.jpeg \n", + " inflating: AD_NC/test/NC/1221363_111.jpeg \n", + " inflating: AD_NC/test/NC/1221363_112.jpeg \n", + " inflating: AD_NC/test/NC/1221363_113.jpeg \n", + " inflating: AD_NC/test/NC/1221363_114.jpeg \n", + " inflating: AD_NC/test/NC/1221363_95.jpeg \n", + " inflating: AD_NC/test/NC/1221363_96.jpeg \n", + " inflating: AD_NC/test/NC/1221363_97.jpeg \n", + " inflating: AD_NC/test/NC/1221363_98.jpeg \n", + " inflating: AD_NC/test/NC/1221363_99.jpeg \n", + " inflating: AD_NC/test/NC/1221673_78.jpeg \n", + " inflating: AD_NC/test/NC/1221673_79.jpeg \n", + " inflating: AD_NC/test/NC/1221673_80.jpeg \n", + " inflating: AD_NC/test/NC/1221673_81.jpeg \n", + " inflating: AD_NC/test/NC/1221673_82.jpeg \n", + " inflating: AD_NC/test/NC/1221673_83.jpeg \n", + " inflating: AD_NC/test/NC/1221673_84.jpeg \n", + " inflating: AD_NC/test/NC/1221673_85.jpeg \n", + " inflating: AD_NC/test/NC/1221673_86.jpeg \n", + " inflating: AD_NC/test/NC/1221673_87.jpeg \n", + " inflating: AD_NC/test/NC/1221673_88.jpeg \n", + " inflating: AD_NC/test/NC/1221673_89.jpeg \n", + " inflating: AD_NC/test/NC/1221673_90.jpeg \n", + " inflating: AD_NC/test/NC/1221673_91.jpeg \n", + " inflating: AD_NC/test/NC/1221673_92.jpeg \n", + " inflating: AD_NC/test/NC/1221673_93.jpeg \n", + " inflating: AD_NC/test/NC/1221673_94.jpeg \n", + " inflating: AD_NC/test/NC/1221673_95.jpeg \n", + " inflating: AD_NC/test/NC/1221673_96.jpeg \n", + " inflating: AD_NC/test/NC/1221673_97.jpeg \n", + " inflating: AD_NC/test/NC/1221674_78.jpeg \n", + " inflating: AD_NC/test/NC/1221674_79.jpeg \n", + " inflating: AD_NC/test/NC/1221674_80.jpeg \n", + " inflating: AD_NC/test/NC/1221674_81.jpeg \n", + " inflating: AD_NC/test/NC/1221674_82.jpeg \n", + " inflating: AD_NC/test/NC/1221674_83.jpeg \n", + " inflating: AD_NC/test/NC/1221674_84.jpeg \n", + " inflating: AD_NC/test/NC/1221674_85.jpeg \n", + " inflating: AD_NC/test/NC/1221674_86.jpeg \n", + " inflating: AD_NC/test/NC/1221674_87.jpeg \n", + " inflating: AD_NC/test/NC/1221674_88.jpeg \n", + " inflating: AD_NC/test/NC/1221674_89.jpeg \n", + " inflating: AD_NC/test/NC/1221674_90.jpeg \n", + " inflating: AD_NC/test/NC/1221674_91.jpeg \n", + " inflating: AD_NC/test/NC/1221674_92.jpeg \n", + " inflating: AD_NC/test/NC/1221674_93.jpeg \n", + " inflating: AD_NC/test/NC/1221674_94.jpeg \n", + " inflating: AD_NC/test/NC/1221674_95.jpeg \n", + " inflating: AD_NC/test/NC/1221674_96.jpeg \n", + " inflating: AD_NC/test/NC/1221674_97.jpeg \n", + " inflating: AD_NC/test/NC/1224466_78.jpeg \n", + " inflating: AD_NC/test/NC/1224466_79.jpeg \n", + " inflating: AD_NC/test/NC/1224466_80.jpeg \n", + " inflating: AD_NC/test/NC/1224466_81.jpeg \n", + " inflating: AD_NC/test/NC/1224466_82.jpeg \n", + " inflating: AD_NC/test/NC/1224466_83.jpeg \n", + " inflating: AD_NC/test/NC/1224466_84.jpeg \n", + " inflating: AD_NC/test/NC/1224466_85.jpeg \n", + " inflating: AD_NC/test/NC/1224466_86.jpeg \n", + " inflating: AD_NC/test/NC/1224466_87.jpeg \n", + " inflating: AD_NC/test/NC/1224466_88.jpeg \n", + " inflating: AD_NC/test/NC/1224466_89.jpeg \n", + " inflating: AD_NC/test/NC/1224466_90.jpeg \n", + " inflating: AD_NC/test/NC/1224466_91.jpeg \n", + " inflating: AD_NC/test/NC/1224466_92.jpeg \n", + " inflating: AD_NC/test/NC/1224466_93.jpeg \n", + " inflating: AD_NC/test/NC/1224466_94.jpeg \n", + " inflating: AD_NC/test/NC/1224466_95.jpeg \n", + " inflating: AD_NC/test/NC/1224466_96.jpeg \n", + " inflating: AD_NC/test/NC/1224466_97.jpeg \n", + " inflating: AD_NC/test/NC/1224468_78.jpeg \n", + " inflating: AD_NC/test/NC/1224468_79.jpeg \n", + " inflating: AD_NC/test/NC/1224468_80.jpeg \n", + " inflating: AD_NC/test/NC/1224468_81.jpeg \n", + " inflating: AD_NC/test/NC/1224468_82.jpeg \n", + " inflating: AD_NC/test/NC/1224468_83.jpeg \n", + " inflating: AD_NC/test/NC/1224468_84.jpeg \n", + " inflating: AD_NC/test/NC/1224468_85.jpeg \n", + " inflating: AD_NC/test/NC/1224468_86.jpeg \n", + " inflating: AD_NC/test/NC/1224468_87.jpeg \n", + " inflating: AD_NC/test/NC/1224468_88.jpeg \n", + " inflating: AD_NC/test/NC/1224468_89.jpeg \n", + " inflating: AD_NC/test/NC/1224468_90.jpeg \n", + " inflating: AD_NC/test/NC/1224468_91.jpeg \n", + " inflating: AD_NC/test/NC/1224468_92.jpeg \n", + " inflating: AD_NC/test/NC/1224468_93.jpeg \n", + " inflating: AD_NC/test/NC/1224468_94.jpeg \n", + " inflating: AD_NC/test/NC/1224468_95.jpeg \n", + " inflating: AD_NC/test/NC/1224468_96.jpeg \n", + " inflating: AD_NC/test/NC/1224468_97.jpeg \n", + " inflating: AD_NC/test/NC/1225000_78.jpeg \n", + " inflating: AD_NC/test/NC/1225000_79.jpeg \n", + " inflating: AD_NC/test/NC/1225000_80.jpeg \n", + " inflating: AD_NC/test/NC/1225000_81.jpeg \n", + " inflating: AD_NC/test/NC/1225000_82.jpeg \n", + " inflating: AD_NC/test/NC/1225000_83.jpeg \n", + " inflating: AD_NC/test/NC/1225000_84.jpeg \n", + " inflating: AD_NC/test/NC/1225000_85.jpeg \n", + " inflating: AD_NC/test/NC/1225000_86.jpeg \n", + " inflating: AD_NC/test/NC/1225000_87.jpeg \n", + " inflating: AD_NC/test/NC/1225000_88.jpeg \n", + " inflating: AD_NC/test/NC/1225000_89.jpeg \n", + " inflating: AD_NC/test/NC/1225000_90.jpeg \n", + " inflating: AD_NC/test/NC/1225000_91.jpeg \n", + " inflating: AD_NC/test/NC/1225000_92.jpeg \n", + " inflating: AD_NC/test/NC/1225000_93.jpeg \n", + " inflating: AD_NC/test/NC/1225000_94.jpeg \n", + " inflating: AD_NC/test/NC/1225000_95.jpeg \n", + " inflating: AD_NC/test/NC/1225000_96.jpeg \n", + " inflating: AD_NC/test/NC/1225000_97.jpeg \n", + " inflating: AD_NC/test/NC/1225879_100.jpeg \n", + " inflating: AD_NC/test/NC/1225879_101.jpeg \n", + " inflating: AD_NC/test/NC/1225879_102.jpeg \n", + " inflating: AD_NC/test/NC/1225879_103.jpeg \n", + " inflating: AD_NC/test/NC/1225879_104.jpeg \n", + " inflating: AD_NC/test/NC/1225879_105.jpeg \n", + " inflating: AD_NC/test/NC/1225879_106.jpeg \n", + " inflating: AD_NC/test/NC/1225879_107.jpeg \n", + " inflating: AD_NC/test/NC/1225879_108.jpeg \n", + " inflating: AD_NC/test/NC/1225879_109.jpeg \n", + " inflating: AD_NC/test/NC/1225879_110.jpeg \n", + " inflating: AD_NC/test/NC/1225879_111.jpeg \n", + " inflating: AD_NC/test/NC/1225879_112.jpeg \n", + " inflating: AD_NC/test/NC/1225879_113.jpeg \n", + " inflating: AD_NC/test/NC/1225879_114.jpeg \n", + " inflating: AD_NC/test/NC/1225879_95.jpeg \n", + " inflating: AD_NC/test/NC/1225879_96.jpeg \n", + " inflating: AD_NC/test/NC/1225879_97.jpeg \n", + " inflating: AD_NC/test/NC/1225879_98.jpeg \n", + " inflating: AD_NC/test/NC/1225879_99.jpeg \n", + " inflating: AD_NC/test/NC/1225896_100.jpeg \n", + " inflating: AD_NC/test/NC/1225896_101.jpeg \n", + " inflating: AD_NC/test/NC/1225896_102.jpeg \n", + " inflating: AD_NC/test/NC/1225896_103.jpeg \n", + " inflating: AD_NC/test/NC/1225896_104.jpeg \n", + " inflating: AD_NC/test/NC/1225896_105.jpeg \n", + " inflating: AD_NC/test/NC/1225896_106.jpeg \n", + " inflating: AD_NC/test/NC/1225896_107.jpeg \n", + " inflating: AD_NC/test/NC/1225896_108.jpeg \n", + " inflating: AD_NC/test/NC/1225896_109.jpeg \n", + " inflating: AD_NC/test/NC/1225896_110.jpeg \n", + " inflating: AD_NC/test/NC/1225896_111.jpeg \n", + " inflating: AD_NC/test/NC/1225896_112.jpeg \n", + " inflating: AD_NC/test/NC/1225896_113.jpeg \n", + " inflating: AD_NC/test/NC/1225896_94.jpeg \n", + " inflating: AD_NC/test/NC/1225896_95.jpeg \n", + " inflating: AD_NC/test/NC/1225896_96.jpeg \n", + " inflating: AD_NC/test/NC/1225896_97.jpeg \n", + " inflating: AD_NC/test/NC/1225896_98.jpeg \n", + " inflating: AD_NC/test/NC/1225896_99.jpeg \n", + " inflating: AD_NC/test/NC/1225971_100.jpeg \n", + " inflating: AD_NC/test/NC/1225971_101.jpeg \n", + " inflating: AD_NC/test/NC/1225971_102.jpeg \n", + " inflating: AD_NC/test/NC/1225971_103.jpeg \n", + " inflating: AD_NC/test/NC/1225971_104.jpeg \n", + " inflating: AD_NC/test/NC/1225971_105.jpeg \n", + " inflating: AD_NC/test/NC/1225971_106.jpeg \n", + " inflating: AD_NC/test/NC/1225971_107.jpeg \n", + " inflating: AD_NC/test/NC/1225971_108.jpeg \n", + " inflating: AD_NC/test/NC/1225971_109.jpeg \n", + " inflating: AD_NC/test/NC/1225971_110.jpeg \n", + " inflating: AD_NC/test/NC/1225971_111.jpeg \n", + " inflating: AD_NC/test/NC/1225971_112.jpeg \n", + " inflating: AD_NC/test/NC/1225971_113.jpeg \n", + " inflating: AD_NC/test/NC/1225971_94.jpeg \n", + " inflating: AD_NC/test/NC/1225971_95.jpeg \n", + " inflating: AD_NC/test/NC/1225971_96.jpeg \n", + " inflating: AD_NC/test/NC/1225971_97.jpeg \n", + " inflating: AD_NC/test/NC/1225971_98.jpeg \n", + " inflating: AD_NC/test/NC/1225971_99.jpeg \n", + " inflating: AD_NC/test/NC/1226456_78.jpeg \n", + " inflating: AD_NC/test/NC/1226456_79.jpeg \n", + " inflating: AD_NC/test/NC/1226456_80.jpeg \n", + " inflating: AD_NC/test/NC/1226456_81.jpeg \n", + " inflating: AD_NC/test/NC/1226456_82.jpeg \n", + " inflating: AD_NC/test/NC/1226456_83.jpeg \n", + " inflating: AD_NC/test/NC/1226456_84.jpeg \n", + " inflating: AD_NC/test/NC/1226456_85.jpeg \n", + " inflating: AD_NC/test/NC/1226456_86.jpeg \n", + " inflating: AD_NC/test/NC/1226456_87.jpeg \n", + " inflating: AD_NC/test/NC/1226456_88.jpeg \n", + " inflating: AD_NC/test/NC/1226456_89.jpeg \n", + " inflating: AD_NC/test/NC/1226456_90.jpeg \n", + " inflating: AD_NC/test/NC/1226456_91.jpeg \n", + " inflating: AD_NC/test/NC/1226456_92.jpeg \n", + " inflating: AD_NC/test/NC/1226456_93.jpeg \n", + " inflating: AD_NC/test/NC/1226456_94.jpeg \n", + " inflating: AD_NC/test/NC/1226456_95.jpeg \n", + " inflating: AD_NC/test/NC/1226456_96.jpeg \n", + " inflating: AD_NC/test/NC/1226456_97.jpeg \n", + " inflating: AD_NC/test/NC/1226457_78.jpeg \n", + " inflating: AD_NC/test/NC/1226457_79.jpeg \n", + " inflating: AD_NC/test/NC/1226457_80.jpeg \n", + " inflating: AD_NC/test/NC/1226457_81.jpeg \n", + " inflating: AD_NC/test/NC/1226457_82.jpeg \n", + " inflating: AD_NC/test/NC/1226457_83.jpeg \n", + " inflating: AD_NC/test/NC/1226457_84.jpeg \n", + " inflating: AD_NC/test/NC/1226457_85.jpeg \n", + " inflating: AD_NC/test/NC/1226457_86.jpeg \n", + " inflating: AD_NC/test/NC/1226457_87.jpeg \n", + " inflating: AD_NC/test/NC/1226457_88.jpeg \n", + " inflating: AD_NC/test/NC/1226457_89.jpeg \n", + " inflating: AD_NC/test/NC/1226457_90.jpeg \n", + " inflating: AD_NC/test/NC/1226457_91.jpeg \n", + " inflating: AD_NC/test/NC/1226457_92.jpeg \n", + " inflating: AD_NC/test/NC/1226457_93.jpeg \n", + " inflating: AD_NC/test/NC/1226457_94.jpeg \n", + " inflating: AD_NC/test/NC/1226457_95.jpeg \n", + " inflating: AD_NC/test/NC/1226457_96.jpeg \n", + " inflating: AD_NC/test/NC/1226457_97.jpeg \n", + " inflating: AD_NC/test/NC/1226508_78.jpeg \n", + " inflating: AD_NC/test/NC/1226508_79.jpeg \n", + " inflating: AD_NC/test/NC/1226508_80.jpeg \n", + " inflating: AD_NC/test/NC/1226508_81.jpeg \n", + " inflating: AD_NC/test/NC/1226508_82.jpeg \n", + " inflating: AD_NC/test/NC/1226508_83.jpeg \n", + " inflating: AD_NC/test/NC/1226508_84.jpeg \n", + " inflating: AD_NC/test/NC/1226508_85.jpeg \n", + " inflating: AD_NC/test/NC/1226508_86.jpeg \n", + " inflating: AD_NC/test/NC/1226508_87.jpeg \n", + " inflating: AD_NC/test/NC/1226508_88.jpeg \n", + " inflating: AD_NC/test/NC/1226508_89.jpeg \n", + " inflating: AD_NC/test/NC/1226508_90.jpeg \n", + " inflating: AD_NC/test/NC/1226508_91.jpeg \n", + " inflating: AD_NC/test/NC/1226508_92.jpeg \n", + " inflating: AD_NC/test/NC/1226508_93.jpeg \n", + " inflating: AD_NC/test/NC/1226508_94.jpeg \n", + " inflating: AD_NC/test/NC/1226508_95.jpeg \n", + " inflating: AD_NC/test/NC/1226508_96.jpeg \n", + " inflating: AD_NC/test/NC/1226508_97.jpeg \n", + " inflating: AD_NC/test/NC/1226810_100.jpeg \n", + " inflating: AD_NC/test/NC/1226810_101.jpeg \n", + " inflating: AD_NC/test/NC/1226810_102.jpeg \n", + " inflating: AD_NC/test/NC/1226810_103.jpeg \n", + " inflating: AD_NC/test/NC/1226810_104.jpeg \n", + " inflating: AD_NC/test/NC/1226810_105.jpeg \n", + " inflating: AD_NC/test/NC/1226810_106.jpeg \n", + " inflating: AD_NC/test/NC/1226810_107.jpeg \n", + " inflating: AD_NC/test/NC/1226810_108.jpeg \n", + " inflating: AD_NC/test/NC/1226810_109.jpeg \n", + " inflating: AD_NC/test/NC/1226810_110.jpeg \n", + " inflating: AD_NC/test/NC/1226810_111.jpeg \n", + " inflating: AD_NC/test/NC/1226810_112.jpeg \n", + " inflating: AD_NC/test/NC/1226810_113.jpeg \n", + " inflating: AD_NC/test/NC/1226810_94.jpeg \n", + " inflating: AD_NC/test/NC/1226810_95.jpeg \n", + " inflating: AD_NC/test/NC/1226810_96.jpeg \n", + " inflating: AD_NC/test/NC/1226810_97.jpeg \n", + " inflating: AD_NC/test/NC/1226810_98.jpeg \n", + " inflating: AD_NC/test/NC/1226810_99.jpeg \n", + " inflating: AD_NC/test/NC/1227239_100.jpeg \n", + " inflating: AD_NC/test/NC/1227239_101.jpeg \n", + " inflating: AD_NC/test/NC/1227239_102.jpeg \n", + " inflating: AD_NC/test/NC/1227239_103.jpeg \n", + " inflating: AD_NC/test/NC/1227239_104.jpeg \n", + " inflating: AD_NC/test/NC/1227239_105.jpeg \n", + " inflating: AD_NC/test/NC/1227239_106.jpeg \n", + " inflating: AD_NC/test/NC/1227239_107.jpeg \n", + " inflating: AD_NC/test/NC/1227239_108.jpeg \n", + " inflating: AD_NC/test/NC/1227239_109.jpeg \n", + " inflating: AD_NC/test/NC/1227239_110.jpeg \n", + " inflating: AD_NC/test/NC/1227239_111.jpeg \n", + " inflating: AD_NC/test/NC/1227239_112.jpeg \n", + " inflating: AD_NC/test/NC/1227239_113.jpeg \n", + " inflating: AD_NC/test/NC/1227239_94.jpeg \n", + " inflating: AD_NC/test/NC/1227239_95.jpeg \n", + " inflating: AD_NC/test/NC/1227239_96.jpeg \n", + " inflating: AD_NC/test/NC/1227239_97.jpeg \n", + " inflating: AD_NC/test/NC/1227239_98.jpeg \n", + " inflating: AD_NC/test/NC/1227239_99.jpeg \n", + " inflating: AD_NC/test/NC/1229284_100.jpeg \n", + " inflating: AD_NC/test/NC/1229284_101.jpeg \n", + " inflating: AD_NC/test/NC/1229284_102.jpeg \n", + " inflating: AD_NC/test/NC/1229284_103.jpeg \n", + " inflating: AD_NC/test/NC/1229284_104.jpeg \n", + " inflating: AD_NC/test/NC/1229284_105.jpeg \n", + " inflating: AD_NC/test/NC/1229284_106.jpeg \n", + " inflating: AD_NC/test/NC/1229284_107.jpeg \n", + " inflating: AD_NC/test/NC/1229284_108.jpeg \n", + " inflating: AD_NC/test/NC/1229284_89.jpeg \n", + " inflating: AD_NC/test/NC/1229284_90.jpeg \n", + " inflating: AD_NC/test/NC/1229284_91.jpeg \n", + " inflating: AD_NC/test/NC/1229284_92.jpeg \n", + " inflating: AD_NC/test/NC/1229284_93.jpeg \n", + " inflating: AD_NC/test/NC/1229284_94.jpeg \n", + " inflating: AD_NC/test/NC/1229284_95.jpeg \n", + " inflating: AD_NC/test/NC/1229284_96.jpeg \n", + " inflating: AD_NC/test/NC/1229284_97.jpeg \n", + " inflating: AD_NC/test/NC/1229284_98.jpeg \n", + " inflating: AD_NC/test/NC/1229284_99.jpeg \n", + " inflating: AD_NC/test/NC/1229457_78.jpeg \n", + " inflating: AD_NC/test/NC/1229457_79.jpeg \n", + " inflating: AD_NC/test/NC/1229457_80.jpeg \n", + " inflating: AD_NC/test/NC/1229457_81.jpeg \n", + " inflating: AD_NC/test/NC/1229457_82.jpeg \n", + " inflating: AD_NC/test/NC/1229457_83.jpeg \n", + " inflating: AD_NC/test/NC/1229457_84.jpeg \n", + " inflating: AD_NC/test/NC/1229457_85.jpeg \n", + " inflating: AD_NC/test/NC/1229457_86.jpeg \n", + " inflating: AD_NC/test/NC/1229457_87.jpeg \n", + " inflating: AD_NC/test/NC/1229457_88.jpeg \n", + " inflating: AD_NC/test/NC/1229457_89.jpeg \n", + " inflating: AD_NC/test/NC/1229457_90.jpeg \n", + " inflating: AD_NC/test/NC/1229457_91.jpeg \n", + " inflating: AD_NC/test/NC/1229457_92.jpeg \n", + " inflating: AD_NC/test/NC/1229457_93.jpeg \n", + " inflating: AD_NC/test/NC/1229457_94.jpeg \n", + " inflating: AD_NC/test/NC/1229457_95.jpeg \n", + " inflating: AD_NC/test/NC/1229457_96.jpeg \n", + " inflating: AD_NC/test/NC/1229457_97.jpeg \n", + " inflating: AD_NC/test/NC/1229464_78.jpeg \n", + " inflating: AD_NC/test/NC/1229464_79.jpeg \n", + " inflating: AD_NC/test/NC/1229464_80.jpeg \n", + " inflating: AD_NC/test/NC/1229464_81.jpeg \n", + " inflating: AD_NC/test/NC/1229464_82.jpeg \n", + " inflating: AD_NC/test/NC/1229464_83.jpeg \n", + " inflating: AD_NC/test/NC/1229464_84.jpeg \n", + " inflating: AD_NC/test/NC/1229464_85.jpeg \n", + " inflating: AD_NC/test/NC/1229464_86.jpeg \n", + " inflating: AD_NC/test/NC/1229464_87.jpeg \n", + " inflating: AD_NC/test/NC/1229464_88.jpeg \n", + " inflating: AD_NC/test/NC/1229464_89.jpeg \n", + " inflating: AD_NC/test/NC/1229464_90.jpeg \n", + " inflating: AD_NC/test/NC/1229464_91.jpeg \n", + " inflating: AD_NC/test/NC/1229464_92.jpeg \n", + " inflating: AD_NC/test/NC/1229464_93.jpeg \n", + " inflating: AD_NC/test/NC/1229464_94.jpeg \n", + " inflating: AD_NC/test/NC/1229464_95.jpeg \n", + " inflating: AD_NC/test/NC/1229464_96.jpeg \n", + " inflating: AD_NC/test/NC/1229464_97.jpeg \n", + " inflating: AD_NC/test/NC/1229529_100.jpeg \n", + " inflating: AD_NC/test/NC/1229529_101.jpeg \n", + " inflating: AD_NC/test/NC/1229529_102.jpeg \n", + " inflating: AD_NC/test/NC/1229529_103.jpeg \n", + " inflating: AD_NC/test/NC/1229529_104.jpeg \n", + " inflating: AD_NC/test/NC/1229529_105.jpeg \n", + " inflating: AD_NC/test/NC/1229529_106.jpeg \n", + " inflating: AD_NC/test/NC/1229529_107.jpeg \n", + " inflating: AD_NC/test/NC/1229529_108.jpeg \n", + " inflating: AD_NC/test/NC/1229529_109.jpeg \n", + " inflating: AD_NC/test/NC/1229529_110.jpeg \n", + " inflating: AD_NC/test/NC/1229529_111.jpeg \n", + " inflating: AD_NC/test/NC/1229529_112.jpeg \n", + " inflating: AD_NC/test/NC/1229529_113.jpeg \n", + " inflating: AD_NC/test/NC/1229529_94.jpeg \n", + " inflating: AD_NC/test/NC/1229529_95.jpeg \n", + " inflating: AD_NC/test/NC/1229529_96.jpeg \n", + " inflating: AD_NC/test/NC/1229529_97.jpeg \n", + " inflating: AD_NC/test/NC/1229529_98.jpeg \n", + " inflating: AD_NC/test/NC/1229529_99.jpeg \n", + " inflating: AD_NC/test/NC/1235535_100.jpeg \n", + " inflating: AD_NC/test/NC/1235535_101.jpeg \n", + " inflating: AD_NC/test/NC/1235535_102.jpeg \n", + " inflating: AD_NC/test/NC/1235535_103.jpeg \n", + " inflating: AD_NC/test/NC/1235535_104.jpeg \n", + " inflating: AD_NC/test/NC/1235535_105.jpeg \n", + " inflating: AD_NC/test/NC/1235535_106.jpeg \n", + " inflating: AD_NC/test/NC/1235535_107.jpeg \n", + " inflating: AD_NC/test/NC/1235535_108.jpeg \n", + " inflating: AD_NC/test/NC/1235535_109.jpeg \n", + " inflating: AD_NC/test/NC/1235535_110.jpeg \n", + " inflating: AD_NC/test/NC/1235535_111.jpeg \n", + " inflating: AD_NC/test/NC/1235535_112.jpeg \n", + " inflating: AD_NC/test/NC/1235535_113.jpeg \n", + " inflating: AD_NC/test/NC/1235535_94.jpeg \n", + " inflating: AD_NC/test/NC/1235535_95.jpeg \n", + " inflating: AD_NC/test/NC/1235535_96.jpeg \n", + " inflating: AD_NC/test/NC/1235535_97.jpeg \n", + " inflating: AD_NC/test/NC/1235535_98.jpeg \n", + " inflating: AD_NC/test/NC/1235535_99.jpeg \n", + " inflating: AD_NC/test/NC/1236085_100.jpeg \n", + " inflating: AD_NC/test/NC/1236085_101.jpeg \n", + " inflating: AD_NC/test/NC/1236085_102.jpeg \n", + " inflating: AD_NC/test/NC/1236085_103.jpeg \n", + " inflating: AD_NC/test/NC/1236085_104.jpeg \n", + " inflating: AD_NC/test/NC/1236085_105.jpeg \n", + " inflating: AD_NC/test/NC/1236085_106.jpeg \n", + " inflating: AD_NC/test/NC/1236085_107.jpeg \n", + " inflating: AD_NC/test/NC/1236085_108.jpeg \n", + " inflating: AD_NC/test/NC/1236085_109.jpeg \n", + " inflating: AD_NC/test/NC/1236085_110.jpeg \n", + " inflating: AD_NC/test/NC/1236085_111.jpeg \n", + " inflating: AD_NC/test/NC/1236085_112.jpeg \n", + " inflating: AD_NC/test/NC/1236085_113.jpeg \n", + " inflating: AD_NC/test/NC/1236085_94.jpeg \n", + " inflating: AD_NC/test/NC/1236085_95.jpeg \n", + " inflating: AD_NC/test/NC/1236085_96.jpeg \n", + " inflating: AD_NC/test/NC/1236085_97.jpeg \n", + " inflating: AD_NC/test/NC/1236085_98.jpeg \n", + " inflating: AD_NC/test/NC/1236085_99.jpeg \n", + " inflating: AD_NC/test/NC/1236425_100.jpeg \n", + " inflating: AD_NC/test/NC/1236425_101.jpeg \n", + " inflating: AD_NC/test/NC/1236425_102.jpeg \n", + " inflating: AD_NC/test/NC/1236425_103.jpeg \n", + " inflating: AD_NC/test/NC/1236425_104.jpeg \n", + " inflating: AD_NC/test/NC/1236425_105.jpeg \n", + " inflating: AD_NC/test/NC/1236425_106.jpeg \n", + " inflating: AD_NC/test/NC/1236425_107.jpeg \n", + " inflating: AD_NC/test/NC/1236425_108.jpeg \n", + " inflating: AD_NC/test/NC/1236425_109.jpeg \n", + " inflating: AD_NC/test/NC/1236425_110.jpeg \n", + " inflating: AD_NC/test/NC/1236425_111.jpeg \n", + " inflating: AD_NC/test/NC/1236425_112.jpeg \n", + " inflating: AD_NC/test/NC/1236425_113.jpeg \n", + " inflating: AD_NC/test/NC/1236425_114.jpeg \n", + " inflating: AD_NC/test/NC/1236425_95.jpeg \n", + " inflating: AD_NC/test/NC/1236425_96.jpeg \n", + " inflating: AD_NC/test/NC/1236425_97.jpeg \n", + " inflating: AD_NC/test/NC/1236425_98.jpeg \n", + " inflating: AD_NC/test/NC/1236425_99.jpeg \n", + " inflating: AD_NC/test/NC/1236679_100.jpeg \n", + " inflating: AD_NC/test/NC/1236679_101.jpeg \n", + " inflating: AD_NC/test/NC/1236679_102.jpeg \n", + " inflating: AD_NC/test/NC/1236679_103.jpeg \n", + " inflating: AD_NC/test/NC/1236679_104.jpeg \n", + " inflating: AD_NC/test/NC/1236679_105.jpeg \n", + " inflating: AD_NC/test/NC/1236679_106.jpeg \n", + " inflating: AD_NC/test/NC/1236679_107.jpeg \n", + " inflating: AD_NC/test/NC/1236679_108.jpeg \n", + " inflating: AD_NC/test/NC/1236679_109.jpeg \n", + " inflating: AD_NC/test/NC/1236679_110.jpeg \n", + " inflating: AD_NC/test/NC/1236679_111.jpeg \n", + " inflating: AD_NC/test/NC/1236679_112.jpeg \n", + " inflating: AD_NC/test/NC/1236679_113.jpeg \n", + " inflating: AD_NC/test/NC/1236679_94.jpeg \n", + " inflating: AD_NC/test/NC/1236679_95.jpeg \n", + " inflating: AD_NC/test/NC/1236679_96.jpeg \n", + " inflating: AD_NC/test/NC/1236679_97.jpeg \n", + " inflating: AD_NC/test/NC/1236679_98.jpeg \n", + " inflating: AD_NC/test/NC/1236679_99.jpeg \n", + " inflating: AD_NC/test/NC/1236721_100.jpeg \n", + " inflating: AD_NC/test/NC/1236721_101.jpeg \n", + " inflating: AD_NC/test/NC/1236721_102.jpeg \n", + " inflating: AD_NC/test/NC/1236721_103.jpeg \n", + " inflating: AD_NC/test/NC/1236721_104.jpeg \n", + " inflating: AD_NC/test/NC/1236721_105.jpeg \n", + " inflating: AD_NC/test/NC/1236721_106.jpeg \n", + " inflating: AD_NC/test/NC/1236721_107.jpeg \n", + " inflating: AD_NC/test/NC/1236721_108.jpeg \n", + " inflating: AD_NC/test/NC/1236721_109.jpeg \n", + " inflating: AD_NC/test/NC/1236721_110.jpeg \n", + " inflating: AD_NC/test/NC/1236721_111.jpeg \n", + " inflating: AD_NC/test/NC/1236721_112.jpeg \n", + " inflating: AD_NC/test/NC/1236721_113.jpeg \n", + " inflating: AD_NC/test/NC/1236721_114.jpeg \n", + " inflating: AD_NC/test/NC/1236721_95.jpeg \n", + " inflating: AD_NC/test/NC/1236721_96.jpeg \n", + " inflating: AD_NC/test/NC/1236721_97.jpeg \n", + " inflating: AD_NC/test/NC/1236721_98.jpeg \n", + " inflating: AD_NC/test/NC/1236721_99.jpeg \n", + " inflating: AD_NC/test/NC/1237590_100.jpeg \n", + " inflating: AD_NC/test/NC/1237590_101.jpeg \n", + " inflating: AD_NC/test/NC/1237590_102.jpeg \n", + " inflating: AD_NC/test/NC/1237590_103.jpeg \n", + " inflating: AD_NC/test/NC/1237590_104.jpeg \n", + " inflating: AD_NC/test/NC/1237590_105.jpeg \n", + " inflating: AD_NC/test/NC/1237590_106.jpeg \n", + " inflating: AD_NC/test/NC/1237590_107.jpeg \n", + " inflating: AD_NC/test/NC/1237590_108.jpeg \n", + " inflating: AD_NC/test/NC/1237590_109.jpeg \n", + " inflating: AD_NC/test/NC/1237590_110.jpeg \n", + " inflating: AD_NC/test/NC/1237590_111.jpeg \n", + " inflating: AD_NC/test/NC/1237590_112.jpeg \n", + " inflating: AD_NC/test/NC/1237590_113.jpeg \n", + " inflating: AD_NC/test/NC/1237590_94.jpeg \n", + " inflating: AD_NC/test/NC/1237590_95.jpeg \n", + " inflating: AD_NC/test/NC/1237590_96.jpeg \n", + " inflating: AD_NC/test/NC/1237590_97.jpeg \n", + " inflating: AD_NC/test/NC/1237590_98.jpeg \n", + " inflating: AD_NC/test/NC/1237590_99.jpeg \n", + " inflating: AD_NC/test/NC/1237740_100.jpeg \n", + " inflating: AD_NC/test/NC/1237740_101.jpeg \n", + " inflating: AD_NC/test/NC/1237740_102.jpeg \n", + " inflating: AD_NC/test/NC/1237740_103.jpeg \n", + " inflating: AD_NC/test/NC/1237740_104.jpeg \n", + " inflating: AD_NC/test/NC/1237740_105.jpeg \n", + " inflating: AD_NC/test/NC/1237740_106.jpeg \n", + " inflating: AD_NC/test/NC/1237740_107.jpeg \n", + " inflating: AD_NC/test/NC/1237740_108.jpeg \n", + " inflating: AD_NC/test/NC/1237740_109.jpeg \n", + " inflating: AD_NC/test/NC/1237740_110.jpeg \n", + " inflating: AD_NC/test/NC/1237740_111.jpeg \n", + " inflating: AD_NC/test/NC/1237740_112.jpeg \n", + " inflating: AD_NC/test/NC/1237740_113.jpeg \n", + " inflating: AD_NC/test/NC/1237740_94.jpeg \n", + " inflating: AD_NC/test/NC/1237740_95.jpeg \n", + " inflating: AD_NC/test/NC/1237740_96.jpeg \n", + " inflating: AD_NC/test/NC/1237740_97.jpeg \n", + " inflating: AD_NC/test/NC/1237740_98.jpeg \n", + " inflating: AD_NC/test/NC/1237740_99.jpeg \n", + " inflating: AD_NC/test/NC/1241179_78.jpeg \n", + " inflating: AD_NC/test/NC/1241179_79.jpeg \n", + " inflating: AD_NC/test/NC/1241179_80.jpeg \n", + " inflating: AD_NC/test/NC/1241179_81.jpeg \n", + " inflating: AD_NC/test/NC/1241179_82.jpeg \n", + " inflating: AD_NC/test/NC/1241179_83.jpeg \n", + " inflating: AD_NC/test/NC/1241179_84.jpeg \n", + " inflating: AD_NC/test/NC/1241179_85.jpeg \n", + " inflating: AD_NC/test/NC/1241179_86.jpeg \n", + " inflating: AD_NC/test/NC/1241179_87.jpeg \n", + " inflating: AD_NC/test/NC/1241179_88.jpeg \n", + " inflating: AD_NC/test/NC/1241179_89.jpeg \n", + " inflating: AD_NC/test/NC/1241179_90.jpeg \n", + " inflating: AD_NC/test/NC/1241179_91.jpeg \n", + " inflating: AD_NC/test/NC/1241179_92.jpeg \n", + " inflating: AD_NC/test/NC/1241179_93.jpeg \n", + " inflating: AD_NC/test/NC/1241179_94.jpeg \n", + " inflating: AD_NC/test/NC/1241179_95.jpeg \n", + " inflating: AD_NC/test/NC/1241179_96.jpeg \n", + " inflating: AD_NC/test/NC/1241179_97.jpeg \n", + " inflating: AD_NC/test/NC/1241191_100.jpeg \n", + " inflating: AD_NC/test/NC/1241191_101.jpeg \n", + " inflating: AD_NC/test/NC/1241191_102.jpeg \n", + " inflating: AD_NC/test/NC/1241191_103.jpeg \n", + " inflating: AD_NC/test/NC/1241191_104.jpeg \n", + " inflating: AD_NC/test/NC/1241191_105.jpeg \n", + " inflating: AD_NC/test/NC/1241191_106.jpeg \n", + " inflating: AD_NC/test/NC/1241191_107.jpeg \n", + " inflating: AD_NC/test/NC/1241191_108.jpeg \n", + " inflating: AD_NC/test/NC/1241191_109.jpeg \n", + " inflating: AD_NC/test/NC/1241191_110.jpeg \n", + " inflating: AD_NC/test/NC/1241191_111.jpeg \n", + " inflating: AD_NC/test/NC/1241191_112.jpeg \n", + " inflating: AD_NC/test/NC/1241191_113.jpeg \n", + " inflating: AD_NC/test/NC/1241191_114.jpeg \n", + " inflating: AD_NC/test/NC/1241191_95.jpeg \n", + " inflating: AD_NC/test/NC/1241191_96.jpeg \n", + " inflating: AD_NC/test/NC/1241191_97.jpeg \n", + " inflating: AD_NC/test/NC/1241191_98.jpeg \n", + " inflating: AD_NC/test/NC/1241191_99.jpeg \n", + " inflating: AD_NC/test/NC/1243833_78.jpeg \n", + " inflating: AD_NC/test/NC/1243833_79.jpeg \n", + " inflating: AD_NC/test/NC/1243833_80.jpeg \n", + " inflating: AD_NC/test/NC/1243833_81.jpeg \n", + " inflating: AD_NC/test/NC/1243833_82.jpeg \n", + " inflating: AD_NC/test/NC/1243833_83.jpeg \n", + " inflating: AD_NC/test/NC/1243833_84.jpeg \n", + " inflating: AD_NC/test/NC/1243833_85.jpeg \n", + " inflating: AD_NC/test/NC/1243833_86.jpeg \n", + " inflating: AD_NC/test/NC/1243833_87.jpeg \n", + " inflating: AD_NC/test/NC/1243833_88.jpeg \n", + " inflating: AD_NC/test/NC/1243833_89.jpeg \n", + " inflating: AD_NC/test/NC/1243833_90.jpeg \n", + " inflating: AD_NC/test/NC/1243833_91.jpeg \n", + " inflating: AD_NC/test/NC/1243833_92.jpeg \n", + " inflating: AD_NC/test/NC/1243833_93.jpeg \n", + " inflating: AD_NC/test/NC/1243833_94.jpeg \n", + " inflating: AD_NC/test/NC/1243833_95.jpeg \n", + " inflating: AD_NC/test/NC/1243833_96.jpeg \n", + " inflating: AD_NC/test/NC/1243833_97.jpeg \n", + " inflating: AD_NC/test/NC/1244529_100.jpeg \n", + " inflating: AD_NC/test/NC/1244529_101.jpeg \n", + " inflating: AD_NC/test/NC/1244529_102.jpeg \n", + " inflating: AD_NC/test/NC/1244529_103.jpeg \n", + " inflating: AD_NC/test/NC/1244529_104.jpeg \n", + " inflating: AD_NC/test/NC/1244529_105.jpeg \n", + " inflating: AD_NC/test/NC/1244529_106.jpeg \n", + " inflating: AD_NC/test/NC/1244529_107.jpeg \n", + " inflating: AD_NC/test/NC/1244529_108.jpeg \n", + " inflating: AD_NC/test/NC/1244529_109.jpeg \n", + " inflating: AD_NC/test/NC/1244529_110.jpeg \n", + " inflating: AD_NC/test/NC/1244529_111.jpeg \n", + " inflating: AD_NC/test/NC/1244529_112.jpeg \n", + " inflating: AD_NC/test/NC/1244529_113.jpeg \n", + " inflating: AD_NC/test/NC/1244529_94.jpeg \n", + " inflating: AD_NC/test/NC/1244529_95.jpeg \n", + " inflating: AD_NC/test/NC/1244529_96.jpeg \n", + " inflating: AD_NC/test/NC/1244529_97.jpeg \n", + " inflating: AD_NC/test/NC/1244529_98.jpeg \n", + " inflating: AD_NC/test/NC/1244529_99.jpeg \n", + " inflating: AD_NC/test/NC/1245611_100.jpeg \n", + " inflating: AD_NC/test/NC/1245611_101.jpeg \n", + " inflating: AD_NC/test/NC/1245611_102.jpeg \n", + " inflating: AD_NC/test/NC/1245611_103.jpeg \n", + " inflating: AD_NC/test/NC/1245611_104.jpeg \n", + " inflating: AD_NC/test/NC/1245611_105.jpeg \n", + " inflating: AD_NC/test/NC/1245611_106.jpeg \n", + " inflating: AD_NC/test/NC/1245611_107.jpeg \n", + " inflating: AD_NC/test/NC/1245611_108.jpeg \n", + " inflating: AD_NC/test/NC/1245611_109.jpeg \n", + " inflating: AD_NC/test/NC/1245611_110.jpeg \n", + " inflating: AD_NC/test/NC/1245611_111.jpeg \n", + " inflating: AD_NC/test/NC/1245611_112.jpeg \n", + " inflating: AD_NC/test/NC/1245611_113.jpeg \n", + " inflating: AD_NC/test/NC/1245611_114.jpeg \n", + " inflating: AD_NC/test/NC/1245611_95.jpeg \n", + " inflating: AD_NC/test/NC/1245611_96.jpeg \n", + " inflating: AD_NC/test/NC/1245611_97.jpeg \n", + " inflating: AD_NC/test/NC/1245611_98.jpeg \n", + " inflating: AD_NC/test/NC/1245611_99.jpeg \n", + " inflating: AD_NC/test/NC/1246441_100.jpeg \n", + " inflating: AD_NC/test/NC/1246441_101.jpeg \n", + " inflating: AD_NC/test/NC/1246441_102.jpeg \n", + " inflating: AD_NC/test/NC/1246441_103.jpeg \n", + " inflating: AD_NC/test/NC/1246441_104.jpeg \n", + " inflating: AD_NC/test/NC/1246441_105.jpeg \n", + " inflating: AD_NC/test/NC/1246441_106.jpeg \n", + " inflating: AD_NC/test/NC/1246441_107.jpeg \n", + " inflating: AD_NC/test/NC/1246441_108.jpeg \n", + " inflating: AD_NC/test/NC/1246441_109.jpeg \n", + " inflating: AD_NC/test/NC/1246441_110.jpeg \n", + " inflating: AD_NC/test/NC/1246441_111.jpeg \n", + " inflating: AD_NC/test/NC/1246441_112.jpeg \n", + " inflating: AD_NC/test/NC/1246441_113.jpeg \n", + " inflating: AD_NC/test/NC/1246441_94.jpeg \n", + " inflating: AD_NC/test/NC/1246441_95.jpeg \n", + " inflating: AD_NC/test/NC/1246441_96.jpeg \n", + " inflating: AD_NC/test/NC/1246441_97.jpeg \n", + " inflating: AD_NC/test/NC/1246441_98.jpeg \n", + " inflating: AD_NC/test/NC/1246441_99.jpeg \n", + " inflating: AD_NC/test/NC/1250826_100.jpeg \n", + " inflating: AD_NC/test/NC/1250826_101.jpeg \n", + " inflating: AD_NC/test/NC/1250826_102.jpeg \n", + " inflating: AD_NC/test/NC/1250826_103.jpeg \n", + " inflating: AD_NC/test/NC/1250826_104.jpeg \n", + " inflating: AD_NC/test/NC/1250826_105.jpeg \n", + " inflating: AD_NC/test/NC/1250826_106.jpeg \n", + " inflating: AD_NC/test/NC/1250826_107.jpeg \n", + " inflating: AD_NC/test/NC/1250826_108.jpeg \n", + " inflating: AD_NC/test/NC/1250826_109.jpeg \n", + " inflating: AD_NC/test/NC/1250826_110.jpeg \n", + " inflating: AD_NC/test/NC/1250826_111.jpeg \n", + " inflating: AD_NC/test/NC/1250826_112.jpeg \n", + " inflating: AD_NC/test/NC/1250826_113.jpeg \n", + " inflating: AD_NC/test/NC/1250826_94.jpeg \n", + " inflating: AD_NC/test/NC/1250826_95.jpeg \n", + " inflating: AD_NC/test/NC/1250826_96.jpeg \n", + " inflating: AD_NC/test/NC/1250826_97.jpeg \n", + " inflating: AD_NC/test/NC/1250826_98.jpeg \n", + " inflating: AD_NC/test/NC/1250826_99.jpeg \n", + " inflating: AD_NC/test/NC/1251421_100.jpeg \n", + " inflating: AD_NC/test/NC/1251421_101.jpeg \n", + " inflating: AD_NC/test/NC/1251421_102.jpeg \n", + " inflating: AD_NC/test/NC/1251421_103.jpeg \n", + " inflating: AD_NC/test/NC/1251421_104.jpeg \n", + " inflating: AD_NC/test/NC/1251421_105.jpeg \n", + " inflating: AD_NC/test/NC/1251421_106.jpeg \n", + " inflating: AD_NC/test/NC/1251421_107.jpeg \n", + " inflating: AD_NC/test/NC/1251421_108.jpeg \n", + " inflating: AD_NC/test/NC/1251421_109.jpeg \n", + " inflating: AD_NC/test/NC/1251421_110.jpeg \n", + " inflating: AD_NC/test/NC/1251421_111.jpeg \n", + " inflating: AD_NC/test/NC/1251421_112.jpeg \n", + " inflating: AD_NC/test/NC/1251421_113.jpeg \n", + " inflating: AD_NC/test/NC/1251421_94.jpeg \n", + " inflating: AD_NC/test/NC/1251421_95.jpeg \n", + " inflating: AD_NC/test/NC/1251421_96.jpeg \n", + " inflating: AD_NC/test/NC/1251421_97.jpeg \n", + " inflating: AD_NC/test/NC/1251421_98.jpeg \n", + " inflating: AD_NC/test/NC/1251421_99.jpeg \n", + " inflating: AD_NC/test/NC/1252024_100.jpeg \n", + " inflating: AD_NC/test/NC/1252024_101.jpeg \n", + " inflating: AD_NC/test/NC/1252024_102.jpeg \n", + " inflating: AD_NC/test/NC/1252024_103.jpeg \n", + " inflating: AD_NC/test/NC/1252024_104.jpeg \n", + " inflating: AD_NC/test/NC/1252024_105.jpeg \n", + " inflating: AD_NC/test/NC/1252024_106.jpeg \n", + " inflating: AD_NC/test/NC/1252024_107.jpeg \n", + " inflating: AD_NC/test/NC/1252024_108.jpeg \n", + " inflating: AD_NC/test/NC/1252024_109.jpeg \n", + " inflating: AD_NC/test/NC/1252024_110.jpeg \n", + " inflating: AD_NC/test/NC/1252024_111.jpeg \n", + " inflating: AD_NC/test/NC/1252024_112.jpeg \n", + " inflating: AD_NC/test/NC/1252024_113.jpeg \n", + " inflating: AD_NC/test/NC/1252024_94.jpeg \n", + " inflating: AD_NC/test/NC/1252024_95.jpeg \n", + " inflating: AD_NC/test/NC/1252024_96.jpeg \n", + " inflating: AD_NC/test/NC/1252024_97.jpeg \n", + " inflating: AD_NC/test/NC/1252024_98.jpeg \n", + " inflating: AD_NC/test/NC/1252024_99.jpeg \n", + " inflating: AD_NC/test/NC/1252848_100.jpeg \n", + " inflating: AD_NC/test/NC/1252848_101.jpeg \n", + " inflating: AD_NC/test/NC/1252848_102.jpeg \n", + " inflating: AD_NC/test/NC/1252848_103.jpeg \n", + " inflating: AD_NC/test/NC/1252848_104.jpeg \n", + " inflating: AD_NC/test/NC/1252848_105.jpeg \n", + " inflating: AD_NC/test/NC/1252848_106.jpeg \n", + " inflating: AD_NC/test/NC/1252848_107.jpeg \n", + " inflating: AD_NC/test/NC/1252848_108.jpeg \n", + " inflating: AD_NC/test/NC/1252848_109.jpeg \n", + " inflating: AD_NC/test/NC/1252848_110.jpeg \n", + " inflating: AD_NC/test/NC/1252848_111.jpeg \n", + " inflating: AD_NC/test/NC/1252848_112.jpeg \n", + " inflating: AD_NC/test/NC/1252848_113.jpeg \n", + " inflating: AD_NC/test/NC/1252848_94.jpeg \n", + " inflating: AD_NC/test/NC/1252848_95.jpeg \n", + " inflating: AD_NC/test/NC/1252848_96.jpeg \n", + " inflating: AD_NC/test/NC/1252848_97.jpeg \n", + " inflating: AD_NC/test/NC/1252848_98.jpeg \n", + " inflating: AD_NC/test/NC/1252848_99.jpeg \n", + " inflating: AD_NC/test/NC/1253141_100.jpeg \n", + " inflating: AD_NC/test/NC/1253141_101.jpeg \n", + " inflating: AD_NC/test/NC/1253141_102.jpeg \n", + " inflating: AD_NC/test/NC/1253141_103.jpeg \n", + " inflating: AD_NC/test/NC/1253141_104.jpeg \n", + " inflating: AD_NC/test/NC/1253141_105.jpeg \n", + " inflating: AD_NC/test/NC/1253141_106.jpeg \n", + " inflating: AD_NC/test/NC/1253141_107.jpeg \n", + " inflating: AD_NC/test/NC/1253141_108.jpeg \n", + " inflating: AD_NC/test/NC/1253141_109.jpeg \n", + " inflating: AD_NC/test/NC/1253141_110.jpeg \n", + " inflating: AD_NC/test/NC/1253141_111.jpeg \n", + " inflating: AD_NC/test/NC/1253141_112.jpeg \n", + " inflating: AD_NC/test/NC/1253141_113.jpeg \n", + " inflating: AD_NC/test/NC/1253141_114.jpeg \n", + " inflating: AD_NC/test/NC/1253141_95.jpeg \n", + " inflating: AD_NC/test/NC/1253141_96.jpeg \n", + " inflating: AD_NC/test/NC/1253141_97.jpeg \n", + " inflating: AD_NC/test/NC/1253141_98.jpeg \n", + " inflating: AD_NC/test/NC/1253141_99.jpeg \n", + " inflating: AD_NC/test/NC/1253769_100.jpeg \n", + " inflating: AD_NC/test/NC/1253769_101.jpeg \n", + " inflating: AD_NC/test/NC/1253769_102.jpeg \n", + " inflating: AD_NC/test/NC/1253769_103.jpeg \n", + " inflating: AD_NC/test/NC/1253769_104.jpeg \n", + " inflating: AD_NC/test/NC/1253769_105.jpeg \n", + " inflating: AD_NC/test/NC/1253769_106.jpeg \n", + " inflating: AD_NC/test/NC/1253769_107.jpeg \n", + " inflating: AD_NC/test/NC/1253769_88.jpeg \n", + " inflating: AD_NC/test/NC/1253769_89.jpeg \n", + " inflating: AD_NC/test/NC/1253769_90.jpeg \n", + " inflating: AD_NC/test/NC/1253769_91.jpeg \n", + " inflating: AD_NC/test/NC/1253769_92.jpeg \n", + " inflating: AD_NC/test/NC/1253769_93.jpeg \n", + " inflating: AD_NC/test/NC/1253769_94.jpeg \n", + " inflating: AD_NC/test/NC/1253769_95.jpeg \n", + " inflating: AD_NC/test/NC/1253769_96.jpeg \n", + " inflating: AD_NC/test/NC/1253769_97.jpeg \n", + " inflating: AD_NC/test/NC/1253769_98.jpeg \n", + " inflating: AD_NC/test/NC/1253769_99.jpeg \n", + " inflating: AD_NC/test/NC/1253770_100.jpeg \n", + " inflating: AD_NC/test/NC/1253770_101.jpeg \n", + " inflating: AD_NC/test/NC/1253770_102.jpeg \n", + " inflating: AD_NC/test/NC/1253770_103.jpeg \n", + " inflating: AD_NC/test/NC/1253770_104.jpeg \n", + " inflating: AD_NC/test/NC/1253770_105.jpeg \n", + " inflating: AD_NC/test/NC/1253770_106.jpeg \n", + " inflating: AD_NC/test/NC/1253770_107.jpeg \n", + " inflating: AD_NC/test/NC/1253770_88.jpeg \n", + " inflating: AD_NC/test/NC/1253770_89.jpeg \n", + " inflating: AD_NC/test/NC/1253770_90.jpeg \n", + " inflating: AD_NC/test/NC/1253770_91.jpeg \n", + " inflating: AD_NC/test/NC/1253770_92.jpeg \n", + " inflating: AD_NC/test/NC/1253770_93.jpeg \n", + " inflating: AD_NC/test/NC/1253770_94.jpeg \n", + " inflating: AD_NC/test/NC/1253770_95.jpeg \n", + " inflating: AD_NC/test/NC/1253770_96.jpeg \n", + " inflating: AD_NC/test/NC/1253770_97.jpeg \n", + " inflating: AD_NC/test/NC/1253770_98.jpeg \n", + " inflating: AD_NC/test/NC/1253770_99.jpeg \n", + " inflating: AD_NC/test/NC/1254168_100.jpeg \n", + " inflating: AD_NC/test/NC/1254168_101.jpeg \n", + " inflating: AD_NC/test/NC/1254168_102.jpeg \n", + " inflating: AD_NC/test/NC/1254168_103.jpeg \n", + " inflating: AD_NC/test/NC/1254168_104.jpeg \n", + " inflating: AD_NC/test/NC/1254168_105.jpeg \n", + " inflating: AD_NC/test/NC/1254168_106.jpeg \n", + " inflating: AD_NC/test/NC/1254168_107.jpeg \n", + " inflating: AD_NC/test/NC/1254168_108.jpeg \n", + " inflating: AD_NC/test/NC/1254168_109.jpeg \n", + " inflating: AD_NC/test/NC/1254168_110.jpeg \n", + " inflating: AD_NC/test/NC/1254168_111.jpeg \n", + " inflating: AD_NC/test/NC/1254168_112.jpeg \n", + " inflating: AD_NC/test/NC/1254168_113.jpeg \n", + " inflating: AD_NC/test/NC/1254168_114.jpeg \n", + " inflating: AD_NC/test/NC/1254168_95.jpeg \n", + " inflating: AD_NC/test/NC/1254168_96.jpeg \n", + " inflating: AD_NC/test/NC/1254168_97.jpeg \n", + " inflating: AD_NC/test/NC/1254168_98.jpeg \n", + " inflating: AD_NC/test/NC/1254168_99.jpeg \n", + " inflating: AD_NC/test/NC/1254369_78.jpeg \n", + " inflating: AD_NC/test/NC/1254369_79.jpeg \n", + " inflating: AD_NC/test/NC/1254369_80.jpeg \n", + " inflating: AD_NC/test/NC/1254369_81.jpeg \n", + " inflating: AD_NC/test/NC/1254369_82.jpeg \n", + " inflating: AD_NC/test/NC/1254369_83.jpeg \n", + " inflating: AD_NC/test/NC/1254369_84.jpeg \n", + " inflating: AD_NC/test/NC/1254369_85.jpeg \n", + " inflating: AD_NC/test/NC/1254369_86.jpeg \n", + " inflating: AD_NC/test/NC/1254369_87.jpeg \n", + " inflating: AD_NC/test/NC/1254369_88.jpeg \n", + " inflating: AD_NC/test/NC/1254369_89.jpeg \n", + " inflating: AD_NC/test/NC/1254369_90.jpeg \n", + " inflating: AD_NC/test/NC/1254369_91.jpeg \n", + " inflating: AD_NC/test/NC/1254369_92.jpeg \n", + " inflating: AD_NC/test/NC/1254369_93.jpeg \n", + " inflating: AD_NC/test/NC/1254369_94.jpeg \n", + " inflating: AD_NC/test/NC/1254369_95.jpeg \n", + " inflating: AD_NC/test/NC/1254369_96.jpeg \n", + " inflating: AD_NC/test/NC/1254369_97.jpeg \n", + " inflating: AD_NC/test/NC/1254370_78.jpeg \n", + " inflating: AD_NC/test/NC/1254370_79.jpeg \n", + " inflating: AD_NC/test/NC/1254370_80.jpeg \n", + " inflating: AD_NC/test/NC/1254370_81.jpeg \n", + " inflating: AD_NC/test/NC/1254370_82.jpeg \n", + " inflating: AD_NC/test/NC/1254370_83.jpeg \n", + " inflating: AD_NC/test/NC/1254370_84.jpeg \n", + " inflating: AD_NC/test/NC/1254370_85.jpeg \n", + " inflating: AD_NC/test/NC/1254370_86.jpeg \n", + " inflating: AD_NC/test/NC/1254370_87.jpeg \n", + " inflating: AD_NC/test/NC/1254370_88.jpeg \n", + " inflating: AD_NC/test/NC/1254370_89.jpeg \n", + " inflating: AD_NC/test/NC/1254370_90.jpeg \n", + " inflating: AD_NC/test/NC/1254370_91.jpeg \n", + " inflating: AD_NC/test/NC/1254370_92.jpeg \n", + " inflating: AD_NC/test/NC/1254370_93.jpeg \n", + " inflating: AD_NC/test/NC/1254370_94.jpeg \n", + " inflating: AD_NC/test/NC/1254370_95.jpeg \n", + " inflating: AD_NC/test/NC/1254370_96.jpeg \n", + " inflating: AD_NC/test/NC/1254370_97.jpeg \n", + " inflating: AD_NC/test/NC/1254386_78.jpeg \n", + " inflating: AD_NC/test/NC/1254386_79.jpeg \n", + " inflating: AD_NC/test/NC/1254386_80.jpeg \n", + " inflating: AD_NC/test/NC/1254386_81.jpeg \n", + " inflating: AD_NC/test/NC/1254386_82.jpeg \n", + " inflating: AD_NC/test/NC/1254386_83.jpeg \n", + " inflating: AD_NC/test/NC/1254386_84.jpeg \n", + " inflating: AD_NC/test/NC/1254386_85.jpeg \n", + " inflating: AD_NC/test/NC/1254386_86.jpeg \n", + " inflating: AD_NC/test/NC/1254386_87.jpeg \n", + " inflating: AD_NC/test/NC/1254386_88.jpeg \n", + " inflating: AD_NC/test/NC/1254386_89.jpeg \n", + " inflating: AD_NC/test/NC/1254386_90.jpeg \n", + " inflating: AD_NC/test/NC/1254386_91.jpeg \n", + " inflating: AD_NC/test/NC/1254386_92.jpeg \n", + " inflating: AD_NC/test/NC/1254386_93.jpeg \n", + " inflating: AD_NC/test/NC/1254386_94.jpeg \n", + " inflating: AD_NC/test/NC/1254386_95.jpeg \n", + " inflating: AD_NC/test/NC/1254386_96.jpeg \n", + " inflating: AD_NC/test/NC/1254386_97.jpeg \n", + " inflating: AD_NC/test/NC/1255144_100.jpeg \n", + " inflating: AD_NC/test/NC/1255144_101.jpeg \n", + " inflating: AD_NC/test/NC/1255144_102.jpeg \n", + " inflating: AD_NC/test/NC/1255144_103.jpeg \n", + " inflating: AD_NC/test/NC/1255144_104.jpeg \n", + " inflating: AD_NC/test/NC/1255144_105.jpeg \n", + " inflating: AD_NC/test/NC/1255144_106.jpeg \n", + " inflating: AD_NC/test/NC/1255144_107.jpeg \n", + " inflating: AD_NC/test/NC/1255144_108.jpeg \n", + " inflating: AD_NC/test/NC/1255144_109.jpeg \n", + " inflating: AD_NC/test/NC/1255144_110.jpeg \n", + " inflating: AD_NC/test/NC/1255144_111.jpeg \n", + " inflating: AD_NC/test/NC/1255144_112.jpeg \n", + " inflating: AD_NC/test/NC/1255144_113.jpeg \n", + " inflating: AD_NC/test/NC/1255144_94.jpeg \n", + " inflating: AD_NC/test/NC/1255144_95.jpeg \n", + " inflating: AD_NC/test/NC/1255144_96.jpeg \n", + " inflating: AD_NC/test/NC/1255144_97.jpeg \n", + " inflating: AD_NC/test/NC/1255144_98.jpeg \n", + " inflating: AD_NC/test/NC/1255144_99.jpeg \n", + " inflating: AD_NC/test/NC/1255412_100.jpeg \n", + " inflating: AD_NC/test/NC/1255412_101.jpeg \n", + " inflating: AD_NC/test/NC/1255412_102.jpeg \n", + " inflating: AD_NC/test/NC/1255412_103.jpeg \n", + " inflating: AD_NC/test/NC/1255412_104.jpeg \n", + " inflating: AD_NC/test/NC/1255412_105.jpeg \n", + " inflating: AD_NC/test/NC/1255412_106.jpeg \n", + " inflating: AD_NC/test/NC/1255412_107.jpeg \n", + " inflating: AD_NC/test/NC/1255412_108.jpeg \n", + " inflating: AD_NC/test/NC/1255412_109.jpeg \n", + " inflating: AD_NC/test/NC/1255412_110.jpeg \n", + " inflating: AD_NC/test/NC/1255412_111.jpeg \n", + " inflating: AD_NC/test/NC/1255412_112.jpeg \n", + " inflating: AD_NC/test/NC/1255412_113.jpeg \n", + " inflating: AD_NC/test/NC/1255412_94.jpeg \n", + " inflating: AD_NC/test/NC/1255412_95.jpeg \n", + " inflating: AD_NC/test/NC/1255412_96.jpeg \n", + " inflating: AD_NC/test/NC/1255412_97.jpeg \n", + " inflating: AD_NC/test/NC/1255412_98.jpeg \n", + " inflating: AD_NC/test/NC/1255412_99.jpeg \n", + " inflating: AD_NC/test/NC/1255836_100.jpeg \n", + " inflating: AD_NC/test/NC/1255836_101.jpeg \n", + " inflating: AD_NC/test/NC/1255836_102.jpeg \n", + " inflating: AD_NC/test/NC/1255836_103.jpeg \n", + " inflating: AD_NC/test/NC/1255836_104.jpeg \n", + " inflating: AD_NC/test/NC/1255836_105.jpeg \n", + " inflating: AD_NC/test/NC/1255836_106.jpeg \n", + " inflating: AD_NC/test/NC/1255836_107.jpeg \n", + " inflating: AD_NC/test/NC/1255836_108.jpeg \n", + " inflating: AD_NC/test/NC/1255836_109.jpeg \n", + " inflating: AD_NC/test/NC/1255836_110.jpeg \n", + " inflating: AD_NC/test/NC/1255836_111.jpeg \n", + " inflating: AD_NC/test/NC/1255836_112.jpeg \n", + " inflating: AD_NC/test/NC/1255836_113.jpeg \n", + " inflating: AD_NC/test/NC/1255836_94.jpeg \n", + " inflating: AD_NC/test/NC/1255836_95.jpeg \n", + " inflating: AD_NC/test/NC/1255836_96.jpeg \n", + " inflating: AD_NC/test/NC/1255836_97.jpeg \n", + " inflating: AD_NC/test/NC/1255836_98.jpeg \n", + " inflating: AD_NC/test/NC/1255836_99.jpeg \n", + " inflating: AD_NC/test/NC/1256135_100.jpeg \n", + " inflating: AD_NC/test/NC/1256135_101.jpeg \n", + " inflating: AD_NC/test/NC/1256135_102.jpeg \n", + " inflating: AD_NC/test/NC/1256135_103.jpeg \n", + " inflating: AD_NC/test/NC/1256135_104.jpeg \n", + " inflating: AD_NC/test/NC/1256135_105.jpeg \n", + " inflating: AD_NC/test/NC/1256135_106.jpeg \n", + " inflating: AD_NC/test/NC/1256135_107.jpeg \n", + " inflating: AD_NC/test/NC/1256135_108.jpeg \n", + " inflating: AD_NC/test/NC/1256135_109.jpeg \n", + " inflating: AD_NC/test/NC/1256135_110.jpeg \n", + " inflating: AD_NC/test/NC/1256135_111.jpeg \n", + " inflating: AD_NC/test/NC/1256135_112.jpeg \n", + " inflating: AD_NC/test/NC/1256135_113.jpeg \n", + " inflating: AD_NC/test/NC/1256135_94.jpeg \n", + " inflating: AD_NC/test/NC/1256135_95.jpeg \n", + " inflating: AD_NC/test/NC/1256135_96.jpeg \n", + " inflating: AD_NC/test/NC/1256135_97.jpeg \n", + " inflating: AD_NC/test/NC/1256135_98.jpeg \n", + " inflating: AD_NC/test/NC/1256135_99.jpeg \n", + " inflating: AD_NC/test/NC/1256397_100.jpeg \n", + " inflating: AD_NC/test/NC/1256397_101.jpeg \n", + " inflating: AD_NC/test/NC/1256397_102.jpeg \n", + " inflating: AD_NC/test/NC/1256397_103.jpeg \n", + " inflating: AD_NC/test/NC/1256397_104.jpeg \n", + " inflating: AD_NC/test/NC/1256397_105.jpeg \n", + " inflating: AD_NC/test/NC/1256397_106.jpeg \n", + " inflating: AD_NC/test/NC/1256397_107.jpeg \n", + " inflating: AD_NC/test/NC/1256397_108.jpeg \n", + " inflating: AD_NC/test/NC/1256397_109.jpeg \n", + " inflating: AD_NC/test/NC/1256397_110.jpeg \n", + " inflating: AD_NC/test/NC/1256397_111.jpeg \n", + " inflating: AD_NC/test/NC/1256397_112.jpeg \n", + " inflating: AD_NC/test/NC/1256397_113.jpeg \n", + " inflating: AD_NC/test/NC/1256397_94.jpeg \n", + " inflating: AD_NC/test/NC/1256397_95.jpeg \n", + " inflating: AD_NC/test/NC/1256397_96.jpeg \n", + " inflating: AD_NC/test/NC/1256397_97.jpeg \n", + " inflating: AD_NC/test/NC/1256397_98.jpeg \n", + " inflating: AD_NC/test/NC/1256397_99.jpeg \n", + " inflating: AD_NC/test/NC/1256802_78.jpeg \n", + " inflating: AD_NC/test/NC/1256802_79.jpeg \n", + " inflating: AD_NC/test/NC/1256802_80.jpeg \n", + " inflating: AD_NC/test/NC/1256802_81.jpeg \n", + " inflating: AD_NC/test/NC/1256802_82.jpeg \n", + " inflating: AD_NC/test/NC/1256802_83.jpeg \n", + " inflating: AD_NC/test/NC/1256802_84.jpeg \n", + " inflating: AD_NC/test/NC/1256802_85.jpeg \n", + " inflating: AD_NC/test/NC/1256802_86.jpeg \n", + " inflating: AD_NC/test/NC/1256802_87.jpeg \n", + " inflating: AD_NC/test/NC/1256802_88.jpeg \n", + " inflating: AD_NC/test/NC/1256802_89.jpeg \n", + " inflating: AD_NC/test/NC/1256802_90.jpeg \n", + " inflating: AD_NC/test/NC/1256802_91.jpeg \n", + " inflating: AD_NC/test/NC/1256802_92.jpeg \n", + " inflating: AD_NC/test/NC/1256802_93.jpeg \n", + " inflating: AD_NC/test/NC/1256802_94.jpeg \n", + " inflating: AD_NC/test/NC/1256802_95.jpeg \n", + " inflating: AD_NC/test/NC/1256802_96.jpeg \n", + " inflating: AD_NC/test/NC/1256802_97.jpeg \n", + " inflating: AD_NC/test/NC/1257943_100.jpeg \n", + " inflating: AD_NC/test/NC/1257943_101.jpeg \n", + " inflating: AD_NC/test/NC/1257943_102.jpeg \n", + " inflating: AD_NC/test/NC/1257943_103.jpeg \n", + " inflating: AD_NC/test/NC/1257943_104.jpeg \n", + " inflating: AD_NC/test/NC/1257943_105.jpeg \n", + " inflating: AD_NC/test/NC/1257943_106.jpeg \n", + " inflating: AD_NC/test/NC/1257943_107.jpeg \n", + " inflating: AD_NC/test/NC/1257943_88.jpeg \n", + " inflating: AD_NC/test/NC/1257943_89.jpeg \n", + " inflating: AD_NC/test/NC/1257943_90.jpeg \n", + " inflating: AD_NC/test/NC/1257943_91.jpeg \n", + " inflating: AD_NC/test/NC/1257943_92.jpeg \n", + " inflating: AD_NC/test/NC/1257943_93.jpeg \n", + " inflating: AD_NC/test/NC/1257943_94.jpeg \n", + " inflating: AD_NC/test/NC/1257943_95.jpeg \n", + " inflating: AD_NC/test/NC/1257943_96.jpeg \n", + " inflating: AD_NC/test/NC/1257943_97.jpeg \n", + " inflating: AD_NC/test/NC/1257943_98.jpeg \n", + " inflating: AD_NC/test/NC/1257943_99.jpeg \n", + " inflating: AD_NC/test/NC/1259842_100.jpeg \n", + " inflating: AD_NC/test/NC/1259842_101.jpeg \n", + " inflating: AD_NC/test/NC/1259842_102.jpeg \n", + " inflating: AD_NC/test/NC/1259842_103.jpeg \n", + " inflating: AD_NC/test/NC/1259842_104.jpeg \n", + " inflating: AD_NC/test/NC/1259842_105.jpeg \n", + " inflating: AD_NC/test/NC/1259842_106.jpeg \n", + " inflating: AD_NC/test/NC/1259842_107.jpeg \n", + " inflating: AD_NC/test/NC/1259842_108.jpeg \n", + " inflating: AD_NC/test/NC/1259842_109.jpeg \n", + " inflating: AD_NC/test/NC/1259842_110.jpeg \n", + " inflating: AD_NC/test/NC/1259842_111.jpeg \n", + " inflating: AD_NC/test/NC/1259842_112.jpeg \n", + " inflating: AD_NC/test/NC/1259842_113.jpeg \n", + " inflating: AD_NC/test/NC/1259842_114.jpeg \n", + " inflating: AD_NC/test/NC/1259842_95.jpeg \n", + " inflating: AD_NC/test/NC/1259842_96.jpeg \n", + " inflating: AD_NC/test/NC/1259842_97.jpeg \n", + " inflating: AD_NC/test/NC/1259842_98.jpeg \n", + " inflating: AD_NC/test/NC/1259842_99.jpeg \n", + " inflating: AD_NC/test/NC/1261605_78.jpeg \n", + " inflating: AD_NC/test/NC/1261605_79.jpeg \n", + " inflating: AD_NC/test/NC/1261605_80.jpeg \n", + " inflating: AD_NC/test/NC/1261605_81.jpeg \n", + " inflating: AD_NC/test/NC/1261605_82.jpeg \n", + " inflating: AD_NC/test/NC/1261605_83.jpeg \n", + " inflating: AD_NC/test/NC/1261605_84.jpeg \n", + " inflating: AD_NC/test/NC/1261605_85.jpeg \n", + " inflating: AD_NC/test/NC/1261605_86.jpeg \n", + " inflating: AD_NC/test/NC/1261605_87.jpeg \n", + " inflating: AD_NC/test/NC/1261605_88.jpeg \n", + " inflating: AD_NC/test/NC/1261605_89.jpeg \n", + " inflating: AD_NC/test/NC/1261605_90.jpeg \n", + " inflating: AD_NC/test/NC/1261605_91.jpeg \n", + " inflating: AD_NC/test/NC/1261605_92.jpeg \n", + " inflating: AD_NC/test/NC/1261605_93.jpeg \n", + " inflating: AD_NC/test/NC/1261605_94.jpeg \n", + " inflating: AD_NC/test/NC/1261605_95.jpeg \n", + " inflating: AD_NC/test/NC/1261605_96.jpeg \n", + " inflating: AD_NC/test/NC/1261605_97.jpeg \n", + " inflating: AD_NC/test/NC/1262194_78.jpeg \n", + " inflating: AD_NC/test/NC/1262194_79.jpeg \n", + " inflating: AD_NC/test/NC/1262194_80.jpeg \n", + " inflating: AD_NC/test/NC/1262194_81.jpeg \n", + " inflating: AD_NC/test/NC/1262194_82.jpeg \n", + " inflating: AD_NC/test/NC/1262194_83.jpeg \n", + " inflating: AD_NC/test/NC/1262194_84.jpeg \n", + " inflating: AD_NC/test/NC/1262194_85.jpeg \n", + " inflating: AD_NC/test/NC/1262194_86.jpeg \n", + " inflating: AD_NC/test/NC/1262194_87.jpeg \n", + " inflating: AD_NC/test/NC/1262194_88.jpeg \n", + " inflating: AD_NC/test/NC/1262194_89.jpeg \n", + " inflating: AD_NC/test/NC/1262194_90.jpeg \n", + " inflating: AD_NC/test/NC/1262194_91.jpeg \n", + " inflating: AD_NC/test/NC/1262194_92.jpeg \n", + " inflating: AD_NC/test/NC/1262194_93.jpeg \n", + " inflating: AD_NC/test/NC/1262194_94.jpeg \n", + " inflating: AD_NC/test/NC/1262194_95.jpeg \n", + " inflating: AD_NC/test/NC/1262194_96.jpeg \n", + " inflating: AD_NC/test/NC/1262194_97.jpeg \n", + " inflating: AD_NC/test/NC/1262195_78.jpeg \n", + " inflating: AD_NC/test/NC/1262195_79.jpeg \n", + " inflating: AD_NC/test/NC/1262195_80.jpeg \n", + " inflating: AD_NC/test/NC/1262195_81.jpeg \n", + " inflating: AD_NC/test/NC/1262195_82.jpeg \n", + " inflating: AD_NC/test/NC/1262195_83.jpeg \n", + " inflating: AD_NC/test/NC/1262195_84.jpeg \n", + " inflating: AD_NC/test/NC/1262195_85.jpeg \n", + " inflating: AD_NC/test/NC/1262195_86.jpeg \n", + " inflating: AD_NC/test/NC/1262195_87.jpeg \n", + " inflating: AD_NC/test/NC/1262195_88.jpeg \n", + " inflating: AD_NC/test/NC/1262195_89.jpeg \n", + " inflating: AD_NC/test/NC/1262195_90.jpeg \n", + " inflating: AD_NC/test/NC/1262195_91.jpeg \n", + " inflating: AD_NC/test/NC/1262195_92.jpeg \n", + " inflating: AD_NC/test/NC/1262195_93.jpeg \n", + " inflating: AD_NC/test/NC/1262195_94.jpeg \n", + " inflating: AD_NC/test/NC/1262195_95.jpeg \n", + " inflating: AD_NC/test/NC/1262195_96.jpeg \n", + " inflating: AD_NC/test/NC/1262195_97.jpeg \n", + " inflating: AD_NC/test/NC/1263330_100.jpeg \n", + " inflating: AD_NC/test/NC/1263330_101.jpeg \n", + " inflating: AD_NC/test/NC/1263330_102.jpeg \n", + " inflating: AD_NC/test/NC/1263330_103.jpeg \n", + " inflating: AD_NC/test/NC/1263330_104.jpeg \n", + " inflating: AD_NC/test/NC/1263330_105.jpeg \n", + " inflating: AD_NC/test/NC/1263330_106.jpeg \n", + " inflating: AD_NC/test/NC/1263330_107.jpeg \n", + " inflating: AD_NC/test/NC/1263330_88.jpeg \n", + " inflating: AD_NC/test/NC/1263330_89.jpeg \n", + " inflating: AD_NC/test/NC/1263330_90.jpeg \n", + " inflating: AD_NC/test/NC/1263330_91.jpeg \n", + " inflating: AD_NC/test/NC/1263330_92.jpeg \n", + " inflating: AD_NC/test/NC/1263330_93.jpeg \n", + " inflating: AD_NC/test/NC/1263330_94.jpeg \n", + " inflating: AD_NC/test/NC/1263330_95.jpeg \n", + " inflating: AD_NC/test/NC/1263330_96.jpeg \n", + " inflating: AD_NC/test/NC/1263330_97.jpeg \n", + " inflating: AD_NC/test/NC/1263330_98.jpeg \n", + " inflating: AD_NC/test/NC/1263330_99.jpeg \n", + " inflating: AD_NC/test/NC/1263792_100.jpeg \n", + " inflating: AD_NC/test/NC/1263792_101.jpeg \n", + " inflating: AD_NC/test/NC/1263792_102.jpeg \n", + " inflating: AD_NC/test/NC/1263792_103.jpeg \n", + " inflating: AD_NC/test/NC/1263792_104.jpeg \n", + " inflating: AD_NC/test/NC/1263792_105.jpeg \n", + " inflating: AD_NC/test/NC/1263792_106.jpeg \n", + " inflating: AD_NC/test/NC/1263792_107.jpeg \n", + " inflating: AD_NC/test/NC/1263792_108.jpeg \n", + " inflating: AD_NC/test/NC/1263792_109.jpeg \n", + " inflating: AD_NC/test/NC/1263792_110.jpeg \n", + " inflating: AD_NC/test/NC/1263792_111.jpeg \n", + " inflating: AD_NC/test/NC/1263792_112.jpeg \n", + " inflating: AD_NC/test/NC/1263792_113.jpeg \n", + " inflating: AD_NC/test/NC/1263792_94.jpeg \n", + " inflating: AD_NC/test/NC/1263792_95.jpeg \n", + " inflating: AD_NC/test/NC/1263792_96.jpeg \n", + " inflating: AD_NC/test/NC/1263792_97.jpeg \n", + " inflating: AD_NC/test/NC/1263792_98.jpeg \n", + " inflating: AD_NC/test/NC/1263792_99.jpeg \n", + " inflating: AD_NC/test/NC/1263811_100.jpeg \n", + " inflating: AD_NC/test/NC/1263811_101.jpeg \n", + " inflating: AD_NC/test/NC/1263811_102.jpeg \n", + " inflating: AD_NC/test/NC/1263811_103.jpeg \n", + " inflating: AD_NC/test/NC/1263811_104.jpeg \n", + " inflating: AD_NC/test/NC/1263811_105.jpeg \n", + " inflating: AD_NC/test/NC/1263811_106.jpeg \n", + " inflating: AD_NC/test/NC/1263811_107.jpeg \n", + " inflating: AD_NC/test/NC/1263811_108.jpeg \n", + " inflating: AD_NC/test/NC/1263811_109.jpeg \n", + " inflating: AD_NC/test/NC/1263811_110.jpeg \n", + " inflating: AD_NC/test/NC/1263811_111.jpeg \n", + " inflating: AD_NC/test/NC/1263811_112.jpeg \n", + " inflating: AD_NC/test/NC/1263811_113.jpeg \n", + " inflating: AD_NC/test/NC/1263811_94.jpeg \n", + " inflating: AD_NC/test/NC/1263811_95.jpeg \n", + " inflating: AD_NC/test/NC/1263811_96.jpeg \n", + " inflating: AD_NC/test/NC/1263811_97.jpeg \n", + " inflating: AD_NC/test/NC/1263811_98.jpeg \n", + " inflating: AD_NC/test/NC/1263811_99.jpeg \n", + " inflating: AD_NC/test/NC/1264016_100.jpeg \n", + " inflating: AD_NC/test/NC/1264016_101.jpeg \n", + " inflating: AD_NC/test/NC/1264016_102.jpeg \n", + " inflating: AD_NC/test/NC/1264016_103.jpeg \n", + " inflating: AD_NC/test/NC/1264016_104.jpeg \n", + " inflating: AD_NC/test/NC/1264016_105.jpeg \n", + " inflating: AD_NC/test/NC/1264016_106.jpeg \n", + " inflating: AD_NC/test/NC/1264016_107.jpeg \n", + " inflating: AD_NC/test/NC/1264016_108.jpeg \n", + " inflating: AD_NC/test/NC/1264016_109.jpeg \n", + " inflating: AD_NC/test/NC/1264016_110.jpeg \n", + " inflating: AD_NC/test/NC/1264016_111.jpeg \n", + " inflating: AD_NC/test/NC/1264016_112.jpeg \n", + " inflating: AD_NC/test/NC/1264016_113.jpeg \n", + " inflating: AD_NC/test/NC/1264016_94.jpeg \n", + " inflating: AD_NC/test/NC/1264016_95.jpeg \n", + " inflating: AD_NC/test/NC/1264016_96.jpeg \n", + " inflating: AD_NC/test/NC/1264016_97.jpeg \n", + " inflating: AD_NC/test/NC/1264016_98.jpeg \n", + " inflating: AD_NC/test/NC/1264016_99.jpeg \n", + " inflating: AD_NC/test/NC/1264767_100.jpeg \n", + " inflating: AD_NC/test/NC/1264767_101.jpeg \n", + " inflating: AD_NC/test/NC/1264767_102.jpeg \n", + " inflating: AD_NC/test/NC/1264767_103.jpeg \n", + " inflating: AD_NC/test/NC/1264767_104.jpeg \n", + " inflating: AD_NC/test/NC/1264767_105.jpeg \n", + " inflating: AD_NC/test/NC/1264767_106.jpeg \n", + " inflating: AD_NC/test/NC/1264767_107.jpeg \n", + " inflating: AD_NC/test/NC/1264767_108.jpeg \n", + " inflating: AD_NC/test/NC/1264767_109.jpeg \n", + " inflating: AD_NC/test/NC/1264767_110.jpeg \n", + " inflating: AD_NC/test/NC/1264767_111.jpeg \n", + " inflating: AD_NC/test/NC/1264767_112.jpeg \n", + " inflating: AD_NC/test/NC/1264767_113.jpeg \n", + " inflating: AD_NC/test/NC/1264767_94.jpeg \n", + " inflating: AD_NC/test/NC/1264767_95.jpeg \n", + " inflating: AD_NC/test/NC/1264767_96.jpeg \n", + " inflating: AD_NC/test/NC/1264767_97.jpeg \n", + " inflating: AD_NC/test/NC/1264767_98.jpeg \n", + " inflating: AD_NC/test/NC/1264767_99.jpeg \n", + " inflating: AD_NC/test/NC/1265863_78.jpeg \n", + " inflating: AD_NC/test/NC/1265863_79.jpeg \n", + " inflating: AD_NC/test/NC/1265863_80.jpeg \n", + " inflating: AD_NC/test/NC/1265863_81.jpeg \n", + " inflating: AD_NC/test/NC/1265863_82.jpeg \n", + " inflating: AD_NC/test/NC/1265863_83.jpeg \n", + " inflating: AD_NC/test/NC/1265863_84.jpeg \n", + " inflating: AD_NC/test/NC/1265863_85.jpeg \n", + " inflating: AD_NC/test/NC/1265863_86.jpeg \n", + " inflating: AD_NC/test/NC/1265863_87.jpeg \n", + " inflating: AD_NC/test/NC/1265863_88.jpeg \n", + " inflating: AD_NC/test/NC/1265863_89.jpeg \n", + " inflating: AD_NC/test/NC/1265863_90.jpeg \n", + " inflating: AD_NC/test/NC/1265863_91.jpeg \n", + " inflating: AD_NC/test/NC/1265863_92.jpeg \n", + " inflating: AD_NC/test/NC/1265863_93.jpeg \n", + " inflating: AD_NC/test/NC/1265863_94.jpeg \n", + " inflating: AD_NC/test/NC/1265863_95.jpeg \n", + " inflating: AD_NC/test/NC/1265863_96.jpeg \n", + " inflating: AD_NC/test/NC/1265863_97.jpeg \n", + " inflating: AD_NC/test/NC/1266356_100.jpeg \n", + " inflating: AD_NC/test/NC/1266356_101.jpeg \n", + " inflating: AD_NC/test/NC/1266356_102.jpeg \n", + " inflating: AD_NC/test/NC/1266356_103.jpeg \n", + " inflating: AD_NC/test/NC/1266356_104.jpeg \n", + " inflating: AD_NC/test/NC/1266356_105.jpeg \n", + " inflating: AD_NC/test/NC/1266356_106.jpeg \n", + " inflating: AD_NC/test/NC/1266356_107.jpeg \n", + " inflating: AD_NC/test/NC/1266356_108.jpeg \n", + " inflating: AD_NC/test/NC/1266356_109.jpeg \n", + " inflating: AD_NC/test/NC/1266356_110.jpeg \n", + " inflating: AD_NC/test/NC/1266356_111.jpeg \n", + " inflating: AD_NC/test/NC/1266356_112.jpeg \n", + " inflating: AD_NC/test/NC/1266356_113.jpeg \n", + " inflating: AD_NC/test/NC/1266356_94.jpeg \n", + " inflating: AD_NC/test/NC/1266356_95.jpeg \n", + " inflating: AD_NC/test/NC/1266356_96.jpeg \n", + " inflating: AD_NC/test/NC/1266356_97.jpeg \n", + " inflating: AD_NC/test/NC/1266356_98.jpeg \n", + " inflating: AD_NC/test/NC/1266356_99.jpeg \n", + " inflating: AD_NC/test/NC/1266559_100.jpeg \n", + " inflating: AD_NC/test/NC/1266559_101.jpeg \n", + " inflating: AD_NC/test/NC/1266559_102.jpeg \n", + " inflating: AD_NC/test/NC/1266559_103.jpeg \n", + " inflating: AD_NC/test/NC/1266559_104.jpeg \n", + " inflating: AD_NC/test/NC/1266559_105.jpeg \n", + " inflating: AD_NC/test/NC/1266559_106.jpeg \n", + " inflating: AD_NC/test/NC/1266559_107.jpeg \n", + " inflating: AD_NC/test/NC/1266559_108.jpeg \n", + " inflating: AD_NC/test/NC/1266559_109.jpeg \n", + " inflating: AD_NC/test/NC/1266559_110.jpeg \n", + " inflating: AD_NC/test/NC/1266559_111.jpeg \n", + " inflating: AD_NC/test/NC/1266559_112.jpeg \n", + " inflating: AD_NC/test/NC/1266559_113.jpeg \n", + " inflating: AD_NC/test/NC/1266559_94.jpeg \n", + " inflating: AD_NC/test/NC/1266559_95.jpeg \n", + " inflating: AD_NC/test/NC/1266559_96.jpeg \n", + " inflating: AD_NC/test/NC/1266559_97.jpeg \n", + " inflating: AD_NC/test/NC/1266559_98.jpeg \n", + " inflating: AD_NC/test/NC/1266559_99.jpeg \n", + " inflating: AD_NC/test/NC/1267882_100.jpeg \n", + " inflating: AD_NC/test/NC/1267882_101.jpeg \n", + " inflating: AD_NC/test/NC/1267882_102.jpeg \n", + " inflating: AD_NC/test/NC/1267882_103.jpeg \n", + " inflating: AD_NC/test/NC/1267882_104.jpeg \n", + " inflating: AD_NC/test/NC/1267882_105.jpeg \n", + " inflating: AD_NC/test/NC/1267882_106.jpeg \n", + " inflating: AD_NC/test/NC/1267882_107.jpeg \n", + " inflating: AD_NC/test/NC/1267882_108.jpeg \n", + " inflating: AD_NC/test/NC/1267882_109.jpeg \n", + " inflating: AD_NC/test/NC/1267882_110.jpeg \n", + " inflating: AD_NC/test/NC/1267882_111.jpeg \n", + " inflating: AD_NC/test/NC/1267882_112.jpeg \n", + " inflating: AD_NC/test/NC/1267882_113.jpeg \n", + " inflating: AD_NC/test/NC/1267882_94.jpeg \n", + " inflating: AD_NC/test/NC/1267882_95.jpeg \n", + " inflating: AD_NC/test/NC/1267882_96.jpeg \n", + " inflating: AD_NC/test/NC/1267882_97.jpeg \n", + " inflating: AD_NC/test/NC/1267882_98.jpeg \n", + " inflating: AD_NC/test/NC/1267882_99.jpeg \n", + " inflating: AD_NC/test/NC/1268293_100.jpeg \n", + " inflating: AD_NC/test/NC/1268293_101.jpeg \n", + " inflating: AD_NC/test/NC/1268293_102.jpeg \n", + " inflating: AD_NC/test/NC/1268293_103.jpeg \n", + " inflating: AD_NC/test/NC/1268293_104.jpeg \n", + " inflating: AD_NC/test/NC/1268293_105.jpeg \n", + " inflating: AD_NC/test/NC/1268293_106.jpeg \n", + " inflating: AD_NC/test/NC/1268293_107.jpeg \n", + " inflating: AD_NC/test/NC/1268293_88.jpeg \n", + " inflating: AD_NC/test/NC/1268293_89.jpeg \n", + " inflating: AD_NC/test/NC/1268293_90.jpeg \n", + " inflating: AD_NC/test/NC/1268293_91.jpeg \n", + " inflating: AD_NC/test/NC/1268293_92.jpeg \n", + " inflating: AD_NC/test/NC/1268293_93.jpeg \n", + " inflating: AD_NC/test/NC/1268293_94.jpeg \n", + " inflating: AD_NC/test/NC/1268293_95.jpeg \n", + " inflating: AD_NC/test/NC/1268293_96.jpeg \n", + " inflating: AD_NC/test/NC/1268293_97.jpeg \n", + " inflating: AD_NC/test/NC/1268293_98.jpeg \n", + " inflating: AD_NC/test/NC/1268293_99.jpeg \n", + " inflating: AD_NC/test/NC/1270020_100.jpeg \n", + " inflating: AD_NC/test/NC/1270020_101.jpeg \n", + " inflating: AD_NC/test/NC/1270020_102.jpeg \n", + " inflating: AD_NC/test/NC/1270020_103.jpeg \n", + " inflating: AD_NC/test/NC/1270020_104.jpeg \n", + " inflating: AD_NC/test/NC/1270020_105.jpeg \n", + " inflating: AD_NC/test/NC/1270020_106.jpeg \n", + " inflating: AD_NC/test/NC/1270020_107.jpeg \n", + " inflating: AD_NC/test/NC/1270020_108.jpeg \n", + " inflating: AD_NC/test/NC/1270020_109.jpeg \n", + " inflating: AD_NC/test/NC/1270020_110.jpeg \n", + " inflating: AD_NC/test/NC/1270020_111.jpeg \n", + " inflating: AD_NC/test/NC/1270020_112.jpeg \n", + " inflating: AD_NC/test/NC/1270020_113.jpeg \n", + " inflating: AD_NC/test/NC/1270020_94.jpeg \n", + " inflating: AD_NC/test/NC/1270020_95.jpeg \n", + " inflating: AD_NC/test/NC/1270020_96.jpeg \n", + " inflating: AD_NC/test/NC/1270020_97.jpeg \n", + " inflating: AD_NC/test/NC/1270020_98.jpeg \n", + " inflating: AD_NC/test/NC/1270020_99.jpeg \n", + " inflating: AD_NC/test/NC/1270100_100.jpeg \n", + " inflating: AD_NC/test/NC/1270100_101.jpeg \n", + " inflating: AD_NC/test/NC/1270100_102.jpeg \n", + " inflating: AD_NC/test/NC/1270100_103.jpeg \n", + " inflating: AD_NC/test/NC/1270100_104.jpeg \n", + " inflating: AD_NC/test/NC/1270100_105.jpeg \n", + " inflating: AD_NC/test/NC/1270100_106.jpeg \n", + " inflating: AD_NC/test/NC/1270100_107.jpeg \n", + " inflating: AD_NC/test/NC/1270100_108.jpeg \n", + " inflating: AD_NC/test/NC/1270100_109.jpeg \n", + " inflating: AD_NC/test/NC/1270100_110.jpeg \n", + " inflating: AD_NC/test/NC/1270100_111.jpeg \n", + " inflating: AD_NC/test/NC/1270100_112.jpeg \n", + " inflating: AD_NC/test/NC/1270100_113.jpeg \n", + " inflating: AD_NC/test/NC/1270100_94.jpeg \n", + " inflating: AD_NC/test/NC/1270100_95.jpeg \n", + " inflating: AD_NC/test/NC/1270100_96.jpeg \n", + " inflating: AD_NC/test/NC/1270100_97.jpeg \n", + " inflating: AD_NC/test/NC/1270100_98.jpeg \n", + " inflating: AD_NC/test/NC/1270100_99.jpeg \n", + " inflating: AD_NC/test/NC/1272868_100.jpeg \n", + " inflating: AD_NC/test/NC/1272868_101.jpeg \n", + " inflating: AD_NC/test/NC/1272868_102.jpeg \n", + " inflating: AD_NC/test/NC/1272868_103.jpeg \n", + " inflating: AD_NC/test/NC/1272868_104.jpeg \n", + " inflating: AD_NC/test/NC/1272868_105.jpeg \n", + " inflating: AD_NC/test/NC/1272868_106.jpeg \n", + " inflating: AD_NC/test/NC/1272868_107.jpeg \n", + " inflating: AD_NC/test/NC/1272868_108.jpeg \n", + " inflating: AD_NC/test/NC/1272868_109.jpeg \n", + " inflating: AD_NC/test/NC/1272868_110.jpeg \n", + " inflating: AD_NC/test/NC/1272868_111.jpeg \n", + " inflating: AD_NC/test/NC/1272868_112.jpeg \n", + " inflating: AD_NC/test/NC/1272868_113.jpeg \n", + " inflating: AD_NC/test/NC/1272868_94.jpeg \n", + " inflating: AD_NC/test/NC/1272868_95.jpeg \n", + " inflating: AD_NC/test/NC/1272868_96.jpeg \n", + " inflating: AD_NC/test/NC/1272868_97.jpeg \n", + " inflating: AD_NC/test/NC/1272868_98.jpeg \n", + " inflating: AD_NC/test/NC/1272868_99.jpeg \n", + " inflating: AD_NC/test/NC/1273042_100.jpeg \n", + " inflating: AD_NC/test/NC/1273042_101.jpeg \n", + " inflating: AD_NC/test/NC/1273042_102.jpeg \n", + " inflating: AD_NC/test/NC/1273042_103.jpeg \n", + " inflating: AD_NC/test/NC/1273042_104.jpeg \n", + " inflating: AD_NC/test/NC/1273042_105.jpeg \n", + " inflating: AD_NC/test/NC/1273042_106.jpeg \n", + " inflating: AD_NC/test/NC/1273042_107.jpeg \n", + " inflating: AD_NC/test/NC/1273042_88.jpeg \n", + " inflating: AD_NC/test/NC/1273042_89.jpeg \n", + " inflating: AD_NC/test/NC/1273042_90.jpeg \n", + " inflating: AD_NC/test/NC/1273042_91.jpeg \n", + " inflating: AD_NC/test/NC/1273042_92.jpeg \n", + " inflating: AD_NC/test/NC/1273042_93.jpeg \n", + " inflating: AD_NC/test/NC/1273042_94.jpeg \n", + " inflating: AD_NC/test/NC/1273042_95.jpeg \n", + " inflating: AD_NC/test/NC/1273042_96.jpeg \n", + " inflating: AD_NC/test/NC/1273042_97.jpeg \n", + " inflating: AD_NC/test/NC/1273042_98.jpeg \n", + " inflating: AD_NC/test/NC/1273042_99.jpeg \n", + " inflating: AD_NC/test/NC/1274602_78.jpeg \n", + " inflating: AD_NC/test/NC/1274602_79.jpeg \n", + " inflating: AD_NC/test/NC/1274602_80.jpeg \n", + " inflating: AD_NC/test/NC/1274602_81.jpeg \n", + " inflating: AD_NC/test/NC/1274602_82.jpeg \n", + " inflating: AD_NC/test/NC/1274602_83.jpeg \n", + " inflating: AD_NC/test/NC/1274602_84.jpeg \n", + " inflating: AD_NC/test/NC/1274602_85.jpeg \n", + " inflating: AD_NC/test/NC/1274602_86.jpeg \n", + " inflating: AD_NC/test/NC/1274602_87.jpeg \n", + " inflating: AD_NC/test/NC/1274602_88.jpeg \n", + " inflating: AD_NC/test/NC/1274602_89.jpeg \n", + " inflating: AD_NC/test/NC/1274602_90.jpeg \n", + " inflating: AD_NC/test/NC/1274602_91.jpeg \n", + " inflating: AD_NC/test/NC/1274602_92.jpeg \n", + " inflating: AD_NC/test/NC/1274602_93.jpeg \n", + " inflating: AD_NC/test/NC/1274602_94.jpeg \n", + " inflating: AD_NC/test/NC/1274602_95.jpeg \n", + " inflating: AD_NC/test/NC/1274602_96.jpeg \n", + " inflating: AD_NC/test/NC/1274602_97.jpeg \n", + " inflating: AD_NC/test/NC/1274735_100.jpeg \n", + " inflating: AD_NC/test/NC/1274735_101.jpeg \n", + " inflating: AD_NC/test/NC/1274735_102.jpeg \n", + " inflating: AD_NC/test/NC/1274735_103.jpeg \n", + " inflating: AD_NC/test/NC/1274735_104.jpeg \n", + " inflating: AD_NC/test/NC/1274735_105.jpeg \n", + " inflating: AD_NC/test/NC/1274735_106.jpeg \n", + " inflating: AD_NC/test/NC/1274735_107.jpeg \n", + " inflating: AD_NC/test/NC/1274735_108.jpeg \n", + " inflating: AD_NC/test/NC/1274735_109.jpeg \n", + " inflating: AD_NC/test/NC/1274735_110.jpeg \n", + " inflating: AD_NC/test/NC/1274735_111.jpeg \n", + " inflating: AD_NC/test/NC/1274735_112.jpeg \n", + " inflating: AD_NC/test/NC/1274735_113.jpeg \n", + " inflating: AD_NC/test/NC/1274735_114.jpeg \n", + " inflating: AD_NC/test/NC/1274735_95.jpeg \n", + " inflating: AD_NC/test/NC/1274735_96.jpeg \n", + " inflating: AD_NC/test/NC/1274735_97.jpeg \n", + " inflating: AD_NC/test/NC/1274735_98.jpeg \n", + " inflating: AD_NC/test/NC/1274735_99.jpeg \n", + " inflating: AD_NC/test/NC/1276990_100.jpeg \n", + " inflating: AD_NC/test/NC/1276990_101.jpeg \n", + " inflating: AD_NC/test/NC/1276990_102.jpeg \n", + " inflating: AD_NC/test/NC/1276990_103.jpeg \n", + " inflating: AD_NC/test/NC/1276990_104.jpeg \n", + " inflating: AD_NC/test/NC/1276990_105.jpeg \n", + " inflating: AD_NC/test/NC/1276990_106.jpeg \n", + " inflating: AD_NC/test/NC/1276990_107.jpeg \n", + " inflating: AD_NC/test/NC/1276990_88.jpeg \n", + " inflating: AD_NC/test/NC/1276990_89.jpeg \n", + " inflating: AD_NC/test/NC/1276990_90.jpeg \n", + " inflating: AD_NC/test/NC/1276990_91.jpeg \n", + " inflating: AD_NC/test/NC/1276990_92.jpeg \n", + " inflating: AD_NC/test/NC/1276990_93.jpeg \n", + " inflating: AD_NC/test/NC/1276990_94.jpeg \n", + " inflating: AD_NC/test/NC/1276990_95.jpeg \n", + " inflating: AD_NC/test/NC/1276990_96.jpeg \n", + " inflating: AD_NC/test/NC/1276990_97.jpeg \n", + " inflating: AD_NC/test/NC/1276990_98.jpeg \n", + " inflating: AD_NC/test/NC/1276990_99.jpeg \n", + " inflating: AD_NC/test/NC/1277389_100.jpeg \n", + " inflating: AD_NC/test/NC/1277389_101.jpeg \n", + " inflating: AD_NC/test/NC/1277389_102.jpeg \n", + " inflating: AD_NC/test/NC/1277389_103.jpeg \n", + " inflating: AD_NC/test/NC/1277389_104.jpeg \n", + " inflating: AD_NC/test/NC/1277389_105.jpeg \n", + " inflating: AD_NC/test/NC/1277389_106.jpeg \n", + " inflating: AD_NC/test/NC/1277389_107.jpeg \n", + " inflating: AD_NC/test/NC/1277389_88.jpeg \n", + " inflating: AD_NC/test/NC/1277389_89.jpeg \n", + " inflating: AD_NC/test/NC/1277389_90.jpeg \n", + " inflating: AD_NC/test/NC/1277389_91.jpeg \n", + " inflating: AD_NC/test/NC/1277389_92.jpeg \n", + " inflating: AD_NC/test/NC/1277389_93.jpeg \n", + " inflating: AD_NC/test/NC/1277389_94.jpeg \n", + " inflating: AD_NC/test/NC/1277389_95.jpeg \n", + " inflating: AD_NC/test/NC/1277389_96.jpeg \n", + " inflating: AD_NC/test/NC/1277389_97.jpeg \n", + " inflating: AD_NC/test/NC/1277389_98.jpeg \n", + " inflating: AD_NC/test/NC/1277389_99.jpeg \n", + " inflating: AD_NC/test/NC/1277390_100.jpeg \n", + " inflating: AD_NC/test/NC/1277390_101.jpeg \n", + " inflating: AD_NC/test/NC/1277390_102.jpeg \n", + " inflating: AD_NC/test/NC/1277390_103.jpeg \n", + " inflating: AD_NC/test/NC/1277390_104.jpeg \n", + " inflating: AD_NC/test/NC/1277390_105.jpeg \n", + " inflating: AD_NC/test/NC/1277390_106.jpeg \n", + " inflating: AD_NC/test/NC/1277390_107.jpeg \n", + " inflating: AD_NC/test/NC/1277390_88.jpeg \n", + " inflating: AD_NC/test/NC/1277390_89.jpeg \n", + " inflating: AD_NC/test/NC/1277390_90.jpeg \n", + " inflating: AD_NC/test/NC/1277390_91.jpeg \n", + " inflating: AD_NC/test/NC/1277390_92.jpeg \n", + " inflating: AD_NC/test/NC/1277390_93.jpeg \n", + " inflating: AD_NC/test/NC/1277390_94.jpeg \n", + " inflating: AD_NC/test/NC/1277390_95.jpeg \n", + " inflating: AD_NC/test/NC/1277390_96.jpeg \n", + " inflating: AD_NC/test/NC/1277390_97.jpeg \n", + " inflating: AD_NC/test/NC/1277390_98.jpeg \n", + " inflating: AD_NC/test/NC/1277390_99.jpeg \n", + " inflating: AD_NC/test/NC/1278606_100.jpeg \n", + " inflating: AD_NC/test/NC/1278606_101.jpeg \n", + " inflating: AD_NC/test/NC/1278606_102.jpeg \n", + " inflating: AD_NC/test/NC/1278606_103.jpeg \n", + " inflating: AD_NC/test/NC/1278606_104.jpeg \n", + " inflating: AD_NC/test/NC/1278606_105.jpeg \n", + " inflating: AD_NC/test/NC/1278606_106.jpeg \n", + " inflating: AD_NC/test/NC/1278606_107.jpeg \n", + " inflating: AD_NC/test/NC/1278606_108.jpeg \n", + " inflating: AD_NC/test/NC/1278606_109.jpeg \n", + " inflating: AD_NC/test/NC/1278606_110.jpeg \n", + " inflating: AD_NC/test/NC/1278606_111.jpeg \n", + " inflating: AD_NC/test/NC/1278606_112.jpeg \n", + " inflating: AD_NC/test/NC/1278606_113.jpeg \n", + " inflating: AD_NC/test/NC/1278606_94.jpeg \n", + " inflating: AD_NC/test/NC/1278606_95.jpeg \n", + " inflating: AD_NC/test/NC/1278606_96.jpeg \n", + " inflating: AD_NC/test/NC/1278606_97.jpeg \n", + " inflating: AD_NC/test/NC/1278606_98.jpeg \n", + " inflating: AD_NC/test/NC/1278606_99.jpeg \n", + " inflating: AD_NC/test/NC/1278640_100.jpeg \n", + " inflating: AD_NC/test/NC/1278640_101.jpeg \n", + " inflating: AD_NC/test/NC/1278640_102.jpeg \n", + " inflating: AD_NC/test/NC/1278640_103.jpeg \n", + " inflating: AD_NC/test/NC/1278640_104.jpeg \n", + " inflating: AD_NC/test/NC/1278640_105.jpeg \n", + " inflating: AD_NC/test/NC/1278640_106.jpeg \n", + " inflating: AD_NC/test/NC/1278640_107.jpeg \n", + " inflating: AD_NC/test/NC/1278640_108.jpeg \n", + " inflating: AD_NC/test/NC/1278640_109.jpeg \n", + " inflating: AD_NC/test/NC/1278640_110.jpeg \n", + " inflating: AD_NC/test/NC/1278640_111.jpeg \n", + " inflating: AD_NC/test/NC/1278640_112.jpeg \n", + " inflating: AD_NC/test/NC/1278640_113.jpeg \n", + " inflating: AD_NC/test/NC/1278640_94.jpeg \n", + " inflating: AD_NC/test/NC/1278640_95.jpeg \n", + " inflating: AD_NC/test/NC/1278640_96.jpeg \n", + " inflating: AD_NC/test/NC/1278640_97.jpeg \n", + " inflating: AD_NC/test/NC/1278640_98.jpeg \n", + " inflating: AD_NC/test/NC/1278640_99.jpeg \n", + " inflating: AD_NC/test/NC/1278852_100.jpeg \n", + " inflating: AD_NC/test/NC/1278852_101.jpeg \n", + " inflating: AD_NC/test/NC/1278852_102.jpeg \n", + " inflating: AD_NC/test/NC/1278852_103.jpeg \n", + " inflating: AD_NC/test/NC/1278852_104.jpeg \n", + " inflating: AD_NC/test/NC/1278852_105.jpeg \n", + " inflating: AD_NC/test/NC/1278852_106.jpeg \n", + " inflating: AD_NC/test/NC/1278852_107.jpeg \n", + " inflating: AD_NC/test/NC/1278852_108.jpeg \n", + " inflating: AD_NC/test/NC/1278852_109.jpeg \n", + " inflating: AD_NC/test/NC/1278852_110.jpeg \n", + " inflating: AD_NC/test/NC/1278852_111.jpeg \n", + " inflating: AD_NC/test/NC/1278852_112.jpeg \n", + " inflating: AD_NC/test/NC/1278852_113.jpeg \n", + " inflating: AD_NC/test/NC/1278852_114.jpeg \n", + " inflating: AD_NC/test/NC/1278852_95.jpeg \n", + " inflating: AD_NC/test/NC/1278852_96.jpeg \n", + " inflating: AD_NC/test/NC/1278852_97.jpeg \n", + " inflating: AD_NC/test/NC/1278852_98.jpeg \n", + " inflating: AD_NC/test/NC/1278852_99.jpeg \n", + " inflating: AD_NC/test/NC/1280292_78.jpeg \n", + " inflating: AD_NC/test/NC/1280292_79.jpeg \n", + " inflating: AD_NC/test/NC/1280292_80.jpeg \n", + " inflating: AD_NC/test/NC/1280292_81.jpeg \n", + " inflating: AD_NC/test/NC/1280292_82.jpeg \n", + " inflating: AD_NC/test/NC/1280292_83.jpeg \n", + " inflating: AD_NC/test/NC/1280292_84.jpeg \n", + " inflating: AD_NC/test/NC/1280292_85.jpeg \n", + " inflating: AD_NC/test/NC/1280292_86.jpeg \n", + " inflating: AD_NC/test/NC/1280292_87.jpeg \n", + " inflating: AD_NC/test/NC/1280292_88.jpeg \n", + " inflating: AD_NC/test/NC/1280292_89.jpeg \n", + " inflating: AD_NC/test/NC/1280292_90.jpeg \n", + " inflating: AD_NC/test/NC/1280292_91.jpeg \n", + " inflating: AD_NC/test/NC/1280292_92.jpeg \n", + " inflating: AD_NC/test/NC/1280292_93.jpeg \n", + " inflating: AD_NC/test/NC/1280292_94.jpeg \n", + " inflating: AD_NC/test/NC/1280292_95.jpeg \n", + " inflating: AD_NC/test/NC/1280292_96.jpeg \n", + " inflating: AD_NC/test/NC/1280292_97.jpeg \n", + " inflating: AD_NC/test/NC/1280797_78.jpeg \n", + " inflating: AD_NC/test/NC/1280797_79.jpeg \n", + " inflating: AD_NC/test/NC/1280797_80.jpeg \n", + " inflating: AD_NC/test/NC/1280797_81.jpeg \n", + " inflating: AD_NC/test/NC/1280797_82.jpeg \n", + " inflating: AD_NC/test/NC/1280797_83.jpeg \n", + " inflating: AD_NC/test/NC/1280797_84.jpeg \n", + " inflating: AD_NC/test/NC/1280797_85.jpeg \n", + " inflating: AD_NC/test/NC/1280797_86.jpeg \n", + " inflating: AD_NC/test/NC/1280797_87.jpeg \n", + " inflating: AD_NC/test/NC/1280797_88.jpeg \n", + " inflating: AD_NC/test/NC/1280797_89.jpeg \n", + " inflating: AD_NC/test/NC/1280797_90.jpeg \n", + " inflating: AD_NC/test/NC/1280797_91.jpeg \n", + " inflating: AD_NC/test/NC/1280797_92.jpeg \n", + " inflating: AD_NC/test/NC/1280797_93.jpeg \n", + " inflating: AD_NC/test/NC/1280797_94.jpeg \n", + " inflating: AD_NC/test/NC/1280797_95.jpeg \n", + " inflating: AD_NC/test/NC/1280797_96.jpeg \n", + " inflating: AD_NC/test/NC/1280797_97.jpeg \n", + " inflating: AD_NC/test/NC/1281566_78.jpeg \n", + " inflating: AD_NC/test/NC/1281566_79.jpeg \n", + " inflating: AD_NC/test/NC/1281566_80.jpeg \n", + " inflating: AD_NC/test/NC/1281566_81.jpeg \n", + " inflating: AD_NC/test/NC/1281566_82.jpeg \n", + " inflating: AD_NC/test/NC/1281566_83.jpeg \n", + " inflating: AD_NC/test/NC/1281566_84.jpeg \n", + " inflating: AD_NC/test/NC/1281566_85.jpeg \n", + " inflating: AD_NC/test/NC/1281566_86.jpeg \n", + " inflating: AD_NC/test/NC/1281566_87.jpeg \n", + " inflating: AD_NC/test/NC/1281566_88.jpeg \n", + " inflating: AD_NC/test/NC/1281566_89.jpeg \n", + " inflating: AD_NC/test/NC/1281566_90.jpeg \n", + " inflating: AD_NC/test/NC/1281566_91.jpeg \n", + " inflating: AD_NC/test/NC/1281566_92.jpeg \n", + " inflating: AD_NC/test/NC/1281566_93.jpeg \n", + " inflating: AD_NC/test/NC/1281566_94.jpeg \n", + " inflating: AD_NC/test/NC/1281566_95.jpeg \n", + " inflating: AD_NC/test/NC/1281566_96.jpeg \n", + " inflating: AD_NC/test/NC/1281566_97.jpeg \n", + " inflating: AD_NC/test/NC/1281567_78.jpeg \n", + " inflating: AD_NC/test/NC/1281567_79.jpeg \n", + " inflating: AD_NC/test/NC/1281567_80.jpeg \n", + " inflating: AD_NC/test/NC/1281567_81.jpeg \n", + " inflating: AD_NC/test/NC/1281567_82.jpeg \n", + " inflating: AD_NC/test/NC/1281567_83.jpeg \n", + " inflating: AD_NC/test/NC/1281567_84.jpeg \n", + " inflating: AD_NC/test/NC/1281567_85.jpeg \n", + " inflating: AD_NC/test/NC/1281567_86.jpeg \n", + " inflating: AD_NC/test/NC/1281567_87.jpeg \n", + " inflating: AD_NC/test/NC/1281567_88.jpeg \n", + " inflating: AD_NC/test/NC/1281567_89.jpeg \n", + " inflating: AD_NC/test/NC/1281567_90.jpeg \n", + " inflating: AD_NC/test/NC/1281567_91.jpeg \n", + " inflating: AD_NC/test/NC/1281567_92.jpeg \n", + " inflating: AD_NC/test/NC/1281567_93.jpeg \n", + " inflating: AD_NC/test/NC/1281567_94.jpeg \n", + " inflating: AD_NC/test/NC/1281567_95.jpeg \n", + " inflating: AD_NC/test/NC/1281567_96.jpeg \n", + " inflating: AD_NC/test/NC/1281567_97.jpeg \n", + " inflating: AD_NC/test/NC/1281631_100.jpeg \n", + " inflating: AD_NC/test/NC/1281631_101.jpeg \n", + " inflating: AD_NC/test/NC/1281631_102.jpeg \n", + " inflating: AD_NC/test/NC/1281631_103.jpeg \n", + " inflating: AD_NC/test/NC/1281631_104.jpeg \n", + " inflating: AD_NC/test/NC/1281631_105.jpeg \n", + " inflating: AD_NC/test/NC/1281631_106.jpeg \n", + " inflating: AD_NC/test/NC/1281631_107.jpeg \n", + " inflating: AD_NC/test/NC/1281631_108.jpeg \n", + " inflating: AD_NC/test/NC/1281631_109.jpeg \n", + " inflating: AD_NC/test/NC/1281631_110.jpeg \n", + " inflating: AD_NC/test/NC/1281631_111.jpeg \n", + " inflating: AD_NC/test/NC/1281631_112.jpeg \n", + " inflating: AD_NC/test/NC/1281631_113.jpeg \n", + " inflating: AD_NC/test/NC/1281631_94.jpeg \n", + " inflating: AD_NC/test/NC/1281631_95.jpeg \n", + " inflating: AD_NC/test/NC/1281631_96.jpeg \n", + " inflating: AD_NC/test/NC/1281631_97.jpeg \n", + " inflating: AD_NC/test/NC/1281631_98.jpeg \n", + " inflating: AD_NC/test/NC/1281631_99.jpeg \n", + " inflating: AD_NC/test/NC/1284408_100.jpeg \n", + " inflating: AD_NC/test/NC/1284408_101.jpeg \n", + " inflating: AD_NC/test/NC/1284408_102.jpeg \n", + " inflating: AD_NC/test/NC/1284408_103.jpeg \n", + " inflating: AD_NC/test/NC/1284408_104.jpeg \n", + " inflating: AD_NC/test/NC/1284408_105.jpeg \n", + " inflating: AD_NC/test/NC/1284408_106.jpeg \n", + " inflating: AD_NC/test/NC/1284408_107.jpeg \n", + " inflating: AD_NC/test/NC/1284408_108.jpeg \n", + " inflating: AD_NC/test/NC/1284408_109.jpeg \n", + " inflating: AD_NC/test/NC/1284408_110.jpeg \n", + " inflating: AD_NC/test/NC/1284408_111.jpeg \n", + " inflating: AD_NC/test/NC/1284408_112.jpeg \n", + " inflating: AD_NC/test/NC/1284408_113.jpeg \n", + " inflating: AD_NC/test/NC/1284408_94.jpeg \n", + " inflating: AD_NC/test/NC/1284408_95.jpeg \n", + " inflating: AD_NC/test/NC/1284408_96.jpeg \n", + " inflating: AD_NC/test/NC/1284408_97.jpeg \n", + " inflating: AD_NC/test/NC/1284408_98.jpeg \n", + " inflating: AD_NC/test/NC/1284408_99.jpeg \n", + " inflating: AD_NC/test/NC/1284808_100.jpeg \n", + " inflating: AD_NC/test/NC/1284808_101.jpeg \n", + " inflating: AD_NC/test/NC/1284808_102.jpeg \n", + " inflating: AD_NC/test/NC/1284808_103.jpeg \n", + " inflating: AD_NC/test/NC/1284808_104.jpeg \n", + " inflating: AD_NC/test/NC/1284808_105.jpeg \n", + " inflating: AD_NC/test/NC/1284808_106.jpeg \n", + " inflating: AD_NC/test/NC/1284808_107.jpeg \n", + " inflating: AD_NC/test/NC/1284808_108.jpeg \n", + " inflating: AD_NC/test/NC/1284808_109.jpeg \n", + " inflating: AD_NC/test/NC/1284808_110.jpeg \n", + " inflating: AD_NC/test/NC/1284808_111.jpeg \n", + " inflating: AD_NC/test/NC/1284808_112.jpeg \n", + " inflating: AD_NC/test/NC/1284808_113.jpeg \n", + " inflating: AD_NC/test/NC/1284808_94.jpeg \n", + " inflating: AD_NC/test/NC/1284808_95.jpeg \n", + " inflating: AD_NC/test/NC/1284808_96.jpeg \n", + " inflating: AD_NC/test/NC/1284808_97.jpeg \n", + " inflating: AD_NC/test/NC/1284808_98.jpeg \n", + " inflating: AD_NC/test/NC/1284808_99.jpeg \n", + " inflating: AD_NC/test/NC/1285188_100.jpeg \n", + " inflating: AD_NC/test/NC/1285188_101.jpeg \n", + " inflating: AD_NC/test/NC/1285188_102.jpeg \n", + " inflating: AD_NC/test/NC/1285188_103.jpeg \n", + " inflating: AD_NC/test/NC/1285188_104.jpeg \n", + " inflating: AD_NC/test/NC/1285188_105.jpeg \n", + " inflating: AD_NC/test/NC/1285188_106.jpeg \n", + " inflating: AD_NC/test/NC/1285188_107.jpeg \n", + " inflating: AD_NC/test/NC/1285188_88.jpeg \n", + " inflating: AD_NC/test/NC/1285188_89.jpeg \n", + " inflating: AD_NC/test/NC/1285188_90.jpeg \n", + " inflating: AD_NC/test/NC/1285188_91.jpeg \n", + " inflating: AD_NC/test/NC/1285188_92.jpeg \n", + " inflating: AD_NC/test/NC/1285188_93.jpeg \n", + " inflating: AD_NC/test/NC/1285188_94.jpeg \n", + " inflating: AD_NC/test/NC/1285188_95.jpeg \n", + " inflating: AD_NC/test/NC/1285188_96.jpeg \n", + " inflating: AD_NC/test/NC/1285188_97.jpeg \n", + " inflating: AD_NC/test/NC/1285188_98.jpeg \n", + " inflating: AD_NC/test/NC/1285188_99.jpeg \n", + " inflating: AD_NC/test/NC/1285276_78.jpeg \n", + " inflating: AD_NC/test/NC/1285276_79.jpeg \n", + " inflating: AD_NC/test/NC/1285276_80.jpeg \n", + " inflating: AD_NC/test/NC/1285276_81.jpeg \n", + " inflating: AD_NC/test/NC/1285276_82.jpeg \n", + " inflating: AD_NC/test/NC/1285276_83.jpeg \n", + " inflating: AD_NC/test/NC/1285276_84.jpeg \n", + " inflating: AD_NC/test/NC/1285276_85.jpeg \n", + " inflating: AD_NC/test/NC/1285276_86.jpeg \n", + " inflating: AD_NC/test/NC/1285276_87.jpeg \n", + " inflating: AD_NC/test/NC/1285276_88.jpeg \n", + " inflating: AD_NC/test/NC/1285276_89.jpeg \n", + " inflating: AD_NC/test/NC/1285276_90.jpeg \n", + " inflating: AD_NC/test/NC/1285276_91.jpeg \n", + " inflating: AD_NC/test/NC/1285276_92.jpeg \n", + " inflating: AD_NC/test/NC/1285276_93.jpeg \n", + " inflating: AD_NC/test/NC/1285276_94.jpeg \n", + " inflating: AD_NC/test/NC/1285276_95.jpeg \n", + " inflating: AD_NC/test/NC/1285276_96.jpeg \n", + " inflating: AD_NC/test/NC/1285276_97.jpeg \n", + " inflating: AD_NC/test/NC/1285703_100.jpeg \n", + " inflating: AD_NC/test/NC/1285703_101.jpeg \n", + " inflating: AD_NC/test/NC/1285703_102.jpeg \n", + " inflating: AD_NC/test/NC/1285703_103.jpeg \n", + " inflating: AD_NC/test/NC/1285703_104.jpeg \n", + " inflating: AD_NC/test/NC/1285703_105.jpeg \n", + " inflating: AD_NC/test/NC/1285703_106.jpeg \n", + " inflating: AD_NC/test/NC/1285703_107.jpeg \n", + " inflating: AD_NC/test/NC/1285703_108.jpeg \n", + " inflating: AD_NC/test/NC/1285703_109.jpeg \n", + " inflating: AD_NC/test/NC/1285703_110.jpeg \n", + " inflating: AD_NC/test/NC/1285703_111.jpeg \n", + " inflating: AD_NC/test/NC/1285703_112.jpeg \n", + " inflating: AD_NC/test/NC/1285703_113.jpeg \n", + " inflating: AD_NC/test/NC/1285703_114.jpeg \n", + " inflating: AD_NC/test/NC/1285703_95.jpeg \n", + " inflating: AD_NC/test/NC/1285703_96.jpeg \n", + " inflating: AD_NC/test/NC/1285703_97.jpeg \n", + " inflating: AD_NC/test/NC/1285703_98.jpeg \n", + " inflating: AD_NC/test/NC/1285703_99.jpeg \n", + " inflating: AD_NC/test/NC/1285929_100.jpeg \n", + " inflating: AD_NC/test/NC/1285929_101.jpeg \n", + " inflating: AD_NC/test/NC/1285929_102.jpeg \n", + " inflating: AD_NC/test/NC/1285929_103.jpeg \n", + " inflating: AD_NC/test/NC/1285929_104.jpeg \n", + " inflating: AD_NC/test/NC/1285929_105.jpeg \n", + " inflating: AD_NC/test/NC/1285929_106.jpeg \n", + " inflating: AD_NC/test/NC/1285929_107.jpeg \n", + " inflating: AD_NC/test/NC/1285929_108.jpeg \n", + " inflating: AD_NC/test/NC/1285929_109.jpeg \n", + " inflating: AD_NC/test/NC/1285929_110.jpeg \n", + " inflating: AD_NC/test/NC/1285929_111.jpeg \n", + " inflating: AD_NC/test/NC/1285929_112.jpeg \n", + " inflating: AD_NC/test/NC/1285929_113.jpeg \n", + " inflating: AD_NC/test/NC/1285929_114.jpeg \n", + " inflating: AD_NC/test/NC/1285929_95.jpeg \n", + " inflating: AD_NC/test/NC/1285929_96.jpeg \n", + " inflating: AD_NC/test/NC/1285929_97.jpeg \n", + " inflating: AD_NC/test/NC/1285929_98.jpeg \n", + " inflating: AD_NC/test/NC/1285929_99.jpeg \n", + " inflating: AD_NC/test/NC/1287192_100.jpeg \n", + " inflating: AD_NC/test/NC/1287192_101.jpeg \n", + " inflating: AD_NC/test/NC/1287192_102.jpeg \n", + " inflating: AD_NC/test/NC/1287192_103.jpeg \n", + " inflating: AD_NC/test/NC/1287192_104.jpeg \n", + " inflating: AD_NC/test/NC/1287192_105.jpeg \n", + " inflating: AD_NC/test/NC/1287192_106.jpeg \n", + " inflating: AD_NC/test/NC/1287192_107.jpeg \n", + " inflating: AD_NC/test/NC/1287192_108.jpeg \n", + " inflating: AD_NC/test/NC/1287192_109.jpeg \n", + " inflating: AD_NC/test/NC/1287192_110.jpeg \n", + " inflating: AD_NC/test/NC/1287192_111.jpeg \n", + " inflating: AD_NC/test/NC/1287192_112.jpeg \n", + " inflating: AD_NC/test/NC/1287192_113.jpeg \n", + " inflating: AD_NC/test/NC/1287192_94.jpeg \n", + " inflating: AD_NC/test/NC/1287192_95.jpeg \n", + " inflating: AD_NC/test/NC/1287192_96.jpeg \n", + " inflating: AD_NC/test/NC/1287192_97.jpeg \n", + " inflating: AD_NC/test/NC/1287192_98.jpeg \n", + " inflating: AD_NC/test/NC/1287192_99.jpeg \n", + " inflating: AD_NC/test/NC/1291468_100.jpeg \n", + " inflating: AD_NC/test/NC/1291468_101.jpeg \n", + " inflating: AD_NC/test/NC/1291468_102.jpeg \n", + " inflating: AD_NC/test/NC/1291468_103.jpeg \n", + " inflating: AD_NC/test/NC/1291468_104.jpeg \n", + " inflating: AD_NC/test/NC/1291468_105.jpeg \n", + " inflating: AD_NC/test/NC/1291468_106.jpeg \n", + " inflating: AD_NC/test/NC/1291468_107.jpeg \n", + " inflating: AD_NC/test/NC/1291468_88.jpeg \n", + " inflating: AD_NC/test/NC/1291468_89.jpeg \n", + " inflating: AD_NC/test/NC/1291468_90.jpeg \n", + " inflating: AD_NC/test/NC/1291468_91.jpeg \n", + " inflating: AD_NC/test/NC/1291468_92.jpeg \n", + " inflating: AD_NC/test/NC/1291468_93.jpeg \n", + " inflating: AD_NC/test/NC/1291468_94.jpeg \n", + " inflating: AD_NC/test/NC/1291468_95.jpeg \n", + " inflating: AD_NC/test/NC/1291468_96.jpeg \n", + " inflating: AD_NC/test/NC/1291468_97.jpeg \n", + " inflating: AD_NC/test/NC/1291468_98.jpeg \n", + " inflating: AD_NC/test/NC/1291468_99.jpeg \n", + " inflating: AD_NC/test/NC/1291745_100.jpeg \n", + " inflating: AD_NC/test/NC/1291745_101.jpeg \n", + " inflating: AD_NC/test/NC/1291745_102.jpeg \n", + " inflating: AD_NC/test/NC/1291745_103.jpeg \n", + " inflating: AD_NC/test/NC/1291745_104.jpeg \n", + " inflating: AD_NC/test/NC/1291745_105.jpeg \n", + " inflating: AD_NC/test/NC/1291745_106.jpeg \n", + " inflating: AD_NC/test/NC/1291745_107.jpeg \n", + " inflating: AD_NC/test/NC/1291745_108.jpeg \n", + " inflating: AD_NC/test/NC/1291745_109.jpeg \n", + " inflating: AD_NC/test/NC/1291745_110.jpeg \n", + " inflating: AD_NC/test/NC/1291745_111.jpeg \n", + " inflating: AD_NC/test/NC/1291745_112.jpeg \n", + " inflating: AD_NC/test/NC/1291745_113.jpeg \n", + " inflating: AD_NC/test/NC/1291745_94.jpeg \n", + " inflating: AD_NC/test/NC/1291745_95.jpeg \n", + " inflating: AD_NC/test/NC/1291745_96.jpeg \n", + " inflating: AD_NC/test/NC/1291745_97.jpeg \n", + " inflating: AD_NC/test/NC/1291745_98.jpeg \n", + " inflating: AD_NC/test/NC/1291745_99.jpeg \n", + " inflating: AD_NC/test/NC/1293292_100.jpeg \n", + " inflating: AD_NC/test/NC/1293292_101.jpeg \n", + " inflating: AD_NC/test/NC/1293292_102.jpeg \n", + " inflating: AD_NC/test/NC/1293292_103.jpeg \n", + " inflating: AD_NC/test/NC/1293292_104.jpeg \n", + " inflating: AD_NC/test/NC/1293292_105.jpeg \n", + " inflating: AD_NC/test/NC/1293292_106.jpeg \n", + " inflating: AD_NC/test/NC/1293292_107.jpeg \n", + " inflating: AD_NC/test/NC/1293292_108.jpeg \n", + " inflating: AD_NC/test/NC/1293292_109.jpeg \n", + " inflating: AD_NC/test/NC/1293292_110.jpeg \n", + " inflating: AD_NC/test/NC/1293292_111.jpeg \n", + " inflating: AD_NC/test/NC/1293292_112.jpeg \n", + " inflating: AD_NC/test/NC/1293292_113.jpeg \n", + " inflating: AD_NC/test/NC/1293292_114.jpeg \n", + " inflating: AD_NC/test/NC/1293292_95.jpeg \n", + " inflating: AD_NC/test/NC/1293292_96.jpeg \n", + " inflating: AD_NC/test/NC/1293292_97.jpeg \n", + " inflating: AD_NC/test/NC/1293292_98.jpeg \n", + " inflating: AD_NC/test/NC/1293292_99.jpeg \n", + " inflating: AD_NC/test/NC/1293329_100.jpeg \n", + " inflating: AD_NC/test/NC/1293329_101.jpeg \n", + " inflating: AD_NC/test/NC/1293329_102.jpeg \n", + " inflating: AD_NC/test/NC/1293329_103.jpeg \n", + " inflating: AD_NC/test/NC/1293329_104.jpeg \n", + " inflating: AD_NC/test/NC/1293329_105.jpeg \n", + " inflating: AD_NC/test/NC/1293329_106.jpeg \n", + " inflating: AD_NC/test/NC/1293329_107.jpeg \n", + " inflating: AD_NC/test/NC/1293329_88.jpeg \n", + " inflating: AD_NC/test/NC/1293329_89.jpeg \n", + " inflating: AD_NC/test/NC/1293329_90.jpeg \n", + " inflating: AD_NC/test/NC/1293329_91.jpeg \n", + " inflating: AD_NC/test/NC/1293329_92.jpeg \n", + " inflating: AD_NC/test/NC/1293329_93.jpeg \n", + " inflating: AD_NC/test/NC/1293329_94.jpeg \n", + " inflating: AD_NC/test/NC/1293329_95.jpeg \n", + " inflating: AD_NC/test/NC/1293329_96.jpeg \n", + " inflating: AD_NC/test/NC/1293329_97.jpeg \n", + " inflating: AD_NC/test/NC/1293329_98.jpeg \n", + " inflating: AD_NC/test/NC/1293329_99.jpeg \n", + " inflating: AD_NC/test/NC/1293823_100.jpeg \n", + " inflating: AD_NC/test/NC/1293823_101.jpeg \n", + " inflating: AD_NC/test/NC/1293823_102.jpeg \n", + " inflating: AD_NC/test/NC/1293823_103.jpeg \n", + " inflating: AD_NC/test/NC/1293823_104.jpeg \n", + " inflating: AD_NC/test/NC/1293823_105.jpeg \n", + " inflating: AD_NC/test/NC/1293823_106.jpeg \n", + " inflating: AD_NC/test/NC/1293823_107.jpeg \n", + " inflating: AD_NC/test/NC/1293823_88.jpeg \n", + " inflating: AD_NC/test/NC/1293823_89.jpeg \n", + " inflating: AD_NC/test/NC/1293823_90.jpeg \n", + " inflating: AD_NC/test/NC/1293823_91.jpeg \n", + " inflating: AD_NC/test/NC/1293823_92.jpeg \n", + " inflating: AD_NC/test/NC/1293823_93.jpeg \n", + " inflating: AD_NC/test/NC/1293823_94.jpeg \n", + " inflating: AD_NC/test/NC/1293823_95.jpeg \n", + " inflating: AD_NC/test/NC/1293823_96.jpeg \n", + " inflating: AD_NC/test/NC/1293823_97.jpeg \n", + " inflating: AD_NC/test/NC/1293823_98.jpeg \n", + " inflating: AD_NC/test/NC/1293823_99.jpeg \n", + " inflating: AD_NC/test/NC/1295347_100.jpeg \n", + " inflating: AD_NC/test/NC/1295347_101.jpeg \n", + " inflating: AD_NC/test/NC/1295347_102.jpeg \n", + " inflating: AD_NC/test/NC/1295347_103.jpeg \n", + " inflating: AD_NC/test/NC/1295347_104.jpeg \n", + " inflating: AD_NC/test/NC/1295347_105.jpeg \n", + " inflating: AD_NC/test/NC/1295347_106.jpeg \n", + " inflating: AD_NC/test/NC/1295347_107.jpeg \n", + " inflating: AD_NC/test/NC/1295347_108.jpeg \n", + " inflating: AD_NC/test/NC/1295347_109.jpeg \n", + " inflating: AD_NC/test/NC/1295347_110.jpeg \n", + " inflating: AD_NC/test/NC/1295347_111.jpeg \n", + " inflating: AD_NC/test/NC/1295347_112.jpeg \n", + " inflating: AD_NC/test/NC/1295347_113.jpeg \n", + " inflating: AD_NC/test/NC/1295347_94.jpeg \n", + " inflating: AD_NC/test/NC/1295347_95.jpeg \n", + " inflating: AD_NC/test/NC/1295347_96.jpeg \n", + " inflating: AD_NC/test/NC/1295347_97.jpeg \n", + " inflating: AD_NC/test/NC/1295347_98.jpeg \n", + " inflating: AD_NC/test/NC/1295347_99.jpeg \n", + " inflating: AD_NC/test/NC/1296519_78.jpeg \n", + " inflating: AD_NC/test/NC/1296519_79.jpeg \n", + " inflating: AD_NC/test/NC/1296519_80.jpeg \n", + " inflating: AD_NC/test/NC/1296519_81.jpeg \n", + " inflating: AD_NC/test/NC/1296519_82.jpeg \n", + " inflating: AD_NC/test/NC/1296519_83.jpeg \n", + " inflating: AD_NC/test/NC/1296519_84.jpeg \n", + " inflating: AD_NC/test/NC/1296519_85.jpeg \n", + " inflating: AD_NC/test/NC/1296519_86.jpeg \n", + " inflating: AD_NC/test/NC/1296519_87.jpeg \n", + " inflating: AD_NC/test/NC/1296519_88.jpeg \n", + " inflating: AD_NC/test/NC/1296519_89.jpeg \n", + " inflating: AD_NC/test/NC/1296519_90.jpeg \n", + " inflating: AD_NC/test/NC/1296519_91.jpeg \n", + " inflating: AD_NC/test/NC/1296519_92.jpeg \n", + " inflating: AD_NC/test/NC/1296519_93.jpeg \n", + " inflating: AD_NC/test/NC/1296519_94.jpeg \n", + " inflating: AD_NC/test/NC/1296519_95.jpeg \n", + " inflating: AD_NC/test/NC/1296519_96.jpeg \n", + " inflating: AD_NC/test/NC/1296519_97.jpeg \n", + " inflating: AD_NC/test/NC/1299107_78.jpeg \n", + " inflating: AD_NC/test/NC/1299107_79.jpeg \n", + " inflating: AD_NC/test/NC/1299107_80.jpeg \n", + " inflating: AD_NC/test/NC/1299107_81.jpeg \n", + " inflating: AD_NC/test/NC/1299107_82.jpeg \n", + " inflating: AD_NC/test/NC/1299107_83.jpeg \n", + " inflating: AD_NC/test/NC/1299107_84.jpeg \n", + " inflating: AD_NC/test/NC/1299107_85.jpeg \n", + " inflating: AD_NC/test/NC/1299107_86.jpeg \n", + " inflating: AD_NC/test/NC/1299107_87.jpeg \n", + " inflating: AD_NC/test/NC/1299107_88.jpeg \n", + " inflating: AD_NC/test/NC/1299107_89.jpeg \n", + " inflating: AD_NC/test/NC/1299107_90.jpeg \n", + " inflating: AD_NC/test/NC/1299107_91.jpeg \n", + " inflating: AD_NC/test/NC/1299107_92.jpeg \n", + " inflating: AD_NC/test/NC/1299107_93.jpeg \n", + " inflating: AD_NC/test/NC/1299107_94.jpeg \n", + " inflating: AD_NC/test/NC/1299107_95.jpeg \n", + " inflating: AD_NC/test/NC/1299107_96.jpeg \n", + " inflating: AD_NC/test/NC/1299107_97.jpeg \n", + " inflating: AD_NC/test/NC/1299200_100.jpeg \n", + " inflating: AD_NC/test/NC/1299200_101.jpeg \n", + " inflating: AD_NC/test/NC/1299200_102.jpeg \n", + " inflating: AD_NC/test/NC/1299200_103.jpeg \n", + " inflating: AD_NC/test/NC/1299200_104.jpeg \n", + " inflating: AD_NC/test/NC/1299200_105.jpeg \n", + " inflating: AD_NC/test/NC/1299200_106.jpeg \n", + " inflating: AD_NC/test/NC/1299200_107.jpeg \n", + " inflating: AD_NC/test/NC/1299200_88.jpeg \n", + " inflating: AD_NC/test/NC/1299200_89.jpeg \n", + " inflating: AD_NC/test/NC/1299200_90.jpeg \n", + " inflating: AD_NC/test/NC/1299200_91.jpeg \n", + " inflating: AD_NC/test/NC/1299200_92.jpeg \n", + " inflating: AD_NC/test/NC/1299200_93.jpeg \n", + " inflating: AD_NC/test/NC/1299200_94.jpeg \n", + " inflating: AD_NC/test/NC/1299200_95.jpeg \n", + " inflating: AD_NC/test/NC/1299200_96.jpeg \n", + " inflating: AD_NC/test/NC/1299200_97.jpeg \n", + " inflating: AD_NC/test/NC/1299200_98.jpeg \n", + " inflating: AD_NC/test/NC/1299200_99.jpeg \n", + " inflating: AD_NC/test/NC/1299912_78.jpeg \n", + " inflating: AD_NC/test/NC/1299912_79.jpeg \n", + " inflating: AD_NC/test/NC/1299912_80.jpeg \n", + " inflating: AD_NC/test/NC/1299912_81.jpeg \n", + " inflating: AD_NC/test/NC/1299912_82.jpeg \n", + " inflating: AD_NC/test/NC/1299912_83.jpeg \n", + " inflating: AD_NC/test/NC/1299912_84.jpeg \n", + " inflating: AD_NC/test/NC/1299912_85.jpeg \n", + " inflating: AD_NC/test/NC/1299912_86.jpeg \n", + " inflating: AD_NC/test/NC/1299912_87.jpeg \n", + " inflating: AD_NC/test/NC/1299912_88.jpeg \n", + " inflating: AD_NC/test/NC/1299912_89.jpeg \n", + " inflating: AD_NC/test/NC/1299912_90.jpeg \n", + " inflating: AD_NC/test/NC/1299912_91.jpeg \n", + " inflating: AD_NC/test/NC/1299912_92.jpeg \n", + " inflating: AD_NC/test/NC/1299912_93.jpeg \n", + " inflating: AD_NC/test/NC/1299912_94.jpeg \n", + " inflating: AD_NC/test/NC/1299912_95.jpeg \n", + " inflating: AD_NC/test/NC/1299912_96.jpeg \n", + " inflating: AD_NC/test/NC/1299912_97.jpeg \n", + " inflating: AD_NC/test/NC/1299913_78.jpeg \n", + " inflating: AD_NC/test/NC/1299913_79.jpeg \n", + " inflating: AD_NC/test/NC/1299913_80.jpeg \n", + " inflating: AD_NC/test/NC/1299913_81.jpeg \n", + " inflating: AD_NC/test/NC/1299913_82.jpeg \n", + " inflating: AD_NC/test/NC/1299913_83.jpeg \n", + " inflating: AD_NC/test/NC/1299913_84.jpeg \n", + " inflating: AD_NC/test/NC/1299913_85.jpeg \n", + " inflating: AD_NC/test/NC/1299913_86.jpeg \n", + " inflating: AD_NC/test/NC/1299913_87.jpeg \n", + " inflating: AD_NC/test/NC/1299913_88.jpeg \n", + " inflating: AD_NC/test/NC/1299913_89.jpeg \n", + " inflating: AD_NC/test/NC/1299913_90.jpeg \n", + " inflating: AD_NC/test/NC/1299913_91.jpeg \n", + " inflating: AD_NC/test/NC/1299913_92.jpeg \n", + " inflating: AD_NC/test/NC/1299913_93.jpeg \n", + " inflating: AD_NC/test/NC/1299913_94.jpeg \n", + " inflating: AD_NC/test/NC/1299913_95.jpeg \n", + " inflating: AD_NC/test/NC/1299913_96.jpeg \n", + " inflating: AD_NC/test/NC/1299913_97.jpeg \n", + " inflating: AD_NC/test/NC/1302056_100.jpeg \n", + " inflating: AD_NC/test/NC/1302056_101.jpeg \n", + " inflating: AD_NC/test/NC/1302056_102.jpeg \n", + " inflating: AD_NC/test/NC/1302056_103.jpeg \n", + " inflating: AD_NC/test/NC/1302056_104.jpeg \n", + " inflating: AD_NC/test/NC/1302056_105.jpeg \n", + " inflating: AD_NC/test/NC/1302056_106.jpeg \n", + " inflating: AD_NC/test/NC/1302056_107.jpeg \n", + " inflating: AD_NC/test/NC/1302056_88.jpeg \n", + " inflating: AD_NC/test/NC/1302056_89.jpeg \n", + " inflating: AD_NC/test/NC/1302056_90.jpeg \n", + " inflating: AD_NC/test/NC/1302056_91.jpeg \n", + " inflating: AD_NC/test/NC/1302056_92.jpeg \n", + " inflating: AD_NC/test/NC/1302056_93.jpeg \n", + " inflating: AD_NC/test/NC/1302056_94.jpeg \n", + " inflating: AD_NC/test/NC/1302056_95.jpeg \n", + " inflating: AD_NC/test/NC/1302056_96.jpeg \n", + " inflating: AD_NC/test/NC/1302056_97.jpeg \n", + " inflating: AD_NC/test/NC/1302056_98.jpeg \n", + " inflating: AD_NC/test/NC/1302056_99.jpeg \n", + " inflating: AD_NC/test/NC/1303143_100.jpeg \n", + " inflating: AD_NC/test/NC/1303143_101.jpeg \n", + " inflating: AD_NC/test/NC/1303143_102.jpeg \n", + " inflating: AD_NC/test/NC/1303143_103.jpeg \n", + " inflating: AD_NC/test/NC/1303143_104.jpeg \n", + " inflating: AD_NC/test/NC/1303143_105.jpeg \n", + " inflating: AD_NC/test/NC/1303143_106.jpeg \n", + " inflating: AD_NC/test/NC/1303143_107.jpeg \n", + " inflating: AD_NC/test/NC/1303143_108.jpeg \n", + " inflating: AD_NC/test/NC/1303143_109.jpeg \n", + " inflating: AD_NC/test/NC/1303143_110.jpeg \n", + " inflating: AD_NC/test/NC/1303143_111.jpeg \n", + " inflating: AD_NC/test/NC/1303143_112.jpeg \n", + " inflating: AD_NC/test/NC/1303143_113.jpeg \n", + " inflating: AD_NC/test/NC/1303143_94.jpeg \n", + " inflating: AD_NC/test/NC/1303143_95.jpeg \n", + " inflating: AD_NC/test/NC/1303143_96.jpeg \n", + " inflating: AD_NC/test/NC/1303143_97.jpeg \n", + " inflating: AD_NC/test/NC/1303143_98.jpeg \n", + " inflating: AD_NC/test/NC/1303143_99.jpeg \n", + " inflating: AD_NC/test/NC/1304067_78.jpeg \n", + " inflating: AD_NC/test/NC/1304067_79.jpeg \n", + " inflating: AD_NC/test/NC/1304067_80.jpeg \n", + " inflating: AD_NC/test/NC/1304067_81.jpeg \n", + " inflating: AD_NC/test/NC/1304067_82.jpeg \n", + " inflating: AD_NC/test/NC/1304067_83.jpeg \n", + " inflating: AD_NC/test/NC/1304067_84.jpeg \n", + " inflating: AD_NC/test/NC/1304067_85.jpeg \n", + " inflating: AD_NC/test/NC/1304067_86.jpeg \n", + " inflating: AD_NC/test/NC/1304067_87.jpeg \n", + " inflating: AD_NC/test/NC/1304067_88.jpeg \n", + " inflating: AD_NC/test/NC/1304067_89.jpeg \n", + " inflating: AD_NC/test/NC/1304067_90.jpeg \n", + " inflating: AD_NC/test/NC/1304067_91.jpeg \n", + " inflating: AD_NC/test/NC/1304067_92.jpeg \n", + " inflating: AD_NC/test/NC/1304067_93.jpeg \n", + " inflating: AD_NC/test/NC/1304067_94.jpeg \n", + " inflating: AD_NC/test/NC/1304067_95.jpeg \n", + " inflating: AD_NC/test/NC/1304067_96.jpeg \n", + " inflating: AD_NC/test/NC/1304067_97.jpeg \n", + " inflating: AD_NC/test/NC/1304645_100.jpeg \n", + " inflating: AD_NC/test/NC/1304645_101.jpeg \n", + " inflating: AD_NC/test/NC/1304645_102.jpeg \n", + " inflating: AD_NC/test/NC/1304645_103.jpeg \n", + " inflating: AD_NC/test/NC/1304645_104.jpeg \n", + " inflating: AD_NC/test/NC/1304645_105.jpeg \n", + " inflating: AD_NC/test/NC/1304645_106.jpeg \n", + " inflating: AD_NC/test/NC/1304645_107.jpeg \n", + " inflating: AD_NC/test/NC/1304645_108.jpeg \n", + " inflating: AD_NC/test/NC/1304645_109.jpeg \n", + " inflating: AD_NC/test/NC/1304645_110.jpeg \n", + " inflating: AD_NC/test/NC/1304645_111.jpeg \n", + " inflating: AD_NC/test/NC/1304645_112.jpeg \n", + " inflating: AD_NC/test/NC/1304645_113.jpeg \n", + " inflating: AD_NC/test/NC/1304645_94.jpeg \n", + " inflating: AD_NC/test/NC/1304645_95.jpeg \n", + " inflating: AD_NC/test/NC/1304645_96.jpeg \n", + " inflating: AD_NC/test/NC/1304645_97.jpeg \n", + " inflating: AD_NC/test/NC/1304645_98.jpeg \n", + " inflating: AD_NC/test/NC/1304645_99.jpeg \n", + " inflating: AD_NC/test/NC/1316573_100.jpeg \n", + " inflating: AD_NC/test/NC/1316573_101.jpeg \n", + " inflating: AD_NC/test/NC/1316573_102.jpeg \n", + " inflating: AD_NC/test/NC/1316573_103.jpeg \n", + " inflating: AD_NC/test/NC/1316573_104.jpeg \n", + " inflating: AD_NC/test/NC/1316573_105.jpeg \n", + " inflating: AD_NC/test/NC/1316573_106.jpeg \n", + " inflating: AD_NC/test/NC/1316573_107.jpeg \n", + " inflating: AD_NC/test/NC/1316573_108.jpeg \n", + " inflating: AD_NC/test/NC/1316573_109.jpeg \n", + " inflating: AD_NC/test/NC/1316573_110.jpeg \n", + " inflating: AD_NC/test/NC/1316573_111.jpeg \n", + " inflating: AD_NC/test/NC/1316573_112.jpeg \n", + " inflating: AD_NC/test/NC/1316573_113.jpeg \n", + " inflating: AD_NC/test/NC/1316573_114.jpeg \n", + " inflating: AD_NC/test/NC/1316573_95.jpeg \n", + " inflating: AD_NC/test/NC/1316573_96.jpeg \n", + " inflating: AD_NC/test/NC/1316573_97.jpeg \n", + " inflating: AD_NC/test/NC/1316573_98.jpeg \n", + " inflating: AD_NC/test/NC/1316573_99.jpeg \n", + " inflating: AD_NC/test/NC/1319246_100.jpeg \n", + " inflating: AD_NC/test/NC/1319246_101.jpeg \n", + " inflating: AD_NC/test/NC/1319246_102.jpeg \n", + " inflating: AD_NC/test/NC/1319246_103.jpeg \n", + " inflating: AD_NC/test/NC/1319246_104.jpeg \n", + " inflating: AD_NC/test/NC/1319246_105.jpeg \n", + " inflating: AD_NC/test/NC/1319246_106.jpeg \n", + " inflating: AD_NC/test/NC/1319246_107.jpeg \n", + " inflating: AD_NC/test/NC/1319246_88.jpeg \n", + " inflating: AD_NC/test/NC/1319246_89.jpeg \n", + " inflating: AD_NC/test/NC/1319246_90.jpeg \n", + " inflating: AD_NC/test/NC/1319246_91.jpeg \n", + " inflating: AD_NC/test/NC/1319246_92.jpeg \n", + " inflating: AD_NC/test/NC/1319246_93.jpeg \n", + " inflating: AD_NC/test/NC/1319246_94.jpeg \n", + " inflating: AD_NC/test/NC/1319246_95.jpeg \n", + " inflating: AD_NC/test/NC/1319246_96.jpeg \n", + " inflating: AD_NC/test/NC/1319246_97.jpeg \n", + " inflating: AD_NC/test/NC/1319246_98.jpeg \n", + " inflating: AD_NC/test/NC/1319246_99.jpeg \n", + " inflating: AD_NC/test/NC/1319247_100.jpeg \n", + " inflating: AD_NC/test/NC/1319247_101.jpeg \n", + " inflating: AD_NC/test/NC/1319247_102.jpeg \n", + " inflating: AD_NC/test/NC/1319247_103.jpeg \n", + " inflating: AD_NC/test/NC/1319247_104.jpeg \n", + " inflating: AD_NC/test/NC/1319247_105.jpeg \n", + " inflating: AD_NC/test/NC/1319247_106.jpeg \n", + " inflating: AD_NC/test/NC/1319247_107.jpeg \n", + " inflating: AD_NC/test/NC/1319247_88.jpeg \n", + " inflating: AD_NC/test/NC/1319247_89.jpeg \n", + " inflating: AD_NC/test/NC/1319247_90.jpeg \n", + " inflating: AD_NC/test/NC/1319247_91.jpeg \n", + " inflating: AD_NC/test/NC/1319247_92.jpeg \n", + " inflating: AD_NC/test/NC/1319247_93.jpeg \n", + " inflating: AD_NC/test/NC/1319247_94.jpeg \n", + " inflating: AD_NC/test/NC/1319247_95.jpeg \n", + " inflating: AD_NC/test/NC/1319247_96.jpeg \n", + " inflating: AD_NC/test/NC/1319247_97.jpeg \n", + " inflating: AD_NC/test/NC/1319247_98.jpeg \n", + " inflating: AD_NC/test/NC/1319247_99.jpeg \n", + " inflating: AD_NC/test/NC/1319248_100.jpeg \n", + " inflating: AD_NC/test/NC/1319248_101.jpeg \n", + " inflating: AD_NC/test/NC/1319248_102.jpeg \n", + " inflating: AD_NC/test/NC/1319248_103.jpeg \n", + " inflating: AD_NC/test/NC/1319248_104.jpeg \n", + " inflating: AD_NC/test/NC/1319248_105.jpeg \n", + " inflating: AD_NC/test/NC/1319248_106.jpeg \n", + " inflating: AD_NC/test/NC/1319248_107.jpeg \n", + " inflating: AD_NC/test/NC/1319248_88.jpeg \n", + " inflating: AD_NC/test/NC/1319248_89.jpeg \n", + " inflating: AD_NC/test/NC/1319248_90.jpeg \n", + " inflating: AD_NC/test/NC/1319248_91.jpeg \n", + " inflating: AD_NC/test/NC/1319248_92.jpeg \n", + " inflating: AD_NC/test/NC/1319248_93.jpeg \n", + " inflating: AD_NC/test/NC/1319248_94.jpeg \n", + " inflating: AD_NC/test/NC/1319248_95.jpeg \n", + " inflating: AD_NC/test/NC/1319248_96.jpeg \n", + " inflating: AD_NC/test/NC/1319248_97.jpeg \n", + " inflating: AD_NC/test/NC/1319248_98.jpeg \n", + " inflating: AD_NC/test/NC/1319248_99.jpeg \n", + " inflating: AD_NC/test/NC/1319400_100.jpeg \n", + " inflating: AD_NC/test/NC/1319400_101.jpeg \n", + " inflating: AD_NC/test/NC/1319400_102.jpeg \n", + " inflating: AD_NC/test/NC/1319400_103.jpeg \n", + " inflating: AD_NC/test/NC/1319400_104.jpeg \n", + " inflating: AD_NC/test/NC/1319400_105.jpeg \n", + " inflating: AD_NC/test/NC/1319400_106.jpeg \n", + " inflating: AD_NC/test/NC/1319400_107.jpeg \n", + " inflating: AD_NC/test/NC/1319400_88.jpeg \n", + " inflating: AD_NC/test/NC/1319400_89.jpeg \n", + " inflating: AD_NC/test/NC/1319400_90.jpeg \n", + " inflating: AD_NC/test/NC/1319400_91.jpeg \n", + " inflating: AD_NC/test/NC/1319400_92.jpeg \n", + " inflating: AD_NC/test/NC/1319400_93.jpeg \n", + " inflating: AD_NC/test/NC/1319400_94.jpeg \n", + " inflating: AD_NC/test/NC/1319400_95.jpeg \n", + " inflating: AD_NC/test/NC/1319400_96.jpeg \n", + " inflating: AD_NC/test/NC/1319400_97.jpeg \n", + " inflating: AD_NC/test/NC/1319400_98.jpeg \n", + " inflating: AD_NC/test/NC/1319400_99.jpeg \n", + " inflating: AD_NC/test/NC/1320212_78.jpeg \n", + " inflating: AD_NC/test/NC/1320212_79.jpeg \n", + " inflating: AD_NC/test/NC/1320212_80.jpeg \n", + " inflating: AD_NC/test/NC/1320212_81.jpeg \n", + " inflating: AD_NC/test/NC/1320212_82.jpeg \n", + " inflating: AD_NC/test/NC/1320212_83.jpeg \n", + " inflating: AD_NC/test/NC/1320212_84.jpeg \n", + " inflating: AD_NC/test/NC/1320212_85.jpeg \n", + " inflating: AD_NC/test/NC/1320212_86.jpeg \n", + " inflating: AD_NC/test/NC/1320212_87.jpeg \n", + " inflating: AD_NC/test/NC/1320212_88.jpeg \n", + " inflating: AD_NC/test/NC/1320212_89.jpeg \n", + " inflating: AD_NC/test/NC/1320212_90.jpeg \n", + " inflating: AD_NC/test/NC/1320212_91.jpeg \n", + " inflating: AD_NC/test/NC/1320212_92.jpeg \n", + " inflating: AD_NC/test/NC/1320212_93.jpeg \n", + " inflating: AD_NC/test/NC/1320212_94.jpeg \n", + " inflating: AD_NC/test/NC/1320212_95.jpeg \n", + " inflating: AD_NC/test/NC/1320212_96.jpeg \n", + " inflating: AD_NC/test/NC/1320212_97.jpeg \n", + " inflating: AD_NC/test/NC/1320538_78.jpeg \n", + " inflating: AD_NC/test/NC/1320538_79.jpeg \n", + " inflating: AD_NC/test/NC/1320538_80.jpeg \n", + " inflating: AD_NC/test/NC/1320538_81.jpeg \n", + " inflating: AD_NC/test/NC/1320538_82.jpeg \n", + " inflating: AD_NC/test/NC/1320538_83.jpeg \n", + " inflating: AD_NC/test/NC/1320538_84.jpeg \n", + " inflating: AD_NC/test/NC/1320538_85.jpeg \n", + " inflating: AD_NC/test/NC/1320538_86.jpeg \n", + " inflating: AD_NC/test/NC/1320538_87.jpeg \n", + " inflating: AD_NC/test/NC/1320538_88.jpeg \n", + " inflating: AD_NC/test/NC/1320538_89.jpeg \n", + " inflating: AD_NC/test/NC/1320538_90.jpeg \n", + " inflating: AD_NC/test/NC/1320538_91.jpeg \n", + " inflating: AD_NC/test/NC/1320538_92.jpeg \n", + " inflating: AD_NC/test/NC/1320538_93.jpeg \n", + " inflating: AD_NC/test/NC/1320538_94.jpeg \n", + " inflating: AD_NC/test/NC/1320538_95.jpeg \n", + " inflating: AD_NC/test/NC/1320538_96.jpeg \n", + " inflating: AD_NC/test/NC/1320538_97.jpeg \n", + " inflating: AD_NC/test/NC/1320572_78.jpeg \n", + " inflating: AD_NC/test/NC/1320572_79.jpeg \n", + " inflating: AD_NC/test/NC/1320572_80.jpeg \n", + " inflating: AD_NC/test/NC/1320572_81.jpeg \n", + " inflating: AD_NC/test/NC/1320572_82.jpeg \n", + " inflating: AD_NC/test/NC/1320572_83.jpeg \n", + " inflating: AD_NC/test/NC/1320572_84.jpeg \n", + " inflating: AD_NC/test/NC/1320572_85.jpeg \n", + " inflating: AD_NC/test/NC/1320572_86.jpeg \n", + " inflating: AD_NC/test/NC/1320572_87.jpeg \n", + " inflating: AD_NC/test/NC/1320572_88.jpeg \n", + " inflating: AD_NC/test/NC/1320572_89.jpeg \n", + " inflating: AD_NC/test/NC/1320572_90.jpeg \n", + " inflating: AD_NC/test/NC/1320572_91.jpeg \n", + " inflating: AD_NC/test/NC/1320572_92.jpeg \n", + " inflating: AD_NC/test/NC/1320572_93.jpeg \n", + " inflating: AD_NC/test/NC/1320572_94.jpeg \n", + " inflating: AD_NC/test/NC/1320572_95.jpeg \n", + " inflating: AD_NC/test/NC/1320572_96.jpeg \n", + " inflating: AD_NC/test/NC/1320572_97.jpeg \n", + " inflating: AD_NC/test/NC/1320573_78.jpeg \n", + " inflating: AD_NC/test/NC/1320573_79.jpeg \n", + " inflating: AD_NC/test/NC/1320573_80.jpeg \n", + " inflating: AD_NC/test/NC/1320573_81.jpeg \n", + " inflating: AD_NC/test/NC/1320573_82.jpeg \n", + " inflating: AD_NC/test/NC/1320573_83.jpeg \n", + " inflating: AD_NC/test/NC/1320573_84.jpeg \n", + " inflating: AD_NC/test/NC/1320573_85.jpeg \n", + " inflating: AD_NC/test/NC/1320573_86.jpeg \n", + " inflating: AD_NC/test/NC/1320573_87.jpeg \n", + " inflating: AD_NC/test/NC/1320573_88.jpeg \n", + " inflating: AD_NC/test/NC/1320573_89.jpeg \n", + " inflating: AD_NC/test/NC/1320573_90.jpeg \n", + " inflating: AD_NC/test/NC/1320573_91.jpeg \n", + " inflating: AD_NC/test/NC/1320573_92.jpeg \n", + " inflating: AD_NC/test/NC/1320573_93.jpeg \n", + " inflating: AD_NC/test/NC/1320573_94.jpeg \n", + " inflating: AD_NC/test/NC/1320573_95.jpeg \n", + " inflating: AD_NC/test/NC/1320573_96.jpeg \n", + " inflating: AD_NC/test/NC/1320573_97.jpeg \n", + " inflating: AD_NC/test/NC/1323146_100.jpeg \n", + " inflating: AD_NC/test/NC/1323146_101.jpeg \n", + " inflating: AD_NC/test/NC/1323146_102.jpeg \n", + " inflating: AD_NC/test/NC/1323146_103.jpeg \n", + " inflating: AD_NC/test/NC/1323146_104.jpeg \n", + " inflating: AD_NC/test/NC/1323146_105.jpeg \n", + " inflating: AD_NC/test/NC/1323146_106.jpeg \n", + " inflating: AD_NC/test/NC/1323146_107.jpeg \n", + " inflating: AD_NC/test/NC/1323146_88.jpeg \n", + " inflating: AD_NC/test/NC/1323146_89.jpeg \n", + " inflating: AD_NC/test/NC/1323146_90.jpeg \n", + " inflating: AD_NC/test/NC/1323146_91.jpeg \n", + " inflating: AD_NC/test/NC/1323146_92.jpeg \n", + " inflating: AD_NC/test/NC/1323146_93.jpeg \n", + " inflating: AD_NC/test/NC/1323146_94.jpeg \n", + " inflating: AD_NC/test/NC/1323146_95.jpeg \n", + " inflating: AD_NC/test/NC/1323146_96.jpeg \n", + " inflating: AD_NC/test/NC/1323146_97.jpeg \n", + " inflating: AD_NC/test/NC/1323146_98.jpeg \n", + " inflating: AD_NC/test/NC/1323146_99.jpeg \n", + " inflating: AD_NC/test/NC/1324187_78.jpeg \n", + " inflating: AD_NC/test/NC/1324187_79.jpeg \n", + " inflating: AD_NC/test/NC/1324187_80.jpeg \n", + " inflating: AD_NC/test/NC/1324187_81.jpeg \n", + " inflating: AD_NC/test/NC/1324187_82.jpeg \n", + " inflating: AD_NC/test/NC/1324187_83.jpeg \n", + " inflating: AD_NC/test/NC/1324187_84.jpeg \n", + " inflating: AD_NC/test/NC/1324187_85.jpeg \n", + " inflating: AD_NC/test/NC/1324187_86.jpeg \n", + " inflating: AD_NC/test/NC/1324187_87.jpeg \n", + " inflating: AD_NC/test/NC/1324187_88.jpeg \n", + " inflating: AD_NC/test/NC/1324187_89.jpeg \n", + " inflating: AD_NC/test/NC/1324187_90.jpeg \n", + " inflating: AD_NC/test/NC/1324187_91.jpeg \n", + " inflating: AD_NC/test/NC/1324187_92.jpeg \n", + " inflating: AD_NC/test/NC/1324187_93.jpeg \n", + " inflating: AD_NC/test/NC/1324187_94.jpeg \n", + " inflating: AD_NC/test/NC/1324187_95.jpeg \n", + " inflating: AD_NC/test/NC/1324187_96.jpeg \n", + " inflating: AD_NC/test/NC/1324187_97.jpeg \n", + " inflating: AD_NC/test/NC/1324201_78.jpeg \n", + " inflating: AD_NC/test/NC/1324201_79.jpeg \n", + " inflating: AD_NC/test/NC/1324201_80.jpeg \n", + " inflating: AD_NC/test/NC/1324201_81.jpeg \n", + " inflating: AD_NC/test/NC/1324201_82.jpeg \n", + " inflating: AD_NC/test/NC/1324201_83.jpeg \n", + " inflating: AD_NC/test/NC/1324201_84.jpeg \n", + " inflating: AD_NC/test/NC/1324201_85.jpeg \n", + " inflating: AD_NC/test/NC/1324201_86.jpeg \n", + " inflating: AD_NC/test/NC/1324201_87.jpeg \n", + " inflating: AD_NC/test/NC/1324201_88.jpeg \n", + " inflating: AD_NC/test/NC/1324201_89.jpeg \n", + " inflating: AD_NC/test/NC/1324201_90.jpeg \n", + " inflating: AD_NC/test/NC/1324201_91.jpeg \n", + " inflating: AD_NC/test/NC/1324201_92.jpeg \n", + " inflating: AD_NC/test/NC/1324201_93.jpeg \n", + " inflating: AD_NC/test/NC/1324201_94.jpeg \n", + " inflating: AD_NC/test/NC/1324201_95.jpeg \n", + " inflating: AD_NC/test/NC/1324201_96.jpeg \n", + " inflating: AD_NC/test/NC/1324201_97.jpeg \n", + " inflating: AD_NC/test/NC/1325533_100.jpeg \n", + " inflating: AD_NC/test/NC/1325533_101.jpeg \n", + " inflating: AD_NC/test/NC/1325533_102.jpeg \n", + " inflating: AD_NC/test/NC/1325533_103.jpeg \n", + " inflating: AD_NC/test/NC/1325533_104.jpeg \n", + " inflating: AD_NC/test/NC/1325533_105.jpeg \n", + " inflating: AD_NC/test/NC/1325533_106.jpeg \n", + " inflating: AD_NC/test/NC/1325533_107.jpeg \n", + " inflating: AD_NC/test/NC/1325533_88.jpeg \n", + " inflating: AD_NC/test/NC/1325533_89.jpeg \n", + " inflating: AD_NC/test/NC/1325533_90.jpeg \n", + " inflating: AD_NC/test/NC/1325533_91.jpeg \n", + " inflating: AD_NC/test/NC/1325533_92.jpeg \n", + " inflating: AD_NC/test/NC/1325533_93.jpeg \n", + " inflating: AD_NC/test/NC/1325533_94.jpeg \n", + " inflating: AD_NC/test/NC/1325533_95.jpeg \n", + " inflating: AD_NC/test/NC/1325533_96.jpeg \n", + " inflating: AD_NC/test/NC/1325533_97.jpeg \n", + " inflating: AD_NC/test/NC/1325533_98.jpeg \n", + " inflating: AD_NC/test/NC/1325533_99.jpeg \n", + " inflating: AD_NC/test/NC/1325568_100.jpeg \n", + " inflating: AD_NC/test/NC/1325568_101.jpeg \n", + " inflating: AD_NC/test/NC/1325568_102.jpeg \n", + " inflating: AD_NC/test/NC/1325568_103.jpeg \n", + " inflating: AD_NC/test/NC/1325568_104.jpeg \n", + " inflating: AD_NC/test/NC/1325568_105.jpeg \n", + " inflating: AD_NC/test/NC/1325568_106.jpeg \n", + " inflating: AD_NC/test/NC/1325568_107.jpeg \n", + " inflating: AD_NC/test/NC/1325568_108.jpeg \n", + " inflating: AD_NC/test/NC/1325568_109.jpeg \n", + " inflating: AD_NC/test/NC/1325568_110.jpeg \n", + " inflating: AD_NC/test/NC/1325568_111.jpeg \n", + " inflating: AD_NC/test/NC/1325568_112.jpeg \n", + " inflating: AD_NC/test/NC/1325568_113.jpeg \n", + " inflating: AD_NC/test/NC/1325568_94.jpeg \n", + " inflating: AD_NC/test/NC/1325568_95.jpeg \n", + " inflating: AD_NC/test/NC/1325568_96.jpeg \n", + " inflating: AD_NC/test/NC/1325568_97.jpeg \n", + " inflating: AD_NC/test/NC/1325568_98.jpeg \n", + " inflating: AD_NC/test/NC/1325568_99.jpeg \n", + " inflating: AD_NC/test/NC/1325857_100.jpeg \n", + " inflating: AD_NC/test/NC/1325857_101.jpeg \n", + " inflating: AD_NC/test/NC/1325857_102.jpeg \n", + " inflating: AD_NC/test/NC/1325857_103.jpeg \n", + " inflating: AD_NC/test/NC/1325857_104.jpeg \n", + " inflating: AD_NC/test/NC/1325857_105.jpeg \n", + " inflating: AD_NC/test/NC/1325857_106.jpeg \n", + " inflating: AD_NC/test/NC/1325857_107.jpeg \n", + " inflating: AD_NC/test/NC/1325857_108.jpeg \n", + " inflating: AD_NC/test/NC/1325857_109.jpeg \n", + " inflating: AD_NC/test/NC/1325857_110.jpeg \n", + " inflating: AD_NC/test/NC/1325857_111.jpeg \n", + " inflating: AD_NC/test/NC/1325857_112.jpeg \n", + " inflating: AD_NC/test/NC/1325857_113.jpeg \n", + " inflating: AD_NC/test/NC/1325857_94.jpeg \n", + " inflating: AD_NC/test/NC/1325857_95.jpeg \n", + " inflating: AD_NC/test/NC/1325857_96.jpeg \n", + " inflating: AD_NC/test/NC/1325857_97.jpeg \n", + " inflating: AD_NC/test/NC/1325857_98.jpeg \n", + " inflating: AD_NC/test/NC/1325857_99.jpeg \n", + " inflating: AD_NC/test/NC/1326332_100.jpeg \n", + " inflating: AD_NC/test/NC/1326332_101.jpeg \n", + " inflating: AD_NC/test/NC/1326332_102.jpeg \n", + " inflating: AD_NC/test/NC/1326332_103.jpeg \n", + " inflating: AD_NC/test/NC/1326332_104.jpeg \n", + " inflating: AD_NC/test/NC/1326332_105.jpeg \n", + " inflating: AD_NC/test/NC/1326332_106.jpeg \n", + " inflating: AD_NC/test/NC/1326332_107.jpeg \n", + " inflating: AD_NC/test/NC/1326332_108.jpeg \n", + " inflating: AD_NC/test/NC/1326332_109.jpeg \n", + " inflating: AD_NC/test/NC/1326332_110.jpeg \n", + " inflating: AD_NC/test/NC/1326332_111.jpeg \n", + " inflating: AD_NC/test/NC/1326332_112.jpeg \n", + " inflating: AD_NC/test/NC/1326332_113.jpeg \n", + " inflating: AD_NC/test/NC/1326332_94.jpeg \n", + " inflating: AD_NC/test/NC/1326332_95.jpeg \n", + " inflating: AD_NC/test/NC/1326332_96.jpeg \n", + " inflating: AD_NC/test/NC/1326332_97.jpeg \n", + " inflating: AD_NC/test/NC/1326332_98.jpeg \n", + " inflating: AD_NC/test/NC/1326332_99.jpeg \n", + " inflating: AD_NC/test/NC/1327191_100.jpeg \n", + " inflating: AD_NC/test/NC/1327191_101.jpeg \n", + " inflating: AD_NC/test/NC/1327191_102.jpeg \n", + " inflating: AD_NC/test/NC/1327191_103.jpeg \n", + " inflating: AD_NC/test/NC/1327191_104.jpeg \n", + " inflating: AD_NC/test/NC/1327191_105.jpeg \n", + " inflating: AD_NC/test/NC/1327191_106.jpeg \n", + " inflating: AD_NC/test/NC/1327191_107.jpeg \n", + " inflating: AD_NC/test/NC/1327191_108.jpeg \n", + " inflating: AD_NC/test/NC/1327191_109.jpeg \n", + " inflating: AD_NC/test/NC/1327191_110.jpeg \n", + " inflating: AD_NC/test/NC/1327191_111.jpeg \n", + " inflating: AD_NC/test/NC/1327191_112.jpeg \n", + " inflating: AD_NC/test/NC/1327191_113.jpeg \n", + " inflating: AD_NC/test/NC/1327191_94.jpeg \n", + " inflating: AD_NC/test/NC/1327191_95.jpeg \n", + " inflating: AD_NC/test/NC/1327191_96.jpeg \n", + " inflating: AD_NC/test/NC/1327191_97.jpeg \n", + " inflating: AD_NC/test/NC/1327191_98.jpeg \n", + " inflating: AD_NC/test/NC/1327191_99.jpeg \n", + " inflating: AD_NC/test/NC/1327456_100.jpeg \n", + " inflating: AD_NC/test/NC/1327456_101.jpeg \n", + " inflating: AD_NC/test/NC/1327456_102.jpeg \n", + " inflating: AD_NC/test/NC/1327456_103.jpeg \n", + " inflating: AD_NC/test/NC/1327456_104.jpeg \n", + " inflating: AD_NC/test/NC/1327456_105.jpeg \n", + " inflating: AD_NC/test/NC/1327456_106.jpeg \n", + " inflating: AD_NC/test/NC/1327456_107.jpeg \n", + " inflating: AD_NC/test/NC/1327456_108.jpeg \n", + " inflating: AD_NC/test/NC/1327456_109.jpeg \n", + " inflating: AD_NC/test/NC/1327456_110.jpeg \n", + " inflating: AD_NC/test/NC/1327456_111.jpeg \n", + " inflating: AD_NC/test/NC/1327456_112.jpeg \n", + " inflating: AD_NC/test/NC/1327456_113.jpeg \n", + " inflating: AD_NC/test/NC/1327456_94.jpeg \n", + " inflating: AD_NC/test/NC/1327456_95.jpeg \n", + " inflating: AD_NC/test/NC/1327456_96.jpeg \n", + " inflating: AD_NC/test/NC/1327456_97.jpeg \n", + " inflating: AD_NC/test/NC/1327456_98.jpeg \n", + " inflating: AD_NC/test/NC/1327456_99.jpeg \n", + " inflating: AD_NC/test/NC/1327480_100.jpeg \n", + " inflating: AD_NC/test/NC/1327480_101.jpeg \n", + " inflating: AD_NC/test/NC/1327480_102.jpeg \n", + " inflating: AD_NC/test/NC/1327480_103.jpeg \n", + " inflating: AD_NC/test/NC/1327480_104.jpeg \n", + " inflating: AD_NC/test/NC/1327480_105.jpeg \n", + " inflating: AD_NC/test/NC/1327480_106.jpeg \n", + " inflating: AD_NC/test/NC/1327480_107.jpeg \n", + " inflating: AD_NC/test/NC/1327480_108.jpeg \n", + " inflating: AD_NC/test/NC/1327480_109.jpeg \n", + " inflating: AD_NC/test/NC/1327480_110.jpeg \n", + " inflating: AD_NC/test/NC/1327480_111.jpeg \n", + " inflating: AD_NC/test/NC/1327480_112.jpeg \n", + " inflating: AD_NC/test/NC/1327480_113.jpeg \n", + " inflating: AD_NC/test/NC/1327480_94.jpeg \n", + " inflating: AD_NC/test/NC/1327480_95.jpeg \n", + " inflating: AD_NC/test/NC/1327480_96.jpeg \n", + " inflating: AD_NC/test/NC/1327480_97.jpeg \n", + " inflating: AD_NC/test/NC/1327480_98.jpeg \n", + " inflating: AD_NC/test/NC/1327480_99.jpeg \n", + " inflating: AD_NC/test/NC/1328524_100.jpeg \n", + " inflating: AD_NC/test/NC/1328524_101.jpeg \n", + " inflating: AD_NC/test/NC/1328524_102.jpeg \n", + " inflating: AD_NC/test/NC/1328524_103.jpeg \n", + " inflating: AD_NC/test/NC/1328524_104.jpeg \n", + " inflating: AD_NC/test/NC/1328524_105.jpeg \n", + " inflating: AD_NC/test/NC/1328524_106.jpeg \n", + " inflating: AD_NC/test/NC/1328524_107.jpeg \n", + " inflating: AD_NC/test/NC/1328524_88.jpeg \n", + " inflating: AD_NC/test/NC/1328524_89.jpeg \n", + " inflating: AD_NC/test/NC/1328524_90.jpeg \n", + " inflating: AD_NC/test/NC/1328524_91.jpeg \n", + " inflating: AD_NC/test/NC/1328524_92.jpeg \n", + " inflating: AD_NC/test/NC/1328524_93.jpeg \n", + " inflating: AD_NC/test/NC/1328524_94.jpeg \n", + " inflating: AD_NC/test/NC/1328524_95.jpeg \n", + " inflating: AD_NC/test/NC/1328524_96.jpeg \n", + " inflating: AD_NC/test/NC/1328524_97.jpeg \n", + " inflating: AD_NC/test/NC/1328524_98.jpeg \n", + " inflating: AD_NC/test/NC/1328524_99.jpeg \n", + " inflating: AD_NC/test/NC/1329968_100.jpeg \n", + " inflating: AD_NC/test/NC/1329968_101.jpeg \n", + " inflating: AD_NC/test/NC/1329968_102.jpeg \n", + " inflating: AD_NC/test/NC/1329968_103.jpeg \n", + " inflating: AD_NC/test/NC/1329968_104.jpeg \n", + " inflating: AD_NC/test/NC/1329968_105.jpeg \n", + " inflating: AD_NC/test/NC/1329968_106.jpeg \n", + " inflating: AD_NC/test/NC/1329968_107.jpeg \n", + " inflating: AD_NC/test/NC/1329968_108.jpeg \n", + " inflating: AD_NC/test/NC/1329968_109.jpeg \n", + " inflating: AD_NC/test/NC/1329968_110.jpeg \n", + " inflating: AD_NC/test/NC/1329968_111.jpeg \n", + " inflating: AD_NC/test/NC/1329968_112.jpeg \n", + " inflating: AD_NC/test/NC/1329968_113.jpeg \n", + " inflating: AD_NC/test/NC/1329968_94.jpeg \n", + " inflating: AD_NC/test/NC/1329968_95.jpeg \n", + " inflating: AD_NC/test/NC/1329968_96.jpeg \n", + " inflating: AD_NC/test/NC/1329968_97.jpeg \n", + " inflating: AD_NC/test/NC/1329968_98.jpeg \n", + " inflating: AD_NC/test/NC/1329968_99.jpeg \n", + " inflating: AD_NC/test/NC/1331222_100.jpeg \n", + " inflating: AD_NC/test/NC/1331222_101.jpeg \n", + " inflating: AD_NC/test/NC/1331222_102.jpeg \n", + " inflating: AD_NC/test/NC/1331222_103.jpeg \n", + " inflating: AD_NC/test/NC/1331222_104.jpeg \n", + " inflating: AD_NC/test/NC/1331222_105.jpeg \n", + " inflating: AD_NC/test/NC/1331222_106.jpeg \n", + " inflating: AD_NC/test/NC/1331222_107.jpeg \n", + " inflating: AD_NC/test/NC/1331222_88.jpeg \n", + " inflating: AD_NC/test/NC/1331222_89.jpeg \n", + " inflating: AD_NC/test/NC/1331222_90.jpeg \n", + " inflating: AD_NC/test/NC/1331222_91.jpeg \n", + " inflating: AD_NC/test/NC/1331222_92.jpeg \n", + " inflating: AD_NC/test/NC/1331222_93.jpeg \n", + " inflating: AD_NC/test/NC/1331222_94.jpeg \n", + " inflating: AD_NC/test/NC/1331222_95.jpeg \n", + " inflating: AD_NC/test/NC/1331222_96.jpeg \n", + " inflating: AD_NC/test/NC/1331222_97.jpeg \n", + " inflating: AD_NC/test/NC/1331222_98.jpeg \n", + " inflating: AD_NC/test/NC/1331222_99.jpeg \n", + " inflating: AD_NC/test/NC/1331870_100.jpeg \n", + " inflating: AD_NC/test/NC/1331870_101.jpeg \n", + " inflating: AD_NC/test/NC/1331870_102.jpeg \n", + " inflating: AD_NC/test/NC/1331870_103.jpeg \n", + " inflating: AD_NC/test/NC/1331870_104.jpeg \n", + " inflating: AD_NC/test/NC/1331870_105.jpeg \n", + " inflating: AD_NC/test/NC/1331870_106.jpeg \n", + " inflating: AD_NC/test/NC/1331870_107.jpeg \n", + " inflating: AD_NC/test/NC/1331870_88.jpeg \n", + " inflating: AD_NC/test/NC/1331870_89.jpeg \n", + " inflating: AD_NC/test/NC/1331870_90.jpeg \n", + " inflating: AD_NC/test/NC/1331870_91.jpeg \n", + " inflating: AD_NC/test/NC/1331870_92.jpeg \n", + " inflating: AD_NC/test/NC/1331870_93.jpeg \n", + " inflating: AD_NC/test/NC/1331870_94.jpeg \n", + " inflating: AD_NC/test/NC/1331870_95.jpeg \n", + " inflating: AD_NC/test/NC/1331870_96.jpeg \n", + " inflating: AD_NC/test/NC/1331870_97.jpeg \n", + " inflating: AD_NC/test/NC/1331870_98.jpeg \n", + " inflating: AD_NC/test/NC/1331870_99.jpeg \n", + " inflating: AD_NC/test/NC/1332317_100.jpeg \n", + " inflating: AD_NC/test/NC/1332317_101.jpeg \n", + " inflating: AD_NC/test/NC/1332317_102.jpeg \n", + " inflating: AD_NC/test/NC/1332317_103.jpeg \n", + " inflating: AD_NC/test/NC/1332317_104.jpeg \n", + " inflating: AD_NC/test/NC/1332317_105.jpeg \n", + " inflating: AD_NC/test/NC/1332317_106.jpeg \n", + " inflating: AD_NC/test/NC/1332317_107.jpeg \n", + " inflating: AD_NC/test/NC/1332317_108.jpeg \n", + " inflating: AD_NC/test/NC/1332317_109.jpeg \n", + " inflating: AD_NC/test/NC/1332317_110.jpeg \n", + " inflating: AD_NC/test/NC/1332317_111.jpeg \n", + " inflating: AD_NC/test/NC/1332317_112.jpeg \n", + " inflating: AD_NC/test/NC/1332317_113.jpeg \n", + " inflating: AD_NC/test/NC/1332317_94.jpeg \n", + " inflating: AD_NC/test/NC/1332317_95.jpeg \n", + " inflating: AD_NC/test/NC/1332317_96.jpeg \n", + " inflating: AD_NC/test/NC/1332317_97.jpeg \n", + " inflating: AD_NC/test/NC/1332317_98.jpeg \n", + " inflating: AD_NC/test/NC/1332317_99.jpeg \n", + " inflating: AD_NC/test/NC/1332407_100.jpeg \n", + " inflating: AD_NC/test/NC/1332407_101.jpeg \n", + " inflating: AD_NC/test/NC/1332407_102.jpeg \n", + " inflating: AD_NC/test/NC/1332407_103.jpeg \n", + " inflating: AD_NC/test/NC/1332407_104.jpeg \n", + " inflating: AD_NC/test/NC/1332407_105.jpeg \n", + " inflating: AD_NC/test/NC/1332407_106.jpeg \n", + " inflating: AD_NC/test/NC/1332407_107.jpeg \n", + " inflating: AD_NC/test/NC/1332407_108.jpeg \n", + " inflating: AD_NC/test/NC/1332407_109.jpeg \n", + " inflating: AD_NC/test/NC/1332407_110.jpeg \n", + " inflating: AD_NC/test/NC/1332407_111.jpeg \n", + " inflating: AD_NC/test/NC/1332407_112.jpeg \n", + " inflating: AD_NC/test/NC/1332407_113.jpeg \n", + " inflating: AD_NC/test/NC/1332407_94.jpeg \n", + " inflating: AD_NC/test/NC/1332407_95.jpeg \n", + " inflating: AD_NC/test/NC/1332407_96.jpeg \n", + " inflating: AD_NC/test/NC/1332407_97.jpeg \n", + " inflating: AD_NC/test/NC/1332407_98.jpeg \n", + " inflating: AD_NC/test/NC/1332407_99.jpeg \n", + " inflating: AD_NC/test/NC/1332450_100.jpeg \n", + " inflating: AD_NC/test/NC/1332450_101.jpeg \n", + " inflating: AD_NC/test/NC/1332450_102.jpeg \n", + " inflating: AD_NC/test/NC/1332450_103.jpeg \n", + " inflating: AD_NC/test/NC/1332450_104.jpeg \n", + " inflating: AD_NC/test/NC/1332450_105.jpeg \n", + " inflating: AD_NC/test/NC/1332450_106.jpeg \n", + " inflating: AD_NC/test/NC/1332450_107.jpeg \n", + " inflating: AD_NC/test/NC/1332450_108.jpeg \n", + " inflating: AD_NC/test/NC/1332450_109.jpeg \n", + " inflating: AD_NC/test/NC/1332450_110.jpeg \n", + " inflating: AD_NC/test/NC/1332450_111.jpeg \n", + " inflating: AD_NC/test/NC/1332450_112.jpeg \n", + " inflating: AD_NC/test/NC/1332450_113.jpeg \n", + " inflating: AD_NC/test/NC/1332450_94.jpeg \n", + " inflating: AD_NC/test/NC/1332450_95.jpeg \n", + " inflating: AD_NC/test/NC/1332450_96.jpeg \n", + " inflating: AD_NC/test/NC/1332450_97.jpeg \n", + " inflating: AD_NC/test/NC/1332450_98.jpeg \n", + " inflating: AD_NC/test/NC/1332450_99.jpeg \n", + " inflating: AD_NC/test/NC/1332469_100.jpeg \n", + " inflating: AD_NC/test/NC/1332469_101.jpeg \n", + " inflating: AD_NC/test/NC/1332469_102.jpeg \n", + " inflating: AD_NC/test/NC/1332469_103.jpeg \n", + " inflating: AD_NC/test/NC/1332469_104.jpeg \n", + " inflating: AD_NC/test/NC/1332469_105.jpeg \n", + " inflating: AD_NC/test/NC/1332469_106.jpeg \n", + " inflating: AD_NC/test/NC/1332469_107.jpeg \n", + " inflating: AD_NC/test/NC/1332469_108.jpeg \n", + " inflating: AD_NC/test/NC/1332469_109.jpeg \n", + " inflating: AD_NC/test/NC/1332469_110.jpeg \n", + " inflating: AD_NC/test/NC/1332469_111.jpeg \n", + " inflating: AD_NC/test/NC/1332469_112.jpeg \n", + " inflating: AD_NC/test/NC/1332469_113.jpeg \n", + " inflating: AD_NC/test/NC/1332469_94.jpeg \n", + " inflating: AD_NC/test/NC/1332469_95.jpeg \n", + " inflating: AD_NC/test/NC/1332469_96.jpeg \n", + " inflating: AD_NC/test/NC/1332469_97.jpeg \n", + " inflating: AD_NC/test/NC/1332469_98.jpeg \n", + " inflating: AD_NC/test/NC/1332469_99.jpeg \n", + " inflating: AD_NC/test/NC/1335384_78.jpeg \n", + " inflating: AD_NC/test/NC/1335384_79.jpeg \n", + " inflating: AD_NC/test/NC/1335384_80.jpeg \n", + " inflating: AD_NC/test/NC/1335384_81.jpeg \n", + " inflating: AD_NC/test/NC/1335384_82.jpeg \n", + " inflating: AD_NC/test/NC/1335384_83.jpeg \n", + " inflating: AD_NC/test/NC/1335384_84.jpeg \n", + " inflating: AD_NC/test/NC/1335384_85.jpeg \n", + " inflating: AD_NC/test/NC/1335384_86.jpeg \n", + " inflating: AD_NC/test/NC/1335384_87.jpeg \n", + " inflating: AD_NC/test/NC/1335384_88.jpeg \n", + " inflating: AD_NC/test/NC/1335384_89.jpeg \n", + " inflating: AD_NC/test/NC/1335384_90.jpeg \n", + " inflating: AD_NC/test/NC/1335384_91.jpeg \n", + " inflating: AD_NC/test/NC/1335384_92.jpeg \n", + " inflating: AD_NC/test/NC/1335384_93.jpeg \n", + " inflating: AD_NC/test/NC/1335384_94.jpeg \n", + " inflating: AD_NC/test/NC/1335384_95.jpeg \n", + " inflating: AD_NC/test/NC/1335384_96.jpeg \n", + " inflating: AD_NC/test/NC/1335384_97.jpeg \n", + " inflating: AD_NC/test/NC/1336238_100.jpeg \n", + " inflating: AD_NC/test/NC/1336238_101.jpeg \n", + " inflating: AD_NC/test/NC/1336238_102.jpeg \n", + " inflating: AD_NC/test/NC/1336238_103.jpeg \n", + " inflating: AD_NC/test/NC/1336238_104.jpeg \n", + " inflating: AD_NC/test/NC/1336238_105.jpeg \n", + " inflating: AD_NC/test/NC/1336238_106.jpeg \n", + " inflating: AD_NC/test/NC/1336238_107.jpeg \n", + " inflating: AD_NC/test/NC/1336238_108.jpeg \n", + " inflating: AD_NC/test/NC/1336238_109.jpeg \n", + " inflating: AD_NC/test/NC/1336238_110.jpeg \n", + " inflating: AD_NC/test/NC/1336238_111.jpeg \n", + " inflating: AD_NC/test/NC/1336238_112.jpeg \n", + " inflating: AD_NC/test/NC/1336238_113.jpeg \n", + " inflating: AD_NC/test/NC/1336238_94.jpeg \n", + " inflating: AD_NC/test/NC/1336238_95.jpeg \n", + " inflating: AD_NC/test/NC/1336238_96.jpeg \n", + " inflating: AD_NC/test/NC/1336238_97.jpeg \n", + " inflating: AD_NC/test/NC/1336238_98.jpeg \n", + " inflating: AD_NC/test/NC/1336238_99.jpeg \n", + " inflating: AD_NC/test/NC/1337907_100.jpeg \n", + " inflating: AD_NC/test/NC/1337907_101.jpeg \n", + " inflating: AD_NC/test/NC/1337907_102.jpeg \n", + " inflating: AD_NC/test/NC/1337907_103.jpeg \n", + " inflating: AD_NC/test/NC/1337907_104.jpeg \n", + " inflating: AD_NC/test/NC/1337907_105.jpeg \n", + " inflating: AD_NC/test/NC/1337907_106.jpeg \n", + " inflating: AD_NC/test/NC/1337907_107.jpeg \n", + " inflating: AD_NC/test/NC/1337907_88.jpeg \n", + " inflating: AD_NC/test/NC/1337907_89.jpeg \n", + " inflating: AD_NC/test/NC/1337907_90.jpeg \n", + " inflating: AD_NC/test/NC/1337907_91.jpeg \n", + " inflating: AD_NC/test/NC/1337907_92.jpeg \n", + " inflating: AD_NC/test/NC/1337907_93.jpeg \n", + " inflating: AD_NC/test/NC/1337907_94.jpeg \n", + " inflating: AD_NC/test/NC/1337907_95.jpeg \n", + " inflating: AD_NC/test/NC/1337907_96.jpeg \n", + " inflating: AD_NC/test/NC/1337907_97.jpeg \n", + " inflating: AD_NC/test/NC/1337907_98.jpeg \n", + " inflating: AD_NC/test/NC/1337907_99.jpeg \n", + " inflating: AD_NC/test/NC/1340855_100.jpeg \n", + " inflating: AD_NC/test/NC/1340855_101.jpeg \n", + " inflating: AD_NC/test/NC/1340855_102.jpeg \n", + " inflating: AD_NC/test/NC/1340855_103.jpeg \n", + " inflating: AD_NC/test/NC/1340855_104.jpeg \n", + " inflating: AD_NC/test/NC/1340855_105.jpeg \n", + " inflating: AD_NC/test/NC/1340855_106.jpeg \n", + " inflating: AD_NC/test/NC/1340855_107.jpeg \n", + " inflating: AD_NC/test/NC/1340855_108.jpeg \n", + " inflating: AD_NC/test/NC/1340855_109.jpeg \n", + " inflating: AD_NC/test/NC/1340855_110.jpeg \n", + " inflating: AD_NC/test/NC/1340855_111.jpeg \n", + " inflating: AD_NC/test/NC/1340855_112.jpeg \n", + " inflating: AD_NC/test/NC/1340855_113.jpeg \n", + " inflating: AD_NC/test/NC/1340855_94.jpeg \n", + " inflating: AD_NC/test/NC/1340855_95.jpeg \n", + " inflating: AD_NC/test/NC/1340855_96.jpeg \n", + " inflating: AD_NC/test/NC/1340855_97.jpeg \n", + " inflating: AD_NC/test/NC/1340855_98.jpeg \n", + " inflating: AD_NC/test/NC/1340855_99.jpeg \n", + " inflating: AD_NC/test/NC/1341792_100.jpeg \n", + " inflating: AD_NC/test/NC/1341792_101.jpeg \n", + " inflating: AD_NC/test/NC/1341792_102.jpeg \n", + " inflating: AD_NC/test/NC/1341792_103.jpeg \n", + " inflating: AD_NC/test/NC/1341792_104.jpeg \n", + " inflating: AD_NC/test/NC/1341792_105.jpeg \n", + " inflating: AD_NC/test/NC/1341792_106.jpeg \n", + " inflating: AD_NC/test/NC/1341792_107.jpeg \n", + " inflating: AD_NC/test/NC/1341792_108.jpeg \n", + " inflating: AD_NC/test/NC/1341792_109.jpeg \n", + " inflating: AD_NC/test/NC/1341792_110.jpeg \n", + " inflating: AD_NC/test/NC/1341792_111.jpeg \n", + " inflating: AD_NC/test/NC/1341792_112.jpeg \n", + " inflating: AD_NC/test/NC/1341792_113.jpeg \n", + " inflating: AD_NC/test/NC/1341792_94.jpeg \n", + " inflating: AD_NC/test/NC/1341792_95.jpeg \n", + " inflating: AD_NC/test/NC/1341792_96.jpeg \n", + " inflating: AD_NC/test/NC/1341792_97.jpeg \n", + " inflating: AD_NC/test/NC/1341792_98.jpeg \n", + " inflating: AD_NC/test/NC/1341792_99.jpeg \n", + " inflating: AD_NC/test/NC/1342528_100.jpeg \n", + " inflating: AD_NC/test/NC/1342528_101.jpeg \n", + " inflating: AD_NC/test/NC/1342528_102.jpeg \n", + " inflating: AD_NC/test/NC/1342528_103.jpeg \n", + " inflating: AD_NC/test/NC/1342528_104.jpeg \n", + " inflating: AD_NC/test/NC/1342528_105.jpeg \n", + " inflating: AD_NC/test/NC/1342528_106.jpeg \n", + " inflating: AD_NC/test/NC/1342528_107.jpeg \n", + " inflating: AD_NC/test/NC/1342528_108.jpeg \n", + " inflating: AD_NC/test/NC/1342528_109.jpeg \n", + " inflating: AD_NC/test/NC/1342528_110.jpeg \n", + " inflating: AD_NC/test/NC/1342528_111.jpeg \n", + " inflating: AD_NC/test/NC/1342528_112.jpeg \n", + " inflating: AD_NC/test/NC/1342528_113.jpeg \n", + " inflating: AD_NC/test/NC/1342528_94.jpeg \n", + " inflating: AD_NC/test/NC/1342528_95.jpeg \n", + " inflating: AD_NC/test/NC/1342528_96.jpeg \n", + " inflating: AD_NC/test/NC/1342528_97.jpeg \n", + " inflating: AD_NC/test/NC/1342528_98.jpeg \n", + " inflating: AD_NC/test/NC/1342528_99.jpeg \n", + " inflating: AD_NC/test/NC/1343715_100.jpeg \n", + " inflating: AD_NC/test/NC/1343715_101.jpeg \n", + " inflating: AD_NC/test/NC/1343715_102.jpeg \n", + " inflating: AD_NC/test/NC/1343715_103.jpeg \n", + " inflating: AD_NC/test/NC/1343715_104.jpeg \n", + " inflating: AD_NC/test/NC/1343715_105.jpeg \n", + " inflating: AD_NC/test/NC/1343715_106.jpeg \n", + " inflating: AD_NC/test/NC/1343715_107.jpeg \n", + " inflating: AD_NC/test/NC/1343715_108.jpeg \n", + " inflating: AD_NC/test/NC/1343715_109.jpeg \n", + " inflating: AD_NC/test/NC/1343715_110.jpeg \n", + " inflating: AD_NC/test/NC/1343715_111.jpeg \n", + " inflating: AD_NC/test/NC/1343715_112.jpeg \n", + " inflating: AD_NC/test/NC/1343715_113.jpeg \n", + " inflating: AD_NC/test/NC/1343715_94.jpeg \n", + " inflating: AD_NC/test/NC/1343715_95.jpeg \n", + " inflating: AD_NC/test/NC/1343715_96.jpeg \n", + " inflating: AD_NC/test/NC/1343715_97.jpeg \n", + " inflating: AD_NC/test/NC/1343715_98.jpeg \n", + " inflating: AD_NC/test/NC/1343715_99.jpeg \n", + " inflating: AD_NC/test/NC/1344288_100.jpeg \n", + " inflating: AD_NC/test/NC/1344288_101.jpeg \n", + " inflating: AD_NC/test/NC/1344288_102.jpeg \n", + " inflating: AD_NC/test/NC/1344288_103.jpeg \n", + " inflating: AD_NC/test/NC/1344288_104.jpeg \n", + " inflating: AD_NC/test/NC/1344288_105.jpeg \n", + " inflating: AD_NC/test/NC/1344288_106.jpeg \n", + " inflating: AD_NC/test/NC/1344288_107.jpeg \n", + " inflating: AD_NC/test/NC/1344288_108.jpeg \n", + " inflating: AD_NC/test/NC/1344288_109.jpeg \n", + " inflating: AD_NC/test/NC/1344288_110.jpeg \n", + " inflating: AD_NC/test/NC/1344288_111.jpeg \n", + " inflating: AD_NC/test/NC/1344288_112.jpeg \n", + " inflating: AD_NC/test/NC/1344288_113.jpeg \n", + " inflating: AD_NC/test/NC/1344288_94.jpeg \n", + " inflating: AD_NC/test/NC/1344288_95.jpeg \n", + " inflating: AD_NC/test/NC/1344288_96.jpeg \n", + " inflating: AD_NC/test/NC/1344288_97.jpeg \n", + " inflating: AD_NC/test/NC/1344288_98.jpeg \n", + " inflating: AD_NC/test/NC/1344288_99.jpeg \n", + " inflating: AD_NC/test/NC/1344400_78.jpeg \n", + " inflating: AD_NC/test/NC/1344400_79.jpeg \n", + " inflating: AD_NC/test/NC/1344400_80.jpeg \n", + " inflating: AD_NC/test/NC/1344400_81.jpeg \n", + " inflating: AD_NC/test/NC/1344400_82.jpeg \n", + " inflating: AD_NC/test/NC/1344400_83.jpeg \n", + " inflating: AD_NC/test/NC/1344400_84.jpeg \n", + " inflating: AD_NC/test/NC/1344400_85.jpeg \n", + " inflating: AD_NC/test/NC/1344400_86.jpeg \n", + " inflating: AD_NC/test/NC/1344400_87.jpeg \n", + " inflating: AD_NC/test/NC/1344400_88.jpeg \n", + " inflating: AD_NC/test/NC/1344400_89.jpeg \n", + " inflating: AD_NC/test/NC/1344400_90.jpeg \n", + " inflating: AD_NC/test/NC/1344400_91.jpeg \n", + " inflating: AD_NC/test/NC/1344400_92.jpeg \n", + " inflating: AD_NC/test/NC/1344400_93.jpeg \n", + " inflating: AD_NC/test/NC/1344400_94.jpeg \n", + " inflating: AD_NC/test/NC/1344400_95.jpeg \n", + " inflating: AD_NC/test/NC/1344400_96.jpeg \n", + " inflating: AD_NC/test/NC/1344400_97.jpeg \n", + " inflating: AD_NC/test/NC/1344417_100.jpeg \n", + " inflating: AD_NC/test/NC/1344417_101.jpeg \n", + " inflating: AD_NC/test/NC/1344417_102.jpeg \n", + " inflating: AD_NC/test/NC/1344417_103.jpeg \n", + " inflating: AD_NC/test/NC/1344417_104.jpeg \n", + " inflating: AD_NC/test/NC/1344417_105.jpeg \n", + " inflating: AD_NC/test/NC/1344417_106.jpeg \n", + " inflating: AD_NC/test/NC/1344417_107.jpeg \n", + " inflating: AD_NC/test/NC/1344417_108.jpeg \n", + " inflating: AD_NC/test/NC/1344417_109.jpeg \n", + " inflating: AD_NC/test/NC/1344417_110.jpeg \n", + " inflating: AD_NC/test/NC/1344417_111.jpeg \n", + " inflating: AD_NC/test/NC/1344417_112.jpeg \n", + " inflating: AD_NC/test/NC/1344417_113.jpeg \n", + " inflating: AD_NC/test/NC/1344417_94.jpeg \n", + " inflating: AD_NC/test/NC/1344417_95.jpeg \n", + " inflating: AD_NC/test/NC/1344417_96.jpeg \n", + " inflating: AD_NC/test/NC/1344417_97.jpeg \n", + " inflating: AD_NC/test/NC/1344417_98.jpeg \n", + " inflating: AD_NC/test/NC/1344417_99.jpeg \n", + " inflating: AD_NC/test/NC/1345109_78.jpeg \n", + " inflating: AD_NC/test/NC/1345109_79.jpeg \n", + " inflating: AD_NC/test/NC/1345109_80.jpeg \n", + " inflating: AD_NC/test/NC/1345109_81.jpeg \n", + " inflating: AD_NC/test/NC/1345109_82.jpeg \n", + " inflating: AD_NC/test/NC/1345109_83.jpeg \n", + " inflating: AD_NC/test/NC/1345109_84.jpeg \n", + " inflating: AD_NC/test/NC/1345109_85.jpeg \n", + " inflating: AD_NC/test/NC/1345109_86.jpeg \n", + " inflating: AD_NC/test/NC/1345109_87.jpeg \n", + " inflating: AD_NC/test/NC/1345109_88.jpeg \n", + " inflating: AD_NC/test/NC/1345109_89.jpeg \n", + " inflating: AD_NC/test/NC/1345109_90.jpeg \n", + " inflating: AD_NC/test/NC/1345109_91.jpeg \n", + " inflating: AD_NC/test/NC/1345109_92.jpeg \n", + " inflating: AD_NC/test/NC/1345109_93.jpeg \n", + " inflating: AD_NC/test/NC/1345109_94.jpeg \n", + " inflating: AD_NC/test/NC/1345109_95.jpeg \n", + " inflating: AD_NC/test/NC/1345109_96.jpeg \n", + " inflating: AD_NC/test/NC/1345109_97.jpeg \n", + " inflating: AD_NC/test/NC/1346089_100.jpeg \n", + " inflating: AD_NC/test/NC/1346089_101.jpeg \n", + " inflating: AD_NC/test/NC/1346089_102.jpeg \n", + " inflating: AD_NC/test/NC/1346089_103.jpeg \n", + " inflating: AD_NC/test/NC/1346089_104.jpeg \n", + " inflating: AD_NC/test/NC/1346089_105.jpeg \n", + " inflating: AD_NC/test/NC/1346089_106.jpeg \n", + " inflating: AD_NC/test/NC/1346089_107.jpeg \n", + " inflating: AD_NC/test/NC/1346089_108.jpeg \n", + " inflating: AD_NC/test/NC/1346089_109.jpeg \n", + " inflating: AD_NC/test/NC/1346089_110.jpeg \n", + " inflating: AD_NC/test/NC/1346089_111.jpeg \n", + " inflating: AD_NC/test/NC/1346089_112.jpeg \n", + " inflating: AD_NC/test/NC/1346089_113.jpeg \n", + " inflating: AD_NC/test/NC/1346089_114.jpeg \n", + " inflating: AD_NC/test/NC/1346089_95.jpeg \n", + " inflating: AD_NC/test/NC/1346089_96.jpeg \n", + " inflating: AD_NC/test/NC/1346089_97.jpeg \n", + " inflating: AD_NC/test/NC/1346089_98.jpeg \n", + " inflating: AD_NC/test/NC/1346089_99.jpeg \n", + " inflating: AD_NC/test/NC/1346152_100.jpeg \n", + " inflating: AD_NC/test/NC/1346152_101.jpeg \n", + " inflating: AD_NC/test/NC/1346152_102.jpeg \n", + " inflating: AD_NC/test/NC/1346152_103.jpeg \n", + " inflating: AD_NC/test/NC/1346152_104.jpeg \n", + " inflating: AD_NC/test/NC/1346152_105.jpeg \n", + " inflating: AD_NC/test/NC/1346152_106.jpeg \n", + " inflating: AD_NC/test/NC/1346152_107.jpeg \n", + " inflating: AD_NC/test/NC/1346152_108.jpeg \n", + " inflating: AD_NC/test/NC/1346152_109.jpeg \n", + " inflating: AD_NC/test/NC/1346152_110.jpeg \n", + " inflating: AD_NC/test/NC/1346152_111.jpeg \n", + " inflating: AD_NC/test/NC/1346152_112.jpeg \n", + " inflating: AD_NC/test/NC/1346152_113.jpeg \n", + " inflating: AD_NC/test/NC/1346152_94.jpeg \n", + " inflating: AD_NC/test/NC/1346152_95.jpeg \n", + " inflating: AD_NC/test/NC/1346152_96.jpeg \n", + " inflating: AD_NC/test/NC/1346152_97.jpeg \n", + " inflating: AD_NC/test/NC/1346152_98.jpeg \n", + " inflating: AD_NC/test/NC/1346152_99.jpeg \n", + " inflating: AD_NC/test/NC/1346240_100.jpeg \n", + " inflating: AD_NC/test/NC/1346240_101.jpeg \n", + " inflating: AD_NC/test/NC/1346240_102.jpeg \n", + " inflating: AD_NC/test/NC/1346240_103.jpeg \n", + " inflating: AD_NC/test/NC/1346240_104.jpeg \n", + " inflating: AD_NC/test/NC/1346240_105.jpeg \n", + " inflating: AD_NC/test/NC/1346240_106.jpeg \n", + " inflating: AD_NC/test/NC/1346240_107.jpeg \n", + " inflating: AD_NC/test/NC/1346240_108.jpeg \n", + " inflating: AD_NC/test/NC/1346240_109.jpeg \n", + " inflating: AD_NC/test/NC/1346240_110.jpeg \n", + " inflating: AD_NC/test/NC/1346240_111.jpeg \n", + " inflating: AD_NC/test/NC/1346240_112.jpeg \n", + " inflating: AD_NC/test/NC/1346240_113.jpeg \n", + " inflating: AD_NC/test/NC/1346240_94.jpeg \n", + " inflating: AD_NC/test/NC/1346240_95.jpeg \n", + " inflating: AD_NC/test/NC/1346240_96.jpeg \n", + " inflating: AD_NC/test/NC/1346240_97.jpeg \n", + " inflating: AD_NC/test/NC/1346240_98.jpeg \n", + " inflating: AD_NC/test/NC/1346240_99.jpeg \n", + " inflating: AD_NC/test/NC/1346789_78.jpeg \n", + " inflating: AD_NC/test/NC/1346789_79.jpeg \n", + " inflating: AD_NC/test/NC/1346789_80.jpeg \n", + " inflating: AD_NC/test/NC/1346789_81.jpeg \n", + " inflating: AD_NC/test/NC/1346789_82.jpeg \n", + " inflating: AD_NC/test/NC/1346789_83.jpeg \n", + " inflating: AD_NC/test/NC/1346789_84.jpeg \n", + " inflating: AD_NC/test/NC/1346789_85.jpeg \n", + " inflating: AD_NC/test/NC/1346789_86.jpeg \n", + " inflating: AD_NC/test/NC/1346789_87.jpeg \n", + " inflating: AD_NC/test/NC/1346789_88.jpeg \n", + " inflating: AD_NC/test/NC/1346789_89.jpeg \n", + " inflating: AD_NC/test/NC/1346789_90.jpeg \n", + " inflating: AD_NC/test/NC/1346789_91.jpeg \n", + " inflating: AD_NC/test/NC/1346789_92.jpeg \n", + " inflating: AD_NC/test/NC/1346789_93.jpeg \n", + " inflating: AD_NC/test/NC/1346789_94.jpeg \n", + " inflating: AD_NC/test/NC/1346789_95.jpeg \n", + " inflating: AD_NC/test/NC/1346789_96.jpeg \n", + " inflating: AD_NC/test/NC/1346789_97.jpeg \n", + " inflating: AD_NC/test/NC/1346790_78.jpeg \n", + " inflating: AD_NC/test/NC/1346790_79.jpeg \n", + " inflating: AD_NC/test/NC/1346790_80.jpeg \n", + " inflating: AD_NC/test/NC/1346790_81.jpeg \n", + " inflating: AD_NC/test/NC/1346790_82.jpeg \n", + " inflating: AD_NC/test/NC/1346790_83.jpeg \n", + " inflating: AD_NC/test/NC/1346790_84.jpeg \n", + " inflating: AD_NC/test/NC/1346790_85.jpeg \n", + " inflating: AD_NC/test/NC/1346790_86.jpeg \n", + " inflating: AD_NC/test/NC/1346790_87.jpeg \n", + " inflating: AD_NC/test/NC/1346790_88.jpeg \n", + " inflating: AD_NC/test/NC/1346790_89.jpeg \n", + " inflating: AD_NC/test/NC/1346790_90.jpeg \n", + " inflating: AD_NC/test/NC/1346790_91.jpeg \n", + " inflating: AD_NC/test/NC/1346790_92.jpeg \n", + " inflating: AD_NC/test/NC/1346790_93.jpeg \n", + " inflating: AD_NC/test/NC/1346790_94.jpeg \n", + " inflating: AD_NC/test/NC/1346790_95.jpeg \n", + " inflating: AD_NC/test/NC/1346790_96.jpeg \n", + " inflating: AD_NC/test/NC/1346790_97.jpeg \n", + " inflating: AD_NC/test/NC/1346960_100.jpeg \n", + " inflating: AD_NC/test/NC/1346960_101.jpeg \n", + " inflating: AD_NC/test/NC/1346960_102.jpeg \n", + " inflating: AD_NC/test/NC/1346960_103.jpeg \n", + " inflating: AD_NC/test/NC/1346960_104.jpeg \n", + " inflating: AD_NC/test/NC/1346960_105.jpeg \n", + " inflating: AD_NC/test/NC/1346960_106.jpeg \n", + " inflating: AD_NC/test/NC/1346960_107.jpeg \n", + " inflating: AD_NC/test/NC/1346960_88.jpeg \n", + " inflating: AD_NC/test/NC/1346960_89.jpeg \n", + " inflating: AD_NC/test/NC/1346960_90.jpeg \n", + " inflating: AD_NC/test/NC/1346960_91.jpeg \n", + " inflating: AD_NC/test/NC/1346960_92.jpeg \n", + " inflating: AD_NC/test/NC/1346960_93.jpeg \n", + " inflating: AD_NC/test/NC/1346960_94.jpeg \n", + " inflating: AD_NC/test/NC/1346960_95.jpeg \n", + " inflating: AD_NC/test/NC/1346960_96.jpeg \n", + " inflating: AD_NC/test/NC/1346960_97.jpeg \n", + " inflating: AD_NC/test/NC/1346960_98.jpeg \n", + " inflating: AD_NC/test/NC/1346960_99.jpeg \n", + " inflating: AD_NC/test/NC/1348108_100.jpeg \n", + " inflating: AD_NC/test/NC/1348108_101.jpeg \n", + " inflating: AD_NC/test/NC/1348108_102.jpeg \n", + " inflating: AD_NC/test/NC/1348108_103.jpeg \n", + " inflating: AD_NC/test/NC/1348108_104.jpeg \n", + " inflating: AD_NC/test/NC/1348108_105.jpeg \n", + " inflating: AD_NC/test/NC/1348108_106.jpeg \n", + " inflating: AD_NC/test/NC/1348108_107.jpeg \n", + " inflating: AD_NC/test/NC/1348108_108.jpeg \n", + " inflating: AD_NC/test/NC/1348108_109.jpeg \n", + " inflating: AD_NC/test/NC/1348108_110.jpeg \n", + " inflating: AD_NC/test/NC/1348108_111.jpeg \n", + " inflating: AD_NC/test/NC/1348108_112.jpeg \n", + " inflating: AD_NC/test/NC/1348108_113.jpeg \n", + " inflating: AD_NC/test/NC/1348108_94.jpeg \n", + " inflating: AD_NC/test/NC/1348108_95.jpeg \n", + " inflating: AD_NC/test/NC/1348108_96.jpeg \n", + " inflating: AD_NC/test/NC/1348108_97.jpeg \n", + " inflating: AD_NC/test/NC/1348108_98.jpeg \n", + " inflating: AD_NC/test/NC/1348108_99.jpeg \n", + " inflating: AD_NC/test/NC/1348596_100.jpeg \n", + " inflating: AD_NC/test/NC/1348596_101.jpeg \n", + " inflating: AD_NC/test/NC/1348596_102.jpeg \n", + " inflating: AD_NC/test/NC/1348596_103.jpeg \n", + " inflating: AD_NC/test/NC/1348596_104.jpeg \n", + " inflating: AD_NC/test/NC/1348596_105.jpeg \n", + " inflating: AD_NC/test/NC/1348596_106.jpeg \n", + " inflating: AD_NC/test/NC/1348596_107.jpeg \n", + " inflating: AD_NC/test/NC/1348596_108.jpeg \n", + " inflating: AD_NC/test/NC/1348596_109.jpeg \n", + " inflating: AD_NC/test/NC/1348596_110.jpeg \n", + " inflating: AD_NC/test/NC/1348596_111.jpeg \n", + " inflating: AD_NC/test/NC/1348596_112.jpeg \n", + " inflating: AD_NC/test/NC/1348596_113.jpeg \n", + " inflating: AD_NC/test/NC/1348596_94.jpeg \n", + " inflating: AD_NC/test/NC/1348596_95.jpeg \n", + " inflating: AD_NC/test/NC/1348596_96.jpeg \n", + " inflating: AD_NC/test/NC/1348596_97.jpeg \n", + " inflating: AD_NC/test/NC/1348596_98.jpeg \n", + " inflating: AD_NC/test/NC/1348596_99.jpeg \n", + " inflating: AD_NC/test/NC/1349784_100.jpeg \n", + " inflating: AD_NC/test/NC/1349784_101.jpeg \n", + " inflating: AD_NC/test/NC/1349784_102.jpeg \n", + " inflating: AD_NC/test/NC/1349784_103.jpeg \n", + " inflating: AD_NC/test/NC/1349784_104.jpeg \n", + " inflating: AD_NC/test/NC/1349784_105.jpeg \n", + " inflating: AD_NC/test/NC/1349784_106.jpeg \n", + " inflating: AD_NC/test/NC/1349784_107.jpeg \n", + " inflating: AD_NC/test/NC/1349784_108.jpeg \n", + " inflating: AD_NC/test/NC/1349784_109.jpeg \n", + " inflating: AD_NC/test/NC/1349784_110.jpeg \n", + " inflating: AD_NC/test/NC/1349784_111.jpeg \n", + " inflating: AD_NC/test/NC/1349784_112.jpeg \n", + " inflating: AD_NC/test/NC/1349784_113.jpeg \n", + " inflating: AD_NC/test/NC/1349784_94.jpeg \n", + " inflating: AD_NC/test/NC/1349784_95.jpeg \n", + " inflating: AD_NC/test/NC/1349784_96.jpeg \n", + " inflating: AD_NC/test/NC/1349784_97.jpeg \n", + " inflating: AD_NC/test/NC/1349784_98.jpeg \n", + " inflating: AD_NC/test/NC/1349784_99.jpeg \n", + " inflating: AD_NC/test/NC/1350223_78.jpeg \n", + " inflating: AD_NC/test/NC/1350223_79.jpeg \n", + " inflating: AD_NC/test/NC/1350223_80.jpeg \n", + " inflating: AD_NC/test/NC/1350223_81.jpeg \n", + " inflating: AD_NC/test/NC/1350223_82.jpeg \n", + " inflating: AD_NC/test/NC/1350223_83.jpeg \n", + " inflating: AD_NC/test/NC/1350223_84.jpeg \n", + " inflating: AD_NC/test/NC/1350223_85.jpeg \n", + " inflating: AD_NC/test/NC/1350223_86.jpeg \n", + " inflating: AD_NC/test/NC/1350223_87.jpeg \n", + " inflating: AD_NC/test/NC/1350223_88.jpeg \n", + " inflating: AD_NC/test/NC/1350223_89.jpeg \n", + " inflating: AD_NC/test/NC/1350223_90.jpeg \n", + " inflating: AD_NC/test/NC/1350223_91.jpeg \n", + " inflating: AD_NC/test/NC/1350223_92.jpeg \n", + " inflating: AD_NC/test/NC/1350223_93.jpeg \n", + " inflating: AD_NC/test/NC/1350223_94.jpeg \n", + " inflating: AD_NC/test/NC/1350223_95.jpeg \n", + " inflating: AD_NC/test/NC/1350223_96.jpeg \n", + " inflating: AD_NC/test/NC/1350223_97.jpeg \n", + " inflating: AD_NC/test/NC/1351301_100.jpeg \n", + " inflating: AD_NC/test/NC/1351301_101.jpeg \n", + " inflating: AD_NC/test/NC/1351301_102.jpeg \n", + " inflating: AD_NC/test/NC/1351301_103.jpeg \n", + " inflating: AD_NC/test/NC/1351301_104.jpeg \n", + " inflating: AD_NC/test/NC/1351301_105.jpeg \n", + " inflating: AD_NC/test/NC/1351301_106.jpeg \n", + " inflating: AD_NC/test/NC/1351301_107.jpeg \n", + " inflating: AD_NC/test/NC/1351301_108.jpeg \n", + " inflating: AD_NC/test/NC/1351301_109.jpeg \n", + " inflating: AD_NC/test/NC/1351301_110.jpeg \n", + " inflating: AD_NC/test/NC/1351301_111.jpeg \n", + " inflating: AD_NC/test/NC/1351301_112.jpeg \n", + " inflating: AD_NC/test/NC/1351301_113.jpeg \n", + " inflating: AD_NC/test/NC/1351301_94.jpeg \n", + " inflating: AD_NC/test/NC/1351301_95.jpeg \n", + " inflating: AD_NC/test/NC/1351301_96.jpeg \n", + " inflating: AD_NC/test/NC/1351301_97.jpeg \n", + " inflating: AD_NC/test/NC/1351301_98.jpeg \n", + " inflating: AD_NC/test/NC/1351301_99.jpeg \n", + " inflating: AD_NC/test/NC/1352191_100.jpeg \n", + " inflating: AD_NC/test/NC/1352191_101.jpeg \n", + " inflating: AD_NC/test/NC/1352191_102.jpeg \n", + " inflating: AD_NC/test/NC/1352191_103.jpeg \n", + " inflating: AD_NC/test/NC/1352191_104.jpeg \n", + " inflating: AD_NC/test/NC/1352191_105.jpeg \n", + " inflating: AD_NC/test/NC/1352191_106.jpeg \n", + " inflating: AD_NC/test/NC/1352191_107.jpeg \n", + " inflating: AD_NC/test/NC/1352191_108.jpeg \n", + " inflating: AD_NC/test/NC/1352191_109.jpeg \n", + " inflating: AD_NC/test/NC/1352191_110.jpeg \n", + " inflating: AD_NC/test/NC/1352191_111.jpeg \n", + " inflating: AD_NC/test/NC/1352191_112.jpeg \n", + " inflating: AD_NC/test/NC/1352191_113.jpeg \n", + " inflating: AD_NC/test/NC/1352191_94.jpeg \n", + " inflating: AD_NC/test/NC/1352191_95.jpeg \n", + " inflating: AD_NC/test/NC/1352191_96.jpeg \n", + " inflating: AD_NC/test/NC/1352191_97.jpeg \n", + " inflating: AD_NC/test/NC/1352191_98.jpeg \n", + " inflating: AD_NC/test/NC/1352191_99.jpeg \n", + " inflating: AD_NC/test/NC/1354011_78.jpeg \n", + " inflating: AD_NC/test/NC/1354011_79.jpeg \n", + " inflating: AD_NC/test/NC/1354011_80.jpeg \n", + " inflating: AD_NC/test/NC/1354011_81.jpeg \n", + " inflating: AD_NC/test/NC/1354011_82.jpeg \n", + " inflating: AD_NC/test/NC/1354011_83.jpeg \n", + " inflating: AD_NC/test/NC/1354011_84.jpeg \n", + " inflating: AD_NC/test/NC/1354011_85.jpeg \n", + " inflating: AD_NC/test/NC/1354011_86.jpeg \n", + " inflating: AD_NC/test/NC/1354011_87.jpeg \n", + " inflating: AD_NC/test/NC/1354011_88.jpeg \n", + " inflating: AD_NC/test/NC/1354011_89.jpeg \n", + " inflating: AD_NC/test/NC/1354011_90.jpeg \n", + " inflating: AD_NC/test/NC/1354011_91.jpeg \n", + " inflating: AD_NC/test/NC/1354011_92.jpeg \n", + " inflating: AD_NC/test/NC/1354011_93.jpeg \n", + " inflating: AD_NC/test/NC/1354011_94.jpeg \n", + " inflating: AD_NC/test/NC/1354011_95.jpeg \n", + " inflating: AD_NC/test/NC/1354011_96.jpeg \n", + " inflating: AD_NC/test/NC/1354011_97.jpeg \n", + " inflating: AD_NC/test/NC/1355445_78.jpeg \n", + " inflating: AD_NC/test/NC/1355445_79.jpeg \n", + " inflating: AD_NC/test/NC/1355445_80.jpeg \n", + " inflating: AD_NC/test/NC/1355445_81.jpeg \n", + " inflating: AD_NC/test/NC/1355445_82.jpeg \n", + " inflating: AD_NC/test/NC/1355445_83.jpeg \n", + " inflating: AD_NC/test/NC/1355445_84.jpeg \n", + " inflating: AD_NC/test/NC/1355445_85.jpeg \n", + " inflating: AD_NC/test/NC/1355445_86.jpeg \n", + " inflating: AD_NC/test/NC/1355445_87.jpeg \n", + " inflating: AD_NC/test/NC/1355445_88.jpeg \n", + " inflating: AD_NC/test/NC/1355445_89.jpeg \n", + " inflating: AD_NC/test/NC/1355445_90.jpeg \n", + " inflating: AD_NC/test/NC/1355445_91.jpeg \n", + " inflating: AD_NC/test/NC/1355445_92.jpeg \n", + " inflating: AD_NC/test/NC/1355445_93.jpeg \n", + " inflating: AD_NC/test/NC/1355445_94.jpeg \n", + " inflating: AD_NC/test/NC/1355445_95.jpeg \n", + " inflating: AD_NC/test/NC/1355445_96.jpeg \n", + " inflating: AD_NC/test/NC/1355445_97.jpeg \n", + " inflating: AD_NC/test/NC/1355446_78.jpeg \n", + " inflating: AD_NC/test/NC/1355446_79.jpeg \n", + " inflating: AD_NC/test/NC/1355446_80.jpeg \n", + " inflating: AD_NC/test/NC/1355446_81.jpeg \n", + " inflating: AD_NC/test/NC/1355446_82.jpeg \n", + " inflating: AD_NC/test/NC/1355446_83.jpeg \n", + " inflating: AD_NC/test/NC/1355446_84.jpeg \n", + " inflating: AD_NC/test/NC/1355446_85.jpeg \n", + " inflating: AD_NC/test/NC/1355446_86.jpeg \n", + " inflating: AD_NC/test/NC/1355446_87.jpeg \n", + " inflating: AD_NC/test/NC/1355446_88.jpeg \n", + " inflating: AD_NC/test/NC/1355446_89.jpeg \n", + " inflating: AD_NC/test/NC/1355446_90.jpeg \n", + " inflating: AD_NC/test/NC/1355446_91.jpeg \n", + " inflating: AD_NC/test/NC/1355446_92.jpeg \n", + " inflating: AD_NC/test/NC/1355446_93.jpeg \n", + " inflating: AD_NC/test/NC/1355446_94.jpeg \n", + " inflating: AD_NC/test/NC/1355446_95.jpeg \n", + " inflating: AD_NC/test/NC/1355446_96.jpeg \n", + " inflating: AD_NC/test/NC/1355446_97.jpeg \n", + " inflating: AD_NC/test/NC/1356105_100.jpeg \n", + " inflating: AD_NC/test/NC/1356105_101.jpeg \n", + " inflating: AD_NC/test/NC/1356105_102.jpeg \n", + " inflating: AD_NC/test/NC/1356105_103.jpeg \n", + " inflating: AD_NC/test/NC/1356105_104.jpeg \n", + " inflating: AD_NC/test/NC/1356105_105.jpeg \n", + " inflating: AD_NC/test/NC/1356105_106.jpeg \n", + " inflating: AD_NC/test/NC/1356105_107.jpeg \n", + " inflating: AD_NC/test/NC/1356105_88.jpeg \n", + " inflating: AD_NC/test/NC/1356105_89.jpeg \n", + " inflating: AD_NC/test/NC/1356105_90.jpeg \n", + " inflating: AD_NC/test/NC/1356105_91.jpeg \n", + " inflating: AD_NC/test/NC/1356105_92.jpeg \n", + " inflating: AD_NC/test/NC/1356105_93.jpeg \n", + " inflating: AD_NC/test/NC/1356105_94.jpeg \n", + " inflating: AD_NC/test/NC/1356105_95.jpeg \n", + " inflating: AD_NC/test/NC/1356105_96.jpeg \n", + " inflating: AD_NC/test/NC/1356105_97.jpeg \n", + " inflating: AD_NC/test/NC/1356105_98.jpeg \n", + " inflating: AD_NC/test/NC/1356105_99.jpeg \n", + " inflating: AD_NC/test/NC/1358529_78.jpeg \n", + " inflating: AD_NC/test/NC/1358529_79.jpeg \n", + " inflating: AD_NC/test/NC/1358529_80.jpeg \n", + " inflating: AD_NC/test/NC/1358529_81.jpeg \n", + " inflating: AD_NC/test/NC/1358529_82.jpeg \n", + " inflating: AD_NC/test/NC/1358529_83.jpeg \n", + " inflating: AD_NC/test/NC/1358529_84.jpeg \n", + " inflating: AD_NC/test/NC/1358529_85.jpeg \n", + " inflating: AD_NC/test/NC/1358529_86.jpeg \n", + " inflating: AD_NC/test/NC/1358529_87.jpeg \n", + " inflating: AD_NC/test/NC/1358529_88.jpeg \n", + " inflating: AD_NC/test/NC/1358529_89.jpeg \n", + " inflating: AD_NC/test/NC/1358529_90.jpeg \n", + " inflating: AD_NC/test/NC/1358529_91.jpeg \n", + " inflating: AD_NC/test/NC/1358529_92.jpeg \n", + " inflating: AD_NC/test/NC/1358529_93.jpeg \n", + " inflating: AD_NC/test/NC/1358529_94.jpeg \n", + " inflating: AD_NC/test/NC/1358529_95.jpeg \n", + " inflating: AD_NC/test/NC/1358529_96.jpeg \n", + " inflating: AD_NC/test/NC/1358529_97.jpeg \n", + " inflating: AD_NC/test/NC/1359836_78.jpeg \n", + " inflating: AD_NC/test/NC/1359836_79.jpeg \n", + " inflating: AD_NC/test/NC/1359836_80.jpeg \n", + " inflating: AD_NC/test/NC/1359836_81.jpeg \n", + " inflating: AD_NC/test/NC/1359836_82.jpeg \n", + " inflating: AD_NC/test/NC/1359836_83.jpeg \n", + " inflating: AD_NC/test/NC/1359836_84.jpeg \n", + " inflating: AD_NC/test/NC/1359836_85.jpeg \n", + " inflating: AD_NC/test/NC/1359836_86.jpeg \n", + " inflating: AD_NC/test/NC/1359836_87.jpeg \n", + " inflating: AD_NC/test/NC/1359836_88.jpeg \n", + " inflating: AD_NC/test/NC/1359836_89.jpeg \n", + " inflating: AD_NC/test/NC/1359836_90.jpeg \n", + " inflating: AD_NC/test/NC/1359836_91.jpeg \n", + " inflating: AD_NC/test/NC/1359836_92.jpeg \n", + " inflating: AD_NC/test/NC/1359836_93.jpeg \n", + " inflating: AD_NC/test/NC/1359836_94.jpeg \n", + " inflating: AD_NC/test/NC/1359836_95.jpeg \n", + " inflating: AD_NC/test/NC/1359836_96.jpeg \n", + " inflating: AD_NC/test/NC/1359836_97.jpeg \n", + " inflating: AD_NC/test/NC/1359936_78.jpeg \n", + " inflating: AD_NC/test/NC/1359936_79.jpeg \n", + " inflating: AD_NC/test/NC/1359936_80.jpeg \n", + " inflating: AD_NC/test/NC/1359936_81.jpeg \n", + " inflating: AD_NC/test/NC/1359936_82.jpeg \n", + " inflating: AD_NC/test/NC/1359936_83.jpeg \n", + " inflating: AD_NC/test/NC/1359936_84.jpeg \n", + " inflating: AD_NC/test/NC/1359936_85.jpeg \n", + " inflating: AD_NC/test/NC/1359936_86.jpeg \n", + " inflating: AD_NC/test/NC/1359936_87.jpeg \n", + " inflating: AD_NC/test/NC/1359936_88.jpeg \n", + " inflating: AD_NC/test/NC/1359936_89.jpeg \n", + " inflating: AD_NC/test/NC/1359936_90.jpeg \n", + " inflating: AD_NC/test/NC/1359936_91.jpeg \n", + " inflating: AD_NC/test/NC/1359936_92.jpeg \n", + " inflating: AD_NC/test/NC/1359936_93.jpeg \n", + " inflating: AD_NC/test/NC/1359936_94.jpeg \n", + " inflating: AD_NC/test/NC/1359936_95.jpeg \n", + " inflating: AD_NC/test/NC/1359936_96.jpeg \n", + " inflating: AD_NC/test/NC/1359936_97.jpeg \n", + " inflating: AD_NC/test/NC/1360295_100.jpeg \n", + " inflating: AD_NC/test/NC/1360295_101.jpeg \n", + " inflating: AD_NC/test/NC/1360295_102.jpeg \n", + " inflating: AD_NC/test/NC/1360295_103.jpeg \n", + " inflating: AD_NC/test/NC/1360295_104.jpeg \n", + " inflating: AD_NC/test/NC/1360295_105.jpeg \n", + " inflating: AD_NC/test/NC/1360295_106.jpeg \n", + " inflating: AD_NC/test/NC/1360295_107.jpeg \n", + " inflating: AD_NC/test/NC/1360295_108.jpeg \n", + " inflating: AD_NC/test/NC/1360295_109.jpeg \n", + " inflating: AD_NC/test/NC/1360295_110.jpeg \n", + " inflating: AD_NC/test/NC/1360295_111.jpeg \n", + " inflating: AD_NC/test/NC/1360295_112.jpeg \n", + " inflating: AD_NC/test/NC/1360295_113.jpeg \n", + " inflating: AD_NC/test/NC/1360295_94.jpeg \n", + " inflating: AD_NC/test/NC/1360295_95.jpeg \n", + " inflating: AD_NC/test/NC/1360295_96.jpeg \n", + " inflating: AD_NC/test/NC/1360295_97.jpeg \n", + " inflating: AD_NC/test/NC/1360295_98.jpeg \n", + " inflating: AD_NC/test/NC/1360295_99.jpeg \n", + " inflating: AD_NC/test/NC/1360551_100.jpeg \n", + " inflating: AD_NC/test/NC/1360551_101.jpeg \n", + " inflating: AD_NC/test/NC/1360551_102.jpeg \n", + " inflating: AD_NC/test/NC/1360551_103.jpeg \n", + " inflating: AD_NC/test/NC/1360551_104.jpeg \n", + " inflating: AD_NC/test/NC/1360551_105.jpeg \n", + " inflating: AD_NC/test/NC/1360551_106.jpeg \n", + " inflating: AD_NC/test/NC/1360551_107.jpeg \n", + " inflating: AD_NC/test/NC/1360551_108.jpeg \n", + " inflating: AD_NC/test/NC/1360551_109.jpeg \n", + " inflating: AD_NC/test/NC/1360551_110.jpeg \n", + " inflating: AD_NC/test/NC/1360551_111.jpeg \n", + " inflating: AD_NC/test/NC/1360551_112.jpeg \n", + " inflating: AD_NC/test/NC/1360551_113.jpeg \n", + " inflating: AD_NC/test/NC/1360551_94.jpeg \n", + " inflating: AD_NC/test/NC/1360551_95.jpeg \n", + " inflating: AD_NC/test/NC/1360551_96.jpeg \n", + " inflating: AD_NC/test/NC/1360551_97.jpeg \n", + " inflating: AD_NC/test/NC/1360551_98.jpeg \n", + " inflating: AD_NC/test/NC/1360551_99.jpeg \n", + " inflating: AD_NC/test/NC/1363593_100.jpeg \n", + " inflating: AD_NC/test/NC/1363593_101.jpeg \n", + " inflating: AD_NC/test/NC/1363593_102.jpeg \n", + " inflating: AD_NC/test/NC/1363593_103.jpeg \n", + " inflating: AD_NC/test/NC/1363593_104.jpeg \n", + " inflating: AD_NC/test/NC/1363593_105.jpeg \n", + " inflating: AD_NC/test/NC/1363593_106.jpeg \n", + " inflating: AD_NC/test/NC/1363593_107.jpeg \n", + " inflating: AD_NC/test/NC/1363593_108.jpeg \n", + " inflating: AD_NC/test/NC/1363593_109.jpeg \n", + " inflating: AD_NC/test/NC/1363593_110.jpeg \n", + " inflating: AD_NC/test/NC/1363593_111.jpeg \n", + " inflating: AD_NC/test/NC/1363593_112.jpeg \n", + " inflating: AD_NC/test/NC/1363593_113.jpeg \n", + " inflating: AD_NC/test/NC/1363593_94.jpeg \n", + " inflating: AD_NC/test/NC/1363593_95.jpeg \n", + " inflating: AD_NC/test/NC/1363593_96.jpeg \n", + " inflating: AD_NC/test/NC/1363593_97.jpeg \n", + " inflating: AD_NC/test/NC/1363593_98.jpeg \n", + " inflating: AD_NC/test/NC/1363593_99.jpeg \n", + " inflating: AD_NC/test/NC/1366966_100.jpeg \n", + " inflating: AD_NC/test/NC/1366966_101.jpeg \n", + " inflating: AD_NC/test/NC/1366966_102.jpeg \n", + " inflating: AD_NC/test/NC/1366966_103.jpeg \n", + " inflating: AD_NC/test/NC/1366966_104.jpeg \n", + " inflating: AD_NC/test/NC/1366966_105.jpeg \n", + " inflating: AD_NC/test/NC/1366966_106.jpeg \n", + " inflating: AD_NC/test/NC/1366966_107.jpeg \n", + " inflating: AD_NC/test/NC/1366966_108.jpeg \n", + " inflating: AD_NC/test/NC/1366966_109.jpeg \n", + " inflating: AD_NC/test/NC/1366966_110.jpeg \n", + " inflating: AD_NC/test/NC/1366966_111.jpeg \n", + " inflating: AD_NC/test/NC/1366966_112.jpeg \n", + " inflating: AD_NC/test/NC/1366966_113.jpeg \n", + " inflating: AD_NC/test/NC/1366966_94.jpeg \n", + " inflating: AD_NC/test/NC/1366966_95.jpeg \n", + " inflating: AD_NC/test/NC/1366966_96.jpeg \n", + " inflating: AD_NC/test/NC/1366966_97.jpeg \n", + " inflating: AD_NC/test/NC/1366966_98.jpeg \n", + " inflating: AD_NC/test/NC/1366966_99.jpeg \n", + " inflating: AD_NC/test/NC/1368078_100.jpeg \n", + " inflating: AD_NC/test/NC/1368078_101.jpeg \n", + " inflating: AD_NC/test/NC/1368078_102.jpeg \n", + " inflating: AD_NC/test/NC/1368078_103.jpeg \n", + " inflating: AD_NC/test/NC/1368078_104.jpeg \n", + " inflating: AD_NC/test/NC/1368078_105.jpeg \n", + " inflating: AD_NC/test/NC/1368078_106.jpeg \n", + " inflating: AD_NC/test/NC/1368078_107.jpeg \n", + " inflating: AD_NC/test/NC/1368078_108.jpeg \n", + " inflating: AD_NC/test/NC/1368078_109.jpeg \n", + " inflating: AD_NC/test/NC/1368078_110.jpeg \n", + " inflating: AD_NC/test/NC/1368078_111.jpeg \n", + " inflating: AD_NC/test/NC/1368078_112.jpeg \n", + " inflating: AD_NC/test/NC/1368078_113.jpeg \n", + " inflating: AD_NC/test/NC/1368078_94.jpeg \n", + " inflating: AD_NC/test/NC/1368078_95.jpeg \n", + " inflating: AD_NC/test/NC/1368078_96.jpeg \n", + " inflating: AD_NC/test/NC/1368078_97.jpeg \n", + " inflating: AD_NC/test/NC/1368078_98.jpeg \n", + " inflating: AD_NC/test/NC/1368078_99.jpeg \n", + " inflating: AD_NC/test/NC/1371438_78.jpeg \n", + " inflating: AD_NC/test/NC/1371438_79.jpeg \n", + " inflating: AD_NC/test/NC/1371438_80.jpeg \n", + " inflating: AD_NC/test/NC/1371438_81.jpeg \n", + " inflating: AD_NC/test/NC/1371438_82.jpeg \n", + " inflating: AD_NC/test/NC/1371438_83.jpeg \n", + " inflating: AD_NC/test/NC/1371438_84.jpeg \n", + " inflating: AD_NC/test/NC/1371438_85.jpeg \n", + " inflating: AD_NC/test/NC/1371438_86.jpeg \n", + " inflating: AD_NC/test/NC/1371438_87.jpeg \n", + " inflating: AD_NC/test/NC/1371438_88.jpeg \n", + " inflating: AD_NC/test/NC/1371438_89.jpeg \n", + " inflating: AD_NC/test/NC/1371438_90.jpeg \n", + " inflating: AD_NC/test/NC/1371438_91.jpeg \n", + " inflating: AD_NC/test/NC/1371438_92.jpeg \n", + " inflating: AD_NC/test/NC/1371438_93.jpeg \n", + " inflating: AD_NC/test/NC/1371438_94.jpeg \n", + " inflating: AD_NC/test/NC/1371438_95.jpeg \n", + " inflating: AD_NC/test/NC/1371438_96.jpeg \n", + " inflating: AD_NC/test/NC/1371438_97.jpeg \n", + " inflating: AD_NC/test/NC/1373547_78.jpeg \n", + " inflating: AD_NC/test/NC/1373547_79.jpeg \n", + " inflating: AD_NC/test/NC/1373547_80.jpeg \n", + " inflating: AD_NC/test/NC/1373547_81.jpeg \n", + " inflating: AD_NC/test/NC/1373547_82.jpeg \n", + " inflating: AD_NC/test/NC/1373547_83.jpeg \n", + " inflating: AD_NC/test/NC/1373547_84.jpeg \n", + " inflating: AD_NC/test/NC/1373547_85.jpeg \n", + " inflating: AD_NC/test/NC/1373547_86.jpeg \n", + " inflating: AD_NC/test/NC/1373547_87.jpeg \n", + " inflating: AD_NC/test/NC/1373547_88.jpeg \n", + " inflating: AD_NC/test/NC/1373547_89.jpeg \n", + " inflating: AD_NC/test/NC/1373547_90.jpeg \n", + " inflating: AD_NC/test/NC/1373547_91.jpeg \n", + " inflating: AD_NC/test/NC/1373547_92.jpeg \n", + " inflating: AD_NC/test/NC/1373547_93.jpeg \n", + " inflating: AD_NC/test/NC/1373547_94.jpeg \n", + " inflating: AD_NC/test/NC/1373547_95.jpeg \n", + " inflating: AD_NC/test/NC/1373547_96.jpeg \n", + " inflating: AD_NC/test/NC/1373547_97.jpeg \n", + " inflating: AD_NC/test/NC/1384712_100.jpeg \n", + " inflating: AD_NC/test/NC/1384712_101.jpeg \n", + " inflating: AD_NC/test/NC/1384712_102.jpeg \n", + " inflating: AD_NC/test/NC/1384712_103.jpeg \n", + " inflating: AD_NC/test/NC/1384712_104.jpeg \n", + " inflating: AD_NC/test/NC/1384712_105.jpeg \n", + " inflating: AD_NC/test/NC/1384712_106.jpeg \n", + " inflating: AD_NC/test/NC/1384712_107.jpeg \n", + " inflating: AD_NC/test/NC/1384712_88.jpeg \n", + " inflating: AD_NC/test/NC/1384712_89.jpeg \n", + " inflating: AD_NC/test/NC/1384712_90.jpeg \n", + " inflating: AD_NC/test/NC/1384712_91.jpeg \n", + " inflating: AD_NC/test/NC/1384712_92.jpeg \n", + " inflating: AD_NC/test/NC/1384712_93.jpeg \n", + " inflating: AD_NC/test/NC/1384712_94.jpeg \n", + " inflating: AD_NC/test/NC/1384712_95.jpeg \n", + " inflating: AD_NC/test/NC/1384712_96.jpeg \n", + " inflating: AD_NC/test/NC/1384712_97.jpeg \n", + " inflating: AD_NC/test/NC/1384712_98.jpeg \n", + " inflating: AD_NC/test/NC/1384712_99.jpeg \n", + " inflating: AD_NC/test/NC/1387180_78.jpeg \n", + " inflating: AD_NC/test/NC/1387180_79.jpeg \n", + " inflating: AD_NC/test/NC/1387180_80.jpeg \n", + " inflating: AD_NC/test/NC/1387180_81.jpeg \n", + " inflating: AD_NC/test/NC/1387180_82.jpeg \n", + " inflating: AD_NC/test/NC/1387180_83.jpeg \n", + " inflating: AD_NC/test/NC/1387180_84.jpeg \n", + " inflating: AD_NC/test/NC/1387180_85.jpeg \n", + " inflating: AD_NC/test/NC/1387180_86.jpeg \n", + " inflating: AD_NC/test/NC/1387180_87.jpeg \n", + " inflating: AD_NC/test/NC/1387180_88.jpeg \n", + " inflating: AD_NC/test/NC/1387180_89.jpeg \n", + " inflating: AD_NC/test/NC/1387180_90.jpeg \n", + " inflating: AD_NC/test/NC/1387180_91.jpeg \n", + " inflating: AD_NC/test/NC/1387180_92.jpeg \n", + " inflating: AD_NC/test/NC/1387180_93.jpeg \n", + " inflating: AD_NC/test/NC/1387180_94.jpeg \n", + " inflating: AD_NC/test/NC/1387180_95.jpeg \n", + " inflating: AD_NC/test/NC/1387180_96.jpeg \n", + " inflating: AD_NC/test/NC/1387180_97.jpeg \n", + " inflating: AD_NC/test/NC/1387539_78.jpeg \n", + " inflating: AD_NC/test/NC/1387539_79.jpeg \n", + " inflating: AD_NC/test/NC/1387539_80.jpeg \n", + " inflating: AD_NC/test/NC/1387539_81.jpeg \n", + " inflating: AD_NC/test/NC/1387539_82.jpeg \n", + " inflating: AD_NC/test/NC/1387539_83.jpeg \n", + " inflating: AD_NC/test/NC/1387539_84.jpeg \n", + " inflating: AD_NC/test/NC/1387539_85.jpeg \n", + " inflating: AD_NC/test/NC/1387539_86.jpeg \n", + " inflating: AD_NC/test/NC/1387539_87.jpeg \n", + " inflating: AD_NC/test/NC/1387539_88.jpeg \n", + " inflating: AD_NC/test/NC/1387539_89.jpeg \n", + " inflating: AD_NC/test/NC/1387539_90.jpeg \n", + " inflating: AD_NC/test/NC/1387539_91.jpeg \n", + " inflating: AD_NC/test/NC/1387539_92.jpeg \n", + " inflating: AD_NC/test/NC/1387539_93.jpeg \n", + " inflating: AD_NC/test/NC/1387539_94.jpeg \n", + " inflating: AD_NC/test/NC/1387539_95.jpeg " + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import torchvision.transforms as transforms\n", + "import time\n", + "import numpy as np\n", + "# import pandas as pd\n", + "import math\n", + "import os\n", + "from PIL import Image\n", + "import numpy as np\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from sklearn.utils import shuffle\n", + "import torch\n", + "from torch.utils.data import DataLoader\n", + "from torchvision.datasets import ImageFolder\n", + "import os\n", + "from PIL import Image\n", + "import numpy as np\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from torch.utils.data import ConcatDataset" + ], + "metadata": { + "id": "_8PBaJLSJSCP" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "\n", + "\n", + "class CustomDataset(Dataset):\n", + " def __init__(self, root_dir, transform=None):\n", + " self.root_dir = root_dir\n", + " self.transform = transform\n", + " self.image_paths = os.listdir(root_dir)\n", + "\n", + " def __len__(self):\n", + " return len(self.image_paths)\n", + "\n", + " def __getitem__(self, idx):\n", + " img_name = os.path.join(self.root_dir, self.image_paths[idx])\n", + " image = Image.open(img_name)\n", + "\n", + " if self.transform:\n", + " image = self.transform(image)\n", + "\n", + " return image\n", + "\n", + "class CustomConcatDataset(ConcatDataset):\n", + " def __init__(self, datasets, labels):\n", + " super(CustomConcatDataset, self).__init__(datasets)\n", + " self.labels = labels\n", + "\n", + " def __getitem__(self, index):\n", + " item = super(CustomConcatDataset, self).__getitem__(index)\n", + " return item, self.labels[index]\n", + "\n", + "def combine(AD, NC):\n", + " # Set labels for the AD dataset\n", + " labels_AD = [True] * len(AD)\n", + " # Set labels for the NC dataset\n", + " labels_NC = [False] * len(NC)\n", + "\n", + " # Combine the datasets and labels\n", + " X = AD + NC\n", + " Y = labels_AD + labels_NC\n", + " # X = torch.utils.data.RandomSampler(X)\n", + " seed = 123\n", + " torch.manual_seed(seed)\n", + " np.random.seed(seed)\n", + "\n", + "\n", + " X_loader = DataLoader(X, batch_size=128, shuffle=True)\n", + " Y_loader = DataLoader(Y, batch_size=128, shuffle=True)\n", + " return X_loader, Y_loader\n", + "\n", + "size = 256\n", + "\n", + "transform_X = transforms.Compose([\n", + " transforms.Resize((size, size)), # Resize the image to the desired size\n", + " transforms.ToTensor(), # Convert the image to a tensor\n", + " transforms.Lambda(lambda x: x / 255.0),\n", + " transforms.Lambda(lambda x: (x - x.mean()) / x.std()) # Subtract mean and divide by standard deviation\n", + "])\n", + "\n", + "\n", + "\n", + "# Replace 'your_nii_folder' with the path to your folder containing .nii files\n", + "# nii_dataset = NiiDataset(root_dir=r'keras_png_slices_data\\keras_png_slices_data\\keras_png_slices_train')\n", + "loaders = {}\n", + "\n", + "for stage in ['train', 'test']:\n", + " loaders[stage] = {}\n", + " AD = CustomDataset(root_dir=rf'ADNI_AD_NC_2D\\AD_NC\\{stage}\\AD', transform=transform_X)\n", + " NC = CustomDataset(root_dir=rf'ADNI_AD_NC_2D\\AD_NC\\{stage}\\NC', transform=transform_X)\n", + " loaders[stage]['X'], loaders[stage]['Y'] = combine(AD, NC)\n", + "# Y_train = CustomDataset(root_dir=r'ADNI_AD_NC_2D\\AD_NC\\train\\NC', transform=transform_Y)\n", + "# X_train_AD_loader = DataLoader(X_train_AD, batch_size=4, shuffle=True)\n", + "# Y_train_loader = DataLoader(Y_train, batch_size=4, shuffle=True)" + ], + "metadata": { + "id": "7zvKyWj2J7Yk" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "\n", + "# Define the model\n", + "class CustomModel(nn.Module):\n", + " def __init__(self):\n", + " super(CustomModel, self).__init__()\n", + " self.conv1 = nn.Conv2d(1, 32, 3, padding=1)\n", + " self.maxpool1 = nn.MaxPool2d(2, 2)\n", + " self.conv2 = nn.Conv2d(32, 32, 3, padding=1)\n", + " self.maxpool2 = nn.MaxPool2d(2, 2)\n", + " self.flatten = nn.Flatten()\n", + " self.fc1 = nn.Linear(32 * 64 * 64, 128)\n", + " self.fc2 = nn.Linear(128, 10)\n", + "\n", + " def forward(self, x):\n", + " x = nn.functional.relu(self.conv1(x))\n", + " x = self.maxpool1(x)\n", + " x = nn.functional.relu(self.conv2(x))\n", + " x = self.maxpool2(x)\n", + " x = self.flatten(x)\n", + " x = nn.functional.relu(self.fc1(x))\n", + " x = nn.functional.softmax(self.fc2(x), dim=1)\n", + " return x\n", + "\n", + "\n", + "\n", + "# Instantiate the model\n", + "model = CustomModel()\n", + "\n" + ], + "metadata": { + "id": "4BuuMeumKHi2" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "criterion = nn.CrossEntropyLoss()\n", + "learning_rate = 0.1\n", + "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4)\n", + "total_step = len(loaders['train']['X'])\n", + "sched_linear_1 = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.005, max_lr=learning_rate, step_size_up=15, step_size_down=15, mode=\"triangular\", verbose=False)\n", + "sched_linear_3 = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.005/learning_rate, end_factor=0.005/learning_rate, verbose=False)\n", + "scheduler = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[sched_linear_1, sched_linear_3], milestones=[30])\n" + ], + "metadata": { + "id": "SztvTT_tKJMA" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "num_epochs = 2\n", + "for epoch in range(num_epochs):\n", + "\n", + " for i, (images, seg) in enumerate(zip(loaders['train']['X'], loaders['train']['Y'])):\n", + " images = images.to(device)\n", + " seg = torch.tensor(seg, dtype=torch.long).to(device)\n", + " outputs = model(images)\n", + "\n", + "\n", + " # Adjust the shapes to match the criterion\n", + " # outputs = outputs.view(-1) # Flatten the outputs to a 1D tensor\n", + " # seg = seg.view(-1) # Flatten the seg tensor to a 1D tensor\n", + " loss = criterion(outputs, seg)\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " if (i + 1) % 5 == 0:\n", + " print(f\"Epoch [{epoch + 1} / {num_epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")\n", + "\n", + " scheduler.step()\n", + "end = time.time()" + ], + "metadata": { + "id": "xeAsMkrnKKij" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 8b28fe8f26c76b81346c41709774cd530a9b6adf Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Mon, 16 Oct 2023 12:28:11 +1000 Subject: [PATCH 02/14] Got images to download in GoogleDrive and added to loader --- Colab version.ipynb | 5309 ++----------------------------------------- 1 file changed, 238 insertions(+), 5071 deletions(-) diff --git a/Colab version.ipynb b/Colab version.ipynb index a914e70c9c..80943a64b2 100644 --- a/Colab version.ipynb +++ b/Colab version.ipynb @@ -1,20 +1,4 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "authorship_tag": "ABX9TyOHMcV3sgfpuOe9f8kAGUP9", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, "cells": [ { "cell_type": "markdown", @@ -28,16 +12,39 @@ }, { "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yzCmT9TUJY10", + "outputId": "898b4f80-2f63-4047-9790-355985414a28" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ], "source": [ "from google.colab import drive\n", - "drive.mount('/content/drive')" + "drive.mount(\"/content/drive\", force_remount=True)" + ] + }, + { + "cell_type": "code", + "source": [ + "%ls" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "yzCmT9TUJY10", - "outputId": "e25f99c9-66c9-41f5-df60-391e55ddcea4" + "id": "T0cDyMtaR_Hi", + "outputId": "1327687d-e185-450a-b06d-0f8c26680638" }, "execution_count": 2, "outputs": [ @@ -45,5059 +52,64 @@ "output_type": "stream", "name": "stdout", "text": [ - "Mounted at /content/drive\n" + "\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n" ] } ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-Lb7CGdEJCQg", - "outputId": "a4c11d2b-0675-4e0b-e73a-86a07cacc29b" + "outputId": "aa6b51ec-c531-4cf2-923a-a1dfe784e838" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "[Errno 2] No such file or directory: '/content/drive/MyDrive/Root'\n", - "/content/drive/MyDrive\n" + "/content/drive/MyDrive/AD_NC\n" ] } ], "source": [ - "%cd /content/drive/MyDrive" + "%cd /content/drive/MyDrive/AD_NC" ] }, { "cell_type": "code", - "source": [ - "!unzip ADNI_AD_NC_2D.zip" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_eAax1mMJpyw", - "outputId": "5e752810-d2dd-41bf-fbee-c95550ec0384" + "outputId": "daf77648-1909-41f9-c05f-0be751ec7d6d" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", - " inflating: AD_NC/test/AD/413796_78.jpeg \n", - " inflating: AD_NC/test/AD/413796_79.jpeg \n", - " inflating: AD_NC/test/AD/413796_80.jpeg \n", - " inflating: AD_NC/test/AD/413796_81.jpeg \n", - " inflating: AD_NC/test/AD/413796_82.jpeg \n", - " inflating: AD_NC/test/AD/413796_83.jpeg \n", - " inflating: AD_NC/test/AD/413796_84.jpeg \n", - " inflating: AD_NC/test/AD/413796_85.jpeg \n", - " inflating: AD_NC/test/AD/413796_86.jpeg \n", - " inflating: AD_NC/test/AD/413796_87.jpeg \n", - " inflating: AD_NC/test/AD/413796_88.jpeg \n", - " inflating: AD_NC/test/AD/413796_89.jpeg \n", - " inflating: AD_NC/test/AD/413796_90.jpeg \n", - " inflating: AD_NC/test/AD/413796_91.jpeg \n", - " inflating: AD_NC/test/AD/413796_92.jpeg \n", - " inflating: AD_NC/test/AD/413796_93.jpeg \n", - " inflating: AD_NC/test/AD/413796_94.jpeg \n", - " inflating: AD_NC/test/AD/413796_95.jpeg \n", - " inflating: AD_NC/test/AD/413796_96.jpeg \n", - " inflating: AD_NC/test/AD/413796_97.jpeg \n", - " inflating: AD_NC/test/AD/414389_78.jpeg \n", - " inflating: AD_NC/test/AD/414389_79.jpeg \n", - " inflating: AD_NC/test/AD/414389_80.jpeg \n", - " inflating: AD_NC/test/AD/414389_81.jpeg \n", - " inflating: AD_NC/test/AD/414389_82.jpeg \n", - " inflating: AD_NC/test/AD/414389_83.jpeg \n", - " inflating: AD_NC/test/AD/414389_84.jpeg \n", - " inflating: AD_NC/test/AD/414389_85.jpeg \n", - " inflating: AD_NC/test/AD/414389_86.jpeg \n", - " inflating: AD_NC/test/AD/414389_87.jpeg \n", - " inflating: AD_NC/test/AD/414389_88.jpeg \n", - " inflating: AD_NC/test/AD/414389_89.jpeg \n", - " inflating: AD_NC/test/AD/414389_90.jpeg \n", - " inflating: AD_NC/test/AD/414389_91.jpeg \n", - " inflating: AD_NC/test/AD/414389_92.jpeg \n", - " inflating: AD_NC/test/AD/414389_93.jpeg \n", - " inflating: AD_NC/test/AD/414389_94.jpeg \n", - " inflating: AD_NC/test/AD/414389_95.jpeg \n", - " inflating: AD_NC/test/AD/414389_96.jpeg \n", - " inflating: AD_NC/test/AD/414389_97.jpeg \n", - " inflating: AD_NC/test/AD/414697_100.jpeg \n", - " inflating: AD_NC/test/AD/414697_101.jpeg \n", - " inflating: AD_NC/test/AD/414697_102.jpeg \n", - " inflating: AD_NC/test/AD/414697_103.jpeg \n", - " inflating: AD_NC/test/AD/414697_104.jpeg \n", - " inflating: AD_NC/test/AD/414697_105.jpeg \n", - " inflating: AD_NC/test/AD/414697_106.jpeg \n", - " inflating: AD_NC/test/AD/414697_107.jpeg \n", - " inflating: AD_NC/test/AD/414697_88.jpeg \n", - " inflating: AD_NC/test/AD/414697_89.jpeg \n", - " inflating: AD_NC/test/AD/414697_90.jpeg \n", - " inflating: AD_NC/test/AD/414697_91.jpeg \n", - " inflating: AD_NC/test/AD/414697_92.jpeg \n", - " inflating: AD_NC/test/AD/414697_93.jpeg \n", - " inflating: AD_NC/test/AD/414697_94.jpeg \n", - " inflating: AD_NC/test/AD/414697_95.jpeg \n", - " inflating: AD_NC/test/AD/414697_96.jpeg \n", - " inflating: AD_NC/test/AD/414697_97.jpeg \n", - " inflating: AD_NC/test/AD/414697_98.jpeg \n", - " inflating: AD_NC/test/AD/414697_99.jpeg \n", - " inflating: AD_NC/test/AD/415180_75.jpeg \n", - " inflating: AD_NC/test/AD/415180_76.jpeg \n", - " inflating: AD_NC/test/AD/415180_77.jpeg \n", - " inflating: AD_NC/test/AD/415180_78.jpeg \n", - " inflating: AD_NC/test/AD/415180_79.jpeg \n", - " inflating: AD_NC/test/AD/415180_80.jpeg \n", - " inflating: AD_NC/test/AD/415180_81.jpeg \n", - " inflating: AD_NC/test/AD/415180_82.jpeg \n", - " inflating: AD_NC/test/AD/415180_83.jpeg \n", - " inflating: AD_NC/test/AD/415180_84.jpeg \n", - " inflating: AD_NC/test/AD/415180_85.jpeg \n", - " inflating: AD_NC/test/AD/415180_86.jpeg \n", - " inflating: AD_NC/test/AD/415180_87.jpeg \n", - " inflating: AD_NC/test/AD/415180_88.jpeg \n", - " inflating: AD_NC/test/AD/415180_89.jpeg \n", - " inflating: AD_NC/test/AD/415180_90.jpeg \n", - " inflating: AD_NC/test/AD/415180_91.jpeg \n", - " inflating: AD_NC/test/AD/415180_92.jpeg \n", - " inflating: AD_NC/test/AD/415180_93.jpeg \n", - " inflating: AD_NC/test/AD/415180_94.jpeg \n", - " inflating: AD_NC/test/AD/415186_75.jpeg \n", - " inflating: AD_NC/test/AD/415186_76.jpeg \n", - " inflating: AD_NC/test/AD/415186_77.jpeg \n", - " inflating: AD_NC/test/AD/415186_78.jpeg \n", - " inflating: AD_NC/test/AD/415186_79.jpeg \n", - " inflating: AD_NC/test/AD/415186_80.jpeg \n", - " inflating: AD_NC/test/AD/415186_81.jpeg \n", - " inflating: AD_NC/test/AD/415186_82.jpeg \n", - " inflating: AD_NC/test/AD/415186_83.jpeg \n", - " inflating: AD_NC/test/AD/415186_84.jpeg \n", - " inflating: AD_NC/test/AD/415186_85.jpeg \n", - " inflating: AD_NC/test/AD/415186_86.jpeg \n", - " inflating: AD_NC/test/AD/415186_87.jpeg \n", - " inflating: AD_NC/test/AD/415186_88.jpeg \n", - " inflating: AD_NC/test/AD/415186_89.jpeg \n", - " inflating: AD_NC/test/AD/415186_90.jpeg \n", - " inflating: AD_NC/test/AD/415186_91.jpeg \n", - " inflating: AD_NC/test/AD/415186_92.jpeg \n", - " inflating: AD_NC/test/AD/415186_93.jpeg \n", - " inflating: AD_NC/test/AD/415186_94.jpeg \n", - " inflating: AD_NC/test/AD/415211_75.jpeg \n", - " inflating: AD_NC/test/AD/415211_76.jpeg \n", - " inflating: AD_NC/test/AD/415211_77.jpeg \n", - " inflating: AD_NC/test/AD/415211_78.jpeg \n", - " inflating: AD_NC/test/AD/415211_79.jpeg \n", - " inflating: AD_NC/test/AD/415211_80.jpeg \n", - " inflating: AD_NC/test/AD/415211_81.jpeg \n", - " inflating: AD_NC/test/AD/415211_82.jpeg \n", - " inflating: AD_NC/test/AD/415211_83.jpeg \n", - " inflating: AD_NC/test/AD/415211_84.jpeg \n", - " inflating: AD_NC/test/AD/415211_85.jpeg \n", - " inflating: AD_NC/test/AD/415211_86.jpeg \n", - " inflating: AD_NC/test/AD/415211_87.jpeg \n", - " inflating: AD_NC/test/AD/415211_88.jpeg \n", - " inflating: AD_NC/test/AD/415211_89.jpeg \n", - " inflating: AD_NC/test/AD/415211_90.jpeg \n", - " inflating: AD_NC/test/AD/415211_91.jpeg \n", - " inflating: AD_NC/test/AD/415211_92.jpeg \n", - " inflating: AD_NC/test/AD/415211_93.jpeg \n", - " inflating: AD_NC/test/AD/415211_94.jpeg \n", - " inflating: AD_NC/test/AD/415587_78.jpeg \n", - " inflating: AD_NC/test/AD/415587_79.jpeg \n", - " inflating: AD_NC/test/AD/415587_80.jpeg \n", - " inflating: AD_NC/test/AD/415587_81.jpeg \n", - " inflating: AD_NC/test/AD/415587_82.jpeg \n", - " inflating: AD_NC/test/AD/415587_83.jpeg \n", - " inflating: AD_NC/test/AD/415587_84.jpeg \n", - " inflating: AD_NC/test/AD/415587_85.jpeg \n", - " inflating: AD_NC/test/AD/415587_86.jpeg \n", - " inflating: AD_NC/test/AD/415587_87.jpeg \n", - " inflating: AD_NC/test/AD/415587_88.jpeg \n", - " inflating: AD_NC/test/AD/415587_89.jpeg \n", - " inflating: AD_NC/test/AD/415587_90.jpeg \n", - " inflating: AD_NC/test/AD/415587_91.jpeg \n", - " inflating: AD_NC/test/AD/415587_92.jpeg \n", - " inflating: AD_NC/test/AD/415587_93.jpeg \n", - " inflating: AD_NC/test/AD/415587_94.jpeg \n", - " inflating: AD_NC/test/AD/415587_95.jpeg \n", - " inflating: AD_NC/test/AD/415587_96.jpeg \n", - " inflating: AD_NC/test/AD/415587_97.jpeg \n", - " inflating: AD_NC/test/AD/415594_78.jpeg \n", - " inflating: AD_NC/test/AD/415594_79.jpeg \n", - " inflating: AD_NC/test/AD/415594_80.jpeg \n", - " inflating: AD_NC/test/AD/415594_81.jpeg \n", - " inflating: AD_NC/test/AD/415594_82.jpeg \n", - " inflating: AD_NC/test/AD/415594_83.jpeg \n", - " inflating: AD_NC/test/AD/415594_84.jpeg \n", - " inflating: AD_NC/test/AD/415594_85.jpeg \n", - " inflating: AD_NC/test/AD/415594_86.jpeg \n", - " inflating: AD_NC/test/AD/415594_87.jpeg \n", - " inflating: AD_NC/test/AD/415594_88.jpeg \n", - " inflating: AD_NC/test/AD/415594_89.jpeg \n", - " inflating: AD_NC/test/AD/415594_90.jpeg \n", - " inflating: AD_NC/test/AD/415594_91.jpeg \n", - " inflating: AD_NC/test/AD/415594_92.jpeg \n", - " inflating: AD_NC/test/AD/415594_93.jpeg \n", - " inflating: AD_NC/test/AD/415594_94.jpeg \n", - " inflating: AD_NC/test/AD/415594_95.jpeg \n", - " inflating: AD_NC/test/AD/415594_96.jpeg \n", - " inflating: AD_NC/test/AD/415594_97.jpeg \n", - " inflating: AD_NC/test/AD/416324_78.jpeg \n", - " inflating: AD_NC/test/AD/416324_79.jpeg \n", - " inflating: AD_NC/test/AD/416324_80.jpeg \n", - " inflating: AD_NC/test/AD/416324_81.jpeg \n", - " inflating: AD_NC/test/AD/416324_82.jpeg \n", - " inflating: AD_NC/test/AD/416324_83.jpeg \n", - " inflating: AD_NC/test/AD/416324_84.jpeg \n", - " inflating: AD_NC/test/AD/416324_85.jpeg \n", - " inflating: AD_NC/test/AD/416324_86.jpeg \n", - " inflating: AD_NC/test/AD/416324_87.jpeg \n", - " inflating: AD_NC/test/AD/416324_88.jpeg \n", - " inflating: AD_NC/test/AD/416324_89.jpeg \n", - " inflating: AD_NC/test/AD/416324_90.jpeg \n", - " inflating: AD_NC/test/AD/416324_91.jpeg \n", - " inflating: AD_NC/test/AD/416324_92.jpeg \n", - " inflating: AD_NC/test/AD/416324_93.jpeg \n", - " inflating: AD_NC/test/AD/416324_94.jpeg \n", - " inflating: AD_NC/test/AD/416324_95.jpeg \n", - " inflating: AD_NC/test/AD/416324_96.jpeg \n", - " inflating: AD_NC/test/AD/416324_97.jpeg \n", - " inflating: AD_NC/test/AD/416335_78.jpeg \n", - " inflating: AD_NC/test/AD/416335_79.jpeg \n", - " inflating: AD_NC/test/AD/416335_80.jpeg \n", - " inflating: AD_NC/test/AD/416335_81.jpeg \n", - " inflating: AD_NC/test/AD/416335_82.jpeg \n", - " inflating: AD_NC/test/AD/416335_83.jpeg \n", - " inflating: AD_NC/test/AD/416335_84.jpeg \n", - " inflating: AD_NC/test/AD/416335_85.jpeg \n", - " inflating: AD_NC/test/AD/416335_86.jpeg \n", - " inflating: AD_NC/test/AD/416335_87.jpeg \n", - " inflating: AD_NC/test/AD/416335_88.jpeg \n", - " inflating: AD_NC/test/AD/416335_89.jpeg \n", - " inflating: AD_NC/test/AD/416335_90.jpeg \n", - " inflating: AD_NC/test/AD/416335_91.jpeg \n", - " inflating: AD_NC/test/AD/416335_92.jpeg \n", - " inflating: AD_NC/test/AD/416335_93.jpeg \n", - " inflating: AD_NC/test/AD/416335_94.jpeg \n", - " inflating: AD_NC/test/AD/416335_95.jpeg \n", - " inflating: AD_NC/test/AD/416335_96.jpeg \n", - " inflating: AD_NC/test/AD/416335_97.jpeg \n", - " inflating: AD_NC/test/AD/416701_78.jpeg \n", - " inflating: AD_NC/test/AD/416701_79.jpeg \n", - " inflating: AD_NC/test/AD/416701_80.jpeg \n", - " inflating: AD_NC/test/AD/416701_81.jpeg \n", - " inflating: AD_NC/test/AD/416701_82.jpeg \n", - " inflating: AD_NC/test/AD/416701_83.jpeg \n", - " inflating: AD_NC/test/AD/416701_84.jpeg \n", - " inflating: AD_NC/test/AD/416701_85.jpeg \n", - " inflating: AD_NC/test/AD/416701_86.jpeg \n", - " inflating: AD_NC/test/AD/416701_87.jpeg \n", - " inflating: AD_NC/test/AD/416701_88.jpeg \n", - " inflating: AD_NC/test/AD/416701_89.jpeg \n", - " inflating: AD_NC/test/AD/416701_90.jpeg \n", - " inflating: AD_NC/test/AD/416701_91.jpeg \n", - " inflating: AD_NC/test/AD/416701_92.jpeg \n", - " inflating: AD_NC/test/AD/416701_93.jpeg \n", - " inflating: AD_NC/test/AD/416701_94.jpeg \n", - " inflating: AD_NC/test/AD/416701_95.jpeg \n", - " inflating: AD_NC/test/AD/416701_96.jpeg \n", - " inflating: AD_NC/test/AD/416701_97.jpeg \n", - " inflating: AD_NC/test/AD/416936_100.jpeg \n", - " inflating: AD_NC/test/AD/416936_101.jpeg \n", - " inflating: AD_NC/test/AD/416936_102.jpeg \n", - " inflating: AD_NC/test/AD/416936_103.jpeg \n", - " inflating: AD_NC/test/AD/416936_104.jpeg \n", - " inflating: AD_NC/test/AD/416936_105.jpeg \n", - " inflating: AD_NC/test/AD/416936_106.jpeg \n", - " inflating: AD_NC/test/AD/416936_107.jpeg \n", - " inflating: AD_NC/test/AD/416936_88.jpeg \n", - " inflating: AD_NC/test/AD/416936_89.jpeg \n", - " inflating: AD_NC/test/AD/416936_90.jpeg \n", - " inflating: AD_NC/test/AD/416936_91.jpeg \n", - " inflating: AD_NC/test/AD/416936_92.jpeg \n", - " inflating: AD_NC/test/AD/416936_93.jpeg \n", - " inflating: AD_NC/test/AD/416936_94.jpeg \n", - " inflating: AD_NC/test/AD/416936_95.jpeg \n", - " inflating: AD_NC/test/AD/416936_96.jpeg \n", - " inflating: AD_NC/test/AD/416936_97.jpeg \n", - " inflating: AD_NC/test/AD/416936_98.jpeg \n", - " inflating: AD_NC/test/AD/416936_99.jpeg \n", - " inflating: AD_NC/test/AD/417901_100.jpeg \n", - " inflating: AD_NC/test/AD/417901_101.jpeg \n", - " inflating: AD_NC/test/AD/417901_102.jpeg \n", - " inflating: AD_NC/test/AD/417901_103.jpeg \n", - " inflating: AD_NC/test/AD/417901_104.jpeg \n", - " inflating: AD_NC/test/AD/417901_105.jpeg \n", - " inflating: AD_NC/test/AD/417901_106.jpeg \n", - " inflating: AD_NC/test/AD/417901_107.jpeg \n", - " inflating: AD_NC/test/AD/417901_88.jpeg \n", - " inflating: AD_NC/test/AD/417901_89.jpeg \n", - " inflating: AD_NC/test/AD/417901_90.jpeg \n", - " inflating: AD_NC/test/AD/417901_91.jpeg \n", - " inflating: AD_NC/test/AD/417901_92.jpeg \n", - " inflating: AD_NC/test/AD/417901_93.jpeg \n", - " inflating: AD_NC/test/AD/417901_94.jpeg \n", - " inflating: AD_NC/test/AD/417901_95.jpeg \n", - " inflating: AD_NC/test/AD/417901_96.jpeg \n", - " inflating: AD_NC/test/AD/417901_97.jpeg \n", - " inflating: AD_NC/test/AD/417901_98.jpeg \n", - " inflating: AD_NC/test/AD/417901_99.jpeg \n", - " inflating: AD_NC/test/AD/417905_100.jpeg \n", - " inflating: AD_NC/test/AD/417905_101.jpeg \n", - " inflating: AD_NC/test/AD/417905_102.jpeg \n", - " inflating: AD_NC/test/AD/417905_103.jpeg \n", - " inflating: AD_NC/test/AD/417905_104.jpeg \n", - " inflating: AD_NC/test/AD/417905_105.jpeg \n", - " inflating: AD_NC/test/AD/417905_106.jpeg \n", - " inflating: AD_NC/test/AD/417905_107.jpeg \n", - " inflating: AD_NC/test/AD/417905_88.jpeg \n", - " inflating: AD_NC/test/AD/417905_89.jpeg \n", - " inflating: AD_NC/test/AD/417905_90.jpeg \n", - " inflating: AD_NC/test/AD/417905_91.jpeg \n", - " inflating: AD_NC/test/AD/417905_92.jpeg \n", - " inflating: AD_NC/test/AD/417905_93.jpeg \n", - " inflating: AD_NC/test/AD/417905_94.jpeg \n", - " inflating: AD_NC/test/AD/417905_95.jpeg \n", - " inflating: AD_NC/test/AD/417905_96.jpeg \n", - " inflating: AD_NC/test/AD/417905_97.jpeg \n", - " inflating: AD_NC/test/AD/417905_98.jpeg \n", - " inflating: AD_NC/test/AD/417905_99.jpeg \n", - " inflating: AD_NC/test/AD/420239_100.jpeg \n", - " inflating: AD_NC/test/AD/420239_101.jpeg \n", - " inflating: AD_NC/test/AD/420239_102.jpeg \n", - " inflating: AD_NC/test/AD/420239_103.jpeg \n", - " inflating: AD_NC/test/AD/420239_104.jpeg \n", - " inflating: AD_NC/test/AD/420239_105.jpeg \n", - " inflating: AD_NC/test/AD/420239_106.jpeg \n", - " inflating: AD_NC/test/AD/420239_107.jpeg \n", - " inflating: AD_NC/test/AD/420239_88.jpeg \n", - " inflating: AD_NC/test/AD/420239_89.jpeg \n", - " inflating: AD_NC/test/AD/420239_90.jpeg \n", - " inflating: AD_NC/test/AD/420239_91.jpeg \n", - " inflating: AD_NC/test/AD/420239_92.jpeg \n", - " inflating: AD_NC/test/AD/420239_93.jpeg \n", - " inflating: AD_NC/test/AD/420239_94.jpeg \n", - " inflating: AD_NC/test/AD/420239_95.jpeg \n", - " inflating: AD_NC/test/AD/420239_96.jpeg \n", - " inflating: AD_NC/test/AD/420239_97.jpeg \n", - " inflating: AD_NC/test/AD/420239_98.jpeg \n", - " inflating: AD_NC/test/AD/420239_99.jpeg \n", - " inflating: AD_NC/test/AD/420398_78.jpeg \n", - " inflating: AD_NC/test/AD/420398_79.jpeg \n", - " inflating: AD_NC/test/AD/420398_80.jpeg \n", - " inflating: AD_NC/test/AD/420398_81.jpeg \n", - " inflating: AD_NC/test/AD/420398_82.jpeg \n", - " inflating: AD_NC/test/AD/420398_83.jpeg \n", - " inflating: AD_NC/test/AD/420398_84.jpeg \n", - " inflating: AD_NC/test/AD/420398_85.jpeg \n", - " inflating: AD_NC/test/AD/420398_86.jpeg \n", - " inflating: AD_NC/test/AD/420398_87.jpeg \n", - " inflating: AD_NC/test/AD/420398_88.jpeg \n", - " inflating: AD_NC/test/AD/420398_89.jpeg \n", - " inflating: AD_NC/test/AD/420398_90.jpeg \n", - " inflating: AD_NC/test/AD/420398_91.jpeg \n", - " inflating: AD_NC/test/AD/420398_92.jpeg \n", - " inflating: AD_NC/test/AD/420398_93.jpeg \n", - " inflating: AD_NC/test/AD/420398_94.jpeg \n", - " inflating: AD_NC/test/AD/420398_95.jpeg \n", - " inflating: AD_NC/test/AD/420398_96.jpeg \n", - " inflating: AD_NC/test/AD/420398_97.jpeg \n", - " inflating: AD_NC/test/AD/420404_78.jpeg \n", - " inflating: AD_NC/test/AD/420404_79.jpeg \n", - " inflating: AD_NC/test/AD/420404_80.jpeg \n", - " inflating: AD_NC/test/AD/420404_81.jpeg \n", - " inflating: AD_NC/test/AD/420404_82.jpeg \n", - " inflating: AD_NC/test/AD/420404_83.jpeg \n", - " inflating: AD_NC/test/AD/420404_84.jpeg \n", - " inflating: AD_NC/test/AD/420404_85.jpeg \n", - " inflating: AD_NC/test/AD/420404_86.jpeg \n", - " inflating: AD_NC/test/AD/420404_87.jpeg \n", - " inflating: AD_NC/test/AD/420404_88.jpeg \n", - " inflating: AD_NC/test/AD/420404_89.jpeg \n", - " inflating: AD_NC/test/AD/420404_90.jpeg \n", - " inflating: AD_NC/test/AD/420404_91.jpeg \n", - " inflating: AD_NC/test/AD/420404_92.jpeg \n", - " inflating: AD_NC/test/AD/420404_93.jpeg \n", - " inflating: AD_NC/test/AD/420404_94.jpeg \n", - " inflating: AD_NC/test/AD/420404_95.jpeg \n", - " inflating: AD_NC/test/AD/420404_96.jpeg \n", - " inflating: AD_NC/test/AD/420404_97.jpeg \n", - " inflating: AD_NC/test/AD/421209_100.jpeg \n", - " inflating: AD_NC/test/AD/421209_101.jpeg \n", - " inflating: AD_NC/test/AD/421209_102.jpeg \n", - " inflating: AD_NC/test/AD/421209_103.jpeg \n", - " inflating: AD_NC/test/AD/421209_104.jpeg \n", - " inflating: AD_NC/test/AD/421209_105.jpeg \n", - " inflating: AD_NC/test/AD/421209_106.jpeg \n", - " inflating: AD_NC/test/AD/421209_107.jpeg \n", - " inflating: AD_NC/test/AD/421209_88.jpeg \n", - " inflating: AD_NC/test/AD/421209_89.jpeg \n", - " inflating: AD_NC/test/AD/421209_90.jpeg \n", - " inflating: AD_NC/test/AD/421209_91.jpeg \n", - " inflating: AD_NC/test/AD/421209_92.jpeg \n", - " inflating: AD_NC/test/AD/421209_93.jpeg \n", - " inflating: AD_NC/test/AD/421209_94.jpeg \n", - " inflating: AD_NC/test/AD/421209_95.jpeg \n", - " inflating: AD_NC/test/AD/421209_96.jpeg \n", - " inflating: AD_NC/test/AD/421209_97.jpeg \n", - " inflating: AD_NC/test/AD/421209_98.jpeg \n", - " inflating: AD_NC/test/AD/421209_99.jpeg \n", - " inflating: AD_NC/test/AD/421402_100.jpeg \n", - " inflating: AD_NC/test/AD/421402_101.jpeg \n", - " inflating: AD_NC/test/AD/421402_102.jpeg \n", - " inflating: AD_NC/test/AD/421402_103.jpeg \n", - " inflating: AD_NC/test/AD/421402_104.jpeg \n", - " inflating: AD_NC/test/AD/421402_105.jpeg \n", - " inflating: AD_NC/test/AD/421402_106.jpeg \n", - " inflating: AD_NC/test/AD/421402_107.jpeg \n", - " inflating: AD_NC/test/AD/421402_88.jpeg \n", - " inflating: AD_NC/test/AD/421402_89.jpeg \n", - " inflating: AD_NC/test/AD/421402_90.jpeg \n", - " inflating: AD_NC/test/AD/421402_91.jpeg \n", - " inflating: AD_NC/test/AD/421402_92.jpeg \n", - " inflating: AD_NC/test/AD/421402_93.jpeg \n", - " inflating: AD_NC/test/AD/421402_94.jpeg \n", - " inflating: AD_NC/test/AD/421402_95.jpeg \n", - " inflating: AD_NC/test/AD/421402_96.jpeg \n", - " inflating: AD_NC/test/AD/421402_97.jpeg \n", - " inflating: AD_NC/test/AD/421402_98.jpeg \n", - " inflating: AD_NC/test/AD/421402_99.jpeg \n", - " inflating: AD_NC/test/AD/422625_78.jpeg \n", - " inflating: AD_NC/test/AD/422625_79.jpeg \n", - " inflating: AD_NC/test/AD/422625_80.jpeg \n", - " inflating: AD_NC/test/AD/422625_81.jpeg \n", - " inflating: AD_NC/test/AD/422625_82.jpeg \n", - " inflating: AD_NC/test/AD/422625_83.jpeg \n", - " inflating: AD_NC/test/AD/422625_84.jpeg \n", - " inflating: AD_NC/test/AD/422625_85.jpeg \n", - " inflating: AD_NC/test/AD/422625_86.jpeg \n", - " inflating: AD_NC/test/AD/422625_87.jpeg \n", - " inflating: AD_NC/test/AD/422625_88.jpeg \n", - " inflating: AD_NC/test/AD/422625_89.jpeg \n", - " inflating: AD_NC/test/AD/422625_90.jpeg \n", - " inflating: AD_NC/test/AD/422625_91.jpeg \n", - " inflating: AD_NC/test/AD/422625_92.jpeg \n", - " inflating: AD_NC/test/AD/422625_93.jpeg \n", - " inflating: AD_NC/test/AD/422625_94.jpeg \n", - " inflating: AD_NC/test/AD/422625_95.jpeg \n", - " inflating: AD_NC/test/AD/422625_96.jpeg \n", - " inflating: AD_NC/test/AD/422625_97.jpeg \n", - " inflating: AD_NC/test/AD/422626_78.jpeg \n", - " inflating: AD_NC/test/AD/422626_79.jpeg \n", - " inflating: AD_NC/test/AD/422626_80.jpeg \n", - " inflating: AD_NC/test/AD/422626_81.jpeg \n", - " inflating: AD_NC/test/AD/422626_82.jpeg \n", - " inflating: AD_NC/test/AD/422626_83.jpeg \n", - " inflating: AD_NC/test/AD/422626_84.jpeg \n", - " inflating: AD_NC/test/AD/422626_85.jpeg \n", - " inflating: AD_NC/test/AD/422626_86.jpeg \n", - " inflating: AD_NC/test/AD/422626_87.jpeg \n", - " inflating: AD_NC/test/AD/422626_88.jpeg \n", - " inflating: AD_NC/test/AD/422626_89.jpeg \n", - " inflating: AD_NC/test/AD/422626_90.jpeg \n", - " inflating: AD_NC/test/AD/422626_91.jpeg \n", - " inflating: AD_NC/test/AD/422626_92.jpeg \n", - " inflating: AD_NC/test/AD/422626_93.jpeg \n", - " inflating: AD_NC/test/AD/422626_94.jpeg \n", - " inflating: AD_NC/test/AD/422626_95.jpeg \n", - " inflating: AD_NC/test/AD/422626_96.jpeg \n", - " inflating: AD_NC/test/AD/422626_97.jpeg \n", - " inflating: AD_NC/test/AD/422736_78.jpeg \n", - " inflating: AD_NC/test/AD/422736_79.jpeg \n", - " inflating: AD_NC/test/AD/422736_80.jpeg \n", - " inflating: AD_NC/test/AD/422736_81.jpeg \n", - " inflating: AD_NC/test/AD/422736_82.jpeg \n", - " inflating: AD_NC/test/AD/422736_83.jpeg \n", - " inflating: AD_NC/test/AD/422736_84.jpeg \n", - " inflating: AD_NC/test/AD/422736_85.jpeg \n", - " inflating: AD_NC/test/AD/422736_86.jpeg \n", - " inflating: AD_NC/test/AD/422736_87.jpeg \n", - " inflating: AD_NC/test/AD/422736_88.jpeg \n", - " inflating: AD_NC/test/AD/422736_89.jpeg \n", - " inflating: AD_NC/test/AD/422736_90.jpeg \n", - " inflating: AD_NC/test/AD/422736_91.jpeg \n", - " inflating: AD_NC/test/AD/422736_92.jpeg \n", - " inflating: AD_NC/test/AD/422736_93.jpeg \n", - " inflating: AD_NC/test/AD/422736_94.jpeg \n", - " inflating: AD_NC/test/AD/422736_95.jpeg \n", - " inflating: AD_NC/test/AD/422736_96.jpeg \n", - " inflating: AD_NC/test/AD/422736_97.jpeg \n", - " inflating: AD_NC/test/AD/422891_75.jpeg \n", - " inflating: AD_NC/test/AD/422891_76.jpeg \n", - " inflating: AD_NC/test/AD/422891_77.jpeg \n", - " inflating: AD_NC/test/AD/422891_78.jpeg \n", - " inflating: AD_NC/test/AD/422891_79.jpeg \n", - " inflating: AD_NC/test/AD/422891_80.jpeg \n", - " inflating: AD_NC/test/AD/422891_81.jpeg \n", - " inflating: AD_NC/test/AD/422891_82.jpeg \n", - " inflating: AD_NC/test/AD/422891_83.jpeg \n", - " inflating: AD_NC/test/AD/422891_84.jpeg \n", - " inflating: AD_NC/test/AD/422891_85.jpeg \n", - " inflating: AD_NC/test/AD/422891_86.jpeg \n", - " inflating: AD_NC/test/AD/422891_87.jpeg \n", - " inflating: AD_NC/test/AD/422891_88.jpeg \n", - " inflating: AD_NC/test/AD/422891_89.jpeg \n", - " inflating: AD_NC/test/AD/422891_90.jpeg \n", - " inflating: AD_NC/test/AD/422891_91.jpeg \n", - " inflating: AD_NC/test/AD/422891_92.jpeg \n", - " inflating: AD_NC/test/AD/422891_93.jpeg \n", - " inflating: AD_NC/test/AD/422891_94.jpeg \n", - " inflating: AD_NC/test/AD/423655_75.jpeg \n", - " inflating: AD_NC/test/AD/423655_76.jpeg \n", - " inflating: AD_NC/test/AD/423655_77.jpeg \n", - " inflating: AD_NC/test/AD/423655_78.jpeg \n", - " inflating: AD_NC/test/AD/423655_79.jpeg \n", - " inflating: AD_NC/test/AD/423655_80.jpeg \n", - " inflating: AD_NC/test/AD/423655_81.jpeg \n", - " inflating: AD_NC/test/AD/423655_82.jpeg \n", - " inflating: AD_NC/test/AD/423655_83.jpeg \n", - " inflating: AD_NC/test/AD/423655_84.jpeg \n", - " inflating: AD_NC/test/AD/423655_85.jpeg \n", - " inflating: AD_NC/test/AD/423655_86.jpeg \n", - " inflating: AD_NC/test/AD/423655_87.jpeg \n", - " inflating: AD_NC/test/AD/423655_88.jpeg \n", - " inflating: AD_NC/test/AD/423655_89.jpeg \n", - " inflating: AD_NC/test/AD/423655_90.jpeg \n", - " inflating: AD_NC/test/AD/423655_91.jpeg \n", - " inflating: AD_NC/test/AD/423655_92.jpeg \n", - " inflating: AD_NC/test/AD/423655_93.jpeg \n", - " inflating: AD_NC/test/AD/423655_94.jpeg \n", - " inflating: AD_NC/test/AD/423659_75.jpeg \n", - " inflating: AD_NC/test/AD/423659_76.jpeg \n", - " inflating: AD_NC/test/AD/423659_77.jpeg \n", - " inflating: AD_NC/test/AD/423659_78.jpeg \n", - " inflating: AD_NC/test/AD/423659_79.jpeg \n", - " inflating: AD_NC/test/AD/423659_80.jpeg \n", - " inflating: AD_NC/test/AD/423659_81.jpeg \n", - " inflating: AD_NC/test/AD/423659_82.jpeg \n", - " inflating: AD_NC/test/AD/423659_83.jpeg \n", - " inflating: AD_NC/test/AD/423659_84.jpeg \n", - " inflating: AD_NC/test/AD/423659_85.jpeg \n", - " inflating: AD_NC/test/AD/423659_86.jpeg \n", - " inflating: AD_NC/test/AD/423659_87.jpeg \n", - " inflating: AD_NC/test/AD/423659_88.jpeg \n", - " inflating: AD_NC/test/AD/423659_89.jpeg \n", - " inflating: AD_NC/test/AD/423659_90.jpeg \n", - " inflating: AD_NC/test/AD/423659_91.jpeg \n", - " inflating: AD_NC/test/AD/423659_92.jpeg \n", - " inflating: AD_NC/test/AD/423659_93.jpeg \n", - " inflating: AD_NC/test/AD/423659_94.jpeg \n", - " inflating: AD_NC/test/AD/423923_75.jpeg \n", - " inflating: AD_NC/test/AD/423923_76.jpeg \n", - " inflating: AD_NC/test/AD/423923_77.jpeg \n", - " inflating: AD_NC/test/AD/423923_78.jpeg \n", - " inflating: AD_NC/test/AD/423923_79.jpeg \n", - " inflating: AD_NC/test/AD/423923_80.jpeg \n", - " inflating: AD_NC/test/AD/423923_81.jpeg \n", - " inflating: AD_NC/test/AD/423923_82.jpeg \n", - " inflating: AD_NC/test/AD/423923_83.jpeg \n", - " inflating: AD_NC/test/AD/423923_84.jpeg \n", - " inflating: AD_NC/test/AD/423923_85.jpeg \n", - " inflating: AD_NC/test/AD/423923_86.jpeg \n", - " inflating: AD_NC/test/AD/423923_87.jpeg \n", - " inflating: AD_NC/test/AD/423923_88.jpeg \n", - " inflating: AD_NC/test/AD/423923_89.jpeg \n", - " inflating: AD_NC/test/AD/423923_90.jpeg \n", - " inflating: AD_NC/test/AD/423923_91.jpeg \n", - " inflating: AD_NC/test/AD/423923_92.jpeg \n", - " inflating: AD_NC/test/AD/423923_93.jpeg \n", - " inflating: AD_NC/test/AD/423923_94.jpeg \n", - " inflating: AD_NC/test/AD/424228_78.jpeg \n", - " inflating: AD_NC/test/AD/424228_79.jpeg \n", - " inflating: AD_NC/test/AD/424228_80.jpeg \n", - " inflating: AD_NC/test/AD/424228_81.jpeg \n", - " inflating: AD_NC/test/AD/424228_82.jpeg \n", - " inflating: AD_NC/test/AD/424228_83.jpeg \n", - " inflating: AD_NC/test/AD/424228_84.jpeg \n", - " inflating: AD_NC/test/AD/424228_85.jpeg \n", - " inflating: AD_NC/test/AD/424228_86.jpeg \n", - " inflating: AD_NC/test/AD/424228_87.jpeg \n", - " inflating: AD_NC/test/AD/424228_88.jpeg \n", - " inflating: AD_NC/test/AD/424228_89.jpeg \n", - " inflating: AD_NC/test/AD/424228_90.jpeg \n", - " inflating: AD_NC/test/AD/424228_91.jpeg \n", - " inflating: AD_NC/test/AD/424228_92.jpeg \n", - " inflating: AD_NC/test/AD/424228_93.jpeg \n", - " inflating: AD_NC/test/AD/424228_94.jpeg \n", - " inflating: AD_NC/test/AD/424228_95.jpeg \n", - " inflating: AD_NC/test/AD/424228_96.jpeg \n", - " inflating: AD_NC/test/AD/424228_97.jpeg \n", - " inflating: AD_NC/test/AD/424234_78.jpeg \n", - " inflating: AD_NC/test/AD/424234_79.jpeg \n", - " inflating: AD_NC/test/AD/424234_80.jpeg \n", - " inflating: AD_NC/test/AD/424234_81.jpeg \n", - " inflating: AD_NC/test/AD/424234_82.jpeg \n", - " inflating: AD_NC/test/AD/424234_83.jpeg \n", - " inflating: AD_NC/test/AD/424234_84.jpeg \n", - " inflating: AD_NC/test/AD/424234_85.jpeg \n", - " inflating: AD_NC/test/AD/424234_86.jpeg \n", - " inflating: AD_NC/test/AD/424234_87.jpeg \n", - " inflating: AD_NC/test/AD/424234_88.jpeg \n", - " inflating: AD_NC/test/AD/424234_89.jpeg \n", - " inflating: AD_NC/test/AD/424234_90.jpeg \n", - " inflating: AD_NC/test/AD/424234_91.jpeg \n", - " inflating: AD_NC/test/AD/424234_92.jpeg \n", - " inflating: AD_NC/test/AD/424234_93.jpeg \n", - " inflating: AD_NC/test/AD/424234_94.jpeg \n", - " inflating: AD_NC/test/AD/424234_95.jpeg \n", - " inflating: AD_NC/test/AD/424234_96.jpeg \n", - " inflating: AD_NC/test/AD/424234_97.jpeg \n", - " inflating: AD_NC/test/AD/424525_78.jpeg \n", - " inflating: AD_NC/test/AD/424525_79.jpeg \n", - " inflating: AD_NC/test/AD/424525_80.jpeg \n", - " inflating: AD_NC/test/AD/424525_81.jpeg \n", - " inflating: AD_NC/test/AD/424525_82.jpeg \n", - " inflating: AD_NC/test/AD/424525_83.jpeg \n", - " inflating: AD_NC/test/AD/424525_84.jpeg \n", - " inflating: AD_NC/test/AD/424525_85.jpeg \n", - " inflating: AD_NC/test/AD/424525_86.jpeg \n", - " inflating: AD_NC/test/AD/424525_87.jpeg \n", - " inflating: AD_NC/test/AD/424525_88.jpeg \n", - " inflating: AD_NC/test/AD/424525_89.jpeg \n", - " inflating: AD_NC/test/AD/424525_90.jpeg \n", - " inflating: AD_NC/test/AD/424525_91.jpeg \n", - " inflating: AD_NC/test/AD/424525_92.jpeg \n", - " inflating: AD_NC/test/AD/424525_93.jpeg \n", - " inflating: AD_NC/test/AD/424525_94.jpeg \n", - " inflating: AD_NC/test/AD/424525_95.jpeg \n", - " inflating: AD_NC/test/AD/424525_96.jpeg \n", - " inflating: AD_NC/test/AD/424525_97.jpeg \n", - " inflating: AD_NC/test/AD/424528_78.jpeg \n", - " inflating: AD_NC/test/AD/424528_79.jpeg \n", - " inflating: AD_NC/test/AD/424528_80.jpeg \n", - " inflating: AD_NC/test/AD/424528_81.jpeg \n", - " inflating: AD_NC/test/AD/424528_82.jpeg \n", - " inflating: AD_NC/test/AD/424528_83.jpeg \n", - " inflating: AD_NC/test/AD/424528_84.jpeg \n", - " inflating: AD_NC/test/AD/424528_85.jpeg \n", - " inflating: AD_NC/test/AD/424528_86.jpeg \n", - " inflating: AD_NC/test/AD/424528_87.jpeg \n", - " inflating: AD_NC/test/AD/424528_88.jpeg \n", - " inflating: AD_NC/test/AD/424528_89.jpeg \n", - " inflating: AD_NC/test/AD/424528_90.jpeg \n", - " inflating: AD_NC/test/AD/424528_91.jpeg \n", - " inflating: AD_NC/test/AD/424528_92.jpeg \n", - " inflating: AD_NC/test/AD/424528_93.jpeg \n", - " inflating: AD_NC/test/AD/424528_94.jpeg \n", - " inflating: AD_NC/test/AD/424528_95.jpeg \n", - " inflating: AD_NC/test/AD/424528_96.jpeg \n", - " inflating: AD_NC/test/AD/424528_97.jpeg \n", - " inflating: AD_NC/test/AD/498565_78.jpeg \n", - " inflating: AD_NC/test/AD/498565_79.jpeg \n", - " inflating: AD_NC/test/AD/498565_80.jpeg \n", - " inflating: AD_NC/test/AD/498565_81.jpeg \n", - " inflating: AD_NC/test/AD/498565_82.jpeg \n", - " inflating: AD_NC/test/AD/498565_83.jpeg \n", - " inflating: AD_NC/test/AD/498565_84.jpeg \n", - " inflating: AD_NC/test/AD/498565_85.jpeg \n", - " inflating: AD_NC/test/AD/498565_86.jpeg \n", - " inflating: AD_NC/test/AD/498565_87.jpeg \n", - " inflating: AD_NC/test/AD/498565_88.jpeg \n", - " inflating: AD_NC/test/AD/498565_89.jpeg \n", - " inflating: AD_NC/test/AD/498565_90.jpeg \n", - " inflating: AD_NC/test/AD/498565_91.jpeg \n", - " inflating: AD_NC/test/AD/498565_92.jpeg \n", - " inflating: AD_NC/test/AD/498565_93.jpeg \n", - " inflating: AD_NC/test/AD/498565_94.jpeg \n", - " inflating: AD_NC/test/AD/498565_95.jpeg \n", - " inflating: AD_NC/test/AD/498565_96.jpeg \n", - " inflating: AD_NC/test/AD/498565_97.jpeg \n", - " inflating: AD_NC/test/AD/505732_78.jpeg \n", - " inflating: AD_NC/test/AD/505732_79.jpeg \n", - " inflating: AD_NC/test/AD/505732_80.jpeg \n", - " inflating: AD_NC/test/AD/505732_81.jpeg \n", - " inflating: AD_NC/test/AD/505732_82.jpeg \n", - " inflating: AD_NC/test/AD/505732_83.jpeg \n", - " inflating: AD_NC/test/AD/505732_84.jpeg \n", - " inflating: AD_NC/test/AD/505732_85.jpeg \n", - " inflating: AD_NC/test/AD/505732_86.jpeg \n", - " inflating: AD_NC/test/AD/505732_87.jpeg \n", - " inflating: AD_NC/test/AD/505732_88.jpeg \n", - " inflating: AD_NC/test/AD/505732_89.jpeg \n", - " inflating: AD_NC/test/AD/505732_90.jpeg \n", - " inflating: AD_NC/test/AD/505732_91.jpeg \n", - " inflating: AD_NC/test/AD/505732_92.jpeg \n", - " inflating: AD_NC/test/AD/505732_93.jpeg \n", - " inflating: AD_NC/test/AD/505732_94.jpeg \n", - " inflating: AD_NC/test/AD/505732_95.jpeg \n", - " inflating: AD_NC/test/AD/505732_96.jpeg \n", - " inflating: AD_NC/test/AD/505732_97.jpeg \n", - " inflating: AD_NC/test/AD/505735_78.jpeg \n", - " inflating: AD_NC/test/AD/505735_79.jpeg \n", - " inflating: AD_NC/test/AD/505735_80.jpeg \n", - " inflating: AD_NC/test/AD/505735_81.jpeg \n", - " inflating: AD_NC/test/AD/505735_82.jpeg \n", - " inflating: AD_NC/test/AD/505735_83.jpeg \n", - " inflating: AD_NC/test/AD/505735_84.jpeg \n", - " inflating: AD_NC/test/AD/505735_85.jpeg \n", - " inflating: AD_NC/test/AD/505735_86.jpeg \n", - " inflating: AD_NC/test/AD/505735_87.jpeg \n", - " inflating: AD_NC/test/AD/505735_88.jpeg \n", - " inflating: AD_NC/test/AD/505735_89.jpeg \n", - " inflating: AD_NC/test/AD/505735_90.jpeg \n", - " inflating: AD_NC/test/AD/505735_91.jpeg \n", - " inflating: AD_NC/test/AD/505735_92.jpeg \n", - " inflating: AD_NC/test/AD/505735_93.jpeg \n", - " inflating: AD_NC/test/AD/505735_94.jpeg \n", - " inflating: AD_NC/test/AD/505735_95.jpeg \n", - " inflating: AD_NC/test/AD/505735_96.jpeg \n", - " inflating: AD_NC/test/AD/505735_97.jpeg \n", - " inflating: AD_NC/test/AD/525734_100.jpeg \n", - " inflating: AD_NC/test/AD/525734_101.jpeg \n", - " inflating: AD_NC/test/AD/525734_102.jpeg \n", - " inflating: AD_NC/test/AD/525734_103.jpeg \n", - " inflating: AD_NC/test/AD/525734_104.jpeg \n", - " inflating: AD_NC/test/AD/525734_105.jpeg \n", - " inflating: AD_NC/test/AD/525734_106.jpeg \n", - " inflating: AD_NC/test/AD/525734_107.jpeg \n", - " inflating: AD_NC/test/AD/525734_88.jpeg \n", - " inflating: AD_NC/test/AD/525734_89.jpeg \n", - " inflating: AD_NC/test/AD/525734_90.jpeg \n", - " inflating: AD_NC/test/AD/525734_91.jpeg \n", - " inflating: AD_NC/test/AD/525734_92.jpeg \n", - " inflating: AD_NC/test/AD/525734_93.jpeg \n", - " inflating: AD_NC/test/AD/525734_94.jpeg \n", - " inflating: AD_NC/test/AD/525734_95.jpeg \n", - " inflating: AD_NC/test/AD/525734_96.jpeg \n", - " inflating: AD_NC/test/AD/525734_97.jpeg \n", - " inflating: AD_NC/test/AD/525734_98.jpeg \n", - " inflating: AD_NC/test/AD/525734_99.jpeg \n", - " inflating: AD_NC/test/AD/844180_78.jpeg \n", - " inflating: AD_NC/test/AD/844180_79.jpeg \n", - " inflating: AD_NC/test/AD/844180_80.jpeg \n", - " inflating: AD_NC/test/AD/844180_81.jpeg \n", - " inflating: AD_NC/test/AD/844180_82.jpeg \n", - " inflating: AD_NC/test/AD/844180_83.jpeg \n", - " inflating: AD_NC/test/AD/844180_84.jpeg \n", - " inflating: AD_NC/test/AD/844180_85.jpeg \n", - " inflating: AD_NC/test/AD/844180_86.jpeg \n", - " inflating: AD_NC/test/AD/844180_87.jpeg \n", - " inflating: AD_NC/test/AD/844180_88.jpeg \n", - " inflating: AD_NC/test/AD/844180_89.jpeg \n", - " inflating: AD_NC/test/AD/844180_90.jpeg \n", - " inflating: AD_NC/test/AD/844180_91.jpeg \n", - " inflating: AD_NC/test/AD/844180_92.jpeg \n", - " inflating: AD_NC/test/AD/844180_93.jpeg \n", - " inflating: AD_NC/test/AD/844180_94.jpeg \n", - " inflating: AD_NC/test/AD/844180_95.jpeg \n", - " inflating: AD_NC/test/AD/844180_96.jpeg \n", - " inflating: AD_NC/test/AD/844180_97.jpeg \n", - " inflating: AD_NC/test/AD/844181_78.jpeg \n", - " inflating: AD_NC/test/AD/844181_79.jpeg \n", - " inflating: AD_NC/test/AD/844181_80.jpeg \n", - " inflating: AD_NC/test/AD/844181_81.jpeg \n", - " inflating: AD_NC/test/AD/844181_82.jpeg \n", - " inflating: AD_NC/test/AD/844181_83.jpeg \n", - " inflating: AD_NC/test/AD/844181_84.jpeg \n", - " inflating: AD_NC/test/AD/844181_85.jpeg \n", - " inflating: AD_NC/test/AD/844181_86.jpeg \n", - " inflating: AD_NC/test/AD/844181_87.jpeg \n", - " inflating: AD_NC/test/AD/844181_88.jpeg \n", - " inflating: AD_NC/test/AD/844181_89.jpeg \n", - " inflating: AD_NC/test/AD/844181_90.jpeg \n", - " inflating: AD_NC/test/AD/844181_91.jpeg \n", - " inflating: AD_NC/test/AD/844181_92.jpeg \n", - " inflating: AD_NC/test/AD/844181_93.jpeg \n", - " inflating: AD_NC/test/AD/844181_94.jpeg \n", - " inflating: AD_NC/test/AD/844181_95.jpeg \n", - " inflating: AD_NC/test/AD/844181_96.jpeg \n", - " inflating: AD_NC/test/AD/844181_97.jpeg \n", - " inflating: AD_NC/test/AD/879209_78.jpeg \n", - " inflating: AD_NC/test/AD/879209_79.jpeg \n", - " inflating: AD_NC/test/AD/879209_80.jpeg \n", - " inflating: AD_NC/test/AD/879209_81.jpeg \n", - " inflating: AD_NC/test/AD/879209_82.jpeg \n", - " inflating: AD_NC/test/AD/879209_83.jpeg \n", - " inflating: AD_NC/test/AD/879209_84.jpeg \n", - " inflating: AD_NC/test/AD/879209_85.jpeg \n", - " inflating: AD_NC/test/AD/879209_86.jpeg \n", - " inflating: AD_NC/test/AD/879209_87.jpeg \n", - " inflating: AD_NC/test/AD/879209_88.jpeg \n", - " inflating: AD_NC/test/AD/879209_89.jpeg \n", - " inflating: AD_NC/test/AD/879209_90.jpeg \n", - " inflating: AD_NC/test/AD/879209_91.jpeg \n", - " inflating: AD_NC/test/AD/879209_92.jpeg \n", - " inflating: AD_NC/test/AD/879209_93.jpeg \n", - " inflating: AD_NC/test/AD/879209_94.jpeg \n", - " inflating: AD_NC/test/AD/879209_95.jpeg \n", - " inflating: AD_NC/test/AD/879209_96.jpeg \n", - " inflating: AD_NC/test/AD/879209_97.jpeg \n", - " inflating: AD_NC/test/AD/879215_78.jpeg \n", - " inflating: AD_NC/test/AD/879215_79.jpeg \n", - " inflating: AD_NC/test/AD/879215_80.jpeg \n", - " inflating: AD_NC/test/AD/879215_81.jpeg \n", - " inflating: AD_NC/test/AD/879215_82.jpeg \n", - " inflating: AD_NC/test/AD/879215_83.jpeg \n", - " inflating: AD_NC/test/AD/879215_84.jpeg \n", - " inflating: AD_NC/test/AD/879215_85.jpeg \n", - " inflating: AD_NC/test/AD/879215_86.jpeg \n", - " inflating: AD_NC/test/AD/879215_87.jpeg \n", - " inflating: AD_NC/test/AD/879215_88.jpeg \n", - " inflating: AD_NC/test/AD/879215_89.jpeg \n", - " inflating: AD_NC/test/AD/879215_90.jpeg \n", - " inflating: AD_NC/test/AD/879215_91.jpeg \n", - " inflating: AD_NC/test/AD/879215_92.jpeg \n", - " inflating: AD_NC/test/AD/879215_93.jpeg \n", - " inflating: AD_NC/test/AD/879215_94.jpeg \n", - " inflating: AD_NC/test/AD/879215_95.jpeg \n", - " inflating: AD_NC/test/AD/879215_96.jpeg \n", - " inflating: AD_NC/test/AD/879215_97.jpeg \n", - " inflating: AD_NC/test/AD/895578_100.jpeg \n", - " inflating: AD_NC/test/AD/895578_101.jpeg \n", - " inflating: AD_NC/test/AD/895578_102.jpeg \n", - " inflating: AD_NC/test/AD/895578_103.jpeg \n", - " inflating: AD_NC/test/AD/895578_104.jpeg \n", - " inflating: AD_NC/test/AD/895578_105.jpeg \n", - " inflating: AD_NC/test/AD/895578_106.jpeg \n", - " inflating: AD_NC/test/AD/895578_107.jpeg \n", - " inflating: AD_NC/test/AD/895578_108.jpeg \n", - " inflating: AD_NC/test/AD/895578_109.jpeg \n", - " inflating: AD_NC/test/AD/895578_110.jpeg \n", - " inflating: AD_NC/test/AD/895578_111.jpeg \n", - " inflating: AD_NC/test/AD/895578_112.jpeg \n", - " inflating: AD_NC/test/AD/895578_113.jpeg \n", - " inflating: AD_NC/test/AD/895578_114.jpeg \n", - " inflating: AD_NC/test/AD/895578_95.jpeg \n", - " inflating: AD_NC/test/AD/895578_96.jpeg \n", - " inflating: AD_NC/test/AD/895578_97.jpeg \n", - " inflating: AD_NC/test/AD/895578_98.jpeg \n", - " inflating: AD_NC/test/AD/895578_99.jpeg \n", - " inflating: AD_NC/test/AD/895579_100.jpeg \n", - " inflating: AD_NC/test/AD/895579_101.jpeg \n", - " inflating: AD_NC/test/AD/895579_102.jpeg \n", - " inflating: AD_NC/test/AD/895579_103.jpeg \n", - " inflating: AD_NC/test/AD/895579_104.jpeg \n", - " inflating: AD_NC/test/AD/895579_105.jpeg \n", - " inflating: AD_NC/test/AD/895579_106.jpeg \n", - " inflating: AD_NC/test/AD/895579_107.jpeg \n", - " inflating: AD_NC/test/AD/895579_108.jpeg \n", - " inflating: AD_NC/test/AD/895579_109.jpeg \n", - " inflating: AD_NC/test/AD/895579_110.jpeg \n", - " inflating: AD_NC/test/AD/895579_111.jpeg \n", - " inflating: AD_NC/test/AD/895579_112.jpeg \n", - " inflating: AD_NC/test/AD/895579_113.jpeg \n", - " inflating: AD_NC/test/AD/895579_114.jpeg \n", - " inflating: AD_NC/test/AD/895579_95.jpeg \n", - " inflating: AD_NC/test/AD/895579_96.jpeg \n", - " inflating: AD_NC/test/AD/895579_97.jpeg \n", - " inflating: AD_NC/test/AD/895579_98.jpeg \n", - " inflating: AD_NC/test/AD/895579_99.jpeg \n", - " inflating: AD_NC/test/AD/898881_100.jpeg \n", - " inflating: AD_NC/test/AD/898881_101.jpeg \n", - " inflating: AD_NC/test/AD/898881_102.jpeg \n", - " inflating: AD_NC/test/AD/898881_103.jpeg \n", - " inflating: AD_NC/test/AD/898881_104.jpeg \n", - " inflating: AD_NC/test/AD/898881_105.jpeg \n", - " inflating: AD_NC/test/AD/898881_106.jpeg \n", - " inflating: AD_NC/test/AD/898881_107.jpeg \n", - " inflating: AD_NC/test/AD/898881_108.jpeg \n", - " inflating: AD_NC/test/AD/898881_109.jpeg \n", - " inflating: AD_NC/test/AD/898881_110.jpeg \n", - " inflating: AD_NC/test/AD/898881_111.jpeg \n", - " inflating: AD_NC/test/AD/898881_112.jpeg \n", - " inflating: AD_NC/test/AD/898881_113.jpeg \n", - " inflating: AD_NC/test/AD/898881_94.jpeg \n", - " inflating: AD_NC/test/AD/898881_95.jpeg \n", - " inflating: AD_NC/test/AD/898881_96.jpeg \n", - " inflating: AD_NC/test/AD/898881_97.jpeg \n", - " inflating: AD_NC/test/AD/898881_98.jpeg \n", - " inflating: AD_NC/test/AD/898881_99.jpeg \n", - " inflating: AD_NC/test/AD/973656_78.jpeg \n", - " inflating: AD_NC/test/AD/973656_79.jpeg \n", - " inflating: AD_NC/test/AD/973656_80.jpeg \n", - " inflating: AD_NC/test/AD/973656_81.jpeg \n", - " inflating: AD_NC/test/AD/973656_82.jpeg \n", - " inflating: AD_NC/test/AD/973656_83.jpeg \n", - " inflating: AD_NC/test/AD/973656_84.jpeg \n", - " inflating: AD_NC/test/AD/973656_85.jpeg \n", - " inflating: AD_NC/test/AD/973656_86.jpeg \n", - " inflating: AD_NC/test/AD/973656_87.jpeg \n", - " inflating: AD_NC/test/AD/973656_88.jpeg \n", - " inflating: AD_NC/test/AD/973656_89.jpeg \n", - " inflating: AD_NC/test/AD/973656_90.jpeg \n", - " inflating: AD_NC/test/AD/973656_91.jpeg \n", - " inflating: AD_NC/test/AD/973656_92.jpeg \n", - " inflating: AD_NC/test/AD/973656_93.jpeg \n", - " inflating: AD_NC/test/AD/973656_94.jpeg \n", - " inflating: AD_NC/test/AD/973656_95.jpeg \n", - " inflating: AD_NC/test/AD/973656_96.jpeg \n", - " inflating: AD_NC/test/AD/973656_97.jpeg \n", - " inflating: AD_NC/test/AD/985197_100.jpeg \n", - " inflating: AD_NC/test/AD/985197_101.jpeg \n", - " inflating: AD_NC/test/AD/985197_102.jpeg \n", - " inflating: AD_NC/test/AD/985197_103.jpeg \n", - " inflating: AD_NC/test/AD/985197_104.jpeg \n", - " inflating: AD_NC/test/AD/985197_105.jpeg \n", - " inflating: AD_NC/test/AD/985197_106.jpeg \n", - " inflating: AD_NC/test/AD/985197_107.jpeg \n", - " inflating: AD_NC/test/AD/985197_108.jpeg \n", - " inflating: AD_NC/test/AD/985197_109.jpeg \n", - " inflating: AD_NC/test/AD/985197_110.jpeg \n", - " inflating: AD_NC/test/AD/985197_111.jpeg \n", - " inflating: AD_NC/test/AD/985197_112.jpeg \n", - " inflating: AD_NC/test/AD/985197_113.jpeg \n", - " inflating: AD_NC/test/AD/985197_94.jpeg \n", - " inflating: AD_NC/test/AD/985197_95.jpeg \n", - " inflating: AD_NC/test/AD/985197_96.jpeg \n", - " inflating: AD_NC/test/AD/985197_97.jpeg \n", - " inflating: AD_NC/test/AD/985197_98.jpeg \n", - " inflating: AD_NC/test/AD/985197_99.jpeg \n", - " inflating: AD_NC/test/AD/991768_100.jpeg \n", - " inflating: AD_NC/test/AD/991768_101.jpeg \n", - " inflating: AD_NC/test/AD/991768_102.jpeg \n", - " inflating: AD_NC/test/AD/991768_103.jpeg \n", - " inflating: AD_NC/test/AD/991768_104.jpeg \n", - " inflating: AD_NC/test/AD/991768_105.jpeg \n", - " inflating: AD_NC/test/AD/991768_106.jpeg \n", - " inflating: AD_NC/test/AD/991768_107.jpeg \n", - " inflating: AD_NC/test/AD/991768_88.jpeg \n", - " inflating: AD_NC/test/AD/991768_89.jpeg \n", - " inflating: AD_NC/test/AD/991768_90.jpeg \n", - " inflating: AD_NC/test/AD/991768_91.jpeg \n", - " inflating: AD_NC/test/AD/991768_92.jpeg \n", - " inflating: AD_NC/test/AD/991768_93.jpeg \n", - " inflating: AD_NC/test/AD/991768_94.jpeg \n", - " inflating: AD_NC/test/AD/991768_95.jpeg \n", - " inflating: AD_NC/test/AD/991768_96.jpeg \n", - " inflating: AD_NC/test/AD/991768_97.jpeg \n", - " inflating: AD_NC/test/AD/991768_98.jpeg \n", - " inflating: AD_NC/test/AD/991768_99.jpeg \n", - " inflating: AD_NC/test/AD/992628_100.jpeg \n", - " inflating: AD_NC/test/AD/992628_101.jpeg \n", - " inflating: AD_NC/test/AD/992628_102.jpeg \n", - " inflating: AD_NC/test/AD/992628_103.jpeg \n", - " inflating: AD_NC/test/AD/992628_104.jpeg \n", - " inflating: AD_NC/test/AD/992628_105.jpeg \n", - " inflating: AD_NC/test/AD/992628_106.jpeg \n", - " inflating: AD_NC/test/AD/992628_107.jpeg \n", - " inflating: AD_NC/test/AD/992628_108.jpeg \n", - " inflating: AD_NC/test/AD/992628_109.jpeg \n", - " inflating: AD_NC/test/AD/992628_110.jpeg \n", - " inflating: AD_NC/test/AD/992628_111.jpeg \n", - " inflating: AD_NC/test/AD/992628_112.jpeg \n", - " inflating: AD_NC/test/AD/992628_113.jpeg \n", - " inflating: AD_NC/test/AD/992628_94.jpeg \n", - " inflating: AD_NC/test/AD/992628_95.jpeg \n", - " inflating: AD_NC/test/AD/992628_96.jpeg \n", - " inflating: AD_NC/test/AD/992628_97.jpeg \n", - " inflating: AD_NC/test/AD/992628_98.jpeg \n", - " inflating: AD_NC/test/AD/992628_99.jpeg \n", - " inflating: AD_NC/test/AD/992839_100.jpeg \n", - " inflating: AD_NC/test/AD/992839_101.jpeg \n", - " inflating: AD_NC/test/AD/992839_102.jpeg \n", - " inflating: AD_NC/test/AD/992839_103.jpeg \n", - " inflating: AD_NC/test/AD/992839_104.jpeg \n", - " inflating: AD_NC/test/AD/992839_105.jpeg \n", - " inflating: AD_NC/test/AD/992839_106.jpeg \n", - " inflating: AD_NC/test/AD/992839_107.jpeg \n", - " inflating: AD_NC/test/AD/992839_108.jpeg \n", - " inflating: AD_NC/test/AD/992839_109.jpeg \n", - " inflating: AD_NC/test/AD/992839_110.jpeg \n", - " inflating: AD_NC/test/AD/992839_111.jpeg \n", - " inflating: AD_NC/test/AD/992839_112.jpeg \n", - " inflating: AD_NC/test/AD/992839_113.jpeg \n", - " inflating: AD_NC/test/AD/992839_94.jpeg \n", - " inflating: AD_NC/test/AD/992839_95.jpeg \n", - " inflating: AD_NC/test/AD/992839_96.jpeg \n", - " inflating: AD_NC/test/AD/992839_97.jpeg \n", - " inflating: AD_NC/test/AD/992839_98.jpeg \n", - " inflating: AD_NC/test/AD/992839_99.jpeg \n", - " creating: AD_NC/test/NC/\n", - " inflating: AD_NC/test/NC/1182968_100.jpeg \n", - " inflating: AD_NC/test/NC/1182968_101.jpeg \n", - " inflating: AD_NC/test/NC/1182968_102.jpeg \n", - " inflating: AD_NC/test/NC/1182968_103.jpeg \n", - " inflating: AD_NC/test/NC/1182968_104.jpeg \n", - " inflating: AD_NC/test/NC/1182968_105.jpeg \n", - " inflating: AD_NC/test/NC/1182968_106.jpeg \n", - " inflating: AD_NC/test/NC/1182968_107.jpeg \n", - " inflating: AD_NC/test/NC/1182968_108.jpeg \n", - " inflating: AD_NC/test/NC/1182968_109.jpeg \n", - " inflating: AD_NC/test/NC/1182968_110.jpeg \n", - " inflating: AD_NC/test/NC/1182968_111.jpeg \n", - " inflating: AD_NC/test/NC/1182968_112.jpeg \n", - " inflating: AD_NC/test/NC/1182968_113.jpeg \n", - " inflating: AD_NC/test/NC/1182968_94.jpeg \n", - " inflating: AD_NC/test/NC/1182968_95.jpeg \n", - " inflating: AD_NC/test/NC/1182968_96.jpeg \n", - " inflating: AD_NC/test/NC/1182968_97.jpeg \n", - " inflating: AD_NC/test/NC/1182968_98.jpeg \n", - " inflating: AD_NC/test/NC/1182968_99.jpeg \n", - " inflating: AD_NC/test/NC/1185628_100.jpeg \n", - " inflating: AD_NC/test/NC/1185628_101.jpeg \n", - " inflating: AD_NC/test/NC/1185628_102.jpeg \n", - " inflating: AD_NC/test/NC/1185628_103.jpeg \n", - " inflating: AD_NC/test/NC/1185628_104.jpeg \n", - " inflating: AD_NC/test/NC/1185628_105.jpeg \n", - " inflating: AD_NC/test/NC/1185628_106.jpeg \n", - " inflating: AD_NC/test/NC/1185628_107.jpeg \n", - " inflating: AD_NC/test/NC/1185628_88.jpeg \n", - " inflating: AD_NC/test/NC/1185628_89.jpeg \n", - " inflating: AD_NC/test/NC/1185628_90.jpeg \n", - " inflating: AD_NC/test/NC/1185628_91.jpeg \n", - " inflating: AD_NC/test/NC/1185628_92.jpeg \n", - " inflating: AD_NC/test/NC/1185628_93.jpeg \n", - " inflating: AD_NC/test/NC/1185628_94.jpeg \n", - " inflating: AD_NC/test/NC/1185628_95.jpeg \n", - " inflating: AD_NC/test/NC/1185628_96.jpeg \n", - " inflating: AD_NC/test/NC/1185628_97.jpeg \n", - " inflating: AD_NC/test/NC/1185628_98.jpeg \n", - " inflating: AD_NC/test/NC/1185628_99.jpeg \n", - " inflating: AD_NC/test/NC/1185714_100.jpeg \n", - " inflating: AD_NC/test/NC/1185714_101.jpeg \n", - " inflating: AD_NC/test/NC/1185714_102.jpeg \n", - " inflating: AD_NC/test/NC/1185714_103.jpeg \n", - " inflating: AD_NC/test/NC/1185714_104.jpeg \n", - " inflating: AD_NC/test/NC/1185714_105.jpeg \n", - " inflating: AD_NC/test/NC/1185714_106.jpeg \n", - " inflating: AD_NC/test/NC/1185714_107.jpeg \n", - " inflating: AD_NC/test/NC/1185714_88.jpeg \n", - " inflating: AD_NC/test/NC/1185714_89.jpeg \n", - " inflating: AD_NC/test/NC/1185714_90.jpeg \n", - " inflating: AD_NC/test/NC/1185714_91.jpeg \n", - " inflating: AD_NC/test/NC/1185714_92.jpeg \n", - " inflating: AD_NC/test/NC/1185714_93.jpeg \n", - " inflating: AD_NC/test/NC/1185714_94.jpeg \n", - " inflating: AD_NC/test/NC/1185714_95.jpeg \n", - " inflating: AD_NC/test/NC/1185714_96.jpeg \n", - " inflating: AD_NC/test/NC/1185714_97.jpeg \n", - " inflating: AD_NC/test/NC/1185714_98.jpeg \n", - " inflating: AD_NC/test/NC/1185714_99.jpeg \n", - " inflating: AD_NC/test/NC/1186516_100.jpeg \n", - " inflating: AD_NC/test/NC/1186516_101.jpeg \n", - " inflating: AD_NC/test/NC/1186516_102.jpeg \n", - " inflating: AD_NC/test/NC/1186516_103.jpeg \n", - " inflating: AD_NC/test/NC/1186516_104.jpeg \n", - " inflating: AD_NC/test/NC/1186516_105.jpeg \n", - " inflating: AD_NC/test/NC/1186516_106.jpeg \n", - " inflating: AD_NC/test/NC/1186516_107.jpeg \n", - " inflating: AD_NC/test/NC/1186516_88.jpeg \n", - " inflating: AD_NC/test/NC/1186516_89.jpeg \n", - " inflating: AD_NC/test/NC/1186516_90.jpeg \n", - " inflating: AD_NC/test/NC/1186516_91.jpeg \n", - " inflating: AD_NC/test/NC/1186516_92.jpeg \n", - " inflating: AD_NC/test/NC/1186516_93.jpeg \n", - " inflating: AD_NC/test/NC/1186516_94.jpeg \n", - " inflating: AD_NC/test/NC/1186516_95.jpeg \n", - " inflating: AD_NC/test/NC/1186516_96.jpeg \n", - " inflating: AD_NC/test/NC/1186516_97.jpeg \n", - " inflating: AD_NC/test/NC/1186516_98.jpeg \n", - " inflating: AD_NC/test/NC/1186516_99.jpeg \n", - " inflating: AD_NC/test/NC/1186714_100.jpeg \n", - " inflating: AD_NC/test/NC/1186714_101.jpeg \n", - " inflating: AD_NC/test/NC/1186714_102.jpeg \n", - " inflating: AD_NC/test/NC/1186714_103.jpeg \n", - " inflating: AD_NC/test/NC/1186714_104.jpeg \n", - " inflating: AD_NC/test/NC/1186714_105.jpeg \n", - " inflating: AD_NC/test/NC/1186714_106.jpeg \n", - " inflating: AD_NC/test/NC/1186714_107.jpeg \n", - " inflating: AD_NC/test/NC/1186714_108.jpeg \n", - " inflating: AD_NC/test/NC/1186714_109.jpeg \n", - " inflating: AD_NC/test/NC/1186714_110.jpeg \n", - " inflating: AD_NC/test/NC/1186714_111.jpeg \n", - " inflating: AD_NC/test/NC/1186714_112.jpeg \n", - " inflating: AD_NC/test/NC/1186714_113.jpeg \n", - " inflating: AD_NC/test/NC/1186714_94.jpeg \n", - " inflating: AD_NC/test/NC/1186714_95.jpeg \n", - " inflating: AD_NC/test/NC/1186714_96.jpeg \n", - " inflating: AD_NC/test/NC/1186714_97.jpeg \n", - " inflating: AD_NC/test/NC/1186714_98.jpeg \n", - " inflating: AD_NC/test/NC/1186714_99.jpeg \n", - " inflating: AD_NC/test/NC/1186737_100.jpeg \n", - " inflating: AD_NC/test/NC/1186737_101.jpeg \n", - " inflating: AD_NC/test/NC/1186737_102.jpeg \n", - " inflating: AD_NC/test/NC/1186737_103.jpeg \n", - " inflating: AD_NC/test/NC/1186737_104.jpeg \n", - " inflating: AD_NC/test/NC/1186737_105.jpeg \n", - " inflating: AD_NC/test/NC/1186737_106.jpeg \n", - " inflating: AD_NC/test/NC/1186737_107.jpeg \n", - " inflating: AD_NC/test/NC/1186737_108.jpeg \n", - " inflating: AD_NC/test/NC/1186737_109.jpeg \n", - " inflating: AD_NC/test/NC/1186737_110.jpeg \n", - " inflating: AD_NC/test/NC/1186737_111.jpeg \n", - " inflating: AD_NC/test/NC/1186737_112.jpeg \n", - " inflating: AD_NC/test/NC/1186737_113.jpeg \n", - " inflating: AD_NC/test/NC/1186737_94.jpeg \n", - " inflating: AD_NC/test/NC/1186737_95.jpeg \n", - " inflating: AD_NC/test/NC/1186737_96.jpeg \n", - " inflating: AD_NC/test/NC/1186737_97.jpeg \n", - " inflating: AD_NC/test/NC/1186737_98.jpeg \n", - " inflating: AD_NC/test/NC/1186737_99.jpeg \n", - " inflating: AD_NC/test/NC/1188738_100.jpeg \n", - " inflating: AD_NC/test/NC/1188738_101.jpeg \n", - " inflating: AD_NC/test/NC/1188738_102.jpeg \n", - " inflating: AD_NC/test/NC/1188738_103.jpeg \n", - " inflating: AD_NC/test/NC/1188738_104.jpeg \n", - " inflating: AD_NC/test/NC/1188738_105.jpeg \n", - " inflating: AD_NC/test/NC/1188738_106.jpeg \n", - " inflating: AD_NC/test/NC/1188738_107.jpeg \n", - " inflating: AD_NC/test/NC/1188738_108.jpeg \n", - " inflating: AD_NC/test/NC/1188738_109.jpeg \n", - " inflating: AD_NC/test/NC/1188738_110.jpeg \n", - " inflating: AD_NC/test/NC/1188738_111.jpeg \n", - " inflating: AD_NC/test/NC/1188738_112.jpeg \n", - " inflating: AD_NC/test/NC/1188738_113.jpeg \n", - " inflating: AD_NC/test/NC/1188738_94.jpeg \n", - " inflating: AD_NC/test/NC/1188738_95.jpeg \n", - " inflating: AD_NC/test/NC/1188738_96.jpeg \n", - " inflating: AD_NC/test/NC/1188738_97.jpeg \n", - " inflating: AD_NC/test/NC/1188738_98.jpeg \n", - " inflating: AD_NC/test/NC/1188738_99.jpeg \n", - " inflating: AD_NC/test/NC/1189614_100.jpeg \n", - " inflating: AD_NC/test/NC/1189614_101.jpeg \n", - " inflating: AD_NC/test/NC/1189614_102.jpeg \n", - " inflating: AD_NC/test/NC/1189614_103.jpeg \n", - " inflating: AD_NC/test/NC/1189614_104.jpeg \n", - " inflating: AD_NC/test/NC/1189614_105.jpeg \n", - " inflating: AD_NC/test/NC/1189614_106.jpeg \n", - " inflating: AD_NC/test/NC/1189614_107.jpeg \n", - " inflating: AD_NC/test/NC/1189614_108.jpeg \n", - " inflating: AD_NC/test/NC/1189614_109.jpeg \n", - " inflating: AD_NC/test/NC/1189614_110.jpeg \n", - " inflating: AD_NC/test/NC/1189614_111.jpeg \n", - " inflating: AD_NC/test/NC/1189614_112.jpeg \n", - " inflating: AD_NC/test/NC/1189614_113.jpeg \n", - " inflating: AD_NC/test/NC/1189614_114.jpeg \n", - " inflating: AD_NC/test/NC/1189614_95.jpeg \n", - " inflating: AD_NC/test/NC/1189614_96.jpeg \n", - " inflating: AD_NC/test/NC/1189614_97.jpeg \n", - " inflating: AD_NC/test/NC/1189614_98.jpeg \n", - " inflating: AD_NC/test/NC/1189614_99.jpeg \n", - " inflating: AD_NC/test/NC/1189640_100.jpeg \n", - " inflating: AD_NC/test/NC/1189640_101.jpeg \n", - " inflating: AD_NC/test/NC/1189640_102.jpeg \n", - " inflating: AD_NC/test/NC/1189640_103.jpeg \n", - " inflating: AD_NC/test/NC/1189640_104.jpeg \n", - " inflating: AD_NC/test/NC/1189640_105.jpeg \n", - " inflating: AD_NC/test/NC/1189640_106.jpeg \n", - " inflating: AD_NC/test/NC/1189640_107.jpeg \n", - " inflating: AD_NC/test/NC/1189640_88.jpeg \n", - " inflating: AD_NC/test/NC/1189640_89.jpeg \n", - " inflating: AD_NC/test/NC/1189640_90.jpeg \n", - " inflating: AD_NC/test/NC/1189640_91.jpeg \n", - " inflating: AD_NC/test/NC/1189640_92.jpeg \n", - " inflating: AD_NC/test/NC/1189640_93.jpeg \n", - " inflating: AD_NC/test/NC/1189640_94.jpeg \n", - " inflating: AD_NC/test/NC/1189640_95.jpeg \n", - " inflating: AD_NC/test/NC/1189640_96.jpeg \n", - " inflating: AD_NC/test/NC/1189640_97.jpeg \n", - " inflating: AD_NC/test/NC/1189640_98.jpeg \n", - " inflating: AD_NC/test/NC/1189640_99.jpeg \n", - " inflating: AD_NC/test/NC/1190195_100.jpeg \n", - " inflating: AD_NC/test/NC/1190195_101.jpeg \n", - " inflating: AD_NC/test/NC/1190195_102.jpeg \n", - " inflating: AD_NC/test/NC/1190195_103.jpeg \n", - " inflating: AD_NC/test/NC/1190195_104.jpeg \n", - " inflating: AD_NC/test/NC/1190195_105.jpeg \n", - " inflating: AD_NC/test/NC/1190195_106.jpeg \n", - " inflating: AD_NC/test/NC/1190195_107.jpeg \n", - " inflating: AD_NC/test/NC/1190195_88.jpeg \n", - " inflating: AD_NC/test/NC/1190195_89.jpeg \n", - " inflating: AD_NC/test/NC/1190195_90.jpeg \n", - " inflating: AD_NC/test/NC/1190195_91.jpeg \n", - " inflating: AD_NC/test/NC/1190195_92.jpeg \n", - " inflating: AD_NC/test/NC/1190195_93.jpeg \n", - " inflating: AD_NC/test/NC/1190195_94.jpeg \n", - " inflating: AD_NC/test/NC/1190195_95.jpeg \n", - " inflating: AD_NC/test/NC/1190195_96.jpeg \n", - " inflating: AD_NC/test/NC/1190195_97.jpeg \n", - " inflating: AD_NC/test/NC/1190195_98.jpeg \n", - " inflating: AD_NC/test/NC/1190195_99.jpeg \n", - " inflating: AD_NC/test/NC/1190397_100.jpeg \n", - " inflating: AD_NC/test/NC/1190397_101.jpeg \n", - " inflating: AD_NC/test/NC/1190397_102.jpeg \n", - " inflating: AD_NC/test/NC/1190397_103.jpeg \n", - " inflating: AD_NC/test/NC/1190397_104.jpeg \n", - " inflating: AD_NC/test/NC/1190397_105.jpeg \n", - " inflating: AD_NC/test/NC/1190397_106.jpeg \n", - " inflating: AD_NC/test/NC/1190397_107.jpeg \n", - " inflating: AD_NC/test/NC/1190397_108.jpeg \n", - " inflating: AD_NC/test/NC/1190397_109.jpeg \n", - " inflating: AD_NC/test/NC/1190397_110.jpeg \n", - " inflating: AD_NC/test/NC/1190397_111.jpeg \n", - " inflating: AD_NC/test/NC/1190397_112.jpeg \n", - " inflating: AD_NC/test/NC/1190397_113.jpeg \n", - " inflating: AD_NC/test/NC/1190397_114.jpeg \n", - " inflating: AD_NC/test/NC/1190397_95.jpeg \n", - " inflating: AD_NC/test/NC/1190397_96.jpeg \n", - " inflating: AD_NC/test/NC/1190397_97.jpeg \n", - " inflating: AD_NC/test/NC/1190397_98.jpeg \n", - " inflating: AD_NC/test/NC/1190397_99.jpeg \n", - " inflating: AD_NC/test/NC/1190623_100.jpeg \n", - " inflating: AD_NC/test/NC/1190623_101.jpeg \n", - " inflating: AD_NC/test/NC/1190623_102.jpeg \n", - " inflating: AD_NC/test/NC/1190623_103.jpeg \n", - " inflating: AD_NC/test/NC/1190623_104.jpeg \n", - " inflating: AD_NC/test/NC/1190623_105.jpeg \n", - " inflating: AD_NC/test/NC/1190623_106.jpeg \n", - " inflating: AD_NC/test/NC/1190623_107.jpeg \n", - " inflating: AD_NC/test/NC/1190623_108.jpeg \n", - " inflating: AD_NC/test/NC/1190623_109.jpeg \n", - " inflating: AD_NC/test/NC/1190623_110.jpeg \n", - " inflating: AD_NC/test/NC/1190623_111.jpeg \n", - " inflating: AD_NC/test/NC/1190623_112.jpeg \n", - " inflating: AD_NC/test/NC/1190623_113.jpeg \n", - " inflating: AD_NC/test/NC/1190623_94.jpeg \n", - " inflating: AD_NC/test/NC/1190623_95.jpeg \n", - " inflating: AD_NC/test/NC/1190623_96.jpeg \n", - " inflating: AD_NC/test/NC/1190623_97.jpeg \n", - " inflating: AD_NC/test/NC/1190623_98.jpeg \n", - " inflating: AD_NC/test/NC/1190623_99.jpeg \n", - " inflating: AD_NC/test/NC/1191372_100.jpeg \n", - " inflating: AD_NC/test/NC/1191372_101.jpeg \n", - " inflating: AD_NC/test/NC/1191372_102.jpeg \n", - " inflating: AD_NC/test/NC/1191372_103.jpeg \n", - " inflating: AD_NC/test/NC/1191372_104.jpeg \n", - " inflating: AD_NC/test/NC/1191372_105.jpeg \n", - " inflating: AD_NC/test/NC/1191372_106.jpeg \n", - " inflating: AD_NC/test/NC/1191372_107.jpeg \n", - " inflating: AD_NC/test/NC/1191372_108.jpeg \n", - " inflating: AD_NC/test/NC/1191372_109.jpeg \n", - " inflating: AD_NC/test/NC/1191372_110.jpeg \n", - " inflating: AD_NC/test/NC/1191372_111.jpeg \n", - " inflating: AD_NC/test/NC/1191372_112.jpeg \n", - " inflating: AD_NC/test/NC/1191372_113.jpeg \n", - " inflating: AD_NC/test/NC/1191372_94.jpeg \n", - " inflating: AD_NC/test/NC/1191372_95.jpeg \n", - " inflating: AD_NC/test/NC/1191372_96.jpeg \n", - " inflating: AD_NC/test/NC/1191372_97.jpeg \n", - " inflating: AD_NC/test/NC/1191372_98.jpeg \n", - " inflating: AD_NC/test/NC/1191372_99.jpeg \n", - " inflating: AD_NC/test/NC/1192854_100.jpeg \n", - " inflating: AD_NC/test/NC/1192854_101.jpeg \n", - " inflating: AD_NC/test/NC/1192854_102.jpeg \n", - " inflating: AD_NC/test/NC/1192854_103.jpeg \n", - " inflating: AD_NC/test/NC/1192854_104.jpeg \n", - " inflating: AD_NC/test/NC/1192854_105.jpeg \n", - " inflating: AD_NC/test/NC/1192854_106.jpeg \n", - " inflating: AD_NC/test/NC/1192854_107.jpeg \n", - " inflating: AD_NC/test/NC/1192854_108.jpeg \n", - " inflating: AD_NC/test/NC/1192854_109.jpeg \n", - " inflating: AD_NC/test/NC/1192854_110.jpeg \n", - " inflating: AD_NC/test/NC/1192854_111.jpeg \n", - " inflating: AD_NC/test/NC/1192854_112.jpeg \n", - " inflating: AD_NC/test/NC/1192854_113.jpeg \n", - " inflating: AD_NC/test/NC/1192854_94.jpeg \n", - " inflating: AD_NC/test/NC/1192854_95.jpeg \n", - " inflating: AD_NC/test/NC/1192854_96.jpeg \n", - " inflating: AD_NC/test/NC/1192854_97.jpeg \n", - " inflating: AD_NC/test/NC/1192854_98.jpeg \n", - " inflating: AD_NC/test/NC/1192854_99.jpeg \n", - " inflating: AD_NC/test/NC/1193331_100.jpeg \n", - " inflating: AD_NC/test/NC/1193331_101.jpeg \n", - " inflating: AD_NC/test/NC/1193331_102.jpeg \n", - " inflating: AD_NC/test/NC/1193331_103.jpeg \n", - " inflating: AD_NC/test/NC/1193331_104.jpeg \n", - " inflating: AD_NC/test/NC/1193331_105.jpeg \n", - " inflating: AD_NC/test/NC/1193331_106.jpeg \n", - " inflating: AD_NC/test/NC/1193331_107.jpeg \n", - " inflating: AD_NC/test/NC/1193331_108.jpeg \n", - " inflating: AD_NC/test/NC/1193331_109.jpeg \n", - " inflating: AD_NC/test/NC/1193331_110.jpeg \n", - " inflating: AD_NC/test/NC/1193331_111.jpeg \n", - " inflating: AD_NC/test/NC/1193331_112.jpeg \n", - " inflating: AD_NC/test/NC/1193331_113.jpeg \n", - " inflating: AD_NC/test/NC/1193331_94.jpeg \n", - " inflating: AD_NC/test/NC/1193331_95.jpeg \n", - " inflating: AD_NC/test/NC/1193331_96.jpeg \n", - " inflating: AD_NC/test/NC/1193331_97.jpeg \n", - " inflating: AD_NC/test/NC/1193331_98.jpeg \n", - " inflating: AD_NC/test/NC/1193331_99.jpeg \n", - " inflating: AD_NC/test/NC/1193762_100.jpeg \n", - " inflating: AD_NC/test/NC/1193762_101.jpeg \n", - " inflating: AD_NC/test/NC/1193762_102.jpeg \n", - " inflating: AD_NC/test/NC/1193762_103.jpeg \n", - " inflating: AD_NC/test/NC/1193762_104.jpeg \n", - " inflating: AD_NC/test/NC/1193762_105.jpeg \n", - " inflating: AD_NC/test/NC/1193762_106.jpeg \n", - " inflating: AD_NC/test/NC/1193762_107.jpeg \n", - " inflating: AD_NC/test/NC/1193762_108.jpeg \n", - " inflating: AD_NC/test/NC/1193762_109.jpeg \n", - " inflating: AD_NC/test/NC/1193762_110.jpeg \n", - " inflating: AD_NC/test/NC/1193762_111.jpeg \n", - " inflating: AD_NC/test/NC/1193762_112.jpeg \n", - " inflating: AD_NC/test/NC/1193762_113.jpeg \n", - " inflating: AD_NC/test/NC/1193762_94.jpeg \n", - " inflating: AD_NC/test/NC/1193762_95.jpeg \n", - " inflating: AD_NC/test/NC/1193762_96.jpeg \n", - " inflating: AD_NC/test/NC/1193762_97.jpeg \n", - " inflating: AD_NC/test/NC/1193762_98.jpeg \n", - " inflating: AD_NC/test/NC/1193762_99.jpeg \n", - " inflating: AD_NC/test/NC/1193770_100.jpeg \n", - " inflating: AD_NC/test/NC/1193770_101.jpeg \n", - " inflating: AD_NC/test/NC/1193770_102.jpeg \n", - " inflating: AD_NC/test/NC/1193770_103.jpeg \n", - " inflating: AD_NC/test/NC/1193770_104.jpeg \n", - " inflating: AD_NC/test/NC/1193770_105.jpeg \n", - " inflating: AD_NC/test/NC/1193770_106.jpeg \n", - " inflating: AD_NC/test/NC/1193770_107.jpeg \n", - " inflating: AD_NC/test/NC/1193770_108.jpeg \n", - " inflating: AD_NC/test/NC/1193770_109.jpeg \n", - " inflating: AD_NC/test/NC/1193770_110.jpeg \n", - " inflating: AD_NC/test/NC/1193770_111.jpeg \n", - " inflating: AD_NC/test/NC/1193770_112.jpeg \n", - " inflating: AD_NC/test/NC/1193770_113.jpeg \n", - " inflating: AD_NC/test/NC/1193770_94.jpeg \n", - " inflating: AD_NC/test/NC/1193770_95.jpeg \n", - " inflating: AD_NC/test/NC/1193770_96.jpeg \n", - " inflating: AD_NC/test/NC/1193770_97.jpeg \n", - " inflating: AD_NC/test/NC/1193770_98.jpeg \n", - " inflating: AD_NC/test/NC/1193770_99.jpeg \n", - " inflating: AD_NC/test/NC/1194377_78.jpeg \n", - " inflating: AD_NC/test/NC/1194377_79.jpeg \n", - " inflating: AD_NC/test/NC/1194377_80.jpeg \n", - " inflating: AD_NC/test/NC/1194377_81.jpeg \n", - " inflating: AD_NC/test/NC/1194377_82.jpeg \n", - " inflating: AD_NC/test/NC/1194377_83.jpeg \n", - " inflating: AD_NC/test/NC/1194377_84.jpeg \n", - " inflating: AD_NC/test/NC/1194377_85.jpeg \n", - " inflating: AD_NC/test/NC/1194377_86.jpeg \n", - " inflating: AD_NC/test/NC/1194377_87.jpeg \n", - " inflating: AD_NC/test/NC/1194377_88.jpeg \n", - " inflating: AD_NC/test/NC/1194377_89.jpeg \n", - " inflating: AD_NC/test/NC/1194377_90.jpeg \n", - " inflating: AD_NC/test/NC/1194377_91.jpeg \n", - " inflating: AD_NC/test/NC/1194377_92.jpeg \n", - " inflating: AD_NC/test/NC/1194377_93.jpeg \n", - " inflating: AD_NC/test/NC/1194377_94.jpeg \n", - " inflating: AD_NC/test/NC/1194377_95.jpeg \n", - " inflating: AD_NC/test/NC/1194377_96.jpeg \n", - " inflating: AD_NC/test/NC/1194377_97.jpeg \n", - " inflating: AD_NC/test/NC/1195471_100.jpeg \n", - " inflating: AD_NC/test/NC/1195471_101.jpeg \n", - " inflating: AD_NC/test/NC/1195471_102.jpeg \n", - " inflating: AD_NC/test/NC/1195471_103.jpeg \n", - " inflating: AD_NC/test/NC/1195471_104.jpeg \n", - " inflating: AD_NC/test/NC/1195471_105.jpeg \n", - " inflating: AD_NC/test/NC/1195471_106.jpeg \n", - " inflating: AD_NC/test/NC/1195471_107.jpeg \n", - " inflating: AD_NC/test/NC/1195471_108.jpeg \n", - " inflating: AD_NC/test/NC/1195471_109.jpeg \n", - " inflating: AD_NC/test/NC/1195471_110.jpeg \n", - " inflating: AD_NC/test/NC/1195471_111.jpeg \n", - " inflating: AD_NC/test/NC/1195471_112.jpeg \n", - " inflating: AD_NC/test/NC/1195471_113.jpeg \n", - " inflating: AD_NC/test/NC/1195471_114.jpeg \n", - " inflating: AD_NC/test/NC/1195471_95.jpeg \n", - " inflating: AD_NC/test/NC/1195471_96.jpeg \n", - " inflating: AD_NC/test/NC/1195471_97.jpeg \n", - " inflating: AD_NC/test/NC/1195471_98.jpeg \n", - " inflating: AD_NC/test/NC/1195471_99.jpeg \n", - " inflating: AD_NC/test/NC/1195531_100.jpeg \n", - " inflating: AD_NC/test/NC/1195531_101.jpeg \n", - " inflating: AD_NC/test/NC/1195531_102.jpeg \n", - " inflating: AD_NC/test/NC/1195531_103.jpeg \n", - " inflating: AD_NC/test/NC/1195531_104.jpeg \n", - " inflating: AD_NC/test/NC/1195531_105.jpeg \n", - " inflating: AD_NC/test/NC/1195531_106.jpeg \n", - " inflating: AD_NC/test/NC/1195531_107.jpeg \n", - " inflating: AD_NC/test/NC/1195531_88.jpeg \n", - " inflating: AD_NC/test/NC/1195531_89.jpeg \n", - " inflating: AD_NC/test/NC/1195531_90.jpeg \n", - " inflating: AD_NC/test/NC/1195531_91.jpeg \n", - " inflating: AD_NC/test/NC/1195531_92.jpeg \n", - " inflating: AD_NC/test/NC/1195531_93.jpeg \n", - " inflating: AD_NC/test/NC/1195531_94.jpeg \n", - " inflating: AD_NC/test/NC/1195531_95.jpeg \n", - " inflating: AD_NC/test/NC/1195531_96.jpeg \n", - " inflating: AD_NC/test/NC/1195531_97.jpeg \n", - " inflating: AD_NC/test/NC/1195531_98.jpeg \n", - " inflating: AD_NC/test/NC/1195531_99.jpeg \n", - " inflating: AD_NC/test/NC/1195981_100.jpeg \n", - " inflating: AD_NC/test/NC/1195981_101.jpeg \n", - " inflating: AD_NC/test/NC/1195981_102.jpeg \n", - " inflating: AD_NC/test/NC/1195981_103.jpeg \n", - " inflating: AD_NC/test/NC/1195981_104.jpeg \n", - " inflating: AD_NC/test/NC/1195981_105.jpeg \n", - " inflating: AD_NC/test/NC/1195981_106.jpeg \n", - " inflating: AD_NC/test/NC/1195981_107.jpeg \n", - " inflating: AD_NC/test/NC/1195981_108.jpeg \n", - " inflating: AD_NC/test/NC/1195981_109.jpeg \n", - " inflating: AD_NC/test/NC/1195981_110.jpeg \n", - " inflating: AD_NC/test/NC/1195981_111.jpeg \n", - " inflating: AD_NC/test/NC/1195981_112.jpeg \n", - " inflating: AD_NC/test/NC/1195981_113.jpeg \n", - " inflating: AD_NC/test/NC/1195981_94.jpeg \n", - " inflating: AD_NC/test/NC/1195981_95.jpeg \n", - " inflating: AD_NC/test/NC/1195981_96.jpeg \n", - " inflating: AD_NC/test/NC/1195981_97.jpeg \n", - " inflating: AD_NC/test/NC/1195981_98.jpeg \n", - " inflating: AD_NC/test/NC/1195981_99.jpeg \n", - " inflating: AD_NC/test/NC/1196891_100.jpeg \n", - " inflating: AD_NC/test/NC/1196891_101.jpeg \n", - " inflating: AD_NC/test/NC/1196891_102.jpeg \n", - " inflating: AD_NC/test/NC/1196891_103.jpeg \n", - " inflating: AD_NC/test/NC/1196891_104.jpeg \n", - " inflating: AD_NC/test/NC/1196891_105.jpeg \n", - " inflating: AD_NC/test/NC/1196891_106.jpeg \n", - " inflating: AD_NC/test/NC/1196891_107.jpeg \n", - " inflating: AD_NC/test/NC/1196891_108.jpeg \n", - " inflating: AD_NC/test/NC/1196891_109.jpeg \n", - " inflating: AD_NC/test/NC/1196891_110.jpeg \n", - " inflating: AD_NC/test/NC/1196891_111.jpeg \n", - " inflating: AD_NC/test/NC/1196891_112.jpeg \n", - " inflating: AD_NC/test/NC/1196891_113.jpeg \n", - " inflating: AD_NC/test/NC/1196891_94.jpeg \n", - " inflating: AD_NC/test/NC/1196891_95.jpeg \n", - " inflating: AD_NC/test/NC/1196891_96.jpeg \n", - " inflating: AD_NC/test/NC/1196891_97.jpeg \n", - " inflating: AD_NC/test/NC/1196891_98.jpeg \n", - " inflating: AD_NC/test/NC/1196891_99.jpeg \n", - " inflating: AD_NC/test/NC/1199310_100.jpeg \n", - " inflating: AD_NC/test/NC/1199310_101.jpeg \n", - " inflating: AD_NC/test/NC/1199310_102.jpeg \n", - " inflating: AD_NC/test/NC/1199310_103.jpeg \n", - " inflating: AD_NC/test/NC/1199310_104.jpeg \n", - " inflating: AD_NC/test/NC/1199310_105.jpeg \n", - " inflating: AD_NC/test/NC/1199310_106.jpeg \n", - " inflating: AD_NC/test/NC/1199310_107.jpeg \n", - " inflating: AD_NC/test/NC/1199310_88.jpeg \n", - " inflating: AD_NC/test/NC/1199310_89.jpeg \n", - " inflating: AD_NC/test/NC/1199310_90.jpeg \n", - " inflating: AD_NC/test/NC/1199310_91.jpeg \n", - " inflating: AD_NC/test/NC/1199310_92.jpeg \n", - " inflating: AD_NC/test/NC/1199310_93.jpeg \n", - " inflating: AD_NC/test/NC/1199310_94.jpeg \n", - " inflating: AD_NC/test/NC/1199310_95.jpeg \n", - " inflating: AD_NC/test/NC/1199310_96.jpeg \n", - " inflating: AD_NC/test/NC/1199310_97.jpeg \n", - " inflating: AD_NC/test/NC/1199310_98.jpeg \n", - " inflating: AD_NC/test/NC/1199310_99.jpeg \n", - " inflating: AD_NC/test/NC/1199414_100.jpeg \n", - " inflating: AD_NC/test/NC/1199414_101.jpeg \n", - " inflating: AD_NC/test/NC/1199414_102.jpeg \n", - " inflating: AD_NC/test/NC/1199414_103.jpeg \n", - " inflating: AD_NC/test/NC/1199414_104.jpeg \n", - " inflating: AD_NC/test/NC/1199414_105.jpeg \n", - " inflating: AD_NC/test/NC/1199414_106.jpeg \n", - " inflating: AD_NC/test/NC/1199414_107.jpeg \n", - " inflating: AD_NC/test/NC/1199414_88.jpeg \n", - " inflating: AD_NC/test/NC/1199414_89.jpeg \n", - " inflating: AD_NC/test/NC/1199414_90.jpeg \n", - " inflating: AD_NC/test/NC/1199414_91.jpeg \n", - " inflating: AD_NC/test/NC/1199414_92.jpeg \n", - " inflating: AD_NC/test/NC/1199414_93.jpeg \n", - " inflating: AD_NC/test/NC/1199414_94.jpeg \n", - " inflating: AD_NC/test/NC/1199414_95.jpeg \n", - " inflating: AD_NC/test/NC/1199414_96.jpeg \n", - " inflating: AD_NC/test/NC/1199414_97.jpeg \n", - " inflating: AD_NC/test/NC/1199414_98.jpeg \n", - " inflating: AD_NC/test/NC/1199414_99.jpeg \n", - " inflating: AD_NC/test/NC/1209877_100.jpeg \n", - " inflating: AD_NC/test/NC/1209877_101.jpeg \n", - " inflating: AD_NC/test/NC/1209877_102.jpeg \n", - " inflating: AD_NC/test/NC/1209877_103.jpeg \n", - " inflating: AD_NC/test/NC/1209877_104.jpeg \n", - " inflating: AD_NC/test/NC/1209877_105.jpeg \n", - " inflating: AD_NC/test/NC/1209877_106.jpeg \n", - " inflating: AD_NC/test/NC/1209877_107.jpeg \n", - " inflating: AD_NC/test/NC/1209877_108.jpeg \n", - " inflating: AD_NC/test/NC/1209877_109.jpeg \n", - " inflating: AD_NC/test/NC/1209877_110.jpeg \n", - " inflating: AD_NC/test/NC/1209877_111.jpeg \n", - " inflating: AD_NC/test/NC/1209877_112.jpeg \n", - " inflating: AD_NC/test/NC/1209877_113.jpeg \n", - " inflating: AD_NC/test/NC/1209877_94.jpeg \n", - " inflating: AD_NC/test/NC/1209877_95.jpeg \n", - " inflating: AD_NC/test/NC/1209877_96.jpeg \n", - " inflating: AD_NC/test/NC/1209877_97.jpeg \n", - " inflating: AD_NC/test/NC/1209877_98.jpeg \n", - " inflating: AD_NC/test/NC/1209877_99.jpeg \n", - " inflating: AD_NC/test/NC/1211451_100.jpeg \n", - " inflating: AD_NC/test/NC/1211451_101.jpeg \n", - " inflating: AD_NC/test/NC/1211451_102.jpeg \n", - " inflating: AD_NC/test/NC/1211451_103.jpeg \n", - " inflating: AD_NC/test/NC/1211451_104.jpeg \n", - " inflating: AD_NC/test/NC/1211451_105.jpeg \n", - " inflating: AD_NC/test/NC/1211451_106.jpeg \n", - " inflating: AD_NC/test/NC/1211451_107.jpeg \n", - " inflating: AD_NC/test/NC/1211451_108.jpeg \n", - " inflating: AD_NC/test/NC/1211451_109.jpeg \n", - " inflating: AD_NC/test/NC/1211451_110.jpeg \n", - " inflating: AD_NC/test/NC/1211451_111.jpeg \n", - " inflating: AD_NC/test/NC/1211451_112.jpeg \n", - " inflating: AD_NC/test/NC/1211451_113.jpeg \n", - " inflating: AD_NC/test/NC/1211451_94.jpeg \n", - " inflating: AD_NC/test/NC/1211451_95.jpeg \n", - " inflating: AD_NC/test/NC/1211451_96.jpeg \n", - " inflating: AD_NC/test/NC/1211451_97.jpeg \n", - " inflating: AD_NC/test/NC/1211451_98.jpeg \n", - " inflating: AD_NC/test/NC/1211451_99.jpeg \n", - " inflating: AD_NC/test/NC/1212969_100.jpeg \n", - " inflating: AD_NC/test/NC/1212969_101.jpeg \n", - " inflating: AD_NC/test/NC/1212969_102.jpeg \n", - " inflating: AD_NC/test/NC/1212969_103.jpeg \n", - " inflating: AD_NC/test/NC/1212969_104.jpeg \n", - " inflating: AD_NC/test/NC/1212969_105.jpeg \n", - " inflating: AD_NC/test/NC/1212969_106.jpeg \n", - " inflating: AD_NC/test/NC/1212969_107.jpeg \n", - " inflating: AD_NC/test/NC/1212969_88.jpeg \n", - " inflating: AD_NC/test/NC/1212969_89.jpeg \n", - " inflating: AD_NC/test/NC/1212969_90.jpeg \n", - " inflating: AD_NC/test/NC/1212969_91.jpeg \n", - " inflating: AD_NC/test/NC/1212969_92.jpeg \n", - " inflating: AD_NC/test/NC/1212969_93.jpeg \n", - " inflating: AD_NC/test/NC/1212969_94.jpeg \n", - " inflating: AD_NC/test/NC/1212969_95.jpeg \n", - " inflating: AD_NC/test/NC/1212969_96.jpeg \n", - " inflating: AD_NC/test/NC/1212969_97.jpeg \n", - " inflating: AD_NC/test/NC/1212969_98.jpeg \n", - " inflating: AD_NC/test/NC/1212969_99.jpeg \n", - " inflating: AD_NC/test/NC/1214021_78.jpeg \n", - " inflating: AD_NC/test/NC/1214021_79.jpeg \n", - " inflating: AD_NC/test/NC/1214021_80.jpeg \n", - " inflating: AD_NC/test/NC/1214021_81.jpeg \n", - " inflating: AD_NC/test/NC/1214021_82.jpeg \n", - " inflating: AD_NC/test/NC/1214021_83.jpeg \n", - " inflating: AD_NC/test/NC/1214021_84.jpeg \n", - " inflating: AD_NC/test/NC/1214021_85.jpeg \n", - " inflating: AD_NC/test/NC/1214021_86.jpeg \n", - " inflating: AD_NC/test/NC/1214021_87.jpeg \n", - " inflating: AD_NC/test/NC/1214021_88.jpeg \n", - " inflating: AD_NC/test/NC/1214021_89.jpeg \n", - " inflating: AD_NC/test/NC/1214021_90.jpeg \n", - " inflating: AD_NC/test/NC/1214021_91.jpeg \n", - " inflating: AD_NC/test/NC/1214021_92.jpeg \n", - " inflating: AD_NC/test/NC/1214021_93.jpeg \n", - " inflating: AD_NC/test/NC/1214021_94.jpeg \n", - " inflating: AD_NC/test/NC/1214021_95.jpeg \n", - " inflating: AD_NC/test/NC/1214021_96.jpeg \n", - " inflating: AD_NC/test/NC/1214021_97.jpeg \n", - " inflating: AD_NC/test/NC/1214909_100.jpeg \n", - " inflating: AD_NC/test/NC/1214909_101.jpeg \n", - " inflating: AD_NC/test/NC/1214909_102.jpeg \n", - " inflating: AD_NC/test/NC/1214909_103.jpeg \n", - " inflating: AD_NC/test/NC/1214909_104.jpeg \n", - " inflating: AD_NC/test/NC/1214909_105.jpeg \n", - " inflating: AD_NC/test/NC/1214909_106.jpeg \n", - " inflating: AD_NC/test/NC/1214909_107.jpeg \n", - " inflating: AD_NC/test/NC/1214909_108.jpeg \n", - " inflating: AD_NC/test/NC/1214909_109.jpeg \n", - " inflating: AD_NC/test/NC/1214909_110.jpeg \n", - " inflating: AD_NC/test/NC/1214909_111.jpeg \n", - " inflating: AD_NC/test/NC/1214909_112.jpeg \n", - " inflating: AD_NC/test/NC/1214909_113.jpeg \n", - " inflating: AD_NC/test/NC/1214909_94.jpeg \n", - " inflating: AD_NC/test/NC/1214909_95.jpeg \n", - " inflating: AD_NC/test/NC/1214909_96.jpeg \n", - " inflating: AD_NC/test/NC/1214909_97.jpeg \n", - " inflating: AD_NC/test/NC/1214909_98.jpeg \n", - " inflating: AD_NC/test/NC/1214909_99.jpeg \n", - " inflating: AD_NC/test/NC/1215566_100.jpeg \n", - " inflating: AD_NC/test/NC/1215566_101.jpeg \n", - " inflating: AD_NC/test/NC/1215566_102.jpeg \n", - " inflating: AD_NC/test/NC/1215566_103.jpeg \n", - " inflating: AD_NC/test/NC/1215566_104.jpeg \n", - " inflating: AD_NC/test/NC/1215566_105.jpeg \n", - " inflating: AD_NC/test/NC/1215566_106.jpeg \n", - " inflating: AD_NC/test/NC/1215566_107.jpeg \n", - " inflating: AD_NC/test/NC/1215566_88.jpeg \n", - " inflating: AD_NC/test/NC/1215566_89.jpeg \n", - " inflating: AD_NC/test/NC/1215566_90.jpeg \n", - " inflating: AD_NC/test/NC/1215566_91.jpeg \n", - " inflating: AD_NC/test/NC/1215566_92.jpeg \n", - " inflating: AD_NC/test/NC/1215566_93.jpeg \n", - " inflating: AD_NC/test/NC/1215566_94.jpeg \n", - " inflating: AD_NC/test/NC/1215566_95.jpeg \n", - " inflating: AD_NC/test/NC/1215566_96.jpeg \n", - " inflating: AD_NC/test/NC/1215566_97.jpeg \n", - " inflating: AD_NC/test/NC/1215566_98.jpeg \n", - " inflating: AD_NC/test/NC/1215566_99.jpeg \n", - " inflating: AD_NC/test/NC/1215774_100.jpeg \n", - " inflating: AD_NC/test/NC/1215774_101.jpeg \n", - " inflating: AD_NC/test/NC/1215774_102.jpeg \n", - " inflating: AD_NC/test/NC/1215774_103.jpeg \n", - " inflating: AD_NC/test/NC/1215774_104.jpeg \n", - " inflating: AD_NC/test/NC/1215774_105.jpeg \n", - " inflating: AD_NC/test/NC/1215774_106.jpeg \n", - " inflating: AD_NC/test/NC/1215774_107.jpeg \n", - " inflating: AD_NC/test/NC/1215774_88.jpeg \n", - " inflating: AD_NC/test/NC/1215774_89.jpeg \n", - " inflating: AD_NC/test/NC/1215774_90.jpeg \n", - " inflating: AD_NC/test/NC/1215774_91.jpeg \n", - " inflating: AD_NC/test/NC/1215774_92.jpeg \n", - " inflating: AD_NC/test/NC/1215774_93.jpeg \n", - " inflating: AD_NC/test/NC/1215774_94.jpeg \n", - " inflating: AD_NC/test/NC/1215774_95.jpeg \n", - " inflating: AD_NC/test/NC/1215774_96.jpeg \n", - " inflating: AD_NC/test/NC/1215774_97.jpeg \n", - " inflating: AD_NC/test/NC/1215774_98.jpeg \n", - " inflating: AD_NC/test/NC/1215774_99.jpeg \n", - " inflating: AD_NC/test/NC/1219059_100.jpeg \n", - " inflating: AD_NC/test/NC/1219059_101.jpeg \n", - " inflating: AD_NC/test/NC/1219059_102.jpeg \n", - " inflating: AD_NC/test/NC/1219059_103.jpeg \n", - " inflating: AD_NC/test/NC/1219059_104.jpeg \n", - " inflating: AD_NC/test/NC/1219059_105.jpeg \n", - " inflating: AD_NC/test/NC/1219059_106.jpeg \n", - " inflating: AD_NC/test/NC/1219059_107.jpeg \n", - " inflating: AD_NC/test/NC/1219059_108.jpeg \n", - " inflating: AD_NC/test/NC/1219059_109.jpeg \n", - " inflating: AD_NC/test/NC/1219059_110.jpeg \n", - " inflating: AD_NC/test/NC/1219059_111.jpeg \n", - " inflating: AD_NC/test/NC/1219059_112.jpeg \n", - " inflating: AD_NC/test/NC/1219059_113.jpeg \n", - " inflating: AD_NC/test/NC/1219059_94.jpeg \n", - " inflating: AD_NC/test/NC/1219059_95.jpeg \n", - " inflating: AD_NC/test/NC/1219059_96.jpeg \n", - " inflating: AD_NC/test/NC/1219059_97.jpeg \n", - " inflating: AD_NC/test/NC/1219059_98.jpeg \n", - " inflating: AD_NC/test/NC/1219059_99.jpeg \n", - " inflating: AD_NC/test/NC/1219675_78.jpeg \n", - " inflating: AD_NC/test/NC/1219675_79.jpeg \n", - " inflating: AD_NC/test/NC/1219675_80.jpeg \n", - " inflating: AD_NC/test/NC/1219675_81.jpeg \n", - " inflating: AD_NC/test/NC/1219675_82.jpeg \n", - " inflating: AD_NC/test/NC/1219675_83.jpeg \n", - " inflating: AD_NC/test/NC/1219675_84.jpeg \n", - " inflating: AD_NC/test/NC/1219675_85.jpeg \n", - " inflating: AD_NC/test/NC/1219675_86.jpeg \n", - " inflating: AD_NC/test/NC/1219675_87.jpeg \n", - " inflating: AD_NC/test/NC/1219675_88.jpeg \n", - " inflating: AD_NC/test/NC/1219675_89.jpeg \n", - " inflating: AD_NC/test/NC/1219675_90.jpeg \n", - " inflating: AD_NC/test/NC/1219675_91.jpeg \n", - " inflating: AD_NC/test/NC/1219675_92.jpeg \n", - " inflating: AD_NC/test/NC/1219675_93.jpeg \n", - " inflating: AD_NC/test/NC/1219675_94.jpeg \n", - " inflating: AD_NC/test/NC/1219675_95.jpeg \n", - " inflating: AD_NC/test/NC/1219675_96.jpeg \n", - " inflating: AD_NC/test/NC/1219675_97.jpeg \n", - " inflating: AD_NC/test/NC/1220921_100.jpeg \n", - " inflating: AD_NC/test/NC/1220921_101.jpeg \n", - " inflating: AD_NC/test/NC/1220921_102.jpeg \n", - " inflating: AD_NC/test/NC/1220921_103.jpeg \n", - " inflating: AD_NC/test/NC/1220921_104.jpeg \n", - " inflating: AD_NC/test/NC/1220921_105.jpeg \n", - " inflating: AD_NC/test/NC/1220921_106.jpeg \n", - " inflating: AD_NC/test/NC/1220921_107.jpeg \n", - " inflating: AD_NC/test/NC/1220921_108.jpeg \n", - " inflating: AD_NC/test/NC/1220921_109.jpeg \n", - " inflating: AD_NC/test/NC/1220921_110.jpeg \n", - " inflating: AD_NC/test/NC/1220921_111.jpeg \n", - " inflating: AD_NC/test/NC/1220921_112.jpeg \n", - " inflating: AD_NC/test/NC/1220921_113.jpeg \n", - " inflating: AD_NC/test/NC/1220921_94.jpeg \n", - " inflating: AD_NC/test/NC/1220921_95.jpeg \n", - " inflating: AD_NC/test/NC/1220921_96.jpeg \n", - " inflating: AD_NC/test/NC/1220921_97.jpeg \n", - " inflating: AD_NC/test/NC/1220921_98.jpeg \n", - " inflating: AD_NC/test/NC/1220921_99.jpeg \n", - " inflating: AD_NC/test/NC/1221051_100.jpeg \n", - " inflating: AD_NC/test/NC/1221051_101.jpeg \n", - " inflating: AD_NC/test/NC/1221051_102.jpeg \n", - " inflating: AD_NC/test/NC/1221051_103.jpeg \n", - " inflating: AD_NC/test/NC/1221051_104.jpeg \n", - " inflating: AD_NC/test/NC/1221051_105.jpeg \n", - " inflating: AD_NC/test/NC/1221051_106.jpeg \n", - " inflating: AD_NC/test/NC/1221051_107.jpeg \n", - " inflating: AD_NC/test/NC/1221051_108.jpeg \n", - " inflating: AD_NC/test/NC/1221051_109.jpeg \n", - " inflating: AD_NC/test/NC/1221051_110.jpeg \n", - " inflating: AD_NC/test/NC/1221051_111.jpeg \n", - " inflating: AD_NC/test/NC/1221051_112.jpeg \n", - " inflating: AD_NC/test/NC/1221051_113.jpeg \n", - " inflating: AD_NC/test/NC/1221051_94.jpeg \n", - " inflating: AD_NC/test/NC/1221051_95.jpeg \n", - " inflating: AD_NC/test/NC/1221051_96.jpeg \n", - " inflating: AD_NC/test/NC/1221051_97.jpeg \n", - " inflating: AD_NC/test/NC/1221051_98.jpeg \n", - " inflating: AD_NC/test/NC/1221051_99.jpeg \n", - " inflating: AD_NC/test/NC/1221363_100.jpeg \n", - " inflating: AD_NC/test/NC/1221363_101.jpeg \n", - " inflating: AD_NC/test/NC/1221363_102.jpeg \n", - " inflating: AD_NC/test/NC/1221363_103.jpeg \n", - " inflating: AD_NC/test/NC/1221363_104.jpeg \n", - " inflating: AD_NC/test/NC/1221363_105.jpeg \n", - " inflating: AD_NC/test/NC/1221363_106.jpeg \n", - " inflating: AD_NC/test/NC/1221363_107.jpeg \n", - " inflating: AD_NC/test/NC/1221363_108.jpeg \n", - " inflating: AD_NC/test/NC/1221363_109.jpeg \n", - " inflating: AD_NC/test/NC/1221363_110.jpeg \n", - " inflating: AD_NC/test/NC/1221363_111.jpeg \n", - " inflating: AD_NC/test/NC/1221363_112.jpeg \n", - " inflating: AD_NC/test/NC/1221363_113.jpeg \n", - " inflating: AD_NC/test/NC/1221363_114.jpeg \n", - " inflating: AD_NC/test/NC/1221363_95.jpeg \n", - " inflating: AD_NC/test/NC/1221363_96.jpeg \n", - " inflating: AD_NC/test/NC/1221363_97.jpeg \n", - " inflating: AD_NC/test/NC/1221363_98.jpeg \n", - " inflating: AD_NC/test/NC/1221363_99.jpeg \n", - " inflating: AD_NC/test/NC/1221673_78.jpeg \n", - " inflating: AD_NC/test/NC/1221673_79.jpeg \n", - " inflating: AD_NC/test/NC/1221673_80.jpeg \n", - " inflating: AD_NC/test/NC/1221673_81.jpeg \n", - " inflating: AD_NC/test/NC/1221673_82.jpeg \n", - " inflating: AD_NC/test/NC/1221673_83.jpeg \n", - " inflating: AD_NC/test/NC/1221673_84.jpeg \n", - " inflating: AD_NC/test/NC/1221673_85.jpeg \n", - " inflating: AD_NC/test/NC/1221673_86.jpeg \n", - " inflating: AD_NC/test/NC/1221673_87.jpeg \n", - " inflating: AD_NC/test/NC/1221673_88.jpeg \n", - " inflating: AD_NC/test/NC/1221673_89.jpeg \n", - " inflating: AD_NC/test/NC/1221673_90.jpeg \n", - " inflating: AD_NC/test/NC/1221673_91.jpeg \n", - " inflating: AD_NC/test/NC/1221673_92.jpeg \n", - " inflating: AD_NC/test/NC/1221673_93.jpeg \n", - " inflating: AD_NC/test/NC/1221673_94.jpeg \n", - " inflating: AD_NC/test/NC/1221673_95.jpeg \n", - " inflating: AD_NC/test/NC/1221673_96.jpeg \n", - " inflating: AD_NC/test/NC/1221673_97.jpeg \n", - " inflating: AD_NC/test/NC/1221674_78.jpeg \n", - " inflating: AD_NC/test/NC/1221674_79.jpeg \n", - " inflating: AD_NC/test/NC/1221674_80.jpeg \n", - " inflating: AD_NC/test/NC/1221674_81.jpeg \n", - " inflating: AD_NC/test/NC/1221674_82.jpeg \n", - " inflating: AD_NC/test/NC/1221674_83.jpeg \n", - " inflating: AD_NC/test/NC/1221674_84.jpeg \n", - " inflating: AD_NC/test/NC/1221674_85.jpeg \n", - " inflating: AD_NC/test/NC/1221674_86.jpeg \n", - " inflating: AD_NC/test/NC/1221674_87.jpeg \n", - " inflating: AD_NC/test/NC/1221674_88.jpeg \n", - " inflating: AD_NC/test/NC/1221674_89.jpeg \n", - " inflating: AD_NC/test/NC/1221674_90.jpeg \n", - " inflating: AD_NC/test/NC/1221674_91.jpeg \n", - " inflating: AD_NC/test/NC/1221674_92.jpeg \n", - " inflating: AD_NC/test/NC/1221674_93.jpeg \n", - " inflating: AD_NC/test/NC/1221674_94.jpeg \n", - " inflating: AD_NC/test/NC/1221674_95.jpeg \n", - " inflating: AD_NC/test/NC/1221674_96.jpeg \n", - " inflating: AD_NC/test/NC/1221674_97.jpeg \n", - " inflating: AD_NC/test/NC/1224466_78.jpeg \n", - " inflating: AD_NC/test/NC/1224466_79.jpeg \n", - " inflating: AD_NC/test/NC/1224466_80.jpeg \n", - " inflating: AD_NC/test/NC/1224466_81.jpeg \n", - " inflating: AD_NC/test/NC/1224466_82.jpeg \n", - " inflating: AD_NC/test/NC/1224466_83.jpeg \n", - " inflating: AD_NC/test/NC/1224466_84.jpeg \n", - " inflating: AD_NC/test/NC/1224466_85.jpeg \n", - " inflating: AD_NC/test/NC/1224466_86.jpeg \n", - " inflating: AD_NC/test/NC/1224466_87.jpeg \n", - " inflating: AD_NC/test/NC/1224466_88.jpeg \n", - " inflating: AD_NC/test/NC/1224466_89.jpeg \n", - " inflating: AD_NC/test/NC/1224466_90.jpeg \n", - " inflating: AD_NC/test/NC/1224466_91.jpeg \n", - " inflating: AD_NC/test/NC/1224466_92.jpeg \n", - " inflating: AD_NC/test/NC/1224466_93.jpeg \n", - " inflating: AD_NC/test/NC/1224466_94.jpeg \n", - " inflating: AD_NC/test/NC/1224466_95.jpeg \n", - " inflating: AD_NC/test/NC/1224466_96.jpeg \n", - " inflating: AD_NC/test/NC/1224466_97.jpeg \n", - " inflating: AD_NC/test/NC/1224468_78.jpeg \n", - " inflating: AD_NC/test/NC/1224468_79.jpeg \n", - " inflating: AD_NC/test/NC/1224468_80.jpeg \n", - " inflating: AD_NC/test/NC/1224468_81.jpeg \n", - " inflating: AD_NC/test/NC/1224468_82.jpeg \n", - " inflating: AD_NC/test/NC/1224468_83.jpeg \n", - " inflating: AD_NC/test/NC/1224468_84.jpeg \n", - " inflating: AD_NC/test/NC/1224468_85.jpeg \n", - " inflating: AD_NC/test/NC/1224468_86.jpeg \n", - " inflating: AD_NC/test/NC/1224468_87.jpeg \n", - " inflating: AD_NC/test/NC/1224468_88.jpeg \n", - " inflating: AD_NC/test/NC/1224468_89.jpeg \n", - " inflating: AD_NC/test/NC/1224468_90.jpeg \n", - " inflating: AD_NC/test/NC/1224468_91.jpeg \n", - " inflating: AD_NC/test/NC/1224468_92.jpeg \n", - " inflating: AD_NC/test/NC/1224468_93.jpeg \n", - " inflating: AD_NC/test/NC/1224468_94.jpeg \n", - " inflating: AD_NC/test/NC/1224468_95.jpeg \n", - " inflating: AD_NC/test/NC/1224468_96.jpeg \n", - " inflating: AD_NC/test/NC/1224468_97.jpeg \n", - " inflating: AD_NC/test/NC/1225000_78.jpeg \n", - " inflating: AD_NC/test/NC/1225000_79.jpeg \n", - " inflating: AD_NC/test/NC/1225000_80.jpeg \n", - " inflating: AD_NC/test/NC/1225000_81.jpeg \n", - " inflating: AD_NC/test/NC/1225000_82.jpeg \n", - " inflating: AD_NC/test/NC/1225000_83.jpeg \n", - " inflating: AD_NC/test/NC/1225000_84.jpeg \n", - " inflating: AD_NC/test/NC/1225000_85.jpeg \n", - " inflating: AD_NC/test/NC/1225000_86.jpeg \n", - " inflating: AD_NC/test/NC/1225000_87.jpeg \n", - " inflating: AD_NC/test/NC/1225000_88.jpeg \n", - " inflating: AD_NC/test/NC/1225000_89.jpeg \n", - " inflating: AD_NC/test/NC/1225000_90.jpeg \n", - " inflating: AD_NC/test/NC/1225000_91.jpeg \n", - " inflating: AD_NC/test/NC/1225000_92.jpeg \n", - " inflating: AD_NC/test/NC/1225000_93.jpeg \n", - " inflating: AD_NC/test/NC/1225000_94.jpeg \n", - " inflating: AD_NC/test/NC/1225000_95.jpeg \n", - " inflating: AD_NC/test/NC/1225000_96.jpeg \n", - " inflating: AD_NC/test/NC/1225000_97.jpeg \n", - " inflating: AD_NC/test/NC/1225879_100.jpeg \n", - " inflating: AD_NC/test/NC/1225879_101.jpeg \n", - " inflating: AD_NC/test/NC/1225879_102.jpeg \n", - " inflating: AD_NC/test/NC/1225879_103.jpeg \n", - " inflating: AD_NC/test/NC/1225879_104.jpeg \n", - " inflating: AD_NC/test/NC/1225879_105.jpeg \n", - " inflating: AD_NC/test/NC/1225879_106.jpeg \n", - " inflating: AD_NC/test/NC/1225879_107.jpeg \n", - " inflating: AD_NC/test/NC/1225879_108.jpeg \n", - " inflating: AD_NC/test/NC/1225879_109.jpeg \n", - " inflating: AD_NC/test/NC/1225879_110.jpeg \n", - " inflating: AD_NC/test/NC/1225879_111.jpeg \n", - " inflating: AD_NC/test/NC/1225879_112.jpeg \n", - " inflating: AD_NC/test/NC/1225879_113.jpeg \n", - " inflating: AD_NC/test/NC/1225879_114.jpeg \n", - " inflating: AD_NC/test/NC/1225879_95.jpeg \n", - " inflating: AD_NC/test/NC/1225879_96.jpeg \n", - " inflating: AD_NC/test/NC/1225879_97.jpeg \n", - " inflating: AD_NC/test/NC/1225879_98.jpeg \n", - " inflating: AD_NC/test/NC/1225879_99.jpeg \n", - " inflating: AD_NC/test/NC/1225896_100.jpeg \n", - " inflating: AD_NC/test/NC/1225896_101.jpeg \n", - " inflating: AD_NC/test/NC/1225896_102.jpeg \n", - " inflating: AD_NC/test/NC/1225896_103.jpeg \n", - " inflating: AD_NC/test/NC/1225896_104.jpeg \n", - " inflating: AD_NC/test/NC/1225896_105.jpeg \n", - " inflating: AD_NC/test/NC/1225896_106.jpeg \n", - " inflating: AD_NC/test/NC/1225896_107.jpeg \n", - " inflating: AD_NC/test/NC/1225896_108.jpeg \n", - " inflating: AD_NC/test/NC/1225896_109.jpeg \n", - " inflating: AD_NC/test/NC/1225896_110.jpeg \n", - " inflating: AD_NC/test/NC/1225896_111.jpeg \n", - " inflating: AD_NC/test/NC/1225896_112.jpeg \n", - " inflating: AD_NC/test/NC/1225896_113.jpeg \n", - " inflating: AD_NC/test/NC/1225896_94.jpeg \n", - " inflating: AD_NC/test/NC/1225896_95.jpeg \n", - " inflating: AD_NC/test/NC/1225896_96.jpeg \n", - " inflating: AD_NC/test/NC/1225896_97.jpeg \n", - " inflating: AD_NC/test/NC/1225896_98.jpeg \n", - " inflating: AD_NC/test/NC/1225896_99.jpeg \n", - " inflating: AD_NC/test/NC/1225971_100.jpeg \n", - " inflating: AD_NC/test/NC/1225971_101.jpeg \n", - " inflating: AD_NC/test/NC/1225971_102.jpeg \n", - " inflating: AD_NC/test/NC/1225971_103.jpeg \n", - " inflating: AD_NC/test/NC/1225971_104.jpeg \n", - " inflating: AD_NC/test/NC/1225971_105.jpeg \n", - " inflating: AD_NC/test/NC/1225971_106.jpeg \n", - " inflating: AD_NC/test/NC/1225971_107.jpeg \n", - " inflating: AD_NC/test/NC/1225971_108.jpeg \n", - " inflating: AD_NC/test/NC/1225971_109.jpeg \n", - " inflating: AD_NC/test/NC/1225971_110.jpeg \n", - " inflating: AD_NC/test/NC/1225971_111.jpeg \n", - " inflating: AD_NC/test/NC/1225971_112.jpeg \n", - " inflating: AD_NC/test/NC/1225971_113.jpeg \n", - " inflating: AD_NC/test/NC/1225971_94.jpeg \n", - " inflating: AD_NC/test/NC/1225971_95.jpeg \n", - " inflating: AD_NC/test/NC/1225971_96.jpeg \n", - " inflating: AD_NC/test/NC/1225971_97.jpeg \n", - " inflating: AD_NC/test/NC/1225971_98.jpeg \n", - " inflating: AD_NC/test/NC/1225971_99.jpeg \n", - " inflating: AD_NC/test/NC/1226456_78.jpeg \n", - " inflating: AD_NC/test/NC/1226456_79.jpeg \n", - " inflating: AD_NC/test/NC/1226456_80.jpeg \n", - " inflating: AD_NC/test/NC/1226456_81.jpeg \n", - " inflating: AD_NC/test/NC/1226456_82.jpeg \n", - " inflating: AD_NC/test/NC/1226456_83.jpeg \n", - " inflating: AD_NC/test/NC/1226456_84.jpeg \n", - " inflating: AD_NC/test/NC/1226456_85.jpeg \n", - " inflating: AD_NC/test/NC/1226456_86.jpeg \n", - " inflating: AD_NC/test/NC/1226456_87.jpeg \n", - " inflating: AD_NC/test/NC/1226456_88.jpeg \n", - " inflating: AD_NC/test/NC/1226456_89.jpeg \n", - " inflating: AD_NC/test/NC/1226456_90.jpeg \n", - " inflating: AD_NC/test/NC/1226456_91.jpeg \n", - " inflating: AD_NC/test/NC/1226456_92.jpeg \n", - " inflating: AD_NC/test/NC/1226456_93.jpeg \n", - " inflating: AD_NC/test/NC/1226456_94.jpeg \n", - " inflating: AD_NC/test/NC/1226456_95.jpeg \n", - " inflating: AD_NC/test/NC/1226456_96.jpeg \n", - " inflating: AD_NC/test/NC/1226456_97.jpeg \n", - " inflating: AD_NC/test/NC/1226457_78.jpeg \n", - " inflating: AD_NC/test/NC/1226457_79.jpeg \n", - " inflating: AD_NC/test/NC/1226457_80.jpeg \n", - " inflating: AD_NC/test/NC/1226457_81.jpeg \n", - " inflating: AD_NC/test/NC/1226457_82.jpeg \n", - " inflating: AD_NC/test/NC/1226457_83.jpeg \n", - " inflating: AD_NC/test/NC/1226457_84.jpeg \n", - " inflating: AD_NC/test/NC/1226457_85.jpeg \n", - " inflating: AD_NC/test/NC/1226457_86.jpeg \n", - " inflating: AD_NC/test/NC/1226457_87.jpeg \n", - " inflating: AD_NC/test/NC/1226457_88.jpeg \n", - " inflating: AD_NC/test/NC/1226457_89.jpeg \n", - " inflating: AD_NC/test/NC/1226457_90.jpeg \n", - " inflating: AD_NC/test/NC/1226457_91.jpeg \n", - " inflating: AD_NC/test/NC/1226457_92.jpeg \n", - " inflating: AD_NC/test/NC/1226457_93.jpeg \n", - " inflating: AD_NC/test/NC/1226457_94.jpeg \n", - " inflating: AD_NC/test/NC/1226457_95.jpeg \n", - " inflating: AD_NC/test/NC/1226457_96.jpeg \n", - " inflating: AD_NC/test/NC/1226457_97.jpeg \n", - " inflating: AD_NC/test/NC/1226508_78.jpeg \n", - " inflating: AD_NC/test/NC/1226508_79.jpeg \n", - " inflating: AD_NC/test/NC/1226508_80.jpeg \n", - " inflating: AD_NC/test/NC/1226508_81.jpeg \n", - " inflating: AD_NC/test/NC/1226508_82.jpeg \n", - " inflating: AD_NC/test/NC/1226508_83.jpeg \n", - " inflating: AD_NC/test/NC/1226508_84.jpeg \n", - " inflating: AD_NC/test/NC/1226508_85.jpeg \n", - " inflating: AD_NC/test/NC/1226508_86.jpeg \n", - " inflating: AD_NC/test/NC/1226508_87.jpeg \n", - " inflating: AD_NC/test/NC/1226508_88.jpeg \n", - " inflating: AD_NC/test/NC/1226508_89.jpeg \n", - " inflating: AD_NC/test/NC/1226508_90.jpeg \n", - " inflating: AD_NC/test/NC/1226508_91.jpeg \n", - " inflating: AD_NC/test/NC/1226508_92.jpeg \n", - " inflating: AD_NC/test/NC/1226508_93.jpeg \n", - " inflating: AD_NC/test/NC/1226508_94.jpeg \n", - " inflating: AD_NC/test/NC/1226508_95.jpeg \n", - " inflating: AD_NC/test/NC/1226508_96.jpeg \n", - " inflating: AD_NC/test/NC/1226508_97.jpeg \n", - " inflating: AD_NC/test/NC/1226810_100.jpeg \n", - " inflating: AD_NC/test/NC/1226810_101.jpeg \n", - " inflating: AD_NC/test/NC/1226810_102.jpeg \n", - " inflating: AD_NC/test/NC/1226810_103.jpeg \n", - " inflating: AD_NC/test/NC/1226810_104.jpeg \n", - " inflating: AD_NC/test/NC/1226810_105.jpeg \n", - " inflating: AD_NC/test/NC/1226810_106.jpeg \n", - " inflating: AD_NC/test/NC/1226810_107.jpeg \n", - " inflating: AD_NC/test/NC/1226810_108.jpeg \n", - " inflating: AD_NC/test/NC/1226810_109.jpeg \n", - " inflating: AD_NC/test/NC/1226810_110.jpeg \n", - " inflating: AD_NC/test/NC/1226810_111.jpeg \n", - " inflating: AD_NC/test/NC/1226810_112.jpeg \n", - " inflating: AD_NC/test/NC/1226810_113.jpeg \n", - " inflating: AD_NC/test/NC/1226810_94.jpeg \n", - " inflating: AD_NC/test/NC/1226810_95.jpeg \n", - " inflating: AD_NC/test/NC/1226810_96.jpeg \n", - " inflating: AD_NC/test/NC/1226810_97.jpeg \n", - " inflating: AD_NC/test/NC/1226810_98.jpeg \n", - " inflating: AD_NC/test/NC/1226810_99.jpeg \n", - " inflating: AD_NC/test/NC/1227239_100.jpeg \n", - " inflating: AD_NC/test/NC/1227239_101.jpeg \n", - " inflating: AD_NC/test/NC/1227239_102.jpeg \n", - " inflating: AD_NC/test/NC/1227239_103.jpeg \n", - " inflating: AD_NC/test/NC/1227239_104.jpeg \n", - " inflating: AD_NC/test/NC/1227239_105.jpeg \n", - " inflating: AD_NC/test/NC/1227239_106.jpeg \n", - " inflating: AD_NC/test/NC/1227239_107.jpeg \n", - " inflating: AD_NC/test/NC/1227239_108.jpeg \n", - " inflating: AD_NC/test/NC/1227239_109.jpeg \n", - " inflating: AD_NC/test/NC/1227239_110.jpeg \n", - " inflating: AD_NC/test/NC/1227239_111.jpeg \n", - " inflating: AD_NC/test/NC/1227239_112.jpeg \n", - " inflating: AD_NC/test/NC/1227239_113.jpeg \n", - " inflating: AD_NC/test/NC/1227239_94.jpeg \n", - " inflating: AD_NC/test/NC/1227239_95.jpeg \n", - " inflating: AD_NC/test/NC/1227239_96.jpeg \n", - " inflating: AD_NC/test/NC/1227239_97.jpeg \n", - " inflating: AD_NC/test/NC/1227239_98.jpeg \n", - " inflating: AD_NC/test/NC/1227239_99.jpeg \n", - " inflating: AD_NC/test/NC/1229284_100.jpeg \n", - " inflating: AD_NC/test/NC/1229284_101.jpeg \n", - " inflating: AD_NC/test/NC/1229284_102.jpeg \n", - " inflating: AD_NC/test/NC/1229284_103.jpeg \n", - " inflating: AD_NC/test/NC/1229284_104.jpeg \n", - " inflating: AD_NC/test/NC/1229284_105.jpeg \n", - " inflating: AD_NC/test/NC/1229284_106.jpeg \n", - " inflating: AD_NC/test/NC/1229284_107.jpeg \n", - " inflating: AD_NC/test/NC/1229284_108.jpeg \n", - " inflating: AD_NC/test/NC/1229284_89.jpeg \n", - " inflating: AD_NC/test/NC/1229284_90.jpeg \n", - " inflating: AD_NC/test/NC/1229284_91.jpeg \n", - " inflating: AD_NC/test/NC/1229284_92.jpeg \n", - " inflating: AD_NC/test/NC/1229284_93.jpeg \n", - " inflating: AD_NC/test/NC/1229284_94.jpeg \n", - " inflating: AD_NC/test/NC/1229284_95.jpeg \n", - " inflating: AD_NC/test/NC/1229284_96.jpeg \n", - " inflating: AD_NC/test/NC/1229284_97.jpeg \n", - " inflating: AD_NC/test/NC/1229284_98.jpeg \n", - " inflating: AD_NC/test/NC/1229284_99.jpeg \n", - " inflating: AD_NC/test/NC/1229457_78.jpeg \n", - " inflating: AD_NC/test/NC/1229457_79.jpeg \n", - " inflating: AD_NC/test/NC/1229457_80.jpeg \n", - " inflating: AD_NC/test/NC/1229457_81.jpeg \n", - " inflating: AD_NC/test/NC/1229457_82.jpeg \n", - " inflating: AD_NC/test/NC/1229457_83.jpeg \n", - " inflating: AD_NC/test/NC/1229457_84.jpeg \n", - " inflating: AD_NC/test/NC/1229457_85.jpeg \n", - " inflating: AD_NC/test/NC/1229457_86.jpeg \n", - " inflating: AD_NC/test/NC/1229457_87.jpeg \n", - " inflating: AD_NC/test/NC/1229457_88.jpeg \n", - " inflating: AD_NC/test/NC/1229457_89.jpeg \n", - " inflating: AD_NC/test/NC/1229457_90.jpeg \n", - " inflating: AD_NC/test/NC/1229457_91.jpeg \n", - " inflating: AD_NC/test/NC/1229457_92.jpeg \n", - " inflating: AD_NC/test/NC/1229457_93.jpeg \n", - " inflating: AD_NC/test/NC/1229457_94.jpeg \n", - " inflating: AD_NC/test/NC/1229457_95.jpeg \n", - " inflating: AD_NC/test/NC/1229457_96.jpeg \n", - " inflating: AD_NC/test/NC/1229457_97.jpeg \n", - " inflating: AD_NC/test/NC/1229464_78.jpeg \n", - " inflating: AD_NC/test/NC/1229464_79.jpeg \n", - " inflating: AD_NC/test/NC/1229464_80.jpeg \n", - " inflating: AD_NC/test/NC/1229464_81.jpeg \n", - " inflating: AD_NC/test/NC/1229464_82.jpeg \n", - " inflating: AD_NC/test/NC/1229464_83.jpeg \n", - " inflating: AD_NC/test/NC/1229464_84.jpeg \n", - " inflating: AD_NC/test/NC/1229464_85.jpeg \n", - " inflating: AD_NC/test/NC/1229464_86.jpeg \n", - " inflating: AD_NC/test/NC/1229464_87.jpeg \n", - " inflating: AD_NC/test/NC/1229464_88.jpeg \n", - " inflating: AD_NC/test/NC/1229464_89.jpeg \n", - " inflating: AD_NC/test/NC/1229464_90.jpeg \n", - " inflating: AD_NC/test/NC/1229464_91.jpeg \n", - " inflating: AD_NC/test/NC/1229464_92.jpeg \n", - " inflating: AD_NC/test/NC/1229464_93.jpeg \n", - " inflating: AD_NC/test/NC/1229464_94.jpeg \n", - " inflating: AD_NC/test/NC/1229464_95.jpeg \n", - " inflating: AD_NC/test/NC/1229464_96.jpeg \n", - " inflating: AD_NC/test/NC/1229464_97.jpeg \n", - " inflating: AD_NC/test/NC/1229529_100.jpeg \n", - " inflating: AD_NC/test/NC/1229529_101.jpeg \n", - " inflating: AD_NC/test/NC/1229529_102.jpeg \n", - " inflating: AD_NC/test/NC/1229529_103.jpeg \n", - " inflating: AD_NC/test/NC/1229529_104.jpeg \n", - " inflating: AD_NC/test/NC/1229529_105.jpeg \n", - " inflating: AD_NC/test/NC/1229529_106.jpeg \n", - " inflating: AD_NC/test/NC/1229529_107.jpeg \n", - " inflating: AD_NC/test/NC/1229529_108.jpeg \n", - " inflating: AD_NC/test/NC/1229529_109.jpeg \n", - " inflating: AD_NC/test/NC/1229529_110.jpeg \n", - " inflating: AD_NC/test/NC/1229529_111.jpeg \n", - " inflating: AD_NC/test/NC/1229529_112.jpeg \n", - " inflating: AD_NC/test/NC/1229529_113.jpeg \n", - " inflating: AD_NC/test/NC/1229529_94.jpeg \n", - " inflating: AD_NC/test/NC/1229529_95.jpeg \n", - " inflating: AD_NC/test/NC/1229529_96.jpeg \n", - " inflating: AD_NC/test/NC/1229529_97.jpeg \n", - " inflating: AD_NC/test/NC/1229529_98.jpeg \n", - " inflating: AD_NC/test/NC/1229529_99.jpeg \n", - " inflating: AD_NC/test/NC/1235535_100.jpeg \n", - " inflating: AD_NC/test/NC/1235535_101.jpeg \n", - " inflating: AD_NC/test/NC/1235535_102.jpeg \n", - " inflating: AD_NC/test/NC/1235535_103.jpeg \n", - " inflating: AD_NC/test/NC/1235535_104.jpeg \n", - " inflating: AD_NC/test/NC/1235535_105.jpeg \n", - " inflating: AD_NC/test/NC/1235535_106.jpeg \n", - " inflating: AD_NC/test/NC/1235535_107.jpeg \n", - " inflating: AD_NC/test/NC/1235535_108.jpeg \n", - " inflating: AD_NC/test/NC/1235535_109.jpeg \n", - " inflating: AD_NC/test/NC/1235535_110.jpeg \n", - " inflating: AD_NC/test/NC/1235535_111.jpeg \n", - " inflating: AD_NC/test/NC/1235535_112.jpeg \n", - " inflating: AD_NC/test/NC/1235535_113.jpeg \n", - " inflating: AD_NC/test/NC/1235535_94.jpeg \n", - " inflating: AD_NC/test/NC/1235535_95.jpeg \n", - " inflating: AD_NC/test/NC/1235535_96.jpeg \n", - " inflating: AD_NC/test/NC/1235535_97.jpeg \n", - " inflating: AD_NC/test/NC/1235535_98.jpeg \n", - " inflating: AD_NC/test/NC/1235535_99.jpeg \n", - " inflating: AD_NC/test/NC/1236085_100.jpeg \n", - " inflating: AD_NC/test/NC/1236085_101.jpeg \n", - " inflating: AD_NC/test/NC/1236085_102.jpeg \n", - " inflating: AD_NC/test/NC/1236085_103.jpeg \n", - " inflating: AD_NC/test/NC/1236085_104.jpeg \n", - " inflating: AD_NC/test/NC/1236085_105.jpeg \n", - " inflating: AD_NC/test/NC/1236085_106.jpeg \n", - " inflating: AD_NC/test/NC/1236085_107.jpeg \n", - " inflating: AD_NC/test/NC/1236085_108.jpeg \n", - " inflating: AD_NC/test/NC/1236085_109.jpeg \n", - " inflating: AD_NC/test/NC/1236085_110.jpeg \n", - " inflating: AD_NC/test/NC/1236085_111.jpeg \n", - " inflating: AD_NC/test/NC/1236085_112.jpeg \n", - " inflating: AD_NC/test/NC/1236085_113.jpeg \n", - " inflating: AD_NC/test/NC/1236085_94.jpeg \n", - " inflating: AD_NC/test/NC/1236085_95.jpeg \n", - " inflating: AD_NC/test/NC/1236085_96.jpeg \n", - " inflating: AD_NC/test/NC/1236085_97.jpeg \n", - " inflating: AD_NC/test/NC/1236085_98.jpeg \n", - " inflating: AD_NC/test/NC/1236085_99.jpeg \n", - " inflating: AD_NC/test/NC/1236425_100.jpeg \n", - " inflating: AD_NC/test/NC/1236425_101.jpeg \n", - " inflating: AD_NC/test/NC/1236425_102.jpeg \n", - " inflating: AD_NC/test/NC/1236425_103.jpeg \n", - " inflating: AD_NC/test/NC/1236425_104.jpeg \n", - " inflating: AD_NC/test/NC/1236425_105.jpeg \n", - " inflating: AD_NC/test/NC/1236425_106.jpeg \n", - " inflating: AD_NC/test/NC/1236425_107.jpeg \n", - " inflating: AD_NC/test/NC/1236425_108.jpeg \n", - " inflating: AD_NC/test/NC/1236425_109.jpeg \n", - " inflating: AD_NC/test/NC/1236425_110.jpeg \n", - " inflating: AD_NC/test/NC/1236425_111.jpeg \n", - " inflating: AD_NC/test/NC/1236425_112.jpeg \n", - " inflating: AD_NC/test/NC/1236425_113.jpeg \n", - " inflating: AD_NC/test/NC/1236425_114.jpeg \n", - " inflating: AD_NC/test/NC/1236425_95.jpeg \n", - " inflating: AD_NC/test/NC/1236425_96.jpeg \n", - " inflating: AD_NC/test/NC/1236425_97.jpeg \n", - " inflating: AD_NC/test/NC/1236425_98.jpeg \n", - " inflating: AD_NC/test/NC/1236425_99.jpeg \n", - " inflating: AD_NC/test/NC/1236679_100.jpeg \n", - " inflating: AD_NC/test/NC/1236679_101.jpeg \n", - " inflating: AD_NC/test/NC/1236679_102.jpeg \n", - " inflating: AD_NC/test/NC/1236679_103.jpeg \n", - " inflating: AD_NC/test/NC/1236679_104.jpeg \n", - " inflating: AD_NC/test/NC/1236679_105.jpeg \n", - " inflating: AD_NC/test/NC/1236679_106.jpeg \n", - " inflating: AD_NC/test/NC/1236679_107.jpeg \n", - " inflating: AD_NC/test/NC/1236679_108.jpeg \n", - " inflating: AD_NC/test/NC/1236679_109.jpeg \n", - " inflating: AD_NC/test/NC/1236679_110.jpeg \n", - " inflating: AD_NC/test/NC/1236679_111.jpeg \n", - " inflating: AD_NC/test/NC/1236679_112.jpeg \n", - " inflating: AD_NC/test/NC/1236679_113.jpeg \n", - " inflating: AD_NC/test/NC/1236679_94.jpeg \n", - " inflating: AD_NC/test/NC/1236679_95.jpeg \n", - " inflating: AD_NC/test/NC/1236679_96.jpeg \n", - " inflating: AD_NC/test/NC/1236679_97.jpeg \n", - " inflating: AD_NC/test/NC/1236679_98.jpeg \n", - " inflating: AD_NC/test/NC/1236679_99.jpeg \n", - " inflating: AD_NC/test/NC/1236721_100.jpeg \n", - " inflating: AD_NC/test/NC/1236721_101.jpeg \n", - " inflating: AD_NC/test/NC/1236721_102.jpeg \n", - " inflating: AD_NC/test/NC/1236721_103.jpeg \n", - " inflating: AD_NC/test/NC/1236721_104.jpeg \n", - " inflating: AD_NC/test/NC/1236721_105.jpeg \n", - " inflating: AD_NC/test/NC/1236721_106.jpeg \n", - " inflating: AD_NC/test/NC/1236721_107.jpeg \n", - " inflating: AD_NC/test/NC/1236721_108.jpeg \n", - " inflating: AD_NC/test/NC/1236721_109.jpeg \n", - " inflating: AD_NC/test/NC/1236721_110.jpeg \n", - " inflating: AD_NC/test/NC/1236721_111.jpeg \n", - " inflating: AD_NC/test/NC/1236721_112.jpeg \n", - " inflating: AD_NC/test/NC/1236721_113.jpeg \n", - " inflating: AD_NC/test/NC/1236721_114.jpeg \n", - " inflating: AD_NC/test/NC/1236721_95.jpeg \n", - " inflating: AD_NC/test/NC/1236721_96.jpeg \n", - " inflating: AD_NC/test/NC/1236721_97.jpeg \n", - " inflating: AD_NC/test/NC/1236721_98.jpeg \n", - " inflating: AD_NC/test/NC/1236721_99.jpeg \n", - " inflating: AD_NC/test/NC/1237590_100.jpeg \n", - " inflating: AD_NC/test/NC/1237590_101.jpeg \n", - " inflating: AD_NC/test/NC/1237590_102.jpeg \n", - " inflating: AD_NC/test/NC/1237590_103.jpeg \n", - " inflating: AD_NC/test/NC/1237590_104.jpeg \n", - " inflating: AD_NC/test/NC/1237590_105.jpeg \n", - " inflating: AD_NC/test/NC/1237590_106.jpeg \n", - " inflating: AD_NC/test/NC/1237590_107.jpeg \n", - " inflating: AD_NC/test/NC/1237590_108.jpeg \n", - " inflating: AD_NC/test/NC/1237590_109.jpeg \n", - " inflating: AD_NC/test/NC/1237590_110.jpeg \n", - " inflating: AD_NC/test/NC/1237590_111.jpeg \n", - " inflating: AD_NC/test/NC/1237590_112.jpeg \n", - " inflating: AD_NC/test/NC/1237590_113.jpeg \n", - " inflating: AD_NC/test/NC/1237590_94.jpeg \n", - " inflating: AD_NC/test/NC/1237590_95.jpeg \n", - " inflating: AD_NC/test/NC/1237590_96.jpeg \n", - " inflating: AD_NC/test/NC/1237590_97.jpeg \n", - " inflating: AD_NC/test/NC/1237590_98.jpeg \n", - " inflating: AD_NC/test/NC/1237590_99.jpeg \n", - " inflating: AD_NC/test/NC/1237740_100.jpeg \n", - " inflating: AD_NC/test/NC/1237740_101.jpeg \n", - " inflating: AD_NC/test/NC/1237740_102.jpeg \n", - " inflating: AD_NC/test/NC/1237740_103.jpeg \n", - " inflating: AD_NC/test/NC/1237740_104.jpeg \n", - " inflating: AD_NC/test/NC/1237740_105.jpeg \n", - " inflating: AD_NC/test/NC/1237740_106.jpeg \n", - " inflating: AD_NC/test/NC/1237740_107.jpeg \n", - " inflating: AD_NC/test/NC/1237740_108.jpeg \n", - " inflating: AD_NC/test/NC/1237740_109.jpeg \n", - " inflating: AD_NC/test/NC/1237740_110.jpeg \n", - " inflating: AD_NC/test/NC/1237740_111.jpeg \n", - " inflating: AD_NC/test/NC/1237740_112.jpeg \n", - " inflating: AD_NC/test/NC/1237740_113.jpeg \n", - " inflating: AD_NC/test/NC/1237740_94.jpeg \n", - " inflating: AD_NC/test/NC/1237740_95.jpeg \n", - " inflating: AD_NC/test/NC/1237740_96.jpeg \n", - " inflating: AD_NC/test/NC/1237740_97.jpeg \n", - " inflating: AD_NC/test/NC/1237740_98.jpeg \n", - " inflating: AD_NC/test/NC/1237740_99.jpeg \n", - " inflating: AD_NC/test/NC/1241179_78.jpeg \n", - " inflating: AD_NC/test/NC/1241179_79.jpeg \n", - " inflating: AD_NC/test/NC/1241179_80.jpeg \n", - " inflating: AD_NC/test/NC/1241179_81.jpeg \n", - " inflating: AD_NC/test/NC/1241179_82.jpeg \n", - " inflating: AD_NC/test/NC/1241179_83.jpeg \n", - " inflating: AD_NC/test/NC/1241179_84.jpeg \n", - " inflating: AD_NC/test/NC/1241179_85.jpeg \n", - " inflating: AD_NC/test/NC/1241179_86.jpeg \n", - " inflating: AD_NC/test/NC/1241179_87.jpeg \n", - " inflating: AD_NC/test/NC/1241179_88.jpeg \n", - " inflating: AD_NC/test/NC/1241179_89.jpeg \n", - " inflating: AD_NC/test/NC/1241179_90.jpeg \n", - " inflating: AD_NC/test/NC/1241179_91.jpeg \n", - " inflating: AD_NC/test/NC/1241179_92.jpeg \n", - " inflating: AD_NC/test/NC/1241179_93.jpeg \n", - " inflating: AD_NC/test/NC/1241179_94.jpeg \n", - " inflating: AD_NC/test/NC/1241179_95.jpeg \n", - " inflating: AD_NC/test/NC/1241179_96.jpeg \n", - " inflating: AD_NC/test/NC/1241179_97.jpeg \n", - " inflating: AD_NC/test/NC/1241191_100.jpeg \n", - " inflating: AD_NC/test/NC/1241191_101.jpeg \n", - " inflating: AD_NC/test/NC/1241191_102.jpeg \n", - " inflating: AD_NC/test/NC/1241191_103.jpeg \n", - " inflating: AD_NC/test/NC/1241191_104.jpeg \n", - " inflating: AD_NC/test/NC/1241191_105.jpeg \n", - " inflating: AD_NC/test/NC/1241191_106.jpeg \n", - " inflating: AD_NC/test/NC/1241191_107.jpeg \n", - " inflating: AD_NC/test/NC/1241191_108.jpeg \n", - " inflating: AD_NC/test/NC/1241191_109.jpeg \n", - " inflating: AD_NC/test/NC/1241191_110.jpeg \n", - " inflating: AD_NC/test/NC/1241191_111.jpeg \n", - " inflating: AD_NC/test/NC/1241191_112.jpeg \n", - " inflating: AD_NC/test/NC/1241191_113.jpeg \n", - " inflating: AD_NC/test/NC/1241191_114.jpeg \n", - " inflating: AD_NC/test/NC/1241191_95.jpeg \n", - " inflating: AD_NC/test/NC/1241191_96.jpeg \n", - " inflating: AD_NC/test/NC/1241191_97.jpeg \n", - " inflating: AD_NC/test/NC/1241191_98.jpeg \n", - " inflating: AD_NC/test/NC/1241191_99.jpeg \n", - " inflating: AD_NC/test/NC/1243833_78.jpeg \n", - " inflating: AD_NC/test/NC/1243833_79.jpeg \n", - " inflating: AD_NC/test/NC/1243833_80.jpeg \n", - " inflating: AD_NC/test/NC/1243833_81.jpeg \n", - " inflating: AD_NC/test/NC/1243833_82.jpeg \n", - " inflating: AD_NC/test/NC/1243833_83.jpeg \n", - " inflating: AD_NC/test/NC/1243833_84.jpeg \n", - " inflating: AD_NC/test/NC/1243833_85.jpeg \n", - " inflating: AD_NC/test/NC/1243833_86.jpeg \n", - " inflating: AD_NC/test/NC/1243833_87.jpeg \n", - " inflating: AD_NC/test/NC/1243833_88.jpeg \n", - " inflating: AD_NC/test/NC/1243833_89.jpeg \n", - " inflating: AD_NC/test/NC/1243833_90.jpeg \n", - " inflating: AD_NC/test/NC/1243833_91.jpeg \n", - " inflating: AD_NC/test/NC/1243833_92.jpeg \n", - " inflating: AD_NC/test/NC/1243833_93.jpeg \n", - " inflating: AD_NC/test/NC/1243833_94.jpeg \n", - " inflating: AD_NC/test/NC/1243833_95.jpeg \n", - " inflating: AD_NC/test/NC/1243833_96.jpeg \n", - " inflating: AD_NC/test/NC/1243833_97.jpeg \n", - " inflating: AD_NC/test/NC/1244529_100.jpeg \n", - " inflating: AD_NC/test/NC/1244529_101.jpeg \n", - " inflating: AD_NC/test/NC/1244529_102.jpeg \n", - " inflating: AD_NC/test/NC/1244529_103.jpeg \n", - " inflating: AD_NC/test/NC/1244529_104.jpeg \n", - " inflating: AD_NC/test/NC/1244529_105.jpeg \n", - " inflating: AD_NC/test/NC/1244529_106.jpeg \n", - " inflating: AD_NC/test/NC/1244529_107.jpeg \n", - " inflating: AD_NC/test/NC/1244529_108.jpeg \n", - " inflating: AD_NC/test/NC/1244529_109.jpeg \n", - " inflating: AD_NC/test/NC/1244529_110.jpeg \n", - " inflating: AD_NC/test/NC/1244529_111.jpeg \n", - " inflating: AD_NC/test/NC/1244529_112.jpeg \n", - " inflating: AD_NC/test/NC/1244529_113.jpeg \n", - " inflating: AD_NC/test/NC/1244529_94.jpeg \n", - " inflating: AD_NC/test/NC/1244529_95.jpeg \n", - " inflating: AD_NC/test/NC/1244529_96.jpeg \n", - " inflating: AD_NC/test/NC/1244529_97.jpeg \n", - " inflating: AD_NC/test/NC/1244529_98.jpeg \n", - " inflating: AD_NC/test/NC/1244529_99.jpeg \n", - " inflating: AD_NC/test/NC/1245611_100.jpeg \n", - " inflating: AD_NC/test/NC/1245611_101.jpeg \n", - " inflating: AD_NC/test/NC/1245611_102.jpeg \n", - " inflating: AD_NC/test/NC/1245611_103.jpeg \n", - " inflating: AD_NC/test/NC/1245611_104.jpeg \n", - " inflating: AD_NC/test/NC/1245611_105.jpeg \n", - " inflating: AD_NC/test/NC/1245611_106.jpeg \n", - " inflating: AD_NC/test/NC/1245611_107.jpeg \n", - " inflating: AD_NC/test/NC/1245611_108.jpeg \n", - " inflating: AD_NC/test/NC/1245611_109.jpeg \n", - " inflating: AD_NC/test/NC/1245611_110.jpeg \n", - " inflating: AD_NC/test/NC/1245611_111.jpeg \n", - " inflating: AD_NC/test/NC/1245611_112.jpeg \n", - " inflating: AD_NC/test/NC/1245611_113.jpeg \n", - " inflating: AD_NC/test/NC/1245611_114.jpeg \n", - " inflating: AD_NC/test/NC/1245611_95.jpeg \n", - " inflating: AD_NC/test/NC/1245611_96.jpeg \n", - " inflating: AD_NC/test/NC/1245611_97.jpeg \n", - " inflating: AD_NC/test/NC/1245611_98.jpeg \n", - " inflating: AD_NC/test/NC/1245611_99.jpeg \n", - " inflating: AD_NC/test/NC/1246441_100.jpeg \n", - " inflating: AD_NC/test/NC/1246441_101.jpeg \n", - " inflating: AD_NC/test/NC/1246441_102.jpeg \n", - " inflating: AD_NC/test/NC/1246441_103.jpeg \n", - " inflating: AD_NC/test/NC/1246441_104.jpeg \n", - " inflating: AD_NC/test/NC/1246441_105.jpeg \n", - " inflating: AD_NC/test/NC/1246441_106.jpeg \n", - " inflating: AD_NC/test/NC/1246441_107.jpeg \n", - " inflating: AD_NC/test/NC/1246441_108.jpeg \n", - " inflating: AD_NC/test/NC/1246441_109.jpeg \n", - " inflating: AD_NC/test/NC/1246441_110.jpeg \n", - " inflating: AD_NC/test/NC/1246441_111.jpeg \n", - " inflating: AD_NC/test/NC/1246441_112.jpeg \n", - " inflating: AD_NC/test/NC/1246441_113.jpeg \n", - " inflating: AD_NC/test/NC/1246441_94.jpeg \n", - " inflating: AD_NC/test/NC/1246441_95.jpeg \n", - " inflating: AD_NC/test/NC/1246441_96.jpeg \n", - " inflating: AD_NC/test/NC/1246441_97.jpeg \n", - " inflating: AD_NC/test/NC/1246441_98.jpeg \n", - " inflating: AD_NC/test/NC/1246441_99.jpeg \n", - " inflating: AD_NC/test/NC/1250826_100.jpeg \n", - " inflating: AD_NC/test/NC/1250826_101.jpeg \n", - " inflating: AD_NC/test/NC/1250826_102.jpeg \n", - " inflating: AD_NC/test/NC/1250826_103.jpeg \n", - " inflating: AD_NC/test/NC/1250826_104.jpeg \n", - " inflating: AD_NC/test/NC/1250826_105.jpeg \n", - " inflating: AD_NC/test/NC/1250826_106.jpeg \n", - " inflating: AD_NC/test/NC/1250826_107.jpeg \n", - " inflating: AD_NC/test/NC/1250826_108.jpeg \n", - " inflating: AD_NC/test/NC/1250826_109.jpeg \n", - " inflating: AD_NC/test/NC/1250826_110.jpeg \n", - " inflating: AD_NC/test/NC/1250826_111.jpeg \n", - " inflating: AD_NC/test/NC/1250826_112.jpeg \n", - " inflating: AD_NC/test/NC/1250826_113.jpeg \n", - " inflating: AD_NC/test/NC/1250826_94.jpeg \n", - " inflating: AD_NC/test/NC/1250826_95.jpeg \n", - " inflating: AD_NC/test/NC/1250826_96.jpeg \n", - " inflating: AD_NC/test/NC/1250826_97.jpeg \n", - " inflating: AD_NC/test/NC/1250826_98.jpeg \n", - " inflating: AD_NC/test/NC/1250826_99.jpeg \n", - " inflating: AD_NC/test/NC/1251421_100.jpeg \n", - " inflating: AD_NC/test/NC/1251421_101.jpeg \n", - " inflating: AD_NC/test/NC/1251421_102.jpeg \n", - " inflating: AD_NC/test/NC/1251421_103.jpeg \n", - " inflating: AD_NC/test/NC/1251421_104.jpeg \n", - " inflating: AD_NC/test/NC/1251421_105.jpeg \n", - " inflating: AD_NC/test/NC/1251421_106.jpeg \n", - " inflating: AD_NC/test/NC/1251421_107.jpeg \n", - " inflating: AD_NC/test/NC/1251421_108.jpeg \n", - " inflating: AD_NC/test/NC/1251421_109.jpeg \n", - " inflating: AD_NC/test/NC/1251421_110.jpeg \n", - " inflating: AD_NC/test/NC/1251421_111.jpeg \n", - " inflating: AD_NC/test/NC/1251421_112.jpeg \n", - " inflating: AD_NC/test/NC/1251421_113.jpeg \n", - " inflating: AD_NC/test/NC/1251421_94.jpeg \n", - " inflating: AD_NC/test/NC/1251421_95.jpeg \n", - " inflating: AD_NC/test/NC/1251421_96.jpeg \n", - " inflating: AD_NC/test/NC/1251421_97.jpeg \n", - " inflating: AD_NC/test/NC/1251421_98.jpeg \n", - " inflating: AD_NC/test/NC/1251421_99.jpeg \n", - " inflating: AD_NC/test/NC/1252024_100.jpeg \n", - " inflating: AD_NC/test/NC/1252024_101.jpeg \n", - " inflating: AD_NC/test/NC/1252024_102.jpeg \n", - " inflating: AD_NC/test/NC/1252024_103.jpeg \n", - " inflating: AD_NC/test/NC/1252024_104.jpeg \n", - " inflating: AD_NC/test/NC/1252024_105.jpeg \n", - " inflating: AD_NC/test/NC/1252024_106.jpeg \n", - " inflating: AD_NC/test/NC/1252024_107.jpeg \n", - " inflating: AD_NC/test/NC/1252024_108.jpeg \n", - " inflating: AD_NC/test/NC/1252024_109.jpeg \n", - " inflating: AD_NC/test/NC/1252024_110.jpeg \n", - " inflating: AD_NC/test/NC/1252024_111.jpeg \n", - " inflating: AD_NC/test/NC/1252024_112.jpeg \n", - " inflating: AD_NC/test/NC/1252024_113.jpeg \n", - " inflating: AD_NC/test/NC/1252024_94.jpeg \n", - " inflating: AD_NC/test/NC/1252024_95.jpeg \n", - " inflating: AD_NC/test/NC/1252024_96.jpeg \n", - " inflating: AD_NC/test/NC/1252024_97.jpeg \n", - " inflating: AD_NC/test/NC/1252024_98.jpeg \n", - " inflating: AD_NC/test/NC/1252024_99.jpeg \n", - " inflating: AD_NC/test/NC/1252848_100.jpeg \n", - " inflating: AD_NC/test/NC/1252848_101.jpeg \n", - " inflating: AD_NC/test/NC/1252848_102.jpeg \n", - " inflating: AD_NC/test/NC/1252848_103.jpeg \n", - " inflating: AD_NC/test/NC/1252848_104.jpeg \n", - " inflating: AD_NC/test/NC/1252848_105.jpeg \n", - " inflating: AD_NC/test/NC/1252848_106.jpeg \n", - " inflating: AD_NC/test/NC/1252848_107.jpeg \n", - " inflating: AD_NC/test/NC/1252848_108.jpeg \n", - " inflating: AD_NC/test/NC/1252848_109.jpeg \n", - " inflating: AD_NC/test/NC/1252848_110.jpeg \n", - " inflating: AD_NC/test/NC/1252848_111.jpeg \n", - " inflating: AD_NC/test/NC/1252848_112.jpeg \n", - " inflating: AD_NC/test/NC/1252848_113.jpeg \n", - " inflating: AD_NC/test/NC/1252848_94.jpeg \n", - " inflating: AD_NC/test/NC/1252848_95.jpeg \n", - " inflating: AD_NC/test/NC/1252848_96.jpeg \n", - " inflating: AD_NC/test/NC/1252848_97.jpeg \n", - " inflating: AD_NC/test/NC/1252848_98.jpeg \n", - " inflating: AD_NC/test/NC/1252848_99.jpeg \n", - " inflating: AD_NC/test/NC/1253141_100.jpeg \n", - " inflating: AD_NC/test/NC/1253141_101.jpeg \n", - " inflating: AD_NC/test/NC/1253141_102.jpeg \n", - " inflating: AD_NC/test/NC/1253141_103.jpeg \n", - " inflating: AD_NC/test/NC/1253141_104.jpeg \n", - " inflating: AD_NC/test/NC/1253141_105.jpeg \n", - " inflating: AD_NC/test/NC/1253141_106.jpeg \n", - " inflating: AD_NC/test/NC/1253141_107.jpeg \n", - " inflating: AD_NC/test/NC/1253141_108.jpeg \n", - " inflating: AD_NC/test/NC/1253141_109.jpeg \n", - " inflating: AD_NC/test/NC/1253141_110.jpeg \n", - " inflating: AD_NC/test/NC/1253141_111.jpeg \n", - " inflating: AD_NC/test/NC/1253141_112.jpeg \n", - " inflating: AD_NC/test/NC/1253141_113.jpeg \n", - " inflating: AD_NC/test/NC/1253141_114.jpeg \n", - " inflating: AD_NC/test/NC/1253141_95.jpeg \n", - " inflating: AD_NC/test/NC/1253141_96.jpeg \n", - " inflating: AD_NC/test/NC/1253141_97.jpeg \n", - " inflating: AD_NC/test/NC/1253141_98.jpeg \n", - " inflating: AD_NC/test/NC/1253141_99.jpeg \n", - " inflating: AD_NC/test/NC/1253769_100.jpeg \n", - " inflating: AD_NC/test/NC/1253769_101.jpeg \n", - " inflating: AD_NC/test/NC/1253769_102.jpeg \n", - " inflating: AD_NC/test/NC/1253769_103.jpeg \n", - " inflating: AD_NC/test/NC/1253769_104.jpeg \n", - " inflating: AD_NC/test/NC/1253769_105.jpeg \n", - " inflating: AD_NC/test/NC/1253769_106.jpeg \n", - " inflating: AD_NC/test/NC/1253769_107.jpeg \n", - " inflating: AD_NC/test/NC/1253769_88.jpeg \n", - " inflating: AD_NC/test/NC/1253769_89.jpeg \n", - " inflating: AD_NC/test/NC/1253769_90.jpeg \n", - " inflating: AD_NC/test/NC/1253769_91.jpeg \n", - " inflating: AD_NC/test/NC/1253769_92.jpeg \n", - " inflating: AD_NC/test/NC/1253769_93.jpeg \n", - " inflating: AD_NC/test/NC/1253769_94.jpeg \n", - " inflating: AD_NC/test/NC/1253769_95.jpeg \n", - " inflating: AD_NC/test/NC/1253769_96.jpeg \n", - " inflating: AD_NC/test/NC/1253769_97.jpeg \n", - " inflating: AD_NC/test/NC/1253769_98.jpeg \n", - " inflating: AD_NC/test/NC/1253769_99.jpeg \n", - " inflating: AD_NC/test/NC/1253770_100.jpeg \n", - " inflating: AD_NC/test/NC/1253770_101.jpeg \n", - " inflating: AD_NC/test/NC/1253770_102.jpeg \n", - " inflating: AD_NC/test/NC/1253770_103.jpeg \n", - " inflating: AD_NC/test/NC/1253770_104.jpeg \n", - " inflating: AD_NC/test/NC/1253770_105.jpeg \n", - " inflating: AD_NC/test/NC/1253770_106.jpeg \n", - " inflating: AD_NC/test/NC/1253770_107.jpeg \n", - " inflating: AD_NC/test/NC/1253770_88.jpeg \n", - " inflating: AD_NC/test/NC/1253770_89.jpeg \n", - " inflating: AD_NC/test/NC/1253770_90.jpeg \n", - " inflating: AD_NC/test/NC/1253770_91.jpeg \n", - " inflating: AD_NC/test/NC/1253770_92.jpeg \n", - " inflating: AD_NC/test/NC/1253770_93.jpeg \n", - " inflating: AD_NC/test/NC/1253770_94.jpeg \n", - " inflating: AD_NC/test/NC/1253770_95.jpeg \n", - " inflating: AD_NC/test/NC/1253770_96.jpeg \n", - " inflating: AD_NC/test/NC/1253770_97.jpeg \n", - " inflating: AD_NC/test/NC/1253770_98.jpeg \n", - " inflating: AD_NC/test/NC/1253770_99.jpeg \n", - " inflating: AD_NC/test/NC/1254168_100.jpeg \n", - " inflating: AD_NC/test/NC/1254168_101.jpeg \n", - " inflating: AD_NC/test/NC/1254168_102.jpeg \n", - " inflating: AD_NC/test/NC/1254168_103.jpeg \n", - " inflating: AD_NC/test/NC/1254168_104.jpeg \n", - " inflating: AD_NC/test/NC/1254168_105.jpeg \n", - " inflating: AD_NC/test/NC/1254168_106.jpeg \n", - " inflating: AD_NC/test/NC/1254168_107.jpeg \n", - " inflating: AD_NC/test/NC/1254168_108.jpeg \n", - " inflating: AD_NC/test/NC/1254168_109.jpeg \n", - " inflating: AD_NC/test/NC/1254168_110.jpeg \n", - " inflating: AD_NC/test/NC/1254168_111.jpeg \n", - " inflating: AD_NC/test/NC/1254168_112.jpeg \n", - " inflating: AD_NC/test/NC/1254168_113.jpeg \n", - " inflating: AD_NC/test/NC/1254168_114.jpeg \n", - " inflating: AD_NC/test/NC/1254168_95.jpeg \n", - " inflating: AD_NC/test/NC/1254168_96.jpeg \n", - " inflating: AD_NC/test/NC/1254168_97.jpeg \n", - " inflating: AD_NC/test/NC/1254168_98.jpeg \n", - " inflating: AD_NC/test/NC/1254168_99.jpeg \n", - " inflating: AD_NC/test/NC/1254369_78.jpeg \n", - " inflating: AD_NC/test/NC/1254369_79.jpeg \n", - " inflating: AD_NC/test/NC/1254369_80.jpeg \n", - " inflating: AD_NC/test/NC/1254369_81.jpeg \n", - " inflating: AD_NC/test/NC/1254369_82.jpeg \n", - " inflating: AD_NC/test/NC/1254369_83.jpeg \n", - " inflating: AD_NC/test/NC/1254369_84.jpeg \n", - " inflating: AD_NC/test/NC/1254369_85.jpeg \n", - " inflating: AD_NC/test/NC/1254369_86.jpeg \n", - " inflating: AD_NC/test/NC/1254369_87.jpeg \n", - " inflating: AD_NC/test/NC/1254369_88.jpeg \n", - " inflating: AD_NC/test/NC/1254369_89.jpeg \n", - " inflating: AD_NC/test/NC/1254369_90.jpeg \n", - " inflating: AD_NC/test/NC/1254369_91.jpeg \n", - " inflating: AD_NC/test/NC/1254369_92.jpeg \n", - " inflating: AD_NC/test/NC/1254369_93.jpeg \n", - " inflating: AD_NC/test/NC/1254369_94.jpeg \n", - " inflating: AD_NC/test/NC/1254369_95.jpeg \n", - " inflating: AD_NC/test/NC/1254369_96.jpeg \n", - " inflating: AD_NC/test/NC/1254369_97.jpeg \n", - " inflating: AD_NC/test/NC/1254370_78.jpeg \n", - " inflating: AD_NC/test/NC/1254370_79.jpeg \n", - " inflating: AD_NC/test/NC/1254370_80.jpeg \n", - " inflating: AD_NC/test/NC/1254370_81.jpeg \n", - " inflating: AD_NC/test/NC/1254370_82.jpeg \n", - " inflating: AD_NC/test/NC/1254370_83.jpeg \n", - " inflating: AD_NC/test/NC/1254370_84.jpeg \n", - " inflating: AD_NC/test/NC/1254370_85.jpeg \n", - " inflating: AD_NC/test/NC/1254370_86.jpeg \n", - " inflating: AD_NC/test/NC/1254370_87.jpeg \n", - " inflating: AD_NC/test/NC/1254370_88.jpeg \n", - " inflating: AD_NC/test/NC/1254370_89.jpeg \n", - " inflating: AD_NC/test/NC/1254370_90.jpeg \n", - " inflating: AD_NC/test/NC/1254370_91.jpeg \n", - " inflating: AD_NC/test/NC/1254370_92.jpeg \n", - " inflating: AD_NC/test/NC/1254370_93.jpeg \n", - " inflating: AD_NC/test/NC/1254370_94.jpeg \n", - " inflating: AD_NC/test/NC/1254370_95.jpeg \n", - " inflating: AD_NC/test/NC/1254370_96.jpeg \n", - " inflating: AD_NC/test/NC/1254370_97.jpeg \n", - " inflating: AD_NC/test/NC/1254386_78.jpeg \n", - " inflating: AD_NC/test/NC/1254386_79.jpeg \n", - " inflating: AD_NC/test/NC/1254386_80.jpeg \n", - " inflating: AD_NC/test/NC/1254386_81.jpeg \n", - " inflating: AD_NC/test/NC/1254386_82.jpeg \n", - " inflating: AD_NC/test/NC/1254386_83.jpeg \n", - " inflating: AD_NC/test/NC/1254386_84.jpeg \n", - " inflating: AD_NC/test/NC/1254386_85.jpeg \n", - " inflating: AD_NC/test/NC/1254386_86.jpeg \n", - " inflating: AD_NC/test/NC/1254386_87.jpeg \n", - " inflating: AD_NC/test/NC/1254386_88.jpeg \n", - " inflating: AD_NC/test/NC/1254386_89.jpeg \n", - " inflating: AD_NC/test/NC/1254386_90.jpeg \n", - " inflating: AD_NC/test/NC/1254386_91.jpeg \n", - " inflating: AD_NC/test/NC/1254386_92.jpeg \n", - " inflating: AD_NC/test/NC/1254386_93.jpeg \n", - " inflating: AD_NC/test/NC/1254386_94.jpeg \n", - " inflating: AD_NC/test/NC/1254386_95.jpeg \n", - " inflating: AD_NC/test/NC/1254386_96.jpeg \n", - " inflating: AD_NC/test/NC/1254386_97.jpeg \n", - " inflating: AD_NC/test/NC/1255144_100.jpeg \n", - " inflating: AD_NC/test/NC/1255144_101.jpeg \n", - " inflating: AD_NC/test/NC/1255144_102.jpeg \n", - " inflating: AD_NC/test/NC/1255144_103.jpeg \n", - " inflating: AD_NC/test/NC/1255144_104.jpeg \n", - " inflating: AD_NC/test/NC/1255144_105.jpeg \n", - " inflating: AD_NC/test/NC/1255144_106.jpeg \n", - " inflating: AD_NC/test/NC/1255144_107.jpeg \n", - " inflating: AD_NC/test/NC/1255144_108.jpeg \n", - " inflating: AD_NC/test/NC/1255144_109.jpeg \n", - " inflating: AD_NC/test/NC/1255144_110.jpeg \n", - " inflating: AD_NC/test/NC/1255144_111.jpeg \n", - " inflating: AD_NC/test/NC/1255144_112.jpeg \n", - " inflating: AD_NC/test/NC/1255144_113.jpeg \n", - " inflating: AD_NC/test/NC/1255144_94.jpeg \n", - " inflating: AD_NC/test/NC/1255144_95.jpeg \n", - " inflating: AD_NC/test/NC/1255144_96.jpeg \n", - " inflating: AD_NC/test/NC/1255144_97.jpeg \n", - " inflating: AD_NC/test/NC/1255144_98.jpeg \n", - " inflating: AD_NC/test/NC/1255144_99.jpeg \n", - " inflating: AD_NC/test/NC/1255412_100.jpeg \n", - " inflating: AD_NC/test/NC/1255412_101.jpeg \n", - " inflating: AD_NC/test/NC/1255412_102.jpeg \n", - " inflating: AD_NC/test/NC/1255412_103.jpeg \n", - " inflating: AD_NC/test/NC/1255412_104.jpeg \n", - " inflating: AD_NC/test/NC/1255412_105.jpeg \n", - " inflating: AD_NC/test/NC/1255412_106.jpeg \n", - " inflating: AD_NC/test/NC/1255412_107.jpeg \n", - " inflating: AD_NC/test/NC/1255412_108.jpeg \n", - " inflating: AD_NC/test/NC/1255412_109.jpeg \n", - " inflating: AD_NC/test/NC/1255412_110.jpeg \n", - " inflating: AD_NC/test/NC/1255412_111.jpeg \n", - " inflating: AD_NC/test/NC/1255412_112.jpeg \n", - " inflating: AD_NC/test/NC/1255412_113.jpeg \n", - " inflating: AD_NC/test/NC/1255412_94.jpeg \n", - " inflating: AD_NC/test/NC/1255412_95.jpeg \n", - " inflating: AD_NC/test/NC/1255412_96.jpeg \n", - " inflating: AD_NC/test/NC/1255412_97.jpeg \n", - " inflating: AD_NC/test/NC/1255412_98.jpeg \n", - " inflating: AD_NC/test/NC/1255412_99.jpeg \n", - " inflating: AD_NC/test/NC/1255836_100.jpeg \n", - " inflating: AD_NC/test/NC/1255836_101.jpeg \n", - " inflating: AD_NC/test/NC/1255836_102.jpeg \n", - " inflating: AD_NC/test/NC/1255836_103.jpeg \n", - " inflating: AD_NC/test/NC/1255836_104.jpeg \n", - " inflating: AD_NC/test/NC/1255836_105.jpeg \n", - " inflating: AD_NC/test/NC/1255836_106.jpeg \n", - " inflating: AD_NC/test/NC/1255836_107.jpeg \n", - " inflating: AD_NC/test/NC/1255836_108.jpeg \n", - " inflating: AD_NC/test/NC/1255836_109.jpeg \n", - " inflating: AD_NC/test/NC/1255836_110.jpeg \n", - " inflating: AD_NC/test/NC/1255836_111.jpeg \n", - " inflating: AD_NC/test/NC/1255836_112.jpeg \n", - " inflating: AD_NC/test/NC/1255836_113.jpeg \n", - " inflating: AD_NC/test/NC/1255836_94.jpeg \n", - " inflating: AD_NC/test/NC/1255836_95.jpeg \n", - " inflating: AD_NC/test/NC/1255836_96.jpeg \n", - " inflating: AD_NC/test/NC/1255836_97.jpeg \n", - " inflating: AD_NC/test/NC/1255836_98.jpeg \n", - " inflating: AD_NC/test/NC/1255836_99.jpeg \n", - " inflating: AD_NC/test/NC/1256135_100.jpeg \n", - " inflating: AD_NC/test/NC/1256135_101.jpeg \n", - " inflating: AD_NC/test/NC/1256135_102.jpeg \n", - " inflating: AD_NC/test/NC/1256135_103.jpeg \n", - " inflating: AD_NC/test/NC/1256135_104.jpeg \n", - " inflating: AD_NC/test/NC/1256135_105.jpeg \n", - " inflating: AD_NC/test/NC/1256135_106.jpeg \n", - " inflating: AD_NC/test/NC/1256135_107.jpeg \n", - " inflating: AD_NC/test/NC/1256135_108.jpeg \n", - " inflating: AD_NC/test/NC/1256135_109.jpeg \n", - " inflating: AD_NC/test/NC/1256135_110.jpeg \n", - " inflating: AD_NC/test/NC/1256135_111.jpeg \n", - " inflating: AD_NC/test/NC/1256135_112.jpeg \n", - " inflating: AD_NC/test/NC/1256135_113.jpeg \n", - " inflating: AD_NC/test/NC/1256135_94.jpeg \n", - " inflating: AD_NC/test/NC/1256135_95.jpeg \n", - " inflating: AD_NC/test/NC/1256135_96.jpeg \n", - " inflating: AD_NC/test/NC/1256135_97.jpeg \n", - " inflating: AD_NC/test/NC/1256135_98.jpeg \n", - " inflating: AD_NC/test/NC/1256135_99.jpeg \n", - " inflating: AD_NC/test/NC/1256397_100.jpeg \n", - " inflating: AD_NC/test/NC/1256397_101.jpeg \n", - " inflating: AD_NC/test/NC/1256397_102.jpeg \n", - " inflating: AD_NC/test/NC/1256397_103.jpeg \n", - " inflating: AD_NC/test/NC/1256397_104.jpeg \n", - " inflating: AD_NC/test/NC/1256397_105.jpeg \n", - " inflating: AD_NC/test/NC/1256397_106.jpeg \n", - " inflating: AD_NC/test/NC/1256397_107.jpeg \n", - " inflating: AD_NC/test/NC/1256397_108.jpeg \n", - " inflating: AD_NC/test/NC/1256397_109.jpeg \n", - " inflating: AD_NC/test/NC/1256397_110.jpeg \n", - " inflating: AD_NC/test/NC/1256397_111.jpeg \n", - " inflating: AD_NC/test/NC/1256397_112.jpeg \n", - " inflating: AD_NC/test/NC/1256397_113.jpeg \n", - " inflating: AD_NC/test/NC/1256397_94.jpeg \n", - " inflating: AD_NC/test/NC/1256397_95.jpeg \n", - " inflating: AD_NC/test/NC/1256397_96.jpeg \n", - " inflating: AD_NC/test/NC/1256397_97.jpeg \n", - " inflating: AD_NC/test/NC/1256397_98.jpeg \n", - " inflating: AD_NC/test/NC/1256397_99.jpeg \n", - " inflating: AD_NC/test/NC/1256802_78.jpeg \n", - " inflating: AD_NC/test/NC/1256802_79.jpeg \n", - " inflating: AD_NC/test/NC/1256802_80.jpeg \n", - " inflating: AD_NC/test/NC/1256802_81.jpeg \n", - " inflating: AD_NC/test/NC/1256802_82.jpeg \n", - " inflating: AD_NC/test/NC/1256802_83.jpeg \n", - " inflating: AD_NC/test/NC/1256802_84.jpeg \n", - " inflating: AD_NC/test/NC/1256802_85.jpeg \n", - " inflating: AD_NC/test/NC/1256802_86.jpeg \n", - " inflating: AD_NC/test/NC/1256802_87.jpeg \n", - " inflating: AD_NC/test/NC/1256802_88.jpeg \n", - " inflating: AD_NC/test/NC/1256802_89.jpeg \n", - " inflating: AD_NC/test/NC/1256802_90.jpeg \n", - " inflating: AD_NC/test/NC/1256802_91.jpeg \n", - " inflating: AD_NC/test/NC/1256802_92.jpeg \n", - " inflating: AD_NC/test/NC/1256802_93.jpeg \n", - " inflating: AD_NC/test/NC/1256802_94.jpeg \n", - " inflating: AD_NC/test/NC/1256802_95.jpeg \n", - " inflating: AD_NC/test/NC/1256802_96.jpeg \n", - " inflating: AD_NC/test/NC/1256802_97.jpeg \n", - " inflating: AD_NC/test/NC/1257943_100.jpeg \n", - " inflating: AD_NC/test/NC/1257943_101.jpeg \n", - " inflating: AD_NC/test/NC/1257943_102.jpeg \n", - " inflating: AD_NC/test/NC/1257943_103.jpeg \n", - " inflating: AD_NC/test/NC/1257943_104.jpeg \n", - " inflating: AD_NC/test/NC/1257943_105.jpeg \n", - " inflating: AD_NC/test/NC/1257943_106.jpeg \n", - " inflating: AD_NC/test/NC/1257943_107.jpeg \n", - " inflating: AD_NC/test/NC/1257943_88.jpeg \n", - " inflating: AD_NC/test/NC/1257943_89.jpeg \n", - " inflating: AD_NC/test/NC/1257943_90.jpeg \n", - " inflating: AD_NC/test/NC/1257943_91.jpeg \n", - " inflating: AD_NC/test/NC/1257943_92.jpeg \n", - " inflating: AD_NC/test/NC/1257943_93.jpeg \n", - " inflating: AD_NC/test/NC/1257943_94.jpeg \n", - " inflating: AD_NC/test/NC/1257943_95.jpeg \n", - " inflating: AD_NC/test/NC/1257943_96.jpeg \n", - " inflating: AD_NC/test/NC/1257943_97.jpeg \n", - " inflating: AD_NC/test/NC/1257943_98.jpeg \n", - " inflating: AD_NC/test/NC/1257943_99.jpeg \n", - " inflating: AD_NC/test/NC/1259842_100.jpeg \n", - " inflating: AD_NC/test/NC/1259842_101.jpeg \n", - " inflating: AD_NC/test/NC/1259842_102.jpeg \n", - " inflating: AD_NC/test/NC/1259842_103.jpeg \n", - " inflating: AD_NC/test/NC/1259842_104.jpeg \n", - " inflating: AD_NC/test/NC/1259842_105.jpeg \n", - " inflating: AD_NC/test/NC/1259842_106.jpeg \n", - " inflating: AD_NC/test/NC/1259842_107.jpeg \n", - " inflating: AD_NC/test/NC/1259842_108.jpeg \n", - " inflating: AD_NC/test/NC/1259842_109.jpeg \n", - " inflating: AD_NC/test/NC/1259842_110.jpeg \n", - " inflating: AD_NC/test/NC/1259842_111.jpeg \n", - " inflating: AD_NC/test/NC/1259842_112.jpeg \n", - " inflating: AD_NC/test/NC/1259842_113.jpeg \n", - " inflating: AD_NC/test/NC/1259842_114.jpeg \n", - " inflating: AD_NC/test/NC/1259842_95.jpeg \n", - " inflating: AD_NC/test/NC/1259842_96.jpeg \n", - " inflating: AD_NC/test/NC/1259842_97.jpeg \n", - " inflating: AD_NC/test/NC/1259842_98.jpeg \n", - " inflating: AD_NC/test/NC/1259842_99.jpeg \n", - " inflating: AD_NC/test/NC/1261605_78.jpeg \n", - " inflating: AD_NC/test/NC/1261605_79.jpeg \n", - " inflating: AD_NC/test/NC/1261605_80.jpeg \n", - " inflating: AD_NC/test/NC/1261605_81.jpeg \n", - " inflating: AD_NC/test/NC/1261605_82.jpeg \n", - " inflating: AD_NC/test/NC/1261605_83.jpeg \n", - " inflating: AD_NC/test/NC/1261605_84.jpeg \n", - " inflating: AD_NC/test/NC/1261605_85.jpeg \n", - " inflating: AD_NC/test/NC/1261605_86.jpeg \n", - " inflating: AD_NC/test/NC/1261605_87.jpeg \n", - " inflating: AD_NC/test/NC/1261605_88.jpeg \n", - " inflating: AD_NC/test/NC/1261605_89.jpeg \n", - " inflating: AD_NC/test/NC/1261605_90.jpeg \n", - " inflating: AD_NC/test/NC/1261605_91.jpeg \n", - " inflating: AD_NC/test/NC/1261605_92.jpeg \n", - " inflating: AD_NC/test/NC/1261605_93.jpeg \n", - " inflating: AD_NC/test/NC/1261605_94.jpeg \n", - " inflating: AD_NC/test/NC/1261605_95.jpeg \n", - " inflating: AD_NC/test/NC/1261605_96.jpeg \n", - " inflating: AD_NC/test/NC/1261605_97.jpeg \n", - " inflating: AD_NC/test/NC/1262194_78.jpeg \n", - " inflating: AD_NC/test/NC/1262194_79.jpeg \n", - " inflating: AD_NC/test/NC/1262194_80.jpeg \n", - " inflating: AD_NC/test/NC/1262194_81.jpeg \n", - " inflating: AD_NC/test/NC/1262194_82.jpeg \n", - " inflating: AD_NC/test/NC/1262194_83.jpeg \n", - " inflating: AD_NC/test/NC/1262194_84.jpeg \n", - " inflating: AD_NC/test/NC/1262194_85.jpeg \n", - " inflating: AD_NC/test/NC/1262194_86.jpeg \n", - " inflating: AD_NC/test/NC/1262194_87.jpeg \n", - " inflating: AD_NC/test/NC/1262194_88.jpeg \n", - " inflating: AD_NC/test/NC/1262194_89.jpeg \n", - " inflating: AD_NC/test/NC/1262194_90.jpeg \n", - " inflating: AD_NC/test/NC/1262194_91.jpeg \n", - " inflating: AD_NC/test/NC/1262194_92.jpeg \n", - " inflating: AD_NC/test/NC/1262194_93.jpeg \n", - " inflating: AD_NC/test/NC/1262194_94.jpeg \n", - " inflating: AD_NC/test/NC/1262194_95.jpeg \n", - " inflating: AD_NC/test/NC/1262194_96.jpeg \n", - " inflating: AD_NC/test/NC/1262194_97.jpeg \n", - " inflating: AD_NC/test/NC/1262195_78.jpeg \n", - " inflating: AD_NC/test/NC/1262195_79.jpeg \n", - " inflating: AD_NC/test/NC/1262195_80.jpeg \n", - " inflating: AD_NC/test/NC/1262195_81.jpeg \n", - " inflating: AD_NC/test/NC/1262195_82.jpeg \n", - " inflating: AD_NC/test/NC/1262195_83.jpeg \n", - " inflating: AD_NC/test/NC/1262195_84.jpeg \n", - " inflating: AD_NC/test/NC/1262195_85.jpeg \n", - " inflating: AD_NC/test/NC/1262195_86.jpeg \n", - " inflating: AD_NC/test/NC/1262195_87.jpeg \n", - " inflating: AD_NC/test/NC/1262195_88.jpeg \n", - " inflating: AD_NC/test/NC/1262195_89.jpeg \n", - " inflating: AD_NC/test/NC/1262195_90.jpeg \n", - " inflating: AD_NC/test/NC/1262195_91.jpeg \n", - " inflating: AD_NC/test/NC/1262195_92.jpeg \n", - " inflating: AD_NC/test/NC/1262195_93.jpeg \n", - " inflating: AD_NC/test/NC/1262195_94.jpeg \n", - " inflating: AD_NC/test/NC/1262195_95.jpeg \n", - " inflating: AD_NC/test/NC/1262195_96.jpeg \n", - " inflating: AD_NC/test/NC/1262195_97.jpeg \n", - " inflating: AD_NC/test/NC/1263330_100.jpeg \n", - " inflating: AD_NC/test/NC/1263330_101.jpeg \n", - " inflating: AD_NC/test/NC/1263330_102.jpeg \n", - " inflating: AD_NC/test/NC/1263330_103.jpeg \n", - " inflating: AD_NC/test/NC/1263330_104.jpeg \n", - " inflating: AD_NC/test/NC/1263330_105.jpeg \n", - " inflating: AD_NC/test/NC/1263330_106.jpeg \n", - " inflating: AD_NC/test/NC/1263330_107.jpeg \n", - " inflating: AD_NC/test/NC/1263330_88.jpeg \n", - " inflating: AD_NC/test/NC/1263330_89.jpeg \n", - " inflating: AD_NC/test/NC/1263330_90.jpeg \n", - " inflating: AD_NC/test/NC/1263330_91.jpeg \n", - " inflating: AD_NC/test/NC/1263330_92.jpeg \n", - " inflating: AD_NC/test/NC/1263330_93.jpeg \n", - " inflating: AD_NC/test/NC/1263330_94.jpeg \n", - " inflating: AD_NC/test/NC/1263330_95.jpeg \n", - " inflating: AD_NC/test/NC/1263330_96.jpeg \n", - " inflating: AD_NC/test/NC/1263330_97.jpeg \n", - " inflating: AD_NC/test/NC/1263330_98.jpeg \n", - " inflating: AD_NC/test/NC/1263330_99.jpeg \n", - " inflating: AD_NC/test/NC/1263792_100.jpeg \n", - " inflating: AD_NC/test/NC/1263792_101.jpeg \n", - " inflating: AD_NC/test/NC/1263792_102.jpeg \n", - " inflating: AD_NC/test/NC/1263792_103.jpeg \n", - " inflating: AD_NC/test/NC/1263792_104.jpeg \n", - " inflating: AD_NC/test/NC/1263792_105.jpeg \n", - " inflating: AD_NC/test/NC/1263792_106.jpeg \n", - " inflating: AD_NC/test/NC/1263792_107.jpeg \n", - " inflating: AD_NC/test/NC/1263792_108.jpeg \n", - " inflating: AD_NC/test/NC/1263792_109.jpeg \n", - " inflating: AD_NC/test/NC/1263792_110.jpeg \n", - " inflating: AD_NC/test/NC/1263792_111.jpeg \n", - " inflating: AD_NC/test/NC/1263792_112.jpeg \n", - " inflating: AD_NC/test/NC/1263792_113.jpeg \n", - " inflating: AD_NC/test/NC/1263792_94.jpeg \n", - " inflating: AD_NC/test/NC/1263792_95.jpeg \n", - " inflating: AD_NC/test/NC/1263792_96.jpeg \n", - " inflating: AD_NC/test/NC/1263792_97.jpeg \n", - " inflating: AD_NC/test/NC/1263792_98.jpeg \n", - " inflating: AD_NC/test/NC/1263792_99.jpeg \n", - " inflating: AD_NC/test/NC/1263811_100.jpeg \n", - " inflating: AD_NC/test/NC/1263811_101.jpeg \n", - " inflating: AD_NC/test/NC/1263811_102.jpeg \n", - " inflating: AD_NC/test/NC/1263811_103.jpeg \n", - " inflating: AD_NC/test/NC/1263811_104.jpeg \n", - " inflating: AD_NC/test/NC/1263811_105.jpeg \n", - " inflating: AD_NC/test/NC/1263811_106.jpeg \n", - " inflating: AD_NC/test/NC/1263811_107.jpeg \n", - " inflating: AD_NC/test/NC/1263811_108.jpeg \n", - " inflating: AD_NC/test/NC/1263811_109.jpeg \n", - " inflating: AD_NC/test/NC/1263811_110.jpeg \n", - " inflating: AD_NC/test/NC/1263811_111.jpeg \n", - " inflating: AD_NC/test/NC/1263811_112.jpeg \n", - " inflating: AD_NC/test/NC/1263811_113.jpeg \n", - " inflating: AD_NC/test/NC/1263811_94.jpeg \n", - " inflating: AD_NC/test/NC/1263811_95.jpeg \n", - " inflating: AD_NC/test/NC/1263811_96.jpeg \n", - " inflating: AD_NC/test/NC/1263811_97.jpeg \n", - " inflating: AD_NC/test/NC/1263811_98.jpeg \n", - " inflating: AD_NC/test/NC/1263811_99.jpeg \n", - " inflating: AD_NC/test/NC/1264016_100.jpeg \n", - " inflating: AD_NC/test/NC/1264016_101.jpeg \n", - " inflating: AD_NC/test/NC/1264016_102.jpeg \n", - " inflating: AD_NC/test/NC/1264016_103.jpeg \n", - " inflating: AD_NC/test/NC/1264016_104.jpeg \n", - " inflating: AD_NC/test/NC/1264016_105.jpeg \n", - " inflating: AD_NC/test/NC/1264016_106.jpeg \n", - " inflating: AD_NC/test/NC/1264016_107.jpeg \n", - " inflating: AD_NC/test/NC/1264016_108.jpeg \n", - " inflating: AD_NC/test/NC/1264016_109.jpeg \n", - " inflating: AD_NC/test/NC/1264016_110.jpeg \n", - " inflating: AD_NC/test/NC/1264016_111.jpeg \n", - " inflating: AD_NC/test/NC/1264016_112.jpeg \n", - " inflating: AD_NC/test/NC/1264016_113.jpeg \n", - " inflating: AD_NC/test/NC/1264016_94.jpeg \n", - " inflating: AD_NC/test/NC/1264016_95.jpeg \n", - " inflating: AD_NC/test/NC/1264016_96.jpeg \n", - " inflating: AD_NC/test/NC/1264016_97.jpeg \n", - " inflating: AD_NC/test/NC/1264016_98.jpeg \n", - " inflating: AD_NC/test/NC/1264016_99.jpeg \n", - " inflating: AD_NC/test/NC/1264767_100.jpeg \n", - " inflating: AD_NC/test/NC/1264767_101.jpeg \n", - " inflating: AD_NC/test/NC/1264767_102.jpeg \n", - " inflating: AD_NC/test/NC/1264767_103.jpeg \n", - " inflating: AD_NC/test/NC/1264767_104.jpeg \n", - " inflating: AD_NC/test/NC/1264767_105.jpeg \n", - " inflating: AD_NC/test/NC/1264767_106.jpeg \n", - " inflating: AD_NC/test/NC/1264767_107.jpeg \n", - " inflating: AD_NC/test/NC/1264767_108.jpeg \n", - " inflating: AD_NC/test/NC/1264767_109.jpeg \n", - " inflating: AD_NC/test/NC/1264767_110.jpeg \n", - " inflating: AD_NC/test/NC/1264767_111.jpeg \n", - " inflating: AD_NC/test/NC/1264767_112.jpeg \n", - " inflating: AD_NC/test/NC/1264767_113.jpeg \n", - " inflating: AD_NC/test/NC/1264767_94.jpeg \n", - " inflating: AD_NC/test/NC/1264767_95.jpeg \n", - " inflating: AD_NC/test/NC/1264767_96.jpeg \n", - " inflating: AD_NC/test/NC/1264767_97.jpeg \n", - " inflating: AD_NC/test/NC/1264767_98.jpeg \n", - " inflating: AD_NC/test/NC/1264767_99.jpeg \n", - " inflating: AD_NC/test/NC/1265863_78.jpeg \n", - " inflating: AD_NC/test/NC/1265863_79.jpeg \n", - " inflating: AD_NC/test/NC/1265863_80.jpeg \n", - " inflating: AD_NC/test/NC/1265863_81.jpeg \n", - " inflating: AD_NC/test/NC/1265863_82.jpeg \n", - " inflating: AD_NC/test/NC/1265863_83.jpeg \n", - " inflating: AD_NC/test/NC/1265863_84.jpeg \n", - " inflating: AD_NC/test/NC/1265863_85.jpeg \n", - " inflating: AD_NC/test/NC/1265863_86.jpeg \n", - " inflating: AD_NC/test/NC/1265863_87.jpeg \n", - " inflating: AD_NC/test/NC/1265863_88.jpeg \n", - " inflating: AD_NC/test/NC/1265863_89.jpeg \n", - " inflating: AD_NC/test/NC/1265863_90.jpeg \n", - " inflating: AD_NC/test/NC/1265863_91.jpeg \n", - " inflating: AD_NC/test/NC/1265863_92.jpeg \n", - " inflating: AD_NC/test/NC/1265863_93.jpeg \n", - " inflating: AD_NC/test/NC/1265863_94.jpeg \n", - " inflating: AD_NC/test/NC/1265863_95.jpeg \n", - " inflating: AD_NC/test/NC/1265863_96.jpeg \n", - " inflating: AD_NC/test/NC/1265863_97.jpeg \n", - " inflating: AD_NC/test/NC/1266356_100.jpeg \n", - " inflating: AD_NC/test/NC/1266356_101.jpeg \n", - " inflating: AD_NC/test/NC/1266356_102.jpeg \n", - " inflating: AD_NC/test/NC/1266356_103.jpeg \n", - " inflating: AD_NC/test/NC/1266356_104.jpeg \n", - " inflating: AD_NC/test/NC/1266356_105.jpeg \n", - " inflating: AD_NC/test/NC/1266356_106.jpeg \n", - " inflating: AD_NC/test/NC/1266356_107.jpeg \n", - " inflating: AD_NC/test/NC/1266356_108.jpeg \n", - " inflating: AD_NC/test/NC/1266356_109.jpeg \n", - " inflating: AD_NC/test/NC/1266356_110.jpeg \n", - " inflating: AD_NC/test/NC/1266356_111.jpeg \n", - " inflating: AD_NC/test/NC/1266356_112.jpeg \n", - " inflating: AD_NC/test/NC/1266356_113.jpeg \n", - " inflating: AD_NC/test/NC/1266356_94.jpeg \n", - " inflating: AD_NC/test/NC/1266356_95.jpeg \n", - " inflating: AD_NC/test/NC/1266356_96.jpeg \n", - " inflating: AD_NC/test/NC/1266356_97.jpeg \n", - " inflating: AD_NC/test/NC/1266356_98.jpeg \n", - " inflating: AD_NC/test/NC/1266356_99.jpeg \n", - " inflating: AD_NC/test/NC/1266559_100.jpeg \n", - " inflating: AD_NC/test/NC/1266559_101.jpeg \n", - " inflating: AD_NC/test/NC/1266559_102.jpeg \n", - " inflating: AD_NC/test/NC/1266559_103.jpeg \n", - " inflating: AD_NC/test/NC/1266559_104.jpeg \n", - " inflating: AD_NC/test/NC/1266559_105.jpeg \n", - " inflating: AD_NC/test/NC/1266559_106.jpeg \n", - " inflating: AD_NC/test/NC/1266559_107.jpeg \n", - " inflating: AD_NC/test/NC/1266559_108.jpeg \n", - " inflating: AD_NC/test/NC/1266559_109.jpeg \n", - " inflating: AD_NC/test/NC/1266559_110.jpeg \n", - " inflating: AD_NC/test/NC/1266559_111.jpeg \n", - " inflating: AD_NC/test/NC/1266559_112.jpeg \n", - " inflating: AD_NC/test/NC/1266559_113.jpeg \n", - " inflating: AD_NC/test/NC/1266559_94.jpeg \n", - " inflating: AD_NC/test/NC/1266559_95.jpeg \n", - " inflating: AD_NC/test/NC/1266559_96.jpeg \n", - " inflating: AD_NC/test/NC/1266559_97.jpeg \n", - " inflating: AD_NC/test/NC/1266559_98.jpeg \n", - " inflating: AD_NC/test/NC/1266559_99.jpeg \n", - " inflating: AD_NC/test/NC/1267882_100.jpeg \n", - " inflating: AD_NC/test/NC/1267882_101.jpeg \n", - " inflating: AD_NC/test/NC/1267882_102.jpeg \n", - " inflating: AD_NC/test/NC/1267882_103.jpeg \n", - " inflating: AD_NC/test/NC/1267882_104.jpeg \n", - " inflating: AD_NC/test/NC/1267882_105.jpeg \n", - " inflating: AD_NC/test/NC/1267882_106.jpeg \n", - " inflating: AD_NC/test/NC/1267882_107.jpeg \n", - " inflating: AD_NC/test/NC/1267882_108.jpeg \n", - " inflating: AD_NC/test/NC/1267882_109.jpeg \n", - " inflating: AD_NC/test/NC/1267882_110.jpeg \n", - " inflating: AD_NC/test/NC/1267882_111.jpeg \n", - " inflating: AD_NC/test/NC/1267882_112.jpeg \n", - " inflating: AD_NC/test/NC/1267882_113.jpeg \n", - " inflating: AD_NC/test/NC/1267882_94.jpeg \n", - " inflating: AD_NC/test/NC/1267882_95.jpeg \n", - " inflating: AD_NC/test/NC/1267882_96.jpeg \n", - " inflating: AD_NC/test/NC/1267882_97.jpeg \n", - " inflating: AD_NC/test/NC/1267882_98.jpeg \n", - " inflating: AD_NC/test/NC/1267882_99.jpeg \n", - " inflating: AD_NC/test/NC/1268293_100.jpeg \n", - " inflating: AD_NC/test/NC/1268293_101.jpeg \n", - " inflating: AD_NC/test/NC/1268293_102.jpeg \n", - " inflating: AD_NC/test/NC/1268293_103.jpeg \n", - " inflating: AD_NC/test/NC/1268293_104.jpeg \n", - " inflating: AD_NC/test/NC/1268293_105.jpeg \n", - " inflating: AD_NC/test/NC/1268293_106.jpeg \n", - " inflating: AD_NC/test/NC/1268293_107.jpeg \n", - " inflating: AD_NC/test/NC/1268293_88.jpeg \n", - " inflating: AD_NC/test/NC/1268293_89.jpeg \n", - " inflating: AD_NC/test/NC/1268293_90.jpeg \n", - " inflating: AD_NC/test/NC/1268293_91.jpeg \n", - " inflating: AD_NC/test/NC/1268293_92.jpeg \n", - " inflating: AD_NC/test/NC/1268293_93.jpeg \n", - " inflating: AD_NC/test/NC/1268293_94.jpeg \n", - " inflating: AD_NC/test/NC/1268293_95.jpeg \n", - " inflating: AD_NC/test/NC/1268293_96.jpeg \n", - " inflating: AD_NC/test/NC/1268293_97.jpeg \n", - " inflating: AD_NC/test/NC/1268293_98.jpeg \n", - " inflating: AD_NC/test/NC/1268293_99.jpeg \n", - " inflating: AD_NC/test/NC/1270020_100.jpeg \n", - " inflating: AD_NC/test/NC/1270020_101.jpeg \n", - " inflating: AD_NC/test/NC/1270020_102.jpeg \n", - " inflating: AD_NC/test/NC/1270020_103.jpeg \n", - " inflating: AD_NC/test/NC/1270020_104.jpeg \n", - " inflating: AD_NC/test/NC/1270020_105.jpeg \n", - " inflating: AD_NC/test/NC/1270020_106.jpeg \n", - " inflating: AD_NC/test/NC/1270020_107.jpeg \n", - " inflating: AD_NC/test/NC/1270020_108.jpeg \n", - " inflating: AD_NC/test/NC/1270020_109.jpeg \n", - " inflating: AD_NC/test/NC/1270020_110.jpeg \n", - " inflating: AD_NC/test/NC/1270020_111.jpeg \n", - " inflating: AD_NC/test/NC/1270020_112.jpeg \n", - " inflating: AD_NC/test/NC/1270020_113.jpeg \n", - " inflating: AD_NC/test/NC/1270020_94.jpeg \n", - " inflating: AD_NC/test/NC/1270020_95.jpeg \n", - " inflating: AD_NC/test/NC/1270020_96.jpeg \n", - " inflating: AD_NC/test/NC/1270020_97.jpeg \n", - " inflating: AD_NC/test/NC/1270020_98.jpeg \n", - " inflating: AD_NC/test/NC/1270020_99.jpeg \n", - " inflating: AD_NC/test/NC/1270100_100.jpeg \n", - " inflating: AD_NC/test/NC/1270100_101.jpeg \n", - " inflating: AD_NC/test/NC/1270100_102.jpeg \n", - " inflating: AD_NC/test/NC/1270100_103.jpeg \n", - " inflating: AD_NC/test/NC/1270100_104.jpeg \n", - " inflating: AD_NC/test/NC/1270100_105.jpeg \n", - " inflating: AD_NC/test/NC/1270100_106.jpeg \n", - " inflating: AD_NC/test/NC/1270100_107.jpeg \n", - " inflating: AD_NC/test/NC/1270100_108.jpeg \n", - " inflating: AD_NC/test/NC/1270100_109.jpeg \n", - " inflating: AD_NC/test/NC/1270100_110.jpeg \n", - " inflating: AD_NC/test/NC/1270100_111.jpeg \n", - " inflating: AD_NC/test/NC/1270100_112.jpeg \n", - " inflating: AD_NC/test/NC/1270100_113.jpeg \n", - " inflating: AD_NC/test/NC/1270100_94.jpeg \n", - " inflating: AD_NC/test/NC/1270100_95.jpeg \n", - " inflating: AD_NC/test/NC/1270100_96.jpeg \n", - " inflating: AD_NC/test/NC/1270100_97.jpeg \n", - " inflating: AD_NC/test/NC/1270100_98.jpeg \n", - " inflating: AD_NC/test/NC/1270100_99.jpeg \n", - " inflating: AD_NC/test/NC/1272868_100.jpeg \n", - " inflating: AD_NC/test/NC/1272868_101.jpeg \n", - " inflating: AD_NC/test/NC/1272868_102.jpeg \n", - " inflating: AD_NC/test/NC/1272868_103.jpeg \n", - " inflating: AD_NC/test/NC/1272868_104.jpeg \n", - " inflating: AD_NC/test/NC/1272868_105.jpeg \n", - " inflating: AD_NC/test/NC/1272868_106.jpeg \n", - " inflating: AD_NC/test/NC/1272868_107.jpeg \n", - " inflating: AD_NC/test/NC/1272868_108.jpeg \n", - " inflating: AD_NC/test/NC/1272868_109.jpeg \n", - " inflating: AD_NC/test/NC/1272868_110.jpeg \n", - " inflating: AD_NC/test/NC/1272868_111.jpeg \n", - " inflating: AD_NC/test/NC/1272868_112.jpeg \n", - " inflating: AD_NC/test/NC/1272868_113.jpeg \n", - " inflating: AD_NC/test/NC/1272868_94.jpeg \n", - " inflating: AD_NC/test/NC/1272868_95.jpeg \n", - " inflating: AD_NC/test/NC/1272868_96.jpeg \n", - " inflating: AD_NC/test/NC/1272868_97.jpeg \n", - " inflating: AD_NC/test/NC/1272868_98.jpeg \n", - " inflating: AD_NC/test/NC/1272868_99.jpeg \n", - " inflating: AD_NC/test/NC/1273042_100.jpeg \n", - " inflating: AD_NC/test/NC/1273042_101.jpeg \n", - " inflating: AD_NC/test/NC/1273042_102.jpeg \n", - " inflating: AD_NC/test/NC/1273042_103.jpeg \n", - " inflating: AD_NC/test/NC/1273042_104.jpeg \n", - " inflating: AD_NC/test/NC/1273042_105.jpeg \n", - " inflating: AD_NC/test/NC/1273042_106.jpeg \n", - " inflating: AD_NC/test/NC/1273042_107.jpeg \n", - " inflating: AD_NC/test/NC/1273042_88.jpeg \n", - " inflating: AD_NC/test/NC/1273042_89.jpeg \n", - " inflating: AD_NC/test/NC/1273042_90.jpeg \n", - " inflating: AD_NC/test/NC/1273042_91.jpeg \n", - " inflating: AD_NC/test/NC/1273042_92.jpeg \n", - " inflating: AD_NC/test/NC/1273042_93.jpeg \n", - " inflating: AD_NC/test/NC/1273042_94.jpeg \n", - " inflating: AD_NC/test/NC/1273042_95.jpeg \n", - " inflating: AD_NC/test/NC/1273042_96.jpeg \n", - " inflating: AD_NC/test/NC/1273042_97.jpeg \n", - " inflating: AD_NC/test/NC/1273042_98.jpeg \n", - " inflating: AD_NC/test/NC/1273042_99.jpeg \n", - " inflating: AD_NC/test/NC/1274602_78.jpeg \n", - " inflating: AD_NC/test/NC/1274602_79.jpeg \n", - " inflating: AD_NC/test/NC/1274602_80.jpeg \n", - " inflating: AD_NC/test/NC/1274602_81.jpeg \n", - " inflating: AD_NC/test/NC/1274602_82.jpeg \n", - " inflating: AD_NC/test/NC/1274602_83.jpeg \n", - " inflating: AD_NC/test/NC/1274602_84.jpeg \n", - " inflating: AD_NC/test/NC/1274602_85.jpeg \n", - " inflating: AD_NC/test/NC/1274602_86.jpeg \n", - " inflating: AD_NC/test/NC/1274602_87.jpeg \n", - " inflating: AD_NC/test/NC/1274602_88.jpeg \n", - " inflating: AD_NC/test/NC/1274602_89.jpeg \n", - " inflating: AD_NC/test/NC/1274602_90.jpeg \n", - " inflating: AD_NC/test/NC/1274602_91.jpeg \n", - " inflating: AD_NC/test/NC/1274602_92.jpeg \n", - " inflating: AD_NC/test/NC/1274602_93.jpeg \n", - " inflating: AD_NC/test/NC/1274602_94.jpeg \n", - " inflating: AD_NC/test/NC/1274602_95.jpeg \n", - " inflating: AD_NC/test/NC/1274602_96.jpeg \n", - " inflating: AD_NC/test/NC/1274602_97.jpeg \n", - " inflating: AD_NC/test/NC/1274735_100.jpeg \n", - " inflating: AD_NC/test/NC/1274735_101.jpeg \n", - " inflating: AD_NC/test/NC/1274735_102.jpeg \n", - " inflating: AD_NC/test/NC/1274735_103.jpeg \n", - " inflating: AD_NC/test/NC/1274735_104.jpeg \n", - " inflating: AD_NC/test/NC/1274735_105.jpeg \n", - " inflating: AD_NC/test/NC/1274735_106.jpeg \n", - " inflating: AD_NC/test/NC/1274735_107.jpeg \n", - " inflating: AD_NC/test/NC/1274735_108.jpeg \n", - " inflating: AD_NC/test/NC/1274735_109.jpeg \n", - " inflating: AD_NC/test/NC/1274735_110.jpeg \n", - " inflating: AD_NC/test/NC/1274735_111.jpeg \n", - " inflating: AD_NC/test/NC/1274735_112.jpeg \n", - " inflating: AD_NC/test/NC/1274735_113.jpeg \n", - " inflating: AD_NC/test/NC/1274735_114.jpeg \n", - " inflating: AD_NC/test/NC/1274735_95.jpeg \n", - " inflating: AD_NC/test/NC/1274735_96.jpeg \n", - " inflating: AD_NC/test/NC/1274735_97.jpeg \n", - " inflating: AD_NC/test/NC/1274735_98.jpeg \n", - " inflating: AD_NC/test/NC/1274735_99.jpeg \n", - " inflating: AD_NC/test/NC/1276990_100.jpeg \n", - " inflating: AD_NC/test/NC/1276990_101.jpeg \n", - " inflating: AD_NC/test/NC/1276990_102.jpeg \n", - " inflating: AD_NC/test/NC/1276990_103.jpeg \n", - " inflating: AD_NC/test/NC/1276990_104.jpeg \n", - " inflating: AD_NC/test/NC/1276990_105.jpeg \n", - " inflating: AD_NC/test/NC/1276990_106.jpeg \n", - " inflating: AD_NC/test/NC/1276990_107.jpeg \n", - " inflating: AD_NC/test/NC/1276990_88.jpeg \n", - " inflating: AD_NC/test/NC/1276990_89.jpeg \n", - " inflating: AD_NC/test/NC/1276990_90.jpeg \n", - " inflating: AD_NC/test/NC/1276990_91.jpeg \n", - " inflating: AD_NC/test/NC/1276990_92.jpeg \n", - " inflating: AD_NC/test/NC/1276990_93.jpeg \n", - " inflating: AD_NC/test/NC/1276990_94.jpeg \n", - " inflating: AD_NC/test/NC/1276990_95.jpeg \n", - " inflating: AD_NC/test/NC/1276990_96.jpeg \n", - " inflating: AD_NC/test/NC/1276990_97.jpeg \n", - " inflating: AD_NC/test/NC/1276990_98.jpeg \n", - " inflating: AD_NC/test/NC/1276990_99.jpeg \n", - " inflating: AD_NC/test/NC/1277389_100.jpeg \n", - " inflating: AD_NC/test/NC/1277389_101.jpeg \n", - " inflating: AD_NC/test/NC/1277389_102.jpeg \n", - " inflating: AD_NC/test/NC/1277389_103.jpeg \n", - " inflating: AD_NC/test/NC/1277389_104.jpeg \n", - " inflating: AD_NC/test/NC/1277389_105.jpeg \n", - " inflating: AD_NC/test/NC/1277389_106.jpeg \n", - " inflating: AD_NC/test/NC/1277389_107.jpeg \n", - " inflating: AD_NC/test/NC/1277389_88.jpeg \n", - " inflating: AD_NC/test/NC/1277389_89.jpeg \n", - " inflating: AD_NC/test/NC/1277389_90.jpeg \n", - " inflating: AD_NC/test/NC/1277389_91.jpeg \n", - " inflating: AD_NC/test/NC/1277389_92.jpeg \n", - " inflating: AD_NC/test/NC/1277389_93.jpeg \n", - " inflating: AD_NC/test/NC/1277389_94.jpeg \n", - " inflating: AD_NC/test/NC/1277389_95.jpeg \n", - " inflating: AD_NC/test/NC/1277389_96.jpeg \n", - " inflating: AD_NC/test/NC/1277389_97.jpeg \n", - " inflating: AD_NC/test/NC/1277389_98.jpeg \n", - " inflating: AD_NC/test/NC/1277389_99.jpeg \n", - " inflating: AD_NC/test/NC/1277390_100.jpeg \n", - " inflating: AD_NC/test/NC/1277390_101.jpeg \n", - " inflating: AD_NC/test/NC/1277390_102.jpeg \n", - " inflating: AD_NC/test/NC/1277390_103.jpeg \n", - " inflating: AD_NC/test/NC/1277390_104.jpeg \n", - " inflating: AD_NC/test/NC/1277390_105.jpeg \n", - " inflating: AD_NC/test/NC/1277390_106.jpeg \n", - " inflating: AD_NC/test/NC/1277390_107.jpeg \n", - " inflating: AD_NC/test/NC/1277390_88.jpeg \n", - " inflating: AD_NC/test/NC/1277390_89.jpeg \n", - " inflating: AD_NC/test/NC/1277390_90.jpeg \n", - " inflating: AD_NC/test/NC/1277390_91.jpeg \n", - " inflating: AD_NC/test/NC/1277390_92.jpeg \n", - " inflating: AD_NC/test/NC/1277390_93.jpeg \n", - " inflating: AD_NC/test/NC/1277390_94.jpeg \n", - " inflating: AD_NC/test/NC/1277390_95.jpeg \n", - " inflating: AD_NC/test/NC/1277390_96.jpeg \n", - " inflating: AD_NC/test/NC/1277390_97.jpeg \n", - " inflating: AD_NC/test/NC/1277390_98.jpeg \n", - " inflating: AD_NC/test/NC/1277390_99.jpeg \n", - " inflating: AD_NC/test/NC/1278606_100.jpeg \n", - " inflating: AD_NC/test/NC/1278606_101.jpeg \n", - " inflating: AD_NC/test/NC/1278606_102.jpeg \n", - " inflating: AD_NC/test/NC/1278606_103.jpeg \n", - " inflating: AD_NC/test/NC/1278606_104.jpeg \n", - " inflating: AD_NC/test/NC/1278606_105.jpeg \n", - " inflating: AD_NC/test/NC/1278606_106.jpeg \n", - " inflating: AD_NC/test/NC/1278606_107.jpeg \n", - " inflating: AD_NC/test/NC/1278606_108.jpeg \n", - " inflating: AD_NC/test/NC/1278606_109.jpeg \n", - " inflating: AD_NC/test/NC/1278606_110.jpeg \n", - " inflating: AD_NC/test/NC/1278606_111.jpeg \n", - " inflating: AD_NC/test/NC/1278606_112.jpeg \n", - " inflating: AD_NC/test/NC/1278606_113.jpeg \n", - " inflating: AD_NC/test/NC/1278606_94.jpeg \n", - " inflating: AD_NC/test/NC/1278606_95.jpeg \n", - " inflating: AD_NC/test/NC/1278606_96.jpeg \n", - " inflating: AD_NC/test/NC/1278606_97.jpeg \n", - " inflating: AD_NC/test/NC/1278606_98.jpeg \n", - " inflating: AD_NC/test/NC/1278606_99.jpeg \n", - " inflating: AD_NC/test/NC/1278640_100.jpeg \n", - " inflating: AD_NC/test/NC/1278640_101.jpeg \n", - " inflating: AD_NC/test/NC/1278640_102.jpeg \n", - " inflating: AD_NC/test/NC/1278640_103.jpeg \n", - " inflating: AD_NC/test/NC/1278640_104.jpeg \n", - " inflating: AD_NC/test/NC/1278640_105.jpeg \n", - " inflating: AD_NC/test/NC/1278640_106.jpeg \n", - " inflating: AD_NC/test/NC/1278640_107.jpeg \n", - " inflating: AD_NC/test/NC/1278640_108.jpeg \n", - " inflating: AD_NC/test/NC/1278640_109.jpeg \n", - " inflating: AD_NC/test/NC/1278640_110.jpeg \n", - " inflating: AD_NC/test/NC/1278640_111.jpeg \n", - " inflating: AD_NC/test/NC/1278640_112.jpeg \n", - " inflating: AD_NC/test/NC/1278640_113.jpeg \n", - " inflating: AD_NC/test/NC/1278640_94.jpeg \n", - " inflating: AD_NC/test/NC/1278640_95.jpeg \n", - " inflating: AD_NC/test/NC/1278640_96.jpeg \n", - " inflating: AD_NC/test/NC/1278640_97.jpeg \n", - " inflating: AD_NC/test/NC/1278640_98.jpeg \n", - " inflating: AD_NC/test/NC/1278640_99.jpeg \n", - " inflating: AD_NC/test/NC/1278852_100.jpeg \n", - " inflating: AD_NC/test/NC/1278852_101.jpeg \n", - " inflating: AD_NC/test/NC/1278852_102.jpeg \n", - " inflating: AD_NC/test/NC/1278852_103.jpeg \n", - " inflating: AD_NC/test/NC/1278852_104.jpeg \n", - " inflating: AD_NC/test/NC/1278852_105.jpeg \n", - " inflating: AD_NC/test/NC/1278852_106.jpeg \n", - " inflating: AD_NC/test/NC/1278852_107.jpeg \n", - " inflating: AD_NC/test/NC/1278852_108.jpeg \n", - " inflating: AD_NC/test/NC/1278852_109.jpeg \n", - " inflating: AD_NC/test/NC/1278852_110.jpeg \n", - " inflating: AD_NC/test/NC/1278852_111.jpeg \n", - " inflating: AD_NC/test/NC/1278852_112.jpeg \n", - " inflating: AD_NC/test/NC/1278852_113.jpeg \n", - " inflating: AD_NC/test/NC/1278852_114.jpeg \n", - " inflating: AD_NC/test/NC/1278852_95.jpeg \n", - " inflating: AD_NC/test/NC/1278852_96.jpeg \n", - " inflating: AD_NC/test/NC/1278852_97.jpeg \n", - " inflating: AD_NC/test/NC/1278852_98.jpeg \n", - " inflating: AD_NC/test/NC/1278852_99.jpeg \n", - " inflating: AD_NC/test/NC/1280292_78.jpeg \n", - " inflating: AD_NC/test/NC/1280292_79.jpeg \n", - " inflating: AD_NC/test/NC/1280292_80.jpeg \n", - " inflating: AD_NC/test/NC/1280292_81.jpeg \n", - " inflating: AD_NC/test/NC/1280292_82.jpeg \n", - " inflating: AD_NC/test/NC/1280292_83.jpeg \n", - " inflating: AD_NC/test/NC/1280292_84.jpeg \n", - " inflating: AD_NC/test/NC/1280292_85.jpeg \n", - " inflating: AD_NC/test/NC/1280292_86.jpeg \n", - " inflating: AD_NC/test/NC/1280292_87.jpeg \n", - " inflating: AD_NC/test/NC/1280292_88.jpeg \n", - " inflating: AD_NC/test/NC/1280292_89.jpeg \n", - " inflating: AD_NC/test/NC/1280292_90.jpeg \n", - " inflating: AD_NC/test/NC/1280292_91.jpeg \n", - " inflating: AD_NC/test/NC/1280292_92.jpeg \n", - " inflating: AD_NC/test/NC/1280292_93.jpeg \n", - " inflating: AD_NC/test/NC/1280292_94.jpeg \n", - " inflating: AD_NC/test/NC/1280292_95.jpeg \n", - " inflating: AD_NC/test/NC/1280292_96.jpeg \n", - " inflating: AD_NC/test/NC/1280292_97.jpeg \n", - " inflating: AD_NC/test/NC/1280797_78.jpeg \n", - " inflating: AD_NC/test/NC/1280797_79.jpeg \n", - " inflating: AD_NC/test/NC/1280797_80.jpeg \n", - " inflating: AD_NC/test/NC/1280797_81.jpeg \n", - " inflating: AD_NC/test/NC/1280797_82.jpeg \n", - " inflating: AD_NC/test/NC/1280797_83.jpeg \n", - " inflating: AD_NC/test/NC/1280797_84.jpeg \n", - " inflating: AD_NC/test/NC/1280797_85.jpeg \n", - " inflating: AD_NC/test/NC/1280797_86.jpeg \n", - " inflating: AD_NC/test/NC/1280797_87.jpeg \n", - " inflating: AD_NC/test/NC/1280797_88.jpeg \n", - " inflating: AD_NC/test/NC/1280797_89.jpeg \n", - " inflating: AD_NC/test/NC/1280797_90.jpeg \n", - " inflating: AD_NC/test/NC/1280797_91.jpeg \n", - " inflating: AD_NC/test/NC/1280797_92.jpeg \n", - " inflating: AD_NC/test/NC/1280797_93.jpeg \n", - " inflating: AD_NC/test/NC/1280797_94.jpeg \n", - " inflating: AD_NC/test/NC/1280797_95.jpeg \n", - " inflating: AD_NC/test/NC/1280797_96.jpeg \n", - " inflating: AD_NC/test/NC/1280797_97.jpeg \n", - " inflating: AD_NC/test/NC/1281566_78.jpeg \n", - " inflating: AD_NC/test/NC/1281566_79.jpeg \n", - " inflating: AD_NC/test/NC/1281566_80.jpeg \n", - " inflating: AD_NC/test/NC/1281566_81.jpeg \n", - " inflating: AD_NC/test/NC/1281566_82.jpeg \n", - " inflating: AD_NC/test/NC/1281566_83.jpeg \n", - " inflating: AD_NC/test/NC/1281566_84.jpeg \n", - " inflating: AD_NC/test/NC/1281566_85.jpeg \n", - " inflating: AD_NC/test/NC/1281566_86.jpeg \n", - " inflating: AD_NC/test/NC/1281566_87.jpeg \n", - " inflating: AD_NC/test/NC/1281566_88.jpeg \n", - " inflating: AD_NC/test/NC/1281566_89.jpeg \n", - " inflating: AD_NC/test/NC/1281566_90.jpeg \n", - " inflating: AD_NC/test/NC/1281566_91.jpeg \n", - " inflating: AD_NC/test/NC/1281566_92.jpeg \n", - " inflating: AD_NC/test/NC/1281566_93.jpeg \n", - " inflating: AD_NC/test/NC/1281566_94.jpeg \n", - " inflating: AD_NC/test/NC/1281566_95.jpeg \n", - " inflating: AD_NC/test/NC/1281566_96.jpeg \n", - " inflating: AD_NC/test/NC/1281566_97.jpeg \n", - " inflating: AD_NC/test/NC/1281567_78.jpeg \n", - " inflating: AD_NC/test/NC/1281567_79.jpeg \n", - " inflating: AD_NC/test/NC/1281567_80.jpeg \n", - " inflating: AD_NC/test/NC/1281567_81.jpeg \n", - " inflating: AD_NC/test/NC/1281567_82.jpeg \n", - " inflating: AD_NC/test/NC/1281567_83.jpeg \n", - " inflating: AD_NC/test/NC/1281567_84.jpeg \n", - " inflating: AD_NC/test/NC/1281567_85.jpeg \n", - " inflating: AD_NC/test/NC/1281567_86.jpeg \n", - " inflating: AD_NC/test/NC/1281567_87.jpeg \n", - " inflating: AD_NC/test/NC/1281567_88.jpeg \n", - " inflating: AD_NC/test/NC/1281567_89.jpeg \n", - " inflating: AD_NC/test/NC/1281567_90.jpeg \n", - " inflating: AD_NC/test/NC/1281567_91.jpeg \n", - " inflating: AD_NC/test/NC/1281567_92.jpeg \n", - " inflating: AD_NC/test/NC/1281567_93.jpeg \n", - " inflating: AD_NC/test/NC/1281567_94.jpeg \n", - " inflating: AD_NC/test/NC/1281567_95.jpeg \n", - " inflating: AD_NC/test/NC/1281567_96.jpeg \n", - " inflating: AD_NC/test/NC/1281567_97.jpeg \n", - " inflating: AD_NC/test/NC/1281631_100.jpeg \n", - " inflating: AD_NC/test/NC/1281631_101.jpeg \n", - " inflating: AD_NC/test/NC/1281631_102.jpeg \n", - " inflating: AD_NC/test/NC/1281631_103.jpeg \n", - " inflating: AD_NC/test/NC/1281631_104.jpeg \n", - " inflating: AD_NC/test/NC/1281631_105.jpeg \n", - " inflating: AD_NC/test/NC/1281631_106.jpeg \n", - " inflating: AD_NC/test/NC/1281631_107.jpeg \n", - " inflating: AD_NC/test/NC/1281631_108.jpeg \n", - " inflating: AD_NC/test/NC/1281631_109.jpeg \n", - " inflating: AD_NC/test/NC/1281631_110.jpeg \n", - " inflating: AD_NC/test/NC/1281631_111.jpeg \n", - " inflating: AD_NC/test/NC/1281631_112.jpeg \n", - " inflating: AD_NC/test/NC/1281631_113.jpeg \n", - " inflating: AD_NC/test/NC/1281631_94.jpeg \n", - " inflating: AD_NC/test/NC/1281631_95.jpeg \n", - " inflating: AD_NC/test/NC/1281631_96.jpeg \n", - " inflating: AD_NC/test/NC/1281631_97.jpeg \n", - " inflating: AD_NC/test/NC/1281631_98.jpeg \n", - " inflating: AD_NC/test/NC/1281631_99.jpeg \n", - " inflating: AD_NC/test/NC/1284408_100.jpeg \n", - " inflating: AD_NC/test/NC/1284408_101.jpeg \n", - " inflating: AD_NC/test/NC/1284408_102.jpeg \n", - " inflating: AD_NC/test/NC/1284408_103.jpeg \n", - " inflating: AD_NC/test/NC/1284408_104.jpeg \n", - " inflating: AD_NC/test/NC/1284408_105.jpeg \n", - " inflating: AD_NC/test/NC/1284408_106.jpeg \n", - " inflating: AD_NC/test/NC/1284408_107.jpeg \n", - " inflating: AD_NC/test/NC/1284408_108.jpeg \n", - " inflating: AD_NC/test/NC/1284408_109.jpeg \n", - " inflating: AD_NC/test/NC/1284408_110.jpeg \n", - " inflating: AD_NC/test/NC/1284408_111.jpeg \n", - " inflating: AD_NC/test/NC/1284408_112.jpeg \n", - " inflating: AD_NC/test/NC/1284408_113.jpeg \n", - " inflating: AD_NC/test/NC/1284408_94.jpeg \n", - " inflating: AD_NC/test/NC/1284408_95.jpeg \n", - " inflating: AD_NC/test/NC/1284408_96.jpeg \n", - " inflating: AD_NC/test/NC/1284408_97.jpeg \n", - " inflating: AD_NC/test/NC/1284408_98.jpeg \n", - " inflating: AD_NC/test/NC/1284408_99.jpeg \n", - " inflating: AD_NC/test/NC/1284808_100.jpeg \n", - " inflating: AD_NC/test/NC/1284808_101.jpeg \n", - " inflating: AD_NC/test/NC/1284808_102.jpeg \n", - " inflating: AD_NC/test/NC/1284808_103.jpeg \n", - " inflating: AD_NC/test/NC/1284808_104.jpeg \n", - " inflating: AD_NC/test/NC/1284808_105.jpeg \n", - " inflating: AD_NC/test/NC/1284808_106.jpeg \n", - " inflating: AD_NC/test/NC/1284808_107.jpeg \n", - " inflating: AD_NC/test/NC/1284808_108.jpeg \n", - " inflating: AD_NC/test/NC/1284808_109.jpeg \n", - " inflating: AD_NC/test/NC/1284808_110.jpeg \n", - " inflating: AD_NC/test/NC/1284808_111.jpeg \n", - " inflating: AD_NC/test/NC/1284808_112.jpeg \n", - " inflating: AD_NC/test/NC/1284808_113.jpeg \n", - " inflating: AD_NC/test/NC/1284808_94.jpeg \n", - " inflating: AD_NC/test/NC/1284808_95.jpeg \n", - " inflating: AD_NC/test/NC/1284808_96.jpeg \n", - " inflating: AD_NC/test/NC/1284808_97.jpeg \n", - " inflating: AD_NC/test/NC/1284808_98.jpeg \n", - " inflating: AD_NC/test/NC/1284808_99.jpeg \n", - " inflating: AD_NC/test/NC/1285188_100.jpeg \n", - " inflating: AD_NC/test/NC/1285188_101.jpeg \n", - " inflating: AD_NC/test/NC/1285188_102.jpeg \n", - " inflating: AD_NC/test/NC/1285188_103.jpeg \n", - " inflating: AD_NC/test/NC/1285188_104.jpeg \n", - " inflating: AD_NC/test/NC/1285188_105.jpeg \n", - " inflating: AD_NC/test/NC/1285188_106.jpeg \n", - " inflating: AD_NC/test/NC/1285188_107.jpeg \n", - " inflating: AD_NC/test/NC/1285188_88.jpeg \n", - " inflating: AD_NC/test/NC/1285188_89.jpeg \n", - " inflating: AD_NC/test/NC/1285188_90.jpeg \n", - " inflating: AD_NC/test/NC/1285188_91.jpeg \n", - " inflating: AD_NC/test/NC/1285188_92.jpeg \n", - " inflating: AD_NC/test/NC/1285188_93.jpeg \n", - " inflating: AD_NC/test/NC/1285188_94.jpeg \n", - " inflating: AD_NC/test/NC/1285188_95.jpeg \n", - " inflating: AD_NC/test/NC/1285188_96.jpeg \n", - " inflating: AD_NC/test/NC/1285188_97.jpeg \n", - " inflating: AD_NC/test/NC/1285188_98.jpeg \n", - " inflating: AD_NC/test/NC/1285188_99.jpeg \n", - " inflating: AD_NC/test/NC/1285276_78.jpeg \n", - " inflating: AD_NC/test/NC/1285276_79.jpeg \n", - " inflating: AD_NC/test/NC/1285276_80.jpeg \n", - " inflating: AD_NC/test/NC/1285276_81.jpeg \n", - " inflating: AD_NC/test/NC/1285276_82.jpeg \n", - " inflating: AD_NC/test/NC/1285276_83.jpeg \n", - " inflating: AD_NC/test/NC/1285276_84.jpeg \n", - " inflating: AD_NC/test/NC/1285276_85.jpeg \n", - " inflating: AD_NC/test/NC/1285276_86.jpeg \n", - " inflating: AD_NC/test/NC/1285276_87.jpeg \n", - " inflating: AD_NC/test/NC/1285276_88.jpeg \n", - " inflating: AD_NC/test/NC/1285276_89.jpeg \n", - " inflating: AD_NC/test/NC/1285276_90.jpeg \n", - " inflating: AD_NC/test/NC/1285276_91.jpeg \n", - " inflating: AD_NC/test/NC/1285276_92.jpeg \n", - " inflating: AD_NC/test/NC/1285276_93.jpeg \n", - " inflating: AD_NC/test/NC/1285276_94.jpeg \n", - " inflating: AD_NC/test/NC/1285276_95.jpeg \n", - " inflating: AD_NC/test/NC/1285276_96.jpeg \n", - " inflating: AD_NC/test/NC/1285276_97.jpeg \n", - " inflating: AD_NC/test/NC/1285703_100.jpeg \n", - " inflating: AD_NC/test/NC/1285703_101.jpeg \n", - " inflating: AD_NC/test/NC/1285703_102.jpeg \n", - " inflating: AD_NC/test/NC/1285703_103.jpeg \n", - " inflating: AD_NC/test/NC/1285703_104.jpeg \n", - " inflating: AD_NC/test/NC/1285703_105.jpeg \n", - " inflating: AD_NC/test/NC/1285703_106.jpeg \n", - " inflating: AD_NC/test/NC/1285703_107.jpeg \n", - " inflating: AD_NC/test/NC/1285703_108.jpeg \n", - " inflating: AD_NC/test/NC/1285703_109.jpeg \n", - " inflating: AD_NC/test/NC/1285703_110.jpeg \n", - " inflating: AD_NC/test/NC/1285703_111.jpeg \n", - " inflating: AD_NC/test/NC/1285703_112.jpeg \n", - " inflating: AD_NC/test/NC/1285703_113.jpeg \n", - " inflating: AD_NC/test/NC/1285703_114.jpeg \n", - " inflating: AD_NC/test/NC/1285703_95.jpeg \n", - " inflating: AD_NC/test/NC/1285703_96.jpeg \n", - " inflating: AD_NC/test/NC/1285703_97.jpeg \n", - " inflating: AD_NC/test/NC/1285703_98.jpeg \n", - " inflating: AD_NC/test/NC/1285703_99.jpeg \n", - " inflating: AD_NC/test/NC/1285929_100.jpeg \n", - " inflating: AD_NC/test/NC/1285929_101.jpeg \n", - " inflating: AD_NC/test/NC/1285929_102.jpeg \n", - " inflating: AD_NC/test/NC/1285929_103.jpeg \n", - " inflating: AD_NC/test/NC/1285929_104.jpeg \n", - " inflating: AD_NC/test/NC/1285929_105.jpeg \n", - " inflating: AD_NC/test/NC/1285929_106.jpeg \n", - " inflating: AD_NC/test/NC/1285929_107.jpeg \n", - " inflating: AD_NC/test/NC/1285929_108.jpeg \n", - " inflating: AD_NC/test/NC/1285929_109.jpeg \n", - " inflating: AD_NC/test/NC/1285929_110.jpeg \n", - " inflating: AD_NC/test/NC/1285929_111.jpeg \n", - " inflating: AD_NC/test/NC/1285929_112.jpeg \n", - " inflating: AD_NC/test/NC/1285929_113.jpeg \n", - " inflating: AD_NC/test/NC/1285929_114.jpeg \n", - " inflating: AD_NC/test/NC/1285929_95.jpeg \n", - " inflating: AD_NC/test/NC/1285929_96.jpeg \n", - " inflating: AD_NC/test/NC/1285929_97.jpeg \n", - " inflating: AD_NC/test/NC/1285929_98.jpeg \n", - " inflating: AD_NC/test/NC/1285929_99.jpeg \n", - " inflating: AD_NC/test/NC/1287192_100.jpeg \n", - " inflating: AD_NC/test/NC/1287192_101.jpeg \n", - " inflating: AD_NC/test/NC/1287192_102.jpeg \n", - " inflating: AD_NC/test/NC/1287192_103.jpeg \n", - " inflating: AD_NC/test/NC/1287192_104.jpeg \n", - " inflating: AD_NC/test/NC/1287192_105.jpeg \n", - " inflating: AD_NC/test/NC/1287192_106.jpeg \n", - " inflating: AD_NC/test/NC/1287192_107.jpeg \n", - " inflating: AD_NC/test/NC/1287192_108.jpeg \n", - " inflating: AD_NC/test/NC/1287192_109.jpeg \n", - " inflating: AD_NC/test/NC/1287192_110.jpeg \n", - " inflating: AD_NC/test/NC/1287192_111.jpeg \n", - " inflating: AD_NC/test/NC/1287192_112.jpeg \n", - " inflating: AD_NC/test/NC/1287192_113.jpeg \n", - " inflating: AD_NC/test/NC/1287192_94.jpeg \n", - " inflating: AD_NC/test/NC/1287192_95.jpeg \n", - " inflating: AD_NC/test/NC/1287192_96.jpeg \n", - " inflating: AD_NC/test/NC/1287192_97.jpeg \n", - " inflating: AD_NC/test/NC/1287192_98.jpeg \n", - " inflating: AD_NC/test/NC/1287192_99.jpeg \n", - " inflating: AD_NC/test/NC/1291468_100.jpeg \n", - " inflating: AD_NC/test/NC/1291468_101.jpeg \n", - " inflating: AD_NC/test/NC/1291468_102.jpeg \n", - " inflating: AD_NC/test/NC/1291468_103.jpeg \n", - " inflating: AD_NC/test/NC/1291468_104.jpeg \n", - " inflating: AD_NC/test/NC/1291468_105.jpeg \n", - " inflating: AD_NC/test/NC/1291468_106.jpeg \n", - " inflating: AD_NC/test/NC/1291468_107.jpeg \n", - " inflating: AD_NC/test/NC/1291468_88.jpeg \n", - " inflating: AD_NC/test/NC/1291468_89.jpeg \n", - " inflating: AD_NC/test/NC/1291468_90.jpeg \n", - " inflating: AD_NC/test/NC/1291468_91.jpeg \n", - " inflating: AD_NC/test/NC/1291468_92.jpeg \n", - " inflating: AD_NC/test/NC/1291468_93.jpeg \n", - " inflating: AD_NC/test/NC/1291468_94.jpeg \n", - " inflating: AD_NC/test/NC/1291468_95.jpeg \n", - " inflating: AD_NC/test/NC/1291468_96.jpeg \n", - " inflating: AD_NC/test/NC/1291468_97.jpeg \n", - " inflating: AD_NC/test/NC/1291468_98.jpeg \n", - " inflating: AD_NC/test/NC/1291468_99.jpeg \n", - " inflating: AD_NC/test/NC/1291745_100.jpeg \n", - " inflating: AD_NC/test/NC/1291745_101.jpeg \n", - " inflating: AD_NC/test/NC/1291745_102.jpeg \n", - " inflating: AD_NC/test/NC/1291745_103.jpeg \n", - " inflating: AD_NC/test/NC/1291745_104.jpeg \n", - " inflating: AD_NC/test/NC/1291745_105.jpeg \n", - " inflating: AD_NC/test/NC/1291745_106.jpeg \n", - " inflating: AD_NC/test/NC/1291745_107.jpeg \n", - " inflating: AD_NC/test/NC/1291745_108.jpeg \n", - " inflating: AD_NC/test/NC/1291745_109.jpeg \n", - " inflating: AD_NC/test/NC/1291745_110.jpeg \n", - " inflating: AD_NC/test/NC/1291745_111.jpeg \n", - " inflating: AD_NC/test/NC/1291745_112.jpeg \n", - " inflating: AD_NC/test/NC/1291745_113.jpeg \n", - " inflating: AD_NC/test/NC/1291745_94.jpeg \n", - " inflating: AD_NC/test/NC/1291745_95.jpeg \n", - " inflating: AD_NC/test/NC/1291745_96.jpeg \n", - " inflating: AD_NC/test/NC/1291745_97.jpeg \n", - " inflating: AD_NC/test/NC/1291745_98.jpeg \n", - " inflating: AD_NC/test/NC/1291745_99.jpeg \n", - " inflating: AD_NC/test/NC/1293292_100.jpeg \n", - " inflating: AD_NC/test/NC/1293292_101.jpeg \n", - " inflating: AD_NC/test/NC/1293292_102.jpeg \n", - " inflating: AD_NC/test/NC/1293292_103.jpeg \n", - " inflating: AD_NC/test/NC/1293292_104.jpeg \n", - " inflating: AD_NC/test/NC/1293292_105.jpeg \n", - " inflating: AD_NC/test/NC/1293292_106.jpeg \n", - " inflating: AD_NC/test/NC/1293292_107.jpeg \n", - " inflating: AD_NC/test/NC/1293292_108.jpeg \n", - " inflating: AD_NC/test/NC/1293292_109.jpeg \n", - " inflating: AD_NC/test/NC/1293292_110.jpeg \n", - " inflating: AD_NC/test/NC/1293292_111.jpeg \n", - " inflating: AD_NC/test/NC/1293292_112.jpeg \n", - " inflating: AD_NC/test/NC/1293292_113.jpeg \n", - " inflating: AD_NC/test/NC/1293292_114.jpeg \n", - " inflating: AD_NC/test/NC/1293292_95.jpeg \n", - " inflating: AD_NC/test/NC/1293292_96.jpeg \n", - " inflating: AD_NC/test/NC/1293292_97.jpeg \n", - " inflating: AD_NC/test/NC/1293292_98.jpeg \n", - " inflating: AD_NC/test/NC/1293292_99.jpeg \n", - " inflating: AD_NC/test/NC/1293329_100.jpeg \n", - " inflating: AD_NC/test/NC/1293329_101.jpeg \n", - " inflating: AD_NC/test/NC/1293329_102.jpeg \n", - " inflating: AD_NC/test/NC/1293329_103.jpeg \n", - " inflating: AD_NC/test/NC/1293329_104.jpeg \n", - " inflating: AD_NC/test/NC/1293329_105.jpeg \n", - " inflating: AD_NC/test/NC/1293329_106.jpeg \n", - " inflating: AD_NC/test/NC/1293329_107.jpeg \n", - " inflating: AD_NC/test/NC/1293329_88.jpeg \n", - " inflating: AD_NC/test/NC/1293329_89.jpeg \n", - " inflating: AD_NC/test/NC/1293329_90.jpeg \n", - " inflating: AD_NC/test/NC/1293329_91.jpeg \n", - " inflating: AD_NC/test/NC/1293329_92.jpeg \n", - " inflating: AD_NC/test/NC/1293329_93.jpeg \n", - " inflating: AD_NC/test/NC/1293329_94.jpeg \n", - " inflating: AD_NC/test/NC/1293329_95.jpeg \n", - " inflating: AD_NC/test/NC/1293329_96.jpeg \n", - " inflating: AD_NC/test/NC/1293329_97.jpeg \n", - " inflating: AD_NC/test/NC/1293329_98.jpeg \n", - " inflating: AD_NC/test/NC/1293329_99.jpeg \n", - " inflating: AD_NC/test/NC/1293823_100.jpeg \n", - " inflating: AD_NC/test/NC/1293823_101.jpeg \n", - " inflating: AD_NC/test/NC/1293823_102.jpeg \n", - " inflating: AD_NC/test/NC/1293823_103.jpeg \n", - " inflating: AD_NC/test/NC/1293823_104.jpeg \n", - " inflating: AD_NC/test/NC/1293823_105.jpeg \n", - " inflating: AD_NC/test/NC/1293823_106.jpeg \n", - " inflating: AD_NC/test/NC/1293823_107.jpeg \n", - " inflating: AD_NC/test/NC/1293823_88.jpeg \n", - " inflating: AD_NC/test/NC/1293823_89.jpeg \n", - " inflating: AD_NC/test/NC/1293823_90.jpeg \n", - " inflating: AD_NC/test/NC/1293823_91.jpeg \n", - " inflating: AD_NC/test/NC/1293823_92.jpeg \n", - " inflating: AD_NC/test/NC/1293823_93.jpeg \n", - " inflating: AD_NC/test/NC/1293823_94.jpeg \n", - " inflating: AD_NC/test/NC/1293823_95.jpeg \n", - " inflating: AD_NC/test/NC/1293823_96.jpeg \n", - " inflating: AD_NC/test/NC/1293823_97.jpeg \n", - " inflating: AD_NC/test/NC/1293823_98.jpeg \n", - " inflating: AD_NC/test/NC/1293823_99.jpeg \n", - " inflating: AD_NC/test/NC/1295347_100.jpeg \n", - " inflating: AD_NC/test/NC/1295347_101.jpeg \n", - " inflating: AD_NC/test/NC/1295347_102.jpeg \n", - " inflating: AD_NC/test/NC/1295347_103.jpeg \n", - " inflating: AD_NC/test/NC/1295347_104.jpeg \n", - " inflating: AD_NC/test/NC/1295347_105.jpeg \n", - " inflating: AD_NC/test/NC/1295347_106.jpeg \n", - " inflating: AD_NC/test/NC/1295347_107.jpeg \n", - " inflating: AD_NC/test/NC/1295347_108.jpeg \n", - " inflating: AD_NC/test/NC/1295347_109.jpeg \n", - " inflating: AD_NC/test/NC/1295347_110.jpeg \n", - " inflating: AD_NC/test/NC/1295347_111.jpeg \n", - " inflating: AD_NC/test/NC/1295347_112.jpeg \n", - " inflating: AD_NC/test/NC/1295347_113.jpeg \n", - " inflating: AD_NC/test/NC/1295347_94.jpeg \n", - " inflating: AD_NC/test/NC/1295347_95.jpeg \n", - " inflating: AD_NC/test/NC/1295347_96.jpeg \n", - " inflating: AD_NC/test/NC/1295347_97.jpeg \n", - " inflating: AD_NC/test/NC/1295347_98.jpeg \n", - " inflating: AD_NC/test/NC/1295347_99.jpeg \n", - " inflating: AD_NC/test/NC/1296519_78.jpeg \n", - " inflating: AD_NC/test/NC/1296519_79.jpeg \n", - " inflating: AD_NC/test/NC/1296519_80.jpeg \n", - " inflating: AD_NC/test/NC/1296519_81.jpeg \n", - " inflating: AD_NC/test/NC/1296519_82.jpeg \n", - " inflating: AD_NC/test/NC/1296519_83.jpeg \n", - " inflating: AD_NC/test/NC/1296519_84.jpeg \n", - " inflating: AD_NC/test/NC/1296519_85.jpeg \n", - " inflating: AD_NC/test/NC/1296519_86.jpeg \n", - " inflating: AD_NC/test/NC/1296519_87.jpeg \n", - " inflating: AD_NC/test/NC/1296519_88.jpeg \n", - " inflating: AD_NC/test/NC/1296519_89.jpeg \n", - " inflating: AD_NC/test/NC/1296519_90.jpeg \n", - " inflating: AD_NC/test/NC/1296519_91.jpeg \n", - " inflating: AD_NC/test/NC/1296519_92.jpeg \n", - " inflating: AD_NC/test/NC/1296519_93.jpeg \n", - " inflating: AD_NC/test/NC/1296519_94.jpeg \n", - " inflating: AD_NC/test/NC/1296519_95.jpeg \n", - " inflating: AD_NC/test/NC/1296519_96.jpeg \n", - " inflating: AD_NC/test/NC/1296519_97.jpeg \n", - " inflating: AD_NC/test/NC/1299107_78.jpeg \n", - " inflating: AD_NC/test/NC/1299107_79.jpeg \n", - " inflating: AD_NC/test/NC/1299107_80.jpeg \n", - " inflating: AD_NC/test/NC/1299107_81.jpeg \n", - " inflating: AD_NC/test/NC/1299107_82.jpeg \n", - " inflating: AD_NC/test/NC/1299107_83.jpeg \n", - " inflating: AD_NC/test/NC/1299107_84.jpeg \n", - " inflating: AD_NC/test/NC/1299107_85.jpeg \n", - " inflating: AD_NC/test/NC/1299107_86.jpeg \n", - " inflating: AD_NC/test/NC/1299107_87.jpeg \n", - " inflating: AD_NC/test/NC/1299107_88.jpeg \n", - " inflating: AD_NC/test/NC/1299107_89.jpeg \n", - " inflating: AD_NC/test/NC/1299107_90.jpeg \n", - " inflating: AD_NC/test/NC/1299107_91.jpeg \n", - " inflating: AD_NC/test/NC/1299107_92.jpeg \n", - " inflating: AD_NC/test/NC/1299107_93.jpeg \n", - " inflating: AD_NC/test/NC/1299107_94.jpeg \n", - " inflating: AD_NC/test/NC/1299107_95.jpeg \n", - " inflating: AD_NC/test/NC/1299107_96.jpeg \n", - " inflating: AD_NC/test/NC/1299107_97.jpeg \n", - " inflating: AD_NC/test/NC/1299200_100.jpeg \n", - " inflating: AD_NC/test/NC/1299200_101.jpeg \n", - " inflating: AD_NC/test/NC/1299200_102.jpeg \n", - " inflating: AD_NC/test/NC/1299200_103.jpeg \n", - " inflating: AD_NC/test/NC/1299200_104.jpeg \n", - " inflating: AD_NC/test/NC/1299200_105.jpeg \n", - " inflating: AD_NC/test/NC/1299200_106.jpeg \n", - " inflating: AD_NC/test/NC/1299200_107.jpeg \n", - " inflating: AD_NC/test/NC/1299200_88.jpeg \n", - " inflating: AD_NC/test/NC/1299200_89.jpeg \n", - " inflating: AD_NC/test/NC/1299200_90.jpeg \n", - " inflating: AD_NC/test/NC/1299200_91.jpeg \n", - " inflating: AD_NC/test/NC/1299200_92.jpeg \n", - " inflating: AD_NC/test/NC/1299200_93.jpeg \n", - " inflating: AD_NC/test/NC/1299200_94.jpeg \n", - " inflating: AD_NC/test/NC/1299200_95.jpeg \n", - " inflating: AD_NC/test/NC/1299200_96.jpeg \n", - " inflating: AD_NC/test/NC/1299200_97.jpeg \n", - " inflating: AD_NC/test/NC/1299200_98.jpeg \n", - " inflating: AD_NC/test/NC/1299200_99.jpeg \n", - " inflating: AD_NC/test/NC/1299912_78.jpeg \n", - " inflating: AD_NC/test/NC/1299912_79.jpeg \n", - " inflating: AD_NC/test/NC/1299912_80.jpeg \n", - " inflating: AD_NC/test/NC/1299912_81.jpeg \n", - " inflating: AD_NC/test/NC/1299912_82.jpeg \n", - " inflating: AD_NC/test/NC/1299912_83.jpeg \n", - " inflating: AD_NC/test/NC/1299912_84.jpeg \n", - " inflating: AD_NC/test/NC/1299912_85.jpeg \n", - " inflating: AD_NC/test/NC/1299912_86.jpeg \n", - " inflating: AD_NC/test/NC/1299912_87.jpeg \n", - " inflating: AD_NC/test/NC/1299912_88.jpeg \n", - " inflating: AD_NC/test/NC/1299912_89.jpeg \n", - " inflating: AD_NC/test/NC/1299912_90.jpeg \n", - " inflating: AD_NC/test/NC/1299912_91.jpeg \n", - " inflating: AD_NC/test/NC/1299912_92.jpeg \n", - " inflating: AD_NC/test/NC/1299912_93.jpeg \n", - " inflating: AD_NC/test/NC/1299912_94.jpeg \n", - " inflating: AD_NC/test/NC/1299912_95.jpeg \n", - " inflating: AD_NC/test/NC/1299912_96.jpeg \n", - " inflating: AD_NC/test/NC/1299912_97.jpeg \n", - " inflating: AD_NC/test/NC/1299913_78.jpeg \n", - " inflating: AD_NC/test/NC/1299913_79.jpeg \n", - " inflating: AD_NC/test/NC/1299913_80.jpeg \n", - " inflating: AD_NC/test/NC/1299913_81.jpeg \n", - " inflating: AD_NC/test/NC/1299913_82.jpeg \n", - " inflating: AD_NC/test/NC/1299913_83.jpeg \n", - " inflating: AD_NC/test/NC/1299913_84.jpeg \n", - " inflating: AD_NC/test/NC/1299913_85.jpeg \n", - " inflating: AD_NC/test/NC/1299913_86.jpeg \n", - " inflating: AD_NC/test/NC/1299913_87.jpeg \n", - " inflating: AD_NC/test/NC/1299913_88.jpeg \n", - " inflating: AD_NC/test/NC/1299913_89.jpeg \n", - " inflating: AD_NC/test/NC/1299913_90.jpeg \n", - " inflating: AD_NC/test/NC/1299913_91.jpeg \n", - " inflating: AD_NC/test/NC/1299913_92.jpeg \n", - " inflating: AD_NC/test/NC/1299913_93.jpeg \n", - " inflating: AD_NC/test/NC/1299913_94.jpeg \n", - " inflating: AD_NC/test/NC/1299913_95.jpeg \n", - " inflating: AD_NC/test/NC/1299913_96.jpeg \n", - " inflating: AD_NC/test/NC/1299913_97.jpeg \n", - " inflating: AD_NC/test/NC/1302056_100.jpeg \n", - " inflating: AD_NC/test/NC/1302056_101.jpeg \n", - " inflating: AD_NC/test/NC/1302056_102.jpeg \n", - " inflating: AD_NC/test/NC/1302056_103.jpeg \n", - " inflating: AD_NC/test/NC/1302056_104.jpeg \n", - " inflating: AD_NC/test/NC/1302056_105.jpeg \n", - " inflating: AD_NC/test/NC/1302056_106.jpeg \n", - " inflating: AD_NC/test/NC/1302056_107.jpeg \n", - " inflating: AD_NC/test/NC/1302056_88.jpeg \n", - " inflating: AD_NC/test/NC/1302056_89.jpeg \n", - " inflating: AD_NC/test/NC/1302056_90.jpeg \n", - " inflating: AD_NC/test/NC/1302056_91.jpeg \n", - " inflating: AD_NC/test/NC/1302056_92.jpeg \n", - " inflating: AD_NC/test/NC/1302056_93.jpeg \n", - " inflating: AD_NC/test/NC/1302056_94.jpeg \n", - " inflating: AD_NC/test/NC/1302056_95.jpeg \n", - " inflating: AD_NC/test/NC/1302056_96.jpeg \n", - " inflating: AD_NC/test/NC/1302056_97.jpeg \n", - " inflating: AD_NC/test/NC/1302056_98.jpeg \n", - " inflating: AD_NC/test/NC/1302056_99.jpeg \n", - " inflating: AD_NC/test/NC/1303143_100.jpeg \n", - " inflating: AD_NC/test/NC/1303143_101.jpeg \n", - " inflating: AD_NC/test/NC/1303143_102.jpeg \n", - " inflating: AD_NC/test/NC/1303143_103.jpeg \n", - " inflating: AD_NC/test/NC/1303143_104.jpeg \n", - " inflating: AD_NC/test/NC/1303143_105.jpeg \n", - " inflating: AD_NC/test/NC/1303143_106.jpeg \n", - " inflating: AD_NC/test/NC/1303143_107.jpeg \n", - " inflating: AD_NC/test/NC/1303143_108.jpeg \n", - " inflating: AD_NC/test/NC/1303143_109.jpeg \n", - " inflating: AD_NC/test/NC/1303143_110.jpeg \n", - " inflating: AD_NC/test/NC/1303143_111.jpeg \n", - " inflating: AD_NC/test/NC/1303143_112.jpeg \n", - " inflating: AD_NC/test/NC/1303143_113.jpeg \n", - " inflating: AD_NC/test/NC/1303143_94.jpeg \n", - " inflating: AD_NC/test/NC/1303143_95.jpeg \n", - " inflating: AD_NC/test/NC/1303143_96.jpeg \n", - " inflating: AD_NC/test/NC/1303143_97.jpeg \n", - " inflating: AD_NC/test/NC/1303143_98.jpeg \n", - " inflating: AD_NC/test/NC/1303143_99.jpeg \n", - " inflating: AD_NC/test/NC/1304067_78.jpeg \n", - " inflating: AD_NC/test/NC/1304067_79.jpeg \n", - " inflating: AD_NC/test/NC/1304067_80.jpeg \n", - " inflating: AD_NC/test/NC/1304067_81.jpeg \n", - " inflating: AD_NC/test/NC/1304067_82.jpeg \n", - " inflating: AD_NC/test/NC/1304067_83.jpeg \n", - " inflating: AD_NC/test/NC/1304067_84.jpeg \n", - " inflating: AD_NC/test/NC/1304067_85.jpeg \n", - " inflating: AD_NC/test/NC/1304067_86.jpeg \n", - " inflating: AD_NC/test/NC/1304067_87.jpeg \n", - " inflating: AD_NC/test/NC/1304067_88.jpeg \n", - " inflating: AD_NC/test/NC/1304067_89.jpeg \n", - " inflating: AD_NC/test/NC/1304067_90.jpeg \n", - " inflating: AD_NC/test/NC/1304067_91.jpeg \n", - " inflating: AD_NC/test/NC/1304067_92.jpeg \n", - " inflating: AD_NC/test/NC/1304067_93.jpeg \n", - " inflating: AD_NC/test/NC/1304067_94.jpeg \n", - " inflating: AD_NC/test/NC/1304067_95.jpeg \n", - " inflating: AD_NC/test/NC/1304067_96.jpeg \n", - " inflating: AD_NC/test/NC/1304067_97.jpeg \n", - " inflating: AD_NC/test/NC/1304645_100.jpeg \n", - " inflating: AD_NC/test/NC/1304645_101.jpeg \n", - " inflating: AD_NC/test/NC/1304645_102.jpeg \n", - " inflating: AD_NC/test/NC/1304645_103.jpeg \n", - " inflating: AD_NC/test/NC/1304645_104.jpeg \n", - " inflating: AD_NC/test/NC/1304645_105.jpeg \n", - " inflating: AD_NC/test/NC/1304645_106.jpeg \n", - " inflating: AD_NC/test/NC/1304645_107.jpeg \n", - " inflating: AD_NC/test/NC/1304645_108.jpeg \n", - " inflating: AD_NC/test/NC/1304645_109.jpeg \n", - " inflating: AD_NC/test/NC/1304645_110.jpeg \n", - " inflating: AD_NC/test/NC/1304645_111.jpeg \n", - " inflating: AD_NC/test/NC/1304645_112.jpeg \n", - " inflating: AD_NC/test/NC/1304645_113.jpeg \n", - " inflating: AD_NC/test/NC/1304645_94.jpeg \n", - " inflating: AD_NC/test/NC/1304645_95.jpeg \n", - " inflating: AD_NC/test/NC/1304645_96.jpeg \n", - " inflating: AD_NC/test/NC/1304645_97.jpeg \n", - " inflating: AD_NC/test/NC/1304645_98.jpeg \n", - " inflating: AD_NC/test/NC/1304645_99.jpeg \n", - " inflating: AD_NC/test/NC/1316573_100.jpeg \n", - " inflating: AD_NC/test/NC/1316573_101.jpeg \n", - " inflating: AD_NC/test/NC/1316573_102.jpeg \n", - " inflating: AD_NC/test/NC/1316573_103.jpeg \n", - " inflating: AD_NC/test/NC/1316573_104.jpeg \n", - " inflating: AD_NC/test/NC/1316573_105.jpeg \n", - " inflating: AD_NC/test/NC/1316573_106.jpeg \n", - " inflating: AD_NC/test/NC/1316573_107.jpeg \n", - " inflating: AD_NC/test/NC/1316573_108.jpeg \n", - " inflating: AD_NC/test/NC/1316573_109.jpeg \n", - " inflating: AD_NC/test/NC/1316573_110.jpeg \n", - " inflating: AD_NC/test/NC/1316573_111.jpeg \n", - " inflating: AD_NC/test/NC/1316573_112.jpeg \n", - " inflating: AD_NC/test/NC/1316573_113.jpeg \n", - " inflating: AD_NC/test/NC/1316573_114.jpeg \n", - " inflating: AD_NC/test/NC/1316573_95.jpeg \n", - " inflating: AD_NC/test/NC/1316573_96.jpeg \n", - " inflating: AD_NC/test/NC/1316573_97.jpeg \n", - " inflating: AD_NC/test/NC/1316573_98.jpeg \n", - " inflating: AD_NC/test/NC/1316573_99.jpeg \n", - " inflating: AD_NC/test/NC/1319246_100.jpeg \n", - " inflating: AD_NC/test/NC/1319246_101.jpeg \n", - " inflating: AD_NC/test/NC/1319246_102.jpeg \n", - " inflating: AD_NC/test/NC/1319246_103.jpeg \n", - " inflating: AD_NC/test/NC/1319246_104.jpeg \n", - " inflating: AD_NC/test/NC/1319246_105.jpeg \n", - " inflating: AD_NC/test/NC/1319246_106.jpeg \n", - " inflating: AD_NC/test/NC/1319246_107.jpeg \n", - " inflating: AD_NC/test/NC/1319246_88.jpeg \n", - " inflating: AD_NC/test/NC/1319246_89.jpeg \n", - " inflating: AD_NC/test/NC/1319246_90.jpeg \n", - " inflating: AD_NC/test/NC/1319246_91.jpeg \n", - " inflating: AD_NC/test/NC/1319246_92.jpeg \n", - " inflating: AD_NC/test/NC/1319246_93.jpeg \n", - " inflating: AD_NC/test/NC/1319246_94.jpeg \n", - " inflating: AD_NC/test/NC/1319246_95.jpeg \n", - " inflating: AD_NC/test/NC/1319246_96.jpeg \n", - " inflating: AD_NC/test/NC/1319246_97.jpeg \n", - " inflating: AD_NC/test/NC/1319246_98.jpeg \n", - " inflating: AD_NC/test/NC/1319246_99.jpeg \n", - " inflating: AD_NC/test/NC/1319247_100.jpeg \n", - " inflating: AD_NC/test/NC/1319247_101.jpeg \n", - " inflating: AD_NC/test/NC/1319247_102.jpeg \n", - " inflating: AD_NC/test/NC/1319247_103.jpeg \n", - " inflating: AD_NC/test/NC/1319247_104.jpeg \n", - " inflating: AD_NC/test/NC/1319247_105.jpeg \n", - " inflating: AD_NC/test/NC/1319247_106.jpeg \n", - " inflating: AD_NC/test/NC/1319247_107.jpeg \n", - " inflating: AD_NC/test/NC/1319247_88.jpeg \n", - " inflating: AD_NC/test/NC/1319247_89.jpeg \n", - " inflating: AD_NC/test/NC/1319247_90.jpeg \n", - " inflating: AD_NC/test/NC/1319247_91.jpeg \n", - " inflating: AD_NC/test/NC/1319247_92.jpeg \n", - " inflating: AD_NC/test/NC/1319247_93.jpeg \n", - " inflating: AD_NC/test/NC/1319247_94.jpeg \n", - " inflating: AD_NC/test/NC/1319247_95.jpeg \n", - " inflating: AD_NC/test/NC/1319247_96.jpeg \n", - " inflating: AD_NC/test/NC/1319247_97.jpeg \n", - " inflating: AD_NC/test/NC/1319247_98.jpeg \n", - " inflating: AD_NC/test/NC/1319247_99.jpeg \n", - " inflating: AD_NC/test/NC/1319248_100.jpeg \n", - " inflating: AD_NC/test/NC/1319248_101.jpeg \n", - " inflating: AD_NC/test/NC/1319248_102.jpeg \n", - " inflating: AD_NC/test/NC/1319248_103.jpeg \n", - " inflating: AD_NC/test/NC/1319248_104.jpeg \n", - " inflating: AD_NC/test/NC/1319248_105.jpeg \n", - " inflating: AD_NC/test/NC/1319248_106.jpeg \n", - " inflating: AD_NC/test/NC/1319248_107.jpeg \n", - " inflating: AD_NC/test/NC/1319248_88.jpeg \n", - " inflating: AD_NC/test/NC/1319248_89.jpeg \n", - " inflating: AD_NC/test/NC/1319248_90.jpeg \n", - " inflating: AD_NC/test/NC/1319248_91.jpeg \n", - " inflating: AD_NC/test/NC/1319248_92.jpeg \n", - " inflating: AD_NC/test/NC/1319248_93.jpeg \n", - " inflating: AD_NC/test/NC/1319248_94.jpeg \n", - " inflating: AD_NC/test/NC/1319248_95.jpeg \n", - " inflating: AD_NC/test/NC/1319248_96.jpeg \n", - " inflating: AD_NC/test/NC/1319248_97.jpeg \n", - " inflating: AD_NC/test/NC/1319248_98.jpeg \n", - " inflating: AD_NC/test/NC/1319248_99.jpeg \n", - " inflating: AD_NC/test/NC/1319400_100.jpeg \n", - " inflating: AD_NC/test/NC/1319400_101.jpeg \n", - " inflating: AD_NC/test/NC/1319400_102.jpeg \n", - " inflating: AD_NC/test/NC/1319400_103.jpeg \n", - " inflating: AD_NC/test/NC/1319400_104.jpeg \n", - " inflating: AD_NC/test/NC/1319400_105.jpeg \n", - " inflating: AD_NC/test/NC/1319400_106.jpeg \n", - " inflating: AD_NC/test/NC/1319400_107.jpeg \n", - " inflating: AD_NC/test/NC/1319400_88.jpeg \n", - " inflating: AD_NC/test/NC/1319400_89.jpeg \n", - " inflating: AD_NC/test/NC/1319400_90.jpeg \n", - " inflating: AD_NC/test/NC/1319400_91.jpeg \n", - " inflating: AD_NC/test/NC/1319400_92.jpeg \n", - " inflating: AD_NC/test/NC/1319400_93.jpeg \n", - " inflating: AD_NC/test/NC/1319400_94.jpeg \n", - " inflating: AD_NC/test/NC/1319400_95.jpeg \n", - " inflating: AD_NC/test/NC/1319400_96.jpeg \n", - " inflating: AD_NC/test/NC/1319400_97.jpeg \n", - " inflating: AD_NC/test/NC/1319400_98.jpeg \n", - " inflating: AD_NC/test/NC/1319400_99.jpeg \n", - " inflating: AD_NC/test/NC/1320212_78.jpeg \n", - " inflating: AD_NC/test/NC/1320212_79.jpeg \n", - " inflating: AD_NC/test/NC/1320212_80.jpeg \n", - " inflating: AD_NC/test/NC/1320212_81.jpeg \n", - " inflating: AD_NC/test/NC/1320212_82.jpeg \n", - " inflating: AD_NC/test/NC/1320212_83.jpeg \n", - " inflating: AD_NC/test/NC/1320212_84.jpeg \n", - " inflating: AD_NC/test/NC/1320212_85.jpeg \n", - " inflating: AD_NC/test/NC/1320212_86.jpeg \n", - " inflating: AD_NC/test/NC/1320212_87.jpeg \n", - " inflating: AD_NC/test/NC/1320212_88.jpeg \n", - " inflating: AD_NC/test/NC/1320212_89.jpeg \n", - " inflating: AD_NC/test/NC/1320212_90.jpeg \n", - " inflating: AD_NC/test/NC/1320212_91.jpeg \n", - " inflating: AD_NC/test/NC/1320212_92.jpeg \n", - " inflating: AD_NC/test/NC/1320212_93.jpeg \n", - " inflating: AD_NC/test/NC/1320212_94.jpeg \n", - " inflating: AD_NC/test/NC/1320212_95.jpeg \n", - " inflating: AD_NC/test/NC/1320212_96.jpeg \n", - " inflating: AD_NC/test/NC/1320212_97.jpeg \n", - " inflating: AD_NC/test/NC/1320538_78.jpeg \n", - " inflating: AD_NC/test/NC/1320538_79.jpeg \n", - " inflating: AD_NC/test/NC/1320538_80.jpeg \n", - " inflating: AD_NC/test/NC/1320538_81.jpeg \n", - " inflating: AD_NC/test/NC/1320538_82.jpeg \n", - " inflating: AD_NC/test/NC/1320538_83.jpeg \n", - " inflating: AD_NC/test/NC/1320538_84.jpeg \n", - " inflating: AD_NC/test/NC/1320538_85.jpeg \n", - " inflating: AD_NC/test/NC/1320538_86.jpeg \n", - " inflating: AD_NC/test/NC/1320538_87.jpeg \n", - " inflating: AD_NC/test/NC/1320538_88.jpeg \n", - " inflating: AD_NC/test/NC/1320538_89.jpeg \n", - " inflating: AD_NC/test/NC/1320538_90.jpeg \n", - " inflating: AD_NC/test/NC/1320538_91.jpeg \n", - " inflating: AD_NC/test/NC/1320538_92.jpeg \n", - " inflating: AD_NC/test/NC/1320538_93.jpeg \n", - " inflating: AD_NC/test/NC/1320538_94.jpeg \n", - " inflating: AD_NC/test/NC/1320538_95.jpeg \n", - " inflating: AD_NC/test/NC/1320538_96.jpeg \n", - " inflating: AD_NC/test/NC/1320538_97.jpeg \n", - " inflating: AD_NC/test/NC/1320572_78.jpeg \n", - " inflating: AD_NC/test/NC/1320572_79.jpeg \n", - " inflating: AD_NC/test/NC/1320572_80.jpeg \n", - " inflating: AD_NC/test/NC/1320572_81.jpeg \n", - " inflating: AD_NC/test/NC/1320572_82.jpeg \n", - " inflating: AD_NC/test/NC/1320572_83.jpeg \n", - " inflating: AD_NC/test/NC/1320572_84.jpeg \n", - " inflating: AD_NC/test/NC/1320572_85.jpeg \n", - " inflating: AD_NC/test/NC/1320572_86.jpeg \n", - " inflating: AD_NC/test/NC/1320572_87.jpeg \n", - " inflating: AD_NC/test/NC/1320572_88.jpeg \n", - " inflating: AD_NC/test/NC/1320572_89.jpeg \n", - " inflating: AD_NC/test/NC/1320572_90.jpeg \n", - " inflating: AD_NC/test/NC/1320572_91.jpeg \n", - " inflating: AD_NC/test/NC/1320572_92.jpeg \n", - " inflating: AD_NC/test/NC/1320572_93.jpeg \n", - " inflating: AD_NC/test/NC/1320572_94.jpeg \n", - " inflating: AD_NC/test/NC/1320572_95.jpeg \n", - " inflating: AD_NC/test/NC/1320572_96.jpeg \n", - " inflating: AD_NC/test/NC/1320572_97.jpeg \n", - " inflating: AD_NC/test/NC/1320573_78.jpeg \n", - " inflating: AD_NC/test/NC/1320573_79.jpeg \n", - " inflating: AD_NC/test/NC/1320573_80.jpeg \n", - " inflating: AD_NC/test/NC/1320573_81.jpeg \n", - " inflating: AD_NC/test/NC/1320573_82.jpeg \n", - " inflating: AD_NC/test/NC/1320573_83.jpeg \n", - " inflating: AD_NC/test/NC/1320573_84.jpeg \n", - " inflating: AD_NC/test/NC/1320573_85.jpeg \n", - " inflating: AD_NC/test/NC/1320573_86.jpeg \n", - " inflating: AD_NC/test/NC/1320573_87.jpeg \n", - " inflating: AD_NC/test/NC/1320573_88.jpeg \n", - " inflating: AD_NC/test/NC/1320573_89.jpeg \n", - " inflating: AD_NC/test/NC/1320573_90.jpeg \n", - " inflating: AD_NC/test/NC/1320573_91.jpeg \n", - " inflating: AD_NC/test/NC/1320573_92.jpeg \n", - " inflating: AD_NC/test/NC/1320573_93.jpeg \n", - " inflating: AD_NC/test/NC/1320573_94.jpeg \n", - " inflating: AD_NC/test/NC/1320573_95.jpeg \n", - " inflating: AD_NC/test/NC/1320573_96.jpeg \n", - " inflating: AD_NC/test/NC/1320573_97.jpeg \n", - " inflating: AD_NC/test/NC/1323146_100.jpeg \n", - " inflating: AD_NC/test/NC/1323146_101.jpeg \n", - " inflating: AD_NC/test/NC/1323146_102.jpeg \n", - " inflating: AD_NC/test/NC/1323146_103.jpeg \n", - " inflating: AD_NC/test/NC/1323146_104.jpeg \n", - " inflating: AD_NC/test/NC/1323146_105.jpeg \n", - " inflating: AD_NC/test/NC/1323146_106.jpeg \n", - " inflating: AD_NC/test/NC/1323146_107.jpeg \n", - " inflating: AD_NC/test/NC/1323146_88.jpeg \n", - " inflating: AD_NC/test/NC/1323146_89.jpeg \n", - " inflating: AD_NC/test/NC/1323146_90.jpeg \n", - " inflating: AD_NC/test/NC/1323146_91.jpeg \n", - " inflating: AD_NC/test/NC/1323146_92.jpeg \n", - " inflating: AD_NC/test/NC/1323146_93.jpeg \n", - " inflating: AD_NC/test/NC/1323146_94.jpeg \n", - " inflating: AD_NC/test/NC/1323146_95.jpeg \n", - " inflating: AD_NC/test/NC/1323146_96.jpeg \n", - " inflating: AD_NC/test/NC/1323146_97.jpeg \n", - " inflating: AD_NC/test/NC/1323146_98.jpeg \n", - " inflating: AD_NC/test/NC/1323146_99.jpeg \n", - " inflating: AD_NC/test/NC/1324187_78.jpeg \n", - " inflating: AD_NC/test/NC/1324187_79.jpeg \n", - " inflating: AD_NC/test/NC/1324187_80.jpeg \n", - " inflating: AD_NC/test/NC/1324187_81.jpeg \n", - " inflating: AD_NC/test/NC/1324187_82.jpeg \n", - " inflating: AD_NC/test/NC/1324187_83.jpeg \n", - " inflating: AD_NC/test/NC/1324187_84.jpeg \n", - " inflating: AD_NC/test/NC/1324187_85.jpeg \n", - " inflating: AD_NC/test/NC/1324187_86.jpeg \n", - " inflating: AD_NC/test/NC/1324187_87.jpeg \n", - " inflating: AD_NC/test/NC/1324187_88.jpeg \n", - " inflating: AD_NC/test/NC/1324187_89.jpeg \n", - " inflating: AD_NC/test/NC/1324187_90.jpeg \n", - " inflating: AD_NC/test/NC/1324187_91.jpeg \n", - " inflating: AD_NC/test/NC/1324187_92.jpeg \n", - " inflating: AD_NC/test/NC/1324187_93.jpeg \n", - " inflating: AD_NC/test/NC/1324187_94.jpeg \n", - " inflating: AD_NC/test/NC/1324187_95.jpeg \n", - " inflating: AD_NC/test/NC/1324187_96.jpeg \n", - " inflating: AD_NC/test/NC/1324187_97.jpeg \n", - " inflating: AD_NC/test/NC/1324201_78.jpeg \n", - " inflating: AD_NC/test/NC/1324201_79.jpeg \n", - " inflating: AD_NC/test/NC/1324201_80.jpeg \n", - " inflating: AD_NC/test/NC/1324201_81.jpeg \n", - " inflating: AD_NC/test/NC/1324201_82.jpeg \n", - " inflating: AD_NC/test/NC/1324201_83.jpeg \n", - " inflating: AD_NC/test/NC/1324201_84.jpeg \n", - " inflating: AD_NC/test/NC/1324201_85.jpeg \n", - " inflating: AD_NC/test/NC/1324201_86.jpeg \n", - " inflating: AD_NC/test/NC/1324201_87.jpeg \n", - " inflating: AD_NC/test/NC/1324201_88.jpeg \n", - " inflating: AD_NC/test/NC/1324201_89.jpeg \n", - " inflating: AD_NC/test/NC/1324201_90.jpeg \n", - " inflating: AD_NC/test/NC/1324201_91.jpeg \n", - " inflating: AD_NC/test/NC/1324201_92.jpeg \n", - " inflating: AD_NC/test/NC/1324201_93.jpeg \n", - " inflating: AD_NC/test/NC/1324201_94.jpeg \n", - " inflating: AD_NC/test/NC/1324201_95.jpeg \n", - " inflating: AD_NC/test/NC/1324201_96.jpeg \n", - " inflating: AD_NC/test/NC/1324201_97.jpeg \n", - " inflating: AD_NC/test/NC/1325533_100.jpeg \n", - " inflating: AD_NC/test/NC/1325533_101.jpeg \n", - " inflating: AD_NC/test/NC/1325533_102.jpeg \n", - " inflating: AD_NC/test/NC/1325533_103.jpeg \n", - " inflating: AD_NC/test/NC/1325533_104.jpeg \n", - " inflating: AD_NC/test/NC/1325533_105.jpeg \n", - " inflating: AD_NC/test/NC/1325533_106.jpeg \n", - " inflating: AD_NC/test/NC/1325533_107.jpeg \n", - " inflating: AD_NC/test/NC/1325533_88.jpeg \n", - " inflating: AD_NC/test/NC/1325533_89.jpeg \n", - " inflating: AD_NC/test/NC/1325533_90.jpeg \n", - " inflating: AD_NC/test/NC/1325533_91.jpeg \n", - " inflating: AD_NC/test/NC/1325533_92.jpeg \n", - " inflating: AD_NC/test/NC/1325533_93.jpeg \n", - " inflating: AD_NC/test/NC/1325533_94.jpeg \n", - " inflating: AD_NC/test/NC/1325533_95.jpeg \n", - " inflating: AD_NC/test/NC/1325533_96.jpeg \n", - " inflating: AD_NC/test/NC/1325533_97.jpeg \n", - " inflating: AD_NC/test/NC/1325533_98.jpeg \n", - " inflating: AD_NC/test/NC/1325533_99.jpeg \n", - " inflating: AD_NC/test/NC/1325568_100.jpeg \n", - " inflating: AD_NC/test/NC/1325568_101.jpeg \n", - " inflating: AD_NC/test/NC/1325568_102.jpeg \n", - " inflating: AD_NC/test/NC/1325568_103.jpeg \n", - " inflating: AD_NC/test/NC/1325568_104.jpeg \n", - " inflating: AD_NC/test/NC/1325568_105.jpeg \n", - " inflating: AD_NC/test/NC/1325568_106.jpeg \n", - " inflating: AD_NC/test/NC/1325568_107.jpeg \n", - " inflating: AD_NC/test/NC/1325568_108.jpeg \n", - " inflating: AD_NC/test/NC/1325568_109.jpeg \n", - " inflating: AD_NC/test/NC/1325568_110.jpeg \n", - " inflating: AD_NC/test/NC/1325568_111.jpeg \n", - " inflating: AD_NC/test/NC/1325568_112.jpeg \n", - " inflating: AD_NC/test/NC/1325568_113.jpeg \n", - " inflating: AD_NC/test/NC/1325568_94.jpeg \n", - " inflating: AD_NC/test/NC/1325568_95.jpeg \n", - " inflating: AD_NC/test/NC/1325568_96.jpeg \n", - " inflating: AD_NC/test/NC/1325568_97.jpeg \n", - " inflating: AD_NC/test/NC/1325568_98.jpeg \n", - " inflating: AD_NC/test/NC/1325568_99.jpeg \n", - " inflating: AD_NC/test/NC/1325857_100.jpeg \n", - " inflating: AD_NC/test/NC/1325857_101.jpeg \n", - " inflating: AD_NC/test/NC/1325857_102.jpeg \n", - " inflating: AD_NC/test/NC/1325857_103.jpeg \n", - " inflating: AD_NC/test/NC/1325857_104.jpeg \n", - " inflating: AD_NC/test/NC/1325857_105.jpeg \n", - " inflating: AD_NC/test/NC/1325857_106.jpeg \n", - " inflating: AD_NC/test/NC/1325857_107.jpeg \n", - " inflating: AD_NC/test/NC/1325857_108.jpeg \n", - " inflating: AD_NC/test/NC/1325857_109.jpeg \n", - " inflating: AD_NC/test/NC/1325857_110.jpeg \n", - " inflating: AD_NC/test/NC/1325857_111.jpeg \n", - " inflating: AD_NC/test/NC/1325857_112.jpeg \n", - " inflating: AD_NC/test/NC/1325857_113.jpeg \n", - " inflating: AD_NC/test/NC/1325857_94.jpeg \n", - " inflating: AD_NC/test/NC/1325857_95.jpeg \n", - " inflating: AD_NC/test/NC/1325857_96.jpeg \n", - " inflating: AD_NC/test/NC/1325857_97.jpeg \n", - " inflating: AD_NC/test/NC/1325857_98.jpeg \n", - " inflating: AD_NC/test/NC/1325857_99.jpeg \n", - " inflating: AD_NC/test/NC/1326332_100.jpeg \n", - " inflating: AD_NC/test/NC/1326332_101.jpeg \n", - " inflating: AD_NC/test/NC/1326332_102.jpeg \n", - " inflating: AD_NC/test/NC/1326332_103.jpeg \n", - " inflating: AD_NC/test/NC/1326332_104.jpeg \n", - " inflating: AD_NC/test/NC/1326332_105.jpeg \n", - " inflating: AD_NC/test/NC/1326332_106.jpeg \n", - " inflating: AD_NC/test/NC/1326332_107.jpeg \n", - " inflating: AD_NC/test/NC/1326332_108.jpeg \n", - " inflating: AD_NC/test/NC/1326332_109.jpeg \n", - " inflating: AD_NC/test/NC/1326332_110.jpeg \n", - " inflating: AD_NC/test/NC/1326332_111.jpeg \n", - " inflating: AD_NC/test/NC/1326332_112.jpeg \n", - " inflating: AD_NC/test/NC/1326332_113.jpeg \n", - " inflating: AD_NC/test/NC/1326332_94.jpeg \n", - " inflating: AD_NC/test/NC/1326332_95.jpeg \n", - " inflating: AD_NC/test/NC/1326332_96.jpeg \n", - " inflating: AD_NC/test/NC/1326332_97.jpeg \n", - " inflating: AD_NC/test/NC/1326332_98.jpeg \n", - " inflating: AD_NC/test/NC/1326332_99.jpeg \n", - " inflating: AD_NC/test/NC/1327191_100.jpeg \n", - " inflating: AD_NC/test/NC/1327191_101.jpeg \n", - " inflating: AD_NC/test/NC/1327191_102.jpeg \n", - " inflating: AD_NC/test/NC/1327191_103.jpeg \n", - " inflating: AD_NC/test/NC/1327191_104.jpeg \n", - " inflating: AD_NC/test/NC/1327191_105.jpeg \n", - " inflating: AD_NC/test/NC/1327191_106.jpeg \n", - " inflating: AD_NC/test/NC/1327191_107.jpeg \n", - " inflating: AD_NC/test/NC/1327191_108.jpeg \n", - " inflating: AD_NC/test/NC/1327191_109.jpeg \n", - " inflating: AD_NC/test/NC/1327191_110.jpeg \n", - " inflating: AD_NC/test/NC/1327191_111.jpeg \n", - " inflating: AD_NC/test/NC/1327191_112.jpeg \n", - " inflating: AD_NC/test/NC/1327191_113.jpeg \n", - " inflating: AD_NC/test/NC/1327191_94.jpeg \n", - " inflating: AD_NC/test/NC/1327191_95.jpeg \n", - " inflating: AD_NC/test/NC/1327191_96.jpeg \n", - " inflating: AD_NC/test/NC/1327191_97.jpeg \n", - " inflating: AD_NC/test/NC/1327191_98.jpeg \n", - " inflating: AD_NC/test/NC/1327191_99.jpeg \n", - " inflating: AD_NC/test/NC/1327456_100.jpeg \n", - " inflating: AD_NC/test/NC/1327456_101.jpeg \n", - " inflating: AD_NC/test/NC/1327456_102.jpeg \n", - " inflating: AD_NC/test/NC/1327456_103.jpeg \n", - " inflating: AD_NC/test/NC/1327456_104.jpeg \n", - " inflating: AD_NC/test/NC/1327456_105.jpeg \n", - " inflating: AD_NC/test/NC/1327456_106.jpeg \n", - " inflating: AD_NC/test/NC/1327456_107.jpeg \n", - " inflating: AD_NC/test/NC/1327456_108.jpeg \n", - " inflating: AD_NC/test/NC/1327456_109.jpeg \n", - " inflating: AD_NC/test/NC/1327456_110.jpeg \n", - " inflating: AD_NC/test/NC/1327456_111.jpeg \n", - " inflating: AD_NC/test/NC/1327456_112.jpeg \n", - " inflating: AD_NC/test/NC/1327456_113.jpeg \n", - " inflating: AD_NC/test/NC/1327456_94.jpeg \n", - " inflating: AD_NC/test/NC/1327456_95.jpeg \n", - " inflating: AD_NC/test/NC/1327456_96.jpeg \n", - " inflating: AD_NC/test/NC/1327456_97.jpeg \n", - " inflating: AD_NC/test/NC/1327456_98.jpeg \n", - " inflating: AD_NC/test/NC/1327456_99.jpeg \n", - " inflating: AD_NC/test/NC/1327480_100.jpeg \n", - " inflating: AD_NC/test/NC/1327480_101.jpeg \n", - " inflating: AD_NC/test/NC/1327480_102.jpeg \n", - " inflating: AD_NC/test/NC/1327480_103.jpeg \n", - " inflating: AD_NC/test/NC/1327480_104.jpeg \n", - " inflating: AD_NC/test/NC/1327480_105.jpeg \n", - " inflating: AD_NC/test/NC/1327480_106.jpeg \n", - " inflating: AD_NC/test/NC/1327480_107.jpeg \n", - " inflating: AD_NC/test/NC/1327480_108.jpeg \n", - " inflating: AD_NC/test/NC/1327480_109.jpeg \n", - " inflating: AD_NC/test/NC/1327480_110.jpeg \n", - " inflating: AD_NC/test/NC/1327480_111.jpeg \n", - " inflating: AD_NC/test/NC/1327480_112.jpeg \n", - " inflating: AD_NC/test/NC/1327480_113.jpeg \n", - " inflating: AD_NC/test/NC/1327480_94.jpeg \n", - " inflating: AD_NC/test/NC/1327480_95.jpeg \n", - " inflating: AD_NC/test/NC/1327480_96.jpeg \n", - " inflating: AD_NC/test/NC/1327480_97.jpeg \n", - " inflating: AD_NC/test/NC/1327480_98.jpeg \n", - " inflating: AD_NC/test/NC/1327480_99.jpeg \n", - " inflating: AD_NC/test/NC/1328524_100.jpeg \n", - " inflating: AD_NC/test/NC/1328524_101.jpeg \n", - " inflating: AD_NC/test/NC/1328524_102.jpeg \n", - " inflating: AD_NC/test/NC/1328524_103.jpeg \n", - " inflating: AD_NC/test/NC/1328524_104.jpeg \n", - " inflating: AD_NC/test/NC/1328524_105.jpeg \n", - " inflating: AD_NC/test/NC/1328524_106.jpeg \n", - " inflating: AD_NC/test/NC/1328524_107.jpeg \n", - " inflating: AD_NC/test/NC/1328524_88.jpeg \n", - " inflating: AD_NC/test/NC/1328524_89.jpeg \n", - " inflating: AD_NC/test/NC/1328524_90.jpeg \n", - " inflating: AD_NC/test/NC/1328524_91.jpeg \n", - " inflating: AD_NC/test/NC/1328524_92.jpeg \n", - " inflating: AD_NC/test/NC/1328524_93.jpeg \n", - " inflating: AD_NC/test/NC/1328524_94.jpeg \n", - " inflating: AD_NC/test/NC/1328524_95.jpeg \n", - " inflating: AD_NC/test/NC/1328524_96.jpeg \n", - " inflating: AD_NC/test/NC/1328524_97.jpeg \n", - " inflating: AD_NC/test/NC/1328524_98.jpeg \n", - " inflating: AD_NC/test/NC/1328524_99.jpeg \n", - " inflating: AD_NC/test/NC/1329968_100.jpeg \n", - " inflating: AD_NC/test/NC/1329968_101.jpeg \n", - " inflating: AD_NC/test/NC/1329968_102.jpeg \n", - " inflating: AD_NC/test/NC/1329968_103.jpeg \n", - " inflating: AD_NC/test/NC/1329968_104.jpeg \n", - " inflating: AD_NC/test/NC/1329968_105.jpeg \n", - " inflating: AD_NC/test/NC/1329968_106.jpeg \n", - " inflating: AD_NC/test/NC/1329968_107.jpeg \n", - " inflating: AD_NC/test/NC/1329968_108.jpeg \n", - " inflating: AD_NC/test/NC/1329968_109.jpeg \n", - " inflating: AD_NC/test/NC/1329968_110.jpeg \n", - " inflating: AD_NC/test/NC/1329968_111.jpeg \n", - " inflating: AD_NC/test/NC/1329968_112.jpeg \n", - " inflating: AD_NC/test/NC/1329968_113.jpeg \n", - " inflating: AD_NC/test/NC/1329968_94.jpeg \n", - " inflating: AD_NC/test/NC/1329968_95.jpeg \n", - " inflating: AD_NC/test/NC/1329968_96.jpeg \n", - " inflating: AD_NC/test/NC/1329968_97.jpeg \n", - " inflating: AD_NC/test/NC/1329968_98.jpeg \n", - " inflating: AD_NC/test/NC/1329968_99.jpeg \n", - " inflating: AD_NC/test/NC/1331222_100.jpeg \n", - " inflating: AD_NC/test/NC/1331222_101.jpeg \n", - " inflating: AD_NC/test/NC/1331222_102.jpeg \n", - " inflating: AD_NC/test/NC/1331222_103.jpeg \n", - " inflating: AD_NC/test/NC/1331222_104.jpeg \n", - " inflating: AD_NC/test/NC/1331222_105.jpeg \n", - " inflating: AD_NC/test/NC/1331222_106.jpeg \n", - " inflating: AD_NC/test/NC/1331222_107.jpeg \n", - " inflating: AD_NC/test/NC/1331222_88.jpeg \n", - " inflating: AD_NC/test/NC/1331222_89.jpeg \n", - " inflating: AD_NC/test/NC/1331222_90.jpeg \n", - " inflating: AD_NC/test/NC/1331222_91.jpeg \n", - " inflating: AD_NC/test/NC/1331222_92.jpeg \n", - " inflating: AD_NC/test/NC/1331222_93.jpeg \n", - " inflating: AD_NC/test/NC/1331222_94.jpeg \n", - " inflating: AD_NC/test/NC/1331222_95.jpeg \n", - " inflating: AD_NC/test/NC/1331222_96.jpeg \n", - " inflating: AD_NC/test/NC/1331222_97.jpeg \n", - " inflating: AD_NC/test/NC/1331222_98.jpeg \n", - " inflating: AD_NC/test/NC/1331222_99.jpeg \n", - " inflating: AD_NC/test/NC/1331870_100.jpeg \n", - " inflating: AD_NC/test/NC/1331870_101.jpeg \n", - " inflating: AD_NC/test/NC/1331870_102.jpeg \n", - " inflating: AD_NC/test/NC/1331870_103.jpeg \n", - " inflating: AD_NC/test/NC/1331870_104.jpeg \n", - " inflating: AD_NC/test/NC/1331870_105.jpeg \n", - " inflating: AD_NC/test/NC/1331870_106.jpeg \n", - " inflating: AD_NC/test/NC/1331870_107.jpeg \n", - " inflating: AD_NC/test/NC/1331870_88.jpeg \n", - " inflating: AD_NC/test/NC/1331870_89.jpeg \n", - " inflating: AD_NC/test/NC/1331870_90.jpeg \n", - " inflating: AD_NC/test/NC/1331870_91.jpeg \n", - " inflating: AD_NC/test/NC/1331870_92.jpeg \n", - " inflating: AD_NC/test/NC/1331870_93.jpeg \n", - " inflating: AD_NC/test/NC/1331870_94.jpeg \n", - " inflating: AD_NC/test/NC/1331870_95.jpeg \n", - " inflating: AD_NC/test/NC/1331870_96.jpeg \n", - " inflating: AD_NC/test/NC/1331870_97.jpeg \n", - " inflating: AD_NC/test/NC/1331870_98.jpeg \n", - " inflating: AD_NC/test/NC/1331870_99.jpeg \n", - " inflating: AD_NC/test/NC/1332317_100.jpeg \n", - " inflating: AD_NC/test/NC/1332317_101.jpeg \n", - " inflating: AD_NC/test/NC/1332317_102.jpeg \n", - " inflating: AD_NC/test/NC/1332317_103.jpeg \n", - " inflating: AD_NC/test/NC/1332317_104.jpeg \n", - " inflating: AD_NC/test/NC/1332317_105.jpeg \n", - " inflating: AD_NC/test/NC/1332317_106.jpeg \n", - " inflating: AD_NC/test/NC/1332317_107.jpeg \n", - " inflating: AD_NC/test/NC/1332317_108.jpeg \n", - " inflating: AD_NC/test/NC/1332317_109.jpeg \n", - " inflating: AD_NC/test/NC/1332317_110.jpeg \n", - " inflating: AD_NC/test/NC/1332317_111.jpeg \n", - " inflating: AD_NC/test/NC/1332317_112.jpeg \n", - " inflating: AD_NC/test/NC/1332317_113.jpeg \n", - " inflating: AD_NC/test/NC/1332317_94.jpeg \n", - " inflating: AD_NC/test/NC/1332317_95.jpeg \n", - " inflating: AD_NC/test/NC/1332317_96.jpeg \n", - " inflating: AD_NC/test/NC/1332317_97.jpeg \n", - " inflating: AD_NC/test/NC/1332317_98.jpeg \n", - " inflating: AD_NC/test/NC/1332317_99.jpeg \n", - " inflating: AD_NC/test/NC/1332407_100.jpeg \n", - " inflating: AD_NC/test/NC/1332407_101.jpeg \n", - " inflating: AD_NC/test/NC/1332407_102.jpeg \n", - " inflating: AD_NC/test/NC/1332407_103.jpeg \n", - " inflating: AD_NC/test/NC/1332407_104.jpeg \n", - " inflating: AD_NC/test/NC/1332407_105.jpeg \n", - " inflating: AD_NC/test/NC/1332407_106.jpeg \n", - " inflating: AD_NC/test/NC/1332407_107.jpeg \n", - " inflating: AD_NC/test/NC/1332407_108.jpeg \n", - " inflating: AD_NC/test/NC/1332407_109.jpeg \n", - " inflating: AD_NC/test/NC/1332407_110.jpeg \n", - " inflating: AD_NC/test/NC/1332407_111.jpeg \n", - " inflating: AD_NC/test/NC/1332407_112.jpeg \n", - " inflating: AD_NC/test/NC/1332407_113.jpeg \n", - " inflating: AD_NC/test/NC/1332407_94.jpeg \n", - " inflating: AD_NC/test/NC/1332407_95.jpeg \n", - " inflating: AD_NC/test/NC/1332407_96.jpeg \n", - " inflating: AD_NC/test/NC/1332407_97.jpeg \n", - " inflating: AD_NC/test/NC/1332407_98.jpeg \n", - " inflating: AD_NC/test/NC/1332407_99.jpeg \n", - " inflating: AD_NC/test/NC/1332450_100.jpeg \n", - " inflating: AD_NC/test/NC/1332450_101.jpeg \n", - " inflating: AD_NC/test/NC/1332450_102.jpeg \n", - " inflating: AD_NC/test/NC/1332450_103.jpeg \n", - " inflating: AD_NC/test/NC/1332450_104.jpeg \n", - " inflating: AD_NC/test/NC/1332450_105.jpeg \n", - " inflating: AD_NC/test/NC/1332450_106.jpeg \n", - " inflating: AD_NC/test/NC/1332450_107.jpeg \n", - " inflating: AD_NC/test/NC/1332450_108.jpeg \n", - " inflating: AD_NC/test/NC/1332450_109.jpeg \n", - " inflating: AD_NC/test/NC/1332450_110.jpeg \n", - " inflating: AD_NC/test/NC/1332450_111.jpeg \n", - " inflating: AD_NC/test/NC/1332450_112.jpeg \n", - " inflating: AD_NC/test/NC/1332450_113.jpeg \n", - " inflating: AD_NC/test/NC/1332450_94.jpeg \n", - " inflating: AD_NC/test/NC/1332450_95.jpeg \n", - " inflating: AD_NC/test/NC/1332450_96.jpeg \n", - " inflating: AD_NC/test/NC/1332450_97.jpeg \n", - " inflating: AD_NC/test/NC/1332450_98.jpeg \n", - " inflating: AD_NC/test/NC/1332450_99.jpeg \n", - " inflating: AD_NC/test/NC/1332469_100.jpeg \n", - " inflating: AD_NC/test/NC/1332469_101.jpeg \n", - " inflating: AD_NC/test/NC/1332469_102.jpeg \n", - " inflating: AD_NC/test/NC/1332469_103.jpeg \n", - " inflating: AD_NC/test/NC/1332469_104.jpeg \n", - " inflating: AD_NC/test/NC/1332469_105.jpeg \n", - " inflating: AD_NC/test/NC/1332469_106.jpeg \n", - " inflating: AD_NC/test/NC/1332469_107.jpeg \n", - " inflating: AD_NC/test/NC/1332469_108.jpeg \n", - " inflating: AD_NC/test/NC/1332469_109.jpeg \n", - " inflating: AD_NC/test/NC/1332469_110.jpeg \n", - " inflating: AD_NC/test/NC/1332469_111.jpeg \n", - " inflating: AD_NC/test/NC/1332469_112.jpeg \n", - " inflating: AD_NC/test/NC/1332469_113.jpeg \n", - " inflating: AD_NC/test/NC/1332469_94.jpeg \n", - " inflating: AD_NC/test/NC/1332469_95.jpeg \n", - " inflating: AD_NC/test/NC/1332469_96.jpeg \n", - " inflating: AD_NC/test/NC/1332469_97.jpeg \n", - " inflating: AD_NC/test/NC/1332469_98.jpeg \n", - " inflating: AD_NC/test/NC/1332469_99.jpeg \n", - " inflating: AD_NC/test/NC/1335384_78.jpeg \n", - " inflating: AD_NC/test/NC/1335384_79.jpeg \n", - " inflating: AD_NC/test/NC/1335384_80.jpeg \n", - " inflating: AD_NC/test/NC/1335384_81.jpeg \n", - " inflating: AD_NC/test/NC/1335384_82.jpeg \n", - " inflating: AD_NC/test/NC/1335384_83.jpeg \n", - " inflating: AD_NC/test/NC/1335384_84.jpeg \n", - " inflating: AD_NC/test/NC/1335384_85.jpeg \n", - " inflating: AD_NC/test/NC/1335384_86.jpeg \n", - " inflating: AD_NC/test/NC/1335384_87.jpeg \n", - " inflating: AD_NC/test/NC/1335384_88.jpeg \n", - " inflating: AD_NC/test/NC/1335384_89.jpeg \n", - " inflating: AD_NC/test/NC/1335384_90.jpeg \n", - " inflating: AD_NC/test/NC/1335384_91.jpeg \n", - " inflating: AD_NC/test/NC/1335384_92.jpeg \n", - " inflating: AD_NC/test/NC/1335384_93.jpeg \n", - " inflating: AD_NC/test/NC/1335384_94.jpeg \n", - " inflating: AD_NC/test/NC/1335384_95.jpeg \n", - " inflating: AD_NC/test/NC/1335384_96.jpeg \n", - " inflating: AD_NC/test/NC/1335384_97.jpeg \n", - " inflating: AD_NC/test/NC/1336238_100.jpeg \n", - " inflating: AD_NC/test/NC/1336238_101.jpeg \n", - " inflating: AD_NC/test/NC/1336238_102.jpeg \n", - " inflating: AD_NC/test/NC/1336238_103.jpeg \n", - " inflating: AD_NC/test/NC/1336238_104.jpeg \n", - " inflating: AD_NC/test/NC/1336238_105.jpeg \n", - " inflating: AD_NC/test/NC/1336238_106.jpeg \n", - " inflating: AD_NC/test/NC/1336238_107.jpeg \n", - " inflating: AD_NC/test/NC/1336238_108.jpeg \n", - " inflating: AD_NC/test/NC/1336238_109.jpeg \n", - " inflating: AD_NC/test/NC/1336238_110.jpeg \n", - " inflating: AD_NC/test/NC/1336238_111.jpeg \n", - " inflating: AD_NC/test/NC/1336238_112.jpeg \n", - " inflating: AD_NC/test/NC/1336238_113.jpeg \n", - " inflating: AD_NC/test/NC/1336238_94.jpeg \n", - " inflating: AD_NC/test/NC/1336238_95.jpeg \n", - " inflating: AD_NC/test/NC/1336238_96.jpeg \n", - " inflating: AD_NC/test/NC/1336238_97.jpeg \n", - " inflating: AD_NC/test/NC/1336238_98.jpeg \n", - " inflating: AD_NC/test/NC/1336238_99.jpeg \n", - " inflating: AD_NC/test/NC/1337907_100.jpeg \n", - " inflating: AD_NC/test/NC/1337907_101.jpeg \n", - " inflating: AD_NC/test/NC/1337907_102.jpeg \n", - " inflating: AD_NC/test/NC/1337907_103.jpeg \n", - " inflating: AD_NC/test/NC/1337907_104.jpeg \n", - " inflating: AD_NC/test/NC/1337907_105.jpeg \n", - " inflating: AD_NC/test/NC/1337907_106.jpeg \n", - " inflating: AD_NC/test/NC/1337907_107.jpeg \n", - " inflating: AD_NC/test/NC/1337907_88.jpeg \n", - " inflating: AD_NC/test/NC/1337907_89.jpeg \n", - " inflating: AD_NC/test/NC/1337907_90.jpeg \n", - " inflating: AD_NC/test/NC/1337907_91.jpeg \n", - " inflating: AD_NC/test/NC/1337907_92.jpeg \n", - " inflating: AD_NC/test/NC/1337907_93.jpeg \n", - " inflating: AD_NC/test/NC/1337907_94.jpeg \n", - " inflating: AD_NC/test/NC/1337907_95.jpeg \n", - " inflating: AD_NC/test/NC/1337907_96.jpeg \n", - " inflating: AD_NC/test/NC/1337907_97.jpeg \n", - " inflating: AD_NC/test/NC/1337907_98.jpeg \n", - " inflating: AD_NC/test/NC/1337907_99.jpeg \n", - " inflating: AD_NC/test/NC/1340855_100.jpeg \n", - " inflating: AD_NC/test/NC/1340855_101.jpeg \n", - " inflating: AD_NC/test/NC/1340855_102.jpeg \n", - " inflating: AD_NC/test/NC/1340855_103.jpeg \n", - " inflating: AD_NC/test/NC/1340855_104.jpeg \n", - " inflating: AD_NC/test/NC/1340855_105.jpeg \n", - " inflating: AD_NC/test/NC/1340855_106.jpeg \n", - " inflating: AD_NC/test/NC/1340855_107.jpeg \n", - " inflating: AD_NC/test/NC/1340855_108.jpeg \n", - " inflating: AD_NC/test/NC/1340855_109.jpeg \n", - " inflating: AD_NC/test/NC/1340855_110.jpeg \n", - " inflating: AD_NC/test/NC/1340855_111.jpeg \n", - " inflating: AD_NC/test/NC/1340855_112.jpeg \n", - " inflating: AD_NC/test/NC/1340855_113.jpeg \n", - " inflating: AD_NC/test/NC/1340855_94.jpeg \n", - " inflating: AD_NC/test/NC/1340855_95.jpeg \n", - " inflating: AD_NC/test/NC/1340855_96.jpeg \n", - " inflating: AD_NC/test/NC/1340855_97.jpeg \n", - " inflating: AD_NC/test/NC/1340855_98.jpeg \n", - " inflating: AD_NC/test/NC/1340855_99.jpeg \n", - " inflating: AD_NC/test/NC/1341792_100.jpeg \n", - " inflating: AD_NC/test/NC/1341792_101.jpeg \n", - " inflating: AD_NC/test/NC/1341792_102.jpeg \n", - " inflating: AD_NC/test/NC/1341792_103.jpeg \n", - " inflating: AD_NC/test/NC/1341792_104.jpeg \n", - " inflating: AD_NC/test/NC/1341792_105.jpeg \n", - " inflating: AD_NC/test/NC/1341792_106.jpeg \n", - " inflating: AD_NC/test/NC/1341792_107.jpeg \n", - " inflating: AD_NC/test/NC/1341792_108.jpeg \n", - " inflating: AD_NC/test/NC/1341792_109.jpeg \n", - " inflating: AD_NC/test/NC/1341792_110.jpeg \n", - " inflating: AD_NC/test/NC/1341792_111.jpeg \n", - " inflating: AD_NC/test/NC/1341792_112.jpeg \n", - " inflating: AD_NC/test/NC/1341792_113.jpeg \n", - " inflating: AD_NC/test/NC/1341792_94.jpeg \n", - " inflating: AD_NC/test/NC/1341792_95.jpeg \n", - " inflating: AD_NC/test/NC/1341792_96.jpeg \n", - " inflating: AD_NC/test/NC/1341792_97.jpeg \n", - " inflating: AD_NC/test/NC/1341792_98.jpeg \n", - " inflating: AD_NC/test/NC/1341792_99.jpeg \n", - " inflating: AD_NC/test/NC/1342528_100.jpeg \n", - " inflating: AD_NC/test/NC/1342528_101.jpeg \n", - " inflating: AD_NC/test/NC/1342528_102.jpeg \n", - " inflating: AD_NC/test/NC/1342528_103.jpeg \n", - " inflating: AD_NC/test/NC/1342528_104.jpeg \n", - " inflating: AD_NC/test/NC/1342528_105.jpeg \n", - " inflating: AD_NC/test/NC/1342528_106.jpeg \n", - " inflating: AD_NC/test/NC/1342528_107.jpeg \n", - " inflating: AD_NC/test/NC/1342528_108.jpeg \n", - " inflating: AD_NC/test/NC/1342528_109.jpeg \n", - " inflating: AD_NC/test/NC/1342528_110.jpeg \n", - " inflating: AD_NC/test/NC/1342528_111.jpeg \n", - " inflating: AD_NC/test/NC/1342528_112.jpeg \n", - " inflating: AD_NC/test/NC/1342528_113.jpeg \n", - " inflating: AD_NC/test/NC/1342528_94.jpeg \n", - " inflating: AD_NC/test/NC/1342528_95.jpeg \n", - " inflating: AD_NC/test/NC/1342528_96.jpeg \n", - " inflating: AD_NC/test/NC/1342528_97.jpeg \n", - " inflating: AD_NC/test/NC/1342528_98.jpeg \n", - " inflating: AD_NC/test/NC/1342528_99.jpeg \n", - " inflating: AD_NC/test/NC/1343715_100.jpeg \n", - " inflating: AD_NC/test/NC/1343715_101.jpeg \n", - " inflating: AD_NC/test/NC/1343715_102.jpeg \n", - " inflating: AD_NC/test/NC/1343715_103.jpeg \n", - " inflating: AD_NC/test/NC/1343715_104.jpeg \n", - " inflating: AD_NC/test/NC/1343715_105.jpeg \n", - " inflating: AD_NC/test/NC/1343715_106.jpeg \n", - " inflating: AD_NC/test/NC/1343715_107.jpeg \n", - " inflating: AD_NC/test/NC/1343715_108.jpeg \n", - " inflating: AD_NC/test/NC/1343715_109.jpeg \n", - " inflating: AD_NC/test/NC/1343715_110.jpeg \n", - " inflating: AD_NC/test/NC/1343715_111.jpeg \n", - " inflating: AD_NC/test/NC/1343715_112.jpeg \n", - " inflating: AD_NC/test/NC/1343715_113.jpeg \n", - " inflating: AD_NC/test/NC/1343715_94.jpeg \n", - " inflating: AD_NC/test/NC/1343715_95.jpeg \n", - " inflating: AD_NC/test/NC/1343715_96.jpeg \n", - " inflating: AD_NC/test/NC/1343715_97.jpeg \n", - " inflating: AD_NC/test/NC/1343715_98.jpeg \n", - " inflating: AD_NC/test/NC/1343715_99.jpeg \n", - " inflating: AD_NC/test/NC/1344288_100.jpeg \n", - " inflating: AD_NC/test/NC/1344288_101.jpeg \n", - " inflating: AD_NC/test/NC/1344288_102.jpeg \n", - " inflating: AD_NC/test/NC/1344288_103.jpeg \n", - " inflating: AD_NC/test/NC/1344288_104.jpeg \n", - " inflating: AD_NC/test/NC/1344288_105.jpeg \n", - " inflating: AD_NC/test/NC/1344288_106.jpeg \n", - " inflating: AD_NC/test/NC/1344288_107.jpeg \n", - " inflating: AD_NC/test/NC/1344288_108.jpeg \n", - " inflating: AD_NC/test/NC/1344288_109.jpeg \n", - " inflating: AD_NC/test/NC/1344288_110.jpeg \n", - " inflating: AD_NC/test/NC/1344288_111.jpeg \n", - " inflating: AD_NC/test/NC/1344288_112.jpeg \n", - " inflating: AD_NC/test/NC/1344288_113.jpeg \n", - " inflating: AD_NC/test/NC/1344288_94.jpeg \n", - " inflating: AD_NC/test/NC/1344288_95.jpeg \n", - " inflating: AD_NC/test/NC/1344288_96.jpeg \n", - " inflating: AD_NC/test/NC/1344288_97.jpeg \n", - " inflating: AD_NC/test/NC/1344288_98.jpeg \n", - " inflating: AD_NC/test/NC/1344288_99.jpeg \n", - " inflating: AD_NC/test/NC/1344400_78.jpeg \n", - " inflating: AD_NC/test/NC/1344400_79.jpeg \n", - " inflating: AD_NC/test/NC/1344400_80.jpeg \n", - " inflating: AD_NC/test/NC/1344400_81.jpeg \n", - " inflating: AD_NC/test/NC/1344400_82.jpeg \n", - " inflating: AD_NC/test/NC/1344400_83.jpeg \n", - " inflating: AD_NC/test/NC/1344400_84.jpeg \n", - " inflating: AD_NC/test/NC/1344400_85.jpeg \n", - " inflating: AD_NC/test/NC/1344400_86.jpeg \n", - " inflating: AD_NC/test/NC/1344400_87.jpeg \n", - " inflating: AD_NC/test/NC/1344400_88.jpeg \n", - " inflating: AD_NC/test/NC/1344400_89.jpeg \n", - " inflating: AD_NC/test/NC/1344400_90.jpeg \n", - " inflating: AD_NC/test/NC/1344400_91.jpeg \n", - " inflating: AD_NC/test/NC/1344400_92.jpeg \n", - " inflating: AD_NC/test/NC/1344400_93.jpeg \n", - " inflating: AD_NC/test/NC/1344400_94.jpeg \n", - " inflating: AD_NC/test/NC/1344400_95.jpeg \n", - " inflating: AD_NC/test/NC/1344400_96.jpeg \n", - " inflating: AD_NC/test/NC/1344400_97.jpeg \n", - " inflating: AD_NC/test/NC/1344417_100.jpeg \n", - " inflating: AD_NC/test/NC/1344417_101.jpeg \n", - " inflating: AD_NC/test/NC/1344417_102.jpeg \n", - " inflating: AD_NC/test/NC/1344417_103.jpeg \n", - " inflating: AD_NC/test/NC/1344417_104.jpeg \n", - " inflating: AD_NC/test/NC/1344417_105.jpeg \n", - " inflating: AD_NC/test/NC/1344417_106.jpeg \n", - " inflating: AD_NC/test/NC/1344417_107.jpeg \n", - " inflating: AD_NC/test/NC/1344417_108.jpeg \n", - " inflating: AD_NC/test/NC/1344417_109.jpeg \n", - " inflating: AD_NC/test/NC/1344417_110.jpeg \n", - " inflating: AD_NC/test/NC/1344417_111.jpeg \n", - " inflating: AD_NC/test/NC/1344417_112.jpeg \n", - " inflating: AD_NC/test/NC/1344417_113.jpeg \n", - " inflating: AD_NC/test/NC/1344417_94.jpeg \n", - " inflating: AD_NC/test/NC/1344417_95.jpeg \n", - " inflating: AD_NC/test/NC/1344417_96.jpeg \n", - " inflating: AD_NC/test/NC/1344417_97.jpeg \n", - " inflating: AD_NC/test/NC/1344417_98.jpeg \n", - " inflating: AD_NC/test/NC/1344417_99.jpeg \n", - " inflating: AD_NC/test/NC/1345109_78.jpeg \n", - " inflating: AD_NC/test/NC/1345109_79.jpeg \n", - " inflating: AD_NC/test/NC/1345109_80.jpeg \n", - " inflating: AD_NC/test/NC/1345109_81.jpeg \n", - " inflating: AD_NC/test/NC/1345109_82.jpeg \n", - " inflating: AD_NC/test/NC/1345109_83.jpeg \n", - " inflating: AD_NC/test/NC/1345109_84.jpeg \n", - " inflating: AD_NC/test/NC/1345109_85.jpeg \n", - " inflating: AD_NC/test/NC/1345109_86.jpeg \n", - " inflating: AD_NC/test/NC/1345109_87.jpeg \n", - " inflating: AD_NC/test/NC/1345109_88.jpeg \n", - " inflating: AD_NC/test/NC/1345109_89.jpeg \n", - " inflating: AD_NC/test/NC/1345109_90.jpeg \n", - " inflating: AD_NC/test/NC/1345109_91.jpeg \n", - " inflating: AD_NC/test/NC/1345109_92.jpeg \n", - " inflating: AD_NC/test/NC/1345109_93.jpeg \n", - " inflating: AD_NC/test/NC/1345109_94.jpeg \n", - " inflating: AD_NC/test/NC/1345109_95.jpeg \n", - " inflating: AD_NC/test/NC/1345109_96.jpeg \n", - " inflating: AD_NC/test/NC/1345109_97.jpeg \n", - " inflating: AD_NC/test/NC/1346089_100.jpeg \n", - " inflating: AD_NC/test/NC/1346089_101.jpeg \n", - " inflating: AD_NC/test/NC/1346089_102.jpeg \n", - " inflating: AD_NC/test/NC/1346089_103.jpeg \n", - " inflating: AD_NC/test/NC/1346089_104.jpeg \n", - " inflating: AD_NC/test/NC/1346089_105.jpeg \n", - " inflating: AD_NC/test/NC/1346089_106.jpeg \n", - " inflating: AD_NC/test/NC/1346089_107.jpeg \n", - " inflating: AD_NC/test/NC/1346089_108.jpeg \n", - " inflating: AD_NC/test/NC/1346089_109.jpeg \n", - " inflating: AD_NC/test/NC/1346089_110.jpeg \n", - " inflating: AD_NC/test/NC/1346089_111.jpeg \n", - " inflating: AD_NC/test/NC/1346089_112.jpeg \n", - " inflating: AD_NC/test/NC/1346089_113.jpeg \n", - " inflating: AD_NC/test/NC/1346089_114.jpeg \n", - " inflating: AD_NC/test/NC/1346089_95.jpeg \n", - " inflating: AD_NC/test/NC/1346089_96.jpeg \n", - " inflating: AD_NC/test/NC/1346089_97.jpeg \n", - " inflating: AD_NC/test/NC/1346089_98.jpeg \n", - " inflating: AD_NC/test/NC/1346089_99.jpeg \n", - " inflating: AD_NC/test/NC/1346152_100.jpeg \n", - " inflating: AD_NC/test/NC/1346152_101.jpeg \n", - " inflating: AD_NC/test/NC/1346152_102.jpeg \n", - " inflating: AD_NC/test/NC/1346152_103.jpeg \n", - " inflating: AD_NC/test/NC/1346152_104.jpeg \n", - " inflating: AD_NC/test/NC/1346152_105.jpeg \n", - " inflating: AD_NC/test/NC/1346152_106.jpeg \n", - " inflating: AD_NC/test/NC/1346152_107.jpeg \n", - " inflating: AD_NC/test/NC/1346152_108.jpeg \n", - " inflating: AD_NC/test/NC/1346152_109.jpeg \n", - " inflating: AD_NC/test/NC/1346152_110.jpeg \n", - " inflating: AD_NC/test/NC/1346152_111.jpeg \n", - " inflating: AD_NC/test/NC/1346152_112.jpeg \n", - " inflating: AD_NC/test/NC/1346152_113.jpeg \n", - " inflating: AD_NC/test/NC/1346152_94.jpeg \n", - " inflating: AD_NC/test/NC/1346152_95.jpeg \n", - " inflating: AD_NC/test/NC/1346152_96.jpeg \n", - " inflating: AD_NC/test/NC/1346152_97.jpeg \n", - " inflating: AD_NC/test/NC/1346152_98.jpeg \n", - " inflating: AD_NC/test/NC/1346152_99.jpeg \n", - " inflating: AD_NC/test/NC/1346240_100.jpeg \n", - " inflating: AD_NC/test/NC/1346240_101.jpeg \n", - " inflating: AD_NC/test/NC/1346240_102.jpeg \n", - " inflating: AD_NC/test/NC/1346240_103.jpeg \n", - " inflating: AD_NC/test/NC/1346240_104.jpeg \n", - " inflating: AD_NC/test/NC/1346240_105.jpeg \n", - " inflating: AD_NC/test/NC/1346240_106.jpeg \n", - " inflating: AD_NC/test/NC/1346240_107.jpeg \n", - " inflating: AD_NC/test/NC/1346240_108.jpeg \n", - " inflating: AD_NC/test/NC/1346240_109.jpeg \n", - " inflating: AD_NC/test/NC/1346240_110.jpeg \n", - " inflating: AD_NC/test/NC/1346240_111.jpeg \n", - " inflating: AD_NC/test/NC/1346240_112.jpeg \n", - " inflating: AD_NC/test/NC/1346240_113.jpeg \n", - " inflating: AD_NC/test/NC/1346240_94.jpeg \n", - " inflating: AD_NC/test/NC/1346240_95.jpeg \n", - " inflating: AD_NC/test/NC/1346240_96.jpeg \n", - " inflating: AD_NC/test/NC/1346240_97.jpeg \n", - " inflating: AD_NC/test/NC/1346240_98.jpeg \n", - " inflating: AD_NC/test/NC/1346240_99.jpeg \n", - " inflating: AD_NC/test/NC/1346789_78.jpeg \n", - " inflating: AD_NC/test/NC/1346789_79.jpeg \n", - " inflating: AD_NC/test/NC/1346789_80.jpeg \n", - " inflating: AD_NC/test/NC/1346789_81.jpeg \n", - " inflating: AD_NC/test/NC/1346789_82.jpeg \n", - " inflating: AD_NC/test/NC/1346789_83.jpeg \n", - " inflating: AD_NC/test/NC/1346789_84.jpeg \n", - " inflating: AD_NC/test/NC/1346789_85.jpeg \n", - " inflating: AD_NC/test/NC/1346789_86.jpeg \n", - " inflating: AD_NC/test/NC/1346789_87.jpeg \n", - " inflating: AD_NC/test/NC/1346789_88.jpeg \n", - " inflating: AD_NC/test/NC/1346789_89.jpeg \n", - " inflating: AD_NC/test/NC/1346789_90.jpeg \n", - " inflating: AD_NC/test/NC/1346789_91.jpeg \n", - " inflating: AD_NC/test/NC/1346789_92.jpeg \n", - " inflating: AD_NC/test/NC/1346789_93.jpeg \n", - " inflating: AD_NC/test/NC/1346789_94.jpeg \n", - " inflating: AD_NC/test/NC/1346789_95.jpeg \n", - " inflating: AD_NC/test/NC/1346789_96.jpeg \n", - " inflating: AD_NC/test/NC/1346789_97.jpeg \n", - " inflating: AD_NC/test/NC/1346790_78.jpeg \n", - " inflating: AD_NC/test/NC/1346790_79.jpeg \n", - " inflating: AD_NC/test/NC/1346790_80.jpeg \n", - " inflating: AD_NC/test/NC/1346790_81.jpeg \n", - " inflating: AD_NC/test/NC/1346790_82.jpeg \n", - " inflating: AD_NC/test/NC/1346790_83.jpeg \n", - " inflating: AD_NC/test/NC/1346790_84.jpeg \n", - " inflating: AD_NC/test/NC/1346790_85.jpeg \n", - " inflating: AD_NC/test/NC/1346790_86.jpeg \n", - " inflating: AD_NC/test/NC/1346790_87.jpeg \n", - " inflating: AD_NC/test/NC/1346790_88.jpeg \n", - " inflating: AD_NC/test/NC/1346790_89.jpeg \n", - " inflating: AD_NC/test/NC/1346790_90.jpeg \n", - " inflating: AD_NC/test/NC/1346790_91.jpeg \n", - " inflating: AD_NC/test/NC/1346790_92.jpeg \n", - " inflating: AD_NC/test/NC/1346790_93.jpeg \n", - " inflating: AD_NC/test/NC/1346790_94.jpeg \n", - " inflating: AD_NC/test/NC/1346790_95.jpeg \n", - " inflating: AD_NC/test/NC/1346790_96.jpeg \n", - " inflating: AD_NC/test/NC/1346790_97.jpeg \n", - " inflating: AD_NC/test/NC/1346960_100.jpeg \n", - " inflating: AD_NC/test/NC/1346960_101.jpeg \n", - " inflating: AD_NC/test/NC/1346960_102.jpeg \n", - " inflating: AD_NC/test/NC/1346960_103.jpeg \n", - " inflating: AD_NC/test/NC/1346960_104.jpeg \n", - " inflating: AD_NC/test/NC/1346960_105.jpeg \n", - " inflating: AD_NC/test/NC/1346960_106.jpeg \n", - " inflating: AD_NC/test/NC/1346960_107.jpeg \n", - " inflating: AD_NC/test/NC/1346960_88.jpeg \n", - " inflating: AD_NC/test/NC/1346960_89.jpeg \n", - " inflating: AD_NC/test/NC/1346960_90.jpeg \n", - " inflating: AD_NC/test/NC/1346960_91.jpeg \n", - " inflating: AD_NC/test/NC/1346960_92.jpeg \n", - " inflating: AD_NC/test/NC/1346960_93.jpeg \n", - " inflating: AD_NC/test/NC/1346960_94.jpeg \n", - " inflating: AD_NC/test/NC/1346960_95.jpeg \n", - " inflating: AD_NC/test/NC/1346960_96.jpeg \n", - " inflating: AD_NC/test/NC/1346960_97.jpeg \n", - " inflating: AD_NC/test/NC/1346960_98.jpeg \n", - " inflating: AD_NC/test/NC/1346960_99.jpeg \n", - " inflating: AD_NC/test/NC/1348108_100.jpeg \n", - " inflating: AD_NC/test/NC/1348108_101.jpeg \n", - " inflating: AD_NC/test/NC/1348108_102.jpeg \n", - " inflating: AD_NC/test/NC/1348108_103.jpeg \n", - " inflating: AD_NC/test/NC/1348108_104.jpeg \n", - " inflating: AD_NC/test/NC/1348108_105.jpeg \n", - " inflating: AD_NC/test/NC/1348108_106.jpeg \n", - " inflating: AD_NC/test/NC/1348108_107.jpeg \n", - " inflating: AD_NC/test/NC/1348108_108.jpeg \n", - " inflating: AD_NC/test/NC/1348108_109.jpeg \n", - " inflating: AD_NC/test/NC/1348108_110.jpeg \n", - " inflating: AD_NC/test/NC/1348108_111.jpeg \n", - " inflating: AD_NC/test/NC/1348108_112.jpeg \n", - " inflating: AD_NC/test/NC/1348108_113.jpeg \n", - " inflating: AD_NC/test/NC/1348108_94.jpeg \n", - " inflating: AD_NC/test/NC/1348108_95.jpeg \n", - " inflating: AD_NC/test/NC/1348108_96.jpeg \n", - " inflating: AD_NC/test/NC/1348108_97.jpeg \n", - " inflating: AD_NC/test/NC/1348108_98.jpeg \n", - " inflating: AD_NC/test/NC/1348108_99.jpeg \n", - " inflating: AD_NC/test/NC/1348596_100.jpeg \n", - " inflating: AD_NC/test/NC/1348596_101.jpeg \n", - " inflating: AD_NC/test/NC/1348596_102.jpeg \n", - " inflating: AD_NC/test/NC/1348596_103.jpeg \n", - " inflating: AD_NC/test/NC/1348596_104.jpeg \n", - " inflating: AD_NC/test/NC/1348596_105.jpeg \n", - " inflating: AD_NC/test/NC/1348596_106.jpeg \n", - " inflating: AD_NC/test/NC/1348596_107.jpeg \n", - " inflating: AD_NC/test/NC/1348596_108.jpeg \n", - " inflating: AD_NC/test/NC/1348596_109.jpeg \n", - " inflating: AD_NC/test/NC/1348596_110.jpeg \n", - " inflating: AD_NC/test/NC/1348596_111.jpeg \n", - " inflating: AD_NC/test/NC/1348596_112.jpeg \n", - " inflating: AD_NC/test/NC/1348596_113.jpeg \n", - " inflating: AD_NC/test/NC/1348596_94.jpeg \n", - " inflating: AD_NC/test/NC/1348596_95.jpeg \n", - " inflating: AD_NC/test/NC/1348596_96.jpeg \n", - " inflating: AD_NC/test/NC/1348596_97.jpeg \n", - " inflating: AD_NC/test/NC/1348596_98.jpeg \n", - " inflating: AD_NC/test/NC/1348596_99.jpeg \n", - " inflating: AD_NC/test/NC/1349784_100.jpeg \n", - " inflating: AD_NC/test/NC/1349784_101.jpeg \n", - " inflating: AD_NC/test/NC/1349784_102.jpeg \n", - " inflating: AD_NC/test/NC/1349784_103.jpeg \n", - " inflating: AD_NC/test/NC/1349784_104.jpeg \n", - " inflating: AD_NC/test/NC/1349784_105.jpeg \n", - " inflating: AD_NC/test/NC/1349784_106.jpeg \n", - " inflating: AD_NC/test/NC/1349784_107.jpeg \n", - " inflating: AD_NC/test/NC/1349784_108.jpeg \n", - " inflating: AD_NC/test/NC/1349784_109.jpeg \n", - " inflating: AD_NC/test/NC/1349784_110.jpeg \n", - " inflating: AD_NC/test/NC/1349784_111.jpeg \n", - " inflating: AD_NC/test/NC/1349784_112.jpeg \n", - " inflating: AD_NC/test/NC/1349784_113.jpeg \n", - " inflating: AD_NC/test/NC/1349784_94.jpeg \n", - " inflating: AD_NC/test/NC/1349784_95.jpeg \n", - " inflating: AD_NC/test/NC/1349784_96.jpeg \n", - " inflating: AD_NC/test/NC/1349784_97.jpeg \n", - " inflating: AD_NC/test/NC/1349784_98.jpeg \n", - " inflating: AD_NC/test/NC/1349784_99.jpeg \n", - " inflating: AD_NC/test/NC/1350223_78.jpeg \n", - " inflating: AD_NC/test/NC/1350223_79.jpeg \n", - " inflating: AD_NC/test/NC/1350223_80.jpeg \n", - " inflating: AD_NC/test/NC/1350223_81.jpeg \n", - " inflating: AD_NC/test/NC/1350223_82.jpeg \n", - " inflating: AD_NC/test/NC/1350223_83.jpeg \n", - " inflating: AD_NC/test/NC/1350223_84.jpeg \n", - " inflating: AD_NC/test/NC/1350223_85.jpeg \n", - " inflating: AD_NC/test/NC/1350223_86.jpeg \n", - " inflating: AD_NC/test/NC/1350223_87.jpeg \n", - " inflating: AD_NC/test/NC/1350223_88.jpeg \n", - " inflating: AD_NC/test/NC/1350223_89.jpeg \n", - " inflating: AD_NC/test/NC/1350223_90.jpeg \n", - " inflating: AD_NC/test/NC/1350223_91.jpeg \n", - " inflating: AD_NC/test/NC/1350223_92.jpeg \n", - " inflating: AD_NC/test/NC/1350223_93.jpeg \n", - " inflating: AD_NC/test/NC/1350223_94.jpeg \n", - " inflating: AD_NC/test/NC/1350223_95.jpeg \n", - " inflating: AD_NC/test/NC/1350223_96.jpeg \n", - " inflating: AD_NC/test/NC/1350223_97.jpeg \n", - " inflating: AD_NC/test/NC/1351301_100.jpeg \n", - " inflating: AD_NC/test/NC/1351301_101.jpeg \n", - " inflating: AD_NC/test/NC/1351301_102.jpeg \n", - " inflating: AD_NC/test/NC/1351301_103.jpeg \n", - " inflating: AD_NC/test/NC/1351301_104.jpeg \n", - " inflating: AD_NC/test/NC/1351301_105.jpeg \n", - " inflating: AD_NC/test/NC/1351301_106.jpeg \n", - " inflating: AD_NC/test/NC/1351301_107.jpeg \n", - " inflating: AD_NC/test/NC/1351301_108.jpeg \n", - " inflating: AD_NC/test/NC/1351301_109.jpeg \n", - " inflating: AD_NC/test/NC/1351301_110.jpeg \n", - " inflating: AD_NC/test/NC/1351301_111.jpeg \n", - " inflating: AD_NC/test/NC/1351301_112.jpeg \n", - " inflating: AD_NC/test/NC/1351301_113.jpeg \n", - " inflating: AD_NC/test/NC/1351301_94.jpeg \n", - " inflating: AD_NC/test/NC/1351301_95.jpeg \n", - " inflating: AD_NC/test/NC/1351301_96.jpeg \n", - " inflating: AD_NC/test/NC/1351301_97.jpeg \n", - " inflating: AD_NC/test/NC/1351301_98.jpeg \n", - " inflating: AD_NC/test/NC/1351301_99.jpeg \n", - " inflating: AD_NC/test/NC/1352191_100.jpeg \n", - " inflating: AD_NC/test/NC/1352191_101.jpeg \n", - " inflating: AD_NC/test/NC/1352191_102.jpeg \n", - " inflating: AD_NC/test/NC/1352191_103.jpeg \n", - " inflating: AD_NC/test/NC/1352191_104.jpeg \n", - " inflating: AD_NC/test/NC/1352191_105.jpeg \n", - " inflating: AD_NC/test/NC/1352191_106.jpeg \n", - " inflating: AD_NC/test/NC/1352191_107.jpeg \n", - " inflating: AD_NC/test/NC/1352191_108.jpeg \n", - " inflating: AD_NC/test/NC/1352191_109.jpeg \n", - " inflating: AD_NC/test/NC/1352191_110.jpeg \n", - " inflating: AD_NC/test/NC/1352191_111.jpeg \n", - " inflating: AD_NC/test/NC/1352191_112.jpeg \n", - " inflating: AD_NC/test/NC/1352191_113.jpeg \n", - " inflating: AD_NC/test/NC/1352191_94.jpeg \n", - " inflating: AD_NC/test/NC/1352191_95.jpeg \n", - " inflating: AD_NC/test/NC/1352191_96.jpeg \n", - " inflating: AD_NC/test/NC/1352191_97.jpeg \n", - " inflating: AD_NC/test/NC/1352191_98.jpeg \n", - " inflating: AD_NC/test/NC/1352191_99.jpeg \n", - " inflating: AD_NC/test/NC/1354011_78.jpeg \n", - " inflating: AD_NC/test/NC/1354011_79.jpeg \n", - " inflating: AD_NC/test/NC/1354011_80.jpeg \n", - " inflating: AD_NC/test/NC/1354011_81.jpeg \n", - " inflating: AD_NC/test/NC/1354011_82.jpeg \n", - " inflating: AD_NC/test/NC/1354011_83.jpeg \n", - " inflating: AD_NC/test/NC/1354011_84.jpeg \n", - " inflating: AD_NC/test/NC/1354011_85.jpeg \n", - " inflating: AD_NC/test/NC/1354011_86.jpeg \n", - " inflating: AD_NC/test/NC/1354011_87.jpeg \n", - " inflating: AD_NC/test/NC/1354011_88.jpeg \n", - " inflating: AD_NC/test/NC/1354011_89.jpeg \n", - " inflating: AD_NC/test/NC/1354011_90.jpeg \n", - " inflating: AD_NC/test/NC/1354011_91.jpeg \n", - " inflating: AD_NC/test/NC/1354011_92.jpeg \n", - " inflating: AD_NC/test/NC/1354011_93.jpeg \n", - " inflating: AD_NC/test/NC/1354011_94.jpeg \n", - " inflating: AD_NC/test/NC/1354011_95.jpeg \n", - " inflating: AD_NC/test/NC/1354011_96.jpeg \n", - " inflating: AD_NC/test/NC/1354011_97.jpeg \n", - " inflating: AD_NC/test/NC/1355445_78.jpeg \n", - " inflating: AD_NC/test/NC/1355445_79.jpeg \n", - " inflating: AD_NC/test/NC/1355445_80.jpeg \n", - " inflating: AD_NC/test/NC/1355445_81.jpeg \n", - " inflating: AD_NC/test/NC/1355445_82.jpeg \n", - " inflating: AD_NC/test/NC/1355445_83.jpeg \n", - " inflating: AD_NC/test/NC/1355445_84.jpeg \n", - " inflating: AD_NC/test/NC/1355445_85.jpeg \n", - " inflating: AD_NC/test/NC/1355445_86.jpeg \n", - " inflating: AD_NC/test/NC/1355445_87.jpeg \n", - " inflating: AD_NC/test/NC/1355445_88.jpeg \n", - " inflating: AD_NC/test/NC/1355445_89.jpeg \n", - " inflating: AD_NC/test/NC/1355445_90.jpeg \n", - " inflating: AD_NC/test/NC/1355445_91.jpeg \n", - " inflating: AD_NC/test/NC/1355445_92.jpeg \n", - " inflating: AD_NC/test/NC/1355445_93.jpeg \n", - " inflating: AD_NC/test/NC/1355445_94.jpeg \n", - " inflating: AD_NC/test/NC/1355445_95.jpeg \n", - " inflating: AD_NC/test/NC/1355445_96.jpeg \n", - " inflating: AD_NC/test/NC/1355445_97.jpeg \n", - " inflating: AD_NC/test/NC/1355446_78.jpeg \n", - " inflating: AD_NC/test/NC/1355446_79.jpeg \n", - " inflating: AD_NC/test/NC/1355446_80.jpeg \n", - " inflating: AD_NC/test/NC/1355446_81.jpeg \n", - " inflating: AD_NC/test/NC/1355446_82.jpeg \n", - " inflating: AD_NC/test/NC/1355446_83.jpeg \n", - " inflating: AD_NC/test/NC/1355446_84.jpeg \n", - " inflating: AD_NC/test/NC/1355446_85.jpeg \n", - " inflating: AD_NC/test/NC/1355446_86.jpeg \n", - " inflating: AD_NC/test/NC/1355446_87.jpeg \n", - " inflating: AD_NC/test/NC/1355446_88.jpeg \n", - " inflating: AD_NC/test/NC/1355446_89.jpeg \n", - " inflating: AD_NC/test/NC/1355446_90.jpeg \n", - " inflating: AD_NC/test/NC/1355446_91.jpeg \n", - " inflating: AD_NC/test/NC/1355446_92.jpeg \n", - " inflating: AD_NC/test/NC/1355446_93.jpeg \n", - " inflating: AD_NC/test/NC/1355446_94.jpeg \n", - " inflating: AD_NC/test/NC/1355446_95.jpeg \n", - " inflating: AD_NC/test/NC/1355446_96.jpeg \n", - " inflating: AD_NC/test/NC/1355446_97.jpeg \n", - " inflating: AD_NC/test/NC/1356105_100.jpeg \n", - " inflating: AD_NC/test/NC/1356105_101.jpeg \n", - " inflating: AD_NC/test/NC/1356105_102.jpeg \n", - " inflating: AD_NC/test/NC/1356105_103.jpeg \n", - " inflating: AD_NC/test/NC/1356105_104.jpeg \n", - " inflating: AD_NC/test/NC/1356105_105.jpeg \n", - " inflating: AD_NC/test/NC/1356105_106.jpeg \n", - " inflating: AD_NC/test/NC/1356105_107.jpeg \n", - " inflating: AD_NC/test/NC/1356105_88.jpeg \n", - " inflating: AD_NC/test/NC/1356105_89.jpeg \n", - " inflating: AD_NC/test/NC/1356105_90.jpeg \n", - " inflating: AD_NC/test/NC/1356105_91.jpeg \n", - " inflating: AD_NC/test/NC/1356105_92.jpeg \n", - " inflating: AD_NC/test/NC/1356105_93.jpeg \n", - " inflating: AD_NC/test/NC/1356105_94.jpeg \n", - " inflating: AD_NC/test/NC/1356105_95.jpeg \n", - " inflating: AD_NC/test/NC/1356105_96.jpeg \n", - " inflating: AD_NC/test/NC/1356105_97.jpeg \n", - " inflating: AD_NC/test/NC/1356105_98.jpeg \n", - " inflating: AD_NC/test/NC/1356105_99.jpeg \n", - " inflating: AD_NC/test/NC/1358529_78.jpeg \n", - " inflating: AD_NC/test/NC/1358529_79.jpeg \n", - " inflating: AD_NC/test/NC/1358529_80.jpeg \n", - " inflating: AD_NC/test/NC/1358529_81.jpeg \n", - " inflating: AD_NC/test/NC/1358529_82.jpeg \n", - " inflating: AD_NC/test/NC/1358529_83.jpeg \n", - " inflating: AD_NC/test/NC/1358529_84.jpeg \n", - " inflating: AD_NC/test/NC/1358529_85.jpeg \n", - " inflating: AD_NC/test/NC/1358529_86.jpeg \n", - " inflating: AD_NC/test/NC/1358529_87.jpeg \n", - " inflating: AD_NC/test/NC/1358529_88.jpeg \n", - " inflating: AD_NC/test/NC/1358529_89.jpeg \n", - " inflating: AD_NC/test/NC/1358529_90.jpeg \n", - " inflating: AD_NC/test/NC/1358529_91.jpeg \n", - " inflating: AD_NC/test/NC/1358529_92.jpeg \n", - " inflating: AD_NC/test/NC/1358529_93.jpeg \n", - " inflating: AD_NC/test/NC/1358529_94.jpeg \n", - " inflating: AD_NC/test/NC/1358529_95.jpeg \n", - " inflating: AD_NC/test/NC/1358529_96.jpeg \n", - " inflating: AD_NC/test/NC/1358529_97.jpeg \n", - " inflating: AD_NC/test/NC/1359836_78.jpeg \n", - " inflating: AD_NC/test/NC/1359836_79.jpeg \n", - " inflating: AD_NC/test/NC/1359836_80.jpeg \n", - " inflating: AD_NC/test/NC/1359836_81.jpeg \n", - " inflating: AD_NC/test/NC/1359836_82.jpeg \n", - " inflating: AD_NC/test/NC/1359836_83.jpeg \n", - " inflating: AD_NC/test/NC/1359836_84.jpeg \n", - " inflating: AD_NC/test/NC/1359836_85.jpeg \n", - " inflating: AD_NC/test/NC/1359836_86.jpeg \n", - " inflating: AD_NC/test/NC/1359836_87.jpeg \n", - " inflating: AD_NC/test/NC/1359836_88.jpeg \n", - " inflating: AD_NC/test/NC/1359836_89.jpeg \n", - " inflating: AD_NC/test/NC/1359836_90.jpeg \n", - " inflating: AD_NC/test/NC/1359836_91.jpeg \n", - " inflating: AD_NC/test/NC/1359836_92.jpeg \n", - " inflating: AD_NC/test/NC/1359836_93.jpeg \n", - " inflating: AD_NC/test/NC/1359836_94.jpeg \n", - " inflating: AD_NC/test/NC/1359836_95.jpeg \n", - " inflating: AD_NC/test/NC/1359836_96.jpeg \n", - " inflating: AD_NC/test/NC/1359836_97.jpeg \n", - " inflating: AD_NC/test/NC/1359936_78.jpeg \n", - " inflating: AD_NC/test/NC/1359936_79.jpeg \n", - " inflating: AD_NC/test/NC/1359936_80.jpeg \n", - " inflating: AD_NC/test/NC/1359936_81.jpeg \n", - " inflating: AD_NC/test/NC/1359936_82.jpeg \n", - " inflating: AD_NC/test/NC/1359936_83.jpeg \n", - " inflating: AD_NC/test/NC/1359936_84.jpeg \n", - " inflating: AD_NC/test/NC/1359936_85.jpeg \n", - " inflating: AD_NC/test/NC/1359936_86.jpeg \n", - " inflating: AD_NC/test/NC/1359936_87.jpeg \n", - " inflating: AD_NC/test/NC/1359936_88.jpeg \n", - " inflating: AD_NC/test/NC/1359936_89.jpeg \n", - " inflating: AD_NC/test/NC/1359936_90.jpeg \n", - " inflating: AD_NC/test/NC/1359936_91.jpeg \n", - " inflating: AD_NC/test/NC/1359936_92.jpeg \n", - " inflating: AD_NC/test/NC/1359936_93.jpeg \n", - " inflating: AD_NC/test/NC/1359936_94.jpeg \n", - " inflating: AD_NC/test/NC/1359936_95.jpeg \n", - " inflating: AD_NC/test/NC/1359936_96.jpeg \n", - " inflating: AD_NC/test/NC/1359936_97.jpeg \n", - " inflating: AD_NC/test/NC/1360295_100.jpeg \n", - " inflating: AD_NC/test/NC/1360295_101.jpeg \n", - " inflating: AD_NC/test/NC/1360295_102.jpeg \n", - " inflating: AD_NC/test/NC/1360295_103.jpeg \n", - " inflating: AD_NC/test/NC/1360295_104.jpeg \n", - " inflating: AD_NC/test/NC/1360295_105.jpeg \n", - " inflating: AD_NC/test/NC/1360295_106.jpeg \n", - " inflating: AD_NC/test/NC/1360295_107.jpeg \n", - " inflating: AD_NC/test/NC/1360295_108.jpeg \n", - " inflating: AD_NC/test/NC/1360295_109.jpeg \n", - " inflating: AD_NC/test/NC/1360295_110.jpeg \n", - " inflating: AD_NC/test/NC/1360295_111.jpeg \n", - " inflating: AD_NC/test/NC/1360295_112.jpeg \n", - " inflating: AD_NC/test/NC/1360295_113.jpeg \n", - " inflating: AD_NC/test/NC/1360295_94.jpeg \n", - " inflating: AD_NC/test/NC/1360295_95.jpeg \n", - " inflating: AD_NC/test/NC/1360295_96.jpeg \n", - " inflating: AD_NC/test/NC/1360295_97.jpeg \n", - " inflating: AD_NC/test/NC/1360295_98.jpeg \n", - " inflating: AD_NC/test/NC/1360295_99.jpeg \n", - " inflating: AD_NC/test/NC/1360551_100.jpeg \n", - " inflating: AD_NC/test/NC/1360551_101.jpeg \n", - " inflating: AD_NC/test/NC/1360551_102.jpeg \n", - " inflating: AD_NC/test/NC/1360551_103.jpeg \n", - " inflating: AD_NC/test/NC/1360551_104.jpeg \n", - " inflating: AD_NC/test/NC/1360551_105.jpeg \n", - " inflating: AD_NC/test/NC/1360551_106.jpeg \n", - " inflating: AD_NC/test/NC/1360551_107.jpeg \n", - " inflating: AD_NC/test/NC/1360551_108.jpeg \n", - " inflating: AD_NC/test/NC/1360551_109.jpeg \n", - " inflating: AD_NC/test/NC/1360551_110.jpeg \n", - " inflating: AD_NC/test/NC/1360551_111.jpeg \n", - " inflating: AD_NC/test/NC/1360551_112.jpeg \n", - " inflating: AD_NC/test/NC/1360551_113.jpeg \n", - " inflating: AD_NC/test/NC/1360551_94.jpeg \n", - " inflating: AD_NC/test/NC/1360551_95.jpeg \n", - " inflating: AD_NC/test/NC/1360551_96.jpeg \n", - " inflating: AD_NC/test/NC/1360551_97.jpeg \n", - " inflating: AD_NC/test/NC/1360551_98.jpeg \n", - " inflating: AD_NC/test/NC/1360551_99.jpeg \n", - " inflating: AD_NC/test/NC/1363593_100.jpeg \n", - " inflating: AD_NC/test/NC/1363593_101.jpeg \n", - " inflating: AD_NC/test/NC/1363593_102.jpeg \n", - " inflating: AD_NC/test/NC/1363593_103.jpeg \n", - " inflating: AD_NC/test/NC/1363593_104.jpeg \n", - " inflating: AD_NC/test/NC/1363593_105.jpeg \n", - " inflating: AD_NC/test/NC/1363593_106.jpeg \n", - " inflating: AD_NC/test/NC/1363593_107.jpeg \n", - " inflating: AD_NC/test/NC/1363593_108.jpeg \n", - " inflating: AD_NC/test/NC/1363593_109.jpeg \n", - " inflating: AD_NC/test/NC/1363593_110.jpeg \n", - " inflating: AD_NC/test/NC/1363593_111.jpeg \n", - " inflating: AD_NC/test/NC/1363593_112.jpeg \n", - " inflating: AD_NC/test/NC/1363593_113.jpeg \n", - " inflating: AD_NC/test/NC/1363593_94.jpeg \n", - " inflating: AD_NC/test/NC/1363593_95.jpeg \n", - " inflating: AD_NC/test/NC/1363593_96.jpeg \n", - " inflating: AD_NC/test/NC/1363593_97.jpeg \n", - " inflating: AD_NC/test/NC/1363593_98.jpeg \n", - " inflating: AD_NC/test/NC/1363593_99.jpeg \n", - " inflating: AD_NC/test/NC/1366966_100.jpeg \n", - " inflating: AD_NC/test/NC/1366966_101.jpeg \n", - " inflating: AD_NC/test/NC/1366966_102.jpeg \n", - " inflating: AD_NC/test/NC/1366966_103.jpeg \n", - " inflating: AD_NC/test/NC/1366966_104.jpeg \n", - " inflating: AD_NC/test/NC/1366966_105.jpeg \n", - " inflating: AD_NC/test/NC/1366966_106.jpeg \n", - " inflating: AD_NC/test/NC/1366966_107.jpeg \n", - " inflating: AD_NC/test/NC/1366966_108.jpeg \n", - " inflating: AD_NC/test/NC/1366966_109.jpeg \n", - " inflating: AD_NC/test/NC/1366966_110.jpeg \n", - " inflating: AD_NC/test/NC/1366966_111.jpeg \n", - " inflating: AD_NC/test/NC/1366966_112.jpeg \n", - " inflating: AD_NC/test/NC/1366966_113.jpeg \n", - " inflating: AD_NC/test/NC/1366966_94.jpeg \n", - " inflating: AD_NC/test/NC/1366966_95.jpeg \n", - " inflating: AD_NC/test/NC/1366966_96.jpeg \n", - " inflating: AD_NC/test/NC/1366966_97.jpeg \n", - " inflating: AD_NC/test/NC/1366966_98.jpeg \n", - " inflating: AD_NC/test/NC/1366966_99.jpeg \n", - " inflating: AD_NC/test/NC/1368078_100.jpeg \n", - " inflating: AD_NC/test/NC/1368078_101.jpeg \n", - " inflating: AD_NC/test/NC/1368078_102.jpeg \n", - " inflating: AD_NC/test/NC/1368078_103.jpeg \n", - " inflating: AD_NC/test/NC/1368078_104.jpeg \n", - " inflating: AD_NC/test/NC/1368078_105.jpeg \n", - " inflating: AD_NC/test/NC/1368078_106.jpeg \n", - " inflating: AD_NC/test/NC/1368078_107.jpeg \n", - " inflating: AD_NC/test/NC/1368078_108.jpeg \n", - " inflating: AD_NC/test/NC/1368078_109.jpeg \n", - " inflating: AD_NC/test/NC/1368078_110.jpeg \n", - " inflating: AD_NC/test/NC/1368078_111.jpeg \n", - " inflating: AD_NC/test/NC/1368078_112.jpeg \n", - " inflating: AD_NC/test/NC/1368078_113.jpeg \n", - " inflating: AD_NC/test/NC/1368078_94.jpeg \n", - " inflating: AD_NC/test/NC/1368078_95.jpeg \n", - " inflating: AD_NC/test/NC/1368078_96.jpeg \n", - " inflating: AD_NC/test/NC/1368078_97.jpeg \n", - " inflating: AD_NC/test/NC/1368078_98.jpeg \n", - " inflating: AD_NC/test/NC/1368078_99.jpeg \n", - " inflating: AD_NC/test/NC/1371438_78.jpeg \n", - " inflating: AD_NC/test/NC/1371438_79.jpeg \n", - " inflating: AD_NC/test/NC/1371438_80.jpeg \n", - " inflating: AD_NC/test/NC/1371438_81.jpeg \n", - " inflating: AD_NC/test/NC/1371438_82.jpeg \n", - " inflating: AD_NC/test/NC/1371438_83.jpeg \n", - " inflating: AD_NC/test/NC/1371438_84.jpeg \n", - " inflating: AD_NC/test/NC/1371438_85.jpeg \n", - " inflating: AD_NC/test/NC/1371438_86.jpeg \n", - " inflating: AD_NC/test/NC/1371438_87.jpeg \n", - " inflating: AD_NC/test/NC/1371438_88.jpeg \n", - " inflating: AD_NC/test/NC/1371438_89.jpeg \n", - " inflating: AD_NC/test/NC/1371438_90.jpeg \n", - " inflating: AD_NC/test/NC/1371438_91.jpeg \n", - " inflating: AD_NC/test/NC/1371438_92.jpeg \n", - " inflating: AD_NC/test/NC/1371438_93.jpeg \n", - " inflating: AD_NC/test/NC/1371438_94.jpeg \n", - " inflating: AD_NC/test/NC/1371438_95.jpeg \n", - " inflating: AD_NC/test/NC/1371438_96.jpeg \n", - " inflating: AD_NC/test/NC/1371438_97.jpeg \n", - " inflating: AD_NC/test/NC/1373547_78.jpeg \n", - " inflating: AD_NC/test/NC/1373547_79.jpeg \n", - " inflating: AD_NC/test/NC/1373547_80.jpeg \n", - " inflating: AD_NC/test/NC/1373547_81.jpeg \n", - " inflating: AD_NC/test/NC/1373547_82.jpeg \n", - " inflating: AD_NC/test/NC/1373547_83.jpeg \n", - " inflating: AD_NC/test/NC/1373547_84.jpeg \n", - " inflating: AD_NC/test/NC/1373547_85.jpeg \n", - " inflating: AD_NC/test/NC/1373547_86.jpeg \n", - " inflating: AD_NC/test/NC/1373547_87.jpeg \n", - " inflating: AD_NC/test/NC/1373547_88.jpeg \n", - " inflating: AD_NC/test/NC/1373547_89.jpeg \n", - " inflating: AD_NC/test/NC/1373547_90.jpeg \n", - " inflating: AD_NC/test/NC/1373547_91.jpeg \n", - " inflating: AD_NC/test/NC/1373547_92.jpeg \n", - " inflating: AD_NC/test/NC/1373547_93.jpeg \n", - " inflating: AD_NC/test/NC/1373547_94.jpeg \n", - " inflating: AD_NC/test/NC/1373547_95.jpeg \n", - " inflating: AD_NC/test/NC/1373547_96.jpeg \n", - " inflating: AD_NC/test/NC/1373547_97.jpeg \n", - " inflating: AD_NC/test/NC/1384712_100.jpeg \n", - " inflating: AD_NC/test/NC/1384712_101.jpeg \n", - " inflating: AD_NC/test/NC/1384712_102.jpeg \n", - " inflating: AD_NC/test/NC/1384712_103.jpeg \n", - " inflating: AD_NC/test/NC/1384712_104.jpeg \n", - " inflating: AD_NC/test/NC/1384712_105.jpeg \n", - " inflating: AD_NC/test/NC/1384712_106.jpeg \n", - " inflating: AD_NC/test/NC/1384712_107.jpeg \n", - " inflating: AD_NC/test/NC/1384712_88.jpeg \n", - " inflating: AD_NC/test/NC/1384712_89.jpeg \n", - " inflating: AD_NC/test/NC/1384712_90.jpeg \n", - " inflating: AD_NC/test/NC/1384712_91.jpeg \n", - " inflating: AD_NC/test/NC/1384712_92.jpeg \n", - " inflating: AD_NC/test/NC/1384712_93.jpeg \n", - " inflating: AD_NC/test/NC/1384712_94.jpeg \n", - " inflating: AD_NC/test/NC/1384712_95.jpeg \n", - " inflating: AD_NC/test/NC/1384712_96.jpeg \n", - " inflating: AD_NC/test/NC/1384712_97.jpeg \n", - " inflating: AD_NC/test/NC/1384712_98.jpeg \n", - " inflating: AD_NC/test/NC/1384712_99.jpeg \n", - " inflating: AD_NC/test/NC/1387180_78.jpeg \n", - " inflating: AD_NC/test/NC/1387180_79.jpeg \n", - " inflating: AD_NC/test/NC/1387180_80.jpeg \n", - " inflating: AD_NC/test/NC/1387180_81.jpeg \n", - " inflating: AD_NC/test/NC/1387180_82.jpeg \n", - " inflating: AD_NC/test/NC/1387180_83.jpeg \n", - " inflating: AD_NC/test/NC/1387180_84.jpeg \n", - " inflating: AD_NC/test/NC/1387180_85.jpeg \n", - " inflating: AD_NC/test/NC/1387180_86.jpeg \n", - " inflating: AD_NC/test/NC/1387180_87.jpeg \n", - " inflating: AD_NC/test/NC/1387180_88.jpeg \n", - " inflating: AD_NC/test/NC/1387180_89.jpeg \n", - " inflating: AD_NC/test/NC/1387180_90.jpeg \n", - " inflating: AD_NC/test/NC/1387180_91.jpeg \n", - " inflating: AD_NC/test/NC/1387180_92.jpeg \n", - " inflating: AD_NC/test/NC/1387180_93.jpeg \n", - " inflating: AD_NC/test/NC/1387180_94.jpeg \n", - " inflating: AD_NC/test/NC/1387180_95.jpeg \n", - " inflating: AD_NC/test/NC/1387180_96.jpeg \n", - " inflating: AD_NC/test/NC/1387180_97.jpeg \n", - " inflating: AD_NC/test/NC/1387539_78.jpeg \n", - " inflating: AD_NC/test/NC/1387539_79.jpeg \n", - " inflating: AD_NC/test/NC/1387539_80.jpeg \n", - " inflating: AD_NC/test/NC/1387539_81.jpeg \n", - " inflating: AD_NC/test/NC/1387539_82.jpeg \n", - " inflating: AD_NC/test/NC/1387539_83.jpeg \n", - " inflating: AD_NC/test/NC/1387539_84.jpeg \n", - " inflating: AD_NC/test/NC/1387539_85.jpeg \n", - " inflating: AD_NC/test/NC/1387539_86.jpeg \n", - " inflating: AD_NC/test/NC/1387539_87.jpeg \n", - " inflating: AD_NC/test/NC/1387539_88.jpeg \n", - " inflating: AD_NC/test/NC/1387539_89.jpeg \n", - " inflating: AD_NC/test/NC/1387539_90.jpeg \n", - " inflating: AD_NC/test/NC/1387539_91.jpeg \n", - " inflating: AD_NC/test/NC/1387539_92.jpeg \n", - " inflating: AD_NC/test/NC/1387539_93.jpeg \n", - " inflating: AD_NC/test/NC/1387539_94.jpeg \n", - " inflating: AD_NC/test/NC/1387539_95.jpeg " + "unzip: cannot find or open NC.zip, NC.zip.zip or NC.zip.ZIP.\n" ] } + ], + "source": [ + "!unzip NC.zip" ] }, { "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "_8PBaJLSJSCP" + }, + "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", @@ -5120,15 +132,15 @@ "import numpy as np\n", "from torch.utils.data import Dataset, DataLoader\n", "from torch.utils.data import ConcatDataset" - ], - "metadata": { - "id": "_8PBaJLSJSCP" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "7zvKyWj2J7Yk" + }, + "outputs": [], "source": [ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", @@ -5152,13 +164,13 @@ " return image\n", "\n", "class CustomConcatDataset(ConcatDataset):\n", - " def __init__(self, datasets, labels):\n", + " def __init__(self, datasets):\n", " super(CustomConcatDataset, self).__init__(datasets)\n", - " self.labels = labels\n", "\n", " def __getitem__(self, index):\n", - " item = super(CustomConcatDataset, self).__getitem__(index)\n", - " return item, self.labels[index]\n", + " item1 = super(CustomConcatDataset, self).__getitem__(index)\n", + " item2 = super(CustomConcatDataset, self).__getitem__(index)\n", + " return item1, item2\n", "\n", "def combine(AD, NC):\n", " # Set labels for the AD dataset\n", @@ -5167,7 +179,7 @@ " labels_NC = [False] * len(NC)\n", "\n", " # Combine the datasets and labels\n", - " X = AD + NC\n", + " X = CustomConcatDataset([AD, NC])\n", " Y = labels_AD + labels_NC\n", " # X = torch.utils.data.RandomSampler(X)\n", " seed = 123\n", @@ -5194,23 +206,23 @@ "# nii_dataset = NiiDataset(root_dir=r'keras_png_slices_data\\keras_png_slices_data\\keras_png_slices_train')\n", "loaders = {}\n", "\n", - "for stage in ['train', 'test']:\n", + "for stage in ['test']:\n", " loaders[stage] = {}\n", - " AD = CustomDataset(root_dir=rf'ADNI_AD_NC_2D\\AD_NC\\{stage}\\AD', transform=transform_X)\n", - " NC = CustomDataset(root_dir=rf'ADNI_AD_NC_2D\\AD_NC\\{stage}\\NC', transform=transform_X)\n", + " AD = CustomDataset(root_dir=rf'{stage}/AD', transform=transform_X)\n", + " NC = CustomDataset(root_dir=rf'{stage}/NC', transform=transform_X)\n", " loaders[stage]['X'], loaders[stage]['Y'] = combine(AD, NC)\n", "# Y_train = CustomDataset(root_dir=r'ADNI_AD_NC_2D\\AD_NC\\train\\NC', transform=transform_Y)\n", "# X_train_AD_loader = DataLoader(X_train_AD, batch_size=4, shuffle=True)\n", "# Y_train_loader = DataLoader(Y_train, batch_size=4, shuffle=True)" - ], - "metadata": { - "id": "7zvKyWj2J7Yk" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4BuuMeumKHi2" + }, + "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", @@ -5243,15 +255,15 @@ "# Instantiate the model\n", "model = CustomModel()\n", "\n" - ], - "metadata": { - "id": "4BuuMeumKHi2" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SztvTT_tKJMA" + }, + "outputs": [], "source": [ "criterion = nn.CrossEntropyLoss()\n", "learning_rate = 0.1\n", @@ -5260,29 +272,173 @@ "sched_linear_1 = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.005, max_lr=learning_rate, step_size_up=15, step_size_down=15, mode=\"triangular\", verbose=False)\n", "sched_linear_3 = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.005/learning_rate, end_factor=0.005/learning_rate, verbose=False)\n", "scheduler = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[sched_linear_1, sched_linear_3], milestones=[30])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "puuj1CSURmJ9", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a60e467c-4d63-4b96-d0df-1925c065d0d6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n", + "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n", + "100%|██████████| 44.7M/44.7M [00:00<00:00, 220MB/s]\n" + ] + } ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torchvision.models as models\n", + "\n", + "class SiameseNetwork(nn.Module):\n", + " def __init__(self, pretrained=True):\n", + " super(SiameseNetwork, self).__init__()\n", + " self.resnet = models.resnet18(pretrained=pretrained)\n", + " self.linear = nn.Linear(1000, 2) # 1000 is the number of output features from ResNet\n", + "\n", + " def forward(self, x1, x2):\n", + " output1 = self.resnet(x1)\n", + " output2 = self.resnet(x2)\n", + " return self.linear(torch.abs(output1 - output2))\n", + "\n", + "model = SiameseNetwork()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": { - "id": "SztvTT_tKJMA" + "id": "ocj6rcu7Rn4-" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "class ContrastiveLoss(torch.nn.Module):\n", + " def __init__(self, margin=2.0):\n", + " super(ContrastiveLoss, self).__init__()\n", + " self.margin = margin\n", + "\n", + " def forward(self, output1, output2, label):\n", + " euclidean_distance = F.pairwise_distance(output1, output2)\n", + " loss_contrastive = torch.mean((1 - label) * torch.pow(euclidean_distance, 2) +\n", + " label * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))\n", + " return loss_contrastive\n" + ] + }, + { + "cell_type": "code", + "source": [ + "import torch.optim as optim\n", + "\n", + "# Set the device\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "# Initialize the network, loss function, and optimizer\n", + "siamese_net = SiameseNetwork(pretrained=True).to(device)\n", + "criterion = ContrastiveLoss()\n", + "optimizer = optim.Adam(siamese_net.parameters(), lr=0.0005)\n", + "\n", + "# Training loop\n", + "epochs = 2\n", + "for epoch in range(epochs):\n", + " for i, (images, label) in enumerate(zip(loaders['test']['X'], loaders['test']['Y'])):\n", + " img0, img1 = images\n", + " img0, img1, label = img0.to(device), img1.to(device), label.to(device)\n", + " output1, output2 = model(img0, img1)\n", + "\n", + "\n", + " # Adjust the shapes to match the criterion\n", + " # outputs = outputs.view(-1) # Flatten the outputs to a 1D tensor\n", + " # seg = seg.view(-1) # Flatten the seg tensor to a 1D tensor\n", + " loss = criterion(output1, output2, label)\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " if (i + 1) % 5 == 0:\n", + " print(f\"Epoch [{epoch + 1} / {num_epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 391 + }, + "id": "4NnXFUDzSWjp", + "outputId": "38eb6735-7694-49e3-8fca-113482e73490" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "error", + "ename": "RuntimeError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0moutput1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x1, x2)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0moutput1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0moutput2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0moutput2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 284\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36m_forward_impl\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_forward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;31m# See note [TorchScript super()]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 269\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 457\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 458\u001b[0m _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 459\u001b[0;31m return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[1;32m 460\u001b[0m self.padding, self.dilation, self.groups)\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: Given groups=1, weight of size [64, 3, 7, 7], expected input[128, 1, 256, 256] to have 3 channels, but got 1 channels instead" + ] + } + ] }, { "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "xeAsMkrnKKij", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 245 + }, + "outputId": "5dd55b9f-d01e-4627-9e35-ba8f1257048e" + }, + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined" + ] + } + ], "source": [ "num_epochs = 2\n", "for epoch in range(num_epochs):\n", "\n", - " for i, (images, seg) in enumerate(zip(loaders['train']['X'], loaders['train']['Y'])):\n", - " images = images.to(device)\n", - " seg = torch.tensor(seg, dtype=torch.long).to(device)\n", + " for i, (images, label) in enumerate(zip(loaders['test']['X'], loaders['test']['Y'])):\n", + " img0, img1 = images\n", + " img0, img1, label = img0.to(device), img1.to(device), label.to(device)\n", " outputs = model(images)\n", "\n", "\n", " # Adjust the shapes to match the criterion\n", " # outputs = outputs.view(-1) # Flatten the outputs to a 1D tensor\n", " # seg = seg.view(-1) # Flatten the seg tensor to a 1D tensor\n", - " loss = criterion(outputs, seg)\n", + " loss = criterion(outputs, label)\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", @@ -5292,12 +448,23 @@ "\n", " scheduler.step()\n", "end = time.time()" - ], - "metadata": { - "id": "xeAsMkrnKKij" - }, - "execution_count": null, - "outputs": [] + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyPullQ1T6knjpYBrXTmmpBA", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" } - ] + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file From 6e899b2a5af05937d78a199586e85ca05f35bbcb Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Mon, 16 Oct 2023 13:18:38 +1000 Subject: [PATCH 03/14] First version of model created, loss is very low but accuracy right now is nan% --- Colab version.ipynb | 322 +++++++++++++++++++------------------------- 1 file changed, 140 insertions(+), 182 deletions(-) diff --git a/Colab version.ipynb b/Colab version.ipynb index 80943a64b2..d5769d6c13 100644 --- a/Colab version.ipynb +++ b/Colab version.ipynb @@ -18,7 +18,7 @@ "base_uri": "https://localhost:8080/" }, "id": "yzCmT9TUJY10", - "outputId": "898b4f80-2f63-4047-9790-355985414a28" + "outputId": "997681ae-a0d2-448e-d290-b00a8dc22666" }, "outputs": [ { @@ -36,48 +36,48 @@ }, { "cell_type": "code", - "source": [ - "%ls" - ], + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "T0cDyMtaR_Hi", - "outputId": "1327687d-e185-450a-b06d-0f8c26680638" + "id": "-Lb7CGdEJCQg", + "outputId": "a693cf6b-0d28-474e-ed69-f7192a4700f9" }, - "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n" + "/content/drive/MyDrive/AD_NC\n" ] } + ], + "source": [ + "%cd /content/drive/MyDrive/AD_NC" ] }, { "cell_type": "code", - "execution_count": 9, + "source": [ + "%ls" + ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "-Lb7CGdEJCQg", - "outputId": "aa6b51ec-c531-4cf2-923a-a1dfe784e838" + "id": "mgx67Xe5dsgs", + "outputId": "b7850ff6-2922-444d-ee4f-48498dcaeacf" }, + "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "/content/drive/MyDrive/AD_NC\n" + "NC.zip \u001b[0m\u001b[01;34mtest\u001b[0m/ \u001b[01;34mtrain\u001b[0m/\n" ] } - ], - "source": [ - "%cd /content/drive/MyDrive/AD_NC" ] }, { @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "id": "_8PBaJLSJSCP" }, @@ -113,6 +113,7 @@ "source": [ "import torch\n", "import torch.nn as nn\n", + "import torch.optim as optim\n", "import torch.nn.functional as F\n", "import torchvision.transforms as transforms\n", "import time\n", @@ -130,13 +131,12 @@ "import os\n", "from PIL import Image\n", "import numpy as np\n", - "from torch.utils.data import Dataset, DataLoader\n", - "from torch.utils.data import ConcatDataset" + "from torch.utils.data import Dataset, DataLoader, TensorDataset, ConcatDataset" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "metadata": { "id": "7zvKyWj2J7Yk" }, @@ -163,126 +163,81 @@ "\n", " return image\n", "\n", - "class CustomConcatDataset(ConcatDataset):\n", - " def __init__(self, datasets):\n", - " super(CustomConcatDataset, self).__init__(datasets)\n", + "class SiameseDataset(Dataset):\n", + " def __init__(self, AD, NC):\n", + " self.AD = AD\n", + " self.NC = NC\n", + " self.labels = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0)\n", "\n", - " def __getitem__(self, index):\n", - " item1 = super(CustomConcatDataset, self).__getitem__(index)\n", - " item2 = super(CustomConcatDataset, self).__getitem__(index)\n", - " return item1, item2\n", + " def __len__(self):\n", + " return min(len(self.AD), len(self.NC))\n", "\n", - "def combine(AD, NC):\n", - " # Set labels for the AD dataset\n", - " labels_AD = [True] * len(AD)\n", - " # Set labels for the NC dataset\n", - " labels_NC = [False] * len(NC)\n", + " def __getitem__(self, idx):\n", + " img1 = self.AD[idx]\n", + " label1 = self.labels[idx]\n", + " img2 = self.NC[idx]\n", + " label2 = self.labels[len(self.AD) + idx]\n", "\n", - " # Combine the datasets and labels\n", - " X = CustomConcatDataset([AD, NC])\n", - " Y = labels_AD + labels_NC\n", - " # X = torch.utils.data.RandomSampler(X)\n", - " seed = 123\n", - " torch.manual_seed(seed)\n", - " np.random.seed(seed)\n", + " return img1, img2, label1, label2\n", "\n", + "# Set the device\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", - " X_loader = DataLoader(X, batch_size=128, shuffle=True)\n", - " Y_loader = DataLoader(Y, batch_size=128, shuffle=True)\n", - " return X_loader, Y_loader\n", "\n", "size = 256\n", - "\n", "transform_X = transforms.Compose([\n", - " transforms.Resize((size, size)), # Resize the image to the desired size\n", - " transforms.ToTensor(), # Convert the image to a tensor\n", - " transforms.Lambda(lambda x: x / 255.0),\n", - " transforms.Lambda(lambda x: (x - x.mean()) / x.std()) # Subtract mean and divide by standard deviation\n", + " transforms.Resize((size, size)),\n", + " transforms.ToTensor(),\n", + " # transforms.Lambda(lambda x: x / 255.0),\n", + " # transforms.Normalize(mean=[0.5], std=[0.5]) # Adjust according to your data statistics\n", "])\n", "\n", "\n", - "\n", "# Replace 'your_nii_folder' with the path to your folder containing .nii files\n", "# nii_dataset = NiiDataset(root_dir=r'keras_png_slices_data\\keras_png_slices_data\\keras_png_slices_train')\n", "loaders = {}\n", "\n", "for stage in ['test']:\n", " loaders[stage] = {}\n", - " AD = CustomDataset(root_dir=rf'{stage}/AD', transform=transform_X)\n", - " NC = CustomDataset(root_dir=rf'{stage}/NC', transform=transform_X)\n", - " loaders[stage]['X'], loaders[stage]['Y'] = combine(AD, NC)\n", - "# Y_train = CustomDataset(root_dir=r'ADNI_AD_NC_2D\\AD_NC\\train\\NC', transform=transform_Y)\n", - "# X_train_AD_loader = DataLoader(X_train_AD, batch_size=4, shuffle=True)\n", - "# Y_train_loader = DataLoader(Y_train, batch_size=4, shuffle=True)" + " AD = CustomDataset(root_dir=os.path.join(stage, 'AD'), transform=transform_X)\n", + " NC = CustomDataset(root_dir=os.path.join(stage, 'NC'), transform=transform_X)\n", + " loaders[stage] = SiameseDataset(AD, NC)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "4BuuMeumKHi2" - }, - "outputs": [], "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "\n", - "# Define the model\n", - "class CustomModel(nn.Module):\n", - " def __init__(self):\n", - " super(CustomModel, self).__init__()\n", - " self.conv1 = nn.Conv2d(1, 32, 3, padding=1)\n", - " self.maxpool1 = nn.MaxPool2d(2, 2)\n", - " self.conv2 = nn.Conv2d(32, 32, 3, padding=1)\n", - " self.maxpool2 = nn.MaxPool2d(2, 2)\n", - " self.flatten = nn.Flatten()\n", - " self.fc1 = nn.Linear(32 * 64 * 64, 128)\n", - " self.fc2 = nn.Linear(128, 10)\n", - "\n", - " def forward(self, x):\n", - " x = nn.functional.relu(self.conv1(x))\n", - " x = self.maxpool1(x)\n", - " x = nn.functional.relu(self.conv2(x))\n", - " x = self.maxpool2(x)\n", - " x = self.flatten(x)\n", - " x = nn.functional.relu(self.fc1(x))\n", - " x = nn.functional.softmax(self.fc2(x), dim=1)\n", - " return x\n", - "\n", - "\n", - "\n", - "# Instantiate the model\n", - "model = CustomModel()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "for load in loaders.values():\n", + " print(len(load))" + ], "metadata": { - "id": "SztvTT_tKJMA" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oW2JaTc0dXLz", + "outputId": "758fa1b1-dbfb-48eb-d982-03b676d628a4" }, - "outputs": [], - "source": [ - "criterion = nn.CrossEntropyLoss()\n", - "learning_rate = 0.1\n", - "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4)\n", - "total_step = len(loaders['train']['X'])\n", - "sched_linear_1 = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.005, max_lr=learning_rate, step_size_up=15, step_size_down=15, mode=\"triangular\", verbose=False)\n", - "sched_linear_3 = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.005/learning_rate, end_factor=0.005/learning_rate, verbose=False)\n", - "scheduler = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[sched_linear_1, sched_linear_3], milestones=[30])\n" + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0\n", + "4460\n" + ] + } ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "metadata": { "id": "puuj1CSURmJ9", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "a60e467c-4d63-4b96-d0df-1925c065d0d6" + "outputId": "532995b4-c399-4666-961a-5513ebf7de7b" }, "outputs": [ { @@ -292,9 +247,7 @@ "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n", - "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n", - "100%|██████████| 44.7M/44.7M [00:00<00:00, 220MB/s]\n" + " warnings.warn(msg)\n" ] } ], @@ -307,6 +260,8 @@ " def __init__(self, pretrained=True):\n", " super(SiameseNetwork, self).__init__()\n", " self.resnet = models.resnet18(pretrained=pretrained)\n", + " # Modify the first convolution layer to accept single-channel input\n", + " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n", " self.linear = nn.Linear(1000, 2) # 1000 is the number of output features from ResNet\n", "\n", " def forward(self, x1, x2):\n", @@ -319,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "metadata": { "id": "ocj6rcu7Rn4-" }, @@ -334,17 +289,12 @@ " euclidean_distance = F.pairwise_distance(output1, output2)\n", " loss_contrastive = torch.mean((1 - label) * torch.pow(euclidean_distance, 2) +\n", " label * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))\n", - " return loss_contrastive\n" + " return loss_contrastive" ] }, { "cell_type": "code", "source": [ - "import torch.optim as optim\n", - "\n", - "# Set the device\n", - "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "\n", "# Initialize the network, loss function, and optimizer\n", "siamese_net = SiameseNetwork(pretrained=True).to(device)\n", "criterion = ContrastiveLoss()\n", @@ -352,109 +302,116 @@ "\n", "# Training loop\n", "epochs = 2\n", + "total_step = len(loaders['test'])\n", "for epoch in range(epochs):\n", - " for i, (images, label) in enumerate(zip(loaders['test']['X'], loaders['test']['Y'])):\n", - " img0, img1 = images\n", - " img0, img1, label = img0.to(device), img1.to(device), label.to(device)\n", - " output1, output2 = model(img0, img1)\n", + " for i, (i1, i2, l1, l2) in enumerate(loaders['test']):\n", + " i1, i2, l1, l2 = i1.to(device), i2.to(device), l1.to(device), l2.to(device)\n", + " i1 = i1.unsqueeze(1) # Add a channel dimension\n", + " i2 = i2.unsqueeze(1) # Add a channel dimension\n", + " output = siamese_net(i1, i2)\n", + " o1, o2 = output[0][0], output[0][1]\n", + " l = (l1 == l2).int()\n", "\n", "\n", " # Adjust the shapes to match the criterion\n", " # outputs = outputs.view(-1) # Flatten the outputs to a 1D tensor\n", " # seg = seg.view(-1) # Flatten the seg tensor to a 1D tensor\n", - " loss = criterion(output1, output2, label)\n", + " loss = criterion(o1, o2, l)\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", "\n", - " if (i + 1) % 5 == 0:\n", - " print(f\"Epoch [{epoch + 1} / {num_epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")" + " if (i + 1) % 500 == 0:\n", + " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")" ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/", - "height": 391 + "base_uri": "https://localhost:8080/" }, "id": "4NnXFUDzSWjp", - "outputId": "38eb6735-7694-49e3-8fca-113482e73490" + "outputId": "8bd9a4f5-1999-4373-9d1b-9f3fe398f29b" }, - "execution_count": 26, + "execution_count": 12, "outputs": [ { - "output_type": "error", - "ename": "RuntimeError", - "evalue": "ignored", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0moutput1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x1, x2)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0moutput1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0moutput2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0moutput2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 284\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36m_forward_impl\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_forward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;31m# See note [TorchScript super()]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 269\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 457\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 458\u001b[0m _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 459\u001b[0;31m return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[1;32m 460\u001b[0m self.padding, self.dilation, self.groups)\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: Given groups=1, weight of size [64, 3, 7, 7], expected input[128, 1, 256, 256] to have 3 channels, but got 1 channels instead" + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [1 / 2], Step [500 / 4460 Loss 3.259337688632513e-07]\n", + "Epoch [1 / 2], Step [1000 / 4460 Loss 3.283045589341782e-05]\n", + "Epoch [1 / 2], Step [1500 / 4460 Loss 1.054049062076956e-05]\n", + "Epoch [1 / 2], Step [2000 / 4460 Loss 1.234111095982371e-06]\n", + "Epoch [1 / 2], Step [2500 / 4460 Loss 1.1719105259544449e-06]\n", + "Epoch [1 / 2], Step [3000 / 4460 Loss 3.3835010526672704e-07]\n", + "Epoch [1 / 2], Step [3500 / 4460 Loss 7.921468636595819e-08]\n", + "Epoch [1 / 2], Step [4000 / 4460 Loss 2.9854402328055585e-07]\n", + "Epoch [2 / 2], Step [500 / 4460 Loss 1.767582347156349e-07]\n", + "Epoch [2 / 2], Step [1000 / 4460 Loss 4.3589712395153413e-10]\n", + "Epoch [2 / 2], Step [1500 / 4460 Loss 8.744237902647001e-07]\n", + "Epoch [2 / 2], Step [2000 / 4460 Loss 2.0802056258095725e-11]\n", + "Epoch [2 / 2], Step [2500 / 4460 Loss 1.8013805913597025e-07]\n", + "Epoch [2 / 2], Step [3000 / 4460 Loss 3.1853244308877038e-06]\n", + "Epoch [2 / 2], Step [3500 / 4460 Loss 2.546377402268263e-07]\n", + "Epoch [2 / 2], Step [4000 / 4460 Loss 2.075099914122802e-08]\n" ] } ] }, { "cell_type": "code", - "execution_count": 17, + "source": [ + "# Evaluation loop\n", + "siamese_net.eval() # Set the model to evaluation mode\n", + "with torch.no_grad(): # Disable gradient calculation for evaluation\n", + " correct = 0\n", + " total = 0\n", + " for i, (i1, i2, l1, l2) in enumerate(loaders['test']):\n", + " i1, i2, l1, l2 = i1.to(device), i2.to(device), l1.to(device), l2.to(device)\n", + " i1 = i1.unsqueeze(1) # Add a channel dimension\n", + " i2 = i2.unsqueeze(1) # Add a channel dimension\n", + " output = siamese_net(i1, i2)\n", + " o1, o2 = output[0][0], output[0][1]\n", + " l = (l1 == l2).int()\n", + " # Compute the Euclidean distance between the outputs\n", + " distance = torch.abs(o1 - o2)\n", + "\n", + " # Apply threshold to determine predicted labels\n", + " threshold = 0.5 # Adjust as needed\n", + " predicted = distance.lt(threshold)\n", + "\n", + "\n", + "\n", + " total += l\n", + " correct += (predicted == l).sum().item()\n", + "\n", + " accuracy = 100 * correct / total\n", + " print(f'Test Accuracy: {accuracy:.2f}%')\n" + ], "metadata": { - "id": "xeAsMkrnKKij", "colab": { - "base_uri": "https://localhost:8080/", - "height": 245 + "base_uri": "https://localhost:8080/" }, - "outputId": "5dd55b9f-d01e-4627-9e35-ba8f1257048e" + "id": "G3TWNpvKdAyn", + "outputId": "40daccb3-3e72-4373-eebb-64c60fd8df2a" }, + "execution_count": 18, "outputs": [ { - "output_type": "error", - "ename": "NameError", - "evalue": "ignored", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined" + "output_type": "stream", + "name": "stdout", + "text": [ + "Test Accuracy: nan%\n" ] } - ], - "source": [ - "num_epochs = 2\n", - "for epoch in range(num_epochs):\n", - "\n", - " for i, (images, label) in enumerate(zip(loaders['test']['X'], loaders['test']['Y'])):\n", - " img0, img1 = images\n", - " img0, img1, label = img0.to(device), img1.to(device), label.to(device)\n", - " outputs = model(images)\n", - "\n", - "\n", - " # Adjust the shapes to match the criterion\n", - " # outputs = outputs.view(-1) # Flatten the outputs to a 1D tensor\n", - " # seg = seg.view(-1) # Flatten the seg tensor to a 1D tensor\n", - " loss = criterion(outputs, label)\n", - " optimizer.zero_grad()\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " if (i + 1) % 5 == 0:\n", - " print(f\"Epoch [{epoch + 1} / {num_epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")\n", - "\n", - " scheduler.step()\n", - "end = time.time()" ] } ], "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyPullQ1T6knjpYBrXTmmpBA", + "machine_shape": "hm", + "gpuType": "A100", + "authorship_tag": "ABX9TyPMy7bIZ9steqmxN0P4lni6", "include_colab_link": true }, "kernelspec": { @@ -463,7 +420,8 @@ }, "language_info": { "name": "python" - } + }, + "accelerator": "GPU" }, "nbformat": 4, "nbformat_minor": 0 From 20f22891abbe0637745a5744afb539bee46207bd Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Mon, 16 Oct 2023 16:46:54 +1000 Subject: [PATCH 04/14] Added classification testing and adjusted some hyper parameters. Still getting 50% accuracy... --- Colab version.ipynb | 364 +++++++++++++++++++++++++++----------------- 1 file changed, 226 insertions(+), 138 deletions(-) diff --git a/Colab version.ipynb b/Colab version.ipynb index d5769d6c13..4f4d8ce601 100644 --- a/Colab version.ipynb +++ b/Colab version.ipynb @@ -131,12 +131,24 @@ "import os\n", "from PIL import Image\n", "import numpy as np\n", - "from torch.utils.data import Dataset, DataLoader, TensorDataset, ConcatDataset" + "from torch.utils.data import Dataset, DataLoader, TensorDataset, ConcatDataset\n", + "import random" ] }, { "cell_type": "code", - "execution_count": 7, + "source": [ + "batch_size = 32" + ], + "metadata": { + "id": "u6EH0wk_CkxF" + }, + "execution_count": 118, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 194, "metadata": { "id": "7zvKyWj2J7Yk" }, @@ -163,33 +175,66 @@ "\n", " return image\n", "\n", + "def get_indexes_by_value(lst):\n", + " index_dict = {}\n", + " for i, value in enumerate(lst):\n", + " value = value.split('_')[1].split('.jpeg')[0]\n", + " if value in index_dict:\n", + " index_dict[value].append(i)\n", + " else:\n", + " index_dict[value] = [i]\n", + "\n", + " return list(index_dict.values())\n", + "\n", "class SiameseDataset(Dataset):\n", " def __init__(self, AD, NC):\n", - " self.AD = AD\n", - " self.NC = NC\n", - " self.labels = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0)\n", + " # Combine the datasets and labels\n", + " self.X = AD + NC\n", + "\n", + " self.Y = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0)\n", + "\n", + " paths = ([str(path) for path in (AD.image_paths + NC.image_paths)])\n", + " indexes = get_indexes_by_value(paths)\n", + " i_indices = [random.sample(indices, len(indices)) for indices in indexes]\n", + " j_indices = [random.sample(indices, len(indices)) for indices in indexes]\n", + " self.i_indices = [item for sublist in i_indices for item in sublist]\n", + " self.j_indices = [item for sublist in j_indices for item in sublist]\n", "\n", " def __len__(self):\n", - " return min(len(self.AD), len(self.NC))\n", + " return len(self.X) // 2\n", "\n", " def __getitem__(self, idx):\n", - " img1 = self.AD[idx]\n", - " label1 = self.labels[idx]\n", - " img2 = self.NC[idx]\n", - " label2 = self.labels[len(self.AD) + idx]\n", + " i = self.i_indices[idx]\n", + " j = self.j_indices[idx]\n", + " img1 = self.X[i]\n", + " img2 = self.X[j]\n", + " l1 = self.Y[i]\n", + " l2 = self.Y[j]\n", "\n", - " return img1, img2, label1, label2\n", + " return img1, img2, l1, l2\n", "\n", "# Set the device\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", "\n", "size = 256\n", + "\n", + "def intensity_normalization(img):\n", + " mean = torch.mean(img)\n", + " std = torch.std(img)\n", + " return (img - mean) / std\n", + "\n", + "def windowing(img, window_center, window_width):\n", + " img = torch.clamp(img, window_center - window_width // 2, window_center + window_width // 2)\n", + " img = (img - (window_center - 0.5)) / (window_width - 1)\n", + " return img\n", + "\n", + "# Example usage in your transform\n", "transform_X = transforms.Compose([\n", " transforms.Resize((size, size)),\n", " transforms.ToTensor(),\n", - " # transforms.Lambda(lambda x: x / 255.0),\n", - " # transforms.Normalize(mean=[0.5], std=[0.5]) # Adjust according to your data statistics\n", + " transforms.Lambda(intensity_normalization),\n", + " transforms.Lambda(lambda x: windowing(x, window_center=size // 2, window_width=size))\n", "])\n", "\n", "\n", @@ -201,56 +246,17 @@ " loaders[stage] = {}\n", " AD = CustomDataset(root_dir=os.path.join(stage, 'AD'), transform=transform_X)\n", " NC = CustomDataset(root_dir=os.path.join(stage, 'NC'), transform=transform_X)\n", - " loaders[stage] = SiameseDataset(AD, NC)" - ] - }, - { - "cell_type": "code", - "source": [ - "for load in loaders.values():\n", - " print(len(load))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oW2JaTc0dXLz", - "outputId": "758fa1b1-dbfb-48eb-d982-03b676d628a4" - }, - "execution_count": 6, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "0\n", - "4460\n" - ] - } + " dataset = SiameseDataset(AD, NC)\n", + " loaders[stage] = DataLoader(dataset, batch_size=batch_size)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 209, "metadata": { - "id": "puuj1CSURmJ9", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "532995b4-c399-4666-961a-5513ebf7de7b" + "id": "puuj1CSURmJ9" }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - " warnings.warn(\n", - "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n" - ] - } - ], + "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", @@ -261,25 +267,20 @@ " super(SiameseNetwork, self).__init__()\n", " self.resnet = models.resnet18(pretrained=pretrained)\n", " # Modify the first convolution layer to accept single-channel input\n", - " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n", - " self.linear = nn.Linear(1000, 2) # 1000 is the number of output features from ResNet\n", + " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=2, padding=3, bias=False)\n", + " self.fc1 = nn.Linear(1000, 500) # Customize the fully connected layers\n", + " self.fc2 = nn.Linear(500, 2) # Customize the fully connected layers\n", "\n", " def forward(self, x1, x2):\n", " output1 = self.resnet(x1)\n", " output2 = self.resnet(x2)\n", - " return self.linear(torch.abs(output1 - output2))\n", + " output = torch.abs(output1 - output2)\n", + " output = self.fc1(output)\n", + " output = self.fc2(output)\n", + " return output\n", + "\n", + "model = SiameseNetwork()\n", "\n", - "model = SiameseNetwork()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "ocj6rcu7Rn4-" - }, - "outputs": [], - "source": [ "class ContrastiveLoss(torch.nn.Module):\n", " def __init__(self, margin=2.0):\n", " super(ContrastiveLoss, self).__init__()\n", @@ -298,61 +299,96 @@ "# Initialize the network, loss function, and optimizer\n", "siamese_net = SiameseNetwork(pretrained=True).to(device)\n", "criterion = ContrastiveLoss()\n", - "optimizer = optim.Adam(siamese_net.parameters(), lr=0.0005)\n", - "\n", + "optimizer = optim.Adam(siamese_net.parameters(), lr=0.001)\n", "# Training loop\n", - "epochs = 2\n", + "epochs = 5\n", "total_step = len(loaders['test'])\n", "for epoch in range(epochs):\n", - " for i, (i1, i2, l1, l2) in enumerate(loaders['test']):\n", - " i1, i2, l1, l2 = i1.to(device), i2.to(device), l1.to(device), l2.to(device)\n", - " i1 = i1.unsqueeze(1) # Add a channel dimension\n", - " i2 = i2.unsqueeze(1) # Add a channel dimension\n", - " output = siamese_net(i1, i2)\n", - " o1, o2 = output[0][0], output[0][1]\n", - " l = (l1 == l2).int()\n", - "\n", - "\n", - " # Adjust the shapes to match the criterion\n", - " # outputs = outputs.view(-1) # Flatten the outputs to a 1D tensor\n", - " # seg = seg.view(-1) # Flatten the seg tensor to a 1D tensor\n", - " loss = criterion(o1, o2, l)\n", + " for i, (img1, img2, lab1, lab2) in enumerate(loaders['test']):\n", + " img1, img2, lab1, lab2 = img1.to(device), img2.to(device), lab1.to(device), lab2.to(device)\n", + " output = siamese_net(img1, img2)\n", + " o1, o2 = output[:, 0], output[:, 1]\n", + " label = (lab1 == lab2).int()\n", + "\n", + " loss = criterion(o1, o2, label)\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", "\n", - " if (i + 1) % 500 == 0:\n", + " if (i + 1) % 21 == 0:\n", + " print((lab1[0], lab2[0]))\n", + " print(label[0])\n", + " print(output[0])\n", " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")" ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 391 }, "id": "4NnXFUDzSWjp", - "outputId": "8bd9a4f5-1999-4373-9d1b-9f3fe398f29b" + "outputId": "aea8ed90-446c-44f5-93e9-e9e5c9b5c683" + }, + "execution_count": 210, + "outputs": [ + { + "output_type": "error", + "ename": "RuntimeError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlab1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlab2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloaders\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlab1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlab2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlab1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlab2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msiamese_net\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mo1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlab1\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlab2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x1, x2)\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0moutput1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0moutput1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0moutput2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0moutput2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 284\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36m_forward_impl\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_forward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;31m# See note [TorchScript super()]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 269\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 457\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 458\u001b[0m _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 459\u001b[0;31m return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[1;32m 460\u001b[0m self.padding, self.dilation, self.groups)\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [32, 1000]" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "correct = 0\n", + "total = 0\n", + "features = []\n", + "labels = []\n", + "with torch.no_grad():\n", + " for i, (img1, img2, lab1, lab2) in enumerate(loaders['test']):\n", + " img1, img2, lab1, lab2 = img1.to(device), img2.to(device), lab1.to(device), lab2.to(device)\n", + " output = siamese_net(img1, img2)\n", + " out1, out2 = output[:, 0], output[:, 1]\n", + " predicted = (out1 - out2).pow(2).sum().sqrt().lt(0.5)\n", + " total += lab1.size(0)\n", + " correct += (predicted == lab1).sum().item()\n", + " features.extend(out1.tolist())\n", + " labels.extend(lab1.tolist())\n", + " features.extend(out2.tolist())\n", + " labels.extend(lab2.tolist())\n", + "\n", + "\n", + " print(f'Accuracy of the network on the test images: {100 * correct / total}%')\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G3TWNpvKdAyn", + "outputId": "bf70c5fd-38e5-49c7-a64f-ba1758f103d9" }, - "execution_count": 12, + "execution_count": 200, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Epoch [1 / 2], Step [500 / 4460 Loss 3.259337688632513e-07]\n", - "Epoch [1 / 2], Step [1000 / 4460 Loss 3.283045589341782e-05]\n", - "Epoch [1 / 2], Step [1500 / 4460 Loss 1.054049062076956e-05]\n", - "Epoch [1 / 2], Step [2000 / 4460 Loss 1.234111095982371e-06]\n", - "Epoch [1 / 2], Step [2500 / 4460 Loss 1.1719105259544449e-06]\n", - "Epoch [1 / 2], Step [3000 / 4460 Loss 3.3835010526672704e-07]\n", - "Epoch [1 / 2], Step [3500 / 4460 Loss 7.921468636595819e-08]\n", - "Epoch [1 / 2], Step [4000 / 4460 Loss 2.9854402328055585e-07]\n", - "Epoch [2 / 2], Step [500 / 4460 Loss 1.767582347156349e-07]\n", - "Epoch [2 / 2], Step [1000 / 4460 Loss 4.3589712395153413e-10]\n", - "Epoch [2 / 2], Step [1500 / 4460 Loss 8.744237902647001e-07]\n", - "Epoch [2 / 2], Step [2000 / 4460 Loss 2.0802056258095725e-11]\n", - "Epoch [2 / 2], Step [2500 / 4460 Loss 1.8013805913597025e-07]\n", - "Epoch [2 / 2], Step [3000 / 4460 Loss 3.1853244308877038e-06]\n", - "Epoch [2 / 2], Step [3500 / 4460 Loss 2.546377402268263e-07]\n", - "Epoch [2 / 2], Step [4000 / 4460 Loss 2.075099914122802e-08]\n" + "Accuracy of the network on the test images: 49.955555555555556%\n" ] } ] @@ -360,50 +396,102 @@ { "cell_type": "code", "source": [ - "# Evaluation loop\n", - "siamese_net.eval() # Set the model to evaluation mode\n", - "with torch.no_grad(): # Disable gradient calculation for evaluation\n", - " correct = 0\n", - " total = 0\n", - " for i, (i1, i2, l1, l2) in enumerate(loaders['test']):\n", - " i1, i2, l1, l2 = i1.to(device), i2.to(device), l1.to(device), l2.to(device)\n", - " i1 = i1.unsqueeze(1) # Add a channel dimension\n", - " i2 = i2.unsqueeze(1) # Add a channel dimension\n", - " output = siamese_net(i1, i2)\n", - " o1, o2 = output[0][0], output[0][1]\n", - " l = (l1 == l2).int()\n", - " # Compute the Euclidean distance between the outputs\n", - " distance = torch.abs(o1 - o2)\n", - "\n", - " # Apply threshold to determine predicted labels\n", - " threshold = 0.5 # Adjust as needed\n", - " predicted = distance.lt(threshold)\n", - "\n", - "\n", - "\n", - " total += l\n", - " correct += (predicted == l).sum().item()\n", - "\n", - " accuracy = 100 * correct / total\n", - " print(f'Test Accuracy: {accuracy:.2f}%')\n" + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Split the data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(np.array(features).reshape(-1, 1), labels, test_size=0.2, random_state=42)\n", + "\n", + "# Train a logistic regression model\n", + "classifier = LogisticRegression(max_iter=1000)\n", + "classifier.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "predictions = classifier.predict(X_test)\n", + "\n", + "# Calculate accuracy\n", + "accuracy = accuracy_score(y_test, predictions)\n", + "print(f\"Accuracy: {accuracy}\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "G3TWNpvKdAyn", - "outputId": "40daccb3-3e72-4373-eebb-64c60fd8df2a" + "id": "MVEvstik8zqK", + "outputId": "c5aed97c-27c1-4685-bf6a-cc2204dbaa69" }, - "execution_count": 18, + "execution_count": 201, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Test Accuracy: nan%\n" + "Accuracy: 0.49166666666666664\n" ] } ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "qA-60lBMGV3i" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "features[0].tolist()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9PrC3ijsFnhE", + "outputId": "42aa07d0-9166-43fb-a67e-713fc99664da" + }, + "execution_count": 148, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.5943959355354309" + ] + }, + "metadata": {}, + "execution_count": 148 + } + ] + }, + { + "cell_type": "code", + "source": [ + "correct, total" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hqYJrbkVfi36", + "outputId": "8e5c2a93-1cd2-47e1-8c29-95e1ceda5ca3" + }, + "execution_count": 139, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(2226, 4500)" + ] + }, + "metadata": {}, + "execution_count": 139 + } + ] } ], "metadata": { @@ -411,7 +499,7 @@ "provenance": [], "machine_shape": "hm", "gpuType": "A100", - "authorship_tag": "ABX9TyPMy7bIZ9steqmxN0P4lni6", + "authorship_tag": "ABX9TyN39gN45mXrH2YYwJ6aKtns", "include_colab_link": true }, "kernelspec": { From 0f1bd3cfbbf5aa74bbf756d1fed45226631a1e12 Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Mon, 16 Oct 2023 20:24:36 +1000 Subject: [PATCH 05/14] Got triple accuracy up to ~70%. I randomize the order of all three cols each batch, rotate the images and use L2 regularization --- Colab version.ipynb | 644 ++++++++++++++++++++++++++++++++++++++------ 1 file changed, 566 insertions(+), 78 deletions(-) diff --git a/Colab version.ipynb b/Colab version.ipynb index 4f4d8ce601..5714621cc4 100644 --- a/Colab version.ipynb +++ b/Colab version.ipynb @@ -18,7 +18,7 @@ "base_uri": "https://localhost:8080/" }, "id": "yzCmT9TUJY10", - "outputId": "997681ae-a0d2-448e-d290-b00a8dc22666" + "outputId": "f4a92c6b-c770-48bf-a03c-3a6b9f924c30" }, "outputs": [ { @@ -42,32 +42,45 @@ "base_uri": "https://localhost:8080/" }, "id": "-Lb7CGdEJCQg", - "outputId": "a693cf6b-0d28-474e-ed69-f7192a4700f9" + "outputId": "7cf9c217-28e6-43e3-aee4-f079779419c3" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "/content/drive/MyDrive/AD_NC\n" + "/root\n" ] } ], "source": [ - "%cd /content/drive/MyDrive/AD_NC" + "%cd" ] }, { "cell_type": "code", "source": [ - "%ls" + "import zipfile\n", + "import os\n", + "\n", + "# Define the paths\n", + "zip_path = '/content/drive/MyDrive/AD_NC.zip' # Path to the zip file\n", + "extract_path = '/content/extracted_folder' # Path where you want to extract the contents\n", + "\n", + "# Unzip the file\n", + "with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n", + " zip_ref.extractall(extract_path)\n", + "\n", + "# List the contents of the extracted folder\n", + "extracted_files = os.listdir(extract_path)\n", + "print(extracted_files)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "mgx67Xe5dsgs", - "outputId": "b7850ff6-2922-444d-ee4f-48498dcaeacf" + "id": "PM8uTdbZpQGJ", + "outputId": "15ea444a-ce03-4ddb-d19e-5a8bb8686b53" }, "execution_count": 3, "outputs": [ @@ -75,37 +88,60 @@ "output_type": "stream", "name": "stdout", "text": [ - "NC.zip \u001b[0m\u001b[01;34mtest\u001b[0m/ \u001b[01;34mtrain\u001b[0m/\n" + "['AD_NC']\n" ] } ] }, { "cell_type": "code", - "execution_count": null, + "source": [ + "cd /content/extracted_folder/AD_NC" + ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "_eAax1mMJpyw", - "outputId": "daf77648-1909-41f9-c05f-0be751ec7d6d" + "id": "x1t1iFV_p2MU", + "outputId": "9d142efb-edd0-4ae2-8e69-1a377fb13ad5" }, + "execution_count": 4, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "unzip: cannot find or open NC.zip, NC.zip.zip or NC.zip.ZIP.\n" + "/content/extracted_folder/AD_NC\n" ] } - ], + ] + }, + { + "cell_type": "code", "source": [ - "!unzip NC.zip" + "ls" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Tp_Fb7Eyp-yv", + "outputId": "45e2140b-5eab-4cf6-dc81-3b6c9fd592f0" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[0m\u001b[01;34mtest\u001b[0m/ \u001b[01;34mtrain\u001b[0m/\n" + ] + } ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": { "id": "_8PBaJLSJSCP" }, @@ -138,17 +174,17 @@ { "cell_type": "code", "source": [ - "batch_size = 32" + "batch_size = 128" ], "metadata": { "id": "u6EH0wk_CkxF" }, - "execution_count": 118, + "execution_count": null, "outputs": [] }, { "cell_type": "code", - "execution_count": 194, + "execution_count": null, "metadata": { "id": "7zvKyWj2J7Yk" }, @@ -175,17 +211,6 @@ "\n", " return image\n", "\n", - "def get_indexes_by_value(lst):\n", - " index_dict = {}\n", - " for i, value in enumerate(lst):\n", - " value = value.split('_')[1].split('.jpeg')[0]\n", - " if value in index_dict:\n", - " index_dict[value].append(i)\n", - " else:\n", - " index_dict[value] = [i]\n", - "\n", - " return list(index_dict.values())\n", - "\n", "class SiameseDataset(Dataset):\n", " def __init__(self, AD, NC):\n", " # Combine the datasets and labels\n", @@ -193,19 +218,17 @@ "\n", " self.Y = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0)\n", "\n", - " paths = ([str(path) for path in (AD.image_paths + NC.image_paths)])\n", - " indexes = get_indexes_by_value(paths)\n", - " i_indices = [random.sample(indices, len(indices)) for indices in indexes]\n", - " j_indices = [random.sample(indices, len(indices)) for indices in indexes]\n", - " self.i_indices = [item for sublist in i_indices for item in sublist]\n", - " self.j_indices = [item for sublist in j_indices for item in sublist]\n", + " # Generate a random permutation of indices\n", + " self.i_indices = torch.randperm(len(self.X) // 2)\n", + " self.j_indices = torch.randperm(len(self.X) // 2)\n", + "\n", "\n", " def __len__(self):\n", " return len(self.X) // 2\n", "\n", " def __getitem__(self, idx):\n", - " i = self.i_indices[idx]\n", - " j = self.j_indices[idx]\n", + " i = self.i_indices[idx] * 2\n", + " j = self.j_indices[idx] * 2 + 1\n", " img1 = self.X[i]\n", " img2 = self.X[j]\n", " l1 = self.Y[i]\n", @@ -217,7 +240,7 @@ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", "\n", - "size = 256\n", + "size = 64\n", "\n", "def intensity_normalization(img):\n", " mean = torch.mean(img)\n", @@ -252,7 +275,38 @@ }, { "cell_type": "code", - "execution_count": 209, + "source": [ + "count = 0\n", + "same = 0\n", + "for i, j, n, m in loaders['test']:\n", + " count += len(n)\n", + " same += (n == m).sum().tolist()\n", + "same / count" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5ceQcKMAPoip", + "outputId": "2b8eb6b1-c1c5-43db-9370-42f0da1531f2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.5144444444444445" + ] + }, + "metadata": {}, + "execution_count": 225 + } + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "id": "puuj1CSURmJ9" }, @@ -265,9 +319,9 @@ "class SiameseNetwork(nn.Module):\n", " def __init__(self, pretrained=True):\n", " super(SiameseNetwork, self).__init__()\n", - " self.resnet = models.resnet18(pretrained=pretrained)\n", + " self.resnet = models.resnet18(pretrained=False)\n", " # Modify the first convolution layer to accept single-channel input\n", - " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=2, padding=3, bias=False)\n", + " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n", " self.fc1 = nn.Linear(1000, 500) # Customize the fully connected layers\n", " self.fc2 = nn.Linear(500, 2) # Customize the fully connected layers\n", "\n", @@ -323,31 +377,137 @@ ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/", - "height": 391 + "base_uri": "https://localhost:8080/" }, "id": "4NnXFUDzSWjp", - "outputId": "aea8ed90-446c-44f5-93e9-e9e5c9b5c683" + "outputId": "ab9065a9-ffad-4405-963d-85ae26293b0d" }, - "execution_count": 210, + "execution_count": null, "outputs": [ { - "output_type": "error", - "ename": "RuntimeError", - "evalue": "ignored", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlab1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlab2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloaders\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlab1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlab2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlab1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlab2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msiamese_net\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mo1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlab1\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlab2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x1, x2)\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0moutput1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0moutput1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0moutput2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0moutput2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 284\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36m_forward_impl\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_forward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;31m# See note [TorchScript super()]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 269\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 457\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 458\u001b[0m _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 459\u001b[0;31m return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[1;32m 460\u001b[0m self.padding, self.dilation, self.groups)\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [32, 1000]" + "output_type": "stream", + "name": "stdout", + "text": [ + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0997, -0.0306], device='cuda:0', grad_fn=)\n", + "Epoch [1 / 5], Step [21 / 141 Loss 0.9753031730651855]\n", + "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(1, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1081, -0.0379], device='cuda:0', grad_fn=)\n", + "Epoch [1 / 5], Step [42 / 141 Loss 1.025275707244873]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0929, -0.0268], device='cuda:0', grad_fn=)\n", + "Epoch [1 / 5], Step [63 / 141 Loss 0.9390543699264526]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1265, -0.0541], device='cuda:0', grad_fn=)\n", + "Epoch [1 / 5], Step [84 / 141 Loss 1.0038050413131714]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0915, -0.0274], device='cuda:0', grad_fn=)\n", + "Epoch [1 / 5], Step [105 / 141 Loss 1.1157597303390503]\n", + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1292, -0.0573], device='cuda:0', grad_fn=)\n", + "Epoch [1 / 5], Step [126 / 141 Loss 1.0057425498962402]\n", + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0953, -0.0308], device='cuda:0', grad_fn=)\n", + "Epoch [2 / 5], Step [21 / 141 Loss 0.9697328805923462]\n", + "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(1, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1175, -0.0509], device='cuda:0', grad_fn=)\n", + "Epoch [2 / 5], Step [42 / 141 Loss 1.004995584487915]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0923, -0.0304], device='cuda:0', grad_fn=)\n", + "Epoch [2 / 5], Step [63 / 141 Loss 0.9395542144775391]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1254, -0.0604], device='cuda:0', grad_fn=)\n", + "Epoch [2 / 5], Step [84 / 141 Loss 0.9549553394317627]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0939, -0.0335], device='cuda:0', grad_fn=)\n", + "Epoch [2 / 5], Step [105 / 141 Loss 1.1070666313171387]\n", + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1300, -0.0632], device='cuda:0', grad_fn=)\n", + "Epoch [2 / 5], Step [126 / 141 Loss 0.9884251952171326]\n", + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0944, -0.0337], device='cuda:0', grad_fn=)\n", + "Epoch [3 / 5], Step [21 / 141 Loss 0.9663693308830261]\n", + "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(1, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1245, -0.0624], device='cuda:0', grad_fn=)\n", + "Epoch [3 / 5], Step [42 / 141 Loss 1.0025595426559448]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1142, -0.0528], device='cuda:0', grad_fn=)\n", + "Epoch [3 / 5], Step [63 / 141 Loss 0.9942120313644409]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1249, -0.0629], device='cuda:0', grad_fn=)\n", + "Epoch [3 / 5], Step [84 / 141 Loss 0.9485695362091064]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0966, -0.0371], device='cuda:0', grad_fn=)\n", + "Epoch [3 / 5], Step [105 / 141 Loss 1.1140365600585938]\n", + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1298, -0.0664], device='cuda:0', grad_fn=)\n", + "Epoch [3 / 5], Step [126 / 141 Loss 0.9871245622634888]\n", + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0954, -0.0360], device='cuda:0', grad_fn=)\n", + "Epoch [4 / 5], Step [21 / 141 Loss 0.96551513671875]\n", + "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(1, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1227, -0.0626], device='cuda:0', grad_fn=)\n", + "Epoch [4 / 5], Step [42 / 141 Loss 1.0030264854431152]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1154, -0.0552], device='cuda:0', grad_fn=)\n", + "Epoch [4 / 5], Step [63 / 141 Loss 0.9911506175994873]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1292, -0.0673], device='cuda:0', grad_fn=)\n", + "Epoch [4 / 5], Step [84 / 141 Loss 0.9427274465560913]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0964, -0.0375], device='cuda:0', grad_fn=)\n", + "Epoch [4 / 5], Step [105 / 141 Loss 1.1125668287277222]\n", + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1283, -0.0657], device='cuda:0', grad_fn=)\n", + "Epoch [4 / 5], Step [126 / 141 Loss 0.9870107173919678]\n", + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0937, -0.0353], device='cuda:0', grad_fn=)\n", + "Epoch [5 / 5], Step [21 / 141 Loss 0.9660702347755432]\n", + "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(1, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1197, -0.0604], device='cuda:0', grad_fn=)\n", + "Epoch [5 / 5], Step [42 / 141 Loss 1.0011703968048096]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1150, -0.0551], device='cuda:0', grad_fn=)\n", + "Epoch [5 / 5], Step [63 / 141 Loss 0.9866605997085571]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1265, -0.0649], device='cuda:0', grad_fn=)\n", + "Epoch [5 / 5], Step [84 / 141 Loss 0.9338618516921997]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0969, -0.0381], device='cuda:0', grad_fn=)\n", + "Epoch [5 / 5], Step [105 / 141 Loss 1.1113743782043457]\n", + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1214, -0.0599], device='cuda:0', grad_fn=)\n", + "Epoch [5 / 5], Step [126 / 141 Loss 0.9903947114944458]\n" ] } ] @@ -382,7 +542,7 @@ "id": "G3TWNpvKdAyn", "outputId": "bf70c5fd-38e5-49c7-a64f-ba1758f103d9" }, - "execution_count": 200, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -421,7 +581,7 @@ "id": "MVEvstik8zqK", "outputId": "c5aed97c-27c1-4685-bf6a-cc2204dbaa69" }, - "execution_count": 201, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -453,7 +613,7 @@ "id": "9PrC3ijsFnhE", "outputId": "42aa07d0-9166-43fb-a67e-713fc99664da" }, - "execution_count": 148, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -467,31 +627,359 @@ } ] }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "hqYJrbkVfi36" + }, + "execution_count": 28, + "outputs": [] + }, { "cell_type": "code", "source": [ - "correct, total" + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "batch_size = 128\n", + "\n", + "class CustomDataset(Dataset):\n", + " def __init__(self, root_dir, transform=None):\n", + " self.root_dir = root_dir\n", + " self.transform = transform\n", + " self.image_paths = os.listdir(root_dir)\n", + "\n", + " def __len__(self):\n", + " return len(self.image_paths)\n", + "\n", + " def __getitem__(self, idx):\n", + " img_name = os.path.join(self.root_dir, self.image_paths[idx])\n", + " image = Image.open(img_name)\n", + "\n", + " if self.transform:\n", + " image = self.transform(image)\n", + "\n", + " return image\n", + "\n", + "class TripletDataset(Dataset):\n", + " def __init__(self, AD, NC):\n", + " # Combine the datasets and labels\n", + " self.X = AD + NC\n", + " self.AD = AD\n", + " self.NC = NC\n", + "\n", + " self.Y = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0)\n", + "\n", + " # Generate random permutations of indices\n", + " self.anc_indices = torch.randperm(len(self.X))\n", + " self.pos_indices = self.anc_indices % len(AD)\n", + " self.neg_indices = self.anc_indices % len(NC)\n", + "\n", + "\n", + " def __len__(self):\n", + " return len(self.anc_indices)\n", + "\n", + " def __getitem__(self, idx):\n", + " anc = self.anc_indices[idx]\n", + " pos = self.pos_indices[idx]\n", + " neg = self.neg_indices[idx]\n", + " img1 = self.X[anc]\n", + " img2 = self.AD[pos]\n", + " img3 = self.NC[neg]\n", + " label = self.Y[anc]\n", + "\n", + " return img1, img2, img3, label\n", + "\n", + "\n", + "# Set the device\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "\n", + "\n", + "size = 256\n", + "\n", + "def intensity_normalization(img):\n", + " mean = torch.mean(img)\n", + " std = torch.std(img)\n", + " return (img - mean) / std\n", + "\n", + "def windowing(img, window_center, window_width):\n", + " img = torch.clamp(img, window_center - window_width // 2, window_center + window_width // 2)\n", + " img = (img - (window_center - 0.5)) / (window_width - 1)\n", + " return img\n", + "\n", + "transform_t = transforms.Compose([\n", + " transforms.Resize((size, size)),\n", + " # transforms.RandomHorizontalFlip(p=0.5), # Randomly flip the image horizontally with 50% probability\n", + " # transforms.RandomVerticalFlip(p=0.5), # Randomly flip the image vertically with 50% probability\n", + " transforms.RandomRotation(degrees=15), # Randomly rotate the image by up to 5 degrees\n", + " transforms.ColorJitter(brightness=0.05, contrast=0.05, saturation=0.05, hue=0.05), # Adjust color jitter with smaller increments\n", + " transforms.RandomAffine(degrees=0, translate=(0.05, 0.05), scale=(0.95, 1.05)), # Random affine transformations with smaller parameters\n", + " transforms.ToTensor(),\n", + " transforms.Lambda(intensity_normalization),\n", + " transforms.Lambda(lambda x: windowing(x, window_center=size // 2, window_width=size))\n", + "])\n", + "\n", + "transform_X = transforms.Compose([\n", + " transforms.Resize((size, size)),\n", + " transforms.ToTensor(),\n", + " transforms.Lambda(intensity_normalization),\n", + " transforms.Lambda(lambda x: windowing(x, window_center=size // 2, window_width=size))\n", + "])\n", + "\n", + "\n", + "# Replace 'your_nii_folder' with the path to your folder containing .nii files\n", + "# nii_dataset = NiiDataset(root_dir=r'keras_png_slices_data\\keras_png_slices_data\\keras_png_slices_train')\n", + "loaders = {}\n", + "\n", + "for stage in ['train', 'test']:\n", + " transform = transform_t if stage == 'train' else transform_X\n", + " AD = CustomDataset(root_dir=os.path.join(stage, 'AD'), transform=transform)\n", + " NC = CustomDataset(root_dir=os.path.join(stage, 'NC'), transform=transform)\n", + " dataset = TripletDataset(AD, NC)\n", + " loaders[stage] = DataLoader(dataset, batch_size=batch_size)" + ], + "metadata": { + "id": "dlrwhZTjTKWf" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torchvision.models as models\n", + "import torch.nn.functional as F\n", + "\n", + "class TripletSiameseNetwork(nn.Module):\n", + " def __init__(self, pretrained=True):\n", + " super(TripletSiameseNetwork, self).__init__()\n", + " self.resnet = models.resnet18(pretrained=pretrained)\n", + " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n", + " self.fc1 = nn.Linear(1000, 500)\n", + " self.fc2 = nn.Linear(500, 20)\n", + "\n", + " def forward_once(self, x):\n", + " output = self.resnet(x)\n", + " output = self.fc1(output)\n", + " output = self.fc2(output)\n", + " return output\n", + "\n", + " def forward(self, anchor, positive, negative):\n", + " output_anchor = self.forward_once(anchor)\n", + " output_positive = self.forward_once(positive)\n", + " output_negative = self.forward_once(negative)\n", + " return output_anchor, output_positive, output_negative\n", + "\n", + "class TripletLoss(nn.Module):\n", + " def __init__(self, margin=1.0):\n", + " super(TripletLoss, self).__init__()\n", + " self.margin = margin\n", + "\n", + " def forward(self, anchor, positive, negative):\n", + " distance_positive = F.pairwise_distance(anchor, positive)\n", + " distance_negative = F.pairwise_distance(anchor, negative)\n", + " losses = F.relu(distance_positive - distance_negative + self.margin)\n", + " return losses.mean()\n", + "\n", + "class TripletLossWithRegularization(nn.Module):\n", + " def __init__(self, margin=1.0, lambda_reg=0.01):\n", + " super(TripletLossWithRegularization, self).__init__()\n", + " self.margin = margin\n", + " self.lambda_reg = lambda_reg # Regularization parameter\n", + "\n", + " def forward(self, anchor, positive, negative):\n", + " distance_positive = F.pairwise_distance(anchor, positive)\n", + " distance_negative = F.pairwise_distance(anchor, negative)\n", + " triplet_losses = F.relu(distance_positive - distance_negative + self.margin)\n", + " triplet_loss = triplet_losses.mean()\n", + "\n", + " # Compute L2 regularization\n", + " l2_reg = None\n", + " for param in trip_model.parameters():\n", + " if l2_reg is None:\n", + " l2_reg = param.norm(2)\n", + " else:\n", + " l2_reg = l2_reg + param.norm(2)\n", + "\n", + " loss = triplet_loss + self.lambda_reg * l2_reg\n", + " return loss\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "id": "c8MhXUlOTHnY" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from torch.optim.lr_scheduler import StepLR\n", + "learning_rate = 0.0005\n", + "trip_model = TripletSiameseNetwork()\n", + "trip_criterion = TripletLossWithRegularization(margin=1.0)\n", + "total_step = len(loaders['train'])\n", + "\n", + "optimizer = optim.Adam(trip_model.parameters(), lr=learning_rate)\n", + "scheduler = StepLR(optimizer, step_size=3, gamma=0.1)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "hqYJrbkVfi36", - "outputId": "8e5c2a93-1cd2-47e1-8c29-95e1ceda5ca3" + "id": "Gs0V-oZe1O46", + "outputId": "adaef8b1-fced-4a59-88e3-e7a58373c6ae" }, - "execution_count": 139, + "execution_count": 9, "outputs": [ { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(2226, 4500)" - ] - }, - "metadata": {}, - "execution_count": 139 + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n", + "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n", + "100%|██████████| 44.7M/44.7M [00:00<00:00, 186MB/s]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from itertools import cycle\n", + "\n", + "test_iter = cycle(iter(loaders['test']))\n", + "# Training loop\n", + "epochs = 10\n", + "trip_model.to(device)\n", + "losses = []\n", + "val_losses = []\n", + "for epoch in range(epochs):\n", + " losses.append([])\n", + " val_losses.append([])\n", + " for i, (img1, img2, img3, _) in enumerate(loaders['train']):\n", + " img1, img2, img3 = img1.to(device), img2.to(device), img3.to(device)\n", + " size = img1.size(0)\n", + " img1, img2, img3 = img1[torch.randperm(size)], img2[torch.randperm(size)], img3[torch.randperm(size)]\n", + " out1, out2, out3 = trip_model(img1, img2, img3)\n", + "\n", + " loss = trip_criterion(out1, out2, out3)\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " if (i + 1) % (total_step // 10) == 0:\n", + " losses[epoch].append(loss.item())\n", + "\n", + " val_img1, val_img2, val_img3, _ = next(test_iter)\n", + " val_img1, val_img2, val_img3 = val_img1.to(device), val_img2.to(device), val_img3.to(device)\n", + " val_out1, val_out2, val_out3 = trip_model(val_img1, val_img2, val_img3)\n", + " val_loss = trip_criterion(val_out1, val_out2, val_out3)\n", + " val_losses[epoch].append(val_loss.item())\n", + "\n", + " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step}], Loss: {loss.item()}, Validation Loss: {val_loss.item()}\")\n", + "\n", + " scheduler.step()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 425 + }, + "id": "A_J3lXLOUDu0", + "outputId": "380a9cc5-fffd-4b7a-ea04-2ac87e26aab3" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [1 / 10], Step [16 / 169], Loss: 6.299764156341553, Validation Loss: 5.821728706359863\n" + ] + }, + { + "output_type": "error", + "ename": "OutOfMemoryError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOutOfMemoryError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mout1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrip_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrip_criterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, anchor, positive, negative)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0manchor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpositive\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnegative\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0moutput_anchor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manchor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0moutput_positive\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpositive\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0moutput_negative\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnegative\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0moutput_anchor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_positive\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_negative\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mforward_once\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 284\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36m_forward_impl\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;31m# See note [TorchScript super()]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 269\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 270\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 271\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaxpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/batchnorm.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[0mused\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnormalization\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0;32min\u001b[0m \u001b[0meval\u001b[0m \u001b[0mmode\u001b[0m \u001b[0mwhen\u001b[0m \u001b[0mbuffers\u001b[0m \u001b[0mare\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \"\"\"\n\u001b[0;32m--> 171\u001b[0;31m return F.batch_norm(\n\u001b[0m\u001b[1;32m 172\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;31m# If buffers are not to be tracked, ensure that they won't be updated\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mbatch_norm\u001b[0;34m(input, running_mean, running_var, weight, bias, training, momentum, eps)\u001b[0m\n\u001b[1;32m 2448\u001b[0m \u001b[0m_verify_batch_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2450\u001b[0;31m return torch.batch_norm(\n\u001b[0m\u001b[1;32m 2451\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrunning_mean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrunning_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmomentum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackends\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcudnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menabled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2452\u001b[0m )\n", + "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 512.00 MiB (GPU 0; 15.77 GiB total capacity; 14.33 GiB already allocated; 28.12 MiB free; 14.47 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF" + ] } ] + }, + { + "cell_type": "code", + "source": [ + "embeddings = []\n", + "labels = []\n", + "trip_model.eval()\n", + "with torch.no_grad():\n", + " for i, (img1, img2, img3, label) in enumerate(loaders['test']):\n", + " img1, img2, img3, label = img1.to(device), img2.to(device), img3.to(device), label.to(device)\n", + " out1, _, _ = trip_model(img1, img2, img3)\n", + " embeddings.extend(out1.cpu().tolist())\n", + " labels.extend(label.cpu().tolist())\n" + ], + "metadata": { + "id": "nnGoOwX-Vk_i" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Split the data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(embeddings, labels, test_size=0.2, random_state=42)\n", + "\n", + "# Train a logistic regression model\n", + "classifier = LogisticRegression(max_iter=1000)\n", + "classifier.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "predictions = classifier.predict(X_test)\n", + "\n", + "# Calculate accuracy\n", + "accuracy = accuracy_score(y_test, predictions)\n", + "print(f\"Accuracy: {accuracy}\")\n" + ], + "metadata": { + "id": "ayGTRCP5Wl0s" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "gaj_LIDgrl6N" + }, + "execution_count": null, + "outputs": [] } ], "metadata": { @@ -499,7 +987,7 @@ "provenance": [], "machine_shape": "hm", "gpuType": "A100", - "authorship_tag": "ABX9TyN39gN45mXrH2YYwJ6aKtns", + "authorship_tag": "ABX9TyPKn/v2mVLUWZMauFxXGvWg", "include_colab_link": true }, "kernelspec": { From 20da493b9f90e7e5b60f06ccca4c861c535e613f Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Mon, 16 Oct 2023 22:07:55 +1000 Subject: [PATCH 06/14] Tuned hyperparameters, will run for 100 epochs --- Colab version.ipynb | 212 ++++++++++++++++++++++++++------------------ 1 file changed, 128 insertions(+), 84 deletions(-) diff --git a/Colab version.ipynb b/Colab version.ipynb index 5714621cc4..00f28d016b 100644 --- a/Colab version.ipynb +++ b/Colab version.ipynb @@ -18,7 +18,7 @@ "base_uri": "https://localhost:8080/" }, "id": "yzCmT9TUJY10", - "outputId": "f4a92c6b-c770-48bf-a03c-3a6b9f924c30" + "outputId": "df7da7be-a49a-44be-ebef-5953c0ada243" }, "outputs": [ { @@ -42,7 +42,7 @@ "base_uri": "https://localhost:8080/" }, "id": "-Lb7CGdEJCQg", - "outputId": "7cf9c217-28e6-43e3-aee4-f079779419c3" + "outputId": "604ba5ed-6fa6-42c3-c3f1-ae6e7e8d94a1" }, "outputs": [ { @@ -80,7 +80,7 @@ "base_uri": "https://localhost:8080/" }, "id": "PM8uTdbZpQGJ", - "outputId": "15ea444a-ce03-4ddb-d19e-5a8bb8686b53" + "outputId": "7ab4b973-0457-4bd1-ba08-287fc9d91540" }, "execution_count": 3, "outputs": [ @@ -103,7 +103,7 @@ "base_uri": "https://localhost:8080/" }, "id": "x1t1iFV_p2MU", - "outputId": "9d142efb-edd0-4ae2-8e69-1a377fb13ad5" + "outputId": "7f825366-4473-4a83-d557-29c3fbd6ec11" }, "execution_count": 4, "outputs": [ @@ -126,7 +126,7 @@ "base_uri": "https://localhost:8080/" }, "id": "Tp_Fb7Eyp-yv", - "outputId": "45e2140b-5eab-4cf6-dc81-3b6c9fd592f0" + "outputId": "153ee02c-1cd9-4262-d0b5-33b7c1857578" }, "execution_count": 5, "outputs": [ @@ -633,14 +633,14 @@ "metadata": { "id": "hqYJrbkVfi36" }, - "execution_count": 28, + "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "batch_size = 128\n", + "batch_size = 80\n", "\n", "class CustomDataset(Dataset):\n", " def __init__(self, root_dir, transform=None):\n", @@ -661,19 +661,15 @@ " return image\n", "\n", "class TripletDataset(Dataset):\n", - " def __init__(self, AD, NC):\n", - " # Combine the datasets and labels\n", + " def __init__(self, AD, NC, transform=None):\n", " self.X = AD + NC\n", " self.AD = AD\n", " self.NC = NC\n", - "\n", " self.Y = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0)\n", - "\n", - " # Generate random permutations of indices\n", " self.anc_indices = torch.randperm(len(self.X))\n", - " self.pos_indices = self.anc_indices % len(AD)\n", - " self.neg_indices = self.anc_indices % len(NC)\n", - "\n", + " self.pos_indices = torch.randperm(len(self.X)) % len(AD)\n", + " self.neg_indices = torch.randperm(len(self.X)) % len(NC)\n", + " self.transform = transform\n", "\n", " def __len__(self):\n", " return len(self.anc_indices)\n", @@ -687,60 +683,86 @@ " img3 = self.NC[neg]\n", " label = self.Y[anc]\n", "\n", + " if self.transform:\n", + " img1 = self.transform(img1)\n", + " img2 = self.transform(img2)\n", + " img3 = self.transform(img3)\n", + "\n", " return img1, img2, img3, label\n", "\n", "\n", + "\n", "# Set the device\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", "\n", "size = 256\n", "\n", - "def intensity_normalization(img):\n", + "def intensity_normalization(img, mean = None, std = None):\n", " mean = torch.mean(img)\n", " std = torch.std(img)\n", " return (img - mean) / std\n", "\n", - "def windowing(img, window_center, window_width):\n", - " img = torch.clamp(img, window_center - window_width // 2, window_center + window_width // 2)\n", - " img = (img - (window_center - 0.5)) / (window_width - 1)\n", - " return img\n", + "class CustomNormalize(object):\n", + " def __init__(self, mean, std):\n", + " self.mean = mean\n", + " self.std = std\n", "\n", - "transform_t = transforms.Compose([\n", + " def __call__(self, img):\n", + " return (img - self.mean) / self.std\n", + "\n", + "transform_train = transforms.Compose([\n", " transforms.Resize((size, size)),\n", - " # transforms.RandomHorizontalFlip(p=0.5), # Randomly flip the image horizontally with 50% probability\n", - " # transforms.RandomVerticalFlip(p=0.5), # Randomly flip the image vertically with 50% probability\n", - " transforms.RandomRotation(degrees=15), # Randomly rotate the image by up to 5 degrees\n", - " transforms.ColorJitter(brightness=0.05, contrast=0.05, saturation=0.05, hue=0.05), # Adjust color jitter with smaller increments\n", - " transforms.RandomAffine(degrees=0, translate=(0.05, 0.05), scale=(0.95, 1.05)), # Random affine transformations with smaller parameters\n", " transforms.ToTensor(),\n", - " transforms.Lambda(intensity_normalization),\n", - " transforms.Lambda(lambda x: windowing(x, window_center=size // 2, window_width=size))\n", + " transforms.RandomRotation(degrees=5), # Randomly rotate the image by up to 5 degrees\n", + " # transforms.ColorJitter(brightness=0.05, contrast=0.05, saturation=0.05, hue=0.05), # Adjust color jitter with smaller increments\n", + " transforms.RandomAffine(degrees=0, translate=(0.05, 0.05), scale=(0.95, 1.05)), # Random affine transformations with smaller parameters\n", + " transforms.Lambda(intensity_normalization)\n", + "\n", "])\n", "\n", - "transform_X = transforms.Compose([\n", + "transform_test = transforms.Compose([\n", " transforms.Resize((size, size)),\n", " transforms.ToTensor(),\n", - " transforms.Lambda(intensity_normalization),\n", - " transforms.Lambda(lambda x: windowing(x, window_center=size // 2, window_width=size))\n", + " transforms.Lambda(intensity_normalization)\n", "])\n", "\n", + "class Normalize(object):\n", + " def __init__(self, mean, std):\n", + " self.mean = mean\n", + " self.std = std\n", + "\n", + " def __call__(self, img):\n", + " return (img - self.mean) / self.std\n", + "\n", + "\n", "\n", "# Replace 'your_nii_folder' with the path to your folder containing .nii files\n", "# nii_dataset = NiiDataset(root_dir=r'keras_png_slices_data\\keras_png_slices_data\\keras_png_slices_train')\n", "loaders = {}\n", "\n", "for stage in ['train', 'test']:\n", - " transform = transform_t if stage == 'train' else transform_X\n", + " if stage == 'train':\n", + " transform = transform_train\n", + " else:\n", + " transform = transform_test\n", " AD = CustomDataset(root_dir=os.path.join(stage, 'AD'), transform=transform)\n", " NC = CustomDataset(root_dir=os.path.join(stage, 'NC'), transform=transform)\n", + " if stage == 'train':\n", + " X = torch.stack([img for img in AD + NC])\n", + " # Compute the mean and standard deviation\n", + " mean = X.mean()\n", + " std = X.std()\n", + " normalize = transforms.Compose([\n", + " Normalize(mean, std)\n", + " ])\n", " dataset = TripletDataset(AD, NC)\n", " loaders[stage] = DataLoader(dataset, batch_size=batch_size)" ], "metadata": { "id": "dlrwhZTjTKWf" }, - "execution_count": 7, + "execution_count": 43, "outputs": [] }, { @@ -757,7 +779,7 @@ " self.resnet = models.resnet18(pretrained=pretrained)\n", " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n", " self.fc1 = nn.Linear(1000, 500)\n", - " self.fc2 = nn.Linear(500, 20)\n", + " self.fc2 = nn.Linear(500, 2)\n", "\n", " def forward_once(self, x):\n", " output = self.resnet(x)\n", @@ -811,43 +833,26 @@ "metadata": { "id": "c8MhXUlOTHnY" }, - "execution_count": 8, + "execution_count": 47, "outputs": [] }, { "cell_type": "code", "source": [ "from torch.optim.lr_scheduler import StepLR\n", - "learning_rate = 0.0005\n", + "learning_rate = 0.001\n", "trip_model = TripletSiameseNetwork()\n", "trip_criterion = TripletLossWithRegularization(margin=1.0)\n", "total_step = len(loaders['train'])\n", "\n", "optimizer = optim.Adam(trip_model.parameters(), lr=learning_rate)\n", - "scheduler = StepLR(optimizer, step_size=3, gamma=0.1)" + "scheduler = StepLR(optimizer, step_size=15, gamma=0.75)" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Gs0V-oZe1O46", - "outputId": "adaef8b1-fced-4a59-88e3-e7a58373c6ae" + "id": "Gs0V-oZe1O46" }, - "execution_count": 9, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - " warnings.warn(\n", - "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n", - "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n", - "100%|██████████| 44.7M/44.7M [00:00<00:00, 186MB/s]\n" - ] - } - ] + "execution_count": 48, + "outputs": [] }, { "cell_type": "code", @@ -856,7 +861,7 @@ "\n", "test_iter = cycle(iter(loaders['test']))\n", "# Training loop\n", - "epochs = 10\n", + "epochs = 100\n", "trip_model.to(device)\n", "losses = []\n", "val_losses = []\n", @@ -889,40 +894,68 @@ ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/", - "height": 425 + "base_uri": "https://localhost:8080/" }, "id": "A_J3lXLOUDu0", - "outputId": "380a9cc5-fffd-4b7a-ea04-2ac87e26aab3" + "outputId": "7c61c7fa-87f6-498f-8cee-a141738d31fb" }, - "execution_count": 10, + "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Epoch [1 / 10], Step [16 / 169], Loss: 6.299764156341553, Validation Loss: 5.821728706359863\n" + "Epoch [1 / 100], Step [26 / 269], Loss: 6.237911701202393, Validation Loss: 6.3210039138793945\n", + "Epoch [1 / 100], Step [52 / 269], Loss: 6.128037929534912, Validation Loss: 6.244564056396484\n", + "Epoch [1 / 100], Step [78 / 269], Loss: 6.11774206161499, Validation Loss: 6.238094806671143\n", + "Epoch [1 / 100], Step [104 / 269], Loss: 6.161336898803711, Validation Loss: 6.252033233642578\n" ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.animation import FuncAnimation\n", + "\n", + "\n", + "\n", + "fig, ax = plt.subplots()\n", + "line1, = ax.plot(np.arange(len(losses[0])), losses[0], label='Training Loss')\n", + "line2, = ax.plot(np.arange(len(val_losses[0])), val_losses[0], label='Validation Loss')\n", + "ax.legend()\n", + "\n", + "\n", + "\n", + "def update(frame):\n", + " line1.set_data(np.arange(len(losses[frame])), losses[frame])\n", + " line2.set_data(np.arange(len(val_losses[frame])), val_losses[frame])\n", + " return line1, line2\n", + "\n", + "ani = FuncAnimation(fig, update, frames=len(losses), blit=True)\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 430 }, + "id": "gD83031xGBYz", + "outputId": "f4d39054-5e51-4ab2-8214-4ed357f5a571" + }, + "execution_count": null, + "outputs": [ { - "output_type": "error", - "ename": "OutOfMemoryError", - "evalue": "ignored", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mOutOfMemoryError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mout1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrip_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrip_criterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, anchor, positive, negative)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0manchor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpositive\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnegative\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0moutput_anchor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manchor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0moutput_positive\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpositive\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0moutput_negative\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnegative\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0moutput_anchor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_positive\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_negative\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mforward_once\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 284\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36m_forward_impl\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;31m# See note [TorchScript super()]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 269\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 270\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 271\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaxpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/batchnorm.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[0mused\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnormalization\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0;32min\u001b[0m \u001b[0meval\u001b[0m \u001b[0mmode\u001b[0m \u001b[0mwhen\u001b[0m \u001b[0mbuffers\u001b[0m \u001b[0mare\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \"\"\"\n\u001b[0;32m--> 171\u001b[0;31m return F.batch_norm(\n\u001b[0m\u001b[1;32m 172\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;31m# If buffers are not to be tracked, ensure that they won't be updated\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mbatch_norm\u001b[0;34m(input, running_mean, running_var, weight, bias, training, momentum, eps)\u001b[0m\n\u001b[1;32m 2448\u001b[0m \u001b[0m_verify_batch_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2450\u001b[0;31m return torch.batch_norm(\n\u001b[0m\u001b[1;32m 2451\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrunning_mean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrunning_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmomentum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackends\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcudnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menabled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2452\u001b[0m )\n", - "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 512.00 MiB (GPU 0; 15.77 GiB total capacity; 14.33 GiB already allocated; 28.12 MiB free; 14.47 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF" - ] + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} } ] }, @@ -967,10 +1000,22 @@ "print(f\"Accuracy: {accuracy}\")\n" ], "metadata": { - "id": "ayGTRCP5Wl0s" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ayGTRCP5Wl0s", + "outputId": "da682072-bc72-4e11-fbc5-f64c96b69050" }, "execution_count": null, - "outputs": [] + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Accuracy: 0.58\n" + ] + } + ] }, { "cell_type": "code", @@ -987,7 +1032,6 @@ "provenance": [], "machine_shape": "hm", "gpuType": "A100", - "authorship_tag": "ABX9TyPKn/v2mVLUWZMauFxXGvWg", "include_colab_link": true }, "kernelspec": { From 67818e09b29e45d9d46cada67708b1630dbab31b Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Tue, 17 Oct 2023 14:40:06 +1000 Subject: [PATCH 07/14] Changed to SGD, fixed classifier to actually use embeddings. Getting ~70% test accuracy --- Colab version.ipynb | 801 ++++++++++++++++++++++++++++---------------- 1 file changed, 511 insertions(+), 290 deletions(-) diff --git a/Colab version.ipynb b/Colab version.ipynb index 00f28d016b..c9ec5a81f2 100644 --- a/Colab version.ipynb +++ b/Colab version.ipynb @@ -12,13 +12,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yzCmT9TUJY10", - "outputId": "df7da7be-a49a-44be-ebef-5953c0ada243" + "outputId": "bdc05ce0-7e5d-4959-8685-ce3b497d3a18" }, "outputs": [ { @@ -36,13 +36,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-Lb7CGdEJCQg", - "outputId": "604ba5ed-6fa6-42c3-c3f1-ae6e7e8d94a1" + "outputId": "0a9018e4-6a51-4c9f-de99-d79cd5078de4" }, "outputs": [ { @@ -59,6 +59,23 @@ }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PM8uTdbZpQGJ", + "outputId": "281ff7ad-897b-488c-f95b-db60efdab763" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['AD_NC']\n" + ] + } + ], "source": [ "import zipfile\n", "import os\n", @@ -74,38 +91,18 @@ "# List the contents of the extracted folder\n", "extracted_files = os.listdir(extract_path)\n", "print(extracted_files)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "PM8uTdbZpQGJ", - "outputId": "7ab4b973-0457-4bd1-ba08-287fc9d91540" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "['AD_NC']\n" - ] - } ] }, { "cell_type": "code", - "source": [ - "cd /content/extracted_folder/AD_NC" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "x1t1iFV_p2MU", - "outputId": "7f825366-4473-4a83-d557-29c3fbd6ec11" + "outputId": "e70aee0f-012c-4647-f4f8-4eec6b9a4397" }, - "execution_count": 4, "outputs": [ { "output_type": "stream", @@ -114,21 +111,21 @@ "/content/extracted_folder/AD_NC\n" ] } + ], + "source": [ + "cd /content/extracted_folder/AD_NC" ] }, { "cell_type": "code", - "source": [ - "ls" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Tp_Fb7Eyp-yv", - "outputId": "153ee02c-1cd9-4262-d0b5-33b7c1857578" + "outputId": "a06d8cb3-1fb3-4833-a6be-6bd8a9840f54" }, - "execution_count": 5, "outputs": [ { "output_type": "stream", @@ -137,11 +134,14 @@ "\u001b[0m\u001b[01;34mtest\u001b[0m/ \u001b[01;34mtrain\u001b[0m/\n" ] } + ], + "source": [ + "ls" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "id": "_8PBaJLSJSCP" }, @@ -164,23 +164,24 @@ "import torch\n", "from torch.utils.data import DataLoader\n", "from torchvision.datasets import ImageFolder\n", - "import os\n", - "from PIL import Image\n", + "\n", + "\n", "import numpy as np\n", "from torch.utils.data import Dataset, DataLoader, TensorDataset, ConcatDataset\n", - "import random" + "import random\n", + "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", - "source": [ - "batch_size = 128" - ], + "execution_count": null, "metadata": { "id": "u6EH0wk_CkxF" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "batch_size = 128" + ] }, { "cell_type": "code", @@ -275,14 +276,7 @@ }, { "cell_type": "code", - "source": [ - "count = 0\n", - "same = 0\n", - "for i, j, n, m in loaders['test']:\n", - " count += len(n)\n", - " same += (n == m).sum().tolist()\n", - "same / count" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -290,18 +284,25 @@ "id": "5ceQcKMAPoip", "outputId": "2b8eb6b1-c1c5-43db-9370-42f0da1531f2" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.5144444444444445" ] }, + "execution_count": 225, "metadata": {}, - "execution_count": 225 + "output_type": "execute_result" } + ], + "source": [ + "count = 0\n", + "same = 0\n", + "for i, j, n, m in loaders['test']:\n", + " count += len(n)\n", + " same += (n == m).sum().tolist()\n", + "same / count" ] }, { @@ -349,32 +350,7 @@ }, { "cell_type": "code", - "source": [ - "# Initialize the network, loss function, and optimizer\n", - "siamese_net = SiameseNetwork(pretrained=True).to(device)\n", - "criterion = ContrastiveLoss()\n", - "optimizer = optim.Adam(siamese_net.parameters(), lr=0.001)\n", - "# Training loop\n", - "epochs = 5\n", - "total_step = len(loaders['test'])\n", - "for epoch in range(epochs):\n", - " for i, (img1, img2, lab1, lab2) in enumerate(loaders['test']):\n", - " img1, img2, lab1, lab2 = img1.to(device), img2.to(device), lab1.to(device), lab2.to(device)\n", - " output = siamese_net(img1, img2)\n", - " o1, o2 = output[:, 0], output[:, 1]\n", - " label = (lab1 == lab2).int()\n", - "\n", - " loss = criterion(o1, o2, label)\n", - " optimizer.zero_grad()\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " if (i + 1) % 21 == 0:\n", - " print((lab1[0], lab2[0]))\n", - " print(label[0])\n", - " print(output[0])\n", - " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -382,11 +358,10 @@ "id": "4NnXFUDzSWjp", "outputId": "ab9065a9-ffad-4405-963d-85ae26293b0d" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", "tensor(0, device='cuda:0', dtype=torch.int32)\n", @@ -510,10 +485,53 @@ "Epoch [5 / 5], Step [126 / 141 Loss 0.9903947114944458]\n" ] } + ], + "source": [ + "# Initialize the network, loss function, and optimizer\n", + "siamese_net = SiameseNetwork(pretrained=True).to(device)\n", + "criterion = ContrastiveLoss()\n", + "optimizer = optim.Adam(siamese_net.parameters(), lr=0.001)\n", + "# Training loop\n", + "epochs = 5\n", + "total_step = len(loaders['test'])\n", + "for epoch in range(epochs):\n", + " for i, (img1, img2, lab1, lab2) in enumerate(loaders['test']):\n", + " img1, img2, lab1, lab2 = img1.to(device), img2.to(device), lab1.to(device), lab2.to(device)\n", + " output = siamese_net(img1, img2)\n", + " o1, o2 = output[:, 0], output[:, 1]\n", + " label = (lab1 == lab2).int()\n", + "\n", + " loss = criterion(o1, o2, label)\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " if (i + 1) % 21 == 0:\n", + " print((lab1[0], lab2[0]))\n", + " print(label[0])\n", + " print(output[0])\n", + " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")" ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G3TWNpvKdAyn", + "outputId": "bf70c5fd-38e5-49c7-a64f-ba1758f103d9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy of the network on the test images: 49.955555555555556%\n" + ] + } + ], "source": [ "correct = 0\n", "total = 0\n", @@ -534,27 +552,27 @@ "\n", "\n", " print(f'Accuracy of the network on the test images: {100 * correct / total}%')\n" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "G3TWNpvKdAyn", - "outputId": "bf70c5fd-38e5-49c7-a64f-ba1758f103d9" + "id": "MVEvstik8zqK", + "outputId": "c5aed97c-27c1-4685-bf6a-cc2204dbaa69" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "Accuracy of the network on the test images: 49.955555555555556%\n" + "Accuracy: 0.49166666666666664\n" ] } - ] - }, - { - "cell_type": "code", + ], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", @@ -573,39 +591,20 @@ "# Calculate accuracy\n", "accuracy = accuracy_score(y_test, predictions)\n", "print(f\"Accuracy: {accuracy}\")\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MVEvstik8zqK", - "outputId": "c5aed97c-27c1-4685-bf6a-cc2204dbaa69" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy: 0.49166666666666664\n" - ] - } ] }, { "cell_type": "code", - "source": [], + "execution_count": null, "metadata": { "id": "qA-60lBMGV3i" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [] }, { "cell_type": "code", - "source": [ - "features[0].tolist()" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -613,34 +612,33 @@ "id": "9PrC3ijsFnhE", "outputId": "42aa07d0-9166-43fb-a67e-713fc99664da" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.5943959355354309" ] }, + "execution_count": 148, "metadata": {}, - "execution_count": 148 + "output_type": "execute_result" } + ], + "source": [ + "features[0].tolist()" ] }, { "cell_type": "code", - "source": [], + "execution_count": null, "metadata": { - "id": "hqYJrbkVfi36" + "id": "dlrwhZTjTKWf" }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", + "outputs": [], "source": [ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "batch_size = 80\n", + "batch_size = 96\n", + "from torch.utils.data.dataset import Subset, random_split\n", "\n", "class CustomDataset(Dataset):\n", " def __init__(self, root_dir, transform=None):\n", @@ -691,12 +689,7 @@ " return img1, img2, img3, label\n", "\n", "\n", - "\n", - "# Set the device\n", - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "\n", - "\n", - "size = 256\n", + "size = 128\n", "\n", "def intensity_normalization(img, mean = None, std = None):\n", " mean = torch.mean(img)\n", @@ -714,11 +707,8 @@ "transform_train = transforms.Compose([\n", " transforms.Resize((size, size)),\n", " transforms.ToTensor(),\n", - " transforms.RandomRotation(degrees=5), # Randomly rotate the image by up to 5 degrees\n", - " # transforms.ColorJitter(brightness=0.05, contrast=0.05, saturation=0.05, hue=0.05), # Adjust color jitter with smaller increments\n", - " transforms.RandomAffine(degrees=0, translate=(0.05, 0.05), scale=(0.95, 1.05)), # Random affine transformations with smaller parameters\n", + " transforms.RandomAffine(degrees=5, translate=(0.05, 0.05), scale=(0.95, 1.05)), # Random affine transformations with smaller parameters\n", " transforms.Lambda(intensity_normalization)\n", - "\n", "])\n", "\n", "transform_test = transforms.Compose([\n", @@ -740,33 +730,69 @@ "# Replace 'your_nii_folder' with the path to your folder containing .nii files\n", "# nii_dataset = NiiDataset(root_dir=r'keras_png_slices_data\\keras_png_slices_data\\keras_png_slices_train')\n", "loaders = {}\n", + "AD_train = CustomDataset(root_dir=os.path.join('train', 'AD'), transform=transform_train)\n", + "NC_train = CustomDataset(root_dir=os.path.join('train', 'NC'), transform=transform_train)\n", "\n", - "for stage in ['train', 'test']:\n", - " if stage == 'train':\n", - " transform = transform_train\n", - " else:\n", - " transform = transform_test\n", - " AD = CustomDataset(root_dir=os.path.join(stage, 'AD'), transform=transform)\n", - " NC = CustomDataset(root_dir=os.path.join(stage, 'NC'), transform=transform)\n", - " if stage == 'train':\n", - " X = torch.stack([img for img in AD + NC])\n", - " # Compute the mean and standard deviation\n", - " mean = X.mean()\n", - " std = X.std()\n", - " normalize = transforms.Compose([\n", + "X = torch.stack([img for img in AD_train + NC_train])\n", + "mean = X.mean()\n", + "std = X.std()\n", + "normalize = transforms.Compose([\n", " Normalize(mean, std)\n", " ])\n", - " dataset = TripletDataset(AD, NC)\n", - " loaders[stage] = DataLoader(dataset, batch_size=batch_size)" + "train_dataset = TripletDataset(AD_train, NC_train, normalize)\n", + "\n", + "\n", + "# Create data loaders for training and validation data\n", + "loaders['train'] = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n", + "\n", + "AD_test = CustomDataset(root_dir=os.path.join('test', 'AD'), transform=transform_test)\n", + "NC_test = CustomDataset(root_dir=os.path.join('test', 'NC'), transform=transform_test)\n", + "\n", + "test_dataset = TripletDataset(AD_test, NC_test, normalize)\n", + "\n", + "split_1 = int(0.5 * len(test_dataset))\n", + "split_2 = len(test_dataset) - split_1\n", + "\n", + "# Perform the split\n", + "test_dataset, cal_dataset = random_split(test_dataset, [split_1, split_2])\n", + "\n", + "loaders['cal'] = DataLoader(cal_dataset, batch_size=batch_size, shuffle=True)\n", + "loaders['test'] = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)" + ] + }, + { + "cell_type": "code", + "source": [ + "len(train_dataset), len(cal_dataset), len(test_dataset)" ], "metadata": { - "id": "dlrwhZTjTKWf" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5RLuD3STujVC", + "outputId": "582bc19f-370e-47ab-d0a2-48b82b190226" }, - "execution_count": 43, - "outputs": [] + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(21520, 4500, 4500)" + ] + }, + "metadata": {}, + "execution_count": 56 + } + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "c8MhXUlOTHnY" + }, + "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", @@ -778,13 +804,13 @@ " super(TripletSiameseNetwork, self).__init__()\n", " self.resnet = models.resnet18(pretrained=pretrained)\n", " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n", - " self.fc1 = nn.Linear(1000, 500)\n", - " self.fc2 = nn.Linear(500, 2)\n", + " self.fc1 = nn.Linear(1000, 32)\n", + " # self.fc2 = nn.Linear(500, 32)\n", "\n", " def forward_once(self, x):\n", " output = self.resnet(x)\n", " output = self.fc1(output)\n", - " output = self.fc2(output)\n", + " # output = self.fc2(output)\n", " return output\n", "\n", " def forward(self, anchor, positive, negative):\n", @@ -805,7 +831,7 @@ " return losses.mean()\n", "\n", "class TripletLossWithRegularization(nn.Module):\n", - " def __init__(self, margin=1.0, lambda_reg=0.01):\n", + " def __init__(self, margin=1.0, lambda_reg=0.0001):\n", " super(TripletLossWithRegularization, self).__init__()\n", " self.margin = margin\n", " self.lambda_reg = lambda_reg # Regularization parameter\n", @@ -829,208 +855,404 @@ "\n", "\n", "\n" - ], - "metadata": { - "id": "c8MhXUlOTHnY" - }, - "execution_count": 47, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Gs0V-oZe1O46" + }, + "outputs": [], "source": [ "from torch.optim.lr_scheduler import StepLR\n", - "learning_rate = 0.001\n", + "learning_rate = 0.05\n", "trip_model = TripletSiameseNetwork()\n", "trip_criterion = TripletLossWithRegularization(margin=1.0)\n", + "val_criterion = TripletLoss(margin=1.0)\n", "total_step = len(loaders['train'])\n", "\n", - "optimizer = optim.Adam(trip_model.parameters(), lr=learning_rate)\n", - "scheduler = StepLR(optimizer, step_size=15, gamma=0.75)" - ], - "metadata": { - "id": "Gs0V-oZe1O46" - }, - "execution_count": 48, - "outputs": [] + "# optimizer = optim.Adam(trip_model.parameters(), lr=learning_rate)\n", + "# scheduler = StepLR(optimizer, step_size=5, gamma=0.8)\n", + "optimizer = torch.optim.SGD(trip_model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4)\n", + "\n", + "sched_linear_1 = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.005, max_lr=learning_rate, step_size_up=15, step_size_down=15, mode=\"triangular\", verbose=False)\n", + "sched_linear_3 = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.005/learning_rate, end_factor=0.005/learning_rate, verbose=False)\n", + "scheduler = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[sched_linear_1, sched_linear_3], milestones=[30])" + ] }, { "cell_type": "code", + "execution_count": 64, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "A_J3lXLOUDu0", + "outputId": "e56e3882-b65d-48f5-a2ae-3280b5c45cdd" + }, + "outputs": [ + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1 / 15], Step [22 / 225], Loss: 1.6861666440963745, Validation Loss: 1.3392752408981323\n", + "Epoch [1 / 15], Step [44 / 225], Loss: 1.1134928464889526, Validation Loss: 1.0987355709075928\n", + "Epoch [1 / 15], Step [66 / 225], Loss: 1.0257302522659302, Validation Loss: 1.1489994525909424\n", + "Epoch [1 / 15], Step [88 / 225], Loss: 1.1023128032684326, Validation Loss: 1.149999976158142\n", + "Epoch [1 / 15], Step [110 / 225], Loss: 0.794222354888916, Validation Loss: 1.2070260047912598\n", + "Epoch [1 / 15], Step [132 / 225], Loss: 0.9533556699752808, Validation Loss: 1.061461329460144\n", + "Epoch [1 / 15], Step [154 / 225], Loss: 0.5997182726860046, Validation Loss: 0.9921392202377319\n", + "Epoch [1 / 15], Step [176 / 225], Loss: 0.8227355480194092, Validation Loss: 0.8853449821472168\n", + "Epoch [1 / 15], Step [198 / 225], Loss: 1.0741984844207764, Validation Loss: 1.1926651000976562\n", + "Epoch [1 / 15], Step [220 / 225], Loss: 1.4481054544448853, Validation Loss: 1.1044371128082275\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [2 / 15], Step [22 / 225], Loss: 0.993647038936615, Validation Loss: 0.9966787695884705\n", + "Epoch [2 / 15], Step [44 / 225], Loss: 1.0237594842910767, Validation Loss: 1.0309518575668335\n", + "Epoch [2 / 15], Step [66 / 225], Loss: 0.883934497833252, Validation Loss: 1.0600988864898682\n", + "Epoch [2 / 15], Step [88 / 225], Loss: 1.0512185096740723, Validation Loss: 0.8157957792282104\n", + "Epoch [2 / 15], Step [110 / 225], Loss: 0.7038865089416504, Validation Loss: 0.8065618276596069\n", + "Epoch [2 / 15], Step [132 / 225], Loss: 0.8788958191871643, Validation Loss: 0.9165557622909546\n", + "Epoch [2 / 15], Step [154 / 225], Loss: 0.5161008834838867, Validation Loss: 0.7060334086418152\n", + "Epoch [2 / 15], Step [176 / 225], Loss: 0.312076598405838, Validation Loss: 0.40925726294517517\n", + "Epoch [2 / 15], Step [198 / 225], Loss: 0.46442490816116333, Validation Loss: 0.510191798210144\n", + "Epoch [2 / 15], Step [220 / 225], Loss: 0.08432803303003311, Validation Loss: 0.13745912909507751\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [3 / 15], Step [22 / 225], Loss: 0.6365734934806824, Validation Loss: 0.9633301496505737\n", + "Epoch [3 / 15], Step [44 / 225], Loss: 0.720203697681427, Validation Loss: 0.2848336696624756\n", + "Epoch [3 / 15], Step [66 / 225], Loss: 0.061845649033784866, Validation Loss: 0.9537119269371033\n", + "Epoch [3 / 15], Step [88 / 225], Loss: 0.09502657502889633, Validation Loss: 0.17387674748897552\n", + "Epoch [3 / 15], Step [110 / 225], Loss: 0.16061733663082123, Validation Loss: 0.0026112645864486694\n", + "Epoch [3 / 15], Step [132 / 225], Loss: 0.1448567658662796, Validation Loss: 0.176682710647583\n", + "Epoch [3 / 15], Step [154 / 225], Loss: 0.11984804272651672, Validation Loss: 0.0029973338823765516\n", + "Epoch [3 / 15], Step [176 / 225], Loss: 0.05067078024148941, Validation Loss: 0.024889525026082993\n", + "Epoch [3 / 15], Step [198 / 225], Loss: 0.08953560888767242, Validation Loss: 0.014550979249179363\n", + "Epoch [3 / 15], Step [220 / 225], Loss: 0.05057196691632271, Validation Loss: 0.14682522416114807\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [4 / 15], Step [22 / 225], Loss: 0.05052595213055611, Validation Loss: 0.2758115828037262\n", + "Epoch [4 / 15], Step [44 / 225], Loss: 0.26822781562805176, Validation Loss: 0.33429664373397827\n", + "Epoch [4 / 15], Step [66 / 225], Loss: 0.05043775588274002, Validation Loss: 0.04388421028852463\n", + "Epoch [4 / 15], Step [88 / 225], Loss: 0.05038832128047943, Validation Loss: 0.06882896274328232\n", + "Epoch [4 / 15], Step [110 / 225], Loss: 0.05032651126384735, Validation Loss: 0.015540232881903648\n", + "Epoch [4 / 15], Step [132 / 225], Loss: 0.05026824027299881, Validation Loss: 0.9465289115905762\n", + "Epoch [4 / 15], Step [154 / 225], Loss: 0.08250364661216736, Validation Loss: 0.0\n", + "Epoch [4 / 15], Step [176 / 225], Loss: 0.07428855448961258, Validation Loss: 0.046117693185806274\n", + "Epoch [4 / 15], Step [198 / 225], Loss: 0.05011678487062454, Validation Loss: 0.022663306444883347\n", + "Epoch [4 / 15], Step [220 / 225], Loss: 0.05006015673279762, Validation Loss: 0.015940412878990173\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [5 / 15], Step [22 / 225], Loss: 0.04997241497039795, Validation Loss: 0.0\n", + "Epoch [5 / 15], Step [44 / 225], Loss: 0.049897659569978714, Validation Loss: 0.0223587267100811\n", + "Epoch [5 / 15], Step [66 / 225], Loss: 0.049830615520477295, Validation Loss: 0.0\n", + "Epoch [5 / 15], Step [88 / 225], Loss: 0.049757134169340134, Validation Loss: 0.0\n", + "Epoch [5 / 15], Step [110 / 225], Loss: 0.0496828556060791, Validation Loss: 0.04544451832771301\n", + "Epoch [5 / 15], Step [132 / 225], Loss: 0.049610793590545654, Validation Loss: 0.0\n", + "Epoch [5 / 15], Step [154 / 225], Loss: 0.04953967407345772, Validation Loss: 0.010974577628076077\n", + "Epoch [5 / 15], Step [176 / 225], Loss: 0.049466632306575775, Validation Loss: 0.0\n", + "Epoch [5 / 15], Step [198 / 225], Loss: 0.04939447343349457, Validation Loss: 0.020791243761777878\n", + "Epoch [5 / 15], Step [220 / 225], Loss: 0.04932032525539398, Validation Loss: 0.0\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGwCAYAAABVdURTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABlIklEQVR4nO3dd3wUBfrH8c+mk0pCCSGEXiT0TsCCioIoTTx6EbEhIoie4v1UbCd6nnecggUsiHQQBEVBREVQmgSUJghEeigCaZAEduf3x6QQCMlu2M1mk+/79dqXm9mZ3Wfcy+XrzDPPWAzDMBAREREpJbzcXYCIiIiIMynciIiISKmicCMiIiKlisKNiIiIlCoKNyIiIlKqKNyIiIhIqaJwIyIiIqWKj7sLKG42m42jR48SEhKCxWJxdzkiIiJiB8MwSElJoWrVqnh5FXxspsyFm6NHjxITE+PuMkRERKQIDh06RLVq1Qpcp8yFm5CQEMD8lxMaGurmakRERMQeycnJxMTE5PwdL0iZCzfZp6JCQ0MVbkRERDyMPS0laigWERGRUkXhRkREREoVhRsREREpVcpcz42IiJQuVquVCxcuuLsMcQI/P79CL/O2h8KNiIh4JMMwSExM5OzZs+4uRZzEy8uLWrVq4efnd03vo3AjIiIeKTvYVK5cmcDAQA1m9XDZQ3aPHTtG9erVr+n7VLgRERGPY7Vac4JNhQoV3F2OOEmlSpU4evQoFy9exNfXt8jvo4ZiERHxONk9NoGBgW6uRJwp+3SU1Wq9pvdRuBEREY+lU1Gli7O+T52WchabFQ78DKnHITgSanQAL293VyUiIlLmKNw4w86lsPxpSD6auyy0KnR9HWJ7uK8uERGRMkinpa7VzqUwf2jeYAOQfMxcvnOpe+oSERG7WG0G6/b9xZKtR1i37y+sNsPdJTmsZs2aTJo0yd1llBg6cnMtbFbziA35/SIYgAWWj4fr7tQpKhGREmj59mO8+MVOjiWl5yyLCgtgQvdYujaOcvrnFdZTMmHCBF544QWH33fTpk0EBQUVsSpTp06daN68eakISQo31+LAz1cescnDgOQj5nq1bii2skREpHDLtx9j5Mz4K/7zNDEpnZEz43l3cEunB5xjx47lPJ83bx7PP/88u3fvzlkWHByc89wwDKxWKz4+hf+prlSpklPr9HQ6LXUtUo87dz0RESkywzA4l3nRrkdK+gUmLN1x1ePuAC8s3UlK+gW73s8w7DuVVaVKlZxHWFgYFosl5+fff/+dkJAQvv76a1q1aoW/vz9r165l37599OzZk8jISIKDg2nTpg3ffvttnve9/LSUxWLhgw8+oHfv3gQGBlKvXj2WLr22NonPPvuMRo0a4e/vT82aNXnzzTfzvP7OO+9Qr149AgICiIyM5J577sl5beHChTRp0oRy5cpRoUIFOnfuTFpa2jXVUxAdubkWwZHOXU9ERIrs/AUrsc+vcMp7GUBicjpNXvjGrvV3vtSFQD/n/EkdP348//73v6lduzbh4eEcOnSIbt268c9//hN/f39mzJhB9+7d2b17N9WrV7/q+7z44ov861//4o033uDtt99m0KBBHDhwgIiICIdr2rx5M3379uWFF16gX79+/PzzzzzyyCNUqFCBe++9l19++YXHHnuMTz/9lA4dOnD69GnWrFkDmEerBgwYwL/+9S969+5NSkoKa9assTsQFoXCzbWo0cG8Kir5GPn33QAhVc31RERE7PDSSy9x22235fwcERFBs2bNcn5++eWXWbx4MUuXLuXRRx+96vvce++9DBgwAIBXX32Vt956i40bN9K1a1eHa/rPf/7DrbfeynPPPQdA/fr12blzJ2+88Qb33nsvBw8eJCgoiLvuuouQkBBq1KhBixYtADPcXLx4kbvvvpsaNWoA0KRJE4drcITCzbXw8jYv954/FLCQb8AJCIWL6eB3bY1eIiJSsHK+3ux8qYtd625MOM29H28qdL3pw9vQtlbhRzrK+TrvopHWrVvn+Tk1NZUXXniBZcuW5QSF8+fPc/DgwQLfp2nTpjnPg4KCCA0N5cSJE0WqadeuXfTs2TPPso4dOzJp0iSsViu33XYbNWrUoHbt2nTt2pWuXbvmnBJr1qwZt956K02aNKFLly7cfvvt3HPPPYSHhxepFnuo5+ZaxfaAvjMg9LKms6BK4FMOTv4Oc/rDhfPuqU9EpIywWCwE+vnY9bihXiWiwgK42rVLFsyrpm6oV8mu93PmpOTLr3p68sknWbx4Ma+++ipr1qxh69atNGnShMzMzALf5/J7M1ksFmw2m9PqvFRISAjx8fHMmTOHqKgonn/+eZo1a8bZs2fx9vZm5cqVfP3118TGxvL222/ToEEDEhISXFILKNw4R2wPGLsdhn0JfT40//nEbhi2FPyCIeFHmDsQLqQX/l4iIuJy3l4WJnSPBbgi4GT/PKF7LN5e7r+9w08//cS9995L7969adKkCVWqVOHPP/8s1hoaNmzITz/9dEVd9evXx9vbPGrl4+ND586d+de//sVvv/3Gn3/+yXfffQeYwapjx468+OKLbNmyBT8/PxYvXuyyenVaylm8vK+83DumLQxaCDP7wL7vYP4Q6DcTfPzdU6OIiOTo2jiKdwe3vGLOTRUXzrkpinr16rFo0SK6d++OxWLhueeec9kRmJMnT7J169Y8y6KionjiiSdo06YNL7/8Mv369WPdunVMnjyZd955B4Avv/yS/fv3c+ONNxIeHs5XX32FzWajQYMGbNiwgVWrVnH77bdTuXJlNmzYwMmTJ2nYsKFL9gEUblyvRhwMmg8z74E/voEF98LfPgEfP3dXJiJS5nVtHMVtsVXYmHCaEynpVA4JoG2tiBJxxCbbf/7zH+677z46dOhAxYoVefrpp0lOTnbJZ82ePZvZs2fnWfbyyy/z7LPPMn/+fJ5//nlefvlloqKieOmll7j33nsBKF++PIsWLeKFF14gPT2devXqMWfOHBo1asSuXbv48ccfmTRpEsnJydSoUYM333yTO+64wyX7AGAxXHktVgmUnJxMWFgYSUlJhIaGFt8H718Ns/uazcUNu8M9H4O3b+HbiYjIFdLT00lISKBWrVoEBAS4uxxxkoK+V0f+fqvnprjUvgn6zwZvf9j1BXx2P1gvursqERGRUkfhpjjVvdXsufH2g52fw+cPm/enEhEREadRuClu9W83Lx338oFtC2DJKAUcERERJ1K4cRKrzWDdvr9YsvUI6/b9hdVWQCtTgzvMnhuLN/w6B754DFzU+S4iIlLW6GopJ1i+/dgVlxJGFXYpYWwP6PMBfDYCtswEL1+467/gxEFQIiIiZZGO3Fyj5duPMXJmfJ5gA5CYlM7ImfEs337sKlsCje+G3lPB4gWbP4av/g5l6+I1ERERp1O4uQZWm8GLX+zM95aZ2cte/GJnwaeomv4Ner4DWGDTNFjxDwUcERGRa6Bwcw02Jpy+4ojNpQzgWFI6GxNOF/xGzQdAj7fN5+vfgZXPK+CIiIgUkcLNNTiRYt+9ouxar+UQs+cG4Oe34LuXFXBERCRfnTp1YuzYse4uo8RSuLkGlUPsm4pp73q0vg+6/dt8vuZN+OG1IlYmIiJ2s1khYQ1sW2j+04XjObp3707Xrl3zfW3NmjVYLBZ+++23a/6c6dOnU758+Wt+H0+lq6WuQdtaEUSFBZCYlJ5v3w1AZKg/bWtFOPCmD4D1Aqx4Bla/Bt4+cOPfnVKviIhcZudSWP40JB/NXRZaFbq+bl7V6mQjRoygT58+HD58mGrVquV57eOPP6Z169Y0bdrU6Z9b1ujIzTXw9rIwoXssAFe7gNvbYuHsuUzH3jjuEbjtJfP5d6/A2klFrlFERK5i51KYPzRvsAFIPmYu37nU6R951113UalSJaZPn55neWpqKgsWLGDEiBH89ddfDBgwgOjoaAIDA2nSpAlz5sxxah0HDx6kZ8+eBAcHExoaSt++fTl+/HjO67/++is333wzISEhhIaG0qpVK3755RcADhw4QPfu3QkPDycoKIhGjRrx1VdfObW+a6Vwc426No7i3cEtqRKW99RTpRB/QgN8OJqUzsBpGziVmuHYG3ccA7c8Zz7/dgKsm+KkikVESinDgMw0+x7pyfD1U1DQ9a7LnzbXs+f97OyR9PHxYejQoUyfPp1L71u9YMECrFYrAwYMID09nVatWrFs2TK2b9/Ogw8+yJAhQ9i4ceO1/zsCbDYbPXv25PTp06xevZqVK1eyf/9++vXrl7POoEGDqFatGps2bWLz5s2MHz8eX1/zZs+jRo0iIyODH3/8kW3btvH6668THBzslNqcRaelnKBr4yhui63CxoTTnEhJp3JIAG1rRXDgrzQGTFvP7uMpDJy2ntkPtKdisL/9b3zjk2C7CD9MNC8R9/KBdg+5bkdERDzZhXPwalUnvZlhHtF5Lca+1f9xFPyC7Fr1vvvu44033mD16tV06tQJME9J9enTh7CwMMLCwnjyySdz1h89ejQrVqxg/vz5tG3b1tEducKqVavYtm0bCQkJxMSY+zdjxgwaNWrEpk2baNOmDQcPHuTvf/871113HQD16tXL2f7gwYP06dOHJk2aAFC7du1rrsnZdOTGSby9LMTVqUDP5tHE1amAt5eF2pWCmfNAeyJD/dlzPJWB09Y7fgTnpqdze26+fgo2feD84kVEpNhcd911dOjQgY8++giAvXv3smbNGkaMGAGA1Wrl5ZdfpkmTJkRERBAcHMyKFSs4ePCgUz5/165dxMTE5AQbgNjYWMqXL8+uXbsAGDduHPfffz+dO3fmtddeY9++fTnrPvbYY7zyyit07NiRCRMmOKUB2tl05MbFalcKZu6DcfSfui4n4Dh0BMdigZv/z2wy/mkSLHvCPILT6l5Xli0i4nl8A80jKPY48DPMuqfw9QYthBod7PtsB4wYMYLRo0czZcoUPv74Y+rUqcNNN90EwBtvvMH//vc/Jk2aRJMmTQgKCmLs2LFkZjrYv3kNXnjhBQYOHMiyZcv4+uuvmTBhAnPnzqV3797cf//9dOnShWXLlvHNN98wceJE3nzzTUaPHl1s9RVGR26KQa2KQcx9MI4qoQHsOZ7KgKnrOZniwBEciwU6vwBxj5o/fzEWtsxyRakiIp7LYjFPDdnzqHOLeVXUVS8HsUBotLmePe/n4H0B+/bti5eXF7Nnz2bGjBncd999WLLe46effqJnz54MHjyYZs2aUbt2bfbs2XNt/24u0bBhQw4dOsShQ4dylu3cuZOzZ88SGxubs6x+/fo8/vjjfPPNN9x99918/PHHOa/FxMTw8MMPs2jRIp544gmmTZvmtPqcQeGmmNSqGMScB9tTJTSAP06YR3AcDji3vwLtHgYMWDIKfp3nsnpFREo1L2/zcm/gyoCT9XPX18z1XCA4OJh+/frxzDPPcOzYMe69996c1+rVq8fKlSv5+eef2bVrFw899FCeK5nsZbVa2bp1a57Hrl276Ny5M02aNGHQoEHEx8ezceNGhg4dyk033UTr1q05f/48jz76KD/88AMHDhzgp59+YtOmTTRs2BCAsWPHsmLFChISEoiPj+f777/Pea2kULgpRuYRnGsMOF1fg9YjAAM+f9gcOiUiIo6L7QF9Z0BoVN7loVXN5S6Yc3OpESNGcObMGbp06ULVqrmN0M8++ywtW7akS5cudOrUiSpVqtCrVy+H3z81NZUWLVrkeXTv3h2LxcKSJUsIDw/nxhtvpHPnztSuXZt588z/YPb29uavv/5i6NCh1K9fn759+3LHHXfw4osvAmZoGjVqFA0bNqRr167Ur1+fd955xyn/TpzFYhhla8Z/cnIyYWFhJCUlERoa6pYa/jxlXkV1LCmdupXNpuNKIQ5cRWWzwZdjIH4GWLzhno+gUS+X1SsiUtKkp6eTkJBArVq1CAiwcwr81disZg9O6nEIjjR7bFx0xEYKVtD36sjfbx25cYOaFYOY80B7osIC2HsilQHT1tt9nyoAvLzgrv9B80FgWOGzEfD7MtcVLCJSmnl5Q60boMk95j8VbDyewo2b1Mw6RZUTcKYWIeD0eBua9jNn4cwfBruXu65gERERD6Fw40Y1KpgBp2pYAPtOphUh4HhDz3egcR+wXYD5Q+CPb11XsIiIiAdwa7j58ccf6d69O1WrVsVisfD5558Xus0PP/xAy5Yt8ff3p27dulfcn8PT1KhgXkWVJ+AkOxBwvH2g91Ro2AOsmTB3IOz73nUFi4iIlHBuDTdpaWk0a9aMKVPsu29SQkICd955JzfffDNbt25l7Nix3H///axYscLFlbqWeQQnLifg9J9WhIBzz0fQ4E6wZsCcAZCwxnUFi4iUEGXsmphSz1nfZ4m5WspisbB48eICL3d7+umnc24klq1///6cPXuW5cvt6zcpCVdLXc3Bv84xYNp6jpw9T+1KQcx9oD2VQx24CuBiBswbAn+sMKdlDv7MvsmaIiIexmq1smfPHipXrkyFChXcXY44SVJSEkePHqVu3bo5N+rM5sjfb4+6/cK6devo3LlznmVdunRh7NixV90mIyODjIzcWTLJycmuKu+aVa8QyJwH2jNg2nr2Zx3BcSjg+PibsxnmDoR9q2DW32DwIqjezrWFi4gUM29vb8qXL8+JEycACAwMzJnwK57JZrNx8uRJAgMD8fG5tnjiUeEmMTGRyMjIPMsiIyNJTk7m/PnzlCtX7optJk6cmDN4yBNUrxDI3Afb039qVsCZup45D7Yn0t6A4xsA/WfBnP6w/weY2QeGfg7VWruybBGRYlelShWAnIAjns/Ly4vq1atfc1D1qHBTFM888wzjxo3L+Tk5OTnPnVBLopiISwLOKbPJ2LGAUw76z4HZfeHPNfDp3WbAiW7p0rpFRIqTxWIhKiqKypUrc+HCBXeXI07g5+eHl9e1twN7VLipUqXKFffXOH78OKGhofketQHw9/fH39+B6b8lxDUHHL9AGDDXvOvtwXXwaW8YthSimrm2cBGRYubt7Y23twbvSS6PmnMTFxfHqlWr8ixbuXIlcXFxbqrItbIDTnT5cuw/ZZ6iSkxy4Coq/2AYtACqtYX0szCjFxzf4apyRURESgS3hpvU1NScO5WCean31q1bOXjwIGCeUho6dGjO+g8//DD79+/nqaee4vfff+edd95h/vz5PP744+4ov1hkB5xq4eVIyLonlWMBJ8S8aiq6FZw/DZ/0gBO/u65gERERN3NruPnll19y7lQKMG7cOFq0aMHzzz8PwLFjx3KCDkCtWrVYtmwZK1eupFmzZrz55pt88MEHdOnSxS31F5eYCPMqqiIHnIBQ86qpqOZw7hR80h1O7nFZvSIiIu5UYubcFJeSPOemMIfPnKP/1PUcPnOemhUCmfNge6LC8u81yte50zCjByRug+AqMPwrqFDHdQWLiIg4ie4KXkpVCzdPUcVElOPPv84xYOp6jiWdt/8NAiNgyBKo3AhSE80jOKcTXFewiIiIGyjceJhq4eYpquyA09/RgBNUAYYugUrXQfIRM+CcOeC6gkVERIqZwo0HMo/gxBETUY4DWQHn6FkHAk5wJRi6FCrUg6RDZsBJOuy6gkVERIqRwo2Hii5fjrkPxlE9IpADWfekcijghETCsC8gojacPQDT74Lko64rWEREpJgo3Hiw6PLlmPNg+5yA4/ARnNAoM+CUrwFnEswjOCmJritYRESkGCjceDjzCI4ZcA6eNgPOEUcCTlg1uPdLCKsOf+015+Ck6j4tIiLiuRRuSoGqWQGnRgUz4AxwNOCUr27emiE0Gk7thhk9Ie0v1xUsIiLiQgo3pUTV8uWY80BuwOk/dZ1jASeilnmKKiQKTuw0A865064rWERExEUUbkqRS4/gHDp9nv5T13H4zDn736BCHTPgBEfC8W1mwDl/xnUFi4iIuIDCTSkTFWYGnJpZAWfAtPWOBZyK9czLxAMrQuJv8OndkJ7kuoJFREScTOGmFIoKM6+iqplzBMfBgFP5OvMITrkIOBoPM/tAerLrChYREXEihZtSyjyCE0fNCoEcPmMGnEOnHQg4kbHmJOOA8nB4E8z6G2SkuqxeERERZ1G4KcWqhAUw98E4alUM4vAZ8xSVQwEnqmlWwAmDQ+thdj/ITHNdwSIiIk6gcFPKVQkLYM4D7XMCjsNHcKo2hyGLwT8UDqyFOf3hggNXYYmIiBQzhZsy4NKAc+RsEQJOdCsY/Bn4BUPCjzB3IFxId13BIiIi10DhpowwT1G1p3ZRA05MWxi0EHyDYN93MH8IXMxwXcEiIiJFpHBThkSGBjDnWgJOjTgYNB98ysEf38CCe+FipsvqFRERKQqFmzImv4Bz8C8HAk7N62HgXPAJgN1fwWf3gfWC6woWERFxkMJNGRQZmnWKqpIZcAZMczDg1O4E/WeBtx/s+gIWPQDWiy6rV0RExBEKN2VU5dAA5j6QG3D6T13nWMCp2xn6zQIvX9ixGD5/GGxW1xUsIiJiJ4WbMiw74NSpFMTRpHT6T13Hgb8cmGNT/3boOwO8fGDbAlgySgFHRETcTuGmjKuc1YOTHXAGTF3vWMC5rhvc8xFYvOHXOfDFY2Czua5gERGRQijcCJVD8gac/o4GnNie0GcaWLxgy0xYNg4Mw3UFi4iIFEDhRoDcgFO3cjDHsgLOn6ccCDiN+0Dv9wELbP4Yvvq7Ao6IiLiFwo3kqBxiTjLODjgDpjkYcJr2hV7vABbYNA1W/EMBR0REip3CjeRRKcQ/T8Bx+AhO84HQ4y3z+fp3YOXzCjgiIlKsFG7kCtkBp17lYBKTzYCT4EjAaTkU7vyP+fznt+C7lxVwRESk2CjcSL4qhfgz+5KAM8DRgNNmBNzxhvl8zZvww2uuKVREROQyCjdyVZVC/JnzYHvqR2YfwVnnWMBp9yB0edV8vvo1+PEN1xQqIiJyCYUbKVDFYPMITv3IYI4nZ9B/6jr2n0y1/w3iRsFtL5nPv3sF1k5ySZ0iIiLZFG6kUNkBp0FkCMeTMxgwbb1jAafjGLjlOfP5txNg3RTXFCoiIoLCjdjJDDjtcgJO/6kOBpwbn4ROz5jPV/wDNrzvmkJFRKTMU7gRu1W4JOCcSDEDzj5HAs5NT8MNT5rPv34KNn3omkJFRKRMU7gRh2QHnOuqmAFngCMBx2KBW541T1OBeZuGzZ+4rlgRESmTFG7EYRWC/Zl1/zUEnM4vQvtR5s9fjIEts1xXrIiIlDkKN1IkFbKajLMDTv+p69l7woGA0+Wf0PYhwIAlo+DXeS6tV0REyg6FGymyiCC/nIBzMsW8isqhgHPH69D6PsCAzx+GbQtdWq+IiJQNCjdyTbIDTsOo0KIFnG5vmrdrMGyw6EHY8blL6xURkdJP4UauWUSQH7Pub5cTcMxTVCn2bezlBXf9D5oNBMMKn42A35e5tmARESnVFG7EKSKC/JidFXBOpWbQf+oGxwJOz8nQpC/YLsL8YbB7uWsLFhGRUkvhRpwmPCvgxF4ScP44bm/A8YZe70Kju8F2AeYPgT++dW3BIiJSKinciFOFZ52iyg44A6attz/gePvA3VOhYQ+wZsLcgbDve9cWLCIipY7CjThddsBpVDWUU6mZDgYcX+jzITToBtYMmDMAEta4tmARESlVFG7EJfILOHvsDTg+fvC36VDvdrh4Hmb3hQM/u7ReEREpPRRuxGXKB14WcKY6EnD8oe+nUOdWuHAOZv0NDm5wbcEiIlIqKNyIS2UHnMbRofyV5mDA8Q2A/rOgdifITIWZfeDwZpfWKyIink/hRlyufKAfM0fkDTi7E+0NOOWg/xyoeQNkpsCnveHoFtcWLCIiHk3hRopF+UA/Zo1onxNwBk5zIOD4BcKAuVA9DjKSYEYvOPabS+sVERHPpXAjxSYs0JdZI9rTJDrMPILjSMDxD4ZBC6BaW0g/CzN6wvEdLq1XREQ8k8KNFKuwQF9mjmhH02phnM4KOL8nJtu3sX8IDF4I0a3g/Gn4pAec+N21BYuIiMdxe7iZMmUKNWvWJCAggHbt2rFx48YC1580aRINGjSgXLlyxMTE8Pjjj5Oenl5M1YozhAX68ul9uQFn4LQN7DpmZ8AJCIPBiyCqGZw7BZ90h5N7XFuwiIh4FLeGm3nz5jFu3DgmTJhAfHw8zZo1o0uXLpw4cSLf9WfPns348eOZMGECu3bt4sMPP2TevHn84x//KObK5VqFBfry6SVHcAZ94EDAKVcehnwOkU0g7YQZcP7a58pyRUTEg1gMwzDc9eHt2rWjTZs2TJ48GQCbzUZMTAyjR49m/PjxV6z/6KOPsmvXLlatWpWz7IknnmDDhg2sXbs238/IyMggIyMj5+fk5GRiYmJISkoiNDTUyXskjko6f4GhH27g18NJhAf6MvuB9jSMsvN7SfsLPrkLTuyE0Gi4dxlE1HJtwSIi4hbJycmEhYXZ9ffbbUduMjMz2bx5M507d84txsuLzp07s27duny36dChA5s3b845dbV//36++uorunXrdtXPmThxImFhYTmPmJgY5+6IXJOwcr7MGNGOZtXCOHPuAgOnrWfnUTuP4ARVgKFLoWIDSD5iHsE5c8C1BYuISInntnBz6tQprFYrkZGReZZHRkaSmJiY7zYDBw7kpZde4vrrr8fX15c6derQqVOnAk9LPfPMMyQlJeU8Dh065NT9kGuXE3BiynPm3AUGfeBAwAmuBMOWQoW6kHTIDDhJh11bsIiIlGhubyh2xA8//MCrr77KO++8Q3x8PIsWLWLZsmW8/PLLV93G39+f0NDQPA8pecLK+fLpiLZFCzghVWDYFxBeC84egOl3QfJR1xYsIiIlltvCTcWKFfH29ub48eN5lh8/fpwqVarku81zzz3HkCFDuP/++2nSpAm9e/fm1VdfZeLEidhstuIoW1woNCBvwBn4wXp2HE2yc+OqcO+XUL4GnEkwj+Ck5H8EUERESje3hRs/Pz9atWqVpznYZrOxatUq4uLi8t3m3LlzeHnlLdnb2xsAN/ZFixNlB5zmMeU5e+4Cgz7YYH/ACatmHsEJi4G/9ppzcFLzv/JORERKL7eelho3bhzTpk3jk08+YdeuXYwcOZK0tDSGDx8OwNChQ3nmmWdy1u/evTvvvvsuc+fOJSEhgZUrV/Lcc8/RvXv3nJAjni80wJcZI9rSonpuwNl+xM6AE17DDDih0XBqtznJOO0v1xYsIiIlio87P7xfv36cPHmS559/nsTERJo3b87y5ctzmowPHjyY50jNs88+i8Vi4dlnn+XIkSNUqlSJ7t27889//tNduyAuEhrgyyf3tWXYRxvZcvAsgz7YkHV38bDCN46oZQacj7uZl4nP6Gk2HQdGuL5wERFxO7fOuXEHR66TF/dLSb/A0KyAE1bO1/6AA3DqDzPgpJ0wJxoPXQLlwl1bsIiIuIRHzLkRsUdIgC8z7mtLy+rlSTrv4CmqivXMIziBFeHYr/Dp3ZBu57YiIuKxFG6kxAvJOkVVpIBT+Toz4JSLgKPxMLMPpNt5ibmIiHgkhRvxCNkBp1WNcMcDTmSseUoqoDwc3gSz/gYZqS6tV0RE3EfhRjzG5QFn4LT1bDtsZ8CJagpDPwf/MDi0Hmb3g8w0l9YrIiLuoXAjHiXY3ycn4CSnX2TQB+v57fBZ+zau2gKGLAb/UDiwFub0hwvnXVqviIgUP4Ub8TjZAad1VsAZ/MEG+wNOtVYw+DPwC4aEH2HuQLiQ7tJ6RUSkeCnciEcK9vdh+n1taVOzCAEnpi0MWgC+gbDvO5g/BC5mgM0KCWtg20LznzarS/dBRERcQ3NuxKOlZlxk+Mcb2fTnGUICfJh1fzuaVitv38YJa8zm4ovnoWpL815UKZfccDO0KnR9HWJ7uKR2ERGxn+bcSJkR7O/Dx8PNIzgp6RcZ9MEGfj101r6Na90AA+aAl695mXjKZXcSTz4G84fCzqVOr1tERFxH4UY8XrC/D9OHt6VtzQhS0i8y+MMNbLU74NwIAVf7L4Csg5rLx+sUlYiIB1G4kVIhyN+Hj4e3yQk4Q+wNOAd+hnMF3VjTgOQj5noiIuIRFG6k1MgJOLWyAs4HdgSc1OP2vbm964mIiNsp3EipEuTvw8f3ZgWcDDPgbDl45uobBEfa98b2riciIm6ncCOlTpC/D9OH5wacoR9uvHrAqdHBvCoKy1XezQKh0eZ6IiLiERRupFQK9DMDTrtLAk58fgHHy9u83BvIP+AYcPvL5noiIuIRFG6k1Ar0M3tw2tcuJODE9oC+MyA06rIXssJOwhqX1yoiIs6jIX5S6p3LvMh90zexfv9pgv19mDGiLS2rh1+5os1qXhWVetzssclMM+8/hQHd/wet7i3u0kVEJIuG+IlcItDPh4/ubUNc7QqkZh3B2XzgKqeoat0ATe4x/9mgK9zyrPnasifh0MbiLVxERIpE4UbKhMsDzrCPNrL5wOnCN7zhCWjYA2wXYN4Qc2qxiIiUaAo3UmaU8/O+LOBsKjzgWCzQ612o1BBSE83bMVzMKJ6CRUSkSBRupEzJDjgd6lx6isoMOFabwbp9f7Fk6xHW7fsLqy2rHc0/GPrPgoAwOLwRvn7KjXsgIiKFUUOxlEnnM63cP2MTP+39iyA/b0Z2qsOsDQc5lpSes05UWAATusfStXHWVVR/rDTvIo4Bd02C1sPdUruISFmkhmKRQpTz8+aDoW3oWLcCaZlW/v3NnjzBBiAxKZ2RM+NZvj2rz6bebbkNxl/9HQ5uKOaqRUTEHgo3UmaV8/Pm/cGt8fPO/9cg+5Dmi1/szD1FdWmD8Xw1GIuIlEQKN1KmbTuSRKbVdtXXDeBYUjobE7Iaj/M0GB9Xg7GISAmkcCNl2omU9MJXunw9NRiLiJRoCjdSplUOCSjaehXqQJ+PAAtsng6/fOz02kREpGgUbqRMa1srgqiwgILuCU5UWABta0Vc+WK9znDrc+ZzNRiLiJQYCjdSpnl7WZjQPRa46j3BmdA9Fm+vq8Sf68dBbE81GIuIlCAKN1LmdW0cxbuDW1Il7MpTVP4+XtStHHL1jS0W6PnOJQ3GQ9RgLCLiZg6Hm+XLl7N27dqcn6dMmULz5s0ZOHAgZ87kczNCEQ/QtXEUa5++hTkPtOd//Zszc0Rb2teKIOOijYdnbiYt4+LVN87TYLxJDcYiIm7mcLj5+9//TnJyMgDbtm3jiSeeoFu3biQkJDBu3DinFyhSXLy9LMTVqUDP5tFcX68Sbw9sSWSoP3tPpPL0Z79R4DBvNRiLiJQYDoebhIQEYmPNHoXPPvuMu+66i1dffZUpU6bw9ddfO71AEXepFOLPO4Na4uNl4cvfjvHxT38WvIEajEVESgSHw42fnx/nzp0D4Ntvv+X2228HICIiIueIjkhp0apGBM/e2RCAV7/axS9/FnIXcTUYi4i4ncPh5vrrr2fcuHG8/PLLbNy4kTvvvBOAPXv2UK1aNacXKOJuwzrUpEezqly0GTwyK77gwX/ZDcaVY9VgLCLiJg6Hm8mTJ+Pj48PChQt59913iY6OBuDrr7+ma9euTi9QxN0sFguv9WlC/chgTqRkMHr2Fi4WcMuGKxqMv/p78RUrIiJYjAK7JEsfR26ZLnKp/SdT6TH5J1IzLvLgjbX5R7eGBW/wx7cw6x7AgLv+C63vK5Y6RURKI0f+fjt85CY+Pp5t27bl/LxkyRJ69erFP/7xDzIzMx2vVsRD1K4UzL//1hSAqT/u5+tthfTT1OsMtz5vPv/qKTi43sUViogIFCHcPPTQQ+zZsweA/fv3079/fwIDA1mwYAFPPaX5HlK6dW0cxUM31gbg7wt/Y9/J1II3uP7xSxqMh6rBWESkGDgcbvbs2UPz5s0BWLBgATfeeCOzZ89m+vTpfPbZZ86uT6TE+XuXBrSvHUFqxkUe/rSQAX9qMBYRKXYOhxvDMLDZzGbKb7/9lm7dugEQExPDqVOnnFudSAnk4+3F2wPMAX9/nEhl/KJtBQ/4U4OxiEixcjjctG7dmldeeYVPP/2U1atX51wKnpCQQGRkpNMLFCmJLh3w98WvR5n+858FbxBRO3eCcfwn8MtHxVGmiEiZ5HC4mTRpEvHx8Tz66KP83//9H3Xr1gVg4cKFdOjQwekFipRUrWpE8H9ZA/7+ucyOAX9qMBYRKRZOuxQ8PT0db29vfH19nfF2LqNLwcWZDMNgzNytLP31KJGh/nw5+gYqhfgXtAEsuBd2fg7BkfDgagiNKq5yRUQ8lksvBc+2efNmZs6cycyZM4mPjycgIKDEBxsRZ7NYLEy8uwn1KgdzPDmD0XPiCx7wZ7FAzylqMBYRcSGHw82JEye4+eabadOmDY899hiPPfYYrVu35tZbb+XkyZOuqFGkRAvy9+G9Ia0I9vdh/f7TvLFid8EbqMFYRMSlHA43o0ePJjU1lR07dnD69GlOnz7N9u3bSU5O5rHHHnNFjSIlXp1Kwbxxjzng7/0f97N8eyHzbNRgLCLiMg6Hm+XLl/POO+/QsGHu6PnY2FimTJnC119/7dTiRDzJHU2ieDBrwN+TC+wY8KcGYxERl3A43Nhstnx7a3x9fXPm34iUVU91aUC7WuaAv5EzN3Mus4ABf5A1wbiXJhiLiDiRw+HmlltuYcyYMRw9ejRn2ZEjR3j88ce59dZbHS5gypQp1KxZk4CAANq1a8fGjRsLXP/s2bOMGjWKqKgo/P39qV+/Pl999ZXDnyviCj7eXrw9sAWVQ/zZczyV8Z8VMuAvp8G4kRqMRUScxOFwM3nyZJKTk6lZsyZ16tShTp061KpVi+TkZN566y2H3mvevHmMGzeOCRMmEB8fT7NmzejSpQsnTpzId/3MzExuu+02/vzzTxYuXMju3buZNm0a0dHRju6GiMtUDgnIGfC39NejfFLYgD//YOg/EwLKZzUYP2leMi4iIkVSpDk3hmHw7bff8vvvvwPQsGFDOnfu7PCHt2vXjjZt2jB58mTAPOUVExPD6NGjGT9+/BXrv/fee7zxxhv8/vvvRb7sXHNupLh8tDaBl77ciY+XhXkPtadVjYiCN9j7Lcz6Gxg2uPM/0GZE8RQqIuIBHPn77bQhfr///js9evTIuWN4YTIzMwkMDGThwoX06tUrZ/mwYcM4e/YsS5YsuWKbbt26ERERQWBgIEuWLKFSpUoMHDiQp59+Gm9v73w/JyMjg4yM3MP8ycnJxMTEKNyIyxmGweg5W/jyt2P2DfgDWPtf+PYF8PKFe7+E6u2LpVYRkZKuWIb4XS4jI4N9+/bZvf6pU6ewWq1X3I8qMjKSxMTEfLfZv38/CxcuxGq18tVXX/Hcc8/x5ptv8sorr1z1cyZOnEhYWFjOIyYmxu4aRa6FxWLh9T5N7R/wB9BxbG6D8bwhkHy04PVFROQKTgs3xcFms1G5cmWmTp1Kq1at6NevH//3f//He++9d9VtnnnmGZKSknIehw4dKsaKpawL8vfh3cGtCPLzNgf8fVPIgL9LG4zTTphXUKnBWETEIW4LNxUrVsTb25vjx4/nWX78+HGqVKmS7zZRUVHUr18/zymohg0bkpiYSGZmZr7b+Pv7ExoamuchUpzqVg7mjb81A+D91ftZvj3/I5M51GAsInJN3BZu/Pz8aNWqFatWrcpZZrPZWLVqFXFxcflu07FjR/bu3Ztnns6ePXuIiorCz8/P5TWLFFW3JlE8cEMtAJ5c8Cv7CxvwF1Eb7vkQLF4QP0MTjEVEHGB3uAkPDyciIuKqjxtuuMHhDx83bhzTpk3jk08+YdeuXYwcOZK0tDSGDx8OwNChQ3nmmWdy1h85ciSnT59mzJgx7Nmzh2XLlvHqq68yatQohz9bpLg93fU62uYM+IsvfMBf3UsmGH/9tCYYi4jYycfeFSdNmuT0D+/Xrx8nT57k+eefJzExkebNm7N8+fKcJuODBw/i5ZWbv2JiYlixYgWPP/44TZs2JTo6mjFjxvD00087vTYRZ/Px9mLywBbc9dZadh9P4ZlF25jUrzkWi+XqG3UcC8d+hR2LzQbjh1ZDaNViq1lExBM57VJwT6E5N+Jum/48zYCp67loM3ixRyOGdahZ8AaZafDBbXBiB0S3huFfgU8hl5SLiJQybrkUXETs06ZmBM90M288+8qynWw+cKbgDfyCchuMj/wCy55Qg7GISAEUbkTc4L6ONbmraRQXrAajZsVzKrWQy70vbTDe8qkajEVECqBwI+IG2QP+6lYOJjE5ndGztxQ+4E8NxiIidlG4EXGTIH8f3ssa8Ldu/1/8+xs7bl3ScSw06q0JxiIiBVC4EXGjSwf8vbd6Hyt2FDLg7/IJxvOGaIKxiMhlHL5aymq1Mn36dFatWsWJEyfyDNQD+O6775xaoLPpaikpiV75cicfrE0gxN+HpaOvp1bFoII3OJ0AUztB+lloMQR6vG0GHxGRUsqlV0uNGTOGMWPGYLVaady4Mc2aNcvzEBHHPX3HdbStGUFKxkUe/nRz4QP+ImrBPR9d0mD8YfEUKiLiARw+clOxYkVmzJhBt27dXFWTS+nIjZRUJ5LTufPttZxMyaBX86r8t7ABfwBrJ8G3E8DLB4Z9CTXyv3WJiIinc+mRGz8/P+rWrVvk4kQkf5VDA5gysCXeXhY+33qUT9cfKHyjjmOyGowvmncQV4OxiIjj4eaJJ57gf//7H2VssLFIsWhbK4Jn7rgOgJe/3En8wUIG/KnBWETkCg6flurduzfff/89ERERNGrUCF9f3zyvL1q0yKkFOptOS0lJZxgGj87ZwrLfjlElNIAvH7ueisGF3G5BDcYiUsq59LRU+fLl6d27NzfddBMVK1YkLCwsz0NErk32gL86lYJITE7nsTl2DPhTg7GISA7dOFOkhNp7IoWek38iLdPKyE51eLrrdYVvpAZjESmldONMkVKgbuUQ/nWPOV7h3R/28U1hA/4gq8H47twG46QjLq5SRKTkKdKRm4ULFzJ//nwOHjxIZmZmntfi4+OdVpwr6MiNeJqXv9zJh44M+MtMgw9vh+PbIboV3PsV+AYUT7EiIi7i0iM3b731FsOHDycyMpItW7bQtm1bKlSowP79+7njjjuKXLSI5G/8HdfRpmY4KRkXGTnTjgF/fkHQbyYElIcjm+GrJ6BsnX0WkTLO4XDzzjvvMHXqVN5++238/Px46qmnWLlyJY899hhJSUmuqFGkTPP19mLKwJZUCvHn98QU/m/x9sJHMeRpMJ6pBmMRKVMcDjcHDx6kQ4cOAJQrV46UlBQAhgwZwpw5c5xbnYgA5oC/yQNa4O1lYfGWI8y0Z8Bf3Vvh1gnm86+fhgPrXFukiEgJ4XC4qVKlCqdPnwagevXqrF+/HoCEhAQN9hNxoXa1KzA+64qpl+wZ8AdqMBaRMsnhcHPLLbewdOlSAIYPH87jjz/ObbfdRr9+/ejdu7fTCxSRXPffUItuTapwwWowalY8f6UWMo3YYoGekyGysTnBeP4QuJBePMWKiLiJw1dL2Ww2bDYbPj4+AMydO5eff/6ZevXq8dBDD+Hn5+eSQp1FV0uJp0vNuEiPyWvZfzKNjnUrMOO+dnh7FTKNOM8E48HQY7ImGIuIR3Hk77eG+Il4oD+Op9Bzyk+cy7TySKc6PGXPgL9938HMPmDYoNu/oe0Dri9URMRJXD7Eb82aNQwePJi4uDiOHDHP4X/66aesXbu2KG8nIg6qFxnC632aAvCOvQP+6twCnV8wny8fDwd+dl2BIiJu5HC4+eyzz+jSpQvlypVjy5YtZGSY5/yTkpJ49dVXnV6giOSve7OqDO9YE4An5v/Kn6fSCt+ow2NqMBaRUs/hcPPKK6/w3nvvMW3atDx3BO/YsWOJn04sUtr8o1tDWtcwB/w9PHMz5zOtBW+Qp8H4pBqMRaRUcjjc7N69mxtvvPGK5WFhYZw9e9YZNYmInXy9vZgyqCUVg7MH/G0rfCSDJhiLSClXpDk3e/fuvWL52rVrqV27tlOKEhH7RYYGMHmgOeBv0ZYjzNxwsPCNImrB3z7OnWC86QPXFyoiUkwcDjcPPPAAY8aMYcOGDVgsFo4ePcqsWbN48sknGTlypCtqFJFCtK9dgae7NgDgpS92sMWeAX9qMBaRUsrhS8ENw+DVV19l4sSJnDt3DgB/f3+efPJJXn75ZZcU6Uy6FFxKK8MweGRWPF9vTyQqLIAvR19PhWD/wjaCz0bA9s8gqBI8uBrCoounYBERBxTLnJvMzEz27t1LamoqsbGxBAcHF6nY4qZwI6VZSvoFek75if0n07i+bkU+ua9t4QP+MtPgw9vh+Hao2hKGfw2+AcVTsIiInVw+5wbAz8+P2NhY2rZt6zHBRqS0Cwnw5b3BrSjn683avaf4z8rdhW/kFwT9Z0G5cDgaD8vUYCwins3uIzf33XefXW/40UcfXVNBrqYjN1IWLP31KI/N2QLAtKGtuS02svCNNMFYREowlxy5mT59Ot9//z1nz57lzJkzV32IiPv1aFaVezvUBGDc/K32DfhTg7GIlBJ2H7kZNWoUc+bMoUaNGgwfPpzBgwcTERHh6vqcTkdupKzIvGhjwLT1bD5whuuqhLD4kY6U8/MueCM1GItICeWSIzdTpkzh2LFjPPXUU3zxxRfExMTQt29fVqxYUfjQMBEpdn4+XkwZ2JKKwX7mgL/P7RjwZ7FAj7dzJxjPG6wJxiLicRxqKPb392fAgAGsXLmSnTt30qhRIx555BFq1qxJamqqq2oUkSKqEhbA2wNamgP+4o8wy54Bf2owFhEPV+Srpby8vLBYLBiGgdVayP1sRMRt4upU4Kku2QP+drL10NnCNwqvCfdkTTDeqgnGIuJZHAo3GRkZzJkzh9tuu4369euzbds2Jk+ezMGDB3U5uEgJ9uCNtenaqAqZVhuPzNzM6bTMwjeqczN0ftF8vnw8/PmTa4sUEXESu8PNI488QlRUFK+99hp33XUXhw4dYsGCBXTr1g0vryIfABKRYmCxWHjjb02pXTGIo0npPDZnC1abHaeaOoyGxveA7SIsGAZJR1xfrIjINbL7aikvLy+qV69OixYtsFiuPvF00aJFTivOFXS1lJRle46n0HPyT5y/YOXRm+vyZNbpqgJlnsuaYLxNE4xFxG1ccrXU0KFDufnmmylfvjxhYWFXfYhIyVU/MoTX+jQBYPL3e/l25/HCN/ILhP4z1WAsIh6jyPeW8lQ6ciMCLyzdwfSf/yQkwIcvR19PjQpBhW+073uYebcmGIuIWxTLvaVExHP9o1tDWlYvT0r6RR6eGc/5TDuueFSDsYh4CIUbkTLIz8eLdwa1omKwH7uOJfPs59vtG8Z5RYPxYdcXKyLiIIUbkTKqSlgAbw1ogZcFPos/zOyNdgz4y5lg3CRrgvEQTTAWkRJH4UakDOtQpyJPdb0OgBeX7uRXewb8XdFgPE4NxiJSoijciJRxD91Ymy6NIsm02hhp74C/PBOMZ2mCsYiUKAo3ImWcOeCvGbWyBvyNmWvngD81GItICaVwIyKEBvjy3uBWlPP1Zs0fp5j07R77NlSDsYiUQAo3IgJAgyq5A/7e/m4vq3bZMeBPDcYiUgKViHAzZcoUatasSUBAAO3atWPjxo12bTd37lwsFgu9evVybYEiZUTP5tEMi6sBwOPztnLwr3OFb6QGYxEpYdwebubNm8e4ceOYMGEC8fHxNGvWjC5dunDixIkCt/vzzz958sknueGGG4qpUpGy4f/ujKVl9fIkp1/k4ZmbSb9gx4C/yxuMN05zeZ0iIlfj9nDzn//8hwceeIDhw4cTGxvLe++9R2BgIB999NFVt7FarQwaNIgXX3yR2rVrF2O1IqWfn48XUwa1pEKQHzsdGfBX52a47SXz+Ypn1GAsIm7j1nCTmZnJ5s2b6dy5c84yLy8vOnfuzLp166663UsvvUTlypUZMWJEoZ+RkZFBcnJynoeIFCwqrBxvZw34W7j5MHM2HrJvw7hHocnfzAbj+UPVYCwibuHWcHPq1CmsViuRkZF5lkdGRpKYmJjvNmvXruXDDz9k2jT7DntPnDgxz13LY2JirrlukbKgQ92K/L2LOeDvhaU77BvwZ7FA97fMBuNzp9RgLCJu4fbTUo5ISUlhyJAhTJs2jYoVK9q1zTPPPENSUlLO49AhO/8LVER4+Kba3B5rDvh7ZFa8fQP+1GAsIm7m1nBTsWJFvL29OX487yWnx48fp0qVKlesv2/fPv7880+6d++Oj48PPj4+zJgxg6VLl+Lj48O+ffuu2Mbf35/Q0NA8DxGxj8Vi4d99zQF/R86et3/AnxqMRcSN3Bpu/Pz8aNWqFatWrcpZZrPZWLVqFXFxcVesf91117Ft2za2bt2a8+jRowc333wzW7du1SknERcIDfDl3cEtCfD1Ys0fp/ifvQP+1GAsIm7i9tNS48aNY9q0aXzyySfs2rWLkSNHkpaWxvDhwwEYOnQozzzzDAABAQE0btw4z6N8+fKEhITQuHFj/Pz83LkrIqXWdVVCee3upgC89d1evvvdjgF/oAZjEXELt4ebfv368e9//5vnn3+e5s2bs3XrVpYvX57TZHzw4EGOHTvm5ipFpFeLaIZmDfgbO9fOAX/ZDcZVshuMB8OF8y6uVETKOoth1wCL0iM5OZmwsDCSkpLUfyPioMyLNvpNXceWg2eJjQpl0SMdCPD1LnzDMwdgaic4fxqaDYRe75jBR0TETo78/Xb7kRsR8Rx+Pl68c8mAv+fsHfAXXgP+ltVg/OtsNRiLiEsp3IiIQy4d8Ldg82HmbrJzvELtTnDby+ZzNRiLiAsp3IiIwzrUrciTXRoAMGHJDn47fNa+DeNGqcFYRFxO4UZEimTkTXW4LWvA38iZ8ZyxZ8CfGoxFpBgo3IhIkVgsFt7s24yaFQI5cvY8Y+dttW/An18g9JsF5SLg6Bb4UhOMRcS5FG5EpMjMAX+tCPD1YvWek7y16g/7NryiwXiqawsVkTJF4UZErknDqFAm3t0EgLe++4Pvd5+wb8NLG4yXPwN/rnVNgSJS5ijciMg1692iGkPa18AwzAF/h07bMeAPshqM+4JhhfnD1GAsIk6hcCMiTvHsXQ1pHlOepPMXGDlrM+kXrIVvZLFA9/+pwVhEnErhRkScwt/Hm3cGtSQiyI/tR5KZsGSHfRuqwVhEnEzhRkScpmr53AF/8345xLxNB+3bUA3GIuJECjci4lQd61bkidvNAX/PLdnBtsNJ9m2oBmMRcRKFGxFxupE31aFzw0gyL9oYOWszZ8/ZMeAPrmwwPmvnrR1ERC6hcCMiTuflZQ74q1EhkMNnzAF/NnsG/KnBWEScQOFGRFwirJwv72UN+Pth90ne+s7OAX+XNhgf2wpfPq4GYxFxiMKNiLhMw6hQXu1tDvj736o/+MHeAX/hNeBv08HiDb/OUYOxiDhE4UZEXOrultUY3L46hgFjHBnwV/smuF0NxiLiOIUbEXG55+6KpVnWgL9HZsXbN+APoP0jajAWEYcp3IiIy/n7ePNu1oC/bUeSeGGpnQP+1GAsIkWgcCMixaJq+XK81d8c8Dd30yHmb7LzKIwajEXEQQo3IlJsrq+XO+Dv2SXb2X7EzgF/lzcYb3jfdUWKiMdTuBGRYmUO+KtM5kUbD890YMDfpQ3GK/4BCWtcV6SIeDSFGxEpVuaAv+Y5A/4et3fAH+RtMF5wrxqMRSRfCjciUuzCyvny7iBzwN/3u0/y9nd77dswp8G4qRqMReSqFG5ExC1iq4byz17mgL9Jq/bYP+DPLxD6q8FYRK5O4UZE3KZPq2oMamcO+Bs7z4EBf+Wrq8FYRK5K4UZE3Or57uaAv7PnHBzwd3mD8b4fzCbjbQvNf9rsfB8RKXUshlG2jucmJycTFhZGUlISoaGh7i5HRIAjZ89z11trOHPuAgPaxjDx7qb2bWgYsOhB2DYfLF5g2HJfC60KXV+H2B6uKVpEipUjf7915EZE3C66fDneGtACiwXmbDzE/F/svArKYoF6t5vPLw02AMnHYP5Q2LnUucWKSImncCMiJcIN9SrxxG31AXjuczsH/Nms8O3zV3kx66D08vE6RSVSxijciEiJ8UinunRuWJmMizZGztpM0rkLBW9w4GdIPlrACgYkH4H9q51ap4iUbAo3IlJiZA/4qx4RyKHT5xk7b0vBA/5Sj9v3xrP7wqe9Ye0kOLpFR3JESjmFGxEpUcLK+fLu4Jb4+5gD/iZ/X8CAv+BI+97UdgH2fQffToCpneBftc0BgBunwam9mpMjUsroaikRKZEWbj7Mkwt+xWKB6cPbclP9SleuZLPCpMZm8zD5/V+ZxbxqauB8+HONeXrqz7WQmZJ3tdBoqHWTeXl5rZsgNMoVuyQi18CRv98KNyJSYv1j8TZmbzhI+UBfvhx9PdXCA69caedS86ooIG/AsZj/6Dsj7+Xg1ovmqamEH8ywc2gDWC+7eWfF+rlhp+b1UC7ciXslIkWhcFMAhRsRz5Fx0Urf99bx6+EkmlYLY/5DcQT4el+54s6lsPzpvM3FodHQ9bXC59xcOA8H18P+HyBhNRzdSp6QZPGCqOa5R3Wqtwffcte+cyLiEIWbAijciHiWvAP+qjPx7ib5r2izmldPpR43e3FqdACvfIJQYc6fMU9d7V9thp1Te/K+7u0PMW2zwk4nqNoCvH0c/xwRcYjCTQEUbkQ8z497TjLs440YBrxxT1P+1jqm+D48+Whu0Nm/GlIuu/TcP9Q8dZV9GqvSdeZwQRFxKoWbAijciHimt1f9wZsr9+Dv48WiRzrQqGpY8RdhGPDX3txTWAlrIP1s3nWCI6HWjblhp3z14q9TpBRSuCmAwo2IZ7LZDB6Y8Qurfj9BTEQ5vnz0BsICfd1clBUSf8s9snNgHVw8n3ed8FpmyKndCWreCEEV3FKqiKdTuCmAwo2I50o6d4Huk9dy8PQ5brmuMh8MbY2XVwk6BXQxAw5tzD2FdWQzGJcNDKzSJOuoTieoHgf+wW4pVcTTKNwUQOFGxLPtOJrE3e/8TMZFG0/cVp/Rt9Zzd0lXl55sNjknrDZPZZ3Ymfd1L1+o1jo37ES3Ah8/d1QqUuIp3BRA4UbE8y345RB/X/gbFgt8MrwtN+Y34K8kSj0BCT/m9uycPZj3dd8g8yqv7MvOIxuDlwbJi4DCTYEUbkRKh2cWbWPOxoOEB/ryxdUG/JV0pxNyT2El/AjnTuV9PbAC1LwhN+xE1NaVWFJmKdwUQOFGpHRIv2Cl7/vr+O1wEs2qhTHnwfb8eiiJEynpVA4JoG2tCLxLUj9OYWw2OLEjtzn5z5/gQlredcKqQ+0bzfk6tW6EEDvvrSVSCijcFEDhRqT0OHzmHHe9vZaz5y4Q6OfNuczc5t2osAAmdI+la2MPvU+U9YLZkJwddg5tNG8AeqlKDXOP6tTsCAFuuDxepJgo3BRA4UakdHnzm928/d2Vdw7PPmbz7uCWnhtwLpWZZl5qnn1PrMRt5L1NhDdEt8ydr1OtLfgGuKtaEadTuCmAwo1I6WG1GVz/+nccS0rP93ULUCUsgLVP3+JZp6jsce602aeT3bNzel/e130CzPtgZYedqOZFux2FSAnhyN9v3RBFRDzWxoTTVw02YB7XOJaUzsaE08TVKWXD8wIjoFEv8wFw9tAlzcmrzXts7f/BfKzCPGVV8wbzkvNaN0HFempOllJL4UZEPNaJlKsHmzzrJdu3nkcrHwMtBpsPw4CTu3PDzp9rIT0Jfv/SfACEROUe1al1E4RFu7d+EScqEQMUpkyZQs2aNQkICKBdu3Zs3LjxqutOmzaNG264gfDwcMLDw+ncuXOB64tI6VU5xL6ekte+/p23Vv3BodPnXFxRCWGxQOXroN1DMGA2PLUf7v8ObnnOvMrK2x9SjsFvc+HzkfDfWHi7FSx7AnYuNU95iXgwt/fczJs3j6FDh/Lee+/Rrl07Jk2axIIFC9i9ezeVK1e+Yv1BgwbRsWNHOnToQEBAAK+//jqLFy9mx44dREcX/l8e6rkRKT2ye24Sk9Kx9//I2taMoHfLaLo1iSKsnJvvTeUuF87DoQ25p7CObgHDdskKFohqlntUp3oc+HngHCEpVTyqobhdu3a0adOGyZMnA2Cz2YiJiWH06NGMHz++0O2tVivh4eFMnjyZoUOHXvF6RkYGGRkZOT8nJycTExOjcCNSSizffoyRM+OBPNcO5Vwt9Z9+zbHaDBZvOczP+/4i+//x/Hy86NywMr1bVOOm+pXw8ykRB7Ld4/xZOPBTVo/Oaji1O+/r3n7m1Ve1O5mBp2pL8FZXgxQvjwk3mZmZBAYGsnDhQnr16pWzfNiwYZw9e5YlS5YU+h4pKSlUrlyZBQsWcNddd13x+gsvvMCLL754xXKFG5HSY/n2Y7z4xc48zcX5zbk5lnSeJVuPsjj+CLuPp+QsDw/0pUezqvRuWY1m1cKwlPVG2+Rjea/ESj6c93W/EHOuTnbPTuVYNSeLy3lMuDl69CjR0dH8/PPPxMXF5Sx/6qmnWL16NRs2bCj0PR555BFWrFjBjh07CAi48vy7jtyIlA1Wm8HGhNN2TSg2DIOdx5JZFH+EJVuPcio19/8jalcMoneLaHq1iCYmQqdiMAw4vT/3flgJP8L5M3nXCapk9vJk3wA0vIY7KpVSrsxcCv7aa68xd+5cfvjhh3yDDYC/vz/+/v7FXJmIFDdvL4vdl3tbLBYaVQ2jUdUwnrnjOtbuPcXiLUdYsSOR/afSeHPlHt5cuUf9OWAekalQx3y0GWHeJiLxt9yjOgfXQdpJ2P6Z+QAIr5n3SqygivZ9ls1q3kU99TgER5o3EdVsHikCjz0t9e9//5tXXnmFb7/9ltatW9v9mWooFpGrSc24yPLtiSyKP8y6/Xn7c25rGEnvFtHc1KASvt5luD/nchcz4PAvuWHnyC9gu5h3ncjGuWGnRgfwD7nyfXYuheVPQ/LR3GWhVaHr6xDbw7X7IB7BY05LgdlQ3LZtW95++23AbCiuXr06jz766FUbiv/1r3/xz3/+kxUrVtC+fXuHPk/hRkTscSzpPJ9vOcriLYfZczw1Z3lEkB/dm0apP+dqMlLMoy/ZV2Id3573dS8fiG6de1SnWhvYsxzmD4UrrnnL+nfbd4YCjnhWuJk3bx7Dhg3j/fffp23btkyaNIn58+fz+++/ExkZydChQ4mOjmbixIkAvP766zz//PPMnj2bjh075rxPcHAwwcHBhX6ewo2IOMIwDHYcTWbxlnz6cyoFcXdWf061cPXn5Cv1JPz5oxl29v8AZw/kfd2nnHkZujUj383BYh7BGbtNp6jKOI8KNwCTJ0/mjTfeIDExkebNm/PWW2/Rrl07ADp16kTNmjWZPn06ADVr1uTAgQNXvMeECRN44YUXCv0shRsRKaqLVhtr955iUfwRvtmZSPqF3NkwbWtFcHeLaLo1jSI0oIz259jjzJ+5R3USfjT7dewx7EuodYNLS5OSzePCTXFSuBERZ0hJv8Dy7Yks3nJE/TlFZRjw0yT49oXC1+3zITS5x9UVSQlWZq6WEhFxl5AAX/7WOoa/tY7h6Flzfs6i+MP8cSKVZduOsWzbMSoE+dG9WVV6t4imqfpzrmSxmP039ji6Ba67C3ztu+WGlG06ciMi4iTZ/TmL4o+w9NcjnErNzHlN/TlXYbPCpMbm4MDCbqIRWBHaPgBt7rf/8nIpNXRaqgAKNyJSHC5abazZe4rFV+nP6dMymjuaqD8HMC8Dn599+5x8bqLRfAAkrIGkQ+bPPgHQrD+0HwWV6hdnpeJGCjcFULgRkeKW3Z+zKP4I6xNy+3P8fbzoHBvJ3S2iubF+Ge/PyXfOTTR0fc28DNx6EXYtgZ8nw9H43HXqd4W4R6Hm9boFRCmncFMAhRsRcaejZ8/z+dYjLI4/wh8ncufnqD8H+yYUG4a5zropsPsrco70RDWDuNHQqBd462hYaaRwUwCFGxEpCQrqz6lTKYi7W1ajZ/Oq6s8pyKm9sP4d2DobLp43l4VWg3YPQathEBDm3vrEqRRuCqBwIyIlzUWrjTV/nGLRliN8syORjIu5/TntakVwt/pzCpb2F/zyEWycCmknzGV+IdByKLR/GMpXd2994hQKNwVQuBGRkiwl/QJfb09k8VX6c/q0jOaGemW8P+dqLqTDtgWwbjKc/N1cZvGG2J7Q4VGIbuXe+uSaKNwUQOFGRDxFdn/Oovgj7M2nP+fultE0iS6j/TkFMQzYuwrWvW3e8iFb9Q5myKl/B3gpHHoahZsCKNyIiKcxDIPtR5JZtOUwX/x6NN/+nF4tookuX86NVZZQidvM5uNtC3LvVh5RB+IegWYDwU89TZ5C4aYACjci4skuWG2svUp/TvvaEdzdohp3NKlCiPpz8ko+Chveh80fQ3qSuaxcBLQZAW0egJBI99YnhVK4KYDCjYiUFsnpF1i+LZFFWw6zfv/pnOX+Pl7cFhvJ3erPuVJGKmyZaV5llX2Hcm8/aNrXnJdTuaF765OrUrgpgMKNiJRGR86e5/MtR1i8Jf/+nD4tq9E4OlT9OdlsVtj1hdl8fHhT7vK6nc2QU7uThgKWMAo3BVC4EZHSzDAMth1JYlH8Eb749Sh/peX259StHEzvrPtbqT/nEoc2ws9vw+9fgpF1mi+yCcSNgsZ9wMfPvfUJoHBTIIUbESkrLlhtrPnjJIvij7By5/Gc/hyLJWt+jvpz8jq9H9a/a562unDOXBYSBW0fhNbDoVy4e+sr4xRuCqBwIyJlUUH9Obc3qsLdLaK5oV5FfNSfA+dOm43HG6ZCaqK5zDcIWgyG9iMhopZ76yujFG4KoHAjImXd4TPnWLL1KIviD7PvZFrO8orBWfNzWqg/B4CLmbB9oXmzzhM7zGUWL7juLugwGmLaure+MkbhpgAKNyIiJvXn2MkwYP/3ZsjZtyp3ebW25lDA6+668gaf4nQKNwVQuBERudIFq40f95xk0RazPyfzkv6c9rUq0LtlNHc0Vn8Ox3dmDQWcD9asMBheE9o/As0HgX+wW8srzRRuCqBwIyJSsOT0C3y97RiL4o+wISG3PyfA14vbYtWfA0DKcfNGnb98COfPmMsCypuNx20fgtAot5ZXGincFEDhRkTEfgX15/RoFs3dLaNpVLUM9+dkpsHW2eZQwNP7zWVevtDkHnNeTpXG7q2vFFG4KYDCjYiI4wzD4LfDSSzecoSlvx7l9CX9OfUqB9O7ZTS9mkdTtaz259issPtrcyjgwXW5y2t3grjRUPdWDQW8Rgo3BVC4ERG5NurPKcThzeYdyXcuyR0KWKmhORSwaV/w8XdvfR5K4aYACjciIs6TdD6rP2fLETZe1p9ze2wVereM5oa6ZbQ/58wB2PAexM+AzKxbYgRVNocCthkBgRHurc/DKNwUQOFGRMQ1Dp0+x5KtR1i05Qj78/Tn+NOjWdVC+3OsNoONCac5kZJO5ZAA2taKwNurFJzKOX8W4j8x70qefMRc5lMOmg80j+ZUqOPW8jyFwk0BFG5ERFwruz9nUfxhvvjt2BX9OXe3rEavFlWJCsvtz1m+/RgvfrGTY0npOcuiwgKY0D2Wro1LyZVH1guwY7F5H6vE37IWWqBBN3NeTvU49eUUQOGmAAo3IiLF54LVxurdJ1m85Qgrd+Xtz4mrXYHeLaLx8fZi3LytXP7HKPvP/LuDW5aegAPmUMA/15hDAf9Ykbu8aksz5DTsCd4+7quvhFK4KYDCjYiIe1ytP6cgFqBKWABrn76ldJyiutzJ3eZQwF/ngjXDXBZWHdo/DC2Hgn+Ie+srQRRuCqBwIyLifodOn+PzLUeYveEgx5LTC13/2W4N6VC3IhFBfpQP9CXAt5Td7iD1JGz6ADZNg3N/mcv8Q6HVMGj3MIRVc299JYDCTQEUbkRESo4lW44wZt5Wh7cr5+udE3TCA/0ID/IjPNCX8oF+RAT6Eh7kl/U8a50gP4L8vEv+sMEL582jOOumwF9/mMu8fKBRb3MoYNXmbi3PnRRuCqBwIyJScqzb9xcDpq0vdL2q5QPIvGjjzLkLWG1F+7Pl5+11SRgy/1k+0I+Iy56XD/QjPCsYhQT44OWO02E2G/zxjTkU8M81uctr3mCGnHq3g1fZurxe4aYACjciIiWH1WZw/evfkZiUfkVDMVzZc2MYBsnpFzl7LpMz5y5wJi2TM5c9P3vuAqcvfX4uM6eR2VFeFrLCjh1hKOt5+XK+zp3rc3SrGXK2LwLDai6rWN+8WWez/uBbNqZCK9wUQOFGRKRkWb79GCNnxgPkCTjOulrKMAzOX7DaH4bSMjl7LpO0TGuRPzM0wOeSU2O5YSg86xRZ+GXP7eojSjpsDgXc/AlkJJvLAitCm/vNR3ClItfrCRRuCqBwIyJS8pTEOTcZF62cPXfBDENpFzh7LpPTWQHoTNolz89lZgWlCySdv1Dkzwv0884JOhFBfnmOGF0ahCr4ZFA1YQFhv36AV/Jhc2Nvf/MoTtyjUKm+k/4NOM6VgxgVbgqgcCMiUjKVhgnFF602ks5f4My5rDCUlhuATp/L5GxaVhjKOnqUfXqtKH1E3ljp7rOJB3y/opGxN2f57yEd2BoziNQqcYQH+RMe5JvTXB3uwj4iVwdUhZsCKNyIiEhJYrMZpGRcvDIMXfI8++jRpcEot4/IoI1lNw/4LKOzVzxeFvPP+jZbTT642I1ltvZcJHcooJeFnCNEl15pdsVVZ5c8L6yPKPvUoisHMSrcFEDhRkREPF12H9HlYch28g9q75tBw+Nf4meYQwFPelVkvlc3ZmTezPHMot+RPLuPKDwwbxgKK+fDB2sSSE6/mO92zhrEqHBTAIUbEREp9dL+gl8+go1TIe2EucwvmIvNB3OmyQj+8q2S98hQVs/Qpc+zjyRdLbQ4as4D7YmrU6HI2yvcFEDhRkREyowL6bBtgXkp+cnfzWUWL4jtCXGjoVqrQt8it48on8vvz2Xy68GzrLfjdhr/69+cns2ji7wrjvz91p25RERESivfAGg5BFoMhr2rYN3bsP8H8+7kOxabdyKPexQa3AFe+V+K7uPtRYVgfyoE539Ka92+v1hvxyDGyiEB17InDlG4ERERKe0sFqjX2XwkbjNv77BtIRxcZz4iaptDAZsPAr9Ah966ba0IosICCh3E2LZWhFN2xR5la3aziIhIWVelCfR+D8b+Btc/DgFhcHo/fPUk/DcWVr0MKcftfjtvLwsTuscCuVdHZcv+eUL32GK9rF89NyIiImVZRipsnWUezTl7wFzm7QdN+kLcKIiMtettNOfGjRRuRERE8mGzwu9fws+T4fDG3OV1boUOj0Ltm83TWwXQhGI3UbgREREpxKGN8PPbZtgxsoYFRjY2j+Q0vgd8/Iq9JIWbAijciIiI2On0flj/HmyZCRfSzGXBVaDdg9BqOARe1iRss8KBnyH1OARHQo0OV70Ky1EKNwVQuBEREXHQ+TPwy8ew4X1ITTSX+Qaal5i3H2lebbVzKSx/GpKP5m4XWhW6vg6xPa65BIWbAijciIiIFNHFTNj+mTkU8Pj2rIUWiG4FR37JZ4Osfpu+M6454Djy91uXgouIiIh9fPyg+QB4eC0M+RzqdgaMqwQbzNcAlo83T1kVkxIRbqZMmULNmjUJCAigXbt2bNy4scD1FyxYwHXXXUdAQABNmjThq6++KqZKRUREBIsF6twMgz+DHlMKWdmA5CNmL04xcXu4mTdvHuPGjWPChAnEx8fTrFkzunTpwokTJ/Jd/+eff2bAgAGMGDGCLVu20KtXL3r16sX27dvzXV9ERERcyNfO2yqk2j8Y8Fq5veemXbt2tGnThsmTJwNgs9mIiYlh9OjRjB8//or1+/XrR1paGl9++WXOsvbt29O8eXPee++9Qj9PPTciIiJOlLAGPrmr8PWGfQm1bijyx3hMz01mZiabN2+mc+fOOcu8vLzo3Lkz69aty3ebdevW5VkfoEuXLlddPyMjg+Tk5DwPERERcZIaHcyroq64+UI2C4RGm+sVE7eGm1OnTmG1WomMjMyzPDIyksTExHy3SUxMdGj9iRMnEhYWlvOIiYlxTvEiIiJizrHp+nrWD1e5u1TX15w278aukortk9zkmWeeISkpKedx6NAhd5ckIiJSusT2MC/3Dr3sHlKhVZ1yGbijfIr10y5TsWJFvL29OX48b5PR8ePHqVKlSr7bVKlSxaH1/f398ff3d07BIiIikr/YHnDdnS6bUOwItx658fPzo1WrVqxatSpnmc1mY9WqVcTFxeW7TVxcXJ71AVauXHnV9UVERKSYeHmbTcNN7jH/6YZgA24+cgMwbtw4hg0bRuvWrWnbti2TJk0iLS2N4cOHAzB06FCio6OZOHEiAGPGjOGmm27izTff5M4772Tu3Ln88ssvTJ061Z27ISIiIiWE28NNv379OHnyJM8//zyJiYk0b96c5cuX5zQNHzx4EC+v3ANMHTp0YPbs2Tz77LP84x//oF69enz++ec0btzYXbsgIiIiJYjb59wUN825ERER8TweM+dGRERExNkUbkRERKRUUbgRERGRUkXhRkREREoVhRsREREpVRRuREREpFRx+5yb4pZ95bvuDi4iIuI5sv9u2zPBpsyFm5SUFADdHVxERMQDpaSkEBYWVuA6ZW6In81m4+jRo4SEhGCxXH5r9muTnJxMTEwMhw4dKpUDAkv7/kHp30ftn+cr7fuo/fN8rtpHwzBISUmhatWqee5ckJ8yd+TGy8uLatWqufQzQkNDS+3/aKH07x+U/n3U/nm+0r6P2j/P54p9LOyITTY1FIuIiEiponAjIiIipYrCjRP5+/szYcIE/P393V2KS5T2/YPSv4/aP89X2vdR++f5SsI+lrmGYhERESnddORGREREShWFGxERESlVFG5ERESkVFG4ERERkVJF4cZBU6ZMoWbNmgQEBNCuXTs2btxY4PoLFizguuuuIyAggCZNmvDVV18VU6VF48j+TZ8+HYvFkucREBBQjNU65scff6R79+5UrVoVi8XC559/Xug2P/zwAy1btsTf35+6desyffp0l9dZVI7u3w8//HDF92exWEhMTCyegh00ceJE2rRpQ0hICJUrV6ZXr17s3r270O086XewKPvoSb+H7777Lk2bNs0Z7hYXF8fXX39d4Dae9P05un+e9N3l57XXXsNisTB27NgC13PHd6hw44B58+Yxbtw4JkyYQHx8PM2aNaNLly6cOHEi3/V//vlnBgwYwIgRI9iyZQu9evWiV69ebN++vZgrt4+j+wfmBMpjx47lPA4cOFCMFTsmLS2NZs2aMWXKFLvWT0hI4M477+Tmm29m69atjB07lvvvv58VK1a4uNKicXT/su3evTvPd1i5cmUXVXhtVq9ezahRo1i/fj0rV67kwoUL3H777aSlpV11G0/7HSzKPoLn/B5Wq1aN1157jc2bN/PLL79wyy230LNnT3bs2JHv+p72/Tm6f+A5393lNm3axPvvv0/Tpk0LXM9t36Ehdmvbtq0xatSonJ+tVqtRtWpVY+LEifmu37dvX+POO+/Ms6xdu3bGQw895NI6i8rR/fv444+NsLCwYqrOuQBj8eLFBa7z1FNPGY0aNcqzrF+/fkaXLl1cWJlz2LN/33//vQEYZ86cKZaanO3EiRMGYKxevfqq63ja7+Dl7NlHT/49NAzDCA8PNz744IN8X/P0788wCt4/T/3uUlJSjHr16hkrV640brrpJmPMmDFXXddd36GO3NgpMzOTzZs307lz55xlXl5edO7cmXXr1uW7zbp16/KsD9ClS5erru9ORdk/gNTUVGrUqEFMTEyh/4XiaTzp+7sWzZs3Jyoqittuu42ffvrJ3eXYLSkpCYCIiIirruPp36E9+wie+XtotVqZO3cuaWlpxMXF5buOJ39/9uwfeOZ3N2rUKO68884rvpv8uOs7VLix06lTp7BarURGRuZZHhkZedUehcTERIfWd6ei7F+DBg346KOPWLJkCTNnzsRms9GhQwcOHz5cHCW73NW+v+TkZM6fP++mqpwnKiqK9957j88++4zPPvuMmJgYOnXqRHx8vLtLK5TNZmPs2LF07NiRxo0bX3U9T/odvJy9++hpv4fbtm0jODgYf39/Hn74YRYvXkxsbGy+63ri9+fI/nnadwcwd+5c4uPjmThxol3ru+s7LHN3BRfniYuLy/NfJB06dKBhw4a8//77vPzyy26sTOzRoEEDGjRokPNzhw4d2LdvH//973/59NNP3VhZ4UaNGsX27dtZu3atu0txGXv30dN+Dxs0aMDWrVtJSkpi4cKFDBs2jNWrV181AHgaR/bP0767Q4cOMWbMGFauXFniG58VbuxUsWJFvL29OX78eJ7lx48fp0qVKvluU6VKFYfWd6ei7N/lfH19adGiBXv37nVFicXuat9faGgo5cqVc1NVrtW2bdsSHxgeffRRvvzyS3788UeqVatW4Lqe9Dt4KUf28XIl/ffQz8+PunXrAtCqVSs2bdrE//73P95///0r1vXE78+R/btcSf/uNm/ezIkTJ2jZsmXOMqvVyo8//sjkyZPJyMjA29s7zzbu+g51WspOfn5+tGrVilWrVuUss9lsrFq16qrnU+Pi4vKsD7By5coCz7+6S1H273JWq5Vt27YRFRXlqjKLlSd9f86ydevWEvv9GYbBo48+yuLFi/nuu++oVatWodt42ndYlH28nKf9HtpsNjIyMvJ9zdO+v/wUtH+XK+nf3a233sq2bdvYunVrzqN169YMGjSIrVu3XhFswI3foUvblUuZuXPnGv7+/sb06dONnTt3Gg8++KBRvnx5IzEx0TAMwxgyZIgxfvz4nPV/+uknw8fHx/j3v/9t7Nq1y5gwYYLh6+trbNu2zV27UCBH9+/FF180VqxYYezbt8/YvHmz0b9/fyMgIMDYsWOHu3ahQCkpKcaWLVuMLVu2GIDxn//8x9iyZYtx4MABwzAMY/z48caQIUNy1t+/f78RGBho/P3vfzd27dplTJkyxfD29jaWL1/url0okKP799///tf4/PPPjT/++MPYtm2bMWbMGMPLy8v49ttv3bULBRo5cqQRFhZm/PDDD8axY8dyHufOnctZx9N/B4uyj570ezh+/Hhj9erVRkJCgvHbb78Z48ePNywWi/HNN98YhuH535+j++dJ393VXH61VEn5DhVuHPT2228b1atXN/z8/Iy2bdsa69evz3ntpptuMoYNG5Zn/fnz5xv169c3/Pz8jEaNGhnLli0r5ood48j+jR07NmfdyMhIo1u3bkZ8fLwbqrZP9qXPlz+y92nYsGHGTTfddMU2zZs3N/z8/IzatWsbH3/8cbHXbS9H9+/111836tSpYwQEBBgRERFGp06djO+++849xdshv30D8nwnnv47WJR99KTfw/vuu8+oUaOG4efnZ1SqVMm49dZbc/7wG4bnf3+O7p8nfXdXc3m4KSnfocUwDMO1x4ZEREREio96bkRERKRUUbgRERGRUkXhRkREREoVhRsREREpVRRuREREpFRRuBEREZFSReFGREREShWFGxERESlVFG5EpMyzWCx8/vnn7i5DRJxE4UZE3Oree+/FYrFc8ejatau7SxMRD+Xj7gJERLp27crHH3+cZ5m/v7+bqhERT6cjNyLidv7+/lSpUiXPIzw8HDBPGb377rvccccdlCtXjtq1a7Nw4cI822/bto1bbrmFcuXKUaFCBR588EFSU1PzrPPRRx/RqFEj/P39iYqK4tFHH83z+qlTp+jduzeBgYHUq1ePpUuXunanRcRlFG5EpMR77rnn6NOnD7/++iuDBg2if//+7Nq1C4C0tDS6dOlCeHg4mzZtYsGCBXz77bd5wsu7777LqFGjePDBB9m2bRtLly6lbt26eT7jxRdfpG/fvvz2229069aNQYMGcfr06WLdTxFxEpffd1xEpADDhg0zvL29jaCgoDyPf/7zn4ZhGAZgPPzww3m2adeunTFy5EjDMAxj6tSpRnh4uJGamprz+rJlywwvLy8jMTHRMAzDqFq1qvF///d/V60BMJ599tmcn1NTUw3A+Prrr522nyJSfNRzIyJud/PNN/Puu+/mWRYREZHzPC4uLs9rcXFxbN26FYBdu3bRrFkzgoKCcl7v2LEjNpuN3bt3Y7FYOHr0KLfeemuBNTRt2jTneVBQEKGhoZw4caKouyQibqRwIyJuFxQUdMVpImcpV66cXev5+vrm+dlisWCz2VxRkoi4mHpuRKTEW79+/RU/N2zYEICGDRvy66+/kpaWlvP6Tz/9hJeXFw0aNCAkJISaNWuyatWqYq1ZRNxHR25ExO0yMjJITEzMs8zHx4eKFSsCsGDBAlq3bs3111/PrFmz2LhxIx9++CEAgwYNYsKECQwbNowXXniBkydPMnr0aIYMGUJkZCQAL7zwAg8//DCVK1fmjjvuICUlhZ9++onRo0cX746KSLFQuBERt1u+fDlRUVF5ljVo0IDff/8dMK9kmjt3Lo888ghRUVHMmTOH2NhYAAIDA1mxYgVjxoyhTZs2BAYG0qdPH/7zn//kvNewYcNIT0/nv//9L08++SQVK1bknnvuKb4dFJFiZTEMw3B3ESIiV2OxWFi8eDG9evVydyki4iHUcyMiIiKlisKNiIiIlCrquRGREk1nzkXEUTpyIyIiIqWKwo2IiIiUKgo3IiIiUqoo3IiIiEiponAjIiIipYrCjYiIiJQqCjciIiJSqijciIiISKny/24Bb2egdMrZAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [6 / 15], Step [22 / 225], Loss: 0.30001387000083923, Validation Loss: 0.0\n", + "Epoch [6 / 15], Step [44 / 225], Loss: 0.049207571893930435, Validation Loss: 0.015120014548301697\n", + "Epoch [6 / 15], Step [66 / 225], Loss: 0.04912778362631798, Validation Loss: 0.007269968744367361\n", + "Epoch [6 / 15], Step [88 / 225], Loss: 0.04904578626155853, Validation Loss: 0.0\n", + "Epoch [6 / 15], Step [110 / 225], Loss: 1.0179965496063232, Validation Loss: 1.0021252632141113\n", + "Epoch [6 / 15], Step [132 / 225], Loss: 0.9388081431388855, Validation Loss: 0.9330480098724365\n", + "Epoch [6 / 15], Step [154 / 225], Loss: 0.8098025321960449, Validation Loss: 0.4797901511192322\n", + "Epoch [6 / 15], Step [176 / 225], Loss: 0.15214264392852783, Validation Loss: 0.6066851019859314\n", + "Epoch [6 / 15], Step [198 / 225], Loss: 0.25942176580429077, Validation Loss: 0.0\n", + "Epoch [6 / 15], Step [220 / 225], Loss: 0.04873622953891754, Validation Loss: 0.02330446057021618\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGwCAYAAABVdURTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABwnklEQVR4nO3dd3gU1dvG8e9uKiEFQkkChF5DlRYBAdEgRar6A5EuIiJN0VfFQlEEu6igCBbERlMERUBEadIJSAeBUISEUCQN0nb3/WMwEEmFZDfZ3J/r2ovZ2TO7T6IkNzPPnGOy2Ww2RERERJyE2dEFiIiIiOQlhRsRERFxKgo3IiIi4lQUbkRERMSpKNyIiIiIU1G4EREREaeicCMiIiJOxdXRBdib1WrlzJkz+Pj4YDKZHF2OiIiI5IDNZiMuLo5y5cphNmd9bqbIhZszZ84QHBzs6DJERETkJpw6dYoKFSpkOabIhRsfHx/A+Ob4+vo6uBoRERHJidjYWIKDg9N+j2elyIWbfy9F+fr6KtyIiIgUMjlpKVFDsYiIiDgVhRsRERFxKgo3IiIi4lSKXM+NiIg4F4vFQkpKiqPLkDzg7u6e7W3eOaFwIyIihZLNZiMqKopLly45uhTJI2azmSpVquDu7n5L76NwIyIihdK/waZs2bJ4eXlpYtZC7t9JdiMjI6lYseIt/fdUuBERkULHYrGkBZtSpUo5uhzJI2XKlOHMmTOkpqbi5uZ20++jhmIRESl0/u2x8fLycnAlkpf+vRxlsVhu6X0UbkREpNDSpSjnklf/PXVZKq9YLXBiI8SfBe8AqNQSzC6OrkpERKTIUbjJC/uXwopnIfbMtX2+5aDj6xDSzXF1iYiIFEG6LHWr9i+FBQPSBxuA2Ehj//6ljqlLRERyxGK1senoBZbsOs2moxewWG2OLinXKleuzLRp0xxdRoGhMze3wmoxztiQ0V8EG2CCFc9B7Xt1iUpEpABasTeSST/uJzImMW1fkJ8nE7qG0LFeUJ5/XnY9JRMmTGDixIm5ft9t27ZRvHjxm6zKcOedd9KoUSOnCEkKN7fixMYbz9ikY4PY08a4Kq3tVpaIiGRvxd5Ihn8VfsM/T6NiEhn+VTgf9Wuc5wEnMjIybXv+/PmMHz+eQ4cOpe3z9vZO27bZbFgsFlxds/9VXaZMmTyts7DTZalbEX82b8eJiMhNs9lsXE5OzdEjLjGFCUv3ZXreHWDi0v3EJabk6P1stpxdygoMDEx7+Pn5YTKZ0p4fPHgQHx8fli9fTpMmTfDw8GDDhg0cPXqU7t27ExAQgLe3N82aNePXX39N977/vSxlMpn45JNP6NmzJ15eXtSoUYOlS2+tTeK7776jbt26eHh4ULlyZd5+++10r3/44YfUqFEDT09PAgICeOCBB9JeW7RoEfXr16dYsWKUKlWKsLAwEhISbqmerOjMza3wDsjbcSIictOupFgIGb8yT97LBkTFJlJ/4i85Gr//5Q54uefNr9TnnnuOt956i6pVq1KyZElOnTpF586defXVV/Hw8GDu3Ll07dqVQ4cOUbFixUzfZ9KkSbzxxhu8+eabfPDBB/Tt25cTJ07g7++f65p27NhBr169mDhxIr1792bjxo08/vjjlCpVikGDBrF9+3ZGjx7Nl19+ScuWLbl48SLr168HjLNVffr04Y033qBnz57ExcWxfv36HAfCm6FwcysqtTTuioqNJOO+G8C3vDFOREQkB15++WXat2+f9tzf35+GDRumPX/llVdYvHgxS5cuZeTIkZm+z6BBg+jTpw8AU6ZM4f3332fr1q107Ngx1zW988473H333bz00ksA1KxZk/379/Pmm28yaNAgTp48SfHixenSpQs+Pj5UqlSJ2267DTDCTWpqKvfddx+VKlUCoH79+rmuITcUbm6F2cW43XvBAMBEhgGn3QtqJhYRsYNibi7sf7lDjsZujbjIoM+3ZTtuzuBmNK+S/ZmOYm5593O+adOm6Z7Hx8czceJEli1blhYUrly5wsmTJ7N8nwYNGqRtFy9eHF9fX6Kjo2+qpgMHDtC9e/d0+1q1asW0adOwWCy0b9+eSpUqUbVqVTp27EjHjh3TLok1bNiQu+++m/r169OhQwfuueceHnjgAUqWLHlTteSEem5uVUg36DUXfP/TdGa6+j/6ngXGXVUiIpKvTCYTXu6uOXq0rlGGID9PMrt3yYRx11TrGmVy9H55OVPyf+96evrpp1m8eDFTpkxh/fr17Nq1i/r165OcnJzl+/x3bSaTyYTVas2zOq/n4+NDeHg43377LUFBQYwfP56GDRty6dIlXFxcWLVqFcuXLyckJIQPPviAWrVqERERkS+1gMJN3gjpBk/shYE/wf2fGn8+uhbcvODYGvhtsqMrFBGR67iYTUzoGgJwQ8D59/mEriG4mB2/vMMff/zBoEGD6NmzJ/Xr1ycwMJDjx4/btYY6derwxx9/3FBXzZo1cXEx/jHv6upKWFgYb7zxBrt37+b48eP89ttvgBGsWrVqxaRJk9i5cyfu7u4sXrw43+rVZam8Yna58Xbvbh/Ad0NgwztQvgnU6eKY2kRE5AYd6wXxUb/GN8xzE5iP89zcjBo1avD999/TtWtXTCYTL730Ur6dgTl37hy7du1Kty8oKIinnnqKZs2a8corr9C7d282bdrE9OnT+fDDDwH46aefOHbsGG3atKFkyZL8/PPPWK1WatWqxZYtW1i9ejX33HMPZcuWZcuWLZw7d446derky9cACjf5q/4DcHoHbP4QFj8GZX6H0jUcXZWIiFzVsV4Q7UMC2Rpxkei4RMr6eNK8in+BOGPzr3feeYeHH36Yli1bUrp0aZ599lliY2Pz5bO++eYbvvnmm3T7XnnlFV588UUWLFjA+PHjeeWVVwgKCuLll19m0KBBAJQoUYLvv/+eiRMnkpiYSI0aNfj222+pW7cuBw4cYN26dUybNo3Y2FgqVarE22+/TadOnfLlawAw2fLzXqwCKDY2Fj8/P2JiYvD19c3/D7SkwBfd4ORGKFMbHlkNHt7ZHyciIplKTEwkIiKCKlWq4Onp6ehyJI9k9d81N7+/1XOT31zc4H9zwDsQzh2EpSOhaOVJERERu1K4sQefAOOOKrMr7FsMm2Y4uiIRERGnpXBjLxVDocNUY3vVeIhY79h6REREnJTCTR6xWG1sOnqBJbtOs+noBSzWDC49NR8KDXqDzQKLBkPMafsXKiIi4uR0t1QeWLE38oZbCYMyupXQZIIu0+Dsfji7BxYOhEHLwNXD/kWLiIg4KZ25uUUr9kYy/KvwdMEGIComkeFfhbNib2T6A9y9oPdc8PSDv7fBinF2rFZERMT5KdzcAovVxqQf92e4ZOa/+yb9uP/GS1T+VeG+2cb29k9hV/o5BUREROTmKdzcgq0RF284Y3M9GxAZk8jWiIs3vlizA7R9ztj+6UmI/DN/ihQRESliFG5uQXRc5sEmR+PaPgs17oHURJjfHy5nEIJERET+48477+SJJ55wdBkFlsLNLSjrk7NZMTMdZzbDfbOgZGW4dAK+H6oVxEVE7M1qMabn2LPI+DMffw537dqVjh07Zvja+vXrMZlM7N69+5Y/Z86cOZQoUeKW36ewUri5Bc2r+BPk53nDirLXC/Iz1inJVLGS0PsrcC0GR36FNa/leZ0iIpKJ/UthWj34ooux0PEXXYzn+5fmy8cNGTKEVatW8ffff9/w2ueff07Tpk1p0KBBvnx2UaJwcwtczCYmdA0ByDTgjGhXPfsF2ALrQ9f3jO11b8Ch5XlXpIiIZGz/UlgwAGLPpN8fG2nsz4eA06VLF8qUKcOcOXPS7Y+Pj2fhwoUMGTKECxcu0KdPH8qXL4+Xlxf169fn22+/zdM6Tp48Sffu3fH29sbX15devXpx9uzZtNf//PNP2rVrh4+PD76+vjRp0oTt27cDcOLECbp27UrJkiUpXrw4devW5eeff87T+m6Vws0t6lgviI/6NSbQL/2lJzcXI9B8s+UkV5JzcIqzYW9o/qix/f0wuHA0r0sVEXFuNhskJ+TskRgLy5+BrO53XfGsMS4n75fDNQNdXV0ZMGAAc+bM4fp1qxcuXIjFYqFPnz4kJibSpEkTli1bxt69e3n00Ufp378/W7duvfXvEWC1WunevTsXL15k7dq1rFq1imPHjtG7d++0MX379qVChQps27aNHTt28Nxzz+Hm5gbAiBEjSEpKYt26dezZs4fXX38db++CtSC0JvHLAx3rBdE+JJCtEReJjkukrI8nFUoWo8eMP9gfGctz3+9mWu9GmEzZnMG551XjrqlTW4wG40dWgXtx+3wRIiKFXcplmFIuj97MZpzReS04Z8OfP5Pjn9cPP/wwb775JmvXruXOO+8EjEtS999/P35+fvj5+fH000+njR81ahQrV65kwYIFNG/ePLdfyA1Wr17Nnj17iIiIIDjY+Prmzp1L3bp12bZtG82aNePkyZP83//9H7Vr1wagRo0aacefPHmS+++/n/r16wNQtWrVW64pr+nMTR5xMZtoUa0U3RuVp0W1UgT7e/Fh38a4mk0s2XWGT9ZHZP8mru7wvy+geFmI3gc/jtEK4iIiTqZ27dq0bNmSzz77DIAjR46wfv16hgwZAoDFYuGVV16hfv36+Pv74+3tzcqVKzl58mSefP6BAwcIDg5OCzYAISEhlChRggMHDgAwduxYHnnkEcLCwnjttdc4evTa1YTRo0czefJkWrVqxYQJE/KkATqv6cxNPgqtWoqXuoQwYek+pi4/QJ0gX+6oUTrrg3yD4H9z4IuusGchlG8Ktz9ml3pFRAo1Ny/jDEpOnNgIXz+Q/bi+i6BSy5x9di4MGTKEUaNGMWPGDD7//HOqVatG27ZtAXjzzTd57733mDZtGvXr16d48eI88cQTJCcn5+ozbsXEiRN56KGHWLZsGcuXL2fChAnMmzePnj178sgjj9ChQweWLVvGL7/8wtSpU3n77bcZNWqU3erLjs7c5LMBLSrxQJMKWG0w8ttwTl28nP1BlVvBPZON7V9egBOb8rdIERFnYDIZl4Zy8qh2F/iWI/PbQUzgW94Yl5P3y67t4D969eqF2Wzmm2++Ye7cuTz88MNprQt//PEH3bt3p1+/fjRs2JCqVaty+PDhW/veXKdOnTqcOnWKU6dOpe3bv38/ly5dIiQkJG1fzZo1efLJJ/nll1+47777+Pzzz9NeCw4O5rHHHuP777/nqaeeYvbs2XlWX15QuMlnJpOJyT3q0bCCH5cupzB07nYuJ6dmf+Dtw6He/WBNNRbYjIvK/2JFRIoKswt0fP3qk/8Gk6vPO75mjMsH3t7e9O7dm3HjxhEZGcmgQYPSXqtRowarVq1i48aNHDhwgGHDhqW7kymnLBYLu3btSvc4cOAAYWFh1K9fn759+xIeHs7WrVsZMGAAbdu2pWnTply5coWRI0eyZs0aTpw4wR9//MG2bduoU6cOAE888QQrV64kIiKC8PBwfv/997TXCgqFGzvwdHNhZv8mlPZ252BUHM8s2p2uSz5DJhN0+wDKhkD8WVgwEFLtd0pSRMTphXSDXnONdoDr+ZYz9od0y9ePHzJkCP/88w8dOnSgXLlrjdAvvvgijRs3pkOHDtx5550EBgbSo0ePXL9/fHw8t912W7pH165dMZlMLFmyhJIlS9KmTRvCwsKoWrUq8+fPB8DFxYULFy4wYMAAatasSa9evejUqROTJk0CjNA0YsQI6tSpQ8eOHalZsyYffvhhnnxP8orJlu1vWecSGxuLn58fMTEx+Pr62vWzt0Zc5KHZm0m12hjXqTbD2lbL/qALR2HWnZAUC82HQec38r1OEZGCLjExkYiICKpUqYKnZ85mi8+U1WL04MSfBe8Ao8cmn87YSNay+u+am9/fOnNjR82r+DOhW10AXl9xkHWHz2V/UKlq0PNjY3vrx7B7QT5WKCJSBJldoEprqP+A8aeCTaGncGNn/UIr0rtpMFYbjPp2JycuJGR/UO3O0PrqnAdLR0PU3vwtUkREpBBTuLEzk8nEyz3q0ii4BDFXUhj25Q4SknLQYNzueah2N6Regfn94MqlfK9VRESkMHJouFm3bh1du3alXLlymEwmfvjhh2yPWbNmDY0bN8bDw4Pq1avfsD5HYeDh6sLMfk0o4+PBwag4/m/Rn9k3GJtd4P5PoERF+CcCFg8Dq9U+BYuIiBQiDg03CQkJNGzYkBkzZuRofEREBPfeey/t2rVj165dPPHEEzzyyCOsXLkynyvNe4F+nszs1xg3FxM/74nio7U5WEvKyx96fQmunnB4Bax7M/8LFREpwIrYPTFOL6/+ezo03HTq1InJkyfTs2fPHI2fOXMmVapU4e2336ZOnTqMHDmSBx54gHfffTefK80fTSr5M/Fqg/GbKw+x5lB09geVawT3vmNsr5kKf63KvwJFRAqofxdxvHw5BxOjSqHx7yzMLi631tRdqJZf2LRpE2FhYen2dejQgSeeeCLTY5KSkkhKSkp7Hhsbm1/l3ZS+oZXYezqWb7eeZPS3O1k68g4ql85m8bXb+sLp7bD9M/huCDy6Fvyr2KdgEZECwMXFhRIlShAdbfyj0MvLK/vFiaVAs1qtnDt3Di8vL1xdby2eFKpwExUVRUBAQLp9AQEBxMbGcuXKFYoVK3bDMVOnTk2beKigmtgthENRsYSfvMSjX27n+8db4e2RzX+ajq9B5G4j5MzvD0N+AffcrW0iIlKYBQYGAqQFHCn8zGYzFStWvOWgWqjCzc0YN24cY8eOTXseGxubbiXUguDfBuMuH2zg8Nl4nl7wJx/1a5z1f1xXD2MGzY/bwNk9sGws9Pgo1+ubiIgUViaTiaCgIMqWLUtKSoqjy5E84O7ujtl86x0zhSrcBAYG3rC+xtmzZ/H19c3wrA2Ah4cHHh4e9ijvlpT19WRm/yY8+PFmVuyLYsbvRxh5V42sD/IrD//7HOZ2hz+/hfJNoPlQ+xQsIlJAuLi43HKPhjiXQjXPTYsWLVi9enW6fatWraJFixYOqihvNa5Ykpe7Gw3Gb686zG8Hc7BQWpU2EHb1stuKcXBqaz5WKCIiUvA5NNzEx8enrVQKxq3eu3bt4uTJk4BxSWnAgAFp4x977DGOHTvGM888w8GDB/nwww9ZsGABTz75pCPKzxcPNq9I39CK2GwwZt4ujp2Lz/6glqMgpAdYU2DBAIjL/eqxIiIizsKh4Wb79u1pK5UCjB07lttuu43x48cDEBkZmRZ0AKpUqcKyZctYtWoVDRs25O233+aTTz6hQ4cODqk/v0zoWpemlUoSl5jKo1/uIC4xm2vJJhN0nw6la0FcJCwaDBZdfxYRkaJJq4IXUNFxiXT74A+iYhO5JySAmf2aYDZn0yx87jDMvguS4+D2EdBxin2KFRERyWdaFdwJlPXx5KN+jXF3MfPL/rNM//1I9geVqQk9PjS2N8+Avd/lb5EiIiIFkMJNAXZbxZJM7lkPgHdWHebX/TnopQnpBq2eMLaXjILoA/lXoIiISAGkcFPA9WoazIAWlQB4cv4ujuakwfiul6BKW0hJgHl9ITEmn6sUEREpOBRuCoGXuoTQvLI/cUmpDJ27ndjsGoxdXOGBz8C3Alw8CouHawVxEREpMhRuCgE3FzMz+jYmyM+TY+cSGDt/F1ZrNn3gxUtD77ng4g6HlsGGd+xTrIiIiIMp3BQSZXw8+Lh/E9xdzfx6IJr3Vv+V/UHlm0Dnt4zt3ybDkdVZjxcREXECCjeFSIMKJZjSsz4A763+i1/2RWV/UJOB0HgAYDNWEP/nRP4WKSIi4mAKN4XMA00qMKhlZcBoMD4SHZf9QZ3ehHK3wZV/YEF/SEnM3yJFREQcSOGmEHrh3jqEVvEnIdnCo3N3ZN9g7OZprCBezB8i/4Sfn4KiNXejiIgUIQo3hdC/Dcbl/Dw5dj6BJ+bloMG4REXjDiqTGXZ+BTvm2KVWERERe1O4KaRKe3vwcf+meLia+e1gNO/+ejj7g6q1M+bAAVj+DPy9I3+LFBERcQCFm0KsfgU/XrvfaDD+4LcjrNgbmf1BdzwJtbuAJdnov4k/l89VioiI2JfCTSHX87YKPNyqCgBPLfiTw2ezaTA2maDHR1CqBsSevrqCeKodKhUREbEPhRsn8Hzn2rSsVupqg/F2Yi5n02Ds6Qu9vwK34nB8PayeZJ9CRURE7EDhxgm4upiZ/lBjypcoxvELlxkzfyeW7BqMy9aGHjOM7Y3vw/4l+V+oiIiIHSjcOAn/4u583L8Jnm5m1hw6xzurDmV/UN2e0GKksf3D43AuB8eIiIgUcAo3TqReeT9ev78BADN+P8rPe3LQYBw2CSq3huR4mN8PknIwKaCIiEgBpnDjZLo3Ks/Q1kaD8dML/+RQVDZhxcUVHvgcfMrB+cPGGRxN8CciIoWYwo0TerZjbVpVL8XlZAtD527n0uXkrA/wLmPMYGx2gwNL4Y/37FOoiIhIPlC4cUKuLmam92lMhZLFOHnxMqO+zUGDcXAz6PS6sb16Ehxbk+91ioiI5AeFGydVsrg7s/o3xdPNzPq/zvPmyhw0Czd9GBr1BZsVFj0Ml07lf6EiIiJ5TOHGiYWU8+WNBxoCMHPtUX7afSbrA0wmuPdtCGwAly/AggGQmmSHSkVERPKOwo2T69awHMPaVgXg/xbuZv+Z2KwPcCsGvb8EzxJwJtxYg0pERKQQUbgpAp7pUJvWNUpzJcXCsK+2809CNg3GJSvDA58CJmP18PAv7VCliIhI3lC4KQJczCY+6HMbFf29OHXxCqPn7STVYs36oOph0O4FY3vZU3A6PP8LFRERyQMKN0VECS93Zg1oQjE3F9b/dZ43ctJg3PopqNkJLElG/03ChfwvVERE5BYp3BQhtQN9eet/RoPxrHXHWLLrdNYHmM3Qcyb4V4WYU/Ddw2C12KFSERGRm6dwU8Tc2yCI4XdWA+DZ73az70xM1gcUKwG9vwY3L2Pum98m53uNIiIit0Lhpgh6+p5atK1ZhsQUK4/O3cHF7BqMA0Kg2wfG9oZ34MBP+V+kiIjITVK4KYJczCbef/A2KpXy4vSlK4z8Jjz7BuP6D0DocGP7h+Fw/kj+FyoiInITFG6KKD8vN2b1b4qXuwsbj15g6vKD2R90zytQsSUkxcL8vpAUn/+FioiI5JLCTRFWK9CHt682GH+6IYLFO//O+gAXN/jfHPAOhHMHYelIrSAuIiIFjsJNEdepfhAj21UH4Lnv9rD3dDYNxj4BV1cQd4V9i2HTDDtUKSIiknMKN8KT7WvSrlYZklKtDPtyBxfis1lPqmIodJhqbK8aDxHr879IERGRHFK4EVzMJqY9eBtVShfn9KUrjPgmnJTsGoybD4UGvcFmgUWDISabOXNERETsROFGAPAr5sas/k0o7u7C5mMXmfLzgawPMJmgyzQIqAcJ52DhQEjN5pZyERERO1C4kTQ1Anx4p3cjAD7/4zjf7cimwdjd6+oK4n7w9zZYOS7/ixQREcmGwo2k06FuIKPvMhqMxy3ew+6/L2V9gH9VuG+2sb3tE9j1bf4WKCIikg2FG7nBE2E1ubt2WZKvNhifi8umwbhmB2j7nLH90xMQ+We+1ygiIpIZhRu5gdls4t0HG1G1THEiYxJz1mDc9lmocQ+kJsL8/nD5on2KFRER+Q+FG8mQr6cxg7G3hytbIy4y+af9WR9gNsN9s6BkZbh0Ar4fqhXERUTEIRRuJFPVy3rz7tUG4y82nWDB9lNZH1CsJPT+ClyLwZFfYc1r+V+kiIjIfyjcSJbahwTwRFgNAF5cvJddpy5lfUBgfej6nrG97g04tDx/CxQREfkPhRvJ1ui7atA+JIBki5XHvtxBdFxi1gc07A3Nhhrb3w+DC0fzv0gREZGrFG4kW2aziXd6NaRameJExSYy4utwklOzaTDuMAWCQyEpxmgwTk6wT7EiIlLkKdxIjvh4ujFrQFN8PFzZdvwfXv5pX9YHuLrD/76A4mUheh/8OEYriIuIiF0o3EiOVSvjzbQHG2EywVebTzJ/28msD/ANgv/NAZML7FkIWz62S50iIlK0KdxIrtxdJ4CxYTUBeOmHfYSf/CfrAyq3gnsmG9u/vAAnNuVzhSIiUtQp3EiujWhXnQ51r2swjs2mwfj24VDvfrCmGgtsxkXZp1ARESmSFG4k18xmE2/3akSNst5ExyUxPLsGY5MJun0AZUMg/iws0AriIiKSfxwebmbMmEHlypXx9PQkNDSUrVu3Zjl+2rRp1KpVi2LFihEcHMyTTz5JYmI2Zw4kz3l7uBoNxp6u7DjxDxN/zKbB2L24McGfhy+c2gyrXrJPoSIiUuQ4NNzMnz+fsWPHMmHCBMLDw2nYsCEdOnQgOjo6w/HffPMNzz33HBMmTODAgQN8+umnzJ8/n+eff97OlQtAldLFeb/PbZhM8M2Wk3yzJZsG41LVoOfVpuItM2H3wvwvUkREihyHhpt33nmHoUOHMnjwYEJCQpg5cyZeXl589tlnGY7fuHEjrVq14qGHHqJy5crcc8899OnTJ8uzPUlJScTGxqZ7SN5pV6ssT99TC4AJS/ey40Q2C2bW7gytnza2l46CqL35XKGIiBQ1Dgs3ycnJ7Nixg7CwsGvFmM2EhYWxaVPGd9S0bNmSHTt2pIWZY8eO8fPPP9O5c+dMP2fq1Kn4+fmlPYKDg/P2CxEev7ManesHkmKx8dhX4ZzNrsG43fNQ7W5IvQLz+8GVS3apU0REigaHhZvz589jsVgICAhItz8gIICoqIzvpnnooYd4+eWXueOOO3Bzc6NatWrceeedWV6WGjduHDExMWmPU6eyWfxRcs1kMvHmAw2pFeDDubgkHvtqB0mpWawIbnaB+z+BEhXhnwhYPAys2cx4LCIikkMObyjOjTVr1jBlyhQ+/PBDwsPD+f7771m2bBmvvPJKpsd4eHjg6+ub7iF5r7iHK7MGNMHX05WdJy8xYck+bFnNSOzlD72+BFdPOLwC1r1pv2JFRMSpOSzclC5dGhcXF86ePZtu/9mzZwkMDMzwmJdeeon+/fvzyCOPUL9+fXr27MmUKVOYOnUqVv3L3+EqlSrOBw81xmyCedtO8XV2DcblGsG97xjba6bCX6vyvUYREXF+Dgs37u7uNGnShNWrV6fts1qtrF69mhYtWmR4zOXLlzGb05fs4uICkPVZArGbtjXL8H8dagMwcek+th3PpsH4tr7QZDBgg++GwMWI/C9SREScmkMvS40dO5bZs2fzxRdfcODAAYYPH05CQgKDBw8GYMCAAYwbNy5tfNeuXfnoo4+YN28eERERrFq1ipdeeomuXbumhRxxvMfaVuXeBkGkWm0M/yqcyJgrWR/Q6XUo3xQSY2BBf0i+bJ9CRUTEKbk68sN79+7NuXPnGD9+PFFRUTRq1IgVK1akNRmfPHky3ZmaF198EZPJxIsvvsjp06cpU6YMXbt25dVXX3XUlyAZMBqMG3A0Op6DUXE89lU48x+9HU+3TAKoqwf0mgsft4GoPbBsLPT4yJjZWEREJJdMtiJ2PSc2NhY/Pz9iYmLUXJzPTl64TNfpG4i5ksL/mlTgjQcaYMoqsESsg7ndwWaFzm9B86H2K1ZERAq03Pz+LlR3S0nhUrGUF9Mfug2zCRbu+JsvN5/I+oAqbSBskrG9YhycynopDhERkYwo3Ei+al2jDM91MhqMX/5xP1uOXcj6gJajIKQHWFNgwQCIO5v1eBERkf9QuJF8N7R1Vbo1LEeq1cbjX4dz5lIWDcYmE3SfDqVrQVwkLBoMlhT7FSsiIoWewo3kO5PJxOv3N6BOkC8XEpJ57KsdJKZkMYOxh4+xgri7D5z4A36daLdaRUSk8FO4Ebso5u7CrP5NKOnlxu6/Y3hh8d6s5yYqUxN6fGhsb5oOe7+zT6EiIlLoKdyI3QT7ezH96gzG34X/zZyNx7M+IKQbtHrC2F4yCqIP5HeJIiLiBBRuxK5aVS/N853rADB52QE2Hc2mwfiul6BKW0hJgHl94fJFiFgPexYZf1qzuLwlIiJFkua5Ebuz2Ww8OX8XP+w6g39xd34cdQflSxTL/ICE8/BxW4j921hoMzXx2mu+5aDj68ZZHhERcVqa50YKNJPJxNT7GlC3nC8XE5IZ9uX2rBuMi5eG5o8Y29cHG4DYSOOW8f1L869gEREpVBRuxCGKubvwcf8m+Bd3Z+/pWMZ9vyfzBmOrBbbOyuSdrh6z4jldohIREUDhRhyoQkljBmMXs4nFO0/z2R/HMx54YiPEnsninWwQe9oYJyIiRZ7CjThUy2qleeFqg/GUnw+w8cj5GwfF53CW4pyOExERp6ZwIw43uFVl7mtcHovVxohvwjl18XL6Ad4BOXujnI4TERGnpnAjDmcymZjSsz71y/vxz+UUhn25gyvJ1/XPVGpp3BVFZiuKm8C3vDFORESKPIUbKRA83YwG41LF3dkfGcuz3+2+1mBsdjFu9wYyDjg26PiaMU5ERIo8hRspMMqVKMaMvo1xNZtY+ucZPlkfce3FkG7Qay74BmV8sE+gfYoUEZECT5P4SYHzxcbjTFi6D7MJ5j4cyh01Sl970Wox7oqKP2v02Oz8CnbPgzJ1YNg6cHV3XOEiIpJvNImfFGoDWlTigSYVsNpg5Lf/aTA2u0CV1lD/AePPjlPBqzScOwB/THNYzSIiUnAo3EiBYzKZmNyjHg0r+HHpcgpD527ncnJqxoO9/KHT1X6cdW/CuUP2K1RERAokhRspkDzdXJjZvwmlvd05GBXHM4t2Zz6Dcb37oUYHsCTD0tFgtdq3WBERKVAUbqTACvIrxod9m+BqNvHT7kg+XncMi9XGpqMXWLLrNJuOXsBitYHJBPe+De7ecGoz7PjM0aWLiIgDqaFYCrwvNx3npSX7MAElvdy5eDk57bUgP08mdA2hY70g2DILlv8fuPvAiC3gV95xRYuISJ5SQ7E4lX63V6JVtVLYIF2wAYiKSWT4V+Gs2BsJzYZAhWaQHAfLnoKildtFROQqhRsp8Kw2OHouPsPX/o0vk37cjwUzdPsAzG5weDns/8FuNYqISMGhcCMF3taIi0TFJmX6ug2IjElka8RFKFsHWo81Xvj5/+DyRfsUKSIiBYbCjRR40XGJuRvX+ikoXRMSzsGql/KxMhERKYgUbqTAK+vjmbtxrh7G5SkwZjA+tjafKhMRkYJI4UYKvOZV/Any88xqTXCC/DxpXsX/2s6Kt0OzR4ztH8dAypX8LlNERAoIhRsp8FzMJiZ0DQEyXROcCV1DcDH/59W7J4BPOfgnAta8lu91iohIwaBwI4VCx3pBfNSvMYF+N16iqlCiGGF1Am48yNPXmNwPYOMHEPlnPlcpIiIFQa7DzYoVK9iwYUPa8xkzZtCoUSMeeugh/vnnnzwtTuR6HesFseHZu/h26O2892AjZvZrjK+nK39fusKcjcczPqh2Z6jbE2wWWDoKLJmsUSUiIk4j1+Hm//7v/4iNjQVgz549PPXUU3Tu3JmIiAjGjh2b5wWKXM/FbKJFtVJ0b1SejvWCGNe5DgDvrDrMmUuZ9NV0fB08/YwzN5s/tGO1IiLiCLkONxEREYSEGP0P3333HV26dGHKlCnMmDGD5cuX53mBIlnp3TSYppVKcjnZwsSl+zIe5BMA97xqbP8+BS4es1+BIiJid7kON+7u7ly+fBmAX3/9lXvuuQcAf3//tDM6IvZiNpt4tWd9XM0mftl/ll/2RWU88LZ+UKUNpF6BH5/Q0gwiIk4s1+HmjjvuYOzYsbzyyits3bqVe++9F4DDhw9ToUKFPC9QJDu1An0Y2qYqABOX7iMhKYO+GpMJukwDV0+IWAu7vrFvkSIiYje5DjfTp0/H1dWVRYsW8dFHH1G+vLHy8vLly+nYsWOeFyiSE6PvqkGwfzHOxCTy7qrDGQ8qVQ3uHGdsr3we4qPtV6CIiNiNyWYrWufnc7NkuhQuvx+KZvDn23Axm1g6shV1y/ndOMiSCrPbQdRuqHc/PPCZ/QsVEZFcy83v71yfuQkPD2fPnj1pz5csWUKPHj14/vnnSU5Ozn21InmkXa2y3Fs/CIvVxvOL92KxZpDbXVyNpRlMLrD3Ozi0wv6FiohIvsp1uBk2bBiHDxun/Y8dO8aDDz6Il5cXCxcu5JlnnsnzAkVyY3zXEHw8XPnz1CW+3nIi40HlGkGLEcb2srGQFGe3+kREJP/lOtwcPnyYRo0aAbBw4ULatGnDN998w5w5c/juu+/yuj6RXAnw9eT/OtYC4M0Vhzgbm8mK4neOg5KVIfY0rH7ZfgWKiEi+y3W4sdlsWK1WwLgVvHPnzgAEBwdz/vz5vK1O5Cb0Da1Ewwp+xCWl8vJP+zMe5O4FXd8ztrfOhpNb7FegiIjkq1yHm6ZNmzJ58mS+/PJL1q5dm3YreEREBAEBGazvI2JnLlfnvjGbYNnuSNYcyuSuqKp3QqO+gM1YmiE1yZ5liohIPsl1uJk2bRrh4eGMHDmSF154gerVqwOwaNEiWrZsmecFityMeuX9GNyqCgAvLdnLlWRLxgPvmQzFy8D5Q7DhXTtWKCIi+SXPbgVPTEzExcUFNze3vHi7fKNbwYuOhKRUwt5ZS2RMIsPvrMazHWtnPHDvd7DoYTC7wWMboGwm40RExGHy9Vbwf+3YsYOvvvqKr776ivDwcDw9PQt8sJGipbiHKxO71QVg9rpjHIrK5K6ouvdBzY5gTYEfR8PVnjIRESmcch1uoqOjadeuHc2aNWP06NGMHj2apk2bcvfdd3Pu3Ln8qFHkpnWoG0j7kABSrTZeWLwHa0Zz35hMcO/b4O4Np7bA9k/tX6iIiOSZXIebUaNGER8fz759+7h48SIXL15k7969xMbGMnr06PyoUeSWTOpWFy93F7af+IcF209lPMivAoRNNLZ/nQQxf9utPhERyVu5DjcrVqzgww8/pE6dOmn7QkJCmDFjBsuXL8/T4kTyQrkSxRjbviYAU5cf5Hx8JndFNR0CFZpDchwse0orh4uIFFK5DjdWqzXD3ho3N7e0+W9ECppBLSsTEuRLzJUUXl12IONBZrOxNIPZDQ6vgH2L7VukiIjkiVyHm7vuuosxY8Zw5syZtH2nT5/mySef5O677851ATNmzKBy5cp4enoSGhrK1q1bsxx/6dIlRowYQVBQEB4eHtSsWZOff/45158rRYuri5kp99XHZILFO0/zx5FMJpwsWxvaPG1sL38GLl+0X5EiIpInch1upk+fTmxsLJUrV6ZatWpUq1aNKlWqEBsby/vvv5+r95o/fz5jx45lwoQJhIeH07BhQzp06EB0dMaTriUnJ9O+fXuOHz/OokWLOHToELNnz6Z8+fK5/TKkCGoUXIL+t1cC4MUf9pKYksncN3c8CaVrQcI5+OUlO1YoIiJ54abmubHZbPz6668cPHgQgDp16hAWFpbrDw8NDaVZs2ZMnz4dMC55BQcHM2rUKJ577rkbxs+cOZM333yTgwcP3vRt55rnpmiLTUwh7O21RMclMebuGjx5tRfnBie3wGcdABv0/wGqtbNnmSIi8h/5Ps+NyWSiffv2jBo1ilGjRhEWFsbBgwepWTOTXxQZSE5OZseOHelCkdlsJiwsjE2bNmV4zNKlS2nRogUjRowgICCAevXqMWXKFCyWTP4FDiQlJREbG5vuIUWXr6cb47uGAPDRmqMcPRef8cCKodDsEWP7pycg+bJ9ChQRkVt205P4/VdSUhJHjx7N8fjz589jsVhuWI8qICCAqKioDI85duwYixYtwmKx8PPPP/PSSy/x9ttvM3ny5Ew/Z+rUqfj5+aU9goODc1yjOKd76wfRtmYZki1WXli8h0xPXt49HnzLwz/HYc1Uu9YoIiI3L8/CjT1YrVbKli3LrFmzaNKkCb179+aFF15g5syZmR4zbtw4YmJi0h6nTmUyz4kUGSaTiVe618PD1czmYxf5Pvx0xgM9feHed4ztTTPgzC671SgiIjfPYeGmdOnSuLi4cPbs2XT7z549S2BgYIbHBAUFUbNmTVxcXNL21alTh6ioKJKTkzM8xsPDA19f33QPkYqlvBgTVgOAV38+wD8JGf//Q62OxvIMNouxcrgl1Y5ViojIzXBYuHF3d6dJkyasXr06bZ/VamX16tW0aNEiw2NatWrFkSNH0s2nc/jwYYKCgnB3d8/3msW5DG1dlZoB3lxMSOa15QczH9jpdfAsAVG7YfMMu9UnIiI3J8fhpmTJkvj7+2f6aN26da4/fOzYscyePZsvvviCAwcOMHz4cBISEhg8eDAAAwYMYNy4cWnjhw8fzsWLFxkzZgyHDx9m2bJlTJkyhREjRuT6s0XcXMxM6VkfgPnbT7E1IpM5bbzLQocpxvbvU+BCznvLRETE/lxzOnDatGl5/uG9e/fm3LlzjB8/nqioKBo1asSKFSvSmoxPnjyJ2XwtfwUHB7Ny5UqefPJJGjRoQPny5RkzZgzPPvtsntcmRUPTyv482CyYedtO8cLiPSwb3Rp31wwyf6OHYPd8iFhr3D01YKmx4KaIiBQ4NzXPTWGmeW7kvy5dTubut9dyISGZ/+tQixHtqmc88OIx+LAlpF6B7jPgtn72LVREpAjL93luRJxJCS93XuxiLAT7/uq/OHEhIeOB/lWh3dXLpCtfgLizGY8TERGHUrgRAXo0Kk+r6qVISrXy0pJ9mc99c/sICGwAiZdghS6HiogURAo3Ilyb+8bdxcy6w+f4aXdkxgNdXI2Vw00uxqrhh5bbt1AREcmWwo3IVVXLePN4u2oAvPzTfmKupGQ8sFwjaDnS2F72FCRqSQ8RkYJE4UbkOsPvrEbV0sU5F5fEmyuzmPum7XNQsgrEnobVL9uvQBERyVau75ayWCzMmTOH1atXEx0dnW5CPYDffvstTwvMa7pbSrKz8eh5Hpq9BZMJvh/ektsqlsx44LG1MLcbYIKHV0DF2+1ap4hIUZKvd0uNGTOGMWPGYLFYqFevHg0bNkz3ECnsWlYrzX2Ny2OzwfOL95JqsWY8sGrbq7eD22DpaEhNsmudIiKSsVyfuSldujRz586lc+fO+VVTvtKZG8mJC/FJ3P3OWi5dTuGFznUY2qZqxgMvX4QZoZAQbVyqajcu43EiInJL8vXMjbu7O9WrZzLJmYiTKOXtwbhOtQF4Z9Vh/v7ncsYDvfyh8xvG9vq3IfqAnSoUEZHM5DrcPPXUU7z33nuZzwMi4iT+1ySYZpVLciXFwsSlWcx9E9IDanYCa4qxcrjVYtc6RUQkvVxflurZsye///47/v7+1K1bFzc3t3Svf//993laYF7TZSnJjb/OxtH5/fWkWGzM7NeEjvUCMx4Yc9q4PJUcB53ehNBH7VuoiIiTy9fLUiVKlKBnz560bduW0qVL4+fnl+4h4kxqBPjw6NV+m4lL9xGflJrxQL/yEDbB2F49CWL+tlOFIiLyX1o4UyQbiSkW7nl3HScvXubhVlUY3zUk44FWK3zeEU5tgZodoc88rRwuIpJHtHCmSB7ydHPhlR71AJizMYK9p2MyHmg2Q9f3wcUdDq+AfQX7Eq2IiLO6qXCzaNEievXqxe23307jxo3TPUScUduaZejSIAirDZ5fvAeLNZMTnmVrQ+unje2fnzFuFRcREbvKdbh5//33GTx4MAEBAezcuZPmzZtTqlQpjh07RqdOnfKjRpECYXyXEHw8XNn9dwxfbjqe+cA7noQydeDyefjlRbvVJyIihlyHmw8//JBZs2bxwQcf4O7uzjPPPMOqVasYPXo0MTGZnK4XcQJlfT155urcN2/9cpiomMSMB7q6Q7f3ARPs+hqO/m6/IkVEJPfh5uTJk7Rs2RKAYsWKERcXB0D//v359ttv87Y6kQKmb/OKNAouQXxSKi//tC/zgcHNofnV28F/HAPJmUwCKCIieS7X4SYwMJCLF40+gooVK7J582YAIiIiNLGfOD2z2cSUnvVxMZv4eU8Uvx08m/ngu18C3wpw6QSsmWK/IkVEirhch5u77rqLpUuXAjB48GCefPJJ2rdvT+/evenZs2eeFyhS0ISU8+XhVpUBeOmHfVxOzmTuGw8fuPdtY3vTDDiz0z4FiogUcbme58ZqtWK1WnF1dQVg3rx5bNy4kRo1ajBs2DDc3d3zpdC8onluJC8kJKXS/p21nIlJZFjbqozrVCfzwYsehr3fQWB9GPo7uLhlPlZERDKUm9/fmsRP5Cb9uv8sj8zdjqvZxE+j76B2YCb/P8WfgxnN4Mo/EDYJ7njCrnWKiDiDfJ/Eb/369fTr148WLVpw+vRpAL788ks2bNhwM28nUiiFhQTQoW4AqVYbz3+/B2tmc994l4EOV3tu1kyFC0ftV6SISBGU63Dz3Xff0aFDB4oVK8bOnTtJSkoCICYmhilT1DQpRcvEbnUp7u5C+MlLzNt2KvOBDftA1TshNdG4e6ponTAVEbGrXIebyZMnM3PmTGbPnp1uRfBWrVoRHh6ep8WJFHRBfsUYe08tAF5bfoBzcUkZDzSZoMs0cC0Gx9fDzq/sV6SISBGT63Bz6NAh2rRpc8N+Pz8/Ll26lBc1iRQqA1tUom45X2ITU5m8bH/mA/2rwF0vGNu/vABxWdxGLiIiN+2m5rk5cuTIDfs3bNhA1apV86QokcLE1cXM1PvqYzbBkl1nWP/XucwHhw6HoEaQGAPLn7FbjSIiRUmuw83QoUMZM2YMW7ZswWQycebMGb7++muefvpphg8fnh81ihR4DSqUYECLygC89MNeElMsGQ90cYVuH4DJBfb/AAeX2a1GEZGiItfh5rnnnuOhhx7i7rvvJj4+njZt2vDII48wbNgwRo0alR81ihQKT91TkwBfD45fuMyHv994djNNUANoefXvyrKnjLM4IiKSZ256npvk5GSOHDlCfHw8ISEheHt753Vt+ULz3Eh++nlPJI9/HY6bi4nlY1pTvaxPxgNTrsCHLeCfCGg6BLq8Y99CRUQKmXyf5wbA3d2dkJAQmjdvXmiCjUh+61QvkHa1ypBisfHC4r2Zr7fmVuzqyuHA9k/h5Gb7FSki4uRyfObm4YcfztEbfvbZZ7dUUH7TmRvJb6cuXqb9u2tJTLHy5gMN+F/T4MwHLxkJO7+E0jXhsQ3g6mG/QkVECpF8OXMzZ84cfv/9dy5dusQ///yT6UOkqAv29+KJsJoATPn5ABcTkjMffM8rULwsnD8M69+2U4UiIs4tx2duRowYwbfffkulSpUYPHgw/fr1w9/fP7/ry3M6cyP2kGKx0vWDDRyMiuN/TSrw5v8aZj543w+wcCCY3WDYOggIsVudIiKFRb6cuZkxYwaRkZE888wz/PjjjwQHB9OrVy9WrlyZeV+BSBHl5mLm1Z71AFi44282H7uQ+eCQ7lDrXrCmwNJRYM3kNnIREcmRXDUUe3h40KdPH1atWsX+/fupW7cujz/+OJUrVyY+Pj6/ahQplJpU8qdP84oAvLB4D0mpmYQWkwnufQs8fOH0dtj2iR2rFBFxPjd9t5TZbMZkMmGz2bBY9C9NkYw817E2pb3dOXougVlrj2U+0LcchE00tn+dBJeyWIRTRESylKtwk5SUxLfffkv79u2pWbMme/bsYfr06Zw8eVK3g4tkwM/LjZe6GD00H/x+hOPnEzIf3GQwVGwBKQmwbKxWDhcRuUk5DjePP/44QUFBvPbaa3Tp0oVTp06xcOFCOnfujNl80yeARJxet4bluKN6aZJTrby0JIu5b8xm6PoeuLjDX7/A3u/sW6iIiJPI8d1SZrOZihUrctttt2EymTId9/333+dZcflBd0uJIxw/n8A909aRnGrlvQcb0b1R+cwHr30Dfn8VvErDyG3gVfjuShQRyWv5crfUgAEDaNeuHSVKlMDPzy/Th4jcqHLp4oxsVx2AV37aT8zllMwHt3oCytSBy+dh5Qv2KVBExInc9NpShZXO3IijJKVa6PTeeo6dS+Ch0IpM6Vk/88GntsGn7QEb9F8M1e6yW50iIgWRXdaWEpHc8XB1SQs032w5yY4TWczoHdwMQocZ2z8+AclZNCKLiEg6CjcidnR71VI80KQCYMx9k2KxZj74rhfBLxgunYDfp9ipQhGRwk/hRsTOnu9ch5JebhyMiuOzDRGZD/TwgS7vGtubP4TT4fYpUESkkFO4EbEz/+LujOtcB4B3fz3MqYuXMx9coz3U/x/YrLB0NFiyaEQWERFA4UbEIf7XpALNq/iTmGJlwtJ9Wa/P1vE1KOYPZ/fAxg/sV6SISCGlcCPiACaTiSk96+HmYuK3g9Gs2BuV+eDipaHD1Z6bNa/BhaP2KVJEpJBSuBFxkOplfXisbTUAJv64j7jELC45NXwQqrYDSxL8OEZLM4iIZEHhRsSBRrSrTqVSXpyNTeLtXw5nPtBkgq7TwM0Ljq+HnV/arUYRkcKmQISbGTNmULlyZTw9PQkNDWXr1q05Om7evHmYTCZ69OiRvwWK5BNPNxcm96gHwBebjrP770uZDy5ZGdpdnbH4lxchLotLWSIiRZjDw838+fMZO3YsEyZMIDw8nIYNG9KhQweio6OzPO748eM8/fTTtG7d2k6ViuSP1jXK0K1hOWw2eH7xHlKzmvsm9DEodxskxsDyZ+xXpIhIIeLwcPPOO+8wdOhQBg8eTEhICDNnzsTLy4vPPvss02MsFgt9+/Zl0qRJVK1a1Y7ViuSPF7vUwdfTlb2nY5m76UTmA11codsHYHKB/UvgwE/2K1JEpJBwaLhJTk5mx44dhIWFpe0zm82EhYWxadOmTI97+eWXKVu2LEOGDMn2M5KSkoiNjU33ECloyvp48myn2gC8/cshImOuZD44sD60GmNs//y0cRZHRETSODTcnD9/HovFQkBAQLr9AQEBREVl3E+wYcMGPv30U2bPnp2jz5g6dWq6VcuDg4NvuW6R/NCnWUUaVyxBQrKFSUv3Zz247TPgXw3iIuHXiXapT0SksHD4ZanciIuLo3///syePZvSpUvn6Jhx48YRExOT9jh16lQ+Vylyc8xmE6/2rI+L2cSKfVH8uv9s5oPdikG3943t7Z/BiY32KVJEpBBwaLgpXbo0Li4unD2b/of42bNnCQwMvGH80aNHOX78OF27dsXV1RVXV1fmzp3L0qVLcXV15ejRGyc38/DwwNfXN91DpKCqE+TLI3dUAWDC0n1cTk7NfHDlO6DxAGN76WhISbRDhSIiBZ9Dw427uztNmjRh9erVafusViurV6+mRYsWN4yvXbs2e/bsYdeuXWmPbt260a5dO3bt2qVLTuIUxoTVoHyJYpy+dIVpv/6V9eD2L4N3AFz4C9a/ZZ8CRUQKOIdflho7diyzZ8/miy++4MCBAwwfPpyEhAQGDx4MwIABAxg3bhwAnp6e1KtXL92jRIkS+Pj4UK9ePdzd3R35pYjkCS93V17pUReATzdEsP9MFk3wxUpC5zeN7Q3vwtl9dqhQRKRgc3i46d27N2+99Rbjx4+nUaNG7Nq1ixUrVqQ1GZ88eZLIyEgHVyliX3fVDqBTvUAsVhvPL96D1ZrFcgt1ukHtLmBNNS5PWS32K1RE5DoWq41NRy+wZNdpNh29gCWrn135yGTLcjli5xMbG4ufnx8xMTHqv5ECLSomkbB31hKflMorPerR//ZKmQ+OPQMzQiEpFjq+Drc/Zr9CRUSAFXsjmfTjfiJjrvX/Bfl5MqFrCB3rBd3y++fm97fDz9yISMYC/Tx56p6aALyx4iDRcVk0DPuWg/aTjO3VL8Olk3aoUETEsGJvJMO/Ck8XbMD4R9rwr8JZsde+V2AUbkQKsAEtKlO/vB9xiam88tOBrAc3HgQVW0JKAvw0ViuHi4hdWKw2Jv24n4x+4vy7b9KP++16iUrhRqQAczGbmHpffcwm+PHPM6w7fC7zwWazMfeNizscWQV7FtmvUBEpsrZGXLzhjM31bEBkTCJbIy7arSaFG5ECrl55Pwa2rAzAiz/sJTEli4bh0jWM2YsBVjwLCRfyv0ARKdKyvGR+E+PygsKNSCHw1D21CPT15OTFy3zwWzZz37QcA2XrwuULsPJ5+xQoIkVWWR+PHI7zzOdKrlG4ESkEvD1cmdgtBIBZ647x19m4zAe7ul9dmsEEu+fBkV/tU6SIFEnZXW4yYdw11byKv30KQuFGpNDoUDeQsDplSbHYeGHx3qznvqnQFEKv3g7+05OQnGCfIkWkSPli43HevW4mddN/Xv/3+YSuIbiY//tq/lG4ESkkTCYTE7vVpZibC1uPX2TRjr+zPuCuF8Ev2Lgt/Pcp9ilSRIqM78P/ZsJSY1b0MXfXYGa/xgT6pb/0FOjnyUf9GufJPDe5oUn8RAqZWeuOMuXng5TwcmP12LaU8s7ievdfq+DrB8Bkhkd+hfJN7FeoiDitX/ZFMfzrcCxWG4NaVmZC1xBMJhMWq42tEReJjkukrI9xKSqvzthoEj8RJza4VRVqB/pw6XIKU34+mPXgGu2hfi+wWY2lGSwp9ilSRJzWH0fOM/KbnVisNu5vXIHxXYxgA8b0FS2qlaJ7o/K0qFbKrpeirqdwI1LIuLmYmXJffUwm+C78bzYePZ/1AR2nQjF/OLsXNr5vnyJFxCntPPkPQ+duJ9lipUPdAF6/vz5mBwWYrCjciBRCjSuW5KHmFQF4cfFeklKzmPumeGno+JqxveZ1OH/EDhWKiLM5FBXHoM+3cTnZwh3VS/N+n9twdSmYMaJgViUi2XqmY21Ke3tw7HwCM9ccy3pwg15Q7W6wJMGPY8BqtU+RIuIUTlxIoN+nW4i5ksJtFUvwcf8meLi6OLqsTCnciBRSfsXcGN/VmPtmxpojRJzP4nZvkwm6vAtuXnBiA+yca6cqRaSwi4pJpN+nWzgXl0TtQB/mDGpOcQ9XR5eVJYUbkUKsa4MgWtcoTXKqlRd/2EOWNz+WrGTcHg7wy3iIi7JPkSJSaP2TkEz/T7dw6uIVKpXyYu6Q5vh5uTm6rGwp3IgUYiaTick96uHhauaPIxf4YdfprA8IfQzKNYakGPj5/+xTpIgUSnGJKQz8fCt/RccT6OvJV0NC7bqEwq1QuBEp5CqVKs6ou6oDMPmnA1y6nJz5YLOLsTSD2RUOLIUDP9qpShEpTBJTLDzyxXZ2/x1DSS83vnqkOcH+XtkfaLVAxHrYs8j405rFzQ75SOFGxAk82qYa1ct6cyEhmddXZDP3TWB9aDXG2F72NFy5lO/1iUjhkWKxMuLrcLZEXMTbw5W5D4dSvaxP9gfuXwrT6sEXXeC7Icaf0+oZ++1M4UbECbi7mpnSsz4A3249xfbjWS9kR5tnoFR1iI+CXyfmf4EiUihYrTaeXvgnqw9G4+Fq5tOBTalfwS/7A/cvhQUDIPZM+v2xkcZ+OwcchRsRJ9G8ij+9mlYA4IXFe0mxZHG7t5sndH3P2N7xORz/ww4VikhBZrPZGL90L0t2ncHVbOKjfo0JrVoq+wOtFljxLJDRDQ1X9614zq6XqBRuRJzIuE518C/uzqGzccxen83cN5XvgCaDjO0fR0NKYr7XJyIF11u/HOKrzScxmeCd3o24q3ZAzg48sfHGMzbp2CD2tDHOThRuRJxIyeLuPN+5DgDvr/6LUxcvZ31A2CTwDoQLR2Ddm3aoUEQKoo/XHmXG70cBmNyjHt0alsv5wfFn83ZcHlC4EXEy9zcuz+1V/UlMsfLSkr1Zz31TrATc+5ax/cc0iNprjxJFpAD5dutJpi43bkR4tmNt+oZWyt0beOfwDE9Ox+UBhRsRJ2MymXi1Z33cXcysOXSOn/dkM1lfna7Gw5oKS0c57NZNEbG/H/88w/OL9wAw/M5qDL+zWu7fJD67CUFN4FseKrXM/XvfJIUbESdUrYw3j139ITXpx33EJqZkfUCnN8HDD86Ew9ZZdqhQRBzt94PRPDl/FzYb9A2tyDMdauX+TfZ+B98Pu27Hf1cIv/q842vGPFt2onAj4qQev7MaVUoXJzouibdWHsp6sG8QtJ9kbK9+Bf45kf8FiojDbDl2gce+2kGq1Ua3huV4pXs9TKb/BpNs7P0OvnsEbBZo1A/+N9f4WXI933LQay6EdMu74nPAZMvygrzziY2Nxc/Pj5iYGHx9fR1djki+2vDXefp9ugWTCRY/3opGwSUyH2y1GpNunfgDqt4FrZ+E+GjjOnmllnb9V5eI5J+9p2PoM2szcUmp3FW7LB/3b4KbSy7PdexZBN8PBZvVCDbdPgCz2bisfWKj0Tycxz87cvP7W+FGxMk9OX8Xi3eeJiTIl6UjW+Ga1Q+x83/Bh7cb/TfX8y0HHV+3+7++RCRvHYmOp9fHm7iYkExoFX++eLg5nm65DB+ZBZt8lpvf37osJeLkXri3Dn7F3NgfGcucjcezHhx94MZgAw6bZVRE8s7f/1ym/6dbuJiQTP3yfnwysOmtBZvb7BdscqvgVSQieaq0twfPdaoNwDurDnPm0pWMB6bNMpoRx8wyKiJ541xcEv0+2UJkTCLVy3rzxcPN8fF0y92b/DfYdC2YwQYUbkSKhN5Ng2laqSSXky1MWLov40EFcJZREbl1MZdT6P/pFo5fuEyFksX4akgo/sXdc/cmuxdeF2z6F+hgAwo3IkWC2WzMfeNqNrFq/1l+2ZfBvBQ5nT3091fh4M9arkGkELicnMrgOVs5GBVHGR8PvhoSSqCfZ+7eZPdCWPzodcHm/QIdbEDhRqTIqBXow9A2VQGYuHQfCUn/6a3J6eyhJzfBvD7wZjVYNMTow0nOZpkHEbG7pFQLw77cQfjJS/gVc+PLIc2pXLp47t5k94JrwabxgEIRbEDhRqRIGX1XDYL9i3EmJpF3Vx1O/2KllsZdUTdMwvUvExQvA82HGbONJsfD3kWwoL8RdBYMhL3fQ1J8fn8ZIpKNVIuVMd/uYv1f5/Fyd+Hzwc2oHZjLO4R3L4DFw64Gm4HQ5b1CEWxAt4I7uhwRu/v9UDSDP9+Gi9nEkhGtqFfe79qL+5cad0UBaU3EQFrg+XcyLqvVmM14/w+wfwlcOnltqKsnVA+DkB5QswN46u+ZiD1ZrTae+W43i3b8jbuLmc8HN6NV9dK5e5M/58MPj10XbKY5PNhonpssKNyIwIivw1m2J5KGFfz4/vFWuJivO1uzf6lx19T1zcW+5Y3p0zOa58Zmg8hdRsjZvwQuHrv2mos7VLsLQrpDrU5QrGS+fU0iAjabjZd/2s/nfxzHxWxixkON6VgvMHdvcn2waTII7n3X4cEGFG6ypHAjAmdjEwl7ey1xSam83L0uA1pUTj/gZmcZtdng7N5rQef8dZe+zK5Q9c6rQedeKF4qL78kEQGm/XqYab/+BcDb/2vI/U0q5O4NCmiwAYWbLCnciBi+2HicCUv34ePhyq9PtSXAN5d3UORE9MFrQSf6ulvQTS5QpbURdGp3Ae+yef/ZIkXMZxsiePmn/QBM7BrCoFZVcvcGf86DxY8BNmgyGO59p8AEG1C4yZLCjYjBYrVx34d/8OffMdzbIIgZDzXO3w88/9e1oBO1+9p+kxkqtjSCTp2uNy68JyLZWrj9FP+3yPh7NbZ9TUbfXSN3b1DAgw0o3GRJ4Ubkmr2nY+g2fQNWG3w+uBntatnpDMrFY0Zvz/4lRmNyGhMEhxpBJ6Qb+OXylLpIEbRibySPfx2O1QZD7qjCi/fWyd0K37u+hR+GAzZo+jB0frvABRtQuMmSwo1Ieq/8tJ9PN0RQoWQxVj3ZlmLudl79+9JJI+gcWAqntqR/rXzTa0GnZGX71iVSCGz46zwPz9lGssVKr6YVeP3+Bk4ZbEDhJksKNyLpJSSlEvbOWiJjEhl+ZzWe7VjbccXEnoEDPxpndE5sJN3t6EENrwadHlCqmqMqFCkwdpz4h/6fbuFysoVO9QKZ/lDj9Hc+ZmfXN/DD4xjBZgh0fqvABhtQuMmSwo3IjVbui2LYlztwNZtYNro1tQJ9HF0SxJ2Fg1eDzvENxt0b/wqodzXodIcytRxXo4iDHIiMpffHm4hNTKV1jdJ8MrApHq65OOtayIINKNxkSeFGJGND525n1f6zNK1UkgXDWmDOzb8A81vCeTj4k3H5KmItWK9bOqJM7WtBp2wI5OaUvEghFHE+gf/N3MT5+CSaVCrJl0Oa4+XumvM32Pk1LBlBWrC59+1C8fdG4SYLCjciGTtz6Qph76zlcrKFKT3rUaW0N9FxiZT18aR5Ff/cne7OT5cvwqHlxhmdo7+BNeXaa/7VrgWdoIaF4ge2SG5ExlzhgY82cfrSFeoE+TLv0dvxK+aW8zcopMEGFG6ypHAjkrlP1h9j8rIDmEi/+EKQnycTuobQsV4Bu037yiU4vNIIOkd+BUvStddKVLrWo1O+caH5AS6SmQvxSfT6eBNHzyVQpXRxFgxrQRkfj5y/wc6vYMlIwAbNHjEuRRWivxcKN1lQuBHJ3LLdZxjxzc4b9v/74++jfo0LXsD5V1LctaDz1ypIvXLtNb9gqNPNuOuqQvMC31sg8l+xiSk8NHsze0/HEuTnyaLhLSlfoljO3yBdsBkKnd8sVMEGFG6ypHAjkjGL1cYdr/9GZExihq+bgEA/TzY8e1fBuUSVmeQE40zO/iVG4Em+bqVynyBjssCQ7lCxRc6WlRBxoMQUCwM+28rWiIuUKu7O/GEtqF7WO+dvEP4lLB1FYQ42kLvf37noQBIRZ7Y14mKmwQaMy1SRMYlsjbhIi2oFfF0o9+LXem9Srhi9OfuXGL06cZGwdZbxKF7mWtCpdAe46EeiFCzJqVaGf7WDrREX8fFw5YuHm998sGn+KHR6o1AGm9zS32QRASA6LvNgczPjCgy3YlD7XuORmgTH1hhB5+AySDgH2z8zHsX8oU4XI+hUaQsuuWjSFMkHFquNsQt28fuhc3i6mfl0UDPqlffL+RsU0WADUCAuPM+YMYPKlSvj6elJaGgoW7duzXTs7Nmzad26NSVLlqRkyZKEhYVlOV5EcqasT84WzjwQGUdyqjX7gQWRqwfU7AA9PoT/OwL9vofGA8GrFFy5COFz4av74c3qsHg4HFphBCIRO7PZbLz4w15+2h2Jm4uJmf2a0LyKf87fIHwuLL3aY9N8WJEKNlAAem7mz5/PgAEDmDlzJqGhoUybNo2FCxdy6NAhypa9cZ2bvn370qpVK1q2bImnpyevv/46ixcvZt++fZQvXz7bz1PPjUjG/u25iYpJJLsfCgG+HgxuVYWHQivi6+kEZzgsqXDiD+OMzoEfISH62msevlCzo3FGp/rdxpkgkXz22vKDzFx7FJMJPuhzG10alMv5weFzr56x4Wqwed0pgk2haigODQ2lWbNmTJ8+HQCr1UpwcDCjRo3iueeey/Z4i8VCyZIlmT59OgMGDLjh9aSkJJKSrv3LKzY2luDgYIUbkQys2BvJ8K+MhSyv/8Hw74/F7o3KsfHoBaLjjL9T3h6uPNgsmIfvqEK53Ny5UZBZLXBy89Wgs9To0fmXW3HjzE9Id6jR3ujtEcljH645whsrDgHw2n31ebB5xZwfvOML+HG0sR36GHR8zSmCDRSicJOcnIyXlxeLFi2iR48eafsHDhzIpUuXWLJkSbbvERcXR9myZVm4cCFdunS54fWJEycyadKkG/Yr3IhkbMXeSCb9uD9dc/H189wkpVpYsusMs9cd469o4y4kV7OJLg2CGNqmKnXL5aInoKCzWuHvbUbI2b8EYk5de821GNQIM+bRqdkBPArAkhVS6H21+QQv/rAXgOc71+bRNrlYR82Jgw0UonBz5swZypcvz8aNG2nRokXa/meeeYa1a9eyZcuWLI42PP7446xcuZJ9+/bh6Xljz4DO3IjknsVqY2vExSxnKLbZbKw5fI5Za4+x6diFtP13VC/N0DZVaVOjdO5WJy7obDY4E26EnP1L4J/j115z8TAuWYV0Ny5hFSvhqCqlEFuy6zRPzN+FzQYj21Xn6Q65WDdtxxz4cYyxHTocOk51qmADRehW8Ndee4158+axZs2aDIMNgIeHBx4euZjBUURwMZuyvd3bZDLRrlZZ2tUqy56/Y5i1/hg/74lkw5HzbDhyntqBPgxtXZWuDcvh7log7l24NSYTlG9iPMImQdTua0HnwhE49LPxMLtBtXZG0KnVGbyyaAK1WozVz+PPgncAVGqpeXeKqNUHzvLUgj+x2WBAi0o8dU/NnB9cBIJNbhXay1JvvfUWkydP5tdff6Vp06Y5/kw1FIvkn1MXL/P5H8eZt+0kl5MtAAT6ejK4VWX6OEvz8X/ZbBB94FrQOXfg2mtmV6jSxpgduXYX8C5z7bX9S2HFsxB75to+33LQ8XVjJmUpMjYdvcCgz7eSlGql523left/DXO+cO32z+GnJ4zt2x+HDlOcNtgUmstSYDQUN2/enA8++AAwGoorVqzIyJEjM20ofuONN3j11VdZuXIlt99+e64+T+FGJP/FXE7h660n+PyP45y7rvm4T/NgBrdyoubjjJw7ZASX/Uvg7J5r+01mqNTKOKPj6gFLR8MN96Vd/aXUa64CThGx++9L9Jm1mYRkC2F1AvioX2PcXHJ4pjNdsBkBHV512mADhSzczJ8/n4EDB/Lxxx/TvHlzpk2bxoIFCzh48CABAQEMGDCA8uXLM3XqVABef/11xo8fzzfffEOrVq3S3sfb2xtv7+xnbVS4EbGfzJqPuzYsx9DWVQkp5+R/By8cvXZGJ3JXDg8yGWdwntijS1RO7q+zcfT6eBP/XE6hRdVSfD64GZ5uOfxvvv0z+OlJY7sIBBsoZOEGYPr06bz55ptERUXRqFEj3n//fUJDQwG48847qVy5MnPmzAGgcuXKnDhx4ob3mDBhAhMnTsz2sxRuROzParWx9vA5Zq1L33zcukZphrauSmtnaz7OyD/HjTM6O7+C84eyHz/wJ6jSOt/LEsc4dfEyD8zcyNnYJBpW8OProbfj7ZHDNthtn8KyscZ2i5Fwz2SnDzZQCMONPSnciDjW7r8vMXt9BD/vicRiNX781A704dE2VenSwEmaj7OyZxF8NyT7cfd/CvUfyP96xO6iYxP538ebOHHhMjUDvJn/aAtKFnfP2cFFNNhA7n5/O/lPEREpaBpUKMEHfW5jzdN3MrhVZbzcXTgYFcfYBX/S5o3fmbXuKLGJKY4uM/94B+RsnIvu8nRGly4n0//TrZy4cJlg/2J8OSQ0F8HmkyIbbHJLZ25ExKEuXU7m6y0nmbMxffPxQ6EVGdSysvM1H1stMK0exEZyY0PxddyKQ5unjDtgtOSDU0hISqXvJ1vYdeoSZX08WPRYSyqW8srZwds+gWVPGdtFNNjoslQWFG5ECqakVAtLdp5h1vpjHHH25uP9S2HBv8vF/HehCxuUrAL/RBi7/ILh7glQ734w62R7YZWYYmHIF9v448gFSni5sWBYC2oG5HBW662z4eenje2Wo6D9K0Uu2IDCTZYUbkQKNqvVxprD0cxad4zNxy6m7W9dozSPtqnKHdWdpPk4w3luyhtT5tfuAnsXwa8TIfa08Vr5JsYcJhVzN/2FOF6qxcrjX4fzy/6zFHd34euht9MouETODlawSaNwkwWFG5HCY/ffl5i1zpj5+GrvMXWCfHm0TRW6NCiX8/lACqrsZihOvgybZ8CGaZBsnM0ipAeETQT/Kg4oWHLLarXx9KI/+T78NO6uZuYMbkbLaqVzdnC6YDMa2r9cZIMNKNxkSeFGpPA5dfEyn26IYMH2U2kzHwf5efJwqyo82DwYH2ec+fh6cWfh91dh55dgs4KLO4QOg9ZPax2rAsxmszHpx/3M2XgcF7OJmf2a0D4khw3lCjY3ULjJgsKNSOH1b/Px538c53y80Xzs82/zcavKBPk5eePt2X2w8gU49rvxvJg/tHsemgwCFycPeIXQO6sO8/7qvwB4t3dDet5WIWcHXh9sWo0x1jIr4sEGFG6ypHAjUvhl1nzcrWE5hrapSp0gJ/67bbPBkV+NkPPvZIClahh3z9TsoF+CBcQn648xeZmxztjL3esyoEXlnB2oYJMphZssKNyIOI9/m48/XnuMLRHpm4+HtalGq+qlnKP5OCOWVAifA79PhcvnjX1V2hrT8AfWd2hpRd2Cbad45rvdAPxfh1qMaFc9ZwdumQXL/8/YbvWE0VvlrP//3gSFmywo3Ig4pz9PXWLW+mMsd9bm48wkxsD6d2Dzh2BJBkxwW1+46yXwCXR0dUXOz3siGflNOFYbPNqmKuM61c5ZwN7yMSx/xti+40nj9n8Fm3QUbrKgcCPi3P5tPp6/7RRXUopQ8/E/x+HXSbDve+O5W3G44wljwjf3HE4UJ7dk7eFzPPLFNlIsNh5sFszU++or2OQhhZssKNyIFA1Ftvn41FZY+Tz8vc147lMOwiZA/V6aBDAfbT9+kX6fbiExxcq9DYJ4/8HbcDHnIKBsnmnMdwRwx1i4e7yCTSYUbrKgcCNStCSmWFiy6zSz1h3j6LkE4GrzcSNj5mOnbD622YwzOKsmQsxJY19QI6Mfp/IdjqzMKe07E8ODszYTl5hK25plmD2gac4WgFWwyRWFmywo3IgUTVarjd8PGTMfX9983KZmGR5tXdU5m49TEmHLR7DubUiOM/bV7mLMmVKqmmNrcxLHzsXT6+NNnI9Pplnlksx9OJRi7i7ZH7j5I1jxnLHd+imjR8rZ/v/LYwo3WVC4EZFdpy4x+z/NxyFBvjzapir3Nghyvubj+HOwZirs+NyYBNDsBs2HQpv/Ay9/R1dXaJ2+dIX/fbSRMzGJ1C3ny7eP3o5vTnq6FGxuisJNFhRuRORfJy9c5rM/0jcfl/Pz5OE7qtC7mRM2H0cfgF9egiOrjOeeJeDO56DpEHB1d2hphc35+CR6zdzEsfMJVC1TnAXDWlDa2yP7Azd9CCvHGdutn4a7XlSwySGFmywo3IjIf/2TkMzXW04wZ+OJa83Hnkbz8eCWVQj083RwhXnsyK9GyInebzz3rwb3vAK1OusXbQ7EXEmhz6zN7I+MpXyJYix8rAXlSuSgQV3B5pYo3GRB4UZEMpOYYuGHnaeZtf4Yx642H7u5mOjWsDxD21ShdqAT/cywpMKur+C3yZBwzthX6Q6j6bhcI4eWVpBdSbYw4LMtbDv+D6W93VkwrAVVy3hnf+CmGcZdbGBcDmz3goJNLincZEHhRkSyY7Xa+O1gNLPWH2Prdc3HbWuW4dE2VWlZzYmaj5PiYMO7xi/f1ETABA37wN0vgW85R1dXoCSnWhk6dztrD5/Dx9OV+Y+2IKRcDn6PKNjkCYWbLCjciEhu7Dz5D5+sj2D53mvNx3XLGc3Hnes7UfPxpVOw+mXYs8B47loMWo02VqT2yMGZCSdnsdoY/e1Olu2JpJibC18OaU7Tyjloxt44HX55wdhu84yx0KmCzU1RuMmCwo2I3IwTFxL4bEMEC7b/ndZ8XL5EMQa3qsyDzSvi7eHq4ArzyN87jLMMpzYbz70DjbM4DfuAOQe3ODshm83GuO/3MG/bKdxcTHw6sBltapbJ/kAFmzylcJMFhRsRuRX/JCTz1eYTfLHpOOfjkwGj+bhvaCUGt6pMgK8TNB/bbHBgKawabyzrAMZinPe8ClXbOrQ0e7PZbEz5+QCz10dgNsGMhxrTqX5Q9gdu/AB+edHYbvss3DlOweYWKdxkQeFGRPJCYoqFxTtPM/s/zcfdG5VnaOuq1Ar0cXCFeSA1CbbOgrVvQlKMsa9mJ+POqtI1HFubnUz/7S/e+uUwAG/c34BezYKzP+iP92HVS8Z22+eg3bh8rLDoULjJgsKNiOSltObjdcfYejx98/GwNlVp4QzNxwkXYO1rsO1TsFnA7GrMjXPnc049CeDcTccZv2QfAC/eW4dHWlfN/iAFm3yjcJMFhRsRyS87T/7D7PXHWLE3Kq35uF55X4a2zrj52GK1sTXiItFxiZT18aR5Ff+cLbboKOcOG5eqDi83nnv6GXf/NH8UXHMwgV0hsnjn3zw5/08ARt9dg7Hta2Z/0B/vGd8fULDJBwo3WVC4EZH8duJCAp9uiGDB9lMkpliBG5uPV+yNZNKP+4mMSUw7LsjPkwldQ+hYLwc9HY50bA2sfBHO7jGel6xsrFdVp5tT9JWs2n+Wx77agcVqY1DLykzoGpL92TcFm3yncJMFhRsRsZd/EpL5cvMJvth4nAsJ15qPW1QtxS/7z94w/t9fnx/1a1zwA47VAn9+C6tfgfgoY1/FFsYkgOWbOLa2W7Dx6HkGfb6N5FQr9zUuz1sPNMSc3dm0DdPg1wnG9p3jjMt1kucUbrKgcCMi9paYYuH78NN8sv4Yx84nZDnWBAT6ebLh2bsK9iWqfyXFw8b3jV6T1CvGvvq94O7xUCIHzbcFyK5Tl+g7ezMJyRbuCQngw76Ncc1uHiMFG7vJze9vJ5l9SkSk4PJ0c+Gh0Ir8OrYtT9+Tde+GDYiMSWTB9pNExyVisRbwf396eBvzt4zaAQ0fMvbtWQDTmxqTAibFOba+HDoUFcegz7eSkGyhVfVSvN/nthwEm3evCzbPK9gUIDpzIyJiR0t2nWbMvF05Hm82gX9xD8r4eFDWx/izjI8HZbyvbf+739vD1fF3Zp3ZafTjnNhgPC9eFu56AW7rX2AnATx54TIPzNxIdFwSjYJL8PUjoRTPblLGDe/CrxON7Tufhzufzfc6i7rc/P52kik1RUQKh7I+OZvkz9fTjbikFKw2OB+fxPn4JA5EZn2Mp5s5XfAp6+OZYRgq7e2Bu2s+nbgvdxsM+gkOLjNuib54DH4cA1tmQYfJUO2u/Pncm3Q2NpG+n24mOi6JWgE+zBncLPtgs/4dWD3J2G73ArR9Jv8LlVzRmRsRETuyWG3c8fpvRMUkktEP3+t7bmw2GxcvJ3MuLonouCTOXf+IN/48f/W1+KTUXNVR0sstk7NA6QNRCS+3mz8blJoM2z+FNa9B4iVjX417oP0rULb2zb1nHvonIZleH2/ir+h4KpXyYuGwFpTNboZpBRuHUUNxFhRuRMTRVuyNZPhX4QDpAs6t3C11OTmV83HJnItPTBeCov8Ths7FJZGaiz4eNxcTpb3/c1nsujD0byAq7e1BMfdMLjtdvgjr3jRmO7amgskFmgwyGnC9c7BGUz6IT0ql7+zN/Pl3DAG+Hix6rCXB/l5ZH7T+baOPCKDdi9D2//K/UEmjcJMFhRsRKQgcNc+N1Woj5krKdaEn8YYzQtGxxp+XLqfk6r19PFyNy14+GYeh8tYzBO94Hc8jPxsHePhC67EQOhzc8ndNrusnTCzh5cZHvx9lc8RFSnq5sWBYC2oEZLNcxrq34LdXjO27XjQmLxS7UrjJgsKNiBQUBX2G4qRUCxfikzM4C5R4QxhKSrXm+H1vNx9ggvvX1OEYABfdAllfaSQXKt1LWT/PdGeG8qJJOqMgCeDpambBYy1oUKFE1m+gYFMgKNxkQeFGRCRv2Ww24pJS058Buu5S2PX9QhcSkrDZwISVHuY/eMZtPkEmY02ucGt1Jqf0I9x27Xb565uk0/UDXd847etBqeIZN0n/ewkws190M7O7BLjuTfhtsrF910vQ5umb/TbJLVK4yYLCjYiI46RarOmapC9eukSFA59y28k5uFuNMyu/ud7BaykPcjgpd4ty/rdJurSPB/O3nSIuMeNm62wnTFSwKVAUbrKgcCMiUgDFRsLvk2Hn14ANXDxIaT6MqPojiE5xz9Mm6f/6dujttKhWKv3OtW8a9YAx23Lrp27+a5M8oXluRESkcPENgu4zoPkw+OUFiFiH26b3Cf7zG4LbPQ+NB4JLxr+yMmuS3nT0Ar8fOpftR0fHpe/FUbAp/BRuRESk4AhqAAOWwuGV8MuLcOEvWDbWuI38nslQPeyGlcfNZhMli7tTsrg7tQKv3fVUv3yJHIWbdBMrrn0Dfn/V2L57gnE3lxQ6WltKREQKFpMJanWExzdB57egmD+cOwhfPwBf3Qdn9+XobZpX8SfIz5PM7rUyYdx+37zK1d4eBRunoXAjIiIFk4sbNB8Ko3dCy1FgdoOjv8HMO2DpaIg7m/XhZhMTuoYA3BBw/n0+oWuI0Uy85vVrwSZsooJNIadwIyIiBVuxEsYlqZFbIaQ72KwQ/gV80NiYgyblSqaHdqwXxEf9GhPol36SwEA/z2szQa95HdZMMV4Imwh3PJl/X4vYhe6WEhGRwuXEJlj5PJwxlrDAtwKETYB6D4A543+zZzph4prXYM1UY1DYJLjjCft8DZJruhU8Cwo3IiJOwGqFvd/BrxMh9m9jX7nG0GEKVGqRs/f4fSqsfc3YVrAp8BRusqBwIyLiRFKuwOYPjdW6k+ONfSHdjctL/lWvjbNa4MRGiD8L3gEQsQ7WvWG81v5laDXG7qVL7ijcZEHhRkTECcVHGw3B4XONnhyzG4QOM9aBilgHK56F2DM3Htf+FWg12v71Sq4p3GRB4UZExImd3W9MAnj0N+O5u/e1MzoZ6fUlhHSzT21yS3Lz+1t3S4mIiPMICIH+i6Hvd1C6VtbBBhOseM64ZCVOpUCEmxkzZlC5cmU8PT0JDQ1l69atWY5fuHAhtWvXxtPTk/r16/Pzzz/bqVIRESkUaoRBpzeyGWSD2NNGL444FYeHm/nz5zN27FgmTJhAeHg4DRs2pEOHDkRHR2c4fuPGjfTp04chQ4awc+dOevToQY8ePdi7d6+dKxcRkQLt8vmcjYvPejJAKXwc3nMTGhpKs2bNmD59OgBWq5Xg4GBGjRrFc889d8P43r17k5CQwE8//ZS27/bbb6dRo0bMnDkz289Tz42ISBERsR6+6JL9uIE/QZXW+V+P3JJC03OTnJzMjh07CAsLS9tnNpsJCwtj06ZNGR6zadOmdOMBOnTokOn4pKQkYmNj0z1ERKQIqNQSfMtx4+IL/zKBb3ljnDgVh4ab8+fPY7FYCAgISLc/ICCAqKioDI+JiorK1fipU6fi5+eX9ggODs6b4kVEpGAzu0DH168+yWR1qY6vGePEqTi85ya/jRs3jpiYmLTHqVOnHF2SiIjYS0g36DUXfIPS7/ctZ+zXbeBOydWRH166dGlcXFw4ezZ9M9fZs2cJDAzM8JjAwMBcjffw8MDDwyNvChYRkcInpBvUvjf9DMWVWuqMjRNz6Jkbd3d3mjRpwurVq9P2Wa1WVq9eTYsWGa8N0qJFi3TjAVatWpXpeBEREcwuRtNw/QeMPxVsnJpDz9wAjB07loEDB9K0aVOaN2/OtGnTSEhIYPDgwQAMGDCA8uXLM3WqsWrrmDFjaNu2LW+//Tb33nsv8+bNY/v27cyaNcuRX4aIiIgUEA4PN7179+bcuXOMHz+eqKgoGjVqxIoVK9Kahk+ePIn5uiXsW7ZsyTfffMOLL77I888/T40aNfjhhx+oV6+eo74EERERKUAcPs+NvWmeGxERkcKn0MxzIyIiIpLXFG5ERETEqSjciIiIiFNRuBERERGnonAjIiIiTkXhRkRERJyKw+e5sbd/73zX6uAiIiKFx7+/t3Myg02RCzdxcXEAWh1cRESkEIqLi8PPzy/LMUVuEj+r1cqZM2fw8fHBZDLl6XvHxsYSHBzMqVOnNEFgPtL32T70fbYPfZ/tR99r+8iv77PNZiMuLo5y5cqlW7kgI0XuzI3ZbKZChQr5+hm+vr76i2MH+j7bh77P9qHvs/3oe20f+fF9zu6Mzb/UUCwiIiJOReFGREREnIrCTR7y8PBgwoQJeHh4OLoUp6bvs33o+2wf+j7bj77X9lEQvs9FrqFYREREnJvO3IiIiIhTUbgRERERp6JwIyIiIk5F4UZEREScisJNHpkxYwaVK1fG09OT0NBQtm7d6uiSnM66devo2rUr5cqVw2Qy8cMPPzi6JKc0depUmjVrho+PD2XLlqVHjx4cOnTI0WU5nY8++ogGDRqkTXTWokULli9f7uiynN5rr72GyWTiiSeecHQpTmXixImYTKZ0j9q1azusHoWbPDB//nzGjh3LhAkTCA8Pp2HDhnTo0IHo6GhHl+ZUEhISaNiwITNmzHB0KU5t7dq1jBgxgs2bN7Nq1SpSUlK45557SEhIcHRpTqVChQq89tpr7Nixg+3bt3PXXXfRvXt39u3b5+jSnNa2bdv4+OOPadCggaNLcUp169YlMjIy7bFhwwaH1aJbwfNAaGgozZo1Y/r06YCxflVwcDCjRo3iueeec3B1zslkMrF48WJ69Ojh6FKc3rlz5yhbtixr166lTZs2ji7Hqfn7+/Pmm28yZMgQR5fidOLj42ncuDEffvghkydPplGjRkybNs3RZTmNiRMn8sMPP7Br1y5HlwLozM0tS05OZseOHYSFhaXtM5vNhIWFsWnTJgdWJpI3YmJiAOMXr+QPi8XCvHnzSEhIoEWLFo4uxymNGDGCe++9N93Paslbf/31F+XKlaNq1ar07duXkydPOqyWIrdwZl47f/48FouFgICAdPsDAgI4ePCgg6oSyRtWq5UnnniCVq1aUa9ePUeX43T27NlDixYtSExMxNvbm8WLFxMSEuLospzOvHnzCA8PZ9u2bY4uxWmFhoYyZ84catWqRWRkJJMmTaJ169bs3bsXHx8fu9ejcCMimRoxYgR79+516LVzZ1arVi127dpFTEwMixYtYuDAgaxdu1YBJw+dOnWKMWPGsGrVKjw9PR1djtPq1KlT2naDBg0IDQ2lUqVKLFiwwCGXWRVublHp0qVxcXHh7Nmz6fafPXuWwMBAB1UlcutGjhzJTz/9xLp166hQoYKjy3FK7u7uVK9eHYAmTZqwbds23nvvPT7++GMHV+Y8duzYQXR0NI0bN07bZ7FYWLduHdOnTycpKQkXFxcHVuicSpQoQc2aNTly5IhDPl89N7fI3d2dJk2asHr16rR9VquV1atX69q5FEo2m42RI0eyePFifvvtN6pUqeLokooMq9VKUlKSo8twKnfffTd79uxh165daY+mTZvSt29fdu3apWCTT+Lj4zl69ChBQUEO+XyduckDY8eOZeDAgTRt2pTmzZszbdo0EhISGDx4sKNLcyrx8fHp/hUQERHBrl278Pf3p2LFig6szLmMGDGCb775hiVLluDj40NUVBQAfn5+FCtWzMHVOY9x48bRqVMnKlasSFxcHN988w1r1qxh5cqVji7Nqfj4+NzQL1a8eHFKlSqlPrI89PTTT9O1a1cqVarEmTNnmDBhAi4uLvTp08ch9Sjc5IHevXtz7tw5xo8fT1RUFI0aNWLFihU3NBnLrdm+fTvt2rVLez527FgABg4cyJw5cxxUlfP56KOPALjzzjvT7f/8888ZNGiQ/QtyUtHR0QwYMIDIyEj8/Pxo0KABK1eupH379o4uTSTX/v77b/r06cOFCxcoU6YMd9xxB5s3b6ZMmTIOqUfz3IiIiIhTUc+NiIiIOBWFGxEREXEqCjciIiLiVBRuRERExKko3IiIiIhTUbgRERERp6JwIyIiIk5F4UZEREScisKNiBR5JpOJH374wdFliEgeUbgREYcaNGgQJpPphkfHjh0dXZqIFFJaW0pEHK5jx458/vnn6fZ5eHg4qBoRKex05kZEHM7Dw4PAwMB0j5IlSwLGJaOPPvqITp06UaxYMapWrcqiRYvSHb9nzx7uuusuihUrRqlSpXj00UeJj49PN+azzz6jbt26eHh4EBQUxMiRI9O9fv78eXr27ImXlxc1atRg6dKl+ftFi0i+UbgRkQLvpZde4v777+fPP/+kb9++PPjggxw4cACAhIQEOnToQMmSJdm2bRsLFy7k119/TRdePvroI0aMGMGjjz7Knj17WLp0KdWrV0/3GZMmTaJXr17s3r2bzp0707dvXy5evGjXr1NE8ohNRMSBBg4caHNxcbEVL1483ePVV1+12Ww2G2B77LHH0h0TGhpqGz58uM1ms9lmzZplK1mypC0+Pj7t9WXLltnMZrMtKirKZrPZbOXKlbO98MILmdYA2F588cW05/Hx8TbAtnz58jz7OkXEftRzIyIO165dOz766KN0+/z9/dO2W7Roke61Fi1asGvXLgAOHDhAw4YNKV68eNrrrVq1wmq1cujQIUwmE2fOnOHuu+/OsoYGDRqkbRcvXhxfX1+io6Nv9ksSEQdSuBERhytevPgNl4nySrFixXI0zs3NLd1zk8mE1WrNj5JEJJ+p50ZECrzNmzff8LxOnToA1KlThz///JOEhIS01//44w/MZjO1atXCx8eHypUrs3r1arvWLCKOozM3IuJwSUlJREVFpdvn6upK6dKlAVi4cCFNmzbljjvu4Ouvv2br1q18+umnAPTt25cJEyYwcOBAJk6cyLlz5xg1ahT9+/cnICAAgIkTJ/LYY49RtmxZOnXqRFxcHH/88QejRo2y7xcqInahcCMiDrdixQqCgoLS7atVqxYHDx4EjDuZ5s2bx+OPP05QUBDffvstISEhAHh5ebFy5UrGjBlDs2bN8PLy4v777+edd95Je6+BAweSmJjIu+++y9NPP03p0qV54IEH7PcFiohdmWw2m83RRYiIZMZkMrF48WJ69Ojh6FJEpJBQz42IiIg4FYUbERERcSrquRGRAk1XzkUkt3TmRkRERJyKwo2IiIg4FYUbERERcSoKNyIiIuJUFG5ERETEqSjciIiIiFNRuBERERGnonAjIiIiTuX/AZ5/GXEkiCNhAAAAAElFTkSuQmCC\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [7 / 15], Step [22 / 225], Loss: 0.04863778129220009, Validation Loss: 0.0\n", + "Epoch [7 / 15], Step [44 / 225], Loss: 0.09685459733009338, Validation Loss: 0.18659985065460205\n", + "Epoch [7 / 15], Step [66 / 225], Loss: 0.08631588518619537, Validation Loss: 1.3988771438598633\n", + "Epoch [7 / 15], Step [88 / 225], Loss: 0.6523657441139221, Validation Loss: 0.6873682737350464\n", + "Epoch [7 / 15], Step [110 / 225], Loss: 0.07394303381443024, Validation Loss: 0.03567586466670036\n", + "Epoch [7 / 15], Step [132 / 225], Loss: 0.048379410058259964, Validation Loss: 0.0\n", + "Epoch [7 / 15], Step [154 / 225], Loss: 0.04830015450716019, Validation Loss: 0.024945590645074844\n", + "Epoch [7 / 15], Step [176 / 225], Loss: 0.04822903499007225, Validation Loss: 0.0\n", + "Epoch [7 / 15], Step [198 / 225], Loss: 0.04814707115292549, Validation Loss: 0.0\n", + "Epoch [7 / 15], Step [220 / 225], Loss: 0.04806003347039223, Validation Loss: 0.0\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [8 / 15], Step [22 / 225], Loss: 0.04794354736804962, Validation Loss: 0.0\n", + "Epoch [8 / 15], Step [44 / 225], Loss: 0.04784921184182167, Validation Loss: 0.0\n" + ] + }, + { + "output_type": "error", + "ename": "KeyboardInterrupt", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mval_losses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloaders\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 631\u001b[0m \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 632\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 633\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 634\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 635\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 675\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 676\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 677\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 678\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 679\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos_indices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0mneg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mneg_indices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mimg1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0manc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0mimg2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAD\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0mimg3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNC\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mneg\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataset.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[0msample_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumulative_sizes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdataset_idx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 243\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdataset_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msample_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/transforms/transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 96\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/transforms/transforms.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m 1534\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdegrees\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranslate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshear\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1536\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maffine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcenter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcenter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1537\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1538\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/transforms/functional.py\u001b[0m in \u001b[0;36maffine\u001b[0;34m(img, angle, translate, scale, shear, interpolation, fill, center)\u001b[0m\n\u001b[1;32m 1244\u001b[0m \u001b[0mtranslate_f\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtranslate\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1245\u001b[0m \u001b[0mmatrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_inverse_affine_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcenter_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mangle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtranslate_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1246\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF_t\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maffine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/transforms/_functional_tensor.py\u001b[0m in \u001b[0;36maffine\u001b[0;34m(img, matrix, interpolation, fill)\u001b[0m\n\u001b[1;32m 616\u001b[0m \u001b[0;31m# grid will be generated on the same device as theta and img\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[0mgrid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_gen_affine_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 618\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_apply_grid_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 619\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 620\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/transforms/_functional_tensor.py\u001b[0m in \u001b[0;36m_apply_grid_transform\u001b[0;34m(img, grid, mode, fill)\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfill_img\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 574\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 575\u001b[0;31m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_cast_squeeze_out\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneed_cast\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneed_squeeze\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 576\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 577\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/transforms/_functional_tensor.py\u001b[0m in \u001b[0;36m_cast_squeeze_out\u001b[0;34m(img, need_cast, need_squeeze, out_dtype)\u001b[0m\n\u001b[1;32m 530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 532\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0m_cast_squeeze_out\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneed_cast\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneed_squeeze\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_dtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 533\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mneed_squeeze\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 534\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], "source": [ "from itertools import cycle\n", "\n", "test_iter = cycle(iter(loaders['test']))\n", "# Training loop\n", - "epochs = 100\n", + "epochs = 15\n", "trip_model.to(device)\n", "losses = []\n", "val_losses = []\n", + "\n", + "transform_train = transforms.RandomAffine(degrees=5, translate=(0.05, 0.05), scale=(0.95, 1.05))\n", + "\n", "for epoch in range(epochs):\n", " losses.append([])\n", " val_losses.append([])\n", " for i, (img1, img2, img3, _) in enumerate(loaders['train']):\n", - " img1, img2, img3 = img1.to(device), img2.to(device), img3.to(device)\n", " size = img1.size(0)\n", " img1, img2, img3 = img1[torch.randperm(size)], img2[torch.randperm(size)], img3[torch.randperm(size)]\n", + " img1 = transform_train(img1)\n", + " img2 = transform_train(img2)\n", + " img3 = transform_train(img3)\n", + " img1, img2, img3 = img1.to(device), img2.to(device), img3.to(device)\n", + "\n", + "\n", " out1, out2, out3 = trip_model(img1, img2, img3)\n", "\n", " loss = trip_criterion(out1, out2, out3)\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", - "\n", + " losses[epoch].append(loss.item())\n", " if (i + 1) % (total_step // 10) == 0:\n", - " losses[epoch].append(loss.item())\n", "\n", - " val_img1, val_img2, val_img3, _ = next(test_iter)\n", - " val_img1, val_img2, val_img3 = val_img1.to(device), val_img2.to(device), val_img3.to(device)\n", - " val_out1, val_out2, val_out3 = trip_model(val_img1, val_img2, val_img3)\n", - " val_loss = trip_criterion(val_out1, val_out2, val_out3)\n", - " val_losses[epoch].append(val_loss.item())\n", + " with torch.no_grad():\n", + " val_img1, val_img2, val_img3, _ = next(test_iter)\n", + " val_img1, val_img2, val_img3 = val_img1.to(device), val_img2.to(device), val_img3.to(device)\n", + " val_out1, val_out2, val_out3 = trip_model(val_img1, val_img2, val_img3)\n", + " val_loss = val_criterion(val_out1, val_out2, val_out3)\n", + " val_losses[epoch].append(val_loss.item())\n", "\n", - " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step}], Loss: {loss.item()}, Validation Loss: {val_loss.item()}\")\n", + " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step}], Loss: {np.mean(losses[epoch])}, Validation Loss: {np.mean(val_losses[epoch])}\")\n", "\n", - " scheduler.step()" - ], + " scheduler.step()\n", + " # Calculate mean of each row\n", + " mean_list1 = np.mean(losses, axis=1)\n", + " mean_list2 = np.mean(val_losses, axis=1)\n", + "\n", + " # Plot the means\n", + " plt.plot(mean_list1, label='Train Loss', marker='o')\n", + " plt.plot(mean_list2, label='Val Loss', marker='o')\n", + "\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Mean Loss')\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "A_J3lXLOUDu0", - "outputId": "7c61c7fa-87f6-498f-8cee-a141738d31fb" + "id": "nnGoOwX-Vk_i" }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch [1 / 100], Step [26 / 269], Loss: 6.237911701202393, Validation Loss: 6.3210039138793945\n", - "Epoch [1 / 100], Step [52 / 269], Loss: 6.128037929534912, Validation Loss: 6.244564056396484\n", - "Epoch [1 / 100], Step [78 / 269], Loss: 6.11774206161499, Validation Loss: 6.238094806671143\n", - "Epoch [1 / 100], Step [104 / 269], Loss: 6.161336898803711, Validation Loss: 6.252033233642578\n" - ] - } + "outputs": [], + "source": [ + "# data = {}\n", + "# labels = {}\n", + "trip_model.eval()\n", + "with torch.no_grad():\n", + " embeddings = trip_model.forward_once\n", + " for stage in ['train', 'cal', 'test']:\n", + " data[stage] = []\n", + " labels[stage] = []\n", + " for i, (img1, _, _, label) in enumerate(loaders[stage]):\n", + " img1 = img1.to(device)\n", + " output = embeddings(img1)\n", + " data[stage].extend(output.cpu().tolist())\n", + " labels[stage].extend(label.tolist())" ] }, { "cell_type": "code", - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.animation import FuncAnimation\n", - "\n", - "\n", - "\n", - "fig, ax = plt.subplots()\n", - "line1, = ax.plot(np.arange(len(losses[0])), losses[0], label='Training Loss')\n", - "line2, = ax.plot(np.arange(len(val_losses[0])), val_losses[0], label='Validation Loss')\n", - "ax.legend()\n", - "\n", - "\n", - "\n", - "def update(frame):\n", - " line1.set_data(np.arange(len(losses[frame])), losses[frame])\n", - " line2.set_data(np.arange(len(val_losses[frame])), val_losses[frame])\n", - " return line1, line2\n", - "\n", - "ani = FuncAnimation(fig, update, frames=len(losses), blit=True)\n", - "plt.show()\n" - ], + "execution_count": 67, "metadata": { + "id": "ayGTRCP5Wl0s", "colab": { - "base_uri": "https://localhost:8080/", - "height": 430 + "base_uri": "https://localhost:8080/" }, - "id": "gD83031xGBYz", - "outputId": "f4d39054-5e51-4ab2-8214-4ed357f5a571" + "outputId": "4c81e07a-cde3-428c-bf42-cc2132a919de" }, - "execution_count": null, "outputs": [ { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} + "output_type": "stream", + "name": "stdout", + "text": [ + "Cal Accuracy: 0.7344444444444445\n", + "Val Accuracy: 0.7146666666666667\n" + ] } - ] - }, - { - "cell_type": "code", - "source": [ - "embeddings = []\n", - "labels = []\n", - "trip_model.eval()\n", - "with torch.no_grad():\n", - " for i, (img1, img2, img3, label) in enumerate(loaders['test']):\n", - " img1, img2, img3, label = img1.to(device), img2.to(device), img3.to(device), label.to(device)\n", - " out1, _, _ = trip_model(img1, img2, img3)\n", - " embeddings.extend(out1.cpu().tolist())\n", - " labels.extend(label.cpu().tolist())\n" ], - "metadata": { - "id": "nnGoOwX-Vk_i" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import accuracy_score\n", "\n", "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(embeddings, labels, test_size=0.2, random_state=42)\n", + "# X_train, X_test, y_train, y_test = train_test_split(embeddings, labels, test_size=0.2, random_state=42)\n", "\n", "# Train a logistic regression model\n", "classifier = LogisticRegression(max_iter=1000)\n", - "classifier.fit(X_train, y_train)\n", + "classifier.fit(data['cal'], labels['cal'])\n", "\n", "# Make predictions\n", - "predictions = classifier.predict(X_test)\n", + "cal_predictions = classifier.predict(data['cal'])\n", + "val_predictions = classifier.predict(data['test'])\n", "\n", "# Calculate accuracy\n", - "accuracy = accuracy_score(y_test, predictions)\n", - "print(f\"Accuracy: {accuracy}\")\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ayGTRCP5Wl0s", - "outputId": "da682072-bc72-4e11-fbc5-f64c96b69050" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy: 0.58\n" - ] - } + "cal_accuracy = accuracy_score(labels['cal'], cal_predictions)\n", + "val_accuracy = accuracy_score(labels['test'], val_predictions)\n", + "print(f\"Cal Accuracy: {cal_accuracy}\")\n", + "print(f\"Val Accuracy: {val_accuracy}\")\n" ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "gaj_LIDgrl6N" - }, - "execution_count": null, - "outputs": [] } ], "metadata": { + "accelerator": "GPU", "colab": { - "provenance": [], "machine_shape": "hm", + "provenance": [], "gpuType": "A100", "include_colab_link": true }, @@ -1040,8 +1262,7 @@ }, "language_info": { "name": "python" - }, - "accelerator": "GPU" + } }, "nbformat": 4, "nbformat_minor": 0 From b9150d2d40388bb41d34e97285a9d04d14f2bc1c Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Tue, 17 Oct 2023 16:53:28 +1000 Subject: [PATCH 08/14] Getting > 0.01 loss on validation after epoch 6 --- Colab version.ipynb | 983 ++++++++++++++++---------------------------- 1 file changed, 350 insertions(+), 633 deletions(-) diff --git a/Colab version.ipynb b/Colab version.ipynb index c9ec5a81f2..ddb2b7e3ed 100644 --- a/Colab version.ipynb +++ b/Colab version.ipynb @@ -12,13 +12,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yzCmT9TUJY10", - "outputId": "bdc05ce0-7e5d-4959-8685-ce3b497d3a18" + "outputId": "8e10d31b-6fc4-4a4a-fdc8-078f4a36b02b" }, "outputs": [ { @@ -36,13 +36,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-Lb7CGdEJCQg", - "outputId": "0a9018e4-6a51-4c9f-de99-d79cd5078de4" + "outputId": "a57f82f8-7816-4bd4-a54e-1e48cab3c3f8" }, "outputs": [ { @@ -59,13 +59,44 @@ }, { "cell_type": "code", - "execution_count": null, + "source": [ + "torch.save(trip_model.state_dict(), '/content/drive/MyDrive/model.pth')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 373 + }, + "id": "ZpU3tv9IGwCB", + "outputId": "11b0d223-3170-4756-825b-922cc5bcbe07" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "error", + "ename": "RuntimeError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrip_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'/content/drive/MyDrive/model.pth'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_use_new_zipfile_serialization\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_open_zipfile_writer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m \u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 442\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 443\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_save\u001b[0;34m(obj, zip_file, pickle_module, pickle_protocol)\u001b[0m\n\u001b[1;32m 663\u001b[0m \u001b[0;31m# .cpu() on the underlying Storage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 664\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'cpu'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 665\u001b[0;31m \u001b[0mstorage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 666\u001b[0m \u001b[0;31m# Now that it is on the CPU we can directly copy it into the zip file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 667\u001b[0m \u001b[0mnum_bytes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/storage.py\u001b[0m in \u001b[0;36mcpu\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;34m\"\"\"Returns a CPU copy of this storage if it's not already on the CPU\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'cpu'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUntypedStorage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 122\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PM8uTdbZpQGJ", - "outputId": "281ff7ad-897b-488c-f95b-db60efdab763" + "outputId": "7b33eba8-1ff0-4df6-d785-81f57a27ba69" }, "outputs": [ { @@ -95,13 +126,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "x1t1iFV_p2MU", - "outputId": "e70aee0f-012c-4647-f4f8-4eec6b9a4397" + "outputId": "21e66a1e-e2c5-422d-ab47-dd1d15d03b89" }, "outputs": [ { @@ -118,13 +149,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Tp_Fb7Eyp-yv", - "outputId": "a06d8cb3-1fb3-4833-a6be-6bd8a9840f54" + "outputId": "2c834d95-b421-4b45-a308-267acefd6334" }, "outputs": [ { @@ -141,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "id": "_8PBaJLSJSCP" }, @@ -174,463 +205,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "u6EH0wk_CkxF" - }, - "outputs": [], - "source": [ - "batch_size = 128" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "7zvKyWj2J7Yk" - }, - "outputs": [], - "source": [ - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "\n", - "\n", - "class CustomDataset(Dataset):\n", - " def __init__(self, root_dir, transform=None):\n", - " self.root_dir = root_dir\n", - " self.transform = transform\n", - " self.image_paths = os.listdir(root_dir)\n", - "\n", - " def __len__(self):\n", - " return len(self.image_paths)\n", - "\n", - " def __getitem__(self, idx):\n", - " img_name = os.path.join(self.root_dir, self.image_paths[idx])\n", - " image = Image.open(img_name)\n", - "\n", - " if self.transform:\n", - " image = self.transform(image)\n", - "\n", - " return image\n", - "\n", - "class SiameseDataset(Dataset):\n", - " def __init__(self, AD, NC):\n", - " # Combine the datasets and labels\n", - " self.X = AD + NC\n", - "\n", - " self.Y = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0)\n", - "\n", - " # Generate a random permutation of indices\n", - " self.i_indices = torch.randperm(len(self.X) // 2)\n", - " self.j_indices = torch.randperm(len(self.X) // 2)\n", - "\n", - "\n", - " def __len__(self):\n", - " return len(self.X) // 2\n", - "\n", - " def __getitem__(self, idx):\n", - " i = self.i_indices[idx] * 2\n", - " j = self.j_indices[idx] * 2 + 1\n", - " img1 = self.X[i]\n", - " img2 = self.X[j]\n", - " l1 = self.Y[i]\n", - " l2 = self.Y[j]\n", - "\n", - " return img1, img2, l1, l2\n", - "\n", - "# Set the device\n", - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "\n", - "\n", - "size = 64\n", - "\n", - "def intensity_normalization(img):\n", - " mean = torch.mean(img)\n", - " std = torch.std(img)\n", - " return (img - mean) / std\n", - "\n", - "def windowing(img, window_center, window_width):\n", - " img = torch.clamp(img, window_center - window_width // 2, window_center + window_width // 2)\n", - " img = (img - (window_center - 0.5)) / (window_width - 1)\n", - " return img\n", - "\n", - "# Example usage in your transform\n", - "transform_X = transforms.Compose([\n", - " transforms.Resize((size, size)),\n", - " transforms.ToTensor(),\n", - " transforms.Lambda(intensity_normalization),\n", - " transforms.Lambda(lambda x: windowing(x, window_center=size // 2, window_width=size))\n", - "])\n", - "\n", - "\n", - "# Replace 'your_nii_folder' with the path to your folder containing .nii files\n", - "# nii_dataset = NiiDataset(root_dir=r'keras_png_slices_data\\keras_png_slices_data\\keras_png_slices_train')\n", - "loaders = {}\n", - "\n", - "for stage in ['test']:\n", - " loaders[stage] = {}\n", - " AD = CustomDataset(root_dir=os.path.join(stage, 'AD'), transform=transform_X)\n", - " NC = CustomDataset(root_dir=os.path.join(stage, 'NC'), transform=transform_X)\n", - " dataset = SiameseDataset(AD, NC)\n", - " loaders[stage] = DataLoader(dataset, batch_size=batch_size)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5ceQcKMAPoip", - "outputId": "2b8eb6b1-c1c5-43db-9370-42f0da1531f2" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.5144444444444445" - ] - }, - "execution_count": 225, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "count = 0\n", - "same = 0\n", - "for i, j, n, m in loaders['test']:\n", - " count += len(n)\n", - " same += (n == m).sum().tolist()\n", - "same / count" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "puuj1CSURmJ9" - }, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torchvision.models as models\n", - "\n", - "class SiameseNetwork(nn.Module):\n", - " def __init__(self, pretrained=True):\n", - " super(SiameseNetwork, self).__init__()\n", - " self.resnet = models.resnet18(pretrained=False)\n", - " # Modify the first convolution layer to accept single-channel input\n", - " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n", - " self.fc1 = nn.Linear(1000, 500) # Customize the fully connected layers\n", - " self.fc2 = nn.Linear(500, 2) # Customize the fully connected layers\n", - "\n", - " def forward(self, x1, x2):\n", - " output1 = self.resnet(x1)\n", - " output2 = self.resnet(x2)\n", - " output = torch.abs(output1 - output2)\n", - " output = self.fc1(output)\n", - " output = self.fc2(output)\n", - " return output\n", - "\n", - "model = SiameseNetwork()\n", - "\n", - "class ContrastiveLoss(torch.nn.Module):\n", - " def __init__(self, margin=2.0):\n", - " super(ContrastiveLoss, self).__init__()\n", - " self.margin = margin\n", - "\n", - " def forward(self, output1, output2, label):\n", - " euclidean_distance = F.pairwise_distance(output1, output2)\n", - " loss_contrastive = torch.mean((1 - label) * torch.pow(euclidean_distance, 2) +\n", - " label * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))\n", - " return loss_contrastive" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4NnXFUDzSWjp", - "outputId": "ab9065a9-ffad-4405-963d-85ae26293b0d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0997, -0.0306], device='cuda:0', grad_fn=)\n", - "Epoch [1 / 5], Step [21 / 141 Loss 0.9753031730651855]\n", - "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(1, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1081, -0.0379], device='cuda:0', grad_fn=)\n", - "Epoch [1 / 5], Step [42 / 141 Loss 1.025275707244873]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0929, -0.0268], device='cuda:0', grad_fn=)\n", - "Epoch [1 / 5], Step [63 / 141 Loss 0.9390543699264526]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1265, -0.0541], device='cuda:0', grad_fn=)\n", - "Epoch [1 / 5], Step [84 / 141 Loss 1.0038050413131714]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0915, -0.0274], device='cuda:0', grad_fn=)\n", - "Epoch [1 / 5], Step [105 / 141 Loss 1.1157597303390503]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1292, -0.0573], device='cuda:0', grad_fn=)\n", - "Epoch [1 / 5], Step [126 / 141 Loss 1.0057425498962402]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0953, -0.0308], device='cuda:0', grad_fn=)\n", - "Epoch [2 / 5], Step [21 / 141 Loss 0.9697328805923462]\n", - "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(1, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1175, -0.0509], device='cuda:0', grad_fn=)\n", - "Epoch [2 / 5], Step [42 / 141 Loss 1.004995584487915]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0923, -0.0304], device='cuda:0', grad_fn=)\n", - "Epoch [2 / 5], Step [63 / 141 Loss 0.9395542144775391]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1254, -0.0604], device='cuda:0', grad_fn=)\n", - "Epoch [2 / 5], Step [84 / 141 Loss 0.9549553394317627]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0939, -0.0335], device='cuda:0', grad_fn=)\n", - "Epoch [2 / 5], Step [105 / 141 Loss 1.1070666313171387]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1300, -0.0632], device='cuda:0', grad_fn=)\n", - "Epoch [2 / 5], Step [126 / 141 Loss 0.9884251952171326]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0944, -0.0337], device='cuda:0', grad_fn=)\n", - "Epoch [3 / 5], Step [21 / 141 Loss 0.9663693308830261]\n", - "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(1, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1245, -0.0624], device='cuda:0', grad_fn=)\n", - "Epoch [3 / 5], Step [42 / 141 Loss 1.0025595426559448]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1142, -0.0528], device='cuda:0', grad_fn=)\n", - "Epoch [3 / 5], Step [63 / 141 Loss 0.9942120313644409]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1249, -0.0629], device='cuda:0', grad_fn=)\n", - "Epoch [3 / 5], Step [84 / 141 Loss 0.9485695362091064]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0966, -0.0371], device='cuda:0', grad_fn=)\n", - "Epoch [3 / 5], Step [105 / 141 Loss 1.1140365600585938]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1298, -0.0664], device='cuda:0', grad_fn=)\n", - "Epoch [3 / 5], Step [126 / 141 Loss 0.9871245622634888]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0954, -0.0360], device='cuda:0', grad_fn=)\n", - "Epoch [4 / 5], Step [21 / 141 Loss 0.96551513671875]\n", - "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(1, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1227, -0.0626], device='cuda:0', grad_fn=)\n", - "Epoch [4 / 5], Step [42 / 141 Loss 1.0030264854431152]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1154, -0.0552], device='cuda:0', grad_fn=)\n", - "Epoch [4 / 5], Step [63 / 141 Loss 0.9911506175994873]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1292, -0.0673], device='cuda:0', grad_fn=)\n", - "Epoch [4 / 5], Step [84 / 141 Loss 0.9427274465560913]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0964, -0.0375], device='cuda:0', grad_fn=)\n", - "Epoch [4 / 5], Step [105 / 141 Loss 1.1125668287277222]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1283, -0.0657], device='cuda:0', grad_fn=)\n", - "Epoch [4 / 5], Step [126 / 141 Loss 0.9870107173919678]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0937, -0.0353], device='cuda:0', grad_fn=)\n", - "Epoch [5 / 5], Step [21 / 141 Loss 0.9660702347755432]\n", - "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(1, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1197, -0.0604], device='cuda:0', grad_fn=)\n", - "Epoch [5 / 5], Step [42 / 141 Loss 1.0011703968048096]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1150, -0.0551], device='cuda:0', grad_fn=)\n", - "Epoch [5 / 5], Step [63 / 141 Loss 0.9866605997085571]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1265, -0.0649], device='cuda:0', grad_fn=)\n", - "Epoch [5 / 5], Step [84 / 141 Loss 0.9338618516921997]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0969, -0.0381], device='cuda:0', grad_fn=)\n", - "Epoch [5 / 5], Step [105 / 141 Loss 1.1113743782043457]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1214, -0.0599], device='cuda:0', grad_fn=)\n", - "Epoch [5 / 5], Step [126 / 141 Loss 0.9903947114944458]\n" - ] - } - ], - "source": [ - "# Initialize the network, loss function, and optimizer\n", - "siamese_net = SiameseNetwork(pretrained=True).to(device)\n", - "criterion = ContrastiveLoss()\n", - "optimizer = optim.Adam(siamese_net.parameters(), lr=0.001)\n", - "# Training loop\n", - "epochs = 5\n", - "total_step = len(loaders['test'])\n", - "for epoch in range(epochs):\n", - " for i, (img1, img2, lab1, lab2) in enumerate(loaders['test']):\n", - " img1, img2, lab1, lab2 = img1.to(device), img2.to(device), lab1.to(device), lab2.to(device)\n", - " output = siamese_net(img1, img2)\n", - " o1, o2 = output[:, 0], output[:, 1]\n", - " label = (lab1 == lab2).int()\n", - "\n", - " loss = criterion(o1, o2, label)\n", - " optimizer.zero_grad()\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " if (i + 1) % 21 == 0:\n", - " print((lab1[0], lab2[0]))\n", - " print(label[0])\n", - " print(output[0])\n", - " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "G3TWNpvKdAyn", - "outputId": "bf70c5fd-38e5-49c7-a64f-ba1758f103d9" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy of the network on the test images: 49.955555555555556%\n" - ] - } - ], - "source": [ - "correct = 0\n", - "total = 0\n", - "features = []\n", - "labels = []\n", - "with torch.no_grad():\n", - " for i, (img1, img2, lab1, lab2) in enumerate(loaders['test']):\n", - " img1, img2, lab1, lab2 = img1.to(device), img2.to(device), lab1.to(device), lab2.to(device)\n", - " output = siamese_net(img1, img2)\n", - " out1, out2 = output[:, 0], output[:, 1]\n", - " predicted = (out1 - out2).pow(2).sum().sqrt().lt(0.5)\n", - " total += lab1.size(0)\n", - " correct += (predicted == lab1).sum().item()\n", - " features.extend(out1.tolist())\n", - " labels.extend(lab1.tolist())\n", - " features.extend(out2.tolist())\n", - " labels.extend(lab2.tolist())\n", - "\n", - "\n", - " print(f'Accuracy of the network on the test images: {100 * correct / total}%')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MVEvstik8zqK", - "outputId": "c5aed97c-27c1-4685-bf6a-cc2204dbaa69" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy: 0.49166666666666664\n" - ] - } - ], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(np.array(features).reshape(-1, 1), labels, test_size=0.2, random_state=42)\n", - "\n", - "# Train a logistic regression model\n", - "classifier = LogisticRegression(max_iter=1000)\n", - "classifier.fit(X_train, y_train)\n", - "\n", - "# Make predictions\n", - "predictions = classifier.predict(X_test)\n", - "\n", - "# Calculate accuracy\n", - "accuracy = accuracy_score(y_test, predictions)\n", - "print(f\"Accuracy: {accuracy}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "qA-60lBMGV3i" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9PrC3ijsFnhE", - "outputId": "42aa07d0-9166-43fb-a67e-713fc99664da" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.5943959355354309" - ] - }, - "execution_count": 148, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "features[0].tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "id": "dlrwhZTjTKWf" }, @@ -686,7 +261,7 @@ " img2 = self.transform(img2)\n", " img3 = self.transform(img3)\n", "\n", - " return img1, img2, img3, label\n", + " return img1, img2, img3, torch.tensor([1 - label, label])\n", "\n", "\n", "size = 128\n", @@ -770,9 +345,9 @@ "base_uri": "https://localhost:8080/" }, "id": "5RLuD3STujVC", - "outputId": "582bc19f-370e-47ab-d0a2-48b82b190226" + "outputId": "ea9b978b-4b8d-4466-81a6-61b943ab8b60" }, - "execution_count": null, + "execution_count": 8, "outputs": [ { "output_type": "execute_result", @@ -782,13 +357,13 @@ ] }, "metadata": {}, - "execution_count": 56 + "execution_count": 8 } ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "id": "c8MhXUlOTHnY" }, @@ -859,13 +434,56 @@ }, { "cell_type": "code", - "execution_count": null, + "source": [ + "cd" + ], "metadata": { - "id": "Gs0V-oZe1O46" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CkYaII4fGtTP", + "outputId": "68ab616f-0cdd-4f74-e160-33c1d4b65f69" }, - "outputs": [], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/root\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "Gs0V-oZe1O46", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "outputId": "b2f0159b-02ad-4335-d7be-25001f9bb682" + }, + "outputs": [ + { + "output_type": "error", + "ename": "RuntimeError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr_scheduler\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStepLR\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cuda'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'cpu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mlearning_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.05\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mtrip_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTripletSiameseNetwork\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/cuda/memory.py\u001b[0m in \u001b[0;36mempty_cache\u001b[0;34m()\u001b[0m\n\u001b[1;32m 131\u001b[0m \"\"\"\n\u001b[1;32m 132\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_emptyCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n" + ] + } + ], "source": [ "from torch.optim.lr_scheduler import StepLR\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "torch.cuda.empty_cache()\n", "learning_rate = 0.05\n", "trip_model = TripletSiameseNetwork()\n", "trip_criterion = TripletLossWithRegularization(margin=1.0)\n", @@ -883,175 +501,134 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "A_J3lXLOUDu0", - "outputId": "e56e3882-b65d-48f5-a2ae-3280b5c45cdd" + "outputId": "9f42b2ed-30cd-41c6-a590-1a81844aa3d5" }, "outputs": [ { - "metadata": { - "tags": null - }, - "name": "stdout", "output_type": "stream", - "text": [ - "Epoch [1 / 15], Step [22 / 225], Loss: 1.6861666440963745, Validation Loss: 1.3392752408981323\n", - "Epoch [1 / 15], Step [44 / 225], Loss: 1.1134928464889526, Validation Loss: 1.0987355709075928\n", - "Epoch [1 / 15], Step [66 / 225], Loss: 1.0257302522659302, Validation Loss: 1.1489994525909424\n", - "Epoch [1 / 15], Step [88 / 225], Loss: 1.1023128032684326, Validation Loss: 1.149999976158142\n", - "Epoch [1 / 15], Step [110 / 225], Loss: 0.794222354888916, Validation Loss: 1.2070260047912598\n", - "Epoch [1 / 15], Step [132 / 225], Loss: 0.9533556699752808, Validation Loss: 1.061461329460144\n", - "Epoch [1 / 15], Step [154 / 225], Loss: 0.5997182726860046, Validation Loss: 0.9921392202377319\n", - "Epoch [1 / 15], Step [176 / 225], Loss: 0.8227355480194092, Validation Loss: 0.8853449821472168\n", - "Epoch [1 / 15], Step [198 / 225], Loss: 1.0741984844207764, Validation Loss: 1.1926651000976562\n", - "Epoch [1 / 15], Step [220 / 225], Loss: 1.4481054544448853, Validation Loss: 1.1044371128082275\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "metadata": { - "tags": null - }, "name": "stdout", - "output_type": "stream", "text": [ - "Epoch [2 / 15], Step [22 / 225], Loss: 0.993647038936615, Validation Loss: 0.9966787695884705\n", - "Epoch [2 / 15], Step [44 / 225], Loss: 1.0237594842910767, Validation Loss: 1.0309518575668335\n", - "Epoch [2 / 15], Step [66 / 225], Loss: 0.883934497833252, Validation Loss: 1.0600988864898682\n", - "Epoch [2 / 15], Step [88 / 225], Loss: 1.0512185096740723, Validation Loss: 0.8157957792282104\n", - "Epoch [2 / 15], Step [110 / 225], Loss: 0.7038865089416504, Validation Loss: 0.8065618276596069\n", - "Epoch [2 / 15], Step [132 / 225], Loss: 0.8788958191871643, Validation Loss: 0.9165557622909546\n", - "Epoch [2 / 15], Step [154 / 225], Loss: 0.5161008834838867, Validation Loss: 0.7060334086418152\n", - "Epoch [2 / 15], Step [176 / 225], Loss: 0.312076598405838, Validation Loss: 0.40925726294517517\n", - "Epoch [2 / 15], Step [198 / 225], Loss: 0.46442490816116333, Validation Loss: 0.510191798210144\n", - "Epoch [2 / 15], Step [220 / 225], Loss: 0.08432803303003311, Validation Loss: 0.13745912909507751\n" + "Epoch [1 / 10], Step [22 / 225], Loss: 1.5358287719163028, Validation Loss: 1.3388497829437256\n", + "Epoch [1 / 10], Step [44 / 225], Loss: 1.4194633120840245, Validation Loss: 1.2655972838401794\n", + "Epoch [1 / 10], Step [66 / 225], Loss: 1.2770680965799275, Validation Loss: 1.2275654872258503\n", + "Epoch [1 / 10], Step [88 / 225], Loss: 1.1987250704656949, Validation Loss: 1.1871682107448578\n", + "Epoch [1 / 10], Step [110 / 225], Loss: 1.1397959481586108, Validation Loss: 1.1396066188812255\n", + "Epoch [1 / 10], Step [132 / 225], Loss: 1.10812256146561, Validation Loss: 1.1233381430308025\n", + "Epoch [1 / 10], Step [154 / 225], Loss: 1.0751701558565165, Validation Loss: 1.0892583557537623\n", + "Epoch [1 / 10], Step [176 / 225], Loss: 1.0456692581488327, Validation Loss: 1.0730071142315865\n", + "Epoch [1 / 10], Step [198 / 225], Loss: 1.0179587769688982, Validation Loss: 1.034343745973375\n", + "Epoch [1 / 10], Step [220 / 225], Loss: 0.9846788463267413, Validation Loss: 0.9897604703903198\n" ] }, { + "output_type": "display_data", "data": { - "image/png": "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\n", "text/plain": [ "
" - ] + ], + "image/png": "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\n" }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} }, { - "metadata": { - "tags": null - }, - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "Epoch [3 / 15], Step [22 / 225], Loss: 0.6365734934806824, Validation Loss: 0.9633301496505737\n", - "Epoch [3 / 15], Step [44 / 225], Loss: 0.720203697681427, Validation Loss: 0.2848336696624756\n", - "Epoch [3 / 15], Step [66 / 225], Loss: 0.061845649033784866, Validation Loss: 0.9537119269371033\n", - "Epoch [3 / 15], Step [88 / 225], Loss: 0.09502657502889633, Validation Loss: 0.17387674748897552\n", - "Epoch [3 / 15], Step [110 / 225], Loss: 0.16061733663082123, Validation Loss: 0.0026112645864486694\n", - "Epoch [3 / 15], Step [132 / 225], Loss: 0.1448567658662796, Validation Loss: 0.176682710647583\n", - "Epoch [3 / 15], Step [154 / 225], Loss: 0.11984804272651672, Validation Loss: 0.0029973338823765516\n", - "Epoch [3 / 15], Step [176 / 225], Loss: 0.05067078024148941, Validation Loss: 0.024889525026082993\n", - "Epoch [3 / 15], Step [198 / 225], Loss: 0.08953560888767242, Validation Loss: 0.014550979249179363\n", - "Epoch [3 / 15], Step [220 / 225], Loss: 0.05057196691632271, Validation Loss: 0.14682522416114807\n" + "Epoch [2 / 10], Step [22 / 225], Loss: 0.5650029859759591, Validation Loss: 0.6480007767677307\n", + "Epoch [2 / 10], Step [44 / 225], Loss: 0.5132254456931894, Validation Loss: 0.5776327848434448\n", + "Epoch [2 / 10], Step [66 / 225], Loss: 0.47316857698288833, Validation Loss: 0.5568574070930481\n", + "Epoch [2 / 10], Step [88 / 225], Loss: 0.43606940572234715, Validation Loss: 0.5899128615856171\n", + "Epoch [2 / 10], Step [110 / 225], Loss: 0.3954015533355149, Validation Loss: 0.5944906234741211\n", + "Epoch [2 / 10], Step [132 / 225], Loss: 0.35609302819339617, Validation Loss: 0.537044107913971\n", + "Epoch [2 / 10], Step [154 / 225], Loss: 0.33128390105610545, Validation Loss: 0.46129965216719676\n", + "Epoch [2 / 10], Step [176 / 225], Loss: 0.2998663415836001, Validation Loss: 0.40397966344607994\n", + "Epoch [2 / 10], Step [198 / 225], Loss: 0.2738230372732035, Validation Loss: 0.3590930341742933\n", + "Epoch [2 / 10], Step [220 / 225], Loss: 0.25180305100300093, Validation Loss: 0.32318373075686396\n" ] }, { + "output_type": "display_data", "data": { - "image/png": "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\n", "text/plain": [ "
" - ] + ], + "image/png": "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\n" }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} }, { - "metadata": { - "tags": null - }, - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "Epoch [4 / 15], Step [22 / 225], Loss: 0.05052595213055611, Validation Loss: 0.2758115828037262\n", - "Epoch [4 / 15], Step [44 / 225], Loss: 0.26822781562805176, Validation Loss: 0.33429664373397827\n", - "Epoch [4 / 15], Step [66 / 225], Loss: 0.05043775588274002, Validation Loss: 0.04388421028852463\n", - "Epoch [4 / 15], Step [88 / 225], Loss: 0.05038832128047943, Validation Loss: 0.06882896274328232\n", - "Epoch [4 / 15], Step [110 / 225], Loss: 0.05032651126384735, Validation Loss: 0.015540232881903648\n", - "Epoch [4 / 15], Step [132 / 225], Loss: 0.05026824027299881, Validation Loss: 0.9465289115905762\n", - "Epoch [4 / 15], Step [154 / 225], Loss: 0.08250364661216736, Validation Loss: 0.0\n", - "Epoch [4 / 15], Step [176 / 225], Loss: 0.07428855448961258, Validation Loss: 0.046117693185806274\n", - "Epoch [4 / 15], Step [198 / 225], Loss: 0.05011678487062454, Validation Loss: 0.022663306444883347\n", - "Epoch [4 / 15], Step [220 / 225], Loss: 0.05006015673279762, Validation Loss: 0.015940412878990173\n" + "Epoch [3 / 10], Step [22 / 225], Loss: 0.059888842261650345, Validation Loss: 0.055629707872867584\n", + "Epoch [3 / 10], Step [44 / 225], Loss: 0.0569748023355549, Validation Loss: 0.04963332414627075\n", + "Epoch [3 / 10], Step [66 / 225], Loss: 0.055947076359933075, Validation Loss: 0.033088882764180504\n", + "Epoch [3 / 10], Step [88 / 225], Loss: 0.05491821848872033, Validation Loss: 0.024816662073135376\n", + "Epoch [3 / 10], Step [110 / 225], Loss: 0.05447610897774046, Validation Loss: 0.0198533296585083\n", + "Epoch [3 / 10], Step [132 / 225], Loss: 0.08655590160439412, Validation Loss: 0.019558300574620564\n", + "Epoch [3 / 10], Step [154 / 225], Loss: 0.10329984067999698, Validation Loss: 0.0819279168333326\n", + "Epoch [3 / 10], Step [176 / 225], Loss: 0.10512340995906429, Validation Loss: 0.07168692722916603\n", + "Epoch [3 / 10], Step [198 / 225], Loss: 0.11643278070095212, Validation Loss: 0.07727163698938158\n", + "Epoch [3 / 10], Step [220 / 225], Loss: 0.11402303913438862, Validation Loss: 0.08088884353637696\n" ] }, { + "output_type": "display_data", "data": { - "image/png": "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\n", "text/plain": [ "
" - ] + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGwCAYAAABVdURTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABvE0lEQVR4nO3dd3RU1d7G8e9Mek8gkAQIvQZCLxewoSgoIqBIr2JHr/VaXguo195QQVFEadIF4YqCgKKCKCUJRRAEAqGETiqkzZz3j4PBSAgpk0wyeT5rzXJyZp+d32GIediz99kWwzAMRERERFyE1dkFiIiIiDiSwo2IiIi4FIUbERERcSkKNyIiIuJSFG5ERETEpSjciIiIiEtRuBERERGX4u7sAsqa3W7nyJEjBAQEYLFYnF2OiIiIFIJhGKSmplKjRg2s1oLHZipduDly5AiRkZHOLkNERESK4eDBg9SqVavANpUu3AQEBADmH05gYKCTqxEREZHCSElJITIyMvf3eEEqXbj566OowMBAhRsREZEKpjBTSjShWERERFyKwo2IiIi4FIUbERERcSmVbs6NiIi4FpvNRnZ2trPLEAfw9PS87DLvwlC4ERGRCskwDI4ePUpSUpKzSxEHsVqt1KtXD09PzxL1o3AjIiIV0l/Bpnr16vj6+urGrBXcXzfZTUxMpHbt2iV6PxVuRESkwrHZbLnBpmrVqs4uRxykWrVqHDlyhJycHDw8PIrdj1MnFP/000/07t2bGjVqYLFY+Oqrry57zpo1a2jbti1eXl40bNiQadOmlXqdIiJSvvw1x8bX19fJlYgj/fVxlM1mK1E/Tg036enptGrVikmTJhWqfXx8PL169aJbt27ExcXx8MMPc+edd7JixYpSrlRERMojfRTlWhz1fjr1Y6kbb7yRG2+8sdDtJ0+eTL169Xj77bcBaNasGWvXruXdd9+lR48e+Z6TmZlJZmZm7tcpKSklK/pS7DY48AukHQP/MKjTBaxupfO9RERE5JIq1Jyb9evX07179zzHevTowcMPP3zJc1599VVeeOGF0i1sx1JY/iSkHLlwLLAG9Hwdom4p3e8tIiIieVSom/gdPXqUsLCwPMfCwsJISUnh3Llz+Z7z9NNPk5ycnPs4ePCgY4vasRTmj8gbbABSEs3jO5Y69vuJiIhD2ewG6/eeYkncYdbvPYXNbji7pCKrW7cuEyZMcHYZ5UaFGrkpDi8vL7y8vEqnc7vNHLEhvx8EA7DA8qegaS99RCUiUg4t357IC//bQWJyRu6xiCBvxvWOomeLCId/v8vNKRk3bhzjx48vcr8bN27Ez8+vmFWZrrnmGlq3bu0SIalChZvw8HCOHTuW59ixY8cIDAzEx8en7As68MvFIzZ5GJBy2GxX78oyK0tERC5v+fZE7psVc9E/T48mZ3DfrBg+GtbW4QEnMTEx9/m8efN4/vnn2bVrV+4xf3//3OeGYWCz2XB3v/yv6mrVqjm0zoquQn0s1blzZ1avXp3n2MqVK+ncubNzCko7dvk2RWknIiLFZhgGZ7NyCvVIzchm3NLfLznuDjB+6Q5SM7IL1Z9hFO6jrPDw8NxHUFAQFosl9+s//viDgIAAvv32W9q1a4eXlxdr165l79699OnTh7CwMPz9/enQoQOrVq3K0+8/P5ayWCx8+umn9OvXD19fXxo1asTSpSWbJvHll1/SvHlzvLy8qFu3bu7inr98+OGHNGrUCG9vb8LCwujfv3/uawsXLiQ6OhofHx+qVq1K9+7dSU9PL1E9BXHqyE1aWhp79uzJ/To+Pp64uDiqVKlC7dq1efrppzl8+DAzZswA4N5772XixIk88cQT3HHHHXz//ffMnz+fZcuWOecC/MMu36Yo7UREpNjOZduIet4xtwYxgKMpGUSP/65Q7Xe82ANfT8f8Sn3qqad46623qF+/PiEhIRw8eJCbbrqJl19+GS8vL2bMmEHv3r3ZtWsXtWvXvmQ/L7zwAm+88QZvvvkmH3zwAUOHDuXAgQNUqVKlyDVt3ryZAQMGMH78eAYOHMgvv/zC/fffT9WqVRk1ahSbNm3i3//+NzNnzqRLly6cPn2an3/+GTBHqwYPHswbb7xBv379SE1N5eeffy50ICwOp4abTZs20a1bt9yvH330UQBGjhzJtGnTSExMJCEhIff1evXqsWzZMh555BHee+89atWqxaeffnrJZeClrk4XCKyBkZKIJZ/8bwCWwJpmOxERkUJ48cUXuf7663O/rlKlCq1atcr9+qWXXmLx4sUsXbqUBx544JL9jBo1isGDBwPwyiuv8P7777NhwwZ69uxZ5JreeecdrrvuOp577jkAGjduzI4dO3jzzTcZNWoUCQkJ+Pn5cfPNNxMQEECdOnVo06YNYIabnJwcbr31VurUqQNAdHR0kWsoCqeGm2uuuabA5Jbf3YevueYaYmNjS7GqIrC6Edv8KVr98m8MwPq3eWKGARYLJAa0IEKTiUVESp2Phxs7XizcP3Y3xJ9m1OcbL9tu2ugOdKx3+ZEOHw/H/X++ffv2eb5OS0tj/PjxLFu2LDconDt3Ls8//vPTsmXL3Od+fn4EBgZy/PjxYtW0c+dO+vTpk+dY165dmTBhAjabjeuvv546depQv359evbsSc+ePXM/EmvVqhXXXXcd0dHR9OjRgxtuuIH+/fsTEhJSrFoKo0LNuSlvbHaD+2NqcV/2wxwl71/+ZMxZ6xGHV2D/dbIzyhMRqVQsFgu+nu6FelzZqBoRQd5cau2SBXPV1JWNqhWqP0feKfmfq54ef/xxFi9ezCuvvMLPP/9MXFwc0dHRZGVlFdjPP/dmslgs2O12h9X5dwEBAcTExDBnzhwiIiJ4/vnnadWqFUlJSbi5ubFy5Uq+/fZboqKi+OCDD2jSpAnx8fGlUgso3JTIhvjTJCZnsMLekSsy32dQ1rP8O+sBBmU9S9vMj3k725xMZV3+JGxb6ORqRUTkL25WC+N6RwFcFHD++npc7yjcrM7f3mHdunWMGjWKfv36ER0dTXh4OPv37y/TGpo1a8a6desuqqtx48a4uZmjVu7u7nTv3p033niDrVu3sn//fr7//nvADFZdu3blhRdeIDY2Fk9PTxYvXlxq9VaopeDlzfHUC/dFsGPlV3tUntc/sPWjiiWV0e4rYPE94BMMDbsjIiLO17NFBB8Na3vRfW7CS/E+N8XRqFEjFi1aRO/evbFYLDz33HOlNgJz4sQJ4uLi8hyLiIjgscceo0OHDrz00ksMHDiQ9evXM3HiRD788EMAvv76a/bt28dVV11FSEgI33zzDXa7nSZNmvDbb7+xevVqbrjhBqpXr85vv/3GiRMnaNasWalcAyjclEj1AO/LtLDwYs5wejf0JHT//2DeCBi5FGq1v8x5IiJSFnq2iOD6qHA2xJ/meGoG1QO86VivSrkYsfnLO++8wx133EGXLl0IDQ3lySefLLV9EmfPns3s2bPzHHvppZd49tlnmT9/Ps8//zwvvfQSERERvPjii4waNQqA4OBgFi1axPjx48nIyKBRo0bMmTOH5s2bs3PnTn766ScmTJhASkoKderU4e233y7S3pJFZTFKcy1WOZSSkkJQUBDJyckEBgaWqC+b3eCK17/naHJGvvdKAPMz27WPX4Hb3EGw93vwCYE7VkC1JiX63iIilVlGRgbx8fHUq1cPb+/L/UNTKoqC3tei/P7WnJsSKOgz2790blAVNw8vGDATaraDc2dgZj9IPlR2hYqIiFQiCjcl9NdntuFBeRNmoLf5id/i2MOs3HEMvPxhyAIIbWxuyTCzH5w97YySRUREXJrm3DhAfp/ZdqgbwvNLf2f2bwk8NDeWhfd2IapGVRi+GKbeACd3wxe3w4glZvARERERh9DIjYO4WS10blCVPq1r0rlBVdzdrLxwS3O6NqzK2Swbd07faK6uCqplBhyfEDi8CeYPh5yC71UgIiIihadwU4o83Kx8OKQd9UP9OJKcwd0zNpORbTMnEw9dCB6+5iTjr+6FUlrWJyIiUtko3JSyIF8Ppo7qQJCPB3EHk3hi4VZzy4la7WHgTLB6wPYvYfmT5p4NIiIiUiIKN2WgXqgfHw1ri7vVwtItR/jg+/M7oTfsDv0mAxbY8An89JZT6xQREXEFCjdlpEuDUF7s0wKAd1bu5uutR8wXovvDja+bz3/4L2z6zEkVioiIuAaFmzI0pFNt7uhaD4DH5m9hy8Ek84VO98BVT5jPv34Ufv/KKfWJiEjFcM011/Dwww87u4xyS+GmjD3TqxndmlQjM8fOXTM2kZh8znyh2/9Bu9GAAYvugn1rnFmmiEjlYbdB/M/mBsfxP5tfl5LevXvTs2fPfF/7+eefsVgsbN26tcTfZ9q0aQQHB5e4n4pK4aaMuVktvD+4DU3CAjiemsmd0zdxNisHLBbo9TZE9QFbFswdCkdinV2uiIhr27EUJrSA6TfDl2PM/05oYR4vBWPGjGHlypUcOnTxXeo///xz2rdvT8uWLUvle1cmCjdOEODtwacj21PVz5Pfj6Tw8Nw47HYDrG5w6xSodxVkpcGs/nByj7PLFRFxTTuWwvwRkHIk7/GURPN4KQScm2++mWrVqjFt2rQ8x9PS0liwYAFjxozh1KlTDB48mJo1a+Lr60t0dDRz5sxxaB0JCQn06dMHf39/AgMDGTBgAMeOHct9fcuWLXTr1o2AgAACAwNp164dmzZtAuDAgQP07t2bkJAQ/Pz8aN68Od98841D6ysphRsniaziy8fD2+HpZuW7Hcd487td5gvuXjBoNkS0hrMnzW0aUhKdWquISIVgGJCVXrhHRgp8+wTku+3x+WPLnzTbFaa/Qt7Kw93dnREjRjBt2jT+vm/1ggULsNlsDB48mIyMDNq1a8eyZcvYvn07d999N8OHD2fDhg0l/zMC7HY7ffr04fTp0/z444+sXLmSffv2MXDgwNw2Q4cOpVatWmzcuJHNmzfz1FNP4eHhAcDYsWPJzMzkp59+Ytu2bbz++uv4+5evO+1r+wUnal+3Cq/3j+aReVv4aM1eGlTzp3+7WuAVYN7k77MecHovzLoVRn9j3tVYRETyl30WXqnhoM4Mc0TntcjCNf+/I+DpV6imd9xxB2+++SY//vgj11xzDWB+JHXbbbcRFBREUFAQjz/+eG77Bx98kBUrVjB//nw6duxY1Au5yOrVq9m2bRvx8fFERprXN2PGDJo3b87GjRvp0KEDCQkJ/Oc//6Fp06YANGrUKPf8hIQEbrvtNqKjowGoX79+iWtyNI3cOFm/NrUY260BAE8v2srG/ec30/SvZm7T4B8Ox3fA7IGQddaJlYqIiCM0bdqULl268Nln5q0/9uzZw88//8yYMWMAsNlsvPTSS0RHR1OlShX8/f1ZsWIFCQkJDvn+O3fuJDIyMjfYAERFRREcHMzOnTsBePTRR7nzzjvp3r07r732Gnv37s1t++9//5v//ve/dO3alXHjxjlkArSjaeSmHHjs+ibsO5HOt9uPcs/MzXx1f1dqV/WFkDpmwPm8Jxz8DRaMgkFfgJuHs0sWESl/PHzNEZTCOPALfNH/8u2GLoQ6XQr3vYtgzJgxPPjgg0yaNInPP/+cBg0acPXVVwPw5ptv8t577zFhwgSio6Px8/Pj4YcfJiur7PYhHD9+PEOGDGHZsmV8++23jBs3jrlz59KvXz/uvPNOevTowbJly/juu+949dVXefvtt3nwwQfLrL7L0chNOWC1Wnh7QCta1AzkdHoWY6ZvJCUj23wxLAqGzAd3H/hzBSx5QPtQiYjkx2IxPxoqzKPBtRBYA7BcqjMIrGm2K0x/lkv1k78BAwZgtVqZPXs2M2bM4I477sByvo9169bRp08fhg0bRqtWrahfvz67d+8u2Z/N3zRr1oyDBw9y8ODB3GM7duwgKSmJqKio3GONGzfmkUce4bvvvuPWW2/l888/z30tMjKSe++9l0WLFvHYY48xZcoUh9XnCAo35YSvpzufjuhAWKAXfx5P48HZseTYzoeY2v+CAdPB4gZb58LK57QPlYhISVjdoOf5u8NfFHDOf93zNbNdKfD392fgwIE8/fTTJCYmMmrUqNzXGjVqxMqVK/nll1/YuXMn99xzT56VTIVls9mIi4vL89i5cyfdu3cnOjqaoUOHEhMTw4YNGxgxYgRXX3017du359y5czzwwAOsWbOGAwcOsG7dOjZu3EizZs0AePjhh1mxYgXx8fHExMTwww8/5L5WXijclCPhQd58OqID3h5Wftx9gv8u23nhxcY9oM8k8/n6ibBuglNqFBFxGVG3wIAZEBiR93hgDfN41C2l+u3HjBnDmTNn6NGjBzVqXJgI/eyzz9K2bVt69OjBNddcQ3h4OH379i1y/2lpabRp0ybPo3fv3lgsFpYsWUJISAhXXXUV3bt3p379+sybNw8ANzc3Tp06xYgRI2jcuDEDBgzgxhtv5IUXXgDM0DR27FiaNWtGz549ady4MR9++KFD/kwcxWIYlWsIICUlhaCgIJKTkwkMDHR2Ofn6Zlsi938RA8B/+7Zg2L/qXHjxl4nw3TPm81s+gLYjnFChiIhzZWRkEB8fT7169fD29i5ZZ3abOQcn7Rj4h5lzbEppxEYKVtD7WpTf3xq5KYduio7g8RsaAzBu6e+s23PywotdHoCuD5vP//cQ7Py67AsUEXElVjeod6W5kXG9KxVsXIDCTTk1tltD+raugc1ucN+szew9kXbhxe7joc0wMOyw8A7Yv85pdYqIiJQ3CjfllMVi4bXbWtK2djApGTncOX0TSWez/noRbn4PmvQCWybMGQSJ5e8+AyIiIs6gcFOOeXu48fHw9tQM9iH+ZDr3zYoh+68VVG7u0H8q1OkKmSkw6zY4vc+5BYuIiJQDCjflXLUAL6aOao+fpxvr953i+SXbL+xH4uEDg+dAWDSkHzf3oUot+nJBEZGKqpKtiXF5jno/FW4qgKbhgbw/uA0WC8zZcJCpa+MvvOgdBMO+hJC6cGa/OYKTkeysUkVEysRfmziePattaVzJX3dhdnMr2aRuLQWvQD79eR//XbYTiwWmjmzPtU3DLrx4eh9M7WGO4NTpagYeDx/nFSsiUsoSExNJSkqievXq+Pr65t7hVyomu93OkSNH8PDwoHbt2he9n0X5/a1wU4EYhsHTi7Yxd+NB/Dzd+PL+LjQN/9s1JG6Fab3MOThNepk3oXLT9mEi4poMw+Do0aMkJSU5uxRxEKvVSr169fD09LzoNYWbAlTkcAOQlWNnxGe/8eu+09QM9mHJA10J9fe60GD/Wph5q7mKqs0wuGVikfc8ERGpSGw2G9nZ2c4uQxzA09MTqzX/GTMKNwWo6OEGIOlsFn0nrWP/qbO0rR3M7Lv+hbfH3z6f3Pk1zB9u3gfnikfM++KIiIhUYLpDsYsL9vVk6qgOBHq7E5OQxFNfbs07w7zZzdD7ffP52nfNLRtEREQqCYWbCqpBNX8+HNoON6uFr+KOMOmHPXkbtB1+YcTmu2cgbk6Z1ygiIuIMCjcV2BWNQnnhluYAvPXdbr7Zlpi3QdeHofMD5vMlY2H3irItUERExAkUbiq4Yf+qw6gudQF4dH4c2w797R43Fgtc/xK0HASGDeaPhIRfnVOoiIhIGVG4cQHP9mrG1Y2rkZFt584ZGzmanHHhRasV+kyERj0g5xzMHgDHdjivWBERkVKmcOMC3N2sfDCkDY2q+3MsJZO7ZmziXJbtQgM3D7h9GkR2Mu9ePOtWOHPAafWKiIiUJoUbFxHo7cHUkR2o4ufJtsPJPDo/Drv9byuoPH1hyDyoHgWpieY+VGknnFewiIhIKVG4cSG1q/oyeVg7PNwsfLv9KO+s3J23gU+IuS1DUG04vRe+uA0yUpxTrIiISClRuHExHetV4dVbWwIw8Yc9LI49lLdBYA0Yvhh8QyFxC8wbCjmZTqhURESkdCjcuKD+7Wpx79UNAHhy4TY2Hzidt0FoQxi2EDz9If4nWHQX2G359CQiIlLxKNy4qCd6NOGGqDCybHbunrGZg6fP5m1Qow0Mmg1unrBjCSx7DCrXThwiIuKiFG5clNVq4d2BrYmKCORUehZ3Tt9EasY/NparfzXcOgWwwObP4YdXnFKriIiIIyncuDA/L3emjmpPtQAvdh1L5aG5cdjs/xidad4Xer1tPv/pDfjt4zKvU0RExJEUblxcRJAPn45oj5e7le//OM4r3+y8uFGHMdDtGfP5t0/AtoVlW6SIiIgDKdxUAq0ig3l7QCsApq6NZ86GhIsbXfUf6HiP+XzxPbBnVRlWKCIi4jgKN5XEzS1r8Ej3xgA899V2ftl7Mm8DiwV6vgYt+oM9B+YNh0ObnFCpiIhIySjcVCL/vq4ht7SqQY7d4L5ZMew7kZa3gdUKfT+CBtdC9ln4oj+c2OWcYkVERIpJ4aYSsVgsvNG/Ja0jg0k+l82d0zeRfPYfK6jcPWHATKjZDs6dMbdpSD6Uf4ciIiLlkMJNJePt4cYnI9pRI8ibfSfTuX/2ZrJt9ryNvPxhyAIIbQwph82Ac/Z0/h2KiIiUMwo3lVD1AG+mjuqAr6cb6/acYvzS3zH+eQM/v6rmNg2BNeHkbvjidshMy79DERGRckThppJqFhHIe4PaYLHAF78lMO2X/Rc3CqplBhyfEDi8CeYPh5ysMq9VRESkKBRuKrHro8J4qmdTAF76egc/7Dp+caNqTWDoQvDwhb3fw1f3gt1+cTsREZFyQuGmkrv7qvrc3q4WdgMenB3L7mOpFzeq1R4GzgSrO2z/EpY/qX2oRESk3FK4qeQsFgsv94umY70qpGXmMGb6Rk6lZV7csGF36PcxYIENn8BPb5V5rSIiIoWhcCN4uluZPKwdtav4cvD0Oe6ZuZnMHNvFDaP7w42vm89/+C9snFq2hYqIiBSCwo0AUMXPk89GtSfA251NB87w9KJtF6+gAuh0D1z1hPl82WPw+1dlWqeIiMjlKNxIrobVA5g0pC1uVguLYg7z0Y9782/Y7f+g3WjAgEV3wb41ZVmmiIhIgZwebiZNmkTdunXx9vamU6dObNiwocD2EyZMoEmTJvj4+BAZGckjjzxCRkZGGVXr+q5qXI1xvaMAeGP5LpZvP3pxI4sFer0NUX3AlgVzh8KR2DKuVEREJH9ODTfz5s3j0UcfZdy4ccTExNCqVSt69OjB8eP5LEkGZs+ezVNPPcW4cePYuXMnU6dOZd68efzf//1fGVfu2kZ0rsuIznUAeGReHNsPJ1/cyOoGt06BeldBVhrM6g8n95RxpSIiIhdzarh55513uOuuuxg9ejRRUVFMnjwZX19fPvvss3zb//LLL3Tt2pUhQ4ZQt25dbrjhBgYPHnzZ0R4puudvjuLKRqGcy7Zx5/RNHEvJZ3TM3QsGzYaI1nD2pLlNQ8qRMq9VRETk75wWbrKysti8eTPdu3e/UIzVSvfu3Vm/fn2+53Tp0oXNmzfnhpl9+/bxzTffcNNNN13y+2RmZpKSkpLnIZfn7mZl4pC2NKjmx9GUDO6asYlzWfmsoPIKMG/yV6UBJCfAzFu1D5WIiDiV08LNyZMnsdlshIWF5TkeFhbG0aP5zPMAhgwZwosvvsgVV1yBh4cHDRo04JprrinwY6lXX32VoKCg3EdkZKRDr8OVBfl48NmoDoT4erD1UDKPL9iC3Z7PCir/auY2Df7hcGInzBkEWWfLvmARERHKwYTiolizZg2vvPIKH374ITExMSxatIhly5bx0ksvXfKcp59+muTk5NzHwYMHy7Diiq9OVT8mD2uHh5uFZdsSmbD6z/wbhtQxA453EBz8DRaMAlt2mdYqIiICTgw3oaGhuLm5cezYsTzHjx07Rnh4eL7nPPfccwwfPpw777yT6Oho+vXrxyuvvMKrr76K/RL7HXl5eREYGJjnIUXTqX5VXu4XDcD7q/9kSdzh/BuGRcGQ+eDuA3+ugCUPaB8qEREpc04LN56enrRr147Vq1fnHrPb7axevZrOnTvne87Zs2exWvOW7ObmBpD/DefEYQa0j+Seq+oD8J+FW4lJOJN/w9r/ggHTweIGW+fCd89qHyoRESlTTv1Y6tFHH2XKlClMnz6dnTt3ct9995Gens7o0aMBGDFiBE8//XRu+969e/PRRx8xd+5c4uPjWblyJc899xy9e/fODTlSep7o2ZTuzcLIyrFz94zNHE46l3/Dxj2gzyTz+a+TYN2EMqtRRETE3ZnffODAgZw4cYLnn3+eo0eP0rp1a5YvX547yTghISHPSM2zzz6LxWLh2Wef5fDhw1SrVo3evXvz8ssvO+sSKhU3q4X3BrXmto9+4Y+jqYyZtpGF93XB3yufv0atB8PZU/DdM7BqPPhWhbYjyrxmERGpfCxGJfs8JyUlhaCgIJKTkzX/ppgOJ52jz8R1nEzLpHuz6nw8vD1uVkv+jVeOM0duLFYYMBOa3VymtYqIiGsoyu/vCrVaSsqHmsE+fDKiHZ7uVlbtPM7ry/+4dOPu46HNMDDssPAO2L+2zOoUEZHKSeFGiqVt7RDe7N8SgE9+2se8jQn5N7RY4Ob3oEkvsGXCnMGQuLUMKxURkcpG4UaKrU/rmvz7ukYAPLN4O7/uO5V/Qzd36D8V6nSFzBSYdRuc3leGlYqISGWicCMl8vB1jejVMoIcu8G9szaz/2R6/g09fGDwHAiLhvTj5j5UqcfybysiIlICCjdSIlarhbdvb0WrWkEknc1mzPSNJJ+7xJ2JvYNg2JcQUhfO7DdHcDLy2XFcRESkBBRupMS8PdyYMqI9EUHe7D2RzgOzY8ixXeLOxAFh5jYNftXh2DZzDk72Je6XIyIiUgwKN+IQ1QO9mTKiPT4ebvz850le/HrHpRtXqW+O4HgFwoF1sHAM2HLKrlgREXFpCjfiMC1qBjFhUGssFpix/gAz1u+/dOOIluYcHDcv2LUMvn5I2zSIiIhDKNyIQ/VoHs4TPZoC8ML/dvDT7hOXblz3Cuj/mXmDv9hZsPqFMqpSRERcmcKNONy9V9fntra1sNkNxn4Rw57jqZdu3Oxm6P2++Xztu/DLxLIpUkREXJbCjTicxWLhlVtb0LFuFVIzc7hj2iZOp2dd+oS2w807GYO5F1XcnDKpU0REXJPCjZQKL3c3PhrWlsgqPiScPsu9szaTlXOJFVQAXR+Gzg+Yz5eMhd0ryqROERFxPQo3Umqq+nsxdWQHArzc2RB/mmcWb+OS+7RaLHD9S9ByEBg2mD8SEn4t24JFRMQlKNxIqWocFsAHQ9pgtcCCzYf45KcCtl2wWqHPRGjUA3LOwewBcKyAJeUiIiL5ULiRUndNk+o8d3MUAK8t/4OVOwrYdsHNA26fBpGdzLsXz7oVzhwom0JFRMQlKNxImRjVpS5DO9XGMOChubH8fqSAbRc8fWHIPKgeBamJ5j5UaQUsKRcREfkbhRspExaLhfG3NKdrw6qczbJx1/RNHE/NuPQJPiHmXYyDasPpvfDFbZCRUnYFi4hIhaVwI2XGw83Kh0PaUT/UjyPJGdw1YzMZ2bZLnxBYw9yHyrcqJG6BeUMhJ7PsChYRkQpJ4UbKVJCvB1NHdSDIx4MtB5P4z8Ktl15BBRDa0BzB8fSH+J9g0V1gLyAQiYhIpadwI2WuXqgfk4e1w91q4X9bjvDe6j8LPqFGGxg0G9w8YccSWPaY9qESEZFLUrgRp+jcoCr/7dsCgAmr/uR/W44UfEL9q+HWKYAFNn8OP7xS+kWKiEiFpHAjTjOoY23uvKIeAI8v2ELcwaSCT2jeF3q9bT7/6Q347eNSrU9ERComhRtxqqdvasZ1TauTmWPnrhmbOJJ0ruATOoyBbs+Yz799ArYtLP0iRUSkQlG4Eadys1p4b3AbmoYHcCI1kzunbyI9M6fgk676D3S8x3y++B7Ys6r0CxURkQpD4Uaczt/LnU9HtifU35MdiSk8Mi8Ou72ACcMWC/R8DVr0B3sOzBsOhzaVXcEiIlKuKdxIuVArxJePh7fH093KdzuO8caKXQWfYLVC34+gwbWQfRa+6A8nLnOOiIhUCgo3Um60qxPCG7e1BGDyj3tZsOlgwSe4e8KAmVCzHZw7Y27TkHyoDCoVEZHyTOFGypW+bWryQLeGAPzf4m1siD9d8Ale/jBkAYQ2hpTDZsA5e5lzRETEpSncSLnz6PWNuSk6nGybwT0zN5Fw6mzBJ/hVNbdpCKwJJ3ebH1FlppVNsSIiUu4o3Ei5Y7VaePv21kTXDOLM2WzumL6RlIzsgk8KqmUGHJ8QOLwZ5g+HnKyyKVhERMoVhRspl3w83Zgyoj1hgV7sOZ7GA7NjybHZCz6pWhMYuhA8fGHv9/DVvWC/zDkiIuJyFG6k3AoP8ubTER3w9rDy0+4T/HfZzsufVKs9DJwJVnfY/iUsf1L7UImIVDIKN1KuRdcKYsLA1gBM+2U/M389cPmTGnaHfh8DFtjwCfz0ZqnWKCIi5YvCjZR7PVtE8J8eTQAYv/R3fv7zxOVPiu4PN75uPv/hZdg4tRQrFBGR8kThRiqE+69pwK1tamKzG9z/RQx7jhdiNVSne+CqJ8znyx6D378q1RpFRKR8ULiRCsFisfDqbdG0qxNCakYOd07fyJn0QqyG6vZ/0G40YMCiu2DfmtIuVUREnEzhRioML3c3Ph7ejlohPuw/dZb7vthMVs5lVkNZLNDrbWh2C9iyYO5QOBJbNgWLiIhTKNxIhRLq78XUkR3w93Ln132neX7JdozLrYayusFtn0K9qyArDWb1h5N7yqZgEREpcwo3UuE0CQ/gg8FtsFpg7saDTF0bf/mT3L1g0GyIaA1nT5rbNKQcKfVaRUSk7CncSIXUrWl1nukVBcDL3+xk9c5jlz/JK8C8yV+VBpCcADNv1T5UIiIuSOFGKqw7utZlcMfaGAb8e04sOxNTLn+SfzVzmwb/cDixE+YMgqzL7F0lIiIVisKNVFgWi4UX+zSnS4OqpGfZuHP6Jk6kZl7+xJA6ZsDxDoKDv8GCUWC7zN5VIiJSYSjcSIXm4Wblw6FtqRfqx+Gkc9wzcxMZ2bbLnxgWBUPmg7sP/LkCljygfahERFyEwo1UeMG+nkwd2Z5Ab3diEpJ48sutl19BBVD7XzBgOljcYOtc+O5Z7UMlIuICFG7EJdSv5s9Hw9rhZrWwJO4IE78v5FLvxj2gzyTz+a+TYN2EUqtRRETKhsKNuIyuDUN5sU9zAN5euZtlWxMLd2LrwXDDy+bzVeMhZkbpFCgiImVC4UZcytBOdRjdtS4Ajy2IY+uhpMKd2OUB6Pqw+fx/D8HOr0ujPBERKQMKN+Jynu0VxTVNqpGRbefO6ZtITD5XuBO7j4c2w8Cww8I7YP/aUq1TRERKh8KNuBw3q4UPBrehcZg/x1MzuWvGJs5m5Vz+RIsFbn4PmvQCWybMGQyJW0u/YBERcSiFG3FJAd4eTB3ZgSp+nmw/nMKj87ZgtxdiJZSbO/SfCnW6QmYKzLoNTu8r/YJFRMRhFG7EZUVW8eWT4e3wdLOy/PejvL1yV+FO9PCBwXMgLBrSj5v7UKUWYnsHEREpFxRuxKW1r1uF126LBmDSD3tZFHOocCd6B8GwLyGkLpzZb47gnEsqrTJFRMSBFG7E5d3athb3X9MAgKe+3Mam/YXcLDMgzNymwa86HNsGc4dAdiEnJ4uIiNMo3Eil8PgNTejZPJwsm517Zm7m4OlCbpZZpb45guMVCAfWwcIxYCvE5GQREXEahRupFKxWC+8MbEWLmoGcSs9izPSNpGYUcrPMiJbmHBw3L9i1DL5+SNs0iIiUYwo3Umn4erozZUR7qgd4sftYGv+eE4utMCuoAOpeAf0/A4sVYmfB6hdKt1gRESk2hRupVCKCfJgyoj1e7lZ+2HWCl5ftLPzJzW6G3u+Zz9e+C79MLJ0iRUSkRIocbpYvX87atRfu3Dpp0iRat27NkCFDOHPmjEOLEykNrSKDeWdAawA+WxfP7N8SCn9y2xHmnYwBvnsG4uY4vD4RESmZIoeb//znP6SkpACwbds2HnvsMW666Sbi4+N59NFHHV6gSGno1TKCR69vDMDzS7bzy56ThT+568PQ+QHz+ZKxsHuF4wsUEZFiK3K4iY+PJyoqCoAvv/ySm2++mVdeeYVJkybx7bffOrxAkdLy4LUN6dO6Bjl2g3tnbWbfibTCnWixwPUvQctBYNhg/khI+LV0ixURkUIrcrjx9PTk7FlzGe2qVau44YYbAKhSpUruiI5IRWCxWHj9tpa0qR1MSkYOY6ZvIulsVuFOtlqhz0Ro1ANyzsHsAXDs99ItWERECqXI4eaKK67g0Ucf5aWXXmLDhg306tULgN27d1OrVi2HFyhSmrw93PhkeHtqBvsQfzKd+7+IIdtmL9zJbh5w+zSI7AQZyTDzVjhzoFTrFRGRyytyuJk4cSLu7u4sXLiQjz76iJo1awLw7bff0rNnT4cXKFLaqgV48enI9vh5uvHL3lM8v+R3jMLex8bTF4bMg+pRkHbU3Icq7UTpFiwiIgUqcripXbs2X3/9NVu2bGHMmDG5x999913ef//9IhcwadIk6tati7e3N506dWLDhg0Ftk9KSmLs2LFERETg5eVF48aN+eabb4r8fUX+rllEIO8NaoPFAnM2JPD5uv2FP9knxLyLcVBtOL0XvrgNMvQRrYiIsxQ53MTExLBt27bcr5csWULfvn35v//7P7KyCjlf4bx58+bx6KOPMm7cOGJiYmjVqhU9evTg+PHj+bbPysri+uuvZ//+/SxcuJBdu3YxZcqU3NEjkZLoHhXG/93YDID/LtvBD7vy/3uYr8Aa5j5UvlUhcQvMGwo5maVUqYiIFKTI4eaee+5h9+7dAOzbt49Bgwbh6+vLggULeOKJJ4rU1zvvvMNdd93F6NGjiYqKYvLkyfj6+vLZZ5/l2/6zzz7j9OnTfPXVV3Tt2pW6dety9dVX06pVq6Jehki+7ryyHgPbR2I34MHZsew6mlr4k0MbmiM4nv4Q/xN8eSfYbaVXrIiI5KvI4Wb37t20bt0agAULFnDVVVcxe/Zspk2bxpdfflnofrKysti8eTPdu3e/UIzVSvfu3Vm/fn2+5yxdupTOnTszduxYwsLCaNGiBa+88go226V/gWRmZpKSkpLnIXIpFouFl/q2oFO9KqRl5jBm+kZOphVhBKZGGxg0G9w8YedSWPaY9qESESljRQ43hmFgt5urSVatWsVNN90EQGRkJCdPFv5GaCdPnsRmsxEWFpbneFhYGEePHs33nH379rFw4UJsNhvffPMNzz33HG+//Tb//e9/L/l9Xn31VYKCgnIfkZGRha5RKidPdyuTh7WjblVfDp05x70zN5OZU4QRmPpXw61TAAts/hx+eKXUahURkYsVOdy0b9+e//73v8ycOZMff/wxdyl4fHz8RUHF0ex2O9WrV+eTTz6hXbt2DBw4kGeeeYbJkydf8pynn36a5OTk3MfBgwdLtUZxDSF+nnw6sgMB3u5sOnCGp7/cVvgVVADN+0Kvt83nP70Bv31cKnWKiMjFihxuJkyYQExMDA888ADPPPMMDRs2BGDhwoV06dKl0P2Ehobi5ubGsWPH8hw/duwY4eHh+Z4TERFB48aNcXNzyz3WrFkzjh49esnJzF5eXgQGBuZ5iBRGw+r+fDS0HW5WC4tiD/Phmr1F66DDGOj2jPn82ydg20LHFykiIhcpcrhp2bIl27ZtIzk5mXHjxuUef/PNN5k+fXqh+/H09KRdu3asXr0695jdbmf16tV07tw533O6du3Knj17cj8WA3MOUEREBJ6enkW9FJHLuqJRKONvaQ7Amyt2sXx7YtE6uOo/0PFu8/nie+DPVQ6uUERE/qnI4eYvmzdvZtasWcyaNYuYmBi8vb3x8PAoUh+PPvooU6ZMYfr06ezcuZP77ruP9PR0Ro8eDcCIESN4+umnc9vfd999nD59moceeojdu3ezbNkyXnnlFcaOHVvcyxC5rOH/qsPIznUAeGTeFrYfTi78yRYL9HwdWvQHew7MHw6HNpVSpSIiAuBe1BOOHz/OwIED+fHHHwkODgbMG+t169aNuXPnUq1atUL3NXDgQE6cOMHzzz/P0aNHad26NcuXL8+du5OQkIDVeiF/RUZGsmLFCh555BFatmxJzZo1eeihh3jyySeLehkiRfLczVHEnzrLT7tPMGb6RpY+cAVhgd6FO9lqhb4fwbnTsPd7+KI/3LECqjUp3aJFRCopi1GkWZJmINm3bx8zZsygWTPzhmc7duxg5MiRNGzYkDlz5pRKoY6SkpJCUFAQycnJmn8jRZKSkc2tH/7CnuNpRNcMYv49nfHxdLv8iX/JTIMZt8DhzRBYE8Z8B0Haj01EpDCK8vu7yOEmKCiIVatW0aFDhzzHN2zYwA033EBSUlKRCy5LCjdSEgdOpdN30jrOnM3mpuhwJg5ui9VqKXwH6afg855wcjeENobRy8GvaukVLCLiIory+7vIc27sdnu+c2s8PDzyTPQVcUV1qvrx8fD2eLhZ+GbbUd5dtbtoHfhVNbdpCKxpBpzZt5sjOiIi4jBFDjfXXnstDz30EEeOHMk9dvjwYR555BGuu+46hxYnUh51rFeFV/pFA/DB93tYEne4aB0E1TIDjk+I+RHV/OGQU7R92URE5NKKHG4mTpxISkoKdevWpUGDBjRo0IB69eqRkpJSrF3BRSqi29tHcs/V9QH4z8KtbD5wpmgdVGsCQxeCh685yfire0EjnyIiDlHkOTdgbsGwatUq/vjjD8C8kd7f94gqzzTnRhzFbje4Z9ZmVu44Rqi/J1+N7UqtEN+idbJnFcweaC4T73g33PiGuXxcRETyKNUJxZfyxx9/cMstt+TuGF5eKdyII6Vn5tB/8np2JqbQNDyAhfd1wd+riHdY2LbQ3EEcw7yj8dVPlEqtIiIVWalOKL6UzMxM9u4t4u3pRSo4Py93po5sT7UAL/44mspDc2Kx2Yv474Xo/nDj6+bzH16GjVMdX6iISCXisHAjUlnVCPZhyoj2eLlbWf3HcV77dmfRO+l0D1x1fsRm2WPw+1cOrVFEpDJRuBFxgNaRwbx1eysApvwcz7yNCUXvpNv/QbvRgAGL7oJ9axxao4hIZaFwI+IgvVvV4OHujQB4ZvF21u89VbQOLBbo9TY0uwVsWTB3KByOKYVKRURcW6EnFIeEhGApYBVHTk4O6enp2Gw2hxVXGjShWEqTYRj8e24c/9tyhGBfD766vyt1Q/2K1klOprn/VPxP4FsV7vgOQhuWTsEiIhVEUX5/F3pZx4QJE0pal4jLs1gsvNm/JQmnz7LlYBJ3TN/I4vu6EuR78V29L8ndCwbNhmk3Q2IczOwHY1ZAYI1Sq1tExJU4bCl4RaGRGykLx1My6DNpHYnJGVzRMJTPR3fAw62InwKnnYDPesDpvVCtGYz+BnyrlE7BIiLlnFOWgovIBdUDvfl0ZHt8Pd1Yu+ckL/zvd4r87wj/auY2Df7hcGInzBkEWWdLp2AREReicCNSSprXCGLCwNZYLDDr1wSm/7K/6J2E1IHhi8A7CA7+BgtGgi3b4bWKiLgShRuRUnRD83Ce7NkUgBe/3sGaXceL3klYcxgyH9x94M/vYMlY7UMlIlIAhRuRUnbPVfW5vV0t7AY8ODuWP4+lFr2T2v+CAdPB4gZb58F3z0Llmi4nIlJoCjcipcxisfByv2g61q1CamYOY6Zv4nR6VtE7atwD+kwyn/86CdZNcGidIiKuosirpWw2G9OmTWP16tUcP34c+z+Gx7///nuHFuhoWi0lznI6PYu+k9aRcPosHetWYeadHfFydyt6R79MhO+eMZ/f8gG0HeHYQkVEyqFSXS310EMP8dBDD2Gz2WjRogWtWrXK8xCR/FXx82TqyPYEeLmzYf9pnlm8vegrqAC6PABdHzaf/+8h2Pm1Q+sUEanoijxyExoayowZM7jppptKq6ZSpZEbcbYfd59g9OcbsBvw1I1NuffqBkXvxDBg6QMQOwvcvMwVVXWvcHyxIiLlRKmO3Hh6etKwoW4FL1JcVzeuxrjezQF4ffkfrPj9aNE7sVjg5vegSS+wZcKcwZC41cGViohUTEUON4899hjvvfde8YbTRQSAkV3qMvxfdTAMeHhuHL8fSS56J27u0H8q1OkKmSkw6zY4vc/xxYqIVDBF/liqX79+/PDDD1SpUoXmzZvj4ZF3z5xFixY5tEBH08dSUl7k2OyMnraRn/88SUSQN0vGdqV6oHfRO8pIhs97wbFtEFIX7lgBAeEOr1dExJlK9WOp4OBg+vXrx9VXX01oaChBQUF5HiJSOO5uViYOaUuDan4kJmdw18zNZGTbit6RdxAM+9IMNmf2w6z+cC7JwdWKiFQc2jhTxMn2n0yn74frSDqbzc0tI/hgcBssFkvROzq9D6b2gPTj5kdVw74EDx/HFywi4gTaOFOkAqkb6sdHQ9vhbrXw9dZEJqz6s3gdValvBhqvQDiwDhaOAVuOY4sVEakAijVys3DhQubPn09CQgJZWXnvtBoTE+Ow4kqDRm6kvJq3MYEnv9wGwPuD23BLqxrF62j/Wph5q7mKqs0wuGWiubpKRKQCK9WRm/fff5/Ro0cTFhZGbGwsHTt2pGrVquzbt48bb7yx2EWLVHYDO9TmrivrAfD4gi3EJpwpXkd1r4D+n4HFat4HZ9V4xxUpIlIBFDncfPjhh3zyySd88MEHeHp68sQTT7By5Ur+/e9/k5xcjOWsIpLrqRubcV3T6mTl2LlrxmaOJJ0rXkfNbobe75nP102AXz5wWI0iIuVdkcNNQkICXbp0AcDHx4fUVHOH4+HDhzNnzhzHVidSybhZLbw3uA1NwwM4mZbJmOmbSM8s5ryZtiOg+3jz+XfPQpx+PkWkcihyuAkPD+f06dMA1K5dm19//RWA+Ph43dhPxAH8vdz5dGR7Qv092ZmYwsPz4rDbi/mz1fVh6PyA+XzJWNi9wmF1ioiUV0UON9deey1Lly4FYPTo0TzyyCNcf/31DBw4kH79+jm8QJHKqFaIL5+MaI+nu5WVO47x+oo/iteRxQLXvwQtB4Fhg/kjIeFXxxYrIlLOFHm1lN1ux2634+7uDsDcuXP55ZdfaNSoEffccw+enp6lUqijaLWUVCRL4g7z0Nw4AN7s35Lb20cWryNbNswdAn9+Z970b/S3ENbccYWKiJSyovz+1k38RMq5d77bxfvf78HDzcKsMZ3oVL9q8TrKOgsz+8LB38A/HMZ8ByF1HFqriEhpKfWb+P38888MGzaMzp07c/jwYQBmzpzJ2rVri9OdiBTg4e6N6RUdQbbN4N5ZmzlwKr14HXn6wpB5UD0K0o7CzH6QdsKxxYqIlANFDjdffvklPXr0wMfHh9jYWDIzMwFITk7mlVdecXiBIpWd1Wrhrdtb0bJWEGfOZjNm+iZSMrKL15lPiHkX46DacHovfHEbZKQ4tmAREScrcrj573//y+TJk5kyZUqeHcG7du1a7u9OLFJR+Xi68emI9oQHerPneBpjv4ghx2YvXmeBNWD4YvCtColbYN5QyM5wbMEiIk5U5HCza9currrqqouOBwUFkZSU5IiaRCQf1QO9+XRke3w83Pj5z5O89PWO4ncW2tAcwfH0h/ifYNFdYC/GjuQiIuVQse5zs2fPnouOr127lvr16zukKBHJX4uaQbw7sDUA09cfYOb6/cXvrEYbGDQb3Dxh51JY9hhUrvUFIuKiihxu7rrrLh566CF+++03LBYLR44c4YsvvuDxxx/nvvvuK40aReRverYI54meTQAY/78d/PxnCSYF178abp0CWGDz5/CD5s2JSMXnXtQTnnrqKex2O9dddx1nz57lqquuwsvLi8cff5wHH3ywNGoUkX+47+oG7DmexqKYw9z/RQyL7+9Kw+r+xeuseV84+zYsexR+egP8QqHTPQ6tV0SkLBX7PjdZWVns2bOHtLQ0oqKi8Pcv5v9Yy5jucyOuIjPHxtApv7HpwBnqVPXlq/u7EuJXgpto/vgG/PCy+fy2qRDd3zGFiog4QKnf5wbA09OTqKgoOnbsWGGCjYgr8XJ34+Ph7agV4sOBU2e5d9ZmsnKKuYIK4Kr/QMe7zeeL74E/VzmmUBGRMlbokZs77rijUB1+9tlnJSqotGnkRlzN7mOp3PrhL6Rl5jCwfSSv3RaNxWIpXmd2u7lyavtC8PCFkf+DWu0dW7CISDGUysjNtGnT+OGHH0hKSuLMmTOXfIhI2WocFsAHQ9pgtcC8TQf59Of44ndmtULfj6DBtZB9Fr7oDyd2Oa5YEZEyUOiRm7FjxzJnzhzq1KnD6NGjGTZsGFWqVCnt+hxOIzfiqj5bG8+LX+/AYoEpw9vTPSqs+J1lpsGMW+DwZgisCXesgOBibtopIuIApTJyM2nSJBITE3niiSf43//+R2RkJAMGDGDFihVUsr03Rcql0V3rMqRTbQwDHpoby87EEmyr4OUPQxZAaGNIOQyzboX0U44rVkSkFBVpQrGXlxeDBw9m5cqV7Nixg+bNm3P//fdTt25d0tLSSqtGESkEi8XCC7c0p2vDqqRn2bhz+iZOpGYWv0O/qjBskTlyc3I3zL7dHNERESnnir1aymq1YrFYMAwDm023bRcpDzzcrHw4pB31Q/04nHSOu2duIiO7BD+fwZHmPlQ+IeZHVPOHQ06W4woWESkFRQo3mZmZzJkzh+uvv57GjRuzbds2Jk6cSEJCgpaDi5QTQb4eTB3VgSAfD2ITknhi4daSfXRcrQkMPb96au/38NW95qoqEZFyqtDh5v777yciIoLXXnuNm2++mYMHD7JgwQJuuukmrNZiDwCJSCmoF+rHR8Pa4m61sHTLET74/uL94IqkVnsYOBOs7rD9S1j+pPahEpFyq9CrpaxWK7Vr16ZNmzYF3kNj0aJFDiuuNGi1lFQmczYk8PSibQBMHNKGm1vWKFmH2xbCl3cCBnR7Bq5+ouRFiogUQlF+fxd6b6kRI0YU/8ZgIuIUgzvWZs/xNKaujeex+VuIDPGlVWRw8TuM7g9nT8G3T5hbNfhWhQ5jHFaviIgjFHtvqYpKIzdS2djsBnfN2MT3fxyneoAXSx7oSkSQT8k6/f5lc5NNLHD7NHPzTRGRUlQme0uJSMXgZrXw3qDWNAkL4HhqJndO38TZrJySddrt/6DdaMAwt2vYt8YRpYqIOITCjUglEODtwacj21PVz5Pfj6Tw8Nw47PYSDNpaLNDrbWh2C9iyYO5QOBzjuIJFREpA4Uakkois4svHw9vh6Wblux3HeOu7Eu4ZZXWD2z6FeldBVpq5D9XJEq7KEhFxAIUbkUqkfd0qvN4/GoAP1+zly82HStahuxcMmg0Rrc2JxjP7QcqRkhcqIlICCjcilUy/NrUY260BAE8v2sbG/adL1qFXgHmTvyoNIDkBZt4KZ0vYp4hICSjciFRCj13fhBtbhJNls3PPzM0cPH22ZB36VzO3afAPhxM7Yc4gyCphnyIixaRwI1IJWa0W3h7QihY1AzmdnsWY6RtJzcguWachdWD4IvAOgoO/wYKRYCthnyIixaBwI1JJ+Xq68+mIDoQFerH7WBoPzoklx1bCPaPCmsOQ+eDuA39+B0vGah8qESlz5SLcTJo0ibp16+Lt7U2nTp3YsGFDoc6bO3cuFouFvn37lm6BIi4qPMibT0d0wNvDyppdJ3j5m50l77T2v2DAdLC4wdZ58N2z2odKRMqU08PNvHnzePTRRxk3bhwxMTG0atWKHj16cPz48QLP279/P48//jhXXnllGVUq4pqiawXxzoDWAHy+bj9f/Hag5J027gF9JpnPf50E6yaUvE8RkUJyerh55513uOuuuxg9ejRRUVFMnjwZX19fPvvss0ueY7PZGDp0KC+88AL169cvw2pFXNNN0RE8fkNjAJ5f8jvr9pwseaetB8MNL5vPV42HmBkl71NEpBCcGm6ysrLYvHkz3bt3zz1mtVrp3r0769evv+R5L774ItWrV2fMmMtv2JeZmUlKSkqeh4hcbGy3hvRrUxOb3eC+WZvZeyKt5J12eQC6Pmw+/99DsPPrkvcpInIZTg03J0+exGazERYWlud4WFgYR48ezfectWvXMnXqVKZMmVKo7/Hqq68SFBSU+4iMjCxx3SKuyGKx8Oqt0bSrE0JKRg53Tt9E0tmsknfcfTy0GQaGHRbeAfvXlrxPEZECOP1jqaJITU1l+PDhTJkyhdDQ0EKd8/TTT5OcnJz7OHjwYClXKVJxeXu48fHwdtQM9iH+ZDr3zYohu6QrqCwWuPk9aNILbJkwZzAkbnVMwSIi+XBquAkNDcXNzY1jx47lOX7s2DHCw8Mvar937172799P7969cXd3x93dnRkzZrB06VLc3d3Zu3fvRed4eXkRGBiY5yEilxbq78XUUe3x83Rj/b5TPL9kO0ZJVzu5uUP/qVCnK2SmwKzb4PQ+xxQsIvIPTg03np6etGvXjtWrV+ces9vtrF69ms6dO1/UvmnTpmzbto24uLjcxy233EK3bt2Ii4vTR04iDtI0PJD3B7fBYoE5Gw4ydW18yTv18IHBcyAsGtKPm/tQpeb/8bOISEk4/WOpRx99lClTpjB9+nR27tzJfffdR3p6OqNHjwZgxIgRPP300wB4e3vTokWLPI/g4GACAgJo0aIFnp6ezrwUEZdyXbMwnrmpGQCvfLOT7/84dpkzCsE7CIZ9CSF14cx+mNUfziWVvF8Rkb9xergZOHAgb731Fs8//zytW7cmLi6O5cuX504yTkhIIDEx0clVilROY66ox6AOkdgN+PecOHYdTS15pwFh5j5UftXh2DaYOwSyz5W8XxGR8yxGiT9Mr1hSUlIICgoiOTlZ829ECiErx86Iz37j132nqRnsw5IHuhLq71XyjhO3wrRe5hycJr1gwAxzbo6ISD6K8vvb6SM3IlK+ebpbmTysHXWr+nI46Rz3zNxMRrat5B1HtDTn4Lh5wa5l8PVD2qZBRBxC4UZELivY15OpozoQ6O3O5gNneHrRtpKvoAKoewX0/wwsVoidZd7JWESkhBRuRKRQGlTz58Oh7XCzWlgce5gP11x864ViaXYz9H7PfL5uAvzygWP6FZFKS+FGRArtikahvHBLcwDeXLGLb7c5aLJ/2xHmnYzB3EU8bo5j+hWRSknhRkSKZNi/6jCqS10AHpkfx7ZDyY7puOvD0PkB8/mSsbB7hWP6FZFKR+FGRIrs2V7NuLpxNTKy7dw5YyNHkzNK3qnFAte/BC0HgWGD+SMh4deS9ysilY7CjYgUmbublQ+GtKFRdX+OpWRy14xNnMtywAoqqxX6TIRGN0DOOZg9AI79XvJ+RaRSUbgRkWIJ9Pbgs1EdqOLnybbDyTw6Pw673QErqNw84PbpENkJMpJh5q1w5kDJ+xWRSkPhRkSKLbKKLx8Pb4enm5Vvtx/lnZW7HdOxpy8MmQfVoyDtqLkPVdoJx/QtIi5P4UZESqRD3Sq8ems0ABN/2MPi2EOO6dgnxNyHKqg2nN4LX9wGGSmO6VtEXJrCjYiU2G3tanHfNQ0AeHLhNjYfOO2YjgNrmPtQ+VaFxC3n96FywORlEXFpCjci4hD/uaEJN0SFkWWzc/eMzRw6c9YxHYc2hKELwdMf9v8Mi+4CuwMmL4uIy1K4ERGHsFotvDuwNVERgZxKz2LMtE2kZeY4pvOabWHQF+DmCTuXwrLHtA+ViFySwo2IOIyflztTR7WnWoAXu46l8u85sdgcsYIKoP41cOsUwAKbP4cfXnFMvyLichRuRMShIoJ8+HREe7zcrXz/x3Fe/Wan4zpv3hd6vW0+/+kN+HWy4/oWEZehcCMiDtcqMpi3B7QC4NO18czdkOC4zjuMgW7PmM+XPwlbFziubxFxCQo3IlIqbm5Zg0e6Nwbg2a+288vek47r/Kr/QMe7zedf3Qt/rjInGcf/DNsWmv/VpGORSstiGJVrVl5KSgpBQUEkJycTGBjo7HJEXJphGDw0N46lW44Q5OPBV2O7Ui/UzzGd2+3myqntC82Jxt5BkP63G/0F1oCer0PULY75fiLiVEX5/a2RGxEpNRaLhTf6t6R1ZDDJ57IZM20jyWezHdO51Qp9P4KwFmDLyhtsAFISYf4I2LHUMd9PRCoMhRsRKVXeHm58MqIdNYK82XcynftnbybbZndM51Y3OHvqEi+eH5Re/pQ+ohKpZBRuRKTUVQ/wZuqoDvh6urFuzynGL/0dh3wifuAXSE0soIEBKYfNdiJSaSjciEiZaBYRyPuD2mCxwBe/JTDtl/0l7zTtmGPbiYhLULgRkTLTPSqMp29sCsBLX+/gh13HS9ahf1jh2v02GXZ9CzYH3TFZRMo1hRsRKVN3XVmfAe1rYTfgwdmx7D6WWvzO6nQxV0VhKbjdoY0wZxBMaAHfvwxJDrzvjoiUOwo3IlKmLBYL/+0bTad6VUjLzGHM9I2cSsssXmdWN3O5t9nzP7+T+bjxDejyoLmzeGqieWfjCS1h1m2w839gc9DqLREpN3SfGxFxijPpWfT9cB0HTp2lQ90QZt3ZCS93t+J1tmOpebfilCMXjgXWhJ6vXbjPTU4m/LEMNk+D+B8vtPMPgzbDoO0ICKlb3MsRkVJWlN/fCjci4jR7jqfS78NfSM3I4ba2tXjr9pZYLJf5iOlS7DZzVVTaMTOw1Olijuzk59ReiJkBcV/kvT9O/W7QbhQ0uQncPYtXh4iUCoWbAijciJQvP+0+wehpG7HZDZ7s2ZT7rmlQdt88Jwt2f2uO5uz9gdx74/iGQpuh0HYkVC3DekTkkhRuCqBwI1L+zFi/n+eX/I7FApOHtaNH8/CyL+LMfoiZCbGzIO3oheP1rjJDTrPe4O5V9nWJCKBwUyCFG5Hy6fkl25mx/gA+Hm4suLczLWoGOacQWzbsXgEx0+HPleSO5vhUgdZDzI+tQhs5pzaRSkzhpgAKNyLlU47NzuhpG/n5z5NEBHmzZGxXqgd6O7eopARzJCdmJqT+bbJyna5myGl2C3g4uUaRSkLhpgAKNyLlV/K5bG79cB17T6TTqlYQ8+7pjLdHMVdQOZItB/asMufm/LkCjPN7Y3kHQ6vB0G4kVG/mzApFXJ7CTQEUbkTKtwOn0uk7aR1nzmbTq2UEHwxqg9VazBVUpSH5sLnKKmYGJB+8cDzyX2bIieoLnr5OK0/EVSncFEDhRqT8+23fKYZN/Y1sm8G/r2vEo9c3dnZJF7PbYO/35mjOrm/BOL/zuFcQtBpoTkIOb+HUEkVcicJNARRuRCqG+ZsO8sTCrQC8N6g1fVrXdHJFBUg9en5uzgxIOnDheM325tycFreCp5/TyhNxBQo3BVC4Eak4Xv1mJx//tA9Pdytz7/4XbWuHOLukgtntEL/GHM35YxnYz2/U6RkALW83g05EKycWKFJxKdwUQOFGpOKw2Q3umbmZVTuPEervxZIHulIz2MfZZRVO2nGIm20uKT+978LxGm3Mj6yi+4NXgPPqE6lgFG4KoHAjUrGkZ+bQf/J6diam0DQ8gIX3dcHfy93ZZRWe3Q77fzZDzo6lYD+/UaeHnxlw2o0yA09xt50QqSQUbgqgcCNS8RxOOkefies4mZZJ92ZhfDy8HW7laQVVYaWfhC1zYPN0OPXnhePh0WbIib4dvJ1080KRck7hpgAKNyIVU2zCGQZ+8itZOXbuuao+T99Uge8rYxjmJp+bp8GOJWDLNI97+ELzW82gU6u9RnNE/kbhpgAKNyIV15K4wzw0Nw6AN/q3ZED7SOcW5AhnT8PWeWbQOfHHhePVm5shp+UA8Al2UnEi5YfCTQEUbkQqtndW7ub91X/i4WZh5phO/Kt+VWeX5BiGAQd/Mz+y+n0R5GSYx929oXk/M+hEdtJojlRaCjcFULgRqdjsdoMH58aybGsiwb4eLBnblTpVXeweMufOwNYF5mjO8d8vHA9tYoacVoPAt4qzqhNxCoWbAijciFR8Gdk2Bn68ni2HkmlQzY9F93clyMfD2WU5nmHA4c2w+XPYvgiyz5rH3bwgqo+53UOdrhrNkUpB4aYACjciruF4SgZ9Jq0jMTmDKxuF8umI9sQkJHE8NYPqAd50rFelYq6oupSMZNi20Aw6R7ddOF61kRlyWg0Gv1Dn1SdSyhRuCqBwI+I6th9O5vbJ6zmXbcPX042zWbbc1yKCvBnXO4qeLSKcWGEpMAw4EmveN2fbQshKM49bPaBZb/Njq7pXgtXq1DJFHE3hpgAKNyKu5dVvd/Lxj/suOv7XmM1Hw9q6XsD5S2YqbP/SnJtzJPbC8Sr1oe0IaD0U/Ks7rTwRR1K4KYDCjYjrsNkNrnj9exKTM/J93QKEB3mz9slrXesjqvwkbjFXWm2dD1mp5jGrOzTtZW73UL+bRnOkQivK72/9TReRCmtD/OlLBhsAA0hMzmBD/OmyK8pZIlrBze/A47ugzySo1cHcuHPHEph1K7zfGn56y9zBXMTFaeRGRCqsv9/UryB1qvhybbPqtKkdQpvIYGqF+GCpDCuMjm435+ZsmQeZyeYxixs0udGcm9PgWrC6ObVEkcLSx1IFULgRcR3r955i8JRfi3xeqL8XbWoHm4/IEFrWCsKvIm3GWVRZZ80RnM3T4ODf/ryCIqHNcGgzDIJqOq08kcJQuCmAwo2I6/hrzs3R5Azy+x+ZBagW4MXTNzZly6FkYg8mseNIMtm2vK2tFmgSHng+7Jihp36oP1ZXnKdz/A9zNCduNmQkmccsVmjUw1xS3vB6cHPhoCcVlsJNARRuRFzL8u2J3DcrBiBPwLnUaqmMbBu/H0khNuEMsQeTiEtI4nDSuYv6DfB2p3VksPlR1vnQE+zrWYpXUsayM2DnUnMS8oG1F44H1IC2w80RnWAX2LtLXIbCTQEUbkRcz/Ltibzwvx15JhcX5T43x1IyiE1IIvbgGWITkth6KImMbPtF7eqH+tG6dnDu3J2m4QG4u7nAuoyTf5ofWW2ZA2dPnT9ogYbdzbk5jXuAmwveAVoqFIWbAijciLgmm91gQ/xph9yhONtmZ9fRVGIPJhGbcIa4hCT2nUy/qJ2PhxvRtYJy5+60rR1M9UDvkl6K8+Rkwh9fm0En/qcLx/3Doc1Q8945IXWdVZ1Ucgo3BVC4EZHiOJOeRdyhJHOEJ+EMcQeTSM3IuahdzWAfc3Tn/EdazWsE4u1RAVckndoLMTMg7gtIP3H+oAUadDNHc5rcpNEcKVMKNwVQuBERR7DbDfadTCMm4ULg2X0sFfs//o/q4WYhqkZQ7kTltrVDKtZS9Jws2P2tOZqz9/sLx/2qmXdAbjsCqjZwWnlSeSjcFEDhRkRKS3pmDlsPJefO3YlNOMPJtKyL2oX6e9I6MiR3OXrLWsH4V4Sl6KfjIXYmxM6CtGMXjte7yhzNaXozuHs5rTxxbQo3BVC4EZGyYhgGh86cy527E5uQxO+XWIreOCwgd+5Om9rBNKhWjpei27Jh9wpzNGfPKnLXqflWhdZDoO0oCG3oxALFFSncFEDhRkScKSPbxo7ElNyRndjLLUU/P3endWQwIX7lcCl6UoI5khMzE1KPXDhe5wpzNKdZb/CowJOspdxQuCmAwo2IlDfHUzLOj+6YgWfroWTOZdsualcv1C937k6b2iE0CQ/Ao7wsRbflwJ6V5mjOn9+BcX4pvU8ItBpsbt5ZvalTS5SKTeGmAAo3IlLe5djs7DqWej7smPff2Xfi4qXo3h5WWtYMvrCVRO0QwsrDUvTkw+dHc2ZAyqELx2t3NkNO877g4eO08qRiUrgpgMKNiFRESWeziPtrdOdgEnEJZ0jJZyl6jSDvC3dVrh1M8xpBzluKbreZK6w2T4Nd34JxfjTKOwhaDjK3ewhr7pzapMKpcOFm0qRJvPnmmxw9epRWrVrxwQcf0LFjx3zbTpkyhRkzZrB9+3YA2rVrxyuvvHLJ9v+kcCMirsBcip5+PvCYc3f+OJqS/1L0iMC/bSMRQmQVJyxFT0k075kTM92cp/OXWh3MuTnN+4GnX9nWJBVKhQo38+bNY8SIEUyePJlOnToxYcIEFixYwK5du6hevfpF7YcOHUrXrl3p0qUL3t7evP766yxevJjff/+dmjUvv6utwo2IuKr0zBy2HU7OnbsTk5DEybTMi9pV9fOkTe3g3L2zWtYKIsC7jG7IZ7fDvh/Oj+Z8A/bzo09egRB9uxl0IlqWTS1SoVSocNOpUyc6dOjAxIkTAbDb7URGRvLggw/y1FNPXfZ8m81GSEgIEydOZMSIEZdtr3AjIpWFYRgcTjqXZ+7O74dTyLLl3TfLYoHG1QPyzN1pWBZL0dOOm6M5m6fDmfgLx2u0NT+yanEbeAWUbg1SYVSYcJOVlYWvry8LFy6kb9++ucdHjhxJUlISS5YsuWwfqampVK9enQULFnDzzTdf9HpmZiaZmRf+5ZKSkkJkZKTCjYhUSpk5NnYcScmduxObcIZDZ/JZiu7lTqvIC5OVW0eGUKW0lqLb7bD/Z3M0Z+f/wJ5tHvf0h+j+5mhOjTal872lwihKuHHqLTFPnjyJzWYjLCwsz/GwsDD++OOPQvXx5JNPUqNGDbp3757v66+++iovvPBCiWsVEXEFXu5u5+ffhOQeO56aQdzfws7WQ8mkZuawds9J1u45mduublXfPHN3mkY4aCm61Qr1rzYf6SchbrY5N+fUHjPwbJ4G4S3NkBN9O3jrH6ZSMKeO3Bw5coSaNWvyyy+/0Llz59zjTzzxBD/++CO//fZbgee/9tprvPHGG6xZs4aWLfP/jFYjNyIiRZNjs7P7WFqebST25rMU3cvdSstaQWbgOT9/JzzIQUvRDQMOrDODzY6lYDv//3EPX2hxK7QbDTXbmZ+pSaVQYUZuQkNDcXNz49ixY3mOHzt2jPDw8ALPfeutt3jttddYtWrVJYMNgJeXF15e2utERKSw3N2sRNUIJKpGIEM71QEg+Wz2+V3RzcATdzCJ5HPZbNx/ho37z+SeGxHknWcbiRY1i7kU3WKBuleYjxtPw5a5ZtA5ucu8h07sLAhrYd43p+UA8Al2zMWLSygXE4o7duzIBx98AJgTimvXrs0DDzxwyQnFb7zxBi+//DIrVqzgX//6V5G+nyYUi4iUnN1uEH8qPc82EvktRXe3WoiqEZg7stOmdjC1q/gWbym6YcDB38yQ8/tiyMk4/018zKXk7UZCZCeN5rioCjOhGMyl4CNHjuTjjz+mY8eOTJgwgfnz5/PHH38QFhbGiBEjqFmzJq+++ioAr7/+Os8//zyzZ8+ma9euuf34+/vj7+9/2e+ncCMiUjrOZuWw7VBy7tydmIQkTqRevBS9ip9nnn2zWkUWYyn6uTOwdb4ZdI7vuHC8WlNzbk7LgeBbpUTXI+VLhQo3ABMnTsy9iV/r1q15//336dSpEwDXXHMNdevWZdq0aQDUrVuXAwcOXNTHuHHjGD9+/GW/l8KNiEjZMAyDI8kZuSM7sQln2H6JpeiNqvvnfpTVpnYIDav741aYpeiGAYc2nR/NWQTZZ83jbl4Q1ccMOnW6aDTHBVS4cFOWFG5ERJwnM8fGzsTUC4Hn4BkOnr54Kbq/lzutIoNyA0/ryGCq+l9m/mRGMmxbYAado9suHK/ayPzIqtUQ8Kvq2AuSMqNwUwCFGxGR8uVEamaebSS2HEribNbFu6LXqeqbZ+5O0/BAPN3zWYpuGHAk1gw52xZC9vmVXm6e0Ky3OZpT90qN5lQwCjcFULgRESnfbHaD3bm7op8h9mASe46nXdTOy91KdM2g3I+y2tQOJiLoH7uNZ6aaASdmuhl4/lKlvrnSqvVQ8K9WylckjqBwUwCFGxGRiif5XDZbDl7YRiI2wVyK/k/hgd55tpFoUSMIH8/zS9GPxJkhZ+sCyEo1j1k9oOlN5mhOvWvMGwpKuaRwUwCFGxGRis8wDOJPpufecyf24Bl2JqZi+8dadHerhWYRgec3CTUDT90AO5bfvzI/tjq86ULj4Drm3JzWQyGg4HutSdlTuCmAwo2IiGs6l2U7vyu6ObITk3CG4/ksRQ/x9cjdEb1rwFGij36F5+8LIDPZbGBxgyY3mndBbtANrMW4CaE4nMJNARRuREQqB8MwSEzOyDN3Z9vhZLJyLl6K3jzUg+GBMVx7djnVzvxtbk5QJLQdAW2GQWCNMr4C+TuFmwIo3IiIVF5ZOXZ2Jqbkhp3YhCQSTp/N06aR5RAjPH+gn/Vn/A1zIrNhsWJp1MOcm9Poeo3mOIHCTQEUbkRE5O9OpmWe3xX9/FL0g0mkZ9nwIoue1g0Mcf+eTtY/ctune4WRGjWYKlfcgWfVOk6svHJRuCmAwo2IiBTEZjf483hqnn2z7Cd2MdBtDf3dfqSKxRzNsRkW4rzasyeyP/7RN9G6bjVqBHkXb98suSyFmwIo3IiISFEln8tm66Ektu4/jnXX17Q7uYSO/J77+jEjmPm2a1jl3YPwOo3N++5EBhNdKwhfT3cnVu46FG4KoHAjIiIlZRgGh/du5+yvn1Fj/yL8c5IAsBsWfrZHM8d2LavsbTGsHjQNDzCXoZ/fSqJeqJ9Gd4pB4aYACjciIuJQOVmw6xtsmz7HLX5N7uFTBDMv5yrm2rqRYITlHg/+ayn6+bDTKjKYIJ8i7opeCSncFEDhRkRESs3peIidCbGzIO1Y7uH4wPYsslzP56eak5Zz8V2QG1b3z7NvVuOwgMLtil6JKNwUQOFGRERKnS0bdi+HzdNhzyrA/FVr+IZyssGtrA3sxY+ngog9mMSBU2cvOt3X041WtS7cVbl1ZDDVAi6zK7qLU7gpgMKNiIiUqaQEiJlpjuikJl44XvdKaDuSU7VvIC4xI3ffrC0Hk0nLzLmom8gqPrkfZbWODCaqRiBe7pXnfjsKNwVQuBEREaew5cCeleaeVn9+B8b5OyX7hECrIea+VtWaYLMb7DmelrsMPfbgGf48nsY/f1t7ullpXjMwN/C0qR1MzWAfl52srHBTAIUbERFxuuTD5rycmBmQcujC8dqdzbsgR/UBD5/cwykZ2Ww9mEzcwb8CTxKn07Mu6rZagFeeuTstXWgpusJNARRuRESk3LDbYM9qczRn93IwbOZx7yBoOcgMOmFRF51mGAYJp8/m2Tdrx5EUcv6xK7qb1UKTsIDcuTttagdTr6of1go4WVnhpgAKNyIiUi6lJELcLNg8A5ITLhyv1dEMOc37gafvJU/PyLax/XBy7kdZsQlJJCZnXNQuyOevXdHPT1auFUyQb/lfiq5wUwCFGxERKdfsdtj3gzmas+sbsJ+fXOwVCC0HmEEnPLpQXSUmnzu/b5Y5wrP1UDKZ/9gVHaBBNb/ckZ02kSE0DvPH3e3iJeuXY7MbbIg/zfHUDKoHeNOxXhWHLWlXuCmAwo2IiFQYqccg7guImQ5n9l84XqOtGXJa3AZe/oXuLttm54/E1NyRndiEM+y/xFL0lrWCcreRaF07mOoB3gX2vXx7Ii/8b0ee0aKIIG/G9Y6iZ4uIQtd4KQo3BVC4ERGRCsduh/0/maM5O78Ge7Z53NMfom83V1rVaFOsrk+nZ12YqJyQRNzBpHyXotcM9skzd6f535aiL9+eyH2zYvhnoPhrzOajYW1LHHAUbgqgcCMiIhVa+kmIm20GndN7LxyPaHV+NKc/eBf/95vNbrD3xIWl6HEHk9h1LDXfpehRNQJpFRnEktgjJJ3Lzrc/CxAe5M3aJ68t0UdUCjcFULgRERGXYBiwf635kdWOJWA7vzTcww9a3ArtRkPNtuCA+96kZmSz7VBy7tydmIT8l6IXZM5d/6Jzg6rFrkHhpgAKNyIi4nLST8HWueZ2Dyd3XTge1sIczYm+HXyCHfbtDMPg4OlzxB48w5ebD/HTnycve857g1rTp3XNYn9PhZsCKNyIiIjLMgxI+NX8yGrHV5BzfnKvu8/50ZxRUKuDQ0Zz/rJ+7ykGT/n1su3KcuSm6Ou8REREpHyyWKBOZ7j1Y3jsD7jxDageBTnnzFVXU6+HDzvDr5Ph7GmHfMuO9aoQEeTNpeKSBXPVVMd6VRzy/QpD4UZERMQV+YRAp3vgvl9gzEpoPcwcwTmxE5Y/CW83hUV3w4FfuGi2cBG4WS2M623eRfmfAeevr8f1jnLY/W4KQx9LiYiIVBYZybB1vjk359i2C8dDG0PbkdBqMPgV76Mj3efGiRRuRESk0jMMOBJjzs3Z9iVkp5vH3Tyh2S3mfXPqXlnkuTm6Q7GTKNyIiIj8TUYKbP/SDDqJcReOV2lghpxWQ8C/mrOqy6VwUwCFGxERkUs4EmfeN2frAshKNY9ZPaBpL3OlVb2rweqc6boKNwVQuBEREbmMzDT4fZE5mnN484XjIXWh7QhzcnJA2MXn2W3mBOW0Y+AfBnW6gNXNISUp3BRA4UZERKQIjm4zJyBvnQeZKeYxqzs0udEczal/rTmas2OpuQor5ciFcwNrQM/XIeqWEpehcFMAhRsREZFiyDpr3hhw8zQ4+NuF40G1IbKjOW/nUltnDphR4oCjcFMAhRsREZESOrbDnJuzZS5kJF2mscUcwXl4W4k+otIdikVERKT0hEXBja+bd0G+4rHLNDYg5bA5F6eMKNyIiIhI8Xj4mEGnMNKOlW4tf6NwIyIiIsXnn8+qqZK0cwCFGxERESm+Ol3MOTUFbZ0ZWNNsV0YUbkRERKT4rG7mcm/gkltn9nzNYfe7KVRJZfadRERExDVF3WIu9w78xwaZgTUcsgy8qNzL9LuJiIiIa4q6xdymoZTuUFwUCjciIiLiGFY3qHels6vQx1IiIiLiWhRuRERExKUo3IiIiIhLUbgRERERl6JwIyIiIi5F4UZERERcisKNiIiIuBSFGxEREXEpCjciIiLiUirdHYoNwwAgJSXFyZWIiIhIYf31e/uv3+MFqXThJjU1FYDIyEgnVyIiIiJFlZqaSlBQUIFtLEZhIpALsdvtHDlyhICAACyWf27NXjIpKSlERkZy8OBBAgMDHdp3eeDq1weuf426vorP1a9R11fxldY1GoZBamoqNWrUwGoteFZNpRu5sVqt1KpVq1S/R2BgoMv+pQXXvz5w/WvU9VV8rn6Nur6KrzSu8XIjNn/RhGIRERFxKQo3IiIi4lIUbhzIy8uLcePG4eXl5exSSoWrXx+4/jXq+io+V79GXV/FVx6usdJNKBYRERHXppEbERERcSkKNyIiIuJSFG5ERETEpSjciIiIiEtRuCnApEmTqFu3Lt7e3nTq1IkNGzYU2H7BggU0bdoUb29voqOj+eabb/K8bhgGzz//PBEREfj4+NC9e3f+/PPP0ryEyyrKNU6ZMoUrr7ySkJAQQkJC6N69+0XtR40ahcViyfPo2bNnaV/GJRXl+qZNm3ZR7d7e3nnalLf3sCjXd80111x0fRaLhV69euW2KU/v308//UTv3r2pUaMGFouFr7766rLnrFmzhrZt2+Ll5UXDhg2ZNm3aRW2K+nNdmop6jYsWLeL666+nWrVqBAYG0rlzZ1asWJGnzfjx4y96D5s2bVqKV3FpRb2+NWvW5Pt39OjRo3naVeT3ML+fMYvFQvPmzXPblJf38NVXX6VDhw4EBARQvXp1+vbty65duy57Xnn4Xahwcwnz5s3j0UcfZdy4ccTExNCqVSt69OjB8ePH823/yy+/MHjwYMaMGUNsbCx9+/alb9++bN++PbfNG2+8wfvvv8/kyZP57bff8PPzo0ePHmRkZJTVZeVR1Gtcs2YNgwcP5ocffmD9+vVERkZyww03cPjw4TztevbsSWJiYu5jzpw5ZXE5Fynq9YF5R82/137gwIE8r5en97Co17do0aI817Z9+3bc3Ny4/fbb87QrL+9feno6rVq1YtKkSYVqHx8fT69evejWrRtxcXE8/PDD3HnnnXl++Rfn70RpKuo1/vTTT1x//fV88803bN68mW7dutG7d29iY2PztGvevHme93Dt2rWlUf5lFfX6/rJr16489VevXj33tYr+Hr733nt5ru3gwYNUqVLlop/D8vAe/vjjj4wdO5Zff/2VlStXkp2dzQ033EB6evolzyk3vwsNyVfHjh2NsWPH5n5ts9mMGjVqGK+++mq+7QcMGGD06tUrz7FOnToZ99xzj2EYhmG3243w8HDjzTffzH09KSnJ8PLyMubMmVMKV3B5Rb3Gf8rJyTECAgKM6dOn5x4bOXKk0adPH0eXWixFvb7PP//cCAoKumR/5e09LOn79+677xoBAQFGWlpa7rHy9P79HWAsXry4wDZPPPGE0bx58zzHBg4caPTo0SP365L+mZWmwlxjfqKioowXXngh9+tx48YZrVq1clxhDlKY6/vhhx8MwDhz5swl27jae7h48WLDYrEY+/fvzz1WXt/D48ePG4Dx448/XrJNefldqJGbfGRlZbF582a6d++ee8xqtdK9e3fWr1+f7znr16/P0x6gR48eue3j4+M5evRonjZBQUF06tTpkn2WpuJc4z+dPXuW7OxsqlSpkuf4mjVrqF69Ok2aNOG+++7j1KlTDq29MIp7fWlpadSpU4fIyEj69OnD77//nvtaeXoPHfH+TZ06lUGDBuHn55fneHl4/4rjcj+DjvgzK2/sdjupqakX/Qz++eef1KhRg/r16zN06FASEhKcVGHxtG7dmoiICK6//nrWrVuXe9wV38OpU6fSvXt36tSpk+d4eXwPk5OTAS76+/Z35eV3ocJNPk6ePInNZiMsLCzP8bCwsIs++/3L0aNHC2z/13+L0mdpKs41/tOTTz5JjRo18vwl7dmzJzNmzGD16tW8/vrr/Pjjj9x4443YbDaH1n85xbm+Jk2a8Nlnn7FkyRJmzZqF3W6nS5cuHDp0CChf72FJ378NGzawfft27rzzzjzHy8v7VxyX+hlMSUnh3LlzDvk7X9689dZbpKWlMWDAgNxjnTp1Ytq0aSxfvpyPPvqI+Ph4rrzySlJTU51YaeFEREQwefJkvvzyS7788ksiIyO55ppriImJARzz/63y5MiRI3z77bcX/RyWx/fQbrfz8MMP07VrV1q0aHHJduXld2Gl2xVcHOO1115j7ty5rFmzJs+k20GDBuU+j46OpmXLljRo0IA1a9Zw3XXXOaPUQuvcuTOdO3fO/bpLly40a9aMjz/+mJdeesmJlTne1KlTiY6OpmPHjnmOV+T3r7KZPXs2L7zwAkuWLMkzJ+XGG2/Mfd6yZUs6depEnTp1mD9/PmPGjHFGqYXWpEkTmjRpkvt1ly5d2Lt3L++++y4zZ850YmWlY/r06QQHB9O3b988x8vjezh27Fi2b9/utPlbRaWRm3yEhobi5ubGsWPH8hw/duwY4eHh+Z4THh5eYPu//luUPktTca7xL2+99RavvfYa3333HS1btiywbf369QkNDWXPnj0lrrkoSnJ9f/Hw8KBNmza5tZen97Ak15eens7cuXML9T9JZ71/xXGpn8HAwEB8fHwc8neivJg7dy533nkn8+fPv+gjgH8KDg6mcePGFeI9zE/Hjh1za3el99AwDD777DOGDx+Op6dngW2d/R4+8MADfP311/zwww/UqlWrwLbl5Xehwk0+PD09adeuHatXr849ZrfbWb16dZ5/2f9d586d87QHWLlyZW77evXqER4enqdNSkoKv/322yX7LE3FuUYwZ7m/9NJLLF++nPbt21/2+xw6dIhTp04RERHhkLoLq7jX93c2m41t27bl1l6e3sOSXN+CBQvIzMxk2LBhl/0+znr/iuNyP4OO+DtRHsyZM4fRo0czZ86cPMv4LyUtLY29e/dWiPcwP3Fxcbm1u8p7COZKpD179hTqHxnOeg8Nw+CBBx5g8eLFfP/999SrV++y55Sb34UOm5rsYubOnWt4eXkZ06ZNM3bs2GHcfffdRnBwsHH06FHDMAxj+PDhxlNPPZXbft26dYa7u7vx1ltvGTt37jTGjRtneHh4GNu2bctt89prrxnBwcHGkiVLjK1btxp9+vQx6tWrZ5w7d67Mr88win6Nr732muHp6WksXLjQSExMzH2kpqYahmEYqampxuOPP26sX7/eiI+PN1atWmW0bdvWaNSokZGRkVHur++FF14wVqxYYezdu9fYvHmzMWjQIMPb29v4/fffc9uUp/ewqNf3lyuuuMIYOHDgRcfL2/uXmppqxMbGGrGxsQZgvPPOO0ZsbKxx4MABwzAM46mnnjKGDx+e237fvn2Gr6+v8Z///MfYuXOnMWnSJMPNzc1Yvnx5bpvL/ZmVtaJe4xdffGG4u7sbkyZNyvMzmJSUlNvmscceM9asWWPEx8cb69atM7p3726EhoYax48fL/fX9+677xpfffWV8eeffxrbtm0zHnroIcNqtRqrVq3KbVPR38O/DBs2zOjUqVO+fZaX9/C+++4zgoKCjDVr1uT5+3b27NncNuX1d6HCTQE++OADo3bt2oanp6fRsWNH49dff8197eqrrzZGjhyZp/38+fONxo0bG56enkbz5s2NZcuW5Xndbrcbzz33nBEWFmZ4eXkZ1113nbFr166yuJRLKso11qlTxwAueowbN84wDMM4e/asccMNNxjVqlUzPDw8jDp16hh33XWX0/6nYxhFu76HH344t21YWJhx0003GTExMXn6K2/vYVH/jv7xxx8GYHz33XcX9VXe3r+/lgX/8/HXNY0cOdK4+uqrLzqndevWhqenp1G/fn3j888/v6jfgv7MylpRr/Hqq68usL1hmMvfIyIiDE9PT6NmzZrGwIEDjT179pTthZ1X1Ot7/fXXjQYNGhje3t5GlSpVjGuuucb4/vvvL+q3Ir+HhmEuffbx8TE++eSTfPssL+9hftcF5Pm5Kq+/Cy3nL0BERETEJWjOjYiIiLgUhRsRERFxKQo3IiIi4lIUbkRERMSlKNyIiIiIS1G4EREREZeicCMiIiIuReFGREREXIrCjYhUehaLha+++srZZYiIgyjciIhTjRo1CovFctGjZ8+ezi5NRCood2cXICLSs2dPPv/88zzHvLy8nFSNiFR0GrkREafz8vIiPDw8zyMkJAQwPzL66KOPuPHGG/Hx8aF+/fosXLgwz/nbtm3j2muvxcfHh6pVq3L33XeTlpaWp81nn31G8+bN8fLyIiIiggceeCDP6ydPnqRfv374+vrSqFEjli5dWroXLSKlRuFGRMq95557jttuu40tW7YwdOhQBg0axM6dOwFIT0+nR48ehISEsHHjRhYsWMCqVavyhJePPvqIsWPHcvfdd7Nt2zaWLl1Kw4YN83yPF154gQEDBrB161Zuuukmhg4dyunTp8v0OkXEQRy6x7iISBGNHDnScHNzM/z8/PI8Xn75ZcMwDAMw7r333jzndOrUybjvvvsMwzCMTz75xAgJCTHS0tJyX1+2bJlhtVqNo0ePGoZhGDVq1DCeeeaZS9YAGM8++2zu12lpaQZgfPvttw67ThEpO5pzIyJO161bNz766KM8x6pUqZL7vHPnznle69y5M3FxcQDs3LmTVq1a4efnl/t6165dsdvt7Nq1C4vFwpEjR7juuusKrKFly5a5z/38/AgMDOT48ePFvSQRcSKFGxFxOj8/v4s+JnIUHx+fQrXz8PDI87XFYsFut5dGSSJSyjTnRkTKvV9//fWir5s1awZAs2bN2LJlC+np6bmvr1u3DqvVSpMmTQgICKBu3bqsXr26TGsWEefRyI2IOF1mZiZHjx7Nc8zd3Z3Q0FAAFixYQPv27bniiiv44osv2LBhA1OnTgVg6NChjBs3jpEjRzJ+/HhOnDjBgw8+yPDhwwkLCwNg/Pjx3HvvvVSvXp0bb7yR1NRU1q1bx4MPPli2FyoiZULhRkScbvny5UREROQ51qRJE/744w/AXMk0d+5c7r//fiIiIpgzZw5RUVEA+Pr6smLFCh566CE6dOiAr68vt912G++8805uXyNHjiQjI4N3332Xxx9/nNDQUPr37192FygiZcpiGIbh7CJERC7FYrGwePFi+vbt6+xSRKSC0JwbERERcSkKNyIiIuJSNOdGRMo1fXIuIkWlkRsRERFxKQo3IiIi4lIUbkRERMSlKNyIiIiIS1G4EREREZeicCMiIiIuReFGREREXIrCjYiIiLiU/wfNYfR5z6VOHgAAAABJRU5ErkJggg==\n" }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} }, { - "metadata": { - "tags": null - }, - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "Epoch [5 / 15], Step [22 / 225], Loss: 0.04997241497039795, Validation Loss: 0.0\n", - "Epoch [5 / 15], Step [44 / 225], Loss: 0.049897659569978714, Validation Loss: 0.0223587267100811\n", - "Epoch [5 / 15], Step [66 / 225], Loss: 0.049830615520477295, Validation Loss: 0.0\n", - "Epoch [5 / 15], Step [88 / 225], Loss: 0.049757134169340134, Validation Loss: 0.0\n", - "Epoch [5 / 15], Step [110 / 225], Loss: 0.0496828556060791, Validation Loss: 0.04544451832771301\n", - "Epoch [5 / 15], Step [132 / 225], Loss: 0.049610793590545654, Validation Loss: 0.0\n", - "Epoch [5 / 15], Step [154 / 225], Loss: 0.04953967407345772, Validation Loss: 0.010974577628076077\n", - "Epoch [5 / 15], Step [176 / 225], Loss: 0.049466632306575775, Validation Loss: 0.0\n", - "Epoch [5 / 15], Step [198 / 225], Loss: 0.04939447343349457, Validation Loss: 0.020791243761777878\n", - "Epoch [5 / 15], Step [220 / 225], Loss: 0.04932032525539398, Validation Loss: 0.0\n" + "Epoch [4 / 10], Step [22 / 225], Loss: 0.18842411464588207, Validation Loss: 0.023324092850089073\n", + "Epoch [4 / 10], Step [44 / 225], Loss: 0.1356671079146591, Validation Loss: 0.011662046425044537\n", + "Epoch [4 / 10], Step [66 / 225], Loss: 0.1225995806920709, Validation Loss: 0.007774697616696358\n", + "Epoch [4 / 10], Step [88 / 225], Loss: 0.10794795511967757, Validation Loss: 0.013592265080660582\n", + "Epoch [4 / 10], Step [110 / 225], Loss: 0.10796696299856359, Validation Loss: 0.011201474699191749\n", + "Epoch [4 / 10], Step [132 / 225], Loss: 0.10170525907905716, Validation Loss: 0.009334562249326458\n", + "Epoch [4 / 10], Step [154 / 225], Loss: 0.09754940814205579, Validation Loss: 0.020729850894505426\n", + "Epoch [4 / 10], Step [176 / 225], Loss: 0.0919922092209824, Validation Loss: 0.019730244923266582\n", + "Epoch [4 / 10], Step [198 / 225], Loss: 0.08834527585316788, Validation Loss: 0.09797394703814967\n", + "Epoch [4 / 10], Step [220 / 225], Loss: 0.0907003279775381, Validation Loss: 0.0924123348086141\n" ] }, { + "output_type": "display_data", "data": { - "image/png": "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\n", "text/plain": [ "
" - ] + ], + "image/png": "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\n" }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ - "Epoch [6 / 15], Step [22 / 225], Loss: 0.30001387000083923, Validation Loss: 0.0\n", - "Epoch [6 / 15], Step [44 / 225], Loss: 0.049207571893930435, Validation Loss: 0.015120014548301697\n", - "Epoch [6 / 15], Step [66 / 225], Loss: 0.04912778362631798, Validation Loss: 0.007269968744367361\n", - "Epoch [6 / 15], Step [88 / 225], Loss: 0.04904578626155853, Validation Loss: 0.0\n", - "Epoch [6 / 15], Step [110 / 225], Loss: 1.0179965496063232, Validation Loss: 1.0021252632141113\n", - "Epoch [6 / 15], Step [132 / 225], Loss: 0.9388081431388855, Validation Loss: 0.9330480098724365\n", - "Epoch [6 / 15], Step [154 / 225], Loss: 0.8098025321960449, Validation Loss: 0.4797901511192322\n", - "Epoch [6 / 15], Step [176 / 225], Loss: 0.15214264392852783, Validation Loss: 0.6066851019859314\n", - "Epoch [6 / 15], Step [198 / 225], Loss: 0.25942176580429077, Validation Loss: 0.0\n", - "Epoch [6 / 15], Step [220 / 225], Loss: 0.04873622953891754, Validation Loss: 0.02330446057021618\n" + "Epoch [5 / 10], Step [22 / 225], Loss: 0.11004065857692198, Validation Loss: 0.20903348922729492\n", + "Epoch [5 / 10], Step [44 / 225], Loss: 0.10677469699558886, Validation Loss: 0.12623683735728264\n", + "Epoch [5 / 10], Step [66 / 225], Loss: 0.09529317644509402, Validation Loss: 0.08415789157152176\n", + "Epoch [5 / 10], Step [88 / 225], Loss: 0.08533265437422828, Validation Loss: 0.06311841867864132\n", + "Epoch [5 / 10], Step [110 / 225], Loss: 0.07885887971655889, Validation Loss: 0.05049473494291305\n", + "Epoch [5 / 10], Step [132 / 225], Loss: 0.0739773078398271, Validation Loss: 0.04207894578576088\n", + "Epoch [5 / 10], Step [154 / 225], Loss: 0.07347074192162457, Validation Loss: 0.07647624505417687\n", + "Epoch [5 / 10], Step [176 / 225], Loss: 0.07329438135705212, Validation Loss: 0.09326270315796137\n", + "Epoch [5 / 10], Step [198 / 225], Loss: 0.07185688417292002, Validation Loss: 0.08290018058485454\n", + "Epoch [5 / 10], Step [220 / 225], Loss: 0.07738829492167994, Validation Loss: 0.0746101625263691\n" ] }, { @@ -1060,7 +637,7 @@ "text/plain": [ "
" ], - "image/png": "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\n" + "image/png": "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\n" }, "metadata": {} }, @@ -1068,16 +645,16 @@ "output_type": "stream", "name": "stdout", "text": [ - "Epoch [7 / 15], Step [22 / 225], Loss: 0.04863778129220009, Validation Loss: 0.0\n", - "Epoch [7 / 15], Step [44 / 225], Loss: 0.09685459733009338, Validation Loss: 0.18659985065460205\n", - "Epoch [7 / 15], Step [66 / 225], Loss: 0.08631588518619537, Validation Loss: 1.3988771438598633\n", - "Epoch [7 / 15], Step [88 / 225], Loss: 0.6523657441139221, Validation Loss: 0.6873682737350464\n", - "Epoch [7 / 15], Step [110 / 225], Loss: 0.07394303381443024, Validation Loss: 0.03567586466670036\n", - "Epoch [7 / 15], Step [132 / 225], Loss: 0.048379410058259964, Validation Loss: 0.0\n", - "Epoch [7 / 15], Step [154 / 225], Loss: 0.04830015450716019, Validation Loss: 0.024945590645074844\n", - "Epoch [7 / 15], Step [176 / 225], Loss: 0.04822903499007225, Validation Loss: 0.0\n", - "Epoch [7 / 15], Step [198 / 225], Loss: 0.04814707115292549, Validation Loss: 0.0\n", - "Epoch [7 / 15], Step [220 / 225], Loss: 0.04806003347039223, Validation Loss: 0.0\n" + "Epoch [6 / 10], Step [22 / 225], Loss: 0.059457464990290726, Validation Loss: 0.01410769484937191\n", + "Epoch [6 / 10], Step [44 / 225], Loss: 0.06400176146152345, Validation Loss: 0.01610004436224699\n", + "Epoch [6 / 10], Step [66 / 225], Loss: 0.06258460139912186, Validation Loss: 0.01073336290816466\n", + "Epoch [6 / 10], Step [88 / 225], Loss: 0.059185962616042656, Validation Loss: 0.008050022181123495\n", + "Epoch [6 / 10], Step [110 / 225], Loss: 0.05729963897981427, Validation Loss: 0.006440017744898796\n", + "Epoch [6 / 10], Step [132 / 225], Loss: 0.055887209505520084, Validation Loss: 0.00799964057902495\n", + "Epoch [6 / 10], Step [154 / 225], Loss: 0.05486652876746345, Validation Loss: 0.006856834782021386\n", + "Epoch [6 / 10], Step [176 / 225], Loss: 0.054090704366734084, Validation Loss: 0.005999730434268713\n", + "Epoch [6 / 10], Step [198 / 225], Loss: 0.05347813244419868, Validation Loss: 0.005333093719349967\n", + "Epoch [6 / 10], Step [220 / 225], Loss: 0.05342629473995079, Validation Loss: 0.004799784347414971\n" ] }, { @@ -1086,18 +663,10 @@ "text/plain": [ "
" ], - "image/png": "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\n" + "image/png": "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\n" }, "metadata": {} }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch [8 / 15], Step [22 / 225], Loss: 0.04794354736804962, Validation Loss: 0.0\n", - "Epoch [8 / 15], Step [44 / 225], Loss: 0.04784921184182167, Validation Loss: 0.0\n" - ] - }, { "output_type": "error", "ename": "KeyboardInterrupt", @@ -1105,21 +674,18 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mval_losses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloaders\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mval_losses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloaders\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 631\u001b[0m \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 632\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 633\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 634\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 635\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 675\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 676\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 677\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 678\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 679\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos_indices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0mneg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mneg_indices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mimg1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0manc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0mimg2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAD\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0mimg3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNC\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mneg\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataset.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[0msample_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumulative_sizes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdataset_idx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 243\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdataset_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msample_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0mneg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mneg_indices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0mimg1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0manc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m \u001b[0mimg2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAD\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 43\u001b[0m \u001b[0mimg3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNC\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mneg\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0manc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/transforms/transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 96\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/transforms/transforms.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m 1534\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdegrees\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranslate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshear\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1536\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maffine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcenter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcenter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1537\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1538\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/transforms/functional.py\u001b[0m in \u001b[0;36maffine\u001b[0;34m(img, angle, translate, scale, shear, interpolation, fill, center)\u001b[0m\n\u001b[1;32m 1244\u001b[0m \u001b[0mtranslate_f\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtranslate\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1245\u001b[0m \u001b[0mmatrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_inverse_affine_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcenter_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mangle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtranslate_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1246\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF_t\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maffine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/transforms/_functional_tensor.py\u001b[0m in \u001b[0;36maffine\u001b[0;34m(img, matrix, interpolation, fill)\u001b[0m\n\u001b[1;32m 616\u001b[0m \u001b[0;31m# grid will be generated on the same device as theta and img\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[0mgrid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_gen_affine_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 618\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_apply_grid_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 619\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 620\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/transforms/_functional_tensor.py\u001b[0m in \u001b[0;36m_apply_grid_transform\u001b[0;34m(img, grid, mode, fill)\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfill_img\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 574\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 575\u001b[0;31m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_cast_squeeze_out\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneed_cast\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneed_squeeze\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 576\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 577\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/transforms/_functional_tensor.py\u001b[0m in \u001b[0;36m_cast_squeeze_out\u001b[0;34m(img, need_cast, need_squeeze, out_dtype)\u001b[0m\n\u001b[1;32m 530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 532\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0m_cast_squeeze_out\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneed_cast\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneed_squeeze\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_dtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 533\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mneed_squeeze\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 534\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } @@ -1129,7 +695,7 @@ "\n", "test_iter = cycle(iter(loaders['test']))\n", "# Training loop\n", - "epochs = 15\n", + "epochs = 10\n", "trip_model.to(device)\n", "losses = []\n", "val_losses = []\n", @@ -1183,66 +749,217 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": { "id": "nnGoOwX-Vk_i" }, "outputs": [], "source": [ - "# data = {}\n", - "# labels = {}\n", + "# # data = {}\n", + "# # labels = {}\n", + "# trip_model.eval()\n", + "# with torch.no_grad():\n", + "# embeddings = trip_model.forward_once\n", + "# for stage in ['train', 'cal', 'test']:\n", + "# data[stage] = []\n", + "# labels[stage] = []\n", + "# for i, (img1, _, _, label) in enumerate(loaders[stage]):\n", + "# img1 = img1.to(device)\n", + "# output = embeddings(img1)\n", + "# data[stage].extend(output.cpu().tolist())\n", + "# labels[stage].extend(label.tolist())" + ] + }, + { + "cell_type": "code", + "source": [ + "class Classifier(nn.Module):\n", + " def __init__(self, input_size, embedding, hidden_size, output_size):\n", + " super(Classifier, self).__init__()\n", + " self.embedding = embedding\n", + " self.fc1 = nn.Linear(input_size, hidden_size)\n", + " self.fc2 = nn.Linear(hidden_size, output_size)\n", + "\n", + " def forward(self, x):\n", + " with torch.no_grad():\n", + " detached_emb = self.embedding(x).detach()\n", + " print(detached_emb.shape)\n", + " x = torch.relu(detached_emb) # Taking the mean of embeddings across the embedding dimension\n", + " x = torch.relu(self.fc1(x))\n", + " x = self.fc2(x)\n", + " return x\n", + "# Assuming you have the appropriate input dimensions\n", + "input_size = 100 # Assuming 100 input features\n", + "embedded_size = 32 # Assuming an embedded size of 50\n", + "hidden_size = 100 # Size of the hidden layer\n", + "output_size = 2 # Size of the output layer\n", + "\n", + "# Creating an instance of the CustomNet\n" + ], + "metadata": { + "id": "LVYZvjpp8q7W" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ "trip_model.eval()\n", - "with torch.no_grad():\n", - " embeddings = trip_model.forward_once\n", - " for stage in ['train', 'cal', 'test']:\n", - " data[stage] = []\n", - " labels[stage] = []\n", - " for i, (img1, _, _, label) in enumerate(loaders[stage]):\n", - " img1 = img1.to(device)\n", - " output = embeddings(img1)\n", - " data[stage].extend(output.cpu().tolist())\n", - " labels[stage].extend(label.tolist())" + "learning_rate = 0.1\n", + "clas_model = Classifier(32, trip_model.forward_once, 100, 2)\n", + "clas_criterion = nn.BCELoss()\n", + "total_step = len(loaders['train'])\n", + "\n", + "clas_optimizer = optim.Adam(clas_model.parameters(), lr=learning_rate)" + ], + "metadata": { + "id": "hKKu2Pnx8_9M" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from itertools import cycle\n", + "\n", + "test_iter = cycle(iter(loaders['test']))\n", + "# Training loop\n", + "epochs = 10\n", + "clas_model.to(device)\n", + "clas_losses = []\n", + "clas_val_losses = []\n", + "\n", + "for epoch in range(epochs):\n", + " clas_losses.append([])\n", + " clas_val_losses.append([])\n", + " for i, (img, _, _, label) in enumerate(loaders['train']):\n", + " img, label = img.to(device), label.to(device)\n", + "\n", + "\n", + " output = clas_model(img)\n", + " loss = clas_criterion(output, label)\n", + " clas_optimizer.zero_grad()\n", + " loss.backward()\n", + " clas_optimizer.step()\n", + " clas_losses[epoch].append(loss.item())\n", + " if (i + 1) % (total_step // 10) == 0:\n", + "\n", + " with torch.no_grad():\n", + " val_img, _, _, val_label = next(test_iter)\n", + " val_img, val_label = val_img.to(device), val_label.to(device)\n", + " val_output = clas_model(val_img)\n", + " val_loss = val_criterion(val_output, val_label)\n", + " clas_val_losses[epoch].append(val_loss.item())\n", + "\n", + " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step}], Loss: {np.mean(clas_losses[epoch])}, Validation Loss: {np.mean(clas_val_losses[epoch])}\")\n", + "\n", + " scheduler.step()\n", + " # Calculate mean of each row\n", + " mean_list1 = np.mean(clas_losses, axis=1)\n", + " mean_list2 = np.mean(clas_val_losses, axis=1)\n", + "\n", + " # Plot the means\n", + " plt.plot(mean_list1, label='Train Loss', marker='o')\n", + " plt.plot(mean_list2, label='Val Loss', marker='o')\n", + "\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Mean Loss')\n", + " plt.legend()\n", + " plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "id": "NN17b-Wm9jHg", + "outputId": "e1009cd6-e393-49d7-c07f-f2ae2cd22aea" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "error", + "ename": "RuntimeError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mclas_val_losses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloaders\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "%env CUDA_LAUNCH_BLOCKING=1\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tVHkbGCDGVmR", + "outputId": "c8644104-3472-4653-a62c-fb7c4038010c" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "env: CUDA_LAUNCH_BLOCKING=1\n" + ] + } ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 73, "metadata": { "id": "ayGTRCP5Wl0s", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "4c81e07a-cde3-428c-bf42-cc2132a919de" + "outputId": "16c820eb-6070-4749-9e47-408606cee484" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Cal Accuracy: 0.7344444444444445\n", - "Val Accuracy: 0.7146666666666667\n" + "Train Accuracy: 0.6839684014869889\n", + "Cal Accuracy: 0.8131111111111111\n", + "Val Accuracy: 0.7526666666666667\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.neural_network import MLPClassifier\n", "from sklearn.metrics import accuracy_score\n", "\n", "# Split the data into training and testing sets\n", "# X_train, X_test, y_train, y_test = train_test_split(embeddings, labels, test_size=0.2, random_state=42)\n", "\n", "# Train a logistic regression model\n", - "classifier = LogisticRegression(max_iter=1000)\n", + "# classifier = LogisticRegression(max_iter=1000)\n", + "classifier = MLPClassifier(hidden_layer_sizes=(50, 20), max_iter=1000)\n", "classifier.fit(data['cal'], labels['cal'])\n", "\n", "# Make predictions\n", + "train_predictions = classifier.predict(data['train'])\n", "cal_predictions = classifier.predict(data['cal'])\n", "val_predictions = classifier.predict(data['test'])\n", "\n", "# Calculate accuracy\n", + "train_accuracy = accuracy_score(labels['train'], train_predictions)\n", "cal_accuracy = accuracy_score(labels['cal'], cal_predictions)\n", "val_accuracy = accuracy_score(labels['test'], val_predictions)\n", + "print(f\"Train Accuracy: {train_accuracy}\")\n", "print(f\"Cal Accuracy: {cal_accuracy}\")\n", "print(f\"Val Accuracy: {val_accuracy}\")\n" ] From 220bc37525bd49a24c45a9d7349871e1f15c6f6f Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Sat, 21 Oct 2023 14:49:25 +1000 Subject: [PATCH 09/14] Add files via upload Moved from jupyer notebook to python files, changed to Random Forest Classifier --- dataset.py | 120 ++++++++++++++++++++++++++++++++++++++++++++++ modules.py | 60 +++++++++++++++++++++++ predict.py | 26 ++++++++++ train.py | 138 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 344 insertions(+) create mode 100644 dataset.py create mode 100644 modules.py create mode 100644 predict.py create mode 100644 train.py diff --git a/dataset.py b/dataset.py new file mode 100644 index 0000000000..5284092131 --- /dev/null +++ b/dataset.py @@ -0,0 +1,120 @@ +import torch + +import torchvision.transforms as transforms + +import os +from PIL import Image +from torch.utils.data import Dataset, DataLoader +import torch + +class CustomDataset(Dataset): + def __init__(self, root_dir, transform=None): + self.root_dir = root_dir + self.transform = transform + self.image_paths = os.listdir(root_dir) + + def __len__(self): + return len(self.image_paths) + + def __getitem__(self, idx): + img_name = os.path.join(self.root_dir, self.image_paths[idx]) + image = Image.open(img_name) + + if self.transform: + image = self.transform(image) + + return image + +class TripletDataset(Dataset): + def __init__(self, AD, NC, transform=None): + self.X = AD + NC + self.AD = AD + self.NC = NC + self.Y = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0) + self.anc_indices = torch.randperm(len(self.X)) + self.pos_indices = torch.randperm(len(self.X)) % len(AD) + self.neg_indices = torch.randperm(len(self.X)) % len(NC) + self.transform = transform + + def __len__(self): + return len(self.anc_indices) + + def __getitem__(self, idx): + anc = self.anc_indices[idx] + pos = self.pos_indices[idx] + neg = self.neg_indices[idx] + img1 = self.X[anc] + img2 = self.AD[pos] + img3 = self.NC[neg] + label = self.Y[anc] + + if self.transform: + img1 = self.transform(img1) + img2 = self.transform(img2) + img3 = self.transform(img3) + + return img1, img2, img3, torch.tensor([1 - label, label]) + + + + +def intensity_normalization(img, mean = None, std = None): + mean = torch.mean(img) + std = torch.std(img) + return (img - mean) / std + +class CustomNormalize(object): + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, img): + return (img - self.mean) / self.std + +transform_train = transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.RandomAffine(degrees=5, translate=(0.05, 0.05), scale=(0.95, 1.05)), # Random affine transformations with smaller parameters + transforms.Lambda(intensity_normalization) +]) + +transform_test = transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.Lambda(intensity_normalization) +]) + +class Normalize(object): + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, img): + return (img - self.mean) / self.std + + + +def gen_loaders(root_dir = '', batch_size = 96): + loaders = {} + AD_train = CustomDataset(root_dir=root_dir + os.path.join('train', 'AD'), transform=transform_train) + NC_train = CustomDataset(root_dir=root_dir + os.path.join('train', 'NC'), transform=transform_train) + + X = torch.stack([img for img in AD_train + NC_train]) + mean = X.mean() + std = X.std() + normalize = transforms.Compose([ + Normalize(mean, std) + ]) + train_dataset = TripletDataset(AD_train, NC_train, normalize) + + + # Create DataLoaders for the two parts + loaders['train'] = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) + + AD_test = CustomDataset(root_dir=os.path.join('test', 'AD'), transform=transform_test) + NC_test = CustomDataset(root_dir=os.path.join('test', 'NC'), transform=transform_test) + + test_dataset = TripletDataset(AD_test, NC_test, normalize) + + loaders['test'] = DataLoader(test_dataset, batch_size=batch_size, shuffle=False) + return loaders \ No newline at end of file diff --git a/modules.py b/modules.py new file mode 100644 index 0000000000..ab28c061c4 --- /dev/null +++ b/modules.py @@ -0,0 +1,60 @@ + +import torch +import torch.nn as nn +import torchvision.models as models +import torch.nn.functional as F + +class TripletSiameseNetwork(nn.Module): + def __init__(self, pretrained=True): + super(TripletSiameseNetwork, self).__init__() + self.resnet = models.resnet18(pretrained=pretrained) + self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn = nn.BatchNorm1d(1000) + self.fc1 = nn.Linear(1000, 128) + + def forward_once(self, x): + output = self.resnet(x) + output = self.bn(output) + output = self.fc1(output) + return output + + def forward(self, anchor, positive, negative): + output_anchor = self.forward_once(anchor) + output_positive = self.forward_once(positive) + output_negative = self.forward_once(negative) + return output_anchor, output_positive, output_negative + +class TripletLoss(nn.Module): + def __init__(self, margin=1.0): + super(TripletLoss, self).__init__() + self.margin = margin + + def forward(self, anchor, positive, negative): + distance_positive = F.pairwise_distance(anchor, positive) + distance_negative = F.pairwise_distance(anchor, negative) + losses = F.relu(distance_positive - distance_negative + self.margin) + return losses.mean() + +class TripletLossWithRegularization(nn.Module): + def __init__(self, model, margin=1.0, lambda_reg=0.0001): + super(TripletLossWithRegularization, self).__init__() + self.model = model + self.margin = margin + self.lambda_reg = lambda_reg # Regularization parameter + + def forward(self, anchor, positive, negative): + distance_positive = F.pairwise_distance(anchor, positive) + distance_negative = F.pairwise_distance(anchor, negative) + triplet_losses = F.relu(distance_positive - distance_negative + self.margin) + triplet_loss = triplet_losses.mean() + + # Compute L2 regularization + l2_reg = None + for param in self.model.parameters(): + if l2_reg is None: + l2_reg = param.norm(2) + else: + l2_reg = l2_reg + param.norm(2) + + loss = triplet_loss + self.lambda_reg * l2_reg + return loss diff --git a/predict.py b/predict.py new file mode 100644 index 0000000000..936cb25daf --- /dev/null +++ b/predict.py @@ -0,0 +1,26 @@ +from modules import TripletSiameseNetwork +from dataset import gen_loaders +import torch +import pickle + +def predict(input=gen_loaders()['test'], triplet_path = 'trip_model.pth', classifier_path='classifier.pickle'): + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + trip_model = TripletSiameseNetwork() + trip_model = torch.load(triplet_path) + with open(classifier_path, 'rb') as f: + clf = pickle.load(f) + outputs = None + + for i, (img, _, _, _) in enumerate(input): + img = img.to(device) + with torch.no_grad(): + features = trip_model.forward_once(img) + + if outputs is None: + outputs = features.cpu() + else: + outputs = torch.cat((outputs, features.cpu()), dim=0) + + pred = clf.predict(outputs) + return pred + diff --git a/train.py b/train.py new file mode 100644 index 0000000000..ae536b1a88 --- /dev/null +++ b/train.py @@ -0,0 +1,138 @@ +import torch +import torchvision.transforms as transforms +import numpy as np +import matplotlib.pyplot as plt +from dataset import gen_loaders +from modules import TripletSiameseNetwork, TripletLoss, TripletLossWithRegularization +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score +import pickle +from itertools import cycle + +loaders = gen_loaders() + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +learning_rate = 0.001 +trip_model = TripletSiameseNetwork() +trip_criterion = TripletLossWithRegularization(margin=1.0) +val_criterion = TripletLoss(margin=1.0) +total_step = len(loaders['train']) +epochs = 20 + +optimizer = torch.optim.SGD(trip_model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4) + +sched_linear_1 = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.0001, max_lr=learning_rate, step_size_up=epochs // 2, step_size_down=epochs // 2, mode="triangular", verbose=False) +sched_linear_3 = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.0001/learning_rate, end_factor=0.0001/learning_rate, verbose=False) +scheduler = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[sched_linear_1, sched_linear_3], milestones=[epochs]) + + + +test_iter = cycle(iter(loaders['test'])) +trip_model.to(device) +losses = [] +val_losses = [] + +transform_train = transforms.RandomAffine(degrees=5, translate=(0.05, 0.05), scale=(0.95, 1.05)) + +prev_loss = float('inf') +epochs_without_improvement = 0 +max_epochs_without_improvement = 3 + +for epoch in range(epochs): + print(f'Learning rate: {optimizer.param_groups[0]["lr"]}') + losses.append([]) + val_losses.append([]) + for i, (img1, img2, img3, _) in enumerate(loaders['train']): + size = img1.size(0) + img1, img2, img3 = img1[torch.randperm(size)], img2[torch.randperm(size)], img3[torch.randperm(size)] + img1_trans = transform_train(img1) + img2_trans = transform_train(img2) + img3_trans = transform_train(img3) + img1_trans, img2_trans, img3_trans = img1_trans.to(device), img2_trans.to(device), img3_trans.to(device) + + + out1, out2, out3 = trip_model(img1_trans, img2_trans, img3_trans) + + loss = trip_criterion(out1, out2, out3) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + no_loss = val_criterion(out1, out2, out3) + losses[epoch].append(no_loss.item()) + if (i + 1) % (total_step // 10) == 0: + + with torch.no_grad(): + val_img1, val_img2, val_img3, _ = next(test_iter) + val_img1, val_img2, val_img3 = val_img1.to(device), val_img2.to(device), val_img3.to(device) + val_out1, val_out2, val_out3 = trip_model(val_img1, val_img2, val_img3) + val_loss = val_criterion(val_out1, val_out2, val_out3) + val_losses[epoch].append(val_loss.item()) + + print(f"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step}], Loss: {np.mean(losses[epoch])}, Validation Loss: {np.mean(val_losses[epoch])}") + + scheduler.step() + mean_list1 = np.mean(losses, axis=1) + mean_list2 = np.mean(val_losses, axis=1) + + # Plot the means + plt.plot(mean_list1, label='Train Loss', marker='o') + plt.plot(mean_list2, label='Val Loss', marker='o') + + plt.xlabel('Epoch') + plt.ylabel('Mean Loss') + plt.legend() + plt.show() + + if np.mean(losses[epoch]) < prev_loss: + epochs_without_improvement = 0 + prev_loss = np.mean(losses[epoch]) + else: + epochs_without_improvement += 1 + print(f'{epochs_without_improvement} / {max_epochs_without_improvement} before early stop.') + + if epochs_without_improvement >= max_epochs_without_improvement: + print(f'Early stopping after {epoch + 1} epochs.') + break + + + + + +output_dict = {} +label_dict = {} +for stage in ['train', 'test']: + outputs = None + labels = None + for i, (img, _, _, label) in enumerate(loaders[stage]): + img, label = img.to(device), label.to(device) + with torch.no_grad(): + features = trip_model.forward_once(img) + + if outputs is None: + outputs = features.cpu() + labels = label.cpu() + else: + outputs = torch.cat((outputs, features.cpu()), dim=0) + labels = torch.cat((labels, label.cpu()), dim=0) + output_dict[stage] = outputs + label_dict[stage] = labels + + +# Example usage: +clf = RandomForestClassifier(n_estimators=400, min_samples_split=300, max_depth=8, critereon='entropy') +clf.fit(output_dict['train'], label_dict['train']) + +y_pred = clf.predict(output_dict['test']) +y_pred_train = clf.predict(output_dict['train']) + + +# Calculate the accuracy of the classifier +print(f"Test accuracy: {accuracy_score(label_dict['test'], y_pred)}, Train accuracy: {accuracy_score(label_dict['train'], y_pred_train)}") + + +trip_model.save('trip_model.pth') +with open('classifier.pickle', 'wb') as f: + pickle.dump(clf, f) + +print('Saved siamese to trip_model.pth and classifier to classifier.pickle') \ No newline at end of file From 61dd2c49cbdc22a28418575256b9ab0cd8e5bbb4 Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Sat, 21 Oct 2023 14:54:45 +1000 Subject: [PATCH 10/14] Add files via upload Moved to correct folder --- recognition/dataset.py | 120 +++++++++++++++++++++++++++++++++++ recognition/modules.py | 60 ++++++++++++++++++ recognition/predict.py | 26 ++++++++ recognition/train.py | 138 +++++++++++++++++++++++++++++++++++++++++ 4 files changed, 344 insertions(+) create mode 100644 recognition/dataset.py create mode 100644 recognition/modules.py create mode 100644 recognition/predict.py create mode 100644 recognition/train.py diff --git a/recognition/dataset.py b/recognition/dataset.py new file mode 100644 index 0000000000..5284092131 --- /dev/null +++ b/recognition/dataset.py @@ -0,0 +1,120 @@ +import torch + +import torchvision.transforms as transforms + +import os +from PIL import Image +from torch.utils.data import Dataset, DataLoader +import torch + +class CustomDataset(Dataset): + def __init__(self, root_dir, transform=None): + self.root_dir = root_dir + self.transform = transform + self.image_paths = os.listdir(root_dir) + + def __len__(self): + return len(self.image_paths) + + def __getitem__(self, idx): + img_name = os.path.join(self.root_dir, self.image_paths[idx]) + image = Image.open(img_name) + + if self.transform: + image = self.transform(image) + + return image + +class TripletDataset(Dataset): + def __init__(self, AD, NC, transform=None): + self.X = AD + NC + self.AD = AD + self.NC = NC + self.Y = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0) + self.anc_indices = torch.randperm(len(self.X)) + self.pos_indices = torch.randperm(len(self.X)) % len(AD) + self.neg_indices = torch.randperm(len(self.X)) % len(NC) + self.transform = transform + + def __len__(self): + return len(self.anc_indices) + + def __getitem__(self, idx): + anc = self.anc_indices[idx] + pos = self.pos_indices[idx] + neg = self.neg_indices[idx] + img1 = self.X[anc] + img2 = self.AD[pos] + img3 = self.NC[neg] + label = self.Y[anc] + + if self.transform: + img1 = self.transform(img1) + img2 = self.transform(img2) + img3 = self.transform(img3) + + return img1, img2, img3, torch.tensor([1 - label, label]) + + + + +def intensity_normalization(img, mean = None, std = None): + mean = torch.mean(img) + std = torch.std(img) + return (img - mean) / std + +class CustomNormalize(object): + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, img): + return (img - self.mean) / self.std + +transform_train = transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.RandomAffine(degrees=5, translate=(0.05, 0.05), scale=(0.95, 1.05)), # Random affine transformations with smaller parameters + transforms.Lambda(intensity_normalization) +]) + +transform_test = transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.Lambda(intensity_normalization) +]) + +class Normalize(object): + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, img): + return (img - self.mean) / self.std + + + +def gen_loaders(root_dir = '', batch_size = 96): + loaders = {} + AD_train = CustomDataset(root_dir=root_dir + os.path.join('train', 'AD'), transform=transform_train) + NC_train = CustomDataset(root_dir=root_dir + os.path.join('train', 'NC'), transform=transform_train) + + X = torch.stack([img for img in AD_train + NC_train]) + mean = X.mean() + std = X.std() + normalize = transforms.Compose([ + Normalize(mean, std) + ]) + train_dataset = TripletDataset(AD_train, NC_train, normalize) + + + # Create DataLoaders for the two parts + loaders['train'] = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) + + AD_test = CustomDataset(root_dir=os.path.join('test', 'AD'), transform=transform_test) + NC_test = CustomDataset(root_dir=os.path.join('test', 'NC'), transform=transform_test) + + test_dataset = TripletDataset(AD_test, NC_test, normalize) + + loaders['test'] = DataLoader(test_dataset, batch_size=batch_size, shuffle=False) + return loaders \ No newline at end of file diff --git a/recognition/modules.py b/recognition/modules.py new file mode 100644 index 0000000000..ab28c061c4 --- /dev/null +++ b/recognition/modules.py @@ -0,0 +1,60 @@ + +import torch +import torch.nn as nn +import torchvision.models as models +import torch.nn.functional as F + +class TripletSiameseNetwork(nn.Module): + def __init__(self, pretrained=True): + super(TripletSiameseNetwork, self).__init__() + self.resnet = models.resnet18(pretrained=pretrained) + self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn = nn.BatchNorm1d(1000) + self.fc1 = nn.Linear(1000, 128) + + def forward_once(self, x): + output = self.resnet(x) + output = self.bn(output) + output = self.fc1(output) + return output + + def forward(self, anchor, positive, negative): + output_anchor = self.forward_once(anchor) + output_positive = self.forward_once(positive) + output_negative = self.forward_once(negative) + return output_anchor, output_positive, output_negative + +class TripletLoss(nn.Module): + def __init__(self, margin=1.0): + super(TripletLoss, self).__init__() + self.margin = margin + + def forward(self, anchor, positive, negative): + distance_positive = F.pairwise_distance(anchor, positive) + distance_negative = F.pairwise_distance(anchor, negative) + losses = F.relu(distance_positive - distance_negative + self.margin) + return losses.mean() + +class TripletLossWithRegularization(nn.Module): + def __init__(self, model, margin=1.0, lambda_reg=0.0001): + super(TripletLossWithRegularization, self).__init__() + self.model = model + self.margin = margin + self.lambda_reg = lambda_reg # Regularization parameter + + def forward(self, anchor, positive, negative): + distance_positive = F.pairwise_distance(anchor, positive) + distance_negative = F.pairwise_distance(anchor, negative) + triplet_losses = F.relu(distance_positive - distance_negative + self.margin) + triplet_loss = triplet_losses.mean() + + # Compute L2 regularization + l2_reg = None + for param in self.model.parameters(): + if l2_reg is None: + l2_reg = param.norm(2) + else: + l2_reg = l2_reg + param.norm(2) + + loss = triplet_loss + self.lambda_reg * l2_reg + return loss diff --git a/recognition/predict.py b/recognition/predict.py new file mode 100644 index 0000000000..936cb25daf --- /dev/null +++ b/recognition/predict.py @@ -0,0 +1,26 @@ +from modules import TripletSiameseNetwork +from dataset import gen_loaders +import torch +import pickle + +def predict(input=gen_loaders()['test'], triplet_path = 'trip_model.pth', classifier_path='classifier.pickle'): + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + trip_model = TripletSiameseNetwork() + trip_model = torch.load(triplet_path) + with open(classifier_path, 'rb') as f: + clf = pickle.load(f) + outputs = None + + for i, (img, _, _, _) in enumerate(input): + img = img.to(device) + with torch.no_grad(): + features = trip_model.forward_once(img) + + if outputs is None: + outputs = features.cpu() + else: + outputs = torch.cat((outputs, features.cpu()), dim=0) + + pred = clf.predict(outputs) + return pred + diff --git a/recognition/train.py b/recognition/train.py new file mode 100644 index 0000000000..ae536b1a88 --- /dev/null +++ b/recognition/train.py @@ -0,0 +1,138 @@ +import torch +import torchvision.transforms as transforms +import numpy as np +import matplotlib.pyplot as plt +from dataset import gen_loaders +from modules import TripletSiameseNetwork, TripletLoss, TripletLossWithRegularization +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score +import pickle +from itertools import cycle + +loaders = gen_loaders() + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +learning_rate = 0.001 +trip_model = TripletSiameseNetwork() +trip_criterion = TripletLossWithRegularization(margin=1.0) +val_criterion = TripletLoss(margin=1.0) +total_step = len(loaders['train']) +epochs = 20 + +optimizer = torch.optim.SGD(trip_model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4) + +sched_linear_1 = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.0001, max_lr=learning_rate, step_size_up=epochs // 2, step_size_down=epochs // 2, mode="triangular", verbose=False) +sched_linear_3 = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.0001/learning_rate, end_factor=0.0001/learning_rate, verbose=False) +scheduler = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[sched_linear_1, sched_linear_3], milestones=[epochs]) + + + +test_iter = cycle(iter(loaders['test'])) +trip_model.to(device) +losses = [] +val_losses = [] + +transform_train = transforms.RandomAffine(degrees=5, translate=(0.05, 0.05), scale=(0.95, 1.05)) + +prev_loss = float('inf') +epochs_without_improvement = 0 +max_epochs_without_improvement = 3 + +for epoch in range(epochs): + print(f'Learning rate: {optimizer.param_groups[0]["lr"]}') + losses.append([]) + val_losses.append([]) + for i, (img1, img2, img3, _) in enumerate(loaders['train']): + size = img1.size(0) + img1, img2, img3 = img1[torch.randperm(size)], img2[torch.randperm(size)], img3[torch.randperm(size)] + img1_trans = transform_train(img1) + img2_trans = transform_train(img2) + img3_trans = transform_train(img3) + img1_trans, img2_trans, img3_trans = img1_trans.to(device), img2_trans.to(device), img3_trans.to(device) + + + out1, out2, out3 = trip_model(img1_trans, img2_trans, img3_trans) + + loss = trip_criterion(out1, out2, out3) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + no_loss = val_criterion(out1, out2, out3) + losses[epoch].append(no_loss.item()) + if (i + 1) % (total_step // 10) == 0: + + with torch.no_grad(): + val_img1, val_img2, val_img3, _ = next(test_iter) + val_img1, val_img2, val_img3 = val_img1.to(device), val_img2.to(device), val_img3.to(device) + val_out1, val_out2, val_out3 = trip_model(val_img1, val_img2, val_img3) + val_loss = val_criterion(val_out1, val_out2, val_out3) + val_losses[epoch].append(val_loss.item()) + + print(f"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step}], Loss: {np.mean(losses[epoch])}, Validation Loss: {np.mean(val_losses[epoch])}") + + scheduler.step() + mean_list1 = np.mean(losses, axis=1) + mean_list2 = np.mean(val_losses, axis=1) + + # Plot the means + plt.plot(mean_list1, label='Train Loss', marker='o') + plt.plot(mean_list2, label='Val Loss', marker='o') + + plt.xlabel('Epoch') + plt.ylabel('Mean Loss') + plt.legend() + plt.show() + + if np.mean(losses[epoch]) < prev_loss: + epochs_without_improvement = 0 + prev_loss = np.mean(losses[epoch]) + else: + epochs_without_improvement += 1 + print(f'{epochs_without_improvement} / {max_epochs_without_improvement} before early stop.') + + if epochs_without_improvement >= max_epochs_without_improvement: + print(f'Early stopping after {epoch + 1} epochs.') + break + + + + + +output_dict = {} +label_dict = {} +for stage in ['train', 'test']: + outputs = None + labels = None + for i, (img, _, _, label) in enumerate(loaders[stage]): + img, label = img.to(device), label.to(device) + with torch.no_grad(): + features = trip_model.forward_once(img) + + if outputs is None: + outputs = features.cpu() + labels = label.cpu() + else: + outputs = torch.cat((outputs, features.cpu()), dim=0) + labels = torch.cat((labels, label.cpu()), dim=0) + output_dict[stage] = outputs + label_dict[stage] = labels + + +# Example usage: +clf = RandomForestClassifier(n_estimators=400, min_samples_split=300, max_depth=8, critereon='entropy') +clf.fit(output_dict['train'], label_dict['train']) + +y_pred = clf.predict(output_dict['test']) +y_pred_train = clf.predict(output_dict['train']) + + +# Calculate the accuracy of the classifier +print(f"Test accuracy: {accuracy_score(label_dict['test'], y_pred)}, Train accuracy: {accuracy_score(label_dict['train'], y_pred_train)}") + + +trip_model.save('trip_model.pth') +with open('classifier.pickle', 'wb') as f: + pickle.dump(clf, f) + +print('Saved siamese to trip_model.pth and classifier to classifier.pickle') \ No newline at end of file From 8a21651d01eb61a983ed486eb633661061dd1496 Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Sat, 21 Oct 2023 14:55:46 +1000 Subject: [PATCH 11/14] Add files via upload Results images --- PCA.png | Bin 0 -> 253124 bytes train.png | Bin 0 -> 38332 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 PCA.png create mode 100644 train.png diff --git a/PCA.png b/PCA.png new file mode 100644 index 0000000000000000000000000000000000000000..91ac8f052aeae3f38078a5d629cc5e2a251fe00d GIT binary patch literal 253124 zcmb@uby(ByAOE}2DP5A%N`ulRBHaccAPu5)*T!h+E{P#2N(l%eu>sO0CCyN}JIBV^ z{QSP>I@h_*pXU$tdT(#%&At16-LL24`FtB=pr=kwa+d@E0FY~Hs2TwPIQ9SlaEgck z^Ga=1{3zxRpqG)l3ZQy~WeanG=lEFnF#zy2iS))AA9GFYu3_p00MPSbe*ra(xDPQe zT01>A@qXs&TLLzx%P*7NAp=`A`7hR$5c_@pJ#zyKRJpJf<)9VR|DQQF=6eV-N`O zZ4rMgV}VTP2^ATCEI;oM6FyhiQ4AFoSvI7+h%>CPut>w;@6SXjXkOPXvI_JWdA_%w zGcU0yu{aSl{2Usz{d{k(;w|W`s`i;z%+nZZ9<6kK!y?sYW1hCl`2StSc#3G@*<)V* z?|oy4ssa7~xiQ8QU*rF$w+0r|Z_x+0jWt&vYQ8qXP9dW{4j% zUZee%s42_YdML`dWi8X}21%%R2t?M2AjOe2x7Q-K;79!opfQHonwx?idioBvQ|h)Xm(&A zcJTmwQLm3|(65JH#z1RVdXq#FNyfLlcR>e1&_}t$-30<>YQ-t2jSzJCqq_;+1wnd^ zjg2ht&yb+o#J}LtprsYi`_>(cgSxENRUS#UBouNn1%ci}G%~w>@90h5>tS|lsJZ@J zQ`gkC=Mu-YU(kwNJWzBLUa+`YQ9|1W4ZnL`e@P$FewlQ8Nijb=Yr5O)X)m5Lv)6>! zpLdot+ijDtYi@S3M1Hdj3Jj)Tm9=BPJz_tA)Xwh-Cz5#n{Yi{mIzYV+nk;6sS5IX5 znuOj=YIM6jcWb(U-J%?)((qkhUkw<~olRT1DWN8mSVQg8gUmVkw@ow)sxMH!JK12*gpM-;6$LQs08wfZ)>H!r~itw0@BDn++vI$4L`t zzRBWi<@&+R$iWOdYK&c|9Uo|dBi??h4fFoGF+lg)>h<+_*7eJ(mkdE7N$$76eWkT$ zirtv?f`XmL!7k&#BFBGE6A+gPU<$7xrP~vw`3>;?#vByA1)a+ZxyqU!y}cQomw;aB z1Z~J6wv_i)`_tm6yR>l&6*9DDbC*ubg4Viu2EGa9u%`*46RCLyR1_~eDCWLcqTU9L z#l5zJUgbcY*pXk^-$74gptjJ90O$sVOFH}(jkulPKKoHG&tfS^U-kS!GpF*_3BjGq zP^M127ZuM1p`-I-#p)<>R(rVL$YWxTiLJ@uxOBgrs-_>Q%YFlDb9;@@I0N30E*y7o z)k`^xJC&@{-8w|MxbPB|YC@CHfX7aC6roh#x5{#Fez1&+O-iNpUmi@-(UT^he53&N zB}F!C&P5dBC9%q_!#ozBmFopq{!))IAQ(72!AwN{EWjBs@JY@EiG9V5_qfxISRjdtD^Y`Z$QT1j3|Aq~@_7%C^3Cx;c)d?Bu4UWf!5-MyyKEFkU z+#>#JoQVCInRjg&rt=s#)(p8rfLnv>68-Uh)}SIG|3SUtPJL?uX7+DakZC`Du}dL> z>n~_@^ki9=LS>Ygn6KZ9mAmBK@`W7pftSMBZ>-pjuOr1SFG6npl%kHQ;6xkG9Ix}P z#R_Jfe}_2U=~!izc8BliEMAP1p7aMHkgsbFI>HGVf^Y_Ze#^|tY75?0S27fa4t3d~ z$LP<+Zc`;CmQL?`0S(CF2sYO*OY~C%MuQy$vqPLuiQ|35Df~#i=+$zw$ROvvpoQL9 zhTskce*}6ShRRJMMIGZR9pZu&$yT$1`?F?j*-jVw|lW#<6Z$i;U*j@a#{Q%kCOxw)PWxs+fKrU>Iz|cD|FuH_K z!ATgt8A1xU6vL4M3ni}1;bfeMU`k{z?5xV|3{gtEVYGiT-L~(8wt(26&0uF{;L7w2 zHym9w(p~!07QAK*wim{i4cXGRl)a6uxg=4FlEhSv+jDb3M^cJWe#nVb$cZ_8YG?F7 zAGG~!!{{ek?)W$h7Ay>T$Uu2=aC-&4l?j>YVv`IRG+Po6Imt!r!qz;SudrqO7Kxeb zd63|nLFT2ysAfntlrM5`x@;ufL-Fbxrj%W+u-{%zOsn9LY#7y@Fti^=#wna!VYl-r z^fM&JJ)Qmekfe{6N!w!}ufXqao;%4FLfGuTaXGa6pea+*A11+R2{&?WuknSsUKa z?J@=03Ox%&%`PeS{O#8VADfF%VQyAZgL?@luY(RVEUz+7u(d2dJNteN1b@M&q7Vt7{GrRbU1qP>~ysJ z4LvFEv-thKZH6LckRt~-dX8LHIuL#T;oIa%Q|2Fd9^V z1a@TpLd1yN9imMx5l+&dW`~gH8VI)@%&59%WG=_A^8FwOulo za$)u(D)z6^$auIX3I8k?`;`W}nPm2(0oz?a(rHejHOG*1M^L*J7$ygHKkYq0?;ps@ z#hYfo6E_yyL!^i%S@mq%&XJuwC|o6Da;87Y{UNc0Gc@9i#w_})zq@ku*>d|*Gt$>l zK_qGn^&%G>yW3oBNAD*p7a}8MI`cXa@$L5JXw1*haiJdLsjrD`oyH*?sw7gPnsFozU*i};4At~$T za&Wwg4qh#xKY$TvCruBC2jYPx`N*T_{Au)^|Sj^o1e~C zkRtp@R|Z&sR)9wT$5zEU*Nl)hGe!{|k9dJ;V5_J@%f@&&$%e$sc(IeX_MgxF)x&CL zo4`9wfeP6)Lz?UKCv?9~nnI9GOEvK{wgaU2UO`I7*VC}2`Thm2I)>VzY}_Hh!Q>;c zuZR?@D0@9a^~7HFbPvZw)&G4V3;4hxK&vo!YrfLyM+${1viFyrHTaVJM@f&bio9uf zV%+H5&iQF+X_!gEn*6fyY2R}4@BU^dRg;8~IlRZDagn63RiGE3ln; z)Q3PVA_5RVjeQM+KK`I_V*n?>Nc1)FyY~n>R4ff>d;=06a$O$csr2BQOcZ}PLP2wN zED79@1riH`O}a}rjG}i(+n@bjS5ZMRi3EQ$BIkhU1lrt{SI7p?o%@MOy(AM~2i&5FM3`G!DNRX( z2(umJffwd+=o$SW8p@Fy_(6h~zsx1J1?#55tZHyfp`ccJ9IeS+2oY~FDY2HH)En*e zLs!0cv@Fbpg!q6@K*8YKix61uK`MFXF1Cb?dU_)EA`%T0Ng_P~&uW4ecg%C(;?^G` zghCT$zT`>sSs(gl^&}!KAd#}*F0cr7Nz2KuOlLs86cw4S^`(JC5epbieSam z=X?KT2Ne$2VXSd$X9kJjWGQVnBQ1^FwrZdexbBZ_y|I&phJiajH!W=i$Dx2qN1?{W zx>46D;k(crk)!;_z^<}ofhf0G*F%qce^>^hA9woQc}A@>0H%A#N&IVu+=0?Wj8TM9 zoR)8hef|4M==(O69B2Nwj1t#W5}X{21ksA2z=cJGGemJfy}xUlk}jJ_XTTLIUc_lF zJevg}H>q0OFu0@4%2Cg}x=X*^+_cvcxdV^=!_XKcC|2Jg2~;_k(;wl3m<;)Aq5u=E z5xWGBPKi&5Iij|hp9zZqPB$#5wg=^lYf{^bDbBBZ7gM+)UI)PtR{MSU`d$_LwAIK7 zv33Fzv!&+Dl3mc=JIftmf}n|X%rM!3T{4=%wqDufd@unso0d9y;H9l8zi76!IFN^YT zFn%|Qe}@iXU${*8*A~W5`;{LJOcPGjs*qm@e#04I#yZr8CSOusMF`WnCaoBpijoY@ z5hH|x#Lz0${alShFbFcl=75~Jzb_6);G|p0026NXWNC&*8n~z+>>%>+zRO?g@l=!F~}k??>;Rm4N{+5QaVd2mn!V&8C1P zgw|?bMfqeTA|Qt8gJ+J!>0&P8#=+6|pxUY(ztvVP+QQx$&=4=CoPb(b!L(~I`)Lcm zd{CEEa5!SVzo+>Uo}y=~?p&e*y8={J%FvWFm&`j+lCKIrAHWF$Ahsn?%wXB5ZK6X zQQCBU=1&rPa5AE29%5mmtnf%8h3z{NSwv`s8-l`4yT)l^uu`W?Z6bj2n)XE4K?_3O z9;^wa;UOkc2Mlm?;Xh11i9fQ62N=W@{sIO;n6C5D(Fyac3d8_jBWeI)i>eaM$7_rL z&n-8V0stU!a9mDrphXb=$=$@g)d`!{87m(mRrtmJK2hj>)dp(f0Y>Z@`f>E6qz3pJ zz&NL2DW-PHW!M>$w9-wo%f7CD9sO?ncEK_RwhYdmBPBjLY90ku=hil7ZJn%w!AEhH zyByH3t2_~Oa)+!70=4b&xXpht)MJ4*R-jt?zQkYnKyo6zvdG0$y;lgah7U_UpwV3f`KEF;+#14)Te{Q1z?{p!>CkDNJ zNGG2Bm@jzaRlmOnXrQDjT{HiJ1bLM&_R(SpxJ_taTWr3qCHIL2MXJ2sZsmMG4PCmV znR}I}zq~)m(;r;K%JL*AO{@?ly>DB9&)5~u>oOxWWV=qf();ZatQZQiXGxuWFq!)f z7qTU>t-^YgQoI-x2|ATH;cSK$t2Udbj^~bAtmGAAP}CY5?M<$gyl82MiiHNTdh<0N zOt=v*xWfvl0*tDKY~Usv7A-LFU`t7n`B)f?4s0xKk@lSK_P%G~)8+#rUcaH3%by*2 zz@LZxU3^sa{^|VFu|JK6|GPbjuKBm9Xxb)pYc^0{Jp@@YF-b=r> zvHI~JSJUQ!h~HR^W+C?zS3dyl?KT~b%u@+o?~aT7?+$(>Ri$TT_rSRID!PTS*+H3U zHoHGfTUvpb5<4OPkeu+hWXGhnd4N54cROSAx-ye?T5^ZXe^d`MXGWxj@h?R7N%-T& zvi|Rn#Q?oB%vp^cNwbEv>8E$9AT^mJ$`=72OPBWVJfnYX`tQA%I(=aLS)jts1+w7n z{ef@yG2@*8S_qOinsM^aNB<8B?k5%s=MI-Wx=Wt)cXZ8D>|pH?{}3)f2F@byvFJ0M z|A^xMb@zMyeAzXt_~(Q{OsV|SiGY0B{KU~{Y~!lPM;()h{%@QE_L&h~%D}YuEYPCw z7P-t2`FsAtJWX`}8ytq%&~ac7)&J&UM4))A?;pWAKR>q_ZSFkiO=7nUj`Q^Ucer5Z zd`*$_QVfOBVv#5eM`}lPu-}BXoqwzOMiFw^Epd$8qL@Y83?l@= zQj-x6ck3R zC897*Ra4WBPfbvM(;5c(=+mn&d|dz|_s+jygyQ+qMY|t{tz``*QKkBm6KP!Ox&@{w ztlii5AjE8f>K8eqh9unWd6yTo2M%n*tWS`b4ajv*ohijf`g&y*^mR*gy*?$O7sQY-fushTK7{T)KXLS=-q?BMJ5c+Meq9}Mk_`?7EOhIx9PIYy2xX|G!Z{1vp} z2i|yE^NlqJQ+cYHs2Fq4&fsre)igP^?Pro-&r9YslnMdfq=;eo(Q%(S#ML?6x{Dy3 z)vlURX1XL);OIge!) zax-`hfF7)9oW(ZrLCd)8n;h|~&9$zV@vO4meFcXRd^0Ir>bs_+ftgtz%WxnP!>k=K zFl}jrBf|?I2cW@oCzK9^S!9t4)K-CgrSGT&}FOlh?xsRjKVIc{(GW$aMIe&ewD&rv9b8mYZ!#t0Y4P3 z`t=*JKp#9(8eiBd_up1{KjipK!AkD;fzR1t-p;V_n%j2U>-ngzP%z9}>mH^WIpR44 zKD;fs;ig9NOC2=TsO9fWE@8vVPqUEhp|B9Cf#i z^RD$+Fy2lP@(}O~1H}){u*5a*aaecPt|rLp=edA6^SKDnb8;iMeJi(hX*76UVsA@7 z$5v~ zd0uYX|DX$QXO~mrs8(!~XQ8pbNu)k_1@@*ro_fubsN-Rd&WHux_j&3gIXBrR?+3P9 zr=@0f#D|fycMvr{fvjp|z9hFIVL+>L4Y7^`(U-N_(qmoH7zYECiz`sXc0sZRf8gTm zES=Uky&HYYfHCRB@~(i^yDPHUcLV4RU8w~_i-W&Ry~+bsFXIvfuRS%M+_ljSl~PgzKjl; z8I`g8Kzh^nzT^h8tiV#Z1tLz))MLbE1lj~izk{8rh5z|+5BtC${+OFY-N3gI^EtT_3EnljT)d3s{j@88(jQ0gw|l6*#PA%oYQmO_O?9QY`AE$y#iGSv zLRxT-V{nwjI|eRMuT#FU%9hc!SAIT^kBJ5_-I|)6VwMp!-sPd|*2fs5Y|;q(Qc9A{ zL1PBU?zZDVSy~yKFO-x<;d>YJp&icQ5Q#sVuiD?Y52=UBO2oH5TXaZKHj6auyiIMn zGeBPyl*~W4MOH?`Eydyt>icKNS1r}|#}u;1+X&<`2QzLYaO(J5 z{x#&)On*8?)p(CTxH)9&^htvd{eJHODYVNBIp(Pn8sURrRZjFPf>o_9DN8 zbv`&4tDTyp%cZWeMZ5^j>A_>@5!0gmue2I=oO+I}X9<{y5;jXk$=T&0-xOEvTY)k( z$Av$XQJL*3a5>HS=t_UYlvzX;cH&|@p(#2aCUH!2bw_m-l-VT5sV`qajZ*-;9&bnd zTX57fi9+a2ocXee|#Ts5T3xW-mHSgpw1o z?)WmdlM}>kjw)#Ki6qn(Saly?)m7)$_50gQ^iPiC2$mWR*U50!)Qt49SxH!O5T-$9 zi!4+RGPI_?{IJ0E#xh+0XLQNU0Nf#yuIq4HNj50;r_EQA5|o8+<_lp}2;tFJUKQS& z%l7@T-MBb{UtS7UZJrcXTN>y4uUh1Ty(_#8t&>_s(1cc5T^rw1q9+e$4N`&UX%mWlMIh zdfkB<$NncIv>$kJx&3Y=SKA5fd{|}F|4LYH>Pfv2$M443V4Eu(;)nBS_Cqbc_bMw`G$rdld_Iugg#mVQN%fTtLC~0cFshN(UNAZrAVT(lXlu0an> zr|HnsI&N`;TlN~~cSX>DMRu`WVq2x1DQ~7pkzl5`7M=sC`_E<~=wx(Y+fjm>eA{hs zXPs8%grKj+>|{=%BI@o$JctD&+3yIT*_<-<`~AMDPQ zK#Htp`4vYcb1^&O>3dhmter!eZ!ZCJt|X0g&O2ri_-qNHr3AhkN)cQL8rcX=MD1cw zwo{{X6Cm;RWhcWN`%MSC^xq-dI zk2HS!wg%t303TfJu8D*+x1rB!E&^{BMp1b+ApweiW;;@iAqI0pVLKwAE$zKwH*h_! zY=rF1nkBjvw+NRk}~(<_zlU!?z^WiE2EJe@I` zbyG!4nW+W*0B{NBOTgW6S(;L(ByoN#Sf_Zmu@d2S85QHL$b9W)oMm8n9lcBxyq79z zF8h>q?KLeh*#qWQ>@Xo-A4BIUBd~s)Ieh~qW*K7EWi38xSM4G*9%Gxm7 zflSyKdM zOV~jg+-`c@ynkI{C1Lx_rP588(&bkq)y=f)_df)VEtJ(47`;}tl!;Xi9zAUCJ)LXN zb0?@nQt&F*9wxVdY-<;+1|KdzMq(EFTS7M_^or8HO9yw*e-_$8oTp=%92iz_yMKRH z-@@O@v0jFCBTxe_;8tn`aOkog9nRu*7oz`?*%DuR?N?*zx9IalZvc^pi$1nmYhSIuWypTr^WdRe_z$Usqya?*uAeG z5SvRR2C^Bz;|5I`gEIbgoNXBUv&pY*$0Ck;-~9XQZ?AlK$+k$zVB0Th>ad1!6V_aI zHHy5yB29l%J~A11ZR8e4E4_{lc`UHRnx^lpi1_LM%Wy5MbcSa!w~9EAO+^KSmVDmT zmrSkVg{8=btsGQ+BL_;GD^2p1)D;SE7<1%!8&drE3^T{+P*q^FO&J8bE6kl@%g z%fEB0TVJ=Jc`TnX|B{JoaJ^)?POz~cm71gZ?O<8`V4Ca7IBCD7`fv8#Pa(dOZg2H7 ziRuc<$#e9Mre;M%ZRK%Rd>w*El++COO*L^FduS{@{#NoYPnsi7Ent>9lgnWI-V01r z3hXMrASC(*8_U_!p=1UEAQUDda6=nh-hy`581BCqYt`9l&weX-k;o?~ej+(5TP$+TOiqa5uSvM9i_#I~6sG+O}q$!dE z6dhP6IGvfCC*9ENZn9i2E1u^qa;8$dM}?Re)g&RmCwT$<)jNH@vu*rMB2fMrPqLC# z3&w;UEO*z&3RD5n{msERtyUyk3?|=tmC#2>g4`^ve{zzIHg8P5xSBj_J`h5F>b}wu zlaz6Oc2=9+gg@eo_v-G*2imw=rB&9qGP&aU$Bn)+bbFIK($F1QuOfcJK>oc=9$0Ch zPF2aZgLp|RJmibDm!thTiL<=liCm}l$*a(@0bRHc-;KzDK6`Is|^U52wc5mzB+wsH?;qPYG#DQKOQf=cWyza!bLIP)Dqxe&TsM(uKLDjBF#&$d98nS^g+S|Fq7!NtzuYg6?SuR0KuHytA#`}Mp{VW)k-j)o*sTE0QY@8vVx%QRct>uGwBlZy7pu9RTbM_)azYuI52|g zAC*Z@Jt812v*+&?qgLKUwwu4!>K3{GorCPBnuUZDoI$33B=hk1+;iqP%xB%~Jd-)+ zIz3*4I|;HG?eBPXAhx3QIzFJYl=GDA%uT&a_5r(CoX{7r-x)$iecV%1!G)}EpU&)c zGRwQ%sR5sk_9+r-NqWnZ z&~KoO)9BpUqfk{xq8(^@f9rK&D~dh;6rk|XGCYTCiU4ZP@a*qC1c>Jl zmrk#0U{kbh=q`&R6)Lrro(3iiyk+kxHSISaF<3|(9{@6bA~Ae2rN6qQm-u~ z&sN&+`D#l&7)8L}=Vi{q5f7T1>#tIM^P2l*QX@Ydbv?+)JPU9_eAc`#cK7U5bZcAN z>ZK8Y05rNBR$j+I&F4?f9fPhC8R%N2x#?JYM)#O;?NIMFU+QM#R1(fQu{(b982y>g zNB_1t&&IA(SKxPY!d~x6hj?0d@p8Pu$x+*UvnJ1G-+gJ-<~NUJa79IDg+#pA3qo{8 zs2T)Uvw0O=Nha9PB@61NgP$wO6wO-CcI&?n3S~<+M+4nzEmT#gHJLOM>LWKEf$$5v zpXU+5Sph`0M4K&)F*SM^N$UxmthJsa1#C17S^M}O?}Hgs5^O&IMMTQzDV^-abAQeZ z5kaK|Np{~%rQ$$MC9~*>v^6_yIq^R2ZAuMU3n_del+2|l*=@PQ8zysRju2W3uFokN zDgO4+zQ3o7%^(15rI`x^{}>_bj#N?~PjbZbO?=v0Kbx90ec-o1==STN0=%u=_6O(T zMS)*;j0j=m6CXX0R@)T#MSwUkqQ1oFsY{!vc9c!@8k-kAWq8%!R>{% z=h+HQ#em;$W~Op^f%tN405P_+(}~y13`0c#iR`C0Wuno4MdP3N)aawdi=K+G#T$$U z=>hag$SXdv21vCLNRA6y@)NTdPwF#(btXm?Uo&J=FVWua6*xajCnX8*&R+AT`O0Ip zi?ibAMyz+P%2tM4J~N78*L%=p(oD+sVxY)^wj^e9{fT@I9^-t~FIQ zk*6FE8KA9=JP8>UuLz?%c?7KaLF@G@7l41O?|}1ER_ZBRe(ZpZ?eA9Fzs{s!=fJy^ z=E|~BXsdbIMHdSO^;?(RM9ouaKOQB+#j>U@cDIt@6{2H5Q$b zbu(GQ+q>I@{LPf2)*;`22QU40Of6b9ih_HQvz%9u0@8NwK^`!E{c!Q3B3Lk{i25jpGF3IGQ#5UiS=MP_HT+q| zHX`cHQQZ9N5@T;3dssXOW!>?S#i0icZjwu9MIU{5RZ|WErfdT98iPN+$5gh5((Ey2W<6v@gVU0}>|3QW zCs^xJKfbm7#zHVNxLBCMrr6{}Jhxcr#h2-HZVk^FbyYDp;YMEs`zasQ8-uc&$mBozc@ptJ4Tk z@BB7h;Klc8;cq|X9t}L=0KOk~V7DZcpH;4S4o}aN-(!dU?abxVonq5t6VD=!; zIgpCF zEkgg@78%S^vpKa-N4D19+%XhSyU$xIx@djJb2bl-Zy3l>4jAQ=&ZQtM$K!wROIpSW zz^}#O5WQPgWyCWlO1$$MT-RuOH_zIJ`a4zavcra)=@Dt<=M2fl72o1)g1PU(8Xjl**`xHzb;#R8blyZlg8>GUa{3z9B# z?_NHxZNS&9J4FkA5gWK7QW{=h-d+v+xiDLH5Er#YThQsKKdz@zjp5=#Jz-KZO`}(jeXZh zq*b^VQ_*7B1W3`cNnD%5p0pr@4AErc(SICCH%QV-EhBq3u{3?|GrOiMp{&@dl$;sB z4q$y#FRGr(!O$DLH*FF>b%1&53C|tCNIFFeVO#lxc+OPG_UjiqrH0PG(Q2Pm^X+2g zXWzO%Ar9NU=85L^0mcR9P%%EIs=d!5%WuT@f>_f$w0rIlq`jGtaQ@v*GPG5j1WKORPCOH}n4E(Wa3ekCFbgMH-cq%~poq|AUDpW=dY91T7 za1j~Nx4QOst>B6Vv;M+aZP|A5?J!ZNXY^}|LZYIpgDOl^Yneo!8J;TU)1UG_%c5>! zjZUFPCP$WfbA6#Dnl6e&64n3xjsgZD_U@8kQvD{1c}Bn^>QpD0(Sas%e?s3sK$38>Sp~YzP^QDo&H6 zGa+_0xSy-5`Tj?d#}B;?mS61>2dNDdzZBSfTvY1JfQw^?5!YXKd0(dXb>fW?NXf+4 zzpSeIgnsHKLNWdL);FWZwl#$2o`JIM!@fBqkST02Uq;^0Lbym2cKRj<8wvz zmpd#XHKOg$61Yh7qkSHOf>B4%F@H)9!?BV2$L8szq@dosSWg#{3+KPK-?5X)Gk_lv z%^UEcPY`AtCc>~@&@!6t;kCZ-QC8q7nVhF9$@{jPgDzC?dOne|x`k#GN&Cl;!6Wi5>ApKB=nEa`1LGuleZ0X&~Vxa<@eX@f`n-Eb3K1e2Ad%Q_PRfF4pO#hF;&MoHbueKX_D>o<~$3%Z`{ zmj$=O#%D5quU^xC+&g9W;;=(eyo;zy;g%MYP@jcypYXX(ulwFp_VvTM5*MZ(JeACR z`8LE{r-FII!e{*WHNBtRvQ(_*W4ptRj$hnwZphaJe%-cmYuvPwF%G|k zj;~87AoLE9gQfkMljrkDqQ0$?s=xxJGgZpDM}{)e^=_})Il6O=gYXaQ0)~R^Rt`4# z!^lS{sLYKXefyJa86>JhOuN-l?7J5C zQ>&YuBU`VPyz+IUc<8=JnW8>STFW~Cy~dy9F%hn-e|+DpZ_HRj>g-CCS1Grn2|9^v zNeja(BxPpAkx9MkRZBJLDOhdFg7OsSOY==^)VnK$n!fGiE1Z5vPm}q34mTZZUXNm* z58SU$ZS7B`2CNf;(mt`E3v|jxtQJ{$?V8-%oP`}rjX)_lHnR_=cu4PajDWu2ILl~r z)xy^+3*1Wx z7kIJm+m5&#)}L9(uHO=(4P=0Y4U$v3%wriCyrP>3*1f)GcIdl2Iijc&nm78vUs~%T z$l^kqi|%RX(jRHk+$UO;Jv>xgi%B%E6%bFWSSP`tW)fV6X`gS zR9&Yea*_r?A8-`nPAPE5g`G)Dd?p(K{5S%fr8|)Q#E9!>#t?3wPn&l?PjlBu^kyD^ z6MRvAWq_Kcp1B7#p1&?8XHL{eAAGd2eL%@Cde;_*b_UB*U;7GoP!Xt#?+tiYaHV7|GAfc?=(XKqR+cMNjOR87*Bj`CLmq`&Wb#O< z^`>E9!b3FsnmyH1d<~zcM$*e|p+7GHYj9%MsyVL8lF*Pf_l$XCQ99S|Q`t0b+cqgW zs%Zko&bdEHje^RlbS9L~8TIq~J1F>7pV*+o?$4(b*%O!36xe?GGOCl=K$^YZ`61Pb zHCG(|%;cebgnJqrXMa#*$YVp?N{yrxYSB)etqL9^>wXR#nt{?-I!_4OamN|{8!KNU zwj(X!a+QytY%}v8=bFCu=kPaP^UQunY{QYn1ooDtKPeu;zey1f+oAen+P8D+rhRnbPj^)$jEMp~=W2t{Gf{R55pTVK*tbKxNuzw0AAO#sutvm!N3RkK#*QJ)k3w z1LldjOXvCry|$ctPKwUk+2pMH4^lG0TNU{hXM9_l+3XmThx@@;??*(l)JUQtc1GK< zIsaw{ZaZkSQ|QNzcyG6`uUJ)$J)^k@^rCpF7V@pO0;ny#$-)V}joMPUmEq)w%v68*^HN$kRGQ;IS! zRkmtCq7Nvv+}YA+cFd=#O8xx001#0=L8Q!;0_X>D1C@a{3tOydHSp(pA-Ljwagr=H zcgVFV@MT&m<7h_km4R=q`tb&w|Lzg%y!gb>2?JPrcI)CltY?DoR;bN&#y6zV^5^0> z$!+5f^~VHwDDkSzDH>s(;mc3nf;AP3-zPjL50S4P!>B4zd4N=LLOK^tH^y*^-fbt4 z+5bcVqUchW!~GHiz2|JFf6{Ymq@oswLTi_Xu~kx*xFrc zfO2P%xhX{OJzP_Em3A=AQ5(988((YXZ>4U~YQb#NlX^VjPHW?{8K+Lxedqc$s92zs}ujRAmAImQgiJcUTrzUiw+3UivOec2M9u>*U8tdFv)^> zZ}MQIi6dRVAPyx_ED_i@Yni8eo-7;BIqGNM92FRAKDYl%K?pa)scU(L`n&mgX>DI# zlb3xdtiiGKN*CP`b=9!0F>CVbggK7m3eY>?XxWBF``kahFopTWkq7oxmap`rW&V-N z8nNo^vg_H?v-o6eWi(cwVli=MGPWG-GsYJBZYZwS z4Im$UIF4fw*=cTX{T9!AClKcy_hBSVo4Z+<_iu@_vV;{40VSncG#{^xQI^30+Dezh zy=%@zQfK*cz++_5zCndq)3oz$asK%aAKCKBtlYO;>(8pGFga_~Kf}yi{IN`=-8az( zm_pBV-LD)aT`cXXkh;Ld@saK%a|?0?O`hs~gPR@ezEOK`S;4lA}V&Ss>ignT!BD5O_rJgXa<>OeYi$ID0^tknQnnh~#NLs5 zG{_9S(+d7Uhwkd`8Ybu2a#tk}C)r-6f34`eB7Q*BFnO|V<}f>^PoqzEftqSAF6x-$ z0^-uL&Yjtj|6tsdggMSy&twz%}q+v7?k7m^Ci zIPre8G`K0vomYJNs^*ABV&*CffuaJC$7|e*nmKOj|369|_Ls<*CK@B|mFtpwgjq*N zBk~Oz8{Qmcqu*aMhhg|aYFSW&%wQq@jQ%AF%9c zK(i1YnDdN*U#%KV2&E3%7LI%?3Ftn8Z4#8w;m<>6JB%rREt=h)xQN=@eE!AhJ$WTAA3S1pD3gt&Y9){|vLWhfMXYtb#IH=@h_>i2syE1+fxLch!})a|Ju`jn z(y|I{)j{AXAXrA(ACVp)Yo4AZ90T%r9#}Wy`tx_*#SH=URS%=n$ACUUZ3Ye3#AOZx zfy@q+x${g0U%e$I_IfTUW-E8l2I>4!1Fkl`_Nz_{{w4mD2!w+zKG~(n0~Jk`^*`_r z5@k3Lnx|{u3wq&Fc}|Ar%I8Ryo$}(YNT$fm9jPwp&gF}D54r}KRx=u!8!i`lf?F*# zwkylWgh}GcUsaHihwGzVC97lGu)BUKkGz0w4`(U)MJ9Bi%g_r=`+?7QT}p%=xWwP_KHR1+n_r-d~`@TeGr!SkBNhtfpwc$ig^+wtlHo$*Ibqy9&m+6O@b^SPK~5+`w9dk$*{#(;$n# zCWxeHPp;Aj)uORlOu2Ru&)jO(g@T*RG3F?aRg7x^J?KV{xL$1kZ`7UdbrkS)o~9$^ z276MJbNA&IVSe|@kpdUhkl9)$E8DqWpwk%g1~sMHVuQXk4uIA4)znz17v$@ntHPAe427cQ$V8_Pri`v77dPB!UekLsQfw}nF z@}G=9J@6VDp2Pj80g@q<=d1l9T@;shIH>)l@KP~$2B>>5=SzzmpQ(Tq*RhZC$cGBL z7v@w|)$XB96V{be6n=S#$Q&e<`Ppr^&FkKaU;@2b;>D`O5$8E%iNMm}4;{D8^aHX$ zouTJ?IKXPzA*Et(2H!Fwg9L3W{5xxebI(l!d5A>zC-n;Y*5kA_$pDF#qBNBJUP~?s zMsl1({Bz&RdVe6(%*9bgGu4^{ul4(;RwjV1AW`-& zpPiGC5R}*mUg8tk8YR=PMM3sJJ;0@Ybgc8S)jPC%eNP5cVcn*6j1G71F0_aPNdDaf2@tx;+ z5C)@+sI0iaO@xS($6V=1kC_Sj9k_{;SF6>yL>w6TEN=LB{22~|&ATEI3f3HCl%t-j)l7)b0&{#H2 z7M6F9h#t|dVoHGL@g!0lYTmL8E7)pidN#zUm|H8jYiV(>Cz11|XuhMWfI!MHf1Ik= zuW8W#IpOma(Pj$Z7mL9YT@p^heOi-q9hIi#u_jqONs(yWevx|3W*SOxLft+we5QsEO!_c z)=?1P-Gkv^T3Zd1jGn$P^6+XcAgdUuuEE8?Cd0s9Tt?)`rVa+1| zj^*zBo@eccA7C5VhpVz$g(k-)cY6jwW^UkHKOv*C5GDGjY*VBTJ>l)Vi~Hi>AkN(L z(fRqb+l~79yyeA7FJP?5W@W}YML5-N{pe?Bc1LgcdyhJ%KjECIShaLHqx-F=S^KZ* zzS7WN3R9fP!>ZD*P0<$i+j}BHLlsfgIX+l9&Z2<2M;9>LDxIp6Hb<>4z}Hmv!wJt1 z=Bh3fVBLoPRsFjVWE6y{_q0yf@YO8MW#h^m9&+c2!q;R>51J60$>+ntmH)juSTykJ z|Ifs_9DfkDF8^~p9(0Y~VaB_kYAdqqCxr4pkEFVXa_w@sUm752Tb7cL(K${?yGluC z__UBlS@~k4kn@tzs4XL&JJu#b>WhVdUqo#V)0^LFWjT%6c0K(&GaObn0o2VYnSf!n z!1l`-1r$AXUfu1>{)Fu^cD(EAR5dHmyDqr36?053fG9q0U!J@+?5_E^T4PujkNNkD zda-W=C;jATZhBMhoSIqK-lGoVh^Fv9YfYvIArJ@sFD~x8wtY@umNR|1n;S?d!0}__T$$`jCQ;oPn5j53PdcG$coz?M%fF@wwa$ zh|CVd1%>-^#eTg9XGYCsnc>yOnrG?`)T7F;<+^L4aZhU6fW9;Q_uYQ{bDXxGq2RTD z9iG1P#DD={`4`74P4K4-iN$_eaNDN&AK^=7cKL z{xE~k+3=TgDM7gUFuJ|FxvVfbyQheYYcr@jHn>JaJaRS71GusT4i34W?3>nR@pC!U_2fSN?*b$^~GL zgO$CG&T?`5 z3#p=#9m^W_NufE6J?T#@bn_mcG_?kw@Zb^N7J(llxn_w1MRpgQ@DaSOlG+@@0U+hF z4uG8$|J$O**{l2Y<)>8Ygm(#(o+Fj`^Y@>gz*ta-iN|Qj^)GI&rs!EF)pUP`6!|}2 z%obwaM16i-#pU;=q{Df(k#O^p@3k8$2S*M;2)d-skI{g<6P485t}&@?>Ozi*Zu@x* zq7>2}USYd@O`=+KD}PjIGm89&>HZ@6)Z=?LF1a#leJS$)!8YrYq$jZOMBjEzvpiL= z>mLL>s-+WYdwy0)*gIJNUWZT>l;dx<)_7=aAcD%8L{J;1AOS4b#{*_yD z!oa#aSJ&DjAx+gS7R$@LTxA;oz5cqp?(9|;fto1Yxf!F&&|P;_DHVcmxBQpUjo3dr z1Y9WcI5B@o#C3iXl0*EW>Bogy=#>ARGnf{&u?oTR6CWzWz9M7xsRQRjH)&qn5NRhj zC9VksEj|34@xh5nTM-MBu7J_qpAn__=96Jc9*4MPSd+P=r$-Hl}{_Ssr~+iU7bzm=rUIuEFFn-9R(Qi=WiO@qX)!(&fd zDgJVnNC`w;%8FUw#4t)VC?i(Dh-#`-DT{z;p`b9Pe)Jh}3s~cfeAMgPz+k}7f60L3zT0vNkL*jXLhR&yvhi9f zLZ~|=Gb0eAHRw@3@{si%U`tP<+As1?@;|XpOVf-0$y{MS-SvOacE|wTI>P2m+{V86 zgr@in)U)LFdGf8Dy2g}T$A1E5(|;K#!X14^g9c7peA~3ZGQ{yr|34`7*&?!FuoZ?G zYeV6t01iLW`4(um4kEs$ql|veL_Tw$!*n*shM}h8%rZeta}F|}QJa9nKg6EnBMnbu z%G(-$(zz4-BH9x`gv7C#HRhnfy{afSkc8GvY8!TcT@oGI!Pl}oz~Sy&yybAEsAz67 zPD<5S&C{F?!)p*2SDCQJs=7{8AULCn@XD?*qlG9laLH0-dwf-+kR!#6EgK!(b@P~5 z6-nsrUq~)zkQ$I`qtmzTE=T@zzCF1-YJW2Ov=ZZsh3b(&=;DasJsp_Ms*vwkf(;|w zW`XrbZ;8*h;M@@PVxrlT9hgC5L~^)u^Ep|~%4B4QciDdOFwvx#iOsj?s+yS;?I$H8 z<5i3-hFK>NDMEQ^51Tt~{g-sh6PI}t!&jo1@u5Ys>@oE@Oy!p^=e`on1_XmsI&1GL zXBAE>VqGS3kW2{N`6~U;j0Pj)-wLaFFNs_j$=Wg#fZD;A6b!+4h?{7Rfv%z$MB6K7 zY->ZZ4hfaQN~KQDO|>U5*I(yZo29o-av#e^d;Fq{`)pWhC@PxuvBNzpAGP0~e){xU zFLwH(M(@dj3$DFU-|^8`;cs2|$f2*P9Pb-6t(i4*WOF<_-6Ypvyk(FNo_*H7Z1?+q ztEQVx-&8Q-di4K=gtJ#)+qe`?5_d5ju)U6iTqgJAguV_{sC3CdViT_2q&zjMJ1%A>vOOK#$jkDSw)gvur-@69VG&D2en!~3eNVx3MvsV&maoJN1XZ}~yA$CXK0S0Y z&;Lf8y%@&`u59b$cVt{tW~JHV16EdLx}n}cyb4`aF_z~v>)Ai|v}y{3+6ZBeyep?! zxEix2bKD2F!->}cA#Tg@JlPI4%HV0v(gPu*?{(|BE!JdLQY^%6_IHoeicwZ8GE_5m zGYRhQ?YgnusRN{nYXTaULwRIEMk*$fQFzBsEKI^U3GroNIARc_EiL=|UNm>L0KO>S zFUlQFK=t_j+oSVE|ENUD66VS5MyZTX{^}v-+=ap4CiSc^jrpRsTDy^q}ONnil)}6}@l~scQ z$5TJ|)H!qv#dMzzVryx)?lV}q0EflTQf)QJ!q5R`7o7Uc5OW|j5$;ISaGj#u8+PG= zFbT__(bfLM^8L&%CQwNKb*Xb=v}pJb6^&;c5WcG7L6H7FQy(RreQbKU-qC*=6~_|_ z?%M}F!ttS5c`F_=C|YFupI@_@g2SqksL~~<_2m!beN+?+6M0vY<%z?w0gWs*3(e2Z zys!A4M15p+#O(U2ejrqwc~-*~%Y}K6=`Qsudp^paZfo=7dB-+${nEQ}vmcq-mqJR{ zmP@{1PR$b+F5bXrj>~N5in&?OR#&+(e-CT7I_8>8SCR{4 zrq?i4z_g=~W!kTqBjYA$u$d7R^f+eCj9bEY3Zzn`E~Ll%{^pDCOJqz6*9x@pz}zVu zJ9?3PAR!2lGwd7jLkar}kdXPSqqf`bDSN&GqR^o+ESCs-g;Je4S#B!3e-`W3q?&$e zqsa&~Ep86j z!4mk(utS22d`Y-H9uPD!0tLr7oN`gb?jq=#DfjaRP0{Z}syt^R7iWf;d7t9s~R zG-xN(jFJA{RnlGa_)XNft)kX$Oo8Q9*q#f|ZIQJxq9L&S`Q$ZveaB(Z`9G#v%|ydBt!LvkfmNlHQ-ehJt_?y6H|A_9k48Y;YREgEl6KSS{Y3tNI} zA`h|OP?5E4fwyz4H11E}5M+2E|6H7!eD?TKt-M)qa` zFc2QQ2fsyqAUbcq_L-VVgrFPKFZwYeMC|tKW0o@cezBq8>@lO|CksJakb%FNbmnw^ za*TU&q5rT60ZSdsisMd<^IwIENNQR#Q*mrMC!fO^IHI>KqN8&m3>N>4EM?IhSLmpQ z-`UV$GhN-r`!HG9W?FYy+eTQSEt;T{bbr9hy68qK-@ez?verGt!wPt}e@DLjUlOCo z*Cev#BA}p~D|pj=<)J6wB^W%FELUuhbK5}tMAdh4R?d-zhXt)BlnoKZjTk?t(|9VV z+Ha|Vlc;3-Og)d*tn7?u2@l6%H)5k=JVVbw!@p1vQv+0yv_wAguJdl!sD})+^quE^G*re2^1c)#}-Z? zmL4$ak|{r=vdymFs03r~>^}^K=xAy8oyAA2F!Et%+jj5c;_*{3ZjIXw(xgVs*xH== z(WVid2lF4Ap}<+w{6fRRDk_8ErYCeY6CL-~3jw+}i@Ok*MvrjeFBfYxww+9m#>#ld z%!@vPxX#*!I)bFLP;75FAkv1n!&A(aWwK>I6n^)_^q&Y5Fj3bNtlr1f>%AI(E?m-nWWj54 z@f*a*Dk1X}rxpdFed(pwENEsxZtDx^hDdHm= zDyqIjZ<%!DXe``1E6p_VkOd6T|Hvx$k3CfnQ*%io<-! zvrutx=#2!sPV6hzfM_oHxf7|?5I8Wb17&&G&2!v3p03<49f{Si*lJXC?L1WvdbCm? zY}lnOi}XkICK%5ln)Y6N&91=svfcinjTPv81|_8Lz^IL%iyvJ!dr#g3#;Ep%<*GQE1`K2Ltu8PNeY1uQ-mQ^JjYWO$MKwA#f63J5UjzbTw&GYhqz8 z%}i{v5X8bCGh2WW)eoSJr~nI6?tZ4^^1!1?H=D*EBNEiQkZ?0Jtsi}6k_=h#2QlW9 zzpgnNv(JWe`A1}={*k!@>coIKY~#mRJn~61{G9%W!BjwE0(&YZ=T524F6=-#iB$VF z8Q=F&3AREg=g=GEqp#gP6>#e0SiHYF?`u}#+tL6arP zBep#;7?+by6o-VnoO!G6pO`drqagPD!rM`J<;olv=j5CAWde_@vwpd_4-P2(+c2;& ztqfGu)5g(G_0yhJiT~5d!Q@XMtL(D(=Xz>%3Vvf0)C;mu?xMOUo7uuh6;50VgkDlM z{w6iLh7AazNe!B~O*&DC$e1m}+r))K4@@ql5qVo|vWncY7L-%7WUPsj;t}~ryHfN= zQbNvCe_MYiK15y^{v%OLn0a`}^XNVbZX-?j)F7sWnmF~e$^K`OUi&EYkyWN4!v(z% z8kb{d7GV6cKM|a6c+5clSD5FJ%n+?CZuecA+yD_oY(`B@{WnG3=bIx=3}gpaA41Kc zv1=QdfpHTK5A*+$m&fQbIvPK#2y;p$q9Tb7s9hkWor)21ZsJ1nHGr!6Q=SOy6?{yp zLEof--Imo~MRwtxauho&_EVsmG-Wy(M0}q?6`m;58dg$-<;@W_g$YGp72_!5{ymX& zx3buDltN+CR~_YYFcg~>$5f3iX(Q$a_iNK~#5RUss$O0(fcRLwluG9!bEJbl0`@{> zSFYpEXydfr&J z{J*8CmY|T$>YTa#>_P^Va!_xn;IOoM3eI?w@>`ze4Yw;Jz@PZ8kIqTlIeNZ+hT@9a z-K_EXgCM`+9&$^$vc!pV)Pd1dl0u2=N(fBGPcRY0gIU3B7(DeF-ZG5~fWau36LzUn z)Tm_2v%y@_QFBm^=?fuW)y7G%lON%zH6hXjQ5su&!?_;j^cwk8a&wP+!V7a=s0?lT zDYr3CY)XS_Vm}FC=;}JsOSMF2giPy-4 zsD_JXeGp;Hi6>&5dPMfWM+h9%+!JgFQHVK$}gI8+c@s^pKqt$P2(n-Fi>m0+N_Lu(VawdZ?24;J^$9Z>z8^Ho|@<1 zY+8v|9%h}-jgvCR4!HRYd8%MMjs94z44SXx9JnJ;?}|p)hD>XieQD zyWdJXwVBch!5w%3w+G=TjU62u9^KE2&5Y}rA_THas?)N?Q_eNhIt#XPGg;uI%^Lg1 z_Y5!GlH0>qM+fr?A)^{6Yf-KI(UdeZ%by~fvZDFJa!j?LTaa@r;6(%Fj9e;XgyAUL zY+A%DWd%GoPfCn!IT2r;{au%vXik#-yQ4wpNE}3#rZbnq-enJZYsgaXr@zmC2xX6z z3)~ZXgtPAlbzv7EAl!ZEeGDXxfMy%_mz67gJpk@7!v!p6GO5?@)rwy2rU!T6ncpq_ z$6;X6&Kml$Y7|FjTQlj$vRXfM2m-DGB)9!kh1-ukti2 zZ!pd>gdLw^pYQF=X(Ui3z48QNkN}ta>&aLm~AL~l>os-Vr_}C%FQp$dZ!;J%D$fcTK&>any9Ai;AixePNTc*=t7XwSMEb#0fy~$N zHdgs6Dex8i(70{-;EN_?kg=0Gz$lGV(vCLb!xO5nqi^2cjs}Mcc_S<9!r7vT)BA7|^W~SxgL@a`9YQUKUZtw(^Z0Q{u!xC(r~)C#6Z_M zO~ptc6iKOFP_(X8+~kCc-h)yrh5oRu<)IWPnnTOx_H522i>{<<(kkxV#`)McBH{&2Gah)rY0o{XhJgZ6mHyUZ-}T2D81&Ra%u^r0lKdtt0rMPc@tT;$qBkfbO=j;o9&-`c43V|{455?UHHh? z&!6y-pI)ZHu7xxT2`fHD-h}c_Dj`1Q0cCJ!FQvQB-LEANXgd$+(I*DE(OsE=t@Nfg zVM>NJiw#W`Ty%9WFWMrs5S439Ndz9!g8FHDXIJs{A^Y#fW#qN0$E9}k#N!S3>XsM9 z@+d~jPACTGyLl$N>c1K(tnX=Sc^p;u8PT!@nATMG;_&fp?!KH_G_PssXsjVRsOj86 z<_ATa6CWqCUpUyO8cuL+Zk9R-$N%A@)tT$7aqIte*bq7BUe|o)&#=n$aO4g7-@a;D zMsOZSWC6d2NuanxJ>N8PEHng_bQJUe`kL2@+g&|1V>VpgU8t*rgq*H|wTlO#@u z$In%Ao^rEKrg|zp7jy3UST=H#78K83%g0x1TF+l?xtLRmwlFO=Tn7u6#s9pJIZ9do z{k(2znewOI;g`sL_)%lQjXkfU++~%Lp$mE+{RuQiE1TajgB~LAo^AC_%wfYTe(Qrs z_^FkJ&cZC7BST2bU0`!|z>tK+9Vr2_ETZza=JL$Sft2*fkGekiJi;zY36Rh1&eqQh zju_rm7xg?WgyqxwYGY8m;X-T1abEXX&)jlL{dl%2qa;PG?(4=93=Xz2R1cK+x(>qHZ!zB3>MTfVZIY%1|VQ6Ig@%ULDG*!`_dGTjB``IN$Fi+<~{~e zSrP4kDdARe2N>^1(!2`75T(f*{O&(UIT$F0@2a(U%)lNg4AE{O23I=&hoZ-!2S<43a$+rJWs}s ztRji$)!fxd+TAC;G>5akBXh`}ms&;zW|Dqfl0$u1loAYeRvd{soPg97QmI`Ta+ zTfE6o@FEs6&nvC2hUKtSaG$32|9tZfGYi80I9wReE=AcUCOxC#;dh#6(zZ(U6By+p zz={+e==xx$?K&XbBbYIxACUnItO?UHL@IMmb5fI*3J=>#Of|FJjpO3LeEIb&?(MB~ zLi1mb{{Ra*#BOvD77ys~=uI1M`a!T&`>FVIA4O8uTU(gY4aetZ`8sPmETdla=$lPnWjMut2-)YGYy9b!G8jAZ*77~6b9heeQ}1=v zZ{O3mv^CR?BlhBWZiO+|GkM6$C1zJ-N(dvkSl5KAW91CWB!SJY)7RfqbTy;c#UEvv zZpt~H<^4uRi9xm|d2vcgstgiFd28)@1T@slPob*S71^5I0`BVSA81(xl}2z+5unmV zog|FVB*d3)tAcg&3bmord{1XXQ(~)ES82A>qb;W8mmGBlPj}65y%(}s)e@gWLe>9G zwigzX5%$ieuB&Q0Ux~c=nV(8v^?dXevjbNGHjA?xI`h1N;EMIJ$kpfO7Jwu*B~>tq ze-mDl1c9Whmf`dlBrK&_I@5r%09_C-`x=2kl1yHK6If%oJDI(ryrFDN=%9uWJVpD~ zE|M8kux-mbH1Fy0fJ@ZNFae{6n(SeiRb%{uXbcRK#yLzgQ+X?_u1&-w!Z+a@LY{d{ zso0^3Nl6&dSDjiIG;)KFOf5@yGs)^FrAS$#f^)xM&oE~##Bz(QfD#UBer|#LnD3Tj zM)>kRSLvg;la;={(rtsGeZG^l@U&w#O-!5BC?a*JI4iAykw_06oDU>E8rEX_bfsG6 zmpBn!EjI$Q0nFa%L_ZMf4MNkYhzgHdou){XP>6y;cT|T}X^{AWCCc4n>4n>h6WD2{ z1Gt;7NTrpnNHeEbZ}b+h{~UM}7Uh~Rtt>n0=(|Q@_U1bJQ04&+G(InTh$!xpn_>P< z199U9dJNj*BJJP(yXlNp$e^FB3u>O!<%2LUnab=|!#=x>+B8O>df#5=a zGK<|sus#9WafX)wVZoklBVcCqB!nfM+D*9SI<@Y8 zK0yms(^HdaKS#P`jY-Tcut$%p3ok$t5Huhzh&u5r93;Di0*n@s8VH>cM3Csu@3~`d zc}l5!bSCn$hbZ<)E!G)J<0}7K;aIr8#bj+ExzCjb&XFH}K8X+h= zj+X1}QZ~LO)3Z=t+o_|h9Vl87Rbo3?^~4%Za0bI5)RLQ~-!w=IBJYEniSD?*U(>e{ zlKhE3_hd7zU2gb;P5t-I&wm|$Jq0(o@d`k&(LzSfi^``T3prjp-vzGh^z`P=I;%X~ zm6+=1$EC*Uya$dDU`Jh%aKfW^Y7m-biR9tt5F-wy+{es5t`C4W>3EFw~#U2KxgE{E_i-mpG87ZEpoH9J$qo19ARr z!V0ldbK7P<{%T(JOFA!ub5oD%N)In0S@Y{ z7NFy;JXjd5{i=Ue^wXr|r2=c%;3Qe=NB7v+QP5`;do>0!CmdV%C$~S^!1RKl2LJD> z(26jpOha2~^FuyT!}_@~C;qz6L7P`|VIdJt#%hC`cz`w#@v06UB^p7u|HFeX_qK*F zF;wy0v7T)CsRlM)2^n?ag~?!tf_Z$_uY0$S8L5hv^x6jyDPS!xuc1?OyD7TKvUfj> z($v$XsxCiZRR=8jyVZ3EbqUIOU6=r7WtUpvK-!y&DT>bs?z+@Sa{%@4VcSY9>nz+<#fpS6v+MhhW zQiKE9#eq^kqD9XabF*6AuWYN?(_#ywNIvo<{SY3?#EoP}Nm*@lJseb$zfwDCEeb}q z(I-CDn8r0~s=_W!8hDz4KbPH;_Jvw;=1)>(WYwH$S=oss9j=*JTc%8@{W#~asjT65 z@C)8_*wq7Z-_a8-D&u6{Zac+jW8)LoM_FVol^XB%JwA4i4m-Nn!?UQ#v(I*Kbv21&EWp)GtOIh~0f8mwnf5$?-P9=G&eHeOxl~u2^1KJ> z(uz~{xld?}1gyS)L9y}0;Q1zVcnD4I`?uljh$v}@5rv@(u`&Fk*-fDjjzY4KahU_l z3$T#~VqGZnRCHn25`JOq7u$QD%UN;NB>V)?#l4%-qbVmhdQhzrZ@bSaX>VJji zJCFYZ_8R|leQGGkaq%boAHP%lYqofvwQ?ROa12EOm5jxE>zeLIgYwXC(X^a>7(KWs{4^dAPd4 znETOVvyZ-n$~>2dgR}~zT)9s%Nv*f$O!~Lj9?tVGuREq5c3O0vN!=n zUQqgUK*xvB2>W*Ak|o@qUBydFCJu;dp*Ura{;Ui`cZYu3*HW$qhudyKD5qaz&^Gwy z#!f!3TewFCbpBg&O0^j4&|7i{n;8KYK-F=k)=aNGF5aUj_U}yM9j)v&9P6-|t-Z9g z&J_bb=Sa8N_Em<1&WT_6Xd`Z`8hniJE*QrX|B+-Y3-e*K?<`B$NYt+Pc`nHRZK_dd z%^Q5IV}N-EcBz%1-<^%fX|8XP5cl+$vp9PkwsGi=?fzZQ)FhUBE}um`Ox$@N zy%`Yu7ERtkd_$Cvrp;`kc@zY^CMAx6R9<~@rH_1st)UW7mqYgODNeb6x2`-JRH!)p zynlO+T-!G4S79~22pD3)yJ<*HrH$R76pxH#;@H!-+!|m7;dr#t9Dbpr!Tu2TI9@7$ zX7Fj42D0`iNpw4HuPY6x^zGp5WT`Xg8JU&Gu}qy*f+}Vmge%@vmnSw%D_>#JM#Xn%g-x z->Kt(Mq`JGnD|L&!DH_zyGEjL$cR6pGS*8sVYVzb5_!r|xps9U9NHfvW1|hC_oAEM zz=*QECCKw?OAd5QD)W%D@HP2%1HA=G;IQL$M|+3ULt-{bDE9h;37@jl_fv zvw+Vv?T(T6QOisyJr;*lS*OoY3(?ZUTq*RK-7J4BTqJS)d?p)>&7tj1LE|rpVI)xs zgW!b;&HZ4=rwn{5Cb*e3^*dsX{HY6JDnBM|jJJyep^2`H^HNuWX_fMGg(?%ao)5Xl z>ifv41GiC1r5yf>@t((FwhSg+pNsy-X#Xds&as-f2NBiBPr+GWkN+j}8LG2vkT?xx zth1u_qe_~-aKssc@ni333KjFgi-;vdVi=)W+2}cgDVKpIiwBT)@Jj51x&G{K(ri(V z)V*9?rW9=cafi9Bx zqmv+yG^p^(IJxm?CoE~wRS&@Qod>_OYcR~uBE9`ZLBtk};ShrxC@`PrmKqTpdj{sd2Gd+5QnN1$*ZToa#6e$`@Fp=6 z*ncP(l!1e0x*DJ*K#UM25?S5w>NJP)Ehsae?ZR{fm(8Q~!<=t`1u2~OnH*PrTqHuQ@L z(%ppkJvn2JCSncRHU#UG;o+nQ1JZm%o2yLo)8sBt<-PSOrf&);``)#Vy|On!19IkP z!k@gF71vs{T^(FOTGJsJTB+PNp>#8&d=gZs!t9#p(ko|{Nb{XWyfvCD= zANk)bfJ3SSL*R=;S{BPL8Q$a0GUG2+s?++9?}qRkp?LkvY32-)R4=}zls3mX2z2r~ z7G$yJ(7w_b)(EOJa@ZmoA;to2aIoGTYkf?782$S+Ve`n zGIS^}j9+g@%-7YXT0Wii5##*595?c5R?fCg00L z&-<(_U6+s_Y0S0mZ4+Q274x>gg(%&c8^5l!>IwrqF(c(PL)i{LR%Qn-3`#u+mqo8HQY`Wrm|enJAs2w)qwL(uyofBwlvmO-() z8Yz0hn6m_$SG@sMw;gff;hPS|{*?A`$eJaTFwwKlLBm-5i!Tm133xwLyctc!1l{}H+Ea79LV zbf_aHD43GR857Ie0T{GjgQ9-mpi2Z_;X!cpxqLyF@s(l&`FR%R(;e<-z8dg4yX!d& zQ)}~Pf0E~*i^zt;cVta0vrshke8PWD_BH!DgTWD7RF+ybnYEtN-yCw5k@9NE@Udu= zMO4cp+tN({2Q;Rkp_SPDi*VRn+V}-Aj8I5b0(ZLIf+bT z#P>036%nn!>V7y8SKr8VaS;E(fel2Jb5<%i7YKY(hR1Cjo7gEPT7r6z@tfWX0Z0`G z<{djzfO#anJLKF^5ABfJY)+R zs3yWZPWs$3!2+eJumOogHa*ipKPp)>GWe+ox7Is*vjQFSthocfM9~XZ>jICj9^n9& zW~PbaG8!#4_p={x+)`ScI5ljeyTp{mcl@oiN0A1pUHjscQXFfz{bVS_9tJfmL14mf z>g9)UF2me=ENzT}S8OOc`Mv^TK6zR~XMVkKD6!pOg(VR1B~6i)BXKzrCwsdCs7%4T z7_HGu*x}E0D~gJCv5)}#9-S46n}fnqFs5ohuxnp@kK_pzqLiY57w%%Z9K24ht?ey$ zGqGZ|=b6jCG=NeM9)m@VHb~hYGd@~$JDU67q2j-X2UAX0jw>P>;CSV}T$rj<#5ZQp zM1tf(1MaCnOb$h_%#yp%D_dRGCAYL+zlz=za>MD5Uh;EoKlxHwZH>QPkE+4bZT}ueoF5MtKJn!RypE~mgt2;n{b}lYJ7}unFa6^dZXq?jz zDAr2UdQ+?D%0zQ?wp61=@W2o{Zvk3(WoQvYoBcSi97ZL=$6n2*2Yv1#yAke{C*gA# zCGQ&SHC(jr#lYl`%a{**J`!$2fG;D(f|iXNga^M5!~>BT99B;d-NDf}YJdfz(HQpb zRXq=|ypQwi`=|BZx!XLqG060BtJd!EKX3{Lr*`p{<@=?(|zzW^Ikkx?)se=%|cGN zLn?-u1Pg^YMQtYu=>?Q!m>X8v9!d<0*w*Qr(d#`!ZtjwMlDS|2_~A0BxfU=h(AOM| zYbdhWIP2~iB3(B}Z3fP{RWFI6x08BP3#0gYJxg%L!yqxOxHW6jdtWvf>rZD1-MyKcGl4dr zS7LZrk!4Hw8nO$~W$Y+3 z3oa;a3hjO9zVkT6^2YSTB&#d!PyPPd>`57i?V%3oilNs&9rfcG=V3x7r{QXjMvRC1 z$x*qV&GSyJdw#v;*Fu}+(!Y;@po<`NohKSs{u+1q zIh0|@mR+sh|2t>21@GBD-y?l73<^SL0i(B~(N3WK$U+kA*WdQFgmnCGQ z`=kqJDyS?UevOW^o!=rT{@K$Z`=XWptNXX)z&z}&Js0Pw1oBmWUPH(4dSsyjsAxwNIXG}-<$d!t#Qpz(21w;`NsK7E*VQtB=+JvJ%{!Cp@^U&^W zI|$)7m=RJa!0f;p;(NrpjDRtB-Uh5r=DH@=C1nM^dSy=@d%ycVz*!U4!wk`bAytDj zN$Ouxg8)+8|4Qv=QVS3ZK0=mUoS>UX0rMdtNYeZ@*R8^;9Mt~Lek8~xNdj-;ZA?h;Bx!}KsA0Lb% zEFWzi4nq_ok)$NOrR`Lkh)H}9>08XnuyQb;lu1exm^C6rJypj*;Ab|qc4ON*&25e1 z%*rJgvy650`^M1^S>yQ4#*$wIc)%z!M~Gqt+^5p8wt->g;;b=BAjW{TE!A$?1i)k* zLlkCD8_Sdt5I}!zI_RCM%`8=>sbQnu*g89H>onBA&5rfkng{)tlQ;_L@Ej8sS) z;klG_jvEFsB`%zgeX(q5et?aue1RN%yYSB;9l%&hAIEvpRB&EWerhhID=|GN=D1ICBGS=Y8E5OdVJ9el-Z);Y($T^vtDYi}g*HXquwn6-Ji zU{(@1uN7@xom+p_vavHY!FP%_!01A#Q0 z-9yR}^{~dBImMcz1Pr7zGb80lxl~nw%#GmRnARLH&4E0O)f7&tn30N3Eq#<}A6+SdF0HKdv?$*Y|DJwdHJCKF=KKIq09xh62wl zeqOR-o)?`RfwN-*XAP^COICx~?CBLmKt4ClSXJYGuyUhiM`~kJfN6+HT-CmtG9~I& z_Z~qaEG)nVi%9T{=Tg(X7A4BHA`z5DxoQLjIUJBy0yYP5L*i2y2236>ILVv46bV_< z$@Fd{4JEZjh)Q#R7b!!0f;c`vGMtggh7N0>bF&%WNcc&1bgpx{Y>$8}S+%%gGl8Ep zK1eVNQqYAw5rF_cjz!6KRo>XjQhH@6f0&WiL7Q&Jy6EM00k5dfVCXr@@u`bMkQ@2?;0Qn-ig@Zkqo zu~<|61d3{h)V8epLv`u1&^FP2L{P%mL4E`#E?Jy>GA8nF;A{rqH3GG@$sFFaJvT#nH zs?9@PudmaD3Z3K-CoxTm}?7R6QbyJ@DW?{ zxxie(WJ_dlp$LOm@;M2xhhe1L5EU6BIEOvG7!2ArAYUEk+VQqc)HidzV8GB#^b#`F zzL5ZnYNU1xNNc>vb;rEm96%p^(r(|Rw%_i3Nq`19^_=-t9|2@B+v6h!8^sF&_kgce zsXhW2m9qlRsb$Tf>#SVWyrIsyx@B2YRWB#g-biJtAMX^HaX^SN-W_1We)p@>bxE)r zfh%|ywO6N~jD6LT8AM!}>Q(kvi2+EX@>X>Ll);m{9wz*h`28fMHz*;2p& zx=MLj3QI~=fGj5kUs&Yb!Vw?SQn$O~j2mV)7?mU#0OkYbhy!_ufJEVst=ZhvZzI(a zvmPc@f)!X4d`rI_lky_Z7Y^C5MKCu5DO*5^sx%{=1dim(DiJRuLAOq+7w16N$z1Q1vAP=A_Vh-25CCWIn}#t73ZCh2gV#u1l- zt4Y*7+-p;o5bJAB5~a*)8FbqncU_cgR3a#ga#e_f2q~#t9!Gd7K7lSwk1$(+7+37> zoWENzQIdnH=!zVLPts8mY7QM>;8TUk4hL>n5C9h`F>sD{wV=0mlEtD3*mRAV-rV~q zZMwc@gOew1`?lL{{lg!y*6km#=GslxZ61{(z12u9(QLlM*4Ex;>+An%+dH=i1hC}L z)T*_bK*e=Z@&{D6x~?JkQbd@UibZMdR7c8|^(x6PRj~e0>RO5%V2VkP z9Atoq<>l+FzWh*6fso5SNZa+Q{Xx~EZ|&BOEAmeJgvvf;Gxb&8SsZ{OZKQwk7bD7X zEWi*STO;K3~O+vh7Lk8OXCv;b3>iNXJ?_1xh_l<3wL9x!z>3N#~h2( zJh|#%Jn=^(<;K{3@&FI8d#sfTQ)Tm#+DKAWfKpxG%c}4FtWtfTRVokC_}owRyT95| z)ffmwYMudt3Gi?wD*y(22av&ZNUWID2u+LFub z#UXdl3i(5--$BjE1Kw|Q5R(9ElD+@JqIzpSFs95^#%`+nxUV??FoSt@;5(z3T_}b6 z^;o7$b6n?e9=Y54njZj5?xQPSXpZKJwYxT2U-#!lU)yYZTWDO*Dt?O+WsgKq7UhZ; z6e^0)Ol# zZb9k|u*C6tXNS!3sw=4wTCf9MXG9RrSz1~acp%CVMgxa)On+pRhY2Dr1uPF~A!=TB zA_cV>CXO@^058m!?r*o~Ge8G@NLfiAu>r~erhv55`<(S7HOSQ0v80|MNPs#607Ft! z_H(UZ_7gbDQx@037i*$A_jO(R3#W5@pkU@;?qFbGEdf)3(ZpwuV@Lr3Q`czh*p~Xd z)4@5sD_{dK#I%jS9{xzMsst$j;M5bUe*o~TT2S$RTwOijpPs5iz=^Rhmn!{^e@SbB?;gUuU=A~mAyo_a&P2y; z>E+$2r2Tjy|nE)m$(1Z0J>U~XZZD;0950W_I^!PLrmwDC$8)^r_qd)C?7 zvZ=;{#K^4N(9xLfY;4$+R54Ma?2&UdT$CtRn4qjs7QTS^B;l~mC;}9AM!)gKjS|AI z2xsQ7^>xmUfRn&YG3THFrVZa7Oquz@)apw)D;1YiP+)>c@xX=xAYFx}0@IVxTYq-2 zX0_>-E!PAT$J@H@$d)T}E83Q&2gj|d&++6#Qe?MCq4aePfv{{xplB#CgIR7%N_0*2 z!H)}P{ou@wscSmk()F79r( z+Oweg5)G)jtLix^aqwTMMBXh}!3|%>a-ISh0d#}e4CM#_pbyb^tfx6se~^fD=)P7Q zyNF!X@8r$ucGtC?5}@fxVRAj9MYnYBh7Dk7`o{zwkE{M9@g@HkS^n|=Vok-S`h{ht zNQ*()v=fj9z?k}2RRk#z!wmoQXi2Zwr-Wul3$1D z#j(H*%u+ThK&qDm_D{wq^`K{NZK^MH{(PYJhO`+3eE@y{_x(QpkSnU^A%R+fHr^X5 z6>Cq@)*HbdFWXS}Uw5Rrvs|!Q5tbD5a|n#!en~(T!o0#1?WtFErDQ_@evCQHXEwL2 z&ywyj%>(rvCS3ZgTvos5%03U1^B4!1)i6)4aM8Uo)Eq0SU#bF@Io)J=jl>cwSM*3T zLcoan>r9tLi4s9sl&eFWsmZbCBM*Dn6YbQ-8hbeK``fpk+n7d(%nW`v+ioTyU4ov8 z;e^u?7??IpOCRu-)_EgmUPhji3WYKgC?FJ!)D^s$kEJZyQiQy@;}gZ(Jj#$jgMqag z8@6`xc9kJeGGJ*WDMWXg$Mi;-0LH0pZ*5qkamu=#kiG#n0X79ka9A1AFW^JtzBU=Y zvtA;gMaoduAtOHRhm9dY>cNAD?7%9ETEn)HEkC4|Acq=`;3zR73CT@{sT6-MlpFm) z`2aHdl=I0?MgB0@D;P>x!h}7+qy>P{SAEC&0>}dprKQLLEGSMXRkd}fK8G>HmkfU* zUv@|vermM2&pMJLmQf0(D17*WzZPavoc2fRKb-36_u#|D7JwwoTVP6@x#7F|3Ue4^ zf$tG*az8EFp>yav9LH$~1SP39aQG*{g8JY?H_%+^bk?+ynD@9fn>4;?ELS zEV0Czl?76NXq!ycmve#w>bgzqwh3n)^k4U%Bo&E3X`|nXWO*WmK2C z<%$4M`kST0*ocS)m<87Pv}m4&#Ip?bV_oHAiVUBCI?SCp zDORG$TCLk{Yx`E!QvgoDZY&UjACeJehqJc898q$Ke^+qE#?*@W4yH*!Mc}E|C6bOT z{+Jd?GlA1P%pc}3B0I_aenQv!u;=R{5|6fVK7HX)ZYT=co?aNKD%5ng53^wT-k2>NlM4GXj}vC!ue!$KfcjCt320Z!b@gFh;|k-GS3jBd!I05kL`Jd^asW^f z_)hh$Z!PO+PK|N`b-9$M??~qbN;H4cnY>5UR?0W%@4DK{a#<_`M$+JnO?4lb08?%z zAUMZQ3^Supv;aD+7@(TGa?Bgr1;E4f319$(vY-1ZOQfjkpIKS4;^7-Cclbst2p~>0 zw+5=AWkU*!@WP|A$#FJ?$qeFP9%f7TYQmExGSdqR;|KV1@DEVY{0(MQ+Nf&`C#p+F zV?kg5$!gU{su1|)iDl8Dr@;`Nt@+(T|l~LM6{7E6f0^qNuTB}YdD*zA7R7Og8#Q{Yy5whG3Ks2xV z;p>KP)u#53tXd`SR88lzp!Z3E!Zj&MoZ-i+7jNbveFSDpn82YFAME37hSG-N2^ry0 z*5h$j?I@`pEC~eo!GsFH!A}TqB&{NVG#rtOtSucg&?az(^R^UEMqtSX>hEsXGJS!B zk*+Q9MpjZ-Etn!0Exaqr8Bc#(6YpEb1_J#?*c3GINce zfP;WM?ZlzJUOQ-q4j%EoXFtqgm31W5f7xNpDpu8&(|-ds3xK~V;CDh}11P^)pSS3m zfEj=&iCmd;JL-SZf@q%rlzP)fW9B1~!t@C*BY|;qU#;A#Rm(@LIIr2Xqgu5!|5^fF zqr8;_2GcrkE+7s|hd-IKQ0n3t44*H>RPnji?GcbMP?>qnlf16UV*7gO(0`e{%E>af z$+M=v)vxLV-v~tE#lua-4&b$0^Njh#+#yk}@7Bf(6@Rmw6mtnvB?)mo$t|W{l@l0A z)(~ZG3Y-lCgFTyuGWDBcguUac1kO^HUB$ zb>BsavP&W;i*gkSrZ$|Yd839k!$bsV5O^4|{vr>4d{j_a!3@TO-<5>`z|PJo>$Eqm z*TuZag5NVYKLN;ik7rTl5HSa1p~o<$9&K}#2{V)>y02ce;S|T~+ok06z!s<=eE?6| za;@qR0%a6@UjRU?901dWlYdi3Wuyd4Qs8wr*@WZf4$5Ew09l}q#6M>uWyYVR_wZ!6 z4kR&k5lNU6Ku}*4P}Xg=3r1mLLpNa(BtY_0jt5AHU=!9!#H^Lo`6%X+6d{h{ zRh<*en3c+^EiE0fN_ojm$LNSUEvs#ed8;8szaypF z5j!gj zFuwwBJdFr|PyjkHrLvE)CE#@S?!D!)h_Mm^DN20rMr8hcQ)pvSi?y>g3GZ9>LFz{&ldv5Pa}> z31OZwm#Mc3@oU4D!2k#IB*7jeP39#gteJ{wHH;m;jQGe+e2#|PZO9W+6u0p|l0ZW4O zamLvd6d}qC1jBOrAYA;0SEX25aqI@M}VC|vEdkwNe-Yv=za(g z;J8c%<=?b%erWka1?$XC*?RX@YmT<816xKEpSBqe_@(Y z3=ifZ9OzMu2#Grj1stD8+TWMbfq4+z(06=$Sicf~Efy*dK2-ol!5V=>QU&0UKAmja zV0cp7nl@4$d`Qakn9WnLSNOFFIJ#L7;MaC=2fIPEW+n|dtg7uLfl&3)48u#=>a`ne zdHJCNXE&-2*Xvk(jj93$u!yV|S&>o!6sr%@6Pq|AsBxIgEi2tSYe@O*^sFc_P*9!I z0x#q3w&l7LD`lm0i&C%x&5V($KA7IJIVq|X0JNz3=6&-uCAFpI>{|pP%(7|+QKv9& zm`(#IqkjORFs9=vetnpFVVnTzMET*Aj@c6BPgee9)q;>J!UzmezXHUwm_VmnHtC(T zbn}$WwvO9;`=nX3VKs(tzN0ZYC6EKKy2;w@4+w>;bD4I=@v z(faLXJ11qbf;As;#^;TgGGTdg zS%5`I#{rl`@#4z^_`|2B*TeLPVgewM@qFy55B%I5%RHetQ#z*YX(33X0c!)x0fXht zliC4T34T~ytM6v9kfH)$HjqLO=`^?w8|+fjSM(?S<4Iz*kL&VDGE^k=IVNIEp0JDF zMXtcvkApMJK;D0YEnK9R(0?P98QLtQrV|r}jFbl?_@zv)&))!)kg5bzHGW+#Wxp=v5)k6oZre{Q5pGAFUi|Pmc7A4AFiJ&aXRWCd=@iCiD z@EBam!ecdp1SUAJjvgxAdCj@^#Jrf|PGd4`LRdCP3i0k>Ua8^ls;r z_Mg&2_>}v)kXfC#_-VRpqg2x-)tqGy)@)Er+tyg%NN>{Z-j=}Ax()j{B+Ih)No7nBFLeuYhyZw`Jf(Tw#lz)+B$WzDbBU^D2kZo>xax_6G9wDFe4b4zo4W5Z?w zX#&v&h79ra~@rss$H(e2DpqU@2&Im4qwxtqd6 z9a|L)Mlh*_2N@5=vmcB(cg`>CVTw78NKBGb>Qti71k`X2#!)^9Wd>g({cq%V^4wI@ zft0tDy2>64IP_cF)|G-CciNT(yr3Ybm^W||XIUG;k9;uYc;siF6al2j;KCWhXAFmE zeh17M$Rcf1$E}3ukRVyA|gLY6HrlAVbECc8gpJ+%2Qy;(?#IZ z6#UtWo}82NN#=S&4}DB{01((O*fUZg1V$%-Sn`(9ANb*MEX)K>+@y(+!qt9#(>Ww# zWrI}<=`a8|C>&4HI)J&6+Hz$HW2L@>>Cmx&J{UomIqD4S2%|^ciGGF2qHbK5?>La7 z#PF4)zbK1yF+GO%vjPB&8~%i_m(&GQcDQZ;S8!yD|C}`o8?2!(wY0dNgD`*m!Pek6 zCFSd8YymQX7jWXR)wAxF6lhx@L;xeB@lX4_;mT8+U^*R;^tS2iRnG8J6L=eRHMW{l zy>`Qfnmcn%M3zH^AoG4$091i%TD zAe{%Gx!qp#G%JnmH34uKtBDoVhlQNR7)Db6)4q0l$C}M`Z$EvyR6nBfs~VRu_XF70 zJ&?(%mu9*zcv-;oP2?tii($zlSi(W4Y3;2w8xMw7t<-I0<)8p!(MH{_4V!xL(3tC@ zYTsS~fCc6?5k@1hX@FJORlpeI#`wX=5|DE_o%<2hVo@dPvsZk|_;`iqQFtbW@jI`$ zDU_8b!ab7JU=r;N`{+wX>As5+Wv}?-B}$YlQ!tH$<$s0)qO&&jcK}Z)@3SOT)Z71x z6wDEUp(DmZ+2zs^DT^DekYCmod~whbgxB|^Y^GyUUjPUIdoTcDiJ@XXZH4U2(*2fA zT3a^U+OW~ij!nBgDP}2jz=k$}*+2@CRI8lRy}$8wg4` z?K7h{^5E#6(;IljDwTt7>YNh=$&x=MQY#!#ok&li^YFoO9f$u-MrHN|zSh(q4Obu` z3XydqH+9Y1^~usXAyYej<$gkQ%$Xxs4wywb4<+C0(XT`z;zTcisWt)5=w~7~Ne9A` zMZvLMprbGFFu=5hc{hM$3}%oY&LZGwM8E2IcCvI2*P44jt1h4$DKnz%a)4m{Ncs*K zBNFKXqVO*RGy@a>qpp+%u1W8ZSAX#vCNA_kAQJ&J!tSXAm5t&jU?G^u0I)=Sy5sz5 zCCYh#5B#J6c|>%oujc|LJ|)!Qt)b;d>Y0Inq{ebS8mXn!Rn5d{T>zamNi{iaO1?9J zvAo)xn~kiZc2-~=F%34m)^0UztUe>TD^a;b=Q3uP#TcWE`Yn@I{WMN2WDlbUlM8UA zz4SLs4P%af6F{uf-BA7Uf!ooG1SV6KWtyt{$NDTndks|5*6d8Ms7UX9VN5C|Kl8vvkA^Lj!TuK%JLWq0iO)d+{QX~#7Y7s^(R&;?E?sUE;RiGi$!V? zfFe1mP~3tv`kisp<@7g>@QfWvUr~&caaRQeAHa~fbaYyY@^}k6HrkQO00nBTeZFy>euP&~x-kWxA9gAHMKF>R6a%l*o<1+z)lT1}Kj$nXz~ z1lxkSmQ)0!1)_ylvm}5p0K9~KU4{^kXQ z6c}NZ0er%;mcdM|HqHb(X)u1^m^v{nV(#K4ATTciHTsS7!>SE}uIa3Uf6`hM$^w%$ zfv1ox1&~ZJj)2gKp3A?msAi zDDZ`8nz3hi{eEH4HJi!X=25{K-!NuDLjQ*`IKMD&@TDVXAAvAc&FiYb822NQr6cRK zJH~pW1j8`4_mp5(Wn~T0ZgAO=ukJwvDo+35KHw!RY=BFSA-b6tp0n%8v264QKy0`8 zp0jsFiQ>XjG)k1aDLjDjTWVRqx2Xpz$xJb=ZR=r8Qq1$y`}l~#){rT`(_XV~hm7iE zcxUMu0fHGo44^TPa*$Hw!KkfNl2SAr=6Ceqyd%Gi@bJ%M$h&mN%B2HR^q4r8J-l8J zN52cW3H;#D-tQil!aZjB*(qDfZd&Sf)L;@>t4n zloKFC39MK?w=BT6Wb>31zBbsfcI$-t>V$qzni&`>YLG)&XGszYvjLL=up*%@tRi3; z;M-AsdQw3A4g&+A!s(k_Y7HGfP&oon0uCizq%38gC(FA)nh=YwqQ3>AxgG%nFdD33 z$s7o&DRdpit*kn%Nb}T3mRg!wdPTsrrnU*U0z~k8;&_4!D2wxaJy2&Y0hgHV1azhR zFr}4C(tzp%T$g2uO^v4V-p z=4@2zS$1`9m4oV^LuJbzK4`YIVi}!Nt{jx&Usa!JE~e{N(0GqFTb63|t##tK4L7ze ziyK#W;(l4VEWm(7#2e}d61xgWYF=a7Eo1I(tFBGm6PlZ?ZpRusJ2n?^9SXP(HI_ZK zd8U_;Oign(!%6@Tvz2nqCY`oTH{NZT&h5GrhE|&HSgL!>Mn`Wk+gQ^ynOcGL8R^5C zH?T;WXK9VQ#?~}{te`oQ0nCyvLu0VOVgT)_u9+&A7$ooS^9%lK+f=9P?5 z4;w)wU|w}1O@lK;JWN##4^a|pt}L^R4kjKV@^Wf(O)oO zgz6`3SJ+^o)OV`4zo{=89R7xR;y1b$Gcl%7);x zM4puG%TnoF_W60*$BA5rMZozRS0FZ)H-ch=%?ih#sTX5PU6MM(JTjjc55|CNv|54n zqwbi@!gXPA0N-H?Y=-{!|GXJqX6Q#0U^tG?)Ag0O2~gm3M}TPa)P{8%9cRR*0;8~E z;Jg``=wZlI*3#0lGduiELfxX~P^DI(SZ8EOozl_V1`uZPArfe1aeMp_VIH|Ze3byz zX{?73$&h-<`ciNL2%Q21ZZM_%>*b8{&3wdjISc?)yEo9Q~ z4g{KXooqg@V=6vh$=64Ej#hIk%q3lueSV!@&oFd2hpNDzK#&GaS842sVI~4_KSokfB*F7Pskk1za zABgA<_ce1h%$>7^IdHyb5YGeBxlkT$fN^6Zpog*H`4J_`UWuSA$|Vp?NF6ZH{RMa zJTP6spIz?xg%a5GW}ac$7nG8|^N>$T(M(YWFm40u_M6u3ty^bsLJBi&H7PohcM^Hl zmm(TTX{9}n){-sNA7It$N2(4-^svQ5x~zH^rMz>tvUJ4i-mhw} zsjk~L9?`G(Y++U;L;a4FBEDewSxwv-+uz3N?8bCY0{_DKN?AYk&t*_^d1Ga2$T zslVs??2l~L?5W;O8}D>%)>fH2eaorNX=llX>NkARdd`Nz=WAQUPJ$_Pb{-IxBwbac%hwx$RdW0d2$)@H^+uIzN6o5O|2c!n` zQBa!n88Nh!sq$T!ns{;`M&slF`g+bcNQquTmWmV-uCTAt9X%3WaMf0qnxtbL?tYq@GQmN{`qGJU7 z$eSk+Sr`fg>0XX2HlCk*t*6XA2-bz-M6fZnOm-z zE@eHnX{76>4;{4hQaubH!?8;kBUmq<3Ecml%-2osFip%qMl8A01Az8<;QoN&MPlFL zL$CQXnc571OR5|IHdAgf?Jg`-T#a&{MxJA8S4w@X@~BxNK3@UanAUy1?G^y0Uc`#4 zYC}bB#E+DO%`6RfKqmv(qC`1cA}EV;w*|!(fQ_D>3*pg)@Yb8KS&0JXlec|RC%_m4 z3CD9Dj9F01q>;c^iidJg;GBo4(9=lhVaeO*`QBcGBu`(IG8>EtZ{HRDMFLDg1HIK_ zG9f1xz9d<_y{7V68%lBZ2e1KsDGdP$07qtS*;1ar+*gZwE-&9`D=YWcL-wE^%oVHE z$XcGaQNJU_cgp%YZaNTn(A)92+qLd?%O*Vm7!(BlJ$jgzD<#irJ{hE>68@3VuI(%2U0w*l<(-Zi@{7A&4-o8fyeL6R!l}MN{-pn!kgsklJle0$B zvzb}ivg*f-6n;jB&NN0is}nH^3q?AJNgt4q7BD0#Q3^`RZPb%;l!8k$o&slKsiF`h z0C?nDuC#P5;Xkhn`v5?I!_%oLw9f@Jt~2oUqJAlXZ5+E{XGq#h@>dvD%6FnoK#n%y zYZy$i_=y3oXtS>DmrmF){#Lu4@xb2?Q&V6c`5pELLD9lEjk^N4`Y$gao-2?O4gaAq zfBSvRq;ty`NkUu|sAk?3+!v5MY0PET@JtJg;pjiZ7Yn~ZOt0$TslX%ApTj}hefd1a zi27kX#2<^f4a=vt=LG5nN(ltX&UEdz+R91@U4dW$xKT%+{l2;qUo?Ow<5LW*6=07@ zzoPcl@&bk#TROO6rKKeSWC2JG0jo)LIs(_Kv))j=|1{mz7aCJo$1(Fo`!pX*ETpdc zWZdnmF8ZDe>sSJAG5crpH7nN+TK(W5D+q*D4 z9I0Wt>RZ4eW_HbqbIxHD^sj$h=sv%IFh>WP&&`$%w*~4wf?D8@hs)wwv>#!v@SMRf zRF4Z4t8@9Ed9z5_5z^Y6Po3)AEUdZur@CyF%ChElSkj7l7$wSHiL)Zg-4}rZ_vzUq z4$O$kVw0Rvl*z-DbM#Q#^RV^r^8lU-g=IbTa#A|5ae6z~!<6KgyuqRrL++|G=bvU? zBtcOVM1bi71#6uNxL)of&`>!27;67aK&bAa;$3~N$r3zB8 zMJXGc>!oac4a6~PLLRho>3#xe_t#tT0av`_i|TEjuyL#5IjU;;oYe&~ia3%>!KTLK zJ%b?$^(T4_Yeu#}J|iiv?s02we85_bo2=WtT~+T10Kn?NDoCNviJ%04ktjQg5zj)uM4?KEsAcfMBBJZegx++(oBqgN`(1K&isk@52=?hs22Wdo7 zR*(|O2qDh}*3#-T(krCdOl81)xo;T2Oe5&PhwBGG2B>mY4n#=W2OSe6qrZ3z@f*>S zVZ#~}RwTeYsj`8>Y6q;I2Y)y_2D?cTSe5VX1>kXhXe<4S*)Bx7QV&>G=0Nz3X%^;= zzN?;Docp1;1g7vE!jT`)&UsS-Xy)Zm3LR7ZRCCehq(52lIg`$+y&0k_JJwHi1&Wre za^QefmP%^bP=I;MMuUdHdc%f*=V3=cq@yyX?&QxrW?f7C8&TqE0zCSfzm2iRNrN%a zxD7Mb-PD|GwspG!6i5-0wVVJTW=nkUrm3NZw(m>&bO7+04^vI7X*TDe4A!SsTeaGO zL#nUtAtF}k&r#btTWgvNu&ns70ffnoR`M5zvF1q8%C>so{#L0TR=W@B9y(-My^NS% zW`^p2)^k)tGz8wU-oLwr`*G|Mzbp^NeN2*K4uv_>oYvfP_V)DLkT8}4Kh8w?SnW^T z)G9zW+}c+EwM@uQbG>5&fwR88&jq3{M%+xDV_7PE#56ZCN2)_ThoC@~gEF7Tdxbg& zrY!^?aU5WJ|7|%({5IAvvFuwaMGm97KwI6Fmp`R2!ATj} zycaejj}~v^tPiLMF>m9TQBmT~m=JDH8B&B&A_M_U#;|fyDgtZS%8FIWq-2;2gUv0k zEn7%SaTW!h2-(lOe-NM`?|wHp1DA~@l4eqflX25#y&XL?RfqAGH9N;`Fy7F^8z_I(A!1Hl(FMVNFJQvmcRO;iPrC$5i(d0$y9Xo|JJK-!UBE1r~hGMsh*n z3rC6%{LWbTnWdXxlIRnBS@^C#!el$rICKRtIs&32DLd(=loWodJGR^qmJ36kl%@KW z{9;qgoEpzG#D~V1yl!)HlcCHP08AtA*Fi%_hiVVw!5{pN;?l+*0n(6%O@(0+60j!! z8f**#1HttnTmjSc1;7vH#^)Jxi@y_~ssB=xHKQCH#z{OXh3DW4V5au!cdkhvg#Mr| z%vH8fHyz}2VywQM0xrRam^i1D&)5s_6gBQeBJ$?eX>SXZb*$iig9LM^1ZU||^njaf zJ(WW`Zxd3fbOr(h8aGYL+PrMF3iFnf8X7y24;M%dTUN8A_<=rI&2Is)sgMq9jiSsk zfz`^ZmYE5}_jFIq0)W%xGeL7tYCFc9*~g?!5X`2HGy2T3(nCl~LegDUV~)t-e_ z_=1AZ+H67)iN+WP6C|xIEQGFc5*R0Wvd>kG0c@b&#dI7C;qxMPC+cc3?bznJ2Z$`y z8cW;+M zlCl6}u-!v-3>!#0yk|tYkI0?O@+eU*EU+q=AaK@qr~Yo!mlGm9J~(3&*+z~o-Vo1z zTJzl%ksf4;d6SKE5JJ*ZEDHm8D%SOOf5euS4(pS}iE*-C^0!meI3_t#IFMH#tDD2>TTOdD@lorg5n|Y)ds2Aq5Zo6R(fhbIw#WE~g zRdp&X+BgWNJt>Z!9`vkJ2@60viHZ6K5W(_F9RW19e!rt@O2bJ(Vb0pvJZ3xFYYx;% zeL(sI&IRmc^DscL7G!S6tO+y6HA0FD^0@VTC}98^$Uy3me%1pThwNmdrK~4eE>@+y zr96id!6DKwNJ*)^St*qHNN}V-^kZQe2u^TT$%|ubf|-y{T`X++#etEJZO|H?wAD`n z6zx7nMWrzz1_g`~!_oQ7_h3%M0Xmq$fj0tX3--jply8`8_27cR;~bUAdWx9xLQb*; zL+5o0&f4KRNgb(ssB?G;ppM~qOpR2Xbs||86eoPlR?JD5Go04}z0`v_HIP#54+MBn z$ct;0$|b8*nMWyWw>74Np%qoXs^%7bK3@ReVu3~1HC{N8smy`;exg2~jMdkgd#P#K zle<=`+R95h9%p-kMXFZLl^HeFO(n7_gZeA1W2*kq_-t!Fu|h>EH?!dc^C0GVSXH%F z&2`YDa~O}gUVsK&&Cdmclu`fYD@7~S!wU(FAoGAWdX6{&`2eDM8PfRPLTvu?V9PL_MN&DT8@RJ?Zm_>c-fGAR4ZBVRZ#_qU&{~PzF+bJ)%k{x_n7}Ftm0@N^ihJ7&D7h7R*Wp z_CNqF)wf)_Ev4F0d)KApwp0%tTTuU{r3ecf)=}Mi<5Q}~x?bSdZIGT=c1eJ>Qm~XB z`nkLmKv56HGPN5JMn1a!fMmf9ojlNS)E{Hcd&+1l_KTM5 zs7>9X<@*K84+ZYf=GtbkXMl!zUdj^i#Tt<{eJ9~*@J&nVA&BC&b@nYcwoz6fyfAb# zWxqSNt%-nserP*OW82QC-_?dpIcF0Ap0UOvTVT9NaLP*rphf!uxLaE`+1a-F82>Mr zMFIbL-DaxqXj^p{2`HwrHYHcts+2nmbHjDZ&)aHG$1=km z%j+ef=zt7XlkHrgyK~yg0CyoG7O7k-%pry&9DyY1%?jh|@B5r*$!Z2ye>*Xom)cwe@0JDq2Gt`6m zwFn%cTzi!)5+?orghPjV$=cYoRI6=O z&GQ;BDC*0>sZ*8`06l$;C|9*)eoV6X`#vovbfdiKO>eTNJmo3=wj1THi~EK+Q`6Ji zB2kdA6Qpg>+cf4tJ)p~Fd~I0!G4c6gJ6qV#v6N9L6a?y|V0imXGd?^_&z94QeV2?U zT^sb9diJrL5lb>jp$-JTv>$~-yVcOwxPn`F~Qw)d2jHUQfl z(J@o1E`m(X?4&C?TK`i&*5dRM_}fc?F$()aeS(&t4gfax1x5#85A(&bIvT%39UEA& zz+nOULfIS<1U%{H*)FUSo9Z=@veiK|qIK0?XP`8P(lD()mqHoz2f)FT)p|b)tmp^$ z!pTUT(6i1?$4{_UN7qwxe>q`{ zNP23`xyA<5-qaaau35;{G)D?H5SYmo@&bV{P+=*dO0lZr%f^yL^~#cE#sX9-i zSYDMs(Mtl6ldyUW;#v24HxX;g=DUFHr39wo^hn`;kgOB$n$mpE2p`VsbWTD02U`jxz~hy{>3kTv3=)v zey8iUi;?g|j1uLd1jX8y!s9u~J2?D9S`TF&sIXe(_QITmQ!7u13q2*+&lJSGS=A?R z#7yADL+nn(!N(XA<)$_kZzd9rI*~VN-Fn^QHt3zy=P_$EZqY-!A;mP(gG_*BwkeQw zLZzgvS_M?;+5!k;0jGXr%gS>pk$J&})0|}s*XyClx|CU)>0!#7Jt;A=St;xMVasLf zs*9Ae0GFkfY=V{+c*(AoY>*nb*%I(bDu>?eqz!G&X2pih%POZ{wd!>bu>w#s!k zSmEFy80c51)SpQV76L`&bSMzct>Q1n=~p%7BgV zGS#PpjHO!hkmxmCw_5g~BXj%#O|1?(|LW z;qEbHk|W(f3FxK~r9`MyfDi&CR4Ua=Jybm)@qiE#52SbjfdoiZ`G7#wQwoqus>(u0 z2oQ*Dk+h&ilLJW(kr6(9)17S|kJ&Nn-*4OfB0R>7%!o|)$o%%_$Ib4sbM~ir96^Se@=;eNy47#`bt0QWW!R!>$ZL!eYj#_-NFAWs1EXHjP!Wb_S^l7{G{PT2r}yxECnt&Kni)IjeI zg4}HqMFwzcb;zq3c4_xMg;Hsd%yk7{uQ>HAS{J&x|lo6$deGqM4| z;l(iaqZefOf@iM)slzy}PMH@d7`GqB>iwUM;^h~j99~AnxPn7~(Z##5xqAWdK-qEA z2w-XtaCThnr{maOUPKNUt3H1g+b?`R)<65xG5-Jj+Zg}Me~#?(E-DM2TS4O)iqqXR zRyUIh%QHIKeb-s!1`|rh` zvID@>0vt7miden0V>xIaz3vZXCa^HoQ)ss3bpronEK= zq+nEt-|tpTOA!jMuT6*DZ9ebPjDi-e&*iLSF_Bw*v*a^gzW?R?zd{R6Lfa)Xtwb)% zS(mTmIMyKrnFVwR$c%escnI^=3L`*C%VgHCqT~3%dX$dqVd!h&GqgnV$s%h5zP4H( z$V_X{JYCXq6NBKw{qm>R*&H|xZ=p?^QN8YrKI+1B* z;r+6Xu&m5uI=_n9e30sz8;thB<2Zi!MpVeJMDS|e&_Bz-y8kS^$oN> z3F?uT{%Z*|Qij$ochYIC&`E!@U&q=L$=kH0UWp~+X1<=sVl$6zjq$@hIcKYyMF(1S z_J9Eol?Q+|4U5t|iFW@Kl-!IS0PUy^a8%&{M3n_~pfC5yf~*3g6MG}=#NEsgtWys% zhvuzrob-*pe-c|j@n`_(nKNGm=*x9Xu7@$d8pU=ni^h5v8T8w^0Rt+zgS45o0g8;t zw1fv`Y2yZfR-#lCfRv2(clD72#2$4#EA|2PXvNm0YLparXL^)Sqt%GciD$5GV=)^r z?r3u>kM-a-S*HvpoI%89BTvhdbAU#1dlfOhi*j%s<;_Lx?=EA1`80O?y<04kPge(! zc-HGgopCpN`ZQM0p2qSyfbQl1R;*_Ou+esd1r0Eq%nI=HQC}^=j7LHlXxU@S1mKQ~ zW<%vk>)$w*lR->Bd^fIt>E~lU9!0axI6gf^>1ZVjTmYJOceiOru^^H`a=5<5bs>~Ye1oSylU6iOU$G+Z@;c3jK3Z4 z&b2gOUZ?Ns#PE5YUZ+FaBg71?;7&y^1EzWoqJsXTNxqPFb*98#? z@Tii1gQTPMmqS&^j1-n%W+pNh1A?ws*5fAao-EcdF17FU?3a_EOTe{H0M2OV`cVPW za>0n8z%o{V8JHTs3D(@DC+Bq8a)$t3(uPg3<(U@mb;79UhC`4Z+jOp-;W!WgI@Qy* z3h$RYosvDD(#;C5+S*3AJ&9wkX8kxka5zIiG!Q+6^=ZqlZN z(QFZ=;Sk#W$h3g8j;TkRzSuxl!zOH~w@a3WPUvxYBn^TG0a}Gq8$+rBICD8fE12!B z0|*33iK=v%U|F!?l35KwYgCLa0$Zz|piC!s1zK9#r++Dq`@G|_o=b(d+gQG2d|XkN zJ7_*d7y~@3dv{MB{A;x20huh3x(eyc+eXyd zN8r?UG_rp5n-9Qu{V;CCZbd2dZQedc2{-{v^wKhaTYt3iJhC|e3ms%VTSKKhPEMXg zuhWkz9ddH?1b}x+8@Ey1&7!PN>>JL;!73IdKI+obtdFb`AAIFy}yHBS~} z&&YIFrPS*JP&2^q^yzzylb5l+dJ)t2KOa{=|I;!0FaIfaFW-+k{pHbJ9l&&h{JSVp zM@#AfVA)?Y9{^}E8b^zK>jG!GD2JCcYtR-zBd9ra9EtJnvR$2L?c;R9fw2W=<0xmt zSPB9G(v`(1vdJy<*r%PCRlukUhw4NHpjTeJjF~4GXqB7J7-O{iE7{C9Ox7^zp3v78RjPL$WB(f@-62292>9GZ~KIZNr245lkb@G0vneAJ#P;!GA2CpQHCMMIsmxU8le<`G2=5WFZp{4 zXwk1iq5IVgLXf1jO2Bu;^Va|`mmYQMhS{W~Wn&hL#TQ~a`$8<{F!%LEtcw?XUh}(k zIV7`Jo7Wn$nBM?kZg}PrusG&71Y_N0MMDGz#|N8YxrnBfSgfM;9%JrjzYyy$d?B{ao<{W+5HJ|VZa9u=JBvmqkL_#{ z^V_@FPN@_1a2I8}`6zl_0}j6pi0UI~0guCjRY~hkxne%8GsYk7nU4YJ5v`6<{sFa1 z#^5`0MC9rqz;TX`=Cl$(96vl zTFZm^QXOAJW&Aw$ckf1JP5qhkwacfm`tS>seHqooEstG5mlv^{52IC104$?uQ%*Lz zi)_mLwU1s&C+J;eu4K%!Dgf}%|0L$KjuRCW$I5gXadREpi}zx4`7Y&Mq>Ti5uN%#y zcC`8c1J-?+dEWpoSz%s-FYO7%n0C=G+c_|Hz5dz#eK1bLml zo71b|@jCssPFh3+Vav#Km%ge|gWmp^+|SzPy*p(;ARK53tba{nWA6Y)0q!1i=ZAg4wRA zughy^=jYKsJxj+d)zNs}wYj7{MsrW+R005IgAoGQgS^{u+(Bs8UCP`c5CB#B{~%!v zb71%l&$EQHqUp|JjG)wNmgraOQa2ba0?#ETW2q%8UcE^>p*NswF#&uav}3K1p$(L= zGu(o>O2C*vk4zpQ3ey$RJ;IblRm;2mnU~f&@9( z0Ic*WyIE#Y-vU(DJa1UIZ4Ejbsr-bp5KgHNEq71JTE`p*_7PVC!6qfgq2C-6(!jr8 zUCEM~?wbzJnOv6ClBX5yRlCe7`4$%RflIWs&v%pT6I9Vh^D)ZBm~l5jfB~lT@8K!m zq<)luu*`MPb{QH#n!q;qw|uT#{zRy*cZ^fUXgYIKYml2kE^cCT3kbUpO6t;Gwk=~x zc~=0m70-G~f=g_!svGaj%?ZU?S{N$E%^?lO^(w|UH?f`5rvQILI2`xS085P+j|Opd zb%BC1j;o7{7`p^!zblggRCHyW_WJb4mT|XCG0{A&L2Ke}wT{W%ZA|rDn)@3{$8yDm zGPRmPALc9dGM1WFx$eRi4{@gHbzNJL|-J>r33ymm0@f60FmPi3tRp)!^=r~KegQ3+& zlYdFakGAXUCNikEThVaol{w(44wh%AmhSnPJCW`me4W1A)9c-ruM^1~iD>sQB#)#y zFj_DFMo1+sPrnjD8USvi(>j8t+JW)(5r+CXxwGsH-qGf7mj=H!*yCR!6G? zZNVWA5!YRM@W>?{)zP5mFTwzgX*o;_6#1p&fhsON8b$sz30=?2^QfUcV8C5$S=J2? zLDnj^+1&7Vll-cDAMz~16j|Pe0h^33<<7Qx{*dpqb#GLBK_x<8XLA6{wwuMOg`n>B zq5?>(=qFS^0U-6D$-&=izk2~AKUii0VH2Z^X!M$)KiSr;{QD|G01&#RJZM_xF0m?H z5O}mr@Kl3VZWPekV7Q8`iU6cOI_w*#ORJDzRSTQTU3HIX@_fQpCARd>HV`hnR|Q}i zoHLizL$f;Ld?Uc1A5w&P1h8_!QvqDk+VdnYhD(7Z?cTYxvI5{B%-bVC!=@J1WhFM$ zv4X~5$+a#5V8)H8uIQIF7oe>RFq?NK2(k%zA*_}3X%q>1EYLdGXxfsjV15T!Q~-YW zfJ^{a6Yz007Jv#sUzPIfd%y(%_YiV|v6%sm_*Y+05B_%9w%AZE`R$IOVFRH^p0y2m zjF7+!fIW4tWt0mIn^WiuFsg!P^evMf8)#s>ud;za)`!uffa)j$M*yhKHdYON(-7$T zPuc#-KAOn&eBX-(pxe_8ByK~RZ+6jfnQ-`S>_;zSUK>Yr_pxRuY*0e#ZkCv3(d5}p zK1r78VtE$j2QN~+>*o&uGEHcxPhJtTVix)7TTwecrGJm3cG8PQy%s9~ zV4J~sQxcTBWdH108f!IZv4M{Xs7g150Ei6wtCej*AJqV)0wBj(74RIxQOrCt>(if( z48W7hf*%R2JmDeZ-Nb0VSrTxeG?mb`fQJc|5=ZLxB76Lp@*TI#Kk{4yXjPc>Wqj4H zy~;4-I6Hx-G5=`D*H7sc%4$*1qa&_f1U#ttrDEQjt z#secrRt_J!Pha|e|F{+lEk}YU?|t;ySLp!e02VTquTl$>NuRV$!BdS+FHlRjQqt=6 z%0gl=)I;uCbqW|A*@eQvnhDgil;xA&hm{En6nE5 zZ8X&8j}vIqT4G%b-s^OF(eCKb-$>paF#??F zOs)S&%c$?IE}*xF<-&u_)usi1hXp;YKMk0_6W3t4Idxj_?i|6Gmhny}u=ofJ$~E<#t4%+^f$tt zx_Vzrnoj2){p8NOVz`OrsEGBnfQE;V2vzz+&~9*DIaaGn-vXMK03|J0l^iexXf)WX zcu@Z<+qa`WudHXF+~km+ON%)z8v;6}+sME%4NYMytwtsNaImV>0UBOSz8fgGWrTj- zbz{YjaZ#hr(%t;Ilfpx+XNwr#-o)?@dhmTQMR`FJkX8aN!Kzo2x{pQ&e?qOgE}0kG zH3|ZKV7u0{xVycG+q;`&dDLRnq|Hm7ozEw+o-Y#cSTUAnqj4<8lbA0Sj4goaW}B?2 z_J@|!&4RX4ht<;G0q@qC`KR-|zD z3+E=>;|CAo@!M}PHtI1LOelXAS06s7-FJ+MTCzk>MuS)Ze#$^tGt5MH_KHl;O1tZ@m?D+W#s!jhst|G5akfALCg!8??x$)jy8@ z+4;euCCGY}sGlIQ*f4)+m-E?nIS<;@H$D56Z1EeWbRvlxT)4g-&wic0yVGk++3S=9 zg@WIu#I(fq5v+ay5b|HNGK8KjUuZ8PuL5#*v zDgR|;+fi&rA4Ku~FGqcP5f!)Y&jwLJg4rf>EjB}BL>cRvJ1$)=)j!HuT8ALH$P?Q3 zDgFjD>;?$mD}D!5!GMhyR&I??)J(v^^9i*O>?$+r;?lAHK3aefHHsK&s7quD`_>)#E&0Qx z&ISUo!I)_R9P1^5u81aM$Dp#dF98s`KP^4-U9ugLHZjM=VacXypwyxg%>m_H?4olG zU|0iU)t!k`om76Rs}#tuNz9AQ7zGMIx(6)p1$%%kGD<)!fhMz%5%6~vAiJiFL%UEAPtU)$ zUEfN2mG7@oz1QjENv|zsuhX}g4uC^GVIa;({yc{rk|jdX1H6-^gd~&EuL+LAZ(2JJ zLfFqk5Koxj*8nn;K0sQR1XvU#5=(deMBs8?fZ{0tehSPEj`UtyiU4#w&j@ts(!71% zdYIqAqLdmy^4KGRTB~&&xz5ndUb>J#)X!K=`kYPBSO>}aR7U`LR-yi0YOe+-1=5hG z8wB(%(b81La;Xr#jmh}k7!H3W=F<;jwY-hl=w&SKUI4ttFxd=jzFQh2aB1O#x`}ZJpJZ8v{@fNtx^E9%pju1ek)=F9k!lg zum?2Ckf~Qf!qmAk*#OoAsanSl7o8N_#WD%<+LGrcTA#`k@N)||+s?>m!Fw*n0w7!r ztpX}^RM#++QOWB4d&QhgthbszfY#+a<1#riDw86WU!5L+8`vZX%deIhLbAg@5<<1P zhSFm|&lTlN7&VNyw92V}9QCRdjIBaJr*k%KydMub=sP3@j12%)4$vyE86!8V*iQjh zlR2Nf;||pw4FujiAP53gb|yo|+o0pta!_fG&VAX~uq#(Dv!%Ha*41BMo~bJNI} z3~oo!o-U(5-$(0q5&hvNy3n`AbL-Jv)TwVy6l*0>U=XVz~qWQr}iLvg3YafTh;M^JqPK6wUK9fFX6f2Tre&W5}h< zeO1L|#keit4=55SDFq746h&ydy^Gm$ba?pmL3BU!J#qZbr=txp%$PgIDcj%NL`1yziH_cG#Xx~k1rjT`&w!H{9!kvp)eGJ9(Uxy!w0T*NLqR7b$!V) z@BjiDVPwbw9n*DSH;&dvpEo5i2eV6-AOQe*D~$wR=225~29RmxO1^654Ir={hThPE zre>aH=l3~QDGv~b2D+oZ2LLC~*?~{A(zr$3kQGP#o{3H1F$l!zMT|xt#&GaH_0Yc! zVFfdGsnYWkbEAH)`F-aeDTH(d~U2T3ffn@`k4#?DkpAX6{J6qrDUAey}uc z0b!f1I||)!aj-OIb?8zNT!AzM!d4iNRhwun16Cne3LUxuYH2T|fMGa~&D}Jbt9{g~ z2;)i~4gGEeEi*xx;8RNuAfZD4=kyH{-J@M%T0}#j#UuHyYPdB38D(lwK(Kibc0Suh zj=-)>D39;qQ9g;8f0>~lg1~Z{_uQ=DRUJ|0^)BicybB=GtcYxdo|>Dr40G za5a{IB*w%NP(wdT7>b0NO6T7^}+{(9gr*_32}t%>XMeuA_W<8Jo|)i0RM08?*oT%Q5`_{pZ-d zcn0_dO|O@+ynPYZ?|(kV?|va-1jwB*uWkpt4}evHqdW8^g$l6)jO@<)QF1jZ)QunW z-a|U$B=(&abku*4=K#Yyz>!1eODu&k2u9I7nC(m=-*TN zo++LD>>fUjI5|sUFD5Turk$77;V>#U*Xekq4FHV}1*rDF?9cX%l67G2?EqxCmR#P` z(kw$!xzj>dj2To~uU{pz6M(tF$NXKg6!?9UVt&&TZCXR0H9&OL2bv#--{ zPO9v`<;C;w?(`r3<9`fSYX8sw^Zx`m2pV3euT2Oc1Yn9y#;DLgw?vOz+ zArzC37w^cFY5TF(C+60+EzppzQ3QA(Yk}eZThK+Fx_?d%@|IAQv}=w@K9DZ zfDxWa2c5elhENl&0xpUp`jdC7d+58}M?TmetW|)R$_#Xep}w#bYUXR zECvs$2cV(=MC=4xv}bz?9qcZW)~bBYy+VKmZQNWz-?S%rtN?vEP1HcEuhX{6__Cf1 zkv&*t{fLp$r~XIx0*tFs&@Qk|?FGUf;8ZhynSz%-*)Ti|XW=qufzYnIo!yPRit2`Pb{?nJPe(fGv$h;vrM{^e+l@J;Qm|@N1NL#$dlXH<=5n2O5Z6(x>U9PZpl~;E6AfdU@lu7yq^0_!W5x_6z4fW6 zot`kT>|ue3yiTRiPx^&?(%@9Fm@@!>{^x0P1sn_?)5l8-+xc3=UD72#?nl)P25Pad)32I5YVFN8-;8I^o-ya>|6fCT z?NjzTeI)54CJ@Ub17Ib;rv%8pnv)zPV~Wjn|E!oBf=PAvsj|hYY73Mdgf;G zeXHv1uMpQt22hm-F#0*ne@89SvMueYREyZ`7GMUK8Y9}!Y^T2z!nOh+6O2?=S!}7- zW}&4K0gXU(cc<~nbSl?cmHd+EO08Yn93a)8KlimL00(se51H#@lhwHo@T$%LxCkdh zYE-nyP4=-U3{(q1c|_mwxvOgRAWulLZSckAK6^$F@rl+NZfT(dByFdFKWGJ3PEO~q z-rLmolqu-*KqkuR(dI7wXnyYIBvk;yZVG4)0VjZljAu7)6yQ1pmgQ4_xP(FlUjQK5 z)u7ul>%>?v0B&U=u;-KN@A$6eWmTnrwGzW8c8hgv)&JN8#q|1HMhhJ5y5a6^f#7+;<@IU7yV__wY5F~E4TARm_=dyJXB z3z8sG>Z7HN_S6_-TL7V8RbjaXaG>7M-|kZ|)XW0VyWzeO;(4dqMmYiGFo$%KZ3iC#L{)1P0^LIE8iEIWy!&zK!X06yu9`V|d5>U{2?Qc}!nkM_n*OT{4ua zM$IFCq|pL}2Bl#(jV@)W0N9rel%qA{%#ApmM-RetS4#j<{pcj>$0rE{G8k!0FQ#{t zcX=Hv6c%YX+s|TE%wj&ej={~#7+yS!n+p`8YZRivD%yaC3eOg6+U(qExFN#%a#-rG z!Y|hJcgRrmh06N|L(!Af9sdC~On<`JW<74_B03xu`QqNpjYXMvtS8)lS zVKgQ;3(95;Om1$X^5Vr|1prWQ_!aXKL@p?A1@EXawwAo7vf#4lS8sCqB&A|O8|i<~ z>-3Zi$FDxEj2xGjve+NyjGJ`>K55_HSbotfiw=S8vUV*Ss z8`c-B+^hg(z34OyZi-Mfd_p;n`Rod&{tPBGiw**^TtAH>z8Hm(H)|^dubTHPS*t3+ zJDFu7yg(ragVd)k14ZO8E)PMjji!+ySj(ecv`+g7{(fv4mDqP{h|UGV072fq2Dm-t z-8<@JzR(RJE-(~4X~oU5W6C&&E@z*|vu8gK+{9 zwQ6W}+N}VnfU3?4?cU|b8TB+=10bPU@C*Q?IRnTcxAbb$ard$5|HptM#?&03)r2+z#k50H;K^renMVnIJnJ%V0ouvO`qyZGlREG9 zsq3SxFm5yMHBdgZR88Ijpgd{B@D2I{8dLyX*$BE%puv=W5x?_nV_pM1@_x<3#p}_Y zLr0W=7C^eLl9IP#zt!r5;<2XgjeeB9e(V7kYXIuzW&%xtKS#&0MDSMu!tIi}OjeOy z-bQb_j;3JX{j2Ey(pA)${+L)k> z+zzP6KHBRN#bpzf@#3IxK+C;H908!pyIY2Z8$9x;_c{kFYjKnK&LKV?{ma}h;Wt?Y zrEP+eG+m@!wSq1W2G`|c4f7E(y9JGVv*MhW6!7g3qt1`f0+C0Blpt(StiucB>KHmJ#r)QNYk=gWDANay9_W$vn|owc5?5)3`w!pG-#NOS`C> z#~W!OXf|4a5szo7N3(_|j)uPR$P|R6))~Q%%Y7*(7|hvVj%w6~D_B!^!z%0%ev1Ww zZnsS#4GxyE9TV4OS{Qs{d>Oz5rkyMi0FW(Autb9gY-u$!gvViWmz1Lqoy(l^cfdsw z8d?@T&kvxsC=oF3y7W+ZfC6=L`}}%kP|I~}X6q;r_&D zpr)oj)<$a12QcJDnQsB5C}V}NT>v!Jiw$KUgm^yVUEf zb+?JxXiXmgB=&zdGJrSr(89^9+Y0@_%N{W9GM;CKYBkexr`4`p0Z{jVMe1&N zj^*4?4l~}{@*4tQ!AAcl|0c#90|m5$aCR&MqAN}fci~d!IzYh7Af-?>`9r#KqP_tP z17ro6w!vMSS|}69W<0xm**`l?OYbKqr_t^861_^wcHGRTvlw69CTrXhaJF0^WT}&u z()GMZOMH%-YNGW%dPgqpSK`^z598|MdE9WleD-dPZm*M{;@#aHWUWN6(~F#OKOanD z49GD!a-PO1^g%f=1d73NwIsTfdek*8A3%@q*0sSiTk3NA@-ptOhqT=>Y9MFF!kWCr z`1Hd{9#W>T7;b!6GvC+CMG7;rS{%-{v|qGHc|4NK+*%A5%q80-O{K3vb*prq0iTT* zR?cQI0>mxJW6F2w)X*XZZHt?m6!6#4legN;5ym!rVCl&KTa`dSBz@{GZ2=rpnE6hh zK%Y50WDO5-&Kon#Wy=&|gXguBjXa~XcN9keG8vTNMx28?05F+S;>VO2o-Vt>fRfMH z>b>`3`r!w$c=L>`xD-@{l9Z)5xXgV;U$AgVXdBfGhb ze1ukxAQ23uv-F)&3DmS@jO>BV5_y1QREIJSaGRWfdM%@47)xBL{h)jg+6iAO;F>~D1BEL zzT+rj@fM^#XUrk!x19=o#HUNMy;kf2wI$%FN;_(kden!Eg}aFMyb-M-?VkutsY4gx zbh=LgTWb>l3FW%PQ}*d^@-80H(l?$&_2fZx?&y=7O=M|zDld!|33M5GOvtDq`8TWM5auBuEEIJ)el%P$}E(1)r zhh=o#*?T#pz9>_-jL!{9ic7gDJ1*z$0fGD36cDyxJhiyTn7f(Aez1scS&v@!AR6Hz z?OK74qE>0f>iTJH1|P<1bRC}8UoywF2!nWHJsF@(F`wr%OQA0r@4^KDke&*yeG915~Fx$(^K9XLY#G%4@aA$d)ek0SjMPVJXf8r;8BL4bc|LgJh|Nh^P&wu{& zUm9!+I{8O`^heY8*Xg%Pf=Q-r{0J8{=6hcYwV#+Ap}6v?8rTwd`LpbVztQp@gc7~9)H?1$s1p@qB6OpAicf5iq3yP$65sq=hw zgIF7k0XJyn`e#v89mZJYz1?&HQ!W#b@c{6=$yI@IuK)&P%0duTU%;3VcA%};EotQq z^?G*@bpTnjJc@SZ7|q)qqiFv-1Tg}qcl>sAA3Toc@d+BZmcMP3`i@bbtaHw@9?#@% zPXMJ-&!r8RqPx_J*)oi?RT;wA5#-IgeF8eI5@uLJ53OuVH%Y+Q6z-ChnP+ zH-O!_{&g^E1odhH-8>U>j?i|wN@r(;vIlMNdh|gPh7M6u*ah<0)9$zfm;%nW?mV24 z{{pZF=x`&!vIbD-!ZNy*sJ6AxL1}kyZUI{h=>|}u&I)qlpzUwVXie*p<@#084#1$! z{zmP07pMMGhNnnChi=;;*xeXVp3vp)ngjgk&7~ifen791V7dZuO9zp=n^&um!il`r z)O)GWBcHtKl412=S=7ptQNBxg$MvW^;kQ#j<1rwG&)T^=Z(XK^{(!H&OK?m2rV1bg z_#4{7rAvdm8cbI!YJkT}z+#1GYtY}bN-Kb4cm6gI=v7g($_C$el0RTN$(tNbw>ASh4 z&F+2_&~Iz|m=)A9S+VFWz>UXy?RYkO(203(7lXlNjJb-*U97HN#`5AKirqGvkItjn z>QS1``n1O-&rUm5n^g)C;<8x4OUhNjRHu9`lv}NP(|OuqHa z7+`lD;W16`eIZsq^M$B=;ga&NQ&c{9$I(1{NSD%|f(*eI|BOv&pxRBZmw;5G z!(67_TH}0I%l-ZikoW=xX*>ZCE}7(v&J#ng#K3_O%>XL zB3f)l@S{7*y@SSXP7(mU;(^~{@^J})jNJ_YrZ+bLWwZxFaA=A9>g@R2dY6}1v3dW! z*t~oRt(PdbG!li>kTU`{PcYEZr;i|&V?xq)@+-J@S`(9{{&o6|PG9D9zmD|J{@Fi^ zzw>wgPBM4@hyU;&;&1$ozmax13e1*F-2df&`Csmz{LZDH1LX?>|EK@ye~Q2NNz%AQ z0095=NklI2d8%4<8DkpDG0Y6DE<>Bk0aXS--FnWX)>y`y;ydk_)X5kjm?0Q< z%L4GE5J#|}9T(FnS~*M{KvxW>hj0+d8Tuqzoinsp1pN@^gix;Q5A<>xO~6?#>qHaH zT#HNP#XS0hO0;+V*iNcxNiNU3H_KR{?dK#lJr;t+EP7D3dmm_?&^3bTF;h}9Y( z9burf)$<7}bHGzta-u%eDS^TpwD=c*x0_j%qt#)F$E8l4ruO_68%n4FN@yo!+%I;~ zUeuEHiRL4Gbx8*}jdU5SVV+Dm{7h$G(` zdZ7_{QIF;p@ZF>1kLs}%%;i)cW=|6iam9uJmI?56s`ZWW0En#&_K}aNDdT3l=I=|y z>m3($73A($2wU3=sB`zE{pe24{V5>8*uQ|k1bV=q$mYX1NqtS3b@~*+k#?BY6|l4w z@YbTQ8@)PU0i_XPqQa0)9-yDBa6$W*5Ny`o#Hz841(jbk*RkZ%QF}960fYgW)G6bg znmZ+FcDR9n{t;w2mc#@BP%3LY0~j<=ut+d7aB{5$UFbUnpZ(BiM#Wtw03w%RjgRH- z!R&TRzwfCtLY;9TDC|JPn&A5R80BLb*@m`t8?ot9iQ8$cuLsbBclR!B1NLVC1$b?P z23K@yrwwI=Hh1!90uZ+|`hQW0qvly0pS~HLW+SGzFPR4e04ViyrzYcbHRo@HvuCLm zC_3#H{on2YVEa+&G@=T?ZTCCOO+b~_pZgeac?Ym{){GKRd;GA^oTUzn5x`{?J)Wx? zjF>rDme8}#ctY^+1VqelcWt)XjISOb#^rbDAkcI9xxss$k?S*V+z^n}Jc0@4Bk-J0 z#?jo=nTG(-(QT}se-N7w-i!Ffr_p%#Dp`GM01RCQYIXahM;Vl5TsF#15%B?F)pjfL zhYuq6gaVfv=?ASrhUn2kw4onYA3TkkN+4s+rN15XHK%O?!`baEG@~4?YFriL$TgW* zHTmNqDG%2uo7*j&TVWoD?hCpxxZ-eFy8SgJl+X-ytVUKxCWxx8T<<6$?5`cXkW9oGaAdGeFaBRNACf&lP}-_IT`-K=u{Cew}{f z(zgZ5{_#Kl$MHY@kN;z`i2c>S`d8zJe&~ndZ~yJToq&_+OU{Dd!SwX$)3hu)MZo_1 zfB)|j_cG5S^p3z`|deRf$;>>3wKvFT2a|`(aKj*U6SX{E%Gos_?y8q>(Ph| zfs_wmT3>vQ_WUA_F3LE%+D7YY5$$K2IDU}>I4Y5EDpA)epi}S|hCOP={<<3T<_hq# ziv~h%KPO`y*dwQ2E$v?kTxHWTEf>`QW}7YFK@P|~Td_I;U}YP+g?j=91GUGy%Yy~~8T2s=v=@Xf!} zLChVTC-lQ{H!YnS$Ewx=Xr9(%bxhk_1_F3B+z&y$vZ1?B85jFX)V$!_8bNab+Ze(9 zUKwo^dBI=;Rbzg<*++LyySv zSy1Gd5baA&STdB zkTn2SJ;qX#dL4CRZhSU?(0T)1=x^h#70ioy=V1)n=drJSn%|C-g0)eh0UR_dc`RTJV$NHU5$L_PA zjr#BS9Z`ShowVapP*S+@$XJW@K0p`1cXWj0;8mL^Jgdczd5nzH_iX`y zSqfU-ekX<0SP5`l22=rqhBFk)^2k#HI#JO@LW?TzmdWiy8OAsk=_j=RO_DzwxOq4T zMLp5GIH z`7i(Fc<(*5mfwK%-~PA%Z32Kl@*_WTf9G}j%}O#6udnA1;XNJa`T1)TzioApMw6Sp zd>P}5tJsXE2d7(@f>HBb(lZ=H8$sh4kcVV`Fb)J+g=e&p?8x762PI6W*MmWmhdUp) z$(b1dLIrd>Hmt*9G>(xDtZ2>IdK1&D0d*K6*93A1!&?O5avh_=Febwh0-+X<&)7mM9rAVBH&CtTDSF@&;Oak}O*uz;Iu zU2bEEU~%)p3L&7?$xsKazOk>40lqSGfDyc0=lXj6z)D<7-14k_E;!TKyqs!OqU`&P z0WF@@jq^#c0b&10=4lJC3mI;PJCarNHIxcTscGG_oi06Oh!L$mgA zFNM$0QrgUo1IBgKf}WkIcbEqP;VS*>2r?9dmNfeZx-72;F?#w!>e62NbvBFPa2RvO zy$5W2=?;)0=F`9W7tP$*vYJPSvX9OlF>kWCVD1f>gDu9yb}>)kFYaz{VuM1`V7$A; zpVz4eK-Y~lI=j0;#lXeYym~lmw6clTX*Zguov8H~w=Mk^0m8QD{50AK@ZKAbqW$O` zAjbRwfOSrfnMciNc*dh30vh%JSLbg$N#|60_N&YKE=9MR?KH>U`1EJv?f>T=j5ojU z{}!ibkCPSAvDxzZHo$>8YMtEAK*ysAK&{4j%b5GjA==XI9LFB8Y7DpD*;(?ntMZ+m zjqclTN9+53N3xWaax2&ZbY>#l=P3-rgn#_JuEeA%6O&fBJPuj9+&` zc*1-=Thg6onZh*P^>Vk8mYOqY+q%DdpY-(s6jd%?MwLnL?#ZS?!6myXOdvmwyhIpm z5h@iKwx9>$i2zg}$t(<4kncNmeoomkn;gwvE6xK1654O$6s8OCK{0LKJ&p3_W$d4Q zJ}MvlVpLxKO4RpL7}*doH$rf2(7LCwv6{VFw98K9fQI@Wfm!P$ZGH`Bo9)ZUmuGR@ zd?$LHPs2K4-Uj=eUB_a66;+-)w0lOr0!qM04>5YwAn)s#?+|wRJj(hGMo)X{Yk=oX ztX4P76QEq>l)CqLzm=99L%%J(e4v-d(=-8qJ2m3%II1w=`mUg!+h|_`C~k{r-7KR6 z*yzmB-T?(#6Wf3otv)VGRH?Bs!{pMd);`v!0G=-O@9-NW+ygWUrcDi@TJYQ!Kvn`w zT1K=ttcJ^wS`bTsfqg|C*1ZCOrvtvH3@lR&rXAg~?jWeNn7QOPqaEPmw6sb)*BgN{ zX)2RadE$C<1GzOm4xlds|nlt7^!s!fH+S38-Hvwo{1h`=; zc8m+bK*b=gPnt0~CYNlUtXLI*g;qO7sb_$?fugmiKQ@clHWpFUr>?S$#+dP?{}uw< z7;BXd^93wYXFQb5l^tMe-3Nr6_hWVDIg0J5?wYZ9s~7bLl)HHutGf?kfA=z40JGY~ zbyNYlwb4A<7kBj8h`yP|tU+HqJdWzo8}vaZis>>6>XY|Q09UOP0%H$IZ5VOC*^7Aq zb{hp_6UAZ8SnoDs%{@10?Dk9M*(fHovHjo_z*ULi;5x?cKs-K*%@M%r?2TwXdM9?> zdK3WQ65v`ip;fCN)$jd(MD5}CMy3DhsGfXJqqU{@eY8a%^q=7dGSFs z1$8QKlR3PC@%`W=<{r%j3~PVxcSbyZE0#I)5a3k#-p@w$xSz^e>*MDVrOJ|iVW+3+ z6A?z^|KK7zR}&No>Qik+w~CTPmMNS{jXDEdm!E!zHjpR4Qp-~UNm`?hj$#k6shsqp zJgV~>kP%u`Q35ml6KhPu4S;v?A~x3m?(64K-d-@?Z&MlOqyODUS!qgCZYLNwfUo6! z6fE&Q#d?I+>6oZ{g-d@+mp280F46A+hK3IEI4rGW?p$5do-J*!KYpA7;HKTq=56+0 zU5D4{S4|&VNQ^)6C;mk8A^T_&nEjQ%@>k-&`EUN4_?^F#$?!2Hmr(!WU;K;lKl~5> zL%P1x;PAI&(hB7njc}wxuvdi&jIbyW%7C%7)b-T~p);RO00;<5eG_ChS_=?VYZ%|+ z{r6)z7@>_WVsbl*wa1%ijh5-bCkFDTNDY4 zH!)~MTBluKS@aMp{nN9=fED*$+SRN#06So`G4D(VVMu|+U=oX$gD7q$QBDxpLxjQg zET%UGs01Xd|5MMcyTr7l>7cHFLb&Ltp6evu($@@tMLAjSrNs!LuVszD^DaQQgJ5gr zF_W%b29sRScRI>@#LyNVUQCvJl0U#E8r2kPA~A3BFt(Gx#Lciq)E9)gQj+tV_0gY8 zOQ-&5`+G*O&sK}FTL@+a&SouQyZbzw_+X^E7->xP}+0j+pj?v?s`0fr0* z;u7jBzcU4kUE_QCioUpB0%Ul9p|z0w*1I&0t=EQUTneoLdfX&nn>Sht5u)a+g~F)+ zg4Y#-yx_AiB!(6{4SqDV35^BZNqMR4058T01L#3xEl&l2WKVu8z(RV)(Xycpb2px> zB3tT@WqI4Qv$Wi8Qyq+EU+8@9a+U2up!zRa@S64LpP$6(TaP0@YDe#=8$H0yF+k|a z!$)b%t`@UcPp5#?Z4`rPOaNjYo#W=2aS>u`0`oIuCHPV&)d$|$x6u3ixqrq^745UFP_Ive_27+N0QSfS+|`>U-g@Rr%g}# zvuP^7Pj$d(1%9h1stQ6niGO;`_eu`|j_2XVd@ozx{9VQ$O`n@i+hG-;5`&fqzvx#DQW`-VfaW z^q>Ay+KBM_n5AzoIb3A8`hXb)-1(bTdob)A;qi4q8Ny(Gbsa62EnsRX(I;-5ueqa1 zkoNZ5NvT@_sLH`JgahStyYhVUub~0g({kGm!KZm0)Rg=tsxYt;p&o#wss}tHV3w6% z2AE9IpkKy%@DheMjNXOIXa$=0tG#&fGLGK)o@fI6q69@WHUN`x zv~KTWrB>Q+Quz;L$UPf!p?|>KMni-LQ z*8xlc#@y*v^P~Y~H~`VOT3dv0{rd3iYTsA~74-p`t~vzh-K`?`h^z(m(;C)7;emGZ&$-;&1pIB{Xwr(= zaLTY;CdDDEv?DL;(45nS&>*4>Mz6BJ3bdOL#N@Ju^=RNeSUR*|@qx_ps(vC%syV^F} z>5z1-l2yUP+n-5%BZZLwbX9d^zq(+o0kUjadCDWonDtQIg1he zqW-&nPxRh-Gq%uS-9v#=xvDjze)=e?4?aa30AqO{{y`t1Ol1bAWz4&Qs9F?JpcGc6 zE8xq(vODBj1v)J0+wP}79o0|2!(jL(NlV<-%UIq$i+UA+4UeJMV>kR_I-9gnICf5B zhq7BWz8WALc+W)OI)5|rciw?lbs3M0;a7<|c#yi5bYrObTN2~YDDgJSFiBX zjye~E!6Ed8n^5>H0Of+U>RWF`+ar|p*L|J7Bk5y1W=TJQZ&aX5gf4CVMkSXbzuEF- z(y*w5b@aNrx_TYa|2vSRt4z|}lXVz}m+{69@oPT9I9?@$(3%0-fI)sq14H{~-2oTn zUA+9@7XfA$N#kFa0JD`_;)}=;)Qb%ON-bW#gCNynBZIC0!Zr#+53U9f)%l^^89a6x zGnicEs2$tKwb%h()`n=`tkATVk&V`9vM@#R(dwaJPDx!GTR?}#M8}_Pg>lwP7_Z?H z$a@QF>UK|%o5zr*0649qP9}yh*sr#+E%kSsMQumfGo=45TK)t^tz{B0<1SZYdI>&^ zIaXN$aM6zQEg(uVAkeoRs`(Hy!)KcSA_1K7zl18D2^V(;$4#BV5IM&I&$-Z_nqM~6ZC zaz;fK2+7JeHnlSL{Z^u%ec7low|2E?PqWa-6Qc!Vp@_`mpa9_@vb2Pym2N|yxWjJ; z*er9}>$#@IB3Y8dFe$@T>@KGOicRtXbPZeUWvP=IgqM;<1KIXMN8`Z><6s-}$sjU=;tnRUd=5Xk2B5es&G^bt90c0s^B45d z6r~3kio#{FZUE4QLZ8Y2?78U$rWe_#J{7e$-Z(5-zDksvG%ug^(jegOYLwE7{$hsW z0pz)Rc00U|)rd*y{DD(wG4m*+b`u)v&qg_A!Pqi?=$ahy&C~o-24>-&V&g=_~f) z#f!9T`u_XxCx14VOx=z2&C=^*mcEn8$;%{8u*1v0zrlothq%lrIMW|6O?))awZ0%| zYQ`+m|3d$oVmgn!1USK_s;3WoddfR6_VJ^0MBTq10q z$KB-z@T*C*+O=2(0&F=(qxOgzirm2zH&X-yLPYIe3!NHx!PseaqO%jc6v?R{$OuT} zrL(g*LCc<yv=2nME5xZw$EIbO<JXVfY!RlCzx>!z{K1P~fdRyx`72iOvD!_6}Q^ci5hgP5mVwGcK0 zmiom}$BZ^g6GLZ67vrv`AhpzuVZ!*JdMC6UKC`lK5vU&6T?NE@I>a78Uaz$P@+cez zK6MGMTZ<~7q;k|{Y)}@U-!L8qOU{OySlt2CXPap500e+aE9<71{k@e$UpSYejXmda zSyh*;0R)TTfHAv^uJ4Tj2-MpEyvE0JjOyfG&+2Ijanq_syVr?jXC6E9t2f=qLVr)@ zu}03XTa2d(O3`)&ja5Q;&J6*8Ej{iw4XrcuafkOF_F~?l&&x(yhHlc1A@f+@xlSKt z-OW6Vf0yYq@5lP~E{gFW7QJRtP@E5yo0l`3Zx@r; zTwO*3dicJUcu#K-$fPqL0j8CsUZg|Jdwc@Q4&WzoxkI9#wI*(c0;Nf8(|5oYN+mi6 z^A}nJa+iR}?R1bf@>I*TPn>x}nT$clX$fdD{D)`Zri}>9eJytzK-h*p)z{X(b9pxd zsAY@+ef9)gE>9XGE%}6fH2Jr3kM>mB@Z2t-Sea?47MY6I>+~H-bUBe&B5ck-O0lY(v1_%e&}}N`&w_8dEJ=w8h0hw??)A z16d^sgo3_S?GiCHL^>k`jp?>qqy3i6XzcsQ-wrZ$$uj`Lq8+WfZ8V0v=mU=Sm*Z%@ zhmg1#M~=|0AYk=e7AjWqYWhsk-~j*vBdttZEmtyc{_OxD`6i1kpg;?XK4m*CUNGf? zeD=r@4^J);l#-!NduxCz-}bYtQ4a4YxT^r4)(v_4JK)A;IfX_|OOcLafS7DTp6-a$ z0tCpav=Cm4eY6LB@=lY&4ILtI63lq~kK{_0GX(XrD=^qc;qJ*U{n6M(-H7^troEOb zK5Lf%AH#t_&ejwG2Ef!>rKQl*1B_W#1;90pv$o`Yo^_bHNg;tiXtn8a4|%oM8bn*g z&@Nq4N_wVBw^f&11t2nOM_X$JpeUW8xh4OJ)>WQGczL>jmc0&bn=S-M3?x zFqQxafDU&AZh2&n;t?*j((Vf1-Sw)avtS%*b#kQSm+RO~ipbskASk0>s^bDcrGmh` zgg!3I8pwBtLSpDa_rGizOU*q3o_18nfXS;dU2VTY+tQ=KHV67iA5T%xHDFOhYYo7d z0*|SyM-sV0(QI_XP&v(KFQXjaGIsbbUjb;u^|M%CK8t)nhh1Lt%!g6GyNPB}W=1Gz z&}Yfq8(q@*3&zDX=9d>y4yV!WA4g?0O@5E%4uIJOY#euD(VtYbcTJ`~;Ti&frr-!pSX=6*P)%8_W1wDW(F^tQU*=hxNX(vnmzJG*5LOEyW zfHedybf9n^zH}U9S8u|K-1=rOTU}oA{+)`la}>AN#R`73OvNwo=JJa$B|mIe)!L ztAM*RJ$ueA_lwKRSdPbO=h$*Gj?wjHtYNsv{SI0=8k<|~0W$7@(w9RAUw1N@&ulzG zyF};%*w!%fE!udppdi(Y%?hwoCfsOP1J^7RDl#tpz)}?Vy<5ENFvLc*Pr7mPsR!}k z?MKl)?L_OS72Tt5w7C?BkvHP_(WB_WaC`j|emjYpQOBu=eu?wRG=>+~ar68#?p|KU z=ynjR**sQg(@V72`FtAdl}`WAaJi28cnZL*L=WKTj?<%VKOUYvj%L=3$pAVG?_vsF zZeHs2dmA%A*yV@M(;0)S(JV@YgWKb$fLR^Il@eO`bgEVWCm$@DEx>xkd;QSBR|D+0 z^pPCVp^HxZRe)^<$f+T~Qyzj}0n!F~>Ri7fKi??;&@k*O&usZUDB1`Sh~)deMW7i! zi+B8v=!h;fwhIg_N^Y@6cyA{N8$jb`uK%I_ojR@4ZwPXi7U@XF8vO&1^HAw(2B@L0 zjg#iFLF-u=ON5seMlFP{rW)d4J136`&{Ur~t_{yEJiilQ;Bp+@&a?Caa#=8W+rb6_?d$JA?Z!!sk=Hjd%558{LW`2UTk|K&eNmp*9% zG?#aS7`*>M+`-rHUc8`BN6}$i?RfX%{V&AT%MW5UyrH*t(72ua162@OtyZ)IB=s8L z9)Q*DMCbf8E!Rn`?RvrddKtG*pT%B_B<)SKm`s=_fL_2)F`guglRjh%HyY``Mmy_{ z8~Sg~J(Ek74M33NcY*AglJEH9A~pb|g1IceGrc;ToS~eSb#nnI{4LE;F&yKp-RT|r zgN~Rlrr@~&;~hRo-^qUszvGxI;4NCf1h}sv54gfmq{GAQMA~_O2^B3{425a{K|YAWxI9H$Yshf+F3Qa%adrAJ%n@{MwdskJc(AN z5ykB+ih3*Roo;L&?4m(=0TW+*{+DC>b3cu+oWM+{QALQJoSnp^K8tmA8}-w6?2g+= zY@~np7~a2*O?we}?-b75jb{A_Mh_sko&iD;mRUD)KvTZt>Q&-ycpI(vZU9T$SZ^0m z>I4h}S0KB5u{n|{BZg$Qca%q$RXh5HF^Oru%XJ>}vu|^w1sxxvjRVAr);2Be6tg1j zhN}UBkFGpaeT(4Y9iG|Lcd>d1$m!BgN4v-Y9EQn=E5N`7)gMALt$0b!9hM}u6qTL^ z;EEM_wx=}&ZV`>UN?Pu78M4yhJ5tEyv9;}VskH-GX>r}ljK!xSI!BFI)d6ihZJle3 z1Ec2~40N|Bqd5UsjVXGxiRy?KX=^$~00EQ?t6(q>?k>8tK_I@QO%@=%Tt=ndMQ2Z6 z){?+WORN)d8=z9_(gWxKFyB&dW4Y;HDBx81j^1E=b{er{{OwlMPaucTHPC1#D1@e2 zGfTc^;I3#C)MM@j4FG|8e>#tTc^vr^;Q3;ftik0m-~l+B-4S|$JNjc!aCmuEA^2%xbu zsRH7(wo%C);FGz&KkdN-g1H7L0-Ww!&>P?*aH<|30a_-}ngf96fak$1R?zFjNcu;Q zVly1X{^f_UEEa$)2GzsEkw;386=P}hIh3W^ajYhr=-$j?adi`&r|>Ka+@i1*t68*L zd2HT!Gg**kC~rp%z~064*a2YnfTDWG_O4l%E_8avL?^*Vi9>DzJiXGZ<{lX1u_ z)B|0gKYyOSduhe`g=A%veU7HOvy=0JARuW6RkfSyWne02NGTkM_D4-6reJ9>F!;!1y+c$c|euKX>xN z9$E3(ecA*9i2w2 zeG<)9A5hamLp_P3vxhLbqu5sJ06~BXQhhyL$KYm^?kNn6Emi`X(0SnS4N)GlnT} zd;H+#MaYrzM0$*A#^ouJ- z+n8O`CohVay_f^?4jT?!*0ZfE&kbDfP~ZVWT27!}wn5+*@?=0z8{yTWTxh(ZY=KP! z@Svu@Tf@K^7HNrd3s_jw2kws5c^=I_S+;mCnKS(4dE{kljIF{m~YaX)MN*7++k+!eh=B zO8__JIcEV#(#ef5)4^R#P&8(MEyG7l8T&e;uNNvMD1ZEO34QqTMNFQ5m^SWgS4+x* zHYkTP=ELB{%NRX-9v%yK^qhU{Csi!o$crg&d&{$|IMITmm$R z$cj0$f>EphyDR3gL6x->Ybjma+(pUXIpf?u%b`IDn9{nxGEed{0J8&01#->3bWD@& zbX9>ltP-nL%asPt+ngZ())yD)$fG%JuW?`Ax99ymW%;fQzbGQ%DTK*tfDg zsXd1Fc}&-KGD(-;Lqm%5=5_j5(zoSP_P74l-->_wPycEB%+LHxI{(rT75Z)c+kg9S z<6r;le;t455B;GOf&Y_`Ui*}NourltQ!yS+L(NXQz<@OUD_E;;uh)ly-{865BmqcP z02Ds65}`7<1}N#9R0Rw?g>ids9NO<3COR1aEG7tM7$h2a`}8RGT{Pk*0?ea*I**ZC zh?5V0F_wd?sAco0=}_BjN4)|l0qo3c^Emm;I}xAy3;-ub*a1$~lc)nss?m*&%WE+F zO^)#Hw4>R1!rurS1ZPw`QEBwiMBk+Oov4i#;bGj3Sr_4U3NU;WMI9m4u0^}LiYWr2 z`TR1n+j-=7FtB6^Tt-_f2~57KzcQb&9Q#16o!KbX7eB!5M4#>;|P{fgZEsE^4`rjMKs{BD$J>Vq}kNAC`xFx*G~c_jsDtpkvxr2vm763|FHA^Gl7k%Gic5FVvu z-6XUCGl4^BlHOZTD9>$Mhu>^hXTZZZWeOS5pqx|>o~en zQvJqAPKORmXM*L{TFNc#-&u|*e<6HZXI=##T-y{hqzyF0O)|lt;b=Z z(2Qswc^JnpGlmt^E6FhLG-G>A8vuY+0J2MSOXf{}#hjxJRRD4gx>1-mOu$_;askbB z5xt8wbkTCi&kS0ZR6VEECBh7_S=E&3c3A4-?#(98u5JfBPaOreRJj7^Yf4Zu`37|{M@WB}qEWo&u-B021Lw20C_o=lLr`34}H1GE;sR&c4d6;?YBw5Mjb>*?4Dxjy!$@S1tRu zc>35J{l|2uOO)@v`)>TnKlvx)v!DHJy#4mu@yE-|Ng&c93oIZ0qHd; z`#K56;t@nun7dBl%%=Mv4s*(hwqtTvVQ^`peZz!Sx0nEO(Aw@};MBN`wml!cMCcll z0YG(q5u3{wY5Am;H&Z}OFhS%n9wU}lJje2y_pWcFHom5x6do?KAQ&ls)c~aEGjrOC z-lyJyY3 zcJX-<&6D%kb-FOqZq$#C0X_O6Uq}50U{X8LL&RgR#gZ&q*1sE@%`6F~AJiL7L$D*zt}3{WRSMYt1EP(+l^Ga>4$Ge|ViUBU6*pl+y2vA?>Dg&`_z z>%EoFkPik$rQcYw;(V3@2|E~^^u0>&1bCSdvD!`Q-Wb^zKc zifMLaI1|crH~8r}&-0rA2|ipFW5$v`tMtLPh>A<0$krvlZMf%ViXG3l&fsgjpR~Jg zmgts%KNlV^rxH$Iy1RHs8Rgwo?58M<^wB=~8`8hYft7ZpiIs9V!eQcoY`@jGD<9Gk=-<<&1ANyl}EdHzi>c2{V|E}NlyW$6b@CQFJ zfXwt7lzp896LRafQL=R+{R-ga#B##9q~SK|Z#uc=F`Z~$hao~+1!xTJ(j?vQrpQPp z{RE*iyp8JU7NG*82kezQBc?CXa^!vr(}UsGM*^OGY$kUA7#-D%-mv2Hp?FuW3m+5qs4UR*_f`!e}@c)sQiX7AEVjqk}{3axnygDur^^{;X# z90FFM-l#NTqSR%Jpu3qw8*+jHqd=%+&jG{Gc)Lg7mIx>WY#rgQua`_%gis)Wx3pUU zr4>R;nZi7#WmL3|6|@Op&;Z0Jz_sLv>5`d;|59{&d1)_;^%?RTK)PQ5%6KG2+m|c| zT7#pe9hmcd3VWa*+p5?_4gk_RW>8vzRkRV*EyFg*#1W>*u__>?i9k2neR7;H%-cHf zI|Vt{6z~es1Ol{}dG;oKT%#!l^=u%hEAF;5&KJNeh1&pBxZ`p++ohneX-v_Fc4Z2l zOWs;yJ*r7Vk$_4glEZXE`<1bVukaA`y}J~<^w}{2RQ^c+crrk30u5-hvE_CE7MGRv zm(#Mg;iC?KF-%ATYE5Vf9a@0Ky#SBOd;FJYlXj-41*nDfyF>{w9 zv~e?m1WIQ&3M8n!K6%k5Ux70B{6|Yrn6ySg(?%^Vvl^Q@V|-QnwP+n5MYY{XW6)*o zeFp%h;9LL*8AFvhH2^GWHFLM0!ri59Mi_NtjG?p$Xk5l?RO$dZ>dX9#X_1DH^{i5F zpHaEk^8VH(`Cc=feadK!g@H~1u1yH<(kVotPifnNIScA505};m)X%SZ3y47hl+Sp+ zrZM`qo>XxMP#Z6zn9RbX)~sV)e!;jYwd^@B`rX*Pc@|Cn8Q!G{NR`gbqMq8^$XftQ z#__6mK$PRdfZoep7IS-p+e*GX?b-CtpO+7Wm;}rFW`N1lrZ>f%d=vU zh5G9mfHr^dkTHldMt5fq9`IW`DVyucUCcH!=2$0=Lxp(EQr{D=QA zMcMz~|M&kre&Q#7BL1)c>;FpEpZ?Q-`u?epH@yaBUneoSN(IDxrjY^0uec@&7MQfK z>8KwcAC@n_Xz7QE~W?&^6kMisxYhhC^iV%P6pr` z@6r;77Ne|w5)GJGWi*S~#U%n`n~qhg0FVr2<=J-Yek;12)7Ur8(dJ;f^*&$=A*BQN z#Z^?FUqt1-_hbL^E;^%i^g&BUBiqo8C*9A&)E~xf3S&nG?GR`U7<6rcR;$1O?1jsO zwtZiZ+3o1C>aB@>Fsita)yR&<4yUs}p|;b}|GVZzL<5AvS9IQQZPE zv?TG1){nHS^@uul88eV5`E5~EecJL){QyiMkQS({K|g>i1XeC%8gw)Ts$G#EbQE*c z09MxD$p0;YErL&g*9O}+?<$dd-X)~Z7u1J-(#n&paK279)-{1V@=C|=@R>j=ZO^r= z0k(iCWX5mQY1M#M2-(e%Rwe6CJKUYA#jjn7DfM&s?+rhK97*-zH?2svKjUT!G|GGW zk5vJX^@}oD^BN=aFfS(&b*l1gtxuoRuk8__VGZEociWtnQSDcRoT=m;Y39M+j=9~$ zhsaMLjv~Iaeb@`Y)+({~!+5wah{B`D$wB1l2fqfmYUo*7ExQONN_)I*0^Roai z`jql&bBN_;1TAm?p2cbzb;fW_3lreR5Fj4=)th7huyHh(OXf6Xl|{5B0NVk4LEuOI zQ_Qry3CKcuqVIO@s09?eyV^Khke)x_8O!P+Zrh4Bc6wq#y)CCZC{2-1V zzZD*(RS~oSWCTuVAyJ;dW6n<~p-d-=v@xUDvzOrGKRWembjV(oaeKIE03o-j_ex&hb__*+139e`G% z1lAiZ*t!Gin&7sQub-t8E`du5nBM5{jVjVzi>y4o0~pmNg4xu_7| z_q1h89#xek?rC*B?0SVh`pzmhsApm{->Jm7Jf!cz{Lq7fUdx~H!OZ**JD=rr3&E7wW6saYT4la3N%qPeu=@|4@g7hJR1JO)?S zaZO%Bn3>jwT1FWNYq$0L_Y7^j9FF7m#YJ+=Z^Es0RDSt|r*ZrADu$QCxPI?NTz`0x zEJSVN2FVb0tfxM6z}HmG4?3ho9>zm@@clmsSX2n^qSNU{6QSWUhHY%LVT7#)f(@+} zrsQtA;q_h20bXu%cZpW5w8?WzwC&l@Pz8X}?IzJ!AF%DJNITl3p*zT02svX`H7L_f z1t}&NeWg_(`RM`Z1Qf}lgZAzck-OZI!@i)vpCC!nZj)aZ!Ut_zE1R1lyo}Osf2Czb zK@IPvQyIv2yC@O#6bZ=DIb5rgWoVsRlZR(NCdi%ur?1c!9`d^uG|w0Hh^H-p%sLuA zK^AH&v|b2ow1)Uw0pPK2{;mVP%bLa-E9YE*8lB%gzUkgLk}O7^KHv*!S&{lDfzEH{ zXIu0$^H6j*TKKNND=XXq$DL%wbn}SqN!C1R-eVjBj9em4zF^$fI=q@Q7xW+0`U=S0 z0HO_9F&{!}+GpsBLpg`~IWD&AEo0Wu2K!h8{#R~fpfAkNI&1OMI%%KQ>wqHY;co@= z&N1bgtL|7d^oGD%OIVZfdGrt<;jTvhE~rO3eA>Q*@P@UR3`Wd>Y06(wwPjxD)X%8l zJ7WyMq~*);CjC-uJKE`TrhRYO$~ebR+NFxHcTBnjTd_ZxOEZ@g$#ZGQ7W&JwowIf7 z*Xp#Xv%YqYs~t*K5o;8)HSZWd%Rb-nIU9{)p)afahQ8OI_3Z3C`Y3>o$;o&UBZPgy zyYeJ=c1~{wad&e=`JUrxc#maFM&nq~-q~~#)7dO3m4dVyz(mwub0Y@d7Yo`=9yREk z!stN1DRmg!-BH#U#J)~GXzmd0oSrfkx+z@FlDT_(bIqKdzrh zk&qZJWB#Lm^pDc= zfARgOFV@kn)?;*i6Yb#|t z@-&F#K2}WbWlTT!W}LtCRxFxt#_^-au|Iu~tQ)J@B)XNe*uc1Jzw|RP{qVzRskMO$ zO8p|{LjcP<@^{*?hDmHS`5&J}>*Orf2!rm)aZJ!i4KI*w-OArgruvF&dNADl84USX zJ|DH~t5{bqV!F6VClYjFT8rI`_8WF0qJ4`7e``#uTC{uZm^YTmho{mKt(57QnXH(n zIA5KG+9X1`0If(&@qK{z4^jj5C+;E5oC%*Mi6Wzg}qjM@k=C8(l3jkz(! zc%Eb+Sp&`%E<@G;AALYfH-~0rRPU%Kd`ydzfT4+y5Y*^f=5kXNK-E}e&}ti%BSSI3 zd)r(BwSxMeBZSfZiwDrjwU-HCTnw<-C4U`-e8r{A8TIu%O zyD80Hz(`#m(zZTjofM$ z7?;$^ot7?#x&+vOK*oa80;#{6XK1>y!|)$k+&m&@yXW^671Z}mNku{KK6RRnq($U?9%dF_x-sYVbFYMG{CL)YFnG=wBd`=hSE8vLB$H@mYgD72a=9o{C%9 z@5Kqq+M2SqhSHhL85aO#=HXWVE`>hxasSTa$UGqZJWJmDRy#_7jC?&Q z765|<&vj0FfWcv`j=PbcokRm6esg&lo$GlD^T8yI5)h>2XZw4ljP zJjJAlMm3N5Y@3#lH;({rWlkS=Vh0Uc)Y;vfj-A5e-W3t zJs2;>W6UyuXstmXycG=;O=cq3w~(NP@!%TSBKEX*hvKZW|CaB|^RqZPrG4~m3gF^8 zzAG47=sxpl_;@W@$+oo9G5HnlE!5Lp{e~i{J$P_$J^MD3AeBBg#K<0*p_~#_x{<5m zYEGL8_>n4n-05QriSa7w;Qt-KcCXXNm6%|y{!z4H3f(79qWkb+is7Ts|Hg^_RoZQ4H#}4&eGLS3udV%-D@31Qy&Q}LSucY6cz}>IZO;8-fZ>Iu-nP`e>Fkt zMQcpK9jR9j;MC}Clvc_WOV=gv$6T#!o*V!*_on<`V!} zIg0|#*fR`sSru*TQg=gjtYDh6rQ78JVJ_!sv2KwEj8li@0F*#$zYT5IM@^a-TEXQ* zcX|m{oP}Dmv~T%$(xAb;Ei~Osi&&0eo&Xv{U?5VVA9@5kd`o&;*`0aZeus!M==Ck4>x>nV1T>iyi5_P zY>zzJdxj+UUFPztA)s9;-^C<(cp1X}xy<;QiF_8f%0IZ5#0L z7@(qm5#zfG7(6^n?v$aY;Ec{$!_Nu;gPS6>7?ukMEKQ=Gn=xO~GNJxj)`#j*r>Yp9 zdub=>8jvz^c^cX>M$^IPlwmBfyyNB>%B2k(+USAcE^{?{tz;qA4~&tMtjP9_z|@_D ziyMR`b8Qb;@SIl9O57uq4f2|H-s(e!z}E*eSrImy=(u!383qydG=o|j0n1f!$x@4> z9YI?K)GhgyJT^k+}_5L`ZN!XP5mwavJc#>K+Y*k3Z;TBu|iMF`$!TC=tC-SOV52L5_Q-B z$ks0XLi2i?{$Omo>fo_fg1~Rs&hN7H9q}pq5C7pm#J~AB|0XS8e(=Eu@hiXbD_^?4 z@B6;*{;7{Qz4j^lCdqk|CK+^L;;BiezrVqJk}-M303q@8S=2sw9vK>TWpERlm)Ehq zxJ6L9Y=*)IV_Cz*v+E&(3Qb-g9RMHFH^pf>K04>`GXyu{5DmHM63b*9b%cRVwL8F? z&anZQ37EP|FPpPBQF4zGbJ!uYb*rjRSFyahjQabZhuOizHUJor55g$6a~SI!4R?be z0T3~%1A)>3?SwoF>ahFb|2rn{{{nTnMPLEQ%2DjE_&%H^0bQdl z9@V7KPJTiNss@@{aSYQ36s@~xvG6O8wgG9thHXU`x5dS4AvvA6lyyDXNJWfOY7in{7j1< zbZF4uRYr`P?C1<(ayBI3Yo39!%iQg zbpEF8Ng$P%po4Ys2&l9hkMZknMt6JGmb8(&d5&m2l{Y{JRn7)Bl)`_q5;hsOIm;s)e%a7nJj$*UdAL!hToj3pw%rXdM(*Fx$!@gBpihN%6o#XKuKT=93YW`> zc@;pgT_gZ)EJTkEskQrLK>HXw1sdk2!GO^s$nRR=Hr5*jBR@Nf-SIg<0{$}6`F{~P zzgOU2hM}l>SUF|rcN!>z^54vOd?-cErTTCixk^B-7wyg&KT|$qtqw@Z+ow_IUY#Gy@T^_ChUilK9NZ~G9$P5czz;5e_x55pg+B#-F zHV-k-PETQH{J`Zw>lH&M`M3SVxG_)t-D=;GKk~Pb7(5;~RT&{eDE~`GNU2EJue3RR zR{@dNeFDX>4vTB1b!}EqM3tMa-|> zNjgL}}aVx9~bY2ofsU$uX_@MK5&{eZl(`8; zz_q1*t1dL_H)H?!Bmon_hVxX*U(UOG!Jk$*KvdIHM*xw97RFX5`p2hfS8)9f&@`Mg zM+G^q)6+k+uYl2|gTMLAPx`T5&jC_5u^o@N?xLKJ7}C`5)TsK*m}&>mN87D4bjnWP zivXUAR&v^=-y$x`rB6co*g@G3{m>7^AOGWjJe^DVU;o$tbqX5$>iVQO`@aTd-(K3mkQR$6pA&dd z5ykj6ii?-gl%ceb(*)Aer4Vc(GVueEJw*eXqgiJF zylJd20gRUugaLrhHs1lXW*`Vam(I(Ez1WbqIAQ_l(^)s%_ZF#S zxdRAkflMI8gULnE)*}rap0^AD1^-XhFqe80h#=dPEXVKW!+m#LCU(uA9!OnzELBx>Qrl04!P-t&cP_A7~-CF(xWs69Cc*mfSXYm%mc~y5uOnuzJSXOB)e1 zBybgiPx{DUt^V%vv;PF_T5R1kkb;id-wGmVui$gsuIwB@k5#Eal7?!0F_t%HKWyIlYPz{|1| z2!Zy=DhPnuqpZ0Lc0ZjnpO?%T1izs{I&g_yJY8cTI`0{jv=0=FqNkN~53P4RQ?1t1T+=mRHkiip`HNV2;=$zw z_86Xh4 zt!Q-5V%K3Dk@pHAiWmZS@GeYly&O{h3{U`*a2XQ~wNfr(bF=~!E&&8+>+8E{?9f;{ z{g4M5SExjS7Q1Ck8r#Uu+R;YfEvU1x!s&)0(zpj_mzmqXR|^uUlL4RIfRL6uExW~UTAyc&H0}y2?ee*nmKs7{08v>v zIPC%PB!CNLZo>fFGOpKLn>UZ5@%DL?C#_hMM}4Tz7tf+Z$u`<`HwY|axJdNdA}cRK$C6Sj{!_Mz_L|~CJNnbG-O^EV3StVdac+2+`8w-QMq|PrY}B>*5)L7 zjeaz?^{DSQ;YkCHm+-?eybB!?3x=kQ4zl}GpNswHzAyFy%ahX-Pc4BWlb}%-kGs1l-I3{P z1?5y-mNjgKJpa)3zqVxE3@74Q%s01D4oCFIj4{n%_P%pj+Q}x1aPW`~AQF_s5_8vwt@JkN@NUh&2P_AN+%V5P#t>{Du1`UZ?Noq$By- z1ASq5$wJ}eLo-rCahv_Yf(Fy$nK{gBaC3!rHbd)EYQqE(h#rQWwHi_PoIV)aWIT%N z>zi)rC zp2mw0-;3K@7$K9O<&h@Tx7F>$$)oc)d3X+}RLUcG-Bym2T&E2Io6!GeHq?o99>eRa zbVlWh`b}X9!^^9ry{^#MJtuPq7&BtJmVsB%wv#1bMLS^Ti^(h&W3*S=TDhWtBOF%WVMecT#=4V~L*V}YQXj<@6k za|LK_TmrNv6ry!U@#(UlJ3`IB4lwwVg~~p#ew4?fsR}7^-|uOoilB{uJKF8iuyk9e z@D{X@x_iPwN!yaI8`nOCh*)* zOZ0`d(p0cySb+q7L2U*dwO(5Ll9h^Q{VsT`fuvn(HSCEYIkpQsfGn*u_0#fa$P_~n zxTIxH?yeM!YN2&gKngQ*KPDV^g3`lzkMxV5nr5C`?9^)JDgTyM5D`qI!p= zRGZ9uTxn^PPc6gFpL8+-eVanVFm?rB3+9WVFq|U|=CX=X)cc=0P=jJvDr#2XfaLEJun4xk$V7-z|%*gQUt z7T`}l)_nXpI!5)UZu6_FDBgQ7u6ce6Sd!`nb)7?l4Lq=zPGY&5#}p8^pw0Tvy5wje zSvN-H@L59}?J0Lf8w7l>lA98e#mPBypQNu#r$>On6vjwGSG3sY(Q0>NI$xql_M+}) zfOaP>x28#HC;C49U}z9GEf@$~3#pr+M%?>o*}v9gJvV?nX=hO>#0sAq`d40Szq|3o zacCQ?hwW~-bor`s-{t8$5)#8`{(s_6{E5T_J*50E{iVMYzx>O;90rcn@&A*MUWdf^ z%Jgc-jDncI(m($?fUfk@eUeGTV3r>y3k~7`fscSg6Wspte~Qh`%R>}@1V#0WcWBoz zJFZ5jA9EO3S6Aaai&>1BxG>!PB3jolm#e$j{GLA;k3RLOwA0P_SFJO&v-X3S&(Zt= zTu*=TXQR1(85@@+&(K19Col#W$Kd^#=|l3U72WD_%x{-byMAt*MT+Vsp|-gm#N_5F zb>7GPoF5Pz`muP)ENg&C$!)Wbd#7$8Jl zqD?+y(8`GM3T;7&j_F#M^ubD%=eM*uK;JL%D!*x!@+bYD(sS@@mqr!5CG~U3bbG`j z)K|dal9WS83!TD$*W?|*g+N42U!Rl3oBqLb)XCkU(xtWmr1F02g8XzA*Q}Kid8CKQ z*79Xf;$GU8wHRA100WuVr_=|4Vu0JU(ZTi!Bwi)Q@jZBROuJN>`b(vi?5+Qnu~B7g zxnaQZzk@BfT$#=I-70XU9MhcO2|u7i^5^4w+2emE+G{nm~?+lC%}LBw#D}w{4Vd8}tv#_Ub zHgeij-?U;oVGchZM>*T1P$QmDPzAK_kC;!6qm$$4^-oa57W93Yz~0^+&+rLNm2QA> zycW!DW4yU3rC|IwA3cc*z@mT-Z99!C>z>38B`X7<=A!Fj|xkEz@yR_}iizHuA< zZa?z3&*`Ef=6Ba=c`s{q>DEnBEQ=a-=rp6MqNh*KN)W_5Fp1su zO$t@fdGbb7Kl9mm__^LZZZSU0UbTEVz|D8;Vj9$NOny5vGmH3=OBa@qxX4>A(S$`H;1wZsUy1f&ZD?0vgs zq?ToR_0Azi6}NfK_ftMR*8r@z>7X)z`QCtD2Gk)rDf2A(d9Zvs1;AxLfdyqHCw|!! z^>@iu`ukmaYl*Qf2Vmp%<>VM8#{>*tJ!2hgn~!QCu{~*7m7?rtow3cER}YE%i9XOZ zS=~s|$9Dv)TY;DKKpq(K!ZBjr25o&+4kh}L`pP~KXzDv?ND-}aX?d8-V~%WZEXZ%l zc=Q}iH)SxUs`vEJ!et)D9@|XUu@U@C7xW3wTDJXkUq@*ru+kxZo2+dJZR(sXbka-O zri=2q@0%{|Iu6otPmCvNr`1V1H$jswcczdXwhs_su;hk*N`Pv^VJxRJ>P9(rgE6=G z9pJ^vr%k61b|Vm^?Vug(FB?@mfaknWrV&+)?>!THU+hH zclrKqkn(YNSbgv`c9S`M3xNe*s}=K$_oP(^ePzpko#`QiNXyu( zv}K_EtAF*c;)j3uhwtyaP9J+xKq$xoPsQbBRBvG-PTXJqr6|7eg{WR$MRs=^TjTXG zc@5MjgY`X^D%}b$CfT_Y5{b0o-$IMY=lanD;_g5U^n252jH zi&!eX&$5THew18 zTYm6ilw6e$ucEFK?uWOrc<)8T-8fFD-0Xu7qVX$VjPCVSpkGHv;P4WG;E_S|W>jv9 zSlldQp`$F?Rd(7>jC@@#V!fZB{V!sh!%zTb8aooe5R?IWwDu{mliv<}Rz~e^HN!?6 zgevz|Fq0XA*02SB4$$TRAC22Gnu9X3s|_?p5Z)lvt_q&v`%(c49TmPZedujS1?ld2 zmphL&>LHjY+c;NR9C(^ZhLGl=SC%UeLr;R4ChMTB<;o?PbfzU&62QJkGgO{rrRHQg zhl9(h2fw9fxF;~tKP+t?xPRDPg!-iA`dR6dMj1xWcQb%vBV{T_x#ub#Z{$JS{;j~@ z>koAUuYX>N_REOQ1^35VyU5esqI{;T~`?@465}-wYvb0l?=glX@X0!FSo%_j2-?o?pIRZH^0E#7~0G`xIi=)e(#`a3# zIT#~5!Hl#s|NDHB#W8`U1HE?l{(kkz!KbYTHmK`oNc-LN(d2t=$S3-&n?kfMrt!sR zvSb44yz&LlAlE&fEA7@5wA*HD$vANw84ATZFYU;RREcOff>){GsZfK zN#$-Cjp06yFC%)t;Lb*VE2}6ehkn_)!Gw7xnAVc%C4g;iQM?R&GMGn;ycz()`i8kg zQ}tObJ;!suilV-X<>NeNPx6?b1I*re64}E?@SU?*4!2QW&!f#4Y>!sa8qH(>4Bqkn zbFLRre|j685$#&Tvx<7O%SN2g-u}yJ#PuxZPj6y%eV2SjMe%h&bLDawE$E+5RDTJ zsR7iO>}X^KnKhMIn^jEb!=#Y|VfbuB-aCspIY(=2Cyla39Wv{9cNeP{FVMCDl=`o& zrpd%!)nbN*p6P%)b$kAbzHP_WWrAj#@2yyC-Jza2^Zd|HyX~sDd8* zRp;?n04ePd6cBjvtTH6=GX$$U5Itv6fZ|e>zaa}#N;~Z6hUC=E-##m-ad|TVx^$C^ z$856_QNZDM`DhBALH)FHDPWUzifZfolLGy>nBuFuBn|(e)ku(Fo70k|zr&aWr$^y(geXxZZ54jwM>vR!T(Fu&A+Jd*s$ z$kV>FZ?sDJFImN`ixo)eaNqh_XK8ov4dc`0SNm3iKpW|Mu&|~gQeU`)otAt7O$l_6 z|ABx!M!Wn?nwU?r$XE~RCQy{7)F_pkz+`%nv5;x`qAo4yj`CE|$FG3SmT7~Ib&(Ynt8qqdyKwWRl-LK z+M+crZ*)@N+G4$k_R(Rbz|BOl1YEZDYo)7bR|;fHVb$fKUa5}-S644$b$1DHh2Jz= z(N|zU1vJ!8 zYnJ}EhOsaN%C3>e7Qj>Q_oF5#Ox7wpUP^p4Rg&e;{9g4_O5~Azk6XaKBk1&1)~?iG z|3}{^zcZD7fvG9w!Mstv$oCzm^kYiajk;=WHmJE4!LO~;Co6sIyDz`zd%h>0Jb9AN z4K%rQ`9J^9|8xAvkNiji0H1{P+a8p`&~yN;JFx38PbQVllMNLbAygGa~GIv~gx zMsCM0^c|Z6ewrNs3fdgJj}Uq^dOpmwdLkE8fX3J zz47U^Way#8n;V$TL_oHSR<|7umkTOr*Y$2R9={3T+9W5=LdJA+8|&-KXcbKMYLU)p zGnk|CeB4#lGKw|Ka{1y4;a`oq+qYp_HSV`sfK2_%=Cm6Dv}x*ik07Wq<=16Qhtp`@ zdBOlfmGZXaX;l5>PXn(|bC-$e`=!rHz0yoerF*wv>%h))@|m=y{~ll@s}b*7m$Wpd zP()bBSQL~g29}nNjlvCp0tVi7DZg1u!bsMV`ZO5pwDE_ZIn`)JO{OKYOwhx#PI%2AAT2b zrT)9W{M$UWZKmaA{1$asuD9U90$t;U! zwg_T@NK)>ahki={4aF$MsB-is()pa$iFsQBrs~jT zkx?djubXxF+Y;U}jsQH>JX=<)6av2}_}nnp-MI%S6CeQh5)WJr;Y-$UyE*hP^vPSb zbZ&1Qepm$*>FX6Q?_zU1j_QbfhUD+^C2f&@&7*#LHUp@x0a_Ktu^?wtr(OJA%u$Zz z0f3{bRw;O*L7g`jFJp$BvMuY&i`ZU1cVGd+DzS#f1;B6)*le|$NkM7sn`u|H%fOyp zT%+!ua!`Nc4G39HZ4k8T+ED|L%G*o(9{y!kKa0Twh-s=G_n%_EkjV(*CzE-}{hWfM4ko#d$u1@&|zm2$;ROzG@Q%Ck=$r@;a;FucJ$q`T>0&QvhRqaze$Aau1i1F!mP+wuM2;i>9>WW|ChS*o|%4` zr=lIM(heWBNEzRbNg?P-=a#{pjmTXASb47oGtm@YJ3CK|X$>$lm}~Q`w<69TFe&Kk z1;A~14Wj@!EiPhsdlNmFO&u-LiBsvk38tvWpz;*3Rga^`pNi&c8`ICf7v(Sfd@OFR zqu1(3=jb#*Y{{gbbz0GS@R=xIk}m+Tg{HS2T*u<+FGp=Pj70?v3ZS${OWcg7k>8=A zHen|EL%jPE0k)6ElQ)x(NeKgQ^n0;jf{*4;O3_EFEi!~c8O;qECt$_R097=8wR)FoR?8Bg2037`E<-9rWv(e`D)ieT ziS(7yBDd?%JqkTuPg*{8Rp4kb*>(|f?L3y94LleIzRMyncxQ@GLJFn>xOqp0l?1TM zvgETh1Rxcty5xt@OUL|BiL8O3r7mjr3h-JZi%pW!CVq!L?hF*zh{qM?3P+}H3J%Kf zAY1Yk(E$#V-xKwHWkK1rc}F2Dn6Yj=Z(KC}v;=Nx$qwdk-Dp92|KRk_gH%IHQSuq1 zOy5n6m;BP-A4z5V06Y>9GE4{e68+L+)L5GM)xWssaxFEY2!119En@<(^u8jK1|}dW z4ascV;=Zxcpo2RrrJ10T;hpN|-vT7Tx0W=0#}tOC|H!5aN^%Esns)TNW%`0k-f8KS zeDsIXZQ!F#OG~TfL7wSv+Ar8l^-4GRN}Ufth*$m2Gn+nrz|UIy($XZq3GmbV{4Fr{ zNU3B&yzd9;$IplQ*>=7s&}7-KhAXG z#(0$+;%kn5WRsxJ!`0nf6Q}*OGgBY8o#6817$uH*LVauS4$tr0iRb||F0mDNlW1lr zw8K$yZm-_lKnr*)d1N}&L#ru|3wY-5JdVx}|3GAKJYfz@;uLD;N!ugWZ)>9d9>et7+a5R19W>l+Ulqk-8Ub^>^O^d{x;FC$E#?( z|1`;8yL&4Tqm5eJ7GL~gG_S8?WBpD~6JM_YoZ?fTI`qXO@(^e~d_dcH8?Y4cb9ZM# z927Yi8vH+c03sQN1u&+KxwfJlEsHy4KEO3Ie?bWS^3}<3AjRz+@4`2ECMgy4uRBNM z4eG-9N$&yBJ!Y?*j+r}*AL`Hqyt;wl5Y7KS!%uMfb{ze`BbKrmo%8Sh-M@=}@=yLr zI&SHwe(I-^Rq7{y@+adD|KUG;|J28ue#=|R9N?hybP%kXse@Z?oH#N`^Do**a;Rkj z89-8pOsy|2QBH4f0czuDIyt$wL0el*CrKl632*LB8@|sG=sCb>F&oE{iR;eG7TT$2 z%NZ+Zrj{q)t@onSJBj|AounDB5o|iv&R<-_5rT}Vr4k4OrQTUPdMACf=S{4hU&R!y z^Z4PDsL1RMTj4QB05Rhsoy$N_|65GX8k1LLKJT`pgCKCX;VNFjALi0A_$9ovIb^#!%WL z!P8Q35{?I6;WNocNdO@K5`6HR%USw_ebhN#p{n54K$EOX9ssQn*W%?el*^pyHy-0% z!$tThV9>&`@i1!yyh~?>Pe_7?GPQUlL2ZSSpyxS;ykkP(b+C?ci#+^HdYOL%d|C)d zgd4OZ4M~gThC>_`hvUiMAz;K0VLh&?X6Oo)@Sv1yXy{J6vh`S1S?ux^(RB zVqnI7zfcDBNi^Vn0a5}X0wlhslq!Fp9~nzW>6_Y);7(wS@9FoHkL@{RaG=3`PP{AV zP4_Ir+mJX}v0nYowQ;%6m|U-bA^8d7)A3*eAjUwl%GwY9PF`so@Gn)>JhhahIv*62 z3c}rTq^m>q)jPi6uG_Sfs>VdJF6nGm>1h=tILRQa(njl7j*@3a+jWuVRjbubW29tm zrR7yK=0ACmM>}azFm_n7GHUVSJ;9vwsDLk}(*mHSP{U_(=AnUzQv`YVh?!aEtlNmr zDL|~-N^PwHpbSald9(JaA$O|KC)FK=&G&7mRyV^+0M`IE@JC)Y|24{76#yBc~5Uq3u#qOV-M*H!@1V(Fs*YzHH(qFU9DuoZ(cC|>hqF~ zS^`B?M|n>`&{`<{14VpzaS`Z*2e%OTrghWFf5L=l0T?zY!&>r6=(xGMjGOmSoG&k_ z3w)AxJAYiR6m)w4d>S7-TcxiJZ0c}t zvEoz9VVb+#uk+ml>1~&R$K8Bz2poH#*C#xE?8}#b<*)pe_^}`Ru@r-8!5|%tM(G+O zS{oMk}BF{$#XK?OmjAjDO;8RvI@eK@e zF^qEbJhpc)Vm-Qy-Fz6;`4qeYbEYl@f^6SwM-66-NcD{9XipcBZ3_hBGGb~(ZkP-C z=K{X(MmkIP_E{8M_iU!=&e~k7f zL8zI5l!P&|Nx`QqO8^1kl)iF9zp|RkmAtRT=#_)KK12e#6e2>|CX;m)z~8x_ej`(@ zKd%6a^ee4iAK_Pn7GuhRCOVpvMY6iMl$pRAc_lz$y%+<@@`P|w+zaYF|Ij7P4T^IL z=t{S3>d@i7?h+({6h3wScBxCSp+!k6jptf=W@6d|V7qwGPpxQLm(tNn_j%|Sl|aft zmZ!v>bbT~goNY}2h%uZlz(7VD<=IIlP{zTMq@~2np#@+?E7B%~o}eb8UD}%Jz$3{| z%Q?Znw0vt`(AVhrsSXUu1bY43PLyVpldMfzxBQPb*v5mk&s*G-?pBO+XmffrwKtW= z-`*F*nqR66_x<$4_S!9ew;ie7_BFu7-;5&fQt}$fnt84ycXiIs7*CFqbd(n3EA3pR zJ+_NVrb;M4ZL5DfHa+6WupnAHJ?6?&6_VwP3M9TDjkTbXPrgLqSW0)7tH_5w@HfYm zZA!;lNn`TXawi?NdTan5?r2Vj($fY{8pxSznKUC`8|BiOikZg)xxY}qDVPaBt4P+% z3>06i=FwQ81X-skj_0Pr+Rebf^mY3hDz7$>3i;Q)Bi)x+r<3?sId`!JPvt0YA$r0(b@O z?O^CeEo*H660`y~qQn3+PS2n%{Be&G+-SvmzK%{GWfkz`DF!8cD%-(_B^Usw?ygJP zxoa|C*YKysBsTosMsZlhB-#L z*s&zXe6kAD5n8#lj@wCIA5GLnOQ^wM6F^ZOeJL@P$-{VOg0O(zX#ktI-_6_d1pZ&0 z?24@uUCE(FxUE0UJ6p1~%fkDn;G5{>K_(l@)&=Q(Va{%W_J1Q>w0bHvv zc!Lfp4JCR!%sDF`=(+CgX{;Oq+%-%9}k zOlWn&?+OwHIrS0z`Hp^kTm4dCl3Fhc7-?n@)l&o>ebl_AA{NJcKA(i=E8(XCPx%L* zDu)2S^IL{+DH#%ir1TzqL!rF^v^#Rx%e7sVv>0G`5wB3RD3N$4k zK^duS+#}-y=nKgQ%&(efDX+BDcuzaavYf+m-Qiy9H*-2*O!l#w@DZ3XbEf;;wWLpEpusdj{=!e8&E}_3iaYRPiM~RqqzWlH2{=a;`Y4P1OR$a zcYU->%Y5$QEoq+zhiAuVBJe)XuCF$kUNg243zICsD5g zim%|Mx}ymAzVO9p!N2zan=Sd403(lJG7QIv{_1_tXXEa(kK*i&C$VyX-VI^nCF7!v zoJ7gqw*Z8UH+cUV9@u*DAgXQx__ZYHYQWcc|AQ2IBaOv9eO+>oIx2IP^i}_jw@?y# z_b+{Sr;j~O**ovN^J^~nb@~n^Gz{1FGfg!BGS5EK;?+6o$FZ7{7BkoJofJ&!I74fD z_$US8+npXq_LOZ^GaHO~ZV7`#(^Y6p7qNZ!c}(8>Fjo3ss7a&qEh~#yZYIg1WjxGIuLA?> z#saOgvOohJ!9ZZrH3Uf7{*5H611>uAF>Sg;)0~G#qIBkNk>4Zh5FW-z>*&7%FfC^= ze||^0D_*M0#yRFc^4ayXm%j zsOlQ-c;<8VJinjM+I!!+b-U(!tE+DHS?B)l-upK^)9+d9`L6Y>XFY%{n#Fw^X+B3Q zBi}cM<*1)CSPth=E7zmyo_P9?og+Zd9E-LqV9d?79o#1%tqiP+);?#1)7pZFCPC8m zkHN<&MNerlSy_+-Xz7~;urbVrp$*ca!g(FX1x)EcPdOYQANVINK}8OZ-dkf}bg&f| z(cD^X97r8N6JYS^^ZIsWI1j-S$>eG}ul301l=Fw=&+9OFs;kZx+NyOTFA~lbo}`gm zHF+4g)pX5=VpD_#FyM2s)e--ec?mEc0MU1#%!m6IhbMTRLx7r~hGREqR4kXkgC~7Q`rl=E3ax$)u;xb_3RJD!;!Zw&#^)^q z`B)_ni;&<7vOKx)Bh$!Ji~2AaEgES_R5<;<0CfUnedzME@5T4VvkB0xr5S8$xq+cuOXSJ?9Yw7?3&JQigmU6BC*QiuKY_48TMDBD8zRlyVa+68hY zrQq8>m=!j!MO8es47%FbrgGZMeJGvMgK2Z$+ak(Rk^a1xmZTpchg@u68THR9!M|?W zUl=S_YX%CBiv+5^ZxYq7a!z_vE#Nal@=$LJCexBfp^svM+?n4xjoDpf&*_jpiri=q zq6R=QEJdN+$w10c1=IqT%InoAG|LM>4c{iGlKupcntNx~>QN)@3IMF!?L|Ym*FB8+ z*+Epg=TQdS%wE48)Angv?xt11+DxBn${~Q;+s(kS0z-YYYTbTRk&DJsY?Ui0q@x(z zyC0oc%7j{-aC&XK(Jh{A$w|k zR(LNcl$38YIHz8X1eGPmg97qAc6LxyN`TcaAP#Ux-CQ~`TvNGjqR{qZba0gK(S0>R zrts~UF~DWgwhcu6Q|>MM_y;5Uk^Xm%D}9vYagSl^gwG@DeHw$_KB5j;?TpESnZKjy ziA-Pq%CG!NhQKf|=xsXE%P+qi-}uHi@^EG5CxJe#)0cBQxxQa8jr&Quk|zU!Dp0wO z7+NfiHpnBIj#{VNa+Df%v=;YVTg%li@MBaEBATZsopx?FQXP+D$nWyy%L&RzG?_EC zW3b3cWdRv zB}P)U!eRS_v1Jw&1n|%>7-xv{9_2PTYXThvFyG6985a;ZXfQZs94~}}F~@47#q@8f zz^8xpVobCMBA+UPT)<#D>?3d-0G&=uu>w%aXz32b-0J|ZK=WD-&?)I4UXaoH1zD!= z)R5)K7st)HQjU?&0X_#rg(SdKzphm%j*JHHbfw^hcVyB|E#;uiBMzX8fs`wLGEk&- z%lvpoAn0_bD=!_j6MQjX=dek)$pPHwESp7efcCdC+Puuue7v6{?_FFXJw9uqQ zI28-dcr)eoVSTf-ItzqnTA`_nJ1fX1`kv)BZrkUgGL;AJ znfIn)m>8}s`!wGLY=E77Pw-UwB--%sRBf#V4Lr2ww_qw-3S3jo0( zD81RtFdh>XN-FCLMnhlB0brIv>)jOjF*Ts;&UzG{c_ymQJrkw11t@b<@B;E@j-m?Q zi785@QEn6s^KKiZkP7buC||!5g)>0a03~+Vi&5`9dW90QcRL1~&8Tf}0;=ZGLEgI< zrI7)BsoM$9o~Q^Tjt-D{$~M|X|E?GT_;EDxqHSCrRn`!_$bgCz0AF#|>3{VY322N7rYD8D-h zlrvu?O=)+Veq=ueGy))fk54l_&h+IIskzSmzx>O;oQ3$qfA|l_PyN(ScwDp*O^0m7A0Mh$l!!L4$tMF1a%(|#E>9cCP!olwSJ?9?k!LvtP+p8%KuV>SKI z5W-ssJkZSF~DJp%$a?rM$I2GGlTr-(W7_u+HT0{8mk$racj?B=GY`*y}Xfu?O1ZJGGO`N85Ds2ug8R8;0 z&F5aHCY>_#zRAe_3rFx1g`*GV&J5O-YfjVWI}>CHI{8b#yzHl>B|rmk7&a!sHwot9 zcg~j#Fl!y@%fb7|_M-llCH+PDT}NRo148;^zzTAqQg`$00_9$SGBQpjaoF!h-=M0r zhrO<^a)`p2pc|R#3fn5_7@i{gF>TSjyc@Oad(qQko)$_i=~ciLvJ0?f*owi?e)RV5 zraMKmRVO>ZUAq&bqm$fWrNX_2>!_2*CJJ*4@L1d1i}lNwP%hW9jQUoVC{ORsy*T{p zH{$5!SEJk=#CZQO#^mG1;QGWCTL{UGjm5r^9L}h?KV>euSYMAF(w*}jhUXpKDYG8w zM~B>Z*8!L&#|;-I=m(}Vf3ALfh+G|MdbyY8LzI2e&_;k=w@vCF9j1R?t5J(W2XJ<< zpBuCb(pzq?hf*znO(tk1$=Khy^hA8hbnbsAax>ab0)1M2$^;X`yLY2{|86pv=IK~)Zg2CztQUf@;5=m!X zEn`L6(bbHDeY78FeFGrZJB!)fz>oIYJm(&K*1Rh#N#X8>Go%@H;e@v*lh z(_EC*q)n6bxL;tEQ@YD*TAdWg2{O1PEi>6Nt7jKw%Aee`Y>A}&Mw{674&bQ>SV!x{ z13LU`m2nWyG^`hO$vV=0zDxVZ!s@b2&-|8UGaaist@`{T4fFRttub0(a_W%ZizgQ3 zb<>#IJ}cnDkt-fa(`nY6uJjs*=g07J<9NZl-ebF%=BgbgEq%)9>j0Jn&w!PjTIA0< zTL;0D?4NDQGdi$a@Z5MCAm#fNTnTIy{idO%&M+M)qjZhQKBqb*eHwjHt!_v33tMPCvARbG*s5? zi{_P9KK=|fqk`bxqy8CwqvRG)^yge4!tE0Vd9nx{-zO+zxmtO%8BM^*u!b@>1{l&p zrCsETev~~FO?ch7hZ6yq3x>{6*x>oW=0^I(jsXk;Izz(@#%_cSsA>T4`rKEjp}=lM z0nkwymKSv&33Vv?>2VC}w8N#XsBAW9M$#-#W9|C&^y3fVmi{rj;cD6Tf6+Z!R{)?*X-e*UGGqjBw-sC@Q&X%*fJ zbuae1aq`-$vC-*8=l=bu(#P}(t6setg&UV++U>+~V-lNR_+kv6eKramt#DIhvI`jK zrSo;#XfK-2KNq#FOsML85ko9D^Ym<*<2)`a*{3+^qgjMm(LGoUHYWa$L>?+2CxbQW2?C8(odQ7lLE*- z&E%l${$Dcz655JpQ_b%#?>1h{RA*HrW<`xoN%yv-#6yT8U>dDvv`BCJ%B84ot|LU& zqh|WNPY33;o!yM>l@X+;lc)pqwm$#)*t&TWpi@l^tJm+PfHN>{Zh{P$>$SU%6tRZ( z)}p^ODO-^=wDJl1PR{^y-0O;@Ai16gv z1dVLe?ZpY;a6sM@1g z-g_C`IH#3POXEZkD45_+(%IX0=&uAEKA-6<2XC?%5B0tAInv|&3PAW@rfuCB)U_O0 zKJU$Z^B(TCn5%TjTL-3! zW;*_Pj^Eji=U&#;-ZX(`CCY@**LJrDwTC@+2a-o27Ci@|2)oS&n;Xu$ojs7`lxH=KARzNOp?_P?LvIu$Ow5njad)8hA ztgSav{@F;~E`X`u0X){|=i}JCd?^}$BduMAKWg8;ljUq}Zsp3%ma=($1BIawjrGmg zxp6c0KKJ?9{_JM~WLv(7tgD@A4A>h`PsbbGJ!XKA)7M{%{r~s>jiWCahjO8)=X;_J zU5KW$zft;g1_s%#0&9TV1#>Lk?PBWE-`}~k=uwYJU;XM=<3IY3{-X?k@z4IV|7`r| zkN#**U*5WPYk9{7ePkps*jSJG^Do5g@|EZzEc-BW)oDl>tXzrd_KtS@x9#f+%h~iC zAaWPO=yR6XkHFA zV@8G9m1_xThA`s1GYE98sGWDCXl$^<)0o~njKbkb6w%-+_Yb35+KyUtCyHnkX&4#; zd?@!s=mQ8Ub;r3~%0|B!>m8J_DMI}m4e_)W4Fu`5G>r!7SMHPFut%pxSXTi}GxF^h zqTc4`DIlypOTUwx-kL22j8fLY}KRc$%*kmMjzZSwDSK z@+PN?yrw)KaKh(J6BX8w2pQaz!{>{XH&@}GUi`mXLttETn!x9vCK;M4k~#IqeaaxsK; z^7%zJ4``KE64y$jIqgO|9<~whHe+_#4%8_j0d;n}sssqui3AMSuy9uQ2kCH9*FE2( zr?P!GSLxDEdt4{UEQ<%%9Q1hB zzBb`~+PCbRT2`q`_TL^kIW0(AWIg!HMJALv-xZlyPV=)sX|-Bjr$m{)TlM%(oqo4{ zO4KR$*W`PKVIlt+;J_mt(@W%1<7t4iN}d+Q+(n{7n%RFSJ873n3+ulNH4NVCN>Mlb zcae@R2Ki1bn8o0;qy+{5kp5tvauv0%1an;!s4PHPU%nv?3KU-SN!=+p3dSV!R{mUh z>8ka7XKoWk!Di$usHmW*c4(u~Q9Ek9>}jhUW3-qGjb})Vx(laxR+kXvn)j6M9Y^8S zJF$7(jooQ2*3PHV-0wuy{;Ea4TqV~M3MS{PD8%JPj0Ol2UCwD(-d6)|j|#{e1J zsyJ(U z<$w8KW&qir{^_5NpZmF=i$C;-{!qO3+G`g+W#2A>F$7`j&p#iVU-+(QKKpF0{&ADr z>ZMCj(`Rg@<*Z0ai&i;1Ka2kU{TKtFs%Tyn2N{I8ff4I#8&Pr^poLa>X%99+KFEM* zlE~K#`ZqL^HOx0V1Zbgs8R&20+B0cEGw@;WXdjL0AWATC_l?^zyvy&yBhDT3iMDnZ zjS69y`_#G8vEGcv+ByQK3e!7aIb~BpdqlGWaJpo?QWc2IV=@G`!Q*8H9tWZ!r9$LK zpAun^)hNEN7O%=V%v!MLL{2VCO);xbK}P zEh7xHB=VN>NcpIj(-N~NkH8_VWE?uJyB*@<#&;$!zuhgK%RKo)i5DlAM7N=AlxtbG z_e`W3o6ep>htEi$#$VX=L%1LXCLKf z`kM*TBfIe8AbWf8s4X<6k?PU{Ld z1(g;Ae$txh*Ibm4S#phMte^GFve*VbgKQ|)Q6>R*6XZ|9@I8PXN}d)n+qT>%ms4ce zVJEj?njYzI)r-M-8)b3;{-C@71;I-ZSrCFcfi7}%)>s2Nt4`^ThOqqzP>3?TwaIxy z7EPiIkQ(2)m-nV(sT@LX`_3~Y(%DJOPY+^zdYoywtAT%wtu4mrGr-t!lt{C&vm2LG zd3)PrW_hGlOp#?840c*{qQ~HMK94GS zR>`jh7%L+ai*594&ZV{Jk+h{8JXdM^+y5LS-h~|F^G3UlEAnVptv#kBjaF$| zrkpP0)52yxiwk~Bz?IEy`d;UqtiZVUEu81Qhq7oHifL zr$3}MQ$XjS#=W-H!s5uy*??TH*L6Zv6@p~ zHt!?djQJg zL=@uL`daP}JUki1>>IB}S;h9={TS_^Ms-?_X0?TEAV0uS|DKBPD=|Gk1w2incifIv zwUK2sxO6FuMQPb8xgJ74z6R|$Ic&%H_Hj(VaVz@2_8W0}=PvS({NIkWLRv;3%* z7eom5eZNa`!C9`RBp>^cJ3JH^JJRAhr7cUXsBG@h2LNJ}uU6Zmxi)!E^bPyucQDCL zk4?^h-*a1rhsdeR4F>J+Sh9mEL}X4)xiHKF!SWH4450wf-z@vLS?0liep-gp6IW=! zG_}+w4WOtgdW5z(?{paekamj6a)kjI!FKOXlmKpVb{2C$T+t=gXhKy4-PDjAYL;s} ze|kHnU;ou89KI28d^;urqFOVi3_w$-830)2Vhe3_jll(>+(RqWV&!O3o5iF#i*}(C z-LvCx^YN7blzZluACz>v}TzmBZW@80uC)%xz{6U8Z=dnPSSTK_kL9Kx0JT00|t}>t!?TjX?nd-=2*6Q z4vZXvCGSg|mBc-(A3n=h$3?l(0pG#d=Q3J4lrh4zUgESIn5A<7kDkDPlz+`js> z^`-X%b|eSVtOK8dS?g!rXN)nq59G26??jY}|Cf1Xqbw+&Q}&{28#crU^M1Ga6Fg8C zEsr_v_8xgirk{B{OrEe!zU6|a-sa?c(8GBo7=a$7;|K3itL_qWJs`VBzT;(ovPfDT z6GU-j{4i0H({_B%X*h4q2j5DI5Fm@x{on7d98C+V2c1(Zlzm&uqR zrA&igmOD4I=hMEoN>fE*vFKt+rfXmG$Rv4Jd?ql;ig7FfmbWHgd^kU!A>A_lxZHs` zUC=Y8Pg5=}*;<-ijp?3^i#AxaQCen6vs5%Jjo^phORKFGGG(0(_Jwr@`we7`Q=;-T zZ7R$k@q4mePm8+&rrlq&Oud|==_K%}sAzY}SulF9xJ7CHk1EP zf<68Fe?Q{2*A~ER)fXKTazPl%GIDp`?WA?inU4AK3Gdd`>9pkot0#|u4GPpN)ERFRIn;C|6v?s!+HfdgbHOzw)pAEAjh(|L>1K_y_-B{LIh% zOj^nQz#sSn>1XzxN&*RY!Pnu@0D(O~Yxe~0<(;&TkB~Hv>tyNDWf?3TRVkY$>JnO< z2YOCkQ$Y8Pm7_x}Dy)vBwMHw(CumWCF*nTB2V`u_8S12I+9lXJ{g21}g{FCa6z6we zkM_YGL}DSTwK@U?;Q`~-YK>?i$R-Q|YqgDNpylbT>eBK)&%4^HwX+uMYt7hlBVa)o z4DG&zE?jUgH1y9H_eL>9FiZiwB?LX-ZFrGQtM zGYGhq3BrA%76;`Z!=aBR1D{&J5*&gJw>rm`djF$36Q<8hzi zUY_$F@6=k9(?lGmC3!x<-s)J=%112Aw>-C^w>$S1GMPLU=_M#zet$QzZkFHa-<)dX zTpof_%WFFCn(@O~znP!k^IagIwMYuas9IJeElK2=(^91U=2P!LwqYtCi-P1zzZJdb z!yk~y!YM!b$u|6!yz+Q6Q%kfgRstBWWS`0Q;|KlD&(-e)Fg%~{{~c*@&1`f6w|}ny zk?+gZjOm!qeHQP{KFb5) zt!dj|a&O(qCe6Nfq(d3;v195u$PPFdNE#ZXFsK0&6I zKrLm0#pd2_Y(Do~Q~-muy-QKn=Z-M}^i0COm+VF6@*Pi4BzWu({m>7^_3PK;ul=>Z z7JuV!{EZyoj92!1e$VeoM*bwww>5YyOfsjVkKNLOwy0OD3wa2zDLLH-$g!h7kk}uD z9tg?hv+tIKtCL)k3{&g#6{lxWbeXgo84T*?vKFl9ioL^g%8O>y0XPDZmJv=dh{6^Oi?%zZ&5Z8thQc)v)GVhV5ZY?d zO|&nL=}r}GxE8G*0=_#$s0>phFS=a*9PQRH7f6=`Hhi0EooP_{mU-|z`50?xgnTgf zoC@NUP|azXY$r##& z^ha7UJk4#3do!u!VEK5oNMczE+b(~P@RYdOieV%o*&sO337SI+zINj#nbcKIT7d|%IeK*~DM9N8}PJADsVzE8Pz zlIQdKo0K4C2})Mq{UzXVO5Glr@0KrFSL-}6t+%G*Esu(dEDML%Rz#99b7u+9lcvf> zhG?;ENkH)C6tn?>v&~8KEnqowrJPPNae7g!1wHt*b3RP@h;`#`{s~%bmaLb++V6Se zl1$e&AszY2MI`=IKIF6~xWR=lhN;j>W}U1{_6ggKBy7b@-<7R=mIu?Q4Cm3{sM3CA z!5zF@DZ|Ww)GFVHPO-IzC~)LMp7Y^2NEpiWb+cha4-7E{02bE9Tf&P(^ezK zbpZ1DVa!pcN24ylu9yC?6+54R4sbVm<>e?0k*|)SfWk4?&iOq8%$0e$mCt@QLol_G z*(3eT95XfmaI{Hjhq40*U8Qfod~3(}kYqndvZFnT1fL9j?7?Hr^q>Fd|M~dyfBw(M zU;pcWJ^q*f<$sCa`8$7S3Zm~wa(=zt$-%6m`4)jC5Uj!!Lm02GbL0mlJ0C(}1_Nk; z8^Z)@E^mUghe$-=y#TTfz$qOqVFoQthLISc?PM@nG$W@4$L+IRsZ^6$|KY){LT`Ey+>5ce#?3^0(dlA*rb!m&u(&n&Co zz3>=h79W%sGk^ctX4G*$Ho)dI7gtQ^JMV7I`2e)a!{nApI%r$xEW`UC?^@OGVUY1r zP_`w1T3pRbaJ~w)@hwDA(z3=o&%TrQ`5V^N8YI|Aa~7$t%Fkcs@zDEPTz>QSiY$kH zH2YiL6z^S0TdEq#Xc%myaX;+5fVkQK@leVD2SZ?)}5$~u8C za)tc7uH`q&Vp;ia2%HWwdpuAcAji&;oyeMOFY+{Og;R^lEc?GQ&wV=Ux_(ez^KExK zLCP6;JCI{LKm5b-+z!tJPezm(ypb+l-?C2sTV>-e-N~69q32oQNMZ>a8rto^&LzC{SyrkaEg>DQRY&27&X$bpCW8K2-VRb zMrxuBgj9_(Q}0R!Gjw|rfF+t}$w0IYCX}BcF2Rh0L1~Im>mzv2hiKX`rp~?yKb=j} zft=T%1t0~V-*uixiLG?dehrn;VURrI1lqI#V#iJHqsctduZ8B-l9P`2W7vjI9gTIm z)^{!4c1YLPo%gHZJ1{K=7!T{?LIACUxu0K^WNMU+0o3#-Mb7b^SHP!fAar;ppM4A> zi3OSNo4?+bEKm9@(UlE5Q0dL1N*BrfE=fYc;qd*qlmY zUiUfL^rHr33nCBSDSas|pfr@0p|@)f+uG~a(R;Eg+-Nw53`+r@c+Sn>?KgR38VryR zC&l+iL2%w>)hpa9t7zf15ag%NRT{!*L}LqPB!6zszQuWYVbQj4rNtTF{QIOV1XZ$* zyaNG{?LzYJNI8v4``IRL4897}fdY*U!{fB!dM(D^_k2`edJev>#%OCJreAz9&c0_a z?mbtJ)6cYG@|mq@UqOM~qy}3HCw^CN&a@b!)D@853wctet_TQ)73Uj<|2aI5%?XMl zZByY~359#}^aT8eOh*ouP>@}K6l<5F)wNR@qKPeAqjUHZpGZ z%r<>!ME@G*exr^bO`Y#I*J4OR7ZF&M=`b3AP66JO@yj$-@JoY!D*UeUpQE%-o_{Xl zGcU&cSw4~dVQe$XrnORw@)IS1v%43}Z{KDtyq#m>jPElSuQ?vlN1j%ajUStS_Gf=K z{?woPQ~CYo&6{bu6PW!^|I`1J=f2Yj)>;e@4+F9#+29Jqx4ZU^gjP64d+|_})F+?> zQwT;}L7ApyH3YS|gZ-Gms3U;#2&SDPEM{8OyuKv0otc)Uav^%>hk&7>crJ$ek8SUy zmNa!KB#i3*Zkn6Ts|oYLg3$v|&MCv(2>t-LA|kR6Sep%ci#;{nnu#>rd(Blv-Sch& zRikCsOZA){aCDfrduV*;2*pXXs8^{oh#H`1e(yBu-9fap)B)yZ{XtBqqgs(iUoG(r zEf($70nP!vIG_T3OK0oTWNv|khU|AkEI3FlR#9?-0nS0r@j>BU0WACJDzDKwd7U75 zZE67~Sf}&7to{`mlXaTDFyS@p(1(cOQB`7%h{|R2Oy5(@C`U^4r_fq7>Y-Y^Ntz zs&}S*KfW(NIab!6_atufS%HxQTdwXMIuP@@oDP1!lsS@yeaXIJ?fsq$Gfam*Yg-Cn zyiXjF{#j{x;c3pB#^b!l1bBc!r*m_&cgpd0*0%?=tbg)~`s%X3%Qpg8$$+xy4zNr| zE0$nSUdpOiT#tnyUA%Ua;CFT%>SuTccRFyzxIVmE;+=*w1deT#n+q3>KhRR74 z*kNZFV`D>7;qoQq?&T{0zpa=kZ+CYiHn(D`lRd!ESZ)o3UO898Y9*}1wA+pD(LLnn zQS?#B=K!zPXP%4ES~CJX`!$z2d|C^l+etk<-aTz(*M4mJ zvw!x_#_#)mzc2pCANeEklRx>B@t^%Fd-IZ|zdPDq{q~Y^s9?0>H2umYp;#bzSrP)@IDr zgsxxb`DO~c@%dRU1SmBd2soaB$tuMf%CEj-)7y&p98!Ql0}$bVUxifwL$_^U3ob<>jTcHRvSUs)oSMSC4vq4 z>%Mm>)G73Xfdk=N$Wf7s$hQoJIv!5}e`@X(wNLlyUjz?;Mmqh>&_OqG0KmP1^5=RzSo0 z_(%d%zgwo|Aej|;LX-olX$mUxgr8>kUgSGjJoA2h<(>pts?|I4Sp2;YvW^yzekh12 zr$VvlFDpN~6}`W@TW88*A2(gTn@(DevXEZn9M_y?P1`bkVBx^OP+s0=$-jksD*J4W z!MY9@Lo8+fc@^Tzqbd74dmun8Ezl}7ob|}`_;1QDHwn+@eV@yIC*S3iEQjo?^?T82 zxYwHn#{vjsqt-!vyYw5=@}%Ed2?3y$TTAtu#4}pF8Wf&mOd4fSX1%t?xfZ~#7$ZQ} z;NU2V$QoCSx~J~oE$3KD>kM z>qYyx9i3ZuV|4c*M!gR8;*HFa+e1Lac!*rAP}UOt0iZ*E(e0&0%)qlqROEjnm0YKA z#{j3oi!VmJ^w|hO*$Xek{F!HH*K*8I$S3!aqelp`&NOO6fOxePWn?m<9hyXQy%DqB zt(fsyd3KMnpw9UzIsyPuP>XCYU%s4eqeAOSXjhNA+OxclZ1z-CDIZ|;85+}o4wju(071uf`wpiOvs0seyl>J%1o(P% zGgv9N%urkDpjnZJ7AjXvdcM$XCIAz}%m+w*g_YBNl*5g9{hp;jfECfm${eLJ8V|xw zt!RuyEC|@v5K>nzN3Vhw2_sBpQ0i7f`zs+FVsks{y8yZ(g2FYB1%O_)NmUzB?V(k+ zxwozMhHzAvG&iEQbtzhVSGjL3>gRCU(I6^D6{oC5*Pl8qOL>Z8oomYoTuHZ zBH2GkMJRS)7P|Gr~Jq@z@jlBt!A-;0$Xo^J5j!AoU-|%kY<0` z-iykOXQS*^ODg36w<2lP`0k>sG75PC0VmL#o}a|HgQ7kMWNmN9^vabeqe%AwE0)Ll z&+Bfhw1KRp9XEDjyt5VE%dHsfqHJHj6zf|k?BrE0)KPX(hz*~?_+eQ}n_E%b-6QR- zm|we=ig|T+o3Y}3jQ3U)P-SSP*uO`c4$_sS$e1(+oXrf?^r$i85pwoMtDf)eEkqyj zT<+ui9%+1%DaYi;r62pTAB+F`zy7at({JOI{jI7O1*?kp^23WoPf3N}7+P<@mXSbhq~6kJOSTrw_TDxV{7!(r%%KSmpI z8MFJgb!7|~t>JVf_q1W`9!%?gXfEq6G#jqspqDJtT0?WH?d?&ojRkn(nSQ$+?Sn&@ zdluCS+82XrMH4sz=KQ=H{d-3-JnJ%mshKZg*Az+>7Z{-k4r0RnGldV=U4i9FRZZm; zg(ZShUx_ioP9bh(jI=%Ib*b(os*Og}1!gX<9~z098*C%fBt5Nnf*uDAGft;p&zr92 zIaX2lGiteUz!0<wX$^p~+XEjiu>-gOn;FF zDN=t2n;hso_c4?GZ55hAF#3@ZS!6ym!F#sZ6Ql%4A0oqiXO`7Zj`vI6sud?gSQ2%fXS033y++Af@t<(~0z1(oV2sU*|~R%l#jglwMP4A9klRym_Yh;aW1X7CYl+0a34zLOrIkDIr!@% zHDcgbVfB7@^^kA<=iZshkr4yDGZ+S#E->H^k!eHya=U=E5pp%lglynhgQOOaN4beV z*Jf2fQPYq@re}cdcCOew{`xoK-q*htXRo~$gS&_6%Utjjo$tH$k%&-$g^C$HEEwCD*BTbDWHN@+Gt zldzGOht9iiXkuQEH61faZ{ogcMK{hx5DofKIOzbCVEVoFSikygl&*XxftG@aif|zC z5H`lIabL3{!eA({Gt>w9YE_#}(O^I()Amsey6xCFJ&!?~av!y0YSes>iH@bS=u~FW zYBmvY07mNQmLOCX!P7wt9mWK0w0Sy@iQ2K#j+J7rda7voa=Ku5J<161k?D8b(gPgU z;9mdfL!#BmF4$BUyJVhr(Yi6BkCOk&A4nmor%lGlg3L$JodGqMl>OQ-%XW7PCi^#n z+Dy`tL|GO$eFDjLpqvV=1;XX$;^V!EXS4nf0f~=YsWnVc0XHsKM}A|tC^aY>WgTffwCY#=>&*ei9AFDk>i!G-pr)gF5s>+ffEZnIfc^Ubqog~2_mn;~7Ctth{l zWR}5|)?+}70HHi9As0#5&=k&an7`E{4P<(wPM ze`kw!EJb5|Jw~nVsBUfL>c`ocp^J*q-{jiHT6A*DCP8bBvNh3Fhq1o%46;wzT8Z(b zA7fWxF%B3BpmfR57|7U-CNh?`1B{hQwP>SE*IvD!0I*(Pi^kri=nERjvqt^98FTFU zm_74M%Ka7JzJ+YJY-e9&o>_cSmWj^z9b|T;nu^f_qVZ+`HWv;Q4WB{3c}D__YE)>o z))v2&#VYWWdAPY9YnLwZVjoKT+f26IjT<-O4Z!SrPWQfldLp1q!1G>o_3G7J1n_au z#|6q39dp^lcAO7G3RS?;^k6>)OioS0$Ro|xEq%du07G=h!^6~|GMFElp8;TVii{)* zc2cykbSfAi5We{eeRzoML13UAy0lxX(Hw2>93i!J^D}5nJAkWR4D@f%mkV%N{>ICx zm93G7CT+E}f=;7KBN%qvJCD&2P=x^Kqq&vu+>bGuY6ZYIL(1fKMhMgf;BvZ-#>X?A zv*VZz09&Z|$PV~+5>15l;ItFf)A2$W>!b`YGk~O#{B_J7wa~8kKKI$)Vk&l`ivf&I z=~6)2y$f~jB|TRUDy?(Hs!geOaSJewD|v{uUnSWHg%fH6AsRv%l|Lv-6-437g|MGaDb)ryHU6A zaWw_~WQex`D9Ncreoak3fe6I-oQn>SS1SO&;1deAL_&@>`jgF#m&R%PWqDMB=NHT- zC{wvv71`?xYZ-FGw5kB0+AQWycRzlEy#@MA#S@G(%ncscU z0_{f4sRpnqs#U;z35ozqcKGtiX#yw(^H>YkmCI2D5EnEv%M|R#YO{6Fk6~VGlOJdZ?ai%{8k-v#{=%j1!y?-2`81cK&o3!}=Fz-=7(Fdu zTA$!e9e~{i$thT=TrFp$epg-Q;(-Atl?i}82aF6!0l%xME1bupY1A_4DZfYW-sKlo zG46Tfw#ELJ@BZv3jT|DFyzDPdo#r$rV!^3@L6(EG{#33S zoLTN6;DGCbnk+Z6!9Fg)Pyxt5rt$=3);hi_`&()8Tmdhau!#xk(2`(oBM0ZV;A#5qbQpscK4ui%25=4b7U0altL zn<=xKd5`LqsG>MeuRWJ8Bm%H$?+hT)kMp%+T)A;QI&RNIxiW^@uoUZS+cDkRqm8Cf zdi8#kkh#Nr1lQ}<2=5O+aV)(0?Xd(X(Tz&JIj){y7=ynz!qYNi3e z)Mr`X<`|Z%IbFB_Kzxf8?NOqk(j|vPic_o@(`x?Ne79~3HY&J#+3>KQ!rAsls_G|`Yof?k}pk>E*uS^5c)?WE~n)xR> z8PPXOprDnT54w(=*7~r1&U*)$|-MoU||<*K2(IPFMkOF zDSK(H2CxK>QGbD$In6njE*zAp2#C^>Hw5t2T8$_hO`r3Ev+9^~lWn|^__6AWOEu5NV9yMXhSScU?;O%BqxL=1= z1R?yc={}m&$(255Jrt)}Mzmf%>y)g!1C*$9YO`afFf^5W1C`)ob;zH7D*V*C_3y}b zsv>Z;pgq#-6}bLApohM`g)-qKk>0y%a-RUoI@ve;qwJR6=CU5ElIPFG*Q6YbxM{H& zxWm!6ua22hzE1%AKm@-4wqRU~CxBe5+2Zel-ivHQfk%Q5`=_6eN%oxtAie>oCKCX0 zB!XYw1m&vKhXC1%Qpu5L0Xu2*^m%wbS6srQ`3#>Az|}jD>86EiNjW=UIe0#0fMl^K zYfh;G?sD}a-*XXEUZf1xf#TpB&%-XJ+M0V{TM;Ranj6-G{cM|Z%_w*yH6r$Ry zN5MTi*GO{>;ASOIgh%5(<>PsPtnnk~$Wx>4_fC#td`4c!#~A~yQip?3c1K#icekVT zz26swo6p9yg>t^J2dG>JNV%|TJu0m&WO+YE0EjV16|kf$Oar-IMRrc^-HYO_TN!)J z;Jm~8_ql!$)vzC%FY*ZYAYT z1+L(9A7jqB>eBH^>sx7iXEC-t@($jXyvKLu{f>#t_xH5Z6S0(i{_~%Yzwj6SLVWM{ ze($1Ve*)=KVJUkTa>dbf|9+HQDxIDBq0Y%Gp3>>n;9QpNs-0S?!ZT=NURJZ?{t3TZ zJ3BE)6Pli$#^kkEBVaD4E~_o$@XurN&2L2i*6paa(aN>}RqOTW%w7435GWx?Cc_w< zA4Tcl6o#HhZBmTBiwCrzA`nfZk7l*CyBmYX98Il<0G&oLLtYf4UaUvA(~b4nID;mZ zPDe3ysW(DKAB^;sLx|>dhe{C=lhsaMJ-TLuIPP`(}$xKPQ>Py|%?NNZ&&OE9@g2r~bS%hssm3dQ>RMoi{_3JOuIH={HfFQ)u> zz}bXh4f)q>L>I(4DwU&k`3f?!mi@YR7v)!Z?CNpKqQz|txYD0?);rCeP$mXWWIRnv z+;Eb!7n8lq(G<8e)>3(Pfz(-lABD9Noz5Bf6vcP56^)lZ7ZvX96gOk<+Km`oc`3os z@ZQ~+w$F2Cm6`4(F8Tr_%>Z7**ItV`buw&GmwK$VS}4$fDC*^k(}M9TsrLwQX}eUp z&mgzOqTsO=1JbQ)fz*YG`i>Z9%a`^5owdcP|IfUT0Lm~Lh9*f17eLQhmf~k#jGDU} z&~BdwVD+(E%KqVh_#ci#ko6}OkbNhR;v)smYV>-DQjn#y=Xp+XI`%^bkhZWLORK0pd)bnd@@ej4X4G?20NFN2xdyz{er_sJRUs9A+c8)KfPG?Tj`ljTh z&H%BT>afn%(F1{}P}S-$YinUEbF4ag-pb#*)B6+(@0HBQNER*ogNN;B#XXn*KT7Z2 zBHvDO;M51u_n7aQ!dY$yVyB(nAIT4Vyw6wpkx>@nJ?MShvv`Z|uQIE5DW9x-wP=m} z{IHbo-~)CNeaSM8pDqggPX1glV1l&lm;PnneD^-+)wH&AujNo4`254YAzNsX)Z#ap zrZrnI<@Z%PbA6INx8}V#k-KSdJuUA`GS3zNNZ!9_&&6{YItUOY!0@S@6|l{hGSY41 z27`Xa;d1dn2DPpLwzPB&x#zqSL-zqj!F9T><{pvA+H%Dp;T(-BV6Petz)))&uw)1k zfcB{0iNWbfoE;oQ4`9`-)pG^t&enEZf99Fk)_;ovmX+xMf)WaF^XAQ{yzoL)0YuH6 z-Kbx=63weua~g6kK;yGc-VL6qk!x`C^6)#v!`-RMz< ztT$~upe$`bp9?AUj}r}K%OS_7v`6{_+n&A$o=*VQXaL66qoQA##)MA+AbVVTB9^jm ze)F60dw=imjTd<7uCUD2HcR^bzyJ3yzkR&)X}6Rm8KPqUASy7JjFg%P3@5Oe=nUzg z24fxHz8%|WKn2<%WBR}(xhE&!Nx?DR-pVP+&dV=H`L$Q0Y1*UH=pG$LVbG2Vf~0VC z5@UG?4Q;x!7CTq2#-z51kT^rIjiPzjiQ_l!#Aa`?3P%fG`h;=0Hvs#tUd91&3faW-Su$t6J{l*cSjA3}0nt%G5spV!DUW`49PX#O*?G#OQUL$SV zLw^VTISeejS`_dwDR>pap9IPU1JKIPKy=OX0ZTbxndJV?!iiH?s-%eJY1UeQ#JHN zy^_Gg{dM*An(1KgHcUMf$I3KHobOZ5QneBVw<{`EqSjaoqvgwu#rSRDw&VW2J1HwGYqjXrN4a3COdT61o#>pN<+PtZS<%{zA~JTu`QBbRuH3v4?dNYq zb!{W2jrFLV_oCv$sEtceYPMqj(sxH;eIrHyr#L#wIAsN7YWMg!njBiq^c^k$$lNX| zF71h2bH7ht(&*$Q{m04zDS(&``Go;s%hqbOqNp$O>1oVR+-o~KIh{B*PA0&w%=0ex z8X1)KnVY12iAG(tYKC-j0osil`TK22i@9YnxG#M~;JTRN^rf%fjxT%p^%FoJyQS=Z z_#gg<`07``8ejhMm*W?I@fYJ4e&H7$92fL$BpHoCtAFQCbO2uc{r%|Z7iP3)g^gg8 z4ziBs1h!cORRo0fI-Lcx(y4V6H7Kj|{S_EgEvSEToY#9WmU6QK5OaGHBwVx*IHq%S z_g-`XYsfydD72(iRu6$f}N``+<5hPKb)V%y>`W$Mz6D=)jtrLJ8T4@U3rRC)H@M)3q9-~^=>4%c^YRS(z1jSW?*G$@eb*ef-e3ocy+2)n;!>$3hQ9#X%$EPTw>1&2OvzW#T)B4}%J{4cVO&Pi8UXcb*RiU2t z0d>=HR9e-jZ?tuK+BA`MP4? zm8C91d!)%1>s!4W9ye_e3mZpZnbB;!pqS zKOMjGcmB?|GQ}r|K24Ugd}SUfEEc$0jd2F}n!Ns6l#kGAV5}+NYNn+OMvy@&u9j&w za?XFsJp=A9yM4xZ6jcCS5sgZptvR0++Gs>apZ~32h{0FC67`Z+DlKWo#(@F62E$a! zXf2~jjA8WB_N8c`4G#O`D4%qr2pF0T5jLb*=nXU9Tp`K58Czzp5x^}pr&gNmjjY}1 zjiU#+YaMs=z)8M12&AAvP{LwTEXh?RtB%AbZYrZ5akMM zo>vCCa?1cBKw(QnqEkLvq_Ss*+)#-#-kD{3xQ#O}J`ITx zbwEU`o)$8l{k38V#@xp=LvSFFGCTvv#5jz|xmgKW2f!L}y^0KSGPgdiBuI;DDJt$S z$#cVN>)}>K-K*{w%zcI=a+eV`=;1W#$mnVM0&8&;xLxJ>wHPBqjRQ8hzRmrBr~RWC zowsAn@DSTqkh{ocV}dar%#r^yKu~FSFD6@yRiXNI>4;xlu70E;qQtl`1&o!x`C86k z6d4bc*8<_<{;fCyAQ`x|?ut<4xw8<{-XO=C0t)@$=Ce__aU4Hlxd?W%DKB0%qeSH;mW?OB~59=F5}3(J2|~tbb$a(;i3TJW2(#t`TK4%ln(hC zAiZErw2zrSc1xL#{_cPINd;t2J;C@vCq}j4vmLY1aSVqr-td^;XbgRT&+XeW{PLG$ z^xErbmR3NN(Q2wP7)+B(kYS`4ju2pIT6C0+?JYF9YWe^a5G)EXWPXg$kkn2p35!`&`*GN1M|rw&MLThWa<*BzL!Gl(#_m$?in=CuWn1{i$ zSDZ@9?b7_gV6Kkl!V)cMIU;g?B}XiWma_zZ+^dyJozU%;RD8X*0*FP*6C8%25y(uD zV?#iap)ibPrt|*d4Hjc!UdY5EfMracV<#RewJ28`v2*2mw6=BueteXJ1_yTmz1=8XyOv=q^h+N02kC=61x%K& z0l=K`K&f}Zn3lX2N8KF+E?>zN|1|(%HK%jQgYp}}-)*TT`}ZT-XB1e#%oW~M09vPq z2fSm0_5#QRkh~B3k8$@lu$^vpBQWLS(p@hAR7 z{MkSIXXDTPxj*;dxS+=%=cGRX z1j1~w(Tv*lYteEg4FX3KwpOojo1Q6c*Azu4O4dos#(kiFq zMfqEB<_ScDeqwI{mRTo$k$`{<4H7_uyh6X5BAVRoBZJ2RTo6tRYnI_6NW;~qZkgs( zrQj+TiI7)X9sNySMdOWeeWvrhu@Ef?hhQs<=l_6#^i87-PP@4PYHCa_mEENRY}v;J ze!H-NJgrBW0#{0v2M+qPjNM`004+dSb#o11S&JEED6cgVlz|MRJ)A}#Y?Aw3Iz>wU zt#Tu(=;g)U2w6@!y8T>5KR!N+>D~J={Q5Uz{Ohm8?6td5?~kI9_aL8-j-v_ya})K! z!C?#mTkbO{nBf)x*LoB#U5?@|AW-nfcyM~>PFkqyhOKzvg(&T8QxEFIcrZQ&_9#0H%X*ujD}oK*@M873|S&Mgwp~X&zI? z31@m3D`u3}snawxa2Sg&4HFX2jR1&M+C_*fShkN?pPKIhu$D6{4?57(M|Q!-CLR3` z4i4h4{FT2FfBw(^`3q3?q{vk(?okQjRY!f8;uyel5`}}aC=F*(5440cz|}s$Yu=Aa ztCmczC2Xia039^73p(ip?PYfDdei_%z1LsM)jRqQxCFX#NHXYL;j0l6NnG{I7G6$e_&!ckpAj${FQ8+=1Y4@V+wC0d< zBkX3=UW^89fE>SPFxkxK5Ns2D0p?LTn@08ZK@{2}Kp6tEj)2gTu7E@s%3~?mm63+O zP(Y2BV9WCg@08q&d-Hz(Q@Abf|FD$lK83UxRz4kMO_pN$si&+5T`CtYe8{fF(4L?6 z%?JIH0ZM-Oq$?g-+GQE7hq{$*BA_z(tL_O}n>?qFmNHYrjLOydR4*(A-J}88i^djdxUzjYdXIk6H$K`|m)-sdZ z)ghN=gK^9OJvzbb>*Xfq`jt(zXaO7w{b80N8YIvx#i)!dTdTypVW93-lu&x-^*ZSr zN35FDm!PD@N<{KfUKhT3FKTz&Q9lB#9*kmbREx^)Ud&O7Ck?0TiYVYGF>nv!Gvu|~ z$PLa>un%I~N0~O1MjwUyL_pI45Fw{$$k`4GGeBv6?`{-Nj~8((%gA~1ECRTSbe>`f zurwsC2EeURijpBZ^w(lc(f`b8#QEu2`f`n&b~NxSinYPV=UTS5Hlwi3Ie?#mZ3`S_ ztzWcPcd93*z;n4*UX^xj+s%Qub8ksyb2eb*7WvkWsf_2i=LAB_X7TY^%cGtXI z=eJ9dbv)JKUhCM=m%bE-U;la>y!L9``SmZy;XRlHW<6;i#Zd_0{ z=?AB`1Kj$XEjsAx!F2QEJ#w%wEjBjH`ZHG-D*aCs};@d zwJ2$&A{}Li*R!2?#!uZBa(4*YNQKmCya|fq6kw)2S;+|H&C2^JEj5hyxeHS=I$=pQLmvsS;$Qn3p5*9)@Gv~HS)8aTm|1gK8O)YaLU_mI|kiO zmQ!D0XF0TTxe{}H|9*5%PosT!5PbmKpmUzU)Ght|?aE2pX6RHS?WS*+A(r$r+q!W* z>Wm>yIW`$j46h`>6d={PPv2qvlr;=Eb=o^QNz0j2!4FgR0j^KD?uvWcQUk;K#(Grv zt>sSNy+!Ls45))a(bErYMcn5#(|VfeiGZ>-UWyHILW62Ga}P`r{|9w&yPze}IR#Y{ zG>VoQ1Pn5PD_q5>iP_+=Wv*2?rbsDw+fS2Ftr`F-7S>fep}{ku^sbfeT9#&~BM!<|{2Ut5dejg9E< zRARV>5NW_9Zmv6^9121BLEnyv+TVIJ!(Q~~{piOqraSfMUTegJ=O#6n`|ho%o*YH7 zkCru`@{EGl>Qj&bfRZX1NYvo3dDvS?TLHA-zC~U$1b#ih)YGW5y4WizvNA<<$qO!AzOq)ktg?9fS zHckdH`{tdf-8+uG{yb{O{ip!0=H2rcQs(hSEvDCZId)>O(~R~UWw8zH(rmpN7@H&)%Qxwb*teXr!@sr8Hx$;=jzW4d0}7n{Q~qRwFY1ng`RS0 zEt@!PDyZAsSac+bGz6{zFil|S^G_*Jv&Sd5So$~r&3`j~hTKyb3uDD2$DDVhX~JE8NAG(C?ex#~zjT&lpRI)5tE=%A-3 zr*R4s%wQhRX-d|gV2Wnchmpo;PI}0X2VFG2ehl14?Hn+-u@+^t&-Ja%*t&8BWh_eH4`8ACk+TETzTaG5h_ui>!Z!w^;mGSPtc74*f<;$PS(FwID6-Q6|h{JeYx1&W(YgW|_!5g(j2#Z<;v8=zC2yhboN+U#W1kO`B`5P`{y`|GUzyh#2>JMWy z(uZQ6vAM=5$ZbHz(VcrSM7i#qoW`&y_*_5(XzJd%8)q-SoZ%)6DPgz| zS5z9hU8w<+g#lyHQNH65Bz~Yu)O1f zJ}BAA(-~9rkDw8d6;Hb{yMM6s2{@0LD;=kFcr9V|&D_7tz1HU3U#kLoV1yzJQMhvl z&E_PkbY55VRJXUHaQmClyY=;$4G*G#hBSWV%hBy0aeWj+G}VIPD(L97^I?>3pTzm` zIO=t@v+W%;wTadq2dx%aM_)^x*sXr;>V7HOjLarhTLFO1}Q zKBonZUwOY3D=7yN6S_=Q=f$Xv3eo7zqq#qf+U;I!98RM!L@K3Dw~!R3<370yw+I@B zbczGA({B}>QQxt76>-x9Ol_26VnFK}tyuyzBga&VXCXrG;Si`u+|2M z9RVr;WF@C6oBH)48(oml?xb&R>13FpFtqF?U__V|^r;23G`hoR^runn0`B^xhv+Mx zbfSukEO*D8NB*7wq>z_Q+NJCQEGd5)YuxR*X%IW_A7gRg%*roZ|t(cpab^fZb+fFDY5$!XHH4eB=rB%$mhqsyJM zsFeU!g=)%uXJ`rlvtrd)YMW6l*J5;d6ysaBqN+;`>5UCOe)nF~)&YP3)Hy&;qe4x$ z8Tv_eFo=eAVSE~Kt-iGtMZ1>_qX|=VSitb1{GBnTVUu#rWzq-eH;d z!N9S;9_7nd(xP_(%AjBQm0yW}=im8v;vf2l{-KQM|HD80!|@Y8@e}da|N38F-f=-6 zi0D%p)DZB~w}a7PXJ;J*4%g-YwPH1jMrTG~3@4SSG`2E+%@iOuLbK@E;h%p#>Ysfn zCZr)i&LDu!*SAX1+-pVu{C1qY_8T!gLp$qtjXYf6aDq5VYb=*c{h!#3TK=fT| zeg6m{SBwe_)@@d!Q!hoI&na5uM8LIPhk4dxdKm`VT1ykY5v+A4t<6g@KL>yvBSc`# z(ODZo(u?_7KMDuuQ91=s_Qoi$I&dN!5rVE{a!)#CwzS#@(eWp(h!2sscus!`e>l~d zpz4wPF6e?jZM3xiEQ})zXpi#7w~dnB-f!NNSw8iC{$(BbzBKSGKBs`GAz*9*7!!~o z2ojiaZyBXE;|`*b!S(r!7WItI5A)k1tMJhRcX{RxT{Y5MHEGWN`}MmUXl@@1oEl~!YsMcQsC7>4g$<_<8fqXp##$D@Yx-t zpRYc)E9+l{vQz*f?x>;?+ALJ4Tbc4AFF|nb6fi}GPO0yVa-PirfosV5W;wdUUUbjS z7#Ai0Kx8W8M46^20vMI!>HkK3-6ONaxoM|Ic{=HMUoiOU-}yVy`^Gm&m$XrgwbBJj z{H)iDfiWW)JL*?1rKQVIN>u~lQidXRaRsRIddm1P+dqu)(NT<$>odw)WPH%4tV14S z(y2O|gEDSFT7!qzjPk#;n?P;0xfv4xm3xVnc6Tz?*jVF%D=zsoKx{A;IiphDSO)|G ze4Q~NojSkkmo7!)@}=BGphOyZ&G(*%|2=*5L_paFFVBen57V#x+ONfB_s+VY4^Pcj z)LeBl9w&$?quSLQn^E7r8Vx$5v1RJ(m*|WeQC!;u2yI7cdn+o>Js0KAd?w~})*`sQHV{KJ;V4i))?mL9qgmIp~)a@Hc5X|e+{%X zrwwYG+tGaPMl^0*jkTNCqOrY6Sz%a!SoJD-Ub~drkQlS4(pp2?YNn4^|KKD7EwI=p zPp4YZ3`=NZZVs|vkIG8F{f7b-<<>K1ZC9;=_ua-XY(#HZ@}}Uqu*SKJae~__Id7oIRW%d zd!rbhc60H8)7!2(b;iM%lDdtQ>Wye2Uz^u1M`62_aUYvb?+GkNlwGCTX+Y%ms5e-E zSM%Y#ploiqA@X$FBOU#V2M2M9e4H-K9aXHBQ;q$8^n2ZC>;aN$0zx3CV9Zvg?vta_ zC;$S>Bn&!3~Ao-Kbo- zlHk*Fl(rnN8Zo6DTHd<%?lB&nr3;Sl=Yjn9g)8$_`cv_Dnk9QdQo6p-7Wv+uGAhs+ zm$m%x^fN#6Gw~|CfI0mvUP3U;dZ><>g%;FFlnJ{ge3-Mn}id zxqCNSt|&qXq$8wua4)L?!O$+VHa=LKp1~9PVW7#OVT|tHiR#H|#9BSZu3)Oqqm3YF zjbW^6Erz7!UTd!6Tz~C22JQxM?~NG305uuNjmFmiRkKN)!6c!-*ydVH8wj&TDaNyL zl;1c*uymt!>N0T{0!Hmp_7;Lf>4&(A$?2UK+&hRG+G0*|C0gT3ka0!I6VK5|QUX)Rw9wvpj(*8I@Pe7aGX?^-z8|mLWM=>sx z1TbjgS8qmj_fqa_sSnt!(*_K!$9$bJ1Q6Qq_u}qo5HEi2^D%U16=ZM)WxUkurDMM< zMAL_ua#?OIV+Oe`0l=IV%{`16H!7POG2}&zHg{v~`i+S0a1nb=z~p-a#1&E6X@ofE znT@MgQRKM}s2twApT>czk4dK=rQtMcXl8S?q(i{ojI>&N+c4w8{I51e zO0lmLUxVl+zUk6}0?gK%gdczvr@ag&{z* z?5e3p$iYE=%$PJRUD}Gt z8d}fgM$B$LAJZGpMcn)h$zG4j8Njbsi%YvN^0**tEo$}cxU@fyt*@L%oXw&&f(g;) zW-bt@BbfHKNEc>CBOLe0yFHGIOVa_8Y3_$%a+&%Vfn{tVG*%sKO9-t3;IDo%ipKdA z4GlikN~X+RPD_}|qvTdkPG{tl1o>Rh1zpg$m$V`o0CrjhvXvGCU^?8(?+yhYU`%`% zzzOVB_sqc43C^^VNu-(Es4c#}D=id4(*`W8m1Fe$M)bb05vN~hM*r$soUa4I1{~da z)CXGLk&RXI1_%kXoW@LOHZP;JR--&YhEb+m9S(@n$Etd$Q~V^V$RT|wW*!w}vKFcu z;6=D#+zn)O5#`sdgg{kMQ5YM3!-Y`=WFQSzn;I{z67%&|TF%@hpzf-KK4t47^UelQ zx_v)Nqf*prTQRNFVr*XZ^(c}T#m+693^(M0szD#1=%3*}HdO8lF|bZ3$JJ^Rg}WHz zvvI7KH=_1Cek5kke_u>nn-RO0qjcjszn_op^&2r-YsD!Ie(r*?FMJ^epZ~6y0;>8> zdrlY+>L}Oil^7_~0d}REn{hfjjQ;sijOWyiv1)B&gL?O(^V;iC2TYZ=x6_CqNY%%= z28h?92%2BKJlFh2=xz42P~@86Bd>2ZeN7wZ2OPELTFT2gpe~*BwXiWhxc!qm4AfAB*LRTn%6x##tO7u( z)x-E+W37LHusNu9P8u%S8l&{YIzr*T=PLRIgl$-RED3a;?Ewv=w{b{aw+xdNtG2cdWL) zmW#o3xyctzU0kH_t;9G%I~RF>dEIf*d-GtN;GAP*29tdl(L0|M>FE1$`)@6OKDD{?S3yG+|%A9(5z~BlM;KvYc z1(PP?%a@|?-CvBt_kK@IpL;$=T%W)&h0lE<;?xiAwi8*5<>Q zRC+P2^wFfIQ9ZpErB}Ze)1$jl>6}DK$Iu}f(F|R2j4L3te?mP z)0eb#`PE}JRhrwe0jB)EyzN_PRhG(xF={Z6#_=d>uXm&VN;k#_d`^%;K)tCb>pZM0WB>ARMkMXC7uOfG^&fx833U1dNZc_V0m8v zjCj_S@)bis0G#H?Jz!nTnw6NeP)@mjwg!mWXyiuR1-F3$_0=~3OP8LB%7%--7y|~A zs3ALD#O3Trsnd@t4N+{Mq;KrRY-=~lS8haHx(=Ya7RBAmQQX<1Y~>hEd(@RY07bJG zUyAvSXJWR!9kXjUVt(mLhFc2cd+d-7U@Y_ryL=@k0Jbr}a{AIsF=3pUZ*72yCNU+g z>2o(3TdqcbZ#yQ}u14{5FGjrde1=Lw(noI_oUql5F#aXL&ZEkA{nB#3X6i6??_@vE z&jDI=q~)77j+qYsGc8^m!^5K(?H|U-smZg`m=6Kd0<}i~S(5!Q`xDpw{DjkEfii=} z{U4Xp^k8v+D}2&YaFpf(*wX|oj#nBGms^`#giB?}0W z*6w-?W^VgY2*bfvXXU5?vihfIF*rT|SfS-1vkEQ1<3=4OT|7H>4x-}99)y^J4oz5X zbP>Zyb2UPc{FTP5rR-|UA6)OgpbL6(l;KuX?BACAK6-k%?=Oa|PY&54E8j0f0U%}p zlF-E#fX0L0ABb{5ee)@;<*Sxo_a5YTIm6(`V+t+;0*9ku2-Y9(aI?O#dY0R91fi327FE!Gb&&Ft|p{DB{c`0nqH66444^5qx+=;HI= z74_frgR%M9&&0-Oo{#k_SJNt3^y)SyAQ`XrvgtAcWF8a1rw*{1p#^0Ml?mx*%rWjwg1OU)*sAGg z@DwoD1${b6Zp-l{zkhrCVfts$_aAWl!{0w4B-`e+qa~wxvb{VHZ)?f=F!WAOty~(+ zQw!FEDb9DGOow9R`?Gjj;0(8+OzSTk^R?i+MN_&;(7tYb?V-#nuOW*CTL3geW+dP+ z&W7@i@y=-Zj(f_evY<;~h4Lu~G#;5UwRw3js_U)j0kVc}^h}B+rKAJB) z7Zre4<#|3|d?Bq@g=cR>T)hN{D@Tu`PyU^=^XLMM+V}278!*(peJlOl3~ixPzf+h_ zTb4fi*(m*{ABYkltYo|~l>XWm0cae(?VT7fCQP&x0)mX2))Q>e9=W0((3*(&A<7UO z)~_7@hWF0CF00u5(F;%|g`1)u`u?O1BSU?Ct_p1PdDB zP)*T5um~`xFXw21^YuoQuWT&L^(8}nRA6MSaDXo(SC5NP8lt&UjM5a%3MuLeE}d@` zgeg%k=z=~5a=nb<85W%P?dyjE2_xVy_Iia6%w16M4($JNk!_OxKg&gOPW`22QEN|) zfs)TB1!fb3r8poDHA(V)uaw;jGpAXRoh3mngSqY%MdThMJfnR%t@#=6(8PG(6$8Tx zQ1taukwk$VRgk}c4_z;c2J|jE4JpV2gaGoKApoUiVASeouE*qyH)3*QH%7Y~=$hQi zJq5!|kk7o;M7iZ%=FEWg;yFWG09qzz?U=cl_^=uD|C=#6WPIpx4nQ)wvK#U2 zwU}(xqrKgXvzKnhAYSlhasV+hjc0j6n~^p}jo}Tj6TfneAs90IP!5vJqf&eIuHC zyD?zQF|?CLgwo!n1X{*fD>s}Hu4g!pfg!+sdTy`uXa3BeiJ$)IpI+YiWXP$@U;gD^j(_|g z|HpF^^zH5K1b*%)kUj_x(S(=M;bm&E^KiNEC!MUr2+gVI2CuVGhN-AjigEwdSE74- zm@mt)7e-t@feA7chG6F1NU6eocKR}HW?Yzpj-Lpmp@CwDXlgK<@@xXg>!KYETpGi&uT>km)LlOw!=qmE5c#e3&UUd zPShe@QKad*5A$hplk{ctdQK^zQBBZPCu*(wpK(qtYdkSr20~YGrb=`{7xZyaVu82H zoR69^H@iK8V3oI1L7y~U5G)!VVnxa|#gGbBxhjBp{PN~rz{&v=ej9o?|A)zTQ2uG+LgHah3|?60A@{R{Ozsu`8vFPD-I9t#q4eZ0095=Nkl z{^x%_e)U&>HU7ze@}G>)eeQGdkNhM5Nc`2m`d8ym|LH%S&i+r5j*gD6LNMAGk=0@&^ zHotoiLmsVN+Kcw*J`?Thm!qv;#H1F}V+0&pq}t+WGK$U&&2G0D<@FRSk0ia9!1Jzn2Qosujtdp^>?i` zWlg^{{$0=oeOjn!r`L+ixa_nWT$EOvJig6jmhZ%a3+o+M;ZyxYwJfUid5qzI!#?DJ zESh?rXCAGBir2gGc*Y|{1nT}EWqQc8dA+dM({icot?1#jQY<)_GuXR=(LE@uE+|4K zHIBzoJmWlah#WGHVx5AP=208yGh2&NYcu9mEnC$njmZ<)Ih!m%!?;SF-Ez)y0rVOG zrP&_$R7D5e2u{8=&A)he|2K7AdoJ<)|Rn zrYP>CzWWBZV?Jz0RiCSNH#&Q3F}`srM%VUYhQjRrl=U$H>0l5u^55OsjE!D7w(j&} z{p&|j+dqir`B_w202w+|@#rvyUDBn2N@Wyx;f^kf@PDuuQwV zA7+^LQ#KYn7ATVhVZZo`zZie+&;7XsV*m61{6FWOm;chg^e-*%cygp4ne?z@zu&dU zJW9q*`zwFtujF=59+vNuP5M^RvFL0%m?rVtts*vDr9y`u&Jkz`IwKTsx`KtynH!eB zllbzq=KY5{@`C804Ky--_fC$Zs{`e*Pp77RtBok4?Trx{W3)2ESOkw7r+PM;#?IBt z(Q?&LxlZRPN9Uv+ok=lTZvC{ohsL%ZRYP1LRGW1Km_81c8^+LKHm!Elp(F#1G+_^N zdAUoPv#b;`f?YPb34Wy-7tE&V;-fz*Pt?oSgZUeE=-Pm&j;49uD*-kBFZDoP98 z(i*k06s>5%r}FEYQr3#BMK6;>{$^(gE>C5}lHw-l4B7?=Q zk_)nI`m*H|s$#)|V*8j@cl}odhX!c%tiG*j*#VR^+#;?DXhPoT%jZf+rwga9B3CKa z`h{H8(xXMkPi42`lrtF0ILD?s&rW+FX+a?JbgWZ9;A@2J9%*?ZoyOW)G;4LP4Kf_i z$f)+`=P}gU#689+bE?$z#xu&-taCH~jm8=q$MMm9>J4D$7FL`w*}osgzMS>X zqI3UNoZr43Z5N^eK#Sxt8FZr0{V^D3`&fU&c@&Du{lysV`$!8JdF0AjKHXze|6{kP za@t#;u_0irZ#(VoMrCg|hFlvvRp}V%xahPl_tj~4$F(Wn%SP{K%psG^>uIG0S3M>< zmH3bTqyK39=#Tzr{1gAgKaqQ0KFX+1g0zZhC42ehmzQTEUVH7e+}i2U{OlBl>S#8b zc|09kE3G8V;zLHUla62%1H+as??wSo6nlHwnT^yxpTg9SXoV}9%iQgDjEr*3F*!%G zqVvyP959pt3Q-0ymHO?N9^K0@8I4?Wy^WS7bF`xb5G$b}7SGA+Y#iq>`4|Q(HMXNT zuEbgi5ccf%ME%+CrffIRSQ}`9F!b?p3Wj+F;f(g^HctgN>II}Zg((x2&`e8xS1rvl zK;vpJyHwTQhVm@MN+o>RRNq3}l+z`GHtTRf7xXD4c_DegzMf~^nNm7DVR^g=F|gpT zN3Hc)w&k%RL)*CZS+R>ea6e1;u(a$B%-*;hi83Y2%9n?qf0TT7KBi_%(Wn1fk#yZ? z+{uI$nVt)V5{Oj{Yr&`ZfbVHdZ=h^8E8OQFa2!QsVkrZ~>W1Nz8vvz>JfscWUvkzR<^I3= z&sG7hHQhDN&!PYrnUuMAqnXyx0^noj)_4AP8@$VFd@loJ07%ID0)T6SMNmeupQOoUR23LYux1iVH9$4!D;T|GPO?4)?y1J%3T%wQLZXuMw#)Wyniod zuY5g*cfJ;Jd@JgdJ7;lRCC~6WDNmvfn9NB1044Y0EC52wy3Du}&5C@#zFZH|FB$lJ zdrRG)asc|Ynss|o+}V!ce5|$7ifC66jAcB_{=&j(XsFS_L2k`dt~VBzvZtJ$2q^n@ zkwEK5e&k2OEu6mmL%uB zA*E}3G2V514m8KnBr1(dvAy>kU>5Kf|4^1Fw*raY@ARho;zq3)xksh~)cg6$znogF zaA-C2;W={F6@9ZVAf=7m?7I?k0+1Y~FR#;ta~DBz)IbqsGo}uP#L)6I2OP}Ldl6^d zj2kvPI*a+yIo}5n9ER2?tu?~Q(SlQJTbq0<#fWEm!$DelfNKC09JM|DA&v-@O|JeYPe5ELXaB zx-mRB1dyF%nj=A3zncpSN{k&tlzxMfyXtg8db4ZSa+T-!>@enEe=XvTd!*N8T~$&V3Y?xo|9VOJlU1 z*JJ-Tel_m@`me_M?bp+RQ$Ut`gt_GeV97neDu5TGOBf5L_r@E5*a~THEFw0qH==rF zJA={cM=&oo5Cm0Z6Ud#^xO z{#F^RET$*(zK1A9f#9Wva_c?H2}iutZ0YZSK{o)O8Y;-s$Y0@}PUWtk6o?g^sw6uV z)}ml$#Jv;bb8nD`vklfk;O0JyuBZfEC75%%a3-(=j3v0^e18Nut5P0-h|`a*W~>1& zGQJ~#qykVY=G5tWRGK>PukpJavkpM#D|2`J;*=61Ap z_eiUmGPQ7g7Q@^3Gmh6}8Oy84_%I-?!k`~j#+ue90Elu}|7jQCHb9}?+l%rx;HTA! z^5x43g7j-^cg~`6(oPFo!D&j`VgfMhIV(~HaIUXq=ntoQ8&|JI1+bMt&3zf~LWX!M zbAR>5Gf{Rrld@=>$lYG_Azw%F=P2ts!@bjLCClY*HPzjnsI0XXp-fIs?I6okpH3>! z!Jn!cSX>?ZT}>$vbMy0-F-YFyrT^W(`*-6%{ZIeX_)CB3FMWciG7%K|a*ez2ewR+U zu){ljsDP-RcjNr%HUgp-8_o3?BLK#LwNhyY;5mz_!C|+yIj-bNp=x~{t*VH|goZcm za`dBqd>W@{N9!-X1aq$;gzC}1dn@|Khq3d@e(b;UO6-p3F*rSnQtupaHHjetp<);d znPfCdKZ*+4Nddu8tRYCQ?!@4X{J-|qnDl$8X&H}90pos_`TQZw471HSSGacw^K)PN zx*_cPXtQd~{CoGe_*SyxrpAkK)~~_ei+%P;Q}yzKF6e?j5)wAas~?14b6`%33BV_L zy^sTM0IZbj`o_z|j}A%xa>`2Jl@9#yw@J<6dskp0JxEV5$DK=e;~D3beJi=(Op4)K zl@*g#32;K1$hN}DyzZ1H#8dv4ma_~h%k_DEaWTUWk+CT{)#s{mLwvXoajEMTasW2U zp|a_APC317`3xYt*2vGv#yTL#xMJH@DlKobDZr3tYXCvF&YQOC(b(LGp+UTRePr`V zOsGebac;7WBF%F}6?DK`6A)IqaXD8g8UkYXh0jDV2F$ePQIX=oQQY~pUyIgg7%k5C zsn>9SAH{eUrFtuajL%>Eye$K$tHc~IHFkjl?KQTo7$?@RUIAFuB98CI$*tF7{l;#b zw>#0;V5|Wk^%!d=qb|ySDSCz7Xu0dmb1wi8H+c?4|KYLOHlgjb*yUlHY778)Cd~lD zhey%7e?JmoH^!Hbh8MZA&4bW9Oh7<6b-Z=O;1foM&i_4w}t)!nNrT0M|!wjiTE= z0hk@c(ZRhqJ~~Q(wGMOJ(GiWwG&h!GlY8L05QedHHaL;j!qCjDD;9z0cL{^2H(`t@-*WP>Ayef;E4weYG6?7foUGf8~*T_S!yX zvHDb|3MfVZn0}`l2lo%+?C3b1`m;^QbkONU=e!*w6!Q!c?(^iM5kdJ|H$+6ewkVt0 zs417q(MHkkq2vl;&hFihdoRBdr;NQlqY|JbkDZd_UdM^`we{H9-AO~nP>Y#>%Cc*v zbBglj%~%K22$UOpd$H~oOMoBSzy$;w?%PQ^O~zfbHjCpmy;+T7V8Mck#LG*_%xwyf$UxB!XD1FU* zS3dU?(}%W{{rHdnc!s`sFZ#wezOnrF@zN*OQbz0ND{%iVfUYKRJEFl<=V%-ym?zEM z&aEHp$ME=8lnaBH?Vm(NeW^5z9>A)2auj`@aT}&`p^+Qlo^5pbT#rI!69DJV3zI0f z@5kZ({n(msL=`6P)6uKNW>ku`n7w)@BOp6{P&hr0`K`NAfZ27bYz=#hcril+27TTQ zX3Ohq$xO*CF!8W^9FrM9u7uzOTsEpTv_rHf(kktrMFCAwty7I=!ZXcNeHjK(?onTv zmu8qcSD>sxQYdgAVd)2?Ov+sXe9TAqOhG;2Hw8>B^#c)FHGhK8?>M6IY`=GF?+d!1 zkCGAq$+Yjneu+Z>J+mtPRD)JuZdIZ4Gy}5Mc8&*WsjyWR)-o+*ZY*9d7Z*WV&4=_# zTGsRlOCI3<8OouGCy7qf}1@1a!pkohI#w(%iJ8(CHb?2MeQ1N_Wx zzL0)XGr(7$a~n6VMQ3*}x8Pfwm11ys5OI1K1;A5is27_A+|2vK=!Utbjnq4Mu++uJs-%K)~eYUS$%R6b->tZu^qKmGv@2{ z80%x#>qmE7iEA&s7^P=kOg}WYf)czL6SF`%0wyhE?rB*#ILvrrhQDyC*qClb>QbeA zpvCM1xx$ir1*-Z}n@)LS6P#DhMPxPVkd`v@T2h8F;g|y~Z7bjJ$3>50Df`f%%*@_t zi=~I@yTALpm)|~K`ow}Vm@^|A-va>A!Kwy!g$#9}qht$>q2LM>H4L3H4_=Gnn2vpP z8nsd-rriUWZa?Z%*m`q2eF`T1g|)3+K8dxhU4YOg!loC4K_@0B_p+}}jt8lIm1G!L zZ#0@k>D8my0tikGiLrkWv)6A$5$2tookvlD0rQ~yAOrzc98&~b3Lb=n*04&w7`=WQ z1{{Ze6fW-`@jxBsG2U5j%~UA?0!yj+)J~1JGm4Va1SI1$f^A>{U9F|olT!Rd06m*0`1??-@YK`8wYCi}=z^XUt@_sc>R0mqy9`Il z8o|}91`tE`pqr*uWX0!c09$=G!;+wY`$$*tHU%}g;?acxTD-i~6`>1|HBZITOq2yq zje6a&!YMtsKJxi8vP-M~*j4Lg($M!z(4c>9f{9^Dl*CEOf2TOL0wnV0;90|qj1LcUrp5PY8ukNc zj?Am*bgR>%%Va(d`dFqfZ-PbzT&v@YU;JXc@WKmm<;sUvm+?y;{ zWs=yeOdZ!tF>7&8owD&y$N${-QpQe@mf~|EEIHhF&H#}UK-sj(_chK}7sA%_xuPMT z%@%%JS_tYl>8-!Mv&d#z9T#*#Pm&T-z>DvTkazEo7QJ;|Qcmy{ndFzuWpODFMTdj6 zp)(4uL{xDDG!&hT=yQJHIr2n-OV49x4-7lB@1d@%}uEdkb?@o5Suo%X5g z2oP4;rvBS8o-$@kk>Q7w>y=lcw!a@0Kx2da4W}`E?e&;^{TsOzQ}ys5itToc8MoXq zAc=}rNq;g}?1TB>9SV{urvYL|TpJx9#Sq|X9jbt@+$?=KTpWx+#@^JQ?&Ahzk4qmK zls!!MXlyqI|2O`PeFq;6767SGj~Wa*KWS&+y_rjrkt_7u7$2R-`Tk+l&`cVe&6t*q zG_9Fw93}Er8!R^JQC+V@w|5f7&v8#CuiUBw2CCJJwm+#MliUkaf2KwaMX(lCK-JXy00{co7K(tD z!eZKUP9n-CEo3=;>a?Z#rnP5L{*nN6jFbf6&BlNyKqV?FK&KQ%WNT&Jjm!(V^_>=_ z45~U5oON<$B4a=b7C8!KzLy56$X0d$N`R`-+GbQP--y<8`gdKW+N6)79_N5R0MnSy z8c8}+Q`lUKcm@#m!V6J-<{4!EE?^RH)azxG|4Oe*6EFPdoW4x3$N7wVDIl_p84MR- zq*YANNxRJ2o!mQ93tJc!z>rVNEtKW}GTn938uk#qMJg-jN0b8~H#3|Q&lGw9)3Y-k z1LPVxfMd#dRM@*jJt=nvk6k|hl#*TLvFVTh@jsq>UmD3+=KFyk_<`I5^SA!i-@3fx zg5HhfQ_bh!LTYAkw_9?^{CQ)o36R+YKyAh4YtO{i#tsVCdQAG`7@c=vq(N>}UPl19 z)Ollj2b@Dk)kF_7jb`H*1I9YNbNJ*ehG;E04Gp7|H(SvgwxfUlPIT_R5xw?tOb~Wc z09@hOYf*gRW>l_Ri`LbvQANWl)(}LLX`*EhfR_7kY5_yQ$aodNnoFu{>l@@*$gP@+ z8vrx}PEmp8>L?C3Dp%{&wNxFk>&bfxQiQ5;e+&+(@X7PbXB6IV zN6Wh|=z>1=^l)F78{{je)jp)>$_JtcSx|(Bb6fE52j{^oH}WD^VTy4Q^l*L&(ByaJ zNd7SvWehAWE|3P442sP^C5%>oXB{%61#%>v_mO2On*c`!X|1iVBikBL=9mij03XH~ z(}^8*IsM7f8i#z#6`}$!Yer^HS(d%u^eBs9QF)%gk^G!K)J?^_m1BY$fulRO7|+a^ z6T@X_4NTu%8NvJ;0x# zCDL7m@8v1u1HWg3QS?#ZJBI0Sdh_bF1XVi!5ANTO(^p=J&X>QOt2K4bAD^B^@9+?i z)y?wH0h5Iy)&sxO`!i|cYzb$p=RZ4Y-1@V<&b7Lc`a@b|M!eQo~- zM6>i5o2CG|8t1j98MetCV_Y!d)Mg0~YUqvv<57m*aEyG)Y0+CBliXJ6Cx7xM6NEX9 zX$&zfXP&>H_n>M$=4%@{-wu~8Pl7t;eSp*L7#EIXFu4m@x)syO{fNOGG>kW5uvv=f zbGtG7-19L;%c);}E~c8Y(JT}yf|T;kGf~`hIddzjg{>&G0d&23jE)fi2WPQv%&=lT zN~dR0KfE7>?)_+15DbI6Q7aCkwY`h>aXDsyD$UyEE0<$(bvJrzt(Y{{(qF6>gBaBM zaULfztDd6e4Pv@Jj=|PE20H+)jY+h*HlCg$XwY;TaN3Plv@fCkDHy1-d$bi`HZ@E1 ztMnIfS~HiayQE!(ltUqF3nhj_QdL2D}awqcedU*7};kVt0>RXncv-63@P!73!*`P9Hbl-KPMIt>}q>GGk(0yLK&? z*}4~{=FIQ;p6|(M`xo>cL|FZ9 z7~vnruheT%Lo0GmG)10i*frg^GJO)#p?;E%rOiew*3g>PHn-#QrK_>IzL^##oj^rK z_wU?^JGb6Q4eadfgm>DBlY_(Pq9Lha^@qd7-efR)V|^{EO|(a}#kGyKxcbahu8rg9 z_<)0Fx*f$0jHuXEtDRD|a%z1^+a;la3Zv%k0wXvCf@TB$EfnO{aA~SNl9yP+-k7WnYdF z<4nttL{8;d_Vr!H0@}gw7OrM&A0Ec&?p?-@T3XO5fFfrv>U^$mZD$CL^V8EfySE?r zzxuVf_e*~}&c60FkSF5@&)$CdWq{9l^toru=eW~NU$7}4%5WR{LQmW~nX#h{NE>m! z3s9nwvpjrBnVydSw2@Y_GUG>`x|XTOXt;=ZSzcd{jpf#XD{>*iobl&?^7qKIe{c{Z zXLPtcjthE6 zQg|4c^~O!KoZT3~VBHy7()ui_H=l_yKnmD`06-8GdodmyN2QFWRpuGaPq<#bem#a8 zrRWtWF_;Wu*ns)al1czEH9D7okH&zcaWO`{a#{wYM%*1EmaUJXE7=u^&(LJV5-7*^1n^d~D<(B^0l09>K&>Q1#-0T@gO zfb_@?t8nswKV79+;wWsxu^qo&N!vMkG!G?h61a(u+X$j0aDH}AG`A=+QoscnU z6+@1#va#)^P4k|D)y6R`_M=FP6k_+uM4;ysVx>d}0Pqw!-@mjI(=U7`IzRl~l&2gG zz@I*1#mkqGgOnLiQ#v}1I^#efis!G~kImCbY*)78N`DrY{=t`{`3wKwh+q7LnE%bc z8CP%LPSEI7XT`ks52Ez7Z$ybPzPZ02HTT18ccWFU#~STW0XQ{WRQATLn4c{oGmO^O zV(hfynm%&81HmsKYy~qS`jY))|JXm4)0PH~{jdJ3 z|7v;11-%#PzhHFb#+A$YxwgB5rdEH@x9rdiDIo76(zR*XlOLXoS7YW;{48j^v=5F*aBd>0C;^;* zDp1Szv3iT}8nmI-w54{H_pfexgx=}=1zpf5fmS@Ah1ete{NhYf_H3BpK`_l!YL@qL zV*(av!B^hDcv@v;waDh%O_nPy=6+dHDrEpk!KB9&#@0IS3P`te%Z<(5n4G#9t|Fbt zdE6sI3!+4Ea(G0Y$qkI>j)IUB4 zID!8PwWw`uMQiVJ6ai)~Hqg?RGanwjn_S$)IN&s?V5f`>9RW5c)U?5~>nQhzhHy+7 z4+qf)+~^OcRcz93r<`}wZ(elzyt#?FzrAPN?!@?&Z^q>OEMu*?5KYQer+~WA8TV*O zvs^8K7Rk^KQ-D#w+lj%Lwn72#^+0`;p?dv#tUvo43U(zXaaI;0O6!_$Qi1FdM3-3{4Chle>Gsg80G+PPw6bkTgJ;ru zZU_v2bL9E_oac6M6GQTB)&WCgs#T6#YvpPbTRQ#oJPKyEO=ID}k6?{4fUQV=Qvltp zu3}k6Q3Zr)G25ucl=SAVVCVOQYqJ*e+m-J;>%yzzu%96>8WX_bxDu@rU~YXUirav) zt2d&!wUZV%P|RXg=y2ii?~2ZOvmUX%$^DI7L1~Oi_rDxd$BA|4nVGBVQSi%qJK>H1 z299+~b9(C*AhW*!X~#zyyu89V(iCu^v@20_T3eSD8fiAsGPzi-KX$s5aca5*eschs z0dk$1tQg34XD6!HuEvbdnPIU2X0xl;5>&ZWl6!B8zzyc>6y(E{Q?pv#l;tIW?#2e- zi}tfVDi@SF0GaL?IulHv97m1w^(qQJivh-Ou5M3Ig^MJRO8Vg}6+o4}~UJGY{Ed>DoHAc58l?X-H1#&hqK-(whhDKsc{ zF5j*p7^*zSh*4+&$`E+QGArFXiP>>GDg&6YGmJS$u>-@SeNJlQv;w;9eS(l1BM9de zn2}zpm{!U4Fg~N7QeMj`9L{+!f3C37syG4gjVuS(Cdjpwu(vI92Z0N^ppSvzX8son z@%aI|;o>824K_xJk=)h(eFG^oFY>IRc?G;sx**4 zz!pHpJkg=ko364%ZgZc$z{;NrfKIZk^}JZeL6;dlCLWwYuf;% zo!uy2xf-=AS5o0000c*j8^um1=7-4iBS4wp%+8=C(C@BplmJFr3&(;%>gR$iS3-s> zFb&TEh!VI>IA7hn1Ss20P*yewIgcC0!uOUwfQ%z$#(;d~0?U)rm>wJ~fYy@il@Iq9 z7lW1bL37*!h)p-QVt)BbzK0>;%=l^gs;%CyQT{7{HQi+dy-y>_Hjhnz@=yNB_;3E3 z|0egLbPCfK@KZnaQ}HuD^E1mkF6jMJA?}UrgchV}lPiwzp$l zaJ9Y`4cfi7z8NE!{rK=GPI^7SNHwb0uSWgG)fmIvBZN)rd7N+V?V+I{klgYE)v{7; zL~Cm|dIs;~o@UKpfE0!6K&^~ph8T(=T8SH@BZJc9uZB0cceL0#1YzWKquP%StWy#Y zP*obW)GDV%G#D6ka_kakt#I=wBaQrY>!uNcVp_6<{Bge0LMU>ibF$j6jLd)OyzeTe zRnJwRDX+QD&>9Qp=H-EqD>W%+jWiJKrjhqw&;@VWb&f2I}Uk$WGL_hGOlZTIi=mLE*`9!+ z5&$GCNWLW$bzmV1YB`D<+X&}{6;A-=EQ;-{IPX~p3RB;3%xI-AR#|X#ejXDSXHf>z zb9<#x2f)+rz!X{x3$3*%0L<(wxxE$V z^2bAD+vj#m*2hME%IHIHv-B_-2IG(Yu|F2S>v#Pw8kG)2WBmNj|9t%3-}`&x&Ye5U zyFOm}#BQ_nwgkX|IY#4i6sQ6HYD~LU5FBgihcM1nIcNg2NmLOKI-HhH`Y|h^^>J=C zrqd%RbduE1p?!9kmaj?yP4CwI)Fd=PpPb$!&3>-JaYNhDul&6riP$Kig}5ZUJ>t6Y z_zTg4x!e|{@XWUC4=|g=pnWgS&koR%dg(7vR69fJz_!Vp`aQ%L!N>Cr*W{nVfJX>` z`2g@XjtM|bfvxY0m*?P z$v*eNm-1mwr{YF;Q5LUULrI-S=j1dB zvk|gZ<(7K@Os>f6SICX;jbbB)$kmzwYDs?Pt^kx>cZ*@KPZ=svYOTk3t(n2NYt8js z&^6uJN{gDYxC-ZIad!JwGxF9?~WO=zWbZMD>g1)itZsY z`D-snt=o^-KpuB{%B(W%I*S7V66-nf1p@W&W2 zG*bRB>VUGoeqe`(X>gfyuMq+QKs6%Y^5x4>{=pxNBEao@Z7nu-cXK7B{$)jgT9tM^ z`}JRs3S&}3KQQVqXz~?h7!2w%`^*cGyCYaB^6KBaAGKR=&|WSILt*cBb2ot^Wh-90 z8b$Kahs>3wmNlPS5ov1}i07W?ozY1iy7cX$k7b*she@Zs@A|Iq$``g;u53t*MBW$l zAqY*bR6%1|CQvA)6i&250vdbkm90cNc>*P@9g;S^@G*^0BH zlQ_9c=SGuiHkvVk$@+Ab^Rp9xOeq8LnU@{dZ8o;hKr~ag(PZr74Ky~xRj4(pQM;77 zhX!?ce?M-$`dS>_KS-dbX0lisj$q*)Wut=1d|iMb(+x>?I2gqMtyAY%wY_R>v4lLW zZltf!ZRi6*o^GpQ8FDH6LrkUc&rd&==M~^u)XIW0&gqL*IY*eVwR4dZDH z05=P_hk0%lH`FIPC{GC)fJCRj^AM%}fr*$!n5Ab>Z=mnL=8PfWbv;sxdQA_(jx-^atW0_9r6#x6o2 zJ*{FnMa(lg^IINWr&jkS@!fVTQ|<(0Pa!=KQ1&~1$M1+g^Jo4{eCbPH%1Hjd_G`Zu zf9g;Dsrbkl~?jOa^CuaJ|Lk56xKL4`3`fW^Tg_)gL8iFK4g6a4gd&l;9LU+ zy-cUS9@~@kSetG|t?`*CO<|(JG+H-yqFb9q`}CV=N&Tqy%29mdB(@GZQJJGH4NFlv z>5{`F`kUkEkZk3~6|}xOnn^b*2!Teg7i(xR&9iA#?u}4Ri%~HEZfgT1w@&d8P-Pu7 zuf*WecFeC{j#7_&N6R#ohi_>C^OSAMH8<72Ys}KX4%s{fLV?OVZ%i$F{X%ANeR{UJh z1${8G>!rWccMz=r&fAd+n-;9p!2w;PCZNVWZAD`fcyDZbajw8Uv|I8a_hmaSl1MTK zgF=_aTL1a36F*0J49FP*^m_39Kz=7pUAdO`WM~X|oab}pq;4uGHD*Ev(!@W3`4cY&&Fm{TH62@l>uZjt^o!=+GF*(DjWdZ|_BU_fqr= zr5Mk9QFeb|+Jq|dSpj4sFWcwQ+un-#b2nr1+>IC^<7YRYiBhwXD=)iLr}n}NQU2WL zk)sM$@~c&%xYk-Y`U7|-fVJV(t1)@;#hCLwRLBv~Q>P{Yl~p^$aFEZokom5deL$4k zDNdvHgJuDGzPQCZns>WV6}aBGjx48)*RDnVrI#WAk%o+NTGs8UihyaSk;l5-2+lYr zwBeXGi0z%lyZ3m?=!uAy{r&y;%YXSV$4~skPsAVkLw_h8{{QM<{i|_CXZpi`_z&mt z*Z$gHTi$&^ADCbeREadp%7{5n=eZ>bE*|ZBX{F>Drymo{@Twb4Kpf0ecblW;S~OtX zLcJNCljAtMb36JlW3F0)SUTJG~!tMhzg1F%$#;r$-M ziStO?>2Q)lvsSO6C6!}NnTts6%4RjH+f4*UE9!tY1?r?bN^`!xYc41dc+Ca~o*vAF z)|X46tti4PS7HjJQhQe5b6$;GKK47+b#SO~u7WVCOc3Ja;egPi?JS6l-I`A zPBb>RVr_RfT30Vcd1Eb*tmH8T!?a^E=!5TOF+K$x-Mg28TV17CEtk^?zkh!}eX|V8 z?W)J#>2bvUyHPQJ>%)DO9d~2diz>=`-+QS_ON&<>kVz>g9e^LerdFWdrBeEh6*o3x z#!;eN>)YGG!?eXL!%Pgw)0O;Ay*l-n)-vuXHr?B}77aj9PD5(dqKs40)Gus!e3Bqi zKQ-mK*2zTg9$fG7p67t549M%d(I**jr8Ujy0$SYMe4G<`F8cGNf0rGq~p&vWDo9Jc}~+TqbIij5elt=3?e z7J#a`9%BH^6b5ae&CF*oK&>1#n6kOH6%B*maD5Kr7mTs9=Khv7?r9>c(Du+^tL0X# zcbn1bpk;Cn=@MnZ5CXD4MYx~^&8EZXmepViF$SC&kEdiz#vvlCkG3ZhWB%up8!Nq(wvYYDTmDWesbG{dZi7S$nLlsgYCYd#~TK+i3l zOnY%Ig>Opp#qSHcpm!w)caH2gpHfPtXG!n3o?5G7IFHgPZKHT{Tx5iiwI)^guEq~t z3VX4%oU1&$C3?>k~{_YIK?_X zK8<1>=L+^I>YX&rm` z9MbM?ePl}+&{l_i{g+)FHXFKNz|a;8P?eUg!2m@aFt&6LIu^8tCbA^k0(PMTej7?AF2cq0Fmt#V}Ni*8b73dE-J77?G0;!5qngTJ#r_Rw))c5vsBXSK2W7=5< zeI56;xY0TCUHOvcCno^Mlf1Xjb2Y}Yl2fCkKcYPiyFtr^8@9(YHxguj(&%Gx^#8e^ z`?+|TmoELj-}n38@wlMJBN=4kF8K(B|37=bYrU^WD#0Ydz0=W%rHeVzjmv-Y)hs4RV7^-c3~3v&I(y7yAIFS8-iDZYIPNTl z<3Y@py0&0|DY>ndF=BFDjYqL$)YMyzXz%Z&zaO_}rz#l&(q*!?Jy)&MLmG8k^g1S# zXN+_-T98Mx8oM{INBj67+5kqEQgxe=hXE5eoe=uFQO&f=4L})$tK38aRZ*ojQkhW*9`&Yucp5aty@8iCetB6 zZJIPvKr%Z$i%UR(69KlEAC7oAaC2qX{)bJPmkSXY=XMfsh0kHG8?N{I3%`=fJRNRdN+Ie}J%#^aE z!9T#vjSOyVSPP&?s|M|_0ZuD`N^Rku2K!IVoo9mbIoeTU5}#4lDL`+6WH!0p zN=J=r^8(SV^gYpA#I!ny(ei=;f;3g`M)T`Gg;aJY+NXy$1e^s8fR=3jLT4<3@7^V3Z| z%*(AQ!4C_zmGw@IvZ#>DGb9(DQwhykFqag(nCY>s%BTTR(41U#Wd3IUzL#{F3>Ds7 z*39jkeA>2vgPFhzL2%Y<#nMwp=$|F`wUb&wSJ$cST1Bf1ZR;T{PKHr=a+)Wols!%f z(6gA#qkex7wW~3897oMnjUk`h@66ZH?UU!_6+mSYHE8o1AW~661bnp~Uq%@kSpwuV zE{0J*68tBCjg-vQQA3)pQpOYJWE_;5G5%zmMN9M|3C??dCG8L{?*b0h~*q*$Az4}U&UVaIlb(Fm8DN){rW-FcDuJ-riihj5Pe4PRME&!5!KIZ_u%FSB< zNH@TJ{=&zyd?`4S(P)Ub4O_sb`uEciepi<-Z9*iP(G?R4u#?8{N?#1bDpE(`e`OQ* zM+pq*{=IuhJ4g-^%4Y)ue#WFOf92JbK#~YCA#}_)yZcV6)_Z$`GnfDqemr~_wLK)f z>OAiLum2f)Q@|BO=KRqcG5+7b5w-qhs<>6}>$$pw#0P|n^8Qz&+C7eX>spMEyyBg2 zLu+R-U5y|ti!_p*TtN)z=ZQd&a=C@PPTJF@OPd!+p-7dWoNBe1L~J#>ii@+;=*|Ii zyjL2|Vs!Q-R_->^9DwH1)y5p+f-D>!Dz5*b?imv4bX6>8J{RT;J#8!maT5LXO$I@x zM)_QD_Wc&5oOOQd#|*0PQb-=zqK$@z7mUupG){jFLOFwKf8GvM_$@OLUVgHP0yCEx`SaDSV6|$I7PKjkpQ9`Z@T4Ct=sEmGJFi^c<4+Kn+VBkx z$TM$ALv8+odG9X6=yHLV><68*>dL7+EZ$#3{}v-1sxA>wOVQBn!$B{q2)QoDd7fo^ zrw4#&X1=S_OJrz(Rl|kPO3d6T37~7JQKPW2n93tr+0 z1Nmuu3rpLi9h;ZoeUka4!YA&h%mc{3^PTT(eih+c-}=@UW5=a~dODBQ<3~}yyoe?M zL=e?t0J%24_8hJS6yk#hh?RC$N*QUOPkI_g=74-AXxOjMmfT8=)U_O)$Na7T8(MxSJthx+Czg{lCUgf$rWBLGT_(+0%pe}~^)QB$Coydx`600^kf55wO4QFF z{^M%Y%Dq^&AQTW7w;#=FrD)#19t~{|n1mjJ+o&~TiA2{Pt)qH5i3S9s17KnbMt!o3 z#%P|C+#^WJ*F4^-6SD^MXt6|^r2NxSEC7QuZ8TbCBp|&-m!dW<#dv2GtAk1`xbM6=JSD4Lg?*`;t5X*8UP7x&qgfj33$8r762y^* zhYNrNgNro+FW_fxSpge$z@!H5)uFoxAz-_`6j~Q&ml2OIV)fuO%7ZCDXccA844%xP z|K;d#uXYK2g?`tdp&H7282EZTN?W$1^W>56a|Uq$&{cOj321aXu@K+^z!rc`1<@I4 zji_IEHs|?i#QjIHLw*f-fVObR+quD@GBPrt=*b{vZ@!)GDLgRT!@D(YnE{mri;T!F~{&fobvn_r*ffHX5_vTEE}al0{tG_{5RCY`E2 z9h}dBsvih~2c)YNlG5S;n?Pj0>@1dN06Hdcn?bJD(Y5)M82ohUn8{U9jjJ)jydSd% zugCcD{UiW8J9|iktps0##}8vMzDUVue5O;v^IV75=yY^805pyPlFjHc*;eU0hoT4~ zsLPDuAsz9|%{qjww4TSj1~I8Fqsi~(%B{&v@LoM8E+;T4J2!7|ogkS~T!^29V%eXh zf1S7*#uABg%oP()1Q?_<+XWz~22pHkfC~s))|6aJ(K_fxa~FWS*U4j-WDq%@8MR*i zNT%U^h=M^zvSoHQxzAE!-^9xiFXPDNPl@3wjs5-YjxpV6_*=u|j7Rim(=A3xnA%~mvWdrU$==gnFMkgJYN{SzN*PlM$ z1mCc{YSp~*k55PjbZVKP?E?khC93b6uYhX1oXWq{=+`@lf9+

h4@Dr?XIPrWRB( zePThI`IhC_(g(|{peXQ48&=bO8-g6RfU!SYCMG&pXKU8V-H#l-SkXK5MR^; zFqIl06yVWlM!nSm7}a7-eKoG{-MksKg9AV$fQmd<2&#fd-)lBn#SZ;%eJd6Z9z>Zm z8+*G^(nbzIHAl#wQ^tkbJ^0Fm@~p{owVuUvIf32-ka%ZKdrikzG2>pj)yRC;yL)K^ zRysOLr?GA@P)q4|?akbTkTzxe`%!~0P2q!ru@djJ0dd-=Jq@(4;8d;J;`A(5=jXYU zEAaa=d`zHBKV?UB*0aIiZ@u-_=GW&qIANzuK*5+&!!|HL6QD6k6Hq-zjB;x*_m>bV z<~HD99uo%W`tjqa0BFlxi&G>PjT@Cv1b^I{@O<(3QL31|HyxkH<-<2){O13OxOkF8 zW8@ZOq&ua$^_z;7)fe0x&V~X1c&XY-XQLsJ2Bhejd8C5diQq%1^GMGMqo3<9E4@Od~0|vAkbA+g@tO{PfRd#4_!X&M5_9e5Q?>+AP0I`6cF=XYscU z&whUzEQbWY_?02SARyPBp&o(dlFs(}0p|8)_`-!3j-wX^1tQ1IhYY^yI9Ah?+KK0A z(R+oh?VCp@d4$pPujSkWEIb|e#LuNlZ!t&p-tQ8aLTYIO!yhxzNJN13ERcJzU<}hu zKxNqxocy#cGd7;4-6WvlJomJ_ zodZsqC&z%RM!u8BJ7ob#lT1ByyE=wm_eW70Ba8wvR)CV4OL|;OBHLCZmqzX}r97@? zP+!X;eRso^JK8-n*qx}2Ry&q@#^2pdBYV$vTpV){8&9P9Uo1eVxskPvfg! z&7+$FV3%~yKwG#;VEXt;lmw>zev~dQV+MxMNv>de1;{O2KAFoGFE}bca<~ICb_sJk z+3l=<_wW8)o{9PS4@^cTWe&Ob7(`6(yfz~_PvqCl#q!?$Sl_=J0mv#nd>B=ow=#)5 z-QP<|YBn0B3c1o195G0y~AYQ+sTQd)56-`0br#C_}Yuf-eHt)zZ{i2FU73A8?~C=sGBjH zPh$ZHTX{UwT;ih?#N>sLEu5H4-dd{~^+Tj7CSL`~XNuHS+TV%lnmB`1G|nMV7a*R? z0YrWl3(8enRH8iL-N6(>r#%SLBy+Jcg_c}E%r2=f#MZ6mUMcm`jKva(pWBNkHm?)E z;1R{ZmYCNlFGA-EK~0A7?jDlx+Bd~BI9A$!B>GDVTReL<$cR!)_gqMrrAYtmGxB%a zzN;rWH+VN0jD`L(d>P&+I7)K7^Iwk3k16~pDW&f~!O)z!LKze^EYsFUU}~KZI+jhq z8=u;QE_*!hFn2WF1q3A+f^MWH+PX031Z}w~fV7?l>zYXDxnPEnv9v=|!%aHraeoMn zak+3f2@kjSEMRwfRy)NmKX-|*wGq`=-+7mLT25`sC|u1A162ggY7D7I8&J0N#FJhl z=54@GvzCrulXg451$mT3A+dGPRp5RZ9?Ol(A2hItCiZOMrRj3fbiVZ7Ppn!z{b3W5LpN0lFOXmwPv(?hk(Rh zy07r8%_Y(&rM2at^TqqprtJ|}jpHr=4_bY=6Jx;G5J9+nup4W@)r@j#2d29vk66;D zZBJMw-;Pf+LTskwz8%@Gd9I4m`DCuGY9gO?4hZ}>tB@cEDQP2!Gp&=ko+(I<-eRumVfat{>AvE zU;3r^NB`&_ZDM?m1CnBmLBG{pJsZ5oD9!=9B{oVEs+je1TxC*L7~pEZF5iADfLn#O z&A42GF^x|rNIgp?eYM$$7Sd08UuM>qnM|`Mk0J*BXw@`StuR4&Z$6I82Y2J_$>W%f zhrE9o!~SJ7Q?kvi`aHyVMCsI$&w0baVjfEZTR z5US~dx=l%&wu0zMPUd64*pQ}`_#!E4r{@xd%M+T}6$B@#Uh!?)>_rpj98Oy@elL~q zrI5Vm?@c#+KWy&h<@aBZJuGW^y^x!5IU_4vKfI7qC&3}2k! zn0?A9KS0R1{u8)3w%$!zK6H2?=y{KjyF=)ZXFUo+ zyi9+)lxkT7k=n+I^E8}qblOqx0C=FY+DBCZAg#k)=v(1FqAFk@oqXToSauTCxPM{Z zvv~qBH&*3RCFRJnSYFyXZL%8myc*El2A>n?q>f+<7a>L6DpkMQDWtO^nZRVDRh5rNV$7G4OM@ zh!YcpuQR=cJ0O{e&M)o3v=s{`p1>y0@M0o*xNBXTE6+1~HZTc{(_qk-U2{i@(npWJ zZ{{l`i1H#0X&V&)76W`ZA3zu~|v_CerJ}gGEgC zke>F{j=dE9qvMzdQd*e_y1EvP-pyEFKZ@zClZbD;7L_|UV+zPxr+U3n)a{@Xqa(^b zn&r0cuA4pH`5w~b1@D&Q@$NVlz1^rD97Vg@&ZCv8qeX6!Z;UI^oG_=akTx$@h4+0# z%Gv}F%lG!xI;xN7(Ri{U*`S!IrCXH6n0_~hg$-d9uR7SZ}8W1cx)n<-+Ej$nqxpY?Gpa2OiFD(Ezf-L@)o*Lkb^I5vYSV2o?(34dQK&O3|K#II_ zxtDlzX%WJwXI?JcP3g*lj%Z!((+mB<9=yTET#?TthF$7@Yc~cj-Hzey8_?ul4BE9A zLWjoFA#Dy|b7!Nz$DoyU>f4>IqkG&!>FuA-DOcm>%_#kU z{y@Z?mvW^<)1BJIdHN44$YLyEW@^j+#lQF$@pu33-;F~C-WSDB zSxQ3(`_VyiSTF!xSJwwl=kO@CQUbROO!Aph0s}WG<0dGH>4Z=1SxN=Z1U)OK!#xR# zH3%~8t)5k>cj}hgl(|k`dL>S7-Hrn$+XQJ!+l!eta2uNjZ4G)z zR{KZSqRsDNe}Ke?6bNC`20~4hYV$lcqm2~BSFfGjy=XT(2*=e}Yga;?E2O2X;UIye zwgDcN>+<75TME-dvQ@&Jc&;W=)Rb~qDUEjhRBB2I)8wow1a3B2LNF+gHX2OKRQVHs zYgdw=wm(vB4Z^r}%Tm~~@Wpg#6Wwbp=c`!E3+XiT;jdto-)93)86lyNcDV<#dwM_9 z_08|cdp5i)JwGHsekH9h!{6Z)%r+- z{1klfNIzCyKKDv*^vtcrj5!7wOZzp;|9sK;ZMNHV4ckwps9fTIpI}*}m0oVL(%4?S zB)!t0UOO)}I=QJy?KEjhC$g-(uR;Itsvl>kr*Uy{5d*-B?tG+m((f6UpL5bzTb52{ z0San&puo}O_YPpC1AW)X-yMpLj!T8aX+npfmEL!W)Fn_?R1&+=UTXdYE!m!wYYL4V ztI2}S?IXY*-MSH-qy6Xt+`4H#_515V+K6 zJ>4wkMuVXzzCa@zJWp2}@S%tI?nnR4x8t0;4**Yv&oIwRu14#|^|F|4X%#tc(>#wVd z^xpm~ePIv|A3g-k6nm3M~Za?0M8IoDUcijc5xBum%tY#T7H{lh1v(+fK z&vs8MF})Q}ee1Ji4Vm1M@SbT8M0I2vJ(B zkv=8n!ZymOjfvDQiGTqKcINhT(&2V$#l{75NQOiVuYxqcI3)3uF_^dgfb?$}Z0@H7 z=0^zT`or@3GJNo0FB)vo4-*t97=3*C`1(T@;&9YXDi7jjjF@_kos3b0oVy=$*tSQ0 zkp8o+^PX))xn|8Z{E(KI5(~zjEcJ&JFVnL=@@+oT&BP|Si6yZAEG5&Up>jTadwaA#4LWA&feX=BXKM=s;Fa{jc1J{bo1T5263OBiKvYM3eejFgm^6s5Oq$5&HVx&k&Q}CFH z9{^+qfKd7!V~{;haTcw1ZYO4H#}uhRNo~r&_T0*{PW|{jd2)(`aT!ZBY8RuJpAI0d zH3)-8gshR?I?Zg2j=6TCas4En4Yp6Nry{wk7S9pW>;I*VohJod?_Y5kadl}bHoE$tDGQcI`0IbCT`$GYD6r_|~3 zAchZ~#0*g8w(T;~W2svh*rv@y4+7X>GV70r%N;rrSZTZ4+FdqDEgi&4r;{mp(kW#Jcb`M06@e%)OD|AIgy8A1}5jJV0q0_7|3`Q{G|AMUL$ z!;8Xu0*wzC1n>#+b3}YRL6G8Q(Vq4-Kc8w?9YbCpC0JYq1=D*r6qP9`InPs|Fq+R^ z6nsEg6nb=ytmY-FXy5Hj!TkW3O)QONO?c!q-kw}uh<4|czHhcY?f#K zwh>YQdNz1J%UApq52}gRb%ny2=TfF^lKvtmGX&Ey&k^{R02<!) znh2o?=aoA5YS1C*nl2=y8kS0pSGlFus}^(8(QbBCb6E{q1$miI5k?V8we=+yc%ewU zy4#BKox`ZUc0JG72vPg%6C;hsO>7tD=79>qq_~c z8Gv$Wo>L64O(+XmTa>e&yiP5j$5VM4fC>OH!wMjrwrC6xk6O|-i2Un|5HlaUdMybA%8GghZop5{>R|ED!g|bQ+WCL*_-7XFagWGA=zM zZFL@vN(V`~6}1PaF}-^~8vUynot;G$qNe|xmB)HqJc|C~2eH_#BH=++=7YF=bP>Bu zTp%L?6B5_(TQP*l)Fwl!)ux)&sJ{I;<{q(hI(BM8L?~B+E(qzI2@BzwAJ7IelGQ9$ zdjIFDi~(OReQ;l5Qyww3kvO%@N`GP!0O>QsOsz-WLm*z#lNP!LlgOk)A|mJCX&P#dlaoay{}i!7sy?fsn@%5pqoM&+(qlRvAM% z-XO6XJn^A|Nti|MR&-pNhd20_tk3TbP_u`Zsge5uVDnsXE@)qtM*EicnX+bvKP*s= zMaMc>D1KWeCZzAXeCg>Jc_{cMsTUq4unj8& zUj?vLjko}i+#T7e=aOn3QIxNj3tBdI_C_XRKN+(w!B6Y5=XRR8osRUoDdUtPn8Ed!3lPb{x&)eQ0GRCKrQf zJ)R;&52HF>M2%1VGrG(uF!c146#{O(-Ax~2%}ys4FC9bMC)5W(S*ij4pp5{u_A$cr z#aUdPT}G!27)yQum;q?jJMCD#bO!+)A@|@Q`S23JSG&53@x^&mrWYyf*K`_t>qed% zx!MD*g$0YD4j2)uMmQ1zH2%vS+PDiPe-U!~n<0N4hCz4m4DEdi4nvITwas*d1M zjaZ&jdI zvk*RUP1)DJ_OHHZ><3RSQGO zDtSW?b$F=3K5}DJAv*OMz_P@on-UkKxfCGQeVW>oB? z;({=0+`p(IJ=0si`UTLr_#WC>VJ(oDsv8%Ve;S#09oex8@EC@v*yYM~V z^ZTbKBy;=}23zTcE%k!p+Tz9a*$i1F3U3|64TbvzZ+w3&eb}%~I}^@4z8@;gr_9S` ze8z6BDn1t{gNZ<%*Sd(&09`=jJZy1~sp$^NXB8Un6-+lZUkmajPv=JF3kXYK$6v}V zTpRf%!RfmIS%&Q_;(zL;fqbz`84#73ke1~m4ej3qQcghuaDp_Wn1+m7dolqM%s)5U z%mf9*pnTddl@$U9F#U6Y)PUb3O)uuuVI2+qW(;Q00x&g*vmVc)K3^l4lJ0bwI~~it zc9eGjGff0e8Yykrn$V>k?h8Tyi)%nyjNDnch}Qj4wBERczFtQ6d>YkDz#VknHjWx} zjB>l{UVyiDhoUZrkVN%qruMByT8%#R_`#DHzwvfD-mW8@*EOaeOd`&%V*LccUbhkN zyb}vuM*wQpy2AjB&H*wC{42F*d;9Q)mtxi1i!t{`H1-tGS%JpSY4-|3b!~1NXj)QE zo>L~Ds+u+gbgvTMX|;6Al-~3Wn|@ew<Z6YsM1%kApZ&93 zbJvmV>#x5a-}~P8@^UBUry<hzi`9=)R zuVT(9H=lZMCsp@4F4W`m`J3-V>A~Y%8r8s3-#e<#nQBOLS8;joZVX=kUX%y@=rSQw zWv#TNLDp-*ZB$_;>*3MNM)gzPEq)EH*+qs{PTcPgSp1AYT z?@G0TtL^&|d48lYv1JW^Q@35`=hYfkYn{zt>4PiZbe zoOIp5t7?5F0I9axj#@M3#H%dmrg!~j()}k7A4Uaw-lDD^{Uq>` zUgib`+G7Y!Z6UZ<_xG~Na+#6m3i@SeZG&{%yWN;d6X64Ecuuq11+a|^S?!i;WF|oTum4|+ zE-s>ac$f#`P6wlyG0EDmyd0IAow$1Q^{D;UZ>0pIwyWCOrLRc)tp_o=xQr%*dNsO; z>7zH9Sm)$3OEq?jey-fO7Sq$ysA;g-VA4YUYSjjyW(7d=0N+{67ek~jkKR~C^9dl5 zvX(qPN_D!+iV%l9z*ZX##)#i5fG@Y4$5lUfeR{Utf=ODfmZA#Sny(=geZ6-tqBBw( zLE|)t&qqq=q~n!ZFA1`4G$2H&9qSVtqN6AATvD?A{LagBs;!xWOQ0^Bg4@!rEQuu) zDi149ah-R#^m*ol(TCb69k#qoU@24e+J>imZu9v`1hq11Gj%vDnR(B$*wprc?M%UQ z3FBi9u7(K|7Id(Eo!Xu3)Q>-}k2`Gp($X@-bDTSlxA$HY1PaASeI^iDLw%cBZF4Rw zGUjb}+avvly-#!4jvoA9%me-?OV$2Wvf2jXq^W}WZ01YmW#3NKY!~HlV}zkHDaxAm zoLt7Q@N&-8O|srA)d5jF7rYqKWsM++&k7)5NqhxN?UJUAWo~4ujft2VJ>Iiiysv}V z6wLqxYu!r#PUdQv00s+>ZqiXV;A1rfOhJQ|omTAb?ZvWOjZyzHstlmDM(hGecQ#4i zNZW(UE1q3R>eB6KLocBp)l1Tu&7zFJyApKSe;Yg4D(&R4M~%Jxn6^8yqt*$6)PM9i z+8#@Haul_bYf(I(DeSdRX9f6d>>tsg41n)`H_Na-z82*@eVn;cK^Xy8bNyAeThLm-m$qG2 zl=^y5`;0m@uU&%y6-N0P1itcthxZFD8t=FSIRV_MovRz)Wl_Mh%ea-Z(*(LD=)Shb zo;r})I@8<*TnduO^0O8`@#V__o$#XT(*npo%fV&g3WId|jPYYJDH!O}Ndl#(V2yhh zj~>N%TI^PF!n(%q?n%#go8NmU){h>>}#91PggV{ObH7#sI#-#Z_E9Ib)s`yNJk?_q31b<<{QH{QCM&>N9u^FPaOY^#1J5Cb5-`>AN>dWvn#ZGXFPj14oXm%J ztdAhj?f7c@Bv=awpTD<&O{p;_xO9a}O+lq8=mQi|wo09{Ly(Ln7w{c<$(AS-+BBtk zbKTthgo63!Fad4d~TggWIJx34h4Tx*KIx((yXaBBLPP9hK8 zI^r+NHSgESki znh5@AGnI(eEnSN6o{$At30SP{?=dHJV!IbDK%u;rR3rTBRT-VFrjq8Gu!F$0eYs=w2?K zw*elD{*we{oy!Ym*edE3h)xG01SMHsK2AG@+QgFsTuOD>u|@iOvD)oL8zQv2yo%am zBsfnH7!RX9nMV`Rx!#{e8);c%QIGqmR(r8*>_z$TRxDpRiTUlrnD5tPc+`x^*O2=D zz>mf7l^djAPfe7*&Z;vo+ko#g$~%o10hZQ#NUQ*}l67x+9$OQl2H@kJB!J}U7M@)C zlz1_JQv*U-)sR7^Q;N^Xs=&CXl)jYq)u}VroTi%X@Yw{+wE(+~O|wI-B&1t{ZsG{! z`CTGg$p4>g$W0S6E1FCVf{cxp$;%@DZfKl-l%Tdl7ZGr&&7BvA^b{|^hM8IJ-;E%n zO#gHMbwSOt&S#zbOp`PsSEEW>+J>LHU=s=;H|1P1MhlJCdym3rpA@a?xZpcuA~j>y zH75*Dc)YkcoJI|yu&gZ`bkSwanX3>2N9#`dt{4DmV##24V?mr8#Ny;Qsym)~ z0xh19ULWFjHI7!DcW&OIjtH+q@|Z3GQ_%YT{a66xmPZFsdgbL<>nh^fab7OrE)lMm zlrv7D) zN|#flOOJxmne4LGNLBm<0^t%sT%5*eHi^!`UUZ`!58k>L-Fr+rhF=arn5D-3B?#$4 z)poCvwrd{dYb_)Wl}mk}k=FnM!u`}7GM~4aqSVYlgdu;$l0`ClvXHP#03hl*lJuPf zRwmq)(DOMG9P&VVFRUpqVW5+(4S;@I{u3C zW%zy(i*jz_6u*nU{@n=M>;(>bm``TUP{{oJv>f!Q*}VV8dJd&v?luyP_&*cqoykz%k9NG`Bj0-@&fEC?QYuW&c;f2&<6ln z)ABk_$SaNd1se9f<>RwL{;ojePR=Qyg?d&R?Of(tLi^?u1Xk$p;`&jv0T{~xLiE{L ztgfzN-f6_%(P0eI-H@&&v|n3Ar?;PWSFRqcXH&(k&{1o-+spo2Nb8}8tH+PBGIh0N zfGPb~8ec(rrm?)hOj~8rv93DSwH-Zm#%|M{QtA3h+HJ`<)JT=!2lj8;xhlhY$ z_zNIuaQ%Ah{lPyNq44igr!?0AP))+iTLd$bPHO+GFSRF6id7oN!ZTwJA`W?_{cc>( zG9@^>e?RN2z1g!t+CG?0qo-+(8w@^U;S*WD{G)&LkEYjt!=L~2fBuEnU-%1uA)kL4 zK4O4aXfsyYN1{TqsW|zVB&#xn0cm3N_;E}gJ&NUUP}ozr6bbQoHt6%Fsk@9$FY0cK zWhN~5grK<` z!R%aDY7i55k#^&1I*$2z7L)ZfCOg$w9kyfADaCkn5p%9JAg$!P8gucvp!{!xi= zMB~W_q$v;rU24bxY;&1IYTfwDrIzizXBgNg8LYk9r9#WJxo1CSvlMOfi3Z1DUYp+p z+cK$+W18`0TjynQe;14F*vu>_i{Mo)q6{|2$o9KseXa}^-TTi};S&g(LQ#kW94Zg& z^AB1m+nXecvEgNVQzt&vb}6Sk8@T<0!Txgmo0h>jU~`e48)%TIpdYjq12LBd6JRly zxR(0Jg5e9ibf2J8YC-W5V7P3;oGFVMDaVR=+YJ`U@>N<0c~!@WW(LqeXqXIJds^d~ z_3~;IuGNhUBy z-`(e>-Ck4>P-|UirGN*zE^yKgORx8Cs*=_U{sc-M{GIM4Zrse}L;>6y+Pef)xjWOH zoC;z!&)2kaYUgg;h`O#f0B|LFhCpn8pID_d!XLl>J>v2X&zk_BhV=B&!zev`Kzjig zJw?SlyzC#%WYlIU@F(D+1Gv;m8fWz6G~(<$U50q}WdgWog3FhZ<~jA604`@-Q{uiz z8%7V&vi|S&#R?zU(0{?;_#1!YZ^Y02+|R{zQSGNRe4?7NZ3`<6Ism1DYPbMFTO#=* zhw&ulr>D6)M!PemDJN#CuaQ(FfL#EXlSh?tzqE+f@j*0pOL6-4ThV)~IOx^vSC3C2 zY)p>UK2l;khFw4q_bT6clL-$9Bi;4qy25kyZ7rv4p>^U$#GA<_KKwXAq>7a@~Y~ ze&<63KRQZLdE?4G#Nf%$7drv{1 z{mR^lX^~!Pup06@`n?(QxUVvOK3RnK$Azj z)4O&YiwAEZ)IW?`|B_??gv^QR%~!HM^P9J$e*1RBOD{*2G}5cSc6-}EI?D^n1OHG< z%CJC?zl_q^Y1H5c=`MqeU6Dz*Akg#$d@mjTIv(%cPYqiA<(CS6=s4%Tr@>4BzTFer zigy33g-_Jb-!=1J`?X(7Bfu}idk0U#SFL+>|9%Ya-HU-nm;*Ih5R4BK`d2X<4iaG6 z2Q@AwNw+1pj*g;(^yO|y)zgBhT_$LqYXzYi>x~-FmdsFKMsjT=u`ZLYhQ!uA1XOu; z8B2K!T-hWFj(C)!h>L#Y%e-o^GCHaB`F9b~(k% zBqc(=lYk0BlT_b()Jr3I=%ue_J7j#`Q|qJ#Fqs(dTDEkr;U95~_hEv7-tsrs2d!>uI6iRfA`}MQw5#-e^Qs@FljFmpI7Xfe zZbJDag5}z@R`IzhbB;m7k1#m0tvA=Uy*-Mo&~|MT{K1e@fcaTrK5oyMHQ4^nQRhb{ z$6ux|;Ndf;nGehY`rvuL`p=RE(0^Hp}Sd9XB7D ztd3xv!@eU}mG-4z3Sje`#2IwQBYrgguXkF}=()oZz?SsI^B-FXjodRG+d@Nn{xtUY z{6}e{t~ETfS8dUPa$1iZL1bz^YTa}Xp(~>b0&072FLsWPV+TgOd*=@FEdp-UO#+G&lhq&$NN{a54W`G9IOg0EM z=L_pOp+BI}SLg7jhY7G6)Ym1?)sx4`v$Sh|SD-eTw?!V?HwZK>}cqz3Kc@nZ%=0o>=B$MEUKiv($3u<)_e_-B6RXVOpE`T2S7 zxO{d8=9hsm9>wzB-IOSLPCR$OFt{rZ?&TW1HXR>0NJN;_3jkfG-bkX9!HEX}(bv~X z0Q1_{qWa@M9%Ux>>IM>y4i&%sMl8b2W3yK_CRzH$&3U+Ko^U=@$*^yzLp);mpz`W2J+g8U$~3kc&9iE-YJ{Y5RR4g3zzs>a{zWSj;=0lzI*J=Btk|v+f2OD7Yej%5==)a=O zTj277U^Ce!n;>%(eC2X98J9^&;FEyd^6+PuJ+CDbEPfKf@t0Mwyp%D4?4~UKZ+U-k zY`nNHxAii50w5b-Vd%N{w;|K#SuQWON-^v3y<3hMb7R>-0BbN`h*Y!{7o;U^(nuR# zntkN>wV#|<&Y`k%*m#0GJ}aYTGzI_`WH{f|Nvj6_YQ7E9FzuFBt!}J3J27+DVKQ~= zLI3fmP*riqBEcPzFLh2iDV|DL0k=>hl^p%qE9&yz@cNy z27)V}rNf={_Mb=M-M$fH%CbXx_5L7N9?BjG8~1@a*L~vG~fXvHsfE zql~aTCLc-=rQQznhPMegoP~;OR*TYi;2GYubxdUVSB(p=W^Z(F{QC z((BFJ)RTd?5d^+S!TI`;!>|APug5R{@-N3v|MXACU;V3pHU8RP`)g0HFMN=gDro?u z+!pMhR3g;Lp3V!k$#QUi@F0OUS^g7{`N^bNTwcb62{Q#4D|M{jdJ6z{o>FIhqdrxN z)V`zM>BZj5Ux}kTFGmAOMZYxeOw`X9~>gR?L}9DAOYL$ z=5lEJ_%Q7cM$}Ku+7jX-ftqQLAc1x#l5Q!OkQIpFd{{VDoKK0*%(m_`^73ps?YU~L zdY&OyX+a9rn!%8i*7Z{+*iu5z!_297E?+u=y{ByoOhoZcd$V$bd-^eRnPhVSdZ|WQ zxdcHga;d6UJC?^fK|o|GDdF3`{8swkGQ;mP?++Jz-zCu(1)0p|OM>jD4A^`>K!z4A z07YerL|uUoQRUeG<3LvcJfyC`>W8>|4iEUYqn& zLr1XN)Mn?BF)wG`OrJD;W(~8CET3c2@yVxJ`@N%s=)k1aSk0j=t~{tQ&x^9ypU&m< z9mb^=Ocy%MRy(@w9s*%AX`xyowPb3Lc!T`2069LU2+C7HfkExK>8fRt&bn;r!QuY4 zjeOs<7QE~Ejyu|j@!k|6`{L{@mrXq?Nw6}#x{Aw3k5hm>fPU^a+mv+`4a%n@?-_J? zs>Y1EO%dFOZnEP2`FIE*gl^^-rwrs@6(Bu)7}NXrpqs;(UY^Aqnmrx%q3^?_shWN4 z0OA^l2XWfJ1YB2QY+YRDY_(#wzaQ7W_OI zOw$z=HIkm4xyV(qZJ+XKHv_PS+PCqkixT;p?Y5#`Zc>xZyK*xl&iM2+MqJY;PhvUf zN4-`}ml*Zy*Q0ymMs(dtO1W|%egVVB;?ZA?*q{89e=`2iANoUiLlWYU)bWjg$m(6*Q; zD1v3? zg#e zH|uRb8t+|!`^l^}v?_yNl2P)7JWTVsw=(D=zz054dyt;ep9|af%vXl8O>0w}7Y55C z)65-D6f7BGGE$#yerH7gd+(6Nu`Tk~j~I9<>qkGPI|uqfN3Qa8%OMloCj4R`YF&xo zAn+v$?ZME?rtfXqfjVW|Z`y5>Tn4{;rKZeRwwL!A>vq_)@juBRNZIyhE@Sh@tSZ{Y z%aYm#n|3HL4|S()beAFEa{T#QL8O>0`Bq*<;61_7=GQiuSJ9sQF(;Qz=GG4Msa1|C zgR(ZFacwsX=$s&ZwZMLcnl&a!YUS(;<89|lrd|9l{9srpV}_c%yTfgwQ?y^n$&1BWT00=mnfGAXb=arOqNTb zEE`IAX4%M>JZl=`^QZPrP!)heK~d2*RsN@3(nVcUAk_MNxg6(;NEuKD^@{Y9Hk#ye zUzN`aoo5x-eE{XTI~31yY0#4c>_CB(Ui7EX@#SJ{5o6H;6{+;?dt} z{h@;m|Es_HtNH0*_!EEPPsGpu?9awu|LcGK>Geeql0D|g3xmTh$-$2@0Ewzvy|S|t z9`zka8VfENi?lcW>Ug%8>-ALzw-ctI+L@2Xc4#ND)EIR!VWOV_f>iT2!Xq!VO{n#d zboL-PEr{{qZY-JD(+(5<DyvrN(XNmN z#CLVcL_fbwyMx);WyHleRogmoRLkXIz6;9ZAWp5`9>kylv4VEa>Z_P_)-i=1toA71 zo=XqANt>^R* z08J2-8VQ?8z{0;I%$vXb4x>n4XFj&ih?kl=nbT$_aX0^a3J|!ThSEiX`8?xnf;kG7 z?3;A|A!rtN`hYaH={y^Jl=)c}>zWzmqny!}GvhpS<$W)ReU@Px-*?G3C0>6%cRd?? z*F0bJ?1zP?@y*n_x$Hx`dvNu(T<+e>69DLY8Ef_-ZIV^9z`o9vY5B!>{2&egQY+)b z=i7MmBm0YBU%o4NpIZscK8BD*D3bip!M;eGqISggv46biSML)c>*=-ax7>9spHy3% z7dN>IjU?@8I?t3VHw73-KiRJz>74(BZ%};KX)t% zqSPF@xH)0Kvb}vp(BPf@f1+>l0UF*rm*Jy$8f`5V9S8=I$J27?4gHMi>a=Y#hIB0u1O?}G%rG9Mo1WKpH88eeLr(P@bR zt}{f)NE%H_tlTlA+Sz4PRmreDz!1c0be?{GYBx^8GdXo6IE@+aBPH&%qH?qs>|G{Jo z+f<(d7M`1CmV0wQZQUqOCi#?MS|-Y#k@Hu+lh<=0H8DioM19Y&ZMSVOzMv=D((f)8 z&(`6A&_1*Nd9XZSU|r{u4_mKoJ@~q{v9ergWKCJ}ZPQS5VrN(<+s}C0z-=F8M$d)q zZPVF;n~xkQbJjE4aC7fR4V!nfU9yD!bR5wJoBqz0q^=e_m!UnVpYfmxKr!B_lWoMM z0hsS?jpK{=i|g57PFe1jLLYN*zZwgG*|<87*-DLewp7Noj_RKDNvL3?c6!_A3CipT z%eKj73yS@@{l0ZIV!2Wq!$bR4pr*DYZKi0?nagNXk6eRJ2&AAlb%2P&LnD2K8OJ|ElAe(mL`ymS)*wiOfR(j?|FT90E|nnBrV0R`b)QjF-{2DE+kR#acR6KgAPO!ugbU<4P-=*^l55Vd!hxy`-9Wju&Op67O}9kb(l z%z8*#JpeE1Cm4eG^{dOm$hHC@BYmap@<=0Hnha)<%g7|Q?&CA@kmuW6&JGCeI-AxvrmSB63M}k`Wp3k{$4+!ww(`;#Os&H|K zUrH5x$guMr`6W?otJGTB4lY9rs(Hs&dCL5s3Ett2;>R<=``fnvf%e~qjKuq-`yMee z_{{dmG8xygBM7$LeBbig_+IZ9ERXfE?>7M5F_~IHerFn{EqJAkT%ug{%DG&>Mj%;t zDhZ}m%~H(tV9zTF$Q|Fr&vHH&vTocL>^djP2tW!#NKR4f!<;8D<`u#b{ZouFiojLT zk05kR4PA~0`e@o+#f<(ooty)flk#ud$bMHV>e6Gnr~pLeQau8px*Eii zOOTVAVd(%VT)*N_)BPx3*({_z(Z#c&sB+Cc>BDnZQKq9v|m6VTpw2k*Vr! z?(Y|B+YdshpASwl-cu{)8DeRC?-3yqKcyEqXc_P)E}5O5$KdU^qXbavbeLQaivEN9 zG45Z*?%`1`S*|EgN~QqBHPYf>s9i%dI?XP`M5Fyu%x4fWz=$?0H3*A4*jkOkXx0m{ zC*w)9Thz71WRtkmnUuRoO^rIT7ioK#?V3wZ-3b`14xE2U`g8IcUJjx^9K<}qz1_78roJUK_<8mbgw2suA`BNs(i);1V^4`q0(Pls>l3Y3>A&tV*$t7jp8d>LkP{cM)0vKN8ZZdMU0htWn)0Kdn zVG~GU^X)x?>2O((XG7+*xqS8mK;)U|PvddPJo%LjFTsvl4mBuz&wjA%d67WgE7CCd z+&ZTLHosji%-^(AYHGNd#o)h;V_kV()%ctCHQlG07Src<=AX}s{Cu|506*)MC@b$h zt-rsmhkfUgmiO~fX6%=I{!zp8{qiE9_XB|_wvoYQaaXO1x)#rhKCsN9j}H*6hgIR) zj+xw?K%LV7-7&{K`@!F8j0LVao?+_RW9fpxacz6&(xP*hHZMBrnPAyB<@7)2tZ-$< zc=18*jLZ(?G3Byo>HdQGBdF23O%?zTIT!_}`w_YB?M?E{!b_rc#qk zP*Wyd@QhN+q>Q7sNe}rMpE9LEzJSSeP0Kn{&&-Qfan)dgp%em`2YTThORcKYMqsxp|+5$;ab^sFTv&(d;uV(^Zot?+|<0om@@Ac%JdvV5f z@$gafc{aQ3KXnsThYofCF1_v!_^+Hkz4T+{i7L8%aJ58!kPfj4Q~`U+v|%SirM}l zgKPX=XJ=>W4nr{JU^_iM&3j&71_A`ABv80>C*qY?qx|YCQPNo+16}EGOSC>rP|H>U z+%1^Ug1wR+l^Nts2L9-4uf^=zajxkvne^)p;Oi%UGSL^6FV=5PHblJR+}n@g*9?5rRFI$pfTq#m^5aPP;Ej(!}eb1!DoPR!Rkv92CP zY5&!z-+C=-FTI@00{zJ_hKS~??rxml*p1;eq^Uh6L&a$slnkO43y9^E=2`49AUiOa zfN)5RnZU7D;+v##@2awh$$Au{3X}b2Cx(CUBt|FX-)h8cry5s{1rjez3W>Bb1e7wz zrPK2YX@Xg|c1P((1F3p}_&CvS4T;{Z{tLB4JX5QY8i);eQIn}2XCK+_P7C_NKX0YxNS6ubQRlh8d3Ei6 z{A_&6m)8dm`IZg%Y~Z)r9G_+2i-5r_GN#Y0BjvLWqW#=d!uXl)Qoc6A1?BURILLBl z9r@?`-e2&p+M}%|FvBL#)Tntq6HG><^lTe~ucSXc$JPcc)vvfF1uzk2NOQ)t?^Kr% zq(}5(yzs2-czHHt#^hl}v~l&4KD$~+b6StuuoSgE0AX5+x;&M5=AQxy*OE?HG|Dm6 z4zx{Cx|NuB0Ck-zW59k^FyX#+1t_i?&;#3V^JnpMJ~#&HOSN9BmW|8z>4J7eaO*Pp zC>*t;$prcZQ;*q#d&C!zr7-AdA@EF_#nNoWJ-*xEV*njxDK!yX5BFmV-Ex6M^h24p`@+?b$acPP`c{PX*f@_b;)W?fx4VTekjCa#hjURGz(5d__T5Zx!GIEI$@POCBhtp`zi^CBj%{$#{!e;`0+F&o1_(l(hM zoAC|;bA$1<04O`oG5|W`)vx3@oddpfZ82-XZ*JU**xQdKpsfO6bd{y_=wa?4t-9g@ z@T*=siQ4ItSU=!CU^>U+7dVs{+#k9L{`8;z)4621-Cghi)H|g^Zr{G05|QEG{F{H1 zpPzL2RGfYJw59p-JTv&P!8Da<0mC5U=-;~^4JJ=r4IGno4NwatI;CH`dVT9wlv^#n zD;=G|R2R{%uVXa0h~?E~N-y=t7tuSo0ij|7KRS;J#H+d*NB`nH%CjrxAA|u~wV2PM z-s?tbFpdF0yuOPx1u$Pcc^uV?cjENkTT$Ap#laEaueyxk-3PHgyNnv3X^Lc4ts#l3 z-D7Ug;4&2mu!a;aF|2pH&~PUBkn-#CT7XlnB{R@>k_2A+i`w}#Dwl=bh~+6mG-p@S zXwV$F%z`7QvI`52_1Y6U#_`04cGp78}9YEqH`eu$8H%IGLpmfv|MqsVc<_55^}!SgS- z_)`XffEvz}##{12+5rOZl8;rf%}l}c1S83O`AaPj@5uH%N=8O15c3^_bq@OLmV8uRHi>8-z%k9!~j!*LU=o`~1s|+E&R{~x>1jwoF;kWUNrAOYi zU$g$3>-!%WeE0dVjk;kg8D4bnX`mpfQKTIc2s2Jh*rjjl>=K&eX9?5RqrArSBD_L_ zW_-#>S(miCKLwfDXI^BOnj0Rdfy(|Q_@<8ye$TepKHH1~f4X$1E!b=a&_XBcL4Vx_ zAar{Xj}T<~gPa>V#!b+Ed5&WY=sOH%*~Y1!3kQHgW|Wcf+A47g0M|1Lq=w~UMMgpb)QN&ppWxYRX+ z$0k*(X=kOjENztl0XE7C8kM%H!(g$$Q4xR;=t$-s-h$32fJIzC}MW3HV)w z$IuDIn55nHTV^~@Jm94wUJhD38P>Ff)KSLgZk!670s1^u|yu zB%1mN;scYZjF&Ngcp9aPv#8eFv4l9x>y12XZUyo6Sde5ab%=Bo;@4P4f2}iH01xGJ zXVSO?aB9z^#=DTMXn68k9qDYBa_<1nxYCefSsbY{y&OjM{0hKBp{Um!B3scujVK;A zT)8N;Xp119flg}Y07&v4ucA4IHBemvKt-VCeLf2Y2(hk0K!~uiyjEJ;!TplEHI3uN z`{~qFkN~4h)FmTH4kLl*!}gcaZJ1mEnu_l}ONQ(}rFaW&TD-nxx<7ItiM$Act2A_{ z9tGp4RHmKZijW!#HGg@N|9GB&g8PMC7I7?-U-_-(Po-KiZlBXGxhvA{rThs>GchjH z61Y*};tWN;n-0%q%EqypI{(dI-1pCaX=~(op-)nCX#~rjFZi1@vLM`cy81lZ)-n`v z4XMrCq-*+5gK@X@&f7&a|jxt08}wi}><01|(T2g$Tl2IMiX=en+#BivD>%ty!4 z)K$9U?(8LP*{EqB#cycpmdliybn3d$5YY0y3eak_ zdkG%P4ron<$KnineRdU%!4TmVS_-XAmmkpcIdrwA-7oE%<{i*- z&l=YCM!nTb4Oh7e&wy9DDWZgMykZj~whONB4UK2Eq(U zq>A+85hE$VP2Gxp3Ne8oGpRJP%!$DL)w~bk9>jQZ1(Ecy??x^!#qZpW3lM$>;?dr{ z&OCv*jVCd>`%bJMy%p7y{g|KZ#=6r2Br)LbJxGa{!~k5SxEMT%%A@;8WJt~sI~})< z)>Fy^@kBhWK4tRE8a-D#v#MKy2HfiN^-EJ&vA)=5XQ z5>D`D1A@IYK9aQBsVd-WaW#wjxr5tFB3%F>J&Vg%DV5u4PnYSzplCFG-BqT%!Idl*6b>u=lGrOEuwvyvJ!()EOb1cQ9HTn1g1WcwJG|E!N+4cTTU zKwSBAmV)o8?JRy3`ENB#8D|r$pLMcPOw;Fsh1TEGf2M@z9+nsm`YByoiR%$4VFFf;} zmt&8A3xHv{!yEw@{ zbG-nxFd5wS=rUs&P*&>IVzvhKJpg0?9GrvJ(|P5XNwC~jLq?<7F6PTK?WX)?eX^~* z?bxf47kMrKl4_>14<`&F^3@*HQhQv`x?7J0_m^Ymzrc}awj~h~tdpkwr~4e;YfM{( zeQO{{E_91Q8&zo+cc_DgiaAT0<&M%eAcw)K4cVMLx&SVLob+_1Q9kztD0E*m02pgP zN>K+gR{$o>*`S~8pGD#`4+h`e3y+Lijz_dF`P?~<#H+>gr1ag=}Z_vI$C3c{?mX=#K%1dzGobV1utAGkm) zX;v(4RqFLir{;B32@wvI(6`L03FqO zELD0BAn5E?8@|&1K~!9&@&2=*%s}7e(NB*aMOhw9K2HPr=tjdO(bn;Cg7K%p`C~a; zZZ$p1`xLZo)Bco&PxS1|FT?LqU{Jbjuv|h|9iT4?P^1Od^z2Ct-+4QR9!xF~;gO!I zb29+%4lgcZeD`iFke23Gp6^zQR(&_ht$i4b8n#+4f0Q8}kX0l%2q1$@39kl$>OTU6 z%?HelMNAe0fLA}p{U;H#3G)nwq(&Q#>OtnIh2gIWLFw%7#0+M%Knm>aAI2KuQ@wT& z^&ZkT#BtFd$Lf3(^&!&I#Uv_MlzHef24IuFWqB3NZYw%FNQg+0^==ah7Rd}*TK{N~ z_A}%LAP|7qOevK@l+r&D1fPEeKZD^{0g?3?q!-*5W7-$O?vYHZE-`k9D~NE?^C_^* zPyS#pDJ2UA{L9k<8U?V)J4r)|XZgPQ3W)jir*!3AAqm&>H-9h`-A;bnsJ>>JlqDs2 zpJ!6vPfLC(qsogv6(3nF-VszNy5<>!5M#AKx};DGq-zC6T7snXde2qViTKjPY-1TH z^`d=CI=K|QW?L1^(Aa|61cl}+o#h>u+Oj1Sn=?T^EWdB~A;_{^2?mJgWnUyXHX&kK z7TzO8cY^5(cmh(r;Zn6m@vFtTy+l_-W}A41@%-v4z14e^o+cv-G_`Io4d!d+MQZ6cjdrX8Z0g6? z@-&r_!tUe8F}pYiG_9k)-vvB204MdBU5(=M|NP&G*Wbv#*W-QX=FLKrWVy91n?h%y zlS^o38Ctjkpr#|(@f?r`;F^qcLsy-4R>SCXmx0#{gaZV4Pn#*bya_;3>*#^#x~h0e zL%6TD%$<_vUpfQ~@>u~m8ABHsUV=L{ZZ2Pr0bP3SU)m0i%WWV{h3d6w(!b9J<9S*_ zwecKSBY1q^`20N69X@=N8`nH9^Ya?sUHdP?j~*OzQYi*}+I2C>JmYJ_3qF>xUY|o# z9+31qF&@7Yv)Mfc@PH=}+l-)}n8z`B`0bcK{Oy?6M=@&vaIQ|H|M)@7nJo1iH)7i9 z#P}ZXGO(9j2uzQO511M~{9eTQTM#{@s}gn4AXS?H2;%b5+cAIpTX8vkJIY6kn7z`7 zenV-|dLq@-4`T(<8bd_Jpqn|;Xyy1IRxe$P;VppPSFgw9WGCkPy%;ZeHfZI!X46++ z0%T2*ut`fI${cH4sJe&XQm%CmfYm9deT$m{S{LPL&LO}^+O3h`kTxbP2lJ>609O+j z#-v#ga+v;n4`6}7=6QYN7C^Pbq;K#Ph6w2{h&DuMAvK0MiZk@`U+Xg|hDgCk`zwu? zQ()t}7nIm-{`2N4X5GT6EhIm+CfZ1C&tyz`2DA2_+9-jE%cTMy?cubG$|b?4vrh)K zA%X|lU$#64Y^5iNsg53A%Tuly_nhuzc05$ByrK3$sU`~lWQxlvs_JMVPQKc=POPO@5=_KH#y*2*Uk!gPC6>B0OP{Wo>Y^K5eESGiPTr&A> zTK4x33%s6}?ijM2H@|KBwDSVcm}il`MlAmohW9*QxORoufshg<@ zh%BOM$$Y&Q2ms4Hm?M174HI(u+;HOfFfaShdKcN``fE$-90fMzLsq#)b zOf`WlB!O|Q`xv!83VUt>n0K_7Dwb8%APFWF0&Oa_xT^`^Wi$t{00^1;X|(Tnu?&7% zeA?d4AxICgfL^8B9l%|s1q~qGOv^$D)NEL3PlX8!60V$&DepX&(G)z>xoZMwfFA%h zXPjCe@~hIuH9(B@LhQ(TX>uXW(mpQsc49&uJ;>YLj&rV&&T4Jw!^0>8U;-dhYPBg! zIi}3F&<>aP+|^jAQl`m}@?7N6a`mIVXb%T5`QCS<{GIPa^~q^e0jfhi2il)+O00!at1>0caDrY5pRpF2MgRwPd=!D7)*@db)x^IX!T` zZr{KQ=YXiKCx8rghc?7JyD`}SVnMbmJY!Eo;8|I%5^bHqPE>Tdra#*=lkJe5v+U;)a?;OVN(Q(xI-O>*kL@e##n21WX+L}}wwa@{hj%7O# zuymt=L@41tdvX>}0F^E&h=PeV2o)q!ze3yVcLd)Qi zTWSV;zPU`FZ~5M{AsH&4#k1mGe)YcP7*oEa4_nuu`SZI8W--1Ba*}N+<4o|`nuGxq`h)E$s|bAI%ayNOLM4!lTlkA{$<-3 z$NLsC%V%7EW{vrhrS!MV)cF1`qCHL9s^rqD@t;=9KcdnOZZfhh1Z&25KQ|PqLG?Yx zLAvnZdj>w9zDs%uV7a`LCueXki(wrza`8P2V%hl2jV4*9qK`~euq3c{+1PVh=e(fH zg<1k#;@D?ij+1;>0LVMfgdz*-TqMcwBEo|H(_nh>j3z>jT^{t!^soruFG~?}oV_5J zXXas_QBlvcweMxfYDMe=wb)*sN4eFe+AfY&=cn_g3RqH$mG8lL)pEC)*L7`R1-w~z z013}EEopifIAt>ZLgQ$g7USDL<7*czT~J%pXtnavm4@ZBe;jjawfrP{ey;!uZ5I#b zo+Cuh^nJ!dY4w78wt{WlsB3r*XaQKvpsj9R^Y?5zNe=_M=9vOa?0mI+9cY>DVfhw- zx&>fH+Et-G6d)Ti&phUmR+e1qr2M+~P|Gv|^b9V~G zCh(S_&&hWJk2$}0H_KjWw-J&@@fct>d<)^4axVdIvy00Zon6pYjU1=V8Z`OZjVN8a z9(&iW#lf9BQGqA8iNP}~b#1YGauT)8z3JI`jMd_e0GyO@^x$FiDZ}9Yy|@A>^(lkn zVouvk1&WlzezXnkTS4rqPkE)&+W?)flYX9pz`HZbYTj+YSY79`uI6lqBmLHQ*_3u~ zFfQzK({9p!^9d5rC4ld97(SM>FFy<6cg4^j=Dof+kCM9$A#@UxrV~@iLe;tkejh0> z2*B)$$vb9Hl9m#mQgGT?jG3(D0a03v&!f_(91<=j-m+Rla<0duI*T#{uv~V#R{`+S zgEeVQmyZhRz21o`5_E0K6zcIFM5VEU$euln(*3t%c6C42lP6IgoPyjgV~j-K{;`+h z;u|+&SVfZDZNwEIs&wNp#<$urZIe-X7L|i8-|I0PKaRm_67xC?2nop@MJpvuLCLHi zLqM65tcwNfHj>))Mhw~z_6if)B_8Ip8ln|3G=yzHuc|ZJx(dOoBgO7^h))sFt{Nei!qRnwU;tXWb+(yKVjGl?cb4R} zXpjzNQj?M3!}x08WVUt<>A8fLE;RTKzD#XFGFkC+g>80YEzVba?UH z;$+_ZJ=I3}p3H^v=!&7TP8d>XwX6^QV!ImOdjb;E@G6!eX}6j{&vz*gWp|@Rb;kQV z&T85g#7mnk%R_#)QEI!?yea2c7L)T?Q6&E3BG?vb*lvN0#Ql6;e0^8gzU!whEQ0ZA znVb{Z=eCKL@f=4xM0U(@hsV^DGE$yQ*S_|P`Ku8Um|27kAV~KajveZ68)skg&-`kP z?>%@TePy;TGImvHh5kmL61DjG2lF+ zKFBu7?B@t()ov%I2#AY4UE_4g2XMsum1>8!uoC3pZbFy87cNf~<;s%T-)c|=*pw!ECct{^msf}8nUZZB&`zQt%MNi*^p|T)IOC2gQUBD z^e|QkypYjoFyK3tR@D3RsC?&cG={STkODFV)|&1(px>QNza{^saf(cbjsLz zZmH?kQV5tN!s~#SJ zrO3b2izfGMzsfsrMg8G}+(gy}7*z$`7Z*`GhxhPq?lj#7<8x`kvKD0fjNe7$Gw{fB z4L>ODaqaI%**u;FWCl+NfVb8gw6T5TvrMA|=`X|gCh)8tUFJZ3_Qg*=d=x`}YV&3I zP=R^ww&yWOU)8RJCnZD%PXmb|4eV8~ZV2j!32V5y>hPKb>$xDXwBr}cNlu;u5hNLY z8YJ#YQWABUo1e+dh_4izk{ji2K`32HkcqfMXFNQQ>1vwZ@)yj78S(oNmk~rs$w)_k zUCOl6>!hRB-QF&Zv5pQvX{X(xyZ}P#J{^zZ;H8)1=GVR!o&AH@MLKIplmWAD+n1^J zIy=#UNVn8x?RTTcB-PoY48}U9S8Rxq;B)P+p`kVmP<;W`5D9lYi#C&9yA3g>8WxRD z1)~kh=aD1}+9{=7>XJ;KMoOt0!mTR|8Hr3TnU$bO4ThkF@0AAaBJ-ic6AaKs3FKt7 zpci+W);lnXHsvFUl{PHd6|}Q$&RqeS%rKA0aofL3S0J7A4`!LT1OurVN#MpkJJ3&= zr|Hrj`OPx&C)4m}@>8zt<4xWCZT>pS%(D=EhbG%SRa@?k73c~I%a@c%xGnG)s7dV7^K3^R`@W4V52PHXr4};VJzp={%d%Lg)WA{4 z0{Z2xqMR9%xIE9el#z!z$y7({3v1!sdO;!t1uJY1sbx%=C=LXV!_Ij;E)Z zPkw7VXkU4x*%X>&G1P3S5h~K%jP-0sL@2Io5#~+}(9{4p%xEi*59&e(cCKHK-oYUW ztMOv2a!IS^8(SgW-)QTkwyC5Bk>}DUK~YJqm-nF)UY>=k0O#_v8=RyWYUGL~cHSYb zNBLM=X{A0J-Rz`mAVHq-^CE34KW7Zl{IrDtpIgia(~%A*VCFA@8+9Eal=sh0sXt?t zXWE!q?E%2(>fytfot+a8y2bOknlbWUTwcWN-o0XjL8cGwAU$pBrlWCO0d#B!TTsWe zbAG$jw zXzuQAK3~&b8tA(jAzdbspM9X)4m&tcRG^%mwBiOV2U4~xv^IZR9`c~`^H~Pk(r?z| z2ZCdY_Ll#ocI??;8m31Zx(bzi)ZgCU2It~7pUuqpyacD>m*GPN2E=3>Zr%3q+NT4< z3C|@+(6BQ7@qEZoN)?EgRPP4|Uctne2%?c%p>ogNGn?mzlm^{4x@SFI8px$LNU90KW4+Ln9Y&&%G^V; zTRy&s>DhpCs9HdpTg_vD1T;H3iY3z3uulGa2hpsxW7$W7oI#8h3eCogn#aJ~K*|iz|N?pW9H(AMVl4>2r=T?wKA5gxJZ{I(@xRxK>6K;oEv~_W5<(Yjqz)DYpk!0mhWc!F_r{4 z3BGL8P1~uRb)$xxRZ>vk7SW%MIWGkicX8I+_0&YB?H3Vqu9`k{CU^60#z*!&^kmrq zB&1{AMwE{C^rI9@1hm<~5pCFx84Q29*`-~UR&5yXI)O?xfrT55Kobd$)0GDFXa$HE z?{=ef=SHk=UW@f^C)WBogwClo(@AaGR8r4UOPez~)Vb@t6pXn;aCbk-JH60(wHt$U zLhG{PiZYd;r{z{V^H;O4?VIyqI-X>E2vW5htGf$vJPciG2+Rb0xl~F{jsAeX9wC6y zR&{7eQ$LID=0uPmTtyY2lNvUKQ2l+;U)oJ2H%Y!yjg~<4vL7YVQ7hHBe?O1Q(;bM~ zu`)bpd47@hRcp%%$SU2u8I@}%xf9Y|ohxW)MNJd}vuz4;OsB0H@UJ}g^V)Hgh`-+3 z&)tvFY3FjTORYKRC`$++ZCBe4VOzVhd}bd(tEZ11rdCUU=+4s|4~$EJbgt~=h`0)k7 zyOP!qA2cv+F}MLR2{lYjBD(^!tfE?VY0|BZ5UwUfqftYO1(+-ZL`vXlwg4loPO71m zf+R~*CXvoMU49LYZX@YJFzQJ4_2DAw{b?-j zodVtCV?#umHLJN{~vddCpYy{O*08Rc8o zA=HO4KRk;04Zzb;Bc?kg$}*12(RmD@2mRVACN5=B)I4Vopev)$F=tSg>X^I+w8ImC zW=R{bCbYZ8g+xc6=&`%q>_p|ZE6HGl(zkpF0mp(*jm6X<6wMoi6iEX1`%}9z3VV_ZeN~nR>Ggvv%1` zFX&~Mq4g4Q_WO#^A{n9S;TL%rtp6*DZnolY;1N#ElZh zGuU(mOs9S_f5(Azj-ikn0RX5P{cokv9OC4oF@D~WIm?K-wB9k^Hf{zfii}-4yrC}1 z*h$B_B~Z0}X5=*^FV}$*L6`^k6SP?_?pZgk3V=pUY!<+DOxJRes7zOvB`)FRG9hi2 z8Y3_3N2-i zzw9?|T7T2Fovc&VoBvW<<~=UQvj+Io)n&D99GUr6qh|lw)+TEiOgA;3d@rggY>VeotI*qn*07;`XL%{}e`E&wt*RW%R$GPT^4 zH+MkV!Q4w{vXt3n(=i`aRpe;uS(b$K!0n92d--_jp{g_?r#kAYZb1WB) zYRo9)44SB+gx>9?gSqUb=Kkf|G2#6Mz`@;;CC?(>hen+UYIcby?W7Lw?k3+L%^>dt zYP6#zbu0)>bY87X3tjf)k|xh}*1Dwp9$loJl^Y6lsWDwGqU?dfM_yk}T_(~qzN1qEMGZ6ujwq0Cv z>NM{UA}%ju<*tA2s;5)htDU6G-J)(B%XduoMZvj5y!qu7?a2h7jLl=fJME`daeaPH zUK7%tz$F+jE_(u`i^6_>0yX+FeAuAMI1O4IK+go3RI$Ccc>dvoXTE8q1qfrZyDX5Z za3+b2t>IdG$)h`%bP!X0@tCKR!EMk=v&}9bYyXgkI(luVvq6t3kr_LYR)Df}=de>R zb`fd7zOzSKJ)UXMI*&O-eK8!R(dhiVAB(eoGyz0yfLmi1Ah&lj>U+1KRoA1n0tEE| zN-I58ccWL{kM+0?L2O0u+U;m~c&V;bQUY&O5=W+jlgIyiTUXuDpwF77|a^txS%hTw$%vgsoSN}DrYz6YOeRO#5+{d^44!5wG#Df)vx6b~ zI{;tWY@{a1_?veGZ0jxznmiZWD|Swr6mfa(a$MoYg0v{_%3Y>>3Pi*Z>0}phX?lVT zKC&*>ONN*LP*A|Lg1{G_Pk|Y?q$&a&^E2e81mhCLwlRvfPwNRk)Clq2U{eWP1Qmj^ zEkH>Z57bkp?aNsl>+7GtEe~yKowLmnIC9^NET?S-=t}P3J)hHWsU1^8LL7mr@8rf0 z%j#!K$C}(EsI{nSpUK=QgfXIUPk>f{wBp{=yf@`|3PSmrpxf`(jqmREP5V3E&9|+S zpe?~0@hMj>Mf%8bK?9}c$FwQCV?q!ES54n&rn1%26h~HV-laVDw;<0=0CR0&Er;!i zAQ#$r(spS_XMn_{bsY_T1cWX2y0JO{{M=8bF2w_el%ZklJbpnZ)FAWqvU>N4Y6!PHAP zuSXp3Q*HnbKu7RX0tBtqq)AT!X;na&D-8>(=l3$e%pHq5xK3~Rw2aI6HGpHCwyYC3 zmt1L+qU?Z3`e|alw1c9KYTPvonbMhR02ctN*=ib-)dZlq8&&F)2KdBDS4Nc0rO5Vc zuSHqF$XFQ<1{tRYog2LQW=u{WJ5XXWyomG1_hbC%UQ7W@+H)l!+u>q7tHpAYf!aA; zb7uixr+2wf@mtPYqH%cz8*j8au}%=OlkAS_fZ&m?7!)M^S$B zom9Ou;_7~y#iGl__c&{Paxr>VSR)e-~74~zL={)Ly*~+pCv4T({yj69f zL9ynZjY&V12J>VrTO)q80}?A*GC@ds`G;v2Tk#79T7qMx9h1vT+;gjZ8lS7B0K~`` z)7aV|(zIz15MW9s7^|= zyq462iKAN!H#6i?C|RVo!7}$rQ_t!$Uw)@XjC|#+rjeQx?pY_3g#BeapC>ctdFEt% zzIYYjfimR_09>^=8$g$F8Dm=`dDBt5WL`zf@*{zO@wjY1+m-8CK<9JYPmN@1E(K0L zGC#{?AMo4dMU&)K<}H9(xVb?dNq?$6%d*%uwqFb2(uo)&pXi59(3Hz<eUaU2BbcNhT%buBf}_-KTVHPZMJ+*vinwbwfj1A#Z=0rloNcD?VVY`7 z9kYzNWiHhT2*{W8@;pvH(|v`@_WmY*F0WZW!KwY5yA=6kOzYl2TO^NLQX_;Y1^{7< z17_8v)hJumY&?tF1~|^UwPd(+weFWp$}8xK00sc$a^wo22OOg_iAy}_3FJcg5rsYPxv&Ie2?xgJiY-2!F<=`-d zeff&ZrGh07!)HJ<@v?OixK${thmX5Vt!)~U+=2Z4K>Q`o>6DyX5(5Q?+(baVSAtuW z3fFH&MeQMJepdelUN!P!CTNPYcB4oK9sR&=_%v9q&_ z@W$YTDX6u&y89q5?>~sey%@cfnN;nr$I`=B&&L++Wt}W z4vtB?m)bLr5~*?3nV=HRH6W}7i0hJPo5|K;Vzj7RcV{oPWc4Z%r03#6>&F8m93(-d zqhiZ^HJ1d;N3&2@wS71Q1!6ccMJ6LmxiFArh4ZNcU zfxkTeQUb+9R|AqRC5W4hRKiW0n@24fG4H6kpnY^T;HPb1UzxtA0~EkUMoZo10Cgs* zgxE2oakNVrDP{AXTl=&8zQg7gzYa0ANKV+iLsU^fOPGdR0&$Va$kxjLQ!` z^CxM`#B`mJ8cVeyy#LfK#->b}HdoPhMgL|#wuR{te0K}pGOlfGT;KIM<|JYZUe5Abl=uhnB7c>sU2Xsf*Zm-Qiz^&x%}fS_h&s{xY1n778&jx8I{_8P-z z$D>K!^LLId$}_huspGc3mO97NrmY+k3J|&3LdMEvtMe-J&iTqu+T1t&OHCXh0YtVz z@u}wAvEX}xF~_0lEO&{azx&-7@JY=8HH@*TZh_9ToK5O6 zf)=X{QXti3gUg?L0Gk@=dbZ>KtFOk^g!D+TOX`V2m_1l&4HBK!|fwPy4xAK^b-NV4E&zU&oD9#!VM4aZ0<~z36Ze z6lu)whB3!Q_CJ$0C0aE2hZWhp~t3XbJe|Kl9{TjF|NS&n91Nce*?%kMv@4J~tLmo!|NlSV+ zZ$_7NhT3)s#;98lp5x}N1Xx@yw^ra2Y9n?H64guW;^J{{YR@~44v z%CoF*-Q`-+$j1AehA%Z`A0;?09E?obP&Ld14nR@aPl$U`%a0{6DA!ETIoIhRN@soQ zP)`>ebq4T4;sgLHH@XlK4T?nOOeVA67Qune%1~C~cl}ew(doJmU-e0EIHtz_h6!j;_$ z(9jQ@%d+m~bSbX7q+B%Z+D!-ybC2zDSvBqOvhPVx07%+t5HAr_D`~t8s&nz2x>H$98U+~+q#UPKxH~mCav5FtPnyS>G{mlc1#VUfBaTp5XfdA zdICeg5^<^xL;m069tkf%%Ol#7QPeIb62& zJYAO(mmWrL`E2_#W4p;bglVr}nx*L|=Ce6_O1#uT7xljYg--LXN>#RA+&728xzX7KHB5iQH)#MYad1xU^M~Ml>zMxt0+&$%t5vLT-42a zEqGo_trvA})uVK{8{?n)W|Y41wOH(QVg~J+P`=4*n!;xV0b50S>&Yo}_KL|k&80>S z^C!y@pY-?MZrYA5Z$YO$s;J$K`mJj*Wq#E<2dU+95xPR2wq>4s$)!sR4e6^7A4HY! z)dvrw3g~T;kK;AZsC47N$+cLLu5F|<^2P36%o$sP%%^5L4ruyFS5fX>tpeIo%er|c zozOOINqjY6?=tUUysUV~-If)2it|A_1kwf<(pQYNDgt}j_I=93mzuIqL6B&us!qxD zcO^i;AUsnfj51)%$>e}FUnO4E-)gFovppO94Y67QxKuZLhM7BQ^dGZi(kUJ3+pM$W z_VhYZ&?JWU@24}!&cRXa-Mk&$BPL`EfTMP#ht#vzidDA}!}CWmeEWMbd;Bo2t}Y-z zi^35mWtmNf%%vIW@yW!iK_HSbK=4Q7e%hBbwEcX174s(mLqONc<3a$3+4mlQ(T&n4b=I4cJ|7S~iuQ3IaY+3Z!0n6bp5hAJkt&ckwK&&K z4Mi@`(3a_FQtl_=7f|IN0fHbvSiAf(E;M5T0GWQezYti-ST@bB9gg2^_N{iI(2POk zeMH=pQq{l!)}%?9rkbs_%VU;cle_=8(lI6N-PmhKtt%LYh(qXP>Q#+-yF3@Dkbz6g zz9$H^e4evsp6M``PmAZht-X+!<4EFYttFafss=}}qehCv5^&f&-V$d+CNq6P3m2yeHp`g!6!`_W!692s%Tp)WI2ko{jUJZb;e$^%eYBKMp-v`Ef}~H z1tOxVRnsx5AivUKPB|7xSPhS@ATX;!W9k5>Dr48S)r~`&@;R2(fGLRdTAk=N0U97H z!MO2jy$+y=^1Dlp0jq$N&Y3B@<+1FJ+cETMsQZVU-_(maIk#|(Q-QEfTy>JIqtTVx zGv@VTFe1H4%u^FT0l+~M0A>13Q-fy~E^pE_(WIPB=A7<;2K}oTU0ufD$)gxNxEG7J z-;5UHVsh_MOwX9ur4D=_9PXw-ZOV zZ^z-Cm!kLDYf*jqrI;%)U%M6+g!Lt5SyE5?+V)c;>865mt6TUEBb_>6=j1285#4Wm zJ(@cFzH=+Ozwhgr#*#5$U2ON2TBr?^*GVj+C*6y*DUT}|0su8rI>7blBdBKNad`lp zJSqOjz7rP!vMFt(rd6A=xyzPrCLle>RJvJV(rdr0 zwoThIpXFerFYOzJPi@RnNbni=p2i_;K7JrrcAcHONUlawFs3^X-b_cg4Co@_=Qw<+ zDf>8sM1+5NecZvpu6JbB-y1-dfzM=U4EU9j1ya>h>BBh!Kw7G@27qaRS>|%fv8wD9 zlT=Ma#mxj2CXmZ{Rlow1bT0UV;M7*N7$J#jVvrT81`Nl&;(JK^rLhg$qa&5 zM!J+gRAr2jL9`!{*2A-?GV#@PRYuEbX|(@%5Y0Yy z9FxyfkO6_E&uSjKBEv;0ENKXP(o10Dl5t%I<#GiDaGA*ClUCYpP!BjqE<-WX!{d2U zmVssjGF&U3D-;L5+4umeWf@m~~+I7ej1lHrXat@7*O*T9TKAeq)hq%B>|D($vXfF+qI}9)9r zLM;P;%sOOUtlNgW>g-spT53@le~t~87+0NMlwhdKI(a(od54tf=UQQ-=(r?V=XPGE zMOn3r^QYwyblDdx!7AzInVJAw?G{yRr|leVqd|TNhUexX$L@OHdjh># zXcu(_sB-*M{?y(AK2m$*vgyuljCoccj;OC~Mnx0+sm-QeVA!^VTFT|9pEh6~&E$^r zPOX(p-LmI_*;bBsm$ctSzPL60i%hc<2I zr+`XY>JqnF)3W8I|8qrwdI`oV)JbQyxfDDzdDaR!xK=eP^8eTs}GN)%FPNn4?q3k3!^Ff5C7pm#NYq>e?R`_-~5~LE5Gt9 zo4a3}kjn>*+^weJ`LK1-u?Lqlt|L>f)I+$U&Djh0{*#v&6>EsKmpz(jd-hg@oJ$Xp^K z@EIY=Q*Vu)-KdbZJP;@i<%=NWDL&F(%@kp#_NMk%6_{FFPIGByjU=DIEQi7Or?5c9 z_w%emt^{P6LUAQIa!a0@-#7i0dGkH}Y2`bnNB%tX zDm0Ybhs$ZRmP?#~Ck22d0C?4Atb3G?{-$OCuFG|_N4zK;lhFv~WODSE08N`0jr&Ui zIhmM!O~&Y{Ljq@=zZKSQPS*@RL3>E?~ck?Atw=JthSsYhXH#cVRDKm6M&N4f81rYk>D(y30 zS^!1C01++Ec6VumaqPw#?Xldr;MlGLB&(MQGBD4Yoz6J13=7(8vC^Jy9MJXn#8Kw4Z`nZrWHi88*;NZBCoeDQP$r*Jez29BD^6qEGsxs4*6%7ndn; zmxl<%0I&JgMNCiiijQ#ozkWU9cfJ#4KxjpNK)p&&o<#iCZ$(LS635e&c4w?rJDsR> zx_NYx0;HO@vUXtS7YO)gX+!A&@4C4VR7*efUsj?%t9|N`CjuNMeVaXa7;Dlm(axo- zs~A6gz^n8V^E3sS7X{{ohs-xfqj`KB&BFuA)6Tj0MF^j%TK>=f`9I@|j{WX;zZ?Jc zzy8#_2p^_icEixhHsv@ZZ#?~+n2z$4)%V#IwuXI|u z%geU#Z>`G;fP@*5^J=g zi6mBo8CrkaNeZZDBK=$0Je0M#kW!Tnx#u~U8i*%>kx2@&>;itL7Qr<5ULtQ$Eua%3 zDpBKE0fzVr@hSk8eaU_5p37n|bI(Jxf6L?TE>8#~&(m;rL*P@>1i{K>Jj$I~JAjHz zd%MJC^2zK4wUo)_C>gZmpAVj)7NtA+`{mRcw3=%*U9 z!X*P^mrut@9@j&Bmvh~@7XyF?fZt4HZfdYBlP*tOsuxF=^z}!73Zw-|wuzcS4ebT# zX>_eNnorwYK}GGdON)Xh+rQXkvPfZ|Sn}k#Adk8T#7eq~5TG$8b16|qL)zcp5I~>M zP#$=#O`Y%AMjDJ86rLPUW^eiJGhg&Gmobe`evO@8RMaMuS8jyK_5pY@mRts$0gPsS z&mElQcy?#xqR$+;qAjEejvI<82=aI&O(ZO;j+NDRYdG&QL(-(&>~wq>t)0DS@>>Ic zHygNoJ_Gm+pIl}74VMI&bM=kpO2cgp(5sDh)I39%T2x#XA9_j&!)*UJ0fI|q0`~O$ zZr;?v>A6Bkbe`LHYHyTD8UQd|eqt^rtcK$F0}45d;qNN23hOUj;p zytp*SXzlIhs?X$V01a$JhxVxfSQnm)JRZ^BrPyy12fA8%LqK^M3+l1}n9X%rLH))6 zQ8!}=Y&{w(=c6>lrE|)ytfND61$&M2J#1VTBk7*R@wh3MOU)(PprXxJTOe(pLBlCi z#-q*a#A`^$oySTL?-h8Qm-96H+==6jFGl!GEnj}~o8OE-_Q(EMyukp|-r=Wz`lmNf z-aq`FEMNX^1_!0(mY_f&Dp2KQp!`+})MLKBW|DGl!n)tj?a$S#%i^?t?Z=2~T5d0% zGTC#7nbNN5%NV#O^HCbmFZVhzXCe(CGL!WvDkq1reB~tK@Hm*6u3F@7n9^Y{)$;Z3 zH5lR!1f(AGLx|Az{g|~WGla77a1z7sy&dJpr%`_EQPi)d=|f1SG*PvUq!}H66@=4m z=<_L60VvhWJ*09yf0rVUDuMZ}4|ig@!##lDS|*Hys?@&%q`AaJQ3&OjcOdfzodj(o zB{lMJ33lzK44A3QlxY|**#H325V`E36z{UBOO(ZNLjn^1Xd;;uN#}WjE=y=&TbAL; z(0SxBG^CIWdYZOglo4WF0+5wq7A5k`4Ho3D1gs65Ofj`Yq$?v-LniQWDK0@K>9$r7 zXxgMYM~ZZ_gP@D|^;IVjsKM|A0eOrJX;jsm&=9soGFQ^mz{Kyf1XQ#!-^awa>wTWi zY!UWT6P191HqV;#o3hR6o;2id^Sut>YP+-vsCJ_O-8b6+E9?Ihh;d7UZiDT!4Qj+p zUyfS*6=h9(Cb=u)qG%7sIPvnkadZvByHoO>3o>c@a*ILUNn0(_dWm#z+cJMjE~C?DmdRbp z!leXiG^F-ud1MgdMpJ-tiFqLCqCUFX(8;r9)}d*IG_vsl8e`gg&0Lx90;CulF74Dn zCQElOQVvQ}Xxr@63GG-T&a#`2x7FgKg&%dC0(F#6MRw5kZN#C0iD zATk{cnTy=B%)CoiM0r@)U^N5~3O}SnzdA6l}4mbjaM}zlj*I$OF&wu{n%$TH20diEeC;Hv#|W)^`%y*!O%OJi1Fl?p1@tnm z`d9s^0iyH|=E38AX*nQIJG3PNavy=YcKt@oc8+4Qdl+&1E7bjFRCfT02iMZRukyX` zB}io0$L#7Vm`0%^+sWk>!tFSkjGN_T94O4BXJbM2x%mD|J@d@5zy zZf?DvAt{v^G#%Qr41w_!ehIV&nx0wN>h>~CB}<(G-gxzuI7z=i9xRL0>2ju%Si%NT zkO8Iy&qTLONf-fr(A?Zco^BbC$lVIx*{JYcUKou4W0Yf$iR#vBx4d@%m+sa&fZ({T zT!tp7(LmO%_C4y}fiS4)*@aMaX%BaDX^^W{M8K_?F}K z4tMe|H4t2uCzoM}t~-k+AgK<+uEFpsH8nB-1JW`bZ3uPoF&!f%tBDcF(bzJnet$r} z!CXz3KS8$d>h8mAlhMKSO_K(o?(|tcuhNbk@^yEd8V@xJZmOu#X7rE1#`JtwfX2IJ zK!EXeJ5iKV`yf9-1!RgE+)5D2`? z&vDdd&UX+>TmmyqFZ;>XV8keY#Hc?^?V%cwG#IyWXwMt3yd2k|0~%fr5b&fG`l<4q zM}0H)hcIaJ=`ugPY}<6v;WC(Gf&A-$kATrH_&o#=OfD{COj-hsOK8;yfaE*chYlV* z$a8j+&XB+57s%L-&RyMd2*gacnCq=sw3!r1^+oX_T zAMi|&=!OBoye=*#T>UrSj6Uz{SXMVgYNpcBEah)Nk8Ni+Y`H0E2YT6Z=OyLRf0t(p z*Is)qI$!&G>|ehbJ9l12_}oQC0hrSs304VeyabRPZQ5uP`?N#5uy_;?{hxMU*y;xGQx5HYPYVx``s7=Mw>8?Vhtbe!^E0H zJjYCuomamSb0+9qO_~ge3DX1^DGjaGlbFs=nOHCw7*J(8iYE}s_W5b(a-v#pLu59~ zE=bdD2;lN^3L$zF)gF^|ryV2G+&{e=kM7-%&X9D*b0ny7)TfIWYMb%o^>p)4-iM%8 zVX%n)nvl8#G2JB(DDU*adCV`*qKcGShmp8^-8&zq_AGAPh%&@u>hgj+p`Zfe&Nyl% z+Jn3_>~3^hQNI|+6_RHA3P46XEOvEnsCwPA>!5WI;Mtv{*oTPC9w7lfJdNrErbPJ{ zNP8MwWZNaEfq~KH6;BhGEs~(w{w~wy(N6SBd9q4IIXR*oAjG-TQR8x%AAmwBilmak zCm_^L4F;;#Mutx6DOI~H=XriIx^*7_P7!p>SyKa~riV5$zjV;rsUanEZv-=$Qm+~L zP>#AwA~G48M3)xn%3`MgQjKK;5Hq9MsNcrBz6Uv{)H^Abf3(Y4*+=9(xh6oX5REpW z9!uI!7YPm8ey+1&l4uMW4`=ge^Z^3otyHYmOz`1XsZB1;)(g<5hC`5(Hdb?5S5D;+ zb_}bs@FnUuPc0MUfvM{u_&Wd8Np9(CMm-BnVF|$DL?{iRBMthb(}?xOFzUu5{p=gs z*QG-@FF0phYN}l6?}|309Nwh6qPn0=lxN=XKJ8DQ?r6r(>!B!0&=;$)h&XmmKXb@DU7<>++x%y@P$meLT6HRgB}k*xlSpL+i=QC(*ujlG>rm)3d0bQbrB;r_`&{jJih; zK~vP+t{1eE_DwK%HG~R4W9Fe6`4S+aQEkNF?YmKbd>VD~S<*h*VArF zdnpEB`ovN*DF_#^$kZ!J38Y!sJzzXlna8sLEoNJ-_K#AiGmS~ddIB&ilQ`JjiOO%j z9+PwE4`W`Sl5|u%P3uoT3ntwx!>o*2hcSiVseP{VLQ@6>n9|W<=o%tUuM!;E_X3+W zv};*k#c(u8O_cU|6=+Elu%}Jign{Vp!q%Nvp@A3BQiMG~6-X&juOzht3Isv!w2*u<4d8~`u)HEZ!X!^h^sct zLGI=O+iHm5-Gj%{d6~xoLC?(#?hqaCA)KF_L?EQs9zV+E(D3jTx-Or|azxHdt7XR=c{=?4=C`+!DVKba39a6gRV$A=z1aKeS91cpB+5dQ?dSIQH6UoT(yjyP`f3`J!6@3}Qq*DQu?N!vut~%#{Q?Ed?_A3roQu#|KgT^M|u_aThc1wfFxCtVyfKFC9xBehGlrQ?fo z(;o@ih;!AWy=VtrGZ+M$jG6+Nt^uEb)XF*i$yqdDQ9T|%4|oM^#qYCmpggCdOcP=b%o*+N{9NwM+1{#`iT)UoiNp~X00G=-GGCGA045I#>w{x>W z1wmp-IcU^yH+BM1>Y{^g$Gz?~91qAhIZxd+skTdSxZdd|fLfkIZvZKJ?05NYLH+yd zaqJx(WvOV10xzJSxV`5ERUEvu1 z#80I2UIF18Q09h_8gtdQl^3VxngQ<8tNG=m`y7VPJSY=9sRB=}!3%=^S0o^+l|Lvb zdp`V+|M5RU^SRFrDBFf@?>#%XVS=ii*KfX+ONCB+rDG?A^HHLA+3Li;6CMfIG#m!D zZ-)HfCR|#EkqIKzs*-OarOP1HeCTh;$c=+;+Dr`J{>`-Sl2~cvsn53dlX1ND?ca&p zFfwfv#`IeWpwnWK&mO*$8cdhX#!PSlNqHaQ4RP}zb^VN$r#yFwRH8V#jH~mrXub`B z5=g>CYLpcog%k_}rE*swn~!+8I&kR>Py?89PS_1D7bGL5{{(((z%KMH@67>=)@M*& z0USD^~%qW=SV_A`D z^XXXAj*ouVR?j6U?ezp;YM+(>?4=qaKvDw+tOjP%AEjonTB2V)tg(i0z_`k@3KeqUjhJnog61qZfSh&Mgo`T)U=e;a`H?-V;R(>Ik!?nMLfsNf-#sjDDq9l zyyGGd&!@k&Uz+ODLH8t-&GB9bJnA*SF=71BE|uO+?rH>`k{9hDGo($lO;aPN&%d0t zw4FN;j7jc(mcTeJS@X>@MiD&~Kau}Hh5UF_*u+(Xbmpq}=dw#|iE zM;$Uzc<}F2j~yMTBm6GOPqURgOOrY8#v8}9CjfZzfn&JfH3af~*Qa6oZ2S2c3LgcO zIrTsBpaiwgpg1}@dg0ZQ2rw|t&d%bkx86!X=91@|Z@!t|KLxn*lM4>m7Ytf81_B9K zeC|Z6LaeptsO><+l5r{hFzI}kQIJg2(cqP7`k!zIrn9UfSxh2R@uSOuqv0T~uFhi&!)PNhRT`892GYBJ zJq})dHFoa26uZ}MrX;L-U*pm0@nLSqcS)t((Rdn(vVRd*XHOUl5GM&R45R@u)W?%* zd5L7_vbWft02@K~oEwVPPLRC1Y+_FDrSYP+q_o-NtDOA!!35sFJq0@gB&Ov%!1^2s!1 zzQoo*+%~HLn(N)2XmxfFBDw$%%1YiVK-3H{sro6GBAbBUlOEExbEIr5!~&wL*Zs;4 z>9&xpHO6;mBz>c|cq$wo$t6>_EnFfKq~uOKn5H{o9Rw0mkCTy*rswrBsdLTyGZ-1n zQFkOXh%QDJ=d@jap1>wIM+j^j2QVV9bZx=yG9b&79hwI_p|81Bs~ofHDrPm(25h+D z0Qi;KG22*>q82nY2r%Rs;6WQk$I={zuj`QntG21A4^TG&!1nuBG2{|>r#~~wqxMW7 zC?L{MU*mB#XKJLpbls7_iYqr?08r)tFGJ1`5hmqG+c|CN09l?dxb~b(wPoZX;IOIO z6{@}2Xab<1jMW}}EgiEhX=k0{0tNH8bFHF=Q-%!)@H7pNFVoF~;HC~Zaw?ifw5qh5 zZ3y_|64<%B)TPxLW2y`|ZIs>kQV*T`E=HQT7vo$qDU+@--|DSi495T@-vvlHc2Y2N z^3z^kxly6Q+;BX~+^+_?$za|e#*{g{a5oNp3!=(pvkK$9rmzHv(nMm$r?y!O=GMfe zOc<5#=QyKY=|>Il+h_v#C}Yy`9K$>lph_?J)!yHU>ft`~3Ob+wBT!&$mUg)hU8vo< zksk5Ohda@`bBlPb7*F~!o>R^kljW50K;8&ttsPzV09={78p&6W4oPP(4>xa;Pn~{J zn>kxBwrF#Y>q@&k`ke|Fq>$TfAV#Ro$ zD7($e5bi=}aV& z+ysT9oNu(NSDwg0mU(@_!e?sty@biLW|EZ?znIMr? zORzjWPiKfS5#vDyd?90uFYZSNB2)&PY0m`d%%#67leAhwdKx^5!IM++SV2etgiO)_ zQq%yT&;*Q+Wj=eo^h;9#?1A)PjFcT9x4OJY4TH;bP{Ozz_oD#=7(+DI+SO1VHCpai zQ)5$l?M^O%4*S%5{{&F>a@0$@y4cGlB#n!6X%Z&oF;yeTzT5m|K1#$gt2j80A%yl} zyA>k{$q*@jAkj)XA)rEHmia<(G$wX=5fqs1D?`?4X+xiCb>cwqDihcuZKc*ycOfo$ z+Kx+idjgP#Bus6hG@7JFq_&3H1K!eBh&tx^bu03KK;}_K+5xqZmV2am2q--1P!8G$ zChKyMZU9;^x@rZ$2{UzBQom(_L6=e|Yto$0M3)fnb^3==BPZZkQwjc0?M-@q=eyb~ zL7NArr<2<$_uSDakl}KvuYN_ofB^5QY0)Q_S|^!mmYLx)2T;u0jhN5|Qy8qEw?rH1 z3rheiVHT{Y%2AW2<5YSkmquN3SyOJ^9i(j+0E!MvNnd+1Vnw@3n+qBiFenmnS!~8{ z0mlx^L@>98;5%%q^pi&IX*Y|mH==R#W>jzAq3z9_a!}vg+%p`JH)F5L{2MTTnD?GAVB2a-=Q5w5xS=)(5SDIj zXjjNiu?z?}R$FU&W$j&Qw+d~cX1WYGn<&^p1J;K}c`!QA8{wyspk^XaIX;Q;$zhD> zr_s@ITmnSiqKJ!{^+T9d;N+gdR=w9t0n+?)GX!N)Qw{W?eW??)Qd(NGJ($;O8JAaT zUuV>{aQ~vvRKELmYSiU!?sTj$=F>*a^l1ONLILfyfS!#J_BUg3a^*!2GpvG_O z6ACkeWXeo4(|R-gLs_5g>ZtqIDA_SUS!-LTBy1bEiPCHX1wTRI}=N_#x% z1H&xCChY*=&OOoL@*E*=kV~%$r&0h0Pe%PL1%=%YyZiD}v3&VKgDP@uVV(^xMOx_> z0g7kC=XUw>hXjpRS4^(u#d$7g+D1xXsXEr?MB)kODJc>{#9!EkjE=Nnc;n3kzUjwl z6MSE7z}1uAi5|pr^5~7YIKKkyw4-}XUpD0!p5KkZ*+m=;VXDJPOinK$F6-D~(yn^7 zLP{IJl$>}Fyn5>ZrV3CP&tj?u?Eagv*lj^%TA_3H;ovGd?>tPv)__Rqbw9YK4R;PU zV*C>?N1OUoPe(BSUUaTOv`-70vhvl#n9vsWF^m!B;}WPKDJKnJa;6$Vuz7qOt)rtT z-?|R*pxj)`x8II<^G)Jucubj@bPa&r{Av{Av$Lq#zSP&rG^#@^0iP9s)9P#-!KFi4 z4aS#UwSO>F80Qi&(bBiu(N3}9;oez-irS2_@jDqLQs-neq|N5Z5a7Eptx@kTdG9Hi zLlh4Hx&Wp1JA<^Nv7c6INvNM~?ec{L)-(G`?RqR=#KzN+FHj-h)jq`KCjG?a9220y zG!yI+$E9jrO!%H5-C4-UXg_Uzx++n4&kYMMm8wNj+cOzS+>H-d>o#LfyDVCy2ouE4%EB|Uty8##$ zB+%Glj5EFjJT9|Z7d2#_ndq*z%A}CS1wtWsAg_vTB|zZv=%s2ChT5PF1tj}2V~Fh) z{V9-zc`29(#LF%E0pPf%9kp>%L!q{7>e3Q@t!8&kJ84WWC~IF%67?WzNeZ6|FJXOyj3-%FvE(LxM+WX0U}+{#*v z0beQxe4=Jo4d4&~v!ho2Yc~_X=ulPRO(CQ72%2^^i0YGb1l1?$z;(c!vhHO-jy@op z%n=V=?_b$R3XqI(1jMeJTY9_EpzP;wzD1j_qX)PZKs&Y<|K~T9XQHX*>gAWCa_uBF zW^ORh#&1Dil%Z{o$t*Ac$Q}AC`f~xeEWh?zx;D{9@$#LwQvj(mo|*tH$C?`b7SBtx z*OR+NX_F#R*Tb}1yoaav zU^soi5>h*i_3+j!0D^W(+e0Sa7)GbD_5zSt+C4#f=Cj<5Is~)~eN0L~78qSX;`8qKo7xD^Hn ze!&j^^l_;JGW&u;Xk&0NO=oxg!nD%u?jE8C|5$gbm~-t8P;4z!#2tVNTBhGSWJ(^_ z2sUy7S!V>hk%h%!H>^9_1XSw-ryrzRykE`woZ4XYnVNJ^HrQh(0ZpqX!ko`NifjV> zGs=(OeTK$Kt&0}U=93g~pt2NHmfK3}WP%d_4d2zTuH^fI<2L>7)d691t+4>{0$5jv zQfj;yAE+)0tpo3#NfTvkD609&o+()1QuoN4{w)wIe5Yt9u> zXFm1K(jt}CBtbI!y?hX0(tEnDf|R+bxG^LG2zf{640LOLGZeQi0?ITxx+G{(T=Che z1Kn$7x_A=ZPhJ4BPUE3JiW&QH)7)l3?Jj`D0^R02+tSY+>$;Vv@t~Et4_9+QTNMN7 zp|%5nyaDV#+(wV@nz(es=azk=3n)DFnSNSfWO08(Uzy+Okmoosl^I`*1;C!gyWx8+ zl}7f$md^_oeR~EF1jt;w7X&gWItzHf8sJ(UPov{;>&Zbh4*?^eJ&*d~NyG%;!b?EqQwB6eWbNN%nHg7zEPq0gGv zcnsNnauM~(c%O3&1z0KS(WT{&>INq9uo-mQ)&gj{8a`Jc1iD3s_w;AYH$Q0RgESJq zxtPV!)I97z#gEp^I{j)h<{ENqfARjupF{g&EMamEBXoD z(4AWJ=%W!&nGS-!dkksBRS&Jvkn{%kS>7%qo!05sO6OL3-9;N6ah?8V`j2%5^g4~h zICcxOi1R*IYSGc;KZjB2jJvqM2f$dq1OQsWBoTIIrrsemTSg4ZNMPCx-q+d(Tg>A@ zBfSa)u0lUkL-0xmSr`p+b#LZJz!wPkF8%0tV0bcX{l+$E@w|+F)km!CdaS_ui~$Gy z)PkqCPzzxeT4)g7rpgci?D)PF0Gt~^&HJNBCOHMaNmo4r4EnxXGKT^Mg(_n(#@QzBof0A4jpA zp}{YC_5wOUAh0Eq9u-V0)}QrY#QC?@R8ek5Yx3MYUZS6SOC6w!e*&XA;8LHl3g&L= zfbKV`ELVLe@smCu>syv>dO+x1HmR znq+#@k4S#P1a`Z@jZ`rL@sHmqe&m;GJBs>%|81N zd6s@;S_iFlYX1a|M!foa3&5RY_Xv~s>hvKutq($Q_MrI%pi+7@C4zau%v_Gm@Cbkhok9~{mpo_jvTN*2c3*o-oZPxur+i&ct=C_&0{`xUV<42BKd=$ zSrgnfTVpWr==!?o7}<~TeS zRjF9RGdh5#!U`cqXS05SaOb=2oG?E7EChCJ(ux*JEtzRa1UO5-LqNs*TyPly%(`R{ z25Vt@o&u%7F?d9%3xK|)os08mF;{K+C~YVG05p4m8PI>s7xTD$xXb>Qyl+&oR=3*C zeQW_w4S0zJl>89D(&ai+Wq5yilO?rGC8Fglo%oq^`N{l#mX7#s_FR+u8V%}EssU4d zzU%ShR{En~Th?U7dT9A--GxO#+my_ zEeDX0$C=6sUM7?kNN5356H;@Lacn)7fk`6Hqi1v!-_@Kiv$eEHhkGO5&_JIfXqs94 zHl1N%ep^8bg0WFYd+JZnkiUqcHHu9Qfu^>n@Ze`3&93+P7ieQgXx4jY(*AJDa~KHB zQU)L++3?b8W@KdJb{)&NcL1A+LA!^*DzeXG7^r!GTH|R{{0y|v()?5kYP3O%8ZXgM zw=j-fOL4Ku{tg9TOkUk1&-oq99gPzP(^PAQ5!Jbe(SfEW*ddVD%wMYks9?_8))G{3 zaW~EXKFh+%G8~W78fuPwS4*2dLx>*)ygpqBgr>|C4Ig1-*`U>s>!$;r(^6IdQ~)wC z%XX(9^=npP1wh+!AAVZcy=jVz4I&ZYl+l>nH*MoFFT{Yh-50ch{evM)PDEyCL+! zef9WSuW!N3&=g?XbQo2SzB4~s#OgC>jJ`1i^x0!Wo`sOt=PQFVJjOccr1O3Nn)C-W zYOT3|h6TL60fJxOBfjQQGxNEyZX-0Z)8aMOshUm#NIj(AKhSNt^-#Hvibx zBi}AGOW)iuK1cAG0vbV)M|uD}KNFbpuoUR2h$P+YpV5})`79M1=t>Kgl^IN^ne|qA zKaYX-#>Q+~0c(Jv`nZ>KX-g39b5Uk2gVrjb-vkg`4~=cz*Ivu-`itqe7SEsaJ!o@2 z=l+{?5Z4N*K&|uKvDx0OaP=3V6d9=3HLnv2e~eb6~Et`s-^40*yjHbrhoAI=a$0eQB=Q8x!{H5#aBL zy${WJ4CfrX?>h%4QT50BDk<=^8ux6s`T!^u3Tau(GwbS4_XB&bZhoy(=LkAEK2HBQ z^Cs3#nX9K~0X6a+`gM05g#k_iR4tU(m(jRi#`5Z#exV%mo?y>?tOZN|uN#!Wt~^=r z=^kW#GRo1kD$8Yb^XMH~)};ZEgci(9%Tt&Fc|9Pvv>q`4cLyyLD8MZMTKV+p{#r;w zze25nz1wSWJ7Ak@J&t9R(PIr;>rKqyr7DW1HcJ0O>0WTjIwzgVH~C$Qby_c>FP~|8 z8#Z$85!p}jY5mo>$FJQ5c>mXmKhF|u_LRxI_x^30IpkExvV0TIQGWfov`0|(5$$IQ z%06gm)nJ2f)_^0Kc7laHmHU4;iIHX<1=G7@Ooev|x#uhVg z1H5M2bxZ*pf-Wrz;svAQ+5ouW0HZ)$zrjV!5d3e^j@vMT#^^Ar!2v=A&6nG^-!9Q6 zm(dwb(Wd*Uv9`-KV?vPD(b^h}LD0ebCiOI5)pCu-Y;7QAg6eP>&C_YrEf;Oa$!Fwicf@K*J+E0g2AYd>rlO z-m19eeebMAVncsQ#v8*nbHI4`-8roU_Xh|o-m9Jtqiv)m%v=jfI_x(*21cmEyabHN z=#9L(TSWCBaOg(!U0RO>fY7I7(aWj_NZlSlr+|))V9}!E&`NK`eYv0gH)=P1@OYNK zk0u4x@}k8jZEK8QtB7)A3t%wS2o!*DLJ)E7dI*3U>3>Jx_-xA>XFfJ)ziAPD=AsAP zg09jry`}T{99jS%?G|gXHaBK`bJl_N(DLX>BtHOIC?kL-0j*;Ylx2NYL9ORc{?2r0 zv27lWqt4ItiSxKzARyqPA6e~!>$iXvl>`BAeX8YY%>AHIz)**|s-FUi`E~<(*Zm-U zN&6^HMly1*1@sMSgMOzeK;2999Kdp(l|7`N3Q#N9YBHZ%5-tCg$Fc%*guKu5I7|zv zmYI_K>S7?RgB@dgILV-%HDhfuIHNhOk7!Q9W^a@}XIlI$er^uJCg9Ngf6YNX17w;8 zXlV8EnerdNY^yWM;DYV-E%XDBkw|&w2KsKXm)xHoH&)XjV*wb@Z!I_5;W!2W8Y6<6 zx-PIUX67e!FAXN}NG-?!yL4v&obsvxb`lr}mf%S>twsWC#-uf8jS|^`PKt^>0r>Jb zQTofG-gK-1@X&uvz{hk(mwso8B52!_0RveKoDewo0beF*&BV6wR(*mEOv%4h1bP0F z>jiS%qoe2o3}JRLgAU9$);&Lm|FItg>j^iY8&6nv z_xD+EnFZSO9l^L{!#>cbFY~8~EE%gb=do)BJL!txh5YyD(jGzCN3@?iD0|;_yE{+5 zAaFt1zDetOG{dGh^E52OHJNFl@BcPtOn{2w9kA_xK{ZCr|SzvZ}XY^xZ7`&^k4Apm63M*A6ZZ<0~XiTZJA8{2JQ!hkKy7L^BR&8EBH z^ZMCAG>@5^K^x(@NzGDUpI#eAi!jSXzFL>iK%4Wu;L^*=4DJFNBivjFYX*9JoX4a7 zL|7C(g(7SHjv4CDv1=k$Szpso$dIFtpa*EAv%FR|=1h>_agWx$&J@9JS_qS@nzBKF z#eACQt6@!40Q8dXXCZXJP*zA_E_cH+T2sIcfKoy3jcZGUDImjRNdGqAK|m%0&+g!Y+_9IRZ$bjKCpi*m?)=G7$D?$lFQE&4>pT|K_fY{_ zV@)!j8T-vBxwfEhGTM(B;biUB`T#{B^MC=`J<81^V1)Pf z_#lQy2T2ztpmlpYizREEZV&*K9?#dGVZ}>Owv_+4mSaFGd}w`nn>qEg{Cdpnn5zOX zxImcm+8T{b|LOoReY2W(^WWUq$&WR0O+{(78{@G-oRS-0Q`4%m$AONj*)ZV8Zm!5z;^&0 zwcBgf=pg4uMM|(EU$IP)ClcmJ-0DjTVA6t_`}Gd~%Xf{KUD1CQFTqsCh2p7=k0h-6MkYc?FM5LXd6(IQJ2ujdSFRB z_%pP>KdOhn0NyfiBjtxL{--bztU^Z!2tX_21~6N)Nh`2`RHc4pU>479FirvI3jPDp zrlKPa)*npZytfv+ZW*w{?b8?7LG9h9kQ>`lh{D#2V~^qHwWKVt*K zplUUQQ=1K-wF{=cxqE=R*tjri8J!oWhUBj@M+CYBzSUIk7t83w+|ntWjkm1{R=Tl1 zYG;kb>aC8cFi?v^*T&I3d=eeN(`*jF84r>DY<7;Vi?iLMGN8!}ShHXB( zkH+)k=pfj3695Gax99^ydS}r&c^>iY*L&fH(5Y7n#3jsboy>7&1g6Jl{VL}5RTLAn z_df!9*zg$o54h3t<+ue|Y4zlvR=MFXl1*Oq7C$&Np@j&;_o<%%(}9@U&RLI4OD1e9v^ zT68=TUN@MfAt2%$;ra9|3V_NS!O_26$9#d1 z0c~88?tGF(|HD0WF<~AD(ByI4v#(4_s;^$(dobr( z!H;C+MOTL^px6M*r6JqK>*`9=b7p{o{iUK=-KvA*XXE9P|@{UZPn zb7?VpttN#>XXocpL!cMqX*5ng3uZFv?iYZgNB36E2=*n!4-G-j@QIxP7tHk<<)9b< z1_AD9b@YXP0o+^9PkB9w8DQ}qfHHh?fs!^t$pWCPtLUjz2*d%$CM{*aX-P5CSm*YZ zZV^c4){1=u;4^{h4gqhlN4Eye-7lh$N1><~yijNF2?R3gmwBsCC$VKund8nHl{MCH zfHKzss0dOGoR9~}4}10geqS|A@lfoKRgX^r|2ndxeA2!8!+p$HcT-A)M~Cd21m5f| zqj7cpFs(y#vjU{^{58OX`IUz@eExh1&pHFJz_YZ}!Y6fR-vCIo7;6m^=r-NI0v-1( zpEUq6Gx)9!^y`zOJkHZXRsw|e*Rr^G2cTAc{&{rIQCI<5rSCJ&4T@_YFjZq8!4#O) z2Y37ZVI` z0c0Le8X%*x;S1El{J>Y4TS2Pnh32gP3c0d^_gEpI?a2f5vNwVLd9_DS_7UyTQpRRs zV>g>b*#)Il ze5RGjV?a$P>w|6@M=>nyDUVA9vgEZs$qvlCqo9WI zuF#rwq@OX?j`^oxOpa5Kqs2!?rRB_Y4ZS0iIKns;4hUk8N*=hs(TFvzPTXf(p~=I{ z);BOxp6`*^8X+XQDp!o^>;E7Ro|=f`Yv-2Z5n8~ z2qKU13g*TF^78?LOP~ax=yLCtXLCobbX;%D3<%jo2cS`BSj9r&R7Xj1Y2KDavo(q|@`s#YMDLI9P-7))YOPxYxn~U@(KZ)hgQD ztI54AZ{v}l-#43m1>or-=@BsMs*b}g#Gr2xb1D-kRAH`^TfKZ3F0lEbsZ`Wz2 z*v>^gn=3m|OM}OB(%tFFJ<+EQB4D6DIpK>8H8NjEc0# zwR@`Ju^@EUK0e8;@=eyLGd#&-#SVKxXYSSAEeaC~fr6HM=-1XkSh_X?EtP=d;lW|F zS6t6~POjA$0{Rd{mb_Om&SQX|*1{Hmb-*TQ13cwF`U<)~-Tx+8wx6beaE(mbtTNIA zc=q58P1dl}0m$;b3J~TIupG-ot1Sk;=>m*+Pu|moM>HplgE<)jT6(P8tCz3h+nbvN zfVy$$*Jt2Jb5ln_gx|53t!9u)AfT)R80qqC*Rlp6Yzzl6IKPN4?+cvF1E*D1D^>{~ zUCaTAC`l%GUZSKq-&s2q8aJbU?esbGJI-TE2Fv`f!0@b(`;dMJ)U1=b5ooeDRtj(p zcF@Rx_350QrM1+f@XR*}Z@h>2y4!QK#HdEAZy%38}m836ZOpjg~MtM~M8 z5$(5c;_d6#@xT{!^?>EG|2J9Qt_D5W+#?1cGzg+B}fIlJFb@0x;Iu%Y9qrs~?!K{~lE(R>#=Biq+x`3jGTfHO^1_iDEzKwy0i zK+q|_y{}`aHQ*L)^#RQs?OR|X7+aO=Xrp1a8l$i-r9lL{-71R8qrbgxQfm&gLEvXH z#{{>4o_pq0|Drm;fqC*cNWZ0d4K1(-a-0HYhRnYOwj0}6dU7*q#)Foo!+z`z#}S`R zWAzL{0%BOAE$fJH-OP5`j1neno+#5vSoGKe*)o9wIS%AY#<%GK=;Ubtf*t)RI()y) zyd$)XT-@+phvjX-kWJlT%|0ELckT_CNTY^OUAq zJ<+7ARrKF7$IM&r1|YO5QtK9Q8{#jZ?NUc7WChu??Y?B0)(4d6t$YsN0kF|EK>~+HcILkr8~D2f+IJ*J`Yf zc+buNCHl1n{Pq}2oL#VH0By!UAC02`q;1*nL&opcH?SaXu5)gJ-%T|#^IE~(l9-H02(~v z6QCj7w+L0OK;}DgFKDSSJX;4-tzW5~vY9q$bP5_%8|W|M#abHFum(DO>fp&nHp&Ny zoDTEbZ@$UeZTh6>J7OknL&~+}l)GgnPc=h!i_K_6Py=DNMkwf0wB4>!tFL>E2NQWf z$X>pAYbJ=aCYY(eww&b=Mt8wRPr*A!!(IT$08Dh_@cbmkPoE+L^?j;P-~p5XhPC;m z5Dr>M97h(8W_)UM3Wq*S8GRPPu?Ij+!NLe;4HF#=bYk^r5-5ZK(Q;<~Dv#YDGC(8! z<7LW@-oXeB*-|@n2IjK^B$y_^@c9m(tzhSqB|^8y=6rZxD&myQVK!iEOn)6L2pVdc9r|-W0FVkK|$=YjcOYUIo z+qVekd#(d#XnE1kQMxf1WmZF=FNKkej7f)Z6$+1c*#-QoXC$!4o?1PdZPvDn&^$iL zzzr)x^e3a30xXOcy}N%KU9FH+Eye+;XD*HAQ`1i$CiY4CX42Po1Hi*|JT(C#6T^0Z z&PP0zwTVG5#waiz5oMkzmPEgv4FIHBAI8BaFXH&K&tv-hJS~o`9e|2GW!a+T-ECT5 z0zmran{Q)lzOAB&H8k%!OLLVVnr5V?{(q|*fFv|GfZl5CBkR=iMvmM1tAnRsMEBq% zm6FU&wPZcn{{rVWAkS0>JC9GL1?h)*W5S~K^plK~?z(pv>j*m6f+h{>gQRa()f`2? z(@Pq*qJ}jg%f)?Km$cv-sGzT-mLCHc?(b$X)LM6Xo>r=AyJn?o8Ng8hh*c8xF;fA{ zNW~$FgTRKdn0liPcyA)$H?BQ%VlKGdwFR>4F{VEyis({h1UgvLAM1q<0deNwvP6)C zG-vJvXdbOCO>(zwr$B7w~ z#N1^n3Y$0l16z$X)(Wm7VzAJ!L7hB>e)4hD*;`t`<+p!H?GcoHM0*5fzuk7lO>AUz zS~O&cxsjJ_7Bymoq6`B8P!|+wEz{|gja2Ml8oXaqV}u#(P14-ZO{Jfj8VxP<6 zb$uP1d$czgyU|)kQZ+2^!pm|ZlHgk5>VOo}L$a5Yj30ks1B0aTv!V%^g)fS~qB&=3cccJ5F0i5hfP|_z_b~~r$3a*Tx({ZR?8H$ zcpS$x_{<$bZG-U1qdq}ZyNqIpFgXB-jQg1cwnT_%f!mxMBTx~T&ac)=u5WKUF}FO= zY|UH)5Xw#L^j$$8p{8$7 zr>vzvS7WZv+al`MchQ+Gqq9VK7@3GrHRV8plEX=?00-UI2(f$Sl=lQ(I{zzl1bf@A zmex&lwg4jTEr+jN&Y}Z=t08Rnxw-%u0?9Qr3q5A4lzuP4kMt>cYXE%e_G?pv-cUMt zM$rfOw8zlzL6pCE5v>=WM&lQs#|$}If4Gf@HA1~r0>wH3iAK9SVoe!qmpO#q)%+cI z&NDzL&&7F`$M-r<>$hh)qUvOl$H8nq6penYps{NCkQSdtp;r+gWN;Ssi>FaLIZG?6 zNr`K%UFMPMUI7pmMJ5QY0RZX%hMLFN(AK^s{)L=CSI{GImd(-)E!P z37QTKA{gf}vn8ae!za-kA0(K{I*ivQm}Wl$zRY_SrpiFs@+hqWOqy7>*&4ncSR?S+k#qBHBg1H$0bXc1Pv@Qs6ueolBJ3ys@LW>!| z5P-O5PE5Q@-;FfYnY_!I31~Kau5wZ^=7r-1Jh6QNllp4bqJ4ZA7CW!Q%O0+-W1uyN z`8N0|gAW*26MC+*zJtRPz6U*&aEg;BQ30|&nst2&uJwq$06c^!F=ggHlu&-ySwby)^cq;!((xMvW)612enwk zhuicw)4s62DqC8jb89rE?>Y4DE(?=w@eus4(mGy`l5M6s+? zOU0fs{-w1?Q1%h+5tRL=r8d5~P9~U@rKMwf8J1^w8Wz@BBDgrvM~` z-ppgi4a^rXu|xn{8mft~FX#7}I1xUL;04%#DmpxOd3h7nH{awDqKvzk!JJ@3I*GRq z>FabDO9ZdhrL^L75GEDhUsYlG6MeawJv4si!tCf4bv7kL!TXtajj?P19CiJ`o*p8U z7*DO28O95kpu=^*YV4U+9e`#Q{WhAo!oGZ1=bUTt(SOCG855+Yt%f;m!VudH`cMuK z)&L_dWioe5VtsoRo42=7&h#e(NHE?qkCn?<1fUEMvp(y_7QtKT7pJvs*ol7knDGyo zD1;AlpX{FX(qf`;u|{UmYcV&1QiPX+4F;aaeh4a8GV^XRA0RD+o7tC`yLvl{vy%wc zt9W|C?LGdh#bDQBju^eFxmg)Ob<9moQz&gTA3m5aEV|*3} zzveoGZ>t2La3ASv3v4gz+-E3V!nhum>9S0dT zq;26jKO$$Wp6!WzM*&4 zY|Hh6V+%QVOuupW5VNbh7y*hM;|v8wfZp~P(S@VmeUHBQRiC$wHBv)MwCM!^gQKIg zo_Va^YL25n7)5V9&7(LS^>q+$K}U7^RFnvIXufLJm^A4#uv)7wiVm~hCYm1@=p*4npVZfDc1e;Wp0OkM< z!Qt}eK97nm%cQT~!`(iZBh#KRMy*ZKbf%Wz`vrSPrQj`m#E4w;ac$mS1H#wL5!XSV zOZJk=XkSGSaAbt07BPWc0j+4=tMPm5_&Ce03AkN16-)b+`y6mH0|fPNZ)44RJ05|M z0Ij_PATf@{(NW$<&}dy>EqUzR=<&BD;q>jZR%Qi{+`N6uI8YM!-dujebp?G~x8O^x zfB&z(%#2?z7Oqz}pep<9nztz)VjQQ>~aK>woJ9|OwXNzupl zb7>y27}Zt6c=x7hH^hEZC_rUaWr+r=?aH^=+-mdYiZKaclb>e6S5~eOKzEndasTEr zwZ^5^Ff}~y<#S{PFiN#UvqASS&ZBGXHJHrc)j%j$Fg|^&x~5t{aMjUHn+L<#9kyfM+Ql4jP_fa> z@sp0#0zJ+BH5eO+u~!3tBFMJ@GLJW#)`40U0rZM#{~)^jYtI`1Grj{jE8xP_5-op* z0N!>Gs0H5z>{N`|B(QhuXu~9vX?N^@D>{HJi=I~Y)!eK^&*dzWa7NQ$C+2I zK0b`z+0*EpKh6270U|0jYysW~fdx1;{X{0&1u$5oT&tGp9-3Pan8&6ydIFH>0p{9k z#y#6c@%k=yU%!pb{Vmr6v?@U$fCqZ(05r`eyIRhZesw&zv=`LL8X(xv?jblN8Gr&I z>9MuOSau(&_2r$JpX0q4J#`hP{Qcm{@7xl?8;JS?4yO(i&{T(BK=AJ=2 z&rYHkBB;5i{&UOQSyWme>6%GYTYvr6F#wo##*_5j8lIoCzX3)g===n_`{YH`o_-P~ zLU`b3Po8EL{5-z(IF5ZbXPtI{ktSfJr-QkEMgmo6eaL4+#vyMKEO_i@1goo5Irw@CE!z&}6MuL7maP zc}U&{sL@9)PCCvfUxUwhyeQH_cNr{TnxH=4DJ$p>(C9e)?wWnH0o<>cKks4sp)8m7Q#AX_xCPYaQnQX`X8dPt z9s}z~_mhCXzp(abDf^it?emo($TTm%Yo^9X?G47?LL1J^{_mQC{QV1+HmwGR zr?pOdK=*z8%lsm11m5t`MzfreBA+*ugcznhbKY-%vmHX561 zKCh-Gbsk>{JP=r6;bFA7RnsWNZ8DDbtcd0}H&OrP+b94sX1A|8FzB68qX>SjGE47y z5Tov4)OKwI;s7xCELtXsM5q-WaW>Fe>A#67jUp{z$N>l#)TaPXwD-mB-scFx)CE8^ zW-w(KU6{&VrB1m5-k1rbl6afJ}fbuQ0$UHTk9*4)r;eO-2 zKHq6P19T4>F|V#;KIz9T;Qg`>sQCP8>^}cIHYWo9i)f(`w2m1Y^N~5hpwo>OwX_JK z6zMYs2<)R61iZjw#JL6W?$Ib9Ej_O`)C1NyYL>t?Up^X z(L$`_d#4LsvbO_6IdVsjr=urSCO~ zNk>qP+rO`=DSAX>s*DW(XVZ1qWFAW`VI(p@n`sy8rX?3~bIvLyp_qm3N z+~42DVqsYPEFNYLadmf-;KmKC-e!orM=doNoXIt1l67VL5(Z0O?Q^fE_NaR-On=a)y|X#{dg>np&xW1$`KO9_w+RCXC$qu(XRQ2JC}Q z;F-|0)q`~cb{_Vj?&cZNQeo))-Stgsw;nY!4f$%fx-l9b#1P;*LGbm=86|*b9Cys~ zf;H9RsS|Y%A)uAQ*WlnFre~*d_TpKbeDXBTzxXuHK6@6U)5AD^avn$Lr#TPy{eXKb zsF(CXe;@1k>APZe0fq2k8byIMxK0RQD? zm9->2PV`9a;N&>QM@KOnPx4s2$D9pKSHS#hJy|03j3(5-a%+SxKMgk7EH^Q`oADfI zOMy(^3hqA#2;N`5iP_bg4CpXm;Nkv(>#Y9>fYQRt9&%oW{GFw$APSSzN>>kBKNyEJ zx8!>_Z?4h^erTdm_L9-L1HR`vSXIEnTzrjMA%zTR^QU3Iy~`0y}6?D*g?&YnI^-!3yYIN)9hD=>GSa(u}Oa zI+N12e9q^)r?d!}sM@vD((IA>0FcuKI7;7#Cns_8>{$j&sDyZwmlYz|hbE|1VK6XC zt}74qNOTKbA22TWsr=3)w7%T`UgN$p07rgjeM)`NR(^JWpEOa?=K*`dpEp)L0JI*Q zoX6SOdGftXqr(%Ak56L=oo4+{?k_#xbC2AFKSgsweKAKV15!iFPafM3v`0(X&$87J z=4?Jq`my9MrMpLFG9Z|08?6~F@`swB!F*(Gm}2^!0i3eIAX4BZz(GJF7(FH^JN=j; z(9+3!2h&54FJ8S3^CLB%J_n$oDL-6BF}sQO&RUdcRDz!XFnN^d(Nqa@uEC5EA`dX3 z&RaCgn{5nD1%dXj4_9$IjAai7Rm_vow3kIJ5UvB5VRwY^)qiD)&>u547*n}2tN%@` zKB>o|yN&wGZ)3H)k5z9O(O1)7B9J!G8mkreqB5^q4Y0au#IQYyfU&IrPql-msfBL8 z{G0TTv4l?b*@+?}%&$9eW^ZaSF zzxey;0UTHV@?ESizl#dN+J8Gs3sX(2LZgi^1r*SiN&5i6`c(|K(`dZj#_nHkqyFtY z>Nm5f&(_fgkQU5$=MC?Jm8+8P?|9~h@3TG%N^2yp&-(O@nHx3ZyX93>*KbllHMf?g z0eDn^Ajm{(L$#iv$uC(?p0Rry>$`8Gc8Q?1e+U{-VAQT>S^c1A)aOuPw#y?*D-L8W z!3{#TYyfb#36d6{n6&gLwgT&0?+y7tj|~KeNkgquSV!RQMgTE{rAL?<@d*9pQ4D0b zV=g_ij#4XGGZSc5UGtPh%y%dbCPqa#gG1;eK!tx=I~ptI3qe$!o}*+mqlSWTzuTm* z*M#eO2=LZ7-$D0WGd_>v;4E%igV->>XnRD<3t+BQx1=LkN~%?l8Dn;Tv=1jSZ!^#O zzVW;jeac`M_Dk)M{v1yMl;cbd(&M`;fSBd3JQCJNsxv%}70N^dVb<6&hrj%%^q*_e zhxXNFtgfz8d1(R8nixbve1jn?11 zVFKBU6?D#=n?tHmXS~}v<6-Uk)7V0H%O|JNzA%--CN?F4e|4W=wkI7`faf*J))5K~ zN=m_AGr6o8;~UV_{LQz~`Y-=SbOCy8)}(rS69JuqIHH^_qW|h;fb3Z34)fN5F6ig= z)!zRR<)I0H($BNae$aIyGuLzf8K2*v@a&#E&6En}xmpho0KnVWbO7~Ccl+WfX5+(n zP_Y{v#|#Cab9@lZ7oWxw0BilnI692_`T0Igi2VaFS1J{N@B~S3U!x#Ra;>)jm?gYx zbb1myfZ4;zMJyXFfEs*M-~r!iobw*wZuP}4qVvh~bPv!%{O|zp0Yq86JZs6S$llyW z3xKOdsSeL7;H^ePSLP)H=r^Pp_=UhmpTG9QUDCfto(weWl6D^MGgn>%(7$3IR2~!4 zU(g{_yjR3)j=#@5_7B;6WpobDqM?OUC6U{EfBE>5d-hb$eePrXgWDftDSHHEKhq|I z*Q%g4R2a?#lrdLX7XeeR%dUU8nWM}jznVgl&6|wkgJu~L&|0*#P{5qD3|YEKCuomG z)$$B57yXu#t!O1%!;Js8B8{&h2jKx@5=8TYXc zq}C37D|!cAF^vHj68_np9CkD|Q-q*z`DAksjfv`~pV z2wLxF-Oj;y0zlMKXTCA+)8~#y)93*PWcKwA<7E6B0T;r^`kcGfjeIidR}X1*f~_Wa zHUqt7mK@v4JZPpPpzl32b1gz$#-2%L6<(~FX%;LyB!lPzBsN+`0F7F3?g1qa%!L|z z4{iGX>LvyVq#dAl_dq|n)?}L2CS0xnNFEP%8ZqDMOLZFyz-@!iv|MEn1nbe#8n^?f zv(}b6(lXRovo7=@s|(cB@2kAt>I{vGW3>HoDg$lk(c>g75g8!i@eA~-^`U)n8iV7b z{TgX;s+mrsmyY6QCO2=M-dG;Hg0LdP`&sW+ca)Ys3ngbt3}{vBgO&TL@NDcT6WHdPR6Ez|9-SJX*qO>oLCINxJ3!THvxMxWZNh zTz!`Ld6pB;c?yV1aVeZ_|w1Aa- z2Xtb8%9_^45&fO!o+yhYO2IZp1FJOD(n{q1o-i*u&ezdU+9)w?Xfmrz++D`y>(>Bm z6ffq&Qcsy>ANuebGs$~&He;XOv%j^b>L0cO3>^SW+RTGdtZ9{Gt+XuJwD`6FH>SJk z92{hd2%X1UPoJc9n~uV-P_6(>gC2VjAlIUA>38MPA#*Hn)S|C7)#RzB)>+@&#FCfk zd?2-0qljh_YXGK4ds@7PTG!Z<`eBZEKYh!f1N|vG35Evgi`F?kh3{Cx&Es~n?*roL zyC)6l;&F*m`u1`k1jE`i90Q-pTDQ=17Iv40U{lF%S?9g)F6y+_b^5!4ZYy0%n4d0m z(q&GpZs5Ae=k-S}{<$h3kflv~#5 z>vsWMFp+8&MH6kjn5PA13zHumpM}X9cii7&xONS|vK#R1Cfc~xIRv0SAD=!A>#;5L zg)6RLj%atKQCu)Q7_b?+D~}5hQoR98^68gRA;1x@j6o{^8s*_CO2A;Xn;~2P4uF^1 zMvDaV1F+F5)hJ-@rcYQP6lcpQ9_Fze!+3i-=T2dEtyp@m+m|p+1b6{MMHm|0XHCF$ zn{m_kc7GV##RDMbK4Qc34yRE)K~pucC!lY`99in6yg_rmyoxUOXfQ7(kL}M9o(R_F zlKYRC%VWT669%gQvT^{xB2EChXuJa17VEgyZwc+$D7#vVc>$z#`Md_06FAoqhRD(A zq;<3xZ9u5t$wjQ0W+Oo*&tmyZ5u(8I_bh$HGsLn@z->kUj9RrY@$8JTcI&YbhcN!d zdJ!F?9%1r~hJVZn&&*OxtnGS&1`oj99wOWUar!wm$A?)yC?h!)Ags?8jlBVYt-ts* z768w-;JC(|cKF@|+Az9oJdNfNfQ_}^IZnW%LUaQ#ZuG{0v<@HyV5(IG0qW7SzCijq zt{7W)b`O2=9w4bbYBPRlWJ!M{ZiJh{w-O++-X(LpowmZ_>~Z}j^S7Cp*Jswvg~z`g zg0(d`$RoL|{|MNr0}QL@$FXA$3+BHG$lLPyW(RO+v!9s$5`Zmufrz6(L0Tf{ho{F; zGQJhUxCSt`>{Xqfwc3C=lqntm+l;$WLkVFm+JKu1oHJ~*uLTDZ*zH}q0(ew7=f0zZ zv#3K8g*oBc0OJWDl)+jj$XssmG&Md=Nyh z1g7+%rlqO|cw=8S*YiA9hCl)3&~dTI1)Km%TUv4e*9J#)m_JKL3A7Bt z*)k_u*bH8APFnX5nHItb(8l3mrj$wlQ@#@i&;jdIu@|8dMh(rvJ4)fQARijaC};SL zV15T6+-_E}{MUcYH^P6%PfJ7bLDT^}!>1>i_Q6d1gDK3idk8S%-dgAi1O^PZ z>U%84bF}awymkI*4t<`Uc;NE?AlNK(MKt@=j zugHX@b3j=OK+`@s%);l}S}%r2r?Fn`q7Kk#A&45Le3)&r2(YZ64&zNA1i;loX9|Zk z0L#qj7Ns`3f8%f%b+vu~q`=g)CAH-P!k`&_Ko1>ACS!1c^&*+1^b0h-L0L1}kNdLL z9y@ExX#pUs`*n0~nYS5p0icyekg|KN+@n0!xMXd%BY^;dKL!a7+N@`<15jbS&9WX# zEj$WEgq!vLx`2lg9_w4{@ka0X|n_iz;-QK078AY3daY7&Dy7CxE});cwQU2&LY*! zQx;rjG!Jb6H{dc(pT*|lSZ&`K# zs_qR995>`#twO zK*J=-rm&D-)urj1+qnFfe~Im@m(kOzBmjpVk`}a5F=r)nnW-lH4*kuf+U#AeZUy%< zS+aQpEtzCgrpKVVcw|9u=DE(8GiM!tC&-)4V)LtC0ZK2U$9GJdp=CFH*Px?H|5|7( zOQh+WMt|4*oqm&M@aJz!uX%)@ImeW1JWs3NOn^HaC_V`f>TmA-2ASZxs=9mH4%P+^o2 zV_8+#nF)Ib;LtMGgs~ePx8dHk`&vwg+y_CWpN*Qo$Fn-jw+}90>db{!iLpSk8J(^3 zahFclCc9k0)YmZJ^o=HjSwwRz;{#mwTPXJ9Fu-8(pjN(%>a~D~ndZBz z%`F0=jIKwT0Ff5LWz%QugC5{;f8?qC$^1oCcM!X`H?h0A%Q3eVEPygY@wW(5ra2jQ zDG*~Gb}e8fz{0}l&36`xvRX0MbpR-!!;};ycR{cTx&#Owt7&fWct)QseG;v>P$@*{ zbFtjAcFZw9TlWYpLCsF!%^Y;==}ew}G=gJf-dZDQ?ffhTzxXm5XBW|69X*b0kF4RSWfoz?dkVN_!yp1^h4nMV zOZ^mJOaHg@t2Vo4-FY^GPT^-DiFN*@^=5yPPU9w+WnuvJ2>%+i0Z|s^si#@kp5&ggZ+vtAvH+dYI`vd(zD^cTq2Mx0)0MZp}*ao<)0WCei zQ0Lj_(FL?x-qqtdk6l;Lt;&Oma$EYcncEC{UqQ(jmZRBnfz}N(|GcD}dCg4Ox3_1Xm zDeN=Z?A=4wer@01vQGdBd}i_M27q_PeS3g}6`*4E`fY44uVa1rI+nNBskCX85#-lR zr0h5k*yFs{0F;@$)^sktC+88{6tDD%|-NlqWVve+iK@-f05zJm^{xL20@AHItylU3| z<^5fPwi@ed&ab9c#5oEg_jf#|?@;j+`0VcP^9WJkmIcvy^#P=9_ND;qp7ql*sl&gy z?aGHc*qiAspnLd(_((u)?z2*N2l%P7OrYqi$@o3uw;YtARt7?tSJDFOT8TY!!Vl1lc6{s0Q`82j-S;8y{Hib)SF0aI4fJn6$c$FXZOHUv%+iM6Er_N6}Q#G-`& zoB=3cS~_ai&Cm7p(`cN35=)rc_7rUtA*LX=(0c)}@Tf{KCZnwb< z8ZFt7`7I!4Tg(x*y*T*f8U1O;?af1UOo^eO;QIw2Pe5291ee@D=al&^S1{g(b%Hb# zi|Tu$ubr967YN5TV6+ApSvMG03jx_fXwomFYg(hadGrdC?(`#M-e|@vfXQk#i_yU$ z;Go9Zn#{IqorhR)j|D<{2VhXp#bgj`z@5i^rZ@oFz}Nv{^h1DRoyu}WXB(!?U_KDM z&64%6H4#jWv}*Pd{A-WUMh6K0L;8t8YK>xJ9Y_S)?hIk{>^SbG-FRS3wOi;2FfKrO zkkL&JqjvlZni~NQ`T!K+D~mWjiA`CfPt7RWS`g2(X5&f$oK+ec(PEqi3}{uIJ-mUI z8gc*WVbq@<#bSt17no^5(MP8XfaPz2hXPYjsV`ac)tgxS^4nlWqj!83g()8@=(Gt< zJ^d=W0J6e-T?a>ihY9^0M>RZTVs^pO#T@Wj4#%<7GQ<8*h=XHe{OM=W{_=0wU+oNJ zQBVrH?pNYM)(eH8jR4j%z1$WtWA4q5r9-;e_Jj93PCbIV07xcKfF9TNX50g^mS?B2 z0mL>>PGfDpD1eLwrx)yl=Gl34pT3B#*3083fU_gOa*y=_G@S=KTy40v*XX@OCt8Fg zdMA1bA_(FojNV7@-7rK+^n|Fua1t*X(i!AFH`tEcxw}cZ(@z;LJ0W9K@Wm@Q39WT3edN?xNbO`qe>$gH=meHi zSzV`=Y-3#%p0I$eOj%HlwRE}b7--i1Soe5hDVO6a>Cm1^3if^zcVw!Ahm>jI(BXe; zrH@CmiRI}(?sPj^vp)f9cr8aNqIuV&dc(W=V+WR_4gx}Vy?W<7Vs0~81wR$^oKB0a zE-wU64>qd5j{{n8&mSRnBdFwv5W7xVWtGJ3XWL>kqRA4c%X=2W>D`N7+%|1k7{YYp zM4^YJqq(~FciUZ7=ym59vmVA>rY=VbOdt!9$p4V)&hy;{ouU}#l>vT^&15S4>5J_3$J7U4K15~Lw!a;tR` zcm#bb)=X!kUn3S^9GPib5*ztn4CR7Mfk48Xwpv0MZ&-@T>o&c!otE6I-Mpdv)=oS3d|b3uV8=eL|~8W3&uVnrBy~LXx5{mYGjR^<<~2HWA~N!>gNBL>V!eF zsKO5{OrF3;jJnPCV2-aMCG6W01pQrJ#%!+GZg{^*6pYAd+vF=Y>AOl2_A`>;#Y)*h z40A6#`M(|?*J|c@xPC+8+7*I%PhLxR=IbeM{hDPKz>lrE*gQL<48;75n^0-670_40 zZZ*xFldTPx5%B6}6&`6mZkG$ZGn;{ZYdR=YQjSJSV@wP4EtKvOK_<#a);mTziugC$ z&R1V{o&Sv0_C_5h@Y<`K1k3yEGfX=lUU&Lm%|%JY+(s)RPOz@ zI-REZgHNlGX#KRYZu+mp@tgXt@tYDOT{s{YPZwn&p%six5@G4Ud&dX=6Jf?K4UMli zNhzwvhIkJmmM#vjB&7Zt6vlzP1%nIKAwD=Gnvz2zLqFt3=qpCz<>@4WU#q`fun0v_ z`|noSC6A=241-+>I?ogQC_@dnrYN6hP56&MRJkbx2|q7)8*+?jtoMH0!$j?#=sE(E zJPrxS=*ED?=sA0GLN?Dx*fQuH$<4@rA{RO<(xm~5Z9x=N0d^x$+_LyDWj@hda^9e& zz4%?YYyIdN&S*-s7iv`q)?-hEPTyHcq5gx(CZS!WPeak@?fW_y?e+-GbB`%p&_5P? zlkVYPeky&oYIBzQx_ofrGqo^g;s4N}mVUA58Sw?{J2Y9Dc#>~RV>{YYt)o5fBjaoC zp0rBLGMNrV7QE0gFFRy@ltTzxcHW-9*X)*~?Wk0KkxJ1@IONGNce>H`&3tkJ>N*sU z7t(*B{eP$OA-$wT{tBShd#-^M%0BIksPK-Jh8-0j-tc%_bspJQKClndd2x*%;RT2W zGj?sdeXqNSNeENhIH%j}yYt8|GJ}DR6wu1nc@wB-edD|qwo6i}hI65;DNtHzGsp14 zh1X$j1L_xdgNMKQ1y#OxzEi`AF)RPvG8(bQR!~dla!y2*2YOPR`o$#lwY`<_U#0`=FiW*TD}Vv zQz(xXG2HbzA#G*(Dbygom!Pon5J$oB<7H!RTHF#-$E%)iAkIHbWT%(0snpQU4{SJ* zxB?w=iQ#Y5PJ@;fp8scq>IA|XbYqDrMI#CG9dhuv)_om)WtW9Np z+PidWC*0vrzFQmJs07mqwFK#*dn+(^KUE zNuVD7G{@=ktaclUenq&9g*TG-Nvp2xD~KgCGNagzI0VVvY=r4ayJ~J7>4k_grvV7c zXHypn+x7VEpeSNU5ePU4H5NEhSWzQMZECVAwYoXcjYZ zp;tQEu+n?5$pwl0E>}E0=RyT6txJPeiC4V1^G|E_8JT-A;Z@iFD)WvvQ|z>zr2?K9 z!r+?;&VWFzQ0KmY8Fe%NPb@sakn&cAU7JyNV;t!8f8s#VnL|UNCc}j;g+iHDXnR{; zM_GnSY3Q>9uFu9Ux!-%)S*TnccQ~LQnkthey^RXL{z3SZr)^ec|4c>#1NiU$!>`H^ z8eiaPedpj!n0{WN{(<5VXUe)*2h5?*1&Vl(>ahHA^HzVl$EWc@O>6#a+j;u)_Ze|} zsf@a(i?xTKPU3AyOAydQjVP?7VyXJ{?WfXk6Lewk4Ko=7&DL2HxpW1e^u6@!_sa&` zMcdU!jI%5c8ZN)I2&EFxmjTmEbG$jj7Y%%uLx=up-(~A3njH1c8&~PseEh7?73t#5 z#IaV;`AZEV4w?0q>l@3P{QvuKUKUBsO~wdsqYsK4EzxPGrc6{ym(sF9OJ*cG_=JWv zbc797SI@vV_>E>kj%8-gcfnjZ%Cduhc6@s>wNyp{ui;ZN&Y$(?Pk^Ig=?o6zdm|YlQFAs8jZ-TOnN)Q zW42HamRqlaM@D-?LfkQ02VE|O^AaS3Y;t9F9)+-nn8IuKfNS@2My9M@U#Zb2?<|M) zb6@S2u5+gWo&_t>a!*3Uq@`n6VP&hyyE7cKdVHK_MB;gLOFQD+(^74|)RAmhG-3i# zUPN-+AQ&!QxL#4R%0%>asF(Q&f3Q$xz_ih&KG-}CQv3<}Pa5gxiRVhIB0}4LH26Y5 zBH)P{(26L6mJ2gtoszJ?O-=G@8q?x*j`wZt-1j>LucXUP*^huRyigSa#GgQ zh9Igd=~`FYocE1tsH~{fq!#s;NDi)p>MPX3ADP8knanc29g)&fwR&u=i|Zo11*o|* zW3Ny)HLzlVR4%y4#k~vryUHS=j2joR!~BzUWn)lD-S0e?P-hd%BL`c#H1-v>eiG$h z@PEsPrc}>A;l|*NaI$n0Q&x%WR`Mn5iSzJ+BQ_XObKf{5!0)5Mo>y%v5fM4x2d$D` zdD6pb?x&z(>#MhPW>`5x%Z4KdK%;tfvNg|I%+ih6c1o0XWZl!)c+fD=-4u>C_${+; zIm+$vQ&&IQMy^ti#w^03JUFEnfy;prkuC32_@iW23df2um%MgwH-1LC!%Ppt84lhd)RU7eopllH)`?Enn^uR7 zj9<*zF-gykkFEAT?qt^xSBqyalr8E({>lh@x9Ao_*da&u*7*rL#h-Lk?^9>6d0jdI z*64ipj>Y!wcuiZr3K?gIL+%l&ZOUvGJoww=v`Z%Mu}taX)bxc8Ru&D|4@0OX1t~fs zz=8hz0Yq%4cL{HEP3c_s(HU5*>@j&|o)xBj6@joma(!#D2)v`Ao_K(OjmNOzvRW}( z#IKCQTP<&+Z&gN<@87*%1g$fi@~(9vsjAr;$p1so(YmLrw%su2D9c z;|_xx>38ld${&)mfGerl;opVW_AqMu$mvpMrwx&;$6A2(w6 zN&NHB|~Im&>Fz9Em(3^c<=|*4}Q&5 z+dWYQx|6-qKl<>0m7o_7MGDiOt2(-%s+)s8&l{7s{bJyX_B*}vqe-X@iv!+zVRhWA zGa^$5F&kq-EG6tl;>wy__3WcZ5uvDpGnSN zkn!)Epr@%W!TjXjWln0Q>{t-s@06wumezq4)P)Pv+rI zFE9HK&%PYGn@I7PlViCmUfjaffl#pHN&NRk@a`QZ8Jla>;(Ih+P0DQGEYXdfQi_ON zv;oC;k%s!>e#G}KGBCOBzs8q5CnH}=eE#)o`o=e1p)~YY+YB%r`72MaquhkP4U)|R z+(BrmUo_bmEZJ{BuuHm2Qn3jgy6`^6VH?nYYQTqGlIEmf&<55S4q@F86uuv~WoZ5! zlVO!j>LrGRfGpUlHhN}L1D^9L{-Vz8pMIqUg~}Pl1m7s0D?^!l^lKe$ul7wf4tj55 z@S+2DTZ1lVn$SyG5NSdGvV2<3BY{)H4M90AnetH#0izMU-^sQ-9~B!k9l5Q1V*Z`* z&QlEiwaaoth)F|lqAdh_Y^xrWv+2+CHPmFS>X8QbOm(+_S{`Oor57&898`f}>?_5W z>6Xbdo_qh)u<6y4z(+d-ryk1BL*_(y>&1`6|G$0O&%khwK+0f=(<1L!S&`=7=q5=q zXGU)5rL9tjDj{{mA#na8CF^$*FF=ArM9?uo?B=MNW zXiC9yHm1UEsR~UU+6GEhdl1Nu;HUcII42A)7jCQE<=5L@z{ifDzJF3t_ehHO}2PuC%PaAx#<|y5|DE9FT-SEi5*ErF@EJ(X9Uo`^F z_YRHv`gYv1Roj!SXQ}H|x(hRLz`!CXhcfCh<@N9JWpDt(Ix{R<%EQh1YnuULnA-_J z(F_Zdt!~$U{YILhKk$b|wt4ecV6k3fivmS*S+@hKIBcgtFpGr+<%%0SPJ<)`5^ zYE#>6W;dY&I4s0{Gpo84HV|~^I;a1c!zEW3yHUW4(Gk}j}DM5Wd=egm@tr%t+&C=ymb%6fZ^C%lkh#~T4 zMrXXhxAa?@z8SVWz}uHk2=u>`ejno=<7GCFSF<&QSz4bprZ@t@+jZJ>vW@L*n*z=(zQ8wdKlmL+%UbSKjwr?y%Q4?q?GxX@t)%9=SpgO zRmGR=+6cz0f~a3mvM`68bVSO9)sx<9B~Ox(4xCPRrt7({wv*Xy=R{e1OrhCg*M`8Q zNt(AVc2Tp**_;l?t1(X|&c>iC4~L16AqVVHNS*g6JczK6_?MaPY3fyxeK65W9e=6% zY@JVZeg#G2v!qUAXA37D_t1`4t6)aBM9@l_W8H*?;Lw>49kB_YbJb^;H_v=j$FM(pif~ zd+vbmKg@S=GO91m?vGz%tUU$!Q2y-wjXMRwoP1&L_hVk#S zk`Y3~lXoGnO@X!jA2KX|ZVmE6c7=*OF1ot3nXE<44-8=yD)yG~k#VGH=J%)@q>MT< z6|g*HDc>i_^t9*4mNS`XX@g6i@3lzxgG_z$PX+#EqqeA@UtJB+5pw)Fc^qa|Ok#pE z$a)RamaWLN7*Wjq&z2$jg^9(e(D& zSUdU|m;(8D_lO_MjZ}nG;>#Jx3zYGhEl1%w>AY1Z5yGe$dt{|;b<9~2|8n9cjJJt~ zRJZQ0J&#EI#7$^L5aw|DrrLy{*`EeBtIWFQM{PcfSYhLeAK*k*1@L1(2L%>5ZMTuc zp1mJ;*JugddoNqaXVP<3$|qD8;)TZ`7#tGospUW2@P&k>0K;sisli}1_K)F;bX9$8 zJPTicJ~y4U#3$~!ahW-P$IWHWaY!%5^X)~x#*il#LwAq&Mj2b~z1KV+>2$e(_02;L zu!f31>_6w)OTP5`?6=?GNrVG2_jR-l{mwQe4L~qH6k8L4BJx;{ zQ5U|-#UE`#8lrjM?oCEA-Y6?o;F6`k*A$v;2&vI>@B}%*>}SL7(^1{&72s@jsZ8>* zNiL5T>k-91B#fC~bwd1ZLyUK{9iL!noPAt`EN6IrmmaYZRkC{amq?2kiSeeS>Z;k? z$$pP6|Lf_C(?UUeub9G|s1;)xzYpPm30_3SIt>1}%q5MF!_LHc>Nz%^Kd7daZqMeB z$TDi_gfb6wW8I40cLy2bu<&mcKDMDg!{p^4p01yyR}r-;>FDy!K*`HiEE5c03dN4# zagdob#f25c8^tvYy+L&f=TVU z$Z}nRJoOGz@j2<3H_SBHYXQXQ*;rX@1~55_A5wQ?;(+mqf#pmBQU|f%PZwt3K4=u{ zR*p51@4lS=D`o#pJ{hez0PlE+ApB`xiPo&GN4bY zNCg^1*04VTEF(jlySZ9;ATq=32jsLMvl!6x8AD*D zkC7l%<$9BKXp~Y3Q@7Nl-nV*}IY$R&7yV%pd4i=e+SDH^vIy$?2waD@{gIw_Z-@#& z*YVI#%wn;We#6izAVrqI_ok&iSowSF$gigpkviefUtY0bH{3U1wp*;CJdzD;Pw^Yk2iS{ELu2CF;4v};ZNkMaE}{|hK<>EoFEYpqIh_vJ&tUulPeL2iv7w9tK3 z*+r{?efs&o-i70(MywLQ-OlO)>6`{K*kf`z60y$i(mStrP#QpSVl{(e--(Mz$Dz-4 z9rujQyZaHRo;I>hAa&*+t5D|dKwG0pU)!b?62*Y~vdkP+x}a`=;1WG{^~zM`wVp1YTXMRjxzQT)t(=Bil?42o|6Z-oD znwh5=F>QlFXKmVvL0UO081`u?8r2r}WZlX)PW3P?te=Q4WtFG8044EfS}7>KQS25$H+qIRQb0{$cQ*Md>KyOh1} zKQe;%#e(rkDL@PphOXN2euh{z^oD~PjlJ9+u9s$t8tbW$7<8eV{dTBtS_NqN#=Lt$ zVf3@(#c8{LsGr$L zA^Np&I6!H27Ep%iFqo>2;P>a;&q6VMm3@m@nvtPDRe3<-+E>q9B^%Md$CVVawx(K^ ztC`4aXkb~3F#&KxolnoWZ&=%!^^hgg|CYDXjL`@8x^&gvTRmRbtvVafE+`qDit`%q}ZjKo_h7`^CNLgp`p^@#+H`& za5E;8#eBZeYUpz1rsn()w(`A+0zH}2s(m|DQP70ubj}*P=d|T=J8AQV4^AX)Q`y3p z%HYr7{Qtv#8Gl-ZgQ@s)L`HgU<&;sW{QSeIkGU;ojdvqtbzdD@KrwhW$Wi z5Q$u|@T(nn8sad%MFY{yP9?(3dnxp{G_!M3y8zn<;%-kAE3uixFTVA zR}arN+nLS6nAGI^v06g`R)QaP!*=pZAC-`B<7vCG>X;UcU{^4>TC9Af|m0om! z$%+7T^vnKOeKzc7SQ`(90oR8vBLj-@jjWa$kjwr?-Kac`c>sl)12zVZLmfyB8QZu2 z42+E6PKPYuYFC-;Yoyo;TR6bp5GpVGKiLdXl^@Tase*}jS3t=!FH#>>u;P%a;YTIS zjHhYf;@(EMs-X*W-qnE!h8^b7UjM<#kOKao1=$uVodT0eX(Y7rX zY_j=dz%?U4D>o-HRLU4l%<@F;4}u- zO|E-jXGA@wAE8Zw$kb8_%lME zn{5}9zJpzt{N8q|5n2&&l_sIbiuBC_<^4O_4~*S52W9*Ec4-s^gjqT4 zhb6Ub*WDwMv|>Kh?E*QuBWyZk!dBzei zHHDktP*;Qh40qzo4lg}S2ovueC4qlh6Mm^!WnS(kJ^j8rbE(096KP+}%cEGvA6^++ zo4n!qR%xn&Z6@q%6RA)XUW2qGc^;R2@ijQml2JMo`R~W>P1g@fn409CjH<(_=EUXy zWu?6ntN%GJg&35O*9SPdr)6VZv%M)vHYbSsklVdNj0NrGvyT_#zx(cQKug!vfyfKB ziDwaOh(nU(hn;n3l|CqD-1I_t>rBh748x4mlKDj!Dq^Xn)VuPKsQuC>g?$kW>Od@@ z5G{NOB10BVp%eN+Aytt(6?H7c9B!^*a`_Jo=)>c%k#fj)gS?(eIg!puX&?{G3IWk= zo`@zZpBSxlLuSp}hMaIgi%vax;QrqhjM#t;&L6w;9ZI6czQvg>WQSsOktX_p2(cS`Px65AZqxt&*!UsP8xWhM!;I%6QMx zVgF4ss#-S!qjpTfczLh7JZVOgIfp@d++B_ko9{%q1Y{0v4 zn9+EJ1wKUBOSwoAD+!Zd>Oyfsl+yJjp&H~JsPK3FX@VCo-ni6ze zLm#G{q!$A5{A#Tl8GNlhCIn4iwg$A=GiH)o|*Nwqn+B7#X6$IIV{my)Z zhRO%$2TdvU_*aSWR3{W=Te6`{((&g%6f0&V>Ooay=QH0L20k{5PRSmLNj~3d6*TAa ztY9zQg!)$EzPxDW_Ua9Y`z;r~xnzub!97Z+V!GwB(szO8jI;=AXgcLyu!8^8^9eaz z4kiw@H7a~Q2de)V;}rac)YOb*BpCSw>*S8{5FfatW^+~_exi2vcy~kl@-(I(EB;+@ z9PH@Cx7XLnzkrzM-aiwl!}?+SRh_B3$9|po7^ZTd%sn`CNIWMaEz<@lu$& zEP>p>e&nUq$^(A${r_JK9g2t%(>rODe$(R;n^d8Aj?w!>pi1S9ABJ!mtetp24s|VX zsl=ih!!H%`ZG_WfxAQ7!plEYFeeIx4ig*4}$40XwB`~z!u=IC~@5)A!^-tBdG?J=O zrX0e)1GgkSPV9cglqFLCO$ySUYX#qwSTo6a5 z`5`3QRcS3E>e^bNsJ51DOx|>XY-h^_z>_se^8CGLLsg|^s+BG1)I}+?T~}BC6;t3sIwNc)f)(sUJC;9*psQBkS2KLE!ac*icUhHW7!r}Q4J2FemVl(NV>Z$GUDK z`)v1!C&l*V9}+CbXfh3I-O>Es-zT)46wv%J;R2s}k~FEhNb|+$5QQPOkU|CK;dZCu zybM-)^lrwYB?k7a@9Qo4?=n~NjEkkg#a+VS%E_9O9YPp8OIQ;Z@Qx?{Dac>(A`7NW z_(SD#Cz7C3ROAbyti=?{;3XsUBCm_opsEEFd-{V>P8N4x!u5`tOcdh{uH4vX&AZbJ ze5f=>r3KVAp}#P}7`Y|9l9fC`(yF}6l_8w}LRZjJZJ%6y~-N;c!?L1GBYrJeH8 z-kb{=xOkgW9?3+n-ppybJ-4Ok`H#<(oh7kS@J}GV#JMp%Nmq;~IzZx@bZR^MT}X*Z zf!kD`?`?X;ixF+KjQ8^Bn&!m+8g(8I#3>OG;>@ayt}DIWKzZ=Rt4K zc9VR790PhqLh$+hvv+cDQP_VaiSJ?~@HRp8K1*Zn`2M@u?_?H&h({9+>fM{bM$Uo zFbyyyL-ja%HD6?#SRkeqj>*T?`qkB19&I~EualaDCwf;}6ZHH}K^^q$s}BGA0hZjP z!V~Jt5fy2QFz*01EN3B}H`EIk^CT=zfv)uXc_AoCm^@=oPViGDL=065c@>cen zV1J_DF8P~$en@`hqVXr($ODbIX3dNo;8$}%aA6FN(keH#AzDBg1_!{T2qOSSO$aNq zEQVcY7Gz7X)Ye(I0c9E;1!z*T#ZJ&PYGuL`Ev4R}0@E(Jou1>K5JF|Z_ znwZ#T8Gwy&UGikR^Wt)?H}z-_);Huw3?@v6{so2EOquG#cDXee<-Y&jE-S2Y@ldN5 z1-THzJ{ioM-%RDrBZ|JYkJyw6Kx_YDWxfUY82IO zqHdg@R7+Wf5-kU_#AgA9*^$>JcDm9A8s|_r0!2TJ8GpVn!&hS}#F~&H?=@ zC2x{r434(x3BH+0A*eQ7%TM(TEA@ri6=lm12D2~Da8V0Qh)`?)SnfQM+MV1t&l4!h zYFy^Rni>upZHhH>PD^TTyY9{g&&3V1#ZKe>VwP+UQkMezDl?=Kq!Z2so4jR@gRRz^ zFzDldp8Kg+T~}NO#!qdHD2@AFo<6*dJucca%Zi2bUfE&C;5Zwm#hH(^=1raC(>NhxIZInQZ=^;Mu^&m^ZQh7@ZZ~;_=146Zi3NSw8t=e zdL2C{dXxUBpsMOaXI#oyRaTl;3pmj1(K-IJX~?SPOf_0%_g3mrMA63_Y>P2M83jpW zdi{(=ooFuYX^0T5ZFrTUViC=mJeM|)1T1N}%T0M>hhh3L!sW`+X~^dpc-b?_RvT0{ ztAuP8I`PWUq*EAGSW0{9RX>mrQ0JDGkzCUCNFaWQuB-w4$G6+b)1@%F)(a}^Sj>2( zmv?=wo+XA{`|sHh#InwEIUcwy_bs?Qh*X%|3z~HJ(>3^zsz(ocePzc}ul+O&j4&WQ z22)GHVtN_=+=tj0F;G|9sc6l7sBaXoUYkc!tg!0+LO?tqE{BPYwowI>^=k>fr>F`5 zdfPAExkO^7x7!2prTi3fFQEY-xJ6@(qx*q`@8`S@Jb)o{F6WW%1b{>CuWV$W`8M}w zFIBFtrO{)vHZ77;Y9f8CnRK?n?G7b~YN*wh1@u$5W*%W)%+BA=$|xitAiOKvDkbKw z3~R?!ois=&S5U)p?$|vVuZdc&DwC3YSj~99Hafe)AGt`nnj{aBouoW5>^!?0o~;f#<-nP+OgyHedI=!@B_-4K zXFkgB?roTxSAMjZipfwX;HmTId_N$H`y?X7@(je1O=yPQ!L-hvQ1GCf%ge{NzV?rk zI;M+FC0@F%=vCm8q*L|y0s68g{3>iS51{*p*9An&j2(NdNmC{Y3gPcTAMi>9%7!!E zH1td7K8N^_MoenoEp71OSwE|e6QKL<@bmRv?C zICAf_PMu_@F7+;nLs($TlysA6F!dD?kRr2n_1MF$!}2b z#K>Wz?9lB_CwXa>?2?Q6mWxss?m`@m9peiI{ zoD@U6QV`%+o}An_H=81spKRvnIt}m>mljw0=*v|f5;SZxHq)l9W)?~xBb>?|Mj2Z; z6VsAr7=4BiD?@(k|)Z|yI6d7`q2a_@d0{$r|{o2{T0flo$9S3Ql@Hni_! z>X?z-^$oZY?BffjD&LpLw{ICPg)9b9v}6p^PITz{d@^hKvV6;GzhcyTky4r0hHT2R zWeG##MJ(BxD&L)suY-;n08hFL7m^~gWq$%z+NxKwa3WqZ$4qPtHCMqb3~FqRy2euC zzpEQMw10(YF980CoJNypPwUi=iSkKP)pk|WJw2+I3WCR}SZsz*>pz(|0%OBdwSU|W z*Ip?QMiBh{{C#X=C6J;#7+v%-=(=?KSwai&q_%FBIki&pGy{}{UWZ(glK&s#3;r_`JHeyX zqWsuUJ#}b{`Ier|H9M={{=aw;Jo5#{7ZXl7;=Z1CO(j1=$jc?nj{U%1R3mnon69Z& z1&uB75!VAF4Lu&}>i%jp0Wkp@vdQJ5t1gtrI;Q;b$@i0AjkZu&*vz8H_qb5Cdw814 zouc%&Cq@O~5!a9#$sDBz4v_5_?uyVagVi3%o3_J+bl27*m1FSGA1?4LR*~KrnG|hF zQMgCH7vZwl`IpYVZ@L04<1A02y9`~SgSTnfWv`xXK&QXMxkYGdQ%#Kz;u+EoFMiZf zQyw$2u{M;&WA9?uP&@H8#p~6e6a}E~Ch@!LqY=R0D zgXIENd_IX&Gf)4WDq+l>TwgocFI=D3pzQT_W{FR6n=x?!ehuC>-|FvJh7%Y;QUK z?Qk@PMwd(IBrj1F$s@`whi7MNZ;h3j^b!^}BZEsfTgb;aUYoqOzqxRQHobpM{g^mj z?@*x)zC3S8oAH;TFSOnSb-8?Q6FK#~JvhT1S+M&dv+%L zY1R>)gV^TQ@%8J~xZTFS;(ql)0xf=D^BqLVW9I(&VsqH{CNpilA&trUzG`5SGo6pH zy}?SOf^Vf~;bWzH8Oa;^$!^~PiT8eSDlR)LcZ#u>=j>Av0ss)OS0eI?OW=3&Mga0<4qrif z=u`bqR_t1*y=vZB!Vhf~^4T>#^f|wam3YK0LHH}iwaV-sFEpAer%a=&i3$XQW(jOI z>H%dRyxEmAjd+R;Yx5NuAsQwySLvU;8-ovapj&Zw9_(U;;>BZ zw~@QvR3h`#((ih7Ic5E?9k@&s9O4}G9pN&%$qUjWeAUiXY1!7K#?w*CJiqd0Tbb?d z#yTR5Bq`_HjmodRfRHUcD4*i`q~orEn_BuW9RVrh)M&Elr9Tg|j6-q0#44727P%`> zx`YsZOnKz<5%;g$9zF!@Ew^egztFNn&)G4a3{+vtRky}df6i-BSJSF)QFY}@fJ{q! zi=|r9m#@7{zQE3%q%7``t-sCfyTvnUs5C*H`lE?49(xZ)`N~v_p;YB>7_yeI_x#~$ zh|l00nY4)BK|{vA;SQ-hg&cyg^j#&Rwx%ziE)0+fNw2q#MLmojF09AA90cc!p23*T zirjDkk3FlM>GBoMDx&3I$>JJry~=k=u*vVQLX*S97+9>mnq~|!5`+G^7Fw{IJcT1eq1gOPYl(r<*HU_`tI-_ zcfR~qY>#GQeJ^>}Z|v!*ma+?7!ReD34!1XuEvV(abX48bOj$;U*c)rN+XGycxI+j} zeCSV7Xm*h1LiM2&XR7SGj$p^y!-=(<_EHw#=a9Gt_=t3Us^V|WY?;`~j(h*?l>^d8 zDMw2DzHDG3-7DYvg@Er?%YPicxJSB20?Uhs;esPRmVGyyE$)gCR2oE2=)9$nM=s*0 zURhzei6IMQsE^)eC_;;N&n&rAX|G;Q&S3By?^W1$j3LS=cL;O!zAeZ$SY+Dh#ZK5# z#qsM|O)fc2aBHL3o_DPxgcopa;qY~%WMqsnhqqv>2h~OM%sQmad1s^zm;I6Iu@8#Y za(+X+mMo(d4XR8&i*TK-nVqr6Ir23#G1A-`N$FG_j(oo( z@okp7JyU_&Y`it?xqiB^uaeq({Y$smfq`BrLChW(ri0TJ(a>*yMd9mi zT5d8KXExoDGO~o1{_m*)Q(3*{PInzgm{(wOHfK|$b(i3I-?d+|XJXd!De-Bd{8Kyg zlT_dS=`vqfiKS5nws3hk-|5(Eap;HZ3L^R4tq8x_21FA3&Ii-)*X^^@+M!tOlU~XH zEJ#lD_t?^ry#3~LuR^j^F6fl5+YVn0_+PrKcT{es`(mtK%2p6Q9B`EH-z=*QJL59t zh9BHOADZg>w}=1N`!wFNPsfd6-w@)qGl((qs$rnW(0AKO_)rwGx&S%;RgQxE^}&1W zyzBH4<_CA{z1Y7`6sf1= zBUeQDp*YiPGY6Xp1t)CR=IqDDvmT1YLGx-G=#029A+kKCc6~$RIVNgqptHlTa#Uj{ zw4zt{nSR{Z_@0xAx&a|ct5ky0V0LIz{twoV#<9aF9+c9gx-?u-G0wG)%e0_aC>_>s z3#40WaIx_5W8A+M6g`!MA9B(s+U(J~-}y&$7Iom!oi;kn>N-9K#Pg$F=M6NUbmeGK ztroBeoTiZc2EU;Ee5Nbs@a9*y4$FtY3j^V7sV{vkIgyi;WNq5QkyCfKTv|Uo3YVt| z>k{){lCPqNUh-o@=n#dQR;`-56Vz5mBg^L^r!oWcsbE8*HnFco$Ta%y>CHvS?}yOV z`6XzFI;8>2p69eP^>Cx8VOwaLj7Z#gzUW?Wp>cFje9%PXzrDX=K{~R?+dnicMI>Zp zzRIyT>H50qCqN}3?Z|vMhU&qKyL3{ui5Z~y3Xi84Yye;WHx8jsF$S#Aiw>Yv|8ogF z(_o6a$Vgg#kyvwC#*rKFi&VZl{9TN7;#HKyVkc_{U*g1yPW0JM z#h&z!46b^ZM@<(_6=K0ii?Q)h-dz`ZgrV$Y<+lI=+GWjH*`1c4IIv%hFuAb4qPS#a zJI&cJI17VH%POd!fEcF*4i|O!Y7tAn^JhF&|F?Q4Ctww*Ose)^OtE$nn4OXguBhYu zBG3HZ>a`f;O@}J%aU|ltEHo3pPW7MQ3e_%an{5dj?@qymS9;;Z`Y4r_` z{ZSLkh=?=hUm>L^Xu*n!gMvkQ2&X>#x*EW%d7X;7ZK5k6YVK##6Y z&hBWr*PK70yFD~sxr{q0ySlu&7jll5v5w#8Yaet4kGdr(F>`#LD3+pbQdu`uA~wq> zFERK;=R@B83aj%a>}#Xajppv}7KXW7XtbGWrxCdolTX@_Acau{wt$e;=bUopQIJ`Y zBy0-^Jpec*MK+~RwLO+@>UW4@y-gJ%Se*ll8vHMHV7^K7qNVqPwg{PrZ-*oncUr7T zBj0P4jvUD%Tr7`=AK0i@?q>fix5eNdrQoFR<+!0Z@&)uuK%@jqPw229vl>!Y;*ye@H6vVQno}om z8F64iGB>jVY_gRdz67STYInXIaW>Zc08FYEVO-AB*a>JH z*<5mCZ~}PbrtGxSE`F)g^+R%?C`{w-X2e@36o>!e0)28a9zOx^VJ~hbY6g4SJXjk8 z2@BkVUM*#lhLi08NV#>2>j~{k0jW$yqrRX43ja>=TAsX+7j6b6IiUMS#lad0@u7RJ zwAq9fCyZLq1D?z@LR16Iu6%@Uq}e|B&x52Jqv~nL_2{Yu)7YL3Kt<#Ue)i|_OyWA? zr%MH`o%Z+U4$JIVn@NuLQOPZhhUCiEuD>bp&~0iu>GCpUI=8JS0;M}qSr6%ev+GfN{jnwZ$BG%KzQKcbTG5AS5X+M2R3_BJ@FalVl8+<>}$WliW|4YCFW1ea)(%79C!>T%~jd%qO)Yh*8HLHK3oEVj7N z0(&WBCOz;?XJ1H#p$4`uryd5b?afE{zjzB5w(*_?FkiUXP6E5pNp0u$*{%PEb= zOB^5$VBzG!*6oD?kfUJ2>TM(4T7T>fNU|{yZr30k$eXVkaQ)iVss=OA`_+zf61rM> z;4f;W44t5hpwE%$vh~q>g+?cO&!sU_7V5x&1JxIGoQ*Zo&7-?;hK{%yyX2PK_JO-0x)v9>20yC_e{5II1jTqEQWRQ|O=`m6DB z-?nGf2FPT%xhQIxOgcnh5*-NDU#6h)etx36Qe|d(>S?1sh?Kk*NGQHS^*wr)sytyENtLDg{s=JC{@`+=~ceTC*6*Q_8CKeb_apudj+ zctZzfMHl6nhYQV{omd&o81_0plnm_sHFaLjx#F9+FNhgPM?2eq{k(5QDj~;X)DKLj znekq|t1aQw(NFT&avMWx*BTU_c3r>VJTBIiQB48NaLa^{ZpBL^SOz#(5d}^$>qB`u z22rm50DE8YPCxqqNf`DUSubHKVk71i>0EL^W|4|fIZ2>OINo^`2S5#9T?3tv@aDOL zoLu;-(-R7%bwB<@V@TnvmK$tRjzN|V>_0RMV<1|hV2`ruGE^M2zvEjfij|(o9x{`ms02!MmMdRBJ&AK_c783 zkT&8?9@~5#td}-fbc6YeP*MP|D+I;VR|z9{QG3%i5S<{&DKwQ(;BGO1oVbu61HBj{ ztR<&BZFE4_Y0!#$A%IrVSU2m`sT^aD8-!o`ad^}|j!<@jt=WT2y~&YD6HbLvW0)$t zqIM3%%FQAg@YXx@ZcFKrR=f*M9qgs$=}WAi^T!iR z;F)&OXqxoun(URZp<;LJ{)c}c=2d(LG!xoSqav%L1=cdzNV4YCfpw%<#))=d%3#J6 z#)S=u$$W*P{-E~{QtBLsopLo@T&~S&HG$u9gV+CZo`gl7 zF4QI;cQW=m?s%{(Qa;MAGJghq|M)(pbL%_%^>8pkGP=`xpB~#BwyZMRbKE)dEOlR; zqUrpnOZ^ia#57KutlhtVw~Y?6mYzh}o-;eKizP)B3qx+%2Wb5(e48|QHTQo8pY;sM zh8Q!o?c9g%$q$FdJ+F3oIVn12RZLkzRISRY**+8J7#b-Q6H;7Ei@xrC?4e*WAhFq7 z>Y67zHF@@GRq^*;z?6;qH2ZA3h16fSDG4#L_?YnkqTUeZ&^gKN5O$$@FMIFKSi?i( z-MP)_#8kTD_y1HI^3*ceB@dt(K0y z6(HgDMdDz>s?&ui%mnbhfQuVxo|I#&_lcLs%*QR&b;|k^SDfqpm4i_tT&gF#5WM-8 zMe%BU!~y3}kH;FjTHx8gZN!(;W!?ZvUT?z=f7;oJGw$w5{*T9vkPf*d(mUI+GL`SY zj)S!z7!n&o#C~HEqo<$Jg|}?}MHtR;DG@|7f@b*$fFR zpa->j-3nlzV=*Xn31VWaNcR4lm2TDH_24r-rk<3URvdOx6MQZG_D!sv8<8PyGT87g zFIkcGk`1K-{lnYG_~pkJPZiwrYgy;z?=y9nSo$4zfT$pQiB8U8Wwg6e*Il4MU?ybh z(02vkMyq1R?<8k!GZFYql~kRMnkn=fQ@I`a@}{jl!6uo4uenuOc z8l_;>_WG-T)+HLEH@SS#RkVwLOoei!%rmE(XV1b<*r9_O5O2CgQ_h75<`Y@LawZ`& zIx;t>u`e~Ge(1sfvIwNp(8rm7f1)Hn6}D$soQXXi;VBTdkw@mzFOwl^nlZi}(3ZnE za>|F2BrkxG+q-{JT4HrpXX-5r(U2 zppr&jxoos-ZMF_lq%SeN&dP7IAfV`M4_0;2qsgIPPcNBIZ$n1X0YO z`_P^5v-hV{H7+%=%QM=2mkxJZGpn8Rio#TBxF+yFlJ|pNpsok)3N%ZHouBAlNg(8n zfizi$zz%}jqyS~$4RwdXtNefLD^dZ9vZZCyu_kWLysiP&MrSnlhu<^Kk2@#6*ee-( z0CtJGJ4G;G@xNHD^41!q(ie2??ReL2k6!FwN9PJD2UOhQ7bhaytSC8UvUF->Pk_?= zrgm62tGN`E5gYt&87)ni+NwFfuH!nsw+yA?;47n#anDk7+hHT)5bl1M+|{f)ZOss* zh*;nR5oEz3jrB3@Gczor0+|3QbuoL5jUX4pQ80JJtc|S+*)X-OB`8pEb<8Ewj1|(U zcs&cdZF}U~?U2Y7p*yxzpH&#WV^rk2f zJ~eIsMSlvv*JBKYls8dt-R{yo$+|yHM?t~gZviDg4RI|@7R1;yicRn+Y;tHYw!$^K z*A^piCZ!~lz=5~l-LBv{&q~R-v8arlFycLMF~^?g2~?T+he%h_qk}%>FH$l$HB7N% zecaOjvwFC9x?IbXyRYcvC$riW{clVB2(7dUP=bRc5ir|zgF4LFa>BxUfe6W4E+4fV z%c!6G{nL22*Uh#i=aPXA7qiCwTQTJAT}}12kZDgA+WUJeI-LBAOEv0;lgx?wmsb&+ z6rc8t6P@wjP3|=itposx@X@8MTY=2Klj*)cT=ATcvPHv<8^>vsgJ$;Q)r>V+p zxnghCSp`7(@d{u1T>$~n)Lq*jfOd;G)1r|v#+2p+sKho zULnFU?fkWGomfrnfYQ+=o~#Mp9qWr0f4_|ms9=v9A|x<))%JLYBUce9;Vt`kBre%F zc;!@&$_<-)Y}cn{T*3lmLFkay0km0v&Pxf#Os-fb(d7<64Z1L1qOUAs9T98RK&NvK z?f_<8+vnMaQ7lgTg}o&jzGvQjC>oAa+rKySfYg1hs$?W`XL8V|Cc6L9VPw^40FSW& z%gbU22Q##4VO>ZzhhADOZ^sBnW&gTXfx#83K)~1M+eR7eg(basY5rFXYILrYq(3lA zu|F*%qVOx&Hkc^KXk_ocdI9&mY{`*7xze=CIy$iJLf z6{sxHpv@X^;VU+_HgFC8O7$ubrk4Vh6v>u|x_PLJtV?8jw}V%%m|VrrzZE(TjYq?Y zFj!RPjGn>rueQCkLre0c3mMC&X>2iO4r3C;PSsUVeCik#faW;RNqM_jh@2l0NpI$o zZZqcQ`9zX+w~Am{(8pb&p#lF^1{1BB$YY`<4!R|aa!6RI49{pE7TA>=7vDe@+zMZVgULGRgp|rxz0`%E+XKc2EJf0DbuK^!X!2&!jo`;mx%&2+Pp<=4 zbMb$(!Ho8&vG~Ac;?+lR?90>z7mdy*(_yikF-DWpO6{0On?Iv}@h0RE8|dmfM~TI? zB3A)0Z6^=0i=9H!^?cPgXZvzV$37qirsq z;vszkC0g$#{^tkiie%A*=ak~;p3J2ETGPiufBeJ~JFujLvlre#VmcI4G?~n@A-Bfx z*9i@SjNTDCI<+j9>jA)GuJ^=5m|%QuMZ@gCChQ2SavAp=wWxu^V^Co3##DJt1UA(p z8K3%+L5ZeEu`)nfp&O z`R^bZd-@$BbO}iHx)h;vo8rY1bTOZcS)XR*M{d-m`ad#Z z?~?PU(hS;|$p@G4FU1u!?a5hBMw-HgQ}>);Q74jd>#V$l-%4uaO$H`S(E$*z*lxj+ zR9;*iTuhHq54y09>-l^C@@DIT8H^tCRy;j%hQNdapsi07Bk zC@z#aKC_8t0G!UV(b8iMB~k>R#6KI0LUZEIE^L&QXFDdnz?_ljl_XOYuo>!qRmSpu z*A7CMYE(+@WEOamKTEniA-=I*%J?{t0hI`^Q@>r{3>r^}Lzm!SR3s%_xbn1@Vf z-;pxXFUqOzoSfYY!8wBP{uH8^j8D8!3w;2wOgZEmHYNu(ESP>|{uq4c)&RIMB>Aya zg_DRpJ&%ya`T7MXm72Zf5@iGpb^z9Vp}!3c+J^R}BWv z{y7vIP6V|}{R@4aqEeADUi)NsekX7<6|Ii5M6vyazHh^aZuM~*l}g7gYu2*7} zgLtMhd^kzqz7DK6Dzuxe`@D zuKDS5(G9?w346ZVUu&A;P`+BhAbj2|#At@4NoY$CSDvLFC$hvZxrhADzDO-m=*nL; z;CKSTWh}Zo&TI{pSDj@HJjC!27XT}{CfX*$`5C(H&&39J7bcCl>pC_!ORw($;YxI> z#joRw$D^+WNTY0JYB68t4wd%T@3prndWsd51K#J1``vs*a6nvu_&e=5aTa}y=Ovr> z=`FuozawR0)zr)}*QjYID9lLAS&`NL68_ZZ)_|US?pEBm(U!*3+ zLyDXXb9$*#eOP6d{yS48D462l75O|#A;^oQ`FOEd5Bl%xOB5}lxvK}^Vu*|u-($~o z$t3b0dD?emZzJc*uUsdhH+M{wL9>7QisW@(N}qG1;Q)QY+SA$v%VLU)m+ge}am?np z0Zhjg0`pt<7X^}Zawuw?g>k7Uct5!YT}Nbo)C5?qnE>y>unQbm6XY-6>#v1Vz-E*w ze?6>L;Ey7alF`SS?&8ficNsmTh1?;NqZbl(ji#GWzQGHt#6hrp9evxCH{Ra+%^M~N z*p9B{%15I?v7N^(Lfwew%Dkg)?N-&k^0=U5-N>~MS!E>ufT&{f;x%px%)X88OiHsL zZzj!H@pg+3AkyA_wsy%d01o_((6fIW4~XAB6pxOOCvh?=nfTcA+lv}7c~uTRdJ@>k z_;PX2qA6S7VLeu&2pXM3mrhoxEK18?j#CJr@PrvH3Gyn3ZoXWnHTM6ZsC|tEkQ>I5 zhSdP}T%cI4-AQ~+(|hQ0dUK@#*9k>f73U!R3gn^o2xcFkFnrKa6)=83;41NWrhAVh zvJ-)K0zU9yX&Vb%RdsG%YA@yFUs-uq0ku>q3gkWfg(y$N3%U?2LZCtb=djFudKqKZ#I^3DJZX@UEby)z?vM*y8Xfxl}UZB9qU2+U_#Mb@ItWrgENARDj=)1tohH`vCRzi4;DrUx_4NTh-$HJ)wp1Qe)jjpx+n@BHxoWz-!NsItY4J zBM!|Q%RXO7ZRk&e*cq~({lj)wWWt;yBG&x`Is2Ug=4#fS$b9B6BC-dL? z-frEBJehKXFxn^9QW-yA&adh8d7J9#` z#IjW}zTQ_iNt+@WAJZax_-j_u5R*nw&M+qh6+?DN0N76bYzH<+Ji4f_>)KF+zH$0@ z&hC&b_*|p=CvcbE{k1A?{v|U3+E8-#&~K@`ih_WMNB4nEt|vhfE%e`08#f(Ru$hp8 z-prKEx;ifOD$76Vaq)^VE8Tt;Q^Ln->J5s<_&R^s7Cc9t8}+V>_StB<&etv{n%PA% zL+alklj6LENW+ytIpP6ytQF<{0oV#3jJvkQkE`e_l&_A}CN2;L=tIdDJzBKIV$Q~P zmkm1LVz=9%9fPM89;D|#6pbfz6@@u5zmKsO=q8iSOe1W$=)|AOnL=RX(QF^UHT8h^$LiyQR7@2_mh1_M)8MIDT5$|E@1tm1Q28quU4F zMm+@Wdi}ZFkKm^?f7gdUtB^T50%9vt{%jWLR+@Xhiy82vG+&`$n(&P+_YAoj_XG3+~KyUhTpZ}!3z4tbLjVM z#fzN5GO}}K~VYe_k zOM|9x&pUO|ivuc$_wq1^c$huz(JyMJ$LW{CfR9o;a#fcrq} z@G8onMxTEkWaxWpD->|*O@gyIr^osdpxKb#^E(f%ob}`YMa>}YOBTnfHs!stsTK4A z-Kg7=Acze}oSO7dfwt11kOt;1NdRX(jgCU>D;>>zF-93er$HVb+0WF;m%w4wj{n*n zWxS>JKq?A{k<8L7glkqpIO8_7FY=xFqw{YG<&k1JrwEqO-+6iC1M|*FPrb&9hfk+c z3a4EUyc)=^r&+eA*{8R50hg9ZIS{Hi;%sw3;A}t++>LJflokXvB@oM}2Yl_x>|T9a zMyrzV@1N|hy&DgirFFa zw@1amYlW#KEc!69ZGjMc!r~{K5oc7vo|q1I0qXm2zCD`MJ7um-x6z?Spg$@ct-Gra>A<031Y!ULo@PXAI$PuIB%v&j3a|YVi?Qk<|I@Zd zdAeDONQQ3Mto|e-?z^GpkuCqZ5?*-3B(?!*8zn@9itKBB`rytwM`MOJ9!}OA{tL6- z>k2Y;dR4P8q6?q8x)UjKXa-MC7+L|v>4V|d!1V~6R>%BRY-ujDpI8y@N;pWLkOqpv z1{DT}@B77XD*+?@hpM6RbDo|WyV{*T{Rm4f6e!yr`pptgUvN#CTJUaI9OfM`e4H#e z_48e->$LM6>%G;-@UgEaGSUFpfPr?#MNoQn&fwm`E9{5}1JREYRyzT^$JfHPS~&M) zfZ8pV6w-_Nf{xE-52VJ@?MHo#fW=^ zRQzA5vS%KL`kI{l=UpurK*8g8J0S23dRT62SBy#5M<^KHLqeNOA~<=J?#_La6?c0S zFC~NV)sm9!dh+_}a^>S6-z-P2b#@*655weD_3eVQQ*w7(^QlhWVUs8hTFxt~dW%)c z0wETdExVt+R27Qmly1@ZYaPXnkk2xEph;?SodY0WkL+ zeGW|S4*$E?dbs@4E`Ti$KzgxaQGtT}KVV0y^Wy7sXZpI~W3$J*w$e^t($hPKdP;MN zuvh6F-+e}ch~oPFzURt2o2>q z@j9D_N1iXU=E(1EB-&(VD!BTPzaQttb!PWEoxJXFoSD&v%N^CEz|r&uTB57|Du2?3wkG$fiOQrB1JV#%x$TV}d98RoTCfR(ocfm^AE*0&j)hrK#U&lM&e1 zfsVH2FHRES99LWB_uj%ySO3M1YaE=g5X{iQNg~2}77gLnHIjO#)E$DxEtp@GGhj_x z`iDfdyhNjZ!K_I$EU~_f6j0!BilR0pZu=wRfwOd6Pr5y?B*Z(GFDR=Hv0+C=F2^0} zGbXcR^L^b2X*GQ_a}@4>nb&+;!pJ3)=WOgqj4Uvg^Soo1J_vO_Z5NXzS(-D#5OZv} zYLDMoJVw!YybjzZJ927`(BJ}Cyu(Z<$`!L(Wo~vPq=_+oR~BQdh5iiFNdhdM{;i(+ zvaLxY;GFlv`xS)8%eA5aJcd_b91gG4$>%|-gB>a%u88jpmS2DC!b{^uv} z#(^UW_}>cqRbze!`n#v|uU49Tx*#T8c2w&YegBBP_Ph}0diTN)zEVQF??QjKJ6umh z4Vll*RNUO3_5zrU3>$XB0SyCgs6?ED64%D8NrEHGmCCoj)TJF%)dk1C0PnDK*o7L3 z8$=fO03L$uhvlNayGQRZrPgxW+jjju4kGlm`I~;Ar-7%qE*B4x8n^d30aF9Dr61~5 zdYt{qUIWU7@COrP_S7m~|D4&q7DbEFk%pNEL_ZcM$UWIjAkq1_x&@r{Bni}$057-Y z@9f$k#FvLG!;lV7(r<9C$(gx>eChq)1>HIMHJ)18Q~lCnUbBUs1?ISSU+6FLUHRf! zT=z3|#t@YEa{#LSBcvPJUpzJqjcT}+LezBTm;05!wY<6aMCm^E0p9;}a^=V*j#zr3 zsw|bh19e{r-Z@w`NtOL=p7LR}0Re*1qL0}^(6K`pd0mC@&)VR?CHs+bZ3b~WjmC*s z>IcV<=siK{7xYY3?eG-o7dxE+!Ub>ltQ#***G*%gpUix&xIhKHHtyjjN&e{6gH6Jt zy?)tb0L&ApEBr2;IzGho&7{s6GY!_S#00+3oZyo6FeKCmzSGr

~Cn3I0}Bv?iH%N2iaz9OZ)>G+zA zZ;J?~7%jG(!ce?z_H${YNPg6K9DJSdUj;bYR9XpvHJ?Ru5J;75{aJky@b2T#wd#+q{e3}4N18ss0@Dr_=`!nD65fT*mecAVZ=y4}hBG;xB8FL8V#L*JS|64nnclmX$ZtHzgi75L$Jnhc9iu{O64O zcoh{qzIUVC*C)G!E%$y2$T!Ei%j?o`n2Wh1+3^>OU+7mFfxa-e{TKB^F?8{w<>x2> z>&$P!deRx1M)h#eL^avci7C!7;`cPYs{wgr@(4KB{)?(}m4E6PK-$1s;;hdZ$yVTUjuVjX9TO3B)f|8!JhRW%#nh z<*2v#$EzAf^g1}Uo6YU**>JOj1jg@Y6#)5i{O+$r+9AijTA~ydS-mU_j?)g-(NU!4 zy7%*^&Y`kD?)yp4Z$@YNcdXqnMTUk&HE?1tV{_ZP{xp2xTp?(nl5NGOok{n0qnUZM zTxqDK2<%Z?HN07EdiFeF08inDfQ86zP;s*T&A4!j`LdNfQ8~Yxr5zDm$G8VyNSiM_ zcHp5J&8HCmyboys7Z1vRW;L#6j&C@ErYahUDp8g+*urwFeLHWg{IoiXIHq%-I!KI8 zSv&BoFkMK~ez7JcrM*hOm?M8r(1`V4SbJKOGV3R1z2xD9x_hbU)F;l*^gWp)XNu`M zaVjZ!3J)DurmeA3+Jew|;EBo0&vjW&u~)K$d#skA;NsZFkR9j9SV#tPp?vvj`s?F0 zRjcHe!*w>R81D)q|94@laIW)96>1kJQ^X<9^oNU7Rw(0WsZ;Ir%Df(pI3!)ZyXm5z zgb>-w+<7e_eN}llElxAgU#Di~Q(I;JmI~tyk%f;FBLZt)5`pwIFsLl{nMdH5Ka#i_s2IbN&y#psX}HGx0wpco;So zzjhhdw!A@)M}UGIUgFC%8*T7{EaH_(1y?QijgFXhx!77gL9Di*szoYuEBKiO6YUAa9RttSXt1hpo)ND*U#kT*Y)P(0|A`{8^uZ;f4WdQkO0E8c-QMW?sy(|XNhXAXLZ zMLV<+{UC$V+s%6##Y-6*TxYL)Do(a|5y1*HDi*aBcn5N+8|p$TTqd@_`&_FC#jrpw zC`#MD7e&gDpd4#A_gyhV3qj<&dY=3M5nHSq$%s?u&e;Qb2QQk}=pOTSeHa2r=&%+3E-qtgkLtWD`P{_>t?5X) zol_CqaZV$Pr+2#J$d+%q=Q2LL!n&q7v4mgLAUzUwS`XP&MA5%;r&>>|CRN-HG-Sl) zW6sU8n!`&+4E&1AzhMW?Ygkmr4bI#zB2lMc)0sV57BC>tycw~F9HhEF@oEf{tu3$F ztzEtEJkBkc>ICN@f8IaNJ-%-m-1fJZLKF{VOnR->@gP5Gjxo)_3`^-X+bYgA8=t5d z57Y_-sqv>RAKqbBG}854>u>v3}z=kMS1@k1>f_@QTylF^8a zSddUyAM|Z!iLv@MgBUF|O!3yPiJ%9vY180K`BMatBn-)xeQs*+FmgGgZGY@Y3=n3W zE(L&9Z&Pcyme*G_*bYIp)4 z@K<>+UYk2gz$*ZG$jLXzs4|#_yK;9AkESWxwM|bh!|r?huRGWQ8tvWx*`+2PDbYpj^1!6^_ufE}J?QZrbWU zg7rcB&0iMTDNffv83E7fPPKrC_Pj9(0c^()OXk3?kcP#z;Xrm0afm%Rq^UbU=tF8l})_-|#^M$mab)^K^ zqRVl@{f)~CrV2<#{A^?`P`P^zg}-h{SyeC}6|UqD9HP{d#Xubivy(5zMWk11D=Trs zS}br9Se=!Q!URE8|9QahK9aYHe<$`|7~e8gOXn&WB>Th0*I2#sPVkq|S!%}{?CPhy z`==UYgx&eAc(x>j%DC54A@D$|5Q%lPgo*##Z>Ai-B3FX69Bdy$P^=-a_pf z<3QBO0&tbF^}{+&3RJUdZrZoubPy@-x2LnzJ|td(1vw zj`fY!is}g@J_KkxSzmRC9{V5PcO5;pvR50j?R2~_rqh{XE2JH)WU4Ts?A%OthG9mOZ~qDu9-KJrEU4TF z4>b%4xBUqk_}F4{x+$5&fH9`2GWe@LPYxeGNbzv5do=j$SHo!Iw1#}J{keR;N~A`v zG9*LPjoqnKaAmI;*^~3hvO-KBYzcWbTv$q=G=zuV%x>K0Alz8Op_3Ee0EUJA#qE4> z@t>VERV}BkNDOpwvR4dgLR7|&eVCTUS1Wr~nDF!K)652Tn%Ind0&YKl%b(N;G_?1U z{7>BrJBw4>Oq~0$m&-R926**NH~dN|w>uuqsm?^;+S)rV|c^p2#e*BLG`&Xz;*4@u$xLi$lj%MAwA%;z0ln$!u@;V)c{eGt+J+O zCjAuc#aI7d=wHaPm^g}6YKF?nD9Q{98BbQYQ1Eh%KtzprrJ~g8V-_gSVU*i>G#Vd+ z_sx>#Qn{>($V*GvSj;$ourkTYfEjU|)Q$C2zwQ8=h2hag9-KOW%VKj+W|!bLQ}hnP z?VzK1)b;vHvgjRVwzZ*363j^0B|5D7+bs)^zExDf(_mQ&caxp5RagwRo&43U$26y) z4`b~-B6Nu26g&GfFYTjV$BYhK-`EN4r^n5s>%+P^}4x_)0E}c!g zblwWv6#pt|$620_7Y{Ss1iJ)@Lc%XwCmb$p3(;&VVGZ$b7eNVqxi##>bK|FLF(sPs z)xd4W%*Y=AIIXADia|kCrm1wD-j4{_gNc@=0i{|U7EOWrDmOm8(>dwqal zIE^qdP%e$IAW%45@kAYMK9)W+!BpiWV~hu(TQ_hgW+>g@+Y6<}pIn<5Cg3D;+Uwnz zW`CM)bo(jXX2XzX`EJ|aPw%f+pKMr|6C3Vi>euz@!{OMG!+p2Dc-7&OY6dL8PXQ*> zLr!>T4^yCA&U!q%Zq>lE*A=>oDZKB@#&kOg3aj=`Hs!p_Bk!p&F8a8}b@Y6{q_~^j zo|{jv2d7Y_&NK*5WF(}KV~4QPwd|bk?k1s0{74i2 z2zKE!DB$-mjWUb92y4r@OQYpkP~uXVvM?xc7*$37Nob)9OT~OO6iR_*R^L8z2%V<< zJNG>}NVmv8WgE4x4J94ZE}UOL^JkXHOz$4bwBg%M>m)NT&}u6kti#?nRYO*!>$}dP zf80_d>(63wQ(cBlCeODg*%`B(C;!6j$a-#xyPE`~a(iFC&%}lEi9K&ddH1_g%r}_A zvz;y;dk@LYdazit$ixi9Uze5t>VHdA^U_@&Tw}&~*IJR1ASgMJmmBe5iaFsjH&|;! zZn1x-mz*g!)9RPe))DlYR&ljLFZo14Gy%F}ZAfsn93k1b3U%@nBlTXtBQ)U|^BJC= z45U7FyS2ke#7F-Ijt4tQ_~C3&ZG&iJ4Wjusg1a)B#RF`I%zAco&s^4Lm5Kt2GpIJj zXyC!zebgprISka1R$WR*tq7jBtpw$RU|gPRL{--c8IJ))Tp(d$xafcqInyieg9ApX z&y}m`VGUTyDYy|yqGRSq`~BPhx(*!-sJ){Sc*v?>$rJiizVn&BY;4jW>(W#f7$7j> ziK}vY?*uOQp0>B$$n-<%Dt(>Nn66z6qe^Zz>ci}{sW!yG=@t&Zad8ftaiop>2tS@C z`qWohRIJu?Wx-L#RBsw~=KWc?A^DZ@yxi$%@kd#!0X*dv;P8x=J`}gc4bq{VNFD8v z?<^M?YX!ToWPHI{{Hf+&nqY<9j>_H)W@)} z>Gnp19(wYsz^{cj#$1Z%^evCyX(qVxZKcG3i@m2AtV1=%eLKY--owL<_(Wm4|C?wdF?NTq=uiuQdNE*Ykzu1 zM-KeXSzf?Qx6nK`^*u8In@{T8=EgPi;P_8tBb!*J$K=Z+DFf?B8geYdN0#`@FsgK- z^2^qNhSjhL%d(-_xJ7$I!rF0ael5vSgL+f$n6+zu>921P`0LN23X5v^YW%L|XNmAX zd-6=#t7p=15|h=3#*lt?l>3NHuCn#kJTkP7IBa<*)uPUHoujQdBDsYe}_F-xo&VycJ7)BgO_>{O!9EdbhSp$%_^&?_YdH4wyDHsNk1sVRC{6AT^aZSaVq62l`2&$HC!rDGCJygL~^rU1YzS7J+lUj zi1_QolGt{7e0+S7xWeR>58}65i5~r=U*_0LGR<&k-PHXn*EO;QPQ%wkPO;zBIx+_m za;x6?H;Uvlfq}nJZBla^T@PQut}I zdCMKa>eUeYHqhWXy(jJ>pXz+r!N2)7ty)Iw0 zX=RzYELYZuGvJHx1t;1QcMCs|(4!Wa$^8cVM!<8j@;5Aa$R|g>hqux+Mozxg0S_s> z^xr9hJ>V6OerCiZiV&WG4=jsOh@miB8V|FvIkkjuwE_dqc+cM-6jVV^omgnB;K%nX zaRBI?q?D6EV&uu37eC{Jq@9yplvv64vm%DCEuK*l7ggJJ)`QB4gzszgw%E(h6ig7X zruYTVNDfIN>)xR4ynbr2rEFDIRp)P^seD2%{vGmqBo#71GCH`a?VoW$1*TQ9;bGB# zz$gxKlfT{-rllFCmd%f{Zg?GED(4YL(l7lfU0H4u0APDA(aX3SIeH+KoZz+YqMJwAkTc*Jjb z%=2cC_dl(6RUG>kvYc+JuQQRAn<^7$QJo2X3Y61Z5O@T@q96yWt5j0qN*|Z2ox+go z?`q5==_OfBr4+;5U1E_`>!7W471JMEX>X=($;=r{DW!I`?7BaH)DPYk`fPsV>7&T% zMM-S-lI^)NrSLv{=bRJ2pTr43Xd1TSG!lbwIo;^@Sn=MS*U@(%-NZRt3n5sTx6W~h zA>)pI(z`P9J$$X4Hn(?i+c4K)uZL-AZ2**e!QEAVZ=maT_a=CpGb)X0to27pRlzWo z4y-Xfd8c~eyH7s;Y(`Aj^MD|k`na2g?WzX|&sU#hUg%t!oKw|nD!5#@Uv0nVZSw`0 zzV5>6&C3FAaP8?J30cj`mt@c!cl9GaVAd{%ewLjlKQtX=LYM+t4}im_jV} zptV-%K~xcYZ}w^>vZ#VhZeDO)+&sn5Bdz0NO8J%_5fXD2$sAwp;^bBBx9qhUcQx^z zV4l)C)71C4!_&X=a-DH`JC3Gpuv?6bOP2xlZ%H<@QB%Xpf6l+;@%QR>jZ>hNH0WhZ#mxpTOn~TiSPv_fjoK z)pBR-GAs(YTk@0}&&bU!9_i3MI1u=%zP@dSp3Cl6znP(Pwk26s_eX8VVu*Pgwqg|$ zVjxrS?wFpd6pDe zGd*!~eI-h;=Xb6okbGFtz0u^b)8#RwVwe2N2n%uOFI$hZ)E%mA?Fu8Lh9*GSd(lPA z2ksN9DO-d`&->~(%g9+hfB_&ZFP8&hY>2n5@WJffYHg8J z$sSyy8*^-a(HN^mC_$7i%@Kq5cJ#T-yev>7m?Vsi8h2dszQPc4Idk<3mv?SE#n|zD zcbp()M^jDWN6!1mm*}Sv;LUysY1$uGy=q$3Uv$gSbCIpHB=;t@fmBHOKbmx;H z>D#DnA)vE$FyO#V=jJ1YHPudLSW;*zNJ?HS|8oCG3E*+?Y@ zxp)FiJMJ3f6})A+v2mbF6qM6o#mU{&pPX#6FmT}gmc-r_uM^{?xoo5=W|RPyV1)Ur3w_dhtY(8{wa8~6&04v*NZgvz z_a9lKWQ{l(LsJ=0CIgV4?zv9Ql?TGGZNNvTqB@fRQvM}FzU)OA^K-_&o9}GIvXRbH ziHMp{Vj7YV~wuVS#W6A%2@6mys92$nApDQasDjtIpoYKx*h8vm`f1shB_j#eZ z+!&Q3OWFu`-Q10QHPgE@b97i^p*F^~4ORQ>g4Y+O_fVK@GKmjqr2g z@GAe|*>8oPli7Hiva7@K35_0wXE%en4Xu=3*OAJq08ovusGP-hvY9EoliGJtp8Zr4 z8dIA+Hs)VWwO#h`RJlT@to21NI=#3zN(?n0?Ef3>dEN~ zm8en5-#S?8L?5)EtHU&=IqDm^%K0#z4=Xy3e`&s*+`lH^%7oOF_N%w&5_n%E1VOzQl&&VQxKruo4G4p6Lf- zS^LiaYlbDQ{;Z5+xS6k4ZGL&uv}KG(`L$*;th^4h#_Ipo8#c49_rmAP%j5+g&N^6A zqa=O8qP_WkI)~tAYanAE!~Z5!`{5OrUUH+G{J>P2itdC3Be5)A1ZuvrDlC!Jg-`3Y z<*b#2W|$X|gt9=!P&V}cK0@6^|93c0cacoe`6{Rfg!#a4sDuCi^%azfm-y7FbY3Eb z{4@4;m!@sMIHMvzpGivRzc$T!Cl?m%-W}E_a_iOh*@405=7R%P%tLfOdyMaH)y!VwXI!1lt>gp?3^n0k&k)(dR0%*nLa8^I=DjgDm0mkp%Lz&Y=^sJEO;b=n3ikWkii*Ag-GyUBzv8$x zfC6*W&IChz39_MSg|XW`^=uZSk4N7+Y!u81fhf1IFYuAs+Z}5!?a1H;sVfz|=f-HmE=~F(LMwQjo>L%7{hnwIwiurvL zPsPs6&%uZ9uMB_tCKQ~sx3>rQlxsI=zQCc_7(osdNqF!rX&CQ)C&sb@7CI0P8Ua_W7QxIK`E0TDVm4E? z2xG9`Sl0ha2VPWHx~n*l9g`iA&BzgO0YcmS*9%P*lVr)GDOhL4=QJSTs1v!1^-8q( zmG9_?-z#rz*2SEo4Sz3}MGylTCUyo*0^{j@9{!{6uW{y{m1S)vo7BF1DSbaMFq4nf zL@uf*icta4E4%d~*$4YJdY?N448FF~`vbe0#L#?jJFqwmHJ}4pz3vgGb@QcKXg4@V zQi?Nq3DovhOmuXq#L45h2joJDz_1HzrMpp-x*zR!RI&+MPZPB>1VMLV2=X!r$^*T7 z_wHcqBwxBOeSd(V{VR9W6wX+NM1#Q%Aw(|f{0+k-HT57oB7F23mD`yH{wlB7&W^vpV zp9i}kyb~M@?RL>;Jz3M#q&J|_Ucf%8E8Zz+)EL#R{66?bE3i;1U@wuI zPxjwpiyUb~$0k8KEcUIJ1e%Xbx!(3WcRFr=n%XoNr|zC#aK*@jVBJePH#itCVPV>F z@Ui#+rmk6eYtiGu>_fg%^2|d%`RB#;w?|7z2Ns_M%&%WfBI$xHjQwx9G7}dd?T`Tr;y%cS)Oox zrO$&Ef{)W2T79N10zX`&s-bC@qQUQeHJ(8vt)cU8_GYfAzUrSO7*jFK_|hEQCv&7G zZzQ+$&H!*yP$vRyx$~{GreI6EwO^a~PAA}KV%^IpOu)xInJa!krPb|!CL7V& zmgT0-bP|i&KxeZCV@QmdMw`y=|Dr{7mFWLWBpf=z%gh5CDCv_CfxIDC;|^9mGMT}F zyqe4@B!d#M-J@!gVFoam3Wvc~w^khE^@XvQN#OuV)ByFgQy)v;W{&&_o$$?{PzAD_ z887l^@q=Gscpe4Yr%u-pIpka)LHXMad&~BOiX6Wk&7C92Ga^4L+r8~CH2Bdb)FC zHXx-;hMxh{WbT>kUF7y8a8*^+S?ubS(|ojepfsE%i%7~DlGhV_6`wx0)<)vx+t6R& zu{?dQb@#62;7)%fMDt(J((1&%uugh{87uWA4(gE$)LuRD6#!2I4U4$S2t*ziaZaJ{ z&7z>v2d-6LX$B$eu`Gjti=E8eejv;}6N)l7yt&;4(x=|M>4I*$I5uRHZ(tFA|W9g9s z(=+F@w`rJX6}|U*>z^$lR2L`7op+wi69DSQu*x#@Pq%u+AM8aCYqTp2++I^1^?b z*{hD601Dym`P*Mn3y$dsyWtx3pC-)B4$5N@2ax^AzJpFUQYbOa2fL7A&~>3QgF`8l zsd)B*j}*!T8S_u=BGYJQoJ=N3tpIeG;my(WLLo z(u3i@bS5O)Xq^yu3$p-w_wpZz$U@4e)oSR?8Yk}grTJ92fODd!DGI~PG958w(H1x~ z(zU_Yt__{78RBd>X!{KmKyHinSc?jT?LoEaM#M+mX77U2AkHYA`uru)*(llA%`Z^v z2?bdAVf(p$9g=@DrQU6g&peA~)pd*CNkrJI6Jrh`0a(0ckk=Ev57hMd{+SP-HF;Gq zafj{Y+LjtIMxfJ{Fs<0OT{Jvt*;-bqRH)e;HP4Zv7T)6veKBx#D8eZr&>Oytd9x3}6+I~9enn1+lT~!XeNf0wV=d;JpRC;a1vl%|}^>4X&)^Udfk3qwjTLib$ zxmeZWSWPNl7NbNkPR(+5CT2s9K?ep@hDr$Ucj?#QgD>f^To+iZ{kG)g->V+%dnU9m zg63Qw9iAlW`o3@_KuQYEBU6{KfOZ~G0d74%9J7StyMQx4z#$jLLC1Nyf%~NDpVW*o zC%@G!{Jp>kQjEe*_{aJoVY-dmDD*rdDDloYP!^AtmX?r3k|)0ZqNlNX3jiJ3Q{khd zqhsUa&x>R3@H3Jcr6rrSAeO?_X1w;?^@kdihq)@CjKd(<Nw{dZKNU(36kj*PgUxyGLxyV>j0>o)(EKeRk}na2DSyE=euMi{=|v6P#l%`1F5rtkDv2NR$n{f= z5>_35daRjeO+vi8T|@pJ0u}>E3?b#@&SyLLg!`g>^RD$G!O7iX=Asfnhl%>}F#L$n zc_D6X+1EviN1jG$@@y5yv*@Vu_9{KmFn&d+B7q}+RLi4ZzkZq5dis9apNUcaAXRNJ z|JnLbR-%kxB=jNeb*E{N(cZ`vAx2k(*Ms%|TbL9#+h29{jfmM7Z>^j1NZK6gsPZi9 zb+!Y)nN^xpwKL4epKsmSdjzG*`08hz&0g9Z%1UfBg84ATub-5YLFvidLCa)N>WS)8 zt)P(-SQm3$f(4#@-Sb!d!f-7~ICxF)Hv138 zagA+iucQ%vIPxUK@5v^#Mjm6qFOVVFr@xSP-uuC>JNgkfzuwnp7j(@0~& zaV$0H1eM}O`t1^?+Xx(#C>dwoZB*Aga#**^mCRqg00?&z_jmXjL`aW{#y^T6gXp{7 z>92=*#L?vHN8b1e%YqVNV`HM{ik{LF7u(9 YbXhl)3k~A7xq!#k%E7YQ+%xh20KkKHDgXcg literal 0 HcmV?d00001 diff --git a/train.png b/train.png new file mode 100644 index 0000000000000000000000000000000000000000..adf944cdc8bdc03e9b4523629ac017939b6ea32a GIT binary patch literal 38332 zcmZsDWn5HW)GpoK-QCCl(j6+&B7$^CgVIQMhaez1bV-+Vm!NcacS^_1J;VQf@9%!N zA5hMC&Yr#3UVE))t>-yLsH-YrV^Cng!NFm_QkK_*gM)_xKVQ*MfKQgj0-b=r;GH#< zWZ^3QQtbm5NS|ch%D}-@$6-F2AOqLvPRbvg;owM^VL$M%G?~tT4^6DKbzQU^texIk zxR{umxR?N+!NGlHFtf9EQg^g=aB*f3)&I)i!63@V#o!72>BIGWCCLBc|9dU)@_+C6 z?|#^|fsd}~q$eC)($*_^8Ep^4<4jclkMHIo*zDsj&V;fb8sUOTNsr>SEc+&d%l(IQ^H@*}y@kFFVM4jd@ zm}h!LOgDIa)gz$^b#S_mz{sJrzfasxSFp=5ql~Vo`bVl$V!ZG;M8f9}Cc6 ze}y2Lv-L??Gi%6@8xSJ@aWbR=ENCLF@H9O=&BnzQJZ}dwf0+$vjJu<$dkW(mTZtZWP$I@ zur0Fj5MgdDHdg}U*AN5n2>G_Uq;jkpTXp{F$e2@ISk|ZWQ&jT^1qF`PU-rU~)4vbr zyB$)2EzondN63lN*$HZPZtXzgTq0mF7dcOpmjvJ6V2o6wNBK$Z=@5HYwV_CPdUGec zi&jY_2mRKCmvr6TxT$=%e*V@_KKMp0nGaZ)RnUb{2i|=-gfCh6;k;LoG;3iY2DjyL z4^3f>B6p-RFAtHMo7)E^W%&DCncw5%uGB!K-;*s;P*-mnM9%dz?BVMGg;HzRs#9Ya zWh9DU%M$+MQ?}YhpI4LzItY#Z-Xlor^X}0Z-JjfnDB(=xsClHIWPP|7SOPC{9DJu1 zI_m0N)C|e+*VP&D@HSQ!w!899%X<#qimhSUfC-frep-I??KU6DmLRm)M|D(^&MFjw zocn)to3|(~DM_#=q@bkSZr<21ZED^6_Qq*5M)>zA(yQ67$lpGa*-NShyEu)?Pi00= z95ppHPP>`zEHhM|;xus}-{LN#Qz3Gt*0NPA3$|Wp-VWNy{jR|6&qWTyZ$5oelLkp2DC!l;2HFX_qG+}(*;S=rekc}VcBQbBk8c<2Em z$<2HJNb7tXS>!wu*WaKln?;;=1`Tm|+iw~A<7jzfFex^T zuaD+aLDrhtpb&1 zGlV$;TxGgx>9508AHJUZrL2D8K! zF{jSp%dD>_O)sGB7r!|RJid_;h=_=21~=L-vcw=RWM6m@ycWeJ#|06eOccCXcWe24 zJ^!ia@alB}GrcdcvH2`VQpGsuN42A`b0q$a2oTzC#J^niIxL@S^{fTu50>N;L=Gjg z4*y$mnS8rLDwAvr8T*E} zji~H>W%6#$IpH8&j>qE`t5m{ynwWb9NM*(Q-`fvq(&vxkalRm9B^Nf6rUTR_MPyPM zVZyHuh|FsUEOqxn3(Y|G{`Nnc?II{GF94+(`6Te(B4!1uC|Y z*bUO$dMHEdBh%%NK$(|vO(m9(qwoKA#dTbLmy9cN>R$R0M5B6}8^!aj@7%KXgICwq zTO+@xD@~Qk_In!;2n23u5URdu&o3-Ag*@I3j*KMrJ!#*|1p2)E`JomClY}dNVDirL zZw7k$AC@-1QcX5?_AcR$ySE<P+A9b1lt$Uo^xEy;1#-rgYQRgLYa zqz4Lwz^i{NR46e}h~N&L&|cfRo2+uWNBR)gH!%uiKP2Kx(k)Rw>yPI$J+1TG%C!SZ zNJsW?cu6y_~SEPa6-b z&~`2#@IlIYa9;;)YA*NLkr(QK<2`aktat#gl40j|!l&~;gOj>dPq<|7@4lYP_6FbL zRwzfAknLE>5U6%%as+PT<;YU@xsRqsy((1Q%0(>U=Y262V)vGmWNyLfxaRX9>4QdM zM|*6MF|i3-y1d4+r-lapU$Im1kyL)UWF(NVFmAIAJvzGRbPuZ>6M>9^0j0ZTO~zQWSb<9n`QSm0Y;4unbEA%+gc@v(@&E^9K>_=w(mDmLQzSezIb06 zOr8c#WeLyac*XjA>8l%yed{U=d1xvqtLIki0icnG93>OhBv80<@lSQ&2pu^!H9Img z&}Ah1V{}=^wS@}TP;`nnPR=h{3>yWt?uS!9C}o@!1f%ENijr&3y`!CAShnJU5=>}nfvr4e zq(e;#7Nb2AFGAqgI%~)kbcJe(v|f}~aYo|x~%cvI)W>X;t6P;yA)xj|~^k#p3oTmYy2sK)(Jf+IPf8P|Hc87rnbGfn71 zTg;Q?Yv!k1wSTj)xOjtfV5q$EfXfN9NA{y7HFN#poWNc$tru17Bbq`0 z+RddIFl;Qm?jtT@A~W*LlnT-N5t*TpP4IA;bQ*o6)jyKUU*h3sOEdp7CUhN~s!$c# zl8h>7@j}*OSl#B@EVc$aouiTQ=a*_2HWj!qIj5zg2%-)lBzAS1Lxn{G4anv1N;Zt%$d%QN z;qcOPXIwi_wlfEwHYlzpLy%xc`sJ3Usm+igTDhlYwR`ajcRW9W4i951UtbEG zl>S4tjhlx8C1?S!&#PEBr8jBA*`Y&E>$rYwtpCWdv`wY^k+BC3onCI?b%R`aWf&PC z|FWS;0;m&C-F5l=5NqS5?ctQo+s!y7RaG1o)r|0mlNJD{QgH&d_*UG2*H_D{*uH=f z^_6rL<|RuT1n@YNbQ4oRv@EP$LO8Lb=c0UoFD(J z4)ia6TkFgeyh-VHXxz+;ll*3QKg`<^jVpO8J+nC({q>2T0|%*|7hH-$;K{(r`36El zPFe>^Uwtg(GWi9CJbZpQZX8B;D-PZOPRI8G#5l?OVlHHle}9Avs=xf~+>p|h*DJit z5WNvUip^)ttHl`ae{DiM7WLu`X^0QYIX_`T>R4OlgV(l<+`{9VG4m{>#{NjKlN%bv ziZ}~XzRr99tZ3`hs!RRUse$UDcrBr#)xM3Ck6+MdDvjbkWqB%+Tup-7w%$!r-GjD| z)%@WG{7$&uN*Gj?5>VRipVpMftaW) zH*O)#PecVG!}yeAeo`WP8}r4GFtC?45@mVgHu}SxoLB5&rbiDR7-I|DpPx(sI6pxM z1+WHB)O@1z>SV^fHm=%+O5erS(@o=Gb~IYrO?EQ^3*vVw^%8^#sR%C%i-IGE)a2w) zg(#At)%)2F2y@g^iM;=7vf=F963Hs=zAruEt=78H?)I$oPe<8|LMK!vs8$xFdpZ6{ zS4mo`3E8`U(pe8@(JyXad8kP}xhw(4tOiF?Lc+j7jEHGT`a z-A=mW3gM_3yu!!cs;jqoVKO?D%f>byvI?vyJpo011tgh6ro=UHe=PseM|tlFXL%j4 z-$|eT+Kdg#^5b(+@Ha@GHJK%B?2NaOo39&6Z8*}~(+cjRS z%gdXzd>Q2L#zHa5RDR@v_A*qA@`p#z%uB=qm%!W=q;DAOm{0ew3CeA2YLfoXIbXqVtwHJ%kK zB&49=E5p9hm=$pCgb_)ZBKLV(E(WKSm;>;XaDQ>Hv}J86^6I)xd~&FT{4kIFly9zj zf(i-5EZ&6JFL8(o_0{cFr5|@2B$A}Bqx+~>eh^}Ts({*Q!!xPE-=r7 zs#ODfq1oGNp^b3e_>(Cv&{i>{jnTo(Tw&60y=jD}6}TXqVO3&e(UAE4d4Sq|5Rxz| zaYLmly3a&jEXDktME#YF(T3VPG;IaxfXJp;#T(3=CqA+mWzMYdtZ?+d;pAeC&uk2BL2h2X7p}ve*oR zJw;VgT7@bJa`Kuj{)6+5Q9<07Xd@&|jm)U$gj;0RE zLGY2nQz|Q?l@Xp`rF#4%S<$^*`Li!GQKx6Tm0G<+Bti5O_z##!0k9=2Ttm($ly~Fj>0gX=RoaqQ7ZM^B~9a})M$0i(b zUnn_??qeJrN=)FnLKEM@M76_!L%xauoV}iqc+7Vv;n|w!W^4~e6coRctvjCz_2^oA zO0YgpUl{Rja#Tj+J#!tme#ouXveeB33>m}fC`PzH0VXdFG&Hs05)Fg6#oThhcH(uG zVs5jiiu!VbruYuG13*s{3zrL`1lL@mhn%XB7E9Ge>1b~+7e$YnbrDj0XcbtwZm@p) zbwljA8LSc72SwBTmKdp`MYMPzHclznqI)I4#|Y`V|dNR7czaJuEp_ir+B3~Qy9<&@VgRT3rVVQHJ0jhtgD!qu`+wG zUR*uaB%O&VY z`3OkFJ$y>Iz{11PIldf|BoV*FyKK^!U&5%gr)pK$%~7#e=>NM5FUWxqSNZ4vj*9W5 zMKqEogud>N`cAL~J_wn3(vn_~8&PnvY|Yh`j^dm(7Kq3usf++-!4KZC9t*Fs%0Qp7 z5)%@<0kfJ0GW&>6Tnu#4PMR(@zXyk=6DYyvF_IA02W3-FHBod+!;X=VZV^CG$P4j# z^ma*qGgiNE(5u(TdJQ=7f8%P<4Hp~V-@X7f3(hU?d~=3yHfb4Wcrd&WZ&{nai^wST zNK&gB1jdcV0}O#+J#}Cd?ia@Dzg(V)rv4ylCdu;W3BeAn`$HqPi5fQ1G;%TMh?f%n zc^*N)JdCGo;Q7671l`cIkuI)ypvZI( zR_iLC!2FO;t}hiIiY(^Na5iTa;iyELr`nf)$>y>V>v*cOj;$C{HL$jD&;FCTg^zy8 zpL{)27jGr_u7Y@Q<&4bD&I8k?SnxB-^I4(*2nnT<$u`xLTer*c$W`9oRg7X{r0?F+ z{A%;{$XG^m_z*Et%vt@ODv=_Xc}M9_vpfD_iP}$~9y3YJF&PPmrhVQtZoZu8ZOp@6 z-s`R-+S(>UJ9qEicFQZvBmU6kX5N2~=QoXO1|~lX?|h#7ATam!P{sShn>T%b(NP~DnM zu8f6R!>VuhT=!FE4Eu0<47h^nGYIx17B;YvKeW@)W3>gmmVBogk@FG%(5zhh&k#7&$}d-)AGy(Xb3B+$HQgCbK!Ukp=ARGnF`917O%m+M&h z*-u(zpR!R?L@0mvT1c;-!__0JM@KL|GbvWa=Tg7Ph>#O8FpX>SO`jSJ!lg0y{wNP| zg_JwJ_;`%~YN(ff#}L5?d;^)KmC5tnU=S;2!xR?Cy?JbEB|=?A9HkHCtqr_*!$(jZ zh;h%E<-fC+12b73@rI+|9)F248C)c3w$5}X^8*6fQ=@s$CraGf4(fZJtj*`(^8%>h z5SgwB`%}7#CyjfO%h7^25^t=qEc16sy7Ff`=hYc!fxaW(hQq04o*e7QYCviT;&boa`+D- z$b956d)D?cAg-SXf)rBz&lW@@ftoNVXjFS>iZnA=>o(?LkCS;qyi$*F=InLo2&CWZ zw~ayMt6^uR2MeDI(i8k@>8@mR7(8w>J~kEYU<9YZHa0O56l+?yKQX?bj(#M(N&9>= z2{3XuSR?+sse+6Q{SpnY4P`oh!t?`%OOwUkcoDeCL~JTt=>*1h@yQ`aLHT41ATHf- zFHLNOlB6+bgzLJ?`dc1LJ`j!H7BsoFO|Kqlx?)ZEL|A;P>f-2A2&~lJ-FlyNd-fvXQ3S% zYy$Ud05)3zd0;CTYrba7ge~Q2g6hT2cftICxqphf^eHLMI~Nx}KO|uloF#l`N5b?OTbTQSs1#n z9Bu4-J}pwLhbWLCVlYIK&;zq3=FEtsriMqB=pt)gH~{wt{*P}v0qY>-e<$-*iPRe( zyL*gv=47}~8Q(^>P`pnvh!Dj$hkJT&wqHHqIYkvbX2rB4}`Tm!hm>;RO3Uv#ldz zd_Olj+>Z4g??(Olkx4)Lt4ZD=0ic+wqUA&sBaFo-3 zhFRS2Q_9@8DxBw|g$Mg$I-o(&x$e}-6V(wS{Bt%BXt37T*I#^f{3~(u(WA|9$!*d8 zxxm0i<+z`_w-BLShDZg~*{4Uz7(BG%|B?3`eIGa>ZjsK5=8<)erhv#F1$k6(Sb z*c;*lU%6m;{-cRgYCr@N1V8crP#GK=k})*Q;`#uD?KgJhp84sQsXA$KmE7rJO`DiG z8hI~$@6A>Ij0XQTR(u_bD5DVP@Se20OZ797E5UQN4-l~1P&0K9N?-qXcTF^$7(eL_ z@DLq0JHUo^w|f3N1Nu}{jZ0rJ+pp^@*V`XX4r`UOOOe|lSCyMUgRKFJHQW1(>F{6= zU8`TM2S$jHL}ZO7c#`AH&8-tJ0wI#btCqO`QpM%QrTfs1^d|h7yeFsXVS#KNlai49 z&V^=W(KN-j-v9pnntkO#OG}Hf?JhbR3!qDWo3%7In?FGyY}(m8e^ARiP-nWtepy^224@+em5Yl0jtG{(OQQNp66ZX$QWB4YlAvmihn)bWfL{yUj_o9l>85%Mld_y==<`K>PviB zq{-Fe51;D2fJYE@HwETFOZWNrNSd7_)2&7Os=Cu)ii?~1zX7lie2*CgMGMq}kXjVV zO&8^s&U4PPN0KB@QRU|sGH$stuI&frUNA|)f@LR=j%4yr`=? z@z>e@41tBPfSwJhz_G}pgoFeZCALdXpFOu`yqLa4GrdUxaCJVose}fg~Bi{1d^P6cYl;b0cMKVrX!=_o5ct3A6vnL)Q!(nbirS+E8{pCFtiwIHY~Ky4~YTw0Y4|7j&vPNTb{Z6C6HFO ztTDIBoFN)t>e1jB4LK;BnGy+hE>)xen{}!D)c|AG=M)aaNSkKnrkXUE%hZdEZQ^$A z{v^ibZ~6{%a2#yF!TS@e11O?$C->#}lB_OBd7r}Cz+@-Vc+}-F;URMWxxpkZU|K$$ z#J~&}O1(!Nmj^mY0}kaIQ`@g%++ktHal8Lf4$D>?5_v-dXS1KGDYNo2Tzdrn(bfjL$%H2gj=a z6##OuBT#J%VjFP-FOtd^4I`%Yvz9<3>2W<{O<#VjA8TF=G3n%fJ}wXdvIO!U)D8Yt zR?>bqw&~Kdi+geoiD|&v+TKDuQAJvoc+TT09k4Io>zjPVPnnQyDbrX>nHjK_W#XHo zrevc7?_xs+?@npD&QtwmROi{VfC`L}DsjmFXhi#E$gP+}yF?#^A_fJembiIJrOw^8 zd-I5BMe13A#UP>gNC{hX(BPHmR6M`5f2>m$+Dxe#D%{XfrFrOUJOoQB|%zHkYYOF#X zhENM%a=ZwZ4~)PD2EuYSMq!2=&Xmjf`bxT;EJd80ID7;ckzY^Oi|1=WxGdMkC{YZp z_bx5O?CjQ7`+G8Mt+OrfL3;V-I}AMzO5k$%YQx3n11^pA`tiz3!pqCcJfUOSM&a{k z{u=9-dM0RmazQ%5;!)&o^pN^Y$y>Q`iOGl3u^w|ynTBbUs9OALFy@AuHzVxuQTfY6 zKwq(D1_L(P^#If?8it0k85vY|E3M=}BkhGP4d51TKzK2tqaI;{=VGA%J75S@sVML_nFq*W|LD`5lBU; z21OHzVd+s*=!xNlkL$^D5pz$~eZ18DGB#0%xS;#Vl=3+!`~mkGl}&GymQ{35zc=D8 zgEREnXKp&-G`f3jifdUFKVlpFaH?EO-;>*Sl5BZ$N+b zlatZlTK?+f=Tv5?z5$)lccka!#1s@1oNM1Tx&k8=*fm$7WLAMsIOPq#+zN_ zHWVN(C-nYCieyJ@{`qP!%B_2@=KmRm77NAVv!AQ0AQuUy$H{9nTyzbSV~|ED@Y3+0gqw2?zo?>Je$%|8{+Zx^z^J5_0!PM z08CL_W%Vw}f?|ErGN&{u6f*yY$ZbxbC|Bi*uFZmSN|y)#X`C>N9u0$gvzzbkyd}-e z&DR1kdHmu4PB zP$h%YA*;DqntV1fsTyjYWWBL z#M-+=0f8Fnh+<5#MIMWX{Z{1btR~0Db!y zK3z`2`u1x84t)){FR>o|38=rL&oW3DPyXwBVTK_dzbR66Bm+YcQ9-vlVI7%*=cSPq z;sRzKm4dcj-owg{%@Kjo-t82lSXkX~v+~u^G)}J8W}4%p*M30_M!*7wdQexp`a9z9 z-@pIbNwej1ToLCoXeJsJ{}=sHiiGZ5cn`!j^w*!9{>a8bPP){WcVeK&nq8r^2!le+ z$MiW#!nQ;T4{h-L(Y4v*(7E(x^v|E~W@9&~1kN^~mkvuW#V-aKFT_{xXBxMNx*p`- zVS&<&EiGASd~Z!eI@LdX_+WZC=btYr#j8Yta250R;qNi;*rH~_`;REhutJylKM%gP zP~Y$a%D6$1r&)lcTUgM8>4$&{%eEuz#-Qm-!4ZRUu4)!E(|v_~0LKF$5DC8?ZNoG? z2524sGEX}l8uRMvhy*_U4VHCYIZ=B5o>a(TiLHpLP+9^{5i{{3dQe?deSQLd)Hlly zX?~DH&mX;=LRZW^=7%Oe42hfp0XT71mGBM*ol|HYu!WcClpbNlPU75Vr#u5bNJ!}S zx4@|>?WWk*I4p0|rap~B#ZFrh;|3ThNhVG;aD#wqF<&i!u?q@>ZT6djEL1>j)Bz9^ zK2+8uWP%z%MZS)C{PkEb2w9OXZETVf$OTRE9HS2R^-e^Cq$H5h%3f0L2kZ zk7NfVt@X3rK5kD3fR;jhD=4m4WfgFK=jo&{Vs(VY1Oy8!&{()Fu0@=jvwBEv9!WSd zA&es@WBCj2^Ya@dJT2>5fy6he^}r_zXD>++VGMmhc(55%>Q)Pt`L@-Hz~o_up7?9& zT;c9a@zTof@YT;{%4ShLbzz`SCUX^Hi*#f=-rLC#m5(L!&-xK>zs{kTj*BO^kZoRsS4mSdlR^g!@LLn81`RkND`$i*nsAS|2dB+HOcodBBREQ;Nppxe7HQ z5I|h!K9A`C8p$fnSd_KeXUGG$skO&yb8~ZV+ImPS zz157&fsQgZQry)&bPMb|d~%C7e-G#Tv^~!@H1us6{?(FwdbQ7;0Gop!X{>-lh^E^D zU-CH&Bp`+?jB^{d>Lz<@1>NSDN^ihg5LfdI9m3j7QUql78`<7^Mt-K0AFmdjCY!YQ z8%+^@wqZ!it|-=3_!2kAohtT!=MRZx44(P*^F%RU+ICycSJqo!z+@!Bc$H?%L;nfz&thU1! z9*}|;pBU;^zDxgnV|T;k25T4eGXRmAL$R>JLo!=U2=0hFl`*O>KgEC{lSXPO%cn1+ z^)Y@ls%jKba7*s0h<2l%m9}pYcR6a|IDO6C^*@3EHU< z;U5wUqLdYpXX?+=5nT@Zz;a^s05H=C95i^8(9gPKK*3pMyT0{ZVD zTD$Ix#+BapJogMdX%g@(CA*THll~{~pux_Q7*+@dgE&GxRxjXFR&fiQN-2(R*V=7Zgqk@$TBy$<@)o)Fz~vzEy?( zkdBZq2Wo4LTir#~^xp(2)h7ubVBK=F1OW0LOqZPlB6kd_+b&$EzkZET_%=Nh&`oc)tUmErj%LN66S6Q}8^8uKWP3*u=Q>vV0;om zZTb4wZ7n=dM+>k3`ExW~Sgj$qhyhA1=C%e?Ew8u!`}^joi|W;2i7>huC+j;8s28}f z9-F2H?Vc`FJV{l8`bPjsF}GMRM#@VW?@Ouwb<&R0_6Kvsj{V7^VFwpb4zA#J3!q#g z{K}_9@FABMVs4q=W%B}D-9(<_w0hDyV->(y3CB*30D(@kTUFPb`` zzN-_()l8~n8?$^H#MnVKbIl$8QomQ=;Ug5rnp-9bu$@DJqQv}tM`R>M4(IvC&bMy_ zBGbIimwdLwM4t-j^4}`F``1Nr&d#?GsiFH3zP<=K;ICJ4&0co;6ToOcFGevB-xu1R z*Edeo(bUuw;W#ob&3aVftyFkxtmx`vYie)AHkLlQKFBT|IUSrFD(WHH2N7?C38+#{ z0f_ilWOogg*0Ut9&i#NhvE|Y}brvV$MHb?b@~M#VXQRm*xgx5)UFk0sY+k|kp2edX zAQCvt3s%qNqyP(m^kAZlWS_@uKZqx}FQ8T&KJlG3a5n(F)(Sk$F*xKVT{)Ro z(1hHjjx^r3`RI#drYHI{WE;&(!s#@>8u9`5(osJFkSxTr0a%SzR}Tqx4iEBpj`OAYAxY>32sQ%iwXSJ%0)JfXOpgvFB-mEIZlyaz=u!% z1LqgT;kt7Jybaxd-gby#ZMX5V=pX zOgEmq1eX>Nw)Zwm0=xrAuULe5&OXz>klRs?u;L9&BWt24hDZQ~C6}7w?9hvXP+um9 z_G)%Y3Tx3#K_`{Zm)e*yPW(F8rnPh0QXHdzYA}}aXjo$ldDa1U#1$-vT78nN!5xn9vuqfC6m=U*2~&|-e&+hLSfv= zsn6-x8r#)hpEs*K&P{`5>E~y&vEVR9#s`Wu_*6Y0T~=7!)R|}lQ$dj(xmFtjPj5f^ zvj`&Fi|NQ>yD&Atnw)vx;UhX!i-0!?1>!R^xdEBq3bbiAYmt=?Ao3B2EIuLVNi;9h z{PKaP%5U*QKz_YX<)ImgZ@(O<&Z_pi7q&1B?b22$m4m~>f+9b@VtHdGyr{nC$~_?B z2o>e2%sRSJh}-y0 zuU-u%?y^-p3=n<+8uG&vSM|2D-KNz{GdNlBo)85Eg=cV>hxE>A>0Lj%;gx@FC%_5rvR~RdHI6w{F5S|>{HPec@Bzb_2Rb7FWSE?r3)sn zdEqB8$7d$d-DfS?FmP{%f#v7{9lww*%MbV%_v16$ioa-aT5~nP(}xK!OhiS!KJFDS zhw2Vy!vwEA(Lz86k9=!yciyz>z|89WU=8~!Zz&sR;$V$e7cFiE!Qik+{VDSt_U6M# zv6*~0bf#SYJf-7NK^6CLD^V@CtcJl;$+!mjkf(Hw6~Rbk5@x^X7hiW`tpc zrKOMTJA|yN@_Kp{fbfgp>v2dwsmFOA>1UwFX!bjaT|KEiy(X;3#6z40)`k1XbL)+o zx#l)yBDlZq^m6U`e%-|r4`bk-c|9^Wgqag{=TUZQMyF?^CmS| zBgwk9)8R#KUJI(K3D(v7?z|nUItv<9=PJyZ*f{GXCO23QS&#C}odDy<5Cstv6|M5V`(jOYxdQ-*9sr41>~~!sFK#2ro1w=j z2K+Hq)M=*9TsCmJ>wVfU^0&n%+4}yDPCyX(Z#gAcvB#8KxD*J7`X;qA zvO#dg;aQOfwKX+cH%xVH0OeZQc1>~8c4O#$yB=0tQIT8@DGBco2Hn63(&Sr*vPaij zhnBqPb>Iin{hs6E~Aj&cX|=Ch}$n^p^K|3<%UJ%!zBYqUueqf~^%Zw=6K=q0I;W@Dxg6 zvoyQ1!lS>RSgiF94qa?|U&{~r`1m+le^eCUUmOVrgV+4aAraM}G9vx}Zm!?M#XpdUemuWqDZR{lo zpWavRxf)wx8sEo`xetd)61O;(SNob18bTH%Bd^h7(q}JQy69jr;9~vaLSs;;k#_Xw zLM)CTB28ale6Aeni!q+Zr%~~J4+mFngz7n&#WN){a;K!s@E5;w9DfF{uCiQqoNAE5 ziZ3%v5~v1>$et@Ujfam9dVQpmwt~JA>4HKyb^e7>nQ&)TT0mXkmzO;o8DcYx6btT0 zu-a>9F1XK+h(H}1O)mSEMCqM2KHY4X8O8d1)OXbK7df9vT*6g91{bDMFSker666p0 z!ioknYS>o|dA>@+ArD2nv_VRf|Ej9M)-mRn&rY*dL=peP=5)5Fk!VzgtAMOw23z`5 zy>8EUC=fdbM7bb&@ey|GNoB{T5yqCWDj&NsVj8$Ujzg6)+w>X5zUgV!jXdPprzH6p0>=pUcP zecz>X_WGfZ(BsOM3#ucEj{?Gqk!qYMC|fUh8q78u>uBhU;Lx=HI>Wi2osR-*a71~a zTZAy4r4HvEVY%ruH4rr|j~J zRlS?b&@*#-GIHOK%VzUqhum2x+b*I_FXIT3ynO~)v4Xmf18AE9F{!Ec-4#5@g5~Qy z{}zd{Rjm7*h@#~Nj{_UaacuI%suN)8cn07L8S~SLsDy19L?0=%XFx;aPxn#ZuqmhEuqce(BE{CiL0g3=RySyX9+pfJvmtm)q$ zJ#SUAh%{X*^Rqw)x)BUZl>C7Bi7bEuVo|uLuBjOaaH&%#4zf&M9x>N0`{aD2$q3ba zt=}o}E?V$#Jo`v&^Y5Z~{ulQ-S?ab~TFTvQo8xFQziZ8ezW@s1xhL62exc>IdB=e<- zwmmsql1BBd*;dF5q|N61qA&@--L~Ndq#E(7?F7fRSxUA~qQ%wH8)&ewl;6D})jwFR zudZPjyAw+@)#%zyc^r3B$v|+?z50v#$FaV##tlX*L&FDCLwV8C^-2T8K_S@x05PHr zcsQ0!R>#{)ISoS|EEY~1H(~I=W|X5iS+{6-@FQA_XgrDQ?~=g8_*+W6Q2K+Q4f`Mp z%TX9Ef-Sk9&zdO7`~!cBdYFCv^xQf+-As0_UkpiU7e=G0g1ivzQcAq3zfyW+YG^;< zGb)EIHa>|+;G*W(>U2-qIsImAqY{p!3|quAWGqz+c!@7OGN14b(n6P2108n};nQs|@uhO`9MwR?CsW$vzP?B6!wT*_VxWs^PNBPHYZ%EoPR?F1#uUCZ--w^g-aNmBf`QU zK04su!QS3pXWM;0lpERRe)*6B?ve>+HH6~!aRm}b3K8!!*Ya6kV3CQJ&o#MnFG<)( zJa^s_(11q^2Xngu#U&`Zaefv@2;Uz6HskQn@9?6=lb&C@ zR1O0C%8b~=!4uyl+2yF%QO+!mQs!z58!8#5tiW$iQp4hdzYQXh*2g|*Y=vjm&i+4K zon=@YO_YUkcbDLv;O_43POuQ%g1fuBJHZ_S1P!jiUBdvuW$>Wee7nE)=R70~-CbRE z>(o8(S$Xe-A(R8|u_-6$Xr1ZnWUeNP{a_qdn%3og$&RtHRSUmvjkS}aQ7owsVwH$5 z+HAOz8S?Kdo?j{N8xa|x+l37Ub*>sa{uM+R#wwksYovv&<5?)qG*w0r2>Qu7C5Zg< zyw=Iy8@n$MA21aEgm}N7I}t+#eK&@mhQC`k)Ji0DFxymQtgu3G{)JM^cHwTZMq5}~ zE-_ygP^HY? zFvI+hQj!D_BLW=)rR%ECn4Jr2>4DA7?LoB;-4;)(nof(_M zzxTp=1NaWCJng=Peok_%{`jvV8$m&+_s#I8s)w<7dy2xCtMQbc?smcT-Efh!lL7Bj zhEqRiTGpzm0GtJfbFg37L=)Lc+Iu+%Pw~d7oj43(duJD0z)4(*iSKUzjPRPj_Tc_L zMXdnz80yl+Fn5lRN-<<7=`1FUQwzz}d9rf_2e&GbpoqleO6WzO?Ls-Hgp@7pgAdt@KTFT^3iPBA zG6NhI#IY8+lEOjLkp*9p)&*_z3>IF_&FddoqeLG3r?A+w3{!efVG9+iM3g?Mc1${7ae7IZd^&mamzORVyzM@BY^#R+r z4{%iC&&0&UV89Uu`gc$;0>Cu>+Hn?g14Q6>`o;k3Re;G~brqmzf-PR*PXQ`grG+*@d9=+6!^ixzYa~?d%s|0|(Pv zRfWW}>5A=towNN1;9U#@0#IC@fCueeq&X{r)9b&3UEqp`?b|lPfm5pyPe20%gGb2d z?pBb~uwbAdtyxtO!o;JPCW~fbR6(*414$9B4rEm)IT!?|P60j*V&+cd=6#h;pJ-hj z{D@!gY)Bs>fMScD11RFNUM&m!!hgCPm;t*ufZ;F*TsVBmY$R}oMspV}0qO;Giq!OU zN}$CS7Z(=*3i)Ycs*e4_?|qQU^}QP7-A~oHe5bKAhVkJnY;W7lnA=!$e;6j;T5o_g z`p5(R$bc2bMHAR%-v9FjbYGy?le9%Y+lUm9 zDc_agvideo? zA3XsW#3j)0shtjWn(+Ipgq$4W_{4+-(0(dsXZon%RTcbml|nP(!$T8{C3qC%slq_UXEAMz} z+x900DoByKpMQz;N(1Q4g9r(v^MY6B%S2}n8{J=MR~a1XUjk}pLz63A)bxchnFKtF zJAa}jH5k}YAe&dL5xmRperz?M`dO=Ye^-rBC*FFSql||~+v%|k9K_<$#Y&@uLrE)S zk}M3MVAtXJe>@fez$y1Nmf@s1#b&P$SQTZic9qs*ITJBOw-a%!7L(#z4}3GnoE7>{ zMB!tvn z+XoDgjfl%9o&Jo+^h82sKL;SF4cH&rYz6;{X&b;f6m2%o_xvilk{o^GjGQRsz+6)~ z)bt3fi(hNRXg~||aq@kYRlXGjn4kb^ppf`*V)*gA{kk5ZJbfozVY-hJ{)@*i3aTYU zl_xZ-f*#DZyF3JXxSfMfkqq89E73{Z2RNlH9cZi;PETlnX8b`${yCz1rGRj?R1Yvh zu_NZ4liM8U>~$2i;86uZEY<5l7c^sghnnhi%NAj{h#lBVysV+`xVT+dW4xJ%I^WB+Ny!Cu&#)7{k; zc+heo292UQN22=LcrE<@Ev|t7p@5HGlYpg#%t8-t?~A3GPcyy|^IIa0>Ku!`pE%i;ac#il3&EQToos%@o{XS7=O0caMa2{9q9SJEl^-#$DxCzLz<$zrfH-vIE=>LQ37|ED7-|JFEbClFEuvZc~k0<^|aBrqu$`#z>QhVq%tlUivjGN%n*Y zhcVPly9DxwqxnmodYJy(sQ+rvAfN=eL1u^eK3=skVL9Qr!`l`4LWDLMb*whmTgNwE6uz7%uwz*H0a_C$;0vqnT#~`(p#HIx zSxw^@lInD-z5D+xqaSD)Uln^1WXvz&N)Y<#VS9P1iYZ~BJ0!k_R+|1w5@)0M${fi4 zWRsev5^ize<8|I}kjYC%O1kT!oN|%$Y zNe4U?%>oj$ad1;s42IID;p}_kuz;uXeyAd`hKpD7QVoS`Hiij= z5~3idyL-F&WHxf)b|_)bU@@48Z&TaTMc5+QP&iW|4pU+Dk(~WcM;eF66=Ixwil^_> z1VBLno;6@Bq5WIFn@oevDxf3QGlwQqgUol>zraa0KG(9+a zv*0bSq4O5Q!?^|B&cp$87}Y?Khwy4(K=&19ZVv=h5*2-aG-32?P*+$v`S&j$yE?Ut z+;$@nVErP8JmKC4KnPH*-Vle#GNCm?#vrqU(^iMtA4Am*xq0yEa1TJS;H-A=Mi%|V zbbmOL*25~5qgjaobjHkEQ8r`)&)9juS}be@@2)h#jW?%*aooB6w0KmD3;O#S6!QKj z6JNbD^c{v>X5SL6DC+ADs?#ma-Zup^`rgi6tA5rG&fbjk{F79!y1Wu@!s|XbyfaV8 zC_6SOHH4_JbM4(|ek;$La~yU*T>|Wq20kDFvq-}%!Y9#B!D=ue2se3OUledFt*dCl zjg5OyTr9N;OGcu$pFOzJb?yuKFr-=G2E+9ea6=HJB}a^7`=yM=IuzfJ`;H)^`EMMN6~r6!OW(5-0(3|MZG?}r6pCcKmI+O3BgxIVDkCE z7J4VaqOKb+7LzhT^6(l)XJX@x;Am$vV4SZPqcy}bQC+m=ArC3zfh$#0?BkomB2+ub`fEPOUA|_0uU%Mv?DZi0LcC#18d&~F z2-AgXwk}Fc5Uo4m;z6}nAGB@60McgY5h#-Oi}C{|z;HMzlKuU(MqLyYp<5j7I>RV| z^LA*s5DK8WUmFdjeRYlca}#!X3U2^D%0^|?*Km3SSvL@HuB?Kvg7KxLqX{))T-Yc? zptEReIY~z&57GCM+FCjL9xtv7Z(I8)i671;M#7IqM_Gbc9EDM&IZ(cSQlv$WmByZ= z%~7QD4#ADy!a+}U;6yUpLFmu>UEy^h+)h+&ih4(0aahDfP*mWCIXs<-EU{$>Mjl(C zJv-UE|E$0T&cd@HPzxY_jx(59Y#^lY;BnPQtqE&R`pk>gF^SZO{iD>xClBZq*xcOI z?eb(>sxv~48A{d_@C2Sx2^uaqLaNEu)eAiJ_p9L3EVpF8zhvm4$K$OyvQ}mbo93Rr z^HkKl^WOz5-CH8QOw|C@67lC8;>1^=Vt-r<{o?Om^On~ko~hB?VDvje1S8QW^M+qn zk#b9AhCneA(VUz%l;kV1D=o()rTgoq*679^r=pE45gr@GXq9g75-+ZWOFH>} z>4k(^i&>mBqxG`{Y;<^N4zp+t{<`cSF2i55+6FtyAZ1QRf6V+_NAcwj+(#=_JDpj3 zmI(7>XIUdp8pc*ds}@=>U=lEg#(RSVgwd-)7#Rx2S52?Lr_Z|a=&dc3r8byqZt9eB z%1Yw8Be-98J&cWFgE=`ETf{;oRv|cB9A_?Mo(3|=7d8W%WJ7S&k;DivFoU11$HBTz zph~A~Butb?OaxENyH3ojCk*f%ISvZ>A2$k|OR5h@qsj2y+7arm>HeHMIAhBYi z_P?6^ziV!)bd`3xZ+6_tI};BSE5?*j!#%_Z62l%K>t)Awp~Iz`7tL=;Op}Ntqp?OR zQh7zs+4duB9#|igznV6`WWWmzmGzo>2%yhoBV+x7jEW6Kiad54tjkF)16P^_X>MAe ziw_n2`PV@Z==N9piBXI8g{WE!4cOKv3Q6Nvb>kn7`42n4k=5`?dz1&Ys%Im&@ysgJ zFczbA>HAwSPl`2(M8yaY?^N+=IRhZf7totKE-FIp*Ay2NJ20A+U-?l8Q;Ler!&e~^ zIq0H&PTd?rCRRftStCy?y&9qmc`PG&k)R_?GsOS_S`;|y;65>sXn)GOzf{XJ_TH~* z;^CW2GiF%m_n1r+pu$CTT-A7T7C=sP*;g9A z^W$qtgBe~KbGN!OQt9HY+n0Ks;9*J(3fj&ashzDG810^af;||zSu3V2Lh>K`(-~VB zpUJF#=mSj?l3_a-Xt0=A!_Zo`p_QQ&hZ$neNzzeVL}FFP!yBWtrj!+PvPW^oo^$z= zQKR`j{Hnt6vJ>&dsB5myV9MA2!l-RXXBW+8-DrWib#S^pX~m8iydP%H5edcL*p$vI1VF4RX%{io;Abyq5`T2whO_$zYT@=8{e>YynV<2oT6*+Q zAeNUDX8b%9kybd$hw);-1ar8k-MvBx{GIW(&@!2(-*n3${OJGQkfv4ItI6Kid;2bp zeZ2f$G0&kWRH$0nY|V|&HD{r;M1e{N1A|xUli8}#&O`r3TMQDvvJDh2_bUn@C1=ZQ zFA%ux$54VcVy$d*W_lr7{_PevS|ZJ|;6Ek8`$d9|N>>=bdQgk`xDSU%0cNK23OCT| zH7P8r5!}dGyJZ8H`)>+06p%4w@TR<{|C9@c8Y2nJontR6WS$qsjDX?>K`hd^plq90co zXwjrdC!%9q8wHX^`;_@%&>L?jLLrmaRptl8V27Vy6OA%u_wbcOtS)nPib5V z*~(&85bCv_zjhr{Vb#ADPK$?D+L|-UZ(>vuNg^~q*w2rskgNB?TF?Dj%c#B68#Q`_ zwL%{J`GcQggBue)g|$8Q8^Txib{qs#YPNDU!u`@2AJ~=UAL60fhuN)E(jW$u4=%IL zton0>-?`Bv;y&K=z(N8gSe$JUufU*VH3I&05EB#f-vvHUrz}DQJB$z2xjFSh$e(j9 zE2Dof3*oWC)?Sd&q1fYjtz34~fWE{TYeQRn89kKfrouISx8x{2uJ{VMkbP58iWNF6Rs^kO- z$`IKh-j?^u3H&j+3nao0rUzWO$3iWR>yb`ALjB?k>`l!{ZGU_TLRF9AV57Rx`|^2M zptxqDrfyYAY|qwb+|GiT@zU@6EPl&Owd;-Y?wqI&#?(uOi6X0T{5ch~l|k<41G-l>`&n-CK>iHjiN=Va2BNX@=O3@;I@yDIk%%& zi*er%(@g{G-3;rUf(sWzBAtTBouWJLkcMaK8+pQ>9TriKJANT?w(e2X-Gj%_KZBH_ z!+c&}2jYy)X~q~XmWndwLkU&wtP&OlL@JER7a0AWf3{&o^Y%=VYX7MB@_dpK&*hF$5okqZM2K4bL@3GVtkIQU8IQL#(C8nUhsKaN5ME$TS%w;d#vu-hvtI#K9d~WXZ4O zz>u!Scs;&zXz}P!+dh?YaY?z1?KH(ROUX)$3*3sYT@Mv2`ZA`IbfpK4TV2g&D69Y= zD0q>j;H13$7UERD>X^x#i9O;-OMVGSBG5jql36TsyKLbiFXf^9#Dh*ZZ5(-8qgYDp zjbCz)ZsSC7$&DSR9xYm85M^o1H&uLM85U^(WrRz-p(KgH-v@t8wu+T*t` zvDJa@JGfECN53JP8YGv>q{R{ZpH(DJ+KQ>?7^etWw2HWifb%tbm)@Y8esxJVZRbeY zFQE1PgY+9p*5s&E5rdQFsBVJ<`Ieaq!n23)VGuDbQRL=hx)v z1VPVZD0v!EaClPGUY2gkh~93@vO;7vTmBZQc~#XO1X`aYMm0i|S*zmB zkoIXJ)0@H00<;fWxpb5uk{h@F3v1lzIm#`GPl`+6tCzJlo1@D@LBsJ>dFe2u+W55} zDDm`M$GFoon_6L!?fB9zNR!&|u5crP#0%;_u!cAeHyP}7>+M+WS}g{w#;^{Q(x!dM z|8LGhrxE>FTz*QTE|T3#W6`&zd0)t`Q{~9n;ywHUZs8NXY8^ zSX#*iz4g3iB%2u;(2)xJvsGxn#mSzhPU9V|IWJk$nR~~RXoJP|Dms1@i(2L7CIrf^ z9pj9W7Sa%s8&qdI-G4fnSzV-ISHm=Jlq#P$m#)J#{`u!at}4bw8hHY)2xG?00eRvf zTqw^GX1I!N1U56KA|A(;Li3=|426*d70QfAP;wWx>&T#bD@Eb7+71(vN;naLcPQHo zElRygDw zQ9=z#nglFHcSDUm+kmsjupPKax^RXaoAFG^(>wPPR|fISut0|?VXe9tSrw+KAjjH| zX4Hd91fNm_wjw?g=yEbcBZ`Q9=68GAu+EOmq8rV?5S54P&u;6WT>p{^?xBhdbx#ZlC@5{9vs=yJL>;3c~-vsTf@vQdYE zLUnjO)MAO65~Lqwy|4p!ZG|vGO+r$-8_S7Y#0<#HV&l2Zh0S|0#>GD5N=u&=FwuM^ zB=@YeCn9xEli`X#!l>@j!%ez7M`JZYlLRzi-#~hG^ou$>`je8ZFGGqym*|`R`Cdk0 zKE_ixF&SZ2>O#h>nfxb&=orJRi{juQ4A7crr-J<+ z{{Jgfr)>*z3iu~a61n1#_V4#nF@HnKgMGe_?0oHh3+bvy3THG4B_+~FWS?`TfC zz6g7622oAMJe-FvT>-qw%+PILiy&UIG+Klb(a={z8C^qJzYOFH-&R60$2vSx7sJ`+f!ThaxP9<0`rDh&?pR6qd7D;)Uab zcvowOgo)1L_K44O647CX;O}-a7&yvC7~!?`g{HSisS(FVsZXNLG_5$fpJFLzEJJ#lnsX9DVx|C!j2;$O2NJfp_))C|Br14JwP- zuI{<)Jf2x=A$B8sQ8=t3mNv!knnzM&wdSXtda8;&*h&8g(^YF#w-#9E@rqE=i+z`A zKoI1elO16{R)h)rhqhZzTE0(}^TD$tMy-(79!ThqU>j(KHz`2WqlyS1#1o2|b4! zuOEeUAIbeXa2aNv^8h4cl2%M?ose5tNzr^3gwQ&u|P{&(JNgiizP)<6E7px26( z>wI}-U&qaAQa#$6Fo@Ql%oo;kZ#;4CnA!ig$%&=H6Wh5yMwq)Q2So4OPudx>#h?8f^+u>79+FuaSO-zH$LdIiA-Qt6~XGD`Wwd zbe|0x(Hw>-tLd$pwZ2SYU!Aq@C-c!yPXuNuCl@Y{2*Jr>VO3krL;=+{V?S&ywsi1-FsqnK0lx{-*!>!+~XaS(nt*pJlvKirRP)lFx*7w zt|XkNd+X*uhTjzWw5gFNmBD$F?dQjYl^Y^RY<+0d79{N0l1wyXnO7{{2opwHutJDS zBP-f}2{0|TERmIAJ{XVgtph_XZmiHyVEP*BAYgw!m`+n$*EK zB_g*LSFVz?ZW+wYXW^#e%{c_h4lafnT&0NHnhUXvBsygYua4N%hk}}kgc19K=@zC# z>0Qf<{^wPfriC&dv9PLwITPVsBCF`~=<7M~6NL^9XpK1OK=N}Z{Q2>3*_bnfap~n} zwP9;r-=oDDK4pcPQ;`zw=St*x;o}}Ko`)Okicd+^}*ViuE=U>adBrl z;FV*`@7_r7gV(!f1mH1X9|`6Eya&1^S3=4V%A6Ld3d{!Xh#trXHEYTRbGIz9(ssXv zJe+gk4^VK1wyL#a;dzD1#ykD2+|+|`XjAZYA2@UFV~PcA=W$GVY%Okl-#yRK-IO?^ zCp#7ADL*qhOPjt(ASyV{edUhEUuLW(ursq(VXVdbsK#2fiQAt-W+$UV&rCYTobXqd zfT4qYsvs?XKEW6wXLLG+Qj!LlS{bLMMX{!w2C-Ecv8dJ``PuOe_b4wXvBWioiH$GX zOki$j$|!WM`4#^0+1fSqS;(1)@X(XNH+V~ka?cMZKp5}-wy`{4tfv~wJ39nI%(eQf zYZU{p{}F?3$0_yR%A+UC{>ue)<7ZS&P$m3Z_%(liaw7#r?9(yQTB_oqqBgJpO&PD4 z_{?L-BlU#^IjdmO)LRxlten!YqA;@70Q(${R&XCBVlaxeCmNnEwQB>e#hKQmcQquM7 zXH}J23tmRmu|#gTMO_i#j^dEA5cHX^asE{~A=w-+kJAD^W&w**nu*8{P`j}0pq@wy z%b+q=ew2}M(TNl-I9oRkqJ(iW`_d-JLkJ-}M6J*u+?p486_v6xS3XhIW~G@67%-WK z4xD87zPBhdRvy4>c}Rf@ntgxFz9S2n`azfOo?syEc;l9Dnk#{4LU z6wNEOmq}zpO<7Mt=&|+{q0w+6tSSvEQw{P5_)?7odijnbl7NTH#$Gj#AX)z~-js@lTz` zGNl+*$;kXsW&1VqD|U^`T^CCebuYxZWZY+{aoHoc!kX+Uj3}gf>8{n(#^E-^S=-y) z`hWnG^?~o;ZtpbOObpSo%|Ms3Y{|danAS_^#<{khq+JA5vz1%E2_FK<=%(QTFs3luRbE%a4l=u@im)w~R_!%cVf6y*&&sFMhhb%{dN6m9Hd`WJXs%&{%~K9$M-eVM;|qV-8wuBVU7+4*)=N~@VQV{o z=9zSoxv`qcOyHOngbfRe`1lvsviQxFEKCgZy3<788DG6sI1c59Wx^lKFHLq!$}r;z zGBa!>V)7OJ4HV8AA;Nm&VsXS_<72)maL#5(9lD}+$YxphOnm8*#@~^&*02Uqxb5bV z%~cJ*I_|O)9`@K)tVzIi&>_hO=U^UL4?O4^ffRj;k}=wtp^4{I((Jh@K}3fnF(XQb zDGwV0)XRis!e7GISw&j1;}eT`H1%hTC!}JEP>j|lyTRB|x{M%ad>uJ0C@}9lw`nMH zUt6aK<+5=I&SDC$!G}lOs=C$eRupD~EQ|o>KVq1@6va}`R1)U;Bh|8qZ5P**$u$ep z{aqTuyCFk=vf$m>lO$PPXKz8=j29;~b=AqoXp=HBOTggpSj zX58%y>*>Z%Y?iLe;OG2MEIf;=JO*eOlVjP2-~P;QhOQBos{u}3}!aT{ZvxL*95NweC|Uw@MSkI@qY94an+4SkC3s^Tm%8F~#yv!g+zTQDW&DGEt5E5C-uaHLV--8(HQnf7ZEaS#Hy2F zb7-Mb(gj7Y;z8n=jyw5Xp!Pudf#5DqOsk7ESv*0ult>nfCQh`1OQijmnSmhpFW(80 zEKA3t2Du_dQrYHFquycyGTuPd`qMOm25-3vgIk{|X1oNPjQJD4;{!;wd zkTGgOX=mN%_J^N)I{zeYL|%pbzfrh=I3CooFX(e%O?lg zK~PC}{ZXDn32^~<_hQUPor2xZ%FXo<>dt?1n-OX0`l~JIT8kt@trQVM%na<4-L4 zSval~&wI5gL4?Tgy9!_g;92YF468}I>7*V?t?r!J2gMbvtb3a;%{;i zwlOr{g&Ip;6%>Rv=T)hc(E{idfZnX`Bh9%+2_8tLVRqM(=KH~fHNu%>kklDD$T~fC z&-0N>CQo(k6WiD=;kQLJXEG3nzq;?ytNSO0m3$vkUo=yQ+j1v4VQttQQ4HK42?L~d z;z?_aJhLuQBZd(kt}%^=xe7a(FBU(~oCwlDDqGK5I!0jtyXnt+z(&8K)Du9``J^m# zt7+&wwB;ZKxO?Xa9)Qrg;N1j~&viaP9@BdRC(^qsaXY0({|6GlvZ3}BF5qOX0`w~& zZv+_j;iJZq?iIx}I6=!~flmEN7F>PB%amGQ;J~Lxmrl}J3tS$5wmhmDykzn)7;AmI znP{PVct06LzWGj$m!|(yrwYv+PAEA|+~Pc1jl89<)8rCUsAt7k(rE+HCDg}d;6!rJ zM)1*s+pL3mZKXd>@mZ6qyMhRkW>ZFGdHVAZ|7Qh(Aexjh8xh-}6!YSmI!^Yk%4 zt3M7EJ}}0iP?(gf{Qk88xX*2NiwgwlHU*q3L4uUXB~SF}vlR$tdF#ae>uE;)4G0$; zlvHA1r6A;#kqi^@D&BNv{RX-m%md29r`L5ND5Tbls<5=bJQ!Kuisn8J76=BQwtm{( zyPO`u&oRLRsU#IorXBj_?^&)%-gDO+&+J~Am}dlYE;9JO&}N3orrdX8hh33AxE%qV z5Cf(N=RRm&SdYviQ6k{cc@C5)15^*LfK4D>S_%$|-gkRqQn>WX8DFCsTA@B7n( zB(7Ifp@bDee$m@k@2O6IYWg_U-I*(@9)}glYnf_V6yq{b5wDwtdD1iypstBmWKa8C`V~d`U}Lik<1c!s z+PUryu9So4;J`6A8}!dS?pX06Z^W5=9@e0WSB{TE1=2QDU}L~{Sl4wWua{tKY|JwB z7K*K$xSrh7+A1INrw^Vibr?zv>fKjg{FBeYn#S77>ep~wKM`uoq}-R_Puew!yEV-Z z2~5d|0$T#QFkl%x)0pXq<$N!eR1(#R=QD9VTcY)unWJz$&#Cwtlu4Gc@ZUCx(D1X4 z)JHGDXKz_hh&Ijh37y8zlC3xQ(Wfc$IGRbw`dTc}983>eKV4YO%?KI0KmPG4h*^2- zRPg*U&14)G&}|$RAIWRkZ=@~tRf;KT{#NbzQUTd{E>@hNQp5s^AKhXjl`C;;)}zA% zq?PQ9ieP?>KJ6E+^2r04OoYZimN&Ocr5geJ_u0dp#eunZs}N@S!}b<3Ux(>MEl8zX zBs${l7KZz7CNhMwTDzi}%wj|H7BgtmW{9j_A1Opxg%F>FhCE6oQMc{BGqO6;MahrK zAKZ00GsJgWb(uQk#V~*#shCx2YNL&SpXZDzDT43_Mlrk@Cg*=4o<57xc2zcmU!(}c`{|o) zeVs;Pts=knQB8^XfU$CUf1tosWgJ0wa6j_ttJ_)2O*eIR!q>9Apm7`FaG0%^s)=gJ z8_A{&?|lM} zJIykC@+q=r)wk1iTH}*#)c?Yzex-e?M~iCoNT)sbXu=ykRpo|I;|I6jfyTR_j5+WS zs-qD2yK2J0-g362>cIlfbaR`X>{qc7Vh{WPv z?GMqm4R-Qg|2v9Bu%cvx3fL`>>Uv+j`5t7Hlu-TenhSO|T&9P(Zxd(9VrNK!RNP)QiGU_G6j*8)(xe_Jam3A}%nxy@^^5&UqdPuF zF1>|{+jOrr4(>Z)`vwg*wnD!BW<`@W;;on&zNUoA1tyyucFqAsv)FB%KX&Wzxy{^a zk^NDlawv&~vKJxJ6&Z%nr}E;W2s`{hijK<0KcQ-iZObpO;>llM_Y^9whJ2(|5g`yi zg;gtm9K=`252J+s!7YMO@D{}F4EjObwo9j?O<83H8la8Yq_>IJ#b@r^6F1~zQLCDh zd43E=5h-VhM1E`e!0agMi+@kf!2LxNVOH4V8_M?U_)mJ?(chbI2f>tejt)SJ>DvoI zC$#lbZb~df&gY?JmAYTc>G2_Ir7=Ag-o}SL>MCg?Ta|+v7miY<71DTK>o^5Bq&Z=S z-*q(&U(-08k4q_6LrQx`r5k1m^8f9sU^um!XF2C|xmT&%Xp>>r*M{u9=HJ zOF6AagY!;$BUX>#9diB2<5M4O-*gfmd>4v32&;CtTkVOJ17zJ?lib{*MC~>^w9@Hw{U48-OmPNK;N-QW zlUi#iHKs9G__Qci=tJ;{u^t0-RoNF~M8deK@D#@fqUK#1zZ63sFw&1`Sx)wsGD6SY z08spZ!qj}GuG*$&9LdfqjPoPW@MGzs9^`Trw?9=HWyPHH9|cGKR1~-Am7m}{xYyFU zvJHt@J4b`d*6bT#4HC;9Rc*fQf@o;kZwB>7vA62fP8FZ-NI>S~H)z`6PV8?@i77=v zz5duJVieT!_`|Ev?P3HB<;uUM(#1YqbQcx$(bo-A<|ll9_{RjCkdt4bmJlUT5R=fe z%7j9;V0=GX$Pb1~!y384RP+l)>DET8R($zuy&xOH>I*~}D}ha{lY&`do>Xmk`sPL& z;gPg4)fs7B7aT|26LdckR_Gw@eL#m(g0cKfn7~nf~5>qgf2@Mv$IiG_o)T3vLe-Bxdvkc!P3+hT`^BGon=gN20zuQyzh2Imc zRNnc1JR45X%7S1?z-2-5&NNh0SEoF=BJ=zMX^rv>e9nM_;M(v3AO_BW^=e}2b#He5 z6)*}#HT&h49sn`gSxG3}p*OV2juK%x6yU6-?+6zmO)&%a_;>lu8Zh+;T1<&UDXZ4r zIA0EvXq4@+#C>^5zhD*cl6eWb_5X0NM((Gg%qy^#bdP4`mCR-6iLgZ&%ZBK4*ttct zBw4K0{D&zP&+0Em!sMI~DElj$3A^d!>iCddrWLG($ss zafVtoMp-v}_p36NGYwk|*}O5mz1qN-6-_$g4bKx-OzvtY?Stu| zjK6r4Qj$w++r%?5YI>&yq0xKq)JVM+5s!1j)oc+S9-U~R#qD>fds^|?x1V1sA#ShB z)q{Z`1<6+Y!_7mB0HA-dQJH-gJ!&#C9Sj+P^FiAB2(suW+F`C{%?B3T6$3{BRr=j> z3_MJ_fe3a}zN^o^*MWtekD)*2j&F_?ff%W~-9Ov~b3cyrgy(Uo>HY ziI^Gh%Ze$-aoC8+<&!9?ve7I-N4HoDzYyyQPHFk~!e5fRgu?~fJK{&_Qzx;5Gw1Fl z6C{b|xUu+nv}viUa7lsPlywx&=SFADp}}IM>xxdLD^mJ1qnu*a4^BnHO_bwSD50}V z`8gsrlmd(4DAkw@ly$CKL9A6nZ?b@MAl7XUS(&i%nRZYUW;_Be9rp|wG0iYq^gtos zhgCIRvRo-&Bi|$Opwx5jR-+P$>4;~NvdML=Zx6w>u9QcvS4@k@6}@85yzoA=E)SGJ zfMu0nDy4)qIziGdIkGEDeUL{b5jdrAaIu{k<0t_L%8Cbvep5kThr5ehRHf~(DYQa4ldh9iP?vDY zDax)(si)))RPYF6-k^iyOLmV0U4{AUjTnzS;HaKq@-m?EGJv0#Z4<^`VuNt5K4{kD z#j?B%o18<7jcCmFbfM#5P1`qcZMzEQMNG*Ye81MfC3x8qEt0tJZF3bl^sri&m5>BH zJU}CUBg}t%&w*Grz+?LEYK;YY{sET)t2tm(06vEkngf1X;PWJbx#G?B8=$#|po=_X z^uC-q{wx3-ttc*&qA$FP#?#KFC%J+V?D&aw7x zv&i)S9vzjfArUP3{Csp}rE5aOpGgZ##0Uf#4j}j5t=X1TQ3su4EBPt9&^#Zh1SAj8 zCEyj$(&qy(F}}WeAiI#gG{qc+CzWl(W83e_%MWmLhAMmTQspHW9(Is=?O|pe`5g#s z6LKv}%}`r#8Y3#n-vQC8)|yJ((i-L4K(gnzSKRD$zZPBtS0m1KMkP@hjfr_f4jJU} zoTK}R@$v8*51H-PskW<^lTA(+;GKmYoEz{S4AJ9=`@w-*!j(p*a3ScD|Ow;)s<>1FvJ4Nb(!L;0tJ-*fqOR*p>PZX!9$9p$x3o^V$ROs3R@AAwskdqeg2O%Yg;oe z#T`w-!NF?xlZW2lzI_AOR6XE=$f{Dih>Ok(9qa~z{s!aB>w znGSI#fYq5YTTZm_`vMumYSx=r$s!Qq*S}T}wQOu8-61#02xZRp{oAso`p{p7w?|9yGo4o~iOIcg= zMr(H5gpVD}{OI2;I?AY1r7O|=+6#Hrdc+jG3l6hrE;zbnTsP$o9}oFHFsc+;N*@%T zSEhzXTxZeDqZ{E53H+PP&d%PuK1SVtOMN@rK1+=+2mX(#j!C1q?J)}kc$#VS& zKwysA?DUw(xQXSMN_?rf=YbgI1#o(=K|sWa+?`TcMk<^^k#HT=Z-~OdX8^#B@vbT% zrho1MWN7SnPP7Sk4z|zP{t&+LAh64c27Jfk7dOGG#yUj5%&EoX9>R$oeg(We8OIXy zDA$!N0eIu5?S14;;$xmy;N8Xs_}jUY6%5z^0MdOtK%DfEB!XCINX+!Y|G(ZK&EjAw(S?=3tjZeLzro}d^y zm@Z)1<|E=f0d%xG_xy6h%D&6jnU^Au)O;R7he04`W{)o%nMm&(pnZ!gDaCP$ zlL2sS#T;%Who?Ste{vbs+KP(A?aEn(p#o5s$QOv&>oVUk;S|&RGsggf9-jg5CZlBTDAo$QGWEsR!_=>{gTMIe&q_BYSArc`m_#EaMMqK+0teATF@R+8w# zx?``=%T-2i&G||rRpZF07`5^(AgE~a*sT864qEn#*tDn?*?ti-cf`q||-mk)@ci|MC-!22QSE#ThPDF9|Gt*(u#(S-$ zE-x3egd|v_5R5pXznx*%- z>2b^FqEehylfpfUsmdETd#WhU{TAI}Jx}WfAXkrZbqtG;%teJKUj6~R$^{mGaLh4q zN?VEqZR7_?otmbSDC;gb{qzNK7+ybWY7_w)yUM<|w{gDjm7Eqr#oEROW1F$-tkm&) z;L-NTi(a?)kB=w>UUhpMrO#IX)7ZJkGu`%aJV|mW8O1b*GBSsVRn9rv7B#242;JS0 z!#YsJCdY}0*$K5fGaEK5I-oRGcPW_;7*dKJr!pPnd@7!c`+mKi=a1))XMgXty?)p4 z`d-)f^ZEU*_s6oN!DGX{g`bnHH=H_$C(-AC#l%+&jC#zh6IYLZ5o~DL-)jBE*iYTf zdb9Is~r7?^0YbxLWOtKT-*RZ`q8z zX6vTT&8pN@tC}2>zPnar2sceL4wx7aWhaC5gB9>OfDIJ94}a@U8OInE+#FmzsIP8C zj`if6rMSCpwf5)wT03gJO=Yof?q8k<4{%KG(l!f(rfcltKnXuic zh>5T>9d&Z!#8URTJtw&|59%pp@dJ3gBbUpKvr0;iFQze-dWnE1cQhC`#%`vp+31BC zLxG%QC)dHLU2?>uXE;dAm?mCqVq%gd9B4b=dpGooajkOG+U)G?5V?{v3c z5|ESn6^@a|zi-UfmkV2?ptg(Ng+=nU^Fy#%ub2F+&^+J43NDkXm-l3i_Dmc}up}>y z&x?Dsf5@uCfQB^ZnW;i>vzM6fdTMc@T4-7)cImuGuMl;}UmcSrB<=FWpvIQz2pqzW*p?f^}d=rDc6cc1Guc}J+3a807#DMJmm zqKRB+CtN><1oTSIS>bd+4({&N_+*~Eb9~3SC>TT;^A=3~mzPyk3HE@UMun=%rTkz< z0cz)q!VAf~@N4GGkli352RlCcSdm3pyQSXS4gH5$E#_OuhxW-GtNx8*mU$IrQ>}@z zR)-cRCHr8-nQQ5Jip0J)>z?8s^R`8|wk5c+h}}cMI6D)Ofhg1>|0Em79tbu6fx4)b zIOX+$@TGFGMoJw`zT@1LLjTmfVKt~rll!a0mdJ$qJ=eRTjz)=e*m37dok*-7U(2Gq z3n>oRt{qTi-y-LxCqyOt%b>y&pAOR&8wWnhPPL)1gl^~dD?Qx`D5%%iF9Vewn?kzs zRGW=T1@!B#($DX5`X}Nn6 z_WceYu@N4dg3-pdIeyH1f52WnuJcz>W(iuI{*r2(UjEyg)lA=XGVT2^) z7^CJ8XIXcCR`mTj+PD(f-U>Y>EcPFZ%VUnCl4f}&y1k2f2MWIKM$Tr!oX=5tr#G5N zpdEHBM7%rTAtgE#!1Ox%DvwlLt-7`Ke&+%Wf`XXC3`(?lVSk}gvOgN6f{t!p_Oy;` zF3;d`2eo0d;dYiO0&CMFOkF!J(QGIkbs-@2QthB0EJ zAE(I_uOHKi&ReGa0hN?sU_3uI;N*Gs%T8LEwwXyo6&)i zL|7KqT9=a}?!A>XeM7p5*5@kNi+N7ZB;Q9j;bUWC!D8t;A>@R)bcNGDh10Qw3&N6b zHQK`!XzASVGWa*5j9gu1^EyPUjii(Y!PzU7z5i7V^+R`cb?MEh1DevTl%6+VDF8$& zcs!-!y?0p757ElJ9Ae4z@y(t5{Oms}Vs~x1MJ;rdV>G2|MZmxGo~i$~*Z*TOc!32S zJz|nBJQfJS3$_Lv8lauJWCyuK&Hg4?qN%q<-S=IG4pt4au)9^d-TfKe_ba9wz36Qe z47vU=AUF@syR?7Ww&b&$gvRp7svO`x5d^1+%@WH`NG$yJh{r|z>-{MMnC|=r=1{FyFbc2xjP}rH&o*IP$Xj^* zLVK-OpJQuIPX$>=T+N>JB9tRB-*hsF(m`r=kA^NpCOG=2`1l<^J0hUs<%bKWu8}qF>a?Uu{I z_~9)GGvxH(lGckT=V&g%MTGUQ38&rlk)`PA z7ob71j7)HTR@E3?Nq^@f;JDoZ_-~TOfzI!lio?sZ%S*+lUZPwF;qjm$^;CxL8Ilyo z_2DNLc-EO!e`?(HUfd)|8ns8w%lSzwc3Z#)=Rn90H)mUr_HZKn!cecbnhWHjd8a|H zcSA)wF`ZeJHTpy}SGoWW3yj3K7)h?RTkSl=G_L1va!+#VcAaHZUAbADmTlQvB=!d>5T}jr)4(PY01qa^s zt+<-MF8vLdb#lcJ6aPW_ZG^=RT!Hysh#v$(*zKmFItyASiLy_Vi?|UOr*grENqB}ND+e9L2clqm|T%sfrfUw1t-ki zG(ubt%Ob})jhDsKq?|!zd`7z=%3S$4J*9N95x%)b$?gU)avg#vQK@20x$Sr&JTj!N zeUkIpxixLH5us9s;}{|$p-fdf%#F?ghS=24netZ~9)+zCO4lBPR?U*cnzgrBHE-&t zQU@^6@W%8R^uzP?`&jEToUSg3X%b{(ha)7>3nUiMo|vE*VWUitO05m+XX?cIi8(}~ zmw3AyaGP=a(P zX+NX%LC+Bkj&9)vbzX&hYWCH#Kn1wm+BcA9c9WQs&hUBd0Wm?(WTK} zTQ0oKm~ooCe)}KlC*uM6`goHWQUG&oY+f4FUMBY|CC?FIWw{`ETmk%7PINfzyoQ*b zai+|{qPjimF0$b}XpxP>l1uxE5w_s(%xQ>^U45 zed)T{WkqSmG3F;1)}0R#5Ak51L8tQ=Df(O4(7c&7N|bi=;m+v4Bc*_m1%!Z= z8^^pbF+@xKbo5a60`8VDLoGO7>ixabwBE_^C4A)5`!N!4Nv)6Y7f4eXvWL$g<7}X^ z-GS#fE&=5_08}W@Wg81jkC8y|5dI)Ijjj!>CFmelA&K9?EtpkBeJBRn;K4WX0n=xo ze=_*;8U5jGUH|%bgksypOf3RxuWBhFfxnkUxTvu$g)dhnoC&xHJf6p3f;k%p)~%XR zp&~#T(UsO30;g#0ORADUiViJke8#jqYz2DyG5kMXR>w{*lVDhWut>nI1tq|d-8J)1 oMI@}`@c!Q=@?YNA)wOfWM+!U~9jbZH*MJY&^`J|Y)9-2j23i45J^%m! literal 0 HcmV?d00001 From 124401d58819bae26cae356577c08d2370b41301 Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Sat, 21 Oct 2023 14:56:56 +1000 Subject: [PATCH 12/14] Add files via upload More images --- inputs.png | Bin 0 -> 73674 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 inputs.png diff --git a/inputs.png b/inputs.png new file mode 100644 index 0000000000000000000000000000000000000000..6c1020bf3011f3a2185703b06fe18767894d18a5 GIT binary patch literal 73674 zcmb@tc|6o#{6A`|MiMi!WH&P?6-jo+HW(x&g9w>XvL{QH8GD&AcG;ptN@N>C_Ldn- zb`>L{h6Wkg#$arBKHu-}&-=%H+4Y zgWm!Hx8>mAwCCX9ssJ2i*SL#@V%R^NLAK_`9Myx8i|hiAr;)W02L~~YZ_oV*yUcqZ zaWja6Lqz_+52uAKboIZ6UUrVbH~hWsTi*$G_izt(XV-CXM1s-2UiYsBc=-nhfpwfC z!C_#Xi|4`N>~HM(|0$_mzVv@v*3i22f8O}t_x~%qV2|8TxW&P7sAT~+vI}#i|D34y z8xNdbGwsFBe0g@h*M25HlZ#I^{TRYOOkx|nlG$Agga-WLn?8Ni_e0Dx3U3MJ$(`$` z&f3;_IRD!*49z`d1pS<~BKYg>YhXAWNR(1{M55w(nDOw+Uuu&8pIQRSBG`oW-`L=oe747}9 zLq?ISUZj?^W##H0mFOLYn!$!zSsIp(?%wHh$A_@rG}T)p36&`pVb;m@iL-ZRh#kNA z1GKPiHgn$o|D$04?^W{uV@v8FmDnx4>rm&| z=IV%YnevwH*KGUEe`qLjq@2@k@oI^o1r&UsGYn@?^)dAdh%))c8Z}_+n(|R;10$;r>iC zt7B!ixS|ID^&WgR*#ElN=XS7elnH$O_j_CPUU#(K{?D2HpCbnw z28SC2`$HM+de7>$=W*Xu!Q=qz6Gxt9LUT$ajneM$Cu zlt{t#hahOgzjYN1-Hut^)Sv_$L-1TVc>i?(>-Fa`wk~E0514Fq$T~y-a;(vVhX*T% z2ZA0IY5E>kl1o;(gW<{7Fz%@r{8gkMIc_08P=CaR^&kyz&lSG$&JY(XOS}~+|H2j- z0_s+BuHjBs4NSd1kW%{J0Jj-v118#h_qAsGA|u{lJHFe8M`Q7nPgHRSD(qS%VK-iW zjNDLi{36_z7j8E{?<8FI<@%9|Q9C5qaCplzn(0}l617H1)cS|y;JPwjbI7Vm%fWtE zvE3h$*&m5_jGi-(nqy=c>|}MTKDaaUHjZI27cD$0_kaGFOIv|+q~cNoz~U{H;b>DK z3DHht@(>UHxE=giOgmUeI|=?iILmHrpxreeX)o-va&N*T-g-^e_PbIhcFB1r!mh5u ziTJGB;!ugp&rbk6P5wy&Em6(fLG+!#xy!tjDus5>qBk9)LsUiU9Q0qjjt7w?OcY>iyU`jSB@gyaibNE>bt^=1&sWT`C&)mH-f)x!c;9g6A$~Iy#ohx;f@^Oqx#QMT zze2PgEU8LHn2Yyj^w`Q|Chs9UG*uqLo^2@^k@2?&JZEg``!`&_Ycr;iSEAf+!#Z7c z!@l2EUOawe51^NInV#LKvubN8Yo|9E0>CRVXFfDPA|bLjRU{1|L-NDY@r^ z>ZnP~KuXV!W_4QUVEvs`mG*v7nNM5n(NB~sDWsA=Jr2iayygtYcnz0{8tLaMx+Wn4ZT1H*6A<6+&bWbKuvH#BeF zI>M<~zwY4lilBt!4CG7d@VJbitcxaKX;bNgUPp$v8>T#ge2s3X_`q%I3fma^^MHr( z)#pl4l|`!IZDy4S^-RvvM|8n!L3O7C$8t~N4GXw^&0HIL1T6-=$5g@JMvW*qTgm;o zo-QP|-+%~#g@j>Y-+&ov-Xn!Ipq3~-;f<;8ifHri2;L%_>9@r7tEz!#iIJV#^ z;m%}nR1!J@Ll;*UbB`W~8EyLf^xfuDUeY|4R6`MMl40ApamkD$pUN#wl>@^2oKI3K zY)#F6RD1&{!JlNEFS8?hTg>HZDRxnC7$H)H3Xq^>mx~kp$TGi4(zAGClg6aU+MwId zJ?F!{1CFgZuzIiCS?2zAlKDI9j4U_*jf4>crZH3YOC% z|Bqs>$s^KJ0{ZvS3I=OJ27&44z()7GAj%%`j$nf~X;G!(cyGmCuS&-kq>cZ!a7sWX>jmdcrsE+^vvWt*$w$yl2Z0Rn>9^*IUH_w5wP=@LAn~J~yi8_q zH+Am1)qi#&Nmtr$EMvXr9vgynNkYBw%g7v{Pr{N|f3b5mZuM?~{wzGYMw()n&@=9a z*vaXP{%~Yx(fm!1AW10|XMfIL16kd3L){0<+P(q^UQxZ}wT%(=wH*@KK zyW;$0a#=dJ-AZ2n0CJ+#?AV%NHVS_F?1;&2WDPA7kq3XOA7y@@$9+*((dj_&^_S~? z$H2>Oz3WvSNljls+v+ywGu}3{lJK1es4G`k5Qjk7>$4W{E8y9&1&ZFrvuOREG3q9n zy?_H%pO64%A+*4mTzx=DRQ}i5o$yfzP>1GVF>2zGaiJu{+N=^8-lNEM$*iBS2i6RF z_Vya^j8qUM>p2Sq?W}SzjVj*k?aP=KmV2dtcqZg1FTd0zYMw;@lBiHC#s!phG86YtSP`MtFQsky1nt)vs=*Hu*E zfF(Uq)s~&@M+xH)YOw!>Dx)i?psDG?=%Yq%b?>9t7U5)};vDp2=4rV?#m0`o1_Z~= zFor`FxU`4(C53+@-HzmRXCY5It|CIM^NX?sPG#K$P8%|hf|lM1>w^p6#6Y!HtgG&*7Ti8Wjz$1LrNwz!q?5O)cJWP_601#0+EDAz|ppR(TrxC3RCkQJS! z#*VnS3upjI)z+jA*G+5&;FKqEr~~ac9e{9HMS=N(tk&UqnNg1d7t{$(@udI;Iffp= zSW%r+S3VjAj^=Sa;>*9OtvN1^%t=jIyGh60?*j6QJlqd%Hxn!iWd4-38}fzRIjB5+ zw-7nXJVr-){N3IQy8LjB<^C{o4Fn#{wADQVy@#)U00w*zMMl{^5(JAwxd5Lr3Gp7u zrd$)RCtqGR>WI-}fs<>~FoNQ(0Lzd>y-28Hl*S*oEHJ>PkQP={e*6yiS$y^(;CZl` z;ccL(l#hAv7-5ex0&X622+?*=mWrU&^~vb_m{nKU`TREVbuCMedVZ&B0wg0b_Sv;m zyLVF(+ejEcCV&yXxVAl;R`fjLZ@K7@$U~{jhm9X!#f3qYOEQ1qXzeT5l{Hi05+gC> zJ({lKAW#W(P2Q~0CCZmM2vP+*dCc{xkdm%t@M2p4Cw4}WJNQIcL6@M2eyK;bN*$$~ ztLni!=>+>-##bAe8ZN<{rD?su(`lH7y)gdOiJ>SakE0B42N(3gq-J@5+a>c`Q7c;~*9{+kIC_ZS2?u$QPPPIh ze@86;I@}YDcaxL#)xkAJZ=EU+H#HLYxK4ONLh2zCFi#=MQ43D-VLu9}==jdym;jCB z{D5fI*vx^fPlur5CuFR1qH2&}gM{g!WU&@SR8xQHZoC1y=VssC8ucGR3u^L&anaUU zLN4NNW;c8mm;}#R=>_kyinTuKJZ{Jln^DmK z`MUErE{%EpJj%E&nB-@$oYgQPu#)`t+64R!qGeowE@!VPmGDK;AnVrrd`lR1t?O_P z*%r2ExHmPlKeblv9oQ5rR-w%4j<^j^0zj2VZT;lpq=O);ENQREZ<}W%cwY-`<4FMh zpjex@Q@}^f6J}64n2(t(2ki!&i~B7@Tph^S0bcQVIi%$hM?ns=kiY4%8~%b>rGiAI zuX<^8=8&tL0TgHVY{RJKBSo!+R(}Du&n(4ysgV9ScBG1FSh7^yA?@EEyO1ut?l)}|Hc zd!!4%Z$~lH9}#=T@+wr^sJ{qTvz3iapsa1d8Zl&S5C6xyGI0eQUK zA$V3y&HM^p+jn!CLWBkVP}E45H5gD+Hv4p!$0o5Ft6_73JZA<99NMPys+MULZ-#IS zI-_L=dFSV`Y?~uL7Rg~@|KM0}X|w_o3&O$%?Xed3kdlGPdxBtD&g=?%FofH!e<5pu z2UxxeZQbcfV^n9jz|Wja7=d&=ES)a4m;buFT2HIutnog2{KVK%cp; zqW8sa{9-{bheux>Ye21@>Q*i>j{w|A#vxA(HqeSz+OZI|qw_Sg9OfV_6JTFRZ4#Ki z&^ar$qSZ6Tnz#1K)STogY&!a)NV>EsEQI2~xy1nh^UB}C4rXu|r-(t%XY>Ti`{NgH zU2W=|zj_mc?M6GW_IKS6#;!hK4=2z&Vp10MU>)m8@S$4br z##Ec`FzA`9FnZc{;b#nzst{Y6vgm29_f=6VAbJ1hZ{-66COg>lxwf7Vu3QY&HGYH2 zOzz4M9?hs!QYfXaO~3S-2jKnjy&|?5tE~Pz-FXL@qWEE>oIU$H6{*0ZE{zMO_W^0C z5*(8~<|k6gAJZI18x*=(Z*$~85Gwu+I3j2#t=6!AtOQhpGK=y3`lnCtQ%>H1mq3=1 zXNS*Z6TCCLs+$GKY@D8c`E>s1w;M-hSU7#g%^%K@{Q}NB+p_erruQdu99Og~3xl5A z?#kdQ0V9q8V@q>BR7iO!6$1&dd6s;{T!ylKBSd*0U|MH|A#o0p6^5a`N??KMST%Vq_fv%#!d>aghSc_nb>WHO>d4L>OsHIJb zdE@OumpBhWaa+TR)E!V+I7*gYi${P3_m%DmHU(`goKAZY`A%76c<5mQqUe;SJ`3@^ z^&8^*TaHjic^Sv$l!GZ>FLZ2nko9B2-(y>^7t$2)S;J%6hMKiIph*$LQNxXE5956Aad!lfVhwej4m}fR?=x6Uc|o(|D$BKd+!3S zp}@2VSYp0C_PKiKbC{-hpm;%~I@;-fwTUPUl09cZ3v>Wi_`l0|LCWf9K?7L;s6Je- z{H<-|JT3;T90cU#LOF0jc+h~?<`b;suUS)zW_k?4#Jm(>AXs7_Q7-e1TY^PSdLdW~ zQx#IJ6JS&ZWGWT;i;toGigx-vuMWX>=|r>8sbsx~t+z*aACU<9qsNhN_XeZ7S)^|@ zCnW313cCnU?4T*lOs&eZD$j#VP z18JCf)qq+73OP2X}c>PRN6zd$S5Q>T?8+e`i6G zmDX4w&K7FDPP+wGW}(uq2oeL0XS9G0M0{(v?^?O-zAnc>hUh$9?(G4$(Qdc0R}m$_ zUu5FjIwy<#A`M=h3i%F8bhU<4fbO9XdreLa#>k5Lkq3>(ut({H$>+5JhY^>0nAGp? z_)TdeTPxPha!c1z*<$E3^eiY#E0>a~3eGEi`m>AS!}|%IiMVcne`1kf3DzUU^xFjh zs-i&`FC_pTy9MHbs>AMt=W9xSYtD*Xs)GTuKpzsQ9PAbNM)oaf!01{<_jGyajW6SE zvBRQ-5c5g~O`O(&?`z)N_T8>9zN3b1+<2vK)88Y!Gik!47(GYbNvj&G2$2pH9j9KR z{A^P^lMlTv>=5mo7F=Ox*@iHrT3GreoN4AAmdiqb?L87?=M5GekmbgN`{^n!E24`L z^e9@sXR3L?;e)K`QgRz0&(H84a-!5bxAy1Lln`zsV~kTx9&fGJl@qu&r3UjLDDCdI z*-m{AhZ9vz`@%vq%USQY=Lk}0<`>D?te8cL&vymX9RcFHi2x<-BJYDceJwvv&>KQl z&nX1%k{?kf`kEXIMb&;X(QAKeNfF(uyh6bK1bCjb=jNu?nZ@)n68Ap90%XP|uT>l1 zDr~2LK;|WwU)8VvOR(EE)JQitE)@M- znX{vL_xYDWScBW(vV{i3G;|Y4{3t1us6VPR9#ELVXb7&eSx_8H0#rHZ!OT_V+*^Wb zaP7bP3Q=WcLoIl8Q4r5`M_eQNqJ1Ud82aIY2-O>KfG{+Cei zs%fultQ{nX#B%w82H5TuNdkG=QJ3hzDsFG^98)}66stb`b$$6L_4TKmp`n8zb?9&) zQCT`!eoPlsxx*s_FIX_8P?eUIB6bMPr# z&&+n@-V>59%kNdE-$Ui%w{P|=-gHN$9b#&i_(WN*4uTtkv5w>$uX1rW=u{22WLzuT zk<-aAOQ@XDdayS6P1H5)x9pvzUz(pD_|9w_KFE-9k1z6day~FDT4VNgm_)$aq-Q1; z?s{IG9XggI;kQ`Jer^#X=5H$QYw~NFRlT2r?#VrNFrqmL0E>o>|IoxCo-HX> z1q7<+I0mS;tjZkamFUlICH)xOOa}a6>3RUjL7k-m;>L zDkUge31uFp^o5C&!ToXyTJpaDR0_P>zZ_KT>8}aeUFv$3=2xSGvESe|^`)O)aT*qF z8FQMYdb7`EVDQIdym+Wxl5{T~o$W|+0?TnE$WF-XTuRpheQSHzvGJ@s+Gjsj0P`#C ziYK?6cy5fmYpICvKB<7M>d^=Ef>O=FIWOvuG$N?I@;osdb0 z)^S?;1$p4-cYA#L3)^Wlwqhv~`TPD`qf6Bai7&KtiJYF-x#Pcq#L z9ssrm?)utP0*fQ`L`{kWT7#>awq5};U6$1C3mH;EFjASo4>h9WTZyD`9pBaY27=FL zNuph`ZX(<0D>9vC{zo#SCnV6-Oxe234s(-Ukwrv0sD1|r2D_WcC)u?JB;QK0LTU8> zff!&4mI`YQDtd^-3N@Vyuh5M5LA@ymy9`_cHj3jkP8a-#k5g7I^qBK(_&Ea~LO+4S|tJ(MZW_MGU%fukM^Ne@Q>06rOP*0m@O=+k?pFC7Q z=936kFn4pK=GCO9?+K$;uV(z;rHxH(NAy8eL)d3Veb~j&#DB6-6Ou@|<)`U-i}S)+ zJQ;ey>5_F%ZfCZ^vpj&uq};BQU&?qJv`=4kXi&mu7$GI1E975%4n?Z$<1%U?;fq^& zsXT&lsyY*jtev69B4EiES=H^u=%3V;zO&gCc_efvrKF&vql@I}hdUSd+B3D?_%8Fu z`>2hl&X~PjOiTEL5Kk`uEKpoxtU9d}{WG|dzt9;nqvT%Hu-_!G_b}^~qQ>{P+Y{a@ zm<|jc=}T(Xro(BpQ!`W_A}PWuFzWhH5dMrRG=qdYVW*sVEfaB>^xD#-6x0`9su-IQ z6AlLhp@u_}aTo|5XH*9^iIbHF>vsrVlA)pmfQSbML4Q>$4J&&-&bD-i?o)H-Xg3qX z^+gE8gtQM}*;z(<@lMb)!1rA5j5ckW%_TWHL|CJ;7p-Shf>OOI6A|#mvnbyFJh}Y4 z=M2ePW<47xK|*!Xe?wb77yt{f>lN`@4==0+RN?9!jOi4E2Gkp|zZpz|gY7IG(80L41^FJQq2pmEczvJ2uqkAtiWs!@lN&Uv+?dUAKDfuV>#6)o&Vx;&y^}E@KGaqH=H6QvRzdXW3DGX^TR5fU;uAPw#PHbSmwLqC*1GJKeZg$weHS5 zM-8k@tKI>97ro}h+h6U0?3I(6uQ2NL%I?QV`Ttl!B#}ZYbSXMS_Kz&f52r$EMctUB-U$SeUDHo_1 z4ZK4X6RJ^-^p{y&K+o)Y&fS&jst=I#`up{%O=nW`{>5#ZslCLvs*^lr%F``>cT zk4cj)mF4XX(nb00_pF&+m~kUbK*SI7cSltFqx(iecp2^fMvojE$Z<(BAO4PJHHxqU zZ)r_S_MRnXfXXYl_ho;#<=znHL}kSEd_!ws;dlJu$8g4A^KT$wXsdR`E#80F>VT?& zGSTwkgj*nA!SI>UgPbPXs&9S7@0(DQORW_+7y z2zyM%o1i(*nhEC3;Rfl5+X6|6F%5Oy=8YtropTxNU|HWb#KdQS?PSj1z1KMq&`G*G zHUF2FoAfHpMKBGk)iu$dw|~ z_kVTTRw#$BHc!fC%7cnk62X9ULwSD-C$N`pPOWV0LTu7wDW(-5tmDPDdJ&zW5(3Km@Bo-rXg7SK$RbaE=)_6YgDkG^Tq8mXV9z{nXLW zBuZfp5E5FD)msrBb!hnHrN zcdIw8*T@b^7_$;?!6c&^d`PbW^!+>#^!vclgtBCCz>2` z#dBx&W%pr)rUv9y&j&pwvJLs9D@9JcnB+Kd&{uHHZq>kK4nP5IB>M^=L0<=yhVAX@@W_29|0=3?@G7ql%msSTvDLn^dLh)} zssIsuRKxDov<&$Ag-gFwunDwVzSQKoZ6Kk6apdGAqw-O7H+@)s(#s$VkE^}bmanIOn(Eki zH;_@)97H7KUWHqT2FhyqT#_yqJiyP69ial7noEyMl-EI|S!0G2Hf%HIKGo)bifTpE zc4kc6=O%WI4IeBNFssVNJ|3(J6M@~Ry9F0Co)&Q&GHX=&>NshmYU8d+?DQ8|;UsM8 z{PumwDHV$O!Mb&B`>*#Kdp|YVZsCJqU>1T*PNqXW@lKcQi?wrQX^2|T4H+tc{x{c7 zNwe31Y7r5-uyIHECt`pY2sIgRCYdI~3lX35pz4XROV_Hr$tOVXn={+o_-e0b^I`~T zuxQ+sTVub))(K+ES!mU|muc0Gf>(LOc8K2(z{+sTexK+D-_iU=(ky%OJifw2&|J(c znMfa-mT>K%W9(C-s3~xFUcX)msn=E64FeUG*(3ekmdgvuEZvg#ZoUlne@siNSxUOP zD_NN~^^h%vEu!TY<;cvVlc0-rPu^$3>*P47ZO~Lnc@?SNQ`Z%I5n^~npu9*YUKI;G zI^(-prM{q&kpYgTJ%#k{7~V&W-eT>A7jB7}v7%crzyCDEZjLzo1WQIum}^mKkahnH zn8v{|Nh5K!CI=2+26}Gc=!eA3Iv-_;7c=6O-SaQ*)U@+-11mZ-3ojS*EJ-rhN9ppDe(GhUfm$sDJ9f&M|V+f&YmQqRlJp zLxlT&Mr)pY9HHGKbR?G^qX*%hHGG)KmkCBBb-=$X(C>(b%NApB#Uf83zuz86e$Cgw zkxm^mLS#C|L2Rl@&AI4#ZjuguUt#OCRi&onM%=URYuOwumtQV(UC_3F!NYGf)LwC4T^2;DRhOhxeY*_QYgLrwiyC58aAtkS8_+3mr2)IM1-gUh0_g zWS({(A?T4LnjHl|z;h)a6VkZ!+F2&x&9>yyOTvk_U$#x4OhHQV%g%}d*|WkjqnZyv zmCbZQHZIF8jpoloB4QoBPwv8tv}y6%14H(j+*o8SJ%6%Bo}Md)e98RgIs5|?g)+)Z zcm@-F)6l)2`PuX9IsRG3azhZ4vAHwzkY2;(e=*%%R10xx^hlL_iiNmEjSba8azEx2 zG{)l_n1|;BOg|x^us`dbFG3^1=}5N0r=zHEN%FRuiglwyDzO-=LE5^tVI_-vCv}G= z$eWrV*wOjgxEe(`wQ$NI;f!=`7Zsh=NfR?UPJ0)fg;{VGXonXL$OYpmJ7+{Lp}D)M zjpMkBqYth&?ek3t);BYLt`>@-bzq9KPJ!=L%eR3reNBno(WjW#sQemP%A&%fsfp2{ z#39lT{7&$)`MBY@wa=0Lhevkb#yFo4tsoGg|2{oAqrBjRp4U=L#(UN5Fh)Hc;t*}p zO;*yp5Kj4$1OlK=J*$i&FB;inW^8s9@wQ_A*0w_W3CC`G!H`{wy+4$lT22eij zYW7$Z!H%qMF%i`=f-~LQQL=9t$e;7JK{PHtRq5zJfTAOnTsyWsI)}OogYk|n?(3g5 zWvGkwP+HO}2_ClItA-wTG-5@}!Gp{nV$tN=&kNG_{RFCNLWYVVipOgeG zXl*?A*DM8R;hlUP;mY0?SCWxf_V#`H**>i;GgD=w;vUoj{nw_MX+>)Wg%>23b?)~) zWJ1)JUUeVniR9WRvxo+zrZ&v7F%7&T4UHCA#_GUV$d*Wf#`05$qCD0Ood_r&_$l|YYXolrU z1D>sy5xhL0k9pcX#VYa)Q?87$E7C3P8<&-@Q^qaX`TbeI7geBuzG}Yblw=gBW8#Ix zZ^4Ki?-bODs+Zx=2czaQ;m`~}jb{=jJ+iR1zXeO7z9-#ia>wTv@y9Y`?Wo&@zX(}y zu+#o+EEYz-(r;RcVq9&cEH@1G>pH5?en#3&xR)VprXl1IEvhm^#_3)>ia zZitTZAS#YFkXM!O2HN>y(6NY@VD)dF<_-c=S|4MD?-PE1QdAxbCqnEF7oV3_K6v4< z{`c)CIym=Ysukdjs^xh<^shWWUTLrL7-+lwC-9y9OmF)OYVJFQynMm^85qFPo8?`C zM|Ha(1%PKWsSO$0`lWITs8kpeVlFJ|E@Sl0fxJ)%8~s&;vvwoBP~R%Nud5;ARp7(V zREYZ)(AHoYTs7hfwqL4W!G`|IJH%N|(FU>*{{> z*QQQoe{^Qig$}T}sLbW2b$xNf;eb?h^MR{@GMH>d1-~_5R6V-i5NaN4gnR zXpntJ%rjh!)EaIke1lLg2JWJk3~6wc`78r7h}1Y{*R(wIU5<>#2R(SVgU5i($jlGI zjS~#8i$<%eHuE&DZ~pel)?lfvoAvJ1Lnqx?@{h+Pi9Y1~3qHZFx5(C&FQWn*Ls8Fu z^n44~VNzV3_*U;oQjRRGDCE?%Cj8ID#I3|Vk)3{tWxSNp&%9OweB(5lc7Go|3>qUZ zOXUYzaH-+h2)q5W1+~ztmt{Z7<2_JnT+11kjf=}Nfqc(E-^HGlde9PGJ17diS*cZD zRWH|Q(bGutgy&(ExmQUFN6{?7N1Gt1hh8g#PH0yMer8iQYF-o4aMb7FloM-8p%_;j zvgR`c>7Z{sDy2Y{A7Mi_JZ9RY*Vupw{!xuZOzP6H?%|FBRbqTItD&?#r2qMLf8d38 zcMl-;K0+^jK6s9F%+FWfb~`eKdM|4wnEhuq%^*tfu4olc`mO2S@@xC@{I(&=a!{y? zXru-C`P{VuyETMAoD*Xf>V%Jd0{~}F!BLwXHo~TbyEAtCWzkVbk&h6TQ<;s8o2K3< zwJyyf{f;bLmsnn+>@r?GW-}@L2LZY;DT-E8!0NNWgZ`IT7MkEpgGh7mJ=G1r06Jbj zO>)OQ(XVA9xH2FqDw1pCF?)!Gk*%9k>uV1nh!QXf#9~;4QihB|XWn_F&8`2A6eG=`s-xfV< zvTjukYVzQYQn+5{b^a6g#_Q+yGA>JL6vi5Uid-hJXonsBo+wEk7T>P-WRl-^>^XJX z>(O{fJtucFG-Vt7FEjP(@WTyzz@V09)47C`Za^r|Cwgs8?NrUsAjaqAjAwkzKnUc) zzm2me7oLJsc`43I(8YVtCbLQ#G{@VzNCD5ashsFy?Gx|_QIygF4^*&yIV;4vS#^Kf?J*L^WGJ)?UyhA$P`;T@h;s03*l>o z%fj{{WBxqB^(N4~kbIwH{Xm}q&AS&+r#iKEnx0$G;3qQDG`L=)lci8m8dLWLFS;~B z%<>|O{h|&&hTqSO|70`*m;pgzZ>NSqWcHUu+3Y;X1}qJAxl_N^ z93yHX99>ipwr=V)sh3D^?P8s9GO^J`dAX{Bc1KfEqL$jy*e+S@{f}`ThJZnrQsrss zV=nKzF(-aHuzh92_p-Y;;G(UBDbsV|E)Rz|J9Cm|&m$>O9DiNGwb+={Ae)Eo`mt%Zmk|v6yg*5P50>9a+O7v>a zaGD#v#m=-(WHE3ZvZ1?w@gF)x{;1WvVb*$^&>KITeW((3uU>HnCpU!>DY~`u8)}Vd z*pv02-U(nPB{W-HbKmoBb?ANaORETew&7{B1JuL~&Hr-xyO?M*OCkmyd=zg{ZIfIN z%w;1|pMQa~>@pMJFl7(6^W$0~Yo1-f6O*ZwdEcmYct9={$fvIX1sdJX%zd&z&%2Rb z`sJ^bn@69$UzFh`OWQ*+++2vVNTa+Ssu1q(Z((Ue&vYLBD$FM}+r@wo0H0Z#+%n8G zDK-tHw4%nl?|CDwnHl6B@1as$bEJ_p{t_mGQdhSB6kWupS~iruj3Z0`T~Vv=WInF) z*P(i+>{;oV1vuwb+xq=L^ak?J#a#>F8 zxtqwL{e$n?y2xSVMU)X3x^8mxL(ZBKwAbH{4&iF7yk3YZf&9+6*xqB=Y55Z-)&y&W zKzgtAt9BKIkI{35sE-k!r{R@G28($=scDjvv%agnjo3Pwu|FFow^08cqm*4Vsw@iG zPIF+UzYZ*tIk6Tn7=d_TJG_dALTr0);joHH)Fk9@hyb47}g2%$MeBILY&Uc9n5jZf;zhBpO?5 zHTuf4I&8svG4^0gb3AX=AdTF0bay}z=qd~;oGekyVJ72LE`4h`N{`>aKloUpLLwRa z%bC9CwJ{`OOAiV~LsH9ds=pIgrAF>LBq#X2>1_WeKB7Xipl%5}6Q?gCj*GUKYrE;m z*^Ac9B(X}4UvIuApUQXo7RBY4JW{k19#}2IxsX+wYIOqt@4flQ9s*oaK6~kd7MHPd z&-JA}>wzFXZBW#{}+DLb zPytkkYHpY^ru)|?Q)BrO5{JdLKSr8hInWasHeI17FzW0nJVFpuynQ4t#~=tB`#R-X zlEwK__>&&gNRpe31k_lts6kBArZX(vi3m?X7#8p!3Kv-!NF+s|k^ z^YI3evy}G@J0;yZk^H~&82_HgtSRhloi_C#E14Y>mX(G4bDR`&rY#%`+$pRii{8Oy zOFJfpfpw#j*+A8p^7B$LJTbg|RmC26ag(?#UjE>L=19qKCvke;iLnJ?D0?5S8CUdou z%$-EkvL32L6D^-=;zQUT02b{x9d#pl#To>PzeK0*bHjcFb?gLI2C#|(V}?GkAf|q* zH=D3^$M*8F1Oh{!X)Dn5PAng3?q#2+ri6zOV_+8XQE#KFdrFChh}o~WHq-Lfa`P+anwW_r4nFC_iHdGHJ@ThdT#@4a z(`mygt${#xgrxv|YCD(tW>S$A9aHZ_Hh%hIMd)VRJr^ix%=2zP_7Y&aOGQOZ8>RcT zHmKR|LV5v2gjioS^PHo7G2j!$x*N1~;W42jn(^ro9UK~HDQ&UnXnU_!tO1TP6W?rp z<3eXc_wN_vi=e@4WwT=acjB=YG06zt;!e^jnMirT;PY|?1z4=VIN+nxwtF8xNmU;& zep61LDEc*-bNzZDHPF|Z9^7B8m9k-#t3MC;w3MQBysE*zgHi@l&fyzX_l zqu$jN;U`PNA?J@CVx^yR+cUO;+#+Mhbk=K@az>%I) z)mUYWMn=%SFQV<>srLbwkqjuekk!^#@csIk(>vK$JRdwd1xdU%h2ru1)R(htcJ0adwIEGJ!; z4qOzI{FNVWB|z~Nz;8+{9sT-)q5E%ReLDc_tGgm~vE4ryX3#iL^DNR`VEbN8Ll^1e zp~U8E=bD$T zJZO2V+7{%F^^aGHioUW>0WD!)`xJoO--fEgeRVZw^r9BE=Chs$?z&I8dQYh3{2b)n zzJfWqjNM2IVrF1M{FA2(Z3LUbM(fS7p`}CL=kG}mmuX_n-z=Ms`}Q3CcfBN$uTCZ) zo)ijfrMM(0dsjvIoe7jL^MF+79CErJidn^pHFPbmmBlR!$MCqDQ`aw)E!2KWTgG( z()OKe;YCSesswrPP_#wwL%fr*!-a?F`&Whza7_v0;^>)MXr}k^E74F?3eOJ#=Ju*% z^wr7<)oICYr`fTKc;r%fJlv9uXrdGn@KRXny(-2#->`ufMf?#zP8S7z$_d)2wBe&Y zZ|VZf1VvMTHA^)^gkXJR*o&QO{m6feIJE5vO(`}oC8m69?zeFcz@>Ij_qAFt|MiH% zt$Q-%I_7r~7O3}>fso={xYN9R=xl0=#mNoJqoE6eSZeyMJ;-HJb(Lib{B07i@%I*> zEGLWt#;g(2lZs1U+ideh3IYGBbtxA?fA(DXiBpVrLhl4CE5MeYX-@}0a05B|=!|2@lgv+>mo9^a!8rKOo-@CR&)!@^|;)D_z$K2=*&?|$nKfjX(c z*d2`5#GNb~pX5`rnP-~ggTO~amycYRYi(vkuWSq*lL(&hk@|V9&RnI~Cl#&3Q~)i! zZw|hTc{V<;wvfxcAK8#PQESeG>$&c1Pi+!}C<~4TqYr06ZY$Be>(sIkeDVIfV?T!} zeGVvBpWiPxhlY%!(L7FNjXrOhk?#lM!!fzU|8fij=S+lj+NhFEX)F{Qxbb|eZC=)H zE*6gAHBGkA5LFB2jP&rZDTyU?czn&N1%bKVT9=VR2k+wHAHuOOUU^=Cj+R13*3Z69 zm2U}zG46a0)Y-YqT|w;^F7v+Wqs&DV#k5*@eSGf-mbOi%>&_1yv>IuxB<`emEyjfp z&Id*sDYgzzRai%nPfvvZxqX3wp`kdiBppl+V${>GhGO`oT3~6QNgq6j4|%9o&qlnz zGicS?DV&AL!n78VoXy?-4qnZFMj4Y9=A z^ap0rVHVKm*;AyA^I|#-%Tva+sTZ zKHAjH0{88mUaCiRVy&u;cQNDa&@}!RH4CAvA=)BC>|FiCS2l$G7u3C?EMFX0!`XK7 zO@-7Ae-z|uFD{H#-5xKXd%{wz=q-iS0TADLtp?~s0cm<~sdtOV?u za4=s>88=yGs~i|rR<-+lZuyaw__5=i=0nc^qEq>5zESdD;5!-I+gs7|52NN8YwX+T zuh=w8<*->exaEy!oE4P!tZM83Mbo+XGyT8+zqB%)Y?D*Y+oV*Oa%N6rb1s?lDU?J? z4mr!AY{ZO&oFXN2obw@+#-^O*Fp)xYE;3?H-+6z2zyIKQd%d2|>v=sN*LB?=eUZ5~ z9;i!W$*++cU8ylWi@u^ZYK3Bv6-12+MNes!t$WRYF5ePOvC{sm;qF}5;H6o$e1BoY zfS%UI;}&GCoss8qU`NqVEbi==V?HHf)ul?!bhM`Vz>U4(f&t;CKuZb>}(YVXVT z$EaqG2U%gc_eVIB%Z}f+TgRU-)+$A-hX!`{%j%6*#V)O)M}&R!sx`F10af=jhTS#W z>8wcERq}HBijUZVlpy}`_S6e4mP1!^#}l@I4;FgO1FjE*U7yqW?V{w~^MzZPA=T!8 za$arZc9k;MTrcvbl`%cT_oFJsfazYmXjIZYIk8ob z`Jim)tZJF!jmED6|yHdmnC0b(gmE1f8h-EUFd?(}ar@spj&Q zxK8=rBFNT~?6Y}l=~a6S98sf|SZ66_7xr|lpqJt^;z0>c++dWk+nk?%)U*C8DOo(M z_eV!Y0um-;kC#Bu^Nf1%15dcU8Y@zkjPBsy+!J+h^)(HHf7>y9mwes9xr5ERAo9i#GtJ-tK4tddy`aM*ZAV8OU`T(0VEO|PF${*r| zE}`=sX`;N=e0VG6vM0Z67&}kj7`t&<%yv2q>AWN38o~SE(=`sXJXd{^Mx%iY7tDZZ z5N9LUmv&}Mx}4GUYP~9#z|rF*I1x3gKoKfqb>+5vwe7n>t^m zVo-w!PP!Zn$k+gSi9UQis5}88W(-CkOk-t{W3P+hUH+0`qG9?p9Cw7p$ClBMNAtor zw?YlpnpW{#^M@Tam{!@9hnZD^afQ2PuX^9?dUlZ!2$)Mq0l{I~rJ~%$zeG%8?eZ~% zw&H!B4R+syfWwD1>-?cvpFjIMx%^T9G}x}XVY9x?W-706^AFCa$Fx#=cNcP?>(5Sc{Q|@TTY~amJFZr^kqNLA2e%RV_I4HoKl=*9zQEY&=G4>_ke zDGI{dM7X*n+)?G61@nXqx=$xNc$RF=L+wKZ8pKbB7){n|%0D=ws~_4&=Egkwjjqks z4NvA$ov)hpWpbXHdeCFGM)^P(&H<6vRTc@>*t!mw`G^*_)HR?Aq3KFp#x4%t*UaYG z9!x;L@96gUvvr|%`1ZJe=Od1-Mw3xZkLF+JxnMlAGAlijtk(U1`+FEvYRn0nTLZ8( z5~?P@QK205*;}#3OaCo>F0qqt_r7Dz&a+E+05NgR{4IClpr=N6 zY)@%q0XM|);WyZJew1!9Lmc|YZuxZ(u;6YN>!2X5kU!uq)^%S~IR2YY`0QDR?c)UI zC@5BQ6IT)Xw&UA0)0W>!BRkzFNq)-Ju60o&8z<}2b2`%i0j7pMT^|J7VM|(ut<^US zJT>F!D!H@?<@Atm>_!uB#JPD8zdwH&0zd7NdKEW#s+XTPe0HIgx{+yBI{9Jv&!}2k z3%rPx`~dPejO&vOMiuYya7AcdSw?hv9{#&2<(19@|DQSS>B#6kz1y;edpiy3pEx?XQ-v5Qv)YiO|9Eag=V2sJh95*tog$KHO#Ay~-u1&88 zXUSn$27KmZ01AS)=V$AT2>Tk$Tp1xc47?BRX-USO=-}$wJxr~^bf~*B4eyY7#ngu5 zoFUlfTQvMBxpLWtXPHjeFUJOGid~y|d<*9uRxTq?+ z#rC}D<+-{?ei#C)OekQ1|Ndgm>g2)t#o0v}9Qczl%2mdi*Q6EgcPY}okI|pOkC>lA z$3ggiLa(EqU4u=`=7jv-Y<+3b)&ZNRcmgilg`zAWCRwaaSw@DsNLs~M;!pd!Q6rbI zn0o``zIKC-yGfS;U+cyIKj*$6ouoAzv+Z8H)~>@W5#pzRd4+GKP}|=s2=%28L~WCsaHbubPCAOylz~U?OoBc^~y6VVk9dt z#en3C+-2B&3HuNpj$?sD-XTsN~?P9XjIGiU1~QfesfBkeL^ z_!aFw>5;9R7s`o`iI>fPlT(_LpU4`ZZX2V;8cteE8qGgb9<@OFG`EWFn1vsKQ!CU#~>y`Nv&%kHgT*?(TJ_$sbB^SH<&2KsOj!qeo^H+!Xj zSS&BjDYT5&br|2*P8CHYuC&tvLLO#TI5|#$>v2sq11@+5{3h3&OTg+NqRiIkqY;4r z5x&=wzj}}IM7HR|do~P5s2buAk=cgC-rtDhA*Pr6pLR*MNG5or@(K4}*@e<+rmH3s zEfyS$iX(x(>lv@bo=Jc%?{|=_=LL)=m7Ax?veIgOvFTl3G?`mki^G)}XEe9xr*8Esa~$El+ZuRYG(0Qw&k5Cy1bWp*hre!#2Go?m zE!X4Dn(`N6*^3M+h;}WW{pIemFu}{-adP!@jVq1Hiz!#X=iYUg+cUB3gsnP^i_bSc zc9_B4c3fE^FQ$9D%IwGlUl!^hHc0ETC$siUg`p=DxBdt-f5mtEiXgSb5T@k-FA?WN z_(&x`LL9&IcxzcOEO|FH1*mVN`7m9v()DD+owd~F2KT{#XZET)=Bj^(Ciy}2E<1z` zgI2do7zw(qK8-G9_lcyE-h`1;sFIzzD^g?vV{Nd-(~iH|sX#$XF`@2Jw-I%d63zMp zWxv=38aJcjD^A!B1j7Hxd{r~UE`FQb+n5$>-QafkConJa(iN{P#?$@iXBtKnvAsp~ z!AthPM{12?{UqxtOFldwE3I6>)0lT|*}jrwgbtxaA+>sHs+9s4afdZAdoPw1QCnP* zS@C2aR)X0!TO_VK*L8V;N6)n06RwE)q+Nv! z2)Hael(8rBFqL=y&2m(sEA~Ag-24R?w2s`3) zl_hv&n6qrt&M11;>kb{rxLTgxyWK;eORlT%Nj*H%l+F~KF4xctEG4%1l=cqKYH#wF z3lW4~S`(#(`u4=Hvl1QnQ?g)|Q|VvDKE@UH-VA@sO5nNEy{^)-$QF=4rt`bttsfQ% zI51@%P7{mwZ4Tv;R43W>bdgeD14bMAswBG82(HidZeX5Q7hj~1Yc^Er1b&wzSwrEh z&7(XNrK@nZ%t_n6o+mK;|VxFFTn$TR!spk?mlT7Jx0Jo&{Y)p~C`V*gL!B&&2% zHHqRmwHpg!GR!k?I=D%o~l4 z%OJSgzOC%v_2*ytJ!wt5Zn7TTJ46?sd+gE#|H0jepYxWOq700nYr;k+2D&~XX^6QZJ646w%8+4 zL65E38&T-wsVOyiD6TMw{x}rkIwc<bDUlL!&d zYSG+p_OT5#ytk*9>Go0;Q!u`&H!|Ib)0XGv>)u+@cVx07&z}(>HQiU$5VKCk@VykUDg@NEI_9l>v&9e!;xeVXX7+ zEYXXth=e7B;XL1H_C{v;X{Z~O@d6Yr1|ow3yrR8TZRuI9xcHn&Dq$s+DYp-a>Z`X~ zXnwo$mJ+x<;WP6?zrfu82AfRd_QIs?&vRD$hubX>&*A%%r6zhF<aO@mPC{}vT*?rZ(vd+m0F z-}zx8WF-B>`x$Y}SG^LfUNTUt@{SR};CgET!fEpBY%w6lFW4AP>(2eeGXEk5ULkDG ziL3d(iXr{F!EtU!@`G8bbmkJiTdno`QMRx<|CfsVwuCyJ*xsL$P4)YJe*#}K21>Q?Qkk2EN@0siPfJROW$9HS#0{0XBLyn|k`gz^1!_bx-A`xUXg$tj14B{X zE%=w{SY>SM43fh`JRIR)Wb%jpc8B{2DGetKt27*hs>t*Gjlf>X=han{4RCWo6uUO( zX9yfRId2V7dC>m6(DS4zQ=PV;0= zd;%c8)4owM6#v~edr|4TfO4&qmxlqbQkZXd!*Om_Rvf!mddDxP+vNgd3`bsN+#V4S znP$0nQON7*BeO;R-Yu>p+PlbUH%Cr`uy$MfQ2yez?WoPnop z+;epiKlB+&)LpB~!``QJz2g!~8>Fvyk*=c*N`!m{bQAqxYV0&%`x4YDLP{tP!A9@`M)tdyS4V)|Y9<9z zL30K#M3?6)wOg&YaLQB3bGQ%c!Cpz?JKt z?VDahvH7Y)vtcd&jvAFb5_TBRbHn4rMP?x;PmZ$&iF13`?-0gmH}b{dG5r1W=#$ch zyrn3HnTx@D(btmDrsQU~H$p{s@`856!;sqRU&65uf?eF5j9DL)Lu`M<3lvhlQ+&Q|3VtkFv2UbckY5f!K-Y(yrmZh+?dm#=iylBfFE zRm%+7+Qy&Yp^DyqH4o|%)Y3Cxjpmqac8@$Pg9m9?*9E+!NsQ{fdEHPiXou)O7_t$3Dg!Vl9A|ZM zzGC)0+yw0cL&+N`XBH^jo>Ye-J=e=dOx{HFHBHhg)66*9PhAncBi3v8;L^E!8<2oe z=aD^LU)SN8opG}@-RaUy4J1*z30xfEIM6-NTh!$feLZ8_xc8rOmjZdhotxP+a=538 zlV1OY9n(1h7M`$1HP2YgqJD)v`3_o(v`d%gQX@QjEL?M@tmX-Un~Oj+mDbVjiN zv5>^c_1q6vY*;?ya`D1e`GTu%`y1)um%WaPM%F%YLOzS=^bd&wl{QCb)!VLV!>;`? z#};9DJ1edHCw4Y^Ren+%q0Phm1Ke~2r3?zp5fPUfE{ELpq+cjwHQwy2{6dF^UN9fTsf1 zxSMwV%I^Q2PYa(h%4R~zdt&}*o3-1~$@q}lzbr$?Mt|*WzyX! z3)mL}^Ci_;rHARBhkV;qBKxvev*VQ~-j}qygFVh(p*C1kgg=7&7GAr{iezSLZoByi1_KbKt&-my&FElWn8)ZIiMSb;%>iQ~sCm z;3H0@7s6^Kn*@^h_N5=}pRX5+s_#lj|Ik=GzR9(9l!aI58Wq+AVxyRpVU}HVnD*na zb2kRevet%>e#Rr;Apy%Lc9HQUTs~n#Hg8@}!tiAvO^RJ@`j^ojR3S-}v@zz1EQB2)nt|V-sHN*Pu^M?#7`xM#?Tq zGaVKVsZ7oGAc(-FPS3CbC|;1pQp7i$Sr*djGQzvmuT;pRwj9$Jw2=iH!;GeADfNHH zS6$#^F}nTz%DP_+tE7*pp0oYuz$VgaXc;^R7tYGHQXhlVsU&|1>ilZ|$7MJA+n)VU z{oQLE+fdA{FEZ@8=5*OeFv*HjY>S3Y+^<^{`4dQ8rE=|-b0!bkuYqDHN@=GGI3>x- zhD(^3vOTYNNFo3whg)*^6FjmyeShY&jK(xL<;AIuE(9aXSm1RrZ+sXk*!O;wHbI2; z0;vQyU)nHeRgkc|#~ZMLO=ZN3T)6vi*` zjW{Q@t{Z%FAvSR~YXk8YW!lm1{w#OO$Ga8hs+zDRyc4Az0UvKX1ZG_dJx8nY-*C(@ zGvo30_LkojE+(b5Sa0Sps{iDPqs|&`0;cEFznF#`@*0<9qZviHB%c7J^RkS@JMqq7 zDTx1)L$=#LYx;fNW>pN+AfOz)qsC!tu%r;FxyUrNbU{CTkn>zpB?{gnS*PMx7>@sA zzhoN>x*4gGhz*PUU7glVlk6hP%?5nm^G`fEam}KC>P*_Y`1z1U{jaL=S0K92fhmay z>}8$xxF;3&FNqK~!(R?JS+Fy9`Sq8S8?nRoAzGXIq#ZoLq*OuBS0;yKgXka9#Pc#u z(#?J!e__o}0C0h|Q4mO*kDC^VjR@#TF!+qAZC!!xMZPG^4QFj;r0j(!5wsYuJ&<%Z zYY{btKF8=sl(BC3OPo@SYYl3LoATxcb#%Wm8_fiyCvg7MR$Z53i#hFh8t4*^aNmc1 z1pp*I_n}BKfR?!m->%e_WXg!RjqGiEjc6NoaDqF%G2Cqu=gzD4if6|TH(gPdFYvG>fyB3NSd`F8gdQs|^ZbP- zw!54XAI6;inelqcE%wyqZ@{n0Lef|>fGu*q_%G~6YFXxud_phgYmR;araAMbl6Jr- zY-%PVc@?#Nm^s$k^q9@Ah22cnPwFEQYLB~5WqD~q4HRdjv?cRpD_FyJ>&%1EhQJZ4 z^{W>p_Ly7#k^jgzEePy1AVFE=Uz$Ynq9xF=CSjRRF!n=^xFWx$z$+0q zz1`aQp*nYI4}SlGV?7hDcI$dg1eQdLglJI_LX3?22^$m>RH)zPq7^W)-8na5>ymBV z3o#e(5LeBRC;3kHE^x*o4=gWaC@1UB7l)7R9wH)0(Adv0x!}tBZI0|k166~I2(a`+ z)I%2hd{lyUw$Z|4KxlMA65SjxF~BhNE||9hF7e%<93>S=+f64(fH+v~_b$0zRF(=o z$H+QyXOQ0=;GR7wrE`Ygx_H;af#^W-bfmmUV1ix=B}`*#sNwgpo~v8NNeWHZN;Dp}fe zF^UJyiJ$5;B8VT=?ETAc(K4P#u-@g{h!)%VG5;U2Q69%buGFK%05@Ok5Ov+Y1bFtH z<5?$Lsl&zkRKLYiR7BQ@MVZl`g+Jo8HSK8EmKa^s9ZdJjCH|0M-NwV+f#dI38@UOh zYqLCugeGQ2Z;u46O1Y4o=9Pz)I_-1oCj=;c<9*+W^a!g8HfY(c3w}YR=ek(49}OmOa{G+_}pMhPT^`~l{9It`>!2pA)b@3=4XPS17~uZ`_0A$;<9sGRfmU=dXEz$ zMMX+myfUWWH|KwH^gQAU^d0LeA65^DEA>Aox2(+`Z)6{x_$l?93?F5)^H`p!ud)Uj zjoZ)@1}v2q$Ez2(=GlWkq7pE;RtXNHnV(H)1e}{+O}#9~t)d5K)pUM6%Ie!h&HCoJ zE_Oe4-Loj#b#e$5svEYswOGf8&0E9OSZZG2gOKln^M>v3QMckOviXHVkV1>bN=-}n zUi(JCSPCs8-FNc8PlS1%eCeyzenVDt&P+-@?YiXKMs3pGuM(&28Yi{gh4Q@x;``(r zajVJiv*fc}MvPkZdM!IHElv9jsl@E@d1w$lbmm{8NGtl_#5zd$yiZ%uwY1 z>+0xs6bKHzK09(!+KZAQi?E9x4z?(!G*E*>BK?+aR#!^>y<3+YTq0OQN5qEB?ODR{ zvTC903)-3?E{bOH)_8+1>wwpyyE?yJX7O%GB!B|7fue-YA5`MgRvk729m(VAzhksvyJ`ZCe{pL0>lHq_mMaS? zN}c+51Mw%RinG0lH*K@4N~wxfF!){Yk?~|b;vS&z7C}sv_{w+c{qa;BT{t&7s6a?q z$2|m6B(T(MnWf<{j%HtUWxNn!1St_-={BS<2N9m{ODTY8LYBZ|L7g(4*h=6>6?q&^ z1g)63<(wNq=@#w713lx>YLXC+#&%PRj$UonbE3Nmuur)1bjTWHt%fnEpcLUXYBr0TKxCH6Ca-TD((q+pUGbie3sA>^$(sRU zL_>3fi`QYQfPp_(yM4yR0D)iMM2mxYG2Sze5?=yyxl9Cf$KMED?eP#Xq*Stj?00(3*MCP;w*}gj~E-dUT=xP;Kv9fPJut4Ok2B<%Jx1*Y;vO|@rOVcr&1~s6JRE8 z1v;dt(y3cQPztU#jzqPIaZYTYjoJSgBsX}$Kh!p`v8ef05iRTBX}@)tk+ ze;Bw^-KB|&{-9+)<$9wXcgr|GX(`V>ez&Ed^*k4b&_@ojVfRx@ zV>|<*6;!DKKyX3O6z`I^F4O@7>5(p##V+YftrqaU~vNn+;jTppJ6 z)}zoeEU2}XKq6h9^Mc}}Ko}(sq~*opS-YFyp)FRC9pM@icCR3=ixp7|^RlJ!8qBLBM-8!@;m^bN>lf58YeXKEo$4NxI-Qz7S;C5!D?bEM)(ovDgOmhU zp}K8v74+Xu-R`wth04{I#%nfWQZJVude)%L)}76|ieEmfra$Tm2uP}5qt(@{{7TP3 z_&`0^5*lnk)673G_Q@PM-g(}D$fdKgxzKBU(9AJO-9NN_;IBg6SM5`4hvbSM+4Zux zr8AW0hKAEP4`YJYc(g1TFu_J#jcnlGkFw3ao%#Xuys*6Kta~%lt9CV{v$8VN-onFk zK2A#{gJ~MqNAL#dHPZ7IyA~gRN)^*!b!?B5vzBGvb9NrGsPmHK8sg2}ZSq$zgU8p_ z*K=srm*-;Mec6)u({{|?|63!MGS|t&n7am(twIZ6L7PDCcxAX3uM{Fz#6Tp787SQ; zvKTK8N&{E5M`bd|W)!s*e#RuWplGTssxWyi2h_kCv?xDb9Y+84MU;wx4rpU+0Z&3L0+W&2CvioO z*yVlQ<3z53Wo~Y$Nff3!6z`jSOo$R1V^Z#0&c~|41`soRW?onN4LW~WjBIIej~3O@ z4J0-QJnOCzRGL(l_q{RDo6`)E4V%V{Df^7K@K86hm1j>F3?x|7hvFv50w09K#xQpF ze*gVbB@RiYd3zT_bFB2k^@N5;Wa-L1s?|)KI6Np-RNhCl;ntOb;)T~j4bVfuI1ci< z;bR$80drJ>x$coEwxzF5(r@KR*DTd}z-YKof8E+F9GmWa9DMfS z{%k4JpA0+3N^HOLOq4_a1%s^pObuO;WRe@%!rZ)==;CaJ?K$?)KRov!xLr?5*LH#N zvBlhKoNguT&7NK;UcFeEiyH^jmN;5Fs(;mQUJ`s!Ss#Q+Lh%vvTv2Uk$|;rs#qDGR zn@{@ZSc_I%!F9BF^!P!*^Js@M#{RD*7!4cY{s}}z0We|tZ#T;eafs9v#IhN z_X+bF^W84(4OGO1vGG!mTtWPOu^Y23Y?~z?nD}8l<#Hz;peK_6I~qo^F6T;$2K<@T z1;*s;iN+JtRXU*Mz{vDoseS^e`2_Y>u&g0xr}%?_tU=F2WxDv*^`e%8KIX8^Ph!aM zqd&$219E~G8Lw??MEBS+uusan_SFlA82l3PHpRV}39eBPsuJ_EEe^Zcl-^yX2^{>6 zql)shK<~y(COqSIU2fIDy~i(nzxdqyB%{jpd)R+wHF>CK)?8iTfE&q1&Pj90w#kEf z5ZqyptBKB#U=}b0i~<;NHn8l%|7gGpfyX=^ui{)En|acg(s`jl~B2pi2- zyt!l`K4Nix7yfOlGaO*tI|hP>0dN)xWCMbR4@|4Vau--zQz?6H53_wM!HY+gR;j_c zTKTQ}iI|ca3tY3AF!8_Gnxd{PpByVw7wFMTlDcsaM$<^{y|v( zFEfkzcjmi3N95l|CsCdiSfq-MG`Ia2R7b4x=h^YN+}oXcqf7bPIB#7y9WHXSJ9L0e zY~pGPb^FrPw{^O~pEG>zCigdRyuc9ZE$|qAWlq2w$!_^d+pfTQYF=o}T zw}atqSU^A4FhpNtQk16Wh<4BDI)Q-5B1?L8fE%@}R-x(>QnU1na#KMKyAvsVRJV)` zYeDFXsbso#2~(%)^bzZpQbpvk2`s4L(Z%}XHLifguqF7VJy2p?vN(ztCUV|X=~!?m)Z7>RqoknS zYnf)WxZ1j}-mjV@{f*gfc4*!vVn*d(b@NDWVj#t2Oc#1?bw!t-}-=U*f(RXd{5+xf$cGec8>~jpq>FQm?zY4^27_2qT3h2&2hEVhqXIT_@XvOmmf8I z*|l!Ix77;n!KS*nXNq}N#3okpgCS%IRMd~H0Z!t((USgRknd=8=fK`ayS1M zEcs~OhI7Imm?;d8$6QJuM$#oiwWkJHi^SDS(IuX+h$o=hakvv1sK+emtXBr%1L#b? zjw-7dI$u>p&@5duV6Z56WG>V$Ell(DOQ`#h*|b%Gt_x+u?#mO-NyVG*O|KmeamX8@ z5(GveH+ZX&YfVLDqq<#ARhC-oL6$In2h)T5DV%Kiq#!tX4$K{f0?|!eB#B0B9{!Yi zl-5Ivy=x+DbSu5AHxQnW-71oST?hxC32S74rIrqssq|NZbt6Y^g+ zBiD#}5tRB+3DoAe%{R9F-|;Ecro?7}@C&`*cj1{ESOGff1-sdz z3+a62?|>Pn%3Wd_+7l2XJ}rTqXHuAtN?ji_mU~lmI+asq@JdUHrmcmsaaj~pNV zaC$OysIvWZ{uwhTYr!k!|C&%)418X0fW)oYVULy|m$zRE7lMAL`62`d#kkf!@sa-x ze>m61FVxDw9oamj_+P41cRX1vBlu*F(s(Jc%(9vQa}PO`*EJzuCCgs^a&y)Sz#P`Q-WW z5wgM@|HYe19Mxj&FHUVchxNo0oHR9jzZW0PpA3~t8+Si2usTY5heHuiaAZFL4m}VU@4bTy|+Y~M_$i;KGAjub&=t`@go>e(=+t=DZ=Iq5-eIameelQe6}2QvT` zn_;rieM|Sr?_RgC>)0%>WSYXa4=)1g*Bw-UE9g>pR%8>sv_ty0?L7I@gkqGtJaU=n zfq&W84w5QMXULnG)Gi|6VbWKcR#XbGT3Q;?M+k-9Ub}r%sZ(KKy}tM%D!pZ{p9J87 zh8>vC9E|E;FFMQ5{cV@QBS;n`O4u!G6<~VLp34Z{`Bz^)ncD}wQ41nzURxP)Xve;% zjBUC&rFvzvY+K{1jt`$@@mTnfU7fh1_;NM={t}qP@ph`Z^UsZ4Sp*epbeV!i{&zrL z8A_XDBPArDWJzTg9Y~BLp@v}?IFm0%7boBpqJ}H8MQ27r!$JL_v2hp?&vpRGi1HM- zDX%o}bohf`9qU{c zzIo?k`iv4GyiGrtq1VP-OXvgLY?+_^CJe{nHf4U};w~$89bOyN8E#3}De34=*rEUR z9@?4m>3aI8ur&HI2czgxw_XL1h^l050>Kg<4#S?M!0N5g=w_>}#tX)|OEE#A2Xaik zrFcHjOJ@==bqIgoI8~1cyNZ|j2vfPN(V2sUXxWD9J)}Kp*}}lpuAKxv&Bi6LZ7|xf;}+UuwIek#uyhw5lqL^6Cl3$Y}J6D3_`t zx6OvqzHRE8xIuw={a3vL`O~^Ji|dCg7e($4x$zc`@ZtIpm8is&z;9+@Ti`-l!dJrv9`L1b3{&qJd*nU9IcIANPwB2g7Mwvy7*epmjD*o&oe87zPEIIq zv7{n)&mTH|mouIf?6fFS;p0>-s9QFrU*U3IpA2eQ$F9d^?{7qkQ0@}WvBud}0r5gn z$>ut`v(n9v&wx1~NjsRM2KxEQldCG^fp}r4E)^$JIXxom7WIIobKxPJ36jzoT)5($ zSW9|=Do6wux_ahQ>(GG!eu!=~oji zu&sKO4{SH&zKO5NJe0c> z^bh}T)4NQC_F1?EboURY`M8|f5(7-`Q{cqcJ|zv=bXMLMgQ2JsfXA18GId8-HMmr7 zpKshg^PZag|9uGlB3p#oGD`SB9EyF_8NP*frgGO8nk zjbo1HxB+(lMzT*Xps3dxFdDjg+sfXhG6$F;(l~J*BQw%97QFe1X4m_vH%^(@2}r@G7pvj0g)AAwNW-wefX^j9-z^?=u*(DCBFmt-3-Zsb^ZY@zM3S;a zDMiH&EZmB1-s{I<<=+03qA)Uy&gUduZc zrB$Joq@okt>H~;MX(~QIgJ+#<=`S18wLdT@a*EA zn9NKG*yu(1jq16C4(OIKB+`IS(qk$3pq>OXc?BtRosyIfbbV5DZ?)wrXxcW6)}uP% zMrpS~nKgFK&3-`wX8n@nqWm4P_FW%xcPnm(m-gl~=8>sl=2(PXYy*g#d;p0)2%dF- z%N|A#AaslyD%uSMA@0~}$*?4ZY4G7G zahjGw?A1dzlW2ryFP@ep41t2-JiNXK8bych=-uhSP}s&NH9cIpq4>10wN02GY{^FC zWqDk|jiV*KK{M{ljDcw}GE`l2V+3lo_^MYwq_9=F-8`!ufTirD%eK_L|8*HHyfqc8 zG2QKo-ud6(DNTBH#Xtmh9Dg~3_&1>=S=>ViO|SUUUVPW){{mL&d7I*=Nt}P3Cm03nJuZ05|>7G z2V8XD7NEUmmW-6A(og4mmihDFhwFp9wzWC4GF?Tyg>e>LqC*A|wIs8w;qUf~i6_5# z`i$KOUa~C|*xGm*DH3u?Mz!07kDCqSnCj4KgFa5y9Dvg9vEMPG4q-Iy{gOTC2!>0z z_%lc>1b5~-=@6I8k!fjV0Q<=a-tGvw|G9RHSQOZoFR?dK!J zNggnoJUe>(^?F=kg#A&8xXVI}>Re_GN|?=WRdVu-*y4}ZG@R2P6pi(|B$~*Mp9uh{ zmNoJ14Ej6;F`&W;y)X|xVXk!voUPK)jg}#bhyY$g&ZXElM_r?<+>Z7pdJwqt*1(>;4_0p-8TdsFRf=|$#l<3pQkGzYiS(%va(WU;!6vqbPfBR`&#`EW zgqp(ks=!+Z&oc}rfD(tnt>GjfDNNd71lA@aI#5{XMl!NSJ1diy8Jt^0}GKgaq8c(u6BSnw8eUq_VzPtsQi6(oC&bAyFnyb*CnRnFd6#ukxGy9+ZN zU1Zc4Dg^6dO9S|A8K{(B$%!p2m)bcN-8Iu}c9=14kEG0X9C73t)%9H}tuGzQ0+El+ zT7@~rmmk>2j)9377dLpnqza^uRA3m~NYb0UyS-+#?+;AYItvuY)1%P!)HQk*3s2xq z+8mZQ?uS=JDl=Hma z=hC7DY%apZq&-rdEqm)veUbNR@WH(a3mA0n&B9rz8SvUp!YBX#Mf1nlf(krR6kMQx z45i2G0|mRiX@k>r#r__S>EG62VFMCFIPuk8SEvYW3=1U4;bxZ`R6u=%K!*|?hR#_Z z(e?sGqRYCq3;YILWrdjF_nA+vq1IHd3J-@Vd2ae*m8_Sw>rfqnu)kCBu`Xb5zaxC4`&0pka= zQOd4;gpy5Xq0wj0=f%m=-HexoW?$Z>cTYDVpx*g}(@kC#c9#;{K|zC$ct8Y&BTWo6 zjiSB=z61&I68q4wHSFxPRDO48uTtUP%>x&w#(D(qD_p7adV<8L!Bg5%ygrH6tHj)G zITNJ+Sz4YVEMa*MGK;~Ng;Qzm)&+bw)ow?3Sz`jacnF(G_hz2dla9^93=mc-e+9QH zO$f@vv)1a23|BS^re6Xqr{VkI;;F{rLR;)FlJmMv>|Mu*RU2JTy91vy8E;{Bp8FPe zBm;VV@;eMXP3z$PHMvn+CsGh~t=MlHdSOcEytC-QZz}+{eHIWMV-dcSDdurlq|pmK zicQ502{h4)1-j;aL^^|poE?@1q!Qb$o}o0i{kpl?(+}a4FD(kH`US(T4n51n5sqsL zvMn1=!zw1(zKRp=?KMXX^xsMbCe7hs;*eCh4!b&wzAbGDh_WX65e<1g3NT{Vi@qj_ zM&+TY_QAlo6$f5hdzYgWv{G2(j+M#TS)m{-?^~&GI9hn|i&UhyunYS&Eyb$hrK^to zu|HYLN&hC~`O39)1FgozYx`|#=peOnePHVN_#~9SfP+bDBmu$4Ij-LtP>9_tp@DKO zbI~`;k9QUF8|d5G`mh1lx3yeY^A*1Yf`TIGb<5b&B|J|9^vbMrRtpJxRMe-cQ?xAx zcMUrsCMxa`bD6Q@Nv}ncRjo>^ip{Y<*UHPKb541(RaSQ&!Gmvo1|A6Q2LmCvO9nGh z)jMZC6aEqj|Kf1c=DNlJCbotgd4~ii%0K+cO+O$24R@SdRaTTrX0Ba|x|E&iTDG|b z1!`=A0~$-~1lXhfHm@};R@~W+2sqpEVT@B2O@thx;DTHwMREt@=wg@CHSrlMB4g!j zM^*NL*B6)9&`bn!MfH*DK}Q~5_h+f_O=(@j9q$x>K?4VgtMG~lF`x}38_MU&um8v1 zo4!N+hyUM^UB!%SS%#UhM2NC8w!t7o2H6`*S+iyvTguqYNQ5Y9u^X~xs|KU&vR5)> z3E3yc_Ivw$?|lD&>vvqobsX0nH;m=IyteZ^pXc*YInpoO;`N7lj{H`NJ3&+lIlu8! zFt~Sy`B}mbReLX7S45LbGLB>EA2TyD$ma*T&C4K_CEvWY!>!y)ji*wTrD;s~fO*Tl zpW^@DIZ`~q=}~7o*kAX|k*pTbX^Jk#F>3E~>)?$45@%k?A{Zf^E5PwX+;v3i0lA#Y)+zos$ZJ1aZb~YE7Vy|l+ z)$}TV=-b<%0kE+PDTC@=XaEx$|6lZGsdb4cFl>D&iiF2r+O}b&>OV0I7!iQJjDIoD ztBkuThPyvL9RH1zM?@{S77wAcoq; zQmIxoNmecWV)OM9ql01Pav=360PPFhntjYoMJU)U29?LPARLN+6EOEEnllBkx&-_| zmrlBmZhN)W|XH3-WUUCs<0THLE`%0Q=#0zfB9Mf03E{!1m zW6VSlpLIP}r6BX?0eo(fg3SH_*t7a*<-Os2&_mm1#L1S*X0@a<7vG!?@k@5lFyL~F zI}bWvxG6l^mR)%Pse(%t5u(jo*H%Ab5X$X0Lo9N2%_ludEwQOhIJFcT0umi`zjg^- zo4RKZyCo1FyuVr8NFv(MM;^Ywa6_Q(()718RD8}-eAjp?Ie^M@Jw51__FbfNQ4BIY zk&QFdfPmtN5tW-uc%fkjL&kk8r6WB~Pq!ZtOSmso%Er1M2)&}Ty`1Y8tJ3?LH^Bqn z;u?U5K}IPimq}7)G73AgBhu&A?01Ow9DhhAW(Yygxn1KJxAVsyu8-Tu#b#kQHvbnc zIVqP8Iml}$>xwK6`Uwje5qDgZI&}G2zldU65-}+H!*^?^(rEDZ*NC{c3V}ZPhWBR~ zVs=>iw6rEsy^TuyCD)y0`3-4aHB%qHjav4^MQXj(&H2HSQwkUeLl*$5PfCz%YR6c- zM1BZmdoIJgHH3QKtD~s_jq^Hn51D7T5Ur7C9t*==LuZMI7Cju`OxvU3%RM-6Q9F3n zJfFcFZSs_GRYGhkjF!FtDM?w;A* z^3|9v)z%O!;O)RuPmkv`whQ~%kZG}>Z_=cNxS{;N!u547s^jSk88HP56%H>wIz*EzKRR(%!BWaIHml)XaA{S z<4}bf%yn6#%TyyzPrYg_QNkXxBp%mH?_c+(^qYfZEcT!pTVYu3@n!CI_)ck2ZLgvl zLhJ2^5bVimlp8=2n4FqH7UI)*LJhL}&>47Xorn@+w0qP>ks`-E8es_rsLGf0AhZFU zibI>pl3$UE=>?Hlf86bVuJyIGhH(l9$;r0(CS{#OfKD(%&K%cFiHK$n%yd@s4h)ID zgO&rodB{l@3Z@A!9WsrynMZ3JGG<48>34SQbru(~Wq9s5vO=L^v|2h^y3pK0{(2QS z*&(Z9>luSiyf^JI@2m3eQHrhBV$|Qpg;dwMKlX=5?@qmh-U*l}G3BY}-Tys;+29m0 z8~R{97w`4@Y~hZ_DXYd*&CT$xKRa}VJ2sXXxFyqPiyGCDjKz&1LPTdL86I9;Hyje3 zg#tJJNeUbuuEgwQSE`MKabNF9N^eoZx#ec%P1%2U0(R}cV&VxcfPpUK+yF$(o$u&;p{9rcqxZU_S6h?1?} z+qY89zd3vUv@Wj~0G9o5j<~0Ju7sbB$g;U+DXdu0c(d0vhmraQ&6A4=M`Adj)ieoK z=wN(IUqn$<`f7mN{_hnzAEgtCT?ge8@Kcv~lGuTB5YhIMEC9Idzz`cQ|NkHUf9D}| z9sMp1*$4q%9}jOk09ft7_}v0VDNj3fw!Vqm8POm)Aoy!?pGZr*<3 zR4jMZ|II7>zjWjO@BCSc-Ns|(=9%qI<<^b6knj~^)|}wN;qO`_hDIKWw~+aoTjsMtRDat(RiXw4tnXkm-Zo=?G3KNYn;zlwa71*-_M%a#X-#6&)V_ zJnEn;N(19>_BKfWQ()4M-EXCpPv5`k`>1*d9G3p~*$kLs6+&dX;!~d%33-PDeslZY z(^T(xqzglj9>8-~fsRD>|BL9i2sMaZc^w}Dvvr9gWVyl(`^Tpbe@s6=TIV=k7aP)l zAGIkFt%gYD+v0anf-=EzQe_?)Uk{X>o+z=`6kt;3E&=RtGvS%o2 zUjQ-ZWhB%^c6D+}f_0yw(?NND$i35A>!;tY9sXR?#Ew0{j_Ew6r5GA#NKR-sq)zny z+YU{o(1$r6P)6TH&-Gz~v0T)jC|#lhchD&ixs<8%mvU=a^0xk{>ZF zXx}!E7djGDt|QTCzbtS&o|V}hlpqESZX1OKnaw>#A|~gd=1AI8AT7S#2cT=WPq4L; zOo-i&9Qi*tQXk|+t}{oizk`C(|M0NF-Yi{FNE+bei>*l1Z-|@jv}$j03R$+F4%>~` z$^~A^`+rqhn=mc^#iiw)hmp&79s4>CXSI%I<3rv~sq_FH9elTh<$TTo+tU~u$xoIH zqv*KdJQ5M`1>2Dl1@u{{B(lU~8l1eX(Ldy=~`+6Azz(olz zOLtEm`X+96zwLP6d@Zy;+)Qg*Pb;wJ)(0D|5}U)d$bTQq*P@R1Ia(k`GmxX42kq?@ zwa6pdjtueVwUyH7j#}uZHm74EsKA4xTSO!-jt`V;{rYXwjTFMqmnBY1;mm)MUTf{w zXsI9W04iRSHD{PHRY;;krvwG_+Ylk*GTY(K0X>ZJ0{gGDXs>32v;m8b2aL*(MmM>% zHn`%mrOnoEAOGSw`W527f8xH+t;F~C_+QlVKSK@7Z0>WL^iN6bF4I5)fg3851=X?C zRDyA0S(q9x+!!fl07(nQUWYYr!)&@T7*ZYx;?+1(cSN<{eR-XBA5`zBJ>KcLcPk^C zD+Yj+{Mi1^S(aH5k}`Y@OwFlb-(#vTgydBoh_4BS=XyOe;i;6N3N2GcZ-E60byRR? zUmkqcI{2)UN=CMIxyQO_1{<1fPw2GcIvq&KEK8gVWO+zygK^%lqCpz+MZ40VVWnJ4 z9!%X2iX53QINmFOaKB^Q0@^PsouJypCB25x?X0N1EW_K&U*#6O5#1Na!$qYt6hw?jH z7u)W@6yw~?;(k}Bl9L}idvHyQ0>>4qA_ZwnFx|`|Tfp#bRy_(?CS^8fyr}-)YVq0H zj|VbEgGqLclIrCBYW7Y>MOT&`W+zAc4CO{Q)YDb!CcMvQV*sR=z04jFjM(oV{LboG zupb8bFKl2#JN?XravLRG13i{9#lX;AIQ`nhI4h4p5X*BGRwB|i`c$7zy;MIjxNpfJ z%#Gujb#LOUjW}Pocw77im6g22VW`$;YQ5Q_9txCGJAR5;p*L&F%R-p3bGdhAbWNg>}4vtoiZ6SuCdv=hFw!{ z6|x~3-%UtLrV)(W63>Z>BpO*?>F#~4Hx58NrFE=W3DDG~dw9Vr^YQB!x6;1tT8sGojL?}!yyyJ-+HC0Uw)PI! zj-!--;;d1nP;CIku_iQ16j_=LFJwBsS(4A;6M=I)-E*;pHNtX(jX zbHhD3ILB=P?Qh-qH-5IaUluFl?Dan_(bX&ZYP?F>l@Ut@UL-|t!1ypU34eo=x{Z;7Ly zm5|;BF6+p_eOYHQB9T#4jTiD`2}aKVjGd{Ig;@yx<>{Bn4|mN^`Iy$h?AI^Is`sixwYbAHnX7K?1WlwjpWGPbYmj?9GC=0s|2tmp<^Q*b& zMD6ISkKC%A_?jB&rZIL$c)w&CaySUlY&l%@)jHbM9$8h4apu|fOpJ&(DqENfMFb;S zcaH&ZXp$!**iv6Ky2P5Hgo9N?+Ew<(>^5FjhlM6_vzUzwSp@kAEfX$>fxDs|#%D_l z z5YR35@jK_^cb+Y)P)4YBF>;8K@!kio{Aw0Qn4=Er3Nlot-d;bJevzvF2sk@7D5k$h z608}aj3b3#zVK?3+xe<>4Ky+8qs%CF)pRK%hG;7m&AC{sab5BGD)~~i>1x=u$d>&b zUk=Gjv@18M*qEZQ-wz$<_(Hcn`p%)s(#|>K`qVA9cI)e;JkyRT(o`QNFM0v|#oY4Y zhDJR612w>UDe$Ay`r5trl+vKEHc#gm-!0e#?GqOEt@0!f=NXUGXJVMKVze$6g=gBW z+3kozNiW%wT%yln$!zKAUrfbHO59EpRSdtgjA%_#cv(@M(#%Kcd^$+f&wV2`U~KcH z;Gl3sSqy?ck%WK#M-e(y-Nh^A_ zx6`t&STB)tbJ58CH_v6@akf3K!+@^y3F-2fChrY-O|ku7dIa3&Ktn{o=C_kw#~Da4+fG z+9g~ff=(siwnvoQGX4*kS%%iKdpi8$39N|OexunEE zoP5}*OTMAMGcGiqKL#ovnaKN+H_g^#AHzDM|0f23Z_|a-ArFQ z5!Wi^#O)JBnhoHLAD=y6Y0<``Gbam9(YOuj8=gjl^BiHtf?{_EJ zHZN?G^JV?zAHz8fxkY2|Uz#up$+V$DK}Ex}TDQ^$^zH1LA}xPAXFzc*e%3~%qfkF3 z7@!=KJEvroRnmImHEmd1GQxpLUT-edcR=;LQi)wtWc>8|gP}i-Duv-;R;O_CKJT02 zl82F~Y<_i79qn)S0=GweHA^B2QUqA|u8aX}#5esrf}NFr+E&+_=G>#G?#>$FDknRE z;=?(D8yfZUs07e6TkWfI(m&ZII7k<3=0I7qqzkV#f@(VkU_=fyt z?`$m&=#xgRfAhceoSFYfA9dm1=#%Jm>2#B{%y%>Twk}U82NL z76c>=IUumS3j!~fNpUNGI6xMTdOh##xaG+5y{T-%js>fmmCZ}6?Y9f>E_nK0JfJrL zKwF!$Q|7Y}KzARxI#5U(SbHsjm zG^fkxMt?37j1uv;sjKmd_h6VJ*+|cXDpKd;P}**hFNCuiYX-=ph6Ojk zU88q>6Kog94Y%G?{@fXHXI*9Q2~w5qn@Tkl9X;{)$hMh9t#GR~FU`?XBnaR(gHA)Qa2P%F6kJ;W9nxt6 zEtB%1mVZ<&0D%X%U)Ac5VlNu<>ga3ec;UILkwpilj(&;HWg(rYeJ^49oeQo z@p%m9a`aCY2@Q$e5ex#2#=mgwf(zuYyt=4Ujo>cf&wDGNTy3{P|D+yKT|G>kW2VT| zgs7%$&8#?7X)O@XySeJ_+|hGR$76IjC0?bjk7k!E5Ix?sS7hGApYX7VXbUp?A09h6zV_78RLL`<|&hVBexGXlw28azW~9f@^Ep*`+!}c**q74IyF@Yo0(o)2d^8 ztE;1fI-IZiRKjmtaB04kF`0Yj9ja8J7dWnW3a(vpd?5DWh^1RTe3B4l{Q+!mGX78a z>Zhxa;`PeJWWFB5^9MSalKUuWf(sDKsA~;xR`5&=>gAzEXo4_Q0N#e-tIY;KZ1i>- z>FFtFYP<3lF0+4>jwSunR79!45AAVou4nteeyp&mA4zhi7#pJ>4ZvZP^gOXUa8CY`(TL^L2F?OE!|ON@UPO!A z3jX*!Cnt3C5h1a@u0T?hJ?*vDNXnXDKV z#6JZ@NZ&g;(y&~<$|_BJ4bpM69T#Ad=V)UXS*7Se+5Z3oa|oqKVgoO(bDm*CM2R)| zdPGcNW-|**3=)nTfz|(e3l=D*8CvE8#>vBL2YqXKC#_u%8PN8fdj^7JX3<>*W_~CO zalN;a2a4Vh4XxAKDK;01`uXR(U~73lubr1@R4WVNyhI`BbgYLrwY@QDC+CyH#zedi z+x8BBjn77Qm$-bJVMG|Fx{r)+&83e*3ZjG-w`6?Uh_w)eoU&Z&xZ6}@LU#&DICr`i z`T7#ohb89dKNULuy5i<#kqjwunNOR$a&%%$QclaPo7v)edl-hPrYY?VBH^bLX;j)6$az(0 zYiV+i^KyG5J+*rDt0-5UUjG$$KPMF2LCO9u0i1sUla+3Cf@NErt{KZUf~XLhlR`I= z^~Z=Z3^W3Q*a`=`&c*8LN?FH6=bhqT*MTx-x~|>F;}NWBPm-5hZSq!(Bjz3olDc@H zlq@rNzZPcgdC4xG6>Rw^{6X@1^*x!D{EXXcFd;*=z%m;~&$2(3(!zCULgd5?h59!o z)LT zrG0B)>etc^n9h)H0!S65#QIGIWjr^l)o^3u)TivCLNOG1uk8Ca-4uCT14|IN1p^_5 zvwCQI`scRcs=BN4+j2+g^Lpjc-v&V`o8 zmwkMY-SxStTM_o^5X$*iwxoMP9rv2CJXw1P_Wkjk_WbEPD_J(cY77>8hAT6^6HPGev+R?N1Ak^}f%W zgE=`AHuHJq`3=8qcaYDCz&`P+BWvSdZ1d)-3cq6gfB_jIQiheITJ!H#$Zs$mM%>-| zRe%`30?%&S>zgz!8k1gC{SZ+#W-1k)x_Xg*Ou{K(SGzE<353h72za497BU9pzCz$A zHOs(H#a-d_($q zh)6ML3NAhifN$jr~F%>*?IqjpTH z@fdCd-#}$K`HgiI9KK4@?){zQtNbof{0(GxUSewHru#ncV7Ow8!}A(B*|j`uyZz|s*e%F`~O+9ymVtU;*$@SB{p+x@21&`6(FBB6G}+>2H=~8HWo(yO|(UQPGoId zQ4iowVRA2Rex8`rOcE~+Ul}umKraO+>0B#OJW3w^H=cTZThuuYE_>W007XiXB9Mi* z(}ZX!b<7tzdm1QRK61Oa)}zB+&@Trv!_O5T6mIy(hMu;tRd+YRER~X{nD;5) zUp2c1Oz>#gdp)%6kUUqAmfi6&HG3zw|Lqu@8l$y^J?E4GY=tggo4(tIV_rj~p*1*Mhr%_=~!UD3^9YaZA*#Ne1bo#(C6pv~zN13}Dh`{eoM zE2-aBYO_IM&WO-B<;TBPx(^O7#;#&z!{@hk89NgG22r=MI65DUzEi}aaV=7p*Ss}1|_YEJG*i(;ng0J8{~^0K@KB1jUeB& z!9KBfM6#`x5Msh0Qsmx5K6P8wARi?OesD;Wpa=1^NU6s~UxI7QB$+XYGa+caFF7x0 zZjEztsiTZd#6zP4mN6XoDsM-YlId0`WG>MvC1Jv~Eqgz{w!^^ujF0TRb;tf0oa=s< z8GM1M^Ug?V_5_g$(P1I>6QNxY@L~;6*!4rzuOA>d_CGo)qn0acIR;FBN3{7!0R)bu zF+ubNuxag_a`K_Z#ZMVMRdosLXhct6-O8f-e7)an61PI2O(E++bj(aAqAvR^PtlUmRANCPccGUklX@mj}dC z6O*8#z0Mu`nntRrqO4Pq6wokO480Nlm#^_R!4U+$6cx@l0bew0J0xiW~w8lXqUYV;FcD^ z7>;6UcsA^Eu>pGbKWithW(n}pFAq*1+2;Bwr9ZdrAwn=gUNS2eYJ92R+%Y|kdtNso z;rMrZfkFaFSIc8$=I-?f&Qg=k(W#Bz{^6ze=Ii5DK&a=eECl4eT4hm$Ih}MI*Z>-? zarCTU@7Y5uF=mCWe_LRFI%B8wYa|C&9Q>e`FvdhK^nH(iGB@m=_oIMTT%h(J+Wcr* zaqh9zczL0o3l2QPx0ZAzQ>1|WScQqGK578QMVl5aX1CgwcM}_++=R2UAgfq1K4%AJ zz?k6bDrJG4w^}OME&HLi&A)tyR2dcZ;`rZmZ?vqYJf~6FPGzEpfXn|$>Si&_1j%bG@Cv;LgBDT-pq z@E&nN3u#^MeyylB>V65M!<3RRZOU*F`3X=9qwSXU%}Pcv3XkuCq%F~kiO+ucT*Wsh z@^Et4kN#nrou6e_SB7PZK~uiO<;#pvTqXb)p+wN&ple zN4VzoZXi7$sV9Q2!UIOQi&qK`328#yAi$Gr4O`N6Tw)VB!Sm`5+9WFq>25|u()T44 z)f+^@N->wyuBHh`ku8$|FHDtYUUU%DDZr|VWu$?Rvo<$cWkT;RHAM*7Q|D~6J^f{z z-7`3&iYGj}j6{d1F>*_N*>7<`vKxP6PMY~HNcvK5{T)*+?$;~jP+Dmt5QzP`vKN6< z+yJV59RsEofSc`fr&^l{ATVf-ll4ik@JI1=iJhX~4oN(KKmJ$DQfplM#bgMUCUuRq z>+xK0&(pb)cu9Dpp;1xN)kRcD_ty%B)KhRz_`A&AlG{b|+;sFWlCF{35n6#;zCyqu&~tgH$>?1}Ep_;8;e*lCRbNcO(soB-t^v32zK8SoOXIwT<`BxW zeaoH`+y11}Vv%!0jFpc%HF8P{;fep^31*f?`BFkfMV~C?p})wMTH&Zuc{SY}U~NGh zFz$wVZ*msNEa7=$N3zbk5}FJ6b{?fP;K2&u8dXgy)OKNh>rtSf1erxON%f_iI6Z~o zF#5$dTC6$=wR@-ERJ__3OOfX&R^>57s|tWhTL!_0qh>Gqi)%Sudlx<&Vdm8_wNWj0 zIysS=Gd22Uk+%lT+qT2g{!TZHtbG>@|EqTYY&5pMZFIJC(cz$ib( zb(lzQXj`Hz@6Rm%`#r#{wfpg*V0t#glVpOCe-78nuqQj`M)!5J!Qf_O z3$$2$dxG93-r(LyxMcE+T*RP14szbA?JQ@!WolV3z7QcXAO0P1J|N+qOkAE8T)ryj ze>Gk%65kr2%&L68YkpX4H73&SjtmI_9Ba^jsj*SYMW}DWi7*AmE>OnqbI<2n9}T*^ z42$OZJbNjIu~OGOtNIbVg&eAN1$|gy+K{~R{>ty+1-G_cA_obPGn~PK`~3#MQJw6G z3}78bR#778jm;_WDb{xt@EpHx(4Yu|TZxno0iW2V?TR|BgFF|ZW-yA1k0QNcvpfqL zA#|B_%f^U#%`uTvOj+%9xtdnqHFJthMc@oq$RF)E1qey^$GL7$@}g^@X*f+vH3z23 ze5FbbnBki@8P9b7xLn3+q3<-Ropp9=qR#rLXC{Mh%tktLmRb}tPMoIxbT7nsr4h)J zIpZQ2uzk2L$;C&xWAA*S*36Bl{Y&TIf3DQ_2lhOeeK~uel>X<{i3J1Pnzx>TFz^zo zo!H8vYh6N?LT|yul!bdC&M7mb{h(q)8LZ}&JLVIa_D)Lpkl=0lc2SW5ufiaT{T z@rrJe?Q<@9QTDkd5RYoo&9>~G1z)7%eC~xc4itfI;x!fbn+}vA!u7@^8R8dlEv6yTNwXIVF{dG5q@KqT zyd$||8hzM`md=E$wGlUKRPeR^>5hJb+_|o59rtZc3S(XRLORCqt{o%raZ1g<5^#lF z3EVML!2gL$OH`&6>W;X{0t;Is^u^D8{?ow{c(tr25vUNHS6&haD=0YK%nt_jq{GgO zh?BSm2T#{kn`fDGS0myu-hsno)gK`r=8@UdPrRC)yHy6X)TQ5F{g(Z`Diqfr-51;{ zFA%;}vq!qHikHh+$*PSRPu{xcn?!f#cH+U(;6hsKQy&B;)@`!?R|Ik1p{?J-B;v2; z->rM*D;!#w`lmj!B$67YZ*-n zQIE4=PUjrH3jQR5tX(_)K@Iv+tbs3G5EVIK_#z}>1AGIV+oh~dejUP@D38eTykz+j zCeB@9y+S6bIL^jE7SIkW)q|ys3uy28Se29!cbPqdcc^aRgbBn@t$5RXH9si9+^OX6 z>dV8FhDDDF#W9WG?+y`<(Tg;anZ!NWV<(u1)tI)qpwm=?5^grGwg1Wb*VOE0rU5gp zReru2W&~H4GWULJ&;~i(h43;iUGVVs6NYp_;RWa!HB@^@TP>1BL|y;~a6YiivMJKmp;av-x9Koet7A;Bb8Ffn@^#b$PO!#ZhNaks0rO%y zLWW}LebXd!8a`gUESOw8zhbK8C}n^c_um?*ew3VEDUgzDt1Rd`8h1*xy4IX#_3o!Y zxFez9=Wybb!x5?rRNf*=uIjhfKpsRSeO5c5WAT5Tvniy7@*%O*NC5I8FaOLuC7dwC){gR$V$+S=jnstK9R}9Jo#$7*az-9hM(%=Tm z)z*I28+AO(ZeK5SP|)l;+WDpqU$Ytg)`$*?=JHe$By zQyI~^G_p`rrL_Oiddv;Ly#<}fXU87NIwio0;m;VJG~)~~d>P~x`(+CY1!I=(q|4~r zC~be$r;^tRNClB@c*S0O7957s*Bwr{#7TGStDwvPD9iY3%Eu44D7L>}L(hlDX_uK( zN`cG`bU};oRyq7L4B@^7Sr5j2PGg!&fO&%J#S&i)e;T~g*$aNG}|>rei+b22rycO}WS zb2h-UeBL&JTa?ApMgVJ zRHN)Z@#xAsLs1tP47&ViV9nU85)_^Lt*3^M9ki6aR~@fQ8p#DQg}Ky6 zjdvcSZ}V~J?C|s*eret$z8IF)@8ikEArmpj+zVQjV_^Qe0k);EMD>sH1)VE6t5B z05qy@jwKT+3yHXV-e_?)$ve0wvu7UH9gdE&INx}Iivt@s>8hC9Ij-m6qe5|=%61JP z+3YaWqSsD@O&Tcj^x;$zxnr68@W9}_lw0uX7075|ecCZ`(O(UDdstRE75yxR_@#+PAqU0tJRrP28K4CLATPdE3RF{>|~;O zr9g~lUs!ok1$HLsm@1BGk+ppEzhzNCq@;2k+(_0$m>8tx%DMr%@yi6H2Dr*CLy+YK z;Nw~e*Ht4(5&RtpPcNa&Hh0KIHr1l3Qo8a6g*c%-qwAzu?&w{TZX1Jgd>bLhv<`oj zpuO)Gcd%5Fx zKf1q!?CN}5p$VqfD&}}PVs!KUGY4urOVC>#pM%4frkjJds-wK%m{T$k7oWI4^)-hj zv2w^nEBrgx$RCnRTep43yJb}*I@^0ql1B%2TzrV1@{Yq?+nVh(6dbCASv4;hv+xFU zKT8tQF#>nLDj9IrEqYiseo+-Sx+y!+8*5}<{1GAYy46j` zI8HWpwo45-xn9WIS`}|k9c|=W%J8;;aI?G>JwAO^R7h#55W!NACUVI)Zi%4R@W<#6H zdEQ$oNhTP9R}!)tYIQvF#uzN-YWynY#pk#3MJ{r*e{AWQ2$z07dY$UNgiyQ}P{JUg zc3-@017Zs1;&=B!6|P4bmar?CqJ*NP^Q*6SGDh#LmHnW7XGw4EPd-LevWzact)_NN zxsOCXxN^A?TT{PU5UKS2#rN-|!N}@+TDxB$dH-sp5y2gOPo*52d%32X&-tD{sqLZ| zj8^*wJ4n^o({`#m>KgE@ zF z%|gg6kDVInJK-Wc7>B4iz_v0hb?+gWl-;t(3odcr*fu${KQn&xVeox0W!$dUJquAV zF+W^+D!rlqvcJT9;#gDJjb`eXVYcvj`>cNR+p9ZgK}m@>G`xoVYVJBxQ*1o}c*e7A z2b<0xJ=>?FC@-XsmOlYSEe){40D-~6s1?QL@a z4^y)bb+y$+c{ZGRPb74n*9#x_Irf0{EmeYFdWzi`VwBN=m%$D1lCrG}kdhWX(jSGE zU2S5GK9LOytG#9IY4EH#uRM{B6G z*3#;-7{|2P7Fqdtvv5!0TY8XVYQ6j+CFz|=by5*bWUKr}!P5r*!)fZ@+ctG`QvY@h z7G&<;#8uPrJG*(Yi^jz<_##GWidNw>-if-g{)r*lsKdtQdPmL<3@qXN- zPW}w8mJTRi%g~i~C*kA`NXZ+$^6qyk(7Bkt21o6Kgy_gYUb;bKQ*-`b^vU^7*^5wjzAa$41w#zksUT%tH8YKOIzg z!#u-p?#mE!YP;rb>aX3dG?Ym0UZJU$J-ywpVfW@w6+QMkJbU;0JZRMdi~beP>oqzO zH`VsOL@Dg3r@|-h_sJCuW!#2M_jWfnE?;SFz;Wuj+aBJ? zQ$G%3CVyUiN<0T?7niHj)p^r$2`LlJm}z<`8_KXruXp_k`u)-lI};}-?Hgl50DtB$ z3x?+4I+(nU*!T#&6~y_xPH~lf7Di(oWb{RadxCJ`UUr7pReJxe^;+nf_lTxhZWdz8 z9cYo#@s1q*nDhSWWY6F-^RrS8oE_I{?{uST-Q&^G3n7|=4nWhtVW-Mwgp%@Boow%5 zp7epVy02-hCwOqME2|=$_H7B8+g9GPH5!{-tDETI$-QKNb07TK6m)hQAm*XBtv?nPU1M5b|f7Hs36E~-PWU;6ev}@rKf*VcKc$!l~`8$Pd@}e7N_v8 zbmq7t^tu+pRIRtp7Kh0Ul+uX}y_fwnnO^l40m-9-*50ADYz19=YX>RVGV(_F@T59k z8DlKA<06&t3B1g$7hu+O5#E-TAm$`B#A)@yrKT;VE7i+f{&Y|jV>aTW$SIg8X-?>-&MQrN zOR}fdX`^%zv#?~(vi94bRj~ABmIGTiu4m{$6V)hv?5nf?*e~*DqU4XfvPOje zyleg(;1vIZvkbCX|F>mqE*w$wIiEN`qLF^tQh|5O2(mx96wvb+;5ZNv3wZvP`4Z&M z?g!qxUU@m^u&WZwPi1O%0 zp0EE1;~?qwJ?1@gTZd8TUAITtz#?#^h36&d?b_ddh?f1;ZIf)X`}TZ~v62w%71{ci z;blCsX%ERH=&77vwg)qug@8S`?zOVDdAvG3A=21-`jHbQdGy)uzdw?(^w(iGo_%DF2yU+=_>HtdN=kHu&_^MT^8V#{C3J4!@q!p&ZL-Sl&{ zXd1i+8xAA>Zp!%_Dg&bh_;`3O&cCXwPga~4zKp?As$$sF$!uPM3$Y{2s!A1SP5WF6 z|2+}@=tZN?6sC)INqME$&i~4}YH}ARvKOnBOH0vtJjwVzr+~)pqu7!Pi!%+9;Kzy-q^?K-z{1)WCi3o{^V1Kb{st6$ zI7lySV@yJp6QQSFr<$I2d~sjVx2LN17;A#{JK?bNRtgsw2E@}G z7!-lDL?z2#g+lTs{M%%P-2HEf(Ogz+KTy$Y9!CZh+jqI1;J7zfhEO9K$?i$m0Ewlw>#M zk4N)`Tpm`^mc}Zy6eN5FtC!V?_f{A6-?Ru=Lv5+6F+VSU-OP;Se)C*VNV`q5sIE1! zvC22!%bvOQUF}9qUA5Dy57YdJpsx^YR3eq0%*1P(Sf^BLS73-_?3PNsZ7Xa8dgRrk zqBAd1Sk1*CEy8|=^Xl~vFE|@+V;G4qc%wm=K1oV$Nyqy6oD*i{Mn3WBLOCZ~P3C&y zLtib0V88zHVuKJY7NCOZ_+APRRF-=k>|Op`$u<35k5!!%O#c)M<(Bu~A7_#kA9#Ch zO{>9d4z-mE{kXIp%CitI)6L1ssfZwD)9M37-Ki$>ofjY6rg3F|qH{2_ig~BsN&i z(24Rfq#e;qU=+;SMO64R118et9{sbG9r#)&INZ^XSogbfo%7M2qz}_=RP2O`TZ#*a zX+Jj0k<^~wh_s5BE6}ZTnRi|Qo#u0oJkUvuc=a$+(O;!YU5R61#SXUWR?ENE8FLq= zj~$f?^(++H6On7pu8|K+w-88MX|JtQ@*(_ifU5Z)D5jo;7t4AEJ-_|d2l)r}%??>K z`ZJ~8xq3o+G%m_3LzKPz(MI{&M4!aw{O|7(KiWSO-~GUxY`ey%bmwkHYgupSzkH6y zNPrgGntlqpv%A#xl8Qk8+KE?nEA?2#uE)jIvbxbdY-R}jh{wiAun4kC2JyeJyDws? zpc4(#ruznyQ*~t!I!^|jhOjxbzT0~>Uw>g-#|+e}y}H3;q;LIao?zMvt53~!U0`YN z?WC>ue_!&@_*9Xt-nOHkms+~#;|(acTz5+s4b{Btc(=q%Bt9Ha+H^-l{AuN&PftzN z#rv5Yz1+L_B5U*A$ib@@+_eTr!d)wMq^MxaTJd?;WJOGpGk5DfPtE6BLdo}fyPJY5 zc%9c6_l=gHf4bxKnMYemL8boI4m(4xrNGb=?ryuUQVe-ir_*GG_Iaf@6bEh5bFNxI z6N5vnxj30=YA*fNshKP#w_kZqUE6$HCr=Av-tDR z-Fo@U_#rPn-2~I!KR1?HK=wylmWN&`@*_OQI`j7opB#D$2KWhmKYu%8URO>?`{|iu zxAltYw}_qMulXX7TXyCP9?EWt;7qy?#o0BkiWncwJ;w4{CB*uTf&iu4B%gQlx6hX0 zYm1WdB4^1_w&Uh@1$16YxyqI`evD63791eY+GwS1O$?e)Fp~byvhy)5339X^pB11} z%s~w8(evt@7}$=E=44T7Qi>@yy&kUltClWSUU-T1yc#+D{aeUAmcR~Mw~x*W^qq~T z7}N3&i#2AUN@`0|HI!0{?T1KZHU{m^ob@El1Uk##kiAhey4HbP$?dXJib|Hh;@j() z+;-1{RO<3qRK;D*R`}LzbN8AIQUdb4y;lhO*UJTlu9)SvlCPDoC!Gzt*%(^O?4W*B zIuUTVUCbRhFk9+;BAC_M&G$B!1(oHlg+6?rwTo|?uSo05I3=|8nI;+GXV#YkJen=$ z<+o@3hTZ?BsP6n!FerO)Cg$5?&l_x2HRK+KxCgJjE#VJC*ygGQV^;XKCrE+ODZZcC z#a#t?PpvE%#=SNO^J1lJ+htyb!rF+mjg?bU#0;lif1-<3W$1 zjH}@&3)@n<4n63U<{#r18D&8aGunHyDpr`QY23@F1FPSrYMTp9_T*|ff1B6s+^I^f z5D*%5z8HXBc$IS&5%k#w2ALR`ZhpB;c3MlkO`|3UbPy>yqSj-1N5hb=$zA-BS9GXi z{XDDNg!R^N*n3F2qR^_*tl9JWWYDtEXC^BKa{;rL{+Ny zo%?3yxeisp^B3XNW~?xsW@fJzUc1*4#T7w5^-R>9AzozUdhmtDs?TpI_QL}rC-@mg1U3)y=+0tFvEr_qui~I9>iv=vYK*XI_ zxn-_YJNwnZ<)phkFXguv)837*cDi|!?}=!)c{)rT@dyw+Ax)|KGdJC6{a>cbiQiBFbEIzf~^j;(n`5WG-ng z(F}76}bHCMG%B6U_gj|Mfq2Ky^e}DSR>$&IS`M93PIr>;+$A|Kz zPi1I?(8e@GiW*&;g!S{eE;0AQxhHWi)z+X%#&5pCl|JC5hdz<3C%S{RZH3g+L0O%G z@}N{Kt8yVnLyAggV*>)sD=SfOo|R{PH(36NLt2dEVCCI>@l6RS-}@J-^_Ju1zmEe8 zl;98l(}_H|mP&9l-YvM=-KRV@o_hVj@^sN94bnc_R}7R@4{pAE_&J1Hosh?+Q#h!p z?z*cT8ly;CH@>LmYoAZUUq&fh3u6Dl?mnd|(!LA}3r4@0(H756Q;Xd1)sed<;y6Jl zpj_H{%F^qsL0tMUK`qsb`$dKPAv$US|240>`U9~L!e>6j<= zp1`0LGQZ3vsLV)0wIYxP9eKH%(HU>ElJX2RlBx1Kmy=p;N1saehDuTHZ%#YJP)8-9 zhAYpL^sw7sL{S18r&^woiKF2pc;TSdpVZYrzSRp^x_Mq&o^$h4Z+K-C@2xS;*vrQJaBvaxyMD#r_ipaBR-ZenN+p4Mx6WFR* zN}V5=IdI4oa46^(z)qRqqMjx4(Wg+DkmS#O`;(*bb-ju~FP=_oef5_nw(9+tdVNY= zWFGu???3mPkO!8jDnWUbTJl3(?^nI6!&N3-1_$MK=NQztZW!jCciVfN5GE6Qn!ObF z#9?@n$J=JDB^_c`S9xBQUb{0o;;>}=&~=kCE4fl6TyUT=Ih01e9utn!7(XC#-EaZM$~7k_z|Sr7=C!kAVFK`q}yb4&A`URlh~JhNgYz zlF2zxW?@4;hm4pBj$86;$+VwS53c{Lh~zZoT&mvzTeXL3*#`ofSh7MCtrojOrrB84 zhP3P%5^=*i6TfF4Z|?9yq7w}$GA6O#k7^==R?2>y@~l0hYYme*rbT1mP*$|urlJ`+ zTy`n+I0RD@OtU{o?FMvAoL|IWFJZJX;Xg5Yv70fM76u0;kqhmm+rL^j-H3h@ecG9K z#t+kFz|u7W&Qg_l-Hftqccd^1rN7l>l^L*(3iXu?`$MjW$AK3O2IE`yuD=zD)|E=J z@hUy?miArS{N^IWpywwe;LkAcf|U?$#*Ib82DMiAV{ldG7kJO5pYL_iy$h9GObTk` zw_hFYH&8(uDTmxby%t6M@L$~#Het>sh@o?k`N?{1!BlU(MwSMj*aixhTN zB%dP*H<_=9uYlYTdBi4$=N3HPducmbBlKcD>-;iU=rH;?k$m`#FFg&){=Ch{%h@W` zdT#Xa-;Q{{%1FBFLY3Uq(z32QR8FsPKT2LVgU0LFXMlebHeM7JR*%Y;c&WPd6j>b& z58ekRA=*X1s(-#Ww7U~`a;9g87ndm0rp6S6S z{P3qn}=U7Gq2l37hb zV5(uOQEJ*c-u#xY%va3Y_FX3(J%VXo^T;)(O}C!|DMn1`_U2d}t?&E9R$nkDX899LI8Opfjmy1;!lghFWorpF}-?P z5*{Ss<^eS{^ju)q3K7yX)m)#_$2~dOEuOR0^GKarci9)Fr~lYgaA|z-6;A#+lAic% zrBLM+(JZJ>kd$e^LM`)m!zjNE1P%4$iun(jTK3Fe%R#RKoxObN%T$iQ? z%b5l9%r_fnpYSj1lNw9k>^lc0@^`YUsmW5badWx58!hekne{Bl_ubXJIoNqYA}e0( zgI3IUJ|V-&_@-^Y2dv4KQ0K0~5ic|^H~8JILa^g`Q58y~4m|!O7=8lU)cey*?nCnW z%8qz3;YOdl)SqZ$eUi`t+2j$K;xVOQz)^;v!OhWk1kx+{5-%_8It%P9KZ?um@gaI{ zEv}lHSVZS*IeH;hZz!hE&c9HKTv0#;`9|!H-qA~Za(Ti!t8$R>68lr520=t7Z#cpH zW0%77CkT4WONKA~7D%H3ZoTl@eZj|r`1kcZ5;Y{P`ilG>BE9_+u%gK;N@}8ix?y}e zH>S;NAR!vGcF`aY$m1(0^{RvM_ znX|u}Ec|+LB~+Ok;F|V{0uY0qixsvdmZvC-$qFjmJTsTz4a!5bOqxGi>Uv4KFuY`L zM_6Eck=%s-g{gAq4>E!JH1P(NhIWz1!lvyTt|Va9Kc`{_CM46-)yux-j-SeL=*QG4 z)2CX-u>gjJ>RpR&}Fu}hVBLyznQI~r*@Vnb0lipTU%?k~~`3I$N@I<~Vt-&5S zI8y=(TdSN`=Sm@a8<=}nM73<_IZ$vt72M`Y1V>wSC2FMJ2y zR#-pkDU5$@t|7bj_(gl&=C?K*TmB@0>PqpRQsfUB{6n?o{rOX6MU2F1dJR(R+MvDNwW zwWRHL{=djDNc)Ze^{7zl)wl;7nF;9UztWIYfuKma;5@MINA@6LZpOP}BGO7urYQ0H2_Mcgr;t*P}t-lY}6H^Heu6Qs`$~h4_K815_viBjU?b zcV;qw&0HR{WZsf5;dOFoZ?sxp5MrF5wb)a^o-vwb<+rPCcYv6Y%xh4Za~k&?njV;V zKR74;k_obVUw+fN4;LIp zGH4Hu^36q&NP^$Em0zVi0CMV3KXkP1*ZLgVsCLZnWFO4DsB%ONdugu_Q(v-GfhCr_ zk0#j|qbc9*c{-&K(KPxjG#rDQS@9RYBBzpU;DalG=S@mc8&cGZx3WvsWTBz z8gna6$`)V2$=kOT_N$#I;%w$cwOSQkXn>vmxN(eZ#@KvP{d3r7-Xf{EF7Z)BdtDe& z#YJFxwtx`QR37EXiupP(Mq5oTHL7x4W7?-u<#T{;!PO0pw^-DVNen1Gc)@wG1b#BiOprUymv9}1S`P9ywx+^}o@vsBrw)9i zsUT#1bsik6?nTm@C=CdRS{tgRf)BiP=~RgdZMeoC7{M9kp0aaWTl+v}Vc+Lt3iUYs z$>WT`)LYfjTIwn-QJXR;Y-{<}16KC%>`^5{!2b3QYx;Om`N zGdOdv@yYYV=w!&Lc{a;VUZzL~>30ckD1PIVhc+P216WAuGE2K8+|pYI2Ap;U0XyEv zI4<|@^s7dsM!R*zbN8LwV~-$vf>&S&U1HMu>KA2An&D$q8F*^J^LKWXO23!>XFyoR%B{W$&UtUx}&= zX*)6zDq>u7ISl!@d-Zhy?<6=$z%;_4qB_r~dq(@68|H!kUsBfe8tUlT&=;Ot5;kVl z-sSz@rdpVDi%lVT35yQ`m*RC1DvgZx092qKO79)c4F5ey`}+=X3TKs8cy9dp86S~k zH7POtLS`NC`FvBar!8FH#EebZ=s#mU9-k&in_PW#lzLVq8&P1v+BJ!p1Hs+$nzvMO zWjX|UcL3tx3iZWd3V>d6K4-}~OP@nWqWb*b_sWWSuPQBH)YP0H7Waty?S^;nIhleR z&8qLiTe2u$gU5#T{nkebbBC8i&WP!a6}=-L+g0hKRyMzl{nyqUx;`uNc-{21f9K)N zR#w<;Q)-{r@z>BpKUe4Rr5f-w;VJ~RK|jejxJI>7C&*m>9R*?rmoi~KcmaQT(+Lhw ziP5sXJnY|(;2iDK56H*==!2dznR7Zim^$&UoKT?O!xEA0sTH_x8y<&{H5IvgJ0GR; zVCR~unGQj?C-F*A2iAaXeWbj@pjyboXPJ>AQ<9%B1lf+J8?~ukgGFhVgyor%+oggXDiKm2XptM_$3WSG{44`3FSXz^+*JL3e&jV z+wxAVC3>1yx)Lw*Rr(J|k?zmJL#UFvd;6O=y+7cX-FZ1!L;sWRGt8UxYo{qAUta7N z=1Tc(_$?38vsCZ4-JFb-^Ie}MJP+g1iGS*49U=18DVIx`{`j5z;|?PpbeJK+x`fL*;vU1g|Uw14L5bhsC*+`Hpd!trX1 z04g~Rz+tmkys`dRlrMGFewUR_mS-HWCUWArEwv>Z*vY>S5^Y<8Kg1IY4U zu|!{};PUk9wbOU%a`cW4M{3edV|nO|FNK2+X!|vGQa$E=#2Z5$=o^9_`iaRm1K^9W zm_P=Fb9vg;dDDEgA%v_r*cwq}S9M};{ps;(W#@>!er<=fmoE7l<}^R$sqYDGRjGzowrYE+Oypy(R_HDpLT{?g8E15(k4`I?3~T z7s4LB>f%~>_Aw_!ERKK0Wi(Py{Pk4faDA@itDWq zSksj)2X~n%r~5-DG=sz6tiGNTZNC(`ys9d4W`>)+zSoJpfSh-;U7;rDcXJJsxw`KA zbQGE~Sb9uNm2@WBuHCKihx_aViC;_AeOS3=s#Cx-<)2E{EN|GWY5S3HNzowb(Qf@j zJhfUpcYmIlyLE^6)P480@NQ_rKOebAs^F+l_32We;k%xZrYT6!)jR@5f?y@#(lo}I zZF`O*8v+SnB3lA|ar0-~b)YM@>SEzYU7YU2;_N|xHYDA|#zRBvXjejoGL?)JuLMgH z);^2NugV!m9+7jw1xwh|EtB)S?wRV=qnr8I)BCL7&ohsd@b5^$9KIhCO8Nc9wYl=(5Gs@MucP&SfFEK_L=p!bQ)0h!ty6(5 zN>UA2xX9rY!mHim1Mo-|zwd*%_{uH@yTSGZ6MhhXou{3w%_PP?xZ)N_Gq_40Fh2?s zuN82zY1&(3*LF50y-M+K*DQY;v9PktzNqNBm@Xg|m%6U7NLgvfVif72e!k!{xzs+M zo3^|CG`~56^=Nupj{Z&Ob_@MYN3+gm3*_b+@;vD|X)JSwS|Ii5553(G*vvMwgcxu< z7|~Tk4jOKJ<7clRM*d$|BcFnMo_Sxz?>h!>RO()1=~{5PZgQ2QH|(Wr55C5{FHub^ zIw*@lbXJJkY48x9)bk#_CwYXM(7N%!7c-RuztAMX3PnhqfN&DYs0G+pd@-D~Pbm#n zk&SQF7%2SD?4IWHQcJV@VbuZJ24o)-9__unD&GK#YZCxd#u65Fx@%X*{ z%AMl7`P;a7tLUrYq z8wdx7r^~akE@N&%ODTZlVZ9#^u4&@Ov`pWFlCzn$Jc6PgtJjFyp!Z)w-Zo!*X)*aW2JIn|Bbm)-th$nCA_D)FBhdSv*Ko{uXU+^Vk8*v1&l8-(w$hDK{nX~ z&}Cv4rJZFeSEPB|p#P_#KYM(t@Z-Boi10Bh(a>Xh#bju8)Jr*H z^UEWw32tcW_N1!F&MxU}5M^xgEvNLSRp26d6)mZ-odZVdO1F-B+i0ZUmrDLJ`+oAC zv5u6Qxkhp1(L`k1x5cWbd3d=`HSF$E65#FJzxExBFa5u5>D7dxU5Vv)^x&J%#9F`O zwjBGmD9*RD4ici{$Bhx_ff$u{< z+|{}>n}N^YPZvw8891palk`}Xt_eTeg+UCkZ&Q4bl0Tseu2T3hCUe1`!utu>x~9^XEy{u{Ag>}($Oo5leFrR%2f z@3*50caDssWg(rSOwiUODBl38A`g4RJ*H~Jwg9j)OpcwRz3J2tOjnG+*?4T%Le%1Q zKYYK~=DVFR#GLJ%DJS?HF?dXfUM!Wui)kyK$#}^;VGS1AfJQi!MGy97_O>=xgg_wU zZh>TG@v!bxS%=cZ;i}HH!v0ChL8EyB8XdDvd{X6NKT+cTs2O`zI~kFO>Gt+t9K4xw zOE`SDp(~C0T=@)v4xo?i-Okq_Tz%;Cz{L(Vzzo!k?mL;&pZ!LLzBb}m!yYZJgFp&X z-Na9`+2Ov86)Cs-Uju+f^76^31)WeOASg{Kt15)!$>BkdK6d*b2c`;kD+Y|&qBzf2 zg7`9?fijueBpda4$%y>}2uh8R_(?JbzG$0=KF%xl{o)?}BP|SynaeO>FxvmjY`RV3 zQOB^4iMilIRrhvSCZO(6^DZxK{D%nyhFNPE-UF2E5kZ9p& zCCqs5Z;(M9`R1apwr*19N%g@tXK~-Ip5(&v&1>gz5puJgR0T}I>1ZkI$Q@NeS0Gv~ z9(*)7{cmh62EB2cCyLD_9V+aYFHoAE^y)m$v=cYNS>7KnBFY4I8dSRmrpf~)nsFBU zN!M2*HrswHi(Lc*PefA8l`$m*Y;7_ zP+49{7wdRMmx*Oe;?c-vB%X5JgWD-*eeQT;uKi$%DZ=&RAM2~~-wz)-MJ(~o5^iQN z4-TmUN^~+yimxOu1WUO$MJwmW=dgIaRhn2RTYt?`^49(F>H>Slg!^@o&O(2s*25r6 z4K~wXY}v^ZEZo%A+Z^B17p~P_J~&;UA!kgjhqusyS~(5q4Z4wy2}0=y=US;vUanqA zw{)2H{Clii{Yx~z)Y@_yy|TI>c#)u$%T+MlZ&zK74tEtnb-w%|xl(K{^SDO!FRefL zrX=bj7c&I@9FHW0!Il#{+MUVmJq$|^^f`gF;gp|~_e4r8NS&ZZNrgtzi5&DtLHp5Z zkux%x2GSg*oigJK4OmvW5x^5{tjtLAsn@$<6TLMoa>F7`coJHV?QY@&D6iV?{hKQf zl&=@ioFxdDsHK20Xb_WmPuASaQSFtgd*Xw1t;ndkUGcB)t4j9|b+5!&hZ7A2*!I-b z#VXM`Z}OZ`23c4H%qzHM<-ePe)+%!cepP7HM;kzE4L4d;hbX=-8B)t934;Ti_T?`Kl_;ZSM?oiy z+U2=REEU;g2yUC#@D<5_B<2pt|Fyk2rey1Lv>_G>jJt)xUXRZ?73h^->&Qq;jkZGX z=N1X@f1zedM(~u_7ZB(R`QTnh`w%*EV{$>@2V$J@n)&BOjl>bAj!Yi6Q7J%5nv;XZ zuPJ}Hy^A09m()oL03K|zU;R_8-WNKb`Kp@q=KNONum3%S^I4yL$_-*_B_-d4 z!RFND-zg5Y8JBW7M*jIahut3jdpMl^H-BsM@A0Nc2YnpHNxNj;!zNl~h^)}0P7Fr> zz4~yvOZOQ!kyH|M88J9$^#r`+Ue>1i&)r8~(JP$Jpw%exw~Lh3k52E0r1;F}Ogj z2Xph)@<(5<<4sk;eCN$(5kY#Ics^%vT8zGwhrbCbm1bHyk0#5LwoxRNt>|hOJi@}Emd(no&syd z2~f>CLJ2ii8q#Q@bmO&LYtkgc_H$ud(aH6Lc~wl0172|DwUPJN4G@3aqkUN0W%jnq?-VQ-M$a37V#RH@P0t@t@b=Kh zi)XIWYYS$HXEzcpoK4Dw=E<-O?w2QzX|`of`yoS>THB@}KZD~ZfEW#P?SX!6&gH}T zOvDISOCe}y)Ty8(X#YOD-+ELNRexsbzkGg16|WQz-QOh0p%@$eU;WEu(glr)ImSEN z;G-2p4`!$9#n7JmZy!G%DJnsiu=)^N^QHQ3xAyzfzCSI>SwPQ-H`6{@SZB#oK1=@r z`o=0cFq~4AY6|y``J16<=AKxZ=@1{*+!J{Mts;u5yC0l!SQ|H!*4oO{+ewT8M@a$| znk_mHcDHInx~fG{FS%pga^5x9U72le-Yo+D;pWO|`R*WrG|ApWIx|td(~~!ogZ-7S z^H19d#-^^W<_mC-GPK~}Pm-=W6IS) zmsi3UZg2Yd*qI9rWujiyv+_x|^XIh9sy?C&S-1hL*GF&$APd}w`gz;*v>vydT%6Fj zi~%4GlKKMRC+2Pfd69l=ImoPuEYFdb+m6Gl_{xCbQSNNnV2V+}9FiSsms{C>6+O%59PkY#*bTz$%AQhZ8 zU+uN*v$iA-aXU#4>GbK z*YjiGD=vh2!!kB2mOny=|M;*~^o+JRd#n z%Le@3d-m~r;X<7K_ow^*ENm}^Ih0qP22FW<4Kul-7ct!nJ41V_2{QNd(HjY{_Ln>? zQ^6JAj9i?opBC3%;`&49qsKm2sd4}A-2JJYW>KlNHJoWOXEu_0AzJc?+O&4rPt$Mfjlfw*fxtzU8tIMBAZ$|H=`xf`WDk8&!pwPL`SYKIw2c3*J_M(F!3ofgi22E>SHp?laH7V|g3p#{;yWZRm^T+M~We1WSyh2FM zyy$vq>Q^26xfj=~D%4jLOgHve*g^}c$}e|j9}>-m=vHs@Y&3XKHtrW*cDzjmt!+8U zKd|vW1>n9$xZ&KTB44m}>N6W>#s1~ntmCbYtgmfWGtn&^kK93m^F$F=l`ViAj!B1b z*2qkV=ZEfBjOmrE+d4P{q4g0{jk`#lhi^C^E&;o81H_2UW>`$A$ek^l`jgcu(+;0R z0XOZaPG{$|rV0yEFyjKqM#pjsM~S~<;4ba6i1Y8!WIVm2dJufbNN(|J?*oNT~<5`Kue6#`x+m0S8_`F3rXywIj#KZdqns%+PmNz zE2&XCpBxGs5Z;pBUgo1UQCP9Cri0acF)p;Dd;;mgP=H{ zYVav!Ug{y7aQM(yveBJl2o~L0{%2uW1a~~nCBpxJjYE<*eX)`w&!F=BkF7`N67&1L zBk;!sp|j1v8eco=xfr$6Pk3&Ljnd^#f@&uC6Z1|UQKG?@wRy26KijG;_jmQ(ts(4b z6~va~E7F1%0aF#?+jR^1=Z`ukq~k~=WzV+ixLiK{$p@Hy8Cir>0~7A-{S+vrp+eG< zOfIRzjwWGAEH2PNkt2ONuR zR3q0G$Oymc=z5+o^`M$9E~iuK9fPLsN4`Hhm>~aziTJ^zNzPiED0zsyHa2nb3r8BX z5QVM^QqnPPRI^7a%x9XOQ*+~JhB|Eq6u=xihIV|4+1mJQ^~2_$A*d&{#RRmxxm85RO>Kl>=%4->$DLihy^=?@P7avweTM&VP5coNLnW zWVINWo&^qCy$Nhonsl0BX9kBEgAoOs*p>ZR>vZ0$z-J8nlL4TX3~Ym&?Yk)K<6d~D zA}8RQG$0cB=2xJ-&W<6JOjmm!=-FlQ(i++A2I%1mJ~jlH+v&{gng!v@jj~km{P{?m zrVzi?c#EsOCs*u&r(F|ke|OUTXAZ1}!j5&Q_uQxj%&wRg})Cf+32|o*8EyAm0*!6~Cz#x2e-_`6t=6Y3dRTpKDar zuZN*k?fqnP1+hB@MGnLl*xj-&YzcO zosU~GLYvqK(y`}xLnQAvl#8Zy0!oUMfB;$E=ctZ7~lppynXr3vyV~JXK zoxcO4(=Q+X>!m!fPXs^#rTeFrn-S4Zl=}K~hyHk6+hS0of7h{z*h0*@{P|8k<07g)USIiN1vogowb;W*AzZ7jpi8o)Ke>S3beu1AcpCKARDMgmoz-+1M>9O{J zfVMp<7r3Ub!T?L$oP=pO0*tf#7zbTtO&0to%w~h?Dg>a0eo1KUzVNBanJ~Td`|l;9 ztJHI-3hAVK{(4k4e4m3SgKD$A@$}+jb&+=1%`<-oJ~A9C+Z9n*A9TpK7O`Q0mPY4` z*pWH@rGH-Z7aNWr;HH_zu9j%Pa|djNR z%+uyuPKcoOpPczOXMG?7jOq%EE)P-O`k=tOR|(Tw5O?+%PhhcbivnJ5M@_^70hsRC zM=)dLt1=r`Rb{A&@~pKWKG}< z8|On`iW=w@ow!`~0BiGHR|51d?>T*Cylg3f9@_B0-vz~mq&4Ts5NrtlOVWNp4h-wg z6;?;-@^ZU~Jr{7)?RzuMO1vN@6qeRFE5Ua6!fCophMO37i0pz~A(;om;L!g8HwaHRsU+*~qx&b)lI4Z2Qx|yGk?T#;W=291nRiL43%)C8|k`UdcTWy;qP~9nX z_hd?9zQhT&V8n`wlguZ{Ek;w z_Nw98c|f521>SGCcR${n-$DqR>^x_xQti}QHLi&a?80;blYg$yIq!YvXET&f6uz^1 zkrN~%)LoLF_JwCrWX#~kE<{NiY9ni8PkSoa^^HAD1F*1~{3y8y>@#jtyUlZSNW*?^ zy#Zd_Gv;h>@bf>y*a!MrK^@?fceap+A*(|w9NRR*R_t0#_ZN&>fSS)ImrSmQvl}g9 zI4rH1i4$PDlUO0?m+Wf5X9EAZ*%1tQ5^X!pJk)L)K&8Ku%)7bcl#C2cPfKu>3*b}W zY8K5CJU+2;`M|^`$}?=7W%#7oVWN#|laKCWe4uRHyif#Q?8l`n{iUMe7kZy*UC+=h zlO=g4{SNOJyg#nxBHvyvF`hGjcO)HZQ44~ZI+fZ#4Ye<;Ii?XI7(J0ETu>6X?2iqI}1#_3iieR#xWCbC0henVaH*A z$9v4%&&9!;O868Ob2e^^5V_1#^Kwr{qh%?f*bJ6eiIzZ-S5I{tQO4mxm3uO;D8gMb z@`c&&LkN^VPtFsIou8QWYA&atWySHmY$?AdH)qadd?vq~b}z|jZt#4Mt<_rDosLRF zuA20@sqQYY>?^J}WdB_rG}i$O^%RA{ZpN-_Z{_I`f5-m&D49-DioCVyKoyp=fe9`r)XnM>@#T~qHQKD~_R_2VDo10&8`r66G#)M)rtg#<_I`gs4# zg~i>s_XZ2xr*4c{{accjdU*zLpH&6DmgWWHjs@UE4ey&nINrTdfxcGO(TVvhEB;!b z-oyu!s3|lO|7@~WbS*}C^LB)H+(Dy;wf>J+6Iz1Qd)7>H+&77Jzh8HHr7?)X8p5>s zY+nb0YQ(8%Ww5WKKuOK{8(i=z(@$DorD!R${w{}AwdxRN55*6=i|Ua3XQ(Hll{`Gt zjZbdlLuRe@f$GZnDNqc6VK~+Y`+1R8(kQ4vOk|~#dJaJH*#(TJTnx9?0VjjCF zIz|8May-Nm!GJzmarutk^z+2xQE8j0Q$-@94QUoBw-p&7{O6Qs z;K3eMF(MU*jk6*aEzGVqksqi4beLCl9-x4;<@MPNI zWga$F>Kxxzz&res98;l7gmAbhrvkQ)Sh`4P&aY%PR{tJ;`nTHZe!T7$3usd;ozN}( z{o0?e?ZZf#>>!tx&qbAEm@%m-@p%9ls(}D9J8{Zz!Ht=!AENii4Bu`a&I^F669jXJ ztDGN{z2Bx%d5f0hT1RsKm$Ek`%%?c$*ul;LDoxrgdbo7)3J(ngx!KaME=F@VDyFWM zDB>PSH}^TU>6^a!pPro|ow?E(S>ahZ8)K$+xXPLCM#53jb9+M7EQGH#+deZKD;C&U zkkw&?M_iw&x^jhsUNuUxl zY`1?cL6w{L%4oK9&onUC5^u9eS6y=s>rG1b5=T5YmW?;>ucmf8%a!zrXs~Mc%FiFVG2ec?IphW22<$DBl*m&5GOZ1t ziR}S>zJerbXn)4bYRP{28@H3&1?v!aru*>s3oGd^L)it%ZHO0K_xQK+Gk(BYCI;L` zSF}SWGl`Eta$#8L)eCmL(jQE#81y6u;r4OQV?E!UpHTuJ??{7BZ$XCvs&nlt&K&E} zVbP><>fdv6%n~LQ6deMHwiMbR%ard7tXkJTFcC%ymopD@3`}1 zL;8sB!RH+0Tz;%^+Q`%1ump?z-ui*)uv0DIyc+O}N093wgG6$hx#iDP=D70+kXUNT z=XmgTqW$+zTaCNdd5^h`{Up^4*ceE6Zbwx!@I$tp>2zLt;tRKBA4Zh&BDZG{CF^9# zRsDjYPnZAiG_>gDuvOt1JI#3X4j?@BF1RwweF;KrCF$in!# z(t?CcN-hhUi6?5~q7y%d`7wlBR&@95vOS@4InW;uEwcqz@6)0sjP@$e>pLH{&HZWX z=ep;15n{)&jng6yo|RbglFFwIyD&4fzc4H!Z=vay-ePgqBO-HKK~E;A63P5 z@aSKBVA@C>sB+A{9LUaQ;6gE2wY8hKUkl|Uvl!v0WMF=BTBesMifnfX{v>z=UNGYZ zFx%Xh6r$Y=>zDr1*1CtPKHjgy4Se<&FQAE?C^J{p4}mf_n!Gv?4|gHnaH%zvG|vmJ zHiK;SLC!z^K!3NT9RCYt!T(G3zJnm5vTq>K*woc;3pJM2=J&dv0@CXODJK7{$Y3t13~4(m&v_2&|MQH<2~W?b4pvn%9}Sq(tkXO6iIr{ zDM2%rh=Wf8dJuTXGqq6oK|HWParO!HCivZ63>Q3wwI$_y9wR?*jKBZ=ZhgPK-Z{fL zCNqOJUmDA*jHqF%Py*v&V+;!X~rH%QQ|l$2u5a54S<5{(vOt#Lkn(zJYw0 zIHGqf&LY`VHZ?`VOkWEzY0!OH{Bb$&6z35I*ql*(EiwlJh8`gebtHuM>7T%~i)Y!4 zU##CRZU`HXd!531w2Q09JUF%UMb%_ApC7vuS35K;5jQ4I8Y*nc+FhDpj_H}FH@MzE zjSQg}Mu{xq^wmt5vK-*M@|FMcM+D_rP6mH!Z{|%Mb8WH-S?!7+H~$pWHG*>Pl?|89 zp;1*3y@g4A(Pe*ZI<~8@D1>}!F*sxO@i23tbwbi}5(Lxl3J?02UQZk*EEQWTyAM7c zqJF|y3#KG)a{TYu_{`ts_;NNMlXz}mD#G9a_aeq6i!O7H0n9t5TqjqgTCN)2NQ^tV zlb9n#q?C{SHiEe?d`a5ID8YX9GuF30inc_b4C(hJ!ETWT#t(ZT=p3I~Imz)S^Lwx> zamwf)k?o%L`X8F4lai^EY95FI*j+zsxzX8wMfessRNF8`K0URG8vxpSd67*93|bkV zEp8Majjq=VGO|luy?C-0lD!L&{Z#*OUu=T8dL`e=IJhq z!`CywNg`(u1uGnzldwhev$*m~ecIS`GLFoU&1q`^zR6C=L zVR{4|`v5SBSm5f4G25PuG&0UN{YkE_7>7cyEC`sf_vk#YsXzBb*i_Lgetbr0_GMW! z6=?nUHOvZp+8yL$N$@&D?dz}a>-TviiNIC+&Yf0{*l14)Mc($ zdGv!h#RIC2adxl1IN$yAHtSZLjm`fqfFoVe$RNZsn!?q$%e=t!8{Kb)7pnK&U@I4u zKbX}}`0s1U)xxvdESw`_U#P5}zxnJAl&W{T{G%w*pmt0%oN`H;u1#ImBn~zO4(7b{ zhZF5%Hv`T=TuPE$dh140Vx`-eUC+FL<^TWhpz?`AHh0Gpf*SG9znfMe)1P9IaTd=g z9HSIn&)~&mTbM)$6JDbuMV2Rh3bGZ8g_BtZ_E>8ucSlbERVWOHwIqeO4v zlF^0G2n*F$CYT7I85jZvl%}iK3=F-X2oxzKrb%<;gqNfOQw(1cO(YZ=4 zzr(vvrCKoMBzx}ZNp%&3FauO)5AgLd{*1|PgHF1kMo>~a(VIcE?33Xsq^$Kus?Gns zYYIn>iJpaZd>k-6TJH>-5oCxJ@2$ZZZ5jl+%jDU?tK;rGeQ#8Y?ylHuic> zK_O(HF+;ZOQ=U;7PlZy*W3Oy8WSN_g$P#6n$dpQtF%>Z=q0o>m%1o3jF=KsiJ-`^%>r)Y}ek?ihJ?1gqvIKUNexuFQDr`Wk#(tniJ8$Q|Tjr0F)J_N8MRXz|Gh zNE8`%#fI)b(ZNM4B_tm4%ckD6C#qSU^|8^;x;5PE-k*{x=ewG{OO@bY!unL)7G=LV z_@-$4BI^mca{b@k3%~g3>H=Q;1HH>(9l-*-bLy93hsEAXeCounuiS#PHvWOMZYsAEbu}a4QP>;@g2(>Dr)yKnP{{+T-fB$hY#s9T+1U=MQ-nCZ8l97 zOQ#MU_3?CBF{`LOh&okYdsq-`cGhY}Qlu<@lcdeBc47Kou3?{)M>w{hly8g491W1n z02|7mMN9{o1!8Ny{eAEow0#|hBDA)mDK$p5ov@nadFyDZ z@End#6|SV=q$Pt<8Qcy&O-aOBbA^pWgNmO5>~&@>7Q5p23{W6))X}&E$ZpF3Pd2>M zv8?T!I>AtR(Zr@v&uc*!KWhX$4TO(B{_9MUIdDH>p7=2BZug42GKem)HtQya{dHmH z&s2=yLA4ewf?X@`Tt35yoLG<W&BoL5NW=q}xES({EbE9iKpX4<# z4eY4}xm(Z{zT!uxI)}9%WU_D8Mo&sawIZFbVyFAAn`0gRAu={?@3S+j-|cR|T+%7r zx!{*^FcuTC^tOEFmbY1D{T#*7P~u{@gKdP}pMbJf^8Bj|DQI%wiN7VNVmHF=`@fwa zE~R)^0UGyo)dbZ{MwLann zSA7>JOBmaRhy)qyKbZzeM;|_vxN{c!4u@EW^eNqzDdLJQ4PWM-_|#{U-dJDas#^Ej z7304360#bVOcF$SpaISAd8sq7^umZ=qL^+sibMW2*)MiV_bA zkI}O;=#T>x*IW5QcGogE?E1HEITRtDWfvK$Hx&Inz>Rw1Y4T^jb-B-x2+x&L#5(s) z)GYI#2z+V_M+fW*xGiw$|K3PJ#l)jeY^(K;3$Oaff7S!!im6#-f{7($^dS)#NQ zFI>af6$Q?>nQzP`UhlYssn&Zn(ri^G-_RlDcV{FEjq^=AQ-pwc@ZJl)Di^HC=hZEj z`_(f%&Hja1ENt`5X1HN5USeMRiWnw2J#u)fM|i@X&KddmAUNCGFCWkxQhSWb#5vC361rfO145yZ`o%d+ zw6_?(&?&TP=pW)U5*;ZONiTfo0s^Tu?1b8XaTY<HjegZ&AR9WNLE&y&y7*M z=re#GN-IW(7~~QTzW(rcqiTix2}eE|mS|IRPGl^H}NF=+HA|L`56skR}y~A8uAO&Z-AA zHjPZ6Pp)8&C0;Uyl}q&>eF3NvW4|JTvA@`P2gnY!vu$_I8Pdq@zh1!^i#9Y@pLMF^IDT~cq@Wf5l$Vp7>B=>bn1}RxwRDcM zRgyovO3D}YSv1A}39fH_Z3i)KJ8oZpt2Z~G`#UB6eA;*SZ1Yfz!#qrTbwm*9KBjUe6KrxA zl~+G_%BFFnP)K!cBL8Gj;6Q=E42-46bFafeU)u5{Tk=ZwRWxq$Yok+l=l*(8y|mjM zYfpOIbGTlu1FI**61&;TAHk&IC(d0;VT9qU?-mD zX@>ZwnedK&$0IB z6r{p#;Hwtrg&S6Uhy7oW=~Jamb&?43Lx{I{3U(E^;VCo;o*t1WqZnf6M0`FmMTJf4 ztSMQ@0!qDBdm&ON^uBGtzVs+WN*V1`i+?nymfjmtD<66HdNB{_WR9$WeA0EvBCfoV zkC+Pl0A!?d9?~qU*o96r_<2arQFTX|yO4%djyEQ-I6s4p=;?41;3#)y$JYKcD;Nrm zv&YCWgzfFfnhpWU^(lNDwJ+m4wW94CsOW9HRQTaJ+rUcW_&*ubB$G!a2Yua@n^}5) z`O3GgW!iRDz(9UIz;YF@S7YUFCdJi`Y>7AmlckE)NU9$5K5JciGR| zV#0W*ML8I-XUGtNL$GKM>jwGk-HhnIbo&_0t7vxj&;%KmiTdsDRerW{W;Ny0AFxsQ=pX zmS2#bJ1$?BAX$QT%J>1CvHMT8L?iws;WK) z@j0n`85J0NpWN?0LHRp4(Yg{XLp#agDqAUuomjfwQ44#5gl@Yx;F7U62m?1;f?cxP ztI~*Bi*_~?<%0DGj^3Nrc44R4Cnz`Mfcjt;TkCL!42_BE6YHxpGZ=Sb)#g#?6|{ha zM-CG5)uxN34uNG@I7yUnU$#M$`Vm3j*jQ$5ei{B}biFv|3>uqPC3QrV0NdUX3Mj{c zasu|Ud4On(Bt*F(LJptQ-aYXt+-GFD)(eWVGE^{{@6mm|XQ9hv*1JOnY?Ih+zBVp% z2pf=U-%mMh_G!LQ0o0!D`pj=vV|XO_q-|MEgu!GSPKfhHGrIt+p~k`0@eGXew>mjD?nS$Gxf%N5f(jf6kKwd^PWmipTrPvwE6tGE3P8Vi3N5KVN7#&`Q0^WSA$?uy}WfuX=O zArz|sAagsmE8g9ILY&GbXG`{`c->I@LFg-eW^A=BCjIellvX;RnMY6tI$c(O(d!0* zy{rP|qWy|fp(vNp?H_PN8mzEwUEZ|>eJ88;H)$?XLsu@&`w zKW9b5rz-hT!YTMDB1+aec!U|Ze8(?sqc|l{&IrxXbHfrXtIU|oKSXsDlF`=d>;aRL zk)Z4QuygrLJ?3;IaDa7e1rsjTp)mp7M?_p|_vW1+=@;q}^e=n=S{e#s@dpQ*mNqyO zpu^X!4LxI~pj9rVhrQ$%Pw2Zc>)>~T3MJkh4Or)bm0)3A{|~dz-@#re=^St|Q%pCe1TGwkpNnjw+jq4~dyESt`i|d4(wI$JPv&v`)h>iNX9Yr%TB@ zV?4=I#|2CUi$_d58qS8!a1i@Mabll%urD=jp(aOKvGNKT&@>q==p^_tNZN~69~B3+ ztOU=1V+4gLJ~qcSLe;IT!%V*|+g@XZx~MGQtYFw;x3{uMunM!Xyq#DjOU%iHt_Noe z_CNQOozmEp2@-1#0)rx*MoVlo+dhp9qsJkEinfqZrC&T&t@DL}3}@K=pxwxKBn^I3 z_FF8)~izuskvKqO_Tmi`3eZVX$otwM&V#YP3nQ^u`dH_te?688>CoB}IH2I_;W zJs=l`>R8>#v{2c;<{tNZn{s|i@c&MM0^`jSS>65_iyvjS*IuEi6wIfan6CyzL4Gi0;jt!LU-8ZfRQtNt zy&dfdA)lMWIMco_##BUQeoL`T!O`nTd2am#lg2x?z62fbtx6C)Nqg)0&6l@!m^1Ji z5Gtl4)ttSxkDYlAvbX-A3#^qF! Date: Sat, 21 Oct 2023 14:58:48 +1000 Subject: [PATCH 13/14] Added Readme --- README.md | 68 +++++++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 53 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 4a064f841b..329d49ac66 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,53 @@ -# Pattern Analysis -Pattern Analysis of various datasets by COMP3710 students at the University of Queensland. - -We create pattern recognition and image processing library for Tensorflow (TF), PyTorch or JAX. - -This library is created and maintained by The University of Queensland [COMP3710](https://my.uq.edu.au/programs-courses/course.html?course_code=comp3710) students. - -The library includes the following implemented in Tensorflow: -* fractals -* recognition problems - -In the recognition folder, you will find many recognition problems solved including: -* OASIS brain segmentation -* Classification -etc. +# Description + +This contains methods to create embeddings via a Triplet Siamese Network, which can then be used to create a classifier for Alzheimer's disease using the ADNI dataset. The model attempts to identify Alzheimer's from MRIs, which would be very helpful for radiology. + +# Algorithm + +The triplet Siamese model works by taking in both a positive and negative input, as well as an anchor. An identical backbone (in this case, resnet) is used on all three inputs to produce an embedded space. The aim of the model is to make the distance between the positive and anchor less than the distance between the negative and the anchor, without knowing which camp the anchor belongs to. The lost function used to achieve this subtracts the negative distance from the positive to ensure a lower loss when closer to positive. This creates a good embedded space to then use to classify inputs. Using this, individual outputs from the embedded space can be inputted into a Random Forest Classifier, which can then be used to identify Alzheimers. + +# Pre-Processing + +Training Images are randomly rotated between -5 and 5 degrees, offset between -5% and 5% and scaled between 95% and 105%. They are then intensity normalized. After that, the mean and standard deviation is found for the entire training set, and both the training and testing sets are normalized using that mean and standard deviation. Finally, the training set is split into two, with 80% being used for the Triplet Siamese Model and the remaining 20% for the classifier. + +# Triplet Siamese Model + +Each epoch, each batch is randomly sorted to have a different triplet each time. The inputs then undergo the rotation, offset and scaling transform from pre-processing to keep the model on it's toes. The loss function is calculated using a tiny amount of L2 regularization, 0.0001. This helps with the generalization of the model. + +The learning rate begins very low at 0.0001, gradually increases to a normal level of low, 0.001, which is reached halfway through training. After the peak is reached, the learning rate decreased at the same rate back to its starting level. + +The optimizer used is a Scholastic Gradient Descent optimizer. When looking into the optimizer I discovered that weight decay and L2 regularization are the same thing, so actually 0.0005 is also used. + +# Classification Model + +The embedded space used for the three inputs in the Triplet Siamese Model is then used inside another model to classify individual inputs. The classification model is a Random Forest model, with 600 estimators, 250 min split and max depth of 12. This allows for more generalization and beats Neural Networks by quite a margin. + +# Dependencies + +Python version: 3.10.12 + +Pytorch version: 2.1.0, Cuda 11.8 + +Numpy version: 1.23.5 + +Matplotlib version: 3.7.1 + +Sklearn version: 1.2.2 + +PIL version: 9.4.0 + +# Inputs and Outputs + +![image](https://raw.githubusercontent.com/FinnRobertson15/PatternAnalysis-2023/topic-recognition/inputs.png) + +Top: 1 (Alzheimer's) + +Bottom: 0 (No Alzheimer's) + +# Results + +![image](https://raw.githubusercontent.com/FinnRobertson15/PatternAnalysis-2023/topic-recognition/PCA.png) + +Triplet loss graph + +![image](https://raw.githubusercontent.com/FinnRobertson15/PatternAnalysis-2023/topic-recognition/train.png) \ No newline at end of file From 61a50611f66b51abec5e1f94caeec0a704824d2f Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Sat, 21 Oct 2023 16:21:37 +1100 Subject: [PATCH 14/14] Update README.md to include accuracy --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 329d49ac66..8d37722724 100644 --- a/README.md +++ b/README.md @@ -50,4 +50,6 @@ Bottom: 0 (No Alzheimer's) Triplet loss graph -![image](https://raw.githubusercontent.com/FinnRobertson15/PatternAnalysis-2023/topic-recognition/train.png) \ No newline at end of file +![image](https://raw.githubusercontent.com/FinnRobertson15/PatternAnalysis-2023/topic-recognition/train.png) + +Classification Accuracy of 65% \ No newline at end of file