yolov4_webcam.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "yolov4_webcam.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aryasmiiti14/IITISoC-22-Auto4-Autopilot-System-Cars/blob/main/yolov4_webcam.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "KRO9B4Z9jl3Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A578sDm_-BJ9"
      },
      "source": [
        "# import dependencies\n",
        "from IPython.display import display, Javascript, Image\n",
        "from google.colab.output import eval_js\n",
        "from google.colab.patches import cv2_imshow\n",
        "from base64 import b64decode, b64encode\n",
        "import cv2\n",
        "import numpy as np\n",
        "import PIL\n",
        "import io\n",
        "import html\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8UlRIVhaYPd8",
        "outputId": "85e32d54-41f6-466a-d85e-977651c3d7eb"
      },
      "source": [
        "# clone darknet repo\n",
        "!git clone https://github.com/AlexeyAB/darknet"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'darknet'...\n",
            "remote: Enumerating objects: 15478, done.\u001b[K\n",
            "remote: Counting objects: 100% (18/18), done.\u001b[K\n",
            "remote: Compressing objects: 100% (8/8), done.\u001b[K\n",
            "remote: Total 15478 (delta 9), reused 14 (delta 8), pack-reused 15460\u001b[K\n",
            "Receiving objects: 100% (15478/15478), 14.08 MiB | 10.34 MiB/s, done.\n",
            "Resolving deltas: 100% (10394/10394), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGNvNdcX9rGp",
        "outputId": "80dafcd8-d48a-4984-80fc-31ba249c513c"
      },
      "source": [
        "# change makefile to have GPU, OPENCV and LIBSO enabled\n",
        "%cd darknet\n",
        "!sed -i 's/OPENCV=0/OPENCV=1/' Makefile\n",
        "!sed -i 's/GPU=0/GPU=1/' Makefile\n",
        "!sed -i 's/CUDNN=0/CUDNN=1/' Makefile\n",
        "!sed -i 's/CUDNN_HALF=0/CUDNN_HALF=1/' Makefile\n",
        "!sed -i 's/LIBSO=0/LIBSO=1/' Makefile"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/darknet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5ogzikW9wk6",
        "outputId": "76f73653-f695-4dc8-8679-e6b5f58fa822"
      },
      "source": [
        "# make darknet (builds darknet so that you can then use the darknet.py file and have its dependencies)\n",
        "!make"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir -p ./obj/\n",
            "mkdir -p backup\n",
            "chmod +x *.sh\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/image_opencv.cpp -o obj/image_opencv.o\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_detections_cv_v3(void**, detection*, int, float, char**, image**, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:946:23:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Krgb\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                 float \u001b[01;35m\u001b[Krgb\u001b[m\u001b[K[3];\n",
            "                       \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_train_loss(char*, void**, int, float, float, int, int, float, int, char*, float, int, int, double)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1147:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "             \u001b[01;35m\u001b[Kif\u001b[m\u001b[K (iteration_old == 0)\n",
            "             \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1150:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "          \u001b[01;36m\u001b[Kif\u001b[m\u001b[K (iteration_old != 0){\n",
            "          \u001b[01;36m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid cv_draw_object(image, float*, int, int, int*, float*, int*, int, char**)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1444:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbuff\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         char \u001b[01;35m\u001b[Kbuff\u001b[m\u001b[K[100];\n",
            "              \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1420:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kit_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kit_tb_res\u001b[m\u001b[K = cv::createTrackbar(it_trackbar_name, window_name, &it_trackbar_value, 1000);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1424:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Klr_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Klr_tb_res\u001b[m\u001b[K = cv::createTrackbar(lr_trackbar_name, window_name, &lr_trackbar_value, 20);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1428:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcl_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kcl_tb_res\u001b[m\u001b[K = cv::createTrackbar(cl_trackbar_name, window_name, &cl_trackbar_value, classes-1);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1431:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbo_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kbo_tb_res\u001b[m\u001b[K = cv::createTrackbar(bo_trackbar_name, window_name, boxonly, 1);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/http_stream.cpp -o obj/http_stream.o\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool JSON_sender::write(const char*)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:253:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = _write(client, outputbuf, outlen);\n",
            "                     \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool MJPG_sender::write(const cv::Mat&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:511:113:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%zu\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "                 sprintf(head, \"--mjpegstream\\r\\nContent-Type: image/jpeg\\r\\nContent-Length: %zu\\r\\n\\r\\n\", outlen\u001b[01;35m\u001b[K)\u001b[m\u001b[K;\n",
            "                                                                                                                 \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid set_track_id(detection*, int, float, float, float, int, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:867:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (int i = 0; \u001b[01;35m\u001b[Ki < v.size()\u001b[m\u001b[K; ++i) {\n",
            "                         \u001b[01;35m\u001b[K~~^~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:875:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "     for (int old_id = 0; \u001b[01;35m\u001b[Kold_id < old_dets.size()\u001b[m\u001b[K; ++old_id) {\n",
            "                          \u001b[01;35m\u001b[K~~~~~~~^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:894:31:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "     for (int index = 0; \u001b[01;35m\u001b[Kindex < new_dets_num*old_dets.size()\u001b[m\u001b[K; ++index) {\n",
            "                         \u001b[01;35m\u001b[K~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:930:28:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "     if (\u001b[01;35m\u001b[Kold_dets_dq.size() > deque_size\u001b[m\u001b[K) old_dets_dq.pop_front();\n",
            "         \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/gemm.c -o obj/gemm.o\n",
            "\u001b[01m\u001b[K./src/gemm.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kconvolution_2d\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gemm.c:2044:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     const int \u001b[01;35m\u001b[Kout_w\u001b[m\u001b[K = (w + 2 * pad - ksize) / stride + 1;    // output_width=input_width for stride=1 and pad=1\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gemm.c:2043:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     const int \u001b[01;35m\u001b[Kout_h\u001b[m\u001b[K = (h + 2 * pad - ksize) / stride + 1;    // output_height=input_height for stride=1 and pad=1\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/utils.c -o obj/utils.o\n",
            "\u001b[01m\u001b[K./src/utils.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcustom_hash\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/utils.c:1061:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around assignment used as truth value [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n",
            "     while (\u001b[01;35m\u001b[Kc\u001b[m\u001b[K = *str++)\n",
            "            \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/dark_cuda.c -o obj/dark_cuda.o\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcudnn_check_error_extended\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:230:20:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between ‘\u001b[01m\u001b[KcudaError_t {aka enum cudaError}\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kenum <anonymous>\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wenum-compare\u001b[m\u001b[K]\n",
            "         if (status \u001b[01;35m\u001b[K!=\u001b[m\u001b[K CUDNN_STATUS_SUCCESS)\n",
            "                    \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcublas_check_error_extended\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:264:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between ‘\u001b[01m\u001b[KcudaError_t {aka enum cudaError}\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kenum cudaError_enum\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wenum-compare\u001b[m\u001b[K]\n",
            "       if (status \u001b[01;35m\u001b[K!=\u001b[m\u001b[K CUDA_SUCCESS)\n",
            "                  \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpre_allocate_pinned_memory\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:395:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\"pre_allocate: size = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K MB, num_of_blocks = %Iu, block_size = %Iu MB \\n\",\n",
            "                                      \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                      \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
            "             \u001b[32m\u001b[Ksize / (1024*1024)\u001b[m\u001b[K, num_of_blocks, pinned_block_size / (1024 * 1024));\n",
            "             \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K          \n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:395:64:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\"pre_allocate: size = %Iu MB, num_of_blocks = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K, block_size = %Iu MB \\n\",\n",
            "                                                              \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                                              \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:395:82:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\"pre_allocate: size = %Iu MB, num_of_blocks = %Iu, block_size = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K MB \\n\",\n",
            "                                                                                \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                                                                \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:405:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "                 printf(\" Allocated \u001b[01;35m\u001b[K%d\u001b[m\u001b[K pinned block \\n\", pinned_block_size);\n",
            "                                    \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                    \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcuda_make_array_pinned_preallocated\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:426:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\"\\n Pinned block_id = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, filled = %f %% \\n\", pinned_block_id, filled);\n",
            "                                          \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                          \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:441:64:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\"Try to allocate new pinned memory, size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K MB \\n\", \u001b[32m\u001b[Ksize / (1024 * 1024)\u001b[m\u001b[K);\n",
            "                                                               \u001b[01;35m\u001b[K~^\u001b[m\u001b[K         \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "                                                               \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:447:63:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\"Try to allocate new pinned BLOCK, size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K MB \\n\", \u001b[32m\u001b[Ksize / (1024 * 1024)\u001b[m\u001b[K);\n",
            "                                                              \u001b[01;35m\u001b[K~^\u001b[m\u001b[K         \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "                                                              \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "At top level:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:287:23:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[KswitchBlasHandle\u001b[m\u001b[K’ defined but not used [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            " static cublasHandle_t \u001b[01;35m\u001b[KswitchBlasHandle\u001b[m\u001b[K[16];\n",
            "                       \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:286:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[KswitchBlasInit\u001b[m\u001b[K’ defined but not used [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            " static int \u001b[01;35m\u001b[KswitchBlasInit\u001b[m\u001b[K[16] = { 0 };\n",
            "            \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/convolutional_layer.c -o obj/convolutional_layer.o\n",
            "\u001b[01m\u001b[K./src/convolutional_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_convolutional_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/convolutional_layer.c:1342:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kt_intput_size\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                         size_t \u001b[01;35m\u001b[Kt_intput_size\u001b[m\u001b[K = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align);\n",
            "                                \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/list.c -o obj/list.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/image.c -o obj/image.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/activations.c -o obj/activations.o\n",
            "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kactivate\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KRELU6\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n",
            "     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX_MAXVAL\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgradient\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n",
            "     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/im2col.c -o obj/im2col.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/col2im.c -o obj/col2im.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/blas.c -o obj/blas.o\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbackward_shortcut_multilayer_cpu\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:207:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_index\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int \u001b[01;35m\u001b[Kout_index\u001b[m\u001b[K = id;\n",
            "                     \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_sim\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:597:59:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_sim(): sim isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", i, j, z);\n",
            "                                                          \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                          \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:597:67:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_sim(): sim isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, j, z);\n",
            "                                                                  \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                  \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:597:75:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_sim(): sim isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, z);\n",
            "                                                                          \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                          \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_P_constrastive\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:611:68:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_P_constrastive(): P isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", i, j, z);\n",
            "                                                                   \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                   \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:611:76:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, j, z);\n",
            "                                                                           \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                           \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:611:84:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, z);\n",
            "                                                                                   \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                                   \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive_f\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:651:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", i, l);\n",
            "                                                                              \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                              \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:651:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, l);\n",
            "                                                                                      \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                                      \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:785:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", i, l);\n",
            "                                                                              \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                              \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/blas.c:785:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, l);\n",
            "                                                                                      \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                                      \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/crop_layer.c -o obj/crop_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/dropout_layer.c -o obj/dropout_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/maxpool_layer.c -o obj/maxpool_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/softmax_layer.c -o obj/softmax_layer.o\n",
            "\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_contrastive_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/softmax_layer.c:203:101:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 9 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "     fprintf(stderr, \"contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d  classes = %4d, step = \u001b[01;35m\u001b[K%4d\u001b[m\u001b[K \\n\", w, h, l.n, l.embedding_size, batch, l.classes, step);\n",
            "                                                                                                   \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                                                                                   \u001b[32m\u001b[K%4ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_contrastive_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/softmax_layer.c:244:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kmax_truth\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                     float \u001b[01;35m\u001b[Kmax_truth\u001b[m\u001b[K = 0;\n",
            "                           \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/softmax_layer.c:423:71:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\" Error: too large number of bboxes: contr_size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K > max_contr_size  = %d \\n\", contr_size, max_contr_size);\n",
            "                                                                      \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                                                      \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/data.c -o obj/data.o\n",
            "\u001b[01m\u001b[K./src/data.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kload_data_detection\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/data.c:1297:24:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int k, \u001b[01;35m\u001b[Kx\u001b[m\u001b[K, y;\n",
            "                        \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/data.c:1090:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kr_scale\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "     float r1 = 0, r2 = 0, r3 = 0, r4 = 0, \u001b[01;35m\u001b[Kr_scale\u001b[m\u001b[K = 0;\n",
            "                                           \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/matrix.c -o obj/matrix.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/network.c -o obj/network.o\n",
            "\u001b[01m\u001b[K./src/network.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_network_waitkey\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/network.c:435:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kema_period\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         int \u001b[01;35m\u001b[Kema_period\u001b[m\u001b[K = (net.max_batches - ema_start_point - 1000) * (1.0 - net.ema_alpha);\n",
            "             \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/network.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_network\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/network.c:660:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet->input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped))\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/network.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/connected_layer.c -o obj/connected_layer.o\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_connected_layer_gpu\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:346:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kone\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Kone\u001b[m\u001b[K = 1;    // alpha[0], beta[0]\n",
            "           \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:344:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kc\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Kc\u001b[m\u001b[K = l.output_gpu;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:343:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kb\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Kb\u001b[m\u001b[K = l.weights_gpu;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:342:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ka\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Ka\u001b[m\u001b[K = state.input;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:341:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = l.outputs;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:340:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kk\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kk\u001b[m\u001b[K = l.inputs;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:339:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Km\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Km\u001b[m\u001b[K = l.batch;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/cost_layer.c -o obj/cost_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/parser.c -o obj/parser.o\n",
            "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kparse_network_cfg_custom\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/parser.c:1777:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1;\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activation_layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/parser.c:6\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ksave_implicit_weights\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/parser.c:1909:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ki\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Ki\u001b[m\u001b[K;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kget_classes_multipliers\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/parser.c:438:29:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kargument 1 range [18446744071562067968, 18446744073709551615] exceeds maximum object size 9223372036854775807 [\u001b[01;35m\u001b[K-Walloc-size-larger-than=\u001b[m\u001b[K]\n",
            "         \u001b[01;35m\u001b[Kclasses_multipliers = (float *)calloc(classes_counters, sizeof(float))\u001b[m\u001b[K;\n",
            "         \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K./src/parser.c:3:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/include/stdlib.h:541:14:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin a call to allocation function ‘\u001b[01m\u001b[Kcalloc\u001b[m\u001b[K’ declared here\n",
            " extern void *\u001b[01;36m\u001b[Kcalloc\u001b[m\u001b[K (size_t __nmemb, size_t __size)\n",
            "              \u001b[01;36m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/option_list.c -o obj/option_list.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/darknet.c -o obj/darknet.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/detection_layer.c -o obj/detection_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/captcha.c -o obj/captcha.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/route_layer.c -o obj/route_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/writing.c -o obj/writing.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/box.c -o obj/box.o\n",
            "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbox_iou_kind\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/box.c:154:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMSE\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(iou_kind) {\n",
            "     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdiounms_sort\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/box.c:898:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbeta_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                     float \u001b[01;35m\u001b[Kbeta_prob\u001b[m\u001b[K = pow(dets[j].prob[k], 2) / sum_prob;\n",
            "                           \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/box.c:897:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kalpha_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                     float \u001b[01;35m\u001b[Kalpha_prob\u001b[m\u001b[K = pow(dets[i].prob[k], 2) / sum_prob;\n",
            "                           \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/nightmare.c -o obj/nightmare.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/normalization_layer.c -o obj/normalization_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/avgpool_layer.c -o obj/avgpool_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/coco.c -o obj/coco.o\n",
            "\u001b[01m\u001b[K./src/coco.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_coco_recall\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/coco.c:248:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbase\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     char *\u001b[01;35m\u001b[Kbase\u001b[m\u001b[K = \"results/comp4_det_test_\";\n",
            "           \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/dice.c -o obj/dice.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/yolo.c -o obj/yolo.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/detector.c -o obj/detector.o\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_detector\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:395:72:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around ‘\u001b[01m\u001b[K&&\u001b[m\u001b[K’ within ‘\u001b[01m\u001b[K||\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n",
            "             \u001b[01;35m\u001b[K(iteration >= (iter_save + 1000) || iteration % 1000 == 0) && net.max_batches < 10000\u001b[m\u001b[K)\n",
            "             \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Keliminate_bdd\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:588:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kstatement with no effect [\u001b[01;35m\u001b[K-Wunused-value\u001b[m\u001b[K]\n",
            "                     \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K (k; buf[k + n] != '\\0'; k++)\n",
            "                     \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:709:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd2\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         int \u001b[01;35m\u001b[Kmkd2\u001b[m\u001b[K = make_directory(buff2, 0777);\n",
            "             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:707:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         int \u001b[01;35m\u001b[Kmkd\u001b[m\u001b[K = make_directory(buff, 0777);\n",
            "             \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector_map\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:1326:24:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kcur_prob\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                 double \u001b[01;35m\u001b[Kcur_prob\u001b[m\u001b[K = 0;\n",
            "                        \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:1347:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_recall\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float \u001b[01;35m\u001b[Kclass_recall\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(truth_classes_count[i] - tp_for_thresh_per_class[i]));\n",
            "               \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:1346:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_precision\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float \u001b[01;35m\u001b[Kclass_precision\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]);\n",
            "               \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdraw_object\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:1890:19:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kinv_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "             float \u001b[01;35m\u001b[Kinv_loss\u001b[m\u001b[K = 1.0 / max_val_cmp(0.01, avg_loss);\n",
            "                   \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/layer.c -o obj/layer.o\n",
            "\u001b[01m\u001b[K./src/layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfree_layer_custom\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/layer.c:208:68:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around ‘\u001b[01m\u001b[K&&\u001b[m\u001b[K’ within ‘\u001b[01m\u001b[K||\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n",
            "     if (l.delta_gpu && (l.optimized_memory < 1 || \u001b[01;35m\u001b[Kl.keep_delta_gpu && l.optimized_memory < 3\u001b[m\u001b[K)) cuda_free(l.delta_gpu), l.delta_gpu = NULL;\n",
            "                                                   \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/compare.c -o obj/compare.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/classifier.c -o obj/classifier.o\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:146:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcount\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kcount\u001b[m\u001b[K = 0;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpredict_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:855:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktime\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     clock_t \u001b[01;35m\u001b[Ktime\u001b[m\u001b[K;\n",
            "             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdemo_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:1287:49:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktval_result\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         struct timeval tval_before, tval_after, \u001b[01;35m\u001b[Ktval_result\u001b[m\u001b[K;\n",
            "                                                 \u001b[01;35m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/classifier.c:1287:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktval_after\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         struct timeval tval_before, \u001b[01;35m\u001b[Ktval_after\u001b[m\u001b[K, tval_result;\n",
            "                                     \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/local_layer.c -o obj/local_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/swag.c -o obj/swag.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/shortcut_layer.c -o obj/shortcut_layer.o\n",
            "\u001b[01m\u001b[K./src/shortcut_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_shortcut_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/shortcut_layer.c:55:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kscale\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float \u001b[01;35m\u001b[Kscale\u001b[m\u001b[K = sqrt(2. / l.nweights);\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/representation_layer.c -o obj/representation_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/activation_layer.c -o obj/activation_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/rnn_layer.c -o obj/rnn_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/gru_layer.c -o obj/gru_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/rnn.c -o obj/rnn.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/rnn_vid.c -o obj/rnn_vid.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/crnn_layer.c -o obj/crnn_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/demo.c -o obj/demo.o\n",
            "\u001b[01m\u001b[K./src/demo.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdetect_in_thread\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/demo.c:101:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kl\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         layer \u001b[01;35m\u001b[Kl\u001b[m\u001b[K = net.layers[net.n - 1];\n",
            "               \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/tag.c -o obj/tag.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/cifar.c -o obj/cifar.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/go.c -o obj/go.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/batchnorm_layer.c -o obj/batchnorm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/art.c -o obj/art.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/region_layer.c -o obj/region_layer.o\n",
            "\u001b[01m\u001b[K./src/region_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_region_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/region_layer.c:63:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kold_h\u001b[m\u001b[K = l->h;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/region_layer.c:62:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kold_w\u001b[m\u001b[K = l->w;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/reorg_layer.c -o obj/reorg_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/reorg_old_layer.c -o obj/reorg_old_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/super.c -o obj/super.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/voxel.c -o obj/voxel.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/tree.c -o obj/tree.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/yolo_layer.c -o obj/yolo_layer.o\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:68:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:75:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.delta, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:106:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:115:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kprocess_batch\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:426:25:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kbest_match_t\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                     int \u001b[01;35m\u001b[Kbest_match_t\u001b[m\u001b[K = 0;\n",
            "                         \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:707:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_anyobj\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Kavg_anyobj\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:706:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_obj\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Kavg_obj\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:705:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_cat\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Kavg_cat\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:704:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Krecall75\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Krecall75\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:703:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Krecall\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Krecall\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:702:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_ciou_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Ktot_ciou_loss\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:701:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_diou_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Ktot_diou_loss\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:698:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_ciou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Ktot_ciou\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:697:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_diou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Ktot_diou\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:696:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_giou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Ktot_giou\u001b[m\u001b[K = 0;\n",
            "           \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:668:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int b, \u001b[01;35m\u001b[Kn\u001b[m\u001b[K;\n",
            "            \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/gaussian_yolo_layer.c -o obj/gaussian_yolo_layer.o\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:72:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:79:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:111:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:120:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4811:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/upsample_layer.c -o obj/upsample_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/lstm_layer.c -o obj/lstm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/conv_lstm_layer.c -o obj/conv_lstm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/scale_channels_layer.c -o obj/scale_channels_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -c ./src/sam_layer.c -o obj/sam_layer.o\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -c ./src/convolutional_kernels.cu -o obj/convolutional_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -c ./src/activation_kernels.cu -o obj/activation_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
            "\n",
            "./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
            "\n",
            "./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
            "\n",
            "./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
            "\n",
            "./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
            "\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -c ./src/im2col_kernels.cu -o obj/im2col_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "./src/im2col_kernels.cu(1354): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1361): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1364): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1389): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1354): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1361): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1364): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1389): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1354): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1361): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1364): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1389): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1354): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1361): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1364): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1389): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1354): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1361): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1364): warning: unrecognized #pragma in device code\n",
            "\n",
            "./src/im2col_kernels.cu(1389): warning: unrecognized #pragma in device code\n",
            "\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -c ./src/col2im_kernels.cu -o obj/col2im_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -c ./src/blas_kernels.cu -o obj/blas_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "\u001b[01m\u001b[K./src/blas_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid backward_shortcut_multilayer_gpu(int, int, int, int*, float**, float*, float*, float*, float*, int, float*, float**, WEIGHTS_NORMALIZATION_T)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas_kernels.cu:1130:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kstep\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kint \u001b[m\u001b[Kstep = 0;\n",
            "     \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -c ./src/crop_layer_kernels.cu -o obj/crop_layer_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -c ./src/dropout_layer_kernels.cu -o obj/dropout_layer_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -c ./src/maxpool_layer_kernels.cu -o obj/maxpool_layer_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -c ./src/network_kernels.cu -o obj/network_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "./src/network_kernels.cu(379): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "./src/network_kernels.cu(379): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "./src/network_kernels.cu(379): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "./src/network_kernels.cu(379): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "./src/network_kernels.cu(379): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "\u001b[01m\u001b[K./src/network_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfloat train_network_datum_gpu(network, float*, float*)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/network_kernels.cu:379:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kl\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[K \u001b[m\u001b[K layer l = net.layers[net.n - 1];\n",
            "       \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC\" -c ./src/avgpool_layer_kernels.cu -o obj/avgpool_layer_kernels.o\n",
            "nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC obj/image_opencv.o obj/http_stream.o obj/gemm.o obj/utils.o obj/dark_cuda.o obj/convolutional_layer.o obj/list.o obj/image.o obj/activations.o obj/im2col.o obj/col2im.o obj/blas.o obj/crop_layer.o obj/dropout_layer.o obj/maxpool_layer.o obj/softmax_layer.o obj/data.o obj/matrix.o obj/network.o obj/connected_layer.o obj/cost_layer.o obj/parser.o obj/option_list.o obj/darknet.o obj/detection_layer.o obj/captcha.o obj/route_layer.o obj/writing.o obj/box.o obj/nightmare.o obj/normalization_layer.o obj/avgpool_layer.o obj/coco.o obj/dice.o obj/yolo.o obj/detector.o obj/layer.o obj/compare.o obj/classifier.o obj/local_layer.o obj/swag.o obj/shortcut_layer.o obj/representation_layer.o obj/activation_layer.o obj/rnn_layer.o obj/gru_layer.o obj/rnn.o obj/rnn_vid.o obj/crnn_layer.o obj/demo.o obj/tag.o obj/cifar.o obj/go.o obj/batchnorm_layer.o obj/art.o obj/region_layer.o obj/reorg_layer.o obj/reorg_old_layer.o obj/super.o obj/voxel.o obj/tree.o obj/yolo_layer.o obj/gaussian_yolo_layer.o obj/upsample_layer.o obj/lstm_layer.o obj/conv_lstm_layer.o obj/scale_channels_layer.o obj/sam_layer.o obj/convolutional_kernels.o obj/activation_kernels.o obj/im2col_kernels.o obj/col2im_kernels.o obj/blas_kernels.o obj/crop_layer_kernels.o obj/dropout_layer_kernels.o obj/maxpool_layer_kernels.o obj/network_kernels.o obj/avgpool_layer_kernels.o -o darknet -lm -pthread `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++\n",
            "g++ -std=c++11 -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC ./obj/image_opencv.o ./obj/http_stream.o ./obj/gemm.o ./obj/utils.o ./obj/dark_cuda.o ./obj/convolutional_layer.o ./obj/list.o ./obj/image.o ./obj/activations.o ./obj/im2col.o ./obj/col2im.o ./obj/blas.o ./obj/crop_layer.o ./obj/dropout_layer.o ./obj/maxpool_layer.o ./obj/softmax_layer.o ./obj/data.o ./obj/matrix.o ./obj/network.o ./obj/connected_layer.o ./obj/cost_layer.o ./obj/parser.o ./obj/option_list.o ./obj/darknet.o ./obj/detection_layer.o ./obj/captcha.o ./obj/route_layer.o ./obj/writing.o ./obj/box.o ./obj/nightmare.o ./obj/normalization_layer.o ./obj/avgpool_layer.o ./obj/coco.o ./obj/dice.o ./obj/yolo.o ./obj/detector.o ./obj/layer.o ./obj/compare.o ./obj/classifier.o ./obj/local_layer.o ./obj/swag.o ./obj/shortcut_layer.o ./obj/representation_layer.o ./obj/activation_layer.o ./obj/rnn_layer.o ./obj/gru_layer.o ./obj/rnn.o ./obj/rnn_vid.o ./obj/crnn_layer.o ./obj/demo.o ./obj/tag.o ./obj/cifar.o ./obj/go.o ./obj/batchnorm_layer.o ./obj/art.o ./obj/region_layer.o ./obj/reorg_layer.o ./obj/reorg_old_layer.o ./obj/super.o ./obj/voxel.o ./obj/tree.o ./obj/yolo_layer.o ./obj/gaussian_yolo_layer.o ./obj/upsample_layer.o ./obj/lstm_layer.o ./obj/conv_lstm_layer.o ./obj/scale_channels_layer.o ./obj/sam_layer.o ./obj/convolutional_kernels.o ./obj/activation_kernels.o ./obj/im2col_kernels.o ./obj/col2im_kernels.o ./obj/blas_kernels.o ./obj/crop_layer_kernels.o ./obj/dropout_layer_kernels.o ./obj/maxpool_layer_kernels.o ./obj/network_kernels.o ./obj/avgpool_layer_kernels.o src/yolo_v2_class.cpp -o libdarknet.so -lm -pthread `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++\n",
            "In file included from \u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:2:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kvoid track_kalman_t::clear_old_states()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:879:50:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                 if ((result_vec_pred[state_id].x > img_size.width) ||\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:880:50:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                     (result_vec_pred[state_id].y > img_size.height))\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Ktrack_kalman_t::tst_t track_kalman_t::get_state_id(bbox_t, std::vector<bool>&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:900:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::predict()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:990:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::correct(std::vector<bbox_t>)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:1025:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> Detector::tracking_id(std::vector<bbox_t>, bool, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:439:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         if (\u001b[01;35m\u001b[Kprev_bbox_vec_deque.size() > frames_story\u001b[m\u001b[K) prev_bbox_vec_deque.pop_back();\n",
            "             \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:454:34:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                     if (\u001b[01;35m\u001b[Kcur_dist < max_dist\u001b[m\u001b[K && (k.track_id == 0 || dist_vec[m] > cur_dist)) {\n",
            "                         \u001b[01;35m\u001b[K~~~~~~~~~^~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:478:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         if (\u001b[01;35m\u001b[Kprev_bbox_vec_deque.size() > frames_story\u001b[m\u001b[K) prev_bbox_vec_deque.pop_back();\n",
            "             \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -fPIC -o uselib src/yolo_console_dll.cpp -lm -pthread `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++ -L ./ -l:libdarknet.so\n",
            "In file included from \u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:23:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kvoid track_kalman_t::clear_old_states()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:879:50:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                 if ((result_vec_pred[state_id].x > img_size.width) ||\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:880:50:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                     (result_vec_pred[state_id].y > img_size.height))\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Ktrack_kalman_t::tst_t track_kalman_t::get_state_id(bbox_t, std::vector<bool>&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:900:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::predict()\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:990:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::correct(std::vector<bbox_t>)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:1025:30:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "         for (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n",
            "                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_boxes(cv::Mat, std::vector<bbox_t>, std::vector<std::__cxx11::basic_string<char> >, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:192:46:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "             int max_width = (\u001b[01;35m\u001b[Ktext_size.width > i.w + 2\u001b[m\u001b[K) ? text_size.width : (i.w + 2);\n",
            "                              \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:201:62:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
            "                 int const max_width_3d = (\u001b[01;35m\u001b[Ktext_size_3d.width > i.w + 2\u001b[m\u001b[K) ? text_size_3d.width : (i.w + 2);\n",
            "                                           \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:183:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcolors\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int const \u001b[01;35m\u001b[Kcolors\u001b[m\u001b[K[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } };\n",
            "               \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AORz9dHs7_kc",
        "outputId": "9a03e2ee-bd0c-4f55-a507-616452461805"
      },
      "source": [
        "# get bthe scaled yolov4 weights file that is pre-trained to detect 80 classes (objects) from shared google drive\n",
        "!wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1V3vsIaxAlGWvK4Aar9bAiK5U0QFttKwq' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1V3vsIaxAlGWvK4Aar9bAiK5U0QFttKwq\" -O yolov4-csp.weights && rm -rf /tmp/cookies.txt"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-08-12 14:02:41--  https://docs.google.com/uc?export=download&confirm=t&id=1V3vsIaxAlGWvK4Aar9bAiK5U0QFttKwq\n",
            "Resolving docs.google.com (docs.google.com)... 142.251.10.100, 142.251.10.113, 142.251.10.139, ...\n",
            "Connecting to docs.google.com (docs.google.com)|142.251.10.100|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://doc-14-84-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/i8r93f89ohpem292ie66l77emrggh68i/1660312950000/17800843676226924807/*/1V3vsIaxAlGWvK4Aar9bAiK5U0QFttKwq?e=download&uuid=805471bd-b687-4da7-bcca-b38b2faaf246 [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2022-08-12 14:02:41--  https://doc-14-84-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/i8r93f89ohpem292ie66l77emrggh68i/1660312950000/17800843676226924807/*/1V3vsIaxAlGWvK4Aar9bAiK5U0QFttKwq?e=download&uuid=805471bd-b687-4da7-bcca-b38b2faaf246\n",
            "Resolving doc-14-84-docs.googleusercontent.com (doc-14-84-docs.googleusercontent.com)... 74.125.24.132, 2404:6800:4003:c03::84\n",
            "Connecting to doc-14-84-docs.googleusercontent.com (doc-14-84-docs.googleusercontent.com)|74.125.24.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 211944840 (202M) [application/octet-stream]\n",
            "Saving to: ‘yolov4-csp.weights’\n",
            "\n",
            "yolov4-csp.weights  100%[===================>] 202.13M  94.5MB/s    in 2.1s    \n",
            "\n",
            "2022-08-12 14:02:44 (94.5 MB/s) - ‘yolov4-csp.weights’ saved [211944840/211944840]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JhrQqPc2HR27"
      },
      "source": [
        "# import darknet functions to perform object detections\n",
        "from darknet import *\n",
        "# load in our YOLOv4 architecture network\n",
        "network, class_names, class_colors = load_network(\"cfg/yolov4-csp.cfg\", \"cfg/coco.data\", \"yolov4-csp.weights\")\n",
        "width = network_width(network)\n",
        "height = network_height(network)\n",
        "\n",
        "# darknet helper function to run detection on image\n",
        "def darknet_helper(img, width, height):\n",
        "  darknet_image = make_image(width, height, 3)\n",
        "  img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "  img_resized = cv2.resize(img_rgb, (width, height),\n",
        "                              interpolation=cv2.INTER_LINEAR)\n",
        "\n",
        "  # get image ratios to convert bounding boxes to proper size\n",
        "  img_height, img_width, _ = img.shape\n",
        "  width_ratio = img_width/width\n",
        "  height_ratio = img_height/height\n",
        "\n",
        "  # run model on darknet style image to get detections\n",
        "  copy_image_from_bytes(darknet_image, img_resized.tobytes())\n",
        "  detections = detect_image(network, class_names, darknet_image)\n",
        "  free_image(darknet_image)\n",
        "  return detections, width_ratio, height_ratio"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Zg82Yi292ku"
      },
      "source": [
        "# function to convert the JavaScript object into an OpenCV image\n",
        "def js_to_image(js_reply):\n",
        "  \"\"\"\n",
        "  Params:\n",
        "          js_reply: JavaScript object containing image from webcam\n",
        "  Returns:\n",
        "          img: OpenCV BGR image\n",
        "  \"\"\"\n",
        "  # decode base64 image\n",
        "  image_bytes = b64decode(js_reply.split(',')[1])\n",
        "  # convert bytes to numpy array\n",
        "  jpg_as_np = np.frombuffer(image_bytes, dtype=np.uint8)\n",
        "  # decode numpy array into OpenCV BGR image\n",
        "  img = cv2.imdecode(jpg_as_np, flags=1)\n",
        "\n",
        "  return img\n",
        "\n",
        "# function to convert OpenCV Rectangle bounding box image into base64 byte string to be overlayed on video stream\n",
        "def bbox_to_bytes(bbox_array):\n",
        "  \"\"\"\n",
        "  Params:\n",
        "          bbox_array: Numpy array (pixels) containing rectangle to overlay on video stream.\n",
        "  Returns:\n",
        "        bytes: Base64 image byte string\n",
        "  \"\"\"\n",
        "  # convert array into PIL image\n",
        "  bbox_PIL = PIL.Image.fromarray(bbox_array, 'RGBA')\n",
        "  iobuf = io.BytesIO()\n",
        "  # format bbox into png for return\n",
        "  bbox_PIL.save(iobuf, format='png')\n",
        "  # format return string\n",
        "  bbox_bytes = 'data:image/png;base64,{}'.format((str(b64encode(iobuf.getvalue()), 'utf-8')))\n",
        "\n",
        "  return bbox_bytes"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zOuLa-Hc4cvd"
      },
      "source": [
        "## YOLOv4 on Wecam Images\n",
        "Running YOLOv4 on images taken from webcam is fairly straight-forward. We will utilize code within Google Colab's **Code Snippets** that has a variety of useful code functions to perform various tasks.\n",
        "\n",
        "We will be using the code snippet for **Camera Capture** which runs JavaScript code to utilize your computer's webcam. The code snippet will take a webcam photo, which we will then pass into our YOLOv4 model for object detection.\n",
        "\n",
        "Below is a function to take the webcam picture using JavaScript and then run YOLOv4 on it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4k7CW_s-V9A"
      },
      "source": [
        "def take_photo(filename='photo.jpg', quality=0.8):\n",
        "  js = Javascript('''\n",
        "    async function takePhoto(quality) {\n",
        "      const div = document.createElement('div');\n",
        "      const capture = document.createElement('button');\n",
        "      capture.textContent = 'Capture';\n",
        "      div.appendChild(capture);\n",
        "\n",
        "      const video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
        "\n",
        "      document.body.appendChild(div);\n",
        "      div.appendChild(video);\n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "\n",
        "      // Resize the output to fit the video element.\n",
        "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
        "\n",
        "      // Wait for Capture to be clicked.\n",
        "      await new Promise((resolve) => capture.onclick = resolve);\n",
        "\n",
        "      const canvas = document.createElement('canvas');\n",
        "      canvas.width = video.videoWidth;\n",
        "      canvas.height = video.videoHeight;\n",
        "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
        "      stream.getVideoTracks()[0].stop();\n",
        "      div.remove();\n",
        "      return canvas.toDataURL('image/jpeg', quality);\n",
        "    }\n",
        "    ''')\n",
        "  display(js)\n",
        "\n",
        "  # get photo data\n",
        "  data = eval_js('takePhoto({})'.format(quality))\n",
        "  # get OpenCV format image\n",
        "  img = js_to_image(data) \n",
        "  \n",
        "  # call our darknet helper on webcam image\n",
        "  detections, width_ratio, height_ratio = darknet_helper(img, width, height)\n",
        "\n",
        "  # loop through detections and draw them on webcam image\n",
        "  for label, confidence, bbox in detections:\n",
        "    left, top, right, bottom = bbox2points(bbox)\n",
        "    left, top, right, bottom = int(left * width_ratio), int(top * height_ratio), int(right * width_ratio), int(bottom * height_ratio)\n",
        "    cv2.rectangle(img, (left, top), (right, bottom), class_colors[label], 2)\n",
        "    cv2.putText(img, \"{} [{:.2f}]\".format(label, float(confidence)),\n",
        "                      (left, top - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\n",
        "                      class_colors[label], 2)\n",
        "  # save image\n",
        "  cv2.imwrite(filename, img)\n",
        "\n",
        "  return filename"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "M_btEC1N-YkS",
        "outputId": "f7571806-b9af-4c04-e03a-392154ac8aea"
      },
      "source": [
        "try:\n",
        "  filename = take_photo('photo.jpg')\n",
        "  print('Saved to {}'.format(filename))\n",
        "  \n",
        "  # Show the image which was just taken.\n",
        "  display(Image(filename))\n",
        "except Exception as err:\n",
        "  # Errors will be thrown if the user does not have a webcam or if they do not\n",
        "  # grant the page permission to access it.\n",
        "  print(str(err))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function takePhoto(quality) {\n",
              "      const div = document.createElement('div');\n",
              "      const capture = document.createElement('button');\n",
              "      capture.textContent = 'Capture';\n",
              "      div.appendChild(capture);\n",
              "\n",
              "      const video = document.createElement('video');\n",
              "      video.style.display = 'block';\n",
              "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
              "\n",
              "      document.body.appendChild(div);\n",
              "      div.appendChild(video);\n",
              "      video.srcObject = stream;\n",
              "      await video.play();\n",
              "\n",
              "      // Resize the output to fit the video element.\n",
              "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
              "\n",
              "      // Wait for Capture to be clicked.\n",
              "      await new Promise((resolve) => capture.onclick = resolve);\n",
              "\n",
              "      const canvas = document.createElement('canvas');\n",
              "      canvas.width = video.videoWidth;\n",
              "      canvas.height = video.videoHeight;\n",
              "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
              "      stream.getVideoTracks()[0].stop();\n",
              "      div.remove();\n",
              "      return canvas.toDataURL('image/jpeg', quality);\n",
              "    }\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved to photo.jpg\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/jpeg": 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/jp9MNrNL/wAtq0Mg/fSmmSed/wBNHf8A5a0+OWbfTJJfK/6aVmV7Qb5x96d/rP3MtMklFLVzNP3hHn/pv+lEn7r/AJY81J5J96jx/wBMP1qCuQKZJEaJfO38eZspkkv/AEx+SgBkc37z97XHftI+X/wzt492b93/AAheq+Zn1+yS12Pmweb52ZK4n9pOYD9n3x4Y/wDlp4L1TP8A4CS1vgP9+pf4o/mgW5xv/BPXZ/wx/wCEPMxt/wBPz/4MLmvZc/8ATf8ASvF/+CfH/Jn3hP5ccX/zev8AxMLmvZZIYf8Abkrqz3/kd4r/AK+T/wDSmOO7C4zjzoqx/GniOz8L+F7/AMVX03+jWdrJPLV+48ny6+VP+CtnxavPhd+yvqUOl6lHBc6pdR2vm+bs/d15VOHtKvIXOZ+U3/BRn9qDWP2lvjRf69qmsJ9gt5dlhbRf8u9fMeof6f8AufOStXxxdQ6pcP8Aapo5P3v/ACzrnpB/Zf8ArZvn/wCWVejOpb3DGFCm/fNvQ9Gs7CTzsxvXSR+LdH0Hm1+//wBda89/4S28jt/JtPLj/wCmlUPNvNauOm964l7Y6eSke06f480fWY/JihkkrN8Qf2PL++lm/wC/leVyeHPFWl/8TK2Mkb/89IpahvPFuvfJ9vmedK6qZyzp/wAhseJNUg8x7LyY/wDrrXMapLD6fPVmPWRqFx/pdlJ+7qGTybr99F/5FrCpTI/e0ynZ2vlR4l/7ZVct/tnzwxD5JKfZ2011cJ5VdDH4NvPs73kU3l+X/wA9azp0zT96Ylv50Vx5EU2ytI2E3yf3/N/55VNHpU52cf6z/lrW3bxQ/Z/3sHz1306BM61UwRYTG4+x3V5/2yrS8PeHBdSfvfuR1paXpdnLcfvPnSrOoS2drG8MU3l0ezJpzY/RItH0G88m1hnk/wCuldDZy6bdScQ+W9cZZxfvPPN5XW+H7r94n7n5KPZl8lU9R+FebXXLb7Vs2SfJX6R/sv8Awv8ADnhzwnba95Lvc6haxzy+ZX5leA/tl/qEPHyeb/qv+elfrR8F9Gh0vwlpsOZJE+yxv5lc9eBvT/hHsHhOUxW6eV9yvKv+CkN40/7PmkJjg+L4G/8AJS5r1DQv+PavJf8AgolIW+AeloqqV/4S22w69D/odzXrcKTvn2G/xFvY8J1Pxx8NfCvjH4taP448If2lqGr6lfQ6Ddf2fDN9im8y6XfukYNH8zxnKZPyZ7CsHxf8LPHXhnT9G1744XeuWmnXOmLDpk0dsL2S02k+XaOkk0fkfJucJu4BwFyHCeh/A34eftQa3od58Wfhz8RdDsP+EruZLjUTeRAyTSJNKpYr9mdU+cyEBSBhh9B2r+Bf26IuD8ZvDX/gKn/yHX39bOKeW4x06NeimrKfNUnzXjBQaivZSjCzWvxc29lcEnI8t8JfDXUJPD/wmS58F6lNb6n4vubjUY7yzEkMsbvaYYKAcRNbwM2JACwjlYZTBqv+1R4S+Gvhz4vatZ2VpqWif8Sy3ls7Oz0OH7JcTGMgmMiWPy4vlQFgrnzPO4+UA+tReBf26T9z4zeGhx3tU/8AkOlfwV+3RF1+Nvhn/wABU/8AkKvOo57Up49Yh4qjZKasqk18c+frRktPh221Vul8jtsecfB+18PfB/VPDvxo/aP+E11rPhC/0CS38P2cNqbqK3ufM48yG6lCr5i/aLgKSY2M++NflPlt8G/ALxvrP/CsDr3g/WG8H+KvH1w1hok9sXeKxk+xF5pLiFUc+bbxOclY/ks3mQBXJHr3h/wp/wAFAC3+gfH/AMJW5/6b2cf/AMgmtW40D/gora7vN/aZ8Fgx9R9ij/8AldXR/b0nOVSOIoKUrq/tKjSVpqPKnT0kua7kneVntdmL0nymr8Bvh7dfDT9vT4haD4R8L3mk+FZvDUF3FbQQypYtNIbZlZQfk/1n21UA4XbMiABWA7H9v5lX9knxbGh4H2D/ANL7avNE0n/goXKnmv8AtQ+CVHrJbRD/ANx9eW/H34gfHA+E9S8AfGf9s34d3FldiN7/AElXCzPskWRf9XYq4wyKeCOmOleG6VGtnGGxlTGUf3fs+b3ptyULXesNW7dX8yLK/MexfHKPwPZ/sLfDzxV4q8PaxJe6XouhnRvEHh9ALvQpnt4c3W/cuIxsAKFlEknkrujYpLH86eDvgR40/aSfxz8QNB1jxL4jj0jRRNp2u65AUudSvo/IJtWQSXDyv9nEyoqO3Ig3FA4U43hD9tfxt8K9BTwr4W/bS8Nx2UGBBb3Je7MKhFRY0ae0dkQKoARSFHOAMnOZ4g/4KlfE7RmKR/tJWd6zdX03w3CS3/AjaLn869jB5g8so1IYfFUW3JtSlOeick7cvs3bbpK2t2mxKpTezPWfHvxh1H9o34XfDv8AY38M/DvVLDxVpWp2tjqh1AHy7eS1ge1yVRTIF2s8spZFMIjYYcAsPfNd/bl8UPa/EW18G/BhLq5+Gmsxx3m+9nlW9sUu5IbiYGO22wyBYwwV34VpJPnEDq3wgf8AgsX8U4rj7LH8dbl0k/5bpoNmUP4+Tn9K734c/wDBSb4yeO9v2f8Aa/8ADukySdI9c0eCNv0snrnxFLLcVGCkqHLFuSXtar96UlKb/hLRpcqXTe7YuaJ6ZL468FfH79qzwZ45/ZE+Fes2HiSHWpNS8XS3cwsobqENAGeR4ZZVhjZPOWVggMhnwVld9pyfiH8af2f/ABp8D7z4N6H+ywfD/wAUL29jt3XTfDUJ8m++3q80UTs32mPdh0WDafL3iEZVQa7XSPiT+2bqGnLqVh+218ORDIN4Jhi/+V1XbXxR+3hdRrNZfteeBHX+Ax2Mf/yurohj8FCdP97StT5eX9/Vv7rbSk/ZWlHWyjZWSsnYu6NT/goJpvijSP2H/h/pnjeSZtbttU0mLWWubkTSG7XTLkSl5AzCRt4bLZOTzk5zX15H/rHEvz1+e/xO+CX7UnxlupJPiX+0hoGqxtOk32OXVLxLRJETy1dLeO1WKNtpIyqAncxPLEnu38U/8FAYk81v2q/CGP8AsDj/AOVteHj8FgsVl9CjDG0eaMqkpayS9/ldo+63ZWe9uhUkfadPji/efujXxKvjb/goBnKftT+EPw0eP/5W03/hOf8AgoHsx/w0/wCE9v8A2Bo//ldXk/2LR/6DaP8A4FP/AOQIsfb8f7rn949P+1Dy/wDU/JXw4fH/APwUFiXEP7TvhV1/6Z6NF/8AK6oT8R/+Cgh6ftMeGm+mjxH/ANx9H9i0f+g2h/4FP/5AOSqfdn7ny/3vyURfweVefJ/zyr4S/wCFmf8ABQzZ/wAnHeHMf9gm3/8AkCkX4n/8FDMean7RXhv/AIDpFt/Swo/sWj/0G0f/AAKf/wAgFj7u/wCWn+p+T61N5vz/AOx9a+Uv2P8A9uTxV4z+E8178Y5JNT1W01ia3W9sraG386HZHIpdVATcDIy/Kqjaq5BOWPr+l/tQeD7+9SzGm3yPJL/0zrx8fha2W42eFq25oOztt8tvyLPTv3Mu/wA2iO1/d0yPzrr++lWbeKszOZ5jo37Y/wCy/wCIfGmn/DnQ/jLpV3q2qzvDYQwCRopZVlki2ecF8oMzxMEBYGQNGU3CWMtrfE79pL4G/BnxJpfhH4n/ABHsdI1TWcGxt7hXbCFwgklZFKwRliR5kpRPlfnCNj80/h/4/wDhHN+zBr3wOf4UT6r8Rtf8SQP4b1q10K3nljiMlmBbpNu88M3lzKERSCZQP4mwfHj4H/Ef4XeIrm9/ar1/xGniDVtKiu9F1CO3TVotSlEIDQT3cl0hjaE+VE20SlRyAU8syfp64Ky3677KpVlBe8lF2cp2t70bL4dddG1Zu9ndR7GNz9JPiL+1R+z/APC22t7rx18WNKtP7Q0pdSsBDI1y11aNJHGk0awhy6sZAV2g7lWRlysUhXEu/wBu/wDZMttS0nSZPjlpJl1eCCa1eOOZ4o1lbaonkVClswP3lmKNGOXCjmvmf4T/ALPE1v8AtJfAnwj4z+EGqzaMnwyuZtbsfFOkRzxRzl9SmmEqhWjVVnuoCqvh4xPbrIFkOK8N/au8G/CPwh8a/H2j+Hv+Ej0a8tPEcg0rw9deG7eO18pvLZ2EqzoYoSWlaBVgYGEQZb5yV5cDwrkmKxSwzqzcuVy5o8trc7gtEm1td62s90aqKcLH6X/FP9pT4GfB3xDpXg/4n/Eex0fUdZwbG3uFdvkLhBJKyKVgjLEjzJSiHY/PyNin8Qf2pf2e/hRFZXfjb4r6VaLfaUupWIhka4a5tGkjjSaNYQ5dWMgK7QdyrIy5WKQr8K22k+E/2c08T3X7YXwI1W/v/HHg63HgF7VTLFpcLWzBrVJL93e1mtgbSAFkllgEPAZWUy63wC/ZX8fr+0V8HvCHx88BX2oaPL4VvNQbSdQ0cpBpsEc99OlvOycSYnlt5XWXBU3scMi4wpy/1VyinR9rVrScYpu65f3louT9nvbltyvmvr16EcqsffXgH4l+APix4ai8X/DLxdY6zp0u0faLKcN5blFfy5F+9FIFdSY3AddwyBXh/wDwU9+Jnj74afs6WGo/DzxbfaLdah4qtrO6vNNnMUxg8i4lKLIvzR5eGPJUgkAqTtZgcz/gnp4D174b/FL42+BY9B1bTPDemeMY4fD9leCfyEXdcnMZlzuY25s2LZLMjQsSQVJh/wCCvf8AybXof/Y823/pHe15mAwGGwvFtHDwfPDmi1ezunFSV+l1f70QqaUyr8bvjH8af2EPh14G+GOmeNLbx/4g8R6reh/EnjibyooYkeLERJmUqubhT5s07BAj5IQosWX4X/4KOfFY/DD4ial46+F2h3PiDwdY2tzZ6j4bvxcaM32maC3jimmimmUyBpvMCpL+8WOVP3TRFin/AAUo/wCJb8WPh9rvxq/tzUPhGvm/2lo3h/5ZH1KMSMPMY7F/eI0SLmQOI47ry9hyzeE3l38UvEvgj4p63+x1pfiPw98GkgtZNW0i7Znd2EUEdxHG26Z9xBeWbZIF+zqBKwXy0P0WWZdl+Y5XSrYilFzm1KU37sW3VS5Lq1pON1yqNuvMjRJNHu3wA/4KD/HTxh8W/Cngz4n+A/C17p3jD91YnwRqCXN7ZOQjCa4hS7maGNFYmRZVjdVDvyYmQ+w/tyftG69+zF8HbXx14R0Gx1LU77W4dPtxqRcww7o5ZWdlQqz/ACwlQAy4Lhsnbtb5R+CHiX4X6r+0d8NbP9g/wH4x0m6sZ2j+IN7rEysdR0vfbCWSYefJEq7VlJwsY8x4vLXeIwup+0T+0/4l/az+AmlePbnwzrXh7wPpPjiOw+IWk6JM9xNc2zrBJBOlw9ukJ2kTL5bNhZXtWdTvjIzxORYatnWHnDDqFLTni3Z6yko3jeVubltGzak7X5bhbU7f4df8FCfjrf3/AIn8LfEPwJ4VutRsPBGoa5o+oeDdQj1CytXtYJpMXrQXcyrG7IqcSI6syKVPnKy0v2UP+Chf7Q/x/wDjno/w01Pwf4HjsbrzZNQaOSezmECIWYwmSeTzZBwwjVGLBWyY1DSp4t4L+N+leF/ijqfiH9lP4SPaeFJvCk9r4m8Ka/PJdWeqSFLjyzMkkkpc5eMLGrBmCyICqySE0fij8ePhf8T/AAd4a8E/AL9n2z8HeJdN1a0ubbVdGgRLoeTDIu37WD5rkOUkMjkEmPexBGa7amUZdUnUprCKPNFXlp+6bi9Zx57x2urNq2t76AoR7Hu3xY8P/Hz9mv8Aas+G1yf2pfFPiOw8deOHhm0rUnZYYLZrq3jeJohIYHzHdFQUiiVCgZFT5dv2dITLH1r4G/ar/aDuPHXxj+D/AIiXRbaFvD/iY3MaRzuRIftNk2CT0H7vt617f/w2vr/mY/4Q+x/66/apK+Gzqu8Rl2CrzS5pRndpJXtOSWyS0SCNLmsfQ/8ArY/9uj97/wA9vnr51k/bN17Of+EV01/+2slMk/bN8VfJ/wAUrpv/AH9kr5jnNLVj6K+1eb+5z/q6h6f9tK+eH/bN8bf9C3pUf/fymSftm+Nj/wAwHSv/ACJWlOuR7D2h9FJEIpE/5aeZRJF+8r5vk/bI8eeY839m6Unl/wDTKT/45Tf+Gw/HX/Pnpv8A4C0e2D2B9HyRCmfuq+b5P2vviF/yy03Sv+uv2WT/AOOUyL9r74kZfH9lf+Av/wBsrT2gQpumfS3+qpkXavmaT9rn4kf9Q7/wFpkv7V/xIH/QN/8AAWufnF7M+mfN83pRJ/0yr5n/AOGq/if/AM9dN/8AACo4/wBqr4kf8/ljH/24Vp7QIH0nJ53fy9lcZ+0f/wAm8ePf+xL1T/0klrxz/hqD4nfP5t5Y/wDgBXM/Fn9oj4i638K/E2jXmqQi2vPD97DKgtkG5XgdSMjpwa3wE/8AbqX+KP5o09nyHq3/AAT4/wCTPfCP11D/ANL7mvXfN+f97XxZ+y38cfH/AIP+BOheHNE1aKK1tvtXlI1ojEbrqVjyeTyxrupP2kfida8xa9/5Kx1159O2eYr/AK+T/wDSmHJc+kLy6/d1+V//AAXR+N/9veLLD4P6Xeb/AOx7X7Vqkf8Az0kk/wBXX1F40/ag+IWjaHc69da98lvFvl/dR/vK/F79pD4++JPjd8TNb+IWvXk7zXl1+6/e/wDLOvNwv8XnCpPkPHPEms3kN5N5u/fHLWb/AGp9qj+2S1f8Qf6VG/7nzP8AprXKmLypPJ/gpzmFP3DYs7Xzbh5o5vL/AI629Dtbz7R/osFc9o9rNLJ+6ikkr0Xwn4cmi2TSw/PJ/wAsvNpe09mb06HtBkFh4k1n9zF5kFM/4VfNL/x//u67y3is7CPH3H/9GVMIvuHya5Z1zq+rnnV54Im+SztZn2VZs/hL5saebDJv/wCeld/Ho03mf6n5627fRpvLT989KdeqaTwh5vp/wqNhqiTedXoul/C+aS3+2eckb+V/qv8AnpWxpegw/aUM0G+vVPD/AISs4tPSGWH5/wDnnUTxfs9jup4Gl7I+b/EHgPzf33kR+dH/AMsqLPwbNdW/+lWeyvbPEHhKHT43mlhT95WPb+HIcvD5PyVvQzGqzD6rSPH7zwR9gjeaLfG//PKsSTQftX/H/Xt+ueHP3f7ry9lcZrHhLypE+wQ73r0adc8qvQSOM0/QfK/4+vueb/ra0ra1mtZP3U0ldJ/YM3lpDLD/AOQqv2fg2b7Qn8aV305nJUdP7Bt/B/S/7Z8UaVpvneQ9xfx/+jK/YPwfoX2CzhhtYfkji/dV+cv7CXwg/wCE8/aA8PaZLpryeXdefL/2zr9ktL+AXlW6fc/7Z1hjocnIFCv7Q830vPl/vcV5J/wUNMi/AXSlePGfFsJ/8lrqvrIfBaaL9zXzj/wVC8DS+Ff2ftKui7MknjO2UE/9el2f6V28LQtxDhv8R0TMn9kDzP8Ahnrw7v8A9X/pf/pXNXo9xdeVz/rK8+/Y/wD+TcfDv/b3/wClk1ej+TD5f73fXicQ/wDI/wAX/wBfan/pbOh/AihJdER/886zbzWYbb9/59Wdcl8qL91DXxz+2p+2RqXgjVJvhv8AC+aOTVfK/wBKvv8AWfZ/+uf/AE0ryoQ9oc/tPZn0P8QPjf4J8L2bzaz4qsYPL/5Z/aq+ePiB/wAFBPDegXk1n4cs5LvzP+WtfHP/AAt+fxRev/ampfbpv+WsstH/AAkfhv7Q/wBvmjfzP9VXbDCmc5npfxE/bw+MGvWc1nY3kGnW0n/PP55K+cvFnxL1i/1R5r/WHuv3u/8AeV0niT+zbrfNYfvHryW40aHX9cfUtQmj8mP/AKayR11w/dkfuqhck8R3kusJeah9yT/npV/WL+ab/Q7WrOn6ND4jjh1jS4f9Gj/5aRVDqEt5En72HYkf/TKonXM/Y00crrn/ABIbf7ZL9yOqHhvXte168ea11KS1SP8A5aVpeNLWz/sdIbqy3vef6qSqfhO1vLWRLP8A1cP/AE0raniqhh7CrOod5Z/EbWNLt4YYvEl9I9v/AMtZZa9C+Ef7a/xa8EXj+V4qnkT/AJZRSy7468T1XyZf30W/fH/zzl/1lc9H431LS/EEPm2cb23m/vawn+8qnR7A/Qj4bf8ABSfx5/aHnePNNS7tv+naLy6+ovgn8ffDfxu8LvrvhyeRHjl2XVtJ/wAs6/KOz+IOjTXkNn5/z/8APSvb/gV8c9Y+EFwniTRh+5k/4+raX/V3FRUwv8htCn9rnP0mju5vM/dVNbS/vCfOrw39n/8AbD8E/F+4TQJZksdYk/1VjJ/y0r2a2l82OuGftftmnJ7Qv3F0PL/dVD5v7vzv9ZTI5TSebN61JfITySij7f8Au6rXNNqfaEch55+yZJt+Hd6n/UakP/kGGvYPD8v/ABNIf+uteM/sqIrfD6839P7Zk/8ARUNeweG5fK1CH+5HLX0/FS5uJcUv736I1i+SKZ9vW8sN1H50X7yvN/2yvih4s+Cn7Nvij4leBZoItWsILeOynuIBIsLTXMUBkCngsolLLuyu4DcrDKn0Wz/54/weVXjH/BSL/ky7xn/3Dv8A042tRk1KnWzfD06ivF1IJp7NOSTRxy3R4R+zL+zN/wAFBfBHwi02/wDgd8cPBui6F4lgh1yKyuovOlDXFvEQXMlhIVbYsYKqxUFTjPJPoDfCb/grN3/ad8Df+C+L/wCVle5/sw/8m1fDz/sRtJ/9I4q7fy/evWx/EeNljqvNQpSfM9XTi27Pq2rshy1PlL/hU/8AwVj2/wDJzngfH/YPi/8AlZUn/Cpf+CtH/Rz3gb/wXxf/ACsr6pkhp9vEP+Wtc/8ArDW/6B6P/gqH+Qcx8p/8Kl/4K0f9HPeBv/BfF/8AKymj4T/8FaJW/wCTnPBBb/sHxf8Aysr6uk/dR0yPvR/rDW/6B6P/AIKh/kHtEfK3/Cpf+CtH/Rz3gb/wXxf/ACsrxb9tLwZ+1fpHwyjH7WX7U/w/fR7a+W8s7DaYppZ1R0DIINPRm+WRxgnHPTIFfVP7Zn7Yfwx/ZG+H9z4k8UTQT6xcRf8AEm0iOX95cSV+Gn7Rn7XPxC/aR+JF/wCMPGWvT3VzcSyeVH5v7u3j/wCecddWG4gxdOoqlOjSjJbNUopr0aREqrR9TWv/AAVl/aM+H+lp4b0b48arqVva7tk11p1pdTPuYsd0t7H50nJONxOBgDAAApv/AMFtP2j1l2z/ABW1JD6t4a0jH6R18C+KL/xV5qTRXk//AF1jrmNY8Ual5fk3U3/XWt3nanJylQotv/p1D/Ij2tXsfqJ4Q/4K4ftD+Kpki/4aYg06J5NhuNR8PWflofcx2r4/AV9P+Bh/wUR+LOgf2/8AD79sj4ea3ZyR7/MsYInyP/BbX4Laf4omtLjzrC8ff/11r3L9nP8Ab1+OX7NGoJqXw+8VeR5n/H1bSfPHJ/2zrlq5238GHo/+Cof5F+0cD731+3/aOttfvLXU/iHpUlzFM4nkS3TaW7kf6OP5Cq8Wn/tAzfc8faV+MC//ABiuE+Df7XOg/tBRvrEs0Frrcnz39lF/6Mr06316H5MzfJ/11ryZ8T1l/wAw1D/wTD/I7YUbo5W7+Ffxd1HWINZvfGNncXNtcefbPLcyssT7g2VUx7VGQOAMcAYrdi0f9ow/c8e6WP8Ativ/AMYrWj16H/X+dVmPxRZ/89q0q8Y4/ERiqtGjJR0V6UXZdlfb5GvsTC/sH9o7f/yPulZ/64r/APGKT+xv2ij18f6V+MC//GK6GPxH2qzH4js/+WtZ/wCs9b/oGof+CYf5BClc5j+w/wBozZ/yUPSNv/XFf/keotasf2itB0m61e78faW0VpbvNIsduhYqqliBmAc4FdZ/b1nLJ+6mqj441+0n8FarEnV9LuFH4xtW2D4jqVsXTpyw1C0pJP8Acw6teRbo2Re+GHibUfFvgTT9c1t0a5lR1ldE2hisjJux0BIXJxxknAA4rb8z2rgvg7rdtafDXTbeRtpXzsn6zOa6v/hI7P8A5bTb68rO1ToZ1iaVNJRjUmklskpNJfIqPwo0o/3X+tp8f+tf/V1jyeKLPPFEfi2zG+vM9oyTYk/6a/cof/Vp5X36zf8AhLdN8vOKZ/wl2mf5NL2gGxF/01/SiT971rBk8UaP/rpZpKb/AMJbpv8A00qedk+zOgrI+IOf+EF1vb9z+yLnH/fpqqyePNN6/wAFYnjH4hWN74Q1WzETjztNnRc+pjYCuvL5/wDChR/xR/NGr2LvwM3f8Kp0rYu4/v8Aj/tvJXWSSj/U15p8JPHGmaP8PtP064ilLx+bkr05lc/1roZPihpojf8AcyPXZxDO2f4v/r7U/wDS2YxheKZyv7XHiibwv8C/EOpWsPmP9g2eV/10r8dvFl/NLrE03nSf9/a/WL9qzxb/AG98E9es9L02SSb7L/qq/KDxhoN5peqP5sP+s/7Z1jgqn7s5pw9pVMq3v737O/m1TNrNf6ikPk1NcS/fE1b3wn8OQ694ghhih8/zJaupU/dHXTp3q2O2+GXw0nv4082z8tK9auPAdlFpaQ2lmn7uu5+G/wAL/sFlD/q9nlf6ut648JHzH/ceW9fOfW6s6p9FTwipnif/AAi81rJ50sNTS6De/wDLLy469UuPCU0X7nyY3qG4+H3/AC28mrhXH7Cieb2/hybPnRQ10mj6DNLH+9+/XVaP4D8288mKGR69F8L/AAbmv+um/P8A9cq0nULhQPK9P8L6l9oh+yw16LZ+HLyXZ5sPz16Fo/wRmiuP9Ks/+2VdD/wq+a1j86WGuf2n2zf+4eJ6h4Dmuv8AljWDqHw5vLAed/rK+gZPCXkyfvfv1j6xoOm/8vU6bKdPFGFSgeAyeHJpJPJlheub8SeF7yw/0wQ/9+69m8UaXZxT/wCi1lXGgi6t3+5X0eBr0jw8dRPGdPls7r9951bdlLDFsMcO+q3xA0G88OXnneT/AMfH/LSofCU3myf675+/m19bhKFKp758djqlWn7h+hH/AARz8J2d/wDGi/8AEkVmk6Wel7PK/wC2lfqnp9r5Uaf36/N//gino0MUepa9FMn2nzdlfplp9h53H7zfXJmNOl9d5DfLoP2XOMjtYZY/3tfJX/BZyzjtv2WdBKrkr8QLVd/r/oV9X2Vp+jXnzmU/9cq+Sf8AgttYvafsoaAzd/iLaj/yRv66uG4Wz/D/AOI74/EeLfshy+V+zj4cP/X3/wClk1dxqmqfZbea8upo9kf/AC0lrgv2S3x+zd4fGO13/wClc1eO/t+ftD6x8OdMtvAWg6l5F5rEUjyxRf8ALOOvmeIKd8/xf/X2p/6WzolX9mkXP2mP22/BPg3S7zQdB1Lz9Sk+SKWP/V1+Wv7QnxG1j5/NvJHv9QlkeWWSWu/8SeJJ5by5vLrzHmuJa8r+MHhz/hLdcs/ss0fnR2v+qrD6r7NHNQ+s4irzwPOtL1nWPs7wxXk8aSf886f4bGseY80U0/8A21lr134d/sv+Nrq48660a72SRfuv3X+sr0XT/wBg/wCJF/G95Fps8ENc08XSpnuU8nxNT3+Q8N8P+PNStY30e1/eP/y182ptY0bU4rPzrCH55P8AnnX0Jc/sb+KordNStdNfzo/kuopf+WddnH+xvDf+G/Ohh8u5ji/1lL69SmEMnqnxt4X8W+KrC8TQftmy2+5LFXc/b7PXo/sY1KDf/wA869U8f/sjXngjS/ItbyOeaT55Za+VPiBFrHhfxJc6b+8j8uX/AFsX/LSt4V6VQ86eEq0zvPHmgw6z9jhtZvL+z/62WOsHVYrywkf/AFnk/wDPSue8P/FXxJo2w+dv/wCutaXiTxb/AMJHZ/bLqf8AfSS+ZLXRM5/3tMJPO1mP7Hpfmf8ATWi48L/6P+9g+en/AA+uobXfMZpNnm/8ta7C3l03VN/77/rlWczeE6R514w0+8sLdNStROnl16j8PviN/angezs9V3xzRxeX/wBc657xZo0+s/8AEtwnk/8ATKsfw/8A8SHVH0yUySf9NI63hU/d8hj7P97znvej+KPsFnbaxa3vmXNn5clrJF/rK+sf2UP2zYfHkkPgn4hXiQXn3LW5l/d+Z/10r4P8L+PNNut/hvzkjm8rf5v/AD0rpPD9/wD2XImpaNqXkXlvLWc4Uq6CnUP1o82Hy05p8c1eA/sn/tQD4vhPCuswbNSs7Xf/AK3f5le/cSpXj/w6nIdXtPaDJP8AVGmRy0+TEozQkXldqY+dnm37K23/AIV7eb+n9syf+ioa9e8Ly/8AE4h/6+o68j/ZU/5J5ef9hqT/ANEw16/4biP9qQ+V9+SWvp+LP+SmxX+L9BR+E+5LPyZY0mihrxX/AIKTqp/Yq8aOy7WH9ncev/Eyta9t0/8A1cP3/wDVV4t/wUrj2/sWeNz76b/6crWq4f8A+R3hv+vkP/SkYz3O4/ZfjhP7NPw5M3/QjaR/6RxV3XvF/wCja4j9lmOJv2a/h056p4F0gn/wCirvY/3smMVy4yH+3Vf8UvzZkM8v3p1LHEfk60XH7rj+CuQOch8r93/qap6xfw6Xp9zqV0fLht4vPlk/6Z1pRxf8Aryv9tfxHeeCP2W/H/iSwlkSa38L3nleX/1zo5zM/Eb/AIKYftOax+0F+0B4k1661j/iW2d/9l0b97/q7eOvjyTxb5moP9lm+euh8Y393rMk15dfvHk/5aS1w0ehaxdagn2Wzk3+bW9Z+zHTp1Wdnp+qXktv5N19yuY8Ufvbh/Kh/wC/Veo/Dv4N+MPEd5baZFpE7vcfJ+9ir6B8B/8ABN2eWzTWNUvJI3k/1sf2r/V1hPF0qZ6NDLcTXqfAfDen+HNelDy/Y50/9qVcvPCWvWGn/wBpX8M8D/8APPyq/Qj/AIZL8B+F4382z8yaP/lrLLvrj/iB+zx8Pb+zmm+xv53lf89f9ZXlf2rS9ryHq/2BU9kfIvwa+KuveA/EkOsaXeOk1vX6WfD/AMeab488L2fiTS/3cN5Fv8v/AJ51+bPxM8ETeCPEj6baQxxv9+Lyv+edfWP/AAT78SalqvhPVdHv/MeG3lj8qWtMV7KpS9tA8v2bw9XkPpCOUxb6s28s0X77+DvVa3iq/bxebH+9+/XJTNR/mzS/7n/TWpE6/hTpIvN5zRHF5XOa0AKqeKJSfDOoj/pxm/8AQDVusTxTqMq6JfWyR7gbOUE+nyGujLv+RhR/xx/NCew34dLv8FWS+Zj/AFnH/bRq3Yv+mv6VyvgLU7638JW8MMSFQHwW/wCujVt/b9Slj/dQx12cRf8AJQYz/r7U/wDS2XT+Behdfr+FJ/raxLi68SRf8esMH/bWjT7/AF7zPJv4YE/65V44qhvVWkim7TVaT/VfjR5I9qOcPZlWT/prUMkXlb/33yVcuAIh+6qncy/f82jnEVpPO8vn7lY/iH/kGXX71/8Aj2fg/wC6a2JJPv8AH61la8fM0e7J/wCfaT/0E12Zb/yMKP8Ajj+aE9ip4P8A+Rbg/wCB/wDoZrXkiFZfgyMN4btiv3vnx/321df4X0b7fef8tK6eIv8AkocZ/wBfan/pbIi+SKZNoHw+/tW3/wBPh8xP+edfAH/BR34S6D8MPi4+m2EMEEN5a/arWOOKv1H0ew8qNIfJ+Svnj/gqJ8EdN8Zfs93PjbT9HT+0vD8vnxS+V/rI/wDnnSwnuVLFT/hn5NXdqJbjyfJr079nPRrO61yGC6s/L/e/9/K4O4sNStdYf7UNj19G/sr+CPN8nxVdf8tPntaeYz9hSDAVqmIqn0P4P0aH+y4YY4f9X/y0rpI/CX2oedF9+ofD/wBjtbdPNm+Su20PXvB9rb/vdSg3/wDTSvk4an2k6lKxz1n8JftUfnA1pD4IzX/7mWaNErV/4XJ4PsJHs4tTg/7+1lah8X9N+0/uvM2V0wmYclKozvPC/wAL9B0bS/7Nimk3/wDTSr+j6Xpug3Hmy+XXl1v8aZv7USz+/wCZ/wBNareIPiZefaHm85I0/wCedaVK51wgj2/UPFug2tu/mlJHzXnXxE+OfhvQbd/36edH8/lebXgnxE/abhsI3hiMk7/8svLrwTx58S/EnjK8mMs0/wD2y/d1eEwlXEHDXr0qZ9FeLP2tIYpHhsPLd/8Ar68yvPfEPxp8VeLbjzvtiQeX/qvKrxzS4ryWPzpZvMrp9Huv9H8mKHzE/wCetexQy2lQPHxWKq1PgOw0vx5r0Vx+9vHkSvS/CeqQ6zGkteS6XdWfmfvYa9O8F2tn9mhmi/5aU/Z0qZya1GM+NHhebVPDX9pReWj2f7zyv+ekdee+A9G/4mCHya+jY9GstZ0N7O68uR/ufvK8c8L6XNYeIHs/3cbx/JLHLX0eT1/sHlZhhKUKp9pf8Ej/ABReeF/2jLPw3L/x56pYSJ5X/LPzP9ZX646HFNLJ/AiR1+RH/BNPRpv+GpNEmi/eJHFcPX69aL/BW+K/eVTkVG1T3Dp9LtYO9fHP/Bd20ji/ZG8Ozp1PxHsx/wCSGoV9k6fJyGxXxz/wXc/5M+8Of9lJs/8A036hXTw7f/WLDf4l+Q4TvVsfOf7J3/JuHhz7/S86f9fk9fBH/BRDxlNrP7RmpWdrN+50+KNPN/8AadfeP7L0kkP7Mnh+V/8AV/6X/wClk1fmB+0Z4ovPiF8XPEOsZkSa81m4eX/v55deTnMuXPsXL/p7U/8ASmdNWHtUonnusfbLq486LzJEr2P9j/4Nw/EHxS+sa9pvmWdn/wA9Iv8AWV51o+lzS6pDZxWe95K+z/gXo1p4S8P20NhBGnmf62SP/lpXzWOx1T4D2sqwp6Lo/wAPtHmvLaeaz+S3i2RRV20ng2zljT9z8lYnhu6/ef67567azv8AzdlfO+0qwPs1D2dEh0v4X6PqmyG601Nn/PXyq7Pw/wDAzwTYaekNroMCeX/0yo8J3+m2sifav+/lenaOdNlt/wB75clejhP75yzn7M+bPj5+yV/wluh3N5pUzv8AupP3ccX7yOvyj/bA/Zk1Lwb4wmmi02+jh8r/AFssX+rr97NYls/L/dfu0rxP44fBH4e/Eu3ms/EejwT+ZXWed7NVKp/PN4s0uLw5epDFNvrodL8OXn/CP/2ldQ+X5n/LP79fop+0J/wSh8B69I958OLSCxmj/wCPW2+1SeXXjmufsPfFTwvH/Y+qaDBJ9n/59pfMq/rfszlr5VTdT3D4w+y+K7rUPJtbN40/56y1fk1TWPDkf2P+0vLevo3xR+zJ8QtLt3vLXw3I/l/89Yv9XXg/jTw5rFpeXN54j0f7K8f/ACylrqp5jSqHmzyqrT9+YeF/iDNLH5N/Nv8A+ulX7fXvNvEmih/5a1wHmw+Z50VaWny6xdSf8tESt/b+0OGdCqdhql/ZRagl5YQ/PW9b+KIbDS3mh8z/ALZVwcdrqUV7/pUNTeIYtYtdHm1i1sv9Gj/4+pf+edaXMPYnpfwj+L2veCPiBD4q8Oa9PA/3IvK/d1+t3wr1698UeBtN1/Wvkubi13yxV+I3wz1S81q887yX/d/8sq/Zj9lfxb/wmXwj0fUZYdn+ix/uov8ArnWFf95SNIf3D0qlj8mpkihzz+8p8cX3zj864jaE/aHlv7JaQt8P74y7v+QxJ0/65Q17D4fi/wCJxbfvvn+1R15B+yTt/wCFe3u/p/bUn/omGvaPDfkjVLbM3z/ao/8A0ZX0/Ff/ACU+K/xfogU+Smj7ks4vKjTyv+2teH/8FL/L/wCGJfGu3r/xLf8A05Wte52f+rSf+OSvEf8Agpk2/wDYi8bN/wBg3/05WtVkP/I7wv8A18h/6UjiW53P7LQP/DMnw5/dOf8AihNI5H/XlFXdxxCuI/ZY/wCTYvhx/wBiHo//AKRQ13v772rlx/8Av1X/ABS/NiK1SeSfepKc/X8K4OQCCS1ryj9tjQZvEf7K/wAQtHH37jwlef8ApPXrlYvjjRovEfhu/wBBv4fMtrywuILqL/pnJHWnIOnTP5v/ANm/4c6P4t8SXP8AbOm/a4dPij/dyf6uvpnwP8DPh7f6gkP/AAhNjJD/ANM7WvN/g34Ss/hx8TPHng+Xy/O0/VJIPN/6d47iSrkn7X2veA5JoNL8Kxu/m/uopK8PMfrNTG+4fVZOsNQw37w+wPhf+zd8MbDZqVho9jBc+V/zyrtpPAemWtm8PnRx+XXwZ4X/AOClnir/AISBNH1Tw3BYv5v73zJZEr07xx+1f4wsPCb+JLDfJDHa75Y5a4p+1n8Z79P2U/fR6d8RNB026t3+wTV4V8SLD+xpPJz8klfP3ij9uv4weKNYm03w5rFrYw+b/wAsv/jlU7f4jeMPEeoJrHiPWJ57n/rrWUMFU9pzkTzGl7TkOb/as8JTaXLbeKopt/mS+R5UX/LOvYP+Ca91eXXhPXvtcKIn2+3/APalc38d7CbxR8J5ryKGTf5sb+V/z0ruf2B4tB8JeE38K3Uz/wBpahdfbbqLyv8AV/8AbSu6Ff8A2b2cz5zHU6lTE88D6Rj/AO2nSrdvD+7qtHdQ+Z/sVf8ANs/L86ogcnIgjlFWY/Jl4I8x6h5ljT+Cpo7HvKKsyK2oS1zeuCaPRNRB6Gzl/wDQDXYeVD5lY3jTSRF4dv5I9ny2UpOP9w16GXf8jCj/AI4/mhPYwPAEjr4Utwfu/Pj/AL7atTkb/wD0bVb4bWVw/hGzmh6nzMf9/GrbuNLm/wCWtepxD/yP8X/19qf+lsUPgRlebNL3qzb2v2rvU0ejTGr9v51rb58pN9eOUQ+XN/qaXzh7Vbx5siebDTJLX949c4FN5fN71Wk4/c5+Srkn7rrVO8uvKk/1FZzNChJEap67/wAgO8/69JP/AEE1ckm82TpVPXf+QHef9ekn/oJrtyuH/ChR/wAUfzQnsM+G9kb3QbaP/f8A/Q2r13wvo0NhbVw3wR0ZJvB1rqB37iZMY/66NXqOlxTYr0+IYXz/ABf/AF9qf+lsz/5dr0L+nxfcFHjzwbZ+PPAeseFL+FJIdQ0uSDy5f+udWdPix++reji+12/+3XDS3IPwW8YeCNS8JeLNS0y6/wBdZ39xB5dz/wBM5K+vfhHoOm+HPAelTRCP/jwjkrK/4Kk/BbTfAf7Qj+KtL03y7bxBax3sscX/AD0/eeZ/6Lrp9P0+HS/hXYGLzP8AkFx/+i6vO/3lKB2ZPTcKpj/ED4q2elxpDp81eP8AiD4oa7ql55P2x3h/55S0zxpdeVI8/wDH9z97XMafFNdS+ddfuIa5MJQpQR3Yr2sw1DxR4kik/c6k8H/XKWrmj+PPHkUfk3WvTzw/885Zax/EHiPwr9oSzsIbuR4/9bJc/J5lMjsPEl/bveWEP+jf89PNro+ro8729WnUPTvBfxQ161k/ezfvv+WUtdb/AMJHqcsX7zz3SSvEPBd/qcusQ2f/AE1r7V+E/wCz9eeLfAaalj55It9YVqFKn8Z7mFqYrFr3D5I8SX95dag/2qzk/wBbWJ5usXV55FrZySf9coq9L+OHgjWPCXiSazNm/wBm/wCetcrpcV5a3GbX53/55R13UPZey9w87FU6tP4zEMusWrvZ/vP+2VdV4PsLyXT/ALZqHmf9cpav6Hfw+Y82qaFHvH/PWun8NxXmvSf6Bo/n/wDTKKus82Cqlbwn4c1HXrhIYofn+/Xs3w/8JXml26fboY4//IldV8I/hVeQ26Tapo8cEP35a7nxBYaDo0f+pSvNnO9TkPRp0/Z+/M5Wyihjjfyq8x1i1s9L+IFzrEtn5k1xLXrtvdWfz+VZ70rhrjyZfGFzN5Lx/vf9VXv5dp7h4+Y/vD69/wCCUeg/avjZc6x5Hz2+jf8AoySv0+0O1/d8n/Vmvzu/4JN2GmnxJrGpRQ/6yKOv0a0e6i/c/wDoquup/FPKn/Oben18e/8ABdr/AJNA8Of9lIs//TfqFfYtpLD5vWvjr/gu2+79kHw4P+qkWf8A6b9Qru4d/wCShw3+JFUp03LQ+af2b/N/4ZW0Pb08u8/9LJ6/Lr4iX+mj4oa2f4I9UuIIov8ArnJX6l/sxCWT9l3QxB96OO8cf+Bk9fkb8YNZsv8AheHiqG1m/c/29cP5f/PP95Xg55O2fYv/AK+1P/Smd8aPtJo6HwtYTS+LLPUj/qfN/wC/dfYfwfuoYtPhF1P5718c/Drm4hmFfWnwTi+1aXDN+8jeP/lpXxOY1z6rKqFRHtOl+T/wOt6zvobW3/11clZ+d9m/dVpaf53lfva82nXPo5nZ6XrM3yceZ5ddnofjK8+0fvfM2V5pYap5Uifud6VsWeqaldf6qzk2V30K5yzpnpcmvQy2/wDrvkrmPEGvWcu8eSm+sqzOsdf3iJUOqRTf8tfM31tOvVOT2f70pySwyyP5uzfXK+MLWGW2mhlgrV1CLyv+Wz76xNduvtUfk/6x64qlRndTpnDaro2jyxvDdQ/J/wBNa8W+Lv7OfgP4jRzQ6po9pvki2faYov3kf/bSvb/EEM3z1zF7F+7x+8rCdSqjqnTpVFY+CfiB+xlN8PtUeaKGeew8391LXPf8KqEtx5MUNfc/jS1+3x+T5CfvK801D4Vabayfuoa6qGYv7Z49fAnzr/wrS80qzeGWHy4ZK9C8N/Ay0uvg3c2eqWf2ubVP38X/AEzjr0iP4VaPc/ub+H/Wf8spa7PR9Ghltks5RHsji2V6tOv7Q8f6p7Oqfnvp+jXvgjxTc6bLC6f6Vsr9Ef8Aglf8fYdet7z4J6zskv7OL7bpcsv/AC0j/wCWkdfLv7Wnw0/sHxz/AMJJa7Nl5FVD9jPxaPh98dPD3jaW8fZZ3/7397XuUKHtKR5Ve1PEn7GfYPN2U8WHm/vq9O0vwZ4PurPzovMkST95FVn/AIVpoPz/ALl/+/ted7PU5ec+UP2QrYzfDW+Zo8r/AG5KCf8AtjDXtvhuw/4mFsP+nqP/ANGVxn/BPTwhpXiD4L6re6iGBXxNMm4Pjj7PbH+tfQ+h/C/QftkP3/8AWx/8ta+l4phfinEv+9+iLj8J9IW8X7v979+vCf8Agpp/yZD42/7hv/pyta92s5PNj/5514d/wUyg2fsMeN5B3/s3/wBOdpTyL/kd4X/r5D/0pGM3zu52/wCyx/ybF8OP+xD0f/0ihrvK4r9lf9z+y78OJvn/AORC0fp/15Q13lvD9/G9K5cf/v1X/FL82ZDD5Pz+b5ntR+5z+78z/prU1vF+7fzf+2tP8r7/APckqCOdFb9z5f8Azz4rm/i5pd5rPwz17QdLmkS5vNLuILWSP/lnJ5ddb5XlR/36p3lrDLb+TL+88ys5mlOf70/CjT/BGsaX8aNV/tSbz7/VLD/SpYv3fmSf5jrhviZ8B5tG1CbWNUmvoLaT/VS23/LOvuH9sj4Gab8G/i5qWvQ6b8kl159rJ/0zkrE8L6NoPi3T0hurOP8Aef8APSvmcVinSxR93gKFLEYY+Cfh/wDALwf4o1iGzl1K+8Qar5u/7TLa7K+wPEHwC0H/AIZnttBuofLv/svkfuv+edehf8Kl+Hvw00ea70bTY7Ty/wDWyy/886reMP2m/wBnT/hX76Pa6xaT3Mf/AB9f6VHJXPPFVa/vnsUaFKgj4t1D9m7wH4E8N3Nnf/DH+1ftH+tuf+Wn/bOT/lnVb4b/AAB/4Si8Q6X4JvrXTbP/AJaXMtfWPwX8U/D34jXF5Z6NeWl9Db/P/wA9PLrb+IEmj6Dp7/YIf9X/AMs4qzrYqqiIZdhvjifK/wAaPBlna+E7nTf+uflR/wDbSrn7J9hN5dzqV1pqRzfc8zyv9ZT/AIwazNqkkNna2fmeZL+9r0X4X+HJtB8Pw+bC8Dyf8s/+edY04Vfac5w4r2VPDTmdJHFMP9b5lX7eL/R6rR58zyfOq55vuK9GB8oWftQ8vyasi/hGz+OqH+qp9vL/AM8oasC/HL+8/wBT8lUvGOP+EV1Pad7f2fNk+3lmp5P3uyaKaSqXia6iPhTU4B1/s6fH/ftq7Mu/5GFH/HH80EoLlZS+FW//AIQSx3dP3uP+/r10Fx+831znwp8z/hB7Pe22PEnP/bV66PzYf+ub16PEX/JQYv8A6+1P/S2Yw+BFaOw5/wBb89P5td/7/wCepuPMT9zUNxD5snNeNzlEPm8dfnpnnN/00p/7mP8A6aUySbzeaAIn6/hVLUP3v+q8yrnm/vKpyedLcP5X3PNqJzOgrfZf3eP3dWdS04t4U1K5k6rps5H/AH7arlno00slbXiDSIbPwFrLDqNJuf8A0U1d+Vw/4UKP+KP5oyew34DWbS/DPTpG2bSZuv8A12eu/s4u1cJ8CI93wr0w/wDXf/0fJXoml2s3evU4h/5H+L/6+1P/AEtmD+BF/T/3vWuk0uLH+u8usSziA/1X366TS4sx/vfnevNh75ofFv8AwV9+HN5f+H/DHjCws/M8uWSGWX/nn/nzK4b4b6DDr3wr0eGWbfN/Y1un9/8A5Z19k/tkfC+H4ofs7+IdH8nzLmzsJL21/wCukf7yvjb9mu/huvC1t++/1cskcsdRjv3mCPTy6v8AveQ8u+Kn7NOpXV5NNo3nyJ9+uDk+HM3g29SHWbP/ALZ/fr9C9H8Jab4jt/sfkpXK/ET9jez8ZfvrUfP/ANda8qhire5M9nFUKdSl7h8DeI/h94D168+2RXn2F/8AnnbVNZ+ErzXo4fDfhyGSdI6+tI/+Cdl5FeJ9q8+f/nrXqnw3/ZG8N+A7fztUs0kf7/lR16qxdKnTPNp4E+S/hX+y1eaNqEPiTWYdj/8APtX3P+zNDo8Xhu50a6+ebyq4Px5a6ZFqH2Pzk/d17H+yf8PjrNxN+5d/Miryq9eriGe5Qp0sP8B83/E3wbFdeMLmbWdNj87zf+WlM0P4GfCvxHcf6V4bg87/AJ617f8AtSfCWaw1i5mEPkTW/wDrY/8AnnXgNnrPirwRcJqX9myPYf8ALWWL/WVn7SqvgLn7J/Gd5pf7G/wf8vzpdBgkeSuw8N/AL4e+Eo0/s/QU2R/8sv8AnnVb4f8Axa03XtPS886ukvPHFn9n5m+Sj63iofGcSoUpmbrlrZ6XbvDaw7EryL4gXU1/eeT/AMsa7zxx4ohurPybU15j4g1SGb/VTfPXVhZ/bFXp0rD9H877O+PuViaPL/xUjzSw+Z+9q5HLNa2f+u8zzKf8P9Gm1TXP3v3PN/5aV9BhKjPlsVTvVPvD/gmnpdnFcQ3kUL7LyKR5ZP8ArnJX35o8UPl/u/M318AfsD3U3/CyP7HtYPLhj0v91H/20jr7/wDD/wDqk/v13U69WpUPNr06UKhvWkMPyebXx3/wXY/5NC8N/wDZR7P/ANINQr7Hsv8AWfvfv18cf8F2U2/sieHT/wBVIs//AEg1Cvd4dd+IMN/iRzQhaSZ87/spxTf8M0+HOfkk+2f+lk9fjN8bL+H/AIXp4q1LzvL8zxRqHmx+b/q/9Ikr9mP2U7a5m/Zr8OG3ljB/0zAk/wCvyevxJ/ac0qbQfj54wh8754/Fuof6z/r4krxs4gnnOO5v+flT/wBKZ6VGooTXMekfCe6m1S4hs4rz5JJa+z/hHL5Vmn36+JP2X4ptZ1RIbU+XN/01r7k8D2HlaXbYPz+V/ra/OMxp1fan22VeyqUuc9OsPOlj/dV0+j6XNdW6QS1zHhPz8/vZqPFHxu0HwRKlnazRz3P/AC1j/wCedctOnVqe4ejXqUqfxnrXhfwH9qjzL5aJXpfhP4X2fyebseGvja8/b2h8JXH70b/+mdPt/wDgrHoNhIkN/Z2jp9yLyvMr3MJgatM8ieaUqj9w+27z4faPF/x6wx1xnjTwbDal/K+//wA86+b9L/4KvfDHWbxIZrzyHj/6evLrv9P/AGufBPjL99Ya9BP5kW/y4pd9aVKdvcmVTqe0LPim1EVu8Pk/PXH6nFN/yyrb1T4l6PrP761vI65jVNZ+1b/ss1eVU0PSpwMHxB9sH7mL95WDHoN5db/3MldbH5Pmfvf3j1vadpemyyf8s6wqQ9obwPFtc8GzRXDiKGuS8QaN/vxv/wAta+ltY8L6PdR/uvLrz3xZ8OdN8zzv+elZ06BE/ZHjmj+SZEire0+1/ef6mr+oeEobCTyYoI/+mUtTafYfZY/Olh+evcwlM8nFwPH/ANqjwtDf+C5prqGORLf5/Mk/5Z18waHa2drKkNr5e+P/AJaV9h/tCWE1/wCC7+b+COLfL+6318c6PYH+0E82vcw9erA+cxVGlOqft/8AsD/GmH48fs36D4qlP+mW8X2K/wD+ukde5fwf7FfGf/BFfS9Stf2ZNSvLqb/Q7jxRcfYK+zIouUlxvor+5UPPPnz/AIJrCUfA3VmH3P8AhLJ8/wDgNa19JaHLN/akP/XWOvnD/gmnj/hRWrbZGDf8JbPgL/162tfSvhuL/icQiX/nrXv8VTtxLif8X6IzWx7ZHFmPya8N/wCCnQJ/Yd8bqs+9U/s0A/8AcTta97t/J8s/6uvB/wDgpz9n/wCGG/HWz73/ABLM/wDgztKjIp3zvC/9fIf+lIzO+/ZSz/wy58N933f+EC0fP/gFFXdR8j/St/7yuH/ZOjB/Zd+HJk6f8IDo/wD6RQ133+t/6aJXFjJ8+Oq/4pfmzMP9Vv8AstPji83/AFtHlc/7eaP3u/tQY/vBn+p/3KZJF+7zVn/XHzpfkpkf+tkrM0/5eHif7WH7LOg/tD+G/Jubz7Dqun/8et9HF5n/AGzr89JLq8+F/ijUvBOszf6Zpd1JBdR+bX643EXnV+a3/BYz4VWfwl8WaJ8bNF1KC1TxBL/Z1/Y/8tJJI4/+PivMxeF+sUz3sqx1XD1eQ8r+Inx+8Kf2HN4bupvtVzeWuz7N/wBM6+fv+Gafg/dae93qnxOsdK/j+wyXUfmR/wDbOuSvdBvPFHiybxJqviq7ghk/5Z20tTan8VvgD4It00fUPh7/AGlc/wDLWW5lk8ySvAwvtKbPtadT2p1Xw7+I3hX9nMXM3he9tNRh+5dS211+8/7aV0l5+0ZB42s/tkX7uGT/AKa14beap4J8eXE154c+GMenQx/9OGyOsTVNUs9Bj8iKZ460nhaVQzqV/YH0V8J7CDxb4omvPJjnhs/9bL/00r1TyoYo3z89eM/sl/FD4e2tm/w91DWII/EN5/pv2aT/AJeK9puIrMb5opvnq+SqkfLV69WpVGW583jNTfvd/eqEcX/T4myppJf+eUz746PaHPyFn/W1Nb+b/wAsap2cv7t5pamjuvN4rTnQchZk5/1u+R6p+I7bb4S1I+lhN/6Aas9dn76ofE9yG8KaqrffNhNn/vg12Zb/AMjGj/jj+aMl8LKXwtaU+A7FHbbGPN5/7avW/IP+W3k/PXN/C69W38D2iuOAJP8A0Y1dDJLDFxFNXocRztxDjP8Ar7U/9LYqfwL0Jo/Op8f+qkqGO/8AKj60R38NeNzl8iCS1hl61DJYQxR5qa4voYqrXF1DLb9ZNlAincHyonGaZZ/vf9Vvp8n/AEy2Vf0TS4f9dip/5eGhseH7A/Z0ml+/U/jOPHgPWz6aRc/+imqxpcXmp+5o8Z2xX4f660nUaPdEf9+mr1sr/wCRhS/xR/NHM9ip+z9bbvhRo849bj/0fJXoNnFNXn/7P3/JI9J+s/8A6Pkr0nT4q9PiD/kfYv8A6+1P/S2KHwIv6XFiTEtdDaRQmNOfnrH0+P8AdpzW9plr/wA8oa8qmRPcm1C1+1aXNZyfvEuIpElr84v+EX1j4I/EjW/AcsMkFn9q8+1r9L7eIbPJlFfEn/BQTwleaD8TbbxJ5Mkdtcf8taupD2nuHVhJ+zq84z4d+PJrT9/51ezeC/iNaXWzzfLr4/0PxR9g2ZmrudL8bzSnyZZ3j/65S187Up2qn1VOv7RH16fFug+Q/wDy0rg/ih8QdN0bT5ryL92n/PWvN9P+Jf2XTkh87/VxVw3xY8eTa9b+TFN+5/661n/Eq2OuFOkvfOh8D2s3xB87xVfw/wChx3WyLzP+WlfQ/wAG/jdo/wAL7NJbWBJHj/5ZS18eSftLaD8NPh5D4blmjkvLe1k8qKWL/WSV4t/w1p48l1B57q88z/pnL5nl16NPC1PsGE6lI+7f2hPjxo/ii5udYv7yBHk/8iV4/pfxL8E6zpc1nfzQf9tJa+UfF/xp1jxHb/vZvMf/AJ5f8s657R/FuvRXHnXWsPJR9Uq+1OSvXpUz6o8F69/Zc80Nreb082uwj8bw/Zn828Tf/wA8vNrwHwdf6xL4bS9qzb+N5vP8m6+5TqYf2hhQxVKoeqa54omlkfM3yVz15rPm3H7mubj8R/8ALbzvkk/55UR6zDKP9dW+HwvszTFVKf2Dqv7UvJf3Pm/JXYfDv+CYffryKTVJopM5+evpz9l/wb4P1nw2n/CW6bI7yfPFL5vl+XXv0/3dI+Yrv96fQn/BPu6P/C3H/ff6vS//AGpX6I6BL5tnD+++evg/9nex+G/wv8eJr/8Ab13A8lr5HlXP7yvtjwJ4y8H69bw/2D4ktJ0/55+bskq6c17U4cV+8q853mny/u/32yvjf/gu1/yaB4c/7KRZ/wDpv1Cvr+z/AI5pYf8AyLXx7/wXWmjk/ZC8Ohf+ij2f/pv1Cvf4d/5KHDf4v0OWlO7Pn/8AZIEz/syaCsXpd/8ApZPX47/8FNPh9/whH7XnjnTYt8aXGqfbYv8ArnJX7HfsgJn9mnw2fX7Z/wClk9fmj/wW8+HOsaX+0RYeKrCzkkttY0uT97/008yvFzy/9u4vl/5+T/8ASmepSpqorSPLv2B7We/8YQ2Uv3/3n/bSOvvPw/YTWsaQkf6uvhv9iPwR8QvBuoP8TtZ8HzpptvYSSRS3P7vzJP8ApnXqkn/BSfTfL/0X4Y+f/wBxTy//AGnXyGNwuJxFU+ywM6WEw3IfRXizx5qVrH/wjegyyJcyf8vMX/LOuYk+GejWunvq+vak8k0n/TWvLvC3xu8efEHS38YaX8MY7WG4/wCPWS5v/wDWVQ8QfGTxVa/udehsY0/5ZRRSyVxU6FXD1TepT+sUjY+IHhyG/geCwmr51+JHgjxJpeoXP8CSS/uq9Uj+LV5qkjj7ba/9tLWStW8i8beI9H/dWejX0P8Az0/eeZXdDFVTnhgaR8nXEXiTS7h/3PmJWx4O+OfxC8EXnnaXd3Vp/wBc7qun8ceA9Stbx/KhgdP+mV1/q64m40v/AEjybqz2V1e3X2zl9h7OqfT/AMI/2udS1SO2m16WT/SK978L/Fuz1n99FN+58qvz68HxXkWqQ2dhN8nm/wDLKvqL4X2GsWvh7zrqaST/AJ5RS142K9lOqelhKlT7Z7T4g+LVnp8ieV5m+sTUP2pYdGk8nydj/wDXWvDfip48m0GR7OO82Tf9Na8K8WfEbWNUkmm+2b3rignMuvXp0z7D1z9ubQbWR5v7ST93/wAs64bxB/wUNh+0J/oUc6f9M7qvja4/4SSXePOdH/6Z1m2fhLxVv/1Pl16NDC/znnVK9X44H234b/bd0HxHeJDr2jvB5n+qljlr2nwP4o0fx5pf2zRryOf/AKZV+cWh6Nq9rcp+++f/AJa19D/sr+LfEnhzVEmE3yf8vUfm/wCsjr1aFD2fwHHXxVWp8Z798dLX7L8P9Sh+T95a/wDLWvhtJfsuoP8AZZkk8v8A5Z191fFS/h1/wPeTWEySeZayeVLXwNZ3/m6hN+5/1ku+vSwvsjzK+x+zH/BIa6huv2N9NhtfL/d6zeJL/wB/K+pbeX95/wAtK+af+CT/AIDvPA/7F/hj+0IX36pLcaj5Uv8Azzkkr6Zj/e/63fRX/iHCfP3/AATP3f8ACitY29f+Ern/APSa1r6Z8NmGXWLaGX/n6jr5j/4Jpf8AJD9V/wCxsn/9JbWvpzw3FN/blnN/09R/+jK93ir/AJKPE/4v8jnPb7f/AFX8deDf8FOpA37DXjcNFhv+Jbk/9xO0r3mS1m+T99JIleCf8FOLfy/2G/G7en9m/wDpztanI/8Akd4X/r5D/wBKREt0egfsqSY/Ze+Gwxv/AOKB0fj0/wBChr0eMf8ATH6153+yqRH+y98NCPvf8IDo+P8AwChr0T/v5vrlx0P9tq/4pfmxAIv+eVEmP+B0/wA3yutJXLzgL5X7uof/ACJU0X+qp/7qs5gU5/J+fza/D3/gvT+0ZeePP22LP4Y2GsR/YPBdrHZfZv8Ap8uP9Z/6Mjr9yLiL/ltCK/mb/bo8Uf8ACe/teePPHku+d7jxlePF/wBc45P/ALXW9Cn7RTmaKdT20DHs/i1eeA/ECWniOzk8mP8AeeXLXp3/AA1f8GZbP/RZtKkm8r/lrF5klZvjD4I6b498Npe/Y97+Vv8ANir5s8afAzUtB1SaG1mkj/e18z7CnUqn1yeKwnun0D40/bI8K/YvJtbOD/Vf8u0VeOR+KNS8ZeLLnUpYfItpJd/l1g+G/AcMWya68zfXeaXoMP2f91D5fl1nCj9Xq85tOdWueXePPGWvaN8aLPWNHvJ4LnT4rfypIv8Aln/y0r9Jvg38S4fi18L9K8bReX9puItl/FFL/q7iP/WV+afj3S4YviI/+sk8yKN6+vf+Cd/ij97qXw3/AIJIv7Ri/wDRclezPCfWMDzwPmalelTx3sZn0n++82n+VDLJzW3BoMMsn/LOtK38Lw/J+teNyI6v3lM577LjZ5VPt4ofnHk108fhKHP+ukqzH4IhEn+u8us4UyOdHKx/98VU8SRGLwrqOPufYZsf98Gu+j8B2cv+trP8a+C4rTwNrV0J95j0m5b8omNeplsP+FGj/jj+aFKa5WcB8Ov+RLsPrL/6Nat+OL93Wn8DfAtprnwt0vUJ5XBk8/ge08g/pXWR/C+zik/1z128RUaj4hxj/wCntT/0tk0/gRwHlYP72mV6R/wrTTf+e3mJ/wA8qi/4Vfpv/Pc15PsS5zPPfJHtUEkNekSfDSz7eZspf+FVaN/z3k/Oj2dUR5zpcXm7P3MldDp8UJjSHyK2L3wvZaNJ+6qtZ2sP2j/Vf+RaIQHzo0tLtfKqPx//AMk+17/sDXX/AKKatC3/ANWmB5dVPiHFj4ea8f8AqC3X/olq9TLv+RhR/wAUfzRzupoZn7PFt53wh0U+Zjm44/7eJK9Is7Xyv9bXn/7N3/JINF+tx/6US16TbxQ4/wBuu/iD/kfYv/r7U/8AS2OHwIv2f/TOuh0+X970/wBZWDpf366HT4v3n+3Xm0zOf85pW5h6edvrkvjp8JfCvxa8B3/hXxRpqTpcWsnlS/8ALSOT/pnXW29r9zyqs/ZfNjrT+Gyedn5C6xa6x4N1y88N+I/+P/T7ryLqKWtLQ/G/b/v1Xc/8FLPBt54N/acvNei03y7PxBYRzxS/89JI/wDWV4hp+vfYI/8AU+Z5lYY3Ce0XOe3l2OpfBM9L/wCE88r/AFs0n7yqd54o+0/62b/WVweoX83l/bIt9ZUfi2aK4TzfuV48KFX2p6s8UbHxY0HUte0/7Z9/7PXlZtZvLeH+CvQvEnij+1NPezlvI0rlfN0eKT/SpvMr3KH8I8qvi/aVPcMGOUxSP5VTafFeXVxjNbEl14VtY/8AQPMkf/nlVP8AtTUppE+y2fyV3QOGp7Wp8Z2ej+MrzQdPTTZfL/7ZVNp2vWeqXjwfx1yVno2sazE/mzeXWro+jTaXefvPLkes61gp1PZnYR/6LH+6mp/2qG26+ZvrEvdZh+z4imqt/ahl2fvvkqKHvnVPFnVW+vTfaE8qvqL9mf4g/atGTwrf7Emt4t9r5X/POvlH4f2P2/XPO8nzHjr0vw/r2peHNYhvLQfPHL/yyr1J0/aUj53FVOc+5/B9/Ddf8f8AN5j/APPWvV/C9/5Uf+izV88fDPxRpvinR4f7/wDy18qvZvAcQ/s//Xb/APtrWNOgclSvb3D3L4dfF/4keHJPJj8SP5P3PKl+eOvHf+CvvxZ1Dxz+y1oOh6lZRpLF48tZmlj4yRZXq4x/wKugs9ZNhcJNdTeXXkf/AAUvuLS7/Z70W5t7jeX8WwZHt9luq+m4bhTWe4dr+YzpVuaokea/Cj9qL4ZfAz4D+EdE8Z6z5Fzdx3jxR/8Ab5PWX+1R4I+G/wC1f8MkvJZrTVbP79hc2Mv+kW//AE0jr4y/atl1PU08HadbyyBYtKmSMR+pu5j/AO1K39H0H9oT9mTQrDxJa6k/2O4/f+V/rPL/AOmclfB8S4qpS4kxdv8An7U/9LZ+g4TK6by5V/I4b9k/xR4w1T9oDUvh74t1i61Gz0+1uIPs1zLvjj8uTy69O/bQ/Zk+E1r8F9V8eeDfBOm6Vqulyxzy3OmxeR9oj8zy5PM8v/rpXB/sb2EOqftCal4jk+/qFrcT/wDfySOSvqv45+Ev+Eo+C/ifw3aw/PeaDceV/wCjK8Spi6v1mE4Hp4Sn7SlznjPgvRptG/Z78K5+RJNBs/8AV/8AXOvn74oedYah/pWpSb5P+etfbHwH8GWfij4CeFYZdkjx6Db2sv8A10jj8uSodc/Y88Ba9J/aWs6DBdv/AM8pIqv29N4n3zSv7X2XuHxb8O/iN8MfAcn9pX//ABNdVj/eRWMn+rjr0W8/4KxeMPDmjv4J0v4V6Ha2flf8srCP93Vz4yfsl+A/CWsf2loOgwQXMf8ArYpP3f7uvAfiL8JdHv4n/svUvIvP+ecv+rr2aEMDY+VxVfM/aml4w/aRs/i1Jc+b4PtIJpPn8yKuDkv/ADbz/XeYn/POrPw78JWfgO7uby6KXdzJFs/6513/AMLvgZrvxg1zydBhjdPN/e/uv3cdW6GFqL3DehWxNT4zgLzS/tVm+paXeSQXNvFvijil8vzKs6P8UPjZa6fD/wAI548vvs3/ADy+1V9IfFz9lDw5+zd8L9S8SeKLzzNSuNLkntbb/nn/AJ8yvof/AIJZ/wDBNjwT4t/Z+h+MHxa8Npff2xF5lhbX0XmeXHXm1KFL2R6tCFU/Mfxp8S/iFf3CXmqTPOn/AD1kqHTte/svZea9N88nz/ZvKr7k/wCClH7BXhvwlod58Qvhzo8GlPpfz3VtZf6u4jr4e1DRpr+PzroO7/8APWscLTpVDPG+0oFzS/HsN/fvDazSbK6SOK98tJotNnkSvLpLXXrCwmvLD935f/POKjw38X/HlhJDDpesT7/+eUn+rr2IUDzvrVL7Z6pZ3+gyyeTLoLxv/wBNJZI69U+Gfhyz1S4/4k2sfZHji3/89K808L/Eb7B/yVrR47qzvItnm20XlyR10/wrl1i18UW01h/pSSfJ5sf/AC0rCVOrBmFT958B7Z4D0H9oTxvZ694P+EHhX/hMf7Hi/wBPjtrWTzLf7RXyvH4XvPC/jhNH8UWc+mzW+qeRqltexeXJH+8/eV+tH/BF+68YaDbfFG8sPhvdX1zeazZpa3MkscFv+7t/9XJJ/wBtK8Q/a0/YY179pb/gp5N4P8R3lp4cTVNB/tvxHLon7zy/3n/LP/ppJ+7r0VQp0KnPA5fbc9PkPvP9mfXvhjr3wX8N3nwg1j7doMdhHBYXP/XOvRfN/d/va4D9nv4LeD/2ePhXpvwl8BzXcmlaX5jxS30u+4krvI/3vMv3K5an7yqYnz//AMEz9n/CiNY3f9DZP/6S2tfTfhPzhrFn5U3+suo6+Yv+CaX/ACQnWPv/API2T/d/69rWvp3wl/yMNj/19R/+jK9/iqF+JMS/736I51se5eb5sleEf8FQlRP2F/HKjr/xLM/+DO0r3SPpx9yvDP8AgqH/AMmLeOf+4Z/6c7SoyL/kd4X/AK+Q/wDSkZ8ljvf2UP8Ak174a/8AYgaN/wCkMNd//wBcq4L9lD/k1r4a/wDYgaN/6Qw16HXLj/8Afqv+KX5sBkcuN9Hf/npTza+b/qvv10+l+Dofs6S3/wB+sOSxnM5uO1vLrZ5UMlbWm+CZrhU+1S7E9q6W30yG1jxFVK41SWHU2g8ofZoIt9zcSy/6ulKdwGXGl6Ro2lzeVFGn7qT95JX8pfxsiml+KniG8mm/1mvXj/8AkSv6h/ihrOpR+AP7N8Dw/wBo3msfu7WPzf8Aln/y0kr+aj9sjwlD4D/aY8eeD/sfl/2X4y1CD/yYr1MDTvSmcVadVYqEz0j4L3/2rwPYeVM8kP2WNP3lZvxI+EujeI45vsvlpNJ/y08qr/7KUV54o8J+TFDvmt5dn+f+/lei+LPAd7a26XnkyV8Fi/a4fFTR+p5d/tGFgfK9x8EdY0G4f7k6f89afcaD9lt/J8mSvoTS/Bs2qSOJbOqel/Ay88W+IPJis/k/65VnTqOo+Q0qYWx8f/EzwlNo2oQ6ldWf/Hx/qq94/wCCWc1nL+1x4S0fWdNjnttYluNOlikl/wBZ5kdbf/BQz4VWfw+8B+BtNih8u8+1XiXX/kOvN/2H7XUov2mfAcNhePBNJ4ot08yP/lnHX6Zl2EpfUPfPyzMsVT+v+4ft/wD8O+/hX432f8IRrGs6VeeVvlili328dcNrH/BPb9oTS7maGw0ex1GH/llc21/HH5n/AH8r7G8JazD4N+Hej/29eX0//LCKWS1/eXH/AG0rS1vXofBGnvNrM2qz20l1+6+zRb/L/wC2lfO1MDSm+c9BYur9s/Ojx58DPip8NLzyfHngm+01P+fmWLzI/wDv5WPbxD/ro9fplH8PpdZ1VPF8s+uXX2uLf/ZupSxpB/1z8uudsP2avgd42OpDxH8AbHTnt5ZEkuPtXlpJ/wB+65J4Q0+tH58R96ofESLHw38QEf8AQEu//RL17H+1Z8L/AAf8G/iQ/hvwb572f2WOfy5Zd/l1438RL7f8OPEC+U/OiXYz/wBsWrXBQ9nmFH/FH80bRfPEpfs1+V/wpPRt3X/Sc/8AgTLXfGHzf3MVeefs3XflfBnRk8pzj7RyP+vmWu9jlm/54yV1cQ/8j/F/9fan/pbHD4EP8nyu1P8AJb/ppTPMm/54yfnR/as0sn+pk/79V45Iz/VUwRQ/7b0+4mml/wCWVMkmliKfuZKDQ5vxZ+9kxisSzl/0it7xJKJZHz8lYmjxfb9Qeb/nnS5AN63Pmx/89KqfEX/kmuv/APYEuv8A0S9aNva1S+IkWPhn4gPpod3/AOiXr0Mv/wB/pf4o/miHT0Mn9muPd8HdHP8A18f+lEtelafFXnv7M0fn/BbRov8Ar5/9KZa9Fs/9Grs4g/5H2L/6+1P/AEtkx+FGxpcVbFv53z15d8QPjToPw+uIdH+xvPf3H/LtFL5de3/Dv4aeMPGVvbXkemultJ/y0ua86mZ1KhW0+LzdkMUPz10+j+CLy6s/tmqf6Inm/wDLWL/WV3Ph/wCF+j+G4/3sPnzR/wDLSWq2oapNFI/2uHZ/0yrqOSpiv5D5R/4KIfsoaP8AHj4PvZ2GpeRreny/atGufK/5aeX/AKv/ALaV+Sl4NR0HUJtH16F4Lyzl2XUUn/LOSv3g8W6z+7ea/h+y/wDbXzK/Nn/gpZ+y/ZeKNUm+OXwv03y7mOL/AIn1jFF/x8f9PFX/ABPcHh8VyVec+UbfWRdR+TWD4ksLyWTybWaT/tnWJ/b15YSfuZq0vD/ij7feeTqn/kOvOqQ9nVPo6dT6wdD4P+BcPijQ/wC0tZ1iff5v7qOOuk0P4LeA9L3w69qX/bXzfLqnF4zmsLfydK+SuP8AFnijWLrf5V5JH/z1rPnqHUoYamd5J4D+DOjXH2yLUv8AV/8AT15lVtQ1/wAE2sfk6NN5n/XSvJZL/UppUhkvPMf/AKaVseG/C+vapqHkeTO710fvA56R3kfiiGX/AI9Yf/IVQ6hLNdR/uhVmz8B6lYWfnXXmR+X/AM9ahvNP46fP/wBdaIe1OGvCkYnlTeW/lTUW8V55iQiaPir95/osnk1No9rZXV4nm11U4HnewZ3ngOL7BZpNF9+Suk+yzSyed/HWD4Xj021j/wBA/dpXVaf5N1Xo0/3ZnXn9iB7N+zHql5DHNpvnSb/N3+V/zzr6Q8H388Vv+6mr5j/Zz/0XxR5H8dxFX0h4Xm/0hPO+RKJ1P3p49ah/OdhHqk11H5N1vry39vTUmuPgdpdp9o3BfE8DY/7drn/GvarOws76z/0T5Hrw79vXSpdO+EGlmZcs3iOHL+v+j3FfQcNzTz3D/wCIijC1RHytr/gBfF9r4U1OSOQ/ZbhojHH/AMtENxk/qa+gfGmvfCv/AIRx9N+I00D20kX72OX/AJaVn+Bvh5Bf/Bbwp4shiy0i3UdwfYXcwH6V5B+1Z8L/ABtf3ltNo0M89tJa/wCqtv8AnpX5txZHl4lxb/6e1P8A0tn61k0qdbLYxfRHGfB//hVf/DRt5e/C/wAxNN+wSJ9mkr6ls7D7fZpD/B5VfIXwb+HHjb4X/ESzvPFtnJAlx/qo6+uvCd9DLZp5v/PLrXhYn2lP3zeEPq/uQOM/ZH+2eF/+En+DF/5kc3hvXpJLWWSX/WWdxJ+7r3uz8JTap/qv3b14/wCOIpvDnjCz+JHgiGCTWLOLyLq2k/dx3lv/AM8/Mr1fwP8AFXwd4os0+yeNo/DF/J/y5eKYvIj/AO/n+rrehD2/vwCc/Z0in48/Z40fxRZvDrOj/av3X+sr5d+Jf7AuhDePDl5qMb+b+68z56+57e6+JEunvNpeg2ms23/PzpF/HP5n/fuuS8SeI9fut8N34JvrR/8Arwkr1acKlOmedM+IfBf/AATx0C11XzvFs12/73/nrXvfhT4S+CPhpbuPDkPkfuv9VXYSaX481S4f7B4P1KR5P+nCSuG+PmqWf7Pvgv8A4Tb4q6lBHfyf8gvwvbXXmXdxJ/7Tjrop+0n7hgqf2zyL42eCPFX7Uvx80H4Gy3nn/wBsSxvf+X/y56PbyeZ+8/66SV+q3wn8Jab4X8B23hXS7OP7HZ2uy1ij/d18T/8ABOf4S3mjaXefGz4g/v8Axh4w/wBKupZYv+Pez/5Z29feHhuX7Bpf+u/1lcFeparyQPYpw/2b3z5O/bU8EQ+I/C9/pt/C7/aPk/ef89K/JHwn8NP7Zkv/AApqn/H/AKXdeRdV+0/7UNhNrP2myi2b/v8A/kSvzQ/aM+GmsfBv46J8YNL0GN9N1C18jXra2/55/wDPSowtT2dU5MVQ9oeD6p8EPEngi3ubzS9Hne2k/wBb5kX+srgNU8L6b/bD3n/CNx2tzJ/rZfK8vzK+8/B9/oN/bw3nnJdW1xF/ossX/LSn+JP2ffhv4yjmvPsflzSfP/rf+Wld3172fuTPOnl1KofJHwTis/8AhLLabxRZ/wBowx/6qK5r6W+Gfwb8K2Gof8JJpem+X/zytv8AnnWPcfsqf2DqH2zQbz5/+WXm17B8K9GvLDR3s7r/AJZxf6ySt6eKVT3BV6H1el7h9D/8EZ9e8STfFT4u+D5f+QPp8tnPFL/08f8A7uvmb/gph+1pqXw5/wCCjHifXvBt7dQXOj2Fvpfm2Mvl/wCr/eSV9Rf8E57/AEz4K+A/jH8Ztfmjjs49U8+6l/6Z29v5n/tSvys/aE+I3/C5PFniH4qa/B/pOueI5L3zfN/1f/PP/wAh16NSfJS9w8HC0PaYo/ZX9kf4yTfHj9n/AMN/FS6h/wBJ1S1/0r/rpH+7kr1H7XD6mvDf+Cdfhz/hEf2L/AGm/vN8mg+fL5v/AE8SSSV7Z/rq4oGdeHs6vIeDf8E0f+SF6t/2Ns//AKS2tfTvhP8Ae65bH/prHXzD/wAE09n/AAorVt+3H/CWT9f+vW1r6b8H3X/FQWfP/L1HX0vFM7cR4lf3v0RyrY9zv9V0rQLZbzWNTt7SF7iKBJbmZY1aWWRYoowWIBZ5HRFXqzMoGSQK+cv+ChXxC8B/En9gLx/rvw98caRr9lFcabBJeaLqcV1Eko1KyYxl4mYBgrKduc4YHuK5P/gs95n/AAy5oO/b/wAj9a/d/wCvK+r5I/aq8UfBfSvEmseGP2D77UB8PtV8IWR8c2lvFfvA88WolklmF8C6bXNoqyDABl2Bv3rq3v8AC3DscU8NjVKXN7S/w+4lCUW1KV9HK/u6atW8zM/SX9ib4g+AvGP7OHgnQvCPjfR9VvdE8E6NBrNnpupRTy2Ev2NF8uZEYmJt0bjawByjDsa7lfix8LLnxTbeBo/iX4fk1u88/wCyaOmswG6n8mSWObZEH3tseCdGwPlaGQHBRsfkz+xR4Ksrb9rP4V3Hh74yW8lzeawl1eReG9N1KS5sRHF5r2lwGgiBWZd8EjxPLEiGV3Plrlum+CX7O/wN8Zf8E5/iL8dvG9r9h8TaD4gkt9H8Qb7qXy9sdi0Vt5EcgjPnSTND5jKdnn7zwgx24/hHA0sbOU68rScLWhqnUlKKum1dJxvpq102bD9W/gt8T/g/8T7m7uvhz8TPDniI2Gz7cNC1mC7+zb92zzPKdtm7Y+M4ztbHQ12nhb4mfDfxtq+reHvBXxA0TWNQ0C5+z67Y6XqsNxNps2518udI2LQvujkG1wDmNh/Ccfhl8FLb4r+APivc+H/2VfiTeeIvFer+FL6LT7z4fXd3aMFEby3MLR3lpFLcMLaCV0jiCuJjbyROZYhGfV/BGl/AWx1/9nVP2AvE+pXvxq/tKF/GaXllqX2VriRIZJlupFMey1hzcROtrG4e185pZMoDKsdwRQpzajWlZq6934bJu9TVcsXy2Ttv001L3P1Y1D4wfC7WfEsXws0v4u+GY9fvmkFppK67A17c+TJNHchIg+9jG9vOjYB2tBIDgowGVrGq3c3idPhFL4UMltqcO+6uZdT3/wCj1+Rfw1/Zd+Hfi3/gnV8RP2ptUvb4eJPDPiy0sdMiiuQsIhMllG4ZNp3BhfMScghoYsEDeH/Tn9jbxpf/ABS+DWm+NPH3jw33irxB4Usbm9lEf2VkL26MzqyAKpLEnCgAHoAK8TOsiw+Tx56NVzSk4Sure8kpaau6s/LVeektcx87eKPiN+1Br/8AwU48QfAr9mnxXoXh/RfDFrp91qFnqHh1b0Xdlize5DLsMjuTdFdqS24KIAJI2+c/Hf8AwUn8D/Ab43ftCfEW4+Hf7O/iDTtY0XxLdReIfFFnqjtHLPE7xzST2yRMiIXil2kycqm4hTlV+1P2bpdM+Gf/AAWS+KWneL/FsUrf8K7tYf7Qu5v9fK8WivgHueuPZa+R/FOh/Ab4iXfxf+JH7XHxWk8KfFuPXdVXS/Anhjw8RYtqEKn5XmhimiZHuCYs+YG/ctI8shl319XluDy+vWhGdFOMaNF2jF8zlO121Gzls7tu0b+ZHIn8R87/ALKlr4o+EGu6lDD4ck12O8w8VnZTP5hffkyHCNgZIGMd+3Gfqn4W+IfD/wAYvCba+NBktPKu3t57Z5RIAyhWyrADcCrr1A5yMcZPm3xG8KaD4L8C+A/H41i88K+N9e0FtTvtMudIlhs7iyFw8VpcxbUO1pliDhQvlOo3gxjYJIfgV8RrD4Y+H9W+IniuTULyTVtUjgYRxh5HYLI5lLO43bizAnrle+TjxeJeEMHXymdbCUbV1KKg7zcpS5mpRs7QatrGS3S1SufQZTmtXDTVOcvc6/8AD2udn4/8fab4b8dXfw18GfCC+1+6sbaOW5ls5W3DcqtkIkbkqA6DcccnGOhOTP8AGbwhpXhzQ9S8GeGXvdU1iRo10e2vFMtq6ts2sEDMSzEbBtG9eeDxVH4/ftCW3iXUJ/AvgC/gtbO5CW2s68qnNzGGb90GQFjAu9ySAS25go2k+ZFo1/4M+DvjvwH8YdEsmu/DAsruwfUbMBXu7+KN1uGaNm3xsBd27kP/AASLtyABUZZwjls8Dha2LwUo1nFtLmn77jTbam72g5ys4wiuZJO/VLqxmZ15SqqNROPotLvp1dlo29NTyf8Aa48ZJ8YbXQbXxf4E1i4m0TV7z7bpNsw8xS0ShSeEbKsjhk+XB2nceVqv+yH8H9Eg/ad8Haz4b8O3doPKmn+zzhpDBdAIYg24sVbLMCMkfLx3J7SfRvix+0x4x13xR4ct7Y3l3JDFcS3NwAsSthIkGeSdkWOmPlPtn3H/AIJ2fDXW7r9ofxDY6rY2EepaHqSQzxvNhEuRJOpjU903I2foK9vMFh8NgMTh6dNR5YU5W3lFycU43evXr3PkFh4ym3b/ADP0x/Z8l1jTPhull8R9e0efZL+6lk/d/vP9yStEWNpoWp6rrOtahJqlneXPn2thb2vmII/+en/kOtjwX4Y1LWvCkvhv4jeHbF9lz0s5d8Zpmva/ef8ACTjSNM8FXqW1pF+9vpLXZb1+aweoTn7MsS3/AMQfEPiS21TwvqWkQ6CYv3n2kSPPP/wCrXjDx5pWi+B9X126t3nhs7aT93c22yOR/wDnnT4Nd1P7TELHU9IWzxs8seY5rwf9or4qaz8VNXtfhj4JvLq0hk1OP7X9tsdlYzKgfOHxU0HxgJE8beKNN8j+1LqTyrbzfM8uvNvib/yTTxF/2Abz/wBEvX2J+0R4X8NWHwL03R7+zR9Yki8/zf8Al4/d18dfFDyf+FaeIsYz/YV3j/vy9TQh/wAKFH/FH80ejR99GV+zDLn4LaLD/wBfP/pTLXpA/wBUn1rzb9mP/kiujfS5/wDSmWvSLOL7/m/cquIf+R/i/wDr7U/9LZrD4EPkl87jyaI4jTJP9Z/sVNF/01/SvHKGeVDnyaZJD+7/AHReppe9VpJc9v8AWVoT7M4/xRFm4eGs3S7XyutbHiz/AI+P3VU9PipVCjS0+LzuDVL4lRCL4Y+IfL6f2Hd5/wC/L1oWcXeqPxP/AOSaeIv+wFd/+iXrsy//AH+l/ij+aE9jJ/Zl2/8ACktF39P9J/8ASmWpvjB8X9N8B6f9jtf3l/cRf6LF/wC1JK4TwX8VtO+HP7O+jOzebeyfaUtrfzMZzcymoP2R/hzeftS/tEW2j+KJpJ7Cz/03xHcy/wDPP/nn/wBtK9LOqfPn+L/6+1P/AEtkS/d0FM9v/YX/AGLtY+LPiT/hor43TSR6V9q8/S7G5i/eXkn/AD0/6519w3l/Z6Nef2b5P/HvFvi/651fs7rR/C+j22g6XDstreLZaxx/8s64z4iazPdapZzWsr/6qRJZP+elcR4tev7QZeePLPVLx9Nihkjmjrj/ABx4ogsJHmM3yR/62n28s0V5NN5Pz1x/iS6vJZE/tSHy3qP4Zy85m+LLqHXvDc15L5kiffirxC4ihuri5s7/AMuT/nrFLXtPiz+2P7H/AOJV5eyvH7yL7LqDwy/fqzamfB/7XH7JdnpfjD+0/AdnJG95df8AHtF/q68E1TwZqXhfWH03WbN4Jo5f3sVfoj8eLWeWyhvNUs/kt7rz/wB3XgPxM8EaDr1w959j/wCPiLfFc/8ALSsJzPawuKdP3D570+1muv3MU9dJ4f8ABum3V55N/wCXsqh4w8L3ng3UHhih8yH/AJZS1lSeN/K2Q+dXJ+9PUp1KX2z0vS/Bvw80uTzpdNTp/wB+63rjxHoOl2aT2uz93Xht58QbyX98byTfUMfie8uo3828rRU6tcv2lKmd/wCLPHk11cfupqwf7e/d/vfv1ytxdTRSfvppHp9vNDhBdffrrpw9n8Zw+3R0Nv8Avbjz7q8/8hVsWc3lS/upt9cxb6h/y2i+5Vy21Sb/AJa+XRXrmkDvNP1mWLZ5U3z11vhvWYZZP3c1eXWevTRScTV0nhfWfN/5bR76wp16rCdPqfSf7Od1CPHCTfvN/wBlkr6Es7//AJZR18u/sn2s1140ubzzpPJjsP8AP/ouvpzT7qG1jSWu6n/FPHxv8Q9C+G93qVhbvDdTfJJ8/wC9rgP+ChMsc/wU0uZAxLeKIfnPf/Rrmu88F69puv2/7quA/wCCgUIt/gdpMYTaP+EnhwP+3a5r6Lhqd+IcMv7xw0v4iJf2dfD0Wufsq6PGfv8AkX3k/wC/9rmx+lcP4P8Air4J1nVLn4e/EHfpWt2cv+i/afk8yvTf2Rdv/DOfhzf0/wBL/wDSyauE/ao/Zk0f4jXD+MLCzeO5/wCWv2aXZJ/10r5Piqnz59i/+vtT/wBKZ9llGO+rzSPIv+Chmjax4N0vwT4r0v8AdpJr0iSyR/8ALSPy/M/9p1N4L8W/8S+GaKb5JIv+WVcr+2J4t+KuqfCuLwT4o0xJ7bQ5Y7q11byv3kfl1xPwj8b3l/pdtDL+7eOvl69P2lLnPpfb+0q+4e/G/m1STzpfuVcs5bOwR/tQ3pXJaH4j/d+T9+srxj4jmtY34kkT/pnXn067w53Qp/WC54s8b+FbXVP3XyPH/qpIpfL8uuYvP2qtY0GR4bXx54x/6ZfZteuP/jlcB4gtfEmsag4tf9T/AM9ah1D4VQ/Z/tmqT/8AbWun69inUI+qYYv+OP2wvipqm+GL4keI5LaSL/j2vdeuJP8A2pXQ/sh/s8eMP2tPGH9veMrx7Twlpd1vuv3v7y8k/wCecdeUR+A4de1R7Swm/wBXL/rfKr6H+Ef7SOm/sjeA/wDhFfJ8tPtUk8Usv/LSuuGNq0/fOr6vhadL3D7h8J6X4P8AA+yzupo7Ty/kijklr0iP45/B/S9HeG/1KCSaP/nnLX4z/tAf8FY7u61D7HoMOou8fyS3MX/LSuV+H/8AwUT8Saz++i16ef8A6hsvySR1EK/tDlnP7B+q3xI+KHhvXrma8im+T/ppXgnxoi8H+MtPez/d7/8Anr/zzr5I1D9uHUr+N/8ATPMf/nl5v+rrzfxR+3DNFqP73WXgf/lr5X+s/wDIdL/l77gpwPY7jQfG3wk8YTQaXrEdrptxdb4rb7L59pcf9s/+Wdd/4b+KGpRRp/b3w9S6/wCmmkaz5H/kOSOvJY/2qvB/xB+F82j6/N5d/HFG9rcxf89KxND+L/8Ay5/vJErq9uqhjCh/IfTlv8VfAfl+ddfDfxVv/wCeX2qz/wDjlTaf8UNSupHs/BvwZkgST/lprevR/wDtOOvEPD/xG+3+SPOr0vwXr3aKatKFc5MVhS/8YfiN8Wr/AOA/irwHf6xpuh6DeWFxPqmieH7X/j8kjj/5bySfvK+Kvhn4N1j4oXmm/D3w5/pV5qF1HBFbR/8APSSvqj4+eI4br4f69Z/bNj3Gl3Cf+Q66T/giv+zxZy65qXxy16zR00f/AELS5Zf+Xi4k/wBZJ/2z/wDalexTr+39w8aH+x++foX8K/CUPgP4f6P4Di2SJo+l29l5kf8A0zjrp4/3Uf70SVTj71N53/Teg8SpU9pV5zwH/gm/u/4Ulqu3dn/hKp+n/XtbV9P+CxDL4ksDKf8Al/j/APRlfL//AATi/wCSIar/ANjXP/6TW1fTvgeXyvElhDj/AJeo6+h4s/5KPE/4v0Rgtjxr/gqjr3xN+NPxO8G/sVfDDwRb6he6hAPEkUrXqRS3EqJexCJTKyRxqkUc7sWYlyyAbdpD+d/tH2X7anhD9nLxbpHjT9jX4ZeAvCWp/YP+Ej1PwfZ2lvOfLvImt8iC9cv+9ZV5RsCRumSR7N8WPM/4fB/CvKK5/wCEGuMDsf3WsV6L/wAFPn2/sNeOB5H+s/sz95/3E7Svq8FmX1GplmBhSg4z5JNvmupSqNNq0kvspq6dmvkTY+f/ANnIf8FCfAfgPw74p+CP7DvwpSK78MWkdl4mjtbO3v8AU7NoonSS4kXUI3dpAscjhguX5KgjA7K/8cf8FX7vQrvwndfsZfDIabfm4N9pwmtvIuftDu8/mRnVNr+Y0kjPkHeXYtkk19J/sof8mtfDX/sQNG/9IYa668v4bDUIdSv4XdI/+WccW/zK8nFcRXzCopYWk2pPVqd9G7fbJlofGfgTWP8Ago38DdcbW/AH7APwG8N6lc2xhe800adaTSQllYxl49VVihZEO0nGVB7Ctuw1D/gq14c17VPHPhL/AIJ7fBjRdd1pZU1TxDocdjBf3PmyCSXzJ49V3uXkVXbcTuYAnJGa+3ddl8IWrp4k1iGCCHT7X/WyxbPL8ysKPx5p2n+I7CHS7Oa6s9Qi8yKKyi3yf9dKp8QSq3m8LSd99J6+T985vao+MA//AAVn0P4c6l4Sm/Ym+Ha6BePcvqlrNqMLJceezNPvjOrYcOzuWyp3F2Jzk11dh8R/+C0kFjaCz/Ym+Gq/Z4lWCaS7t/M2dl+bVv0xmvrHxXH4jj0jUj4hmS10pzstrKL55JPn/wCen/LOr1np+p+HL3UvFUM2v6i+P+QTLLH5f/bOuSfEFOpvg6L/AO3Z/wDyZcXfofnbF8Ev+CxOn/tS6n+1toX7Puhaf4j1yC2tdWsINT0iSwubWJbZPsxjmvHdFcWsW5kdZPvbXTNdB4z+Hn/BT7x74zuviJ8RP+CcfwR1nUrxU+13WsadpE7yhEVFLSSaiZGwiqoyxwFA6AV992uk2fxE0+213xP4bnh8v/j2tpL9/wDvv93VoHxRPrT6RcaFZDSvK/4+ZZN5kj/554raXFlecot4WleKUU0pp8q2V1O9l5mlj84/i/pn/BS/446vY+JviZ+xF8K9Vks7VoLU3UllcIqjGcebqT7TyvQjOB1xXzN4z+CH7T/gXx5e3Piv4IaTFLeut22gRraXNpCpXanlxxyuUULwAG4HFfs34/8ACes3esaX/YUsH2P7l1bS2v7vy6+Lf+CsXh3WPDtn4Z17wlryaPNJLJa/brYyJs8v/lm//fyvVy/P6deCpvD01baylZenv6HjY/FVcGuaEU/v/wAz5B8FeF/2hfjV4qk0zw3+z54S1jU2tTdzm8gt4yYYxhnaSa4UBQABy3biu78c6B+3V+0jp2mfAvRfgH4Y0jRtAm3xeGPC97bWVgSzmQh994V5cljhgSxJzk1g+F/2zfhj8IPA9/Da+JLSDUtQtY4PEepfav8Ann/rPLj/AOuleaah/wAFh/Dfwg1SbWPhpryXU1x88v2mLf5klck84qUJOMcNStHbSeltrfvNLeR7cccsbTi5N3Z9LaHov/BSfwvZD4a/Cj9jrwh4UvYIEnE2haRamccrmdvtN1LHI7KoQs6tx0wQCLnwI+Ff/BVD9ljxn4p+L9l+zR4Y1XV/F0v9o6pfeJ9S08mOQPM7yRR299CIyzTuSu3soCgDFfFXiT/gv9+11qeuarr3w4l0rR7nWPLS6uY4t9xH/wBc/Mryvxp/wU7/AGwPihI974o+P3iqOa4/1vla9JH/AOQ461jn+IlSnT+q0bTtze7K7s01d8990nuc06vs/iR+lPj7/gqV+3fJnSvFvwi+G1hLDN/r1eeJlb0D/wBpY/WoG/4Kiftq+N9NPh+2+FXwruYTbeTsTU5m/d+mW1U1+NHjz9ofxh9tebWfGGo31zJ/y1ubqSeSSue0r4oeMNU3zf8ACVXUCf8AX/XH/alJVLfU6P8A4DL/AOTMfrPkfvR8Fv23v+CoPjTQ4/Dnwk/Zj+H1/baei71SJ4yg7b9+pLj8cVzuu/Gr/gpt4g+L8+qn4CeDrbXNOGbu0tbqLyRJs++zPfsDJj0fPtX5IfBf9vD9qj4IR5+HXxb1W1T/AJa20V/J5cn/AF0r6c/ZT/4LU/FTwJ4o+2fFXQYL5LyXZdS/ZfL/AO2lOWY0kub6nR/8Bn/8mX9Y5Oh+hj/FX/gsB4k+G80X/DKHgY6WsAgnvZL6NZyqeu/VNxI9lr5Y+JWoftXaRomuaF4z+F2i2EMVndR6o8NyjNFGY2EhBFywJC5IwD+NffXwf/4KMfs0/tX+GLPwf4S8YSSTahFH5umxzRp9n/7aR14p+3x4XGgWmuRRabHaQyeE55Io45t//LF6xpZxS+uU4/U6OslryzutVt7+524efMj5b+DfiH9pGy+Hmn2fw/8AAOk32lJ532W5uplV3zM5bINwnRyw+6OB3611sXjP9sUJhPhN4dI/6+U/+S62P2X/APkiGi+b9z/Sf/SmWvQ4/wB7H+6rDO88o0c5xNN4OjK1Sau4zu7SertNK73eiOmL91as8k/4S79sv/olOgf+BSf/ACXTF8Y/ti/Nj4UaB7/6Sn/yXXsMcpp/EUnf95Xmf6wUf+gGh/4DP/5YXyPuzx1/GX7ZB4b4T+H8+v2lM/8ApXTX8Y/tiy8H4UaBn1+0p/8AJVeySQ+b2+ej6/8AbWj/AFgo/wDQDQ/8Bn/8sK9j5s+f9a8SftRSs32/4b6LGe+ydP8A5JNQWvir9phf9T8PdHP1mX/5Ir1rxhEftn+xWbpv+trOXElFVLfUKH/gM/8A5YTyPuzh7XxP+1cvEPwy0Q/W4T/5Jqp488UftPt4O1OLxR8ONGt9PbT5xeTRTqWjidSrMALhuQDxwfoa9n0e1rD+OzSxfDHXmO/DaRODnp1r0cFn1GeMpx+pUVeS1UZ3Wq298bo2W7Phbxl8WvBXh9v+Ea8Q+LWs54YvlWGB3eJGO7+FGABzn8a+qv2AfDf7W3w1+FM/xB/Zw+Dmi+JdD8WXaXw1vxHexLLNsxGoVTdQMqDOOUz8555FfnZ8YtLe8+N2pwQykS3Jt1QnoMW8dfvT+xv8PtH8G/s9+Ffhjqnlx/2foNmn72XZ+88v/wCOV72d5jSw2OxF8JSl789WpXfvPV++tX1OCvKooqJ5ZP8AHT/gqg8n2aT9nHwTuzv2i9Q/+5KsnVPjZ/wUzdwt7+z14NQydCt2nP4/2hX0zo/iP+xtcm8K6z8j/wDTWsrxZf2drqf2KKaT9588UdeRDPKL/wCYKj/4DP8A+TPM9ov5UfM4+Jf/AAUhtrtpz8A/CgeTqDeJj/0vrnvE/wC0h+1Po3jzwt4a+N3wx8OaZa+ItRFratp0+6RsuiMwZbmYLtMqHDAbhkAjqPpLVNUmurhIpbP9zXz7+3ZDb6b8TPg+ttGwSPXpyAe+J7GvRyvFYTM8Z9WqYSlFSjPWKkmrQk1Zub6rsVSlSqO3Kj1v7VD9nTzZo/3leUeLLX7L4kmm875PNr0WOw02/ih1L+OOuD8eRQ3WqTQ/+RK+LJOA+KnhybxH4bc2EsnnR/8ALOL/AJaV4P4g+H15L4bfUovM36fFI/lR19S/2XDa6e+JnrhpNB03RrO/h8mTZcf62g2Vc+PPih4SOveC7m8+eea3/fxeVXzTqFhNdag/775P+WXlV9V6hL/ZfiHUvDcs0ey3upIK+ePjJ4cn8B+KHm0rf/Zt5L+6/dfu6J0/3p3UMVTfxnK/Zf8ATPscs1PjH2WTmbzEoj1T/ltFUNxdeaP3X363pz9mdfsGy5HdzeZ+9h2VNJL5Un/H5Va3/wCPf97NvojuvKfr89FT94dVHC8hsW8vmx/66tKPVJvLSGWuSl1SbzEq/wD2p50f7qoqUDT+GdImqf6R/sVpaPr0sUiTH7lcZp93eeZ5MsO/zP8AnlXYeC9LvNZ1W2021h+eSXZFWcPZUzKex9e/sZ2E0XhubxJL/wAxC6/8hx19IWfk/Y/JH368u+Ffhyz8L6PYaDEf3NnFsr1fyv3SfuaPb/vTxK8KftQ8BazD4X1B4bX7knzy1R/by1FNV+AmkXsbMFbxRBhG6j/RrmoLyWaLVEm8ny0jqj+2bq0Wofs+aOltFiP/AISaFg3v9nuK+j4Yhz8Q4af94wpwtNM7X9kKXyv2dPDp/wCvv/0smrsPEn72zm/c1xX7I1xHH+zv4eVl3Efa8j/t7mrX+NnxC034X+ANV8bapD+50+1keKL/AJ6Sf8s468XP6ftc+xcP+ntT/wBKZ67qKlFTPCv2rPFvg/8A4U34h0e/8iS5ksJE8uSvhj4d/Eb7LrFtNdTR+T/y9RRViftEfGTxh4ouNV1i/wBYk2Xl1JdS/vfM/wBZXl154j+yx2epafP5iSf62vnq+XVcIuSsfQYfNcLXUJQPtvS/iNpt1/x6zfP/ANNKvx6zBf8A/H1XzT8K/iDDqhSCWb54/wDlp5tex+F9Us5YvO87zK+cr0+T4D3aGL9od59v0fRv33npXK+KNUm164+x2E3+slqt4k1mEx+T5/z/APPWuV8QfEGz8L2//H5B53/TWigqtT3IHVelT9+Z7x8J/h1oNhAnnQwO/m75Za1f2kPg34b+JfwjvNBu/If/AJbxfvdn7yvl3S/2vv7LvENrN5f8EvlVpSftG+JPiDJ/ZsXiS7Szj+fyv+elepToVaZyzxx8l/Ejw5/wrnxBNo+swzzvHL/zyrm9P8ZQeZ/osKRv/wAsvLr374seA9N+JfixLy/vE87ytlcN8RP2WtS0uzh1LwvqVpd+ZL+9i+55ddqoHm/Wqvtec4DVfG97an/XfJ/y1pmh2uj6zvmh+Sb/AJ5S1z3jjwbr2g6olnf2bxvVDS/ttg//AC0/661l7BGU8xq+1PcvCX+gSeTL9ySu2ktby12TWv8Aqf8AnpXiGh/FD7KEhuvLkmjr0vwX8ZLO7t0s7+CvNnT9mdlPMaZ3PhfxRNYXiTed0r1Twn42mit/9dXhVvdQi4+2XU0cf/XKuzs9ehis/wDXVvhaZpWxFJneeMNUm8ZafNoNrsjmvP3HmSS1+l/7J/wq034N/BvQfBOjTb7a3sN8Xmf9NP3klflZ8K5f+Ey+KnhvR4oZJ/tGvWaeX+8/56V+yXhew+w6HYWfz/6Paxp+9r3KFP2dI+cx1T96badPxqWq6f638Kmjl++M/nWx5czwb/gnB5n/AApHVdnT/hKp/wD0mtq+l/Bcv/FQWYi/5+o//RlfNH/BOL/kiGq/9jXP/wCk1tX0j4S84a5Zwxf8tLqOvoeLP+SjxP8Ai/REqpyJHEfF7yv+Hv3wp3dP+ECuN/18rWK9D/4KcS+b+wx46Pvpn/pztK89+MG3/h8D8K8Sf8yFcZPv5WsV3/8AwU3k3fsMeNx6/wBm/wDpzta9nl/4Vso/w0v/AE7Izkeh/sqRA/ss/DYj73/CA6Pjn/pyhru9Htby68SQwm8ePy4v9XF/y0rgf2UwV/Zh+G0pn2L/AMIDo+f/AAChrr4/tltrkN5YaDJqM3m7PL+1bK+er/8AIxq/4pfmyan8I6/RvB3iS1sJv7e8VSXzyeZ/o1zF+78uuHvNegtfGFz8PtB0efSprfy0lkspY/8AR45P+XiOum8TeF92tw+MZvEmpaU9n5c91bSyfarSuf8AiRf6Dfx22vHyJ4ZLqRPtNldf6z/nn5lXQvUOKcKfxmp40uj4I+Glh4Wh1lLu/wD7Ut0uriX/AJaeZJ5nmPXQ6T4n8Y6XPfax46OlWukRRZEdtdSTzpXlPjCw17xR488JQy6/a/8ACOR6pG91psdr/wAfEfl/8tJK9T0X4i/2h43n8K2Hg++NvH8kmoy23lx//bK4qlOobc6Jr+9l+I2nwX3gnx1e2NvFJ+8ltrD/AF//AH8qSHxfNBrkfgSfTdZmuxFiTURaRpG4/wCe9O8S/wDCaaTpVvY/DXQtOTy/9bHeSbEjH4VbjvddMEFzf2dlavJaf6V++3+XJ/v/APPOlFXDnRz2taV4i8O3VtpHhLw9b3FtPcySGS4v5PM8z+OR3r8xf+Dhb48j4O+DdD+Dfhaa7nvL26+1X9zdan53lx+X71+mUPi2HwxFMvinx1De3YuvOl+z2vyW/wD0zr+cn/gtB8X7z4jftsfEIWvjB9RsNP1n7La3Msv/ADzjjruws6tD3zlnT+sfGfG3iz4l6ldao811XN3njya6kSGGaSs28ixcTTXU3mVQk1SGKTyYoan63UO2nQ9md54X1SaaN5vOf/v7XQyWvjDWrf8A0X93D/6MrgPDd15Z86X79ei+H9Z82y8n7Y8nmf8ATX93WH1oxqE2uXWpaNpaaPf/AGS0eSL/AFfm+ZJXnWoapqVgHhihkRP+msVeqXfhzwdpdn/bF1eRxv8Af8yKKvMfGHjKzluH8qbz/wDprLWt7ip0zK/4TfXopPON47v/AMsq7P4d/GnxtdXEOg3U0E8Mkuz/AEmKvMbzWZrr/j1s4/8ArrV/w/4jm0u8Sb54/L/5aRVUK/szSdD2h7f8P/j74w+DfjxNS8OaldaVqUd1vtb2yl/9p1+ivwb/AOCg/j39pD4Yah4G+L11Z3l7B4YuxYarCNktx+5f7471+W/h/wCKvhrzU/t7QZ77zP8AWyyy/wCr/wCmlez/AAD1SzsfGGnXVt4njt7e5v1ijhjl/wCPhmOAv4k4q6VT2+OpR/vR/NHLhYewq2P1T/ZgnhT4JaKkvb7T/wClMtejxxf98V5P+yV4n0jWfhbDoFlLuvNJmlgvE9BJI0o/RxXrcf8A01m+SvL4ji4cRYp/9PKn/pTPdgueKQVJUcvel84e1eUaDk6fjTJP++6ZJKaef46AOP8AGH7q84FU9Oi82ROaueMP3V5wKh0X+ClyFUzqtHixHXO/G+HzPhZ4iX+7otw1dXo8PlRp51ZHxhigf4R+Jyeh8PXbRfhCa78r/wB/o/4o/miZVND83PDnhGXxl+1ZpPhZYN66trthbN/uskYb9AK/a/T7DzbiGK1+Ty/+PXyq/KD9j7RItZ/b38JQTwb1GsJKf3efuWpNfqnJLN89nYQ/ubevo+Jf+RzW/wCvk/8A0pnj4mXMrHT+LP7N8ZeH/wC0r/8A0TW9L/5aSVxml+PLTXo0s9Z8uO/t/k8uX/WSVt6pf6b9jtte0uGP7N+7S/i83f8Au65v4gaDDfxw+KvDcKPNb/6r/ppHXhHD+7IdZl/0h/sv3/v18+ftpX1/dfET4Pf2lt82PX5wcf8AXexr2IeKYb+zh1iKb5PuV4x+2fcNd/E34SLDPhz4hmIk9CZ7LBr6Phmd84h/hqf+m5joQtXuepeH7+aUeT9j+SsHxxFN880UPmeXXSaHdf6Hj+CsfXJZh532qavmOc1OMt7qa6t/9TWJcReVK811W9JDefP9l8usHx7a3l1p/k2t55D/APPWtAPjD40iHS/jhrEMUPz3Eu+uM8eeDbP4g+F5tHu4dk334pf+ecld/wDtMSz+HPiDpV5dQ70k8xJZIv8ArpVPVIoYrdPKrOp7U2p+yPjO4i1LRrybTb/5Hjl2U+4v/Ki6b3r2/wCNfwfh8R26a9pf/H/H/wAs4v8Al4rwq4tBFJ5Hz7/N/wCWlaUKntD1Kdf+Qmt7+bGKf9qmEnB2JVaOKbzP+WeyrPlfc/fV1e0OunOrUJrfzof33+sNX7eWaUfvZvLSi3ih7zfJVyzihll/1PyVhXnUL5Cz4fim/wBdFDv8yvpb9lP4ezRSf8J5qkP+r+S1jk/5af8ATSvMfgn8JbzxvKmsXXmQabHL/rf+elfWPgfS4f3NnYQxwW1v/qraP/lnUT9lY4sVXp06fIeqfDu1s9UjSGUV38nkxXCRRVxPhOX7Lb/6LD5ddJZy3k0f+lVhyI8adT96auqRQ3Vn/wAs68t/ad1L7R8ErKw/55eJIT/5L3Fdj9v1j+2HEs3+jV5/+068g+HlpGxwP7ajJHv5U1fTcJzvxBhl/eJhO9VI7b4GfFLw/wDD79nHw/Lqmr2tuy/asiZZW/5e5j0jr5n/AG+P2m7z4jafbeEPDfjCC7sI/wB/dR2UUkH7z/lnHJ5lQap8YvihD4Vh+Hvww+NF7YXGnoz3Oh2HhtWKozs/zXD8Nndu9t2O1fNXizxbrHijUJtS1q8+3X9xLvupf+ele9icqpf2zia0+tSb/wDJmedmeYOMnSPN/ipLNf8Ag/UoYjsfyt//AH7rhtC1Sa/0OHPlx+X8kvlV3/jzzrrT5vK/5aRV4Voniia1kms/9XXzWeU/aHp5NU5PgPb/AAXdfYLfMX369U8OeMoZbdPK+R6+ftD8SQxaP50Pl74/+mtaWn+MvNjS8tbz5/8AplXxFenT+A+7wlU+h/EHigy6f9sivP8AVxfvfNr54+Jnxkm1TVJh9xI/9VWlF8S9Suo002K8fZJ/rY68i+Ilr5uuTeVDs8yX/V0sLT9mb16/tEM1X4g6l9sT7LeSb67PwX8ZNYtbP7HFMklzJ/y1rkvD/wAKtS1nyf3X+sr2n4H/APBPvxt8S995/bH9mw/8sr3/AJZyV13VQjC0MTUKfh++8VXUian+887/AJ6Vvahf/FS1t/tl15l1Z/8Afzy69vs/+CRv7VPhzw39s8B+KtK1y2+/5dtL+8krifGHwM/ao+Gll/Zvjf4V30EMf+qltoo3rCftfaHq06FWHuch4/4o0vxJ4tj+23egybP+eslcHrlrDaxubqGON/8AllXseqWvxCls/J/4Q/Uv3f8A04SV5v4k+C3xI8Ufv7qG7gT/AKebXZ5dFOFT7Zy4ulVX/Ls8o8QX/wBluPOiqHT/ABReWtwk0XmVpeLPhprPhyTydUMkj+b/AN/KxJNLmtY/3v7uuqFOkfOV4VadU91+Gfjz+2beGGWCTfH/AMtfNrubjXptQkSGL5K8K+F+oTRf8t69I0/VP3ieVs31j7D2lX3Dqpv2dL3z6o/4Jz6N/wAJR+1Rol59jkns9H8ye6/df6v/AJZx1+u9v9yvz9/4Iz+HPDel+F9e8Yax5n9q6pqnkWsv/LP7P/zz/wC/lfoFZ/6r919yvV9g6dI8avX9pV9wtJ1/CrEf/TWq3772qzH51ZEng/8AwTg/5Ilqn/Y1z/8ApNbV9J+E/O/tyz/1n/H1G/8A5Er5r/4Jxf8AJENV/wCxrn/9JravpHwn/wAjBaf9fUf/AKMr6Hiv/ko8T/i/yJ+yjh/i7x/wV++Fnm7X/wCKDuc+h/c6xXf/APBTgsv7D3jeNR8o/s3H/gzta4L4yR+V/wAFgPhWv/UhXH/orWK7r/gpq0r/ALEPjctsx/xLen/YTta9v4s2yh/3aX/p2QdGeh/spEf8MwfDfzIcj/hAtH/9Ioa7rwxqmr/25qUOl6lZed9lkeK2vYv3cf8A20rh/wBlePP7Lvw2Pn7P+KB0f/0ihrso/FkPg2T7ZLoN9dJeS+RdSW1rv8uOvnMRD/bqv+KX5s5a09Do/DV14wm0ea08eWVrJNJ/y106TfHJHJXnfxUuvh74c0ubwf4SmtP7Vs/9Kisv+ekkf/PSuw8YeFrPxP4nttYsNZk057eL/SvsV1skk/551j/FCws9U0u512WztNRmt7X/AEDzf9ZH/wA9P3laU5+zOM8u8N/FrUvC/hNPG3jLR49Oms5fP82P9/8A6z/pnVaT9sPUr+5c6N9unT7/APrfIj/8h1x/ijWZtft3s9UvPItv3c8VjLFTNL+x2Gn/AOgaP8//AD18qipNHSqdI2NQ/aM+JF19mmi8Hx75PM+1faZbh/8Arn/y0qnqPxk+KusmH/iQ6bB/ov73/RfM/ef9tKI7/XtZk/dRWmz/AK+v3lQ3A8VWG+8uvsMCf89PNkk/9p1hCj/IEJ/znDfHz4n/ABI8G/DbUviF4j8bSWMOl2Ej+VFL5ccn7v8A55x1+APxo8W6l498Wal4qv8AUp7qbULqSeWWT/lpJJX6p/8ABZz48Xmg/CfR/hVFef6Z4oluPtXlRf8ALvHX5Ea/pepeY/77/V1pUn7OkZw/inE+JLuaKR4fJrHiuv8ApjXSXmjaldXCQ+T5ldh8L/gj/wAJReJFdQ3f7z/nlF/rK82piPZ/Gd9OhVq/Aeb/AG/UvL/dfcqzYa94ksf+PWaSN/8AplX2N4T/AOCc+peMrO2vJf8ARHk/5ZSV6v4X/wCCMHjDWdP+2ReKrSDzP+etr5dcn16lU+A9D+yqp+cWseLfFV1b+Tf3k8//AF1q54L0aHVP+P8AmffJ/wBMvMr758Wf8EUfHmgxzanqvjyx8n/p2ta8x8Uf8E59Y8Lx+fa+JP8Av5+7rrp4unTMYZPV+wfJfiiws9Pk8m1m8zy6wftXldq+hPEn7I2v6N5011pnnw/9O11XkXjD4X6l4ckeG6s3j/66xVn9bpVAnga1P4zH0fVJopf3U3z16v8As+aTrTfE/wAMasl5B5J16z3xSS/vMeema8Zt4vK1BLOb93XqvwI0XU5Pir4RvGuC2zxPZM6P1AE6ZNeplf7zG0v8UfzR5s9z9J/2cPivY/Cn4n65qvi3XPsGlTMkYEn3ZP3EeXH0OR+FfQv/AA2v+z31l8eQI9fnD8R7/UtS+Nmu6Nq1lcCxt4YBZXKHIJa3jZkx2+Ymucj+GkN1cf8AIS2f9NfstaZ/TpVs+xKn/wA/J/8ApTPTofwj9Qbz9uH9nWKNP+K23/8AXK18yq0n7ev7NMVx5P8AwsiDf/zzlr4z0P4GfAa28P6V/wAJb+2DqVjc3lhG8um6boPnx2/mf8s5P3lcl44/Z4/Zdi+0/wDCI/tOarqOq+b/AMe174X+yR/9/K8R4Wl9gX732p97Sft6/s0+Z/yPkf8A5D/+OU8/tzfs93Wz7L4wjf8A65V+V8ngiGL/AFU3mf8AbKj/AIReWw/ffbJP+uVYew9maezP1fk+I2g+Nwl3oO/7NJ/y1l/5aV0Phn/lnXzr+yxqH2r4d+Hv33mJ/ZcaeV/zz/d19G+F/wDVJUl/wzvND/1f72sP42RvF8LfExXZlfD9+Gx6eUa39H+4n9+sL4zAyfCPxV5nfQLxh+EJrtyv/fqX+KP5mFSHOrnx/wDsKtHF+3j4bu02ZF6Ypc+9lKa/SjXL/wDsvVH+wff/AOeUtfmP+yrNDpP7YPhPWIlAmTXYGDjqMItfp944tLO11yb7VNHB9o/8iV9BxDP/AIWMT/18n/6UzyMRDlmmc9Jfw+CPED6xfn/iVap8ksv/ADzkqbR9eh0a8TwrdfJDJ89hc+b+7uI6x9UlvP32kX+mx3VnJ/0yrmLyaHQbeHQde8z+zfN32Fz/AMtLevC5PaHByFzx5oMOg6xczf6uw1D/AI+ov+mleMftOXVhN4/+E1rZzbo4tbbK/wB3M9p/hXtWoSw6pZvpniibz38rZa3Pm/u5K+f/AI/bbX4n/DezZt7W+vkN7/v7WvouFv8AkcR/w1P/AE3I0o/xPv8AyPbtLtfsHnTfbKrax/p9m8XneYkn/LSKWmWesw3Ujw+TJVDWNZs9Ft/J/g/6ZV87OYoQZTj8mw34mrB8Q2v2rfN51bdxLDdRpNLWDqGqw/2h/ZsUMj/9NKzL/dnyp+2B4R+1arpviS18zfby/ZfLj/7+Vz2n2E0ujp9q2edXrX7UsUMuj3kMUPz2/lz+bXmmiWsOqaOn+sR/v05/vCjB1DS/9Hea0hrzH4ifCrR/GXnalL/ompf89P8AlnJXsFnL5t6+j/bP33/PKub8QaN9gvPOk+5J8/8A1zrnhCx04f2v2D511z4ceJfDkb/a9HnkT/n5tot8dZVvDNL/AKqzf/tpFX0tcSzy2/8AovmSVm3EV5/x+Sw/PRTqHsU654z4f8CeKtauP+QO7p/z0r0v4f8Awls7V/O1mbz/APpn/wAs62NPi1i6uH5+Su28N6D5WzzfLjf/AKa1v7T2hjXxdX7B0/g/Sz5cNnawxwW0f+qj/wCedeneH7CGw2fYIY99cr4fitLW3TP3467DwPL9q1yGzls/n/65VjOo/gPN/ie+ei+B5Zpd4+xyR+X/AK2SunHneX+6zsxWbZ2s1rZv5Xl76s+H4tYxN9qm+SrgcxD/AK64eGWbY9ecftOxNb+BbSFpMn+14zj/ALZS16VHow/tR7yXzN9ed/tTxlfAtm427f7XjAx/1ylr6Thf/ko8N/i/Rl0/jR5Uk/ijw18NrnTvhJ4Vk1LxT4tt5g81jDiW2t4SU+Z/+BflivjPxZql5FzLDseP5PLr7g0K8ksPhZJ4V+HrNJ438b3Emj287Nn+ztOIJmdT/wAs95JGfaviX40eEpfhz8QNY+HsWpQXz6PdeRLc23+rk/d19pmP/Iwrf45fmzxM3VScmoHDapdTXVnN5Xmb68Tj8L3mqeNP7Htfk+0S/wCsr2O4uh/qfOkjrzrV9U/4Rzxwl75MjvHLHP8Au5dlfI5zQ56XPA6cmqYf2vJMreLPDnir4fSJo+swzokn+qlk/wBXJVDR7/UopP3Ur1794g/sf4v+C3+1f8tPn/65yV8/SWupeEtYm0e/h+eOvh4T52feTo+w+A6Gz+2aoOslWY9Gs7+VIftnlzVT8P69DDced5PmPJ/01q/b6z/xNMWsPzyVharUOmhM9v8AgX4X026uEmlh8xP+edfQlv4ym8EWaTWF55Hlxf8AfyvFvgH/AKvz9U/55RpF5cVewappcOq2f2PyfMrkxUHTqc8D6DCVFM6H4f8A/BQnUvAcj2Z1LyP3v/LT/V1N4s/4KE+GvFtxNDLeI7x/8tftX7uSvn74qfCrXrXT31O1013SSvnXxZ4c17S5HmurOSCnTxVWoepTxSw9Tnmfe1v+2b8N9B0+5+1Q2M95JLvik+1f8s68Q+KH7V+g+I7iaz0uzg86T/lp5vmV8u2/9sy3iWcs0iPJXSeH/C95LeJ+53vVHLis0+sG3rlr/wAJRcf2ndTb3k+f/rnXDeNfDkMVv/yz/wCuleu2+gzWtun9oWfl1w3xItYc+d3rogfOV/5zz3R5ZrWN4Ypq6TwfLr2vaxDpul757mT/AJ5VzF5dQ+Z+6hj/AOutei/BfQdftbabxVpfirStKTzdkt9J5kk9vXrYT+KeHi6/sz7z+FejaF4X8P6boMXhtPt9naxp9utpf9Z/20jr2zwP+0Z8QvBH2aLS7yd7aP8A1vm6pJJ/6Mr4S8H+Nx4t8mbQPEniPxdcxxbIorHwvGlhJ/10r0WPxl8W9L09JtU03Q/Dif8AUS17y4/+/dfRKdKouSZ877Sr7U/Uf4H/ALRnhv4on+zb+H7DqX/LKKWX/WV6jHNX5NfCv9pHw3oPN9rGmx39v+/+3abFcSeZJ/0zkjr9FP2U/j7Z/HjwH/afnf6Zp/7i68z5PM/6aeXXm4vCKn78DtwuK9pV5JnMf8E4PL/4Ufqu/Of+Ern/APSa1r6T8Jyn/hILP9z/AMvUf/oyvmz/AIJwHPwR1Vcf8zXP/wCk1tX0v4O/5GG0/wCvuP8A9GV28V/8lPif8X+R0udopHBfGAY/4LAfCt/m58BXDe/+q1ivQf8Agp1n/hhrxxt2Z/4lvmY/7CdrXn/xVTH/AAWD+FS/9SHc/wDonWK9G/4Kgf8AJjHjj/uGf+nO0r2/+ZtlH+Gl/wCnZDWzO4/ZWAk/Zc+HIkCY/wCEA0bGf+vGGvQfA8Wmm8v7OLWJ/tn3/Lil/wBXXAfssnd+y38NYvXwBo3/AKQw1sapr2m+F7O51iws7HVdSt5dnl2115c/l/8APOvnMRP/AG6qv70vzZzV9jS/sHxtqkd//wAJ5Npt1bXHyRReVskkj/5Z+ZXB+MPEeg/CWO28K6XDqNpbXn+qjk/f2/8A1zjrp/EFrqXij+zbyXX9R0CaP/j6tvN8+D93/wA9K4/4+ePNB0bw/wD2l4c1ix1mazi/1dj/AKyOT/lp+7rb+Gc9M8c8Sazr0vxEvB+4js/N/wBFlilqa58UXl1rCab/AMsfKrm/Gni7R7DS7bXtUhu45rz9/LFFa1zFn8eJpdQhs9B8HySPH8n2m5/eSf8AkOkI9XjlxeJ/YwdJvK/1tVtDi17+xpv7Uhn2R3X+qll8yuAk8ZfGC61R/wCxpoIP3v8Ax7RRR1Zj8SfGyLZ/bPiqCD/Wf8tbdPMjoHP3z4D/AOC3kt5/wvDwfey+Yln/AMI5cJa/9dPtH7yvhvVLCG6j87zo/wB5/wA8q/TX/grB+yr48+KH7P8AN8ZrrWJL6/8ACfl3v7r95JJZ/wDLT/0ZX5QW+talYSf8tJE/5ZebWNf+EdFCnSqHrvwP+F+ja9rCf2p5ez/nlJX1R8P/AIaeFdBk86w02BK+Xf2W9QvNU13zhNH+7/1vmV9deD5f9T5v7yvi8xr89XkPu8jw9KFL3z134Xy6bEUxD/38r3jw/r0P2dK8W+G+jfatk3k17B4b0GaWP91DWeEOvFUy/wCMPsd/pb/av9TXjnjDQNBlt3/0ON/+2Ve6634XmutOfypkrzfVPC8P/LWuqvMzoQPkv4qeDdH/AH3laPsSP/nlXiHiX4X6Pr2+C/035P8AprX238QPAem3Ub5+Ty/9b/00rwHxZ4I/4mE0tr5deenT9qds6dH2Xvnxn8ZP2QZrCzufEmgDYkdcd+zfFqdt8XPDWmi8f7Pb67afuz/12SvvqP4ff8JR4XvNAuhseSL/AFlfFdv4d1b4dftS6L4VvYERh4qsFbHobhK+syOtVqY2l/ij+aPi8yoUlV9w9U+L2v24+P17osMWZFMHmn628ZH862vOg/6Z1wXxUt5Lv9r/AFZnx5dqbSb5+n/HpDXVyed5n+pr1uI/+R1if+vk/wD0pmGEhcZqFhDdRvx/2yqh9g+yxf7FWbi6mqtJLD9oz51fPwrnf7Mmt+j/ALny6p6pYXl1/qoZP+2dXLi6PT59lPs9U+y7/wB9so5/3gTp0j66/Y/l/wCLX+HjL9z7LX054T5kh/fV8u/sn3X/ABbfSpvOk2fvPK/7+V9J+B7SHWdQtobrf5P/ADzqOT96c8z1rR/+Pf8A1Pz1g/GkyD4SeKYniww8OX2T/wBsDXSaf+9/c+TWN8ZrcL8JfFjN98eGNQz/AOA7V6GX/wC/0v8AFH80cz2Ph39l21S8/ay8NxxROZY9Q85iP7iW7Gv0x1yKa/8AB9nrGs3nnvH/AMvNfnf+xTbtdftT6asUG+SGG4mx/sfZ/LP6yV+jnw/8nxTZ3Pg+6vPL8v54opP+Wle3xD/yOcT/ANfJ/wDpTPIxHxI4aTXryxjf9yl3bf8APX/nnVDxL4XPinS/+JXqUc/mRf6vzfMqbxRf6x8M7y5hurOOez8397bS1myWGm+N5IdY+FXjaDSryP8A4+tNlrzKfsjz+vuHHx6pqXg2Sbw3r1n+5uP+XaX/AJaV5X8a47W3+KPw+e2v3mgOvhl8z76fvrbINe9+KfEfxO0G3fTfHnwxg1yz/wCfm2i8uvnb44eM/h34n+JHguT4fxajazWutn7baX8WzyCZbfYU9jhvyr3OGZ3ziH+Gp/6bmawhaovn+R7pJHN881r9/wC/Wbcf6v8A0ry98lWfDd1NFp7/AG+8895KoeILWW6CTWsz18wTAytYv5otnlfOlUI5vNk86ptUuvvw/u98dYOh/wBpWuoTS3M3mJQHOUPixop17w3eQ39n5n+iyJXzN8O5vsGlPo91NJvjuq+t/EkX2rT3r5p1TRptG8ealZzQ/uZJfOikoNTm9Y0/7BrCax5P+rqtrMsXiO3/ANT5dbfiCX/SPJMMlUI7r7LbvD5Pz1hUNaHtYHH291Do0f2OX7lWf3OqR+T5LvWlqml2d/b/AOlQ1j/aprDfDFDJH/7UqKZ1zmX7O6stGj8nOxP+estdDp+lzapsml37K5LT5vtcnk6pZ/JXbeH9ZhikT9zJJ/0zjqg9odb4btZvtEMPkv8AvPkr2z4f+HILXZN/HXPeD/hpN8n2rZvrvNP0u80u4TP+pp+zq+15zknMueVqX2j/AKY10Olxfu3h87zHrHjv4fM/5aVpeH7DytQe8EtdFOBjOAy8lhi1Dyf468y/awt7JPhpZTWvU65GD/35mr1nWNF/0d9RrxT9py/F14FtodjDy9Yj5b/rlLX0XCvtf9YcNf8Am/Q0p/Gjx/4ev8Q/DepapdeCgtrqniOMWema9dx5TSLZEBkmi/295avnf9qjRvhX4X1228N/CWG61VNLikfxH4kl+ePULiT/ANp//HK9d+JPh74u+MtPsdRsPH3h3wz4Y0PTJbMLqevNBNqMksryTMI15wFZY6p+JLD4tfFr4B2fwx8B/s03X9iWdrG/9pW2s28H9oXn/PxPJ/y0/wCudfYZlO2Y1v8AHL82cGOpU5Jx8z42SWH+1Hhl8zfJXnvxcigsNU86L93Xql5p82g65c6Nr0Pl3lndSQXUXm+Z5cleb/HDyZbiH7BD/wAsv3sleDjoe0pGGBp0sPV5y/8AC/4gw/2O8N1eQI8f/PSWqfxIlh1SRJh883/PWvOtHuprC4rp/t/2q3TmvgK9D2eJPvMJivaUvfMT+3vst4mYfkrsPC+qw3V5D/c/6ZVx+qaX/wA86oR3+paNvmhq4V0X7SrTPs/wH430HS/D9npthqUbvH/yyli/eV7x8E/FHhzVLyGHxHMkf72P/Wy/6yvzr+H3xVmsLhJrrzP3de3+C/j7o9/p8M13NJBN/wA85Za5alO510K7Xvn6Hahpfw9urfyfOg/ef9Na4n4ifDn4SxWfkXXhu0k8z/lrJa+ZXzNZ/tN+bHDpsV5JJ5f/AD0qt4k/abvL+P8As3VNYgkeP/llFLXnVIez+A7aeK9p8Z6dqHwb+D8UiQ/2DBvkqh/whHg+wuH8qG02R/8APWKvB7j456lLsmlvI5P+2tU7z45/ZdH/ANK1J3T/AKZVFP2s2bzqUj0v4mS6D9neHQfLT/nr5lfPHjzWZov3N1NH+8qbWPjd9vkf99J+8/56y15j4l8Ufb7h/sp8vzP9bXtYfCqoeHjq5sWcsNzeP5QST/pn5Vdz8H/i1rHhfVE03XvGF9aaJ9q3y6bY6XH+8/8AIdeXeC7/AMrWE82uk8WWt5YXH9sRef5P/PWW6jj/AO/detT/AHfwHgT/AH9X3z6H8SRXus6cniTw5N8RtVsLj/lxii+yR/8AkOrPhvVNN0uNIb/4b+GNK/dfvb3xRrP2qST/AMiV5F8H/jLN9o/sfVNH1/XIZP3f2L/hKJErv9c8Jf8ACOXD6lYeG/CNin/PPUte+1yR1v7Q5PYUva856j4P+LXmxvpsXxged4/+Yb4N8ORv/wCRPLr69/4JdeKLzwb8WLnw3dabr8EPiiKSf7d4kuvMuLiT/rnX5++H/iheXV4lmfHmoyJH/qrbwvo3kRx/+i6+lv8Agn3rOpWH7SHhia/8NyWLyap/x+6lrPmXdx/2z/7aVvU/eUiKcKntT71/4Jw/8kS1X/sa5v8A0mtq+kfB/m/8JZpvHySX8f8A6Mr5p/4JzH/iyuqfvMf8VTPx/wBu1tX054Hi/wCKs00fwfb4624q/wCSjxP+L/I9WPwnDfFpP+NwfwoXzOvgG55/7Y6xXo3/AAVCj2/sL+OT5+//AJBnP/cTtK88+LU2f+CxXwok9PAVz/H/ANMdY716D/wVB/5MY8cfXTP/AE52tev/AMzbKP8ADS/9OyH9mR237McV2f2Tfh0dO8vzT8O9K8nzP+en2GGvnP8Aa8uvjl4c+FHiTWPDnwB1jUfGdvayPo2paRYefH5n/Px5lt/0z/56V9Mfsof8mtfDX/sQNG/9IYa6vxRdQ6Xod5qUvyJb2sj/APkOvmMXU9njqv8Ail+bJnC6Pw0+Gf8AwXC/bG+CPiB/+E88VWPiewjl8u603W7X/V/9tP8AWV674Q/4LP8A7MfxLkSz8b6DP4R1W8l33WpS/v7fzP8Arp/rK/P340fC/wAba98RNVvLDw3dSW15qlxdRSfZf+WcklU7n9kvWLrSvtsXiT7Lc/f8qSKtp47Az+My/s7FezP1rs/iX8PfiN4g0rQfBvxg0rxHDcRefLbWV/HPHHXeado3g/VLhNOsNS2TW/8AyztpfLkjr8hf+Cd//CVfC/8AbM0GHWd8nmWt5BLHH8/mf6PJX6m+E/HF5Lcf6L4bjgmkl/1n+r8z/rpJR09w469CrT+M9OvNB0fQZLzXrXQXkeT5/wB3a+Z5n/XOtLQ7XTde0O2vNU8KvG8cv+rli8uuVt7Dx5dadDZy6lY/vPM8r/WPJXQ6P4c166jhvLrxtB+8l8yKSLS/9Z5cflyf8tKRcDH/AGgdL1jxv8P/ABP4Vv8ATYP7N1DQby1/4+vMkk/dyV/P3qlhpv8Az22P5tf0P3nhzTbqztrPVPHk+y88yOLzPs6f6v8Ad1+BXxo8BaPYfFTXvDdhN/o1nr15BF5X/LSP7RJU1Kn7o6aEqdNnf/s16Npvhfw3f69r95HBbSXX725ll2fu69p0/wDal+Ceg2fk2t3dyeX/AMtfsteOfEzwbef8IXYQyzRxwxxR/av+mkleUXnhfUr+z/0DWPISvlalClXq88z6yniqtCjyQPtjw/8A8FK/g/4Sk8mXzN//AE0r2bw//wAFJ/h7daPbTfbP31xX5KeMPhp4k0GN9e1n7dIn/Pz9lk8uqel/EbUtGjhhtdSnfy/9V/0zqK+Fp0/gNKderU+M/Z6T9rqG6097z+0o3T/llF/q68Z+Ln7fUPhKz866s4E/6a18MeC/jn48ls/sel6ldyP/ANM65L4qfEHXtfkf+2fP/wCmvmVwTnV9qelz+zpc5798YP8AgqLrGqb9N8LwwP8A9Nf+edeP6x+2l8Tr+4/0qZ54ZP8Allbfu68WvL+GWTEUOxK6Hw/f+G/KSzi1hPtn/LWP7LXpUKdI8atUq1KvPzntPwf/AGoPjDpfiCzstL1L/Rry6jT7Dc2vmV6/+1l8DLe/8UeCfj9osIjuo/GGkQarHHHxJFJcR+XJXyl4P8UQ2viBIbUSb45d/mRV97fDDVovjJ+zxJYXjebNplzb3at5fQRTJcp/6Lkr6PJ/ZfXaVv5o/mjgxENOc+W/iJp8V3+1Vr8kiuxSK3wB0/484a6O38mLfDKPn/6a1zHxusdbt/j14j1fSGdZTHbCIw/fOLWIVsaHf3mtaHbXl/M73Mlr+982teJ/+R7if+vk/wD0phl8KnKmMvfJ+f8AgrK1T/ge+rmoWv7yobi182vDOznp+1IbOWb5If3myjWIpzp7/ufnqz9gm+0eTEPnkq5HoputDvPtXmf6PFvpUxVD6r/Zf/e/Cvw9/wBev72vqXwGfKvLaaKvlf8AZKimi+Dfh6aXzN/2X/WS/wDXSSvpzwXdeVcWcx/6ZvW1M8uf8U9p0v8A1VY/xnj3/CDxW3p4Y1E/+SzVt6Z3rK+N3/JHfFf/AGLep/8ApK1dGW/8jOl/ij+aIex8hf8ABPiAy/tc3Eipu2eF7nj8Ya+ydc8RzeHNchvIZpI7mP54vLr41/4J0X4i/bJubWf7k3h2eIfj5Jr7K8ceEtN1m4eDVJtiRy/6yKvdz+F86xP/AF8n/wClM8fFz91HVR3Xhv4teH31LQbyOPWLf57rTbmX/WV5L4s0HTf7Y+xy2d3p1/5u/wC0xfu5I6x/Fnhf4wfD28TxV4Ih/tmz+/Fc2P8Ax8R1pad+0tpvjy2SHx54Pkgv4/kupPK2SV5H8M4qftR9nrPx48OXDzaN4kfVbP8A55X37yvDv2jvGV343+IHhFfE/wAOVsruDUnS9FnDh9QVpIMIB3YYIH+/X0rp8vwx8Rx+T4c+JGo2r/8APtJFXhn7Tuk+HtG+K/w8Sz8dFidcJvbl4cfYcTW37zHfjJ/4BX0HDP8AyOI/4an/AKbmbUvj/rsdd4X1SHWbNJ/JeD/pnLWlqF1ZRFLOWb565641nR7DWPsel+Kk1n+P7TFF5ddPZxabdW/nXVn5j/8APSvmDKBx/iCwi8x7zznrH0fWbO/uPJj/AHldPrkX+kPD9j2J/wCjKytH0uG0uPOihrSZH/L4vyWv2CzSHH/fyvnL4weHNZ0HVLbUv7Sd/wB7IksnlV9XeJP7NutL86Ky8t68Z+LGjHXvDd5DFN/yy8zzYqz5zsPHI4vtVv50v36wbyL7LqH/ABMIdiVt6fpepWtx50upb4f+eVU/Elh/aEb/AGX/AL+xVHs/aEz/AHZj6raz/uZbH95UN5pdnfx5l+/Wl4Xi+y6Wmm3V55k0f/PSmahY/YLhJv8AWJJWfsy/aHGXl1eR3n9m/wCrSP8A5a/89K6HwffzWmoQy2s3zx0/ULW0upMfJWbcaXNLJ/rng8uX/WxS+XJRTInD259D+A/i1rMV5Z2cXkeT5v8ApXm16dH43/tT9zFN/rP9bXzN4I1DyreGH7Ykj/8ATSvSPD/nXVxDN53+rrfn9oZw/dnrunxf6Z9t/wDatben69Zm8/s3zo99cfp+vQ+Yln50m+tiztbMXv8AaUv+u/56U/3o/aHotuftVv5MVeD/ALXUAg8IWqsm1v7YjyP+2UteteF/Ef8Apn2PyZNn/PWvN/21dGmtfBVlq4mbyptZjVYj/AfKmP8ASvf4S/5KHD/4v0NaftJSTZ8b3d7+zx4Y+IF7rvib4T658QvGEzwfY/DsU0qWkJCoFysf3yQAce9dZ4k8L+MPFFlDr3x4+Hvh/wAHeGLe1k+weF9J8UfYZ7j/ALZ+ZWd4s8M/FgWM/ivwqNB8E6GsezUfGSagFv8AUjleCF+dAn3BjqEB715brPin4G2twl54S+G/jT4t69/y/wCpalLceRHJ/wBs6+0zH/kYVv8AHL82eU41I4ub83+Zx/7QfwgmtbdPiT4D+Ceo+HPCX7uD7Tc6pJdfaJJP+WnmSV4D8WB5Wnp5X36+uri11j4026TfFrw1aeCtB0/zPsttFqnlyRyf9c5P9ZXyd8WLW8/sfyZbP5zL/ra8PFQ/dHHCpVp4n34Hksf+s/56Vq6Pqk3zw/6ys2Tzov8AljRZyzfaOlfJY6mfW5bU9odVJDBLH5Mvl76wdUsJpY/9d8lXLO6ml2ebNVyX/VV4B9N/EpHK8Wv7ryfLqzZ6zqVqP3U0lX7zRvN3zReXVAWE0X7mWH56vnOXkq0yzH4y1KwuPOim+emSeLbu6l8/+OT/AFvm1DceRF/rYam07S4ZY/3tHIYc9Umj8b3nl/Y/4Krahr2sS/8ALZ0hq/JpUNrb+bFDHvrB1SWaWTijkpFqriRmqed5aTRXlVrOUf8AA6uXFt5Ol+dj/WVTt/uVvQmctf2xsaHLDFdp5v7uvSLiKzv9KSG6s9Nkfyv+PnVrr93H/wBs6800fyfMT9zXcx6zefZ0EU0G/wD56S2Hn16v8SkeaYkXk2txm11Kxf8A56/ZopEr1H4Z/EazutL/AOEb8nwdYpH/AMxLUrXz5/8Av3XH3Oqf2oifb/Emo3bx/wDLK20aPy6Z4fsJrC4+2Ws19G8f/ULo5/Zka1D1o6pNdSf6LrHiPVUj/wCfGw+w29e6/sDyz3X7THgb7Lo9jA8mvW/myxXXnz/9/K+aY7+bVLNDdfa77/nrFe6p9kj/AO/dfYf/AASb8JXmvftN+FbP+zNOSHT/ALRqMsVl+88uOO3/AOen/XSSt4VA29w/QP8A4Jx/8kP1b/saZ/8A0mtq+mPB/nf8JTpp8n/l/jr5n/4Jxf8AJEtV/wCxqn/9Jravp7wVmLxJYfvvk+1R118VTtxNiV/e/RHWtjgfi1Dt/wCCwvwpj8xufAVzyev+p1ivRf8AgqB/yYp44/7hn/pztK85+K5En/BYP4UY6/8ACBXOfr5OsV6H/wAFPiy/sL+OYn6j+zP/AE52lez/AMzbKP8ADS/9OyHy6M9B/ZU/5Na+Gn/YgaN/6Qw1Q/bA8b/8IH+zv4n1LzvLmuLCSytf+mklx+7rR/ZQ/wCTWvhr/wBiBo3/AKQw14v/AMFQNZ8nwH4Y8K+d/wAfmvefLF/0zjj/APtlfG5l/vtX/FL8zXDQ9piVA/P3xJ4W+waX/cryXXYpvMmr6H8eRQ/2G/lfcrxn+xv3jzffrwKlTnZ96krHnv7PfgO8l/aE/tjz0tbm3tbjyrmWX/V/8s//AGpX6KfAv9kvXvEccMOqfGCSC/uIt/2KOw/d18Z/AP4c/wBvfHm2m0u8ntJpJY/N83/Uf6yP/WV+l/xk0v4b+F/DdtqV/wDtFXV9f/b4/K03wtaxp5f/AG0r38DP90fG5xQp/WSGz+DfhXS/GFt4P8UfGDVUvNPi87/RpfL8uPy/+udXLOL4P+XbWfgPwT4x8YvH5jxf2b9ofy/+elcNp/wMm+Pt59s+F/wl1nWfMl/0rxB4k17y4/8ArnJJ/wAtP+2de/fCD4L/AB91PQ30GL4tR+GNK0f5IrHw3pf7iT/rnPJ5dd07ezPGPgz/AIKqfFr4kfCD4P6bpvhf9mm+8Jf25rP2Ualrdr+8uP3f+rjjr8uPGGlalL4gmHiOz+yX/m/6V5tfs7/wVv8AgH438MWXhXU/FHju+1+z+33H7y+l/wCWnl/u5K/Lz9pC51LVPFFh4V0zwrv/AHu+W9kirycVivZ1OSB9PgMDSr4LnOh1j4X6n4y+HdtZ2HmfafKj/df9s68T8UfBb4weCNQfypoI0k/5ebb95JHX1d8J7rzY4dMupk3+VGnm160n7Ofgnxlbedr293/5a/Zrry68GpivYVeQ9yhhPaUj80I/gP8AHLxbrEP/AAlHjae+03zd/wDpOqSf6v8A6511Vv8AsMzeI9cfWLXUktLaT/l28rzK/RG0/Zz+FfhIvNa+FEnf/npc/vKxPEFro8VwlnFD8n/TOlXzE0oZdqeV/sz/ALJfgPwlvs/+EPgvnki2ebexeZXnX7Wn7Kug3/jD91o8din3PMsoq+/PgX4Ds9U0v7Za/wC5XAftUeA9N0YzaxLsn8v5K872mJqPnPb9nRqUuRn5++C/2LdGtbKaa/1K7u08r97FFa/6yuevP2N9N0DWHm8OWeq/9vNr5lfWnw3v9Nlkf/WQfx/vIq9j8P2FnLGk3nJJXbTxtU8epl1I+G/hX+y/No376/8ADc8CXHz+bc//ABuvof4WeHT4e8N61YWsbrG+kygoeyqhYfqK7/4mX+m6Dp7i1hTfXlfhj4meZc6jpvm7PtFnLF5X+8hH9a9PJcVVqZvS/wAUfzRx5hgadPCNwPmz4peNbzwv+0rIsGleb89vtbdt37oYlxn8a7Dxhp+g/Z/O0uHyH82m+LtA03xB8WdRluBFv0ww3Jdo8lP3MVc74g1SbVbjzovkT/nlXq8UTtxDil/08n/6UziwUeXCxXdIypIvNvP3uxKfqFqfMT7mz/nrRx5n+3UM/wC7k/e15UCPZFm2/wCmU0laWl/2l/Z+pQxb3SSwk83/AK51m+bD5eKuaf4i+wWdz533JItlbU/4pyVz6f8A2Z7X7V8G9Bhtf+fX/VR/9dK+ovh3o3nRw/b4fLSPy/3fm18x/sjyw3Xw30Sa1h+STzP/AEokr608DxYEMMVaT/d1Ti+P4z1TQ7WH7OnlQ1i/HGz3fBrxXJn7vhi/P/kq9dZ4XtZpbesz486TLH8DPGMhh4XwrqRz6f6O9dGV/wC/0f8AFH80Q9j4p/4Jt+HtP1f9s2/ub+8MBtvC0ssG7pI5aBP619u/Eyw1LS9UT7fD8lxF/rP+elfGv/BNfQLTWf2iPFuqTTjzLDw1FFBG/TdLIpb/AMdQV9rXnijWPDln9j1TR4NRtv8An2ua+gz58me4l/8ATyf/AKUzxcScfc3V5peybS7yfZJ8/l1z3ijxJqUUbiLwHY3TyfPLcyRf6yuzvP8AhTOvXH2y6n1Xw/NJ/wBtI6oaj8Ofh7f/APHh8eP3P/PKWwryZnLCZ5XcfCr4heLf+Kk0GGxgfG/yvN2eXXl/xhvby8+Ivg3R/iLof2V7XWjHqcn/AD3iMsG5vyDfnX03b6N8PPDkbwn4zX13/wBe1hXjP7VWo+Ah8Q/hk1ppmoX9vb61I2qversa9j860yi/gGH/AAIV7nC7585g/wC7U/8ATczWl8f9diHWfhz8JfBGsf8ACSfD7xV/x+f62xlut9dP4T1ma6k/ezfuareLNGs9e097zwb8JdOtXk/1Vzcy+ZXH+F/F01rrCaDfnyJvueV/00rw5mZ6XrkX2q3/ANEl8t6x9Dtby1jcX4jkmq5b3UNrZPNLNJs/661D9qhuo/3X/bKucCHULrUrr9z5PyVx/iDybWzf9w//AGzrtriWaW3fyqwbySGX/XQ/PWhf8Q8H8QaX9g1iaH50ST5/3lc3rkUOl27zEPsr2P4oeHLy/tk1K1s/M8uvMdUim+x+ddQ/PJ8ksVcv7wv+IcNYXUP2zmbzK2Ly/hv4/sf8dZuoaXNa3H+iw1pWcXlfvxD89VMj+Gc9b6X/AGVqL/vvkkq/eaNDqlv/AKLN5b/9M60pLC81m4f/AIluxPSqEl+fC++GWH5KzLhMh8P2GpaNeJ/y3/6616jo/jI2FxDZ+TJ+8rj9Pv8ATdZt0mtav2d/Z2FwlVD+cufvnsfh+6hl/wBMz89aUGval9o8kw/JXm+h+I7v7RCIv9T/AMta7/T9Usrq3/0Wb563nUMjsPD+veVIlYn7Xl41z8EtOy6t/wAVJCQV/wCve4rK0qw1/VNQfyppI0jrB/aE1rVH8BWvh+8Mjxw6ujpJImMkRSj/ANmr3+FI1JcRYZv+Y0oqpBpM+fpp7jWvGcPhqX9n3QNVgiCSf27rGs/ZY5sn+Nv4Av3cf7Oe9Y/xRi+LVhpdzDr3xy8M/D3wr/0DfDd1HPJcf9c44/8AWVwHx/0r4d6j4lvbU/Brx3r2sTGP7RfW2oSppiv5SbFjSPvs27v9rNX/AIb6Zr1/GnhXw3+xPo8l/Z2v+lXPiDVJJPL/AOmn7zy46+tzOdNZlWv/ADy/NnLXo3m3zngmoWHw2l8Uf8Ub4b8T+MYY5f3t7e3X2TzP/IdM+PGqTeKPCcM0XwZg8K2dv8kUsd1JJ5n/AH8ruf2hPDnirRpJpviX8SPDOhp5X7rw34NljkuP+/cf+r/7+V4zrl1oP/CNzf2XpviB4fN/4/dXl8zzK8Sp/cMbc9P3zyu8/cyVTuJR5f7nfV+8tYf+ulU7yKb5/wC5XjYqh7Q3wlf2Y/T7qaL/AFU2+ti2l/d/7dc3HH5X77zqv29/NXzFeh7OqfVYTFe0Ohjuh5f72GOg/Y5f31ZUd1Me/wAlPkuq5dTv5PaUi5HYWd1J1p/lQ2sn/LPZVP7VDFG/m/fqGSWtF7Yz9hTplzWNU+1bIYvkT/lrWDcf638a1fsv7v8Ae1lXA8m4/goBQ9pVNK40v7V4bf8Acx/u65uPPfy469O8P2Bv/D00EMUcn/XWvN5Ivss7+bDV4Gv7Sqdud5VUwmFp1v5y/o3k/aE82uk+1TRRoLWaeBP+md1srntP8n/XVvf8w/8A1L/9sovMr3YHyxNZ3V7FJ/oupXUb/wDPW2uvLqzJql5Js+1Xl3P/ANfN1vrHjl8qPj7/AP01iqb7UevnfPWfPcPZ1TqvD8tn9oQS+RGn/Xr5lfr1/wAES/gZ/Y3ge/8Ajlqg8x9Ui+y2sssWz93/AKySvyd/Z7+H2vePPiJYaDYQ/avtF1Gnl/f/ANZX9Ev7P3wv034N/Bvw38MdL+5o+lxwS/8AXT/lpSn+7FTp+0PN/wDgnB/yRLVP+xrn/wDSa2r6Z8F+TF4ksOPn+3x/+jK+Z/8Agm//AMkP1X/sa5//AEmta+mPBfkxeKbD9z8/2+P/ANGV6/FckuI8T/i/RGkfhOG+LKY/4LC/CZZO/gC4z/361ivQ/wDgqDJE37CnjgKef+JZ/wCnO0rzr4s/8phvhT/2IVz/AOidYr0P/gqB/wAmMeOP+4Z/6c7SvV/5m+T/AOGl/wCnZBH4Wd1+yifL/Zf+HB/6kHRv/SGGvmn/AIKcaz9q+LHhLw2Zv+PfRri6l/e/89JP/tdfS/7LH/Jrnw1/7EDRv/SGGvib/goR4o+3/taXmmxTfJp+jWcH/tT/ANqV8bm8/wDaqv8Ail+bPQyuF8ajyLxRF/o/kn94lee6x5NsH837lehapL/o/SvNPHl3DFbzTf8ATKvlV+8rH2ux7x/wTr+Bnir4gyeJPiRoH9lR6bby/Yr+51KX95Z/u/M8yP8A7+V9IfAvw54b8R+LJobDwFaeI30+X/StS1u62W+nx/8APT/rpXxh+zH4t8eS+D7bwr4cOpbLy/k+1fZrr/R7iTzP+Wkf/LSv1W/Zv/ZzHw5+GeiaNLpsd3/aEUc+qxSWuyP7RJ+8kkk/56f886+wwn7ukfC5jW58TM9J8EaL4J02zWx+Het6atqkvnX8VvL57xv/ALH/ADzroZbHw541tI/PgedIJd48yOROa5a8+Cek2EtpqPw61WHw4lvJm5Om2o/eR+lX/DmqWXiIJNqlo97Np8siWt1HF/x8eXR8Z5Jxn7Vfwh0b43/CnWvAfju107S9KFpv0zVrm52PZ3kf+rk/3K/F/wAcaNpEVxrem39nH/aWl/uL+28r/wBF1+9Ona7e6xb3Nv4i8IT6dETsjN7dW7/aE9f3chxX5+/8Fh/2dLOxi0348+BPAUaWCWv2LXrnTRv8z/nnJ5cf/PP/AJ6VxYqh7Sme5k+O9hU5Jn5m+F7qz/tBPKvPkr3L4f8AxV03RrP7H53lpH/y182vlfxrr0Gg3F5No00/2bzZPKjlrm9H+NOsS3H7nf8A9ta+fxVCr9s+ywNT2h9n+PPjd51n/oE3yf8ALWuV8J/F/wCHt1cTf8JbN9h8v/VSS/8ALSvIvh/4t8VeN9Qhhv4f9Gjl/wBKkrS/aQ+Evhvxb8N7yG11L7LeW9rI8UsVcioe09yZvPF0qZ6Lrv8AwUn+GPw01mbwr4S8YRv9n/5a20teaeNP+Ch3gnxRrCabr155kPm/vZftVfHmjfALTftjw3+vPa/88vKi31Q8SfAy80HUH/4n0c//ADy/5Z11rA1aZyvMqUz6x8eftS+CfEccOm/Dmbe/mxp5sUteqeB/i1/YunpDf3iSP5VfDHwf8LzaZ4ktpr/Z5Mcv/PWve/Eml+bp6XmjXmzy6wrU3TNqGKpVz0v4sfG7TZbd4fOrzfw9rx1/xXa3dku398u9/UZGRXj/AMQNZ8VWuoeTdTv5P/XWu8/Z/u5728haT92ocf8AbTmvWyGHJmFH/FH80cWbz/dvk7Gvrd1p9v8AGXV4bmPz0mSAXEHoPKirnNYsP9I/1NbvxBhtIfiXf3cURjmdEEkydWHkpgVkahL9/wD1letxDCo+JMX/ANfan/pbPLws7YSPovyMS4tZ8uIpvLqhJdQiTyf46v3Hm/PWbefurhP79eYVz+0LMkR8vEVWbSw+327w+d89U/tcMP8Ayx+enx3U32d7zya2h/EOepQPr39juL/i2mjw/wAdv5nm/wDgRJX118O/3vkivkL9ju6834V6Px8/m3H/AKUSV9gfCOL/AEyEVpP+IeSfQngfQRLZpN5NUP2jdO8v9n3xw/k4x4N1I5x/06yV3/gfRv8AiTwzViftPacY/wBnHx+6xYUeDNUI/wDASWvVy2H+3Uv8UfzRzVJn5J/soeNrnwl+1nq1xYXO14vDc0zj0ZUgKfqa+iPg/wD8Fi/2e/G9wmg/FqK+03/Vp9uitfMj/wCuklfJvwSYt+1RrcMb7ZG8NzAn1TyrfNfJ9v8A6Lrj3ks3zyf9Na9LiD2VPPMTOf8Az8n/AOlM7ZYCnisLTkfup4b+IPwB+L/7/wAEfELQ9VtpPni8u/j8z/v3W3ceCNNtZE+ywpIkn+qlir8fPhXr1n9jhhurPzP+2Ve/eA/Ft59n8mw1i+tP+uV1JHXzf9q0vamP+rmJn8B+gtl8NNHtfOvIoUj+0S75fMrwr9qeTQtP+MPwvSa7hjit/EbNeSvIoCR+faEsxHQYDcn0NeS2d1Df/wDH/q93P/11upJKyPGtvaR+IdAaJVZXvfnA7jfH/jXt8K5jSq57CMP5av4UpsHw9iMPD2tSW1l97t+p9N/ED9oz4A+HNPubPStSu9Vm8r91/ZNr/wC1K+VPF/7Q8N140+2S+G/7Ktv+WUn2rf5n/XStjxBNZ+Y8Ig2V4b8YJYf+PyL949v89fL081pVKvId0+HPq9LnPsz4Z+LYfFGhw/v/ADPMirb1SL7Bbp9lgk2f88o6+Xf2X/jT/wAJHof9j6NPsvLf/l2lr6i0+W8utHh+1Tb5vK/e16lM+VxSq4eryD0i8q3efzq5jxZDPdRp9gvPLreuLqbHky1yviTWbO0uPJlmrQzplyPydUs3s7qvLviJ4SmuvOtIZvL8uXf+7rv/ACv9MS886qeuS6bLI/77e9Zj52eJ3lpDayfYx87yVTt9BvPtjzSzfJ/zyrvPEnheHD3kUHz1yUcV5NePDLZyRpH/AMtKiZqTaPJZxSvZ+b++o1zwv/alnN5vlu9ULi1/s/UPOz/rK6HQ9Vh1Tfpsv36yJh/EPK7y6vdBuPJiPmeX/wA861bfVIdZ8k3UP/XWKWWu21T4fabLG5tYf+/dee+IPAepWEiala+fBD/zylrSYTgd/pWvQxXCQ+d88ldJpd//AMTBJxeyV45pfijy5POupv8AV/8APWuks/G/7yHy60p/wjD+GfQngv40+D/C2oeR4j1iTyZIv3vlf6yP/ppXOftHePvB/jHw1aReFb3zlS/VySm042OOleJeN/DmveI7ea80EfP/AM8vNrlPh1N4jtvEU+k67PONtqziGR8gHcoz+p/OvoeF6qXEeGjH+b/M7KNapyqDPNPix+1feeD/ANoF/h7471vWrXwtoXlFLXw5F++ud8MczGQ9/mkIHsBWFqlh4P8AiV4X8VfGb7ZqtjYWd1vsNIudU/eSR/8APOP/AKaV2v7VPwAs/ifpkviG1ijtb+3CiLUohiSPAH+sH/LSP2r5w8B/Dn4heDfFCQ+I/hXrPiC/kuv9Ftv3klpJ/wB+6+gzWp/wqV4f35f+lM5nLnqNebOwk1Pw3f8Ahvyfhz+zT4g+3/8AQX1vzJ44/wDpp/zzrg/HEXiSxkf+3vHj6jc+V+9sYv8AV2//ALTr1rx3H8YNQt/+LoeNk8AaJcfJL4f026/1kf8A1zryLxRf+D/DmqTab4Cs/t1h5X/ISvf+Pi4kryK07VPfH+6+A8u1C6Etw/Pz1Tkim8z975kiVpXssMt5MM/PUMn+i/6395XDMzpwsZskXlUyOUVNeSiWNIfJo+webHXh42B7mBnUGSXU3l0+3upqmj/0T/ljvqYxeb/yxrzuc9xQZWjl/wBI/e1ZjihqtJFD5n+p+ei5l/55TbKOcJwLOoapDax/7dVtHuobq886/hke2rN1C648nyfMeSt7wtFFFZ/vdjw3H7i6jl/5Z0ez9oQsWqZ23h+6s7Czm/uR/wDTWvOvEH2OXXLma1h/1ku+ti3xo14nk/8AHzb3X/XSOStvVPBGm+LdPh8VeCfk/wCWGs20n/LnJ/z0/wCudRhMFVw+J5ztzHO/reFhROM0vH2hPOzvrrbyGaHT4YZfkeSuh8N/C/w3azwzReXff8/VzLL/AOi46uePNBs4tL/tLS7OREjir6A+SmeY6xdTfaPJP/LP/W1c8P6NNfyJ/wAtEqtbWvm3jzSw/JXbeC7WbVNZttH0uFN8kuz95Sp09S4VD7t/4Iz/AAM03xR+0BpWpf2Z/ofh+1/tG6lli/1kn/LvX7Bx4/4HXx5/wR7+AV58L/gHN481Sz2XniS6/debF/y7x19hxfcrPFP95yGlOHszwb/gnF/yRDVf+xrn/wDSa2r6X8Dy/wDFU2Hm/P8A6fH/AOjK+Zf+Cc3/ACRHVf8Asap//Sa2r6X8Dy58Uab/ANf8dezxX/yUWJ/xfoi4fCjivi6PL/4LBfCfP/QgXH/orWK9D/4KgSbv2GPHA/7Bn/pztK85+MBjl/4LAfCvG7B8BXGfX/U6xXoH/BTpSn7DXjhf+wZ/6c7SvR/5nGT/AOGl/wCnZC6SPQP2WZcfst/DUf8AUgaN/wCkMNfnd+15qn9vfteeNrzzn/d6z5H/AH7jjjr9DP2W5N/7Lvw2X5+PAOj9P+vKGvzG+IGszeKPjR4n8S+d5iXniO8n83/t4kr4XPZ+zxVX/FL82ezk0P8AaEGsXU0Vn/2y/wBZXl3juWa6t3gH35K9I8S3R/s7yZpq8l8US+bqiQ/PP5f/ACyir57D64k+nxE/Z0ec+qP+Cb/wM/t6KHxJrMOoyPZ6zGlhbf8ALO4k/wCelfrJ4R0fUdK0r/hF9UineGKLbLe3N1+8uJH/AOudfFH/AATu+H3m/DjTfOmntNS+wRz/AGaP/ln+8r7Xt/Do8UaXDe+JtBgS+j+e1iuJZJPL/wB+vtOT2dJH51iK3tKxNLJoXgSJftuv2Ok6dGP3Mc0sab/+ByVF4il0O2WLx3fa9d/YbK3877NbyF45P7kmyPrSw3/9l2cNz4xu9Pjv55cbI4uv/TOuUjk17R/FF/r3ij4m30+kW432tlJ9nSP/ALafu6iEOc5zQiufhz8evDc0N9oN8Us5PuX9hJHIh/2PMFWr3WfDmhanbeELT4T6jPZ2/wAkVzbaP5lvBWDrep6D4o8P2x8L/EeeB4JdkdzFrOzzPMk/1cnl1R1z4hQWGup4Jl16Tzo4vI/d6pJJJ/20/wCmlachmfmr/wAHDPwf0PRfiX4W+JukeGHsl1SOWyupfJ8uOeSPy/njr819L0aztdUSb/Wf9Mq/XH/grh+zdZ6z+y3r3j3RtfvtRv8Aw/qlvdWttc3VxP8AZ4/+Wkf7yvx/t/FEP2h/33lvHXDmOFvSPqMqxdqXIX/iB8eNe+HF5baPoN55Hmf6q2/56VzeoftBax4js7mHXtSkkv8Ayv8Aj2j/AOWkddD4X+Gln8QfFH/CSX829LeX915te2eD9L8K+F5IZv7NgR7f/VSeVXj0/ZYc9iFCriD5I8P6pr11qH226s5I4fN/1UvyVN498R6l4juPJ0bw3J+7/wCWsfmSSSV9sXH7SPw38Lxzfb7O11H/AKZVg3n7WnhX7G8MXhuCx8z/AFUsUsaV1Tr0/Zc5v/ZdKf2z4h/4uRYR+ddeA9cg/wCmn9lyU/S/iN480vVIbPyZJPM/5ZXMXl+XX1FcfEvQdeuPOx57yVg/ED4feG7/AEN9X/s2DZ9//ppXD7alUIngfqfwTPLvGmoQ6zs82aul+CBWw1qxiX7z3UYP7zsWFcXqnkxSPDFv/d10HwkvZU8YaXbHvqMA/wDIi17OV06dPH0v8UfzRw16tSpTcfI7/wCIwRfHl9JMu5QIsD0/drXOXl1DF/uVs/E69W3+IV+rxZH7rn/tklY0fnXX/HrZySP/ANMq9PP4uefYtf8AT2p/6Uzio1VTox9EY95dTfaP9Fh8xKZced8g8mSrmqRTWo8mWHy/Mlo/feXjyfnrwfZVh+1VT4DKeI+Z+6mk30yOWW1ieE/PVy84l4h+es3VD9lt3mrShoazftKR9h/sTmb/AIVXpvm/fkurj/0okr7J+D8v/EwsP3O/97XxV+w/qk1/8M7Dzf3iR3Vx5X/TP95X2x8G/I/tiz/1e/zaIT/enm1z7V8D2sP9hwiL/nlXLftV/wDJtPxC/wCxH1b/ANI5a7LwPa58Pw+bs31yf7WMZH7NfxDI+6PA2rf+kcte5lv+/Uv8UfzR5vwH4l/AG3Dfth6oy/ek0CZP/JaOvk6eKH/hIH/ff8tf3tfVHwbvxp37ZEiLs3T6Fcdf+vXFfLt54b8SRa5eTf2PfbJLqR/+PWT95W/FUP8AhXxP/Xyf/pTPfwc7UqZ23g/VJtPkT998lex+A/G/nSeT/HXg+n2t7a2fky74/wDrrXZ+D7+aHZ++dH/661+f14H0OFn7SqfSGh+I/N5lFSa9fySahpkjSZ8ufIPp8y/4V554b16aW3/1z763bXUJJ8SNJnyzkH0/zivb4L/5KOn/AIK3/pmoXmULYGXrH/0qJ2Ou3/2qPp5leXfEjyfsc37munkv5pY3/ffJXK+JIvtVu/76vlsJud1eHJTPNPgXF4wsP2iNK0fwlsnubzzP9Gk/5eP3fmeX/wCQ6/QX4f61Nqll/pUzx+X8nlSV8H/Cu/8A+EN/aE8JeNv+gX4jt3ll/wCmfmeXJ/5Dr9Ef2gPBv/CufiJZ+KtGs/8AiVax8l1/0zuK+1wk/aUj85zzD/veczdUv/vw2s3z1x/iD/ntdBK7DULqyFm+pY/8hV574oi/t7ZP5zxpH/zyq5nzsCbTtZml/cy1safYWd1v8qH564z7fDbSeT53z/8ALKtjwXfanFrDzXUyeTJ/5DrnOg0tU0C8it3hlh+SuD8QaXNFJ+6idK91OjQ69p/7qb5P+etcZ4k+H0NhHc3h8yRPvy1rCFT4xzmeJ6hazTR+dJ9+OmaPffZXfP366HxJoJMmIof9vza5u8sJ4pH8r5KwqEUzrdD1793m/wBlWfEFpDrOnvDEU3yf6qvOv7Zm8t4ZoZE/66U8+Lf7GjTiSRP+mdUdRD4s+Ev2qN5opo/Org7ybxJ4cvPJuvuV6XJ48hv7fyZT8lYniC/0e/t/Jv8A/tlQaU/ZfbMrwf8AEb+y9Uhmv7yf/rlXofifV/D3iK3j1ewgjN0G2ef/ABtGQSQfxArx7xJ4chtf+JlYXlaPw08RXd3q8uh3E+8JamUfgyj/ANmr3eEZ34lwq/vf5kT/AIpseP216202O80VlURNvlL9Pz7/AO5361474gtdSsPs01r4k1HSrO4l8iWTTZfL+z/9M69W8U6zdw6lLYFY5rXeqzIseX5UHYP9s5yPY15B4ov5rWSbR5T59neS/wDLWvpc4qezziv/AI5f+lM4KkIOb5dytrH/AAy7a2b6b4D+FfxD+IWtx/8AH/fXMXkQeZ/00jtq8W8Wy+JBZ3mg+KNH07Rkt5fPi037L9lnj/8Aan/LSvpP4d+Mvj9deA38E/8ADV/hX4eaPp8uy1/e+Rd3kf8Az0/d18x/HT/hFdL8UTf2N8VJ/GN5/wAv+r3NrInmf9c/M/eSV5leHtPfIhX5HyHkt5Kftjw/6t6hkl8r/XTVfk8mW4x53z1m6pFXDOZuoVPjH3Ev3BRp9z9lkfzfv1Tjlm/4HT8fapM+dXnYqnzndh5/vDVjm82R+E2Uy4x/yyqG4tZtLt0ml+5JVOS/mljx53yV4dSn+9Po6Fel9sfqF1DF1+/VO4v/AJKhuJfN3+lVpJZqunTMK9fsXNLsLy7uMeTJJXbaP5N1++lh8yzki2apL5X/AB71zfhO2mlk8n7k0n+q/wCmkld/ZxTXV4+sWmm/6TH8mvaRJ/y8R13U4HlV9zNj0b/SP7Hlm2Xlv+/sLnyv+PyOuz0O6s47dPElrC8cNxa/Yteil/5d5P8AlncViW+l6bYXH+gal5+myS/8Su+8395Zyf8APOtX/iZXW/7dN5F5/qLryv8Al8/6aV1hP3KRt6faiwkcyzeZ5f8Ay1/56VzHxJ8RzRW/9mxXj/vP9bFFLW3H/wASvS3ml+5HXmmsap9v1CaaWb55K6Dlh7UNLsPtVx+93p5n/POvqX9gf9nOH4tfHTw34JtLKT/iYS/6Vc/884/9ZJ/5Dr5y8GRTX+oQ2UQkneSXZFFFF5kklftD/wAEm/2I/EnwH8D/APC5vi1Zva+J9ctdml6Rc/6zS7P/AKaf9NJKrSnS5w9g5n2T4b0vTfDmj22g6NZpBZ2drHBaxRf8s4461YvuVTh/1X/bWpv9bHmXzK8qp+8qneeE/wDBOb/kiOq/9jVP/wCk1tX0v4Hmx4os/wDr6jr5o/4Jyy+V8E9UP/U1T/8ApNbV9KeCx5viSz/6+o//AEZX0HFn/JR4n/F+iMo/CcP8WHjP/BX74VNP0HgO43/9+dYr0H/gp55f/DD/AI12s7HGm8n/ALCdrXn3xcf/AI2/fCtv+pDuP/ROsV3v/BTcbv2IvG8nr/Zv/pyta9SX/I4yf/DS/wDTsgj8LO1/Z8vzYfsheArzOz7P8MtPfP8A3Doa/Kuzuvtdx9s+/wCZLvr9QPhTeCy/YX8MXIPzRfCSzcf+Cpa/L/R4v9D/AHP7yvhM99/FVf8AFL8z6LIfjmP8YapD9g/dVzHw20bUvGPxJsNB0+zed7i6/wBZFLs8upvHF/5Unk+dsr1f/gnl/wAIr4X1fXvid48mtEtrOWOG1ivZf+Pj/rn/ANNK83LaH+0nfnFf91yH3P8ABv4oaP8ADnwnpug/D74e6xrmtx2v737N+7jj/wCuklauuftN/H7wR4pTUr/4AyPqWqfJFFc+LZP9X/1z/wBXHXQ/AeKbXvC7+FPEfiR/CWq6hLHdaNZabLskjt5P9XHJ/wBNK7/UvEln4Unl8FDx5PqN5HYSJFem5t3uPM/+OV9dD2Z8PM8wT9p/4ZeI7m3vf2i/2cbixv0k8gXvk/box/seZXuPhiPw54+sLm8u9I0w6RLF5f2K90rZJJH/ANNPMkrhvCtlp3jDw2+neKPHVvJrN3cmG2sr2wt8xyfSuI1z9kb4hX+oar4wuvFVpPbR/PFpskX+s/56eZWfsyJzPX/EfwX1KbR7OX4TeL7HRohJvl+zaNbyR3H/AE0re0rUrPQtTh8KXHima+vDYcyS+WjySf8APT/rpXzJ4f8A2lviF8NdL03QfBvhXSpLDzf3v2mK4/dyf89KPiz+3L+zf8L4pvFHiiLQLTW/svnReVpnn3H2z/npsrqoYTFYj3EclfEUqS52e1+LNB8IaN4T8QD44/E2a60m8tbi1NtfS2//AB7vH/1z/wCPiv5tP2uPhfZ/Af8AaE8Q/D3Rp/8AiVR3Xn6NJ5v+ss7j95HX6EfGj/gt94qv/tnhvS/hXoGo20kvmWtze2EifvP+enl+ZX57/tsfF/xJ+0Prk3xa8R6PY2OsfZY4IrbSIvLt4446jMcqxuDw3PWN8nzKli6vuFP4Z+I/7MvEmtZvM8z/AFsUVekeIIvEms6X9s0abZ/0y82vk7wv8S/sFx++m8t69v8AB/xpmh0v91qUEkP/AC1r46tCo2ff4Wv+6MTxZ8KvG1/cP/ZdnPv/AOecX7ytLT/2ZPi0NLSa/wBH8j+P97dV0ln+1z4b0aR5vscEbx/8sopayvGH/BROzv7N7OOzTf8A9cqOerU9w3pwp/HMp+E/BF5Yaj52qXfkJb/62OStjxB4y03VLP7JaTfJXjmr/tQabr1xNNdQyQfvf3v/AE0rnr343ab5nnfbPk/55VFOhVhVM8Xi6XsjpPiDdQ2EnnRffkrR+CervbeMNHe62Yn1S3jXHqZFH9a84j16bxP9v17yZPs1vFv/AHtavwt8QPffEjwulqNit4lsQw9vtCV99w1l1OeNhUq91+Z8JnGY1IWp0u6PR/2h/iNrmkfGDU9D0qcIIBBnbHk8wRt/Wsvwv431KL97daxPG/8A0yrlv2qtR1aL9pPxFBpv7tl+yYl/7c4TUfhOIRW6TareefNX6JXy/DTzGtJw1c5fmz8+zXFYyOJqLnsk3b0uex6X8UIfL8mXR3vrb/n2uYq3rPXvB1/H/pXw9gg/69rqSOvOtH1SHy/3Vatvr3m/uPO2PXprIsBi179M+YqZzmmE9+Fc7CPwR4D8SXn/AC/Wn/b1XJfEj4fXnhyOaHS5vt1tJF/rbb/lnWxpeqTRRvN/HV/w/fzRfabz78Mn7uWKT/lpHXl5jwXls6f7k9jJuPcyoVf9p9+B7f8AsJ2EMPwj03+/9quPN/7+V9pfB+I/2pZ+V9/zY6+PP2V7XTdL8F21noWpPPDHfyP5f/LSPzJP9XX178Fz5uoWE0v/ACzr8fxuDqZfjpwmfrWHx2FzHC+2on3V4L40OHM3z1yn7VJ2/sz/ABEi+TjwLq/T/rylrf8Ah/L/AMSO2mE3z+VXN/tVyZ/Zu+IY/wCpG1b/ANI5a9DLf99pf4o/miHsfhDoXijRPCX7XK6z4h1m2srVNKkSSW7uvLTm2zXiNn8Rvi1rt5eTS69fXdh9q2f8fXmRx1q/tVzw/wDCx9RtJf4ltz/5DSub+E/jy88JaHrfhuwhfZrHl/vfN/1ddHFXs55viL/zz/8ASmexgp/uUbEeszdJfv8A/LXza29L1T/ptXHyXU32jzpYa6rSrDzI4byX/lp/qq+Gqfzn0GHn7M9O8L3/ANyE13Wkt5kE3y7Mgfh1rzPwufKuEHnV6NokmbW4lP8AcB/Q163Bv/JRU/8ABW/9M1DTMZ3wT9Y/+lIsed/o1Y+sS/I8P8daUn73p+7rH1DBkf8Ac18jTqWPR/iHE+KIfK/ff6t/v1+stxo1n+0F+zfps3neXNrGg2d7YXP/ADzuPs/+sr8mvFEQlk8nzq/U3/gnn4tHi39kPwZN/Hp9hJp0v/bvJ5dfTYGa9lznyOcU/aVD5p8F69rFrqGpeG/FF48dzZy+RLYyf8s6oeJPEc2l3H+gQpIklel/8FGPgtrHhzVLb48eDYXjS4Pka9FF/wCQ5K+Y7f4gzS26fa5vnr1amtL3D5KvQ9h8B6Eb+G/k84ffqbT/ABRDLceVL5m+ue0vVYZYEm875JKfJfQxXDw2s0e+sKdM5JzqnvHw38UWel6X5Mok2eb+9rtriws9e0tLywh/cyf89a+ePAfii8hvE+3zfJ/y1r3Xwf4ohuoEhtZvkq+SrTCnUOA+LHgO8+xpNpkMaPHL+9/6515Rrmn+V/rf9dX1pqGlxXVu/m/8tK8T+Mnw0+wf8Ti1+5/zziqJmlQ8NuLr7VcTWcX/ACzrE1i6mtLf/St9dnqlrDEHz9+uYvLCExzTX/8Ayz/1tZGlOucreXV7Nvm0u8rH1jVJvkilvK6e48m6jf7B5aJ/z0rntUsZrD/W/vKmobQmZUmqalLH5IvJNldF8J5Gl8SSu0WP9Af5v+BpWT/rY+IfLet34YpKniGUGPCfYmwffele1whO/E2F/wAS/U2Xwsn8fw6hZ3Vzcy6f5dtdFV88Di4QKAU3dnBzj2xXj/jTXvtW+8/176fLvl/+OV2Xxb13VtK8Qa/N4A1me6ubdYH8QeGrok+cvkpsuLcHkALtVtnO4GvC/GHi3/iYQ6lazeZDqHyeVX0OdqpPNsR/jn/6UzinyWfLvc29D8R6PF4seaX4ET+NPtEX+i2UV1cfu/8AwGrkv2gP+Ek1nZ5vwHg8D2EfzxReVJH5n/bST95JTPh/4j1/RrxNB0b4tSeEYY5dl1e+bJ/7T/eVg/Gy/h1/VU/4uT4j8cXkcX7q+uYpEjj/AO/lePOv9g86dCr7X3zyK4xFcP5vyPTLi6huo+v/AC1qzqlrDdf8fU0+/wD55eVUP2D/AEfzvOSBP+edY+3onoqhVsVvK+/maSmR/vf9h6m/0Py/3V5vqazlmP7n7Z5Cf9cq51aoddOn7Omdz8F7DTPFsdz8PfFv7v8Atjy/7LuZIv8Aj3uP+WclY/iT4LeKtBvJtNl02SO5t5dl1HXf2/7S3hXVPC+j+G/FvgN0udDi2Wvimx/d3Enl/wDPSOuz8B/tVfDf4jSQ6R8afDdr53+oi8SW37i4j/6aSf8APSt54TC1C51KsPgPl3XNG1jRpPIutNkj/wC2VZv+tkQV7x8UfFvw91TUNS0GLXoLuzt7qT7BcxWv/HxH/wA9K8rt7DTdZ1zyfC8PmPJL+6j/AOelcP1X2dU0hUq1Eavhuws/saWeqeZBDcS/ur6KL/VyV2elx6xLeJDfzJa+IbOL/RbmX/V6hH/10qho+jf2Xob3nk/brD/mKabJL+8t/wDppVmOKzis/wCzfP8AtUP/AC6yyS/6Rb1dOmEya3/0qSa8isvsn9of8f8Apsn/AD0/56Vf0/RpppE+y/8AfqpvD9tD/wAtYa+gf2I/2brz9of46ab4OELyabZ/6br0v/PO3j/+OV1QhVtcx5D6f/YT/wCCX/w38efAv/hMP2lvDf8AaVz4ki32Fj5uz7PZ/wDPT/rpJXYSf8EEP2G7/UHvIv8AhLrWH7/2a216Py4//IfmV9jaXa2dhZppthBGkNvFGkUf/POOtiP91HjFcU69U60eIfs5/wDBOH9kD9mTUIde+F/wltP7Yt/9Vrer/wCl3Ef/AFzkk/1de/R96rp1/CrEc1Yc9Wfxk8/tCzby/cz9+p06/hVLzfcVNbyzS/uaRJ4b/wAE7P8Akimqf9jTP/6TW1fTPgfEvizTYpfufb46+Zf+Cdox8FtUk9PFM3/pNbV9KeC/+RosP+vuOvoeLP8Ako8T/i/RFQ+FHFfF5/O/4K8/Cw/9SHcf+itXr0D/AIKbf8mP+N/+4b/6c7WvO/iy/wDxt1+Frf8AUiXH/orV67//AIKYSbv2IfGw/wCwb/6crWvU/wCZxk/+Gl/6dkKHwS/roaXhfUPsH/BPfRrz/nn8HLT/ANNS1+aejy+VZ/66v0D8V60dG/4Jj6fqRn2L/wAKl0yA/wDbSyijr8+f3Pl/uh5iV8Dnv+/Vv8UvzPo8hp88pSOS+IF1/pH/AD0r6B/4Jp/s53fxV+JEPjHXrxLGw8N/6V9pl/5+JP8AV14hb6FN4o8eaP4Vih8ybVNUt7X/AL+SeXX63fCv4D6D8G9UuZ/Ad5aaVolxL5+qW0kUb+ZJ/q4/3klZ5XT/AHnOVn9dU6XIel6rJo3iTw/Z674d8UvBNp+qRxy6lHLH5nlx/wCsrakj8I+LbC/8R/DnUdGe+t5dkV9/ZlvIY5P9+sLXPhr8QbnULaHQfFWlf2PJFvura50uP/0Z/wBNKZH4W1jwv4XSb4c+CfD/ANvuP3GqR/ZZIPMj8z/pnXvqGp8Vzop+Jfgbp3jLRLC88Za7o1jr0l1vsJbKw8tPM/1nl/6z95XDfEj9oLTfgrbaxo/7Wn/CPwaJ9l8+1vfNk/4mEkf+rryX9v8A/bx+Bv7KMlmfFvhTQPEfxCjtZP8AQdN1ST/iX/8APPzK/Jf9pj9sj4zftQeLZvGHxL8Sfa3k/wCPWxtv9Rbx/wDPOOOvqsmyDFZnU9/4D5zO+IsLllLT4z60/bI/4Ki6b4os/wDhD/2ffBNr4S0f7VvutSjut93cf/G6+M/GHxMm8ZXD+beTzzSS/vbmSX/WV57++upEmv5ZJKm/tSzsN/lD55K/UMtybA4Cl7kD8szTPMdmVTSZc1jWYIv3Jh8+b/llWb4osJv+EfeG6m+fyv3tX/Cf+k6w80sG9I/n83zafrnk3UfnRV+ZeI+a0lVhg0fq/hpk+Kp4aeMn9s+TviRYTaBrlz5U3l/88vLrno/iNr1r/wAvj17l8WPBv9qWc3m+XvH/AC1iirwrxB4XmsLjH36/MKFelUP03FU6tP4CHVPiDrF/I83nSVjyeKL2WJ6muNLmqt9g/efva6uSkcM6+KsMt9UvJP8AVTPW9p+l3ktuk119+odD0aE3KV0knkyf6qH5PuVcJqmZ0/a1DpPD+vWei+G5tNuofnuItn7v/lpUPwS1CW2+M3ha0PR/E9gPzuI657ULr93/AK756vfA+7Nz8cvB5H3f+Ep0/H/gTHX2nD1e+Ih6r8z5/N4fvI+qO+/az1iWy/aY8Swr0Bs//SOCszQ9UvLqOEffqx+2NDGf2lPE8jdSLP8A9I4K5/w9532dPKO9K+/lWtmNVf35fmz47N6N6k35v8z0Kzuppo/9dsres9U83ZzXH6XdQylP79b1v/z2/eb6+gw9fQ+BxdA7bT9em+x/uqv+C9em/wCe3yVyWl38vzw/vK0vDd1MN/Pmfx13Q/eHjzh7Nnvfwv8AFH/CB+JIdS81/sFx/wAf8cX/AC0r7n+Bl/Z6p9gvYpvMs7jy/Kkjr8zfD/iiaKTybrzI0r6x/Yb+NP8AYHiSz8H6zeb7O4lj+y+bL/q6+E4tyBYql7aj8Z9vwlxM8Divq0/gP1o+Hcvm+H4ZjXN/tU+b/wAM1+P84/5EbVs/+ActdD8P7qH/AIR+2+yzf6uKuc/anlMn7N3xC8zr/wAIPq2P/AOWvzPA06yx1K/80fzR+yw9m486P5wf2v8Azo/ipeCL7j28DS/giCvOvBcs0VxXpv7XcKy/E+4V4sg20fP/AGyWvLPC8Xlaw/2X79XxVO2cYn/r5P8A9KZ6+Bp89BHYXEs3mJ5v7uvVLfRpovAfhvWPO/4/LWT93/1zuPLryiS6mEn72H5P+ete2W8sN18F/A3/AEz/ALQ/9KK+VPUXtS/4Xtf3fnV3ug7P7Ol2fe8v5vyNch4P/wCPeP6V29rbyQWk6lMEx8e/Brs4R/5Ken/grf8ApmodeP8A9yfrH/0qJQ83yv8AW/crH1y6hlj4mj3/APPKrOsXX2SD96a4zxJrMMUn7qavjz1Ch4sl/wCm3mPX6C/8Ed/GX9vfs9ax4Vlm8x9D8USJ5cX/ADzuI/Mr839Qv5rpH/fV9pf8EV/GXleNPG3gPz4995YWd7F/2zkkj/8Aale9gf4R85mmx95/ETwbo/xG8F6l4J8SWfn2GqWskEsctfkv4p8Of8K58YX+j3V5I72d1sl/6aV+uPiy/wD7L8L6leH/AJd7C4k/8h1+Xf7QFhNr14/iqWyjg/5+vKr2oT9nSPlcVT/dmbpevTXdun2D7lXI/J+2eddQyb5P+Wvm1weh6z5UiQxV0+n/AGy6vEn+2f8AbOohXPOnQOt0+/8A+WMUz769C+F/i6bRjDDqGpSSf89ZZa8ut7oRb/K/ePU2l6zeRbPt/wB//lr5Vb+09ocnsPZn2T4O1m0163Sb/Wf9ta3rzwvZ+I9P+xxQRyJXzl8O/i1eaDHDZWMPmJ/zzr6N8Gaz/bVvbXlrsj8z/WxVmdJ8/fHT4I3mg3D6npkPmJJ/yyrxO8i82N/Nh3pX6Ka54I8Na9Ik2oQx+dHFXi3xU/ZBh8R3FzeeF4UsX/5ZS/8ALOSon+8Mz45vLCGW3xawpHXN6hYTD99dQ+Z5deweOPhB428GyXNndaPPOlv/AK25tovMjrzrWJZtKkeG6hk31y1Dogc3JosMuyatbwLZPba/K4+4bVgPruWsnVNe021/5Y/P/wA8qk+GHiY6x4vubPbsCWLP5e/OPnQf1r2+EYW4nwr/AL6/Jm8XzK5zX7R9npFrer4ks3xqVnIjvLnYYV2jALdkfofcV8x/EzXrOXS3hhmS0+0XUbxRS19aeO/G3iv4a+Ob7xl4e03RfFOmpbeXr3hy8tfMnt18pcFfYjDfjXyd+0RYeD7/AFiHxh8OZrtNKvJfP+w33+st/wDpnX0XEj5Mwrv+/P8A9KZjhp/vZLzZzely6zLrj6xYeFf7V8z/AFtjVDxT4j83UJtN1S8fw55f/LtY2vmVlaf4j+1RzQy6/dWP/TO2/wCWlFvf6la3PnWGs6VI/wDz1uZfM8uvjzesTRjTIrf7HpfxIggh+/L/AGlFWPcywxXnk6NrH27/AJ63Mlr5cdaV5rPnXD2d1Z+Fbt5Iv3tz9lqn5umw27w/2x57x/8ALjZWv/LT/rpUT/eFwn+65CneRTShP7U17z5v+ef2WqEn7qXmbZ/HXQ/2pqV3p7/2pr0Fp/042Nr/AKz/AK6VjyWHmxed50//AG1iq6Yx9ldTX9v5N/Nd3af8844qZJoOpWtwn/EtkSa4/wBVbVNp10f+WUMkj/8ATOXZWrZxfZbf7X9stbRJP9b/AB3FddOpVNDNj8MTXUc3lb53t/nv5P8AnnVPRvsdrqifZfMgfzf3UstdJrGqQmz+x2EL/Zv+faKX/Wf9NJKyvCel/b7jyYoY7tJP9bFL+7q/+Xhyc9U7y4upr+SHUpYbrTb+OL97c23+ovI6m0u1vLD9z5KSQyVW8N2sNhJ5P2Py0/5ZR+b5ldHVFwn9g0PA9reeI7yHQdLmke5vLqOCKxjtf9ZJX7AfsF/stab+zd8L4YdU/wCRh1j9/rNz/wC06+Tv+CU/7G8OseILb9pzx5oPlw2fmf8ACLxSf8vEn/LS4r9HbeKlXxXs6XJA6uT2ZsW/k/8A66sxyms2PEQzVy3lmljrzCjT/wCWdOg71Qt5ZunnfJVlJf8AnlQBZj6Pj79TRymq0fnfP/Spo+9ZgeI/8E8nkX4MamF6f8JRN/6TW1fSXw//AHvjCw80/wDL1XzX/wAE9v8AkjGp/wDY0Tf+k1tX0p4DzL4tsB/09R19LxZ/yUeJ/wAX6IiELxTOF+KLyH/grX8LWPUeBbjH/frV69C/4KX/APJkvjX/ALhv/pyta86+Kbyf8PafhexO8/8ACDXH/orVq73/AIKSSbv2KfGw27f+Qbx/3ErWvU/5nGT/AOGl/wCnZB0kYHjvRr/xZ/wT78A+CILvC6p4U0D7XJ/zzgjsopP/AGnXyT8YPhVpvw+8P/8ACSaNeO9tb3UcF19p/wBZ+8/1dfSviD43+FPgH+z78IPEnjtbt9J1DwLpkFzDbQedv8uyik6dvpXwz+0R49h+Pv7Smsal8NPFWqwfD24ijn+xalFJHcXFx+7/AHfl/wDPPzK+Lzb2VTHVYT/ml+bPpcjp1KdLnOq+Df2Pwl+0x4J1jxv/AKLZ2+vRz+Zc/u/9X+8r9ZfhvF/wnngu2vLq906e51T/AEq1025tf3kcdfjz8P7nTbD4waDqfiiaCeGO/wD+PnW/39vH+7/1knmV+qHg/wDah/ZM0bwa/in4afHD4dazf6XaZz/akaTyRxx/vKxypValPQy4mo0krxPS9KHxCl0e/wBN+IGm6H5McvkWsdtFInmRx/6uT/WV8Jf8FBP+Cvll8A5NY+A/7Pvkf2xZ3Wz+2rbVJJPs8n/LT/tpXi3/AAUE/wCCwOseLZH+GPwM1KSxh0/9xf6vZapJJ5kn/TOvzZ8aeMry/uJpr+9eeaT55ZJf9ZJX6nkXD/t/3uJPyLOOIqdD9zRNj4gfF/XviD4svNT1nWH1G8uLrff3NzL5kkkn/XSqf2/yo0/fPXDafdQ2Ej3nnfPV/S/Ednr0nk+fX6LTVLDrkpn55j4PE1ednVXGvTXWz7CP+2lULiW8luEvPPplxdQ2Fn9jiMcaVD/y5/upo9n/AF1rWdc86nhP3p1vw71SeK3v/wB9/wA86uahqn7vn9a574by/b5JrMTR/wB+tLxPa/YI/wB0P9ZX89cb+1/tic5n9LcDzpU8jhAx/FlrDf2f7qvEPihF9guExDXrWqap+78n95HXm/jiKGUed9+vh6e59jXhc8rku/37w/wU+OLzf33nJVzWLCGX99F+8es2z0u88/8Ae16POeVP94Wbe6/eeT/q60ox5Uf+uqnLawxf79MuLr7n860JLN5L/o7iWaOtb4AP5fxw8IL6+KNP/wDSmOuL1i/839z5zxv/ANMq7D9nKPHxn8If9jPp/wD6Ux19pw7B/WYeq/NHy2cOlOa9T0P9saLzf2kvEI97P/0jgrmtDihta6/9r7P/AA0V4g8r7+bT/wBI4a5DSvOlj/eiv0icP+FGs/78vzZ8Pmdf/aJw83+ZvaWPKuPOhrp9P/eyJ53365XS4vKl/wBdXVafFDEiTS17WHPj8Ub1n/qvxqtHrM1hcOIqrW8ol7fJWbeS3kOoJ9z/AFtelTqHh1P3h6LZ3/2qNJovuV23w38ZzaNqkMMU3z15Xp9/+7/dVq6Xqk1rJ50v3K2aVVWOGcKtKr7SB+1v/BNv9quz+KHhOH4V+KNS/wCJ9p8X+iyyf8vEde6ftT/8m3fEH/sR9W/9I5a/E39m/wDaH1L4aeOLDxJo2pSQPZ3Ub/8AXSv1u1b48eG/j7+xL438YaNPH53/AAhOqpqltHL/AKu4+xy1+U8Q5NVwGaUq1H4OaP5o/ZOE88pY6j7GpP3z8BP2x52T4oXayNtAs4fLPvsFeb/DO1+366kMR/1nyV2/7cTzL8WXH8H2GL/0AV5t8M9evNL1xLy1n8ua3+e1l/5518VxX/yOcT/18n/6Uz9Qy+taCR67qnhy8td8N1DXf2d19l+FXhWzlheNI/tnlSf9vFcx4f8AEfxgutLTWLrQbuezk/5eZbXzPMjr07WNG839m/wl4jutN8tI9Z1BP+ufmXEn/wAbr5z2DqUj0Kdf977hZ8B/6Vbp/fjrukdDbkt0AO76VxfwyhhurNPsu+u2dJFiYMu3KnFb8G06tPieHP8AyVv/AEzUPTzP/cn6x/8ASkcT40u7Py38qb5K8l8Ya9/pHkxV6R8QDefaPJi/eJXnWsaDNqlwk0VfN0/3h2V5lazm82z/ANuSvp//AIJN6pNoP7XFnZxfu/7U0G8g/wDan/tOvnW38JXmlx+ddQ70kr2//gnPdzWv7ZHhKGL/AKfP/SOSvZwLqfAfOZj7T2R+onxcv4bD4b63NdTbHksJEii/56fu6+BvGFrDf6Xc6bfwxyfaItksdfRvx88eTazeXmjy3k8aWfmQV8weINe+wfbJtU8uNI/+Wtd3tD5qe54h4ktf7B1l7Ow+5U2h6prE2oJeRXmzy/8AWxS1N4g1n/hI7ib7LD8nm/62Ksf/AEz5IYvkfzf9bWcK4q3s7Hf6fqnmyP8A+0qs291eap/rYdiViaXLNaxpN8kk3lf62rlvf3l1bvDF5kb1sckzpNL17/hF9n+mSf8AXT79e0/B/wCMktreWc11r8+yOX/V/wDPSvmz99YWf+lTef8A9NKuWfii8sNn9l3v/bSqoGP7w/TLwn8QfDevW8MEUySXPlb/AN1Wl5uLl/N/1NfD3wf/AGhofC8kNnf6k/8Ac/v19LeA/irZ6pcedLqSSfaIv+etbQM5zO//AOEX8N39xNeeT88n+trgPGH7NPgnxReXP9qeG7F4ZP8AVV3nh/XrPWt/2A+X5f8Ara1be1mi/wBb+8qPZ/vSPaM+S/FH7BXwx1nVHhi8KyWs0f8Ay1j+SvLfiV+yrpHwPEfi7TVlU3En2Mq75GGBf/2nX6E+V5sb+bD8lfPv7fH9jD4W6fHa+V9pTxHCH2ddvkXH9cV9DwtC3EeGf941ozvI/Jj9obxK3hr9ovWNX8Oan9m1K3FsJYpZMJOPs0RH6ED8K8u+Mmswaz4f/wCJXDHawyfP5UUX+rkrvP2w/DiP8Y9b8Q2n/Hwots/hbRCvDdc1me6s3827k3/88q9HiOlfNK7/AL8v/SmKlU/fSh5s564uvssfkyzI+P8AnrRpcU0sn+i2drP/ANdJaoahdTSyefF9/wD6axVNJFNKE/4lsF1/0ytpfLr4r2fJVPUp/vNCa8hmtdUSeWztP+uX+srVF/DFH+91ixtfM/5ZWVr5dYmoWs1hIn+hyWn/AEzkqzoevXlqX+y3nkeZ/wAtfsu+tqZHIXPsFnaf628dEk/5a/8ALSSmXNpNdb5v+JjOn/TWr/2+a/t/tl3qV1dfZ4v+P65i8uOOOobi1vNZLy2sF3qvl/8ALzcy+XHW8/cOgzbewmtf9b5n/bSr8drDLH+9hn3/APkOs2SLyv3Pkoif9MrqtKztbyUedF/5PXX7unzlQnSM3XL8Wtn5PyVveB7Cb7Gl5FDBJ5n/AC182uY1yKa61RIPO3v/ANcq7nR5fstn5P7j/rnbUQOSudVHa+VZJ/fk/wCedbfhPQRLI811v2VQ8JxWd1p8I+xx/wDbKut0eH7VeJDF+4S3+fzP+mldVOdIzp06ntD9j/2f9Gs9B+EfhjTbGH5I9Bt/9V/1zrvLciWRPKFeRfsj+Lf+Eo/Zz8H6953mPcaNGkv/AF0j/d161HL+7Typq4a8P3p08hpRReV/ramt5aoW4mlk/wCWdWq4zQvRc/8AbSpou1Vrf+lPMv8Az1p0zM0P+WlO/febVa36fwVZ83/vqrNqZ4l/wT6dV+DGqB+n/CTzf+k9tX0j8O/+Rw03/r63181f8E/n2/B3Ux/1M03/AKT29fSXw6/5HDT/APr6r6Hiz/ko8T/i/wAjKELxRxPxWYN/wVl+GDj+LwNcf+itWruv+CkMm79ivxqPfTv/AE5WtcB8U3/42u/DBvTwPP8A+itWruv+Cj8kjfsYeMg0WB/xLuf+4jbV63/M3yf/AA0v/TsivsyPDv2jtA1u+/Zx+CXiNLCDU9Is/BdjBJpcsOPKnk0+L9/vr5R1jwbqWl/ECbWLXxtoej6b+7eKxsrWS+uP/jdfW/7Y9ytp/wAE0vhsFbY1xpOgxg+v+hivzQ+KHx9/suyTQfCXiS0R/NkS6uYvnr47E5bicwz2XJ/NL82evSzzDZXlc51T0X40ftLWfg28eHRte1LUtVjikT7de3X+r/65x/6uvmz/AIWD9kjeHw5pv2FJP9b5cu+ST/tpWPrniP8AtmTzrr95+6/1sVYkd1NFJ+6/561+n5Hw7hstpa/Gfj2f8YY7N8TyQ+A0pNUm8t4Zf/RtVo4tRv5Hmv7J44f+WXm/8tKP7Uh/5475qP7e/dv5sL19hTnZHx3JyPnZg6zLZy3/APY/2P8A7aVveF7XQdGt0P2OPf8A8tf+mlcrJfw3/iN7z93+7+SrlxdfZdUtprT9/DJ/rfKrop/vDOv7SodPqF1Df3nnf6tKh82GWTyYvuVT+1wxxvNL+7qtZ6p5sn7ut5mNM6HwHrMPhfxok10I/Jki2eV5tdz44uoZZH/sqGTZ9+vH/Hlr5ul/2xpf/HzH/wA8v+Wleo/s/wDi7w38S9ITw3f+Xa6rHFs/eS/8fFfknHuVVcR/tMD9e4FzWlTpfVpnDXkv71/tXlpXMeKIoZd/m/vK9a+KHwl1jS5JptPs/MSP/W15dqmjXg3wSwyb6/JPZ2P1jdHj+uRXlrevPF9yqH2uXPn+Sm+uz8UeHbyKR8eXsrj7yw+y3nNb05nBOBTBml/fS1W1C/8AJi8n+OrN5L5Ub1lSS+bJ+9r0sJQ+sVThxVf2aCP97/ra9M/Z3iij+MfhPH/QzWH/AKUR15tFF+8Tyq9L/Z+ix8X/AAif+pnsP/SmOv0rIsP7OvD1X5nxGNqe0qr1R3n7Wxj/AOGnNchSXEkn2T/0khrldLim6RQ/PXUftZafLcftM+JrpcYjFn9/p/x5wVy/hOWeWP8AdeZ/rf8AlrX2k/8Afqv+KX5nzGaf7zP1f5nSaPazfJNLA++t6TyYremeE9LvNZuPJsId83/PKtjVPhz48P8Ax9aDd7PN/wCWcXmV2QxFGl8cz5mvhK1X4DHs7+H5/KrNvLqH+1Ic/frbtvBviTRpP9K0e73/APTWKuM8YSzf25Z2Uln88kv+55ldFPF0ThhltWB39nf/AGXZ++8urMmqeV+5x5nmVQ0uLyrP/Sofno0vzpbl5pZvLSvSp1Lnm19PcOz8N6zDa7K+pP2Y/wBp/WPAfhXxL4PF7JNaeJdBvtMuLeSTOHlt3QSfm1fIdnLDFH++mff9K7b4Xaox8SWUTXGFaZQG/vZI4rmxWDpYmFp9GvzOnLav1PMaM4dWl+J5b+3DBs+LPnOjEvYQlNnVgByK8m8ByzS64/2WaOObyv8AlpX1T+2N8JG8e+FJPFvh+3mbUdFjzdwQ9biA85/4BnP418oeB7DyvEH2yL5/+mtfjPGGX1MPmlar/NKT+9tn9DZNmNPFc8P5W19zsfbHwH8JeD9e8NJDYfFS++3+V+9sb6LZH/2zr0jxx4S8n9j9/tV5HPNo/i3f5sX/AC0/ef8A2yuV+CcWr/8ACo9Nh8UfDe1ewkij+yyW0u+Ty/8AnpJXpGhxaDr37D3jmz0aGRLa3urx7Xzf+WckflyV8eetTnV9qeUfCu6Hl+TL9zzf+WteiTxRTyWiL9ySTb+orxT4X6p9l3+V9z/nrXreka01/BbMsPlmF8gfUj/Cr4X/AOSsh/grf+mah9PmD58sb84/+lId4o8Oab5X72H5K5LUPC9n8nlReW//AD1rttU1TzY34rnriX7+PuV8TCp7OqdvszE1C1FhpeZa7b/gm/pk2s/tqaVeH7mn6XqE/wD5L+X/AO1K4DxJfwy2bw/6z/rlXsH/AASj0qa//aU1vUvJ+Sz8JXH/AJEuLevYwM37U8bNf4Z7B+1JrP8AwjnxQ1XTYh/rPLn/APIdfMHxM8R6lLcP5X7uH/lr5VfSf7eGgzWvxE/tKKZ/9M0uN/Kj/wCWnl/u6+P9Y1ma61SaC68yOaP/AJZXNe5Ch7Q+ExU/3vuGbp+vfZdQ/wCWiQ/8ta0vEFreWsiXkXyQyf8ALSsS8i82RPNs/nrp7f8A4nOl/Y/O+fyqPYEc/tB+hy/6H/rv+/tXI/FEMV59i/8AItcZ++sJHhupqv6f4ihtf3MsG+kZTOnuJZtUs5vKPmJ/y1rnry/1iwj8m1hqYeKLyK3fzYdkMn/PKKof+Eo02W3fyoadOmZlaTxHqXmJP/q/+2td54H/AGkdS8OagmJvM/56yyf8s64DVLXQbqBLyKby6wdUurOKn7Qfsz7e+Gf7ZFnFHDefbJJEk+SX/lnXuvgf9pvw3daek11qUcn/AG1r8mpLryrlLzHz1seH/HniTQf+PC8k8n/lrFV+0pGFNVPan612f7Q/gnWrd5v7SRHj/wCWXm14f+2R458M+K/A9pHok0ckh1uOR3j7qIZh/MivijS/2h/FVhI4l+5XWeFPipqHji5bSZk2wxxGYD3BC/8Asxr3+FqylxJhVH+b9DalC0jF+O/7DfiP4k+Fv+F1/DbVPO1O9T/T9GvnUJP5ZMSmFn4ztRcp3OT3r4D+LGg3nhzxBc6ZdWclpc28uy6tpP3flyV+23wksoLj4MaU0vcT/wDpRJXxz/wUs/ZV/wCEo8NzfE7wbpsEmpaf+/v/ACrX95cW/wD9rrzs7zia4gxdGpsqtRL5TZ9JUymE6cKsd2l+R+bPF1/y2k2VNp+lw/OPsfn/APXOWn6hYeVI80X/AJDpkUU11Hj5/wDtnLXBTqe0qnnTw9XDlPWLqa1Hkxeen/bWn6PdfZdn76T95UOuf61P9Zv/AOmtQ/uYo60nP96XTOwj/wCJpGn2rTb6+8v/AFUUnyRx1fkP2/8A4+4bvVbz/llptt8lpbx1zGj6pZ3Xlw3X3P8Ann9qkrpNLurOWP8As3zp44f+fHTYv9Z/10reFQz/AOXhW1i1hijSzli0q1m/55RxefJ/38qn/pkUaQxTRyf9NPKrbvPO0b9zdalY2M3/ACyi02Lz7iof+Jlf/wCt/tWP/pr9l2eZTmH7w5u4+2XWsJLJNJv82uh+33nyaPpcP/LWqdn4cmv9Y8m1mnf/AKay122n6N4b0aT/AEqFL65j/wCXaOX/ANGVnTgSbHguWaw8PpZ+dBO/m/8ALOut+1f2Xp9tD5PmTXkuz/45WD4fj+3W6TXUMCf9M7aLZHVxJZta8U/vZvskNnL5EX/TST/lpXXTp0qZyV69Wm/cP1H/AOCafiOHXv2b7OH7iWeqXEHlxf8ALP8A5aV9J6dFD5dfIv8AwSnv55fhv4n02L93Db69G8UX/XSOvrq386sMX/EOuh79LnmWfK/ef/WqzHLN5f8AqaZb+d8/m1NbxeV+5rk50aFm3/rVm3lMQzVbzfK614t+0h+3D8NvgFqH/CN3UL6rrf35bK2l/wCPf/rpJUcntAPcvN/55fcpl54o0fS43m1TWLSBP+nm62V+bPxR/wCCk/xy8b+dDo2sJ4fs/wDnlpH7uT/v5Xi2sfG7xtrMnna9r13O8n/PS6rqWC56Yvb+zP0R/Ys+J/gbwR8LdQ0zxN4qsbG4l1+WVIbibDlDBAN2PTKkZ9jXt/g/9qr9nvSvFFnNdfGDRo0t5d8v+lV+UHxt8W3/AIe8X2sNrfNCp09XIXrnfIP6Vx8fxQ1L7R5MUsmySvd4qw3PxFiX/e/yKjW5YpH6u/ET9o/4Kaj/AMFEPAPxc0/4h2DeHNL8JTW19qy7/KglMeogIfkzkmWMdD98V7N8V/2j/wBiX41fDbVPhr44+M+nNpOrQLHObWeWOWNldZI5EYR/eR0VxkFSVwwZSQfxw0nxXcN8HNX1xbhi0GoKitG/IBaHv/wI1wsnxQ1eKT91rEkb/wDTWWunN6FSmsDUpu0o0o2a3TU5tNGE8Sqd79T7K/b6tf2Rf2fvg9B4o/Zq+LV/4n8RT6ksRtdXmR7dISCNxWKGNs52/wAWOvFfDHiT9sz47afI02j+HfC8kAXcBNY3O7H4TisD4uePLzXpIbKW8kn+z/8APWWuJkv5pY/+Wb19bleZZyqXPWryl6nyuOzSr7e1OVonZ2X/AAUY+K8V6LHXfCnh2Et0dbS4A/Wc13nh/wDbG8b65Fvj0rRSf9i2m/8AjteCa54O0fxRaZl/dvHXMW//AAlXgi986K7nezjr1aObZnGpapJ2POq43GT/AIdZn1ZdftV/EqHa0eiaIQXxzbTf/HarXX7X3xDt2wNE0T8beb/47XkPhfxRZ+I9LT99H/01q5qEUMu/99Xrwx+Jf2mePPNs1pfFWZ2l1+3B8XFupIrTw74dZUfA3Wk+f/R1Saf+258WLltlx4d8PA/7NrP/APHq8UvJfsGsfupqv2/k/aPOxVxx2IjvIVTN81t7lZnur/tffEZV/wCQJomf+vab/wCO1a079rLx7ONtzo2kM3pDbS/1lrxuOWG66Q1ft4pov3tbfXK38xzzznN1/wAvmeww/tP+PJ5NsekaOw9RBL/8crn/ABp+138Z9Aja50jw74dnRV3FHtJy2PqJhXFafqk0v+q/d1T1yb95+9h8yGrdas4Wc2mZR4jzalvUbPVPhP8At0aX4kuV0j4jaNFp10ektruEbfQMSf1r1XU/H11qOlf2x4B1jSb6I9PNjc/ycV8a+KPh9DrNn9s0bZB/10rmNLv/AIkeCLjztG1i+jSP/nnXx2cxzpzvh8XOC9T7vIuKISo/7RBTZ9T+Nf2kvit4UlZF0XQmA/v2s39Ja5g/tmfGBNxl0Xw0AvT/AEO4/wDj9eC3nxk8bX//AB/w/a3/AOWv2msfVPiD421S3eztfuf8844q+WdXjCFTXGy/D/I+reeZXUp3jRSPZNf/AOCifxq0eWRB4W8MKE6+ZYXJ/lcCshf+CmXx3d9o8JeEf/AC6/8AkmvDtU0bXpYvtuqf8tKoafYTS/6r/lnXoUcz4gpfHiJM8ipj4VPh0PoqH/gpN8cWlWKXwl4WBPXFhc//ACRWlbf8FDPjRIm6Xwv4WH0tbj/4/XzrZWHmyfvf+WdbGlxebJ5P8de3hs1zSW9VnhYnMsTDaTOz8deONe8f6pqPjfxNIrahqcoaUQRbEQBQqIo9FVVUZJJAySTklngv/UJ9r+/WP4o/4lenwwib566HwX5MtvDNL9+uuMpTfM3ds8CUpV5OUt2e6/sl6D/anxIh82DfDHFvlr6xHhzTbC4T7B9ySvmD9nfXv+ENuLnXrr/UyRbPKir6N+H3jyz8W/vbX/ln/wCQ6+KzupWnifcPqcnpqGG989X+E/hLwHf6qkPijQYLuGT5PLlirs/iZ+wp+zr8RvB/9paf4Pgtby3/AOWlt/y0/wC2dcZ8P7rzdUhs5a910/xH5Oh/2PXjwxGLp/bPYhSwjPgz4ifsW6bYSTWel3j2vl/8svKrx/xB+zTr2g3H+gal5j/9NK+9viJD9quLn+/Xj+saN5uqcw+Z5lepheKcywm8zysVwzluM98+PNY8B+KtLvPKv9Nk/d/8tK0fBF3JbePNIhaR1Z9Ut1IPfMi19ceOPBGgS+B7n7VZx7/sv72WWKvlKeKztfjFpdnp/wDqo9btsf8Af1a+84e4iebNQnA+KzLhlZfXp1oT+0vzR6PrHiX7J4/1HTPNA2mLh+nMSH+teCfGT4QTeA/Gia9pdn5em6p+8/df6u3k/wCWkdek/E7Vxpnxc1FoE3MvktIP+2MddH9v03x54X/s3VNkiXH/AD1/5Z10cT5U8fQly7iy7OpZPxNiHU+B1J/+lMv/ALP+mf8AFFWc3hL4kXcF5Ja77q2l/wCelex+D5dSl/Y7+KMOsj/SY5dU+1Sxf89Ps9vXAfCvRvEml+C7bR/FvgOx/s2z/wCPC5tv9ZHHXoXwzis7r9kv4l6bYeY8P2rVP3sn/PP7PHX4HiKdWlV5Jn7xSxCxNKnUgfIvgPXppdkOX/eV7d4K1b7VpFzNu2+VGGz6cN/hXzn4blm0u86eZXtnwuuGPhrW2k3fJaA8/wC7JWvDMJriim1tyVv/AEzUPfrVebK5R84f+lxOnjv5/wDntWJqmqTWuzE3mJJWb/an2q3fypvLqhcX8P2f/X+Y9fCQPdqDNc1T7/8Afr6H/wCCQd//AMX48Ww+d5nmeF//AG4r5U1zVPN3/vvkrqv2U/2qtN/ZQ8YeKviTdQ75v+ENuILW2k/5aXHmRyR17OW+0nV5D5zNYc9I+z/+CrHxz+GPwv8ACej6brWvQQa9JLJPFFH/AMfEdv8A/G6+DLP4oeG/iMnnaNqUc955uyWWKvlf4+fH3x5+0F8SNS+IfxB1h7u/vJd8snm/+Q65Xwn481LwHriaxpd5Jvjl/wBV/wA9K+qnhbUvcPhK8+er7h97aXqn2WT7HqkKb66HT7WG6/0vS5o9kf8Ara8Q+F/xp0H4jaVDeRXknnR/JdW0n+sjkr2zwHqem6Xshv5t9tef8vMX/LP/AK6VxfvTk9oYnjD7J9oea1m8usSz1mGLp5cld/448EWdhA+saXDPOkn/AAOvMbyWGWR4vJ8t6zn7Q66apVKR1tvdfabf97D8kn/POqf/AAhlnEHvNLvJ5Hk/5ZyVlW+qTQx+T9//AK5Vfs9QhsN94PM30QqGenwGbqFhqUv7iWF40rHvLC8ynlQ/9/a6qfxSbqB4bqHYn/TSKn3HiPQZdP8AJtbNN/8Az1llqS/aUjhrz/ReceW9Fnqc2POz5aV0/iCXw39n6x75K577LZn99FDQOAR3U3mP5v7xK774GuW8V3Cj7v8AZz4/7+R153b/AGzDjyY9n/XWu/8AgPOD4sngc4f+zXbZ6DzI/wDGvo+Ev+Skwv8Ai/RmqhaLZ9v/AAZG/wCD2lL7T/8ApRJVXxZo1ndWc0Etn5nmf62KWtX4FwrL8INH2ttb/SOf+3iSrPiS1mikevkeJJ/8ZLjf+v1X/wBLkfd4WF8FS/wx/JH5F/t6fs03nwb8aXOvaNB/xJNQl/dSf885P+edfPdnYeVF51rZySPX7E/tIfBHw38afA9/4P8AEcPyXH/LT/nnJH/q5K/Kb4u/DnX/AIVeOLzwfr0MnnWcv+tii8vzI/8AnpSy7Efu+SZ4uaYWrTqc5574ghmlkQeTJG//ADylqnbxeVs82tLxD5Mtx50UM8b/APTWWqEcQr0Dwecs2csMT/uofnrodD1m8ijeHzpJIZP+XaL5K5XzYZbj/U7Kv2d19lk/56VrTqGf8Q7O38mwj/0Ca0055P8AllbfvJ5P+2lF55/lvDKdVn/6a6lf+X/5DrE0vxRrFhcf6LeeR/1yijqHUNevL+8ee/ned/8Anpcy760/iB/DNKOK8sb3+0pppPs0nyfuq2NLv4ftCQ2umx7P+mf/AC0rno9Z82PMs0n7uuz+GdrZ/aX1L95I8f8Aqq6qBnUqezO/0+/mit0hl+5H/qv+mdVtG+2Ragl7dQ+ZN9qkn/56fvJP/tdTebZfY3m87f8A89fNo8PxfZdUQRf7ksUtdc4e0OX25+hf/BJrUIYtD8Z2f/T1Zv8A9c/3clfZn2/7g+TfXxD/AMEq7WYaf4wvIpv+Wtmksf8A38r7Mtz7/wCsrzcVanVPRh/BN77V6/PT45Zqp2VTSfuv+W3Fc5qeXftgftLQfs+/C+5161hSTWLz9xpdtL/z0/56f9s6/LvxJ4t8SeLdUude168e7vLyXz7q5l/5aSV6j/wUR+N3/Cy/2jNS0e11J/sfhuX+y7W2/wCWf7v/AFkleRWdr9zza1p0zmn/ADkMhml/1X/LSmR6DNKPO+euk07QftUf+iwpWxb6D5UfSu1QqGHtB/7SGltd+OLa5zwukxr/AORZf8a4O30ab/njvr2X4saJ/aniOKX0sFH/AI+/+NYVn4M/56w19DxNTvn2If8AeJkuaViHw/pzzfADXLMx8yaohx/wK3/wrzfUdBmtrd55NkaRxV7tY6N5fw71DT8bPMu1b9Y/8K8k+NEv/COeE5uPLeSuvMIX+p/9eY/+lzOLG/wkeCeONeEuoP8A9M6Zp+qQ/Y/OimTf/wA8qytQ/wBKu3nlm8yseS6msNQ/5abK9rCeysfIV4HbWd/N5b+bDvSrmsRf2pZ/7f8Azzqho915ulocVf8AtQ8vHkV6m5wQn7OqcG9/qXgTWPOtZpPs0n/LM16RoevQ69p/2yKZHf8A6Zy1yviO/wDDgsHs76zrn9A1qLwprCNbGT7NJU03VoVTor01iKR1PivMWoJd9f8AppVnS7qzupEhmqbWPJ1TT4by18uRPv1lafL9luE8oV3U6ntDyvZ+zOt/49dmPuVq2csOqWf72WSOse4l/wBHh8288x/+mVWdLv5opP79ddM5/Z/zjNPuobC8+x+dJV+WL7VG4m+5VPxBbTeWl5FD89TWcsN1bIP9XXVzo4p0ytHqk2jXn2O5vPMtpP8AVeZ/yzo1i1mlt3msIf3NTapoNnfx+dLDJ/11qtpes/ZZP7Nuvuebsrkr06TO2hU+rnPW9hDFef8AEzs32f8ATKr/APxIbX99/H/01irpLz+zvs7/ANyvN/iZ4j02KP7Ho/l7z/rfKrzq9ClT+M9Khi6tSpyQMvxRqR8W+Ik0nRvuSdI466DV/Dmj+F/C6QxfPNJVT4M6FnztduYfmHTzK2viJfw3Rh00Q/7fmVh9UpVKXOa18VUp1uSBwcenzfZ66TwP4XmurhJpYZN9dP4X8JWeoIkPkx10/wDZem+F7N/Kh8utKGBObFY48u8cfutUSGWuh8L6f9q8mC1+5XK66IdU8QTTRTfJ5teheANG837N5UMlXUp+z+A6Kc/3Z7x8N/Bt7f8Aw/tpv9W8n/kSvXfgn4D1jw5cvN/aUciXHyfZq5v4f6po9rodtoOsw/ZfLi2eZXoXge1s/wC1E/svXo5PM/6a18Xip1KlWZ9vQhSp0qfIe0/Cfwbr0WsQ6lLpu9P+mn+rkrv/ABBdzaXvmtYa6r4Hy+KvBvw/h+36b9uh83z4vN/5Zx1xPxQ+I00usXM0VmiJ5v8Aq/8AnnXlezO04DxR4ps/tDzS/u3krzfxZ4js9Gk+2ff/AHtdJ448R6bKHvJYfLrzHxJF/b0nnRXn+r/5ZVyew56prz/uvcOk8efEKzv/AIdveCaPZJ+78qWvlTRbttS+K+mXzfdfXYsf9/Vr1r4oa9/Zfgv+zbr/ALaxV4Z4P1Z9S+JGgJAHRRrVqSD6ectfdcGUOSvKZ8lxLX/e0If3l+aNz48XLQ/FXVgnX9x/6Ijqh4P8R6xa3CETPHDWl8brG0Hxe1S8utQ2kmDEX/bCMVzdvLeXVx5P7tEr9GrLnmz89zz2n9qV7fzy/wDSmfTnwrl/tTR3m0HXpI5o/wDlnLL5kcler/DeLWL/AOAfxI03VNNggm/0zyvs3/LSP7H/AKyvlT4N/EGbRdch8N2t5BvvJY08qSvr34DxTS+B/FWjXV55/mSyf9+/s9fi/G2XUsJivbQ+2fqnAOaYrGYbkrfYPz30/wA6KSvX/hHPHP4V8RFegsxn/viWvGtMlmlvPJuoZP3fyS17F8Jdv/CKeJNn3fsIx/3xLXyXDE78TU1/crf+mah+uYl/8Jr9Y/8ApSMiSXyY8RfJVO4uYZY/J86i487zPJlPyVTuPJi/fS18Oj1/aezKGuCaKP8Ae1q/svfBbwR+0Z8eNK+DPxB8+PTfEEVxBLLFL/y08vzKx9Y/e29dn+w3f/2X+154AvIvkT+3tn7z/rnJXsYGH708nHfvKR80/wDBQz9lXTf2Lf2iLz4PaN4rn1y2j0u3vft1zFskj8z/AJZ14bb39nNxX1R/wWM1TUtU/b48c/b9+yP7Glr5v/PP7PHXydcWtn/rv46+rnR5FCZ8DX/d1eQ6fwv4o1Lw5qkOpaDqUkE1vL/yzr6u/Z3/AGltB8ZQQ+Fbowabrcn+tiuZdlveV8Wxxala/vrUySVZs9evLWTN/wDu6wn75n7OlM/TiTxleWujzaPr1n5CSfJLHJ/yz/651wfijQdNv5HmsLyOT/nl5Uu/zK8l/Zr/AG0rPRtPtvBPx4hk1/QfN2RX0v8Ax96fH/10/wCWkde36p8INH8W6XN8QvgF8Q9O1nRPKkn+zRfu5I/+mdYT/d/GFP2tM4a3u9Y0uR/t/wC7StLR/Edn5jmWHf5lcx/wlHmn7HrJntJo/wDWx3MXl1cs7rRs/wC3/wBMqwnD2Zc5nbXFhZ69Z/67y6wdQ8B6lanybDUvn/56S1Npelwyx+df6l5cP/PKsfWPiDoOjW/k2N55n/XKpMIQ/ej9Q8L6lYRpNdXkGz/np5tZus6ppugjzrrUk31g6x8QfFWvWz2el2flwyf8tZaoeH/h9r2vXCTXV7/rJf8Aj5uZfLjjrn5zrH6h8RtYuriaz0aznjTzf9b5Vek/sm2usP8AEC+1TVjNubR5E2y9v3sR/pWv4H8B/A3wwPO8W+Kv7S/56/Zv3n/kOvW9D8Y/Aq/VvCvwq0CSG6j/AH895JCy+ZGPlIy3PVlP4V9Hwj/yUuF/xf5ieIUp8sT6m+AX/JG9K+Td/wAfHH/bxJW5qkJ8t/8AlpXMfAq/8j4T6XF6ef8A+j5K6eSaGX/lhXxnE3/JT47/AK/Vf/S5H6XgP9xpf4Y/kjhvEml/ag/7n5K+S/27P2VIPih4TfxV4X0KS71vT4v9VF/rLiP/AJ519paxFDXMeJNGhu7d4c15FOp7Orzl4unSqUj8NPFFr/ZeofYrrTZLT/p2ll/eR1iebDj/AGP+mlfZn/BRz9kH/hEtQm+MHgjTv9Ak/wCQzbW3/LvJ/wA9K+Nri18qSvpsLX9pT54Hw+Loezq/AVrgebIn3KuRzGKPyf3dQx/x58v/AL+0/wDueZXackyzn95/rqfHFn/npVaP97L12Vf+yzS26eVN5da05klmz8n55vtiRvXpfwji+1aXczX+/f5teV20MMUn7379el/DfVLOK3ezE3l+Z/yzruwk/wCc56x6F+4isEm+SOqenTCK4eb+OT/lpTLy/hsLdIa5vWPEZiuPJtZtldf/AC8OGnT1P0p/4JL2E3/Cu/Fusf8ALG41m3gil/65x/8A2yvsCPvXyp/wTvupvhV+yv4e0y60f/SdQ8zUbr/t4r36P4of89Ya8nF1KXtT2KZ3n2v7n9yq3izWYdB8H6lr0v8Ay52FxP8A9+465L/hb+j5/wBSm/8A66151+1R+0Fpth8APFVnazbLm80aSC18q6/56VyqpqHIflxqnjE+I/FlzrFz5k73l1JPL/008yuq0aX7n7mvNJLqz/tR/sH3JP8AVV3PhO/vPk+1TV6NOZy1P5D0jwubzy3m+5XVafaxS7PtX365Xw/f+dGn77/tlXSf2mIv9dXdA4f4Z1viqFpNSRl/54D+ZqhHYc/6356l8b3txb6xHHGishtgSD67mqpb69D0l+/Xv8QwTz3Ef4v8i6lS02jdjtdvh+eHy+sgOPxWvmT9rTWYYvFlto9hN8kdrvuov+mlfSN9q5k8H3l5H/yzlVR9Mp/jXxB8cPFE2s/FDVdStbtP+PrZ+6/6Z/u67cXC/wBU/wCvUf8A0qZyY2fNSXoc3qHnRXCTf6tKZeRWeoWfnRffjrH1jWZvMxLRpeswm4T/AKaV2UP3Z4FSgaWh3/lXH7qZ66HUNZhis/3U3z1yUkvlXnnRfIlU9U877QnlTSbK7vbnFPC6jPEF3NcyOZazYJZpZPscv/LT/VVZ/wBbeJVnWLD/AEdLy1h+eOs51PaHUoU6aNbwR4nmurdvDtz/AK5P9V/t1u6XLDa6gkMu968+jM/nw65Yw/PH/rf+mldfpeqfcmx89deFr/YOLGUF0OwuPJ81Pv1ct/3UieV5m+s2zv8AUpY3xD8n/LWpreW7lkzXq06h41eB0Md3+7f99G9Y8l1LYXn7qb/WVc0/zven3EUMu/n5664Q9oebzl+3l/55H/WVleJNGhl/0yX7/wD11p8d1DayfvZq5jx34yNtaOIrys6/s6dI3w9OrUqmR4u8YzWVqwi4rkNG0+78WawkHn7PMP8ArJKzJJb3xDqKRRyvIZJa9i+Gfgifwvp7zny5HrxqftMfVPpJ06WAo83U1bfS/wDhEvDf7qbz/L/561xlvKfEfiT97C8n/TOuh+IniPytP8nztk0n/PSqfwv0GG63zS/I/wDz0r0qdP2dTkPKf8PnPRfDel/Zbf8A1Pz1lfEjVfK09z+731t/udG0+vNPiR4o+13Hkiau2vNU6R5VBfWMVzmJ4XsDf6x52fM/6ZV7l8M4obW8tofJf95XjngeL7Veed9yvafBcX7tPNm8zy68ep/CPbnP97BHeeJNQmi/c/JVDwnrN5pdwktrNIjxy7/MqE2sJuMX95HH/wBdas3nhy80aSGGSGSPzPnil83/AFleB+6gfR0van1v+zt+258WorfSvAeYLqHzY4PNktf3nl+ZVz42ePde8G+LH/tSHzIbz9/F+9r5v+D/AIovNB8caPqUcMkiW9/G8sUX/LSvV/jxLefEGN9Y87y/L/49f+mlfO17fWD3Kf7ykY/ij456DL/rPk/6Z1W0fxb4V1S3f7JeeXN/00rw3xJ/aUX7nyfkkqbwv9siuPOuoZI/+eVdP1KlUpHKsVVpvkOh+OmvQ33/ABLZZvk/5a1w3w5QL4z0aOyt9sY1W3y/r+9WrPjzVfKPnS/8tP8AnpXK+E/iz4bg+KHhvw9YWCma616zi3LL8g3TouR+dfW8O08NhKKlKetz5jNnicbm0FGGia/M7D47xwwfFPVLuWXGfI4/7YR14/4w+NNnpcb6b4ch+3Xkn/PL/lnXV/tKeB/iT8TP2idc0Lw5FdGytxajA/1ZJtYmP6mt3wV+zJoPgiNLzU4fPuY/+ev/ACzrzuJuOaGVYipQoe9UTaflZn0eX8ASzPMquIr7SlJr0bueb/DPw542u/EEPirWdSnSaP5/K82v01/Yv8b6B4y8H3+j3XmR6lJF+9il/wCWn/LOvjO8sLO1j/0WGNP+uddP8H/jJqfw08WWesWt5JHDH8l1bf8APSOvyLF55is0q89Y/RsPw/hsqw3JRgeafEG1m8OfEjW9Hl8xPseqXEHlf9tK9I+Ecs03gvxSduw/2cNv18uauG+Nl/Z6n8dNevLC8jnhvL/7VFJF/wAtPMrs/g+gHg/xeI42GdOGAe/7uau/hmH/ABktOf8Acq/+mah6E/8AkXP1j/6VE5vzZukvmUySWESP/wAtKYbWYx+dFDH/ANdaLf8A1v8At18CfSr2JT1CWby8eTsSun/ZOuppf2pPAEH8H/CUW71zeqSzbP8AlnW3+zPdTWv7TngYRfIn/CUW7y16eEdqp5uKp+0If+C0nhf/AIvp/wALOih8tNUupLKX/ppJH/q6+GLjS/Okeb/WV+lP/BXSLR/FvwrudYtf9db+I43/AOufmeZX5sxy3kV7n95JDX2FOv7SkfCZrQ9nVH29/Na/ufJouLqa6/1UMdauny6bdSf6V5e+r8vhabzP3Vm/7v8A55VqcEDmJLrUoov3sNHh/wAeeMPBuvp4k8L6ld2NzH/y1tpa62Tw3DLp7+d+7rB1Cw+5B5Mez/pnUz9oaeyO5/4av8SeMpIYfHlnBI//AD/R/J5ddJb+LbywjS8sNYfZJ8/mRfP5leISaDN8/lfco8P6p4k8L6gk9hM8af8AoyuKdCqafuT6QtPihrGqafDZ3UP7n/nrVCPzrrUHgi+/JLWJ8P8A4teFfEciab4jhjtbn7n73/VyV9gfs5/s0/s9+KJLPxj43s7u+f8A58orr9xJ/wB+6wnQqnLCtTPIvAX7PvxC+I2s/wBm2HjCDTk/5+ZZf3cde8eA/wDgnF8B/LT/AITf9pzXL6b/AJa21lLHBb19CW/7N37K+qaW/wDwjngnyPL/AOfbVLhP/alaXhv9mX4Sy2/+n3mqxp/zyiuqUIc5z16lT7BwHhv9gX9iHS7dIZde1W6T/nrL4jk/9p1Z+Jf7K3wK+Bvh6LxX8LbfUEvLq+FpK1zqr3EbRFGckA9DuROfr613Nx+zl4P8OWfneHPFU89z/wBP11XJ/GL4Xa74P8O2/iDUfGcN9DJerAlnFzsJR2353nptI/Gvo+FafJxJhf8AETQq1HJJna/BO9RPhnp1udu799jf0/1z1vXOq/8ALGKWuU+Evlf8K009m6jzv/Rz1a1C6vLWR72KGvzzir2n+tGO/wCv1X/0uR+wYH/cKP8Ahj+SN6SYeX50v36ytQuofn/v1lf8JReTf62HZUMmqeb++86vLU0aTgZvjzwRo/jPw/c6PrOmxzw3EX73za/Jr9rT9m6b9nj4mXmgxee+j3Evn6Ncy/8ALSP/AJ5/9s6/XT+3ppf3F19yvDf2zPgFZ/HP4ZX+mWEMn9pW/wC/0v8A66V3YSv7OqfP5jQqzPyUvP3MlPji/wBHq/rFhNYXD2d/ZzwXMcuyW2uYtkkclU/3UX74/vK+pp7HyVSmEcU0X/LbfU0l1N/wCofN83pRHLDF0ro/wBCBN9pHmedmuh8J6zNa3n2z7lcxH/rRW9p9rN9nxLWkC/ZnT3Hii8v98P2zzErb+Eel/wDCR+PdK0fWZk8m8v7dLqSX/nn5n7z/AMh1zfhvQZpY/wCCvUfgv4c8rxZDeffSOKSrqVPZmMPZe1PvDxR+1V4V8OW//CN+A9Mjunt4vIill/1Edeb+IP2h/ipr3/IQ8SeQn/PKyi2eZXnsf2PzPOi+/T7jVIf+WtcM/ZF15m3/AMJlr11/rdSnd/8AnrXE/FT4oTWuhvpl1r3mPJ8n+t/eVlfED4q6b4Xs/wDbk/55V85eNPihrPiPW0mlm8uH/nnW9OnSPOg6sDs7O5/0iu88Jyw/Z/Jl+/8A9Mq8o8N388UieV5f7z/npXp3hv8A0rnzo0rrpwCc/aHpGjS+VbpzW9Z6pD5f+3XGWV/Dax/65KP+Ej4f/lolb84Qp0/tnrXxh17+yvEUUPrYK3/j7/4VysniObyk/ff6ypP2lPEC6P45sonjyG0tGz/20k/wrzm48UebxF+7r3eI/wDkfYj/ABBVhd3PY5/EElv8DtZ1qOTmG5Uhv+BQ/wCNfCXiC/8AteoPN5zyPJLvlkr621fWjP8Asi+Lr1JPuajHGD7F7Uf1r5AvJfNk/dfJXsVv4WE/69L/ANKkeXj/AGnPG3b9WWbeKG6t/wB79+s3UNBmP/Hr9+rmn+T8/m/fqzcRTD/VeZvpUzzTCj1ia2j/ALOvv+/lalpLDqmn+TF+8esrU9MNzLm5qnbzXel3HmxS/JWkK5nOmbel6XN9ofza3pIobuPMXmVm2d//AGpafbLX5Jv+WsdUbfx9HG/lSjZW3tEcs6dZ7EFxa/YLh4fKfZ9+tLw3fzfbIZfJ31Q1TxFp11+681HetLwvdfarj/RYa6KbFU9r7LU7azlm/wBfLDIiCtKzl82TyYoa5v7fN/002VsafL5uyaKvWoHjV6ht6P532h4JYdj1c1TVIYY/3RrHt/8ARZHmlmrE8YeI9Nij8mWf5/8AnlHLXT7f2ZxU8JVxFX3BnjDXoYo38379eb+IL+bU5/Ii+5Rr+vz31x5Q4rovhz4AvdRf+2Jh+5/55/8APSvL+uvFvkge/QwiwC55mv8ADD4fQ21v/aV19/8A6aV6F9q+wW/7rZsp9na2cVn5P+r8uuG8eeI57WR7OKb/AFldtOH1c86pUq4iqZWqRT+J/Enkyfc83915deneD/Dn2CzT78iVyXws8OyymTWL7733K7bxJ4jg8OaX/rtldVCp1OfFVvafuyn448UQ21v5Es35V5BrusfbtQeU9at+N/GZ1O5eL7Z5iVxt5rxj3gTVw47H0jsy3LakPjPTfhvdWelyPqWs3kECR/8APWvoT4F6D/wsbS7nWNLh/c28uzzZf+WlfFul3U2s3GJbt+K+9v2U5f8AhHPhHpsMlnHvuIvP83yq+ZzHO/Z4b3D38Dk9OpiueZxPxQ0HXtK1SaG6/wBTH/y1irV0/wAUaldeF7DQMeZDb/6qST/WR16XrlhqWvaW+sX9nvtreXZW3o/wH8K+PPDf9veHP3F5Z/8AH1pv/LSSP/npHXzkM1q1D6b6jSp1Dlfg/qn2DVEvLr7kde5fHj43fD3xH8N7DQfCWgyWt5+7+1SeVHH5fl1xNn8G/wDhHfDf9pWEySTSf8spKp654SvNGkhmv7PzEk/5aVnOvUvzl/uqfuHmOuan9g1T7ZdQ+f5n+trbEWm3Wn+dF5ccMkW+KSub+IkX/E0eGKHy4fN/dURf2lYaPDDL9ysamLqkQwvtKp89/tWePPEmleL08H2t5JBDHF58Usf/AC08ytD9kr4bS+IfHujeLdZnAW01a2nh3ffmdZVIP4EV03xw+H8PxB0v91Cn2yz+e1k/9p1jfs5eKp9I8e6B4Wvx5Eya5bQ+V9ZVFcEM1xVTGUaf9+P5o+iwGBwy5n5M+otc1Cx0vx/fTMgDSNGsm3q37pP6YqLVNes9UDxRQ/JXH/FjxXBpXxQ1WzdnVkMOHHbMEZpmh+I4b+VP33mJ/wBMpa+Z4pm/9ZMb/wBfan/pcj7PLocuDpuP8sfyQ/XNPmlL+VD8lcfqH+i75pYa9L1ibzdPx5KbK4PxRa3kW8xQ14eE9r7T3zpxUOdHE2d15usPNL5m+vbvg7cznwd4pkfbsTTx5ePTZNXh32r7BrvnS/fr3T4JSQ3fg3xF5XQ2Sg/98S19vwlPn4hp/wCCr/6ZqHj46jOnhHfvH/0pHJy/6VbvNVOP91v/AL9dDqFh9lH+lQ/9+qypNBh+0edz+8r4WcD3IU6pj3GoTiN5vOjSn/BfXptB+OHhjWLb/j5/tmP/AMifu6p65a/2fvhIkdK4nVPHk3gPWLbxhawb5rO6jnijl/56R16OEnyVThxf8M9U/wCCpnjeGK40H4e2upf8tZNRv/K/79x18cj7HL+5rV+Inj3WPiN4wv8AxTr17595qF1vll82seMQxSV9VQhT9mfF46v7QZJpcMv761m/fVct/G+vaNGlnLeSR1WkihtZPtks3mUXFhNqkf8AfroPJNsa99vk/dTf9df+mlMk+xzf6n/yLXKyRalpf+q+dKfHrM0v+t/d1ftAOqt7CGUfujHsqnLo3m3D+VD8lYMl/PFv8qZ9/wD0yo/t7UrXZ++ffV+0A3h4N+1SfvYa7n4T/FD4qfAz998PvG09rD5u/wCw3P7+CT/tnXlH/CZax5ifvqfJ4yvP+WtHPS+2ROn7Q/QL9n//AIKn/wDCL3Dw/FX4bwSQyS/6VfaTdbP/ACHJX0t4b/4KO/si+LbNP+K8n015P+WepWEiV+MP9vTX/wDrZqf/AGzeQ/6u8krSdDAz+A5fYVT9qrv9o34A6pEk2lfHLwxJ5n/LL+2Y46wPHvxD8JeK7CPSvDfjrStVMc4laLT9TimKgKw3FUckD5gM9OfevxxuZTqkfk3c3yV9Af8ABM+xSz+POrLDIpT/AIRGfGP+vq1r0eGcNThxDhmp3947qdGEad3ufqX8Kr7y/h9psPlZ2mXn/tq9dbb2E2qW/nS/crkfg3t/4Quz39P3mP8Av41egfvpY/Ji+5X5vxTV5uJscv8Ap9V/9LkfqWChfAUX/dj+SMe40HTYo6xNY0H935VrDXZyWH7vyarXmlzSx/uq8H2hqeRa5LqWn3iQj7n/AC1plvqEF/G/m/fr0LVPC9nfxP5sPz/89K898YeDdS0svNYTf9da19vTM69P2h+e/wDwUM/Z9h8OfEH/AIWdpf8Aqdcl/wBP/wCvyvmCTS4vM/e+ZX6ZftKeErP4lfDu/wDCuvWn763+e1ufK/1ckdfnprnhfWNL1Sazv7N0eOX/AFclfUZVUWIpnxeOw/1eqcwlhOY38r/nrT/7LHl/8tN9bEelzGR4Yofnqzp2lTyyeR/HHXrHnT9mZVnoP7xJv4663StLsoo0Bm/7+1Dp+lzRSP8Aaof9Z/y1rbuIvslv50X/AD1/1VdBz06nOTfb/wCy7dIR5eyT/lqa7P4T2Gm/bE1Kwhe6f/n5ll/9Fx151/bV5LeJ9ghSRP8AlrLcy17B4Xi/sbR0Hnf6z5/MrnmcM5/vTp/7U+y7/N/dv9+uJ8efEuG1s5rOx8x5v/RdUPHHxBhtbd/9MrxnxJ4ymluH8n7klcvszrh+8JvGHjea/vHvJpnkmribjVP+Jp51Wby6+1b/ADZvnkrKl/4+PwrYj6r+9uel+B9U82T/AEqF5Er1rw/EJY0+ymOvHPh+PtVer6fdT2saQ+dsSuugLkOtj1SGGN4Zd++qF5rP2WT91NH/AN/azZLmH7O/9+s3ULqzijeY3kddXOiJ0z2f9rvVmsPiNZxGTAOhRH/yNNXjcl/+8fzJq9F/bk1QWHxXsFkK7D4chJDf9d7ivE7jXv8Alt5z17vEs7Z9iF/eJqwuz22aZpP2GvGTrJ8x1uAMf+2tlmvk2SbzZOlfUGkah9s/YK8a3McnTxFAAf8AtrYf418xCX/SH/5Z16eKdSFLCW/59R/9KmcOK2j6f5k2j3UMtw8Mtb1vL5v7n/yJXMXH7rf9lrb8P+I/KjSG6h+f/nrWtCftDyJw9mM1Cw8799VCXS4ea6SO0s7+TMV589U9V8MTX++Hzvnk/wCWtaToBTMezl+y3H7qaotf8Hm+R9R0s75f+eVaP/CB3kQf/TE87/ppT5ItS0FPJlvI5ErkqQ5zZOx5/HBNFP8AvYsY/wBZXdeBz+7f96++T/llWILXUtev3EP3P+elbFvN/ZUaWcU//XWtMJCrCqc2LftKZ21nJNax+TLDVmPzotk0X3K57S7+b5BLvkStK58R/ZbN/wDV7K+loV/Zo+crUKtRlnxB4tgtbfib5/8AnnXm+uazNql4/leZVm8udR8R3L/YR8n1rq/AHwr86T+0tUCSf9O1c1ev7f4D0qHs8HS98yfAfgO81S8Sa6+5XrWj6f8A2Dp/2L+CobeLTdBj8793H5dY/iD4oaRa7/8ATK6aEFSpHJXrVsXVJvEHi2G1jeCKb/tlXH6HajXtbfUtZnTyY+v72uW8bfEAapcbrHEY/wCmdc2fEeo3J8mKZ0FY1syw1P4zswuVVbHump/F/wAN6Db/AGPS/Lnf/lrFHXB+MPiNea9ePNawvs/56S1zGj+F/El1bvqVrDJs/wCWtQ6hLeWsnk38Hlv/AM8q8bFZ8vggepQyOlT98fqHnS8+d89VtD0ubXtU+x3X7tPv1c8P6p9lvEmimq5pd1DdeME4/wCPiX/WV81Xx1Woz1aeE9mP0vRZrDVfJtPn/e1+jvhfwbDo3h6z0e1/dpHaxp5X/POvjzQ/hLNF4s0rzbN50uLqPzYv+en7yvtLRorywt/Kl8vZ/wAsq8nMKh6mBoUvac5sXngjWJfh3c/YZvMf/nlFXGaH4z8SeHLjybWb7JNH8ldtH8WrzwRG+m/2al1bXEWy6trn/V15jpc03iPWPsc2pfP/AM9ZKzpw/kN8VP8Ae8h3kfxG8rT3m1C8d3rKuPiX/alu/wC+3+X/AMspK7P4D6f+zfrPgPxJD8adYjtdVt5f+JXJLdbI5I/L/wCWcf8Az0rwHxRf6dLqNzZaDNJ5Mkv7rzP+edetT/vnncnJ7522h3eg+KLx4LmH5/8ArrVzx54N0HRtHfUrWby/Li3/ALv95XmPhLS9Ytbh/K1iOT97/wAsq6T4iaprGl+D/wDT9Y2eZ8n/AF0rPFU/Zr3C6FT96cfJdQ+a9Y2m/DKz1X4qeG/GmhwrFdWviOxkvMfdmT7Qm4n/AGwMmrVvdfak8/zq6PwPj/hLNH8vp/alvn/v4tePg6PPj6Tn/NH80epGvUhN8pxX7RPiqBf2ivEOhSsFaL7Iql+mGtIW/rT/AAXrEVhIn9yj9sH4Xyat8VdS8Z+G0Zb8CBbmNOtwBBGAPyAFeXeB/GV5L+586uDijAc2f4uXerUf/k7PrsqzHmoRp9kl9yPpzRvEcN/GlnKZPs1HiDQbOWDzrX7lcf8ADvS9Yv7PzvOkSGT/AFVei2+jTRaf+9+evmuSx6c5+0PEPGNhBYaok3k/PJXsf7O8vm+DPEZwyf6Inzn/AHJea86+MGlw2v8AxMptiPHXc/s1XO/wL4nl35xYo30+SavpuDnzcRU3/crf+mah5mP9p7B37x/9KRfuIv3b+bVO9sJvL/dfcq5J5t1H/wDHap3ks0Vs/wC+j2f89a+K50fQU585xniz97H/AKL9/wD5a15L8XNQ83Q3s4oY9/8Az0r13xBL5XneTsk/dV458XJf9DfzN6PXo4T3Kp52O/hHjl5/rX/ff9sqfZ6p5Un72H5KLi0/0j/RZvnqaOKbKebDHsr66gfAV4amrp+oabdb/K8vf/00q/5UOf3UNY9v4cMv7750SppJby1/1Vd0IUjln7UuR2H+kPDLDJJWPrPhyaaT91DWlb69NFs838KvyX8N1smFHszPnRw0kX2Uv9/fUMZmlk/56JXbXlhpt0EzDUNn4c02I/8APOonTI56pyUkv7zyYoqJYvuCuquNChm/36P+EXhht/8Abrn5P3ptA4/MMUj/AOs31DHd+bI/7mSuz/4RKziPnfu99Q2+iwxSfvYac9DTkOYt4ryWN+Pkr6S/4JlW0kPxy1Zn/wChTnH/AJNWteLyaN+8r6D/AOCeQtl+NGprbwKmPC8/3en/AB821e7wpO/EGH/xGkz9LfgxCJ/CVpt+8pkx/wB/GrvLPzf+WW+SvL/hJrH2fRILSX7g3/8AobV6Vo9/DLH/AByV+c8TQvxPjv8Ar9V/9Lkfp2BnbL6P+GP5I1Y4pvMTzamuLWGL/Wiizuv3b0XEv2rvXi8iNjKvLWGXf9+sTWNBhurN/wBz5iV095F5UfWofss11Gk0v7tKzqF8h4h8RPBumfZ5ofsafvO9fIX7SH7Ps0txc+JLDfv/AOeX/POvv/xxo32+Pr+5jrzHxR4I0eWTzorP5/8A0ZXRh6v1P3zhxeEpYj3Jn5j3nhLTbCXyr+8/7a1D/Y1na2/7qZE/6619D/tOfAebS7ybxho2jxx23/LW2ji/1dfM3ijVJrW5f+Py/wDllX1uBxX1inznw+LwnsPcLkmsw/Z/scX/AJFrm9c1m8lk8n95/wBsqyrzXhFJ/HH/ANc61dDsJpf30vkSTf8ATzXpHhzgbGh6VZ2tvDqWqXkEif8APL7/AJf/AGzrrf8AhMry68PzWdrM8n2eL/j5l/5af9s65uPT9Yv40+32aIkn/PtFsrqtDtYYtDeztYY4LaP5PNuZf9ZRyBOB5Fr/AIjmv7jMs0j/APbWubu7r95/t0axdeVqE0P7v/Wyf6qs2Tz/APXVzzCn7UmuJf3WZap+VN9oSjzfcUQf8fAoOs7zwfqEMUcJi+/Xoun6pDLbp+++evJfDcvld/nrttL1T/R082uunMzmdbqGoTxWfnRfvKxLjXpvLx9kjeoY9Umut9nFNHJVO81TytkNR7SqEIHt3/BQWfy/i7p0fl5z4ZhOf+3i4rwWOXzY69w/4KH3Pk/GrS09fC8J/wDJm5rw/wDc+X+69a+l4lnbiDEL+8YV9j27w08kf/BPfx0x6jxPBj/v7p9fNXm52V9IeEp/O/4J6+PW9PFUA/8AImnV862f2PFe1jf93wv/AF6j/wClTPPxE7VI+n6smt5YfL/eiph5P+uPyUf2NNL++tZqPsGpeX+9hR3rChUOCvAs2eqQxdKufb7y6j/dTSb6zf7LvIo/3Vn8lWdPi1iEfurOvVpzOOcPZlO//teFPOjm4qhN/a8x8qQSO/pXc6fpf2qPzpfLqn4o1Wz0uJ4YrN9/lf6yuTEE03TqVLHGDxYdLt/7NA2VDZ69afaPPM3z1y2rzTy6hNNKd/72obck968f+0adOqewsuVj1DT7rzbfzopv9usrXPFvmnyTXH2+qXlrF5NWdLtZru4xLXVPOaRh/ZX73nOk0vxb9g/1UNaV38S/EcsaeXdyR+X/AM8/kqhJ4INtGk11donmf8s6ytYtZrGTyZf3n/TSop5xUphPKqf2yzc+KNX164f7devJ/wBNJJahvLqa6kSzpml+TFcZ/d16L8UZfDeqeF7a80bTbSC5j/59ovLrkr5riq51QwlKnTPPdQ8G6lYWaaldFPJk/wCWlQ+G9LiutYhhlm2J5uyt6S6m1Twu8Pnb0j/1tc9oU3lXnnS/vPLrzvrVWoddOgei+INU1Lwvbw6bpcyfvP8AlnWJ48sPM0uHWJYd7/8APSut8eeHPt/w/s/FVhD8/wC7f/W/89KzdY0v+1PhkmpXU/zxxfva56lQuftaZ5jZyzS3ieSa7aPw5NYfZtStZ64nT4pvtnSvor4V+HLPXvg5N/HeSeZ/yy/55/6utKf84TqfYPoH4BappviPT9H1iKz89Pssfmy20X+r/wCeleweMNQh/s5IdFhTfXhv7Acs1/4P16G/h8tLPVI/K83/AKaR/wCrr3u48OTXf/IKheR5P+edcmK/inpYKn7Nc5wdxa6lr3+hyzR+dTPA/wAG/G11rl5DdWclp9ni/wBbc/8ATSuS+KMXjDw54w+2RajJs/6Z/u/LrpI/jn411SRJopvss0dr5HmR/wDPOuimcrr06lU4bxJ/aVhrk0Upk3xy1j3AvL+3m1Lyf9XXVa5qlnf6hNNf/vJpP9bJ/wA9K7D4T6p4J8OapDN4ys4PsFx/z0i311QOCvU/e8h5L8O5dYuvEH72Z9kfmebVz4seN9Si8Lw6Pc/6m4uv9VL/AMtK+sdQ8b/su+LbjTbPwloMkF/+883zdLjSO4/7918f/tyy6D4N+LFtoPhy88y2ksI7ry4v+Wfmf/u6zrzq1Bwh7P3zmNA1SGNP9b8n/PKu4+G2tRS+OtKiHVtUtwPxkWvJtHlm8zzjN8ld78LZIJvH2hS2w4/tm1z/AN/lrz8H7X6/S/xR/NHowqe0O0+OE3/Fy9Vj+TgQ9f8ArjHXmWj/AA5s7/xRNeWtn/x8fPLXp3xb0HU9e+MOrWVhHnf5HP8A2wjre8F/D6HRtlnLD/11lrl4pxVNZ7i4P/n5P/0pn0uS4Tnmpk3gvQZordIc/JHF/q66e80//Q/9d8n/AEzrY0/QYfLTy4f+2lP1jS/9H/dffr5E+vsjwT4yaXNdaPc+dD57x/6rzK0v2ZLrb4D8bSRpsaPTI2/8h3H+FX/i5/oGh3n7nzPMi/1VY/7L/nJ8PvHkkqhn/stDhu/7q4619JwXU5+IYf4K3/pmoedmkKSwz5O8f/Skbel6pNKn72H56ragZpd/76q2j3811bp52zZUOp3U0W+Hzq+N/eHscns6pieIZfssj14z8aPO+x/6LN8n/TWvVNcv5pf+W1eS/FyWb7O/76PZ/wAtY/Nr0cDDkqnlY7+EeM/b/wDSK0rPWZvM/wBckiVlXkX36I7Wby/3UPlvX2GEhSPgK89Tqo9ZvPkP8FaX9q2eqR+T/q64C31TUrWR/wB9Jsq/b+N5tLk/e2cc6V1/uzk/e1DuUsIfL8nyY3eSqdxp/wBluPJlFYMfxQh8zPkyR0+T4jWd0U82b56on2B0lpFNLJiKmXksNrJ/t1g/8J5Zj9zFDHH/ANNah/4SiziP7qaTfWVOobTofyG3capD5n72aOpv7Uh2f8s9lcld6p5v76Wb5KzZPERik4p6GE6dU7mPVIDccTfJUN5rMPmOPv1xNx4jm8z91VaPxH5tw/m1lOuaU6FU6258R/u/JNe//wDBNbUpLr44arbt0HhSdv8AyZtf8a+UJNe/ePX0l/wSyuri5/aC1gv9z/hDbjH1+12le3wv/wAlBhv8R0fZP0l8D3g+zxW6yYZScD8TXqfhvWZoo0hirwzQr6e01Bcyqsfcs2K9P8J+IvD0KLJqHiSzQ9w10g/rXxHEWW5hPijGz9jNp1ajTUXa3O7dD77L8RReDppyWkV18kd/FdebIkx+R627KKb/AF38FclpvjbwcV23HivSgvodRiH/ALNWv/wsbwNFbeVF4z0nd/2EYv8A4qvH+o5nOp/An/4DL/I9D6xQ/nX3o07zzrr/AFVXNPtcR/vRiszTfiB8Po4d7+O9GVvQ6pEP/Zqnb4k/DlPu+OdHf66nF/8AFVnPK8z/AOfE/wDwGX+RnDEUP5196KeuWEMtm8M0PyVweueF5ornzrWGu7vvHngO4Pmv470Yn0GqRf8AxVZFx4s8AzBzD4y0pGPf+0Yv/iqieUZn/wA+J/8AgMv8iPbw/nX3o8o8eeA7PVLeY/Y03yf63zP+WlfDH7WP7NM/hy8ufFXhfR/9Gk+eWL/nnJX6MeK9b8JvHm38UadO/wDsXsZ/ka8k+Jeh6T4gt7kLfwzpLFsESYP8q68twWZ08R79Cf8A4DL/ACOavRwdT7a+9H5Tf2XeWF553k7Hj+f93/rK6HS5by1jTzbODZ9/91/7Ukr1z40/s4+PPDOsyWPhLwdqup2M53lrLT5JQD/wFTXIW/wb+L5mV5fhV4hVV+4ieHbgAf8AjlfYUcHj570pf+Av/I+MxWHoQqcsGU9Hv/NkSbyfP8z54v8AlnHH/wDHK27eWG71DztevJN8n+q/+Nxx1Knwx+MUMv2pvhZ4jY/3hpFwX/8AQKYPhH8VZ7plf4T+IyWO50GkXCKT/v7K6PqmO/59S/8AAWefOB4p8WPBH/COeKHmsP8Aj2uPn/ey+Z5dclcTfvK+iPiP8M/iP4g8OvpVn8E/F8l1Gd1oIvDtykEZ/wC+K8juv2ev2g137PgV4yPpjwxd/wDxuuWvgMc9qUv/AAF/5F0IJU9TipO1VriWY3H7qu5P7PH7Qkn3vgR4z/8ACXu//jdRf8M5ftC78/8ACiPGf/hL3f8A8bpQwGO/59S/8Bf+RvOKKXheWby0+112FvdQ/Z8Ws1Q6H8Bfj7EuZfgf4wVvVvDV0P8A2nWnb/Az4+xO/wDxZnxX7f8AFO3P/wARW0MBjv8An1L/AMBf+RhpAS31r7LbvD+7qheX8Mv+/Wq/wS+Oqb9vwW8WHPTHhy6/+N0W/wAFfjtE28/BXxbt9P8AhG7rP/out/qmN9l/Cl/4C/8AIx1mexf8FEfL/wCF16Xvfaf+EWg5/wC3m5rwWC7/AOeNe1/8FJpGT45aSF6/8InB/wClN1XgUd16fJXpcUTtxDiP8X+QOF2fQ3gv95/wT28eH18VQn/yJp1fPMdrDFIlfSHwe0XWvE/7BHjXRvDejXeoXs3iqPyrSyt2llkIawY7UUEnCgk4HQE15JJ8CPjWYnYfB7xTjsv/AAj9zn/0CvbxvtHQwkoQbXso7Jv7U+xw18M5VI+SOTFhDdf6qby6s2+n6lFz9s+T/rrUsnwL/aHE7mL4LeL8dj/wj91/8bqxafBr9pUP5Z+DPiwj1bw7cj/2SroOXWnL7n/kcNfCVh1n9s/5azVZ/wCJlF/y2rY0z4HftA6ivl3Hwl16Ef8ATXRZ1/mldDpv7NfxwSPE/gW+H1sZf/ia7vaRh9mX/gL/AMjhqZfja3wwOJ/0yZP9uue8YR6kNPfzZn/d163cfs6fGy1Lr/whWpMh7Q6XKf8A2WuC8e/BD9oDzZbe1+EXi26U9Gh8PXLA/klcWIxNX2XuRl/4C/8AI1wmU4inU9+B4ncS/wCkURffrs5/2cf2hi7EfAbxmfp4Xu//AI3TP+GbP2hvLx/wofxn/wCEvd//ABuvkq+Fxrq8/spf+Av/ACPpKdOxyXm+4rY8L3XlahDKP+WdbH/DOP7Q3GPgN4z5/wBZ/wAUvd8/+Q6fB+z3+0Za3PnRfAbxn/4S93/8brCGEx3/AD6l/wCAv/I6+VEPiDWZv7QT/WbKreIL/wA23SbHz1vXnwK/aGuY0H/ChvGfv/xS93/8bpLj4B/tCXVjsl+BHjLPp/wi93n/ANF1v9Vx3/PqX3P/ACMJwTOKt7r7n76u/wBDv7O60yGG63yf9dKx4v2dP2gohn/hQvjTd6/8Itd//G66PR/gR8f4bJyfgl4uRj/C3hq6B/8ARdX9Wxv/AD6l/wCAv/I5Z03DYr29hpulR3kN1DvST/VVyVnaQxXn7mvWNE+A3xivfD88uo/CHxWtysm1Ul0C5UkeoBSuYuP2evj1YXEvlfBjxdMh6bfDV0c/+Q6dPCYn2l/ZS/8AAWXBM7Xw34oh1n4XzaFLD5jx2skFcf4Hl1PXv+KVi3yPcfJFFVrwT8M/2jPD144uPgZ4xaF+oPhi7/8Ajdet/s7aF4g+E3iC58beJ/2efGV3eHy/ssUXhW6/d/3/APlnW39n4ivvTl9z/wAg56p4LqHgjUvC+oTabrNlIk0cuz97FXW/Bv4oXngPUHs7uFJ9NuJf9Ki/55/9NK9f/bBl8T/G2bRNV8CfATx7FdWVrJHdyXHhWZDJ/wCQ68Osvgv8f0fcPgn4wVvVvDN2P/adP+zcf/z7l9zMZ0qcz7n/AGe7Cz/4Q+bxJo0LpZ6pL5n+qqzrHxz1jwR4oSbQZoJHs5d/7z/npXcfAcw+Avgh4d8Hap4cX7TBpEDXoljO5bkHeQR1GDxXnXxP+EUmtXh17wjBcB57nM8E0DAqPUcc1yTwmN9r/Cl/4C/8j1KFT2eGsZ37QH7QXgn4l/FRPGFhpv8AZ02oWEf2/TY5d8f2j/lpJHWl8I/CXhv4jePNK8K3+sR6dZ6pdRwfbf8AnnXmHiz4J+NDraXFh4VvpIx1MdjIf5Cus1PwB4+0W2t7XSNHvWC/eeK3ZiPyFd9PBYudTWnL7n/kedNP2h7L+1Z+y/4J/Zf+Kdn4btfFT6qklrHdS+Z5cnmRyf8ATOuS8aeFvB/iizTU/C95HAkfyfZoovLryqPw18Wr/XEu9Q8N63Pxska5tJcY+u2tf+zviVFfIkXg3U/LHU/YJMf+g11zwdf7FOX3M5Z0pe15zr/h/wDDrxJa6p9sxJs/5ZSRV8i/taeMtSuv2iPENndTb30u6+y/9+46+4/C/jjxL4cXSr+/8GX0zW3l/a4YYXJk/Svz9+K3wr+P/jX4n614sj+DHjKUajrNxdiV/DV0f9ZJn/nnXLWweL/59y+5ndhVT+2afg/xb5uyGX/v7XrfwbjuV+Imiw2ke6I6tbMx9AJVrx3wt8JPjlbMi33wY8WKR/EfDtyB/wCgV9FfAD4e+NNJ1bTtW1zwhqlq/wBtiMv2nT5E2DeMk7lGBXlww2KhjKMlSl8UfsvuvI9GjTouTiet63pkNv40vtUij8p5Wj8yX/noBGg/pVy0+yeZ/t0/xF4N17VNcm1GzuLURSFdqyuwIwoB6Ke4qrZ+BPFds+4ajaKP9mR//ia4uIuEOKsTxDiq9HCTlCVSbTSWqcm09+qPrstxuW4fCRTqJSsr+tjfs9U8qPHyVleKPGWm6Xbv5syb6nTw74mVWU3Vqc9P3jf/ABNcl4n+D3jrXZt0ep6aqejzSZ/SOvJ/1L4v/wCgKf3L/M9CGaZYt6qPKvij4tvdZjuZpZvkq7+y/qEkvw3+IhaZXMOjIRj/AK43P+FaXir9lX4q6vp8lrpXiHREduhmu5gP0hNafwA/Zr+Inwv8F+MvDvirWNHnn8Q6esFi9jcSsiMI51JcvEpAzKvQHofx+m4R4S4kwObwq18LKMVGoru27pTS69W0jyczxuBqU26dRN3X/pSZxuh6r5Vv+6mqzeX8MsfE0nnV0th+zD8QbZds2taQf924l/8AjVXJv2c/HbIyxarpIz0zcS//ABuvA/1J4r/6BJ/cv8ztWcYJ/FUR5D4olhikSYfu/wDnrXkXxcuvtSYi+5X1Bqv7JvxH1Hdu1rRGz/fuZh/7Srh/G/7Anxk8RW32fSvEHhaIf9Nby4H8oDXThuDeKoVLvCT+5f5nLjMzwNSlaM0z5A82GKTzqsxap+7/AHtfQJ/4Je/tBAbl8YeDt3qdQu//AJGqxF/wTG+PKBWPirwfuHU/2hdf/I1e7DhbiRf8w0v6+Z8g6iufPNvFZ+ZU0kWm2v8Ax9WdfQyf8E0Pj2r7j4t8I/8Agfdf/I1XG/4JwfHJ7fyJfEvhGT3e/uv/AJGro/1W4g/6BpGd0fMMv9j+Z/x5bEpl5/wjfl/urOTfX1Av/BNf4yMrLNr3hI56Yv7r/wCRqgf/AIJq/HL/AJZeIfBo+t/df/I1ZT4X4je2Gl/XzIPlS8ihl/5B/mR1TlN5Ec4evrE/8Ezfj2N3k+K/B6Z6Y1C6/wDkaobj/gmL8fpYlRfFvg7I9b66/wDkap/1V4i/6Bpf18zeE6aPk6SWaXvTP+WtfUk3/BKr4/M7PF4w8HAnpnULv/5GqD/h1J+0Rv3/APCaeC8/9hC7/wDkWtP9VuIf+gaX9fMOaJ8y/vv+mtMr6f8A+HVP7RH/AEOfgv8A8GF3/wDItMb/AIJRftEO+4+M/Bf/AIMbv/5FrH/VLiF/8w0v6+Zr7aJ8x+VNdSV9Tf8ABKzT5LT4+6xJKm0nwdcDH/b3aU7Tf+CV/wAeLVv9J8VeDSP9nUbv/wCRq9j/AGUP2T/Hn7NPj3VfiR8R/FXh3+y/+EcnglltL6X9z+9hlMjmWJFVAsTEtnjjtkj3OHuHc7wed0K1ahKMIyu29kjCU01Y/9k=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r4j-Wl_95e0Z"
      },
      "source": [
        "## YOLOv4 on Webcam Videos\n",
        "Running YOLOv4 on webcam video is a little more complex than images. We need to start a video stream using our webcam as input. Then we run each frame through our YOLOv4 model and create an overlay image that contains bounding box of detection(s). We then overlay the bounding box image back onto the next frame of our video stream. \n",
        "\n",
        "YOLOv4 is so fast that it can run the detections in real-time!\n",
        "\n",
        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)\n",
        "\n",
        "Below is a function to start up the video stream using similar JavaScript as was used for images. The video stream frames are fed as input to YOLOv4."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZk68RUQW3Xq"
      },
      "source": [
        "# JavaScript to properly create our live video stream using our webcam as input\n",
        "def video_stream():\n",
        "  js = Javascript('''\n",
        "    var video;\n",
        "    var div = null;\n",
        "    var stream;\n",
        "    var captureCanvas;\n",
        "    var imgElement;\n",
        "    var labelElement;\n",
        "    \n",
        "    var pendingResolve = null;\n",
        "    var shutdown = false;\n",
        "    \n",
        "    function removeDom() {\n",
        "       stream.getVideoTracks()[0].stop();\n",
        "       video.remove();\n",
        "       div.remove();\n",
        "       video = null;\n",
        "       div = null;\n",
        "       stream = null;\n",
        "       imgElement = null;\n",
        "       captureCanvas = null;\n",
        "       labelElement = null;\n",
        "    }\n",
        "    \n",
        "    function onAnimationFrame() {\n",
        "      if (!shutdown) {\n",
        "        window.requestAnimationFrame(onAnimationFrame);\n",
        "      }\n",
        "      if (pendingResolve) {\n",
        "        var result = \"\";\n",
        "        if (!shutdown) {\n",
        "          captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);\n",
        "          result = captureCanvas.toDataURL('image/jpeg', 0.8)\n",
        "        }\n",
        "        var lp = pendingResolve;\n",
        "        pendingResolve = null;\n",
        "        lp(result);\n",
        "      }\n",
        "    }\n",
        "    \n",
        "    async function createDom() {\n",
        "      if (div !== null) {\n",
        "        return stream;\n",
        "      }\n",
        "\n",
        "      div = document.createElement('div');\n",
        "      div.style.border = '2px solid black';\n",
        "      div.style.padding = '3px';\n",
        "      div.style.width = '100%';\n",
        "      div.style.maxWidth = '600px';\n",
        "      document.body.appendChild(div);\n",
        "      \n",
        "      const modelOut = document.createElement('div');\n",
        "      modelOut.innerHTML = \"<span>Status:</span>\";\n",
        "      labelElement = document.createElement('span');\n",
        "      labelElement.innerText = 'No data';\n",
        "      labelElement.style.fontWeight = 'bold';\n",
        "      modelOut.appendChild(labelElement);\n",
        "      div.appendChild(modelOut);\n",
        "           \n",
        "      video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      video.width = div.clientWidth - 6;\n",
        "      video.setAttribute('playsinline', '');\n",
        "      video.onclick = () => { shutdown = true; };\n",
        "      stream = await navigator.mediaDevices.getUserMedia(\n",
        "          {video: { facingMode: \"environment\"}});\n",
        "      div.appendChild(video);\n",
        "\n",
        "      imgElement = document.createElement('img');\n",
        "      imgElement.style.position = 'absolute';\n",
        "      imgElement.style.zIndex = 1;\n",
        "      imgElement.onclick = () => { shutdown = true; };\n",
        "      div.appendChild(imgElement);\n",
        "      \n",
        "      const instruction = document.createElement('div');\n",
        "      instruction.innerHTML = \n",
        "          '<span style=\"color: red; font-weight: bold;\">' +\n",
        "          'When finished, click here or on the video to stop this demo</span>';\n",
        "      div.appendChild(instruction);\n",
        "      instruction.onclick = () => { shutdown = true; };\n",
        "      \n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "\n",
        "      captureCanvas = document.createElement('canvas');\n",
        "      captureCanvas.width = 640; //video.videoWidth;\n",
        "      captureCanvas.height = 480; //video.videoHeight;\n",
        "      window.requestAnimationFrame(onAnimationFrame);\n",
        "      \n",
        "      return stream;\n",
        "    }\n",
        "    async function stream_frame(label, imgData) {\n",
        "      if (shutdown) {\n",
        "        removeDom();\n",
        "        shutdown = false;\n",
        "        return '';\n",
        "      }\n",
        "\n",
        "      var preCreate = Date.now();\n",
        "      stream = await createDom();\n",
        "      \n",
        "      var preShow = Date.now();\n",
        "      if (label != \"\") {\n",
        "        labelElement.innerHTML = label;\n",
        "      }\n",
        "            \n",
        "      if (imgData != \"\") {\n",
        "        var videoRect = video.getClientRects()[0];\n",
        "        imgElement.style.top = videoRect.top + \"px\";\n",
        "        imgElement.style.left = videoRect.left + \"px\";\n",
        "        imgElement.style.width = videoRect.width + \"px\";\n",
        "        imgElement.style.height = videoRect.height + \"px\";\n",
        "        imgElement.src = imgData;\n",
        "      }\n",
        "      \n",
        "      var preCapture = Date.now();\n",
        "      var result = await new Promise(function(resolve, reject) {\n",
        "        pendingResolve = resolve;\n",
        "      });\n",
        "      shutdown = false;\n",
        "      \n",
        "      return {'create': preShow - preCreate, \n",
        "              'show': preCapture - preShow, \n",
        "              'capture': Date.now() - preCapture,\n",
        "              'img': result};\n",
        "    }\n",
        "    ''')\n",
        "\n",
        "  display(js)\n",
        "  \n",
        "def video_frame(label, bbox):\n",
        "  data = eval_js('stream_frame(\"{}\", \"{}\")'.format(label, bbox))\n",
        "  return data"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UfaHiGn461oG"
      },
      "source": [
        "### Running on Webcam Video"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "RPDr23YFW_7c",
        "outputId": "4b5e0aa4-1e06-404f-9b3b-0e57a4459a52"
      },
      "source": [
        "# start streaming video from webcam\n",
        "video_stream()\n",
        "# label for video\n",
        "label_html = 'Capturing...'\n",
        "# initialze bounding box to empty\n",
        "bbox = ''\n",
        "count = 0 \n",
        "while True:\n",
        "    js_reply = video_frame(label_html, bbox)\n",
        "    if not js_reply:\n",
        "        break\n",
        "\n",
        "    # convert JS response to OpenCV Image\n",
        "    frame = js_to_image(js_reply[\"img\"])\n",
        "\n",
        "    # create transparent overlay for bounding box\n",
        "    bbox_array = np.zeros([480,640,4], dtype=np.uint8)\n",
        "\n",
        "    # call our darknet helper on video frame\n",
        "    detections, width_ratio, height_ratio = darknet_helper(frame, width, height)\n",
        "\n",
        "    # loop through detections and draw them on transparent overlay image\n",
        "    for label, confidence, bbox in detections:\n",
        "      left, top, right, bottom = bbox2points(bbox)\n",
        "      left, top, right, bottom = int(left * width_ratio), int(top * height_ratio), int(right * width_ratio), int(bottom * height_ratio)\n",
        "      bbox_array = cv2.rectangle(bbox_array, (left, top), (right, bottom), class_colors[label], 2)\n",
        "      bbox_array = cv2.putText(bbox_array, \"{} [{:.2f}]\".format(label, float(confidence)),\n",
        "                        (left, top - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\n",
        "                        class_colors[label], 2)\n",
        "\n",
        "    bbox_array[:,:,3] = (bbox_array.max(axis = 2) > 0 ).astype(int) * 255\n",
        "    # convert overlay of bbox into bytes\n",
        "    bbox_bytes = bbox_to_bytes(bbox_array)\n",
        "    # update bbox so next frame gets new overlay\n",
        "    bbox = bbox_bytes"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    var video;\n",
              "    var div = null;\n",
              "    var stream;\n",
              "    var captureCanvas;\n",
              "    var imgElement;\n",
              "    var labelElement;\n",
              "    \n",
              "    var pendingResolve = null;\n",
              "    var shutdown = false;\n",
              "    \n",
              "    function removeDom() {\n",
              "       stream.getVideoTracks()[0].stop();\n",
              "       video.remove();\n",
              "       div.remove();\n",
              "       video = null;\n",
              "       div = null;\n",
              "       stream = null;\n",
              "       imgElement = null;\n",
              "       captureCanvas = null;\n",
              "       labelElement = null;\n",
              "    }\n",
              "    \n",
              "    function onAnimationFrame() {\n",
              "      if (!shutdown) {\n",
              "        window.requestAnimationFrame(onAnimationFrame);\n",
              "      }\n",
              "      if (pendingResolve) {\n",
              "        var result = \"\";\n",
              "        if (!shutdown) {\n",
              "          captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);\n",
              "          result = captureCanvas.toDataURL('image/jpeg', 0.8)\n",
              "        }\n",
              "        var lp = pendingResolve;\n",
              "        pendingResolve = null;\n",
              "        lp(result);\n",
              "      }\n",
              "    }\n",
              "    \n",
              "    async function createDom() {\n",
              "      if (div !== null) {\n",
              "        return stream;\n",
              "      }\n",
              "\n",
              "      div = document.createElement('div');\n",
              "      div.style.border = '2px solid black';\n",
              "      div.style.padding = '3px';\n",
              "      div.style.width = '100%';\n",
              "      div.style.maxWidth = '600px';\n",
              "      document.body.appendChild(div);\n",
              "      \n",
              "      const modelOut = document.createElement('div');\n",
              "      modelOut.innerHTML = \"<span>Status:</span>\";\n",
              "      labelElement = document.createElement('span');\n",
              "      labelElement.innerText = 'No data';\n",
              "      labelElement.style.fontWeight = 'bold';\n",
              "      modelOut.appendChild(labelElement);\n",
              "      div.appendChild(modelOut);\n",
              "           \n",
              "      video = document.createElement('video');\n",
              "      video.style.display = 'block';\n",
              "      video.width = div.clientWidth - 6;\n",
              "      video.setAttribute('playsinline', '');\n",
              "      video.onclick = () => { shutdown = true; };\n",
              "      stream = await navigator.mediaDevices.getUserMedia(\n",
              "          {video: { facingMode: \"environment\"}});\n",
              "      div.appendChild(video);\n",
              "\n",
              "      imgElement = document.createElement('img');\n",
              "      imgElement.style.position = 'absolute';\n",
              "      imgElement.style.zIndex = 1;\n",
              "      imgElement.onclick = () => { shutdown = true; };\n",
              "      div.appendChild(imgElement);\n",
              "      \n",
              "      const instruction = document.createElement('div');\n",
              "      instruction.innerHTML = \n",
              "          '<span style=\"color: red; font-weight: bold;\">' +\n",
              "          'When finished, click here or on the video to stop this demo</span>';\n",
              "      div.appendChild(instruction);\n",
              "      instruction.onclick = () => { shutdown = true; };\n",
              "      \n",
              "      video.srcObject = stream;\n",
              "      await video.play();\n",
              "\n",
              "      captureCanvas = document.createElement('canvas');\n",
              "      captureCanvas.width = 640; //video.videoWidth;\n",
              "      captureCanvas.height = 480; //video.videoHeight;\n",
              "      window.requestAnimationFrame(onAnimationFrame);\n",
              "      \n",
              "      return stream;\n",
              "    }\n",
              "    async function stream_frame(label, imgData) {\n",
              "      if (shutdown) {\n",
              "        removeDom();\n",
              "        shutdown = false;\n",
              "        return '';\n",
              "      }\n",
              "\n",
              "      var preCreate = Date.now();\n",
              "      stream = await createDom();\n",
              "      \n",
              "      var preShow = Date.now();\n",
              "      if (label != \"\") {\n",
              "        labelElement.innerHTML = label;\n",
              "      }\n",
              "            \n",
              "      if (imgData != \"\") {\n",
              "        var videoRect = video.getClientRects()[0];\n",
              "        imgElement.style.top = videoRect.top + \"px\";\n",
              "        imgElement.style.left = videoRect.left + \"px\";\n",
              "        imgElement.style.width = videoRect.width + \"px\";\n",
              "        imgElement.style.height = videoRect.height + \"px\";\n",
              "        imgElement.src = imgData;\n",
              "      }\n",
              "      \n",
              "      var preCapture = Date.now();\n",
              "      var result = await new Promise(function(resolve, reject) {\n",
              "        pendingResolve = resolve;\n",
              "      });\n",
              "      shutdown = false;\n",
              "      \n",
              "      return {'create': preShow - preCreate, \n",
              "              'show': preCapture - preShow, \n",
              "              'capture': Date.now() - preCapture,\n",
              "              'img': result};\n",
              "    }\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}