fake_articels_LSTM_&_ANN (2).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "taB4j1M8DgZ1"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.utils import plot_model\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import itertools\n",
        "import seaborn as sns\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization.\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Now you can use the file path to access specific files\n",
        "file_path = '/content/drive/MyDrive/gpt fakearticles/Fake.csv'  # Replace with your file's path\n",
        "file_path = '/content/drive/MyDrive/gpt fakearticles/True.csv'\n",
        "# You can then open and read the file as you would with a local file\n",
        "with open(file_path, 'r') as file:\n",
        "    content = file.read()\n",
        "\n",
        "print(content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "quPdwkRGEeez",
        "outputId": "83f7d966-924f-4805-902e-ad82f337b70d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "IOPub data rate exceeded.\n",
            "The notebook server will temporarily stop sending output\n",
            "to the client in order to avoid crashing it.\n",
            "To change this limit, set the config variable\n",
            "`--NotebookApp.iopub_data_rate_limit`.\n",
            "\n",
            "Current values:\n",
            "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
            "NotebookApp.rate_limit_window=3.0 (secs)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_true1 = pd.read_csv(\"/content/drive/MyDrive/gpt fakearticles/True.csv\")\n",
        "df_fake1 = pd.read_csv(\"/content/drive/MyDrive/gpt fakearticles/Fake.csv\")"
      ],
      "metadata": {
        "id": "UfaA_yMrEehd"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_true1[\"text\"][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cIwtGpYtEekZ",
        "outputId": "4b9cf74a-1bbb-44e2-a619-a120e39a6b6d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WASHINGTON (Reuters) - The head of a conservative Republican faction in the U.S. Congress, who voted this month for a huge expansion of the national debt to pay for tax cuts, called himself a “fiscal conservative” on Sunday and urged budget restraint in 2018. In keeping with a sharp pivot under way among Republicans, U.S. Representative Mark Meadows, speaking on CBS’ “Face the Nation,” drew a hard line on federal spending, which lawmakers are bracing to do battle over in January. When they return from the holidays on Wednesday, lawmakers will begin trying to pass a federal budget in a fight likely to be linked to other issues, such as immigration policy, even as the November congressional election campaigns approach in which Republicans will seek to keep control of Congress. President Donald Trump and his Republicans want a big budget increase in military spending, while Democrats also want proportional increases for non-defense “discretionary” spending on programs that support education, scientific research, infrastructure, public health and environmental protection. “The (Trump) administration has already been willing to say: ‘We’re going to increase non-defense discretionary spending ... by about 7 percent,’” Meadows, chairman of the small but influential House Freedom Caucus, said on the program. “Now, Democrats are saying that’s not enough, we need to give the government a pay raise of 10 to 11 percent. For a fiscal conservative, I don’t see where the rationale is. ... Eventually you run out of other people’s money,” he said. Meadows was among Republicans who voted in late December for their party’s debt-financed tax overhaul, which is expected to balloon the federal budget deficit and add about $1.5 trillion over 10 years to the $20 trillion national debt. “It’s interesting to hear Mark talk about fiscal responsibility,” Democratic U.S. Representative Joseph Crowley said on CBS. Crowley said the Republican tax bill would require the  United States to borrow $1.5 trillion, to be paid off by future generations, to finance tax cuts for corporations and the rich. “This is one of the least ... fiscally responsible bills we’ve ever seen passed in the history of the House of Representatives. I think we’re going to be paying for this for many, many years to come,” Crowley said. Republicans insist the tax package, the biggest U.S. tax overhaul in more than 30 years,  will boost the economy and job growth. House Speaker Paul Ryan, who also supported the tax bill, recently went further than Meadows, making clear in a radio interview that welfare or “entitlement reform,” as the party often calls it, would be a top Republican priority in 2018. In Republican parlance, “entitlement” programs mean food stamps, housing assistance, Medicare and Medicaid health insurance for the elderly, poor and disabled, as well as other programs created by Washington to assist the needy. Democrats seized on Ryan’s early December remarks, saying they showed Republicans would try to pay for their tax overhaul by seeking spending cuts for social programs. But the goals of House Republicans may have to take a back seat to the Senate, where the votes of some Democrats will be needed to approve a budget and prevent a government shutdown. Democrats will use their leverage in the Senate, which Republicans narrowly control, to defend both discretionary non-defense programs and social spending, while tackling the issue of the “Dreamers,” people brought illegally to the country as children. Trump in September put a March 2018 expiration date on the Deferred Action for Childhood Arrivals, or DACA, program, which protects the young immigrants from deportation and provides them with work permits. The president has said in recent Twitter messages he wants funding for his proposed Mexican border wall and other immigration law changes in exchange for agreeing to help the Dreamers. Representative Debbie Dingell told CBS she did not favor linking that issue to other policy objectives, such as wall funding. “We need to do DACA clean,” she said.  On Wednesday, Trump aides will meet with congressional leaders to discuss those issues. That will be followed by a weekend of strategy sessions for Trump and Republican leaders on Jan. 6 and 7, the White House said. Trump was also scheduled to meet on Sunday with Florida Republican Governor Rick Scott, who wants more emergency aid. The House has passed an $81 billion aid package after hurricanes in Florida, Texas and Puerto Rico, and wildfires in California. The package far exceeded the $44 billion requested by the Trump administration. The Senate has not yet voted on the aid. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = tf.keras.preprocessing.text.Tokenizer()"
      ],
      "metadata": {
        "id": "BSo09TVdIAcU"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fake_articlesdf = pd.read_csv(\"/content/drive/MyDrive/gpt fakearticles/chatgpt_fake_articles (1).csv\", encoding='unicode_escape')\n",
        "\n",
        "fake_articles = fake_articlesdf[\"text\"].to_list()\n",
        "\n",
        "fake_articlesdf2 = pd.read_csv(\"/content/drive/MyDrive/gpt fakearticles/chatgpt_fake_articles.csv\", encoding='unicode_escape')\n",
        "\n",
        "fake_articles2 = fake_articlesdf2[\"text\"].to_list()\n",
        "\n",
        "\n",
        "\n",
        "true_titles = df_true1[\"text\"].to_list()\n",
        "\n",
        "fake_titles = df_fake1[\"text\"].to_list()\n",
        "\n",
        "texts = true_titles + fake_titles + fake_articles\n",
        "\n",
        "#add a new dataset to this one to test (so the tokenizer has seen the words before)\n",
        "textswithfakearticles = true_titles + fake_titles + fake_articles + fake_articles2\n",
        "\n",
        "# Create labels\n",
        "true_labels = [1] * len(true_titles)\n",
        "fake_labels = [0] * len(fake_titles)\n",
        "fake_article_labels = [0] * len(fake_articles)\n",
        "\n",
        "labels = true_labels + fake_labels + fake_article_labels\n",
        "\n",
        "\n",
        "tokenizer.fit_on_texts(textswithfakearticles)\n",
        "\n",
        "tokenized = tokenizer.texts_to_sequences(texts)\n",
        "\n",
        "\n",
        "\n",
        "print(max(tokenizer.word_index.values()))\n",
        "\n",
        "vocab_size = max(tokenizer.word_index.values())+1\n",
        "\n",
        "print(vocab_size)\n",
        "\n",
        "\n",
        "#print(tokenized)\n",
        "\n",
        "print(len(tokenized))\n",
        "print(len(labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SS17hpHjIAeo",
        "outputId": "7fd906eb-c14f-4262-f5f4-9f3be127cb73"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "138053\n",
            "138054\n",
            "44948\n",
            "44948\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "word_counts = tokenizer.word_counts\n",
        "sorted_word_counts = sorted(word_counts.items(), key=lambda x: x[1], reverse=True)\n",
        "most_common_words = sorted_word_counts[:100]\n",
        "words, counts = zip(*most_common_words)\n",
        "plt.figure(figsize=(20,10))\n",
        "plt.bar(words, counts)\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 916
        },
        "id": "o2lgtw3wIAh2",
        "outputId": "a29612f9-8fe2-4285-826d-632c8ca105a1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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4bSMEycBll10WN998c/zqV7+Kk046qWJ5ly5dYtSoUQU7ueeff37ceOONMXz48Nhtt90iIuLxxx+Piy66KP71r3/F5ZdfXmn90aNHx+DBg2PChAlRVlYWv/zlL6vdSdV0oviMM86ITp06xaRJk6JTp07x9NNPxwcffBBnnXVWXHXVVZXWXbGj9cEHH8Rll10Wffr0iV69ekVExJQpU+KBBx6ICy64YKWvUW07Lava+fjss8+iefPmERHx4IMPxmGHHRb16tWLXXbZJWbPnl1jG48//vho0KBB/OUvf4lNNtmkVifZLrvssjj77LPj0ksvjR133DHWX3/9Sv9fU3L/ox/9KPbcc8847bTT4vPPP4+ePXvGrFmzIqUUt99+e/zwhz+sWHfmzJkV/37uuefi7LPPjnPOOafSezFixIj41a9+VWN7S/1yXMoB/cQTT4ztttsunn322Yovhx999FEcf/zxcfLJJ1cJm5Y766yz4oQTTogrrriiqC/AqxKAjR49Om699daIiHjooYfioYceiv/93/+NO++8M84555yC06mVGp7ce++9cfnll8dee+0VAwcOjB/+8IdVwoLqnHPOOXHllVdGRMQLL7wQZ511VgwePDgefvjhGDx4cIwdO7ZgbamdiNmzZ8eBBx4Yc+bMicWLF8f+++8fzZs3jyuvvDIWL14co0ePrraulBM4p59+ehxxxBExdOjQaNeu3Upfj+X+8Y9/xE477RQREXfeeWd06dIlnnjiiXjwwQfjlFNOqfaL0YpXJL744osxb968ip+XLl0aEydOLBgKRkRcf/31MXTo0Pj5z38el19+eUXnrVWrVjFq1KiCIcgDDzwQhxxySHTv3r1iv//EE0/EdtttF/fdd1/sv//+1dadcsop0bNnz5gwYUKt908REW+99Vbcd999MW7cuNh7771j8803jwEDBkT//v1j4403rrG2mIDoueeeiy+++KLi34UUavd5550Xzz//fEyePDkOPPDAiuW9e/eOiy66qNLJ5Ycffrji37NmzYohQ4bE8ccfX2l/ePPNN8ewYcNqfH6FOvqffvppjVfs3nPPPZV+/uKLL2LmzJnRoEGD2GKLLap83lb8ovrAAw9Ey5YtK35eunRpTJo0KcrLyyvVrLhOsZa//imleOGFFyrtoxs2bBjdunWLs88+u2D9z372sxg8eHDMnTu32uPa16/QXdULJj766KNKP3/xxRfx3HPPxQUXXFClH1SdYk+KRJS+Xyvmc7pc//79Y9asWbHbbrtVOpbXxqr0MUr58l/qfmbIkCFx2WWXxeDBgyv6YBFffQG99tprV/ocS+lDDx8+PM4555y4/vrra32Fb7EnxFbc1xSrWbNmFReKTJ48uWL/WFuF9k9vvvlmjfuHwYMHx/HHHx+/+tWvKr0X3/ve9+Loo4+usn4p+6cVlfqZWd7WRx55JP785z9X+h40aNCgOOuss+L6668vWPvKK6/Eb3/720ojjU8//fSKE0HV2XTTTWPvvfeOvfbaK/bee+9anSBarpT+V6mf7YiId999N4466qiYPHlyxQmcjz/+OPbZZ5+4/fbbC46Wufnmm6NNmzbRt2/fiIj4xS9+Eb///e+jc+fO8cc//rFguH3++efHf/7nf8Yf/vCH2GCDDWr1mkQU16dZHd9lCv1dfPDBB1X2j9Up9uR0qa/LGWecEePGjYu+fftGly5dav138eqrr1ZcpHDXXXfFnnvuGbfddls88cQTcdRRR9UYgpx44onxyCOPxLHHHlvU3+I111wT559/fhx//PFx7733xoABA+KNN96IZ555Jk499dSCdR988EEMHTq04PG30AWSpVx0GBExZsyY+OlPfxpt2rSJjTfeuMpFcoWO98W8h8u/R6aUYvTo0ZWupm/YsGGUl5cX7B8sV+r3g2KPaf369av084oXty7/eblVvch1+euy3HvvvRefffZZpX1T06ZNo23btisNQUq5ECyitItQzjzzzGjQoEHMmTOn0gWmRx55ZAwePLhgCDJo0KDYfPPNY8qUKRWfmQ8++CCOOeaYGDRoUEyYMKFKzaqcwzrmmGMipRQ33XRTtGvXrtbvTyntXK7UgOjiiy+OSy65JHr27FnUfub444+POXPmxAUXXPCN9S9LvdBxVfoK1FJinbfFFlukv/71rymllJo1a5beeOONlFJKL730UmrVqlXBuk022STde++9VZaPHz8+tW/fvsZtlpWVpXnz5hXd1g033DA9//zzKaWUWrRokV5++eWUUkqTJk1K3bt3L1h32GGHpd/+9rdVlv/2t79NP/jBD2rc5u9///tUv3791K5du9StW7fUvXv3ikePHj0qrVteXp7Ky8tTWVlZ6tixY8XP5eXlaauttkoHHHBA+r//+7+C2+ratWu6+uqr05w5c1KLFi3Sk08+mVJK6dlnn03t2rWrsZ1NmzZNL730Uo3rfF1ZWVnFo169ehWP5T/XpF27dmn69OkppZRuvfXW9N3vfjctWrQo/e53v6vxvfiP//iPNGHChCrLJ0yYkHbYYYcat7nffvulc845J6VU+bP6xBNPpM0226zK+ldffXW6+uqrU7169dLll19e8fPVV1+dRo4cmfr161ewrY0bN07/+Mc/qix/4YUXUuPGjQu2sWnTphXtKkaTJk3S7NmzU0opHXHEEemiiy5KKaU0Z86c1KRJkxprGzdunObMmZNSSmnQoEHp5JNPTiml9Morr9T4N1xeXp6eeOKJlFJKDz74YGrVqlV64IEH0sCBA9P+++9f4zanTZuWTj/99NSmTZvUqlWrdMopp6Snn366xpr1118/zZw5M6WU0oUXXph++MMfppRSmjp16ko/37vssksaMWJESqnye//UU0+lDh06FKz7wQ9+kI455pi0ePHiSnUPP/xw+u53v1uwrqysLL377rtVlk+fPj21bt262prmzZun119/vcbnUZ0VX5eDDz44DR8+PKWU0uzZswt+1lb8m13x73j5o2nTpunGG28suM1tt9023XPPPSmlyq/nCy+8kDbccMOCdd27d0/nnntuleXnnntulf3hipo2bZpee+21gv9fG/PmzUtXXXVV6tq1a1pvvfXSwQcfnMaPH5+WLl1aab3nn38+Pf/886msrCw9/PDDFT8///zzadq0aemKK66odn+xKjbddNM0ZcqUlFLl1/O1115LzZs3L1i37777pttuu63K8ltvvTXttdde1daceeaZ6cwzz0z16tVL/+///b+Kn88888w0aNCgtPPOO6ddd921qPYvWLAgHXrooemWW26p8n8rHiO+/jlr2LBh2mqrrdJ9991X1PZq4/jjj08LFiwouq66v4eajmvjxo2reIwYMSK1bt06HXXUURXHiqOOOiq1bt06jRw5sqh2TJ48eaXHtGL6Fysqdb9Wyud09uzZ6csvv6zxeRSyKn2MY489NvXp0yfNnTu3UlsnTpyYOnfuXG1NqfuZ9ddfP82YMSOlVPl1mTlzZmrUqFGNtaX2oVu1apUaNmyY6tWrlxo3bpxat25d6VHIDTfckL73ve+lDz74oKjnWKzDDjsstWvXLu29996prKws7bbbbmmfffap9rGi5Z/devXqpa5du6YePXpUPLbffvvUvHnzdMQRRxTcbosWLSqOoyu+nrNmzar2vVjV/dOqHJs23HDD9PDDD1dZ/re//S21adOmYN3dd9+dGjRokHbZZZeKfXevXr1SgwYN0t13312w7g9/+EM66aST0pZbbpnKysrSd77znfSTn/wk/f73v0+vvvpqjW0tpf9V6mc7pZR+9KMfpZ49e6YXX3yxYtk///nP1LNnz3TUUUcVrNtqq63SpEmTUkopPfnkk6lp06bpv/7rv9LBBx+cDj300IJ13bt3T82aNUuNGjVKW221VaXPXU370lL7NMV+lzn00EPToYcemurVq5e+973vVfx86KGHpkMOOSSVl5enPn36FNxeSin97ne/S23atEmXXXZZatKkScX7MXbs2LT33nsXfH6lvC4bbrhhtc9vZZo3b17xWezdu3caNWpUSqnm/uxyLVu2TI8//njR29x6660r+lErfk4vuOCCdOqppxasO+igg9KWW26Zhg8fnsaOHVupLzBu3LiCdbvuumvac8890/3335+ee+65NH369EqPQjbddNOK/n0xSnkP99577/Thhx8Wva2USv9+UOoxLaWUHnroobTDDjukiRMnpgULFqQFCxakiRMnpp49e6YHH3yw0rqr2me79dZb02677VZxDimllF5++eW0xx57pP/+7/+usZ0TJ05MDRs2TDvttFPFvnunnXZKjRo1qtLOrxs0aFDacccd02OPPZbWX3/9itd1/PjxBc9FrHiuZcX34o033kjrr79+wW01bdo0/f3vf6+yfPr06QXrVuUc1vrrr1/p9aytUtq5XCl9xJRS2njjjav9rrMyzZo1S88991zRdaX0L1f1e+yq9BWoHSNBMvDWW2/Fd7/73SrLly1bVuOVXx9++GG1ifA222yz0umlZs6cGQ0bNowRI0ZUmgNw4MCBK503cHmK2qZNm3j77bdj6623js0226xiPt7qPPDAAxVXQK3owAMPrDZ5X9Fll10Wl19+eZx77rk1rrf8eUV8Ndz5T3/600qn9/m6oUOHxtFHHx1nnnlm7LfffhVXFz344IPRo0ePGms7d+5c7TQ8NVmVqwQXLFhQkdpPnDgxfvjDH0bTpk2jb9++cc455xSse+GFF6qdGqNTp07x4osv1rjNZ555Jv7rv/6ryvIOHTpUuhJ+uVW5ImarrbaK+fPnV5lm6d13363272W5Pn36xLPPPhubb755jc/l67773e/G+PHj49BDD40HHnggzjzzzIrtrWxe7tatW8fcuXOjY8eOMXHixLjssssi4qvnXdMVo/PmzYuOHTtGRMRf/vKX+NGPfhQHHHBAlJeXx84771zjNnv06BE9evSIESNGxH333Rdjx46N3XbbLbbZZpsYOHBgHH/88VWu9mzYsGF89tlnERHx17/+tWI0xQYbbLDSuctfeOGFuO2226osb9u2bY2f+8ceeyyefPLJKiN+ysvLq50io0ePHlFWVhZlZWWx3377VRqqvHTp0pg5c2alK6hXdPjhh8fkyZOLvuJiu+22i9GjR0ffvn3joYceiksvvTQivpq79+vzmy83c+bMSCnF5ptvHk8//XSlqyobNmwYbdu2rXE+1ZkzZ1a7T2nUqFHBey1ERLz00ktx5513Vll+wgkn1HiF38477xyvv/56jX87K9OuXbvYfffd49VXX41XX301Xnjhhejfv3+0bt06xo4dWzEt0fIpK8rKymLfffet8nuaNGkSv/3tb0tuR3Xee++9au9xtWjRohqvFpoyZUq1+6CePXvGiSeeWG3Nqo6UqE6LFi3i4osvjoMPPjiOPfbYSv+3/OrITp06xTPPPBNt2rQp6neXqqaRYTUpdrRC//79K/79wx/+MC655JI47bTTKpYNGjQorr322vjrX/9asV+ujXbt2tXYL4korn+xomL3a8uV8jktLy+PLbfcMoYNG1bUNGcRq9bHePDBB+OBBx6ocq+5LbfcsuDI2FL3M61atYp33nmnSt/kueeeq3FEXUTpfeia9pc1ufbaa+P111+P9u3bx2abbVbl6vEVp35ZlXsY/Pd//3fcfPPN8cYbb8QjjzwS2223Xa1Gty6/unf69OnRp0+fSleTLu93rThS+OsaNWpUbX/g1VdfrXb0wKrun1bl2PTZZ59VO+qzbdu2FX2d6vziF7+I8847r8rUhBdeeGH84he/KPj6HHPMMXHMMcdERMQ777wTjzzySPzlL3+Jn/3sZ7Fs2bIa+3ul9L9K/WxHfPW94K9//Wulq5eXT71V033u5s6dW7HN8ePHxw9/+MM4+eSTY7fddqt26sHlvn5VeW2V2qcp9rvM8v5wSimaN28eTZo0qfi/hg0bxi677FJptE11fvvb38aYMWOiX79+MXz48IrlPXv2LHjML/V1adiwYUl/Ez179ozLLrssevfuHY888kjFaKiZM2eudIR069atixqtstycOXMqpkpt0qRJfPLJJxERceyxx8Yuu+xS8Grrxx57LB5//PGipgyO+GrfNnXq1FpNK7Wijz76KI444oiiaiJKew+/fuxdunRpvPDCC7HZZput9NxEqd8PSj2mRUT8/Oc/j9GjR1dM1xfx1ffppk2bxsknn1xxrihi1ftsF1xwQdx9992VRt1tvfXW8Zvf/CYOP/zw+MlPflKwnUOGDIkzzzyz0t/f8uXnnntuwdHwEV/tz+64447YZZddKvW3tttuu3jjjTeqrVm0aFG1x90PP/ywxhkYGjVqVPF3sKJPP/20ximtIko7h/Uf//EfMXfu3BpHMq6udi5XSh8x4qvpyIqZWnm5jh07FpxZpCal9C9X9XvsqvQVqB0hSAY6d+4cjz32WJUhxnfffXeNJ967desW1157bZV5Fq+99tqVdijee++92GGHHaJJkyYV08CMHDkyLr/88njggQdixx13rLauS5cu8fzzz0enTp1i5513jl/96lfRsGHD+P3vf1/jSecNN9ww7r333jjrrLMqLb/33nsLnmRcrpROS6lf/A8//PDYfffd45133qn0Gu63335x6KGH1lh75ZVXxi9+8Yu44ooromvXrrHeeutV+v/qTqTvtddeJbUz4quDwfLhixMnTozbb789Ir56vWqaimXbbbeNYcOGxQ033FBxgFuyZEkMGzasyr1Evq7YL8crHtDvueeeouazHjZsWAwaNCguuuii2GWXXSLiq7mjL7nkkrjyyisrtWPy5MkV/14eAr344ovVvg+F5jVdlQDssMMOi6OPPrpiPtaDDjooIr46wNb0JabU8GRFKaX44osvYsmSJZFSitatW8e1114bF1xwQYwZM6Ziqq2IiN133z0GDx4cu+22Wzz99NNxxx13RMRX79/XOzFfV+pJqkIH+zfffLPSsNTlVuUEzrXXXhtHHHFEPPbYY9W+94WGWF955ZVx6KGHxq9//evo379/xd/+n//854r949ct318vPwH04osvVjsXcqHPW6dOnaq9b8LEiRNr/DvcaKONYvr06VXm/J0+fXq1J1eXO/300+Oss86KefPmVfvaFLqJaMRX87D+4Q9/iLFjx8aMGTOiX79+8Ze//CV69+4dixYtiksuuST69+9f0eld1YCoFMunUzn99NMj4t/D+G+44YaKv+XqdOzYMcaMGVNl+owbbrihIqD8uuXHlwEDBsTVV1+9SjcvXtGCBQtiwYIFBf+/mHBheZhYG6t7vu5VuRdIKRdMfP1Ec0op3nnnnRg+fHiN9y6JKP2kSLH7teVK+Zw+/PDDMXPmzLjrrruKDkH22muv+Pjjj+PGG2+sNHXEwIEDVzplWm2//K/4+pe6nznqqKPi3HPPjbvuuivKyspi2bJl8cQTT8TZZ59d4w28lz+fUvrQK57IKUYxJ8RWvIdBTaqbwqVJkyZxyimnRETEs88+G1deeWWt+lAXXnhhRETFVJvF3kz7kEMOiUsuuaTixHRZWVnMmTMnzj333BrDk2LDz+VW5djUq1evuPDCC+OWW26peJ6ff/55XHzxxTXu9995551qP1fHHHNM/PrXv66xvZ999lk8/vjjMXny5Hj44Yfjueeeiy5dutQYEESU1v8q9bMd8dU+6uuvZcRXU7p9fdqhFS2fhm3TTTeNBx98sOIeUY0bN67xZs7LP3fFKrVPU+x3meWhfnl5eZx99tm1mvrq60o5OV3q63LWWWfF1VdfHddee21RU8SNGjUqfvKTn8T48ePj/PPPr/gOcvfdd6/0xOOll14aQ4cOjZtvvrmo6YQ33njj+PDDD2OzzTaLTTfdNP7v//4vunXrVtEXLGSbbbYp6QbhpVx0GBFxxBFHVExzW4xS3sOf//zn0bVr1xg4cGAsXbo09txzz5gyZUo0bdo0/vKXv9S4vyj1+0Gpx7SIr6aYqu74snz660JK6bO988478eWXX1ZZvnTp0pg/f36N7Sw1NI0o7SKUPfbYI2655ZaKi+OW909+9atfxT777FNwW9///vfj5JNPjhtvvLHie+RTTz0Vp5xyykrvr1JKgHbDDTfEKaecEm+99VZ06dKl1sfRVWlnqQHRiSeeGLfddlutpsNf0ahRo2LIkCHxX//1XzVOsfl1pfQvV8f32FL7CtRSXQw/YfUaP358atmyZRo+fHhq2rRp+vWvf51OPPHE1LBhwxqH9k2ePDmtv/76adttt00nnHBCOuGEE9K2226bmjVrlh599NEat7n77run448/Pn3xxRcVy7744ovUv3//tMceexSsmzhxYvqf//mflNJX0zdsvfXWqaysLLVp06Zi+HR1xo4dm+rXr5++//3vp0svvTRdeuml6fvf/35q0KBBGjt2bI1tPeGEE9L1119f4zrVmTt3brruuuvSueeeW2m6kjPPPLPo31UbpU478dFHH6WrrroqDRw4MA0cODCNHDkyffzxxyvd3nXXXZcaNGiQWrVqlbbffvuKKWmuueaagsOyU/pq+qK2bdumjTbaKO23335pv/32SxtttFFq27Zteuqpp2rc5sCBA1O/fv3SkiVLUrNmzdKMGTPS7NmzU48ePdIZZ5xRad0zzzwzffrppymllH7+859XeQ9W9n5U93pW93Oh6YgKTclSk3feeSdNmzat0vQ+Tz311EqnOVuyZEn69a9/nQYNGpSmTZtWsXzkyJFpzJgxBetOPfXUtNlmm6XevXunDTfcMH3yyScppZT++Mc/1jhMPqWvpmg79dRT0wYbbJA22WSTdO6551aaUuKaa65Jbdu2rVQze/bs1Ldv37T99tunG264oWL5z3/+83T66afXuL2zzjor7b777umdd95JzZs3T6+99lp6/PHH0+abb14xdVh1fvSjH6WTTjoppZQqPjOffPJJ2nfffdPxxx9fsG7cuHHp888/r7FNX3fDDTekBg0apGbNmqXNNtus0lDiTp061Vj75ZdfVhm6PnPmzDR//vwa62bMmJG6detW5bO4/DNayJgxY1KHDh3S7bffntZff/30xz/+MV122WUV/y7k4osvTq1atUrDhw9Pjz76aHr00UfTsGHDUqtWrdIll1xSsK7Uv4vvf//7ab311kvbbbdd+s1vflPtFDDz589PZWVlBX/HN+Gxxx5LzZo1S6ecckpq3LhxOuOMM9L++++f1l9//fTss88WrJswYUJq3Lhx6tKlS8U+uGvXrqlx48YlTUVRGytOC3j11VenUaNGpXPPPTe1b98+/fjHPy5Yd/HFF9f4WNFFF11U68fqcO+996YlS5ZU/LumR0023XTTdNVVV1VZftVVV6VNN9202ppCx4FevXqtdN9dav+i1P1aqZ/TUj3zzDNpww03TB06dKiY+uU73/lO2nDDDdPUqVNrrD3ooIPSL3/5y5TSv5/j0qVL0xFHHFExlU9KhV//2kyFttzixYvTiSeemBo0aJDKysrSeuutl+rVq5eOOeaYlU4FVmofOqWUXn/99XT++eeno446qmJff//991c7FWcpZs2aVevH19W2DzV48ODV0tblPv7449S7d+/UqlWrVL9+/dSxY8e03nrrpT333LOiPYV8+umnacKECen666+vsp8rpNTPTEpfTQ/Tvn37tOGGG6Z999037bvvvhWf95rew4MOOijddNNNVZbfdNNN6YADDihY16tXr9S4cePUo0ePdOaZZ6bx48fXesqbUvpfq/LZPuSQQ9Kee+6Z3nrrrYplb775Ztprr71Sv379CtYdffTRaYcddkgDBw5MTZs2Te+//35K6av9+nbbbVfjNj/66KM0ZsyYNGTIkIq+wtSpU9Obb75ZsKZQn6Zly5Y19mlW5btMqbbddts0fvz4lFLlaVWuueaalfbZi9WvX7/UsmXL1KlTp/T973+/0vRdNU1LVsjnn39ecYwupHv37ql58+apWbNmqUuXLrWeumvgwIEVfYlrr702NWnSpGIfcsIJJxSse/rpp9O+++6bJk+enN5///2KaZiWPwqZNGlS6tWrV3r44YeLqrviiitSmzZtUv/+/dNVV11V631USsV/ttu3b5+eeeaZlFJK99xzT2rfvn165ZVX0i9/+cuVTpda6veDFX3++ee1fl1SSmmPPfZI+++/f6Wp0ufNm5cOOOCAtOeeexasK6XP9v3vfz/16NGjUh/k2WefTTvssEM6+OCDa2znd77znXTnnXdWWX7HHXekjh071li7xx57pGuuuSal9O8+TUopnXbaaQWnwnvhhRdS27Zt04EHHpgaNmyYDj/88LTtttumdu3a1Tj98kcffZQOOeSQimkhGzZsmMrKylK/fv3SRx99VGM7zzjjjIpjxJdffpl23XXXVFZWltZff/1qp39MKaUpU6akTp06FX0cra6d9erVS/369Vvpuaja9hG/btCgQalVq1Zpzz33TKeddlqtz9GtON1bs2bNaj3d26r0L0u1Kn0FaqcspRLGBbHWeeyxx+KSSy6J559/Pj799NPYYYcdYujQoTUOWZ4zZ040aNAgrrvuunj55Zcj4qsrY372s5/Fl19+GZtuumnB2iZNmsRzzz1XZSjpiy++GD179qxxGPnXffjhh7W6SdZTTz0V11xzTaWbEA4aNGil0/4MGzYsRo4cGX379q311d2TJk2KQw45JDbffPN4+eWXo0uXLhU3Dd9hhx3ib3/7W62fX2098sgjNf5/daM+nn322ejTp0+lETnPPPNMfP755/Hggw/GDjvsUOPvnDp1asyZMycOOOCAiquaJkyYEK1bt67xip9FixbFrbfeWulzc/TRR6/0yqgFCxbE4YcfHs8++2x88skn0b59+5g3b17ssssu8b//+7+V6lcc/VHT1RJlZWXVvh8rez1XtCojalanYkcDfPHFF3H11VfH3Llz4/jjj6+4wuw3v/lNNG/evOB0PF27do2XX345DjjggDjppJPi4IMPrnJFwvvvvx9t27at8Yq/YixZsiROPfXUGDduXCxdujQaNGgQX375ZfzkJz+JcePGFbwi4s0334w+ffpESilee+216NmzZ7z22mvRpk2bePTRR2u80i/iq8/4ilP21XT148YbbxyDBg2KIUOGRL169Up/skVY/trfcMMN0alTp3jqqafiww8/jLPOOiuuuuqq2GOPPQrW3nrrrXHRRRdVDMNu3759XHzxxTXe7DSlFKNGjYoRI0bE22+/XVF3zjnnxKBBgwruh2samhxR+Or9gQMHxoknnljjVbUppZgzZ06V33HLLbfUuM2VXeFdrBkzZsSwYcMqHUfPPffc6Nq1a411c+fOjeuvv77S/vCUU04pOBJkuUWLFsXw4cNj0qRJ1d7Uc8aMGdXWfX00Vb169WKjjTaKfffdN84777yCIwm+/tn/+g3VV/eIjmLUq1cv5s2bF23btq3xb6+6K95XNG7cuDjxxBPjoIMOqugbPPXUUzFx4sQYM2ZMHH/88VVqvv7ZXv561uYK+FL6FxGrtl8r9XNaij322CO++93vxpgxYyqmFvzyyy/jxBNPjBkzZsSjjz5asPYf//hH7LfffhV9pkMOOST++f+1d+dxNab//8Bfp037QmWNVhFlHcNYw5AlZB1K9n0qSWSGCEMfI2UbW6GQdUYYM0RRCoVSWaOQfbKEitFy/f7od+5vp7N0zunUkXk/H48eD+7O3X2dus99X/f1vq73++ZNvHnzBomJiVzawcquLeVVtkooJycHN27cQH5+Ptq1ayc0O1wcefrQcXFxGDBgALp27Yr4+Hjcvn0blpaWCAwMxNWrV3HkyBGx++bl5eHIkSPIysqCr68v6tati5SUFNSvX7/S9F2i+gk8Hg/Ozs4Cr6tqH0pFRUViv7yy1aaJiYkCv8++fftKfH1qaioGDhyIwsJCFBQUoG7dunj16hVX7Fbc9VDeexNfYWGhUH/W1dVVIN1RRVu3boW/vz9Gjx4tsNL48OHDCAgIQKNGjbjXlu+/1a1bFyoqKujXrx969eqFXr16oXnz5hLbV1XynNtA2X2N/5nl38tycnJgb2+P48ePi119kpeXhyVLliAnJwezZs3i0o8uXboUGhoa+Pnnn0Xul56ejr59+3Izx+/evQtLS0ssXrwYOTk5YvsDovo0jRs3xvz58yX2aQD5nmVevnyJ+fPnc/ftisMokj4XoaGhWLZsGYKCgjBlyhSEhoYiKyuLW5Hyww8/CO0j7+dw0qRJYvcBJKerlPf6FBAQIPGY4lZElJaWorS0lLvHHDhwABcvXoSNjQ1mzJghNq3OvXv3MG7cOKG+C6ukwDm/j1Hx91rZfqLSp/HxeDyx1yh5zm1NTU3cv38fTZo0wfTp06GtrY2QkBA8ePAAbdq0qTQFsTzPBwUFBVi4cCEOHTqE169fC31f0rl9//59uLi4IDMzk7tePH78GDY2NoiKihKb1UCePltubi4mTJiAU6dOcX2u4uJi9O/fH7t375bYf1q+fDmCg4Ph5+fHjXMkJibif//7H+bNmydxdUFCQgIGDBgANzc37N69GzNmzMCtW7dw8eJFxMXFic2C8u7dO2zatEngGjxnzhw0bNhQ7LH47t+/LzDuJU2Ku8aNG+PYsWPo2LEjoqKiMGfOHJw7dw579uxBbGwsEhMThfaxs7NDy5YtsWDBApGF0Su7j967d0/gOipNO6XtI1YkT38GAFfQXpzKVkLJ078MDw+HsbExBg0aBKAsleb27dthZ2eH/fv3i/29KqOv8F9DQZD/MFVVVTx//lzoZvH69WuYmppKvNnVr18fe/bsEepEnz59Gu7u7pUuR6xJ8nRaOnXqhAEDBiAgIAB6enpIS0uDqakpXF1d4eTkhFmzZlVLW2VNOyHrAMW8efOwYsUK6OjocEvUxVm3bp0C3pFosj4c16SIiAiMGTNGaCnm58+fceDAAYkDr1evXsWhQ4dEBjL++OMPsftlZ2dj+PDhyMjIAADuoYrfCVF07scVK1Zg8uTJaNy4sdCxpPXp0yeh9yhNWp/Hjx8jIyMDBQUFaNeunVQdpeLiYhw4cADp6encOVPZIMU///yDH374AefPn+eWaOfl5cHR0REHDhwQmX6tbt26uHLlilQ1Qdq3b4+YmBgYGRlVmjpI0uCysbExYmNj4eDgAAMDAyQnJ8PW1haxsbHw8fHh6keUV1xcjMjISPTv3x/169dHYWEh8vPzKw0IVcTP4yop/U5F0g7AifLp0yeZUqtUXLZdVFSEwsJCaGhoQFtbu9LaVbJwd3eHo6MjevToIXNNGHmNHTsWcXFxGD9+PBo2bCh0Dnl5eVV7G96/f4+JEyfCxcVFqJZIbSXvhAl5yDsoAsh+XSsqKsKMGTOwZMkSicdVpKpOeKnKw/+XrkuXLhg1ahTmzZvH9RP5qQ+GDx+OJ0+eiNxP3sHe7OxsuLi4ICMjQyBFVnX1E6KiogSuSUVFRUhNTUV4eLjEwTR5+1D8h/ytW7fCwMAAaWlpUFdXh5ubG7y8vGRO5VadpJ0kUXFAlf3/OlDnz59HXFwc4uPjoaGhgZ49e8LR0VFiTQlZn9mKi4uxatUqTJ48udJ0peIwxhATEyNwLZXUX6/KMfv27Yv27dtjzZo1Ap+nixcvYty4cWJT6nz8+BGMMWhra+PDhw948OABYmJiYGdnh/79+8vUBmkMGDAAOTk5+PHHH0Xet4cOHSpxf1kHp48dOybwf2k/h/JKT09Hnz59YGhoKNP1SRk6deoENTU1eHl5iRy4FTfBTZ5Jh1Uhz7ndrFkz7NixA3369IGFhQW2bNmCQYMG4ebNm+jWrRvevn0r1bFleT7gD5avWLEC48ePx+bNm/H06VNs27YNgYGBEmttAGXXizNnzggMhvft27faJrlmZmZyx2rRooVUg8TyTgTjy8rKQmBgoNSTUHJycmBmZiby5+bk5AhMOK5sXKY8SWM08gTQdHR0kJaWJnMdoezsbJlrqJb3NfcRgbJaNVu2bEHv3r1x6dIl9OnTByEhIfjzzz+hpqYmdmyoKn0FIh0KgnxFPn/+LHI2qbgVHeVnXpb36NEj2NnZSSye5enpiaNHj2Lt2rUCkXRfX1+MGDGiSsW1xCktLcX9+/dFvscePXoo9Fh6enq4fv06rKysYGRkhISEBLRq1QppaWkYOnSoxPyW8rp69SqcnJygqakp9aoOWQco5J0ZePz4cQwYMADq6uo4fvy4xPdRWQ7ImJgYsTOfd+7cKXFfWX369Anp6ekijyWunfIGB/kP9/3790d0dDT69euHzMxMvHz5Ei4uLhJnXVVcDZCcnIzXr1+LXA2gqL9FWFgYgoODce/ePQBlhcjmzp0rdvUIULVZQvIeU9aBc74xY8YgOzsbERERXA7cW7duYcKECbC2tsb+/fuF9vH29oaJiQl++umnSn9+QEAAfH19oa2tLffsN6BsoD8lJQUWFhawsrJCaGgoHB0dkZWVBXt7e7GDjNra2rh9+3aV6ifIQt4BuNLSUvzyyy/YunUrXr58iczMTFhaWmLJkiUwNzeX+QH+3r17mDVrFnx9fRU6wDF16lTEx8cjKysLjRo1Qs+ePdGrVy/07NlTaLZPVYoVl2doaIiTJ0+ia9eucrdbETIyMuDs7CxwX6tbty4yMzNhbGxc6UpNRQajKpL38y+ruLg4rF27VmACgq+vr8SVWMpgYGCA69ev11gQRN4JL0VFRXBycsLWrVulXo0BlK2sqV+/PiZPniywfefOncjNzRVbgL6kpAS7d+8W27eQZvWurH1oXV1drrhy+YGthw8fokWLFvj06ZPI/eQd7K3KqkFFioyMxMGDB4UGZ/nk7UMZGhoiKSkJtra2MDQ0xKVLl9CyZUskJSVhwoQJ3GCXKHv27MHWrVvx4MEDXLp0Cc2aNUNISAgsLCwkDkrLO0tTERhjuHbtGjZt2oR9+/ZVWuxU3DPbs2fPYGVlJbI2gq6uLm7cuCFT/vPy5Omvy3tMAwMDpKSkwMrKSuBz8ejRI9ja2or9PPXr1w/Dhw/HzJkzkZeXhxYtWkBdXR2vXr3CunXrBCatVdZnLk9c/1lPTw8XLlyotF5UZeSdvMJX2eeQLzc3F3fv3gVQNiAnagJQefJen6pKnuc1bW1tpKamylzMuabJc24vW7YMISEhaNiwIQoLC5GZmYk6depg586d2LFjBy5duqTwdjZt2hQRERHo1asX9PX1kZKSAmtra+zZswf79+/HX3/9pfBjKpOsE8Fu3LiB1q1bi/xeVFSUyHpfstwPK47LpKSkoLi4mDu/MzMzoaqqig4dOkjs08gTQHN2dsbEiRMl1u0SRUVFBU2aNBF4ZpI1kFKTSkpKEBUVJZAhYsiQIRLrczDGcOTIEZw7d07k9UnSJFdtbW3cuXMHTZs2xcKFC/H8+XNERETg5s2b6NWrF3Jzcytts6x9BSIdKoz+Fbh37x4mT56MixcvCmwXt6yTH2nm8Xjw9/cXKEpUUlKCpKSkSjt3a9euBY/Hg7u7O1ecSl1dHbNmzUJgYKAC3pWgy5cvY9y4cXj06JHQ0mNx71GaFQ88Hg9BQUFC23V0dLiZzg0bNkRWVhZatWoFAHIVU5OGt7c3nJ2dRa7qmDt3rsi0E/r6+sjJyREKgjx+/FjkTb18sSxZir8PGzaMe/iSVNSzslQlAQEBWL58OTp27ChyBpUinTp1Cu7u7iL/XpLayf/cVPTkyROJhWBXrVqF4OBgzJkzB3p6eli/fj0sLCwwY8aMSmc0XLp0CbGxsTA2NoaKigpUVFTQrVs3rrh7+dUAivhb+Pv7Y926dfDw8ODSFF26dAne3t7IycnB8uXLRe63YMECnDt3Dlu2bBE5S0gSeY9pamoKFxcXuLm5oU+fPlLPwDx16hTOnj0rUATQzs4OmzdvFpsGoqSkBGvWrMHp06fh4OAglNqm/Myb8oENeQtXAkDr1q2RlpYGCwsLfPvtt1izZg00NDSwfft2ibNrOnXqhNTUVJkHaeRN5eDl5QULCwvExMSIHIATZ+XKlQgPD8eaNWsEZq60bt0aISEhMgdBbGxsEBgYCDc3N4mDYrIKDQ0FADx9+hTx8fGIi4tDUFAQ9/ktP6u7KsWKyzMyMkLdunUV8waqQFRBdX5KPQDVMqlBkpKSEqxatarKgTNpV6vt3bsXkyZNwvDhw7n0VYmJiejTpw92796NcePGVXqsz58/48GDB7CysuLu3xUpYgBu2LBhiIqKgre3t9Q/qyrGjBmDKVOmiJzwMnbsWLH7qauryxQs5Nu2bRsiIyOFtrdq1YorTimKl5cXdu/ejUGDBqF169Yy9S1k7UPzGRoa4vnz50IBqdTUVIkpY65cuYJt27YJbW/cuDFevHghdr+K/QRVVVWx/YTq1LlzZ0yfPl3s9+XtQ6mrq3P3d1NTU+Tk5KBly5YwMDDA48ePxe63ZcsW+Pv7Y+7cufjll1+4v5ehoSFCQkIkBkFWrVqFLVu2ACj7/W7atImbpent7S12cENcfwUou+6LS6mSkpKC8+fP4/z580hISMCHDx9gb28PDw8PsbPPN2zYwP3c0NBQ6Orqct8rKSlBfHy80DMAX58+fRAXFydXEETe/rq8x6xTp47IGcqZmZkSB+5TUlIQHBwMoKx4d/369ZGamorff/8d/v7+AkEQSX3m8iR97s3MzCq994tTftWKtrY2cnNzERISAjs7u0rTk1VU2eewoKAAHh4eiIiI4AbtVFVV4e7ujo0bN4otXC7v9QkoOx+Dg4PFrogXN2FC3ue1jh074vHjx3IHQQoLC0W2U9LklSdPnuD48eMi9xM3O1+ec3vZsmVo3bo1Hj9+jFGjRnEr61RVVcUWDOd7/fo1/P39xQ7aivs7vHnzhnvu0NfX517XrVs3kRkw+NcmaYhLDQqUra7YtWsXsrOzERISAlNTU/z9999o2rQpN/aiqFUSvXv3xh9//AFDQ0OBcZL3799j2LBhEoML/fv3R0JCgtA9//fff4e7u7vIycPi7of5+flCE3zKj8usW7cOenp6CA8P51bFv337FpMmTap0wsOkSZMwevRo7rrNX72XlJQk9l7h7OwMb29vZGRkiEztKq5P+vjxY261Av85jz+RzNHRUeIkx1OnTkFXVxfdunUDAGzevBk7duzgntXFFXF3cXER+Tvl8XjQ1NSEtbU1xo0bJ3RduH//PgYOHIinT59y31u9ejXMzMxw8uRJsVkA5s6di23btsHR0VHkijNJdHV18fr1azRt2hTR0dHceaypqSly4gKfPH0FIqPqLDhCasZ3333HevTowf766y+WmprKrl+/LvBVUa9evVivXr0Yj8dj3333Hff/Xr16sX79+rHp06ezzMxMqY5dUFDA0tPTWXp6OisoKFD0W+O0adOGjRo1it26dYu9ffuW5eXlCXxV1KtXL65wVPn3V/HL0dFR5PGGDh3Ktm/fzhgrK+hsbW3NVq5cydq3b8/69OlTLe9RU1NTZBHWmzdvMi0tLZH7eHh4sCZNmrADBw6wnJwclpOTw/bv38+aNGkiVGj8S9CgQQMWERFRI8eytrZms2fPFijSJknbtm1Zu3btmIqKCrO3txco5ufg4MD09PTYqFGjxO6vra3NHjx4wBhjrG7duiw9PZ0xxtitW7dYgwYNJB7b0NCQK7BmaWnJYmNjGWNlRVfF/e2rwtjYmEVGRgptj4yMZPXq1RO7n5mZGVdUjV/cnDHGIiIi2IABA6rlmH/88QcbOXIk09LSYg0aNGBeXl5csUBJdHV1WWpqqtD2lJQUpqenJ3Ifea4V5f3777/s8ePH7NGjRwJfkpw6dYr9/vvvjDHG7t27x2xtbRmPx2PGxsYsJiZG7H4HDx5klpaWbOPGjezixYssLS1N4EscJycnZmdnx3777Td29OhRFhUVJfAlTr169bifq6+vz+7cucMYKysw2bZtW7H7WVlZsbNnzzLGBAuB3r59mxkaGordT5LU1FSxf8OqKigoYKdPn2Z+fn6sc+fOTENDQ+j9VaVYcXl79uxhI0eOrNZ7Z3nyFlQfP348CwsLk1jEUZECAgKYpaUl27t3L9PS0uLOmQMHDrDOnTtL3LegoIDNmTOHmZiYMBUVFaEvUVq0aMHWrVsntD0oKIi1aNGi0uNNnjyZqaqqMlVVVa6tP/74I1u9erXAa8UV/hZV1FmcFStWMENDQzZixAi2atUqmQqzyuPff/9lnp6eXDFJFRUVVqdOHTZ37lz26dMnifvOnTuXLVy4UKbj1alTh7sXlpeVlcXq1Kkjdr969eqxkydPynQsPln70Hw+Pj6sW7du7Pnz59z9MCEhgVlaWnJFfkUxMTFhKSkpjDHBa2J0dDRr0qSJ2P1qup8gSmFhIfPy8mLNmzcX+l5V+1Dff/8927dvH2OMsalTp7JOnTqxvXv3sv79+7NOnTqJ3a9ly5bs6NGjjDHB32dGRobEvgVjjGlpaXH36AULFrDx48czxhi7ceMGMzY2Frtf27ZtBb5atWrFtLW1mb6+vsQC0Kqqqqxjx47Mx8eHHT9+vNLCsYwxZm5uzszNzRmPx2NmZmbc/83NzVnz5s1Zv3792OXLl0Xuu2XLFtagQQPm4+PDIiMj2bFjxwS+JJG3vy7vMadMmcKGDRvGPn/+zBXJffToEWvXrp3E55nyf8NRo0Zxn72cnJxq+VycPn2a9evXj+vvy+L7779nW7ZsYYyVFRQ2NTVlTZo0YZqamuy3336T+udI+hzyTZ8+nVlaWrK//vqLK2x98uRJZmVlxWbOnCl2P3mvT4wxtmTJEtawYUO2du1apqmpyVasWMGmTJnC6tWrJ/H+JOvzGt+hQ4eYnZ0d27VrF7t69arU/eB//vmHDRo0SGQfQdL99+zZs0xbW5u1bt2aqampsbZt2zJDQ0NmYGAg8RlB3nNbXgMGDGA2NjYsMDCQ7dq1i+3evVvgSxx7e3t2/vx5xhhjffr0YT4+Poyxsr5j48aNhV5f/lok6cvCwkLsMc+fP8+0tLRY3759mYaGBne+rV69WqA4tqTnM1me1Xg8Hnv58qXQ9pcvXzI1NTWJ+/r7+zNLS0v2/PlzbtuBAweYtra2ULF1fpFuFRUVNmPGDIHC3Z6enuzbb7+VWOC+UaNG7MaNG0LbMzIyWMOGDSW2kzHGDh8+zNatW8ceP37Mbdu9e7fY5zx5+6QVZWZmsgkTJjA1NbVK92vdujXXb0tPT2caGhps0aJFrHPnzmzixIli95swYQIzMDBgzZo1Y8OHD2fDhw9n5ubmzNDQkI0ePZrZ2tqyOnXqsISEBIH9BgwYwJycnNjr16+5ba9evWJOTk5s4MCBYo9nZGQkd/9y3LhxrH379mzKlClMW1ubvXr1ijHG2LFjx1irVq3E7idPX4HIhoIgXwFtbW2Rg+eVmThxInv37l01tEjxtLW1uQHXmpCVlcV1oPLz89mMGTOYvb09Gz58eKUDW/IyNTVlp0+fFtp+6tQpZmpqyv0/LS2NlZSUMMaqNkChDHXr1q2xgTQ9PT2ZjrVs2TK2bNkyxuPx2Pz587n/L1u2jK1atYpFRkayf//9V+z+jRs35gIf9vb23ID/xYsXmb6+vsRjd+vWjXuIHzt2LHNycmIJCQnM3d1d4k2SsbJO+aJFi9iUKVPYpEmTuK/JkyeL3cfAwEBkoPPu3bvMwMBA7H46Ojrcw2bjxo1ZUlISY4yx7OxspqOjI7Gd8h6T7/3792znzp3s+++/Z6qqqszGxoYFBASIff2QIUNYjx492NOnT7ltT548YT179mTDhg2r9HiyuHv3LuvWrZvQg5SsnUe+169fs9LSUomvkbfDKi44VBl5B+A0NTW5a2b5B+qbN29Wes5UHDyJiopiW7ZsYa1atWJOTk4yvwdJFi1axLp06cI0NTVZu3bt2Ny5c1lUVBR78+aNQo9TXtu2bZmenh7T1dVlrVu3Fhg0lDSQJq+KD6aWlpbs22+/ZYsWLWLv378Xu9/UqVOZjY0NU1FRYU2aNGGurq5sx44dUk+WkFVVAmezZ89mLVu2ZEeOHGFaWlps586dbMWKFaxJkyZs7969IvfR0NAQ2b+4d++exIF3xhjz9PRkHTp0YBcuXGA6OjpcW6OioiQGB+Ul70BDVckz4eXHH39k+vr6rEOHDmz69OkCgwDe3t4i97G2tmZ79uwR2h4RESHx/TVs2JDdvXtXujdTgbx96H///ZdNnTqVqampMR6Px9TV1ZmKigpzc3NjxcXFYveTd0CsKv0EeRgaGjIjIyPuy9DQkKmqqjI9PT2Rg9lV7UNduXKFu6+8fPmS9e/fn+np6bH27dtLDEaJu8dkZmYyTU1Nie+x/IBv27ZtuUH/+/fvV3p/qujdu3fMxcVFYuCgKs9c5Sd3SasqA1vy9tflPWZeXh7r27cvd56ZmZkxdXV11qNHD5afny92P3t7e7Z+/XqWk5PD9PX12cWLFxljjF29epXVr19f5vZXxtDQkHvm0tXVFfiMGBkZSdy3Xr163MDmjh07mIODAyspKWGHDh0SG3CX9XNY/lj8SUvlxcbGSgzwVWXA3tLSkv3555+MsbLPIv/8Wb9+vcSJFrI+r/GJO8cqO9fGjRvHunbtyq5cucJ0dHRYdHQ027NnD7O1teXaL8o333zD/P39ufeXlZXFPnz4wIYMGSIxiCXPuR0QECDxSxJdXV2J10xx1q1bxwWrzpw5wzQ1NVmdOnWYiooKCwkJkfnnSaNz584sKCiIMSZ4/U5KShIZeJEXPzDG4/HYuXPnBIJlKSkpbNWqVaxZs2aV/pwff/yRtWrVir1+/Zrt27ePaWlpsSNHjgi9rqoTjnV1dcV+fnV1dWV569WKP3Gs/DNU27ZtuWcoSXR0dLhg8tKlS7mg17Vr1yReuxcuXMhmzZrFjYUxxlhJSQn78ccf2aJFi1hpaSmbPn0669q1q8B+2tra3DhNedevX5d4vzc3N5erj8hYWbB7zpw5bMiQIezvv//mtvv7+7OVK1eK3a+2jM/WZpQO6ytgZ2cnV4omSTUKvjTffvst7t+/X2N5BsunodHR0cHWrVur/ZjSpp1o164dl1+yRYsWuHLlClavXs0V2bOyshK7zFlRCgoKEBcXJ3I5sKQlr1OnTkVkZKTYVAGKNHLkSJw/f17qIsf8lEbm5uYYM2aMzHnoe/TogTNnzsDe3h6jRo2Cl5cXYmNjcebMGfTp00fivosXL+aW0S5fvhyDBw9G9+7dUa9ePRw8eFDsfvKmKxg/fjy2bNkitGR4+/btEgvfWVpa4sGDB2jatClatGiBQ4cOoVOnTjhx4gRXfFzRx+TT09PDpEmTMGnSJNy6dQuurq4ICAiAv7+/yNdv2rQJQ4YMgbm5OczMzACUFaGzt7fH3r17Kz0eP/2RNMU9J02aBDU1Nfz5558KSfMmTYqkBw8eyPWz5U3lIG/aLjs7O1y4cEEobdeRI0fQrl07icesmLqCx+PBxMQEvXv3FpnGsCoCAwNhYmKCpUuXYvjw4VIVWARQaZFQcUWAAelTcyiKvOfMjh07AEiXKkwRnj59KvJeX1paiqKiIon7njhxgstpzU8bYG1tjWbNmmHfvn0irzVmZmaIiYkROubZs2e5a4c4UVFROHjwIDp37izwuW/VqhV3T1Ykef+GVaWtrS228Kc4N27c4GqZZWZmCnxP3DVy2rRpmDt3LoqKitC7d28AZXUJFixYAB8fH7HH8vHxwfr167Fp0yaZr7/y9qE1NDSwY8cO+Pv7IyMjA/n5+WjXrl2lNVCCgoIwcuRImJqa4uPHj+jZsydevHiBzp0745dffhG7n7z9BHkFBwcL/C5VVFRgYmKCb7/9VmSaiqr2oTp27Mj929TUFKdOnZJqPwsLC1y/fl3oHnPq1CmBdJiifP/995g6dSratWuHzMxMDBw4EABw8+ZNmdM56evrIyAgAM7Ozhg/frzY1+Tl5eHIkSPIysqCr68v6tati5SUFNSvX18ojVr51L5t27aVmIZLVAqYimlwZCFvf13eYxoYGODMmTNISEhAeno6VyRXUiF2oCzV6rhx4+Dt7Y0+ffpw6Vajo6Mr7WPI8yxTlfSQhYWFXAqe6OhoDB8+HCoqKujcuTMePXokch9ZP4flj1W/fn2h7aampmJrzQHir09dunSReH0CgBcvXnD3CV1dXS7N5uDBgyWeR7I+r/HJez+MjY3FsWPH0LFjR6ioqKBZs2b4/vvvoa+vj9WrV3M1giq6ffs2V09QTU0NHz9+hK6uLpYvX46hQ4eKTBkFyHduHz16VOD/RUVFePDgAdTU1GBlZSX22QcoKxIuKdWOOOXTbPbt2xd37tzBtWvXYG1tLTFFWFVkZGSITIFpamoq1X1Z2mc1fhpbHo/H9S3K09LSwsaNGys93saNG+Hq6orOnTvj6dOn2L9/v8iUi/zUVpMmTcL69etFpmKVxMXFBZMmTUJQUBBXJzYpKQm+vr4YPnx4pftXpf6qLLX4DA0NYWRkBFdXV/j5+aF79+4Sr0vlaWhocNeis2fPcs9LdevWFZk+ji8sLAyJiYkC6bFVVFTg4eGB7777DqtWrcKPP/4olDasTp06XB2Y8vLz86GhoSH2eMuWLUNAQAB27twJLS0tqd4bn6GhITZt2iS0vbJaovzz5dq1awK1CkXVBybyoSBILVX+4vC///0PCxYswKpVq0Tm8ZP1wvsl8vDwgI+PD9fBqvgeq+vmLGuhzKqQts6KoaEhHjx4AFNTUzx8+BClpaVyDVDIKzU1FQMHDkRhYSEKCgpQt25dvHr1Ctra2jA1NRV6cCifx7O0tBTbt2/H2bNnK625UFWbNm3CqFGjcOHCBZHnjLgHnAkTJsh9PH5xu59//hnq6uq4ePEiRowYgcWLF0vct3yBZ2tra9y5cwdv3ryptCDx1q1bsXv3brEP3OWV/zvwc0tHR0ejc+fOAMo6Vzk5ORIHbSdNmoS0tDT07NkTfn5+cHZ2xqZNm1BUVCTyb6eIY/J9+vQJx48fR2RkJE6dOoX69evD19dX7OvNzMyQkpKCmJgYrgPRsmVLiQ8dpaWlWLlyJYKCgpCfnw+gLPji4+ODn3/+WWw9kuvXr+PatWtic61WB/6Az61bt4Qe4Hk8nthaISEhIfDz88O2bdtkGuSRdwDO398fEyZMwNOnT1FaWoo//vgDd+/eRUREBP7880+Jxyx/3eX/W9qaMLJKTU1FXFwczp8/j6CgIGhoaHCF/nr16iU2KOLl5SXw/6KiIhQWFkJDQwPa2toSz+2q1JKpKlmCfHxGRkaoV68ejIyMYGhoCDU1tUqLrMqjKoEzWXNaA2UD6J6enrh+/brABITdu3dj/fr1Eo+Xm5srsrBtQUFBpYPxMTExCA4OFrg+zZ07t9JBP0C6GiTKJkvdMT5fX1+8fv0as2fP5q5pmpqaWLhwIRYtWiTw2ooDAbGxsfj777/RqlUrofu9pMKV8vahly9fjvnz58PMzEwgWPbx40f8+uuvYgep+ANiiYmJSEtLk3qwV95+grwmTpyIvLw8hIWFCTyE8weZxZG3DyWvefPmYc6cOfj06RMYY0hOTsb+/fuxevVqrtaTOJs3b8bixYvx+PFj/P7776hXrx6AsoEHSTVvxBFVX6m89PR09OnTB4aGhnj48CGmTZuGunXr4o8//kBOTo5QUD01NZUL/F6/fl3sz5Xm7y/LwBb/9VXtr8tyzMePH8PMzAzdunXjcsRLY+TIkejWrRueP3+ONm3acNv79OkDFxcXsfvJ+izDV5Xz29raGlFRUXBxccHp06e5Qed//vlH7HVG3s9hly5dsHTpUkRERHB/g48fPyIgIEDivvJen4Cy/sTz58/RtGlTWFlZITo6Gu3bt8eVK1e4mhaiyPu8JmtNPL6CggLuvm1kZITc3Fw0b94c9vb2SElJEbtfVeuFynJui6rx9P79e0ycOFHieQ0Av/32G/z8/ODv74/WrVvLNS706dMnNGvWTOrf8eTJkyV+X9zAuzy1teR5Vnvw4AEYY7C0tERycrJA31VDQwOmpqYii2OLquU2fPhwXLhwAWPHjgWPx+NeI6puhrwTjrdu3Yr58+dj3Lhx3D1ATU0NU6ZMwa+//ipxX3kmSMpbi2/gwIFISEjAgQMH8OLFC7x48ULiM1N53bp1w7x589C1a1ckJydzz5KZmZkSn02Ki4tx584doWPcuXOHqx+kqakp9L4HDx6M6dOnIywsTCCwNHPmTLE1TwBg9OjR2L9/P0xNTWFubi70eZJ0zeCTtf7QP//8gzFjxiAuLo6bZJqXlwdHR0ccOHCgWp69/mt4TJ4poUTpVFRUBD7cTEThJVZJUcfaRNLAV3W8x8zMTEyZMkXmQpmKUFhYKHFVx/Tp0xEREYGGDRsiJycHTZo0EXnjBoDs7GyFt49/c9u6dSsMDAyQlpYGdXV1uLm5wcvLS2hgwtHRUaqfy+PxJBYkk1VYWBhmzpwJTU1N1KtXT+DzwePxBH43devWRWZmJoyNjSsdUBBXUM7d3R2Ojo7o0aOHzLOZ5FWvXj0kJydLdbzq+Ds8evRI4iwhRRzz9OnTiIyMRFRUFNTU1DBy5Ei4urqiR48elf5cWWfCLFq0CGFhYQgICEDXrl0BAAkJCVi2bBmmTZsmdhbcN998g+DgYJke3KsqOzsbLi4uyMjIECjSzT93y1+jKp7TBQUFKC4uhra2tlBnTtz5LYq0A3AXLlzA8uXLBR6o/f39pSoEGhYWhuDgYNy7dw9AWWH0uXPnSiy2pwhpaWkIDg7Gvn37UFpaKtM1/969e5g1axZ8fX0FBi5FkWVWcFXJG+T76aefcP78eaSmpqJly5ZccKhHjx5Sz/iSxbFjxzBhwgQsWrQIy5cvR0BAgEDg7Pvvvxe7r4ODAzZu3IiePXuib9++aNu2LdauXYsNGzZgzZo1YletHD16FEFBQQIBCV9fX4lFlYGyFYCjRo2Ch4cH9PT0kJ6eDgsLC3h4eODevXtiZ7P/9ttv8PLywsiRI7kBqcuXL+PIkSMIDg7GnDlzRO5XWFgIDw8PhIeHAwD3oOrh4YHGjRtXWjBVWWQNuuXn5+P27dvQ0tKCjY2NyAG0SZMmSX18SQMR5c97Uf1qcZ99VVVVblVuea9fv4apqanEa0ZVZmnWlKtXr8LJyQmamprcgMGVK1fw8eNHbnBTFHmLI798+RLz58/nfi8VH08l/T737duHZcuWcX3nRo0aISAgQOzATVVVLAjMGMPz58+xZ88e9OzZU+TMZqBsdnX79u2xZs0a6OnpIS0tDZaWlrh48SLGjRuHhw8fKrSd8g5sAZL7b5L6bPIeU1VVFd26dYObmxtGjhxZLfeW8mR9limPX8g5KysL69evF1nIWZQjR45g3LhxKCkpQZ8+fRAdHQ2grDhvfHw8/v77b6F95P0cZmRkwMnJCf/++y8XHEpLS4OmpiZOnz4ttp0REREYM2aM0DX38+fPOHDggMSJHX5+ftDX18dPP/2EgwcPws3NDebm5sjJyYG3t7fAhL7yZHleO378OAYMGAB1dXWRA9TliRvY/Oabb7By5Ur0798fQ4YMgaGhIVavXo0NGzZw/TFRhg0bhkGDBmHatGmYP38+jh07hokTJ+KPP/6AkZERzp49K7YtirrmZ2RkwNnZWeK14t69exg3bpzQ4Gxl97SqXC8qBmaKiopw48YN5OXlccXIRZk/fz6SkpJw+PBhNG/eHCkpKXj58iXc3d3h7u4ucrKQvM9q8pB24lX53+vw4cOxe/du6OvrV7pqQ9IEDaDsea38mJCOjk6lbWnYsCHWrFkj1QRJvuXLlyM8PBzLly/HtGnTcOPGDVhaWuLgwYMICQnBpUuXJO6fnp6OuLg4xMXF4cKFC1BTU0OvXr2wb98+sfvk5ORg9uzZePz4MTw9Pbnzy9vbGyUlJUL3WT5PT0/s378fP/30E7755hsAZdfEVatWYdy4cVi/fj1CQ0Oxe/duJCQkcPvl5eVhwoQJOHHiBPfsW1xcjCFDhmD37t0wMDAQebzRo0fj3LlzGDlypMjC6JImtOXm5mLixIlinwXEfRbHjBmD7OxsREREcCtab926hQkTJsDa2ppbkUbkR0GQWiouLo7798OHD2FmZiY0EF5aWoqcnJwan5VVHcQtE+aTdzaIOF27doWamhr8/PxERtHLzzZShlOnTuH+/fvw9PTE8uXLueXVFVWcpawIhoaGSEpKgq2tLQwNDXHp0iW0bNkSSUlJmDBhAu7cuaPwY8qjQYMG8PT0hJ+fX6WdmPDwcPzwww+oU6cON7gkjrjP09SpUxEfH4/79++jcePG3EBhz549K02PIa+FCxdCV1e3RtKL8dX04I22tjYGDx4MV1dXDBw4UGjQXpzKZsJUXGoOlA2cbN26VejB6dixY5g9ezaePn3KbSu/Gu/q1atYvHhxja7Gc3Z2hqqqKkJDQ2FhYYGkpCS8efMGPj4+WLt2rcAy4MrO6fK+pPuFv78/1q1bBw8PD26g+NKlS9i0aRO8vb0lpgaRFWMMqampOH/+PM6fP4+EhAS8f/8eDg4O6NmzJ4KDg2X6eVevXoWbm5vE62F6ejr69u0LAwMDPHz4EHfv3oWlpSUWL14sclZwVcn74MhPv+Ht7S1TqrCqkDdwFhwcDFVVVXh6euLs2bNwdnYGY4xbrSbqnjhhwgRMmTJFqsBqRQkJCRgwYADc3Nywe/duzJgxA7du3cLFixcRFxeHDh06iNyvSZMm8PPzw48//iiwffPmzVi1apXAtaY8Ly8vJCYmIiQkBE5OTkhPT4elpSWOHTuGZcuWiZw9qizyBt3k8fHjR5SWlnIDBA8fPkRUVBRatmxZaSCyfH9alJ49e4rcrqKigpcvXwrNyIuNjcWYMWOQm5srcj957k3KwE8lt2PHDm61UXFxMaZOnYrs7GzEx8eL3M/f3x+hoaHw8fHB4sWL8fPPP3N/D39/f7GzugcMGICcnBz8+OOPIn8vlQUkgbIgYX5+vsjVWXzp6emV/hw+cbM0K85a5l8je/fujUWLFontkxsYGCAlJQVWVlYCQZBHjx7B1taWW02sKFUd2KrJY6ampiIyMhIHDhxAbm4unJyc4ObmBmdnZ4mrCOQl77NMXFwcBgwYgK5duyI+Ph63b9+GpaUlAgMDcfXqVRw5ckTicV+8eMGtWuFfA5OTk6Gvry9yNbG8n0Og7POwb98+7r20bNkSrq6uElO6VCW4W9Hly5dx8eJF2NjYwNnZWezrZHleU1FRwYsXL2Bqair3BMm9e/eiuLgYEydOxLVr1+Dk5IQ3b95AQ0MDu3fvxpgxY0Tul52djfz8fDg4OKCgoAA+Pj7c+1u3bp3YsQhFXvMTEhLg7OyMt2/fin1Np06doKamBi8vL5GDtuLuaYq+XpSWlmLWrFmwsrLCggULRL7m8+fPmDNnDnbv3o2SkhKoqamhpKQE48aNw+7du0VO8pTlWU2Ue/fu4dy5cyKfYyWlGZPWpEmTsGHDBi6NsyTVkZpelgmSfNbW1ti2bRv69OkjcG+6c+cOunTpIvF8A/7vGercuXM4d+4cTp8+DcYYl9lEkUpKShAYGIhNmzbh5cuXAID69evDw8MDCxcuhKqqKnJycqCioiJy4s29e/cEromVpdrX0dHB6dOn5Zro6OrqikePHiEkJAS9evXC0aNH8fLlS65vLC71noGBAc6ePcsFefiSk5PRr18/5OXlydwWUkEN1yAh1UBFRYW9fPlSaPurV6/kKsr7Jbt58yb7+++/BYrlHj9+XOHHkbdQZk2bOHGixIK21cHY2Jgr5mVjY8NOnTrFGCsrWqutrV2jbZHEyMhIrkJ748ePZ2FhYXIXcH/y5AmLjIxkM2bMYC1atGAqKioKLe5WvrCsl5cXMzQ0ZD169GA//vijVIVnq2LZsmVMRUWFderUiQ0dOpQNGzZM4Ks6yHt+N2jQQGKBUlHq1KkjsrjunTt3hAqs8gsvViyCrojC6NKoV68eS0tLY4wxpq+vz+7cucMYYywmJkZiQeaqnt/y+vfff9njx4/Zo0ePBL4kMTY2ZpGRkULbIyMjWb169RTaPkNDQ6ampsY6dOjA5s2bx44fPy5zEdryUlNTmZ6ensTX9OnTh/n6+jLGBAtCJiYmSlWgUVYNGzYUWUg1KiqKNWrUSOx+169fZ+vXr2cuLi7M2NiYNWrUiI0dO5Zt27ZN7mLUNeXhw4fs999/5z4rogwdOpSpq6sza2tr9ssvv7CnT5/KdIz79++zqVOnsm+++Ya1bNmSubq6iiy+WJ6Ojo7IYuyZmZkSCzQ2bdqUXbp0iTEmeM7cu3ev0vOtpvn5+TETExP222+/cQVIN2/ezExMTNhPP/3Evc7FxYUrAuni4iLxS5zvv/+ebdmyhTFWVoiyfv36rEmTJkxTU1NiwVq+t2/fsrVr17IpU6awKVOmsKCgIJaXlyfytfxCxSoqKkJFi/X19ZmKigqbPXu22GPJc29SBk1NTZH94Js3bzItLS2x+8lbHFlXV5elpqZWrdFSKF88uSqFw+VRvhB7+c9vdHQ0a9KkicKPZ2Vlxc6ePSt0vNu3bzNDQ0OFH08RxywtLWWxsbFs6tSpzMjIiBkYGLBJkyYpvJ3yPsvUVCFnPnk/h3FxcayoqEhoe1FREYuLixO7H4/HY//884/Q9uvXr1da+H3VqlUsLCxMaHtYWBgLDAwUu5+8z2uKUlBQwK5du8Zyc3Or5efLc81fv369wFdISAhbuHAh1/+SREtLi3smkEV1XC/u3LnDGjRoUOnrcnJy2MmTJ9nBgwclFgxnTLZntYq2b9/OVFVVWf369VmbNm1Y27Ztua927dpV2s7aYMGCBWz58uUy7aOpqckePnzIGBP829+8eVNinzQoKIg5OzszIyMj7hnK29ubHTt2jL1580biMSs+D8ryfMj37t27ai8ibmtrK/EZQpIGDRqwpKQkxhhjenp63Hl77NgxocLt5YnrD6WkpHxxff3a6stMJExkwkSkwgLK0gnIWpjwSyVL6hdFkLdQZk1TRnH7du3a4cqVK7CxsUHPnj3h7++PV69eYc+ePWjdunWNt0ecCRMm4ODBg/jpp59k2q9OnToIDAzEtGnT0KhRI5lXdFR33vyKM33btm0LoKwIbXnVkSNclhokilJ+RuWnT5+EUmuIW2Xx+fNnLr+/tNq0aYNNmzYJLcHdtGmT0Oqv8vnuK1uNVx1KSkq4342xsTGePXsGW1tbNGvWDHfv3hW7X1XPb1ndu3cPkydPliu1YFFRkUCxXL4OHToofHbR3r170b17d5lX7VRMx8D+f1qUTZs2castxLly5Qq2bdsmtL1x48Z48eKFTO2Qxps3b0TONG3RooXENGht2rRBmzZtuBnc/FRhc+bMkTlVmCzkrckl62q1qKgo5ObmYs+ePQgPD8fSpUvRt29fTJ48GcOGDat09ZmVlRVXPF5aQ4YMwdGjR4XqGh07dgyDBw8Wu19VapDUtPDwcISGhgrM1nRwcEDjxo0xe/ZsbuWRgYEB13Zx6Qgqk5KSwq3WOnLkCOrXr4/U1FT8/vvv8Pf3F1sPBhCdbiY4OBirVq0SmW4mJCQEjDFMnjwZAQEBAm3W0NCAubm5xJz78tyblEFfXx85OTlC14zHjx+LXekAyF8c2czMTCgFljRkTaMlbzFlRRgyZAiWL1+OQ4cOASjrp+Xk5GDhwoUYMWKEwo/39OlTkbNcS0tLuTzzX9oxeTweHB0d4ejoiFmzZmHKlCkIDw9X+EpjeZ9lqlLI2dHRUeJ1WlSKMXk/h46OjiJXdLx79w6Ojo5Cn4t27dpxhaP79OkjUGuqpKQEDx48gJOTk8T3t23bNpG/m1atWuGHH37AwoULRe4n7/MaIP5+z+PxEBYWJtXP0NbWlqngsKx9E3mu+RVXH/NXm/FThUrSsWNHPH78GLa2tjIdszquF1lZWRL76/LU1pLlWa2ilStX4pdffhF7Lla0YcMGTJ8+HZqammLTM/GJW+VY0+Sp5yRvLb7Vq1dj/PjxmD59Orp37871hxhjePz4scS0hubm5hKvh9I8W1T2zDZv3jysWLECOjo6AnVKRRFX5yooKAgLFizA1q1bZaqlCchff6h3797w8vLC/v370ahRIwBln09vb2/06dNHpjYQ0SgIUovxP8w8Hg9LliwRqB1RUlKCpKQkboC0tvPy8oKFhQViYmJEpn5RhP9asXl5rVq1Ch8+fAAA/PLLL3B3d8esWbPQvHnzSotQ1qSSkhKsWbMGp0+flqmoI38w6+nTp4iPj0dcXByCgoIwY8YMNGzYUGxOeVF58/38/BSeN1+eYrOKoozBm4KCAixcuBCHDh3C69evhb4vrpM0depUREZGypQqbM2aNRg0aBDOnj0rkH7p8ePH+OuvvwReW345ee/evcWmD+jbt2+1pJhq3bo10tLSYGFhgW+//RZr1qyBhoYGtm/fzhWGFkXe81teEydOhJqaGv7880+pC/TxjR8/Hlu2bBH6rG7fvh2urq4Kbae4JcmVGTZsmMD/eTwelxYlKChI4r516tQRuO/wZWZmVkvRO3kfHFklqcIUrSqBM3kKQgKAiYkJ5s2bh3nz5iElJQW7du2Cu7s7dHV14ebmhtmzZ4sNEvLzw2dnZyMkJESq/PB2dnb45ZdfcP78eYGaIImJifDx8RH4G5V/sO7YsSNOnjwJDw8PAP8X7A4NDa20UG5NkzboVn4yh7wTOwoLC7kBwejoaAwfPhwqKiro3LlzpelUvb294ezsLDLdzNy5cwXSzbRv3x4xMTEwMjJCeHg4Jk+eDF1dXZnaKs+9SRnGjBmDKVOmYO3atdx9PzExEb6+vhKLhstbHDkkJAR+fn7Ytm2bTAMNEydORE5ODpYsWSLVZ15Uyppbt24J1S/h8XgKT7UbFBSEkSNHwtTUFB8/fkTPnj3x4sULdO7cWaF57PnkHdhS5jGfPHmCyMhIREZG4saNG+jSpQs2b96s8HaKe5axsbGRGHCRp5AzX8Vn8qKiIly/fh03btwQ20+U93MoboLk69evRdYV4Pdlrl+/jv79+wtc1/jB3coCdS9evEDDhg2FtpuYmOD58+di95P3eU3e+/2IESPQqVMnoYHwNWvW4MqVKzh8+LDI/eStFyrPNb8qwVoPDw94eXnB19dX5BiGuDR/VfnsVhxg5k8IOnnypMRnoICAAMycOVOo/mlhYSECAgJEBkFkeVar6O3btxg1apTE15QXHBwMV1dXaGpqSkyLy+PxRAZBqlLrSl7p6ekyT5D09/fHhAkT8PTpU5SWluKPP/4QqMUnzuvXr+Hn5yf0/PvmzRtYWFhIfH8VJ3UWFRUhNTUV69atE7oflu978QO24pQPLqSmpnIBPHnTxbq5uaGwsJCr0ytLLU1bW1vcvXsX5ubmaNOmDde/2bp1q8hrJd+mTZswZMgQmJubc8HBnJwc2NvbY+/evXK9DyKIgiC1GP/DzBhDRkYGNDQ0uO9paGigTZs2mD9/vrKap1CXLl1CbGwsjI2NoaKiwhXQW716NTw9PRWSB9vQ0FCoKGbFaKs0gzBfu1atWnE3cVNTU2zduhVHjx6FnZ3dFxV0y8jI4Dpt8qySkHVFR2BgIExMTLB06dIay5tf05QxeLNgwQKcO3cOW7Zswfjx47F582Y8ffoU27ZtE1tkEZBvJkzPnj2RmZmJzZs3c/lChw8fjtmzZ3MzMURRxmq8xYsXo6CgAEDZTKrBgweje/fuqFevHg4ePFjp/tW9Yonv+vXruHbtmsjBUFHKP0jxeDyEhoYiOjoanTt3BgAkJSUhJydHYnHOmlRxJqAsanpWsLwPjnXr1kV+fj7atGmDnj17Ytq0aejevTsMDQ0V3kagaoGzqq5We/78Oc6cOYMzZ85AVVUVAwcOREZGBuzs7LBmzRp4e3sLvL5ifviVK1fC1NQUaWlpCAsLE5sfPiwsDEZGRrh16xZu3brFbTc0NBSYvVrxwXrVqlUYMGAAbt26heLiYqxfv16gBsmXpCqzNWVlbW2NqKgouLi44PTp09zf6Z9//ql00srVq1cFAiAAoKamhgULFgitRLt9+zYKCgpgZGSE+Ph4fPz4UaogSPnrWmlpqcz3JmVYu3YteDwe3N3duZm86urqmDVrlsR7r4uLC2JiYvDtt9/Cw8MDbm5uCAsL44ojl2dkZCTw+S4oKJB5oCEhIQEXLlyQq/9Z06vMDQwMcObMGSQmJgrUO+rbt69Cj8Mn78CWMo7JX0WQkJDA1a44duyYwgNRfOU/26ampmIL11bEX9Fw+PBh8Hg8lJaWIjExEfPnz6+0XyJuEHXZsmVc3aSKZP0c8gsx83g8TJw4USDwWFJSgvT0dJGTmfjFfc3NzTFmzBi5+q5mZmZITEwUChAlJiZK7EPL+7wm7/0+Pj4ey5YtE9o+YMAAiZNXJk2aJHXfRJ5rviwFtXV1ddGqVSvMnDlTaAUlv6bJ5MmTuW3865ukMYyqXC8qjsPwV64EBQUJtKMicc9PaWlpqFu3rsh9LCwsxD6rVbZKfNSoUYiOjsbMmTMlvo6vfDBKnsCUrEF6RZBnsuTQoUNx4sQJLF++HDo6OvD390f79u1x4sQJfP/99xL3lff5V1Q/sGPHjmjUqBF+/fVXgc/A0KFDuWtZxclnkpT/Xcg7iTQkJESu/YCySdz8APDSpUvh5OSEvXv3QkNDQ2K9TjMzM6SkpCAmJga3b98GUFa/pLr6Cv9FVBj9KzBp0iSsX7/+q16dYGRkhJSUFFhYWMDKygqhoaFwdHREVlYW7O3tUVhYWOVj/NeKzcurX79+GD58OGbOnIm8vDy0aNEC6urqePXqFdatWycx7URtIGpFR69evSpd0ZGWloa4uDicP38eFy5cgIaGBrdvr169am1QpGJHPjw8HA4ODjU2eNO0aVNERESgV69e0NfXR0pKCqytrbFnzx7s379f7OCto6Oj2J/J4/GE0g4UFRXByckJW7dulTotFP93s379ekybNk3kajxVVVUkJiZK9fOq6s2bN0KDShXJe37L65tvvkFwcLDUBeUk/d3KE/U3VLaKA2iVeffuHUaOHImrV6/iw4cPaNSoEV68eIEuXbrgr7/+Ejlbs6qePXsm8ODYsmXLSoN8J0+elCtVmLx0dHRkCpyVJ09ByKKiIhw/fhy7du1CdHQ0HBwcMHXqVIwbN457z0ePHsXkyZOFikN26dIFo0aNwrx58wSKSSYnJ2P48OEKX1kFlA3crl69WmAQdeHChVwKoi9FXFwcBg0ahKZNmwoE3XJycvD333+je/fuQvvIO2vyyJEjGDduHEpKStCnTx9ER0cDKEvVEB8fj7///ltsO+vXr489e/agX79+AttPnz4Nd3d3rvAmUPb31tXVRbdu3RAQEID58+eLDYKUn8Fam69rhYWFyMrKAgAuQCELScWRJQ0CVCSu321nZ4d9+/bJtbLB2dkZqqqqCA0NFbnKXNQ5WlWypuurqgsXLmD58uUC1wt/f3+h813ZxzQzM8PYsWPh6uqq8CCpIslTyLky9+/fR6dOnSTOKJb2c8gvxBweHo7Ro0cLFEHnr+iYNm0ajI2NZW5nZdasWYM1a9bg119/Re/evQGUne8LFiyAj49PpWmcZCXP/R4AtLS0cP36daF0UXfu3EG7du3w8eNHkfvJ0jeR9poP/N/grCwFtf/9919cunQJ9vb2QmlZK1v9KCmwWFPXC/6zyrt376Cvry/Qby4pKUF+fj5mzpwpchWYqqqq2NX3pqamEoPXq1evxrp16zBo0CCRq2QqruaoLIUSH4/HExlA09PTkztI/yWrzuff+/fvo02bNtxEP0WZPHky1q9fL5RGsKCgAB4eHtVy/62osLAQd+7cQdOmTSu9Btd0X+G/hoIgpFbo3r07fHx8MGzYMIwbNw5v377F4sWLsX37dly7dk1o5khVVeUG+7UzNjZGXFwcWrVqhdDQUGzcuFEg9zY/Yl1b8WeveHt7V2lFBz9v/r59+6o1b351U/bgja6uLm7duoWmTZuiSZMm+OOPP9CpUyc8ePAA9vb2YmfPycPExIQbrJEG/3cTFxeHLl26CK3GMzc3x/z586ul1oa8FHV+S1I+xdPVq1exePHirzq1YEREBH799Vfcu3cPANC8eXP4+vpKPTsxISEB6enp1T4ruLaQNXBW3sKFC6GrqyvTajVjY2OUlpZi7NixmDZtmsiH1by8PLRr105oJqCuri4yMjJgYWEhEAR5+PAhWrRogU+fPlV6fFmCZ+7u7nB0dESPHj1kHvhRhqdPn2LLli0CM9kkBd0GDBiAnJwc/PjjjyJnTQ4dOlTssV68eIHnz5+jTZs2UFFRAQAkJydDX19f4qCVp6cnjh49KjLdzIgRIwRmAd69exdLly5FVlYWUlJSYGdnJ7CChI/H40nM9/w1i4+Px3fffSf0eykuLsbFixfRo0cPkfu5u7tz9alkObejo6MRFBQkcxotoOyzHxsbCwcHBxgYGCA5ORm2traIjY2Fj4+PQlaZl1dZ+p6jR48q9Hi1CWMM7969Q1hYGHe9sLOzw5QpU+SuFSRJVdPU5OTk4MaNG8jPz0e7du2q1M/bs2cPFi5ciGfPnsn9MyriB2llnUxRUlKC4OBgHDp0SChFHCA59QtjDH5+ftiwYQO3n6amJhYuXCgyrVFVyXO/B4BOnTph8ODBQm1atmwZTpw4gWvXroncryp9k+pw69YtfPPNNwobLJ4wYQKmTJki9hotjdzcXK4uoa2trdgV5uHh4VxtrZCQEJlqa6moqODFixdCYzSPHj2CnZ2dxN9HxVVK5fF4PGRnZwtsq+ozcFWC9LKQZRXRH3/8IfZ70ta7UcTzb8V0wPwUasuWLcOdO3dw/fp1ie9DVuLG9l69eoUGDRoIrCJ6//4993wqKm1xeRWfY6UNnAHyp/r7L/cVFIXSYZFaoaqpX2T1Xyg2L6+q5N6uSfIUIATKlvTyV3QEBQVJvaKjpvPm1xRl1iABAEtLSzx48ABNmzZFixYtcOjQIXTq1AknTpxQeDoeftoOSak+yis/e6u2rMaT9/yWxX8pteC6deuwZMkS/Pjjj1wh9ISEBMycOROvXr0SSv8iSrdu3Wrsofrt27dCg0yTJk0Sm3agplSlJldVUw0FBwdj1KhREu/thoaGIlMhVCU/vDzBMw0NDaxevRpTp05Fo0aNuM9uz549v6hgK1+9evUwZMgQdO7cmXuovnr1KgAIFEznq0pqowYNGqBBgwYC2/iFziWRJd2Mra0tDhw4AKBsICYmJkZkofr/MlkLMvPVqVMHgYGBmDZtWqXntiLSaAFlA778/qyxsTGePXsGW1tbNGvWjBvIU6SqpuuTl6yFnKvC0tISV65cQb169QS25+XloX379kKDjHwpKSno378/NDU1uc9tcHAwVq1axdWVUaSqpqlp2rSpzL+/ioOT/EG/q1evKjzNLD+9lawCAgIQGhoKHx8fLF68GD///DMePnyIqKioSgMZPB4P//vf/7BkyRLcvn0bWlpasLGxkVgLiO/q1atiAy/lB20VkVpwyZIlGD58OLKysgRWrOzfv1+oHogi6oVW1wx0W1tbofokfFlZWQgJCRHo63l5eUkMML979w59+/ZFs2bNMGnSJEycOFHiKuHy+O8lIiKCu86oqqrC3d0dGzduFFq5xF/ZZ2Fhge+++07odylK+Vq4/v7+ctXClTWlVVWfgeWtdSUrAwMD7homT9BY1lp8inj+rfisyD+emZkZ188SRdZA7fv378EYA2MMHz58EOjrl5SU4K+//hLqrxgZGXH9GFHt5LdV1O9G2skT1ZHqj0iPVoKQWkua1C+y+tLS23yJ+KlCXFxc0Lp1a5w6dQpdunTBtWvXMGjQILx48ULZTQQAocHHigUI169fL9XPkXZFh5GRkUDe/F69elVr3vz/iuDgYKiqqsLT0xNnz56Fs7MzGGMoKirCunXr4OXlpbBj8TvwNjY26NChg9AMui8lV7siVceKpf9SakELCwsEBAQI5QIPDw/HsmXLhB64KtZHkERUkcWqiI+Ph7OzMwwMDLh86NeuXUNeXh5OnDhRpdl/VaWioiIUOBP1cCTqgUOZq9Xmz5+PpKQkHD58GM2bN0dKSgpevnwJd3d3uLu7ix2IEhc827x5M1auXFlp8Ozp06eIj49HXFwc4uLikJmZiYYNG1ZL+i15nTp1Cu7u7nj9+rXQLGtxAdCamjUpirxpn8QV1a6Y+um/QkVFBS9fvhSaAZyZmYmOHTtWOqtSmnNbEWm0gJpfZS5v+h55yTqwpQjiZmm/fPkSTZs2xb///ityv+7du8Pa2lqgPk9xcTGmTp2K7OxsxMfHK7Sd8qapYYzhyJEjOHfunMjAkqRZ1hVTHPFX5vbu3Vvh6YbkXeliZWWFDRs2YNCgQdDT08P169e5bZcvX0ZkZKRC2wkABw4cgLu7O/r374/o6Gj069cPmZmZePnyJVxcXLBr1y7utYq63588eRKrVq3C9evXoaWlBQcHByxdulRo0lpV+iZ8ssxAV4TTp09jyJAhaNu2Lde/4NchqqzGQ25uLvbs2YPw8HDcunULffv2xeTJkzFs2DCJgYoZM2bg7Nmz2LRpk0CfxtPTE99//z22bNnCvVbeWfbyrj6YN28eVqxYAR0dHYmz9MWltJKVqCB9cXGxzEH6mtS1a1eoqanBz89PZFC4OlIUVqxjx78eWltbi1xhy+fv7y8xUFvx2aniZ7giHo+HgIAA/PzzzwJt4/9OwsPDa/w5tqb7Cv9FFAQhpJzamN6mplUl9/aXgF+AcO3atSK/X9mKDnGFDWs6b/5/1aNHj3Dt2jVYW1vDwcGhyj8vPT0drVu3hoqKisx1RGojec9veX3tqQU1NTVx48YNWFtbC2y/d+8e7O3thdIhVVwxkJubi8LCQi5YmpeXB21tbZiamoqdMSsve3t7dOnSBVu2bOE68yUlJZg9ezYuXryIjIwMhR5PFrU1cCYqP3xxcTFcXV0l5oeXNXhWUWFhIRISEnDu3DmcP3+eS82k6PQ9VWFjY4N+/frB398f9evXl2qfqqQ2qmkPHjzAsGHDaqyo9peOP8v92LFjcHJyElmQ2dbWttIC1LKe2/Km0QLKBgsLCgowfPhw3L9/H4MHD0ZmZia3ypw/S1xR5E3fI6+aHNji1yUYNmwYwsPDBWYjl5SUICYmBmfOnBG7wkZLSwupqalCqetu3bqFjh07KqT2Y3nyBly9vLywbds2ODo6on79+kK/0/ID9sokb2pBHR0d3L59G02bNkXDhg1x8uRJbgVPu3bt8O7dO4W31cHBATNmzMCcOXO4tJIWFhaYMWMGGjZsiICAAIUfU1pV6ZvwZ6AbGRnh3r17AoHhkpISnDhxAn5+fgpNgwYA7dq1Q//+/YVWMvr5+SE6OlrqVI0pKSnYtWsXQkNDoaurCzc3N8yePVvkGIixsTGOHDmCXr16CWw/d+4cRo8ejdzcXG5b+ecCcQPUkgJLsq4+cHR0xNGjR2FoaFgjz3mKCtLXpKrU4qtpsgZq4+LiwBhD79698fvvvwusfNfQ0ECzZs0krnhSxnNsTfcV/osoCEKICLUpvY0yyJt7+0tQWQFCWtHx5anO4mDlOzfi0jh8TWr6/BY3K1ia3L21QevWrTFu3Dj89NNPAttXrlyJgwcPSgwsREZG4rfffkNYWBhXoPPu3buYNm0aZsyYAVdXV4W2VVwx0Lt376Jt27Zii4HWtNoYOHv8+DEyMjJQUFCAdu3aCQXFKpI1eMb3008/4fz580hNTUXLli25z3CPHj1gZGSksPejCPr6+khNTa10YLo2zpoEhItqJycn4/Xr19VaVPtLVtWCzPKe29OmTUNcXByysrIUkiKuOlaZ83l5eSEiIgIODg4ype+RV00ObPGfBcoHBPnU1dVhbm6OoKAgDB48WOT+9evXx549e4RWRJw+fRru7u54+fKlQtsrb8C1bt262Lt3LwYOHCj3sa9du8alKWrVqlW1rHyTd6WLra0tIiIi8O2336Jbt24YPHgw/Pz8cPDgQXh4eOCff/5ReFt1dHRw8+ZNmJubo169ejh//jzs7e1x+/Zt9O7dG8+fP1f4MeUha99EnhnoiqCpqYmMjAyh619mZiYcHBykqlX2/PlzREREYNeuXXjy5AlGjBiBp0+fIi4uDmvWrBFaraqtrY1r166hZcuWAttv3ryJTp06CfT1y8+yr7gaoKLanE4aqFqQviqOHDkiNl2UqCCYMuvdiFpNC4hOmQrIH6h99OgRmjZtKvO9XRnPsTXdV/gvopoghIjwpczk+VLJm3v7S3Dp0iWJud/37t1LKzq+IJUVB6sqfq5/U1NTPHz4UCjI8rWpqfO7fO7eJUuWyJW7tzYICAjAmDFjEB8fL5B2ICYmBocOHZK475IlS3DkyBGBoIStrS2Cg4MxcuRIhQdB2rdvj9u3bwsFQW7fvl0tS93lVdtqcoWFhSE4OJir7WFjY4O5c+di6tSpYvextrbGoUOHhIJnBw8elDhwGxgYCBMTEyxduhTDhw9XSA2f6jJy5EicP3++0gf/8oXHa5NLly4hNjYWxsbGUFFRgYqKCrp164bVq1fD09Pzi1qVUxP4/WYTExMsW7aMu+bz01S0bNlSbAAEkP/c3rFjBwDBNFpBQUHcLHJZU8RVZ32k9PR07r5XMdVWdQRd7Ozs8OrVK4X/XFH4fScLCwtcuXJF4t9alDFjxmDKlClYu3YtvvvuOwBl91JfX1+MHTtW4e0dM2YMCgsLZa4lY2BgAEtLS7mO+c8//+CHH37A+fPnBVZ/Ojo64sCBA2KLSMvDzMxMKBglDRcXF8TExODbb7+Fh4cHVysvJydHqhpn8jAyMsKHDx8AAI0bN8aNGzdgb2+PvLw8ha0Aqlu3LjIzM2FsbFxpkFPc317Wvsm5c+eqNANdXiYmJrh+/bpQX+L69esSa1gVFRXh+PHj2LVrF6Kjo+Hg4IC5c+di3Lhx3DPD0aNHMXnyZKFzoUuXLli6dCkiIiK438XHjx8REBAgVOC8fGCjtgc5KiNLrStF2bBhA37++WdMnDgRx44dw6RJk5CVlYUrV65gzpw53OsUUe+mKrKzs+Hi4iLzatomTZrg+fPnaNq0KaysrLiaUVeuXJFYfyg2Nha6uroYNWqUwPbDhw+jsLBQaFWOMp9ja7qv8F9EQRBCyFdJ3gKEgwYNqu6mERlUd3GwESNGoGfPnlyApWPHjmJT2Cg6PZEy1NT5zR8AZIwhIyNDKLVgmzZtMH/+/BppS3UaMWIEkpOTsW7dOkRFRQEAWrZsieTk5Epndz5//lxkLuiSkhKFzXpNT0/n/u3p6QkvLy/cv38fnTt3BgBcvnwZmzdvFkqboAy1MXDm7++PdevWwcPDg3vQv3TpEry9vZGTk4Ply5eL3E/e4Flqairi4uJw/vx5BAUFQUNDg3uo7tWr1xcVFNm0aRNGjRqFCxcuiHyo5udtLv/gqaxZk/Ko6aLatUVqaioiIiIwc+ZM5OXloXPnzlBXV8erV6+wbt06zJo1S+x+VTm3jYyMUK9ePRgZGcHQ0BBqamoKHVRWhKoW2ZWGsge2ZC08zLd27VrweDy4u7tz90V1dXXMmjWrWu5P8gZfly1bhoCAAOzcuVNgtZM0PDw88OHDB9y8eZObMX/r1i1MmDABnp6e2L9/v1xtEkXegszlf9djxoxBs2bNcPHiRdjY2FRbnaMePXrgzJkzsLe3x6hRo+Dl5YXY2FicOXMGffr0UcgxgoODueu1rH97efsm/AH+Bw8eQF9fHzt37hRYAWRnZyfHO6nctGnTMH36dGRnZwsEFAMDA+Hj4yN2v4YNG6K0tBRjx45FcnKyyPfk6OgocuV4SEgInJyc0KRJE25STVpaGurUqcOlzRYnLy8PycnJIlf7V0wZWtsoOkgvjd9++w3bt2/H2LFjsXv3bixYsACWlpbw9/cXCPBVLPjNGBP6vFVn/SgvLy9YWFggJiZG5GpaceQN1K5evRrbtm0T2m5qaorp06cLBUGU+RxbE32F/zpKh0UI+SrVZAFCUn1qojjYqVOncP/+fXh6emL58uXcg1JFiizC/l/xtacWdHd3h6OjI3r06CHzOers7IynT58iNDQU7du3B1CWJmP69Olo3Lgxl2O9KvjpGCrr6lXXQ44samNNLhMTE2zYsEFopvL+/fvh4eEhcRb2tWvXEBwczA2KtGzZEj4+PjKlRklLS0NwcDD27duH0tJSpf8NywsLC8PMmTOhqamJevXqCTxs83g8kUFlRac2qk41XVS7tjA2NkZcXBxatWqF0NBQbNy4Eampqfj999/h7+/Pne+Vkfbcrk0p4mqCIgo5V4W4wC+fv7+/xO8XFhYiKysLALhVGl+Sjx8/wsXFBYmJiTA3NxcKLEmqt2BgYICzZ8/im2++EdienJyMfv36IS8vT2HtNDIyQmFhocypBePj4/Hdd98JFSYuLi7GxYsX0aNHD4W1sXxbPn36hEaNGqG0tBRr1qzhAi+LFy9W+ue4qn2Tq1evwsnJCZqamlzWhCtXruDjx4/cLHZFYowhJCQEQUFBXL2Rxo0bY/78+fD09BQ7k3zPnj0YNWqU3CtuCwsLsW/fPty5cwdAWZ/G1dVVYrDwxIkTcHV1RX5+PvT19YX6CV9SCsyqqMk6btra2rh9+zaaNWsGU1NTnDlzBm3atMG9e/fQuXNnvH79GoDya/EZGxsjNjYWDg4OMDAwQHJyMmxtbREbGwsfHx+pfzeXLl3CpUuXKg3Uampq4s6dO0JB4YcPH6Jly5ZiUwJ/7c+x/1UUBCGEfHVKSkqQmJgIe3t7pXeeSdXUZHGwSZMmYcOGDWKDIIRUNHXqVMTHx8s1aJubm4sJEybg1KlT3ABFcXEx+vfvj927d0tMWyCtR48eSf3aZs2aVfl4ilCbHjgMDQ1x5coVkbm3O3XqpNBBLaBscCM1NRXnz5/H+fPnkZCQgPfv38PBwQE9e/ZEcHCwQo9XFQ0aNICnpyf8/Py4egHSKj9rMi4uDpmZmdU2a1JeNV1Uu7bQ1tbGnTt30LRpU4wePRqtWrXC0qVL8fjxY9ja2opNbyPvuc2f4OLt7f3Fp4irCcoe2KoYxC0qKsKDBw+gpqYGKysrqYsy14SSkhJERUUJzM4fMmSI2NXAADB69GicO3cOI0eOFFkYfenSpWL3FVenIzU1FT179hRYxVNVlRVnFve3r401uaQhy+9WXN9D3r5J9+7dYW1tjR07dnDBpeLiYkydOhXZ2dmIj4+X6edV5uPHj2CMQVtbGx8+fMCDBw8QExMDOzs79O/fX6HH4lu9ejXq16+PyZMnC2zfuXMncnNzsXDhQpH7NW/eHAMHDsSqVau+uICnIigjSG9paYnff/8d7dq1Q8eOHbk6g9HR0fjhhx9EBpaU8bk3MjJCSkoKLCwsYGVlhdDQUDg6OiIrKwv29vYKS4XH17RpU2zatEmo1sixY8cwZ86cL6p/SaofBUEIIV8lTU1N3L59GxYWFspuCpERf+k5UPawHh4eTsXByBetKoO2mZmZ3My5Fi1aVPsgnqgihDwer9pSXXzNPDw8oK6uLnQdmj9/Pj5+/IjNmzeL3TcrKwu7du1CdnY2QkJCYGpqir///htNmzZFq1atRO5jZGSE/Px8tGnThnuY7t69u8j0FMpWt25dXLlyRa5VfDU5a1KRqrOodm3h4OCAqVOnwsXFBa1bt8apU6fQpUsXXLt2DYMGDcKLFy9E7ifvuZ2Wlsal0bpw4cIXnSKupn0pA9rv37/HxIkT4eLiUm2pTWV1//59DBw4EE+fPuXqZN29exdmZmY4efKk2OuWjo4OTp8+LVcB4aFDhyIvLw/79+/nakE8ffoUrq6uMDIywtGjR+V/QwoirghwZmYmOnbsqNBATXklJSU4evQoF5Cys7PD0KFDhVakyKuyIuVA9a2Q0tLSQmpqKlq0aCGw/datW+jYsaPCB3v79euH4cOHcykJW7RoIVVKwqowNzdHZGQkl36LLykpCT/88IPYNHk6OjrIyMiQu87Ol04ZQfqpU6fCzMwMS5cuxebNm+Hr64uuXbvi6tWrGD58OMLCwkS2s6aLf1dlNe3du3exceNGgZXUHh4eQjUPy1u4cCEOHjyIXbt2cSva4uLiMHnyZIwcOVJiCi7y9aGaIISQr1Lr1q2RnZ1NQZBaqOJAFxUHI1+6quSjb968eY08GMlbhJAIKh+k5fF4CA0NRXR0NFdnJSkpCTk5ORJzWcfFxWHAgAHo2rUr4uPjsXLlSpiamiItLQ1hYWE4cuSIyP327t2L7t2714pVMhMmTMDBgweFir9LImrWpJ+fX61JbVSdRbVrC39/f4wbNw7e3t7o06cPVysnOjpaYqo3ec/tNm3aoE2bNlyNGX4arTlz5nxxKeJqmqyFnKuLvr4+AgIC4Ozs/MUEQTw9PWFlZYXLly9zn9vXr1/Dzc0Nnp6eOHnypMj9zMzM5L7+8mchm5ubw8zMDACQk5MDe3t77N27V743Us779++5tlUWrKj4Hvh1FHk8HiZOnChQYLikpATp6elCA9yKcvPmTQwZMgQvXrzgBjH/97//wcTEBCdOnEDr1q2rfAxl5tjX19dHTk6OUBDk8ePH1bLyPCUlhVs5d+TIEdSvX18gJWF1BEFevHiBhg0bCm03MTHB8+fPxe7Xv39/XL169asNgiijjtv27du52ipz5sxBvXr1cPHiRQwZMgQzZswQeK0ya/EtXryYC64sX74cgwcPRvfu3bnVtOL8/vvv+OGHH9CxY0euf3H58mW0bt0aBw4cwIgRI0Tut2LFCjx8+BB9+vThgqulpaVwd3fHqlWrFPzuyJeOVoIQQr5Kp06dwqJFi7BixQp06NABOjo6At+vDYNIhJAvm6xL3efNm4cVK1ZAR0dHYDBdFEWvcnJ2doaqqipCQ0NhYWGBpKQkvHnzhitC2L17d4Ue72vFzw9eGR6Ph9jYWJHf69KlC0aNGoV58+ZBT08PaWlpsLS0RHJyMoYPH/5VLMv39PREREQE2rRpI/UqPkpt9HV48eIFnj9/jjZt2nCp0JKTk6Gvry80EFhVtSlFXE3h31vWr1+PadOmiRzYUlVVRWJiYo21KSEhAc7Oznj79m2NHVMSHR0dXL58Gfb29gLb09LS0LVrV+Tn54vc7+TJk9i4cSO2bt0qU8FxPsYYYmJiBGYw9+3bV+afI0r5lT/iVj6IW+3Ar6MYHh6O0aNHC9Rx4Ne9mDZtGoyNjRXS1vK6dOkCExMThIeHc/2mt2/fYuLEicjNzcXFixcVfsya5OnpiaNHj2Lt2rUChcp9fX0xYsQImQu1V0belIRVYWNjg6VLl8LNzU1g+549e7B06VKRNcCAstphy5cvx6RJk2Bvby/UT6iYuqi2+9LquH1ptfikWU1rZWUFV1dXofpTS5cuxd69e7maUuJkZmYiLS0NWlpasLe3/2JSAZOaRStBCCFfpYEDBwIo60CJKhKp7I4HIaT2CwwMhImJCZYuXSrVoG1qaiqKioq4f4tTHaucLl26hNjYWBgbG0NFRQWqqqro1q0bVq9eDU9Pzy8+1dCXQhEzSjMyMhAZGSm03dTUVGIx9dokIyODm/kv7So+ZcyaJIrXoEEDNGjQQGAbvyCwotWtW1cgjda0adO+2BRxNYV/LWeMISMjQ2hgq02bNpg/f361HHvDhg0C/2eM4fnz59izZw8GDBhQLceUR506dfDhwweh7fn5+QK/r4rc3NxQWFjIFW2XtuA4X2xsLGJjY/HPP/+gtLQUqamp3L1g586dcrwTwZ/NX9Ui631q165dAMpm7i9btowLnD18+BBRUVFo2bJltQRAAOD69eu4evWqwMQRIyMj/PLLL0JF5BXl7du3CAsLE0i/NWnSpGpZzbd27VrweDy4u7ujuLgYAKCuro5Zs2YhMDBQ4ceztrZGVFQUXFxccPr0aXh7ewMA/vnnn2qbADht2jTMnTsXRUVFXD2smJgYLFiwAD4+PhL3AyA0oA3gq3hWryxIXx3S09NFbufxeNDU1ETTpk25lV7864QyavG9e/cOJSUlAp+5unXr4s2bN1BTUxPblufPn4tcae3m5oZff/210uOam5uDMQYrKyuFpdsjtQ/95QkhX6Vdu3ZJLAhJCCFVJeugbfmBiZpOz1BSUsKlXjA2NsazZ89ga2uLZs2a4e7duzXalv86Q0NDPH/+XChdY2pqKho3bqykVimWPOc3pTYisqpNKeJqijIHtiquvOGv7powYQIWLVpUY+2ozODBgzF9+nSEhYVxAbqkpCTMnDlT4uzzqszaDwgIwPLly9GxY0c0bNhQ4ZMdyg+qyjvAmpqaioiICK6eROfOnau9nkTz5s3x8uVLoVpY//zzD6ytrRV+vPj4eDg7O8PAwAAdO3YEUBa8W758OU6cOMHVC1AUDQ0NrF+/HqtXr+ZmqfODaNVB3pSEVeHr64vXr19j9uzZXL05TU1NLFy4UOLnnp+26WuljCB927ZtRU7+5FNXV8eYMWOwbds2Li0iPwhak3744Qc4Oztj9uzZAtsPHTqE48eP46+//hK5X69evXDhwgWha0NCQoLEFe2FhYXw8PBAeHg4gLIVIZaWlvDw8EDjxo3h5+dXxXdEahNKh0UI+Sp9KQUhCSH/HVVZ6v7+/XvExsaiRYsWCk8ZA1StCCFRrPnz5yMpKQmHDx9G8+bNkZKSgpcvX8Ld3R3u7u5YunSpspuoFJTaiBBSE/Ly8jBhwgScOHGCW81RVFSEoUOHYteuXdUySNmwYUOsWbOm2uqiiJsBLoqDg4PI7cbGxoiLi0OrVq0QGhqKjRs3CtST4K+cqKryNUsSEhKwYMECLFu2jKutdfnyZSxfvhyBgYHcyn5Fsbe3R5cuXbBlyxZuolxJSQlmz56NixcvIiMjQ6HHU4aaTElYXn5+Pm7fvg0tLS3Y2NgI1JYRRdQKED5+nYra7OTJkzUepD927BgWLlwIX19fLsCbnJyMoKAgLF26FMXFxfDz88OYMWOUWgy8bt26SExMRMuWLQW237lzB127dsXr169F7rd161b4+/tj9OjRAteLw4cPIyAgAI0aNeJeWz6g7eXlhcTERISEhMDJyQnp6emwtLTEsWPHsGzZMloN/x9DQRBCyFdJRUUFL1++FCpO/OjRI9jZ2XHFuAghRF5VGbQdPXo0evTogR9//BEfP35EmzZt8PDhQzDGJBb3k9fp06dRUFCA4cOH4/79+xg8eDAyMzO5IoT8FAak+n3+/Blz5szB7t27UVJSAjU1NRQXF8PV1RW7d+8WWsH4X2FkZCQwa7JXr17/+dRGhNRG9+/fR1ZWFnr06AEtLS2xRdqV7f79+wL1OaRZeVBaWor79+9zKa3Kk7SKoF69ekhOToaVlVXVGi0Gvw5IZUM7ktIM1VQ9iYo1S/ht5m8r/39FT1rT0tLC9evXuSLsfHfv3kXbtm3x8eNHhR6PiFdxZUpRUREePHgANTU1WFlZISUlRUktq706deqEFStWoH///gLbT58+jSVLliA5ORlRUVHw8fGptH5GdRJXlykjIwPffvut2GsNP6hXmYrXjmbNmuHgwYPo3LmzQC2++/fvo3379gKBWfL1o3RYhJCvCr8gJH8GiaiCkG3btlVS6wghX5OqLHWPj4/Hzz//DAA4evQoGGPIy8tDeHg4Vq5cqfAgSPkHImtra9y5c0eqIoRE8TQ0NLBjxw74+/sjIyMDBQUFaNeuXbWk/qhNKLURIbXb69evMXr0aJw7dw48Hg/37t2DpaUlpkyZAiMjIwQFBSmtbfznA3HKp/Bbt26dyNdcvnwZ48aNw6NHj4SCDZUN2E+dOhWRkZHVNrv9wYMHVf4ZNVVPoqbTgZbXvn173L59WygIcvv2bbRp00ZJrfpvEjX7/v3795g4cSJcXFyU0KLaLyMjQ2Sx72bNmnGrnNq2bYvnz5/XdNMEdOrUCdu3b8fGjRsFtm/duhUdOnQQu5+8KdRyc3OFsoMAQEFBAT0D/QdREIQQ8lVRZkFIQsh/S1UGbd+9e8cVBDx16hRGjBgBbW1tDBo0CL6+vopuqkjVUQSUSCcsLAzBwcG4d+8eAMDGxgZz587F1KlTldwy5Rk0aJCym0AIqQJvb2+oq6sjJydHIM3JmDFjMG/ePKUGQSoOuKakpKC4uJgbDM/MzISqqqrEAbiZM2eiY8eOOHnypFR1PcoHXkpLS7F9+3acPXsWDg4OQkXVxQVepFV+4HP16tWoX78+Jk+eLPCanTt3Ijc3FwsXLhT5M2qqnkTFmiV5eXlChcqnTJkCAwMDhRyvfKowT09PeHl54f79+wLpdDZv3lwthcqJbPT19REQEABnZ+dqSx33NWvRogUCAwOxfft2bgykqKgIgYGBXCq0p0+fon79+spsJlauXIm+ffsiLS0Nffr0AQDExMTgypUriI6OVvjx+NdtDw8PAP+36iw0NJS7zpH/DkqHRQj5KimjICQhhEirefPmWLlyJQYNGgQLCwscOHAAvXv35h4IXr16pewmkmri7++PdevWwcPDg3v4unTpEjZt2gRvb2+JebIJIeRL1aBBA5w+fRpt2rQRSDmSnZ0NBwcH5OfnK7uJAMoCDufPn0d4eDiMjIwAAG/fvsWkSZO4+lmi6OjoIC0tTepVe46OjlK9jsfjITY2VrrGS8Hc3ByRkZH47rvvBLYnJSXhhx9+kLhqpKbrSVy9ehVOTk7Q1NTkahhcuXIFHz9+RHR0NNq3b1/lYygiVRipOQkJCXB2dsbbt2+V3ZRa5+LFixgyZAhUVFS42j8ZGRkoKSnBn3/+ic6dO2PPnj148eJFjU24Euf69etYs2YN0tLSoKWlBQcHByxatAg2NjYS97ty5QrOnTsnMiWhuGByQkICBgwYADc3N+zevRszZszArVu3cPHiRcTFxUkMfpOvDwVBCCGEEEJq2G+//QYvLy/o6uqiadOmSE1NhYqKCjZu3Ig//vhDqekiSPUyMTHBhg0bMHbsWIHt+/fvh4eHBwXACCG1kp6eHlJSUmBjYyMQBLl69Sr69+8vtthtTWvcuDGio6PRqlUrge03btxAv3798OzZM5H79e7dGwsWLICTk1NNNFNumpqauH37NiwsLAS2Z2dnw87ODp8+fVJSy4R1794d1tbW2LFjB9TUypKUFBcXY+rUqcjOzkZ8fHyVj/Ho0SOpXysqlRCpHhs2bBD4P2MMz58/x549e9CzZ09ERkYqqWW124cPH7Bv3z5kZmYCAGxtbTFu3Djo6ekpuWVVt2rVKixevBi2traoX7++wGq8yoLJWVlZCAwMRFpaGvLz89G+fXssXLhQqC4J+fpROixCCCGEkBo2e/ZsdOrUCY8fP8b333/Pzbi0tLTEypUrldw6Up2KiorQsWNHoe0dOnRAcXGxElpECCFV1717d0RERGDFihUAygalSktLsWbNGqlXRdSE9+/fIzc3V2h7bm4uPnz4IHY/Dw8P+Pj44MWLF7C3txdKacWfea1sZmZmSExMFAqCJCYmolGjRkpqlWhXr14VCIAAgJqaGhYsWCDyPikPCmx8mYKDgwX+r6KiAhMTE0yYMAGLFi1SUqtqPz09PfTo0QPm5ub4/PkzgP+rwzNkyBBlNk1AVlYWdu3ahezsbISEhMDU1BR///03mjZtKhSg5lu/fj127tyJiRMnynw8Kysr7Nixo4qtJl8DCoIQQgghhChBx44d4eDggAcPHsDKygpqampUF+E/YPz48diyZYvQsv3t27fD1dVVSa0ihJCq+fXXX9G7d29cvXoVnz9/xoIFC3Dz5k28efMGiYmJym4ex8XFBZMmTUJQUBCXgikpKQm+vr4YPny42P1GjBgBAAK1Nvhplr6kVErTpk3D3LlzUVRUhN69ewMoy7e/YMECsam+lEVfXx85OTlCqbYeP35cLTPXIyIiJH7f3d1d4cckoklKy0bkk52dDRcXF2RkZAhcm/i+lGtUXFwcBgwYgK5duyI+Ph4rV66Eqakp0tLSEBYWhiNHjojcT0VFBV27dpXqGO/fv5e6PZQ+/b+F0mERQgghhNSwwsJCeHh4IDw8HEBZUVZLS0t4eHigcePG8PPzU3ILSXXx8PBAREQEzMzMuMKsSUlJyMnJgbu7u8Ds4qoWyyWEkJpQVFQEJycnrF69GmfOnBFIOTJnzhw0bNhQ2U3kFBYWYv78+di5cyeKiooAlK0+mDJlCn799Vfo6OiI3K+ytEpfyooDxhj8/PywYcMGbia4pqYmFi5cCH9/fyW3TpCnpyeOHj2KtWvXcjVMEhMT4evrixEjRiAkJEShx+PXgOErKipCYWEhNDQ0oK2tjTdv3ij0eITUJGdnZ6iqqiI0NBQWFhZISkrCmzdv4OPjg7Vr16J79+7KbiIAoEuXLhg1ahTmzZsnkDoxOTkZw4cPx5MnT0Tut2bNGjx79kyq6wK/FpAkX1oAm9QMCoIQQgghhNQwLy8vJCYmIiQkBE5OTkhPT4elpSWOHTuGZcuWITU1VdlNJNVEWcVyCSGkOpmYmODixYuVFrb9UhQUFCArKwtAWaoUccEPoGywvEWLFvjzzz/RsmXLmmpileTn5+P27dvQ0tKCjY0N6tSpo+wmCfn8+TN8fX2xdetWLh2kuro6Zs2ahcDAwBpp87179zBr1iz4+vqif//+1X48QqqLsbExYmNj4eDgAAMDAyQnJ8PW1haxsbHw8fH5Yp4tdHV1kZGRAQsLC4EgyMOHD9GiRQuxdYtKS0sxaNAgZGZmws7OTigl4R9//MH9Oy4uTur29OzZU743QmolSodFCCGEEFLDoqKicPDgQXTu3FlgplKrVq24QRnydaKi94SQr5GbmxvCwsIQGBio7KZIRUdHR+o6Hurq6l9UQXFp6Orq4ptvvlF2MyTS0NDA+vXrsXr1aoGAlLa2do21wcbGBoGBgXBzc8OdO3dq7LiEKFpJSQmXRs7Y2BjPnj2Dra0tmjVrhrt37yq5df/H0NAQz58/F6pblJqaisaNG4vdz9PTE+fOnYOjoyPq1asncaVHxcDGhQsXsG3bNmRlZeHIkSNo3Lgx9uzZI9QG8vWjIAghhBBCSA3Lzc2Fqamp0PaCgoJKl28TQgghX5ri4mLs3LkTZ8+eRYcOHYRWVtT29H5z5szB//73P4SGhgoU8iZVp62tDXt7e6UdX01NDc+ePVPa8QlRhNatWyMtLQ0WFhb49ttvsWbNGmhoaGD79u2wtLRUdvM4P/zwAxYuXIjDhw+Dx+OhtLQUiYmJmD9/vsS6POHh4fj9999lrp/4+++/Y/z48XB1dUVqair+/fdfAMC7d++watUq/PXXX1V6P6R2obs3IYQQQkgN69ixI06ePAkPDw8A4AIfoaGh6NKlizKbRgghhMjsxo0baN++PYCyOlflfQ3B/StXriAmJgbR0dGwt7cXCvKUT8VCvkzHjx8X+D9jDM+fP8emTZukLrhMyJdq8eLFKCgoAAAsX74cgwcPRvfu3VGvXj0cPHhQya37P6tWrcKcOXNgZmaGkpIS2NnZobi4GK6urli8eLHY/erWrQsrKyuZj7dy5Ups3boV7u7uOHDgALe9a9euWLlypVzvgdReVBOEEEIIIaSGJSQkYMCAAXBzc8Pu3bsxY8YM3Lp1CxcvXkRcXBw6dOig7CYSQggh5P+bNGmSxO/v2rWrhlpC5KWioiLwfx6PBxMTE/Tu3RtBQUFo2LChklpGSPV48+YNjIyMvshA9OPHj5GRkYGCggK0a9cO1tbWEl+/a9cunDp1Crt27ZIpZZ62tjZu3boFc3NzgRok2dnZsLOzq3WpDknV0EoQQgghhJAa1q1bN6SlpWH16tWwt7dHdHQ02rdvj0uXLik1JQQhhBBChFGQo/YrLS1VdhMIqVF169ZVdhNECgsLQ3BwMO7duwegrDbP3LlzMXXqVLH7bNiwAVlZWahfvz7Mzc2FCqOnpKSI3K9Bgwa4f/8+zM3NBbYnJCR8UWnCSM2gIAghhBBCSA0qKirCjBkzsGTJEuzYsUPZzSGEEEKIFIqLi3H+/HlkZWVh3Lhx0NPTw7Nnz6Cvrw9dXV1lN4/IgJ8Q5UucIU/I18zf3x/r1q2Dh4cHlwL40qVL8Pb2Rk5ODpYvXy5yv2HDhsl1vGnTpsHLyws7d+4Ej8fDs2fPcOnSJcyfPx9LliyR922QWorSYRFCCCGE1DADAwNcv34dFhYWym4KIYQQQirx6NEjODk5IScnB//++y8yMzNhaWkJLy8v/Pvvv9i6dauym0ikIM8MdEKI4piYmGDDhg0YO3aswPb9+/fDw8MDr169UujxGGNYtWoVVq9ejcLCQgBAnTp1MH/+fKxYsUKhxyJfPpXKX0IIIYQQQhRp2LBhiIqKUnYzCCGEECIFLy8vdOzYEW/fvoWWlha33cXFBTExMUpsGZGWv78/vLy84OzsjMOHD+Pw4cNwdnaGt7c3/P39ld08Qv4TioqK0LFjR6HtHTp0QHFxscKPx+Px8PPPP+PNmze4ceMGLl++jNzcXAqA/EfRShBCCCGEkBq2cuVKBAUFoU+fPujQoQN0dHQEvu/p6amklhFCCCGkonr16uHixYuwtbUVKK778OFD2NnZcTOMyZerpmegE0KEeXh4QF1dHevWrRPYPn/+fHz8+BGbN28WuV9JSQmCg4Nx6NAh5OTk4PPnzwLff/PmTbW1mXw9qCYIIYQQQkgNCwsLg6GhIa5du4Zr164JfI/H41EQhBBCCPmClJaWoqSkRGj7kydPoKenp4QWEVnV9Ax0QkiZefPmcf/m8XgIDQ1FdHQ0OnfuDABISkpCTk4O3N3dxf6MgIAAhIaGwsfHB4sXL8bPP/+Mhw8fIioqilZyEanRShBCCCGEECWi4pyEEELIl23MmDEwMDDA9u3boaenh/T0dJiYmGDo0KFo2rQpdu3apewmkkrIOwOdEFI1jo6OUr2Ox+MhNjZW5PesrKywYcMGDBo0CHp6erh+/Tq37fLly4iMjFRkk8lXioIghBBCCCFKQMU5CSGEkNrhyZMn6N+/PxhjuHfvHjp27Ih79+7B2NgY8fHxMDU1VXYTSSU8PDwQEREBMzMzkTPQ1dXVuddWDJQQQpRLR0cHt2/fRtOmTdGwYUOcPHkS7du3R3Z2Ntq1a4d3794pu4mkFqB0WIQQQgghNczf3x/r1q2Dh4cHunTpAgC4dOkSvL29kZOTg+XLlyu5hYQQQgjha9KkCdLS0nDgwAGkp6cjPz8fU6ZMgaurq0ChdPLlunHjBtq3bw8AyMrKAgAYGxvD2NgYN27c4F5HK3MJ+fI0adIEz58/R9OmTWFlZYXo6Gi0b98eV65cQZ06dZTdPFJL0EoQQgghhJAaRsU5CSGEkNrj06dP0NTUVHYzCCHkP8nPzw/6+vr46aefcPDgQbi5ucHc3Bw5OTnw9vZGYGCgsptIagEKghBCCCGE1DBDQ0NcuXIFNjY2AtszMzPRqVMn5OXlKadhhBBCCBGir68PFxcXuLm5oU+fPlBRUVF2k4ic7t+/j6ysLPTo0QNaWlpgjNHqD0JqmcuXL+PixYuwsbGBs7OzsptDagm6cxNCCCGE1LDx48djy5YtQtu3b98OV1dXJbSIEEIIIeKEh4ejsLAQQ4cORePGjTF37lxcvXpV2c0iMnj9+jX69OmD5s2bY+DAgXj+/DkAYMqUKfDx8VFy6wghkqxevRo7d+7k/t+5c2fMmzcPubm5+N///qfElpHahIIghBBCCCFKEBYWhtatW2Pq1KmYOnUq7O3tsWPHDqioqGDevHncFyGEEEKUy8XFBYcPH8bLly+xatUq3Lp1C507d0bz5s2pjlct4e3tDXV1deTk5EBbW5vbPmbMGJw6dUqJLSOEVGbbtm1o0aKF0PZWrVph69atSmgRqY0oHRYhhBBCSA1zdHSU6nU8Hg+xsbHV3BpCCCGEyOrWrVtwdXVFeno6SkpKlN0cUokGDRrg9OnTaNOmDfT09JCWlgZLS0tkZ2fDwcEB+fn5ym4iIUQMTU1N3L59GxYWFgLbs7OzYWdnh0+fPimpZaQ2UVN2AwghhBBC/mvOnTun7CYQQgghREafPn3C8ePHERkZiVOnTqF+/frw9fVVdrOIFAoKCgRWgPC9efMGderUUUKLCCHSMjMzQ2JiolAQJDExEY0aNVJSq0htQ0EQQgghhBBCCCGEEDFOnz6NyMhIREVFQU1NDSNHjkR0dDR69Oih7KYRKXXv3h0RERFYsWIFgLLVtqWlpVizZo3UK3QJIcoxbdo0zJ07F0VFRejduzcAICYmBgsWLKCaPkRqlA6LEEIIIYQQQgghRAxtbW0MHjwYrq6uGDhwINTV1ZXdJCKjmzdvonfv3mjfvj1iY2MxZMgQ3Lx5E2/evEFiYiKsrKyU3URCiBiMMfj5+WHDhg34/PkzgLIUWQsXLoS/v7+SW0dqCwqCEEIIIYQQQgghhIjx4cMH6OnpKbsZRE5FRUVwcnLC6tWrcebMGaSlpSE/Px/t27fHnDlz0LBhQ2U3kRAihfz8fNy+fRtaWlqwsbGhVHZEJhQEIYQQQgghhBBCCJGgpKQEUVFRuH37NgDAzs4OQ4cOhaqqqpJbRqRhYmKCixcvwsbGRtlNIYQQogQUBCGEEEIIIYQQQggR4/79+xg4cCCePn0KW1tbAMDdu3dhZmaGkydPUiqlWsDb2xt16tRBYGCgsptCCCFECSgIQgghhBBCCCGEECLGwIEDwRjDvn37ULduXQDA69ev4ebmBhUVFZw8eVLJLSSV8fDwQEREBGxsbNChQwfo6OgIfH/dunVKahkhhJCaQEEQQgghhBBCCCGEEDF0dHRw+fJl2NvbC2xPS0tD165dkZ+fr6SWEWk5OjqK/R6Px0NsbGwNtoYQQkhNU1N2AwghhBBCCCGEEEK+VHXq1MGHDx+Etufn50NDQ0MJLSKyOnfunLKbQAghRIlUlN0AQgghhBBCCCGEkC/V4MGDMX36dCQlJYExBsYYLl++jJkzZ2LIkCHKbh4hhBBCKkHpsAghhBBCCCGEEELEyMvLw4QJE3DixAmoq6sDAIqKijB06FDs2rULhoaGym0gIYQQQiSiIAghhBBCCCGEEEJIJe7fv4/bt28DAFq2bAlra2slt4gQQggh0qAgCCGEEEIIIYQQQogY8+bNE7mdx+NBU1MT1tbWGDp0KOrWrVvDLSOEEEKINCgIQgghhBBCCCGEECKGo6MjUlJSUFJSAltbWwBAZmYmVFVV0aJFC9y9exc8Hg8JCQmws7NTcmsJIYQQUhEVRieEEEIIIYQQQggRY+jQoejbty+ePXuGa9eu4dq1a3jy5Am+//57jB07Fk+fPkWPHj3g7e2t7KYSQgghRARaCUIIIYQQQgghhBAiRuPGjXHmzBmhVR43b95Ev3798PTpU6SkpKBfv3549eqVklpJCCGEEHFoJQghhBBCCCGEEEKIGO/evcM///wjtD03Nxfv378HABgaGuLz58813TRCCCGESIGCIIQQQgghhBBCCCFiDB06FJMnT8bRo0fx5MkTPHnyBEePHsWUKVMwbNgwAEBycjKaN2+u3IYSQgghRCRKh0UIIYQQQgghhBAiRn5+Pry9vREREYHi4mIAgJqaGiZMmIDg4GDo6Ojg+vXrAIC2bdsqr6GEEEIIEYmCIIQQQgghhBBCCCGVyM/PR3Z2NgDA0tISurq6Sm4RIYQQQqRBQRBCCCGEEEIIIYQQQgghhHyVqCYIIYQQQgghhBBCCCGEEEK+ShQEIYQQQgghhBBCCCGEEELIV4mCIIQQQgghhBBCCCGEEEII+SpREIQQQgghhBBCCCGEEEIIIV8lCoIQQgghhBBCCCGEEEIIIeSrREEQQgghhBBCCCGEEEIIIYR8lSgIQgghhBBCCCGEEEIIIYSQr9L/A0hh2y+PSOW0AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lengths = [len(item) for item in tokenized]\n",
        "\n",
        "max_length = max(lengths)\n",
        "min_length = min(lengths)\n",
        "\n",
        "max_length_index = lengths.index(max_length)\n",
        "min_length_index = lengths.index(min_length)\n",
        "\n",
        "true_tokenized = tokenizer.texts_to_sequences(true_titles)\n",
        "\n",
        "fake_tokenized = tokenizer.texts_to_sequences(fake_titles)\n",
        "\n",
        "true_lengths = [len(item) for item in true_tokenized]\n",
        "\n",
        "fake_lengths = [len(item) for item in fake_tokenized]\n",
        "\n",
        "print(\"Distribution of article lengths\")\n",
        "\n",
        "#sns.kdeplot(lengths, label='Overall')\n",
        "sns.kdeplot(true_lengths, label='Real')\n",
        "sns.kdeplot(fake_lengths, label='Fake')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "Qbtv056tIAk6",
        "outputId": "8ecdeb6a-e379-4a4f-8ba4-ad11fd0508a5"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distribution of article lengths\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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fxKxZs+ByuTBs2DDk5+fjm2++QUhICCZOnNhkc1d9eweSyBktvRRo1W/p0JPR8gRazGgREVHjGTBgAF555RW8/PLL6NevH9577z25dtsXvV6P999/HzfddBPuuOMOXLhwAS+88ALmz5+PlJQU9OnTByNHjsTmzZubvD8XM1oEoGJTabNBir3rdNdhlaVDBlpERHRtVq9e7fP4rFmzMGvWLMWxhx9+WP66clmQXq/HRx99pBg/Y8YMzJgxo9HmWhfMaBGAiqVCk8GT0arH0qGc0WIxPBERkTcGWgSgao1WnRqWepYOWQxPRETkEwMtAuBdo1WfpUN3V3guHRIREfnEQIsAeNdoue86rFMxfOWlQ3eg5eBdh0RERAADLXIr9+oMD9R36bByewdmtIiIiAAGWuRWpY9WbUuHQvCuQyKiFk400obOatRYvzsGWgRA2RkeqMNdh6VXAVe59LVn6dDotXTocjbFNImIqA4MBgMAoKioyM8zab0cDgcAqQHqtWAfLQJQtUar1oalnmVDfQBgdO9j5cloAVKwZbY09jSJiKgOdDodQkNDceHCBQBAYGAgNBqNn2fVerhcLly8eBGBgYHQ668tVGKgRQC8M1p13IKn8rIhAAcM0GsN0LrKpG14GGgREfmN1WoFADnYovrRarXo2rXrNQeoDLQIQEWNllwMX2tGy93aIaAi0Hrn29N4wGlCB00Z67SIiPxMo9EgIiICYWFhKCsr8/d0Wh2j0ShvYH0tGGgRgIoMVp2L4T0tHLyWC4/arqJABKCDpoCBFhFRC6HT6a65zogajsXwBCEEPN0cKpYOa8lolbkLLA0B8qGLBaUohPt7bsNDRETEQIsqlg2BeiwdegItTyE8gItXS3FVDrSY0SIiImKgRYrsVUBdN5V2eDJaykCrQDDQIiIi8mCgRYqMVp1rtMqKpWd3oOV0CVwudKDAk9HiNjxEREQMtAhwCu9Aq65Lh4XSszvQyi10wOkSzGgRERF5YaBFcDq9a7Tq2EfLk9Fy12hdvFoKABUZLRbDExERMdCiihotjQYwuovhy2vLaDmUdx1eLHAHWsxoERERyRhokVyjpddqoNdJHXBrLYaX2zsEAaia0RIMtIiIiBhoUUWNllajgVFXz/YOnoyWO9DytHdwFXPpkIiIiIEWyTVa3hmtWmu0HO5ieGOljJZ76dBZwowWERERAy2SgyqdVgO9tiKjJUQNWS25vUOlGi156ZAZLSIiIgZaVFGjpdPCoNNUOe5TmbJh6cWrJQCAQmEGAGhYo0VERMRAi5Q1WnpdxT+JGvc7rBRoXZBrtKTvNWxYSkRExECLKlo56LUa6LUVGa0a7zx0KPc6vOQOtFzuuxB1ZQy0iIiIGGiRvESo02pg8Mpo1XjnodcWPEIIFDqcAIDAkPYAAK2rDCgvbZoJExERtRIMtEheItTrNNBpNfAktard71AIxRY8ZU4hB2uBwZaKcazTIiIilWOgRRUZLY0UYXnqtMqqq9FyOgDhDsKMgSguc8qnOrULRKEwSd/wzkMiIlI5vwdaK1asQPfu3WE2mxEbG4tdu3bVOH7Dhg3o3bs3zGYz+vfvjy1btijOCyGQnJyMiIgIBAQEID4+HseOHVOMWbhwIYYOHYrAwECEhoZWeY/Vq1dDo9H4fFy4cAEAsGPHDp/nbTbbtf1C/MB76RAADO7najNanh5aAGAIRIk70NJrNbAEGrz2O2RGi4iI1M2vgdb69euRlJSEBQsWYM+ePRgwYAASEhLkYKaynTt3Yty4cZgyZQr27t2LxMREJCYm4sCBA/KYRYsWYfny5UhNTUVmZiaCgoKQkJCAkpISeYzD4cCYMWPw+OOP+3yfsWPH4vz584pHQkIChg8fjrCwMMXYo0ePKsZVPt8aVA609LV1h/fUZ2kNgM6AYnd9VoBBh2CTnvsdEhERufk10HrllVfw6KOPYvLkyejbty9SU1MRGBiIlStX+hy/bNkyjBw5Ek899RT69OmDF154Abfccgtef/11AFI2a+nSpZg3bx5Gjx6Nm2++Ge+88w7OnTuHjRs3ytd57rnnMGvWLPTv39/n+wQEBMBqtcoPnU6Hbdu2YcqUKVXGhoWFKcZqtX5PEtabp2Gppyu8obbu8JVaOxS5Ay2zUYcgo17ehoeBFhERqZ3fogKHw4GsrCzEx8dXTEarRXx8PDIyMny+JiMjQzEeABISEuTxJ0+ehM1mU4yxWCyIjY2t9pp18c477yAwMBC//e1vq5yLiYlBREQE7rrrLnzzzTc1Xqe0tBR2u13xaAmq1Gh5usOXV5fRUrZ28NRoBRh0CDLpvDJabPFARETq5rdA69KlS3A6nQgPD1ccDw8Pr7bOyWaz1Tje81yfa9bFP//5T/zud79DQECAfCwiIgKpqan46KOP8NFHHyEqKgq333479uzZU+11UlJSYLFY5EdUVFSD59SYqtRo6aXnsuoyWg7lhtIlZZWWDt1NS1kMT0REaqf39wRauoyMDBw+fBj/+te/FMd79eqFXr16yd8PHToUP/30E1599dUqYz3mzJmDpKQk+Xu73d4igi15Cx53Jsvgfi6vtkbLE2hJzUmLvZcOTXoUQtqGh0uHRESkdn7LaHXq1Ak6nQ45OTmK4zk5ObBarT5fY7Vaaxzvea7PNWvz9ttvIyYmBoMGDap17JAhQ3D8+PFqz5tMJoSEhCgeLUF5lWL4Wu46LFNmtDxLh4HujNZVFsMTEREB8GOgZTQaMWjQIKSnp8vHXC4X0tPTERcX5/M1cXFxivEAkJaWJo+Pjo6G1WpVjLHb7cjMzKz2mjUpKCjABx984LMI3pd9+/YhIiKi3u/jb1XuOtTW0kfLc9ehp0bLc9ehO6PF9g5EREQSvy4dJiUlYeLEiRg8eDCGDBmCpUuXorCwEJMnTwYATJgwAV26dEFKSgoAYMaMGRg+fDiWLFmCUaNGYd26ddi9ezfeeustAIBGo8HMmTPx4osvomfPnoiOjsb8+fMRGRmJxMRE+X2zs7ORm5uL7OxsOJ1O7Nu3DwDQo0cPBAcHy+PWr1+P8vJy/P73v68y96VLlyI6Oho33XQTSkpK8Pbbb2Pbtm34/PPPm+i31XSq1GjVltFyVHSFB6oWwxd6MlrcWJqIiFTOr4HW2LFjcfHiRSQnJ8NmsyEmJgZbt26Vi9mzs7MV7RKGDh2KtWvXYt68eZg7dy569uyJjRs3ol+/fvKY2bNno7CwEFOnTkVeXh6GDRuGrVu3wmw2y2OSk5OxZs0a+fuBAwcCALZv347bb79dPv7Pf/4TDzzwgM+mpg6HA3/+859x9uxZBAYG4uabb8YXX3yBESNGNNavp9lU1GjVs49WpUDL7F46LIS7MzwDLSIiUjm/F8NPnz4d06dP93lux44dVY6NGTMGY8aMqfZ6Go0Gzz//PJ5//vlqx6xevRqrV6+udW47d+6s9tzs2bMxe/bsWq/RGlSu0fJktMqqrdHyZLTcNVry0qEWJr13RqvQ16uJiIhUo/V116RG53S3cagItNx3HVbbsNRToyXddehp7xBo1MOg08h3HQr20SIiIpVjoEU+iuE9Ga1qlg4r9dGSO8MbdDDqtQy0iIiI3Bhokbx0WLlGq/o+Wp6lQ3cfLa9ieINOi0LhrodjjRYREakcAy3yymi5G5bWutehpxhe2UcrwKCFUadFERhoERERAQy0CN7F8NL3ch+t2pYO3X20Srz6aGm1GpRopABMw2J4IiJSOQZaBFeljJa+1rsOlVvwFMmBlnQTa5leCsA05cWAy9kkcyYiImoNGGhRlRoto1yjVb8teAIMOgCAQxtYMZZZLSIiUjEGWlT1rkNdLXcdllVaOqwUaEFvQrlw/9NinRYREakYAy2quqm0tpY+WnJ7h0pb8Bil1xn1OrnFAzNaRESkZgy0CC6hXDqs2OuwjlvwePXR8rxeDrS4sTQREakYAy2SAypdnfc6rLSptKOiMzwAGPVaFAlmtIiIiBhokbwFj5zR0tZ216FnC55KS4dyRkuLAvbSIiIiYqBFFTVa2rrsdegsB5wO6WtDIMqcLvn1nkCLGS0iIiIJAy2qUqNV49Kh545DADAEytksADAbPZ3ltSiE1PqBGS0iIlIzBlrkVaNVaQseX0uHcqClAfQmuSu8VlPRf8uk16IQJmkYN5YmIiIVY6BFch8tOaPlqdFy1ZDRMgYBGo3cFT7QqIdGU7H0WCg8GS0uHRIRkXox0KKqfbRq6gzv8N0V3tPaAZAyWxV9tNjegYiI1IuBFlXpDF9jH63KPbQqNSsFAINeiyLP0iEzWkREpGIMtMhHoCX9s3D4rNFS9tDy1GgFVMpoFXiWDlmjRUREKsZAi6psKl2xdFhDRquaHloAYNRrUMQteIiIiBhoUUXDUl2lhqU++2g5lBktTzF8gLEi0DLotCgQbFhKRETEQIuqLYb33UfL9z6HlZcOi9gZnoiIiIEWVTQsrQi0asholSnvOix0lAMAAk16eYhB733XIZcOiYhIvRhokVyLpfc0LNXWUKPlCZyMQQAqlg6DjJXaO3iWDlkMT0REKsZAi6rcdejJaPm+61C5dFhY6s5oGSsyWkZFRouBFhERqRcDLapSo2Wo8a5D5dKhJ6MV7LV0qMhoOQoA4eM6REREKsBAi6psKl3jXoeVlg7ljJbJ+65Dr/YOrnLA6WiKaRMREbV4DLTIa1Npz16H7rsOa9vrEN41Wt5Lh7qKpUOABfFERKRaDLSoyqbSdcpoeWq0PHcdGpUZLSd0cGiM0oFS7ndIRETqxECL5DYO2rp0hq9812GpO6NlUhbDA0CxJkD5GiIiIpVhoEXwrBDKW/Boa7rrULl06CujZXQHaiVyoMU7D4mISJ38HmitWLEC3bt3h9lsRmxsLHbt2lXj+A0bNqB3794wm83o378/tmzZojgvhEBycjIiIiIQEBCA+Ph4HDt2TDFm4cKFGDp0KAIDAxEaGurzfTQaTZXHunXrFGN27NiBW265BSaTCT169MDq1avr/fO3BOWVtuDxZKTKfdVoVbMFj3dGy3PXYjFbPBARkcr5NdBav349kpKSsGDBAuzZswcDBgxAQkICLly44HP8zp07MW7cOEyZMgV79+5FYmIiEhMTceDAAXnMokWLsHz5cqSmpiIzMxNBQUFISEhASUmJPMbhcGDMmDF4/PHHa5zfqlWrcP78efmRmJgonzt58iRGjRqFESNGYN++fZg5cyYeeeQRfPbZZ9f2S/EDZ6WGpZ6MltMlICq3ZpCXDoMBePfR8t5UWrpOIdwZLTYtJSIilfJroPXKK6/g0UcfxeTJk9G3b1+kpqYiMDAQK1eu9Dl+2bJlGDlyJJ566in06dMHL7zwAm655Ra8/vrrAKRs1tKlSzFv3jyMHj0aN998M9555x2cO3cOGzdulK/z3HPPYdasWejfv3+N8wsNDYXVapUfZnPFnXSpqamIjo7GkiVL0KdPH0yfPh2//e1v8eqrr177L6aZeTJX7jhLrtECfOx3KAdalTJaxpoyWqzRIiIidfJboOVwOJCVlYX4+PiKyWi1iI+PR0ZGhs/XZGRkKMYDQEJCgjz+5MmTsNlsijEWiwWxsbHVXrMm06ZNQ6dOnTBkyBCsXLlSkd2pbS6+lJaWwm63Kx4tQeW9Dj13HQI+9juUG5YGQgjhtddh1YxWAZcOiYhI5fS1D2kaly5dgtPpRHh4uOJ4eHg4jhw54vM1NpvN53ibzSaf9xyrbkxdPf/887jjjjsQGBiIzz//HE888QQKCgrwpz/9qca52O12FBcXIyAgoMo1U1JS8Nxzz9VrHs2hvFJ7B88SIlApoyWEYumwpMwlN31X9NFyZ7QU3eGJiIhUyG+BVks3f/58+euBAweisLAQixcvlgOthpgzZw6SkpLk7+12O6Kioq5pno3B5Q60NBofGS3vOw/LigG4IytjoJzNAoAAQ9WM1lXBpUMiIlI3vy0ddurUCTqdDjk5OYrjOTk5sFqtPl9jtVprHO95rs816yo2NhY///wzSktLa5xLSEiIz2wWAJhMJoSEhCgeLYHn5kKdO9DSaDTyMqIio+VZNgQAQ6DcQyvQqJN7cAEVgVqBMEkHWAxPREQq5bdAy2g0YtCgQUhPT5ePuVwupKenIy4uzudr4uLiFOMBIC0tTR4fHR0Nq9WqGGO325GZmVntNetq3759aN++PUwmU53m0po4K20qDVQES2XeGS3PEqA+ANDqvHpoKROjckbLZVK+joiISGX8unSYlJSEiRMnYvDgwRgyZAiWLl2KwsJCTJ48GQAwYcIEdOnSBSkpKQCAGTNmYPjw4ViyZAlGjRqFdevWYffu3XjrrbcASJmYmTNn4sUXX0TPnj0RHR2N+fPnIzIyUtGaITs7G7m5ucjOzobT6cS+ffsAAD169EBwcDD++9//IicnB7feeivMZjPS0tLw17/+FU8++aR8jT/+8Y94/fXXMXv2bPzhD3/Atm3b8MEHH2Dz5s3N88trRE7huevQK9DSalECl7KXlqPyPodSoBXkVQgPVNRo2V0mKZRnoEVERCrl10Br7NixuHjxIpKTk2Gz2RATE4OtW7fKRebZ2dnQehVmDx06FGvXrsW8efMwd+5c9OzZExs3bkS/fv3kMbNnz0ZhYSGmTp2KvLw8DBs2DFu3blW0ZkhOTsaaNWvk7wcOHAgA2L59O26//XYYDAasWLECs2bNghACPXr0kFtReERHR2Pz5s2YNWsWli1bhuuuuw5vv/02EhISmuz31VQ8NVqepUMA0Pva77BSa4dCeelQ+c/I096hgDVaRESkchpRpSMlNRe73Q6LxYL8/Hy/1mtFz9kMIYBdz96JsHZScPSLhV/g4tVSbPnTbegb6Z7biR3AO6OBzn2Aad9i64Hz+OO7ezC4W3t8+PhQ+XqFpeW4acFnuFf7Lf5uXA50+yUweYuPdyYiImp96vP32+9b8JB/CSHkFg3eGS2DexlR0Uer0tKhnNEy+c5oFXn6aJVebexpExERtQoMtFTO6VWD5V0M7+kOX1bD0qFco2VU1mhV3HXIPlpERKRuDLRUzum1cuyrRYOyvUOlfQ4dvmu0NBoNjDqtV0aLgRYREakTAy2V814ZVCwdujNa5d6BliejZXBntEp933UISC0erno2lWZGi4iIVIqBlsp5Z7SUS4fujJbPGi33XYfVZLQAKSNWKNyBVlkR4HI25rSJiIhaBQZaKuddo6X1bu+g9ZXRcmem3EuH1dVoAVJGqxAVLTWY1SIiIjVioKVyrmqK4Q2++mh5tuAxVOqjZfKV0dKiFAYIjfsc67SIiEiFGGipnKIYviLOkjNaDmf17R1qy2gBGjgNQe7XMtAiIiL1YaClcp6MllYj3S3oYdDXtHQoBU8F7mJ4XxktzzY85Xp3oMWMFhERqRADLZXzrBx612cB1TQsLVNmtDyBVrCPuw7luxbljBablhIRkfow0FI5XxtKA153HdbQ3uFKYRkAIDTQWOW6Rk9GTMeMFhERqRcDLZXztaE0UNEZ3vem0lLwlF/sDrQCDFWuKzc81UtBGbfhISIiNWKgpXKe9g46bXVLhz4yWsYgOMpd8tJhe58ZLWk50aF1B1oshiciIhVioKVy8tKhMs7y2uvQewueivYOnmyWRgOE+MhoGd0ZrVIdM1pERKReDLRUzlVdRkuu0fK1dBiMvCIHACDEbKjyWgAwuTNapcxoERGRijHQUjlPRqtqoFVTjVYg8jz1WYFVs1lARTF8iZbF8EREpF4MtFTOKffRqlQM725YWuap0Sp3AC4puIIhEFcKpYyWrzsOgYo+WiVabixNRETqxUBL5TxtsqpbOpQzWmWFFSeNwRUZLR/1WUBFRqtI4w60WKNFREQqxEBL5SqK4Wvpo+XZfkerB/RGuUarfS1Lh0VgRouIiNSLgZbKVdfewbN0KHeGr9QVPq+o+malgI9AizVaRESkQgy0VM5VbTG8O6NV7slouQMl95Y6V4pqLoY3uQOtQpiVryciIlIRBloq53T57qPlueuwzJPRcigzWvnFnqXDmjNaBYIZLSIiUi8GWipXXR+tii14PBmtitYOgPc+h9XUaLlfX+DJaJXaG23ORERErQUDLZWrrhhevuvQVemuQ/fSYUUfLd8ZLc/SYYHLa+lQCJ9jiYiI2ioGWirnaZNVbR+tKhktTzG8u49WLe0d8j2BlqscKC9trGkTERG1Cgy0VK76pcNKfbTkGi1p6dBz12FtNVpXXaaKgyyIJyIilWGgpXJyMXy1ex26M1plFfsclpQ5UVzmBABYqr3rUNrrsMSpAQzcWJqIiNSJgZbKyXsdVnfXoZzR8tRoBcrZLJ1WgxCz3ud1PcXwpU4XYAx2X4MZLSIiUhcGWipX7dKh3LC0Umd4YxCuuOuzLAEGaCrVdnl4lg4d5S7A5A602OKBiIhUhoGWytV616Gc0XIHScYgXC6QAq1Owb7rswDvQMvJjBYREakWAy2Vq3YLHl2luw49W/AYAnGxoAQA0LmdCdXxBFql5S7A1E46yBotIiJSGb8HWitWrED37t1hNpsRGxuLXbt21Th+w4YN6N27N8xmM/r3748tW7YozgshkJycjIiICAQEBCA+Ph7Hjh1TjFm4cCGGDh2KwMBAhIaGVnmP77//HuPGjUNUVBQCAgLQp08fLFu2TDFmx44d0Gg0VR42m61hvwg/qXULnso1WsYgXLBLbRo6B9cQaOm8lg6Z0SIiIpXya6C1fv16JCUlYcGCBdizZw8GDBiAhIQEXLhwwef4nTt3Yty4cZgyZQr27t2LxMREJCYm4sCBA/KYRYsWYfny5UhNTUVmZiaCgoKQkJCAkpISeYzD4cCYMWPw+OOP+3yfrKwshIWF4d1338XBgwfx7LPPYs6cOXj99derjD169CjOnz8vP8LCwq7xt9K8PHFU5aXDAIN016Dn7kLvQOviVXegVUNGy2xwB1pO1mgREZF6+b5lrJm88sorePTRRzF58mQAQGpqKjZv3oyVK1fimWeeqTJ+2bJlGDlyJJ566ikAwAsvvIC0tDS8/vrrSE1NhRACS5cuxbx58zB69GgAwDvvvIPw8HBs3LgRDz30EADgueeeAwCsXr3a57z+8Ic/KL6//vrrkZGRgY8//hjTp09XnAsLC/OZFWstqiuGDzC62zN4Aq2yimL4iwVSoBXWzlztdY066fWKjBaXDomISGX8ltFyOBzIyspCfHx8xWS0WsTHxyMjI8PnazIyMhTjASAhIUEef/LkSdhsNsUYi8WC2NjYaq9ZV/n5+ejQoUOV4zExMYiIiMBdd92Fb775psZrlJaWwm63Kx7+Vl0xvJzRclTKaBkC65TRUt516K7RcjDQIiIidWlQoHXixIlrfuNLly7B6XQiPDxccTw8PLzaOiebzVbjeM9zfa5ZFzt37sT69esxdepU+VhERARSU1Px0Ucf4aOPPkJUVBRuv/127Nmzp9rrpKSkwGKxyI+oqKgGz6mxVBTDK497Lx0KIYASd1BoCqlXoFXuEnC590fk0iEREalNgwKtHj16YMSIEXj33XcVtU9t0YEDBzB69GgsWLAAd999t3y8V69eeOyxxzBo0CAMHToUK1euxNChQ/Hqq69We605c+YgPz9ffpw5c6Y5foQaVVcMb3YvHbqEu87Ks+xnDpGXDusSaAGA0xNosRieiIhUpkGB1p49e3DzzTcjKSkJVqsVjz32WK13C1bWqVMn6HQ65OTkKI7n5OTAarX6fI3Vaq1xvOe5PtesyaFDh3DnnXdi6tSpmDdvXq3jhwwZguPHj1d73mQyISQkRPHwN3kLnmqWDgGgpLQcKJUyWqX6ILkzfF3uOgSAMh0zWkREpE4NCrRiYmKwbNkynDt3DitXrsT58+cxbNgw9OvXD6+88gouXrxY6zWMRiMGDRqE9PR0+ZjL5UJ6ejri4uJ8viYuLk4xHgDS0tLk8dHR0bBarYoxdrsdmZmZ1V6zOgcPHsSIESMwceJELFy4sE6v2bdvHyIiIur1Pv5WXR8tg04rt3goKbQDkMZdKje7z2sQWs0+h57zntitjBktIiJSqWsqhtfr9XjggQewYcMGvPzyyzh+/DiefPJJREVFYcKECTh//nyNr09KSsI//vEPrFmzBocPH8bjjz+OwsJC+S7ECRMmYM6cOfL4GTNmYOvWrViyZAmOHDmCv/zlL9i9e7d8J6BGo8HMmTPx4osv4j//+Q/279+PCRMmIDIyEomJifJ1srOzsW/fPmRnZ8PpdGLfvn3Yt28fCgqkQODAgQMYMWIE7r77biQlJcFms8FmsykCyKVLl+KTTz7B8ePHceDAAcycORPbtm3DtGnTruVX2uxc1RTDA4DZndUqKciVDmgNuFgkjescbKp2+x1A+iw8Wa2KjBaL4YmISF2uqb3D7t27sXLlSqxbtw5BQUF48sknMWXKFPz888947rnnMHr06BqXFMeOHYuLFy8iOTkZNpsNMTEx2Lp1q1zMnp2dDa22IhYcOnQo1q5di3nz5mHu3Lno2bMnNm7ciH79+sljZs+ejcLCQkydOhV5eXkYNmwYtm7dCrO5ohVBcnIy1qxZI38/cOBAAMD27dtx++2348MPP8TFixfx7rvv4t1335XHdevWDadOnQIg3TX55z//GWfPnkVgYCBuvvlmfPHFFxgxYsS1/EqbnWcrQ1+BVoBBh6sl5SgrypcOmENw0b39Tk31WR5GvRal5S44dIHSAWa0iIhIZTRCuFMa9fDKK69g1apVOHr0KO6991488sgjuPfeexVB0c8//4zu3bujvLy8USfcltjtdlgsFuTn5/utXmvF9uNY/NlRPDj4Oiz67QDFueGLt+P05SJsfcCA3lvGAO2jsfbW/2Duv/cjvk8Y3p74ixqvPfjFNFwqcGD779sj+sN7gGAr8OTRpvxxiIiImlx9/n43KKP1xhtv4A9/+AMmTZpUbU1SWFgY/vnPfzbk8tSMqmtYClQUxDs9GS1Tuzq1dvDwLB2WaJjRIiIidWpQoJWWloauXbsqMliAtM/gmTNn0LVrVxiNRkycOLFRJklNp7qGpUBFjZbL00PLbMGFq54NpavvCu/hafFQog2QDjgKAJcL0Pp9i00iIqJm0aC/eDfccAMuXbpU5Xhubi6io6OveVLUfOqS0XKVeDJadWtW6mHSu4vpPRktACgrvJbpEhERtSoNCrSqK+sqKChQFJ1Ty1dTRivQ3bRUI2e0vJqV1tBDy0POaAkjoHH35eKdh0REpCL1WjpMSkoCIN26n5ycjMDAikyF0+lEZmYmYmJiGnWC1LScLunZV0bL0x1elNZv+x0PT6BV6hSAKRgoyWfTUiIiUpV6BVp79+4FIGW09u/fD6PRKJ8zGo0YMGAAnnzyycadITWp6rbgASqWDrXuLJTwKoYPq0cxvMPpAkwWd6Dl/420iYiImku9Aq3t27cDACZPnoxly5a1iC1k6NpUtwUPUBFo6cqkQKtUH4zScikFVp+MlqPcBZjaSQcZaBERkYo06K7DVatWNfY8yE8qtuCpei7AvXSodwdadiEtFbcz6+U7EmsiLx2WO70CLdZoERGRetQ50HrggQewevVqhISE4IEHHqhx7Mcff3zNE6PmIS8d1tDewVAm1VXlOaU2DXXJZgGVMlpmd/azhBktIiJSjzoHWhaLRd7bzmKxNNmEqHnJS4c+arQ8dx0anVKgddkp3VFalzsOAcDkc+mQGS0iIlKPOgda3suFXDpsO2rKaHlqtEzlUqB1sUwKsOqa0WKgRUREategPlrFxcUoKiqSvz99+jSWLl2Kzz//vNEmRs2jpoyWJ9Ayu6Qmozml0l2mdV46VNx16F46ZDE8ERGpSIMCrdGjR+Odd94BAOTl5WHIkCFYsmQJRo8ejTfeeKNRJ0hNy9NHy+cWPEYdAIEAd6B1rtgAAAirw/Y7QOW7DhloERGR+jQo0NqzZw9uu+02AMCHH34Iq9WK06dP45133sHy5csbdYLUtISo4a5Dgw6BKIUOUjT2szvQqm8xfCmXDomISKUaFGgVFRWhXTvpD+fnn3+OBx54AFqtFrfeeitOnz7dqBOkplXTFjwBBh3awb1ErNHhjDtGqvvSobT0WOp91yEDLSIiUpEGBVo9evTAxo0bcebMGXz22We4++67AQAXLlxgE9NWxlnTptJGLYI1xdI3pna4VOgAUI+7Dg0+iuHZ3oGIiFSkQYFWcnIynnzySXTv3h2xsbGIi4sDIGW3Bg4c2KgTpKZV8xY8eoS4M1rCHILLnkCrQcXwXDokIiL1aVBn+N/+9rcYNmwYzp8/jwEDBsjH77zzTtx///2NNjlqejVuwWPUoZ1GCrTK9O0ghBQ8dQwyVhnrS0UxvNOrGJ6BFhERqUeDAi0AsFqtsFqtimNDhgy55glR8/LcdVjdptLtIC0dluiCAACRoWafrSB8Ud51GCod5F2HRESkIg0KtAoLC/HSSy8hPT0dFy5cgMvlUpw/ceJEo0yOml5tDUs9Ga2r7n0OI0MD6nxtU3V3HbpcgLZBq9ZEREStSoMCrUceeQRffvklHn74YURERMhb81DrU1PDUrNRK991mOvefqdLPQItuUbL+65DCKCssCLwIiIiasMaFGh9+umn2Lx5M375y1829nyomblq6KNl1GkR4s5oXXBIBfBd2tcjo2XwymjpzYBWD7jKpTsPGWgREZEKNGj9pn379ujQoUNjz4X8oKZieI1Gg/a6EgDAuRKpWWl9MlqBRimOL3KUAxoN7zwkIiLVaVCg9cILLyA5OVmx3yG1TjX10QKAUK0UaJ0tkoKm+gVaUsPSYodTOsA7D4mISGUatHS4ZMkS/PTTTwgPD0f37t1hMBgU5/fs2dMok6OmV1MxPABYtEWAE7gKqRi+PkuHnkCrsEqgld/A2RIREbUuDQq0EhMTG3ka5C81FcMDQAddqRRoiUBoNECEpf5LhxUZLS4dEhGRujQo0FqwYEFjz4P8xCnFWdVmtLoElAEOwI4AtA80yr2x6sKT0XI4XShzumDgfodERKQyDW5mlJeXh7fffhtz5sxBbm4uAGnJ8OzZs402OWp6rlpqtCxaqWHpVRGIXPcWPHXlyWgBQJHDyf0OiYhIdRqU0frhhx8QHx8Pi8WCU6dO4dFHH0WHDh3w8ccfIzs7G++8805jz5OaiKdGq7pWaBp3J/erCMT/DYis17WNei30Wg3KXQLFDicsXDokIiKVaVBGKykpCZMmTcKxY8dgNpvl4/feey+++uqrRpscNb0a7zoUQs4+zbpvEObf16fe168oiC/nXYdERKQ6DQq0vvvuOzz22GNVjnfp0gU2m61e11qxYgW6d+8Os9mM2NhY7Nq1q8bxGzZsQO/evWE2m9G/f39s2bJFcV4IgeTkZERERCAgIADx8fE4duyYYszChQsxdOhQBAYGIjQ01Of7ZGdnY9SoUQgMDERYWBieeuoplJeXK8bs2LEDt9xyC0wmE3r06IHVq1fX62dvCWq867C8BHCVAQDuGdQLYe3MVcfUQlEQL2e0eNchERGpQ4MCLZPJBLu9ap3Njz/+iM6dO9f5OuvXr0dSUhIWLFiAPXv2YMCAAUhISMCFCxd8jt+5cyfGjRuHKVOmYO/evUhMTERiYiIOHDggj1m0aBGWL1+O1NRUZGZmIigoCAkJCSgpKZHHOBwOjBkzBo8//rjP93E6nRg1ahQcDgd27tyJNWvWYPXq1UhOTpbHnDx5EqNGjcKIESOwb98+zJw5E4888gg+++yzOv/8LUGNdx3KtVQawBjcoOvLGa1SZrSIiEiFRANMmTJFJCYmCofDIYKDg8WJEyfE6dOnxcCBA8WMGTPqfJ0hQ4aIadOmyd87nU4RGRkpUlJSfI5/8MEHxahRoxTHYmNjxWOPPSaEEMLlcgmr1SoWL14sn8/LyxMmk0m8//77Va63atUqYbFYqhzfsmWL0Gq1wmazycfeeOMNERISIkpLS4UQQsyePVvcdNNNiteNHTtWJCQk1PJTV8jPzxcARH5+fp1f09huX7xddHt6k9h18nLVk7YDQiwIEeLl6AZff9Tyr0S3pzeJbUdyhNj3vnS9dxKvYcZERET+VZ+/3w3KaC1ZsgQFBQXo3LkziouLMXz4cPTo0QPt2rXDwoUL63QNh8OBrKwsxMfHy8e0Wi3i4+ORkZHh8zUZGRmK8QCQkJAgjz958iRsNptijMViQWxsbLXXrO59+vfvj/DwcMX72O12HDx4sE5z8aW0tBR2u13x8LeatuBB0WXpObBTg68faHBvw1PKuw6JiEh9GnTXocViQVpaGr755ht8//33KCgowC233FIl8KjJpUuX4HQ6FcEMAISHh+PIkSM+X2Oz2XyO99SFeZ5rGlMX1b2P93tUN8Zut6O4uBgBAVUbe6akpOC5556r8zyaQ43F8IWXpOfAjg2+fqBJWjoscpQD7bh0SERE6lLvQMvlcmH16tX4+OOPcerUKWg0GkRHR8NqtUIIAU11fQIIc+bMQVJSkvy93W5HVFSUH2dUSzG8J6MVdA2Blme/wzInO8MTEZHq1GvpUAiBX//613jkkUdw9uxZ9O/fHzfddBNOnz6NSZMm4f7776/ztTp16gSdToecnBzF8ZycHFitVp+vsVqtNY73PNfnmvV5H+/3qG5MSEiIz2wWIN1EEBISonj4W0UxvI+TckbrGpYO3XcdFpYy0CIiIvWpV6C1evVqfPXVV0hPT8fevXvx/vvvY926dfj+++/xxRdfYNu2bXVuVmo0GjFo0CCkp6fLx1wuF9LT0xEXF+fzNXFxcYrxAJCWliaP92TWvMfY7XZkZmZWe83q3mf//v2Kux/T0tIQEhKCvn371mkurYWc0fK1dFjkDrSCriXQcme0HOWA2SIddFwFXM4GX5OIiKi1qFeg9f7772Pu3LkYMWJElXN33HEHnnnmGbz33nt1vl5SUhL+8Y9/YM2aNTh8+DAef/xxFBYWYvLkyQCACRMmYM6cOfL4GTNmYOvWrViyZAmOHDmCv/zlL9i9ezemT58OANBoNJg5cyZefPFF/Oc//8H+/fsxYcIEREZGKjbCzs7Oxr59+5CdnQ2n04l9+/Zh3759KCgoAADcfffd6Nu3Lx5++GF8//33+OyzzzBv3jxMmzYNJpMJAPDHP/4RJ06cwOzZs3HkyBH8/e9/xwcffIBZs2bV51fqd3KNlq+lw8bMaHn30QIAR0GDr0lERNRa1KtG64cffsCiRYuqPX/PPfdg+fLldb7e2LFjcfHiRSQnJ8NmsyEmJgZbt26Vi8yzs7Oh9VrTGjp0KNauXYt58+Zh7ty56NmzJzZu3Ih+/frJY2bPno3CwkJMnToVeXl5GDZsGLZu3aroYJ+cnIw1a9bI3w8cOBAAsH37dtx+++3Q6XTYtGkTHn/8ccTFxSEoKAgTJ07E888/L78mOjoamzdvxqxZs7Bs2TJcd911ePvtt5GQkFDnn78lqLGPllyjde0ZrSKHE9CbAJ0RcDqk5UNPhouIiKiN0gjhXjuqA6PRiNOnTyMiIsLn+XPnziE6OhqlpaWNNsG2zG63w2KxID8/32/1Wv0WfIaC0nLsePJ2dO8UpDz59zjgwiHg4X8DN9zRoOu//b8TeHHzYYyOicSyhwYCi26QliQfzwDC+zbCT0BERNS86vP3u15Lh06nE3p99UkwnU5XZZsaatnq1t7h2pcOixzumiwWxBMRkYrUa+lQCIFJkybJdUqVMZPV+jhFNUuHLlejLh0WM9AiIiIVqlegNXHixFrHTJgwocGToebnWTmuktAqyQOEOzi6loalnr0OHe5Mp6cuixtLExGRCtQr0Fq1alVTzYP8pNq7DotypWdTiFTE3kCepcMqGS1uw0NERCrQoL0OqW0QQsAdZ1VdOiy69u13gIoteCoyWqHSc0neNV2XiIioNWCgpWIur/tNq2S0GmGfQ8BHjVZAqPRcnHdN1yUiImoNGGipmNMr0qo2o3UNhfAAEGjw2oIHYEaLiIhUhYGWirm8WqhVae/QCK0dgIqlw+IyJ1wuwYwWERGpCgMtFfPOaFUthve0dmicpUMAKCl3VmS0iq9c03WJiIhaAwZaKuYU3kuHlU56Aq1rzGiZ9Tp4YrjCUmdFRotLh0REpAIMtFTMVVNGq7BxarS0Wg0CDF4F8QHtpRNcOiQiIhVgoKViiqXDJmrvAFT00ip0lLMYnoiIVIWBlop5lg41GkBTJaPVOEuHABBilgIte3GZ19KhXdrmh4iIqA1joKVinjinyrKhEF7tHa49oxUaaAAAXCkqq8hoQXAbHiIiavMYaKlYtRtKOwqB8hLp60bIaHUIMgIArhQ5AL0RMARKJ1inRUREbRwDLRVzVbvPoXvZUG8GjEHX/D7tA6VAK7fQIR1gnRYREakEAy0VkzeUrrYQvhNQOQhrgPbujFZekTvQYtNSIiJSCQZaKubyKoZXkAvhOzTK+1RktMqkA2xaSkREKsFAS8U8gVaVjJb9Z+k5OLxR3qdDkFQMXyWjxaVDIiJq4xhoqZizursOz+2Tnq39GuV9Qj0ZraJKNVpcOiQiojaOgZaKeWq0qtx1eH6f9Bw5sFHeR77r0FMM7+kOz4wWERG1cQy0VExeOvTOaJWXAjmHpK8jYhrlfTw1WleK3DVaLIYnIiKVYKClYj7vOsw5CLjKgIAOQGjXRnmf9u6GpfnFZSh3utjegYiIVIOBlopVNCz1Onhur/QcGdMorR0AwBJgkC+V570NDzNaRETUxjHQUjGfDUsbuT4LAPQ6LSwBXnceMqNFREQqwUBLxXwWw3syWo1Un+Wh6KXFjBYREakEAy0Vc1Yuhi8rAS4clr5uxIwWUFGnlVvoYHsHIiJSDQZaKuby9NHyZLRyDgKucmnrHct1jfpeHby34fFktErzAZezUd+HiIioJWGgpWJyMbxGAzgKgWOfSScasRDeQ9G01JPRAoCS/EZ9HyIiopZE7+8JkP94iuEfK/4HkPJfQLhTXI28bAh4Z7TKAL0RMAQCZUVSQXwj7alIRETU0jDQUjGpGF7g7tLPpSArOByIGgIMmtzo7+UJtC5dLZUOBLSXAi3WaRERURvWIpYOV6xYge7du8NsNiM2Nha7du2qcfyGDRvQu3dvmM1m9O/fH1u2bFGcF0IgOTkZERERCAgIQHx8PI4dO6YYk5ubi/HjxyMkJAShoaGYMmUKCgoK5PN/+ctfoNFoqjyCgoLkMatXr65y3mw2N8JvpHk4hYAFhQgQxdKBGd8DY98FLF0a/b0iLNLv5ec893uxxQMREamA3wOt9evXIykpCQsWLMCePXswYMAAJCQk4MKFCz7H79y5E+PGjcOUKVOwd+9eJCYmIjExEQcOHJDHLFq0CMuXL0dqaioyMzMRFBSEhIQElJSUyGPGjx+PgwcPIi0tDZs2bcJXX32FqVOnyueffPJJnD9/XvHo27cvxowZo5hPSEiIYszp06cb+TfUdFwuges0l6RvgjoDhoAme6/r2gcCAM5ecQdabPFARERqIPxsyJAhYtq0afL3TqdTREZGipSUFJ/jH3zwQTFq1CjFsdjYWPHYY48JIYRwuVzCarWKxYsXy+fz8vKEyWQS77//vhBCiEOHDgkA4rvvvpPHfPrpp0Kj0YizZ8/6fN99+/YJAOKrr76Sj61atUpYLJb6/cBe8vPzBQCRn5/f4Gtci/9+f1ZMnbNAiAUhQrx5e5O+ly2/WHR7epO4fs5mUVbuFGLtOOl9v/tnk74vERFRY6vP32+/ZrQcDgeysrIQHx8vH9NqtYiPj0dGRobP12RkZCjGA0BCQoI8/uTJk7DZbIoxFosFsbGx8piMjAyEhoZi8ODB8pj4+HhotVpkZmb6fN+3334bN954I2677TbF8YKCAnTr1g1RUVEYPXo0Dh48WO3PW1paCrvdrnj4U7lToIsno9VI+xpWp3OwCUadFk6XgM1ewowWERGpgl8DrUuXLsHpdCI8PFxxPDw8HDabzedrbDZbjeM9z7WNCQsLU5zX6/Xo0KGDz/ctKSnBe++9hylTpiiO9+rVCytXrsQnn3yCd999Fy6XC0OHDsXPP//sc+4pKSmwWCzyIyoqyue45lLu8g60mnYuWq0GkaFSndbZK8VeTUuvNOn7EhER+ZPfa7Rag3//+9+4evUqJk6cqDgeFxeHCRMmICYmBsOHD8fHH3+Mzp0748033/R5nTlz5iA/P19+nDlzpjmmXy2ny1URaFmaNqMFAF3aSzVgZ/OKKzJaLIYnIqI2zK+BVqdOnaDT6ZCTk6M4npOTA6vV6vM1Vqu1xvGe59rGVC62Ly8vR25urs/3ffvtt3HfffdVyZJVZjAYMHDgQBw/ftzneZPJhJCQEMXDn8qcAl00F6VvmjijBQBdQqVA6+crxVJ7BwAoym3y9yUiIvIXvwZaRqMRgwYNQnp6unzM5XIhPT0dcXFxPl8TFxenGA8AaWlp8vjo6GhYrVbFGLvdjszMTHlMXFwc8vLykJWVJY/Ztm0bXC4XYmNjFdc+efIktm/fXmXZ0Ben04n9+/cjIiKi1rEtgdN76dDSHIGW152HQZ2kg0WXm/x9iYiI/MXvDUuTkpIwceJEDB48GEOGDMHSpUtRWFiIyZOlppkTJkxAly5dkJKSAgCYMWMGhg8fjiVLlmDUqFFYt24ddu/ejbfeegsAoNFoMHPmTLz44ovo2bMnoqOjMX/+fERGRiIxMREA0KdPH4wcORKPPvooUlNTUVZWhunTp+Ohhx5CZGSkYn4rV65EREQE7rnnnipzf/7553HrrbeiR48eyMvLw+LFi3H69Gk88sgjTfgbazyitAAdNO7eYc2Q0brOe+kw0B1oFV5q8vclIiLyF78HWmPHjsXFixeRnJwMm82GmJgYbN26VV6my87OhlZbkXgbOnQo1q5di3nz5mHu3Lno2bMnNm7ciH79+sljZs+ejcLCQkydOhV5eXkYNmwYtm7dqmgm+t5772H69Om48847odVq8Zvf/AbLly9XzM3lcmH16tWYNGkSdDpdlblfuXIFjz76KGw2G9q3b49BgwZh586d6Nu3b2P/mppEYPE5AECxNhgBZkuTv5+nRuvnK0VAkDvrV8RAi4iI2i6NEO6dhanZ2e12WCwW5Ofn+6Vea9OHq3HfgRk4Z74Bkc/safL3O5NbhNsWbYdRp8WRZwZDu6SHdGL+JUBnaPL3JyIiagz1+fvNuw5VLLD4PAAgz9g8NWURFjNMei0cThdOF5sAaKQTLIgnIqI2ioGWigWXSEuHdpPvOzwbm16nRe8IKfI/aCsAAjtKJwovNsv7ExERNTcGWirWrkTKaNlNzXeX5E2R7kDrnN3rzkPWaRERUdvEQEvF2pVKXfALzP4KtDpLB3nnIRERtVEMtFQspFTKaF1txkCrr3vp8NC5fAh56ZCBFhERtU0MtNSqvBQhZVKAU9SMgVZvawi0GuBSgQPFhg7SQdZoERFRG8VAS63sUiF8iTDAYerYbG8bYNThhs7BAACbM0g6yBotIiJqoxhoqZV7M+craAe9vnn/GfTvIjVHPVEkNTDl0iEREbVVDLTUylEIACgSJui1mmZ969jrpSXDfZfdGxMw0CIiojaKgZZalUp7HBYgALpmDrSG3iC1dci65A60uHRIRERtFAMttXJIgVahMMOga95/BlEdAtElNAAXXO2kAyyGJyKiNoqBllp5Ai2Ymz2jBQBDb+iIXOEOtErygXJHs8+BiIioqTHQUqvSikCruWu0AGBoj47IQzCcnn+CRZebfQ5ERERNjYGWWrmL4QtFAPTNvHQIAHHXd4KAFleE1OqBdVpERNQWMdBSK8dVAP7LaFktZlzfKQiXhNTqgXVaRETUFjHQUis5o+WfGi0AiPOu0yrk0iEREbU9DLTUyqtGy6DzT6A19IZOuAxp70MuHRIRUVvEQEutFHcd+uefwa3Xd8BlIQVaRVdsfpkDERFRU2KgpVZyH60Av9RoAUDHYBO0wZ0BABdsZ/0yByIioqbEQEut5KVDE/R+WjoEgDBrFwDA1cvn/TYHIiKipsJAS608xfB+2ILHW3S37gAAZ8FFuFzCb/MgIiJqCgy01Mq9dFggzND7qUYLAG7o3g0AYHHlYf/ZfL/Ng4iIqCkw0FIr99JhEcx+XTrUt7MCADpp7Nh+9ILf5kFERNQUGGipkRBeGS3/FcMDANyBVjtNMXYeOuW/eRARETUBBlpqVF4CCCcAoAgmv9ZowRQMl1FqWnr5/Cn8fKXIf3MhIiJqZAy01MhdCA94Gpb695+BNiQCABCmycPnB3P8OhciIqLGxEBLjUqlfQ6LYIKA1r8ZLQBoJwVaVuRi60E2LiUioraDgZYaueuzioQZAPxbowUAIZEAAKvmCnafykVuocO/8yEiImokDLTUyL10WIAAAIDez0uHnoxW76ACuATw9XHue0hERG0DAy018nSFFyYALSCj5Q60egVJ8/rfjxf9ORsiIqJGw0BLjTytHTwZLX8HWu5i+EjtFQDA/45dghDsEk9ERK1fiwi0VqxYge7du8NsNiM2Nha7du2qcfyGDRvQu3dvmM1m9O/fH1u2bFGcF0IgOTkZERERCAgIQHx8PI4dO6YYk5ubi/HjxyMkJAShoaGYMmUKCgoK5POnTp2CRqOp8vj222/rNZcWSd5Q2lOj5e+lQ6lGq53jIkx6LWz2Ehy/UFDLi4iIiFo+vwda69evR1JSEhYsWIA9e/ZgwIABSEhIwIULvruE79y5E+PGjcOUKVOwd+9eJCYmIjExEQcOHJDHLFq0CMuXL0dqaioyMzMRFBSEhIQElJSUyGPGjx+PgwcPIi0tDZs2bcJXX32FqVOnVnm/L774AufPn5cfgwYNqtdcWiSvrvAAoPNjZ3gActNSTUEOYrtbAABfHWOdFhERtQHCz4YMGSKmTZsmf+90OkVkZKRISUnxOf7BBx8Uo0aNUhyLjY0Vjz32mBBCCJfLJaxWq1i8eLF8Pi8vT5hMJvH+++8LIYQ4dOiQACC+++47ecynn34qNBqNOHv2rBBCiJMnTwoAYu/evdXOvba51CY/P18AEPn5+XUa32i+XCzEghDx/rO/Ft2e3iSKHeXN+/6VlZcJ8ZdQIRaEiH99nim6Pb1JTFyZ6d85ERERVaM+f7/9mtFyOBzIyspCfHy8fEyr1SI+Ph4ZGRk+X5ORkaEYDwAJCQny+JMnT8JmsynGWCwWxMbGymMyMjIQGhqKwYMHy2Pi4+Oh1WqRmZmpuPavf/1rhIWFYdiwYfjPf/5Tr7lUVlpaCrvdrnj4hWfpsKXUaOn0QFAYAOCXVqm1w7cnLqOkzOnPWREREV0zvwZaly5dgtPpRHh4uOJ4eHg4bDbfjSttNluN4z3PtY0JCwtTnNfr9ejQoYM8Jjg4GEuWLMGGDRuwefNmDBs2DImJiYpgq7a5VJaSkgKLxSI/oqKifI5rcu72DoWQ7jr0e8NSQC6I727IR+d2JpSUuZB1+oqfJ0VERHRt/F6j1VJ16tQJSUlJiI2NxS9+8Qu89NJL+P3vf4/Fixc3+Jpz5sxBfn6+/Dhz5kwjzrge5PYO0obSGk0LCLTcLR40V8/jtp6dAABfHWObByIiat38Gmh16tQJOp0OOTnK/e1ycnJgtVp9vsZqtdY43vNc25jKxfbl5eXIzc2t9n0BIDY2FsePH6/zXCozmUwICQlRPPzCIW3BUwhzy8hmAXKghas2DL+xMwDgy6MMtIiIqHXza6BlNBoxaNAgpKeny8dcLhfS09MRFxfn8zVxcXGK8QCQlpYmj4+OjobValWMsdvtyMzMlMfExcUhLy8PWVlZ8pht27bB5XIhNja22vnu27cPERERdZ5Li+VZOhRm/9dnebiXDmE/j9t6doZRp8UR21X88HOeX6dFRER0LfT+nkBSUhImTpyIwYMHY8iQIVi6dCkKCwsxefJkAMCECRPQpUsXpKSkAABmzJiB4cOHY8mSJRg1ahTWrVuH3bt346233gIAaDQazJw5Ey+++CJ69uyJ6OhozJ8/H5GRkUhMTAQA9OnTByNHjsSjjz6K1NRUlJWVYfr06XjooYcQGSn1dFqzZg2MRiMGDhwIAPj444+xcuVKvP322/Lca5tLi+VZOoTZ/9vveLh7aeHqOXQIMmLUzRH4996zWLPzNJY8GOrXqRERETWU3wOtsWPH4uLFi0hOTobNZkNMTAy2bt0qF5lnZ2dD69VQc+jQoVi7di3mzZuHuXPnomfPnti4cSP69esnj5k9ezYKCwsxdepU5OXlYdiwYdi6dSvMZrM85r333sP06dNx5513QqvV4je/+Q2WL1+umNsLL7yA06dPQ6/Xo3fv3li/fj1++9vf1msuLZJcDN+CMlruXlqwnwcATIjrhn/vPYv//nAOc+/tjY7BJj9OjoiIqGE0QnCvE3+x2+2wWCzIz89v3nqtpf2BvGwklj6Pc8E3Ydez8bW/pqldOAz8/VbAHAo8cxpCCCSu+Abf/5yPSUO74y+/vsnfMyQiIgJQv7/fLWTdiJqV19KhocUsHbprtErygLJiaDQaPJXQGwDwr29P44jNTz3HiIiIrkEL+StLzcqrGL7F3HVotgCGQOlr+zkAwLCenXBPPyucLoG/ffajHydHRETUMAy01MZZBjhLAQAFLalGS6MBLNdJX+dly4eT7roRAPDVjxdhLynzx8yIiIgajIGW2ri33wGkTaX1/t5Q2lv7aOn5ykn5UM/wdrihcxAcThe2H/G90TgREVFLxUBLbdz1WS6tEeXQQ6dtQf8E2neXnnNPKg4n3CTdkfj5wRwQERG1Ji3oryw1C3dGq9wQBAAwtKSMVoeqGS0AGNlPCrS2H73AjaaJiKhVYaClNu5CeKdOKjxvMcXwgNfS4SnF4f5dLAhrZ0KRw4kffs5v/nkRERE1EAMttSmV9jks00sZrRZTDA9UZLRyTwFe7d00Gg1uvi4UALD/LAMtIiJqPRhoqY07o1WulzJa+pZUoxXaDYBG2vS66LLiVP8uFgDAQQZaRETUirSgv7LULNw1WmXupcMWddehwQyEuPc8rFQQ36+L1HmXGS0iImpNGGipjWfpsCXWaAE+WzwAFRmtny4WoMhR3tyzIiIiahAGWmrjzmg5dJ4arRb2T6CaFg9hIWaEtTPBJYBD57gdDxERtQ4t7K8sNTl3Rqu0JRbDA0CH7tJzpTsPAaCfO6vF5UMiImotGGipjSfQ0kqBlq4l1WgB1S4dAhWBFjNaRETUWjDQUht3Z/hS99KhocVltDwtHqoGWr2t7QAAR3OuNueMiIiIGoyBltqUStmgUq2nGL6F/RPwZLQKbICjSHGqlzvQ+jHnKpwuUfmVRERELU4L+ytLTc69dFii9fTRamEZrcAOgFlaIqxcp9W9YxBMei1Kylw4k1tU9bVEREQtDAMttXEHWsXaFthHy6OaOi2dVoOe4cEAgCM2Lh8SEVHLx0BLbTyBlqaFZrSAijqtyz9VOXVjuLtOi4EWERG1Agy01MbdR6tI477rsKXVaAFA+E3Ss21/lVMVBfG885CIiFq+FvhXlpqUnNEyAwAMLXHpMCJGej7/fZVTvazSVjzMaBERUWvAQEtNXE45o1WIFroFDwBEDJCeL/0ot6Pw8GS0Tl4qREmZs7lnRkREVC8MtNTEURG0FHlqtHQt8J9AcBjQLgKAAHIOKE6FtTOhY5ARLsGCeCIiavla4F9ZajLuZUPojCiFAUALLYYHKrJalZYPNRoN+kZKy4fsEE9ERC0dAy018QRapnYod7oAtNClQ6DGOi1PoHXwHPc8JCKilo2Blpp46p2MwXJn9RZZDA9Um9ECgJsipYamB5nRIiKiFo6Blpq4t9+BKQTl7kCrRbZ3ACoCrQuHgbISxamb3BmtIzY7t+IhIqIWrYX+laUm4b106JKWDltsjVZIJBDYCRBOIOeg4lT3jkEIMOhQUubCyUsF1VyAiIjI/xhoqYmiRkvKBLXILXgAQKPxWj7cpzil02rQJ0Jq83DgLJcPiYio5WKgpSaKjJY70GqpGS2g2kALAAZEhQIA9mRfab75EBER1VOLCLRWrFiB7t27w2w2IzY2Frt27apx/IYNG9C7d2+YzWb0798fW7ZsUZwXQiA5ORkREREICAhAfHw8jh07phiTm5uL8ePHIyQkBKGhoZgyZQoKCiqWoXbs2IHRo0cjIiICQUFBiImJwXvvvae4xurVq6HRaBQPs9l8jb+NJuTpo2UKbvk1WgDQZZD0fHpnlVO/6N4BAPDdKQZaRETUcvn9r+z69euRlJSEBQsWYM+ePRgwYAASEhJw4cIFn+N37tyJcePGYcqUKdi7dy8SExORmJiIAwcqGlsuWrQIy5cvR2pqKjIzMxEUFISEhASUlFQUVY8fPx4HDx5EWloaNm3ahK+++gpTp05VvM/NN9+Mjz76CD/88AMmT56MCRMmYNOmTYr5hISE4Pz58/Lj9OnTjfwbakRyMXxFe4cWe9chAETfBmj1wOXjQO5JxanB3dsDkAri84vL/DE7IiKi2gk/GzJkiJg2bZr8vdPpFJGRkSIlJcXn+AcffFCMGjVKcSw2NlY89thjQgghXC6XsFqtYvHixfL5vLw8YTKZxPvvvy+EEOLQoUMCgPjuu+/kMZ9++qnQaDTi7Nmz1c713nvvFZMnT5a/X7VqlbBYLHX/YSvJz88XAER+fn6Dr1Ev//mTEAtChNjxskhc8bXo9vQm8en+883z3g218h5pzrv+UeXU8EXbRLenN4ltR3L8MDEiIlKr+vz99mtGy+FwICsrC/Hx8fIxrVaL+Ph4ZGRk+HxNRkaGYjwAJCQkyONPnjwJm82mGGOxWBAbGyuPycjIQGhoKAYPHiyPiY+Ph1arRWZmZrXzzc/PR4cOHRTHCgoK0K1bN0RFRWH06NE4ePBgNa9uAbxqtIod0j6BgUadHydUBz3ulJ6Pp1c5Ndi9fLj7VG5zzoiIiKjO/BpoXbp0CU6nE+Hh4Yrj4eHhsNlsPl9js9lqHO95rm1MWFiY4rxer0eHDh2qfd8PPvgA3333HSZPniwf69WrF1auXIlPPvkE7777LlwuF4YOHYqff/7Z5zVKS0tht9sVj2blCbSMwSh2b8gc0OIDLXfAfOJLoLxUcWqIO9DadZKBFhERtUx+r9FqDbZv347JkyfjH//4B2666Sb5eFxcHCZMmICYmBgMHz4cH3/8MTp37ow333zT53VSUlJgsVjkR1RUVHP9CBIfGa0AQwsPtKw3A8HhQFkhkP2t4tSt13cEAOzNzkNBabk/ZkdERFQjvwZanTp1gk6nQ05OjuJ4Tk4OrFarz9dYrdYax3ueaxtTudi+vLwcubm5Vd73yy+/xP/93//h1VdfxYQJE2r8eQwGAwYOHIjjx4/7PD9nzhzk5+fLjzNnztR4vUbn2YKnNS0dajTADZ7lwy8Up7p2DET3joEodwlk/HTZD5MjIiKqmV8DLaPRiEGDBiE9vaL+xuVyIT09HXFxcT5fExcXpxgPAGlpafL46OhoWK1WxRi73Y7MzEx5TFxcHPLy8pCVlSWP2bZtG1wuF2JjY+VjO3bswKhRo/Dyyy8r7kisjtPpxP79+xEREeHzvMlkQkhIiOLRrNx3HQpTOxS1lqVDwKtO64sqp27r2RkA8NWPF5tzRkRERHWi9/cEkpKSMHHiRAwePBhDhgzB0qVLUVhYKNdCTZgwAV26dEFKSgoAYMaMGRg+fDiWLFmCUaNGYd26ddi9ezfeeustAIBGo8HMmTPx4osvomfPnoiOjsb8+fMRGRmJxMREAECfPn0wcuRIPProo0hNTUVZWRmmT5+Ohx56CJGRkQCk5cL77rsPM2bMwG9+8xu5dstoNMoF8c8//zxuvfVW9OjRA3l5eVi8eDFOnz6NRx55pDl/hXXnXjos0wfD6ZICk0CD3/8J1O6GOwCNDrhwCLj8E9DxBvnUr27sjH99expfHWOgRURELY/fa7TGjh2Lv/3tb0hOTkZMTAz27duHrVu3ysXs2dnZOH/+vDx+6NChWLt2Ld566y0MGDAAH374ITZu3Ih+/frJY2bPno3/9//+H6ZOnYpf/OIXKCgowNatWxXNRN977z307t0bd955J+69914MGzZMDtYAYM2aNSgqKkJKSgoiIiLkxwMPPCCPuXLlCh599FH06dMH9957L+x2O3bu3Im+ffs25a+sYYSQA60STaB8uFVktAI7ANffLn194GPFqVuv7wC9VoPTl4tw+nJh88+NiIioBhohhPD3JNTKbrfDYrEgPz+/6ZcRy0qAhVLwanviR9z6ym7otBocX3gPNJoW3LTUY+97wCdPAJ37ANOURfFjUnfiu1NX8NID/fHQkK5+miAREalFff5++z2jRc3EUbG9UJGQMnuBBl3rCLIAoPcoQGcELh4Gcg4pTsW57z789gQL4omIqGVhoKUWnu13jMEocndCaBXLhh4BoUCPu6SvD3ykOHWrHGjlgglaIiJqSRhoqYVXD62S1nTHobd+7vq4Ax9JNWduA7u2h1Gnhc1egtOXi/w0OSIioqoYaKmFV1f4otbSrLSyXvcAhkDgykng3F75cIBRh5ioUABcPiQiopaFgZZaeGW05ECrtWW0jEHAjSOlr6ssH0otNzK5HQ8REbUgDLTUwqsrvGfpsMV3hfel32+k54P/Blwu+bBng+ms01f8MSsiIiKfGGiphacY3juj1RqalVbWIx4whQD2s8CZTPlwTNdQaDRAdm4RLlwt8eMEiYiIKjDQUgvF0qF022GrWzoEAIMZ6H2f9LXX8mGI2YBe4e0AAHtO5/lhYkRERFUx0FILH3cdBra2YngPz/LhoY2As1w+fEu39gCAPdlcPiQiopaBgZZatIVieI/rhwMBHYDCi8Cp/8mHb+nqDrRYp0VERC0EAy218Aq0iltrHy0PnQG4KVH6+vt18uFB7ozWD2fz5awdERGRPzHQUguvzvDFjla+dAgAA34nPR/6BCjJBwB07xiI8BATHOUufHeKbR6IiMj/GGiphf2c9NwuovUvHQLAdYOBTr2A8mLgwMcAAI1Gg+E3dgYA7Dh60Z+zIyIiAsBASz3yz0jPoVGtf+kQADQa4JaHpa/3visfvr1XGABgx9EL/pgVERGRAgMtNSgrlgrHAcASVbF02JoDLQC4eSyg1QNndwMXDgMAftmjE3RaDX66WIgzudz3kIiI/IuBlhrkubNZxmAgoH1FRqs112gBQHBYxZY8e/4FALAEGHBL11AAwNYDNj9NjIiISMJASw3ys6VnSxSg0XjVaLXCzvCV3TJBet6zBiiUNpS+f+B1AIC/7ziO/OIyf82MiIiIgZYq5FXUZwFAsbszfKtfOgSAHncB1psBRwHwzasAgAcHX4ceYcG4UlSGV9N+9PMEiYhIzRhoqYGnEN7iDrTaytIhAGi1wB3zpa93/QOwn4dep8Wz9/YBAKzeeQoLNx+CEMKPkyQiIrVioKUGckarKwC0jfYO3nreBUTFAuUlwP/+BgAY0TsMs0f2AgD8438n8dLWI/6cIRERqRQDLTXIr7x02IYyWoDU6sGT1dq9Cjj6KQDgidt74KUH+gMA3vzyBNbsPOWnCRIRkVox0FIDT0bL0hVlThfKXdIyWpuo0fKIvg0Y+HtAOIENk4DsTADAQ0O6ypmthVsO4/iFAj9OkoiI1IaBVlvnLAOuurvCh0bJy4ZAG1o69LhvKdAzQVpCXDsG2PkaUGLH48NvwK9u7AxHuQtPffg9nC7WaxERUfNgoNXW2c8CwgXojEBQmLzZslYDGHVt7OPXGYAxq4GoW6X9Dz+fB7zSF5oPJ2N5z33oabqCvdl5WPn1SX/PlIiIVKINNFKiGsnLhtcBWi2ulnhaO+ih0Wj8OLEmYgwEJv4H+OEDKaN16Shw8N8IPfhvpGmAH41d8NUXt+BsyAR06T9CCs6IiIiaCAOttq5Sa4fTlwsBANe1D/DXjJqe3iTtgxgzHvh5F3BiB3BiB8SZXbhRexY34iyw8b8Qn7aD5oY7gJ53A73vAwJC/T1zIiJqYxhotXWVWjv8dFEqBr8hLNhfM2o+Wi3Q9Vbpcfsz0BRfQd7+z/C/T9cizrUXnUrtwKFPpMfmJ4H+vwEGTwG63OLvmRMRURvBQKut82y/4w60PHfd9eisgkCrsoD2CB3yECyhd2LIqm9xM07gqetPY6hjJzQXDwN735UekQOlrX1uvAcIifD3rImIqBVjoNXW5SmXDn+6KC0dqiKjVY1f3dgZz42+GfM3ajH+px64u884vDi8AGFH1wKHNgLn9koPzALC+0mBl/VmICQSCOwABHSoeNbxf0JERFQ9/pVo6/I8Ga0oCCHUndHy8vCt3eB0uvDi5sP4/PAFfHEEiO/zGJJ+Nwe9bf8FDv8X+Hk3kHNAeviiMwJhfYHIGCB6OHDDCCCgfbP+HERE1LIx0GrLLhwGrpySvu5wAy4XOpBfXAaNBri+c5Bfp9YSTPplNGKv74gXNx/CN8cv4/NDOfj8UA6mDLsLT0/6fzCWXgFOfwPYfgByDgIFF4DiXKAoFyjJA5wO4Pw+6ZG1GtDogKghQI944PoRgLU/oDf694ckIiK/ahGNlFasWIHu3bvDbDYjNjYWu3btqnH8hg0b0Lt3b5jNZvTv3x9btmxRnBdCIDk5GREREQgICEB8fDyOHTumGJObm4vx48cjJCQEoaGhmDJlCgoKlF3Df/jhB9x2220wm82IiorCokWL6j0Xv9r2IgAB9Pk1EBIhZ7Ouax8Ac1vZfuca9YkIwXuP3Iovkn6F+26W6rH++fVJjH0rA2fLAoG+vwbumAeMex94NB34017gmdNAcq709Zg1wK3TgE69pK702RnAtheAt+8AUq4D/nk38NmzwIGPgZ+zpKXcgguA/TyQf1bKOOaflRrLEhFRm+P3jNb69euRlJSE1NRUxMbGYunSpUhISMDRo0cRFhZWZfzOnTsxbtw4pKSk4L777sPatWuRmJiIPXv2oF+/fgCARYsWYfny5VizZg2io6Mxf/58JCQk4NChQzCbzQCA8ePH4/z580hLS0NZWRkmT56MqVOnYu3atQAAu92Ou+++G/Hx8UhNTcX+/fvxhz/8AaGhoZg6dWqd5+I3Z7OAI5sAjRYY8SwArzsOVb5s6EuPsHZ4/Xe34NcDbHhyw/fYm52He5f9D+Nju2Jg1/awBBgQGmhAhMWMdmYDoNUBHa6XHjclAvgrcOU0cPwL6ZGdARRfAc5kSo9aaYCgTkC7COlhagdo9VINmNbg/togPdpFAu27Ax2igdBugMHctL8cIiJqMI0Qwq/7kcTGxuIXv/gFXn/9dQCAy+VCVFQU/t//+3945plnqowfO3YsCgsLsWnTJvnYrbfeipiYGKSmpkIIgcjISPz5z3/Gk08+CQDIz89HeHg4Vq9ejYceegiHDx9G37598d1332Hw4MEAgK1bt+Lee+/Fzz//jMjISLzxxht49tlnYbPZYDRKyz/PPPMMNm7ciCNHjtRpLrWx2+2wWCzIz89HSEhIA3+D1XgnETixHRjwO+D+N5BfXIaH3voWh8/bMfVX12PuvX0a9/3akDO5RZi2dg9++Dnf5/kOQUZEtQ9AVIdARFjM6BBkQsdgIzoFGxFiNsCk18Go0yCk6DRCLu9DQE4WtLYfgKs5QEEO4CqXAmCtTnp2lUuPhmoXKQVd7bsD7d3PxkCvARoAQnoPZxngcla8n1YnLXlqtYAQ0pJocZ7UWd/76/JSKcjT6t1Bn1F6D2MQYAyWHqZg5ffGIGlsZdU2yq10XLgAR4F7Lu6Hs0zqk2YMAgI7AkGdKx6mYGle0EjLuk6HNN7pAJyl0tcaLWAIBAwB0rPO4Hs+QkivKyuWtnTyPAshjddovR7u71H5uLbSWB/jtDr375QZZqLWpD5/v/2a0XI4HMjKysKcOXPkY1qtFvHx8cjIyPD5moyMDCQlJSmOJSQkYOPGjQCAkydPwmazIT4+Xj5vsVgQGxuLjIwMPPTQQ8jIyEBoaKgcZAFAfHw8tFotMjMzcf/99yMjIwO/+tWv5CDL8z4vv/wyrly5gvbt29c6l8pKS0tRWloqf5+fL/0ht9vtNfyWGuDUN8DhbYBGj68tDyL1lTScvlyIK0Vl6BhkQGLf9o3/nm2IRQ+sGt8P2w5fwGcHz+NcXgnsJWXIKyqDvaQcl0qLcCk3D3t/qsvVzAB+CbPhNpj1WjiFgEsATpeAUwDCJeASTlhEAcI0VxCmyUOYJg8BcECPcujhQpBBINgAGDUuGFGGTq7LCHflwOqyIRAlQOlZ4NJZAF837S+mDXJCi1IY4dCYoBEuGFAGA8phwDUEvg3gggZO6FAOHcqhR7nGACcMKNPo4YQOGgho4IIGgAYu6IULWpRDJ8qhhxNauOCCFi5o4dTo4IIWAlrpjEYrn3O5z7mgg0sjVY5ohGcGcL+HkB9a4QIgoIVQPEtfSaNdGh2EO3js1jG4IogUTsDp/o8IlxMQ7meXd7Cv8QpGNagIQjUV56Bxx+CaqsdReZw7aPYZ6Hod81DkGUQzHkc1x+tynUrq9BqX9B8O5V7/AaLRuP/jyZ0p1+kBrVGZPXf/G6m4lvD9fY1janidr99Djdfxfq9K5z2ftVbn/lpX8R+SYb2Be16uep1r4PkbWpdclV8DrUuXLsHpdCI8PFxxPDw8XM4aVWaz2XyOt9ls8nnPsZrGVF6W1Ov16NChg2JMdHR0lWt4zrVv377WuVSWkpKC5557rsrxqKgon+MbRcodim/PALhpYdO9HRERUcuyA0Dtq0wNcfXqVVgslhrH+L1GS03mzJmjyIC5XC7k5uaiY8eOftl30G63IyoqCmfOnGn8pUu6JvxsWjZ+Pi0XP5uWra18PkIIXL16FZGRkbWO9Wug1alTJ+h0OuTk5CiO5+TkwGq1+nyN1WqtcbznOScnBxEREYoxMTEx8pgLFy4orlFeXo7c3FzFdXy9j/d71DaXykwmE0wmk+JYaGioz7HNKSQkpFX/g2/L+Nm0bPx8Wi5+Ni1bW/h8astkefi1vYPRaMSgQYOQnp4uH3O5XEhPT0dcXJzP18TFxSnGA0BaWpo8Pjo6GlarVTHGbrcjMzNTHhMXF4e8vDxkZWXJY7Zt2waXy4XY2Fh5zFdffYWysjLF+/Tq1Qvt27ev01yIiIhI5YSfrVu3TphMJrF69Wpx6NAhMXXqVBEaGipsNpsQQoiHH35YPPPMM/L4b775Ruj1evG3v/1NHD58WCxYsEAYDAaxf/9+ecxLL70kQkNDxSeffCJ++OEHMXr0aBEdHS2Ki4vlMSNHjhQDBw4UmZmZ4uuvvxY9e/YU48aNk8/n5eWJ8PBw8fDDD4sDBw6IdevWicDAQPHmm2/Way4tWX5+vgAg8vPz/T0VqoSfTcvGz6fl4mfTsqnx8/F7oCWEEK+99pro2rWrMBqNYsiQIeLbb7+Vzw0fPlxMnDhRMf6DDz4QN954ozAajeKmm24SmzdvVpx3uVxi/vz5Ijw8XJhMJnHnnXeKo0ePKsZcvnxZjBs3TgQHB4uQkBAxefJkcfXqVcWY77//XgwbNkyYTCbRpUsX8dJLL1WZe21zaclKSkrEggULRElJib+nQpXws2nZ+Pm0XPxsWjY1fj5+76NFRERE1Fa1iC14iIiIiNoiBlpERERETYSBFhEREVETYaBFRERE1EQYaKnUihUr0L17d5jNZsTGxmLXrl3+nlKbk5KSgl/84hdo164dwsLCkJiYiKNHjyrGlJSUYNq0aejYsSOCg4Pxm9/8pkoT3OzsbIwaNQqBgYEICwvDU089hfJy5V58O3bswC233AKTyYQePXpg9erVTf3jtSkvvfQSNBoNZs6cKR/jZ+NfZ8+exe9//3t07NgRAQEB6N+/P3bv3i2fF0IgOTkZERERCAgIQHx8PI4dO6a4Rm5uLsaPH4+QkBCEhoZiypQpKCgoUIz54YcfcNttt8FsNiMqKgqLFi1qlp+vtXI6nZg/fz6io6MREBCAG264AS+88IJizz9+NpX49Z5H8ot169YJo9EoVq5cKQ4ePCgeffRRERoaKnJycvw9tTYlISFBrFq1Shw4cEDs27dP3HvvvaJr166ioKBAHvPHP/5RREVFifT0dLF7925x6623iqFDh8rny8vLRb9+/UR8fLzYu3ev2LJli+jUqZOYM2eOPObEiRMiMDBQJCUliUOHDonXXntN6HQ6sXXr1mb9eVurXbt2ie7du4ubb75ZzJgxQz7Oz8Z/cnNzRbdu3cSkSZNEZmamOHHihPjss8/E8ePH5TEvvfSSsFgsYuPGjeL7778Xv/71r332SxwwYID49ttvxf/+9z/Ro0cPRb/E/Px8ER4eLsaPHy8OHDgg3n//fREQEKDol0hKCxcuFB07dhSbNm0SJ0+eFBs2bBDBwcFi2bJl8hh+NkoMtFRoyJAhYtq0afL3TqdTREZGipSUFD/Oqu27cOGCACC+/PJLIYTUFNdgMIgNGzbIYw4fPiwAiIyMDCGEEFu2bBFarVZu4CuEEG+88YYICQkRpaWlQgghZs+eLW666SbFe40dO1YkJCQ09Y/U6l29elX07NlTpKWlieHDh8uBFj8b/3r66afFsGHDqj3vcrmE1WoVixcvlo/l5eUJk8kk3n//fSGEEIcOHRIAxHfffSeP+fTTT4VGoxFnz54VQgjx97//XbRv317+vDzv3atXr8b+kdqMUaNGiT/84Q+KYw888IAYP368EIKfjS9cOlQZh8OBrKwsxMfHy8e0Wi3i4+ORkZHhx5m1ffn5+QCADh06AACysrJQVlam+Cx69+6Nrl27yp9FRkYG+vfvj/DwcHlMQkIC7HY7Dh48KI/xvoZnDD/P2k2bNg2jRo2q8vvjZ+Nf//nPfzB48GCMGTMGYWFhGDhwIP7xj3/I50+ePAmbzab43VosFsTGxio+n9DQUAwePFgeEx8fD61Wi8zMTHnMr371KxiNRnlMQkICjh49iitXrjT1j9kqDR06FOnp6fjxxx8BAN9//z2+/vpr3HPPPQD42fji102lqfldunQJTqdT8ccBAMLDw3HkyBE/zartc7lcmDlzJn75y1+iX79+AACbzQaj0VhlY/Hw8HDYbDZ5jK/PynOupjF2ux3FxcUICAhoih+p1Vu3bh327NmD7777rso5fjb+deLECbzxxhtISkrC3Llz8d133+FPf/oTjEYjJk6cKP9+ff1uvX/3YWFhivN6vR4dOnRQjImOjq5yDc85z762VOGZZ56B3W5H7969odPp4HQ6sXDhQowfPx4A+Nn4wECLqBlMmzYNBw4cwNdff+3vqRCAM2fOYMaMGUhLS4PZbPb3dKgSl8uFwYMH469//SsAYODAgThw4ABSU1MxceJEP89O3T744AO89957WLt2LW666Sbs27cPM2fORGRkJD+banDpUGU6deoEnU5X5e6pnJwcWK1WP82qbZs+fTo2bdqE7du347rrrpOPW61WOBwO5OXlKcZ7fxZWq9XnZ+U5V9OYkJAQZkyqkZWVhQsXLuCWW26BXq+HXq/Hl19+ieXLl0Ov1yM8PJyfjR9FRESgb9++imN9+vRBdnY2gIrfb03/P2a1WnHhwgXF+fLycuTm5tbrMySlp556Cs888wweeugh9O/fHw8//DBmzZqFlJQUAPxsfGGgpTJGoxGDBg1Cenq6fMzlciE9PR1xcXF+nFnbI4TA9OnT8e9//xvbtm2rkgYfNGgQDAaD4rM4evQosrOz5c8iLi4O+/fvV/yfUlpaGkJCQuQ/RHFxcYpreMbw86zenXfeif3792Pfvn3yY/DgwRg/frz8NT8b//nlL39ZpRXKjz/+iG7dugEAoqOjYbVaFb9bu92OzMxMxeeTl5eHrKwsecy2bdvgcrkQGxsrj/nqq69QVlYmj0lLS0OvXr1a1dJUcyoqKoJWqwwddDodXC4XAH42Pvm7Gp+a37p164TJZBKrV68Whw4dElOnThWhoaGKu6fo2j3++OPCYrGIHTt2iPPnz8uPoqIiecwf//hH0bVrV7Ft2zaxe/duERcXJ+Li4uTznhYCd999t9i3b5/YunWr6Ny5s88WAk899ZQ4fPiwWLFiBVsINID3XYdC8LPxp127dgm9Xi8WLlwojh07Jt577z0RGBgo3n33XXnMSy+9JEJDQ8Unn3wifvjhBzF69GifLQQGDhwoMjMzxddffy169uypaCGQl5cnwsPDxcMPPywOHDgg1q1bJwIDA1tlC4HmMnHiRNGlSxe5vcPHH38sOnXqJGbPni2P4WejxEBLpV577TXRtWtXYTQaxZAhQ8S3337r7ym1OQB8PlatWiWPKS4uFk888YRo3769CAwMFPfff784f/684jqnTp0S99xzjwgICBCdOnUSf/7zn0VZWZlizPbt20VMTIwwGo3i+uuvV7wH1U3lQIufjX/997//Ff369RMmk0n07t1bvPXWW4rzLpdLzJ8/X4SHhwuTySTuvPNOcfToUcWYy5cvi3Hjxong4GAREhIiJk+eLK5evaoY8/3334thw4YJk8kkunTpIl566aUm/9laM7vdLmbMmCG6du0qzGazuP7668Wzzz6raMPAz0ZJI4RXO1ciIiIiajSs0SIiIiJqIgy0iIiIiJoIAy0iIiKiJsJAi4iIiKiJMNAiIiIiaiIMtIiIiIiaCAMtIiIioibCQIuIiIioiTDQIiIiImoiDLSIiIiImggDLSIiIqImwkCLiIiIqIn8f7ZyIXISk3YmAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "\n",
        "padded_sequences = pad_sequences(tokenized, padding='post', maxlen=max_length)\n",
        "\n",
        "print(padded_sequences)\n",
        "\n",
        "lengths = [len(item) for item in padded_sequences]\n",
        "\n",
        "max_length = max(lengths)\n",
        "min_length = min(lengths)\n",
        "\n",
        "max_length_index = lengths.index(max_length)\n",
        "min_length_index = lengths.index(min_length)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fXPh_fimIAnz",
        "outputId": "b95ec220-e3d9-4d60-9470-f53379ed4b13"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 107   67    1 ...    0    0    0]\n",
            " [ 107   67 1549 ...    0    0    0]\n",
            " [ 107   67    1 ...    0    0    0]\n",
            " ...\n",
            " [   1   15  155 ...    0    0    0]\n",
            " [   1  163   21 ...    0    0    0]\n",
            " [   1   15  155 ...    0    0    0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_length = 44\n",
        "\n",
        "from tensorflow.keras.callbacks import LearningRateScheduler\n",
        "\n",
        "def scheduler(epoch, lr):\n",
        "    if epoch < 1:\n",
        "        return lr\n",
        "    else:\n",
        "        return lr * (10**(-epoch/2))\n",
        "callbacks = [LearningRateScheduler(scheduler, verbose=1)]\n",
        "\n",
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Embedding(vocab_size, embedding_length, input_length=max_length))\n",
        "model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(256, return_sequences = False)))\n",
        "\n",
        "model.add(tf.keras.layers.Dense(256, activation = \"relu\"))\n",
        "\n",
        "model.add(tf.keras.layers.Dense(2, activation = \"softmax\"))\n",
        "input_array = np.random.randint(1000, size=(32, max_length))\n",
        "model.compile(tf.keras.optimizers.AdamW(weight_decay=0.001), 'binary_focal_crossentropy', metrics=[tf.keras.metrics.BinaryAccuracy()])\n",
        "output_array = model.predict(input_array)\n",
        "print(output_array.shape)\n",
        "model.summary()\n",
        "plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 880
        },
        "id": "zBQJtfycEem-",
        "outputId": "61ddb608-2a1a-49a7-e100-242f5a15a5fa"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 5s 5s/step\n",
            "(32, 2)\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 8375, 44)          6074376   \n",
            "                                                                 \n",
            " bidirectional (Bidirection  (None, 512)               616448    \n",
            " al)                                                             \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               131328    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 2)                 514       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6822666 (26.03 MB)\n",
            "Trainable params: 6822666 (26.03 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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uvRpMVCqVn59fSkqKcd2A1NfXMxiMgwcPaia+WevIhivV1dVhYWGrV68mUmg0Gp/PVyqVeuzQ9TrqPMQxCf8VCsVPP/1UWloKx7Pd3d337t27d+9egw4aaEelUmVmZspkspCQEGP5oElsbOzEiRPhGnUcx6uqqu7cuVNWVmYsf5CQ6Q0mk0mlUlNSUmpqapRKZVVV1YkTJ2JiYiZOnMjj8bp+LAUyROISoUlDQwN8afzTTz+FKVFRUcHBwSEhIcZ6PzwnJ+fChQvZ2dnaV7QNDocOHSooKLh69Spc2pmVlQVfGtf8EMhgo98HvDe8a5mbm/vuu+9yuVwymczj8Xx9fZOTk5VKpV6MR0VFwWWTLi4u586d04vNrgCDdS0Hx38t6Ni11MJPP/20fft2ffljomRmZiYkJHR0dPTbgiFiDMP1+s7n2bNnly5dql+biMEEw7CMjAy0HRzCcBgixlDXEoFAmDxIyBAIhMmDhAyBQJg8SMgQCITJg4QMgUCYPBRDGB2cPUEQBmLp0qXdfnhveICCc1hiECHT8SvjiCHI0qVLw8PDNT9yMGw4fPgwAKDr29GIQcYQf5MGEbJhuQrpDWHp0qU+Pj7D8g7CFWTD8tJMC0MIGRojQyAQJg8SMgQCYfIgIUMgECYPEjIEAmHyICFDIBAmj6kK2W+//TZ27FgSiYRhmJ2dXXx8vKFrvHDhgkAggB8RGzFiBNwtGDH8WLduHfG1uE53+caNG1FRUZqR8Mknn2gWmD17tpmZGZlMHjduXLcf1zcoaWlpcPN2Z2fnVatWVVdXw3SRSOTh4cFkMtlstoeHx65du6RSKcyaOXNm12/kwS3s4uPjO6XD3TYvXbokEok0P5CZmZlJlLG2th7kq/5/6PerQIP8PTK40UtjY+Og1ejm5sbj8QatusEHGPJT18ZF909dW1paZmdnFxcXwx2XITExMQsWLJBKpfDQzc0N7s9w+fJlzdOzs7OJb/YPJunp6QAAkUjU1NSUn58vEAgmTpwIv4U3f/78gwcP1tbWymSys2fPUqnU9957D541Y8aMrpowZ84cHMeJfe0Ixo0bB89KTEycMWMG8btTq9WVlZW5ubnz5s1Dn7oeiigUCl9fX2N7MUzQS2MOzh1hMpnwC7HENo779+9PT08/e/asmZkZUSwpKYlEIgmFQmN9NlaTb775ZuTIkdu2bePxeBMnToyIiCgoKIC7mtNotI0bN9rY2HA4nODg4EWLFv38889wZyYGg0FIM0QoFH7++efQZqdtCoqKimD65s2bJ0yYMG/evI6ODgAAhmHwC7GjRo0y0tWbbNdycEhJSem0szyi3+ilMY1yR8rKynbt2rVnz55OG3H7+vqGh4e/ePFi69atg+xSVyQSib29PfECFty2Eu6qd/HiRU3PHRwcAABw/4Fr165pSrNEIikqKpo1a1av1cXGxhYUFCQmJur1IvrP8BGyo0ePstlsFouVlZU1d+5cLpfr6Oh45swZAEBSUhKDwbC1tV23bp29vT2DwfD19YV/VmFhYTQaDW5oBgDYuHEjm83GMAxurhUZGVleXo5hmLu7uy4+3L5929PTk8fjMRgMLy+vn376CQCwZs0aOHzg5uYGd29dtWoVi8Xi8XiXLl1SqVQxMTF8Pp/JZL711luwb/6vf/2LxWKZmZnV1tZGRkY6ODgUFxcbqN36AY7jhw4dGjt2LJ1Ot7CwWLRo0ZMnT0BfGlNfd+TatWuDsMdlUlISjuOBgYFds+Lj40ePHn3ixIkbN250ze2pobTEKgCg25DoFYFAoCnxcIBMIBB0LVlaWmpubu7s7Nw1a//+/Zs3b9alOgsLixkzZiQmJuJD5HPQ+u2pGneMLDo6GgBw8+bN5ubm2tpaPz8/Npvd3t6O47hQKGSz2X/88Udra6tYLIZjohUVFTiOL1u2zM7OjrB54MABAEBdXR2O40FBQW5ubpo1ah8jO3fuXGxsbENDw6tXr6ZNm0aMFwQFBZHJ5BcvXhAlP/7440uXLuE4vnXrVjqdfv78+cbGxh07dpBIJLilM7yWzZs3Hzly5IMPPvjvf/+rpzbrBaDD+EVMTAyNRjt16lRTU1NhYaG3t7e1tTXcMVP3xtTLHbl8+bKZmdnevXt1ubR+bwcnEAg8PT07FXNzc3v27BmO43fv3iWRSC4uLi0tLfj/HSPT0lBaYrWnkNBOTk4OlUpNSkqSSqVFRUVjx46FQ10E7e3tlZWVR44codPp3W5tV1lZ6enpqVKp4GFcXJyjo6O5uTmVSnVxcVm4cOH9+/c1y0dFRQEA8vPziRS0r2U/6VbIFAoFPExOTgYAlJWV4TguFAo1BejBgwcAgD179uB6FTJNEhISAAC1tbU4jsO/a2JP2ebm5lGjRnV0dCgUChaLFRISAtPlcjmdTt+wYUPXaxk0eg0yuVzO4XAIn3Ecv3//PgAAqkmfhEwvd0R3+idkLS0tGIYtWLCgUzFCyHAcj4yMBADAzXoJIdPeUD3FqpaQ6JWdO3cSDyiOjo4SiUQzF+5vbWVl9eWXX0LF7MSmTZu+/vpr4hBujSyTydra2vLy8iZNmsRkMouKiogCJ0+eBAB89913RAra19IgwD17ut1TcsqUKSwWCz7nGwi4UxacpZ41a9bo0aNPnjwJ72J6enpISAiZTC4uLpbL5XBWGwDAZDJHjBhhUK8GjlgsbmlpmTJlCpEydepUGo0GO4b9ZhDuSP+Af0XaN2GLj48fM2ZMcnLynTt3iMQ+NRQRq/0Oiejo6GPHjt28ebOlpeXp06e+vr4+Pj4SiYQoIJFIamtr09LSvv3220mTJnUaaqyqqrp06dLKlSuJFCcnp0mTJnE4HBqNNm3atNTUVIVCAQUXAtukpqamV98GgeEsZNqh0+l1dXX6tXnlypWZM2fa2NjQ6XRi6gcAgGHYunXrnj59evPmTQDAd999B/fxff36NQBg586dxDKc58+fy+Vy/XqlX5qamgAAcKkRgbm5uUwmG6BlQ9yRgdPa2goAIKYvu4XBYKSmpmIY9umnnyoUCpjYv4bqX0i8fPlSJBKtXbt21qxZbDbb1dX1+PHjVVVV8GEWQqVSbWxsZs+enZ6eLhaLYY+BQCQShYaGdprN0MTLy4tMJpeUlBApTCYT/NU+RucNFTKlUtnU1OTo6KgXa7m5uYcPH66oqFi8ePGIESPu3bvX3NwsEok0y6xcuZLBYJw4caK4uJjL5cKhVhsbGwDA4cOHNR+S8/Ly9OKVgTA3NwcAdPo1Drwx9XtH9Aj8ufa6QbqPj09ERERpaSmx/Kp/DdW/kCgtLVWpVCNHjiRSuFyupaWlWCzuWtjd3Z1MJmtmVVdXp6WlbdiwQUsVarVarVZrCjrchh22j9F5Q4UsJycHx/Fp06YBACgUSrfdT935/fff2Wz248ePlUrlhg0bBAIBg8Ho9CVSCwuLpUuXZmZmHjx4MDQ0FCY6OTkxGIyCgoKB1D7IjB8/nsPhPHz4kEi5d+9ee3v75MmTwQAaU793RI/Y2tpiGKbLSrG4uDgPDw84MQ16a6ie6F9IQHGES8MgMpmsoaHBycnp1atXH3/8sWZhqHpwfQZEJBItX77c0tJSsxgcgCaAEw6aX9yEbQKH3ozOGyRkarW6sbGxo6OjsLAwPDycz+fDEQF3d/eGhobMzEylUllXVweX3kAsLS2rqqr+/PNPmUzW7U9LqVTW1NTk5OSw2Ww+nw8AuHHjRmtra2lpadehkPXr17e1tV2+fHnBggUwhcFgrFq16syZM0ePHpVKpSqVqrKyUjMchyAMBiMyMvLixYunT5+WSqWPHz9ev369vb29UCgEfWzMgd+R7OxsQy+/YLFYAoGgsrKy15Kwg0kmk4lDLQ2lxUhPIRESEmJnZ9ftm0+urq7+/v7Hjx/Pzc1VKBQSiQTWsnr1ajabff369Vu3bkmlUqVSmZ+fv2LFCjabHRERAc+tqak5efJk1w/nvnjxIj09vampSalU5uXlrVmzhs/nr1+/nigA28TLy6vXlhkM9Dt3MGizlr/99tu4ceNIJBIAYMSIEfv27UtOToajj6NGjSovLz927BiXywUAODs7l5SUCIVCKpXq4OBAoVC4XO6iRYvKy8uhqVevXvn7+zMYDFdX188++2zbtm0AAHd3dzhr4+zszGQyp0+f/vXXX7u5ufXUjBcvXsRxfPv27ZaWlubm5sHBwV999RUAwM3NDS4pgEyaNCkqKkrzQtra2rZv387n8ykUio2NTVBQkFgsFolE8Indycmp25lywwF0mFFSq9UHDhwYNWoUlUq1sLBYvHhxcXExzNKxMaurqwd+R6qrq69evWpmZkZMB2un38svwsLCqFSqXC6HhxcvXoSRYG1tDWcqNdm2bRux/KKnhtIeq92GBI7jixcvBgDExMR06zZcZ+fu7k6n0zkczjvvvPPDDz/ArMDAQFdXVw6HQ6fT3dzcQkJCHj9+TJwYERGxfPnyrgYjIyPd3NzYbDaFQnF0dAwNDa2qqtIsMH/+fAcHB7VaTaSg5RcGB75AZ2wv8Hnz5j19+tTYXmjDEEHWLYN/R/otZKWlpRQKZZD/UbqiUqn8/PxSUlKM6wakvr6ewWAcPHhQMxEtvxgMeh2vNRBEn7SwsBA+ZRjFjSGIse5IrygUip9++qm0tBSOZ7u7u+/du3fv3r3wtR6joFKpMjMzZTJZSEiIsXzQJDY2duLEiWFhYQAAHMerqqru3LlTVlZmLH/eICEzFtu3by8tLS0pKVm1alXXLwoghiANDQ3wpfFPP/0UpkRFRQUHB4eEhBjr/fCcnJwLFy5kZ2drX9E2OBw6dKigoODq1atwsWRWVhZ8afzKlStG80m/D3hDs2sZFRUFFxy6uLicO3dukGuPjo4mkUhOTk7wnaQhDhiUrqVR7oiOXUst/PTTT9u3b9eXPyZKZmZmQkJCR0dHvy0YIsYwXK/vfJ49e3bp0qX6tYkYTDAMy8jIGJZ7pgUHB4O/NoVDGBFDxBjqWiIQCJMHCRkCgTB5kJAhEAiTBwkZAoEweSiGMHr27FlDmEUMDkP8rfV+A1+pQcE5PNHvJKiOn+VFIBBvMkN9+QUC0Qk4y46egxAGBY2RIRAIkwcJGQKBMHmQkCEQCJMHCRkCgTB5kJAhEAiTBwkZAoEweZCQIRAIkwcJGQKBMHmQkCEQCJMHCRkCgTB5kJAhEAiTBwkZAoEweZCQIRAIkwcJGQKBMHmQkCEQCJMHCRkCgTB5kJAhEAiTBwkZAoEweZCQIRAIkwcJGQKBMHmQkCEQCJMHCRkCgTB5kJAhEAiTBwkZAoEweZCQIRAIkwcJGQKBMHmQkCEQCJMHCRkCgTB5kJAhEAiTBwkZAoEweZCQIRAIkwcJGQKBMHmQkCEQCJOHYmwHEMON3NzcvLw84vDJkycAAJFIRKT4+Pj8z//8jxE8QwxfMBzHje0DYlhx8+bNd999l0qlkkidn/fVarVSqbxx40ZAQIBRfEMMV5CQIfSMWq0eMWJEXV1dt7nW1tbV1dVkMnmQvUIMb9AYGULPkEikZcuW0Wi0rlk0Gm358uVIxRB6BwkZQv989NFH7e3tXdPb29s/+uijwfcHMexBXUuEQXBxcXn+/HmnRCcnp+fPn2MYZhSXEMMY9ESGMAiffPIJlUrVTKFSqStXrkQqhjAE6IkMYRCePHkyduzYTolFRUXjxo0zij+I4Q16IkMYBA8Pj3Hjxmk+f3l6eiIVQxgIJGQIQ/GPf/yDmKCkUqkrVqwwrj+IYQzqWiIMhUQicXZ2hgGGYdjTp09dXFyM7RRieIKeyBCGwsnJ6e233yaRSCQS6e2330YqhjAcSMgQBuSTTz7BMIxEIn3yySfG9gUxnEFdS4QBqa+vHzFiBACgqqrK1tbW2O4ghi1GEzK0ngiBGH4YS0+M+Rmf8PBwHx8fIzpguuTl5SUmJmZkZBjbkd7Jzc3FMMzPz0/3U5YuXYpiw+SAMWms2o35RJaRkfHhhx8apXZT5+zZs7b620YAACAASURBVEuXLjWJYQGZTAYAMDMz0/0UFBumiHFjEn1YEWFY+iRhCET/QLOWCATC5EFChkAgTB4kZAgEwuRBQoZAIEyeN0LIpk6dSiaTJ06c2G8La9asMTMzwzCsoKBAe9bVq1d5PN6PP/44II8Nw1D2bRC4ceNGVFTUhQsXBAIBhmEYhnV632D27NlmZmZkMnncuHGPHj0aZPfS0tKmTp1qZmbm7Oy8atWq6upqmC4SiTw8PJhMJpvN9vDw2LVrl1QqhVkzZ87EusDhcAAA8fHxndLHjx8PALh06ZJIJFKpVIN8dYbmjRCyBw8e+Pv7D8TCiRMnjh8/rkvWUF4SMZR9MzS7d+9OSkrasWNHUFDQ06dP3dzcrKysTp8+feXKFaLM9evXz507t2DBArFY7O3tPZjuZWRkLFu2LDg4uLKyMisrKzc3d+7cuR0dHQCA27dvh4aGVlRU1NTUxMXFiUSiJUuWaDE1ffp0LbmBgYEMBiMgIKCpqUnP12BU3gghgwzOuwTz589vbm5esGDBINTVVwztm0Kh8PX1NZDxgbB///709PSzZ89qrgVJSkoikUhCobC5udmIvkG++eabkSNHbtu2jcfjTZw4MSIioqCg4N69ewAAGo22ceNGGxsbDocTHBy8aNGin3/++eXLlwAABoMhlUpxDYRC4eeffw5tnjp1SjOrqKgIpm/evHnChAnz5s2DQjk8eIOErNOXl/uKFh3Ui0TiOH7u3Lljx44N3JSxSElJqa2tNbYXnSkrK9u1a9eePXsYDIZmuq+vb3h4+IsXL7Zu3Wos3wgkEom9vT0RSE5OTgAAuOnBxYsXNT13cHAAALS0tAAArl27pinNEomkqKho1qxZvVYXGxtbUFBgxIX4escEhEylUsXExPD5fCaT+dZbb8H3chITE9lsNolEmjx5sp2dHZVKZbPZ3t7efn5+Tk5ODAbD3Nyc+GuClJWVeXh4sNlsJpPp5+d3584dLfYBADiOHzhwYMyYMXQ6ncfjbdu2jTDVU9adO3f4fD6GYV999RUA4OjRo2w2m8ViZWVlzZ07l8vlOjo6njlzhqg3ISFhzJgxTCbT2tra1dU1ISHBcMvZdfctKSmJwWDY2tquW7fO3t6ewWD4+vrCp4OwsDAajQbfAwcAbNy4kc1mYxhWX18fHh4eGRlZXl6OYZi7uzsA4Nq1a1wud9++fQa6Ih1JSkrCcTwwMLBrVnx8/OjRo0+cOHHjxo2uuTiOHzp0aOzYsXQ63cLCYtGiRXDX9F5va7fhpB2BQKD5HwAHyAQCQdeSpaWl5ubmzs7OXbP279+/efNmXaqzsLCYMWNGYmLi8BltwI0EACAjI0OXklu3bqXT6efPn29sbNyxYweJRHrw4AGO47t37wYA3Lt37/Xr1/X19e+//z4A4MqVK3V1da9fvw4LCwMAFBQUQCMBAQECgeDZs2dKpbKoqOjtt99mMBglJSVa7EdHR2MY9sUXXzQ2Nsrl8uTkZABAfn6+9iyJRAIAOHLkCKw3OjoaAHDz5s3m5uba2lo/Pz82m93e3o7j+L59+8hkclZWllwu//333+3s7GbOnKlj68GfR58avE++CYVCNpv9xx9/tLa2isViOAhdUVGB4/iyZcvs7OwImwcOHAAA1NXV4TgeFBTk5uZGZF2+fNnMzGzv3r199VP32NAFgUDg6enZKdHNze3Zs2c4jt+9e5dEIrm4uLS0tOA4np2dvXDhQlgmJiaGRqOdOnWqqampsLDQ29sb7i6Ma226nsJJOzk5OVQqNSkpSSqVFhUVjR07ds6cOZoF2tvbKysrjxw5QqfTO/UZIZWVlZ6eniqVCh7GxcU5Ojqam5tTqVQXF5eFCxfev39fs3xUVBQRtHqhfzGpL4a6kCkUChaLFRISAg/lcjmdTt+wYQP+l5DJZDKY9e233wIAHj9+DA/v378PAEhPT4eHAQEBEyZMIMwWFhYCALZu3dqTfblczmKx3nvvPeIU+Jebn5+vJQvvQSwUCgU8hJJXVlaG4/jUqVP/9re/EUbWrl1LIpHa2tp0aT09Clm3vgmFQh6PR5z44MEDAMCePXvwvghZv9GjkLW0tGAYtmDBgk7phJDhOB4ZGQkA2LRpE64hZHK5nMPhEIGB/xVRUJd7ajot4dorO3fuJB4vHB0dJRKJZq6dnR0AwMrK6ssvv4SK2YlNmzZ9/fXXxGFFRcWjR49kMllbW1teXt6kSZOYTGZRURFR4OTJkwCA7777ThffdMG4QjbUu5bFxcVyuRzOHAMAmEzmiBEj4BN+J+DW1sT4JRwRUyqV3Zr18vLi8XiFhYU92S8rK5PL5QEBAV3P1ZLVK9BJ6FVrayuu8WCvUqmoVKoRd+HW9K0TU6ZMYbFY3Tb7EKe2thbHcRaLpaVMfHz8mDFjkpOTidEGAIBYLG5paZkyZQqRMnXqVBqNBrvYnSCaTvdw7UR0dPSxY8du3rzZ0tLy9OlTX19fHx8f+McDkUgktbW1aWlp33777aRJkzqNRVZVVV26dGnlypVEipOT06RJkzgcDo1GmzZtWmpqqkKhgIILgW1SU1PTq28mwVAXstevXwMAdu7cSSyHef78uVwuH7hlKpWqVCp7sl9ZWQkAsLGx6Xqilqw+MW/evN9//z0rK0uhUDx8+DAzM/Pvf/+7EYVMO3Q6va6uzthe9JnW1lYAAJ1O11KGwWCkpqZiGPbpp58qFAqYCFcnwDVZBObm5vBjHj3Rv3B9+fKlSCRau3btrFmz2Gy2q6vr8ePHq6qq4NMuhEql2tjYzJ49Oz09XSwWJyQkaFoQiUShoaGdZjM08fLyIpPJJSUlRAqTyQR/tc8wYKgLGdSLw4cPaz5G5uXlDdBsR0dHQ0MDn8/vyT6Miba2tq7nasnqE7GxsbNmzVq5ciWXy/3ggw8+/PDDnpaqGR2lUtnU1OTo6GhsR/oM/Ln2uv7Tx8cnIiKitLQ0Li4Oppibm4O/vkFE0Gsj9C9cS0tLVSrVyJEjiRQul2tpaSkWi7sWdnd3J5PJmlnV1dVpaWkbNmzQUoVarVar1ZqC3t7eDv5qn2HAUBcyOAXZdT39APnll1/UarW3t3dP9sePH08ikX799deu52rJ6hNisbi8vLyurk6pVFZUVBw9etTCwmKANg1ETk4OjuPTpk0DAFAolJ467EMQW1tbDMN0WSkWFxfn4eGRn58PD8ePH8/hcB4+fEgUuHfvXnt7++TJk7UY6V+4QnGES8MgMpmsoaHBycnp1atXH3/8sWZhqHpwfQZEJBItX77c0tJSs9icOXM0D+GEg+a3KmGbwKG3YcBQFzIGg7Fq1aozZ84cPXpUKpWqVKrKykrNW6477e3tzc3NHR0djx49CgsLc3Z2XrlyZU/2bWxslixZcv78+ZSUFKlUWlhYSKzw0pLVJzZt2sTn8+GCoCGIWq1ubGzs6OgoLCwMDw/n8/lwCMbd3b2hoSEzM1OpVNbV1cG1ThBLS8uqqqo///xTJpMplcrs7GyjL79gsVgCgQCOBmgHdjCJrj2DwYiMjLx48eLp06elUunjx4/Xr19vb28vFAq1G+kpXENCQuzs7Lp988nV1dXf3//48eO5ubkKhUIikcBaVq9ezWazr1+/fuvWLalUqlQq8/PzV6xYwWazIyIi4Lk1NTUnT57csmVLJ5svXrxIT09vampSKpV5eXlr1qzh8/nr168nCsA28fLy6rVlTANDzyb0BNB5ZqqtrW379u18Pp9CodjY2AQFBYnF4sTERDha6eLicvv27f379/N4PACAnZ3d999/n56eDv9qLCwszpw5g+N4amqqv7+/ra0thUKxsrL66KOPnj9/rsU+juMymSw0NNTKyorD4UyfPj0mJgYA4Ojo+J///KenrNDQULjGisViBQYGJicnQydHjRpVXl5+7NgxLpcLAHB2di4pKbl165aVlRVxI6hU6tixYy9cuKBLm/RjhujIkSO6+yYUCqlUqoODA4VC4XK5ixYtKi8vh3ZevXrl7+/PYDBcXV0/++wzuIbO3d0dTpM5Ozszmczp06dXV1dfvXrVzMwsPj6+T37i+l5+ERYWRqVS5XI5PLx48aKbmxsAwNraGs5UarJt2zZi+YVarT5w4MCoUaOoVKqFhcXixYuLi4txHNfedD2F0+LFiwEAMTEx3ToJF+K5u7vT6XQOh/POO+/88MMPMCswMNDV1ZXD4dDpdDc3t5CQEGJqHsfxiIiI5cuXdzUYGRnp5ubGZrMpFAqMzKqqKs0C8+fPd3BwUKvV/WjSbkHLL95QkpOTw8PDicO2trYtW7bQ6XTiJ6cFQweNUCi0tLQ0nH3t6Dc2SktLKRRKt2uvBhOVSuXn55eSkmJcNyD19fUMBuPgwYN6tImWX7yJVFdXh4WFrV69mkih0Wh8Pl+pVA6REahh84EEd3f3vXv37t2714i9eJVKlZmZKZPJQkJCjOWDJrGxsRMnToSLxocHSMiMA5PJpFKpKSkpNTU1SqWyqqrqxIkTMTExISEhsJ+C0CNRUVHBwcEhISHGej88JyfnwoUL2dnZ2le0DQ6HDh0qKCi4evXqAN8+HlIgITMOPB7v+vXrRUVFo0ePZjKZnp6eqamp+/fvh+8nGJcdO3akpqY2Nze7urqeP3/e2O7oh3379oWFhf3zn/80Su0BAQHff/898Y6qEcnKympra8vJyRmyU+T9A+2iZDT8/Px+/vlnY3vRDQkJCZ3WWw4PZs+ePXv2bGN7YWQWLly4cOFCY3uhf9ATGQKBMHmQkCEQCJMHCRkCgTB5kJAhEAiTx5iD/QN/9/uNBTbd2bNnje2IoUCxYXIY95ZhuJG+dTs4W4EgEIjBxFh6Ysyu5Rv+itJAMO7rIIYGxYYpouPuBAYCjZEhEAiTBwkZAoEweZCQIRAIkwcJGQKBMHmQkCEQCJMHCRkCgTB5hq6QXbhwQSAQYN3h4uLSV2tTp04lk8kTJ07stz9r1qwxMzPDMKzr1hKdsq5evcrj8X788cd+14XQnRs3bkRFRWlGyyeffKJZYPbs2WZmZmQyedy4cd1+Mt+gpKWlwX3anZ2dV61aVV1dDdNFIpGHhweTyWSz2R4eHrt27ZJKpTBr5syZXWMebkwXHx/fKZ3YQ1N3WltbPTw8NPcD7jbr0qVLIpHIVL6vOXSFLCgo6OnTp25ubsSW1x0dHXK5vKamph9fp3vw4IG/v/9A/Dlx4kRP27V1ysKNtCbwDWT37t1JSUk7duwgosXKyur06dNXrlwhyly/fv3cuXMLFiwQi8Xe3t6D6V5GRsayZcuCg4MrKyuzsrJyc3Pnzp0L95C+fft2aGhoRUVFTU1NXFycSCRasmSJFlPTp0/Xl1fR0dHFxcW9ZgUGBjIYjICAALjF5xBn6ApZV8hkMpPJtLW1HT16dP8sDM7rBPPnz29ubl6wYMEg1GUIFAqFr6/vUDCinf3796enp589e9bMzIxITEpKIpFIQqHQWB+D1eSbb74ZOXLktm3beDzexIkTIyIiCgoK4F7lNBpt48aNNjY2HA4nODh40aJFP//8M9xvicFgSKVSzbWmQqHw888/hzY7bT5QVFTUJ5fu3r3b0yldszZv3jxhwoR58+ZB8R3KmJKQEWRmZvbvxAF+21eLDupFInEcP3fuXP82l9MjKSkptbW1Q8GIFsrKynbt2rVnz55O22v7+vqGh4e/ePFi69athqtdRyQSib29PREbcDNKuIHexYsXNT13cHAAAMBdBa5du6YpzRKJpKioaNasWQP3R6FQbNu2LTExUfes2NjYgoKCbk8ZUpikkEESExPZbDaJRJo8ebKdnR2VSmWz2d7e3n5+fnCfVHNzc+J/DFJWVubh4cFms5lMpp+f3507d2C6SqWKiYnh8/lMJvOtt94iXrbAcfzAgQNjxoyh0+k8Hg9ufaY9686dO3w+H8Owr776CgBw9OhRNpvNYrGysrLmzp3L5XIdHR3PnDlD1JuQkDBmzBgmk2ltbe3q6pqQkPDhhx/qq4lwHD906NDYsWPpdLqFhcWiRYuePHkCAAgLC6PRaMSXlzdu3MhmszEMg5uSRUZGlpeXYxjm7u6elJTEYDBsbW3XrVtnb2/PYDB8fX3hM4XuRgAA165d0+8el0lJSTiOBwYGds2Kj48fPXr0iRMnbty4oXub9Hqnuo0Q7QgEAk01hwNkAoGga8nS0lJzc3NnZ+euWfv379+8ebMu1fVKdHQ0fAzUPcvCwmLGjBmJiYlDfcDE8O9gdQ/Q7X06zTEyHMc3b96suanf7t27AQD37t17/fp1fX39+++/DwC4cuVKXV3d69ev4SYxBQUFsHBAQIBAIHj27JlSqSwqKnr77bcZDEZJSQmO41u3bqXT6efPn29sbNyxYweJRII7M0dHR2MY9sUXXzQ2Nsrl8uTkZABAfn6+9iyJRAIAOHLkCKw3OjoaAHDz5s3m5uba2lo/Pz82m93e3o7j+L59+8hkclZWllwu//333+3s7GbOnKlL6+n4rmVMTAyNRjt16lRTU1NhYaG3t7e1tXV1dTWO48uWLbOzsyNKHjhwAABQV1eH43hQUJCbmxuRJRQK2Wz2H3/80draKhaL4dB1RUVFn4xcvnzZzMxs7969ulydLrEhEAg8PT07Jbq5uT179gzH8bt375JIJBcXl5aWFhzHs7Ozid0qtbSJljvVU4RoJycnh0qlJiUlSaXSoqKisWPHzpkzR7NAe3t7ZWXlkSNH6HR6txvWVVZWenp6qlQqeBgXF+fo6Ghubk6lUl1cXBYuXHj//v1e3YDcuXMnMDAQx/G6ujoAQHR0tC5ZOI5HRUURsa0FtK+lNuBeqpp0FTKZTAYP4c4dRIH79+8DANLT0+FhQEDAhAkTiHMLCwsBAFu3blUoFCwWKyQkBKbL5XI6nb5hwwa5XM5isd577z3iFPj/nJ+fryUL70HIFAoFPISSV1ZWhuP41KlT//a3vxFG1q5dSyKR2traem0WXYJGLpdzOBziuogGgWrSJyHT/C958OABAGDPnj19MtIneo2NlpYWDMMWLFjQKZ0QMhzHIyMjAQBwC15CyLS3SU93qqcI0eVaNCcHHR0dJRKJZi7cRtrKyurLL7+EitmJTZs2ff3118Qh3AVZJpO1tbXl5eVNmjSJyWQWFRX16oZcLp8yZUplZSXeRa20ZEFOnjwJAPjuu++0V4H2teyFTk9kWkrSaDQAADEwCUfEetom0svLi8fjFRYWFhcXy+VyYhqbyWSOGDHiyZMnZWVlcrk8ICCg67lasnoFOgm9am1txTWe2FUqFZVKJZPJ/TDbFbFY3NLSMmXKFCJl6tSpNBoNdgz7zZQpU1gsFuyOGYva2locx7VPXsfHx48ZMyY5OZkYQAB9bBPiTvUUIb36GR0dfezYsZs3b7a0tDx9+tTX19fHxwf+z0EkEkltbW1aWtq33347adKkTqOKVVVVly5dWrlyJZHi5OQ0adIkDodDo9GmTZuWmpqqUCig4Gpnx44da9euhSNxumdBYDvX1NT0WosRMQEh0yQxMbEfC2d6gkqlKpXK169fAwB27txJrM15/vy5XC6vrKwEAHQ7oKAlq0/Mmzfv999/z8rKUigUDx8+zMzM/Pvf/64vIYOz5nD9EYG5ublMJhugZTqdDv+6jUVrayt0Q0sZBoORmpqKYdinn36qUChgYv/apKcI0e7ky5cvRSLR2rVrZ82axWazXV1djx8/XlVVBZ9bIVQq1cbGZvbs2enp6WKxuNPmVSKRKDQ0tNNshiZeXl5kMrmkpES7J3fu3Hn8+PGaNWv6lEXAZDLBX20+ZDExIdMjHR0dDQ0NfD4f6tHhw4c1n1Tz8vJgALW1tXU9V0tWn4iNjZ01a9bKlSu5XO4HH3zw4Ycf9rRUrR+Ym5sDADr9RJuamhwdHQdiVqlUDtzIAIE/rV7Xavr4+ERERJSWlsbFxcGU/rVJTxGivfbS0lKVSjVy5EgihcvlWlpaisXiroXd3d3JZLJmVnV1dVpa2oYNG7RUoVar1Wq1dkEHAKSkpNy8eZNEIkEVhpezb98+DMPWr1/fU9bDhw/h6e3t7eCvNh+ymKSQvXz5ctWqVQM08ssvv6jVam9vbzjF2XW9/vjx40kk0q+//tr1XC1ZfUIsFpeXl9fV1SmVyoqKiqNHj+px29Tx48dzOBwiHAEA9+7da29vnzx5MgCAQqH01OnWTk5ODo7j06ZNG4iRAWJra4thmC4rxeLi4jw8PPLz8+Gh9jbpiZ4iRDtQHOHSMIhMJmtoaHBycnr16tXHH3+sWRiqHlyfARGJRMuXL7e0tNQsNmfOHM1DOOHg4+Oj3ZPU1FRNCdYcCNMcce6URXTAYTvD4bwhi4kJGY7jCoXiwoULXC63H6e3t7c3Nzd3dHQ8evQoLCzM2dl55cqVDAZj1apVZ86cOXr0qFQqValUlZWVL1++tLGxWbJkyfnz51NSUqRSaWFhIbHCS0tWn9i0aROfz4erh/QOg8GIjIy8ePHi6dOnpVLp48eP169fb29vLxQKAQDu7u4NDQ2ZmZlKpbKurg4uboJYWlpWVVX9+eefMpkM6pRarW5sbOzo6CgsLAwPD+fz+XDgRncj2dnZelx+wWKxBAIB7OD32gipqalEb117m2gx0m2EAABCQkLs7Oy6ffPJ1dXV39//+PHjubm5CoVCIpHAWlavXs1ms69fv37r1i2pVKpUKvPz81esWMFmsyMiIuC5NTU1J0+e3LJlSyebL168SE9Pb2pqUiqVeXl5a9as4fP569ev1+7JAIHt7OXlpXfL+kSPEwd9AvQ2M3Xx4sWuU5YEO3fuTExMhMOQLi4ut2/f3r9/P4/HAwDY2dl9//336enp8D/EwsLizJkzOI6npqb6+/vb2tpSKBQrK6uPPvro+fPnsK62trbt27fz+XwKhWJjYxMUFCQWi3Ecl8lkoaGhVlZWHA5n+vTpMTExAABHR8f//Oc/PWWFhobCpVUsFiswMDA5ORk6OWrUqPLy8mPHjkEJdnZ2LikpuXXrlpWVFXFRVCp17NixFy5c6LX1dJwhUqvVBw4cGDVqFJVKtbCwWLx4cXFxMcx69eqVv78/g8FwdXX97LPP4Do4d3d3OC/m7OzMZDKnT59eXV0tFAqpVKqDgwOFQuFyuYsWLSovL++rkatXr5qZmcXHx/fqM67bjHZYWBiVSpXL5fCQiBZra2s4U6nJtm3biOUXPbWJ9jvVU4QsXrwYABATE9Otk3BJnbu7O51O53A477zzzg8//ACzAgMDXV1dORwOnU53c3MLCQnRfDiKiIhYvnx5V4ORkZFubm5sNptCocBgq6qqglnaPdGk26lJLVnz5893cHBQq9XazaLlF28uycnJ4eHhxGFbW9uWLVvodDrx++yJwQwaoVBoaWk5OHVBdImN0tJSCoXS7dqrwUSlUvn5+aWkpBjXDcN5Ul9fz2AwDh482GtJtPziDaW6ujosLGz16tVECo1G4/P5SqXSKANPWhiCn0Bwd3ffu3fv3r17DdQx1wWVSpWZmSmTyUJCQozlg6E9iY2NnThxIlxbPpRBQmY0mEwmlUpNSUmpqalRKpVVVVUnTpyIiYkJCQnp3wjgm0ZUVFRwcHBISIix3g/Pycm5cOFCdnZ2Pz7HYhKeHDp0qKCg4OrVqwN8SXkwMNajIEBdSxzPzc199913uVwumUzm8Xi+vr7JyclKpbLXEwftMT4qKgquC3VxcTl37twg1Ij3MTZ++umn7du3G9SfN5PMzMyEhISOjg4dyxu3a2nMncYRfn5+P//8s7G90EZCQkKnVZpDjdmzZ8+ePdvYXgxDFi5cuHDhQmN7oSuoa4lAIEweJGQIBMLkQUKGQCBMHiRkCATC5MFwI334EcOwadOmGffdY9OlsrLyt99+075dhely/vx5FBsmB4xJo+mJsSoODg42Sr2IQebx48dg6L+ph9AT586dM0q9RhMyxBsC3ILg7NmzxnYEMZxBY2QIBMLkQUKGQCBMHiRkCATC5EFChkAgTB4kZAgEwuRBQoZAIEweJGQIBMLkQUKGQCBMHiRkCATC5EFChkAgTB4kZAgEwuRBQoZAIEweJGQIBMLkQUKGQCBMHiRkCATC5EFChkAgTB4kZAgEwuRBQoZAIEweJGQIBMLkQUKGQCBMHiRkCATC5EFChkAgTB4kZAgEwuRBQoZAIEweJGQIBMLkQUKGQCBMHiRkCATC5EFChkAgTB4kZAgEwuRBQoZAIEweJGQIBMLkQUKGQCBMHiRkCATC5MFwHDe2D4hhxXfffXfo0CGVSgUP6+vrAQDW1tbwkEwmR0RE/OMf/zCaf4jhCBIyhJ4pKSkZM2aMlgLFxcWjR48eNH8QbwKoa4nQM6NHj54wYQKGYV2zMAybMGECUjGE3kFChtA///jHP8hkctd0CoWyYsWKwfcHMexBXUuE/qmqqnJyclKr1Z3SMQyTSCQODg5G8QoxjEFPZAj9M3LkSF9fXxLp/0QXiUR65513kIohDAESMoRB+OSTTzqlYBiGJisRBgJ1LREGobGx0c7OTqlUEikUCqW6utrKysqIXiGGK+iJDGEQLCws3nvvPWLIn0wmz5kzB6kYwkAgIUMYiuXLlxPj/TiOL1++3Lj+IIYxqGuJMBRyudzKyqq1tRUAwGAw6uvr2Wy2sZ1CDE/QExnCULBYrMWLF1OpVCqVunjxYqRiCMOBhAxhQD7++GOlUqlUKj/++GNj+4IYzlA0DyorK+/evWssVxDDD5VKxWKxcByXSqVnz541tjuI4YOvr6+jo+P/f4xrkJGRYTzHEAgEQlcyMjI0tYvStQQa/kdogmFYRkbGhx9+2L/Tf/31VwzD/ud//ke/XumF4OBgAMC5c+eM7Qiib3T9JEE3QoZA6BE/Pz9ju4AY/iAhQxiWTm9cIhCGAAUZAoEweZCQIRAIkwcJGQKBMHmQHMUkAwAAIABJREFUkCEQCJNHz0I2depUMpk8ceLEbnOvXr3K4/F+/PHHrllr1qwxMzPDMKygoEB7Sb1gOPsHDx60tbXFMOzf//53p6wbN25ERUVpKaAXLl26JBKJiE2MjIKhb5+xgHfwwoULAoEAwzAMwzp9dm327NlmZmZkMnncuHGPHj0aZPfS0tKmTp1qZmbm7Oy8atWq6upqmC4SiTw8PJhMJpvN9vDw2LVrl1QqhVkzZ87EusDhcAAA8fHxndLHjx/fV5daW1s9PDx27typPWvgQatnIXvw4IG/v39PuVpWqJ04ceL48eO6lNQLhrO/devWbt+O2L17d1JS0o4dO3oqoC8CAwMZDEZAQEBTU5PhatHOsFyKSNzBoKCgp0+furm5WVlZnT59+sqVK0SZ69evnzt3bsGCBWKx2NvbezDdy8jIWLZsWXBwcGVlZVZWVm5u7ty5czs6OgAAt2/fDg0NraioqKmpiYuLE4lES5Ys0WJq+vTp+vIqOjq6uLi416yBB61Bupbd7qADAJg/f35zc/OCBQt6taB7SR1RKBS+vr6Gs6+d/fv3p6ennz171szMTMdTOjncJzZv3jxhwoR58+bBOB58DN28A2mc/tHtHUxKSiKRSEKhsLm5eTCd6ZZvvvlm5MiR27Zt4/F4EydOjIiIKCgouHfvHgCARqNt3LjRxsaGw+EEBwcvWrTo559/fvnyJQCAwWBIpVLNJfJCofDzzz+HNk+dOqWZVVRU1CeX7t6929MpXbMGGLQGETIqldqPs3qSP72QkpJSW1trOPtaKCsr27Vr1549exgMhu5nDdDh2NjYgoKCxMTEflsYygzy3ezpDvr6+oaHh7948WLr1q2D5kxPSCQSe3t74kfk5OQEAHj+/DkA4OLFi5qew20TWlpaAADXrl3TlGaJRFJUVDRr1qyB+6NQKLZt29ZtBPaUNZCgNYiQlZWVeXh4sNlsJpPp5+d3584dAMCdO3f4fD6GYV999RUshuP4gQMHxowZQ6fTeTzetm3bYHqnkv/6179YLJaZmVltbW1kZKSDg0NxcbFKpYqJieHz+Uwm86233tJ8S/TUqVNTpkxhMBhsNtvFxSUuLi48PDwyMrK8vBzDMHd39249OXTo0NixY+l0uoWFxaJFi548eQIAOHr0KJvNZrFYWVlZc+fO5XK5jo6OZ86cgWfdvn3b09OTx+MxGAwvL6+ffvqp29ZISkrCcTwwMLCn5vr111//9re/sVgsLpfr5eUllUo7OZyYmMhms0kk0uTJk+3s7KhUKpvN9vb29vPzc3JyYjAY5ubmxL8oxMLCYsaMGYmJiYPfy+vUvFraMCkpicFg2Nrarlu3zt7ensFg+Pr6woeIsLAwGo02YsQIaHPjxo1sNhvDsPr6+k6NAwC4du0al8vdt2+fga5Iyx2Mj48fPXr0iRMnbty40TW3f3GlJba1IBAINMUdDpAJBIKuJUtLS83NzZ2dnbtm7d+/f/PmzbpU1yvR0dHwMVD3rAEFbdeXxvGBERAQIBAInj17plQqi4qK3n77bQaDUVJSguO4RCIBABw5cgSWjI6OxjDsiy++aGxslMvlycnJAID8/PxuSwIANm/efOTIkQ8++OC///3v1q1b6XT6+fPnGxsbd+zYQSKRHjx4gOP44cOHAQD//Oc/X7161dDQ8M033yxbtgzH8aCgIDc3N8LJTvZjYmJoNNqpU6eampoKCwu9vb2tra2rq6uJqm/evNnc3FxbW+vn58dms9vb23EcP3fuXGxsbENDw6tXr6ZNm2ZlZQWtlZaWAgC+/vpreCgQCDw9PTWbSLNAS0sLl8sViUQKhaK6uvqDDz6oq6vr6vDu3bsBAPfu3Xv9+nV9ff37778PALhy5UpdXd3r16/DwsIAAAUFBZq1REVFEe05EECXF3R7pdvb120bCoVCNpv9xx9/tLa2isViOFZdUVGB4/iyZcvs7OwImwcOHAAAdNs4ly9fNjMz27t3b18vbcmSJUuWLOm1WNc7iOO4m5vbs2fPcBy/e/cuiURycXFpaWnBcTw7O3vhwoWwTP/iqqfY1k5OTg6VSk1KSpJKpUVFRWPHjp0zZ45mgfb29srKyiNHjtDp9E59RkhlZaWnp6dKpYKHcXFxjo6O5ubmVCrVxcVl4cKF9+/f79UNyJ07dwIDA3Ecr6urg8qlSxauc9B2jUmDCNmECROIw8LCQgDA1q1b8f8b33K5nMVivffee0RJ+I+kRcgUCgU8VCgULBYrJCQEHsrlcjqdvmHDhvb2dnNzc39/f8JmR0cHFHgtQiaXyzkcDmENx/H79+8DAOAPo1PVUG3Lyso6XXVCQgIAoLa2Fu+iUxiGLViwQLOwZgE4UnD58uVOBrsVMplMBg+//fZbAMDjx481HU5PT9e0cPLkSQDAd999hw8MfQlZt20oFAp5PB5x4oMHDwAAe/bswfsiZP1GFyHr9g7iGkKG43hkZCQAYNOmTbiGkPUvrnqKbV0uR3Ny0NHRUSKRaOba2dkBAKysrL788kuomJ3YtGkT8e+L43hFRcWjR49kMllbW1teXt6kSZOYTGZRUVGvbsjl8ilTplRWVuJd1EpLFkTHoO0akwZfR+bl5cXj8aCcaVJWViaXywMCAvphs7i4WC6XE5PBTCZzxIgRT548KSwsbGpqmjNnDlGSTCb3+qgsFotbWlqmTJlCpEydOpVGo8E+TidoNBoAQHNzIAgcFuw6fwyljcVi9VS7QCCwtbVdvnx5bGzsn3/+qd3VTm4Qw6Kw9k5ewUpramp0tDlo9NSGAIApU6awWCzY/xoi9HoHAQDx8fFjxoxJTk6GoyiQ/sVVT7Hdq5/R0dHHjh27efNmS0vL06dPfX19fXx84D8KRCKR1NbWpqWlffvtt5MmTeo0yFhVVXXp0qWVK1cSKU5OTpMmTeJwODQabdq0aampqQqFAgqudnbs2LF27dpuNzDVkgXpd9AOxoJYKpXaNWorKysBAN12oXvl9evXAICdO3cSK1yeP38ul8vh6hhzc/M+WYMzvnDtDIG5ublMJtN+4pUrV2bOnGljY0On0zsNURHAL9bT6fSejDCZzFu3bk2fPn3fvn0CgSAkJEShUPTJfy2WCQdMCDqdDv+rhwi93kEAAIPBSE1NxTDs008/JW5f/+Kqp9jW7uTLly9FItHatWtnzZrFZrNdXV2PHz9eVVUFH2MhVCrVxsZm9uzZ6enpYrEY9iEIRCJRaGiolvkoLy8vMplcUlKi3ZM7d+48fvx4zZo1fcoi6HfQGlzIOjo6Ghoa+Hx+p3TYZG1tbf2wCeXv8OHDms+WeXl5I0eOBADU19f3yRoUvk7h1dTU9H++P9mFioqKxYsXjxgx4t69e83NzSKRqNti8MZoX+k3bty4H3/8saqqavv27RkZGQcPHuyT/z3R3t5OOGAqKJXKXlt+kNHlDgIAfHx8IiIiSktL4+LiYEr/4qqn2NZee2lpqUqlgvEP4XK5lpaWYrG4a2F3d3cymayZVV1dnZaWtmHDBi1VqNVqtVqtXdABACkpKTdv3iSRSFCF4eXs27cPw7D169f3lPXw4UN4er+D1uBC9ssvv6jV6q6LA8ePH08ikX799dd+2IRTdfAdAE1cXFwsLS2vX7/eJ2vjx4/ncDhEUwIA7t27197ePnnyZC1nPX78WKlUbtiwQSAQMBiMntaOwEX8WtYZVVVV/fHHHwAAGxubf/7zn97e3vBw4MBK4ciIqZCTk4Pj+LRp0wAAFAql2+7nINPrHSSIi4vz8PDIz8+Hh/2Lq55iWztQHOHSMIhMJmtoaHBycnr16lWnDROg6sH1GRCRSLR8+XJLS0vNYppDNAAAOOHg4+Oj3ZPU1FRNCdYcCCOGdLtmER3wfgetQYSsvb29ubm5o6Pj0aNHYWFhzs7Omn1viI2NzZIlS86fP5+SkiKVSgsLC48dO6ajfQaDsWrVqjNnzhw9elQqlapUqsrKypcvX9Lp9B07duTm5oaFhb148UKtVstkMqgLlpaWVVVVf/75p0wm6/TzYDAYkZGRFy9ePH36tFQqffz48fr16+3t7YVCoRYf4DPmjRs3WltbS0tLux34AACwWCyBQAD70d1SVVW1bt26J0+etLe35+fnP3/+HP6MtTisI7BSLy+vfpw7mKjV6sbGxo6OjsLCwvDwcD6fD6PF3d29oaEhMzNTqVTW1dXBJVGQTo2TnZ1tuOUXvd5BAtjBJPYk7l9c9RTbAICQkBA7O7tu33xydXX19/c/fvx4bm6uQqGQSCSwltWrV7PZ7OvXr9+6dUsqlSqVyvz8/BUrVrDZ7IiICHhuTU3NyZMnt2zZ0snmixcv0tPTm5qalEplXl7emjVr+Hz++vXrtXsyQPoftJoaqZdZy9TUVH9/f1tbWwqFYmVl9dFHHz1//hzH8SNHjsBlQSwWC86/ymSy0NBQKysrDoczffr0mJgYAICjo2NoaKhmSZFIBB81nZyciGnjtra27du38/l8CoViY2MTFBQkFoth1ldffeX1/7V352FNXXnjwM8N2TcIsspOghviVhdArXWcMmN9FXEjjvob9dEnattIRaWI5UXArVBhsDh9XcrzVn0EBAaXAdtaix0rVTtCQRhRqWCFIousJkIg9/fHeXsnEyCEQAiB7+cvcu/Nud97z8mXu5x7j48Pm81ms9nTp09PTk4mSfL+/ftubm4cDmfevHn79+/XikStVsfFxXl5eTEYDJFIFBQUVFZWRpJkcnIyvvro5eVVXl5+8uRJoVCIEHJzc3v06FFYWJi1tbWVldXq1atxnymxWBwSEoL/n/B4vBUrVpAkKZfLGQyGQqHA4X3yySeaC1RUVPj7+4tEIgsLi7Fjx0ZERHR2dmoFHB4ejsNwd3f/xz/+ceTIEUtLS4SQvb39+fPnU1NTcYEikejChQtURSxZssTJyUmtVg+wQlE/71pqVbTufSiTyRgMhpOTE51OFwqFy5cvLy8vx+U0NDQsXLiQzWZ7eHi8//77uJuhRCLBd9OonVNTU5OTkyMQCGJjY/u7aXp2v9CqwaysLLFYjBCysbHBdyo17dmzh+p+YVi76q1tBwUFIYQiIyN7DBL3sJNIJCwWi8/nz507929/+xuetWzZMg8PDz6fz2KxxGKxVCrVPDjatWvX+vXruxcYGhoqFot5PB6dTse/yurqajxLdySaerw1qWOWno22e5sc/EQGtDx+/JhOp/fYc8d46uvr2Wx2fHz8wIvqbyLrF5lMZm1tbaTC+6RnIjNJDXbX1dU1f/78M2fOmDYM40Wif6Pt3ibhNT5GJ5FIoqOjo6Oj8UMhQyMqKmratGm4o+wwZ9oXdejDJDWopaurKzs7u7W1VSqVmioGY0cykEYLiWwohIeHr169WiqVDs3TxceOHSssLMzJyTHsoVfQ3RDXYHd5eXmZmZm5ubm6e7SZbyQDbLSQyIbIwYMH5XL54cOHjb2iS5cutbe35+XliUQiY69rgPbt25eSktLc3Ozh4ZGRkWHqcPowZDXYo0WLFp0/f556+NSEjBHJwBstQWo8n5menh4cHEyOxJdJAYMNcFzL4QzGtTRT3dskHJEBAMweJDIAgNmDRAYAMHuQyAAAZo/efRK+AgoAJSEhYUReEf/hhx8QNPgRAY7IAABmr4cjshH5vxcYjCCIDz74ALpfgOGj+8tm4IgMAGD2IJEBAMweJDIAgNmDRAYAMHuQyAAAZq/fiSwzM9PT05PQQKfTbWxsfv/732dlZVGL5eTkWFpaXrlypXsJW7ZsEQgEBEHgF5PrWHJQGK/8+Ph4/EL3zz77TGvW9evXw8PDNfeVg4PD+vXreyznp59+kkqlHh4eLBbLxsZm6tSpsbGxCCGpVErotGnTJqr8jz76qMfCjx07RhAEjUabMGHCd999d/ny5aNHjw7/V4CNJN0bw4YNGzQXCAgIEAgEFhYW3t7exnh/tA6xsbFajYoaiQ5Tq9UJCQn+/v6aE6OjoydNmiQUClkslkQi2bt3L35Tmylbl+ZbFvV/Q6xYLKbGVX358uX169cnTJiANMaIvXr1qlAovHz5co9f1xyLV/eSA2fU8rUGFcciIyOXLl3a0tKCP2ruq+6Kioq4XO7OnTufPn2qVCrLysr27t27aNEikiSDg4O/+uor/NJ0/Nb2ZcuWdXR0vHr1qra2duvWrVeuXMHlI4QcHBy6j7ra2dnp5uaGEMIFYomJiQsWLGhsbNRzG5Ex3xBrWnq+IXYgujeGMWPGoG5DMmuOTz6UqDGfKN7e3tTcR48ezZ07FyGkOeQ2SZILFixITk5uaGhoaWlJS0tjMBh//OMf8az+ti7DdG+Tg3BqKRKJFi1a9Je//AUhlJ6ejicuWbKkubl56dKlfX5d/yX1pFQqNf+BDHr5uh05ciQ1NTU9PV0gEOizfHx8vJWVVWJioru7O5vNHjduXExMDB6jgCCIuXPnWlpa0un/192PIAgGg8Hlcm1tbTUH43njjTdqamqys7O1Cs/MzOw+GOrOnTunTp36zjvvUOP7mopWTZmwECPpsTEkJSXRaDSZTGaqdzRq0XqF94MHD/D0n3766cMPP9y+ffu0adO0vsLn8/E7ygUCwZo1a4KCgq5du4YHAzZV6xq0a2Tu7u7ot0FJ+9Tb4GmD4syZM1qjKA+ZJ0+efPTRRwcOHNAx0KmWhoaG5ubmly9fUlOYTCY+Eb5w4YKOl3DKZLL/+q//wn/jEQn/+te/ai1z7Nix0NDQ7t+NiooqLCxMTEzUM0gjGZSaMmF169ZbY/D39w8JCamqqtq9e7epYtPH1KlTMzMz161b130sy6tXr1KDRSGEbGxsEELUKMImaV2DlsiKiooQQgsWLEAI3bp1y9XVlSAIPLYQQogkybi4uPHjx7NYLEtLSzwiTvclP/74Yy6XKxAIamtrQ0NDnZycysrKurq6IiMjXV1dORzOlClT8Pkvdvbs2ZkzZ7LZbB6P5+7uHhMTExISEhoaWl5eThCERCLpMZJjx45NnDiRxWKJRKLly5fj8ehPnDjB4/G4XO6lS5cWL14sFAqdnZ3xKTBC6B//+MekSZMsLS3ZbLaPj8+XX37Z405ISkoiSXLZsmX677dZs2a9evXqd7/73ffff9+f/f0ffve7302cOPHbb78tKyujJn7//fcKhSIgIKD78iKRaMGCBYmJieQgvUSzt70ql8uZTCb1NtF3332Xx+MRBIGH/NGsqaSkJDabbWdnt23bNkdHRzab7e/vjwfZ078QhNC1a9eMNzRcv+hoDLGxsePGjTt9+vT169e7zzWsier4mRhbVVUVh8Px8PDAHwe9delF86jSsGtkCoUiNzfXzc0tICCgra0NT8THmcePH8cfIyIiCIL45JNPGhsbFQpFcnIy+u0aWfclEUI7d+48fvz4ihUr/vWvf+3evZvFYmVkZDQ2Nu7bt49Go+GxQhMSEhBChw8fbmhoePny5f/8z/+sW7eOJMmVK1eKxWIqVK3yIyMjmUzm2bNnm5qaioqKZsyYYWNjU1NTQ636m2++aW5urq2tnT9/Po/HwxeeLl68GBUV9fLly4aGBl9f3zFjxuDStK6ReXp6Tpo0Sce+6k6hUFCjk06aNOno0aMNDQ3dF8PXyHq8jCIWi58+fYpP7UNCQqjpQUFBKSkpeKRrzWtkWHh4OFUFuiE9rpHp2Kvr1q2zt7enloyLi0MI1dXVkd1qSiaT8Xi80tLS169fl5SUzJo1SyAQPHv2rF+FXL16VSAQREdH97ldpJGvkfXWGJ4+fUqS5O3bt2k0mru7O/7JaF4jM6yJ9vYz0S0mJsbZ2dnKyorBYLi7uwcGBt69e1drmTlz5mhdI9P06tUrgUAgl8s1J+rfugzTvU0ansi0EqKPj8///u//tre34wU004dCoeByuW+//Tb1dc2L/T0mMqVSiT8qlUoulyuVSvFHhULBYrF27NjR0dFhZWW1cOFCqszOzk78T0BHIlMoFHw+nyqNJMm7d+8ihHC711o1zrZPnjzR2vZDhw4hhGpra8n/TGRtbW0EQSxdurT7vtKRyEiS7Ojo+Mtf/oLvliCE7Ozs8IDbmvpMZE1NTTweTyQS4eEXy8vLnZ2d29vbe0tkn3/+OULoiy++0BEY1mci071X+5XINHfUvXv3EEIHDhzoVyH9YrxEpqMx4ERGkiQ+68cjY1KJzLAm2tvPpM848SChra2t7e3t+fn506dP53A4Dx480FxGdyKLiIgYN24cdTcD0791GaZ7mzT81JJqcyqV6vnz5x988IFcLp8yZUp9fb3Wkk+ePFEoFIsWLTJgLWVlZQqFgrolzOFwHBwcHj58WFRU1NTUpDmqu4WFxc6dO3WXVlJS0tbWRh0BIYRmzZrFZDJ7HCecyWQihLqP8o1Heel+jxmnNgOGlmEwGHK5/F//+tcPP/ywfPny2tra1atXNzY29qsQS0vLP/3pT42NjampqQihhISEHTt24E3oEY7zxYsX/Y22u37tVf3NnDmTy+Xisyqzo09jiI2NHT9+fHJy8q1bt6iJhjXR3n4mfcbp4uIyffp0Pp/PZDJ9fX1TUlKUSiXOj/rIyspKT0//8ssvtW5tDWLr0tMgXCOj0+lOTk6bNm2Kj48vKyvrPswMHgbd1tbWgMJfvXqFENq/fz/Vz6WyslKhULS0tCCErKys+lUavhfB5/M1J1pZWeHDFh3+/ve/v/XWW7a2tiwWa+/evT0u8/r1a4RQ9yuj+pszZ87f/va37du319XVffvtt/39Or7k/9lnnzU1NV28eHHbtm06FsY3RnHMA2TwXu0Ti8XC41GbHX0aA5vNTklJIQhi8+bNSqUSTzRsZ/b2M+lv2D4+PhYWFo8ePdJn4dTU1CNHjuTl5eEbfZoGsXXpaTB79vv4+CCESktLtabjuzbt7e0GlInTX0JCguZhZH5+/tixYxFC3Y/+dMOJT6tNNDU1OTs76/jWs2fPgoKCHBwc7ty509zcfPTo0R4Xw5WnZ2/A7777Dl/jW7lypdaNatxb0oBWOG3aNF9f37t378pkstWrV+seWaujo4OKeYAM26t9UqlUAy/EVPRsDH5+frt27Xr8+DHVn8uwndnbz6S/YavVarVarc8/4+PHj587d+7GjRv4l6hlEFuXngYzkf3zn/9ECI0fP15r+uTJk2k02s2bNw0o08XFhc1m42cANLm7u1tbW3/11Vf9Km3y5Ml8Pv/HH3+kpty5c6ejo0OzT1Z3xcXFKpVqx44dnp6ebDa7t74juJe/np2D/vnPf/J4PIRQe3u7VurHdx6nTJmiTzla8EFZRkbGBx98oHtJHKe9vb0Ba9Gie6/S6fTup+f6wBcKfX19B1KIqejfGGJiYiZMmFBQUIA/GtZEe/uZ9Enz4gxCCN8f8PPz0/EVkiTDwsKKi4uzs7O1jhwpg9i69DSgRKZUKtVqNUmS1dXVKSkp+/fvt7Gx6f4TsrW1XbVqVUZGxpkzZ1paWoqKik6ePKnnKths9qZNmy5cuHDixImWlpaurq7nz5//+uuvLBZr37593333nVwur6qqUqvVra2tOCNYW1tXV1dXVFS0trZqtX42mx0aGpqVlXXu3LmWlpbi4uLt27c7OjrKZDIdMbi6uiKErl+//vr168ePH/d26YfL5Xp6euLzaB1UKtWLFy/y8vJwIkMIBQUFpaenNzU1NTc3X7p06cMPPwwMDDQska1Zs8bGxiYoKMjT01P3kjhOfBA9QLr3qkQiefnyZXZ2tkqlqqurq6yspL7YvabUanVjY2NnZ2dRUVFISIirq+vGjRv7VUhubu5w6H6hZ2NAv51gUt2yDGuivf1MEEJSqdTe3r63J5+qqqpSU1Px0yP5+flbtmxxdXXdvn27jnWVlpZ+/PHHp06dYjAYms82xcfHU8sMYuvSl+axqD53LbOysrrfsmSxWF5eXjt27MA3y48fP457/XC53GXLlpEk2draunXr1jFjxvD5/Hnz5kVGRiKEnJ2dt27dqrnk0aNH8eGoi4sL1eG4vb09LCzM1dWVTqfb2tquXLmypKQEz/r00099fHzYbDabzZ4+fXpycjJJkvfv33dzc+NwOPPmzdu/f79WJGq1Oi4uzsvLi8FgiESioKCgsrIykiSTk5PxFUovL6/y8vKTJ08KhUKEkJub26NHj8LCwqytra2srFavXo27pInF4pCQEPw/h8fjrVixgiRJuVzOYDDwfcPe9hUlKyuLJMmvvvoqODhYLBazWCwmkzl+/PioqKjXr19TO7ylpeXNN9+0trZGCNFoNIlEcvDgQa3ybWxs8M0vkiT37t17+/Zt/De1+TQabdKkSf/4xz+oYpcsWeLk5IT/D+mG9Oh+0dteJUmyoaFh4cKFbDbbw8Pj/fffx10IJRIJvl9G1VRNTY1MJmMwGE5OTnQ6XSgULl++vLy8vL+F5OTkCASC2NjYPreLNHL3i94ag2ZlUfbs2UPdkjasifb2MwkKCkIIRUZG9hhkaGioWCzm8Xh0Oh3/Hqurq/Gs/Pz8uXPnOjo64ubq4ODg7+9/8+bN4uLiHttzXFwcVaz+rcsw3dukgd0vQI8eP35Mp9O1nvkYhurr69lsdnx8vD4L65PIBgV+6mUIVkQxaiIbJo2hq6tr/vz5Z86cGbI19qt1GaZ7m4TX+AwmiUQSHR0dHR2NXwYwbEVFRU2bNk0ul5s6EG0j6bUcw6ExdHV1ZWdnt7a2SqXSIVupSVoXJLJBFh4evnr1aqlUOkweCe7u2LFjhYWFOTk5uEMcMB6TN4a8vLzMzMzc3FwDujcaxlStCxLZ4Dt48KBcLu/en244uHTpUnt7e15enu7OGUNv3759KSkpzc3NHh4eGRkZpg5n0Ji2MSxatOj8+fPUY6rGZsLWRZAaD3amp6cHBweTQ/moJxj2CIJIS0uD4eDA8NG9TcIRGQDA7EEiAwCYPUhkAADL1z6aAAAgAElEQVSzB4kMAGD2IJEBAMwevfsko75QH5ij4ODg4OBgU0dhLNDgR4D/6H7x/Pnz27dvmzAaMPLgtxX1+TYOAPrF399f89VGBPQaA0aFO/tQ4wQCYAxwjQwAYPYgkQEAzB4kMgCA2YNEBgAwe5DIAABmDxIZAMDsQSIDAJg9SGQAALMHiQwAYPYgkQEAzB4kMgCA2YNEBgAwe5DIAABmDxIZAMDsQSIDAJg9SGQAALMHiQwAYPYgkQEAzB4kMgCA2YNEBgAwe5DIAABmDxIZAMDsQSIDAJg9SGQAALMHiQwAYPYgkQEAzB4kMgCA2YNEBgAwe5DIAABmDxIZAMDsQSIDAJg9SGQAALNHN3UAYKSpr69vaWmhPr569Qoh9PPPP1NThEKhjY2NCSIDIxdBkqSpYwAjSkpKyubNm3Us8Pnnn2/atGnI4gGjASQyMMiam5ttbW1VKlWPcxkMRl1dnaWl5RBHBUY2uEYGBpmlpeU777xDp/dw1YJOpy9ZsgSyGBh0kMjA4Fu/fn1XV1f36Wq1ev369UMfDxjx4NQSDL7Xr1/b2Njgy/yauFxufX09h8MxSVRgBIMjMjD42Gz2ihUrGAyG5kQGg7Fq1SrIYsAYIJEBo/jTn/6kdb1fpVL96U9/MlU8YGSDU0tgFJ2dnfb29i9fvqSmWFlZ1dXV9XgTAIABgiMyYBR0On3t2rXU2SWDwVi/fj1kMWAkkMiAsaxdu5Y6u1SpVGvXrjVtPGAEg1NLYCwkSbq4uFRVVSGEHB0dq6qqCIIwdVBgZIIjMmAsBEFs2LCByWQymcw///nPkMWA8cARGTCioqKiqVOn4j98fHxMHQ4YsUbRxdfVq1ebOoTRiM/nI4Sio6NNHchodPHiRVOHMERG0allRkbG8+fPTR3FQD1//jwjI8PUUfSDm5ubu7u7nguPjDoaDsyunQzQKDq1JAgiLS1tzZo1pg5kQNLT04ODg82o1vCbyDw9PfVZeGTU0XBgdu1kgEbRqSUwCT1TGAADMYpOLQEAIxUkMgCA2YNEBgAwe5DIAABmDxJZH7Zs2SIQCAiCKCwsNHUshsvJybG0tLxy5YqpAxlM169fDw8Pz8zM9PT0JAgCP0iguUBAQIBAILCwsPD29r5///5QxhYbG0v8p8mTJ2suoFarExIS/P39NSdGR0dPmjRJKBSyWCyJRLJ37962tjaE0OXLl48ePdrjS3cBBomsD6dPnz516pSpoxiokXcb/r//+7+TkpL27du3cuXKn3/+WSwWjxkz5ty5c3//+9+pZb766quLFy8uXbq0pKRkxowZJoxWy+PHj998881du3YpFArN6Tdu3HjvvfcqKirq6+sPHTqUmJiIe3EvW7aMzWYvWrSoqanJRCEPd5DIRoUlS5Y0NzcvXbrUSOUrlUqtgwujOnLkSGpqanp6ukAgoCYmJSXRaDSZTNbc3Dxkkehw9uxZUsODBw/w9J9++unDDz/cvn37tGnTtL7C5/NlMpm1tbVAIFizZk1QUNC1a9d++eUXhNDOnTunTp36zjvvdHZ2DvWWmANIZH2Dp537dObMmdra2qFZ15MnTz766KMDBw6w2WzN6f7+/iEhIVVVVbt37x6aSAwzderUzMzMdevWsVgsrVlXr161sLCgPuJhjKmjtqioqMLCwsTExCEL1YxAIusBSZJxcXHjx49nsViWlpZ79uyhZnV1dUVGRrq6unI4nClTpqSlpSGETpw4wePxuFzupUuXFi9eLBQKnZ2dL1y4gL9y8+bN2bNnc7lcoVDo4+ODR+HusRwjuXXrlqurK0EQn376qe5ok5KS2Gy2nZ3dtm3bHB0d2Wy2v7//nTt3EEJyuZzJZDo4OOAy3333XR6PRxBEfX19SEhIaGhoeXk5QRASiQQhdO3aNaFQePDgQWNsTlJSEkmSy5Yt6z4rNjZ23Lhxp0+fvn79eve5JEkeO3Zs4sSJLBZLJBItX7784cOHuncIGtqa0lJVVcXhcDw8PPBHkUi0YMGCxMTEkXehYBCQowZCKC0tTZ8lIyIiCIL45JNPGhsbFQpFcnIyQqigoIAkyd27d7NYrIyMjMbGxn379tFotHv37uGvIIS++eab5ubm2tra+fPn83i8jo6OtrY2oVB49OhRpVJZU1OzYsWKuro6HeX0Cf+Q+rvt+PTk+PHj1Ab2GC1JkjKZjMfjlZaWvn79uqSkZNasWQKB4NmzZyRJrlu3zt7eniozLi4OIYQ3Z+XKlWKxmJp19epVgUAQHR3d3zj1qSNPT89JkyZpTRSLxU+fPiVJ8vbt2zQazd3dva2tjSTJ3NzcwMBAvExkZCSTyTx79mxTU1NRUdGMGTNsbGxqamp07xDDaiomJsbZ2dnKyorBYLi7uwcGBt69e1drmTlz5kydOrW3El69eiUQCORyuebE8PBwqinqZlg7MV+jaVP1S2QKhYLL5b799tvUFPzPuaCgQKlUcrlcqVRKLclisXbs2EH+9ktQKpV4Fs59T548wVdGrl69qrkKHeX0aRATWfdoSZKUyWSWlpbUF+/du4cQOnDgANmfRGawPuuora2NIIilS5dqTacSGUmSoaGhCKH33nuP1EhkCoWCz+dT+5wkybt37yKEcLbtbYcYXFPPnj27f/9+a2tre3t7fn7+9OnTORzOgwcPNJfRncgiIiLGjRvX0tKiOfHzzz9HCH3xxRd9BjDaEhmcWmp78uSJQqFYtGhR91llZWUKhYK6j87hcBwcHPDpiRYmk4kQUqlUnp6ednZ269evj4qKqqio6G85Q4OKtvusmTNncrlcE8ampba2liRJLperY5nY2Njx48cnJyffunWLmlhSUtLW1jZz5kxqyqxZs5hMJj5x1kLtEINrysXFZfr06Xw+n8lk+vr6pqSkKJVKnB/1kZWVlZ6e/uWXX2rezUAI4Q1/8eKFnuWMHpDItOHXyNja2nafhUec3b9/P9U5qLKyUusOuhYOh3Pjxo158+YdPHjQ09NTKpUqlUoDyjEhFotVV1dn6ij+z+vXrxFC3S+Ta2Kz2SkpKQRBbN68WalU4om44wJ+ORrFysqqtbVVR1GDVVM+Pj4WFhaPHj3SZ+HU1NQjR47k5eV1f/0RHhUU7wSgCRKZNnwvrL29vfssnN0SEhI0j2nz8/N1F+jt7X3lypXq6uqwsLC0tLT4+HjDyjEJlUrV1NTk7Oxs6kD+D/4l99k11M/Pb9euXY8fP46JicFTrKysEEJaaavPTRusmlKr1Wq1Wnf+xY4fP37u3LkbN26MHTu2+9yOjg70204AmiCRaZs8eTKNRrt582b3WS4uLmw2u19d/Kurq0tLSxFCtra2hw8fnjFjRmlpqQHlmEpeXh5Jkr6+vgghOp3e4+nnULKzsyMIQp+eYjExMRMmTCgoKMAfJ0+ezOfzf/zxR2qBO3fudHR0vPHGGzoKMbim/vCHP2h+xPcH/Pz8dHyFJMmwsLDi4uLs7GytI0cK3nB7e/v+xjPiQSLTZmtru2rVqoyMjDNnzrS0tBQVFZ08eRLPYrPZmzZtunDhwokTJ1paWrq6up4/f/7rr7/qKK26unrbtm0PHz7s6OgoKCiorKz09fU1oJyhpFarGxsbOzs7i4qKQkJCXF1dN27ciBCSSCQvX77Mzs5WqVR1dXWVlZXUV6ytraurqysqKlpbW1UqVW5urpG6X3C5XE9PT33eIotPMKluWWw2OzQ0NCsr69y5cy0tLcXFxdu3b3d0dJTJZLoL6a2mpFKpvb19b08+VVVVpaamNjU1qVSq/Pz8LVu2uLq6bt++Xce6SktLP/7441OnTjEYDM1nm+Lj46ll8IbD6Ac9MPbdhOED6d39orW1devWrWPGjOHz+fPmzYuMjEQIOTs7//TTT+3t7WFhYa6urnQ63dbWduXKlSUlJcnJyfgqrJeXV3l5+cmTJ4VCIULIzc3t66+/9vf3F4lEFhYWY8eOjYiI6OzsJEmyx3L0ic2Au1HHjx/H/b+4XO6yZct0RPvo0SOZTMZgMJycnOh0ulAoXL58eXl5OS6noaFh4cKFbDbbw8Pj/fffx93rJBIJvkPn5ubG4XDmzZtXU1OTk5MjEAhiY2P7FSepXx3J5XIGg6FQKPDHrKwssViMELKxscF3KjXt2bOH6n6hVqvj4uK8vLwYDIZIJAoKCiorKyNJUvcO6a2mgoKCEEKRkZE9BhkaGioWi3k8Hp1Od3Z23rp1a3V1NZ6Vn58/d+5cR0dH/AN0cHDw9/e/efNmcXFxj7/QuLg4qtglS5Y4OTmp1eo+9+Rou2s5mjZV70Q2nBm7geJHZIxXvm761NHjx4/pdLrWA0BDr6ura/78+WfOnBmyNdbX17PZ7Pj4eH0WHm2JDE4tgbZh/pYFiUQSHR0dHR2N3wxhEl1dXdnZ2a2trVKpdMhWGhUVNW3aNLlcPmRrNCOQyID5CQ8PX716tVQqNdXz4Xl5eZmZmbm5ubp7tA2iY8eOFRYW5uTkMBiMoVmjeYFEBv5t3759KSkpzc3NHh4ew3wwsYMHD8rl8sOHD5tk7YsWLTp//jz15KmxXbp0qb29PS8vTyQSDc0azQ6MogT+7dChQ4cOHTJ1FPoKCAgICAgwdRRDITAwMDAw0NRRDGtwRAYAMHuQyAAAZg8SGQDA7EEiAwCYPUhkAACzR5Cj5rW58Op9MNqMnl/36Op+ERISovsNBMNffn5+YmLiUL45figFBwePgDoaDnA7MXUUQ2d0JTI/P781a9aYOoqBSkxMHAFb0aPg4OCRUUfDwahKZHCNDABg9iCRAQDMHiQyAIDZg0QGADB7kMgAAGYPEtm/ZWZmenp6ar4unclk2tnZvfXWW3FxcY2NjaYOEPyH69evh4eHa9bahg0bNBcICAgQCAQWFhbe3t69vVzfSKKjoydNmiQUClkslkQi2bt3L/UayNjYWOI/UeNmIoRUKtWhQ4ckEgmTybSyspo8eXJFRcXly5ePHj06zF94aWKmfkXt0EH6vepaLBbjobbxGBzffvvtxo0bCYJwdHTEY+GY1sh+hbGedUSSZGRk5NKlS6mBuMVi8ZgxY1C3Qd2pkcaH2IIFC5KTkxsaGlpaWtLS0hgMxh//+Ec8ixqhjuLt7U19MSgoaPz48T/88INKpaqurl62bFlxcTFJkomJiQsWLGhsbNQzgJHdTrobTZvaz0Sm6eLFizQazc7OrqmpyTjR6cuoDVShUPj5+ZmwED3r6PDhw+PGjVMqldQUsVh8/vx5Go3m5OSkWUemSmRLlizBo8xguGfcs2fPSJKMiYnpbcCBCxcuEARRVFTU41y5XO7n56dSqfQJYLQlMji11MuqVas2btxYW1v72WefmToWIzpz5kxtbe1wKESHJ0+efPTRRwcOHMBDKVP8/f1DQkKqqqp2795tvLXr6erVq9RIdAghGxsbhFCfQ5T/9a9/nTFjRm+jvUVFRRUWFo6qbq76g0SmLzy2Y25uLkKoq6srMjLS1dWVw+FMmTIF//c7ceIEj8fjcrmXLl1avHixUCh0dna+cOEC/vrNmzdnz57N5XKFQqGPj09LS0tv5QwKkiSPHTs2ceJEFoslEomWL1/+8OFDhJBcLmcymdQ7mt99910ej0cQRH19fUhISGhoaHl5OUEQEokkKSmJzWbb2dlt27bN0dGRzWb7+/vfuXOnX4UghK5duza4Y1wmJSWRJLls2bLus2JjY8eNG3f69Onr16/rv090V9yg1FFVVRWHw/Hw8NCxTEdHxw8//DBt2rTeFhCJRAsWLEhMTCRHzROU/WDaA8KhhAZwakmSJE49Li4uJEnu3r2bxWJlZGQ0Njbu27ePRqPhy2cREREIoW+++aa5ubm2tnb+/Pk8Hq+jo6OtrU0oFB49elSpVNbU1KxYsaKurk5HOTroecoQGRnJZDLPnj3b1NRUVFQ0Y8YMGxubmpoakiTXrVtnb29PLRkXF4cQwvGsXLlSLBZTs2QyGY/HKy0tff36dUlJyaxZswQCAT4/0r+Qq1evCgSC6OjoPmMm9asjT0/PSZMmaU0Ui8VPnz4lSfL27ds0Gs3d3b2trY38z1NLHfukt4ojDaojLa9evRIIBHK5HH+MiYlxdna2srJiMBju7u6BgYF3794lSfLp06cIoWnTpr311lsODg4sFmvChAmffvqp5iiW4eHhCKGCgoI+VzraTi1H06YOLJGRJEkQhJWVlVKp5HK5UqkUT1QoFCwWa8eOHeRvvwfq2k1ycjJC6MmTJw8ePEDdrkPrKEcHfRqoQqHg8/lUySRJ3r17FyGEs0m/Epnmrrh37x5C6MCBA/0qpF/6rKO2tjaCIJYuXao1nUpkJEmGhoYihPBgvVQi071Peqs4w+pIS0RExLhx46j7Eng849bW1vb29vz8/OnTp3M4nAcPHuABet9+++3vv/++oaGhqanpww8/RAidO3eOKurzzz9HCH3xxRd9rnS0JTI4tdTXq1evSJIUCoVlZWUKhYK6Zc7hcBwcHPBJihYmk4kQUqlUnp6ednZ269evj4qKqqiowHP1L6e/SkpK2traZs6cSU2ZNWsWk8nEJ4YGmzlzJpfLHZQIDVZbW0uSpO5B2GJjY8ePH5+cnHzr1i1qYr/2CVVxA6+jrKys9PT0L7/8UiAQ4CkuLi7Tp0/n8/lMJtPX1zclJUWpVCYnJ7NYLISQt7e3v7+/tbW1paXlgQMHLC0tT548SZWGN/zFixf6BzBKQCLT16NHjxBCEyZMePXqFUJo//79VD+gyspK3ddxORzOjRs35s2bd/DgQU9PT6lUqlQqDShHT01NTQghPp+vOdHKyqq1tXWAJbNYrLq6ugEWMhCvX7/GYehYhs1mp6SkEASxefNmpVKJJxq2TwZYR6mpqUeOHMnLy3N3d+9tGR8fHwsLi0ePHjk6OiKE6uvrqVlMJtPNza28vJyawuFw0G87AWiCRKava9euIYQWL15sa2uLEEpISNA8ss3Pz9f9dW9v7ytXrlRXV4eFhaWlpcXHxxtWjj6srKwQQlo/0aamJmdn54EUq1KpBl7IAOFfcp9dQ/38/Hbt2vX48WOq05Zh+2QgdXT8+PFz587duHFj7NixOhZTq9VqtZrFYvH5fC8vr9LSUs25nZ2dlpaW1MeOjg70204AmiCR6aWmpiYhIcHZ2Xnz5s0uLi5sNruwsFD/r1dXV+MGamtre/jw4RkzZpSWlhpQjp4mT57M5/N//PFHasqdO3c6OjreeOMNhBCdTlepVAYUm5eXR5Kkr6/vQAoZIDs7O4Ig9BlgPCYmZsKECQUFBfij7n3SG8PqiCTJsLCw4uLi7OxsrWNAhNAf/vAHzY/41gF+l2RwcHBBQcHPP/+MZykUisrKSs3eGHjD7e3t+xXPaACJrAckSba1teG7RXV1dWlpaXPnzrWwsMjOzhYKhWw2e9OmTRcuXDhx4kRLS0tXV9fz589//fVXHQVWV1dv27bt4cOHHR0dBQUFlZWVvr6+BpSjJzabHRoampWVde7cuZaWluLi4u3btzs6OspkMoSQRCJ5+fJldna2SqWqq6urrKykvmhtbV1dXV1RUdHa2orzFH68obOzs6ioKCQkxNXVFXdD0b+Q3NzcQex+weVyPT09nz9/rs9OSElJoTpz6d4nOgrprY6kUqm9vX2PTz6VlpZ+/PHHp06dYjAYmo8ixcfHI4SqqqpSU1ObmppUKlV+fv6WLVtcXV23b9+OENq1a5ebm9vGjRufPXvW0NAQFhamVCrxJX8Mb3hvHc1GNaPeShhWUF93xC5fvjxlyhQul8tkMmk0GkII36acPXt2dHR0Q0MDtWR7e3tYWJirqyudTre1tV25cmVJSUlycjK+Fuvl5VVeXn7y5EmhUIgQcnNz+/rrr/39/UUikYWFxdixYyMiInC37x7L0b0Vet6NUqvVcXFxXl5eDAZDJBIFBQWVlZXhWQ0NDQsXLmSz2R4eHu+///6ePXsQQhKJBN9Nc3Nz43A48+bNq6mpkclkDAbDycmJTqcLhcLly5eXl5f3t5CcnByBQBAbG9tnzKR+d5blcjmDwVAoFPhjVlaWWCxGCNnY2OA7lZr27NlDdb/obZ/oqLhHjx71VkdBQUEIocjIyO4R4vuP3cXFxZEkGRoaKhaLeTwenU53dnbeunVrdXU19d1ffvll7dq1IpGIxWLNnj07NzdXs+QlS5Y4OTlpdsjozWi7azmaNlXv5/iGs6FsoDKZzNraemjWhelTR48fP6bT6b095TNkurq65s+ff+bMmSFbY319PZvNjo+P12fh0ZbI4NQS6DIM37ggkUiio6Ojo6Op90kMva6uruzs7NbWVqlUOmQrjYqKmjZtmlwuH7I1mhFIZMD8hIeHr169WiqV6nPV3xjy8vIyMzNzc3N192gbRMeOHSssLMzJyWEwGEOzRvMCiQz0bN++fSkpKc3NzR4eHhkZGaYOR9vBgwflcvnhw4dNsvZFixadP3+eetrU2C5dutTe3p6XlycSiYZmjWZndA0HB/R36NChQ4cOmToKXQICAgICAkwdxVAIDAwMDAw0dRTDGhyRAQDMHiQyAIDZg0QGADB7kMgAAGZvdF3sH5RHsk0Lb0J6erqpAzGWEVBHw8Fo240EOWpem0sQhKlDAGBIjaJf9+jZVGASeAChEXwICYYDuEYGADB7kMgAAGYPEhkAwOxBIgMAmD1IZAAAsweJDABg9iCRAQDMHiQyAIDZg0QGADB7kMgAAGYPEhkAwOxBIgMAmD1IZAAAsweJDABg9iCRAQDMHiQyAIDZg0QGADB7kMgAAGYPEhkAwOxBIgMAmD1IZAAAsweJDABg9iCRAQDMHiQyAIDZg0QGADB7kMgAAGYPEhkAwOxBIgMAmD1IZAAAsweJDABg9iCRAQDMHiQyAIDZg0QGADB7dFMHAEaa7777Lj8/n/r48OFDhNDRo0epKX5+fm+++aYJIgMjF0GSpKljACPKN9988/vf/57BYNBo2sf7arVapVJdv3590aJFJokNjFSQyMAgU6vVDg4OdXV1Pc61sbGpqamxsLAY4qjAyAbXyMAgo9Fo69atYzKZ3Wcxmcz169dDFgODDhIZGHxr167t6OjoPr2jo2Pt2rVDHw8Y8eDUEhiFu7t7ZWWl1kQXF5fKykqCIEwSEhjB4IgMGMWGDRsYDIbmFAaDsXHjRshiwBjgiAwYxcOHDydOnKg18cGDB97e3iaJB4xscEQGjGLChAne3t6ax1+TJk2CLAaMBBIZMJb/9//+H3WDksFg/PnPfzZtPGAEg1NLYCy//PKLm5sbbmAEQfz888/u7u6mDgqMTHBEBozFxcVlzpw5NBqNRqPNmTMHshgwHkhkwIg2bNhAEASNRtuwYYOpYwEjGZxaAiOqr693cHBACFVXV9vZ2Zk6HDBykSNOWlqaqXcqAMNXWlqaqX+jg2/EvsZntKWz4ODgkJAQPz8/Uwei7bvvviMIYv78+QaXkJCQgBD64IMPBi+o0Ss4ONjUIRjFiE1ka9asMXUIQyo4ONjPz28YbvXixYsRQgKBwOASLl68iEZfhRoJJDIADDGQFAaAnuCuJQDA7EEiAwCYPUhkAACzB4kMAGD2IJEhhNCWLVsEAgFBEIWFhaaOBSGE1Gp1QkKCv7+/UdeSk5NjaWl55coVo65l6F2/fj08PDwzM9PT05MgCIIgtJ4rCAgIEAgEFhYW3t7e9+/fH8rYoqOjJ02aJBQKWSyWRCLZu3dvW1sbnhUbG0v8p8mTJ1NfVKlUhw4dkkgkTCbTyspq8uTJFRUVly9fPnr0aFdX11BuwvAEiQwhhE6fPn3q1ClTR/F/Hj9+/Oabb+7atUuhUBh1ReRIfKjjv//7v5OSkvbt27dy5cqff/5ZLBaPGTPm3Llzf//736llvvrqq4sXLy5durSkpGTGjBlDGd6NGzfee++9ioqK+vr6Q4cOJSYmrl69Wp8vBgcHf/HFF+fPn1coFP/617/EYnFbW9uyZcvYbPaiRYuampqMHfkwB4lsePnpp58+/PDD7du3T5s2zdjrWrJkSXNz89KlS41UvlKpNPZBpZYjR46kpqamp6dr9vlISkqi0Wgymay5uXkog+kRn8+XyWTW1tYCgWDNmjVBQUHXrl375Zdf8NyzZ89q9lZ/8OABnp6ampqdnX3x4sU5c+bQ6XRHR8dLly7h47WdO3dOnTr1nXfe6ezsNNlWDQOQyP7PMHkF89SpUzMzM9etW8disUwdy0CdOXOmtrZ2yFb35MmTjz766MCBA2w2W3O6v79/SEhIVVXV7t27hyyY3ly9elVzECkbGxuEUJ+H3n/9619nzJjh4+PT49yoqKjCwsLExMRBjNPsjN5ERpJkXFzc+PHjWSyWpaXlnj17qFldXV2RkZGurq4cDmfKlCn4aacTJ07weDwul3vp0qXFixcLhUJnZ+cLFy7gr9y8eXP27NlcLlcoFPr4+LS0tPRWzjBx69YtV1dXgiA+/fRTpHPrkpKS2Gy2nZ3dtm3bHB0d2Wy2v7//nTt3EEJyuZzJZOLHwhFC7777Lo/HIwiivr4+JCQkNDS0vLycIAiJRIIQunbtmlAoPHjwoJG2KCkpiSTJZcuWdZ8VGxs7bty406dPX79+vftckiSPHTs2ceJEFoslEomWL1+OR0fXXeODUrlVVVUcDsfDw0PHMh0dHT/88IOOI3SRSLRgwYLExMQRea1AXyZ4vtPIcJPqc7GIiAiCID755JPGxkaFQpGcnIwQKigoIEly9+7dLBYrIyOjsbFx3759NBrt3r17+CsIoW+++aa5ubm2tnb+/Pk8Hq+jo6OtrU0oFB49elSpVNbU1KxYsaKurk5HOfqYM2fO1KlT9d9q1P+HgfEZzfHjx6kd0uPWkSQpk8l4PF5paenr169LSkpmzZolEAiePXtGkuS6devs7e2pMuPi4hBCePNXrlwpFoupWVevXhV9kHQAABIBSURBVBUIBNHR0f0KkiTJVatWrVq1qs/FPD09J02apDVRLBY/ffqUJMnbt2/TaDR3d/e2tjaSJHNzcwMDA/EykZGRTCbz7NmzTU1NRUVFM2bMwKMI694nA6lc7NWrVwKBQC6X448xMTHOzs5WVlYMBsPd3T0wMPDu3bskST59+hQhNG3atLfeesvBwYHFYk2YMOHTTz9Vq9VUUeHh4VTr1c2AdmIWRmkiUygUXC737bffpqbg/7QFBQVKpZLL5UqlUmpJFou1Y8cO8rdmrVQq8Syc+548eYKvZVy9elVzFTrK0YepEln3rSNJUiaTWVpaUl+8d+8eQujAgQNkfxKZwfRJZG1tbQRBLF26VGs6lchIkgwNDUUIvffee6RGIlMoFHw+n6omkiTv3r2LEMIJt7d9MsDKxSIiIsaNG9fS0oI/Pnv27P79+62tre3t7fn5+dOnT+dwOA8ePCguLkYIvf32299//31DQ0NTU9OHH36IEDp37hxV1Oeff44Q+uKLL/pc6UhNZKP01PLJkycKhWLRokXdZ5WVlSkUCurON4fDcXBwwOcaWvBg2iqVytPT087Obv369VFRURUVFf0tZ3iitq77rJkzZ3K53GG1LbW1tSRJcrlcHcvExsaOHz8+OTn51q1b1MSSkpK2traZM2dSU2bNmsVkMvG5sxZqnwy8crOystLT07/88kvqvoSLi8v06dP5fD6TyfT19U1JSVEqlcnJyfhqqbe3t7+/v7W1taWl5YEDBywtLU+ePEmVhjf8xYsX+gcwwozSRPb8+XOEkK2tbfdZr169Qgjt37+f6s5TWVmp+3Ish8O5cePGvHnzDh486OnpKZVKlUqlAeWYERaLVVdXZ+oo/u3169cIId13SNhsdkpKCkEQmzdvViqVeCLuuMDn8zWXtLKyam1t1VHUACs3NTX1yJEjeXl5Ol7/7ePjY2Fh8ejRI0dHR4RQfX09NYvJZLq5uZWXl1NTOBwO+m0njE6jNJHhG1vt7e3dZ+HslpCQoHngmp+fr7tAb2/vK1euVFdXh4WFpaWlxcfHG1aOWVCpVE1NTc7OzqYO5N/wL7nPrqF+fn67du16/PhxTEwMnmJlZYUQ0kpbfW7dQCr3+PHj586du3HjxtixY3Usplar1Wo1i8Xi8/leXl6lpaWaczs7Oy0tLamPHR0d6LedMDqN0kQ2efJkGo128+bN7rNcXFzYbHa/uvhXV1fjdmZra3v48OEZM2aUlpYaUI65yMvLI0nS19cXIUSn03s8/RxidnZ2BEHo01MsJiZmwoQJBQUF+OPkyZP5fP6PP/5ILXDnzp2Ojo433nhDRyGGVS5JkmFhYcXFxdnZ2VrHgAihP/zhD5of8a0D/KbM4ODggoKCn3/+Gc9SKBSVlZWavTHwhtvb2/crnpFklCYyW1vbVatWZWRknDlzpqWlpaioiLriwGazN23adOHChRMnTrS0tHR1dT1//vzXX3/VUVp1dfW2bdsePnzY0dFRUFBQWVnp6+trQDnDmVqtbmxs7OzsLCoqCgkJcXV13bhxI0JIIpG8fPkyOztbpVLV1dVVVlZSX7G2tq6urq6oqGhtbVWpVLm5ucbrfsHlcj09PfEVA93wCSbVmYvNZoeGhmZlZZ07d66lpaW4uHj79u2Ojo4ymUx3Ib1VrlQqtbe37/HJp9LS0o8//vjUqVMMBkPzUaT4+HiEUFVVVWpqalNTk0qlys/P37Jli6ur6/bt2xFCu3btcnNz27hx47NnzxoaGsLCwpRKJb7kj+EN762j2ahg7LsJQ0/P7hetra1bt24dM2YMn8+fN29eZGQkQsjZ2fmnn35qb28PCwtzdXWl0+m2trYrV64sKSlJTk7Gl1S9vLzKy8tPnjwpFAoRQm5ubl9//bW/v79IJLKwsBg7dmxERERnZydJkj2Wozuq/Pz8uXPn4ssiCCEHBwd/f/+bN2/2uTmon3ejjh8/jvt/cbncZcuW6di6R48eyWQyBoPh5OREp9OFQuHy5cvLy8txOQ0NDQsXLmSz2R4eHu+//z7ujieRSPA9ODc3Nw6HM2/evJqampycHIFAEBsbq3+QmJ7dL+RyOYPBUCgU+GNWVpZYLEYI2djY4DuVmvbs2UN1v1Cr1XFxcV5eXgwGQyQSBQUFlZWVkSSpe5/0VrlBQUEIocjIyO4R4vuP3cXFxZEkGRoaKhaLeTwenU53dnbeunVrdXU19d1ffvll7dq1IpGIxWLNnj07NzdXs+QlS5Y4OTlpdsjoTX/bibkYvYlshDFqA8VP1Rip8D7pmcgeP35Mp9O1nvIZel1dXfPnzz9z5syQrbG+vp7NZsfHx+uz8EhNZKP01BL01/B/xYJEIomOjo6OjqbeJzH0urq6srOzW1tbpVLpkK00Kipq2rRpcrl8yNY4DEEiG1IPHz4kejeUrX9ECg8PX716tVQqNdXz4Xl5eZmZmbm5ubp7tA2iY8eOFRYW5uTkMBiMoVnj8ASJbEhNmDBBx+FxamqqqQPswb59+1JSUpqbmz08PDIyMkwdTh8OHjwol8sPHz5skrUvWrTo/Pnz1MOnxnbp0qX29va8vDyRSDQ0axy2YBQl0IdDhw4dOnTI1FH0Q0BAQEBAgKmjGAqBgYGBgYGmjmJYgCMyAIDZg0QGADB7kMgAAGYPEhkAwOyN2Iv96enppg5hqI2MJ9K7w8/fjMIKBf0wZF1vh8yweqM0AMPNiOzZP2KPyMhR9v5ygiDS0tLWrFlj6kAGHx4w7eLFi6YOZCQYJoPsDDq4RgYAMHuQyAAAZg8SGQDA7EEiAwCYPUhkAACzB4kMAGD2IJH9W2Zmpqenp+YLwphMpp2d3VtvvRUXF9fY2GjqAMFAXb9+PTw8XLOiN2zYoLlAQECAQCCwsLDw9vbu8b37xhMdHT1p0iShUMhisSQSyd69e/EbIi9fvnz06NHh/2JLEzN1R7bBN8BXXYvFYjyqNh5u49tvv924cSNBEI6Ojnhgm+EJjdCOjqTer7ruU2Rk5NKlS6mRvcVi8ZgxY1C3IeKpQciH2IIFC5KTkxsaGlpaWtLS0hgMxh//+Ec8KzExccGCBY2NjQNfy0htJ3BE1iuCIKysrN56662UlJT09PQXL14sWbLEVK8eNSGlUunv7z8cChmII0eOpKampqenUyN7I4SSkpJoNJpMJhsO1crn8/HYCAKBYM2aNUFBQdeuXfvll18QQjt37pw6deo777zT2dlp6jCHKUhkelm1atXGjRtra2s/++wzU8cy1M6cOVNbWzscCjHYkydPPvroowMHDuCBmSn+/v4hISFVVVW7d+82VWyUq1evUoPUIYRsbGwQQtTo5VFRUYWFhYmJiaYJbtiDRKYvPIxjbm4uQqirqysyMtLV1ZXD4UyZMgWfzJ44cYLH43G53EuXLi1evFgoFDo7O1+4cAF//ebNm7Nnz+ZyuUKh0MfHp6WlpbdyjIckyWPHjk2cOJHFYolEouXLlz98+BAhJJfLmUwm9YLmd999l8fjEQRRX18fEhISGhpaXl5OEIREIklKSmKz2XZ2dtu2bXN0dGSz2f7+/nfu3OlXIQiha9euGW+My+6SkpJIkly2bFn3WbGxsePGjTt9+vT169e7z+1tj+mu60Gp1qqqKg6H4+HhgT+KRKIFCxYkJiaSo+zZO32Z9szWGAbrGpkWnHpcXFxIkty9ezeLxcrIyGhsbNy3bx+NRsOXzyIiIhBC33zzTXNzc21t7fz583k8XkdHR1tbm1AoPHr0qFKprKmpWbFiRV1dnY5yDID0uPYRGRnJZDLPnj3b1NRUVFQ0Y8YMGxubmpoakiTXrVtnb29PLRkXF4cQwkGuXLlSLBZTs2QyGY/HKy0tff36dUlJyaxZswQCwbNnz/pVyNWrVwUCQXR0tD6bNvBrZJ6enpMmTdKaKBaLnz59SpLk7du3aTSau7t7W1sb+Z/XyHTssd7qmhyMan316pVAIJDL5ZoTw8PDEUIFBQUG7gWSJEfuNTJIZNp6S2QkSeKrZkqlksvlSqVSPFGhULBYrB07dpC/NW6lUolnJScnI4SePHny4MED1O2iso5yDNBnA1UoFHw+n1odSZJ3795FCOFs0q9Eprl/7t27hxA6cOBAvwrplwEmsra2NoIgli5dqjWdSmQkSYaGhiKE8Di+VCLTvcd6q+tBqdaIiIhx48ZR9yWwzz//HCH0xRdf9KsoLSM1kcGppb5evXpFkqRQKCwrK1MoFJMnT8bTORyOg4MDPuPQwmQyEUIqlcrT09POzm79+vVRUVEVFRV4rv7lDIqSkpK2traZM2dSU2bNmsVkMvGJocFmzpzJ5XKNF/bA1dbWkiSpe3y22NjY8ePHJycn37p1i5rYrz1G1fXAqzUrKys9Pf3LL7/UvC+BEMKb8OLFC/2LGj0gkenr0aNHCKEJEya8evUKIbR//36qu1llZSV1UbZHHA7nxo0b8+bNO3jwoKenp1QqVSqVBpQzEE1NTQghPp+vOdHKyqq1tXWAJbNYrLq6ugEWYjyvX79GCLFYLB3LsNnslJQUgiA2b96sVCrxRMP22ACrNTU19ciRI3l5ee7u7lqzOBwOtTlACyQyfV27dg0htHjxYltbW4RQQkKC5pFtn29n9fb2vnLlSnV1dVhYWFpaWnx8vGHlGMzKygohpPUjbGpqcnZ2HkixKpVq4IUYFf7999mh1M/Pb9euXY8fP46JicFTDNtjA6nW48ePnzt37saNG2PHju0+t6Ojg9ocoAUSmV5qamoSEhKcnZ03b97s4uLCZrMLCwv1/3p1dXVpaSlCyNbW9vDhwzNmzCgtLTWgnIGYPHkyn8//8ccfqSl37tzp6Oh44403EEJ0Ol2lUhlQbF5eHkmSvr6+AynEqOzs7AiC0KenWExMzIQJEwoKCvBH3XusN4ZVK0mSYWFhxcXF2dnZWseAFLwJ9vb2/Sp5lIBE1gOSJNva2tRqNUmSdXV1aWlpc+fOtbCwyM7OFgqFbDZ706ZNFy5cOHHiREtLS1dX1/Pnz3/99VcdBVZXV2/btu3hw4cdHR0FBQWVlZW+vr4GlDMQbDY7NDQ0Kyvr3LlzLS0txcXF27dvd3R0lMlkCCGJRPLy5cvs7GyVSlVXV1dZWUl90draurq6uqKiorW1Fecp/MxDZ2dnUVFRSEiIq6sr7puifyG5ublD1v2Cy+V6enriF//rhk8wqc5cuveYjkJ6q1apVGpvb9/jk0+lpaUff/zxqVOnGAyG5kNy8fHx1DJ4E3x8fPq1+aOFce8lmILBdy0vX748ZcoULpfLZDJpNBr6rXP/7Nmzo6OjGxoaqCXb29vDwsJcXV3pdLqtre3KlStLSkqSk5Px5VgvL6/y8vKTJ08KhUKEkJub29dff+3v7y8SiSwsLMaOHRsREdHZ2dlbOYZtNdLjbpRarY6Li/Py8mIwGCKRKCgoqKysDM9qaGhYuHAhm8328PB4//339+zZgxCSSCTPnj27f/++m5sbh8OZN29eTU2NTCZjMBhOTk50Ol0oFC5fvry8vLy/heTk5AgEgtjYWH02beDdL+RyOYPBUCgU+GNWVpZYLEYI2djY4DuVmvbs2UN1v+htj+mo60ePHvVWrUFBQQihyMjI7hEWFxf3+POMi4ujllmyZImTkxP+/2owfdqJOYJENkIMWQPFj9EMwYooA09kjx8/ptPpZ8+eHayQDNPV1TV//vwzZ84Y8N36+no2mx0fHz/AGEZqIoNTS9BvZvcmBolEEh0dHR0djd8nYRJdXV3Z2dmtra1SqdSAr0dFRU2bNk0ulw96YCMDJDIwKoSHh69evVoqlZrq+fC8vLzMzMzc3FzdPdp6dOzYscLCwpycHAaDYYzYRgBIZKAf9u3bl5KS0tzc7OHhkZGRYepw+ufgwYNyufzw4cMmWfuiRYvOnz9PPYuqv0uXLrW3t+fl5YlEImMENjKM2HEtgTEcOnTo0KFDpo7CcAEBAQEBAaaOon8CAwMDAwNNHcVwB0dkAACzB4kMAGD2IJEBAMweJDIAgNkbsRf7V69ebeoQhlpCQsLFixdNHcXg++GHH9CorFCgP4IccW/Ozc/PP3bsmKmjAGCY2rVrl5+fn6mjGGQjMJEBAEYbuEYGADB7kMgAAGYPEhkAwOxBIgMAmL3/DyrdUNAlVK6yAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(padded_sequences[20])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m71OMUEJEeqC",
        "outputId": "4e0abfc0-cf50-4e98-d15e-64441f950962"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[107  67   4 ...   0   0   0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = np.array(labels)\n",
        "\n",
        "one_hot_labels = to_categorical(labels)\n",
        "\n",
        "print(one_hot_labels[0])\n",
        "\n",
        "sequences_train, sequences_test, labels_train, labels_test = train_test_split(padded_sequences, one_hot_labels, test_size=0.2, random_state=43)\n",
        "\n",
        "model.fit(sequences_train, labels_train, epochs = 4, batch_size = 64, validation_data=(sequences_test, labels_test), callbacks=callbacks)\n",
        "model.evaluate(sequences_test, labels_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Rm4mdTwEesb",
        "outputId": "4134be4c-f148-4d4a-a0b6-fd26495ed130"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 1.]\n",
            "\n",
            "Epoch 1: LearningRateScheduler setting learning rate to 0.0010000000474974513.\n",
            "Epoch 1/4\n",
            "562/562 [==============================] - 698s 1s/step - loss: 0.0090 - binary_accuracy: 0.9885 - val_loss: 7.6620e-04 - val_binary_accuracy: 0.9992 - lr: 0.0010\n",
            "\n",
            "Epoch 2: LearningRateScheduler setting learning rate to 0.00031622778103685084.\n",
            "Epoch 2/4\n",
            "562/562 [==============================] - 691s 1s/step - loss: 2.8939e-04 - binary_accuracy: 0.9998 - val_loss: 7.4421e-04 - val_binary_accuracy: 0.9992 - lr: 3.1623e-04\n",
            "\n",
            "Epoch 3: LearningRateScheduler setting learning rate to 3.162277862429619e-05.\n",
            "Epoch 3/4\n",
            "562/562 [==============================] - 683s 1s/step - loss: 1.1613e-04 - binary_accuracy: 0.9999 - val_loss: 6.8884e-04 - val_binary_accuracy: 0.9992 - lr: 3.1623e-05\n",
            "\n",
            "Epoch 4: LearningRateScheduler setting learning rate to 1.0000000409520217e-06.\n",
            "Epoch 4/4\n",
            "562/562 [==============================] - 680s 1s/step - loss: 9.7533e-05 - binary_accuracy: 0.9999 - val_loss: 6.8809e-04 - val_binary_accuracy: 0.9992 - lr: 1.0000e-06\n",
            "281/281 [==============================] - 81s 286ms/step - loss: 6.8809e-04 - binary_accuracy: 0.9992\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0006880914443172514, 0.9992213845252991]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(cm,classes,normalize=False,title='Confusion Matrix',cmap=plt.cm.Dark2):\n",
        "  plt.imshow(cm,interpolation='nearest',cmap=cmap)\n",
        "  plt.title(title)\n",
        "  plt.colorbar()\n",
        "  tick_marks=np.arange(len(classes))\n",
        "  plt.xticks(tick_marks,classes,rotation=45)\n",
        "  plt.yticks(tick_marks,classes)\n",
        "  if normalize:\n",
        "    cm=cm.astype('float')/cm.sum(axis=1)[:,np.newaxis]\n",
        "    print(\"normalize confusion matrix\")\n",
        "  else:\n",
        "    print('confusion matrix,without normalization')\n",
        "\n",
        "  thresh = cm.max() / 2\n",
        "  for i,j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"yellow\" if cm[i, j] > thresh else \"red\")\n",
        "  plt.tight_layout()\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Predicted label')\n",
        "\n",
        "pred_lr=model.predict(sequences_test)\n",
        "score = model.evaluate(sequences_test, labels_test)\n",
        "pred_lr = (pred_lr > 0.5).astype(int)\n",
        "labels_test_single = np.argmax(labels_test, axis=1)\n",
        "pred_lr_single = np.argmax(pred_lr, axis=1)\n",
        "print(f'Accuracy: {round(score[1]*100,2)}%')\n",
        "cm = metrics.confusion_matrix(labels_test_single,pred_lr_single)\n",
        "plot_confusion_matrix(cm, classes=['FAKE NEWS', 'REAL NEWS'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "HWQhu_zoI39s",
        "outputId": "e7010bab-a28c-4581-a68c-adba45d0098f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "281/281 [==============================] - 80s 280ms/step\n",
            "281/281 [==============================] - 80s 285ms/step - loss: 6.8809e-04 - binary_accuracy: 0.9992\n",
            "Accuracy: 99.92%\n",
            "confusion matrix,without normalization\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "misclassified_indices = np.where(pred_lr_single != labels_test_single)[0]\n",
        "\n",
        "incorrect_labels = []\n",
        "for i in misclassified_indices:\n",
        "    incorrect_labels.append(pred_lr_single[i])\n",
        "\n",
        "misclassified_sequences = []\n",
        "for i in misclassified_indices:\n",
        "    misclassified_sequences.append(sequences_test[i])\n",
        "misclassified_texts = tokenizer.sequences_to_texts(misclassified_sequences)\n",
        "\n",
        "for i in range(len(misclassified_texts)):\n",
        "    if incorrect_labels[i] == 1:\n",
        "        print(\"Predicted Real\\n\")\n",
        "    if incorrect_labels[i] == 0:\n",
        "        print(\"Predicted Fake\\n\")\n",
        "    print(misclassified_texts[i])\n",
        "    print()\n",
        "\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d79v8wwwI4Ab",
        "outputId": "d13855b1-494c-434d-b655-6a4a95273c4a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Real\n",
            "\n",
            "according to reuters a grand jury in oklahoma found that jail staff did not verify what drugs they were using for lethal injections and were unaware when the wrong drugs were administered this report is another damning piece of evidence that shows that there are many flaws in the supposedly humane systems in place for carrying out capital punishments in 2014 the news broke that the execution of an inmate named clayton lockett who was sentenced to death for murder an executioner failed to properly put an i v line into lockett the needle used to inject the lethal drugs into lockett began to spray all over the execution chamber that he was in he died after 45 minutes of agony not because the toxins that were pumped into his body worked as intended but because they caused lockett to die of a heart attack after a massive outcry from the public which included a scathing condemnation from amnesty international oklahoma made some minor changes to their policies regarding executions in 2015 two more botched executions were performed one of those executed was charles werner his final words were my body is on fire one of the major factors contributing to these botched executions is due to the absence of sodium thiopental which is the executioner s drug of choice for lethal injections in the u s the drug was taken off from the market in 2011 due to increasing international regulations on lethal injections states who have yet to abolish capital punishment have been scrambling to find alternatives that means once again in its history the u s is desperately clinging to a practice that the rest of the world is moving towards abolishing there are many good reasons for abolishing the death penalty but there is one reason in particular that even the staunchest advocates of the deathly penalty must admit is terrifying a recent study found that 1 in 25 prisoners who are sentenced to death are actually innocent no matter what protocols are put in place we can never know for sure if they are truly innocent i share the same stance as many capital punishment abolitionists that capital punishment is always wrong no matter what whether the convicted individual is guilty or innocent the idea that innocent people might be subjected to what is increasingly becoming not only a death sentence but a sentence to living their final moments in a state of hell my body is on fire is unconscionable oklahoma you have already proven that reforming your policies on capital punishment are a facade it s time to for you and the rest of the states that have yet to abolish the death penalty to give up this barbaric practice right now all executions are on hold in oklahoma since then not a single execution has been botched and not a single innocent person has been murdered by the state it s sounds as if they have found the perfect solution to their problem now they just need to make it permanent featured image from photo by joe raedle newsmakers\n",
            "\n",
            "Predicted Fake\n",
            "\n",
            "the democratic and republican nominees for the nov 8 presidential election are decided in a series of state by state contests the key to winning the nomination for each party is ultimately not about the popular vote but about securing the number of delegates needed to win the nomination at each party’s convention july 18 21 in cleveland for the republicans and july 25 28 in philadelphia for the democrats the following is a guide to the nominating process q is the delegate selection process the same for the republican and democratic parties a no the parties set their own rules one thing that is the same is that at each party convention a candidate needs to reach only a simple majority of the delegate votes to win the nomination q how many delegates are there a the democratic convention will be attended by about 4 763 delegates with 2 382 delegates needed to win the nomination the republican convention will be attended by 2 472 delegates with 1 237 delegates needed to win q i keep hearing about “superdelegates ” are they different from other delegates do both the republicans and democrats have superdelegates a superdelegates officially known as unpledged delegates are a sort of wild card in the nominating process but only the democrats have them the category was created for the 1984 democratic convention and according to political scientists they are a legacy of the 1980 convention when there was a fight for the nomination between president jimmy carter who was seeking a second term in the white house and senator edward kennedy of massachusetts members of congress were frustrated by their lack of influence because delegates elected to support one candidate could not switch to support another so democratic members of the house of representatives led an effort to win a role for themselves that resulted in the creation of superdelegates unlike other delegates superdelegates may change what candidate they are supporting right up to the convention there is no fixed number of superdelegates because the group is defined by various categories whose members change from one election cycle to another here is who gets to be a superdelegate all democratic members of the house of representatives and the senate the democratic governors the democratic president and vice president of the united states former democratic presidents and vice presidents former democratic leaders of the u s senate former democratic speakers of the house and former democratic minority leaders throw in the members of the democratic national committee and the former chairs of the dnc and you finally have the whole pool of superdelegates q what about the other delegates do they get to choose which candidate to support a both the democratic and republican parties send delegates to their conventions based on the popular vote in the primary elections and caucuses held in each of the 50 states but the parties have different rules on how delegates are allotted to a candidate the democratic party applies uniform rules to all states in each state delegates are allocated in proportion to the percentage of the primary or caucus vote in each district but a candidate must win at least 15 percent of the vote to be allocated any delegates the republican party lets states determine their own rules although it does dictate some things some states award delegates proportionate to the popular vote although most such states have a minimum percentage that a candidate must reach to win any delegates some other states use the winner take all method in which the candidate with the highest percentage of the popular vote is awarded all the delegates other states use a combination of the two methods states that use the proportionate method may instead use the winner take all method if one candidate wins more than 50 percent of the popular vote in addition the republican party requires that all states with nominating contests held between march 1 and march 14 use the proportional method meaning that all the states holding votes on super tuesday will have to award delegates proportionally q what happens to delegates if a candidate drops out of the race a another good question because we have certainly seen that happen this year for the democratic party in every state delegates are reallocated to the remaining candidates for the republican party it varies by state in some states delegates are required to stick with their original candidate at least through the first ballot at the republican national convention in some other states if a candidate drops out his or her delegates may immediately pledge to another candidate there is also a middle ground in which those delegates are reallocated to the remaining candidates republican national committee here here democratic national committee here battlopedia here 2016 vote for bernie here www realclearpolitics com here here www demconvention com\n",
            "\n",
            "Predicted Real\n",
            "\n",
            "judge neil gorsuch a strict conservative similar to justice scalia a strict constitutionalistjudge neil gorsuch 49 is president donald trump s pick for appointment to the supreme court to fill the seat vacated a year ago by the death of justice antonin scalia the announcement from president trump breaking donald trump reveals that his choice for us supreme court is neil gorsuch 49 https t co d91eyacimz https t co dbcip2bp2u cnn cnn february 1 2017gorsuch has the typical pedigree of a high court justice he graduated from columbia harvard and oxford clerked for two supreme court justices and did a stint at the department of justice since 2006 he has served on the 10th circuit court of appeals in denver colorado his supporters note that he is an outdoorsman who fishes hunts and skis on the court conservatives hope he could become the intellectual heir to scalia long the outspoken leader of the conservative bloc the real appeal of gorsuch nomination is he s likely to be the most effective conservative nominee in terms of winning over anthony kennedy and forging conservative decisions on the court said jeffrey rosen of the national constitution center he s unusual for his memorable writing style the depth of his reading and his willingness to rethink constitutional principles from the ground up like justice scalia he sometimes reaches results that favor liberals when he thinks the history or text of the constitution or the law require it especially in areas like criminal law or the rights of religious minorities but unlike scalia he s less willing to defer to regulations and might be more willing to second guess trump s regulatory decision read more politico\n",
            "\n",
            "Predicted Fake\n",
            "\n",
            "skopje macedonia s parliament has adopted a 2018 draft budget lowering the deficit to 2 7 percent of national output from 2 9 this year and forecasting growth of 3 2 percent the budget totaling 221 7 billion denars 4 28 billion and adopted late on friday allocated spending equivalent to about 400 million euros 474 28 million for capital investment while average annual inflation was expected to be 1 7 percent revenues were forecast at 193 5 billion denars the main opposition vmro dpmne party boycotted the budget vote in protest at the arrest of a former interior minister several lawmakers and political activists on charges related to violence in parliament in april which pushed the nation into a political crisis macedonia s central bank revised its economic growth forecast for 2017 down to 0 5 percent from 2 5 percent citing a poor economic performance in the first half of the year because of political instability macedonia s two year crisis over a wiretapping scandal that toppled the government of nationalist prime minister nikola gruevski ended in june with the appointment of a pro western government of the social democrat prime minister zoran zaev finance minister dragan tevdovski told parliament that the government planned to finance the deficit through a combination of domestic and foreign loans the borrowing abroad may be done through a eurobond or favorable foreign loans tevdovski said the economy of the landlocked balkan country grew by 2 4 percent in 2016 but national output contracted 1 9 percent in the second quarter of 2017 the first decline since 2012 macedonia which won independence from former yugoslavia in 1991 has made little progress towards membership of the european union and the nato alliance due to a name dispute with greece 1 0 8434 euros\n",
            "\n",
            "Predicted Real\n",
            "\n",
            "judge neil gorsuch a strict conservative similar to justice scalia a strict constitutionalistjudge neil gorsuch 49 is president donald trump s pick for appointment to the supreme court to fill the seat vacated a year ago by the death of justice antonin scalia the announcement from president trump breaking donald trump reveals that his choice for us supreme court is neil gorsuch 49 https t co d91eyacimz https t co dbcip2bp2u cnn cnn february 1 2017gorsuch has the typical pedigree of a high court justice he graduated from columbia harvard and oxford clerked for two supreme court justices and did a stint at the department of justice since 2006 he has served on the 10th circuit court of appeals in denver colorado his supporters note that he is an outdoorsman who fishes hunts and skis on the court conservatives hope he could become the intellectual heir to scalia long the outspoken leader of the conservative bloc the real appeal of gorsuch nomination is he s likely to be the most effective conservative nominee in terms of winning over anthony kennedy and forging conservative decisions on the court said jeffrey rosen of the national constitution center he s unusual for his memorable writing style the depth of his reading and his willingness to rethink constitutional principles from the ground up like justice scalia he sometimes reaches results that favor liberals when he thinks the history or text of the constitution or the law require it especially in areas like criminal law or the rights of religious minorities but unlike scalia he s less willing to defer to regulations and might be more willing to second guess trump s regulatory decision read more politico\n",
            "\n",
            "Predicted Fake\n",
            "\n",
            "baltimore the prospect of running for re election with donald trump at the top of the republican ticket was worrying some party lawmakers as they huddled for an annual retreat where a few forecast trouble if trump becomes the party’s presidential nominee “heaven help us ” representative peter roskam said when asked about it by a reporter but the illinois republican quickly added “i’m not going to comment on the presidential candidates at this point ” that reticence was shared by many lawmakers asked about running on trump’s coattails during an event in baltimore where senate and house of representatives republicans gathered to talk through their priorities for a coming year whose highlight is the november election some were not reluctant to express concerns trump would not help republicans if he topped the ticket despite his appeal to americans fed up with politicians said representative justin amash a favorite of the conservative tea party movement who has endorsed kentucky republican senator rand paul for president “he does appeal to the anger ” amash said of trump the outspoken real estate mogul who is front runner to be the party’s presidential nominee but he said trump’s “policy prescriptions will take us in the wrong direction and it won’t be long before many of those people are angry at him and angry at republicans again ” the idea of trump capturing the republican nomination seemed highly unlikely just months ago but it was sinking in as an actual possibility this week across the republican party in addition to voting for a new president americans will elect members of the senate and the house on nov 8 in u s politics a party’s presidential nominee has a big impact on the campaigns of candidates for both chambers of congress at the party gathering republican leaders were keen to talk policy not presidential politics “what happens above us on the presidential ballot you really don’t control ” senator john thune a senior party leader from south dakota told reporters house speaker paul ryan of wisconsin said “we are not sitting here thinking about who the nominee is going to be we don’t have time to think about that ” but in the hallways of the retreat at a hotel on baltimore’s restored waterfront trump was often the hot topic he is leading national opinion polls ahead of the feb 1 iowa caucus the first step in the nominating process representative jason chaffetz said it was important to have a presidential nominee who will not “embarrass” the rest of the party’s candidates as they defend the republican majorities in both congressional chambers chaffetz who is from utah has endorsed marco rubio a republican senator from florida polls show rubio in third place behind trump and texas senator ted cruz in iowa “marco rubio is not going to embarrass us he’s not as sensational in his comments as the others but there’s a good quality to that and i think that’s also something you look for in a president ” chaffetz said among other proposals trump has called for a temporary ban on muslims entering the united states and a wall along the u s border with mexico to stop the flow of illegal immigrants representative tom cole said republicans’ fate will be linked closely to the republican nominee whoever it is trump would be a wild card said cole of oklahoma “i don’t know what he trump will do i can see a situation where he could be a great asset because i do think he will bring some people out to vote who have not been brought before ” representative peter king of new york agreed that trump is “more of a gamble” at the top of the ticket “the conventional wisdom is that he would hurt more than he would help ” said king who was unable to attend the retreat but spoke by telephone “but on the other hand he is tapping into something among voters out there ” editing by kevin drawbaugh caren bohan and frances kerry sap is the sponsor of this coverage which is independently produced by the staff of reuters news agency\n",
            "\n",
            "Predicted Real\n",
            "\n",
            "washington it sits on the corsica river along the eastern shore a 45 acre russian compound known as the washington dacha to those who use it when it was closed on thursday as part of the white house s response to what it called russian interference during the presidential election questions began to swirl around what took place at the estate in queen anne s county the main reason it was used was to get rid of spy gear and to possibly have private conversations said pete earley author of the book comrade j the untold secrets of russia s master spy in america after the end of the cold war the property in centreville maryland consists of two georgian style mansions six guest cottages a pool and tennis court it was mainly a retreat for russian diplomats stationed in washington according to earley and wasn t used as a site for spying activities the site however did play a small role in russian espionage earley said as the story goes according to earley russian generals at the compound wanted a speed boat to use on the water but the only one docked there was for diplomats to sell the kremlin on the idea of buying a boat for military officers the generals came up with a plan to use the boat to discard old spy gear they would take speed boat out into the middle of the river and they would dump all this smashed spy gear into the water earley said read more wtop\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "position = 21416\n",
        "\n",
        "position = 21417\n",
        "\n",
        "input_array=padded_sequences[position]\n",
        "print(texts[position])\n",
        "print(input_array)\n",
        "\n",
        "input_array = np.expand_dims(input_array, 0)\n",
        "print(input_array)\n",
        "\n",
        "output_array = model.predict(input_array)\n",
        "print(output_array)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KCnAfff8I4DX",
        "outputId": "c29b8843-3c88-4cf3-d272-8dd241c17c3b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Donald Trump just couldn t wish all Americans a Happy New Year and leave it at that. Instead, he had to give a shout out to his enemies, haters and  the very dishonest fake news media.  The former reality show star had just one job to do and he couldn t do it. As our Country rapidly grows stronger and smarter, I want to wish all of my friends, supporters, enemies, haters, and even the very dishonest Fake News Media, a Happy and Healthy New Year,  President Angry Pants tweeted.  2018 will be a great year for America! As our Country rapidly grows stronger and smarter, I want to wish all of my friends, supporters, enemies, haters, and even the very dishonest Fake News Media, a Happy and Healthy New Year. 2018 will be a great year for America!  Donald J. Trump (@realDonaldTrump) December 31, 2017Trump s tweet went down about as welll as you d expect.What kind of president sends a New Year s greeting like this despicable, petty, infantile gibberish? Only Trump! His lack of decency won t even allow him to rise above the gutter long enough to wish the American citizens a happy new year!  Bishop Talbert Swan (@TalbertSwan) December 31, 2017no one likes you  Calvin (@calvinstowell) December 31, 2017Your impeachment would make 2018 a great year for America, but I ll also accept regaining control of Congress.  Miranda Yaver (@mirandayaver) December 31, 2017Do you hear yourself talk? When you have to include that many people that hate you you have to wonder? Why do the they all hate me?  Alan Sandoval (@AlanSandoval13) December 31, 2017Who uses the word Haters in a New Years wish??  Marlene (@marlene399) December 31, 2017You can t just say happy new year?  Koren pollitt (@Korencarpenter) December 31, 2017Here s Trump s New Year s Eve tweet from 2016.Happy New Year to all, including to my many enemies and those who have fought me and lost so badly they just don t know what to do. Love!  Donald J. Trump (@realDonaldTrump) December 31, 2016This is nothing new for Trump. He s been doing this for years.Trump has directed messages to his  enemies  and  haters  for New Year s, Easter, Thanksgiving, and the anniversary of 9/11. pic.twitter.com/4FPAe2KypA  Daniel Dale (@ddale8) December 31, 2017Trump s holiday tweets are clearly not presidential.How long did he work at Hallmark before becoming President?  Steven Goodine (@SGoodine) December 31, 2017He s always been like this . . . the only difference is that in the last few years, his filter has been breaking down.  Roy Schulze (@thbthttt) December 31, 2017Who, apart from a teenager uses the term haters?  Wendy (@WendyWhistles) December 31, 2017he s a fucking 5 year old  Who Knows (@rainyday80) December 31, 2017So, to all the people who voted for this a hole thinking he would change once he got into power, you were wrong! 70-year-old men don t change and now he s a year older.Photo by Andrew Burton/Getty Images.\n",
            "[70 15 82 ...  0  0  0]\n",
            "[[70 15 82 ...  0  0  0]]\n",
            "1/1 [==============================] - 0s 178ms/step\n",
            "[[9.9958581e-01 4.1420734e-04]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ANN"
      ],
      "metadata": {
        "id": "J8ZCtCLVVBdn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.utils import plot_model"
      ],
      "metadata": {
        "id": "5vuqA2FEU8HI"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lengths = []\n",
        "\n",
        "for i in range(len(tokenized)):\n",
        "\n",
        "    lengths.append(len(tokenized[i]))\n",
        "\n",
        "\n",
        "print(max(lengths))\n",
        "\n",
        "print(min(lengths))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PMS94CyOVEPF",
        "outputId": "878a260b-2a3e-43b2-ce3a-aed215adabf5"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8375\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lengths = [len(item) for item in tokenized]\n",
        "\n",
        "max_length = max(lengths)\n",
        "min_length = min(lengths)\n",
        "\n",
        "max_length_index = lengths.index(max_length)\n",
        "min_length_index = lengths.index(min_length)"
      ],
      "metadata": {
        "id": "cUj1HvfVVESL"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "# Assuming sequences is your list of lists, where each inner list is a sequence.\n",
        "sequences = [[1, 2, 3], [4, 5], [6, 7, 8, 9]]\n",
        "\n",
        "# Pad the sequences\n",
        "padded_sequences = pad_sequences(sequences, padding='post')\n",
        "\n",
        "print(padded_sequences)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySiztYYLVEXm",
        "outputId": "71cb31d0-22eb-4da1-8e59-c71ede909d10"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 2 3 0]\n",
            " [4 5 0 0]\n",
            " [6 7 8 9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "\n",
        "padded_sequences = pad_sequences(tokenized, padding='post')\n",
        "\n",
        "print(padded_sequences)\n",
        "\n",
        "lengths = [len(item) for item in padded_sequences]\n",
        "\n",
        "max_length = max(lengths)\n",
        "min_length = min(lengths)\n",
        "\n",
        "max_length_index = lengths.index(max_length)\n",
        "min_length_index = lengths.index(min_length)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_dbSRv9VEa_",
        "outputId": "32eeac63-0d2a-4f7f-a427-ed29031f2486"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 107   67    1 ...    0    0    0]\n",
            " [ 107   67 1549 ...    0    0    0]\n",
            " [ 107   67    1 ...    0    0    0]\n",
            " ...\n",
            " [   1   15  155 ...    0    0    0]\n",
            " [   1  163   21 ...    0    0    0]\n",
            " [   1   15  155 ...    0    0    0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_length = 44\n",
        "\n",
        "\n",
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Embedding(vocab_size, embedding_length, input_length=max_length))\n",
        "\n",
        "model.add(tf.keras.layers.Dense(256))\n",
        "model.add(tf.keras.layers.Dense(256))\n",
        "model.add(tf.keras.layers.GlobalAveragePooling1D())\n",
        "model.add(tf.keras.layers.Dense(2, activation = \"softmax\"))\n",
        "input_array = np.random.randint(1000, size=(32, max_length))\n",
        "model.compile(tf.keras.optimizers.AdamW(), 'binary_focal_crossentropy', metrics=[tf.keras.metrics.BinaryAccuracy()])\n",
        "output_array = model.predict(input_array)\n",
        "print(output_array.shape)\n",
        "model.summary()\n",
        "plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OK2Jur5RVV6m",
        "outputId": "aebd5850-f2be-4c31-ae1e-6b741f279a5d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 109ms/step\n",
            "(32, 2)\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, 8375, 44)          6074376   \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 8375, 256)         11520     \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 8375, 256)         65792     \n",
            "                                                                 \n",
            " global_average_pooling1d (  (None, 256)               0         \n",
            " GlobalAveragePooling1D)                                         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 2)                 514       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6152202 (23.47 MB)\n",
            "Trainable params: 6152202 (23.47 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(padded_sequences[20])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aXVHClkMVEgv",
        "outputId": "4509c18c-d2fa-47e3-b23c-67e4dc2af6d0"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[107  67   4 ...   0   0   0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = np.array(labels)\n",
        "\n",
        "one_hot_labels = to_categorical(labels)\n",
        "\n",
        "print(one_hot_labels[0])\n",
        "\n",
        "sequences_train, sequences_test, labels_train, labels_test = train_test_split(padded_sequences, one_hot_labels, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "model.fit(sequences_train, labels_train, epochs = 4, batch_size = 256, validation_data=(sequences_test, labels_test))\n",
        "model.evaluate(sequences_test, labels_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3k3XWf_LVfeM",
        "outputId": "d90a8ce7-4547-4133-a8cf-2443e759e896"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "position = 21416\n",
        "\n",
        "position = 21417\n",
        "\n",
        "input_array=padded_sequences[position]\n",
        "print(texts[position])\n",
        "print(input_array)\n",
        "\n",
        "input_array = np.expand_dims(input_array, 0)\n",
        "print(input_array)\n",
        "\n",
        "output_array = model.predict(input_array)\n",
        "print(output_array)"
      ],
      "metadata": {
        "id": "5yDRPe7SViI0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hard_test = tokenizer.texts_to_sequences(fake_articles2)\n",
        "\n",
        "padded_fake_articles = pad_sequences(hard_test, padding='post', maxlen=8375)"
      ],
      "metadata": {
        "id": "hEg7r7mmViPe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#positions = [1,2,3,4,5,6,7,8]\n",
        "for position in range(len(padded_fake_articles)):\n",
        "    input_array=padded_fake_articles[position]\n",
        "    print(fake_articles[position])\n",
        "    #print(input_array)\n",
        "\n",
        "    input_array = np.expand_dims(input_array, 0)\n",
        "    #print(input_array)\n",
        "\n",
        "    output_array = model.predict(input_array)\n",
        "    print(output_array)"
      ],
      "metadata": {
        "id": "rkNeJfCRViSh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hard_test_labels = [0] * len(padded_fake_articles)\n",
        "\n",
        "hard_test_labels = [1] + hard_test_labels\n",
        "\n",
        "hard_test_labels = np.array(hard_test_labels)\n",
        "\n",
        "one_hot_hard_test_labels = to_categorical(hard_test_labels)\n",
        "\n",
        "one_hot_hard_test_labels = np.delete(one_hot_hard_test_labels, 0, axis=0)\n",
        "\n",
        "\n",
        "#print(one_hot_hard_test_labels)\n",
        "\n",
        "model.evaluate(padded_fake_articles, one_hot_hard_test_labels)"
      ],
      "metadata": {
        "id": "C3EFdRSQViYW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RNN"
      ],
      "metadata": {
        "id": "3EpkxrPdXmUY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import re\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "import tensorflow as tf\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score\n",
        "import seaborn as sns\n",
        "plt.style.use('ggplot')"
      ],
      "metadata": {
        "id": "0YFvV-zjVibk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fake_df = pd.read_csv('/content/drive/MyDrive/gpt fakearticles/Fake.csv')\n",
        "real_df = pd.read_csv('/content/drive/MyDrive/gpt fakearticles/True.csv')"
      ],
      "metadata": {
        "id": "OmEk2BRpVifH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fake_df.isnull().sum()"
      ],
      "metadata": {
        "id": "j6swM0ejViic"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "real_df.isnull().sum()"
      ],
      "metadata": {
        "id": "gmAhh5N2X8E0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fake_df.subject.unique()"
      ],
      "metadata": {
        "id": "sVrq_c3WVil2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "real_df.subject.unique()"
      ],
      "metadata": {
        "id": "-PZ16ziLYFDs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fake_df.drop(['date', 'subject'], axis=1, inplace=True)\n",
        "real_df.drop(['date', 'subject'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "7hhcMWHzYFHD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fake_df['class'] = 0\n",
        "real_df['class'] = 1"
      ],
      "metadata": {
        "id": "1NZe4qnHYFKB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.bar('Fake News', len(fake_df), color='orange')\n",
        "plt.bar('Real News', len(real_df), color='green')\n",
        "plt.title('Distribution of Fake News and Real News', size=15)\n",
        "plt.xlabel('News Type', size=15)\n",
        "plt.ylabel('# of News Articles', size=15)\n",
        "\n",
        "\n",
        "total_len = len(fake_df) + len(real_df)\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.bar('Fake News', len(fake_df) / total_len, color='orange')\n",
        "plt.bar('Real News', len(real_df) / total_len, color='green')\n",
        "plt.title('Distribution of Fake News and Real News', size=15)\n",
        "plt.xlabel('News Type', size=15)\n",
        "plt.ylabel('Proportion of News Articles', size=15)"
      ],
      "metadata": {
        "id": "GNod5T5NYFNk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Difference in news articles:',len(fake_df)-len(real_df))"
      ],
      "metadata": {
        "id": "2ZsFu8mvYQQi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "news_df = pd.concat([fake_df, real_df], ignore_index=True, sort=False)\n",
        "news_df"
      ],
      "metadata": {
        "id": "bq4UGj8kYcaY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Combining the title with the text\n",
        "news_df['text'] = news_df['title'] + news_df['text']\n",
        "news_df.drop('title', axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "IBPoWdJNYfwR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Split into training and testing\n",
        "features = news_df['text']\n",
        "targets = news_df['class']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(features, targets, test_size=0.20, random_state=18)"
      ],
      "metadata": {
        "id": "8nJ4iFqpYrO8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Normalizing\n",
        "def normalize(data):\n",
        "    normalized = []\n",
        "    for i in data:\n",
        "        i = i.lower()\n",
        "        # get rid of urls\n",
        "        i = re.sub('https?://\\S+|www\\.\\S+', '', i)\n",
        "        # get rid of non words and extra spaces\n",
        "        i = re.sub('\\\\W', ' ', i)\n",
        "        i = re.sub('\\n', '', i)\n",
        "        i = re.sub(' +', ' ', i)\n",
        "        i = re.sub('^ ', '', i)\n",
        "        i = re.sub(' $', '', i)\n",
        "        normalized.append(i)\n",
        "    return normalized\n",
        "\n",
        "X_train = normalize(X_train)\n",
        "X_test = normalize(X_test)"
      ],
      "metadata": {
        "id": "IvoRh74uYxvH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_vocab = 10000\n",
        "tokenizer = Tokenizer(num_words=max_vocab)\n",
        "tokenizer.fit_on_texts(X_train)\n"
      ],
      "metadata": {
        "id": "UQRANxSvYFQm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#onvert text to vectors\n",
        "# tokenize the text into vectors\n",
        "X_train = tokenizer.texts_to_sequences(X_train)\n",
        "X_test = tokenizer.texts_to_sequences(X_test)\n"
      ],
      "metadata": {
        "id": "HLk9l69wYFTY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = tf.keras.preprocessing.sequence.pad_sequences(X_train, padding='post', maxlen=256)\n",
        "X_test = tf.keras.preprocessing.sequence.pad_sequences(X_test, padding='post', maxlen=256)"
      ],
      "metadata": {
        "id": "Dg3gfN2pZM6_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Building the RNN.\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(max_vocab, 128),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64,  return_sequences=True)),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(16)),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "yFZc1A_UZM-T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=2, restore_best_weights=True)\n",
        "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "              optimizer=tf.keras.optimizers.Adam(1e-4),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(X_train, y_train, epochs=10,validation_split=0.1, batch_size=30, shuffle=True, callbacks=[early_stop])"
      ],
      "metadata": {
        "id": "paXUE45nZNA6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict = history.history\n",
        "\n",
        "acc = history_dict['accuracy']\n",
        "val_acc = history_dict['val_accuracy']\n",
        "loss = history_dict['loss']\n",
        "val_loss = history_dict['val_loss']\n",
        "epochs = history.epoch\n",
        "\n",
        "plt.figure(figsize=(12,9))\n",
        "plt.plot(epochs, loss, 'r', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss', size=20)\n",
        "plt.xlabel('Epochs', size=20)\n",
        "plt.ylabel('Loss', size=20)\n",
        "plt.legend(prop={'size': 20})\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(12,9))\n",
        "plt.plot(epochs, acc, 'g', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy', size=20)\n",
        "plt.xlabel('Epochs', size=20)\n",
        "plt.ylabel('Accuracy', size=20)\n",
        "plt.legend(prop={'size': 20})\n",
        "plt.ylim((0.5,1))\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "VYuA5BR5ZNDi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "id": "cfd0CKH9ZNG0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model.predict(X_test)\n",
        "\n",
        "binary_predictions = []\n",
        "\n",
        "for i in pred:\n",
        "    if i >= 0.5:\n",
        "        binary_predictions.append(1)\n",
        "    else:\n",
        "        binary_predictions.append(0)"
      ],
      "metadata": {
        "id": "t6tmc_PSZNKK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy on testing set:', accuracy_score(binary_predictions, y_test))\n",
        "print('Precision on testing set:', precision_score(binary_predictions, y_test))\n",
        "print('Recall on testing set:', recall_score(binary_predictions, y_test))"
      ],
      "metadata": {
        "id": "aMqs0_F7ZNNJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "matrix = confusion_matrix(binary_predictions, y_test, normalize='all')\n",
        "plt.figure(figsize=(16, 10))\n",
        "ax= plt.subplot()\n",
        "sns.heatmap(matrix, annot=True, ax = ax)\n",
        "\n",
        "# labels, title and ticks\n",
        "ax.set_xlabel('Predicted Labels', size=20)\n",
        "ax.set_ylabel('True Labels', size=20)\n",
        "ax.set_title('Confusion Matrix', size=20)\n",
        "ax.xaxis.set_ticklabels([0,1], size=15)\n",
        "ax.yaxis.set_ticklabels([0,1], size=15)"
      ],
      "metadata": {
        "id": "q2-Vhs4Hi6up"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FgkcPBmei66A"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}