Codeway Inter Task 1.ipynb

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   "id": "3959b894",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b824595d",
   "metadata": {},
   "source": [
    "# Genre Classification Dataset IMDb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bfc04e80",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\abhin\\Downloads\\archive (4)\\Genre Classification Dataset\\train_data.txt\",sep=':::',header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4362dde0",
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>Oscar et la dame rose (2009)</td>\n",
       "      <td>drama</td>\n",
       "      <td>Listening in to a conversation between his do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Cupid (1997)</td>\n",
       "      <td>thriller</td>\n",
       "      <td>A brother and sister with a past incestuous r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Young, Wild and Wonderful (1980)</td>\n",
       "      <td>adult</td>\n",
       "      <td>As the bus empties the students for their fie...</td>\n",
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       "      <td>To help their unemployed father make ends mee...</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
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       "      <td>54211</td>\n",
       "      <td>Dead Girls Don't Cry (????)</td>\n",
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       "      <td>The NEXT Generation of EXPLOITATION. The sist...</td>\n",
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       "    <tr>\n",
       "      <th>54211</th>\n",
       "      <td>54212</td>\n",
       "      <td>Ronald Goedemondt: Ze bestaan echt (2008)</td>\n",
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       "      <td>54214</td>\n",
       "      <td>Nature's Fury: Storm of the Century (2006)</td>\n",
       "      <td>history</td>\n",
       "      <td>On Labor Day Weekend, 1935, the most intense ...</td>\n",
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       "<p>54214 rows × 4 columns</p>\n",
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       "           0                                             1              2  \\\n",
       "0          1                 Oscar et la dame rose (2009)          drama    \n",
       "1          2                                 Cupid (1997)       thriller    \n",
       "2          3             Young, Wild and Wonderful (1980)          adult    \n",
       "3          4                        The Secret Sin (1915)          drama    \n",
       "4          5                       The Unrecovered (2007)          drama    \n",
       "...      ...                                           ...            ...   \n",
       "54209  54210                              \"Bonino\" (1953)         comedy    \n",
       "54210  54211                  Dead Girls Don't Cry (????)         horror    \n",
       "54211  54212    Ronald Goedemondt: Ze bestaan echt (2008)    documentary    \n",
       "54212  54213                     Make Your Own Bed (1944)         comedy    \n",
       "54213  54214   Nature's Fury: Storm of the Century (2006)        history    \n",
       "\n",
       "                                                       3  \n",
       "0       Listening in to a conversation between his do...  \n",
       "1       A brother and sister with a past incestuous r...  \n",
       "2       As the bus empties the students for their fie...  \n",
       "3       To help their unemployed father make ends mee...  \n",
       "4       The film's title refers not only to the un-re...  \n",
       "...                                                  ...  \n",
       "54209   This short-lived NBC live sitcom centered on ...  \n",
       "54210   The NEXT Generation of EXPLOITATION. The sist...  \n",
       "54211   Ze bestaan echt, is a stand-up comedy about g...  \n",
       "54212   Walter and Vivian live in the country and hav...  \n",
       "54213   On Labor Day Weekend, 1935, the most intense ...  \n",
       "\n",
       "[54214 rows x 4 columns]"
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    "df"
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   "id": "432eb577",
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   "outputs": [
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       "   ID                               TITLE       GENRE  \\\n",
       "0   1       Oscar et la dame rose (2009)       drama    \n",
       "1   2                       Cupid (1997)    thriller    \n",
       "2   3   Young, Wild and Wonderful (1980)       adult    \n",
       "3   4              The Secret Sin (1915)       drama    \n",
       "4   5             The Unrecovered (2007)       drama    \n",
       "\n",
       "                                         DESCRIPTION  \n",
       "0   Listening in to a conversation between his do...  \n",
       "1   A brother and sister with a past incestuous r...  \n",
       "2   As the bus empties the students for their fie...  \n",
       "3   To help their unemployed father make ends mee...  \n",
       "4   The film's title refers not only to the un-re...  "
      ]
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      "(54214, 4)\n"
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       "      <td>Edgar's Lunch (1998)</td>\n",
       "      <td>L.R. Brane loves his life - his car, his apar...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <td>La guerra de papá (1977)</td>\n",
       "      <td>Spain, March 1964: Quico is a very naughty ch...</td>\n",
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       "      <td>His father has died, he hasn't spoken with hi...</td>\n",
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       "   ID                          TITLE  \\\n",
       "0   1          Edgar's Lunch (1998)    \n",
       "1   2      La guerra de papá (1977)    \n",
       "2   3   Off the Beaten Track (2010)    \n",
       "3   4        Meu Amigo Hindu (2015)    \n",
       "4   5             Er nu zhai (1955)    \n",
       "\n",
       "                                               GENRE  DESCRIPTION  \n",
       "0   L.R. Brane loves his life - his car, his apar...          NaN  \n",
       "1   Spain, March 1964: Quico is a very naughty ch...          NaN  \n",
       "2   One year in the life of Albin and his family ...          NaN  \n",
       "3   His father has died, he hasn't spoken with hi...          NaN  \n",
       "4   Before he was known internationally as a mart...          NaN  "
      ]
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     "output_type": "display_data"
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      "None\n",
      "(54200, 4)\n"
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       "      <th>DESCRIPTION</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Edgar's Lunch (1998)</td>\n",
       "      <td>thriller</td>\n",
       "      <td>L.R. Brane loves his life - his car, his apar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>La guerra de papá (1977)</td>\n",
       "      <td>comedy</td>\n",
       "      <td>Spain, March 1964: Quico is a very naughty ch...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Off the Beaten Track (2010)</td>\n",
       "      <td>documentary</td>\n",
       "      <td>One year in the life of Albin and his family ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Meu Amigo Hindu (2015)</td>\n",
       "      <td>drama</td>\n",
       "      <td>His father has died, he hasn't spoken with hi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Er nu zhai (1955)</td>\n",
       "      <td>drama</td>\n",
       "      <td>Before he was known internationally as a mart...</td>\n",
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       "   ID                          TITLE          GENRE  \\\n",
       "0   1          Edgar's Lunch (1998)       thriller    \n",
       "1   2      La guerra de papá (1977)         comedy    \n",
       "2   3   Off the Beaten Track (2010)    documentary    \n",
       "3   4        Meu Amigo Hindu (2015)          drama    \n",
       "4   5             Er nu zhai (1955)          drama    \n",
       "\n",
       "                                         DESCRIPTION  \n",
       "0   L.R. Brane loves his life - his car, his apar...  \n",
       "1   Spain, March 1964: Quico is a very naughty ch...  \n",
       "2   One year in the life of Albin and his family ...  \n",
       "3   His father has died, he hasn't spoken with hi...  \n",
       "4   Before he was known internationally as a mart...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "(54200, 4)\n"
     ]
    }
   ],
   "source": [
    "train_data = pd.read_csv(r\"C:\\Users\\abhin\\Downloads\\archive (4)\\Genre Classification Dataset\\train_data.txt\",sep=':::',header = None, names=['ID', 'TITLE', 'GENRE', 'DESCRIPTION'])\n",
    "display(train_data.head())\n",
    "print(train_data.shape)\n",
    "\n",
    "\n",
    "test_data = pd.read_csv(r\"C:\\Users\\abhin\\Downloads\\archive (4)\\Genre Classification Dataset\\test_data.txt\",sep=':::',header=None, names=['ID', 'TITLE', 'GENRE', 'DESCRIPTION'])\n",
    "print(display(test_data.head()))\n",
    "print(test_data.shape)\n",
    "\n",
    "test_solution_data = pd.read_csv(r\"C:\\Users\\abhin\\Downloads\\archive (4)\\Genre Classification Dataset\\test_data_solution.txt\",sep=':::', names=['ID', 'TITLE', 'GENRE', 'DESCRIPTION'])\n",
    "print(display(test_solution_data.head()))\n",
    "print(test_solution_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0a58cf5",
   "metadata": {},
   "source": [
    "# Performing some data Visualization method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "93dd680c",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(19,7))\n",
    "sns.countplot(y=train_data['GENRE'], order = train_data['GENRE'].value_counts().index)\n",
    "plt.title('Number of Movies per Genre')\n",
    "plt.xlabel('Number of Movies')\n",
    "plt.ylabel('Genre')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "461be4bf",
   "metadata": {},
   "source": [
    "# Bar Representation of Genre vs Description length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10dca3dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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09NNPp5QaxpPqeeedlwqFQhoxYkRKKaXHH388XXrppWmrrbZK3/72t9Mnn3ySUvr/v1A0Zl9+QV/07/Ovf/0rbbTRRumhhx5KKdXfE87iX3/atGlVrnvqqadS9+7d0wMPPFAvX7umxo8fn1ZdddV01VVXpZRSeuCBB1KhUEhnnnlmiSernS//AjFhwoTUpk2bdMkll5RooqomTpyYjjjiiLTKKqukn/3sZ8XlDeG5ZHEXX3xxOvzww9PTTz+dBg4cmNZcc8307LPPppQazy/Oi+7HSy+9lDp37pzOP//8NHr06LT11lunHj16FJ/nc3vrrbfSfffdly6++OKU0hePjSeeeCJ17tw57b333mnu3Lklmas6lvUcPGXKlDRw4MD0q1/9qsryUnnggQfST37yk3ThhRemrbfeukG/Vp5++ulp4403TmeddVZq1qxZ8TVk5MiRqWvXrg06SFZUVCyxbMiQIWm//farsqzUj4e6UFlZmebOnZsOOOCAdP7556eUUpo8eXJaa6210nHHHVfi6ZbtscceS126dEnnnHNO+s9//pNSSunII49Mhx56aProo49KPN3/f04pLy9PP//5z9Mf//jHlFJK8+bNS3vvvXfaaqut0qhRo0oaJJf1nPHBBx+kzp07F2du6BZ//n7zzTerXPfoo4+mDTbYIN1///0ppYb7MztlypS01157paOPPjql1LB+X7niiivSXnvtlVL6Ys6777477bPPPmnHHXdM119/fYN+bf86i3+fv/zzsMMOO6Qf/OAHuUf6Wv/73//S5ptvXvw7aNasWaldu3bplFNOKfFktXP11VcX71NKKR122GGpR48eDWanjIMPPrjkQXLmzJlp9OjRacMNN0zbb799cXl9B0kxssQ+//zzdNJJJxUL9DvvvJPWW2+99KMf/Sitt956aeutt24wQXLRD+r06dPT5MmTiy+6n376adp1113Taqutll599dWUUsN4Qb7ssstSkyZN0qWXXlpc9vrrr6fNN988ffvb305Tp04tLr///vuLf3Q1Jos/ud5zzz3F/wu3yKBBg9LWW29dL197n332Sddee23x8+HDh6c+ffqks846q/i9/+yzz9J2222Xzj333JRS6R83o0aNSnvvvXdKKaX3338/rb322mnw4MHF6ydOnFiq0ZbbqFGjUu/evdPBBx+cPvroo+KL3DnnnJP69++f3nvvvRJP+IX//e9/6bjjjksbbrhhuvDCC4vLS/mYWPxrjxkzJnXr1i09//zzKaUvXrR/8IMfpDXXXDO9+OKLxfXOP//8VF5enn3WujR+/Ph01113pbPOOqu4bOHChal///6pe/fu2YPkpEmTUsuWLdNKK62UzjvvvCrXPfHEE2n11VdP++67b4P/o+Wyyy5LN910U5o8eXJx2QUXXJA6d+6cpkyZklIq3S/EzzzzTGrTpk26/fbbU0pfvFb26dOnwb5Wvvfee2mbbbZJTZo0SSeeeGKV6xYPkg3l+W2RM844I+21115VHquVlZVp5513TkcccURKacnnvLfffjvNnj0765y1tfjvi5WVlWnLLbdMt99+e/roo4/SmmuuWeV/Ot12223p0UcfLdWoVVRWVqbKysp0yy23pDZt2qSdd9459erVK915553pvvvuS127dk233XZbSqk0e+8v/vzw9NNPp+233z5tueWWVfaunjFjRho0aFDacsst0+jRo7M/Ly76H3SLjBkzJk2YMKEYRmfNmpV+9rOfpUMOOSSlVPrf+77K4rM9+uijqVAopJ/97GfFx0BKKR199NGpV69exb1oS2XxWUeNGpWGDBmSPv300+KRA/fcc09aaaWV0hNPPFGqEatY9Dq4aG/8O+64Iw0YMCDttttu6cADD0x77713WmedddKHH35Y4klr7+qrr07Dhg2r8j/IHnroobTpppsWf3ZLHcMW+eijj9LGG2+cKioq0jvvvLPE8/Wjjz66wv2O++6776ZCoZAKhUI64IAD0htvvJFee+21tPvuu6cTTzwx23P5on/jt99+Oz311FPplVdeqfK8cdBBB5U8SE6bNi3dcsstabXVVqvyP0jr83skRpbYggUL0r/+9a/05ptvps8++yxtscUW6cgjj0wpffED36JFi7T99tunRx55pKRzLvoBuvvuu9OAAQPSWmutlfbff//029/+NqX0xf+d3WOPPdJqq62WXnvttZRSaX7B+PKT+cUXX5wKhcIyg+STTz6ZTj311NStW7cVMjR9lcW/FzfffHPabbfd0korrZROPfXU4mHHzzzzTOrbt29xr5K6ejGcPXt2OuaYY1LTpk3TLbfcklL64sm3rKwsbbvttmmNNdZIv/rVr9Lbb7+dbrnlltS2bdsGsfv/pZdemg444ID07rvvprXWWiv9/Oc/Lz6OH3rooTR06NAqf5g3RIsfOnzbbbelBx98MF155ZVpq622Sj169EiDBw9OL774Ynr22WdTjx490tixY1NK+X4RWvR1nn322XT99dena6+9tvic8fHHH6djjjkmbbnllumiiy4q3qbUf6zce++96eSTTy5G80X/h/vzzz9Pu+66a1p99dXTlVdemfr375969erVoA4xr6m5c+emjTbaqPhL2+KPi0VBcv311896aPG8efPSX/7yl7TWWmst9W0dnnrqqdS0adN00EEHZZupOhZ/3H722Wfp0EMPTS1btky77bZb+uUvf5kWLFiQZs6cmXbbbbd06qn/r70zD6gx+//4eSoq7ZtW2kmLbNFCsiQtaLMvichOSJbsexjrEI19GbvssmenLNk1QqRClFJJy/v3R7975j7dzPjO1L03c17/zLj3qc597nnO8j6fz/sTIbHNyJMnTxATE0MPRYVfFwiS0jZXPnz4EKqqqnBwcICLiwtOnTrFe3/dunUwNjbGyJEjkZqaKqFWivLy5Uvcv38fQPn4IWDixIkwNTWl91bQd9LT0zF9+nQ8fPhQ/I39l+zfvx9DhgzBH3/8gcDAQERERMDU1BRDhgyhfT0rKwuDBg1CTEyMxMd54M/v5NOnT/D29kavXr1w9uxZ6OvrIyoqCk2bNoWRkZHEngHBfUtISMCzZ8/g4OAAjuPw66+/8q7Lz89Hjx49YGlpiZ07d4qtfQJLpOPHjwMoPzRQUFCAo6MjfH196frk5s2bqFWrFq5fvy62tv0bQkNDER4ejrNnz8LT0xN2dnZo2bIljh49igMHDsDPzw8nTpwAIPm9T3R0NObOnQtDQ0M4OTkhODgYz58/R15eHgYOHIiIiAh8+/ZNYs9bWVkZ7t+/D2NjY2RmZgIAgoKCUL9+fQwaNAiXL18GUL6vtLa2ppHJNZlevXrB1dUVKioqmD9/Pg0yatKkidRlXt27d4+mMJuZmWHw4MG0rzx69AjBwcEigS3SRmV9e8GCBfD390erVq3Qp08fjB8/HqGhoejXr59YMpyEbfnq1asHKysrqKmpYezYsbz72a9fP9jY2GDNmjViOeC4e/cufv/9d+zatYtG/efl5eH3339HvXr10KtXL3ptde1tmBgpZirr6IIQ4SNHjqBFixZ48eIFAODEiRNo06YNXFxcpGIxffz4cSgqKmLJkiW4evUqhg4dCnl5eSpmpKWloVu3buA4TmJeaoL7++nTJzoYLV26VCRCMiUlBa6urqhXrx4sLCxETnJrOsL9bOrUqXBzc8OjR4+wf/9+dOrUCQ0aNICXlxd2794NMzMzTJ06tcrb8OXLF0yZMgUyMjLYvn07gPKBrKioCFFRUfDz84OysjL69OkDDQ0NmjIsyQ3Jjh07YGBggLp16yI0NJT33vDhwzFgwACJn37/FQkJCWjatCnWrVuHsLAwcBxHF3sAsHLlSgQGBqJ27dqYM2cODAwMYGtrK/YTuH379kFTUxONGzdGgwYNUKtWLaxbtw5A+eZ7xIgRcHFxwezZs8XarsrIysqCvr4+OI6jkUvAn/20rKwMffv2haOjI7y9vWn0hzRsrP8pb968gZubG8zMzJCcnAzgzzGltLQUTZs2RePGjastWquyebKkpAR79+6FoqIiL1pZwK1bt0RS6CSJ8Pd/+vRpOm4kJSVh2bJlMDU1RYsWLRAaGgp/f3/06NGDitziPBjIzc2FgoICOI5DSEiIyDUpKSlo27at1M2VBQUFuHPnDm7fvo2AgAA4OjrSVEkBv/zyC6ytraUmikN4IX/mzBm4u7vTg5grV67A0dERPXv2pJFAZWVliIyMhKmpaY2JDhL03devX6N+/fpYv349gPJILY7j4OzszBNhp06dCnNzc7rulSS3b99Gly5daJTmixcvoKenh7i4OKSkpGD8+PHo2LEjOI7DxIkTJTbGC+xjbt26hXfv3qF169Zo1aoVTp48yRs78vPzMWDAALHe2ydPnmDYsGHQ0NDAsWPHAJTbEmzfvh1eXl7Q09NDz549sX//fvTr1w/BwcFSGfUrfB+vXr0KIyMjeuCRnZ2N5ORk9OjRA+3atYOhoSGNmJR0W+fPnw9tbW08e/YMX79+xYoVK+Dl5QVNTU2EhYXBwcEBTk5OyMnJEflZcWNjY0MzkYDy7BhhJk+eDHt7+0ptLaQZ4XRs4TEiPz8fa9asgYeHBwwNDTFu3DgMHToUWlpaEj9sunjxIlavXk3/3b17d3Ach6CgIN51kydPRrNmzaTCquJHOHz4MJ13b9y4gQEDBuDEiRM4fPgwxo4dCxUVFXAcR4PAqpu4uDhoaGjQe71u3TooKyujV69evAP+bt26wcHBgT6n1cWBAwdQr1492NraomXLlqhfvz7NcBUIkqampvD29q7WdjAxUowIBv34+HgsWrQIGzdu5J2ubty4EQ0bNqQdITIyErNmzZLIRC34m6WlpSgrK0NBQQF69uxJPX+ys7NhYGCAMWPG8H4uPT0dPXv2lOim8OrVq2jRogXOnTtHJ4IlS5aICJJAuXgjLRuVqqKyNB7hU5esrCw8ffoUnp6e6NGjBziOg6KiYpWdUAtPxHfv3kWfPn0gIyND0/8E5Ofn4/z58/D394eBgQHs7Oyq5O//CIJ7dP/+fVy7dg23b9+m7w0YMAAcx+HYsWP48OEDPnz4gIiICOjo6NBnU1p58+YNRo4cCX19faipqdGNrnCBj+LiYhw8eBDe3t7Q1NREy5YtxbqpevDgAXR0dLBp0ybk5eUhNzcXc+fOhZycHPXNff36NQYMGICOHTuKvZhDZYvz5ORkNG/eHDY2NjzBQ1hcyMzMpD8rbf56f4WwyCjc7vT0dFhbW6N58+Y01VX42upKfxX8jUuXLmHJkiUYOXIkLl++TEX13bt3Q0FBASNHjqyWv18VVDwMMjMzw8qVK3npkkVFRVi8eDGCg4Np+pBAuBE3SUlJqFu3Lho3bvzdMS4xMVFq5sqKp/OXL1/+riAprZHsf/zxB+Tk5ODl5UVT9zZv3oy2bdvC0NAQvr6+6NChAzQ0NKg1RE3h7NmzWLNmDYYNG8abexYvXgwZGRkMGjQIISEhGDBgANTU1HD37l3JNRb8iJUePXqgdu3amDVrFpKTk7FlyxYMHDgQb9++RV5eHu7du4du3brRuVXcvH79Gr///jtvLfv+/Xs4OjqidevWOHHihEQEJuHorhcvXmDo0KFQU1NDbGwsgD/v8b59+zB58mQoKiqiTp06MDIyomO7NB7gbdmyBaNGjaK+vhXv7YMHDxATE4PGjRtDV1dXov7nN2/exPDhw6kPvDDbtm3DxIkT6cFqxSh4cfHhwwf6PZ89exYODg4ihWtOnjyJESNGQFNTs0aNfe/fv+f1j02bNmH69OlYunQp78A/PT0d169fh5ubG5o3bw6O4xAdHQ1AMtYP+/fvh5aWFoKDg+lYfO/ePXTs2BH6+vo4evQotm3bRsU7YVsiaeb169eQl5dHq1atsG3bNpSUlCAqKoqXvbR582aoqKjAwcGh2sfN3Nxc9O/fn46VqampMDc3R/v27WFpaQlfX1/ePryiOF/VXLhwAZqamnTdefnyZXAcB21tbdy6dQtAuSC5ZcsW2NjYVOuBKBMjxczRo0ehrKyMJk2aoGHDhmjSpAmePHkCoDztSFtbG02aNIGjoyNUVVUl8tBv2bIFffr0EREBWrdujePHj+P169cwNDTE0KFD6XuxsbG4du0aAMlXwc3JyYGFhQWcnZ0RHx8vEiG5atUqqVz0VDU7duxA9+7d4e/vLyI0CLh//z6WL1+OunXr0rTYqro3kydPRosWLeDr6wstLS3IyMjwKvkJ/s6XL1+QlJQECwsLrFq1qkr+9o9w4MABaGhowMDAAI0aNeKZM3fr1g26urowMDCAs7MzjI2NpX5RJJhIf/31V9SpUwe2trY8s2ZBxJ7guvfv3yMxMZE+r+J6Jk6fPg1ra2tkZGTwJv9Zs2ahTp06tGJzRkYGMjIyxNImAcL34PPnzyguLqaR648ePYKNjQ28vLx4/mYVnytp8f35EYSrOg8dOhSurq5YtmwZtQVJT09Ho0aN0Lx5cxqdL47Pt3//ftSpUweenp6wsbGBkZERBg0aRCPu9+zZA1VVVeo5Jq1Mnz4d2trauHbtGvWErIzTp0/D398fgYGByM/PF0uqkHBkLwDcuXMHSkpKCAgI4AnNkpzPly9fji1btoi8LmhTeno6jSi5dOkSAgMD0bp1axqRBUjP8yhox8uXL+kmIyUlBVpaWujYsSNev34NoFwYXrBgAfr374/p06dLVbTvjzJ8+HBwHMcrgCRg586dGDBgANzd3TFx4kSJiXrA9/vG1q1bYWVlhYCAAPTt2xcDBgzAvn37xNw6Uf744w+oqKhAR0eHHtwJDjgEgmS7du1w+PBhsfb7W7duoU2bNrzDluTkZCpIHj16VORnnj59ioULF8LMzEwkC0VaePXqFTp37gxlZWVabEngKVpxvfTkyRO0a9eOFlgTN4cOHYK9vT0sLS3x/PlzAOXjZEWbleTkZPTq1Qvu7u5iPzS9du0a5OXlMWvWLCq6DBo0CH5+fsjNzQVQLsAsXboUHTt2rFHp2X369IGbmxudO2fMmEHXMBzHoVOnTiKpzUVFRXj27BmCg4NhZGQkEY/AxMREaGho8Pz9gfJ+/vjxY/Tp0wf169eHnZ0dvLy8kJSUJPY2/iiV7WFyc3MRHBwMV1dXeHl5IT09HV5eXrziaY8ePRJZD1UXZ86cwdOnT/Hp0yc0btyYZlutXbsWysrK8PHxqXYLpLKyMnz58gURERE0+ywtLQ3169dHUFAQfHx8oKGhQfWnL1++0OezumBipJiZOHEiPQWKj49H165dYWRkRAfd27dvY9KkSZgyZYrYF2mCh3HGjBlwcHDA8OHD6QYqJycHbm5uGD9+PMzNzRESEkIf2g8fPmDAgAHYuHGj2Bf+3/t7nz9/RpMmTdCyZUueIPnLL7/wTqF+ZsLCwqCtrQ0TExOa5lBxIypg2bJl0NPTq7KQ8D179kBJSQnXr19HQUEBnj59itGjR0NGRoZ6SAoWdUB55F6PHj0wYcKEKvn7f0VZWRk+f/6Mtm3bYvv27UhISMCyZcugq6vLS/88fvw4tm7dipMnT9aYFDmgfHFx8+ZNjB49Gi1btsQvv/zytz8jDsHhyJEjSEpKwvnz5yEjI0M35IINTHp6OkxMTKifqbgRfiYWLVoEDw8PNG/eHEFBQVSIfvDgAaytreHt7U19jWoaFcfM2NhYyMvLY+jQoejVqxeaN2+OVq1aYc+ePQDKvxd7e3uYmZlRwaQ6SUlJgYWFBTZs2EDbGhMTg/bt22PIkCHIyspCaWkptm/fDgMDA7EL1j9KamoqTZ0Eyv2vEhMTMXHiRJw6dQqfP3/mXX/y5EkoKSlV6wZMcD/Pnj2LCRMmoFevXli/fj1NxU9ISECdOnUQGBgo8cIv7969Q2hoKG2bAMF48erVK6ioqPA2/5cvX0anTp3g7u7OSwWWNIL7HhsbC0dHR6xatYpGbKakpEBTUxPu7u5Skar8TxB8PuHN9PTp0yEjI4OtW7eKXC8QQSR5KCycqTRnzhyMGTMG+/bto2ugxMREzJ49G40bNwbHcVBVVZX4WPPmzRtMmzYNampqvMg2YUHSysoKnp6eYrWTEV7LCYu2woKk4ICgrKyMfv9FRUVYs2YN3NzcpCKCubL9xLlz5+Dt7Q01NTU65wtfJ/zZZ8yYgcaNG0vEyufSpUvo2rUr5OXlsXHjRl77BAiet9TUVMjLy9OoVXGxZ88eyMnJwdvbG926dcOyZcvw+vVrKCkp8QIRsrOzqz09tapJTEyEqqoq/P39kZiYCG9vbyQkJAAoF1jNzc3RoUOHSr0WBQVjhA/RxMWWLVvQoUMHFBYW0v4hCFwQkJKSgi9fvkisoMqPIDyX3L9/H48fP6ZrqeLiYpw5cwZdu3aFmpoaPD094eDgICL6VfU+SPDsJSUl4e7du/j27RvNFNiyZQtat25Ns0127twJW1tbeHl5VXtEpIDz58/j+vXr+Pz5M1q2bEkPhQQ2IBzHic2Wh4mR1YygM7558wbp6ekICAigHotAeRqrj48PDA0N6YMjqUgEQdRJaWkpoqKi4OTkhNDQUBohuXv3bsjIyMDJyYn3c1OnToWFhYVYF9IClV7YzFsQYSogJycHjRs3RtOmTXHp0iU6WK1evVqip/HVwfdE2UWLFqF+/foYN24cXUgLXyvoaw8fPoSdnV2VRWEsW7YMzs7OvNc+fPiAgQMHQlZWFkeOHBH5GX9/f3h5eaG4uLhaRG3B7ywsLMSnT5/Qr18/mh6Uk5ODDRs2QEdHB8OGDavyvy1OBJ8zLS0Nw4YNQ8uWLbFy5Ur6/qJFi8QuNNy4cQMcxyEmJgZ5eXlo3bo1/P39eWmfWVlZsLKywuHDh8XatopMmzYNWlpaWL16NcaPHw9PT0+oqKjQyO+HDx+icePGaNWqVY1JV/keHz58gJOTE5YuXUpfS0hIwJAhQ+Dk5EQ/85s3b+Ds7FzlY/zKlStFxOcHDx7AwMBAxDYiOjoahoaGNI2opKSk2k9r/w1v376FpqYmNmzYgNu3byMoKAiNGzeGra0tZGVlaT8XXkTb2tpWuxh/8OBByMvLY9CgQWjTpg1atWoFOzs72pcTExOhpqaGTp06iUV8/isEC/dr165hw4YN9PW3b99CQ0MDw4YNE5krrl27JhUFdioSGxsLBQUFrFixQuRw648//oCmpia8vb2l3grke1y8eBFeXl68zcuoUaOgoKCAQ4cO8a6VlmjVAwcOQEFBAT169IC1tTWaNGmCtm3b0mjO3NxcvHjxAh4eHtDQ0OBVwhUHld2n9+/fUy9u4aI1AkHyw4cPYm+ngBcvXoDjOJ4PoLAgKShqU1paSse9xMREaGtrS7zfC4/DWVlZvLnuzp078PHxQZMmTeicWFGQBMqrardr167aD0K+J+InJiaiW7duaNGiBc8WqbI1v6OjI3bs2FGt7RQgfPDWq1cvWFtb48qVK7CwsMDw4cPRtWtX6Onp1ZhiRhUR3NP79+9DSUkJHTp0QOfOnXlR4YKU3I4dO9IUWAHfvn2DmZkZNm/eLM5mAwDmzZuHevXqUWs24b4i6OvSjnCbJ0+eDAsLCxgYGEBHRwcRERG853Ht2rVo1qwZOI6jBSmrs00HDhyAnp4eFi5cyFtPrV69Go0bN6Z77smTJ2Pp0qXVfihz+fJltG7dmvfalStX0KpVK96hdEBAAIYOHSqiq1QXTIwUAwcOHICuri6aN28OdXV1kYXZ3bt34evrC0VFRYkVfjl69Cj09PToBFZaWopFixbByckJQ4cOpYPqggULaCGH0NBQDBw4UOyeP7/99huGDh1K0wYLCgqgqakJR0dHETHt8+fPMDQ0hLu7O+Li4n7K9Gzhz/Tp0ydkZWXxBO3IyEg0bdoUU6dOpeJPxUXutGnTULt2bV6xk3+DwIdD2IgfKDcTFpy4CHt73blzBy1atKj2fnT48GG0bdsWfn5+sLS0xPv37+l7nz9/xoYNG2BgYCBi2iyNVEwrEE7JSUxMRFpaGtLT0zF8+HA0b94cgwYNgre3N/T09MR64PH06VMsWLAACxYsoK8JvNG6deuG58+fIzk5GdOnT4eBgYFEi3WlpqaicePGvIiB1NRU9OvXD3Xr1qXjy/3799GvX78aNZ4sXLhQxCfq48ePMDY2pil/Am7dugVbW1ueAFTVfebNmzcYMGCASORbYmIijIyMqO+VcOqfiYkJZs6cWaXtqAoq6welpaWYOnUq1NXVUadOHYwbN46mK3bs2BFhYWG861esWAFZWdlqFRIyMzPRpEkTXrR0fHw8/P390bRpU6SkpAAo//4NDQ0lFhEuGMfKysqQn5+P/v37w87OjqaSXb9+HVFRUZVG/UgjGRkZcHBwoNE/X79+RVZWFvbu3UsjZf744w9wHIfAwECRyJSagKC6ua+vL28eHzFiBBQVFcUehfV3vH79Gg0aNOBFZAnWBx06dOAddJSVlYndL1XQty9evIglS5YgODgY586dQ1ZWFr5+/Ypp06ZBVVWVZ8UiPFaKs40CSkpKEBcXh7p16yIwMJC+npycjNDQUGhpaYl4h69cuRJaWlpiiwaqjIp2MY6Ojqhbty48PDywdetWlJaW4sqVK/D390ezZs1ERLOysjJkZWWhcePG1R5JJDzOHTt2DFu3bsVvv/1Go9YSExMREBAAV1dXHDx4sNLPKCgmJbDEqU5u3ryJnj170gjpz58/o127dti8eTMKCwsxevRoWhQqLCysRvltCyMsSGppaaFOnTrUi164qFfDhg1hb2/PC4g5evQo5OTkJLL/379/P8zNzREbG8urFVFSUoKAgACR9G1pZunSpdDS0sKFCxcQHx+Pbdu20YNX4Tk1KSkJ69evr/a+duHCBSgrKyMmJkbE9u7YsWOwtLRE+/bt4e7uLrasmKSkJJibm/MCVPbs2QOO4+gYPG3aNPj7+1OLKnHAxMhqQvDFP3/+HPXr18fKlSsRHR0Nb29vqKuri0TUJCQkoFevXiIbM3Fx5coV9OvXD3Z2djQyQ1iQDA0NpYr9/v374evrCy8vL4wbN07sUYYzZsyAnZ0dwsPDaYTXy5cvYWBggA4dOogM6IIiLR4eHmJ9uMSB8MJk7ty5aN++PXR0dDBx4kSekfa0adPQrFkzREZGVloFbdu2bf9oEfW9DWBqaipatmyJESNG8MSlhIQEDB48GJs2beJNBILNWVUjvAC7cuUKVFVVMXToUAwYMABKSkoIDg7mXf/582esWrUKFhYWVSbMVjeC7034JE5bWxsXLlwAUB4hOW/ePHTu3Jm30a2uzbvwPX/y5Anc3d2hq6vLM9wvLS3Fli1b4ObmBo7j0KhRI5iYmPAKCUmCx48fQ15enmcAL/DOcXBw4KUOC5BmEURAfn4+fvnlF5FTzszMTLi4uGDOnDkoKSnhfRZvb2/4+flVaxSTYDy+fv06du/eTV9v3749rK2teT6LBQUFcHFx4aWgSQPC9yw5ORkJCQm8KIM7d+7wxJmSkhLqzSnMrVu3cP/+/Wpta0pKCurWrYsTJ07Q18rKynD+/Hm0aNGCWmgA/KJX4kbQ5wSC0LNnzzB48GA4OTnxokekJcLu7/j8+TPs7e2xbt06FBYWIjIyEi4uLtDT04OcnBzNFHj58mWN9IgUfA9PnjyBkZERfHx8eH1+7Nix4DiuUu9ASZGUlARdXV2aSgmUi3kHDhyAvb09PSyVpDhy4MABqKqq0oNEe3t79OrVCwUFBUhPT8f06dOhqakpMpaIA+Fxr7CwkJd6f/r0aWhqavIEyT/++AM9evRAp06d6HXFxcWYP39+tY97P8qcOXOgq6uLffv2ISMjA/b29rCxsaGiXXx8PAICAmBoaFhp9WNxFhudMGECDAwMYGVlBWNjYxgYGOD8+fMAygXAwMBAtGvXDjt37hT52a9fv1Z7xJNgTDh58iSCgoKgq6uLYcOG4cmTJ1i1ahVGjBiB9+/fo6ioCLdu3cLw4cNrXMba99Z+jx49goqKCrp27UojbAX349WrV+jZsyfvZ+/evSu2zMIHDx7g+vXrvHTxtm3bolGjRti3bx+ys7Px8eNHTJs2DYaGhhLTJP5XysrKEBAQIHLgfunSJXAc9916BFU1vldW8X3cuHHo0aMH7zXhQ/3du3dj1KhRGDRoULVXUxes5/Ly8hAeHo4uXbrQw+ecnBy0bdsWtWvXRuvWraGkpCR2b1AmRlYjFy5cwMaNGzF+/Hj62tu3b+Hn58czBxUgicW/sDn83bt3ERQUBGtraxFB0tHREaGhoVQwEniiSCqlfNmyZXBwcEBYWBg1a379+jXq1q2L9u3b8ybaiIgIXLhwocb6Mf0IkZGR0NbWxrZt27Bjxw60bNkSbdq04UXhTp8+HUZGRlVWsVV4Mo2JiUFYWBiCgoLw+++/o7S0FBs3boSjoyN69uyJS5cu4e7du/Dy8uINztWVkl2Zufju3buxcOFCAOX99+DBg1BWVkZISAjv2tzcXKnwL/oRLl++zKu2evDgQcjIyGDdunUA+FGTpaWlYq32fO7cOWzfvh3h4eGoV68eOnToIDLGlZaW4uLFi7hz506lInl1UlmaVX5+Plq3bo1JkyaJ+D41b96cV+SopiF4JuLj4zFu3Dj6+owZM6CoqIijR4/yxnM/P79q+7zCffHr1680Mk8ghr179w6NGzeGlZUVYmNjcfr0aUydOhWamppiieb4UYT70LRp09CwYUOoq6ujWbNmWLt2Le80/MuXL7hz5w4VFQTPoLDnWHWTlZWFVq1aISoqSmTubtKkCc8zV1JCn+DvHjt2DN27d6dRSE+fPkVQUBAcHR2rNWK3Knnz5g0+ffqEgoICBAUFoUmTJlBWVka3bt2wevVqZGZmwtPTEwMHDqyR0ZD379+nKfGC7+3x48cwMDBA586deWvc8PBwsaV8/Qhv3rxBo0aNRCr5FhcXo169etTYX1IkJyfD0tKSRq1//PgR8vLyvKrVWVlZCAsLQ7169ZCdnS2RZ3bevHno1q0b2rVrh+vXr9M2CATJ7t2702vfvHlD5yFxFYz4EcrKypCZmQknJycaTXjx4kUoKSmJZA2cOXMGU6ZMqXTcEddn2bFjB7S1tXH37l1kZWXh06dPCAgIgJaWFl0LXrt2De3bt+eN6YD4xPWKa73Tp0/D2NgYvr6+GDhwIJo1ayaRtOSqQniPceHCBezZswdJSUnUDuvu3btQUlKCn58fzXao2D/EfdCxf/9+qKmpwdTUFEpKSpgxYwZ9z9vbG40aNYK6ujqcnJygr68v9UU7BZSVlaGwsBB2dnZUbykrK6Nz6sSJE+Hm5ob8/PxqWS9MnToVvr6+vMj0srIydOrUiRaoqbgnFQiBlb1X1Zw+fRpWVlaIj49HcXEx3rx5AyMjI0RGRtK2vnr1CkuWLMH8+fMlciDKxMhqpE+fPuA4Di4uLryNrUCQrFu3rtjMQSvjzp07aNiwIU+ku3HjxncFSScnJwwfPpx3AiDuhYRgcMnKykJISAgaNGiA8ePH0wjJ1NRUGBgYwNXVFRMmTMCoUaOgrq4udqFDnJw4cQKNGjXCjRs3AJSfBNWqVQvNmzeHs7MzLxph/fr1VT4Yh4eHo27duoiMjMSAAQNgamqKUaNGAQDWrVsHLy8vcBwHS0tLNGvWTKSqc1UzZcoUWjQCKE9dV1ZWBsdxCA8Pp68XFxdTQbKm+kQ+ePAAjRs3xt69ewGUL1L/TmwWxzN77do1cByHEydOIC8vDwsXLoSdnR3GjBlD/VskKSIIT/75+fk8T6Np06bB3t4eGzdupH01Pz8fTk5OP1QMSFoQTnUVZvHixahXrx7vkGzIkCFQVFREREQElixZgrFjx0JFRaVKfbwqRrsB5YL1H3/8gXv37qF3795o06YNLZyTlZUFT09PmJmZwdjYGPb29lK7OJ4zZw709PRw7NgxfPv2DZ07d4aZmRlmz55NozsPHjwILy8vtGvXjvar6noGKh48CPd3wfx+4cIF3uvdunXD/Pnzq6U9/yuHDh1CnTp1MH36dF7EwLNnzxAUFAQnJyepTh8rKyvDkydPoKGhQSMyXrx4gdjYWGzevJnnYeXv74/p06dLqqk/TMUqwrm5uZCTk0NgYKCIHcvjx49Rp04d9O7dm65LJMX3xP4vX77A09MTrq6uvHFFsIkU9mMUBxXbeOvWLdjb26OsrAzJycmoX78+hgwZQt+/ffs2SktL8f79e57djDhZuXIldHR0MHnyZLRv3x516tTBpk2bqBB1+vRp6OjooH379ryfk3Q2QWXVsN+/f49GjRqhqKgIR48ehbKyMj3Uzc/Px5YtW0T2EeJaw1Rs6/z58+Hl5SXynoeHB5o2bUrbJVwlWJycOnWKVjAODw+nB0oCv9OAgACxF8ioLiZMmABtbW3UrVsXFhYWaNOmDR1P7t69CxUVFQQGBkr8EDU7Oxt2dnbYsmUL7ty5g/Xr16NWrVoYM2YMvebKlStYv349Dh48KFG7pL/je3163rx5MDY2pv1NMKbOnDlTZAyqSl6+fEmju4Xn9okTJ8LU1JQe2AnanZ6ejmnTplV7NKSAMWPGgOM4ODs7IzIyEgkJCbh8+TLU1NR4WTKA5A6HmBhZjRQVFWHMmDFQUFDgiSNAeWfs0KEDTE1NJZYOVVBQQCPAhCeE7wmSUVFRaNSoEcLCwiS6mPj9999hb28PPz8/NGjQAAoKChg/fjw9aXj79i0CAwPRpk0bODs71/giE39HUlIS5s2bB6C8ArSWlhY2bdqEmzdvQkdHp1Kj6qpaRJ0/fx4WFhY05P/QoUNQUFAQiTa4ffs27t+/T/tNdZ4I9u3bl04MgoH10qVLqF+/Pjp06MCLViopKUFsbCw4jsPYsWOrrU1VwfeeufHjx8PY2JiX0ipJnjx5gr179/K8/YqKijB79mw4Ojpi7NixdMKW9KZE0CYzMzOEhobSfhMSEgJbW1t07twZU6dORZs2bWBjY1Pj/IwyMzOp3cCBAwewe/du5OXlYcmSJbCxseH1+Xnz5sHDw4N+7uoYN9+9ewcDAwOcPn0ae/fuhaysLC1qkJiYiB49eqB169ZUkATKI+JevHhRLTYO/xThBdu9e/fg5ORED33Onj0LZWVltGvXDiYmJpg3bx6+fPmCnJwcnDt3jo691dGXBCmngufq+PHj8Pf3R2BgIC+a0NXVFdbW1pg5cyZ2796NcePGQVVVVWKe1cIIItZWrFjBe13wmZ4+fYrBgwejUaNGlVZqliaGDh0KXV1dREdHi0Raf/jwAVOnToW2trZURQxWRNDXhdt/8+ZN5OTk4MqVK1BSUsKAAQNEigYJfOD69OkjkTWuIGNG8LydOnUKISEhGDJkCLWwycjIgKWlJdq0aYNVq1bhwoULmDhxItTV1SUmHgiKHJw5cwZNmzZFWloajI2NERISQp+B69evY8yYMWLP9qk4Xy9dupTnSzh+/HjUqlULGzdupN/5kSNH0LlzZ4nP9cII+/Lu3bsXycnJKCgogJmZGXr16gU1NTXeoe7Tp0/Rrl07OldJCkEgSHh4OMzMzOjrgnsdFxcHY2Njkcgmcd77Q4cOQVFRETNnzsSiRYvg4+MDFRUVuj/7+vUrHj16hMGDB0NNTU1ixZb+KcJz/5kzZ2Bvb4/Lly8jKysLhw4dQrdu3dCgQQOa5pqUlASO40TSh8XJqVOnMHXqVIwYMYI3ju/Zswe1a9eW+r2PgIr9+ObNmzhz5gwNJnjw4AF8fHzg4eFBD8Hy8vLg4eGBAQMGVEubhPfSZ86cgbu7O7UbuHLlCs0OFD6wi4yMhKmpabV7cgvvtz08PNC2bVvMmzcPzZs3x5AhQ9CvXz/06dNHKizJmBhZRVSW8geUd4a+fftCXV2d50MGlG8UJVX1UfihTk9Ph7GxMby9velrlQmSJSUlWLFihUQnj8ePH0NHRwcxMTE0wiYyMhI2Nja8CMnCwkJ8+/aNmjr/LDx8+JAOHBMnTqQick5ODgoLC9G5c2fMnTuX9sG2bdvC0tKy2iab3bt3w9HREQCwb98+qKio0NPk3NxcnD17VkT4rK7T5IonOnFxcTh8+DBdqF25cgVqamro06cPLzqruLgYx48fl+oNoTAZGRm8e/js2TM4Ojpi165dACQr8GVnZ9Mo1IrpQV+/fsXs2bPRunVrhISESMS/VfjeLFmyBFpaWli4cCGWLVsGCwsLtG3bFmfOnAFQbj3Qv39/dO7cGcOGDav2aLaqpKysDHl5edDR0cHIkSOxYcMGcByHbdu2ASiPOly0aBGsra15Y0N2djby8/NFhJOqJDw8HMrKypCTk+PZhAB/CpJt2rTheUhKExW//6KiIuzatQv5+fm4dOkSdHV1qfDXtm1bmJiYYMKECby5qDqe0evXr4PjOCrinT59GoqKiujfvz/8/PzAcRwmTJhArx8+fDhat24NMzMztGnTRqxF6P6KZ8+ewdzcnIrhlUW2PX36FCNGjJCqjez3IgrGjBkDbW1trF+/nm6aDh48iIEDB8LY2Fhqo32FycjIgJmZGW7duoVTp05BUVERFy9eBFBuFVK7dm0RQTI8PByHDx+WiKgXFxcHjuOoF+exY8egqKiIrl27ok2bNuA4jkY+ZmZmwt/fHzY2NjAxMUGzZs3E+p388ccf1A5j//79aNOmDTIyMlBUVARzc/NKD0onTpwIV1fXSn3Kqgvh/n3s2DFs2bIFAQEBIgLd+PHjIS8vz4uQFCANgmRCQgJMTEwQGxuLiRMnQlVVlYq6a9euhba2NgICAgCUf+aCggJ4e3vD3d1d7HP/mTNnaDbGiBEjqMf5vXv3YGFhgcmTJ/Ouv3jxIho0aECFeHHz8eNHuLq60uIYmZmZ0NfXx8iRIyu9XpoOGP9Xtm/fjlGjRolkVd28eRMeHh4IDg6mh+4pKSkSO8guKyvD0qVLwXEcGjRoIOJrumfPHigpKSE0NFQi7ftRJk2aRC3AgPJxRl9fH8rKymjcuDG1+Llw4QK6deuGOnXqoHnz5rC1tYWdnV21Z+QB5WO5nJwcvLy86NpEUKzT0NAQvr6+6NChA89aq7o4c+YMVq5cSddRe/fuxaBBg3Du3DkkJibCzs4OOjo6vHlSkjAxsgoQdO5z585h9OjR6N69Oy/FDyhP2VZXV6cGw5JG0OYrV64gLi4OW7duhZmZGc/PTyBICj/okub69evQ1dUVidqZNm0a5OXlER4ezvNi+FkoKyvDo0ePoKWlhaioKAwbNgwcx/GMv7Ozs2FpaYk1a9YAKE9P7tOnD28AryoEi7ItW7aga9euNK1FuLLjsWPHEBYWJtYUeeHPOWjQIHAch2PHjlEvj0uXLkFVVVVEkJRmhCfP48ePg+M4jBw5km4CysrK0LNnT7i7u0uqiTwuX74MY2NjuLi4UOFc2B8wIiIC7u7uEj2Nu337NpYtW8bzVH316hU6deoENzc3XuVU4XG8pkVGXrlyBfLy8pCVlRUx8M7KysLixYvpQY64EBiKy8vLi6SIAOWCZO/evWFra8v7fqSBM2fO0IqHY8eOpSKBQGQaNGgQRo0aRcfH4OBgNGzYEMOHD6/29JeCggIsWrQItWrVwtq1a7F37166KRQIpvLy8rwq3vn5+cjIyJCqsTApKQmysrL08FZYjExISMC1a9cAQCo9Fs+dO4fDhw+LtE1gexAdHY2ioiK8ePECGzZsqDE+1s+fP8fgwYOhrq6O2rVr02g4wee8fPky6tSpg8DAQCxevBgTJ05E3bp1JSY2fPr0CcOHD4eioiJOnjyJ6OhoekgqsA0RHhMFBfSeP38uVr9oQSG3WrVqoUuXLrwDI6B8DWVubg5vb28kJycjPj6eCmjiLPoiPHZNmjQJioqKsLa2BsdxGD58uMhcHh4eLnUFiwQ8fPgQo0aNgra2NtTV1XnpqK9evcKECROgrq6OwMBABAcHo23btrC1ta32wn8VycvLQ3BwMBwcHODu7g4VFRWa1pmbm0urfo8aNQrv37/Ho0eP4O3tjQ4dOkhM9BUcWiQnJyMtLQ1GRkY8a4GDBw/yosGkwS/0nyKI/HZ0dBSpYj9nzhxYWlqKzKviXD8K39vPnz9j7dq1kJWVxZIlS0Su3bZtG3R1dXnrXmni69evsLOzQ6tWrXD48GGcOHEC9vb2OHfuHJKTkxEQEAB7e3vExMSgrKwMHz9+RGxsLObNm4eYmBh636v6/gvu8cuXL2kl6pSUFGhpaaFjx440yj0pKQkLFixA//79MX36dLF4MkZFRcHKygqdOnXCjh07kJubCz8/P0RFRQEo7xMC+z1pKFLExMgq4tChQ1BXV0ePHj0wfvx4yMjIICIigjfR9e/fHxzHIT4+XmLtFB6gzp07B47jcPr0aWRnZ+P3339HvXr1eILkzZs34e/vD0dHR+Tl5Ul88rhx4wYMDQ1x5coVAHyTZDMzM+jr62PatGk1TjT4UZYtWwZ1dXUoKCjg7NmzAP70v3n//j18fHzg5+eH5cuXw8PDA87OziKG4f+E7/3sy5cvaSSccJXbwsJCeHp6YsCAAWLvM3v37qUnsb169YKGhgaOHDnCEyS1tLTQpUsXqY+cTU1NpcLGsmXL8PLlS6xbtw7BwcFQUFBAr169sH//fjx+/BgmJia8lClx8L3vVuBH0rt3b3qPBdcWFRWJNaKjIgI/S1lZWWzfvh3An+L6mzdvoKamVmnlPUmPff8rxcXFyMjIgIyMDGRkZBAWFkbN1QVkZWVhyZIl0NPT4xVGqGoE9y4nJwdAeRGdyZMnQ15envqdCt/f+/fvY+DAgVIT+SYwSNfT00Pz5s3Rv39/qKuriwgCXbt2xeDBg+n806tXL5w6deq7/p1VTWFhIRYvXgwZGRnUq1dPxD92165dqF27ttQUYhLcjzt37uDy5cvIz89Hfn4+vLy84OvrK3LoOHToUAQFBUm00vdf4e/vDzk5OeodKkxgYCDq16+PFStW4OvXrzVuPNmzZw84joOSkhKuXr0KoHzcFHzOW7duoV27drC3t0eTJk0kHmmbnZ2NkSNHQk5ODjY2NnScAf70QZeRkeEdoEqCsrIyhIaGguM4WmlawJcvX3Dy5ElYWVlBX18fDRs2hKOjo8Tu7fXr1+Hl5YUrV67gy5cvtCjikiVLRISM1atXS9U6XPh5i4qKAsdxMDExoZlfAt69e4fDhw/D09MTwcHBmDVrVrWJGX/Hx48f0bRpU3Ach4iICN57Hz58QFRUFLWqsrKyQqtWrcQumgrz9u1btGvXDrt27YKxsTGGDh1K11YvX77EoEGDcOrUKbG369/yvbF64MCB0NbWxrp163jCo+CZFWTqSYKioiJeu/Py8miE5PLly0Wul6YDSWEE/fjLly/o2LEj2rVrh6lTp/JsoEpLS9G/f3/Y29tjw4YNlWb2VHVUs+DexsbGwtHREatWraIHWSkpKdDU1IS7u7tEDxxv3ryJmTNn0kCtefPmQV5enmY1fPv2jedxKUmYGFkF3L59G8bGxoiOjgZQviFQVVUFx3EIDg7mpa4MGTJEKnyZ3r59iw0bNmDBggX0tYKCgkoFycTERKr6SwOtW7dG06ZNeYPnhw8fEBgYiIkTJ0p0AqguBANpbGwstLW1oauri6ioKBHPiUOHDsHLywu2trbw9PSskoWJ8IQWExODCRMmYMOGDfQ0Zf/+/VBRUUFISAhOnjyJEydOwN3dHXZ2dryKseLg6dOnaNCgAc98PjAwUESQPHfuHOrXry9V/boi165dQ61atXDmzBmMHj0aCgoKNOq3pKQEt27dQlBQEOzs7FCvXj3o6urSCDdxLEQF3+n58+cRGRmJvn374sCBAzQtLz4+nkahVhQkxUnFe1FUVISVK1dCSUmJpq2WlpbS6zw8PHjRYzWdly9f4vz585CRkcGIESNEBMn8/HysWrWq2lK7BN/50aNH4ePjw9v8hYWFQV5envfab7/9huTkZKlNh9fQ0EDt2rWxb98++prgM44dOxb29vbo0aMHnJ2d0ahRI/o5qvOZFH6uvn37hpUrV0JeXp6KjsLv7969GxzH8RbzkkDQpoMHD6Ju3bqIioqih7fbt2+Ho6MjvL29cfjwYZw/fx5hYWHQ0NCgkanSSo8ePaCpqcmbb4DyqvUaGhpo0KCBWKPv/i2CfvvgwQNs2bIFw4cPh7q6OuLi4gCUCzSCeV5g8SDJzyfc13NzcxEREcFLyxau4iwQpSp6XIuznaWlpZg3bx6CgoJQt27dSlNai4uLcfv2bTx//pzneS1Otm/fDn9/f/To0YM3lkVGRqJevXpYvHhxpZFV0iBICrc3Ly8PCQkJOHfuHMaMGYOGDRuKeKpXhiSK1WRlZaF///4IDAyEi4uLiIhUXFyMoqIinDlzBgkJCdXqR/w9Kt4XgSVIz549ea9HRESgcePG1e6TV9UIfx/fvn0TOQjz9/dHo0aNsHDhQrx48QIpKSno0KED2rZtK7EDp9OnT6N79+7w9vbG0KFD6etfv37FkiVLwHFcpQfu0khZWRntY3l5eWjXrh04joO/v7/IdQMGDECLFi2wZMkSkXT06iA2NhYKCgpYsWKFSL/+448/oKmpCW9v7yotBPkjVOx3CQkJaNq0Kfr06QMVFRW4u7tL3d6XiZH/EOEv+8SJE7Qa4uvXr2FsbIzx48fj+PHjkJWVRVhYmFSlDqempoLjOKiqqmLRokW89woKCrB7926YmZnBw8NDQi388/4+fPgQR44cwblz5+hG5c2bN7CysoK9vT3i4uJw7do1TJ06FS1btqxRi/wfoeIGNj8/n0a/GBkZYc6cOZUOKllZWbxqqlXBjBkzoK2tjdatW8PW1hbt2rWjkSuCdKL69evDwcEBfn5+YvfYu3//PiIjIzFkyBCUlpbyJiOBIHns2DG6mJCEZ+GPIDxWDBw4ECoqKlBSUqLFKYQrQRYUFCArKwsRERFo1aoVlJWVxVqw6cCBAzRCs2XLlrCxsUGnTp2ol6lwFGp1+hB+D+HnZ//+/Th//jy+ffuGwsJCekosnLZSVFQEa2vrGlHdtjKEi01U3LQePnwYMjIyGD16NLVOWLBgAS9iqLqIjY2FvLw8li1bJiImCQTJuXPnYvjw4ahTp45U+rcWFhYiJSUFpqam0NfXh7Ozs0iEUklJCcLCwtC7d28MHDhQLGOg4Du/ffs2rly5ItK/K9t0HDhwgJqsS5K4uDgoKysjOjpaJEo9Li4OAQEBqF27NqysrNCsWTOJR9sJI7jvr1+/RlpaGm/t4efnBw0NDRw+fJim8E+ZMgUXLlyQ2lS4ighHMws+AwAkJycjODgYGhoaNDsDKB9fxJk6XBmCNj948IAWkMjOzsbYsWNpxKrwdWVlZVixYoXYnwXB37958ybi4+ORk5OD4uJibNiwgfr8CiPpSrxAeUVaXV1dmJmZ0fRDAdOnT4epqSkiIyOlppCeAOE1wPz58zF69GgaIHL//n2EhoaiYcOG1HMbKI/qFKRSilNQEm7rgQMHqMCRmZmJIUOGwNHRkVfYq6ysTMQKQZyHeKdOnUKPHj0QEhKC3377DUD5fkPglbxq1SqsW7cOw4YNg4qKSo0rJlrRY7x79+6wsbHBhg0beAJTYGAgatWqBX19fQQGBqJbt250nyHuCNVDhw5BVVUVI0aMwPLly6Gvrw9vb28apPP161csW7YMHMdR6wppRfjeCawg8vPz4ePjAwsLC+zbt4+XgVBWVgYfHx8MHDiw2p/bjIwMODg4iFh97N27lxZ0/eOPP8BxHAIDAyVmKyO4h+/evcOqVatgY2MDbW1tiWanVQYTI/8F27Ztw9atW5Gfn4+kpCR8+/YNXbt2RXBwML5+/Ypv375RX5WRI0dKVaRHdHQ0ateujeDgYBFRpqCgAFu3boWtra1ETrEEg8iBAwegp6eHpk2bQl9fH927d6en8enp6XBzc4OxsTEMDAxgYmLCqwj+MyA8EMfHx+P8+fM8b4c5c+bAyMgICxYsoN+Tn58fb8NWVanZJSUlGDhwIL3Hx48fh4+PD1q0aIHbt28DKPdpevHiBTIyMqpcCP27dn758oX66rRt25a+J3yK2atXL3AcJ1LZXprw8/PD9OnT6cQlKDyirKyMEydOUIG1sqIOz58/R7du3RAZGckTLKuLN2/ewNbWlnqUAuWik5+fHzw9PWl6wvnz52FsbCz2kzjh+xMREQEDAwNs3bqVTsIFBQU0pdXHxwcjR45E165d0ahRI6n0o/s7BJ/32LFj6NChA2xsbODn54fr16/zKpvWrl0bXbt2RY8ePSAvL0+f3+oiPT0dLVq0oEb8AoT758yZM2FrawtnZ2epKujxvWfo27dvMDY2RqtWrXDv3r2/XPhW5xhYMbpw8eLF9NAuPz8fixcvltooiJKSEvTv3596in358gUPHjzA5MmTsXDhQiravXjxAmlpaVIncgDlBxwWFhbQ0dFBv379eBG+gYGBMDQ0hLe3N/z9/VGnTh2pEJX+F2JjY9G8eXPY2dkhKCiI2iykpKQgODgYqqqqiImJweTJk6GmpibRrBThZ8HIyAgrVqygc86nT58wcuRI1KpVS0SQlFQ7Dxw4AE1NTcyZM4eKezk5OdiwYQO0tbVpAbgZM2agffv2Yj1o/969Wb16NSwtLTF69GiR73rMmDHw8/OTWvuB8PBw6OvrIzo6mrevefjwIUJDQ2FiYoLp06fDy8sLFhYWYheRhO/b5MmToa+vj19++YUe0rx69QpDhw6Fs7MzoqKiUFxcjI4dO0osi+P06dOoXbs2evfujU6dOkFTU5OmkpeWlqJ3795wdHSEra0tAgMDJX5Q8W+YMmUKdHR0sGjRIiq8Dx48mAYIAMCAAQOgr6+PmJgYevBe0Uuyunn06BGsrKzomjwjIwOGhoaQl5eHg4MDXRsUFhZi9erVUnEg+T0qCsFBQUG0vfn5+ejQoQNatmyJQ4cO8dZYwnuf6hyLPn/+DHt7e6xbtw6FhYWIjIyEi4sL9PT0ICcnR4vCvHz5UiwekX+FsFVbVlaWxAon/xVMjPwfEXTuP/74AwoKCrzIwpycHLRs2ZKesBUWFmLs2LHYt2+fxB76kpIS2uaKYujatWvBcRwWL14s8nOFhYUS9ZA4d+4ctLW1qZ/P3r17oaKiAhcXFxw+fJhel5SUhAcPHoikHtZ0hAfRCRMmwNDQEMrKymjbti2WLl1K35szZw5MTEzg5+dHB8KqEFKEJ4J79+7h8ePHaN++PW9QPXv2LHx8fODg4FCpEFzdiznhKDCg/Jn09fWFnp4eL+1KeEEQFBQkFTYJ3+PSpUu0vXl5ecjOzsanT58wcOBAqKqqYv/+/X/plzZ27Fh07NhRLG39448/oKenh9OnT/NeP3DgAKysrGhVakCyUaiLFy+Gnp4erl+/zhsDBf1zyZIlUFBQgIuLC65evUqfH2lIL/tfOXLkCFRUVBAREYEzZ86gcePGcHFxwb59+6iQHRcXh549e6Jnz55i2SCkpKTA0NCQHiQBlS8S09PTpcq3SLiN+/btw4IFC3Du3DkqHHz69AnGxsZwdnZGQkICioqK4O3tzfPeFMfG/K+iC4Hy6NfatWtXOs9LkrKyMgQFBaFPnz64ePEihgwZAg8PD1hYWMDZ2RmdO3eWqv5QkZcvX8LS0hLr1q1DTEwMvLy84OrqyqsQv2DBAgQHB6N79+5Sn15ekRs3bkBDQ4OKw0ZGRnBxcaGHTKmpqRg3bhwMDAzQtGlTqTgMPnHiBJSUlPDrr7+KiHdFRUUIDQ1FnTp1xO6tXJEzZ85AWVkZGzduFMkY+PbtGzZv3gxlZWU0aNAAmpqaPNGjuhFet3348AEZGRm8NeXChQvRtGlThIWF8TzxAX7EqTSxf/9+6Orq8g66cnNzkZaWhtLSUmRkZGD69Olo0qQJfH19xe67KHy/5s6dC21tbSQkJFA/N8H7aWlpGDt2LExMTGBsbAw7OzuxC15AeTT43r17sXr1agDlmVjR0dGQlZXl+RFnZWUhLy9PLCmz1cW+fftgZmaGW7duASgfFzmOg7m5Ofr168cL/ujatStsbW2xa9cuifjRX7p0CZGRkQDK+4qZmRmGDRuGx48fQ1dXFz4+PjWmaJoAQTG0nTt38rLG8vLy0L59ezg4OCA2NlZkvV5dz+6bN2/w6dMnFBQUICgoCE2aNIGysjK6deuG1atXIzMzE56enrzMGMbfw8TI/wHBRvbWrVv45ZdfRAyF37x5A3V1dcyYMQMPHjzAtGnTYG5uLpEF9fPnz3kb7/PnzyMiIgIRERF48uQJfUhWrVoFjuMQFRUlNQuIwsJCjBgxgvq5paamwszMDD4+PnBzc0OLFi1oJeGfEeHv4caNG2jSpAlu3ryJy5cvY+TIkWjWrBlmz55Nr4mOjsaYMWMwdOjQKjfanjRpEtTV1WFubg41NTXcuHGD9/7Zs2fh6+sLY2NjsVbkEtyjEydOoHv37jQlKyUlBZ6enujQoQP27NlDr5fWggcCjhw5whPsVq5cia5du9LPBQB9+vSBqqoqYmNj6QJ07NixSEtLo/dj/PjxaNGihVhSol+8eAEbGxts3boVAH/yb9SoEcaMGVPtbfg7CgoK4OXlhYULFwIojy44efIkevTogbFjx9LojuXLl/MiyGqiEJmSkoKmTZvSNK78/HzUq1cPdevWRaNGjXDgwAG6KSgoKBDbJiYpKQlGRkZUjBSel27evEmLCEkTFavHampqwsbGBnp6eggNDaUbkE+fPsHS0hINGjSAtbU1bGxsxLoA/V504ZQpUzBv3jwqnM6YMQOamppSF124ceNG2NnZQUVFhRbjKisrw5IlS9CpUyeJVYWtjIrR6KmpqRgwYADtz/fu3UPv3r3RunVrniAJ1Lzx5PHjxzh48CDmzp1LX0tPT4eZmRmcnJx4G9o3b95IrGq2MIWFhejSpQv1Tc7Pz8fz588RFRWF6OhoOvb1798fOjo6ErENETB69GgEBQXRdiYmJmL06NGYNWsWTfF79uwZtm7dKtYiXsL9dPbs2XB1dYWamhpGjhyJ2NhY+t6CBQvQrFkzTJw4UUTckIZ9RMVx49dff0Xnzp0BlKdmL1q0CBYWFrCwsMC4ceOo6FdQUCDWrJ4ZM2bwojSzs7Ph4eFB11Rv3rzB+fPn0bt3b/z66694//49cnJycO3aNezcuVMiHpHPnz+HiooK9PX1eV6bX758QXR0NOTk5DB58mSxtaeqEe47X79+xdmzZ6mVz5EjR6Curo4tW7Zgx44dqF27NoKCgmhBVaDcM9jQ0FAs9jcVKSsro3uGXr16oU+fPigqKsLXr1/Rtm1bcByHdu3a1Zj56MCBAzAyMuIddH39+pVWlc/Pz0fHjh1hYmKCS5cuVWtbysrK8OTJE2hoaNB9wosXLxAbG4vNmzfzCsH4+/vXWKsnScHEyL9h6dKlmDJlCv13ZmYmunTpAkVFRfTr1w8Av/jBli1b6KmJnp6eRFLOtm7dCmtraxw9ehRAeZShrKwsunXrBg0NDTRt2hSbN2+mAs2qVasgLy/PE7gkTVJSEu7cuYPPnz+jWbNmGDx4MIDyFJw6derA1taWFyH5M7J371707dsXEydOpK+9efMG4eHhaNq0KebMmUNfF94A/5uJpqIQampqivPnz2PTpk1wd3eHjo4OTyADylO2J02aJHYbgoMHD0JZWRlTp07lpZomJyejc+fO6NChg0QWBP8rq1evhpmZGVauXEkFokOHDsHAwACDBw/mfba+fftCXV0dERERcHNzg4mJCf2+BcbZVTXmCC/Kvxdd7e/vD1NTU17UT0lJCTp16iSSlisJ8vPz0alTJwwdOhQbNmxA165d0bFjR3Ts2BHOzs7o1asXiouL8eXLF0RFRUllBNmPkpKSgqVLlyInJwfp6ekwNzfHqFGj8PXrV1haWqJVq1bYvn27RCIpWrdujSZNmvC854Dy1Ln+/ftLVBj4KxISEuDj40MPYX777Tc4Ozujb9++9DnLycnBihUreNVjxbXY/6voQhcXF3Tu3BmFhYUoKCiQWOGLv+Pp06d0jBOso8LCwuDl5SU1lR6BP8fD06dPIyQkBMOGDUO3bt141wgESTc3N8TExEiglf+ez58/Q11dHRzHYezYsbz3BIKkq6srnj17JhXCk4CSkhL4+/tjzJgxuH37NkaNGoWOHTvCwMAA9vb2GDBgAMrKypCdnU39xyTVzn79+qFz5864ePEiBgwYgE6dOsHW1hZubm7w9vYW+7N67do13r+nT58OHR0d/P777zhy5AicnJzQsmVLnvi0aNEiGBoaSqUFhIATJ05QHzeO4xASEgJjY2Mq7s2bNw8GBgYi9gniOAQ5cuQIgoODeXPFp0+fUK9ePYSFheHChQsIDAyEo6MjWrduDS0tLXqoKoy4193p6emYPn061NTUMGPGDN57+fn5iImJAcdxvP1JTWTq1KnYunUr0tPT8f79e3z8+BEuLi6IiooCUD7HW1hYoG7dupg/fz7ve+zfv7/Y6kS8e/cOX7584QVc5Ofnw9nZmfp4AsCIESMQHx8v1sONf8vy5cvh5OQEoDwFffHixWjYsCFUVFTooVNubi5Gjx4ttudg6NCh0NXVRXR0tMi69cOHD5g6dSq0tbWl0vdcmmFi5F9QUFCAWbNmoU6dOryq04cOHUKnTp2grq5ON+HC6dAPHz7EjRs3JFat6MOHD2jVqhVat26No0ePIjQ0FOvXr6ftDAgIgIODAzZu3EgHsMWLF0NTU1NqNiyCxcDhw4fRsmVLenp47tw5ODs7Y+jQoSIpIj8THz58gK+vL7S0tBAYGMh7Ly0tDeHh4WjRogUvJaIqWbZsGebPn4958+bR15KSkuDr6wt9fX0RQVKAuCaE1NRUWFpaighegr+fnJwMHx8fNG/eXOIpWX/Hly9fMGjQIDg5OWH58uU0QvLEiROoX78+Bg4cyBMkx48fj27duvFMkQXPS1VGYVdMu6vMrBwAWrVqBRMTE6xZswb79+9HeHg41NTUJO6TImD9+vVwdHSEhoYGZs6ciatXrwIoT//o06cPva6wsBCzZs2SygiyH6G4uJhGqYwcORI9e/ak/aFv376oVasW3N3dqy1S/6+Kjj1//hwNGzaEjY0N9u3bh3379mHs2LFQUVGRWi+p7du3IyAgAIGBgbyNxtatW+Hs7CySoiVA3JvDv4ou7Nixo1QJRsJU1q5Hjx5h0qRJUFVVlcp+ERcXB1lZWeorJysrS61kBCQlJcHb2xudO3cWEd9rCjdv3oSFhQVat25NPXYF31dGRgbU1dXRuXNnqUtDmzFjBmxsbCAvL48ePXpg165dyM/PR3h4OHx9fSXSJuF+Lvj/q1evwsjICDo6OujVqxddo0RHR8PR0VGsmRytW7fG8OHD6Rh39uxZNGrUiAqUly5dQu3atdGyZUs4ODjwMk62bdsmVV74AsrKynDnzh1oamrS9Np169ahZ8+e2LhxI52X3r59iyZNmkisMJZg3Xbw4EG6Xtq2bRu0tLSgpqaGiIgIWiRqyJAh6N27t0TaWZGMjAzMmDED8vLyImL0ly9fsHnzZqn2I6wMYQH69OnTUFZWpn0HKI+Cs7CwoEE+r1+/xqBBg7Bt2zb6s+IeDw8ePIgmTZrA0tISERERdM1eXFwMKysrdOnSBTdv3sT48eNhZGRECxdKO4Jx8vjx4zAwMIC3tzcsLCzQr18/LF++HNu3bwfHcSJ7lKoei763dhozZgy0tbWxfv16OscfPHgQAwcOhLGxsVT5ntcUmBj5N7x//x5LliyBhoYGZs2aRV+Pi4tDhw4d4ODgQEOGS0pKJJ5WJBgMs7Ky4OLigrZt28LZ2RmXL1+m1+Tn56N79+5o0aIFNm3aRNNXpHEDvnfvXhgZGdEw+KlTp2Ls2LE/fdVsoHxTHxQUBENDQ2zYsIH3XlpaGoYOHVotVcPy8/PRpUsXcByHgQMH8t5LSkqCn5+fSOi8uElKSoKlpSWv4mFFv6LHjx8jMDBQqkVrweSZn5+P4OBgtGrVCr/88gsVJI8fP16pICm8yRWOXKwqLl26BI7jaMrvmTNnvmtWDgC9e/dGs2bNYGpqCicnJ6mYjIXvSVpamohps4eHB4YNG8a77uvXr1JzIPNXCNr86tUrZGZmikT6+Pn5ISwsjF43btw4nDx5UqQKalW3p7KiYwJP0ffv38PDwwNWVlYwMzNDmzZtpLq65owZM6Cnpwdzc3OR+7Zt2za4urrCy8sLz58/l1AL/+SvogulNeq0Infv3oWXlxeaN28ulf0iNTUVO3fuxK+//goAePLkCUaNGoVGjRrRw14BDx48kEjxv3+C4NktLCxEWVkZjZy+efMmNDU1ERAQQAvXCK7NzMyUqmI8wtHeT58+xYULFwD8+SwMHz4c3bt3x9evX8Uqzgv+1rlz5xAZGYlu3bphz549KCgowOfPn2lFXsF1kyZNQseOHcVm7bR27VoYGhrS9UZhYSFevHhBvfBPnjwJbW1tbNq0CY8ePaL+oNHR0bzfI42CJAB4enrC3d1dRCwqLS1FQUEBOnfuDDc3N4kWq7l79y7s7OzQvXt3GrX25s0b3vNVWlqKjh07SlX6c2ZmJmbOnAkVFRWpjo79X4mJiUF0dDTPmx8o34/Z2toiIiICsbGx8Pb2hoeHx3ezhqqbx48fQ19fH8uWLcOkSZPoekQw9l2/fh36+vowMTGBiYmJVKzJfxTBPf3w4QO2b9+OwMBAbN68me7lnj59CkdHR7EEPJw7dw6HDx8WEZoFh+nR0dEoKirCixcvsGHDhhrnySktMDHyO5SWltIHIikpCTNnzgTHcVi+fDm95tixY/Dy8kKrVq3ookISYqTw3xSe5HJzc9GxY0dwHEeNhgUUFhaid+/esLCwkErPLgEJCQlwdXWFjY0NnJ2doays/N2ovJqK8Pf3/PlzPH78mArDL1++RP/+/eHs7IyNGzfyfu79+/dVYhhe2c++efMGISEhUFJSEknhefDgAdq2bYuuXbv+47/5b7l9+zYUFRVx/vx5+prgc1y6dIluZqUtcqMyhAXJgQMHVipIGhsbY9CgQbyTWqD6/JkEqTgaGhpYvXo19uzZU6lZeXh4OO9n0tPTpeqgoOL9ycnJQXx8PDw9PWFra0ujQSR9iPRPEPjpWFhYwM3NjUZQfP36FT4+PnB2dqZ+surq6tUujvxV0TFhv7FXr14hIyODChzSwD+pHrtu3ToMGzZMon2nJkYX/hW3bt2SWEbJXyHwSdPT06MFCoFyX78xY8agYcOGNTItW9B/Tp48iQEDBsDFxQXjxo2jG9qbN29CS0urUkFSGigrK6Pz54sXLzBu3Die9/KrV68QEREBNTU1iRUQOnjwIFRVVTFo0CCMGDEC+vr6CAwM5BVdvHbtGiZPngxVVVWxrm8XL16M5s2bo7S0FKNHj8aSJUtQXFyMz58/o6ioCD4+Ppg1axYd4zp16gQrKyuMGTNGqvpBxTFYEFl67NgxtGrViq6bSkpKUFhYiOXLl6Ndu3Zo1qyZRIvVCFi3bh3c3NzQq1cvngiZm5uLixcvwsfHB3Z2dlLn9ScQJDU1NWn6ck3m48ePsLKyAsdx1PNc+PtaunQpGjVqRA9TBX1HEs/CvXv3MGzYMPrvEydOwNPTE506daLBR7m5uUhKSqLR7TUFwbMoEOcF/y4pKUFeXh68vb3Fdojg7+8POTk5HDt2TGQ/GRgYiPr162PFihViP+j62WBi5N9w4MABODg4ICAgAGpqapCTk+OZeh87dgxdunRBw4YNJeoRkJycTE/n9+zZAx8fHxQXFyM7Oxtt27aFg4MDjh8/znt4CwsLMXDgQKlX8s+ePYtFixZhwoQJP50Pg/DgNWPGDFhbW8Pc3JyeeBUUFODZs2d0oyBcJbqy3/G/ItwfcnNzeRFWOTk5CAwMhIqKiogIlpKSItFNeHp6OpydnTF48GCR6tghISEIDg5GUVGR1E4O37t3X758QVBQkIggeeLECcjLy/PS5qsbwUJTXV0dxsbGP4VZ+eXLl9GxY0dexUxpjer4K16/fg0LCwusX78eMTExGDhwILS0tHDkyBEA5T5CzZs3R5MmTWBra1vtaWg1uejYv6keW9nvkCTSHl34PaR1nBbwVz5pz549Q1hYGHR0dEQK19QEYmNjoaioiNmzZ+OXX36Br68vFBQUaMTvrVu3oKurC3d3d4mlnVfsH7dv3+Zl8rx69QoGBgY8kSw+Ph4eHh6ws7OTWBruy5cvYW1tTTNbSktLoaioyPOhT0tLQ5cuXdCsWTOxH7S/fv0aampqaNy4MWRlZXl//8uXL7C2tqYeynl5eejTpw92795Nxztpe25Pnz7NG4uzs7NhZWWF0aNH09cKCgqwa9cujBs3Tuwev8IRvAUFBTzhfN26dWjTpg169epF92QXLlxA165deZYI0rZeyczMRHh4OOrVq4dPnz5JXZ/4Kypr68OHD9GpUyfUr1+fZkQIrwdevHiBFy9e0H4mboH4woULmDVrFsLDw2ktBQEnTpxA586d4eXlRTNSagqC70JwX/fv3w+O42jGh+C5dXNzE/shQo8ePaCpqYkjR47wnuEZM2ZAQ0MDDRo0kKogjJoIEyP/gvv376NOnTpYu3YtPn36hEePHiEiIgKqqqo8QfLgwYO8EHtxU1JSgkWLFoHjOAwZMgQcx/EWxQLjXRcXFxFBUpqpSZPav2XhwoXQ1dWlE4ifnx/q1q1LF4dPnjzBwIEDYWlpST1L/i3C93fOnDlo06YNdHR00KNHD+oLlJubi8DAQKipqSEhIUHkd0iyL23atAkWFhYICgrC0aNHcfPmTYwbNw4aGhrUOkEaEb5n8fHx2LVrFxISEmhEUF5eHhUkly9fTm0Url27JhGz8nnz5qFOnTo/jVn5o0ePJLaQ/DcIP69v3rzBiBEj6L+fP3+OoUOHQk1NDYcOHQJQvpl8//692BZJP1J0TCCWSgs/S/VYYaQ1urCm81c+aY8fP8bkyZOlImX/f+HTp09o164dteN49+4d9PX1MXLkSN51V69ehZmZmYjVhTioOFY/fPgQxsbG9DnMzs6GkZGRiOUGUG6nVF3WFD/C8+fP0axZMxQXF+PZs2cwNDRESEgIfV+QOpmSkiJ2PzfBWsLf3x8cx6Fz5868atJZWVkIDAyEt7c35s6di06dOqFly5b0+5C2fcTly5dRr149mJqa4tdff6WHMQcPHkSjRo1w/fr1Sn9OHGuqigdxCxYsQNu2bdGlSxesWbOGvi4QJHv37k377b1796R+vfLu3bsaG30HAPPnz8fixYtp33/y5AlatGgBKysrattTWZaVuJ+BI0eOoFatWnBwcICOjg4UFRVx7tw53jWnTp2Cs7MzAgICeM+ztPFX9+7o0aOoVasWz4/5w4cPiI6OxtSpU6vtEEFwr16/fo20tDTe2tnPzw8aGho4fPgwPZSbMmUKLly4gHfv3lVpO/6LMDHyLzh06BAaNGjAOw1OT0/HhAkTICMjw1uQStqXqaCggC4qBgwYQF8XpCsIe0geOnRIageo/xplZWX4+vUrPD09aRr2kSNHoKamhnXr1gH4cxK8f/8+5s6dW+WLp5kzZ0JLSwu//vorNm/ejLZt28LFxQUrV64EUC5m9+7dGxzHSUVkqnDf3blzJzp37gwFBQVYWVlJNAriRxBue0REBOrXrw8LCwtYW1sjKCiIbk7y8vIwcOBAODs7Y86cObzTOHELkj+LWXnFMU/aNlN/haDtp06dwrBhwxAUFAQ3NzdeXxAIktra2ti3b5/Y21iTio79jNVj2Zxe/fyVT1pNsASpSEZGBszNzfHo0SOkpaXByMgIQ4YMoe8fOHCAHrILDsXESUxMDBo1asSb/16+fAkbGxvk5OTwCoAI939Jje0Vn8GLFy/C0NAQ9+7dg5mZGYYMGULblpCQgP79+0v04PTjx48YO3YsduzYATU1NfTo0YOXHXP69Gn4+/ujefPm6NKli9hTmv+Kim3Iy8tDVlYWxo0bh06dOkFDQwNz5szBunXr0KFDB2zbtg2A+NdPu3fv5lllLV26FNra2pgyZQp69+4NNTU1XiHK6OhotG3bFh4eHrxUfmm45z8LwvcyOTkZffr0AcdxPG/+p0+fonnz5rC2tqaCpCTn2Pfv32PlypXUDiQ+Ph6BgYGwt7cXESTPnDkj0UOYv0P4/m/ZsgVjx45FaGgo9u7dC6B8T7p161aRnxOeg6rrOd6/fz8sLCygo6ODfv364cCBA/S9wMBAGBoawtvbG/7+/qhTp45UeSfXZJgY+RdcuXIFKioqvOIvQHmKiJKSEjiOw8KFCyXUOj7fvn3DkCFD4O3tDVVVVd5CWViQtLOzg7u7u8TFU0Y5ZWVl+PjxI8zNzZGSkoL4+HgoKytTg/DCwkJERUWJGPVWxUBcVlaGN2/eoEmTJnQSAMo3KMOGDYOjoyNu3rwJoDyVaMaMGVJzMis8meXl5SElJQXPnz+vEcVHACAqKgqGhoa4dOkSAGDChAlQVVWFj48PTYnPy8tD165dMWTIEIkLDT+rWXlNIi4uDnXq1EHHjh3h4uICjuN4lU2B8gibPn36wNjYGF++fJFIv5H2omM/Y/VYhvj4GXzSXr58iaKiImRlZaF9+/bYtm0bTExMMHToUNq/U1NTMWjQIInYK5SVlaG0tBTHjh2DjY0N2rRpQwXJy5cvU4FSGgWay5cvo02bNvTfHh4e4DgO/fr14103efJkODk5iRQfEzeC/cHNmzehqqqKHj168KI0c3NzkZ+fT+cSaVgDCn/vt27dEvHFe/PmDXbs2AFnZ2e4u7uD4zhYWloiKytL7G19+/YtZs2aBTU1NaxcuRJr1qzBqVOnAJRbIQnsboQFyaVLl2L48OFS2b9/JsLDw2FnZ4f+/fvD2toaMjIyNEocKBckW7ZsCU1NTYlZVADlPv2qqqqwtrbGmTNn6OtXr15Fz549YWdnR31+axKC9P7BgwcjPDwcHMfh119/lZif+MuXL2FpaYl169YhJiYGXl5ecHV15WWaLliwAMHBwejevbvEfIh/RpgY+Re8efMGrq6uGDZsGE8MSktLQ0BAAFasWCGWak4/SlFREXJycqhoIIhsE1BQUIC8vDwRI36G+Pje4qJbt25wcnKCkpISNm/eTF/PyMhAmzZtqsyLqqI48fHjR5iZmdHfL9iIfPr0CfXr18fs2bNFfoc0LEaBPz+LpIW6/5X09HR06dKFntQfO3YMqqqqCAkJQePGjeHl5UV9UgoLC6XGn+ln2ITXVN69e4dffvmFpq2kpaUhLCwMcnJyOHjwIO/aly9f8iIqxI00Fx372avHMsRDTfZJu3//Ppo3b04jOgRZD927d+ddFxERAVtbW4mkZgsKQn779g1nzpyBvb09nJyc8PXrVyQmJsLc3JyKaNKCcMFLc3NzKmqcPHkSLi4uaNasGW7fvo3jx49jwoQJUFFRkZpxUThaU1VVFT179qx0DpE2cSw8PByGhoaoU6cOunfvLpIRkJaWhrt372LAgAGoV68eXeeK+3NkZmZi1qxZ0NDQgLa2Nj2EBv70365VqxbPf7uihx6jajl48CCUlZVx8+ZNFBcX49OnT5g7dy5kZGR4e+cHDx5g0KBBEp3zBTZd8vLy2L17N++9a9euoW/fvjAyMuL1K2nnzJkzqF+/Pj0EPnnyJDiO4+1/gerd95SVlfF+f2pqKgYMGEC/63v37qF3795o3bq1yB5cWvbBPwtMjPwbtm7digYNGmDIkCE4ffo00tPTERERARcXF6mNwsrIyMDMmTOhqqpKB9VZs2bB19dXIqk2jHKEFxWvXr3iLfJ37doFc3NzuLm50ddyc3Ph6emJtm3bVslEKPz3BX3306dPsLGxoR5RpaWl9LoePXpg6NCh//rvMkSJj49HRkYGEhMTUa9ePeobNGXKFCgpKcHJyYlXCVdaFqQ1eRNeU/j111+RnJxM/52cnAyO42BkZMQrYJWTk4OwsDDIysry/A2lAWktOvazVI9lSJ6a6JMGlEfc161bF+PGjQNQPrd4enrC0NAQy5Ytw8qVKxEaGgoVFRWJFEE6f/48OI7Dzp07AZQLkqdPn4a9vT3c3Nxw8eJFeHl5YcOGDbhw4QKuX7+OM2fO4MCBA0hJSRF7ewXjgkAczcvLQ3h4OHx8fJCWloaSkhIcP34cnp6eUFFRgbW1NVxdXaWuwJRgzEtMTISGhgbc3d15RYKkAeEx+Pr167Czs8P169exb98++Pn5oU2bNjxrDeHre/fuDXd3d7G2V5j09HTMnz8f8vLyIhl1+fn52LBhA40ME8DmnOpjw4YN1M9V+D5PnjwZsrKyNB1aGEkKks+ePUPfvn1Rp04dnD17lvfepUuXMHjwYImMf/+U7du3w8fHB0B5arSysjItwpuTkyMWuy3B93769GmEhIRg2LBh6NatG+8agSDp5uZWaZ9gVA1MjPwOwoPTtm3b0LFjRygqKsLS0hLa2tpS7UsHlIsG8+bNA8dxaNGiBZSUlJCYmCjpZjFQPtlZW1tDXV0dERERSE1Nxbdv3zB37lxYW1ujUaNG6Nq1K1q1aoUmTZpUSRU9YTFr3rx58PX1pT5/hw8fhqysLJYsWUKvKSoqQosWLTBr1qx//DcZfy8izp49G35+fjQFbfny5WjXrh2mT58uNQJkRWrqJlzaKSsrQ3Z2NkxNTUV8aObMmQOO40T6RU5ODiZOnAiO43Ds2DFxN1kEad88/WzVYxmMv6Ji9oBgntm2bRuaNWtGi9KVlJQgJCQErVu3RuPGjdGzZ0/eYZg4+fz5MyZOnIjatWvj999/B/CnINm0aVNwHAd9fX20bt0axsbGMDc3h4mJCRo2bMg7xBEnp0+fhpWVFeLj41FcXIw3b97AyMiIVzUbKC++8/HjR4mlIf4dgnHu2rVrcHd3l6o1SMW23LhxA6GhofTfd+/epVFMAiEb+FMkPn/+PBo2bChRLz2B/7aioqKI3U1eXh5iY2NZxJWY2LNnD+Tk5KgnrmB/de3aNcjIyKBWrVr08FcS8/79+/dx5swZ7N27l/bhtLQ09O/fHxoaGiKCZE0LNNq+fTvat2+PLVu2QEVFhdZIAIDY2Fj06dNHLBYWcXFxkJWVhZeXFywsLCArK8srnAOUR7t7e3ujc+fOEk3X/5lhYuRfIDwAvX//Hnfu3MH58+epMb+0U1BQgPj4eCxdurTGVXn8WTl48CDMzMywe/durFy5EnXr1kWvXr2QnJyMkpISXL9+HePGjcPEiROxcuXKKq8aNnnyZOjp6WHLli28dP21a9eC4zh4eXmhV69ecHNzg7W1NVsY/QuEF8+bN2/GjBkzEBoaivPnz1PP1oiICLRo0YIuiPz8/PDrr7+yFJ3/IBU9uW7evMmLKpw1axZkZWV50ZFAeTXZqVOn1ogiQpLkZ6sey2B8j9zcXAB/9tn379/z3r937x4aNGjA2wAC5UJgfn6+xFOg8/LyEBERARkZGezatQtAuSB56tQpdOzYEZaWlnTznZ2djdLSUol60o4ZMwYcx8HZ2RmRkZFISEjA5cuXoaamhpMnT9LrasIYUrGN0tbmxYsXw8vLC15eXujfvz/vvXv37qFPnz5wdXUViWIaMmQITE1NJS4EC+xuVFVVaVGbirB1d9Xxvf6bnZ0NNzc3dO3alRdR+OzZM4wePRqzZs2CgoKCRHwB9+3bB21tbdjY2EBeXh5NmzbFjh07UFJSgjdv3iAoKAh169bljS3Syvfu/7Vr19CkSRORSOGCggJ4e3tj8ODB1S4Cp6amYufOnTQa+cmTJxg1ahQaNWpEozQFPHjwoMZoPzURJkb+DSwSgvFvqDgQnz17lue3d/HiRZiamqJnz57fnfSqKjXg5s2bMDEx4RkgC3Pt2jUMGTIEffv2xfjx4+mCiPmj/TvCw8NpWpynpycaNmyIiIgIAOWnsw4ODrC2toadnR2srKzofWdjz3+T0tJSfP36FVpaWmjVqhWePn1K35s+fXqlgiTrKz9GTa4ey2D8CL/99huvcv2dO3dgbW2NCRMm4OXLl3SsmDt3LnR0dPDixQtJNve75ObmigiSRUVFiIuLg62tLdzc3HiFGMU5BlY8LCwuLoaHhwfatm2LefPmoXnz5hgyZAj69euHPn364N27d2Jr28+G8NgbFRUFNTU1hIaGolmzZlBQUKg0isnDwwPDhg0D8Od3NXz4cFy/fl18Df8LhD0kpaUI6s+IcN/Zvn07Zs2ahcjISFy8eBFAuV9727Zt4erqirNnz+Ly5cvw9PSEr68vXrx4AUNDw0qrOlcnt2/fhpaWFjZv3oyMjAxkZ2fD398fjo6OtJjes2fPEBgYCFNTU15xKWlDuF2//fYbFi1axCsQtHjxYtStWxdjxoxBXFwc4uLi0KlTJzRu3Lja90HPnz+HiooK9PT06PwClN/bMWPGoGHDhiwtW4wwMZLBqCaEB9G1a9di+PDhcHV1xYwZM3jXXbx4EWZmZujXrx81860Ojhw5AnNzc16YuWCy/l4qODuh/XccO3YMJiYm1CLh6NGjkJOT45lQx8bGYuHChbxq5UwAZrx69QoGBgZo3749L0Jy+vTpUFRUFNmEMX6Mmlg9lsH4UWbMmAE7OzuEh4fj7du3ePXqFVavXo169erB2dkZffv2RVpaGp4+fYpu3bpRX3FpFNyzs7MrjZA8e/YsjI2N0alTJ4m17cyZM1i5ciX1fty7dy8GDRqEc+fOITExEXZ2dtDR0QHHcThy5IjE2vmzcOPGDaxatYoepv/xxx8YPXo0rKysRKKY/vjjD9qfpXUtlZmZifHjx8Pd3V1qxaSfhYkTJ0JTUxP+/v4wMTGBnZ0dwsLCUFZWhrNnz8LPz49WXG/ZsiVKSkpQWFgIa2trkQKB1c3OnTthY2NDI76Bck/RLl26wMHBgV73/Plz3rpFmpkxYwZUVFTQrl07KCkpoVOnTjRCefHixejQoQNkZWXh4uLCOwSuzmc3PT0d06dPh5qamsie/NmzZwgLC4OOjk6VFY9l/DVMjGQwqgHhxcXcuXOhoKCAgIAAKCgowNbWFnFxcbzr4+PjoaioWK0ejWfPnoW6ujpu3bpFXystLUVZWRkOHDggNZUdazIVJ8+NGzdS0/Tdu3dDVVWVikifP3+u1MReWhfPjOpDMF7k5eUB+LMPvH79GnXr1kW7du14guTEiROhra0t8ZSzmkpNrR7LYPwIy5Ytg4ODA8aPH083rFlZWVizZg1cXV2hr6+PiRMnwsrKilc0T1IIxr9nz54hMTER8fHx9L3c3FxMmjRJJELywoULEi3YEBUVBSsrK3Tq1Ak7duxAbm4u/Pz8aObL58+fsWjRIjg5OUnMy/Jn4fLly+A4DhoaGrh8+TJ9XRDFZGVlhQ0bNoj8nLSP3x8/fhTxdWX8e4TvZVxcHIyMjHDjxg0A5WurJUuWoGXLlpg2bRq97vHjx3j9+jX92UmTJsHS0pJXaLQ6uXz5MnJychATEwNTU1N6aFpQUAAAePv2LWrXro1Tp06JpT3/BuHn7tu3b+jVqxdu3LiBwsJCPHz4EMbGxmjTpg211vjy5QuePHnCex7EcQgs8HCVl5cX8XB9/PgxJk+ezCzuxAQTIxmMauTGjRsYOXIkLl26BKC8AqCzszP8/PxE0qXv3r1brULUs2fP0KRJE4wYMYKX+llcXAw3NzfexMz4d2zcuBE5OTlYvHgxAgICcPnyZaioqPAqJe7evRtTpkyhlc0Z/22OHj0KLy8vtGnTBjExMXSjnZqaSgVJ4eeWFRH6d9SE6rEMxv+CIKIkKysLISEhsLS0xPjx42nKtmCjt3z5cgwaNAgcx4HjuEqFeHEhaNOhQ4dgYWEBCwsL1K1bF3379qXel3l5eZg0aRLk5eVFLCokyc2bNzFz5kzIy8sjPDwc8+bNg7y8PE0D/fbtG/Lz8yXcyppPWloa5syZAyUlJZG05uTkZISFhUFdXR2HDx+WUAv/HUyIrBpCQkJExKMtW7bA3Nycd3D7+fNnTJkyBc2bNxdZf1+9ehUjRoyApqYm7ty5I5Z2nzlzBhzH4cKFC3jx4gWUlZUxadIk3jXJycmwsrKiRcekFWEh8smTJ7hx4wYGDx5M5yCg/LMYGxujbdu2lVpYiPMQQeDhqqKiIiJICuZTRvXDxEgGo5o4dOgQ7O3tYWtryzO+jY+Ph4uLC3x9fUUqogHVGxn322+/oUGDBujRowd+++037N+/H+3bt4e9vT1LR/wXCE+ey5YtA8dxePnyJZ4+fQolJSVwHMdLzS4sLISnpydCQkLYQpSBa9euQUFBAZMmTYKPjw/s7e0xZMgQWpQmNTUVhoaGaNasGZ49eybh1v48SHP1WAbjn/D777/D3t4efn5+aNCgARQUFHiCpIDc3Fxcu3aNd8AhKU6dOgVVVVWsX78e2dnZiI2NBcdx6N69O7KysgCUC5IjR46EpqYmPn/+LNF5s+LfTkhIQNOmTdGnTx+oqKjA3d0db9++lVDrfk4yMzO/W4n60aNHWLFiBcsq+Q9z48YNjBgxQkRAOnToEBo2bIhHjx4B+HPOf/HiBTiOE8lSe/r0KRYtWiS2dVZqaip2796N5cuX09c2b96MOnXqYMKECcjKyqIRfMbGxjVmXJk4cSIMDAxgZGQERUVFnDp1ijduJicnw9zcHHZ2dhI/BBYIkpqamryaDgzxwcRIBqOKqLiRPXfuHLp27QolJSVs376d996lS5fg6uqKNm3aiOWkS3gS2LNnD3r27AllZWW0bNlSbB4d/wVu3LiB1atX4+jRo/S16OhoqKmpISwsDHfu3MG5c+fg4eEhFpNmhvTz8uVLzJo1C0uXLqWvrV27Fs7Ozhg0aBAVJF++fAlLS0u8evVKUk39KZH26rEMxo/y+PFj6OjoICYmhkYVRkZGwsbGBuPHj+dtZKWln+fk5CA4OBgLFiwAUG5NYWpqisDAQOjq6qJLly60GviXL1+krhiM4D6+e/cOq1atgo2NDbS1tVnkejXwI5Wo2Rr2v4tgHb1p0yZqgZSWlgZdXV3069ePpgUD5b6idnZ2uHnzpsjvEVdgxvPnz6GqqgptbW2e7+nnz5+xbds2qKurw8jICA0bNoShoSFu374tlnb9E4TnkyNHjsDGxgZ79uxBbGwsLCws4ODggPv37/N+5vHjxwgICJCKZzYzMxPh4eGoV68ePn36xPZkYoaJkQxGFbNnzx46MN+6dQu+vr5wdnbGgQMHeNedPXsWw4YNE9umQHhwLSsrw7t378Tu0fEzI/A1UlZWxsmTJ+nrWVlZ2L59OwwMDGBgYICmTZsyAZgBoPx0uGXLlqhXr57I5mrt2rVwcnLCkCFD8ODBAwDsGWUwGN/n+vXr0NXVFfEinjZtGk0lfvnypUTaJrzOEaw5BFGPO3bswLNnz5CVlYWmTZtiyJAhAMrtTjiOg4eHh1TbmQg+W2lpKbKyssTmM/dfhFWiZlREeG+TkpICZ2dnNG/enPrgX7lyBYqKiggICMCOHTtw6dIldO7cGS1atJDo+ltQREVDQ0MkLRso9zQ8cOAATpw4gdevX0ughf87hw4dwoQJE7Bo0SL62qdPn2BiYoJWrVp9tzaBNOyD3r17xw6RJAQTIxmMKuTx48eoV68eOnfuTBeoV65cQWBgIFxdXb9bmU3cgmTFUx92CvTvSUtLw9y5c6GsrFxpIaLs7Gw8ePAAqampTABmUKZPnw5dXV34+vqKRP2sX78eVlZWGDVqFIqKiqQmmonBYEgfN27cgKGhIa5cuQLgz8rxAGBmZgZ9fX1MmzZNYvNOcnIyjQDas2cPunXrhs+fP9P27Ny5Ey4uLnTj/fvvv8PNzQ3W1tYiaeaM/y6sEjVDQGXf/9GjR+Hj48MTvxISEtCyZUuYmpqiUaNG6Nixo1QEBAhSsGVlZXn2AzXRr/DLly9o0KABOI7DgAEDeO9lZ2fDzMwMzs7OSExMlFALGdIKBwCEwWD8IwAQjuPov79+/UoOHTpEli5dSgwMDMjhw4eJjIwMuXr1KlmxYgX5+PEjCQkJIX369JFgqxnVRWZmJlm3bh1ZuHAhWbZsGRk9ejQhhJDi4mJSq1Yt3rVlZWVERkZGEs1kSBlz5swh+/fvJ126dCFjxowhurq69L3NmzeTdu3aERMTE8k1kMFg1AjatGlD8vPzSXx8PFFRUSGEEJKVlUWGDx9OTExMyKhRo4ixsbHY21VaWkqWLl1KpkyZQkJCQshvv/1GNm/eTIKCgug1M2fOJHv27CFPnz4lhBAyZcoUUqdOHTJ58mSR+ZPx3+bTp09EQ0ODcBwnsg5n/Df49u0bqV27NiGkfO9VUlJClJWVCSGEnDlzhixbtozk5OSQ9evXE3t7e5KTk0Py8/NJYWEhMTMzIzIyMqSkpITIyclJ8mOQ9+/fk7Vr15Lly5eTefPm0X1DTdwjfPjwgfTs2ZO8f/+eLF68mHh6etLPkJOTQwwMDEi/fv3Ihg0bJNxShjTBxEgGo4opKioihw4dIgsXLiT169enguS1a9fI9OnTiZWVFfn1118l3UxGNSG8sJg/fz4ZNWoUIURUuGb8txB8/48ePSIvXrwgSkpKxMLCgtSvX58QQkhkZCQ5ceIE6dy5Mxk7dixPkGQwGAwBfzWWpKWlEXd3dyIvL0+ioqKIiooKOXbsGDl79iyJi4sj6urqEmt3YWEh6devHzl06BDp378/2bp1KyGkXKiUlZUlDx8+JE5OTsTOzo6oqqqS69evkytXrhA7OzuJtZkh3bB11X+PEydOEC8vL/rv+fPnk5MnT5LCwkLi6OhIJk+eTOrVq0fOnz9PoqKiyOfPn8mGDRtExhFpEvsyMzNJdHQ0Wb16NZk8eTIJDw+XdJP+EuF7V/EZzMzMJN26dSMKCgpkypQpxMPDg76fn59PFBQUiKysrETazZBOmBjJYPxLYmJiyKVLl8j27dvpawJBcsaMGcTe3p7s2bOHyMjIkAcPHhAbGxupmQAZ1UNmZiZZv349WblyJZk0aRKZPHmypJvEkCCCxdrBgwfJyJEjib6+PsnMzCStW7cmAwcOpAvryMhIcvr0aeLk5EQiIyOJjo6OhFvOYDCkib8aS0JCQkinTp1IRkYG6dOnD3n58iUpLi4mtWvXJvv37yfNmzeXaNuLi4vJyJEjSXp6Orl8+TIvCkiQPXDt2jXy66+/ElVVVTJy5Ehia2sr0TYzGAzpYc+ePaR3795k1apVZNSoUWTJkiVkwYIFZNy4cURWVpasXbuWmJqakmXLlhFHR0cSFxdH1qxZQx4/fkzOnj1LTE1NJf0Rvsu7d+/IsmXLyO7du0lSUhJRV1eXSqFdWIhcs2YNSUpKIq9evSLjxo0jTZo0IYaGhiQjI4P4+voSRUVFMnXqVOLu7s77LIIDKAaDECZGMhj/M8IDcXFxMVm6dCnZtm0b6dChA1mzZg29DgAZNmwYiYmJIW3atCEXL16kg7E0ncgxqod3796RqKgo8uDBAxIXFyeViwqG+Dh//jzp2bMnmTNnDhk+fDjZt28fGTx4MLG3tydhYWHE39+fEELI+PHjyZ07d8i+ffuYGMlgMET43ljSuHFjMmnSJNK1a1dCCCH3798nMjIyRFtbm+jp6Um41eV8+/aNFBYWkuXLl5NffvmFzJs3j4wZM4a+n5WVRbS1tdkaicFgiJCenk5iYmLI8uXLyaxZs0hxcTFp1KgR8fHxIYSUjx/u7u5EVVWVnD59msjLy5NDhw6Rq1evksWLF0u9APb+/Xs6Zks7kydPJps2bSIhISHk9evXJDExkfj6+pLQ0FBiampKMjIySEBAAPn8+TPZtGkTadWqlaSbzJBSmBjJYPwPCC+Qb9++TczMzIiCggKJiYkhv/32G3FxcSHr1q2j169atYqcOXOGGBsbk1WrVrHF9X8M5mvEIKTcz2jChAlEUVGRLF26lLx+/Zq0a9eOWFtbk/z8fJKXl0dmz55NIyQ/fPjAhEgGgyHCX40lX758IV++fOGNJdKKIC1x+fLlZO7cuWTMmDFk9uzZ5MGDB2Tr1q1ESUlJ0k1kMBhSyLt370h0dDRZtWoV+fr1K9m1axfp1q0bKSoqIvLy8iQjI4M0bNiQLFiwgNokCWAReVXD9u3bycyZM8n+/ftJs2bNyJUrV4irqyuxtLQkPj4+ZMyYMcTY2Ji8ffuWzJo1i0RHR7P7zvguknVtZTBqEAComDhlyhRy9uxZMm7cONK7d28yYMAAUlZWRrZs2UJCQ0PJ2rVrSX5+Prl+/Tpxd3enJ//stP+/haamJiGE+Rr911FQUCChoaGktLSU5ObmEj8/P9KuXTvy22+/kUOHDpF+/fqRiIgIUlRURPz8/JgQyWAwKuVHx5KSkhIaISmN6OnpkeHDh5NatWqRcePGke3bt5MnT56Q+Ph4JkQyGIzvoqurS4YOHUpq1apF5s2bRxISEki3bt2IvLw8KSkpIXp6eqRp06bkw4cPIj/LBLF/T2lpKZGTkyPDhg0jzZo1I7GxsSQ4OJjExMSQt2/fksWLFxMZGRkyZMgQ0qBBAxITE0N/jt1/RmUwMZLB+EEEYtK8efPIxo0bya5du4iDgwORkZEh6urqJDg4mNSuXZssX76c6OjoEENDQ1JWVka9JIXFTMZ/CyZEMmxtbYmMjAw5cuQIkZOTI7NnzyaEEKKmpkaaNGlCbG1tJe7pxmAwpJ8fGUuaNGki2Ub+ALq6umT8+PGkTZs2JCEhgfj6+hJzc3NJN4vBYEg5+vr6ZNCgQaSoqIjMnz+faGlpkbCwMCInJ0fKysrIhw8fmPBVTcjKyhJXV1ciJydHMjIyyPz588n06dPJ4MGDSU5ODomOjia7du0i9evXJw0aNKDBGOz7YHwPJkYyGD8IAPLu3Tty+PBh8ssvv5COHTvS90pLS4mamhoJCQkhnp6eZP/+/URVVZUMHjyYyMnJsRMhBuM/juAgoqioiKSnp5NXr14RQ0NDcu7cOeLg4EBmzZol0Uq3DAajZvAzjSWKiorE1dWVuLq6SropDAajBqGnp0dGjhxJCCFkwoQJ5ObNm0RXV5e8fv2alJaWkqlTp0q4hTUf4Wy+lStXknfv3pEFCxYQQ0NDQgghd+7cITk5OcTZ2ZkQQsjbt2+Jh4cHadmyJRk6dCghhAVjMP4eJkYyGD8Ix3GktLSUpKWlkbp16xJC/hyoZWVlydevX8mHDx+IqakpCQ8Ppz/HhEgGgyHA1NSUmJmZkdDQUKKmpkbu379Prl69WmPEAwaDIR2wsYTBYPyXqVu3Lhk5ciSRlZUlq1evJnp6emTNmjXExcWFyMnJkZKSEiInx6SOf4KwEHnz5k1y//59snnzZqKnp0etx/Ly8oisrCy5evUqIYSQ+fPnE2VlZTJ8+HBCCNv/Mn4MVsCGwfgOlfn8ff78mTRo0ICMGDGCzJw5kxDy54B969Ytcv36ddK/f3/qFchgMBgVOXfuHElMTCQfPnwgISEhxMrKStJNYjAYNRA2ljAYjP86GRkZZMWKFSQpKYmcPHmSBo8wIezfExERQS5evEgsLCzIjRs3SFpaGpkxYwaZNm0aIYSQ4cOHk7i4OFJcXEwMDQ3J5cuXSa1atZhXPuOHYWIkg1EJwidChYWFpFatWtTzYvHixWTFihVk5syZZNiwYYQQQoqLi4mPjw/R0tIiO3fuZAMwg8EQgS3OGAxGVcDGEgaDwfiTjx8/Ek1NTcJxHCsWWkUcOnSIBAUFkZMnTxInJyeSmppKNm/eTFauXEnCw8NJZGQkIaQ8XZvjONK4cWMiKyvLIlIZ/xOspzAYFRCexKKiosj169dJamoq6dSpE+nTpw8ZO3Ysef/+PZk0aRK5cOECUVFRIcnJySQ7O5scO3aMcBzHNgoMBkMENiYwGIyqgI0lDAaD8SdaWlqEEFYstCp58eIFsbS0JC4uLoSQcmuQYcOGkZycHDJjxgyipKREwsLCSLNmzei+V1Btm8H4UdjTymD8P4IgYcEkNnXqVLJo0SLSunVr4uDgQG7fvk38/f3Jw4cPybJly8jWrVtJbm4uKSgoIC1btiR3794ltWrVIiUlJWyjwGAwGAwGg8FgMBhigu2/qg5zc3Py7t07cvfuXfqagYEB8ff3J7Vq1SKRkZFkyZIlhJA/7ztLjWf8rzDpmsH4f4RD+5OTk8mRI0fIzp07iaenJyGEkLt375Lly5eTQYMGkYMHDxI/Pz/i5+fH+x3sRIjBYDAYDAaDwWAwGDUVS0tLYmBgQDZv3kyUlJRIgwYNCCGEaGtrk+7du5NGjRqR7du3k44dO5KmTZtKuLWMmgqLjGT85+nfvz+JiIgghPwZFVlUVERevnxJ5OXl6XVNmzYloaGhhOM48vjxY0JIufgoDDsRYjAYDAaDwWAwGAxGTcXGxoaMGDGCxMXFkYULF5K9e/eS+/fvk/DwcFKrVi3SuXNnkpqaSl6+fCnppjJqMCyEi/GfJi8vj5iZmZHVq1cTNTU1MnXqVEIIIZqamsTOzo7cu3ePODs7EwUFBUIIIS4uLqSkpIQkJCSQrl27MvGRwWAwGAwGg8FgMBg/BQIPyIEDB5LatWuTvXv3kqCgIFKvXj2ioqJCjhw5QgAQU1NTukdmMP4JTIxk/KdRUVEhY8eOJcrKymTx4sUEAJk2bRoxNDQkdnZ2ZM2aNaRBgwakU6dOpHbt2iQ3N5coKysTIyMjSTedwWAwGAwGg8FgMBiMKkO4GGufPn1I165dSUZGBiktLSUNGzYkHMeRiRMnktzcXGJvby/p5jJqMBwEVTsYjP8YJSUl1N/x/Pnz5ODBg2Tt2rVk6dKlZPz48YQQQrp160YePnxIXFxciIWFBbl48SL58OEDuXv3LvOGZDAYDAaDwWAwGAzGT4dAkBTm4sWLZMOGDeTcuXPk1KlTzC+S8a9gnpGM/ywCMTEiIoJMnjyZZGVlESMjIzJlyhQye/ZsQgghhw8fJoMGDSLfvn0jFy5cIObm5uTOnTtETk5OxC+SwWAwGAwGg8FgMBiMmk5l1cltbW2JkZERiY+PZ0Ik41/DIiMZ/2kOHz5M+vfvT06ePEkcHR1Jamoq2bx5M1m1ahWZOHEimT59OiGEkLKyMlJcXEwL2ghHVTIYDAaDwWAwGAwGg/GzUlZWRmRkZOh/GYx/C1NTGP9pUlNTiZmZGXFxcSGEEGJmZkaGDx9OsrOzycyZM4m6ujoZPXo0kZGRoUIkACZEMhgMBoPBYDAYDAbjP4FAgGRCJKOqYD2J8Z/GzMyMvHv3jty5c4e+ZmBgQPz9/YmcnBwZO3Ys2bRpE+9nKgtZZzAYDAaDwWAwGAwGg8Fg/D0svIvxn8bc3JzUr1+fbNmyhSgrK5MGDRoQQgjR1NQkAQEBJCAggPj5+Um4lQwGg8FgMBgMBoPBYDAYPwfMM5Lxn2fLli1k4cKFxNHRkXh6epKGDRuSqVOnEjU1NfL7778TjuOYRySDwWAwGAwGg8FgMBgMRhXAxEjGfxYANOX6999/J/v27SMnTpwgJiYmREVFhVy7do3UqlWLdx2DwWAwGAwGg8FgMBgMBuOfw8RIxn8aYaGxoKCAZGRkkK9fv5JGjRoRGRkZFhHJYDAYDAaDwWAwGAwGg1GFMDGS8Z/ne5GPZWVlrFoYg8FgMBgMBoPBYDAYDEYVwsRIBoPBYDAYDAaDwWAwGAwGgyEWWNgXg8FgMBgMBoPBYDAYDAaDwRALTIxkMBgMBoPBYDAYDAaDwWAwGGKBiZEMBoPBYDAYDAaDwWAwGAwGQywwMZLBYDAYDAaDwWAwGAwGg8FgiAUmRjIYDAaDwWAwGAwGg8FgMBgMscDESAaDwWAwGAwGg8FgMBgMBoMhFpgYyWAwGAwGg8FgMBgMBoPBYDDEAhMjGQwGg8FgMBgMBoPBYDAYDIZYYGIkg8FgMBgMBoPBYDAYDAaDwRALTIxkMBgMBoPBYDAYDAaDwWAwGGKBiZEMBoPBYDAYjGolMzOTjB07llhYWBAFBQWiq6tLWrduTaKjo0lBQYGkm8dgMBgMBoPBECNykm4Ag8FgMBgMBuPn5cWLF8TFxYWoq6uTBQsWEDs7O1JSUkKSk5PJpk2biIGBAenatWu1/O1v376R2rVrV8vvZjAYDAaDwWD8M1hkJIPBYDAYDAaj2hgxYgSRk5MjiYmJpEePHqRRo0bEzs6OBAQEkOPHj5MuXboQQgj5/PkzGTp0KKlbty5RVVUl7du3J0lJSfT3zJo1izRp0oRs376dmJiYEDU1NdKrVy+Sl5dHr3FzcyOjRo0i48ePJ9ra2sTd3Z0QQsjjx4+Jl5cXUVZWJrq6uqR///4kKytLvDeCwWAwGAwGg0EIYWIkg8FgMBgMBqOa+PjxIzl9+jQZOXIkUVJSqvQajuMIAOLt7U0yMzPJiRMnyO3bt0mzZs1Ihw4dyKdPn+i1KSkpJDY2lhw7dowcO3aMxMfHk0WLFvF+39atW4mcnBy5evUqWb9+PcnIyCBt27YlTZo0IYmJieTUqVPk3bt3pEePHtX62RkMBoPBYDAYlcPStBkMBoPBYDAY1cLz588JANKwYUPe69ra2uTr16+EEEJGjhxJPDw8yIMHD8j79++JvLw8IYSQpUuXktjYWLJ//34ydOhQQgghZWVlZMuWLURFRYUQQkj//v3JuXPnyPz58+nvtrCwIFFRUfTfM2bMIM2aNSMLFiygr23atInUq1ePJCcnkwYNGlTPh2cwGAwGg8FgVAoTIxkMBoPBYDAY1QrHcbx/37p1i5SVlZG+ffuSoqIicvv2bfLlyxeipaXFu66wsJCkpKTQf5uYmFAhkhBC9PX1yfv373k/06JFC96/b9++TS5cuECUlZVF2pWSksLESAaDwWAwGAwxw8RIBoPBYDAYDEa1YGFhQTiOI0+fPuW9bmZmRgghRFFRkRBSHvGor69PLl68KPI71NXV6f/XqlWL9x7HcaSsrIz3WsV08LKyMtKlSxeyePFikd+tr6//w5+FwWAwGAwGg1E1MDGSwWAwGAwGg1EtaGlpEXd3d7JmzRoyevTo7/pGNmvWjGRmZhI5OTliYmJSpW1o1qwZOXDgADExMSFycmzpy2AwGAwGgyFpWAEbBoPBYDAYDEa1sXbtWlJSUkJatGhB9uzZQ548eUKePXtGduzYQZ4+fUpkZWVJx44diZOTE/H19SVxcXHk1atX5Nq1ayQyMpIkJib+q78/cuRI8unTJ9K7d29y69Yt8uLFC3L69GkyaNAgUlpaWkWfksFgMBgMBoPxo7DjYQaDwWAwGAxGtWFubk7u3r1LFixYQKZMmULS0tKIvLw8sba2JhMnTiQjRowgHMeREydOkGnTppFBgwaRDx8+ED09PeLq6kp0dXX/1d83MDAgV69eJREREcTDw4MUFRURY2Nj0rlzZyIjw87lGQwGg8FgMMQNBwCSbgSDwWAwGAwGg8FgMBgMBoPB+Plhx8EMBoPBYDAYDAaDwWAwGAwGQywwMZLBYDAYDAaDwWAwGAwGg8FgiAUmRjIYDAaDwWAwGAwGg8FgMBgMscDESAaDwWAwGAwGg8FgMBgMBoMhFpgYyWAwGAwGg8FgMBgMBoPBYDDEAhMjGQwGg8FgMBgMBoPBYDAYDIZYYGIkg8FgMBgMBoPBYDAYDAaDwRALTIxkMBgMBoPBYDAYDAaDwWAwGGKBiZEMBoPBYDAYDAaDwWAwGAwGQywwMZLBYDAYDAaDwWAwGAwGg8FgiAUmRjIYDAaDwWAwGAwGg8FgMBgMscDESAaDwWAwGAwGg8FgMBgMBoMhFv4PYpge+mHtx6EAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x1100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['DESCRIPTION_length'] = train_data['DESCRIPTION'].apply(len)\n",
    "plt.figure(figsize=(16, 11))\n",
    "sns.barplot(x='GENRE', y='DESCRIPTION_length', data=train_data)\n",
    "plt.title('Description Length by Genre')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Genre')\n",
    "plt.ylabel('Description Length')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "887be936",
   "metadata": {},
   "source": [
    "# Now to get to know about top genre which mostly people watched"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07d6ff3c",
   "metadata": {},
   "source": [
    "**now training and testing of the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "692e9906",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['DESCRIPTION'].fillna(\"\", inplace=True)\n",
    "test_data['DESCRIPTION'].fillna(\"\", inplace=True)\n",
    "\n",
    "t_v = TfidfVectorizer(stop_words='english', max_features=100000)\n",
    "X_train = t_v.fit_transform(train_data['DESCRIPTION'])\n",
    "X_test = t_v.transform(test_data['DESCRIPTION'])\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "y_train = label_encoder.fit_transform(train_data['GENRE'])\n",
    "y_test = label_encoder.transform(test_solution_data['GENRE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b98a805",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy: 0.5836945494789265\n",
      "Validation Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.44      0.32      0.37       263\n",
      "           1       0.74      0.44      0.55       112\n",
      "           2       0.45      0.21      0.28       139\n",
      "           3       0.47      0.15      0.23       104\n",
      "           4       0.00      0.00      0.00        61\n",
      "           5       0.53      0.59      0.56      1443\n",
      "           6       0.39      0.07      0.11       107\n",
      "           7       0.69      0.81      0.75      2659\n",
      "           8       0.56      0.72      0.63      2697\n",
      "           9       0.36      0.17      0.23       150\n",
      "          10       0.13      0.03      0.04        74\n",
      "          11       0.82      0.68      0.74        40\n",
      "          12       0.00      0.00      0.00        45\n",
      "          13       0.65      0.66      0.66       431\n",
      "          14       0.61      0.53      0.57       144\n",
      "          15       0.25      0.04      0.07        50\n",
      "          16       0.43      0.05      0.10        56\n",
      "          17       0.20      0.06      0.09        34\n",
      "          18       0.49      0.25      0.33       192\n",
      "          19       0.36      0.06      0.10       151\n",
      "          20       0.50      0.28      0.36       143\n",
      "          21       0.44      0.36      0.40      1045\n",
      "          22       0.60      0.41      0.49        93\n",
      "          23       0.62      0.25      0.35        81\n",
      "          24       0.30      0.16      0.21       309\n",
      "          25       0.50      0.05      0.09        20\n",
      "          26       0.85      0.83      0.84       200\n",
      "\n",
      "    accuracy                           0.58     10843\n",
      "   macro avg       0.46      0.30      0.34     10843\n",
      "weighted avg       0.56      0.58      0.56     10843\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train_sub, X_val, y_train_sub, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n",
    "\n",
    "clf = LinearSVC()\n",
    "clf.fit(X_train_sub, y_train_sub)\n",
    "\n",
    "y_val_pred = clf.predict(X_val)\n",
    "print(\"Validation Accuracy:\", accuracy_score(y_val, y_val_pred))\n",
    "print(\"Validation Classification Report:\\n\", classification_report(y_val, y_val_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b6839f33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.09357933579335793\n",
      "Test Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00      1314\n",
      "           1       0.00      0.00      0.00       590\n",
      "           2       0.00      0.00      0.00       775\n",
      "           3       0.00      0.00      0.00       498\n",
      "           4       0.00      0.00      0.00       264\n",
      "           5       0.00      0.00      0.00      7446\n",
      "           6       0.00      0.00      0.00       505\n",
      "           7       0.00      0.00      0.00     13096\n",
      "           8       0.00      0.00      0.00     13612\n",
      "           9       0.00      0.00      0.00       783\n",
      "          10       0.00      0.00      0.00       322\n",
      "          11       0.00      0.00      0.00       193\n",
      "          12       0.00      0.00      0.00       243\n",
      "          13       0.00      0.00      0.00      2204\n",
      "          14       0.00      0.00      0.00       731\n",
      "          15       0.00      0.00      0.00       276\n",
      "          16       0.00      0.00      0.00       318\n",
      "          17       0.00      0.00      0.00       181\n",
      "          18       0.00      0.00      0.00       883\n",
      "          19       0.00      0.00      0.00       672\n",
      "          20       0.00      0.00      0.00       646\n",
      "          21       0.09      1.00      0.17      5072\n",
      "          22       0.00      0.00      0.00       431\n",
      "          23       0.00      0.00      0.00       391\n",
      "          24       0.00      0.00      0.00      1590\n",
      "          25       0.00      0.00      0.00       132\n",
      "          26       0.00      0.00      0.00      1032\n",
      "\n",
      "    accuracy                           0.09     54200\n",
      "   macro avg       0.00      0.04      0.01     54200\n",
      "weighted avg       0.01      0.09      0.02     54200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "print(\"Test Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "print(\"Test Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "739f72f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "NB_classifier = MultinomialNB()\n",
    "NB_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1bfe3e11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 8, 8, ..., 8, 8, 8])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NB_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47214c15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=400)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=400)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=400)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log_classifier = LogisticRegression(max_iter=400)\n",
    "log_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5a1902d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 8, 8, ..., 8, 8, 8])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1be8990d",
   "metadata": {},
   "source": [
    "# Now designing a function show we can predict the genre of the movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b0bff4ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " action \n",
      " drama \n"
     ]
    }
   ],
   "source": [
    "def predict_movie(description):\n",
    "    t_v1 = t_v.transform([description])\n",
    "    pred_label = clf.predict(t_v1)\n",
    "    return label_encoder.inverse_transform(pred_label)[0]\n",
    "\n",
    "sample_descr_for_movie = \"A movie where police cashes the criminal and shoot him\"\n",
    "print(predict_movie(sample_descr_for_movie))\n",
    "\n",
    "sample_descr_for_movie1 = \"A movie where person cashes a girl too get marry with him but girl refuses him.\"\n",
    "print(predict_movie(sample_descr_for_movie1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9812e94a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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