FinalProject.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a2c7e14c",
   "metadata": {},
   "source": [
    "# Spoken Language Intent Detection Classification Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c9e3b47",
   "metadata": {},
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f898c11a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.10/site-packages/requests/__init__.py:109: RequestsDependencyWarning: urllib3 (2.2.2) or chardet (None)/charset_normalizer (3.0.1) doesn't match a supported version!\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io.wavfile as wavfile\n",
    "from scipy.signal import spectrogram\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.svm import SVC\n",
    "import numpy.fft\n",
    "import librosa\n",
    "from scipy.stats import zscore\n",
    "from sklearn.model_selection import GridSearchCV, KFold\n",
    "from warnings import simplefilter\n",
    "simplefilter(action=\"ignore\", category=pd.errors.PerformanceWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28aa5823",
   "metadata": {},
   "source": [
    "## Functions definition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b06f58bb",
   "metadata": {},
   "source": [
    "### Audio tracks handling functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7aaf60a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sr_audio(filepath):\n",
    "    \"\"\"Get sample rate of audio track.\"\"\"\n",
    "    sr, audio = wavfile.read(filepath)\n",
    "    return sr, audio\n",
    "\n",
    "\n",
    "def load_dataframe(path):\n",
    "    \"\"\"Load and describe DataFrame properties.\"\"\"\n",
    "    \n",
    "    df = pd.read_csv(path)\n",
    "    \n",
    "    # Dataframe exploration\n",
    "    print('***** DATAFRAME INFO *****\\n')\n",
    "    print(df.info())\n",
    "    print('\\n\\n***** HEAD *****\\n')\n",
    "    print(df.head())\n",
    "    \n",
    "    if('action' in df.columns):\n",
    "        print('\\n\\n ***** POSSIBLE CLASSIFICATION OUTCOMES *****\\n')\n",
    "        print(df.groupby(['action', 'object']).size())\n",
    "        \n",
    "\n",
    "    sampleRates = list()\n",
    "    audios = list()\n",
    "    for i in range(0, df.shape[0]):\n",
    "        audio_path = df['path'].loc[i]\n",
    "        sr, audio = get_sr_audio(audio_path)\n",
    "        sampleRates.append(sr)\n",
    "        audios.append(audio.astype(np.float32))\n",
    "    df['SampleRates'] = sampleRates\n",
    "    df['AudioData'] = audios\n",
    "    \n",
    "    print('\\n\\n ***** SAMPLE RATES *****')\n",
    "    print(df['SampleRates'].value_counts())\n",
    "    \n",
    "    print('\\n\\n ***** Self-reported fluency level *****')\n",
    "    print(df['Self-reported fluency level '].value_counts())\n",
    "    \n",
    "    print('\\n\\n ***** First Language spoken *****')\n",
    "    print(df['First Language spoken'].value_counts())\n",
    "    \n",
    "    print('\\n\\n ***** Current language used for work/school *****')\n",
    "    print(df['Current language used for work/school'].value_counts())\n",
    "    \n",
    "    print('\\n\\n ***** Gender *****')\n",
    "    print(df['gender'].value_counts())\n",
    "    print('\\n\\n ***** Age range *****')\n",
    "    print(df['ageRange'].value_counts())\n",
    "    return df\n",
    "\n",
    "\n",
    "def edit_sample_rates(df, goal_sr):\n",
    "    \"\"\"Given a DataFrame of audio tracks, change the sample rate to the goal_sr value.\"\"\"\n",
    "    \n",
    "    all_audios = list()\n",
    "    all_srs = list()\n",
    "    for index, row in df.iterrows():\n",
    "    # Load the audio file\n",
    "        sr = row['SampleRates']\n",
    "        audio = row['AudioData']\n",
    "    # Check if the sample rate is equal to the target sample rate\n",
    "        if sr==goal_sr:\n",
    "            all_audios.append(audio)\n",
    "            all_srs.append(sr)\n",
    "        else:\n",
    "        # Convert the sample rate to the target sample rate\n",
    "            resampled_audio = librosa.resample(audio, orig_sr=sr, target_sr=goal_sr)\n",
    "            all_audios.append(resampled_audio)\n",
    "            all_srs.append(goal_sr)\n",
    "    df['AudioData']=all_audios\n",
    "    df['SampleRates']=all_srs\n",
    "    print('Sample rates after resampling:\\n', df['SampleRates'].value_counts())\n",
    "    \n",
    "\n",
    "def clean_df(df):\n",
    "    \"\"\"Perform ordinal encoding on categorical features of DataFrame.\"\"\"\n",
    "    \n",
    "    catFeatures = ['Self-reported fluency level ', 'First Language spoken', 'Current language used for work/school', 'gender', 'ageRange']\n",
    "    dicts = list()\n",
    "    dic_fluency = {'native' : 0, 'advanced' : 1, 'intermediate':2, 'basic': 3}\n",
    "    dicts.append(dic_fluency)\n",
    "    dic_first_lan = {'English (United States)': 0, 'English (Canada)': 1, 'French (Canada)' : 2, 'Spanish (Venezuela)':3, 'Telugu':4 }\n",
    "    dicts.append(dic_first_lan)\n",
    "    dic_current = {'English (United States)': 0, 'English (Canada)': 1, 'English (Australia)' : 2, 'Spanish (Venezuela)':3 }\n",
    "    dicts.append(dic_current)\n",
    "    dic_gender = {'female': 0, 'male': 1 }\n",
    "    dicts.append(dic_gender)\n",
    "    dic_age = {'22-40':0, '41-65':1, '65+': 3}\n",
    "    dicts.append(dic_age)\n",
    "    for i in range(0, len(catFeatures)):\n",
    "        dic = dicts[i]\n",
    "        feat = catFeatures[i]\n",
    "        df[feat] = df[feat].replace(dic)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f3ce81d",
   "metadata": {},
   "source": [
    "### Visual representation functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4459378",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_spectrogram(audio_data, sr):\n",
    "    \"\"\"Given a track audio_data, plot its spectrogram.\"\"\"\n",
    "    \n",
    "    frequencies, times, spec = spectrogram(audio_data, fs=sr)\n",
    "    plt.pcolormesh(times, frequencies, 10*np.log10(spec)) #changed the scale to dB\n",
    "    plt.colorbar()\n",
    "    plt.xlabel(\"Time (s)\")\n",
    "    plt.ylabel(\"Frequency (Hz)\")\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "\n",
    "def plot_time_dimension(audio_data, sr):\n",
    "    \"\"\"Plot the audio track in time domain.\"\"\"\n",
    "    \n",
    "    audioArray = np.array(audio_data)\n",
    "    duration = len(audioArray) / sr\n",
    "    # Create the time axis in seconds\n",
    "    time = np.linspace(0, duration, len(audioArray))\n",
    "    plt.plot(time, audioArray)\n",
    "    plt.xlabel(\"Time (s)\")\n",
    "    plt.ylabel(\"Amplitude\")\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "    \n",
    "def plot_freq_dimension(audio_data, sr):\n",
    "    \"\"\"Plot audio track in frequency domain.\"\"\"\n",
    "    \n",
    "    audioArray = np.array(audio_data)\n",
    "    freqArray = np.abs(np.fft.fft(audioArray))\n",
    "    decibels = 20 * np.log10(freqArray)\n",
    "    plt.figure(figsize=(10,4))\n",
    "    plt.plot(decibels)\n",
    "    plt.xlabel(\"Frequency (Hz)\")\n",
    "    plt.ylabel(\"Magnitude (dB)\")\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd5ca641",
   "metadata": {},
   "source": [
    "## Data loading and preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d8e8f36e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train (dev) and test (eval) file paths\n",
    "\n",
    "dev_path = 'dsl_data/development.csv'\n",
    "eval_path = 'dsl_data/evaluation.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3bfd2b48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** DATAFRAME INFO *****\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9854 entries, 0 to 9853\n",
      "Data columns (total 10 columns):\n",
      " #   Column                                 Non-Null Count  Dtype \n",
      "---  ------                                 --------------  ----- \n",
      " 0   Id                                     9854 non-null   int64 \n",
      " 1   path                                   9854 non-null   object\n",
      " 2   speakerId                              9854 non-null   object\n",
      " 3   action                                 9854 non-null   object\n",
      " 4   object                                 9854 non-null   object\n",
      " 5   Self-reported fluency level            9854 non-null   object\n",
      " 6   First Language spoken                  9854 non-null   object\n",
      " 7   Current language used for work/school  9854 non-null   object\n",
      " 8   gender                                 9854 non-null   object\n",
      " 9   ageRange                               9854 non-null   object\n",
      "dtypes: int64(1), object(9)\n",
      "memory usage: 770.0+ KB\n",
      "None\n",
      "\n",
      "\n",
      "***** HEAD *****\n",
      "\n",
      "   Id                                               path         speakerId  \\\n",
      "0   0  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0a312...  2BqVo8kVB2Skwgyb   \n",
      "1   1  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0ee42...  2BqVo8kVB2Skwgyb   \n",
      "2   2  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/1d9f3...  2BqVo8kVB2Skwgyb   \n",
      "3   3  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/269fc...  2BqVo8kVB2Skwgyb   \n",
      "4   4  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/5bbda...  2BqVo8kVB2Skwgyb   \n",
      "\n",
      "            action  object Self-reported fluency level   \\\n",
      "0  change language    none                     advanced   \n",
      "1         activate   music                     advanced   \n",
      "2       deactivate  lights                     advanced   \n",
      "3         increase  volume                     advanced   \n",
      "4         increase  volume                     advanced   \n",
      "\n",
      "     First Language spoken Current language used for work/school  gender  \\\n",
      "0  English (United States)               English (United States)  female   \n",
      "1  English (United States)               English (United States)  female   \n",
      "2  English (United States)               English (United States)  female   \n",
      "3  English (United States)               English (United States)  female   \n",
      "4  English (United States)               English (United States)  female   \n",
      "\n",
      "  ageRange  \n",
      "0    22-40  \n",
      "1    22-40  \n",
      "2    22-40  \n",
      "3    22-40  \n",
      "4    22-40  \n",
      "\n",
      "\n",
      " ***** POSSIBLE CLASSIFICATION OUTCOMES *****\n",
      "\n",
      "action           object\n",
      "activate         music      791\n",
      "change language  none      1113\n",
      "deactivate       lights     552\n",
      "decrease         heat      1189\n",
      "                 volume    2386\n",
      "increase         heat      1209\n",
      "                 volume    2614\n",
      "dtype: int64\n",
      "\n",
      "\n",
      " ***** SAMPLE RATES *****\n",
      "SampleRates\n",
      "16000    9554\n",
      "22050     300\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      " ***** Self-reported fluency level *****\n",
      "Self-reported fluency level \n",
      "native          9444\n",
      "advanced         346\n",
      "basic             56\n",
      "intermediate       8\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      " ***** First Language spoken *****\n",
      "First Language spoken\n",
      "English (United States)    9642\n",
      "French (Canada)             148\n",
      "Spanish (Venezuela)          35\n",
      "Telugu                       21\n",
      "English (Canada)              8\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      " ***** Current language used for work/school *****\n",
      "Current language used for work/school\n",
      "English (United States)    9598\n",
      "English (Canada)            156\n",
      "English (Australia)          65\n",
      "Spanish (Venezuela)          35\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      " ***** Gender *****\n",
      "gender\n",
      "male      5265\n",
      "female    4589\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      " ***** Age range *****\n",
      "ageRange\n",
      "22-40    7496\n",
      "41-65    2115\n",
      "65+       243\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = load_dataframe(dev_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1ffea66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample rates after resampling:\n",
      " SampleRates\n",
      "16000    9854\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#bring all srs to 16kHz\n",
    "\n",
    "target_rate = 16000\n",
    "edit_sample_rates(df, target_rate) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f5bb543",
   "metadata": {},
   "source": [
    "### Single audio assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5082339d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the first audio\n",
    "\n",
    "audio_data = df['AudioData'].loc[0]\n",
    "sr = df['SampleRates'].loc[0]\n",
    "\n",
    "plot_time_dimension(audio_data, sr)\n",
    "plot_spectrogram(audio_data, sr)\n",
    "plot_freq_dimension(audio_data, sr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "953c7881",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/k1/d0rvjnq127s1n3prqzn3314m0000gn/T/ipykernel_51786/4153402632.py:93: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df[feat] = df[feat].replace(dic)\n"
     ]
    }
   ],
   "source": [
    "# data cleaning (encoding of categorical features) \n",
    "\n",
    "df = clean_df(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a2d243f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>path</th>\n",
       "      <th>speakerId</th>\n",
       "      <th>action</th>\n",
       "      <th>object</th>\n",
       "      <th>Self-reported fluency level</th>\n",
       "      <th>First Language spoken</th>\n",
       "      <th>Current language used for work/school</th>\n",
       "      <th>gender</th>\n",
       "      <th>ageRange</th>\n",
       "      <th>SampleRates</th>\n",
       "      <th>AudioData</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0a312...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>change language</td>\n",
       "      <td>none</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16000</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0ee42...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>activate</td>\n",
       "      <td>music</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16000</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/1d9f3...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>deactivate</td>\n",
       "      <td>lights</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16000</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/269fc...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>increase</td>\n",
       "      <td>volume</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16000</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/5bbda...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>increase</td>\n",
       "      <td>volume</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16000</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id                                               path         speakerId  \\\n",
       "0   0  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0a312...  2BqVo8kVB2Skwgyb   \n",
       "1   1  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0ee42...  2BqVo8kVB2Skwgyb   \n",
       "2   2  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/1d9f3...  2BqVo8kVB2Skwgyb   \n",
       "3   3  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/269fc...  2BqVo8kVB2Skwgyb   \n",
       "4   4  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/5bbda...  2BqVo8kVB2Skwgyb   \n",
       "\n",
       "            action  object  Self-reported fluency level   \\\n",
       "0  change language    none                             1   \n",
       "1         activate   music                             1   \n",
       "2       deactivate  lights                             1   \n",
       "3         increase  volume                             1   \n",
       "4         increase  volume                             1   \n",
       "\n",
       "   First Language spoken  Current language used for work/school  gender  \\\n",
       "0                      0                                      0       0   \n",
       "1                      0                                      0       0   \n",
       "2                      0                                      0       0   \n",
       "3                      0                                      0       0   \n",
       "4                      0                                      0       0   \n",
       "\n",
       "   ageRange  SampleRates                                          AudioData  \n",
       "0         0        16000  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "1         0        16000  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "2         0        16000  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "3         0        16000  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "4         0        16000  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02568eb4",
   "metadata": {},
   "source": [
    "### DataFrame composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "de9ad1f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_all_audios(df):\n",
    "    \"\"\"Plot every track in the DataFrame df (overlapped).\"\"\"\n",
    "    \n",
    "    fig, ax = plt.subplots(1,1)\n",
    "    for ind in range(0, len(df)):\n",
    "        audio = df['AudioData'].loc[ind]\n",
    "        sr = df['SampleRates'].loc[ind]\n",
    "        audioArray = np.array(audio)\n",
    "        duration = len(audio) / sr\n",
    "        time = np.linspace(0, duration, len(audio))\n",
    "        ax.plot(time, audioArray)\n",
    "    ax.set_ylabel(\"Amplitude\")\n",
    "    ax.set_xlabel(\"Time [s]\")\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "    \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "364bfb33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PpK1G/ImxgMnhFEwKBS97Q/iJfQCARqXrj1RqXSnuOPY7vuk9AYh2/Rzuzkn5cwsk9HwdX7A/Y7A/HxD8z0jP53vd7nSoAOvAgQPIzMyETqdDeHg4fv75Z/Tr1w/5+flQqVSIjo7mlU9MTERJSQkAoKSkhBdctZxvOeeqjFarRVNTE6qrq2E2mwXLHD161GXbX375ZTz//PMOxzds2IDQ0FD3D++BeJb/y1996TinZc3hHC7v6jwZqNnDRYQ14Srg9n2IbeyEqmNZDudf/et9xOjrMajiFF6Y8ggAwJTIofX8+7Vr17q9V05OjmeN62Do+Tq+YH/GYH8+IPifkZ7Pc42NwivyW+tQAVbv3r2Rn5+P2tpa/PDDD5gxYwb++OOP9m6WKAsWLEB2drb1vVarRWpqKiZOnIjIyEjJ7nN020HUtTrWqLalSlDoj+No1zqMNDcHda0GiRmoANh6wEytAiyG4xBeX4s/h1+N6bsc7797PDCiZxUS8INggBWjt/SkddLZss2bozk0thpdnDx5suDzAZZ/PeTk5GDChAlQKpVOy3VU9HwdX7A/Y7A/HxD8z0jP572WESh3OlSApVKp0KNHDwDA8OHDsWvXLixduhS33XYbDAYDampqeL1YpaWlSEqybDyclJTksNqvZZWhfZnWKw9LS0sRGRmJkJAQyOVyyOVywTItdTijVquhVjvOjVIqlZL+8uuO7nY4ZuxtSzAaU/oCtg8Aph0pFrxeobL1fj154T4stRs5VMGEW8v+huZECfT1tYDiPofryzs5b5umSSjBKIcKWRk+TVNiafM6gRp9pNufSfJXX6N0xfdI/+q/YGQdZiqhR6T+bASaYH8+IPifMdifDwj+Z6Tn865OMTr0NxPLstDr9Rg+fDiUSiU2btxoPXfs2DEUFhYiM9Oyai4zMxMHDhzgrfbLyclBZGQk+vXrZy1jX0dLmZY6VCoVhg8fzivDsiw2btxoLdPemPoihGj4KxplSY4r84qVwukQ5Cpb2au0I1GmsX1EesgroGBMMEXHIbq+Cr1OfYNuG2aJbtuwPXv594IZz+EtvFS1CeMbbF2uy/fd67auiAMHoNuzB/rjx1G7eg1q//c/0e1whqPMp4QQQiTSYXqwFixYgGuuuQZdu3ZFXV0dvvnmG2zZsgXr169HVFQUZs6ciezsbMTGxiIyMhJz585FZmYmxowZAwCYOHEi+vXrh7vvvhuLFy9GSUkJnnnmGcyePdvas/TII4/g3XffxZNPPon7778fmzZtwvfff481a9ZY25GdnY0ZM2ZgxIgRGDVqFN566y00NDTgvvsce3PaQyfTcejlegC23rJDTXKg1TSvw00pGIN8AIDepIJawc+RVXd+COrM/IBDYbfVTt/bT0EWfhTsfPH/Mmg9nSsVxeA4wKBV4Fq20RruF9Smi66T1etR9I9/AADCx46FPCrKadk6nRHhagUYxnFi2YKfDmDbqQr89tjlCFV1mD8WhBBCAlSH+SYpKyvDPffcg+LiYkRFRWHQoEFYv349JkyYAAB48803IZPJMG3aNOj1emRlZeG9996zXi+Xy7F69WrMmjULmZmZCAsLw4wZM7Bo0SJrmYyMDKxZswbz58/H0qVL0aVLF3z88cfIyrLNJbrttttQXl6OhQsXoqSkBEOGDMG6descJr63G7MJaLUgsNTk2FGZXmwZIlx2aTI2HeyHrAjghu7roGkyo++JeuxpjMKmOhNCw1m09GkNKT8BdAkDAKgjLUOJps7Oe32Ko+rQuYIByzAoTk5G5yL+Js5xqEJtQQiKd8YgPEWH1CuqPH9eu0SxrE7nNMDacboSt324HXeM6oqXbxrocP7bnYUAgF/3FeG2kV09bwchhBBip8MEWJ984rhPnj2NRoNly5Zh2bJlTsukpaW5XZ02duxY7N2712WZOXPmYM6cOS7LtBfWyAIOO+Xwg6D0Eg7j8izP+NeQCqiwFb+eehw3dF+H/sfqEK01YTx+xDHchbg6FuebPyUJKU2oQJjotlSO3w3Zukic7N4De0cMw23frXAoU3UsHABQX6RxOOeM2KG8Mr0RRxt0uCw6FIt+/gVAAr7dWSgYYNnqFt0MQgghxKkOPQeLOGLhmI39pbAfMZ37DGiy9Pb0Pu8YRTCMZYhQrbddr446zwvNTFw36+vG8h4oP3g9ODAwcwwOhfdFnTycV+fN8m/Qr2sJilKu8OWRHBj+/B8gt7TTXNd6zaTNiNzDuHXfKfx8/hAKa0N452qMJly58yiWnCmRtG2EEEIIQAFW8OOARFkdJmM1NFvtVj8qQ8FG2a18FMh3lZHFz9tl4mxjj3XnRqDy8HXYn/IPHDQnYVP8WHzd5TaHOtTR58DKhnnU5DCDbcK7WauHdss5mBuaVzee3oKNWz7B7dcuwv5O3XD+EeeT7A3N3VFba1nUGSJ45z44V45jDTosLqAAixBCiPQowLqIyBnbJPXwyUvQMNE2zClTuw80muosc6gYjkFoRBk4thEVhsM4Z7b0XBllDmOTNpxjBCe0PU7tmRB8t2YRypYuBQCUf3wA2nVnUPXdUUBfD3x5Ax4zzkUtE46FmQ84XG+qbEL1qpMw1eodzrVgWQ5GGgskhBDiRxRgBRlOqCuq2cR+yyE3c+h5gQPXVAOZthwz15sRq+UQkrLSSX0sGLCYpDoKNOfIutEwCiMK7oFCn4vPJuSgSm1LuqYwxsBgsEtsGlKD85HHsLrrr6LaX7LbMkm98v3lAABTmSV3lv5EDb5+7E4c1/ITbZkZ2zRCtsmIktd2o2F7Mcrets2jW1HBv8e2U5Wi2kIIIYR4iwKsICVnFUip7QGGtf2K/2LP4a7NDH4dI8NPFQsQtn4psvZwyP7Z7LSeUJUBqbJaJMnqoAjthF6mZMRylh6rFI3jij1Nw2XQvhuBpvfUKD8QA1VEEVb3fw+N6kgcTu+O2rAIh2tuXaDArQsUMDic4SuuZvFr0XDesYL0KdbX5R8esL5mG/jbBdn3V+mM/Od9v7AMrMZxL0aTSWATRkIIIUQECrCC1LgTd+H6w3Mx5tz1vOOHurI4m8jgkyw5dP1YlP/DiG4mDkatZWVdoTkBZrvhvFA2xDqUd7f+Clxh6mc9F5bUqmsIgMlUiUEna9H9IIeyrfE4/2csmsLHo6rzEsx+6kVMff1DXvldabaP4FdR/OCr9WrBH5OnQhnG30KnKqaP9XXroEqs508VwXBlEjiZ7bnPnz+PF198Mej36SKEEOIfFGAFqe5VQwEAA0ouBwDUG0Jh1sdjdy/br7xqjgnGbhyqZpqguzAdv5+aiKz6JZhjfNRaphMYTGdjoOYU+EN5mLdKsau+Ea+UVcC+f8jM2Hp9GI5DzalINETfLNjG32Td8eIUW0JUU6vhzdf2n+O9L9YkQ65M4x1TKoTnfXXRXCt4HLAEbkU6x/4yLszWi/XTTz8BAP7++2+n9RBCCCHOUIAVbJzM3X5syytoPP244Dk2zHLRt6euhYwzY1dTd2s+qHnQYDKioGdMOCEvxgm5bTJ8CG7DNfUMuhgthaNRB43dQF9RQhn2da9x2tQatetBwbr151ye1ytU0KochymVzEmn1zDgsGfddzh2+rTLuquqWiU95TjgbC7QWMU/tv5fQP43LusihBBy8ekwiUaJ/1RGMpCHnoS5sQcePv41Rl04hQNDkgCY0KlVDK4DfxiuznwJ5KwaKhiRr3kYAHBIlgIZC9RE6LG3txFp5jM4I+sPhm0CJwvBtoHDcMmBPW7b9XCVY04vV4wsBzUAley40zIRjA5NdTVobNIB6ub6lS3P6HyBAI6tBb670/L635WAXAGc2gTkvms5NuROj9pKCCEkuFEPVpAxCXRhcRwQz9QjknHc9LlFUvJ/AQDXH9mPJG0DmvaqnZZtUSWvxRU9jiGWPYjbztk2wP79mglg7fb7m8M9i7DqrxB3/iFEVH6Af/3fE4IdbZ+Yr0GDwnZfQ2ix2zbYM3GW/FkxyvfclASKIjtBs6kYmk3FAGtpDRvvIpv8UbsdAF7oBJQe4vdmEUIIIXYowAoy+lb5pkyKBvz1592Yoj6Cm9QHnFwFxDD16Cyrtb5XGWy9R2a7eVecXWgUa44CywDH+23EPUfWWY/XR0SgITwMSjNwh7YOQ/bUovcFy4bZmoa/AAClsXEObajmIrE+dTQABkxYAgpHvOxQhrW7f7bie4yNtAVT9QVfAVyD02e0p2fsNqk2Wp6PU8vE75Xz/iXiyhFCCLkoUYAVZIxmy5YwrLkSZsMJ1EeeEH3tRNUxweNNdvOqTIzzlA4tWibCX7uTxdOVNeirN+F+LX9Lm7+GjIRcc97hWjNkUA+8BeETXkRU0ZUO508obcHeo4pV6Blim4Qeeuo0NFXv8srHaGsgN7tOtyAvs/TscSEK4NxO4UINZS7rIIQQQuxRgBVkWJbF7oghMGi/gLHhV3CcwERyDlCzSt6h0HolEsz22dwZKIzu50D1bezmMLE+N2EbAKBLBTBh+Ee4YuQXULXqWcvv2Rfq+N95xyaFbENcv2Kouo8HAMSfnAajuppXRse47mGq3mubvJ5WfB4/PTULH7z8T2dz/wEAysM1AJqHCOudZLQ/scHlfQkhhBB7FGAFHRn2RQywe++YG+ofRTOw6thS63uGA0bnpqHx+AW7Uhz6bDJgj34j3kv+DseihHu3lpz9By6tG4Kd3VKsx6bqT6MkQoP9XZNxKLwnToV2RbmyCwzVI12OwIV2qUTPrENojDliPVbR83u3T2zPVG+7wbjduQCA7hfOwZwe7uwSvpMb3Zexou12CCGECKMAK8iYORnMjG2iuFntGARcrR3Nex+tdCzDcgy+OjMUJ4p24yCXj4OxB1GlEp7UfWndEOSlmzC5SzJOKRV4qFaLE/FRKEiItpaZyFZCXzINprpBAACd2jahXG7m0O8sC3lzLGgItW1KbQyxbWvTjSnyOqQxZ7TOIO+sJgqaCCGE+I4CrCDDcgyMjPBquEZ5o1d1RjVYhhP1cucbKOcM1OKcUompXWw9WSa5YxYQVmc5n9d3oPXYXZtYPPcNi8u+twwjGk9uRsPGZ8HqanjXfqN6CTmh7rO1j09NwU/hYfyDKstHPZZpcnltKHRgWZHpIWjDaEIIIU5QgBV0HL/0Q0Jqcaj7avzW9Tefao6o14HV1bov2Ox4t/6iyk3ZbWlz+gFLgKVZXwy2rhiGI7+gKeY4GPVBAAYkMdUO155ikx2OFam74tn4Tg7HOTmDqE7NE96dxEZXyA/glVdeEdVuQgghxBkKsIKMUNwQ3y8HR1nnvU9ihOg4zFy5E31XLxd9jdmuB4tzksSzWzG/xfbvzLXn0KnSgM7M00hQPeFw7UE2DTcYXnQ4XtX5VehCL3U4buodBTbGMnwqLxbuzYtmGmAw8BcGsKwB2nCF48/20M+CdRBCCCEUYAUZnaK5h0YGJGRZ5kz9reOnVtgTI299mQOjnL/KMKnGeVkFK7wfoNAQmrFmBO/9kNP8Mm9wT1tfszVnMSjfkt5BJTuFRw2zrefkAI6ZrkeEk8CtLu4Rh2NslO2ZGBErJFscODgXu4ZF42xqCP/Ecd96BAkhhAQvCrCCDAsODDhUTElBSrplsvjORv5cqEcHyaA/vAqKc863hjEpxO+ilF41SHRZzux6Nd/jz37Oe3/sx2RwzbHQL6ytV+ouqDCazcJ7skjR9+bxYBeeigpLOolTGWFuShJCCCEWFGAFGQZygANqUxwzpbe4b/VKGI6vRcLLSqdlwLjYl8+POlc4rlQs16ixl+3OO3Y1LG1Pkjvf0kdudp4UVX5eXMZ3Meo5Dc5USFcfIYSQjo8CrGDEul5p1+PcKfdVyAJnH/Ad8V1xo+EF3jFGXuektM3Ak0ednmNMrYYvde4z1DuTqX8HY1/fgmMl7ttECCHk4kABVhAaV7vX5XmmVXZ3xQUGVx45C5XRBK3GMp/KYDdEGKGLdZg9bwKHUzDz9iZ0uI/da85Jh1h8rftUB0VMLL9eVTkYheuUE4mV5Rh0Sjg5qhDG4H2AVQfL0OEfx2k7HUIIIRYUYAWhWJPW+toocz+hPeElJXRKBQxKBf7qnQpzq+HB4eev4b1nweIFNGEGGrACAlvxeODqfe4DrF6d+D1uDGNCuj7FSWmLf3/yjsMxLtzFkKgETu0th0Hnet9DQgghFwcKsIJMS7gi1ytgaorC3q69RF1Xp7GtBDxll4G9RXyjLafVnxF7sBGWQOJrHwMsUTggra4YTPNsd1WnLS6Lq40GpJYWOZ6Q+XdeWcnpWuxaXeDXexBCCOkYKMAKMnowYDkGff43Fid/fR11apGr7Oxij5NJsTDI7YMRM4aWPWh9V660Jfx0tRhPqjznYVtkWL7xDczfa9mXUBm1z2X5+7athdokPA/N31P3a8tdZ4onhBBycaAAK8iYIQPL2sKIiEbHkGLAKcdeFjPD/yi0Do4YJx+VWtFhlPehTfhmyzDnhMLdosobOWk+1nKYIYf3c7MIIYRcvCjACjJquM/YrvQgB5QYVRCed9RWiR7UQ+7mvf9aPxxqg+9Dl7O4L/AEPvC5HkIIIRcfCrCCjKJVsFOrCfX7PStV/k1PwBhtoVo4HFcPqtIvByN2KNTZPVqnbQAwQv8+9Jz4dBU0vZ0QQkgLCrCCTavtaRraIMByRiFzngQUJu+60d5XviV4XD3wNlHXy1nhIT/5Ka3g8Ty2p6h6ASBfbULRiRrR5QkhhAQvCrCCTfskYLdSs7bAaWDM5U7Lyaq823z6cvlBweOMRlwPVoK2WvC4vEp4SJFjxE/Vb5AB+kbqxyKEEEIBVhCSau2ed368UAIASNLUISdJAYXJEnBwrSI/WaV3AZavlGYPAyA3AavAftaEEEIIBVjByNPv/LrwLihJHMk7pleI/2jY3y+tOaDqFVkBlB9Hzty7cee6VQ7XyGraIH+WgLgG4aFAdyKKRyMlfy5YLoR3/CTXWYpmEUIICTIUYAUhzj4Tu4ghw13Dn0JDOD8zekGcbe5WuKySd25ToriJ39lffwwAePB/K1o10Ldun6EnfVwG6cHt85s3mU45MAsRZcNRZ5rGO29A4OzZSAghJHBQgBV0+LsDagwiognG9cdgYOhvvPcHoltvv+NZwHRVyZcelW9t4c5LHI7JY3uIuraHOQnT9ZejP9xvIQQAH5mn8N6z8G21IiGEkIsDBVhBhm21Su7OP3xPoSBn+POl5CZ+j5aeM2LMwViojOI+TsaGdV63RWceCs3QexyOMyL2XASAscb+CIEK/0GI+8IinOMSHI411bXP8CchhJDAQQFW0GFRXzPA+k4hk34yubpxO+99VGMd+hRGYMSRGN7xrmXF1tf2k9wVnNnrYcJG81VeXSc/xQ80xS62NLkZAvydHeZw7I9vjoltFiGEkCBFAVaQYcCgvGSy9X1Ueq7L8iGN3mwFww+OimWWICSqQdx8pDJGDRnr3TyqRnacV9cpT3o3uT0+pMLleS3nmGesqrjBq3sRQggJHh0mwHr55ZcxcuRIREREICEhAVOnTsWxY/yeAp1Oh9mzZ6NTp04IDw/HtGnTUFpayitTWFiIKVOmIDQ0FAkJCXjiiSdgMvGX7m/ZsgXDhg2DWq1Gjx498Pnnnzu0Z9myZUhPT4dGo8Ho0aOxc+dOyZ/ZG60zuSvDXAcIl+wWzgtlT6i3h1PLYOwfDTZSiU3KcE+aiIqasW2f3oDjEN7keeATrXYdmG1gR7o8Twgh5OLUYQKsP/74A7Nnz8b27duRk5MDo9GIiRMnoqHB9qU5f/58/Prrr1i5ciX++OMPFBUV4aabbrKeN5vNmDJlCgwGA7Zt24YvvvgCn3/+ORYuXGgtU1BQgClTpuCqq65Cfn4+5s2bhwceeADr16+3llmxYgWys7Px7LPPYs+ePRg8eDCysrJQVlbWNj8MF7rISuzecejUZ73TsmLtrkxsdYSBcWAMzF3CYMh0nIMEAPUhRqf1VSddBv3oBJjlMU7LSC2loQJXH9vTZvcjhBByceswa8zXreNPjP7888+RkJCAvLw8XHHFFaitrcUnn3yCb775BuPGWYaRPvvsM/Tt2xfbt2/HmDFjsGHDBhw+fBi///47EhMTMWTIELzwwgt46qmn8Nxzz0GlUmH58uXIyMjAG2+8AQDo27cv/vrrL7z55pvIysoCACxZsgQPPvgg7rvvPgDA8uXLsWbNGnz66ad4+umnBduv1+uh19vmQ2m1lp4Ro9EIo9F5MOIp+7lOYckHJKmzzqSBptUxNlzpUC6uRg0kWV5XRhiR5GSlnrFLbwBAY9SNAD6UpI1iNCldbN0DAGZputU4jvPpd9pyrcFgQPG8+VAkJCD+nwskaVsgaHk+KT/3gSbYnzHYnw8I/mek5/O9bnc6TIDVWm1tLQAgNjYWAJCXlwej0Yjx48dby/Tp0wddu3ZFbm4uxowZg9zcXAwcOBCJibYemaysLMyaNQuHDh3C0KFDkZuby6ujpcy8efMAWL708vLysGCB7QtPJpNh/PjxyM11Pt/p5ZdfxvPPP+9wfMOGDQgNlW6/wHS718rQSmfFfMJwHG8a1pAyS/Ar55xPHRc6ZVa07hmTFqOJhjwmA6bifETr65HbbShw3sXcLw/2RyziYlEnMP8KAOrr67F27VpPm+vgzy+/RNrGjQCAXUMG+1xfoMnJyWnvJvhdsD9jsD8fEPzPSM/nucbGRlHlOmSAxbIs5s2bh0svvRQDBlhWzJWUlEClUiE6OppXNjExESUlJdYy9sFVy/mWc67KaLVaNDU1obq6GmazWbDM0aNHnbZ5wYIFyM7Otr7XarVITU3FxIkTERkpXW6lQ3kveFS+gfF880J1qwlUUfpihzIauRFw6PfiM2r6eXxvT4RlvQKGkaFpz2cYU3II+1VjADRJUvcl+nedngsPD8fkyWO9rttoNCInJwejR4xAy4Dv5MmTXV7TkbQ834QJE6BUOvaEBoNgf8Zgfz4g+J+Rns97LSNQ7nTIAGv27Nk4ePAg/vrrr/ZuimhqtRpqteMQlVKplPSX32jI8Kj8xC4puNMxPvJYn6IKHE2Js74Pk7d/tzPTnEBVEd8X0J6WpM6GUDlYQ4Sb+zKS/E4VCtsfz2D8C1Dqz34gCvZnDPbnA4L/Gen5vKtTjA4zyb3FnDlzsHr1amzevBldunSxHk9KSoLBYEBNTQ2vfGlpKZKSkqxlWq8qbHnvrkxkZCRCQkIQFxcHuVwuWKaljvbEsa57jVqrk8mar/O+Z6esUxJSqut5x5Tts5ezXQOkSSTaWk2U0unQICGEENKiwwRYHMdhzpw5+Pnnn7Fp0yZkZPB7aoYPHw6lUomNzXNWAODYsWMoLCxEZmYmACAzMxMHDhzgrfbLyclBZGQk+vXrZy1jX0dLmZY6VCoVhg8fzivDsiw2btxoLdOuZJ7ll2oZIOzElGNS55kIU0S7vwWn5yUKrYi1DJcqGVtOLYWJP/TIiU7tKY2Q4TN9rkMBM9Iiz0nQGkIIIRebDhNgzZ49G1999RW++eYbREREoKSkBCUlJWhqsvS8REVFYebMmcjOzsbmzZuRl5eH++67D5mZmRgzZgwAYOLEiejXrx/uvvtu7Nu3D+vXr8czzzyD2bNnW4fvHnnkEZw+fRpPPvkkjh49ivfeew/ff/895s+fb21LdnY2PvroI3zxxRc4cuQIZs2ahYaGBuuqwnZlF8coNO63yWGa46Srku9AlCoO16Y+LOIa4a1gJqXY8pJpZfyPVokqDolcldu6paJIGmR9LYtKRaiIAI9p4OcQm6g6hm5RZ6RuGiGEkItAh5mD9f777wMAxo4dyzv+2Wef4d577wUAvPnmm5DJZJg2bRr0ej2ysrLw3nvvWcvK5XKsXr0as2bNQmZmJsLCwjBjxgwsWrTIWiYjIwNr1qzB/PnzsXTpUnTp0gUff/yxNUUDANx2220oLy/HwoULUVJSgiFDhmDdunUOE9/bm0wpbqWDN+SFDZDXGmAYHoeaiBhwDNAr0rZqsV7OD7C+abgO04p24uOkq6HzW6uEyTTRuBEqvOWuXCM/wEqQeZeRvc2TqBJCCAk4HSbA4kR8a2k0GixbtgzLli1zWiYtLc3tEvqxY8di7969LsvMmTMHc+bMcdumdueHueYZRiPKzljmXMkvNODvUeOBrz7nlWn92+p2YAsA4MqaA/iVl0yibYj5/AgR6vdiHJ6Or6bUf4Fta+UGI+KUCjBerAYVcrxBh7+q63B3ShyUsrYd1iWEkGDSYYYIiXeadvj+K2Y4Dl3Ob7G+f6PMbvsd1hJsKJLE5WlSsm3df2UjZ90EWQLxhIZxjFBPc8kStcgNN0HT9yVVGPj3ITx7skiyW16x8yj+eeIC/nXivGR1EkLIxYgCrCBjZuw6JRkOFZwMEY0cpm82o3OFY4ChcLPXc//z5bhm/2n0OrnSeizG7DiRXj34Tt57JsCGyYwnNyDa4HmjZAK9VbvYPlI0yWfPnbwAAPjwfLnkdX9Z5J8ktYQQcrGgACvICIUQ//7WjBu2c3j9Y8doathJ10FHWqVjQrUwgeG28w38JKsRcLGakeP8OlFJeIMe4IoTZzyuS2j142a27bOqswbhhQWEEEICEwVYQUYoIEhvzkohF4hpZB7GOQZF62l7lvttNVbwjnbihLvGOI7DW28uwruvPeu3IGtc66mFzUNtj5UJb0ztitB8qyNcujfN8knD1q1tfk9CCCHeowAryOhktkSj7iZje2PZLfcIHj/jqsfKjjmBw+ATR9G/4ATiaqulbJpVuJOUDBrOWd9Wx1RldDO+SwghpN1QgBVkNKHSz8ex98sVE6yvlTA57YVy1jPW/2S+9XV7TtNydm8z/ZEghBAiAfo2CTKMzOS+kH15DpgSJT5bR2xtNfIi+qEzynFCcw/ernzDUk+rkKWTNsBmuYvAAfg+MctlGZmJtskhhBDiHgVYQUZm5AdYjJtcmQwAhZN0AHqF47DfPWt/wpRh72OGYgMAYFrDZsFrk0KFJ2V7OuerLcmazLig4c/TCgV/U8XIkjFt2SQbifJcEUIIaRsUYAUZuZk/L4dxN01HIODRyMMAAHl9BOZIOQmQlCw/X5RSIVywSxV/VWL8YC26XVMGRiCYa2uy4iaHRQI9ZZQPihBCiOcowLrICfWL9I4aBQCoCXdMsimr0AFmDpfJDvKO92k4Iep+aruJ2RzDIK5vPdRRJkR3a7vs587IGk3YHTmAd6wGkZLew1hUhJNXj0flZ597dl1xiaTtIIQQ4l8UYBEHsWpLpvKyWL3DuesMf2PMiZ3oJzvr8336nz4BU5PlIyhmBEwe293LO4kfXvsjdiTvfQOknXNV9sYSGC9cQNmrr7otazh1ynbdkiWStoMQQoh/UYAVdDybqyOUcT1M7rzXJpqpx6rSJz1tFKIbHLfIWfThmzjxvyTRdagH3iaqXEyTY3LUtmJwszaSM4lfhKA/esz2xuiHTSUJIYT4DQVYwczL/WrClFEeX2OMiMb3mAIdVILnL2ne1kXINnM/3JXxLxyJ6eryHoxGXLvuOP67qHL+0CDhLH62xj95wgghhPgfBVgXOanWpum69MBh9MJWjPb4fncan8EJTSqevmyWJG3x9z6IXcKdB4tSqvt1tfU110F7sFiOw1/Vdag1epY+hBBCOjoKsIIOP7oIcTNadv124dV7lRGu977bjiGoQQQAoGfDGetxX+YsGeRKr6/1F7XaMc/F85e8Cpnb5Zltj+M4HGvQwcBKsyJzW3W9z3V8fL4cN+efwthdx9wXJoSQIEIBVtCxBVicuRpqN4vzulQKH/97kPAJQ60lKek6XIUPcafl9aH7RbWMYf3btcSowgEA6gHTJKszIqJK8HisxsvhOz/ms/qxtBpX7jyK6ftPS1Lfuopan+tYeLIIAFCs75g9cIQQ4i0KsIKY2VTo9JxJrkFlTF+wjGcfAWOjbT+/xubequ0jYxAfX+D2WpXZ7HaDZ4NMfFb51mSRncEBYBQat2VVkg2OeqZx1y7ra07gZ1FXfxTV1X97Vfcn5y0bbv8pQc+TP1zQue4VJYSQYEIBVpDhOHGBQ/7gOdg3eA7OdnW9NYzjDYQPp3Q+2qodwJ+XXcor3r3Ufa9Plca3vFOlkWGiyoX4dBfvmSvtegbNjsOMO3dOwYGDM8EwZR7XLXX/oNQh6IuniiSukRBCAhcFWEGmWtfX+poBBwhMx6mO7gltZAYAoDjJxaR0J71Ny6MjsTtuNzhwTr/Uz22NRWbudlRHR1uPpVdqEd3omFvLHuPjKryKiPYKnaQlk3m+aTcrcYjVKNFcrhZGN72XhBASTCjACjI6U5z1dUicDqrzjr/ivUPmeV2/sVGBZTHROBtxFhWaCpyCcGqFhmINFGYzlK1Wv0U3uQ6wkkf7lppAr5S7LwRA0U5DhH4lcfzy3yInE/S8dNpNcE0IIcGEAqwgFpbomNwzpVK6b2ETY4IeapdlDApxAU+L0E6+TYauCJc287ozjOQDcr47KpDMNZAcDvD2EUKIlCjAusiE6ADWXA2O4yADkBEWzjsvRxkA8SkIShAPAFCazOiDk5C1GpO8EBMudBmP0izdCjOzvG0+0oHY/2WgIThCCAkY3i/ZIh3C+TgGaeW2L97O5ZEwaD+DXD0YU1MmAbDlnopWvItwxToAAIMeour/E6NxOY7j8rMFiMIJHEc677ynqxS5gAxdxGv5SbMsC5mM/v1CCCEXK/oGCDKt+zCiGvhHMopiAABm/T6Ha1uCK7FYuxWLUXWWTN1d0X4rxWRRqe127xZnFSy0Ucfw2muvQaejITFCCLlYUYAVbFqtwhvgPBWWG+6Hm46YE7yt3C9UGWOdnGm7XjGtjIM+pBRNTU3Ytm2b3+5jrqvDjNU/ILXEu4D2nbOlePLYOcFcXIQQQnxHARYBACiY863eu1+iX8xZNl9WGqRdzu8t50kj/EDEhof5+fl+u33piy/h3jU/4rNF//Dq+pdOF+PLokrk1zX51A6jn7PzE0JIR0UB1kWGcdKbk6R+hPeeg/h+n8ZzKb41SiRZSIzL86wft6ERy5NwQ3/8uNf30a6zDOfKnfRAaU3iFiosPVvidRt+LatB6h/78EOJ8HZChBByMaMAK8iITOQOmZtfvU4jfiXh9gsTUA3vM7D3rTrr9bU8bRhgSXGngpu83zOR07vOKdXrzwP4tazGbT3rKtzsBu7Cg4fOAADmHPF6HJoQQoIWBVhBR9xXf1r4ANe1iBgCs1cHZ1vUuK8nQ1vsQWnS2jXbNqPkpf84ZN7/54nzguX9Me/qrJsEsoQQcrGhACvoiPvyDFdGuzwvExNgcS7ftj2JAgdGaH+hVrpFnZHkXlJ48r8fovq//8WIIwd4x8sNJsHyR/yQ8HP09iOS10kIIR0ZBVhBRuzQlUbuOuO5pz1Y+lAOm66Iw5mM1pndGSjTr4Rm5EMA41lWd49JNETYBe7nJXUKCbx5RxGNDaLK6f00MX1fXaNf6iWEkI6IAqwgwynEruiTdr5S4QhLb0lFPD93rTk8Epoh06HsPAKKLqMkvaf/2AUgZiD6UzkMBVt4JQJxq5z29sE5zzeoJoSQYEUBVpBhQ6TZdsaT8EEut91zTF4Nvz0aW08Zo9CIvldDZ+m2z/GlZytsqwyhu+XQ7/vG42u1Wu8nkPvTjpp63vsSvTQ/6yZzYKTrIISQQEABVrBRil/957PmXFlpafnWQwqzND07NWnC84dcMTnZh5BRuN6QWuAK66uolcK7SbV/QgjvvXW2lPd+yLZD7dQSQggJXhRgBRlWoqGrarncbTeWqtNWAEDnLkdF1uqsQlu40mC+CvBw/8IWMkWIV9d5gxGRiDVQ1YjMkUUIIcR7FGAFGXNzEONuKX68povP91KEnvG5jta05ulQpl8uJlG6A7WbiftChHui3N/c2SpLT5vNGQzOznhYEyRbRemtnMradr0/IYQEEgqwggxrFBdkRChjvb7H6KOW3huG4xCiqPHgSidBiV2UcwAmyKPT8cFQ+/laDJQ9JnjcTneeggb/QzgiWh0PC3O/QlDOSNMLdHLSJEnqAYDwJsdVhMf8kJLBGRPN+yeEEKsOFWBt3boV1113HVJSUsAwDFatWsU7z3EcFi5ciOTkZISEhGD8+PE4ceIEr0xVVRWmT5+OyMhIREdHY+bMmaiv50/63b9/Py6//HJoNBqkpqZi8eLFDm1ZuXIl+vTpA41Gg4EDB2Lt2rWSP6836i9c5vd7PP4zC6WJw4pXzLj+q/WQ1VmOMwLL/00yOTgARyNk0CmE5zPZ2wgTIAOORCmtxxRdRkIz4Bapmm91HVSIhQzXQMU73qPHLrfXspw0f3RMRcXuC4n00KrvHI7NOSxRlnxCCCEe6VABVkNDAwYPHoxly5YJnl+8eDHefvttLF++HDt27EBYWBiysrKg09n+FT99+nQcOnQIOTk5WL16NbZu3YqHHnrIel6r1WLixIlIS0tDXl4eXnvtNTz33HP48MMPrWW2bduGO+64AzNnzsTevXsxdepUTJ06FQcPHvTfw/uV510PY47argnbZMlvlVzqmM37rx6DMDIrAnddEobx2Q+4rZcBoEy9FEojA5PM0rUli0jyuH2ekPIPQYPM+c9Sf+qUhHdyFN7kmIeqUeTKvvcLy6RujiB/ZJEnhJBA5L5LIYBcc801uOaaawTPcRyHt956C8888wxuuOEGAMCXX36JxMRErFq1CrfffjuOHDmCdevWYdeuXRgxYgQA4J133sHkyZPx+uuvIyUlBV9//TUMBgM+/fRTqFQq9O/fH/n5+ViyZIk1EFu6dCkmTZqEJ554AgDwwgsvICcnB++++y6WL18u2D69Xg+93f5xLUv4jUYjjEYJUxI0Y43iNxJWMZ5vOiw0BUmtdz9sZmYYhw2KObuZUC2vpud0xYaBwDX7/BuUAECEwEwslaoRBoPjcGsTo8MZvQyck2zvR1RmjLZ7b9r9BbjOI4G4njg95VrBa8T+/r35nHDgRF33/KkiPJDsuJm2p/d0V/630mqMjdR4VXdH0vJswfqMwf58QPA/Iz2f73W706ECLFcKCgpQUlKC8ePHW49FRUVh9OjRyM3Nxe23347c3FxER0dbgysAGD9+PGQyGXbs2IEbb7wRubm5uOKKK6BS2YaNsrKy8Oqrr6K6uhoxMTHIzc1FdnY27/5ZWVkOQ5b2Xn75ZTz//PMOxzds2IDQUM8nZztnmVFkbFgv+gqGEZcB3JmWIUIhapY/ifuJR/+JJUtf4h2zD7e2weT0XJtoDhLT0vNx4vglDqefSH8Tp8o06C8T1+OjWP0oAOB/Q79ELydl7IeXw1tPCHNSzlldrZ1p1OOHtb9hjzIMQ42NCAMLRKY5r7/VOZdD3wL1OJRvVebr/IMw6qsBADk5OSKeoGML9mcM9ucDgv8Z6fk819gobteKoAmwSkos25skJibyjicmJlrPlZSUICEhgXdeoVAgNjaWVyYjI8OhjpZzMTExKCkpcXkfIQsWLOAFZVqtFqmpqZg4cSIiIyM9eVSXPvztz+ZXnueR8lbYNjlq7xLuvUoyVPDe7+3juMm0kg2ctAFMcx6vsNAawfOnNOcAAEWMZ1nLJ0+ejJNPPe30XIutf2YLlmldzlldrbEMg1Vd++GPmnoUJHXFtwPTgb+E815NnjzZ4Zz9PR0I1ONQvlWZ9Ix0TEgdgZycHEyYMAFKpRLByGg0BvUzBvvzAcH/jPR83hObRDpoAqxAp1aroVY7JrxUKpV+/3CzjAwVnQYiuvakR9e5SqYZJeG2c7cf/x2relwheM6gkCNELV0A6gm1ut59IZFc/Y7F/v69/Zz80Zy5/a/aBo/b4ek93ZU/1KC3lmmLz357C/ZnDPbnA4L/Gen5vKtTjA41yd2VpCTLROjSUn6W6tLSUuu5pKQklJXxh3ZMJhOqqqp4ZYTqsL+HszIt5wPN2a6TcHDAQ/jrUsfVkN66exN/DtKesoFoMIYB4KdjCjO5TxMQZXAerVWHaaBKFw6+/KJ5iFATUodRo39uu/uKsLXKxVhss82z7kBW7h9t0Brv1JkDp7eSEEL8KWgCrIyMDCQlJWHjxo3WY1qtFjt27EBmZiYAIDMzEzU1NcjLy7OW2bRpE1iWxejRo61ltm7dypvElpOTg969eyMmJsZaxv4+LWVa7hNozqS5y7Xk+8Yvy/IfxP5zgwEA2rO2jOozi370uW5/s5/nJauxzBlTKp0lAG0jAr+SW0VO+H/6S+GFFoGAFhESQi4WHSrAqq+vR35+PvLz8wFYJrbn5+ejsLAQDMNg3rx5ePHFF/HLL7/gwIEDuOeee5CSkoKpU6cCAPr27YtJkybhwQcfxM6dO/H3339jzpw5uP3225GSkgIAuPPOO6FSqTBz5kwcOnQIK1aswNKlS3nzpx577DGsW7cOb7zxBo4ePYrnnnsOu3fvxpw5c9r6RyIKJ5O3wU04TFdYgs6a09JN2i+Ij5asLin5sH/0RY3iK0LIxcKrAKumpgYff/wxFixYgKoqS9brPXv24MKFC5I2rrXdu3dj6NChGDp0KAAgOzsbQ4cOxcKFCwEATz75JObOnYuHHnoII0eORH19PdatWweNxpYV/Ouvv0afPn1w9dVXY/Lkybjssst4Oa6ioqKwYcMGFBQUYPjw4Xj88cexcOFCXq6sSy65BN988w0+/PBDDB48GD/88ANWrVqFAQMcJ3BfLHpXF/ql3powjftCAahRqUZe114oVXmfMT8YUYBFCLlYeDzJff/+/Rg/fjyioqJw5swZPPjgg4iNjcVPP/2EwsJCfPnll/5oJwBg7NixLhMVMgyDRYsWYdGiRU7LxMbG4ptvvnF5n0GDBuHPP/90WeaWW27BLbdIn11cajp1tNsyMvg+mTteucPnOjoW16HCbwPGoDwyBvd0ehnr9z7cRm0KLEaBzP6EEHKx8LgHKzs7G/feey9OnDjB6xmaPHkytm7dKmnjiO84xv3wYITiB5/vc+PxnYLHG8tUSNF5niU8VO4iIZTEWocBVYxjwk1PlUda6tgX2cfLGgIjOKk0eJ/uwyTwj6HAeCpCCPE/jwOsXbt24eGHHf9F3rlzZ5d5oEjgYuB7ptt+54SPa8+G4v/Of8s71qAJES5sp3+M//dUbNGj1R+DGll0m907kCRtznc41v9v77d/KtI7LhRoy82nCSGkPXkcYKnVasEkW8ePH0d8fLwkjSLBRWPm71F4KKOn22u6RQxC36gxkrWB0UQ7PXc57HKaiBzWkjPi9vi7mOVpJUyWRgghHYzHAdb111+PRYsWWdMYMAyDwsJCPPXUU5g2bZrkDSRtQTioENpv0Bt3lazhvf9+gvCefK0Nir1SmgYAkIUluC8EoPu5U+hVWgaZ2fXDR6rFZfIVgzZADgwcx6G4uJi3ZyghhHjL4wDrjTfeQH19PRISEtDU1IQrr7wSPXr0QEREBF566SX3FZA2xUmQ40ossSkk8/oO9FsbNPIw4RMifwxbmXkYcqEI3c80tNl8IVNRURvdibhy4sQJfPDBB043bCeEEE94vIowKioKOTk5+Ouvv7B//37U19dj2LBhvE2WSXBQeJh0u0HGILKdV471jhwpST0KrQb/h0/xPzwiSX2utPRg2fdkyWVOJrURvzl40DLfrLq6up1bQggJBl7vRXjZZZfhssvabiIyaXtDTnsWLO3SaHB1Y5OfWiMOw0iTO7cUSdAyUZLU5VZzYFVTY1uJKZMX++VWOjPNHSOEkLYgKsB6++23RVf46KOPet0Y0j6UTnpLZB52Ru3RqNs/wHI6Fuh6jDDC0IA6lW14sS2HVlsCLJOp1m3RBrMZN+45iaVe3mrJmbZZ6dtgNsNMc8sIIRcxUQHWm2++yXtfXl6OxsZGREdHA7Bkdg8NDUVCQgIFWAHE5+83D6/vuVuBiqZwUWUNCiVUJt/TQ7SWFt7Pq+sez/sOz2XOtL53F2B5Gn7VVblIT2AdIrTvXRL+2aworsL+eu+D2I/OV3h9rVhVRhP6/eV9eof2wtD+R4QQCYkaTykoKLD+99JLL2HIkCE4cuQIqqqqUFVVhSNHjmDYsGF44YUX/N1e4gGtIcKnTfM8vbL3ThXKD0SCNTpe2aeev1HxM4887nW7XFHLvdsHMVPv3563n17b7fxkc4BlNjdYDzFMg2BRoeSdnmhi/T9EuLWqzu/3IISQQOfxhJV///vfeOedd9C7d2/rsd69e+PNN9/EM888I2njiG98HebyNk2Dod6xY3Rg/Qne+139B3tXuZ+Ejf0nL+GomEcPV4rfYqix1jHpZmsc767CvzvnQ6CBQ99Bt8ihHixCiJQ8DrCKi4thMjlun2E2m1FaWipJo4h0OIkmfXuCNfLvGRhTcdx/eQ6CbVshTsSXbc/o06Lv7qrjyLp6kPdzEr6/ITB+mC69W0h/DxBCiMffvldffTUefvhh7Nmzx3osLy8Ps2bNolQNAag2MsPraz2d5O7M0RUpCK3zLqt3hDJWmkaIYB/SiOr9kyoTqzVoct+D9eE5z/d1bGsnGilRJyGEeBxgffrpp0hKSsKIESOgVquhVqsxatQoJCYm4uOPP/ZHG0l7kbCzZNy6bQ7HtKFOkoLamdzlQUnu7+mjuAuwOHg+R60mspuTyponucO+m0u49jKRmy93qqnypGkENERICJGWxwFWfHw81q5di6NHj2LlypVYuXIljhw5grVr1yIhQdx2JMS/WoacmlhvwgAbKb9u+lafcjj22yVjJbyDa41q9wtmPZnfVGdmwIgM2wyM5d4nejjZSkpw2E+4LWIDxVGH94ks6R22AwxVEkJIe/J6gk6vXr1w/fXX4/rrr0evXr2kbBORSOu5xhznWVoEqUbAnFk+7S4UJib79ybNxMypConMh75RhrrzGrf7A5aZxP/RKVbHQcPUoi4yXfB8U3MGcV6gxfkW3k7btM6n6925oJc+xQYhhAQTjzO533///S7Pf/rpp143hkjLaFZbX3NsI/S1gbfH2oznlmDzrDv8fh+5KtJtmYe1Y3D6lyQAQF/TKWCYmwtERqAs5EhRHcZpfabgee3atYieOlXUEKFY3S8U+nS9O9VGcUOVHQkNERJCpORxgNV6ny6j0YiDBw+ipqYG48aNk6xhxHd7G8KQKrcEWWbjSY+vD6avm06DZnhUvqFU7bbMsIR9OFLZG40m17m3fo0fi17FzoMx/cnm302ADrsZWQ5KGf/TsLigbTLCE0JIR+VxgPXzzz87HGNZFrNmzUL37t0laRSRRhNnxrkuVzW/8/zL299DhPx7se2SUqI1Vd/rYTi6RlTZMcl7MCZ5Dz49OB1/F412Wq5K6br3jNW2JOZ0v4rQE1889zjm/uNZaMPd99658umFcjycyp9fWUM9WIQQ4pIk32gymQzZ2dkOW+qQdsJZvrDPmJtgUlh6V1jjmXZskHsqc2B8Yat7Xwtld896Yu8f8LX1tQJmh/PuAke23pKwlJNy2SaArqVFuGPDrz7Xs61GfEJVMSqDMDgjhJDWJOsyOHXqlGACUtL29Nr/AgDKOMv2LxyrA2t0XMXnjlR5sMRgA6j3QBae5PW1gxTFDsc+6HIrGBHdgXKZ/bCkND/8zmW+D+V5MnKpF7EVj9bkGIQSQkiw8XiIMDs7m/ee4zgUFxdjzZo1mDHDs3kuxE84fqJHjq31sh4J2tK6Lo6zvG41p0fMKr+OIILxPsmmwWib3yhXnAag8rk9l+9zsQeiHxyqc7+nozFA55rRECEhREoeB1h79+7lvZfJZIiPj8cbb7zhdoUhaQ/ef5n54+tGmV8FWZUe+iuSAKWlA7UkNs7nfROlxKjCJa9TDtd7EZqqq3Hq1GLJ7yuFDZVa0WX3iMjY//TJYjzgS4MIIaQD8DjA2rx5sz/aQfzGh0SjUnc0cBzkZToAgLysCebOlkzu9SGhEs8+8o0yZShQDkAvfihrROIe7C4d5jT56LCwn3FMd5XgOQBo3LkLUHra0rajM7PQyC0BMctx2K0VDqRKROTH2qVtDMgAi3qwCCFS8ngO1rhx41BTU+NwXKvVUpqGAMBxrXpKGABwPy/G7zggTVdk/5YvwL7cOEYJebH74a4WswZ/DgBIl1cLno9VnnN5/YXHHnN7D7mXCwFUBte9Z2KY7Ib1VpQ434bnoIghQuJeU1MTDBL83ggh7cfjAGvLli2Cf/B1Oh3+/PNPSRpFvGc2HHE4ZmxYz3vfOdR15n2OA6bsZDH0lLT9SgwrXSJNfzOZE6E85tncNaUsML8QUypKJa3PVQ6sLdV1Ts/ZOyl3n2fsYqXX6/Hqq6/i1Vdfbe+mEEJ8IHqIcP/+/dbXhw8fRkmJ7S9Zs9mMdevWoXPnztK2jnhOYAIxx/J7HC5LvNFlFY1lKszYLH2vl/xsA4AIyxu7+Ko6Mgo9as7jeExXye/pLV2l5xPM0yLPATo/NMZHM9b8iOcfnOdTHfbhcLEE2+S8FpYE2blyzO+W4nNdUgmUIcLSUktAbDbTaktCOjLRAdaQIUPAMAwYhhEcCgwJCcE777wjaeOIN/iBkTeTx40NcqkawyMr16ElwFKcb4AhxZKja/E9j+CVV17CnKuyXVzdtuSdenh8zYJRS/Hn1rv90BqL9KLzOJWa7rf6XTmnN6CPIkTSOl89W4Z5GckBE9gEisOHD7d3EwghEhAdYBUUFIDjOHTr1g07d+5EfHy89ZxKpUJCQgLkcv98MRPxTLodvPdmib8UfcHpbcGfrNo2nFYRHYvutUVCl7QbzdB7AIhfPdcWVEbveo4YCdIizNhfgB2Z/Xyup7VNVXW4upNvmeZ9pdVqsWPHDtTWepnORGJlZWXW1yaTCQqFx2uRCCEBQPSf3LS0NACWbXFIAONcTzKWgR8EyxnHwIZj/dWjQD0VvpB5GSipjb7PDTur88/8sun7T2NZ366YlhTrl/rF+Pbbb1Fc7JggNhAcPnwYgwYNau9mEEK8ICrA+uWXX3DNNddAqVTil19+cVn2+uuvl6RhxD9aD8ckqx9yKFN5VPo8UO6Cq0BK0xCoGM67f9yMOZgvbUMkNvtIYbsGWGKCq9raWkREREAma9v9MmkeFiEdl6gAa+rUqSgpKUFCQgKmTp3qtBzDMPQXQoAo7iTck8Xf7044rDHW+2tIwnmQtX3AUD/d03uXtPrjsT7J95/L6fRr0e3Maq+uVRsNGHLsEA527+1zO4h4R44cwYoVK9CnTx/cfvvtfr8fZ9dTefr0aQwdGnh/Nggh7on6xrAfFqQhwo6hLFrMli1t97tsLHe9Ku9/V0zAlTv34o/OQwJmJHExQmGfdOBfg0MQX+hbnWfSr4FRGY7eJ77z+NrX334ZAPDL5Vf71gjikW3btgEAjh492ib3sw+wDhw4gGnTprXJfQkh0mrb/m7SZiL1TiZo23VaKRjXyS+lxLEMnlQ4Dyp2DByKp3d/jdn7f2qzNrWXC50v9+n66//c6PlFAbr/X0fQ1qsc6+vr2/R+hBD/ENWD9fbbb4uu8NFHH/W6MUQ6BoUZ7uLnBNXTbdOYZiHQA+CQgkoUIU6wzLUFucDg+9q0XS7JVYDZMsF70rYtyOsiTbXfXy7DrX+2XQ+ijOPABnA6hDK9EQnqwNwriDKqE0K8ISrAevPNN0VVxjAMBVjtTGU0YtSFIpzqItxjEaNOtL6WMY7/Um7Q+y/VxgT5HpyRTwcALDXdhJfZRwFZ4H7pA0DEde+ibtXDADg89d8PcOsC7+Zhpeu+wRN273f0749b/zwgSRtFkaAHy8xxkPspSKsxmQM2wLJPqtwWOOptJCQoiPq2KCgo8Hc7iETGHS+EzATMXQNMyDehqCf/vIxx/SV2f2gynvPz3CyOBR6V/4SPDk1FxcA0h/P6479B3esav7bBE5pRD8NceRLmyhMAdwEqTgmDzPOcVBw4MM0TzKYcfQTAbIlb6lzvwgIcyXCdPLVHcSG6nS/E78MvASuwWk7PcgiV+yfACrQwu76+HuHh/lhN67mzZ89a0+R0VAaDAUqlkpLKkouKT3OwOI67qP+1tWzZMqSnp0Oj0WD06NHYuXNnezcJe3ulWl/3uQCM2zIbk/edwuR9pxCRYMDpjHhsVh5EI4QnwYeImRvvJWOTDCY9g6Pfp+DoihTsKeQPBTZoLElRDYd/Rt2qh9CwNTD2YlOmDINm4K0IG/sv/Hb0Pfzv2FLEG2M86hRiQ+XISzqEsCRbr5XZzXeNIU26QPe9xf92eT62vhbjj+9Bt8YKvLD8DcEyvf7cDyPr2593ueEcQmtWgmEbecdrTW2/+vi5557Dc889J3ju9ddfFzze0NAgqu7y8nKUlZV5taq69d+pn332mcd1BAqO41BWVob//Oc/eP7556HX+/EvGEICjFcB1ieffIIBAwZAo9FAo9FgwIAB+Pjjj6VuW0BbsWIFsrOz8eyzz2LPnj0YPHgwsrKyeFmY28Pww84nrl+ecw63fbcCI1a+DfOqudh5PpKftIEDnvrBf71X9Rc0qD5u6xU4tToRtxb8gEijZUL+tW9+Cg4clEkG1IWpoel5CEWn/ocmeUv7AmcF65TqK3BUx//jc0InQ4OiAUUhRVA28Yf/DJcn4a4rliL1irfR59YHkXrlElTMNoFVOg9YKp4yedQmDoA2MgI6tecbKd+St9n6+lz/noJlTByQ+sc+j+u2F1vyT4Rpf0F41SeQmWsgM9eAMdfj2j0n8MWFCph8DODE+vzzz0WVW7x4Me/9l19+CcCymprjOOh0OhQUFKCpqQkmkwmNjY1gWRYffvghLly4gO3bt3vctqqqKveFApzRaMRzzz2H559/Hu+99571+Msvv2w9r9UG1k4JhEiN4Tzsglq4cCGWLFmCuXPnIjMzEwCQm5uLd999F/Pnz8eiRYv80tBAM3r0aIwcORLvvvsuAMtfuKmpqZg7dy6eftr95HGtVouoqCjU1tYiMlK6rUKO9Onr1XUl0UBSjWTN8Fh1GMA19+hwjCVYiKtzXl4ezzaXZ8CBwaGwHmA4IEVbBb1Gg2NDhvm1vfWsHlqOJj8TQkgg4zRxeP7VFyWtU+z3t8czdt9//3189NFHuOOOO6zHrr/+egwaNAhz5869KAIsg8GAvLw8LFiwwHpMJpNh/PjxyM3NFbxGr9fzusdb/vVmNBph9HKPudY+/M8i2G/DvXZwd+trE1jo4tW49XfhXD7tGVwBQIy4kRcrczm/96gfTvLeJxQF1t6GhBBC2t5314yT7Du2hdj6PA6wjEYjRowY4XB8+PDhMJk8G9LoqCoqKmA2m5GYmMg7npiY6DQZ4csvv4znn3/e4fiGDRsQGhoqSbuKS+tx5OpY9N1YhSMj5IDdZ0ABGUJrZci75TEMNqUjhgtDhOx6nCkPgyrChO1qDU7WheKu3/08RCPjgFZ7Hb5/pxIKxXCUsjWQKxMB8x+okcnxr52NiDxsGe46kZ6MnmcsW5rUh6txrF8ybPNlGaDKBIbjkFRVCpYBGjX+3Xj84p15SAghHYhMhbVr10paZWNjo/tC8GKIcO7cuVAqlViyZAnv+D/+8Q80NTVh2bJlnlTXIRUVFaFz587Ytm2bdZgUAJ588kn88ccf2LFjh8M1Qj1YqampqKiokHSI8O27bnR5/tKEG9ElrBcAoIvmWofzAzO64vuX/RMo97yxGFVHwlF5NAIAwKoZXP3WN9bzz370FsbucfzZRUz90C/t8cVK/RJwZccQFschXs5i9RgZdjXx/71SH30bmiKvtUxuM3P4EndALjeB42ANDk+dl6NguxJX7mehXOQ45Bj9mRwhu2SAEmgazkJ5WgZZOdCgYRCmY1AdG4u4ykoAQGlCAraMuwoAcOt3KxxW5l31/rcAAJnZhLTiH3EufiRM6m4AgEf+WMUr+3nmJBjlSjDgYJLJASlWf3EcOp1/CDJOB5MiGUZ1D4Q0/AkAKE/90nqPg2P6IEohLkBuMjWhUleJLuG2BGUcx7ldrfbSSy/BDDPkcH6f+Ph4lJeXOxy/9957Rc3hSkhIwB133IGwsDCPVs+99NJLDsfGjBmDq69umwz+LMuiuLgYKSkpAIQTrRqNRuTk5GDChAlQKh1XJv/yyy84cMB5GpKBAwfiwIEDePTRRxERESFd4yXk7hk7Ono+72m1WsTFxUk/RAhYJrlv2LABY8aMAQDs2LEDhYWFuOeee5CdnW0t1zoICxZxcXGQy+UoLS3lHS8tLUVSUpLgNWq1GmqBycdKpVLSX/7o4nrsSHa+vLygbr81wBLSxWiEvxbNy1Uc4gbUQ1ejBGtiMHHGB7zzQsFV2MSX/dIWX+X2YjFjih6a5pHK6QBu5wz4+8+7AAAfXHEDwDSfZBjIqnSQx1sC15qTV6F07x3ot28OLq8GLm9Oi+EwqGkAQnc1BwBGIHS7LRiIbAIAzhpcAUBiWRlu+26F27azcgUKutzGO/Z394G49JTlC7HXwcPQXTlV8NpzVw72fqI7w6CyyweQmavBKjoBAOo72TYbL7lqiMdVKpVKRIZ4/g+U4cOHIy8vz2WZ2bNnw2w244UXXrAeu/3225Gens5bfciyrMMm0EajEWvXrkV4eLjHf76VSqXDEMSkSZM8qsNX6enposo5+/tr2rRpuPTSS7F8+XLe8SeeeAJhYWHWMh2B1H9HBxp6Pu/qFMPjAOvgwYMYNswygfjUqVMALAFHXFwcDh48aC0XzPlOVCoVhg8fjo0bN1o3v2ZZFhs3bsScOXPatW3KpmpcdbgCZZFh6Fxdhz8ufwv6Glsm/iqD66SJa88X4yhS/NI2hgEYBYeuY6twr+FJaDM6OS0rC09CyJjZkIU6L9PWdAdX4uyIkZBzMhwJOW0NrlrIGVjzXFmDq2aqvVV4kHsbT+qMMOstAUG48H7cVtHfSjfM+eXkm1yeP9C5G27I3YTIOi3WjLxSsMwl0eFQ+poYlpFZgyt7W0f18a1eD02YMAFHjhxBeHi44MrfqKgoAIBczv8d9Orl+I+T1sGVr8LCwlBTU2N9f/PNN0taf1tJSkrCc889h/r6eqxYsQJDhw61BleEXAw8DrA2b97svtBFIDs7GzNmzMCIESMwatQovPXWW2hoaMB997XvNi87uqZg2Jli7Opdh/0mIIzh/4rNnOvhP3+GxUfYVLxkugu5bD+YIQfkti+mz57/h/V12KTXIdNIN2zqK8PJHOgPrgQAzLvF+8//tY0qmI0h1vfhOtflQ3ZI98W99pKxrgswDJaPvwm9CguwbdBwwSLf2y2akFrPUM9TS/hCo9HgySefBADBXFh33XWX4HVSB1NCWvd09+/f3+/39Kfw8HDMnDmzvZtBSJvzbt8Pgttuuw3l5eVYuHAhSkpKMGTIEKxbt85h4ntbMyrk2NGjC84kFmBnHxkeabWo0X7KnYFNh0p2xqGOqG4NqD0t/b80ezPn8Rc7UPBceskF6+tACq50+76GseAPSerqZbR9OVeGul/lyLRxfvOKmE6oiHHeY6jw47ZGgdbjHR8f3273Zll+vrdA+9kQQsTxOMDS6XR45513sHnzZpSVlTn8ZbBnzx7JGhfo5syZ0+5Dgs4k1ggf5+zWv1UankWyxrHHLWGw1i8B1pfmCYLHN8+ypfx4+tKHEUjLJOyDq2U33w3gW6/rsg+YBu4XzhROAs///d//4b333kNycnKb3O9i3h2DkGDicYA1c+ZMbNiwATfffDNGjRpF/7oKUKF1qQAuOBw3c7bJs2bE44JuBTpr+BOeFWoOa0YymLJL2r/oKznXPVNf9snCvnjhLOKB4HS69/OEzmjuxDL8bH3f57z3W4asG3MFJm3f6tE15S56pi524eHhqK933Pi8RUJCAp555hkoFG3T4U8BFiHBweO/MVavXo21a9fi0ksv9Ud7iERCjOJ+tRyEe6q+GC9HbJ0ZmUel+8ve7GZnpm/7CPdwtZfX0YSH7d6/v1uHazOdFhct9dxGr69ddP9c7BgwxKMA61TnVMHNm4nFXXfd5bDarbW2Cq4ACrAICRYe/63buXPngM1bQqTFSPj3vCbGgL/lwvOvAtUqtMrW60NnbYM5BgCQdnYdep76yas6DAoFNo+8BDq1xqPr/h7kmBiY2CQlJWH69Ont3Qwr+wBr9OjR7dgSQogvPA6w3njjDTz11FM4e/asP9pDAsiO3hIO/zJAXUKM09PHo7s4PRc4vP95VJq6AgCia095Xcen190KwPMs8vUS7RQQzAKp18h+oYyzvHqEkMDncYA1YsQI6HQ6dOvWDREREYiNjeX9RwJfrcExO7WQ+hD3ZTxhTHWeAHVXonebVLclWYjzALEt1IVahnM5D+c9rr6sbTKAd2SBFGD17Gmbh9iWQ5OEEGl5/Kf3jjvuwIULF/Cf//wHiYmJNMm9A9pc/B2mps1t8/uydtufGPtF885Vq50HX4GCUXm/srLlT4mmqcLrOqqiopsr8+zPXJOHQ4q+iFcpUG7wbKul7PT2TW0CBFaAZZ8lulu3bu3YEkKILzwOsLZt24bc3FwMHjzYH+0hbUDPituoUlIMwNkNsXFKfucpK/Pv5sztz/IFHtbkmDVcLJbxcqK6xP8ISg9R4UyT476JAPBYWiKeOeG4etWVJ9LbfxgsNTW1vZtg1b9/f/z0k2WeXkiIxN3IhJA243GA1adPHzQ1udnjg7QbRchVMDUFXrZ9BgBr/0XfKlYInP4D6cUYayWpR86aJanHV0oXAVuoF6sVA6EXPCwsDN26dcPp06fbuymQy+V45plnwDBMm2SOJ4T4h8d/el955RU8/vjj2LJlCyorK6HVann/kfYlVw9q7yY4xUIGU/cImOM1YOP5w1b74/y3DUt7izXWgpEghJS1SuobkNo/VvJaaAAtBlAoFA77IBJCOhaPe7BadnW/+mr+xFmO48AwDMzmwPhX9sWKYST8S1nybiUGph78ZKNfPjsfAFAU3n5bk/ibTMT8nk4PP4wivONzPW3B5RY+gdFEQghpd5Ju9nzgwAGfGkOCGNNqiLBZeFM7zAcToeGPl4ErZ0tSFwPXPU8R10xC/LzHgM22AMtsTgNQzCu3fcBQj++9YdRlHl/jTrxKgeMS/dpuSmzflZmEEOIvHgdYV155Je99XV0dvv32W3z88cfIy8sL2L35LmaK0PEwNf7e3s1AmcoxjUdMXWAOK7P1pTD2iZKwRuddO/Fz5wrMQ3IMRo12q8tE39UP85ve7JOKUduPSFJXksrzZyKEkI7A6xmUW7duxYwZM5CcnIzXX38d48aNw/bt26VsG5EII4tu7yYA4GCQqdq7EeIZG2FO8yx1RHFDgpc389/EpZ39pVntu254L+vrriFqfDkwQ7hgB56DRQghUvKoB6ukpASff/45PvnkE2i1Wtx6663Q6/VYtWoV+vXr5682Eh/IzXq4mxVnYpOhkBW7KXXxMNeehzrK6L5gKxfqUpyeY1ztO8T5b/L6hXhpUiAMiRQ5AdzDOVjRSprITQgJTqJ7sK677jr07t0b+/fvx1tvvYWioiK8847rSbmk/an0tXDXrcChfYZpRhze3y73dUeX9wkYueeztVkXP+diQx/nFwbI5PW2Ni4mHDO7xLV3MwghxC9E92D99ttvePTRRzFr1izeVg4k8AXEqI1AI7qWeJaQ0hesWQ+ZXO2yTB3HQXViPVjtBcCLXZ9KGhIhNGWbA4M9DTcho2C14HVcc/qFiIgBqKs72HzM+6zx9iqiA3cS+Rf906CkVASEkCAlugfrr7/+Ql1dHYYPH47Ro0fj3XffRUWF99t+EP+Rq/rb3jAMGHknr+rxd2B286bf/HwHG2PtObdlfmQMMBz+yav61xaMx9qC8YLn7i1aBSMXCpXByYT+5g6srl0fsB4yGjO9akdrldGBuT9oktnzIVhCCOlIRAdYY8aMwUcffYTi4mI8/PDD+O6775CSkgKWZZGTk4O6ujp/tpN4QKboan3NAWBkrufP6NkBfm6RcLCWXClu02kp+DuH1I8nroeRtU3iV5ht+/Gl64oAAEqTs9wGlrYxjH2Hcsfs2eEAdFK67xi/3Eh/XxBCgpvHqwjDwsJw//3346+//sKBAwfw+OOP45VXXkFCQgKuv/56f7SReEimTPeovI4d6Z+GBBBGRIDlSY9dvMI2Mf23gnHirinPFz7RETK0txIm920Ll6sMFGARQoKbT39L9u7dG4sXL8b58+fx7bffStUm4jPG4bVcM0pk+YuXJz8F+z84e8tcp0L4o+Kx5vqFgzxFQkLzefsWBPbE96GRvs0R65j9c4QQIp4kO4nK5XJMnToVv/zyixTVET9wlQuLkyDA2jTITR2tTn/00tM+39MfZEpLb1JYkt5lOU9+YgllIS7PKzp5N0eurTwgsNLP1fNzAR4cEkJIW6Ct2i8a/u2l+jnT9UepdQqDHufPiqp3R/kar9vEJ+5Lv9s1ZUgeVQ39MNdBkSe8+8kHTpCiYuivCUII8RT9zXmRkKt68d6frT9k987zEKCs9S4ybqooCOni8T0A4Ez9Qa+u8wYDQBnKIrpbEzzZM7u4IdFtmeTiv0W2IPB4utuOy82gA5jjdkWEEOI9CrAuEgyjhCribut7I2twe43BxWKwkhj+l5HeTa5Sk8wWsfQ/dcztvdufuC/bf/zxPBpN7rOcd09v+7QEzz44r83vSQghxIICrGAn6l/lwmWOdHV+7es32T46jz4sh8nNJ8l+wOsfX30kok0SEzHi9idM7gvZKdR2RrVeXCLPCLmzFA12JO5A2TpstLQVSmRgmKa9m0AIIX7n0V6EpGORuYwqbOfMnHCQwLkIznRqBrcusH18whtt9SlCzDA18cfYapUR1tfpbZjB3cZ9hFUEz9IlNBjFr6TTaNRwPW2+tcCZgyX1wFm8iv7aIYQEP+rBCmLjI02Irjlhd4QTeAWYuHSf72W2i6cYmWNwoJepHI4FGo732nVY4Sz2PGW2ZE6/+kgeAGDC3kYkpEVA3b27FE0MCnckBe72PYQQIhUKsILYmDATYqrbZr5Tk5rBkUwD4gdpIVc59gS56g1rG+57hGo96DVylre0JTDLqCzGA3/+gg8fHIFpTw5HxAThbXTsddTJ4Z5K0wR+sE0IIb6iACuIOX5dc05eS+P0UBPi+tVLXm970KDJbZltxa4z4CtYFiERKsjkMjCajjvv6OII+wghRFoUYBHJMC5itjMpqW3SBjNnlqQeBdyv+ttZPMzhGMcJhyPK5mzt4gXOHCwhFHQRQohrFGAFMYVDxGP3teiH729XX7qfXnuL9DcUUGsoEzxuLMz1qB4VXKexYBhpMuC3qlXi+qQxMMIxDYWzj09HnsDet29fAEB4eHg7t4QQEgw67t+GxK2IVskyGXl829y4VZywbfYo6NpoiMzZPCZW69nKRRnD4kPubpdlhPYWvMBGoScqPLpXoLsuvnVWWecyo8M77FY5ffv2xf3334/4+Db6c0IICWoUYF1E7DNVu/oSzMMAAFrJ7ns62jY8OObAHsnq9bcwNALwbEL2GdaXFXKB2YPlSYbzwHwCcRiGQdeuXdu7GYSQIEFDhMSBFt4NkQj16ADAf1Out74O1bmfPG4vTOc+43zrVnjjVRGT2v15f0e+9QI1qdQStYMQQog3KMAKRp5spCegBJ5OyG6+rZPjBl4OLM8CkIzyGo/Ks/B8kvsdqMevrSa1iwlvOIn/+ISEtM1CAEIIIf5HAVYQYhg3GwMCuNB4wum5QnT26r6hrPuwZOThfR7VGdOg86g8ywlnY+eMzreqYQXCKTF5u8ycb4Fsa+HhvW1vnNz+oS6BMT/Il/61xA48EZ4QQsSiAOsiVWtwPhG7Cd5NSJ+urXNbZsjxw6LrO6nd63EbnIVFXGOlqOvnJjyOepUKB/tGuCzXXnON+oeHiCvYjpOhOildB1Cxbs4TQkgw6DAB1ksvvYRLLrkEoaGhiI6OFixTWFiIKVOmIDQ0FAkJCXjiiSdgMvE38N2yZQuGDRsGtVqNHj164PPPP3eoZ9myZUhPT4dGo8Ho0aOxc+dO3nmdTofZs2ejU6dOCA8Px7Rp01BaWirVo0oqvF549ZyebcTO8rXILftFsnuFOUtvbiepSvwKu5KmAgCA4fRmr9vkqR8jr8Yvw/uiPtz3IGDQoEFuy6j79HFyxrc5WEa5Z+3/bnA3j8q7it+GR4rfo5EQQoJVhwmwDAYDbrnlFsyaNUvwvNlsxpQpU2AwGLBt2zZ88cUX+Pzzz7Fw4UJrmYKCAkyZMgVXXXUV8vPzMW/ePDzwwANYv369tcyKFSuQnZ2NZ599Fnv27MHgwYORlZWFsjJbfqX58+fj119/xcqVK/HHH3+gqKgIN910k/8e3geMiw2MC+oPoLDhiGT3MvphOxw5x4FrqhZdXm/2fLK6wACh07Jja0cAAMZHuk9EGhLivrcp4R//cFvGntgf8cHuvd0XsjM2NhLL+kqzgq7dd0UihJAA0GECrOeffx7z58/HwIEDBc9v2LABhw8fxldffYUhQ4bgmmuuwQsvvIBly5bBYLCsRFu+fDkyMjLwxhtvoG/fvpgzZw5uvvlmvPnmm9Z6lixZggcffBD33Xcf+vXrh+XLlyM0NBSffvopAKC2thaffPIJlixZgnHjxmH48OH47LPPsG3bNmzfvt3/Pwg/0ZktvS2H0NPrOpQtPVgSfcHWGasRajCBbaoSfY2J83TVYStuooMniu7Fs8lNGBbqfjI9J6JHj1E6my8nfK1SZPTSXns/UmxFCCEWQTMZIjc3FwMHDkRiYqL1WFZWFmbNmoVDhw5h6NChyM3Nxfjx/E13s7KyMG/ePACWXrK8vDwsWLDAel4mk2H8+PHIzbVkAs/Ly4PRaOTV06dPH3Tt2hW5ubkYM2aMYPv0ej30er31vVZryTNlNBphNLrvDfGeuK+8SuM/oTHvwP9U3gUof54979V1rmiNluFE0/ld0EekQN17soirfPyKV7r+N4cMMsQoxA3fsSzL+90yKhU4A//nK+va1aPf/4ToUAyNcN8z1hDimH3dFaPRCLNZOGgUap/RLNwzajQZwbLOe03t6/Pv5759BfszBvvzAcH/jPR8vtftTtAEWCUlJbzgCoD1fUlJicsyWq0WTU1NqK6uhtlsFixz9OhRax0qlcphHlhiYqL1PkJefvllPP/88w7HN2zYgNBQz74MXXM9OdsZDuFoYMfBgE1eXR/t5kvVNxwMR1aJDLB8J1UvzJkzZ7B27Vrre+Wjc5Hx+hu8Mht27uC9D3fz69u4bh0eEXHv5TfeKbaZAIC1a9ciXxEGhMYJnmvNAAaIdBxSXL9+A85rYgGV81xqOTk5vP8Hs2B/xmB/PiD4n5Gez3ONjc5Xpdtr1wDr6aefxquvvuqyzJEjR9DH6UTgjmPBggXIzs62vtdqtUhNTcXEiRMRGRkp2X0+/O1PyerylhQBSjFTimEFxV7c27e7cwrfro81265PT09HVlYW7/zJVgHW5Mn8oHHrn9lwpaX8yaeedlmuOiraXVMd6tWX1QDHHRdFtG4jADSZWSDXcf5eVtZEnC+qwt9nhfeEBIAJEyYgJycHEyZMgNLpEGnHZjQag/oZg/35gOB/Rno+77WMQLnTrgHW448/jnvvvddlmW7dxK1uSkpKcljt17KyLykpyfr/1qv9SktLERkZiZCQEMjlcsjlcsEy9nUYDAbU1NTwerHsywhRq9VQqx2zayuVSr9+uDU68fOXvDW9tl7yOh/ovQjf/dw2Xdf2A2NctAotOUcrKzsjBeUe1ZVstg0xymQyt79bZ+dZNlHwuL8+K0qlEn2drP4TuqdRJtxjqVQq8X9pSagysfjkgvCK0Zb6/P3ZDwTB/ozB/nxA8D8jPZ93dYrRrpPc4+Pj0adPH5f/qVTi9oLLzMzEgQMHeKv9cnJyEBkZiX79+lnLbNy4kXddTk4OMjMzAQAqlQrDhw/nlWFZFhs3brSWGT58OJRKJa/MsWPHUFhYaC0TSBRmHfod+dyv9xjh4fY3YrCMd2kKjB5Mcl8JPVZCj1L7CeUMY93y5/ChcV61oYWYSe6tjRm9HgMHfAbOSYDlT91Dxec/c7WXZYhchpd6dUGsUtpErIQQ0pF0mDlYhYWFqKqqQmFhIcxmM/Lz8wEAPXr0QHh4OCZOnIh+/frh7rvvxuLFi1FSUoJnnnkGs2fPtvYcPfLII3j33Xfx5JNP4v7778emTZvw/fffY82aNdb7ZGdnY8aMGRgxYgRGjRqFt956Cw0NDbjvvvsAAFFRUZg5cyays7MRGxuLyMhIzJ07F5mZmU4nuLc3tU5cmgMZGPQ0J+OUzPlcMiFXN/oWYG3uMtTh2NgGzzdaBgCdSXxv2lLo3RdqY2FhPaBSpQFwnPckhTFRYdhe2+CXugkhhNh0mABr4cKF+OKLL6zvhw61fClv3rwZY8eOhVwux+rVqzFr1ixkZmYiLCwMM2bMwKJFi6zXZGRkYM2aNZg/fz6WLl2KLl264OOPP+bNk7nttttQXl6OhQsXoqSkBEOGDMG6det4E9/ffPNNyGQyTJs2DXq9HllZWXjvvffa4Kfgf1ca++FK9MNK/CD6GlezllZfepXb6w92ynA49mp5Jc4iWXQbpCTnPN/PUIg3PVj+FiKXYVBECPbX2YLiD/unA3DdK9War3PdCCEk2HWYAOvzzz8XzLpuLy0tTXDFk72xY8di717XW7DMmTMHc+bMcXpeo9Fg2bJlWLZsmct6Lgajz5kcjqljjGiqtPQ+7eo32Hp86nnh4TtO4Ms61MvgxMD63iuViBIYm0fPOQUHxhRcwUTrp7k61ruVp8J1B9fPihBCvNVhEo2SwHTTYX7QxIJBwmDhFRb/OuQk+BFIisl6GdScqvN8/8LW7O9sTA68Xihf3ZIUy3sfprDMlaLgiBBCpEMBFvGJKoLfg2UGA7lSOCjRH/he8LhQ6eoT3uUGM3OOPWq+MCUFX4DVJ8y7zbzteTKcSAghFyMKsIhPIrs24UyqfWZxfi8IYzfUxxmFJ1cLDRFWHJJu2MoXxjQKJAghhHiuw8zBIoGjwi4vKiMH6sNsHyNvBpmEQhjWFBixf8OVLMCYkFz5oqjynewSjbqb5B46apRPbWsL/+nZ2aPyNMhICCEWgfEtRiTDyKVP+tnaj5fwPzb2YQQDL7bMsZuDdZcXqRn8Sg40XMVCHpkiqniaKbhyP93fJb69m0AIIR0SBVhBJjp9o8OxiPpzAACFUdz+SW7ZdVOEsBzvvTc9GDVq27513dGxA5RA6MHZOOISp+c4LjDaSAghwY4CrCCjCHXcnkRh1uOKPx/HpbkLJLnH7h62r+j+BvGZ09k64QSmuxJte01e4uOodZdKcXtEtQWp82CJzYzOypyHUHEqhSSrBdUM/dVBCCGu0N+SQSY8eafgcYVZBzkrzQq72nDbFzQDoCra+b5MyeW2zYPZ6gLBMlzzl3UyGIT5+OXf38n+d8FgYLh3Kyvt3dc5jvf+0uhwJyVdUzgJ4qh3jBBCLGiSe7Bph284+x4TptWU9eEHd6BBzmH65i1Or58PDd5na/GsLMbntsgDMHu6M8quqR6Vf7tvV1Hl9vfo6/ScWsbAYPczilf5tkCBEEKIMOrBClKMLNZ9IYlwbuZgDT+0E4nVlU6vnwYVNp94DAMusng/fvZsj8onqsXt4L59wBCn53q02tD57pROHrXBEzcl+h4wE0JIR0UBVhCK678KMkWXNrsf52LOD8DPheWMnOHA6l3Pn6pf96RH7Qp0srAw/1QskBm/hUYuQ49Qte29zH9/BYyO8m74kRBCggEFWEFIFVkMRejlbXrPTZc57wkZUliGv3t2xvkYN1+4buIwTlfjecPakdtJ7i4CIX+KV4nrCfNGOz0SIYQEHAqwgkzL9xvDqF2Wk5qzXqzeF8oQqTOgNlSD/V0TXdZhOLkeAGAs2iN5+wJSG0cjmdGOPWbhCv+lxaBYixByMbu4Jr1cDALsWy1C52SDZwHGkzkwlx8Bqy2WtA2mssOS1idWRITr7X7k4f4ZQtMrhZO1jo2xpeB/uVcXlOmN6C3BvoSEEEIcUYBFvGbWJQMo9LkeBXPO+pqtPe9zfQ5Ys/R1OsFwQGR1f3S7SonLLrusze7b4tW7H0ZdmHDgZt9h1jpdAyGEEGlRgBVkGFnbpSlQwha4RNUava5HzlS5m37lE7aN526p9Z0wdeq4Nr0nAMyb/2/s69XP6+ul6PwMsA5UQghpNzQHK8iwZv5XnM4P85n/XVEF1hSOTyuOWY/JWHEh0tFkx/QRMkizfyLb6CTJqEQJVgHApKzz6XpN//6StEMe6/hzNMt9m0+lkdNfB4QQIhX6GzXYtIpzzJGuAx+5ZozgcbXB+Ufj1rp61J9biOFGu2BDZNeFQSAICGV+Qc1p37OUG8/tcHJGXONa8nmZ5AwMBuG5SWZ1jag6nBEKjLwicoL8PXZ5rlJE5tEihBDiOxoiDHKcixBaFTEdjDxB8JzCzMDV9HRznAaQaFeaMz/XAmz7b/LMJoVY/i9nAC+nbcWbA2uQ7IWenaFuXuF5Yxsn/qSUDYSQixkFWEFOexmL8J+FgxdGHo+E8r1AzGiP6zVnRHgXYAl96bKsFxWJrdw7xUW9XJ43OQkIZQE2C0ktk+GFnm2XdFaKjaQJISQY0BBhkOPG87tizHL+MFFc5QHv6lV490Xq3yn4ltr3nt7IP+xhU/ftm4jCwoFYq+/jtIzBTMNt7lCoRQi5mFGAFWTcDcuwflplGNbgfEwteUKVX+7pyPLw5yuPuSnnmrY2EYAMZVwETtWkSdAu6cU99CAAYOOIzHZuCSGEECE0RBhkWDd7y+mUeiiNluEthpEmvu56rhEZhU1Oz6uizECZ+Pr+N5rBDTu8DwRLwlpv2yOuL4WNdcx+z3LCQ4F6c9tmytcMHMh7H3PPPZisiEVhYgqu3p3bpm1pcUtSDLZV1+OC3paiw8uOTUIICTrUgxVk3A9d8QMXs9efANs3ac+CRijM0vWMuVuJ5872pNa5oLwPsJx5Z++DHrTId4yC/28hhmFwJiUVrI+pGXzxTt807Mq0/az7h2vA0Mx2QggBQAFW0Cm80Nmj8qtHCX8ERhz1bMXZFvNgj8r7E+fHL3muOUA9W9fVb/cQvrHzADav9wAou3TB0bTuvOM3JkT7uVGAzO5nHWgT/AkhpD1RgBVkjBDeh64F16oHq/X7FqmlrvNSLT/yHO99HULcN04kBoDWh+oYJ8HIvtDj3lcawP7x2D/Rff06mOx6uTaO7I13+rbN/LGXenZGlEKON/qktsn9CCGkI6AAK8hwbnoROEZcVnMFy+CGrclOz0+q3uZRu1qI7eN49i4Jh76ab7qoy3JR5TochgHTaqiwf3gIFLK2eaCZXeJx5LIBGBThe7JYQggJFhRgBRkpv1Jj6p33hmkY7/cedEcbyqDJya0NrPPJ9O40ynVeX9vuAnxukyzA20cIIW2NAqwgo4AK6sgSp+f396gCGDUUIZd7fY8zbKLDMXc9Z0KqIiIFj68bzkBPaaY6PAq5CCEXMwqwgoyKi4E6qsjp+VOdTVBH/R8UmpFe36OQS0BBqyDrSngeEU171XHI7tYFChgVDBpChL+eXX5pO+lFMZU4JlP90uVGQIEh5qGHbG9cTHInhBASeCjACjKuVnJdaN5n2Nel9HUIQZZhMZ4x3Gs95lWHkxftkHmRKNVcss/6+prLVXhsWAg+FBlgNZmEN31uC+p+rdNNEEII6SgowAoyCoHEmIunyXAmAVhyk8DEcYZFvYc5rBYZ74EBStQggnecNbvPW1scFY7a0BD8lnmlR/dsISYkK4xwHMJsUa5h8He8+Py6O0qGiy7b3ibFCQ+5EkIIaXsUYAWhMyVRAACOtfx6d/eS4cmZCpyLdwxPqtSHUGrybLPlUsQ6HDOy3VBeeJnba81yGb64eiwW3/MIIsuXeHRfdZSbifWsZbueJqVjr9PnV3v3Ud9R7Bhg7Swe5lVdvuj0kPvEpv3DpUuVQQghxDcUYAUdM0qqw5E2/kWAcd8zxTHO9xAU8kvGpbZr7fqTGszjYdBFi6ukZWiQEx/Y9ZpWjIxJ5ahVfem0DGdsEDx+4rrpWOskoao7nMAfkU8OTfeqLl8o4hPa/J6EEEK8RwFW0LEEVSGxZ/1+J/vkoiyiYWhqvQeghZxxFkiJn4MlV3JgGOCsrBHxqmy35b+xm2OVoukv+j5imNi2X+LIKNpvSxxCCCGeowAriOkN4oaM6jzrxLL6kx2I70xjsVAzE6xO67ScWm7GmKTzDscvP+TZ0CQANCAEKuak23IfQI95aMB01MFkl62+d4nzwDOQ1+mp+/RxW2ZMVHgbtIQQQogYFGAFseLybqLKlXs4B8ucYgncOMjwtOkhfKaeArCu50cNiCt3OHbJYc9DGg4yME57xIBnMh+wtBHAbphxFhzugm3o8LKTBzC1dLXH921vYlZ+Xh4bgRWDu2N3Jq0+JISQ9kYBVhCra3CcjO4rNkIJY3/HjaCN53eiTHfOo7okT0TJsshLdOzpse+gk4FD31L/D5+2lytjI9BF43o/yvY0I0V4GJkQQoJNhwiwzpw5g5kzZyIjIwMhISHo3r07nn32WRgMBl65/fv34/LLL4dGo0FqaioWL17sUNfKlSvRp08faDQaDBw4EGvXruWd5zgOCxcuRHJyMkJCQjB+/HicOHGCV6aqqgrTp09HZGQkoqOjMXPmTNTX10v/4F7imiOXPj9dwO+YIGndq6/NAgT2uDMc+QUVOsdhwBbHfnaeOkEqxsK/3ZYZgXy/t8OV2LssE+TDLrmkXdshxj0+BkOMQAgdSXPJCCEXiQ4RYB09ehQsy+KDDz7AoUOH8Oabb2L58uX45z//aS2j1WoxceJEpKWlIS8vD6+99hqee+45fPjhh9Yy27Ztwx133IGZM2di7969mDp1KqZOnYqDBw9ayyxevBhvv/02li9fjh07diAsLAxZWVnQ6Wz72E2fPh2HDh1CTk4OVq9eja1bt+Ih+6zb7awlF2f6uXP4DddKWvep1HTeezamubeEcz2RS2g1nuTMBrdFFBC32XVNSJj1dVKpdHsYhl95JXps/B2pH34gWZ3+khrAPWGEEBLoxGdcbEeTJk3CpEmTrO+7deuGY8eO4f3338frr78OAPj6669hMBjw6aefQqVSoX///sjPz8eSJUuswc/SpUsxadIkPPHEEwCAF154ATk5OXj33XexfPlycByHt956C8888wxuuOEGAMCXX36JxMRErFq1CrfffjuOHDmCdevWYdeuXRgxYgQA4J133sHkyZPx+uuvIyUlRfAZ9Ho99Hrbyjat1jIp3Gg0wmiUcONkRo/+Rv/mQ/ri4AI8jDkAAE4tx9DSYwCAJlVgbiDImWwJUeWc+4HJo4ldsSetN7C1BvMUP6DfsXpc8PCeLn+nCQkwcRzgpEzLtWazyeGYV/fzEsuafarXZHYMZlmWtdbpjzYHimB/xmB/PiD4n5Gez/e63ekQAZaQ2tpaxMba5hjl5ubiiiuugEpl+1d3VlYWXn31VVRXVyMmJga5ubnIzuYv8c/KysKqVasAAAUFBSgpKcH48eOt56OiojB69Gjk5ubi9ttvR25uLqKjo63BFQCMHz8eMpkMO3bswI033ijY3pdffhnPP/+8w/ENGzYgNDTUq5+BMA4qu6EZd9PIOS/WzmVVbgOaAywAUDT3Xp2LjXByhXdOJAM9iyWoiFOi/vi/cLt6H2TY77b4lj6WRKIa1CACjbyBrmqRub5aDz17I3/fPnR2VV9kmqT3a13n0aPHsHb/Lq+ryleEAKH8/F2nTp1CzuE8AEBOTo7XdXcUwf6Mwf58QPA/Iz2f5xobG0WV65AB1smTJ/HOO+9Ye68AoKSkBBkZGbxyiYmJ1nMxMTEoKSmxHrMvU1JSYi1nf52zMgkJ/C8NhUKB2NhYaxkhCxYs4AV3Wq0WqampmDhxIiIjpdvi5LMPfgLD2gdNrntsOEbckJkrclPz8KAHewsmlZ3HyMOug50GDQOxyRMat73t8jxnjoCaVbuuRO7Y/tZHGkxhAIQTmtqbPHmy2zLOGI1G5OTkYMjgwSjHf53X99chAMCgcA0mX+b9/YTqBIDevXtjcmq811XJK7XAEf7ChwE9e2JCcjRycnIwYcIEKJWB2evpq5bfYbA+Y7A/HxD8z0jP572WESh32jXAevrpp/Hqq6+6LHPkyBH0scsBdOHCBUyaNAm33HILHnzQ/fYhgUKtVkOtdvyCVyqVkv7yQ+VNsI+ZGEbmNERR66oluWdSoftuJs4u+ArVc7j7p+VQsPx5W6/f5P7jWIo4wePmsoOCx1tjwIFlndxHZTcBu8m3wFOK36lcbmuPq/oYhvHLX5ByudynehVyx5/zI2mJUDZn8Jf6sx+Igv0Zg/35gOB/Rno+7+oUo10DrMcffxz33nuvyzLdutlyORUVFeGqq67CJZdcwpu8DgBJSUkoLS3lHWt5n5SU5LKM/fmWY8nJybwyQ4YMsZYpKyvj1WEymVBVVWW9vj2Fc+K6LltEyzmwrSKwWoNjzioAePG+OYLHZZxnw4xGoyWw2p2ehBFnbL1+T3NLcBNsvXzfjpVhSIEZTVcZoVcyUBs5VCMaN+gXYZlHd+SrrXW+7YyMM4Nl5FDtq/LhDsGhk0r6vx7CFXIYjZ4nmCWEkI6mXVcRxsfHo0+fPi7/a5lTdeHCBYwdOxbDhw/HZ599BpmM3/TMzExs3bqVN/ksJycHvXv3RkxMjLXMxo0bedfl5OQgMzMTAJCRkYGkpCReGa1Wix07dljLZGZmoqamBnl5edYymzZtAsuyGD16tIQ/He+0HqXj3AwR3hRtgK5VfHSg+k/Bsg0hwpPnRWx5iM0DbRsws829Q2VRYbwyIRx/k+aCJAanngWqb+Fgthu+O8F1dX9Dl5z/TMLQnG7DGMh53duGXPpMZYQQctHoEGkaWoKrrl274vXXX0d5eTlKSkp4c57uvPNOqFQqzJw5E4cOHcKKFSuwdOlS3rynxx57DOvWrcMbb7yBo0eP4rnnnsPu3bsxZ46lZ4ZhGMybNw8vvvgifvnlFxw4cAD33HMPUlJSMHXqVABA3759MWnSJDz44IPYuXMn/v77b8yZMwe333670xWEgUwj8AnQmz3oBRP5HXwm0S6FgovYRR8yjPc+pPk6jd7W65Eq835o013A2QDLdjOMzsv9gwAoNW2b66l7qMZ9IS9cnxDtl3oJIeRi0CECrJycHJw8eRIbN25Ely5dkJycbP2vRVRUFDZs2ICCggIMHz4cjz/+OBYuXMjLT3XJJZfgm2++wYcffojBgwfjhx9+wKpVqzBgwABrmSeffBJz587FQw89hJEjR6K+vh7r1q2DRmP7Evv666/Rp08fXH311Zg8eTIuu+wyhyHL9sUJvLLpfexbyMwG9D/yGQw9xffUNGhsqx3NCRqwIXIYe/In6M8IT0ONB5PdW2uMvE7wuMyumQJ5Tj1mGC6cRJNlLMER03rc1AOjrxO3RZGvVg/riTuTY/FCj87uC3shRO7bXw9DIqVcHUsIIR1Lh1hFeO+997qdqwUAgwYNwp9/Cg9vtbjllltwyy23OD3PMAwWLVqERYsWOS0TGxuLb775xm172ou71Audi/9CSvHfYMChUaCzpVJfJHjdgR69ra/ZWDXMQy1BiiE2FZv7WobtDkYxOFQWB4dNeiQIilZhIgDgPBvlc12cwIpBqSjVbdODNSIqDCNaDbMGksRWc7iUPgTehBDS0XSIAIuI17OpAIUy+6FK4S81xkUQ5jRAc/IF2Rgdha8Hl8Ek52BQsWDt7tmpb11znSK/XBnhj+Qy3I3y5hWEdZz3Q2KunpsQQgiRSocYIiTiJRj5q99EBzYeqEQ05KVNljc6MxpCzNCGm9AY4jhvSR3pWboDkzJN8Hi5k/QM9k53y3BbpgUX6b9tYMJj3OTbIoQQEvSoByvIeJOZ3RPPn3wXACCrNkD1dymYJrPAp4jfhr0RfQCx2/l5OYz047SbYFIoAL37sgAEk4oKNseDn2dnk+XfK6n9HAZIOxyNFBPdCCHkIkY9WEHG3OpXOvj8SbfXDNu7RFTdm3fchIcvrLS+l9WbwJjdByAfdr5ZVP2+MCmVXgdnrngSYKmbizI014gQQi56FGAFua6V/O17GIHNjqNrT4mqK8pg2R6gEJ6lozDJFDjexX8ftQRVtvtCrXThCv3QEiAsmoYHCSGEUIAVfGT8xJ9ylp81WyjAstdgcr7H0mfs7diIS7AOY3nHB6ucZz1XhlnmZZVHub6vossol+edM0MlOy66tByWn0dnnHNT0jvJPXxf4RgIZNQLRwghPqEAK8jowF9hF2bgT0pytuFxfuUm1Bmr8XvRl07rrpdF4E+Mhh78OtQMf3I7AyD3tuFIHF6D0AQDxJCFut5UOMfQEwCwxcDPMeXpJH4VjO4LCZDBEkRug+tJ+x09LPmofzoSVAp8O0j6XF4TOkm3qTkhhAQ6CrCCjLtd3oZWDBU8XnNuC9ae/xA6c4PTa3Vyy0z1sYb+vOOHQhyHGFOaziC2p2f7IrpygY3G57qROMPyE4RyHHB2k3DSUF45N5GPsa/rnqdE9TzEKJfgM9Gz6Dum6xKise+S/hgdHS553Uv7+rrFESGEdBwUYAWpdWfGAQBMJv6u3yFm4f0ExWCa+2d6sPxNrXeFH+K9jzUCEQ3Ohxq1yghR94uWu98UWA8TGst8n/fERrjeHV3OVCFMvsnL/q+OxV+T9DUy+uuGEHLxoL/xgtTK41MBAGazCnHxN0lSZ4TRSWDU6vu4IloPhrOFIrXycDCcDutGleJ4l3psjxE33ypD7TzAWs1sxbEIGf5nynNaxh3DYLt0ChIFFVEJ0m0Po0pPl6yuQODH5PmEEBJwKMAKMgaZwiHgORJyo2DZugT73iz36QgYkTOM6kJNCNfXWd9PjF4BhjOgJE6HbYMqYZB51+NkvwHNBaYM0y8Jw65kcfN62ETHnjs2yfvePCGRcSEYNkk4Uao3VN27I/WjD5Hxv1WS1dmeaOI8IeRiQgFWkGlUug5eVGZbBnOTUY2K9X19vme4kR+ctf4azWAKfL4HAKQLfFz/GDba5TXGftEw9rf8BwCnwZ8H1LInIRfue87d2OQwKFXS7kMYfvnl0PTu7b4gIYSQgEIBVpBT6S2TsjuHTMXisgqEmvlDWIYL0V7V29+uP2nmGcehvMoG4TlNRmU6jL28W00m2P/hpleEU8pg7hIGKCwf9XPNObzCUQ8A0F+VDN3VydbzrWkgbhUkIYQQYo8CrCDDAJDLbGkTJv22DgCQH3cTaiKu9Lre4oTOvPdZsAVQCQIL6zTH1Tj/dwyOHOTvIcjJ1DBniJvk3mJv2QCPyouhagmc5IzT4AoAYpk6p+cIIYQQZyjACjJ1slBAZxvuCtE1bwLIMJjf+2mH8nHlFZYXbqZg1cbyl+27++BENzKoOxcCvc710FtTsqUHSpFqm/jeFDYWAHDDThaVTTH49qhlq51ku7ua4dtmzeXllDKAEEKI/1CAFWSOhKaCZSy/1uJQ15sO10SHIO3sWZdlmpSWAOlcaqrTMkozf4jw+wjxOZQ23tobx+98C/KIZOux+k4z8dVrJvT7VYbNf76ESl0sujFFyLLbVfqE7hLR9xBiMIT5dL2QAZ2DI4u7VGhPRkLIxYwCrGDDMIjXlbsssmHiBBzr1Qt7RqW5XRf4R59UbOmTiqpO/GSeo+2CnaSaet65vBB+NnlXfi2aDH2I45Chqjlh+j8Rgu3q2dik/gf6MCXo0fyR7aX3bJixLVzRM859IUIIIRcF35dOkYCjZl1PzK6OjUV1bCw49Xm3dbEyGRrVjsNxUV5uCmPU9OO9Z2oNeDO0ERkaDb7IcLwPAwZJTDUAIJ0pxUdcKirBYYfZ7FBWUKvkS4yIdBSEEEKIr6gHKxi52xemmVkhLqXA0bCe1tfdzYkO5xXuE65bGdX8lANDa/ejuFaP6zJD8ENXxwArRPY3770SDJIEPradKioE78fG+Z7lnRBCCPEUBVgXkWgjf/uaZH2G22vOq1OwOc62+rAz6zivy1WYxgokNu9ZaMuLlaIrQoSpDqom21JEld0G1TLG+ZY7LZ6a/RQ4nZNNmH2dBySjTl5CCCGeowAryDAC4U5SRRkA4KWTS3nH43S2eVWpVcLpCLTKCIwpPeLynnVFjnOuvr+Ugb47i9pb+YHP9N9+xmtv/8f6Xg4O95z7GoMKbFveGJQqqAfdjpDL/uHyvuMPWIYOdw4Ygu8nXOuyrNf6C2fBJ4QQQlyhACvIMIxtSKxlvtFH/1mAt19/DjeV/e5QPn2iZUJ8mN75vC1385YMdY5JRQ+nMah83ITWHV6Ttx9CVINtUrwiZAJk4NC16Iz9Q0DVbRwUcb1c3jemwRa8VYeKX7noiSExlGiUEEKI5yjACjIcZ+lNMozoBK45gWZ4UyMGnjrmMC29WFaDkFjLpsyhBidDbACSGqtc39Nsq7mXi0ANALYMbDXpXG5Jl9Ct8JjL6wBAzpQ5PVcZHo3f+rveNqdFX5wQVQ4AEjQiJ9OD0hIQQgixoQAryJjNlh4stpMGUAr8ejnbjPQaWQMaoQHAuVwTqGSdB1+thXCW3i4mUrjXSzdSeEY8A+DxrzZgxglL5vkPu6sgQxUiFd9ZyxjMP+BM/X78WSO8+vFsXLLgcXsGKHEz1mAAd9RtWQBA52FA0kBRRYemRourkxBCSNCjACvImEMvdXle3tgAAGBYy1ytSsQ4lImra+S9j9A1WF93NlvG/OzX+0UZGvDviiokmEx4rsLS25Waxu/5mRhpRIKCRZa8J5xRsRqkKw5jIfcv9O/+KJLV90DOVFrPbyxJwY7y31BStxUA0O0Cg3hDJa8OwzB+vi5HDOTgsODcR27KNUtz/fO0J5NRDxYhhBALCrCCjCbK9RYwmqLTUFaWILpyqMO5XsWViGzUY+TpYusxrSISk87swE0//IjrV/0PYbAMQVb2/hoPaNUI7fYG3rvOhFvr6vH7uSL0MFqGHCdE8nu9JkcZ8c9kHbqefwCaEQ9Yj5+O2Qdl2PWQq4dCrrLkyOqNo4hDhXUB4G5uILSFGgzeUQYZy4I1nUN0WQ6G5X+CvX/fzLsPpxGXeiLeWINoc43gOdkFuwBTrgSU0md9J4QQEtwowAoyjFw4rYAy3BLwyExGaMrOQ2F2zJ/Qo6wGl504DwbAAGMV8iMHYk/UEGjMRihNJuu+hqxch5q037FfZYZcXY7tfS0fI/v+G7mTzhylPhbKLrZ9B3vGr4Vc1QPK0KvAMDJAzp/D9V/ciDXcOFzYFgsZx2DSgQJM3ncKww6vQkJFPmRcqyFHhe3GnNr1x3texYeCxxVnWq2ovGGZ9eVriuUu6ySEEEIACrCCjiYqDkITquTXNzgebNZ1bCVkShZ7u9qSiDb24fBnp8tgdpEHaqfGBJUpBACwTcNP1bBk52OC1xTDDBMMeLjHbtzbbTdmN1WgCBus55lwfsLQU0h3ev/mK3jvuBBbe82JIS6vHGva4qbuZnE9rC9vUWzFcfXd4q4jhBBy0aIAK8gwDANliGWYrDzaliPh9KXOg42wJAN63VSC4phw5HZPQfEoJdI1N6Nb2An8A873FWSUVVA3B1iPJ8bh507huLFzEgCgPn4Fzjc59pLdjgb8If8I4comdFI3odB4L34ZuhPlMhZ7VOIn07c4dbi3wzHD8E4wp4TC1CPSekxtqra+/gS3YhMyUZQsfs9EeypG/MpCQgghFycKsIIQ0/xbXWGXfLO8IdX1Nc0dQdXhITiY2hu/H9uF8q6fYCoct69pIR+yGzFNzQGVTIbP4qJwUmUpX6XQ4q0Kx5WEZgBXK3ZZ32/k4gBNOT6P1GNjqNGhfPixPYg4ZZvIvrdXAu/8hXLHSfNsnAbGgTF45Vi+9dib8mzr63PojMoBDeAYBpO5/zl9vpnytU7PEUIIIa5QgBXEdCpb0tGxx0/iuwjHZJxGgf2+6+oTwHJ6h+PyVok/796kxNUn7rG+r9Hxe8nqS67Ht0dv4h1TyWrRxNruOWrdepfPwLAsOOMZ63stw99b8ET3aRhZ6djztbNTMsZf6Ild6+tw9spBGN37Kd75jIw5AIBUFDq993zFDy7bZu/37CvdF7oITexk6UWcFBfppiQhhAQXCrAuElvPdsVXSZE4NagCBzNqcTomHwBQgnhrGX1cCkwh4TBGdYLcbBsGCxv/ApQ9JkAz4kEAAKtoAgDcunEN1OYQXHdoDh4IiUBYo62uxrMPwFidiTBlrfXYRwfuxrDYF2CfzUDWyE+z4C5XpzHKloaBCY2DQR2F6WdsE+O7FRvwdEYS4mWWYVIGgFomQ+fOt/PqiY4ajq6pM3EptjrcY6Z8LT5TLka4wkkG+2vftL586cYB+HXOZeiR4J9M8h3de/3S8H6/NLzbN629m0IIIW2KAqwgM7FfAtC8tY1JYespKmmMwN3xRpR210KFWoTUfQ8AKIBt6NAUHoWm9D6ATMabOi4LT4RmwC2QaaIAAMX9P7GeG7j/PcSr61F7dBzsJ5ybG3sAkOFvra3nYnvxSJgEAqih564QfJb9/SIcjhk6JSNkzFzIolKhGXqPw/mBZw2Yl54kWJ89uVyOjIzH0DnpeuzoW4FPBqRbz916aT9c9eBiIPuw7YKY5o2x43oBI+63Hk6JDsHALlFu73exClfIcWNiDMIV4tJnEEJIsHC+RIx0SFEhSjRGlAIADnTvja1DRqKmVglFfSmYE5H4v761SFnFgQlT47vrgBPIwH9xI/ab05Fwfh+MMQlQVpdb6/tv3K+4u+I6AMAcNGBB7+/QGHfQej6+6hBq5QMAVgEI7FlYawzFgq1Po94UDQCokcl52eQBoHNtd7TsUf1XtRw9ooF+R+twtKdwr5AiaSAUSQPB6mqReuJ3NPbwfKPn1NRUKBQK9O/3BgDAvn9FM/ROIKJVcHfPKmD7ciDz//j1xLheqUgIIeTiRD1YQYhhLAEMJ5Ph2YezMbzMsi3Mua0ptkLW7W8YnEI6qhGFyshYqMsvQGayDLldsTcOpVyJ9ZJ8mHHg2GkAwE/Hr3O8sdCIGmNElS4ajabQ5rsBW0yDAABnWcuEdZld50aeQYHL/qxAcrUCrLNkWnZ6nvoZXRqFt99xZsqUKVAoHP9tsbB7CuZ2TUBG6+AKAGLSgWteAaItiVx/nJWJ96cPQ48EgbKEEEIuehRgXQQSm6odjnFN/A2cw+RN+GEyf8itW3EYRh+O5R2rLYjE/k96Y/4Z2+T1XscsGzVH19RYj6kT/weZ5hzUcZvQT/kZonV1eGrXVwCA7+svxRzDXNxkeB4AwNp9DGfF6/HX5XFA1kuCz8K0TiwKIL1RILJTOP9oO9uU+f+6JuBf3VMEz7U2PC0W1wx0v/chIYSQixMFWBeZyo2PIS8tEeXh/KGtkqQk1IfbzSViLNvDtGxfY481yaCxm281ZG8+VuoGIaLOlgFdFZuLsIxlYBSNiGLO4pt1z2PshXwAwGWVuWg8q8S//vgCAMBxto+hquXlsHsQG3u5w71jq6pgqjgOADCe/dvpc2p6xUDTJxYR41ynpyCEEEL8ocMEWNdffz26du0KjUaD5ORk3H333SgqKuKV2b9/Py6//HJoNBqkpqZi8eLFDvWsXLkSffr0gUajwcCBA7F2LT/XEcdxWLhwIZKTkxESEoLx48fjxIkTvDJVVVWYPn06IiMjER0djZkzZ6K+vl76h/aDsvI0lEaHY1ernpod3frz3itDmqCOfhRypfPNmVswAJKMruci2fcZRbB6/NOsQN9qS4qE+oizjhfI5Bg8SHhD5qbt7+BEnzpETO0HZZcuvHOjb+hmuZ+MQdy9/RE1Md1t+wkhhBCpdZgA66qrrsL333+PY8eO4ccff8SpU6dw8822jX61Wi0mTpyItLQ05OXl4bXXXsNzzz2HDz+07Te3bds23HHHHZg5cyb27t2LqVOnYurUqTh40DZpe/HixXj77bexfPly7NixA2FhYcjKyoKueR8+AJg+fToOHTqEnJwcrF69Glu3bsVDDz3UNj8IL7QeQDud0ROnU2xpGKLCq6BT8vNLmY2hYBj+PKXrSjdaX5fpzvHOhTfuwy1/WYbvBhQ4nxPV+4xlzpKyZVUeALPCtrmyWm7LvyWTKYUrMemhjTEi5s7b0eP3HN6pyFjvsrMTQgghUuowqwjnz59vfZ2Wloann34aU6dOhdFohFKpxNdffw2DwYBPP/0UKpUK/fv3R35+PpYsWWINfpYuXYpJkybhiSeeAAC88MILyMnJwbvvvovly5eD4zi89dZbeOaZZ3DDDTcAAL788kskJiZi1apVuP3223HkyBGsW7cOu3btwogRIwAA77zzDiZPnozXX38dKSni5vC0J31oCHb0PY9pBaMBAMPrj+Ceov/h0pq9ONlchjM7BjfdTAqwAGSQ48+SHzCRd5ZDl0rgm1dN2DKIwcEMh8sBAAqOgRHAswPTcKnxUjSGhgIwQp2wBqwxCqkRRcIXitQ/nFb1EUIIaX8dJsCyV1VVha+//hqXXHIJlEpLIJCbm4srrrgCKpVta5esrCy8+uqrqK6u/v/27j0qynLfA/h3BmYGYWCGi8yAIuIF8CiQWiK2uyle2wZq3mppFOlO0R07K3KdgnR30nLXWZZll5VapzKzlErbKIqSufGKeMsIORzMrcBW4yYi48xz/mAzMjIzzOAAM9P3s9asmPf9vc88v3ne1/n1XuHv74+CggI888wzJm2NHz8e2dnZAICysjJUVFQgMTHROF+lUiE+Ph4FBQWYNWsWCgoKoFarjcUVACQmJkIqleLgwYOYMmWK2T5fv34d16/f3DtTW1sLANDpdNDp2j4ipqM60pYUBrxe8iYA4A20Pe+phcz7AdyQ+kDfeBgGYf55fJ42XNCnB7Az2BN9jYf3KiEP3GcS05KHRCqF0Jv/rNa5bt1Xj5phQRig8Gz3O9Dr9Q79zh2tpW/O3Mfb4e75Ae6fo7vnB7h/jszv9ttuj0sVWBkZGVizZg0aGhowcuRIbNu2zTivoqICERGmu000Go1xnr+/PyoqKozTWsdUVFQY41ovZykmONj0eXienp4ICAgwxpizYsUKLFu2rM30nTt3wtu77UORb8cwK/P0TYU471dsMu263sN4HyprJBIFPLyHQt942GpcLFRQ35Ci2rO5iOzV6mbt0lYF2HWZJxS6to+5uXEjynhuXO/xyaj+6Thwovn2EAa5HNUjRgAAcnObDw8ORwDCGgTkpb/i+++L27R3q5ZC2tm15Oeu3D0/wP1zdPf8APfPkfnZr6Ghof0gdHOB9cILL+C1116zGnPmzBlER0cDAJ577jmkpqaivLwcy5Ytw9y5c7Ft2zaLl907k6VLl5rsPautrUVYWBjGjRsHPz/HPadNp9Ph8qeW5+uvb8E//TvevlAoUd5/GJ7VbcDV4z7G6QrDzb1z/pDj7fMv4uiIhdjXoMbMH8xfAKCXmh+3qKjp6BU66eZn6h9B6bc7AQDe0dGIW7gQubm5GDt2LGQyGSoLDgAAIvr2Reykvhb7fs8996Curs7pD+PqdDqT/NyNu+cHuH+O7p4f4P45Mr+OazkC1Z5uLbCWLFmClJQUqzH9+vUz/h0UFISgoCBERkZi0KBBCAsLw4EDB5CQkACtVovKykqTZVvea7Va43/NxbSe3zItJCTEJOaOO+4wxlRVVZm0cePGDVy5csW4vDkKhQIKhaLNdJlM5vDBrwqS3zLlZiETXyyw+V6g3vvmtNK6AIxRA5d+Uppe7meWBEXxSehzbDXO4GaBNbSmyPi3IcwX2wf+ipEKA3rJrsBLd2t/rPPw8DT5ToTHzd1rXsHBxnm3fndSqdTqdxkQEICAgACL851NZ6wbzsTd8wPcP0d3zw9w/xyZX8fatEW3XkXYs2dPREdHW321PqeqNYOh+VhTy3lNCQkJ+OGHH0yOjebm5iIqKgr+/v7GmN27d5u0k5ubi4SEBABAREQEtFqtSUxtbS0OHjxojElISEB1dTWOHj1qjMnLy4PBYEB8fPztfiUOUa22XNB4NwEfrNYjqjrKOO3SdSWKv9biXyda70lre+jOGrm4gVPTlPgt5Qa+HavFcW11c1/sa8YsiVQKj1Wv4+xjc5E4dWr7CxAREXUzl7hNw8GDB7FmzRoUFRWhvLwceXl5mD17Nvr3728sfB555BHI5XKkpqbi9OnT2LRpE1avXm1yWO7pp59GTk4O3njjDfz88894+eWXceTIESxatAhA8x2+09PT8corr+Dbb7/FyZMnMXfuXISGhiI5ORkAMGjQIEyYMAHz5s3DoUOHsH//fixatAizZs1y+kNPLTwBDPltiMk0g850VRCGGrvbve4nxbURBjS1uov6raen97zSfJuGUcePQHnNtuPYABA5eTImL10KpdL88wmJiIiciUsUWN7e3tiyZQvGjBmDqKgopKamIjY2Fvn5+cbDbiqVCjt37kRZWRmGDx+OJUuWIDMz0+T+VKNGjcLnn3+ODz74AHFxcfjqq6+QnZ2NIUNuFhvPP/88Fi9ejPnz5+Ouu+5CfX09cnJy4OV18/5Kn332GaKjozFmzBhMmjQJf/jDH0zut/V79yPuM/59o9VNuE71CoRcL4MA8MSOrzDwfPMd2b162HY8m4iIyFW4xFWEMTExyMvLazcuNjYW+/btsxozffp0TJ8+3eJ8iUSC5cuXY/ny5RZjAgIC8Pnnn7fbn9+rnyWDESyaz3WrMdys4Zv+/YBlmUGPiNoKXD9XAY+JVaj0qgKuuMSqSEREZBOX2INFjrM07TlMXbm2zfS8QeHwvv8lm9r4Tfcvmz/v52tt7//gaWg+cCgVBgQGnoeHh+mJWh7SthcDEBERuRIWWG6qz/lSAEBk6SmT6SVhEfhNpTaZ5llfjUa5JzzUNjwYWSqBRJjeTbRY23xl3iX0xOeYYzLv1sf0AIBUmE71lt587++fAK02qf1+EBEROTEel3FTSTs34mzfQYg+e8pqXP2ly/BtakSgovkE/atePvBpvGqcX6O/WfwopUBFmBwKXR0AYGBSBQp+i0ZptRoAsE9yP/ZLmu+CL8zc7+HOyVOQ/1MRhhw4ZjI9TC6wMG4hQpWhGDaAxRUREbk+7sFyU15NjRjyyzEoLDzSpsVp31iEBo3FPZrmB2df8e9pMv+aAHbXNt/6YoyfDMJLimc2rgcAePYwIHJYMGDmRq8T8V2baWGj7sX8d9dDE9L8iBx5n3DjvAV3LEDSbRZXsl68wpCIiJwDCyw3J7nlhmgZEaY3Q9VLPZHt2wsKjx7YECHH9tEPAwol5MqbNxGtb3VE8NHj3+KOkjOo+2fzeVKasenwlDXfd6ssLNIYp8ZvAIAzjVL0XvsugjMy4D1sKACgz4cfIPDJVES88iE8PHzg5dUbt0OTPgzqKQPgfUdw+8FERERdgIcI3ZSHfDD0TacRmzgVgeE/AyXNh+VSo/ph5sAb+O/85rhG4YlfcRU9R3yB3eoEhBkMGJn0IOovV+L/Thlwo8m0BlcFXEA9gPP7AtDr1f+EX/8HMH/tcNT+qwqrypsfidNLIcPO2kH43yvHcVkvhe8DD5i0IQsNRfCzzwIA7g05DJsehGiFTOsDmdan/UAiIqIuwgLLDV1reBKxk/tDXzcZ980ZBalUikXXGiAB4OHpCRU8MW3aNOw/XgzJP/3x/n16KO58E++Vvg5IJIiKfBEAUFFWgz3/8zPuHOYBxflfkSNC8F/95uH50Lsx4fz/wveh2QCAHr5+6OHrhx+1jSiqbcA0jT/O1a1A+p50rIx50mpfpbxikIiI3BALLDek1/8H7pkyyeR5SYoe3iYxMTExiImJwVOtpkVFmd6mQRuhwuzMlsf/3Ik5QPM1guPvNvu5A7y9MMC7+Yas4X7h2Jq09fYSISIiclE8B4uIiIjIwVhgERERETkYCywiIiIiB2OBRURERORgLLCIiIiIHIwFFhEREZGDscAiIiIicjAWWEREREQOxgKLiIiIyMFYYBERERE5GAssIiIiIgdjgUVERETkYCywiIiIiByMBRYRERGRg3l2dwd+r4QQAIDa2lqHtqvT6dDQ0IDa2lrIZDKHtu0s3D1H5uf63D1Hd88PcP8cmV/Htfxut/yOW8ICq5vU1dUBAMLCwrq5J0RERGSvuro6qFQqi/Mlor0SjDqFwWDAhQsX4OvrC4lE4rB2a2trERYWhl9//RV+fn4Oa9eZuHuOzM/1uXuO7p4f4P45Mr+OE0Kgrq4OoaGhkEotn2nFPVjdRCqVonfv3p3Wvp+fn1tuNK25e47Mz/W5e47unh/g/jkyv46xtueqBU9yJyIiInIwFlhEREREDsYCy80oFApkZWVBoVB0d1c6jbvnyPxcn7vn6O75Ae6fI/PrfDzJnYiIiMjBuAeLiIiIyMFYYBERERE5GAssIiIiIgdjgUVERETkYCywXNA777yDvn37wsvLC/Hx8Th06JDV+M2bNyM6OhpeXl6IiYnB999/30U9td+KFStw1113wdfXF8HBwUhOTkZxcbHVZTZs2ACJRGLy8vLy6qIe2+fll19u09fo6Giry7jS+AFA37592+QokUiQlpZmNt7Zx++HH37A5MmTERoaColEguzsbJP5QghkZmYiJCQEPXr0QGJiIkpKStpt197tuLNYy0+n0yEjIwMxMTHw8fFBaGgo5s6diwsXLlhtsyPreWdqbwxTUlLa9HfChAnttusKYwjA7PYokUiwatUqi2060xja8rvQ2NiItLQ0BAYGQqlUYtq0aaisrLTabke3XVuxwHIxmzZtwjPPPIOsrCwUFhYiLi4O48ePR1VVldn4f/zjH5g9ezZSU1Nx7NgxJCcnIzk5GadOnerintsmPz8faWlpOHDgAHJzc6HT6TBu3DhcvXrV6nJ+fn64ePGi8VVeXt5FPbbf4MGDTfr6448/Wox1tfEDgMOHD5vkl5ubCwCYPn26xWWcefyuXr2KuLg4vPPOO2bnv/7663jrrbfw3nvv4eDBg/Dx8cH48ePR2NhosU17t+POZC2/hoYGFBYW4qWXXkJhYSG2bNmC4uJiPPTQQ+22a8963tnaG0MAmDBhgkl/N27caLVNVxlDACZ5Xbx4EevWrYNEIsG0adOstussY2jL78Jf/vIXfPfdd9i8eTPy8/Nx4cIFTJ061Wq7Hdl27SLIpYwYMUKkpaUZ3+v1ehEaGipWrFhhNn7GjBniwQcfNJkWHx8v/vSnP3VqPx2lqqpKABD5+fkWY9avXy9UKlXXdeo2ZGVlibi4OJvjXX38hBDi6aefFv379xcGg8HsfFcaPwBi69atxvcGg0FotVqxatUq47Tq6mqhUCjExo0bLbZj73bcVW7Nz5xDhw4JAKK8vNxijL3reVcyl+Njjz0mkpKS7GrHlccwKSlJjB492mqMM4/hrb8L1dXVQiaTic2bNxtjzpw5IwCIgoICs210dNu1B/dguZCmpiYcPXoUiYmJxmlSqRSJiYkoKCgwu0xBQYFJPACMHz/eYryzqampAQAEBARYjauvr0d4eDjCwsKQlJSE06dPd0X3OqSkpAShoaHo168fHn30UZw7d85irKuPX1NTEz799FM88cQTVh9q7krj11pZWRkqKipMxkilUiE+Pt7iGHVkO3YmNTU1kEgkUKvVVuPsWc+dwd69exEcHIyoqCgsWLAAly9fthjrymNYWVmJ7du3IzU1td1YZx3DW38Xjh49Cp1OZzIe0dHR6NOnj8Xx6Mi2ay8WWC7k0qVL0Ov10Gg0JtM1Gg0qKirMLlNRUWFXvDMxGAxIT0/H3XffjSFDhliMi4qKwrp16/DNN9/g008/hcFgwKhRo3D+/Pku7K1t4uPjsWHDBuTk5GDt2rUoKyvDPffcg7q6OrPxrjx+AJCdnY3q6mqkpKRYjHGl8btVyzjYM0Yd2Y6dRWNjIzIyMjB79myrD9C1dz3vbhMmTMAnn3yC3bt347XXXkN+fj4mTpwIvV5vNt6Vx/Djjz+Gr69vu4fPnHUMzf0uVFRUQC6Xtyn62/ttbImxdRl7eTqkFaJOkJaWhlOnTrV73D8hIQEJCQnG96NGjcKgQYPw/vvv469//Wtnd9MuEydONP4dGxuL+Ph4hIeH48svv7Tp/yhdzUcffYSJEyciNDTUYowrjd/vmU6nw4wZMyCEwNq1a63Gutp6PmvWLOPfMTExiI2NRf/+/bF3716MGTOmG3vmeOvWrcOjjz7a7oUkzjqGtv4uOAPuwXIhQUFB8PDwaHNlRGVlJbRardlltFqtXfHOYtGiRdi2bRv27NmD3r1727WsTCbD0KFDcfbs2U7qneOo1WpERkZa7Kurjh8AlJeXY9euXXjyySftWs6Vxq9lHOwZo45sx92tpbgqLy9Hbm6u1b1X5rS3njubfv36ISgoyGJ/XXEMAWDfvn0oLi62e5sEnGMMLf0uaLVaNDU1obq62iS+vd/Glhhbl7EXCywXIpfLMXz4cOzevds4zWAwYPfu3SZ7AFpLSEgwiQeA3Nxci/HdTQiBRYsWYevWrcjLy0NERITdbej1epw8eRIhISGd0EPHqq+vR2lpqcW+utr4tbZ+/XoEBwfjwQcftGs5Vxq/iIgIaLVakzGqra3FwYMHLY5RR7bj7tRSXJWUlGDXrl0IDAy0u4321nNnc/78eVy+fNlif11tDFt89NFHGD58OOLi4uxetjvHsL3fheHDh0Mmk5mMR3FxMc6dO2dxPDqy7Xak4+RCvvjiC6FQKMSGDRvETz/9JObPny/UarWoqKgQQggxZ84c8cILLxjj9+/fLzw9PcXf/vY3cebMGZGVlSVkMpk4efJkd6Vg1YIFC4RKpRJ79+4VFy9eNL4aGhqMMbfmuGzZMrFjxw5RWloqjh49KmbNmiW8vLzE6dOnuyMFq5YsWSL27t0rysrKxP79+0ViYqIICgoSVVVVQgjXH78Wer1e9OnTR2RkZLSZ52rjV1dXJ44dOyaOHTsmAIg333xTHDt2zHgV3cqVK4VarRbffPONOHHihEhKShIRERHi2rVrxjZGjx4t3n77beP79rZjZ8mvqalJPPTQQ6J3796iqKjIZJu8fv26xfzaW8+7mrUc6+rqxLPPPisKCgpEWVmZ2LVrlxg2bJgYOHCgaGxsNLbhqmPYoqamRnh7e4u1a9eabcOZx9CW34WnnnpK9OnTR+Tl5YkjR46IhIQEkZCQYNJOVFSU2LJli/G9Ldvu7WCB5YLefvtt0adPHyGXy8WIESPEgQMHjPPuu+8+8dhjj5nEf/nllyIyMlLI5XIxePBgsX379i7use0AmH2tX7/eGHNrjunp6cbvQ6PRiEmTJonCwsKu77wNZs6cKUJCQoRcLhe9evUSM2fOFGfPnjXOd/Xxa7Fjxw4BQBQXF7eZ52rjt2fPHrPrZEsOBoNBvPTSS0Kj0QiFQiHGjBnTJu/w8HCRlZVlMs3adtyVrOVXVlZmcZvcs2ePsY1b82tvPe9q1nJsaGgQ48aNEz179hQymUyEh4eLefPmtSmUXHUMW7z//vuiR48eorq62mwbzjyGtvwuXLt2TSxcuFD4+/sLb29vMWXKFHHx4sU27bRexpZt93ZI/v2hREREROQgPAeLiIiIyMFYYBERERE5GAssIiIiIgdjgUVERETkYCywiIiIiByMBRYRERGRg7HAIiIiInIwFlhEREREDsYCi4jo31JSUpCcnNzln7thwwZIJBJIJBKkp6fbtExKSopxmezs7E7tHxHZz7O7O0BE1BUkEonV+VlZWVi9ejW66+EWfn5+KC4uho+Pj03xq1evxsqVK13mAcpEvzcssIjod+HixYvGvzdt2oTMzEwUFxcbpymVSiiVyu7oGoDmAlCr1docr1KpoFKpOrFHRHQ7eIiQiH4XtFqt8aVSqYwFTctLqVS2OUR4//33Y/HixUhPT4e/vz80Gg0+/PBDXL16FY8//jh8fX0xYMAA/P3vfzf5rFOnTmHixIlQKpXQaDSYM2cOLl26ZHef3333XQwcOBBeXl7QaDR4+OGHb/drIKIuwgKLiMiKjz/+GEFBQTh06BAWL16MBQsWYPr06Rg1ahQKCwsxbtw4zJkzBw0NDQCA6upqjB49GkOHDsWRI0eQk5ODyspKzJgxw67PPXLkCP785z9j+fLlKC4uRk5ODu69997OSJGIOgEPERIRWREXF4cXX3wRALB06VKsXLkSQUFBmDdvHgAgMzMTa9euxYkTJzBy5EisWbMGQ4cOxauvvmpsY926dQgLC8Mvv/yCyMhImz733Llz8PHxwR//+Ef4+voiPDwcQ4cOdXyCRNQpuAeLiMiK2NhY498eHh4IDAxETEyMcZpGowEAVFVVAQCOHz+OPXv2GM/pUiqViI6OBgCUlpba/Lljx45FeHg4+vXrhzlz5uCzzz4z7iUjIufHAouIyAqZTGbyXiKRmExruTrRYDAAAOrr6zF58mQUFRWZvEpKSuw6xOfr64vCwkJs3LgRISEhyMzMRFxcHKqrq28/KSLqdDxESETkQMOGDcPXX3+Nvn37wtPz9v6J9fT0RGJiIhITE5GVlQW1Wo28vDxMnTrVQb0los7CPVhERA6UlpaGK1euYPbs2Th8+DBKS0uxY8cOPP7449Dr9Ta3s23bNrz11lsoKipCeXk5PvnkExgMBkRFRXVi74nIUVhgERE5UGhoKPbv3w+9Xo9x48YhJiYG6enpUKvVkEpt/ydXrVZjy5YtGD16NAYNGoT33nsPGzduxODBgzux90TkKBLRXbctJiIiAM2PyklPT+/Q+VUSiQRbt27tlkf8EJFl3INFROQEampqoFQqkZGRYVP8U0891a13nici67gHi4iom9XV1aGyshJA86HBoKCgdpepqqpCbW0tACAkJMTmZxgSUddggUVERETkYDxESERERORgLLCIiIiIHIwFFhEREZGDscAiIiIicjAWWEREREQOxgKLiIiIyMFYYBERERE5GAssIiIiIgf7fylUQfhr/XXXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all_audios(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "913a27fa",
   "metadata": {},
   "source": [
    "### Normalisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "91a9568a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# By normalising the amplitude of the audios it is possible \n",
    "#to lower the impact of different recording conditions:\n",
    "\n",
    "def normalise_audios(df):\n",
    "    \"\"\"Perform z-score normalization of audio data.\"\"\"\n",
    "    \n",
    "    normalised = list()\n",
    "    for idx, rec in df.iterrows():\n",
    "        normalised.append(zscore(rec['AudioData']))\n",
    "    df['AudioData'] = normalised\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ff50d288",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = normalise_audios(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4bb23054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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M+h50Ee0hoTa2Q8Lg1aqcN0T0lkUZUiAqIUCZKQG/hYDjADZQmQmi0lgEGZHj2LQScXhCaKxQRDovRJowO0ekba1tkq6WrwiNlV0idIBOWUh6kUL0RE9qRMNNFyUrZ5NU8dBbpWrA/ckLQafzpIXyjEy07EKie1iXhkL9BkkEmDRXPBQsXYtDqz+dAC8ipWus8mJa2d7RIbTdFGXrGLYORIBS0cDCBZJ8MJ4TLspPlGiGsuc4g69aII2aNqkfIu0mzj7jbJ0Q6eGYXZR1RZl3Nt1kepQ6keYV8s01BXyLZM7oNLd1opDKpEfgD8kxK5yxmJY61Vbmm2kBf1HqcKYkaLsVkGNmSEuHx4u1sxq0st9xALr03piM6fZMSo5vElVnSqfPZGXsqpRx5k/Tc6aXHnJZqVPspgXPdL1M7fH4ckYrZ8kyHc/PNABoea6AStnGxHGk/yO3oEfnh48f3066Qwtqfevsu/jxnKEWLc8d6oVHQUFBQUHhMIUNB5Z64ZkTlFu6goKCgoKCwqseKsKzF+CQf3WWVfyc6cK2DOWk1rQO02FMO3DGFoul2QEKt9PxeURZgn1XSovhLziuJsXUchI348j9GOmEpKQDrJg0kDNBPMJiJBKYq+6ubwR4tUH8FOqfmpBMkFBMwt1Voh7aEtKRI+NJBOEDYkCkPe9q3DgeTQ86F2UA8fZwgDK5iMZiBIZl0Crx5iH3Mqke9D4oHbozQllp7UINREhsMEg8Z4PW8esWpkpCDeQzlNm0uHkbWJCNx7RAatIskMiWE9mFUmZ6jvT5oLFgXYv0Z2FS4v4FGtNoRMaxJ1ICcsCCzimM0nUx7Zg3yZ6AMpsCYaLVOGuMsq6YEo5vlXKeVKDLi4V+NbZS9hAd3z8pxy/NI7qQMqqMpFxXNh8CQrWfvoB0uplnuwdpHNOg1SDRUpRRZbPIO2ddkbWE0yrz1pqR4/tb5Rr53uQMuEJW+pkzExuaOYajAxVgJJ9AWbPQF0+7dTjjstRKVJQp15KISRumplPS5n7yeyCqGpT1ZmyjjDN6/jI1ycsIWGyTM11ZYLNBS+n146WXh1GuOp57hLNnOQssQNRYpo8apO3y8xWAorTmDhXhUVBQUFBQOEzRyNLal38HClu3bsVFF12EgYEBhMNhLF68GJ///Od3c3h/6qmncMoppyAUCqGvrw833njjAWmPivDsATSr9i88IV/SWXq7569kHyltcjTGR7oRHBFinZwKGX2y2Z2RlfPm5pEODWtOUJnPG9/h1VqYWeRzI0+OT76aeNHzbNaynogSf+nrpHxLiyTHfLIAdV5MpOwf3tbvlpNxudAwfRHrvuY3YyknX6O5kpSNcMXV4TH8lqvOzJYEmTFpT6pboiuxoFz8VEE+p1mbRaeFu5Vu6iz6co+ItA8KJOS980206HS+TIRCWba3ks1EjsITU7kI4Kv97O8Q1XCTDCIrI6TZQiEYjh7yAk4u8yLM0BQZcdKiUI2iLrxQ3t/X3Piya77cEOMvtrllY558lhcrhvtzhkxRk9QPrOeTpcWukZD0f4DMSfNpXh0uxRJ5P1XJKsKZoQXP8+SYHDWyO0kDZ5qiDWWZVxVKZvA5OhACrJIfJvWhzovnKcITWChhNYsiLRot3LXDpE00LdurKTlmkBb5a2Swy7pV9ogcv0z7ttHCcl4Mn52shTmC8AGtgGVrsDQNmQrdg5SYkM9LlKyH9HmGJ8lAi3WNSvJAMWgBMys8c7SN9c48Wmbs20WPDY7w8LOysSA52DilVvvHz+7whNQveUxIKUpGkc/weN2EmSXvX8N4/vnnYds2vve972HJkiVYv349PvzhDyOfz+PrX/86gJqY8JlnnokzzjgDt912G55++mlceOGFSKVS+106Rr3wKCgoKCgoHKawgX0UHqwhk8l4ts9msbQnOOuss3DWWWe5/1+0aBE2bNiA7373u+4Lz5133olyuYwf/ehHMAzD9dW86aab9vsLj6K0FBQUFBQUDlNY9SytffkHAH19fUgmk+6/G2644YC0d2ZmBq2tIv/+0EMP4dRTT4VhSMRz7dq12LBhA6anZ3Ff3kuoCM8eQHNq//I9RGOl5fcc5i8TLcU0ES9y9uqgNF/lxsfMLCT5fGIRmGJryKTX/kPFXUY6OC3WEsVOsZbgfQx64S+200JlfunnxaK0PRiW2HE4KOWeENkodEm8eIYWrO7YLs7L8+aLEMZEVlYQpmghMS+MNHNBAD4gWTO7bFyOTrQI66iw7cV4RmLi/W2y0nf7lKw8Lg3TKsYYixAJmMZieOwMaKFvLkuTghYSD+bkvKf0bQJ2HoNT+jZhpEz2FkR/+Ii6YnpRn6IFt9K1Hll+1oGaWUwUDC0ELdFCcR9ROWxhkGgVuortQlqXSH/mi9Lm0R0tQEft56LFO+VcVRlTXtCOFJoiv1PGzgmRoScxC+Wk9Am7qBeOJod3so3QyLLBx/YTvMA4Tot7AzTH6lSsHqoiRBSt7zFZhF88ormjtk46PHx7aXmhe+xQ82/6yg6Zn1qXHJ+1klqXyliwiedkWvpwfqf8oWmP1e41w9GB3FIkQiWUNQsTBTkXL3IOEK22Y6dQmfPnyb2840XxX2H6NTIs12IFWWysOY01m4UPPx95EX5kTK63HKtbStSPZ/s12BpA8lqehcr8DA3kZWJZQWnQ9BG1MWItogMNy8E+uqXXfm7fvh2JhAhy7Wt0pxk2btyIb33rW250BwBGRkYwMDDgqdfV1eX+bn/6XaoIj4KCgoKCwmsciUTC8++lXniuvPJKaJr2kv+ef/55zz47d+7EWWedhfe+97348Ic/fKAvpylUhEdBQUFBQeEwxf5aw7MnuPzyy3HBBRe8ZJ1Fixa55aGhIbzlLW/BG97wBnz/+9/31Ovu7sbo6KhnW+P/3d2zhMv3EuqFZw+g2bV/xEZ4MgNiOymLqpfC3pQBw1YMnD3AYDfg5DbSL6Hsn+x8ymaIyblaNki2U65PKAUz6Q3mleOAVj+EMQM49dNwWJitKZgaSG6lrJllFEul+HspL7HmUoZ0VHootB6UuPN4XjqytUtoL3ZbNumYUcrQ4XJ7PF8LvReBomnArN/O7LLNtB2H4m1K6Xlhc49bXrRQbsYtY5S9RRk07B7OmSAezRDOimoherKNroWsNzrDEot/YaYTZ9R/FukC2IYAnPWTJb0Rmk88jhUaX6aumA4op9iiQtpsTNNYp6R+iTLOKkQP+cLS/y0xOUE4XgQKSzCvfwIjGWlQPCQNSuflRjHJQiJIzuyBDjl+ZVLmvYfmyDfvE5voB19JdtAKRCHpci1ON3UW6+ow7RKu6z/Zmidj0Xe0XPts4XVtkB4MnZyCyfpCRLcVqZ1EvTk50vOh+hPDki3V1ZuWw5MVRU9E+Pb1Y7U/OsF6i+MBExWfhTTR0GHSj2L6OJKU5xFbtOgtMuer1M7pVWTtQf3pI62h+DbZzs9fnydDVcpMwefo+eOep37ocgowLa9+mWU0z0zMzZP/8Bwz0vX9XlFrCQ3WPgj/2Huxb0dHBzo6Ol6+ImqRnbe85S044YQTcPvtt8Pn8878NWvW4LOf/SwqlQoCgVq/rlu3DsuXL9+vdBagKC0FBQUFBQWFA4CdO3fizW9+MxYsWICvf/3rGB8fx8jICEZG5Ov93HPPhWEYuOiii/DMM8/g3/7t33Drrbfisssu2+/tUREeBQUFBQWFwxS2U/u3L/sfKKxbtw4bN27Exo0bMX/+fM/vnLrgYTKZxD333IOLL74YJ5xwAtrb23HNNdfs95R0QL3w7BFCUw78AQeZfgkBtrxA4l4U8uTMAM6M4WyY6Kjsm15M8vws7d9H7srEynjc2ClTYfw4CTNrNJFZTA6o0RaNLC3bL0kQwQyFxMkVnWkgzthCUC4oMCQNCa1Mu+Vl7ePSVkqxGCuSIzYJ+pnkKL1tVLI8BubJcbZPkIN5QmiCHdvaEdJ8APU54KWuEt0ks0+dNDUl7Ul2yCAMp1kwjULu0zTg1D+zWYawm7OPMm5CPRKLL9tyS27NSOrm6tYRIAcsS45hfUb6hB3kfTRcnGFXksN45kSA6jDtSolrCJKlQjkl2802ykgiWq3K2SnUnvFx6cOODhGftOqNtmwfQob0w8iY1PfTHGMai4UHef6Mj8o9wGKgFUmQQoAyJDlXyknKuXxj5DAep3EvsP0KUR4xWg3hyE8fU1EknOhUSDyUbCmsENGIZP1gE8Wmx6Qf2IYDRF1pREHqozIRff1yv2Qok86g7KqxonB+R9aFLgO2DkwCfs2Go9lIkZ0Eu6IvaJFO37BD0g67O2XcixXp2ww/mkboJqF+4zILs4bH6XlFU6/YJnU4G5aXFDSeoY3noJEGYAEWCbb6S5zqSpQlT3O6XwL5Wn1f+ZWza7D2kdLal31fDhdccMHLrvUBgGOOOQYPPvjgAWtHA4rSUlBQUFBQUHjVQ0V4FBQUFBQUDlMcyhGeQw3qhWcPUGzToAc1GBKZRaGDwtIULi0SpcJ+WFz2VUjMjF3RiQZpeVF+wZlWnBkQHZE6mYUypDqda1e39MiYjUYyhRUELKtx7uaCiZwN4QnnUmaHPSAnLBMtZVDHjJSEquAspCGijXpT0sERojkGx4TKSSWFN2oJS4jemSeuzn1t06hou3vapItCeRjk4t3SIscslCjLbFT4nmCXnItYRRjbpX7rBjnm6GrpBytI/motcl0DLSIEZ1HYfElShBm35VvdnzF2cm+VPpykjKGZldK2+HOy3SR6K0J0T4HZOc5CJDbPDhB9EJNr5OyeZELmAPugjWyVsZvhrCufBujAdD6MnElZYERdxSNCOgWIutKInxseomwOEoQs9MiFceaaPYtIo3+YfLIo48k/Rd5VrUR7ZZoLJDb8GB1Hg0V1QIKBkTbKViNxzuJ86dsiC11yPH6cqJ+U7NvWKTxllfyzZoJyA1uUPRcmz7JQQK5r65A8wPLtNU7IqN/4T+ycBxM2UkQlM2XMNNbiXqGht03K5CtTJp0vTvQcOaRrZnMCokxD7S/O8seadyV2iWnNxvRxn2cOANtbx6bnYYOuAgCbBF41Mt/U61SWU3nlKC3b0bzinHux/2sFitJSUFBQUFBQeNVDRXgUFBQUFBQOUyhKa+5QLzx7gGoYcIKAQ/QOh8Y5O6fzMQnxTq6USrzqv9AhdIdGCR46CWgVOok/4igpzdFiu9QJj5MoYJ8E8GJD3hBrdr4PwXpYVrOEUiu1USVqE2f3cLaYRrSFjwTHOOMmTmJ6eeLr2FdrS0ROXKhInVxJVMP8lEXSGRUq5/ntpMbp1AXS4kDMX0bFV7uwskV9zdZMQaFgFiXE6ydTkZD7k3lJp2yPyyCPlCnjhoZpfBULwcl2H3kG2UQDpQxpQ9WWfVsNOVelbvAT0SvuNQE1ykQOSmUal3wfhdxLUscipqWSIPqGBPp4XjJlq03LhVUok8g0ZHtmWuiYxcuG3PJYVjiDlmAVKAEt0SIiUcpaJNqLvdIKdL3FnMwNp0qCgQb1D9FwLDaom3SNJCBpURt0okv8Beq3JAkYtkinaDpnadXb4wBalCgwGvdiRuZYNUJCojM051vlORKkrDST+gTTUl+nG1UnupazvXj+My3IGYvzuoXvbMyxRlcu6x5DRbOwMyt8Z5EEJ3s70275xQ3z3PKCJSLgmQuTsGRaKGOmU5l/CGRo6QA9H3MLpMw+XCzqyreIn55dZqP59a73WQ58luOZG5x5W2ql+UDLBTgjsuG95VAbDzQs+GDtA1mzO+n/6oV64VFQUFBQUDhM4ezjGh5HreFRUFBQUFBQUHj1QEV49gJhSTxAmVb0s8fUzCKhZZgOys2Tt+nURqnvpzCqmWDqg847JfWrIQqxBynMH+UwvOzLYl1ALbTr1xvnliwtm+KbRpYE0+gcpkSyPeHxCoXWM2UJ0U+2CpewICIZSeOmcINRQ0LcW54QCqnlCKGZ+CvGT1wLZ1f1xDIIODqQPgITpSjKda6Ohc6YDluRlDB7IxMKAAyijVb0S6x8OCt+TxZdo0NUiEZxeTqMZzw0EutLlyX+PlmS8L4/KTuX61RX2dYxQ9fSFpFrT5PCoE0CdB76ZpaMFSPdPIzP1CmL7GlMDZD4YYboibYOifXvnE655faE9L9Vqc0Zy/ZhZFT61ke0lE0Zf6GYzBOmh3p7ZF7tHJU0nuAkfdMR41Tslv+w2CBIENJfkH1LnZxG2fyLmAUGnXqGkWP5ECY/qQLRWCzY6Cf6CSRCWCEfLlOjjDCi8DibjKmlbJp4Hcrg82QaUv2ZKblPW9tljHKP1ejmoE8DVta8l2xonrnH9+Z0UeZAa1/aLY+kybyN5iGPrz8utJ1lyPYqTUrfNAkqUqokZyAyNcaCnyzq2thXr5/G9muwtV0EXqnMx/EXmwshFjvqQpqzZJgdCKg1PHOHeuFRUFBQUFA4TGE5PljOPqzheeUy6A86FKWloKCgoKCg8KqHivDsAfxFQLe9mSsc/s8skPdHzgZg0b6QaMnNKlrocGYPUwcJqc9ZCJGx5lQX0xfsLQMARo5+b0uWlkWeSuWY7MOZYxxGrpKvULBVGsvZWx1BMbMZKqbcMofBWXgwsUwyRDhDZ6BVaItsWeizJHn6JIwS/HUecOe2dphOrW/iXRLLbg1KSP/nLx7llpd3jbnlZ4ck82tRlwxaequ039dK9EpRKMxKnDN9ZMyYnqyMSEdvCwsFc0rPZrfcH5bzzgQKwDSwMjGMuzeLqmBnUvrWJmrGPy79xtqLnF3C8IhJ0ryJiKkx0kfTJKDpFE7KuCdIJLBUkQvmbCCmnFLRCqABeTOAji4RnCyUyfuJ9g0FpA1+8q6ayMhN5hB9U26ROoGZ5t93DnlOaZTpxvtqRGPx/a8RzWf5pM875mWBKtDSnsW0Sf1AtFeAMqdKBRI8JMHACAk5miSGyT5cWkAa5CNhxrYOmRtTuvSPSUKaiflCO6a6iHojqmvxqVtr7XV0ID0As+pHWdMQ8st82zQmQoXxqMyBiWG5r9u65VwmzQ2TshfZv48pSyss5YrFtH1zOobp4+hOyozt2t1jq0HtV8NAxfLeC9ER2dck8da2Z+TeH18lXFfjXmuid3rAUKMY9z52YeO1E+JRLzwKCgoKCgqHKdQanrlDvfDsAULTDnTD8egxBMlmwiPJT5ESXihXZql+6v3ocHOtFN0k+wl6EbcoklOO8xeq1NFpYZ1ONhFAzb6iIfliG0BDpiaYljqsyWPRF0vbM/KVXeogR+mIfPHF4/K1mK3KF1AnRXu20iLhI7tk8fBQThY3coTnhdFOt8wRpBPmbXfLjwwuQBA+/P+CwLz+CXfRcpV0eHKVYNPjsK7I6YtfcMsvZuTrNdmfdssz5K7uccpmZ+ew9IkVpmgPWQzo9FVepJDhNvIn2ZnrwGoAz2R60N8mka6pAk00upYqWT/4s6RBROPIkUe2veBFztl+tj+hL+sZOWbJknAjO3cHSTeJrRPipMFiFiKADti2z2NPYFYpSlNp/pgyIMcvkwt5YJL0kWhBL7uQByd4QbIck7WS2Cm+1EEWKgb1VSdF+WgKFEwD0Gs/zTFaqNxCC5JJxylCi7Fz47J4uLBTVpZH5sm9Uw2QZQktGq/QAuBkWCIthQj1Lc0Ttm6p2NInHIF5YbjWQUH4gBAQD5io+CxsTUukjqM63IZoq4Rapran3HL3QklGKIYkcmVnSNgsRNpBdE9Ft9MiYRq7IFmlcOS9HG8enWtIgjUeDf4CYFdrVjsN8LOen9HTy2VMPfGR+rE4SqRw6EC98CgoKCgoKBym2PdFy4rSUlBQUFBQUDjE0ZAJ2Jf9XytQLzx7gFJrzS2dX6b1UvMFcbywV6MFyZ3rJbQ8+noJ3/JiY160zEI3HI4NTtNiOpK658XSZdLz2XVOa5Yc2vaLK4GZkjrsls7HnVoh8eJyO10c6dKw2/hIQcLyUdLZyJGFhFMmuX5atNmTlIWOIzNCdUWJFlk/1uOWO1K5mqtzCYgEygjUOZztObkwppAWtEocnBdRPzMti5YrRIfNTwiHObNTKDAjLW0utxCdlJF9fXSNDi0QbSW3d3aWnygLtbEwOglM137+eVqutz0qWigTGeEgnYQcR59sbk/CYf/QBFE5admePkLKfrKTYHl/drVmipCpjTBRJwYtdjXCJlCu0VwVstUoEZVZpgW9JeJ1owlaJK8z9SZtY4oqIIyQ18KA9vUVmtNYvOCZ7+1ipLnDe9ioAlbtuv0LpJ0zWaH/DKL5IkTnlWK0YDspbSjRwngjKH1YHRZ6pRAkW5aFdG8S2AKGLVdYV4fpxcb8b9xXZUdHxQYMXTraIB0h3lenh9aWaaFf8ybplNFY+JKyr7aTrDdScvzsEbQ4n+akx+aHFi2b5K7O1L6vfgs2pqxedqBXHdgBmQMVsiPJ9RK9RYuZLarfSO7QdllCoHBoQL3wKCgoKCgoHKaw99FLS2VpKSgoKCgoKBzyUGt45g71wrMHcLTaP5YYD86QPkRcwsOxnbI91yOTMdMvcdcIrfq3DdLzIAYikCcX6wLRWCk5ZnyHhHtz80iOnSXVyckdqGXo6JRR0Dgyh+tDRJsV2okTo/uD7zOPszZRD+0hiS/3htJuOUou6k9MiatyOieh71SHUD+lvPRdS0yOyZlcZtUPp96oyUIE5Xqj/BTGH5+R9KTOFPEchBJlCWVyQkOYRNM4RIVUSXvHn5M63J+clVclR+xIr1Abi8i35H+KS6Q9dY+KkmWgPy403FOjvdKeJPFMJjm2E3XFc5epH3YSL8wj1/sxzmYivSfWk+kQysahLK1wmGgayvqZzsr4tkdrHWQ5QJG0d0JE91hEcWqDMhbBNkl/LBLtVY1L28JDzS0zOh6X4xfmU8YTZXLx3C53kAO7n/V5iMqcks4N9acBCwgFqgj6m1M8nInGlF9Pe1quhR4GMwU5PvdJcKHM4TJlfvF9wfeOk5HtZPTt0bMK+6U9SaM2voEGVaxXUPFZSIRIc6kqx0wXZIxCAaGfWrvkbLkiOd0zzUoZZ8FFMr72Tpmg7JzOulKsTcaZsYHmt7ibSeurn9LIO0DFQYk0yww6DlNjbLXDz2jHV9uuNWcTDwhs+JQOzxxxUJWWFy5cCE3Tdvt38cUXAwBKpRIuvvhitLW1IRaL4ZxzzsHo6KjnGIODgzj77LMRiUTQ2dmJT3/606hWvbPtt7/9LY4//ngEg0EsWbIEd9xxxyt1iQoKCgoKCgqHAA7qC88jjzyC4eFh99+6desAAO9973sBAJdeein+67/+C3fffTd+97vfYWhoCO95z3vc/S3Lwtlnn41yuYw//vGP+PGPf4w77rgD11xzjVtny5YtOPvss/GWt7wFTzzxBC655BJ86EMfwm9+85tX9mIVFBQUFBT2MyxH2+d/rxUcVEqro6PD8/+vfOUrWLx4Md70pjdhZmYGP/zhD3HXXXfhtNNOAwDcfvvtWLFiBf70pz/hpJNOwj333INnn30W9957L7q6urBq1Sp84QtfwBVXXIFrr70WhmHgtttuw8DAAL7xjW8AAFasWIE//OEPuPnmm7F27dqm7TJNE6YpXEQmUwvFhnwadJ/mMVuzuqQLdbZlWCihWTafhsdxtzlNpBEzUaHML0/gig7qkMpV67gciK0uQJQFUAvjGnrt2C1jDsrV2n75HuaoKLRLGRAeIUVqh0azKUAZN/MMdvROueWZisSgS+QS3hmVvp8hd/IBDomXKLw/LRRVe994LfReBfLbWmHW4+WhbqHAelgUMU/igUTHJMkiwaD25EsSig/R9Wo+onKylOVB4n5sORGOCkUykpbsqs2GtK1SkWvsj9Worv5gFhtLkh22mLLYXigy5UFZV1T2MR1JmSQslBakzLIyuYob5AjPGXmg7KEWopkKJtF2QTlZgkT2qmYQ0ACnHEQrZZyVie5pScnYTVI/8xwL5EgtLkFWC0XKLKPrTZ9I9JktnRIjUbvsQnJUp3uBPxN1yszSiTa16/ScXTYQN+R6Q2QnkaVOj9HADE4Id9KWkD6JE5VWpkaYNCdbItS3Nqc/0b3cLXO7m+ZPuUo0ky3tjIdqbfDX76UNO/pgwkZrUto2LybZi1uKcl9rRHWViBrWpinjLCF92LVO6o+/neYePVvZ5sagzEedpiTpLHoEVDm7sNGFRr1vnFYfnKqDMN0v8THph6k2OQEZuXvuKStcp7T0V+4lwtrHRcvWa4jS0hzn0FixVC6X0dvbi8suuwz/+I//iPvvvx+nn346pqenkUql3Hr9/f245JJLcOmll+Kaa67Bz3/+czzxxBPu77ds2YJFixbhsccew3HHHYdTTz0Vxx9/PG655Ra3zu23345LLrkEMzMkk0y49tprcd111+22/a677kIkEmmyh4KCgoKCQg2FQgHnnnsuZmZmkEgkXn6HvUAmk0EymcQdjx+LCK0f3VMUshYuOO7JA9rWQwWHzKLln/3sZ0in07jgggsAACMjIzAMw/OyAwBdXV0YGRlx63R1de32+8bvXqpOJpNBsVhEOBzGrrjqqqtw2WWXuf/PZDLo6+vD1x7aBj0YciXJAcBIS5ltI/grxPNGSS/+HiNHjvDQh3RFJGw8BqMWa6hMkmR+dpYIzy6vtY6/FuG58nUL8a2fb24a4WHtHc8CaIrwTL9OLlSjL9AA6YT8r4Hn3bJNK0E5wvP8tET7+FulQoszo7SQNUc6P5lJCaP01yM8HymciM8NbW4a4UlFKcJDX8ezRXjK1AaO8JgZsqighcrhUak/W4THl6KIBxlEntC5wy0PFlJu+ZSW7Thi63l4fuGdngjPeElO8MKI6OxbBZkgwaHmUQ5elOuRwqc5Wk5RlIOiUp4IT0jG2hvhoQX6FOHxswWGGcRV2tG4wXkakVkiPKybNJmRUCVrMaWH6UFNdhLGaPNr5/uh3DKHCA+Hacmsc7YITypYxafsVfi67wnEY2QJQYuQs7RIuyUsc2BwQixXOMJTpAXJ3D9mSRoXnSXCYxaljkHj5YnwWGTP4ZOxbm9EeGwd50yfhmuLG3aL8PRyhGda2u+jsc5ShMeiCI/GEZ5fc4RHrsXKSX2dNJGMGUr2oEd5VG6jOUV4rjphIW5etwXlqoNqmCI8lBAytULGLiS5BfBVSZOnvm+1TA/PAwzb8Xmeq3u+/yER83hFcMi88Pzwhz/EW9/6VvT29r585QOMYDCIYDC423az6kDXHeiU8ZSngA9FgRGkl4LYThKTO4q6nF5mPIJYVA7Qy0y+g8LSFJwyacLmuukBQE/1XV2y7YBkJ5St2j8AqJL/VpHax0Jeeo7E5dKUkdQrD6hSXh5ck/SG2EEd89T4fKkzIX/IQjHpAH4JsUiwLkt/cHsXiKX3pid6EdQ0YDGArjyA2h+hThIYrHh8tUjcjDJEQhGmD0iMjvq6zE7N9EewtEAmgo9ehGx+kyWxviVJ8caKBeUP0KmxIbf85PRiHAHgqVwnHhuTl8PelEyECv2Rsqdk/vpIHDO1kV4ejqbsJ5q7TKmaRPf4ZhEwZC+nEeJOdXrpLXGKFHVbT6wIlIBguIgqvdGPZOU4PsoIA12jQWWTsoH0CRItZE+xnFwvL1tw6A/WOL3kRIbphYFeipwemZ9lEjZsIao0bFSAPBAOF/DsdvFE6+iQ8U2E5UFSoaxGIyo3W1nj7DCpk56KUH1pT1tK5vmGbSJQaRDHY7PL+aS8KIZDMvAhGq54/V5oKPLO6x5FRbOQK8sc25STh4VOwoNBcrcveLzGSFCVxAPTfy3tL5FIo0PPLydI2YJB+kNPc9iij88Q5blwlmLjsaQ1noOVOrVfkX4eXirH99GzkedwKCftybbV6lv6K/cSoSitueOgLlpuYNu2bbj33nvxoQ99yN3W3d2NcrmMdDrtqTs6Ooru7m63zq5ZW43/v1ydRCLRNLqjoKCgoKCg8OrDIfHCc/vtt6OzsxNnn322u+2EE05AIBDAfffd527bsGEDBgcHsWbNGgDAmjVr8PTTT2NsbMyts27dOiQSCRx55JFuHT5Go07jGAoKCgoKCocrbOxbppb9smd49eCgU1q2beP222/H+eefD79fmpNMJnHRRRfhsssuQ2trKxKJBP7hH/4Ba9aswUknnQQAOPPMM3HkkUfi/e9/P2688UaMjIzg6quvxsUXX+xSUh/96EfxT//0T/jMZz6DCy+8EPfffz9++tOf4pe//OUet9VXBXw+71odni0UYYePwqJMYwWIGgqmia7qIZ8lDrfTan+PiB37xlC418gQ5UJeWtYuDF1wxkGg3qxClwaz0XaO2tKaIc9xYyTmFqZKFRJDi0tjByITbnm6IqH413UOuuVRWiz3/JisRYmEJSQ+PS00h03rasb8so4lsnQGwTqfHQ+ZCNYpgSJli+wYElI/SBlDIRLKq9D6hxKtnbCqRIsY5N9E65f8tK6jGiRugGgsh+aNn9aosIDYw+kBt/zG5FZgAjghuRVhogXzVZkI2xxZO2G1yrho09KGsdXku0Zz0cO2tdMalQKNqbANyPeROGGPUDOlcRlfDpU7UxJN1efJiZ36ZHcczbNuhGks7nNeo2KSmGE4KWsmzCDdiJO0RitAa4eSzOERbReS7YUArSMijyd7mjL1RoXOmxadSOSLBtABbNvaibZ5QlmWKoGm5faYjCn7ahWpTjYvfExnl1CZUxnp8+EMrWWiMQ2HiGYi3ysWPOQ1PzqtvRkt1O4vo77QK+SrQvdZKPqkbXkSElzcLvf7s4NCqzEN1Noh65qm/XJf5ws0XvQ80ZjVrPBzjcaOMrb4XEV5nEBvtm6y/tNXrf0rx2npAIkWsvArt6fUQrRX/bZzDivhwUMi7vGK4KC/8Nx7770YHBzEhRdeuNvvbr75Zvh8PpxzzjkwTRNr167Fd77zHff3uq7jF7/4BT72sY9hzZo1iEajOP/883H99de7dQYGBvDLX/4Sl156KW699VbMnz8fP/jBD2ZNSVdQUFBQUFB49eGgv/CceeaZmC0zPhQK4dvf/ja+/e1vz7p/f38//vu///slz/HmN78Zjz/++D61U0FBQUFB4VDDvntpqQiPQhMkBqvwB6qYOJJSXSmjhVPRSy0UFiXDmgoJAFL03JPqzQkt7etJhG+AhMFojvK5AhSy5cwbTlcHACukwW4ijsXXw2meTLlFyQMMlDERoHTXGKUMj5gSZl8UlnD3I+mFbnlLWugYRpkygPxbqUHLKf05S6kXJR1lzQd0AJliCGY9rh+i0L1DIXGmxqIU9q+QZ1YhTXQMUXjRLZQa3Er0zaQMYMtmOVf6CMoA6pNYeYw8xYKUIhWh1L3putnVdDWKis1ZZsRVbhNqwyFKq8zSBpXmlBaXLYOoN8oMKvbSuFMqsU0cbLCDMowoFZrnYoVS5keLBpAERscSQEzouWREJvJMXvo/l5Ox1oh2sUoyFkwv2mHKcgoQRUJ0j0bp9nZJ+lajfZleCXbKNZpxuZaudrnR/bYfKAMd89KYelCytJzXSR2WPsiYcl1louqq7NVF/TlKkgihlByH08A1yhxMj8g9qBHl10lZY4tTk2758R3ibbeip5b0EXB0oADszCVQ1mzMUBaVTbQjU7TdnUK9sb/eju1CK0dbpT/Z865EtGa5IH1ih8nLrMzzmXzu6Pno8dJiWZD6aRvMpRXUYOm7PtOpDfHmlBnrOzaWKVjlVy7zyYbmZtDt7f6vFagXHgUFBQUFhcMUKsIzd6gXnj1AerEfetAPh3uNFqexm66fIjasscOL3XhRsZmS7RHKop9ZKF9yRflQ9Lifc1SHBQl5oXJiG31iAyi16misEQ9NOq54Vm4efTHROfgL3SGZ+hAtTo6H5UuTbyIWxdpekkjOUF6+OlPk1DyYkTo6LwZeIV+jRbIScMgZPNhSglFfhFeeDKNcXx1sBkgkkL7i/a20SJXEDNmt25ehRctl0mahqI5FX52pZ8nheqnUCY3Lvoll0m/bcjJxwhQmPDax3S3vyNdkFrLVEAq0UDmkywSsdtNEKzePZti8yJOiPbwQnzWeOKpjsfAg9XmZpPgd+tIPTFGkopPaZvOXsuZu42jbDDlusxZTnLRuWMSvtJ1E7UhDyV8kkbo03W80dhwF0iiiBV6gTpETFpzkBersZh5wdEAHMsUg9JPS1GY55hS5xrP7eZU0rDiKxR4GOkVTyywySXpEC3skmrplUFbuciR2YkoW/IdIM2dxFyUamLW+bSxaXpScRtVXxcOjsqj+xOVb3PJfBvvc8rIeyaCdyEt4O9YmUR0WlsxMUgicrz1Ci/DJLiQwQzpXLPVEU7VKWmmsQ2UZtfnQiPA4Wu0fR3XypGvGUXi+p8g5B9VQrb7le+1ETQ4nqBceBQUFBQWFwxT7LjyoIjwKCgoKCgoKhzhsR/Oso9ub/V8rUC88e4BKDLCD3kVtYbJ+KNLEiY1IzLPYRgsPSSYjvp3C6rSA2GR6gRbcBWbRTWFw+DY2xAvuvG/xxXZZtGymRIeHtX4i5Lye6yXHbdbnoRA6L6Y1dIkpF4ln25SVxYqLErJIcoR8LAyyJKiQBg5LW/iI6gonvL41gbqwRrQrB3990XIxT148cbIGMKVtNtkE8HXpPUKj+DeQFhDRQ0xjZZbR4uQd0u88HtrJ5I5N1gNHJIUCYCqwtR43bzXysGgA1qe73TLydDsbFLoPk20B0Tr2LPQny+/reaJ12qTftEnpT0ejuUVWDg4tOnWIlgqQdYhRn4NGvIwC9b81KY1waPGtyQuS6ZjaArk5NLIdsUgrpmTQtRM9p5NOET/7g1OkxTTARlwCP7WnRHPMH7IBvXbdGvHYTMUyteQnF3WLKMtkrzwAZqZk7oXJTiKfkb4qkHN9lPzLku3Cx7Dxe4nmf47c7dmrqwFfndKaMsMoaxaOXiyGVU+NiN5OXzvZQ7Dn1yz+aKUyTUSaM8kW4YqyGaI4qT9ZWyk0QV54W+T4hQ6aw8084+o/AwUHTtXx3Au8HIHvF15SwJpojeevxedROGSgXngUFBQUFBQOU9j7SGkp4UEFBQUFBQWFQx777pauXngUmsD2A1rAK0/Oq/gjYxJe9YRRaT5xqLWUosyMWXxMbRohzsBq3Szh8MwCqdT1qHBS+W6JtRY6vZNasyW07fiAhvYjU1pZytgKUuZOSVgpgLJLipS90tctcvp+sgw4MikpaD7i5dJl6QCmAAzKliqQPYE/RrwiN8eqf+3ogA+Oq/3BOiE2UR6cbWS0y8DmScbfojq+xVKHHcmzi+SQ7MrN/TmzSPqzjUL6KxftdMuJgNBzj870y3EsP44F8HymG73xcXc70xBGh7TNnJb2+8iZuhKjvs0QJUR8YZUTZaibmVK0Q5ymwv4qUmR7C3aNr+o0kaMNLX4vreNrk36oUNYSq5vEo1JnckIoUS1N9SNsoU3jGGTtHbIGIPuPcpIoOcp68xMlWiGdqHCM9ZRqfRsMVJEmmslhu4QIZX4R1WW0ynUxjcXiL4VBuV5/l4x7LEq6XZQ1xvo2M5QJtXiB3I+b14v2zqknPuuWG/dmwNYBs0ZH+TUbJUueO0wN8/0bC0hfRZIymdJm8/vdnJF+mJmQDDKmuowxormjNEb0rMz10ngVmm9v0JfVOq1a7KxR+/FB6atKlGhWoq500tnh53sj89FqzoAqHGSoFx4FBQUFBYXDFBY0WPsgHrgv+x5uUC88CgoKCgoKhykUpTV3qBeePYBm1/7xqnzOaDGTJNpHiUNkEO4R7eNsgCDZT7DLsUmOCz6mRxaSlD7F+XPzpHH5LqKk0vBCA/T6IcpJoFw/Z2RYqnCGAodwjRk5bpEdlomSqBB9oFMD+8laYmNBxNDKlOFVzFJ4PyphcI2okyQJ0GXIbqBq+uHABySA7FTUtZbwU1ZLlbJp2N/DJgE9ptKqRAf4yWm6Spkj1ThljQ03l6CvUIQ+W5JrjBsysItCkqXVGZBJ8czMQgBAQLfwYrbD3d4blzqFslxXlSikqDBmKBIFyzYnHtB8YluUAvWPRhQP01VWlJzWs1KHxQA16k97axRYWvtZ7pYxDQzSjdVC8yoo837apMcXUZY6iQ1y/2sktGhslOOXelk9VIqcZaaxezs5sGtksQG6n7MzESBZ+xlOSYYUiytWSEBPN+ga6bp8lKFmEbUHg0QIKVOMBQxDhlzX1LaUXBfdRybRUn1Hjrhlzpps2F40hAcXRNKo+qrYWZB00lULJGOr4a4OeLM1t2ekDSxyyHW4nxk+mjOVJM0lqm4Fm89tnZ7FTM03nrN6ffr684BtAfme5ksN+L3AXyIaLkHZfHW636J5pHDoQL3wKCgoKCgoHKawsG+0lPXyVV41eO3EshQUFBQUFF5laFBa+/LvlYBpmli1ahU0TcMTTzzh+d1TTz2FU045BaFQCH19fbjxxhsPSBtUhGcPoJuADi+1xOHS1uclTDt+jISfU5tJhLCdsjQ4gYSi1fyyzr5GLPjHAoPs1ltqlZ2ZkoqOco4NkO+hcLoJNCLGLMzFtBw7sie3SMNzRCVUKRQfoDD1lmxzJ/TpslyEnzK22D+IHZY57F9iQULyEgpEKwg03uP9Nhpx6wplkEXbRTWMndaZGggQdVUkt3SHqDGLaCxjmrN75Bo5S4vj7/EQZdNVKJuO+FL2HZsfTrs/M3nZHiLvLXaEL3TI8YsFztiS5lgUrudMFiMt5bwk7kAfk7ZpJPrnERhk6idJ4n7Z5mKAzvyS+9NPc4YF5XxJ6XMeoypl2+mTzR9lGokZ+shZ23MPZ8ghnWg7UP1qXO4fJ0jjTuKBhRGhcmJ1kcZAuIJKlbLDiJrx0TVWyO1dH5a5yv5oAbrGaq9cgJkjwcOkV4TTvS6i3jpaRcwwaUj9jRMigMlzaVlrjYYO2DpQArLVIKo+HQna99mJLrc8MyTqqtV+uXamsQKUuVnSyAss3vy6WLjSSBMtmJA+ZO8/ns/scUjMORo2dCzECMf7nA1P0BjFiMpvnYU2rXp/vhI4XMxDP/OZz6C3txdPPvmkZ3smk8GZZ56JM844A7fddhuefvppXHjhhUilUvjIRz6yX9ugXngUFBQUFBQUDhh+9atf4Z577sG///u/41e/+pXnd3feeSfK5TJ+9KMfwTAMrFy5Ek888QRuuumm/f7CoygtBQUFBQWFwxQONNj78M+ph6gymYznn2nun5XXo6Oj+PCHP4x/+Zd/QSQS2e33Dz30EE499VQYhkT01q5diw0bNmB6enq3+vsCFeHZA0SHHeiGAzNFoW5KJil2SHcakpiBcpzC//SKGZ5ymtYpUQiWPbMCdEw+r2e9GtX303zNzfMOdSUKaPXwrmYJvcZ+MewrxO2YWSRxYc46iZKnlUU82+vbB90yZ4X0xCUl7IHRpW45RLQRC6bpVK7SedknyCwH0GhdKFqGVu+QEuRmYj8jlCjGTZkdHhoi1Dw+7RAlwdQVZ4hwFolekg4N+ykLjPrKpAFIsXlaXfXMBxuZsrR/sJRyy4USUQAZOQ7Trjy+Bj1LmIbjLC0W2bTaiK6i7CGQL5WWlfH1kw9XNU78LVGWVtkHhGs/K6DGdVF2HmVFVfxEn1G2kZ+oq2qM6FHKzGLvs8I8ukgaJB8JA9okWqjl6P4heqjMHm0pmbcN6oopLMDrvcX5SJ4srTbycZuizLsUCTOypxiJH7bFhNfZOS4PEs6gnEwL9caZffNb0m55y5Pz3fLEMbV52MjSGsy2oKxZmB8Tvj2XE3400iltyBRl7AZaZfv2mZRbzs7IvsGI9C2LNIKoxkqchA23khij2HnBpLlqTM/y/J2s9aHhr/2+4aXFywL4mRtME70VoTp52d7Yl5nRA439RWn19fV5tn/+85/Htddeuy9Ng+M4uOCCC/DRj34Uq1evxtatW3erMzIygoGBAc+2rq4u93ctLS277bO3UC88CgoKCgoKr3Fs374diYSsvwoGg7PWvfLKK/HVr371JY/33HPP4Z577kE2m8VVV12139q5L1AvPAoKCgoKCocpbEeD7ex9SKmxbyKR8LzwvBQuv/xyXHDBBS9ZZ9GiRbj//vvx0EMP7fbytHr1apx33nn48Y9/jO7uboyOjnp+3/h/d3f3HK9iblAvPHuAaliDY2gwMhTaJCqKaSmP7wqLEBLdUY41n6RMg4QmqT7NRc6gioyR8JghxzQTdPxd9Lz0sghuObrQVz4K4XKU1OsHJuHxwmrZXiyQ6CFlNuVah9zykogI61U4JYzg0M1bKMiN4hA7wH5YoXYJg5vFAAAfEKt59Gj1C9eJgvGTIFuVKJJoSCiJHJ3XCEr9MovCkQdTqUOOEx7h8Lu0OSSaizAoS6U9JKH+GE2WAk2iljq3lNSLGIiLT9kmR4zN8kQfMN1jBcnvSWy4PLQozzkWSOT6piTxeGjU0BZpZ4W8jaqUpeVQ/7OPlVafWJrPgZaWvmU6iUX2WCjST1RjhbJ4Wv8ix8kLM+PxXfIX6F5lHzu2CBuV41ghuq4IZVrS/AnQvNLqyyM1eKnYUkloO5soGx8dJ5SSOVCN0j1CmVw8//VgcyWVKHmNZShzijPpli0QVUr2xlpwtNyzjfux8TNTDMKE7fHqWtYrf7BGczLppyZlMm3XU9Ie8ggLEI1l0zgGyLOsWpR+s6j9pTby8KJuCE3QcThjS5qAUkut/526l1YlpqFseTOsWEyWhWI5k6uxPyBCna+k8KC1j27pe7NvR0cHOjo6XrbeN7/5TXzxi190/z80NIS1a9fi3/7t33DiiScCANasWYPPfvazqFQqCARq47lu3TosX758v9JZgHrhUVBQUFBQUDgAWLBggef/sVjtBXjx4sWYP7/2NXLuuefiuuuuw0UXXYQrrrgC69evx6233oqbb755v7dHvfDsBVjCnCOJrPHA27k+L/5ld9/4dtLqIad1ljaPjLOcOUVByAk9sZUcqukLko9Zayzcr3StKl9HHJEI0qJW1vTJ9ciFctTFT1+avMw35pedQ5p8zb2Ql3Blpihfi0Vy+g63yFdqibR09LCcYWZYvl71eAW+XcNZADRyJ69ulq9ObYF8/rFFRSIm553eJF8ZdliOEx2laBotkuTAVYgiJEWRKvFYacRpdfm/7zzOLZ/Qtl3a7ISxFMB4Ne65Po6GVTfRdVF0xbMImb48ixSxYQsJtjPheRwgqwheGFxOyQnYRd1HFg82zZPQFopyzHeAOODkA9DIIV1LS9TI12QxMADYpAEFigxMraIoEDnX8wJmho+ipTo5y3NEyOaF2eSorpEFSWWL9H9kSS1Uqus2shT5dCYoCseO8wm5xhLNfz1P91qsuQWDRff50KSsPu9tk0XF1S56FuSlDcM5uXfSeVo8TDo8nbHatTSCUMvbx1HxWdg4JdHFdEn2tWgsujulDVM5ydBp68xQfWlbmiI/nqhsmBb5U5SsPE+2x56T7aY0bRc3cym7+jz131ciQMUCfHzr8BSjCE98p7StQNpqgXqw1vcKuqXvL0rrYCGZTOKee+7BxRdfjBNOOAHt7e245ppr9ntKOqBeeBQUFBQUFA5b2PDB3gdKa1/23VMsXLgQjrP7B+kxxxyDBx988ICfX+nwKCgoKCgoKLzqoSI8e4BAzoY/YCO7QN4TWcLcT5ol+V4ps6M6h1SZamCHXq25ZIlnUTTrqfAx2TKCF2AGct636tw8zX3dDU7LQr0yUVp8DqZCmIrj0Dq7P7OWDmvvDJdTbjlblRjxvKSEvjMj0ojKVqEJ9F7S+aFFmxyRNYIVGPULs2yfa4wXpIXH0aNk9XC22Dz1MscLgMlWwE9UWjVKVhrZ5osbZ1u8zn3ipxj6X817VPal75FsXSgnrpfg+GR70E/kYb9MRp1ogirRIlNH0cJdont4QTzbmTC95Vl8T/PEIcrJo2vEkXKeJ7QoWm8srjZsL0VF9X3byZW+RyaiQTRHhWwgglPMQ0iR70Om8IqUCFIlbaVAhugtq7kjvEYUldUqY5EfjwIdtZ96Uh4MNll+2KQdxAubHdL8cdqkvk7nCiyQ7IIyWavMb0u75aG00FvBgLSto02sJUa2CPfTukD25Xk1VaxRUUad3ylZflRsDcd0iI7W5owcpzchnctUl060MlOx6TGZTPE2WcCfHZLtWpz1eUjPagfRo/S84uUFTEVFRnbXPvPX6wZygGPVqC33XDSHg5Ir4EkOSWynJI6O5okYBxKWo8HaB1pqX/Y93KBeeBQUFBQUFA5THO5reF5JqBceBQUFBQWFwxTOPjqeO6+QeeihAPXCswewghpgaB5XXqZ3SpQZECEdJdZ+4PAqOzYzvcVZPry+y1+U/0TGSBck3PwNPd9F2WG7aJ0bWcCon6fYCZj1aDPTFvHN5ATdSfozxGCwizHTW2wJka5IWDtBvN9gVrKfNBKC0Yg2cqISyjZI5yRIGS5FTTrVdmr+MLU2WLDqnEZuQuLUlaR0cJCOWaTsj0qWuCi22Ngqx+EsHqY/uA+ZnmT7BnaLzlaEa9lqirZFwZL29AdqNERAq0LMM4AZogy4zRrp8PhoXIwZypyiecZ0LFMDQdKB8mS+JIlHJSrTk5nFGVukWeShpep2D3qkAidHruVydI+eD2djmTMy7sGMHJ8/WFNb5NrTS2muEhOoF5kOo1NRP0R3UBbYG8nVnfSC2Ok7Xu/zeEcOk+z6TZYo/lHSlumje2cePWDYvL2FLEgoY9E/SvYT3VKnLS7HyZWIeqMOmrdY0gjLZKeSCsmE2LCzll4YhA+IAH7NhuOz8WK6uQ4L01hmVeYGt2ciI7xmgqxhimSP4qPMNYtoO42yQc1WmWNMZTLNz/OcdXUaz9xGd1ghoGp59w2RnUShg+5xenanF8sJXDp493W5CocA1AuPgoKCgoLCYQoLGizswxqefdj3cIN64VFQUFBQUDhMYTv7tg7Hfg1Fo9QLzx5ALwM6vGKAZbKKYLlxzs5hCohFzqqzZI0YOa/DcgPFdqYjmmdssbAhb08Me2d1erEGrZGlNQM00plsmhHjx8p/yBHCA43SyCJhuVAOjz82Ivr+hQ7pmERQOoOtFsYjEu62KHPHJHl5Y5N0ntNFIW5HA+ADEkBbrIBynVMqFeW8LF9fMilUztleFE5nWrFCZV+BMndIXJKztHg8OLunQnF2H9F5R0dEbHBjSZQKtxXbcUT95yRRXYWylCOtQkMUydqDOR4O1zOlGiIqhzMMOYPJE6bnTCKyfghOUd8SBcD20cFtRK/Mt4BQjbLQyNGbs3hA2T083/wxspZIEB1G7Uwvab4+oeLJRiRBzxayBWmh7Jt+Enuk7CpuD2dLFQIAQjUH+0hMOrqQI/p1vsx/toqwytJmg2wXWHyP52p1fvPsJy6bZRmvWEjaw1Qyz8mIn4QHW2t0quHogAkEdQu6r4rlLfJQSJdlvr0wLlRXa0wyByez8rCMkI1LgWgsvt+ZImcay6H+YWGVUqfUCY3RfPBkq0rZzRasd5NeAvyW9xlYbJc+jI7yugPqN5oP1Trjbb3yyVoKc4B64VFQUFBQUDhMYe/jouV92fdwg3rhUVBQUFBQOExhQxI19nb/1wrUC88eoBLRYBsaChTyj22TMou0eVyyiS4oUmIDC7xxBk81Km/c4QkK61KySomOk9hKIXnKQojtpHB71Dup/UUR3NIsOXaV2s2ZCLGdQh9k5zefNgE/eWlRNso7Bta75RBxKr8bW+KWOWOrPCSh72CvZHZUJ4XGCh4nRl+lUWm0jxzSd4y2wKxnabW2SiybHZxtU2LPyU6pk8/Luao5oSo4C4nBTuikrejJ5GKWpkIx72cnhbpK+IXmyBEvatfLecvABGXB9JDI29ZJmYD+CXLWJmqT6VjOJmNfrdiglHN9Ui63sBEc0ToU3i/2NlfNdPxEr6TIj2wqALQC/ind46lUTdC5SFQQfqJviCZzyG0c1Zene/na7SBTFVIMpGWMKt1CwWhEZfLfCkeX9luar0bVZQyUiUZkmilAYpicRGmReCNnY3Hb2GPOpr4tVuSCWTwzEGh+b8YMuclzptQfLcg9clRbTWDQb/uBYSCkV1D1VfHwjn63DntvpaJCrfI899F8MCsyP1l00UOb7qTJyhl/REnzPcVCmkzdclatv0TZrfVnYoPVq4ZrXlrhKamT76F5y87pJDzodU6vF1477xCHFQ56LGvnzp3427/9W7S1tSEcDuPoo4/GX/7yF/f3juPgmmuuQU9PD8LhMM444wy8+OKLnmNMTU3hvPPOQyKRQCqVwkUXXYRcLuep89RTT+GUU05BKBRCX18fbrzxxlfk+hQUFBQUFA4UGkrL+/LvtYKD+sIzPT2NN77xjQgEAvjVr36FZ599Ft/4xjfQ0iJf+zfeeCO++c1v4rbbbsPDDz+MaDSKtWvXolSSL5zzzjsPzzzzDNatW4df/OIX+P3vf+9xWs1kMjjzzDPR39+PRx99FF/72tdw7bXX4vvf//4rer0KCgoKCgr7E401PPvy77WCg0ppffWrX0VfXx9uv/12d9vAwIBbdhwHt9xyC66++mq8853vBAD88z//M7q6uvCzn/0M73vf+/Dcc8/h17/+NR555BGsXr0aAPCtb30Lb3vb2/D1r38dvb29uPPOO1Eul/GjH/0IhmFg5cqVeOKJJ3DTTTc1taA3TROmKaHeTKZGG/jDGvSghhCFXQ3qQR9H8ykzK2DJDixyFjBle+ufyX9mpRgbVVplMoYodBofIq8hDodH5D/xMQkDW+zhBSAf98Gop3roUQ0NNipANBaLs2WPlVA5ZzokKHuiWhIaqIdExvJlCY87ujTkuKRkeYwG5cSPZiWU7YyQQBkJi1VbRAAwHJbrDEWqNYE0AIalw6nHlitFOWaQ3/OJzgjSje9Q5k6ZHwiUjaXnKMRN1I/BQnal3YXOACBMaTarOiUzaz7xnKYjff7Q2HIAwHQhCZ0opAxln+llEh4kNsBH3kNGvnk2Gc+hstyCCNGcBh+Tsodsk24Czk5ha6xpykIi6kfvsN2fui3jqHO2DrXfKVD6o5/pXs5EozGifia20OOrpRNFZTM1RqeKbJfOKs2jG4Mz++JMe9XqB4OOR0k0QDexTp3OcyyUknvBpAymAIlkmgVpT4SO2RGUNgSpcUVTjhOna8wRddVCtJdNmYCbpmocvlG/jkwpjopmYVGLPLMyRLPGidq2HRpTygKbniJDtbScK9YrWV12nwyYJ5tshJ4PRFkGiBL10fgSC4rqfHr+1rf76xmvkXLtOWhRZhZNc1BiLDR6TrIPnV3vZsd87URNDidoTjOv9lcIRx55JNauXYsdO3bgd7/7HebNm4e///u/x4c//GEAwObNm7F48WI8/vjjWLVqlbvfm970JqxatQq33norfvSjH+Hyyy/H9LSs6ahWqwiFQrj77rvx7ne/Gx/4wAeQyWTws5/9zK3zwAMP4LTTTsPU1JQnogQA1157La677rrd2nvXXXchEonstl1BQUFBQaGBQqGAc889FzMzM0gkEi+/w14gk8kgmUzir+57P4yo8fI7zIJyvoyfnv4vB7SthwoOaoRn8+bN+O53v4vLLrsM//iP/4hHHnkEn/jEJ2AYBs4//3yMjIwAALq6ujz7dXV1ub8bGRlBZ2en5/d+vx+tra2eOhw54mOOjIzs9sJz1VVX4bLLLnP/n8lk0NfXh68/uA16MIQSLfCMDEm5ypo89GUcyFK0gBa+8fbWJ2eJ8MSay+EHChThIXGYAknLJ7ZKI6yA94sj3+uDoWv4zBsW4saHtqJcj0Lxwk4+H3/FeLQs3iTt5kWn7RThWdkirsoRivBUqP5oSVZtP7pFwiVORj7hWDq+uoQWbdLXfShSRhA+/KPvKHxu5kV30XIwRhpBpJfCiCdksWWJFn+Wp+hz0Wke4WGLEY7isSYMR3g6Vov3yKrWHW55flBe3HeN8FyUW4Mfxh5Cib5RshTVmcnIBLQnSKKfIzzpWSI87N6gN99e6pH/zBrh4eulRar6tNSxaCF3MOTgC8kl+NzMRpg2f4pThKfUvJ89ER6q46NFzjqVefEqR3g8Ltv+5t9/AYqMzR7hIX2bQhBfSC6tzUG6qTwRHlqsW6Y+DJGe1ewRHoq40jF7WyRCmKFFyBzhYW2cIi0eDnkiM9Sf9UlsODr+vrQa/5p8EBXNQpkmiifCQ/pafBxuz2wRHoMjPLS4etYID/2dD0zPsmhZDum1ial3v6FruPJ1C/GN+2vPQZ4bDI9lDK+pbxLhsUyqfIDh7GOWlvMaWmF9UF94bNvG6tWr8eUvfxkAcNxxx2H9+vW47bbbcP755x+0dgWDQQSDwd22V0sOHNtBmeiqakrKTGPxSv9cgv7I0I1bons++0a5awLs1UUvM/wCQs8plFvpD/6k3IljfcQp7DKnraDj/k0xbQdmXW6TRbccFtDja6Y/iA2vKgDQ6YHp6BRaD8iLxLMZeTnti8of9xez9NSIUEifHixmjF7yKFTuJ97F9ldqnLQDmI4Ds/4X2MzTgz0qxy9RFky1JHUMyjopE1XnkNhdgET/HHrAGpQdV+iR7Sw8mB1KueXXd8ki/OfJkK1ItMJpXU8DuTU4retpPFmUPtyalcyssYz8IaiSH5mfrsuM00sIhd0500SnPxAcuucsRFgkPBiQ69IqNOc49aiNXgZYPHA0BCQBa9wHs5081NhbLUB/+GIsvid1bHrxC2VlglaJmvSRuCcNKaqUAWTQH81SD3VKK38JUJFeWqr0chiLlertLSEWlvk/Q3RtdUwaYXRInXSe5lWespzi1M90r1n0ArxxPOWWO1vlgisVotUMOdfmYVGW7OyU+stb5IV8qFC7N32ODpQATS9D81nQKAMrZ8t1zeQkCs5jFApIf9okPGh3ypiWhuU4/FLh0Bjp1FdV6qtyhMUGSTBwmp6P9FxuvMz761luJurPC5rzno8Ceu5FpqQ9NnG3lfo3h2U2f3E+EFBu6XPHQV2t1NPTgyOPPNKzbcWKFRgcrOXFdnfXbsbR0VFPndHRUfd33d3dGBvzygBXq1VMTU156jQ7Bp9DQUFBQUFB4dWLg/rC88Y3vhEbNmzwbHvhhRfQ31/TdxgYGEB3dzfuu+8+9/eZTAYPP/ww1qxZAwBYs2YN0uk0Hn30UbfO/fffD9u2ceKJJ7p1fv/736NSkS+JdevWYfny5bvRWQoKCgoKCocLVJbW3HFQKa1LL70Ub3jDG/DlL38Zf/VXf4U///nP+P73v++mi2uahksuuQRf/OIXsXTpUgwMDOBzn/scent78a53vQtALSJ01lln4cMf/jBuu+02VCoVfPzjH8f73vc+9PbWsgvOPfdcXHfddbjoootwxRVXYP369bj11ltx880371F7K2HADnrXEYQocGTSu1OJaKZgmsLzLJrF1BBx0Ymt8mKWnU9eT+TyFszwGh7ycgnTeo2sHLOwSyDLVxU/HmPGAaq143k8oSjsz6KHvE4jOy3h69Yu4gwIBaJmEoZw223M3RE4xKob7K9E7SFaJEihcr9uQ3cAVIGurhnXS4vXC2ULcmE6UT8sCleibCAnSdQGodJG6zGIRsktkDpGhsaGRB1B8yBflbh5Bw1ahGjB6WoUAwCmrSiqtHZi53RK9m2RfUcmZaFZtYX6Z0YmL1OwPKcdGmue07xmhucA01i+Avm9Ufacb4pEFA2igeo0VrW96vHnCpCIXLWH6LBx6av4EqFEM1PkUeVZTyXlClHIHtBYBEgMlDultEg6Sx8nyqmH1qvQuqOSqQNJoJQzUKYssADNNz1F68qImgEJD3LbbKKljIjsy6KCEYMGdRZU6V44emCnbKc/fBOmrAfLl2t9W6k/sDZPtcHUbGTJr+3YRbIObXsmJe3UpW3pvNSPEW3NtFeeUqrYO89P1+inOlVO02L7NeqGCrHlDqVaNdbkNLY5em1a+2nOsN8hrwXK01pJXhvWWA/Gy9EONBSlNXcc1Bee173udfj//r//D1dddRWuv/56DAwM4JZbbsF5553n1vnMZz6DfD6Pj3zkI0in0zj55JPx61//GqGQzMQ777wTH//4x3H66afD5/PhnHPOwTe/+U3398lkEvfccw8uvvhinHDCCWhvb8c111zTNCVdQUFBQUFB4dWHg24t8fa3vx1vf/vbZ/29pmm4/vrrcf31189ap7W1FXfddddLnueYY47Bgw8+uNftBAArAiDoXUzHUv3x7ZSNFWv+1hwhbZx8j3wlRMiJd+Io0qUgl/PokHwVpZfIC5/HmZ0Xn9KC5+QWbzvy3ZprQl1JaCjXP6D4ZZ+zsQJFWmydokq0aJOl7HtiEu3hCEZPSLaPmvLplacsEnYPBy3OrAxLNKltyaRbHhtOSfWGDk8YyJYMN0urSC7V8SQteiRNmGREvtbNqtwaU+McmhFopuwboEgOR4j5S5P1ZOb1iRfFxpxEY+ZFJMTwhsRGOmatzQuMSTyVE1+ReS1pt7xjksIxEXKO3kFRQpofNi3IZAsMbYojhrSdFgw7FM0A9YMVl/Nqnu1yYo32NaJl96dJ6VKVIC3QpUlpU7TN5EXjUTovZfO1Pifby3HZXuiUY1aSlPW2jG5uWgzvHyaNo0XySW9TBIbtFRruMPFkEdPF5lmBDJ30rEARMCtNNzctAi/naTtlIFb8EgUKU3vYpX3cJ6Gu5W3jbjlTkTp+eshlS3VNofoKiIUtU6j4LGz3p9w6rUHpk1xY2rZ5SOZqR5tEIIs0dhXKgijl6DlAi+EtcpOv0rXrWblP/ZRJx9EYvh+53IgENgKmvjKgV71ZexRk9WyPjFFiQgdF1TON9uIVg/LSmjsO+guPgoKCgoKCwt5BUVpzx2tntZKCgoKCgoLCaxYqwrMHsHVA073CbBzmLLY2f1NuWy8UyvgqoWU4vMpOvOHJ5hoOE0eTgzcJPoeE3fHoRpQDs7+5+8qiK+HPA3adKSiR1oqvSmFbklv3iOxR+J0XD5dIpyVMcWFzlu0LW8RS/tknxYU5QEJkLYtlkWqZNEACFNJviRdqEvgVoC1WcBctj1D9AtkxhEMS9h9Pk4s60S7+MIklUsjdmJE6ZhvpAtGC21IHjaVOruIkbDgQl2tfFpFV8MOVlLTTAdoBzFhhxPxCbY6RNQAv/gwSjcVzlL9wuh+W8Rp5Henq0PhaEaJ4SMvIT/YHvHA0QIKEOi8uLZM7Ni1ArZQMIABUTD8iLTLWOi1KDxCtmSchvjLpC4U3E0VCDOT0MtJmYXFFFqxLE7U8ItvTJ5Kb+XyhO1mgjxe3W345Tr4UACJAvhBEhNzD85tFYJMFKivd0m8+9pAJMn9OC26JyqnSwv5YUsbFRyvLWZwwQ4kGjwwvkjotsu+81rRbPrKjNicDtg5MAhvGOmHCxtJOocNKlJhQqkr5yD4RHd2ZlWs36Flh8aJxWtRdmaAVw0wTJ4gvokX4nqUGJALLC9F5jXMjB6TxPNdNB3rV8VJgZNXDTDv/DdBJt8eoi8laZaXDcyhCvfAoKCgoKCgcplAvPHOHorQUFBQUFBQUXvVQEZ49gObs4o0ELy1VloitR8th55vI2VuiwB4wRWX6edU/6+3Q8SkLwZNVQBYufExtF10IzRYdHgZZOaHQTaFmythimwk/+ftkJyWOXDKlUW9olxSxrQWxTtiRlw6LBeSg4T7J5iiMyzEjA7I9U5Rwd2uCaK9wAQFHB2aA0Ue6XcuM+PGSFRU3JAa9bVjas6xP6KQXh8S+IRhsrtVT7nWLCIwQrUO6HMTkoEr0UHeURJIIT2bnu+Wj4qKRUrBq6YAFO4j2oAxGuEUmxZ/LIgCUXkC6MRPkVUReWiOvb+6BxXOrnGIqUyp5MmWmiF4hKqpqStyfPyLtkPynPVEEHCAeL2KSM3SIDitC2s8UZEWT8xYWEpWWoesiR3If3RuszVLuJHolTG0mfSGDqCLO+GOqqDwmKZs9/TNABUgl8xgeI9qRaM1Kj7Q5lCC7kxmyMqHMLL5draAcpyMl86ElKA8eznZMh8jrKsL0HFmlkHEa06PPT9TuhWD9YXdMzzCqviq2zAj/zXRVLCjX8vyw+CDyMfme5XvKoOdJmWxBNMrYcogCQzvZ0BgyT6LbZRw5G9Egeiu+o3a9RsAHnAj4Sw7sqoMK0VgVosaSW2hOtjePFTSWJljlVy5qoiI8c4d64VFQUFBQUDhM4WDfUstfudVGBx/qhUdBQUFBQeEwhYrwzB3qhWcP4KsAPp9XvI0pLaaTeOU+i1RVKZzfTAQL8NJHjVX/AFBq2V0WHfBm1TCNxa/ugbz3Pd4Kau43geMjmwuivpjeYoFFjTIUWKyvSg7ILHGfrkijTkxudsvHJiQE/Uh6wC1Hg7JvKSY0x3RejtMSlZB4W0jKtqOhEQmPHTsJo87l6eysTV9DJy/d5Jb/vFMoIXuG0nhIxt8qUrYRZSRpxCtyX3EmTpUoT5bxbzWEA0v4pT+3FES07bh6+lCbP4fH04vd7V0k5Mgy/hpl8diGXHtqowzwyBrKRCtQGJ8sGNg9vLKEHc+ljt4v2/1MaQVloliUUcV2IZnpKJCq/TTiQsdYFokWUsYcW5l0d6fd8nRQOr3kSNmmLJ4y2as4RC2hTDYWSepDqsNCf36i1XzPE+exUPohVwwB/vpP6hO0kCUEiQ2yjQLPq2qWaL4i2XakmtudMI21NS1ClGHKiuoiOnXzlFC6fN8ZRHUd1VnLtPLbfmAc2DzTgrJmo0AZc/NJADPil/Z39cnDbLwofTWZkzJn4TEVrtE8YULPmaG+oj7hjEJeXmATNRYeleMUOmrzs1pfQlDs8MG0HM8yAn4GZufT/ULPX86MbdDBTvPhUTjIUC88CgoKCgoKhylUhGfuUC88CgoKCgoKhynUC8/coV549gC6CegArFm8Vjzu5yxI2NZ8QnFY1CnxdsrkoIwBDp1yxpZusos1+yBxe7xtqEREeFAv1wS3dm0rZ3Yx/RYlf68yOS/PZIlKIC+xNkPC2hNkGT5CXlo7chKDbo0IRVUigb7MhITBWaiwTCpgzw921zx/4kAqVHKFB7sjEsZnn6CHd4jIYXtcqKUMhdk51A+yq8pPCb1itUl9ph50szmFOTQj13tEQrLDIsSFRklgcLwaR2/9Z0dQruVPIwvdcpHoAIfC+HpWTjy9XMraLGF3HneLvLRs8jNiOGnJKgr2S9tYeFCPSh9yVk7DQ0oPWvAT7RIlMckq0VsVGusR8lBjUUQPFUU+We1PSPUq3VeTq+W8viIJFUblJrOZkmNHb6KxDBKoLOdCQAoo5wwYUXJU3yx8YaWTKCS+XupnjWgvfwvRxztl7mVj0v+hlNwXVRLbtGk+Pz8mGYgtJIqYIS+8eSk510Spdt8F6g+4qZEkSo6NVcsH3TrTptz7nWG537dmJJPL0KWf86NyL/vi0m/sit7aLsdhccJSTOa5OSL9oJEwI9NbARIILQlL7NL3jee24wMcBwilSTAzLMc0W4g+42cjHbKR1WWpv6yHJNSwKCgoKCgoHKZQEZ65Q73wKCgoKCgoHKZwHM0TNd2b/V8rUC88e4ByHNCDXhqLhQgpguyRsObwpkX2MMEZ2Zm9tEwSe/OTiB2Dz1vsIHE1orrY76UkyRi13wVqGWcAkO3XYNZDtExnBEgbj6+5QmHe6WkJTfd3i6kXUw8PTUoG1ukdG+T4dLJEUELoO4numZ9Ku+WNRNmwYNpjW/rc8rK+0brw4BHojWRQ9dXqsaja77dJllMyIiH9bElC+uy3xWUWT4NOlE2OKA+isUKid4gKjQF/VU2VpQ+DPgr7V6U9kXomiWkHMGkKLdKXSLvlLdNCHxQjchz/mByHRSo91BUlpQWFFYHNVChlMyFE3mF9Mkl9lJFUzMrJIgkZ3zxRkz7NB4QBKx9AwaTt7XKcXJ6E/ojy8Ij1kcghe05ZCemH6RUkDklzm0XtfNOU3Uaif5w5ZWalPRrNAZOovY6OGi0bTxUwMUq0Sy+n90h/lkl0UQ/JNepD5B0Wpv4ckX27jhI1vXRZ2tCdkAy+yYL0bW9K6k8VpG2ccTlVlO1LUrX72l9PJT126SAqPgtpU87FgofbssL7skBodkTm7YLFY255cKfcGBbNH77Xsllpj02CkIEZGfdyh4x18lm6H2fLqKpvN+qHsAOA7QOy8+SYnLHoY8FVFn6le6dBadm7CL0qHBpQLzwKCgoKCgqHKWxo+yQ8uC/7Hm5QLzwKCgoKCgqHKdQanrlDvfDsAewAoBleWoCF+sITzUW6y0RXEWOBQpdsj4yQwCBlSrGXi4/O5S86VKZjEr3F3i/ZPq/3S6AANHThNFvojaCwUjApI8nXXAMMGoWgs2WKFxNO637BLU9QjLjIYn2U7eUnmmDrpNA0Jy8UkcDBnDSutVWyOXLlIIx62sV0OYxKPUvr6WclGyvSJfWZeuNsLM4Myk9KOF0nqsg3KbHsaqts1ybltirSGMOQ6+pNCq3AX1g5irkviUjYf6accsurktvd8o/Wr3HLnOHCqSNMXRnCcnhoTs4g84iqUdlOUlYReR5ZNHb5YRlfo6PYtA5nHmn1Sa2FqoBNcyknVIhO86GYpYykGKl7srgfPdWYrqrOl4upkIAkjyOLeNpm86w0UMaQXZU6ek7KWScGdALZsRh0ot6qlE0WpqwrRmlSMp4C/cydyHmLtvRDqSr3EQsP8h+yNPncpebJuCxIpt1ywpD2cHZV4ziOI+fwaY5H6JIFDEMBof/CBpUXiIrfjlG5fwfmi8HgjknZPjki1LafsvwY1QXSZo0ECQskMsn+WUxLNZ6tjWxVX6X2nOP7JTLKNxKdlwRkG55cAJCdr+92HoVDB+qFR0FBQUFB4TCFWrQ8d6gXnj2BVvvHXwAcseHFwyGKlLDNRJUWLfOXh5GVg5Za5UuRIyu8cJj1IVhvJ7FNjlPooq/qXd3SLflg8RcAq34eXlTd+rzslO+hEAB99ITC0qg86XhUyQ5g2JQvtcUUtZhHuu28kLiN9HDyJF/PUR3+ulxAC3c7grna4soisHW6FWY9BNexQFbi6r7mkbgYuagPDcoXq056LAw7SIuWyaFbo8WunmcJfaFzRKuTdHWqFHHqN2TF83C9foc/gycLoqPS0yaTaGg85ZbDG2Usygm5Xl+5ebSRF7tnF1L/0LBrdDGFKZl0yU6JmHUslnKpKn3C4zVMi36deiTNqfgQTko0g+0GyhU5zrweGcedm0hUJSL1A7TQ14qT/o/dfFzsNrqxChT5oUgU0mTx0CrzhCM8Tgfd6A1HdVuDEZTOtWjhtzlEi7RL5Bi+UOZ/maI9CFNkTLYiSPo22ylCcsJ8iQSWe+S62sLSz49uEjuVE5dsdcsL4nJvPjNeC5c03NKnShGUNQv5MkU4KSIapWjMjsmUW7ZG5VoWrhxyy2NZ0ebihcqhpERvSmTtEYnLdpOeD44pbeCF97MtUWk8E7X674PTDrSqAytIEdc+KbPNRHiCntcp327bq5VXbtWyorTmDvXCo6CgoKCgcJhCRXjmDt/LV1FQUFBQUFBQOLyhIjx7AF+ltsCtEiOtCNL8CFEYtRynHekFOjYkoc5cL+lwxNmOgM5JtENwRvbNLJD6TEeYFF7lhc1sUVFrqw2jru2im0CDcWDtHm6fbxYbAv46iIal4eykHKYGrs/Oc8txWh3L2iBxopaGR1Ju2SZ9IoO4vqUxocl+P7IEhqNjLYDWaMG1lkgXJJweoYWUcdL/GZwiyiwpbbB2kAV9Fy00DZN+S5acwcmOIThJC9CJ6uoMC21RJB+SvpDEzZ8qiL7Qf6x/Pb4SBb731Cl447Ln3e3zorIKeWgsJcckZ/PAEC0mp2nA2jtsixIeIxn/NrkWi3R1eJGwSZQT6/A4xIxViPrp6SFuoBoAKkBrZwY6jXvOlDYbZDmRM8m1vJWsFmakvhWkNkySBhHRXm2PSpunjqfLIlrEIQ0WjWmsGeo4P53LT/RHXYcn2FHADC1650XUTlLmYZQWJ+fzwiv7SNPJR+e16RoLFdpOtF0yIIuTAz6xcZkkjZ153TIWU2QPkSWNnYbVi+HoQB5oD+VR8VkI0+LkMomQBUkjqyMlFOcYPSu2bOuSNswX/p8pLbaAyYflGifTMjA2zUk6rcctnektXiLQ0GLy16eC2aLBtIDwJLmr073gsQ4i2ot108r1/rfMVy6W4OwjpaUiPAoKCgoKCgqHPBzUPi72+t8r0MZf/vKXOPHEExEOh9HS0oJ3vetdnt8PDg7i7LPPRiQSQWdnJz796U+jWp3lK3sfoCI8CgoKCgoKCgcE//7v/44Pf/jD+PKXv4zTTjsN1WoV69evd39vWRbOPvtsdHd3449//COGh4fxgQ98AIFAAF/+8pf3a1vUC88eoOGWHpwi6XqWFacQuE09y6v7891EE1F4lcOi7F5gTJN0fYKyEFhbosLu0M2zCrRdXuOL7T7Yeq1uQ1Id8GavsPx+hVidyLgcLD0joW/Wz4kTvbUoLDobPk2yn0ZKEnfeukGEM3qWSHbSiUu3uuWHn1zilt954qNu+fHp+W45api10LtZs5Nw6pTWinahvbbMiMZIjrSDEmGhSDiTq7BEsk5mxmSQNcpEq6Yog6ZCeim9ZMGQlpj406NyvW8beM4tP5+T7W9te9otx45+GNi8Ah84+mHYRBFuQbtbDmyijJ6VMniWRhlbRLUyTckZgJyp56Nr8aVkTNsSQsmNDEp/moHm9JlBmTucdeUrG0AYmMlEYULa74xKI7pWyNixVUE0Iu3JVMgZnDKh9CKH66XO1HF08UQzaXwcythygs2zbnRy+ta2SftNTQNaADMTRLhVqKVISPpBJ8qGtZg4q8ukRDSSKYJFOkKc8fe6BeJgzjYlTBP7SdCLqeFMRcZuReuIW35msqd2njqnsyXTgrJmezKzlrXKPfvClMzJlrBce4xc47WYlFmjqULlUlnoqnya5jbRdhrpYgW3UJ9Q9dloXNc1pf7TXwDsKjwhj9iQHH/ySDl+QJi6pnYVr6R4sQ0N2n5QWs5kMp7twWAQwWBzbbW5olqt4pOf/CS+9rWv4aKLLnK3H3nkkW75nnvuwbPPPot7770XXV1dWLVqFb7whS/giiuuwLXXXgvDMJodeq+gKC0FBQUFBYXDFI0srX35BwB9fX1IJpPuvxtuuGGf2/bYY49h586d8Pl8OO6449DT04O3vvWtngjPQw89hKOPPhpdXbKua+3atchkMnjmmWf2uQ0MFeFRUFBQUFB4jWP79u1IJGRx+75GdwBg8+bNAIBrr70WN910ExYuXIhvfOMbePOb34wXXngBra2tGBkZ8bzsAHD/PzIystsx9wXqhWdPYNf+RcYp04ooKovCqB7XcorIaURjMe0VIFd0phSKbZQ1QlkCbAXAgnC8PTIm4diZKPth1M7dyMiydXHI8GRjscBgmrJyiH5jx+pcRhqeoGymQVMojwRlZrHA4NO9QuXoFHIfK4o425mrheLZUZSMKs5SyZcNVyAt7K/AX+/wJ4Z73ToxotuqJI7H2SWRgFAP49MkjBaQfgiMk6vyPDmmNkEidSQoZyVk8Nvp2n3kTxIPSP9kaSLsLKVwZP1nZ1iyWkaL0raONcNSn6T7QzS3eM6Z7ewXIvBTxpkdkutNEIVkEa2wcNGoW67SBGSH+iKN0RQ5XyeiRcAGYtEiCtNyA7UuldSa7qjQcwviabfMFEyRsrcscr3nbCabsup0ukajXWiX6hjdrGQFopGwJFplbliUOaX3s8dLfbutwSzKvVcuEZ3np4wttlYh2w62bnEoyy8wIce0yL4kXZY+HCTX8pEp+UO2sFPmT29EaIyQLn3y4BYRAm1kWun150w2H4YJGy1xEjDcJhmF/NxgSovFJ9nShTO8cgUaO5pjwTjds5uFVrYouzW/SPrNGJV+5qUGEZmqu1lLOFrtX7GdBDY75TixIXoG0tIBtvZpuOXYzW+tAwLb0Tx/A/ZmfwBIJBKeF56XwpVXXomvfvWrL1nnueeeg123jf/sZz+Lc845BwBw++23Y/78+bj77rvxd3/3d3vd7r2BeuFRUFBQUFA4TNHIttqX/fcUl19+OS644IKXrLNo0SIMD9c+wnjNTjAYxKJFizA4WFtv1t3djT//+c+efUdHR93f7U+oFx4FBQUFBQWFOaOjowMdHR0vW++EE05AMBjEhg0bcPLJJwMAKpUKtm7div7+mqHzmjVr8KUvfQljY2Po7KzZ5qxbtw6JRMLzorQ/oF549gS+2r/ppZQtlZZf+8nYODJGofRZqCg70JyK4pAwO/GmF8uBAnQuzryJ76AMqiUS9g5NeV/jC51EtQQBqx5dJ/smjyt8NUqZERSuLeUkBJ1qk9SF1pA00CQehcst1GGn9okT+rNpeavfslHKpYXSoMmMUF19bZKOtiQ5UfPSGvV6aXEGViIk5fGcxLt7YtKe7TMpvBwqLdIRvmlpmx2hjB5Pyp1snyl66Sp3X/arCkvfhvSK+3OIvMk444YFxAIhCe8zjcXJHD7KZrKpbYt/mnbLGz5K7vamXGOF6I9poqgC5NieikqsnwUemQ4rl8KADyhX/VgwT6iWliBl9/hJ9I8uIEPieNY2mQ98LxlpooQzJPTZKf3DPk1OG2VdsX0ciQSCsvPY34q/lONteffnRIa8wzzUlewbDMk1stDiZEYoBiMmVFqFnN81GlPO2FqSlJuZ6UXGxhnJqDq6VSjRFT3C/YTqXl0BWwdKNY+uqq+KsaLMjf5+uQefnxCvN6auooa0P0qUMdOgnrlB2XzFURlfdDb3tkNRzlWNymDEBkkkUFg+19HcpfZDNU9BZoeIgYeZmMWLjUU76xmsVvmVULept+UQtpZIJBL46Ec/is9//vPo6+tDf38/vva1rwEA3vve9wIAzjzzTBx55JF4//vfjxtvvBEjIyO4+uqrcfHFF++XdUQM9cKjoKCgoKBwmOJQfuEBgK997Wvw+/14//vfj2KxiBNPPBH3338/Wlpqb5+6ruMXv/gFPvaxj2HNmjWIRqM4//zzcf311+/3tqgXHgUFBQUFhcMU+2vR8oFCIBDA17/+dXz961+ftU5/fz/++7//+4C2A9iLF54tW7bgwQcfxLZt21AoFNDR0YHjjjsOa9asQSgUevkDHM5wav9IL8yT1cQUULGtOQVkiu4eojvoOBUOgcq++W6JlyYG5cTZ+RIGjo40D5/yeTnDCwCqEUCvHyI8KSKILDDooT9miSL7iMLgsLmPqJwqxXx91HlJSm8YJ15uB2UYJXsli2R4NOWWly+QdMW+aNotvzDTURMeBLCwZQqVeidw2Jw9ek7ve8EtPz4pAoapiLStSL5gto86JUp0EmUhaWUeewrX69InC5LSZm7PgohkJ7X6hSIc0Wp8ua45CNLAPj8m9EGRxNn0iAyYk5TzhsalbR5KVZfyix8QyszH2YNJuZZkRGL9RaKE4gmhBWcK8jzgTJyZrLRzXrIIlGrHG54W+mawQDcK0RyJNmkQC11a7SToR5lWVfI1cwwp+wp0Q8Skryyi+ZCXOnoLZWZl5EYPtFD20IhcV2WeDehAxdIRTbCgpbS54VEF1LILG2BxRc6CLJvyuE79UeoUTpf2D3TJ/NlZIEMpQmIWEcIK3SNdYcmMe2ysdl8Y9d/nKwYqPt1Dn704LdRYjOi5YoX6KijztmTJtUyTtxeLDTo07r4EKWMynURZcqERyvxqoXGn+jplVDWWDjQeVdUgULUAg0QFeV9+hvISgfCE/BEYX1Vrv2Ue2JcIhb3DnIUH77zzTrz+9a/H4sWLccUVV+BnP/sZHnzwQfzgBz/AWWedha6uLvz93/89tm3bNueTX3vttdA0zfPviCOOcH9fKpVw8cUXo62tDbFYDOecc467eruBuXhw/Pa3v8Xxxx+PYDCIJUuW4I477phzGxUUFBQUFA5V7JOP1j5meB1umFOE57jjjoNhGLjgggvw7//+7+jr6/P83jRNPPTQQ/jJT36C1atX4zvf+Y67IOnlsHLlStx7773SIL806dJLL8Uvf/lL3H333Ugmk/j4xz+O97znPfif//kfAHPz4NiyZQvOPvtsfPSjH8Wdd96J++67Dx/60IfQ09ODtWvXzqmNCgoKCgoKhyJqLy37soZnPzbmEMecXni+8pWvvOTLQTAYxJvf/Ga8+c1vxpe+9CVs3bp17g3w+5vm2s/MzOCHP/wh7rrrLpx22mkAaoJFK1aswJ/+9CecdNJJc/LguO222zAwMIBvfOMbAIAVK1bgD3/4A26++eZZr8k0TZgm+fTUPUbCtgbd1gD5FfzsPUUTh+ktUJmzqxyJAsNHIVDODjFmKNurVWKqEabSOoimoO0hFj9s8d4QLWMOjPro+2IaGppgPjo3+9HodM3x7eQBRDTBPKKBTBJAa4tKmH1BUDJxBonfG8uLOOERPVI/5pdQ9tOUmbUqIf5cz8zI/KmYIWj10HuLXkW1TrMdHdvp1ik4Egb/n/FFbtlPIX3mtdujcvEzlrSBBeKqfqJFyOcI5OUUoM7VKXXqyLBk04yWhdYZh1AAxwZH3J9D1IY39Ay55R0JoTC2jUjf8nyy+mS8dJofPk6GYNso8pma1yJ0EtOXC/uEXpwyhZ7obhE6cjwvbZ6XlHkSrqcYhTUNCTpXjqjDFNFYQb+cN0R9brTJjTVN2WThHURLESvC2ToVolECJvEWEaJrB+W6Kl1yXv+gXJcvRRRnLgQkaz+DSWlbjDKwDOJIquTzFaTAO9N24bDsWzhKxjFF2V47spIqnAjIvM1WKKONBA8tomKzRTnmyqRkbJmp2v3ot3VgEpjKpVDWbESJGqMkTiyJi9Hf85NCufJ1DU3JAIQpe6uXPLaKnKVF86FKWXJ2mcaLxjTI3AUJCUbEls2l/I06dRapavBbgE0JYZylFcgQJUrXW1oqfdhgkq2KorQORWiOc/De76699lp87WtfQzKZRCgUwpo1a3DDDTdgwYIFuP/++3H66adjenoaqVTK3ae/vx+XXHIJLr30UlxzzTX4+c9/jieeeML9/ZYtW7Bo0SI89thjOO6443Dqqafi+OOPxy233OLWuf3223HJJZdgZoYcOHdp13XXXbfb9rvuuguRSKTJHgoKCgoKCjUUCgWce+65mJmZmbN68Z4ik8kgmUxiyb9cBT2y9+tnrUIJG99/wwFt66GCPV60fNppp+FNb3oTPv/5z3u2T09P45xzzsH9998/52OdeOKJuOOOO7B8+XIMDw/juuuuwymnnIL169djZGQEhmF4XnaAmsdGw19jLh4cs9XJZDIoFosIh8PYFVdddRUuu+wy9/+ZTAZ9fX248U/boAe9E4u1d2z6SrZ3WSTsghfB0VcIR1Cc2SI8FE5iuwqPdQVFeDz2Fl5nCfgLDgy/hkvPHMBXH96KsuXsdu65RHim3ilf64vbJVJh0qLEVS0SXZktwvNcRsaoTJ3nifBsF3uI/71CbCY4wjNZiMBwfLisejx+3r5ujyI81iwRHv7SnMnMEuEpSx1vhIc1W6TfViyS9qxp3eyWOcLTa8gLea+eR/TFTyC/9JueCM9z+R63vCM/S4RnijSCeK7Q/HCCHJ7ksCVFePpk7DjCkzTkM5gjPB57DorwGBy10DR8rPg6fDf8CCbIgiGXlfss1fLyEZ6ZktSfHpJ+mFOEhyI2vFDZE+EZCzSt758gh26K8AQDDr6QXIrPzbwIP0d4gtIIbj/bo2SK8iDhCA9/mhaGRQMn1SfzpC0qfcURnqGc9EkqLO3h86YMuZc5wrM5XwtF+20d75o8A7cGHtstwpMzpc1LWuQ5wBGeFNlMDKVpjFifh5ziPREesg7xRHhooTjrl4EsOfxZqd80wqNruPwtC/G1/6k9B/nZ7YnwZCnCY7HVDiUm1JtpmbTjAUY9l2af9n+tYI9feH7729/i6aefxuOPP44777wT0WjtQVYul/G73/1uj4711re+1S0fc8wxOPHEE9Hf34+f/vSnTV9EXikEg8GmgkdO2oFjOMjL312Q/prnZYPLkfHmHiyc7VWlP0Q6ZcakifaKDskOpRB5uQhz4PHzipLnV3bAuz7djknYt1p0UKnfwKxPVuFdJGkDEz0kjleSSpszkmnVnxIhspGq9OWquNBVL5Tlr06RbrsXhuSP9clLRJDwr495yC1vykvo3qJUuSM6dtSEB0eOx4ktG+DU/8rtKAtl9lhaBtAf4BA6jTlndhBlUNHJv4kyfYIR+kMWkwHMUkaSnZFBDtIflx1k9uMnnmlRZLtbfja/CEcD2FxNIU4vQpsLsu+WjUQfdMgftWKkuQdTtZUmKWUnBbJEH/TRHyD6i5ulF7wipE8C9Fbh0Bv3ojahvQo02Qul2vPD0cvo75C5EeqWfcv08jxlsmEdvXiXZO45E3ItmRap0/WwtL9E9IoZ5S8MugHoZjC7yVeLXkJMEv1MdcuNGLB0wAGCqRwmc+T1RoePUQZZkai0EAljZnJERY3KtTtx6Z9ASOYS3wsj9BISDkvq0basNPp13TLHDNp3lO7Z7fUsqgYFt6prMyxfFevT8rJthOTCbBr30Ul5Ac6RB19xp1xXsVv6YTIv59UpA1SnseCE0WpKXrp0Es+0x+U4bZvoOUi+Y5HR+jOv/iFZ1B2YcDzPZT5Xnl6Syc4ORaL1G99Mlu+19Bpx+GDOWVqMe++9FyMjIzjppJP2aL3OyyGVSmHZsmXYuHEjuru7US6XkU6nPXVGR0fdNT/d3d27ZW3t6sExW51EInFQX6oUFBQUFBT2FQ3hwX3591rBXgkP9vT04He/+x0++MEP4nWvex3uvvturFixYp8bk8vlsGnTJrz//e/HCSecgEAggPvuu891Wd2wYQMGBwexZs0aAHPz4FizZs1ugkbr1q1zj7EnMFOAHvRGUdhagl15Q1O0nVb0MU1kUNTEI2duyteBNklfRbT4jvct03l56np0VnYZaV9FXILtANCQ1Gh9nmiIVRRGpqVLTA2wmzOH3z20FO3weKHfLb+QkYhEZ0i+QCML5bvqxbREcnpCEtl4ekK+Lle2S/TgdYmt0CwDGAGeLfS6X7wTZaFUpkpyMQsTMlCsDbI4Lp9w929eimbgB0WUHNgDFHnIs1YMLRZdPyY03HsXC32QI4f09UXJhpwXSAMAegJpPJST7SmybJi/WOL1O16kaM+UjIXZxZwnzbO4bK/QY8Ehem5oh0TJWrpkAobpOzhLdg9cniDX+86IjPX86AyQq/3clJfIA+vS6J45Jl/64xT9CBkUiZIr9GhRpckSptgtv/C3k+3FmNygWlSOyc7mbOUQTsjZ8uT03Zuo6QtFghXEYtJXIb/08xDZRvQkJTrEdgwa6VnlqA0max8RtTQvIvdIOwnKjFIoKleWdk6XiYKk+3SoQG2L1trmt/1AHihZBqqOD90RuS7WkmJa83VLt7rlbRkJkbQcKR+hJlFXYRpHppUnszJ/2L7ET31SGuGHlPRbvpcoMKL2G9pT8rNG77AtUDlOju20vKAabu6W7i+88tYSitOaO/Y4wqPV7/ZgMIi77roLn/zkJ3HWWWfhO9/5zh6f/FOf+hR+97vfYevWrfjjH/+Id7/73dB1HX/zN3+DZDKJiy66CJdddhkeeOABPProo/jgBz+INWvW4KSTTgLg9eB48skn8Zvf/GY3D46PfvSj2Lx5Mz7zmc/g+eefx3e+8x389Kc/xaWXXrrH7VVQUFBQUDiksK/RHRXhmR27JnVdffXVWLFiBc4///w9PvmOHTvwN3/zN5icnERHRwdOPvlk/OlPf3JdWG+++Wb4fD6cc845ME0Ta9eu9bxYzcWDY2BgAL/85S9x6aWX4tZbb8X8+fPxgx/8QGnwKCgoKCgovIawV9YS7e3tnm3nnHMOli9fjkcffXSPjvWTn/zkJX8fCoXw7W9/G9/+9rdnrTMXD443v/nNePzxx/eobc1gBQEEgQAtEmYai0ODnmwnzt6ikKpOYc/QNC0Inccu1lI/QJLnviplbxF1FZ6Q7QXS5/FIqqNmieFvUFp+SczJ9MsJPbQZLc70ZH+RDsbUpHRGpSrb17RtcctJaoiZkOn3Xy8c5ZZP6BPPjb542i3/cXzALZ/SI5lNJyfEHmKkmnR1eMZKcVR8u1tLdEflwuLkxD2vVeiA3w0tdsvscK1TltCCXqG9RsgWobdFjlMlO4BAXCiDozqFhpupNl9LtqMkFMBThQG8H8Cvxo+CTtk3Bi3mHJ6UzBetSjRqZBbrEVqV7pD4jofiKciEPXqJLHBtDcqiaM4OK2tyvRXqtxJl2WT8QdoecY9RIGqDaaOxKZl8K+cL/VeuSv0WyjzanpV+YyuX4rzmjuE2Zf04RO05aWmzFbLRDH5/82NmikFAq/2cHky5249dIUr0C2lhP1N+TN8wVcp2LcjsknZZx2hR+mp+SI4/RDYTnGHXFZKHWYQWzB8ZF32n3wzXlgc0Fi3HAyVYvipM0pIqWdKeDWR3wljaIdpZL44LVd2dkjZM0LUbRF0Vs/wQJWqfHOQ1q3mkokq6Oj6i4xu3V0MOSavWKFCmQS16/mp0Xp2WHZi0ZMGpT1wr8MpFTfZVLVkJD74E+vv7m24/6qijcNRRRzX9nYKCgoKCgsL+x6Huln4oYc4vPO95z3vmVO8//uM/9roxCgoKCgoKCgoHAnN+4Ukmvc67d911F97xjncgHo/PsserD5pTd9ZljRYS5AsQBcR0FQsG+kiokLO3uMzZANExchhOUbYNaVqwbgTr/PBxyknvW3x8hwUjUAvlO36h4Pwlim/SLpYh/ynzVCD9ls6+abwcJjwcoODMJc+75QDFlBOk/NURkg4+NSn1C6T4eP/EEQjYOt4PoDeSdrO0pihLayAiwmicFfXvT69yy8csFGHAqRlp87G9EuovVCWMH2yXbK8tY8KjsFO2Rc7p7Bq/NS/1V6eE8jg+utUtr9cXA9PA4vgEtpLmzGA25Zb9RAGYTM3k5Tb3z1AGUJ9MxuqM9KEzLBRbZCHxt4T1k5JltqxFqAqQ3gvTbZxxsyUr2V4TM0nAXxOiO75PaMogacJEu0lrhWgdptXSZGVS7JW+ZcsWvnaLRCBjcdK9GZTJHewRPSWbxs4gwUmbaI4eokSjGoAs0J3IIkv3yDND0m/J+C48cx0tMbkuprfYPZwR0qU9KYP2pfTNFIlDRiLSn32UTvpsVvSpLBqv1e01KlOvZ2k1MFGSts2U5T5i6sqgrDrOguxrJTqPRCmTEWnn8GZZOpGcL31bZkFCprpID4czEEPbdhcGBMTmJ1CfFlYQsGwgUKD5Q9Q8P2d5+QL/DXCfma9k0GRfFx6rCM/uuP322z3//3//7//hxhtvxKJFi2bZQ0FBQUFBQeFAQq3hmTv2SnhQQUFBQUFBQeFwwl4JD75WoZdqelahyeYCVMSOwCEai1kcnSxW2HuLXdQrMbJuYGMX9uGaxZOLmABUyVKAsxMAoNimw6q3MbbDgVHP+uJzc9iWr8FPYW2NHJxzJYkXdyWEfvKE1ulC//PZY9zy8vkiRMZUBYv4rY5tdcujFaEefjO+0i2f0DIInx0ApoH5xjSceuZJD9kxDBMn9/jkfLe8aJ6E4rfNSKaPTdkfGbKfqFIW0s7plFuOhKWzS+TEHSSnbLZXOIm8tDiLbcpqTv/1RYQOeG5CMmISYZlceeq3gkZZYG1CMXA7K0SHVYk6teganyU6ZlmPiBxuz6XccrEi18v79ielzV0kPLg4mgXGjsLxvTuQo3TGAFk8sIUBez8ZROWMzEiWnEMWA3qGMr84oYrSwDLTlMZD3mFMY0WJBmIaq1iQm37SJ8cZTUeBFLB5eyf6yE1+yzbxjGvtlCy/oRnyuiIxw2pE2jA+SlwyWUuEKLsqFZB9mSrlDC+mBacr0mbOZBwpSX+21Gky3fb+ubDpgTQvKtRnnOxatuXkPqpYMq94vBgREh6ct1juxxxl+cUpc80k/7XwVjl+frnMbfZN42dX4xna+OkvApYFmAnK2qPHr6/CFhL0bKVnbuOZ6ezyvD2gUMKDc4Z64VFQUFBQUDhMobK05o45v/D8/Oc/9/zftm3cd999WL9+vWf7//7f/3v/tExBQUFBQUFBYT9hzi8873rXu3bb9nd/93ee/2uaBstqLsT1akBkzIE/4MBMUMYSRWaDM012grjyAl7RvlIrZZAUKKuLMqI4M4BFC8NTJHyVaF4/NCX/4dAsAGT6fbttA7wCg3w+v8egSIpGhELrlL3SSg7OQyVvhl8Dxy0UgcE0+S6xO/afJxa45b55QgFMk5rYkriEvucbUzUvLQBxveS6pa+bFNqrLSiUCvsQtZOf15FJodjWmcvd8jD5H+Wy0uaeDhl89j/yUbbI9Kh07oL5G93yZIVc5g25xixxpE+ne/CG+s8FCWkbu9JzpkyeKACN/IYcmh9MOcVC0g+5klBLnDWzvEVoLM6ierYsNM10WsaFxQO3U58EyU+qVIjgPRrw1GgPAuQSPuSTfuZ2BijrZzQj/VatNqd+WXTRV6ZfEC3lIzqPE3E4G4uzjcqQc7WkhCPJFaXflvaPADPLsLR/BNsyQk0uXCB92BqUeyTWLhzIlrRkselETYaT0j/FnJyLs6U2TAnF2RaRtvnpOIZP5sYMZbfNJ6p0YUgyGcfqDzmf7RU7TJAAJmcsPrZDaOKWuNDTPH9KVH9iRtpvkpgkZ/bZNAfS5D5vV2Qsil3EzVBmIlNOHu/D+mkbTJvPqv3jrNoqLVNIbaQsyKScNzefnr/1c1mcufVK4DVES+0L5vzCY9vNlUYVFBQUFBQUDg4UpTV3qDU8CgoKCgoKhyvUouU5Y04vPH/6059ch/KXQ6FQwJYtW7By5cqXr3yYwQ7UhKtYeI+zn0yit5i6AmVLsR1OcouESGcWSog0zFlgQQrrUkSZEyaMrNO0DiPf41UgCE0CRv0Y2T4NZj2Ax9fD/lueBA26BjMv4WW/LlHAwZmUWz677xm3PEKdlClLWJ6zSDalJbuEPZK2lkSIrMUv20+KbYIHdd5mZ6UFtlUbiDJlu81UmntXBYgPrFDGSjEtcW07KbHq3s60W06SsNtwTq6xTCF3X0HKL2TFS+iszmfd8r8MyX12Qe8fqc4zQOYUnNX5DF40hfLoCks23EhOKJ7CiNAo8V6pEw1SZhbRBAZ5QlWozfGgXFeUfMfKNCGWJoVSbAvJuET8cq4q9f/6Ucn2CteboGlAf0IoldGitH8B+amxeF2KKKGpUsQtD1Pmjl2hr1fKrDEm5drL/MAv0bj7ZW7PplWSJEqXs+Q6gnn3p5Wq7rYfADw1Itln4SAdh6i9nCn3CNOO5bL0A2cLvr5LhCuLpLLHfmd8r3GGF2dvhTTZ3mnUMrC0ul/WTCWMqq/qEc9kWjlGVNcL0zLPC9SeAmUv9neI+CFn4YX80gbOguSIhB6UeWsFZY4Zk1L2+BrSrd+g9Bs/fSVAr3qf72Fh9jBxtPR5kDRWKQEUjUeUpQRfDknMaVje//73Y+3atbj77ruRz+eb1nn22Wfxj//4j1i8ePEem4gqKCgoKCgo7A20/fDvtYE5RXieffZZfPe738XVV1+Nc889F8uWLUNvby9CoRCmp6fx/PPPI5fL4d3vfjfuueceHH300Qe63QoKCgoKCgqK0poz5vTCEwgE8IlPfAKf+MQn8Je//AV/+MMfsG3bNhSLRRx77LG49NJL8Za3vAWtra0vf7DDGNWwBsfQ3JX4gJeiItsnj2cLWQp5hK8y/RJ2TW2Wg1aiEngLFeUEuXnkxUTUU7Gj+Rs6+18F095ZbaY0+OqnT26xUa4LD5Za5BwlGk5PpoNEoKGlJTRdoM4oB5tPLc7uKVKmBovRndIuFNU4pY0VLal/UmxY2kah9Z+MnoiAreNvAKyf6UWl3vns69QXlnj0ilbxxtpYkmyjYVPi2vP7JHOKvY1Gp4W66pon7eeMmCqJrWltEuofJfqp0iF1Tu8QjzCGXY/L244PFn2R5avNKUUW38ttI4ptnkzA3pTE4jlrprtVROQ6yRsrWxFqj72rNk0KBclZXZGATNL5UTnX6t7tbtlnB4CxY3F05zBK9ORlaoyzdZgGZYxNU3phXvozSNQG3zMeEcJxuS5fJ3mf5aVPQilpz8o2ERKcMoVK6yR6sVqnTauOD+1B6fOhAs0ZEufsCJNvF41vkMQVs5TJaBUpC4mzAqn85Lh4YzE1zFmQveG0W36RaFYf5as17lmfHUA/gB2FJCqahde3Cn1mEsW5k+iz4zrEk86k87IfFrc5QRQqb09Fhb40KSPPJGrMismzywyS+GRO6vsLPirXf9Z/XU4CpgWEJ2hJAS1HYBqL549Dj7roztq+Vvk19BZxGGGPFy2vXr0aq1evPhBtUVBQUFBQUNgTqAjPnKGytPYAtg5ofu8bPeuaBGnxcInczKvyEeiJ8PDC5pkBksDnNY7szE4RJF58x1oRAfkg91hdxAe9CyfNVIDKPphWre28ODlMJtgVugabolcc4eIFhAvIPfzpzDzZl77Wj0yJnsyLGVmQvMkvX5orYxKBiVEHlBxp/2+nRSenxSjAX7+IFYkR2PVOPiYyKNdCA/hwVsxvHxodcMuhAC0ipa/OncMS9urplk++p4dkAWqAXcunZRD0qByzL5F2ywXq0DGKaD08LW3rD2UxAGBHucUTaeHIVaZI5wqRWzotlq6Umt/yo5MSeVgxTyIYz01K1It1Xfjru4W+vj32AaRZtH1K9P0tWhS9oLU2YQezLShRVCFG+kg8ZyxagTqdl0nJ2kcI8+c32WRQcKgapahIj0Q/wiEZo0V9Mvd6whKhGjcpmkT4w9bFbnl5R21uTJfD6KD+YX2nibJECzdOy/xnDSWOAvFCX8aWEdmXI2kr22Ucd+Yk6sIWEhl6eLDrOo/vrnhz24uw9Qoenel3t61Kyv31pk7RmLpz/evkWtokcthD0cUYRQJZz2e6KOPL0cKxbbLoPdghY+dU5Lp0srzxD8sxfaSP01hH3/gZGXXgrzqexA+21OHnsiGX4nn+NvTKXqL79j+UW/qcodaSKygoKCgoKLzqoSI8CgoKCgoKhykcZ3bZhLnu/1qBeuHZA9g6oOk1d/EGSm3NbR04SBgZlvrsRs4LgVkrwsg7TbfzAjoPdUWheqabeKHy1IpdwuGOULflBFCuswBsIeEvyf7RUbm4clwaZQekjp9sFPK0uJRl+VcmJcweplWk0yEJXz8y1OeWMx0SLz674ym3vM2UMD47RKcrYWj1EO3xkW2AXothv2gKNfPQtFAPrEOyOCWiG6wbw1YUz0EWgk7MSLybqSIzI9eusU5Ivjkl8dONx7nlLx79n255KkbHr/Oii0Pj6AxJ/28siJVAPiN9laBFtlkKWbe3yrWwxktvR9otP7dTKIMT+mWBccM1G/AuPudF1Ey3eagTsr1gzZxQXf8n5K9gIifb2d7iqE5ZoM76PwGi8yxa6L/dFtqxEqd7yd/8yR6LCs+RmRGueNgQSm7zFDmP06L0JGnvtCelbxvUXsXSkZlF92nLjBxzNhqLNWfYSTw+Tyhjtt7YliVrcMLgmPRJsFcsHgxf80Xgw2QHszhS47Z9Wu0828xWWL4qcqSZM1SS827OyXX1d8mCf6auJopC5yViabf8/JjMZ4YVoidqROYeW2+wlYk9I23T2eaBnqeNJQWNTWaLBtPyLkjmZQT8LO56QJ5jO/633C/VcK0Rlv4K0kRqDc+csceU1ubNmw9EOxQUFBQUFBQUDhj2+IVnyZIleMtb3oJ//dd/RalUevkdFBQUFBQUFA4MGouW9+XfawR7TGk99thjuP3223HZZZfh4x//OP76r/8aF110EV7/+tcfiPYdWqiLUnJosyKRWUSJuuJJxFQXO5QHJ+QXmX7ShyA7idA0abqEZXtiUMLDji7h2/Ck1GcXdbaraLSpYS1hzABOPYzL15abJ/ubCZJtzxFNQHoXkZC0KUlu6UcnJduFQ+VMRXF2Rmec+DrCf40d65aXkpR9D2m7vyH+Yk0EaeTt+NX00ajWqZfWAGmhkPXDspQcZ6oslMqimNBbT05JlhmK0g/VDGXWUTtD46QH0k6DH5dQ/LQpNMfrSJfmz3mh2+YbQlu06jU6KakX8Otx6Yd5kbRbXtgrbd76ooTZtZhQIRbNyyrp31eIwkskZFw4W2dnQcYuWxEqhDVk/DTZmS7sj8m19EQkxcWqhIAs0B3Oop/ozt6gjGmOOFueP1uJRklnZOysrMwljWgsh+hX1ilKTwp12Nkp52W9lwBZb3TFhHLiDLI4WSqkdBvIAe2hAqrUJ4M5aTNbaaRDQvkx9cP9uS0j+06QHlSU7rsO0rPKEq2cSgodOVWQvuqLpt0yz43ZaDgAKFSDqPp0j17Q1rxQZmN56U+2yWBbkJWtMtbjJanPFF53UubJ9nE5fjRBxywSdRVmmlXOxZYTnIXqGsLXp4W/ANhVbwZshRLyeClDeX5KfkG3eGPfV9JaQnP2LSvsFc0oO8jY42FZtWoVbr31VgwNDeFHP/oRhoeHcfLJJ+Ooo47CTTfdhPHx8Zc/iIKCgoKCgsK+w9kP/14j2Ov3UL/fj/e85z24++678dWvfhUbN27Epz71KfT19eEDH/gAhoeHX/4gCgoKCgoKCgqvAPY6S+svf/kLfvSjH+EnP/kJotEoPvWpT+Giiy7Cjh07cN111+Gd73wn/vznP+/Pth50+E1Ad4BCZ3O6KkvWDyxOyPUDEln27Mu2FEx7VUPN6a30EnJyptfWmYXyHw5VRsa8r/GVsAbNXzu2r+xAr1tLlJN0Pkmw2EXokNyKSbY9G5FKTIX8vrTELbP7dq4qIXcOg58+7wW3vCUv2VgRcutuIQXH5SF5uX4wuxy67ccbUMsMalhLsAXD/JjQFl1BCZsvCAvt8tSM0FgsiZ/olfosj28WhEYpBYhSKRPZZZLNAcvp0+AzRTJVlT5J2wEcDWCz2YkVCaEDNmQl+4yzovQUUZ40z/JF6fMQOXSH/EIHsIjfaEHakC9LH7IAYJQomPIswoMdMaFawiTq2E000NNTkgH3p/JCt9wTlz5nZ/DJCeIbqkQJT8u4cNYh3yelHrnGBfMkKl0kcT/OLuylNrDYI9NMXVGhunrqlg094TS2lKQfTmrb6pYzdFMNRIWO/Pnmo+RcJGIZC0pfdSSkP02ahzwHhmfkvMGAjG9LjIQWSf10sCDXwkKLW4q1rCu/7cfRAJKBAixfFYO5lFsnRff1slbpT6bkGJ5sPhpTtpDYNiLZXoGgtD9iyHzLTQg9p1P2lkb2ItwEj2BgnRXU61Ur0Vq2KlsB6TR/CmTh46Nnl5/FDOtD4ZCo7AGHEh6cM/b4heemm27C7bffjg0bNuBtb3sb/vmf/xlve9vb4PPVJu3AwADuuOMOLFy4cH+3VUFBQUFBQYGh0tLnjD1+4fnud7+LCy+8EBdccAF6enqa1uns7MQPf/jDfW6cgoKCgoKCgsL+wB6/8Lz44osvW8cwDJx//vl71aBDGVYAgOENjQeyVIEig5TE4nFpZrqq0EUUGB1GN+V/gbzEY4sdzcO0wTSJAibkmOzYXmrxhi2NrAO9fprQjANfpXYMs0XOURC2xOOrxR5ddkpitzpRIRxmZxyRENGzB4fFK6pA2Raj5FW0Ii501WRZ6BWmtH6TFgpgvBRHoJ5xFA2YbpZWm8FhfBkQppCez0pmE1NOMwW54ApdF1M/DvlD+VskvF/N0yBU5Fw7RoU+OKpFrpEzzibIyGdlsNZvi4JjeMYUrzFu//BEyi1bZZpoRDtW2mhuUeZRmrLGKmW5xmW94v00kBCOk/szR3ThzoK0YVGr1GexwaAj1ENDSLBs+zGdlza0x2V8mSZjmolhxIVXKJN3mJGR+6GclGvXKEtrPCP9zCJ+IRL6Y5d2P4nd8TzhLKTHJvtwSv3nkhbJUpyktM4smTCNEO21vENutjQ5pPN5Nw3JHDDIN61AtCNnb/G+LAq6MSeUcWdIaLIOg0QU6w8tvW4y9YehRTA1G29d8Jxb57mM3Ds8Jwv0ENowKW1+Q+8Wt5yhh2VPTDinUpWo5FBzCRR/rDl3ZJMIqufh+hLRjEa2U+pFOebYaqI4iQ4zifo3U7I93Hi80TP/gENFeOaMPV60fPvtt+Puu+/ebfvdd9+NH//4x/ulUQoKCgoKCgpzgMrSmjP2+IXnhhtuQHt7+27bOzs78eUvf3m/NEpBQUFBQUFBYX9ijymtwcFBDAwM7La9v78fg4OD+6VRhyoqMcAOev2qmN4JSZIPiHHxZIoQSwGdwp5+KjNdle8mqqTY3MMrkJdymRJXNPLqikx4X+MLnRqcut/L1HIfTFvbrd2JbbIPZ2bxcVGS9lmktlWgrJkzVz7vltenZd3X6i4R3HtkRPyznhyXDKn1mtR/T/8TbnmnKZQQZ3z4NMf11OkPT8H27R7yHjJFvO7FjITZI36py8KAhZ3Sqa0DIhY3NUGDWaX+2URqlAkZTCMtfdLaL5Pl3s3L3XJsqVAz7WSYNlGNIVr/+UJOuMYYZTm1paQ+03BmXuikWLQ5NVDKSp3WjmzTOmNF6YeSJaH+vrD0SYwoSz+ZEoVapG93kGCgVj9OxG/i/cskq9Mm3pipvTQJ4qW7pDxJQnyIyHl9ZvNvOv+I3MR0W3mufSYrx+9PyTX2hIXbSBB9OVqS/vEHi0AB6I9N4392yvOyMy7H3zmdcssruoQ6DOlyg7WHhDpkoT8/ebSx4GeQsu2YbhtLS9sWtMvcG6JMOp7/TO+OFmv9bzg6Xg/g5N7NsHxVPD49X9pGNBbPyQmiMpe3CVU3TrS1j0IMnNUVpWysyZyMLwtmWpQd6bkHbSmz12BFLtcVHgzUH2GOXgt2TB4lx/RRBhY/G31VejaSIGwj88va6/znvYDK0poz9jjC09nZiaeeemq37U8++STa2tqa7KGgoKCgoKBwINBYe7Qv/14r2OMXnr/5m7/BJz7xCTzwwAOwLAuWZeH+++/HJz/5Sbzvfe87EG1UUFBQUFBQOAzxwgsv4J3vfCfa29uRSCRw8skn44EHHvDUGRwcxNlnn41IJILOzk58+tOfRrVaneWIe489Drx94QtfwNatW3H66afD76/tbts2PvCBD+zTGp6vfOUruOqqq/DJT34St9xyCwCgVCrh8ssvx09+8hOYpom1a9fiO9/5Drq6JKQ/ODiIj33sY3jggQcQi8Vw/vnn44YbbnDbBgC//e1vcdlll+GZZ55BX18frr76alxwwQV73EbbD2iBmudKA7xC379TXpUrUQqvOpQZk5PtLE5ocDZWG4moFZsfM769ucdWeKJ5fRbNArxRUCMrXlqcoTB1BGUozJKNBsp2MeISgq7ulFA20xDjJDDYHZaDRgwWwWPvJ+mL7/3lVLf8lhUb3PIYefG8o+spaJYBTPwvmLYfdj1c/pfpBXJ8ogwyJaF+QlHZ3h8XCqO8WAYqTZlETOdpRJ0QkwMfZWbZFH3nDK/TBkRo0aZvkJ00uZYZtfbocDxZURMzQg+VKnICiyhF9pAySViPkWiVeD1n2DFVsWOG2tMu9MR9Q8vccnuEjkNZNse27nTL7GsWDtSO3xuawRMzMkYsMvnA40e6ZaNVKKTOlMyfSknOFd5CIpDCWMJfoHuPPvWSnUJRxQ0579IWEQPsDkn2XIAynn6z7Qi3fMYCmZNmucZthPUKlrTJcZ4blWfXsT2SvTVFFOoYiT0yLcVjEQ42zzRkL6pitfnjfRv5Ui3ukrYxtZQ0hIfvrYso6rYfyANDxRQqPgvLyM+Os8x25sjvbLDTLY+2yIMzSu3nDKyEIWWD7lOuz3RtW7eMS3pGaC8nT2sNCAHKtGqIBDZs5HQT0C0gMirjW6JnMS9H4Icg+3PFhmp9WK28gmGTQzxL6+1vfzuWLl2K+++/H+FwGLfccgve/va3Y9OmTeju7oZlWTj77LPR3d2NP/7xjxgeHsYHPvABBAKB/b4ueI8jPIZh4N/+7d/w/PPP484778R//Md/YNOmTfjRj34EwzBe/gBN8Mgjj+B73/sejjnmGM/2Sy+9FP/1X/+Fu+++G7/73e8wNDSE97znPe7vGx1VLpfxxz/+ET/+8Y9xxx134JprrnHrbNmyBWeffTbe8pa34IknnsAll1yCD33oQ/jNb36zV21VUFBQUFB4tSGTyXj+mab58ju9DCYmJvDiiy/iyiuvxDHHHIOlS5fiK1/5CgqFAtavXw8AuOeee/Dss8/iX//1X7Fq1Sq89a1vxRe+8AV8+9vfRrm8f/P793pp1bJly7Bs2bKXr/gyyOVyOO+88/B//s//wRe/+EV3+8zMDH74wx/irrvuwmmnnQaglhK/YsUK/OlPf8JJJ53kdtS9996Lrq4urFq1Cl/4whdwxRVX4Nprr4VhGLjtttswMDCAb3zjGwCAFStW4A9/+ANuvvlmrF27ds8aW3dLp/WynoVs+Z7mX5B6SbYbefraJjdzjurotK6U3c9tXSIKpdbm76p8Xn5zjw15Jd5nFvvQkBwxWwCz/muO6pCzAeijFqSqDlSat8OYLx3z1IRYBrRRBGBzRtZ8cVRnMem9DBfky/GEJbIoPkxO3H/b+ye3vMnsgs8OYD6AETPh6vDkSHvEF6RFkrpcGGuVZMryhViiiAdHTrQyabbM4pCu52mBZVS2D2+STEd2jt5Euijz61/WADBWTWB+/ScvLuWvY46oBMiSoGrJPCvl5KMkGJOHSTYjEYYA6bqw7P+RHbywVtrAUZGtkxI9YC2dwYJs54WpAVvH8QBezHZilDRn5sWk/dEuOU4iLNfLC5V1ut7CQooW7pD5HJBgAOI7pQ1DbXJDBxbL3ONr5EXUj07KfF5Ai5n/Z0R0pY5tqUU/Ko7Ps4iXFxiz/gzrC6VLzXWH+HodWpTL0a0FvdL+kTGJtLS2yYJ2PiY7p+cDBtWROVCph0D89VDGWDGGsmZh/YgsbO5NSeeWqtLnSwdEY4ojuluyMh9YF6gaLDbdns6RbQ1p7BRNOZf/Baljtsn49vyP1M/Ol3Fs5DM0bnsrCFgWkO8m7aaUW/Rqn8mwe1CJ1MbFKmvNKxwAaNhHt/T6z76+Ps/2z3/+87j22mv3/sAA2trasHz5cvzzP/8zjj/+eASDQXzve99DZ2cnTjjhBADAQw89hKOPPtrD3KxduxYf+9jH8Mwzz+C4447bpzYw9viFx7Is3HHHHbjvvvswNjYG2/b+Ib3//vv36HgXX3wxzj77bJxxxhmeF55HH30UlUoFZ5xxhrvtiCOOwIIFC/DQQw/hpJNOmlNHPfTQQ55jNOpccskls7bJNE3P220mU4uDBn0adJ8GShKAn8o8xfnFw/A3L0OnFyTSifNx/QCFVP10BqrPmO2Fx/B7b8CgT0PQp7nlBgJ0XJ2vgbez15cmlQwKGHJ43KBGGXShNpUNov38FCMOUJ2ALXV0jiOTuJnPDsBX5474OHzegOe8fHxqG41mkNpfpWu0NapDfQjartN2i7bzvp7rpTbwNTaoDZ/t99Th9nM7fZ528qSQcpDKGpUDaD5e3E6/5jStE5xlX26z33PtuvtztnPNdswgml+vTtfL48JzmO+HILVntjbos84lu+n2xr5+2+95yPIY8Tyc/X6hfekaHZqfvjn0z2zHtBy+9ub3YOMFtbGtcY7ZxsWa5V6b7X70zTJPZh9rh8p0LbPcgwaxuEF65jZedIz6tsZPH80TbZbne5DqkC4mGispqvYr98Kzv7B9+3YkEuS/Fgy+RO25QdM03HvvvXjXu96FeDwOn8+Hzs5O/PrXv0ZLS+1DY2RkxPM3HID7/5GRkd2OuS/Y4xeeT37yk7jjjjtw9tln46ijjoKm7f3A/uQnP8Fjjz2GRx55ZLffjYyMwDAMpFIpz/auri63E+bSUbPVyWQyKBaLCIfD2BU33HADrrvuut22X31kPyKRyG7bD3d89qiFB/YElVnKsyH38lVmw7FUPmvsrOaVis03o3nGthe8NGD+LOU9xfDiOVddNfjXWDWXivyXoGvWWoLYLNuzs5RnA7+IF2cpN8E506d5N1Dk9D28nceIrzE4S7kVewaTxmK0eZW3zOU49ez8WecgAMzM/isXfL/M9rTmlQTUb6AUbFRnKc92rsIsdQB8rPi6WoHHerZ7llmRzCx1ZqvPaL70zIujZ9n+13PYF8AVJy6cW8U5oFAo4NxXyl1pP6WlJxIJzwvPS+HKK6/EV7/61Zes89xzz2H58uW4+OKL0dnZiQcffBDhcBg/+MEP8I53vAOPPPLIrPZUBwp7/MLzk5/8BD/96U/xtre9bZ9OvH37dnzyk5/EunXrEAo1X2B2sHDVVVfhsssuc/+fyWTQ19eHLz6zDXoo5NHhYTddXtjLVFeVJEI49MiLW7nMC5Vnc1rnAAdTYBXS4THSUi5LdBtALRJk6BquWr0QX/3TVpSt2jn52nh/3s6Lb/O9tKgyKU/SQFSenh1JeRouT4lOC4e1WT9kSYx8LAhluugTYtvc8tMF0e3Zmm9DwNFxbvpU3N3yAKp16+NnRiX8/uZ+sUfpDcpfnW1FodgG8ym3PF2Ul+IZ0qthWsG/hewnZtHeqSQpzH6EXGN/TOLj7HbNDu99wTxOG3k77u/+BV4syGBWPe7kMvgVkxYep+U4vHA3slSu3SCbibwp9ZmqSAWaLy5lcJtTZD8R9Mn47iBX7p3T7fiUvQpf9z2Bty18wt3OY9FmyM00Q5pL02W5sZ6bkMWx+UkZI2Ok+SOODOpRGJC2JYj6iQSbv50Hie4JkfP7aFb6f2liBu+dfgvubnkA24vSzgJRMNznHGlpjUi/bZ+UvmI6LL1d5oDeItvbSYupQvMzu176c8Hrdrhlpo1aQs3fchoRnoCj433pN+Gu1O9R0SzsmJE2hIhSNKvNw88DKdH/YTovU5L7iyMjQeqfKbrvmNKqkKWLRdSelpXtoQmKdtKzq2GXY+gaPnXqQtx8zxaUqw5sg3R1+HuYnt0BfrEkNBY2W+Zcvp72Ew7CouXLL7/8ZRN/Fi1ahPvvvx+/+MUvMD097b5Mfec738G6devw4x//GFdeeSW6u7vx5z//2bPv6Gjta6O7u3u34+4L9viFxzAMLFmyZJ9P/Oijj2JsbAzHH3+8u82yLPz+97/HP/3TP+E3v/kNyuUy0um0J8ozOjrqdsJcOqq7u9vdxnUSiUTT6A5QC+U1C+eZtgPddsAsHi0BcTOdAMCmr6gKv9jM4YXHJlErk7dzmV7o6V0BZbYaou18HACg6DXKlgOz8cIzy/4ctqVlHTDpQDbspuUyXVyV/vDxdh+VLV/zT1De6pBqI9eveM5loVJ/4TGpPVyfhQmrsxynTOQ9H4dpBYvotgr1iTPL9tn6hF94Kho9zOt1LF/V0zZKAvO0rcJjQbQLe0XpvCiBjuk5Dp/L17zNjKqP1ixRHc8Y+Xbv27JmzzoWlme7v2kdbnOJrpf7n2HRpXP9IPUJCycyNNquc7/Rvo0X7arP8oy1qTFfQnMePO7Nx4LHy6Q267PcaxXiY0yeh565bTXdznAa3I8t9Sqa5blen+cam0cbeNxnawNT5Nos/cCUVoXvQaqj0X2k0bXzs9u3y+WWqw7KVcfzrKtyHbbnmiVK1nj+WtarW9ymo6MDHR0dL1uvUKi9RPt8Ps92n8/nLodZs2YNvvSlL2FsbAydnbUPl3Xr1iGRSODII4/E/sQeZ2ldfvnluPXWW+E4+zagp59+Op5++mk88cQT7r/Vq1fjvPPOc8uBQAD33Xefu8+GDRswODiINWvWAKh11NNPP42xMYka7NpRa9as8RyjUadxDAUFBQUFhcMWh7CX1po1a9DS0oLzzz8fTz75JF544QV8+tOfdrOnAeDMM8/EkUceife///148skn8Zvf/AZXX301Lr744v2yjoixxxGeP/zhD3jggQfwq1/9CitXrkQg4CVX/+M//mNOx4nH4zjqqKM826LRKNra2tztF110ES677DK0trYikUjgH/7hH7BmzRqcdNJJALwddeONN2JkZGS3jvroRz+Kf/qnf8JnPvMZXHjhhbj//vvx05/+FL/85S/39NJFlZLf9GfpwWCGXc6bZ2DZNJbRUTlovouoEqLMPDQtlVk6nY/PXzb+4i5u6TnHXUBdTgBle/f9yYHB8zXEdJod5k8mageFvtrDEv/l0D1niCwhZ+3/fPgEtzywTLI8zugSnZP/HFsl10KNe2G8o7bAUQeWx0fdqMFbWsXeYqIq1MOzOeGQRwqyvVARamZyXHhthzKz2KnZilBf5+jLuls+BR1d6nDGzbyoLG7YmBbq4dSeTW45XUwBAPLVINpD0p+b0pLVFSYKxpwhmpisFtAuA1wiTZ5iiWgvohKKlHHDDt2Le4WS2zQs2/u7ZBw5060nItc4EJU6y8JpYPB4nDbvRYzQhJsfEppvjPxSmMZi7ZpCTm4CPUdZdWHp8/AoZauRKHwwKQtHuB9KRD8xjbKyWxZSsps5ZyBunGx3fyYTzRfrsEVCmKixjTuEngvHpG3pKbn2+Hzhz7NpuY8sCv2yJg8WS9u2USYdU5bDOZnnfC2hegjZX3/oTJciKGsW2qNSZygtY1fMylhoNOdHg3ItOVPqlImWKm2TNtik8eVLSURX2yl9zuPLmlfGTHMrHA4+NZ51jaCh2arBtLzPXH7ucXYuU1pFGS53WYPTPFh2QLCvaskHUmm5vb0dv/71r/HZz34Wp512GiqVClauXIn//M//xLHH1lZc6rqOX/ziF/jYxz6GNWvWIBqN4vzzz8f111+/39uzxy88qVQK7373u/d7Q5rh5ptvhs/nwznnnOMRHmxgLh01MDCAX/7yl7j00ktx6623Yv78+fjBD36w5ynpCgoKCgoKCnuE1atXv6zuXX9/P/77v//7gLdlj194br/99gPRDgA1RWRGKBTCt7/9bXz729+edZ+5dNSb3/xmPP744/ujiQoKCgoKCocODnGl5UMJeyU8WK1W8dvf/habNm3Cueeei3g8jqGhISQSCcRis+W3vnrgWWxM4dLgDAlczSMtkFnST3lhs5mk7JlxEtYiV3QWDzSTswj+ZcnGguwq0q27Zk5o0Oq6E3pZFl+zuTi3I7OQ2rGD6DdxAwBKROXoJGBIC2W3kNggi/5tmpbtPYtI7p7irX+eXuiW1+8UKqq7VeiSdyx+pqaZMng02vxZOHXxuKdyIqr1222y6D4Zkfg1i/ixhcSaZUItPfS8pC1bZbKWIHETn0nhdKLAOHQc6ZUQfeH/396fh9lVldnj+Dp3nu+teUhVKiMZMEAAgSBqgEBAmi/atg0ONCjoTx60G7BF/TTNoG2j2IjYony6HYKt4jx8WpSZgEiYEyAJhAyVVIaaq27deTrn/P649553neReUkXmZK/nqSc7p/bZZ09n1z7v2u96iTY6rkGoIhbrMyveTxNFn+3AZ5BE/4a2i23dG5W2lLbKO6n1C2VT6iHbPZn6i1lZFvI+Kb+rTWimCfKsaW0UeoVpym0kZphuI4owLdTMgoZxnIGy4KKTvMBSpK3EUdqHKDTJREbKZ+FBPlBqEwAl0VYnuT/nR4Ui8TZLnzQEJc39/OaIUHgF8khi0viElhFgDJjVOIpREoScHo5baaYvU+QZF4mRdxvRi5m4tDe5Q/pEi8hLy33CEdg35GRulEg8k70jp3lkoUoRpVv10qrqG4W9ORQ13fZeN5HI5DB7nIWkLQXqh2lheda6LeJlGZtTW9EvvpPcpZulvVqSvBGJXi+RIGrTJrk1NW3PyOZVXTGtVF7bOYQE01vUPbZjBDYRwkrx7A9wwKE2PJPGlDc827ZtwwUXXIC+vj7k83mcd955CIfD+PrXv458Po977733QNRTQUFBQUFBQeFtY8peWv/0T/+EU089FePj4za37g984AN7eEMpKCgoKCgoHDhUDy3vy8+xgilbeP7yl7/gmWee2SNQ6IwZM7Bz5846dx0dqApaMu3DYoOFUG2RwBypvbIpnQUJfeK4YsVjAQAPqZOWfESVsHMU6fZwhPRsC4VK2C2uqyMhE907DqBqOWddlxiFSKino+Xi+BVSqcZGEUDjMA1uiuPDmjBucodoJAE0vjeny3Q9qVvm2pIGoZzyFFpiY7bN0n+ZoIjtbGZv8Es6TWb8Oc1Cqw1lycZN/WOSV4ijRHog/tr0lu6T6+yhs2VMqI2T2qVdbT4Z/N54mcLLltzIkmvgIIkNOlj8bZgUwRspJhRRAG6P5A8FZGJmyFOJvVo6AjLZPaSBE6TI5lmiotztJGZIgoRMew1WPOMGM2G0hqWeu/KSh73SfETBlMLSP6M5yT/K/dAn/cOeWbE3yXuItM0KGaEXR4j66U/QS0weWz09QkFu2yZU10ZHM6ABG0ebEU9LmeMN0pYi0WHJCYoV5SI9mYhwKrEWeafyBRnHLI11tEnGiCOzl/LyrI4W4WZ6RyienUf6P50RzqZzRrmfq15aAWcRRYduo/bYO4xp6AKJ2nCU+VESY3QFaH5SOWmKeO4k2s5JNF+BxiLwJsXkouk/drykQ9sl7cqU73VXutJ0ldd3b1zyZEilnNduN8e05IgWhv3fg4L9pLR8LGDKGx7DMKDre/rc7dixA+FwuMYdCgoKCgoKCgcE6gzPpDFlSuv888/Ht771Lev/mqYhlUrhlltu2edwEwoKCgoKCgoKBwJTtvDceeedWL58ORYuXIhcLoePfOQj2LhxI5qbm3H//fcfiDoePtDKP8x5lkjfzS8siO2kv5sC63GsK463VSAnBAeZS5mK4lAGbIT0SYiaus/Vdwu+p/uBamDiQlQk0fkeClxs83bRvSSMGCep/3ax+bKonS8iNITHXVuRK07xqmaFhN9zkG14R1aUv5jGGqFOHcyH4TJcWAggVfRZ4QfYI6YzJFTItEDcSrvpWVvTQmHEySPJJO8P3y5pezFCwoMUr6oUohAA7L1FA9gVkzpkiPqJk7Be0FWw/t02Jv2Qz0o/G+Q15m0Rqi43Rt5MLTJGGtEBY4O1gwa2R6WvtiXkuUxhdBK1FCFPKw7NMCsgL8ecGUIDuU0n0Hsazu3YYAsf0Epc7jPj4lXncwq1kaOIkjv6iXJKULyqFAnxUZ+PL2DlThoXEsrTSRDPG5N2zW6VthhU6MnzJL5bPB0FskAskEM4LDRTuiDj6ycKqRSq/e3Jonw8Z0rsHZaXe4MeWTw4JtdIRiikOHlyTWuIW2kWA+xop/7fOAtAOWL534aBXakoCpqOVlu7SGyQFsgMrQMTSaLtaO6ZRG0zze3yUOiQtPSbTu8gSGSS9CltRw14TeP1unq9+tqbzrKxg1hZ25GCwJDUOUd0v23trgyp/rb8n98eDmfhwcMNUx6Wrq4uvPLKK/j5z3+OV199FalUCldddRU++tGP1o1NpaCgoKCgoHAAoCitSeNt7UNdLhc+9rGP7e+6KCgoKCgoKCgcEEx5w/PjH//4LX//D//wD2+7Moc7NKP8YxA95CKPLT7Rz6f0Oe4KU1QsZMWeXwx3hjx7yEuLBQaL5B3G5limxlh0DSgLbFXvcqckSjpTYlwn9jrjckthoS1c/aTGRcJ37LWRKEoeFq9jU/z6CenIZvLYOqtBFMSejc+y0gHyEmrxpOCsDJDLoVvBcE5vE7qhiQaEKYlxcu1o9Mpz+yZiVpopj0IjedPoPAaSx5kW87tJHm0hiitkE3DzSt2C5Bby5ngXALu3EwB4fByFXK7n4mK79wyTOBvF/+JYS03tMhnTWRmjcaIa80RPRAK13fZSJbn3laFOK81UxXHNQmlN85T7eSgfxpoJ8fqZG5U8rV7hJ14ZlzLDbumfKHk/TRgyiR1D0l9MOzoKxA/5ZQ77/DIPQ77dXpoKNg9J/DKOOxalPpkVTABZoDOYwBh5KnmdMl4cp2x+K3lOxYWey2Sl/uzBZJAHmbuD6Esqk+PBJcnjiT3ydowKTTmnTfp8C3lvnTa7/O64DBcwMh+N/jSKmo7eMamnj+inOMX24veltUHGcWSCxogESItE4ZUo7SSPQj1BnoAJyaMHpJych/rKJWkWCfSkyvk97nJfutKArttFBXUiLZJE5TPVxd5b1fWTI64fcOyra7my8NTHP/3TP9n+XywWkclk4PF4EAgEjuoNj4KCgoKCwmEFRWlNGlP20hofH7f9pFIpbNiwAWedddbRf2hZQUFBQUFB4YjEfjlLPnfuXHzta1/Dxz72Mbzxxhv7o8jDEs484ASg00l/precHJqIhQHJMu6J073ETrDHFunK2TyiSJvPdr3ANBTlYUrK2G2kTYfd86pWXbkNRRJJdDG9RfGzMLO2qFqOYuiwx9PQsLg3tJNJ30kCZW+MCs3B1BV7b3X54la60ZWGVnFJWxDsh1Hx6smQnTqzuwpjBSxOWCA3C25LtEXM8hNO6fjGp6XMkTNq85PsFcUeMdsHhSKZd9KQlR4rkGdNzmf96yYapUhxozTqNxD9wSZ3nalWWznSRo9bCmVPn9YmGd8Gj6ST5PqSKEj6+JYBKz2QERcapvBKptP6t8ErL1C8ILRIT1TcEJe1bbDSu/JRKx3xCJ30HHkDZaeTYB159DhoDptED6XH5Ln5oLzcXq+UUyDPuEKWqMNp0p9bJhqsfwN+6Sue/yzwOJaS57aGxa0omZLyTRpTjV67fELm0uhGmZOtp0r/M/U2Pi7zqpnioO2Ix6w009BV7zxPZaw2DbUgDwM+6hOmzFg40Unp/gGhz8IULyyVkHvdRNH6A7IY8fzU3VKmm6glncQG3eSdx2srx0GMzyqX6a3EFDTcgOEAQv3kZdZKdFicYhw2U/m8Hla9tOidO+BQFp5JY785z7lcLuzatWt/FaegoKCgoKCwFyi39Mljyhue//f//p/t/6Zpor+/H9/5znfwrne9a79V7HCE4QY0924RmOnsJh9wY2sPW0fqSY5nJZhx3ai8bPkJ7pJZqrGsO33x2/Qh6DpQPtxcPVjnSZhAJTwFh8fgZ2u73V+FEZJPJo0OwZYoSvjOpHyJ85ejPySfQWGvdGSerCuzGuTrPkMHYucExRLyDv8OK50z3YBW/nLuyzVZoSVidOo6TIM2ROIdHRTKYThP1hsKRTGSIHMahWkYPVEG1ttPZj9aTAotFPmavu6jFDJge1q+gjmK+rvae4Htp+Jd7b34/ab51vV57dIPfIi0COmr3CyOZ0IHQckilKTI5v6g5PdQKJASTfydmZiVZuuKi8pkTaGzWrZI3UzSkKmcsm9wZ7A4utG63u6Wl6BA+YdLMqk57Eia5gZbFQw6NGt45bqpUR0oj2cXWW+m02Fass51tMWtNB9CtunPZMrzxDAc6J+QOvvJKhKjaOwhisaezJMJmeCig7tessIlKbp9abqMBVtWM0O0CLmlnmmK0p7PS9unNcet9HBlznvhABxAd9M4CpqOEYp6X8zJs1xeOkifl+sestgkKfK700sHkinkClvS2GLpGpQ6Z2bIs3z91A+0FpfayVmgT8oJDJeveyq3GZ6y8wZbb9gilJgp1xveoHen27FHfv0Y2kQcSZjyhuf973+/7f+apqGlpQXnnHMO7rzzzv1VLwUFBQUFBQWF/Ya3FUtLQUFBQUFB4TCAOsMzaRxEAewjH7oPMH12M6eDD6fRXtCme0MTig+Qso+cb4yk1jmMA2nvMH3GkcxZRj0fkzRrRTCtBlRk1CtW/eR0DflK3fmgs5dCVrAsPx+SZrBcfJGiM8eH5YauLgkbwRHDOSL2nKDogaSIV3uVNFgube2tWYcX0rPgNFw4E2UKpkrDMI3S6JBnPbFjrpX+3PxHpc5eOQG5avsMK52PS30cMRlkV5/QEMWITARXigaTIqr3NEnnjpLsf4q0U9opOnmuchA7p7ttIQaYQkqPSp39jUKX5IZIF4UikheK8vq3UJTt0biMV4pCBvQnhJqZ2yThFXIUt4Qpy2GKiP1kXsJDzI8KDRepnLIvmg7c8+Z7resXTH/dSm+nkCIjOSnTR3RSgeKgRELS9lGmRYjSctL8NHnBny/0YktAymGdKNa62TUudG04KC/ovNgoMArMjo1Cp3qO5FjrScrfQIfzU0mZS5Go5OEI5qzPgyJRLW0yt1Okp6QFifohaslFGjhZEnIanBCqt6ov5DGdQAEYTQeQ1wx46SA009PZJL0j41JPs5NO99K776bQ4zwUrDXEB7b1IOlfEXWe65Fy/JtJv4g0eXh9LETL172O8r+OQtnpg87Uw0VrbnQTH2amg/e0tlbXTLPOEYADAXWGZ/KY8obnhhtumHTeb37zm1MtXkFBQUFBQUFhv2PKG57Vq1dj9erVKBaLmDdvHgDgzTffhNPpxMknn2zl0/gzVEFBQUFBQeHA4Biy0uwLprzhufjiixEOh3HfffehoaFsah4fH8fHP/5xvPvd78bnPve5/V7JwwVV7RqfsDI2zywX6/BwUHDa+7GnVZF0I8yoZArtpHASVD5FI7Dp/zANxd5Ytkjru2nuNGzUy5Lqp5Xvqer3MEWXEys7QjsopEKYPCbGpEEahTAIHi9eNg1BMWVHPbVDEmxPxaw0e/fMCw9a6Y9Oe95KbymIW5ubOtul6XBWREqOCwzArOjw5KnDTvZvtdLnnCjUydaSyOk/OPoOK90Zk7b0a7WjiucbpXyTdEJslm2yHXNYjaaA0BAzQ0J1+YkXTeXLz83qHpw1QyLFJ0g7KNgk/WzzyiGqlakB1lGJp6ScaETKaSYNmfag0F5t5NE2Sh5tgxlJc+iKBgoRwh5wZiUK/ETRj2lR6eeduZiVLhFdxaFA1u3osNJdrRIzIOYjPR+iJnWisfJN5OVIVA5TlgWv9H8+I+VkycPOSZQQU4RVDzinw8CmCfGeY2/ErRNC1cWIPptJnomjRIHlyIvKTXMsG5T6pzLsWilJ1sZhyqlIujfT2qUPWRdoWrg8Lm7DCYwBHpcBaDoa/DKmrK2UJxqxY6G8v8Ps4UjvAvch04vmhJTjaqZQNUEZl8I4ebSRt11mhsxt9pr0kQesJ0FeWosr67sJOGk9Zc21fCOt0dvlWd645KnSZAeVJlJneCaNKSst33nnnbj99tutzQ4ANDQ04N/+7d+Ul5aCgoKCgoLCYYkpW3gSiQSGh4f3uD48PIxkMlnjDgUFBQUFBYUDAXVoefKY8obnAx/4AD7+8Y/jzjvvxGmnnQYAeO655/D5z38ef/u3f7vfK3g4wVEAnA4g08l2V0pOENWTovtYDDAmaT/tG0scrsJVO4+NlqJovIUoXSbGiL0NsNuRqlzMYUUR1kpCwflIPj3nkJuS3bW9wmzlEnWSnpAG5cijJBsViuRvp79ipbfnxGLIoQoe3TnPShc7pdHz/f1WOkA8XMrpg6PSmGXBDXBWOsTLonDUkY+kF1jpCYqWPs0vtm+D8nP08BTJ6XtGKcrzDDG5GxzCnOZKS0AmiM8lEyRI4TO2pCTkRGPF08cwNRvFw15dHJJgmMYlvVOoBB/RAW6KQ9Iaq/2xMkQU1YlNO600U0ssAnlStM9KByhOCfftACliZiv0Vslw4rKOF6zr2/LSdo5i7yGPp+lziILJC4XH9Mr8DqFU1m3ukvr7KTxBo7w0xXGhe5IUosIclevdCyRkA1O0PBaMKFFs7FnW1TRRKzvWjrZb6UaijaY1xK30UFK8qFiQkGlEF4lG5tNSf3dIxmVmq/DzLPh5SoeIee7KlMerGlqiMzSBokNHgjz4OkJCcYaIey9S2PAwRZMPE02cIKFF9tz0tEm/+YleTFA0dvhI+DROPD8JohYaJe0bk/pkmyohJSqhJaCVf3idpVfNRven2znkhFyv6l/yiYYDDkVpTRpT3vDce++9+Od//md85CMfQbFYJjtdLheuuuoqfOMb39jvFVRQUFBQUFBQ2FdMecMTCATw3e9+F9/4xjeweXP58OTs2bMRDAb3cqeCgoKCgoLC/oSitCaPty082N/fj/7+frznPe+B3++HaZpHvSu6swA4NYAs7HDmKI4VmTyDg+QV0SQ20qb1YuwcXSD2UjaLZshcGtlGXiDkjcF1CAzIjOUo6uzJVdptP2p4NMuU6x824azE0nIWasfo8ovOnM0DTSdqoNQm5noXeX+8c7rQHD5ygVibFCFBpo1cRLVc2LXeSnOU8xaXmNCD1PHnBfpQ0n14CUARDuiVc/n/mzzeytPoFFqNvbd2EK3GsbcWhIU+a/UK9fPXgZlWOjlX+o30F+FqErO8203RtEfFI6wtImU2URTy6QGhbBor/TY9MIZNOeEwT4wJzbRuQryWxkiQ0NcubXHQdRadY3orS9TMgkahbziaPI/j73tPsNLzW4TeChE9d1xQqKVZxNPq7iQwBMwP9ePlVI91vckt/ZAlYcN4XuowRh5MHBPKR3GmghSjCiRShwCJFibk3ubuuJXmOGKN00g0jzBO9ZkWlDkZc5fpm5gng9mRnXvcBwCPDghde0rTdisdIU8uFn7keFt+D7kSEfg606ZbIPMtMyZ1zjfJnwC/S+59fud0K33atPL76zJcQBJo9GZQcpTgoTnDcdaGiQZlTy4Wpdw6Kp5rHFPM45Q1h73emKpz++mMAMeqA12msea/ShzLsNpcVzWm4ASAEpBpozwUaZ09bGlqIy9NEUzZHWgfoCitSWPKwzI6Oopzzz0Xxx13HN73vvehv7/8x+Cqq646ql3SFRQUFBQUFI5cTHnDc/3118PtdqOvrw+BgHxhXXrppXjwwQf3a+UUFBQUFBQU3gLmfvg5RjBlSuvhhx/GQw89hK6uLtv1uXPnYtu2bfutYocjSv5yLC1nXoykwV3yexYAZBqLva6yjbX3mCwwyHGr8lEqhzwGXGRhZyFAhidFsa0C9jz5KCxPL92roepMkW0mio6oK47FxfUwApLJSx5AHFfLRXZkr6N2kBk/USSnh7dY6SQpf42VxFQ+3yPUSYyetb4YhlmJv/VmocUKauakt7q/GLPSbW7xFnE5xOx/SnCr1MGQOryeJNpoq5TDcbWMknSQNiGDX4hJG1tbhP5oI+rhpWF5rxY2Cg0UqtABBcNlE2bsTYs3E1MJQfJqYVE79q5qIVHB9YNix1/YJs/NlIRSYaox5pYy39EqtJeDxnpzgmg7LwV2g0z2wUwz5gNYn+qE3yP9MEoTbjgvHkkcMyuelnJyGemTNInspX0kEhgjHoL6wUNxoHIFoc8iYaGWMhQ/iym/XEnGt7ck3Ma0ljJF5dZ07CIRxUaiLN/RIFTpS6PdVnpmWIQHmSpiSojjcPVnpH/a/NKHu9LiDce0kZ8EJ9mLiufGCe1St+pzq/++Pt6GgqYjQDGw+N48zf90Hc+1lojUs39MKNq2BpknDSFZ5Pq3yVxyRuS5GgsYkreaniDRwjSJIpJHa8ur5fwed/n3+QYgrwPNr8kalWmR8c0L4w16LWxCtNmKHiqF7jvgUGd4Jo8pb3jS6bTNslPF2NgYvF5vjTsUFBQUFBQUDgjUGZ5JY8qU1rvf/W78+Mc/tv6vaRoMw8Add9yBs88+e79WTkFBQUFBQUFhf2DKFp477rgD5557Ll588UUUCgXceOONWLduHcbGxvDXv/71QNTxsIHpLNM5YXE6QpbiTfGJfvYGoJBIyMfE9MveTuQUZBO+Koi1GuEdUmhyGpm6qRwusxCqTU8BZY8ET2X0XTkTRsVLKzgoGVMd5CVBlBt7IWl5qUchRfTBCHk/xcSO3EmeLG+OSeed1SE01o6CUANzvEKvnBd8Q+pADN09o2dY6RMC2wHdAx+AjOEFKp6DzeTV9VpG6IP3UJnzGsWMv7Eg4m9/iR9npcfIKwfkocY0llmkynklD5vfUxSPacgj9M2cmNjHq4JvAJDOh3AGgA3JVhhMC9ZxjMwT1ZIhmmZGTDy/mGo8u2ejlX5pSPrn/GnSP0xHNpKyJotAjpRkwp4SFYrbpwmN0k/qm1URxZLhxJyA0JQDlIe9vXYmZS55PVIfpmzc5F3F3me+Bsnfn5K+tdExBem3kZRQhBzvib23qnGmALuA5K5szPq3JSAUVYJENfPEdZ/RIn2lU324bn/pn22lFzUT5USf6Ow9x3HQdlC/ZSkmF4sE8rOYSts0UaZNq8KDA4NR5GFgdreMF4O9//pH5blGiTynaBHRM9IP2ZDUjdvlJjqyRDHRAmG5XqCxM0vSFpMXLFo4Uh3lcrwVzzBXFtB1ID5LyuFYWjYPL1qvs8Iqw1/pEp1dxg40lIVn0piyhecd73gH3nzzTZx11lm45JJLkE6n8bd/+7dYvXo1Zs+evfcCFBQUFBQUFPYLqmd49uXnWMGULDzFYhEXXHAB7r33XvzLv/zLgarTYQtNL1tKinU0Fm0Th768SUbEFlHdlaOo6L7a+jmxTWRF8EgejujLUddZz6dho3z9jC60n68qhAGt8qGUadWQ16v1kClhO0jNEuv0bD4QWPRRXdvl2b07xJJjTpM2nNgiJ77TdApwICdf3wN0yrCxQawKcUMaPd1LpwYJMWcWcJbrscgjlqIz/GKiGyBBo9fyYtnYkBELT451YCi0hMMrX/T89eoK0uHJAll+KOSB21VbfJ4Px3IYgkoUELg0A5vGxQLWFJSv+ERGPkfZ4hGlNJfJB4xZB4m/+js8cSv9m12LrXQzRT9v8Ei60yf5vZCJ0kjmzx6PiDqd5t8JjJyHv299AcOmWL3GNXnJuP9Z16U/KfOkfzBmpUFjEWmWORP2SbtcpEfEh4G7I1J/1thJ5WV+ch2mBcTCw22PoQSMAmfEtmBVUubV+5pfRS3w4fwnx0Sfh+vGVp2RnPRPuiD99twO0TJqjYq+0+iw9BUvVNvTpG3VLPkH0mKp6wpRtPQscPyMXShqus3axtBJv8tLB6TzIOsNWV20kOQZG6OI6gSTrEB8aLn0KtVhvswx/4DUQadz003rSdes4uzhrBQdGDTgKpkYWyjzh0P1sPWc10Ynn4V32P9VOLwwpWFxu9149dXaL+zbwfe+9z2ccMIJiEQiiEQiWLJkCf785z9bv8/lcrj22mvR1NSEUCiED37wgxgcHLSV0dfXh4suugiBQACtra34/Oc/j1LJ7gm0cuVKnHzyyfB6vZgzZw5WrFix39qgoKCgoKBwyKDc0ieNKe9DP/axj+EHP/jBfnl4V1cXvva1r+Gll17Ciy++iHPOOQeXXHIJ1q1bB6Cs+fO///u/+NWvfoUnn3wSu3btsgUo1XUdF110EQqFAp555hncd999WLFiBW6++WYrT29vLy666CKcffbZWLNmDa677jpcffXVeOihh/ZLGxQUFBQUFA4VFKU1eUz50HKpVMIPf/hDPProozjllFP2iKH1zW9+c9JlXXzxxbb/f/WrX8X3vvc9PPvss+jq6sIPfvAD/OxnP8M555wDAPjRj36EBQsW4Nlnn8UZZ5yBhx9+GOvXr8ejjz6KtrY2nHTSSfjKV76CL3zhC7j11lvh8Xhw7733YubMmbjzzjsBAAsWLMDTTz+Nu+66C8uXL59S2x1FwOGwS4mzmTPaK/9J9NQWYmAtByNNB+uIAmMTqUOX2Zhsk/0pP2uMQlQUySKse8g0u9shulJM9HSceaB6DpMPSTO4TjbTboRO8tGLw/TNtA45tNlEdMALA2Lq74oINdDso9PfhNXZGVa6wy2Hb3OmmMrnegZh6D7sBHCKdxiuik16Gx0WXZefZqVfTEmZTKuN5YXqCrml8c1EIWWISkiPSX6fn0IDpIlvpDEOUAiAEOmZsM7Mrgkx18+LxQGUD5YyjVWk/MWipDlS9oZe0Q5614JNVnoaUTB8gPbsVjnAvD4j4T9OaBAKcm1cyuQ6vzHRaqXnR+VQ6zgdzP7Za++00h+Yvx7vBfBk4jgM19H82ZGSfmDdmxRFAIdOzgApqU8uInODI3HrND+9FIoi5ZYxDVMoh+5Q3Eqv6Zf500mU1uODcrh9YXgcMwG8kenA3zSvsa5HiCNJEI3lppfqzIbNVnqwKFTUz1dLvy2aJdHMOZwEz4fx9J7yIQDgGJE2GjFpO+vnNAak7S+8OQMA4IUDlzYA2+Mx5DXDRkt1hMUpYCRNdCQdmG+JCb3IB+n5XRgnbaXidlrM6FklTeqvt5M1f0T6szhTrge2yZwZrUFX6ZVDy+lOB/K6aR08BuzrYZHSka1UH7/MvVzlALNO66XC4YMpb3jWrl2Lk08+GQDw5ptv2n63L7G0dF3Hr371K6TTaSxZsgQvvfQSisUili1bZuWZP38+pk+fjlWrVuGMM87AqlWrsGjRIrS1iWja8uXLcc0112DdunVYvHgxVq1aZSujmue6666rW5d8Po98XmZsIlF+mb2aBqdDA62btt1xVcAKkJP/u8NJ97ppT8QbHuZ/65Vpu06eBw4qk/N4dq+PE/BU7uPfGXUEs5x0nUvyao7av6Ddj4cOALnpAV5qqJvyuOiPr5PSDj4M5ZBFT6Prhu6DUREe1HUvXef85E1G5fNz69WHr3vJQFqitO16nf6x9QmlTfOt+8dtOm33anXq46lz3d63bkpLfoftOuWn97vemHrq9BuXyfWplu80XLb8LtR+Fp81spVDaV6H3Nx2mpM6j5dZu9/cJolnUt3q9WettrsMl/0QCX8V8Jw0OM39X7vfeM546swZR5356aD+MSbRD9XnVv/1VPI5qH/q1qfOnNT38qzd62lbH/mdYvDfH1qYeX2kfa61plXXv+q/trWO0rx2e+gvp5PX1koe3fH2/xZOGcpLa9KY9IZny5YtmDlzJp544on9WoHXXnsNS5YsQS6XQygUwu9+9zssXLgQa9asgcfjQSwWs+Vva2vDwEBZ2XVgYMC22an+vvq7t8qTSCSQzWbh9/uxO26//Xbcdttte1z/l0U9NUUXLSzca3OnjndN8TrjjL1nAYDPv3vGJCvzNpGtk2bE337xdBwTHKZxzatfq5mfY/0tq5njbaBWAEEAFLPRjlyddD3Ey/9cFn9v/TzkQguOdUlWRQwdh6lgUZ3rb1ELQW1DHU7lc6m75gMAztr1/slXqgpvnXTD7hlrgDf2vOCnds+4J/6O/94OnFA7U6Xt5w1eCAxeWDNLpE6aMZPSp8XoP+NTG0dE66QZZKy19cNu/XkjTiz3GZ+7j9cpk/u5ntWDr3P+nt0zvk207j0LANy4ZMbky6z3YlSQyWTwkcmXtm9QG55JY9Ibnrlz56K/vx+treXZc+mll+Lb3/72HpuJqWLevHlYs2YNJiYm8Otf/xpXXHEFnnzyyX0qc1/xpS99CTfccIP1/0Qige7ubvz7K9vg9PpsVh32qPIKy2LzauIPPA8r7Nf5CGCvK9bnKbCFl2kl+kPnoj+ebnG6sOkFAYB/uPxFc8OyGfjWg70oVHR4chRagi1NDqLE2NNs4jjmsSjZLDeEw3JDd4WaAYC+cVlJ5zSJ587iqHhRbcrIarUoJGb847wSzmAGhYcwzLJlZ8var2Fdz/0wKi5ls7xip34lI5GgPZqYvkepg4vU+GYKB/AaRSTfuEGoDUeWviK95IESFnO9mZXXrXFa3Er7iVKJUcgAjgyuFzy4oXQyvul6GSXyrkrm5C99kLyQxsgrx0taJYvapN8CpG/T6qWwDgWhJDjMB1OHDKZm4iW51088KuvPrJ8QD7jjgnGc3X8xnuj4X5v736aUiJsw/TSWkZcjmaL+yckL54vU1pbxuaX8AlFjRdJv0Ys06Unv5cR5opPDGjV+8nrL6lLOkugOHLf1crw5439Qopc1Tl6BecrfRYuHn162l5PyV38wJ/OT68/1GU1K/4f95KUZl+vGuCxIgU4Z9+wOKT/SI+9UrELRuk0nPpU5Hd/xvoiCZthCS+yKkz4S9TOPkYe8Gv0U+mScPLO0BGnp8PoYlnu9fVL/fLv0rSNJVj7avAX7ybuV1q7qmuh1aPiXE2bgrkfK62Cmnd5ljpBO9/J6aLABrwI9P5mvGIWDjUlveEzTvg3805/+hNtvv32fK+DxeDBnzhwAwCmnnIIXXngBd999Ny699FIUCgXE43GblWdwcBDt7eUFs729Hc8//7ytvKoXF+fZ3bNrcHAQkUikpnUHALxeb80wGXnDhNMw7Rse/iqiTQj3ls7X+auo3oaH8hgsJEhp24aH6mB7FtHbea4nAFqnUSiZ1oYnz/ezXledOuXNOhse+lz00OpTpLMKeb5ODzDI575EYncmXdfoUJGTzkXYxsZRtDY8fIhJpzINrfaz+I+ITn+IbfU3ybWZ8ht0XaN+MOl6gdrupDK5/AKl9Ur+gmagxHWg8t1UZt72uS5pWxspzW2097n02+5rgJROVIUh5RhUpu6gDQa3q/Is3VEC6Lmcp0SDmq/TRp1eJu5zg6/TvQXKU7SVQ6C/uDw/SwZROVxng6mTovWv4dizvQCg81yluW3SX9NSnT4pEH1ToheP+8RbZ54YNI5Ous7vMvczz8Py/w0UNB2uOuWjzhjxmuCsk0ejOthnG5VP/Z+jd8pJ42UTCaTqu95iTayug3yd13e9zvpr2LunkvfgmU001P1TMun7jxUcdmoBhmEgn8/jlFNOgdvtxmOPPWb9bsOGDejr68OSJUsAAEuWLMFrr72GoSH5en/kkUcQiUSwcOFCKw+XUc1TLUNBQUFBQeGIhXJLnzQmbeHRNG2PQ8n7ckgZKFNHF154IaZPn45kMomf/exnWLlyJR566CFEo1FcddVVuOGGG9DY2IhIJILPfvazWLJkCc44o3w45fzzz8fChQtx+eWX44477sDAwABuuukmXHvttZaF5tOf/jS+853v4MYbb8QnPvEJPP744/jlL3+JBx54YMr1NZ3lH2JQbCZPVvx3pmUWcaRylirnUBRMgXE6OEBfh+T55ZmQ8h2F2uMQHKAvdUf9oS5ERXjQRsVRXT1Ej7HolmeMvoIjUicjTSb3oOyrR7JiWme5/td2CVU0NyQb2HdFxauoxy20F4cq2ELRzwdKMUD3IAZgc7rF+kJuJn6v1S284nNxOSVxEYnC+cg68ZuhU6z0cJrCDTSIlUknwUYQLaKNiKXQ9JAlgWgak75M+yakLUx1NfhyQBGI+XKgqYWRMXEdGc9THcjaUKTrOb12+vUxoZk89Bnc5ROqZUdOKMjpPvG8i5LOvo/CPXipD6d7RBxyrl8sroUKfzvbNwyNKLYmipy+Ob0bH1uBTt4DCQoDkRsS2sjTIjxEOiNj4fNJ3TxBsRCyOGGIvLTafDJnlkTEi8pBlgf2wHp2ovzBFS8FEbLx2IIUcd2vp6X/R4i62haXPu+ISDn9ExQag/phTtuwld4yIgfIjDGyWEfImkfR0l3t0ldeMouMVryuqoeaM3k38prT5mlVII8q5wxqL81VG3VInlksFGk66QYS8PRulfrne2RctCTRrHSvZ5zKpOWRjyCEdpbze1wATgKyLZrNwg0ApLuJYp2zTyzEWjWXsHX9QONwj5b+1a9+FQ888IB1Ljcej++Rp6+vD9dccw2eeOIJhEIhXHHFFbj99tvhcsmcWblyJW644QasW7cO3d3duOmmm3DllVdOqS5TorSuvPJKayORy+Xw6U9/eg+39N/+9reTfvjQ0BD+4R/+Af39/YhGozjhhBPw0EMP4bzzzgMA3HXXXXA4HPjgBz+IfD6P5cuX47vf/a51v9PpxB//+Edcc801WLJkCYLBIK644gp8+ctftvLMnDkTDzzwAK6//nrcfffd6Orqwve///0pu6QrKCgoKCgoTA2FQgEf+tCHsGTJkpoaflU9vfb2djzzzDPo7+/HP/zDP8DtduPf//3fAYie3qc//Wn89Kc/xWOPPYarr74aHR0dU/pbPukNzxVXXGH7/8c+9rFJP6Qe9iZg6PP5cM899+Cee+6pm6enpwd/+tOf3rKcpUuXYvXq1W+rjgoKCgoKCoctDnMvrarHc70IBwdTT2/SG54f/ehHky70aIXpKFM+LEDF5kyOsZVrongyYv23HSUlhxYbveWmdC5GWhocvyVHByTreIHlGkm/ZLeRLgVguYBquogJuskdNbaRDklGSB+DKLTkLDqUSJQNm6Y5inHKIabpLvLYGnFIZ2zLiI93f468Pxqksyf02vIA3Z4xVI+eTg+MWYdHZ1MsrZhDTPd/ExYaK0kd+WxWAuFyVHGG2010Y5zokl3S79nu2rZtJ8VyGk4IHdAZE76Uo6sbFa7RMDVbrCJfgEQLKQq2M8B2dsEuij81v1GowwUd4r3FB1lZyG42RTPfnpMx6s0KdZIh8cCXd3RZ6ROmiWghRz+POHTMBLA514I/bRJX6xO6RWBgKCP9s3MrhaYu0JyMST84XDQPqa9MEicsksie109R4ClG2Jyw0EPTyItqR0Ha/vKEePwtDEusq2WRtUjhUiyLrMUEHep+PSeefScHt1rpOM3nVx3SbxyZPUAHyEsN0vYtu4Ty2zYmFBgLKuYjtSkkH1FL+SLpJtHcy1XmlQkH4Cl7f3k13RZfjBe2XI50sSiWHFNOyYQsZiYfMG4o1Lye66C4ciSc6MjLmEY3S/mJWVQmrWmFCKfL91Y1c6rrIMfP4hO9oe3UJ43yC/aMdVbiI+qFg3wwZj88rqo3V0U95539jQOlp1cLh92hZQUFBQUFBYWDi+7ubkSjUetnf3hhTwb7Q09vspiy0rKCgoKCgoLC4YH9dWh5+/btiERIv+strDtf/OIX8fWvf/0ty3399dcxf/78t1+xAwC14XkbIK06tKwRk/DYAjHlakRLMeXEQoIU+QDBIYqNNY+pKDLZ9pJ2RZSMc2T6ZXFCjdwT2PsKKNNyjkoRhQhQqJTBnmaZVrmpQB4KNs80MinrFFfLSV4eLqIYWPwtWZAO4HhA7eQREyW1r968iBAGSIdnhDjGud4BmJqGPICrG160NHpypO+RpAY8kl5gpV9Pi6dYp1eopfXD8mWRIU8fmyyNT9qbm03m92GKW9RGXl1GHVqBPKfSebl3VqTsZeZ3FbF1VGiLEtGFTAE43RQWgcaiMSATkGmLdSSo2B0U+maGT7yrWATSSxNlLC905GBaxoJprAYPSz8L4oWA9a/PT0KFNDcGxmQRdoRI54foEpeHqB8S3EtMUEy0sHAPmaz07dgIeRixqCPV4emCcCTvbBNhzAJ5OaXohX4gfiLeW/m3OyB06ggFu1s5PNdKN5Lg5PFh6bcseXLtSMtLOJqRPmeqjr3kEilxs7TFz/JJG7kfODbW0KiMYzBU7s+ql1Yi40NeM2yxyTreIW3keFhOGtNkv5Rp0LOcQdJrInHOwEZZT7MdpCPkkXt9Q9L2xEzS4aF1rERrou3oQGVKeqpDWBG04TUwSyrNJoWQ4DzE4kKrjIVhU008wNhPZ3gikYhtw/NW+NznPrdXD6lZs2a95e+rOFB6erWgNjwKCgoKCgoKk0ZLSwtaWmrLRUwVS5YswVe/+lUMDQ1ZkRxq6ent7pz0dvT01BkeBQUFBQWFIxRVSmtffg4k+vr6sGbNGvT19UHXdaxZswZr1qxBKlU+Tc56eq+88goeeuihmnp6W7ZswY033og33ngD3/3ud/HLX/4S119//ZTqoiw8U4AnATjzQHBAZsjA6RTNmLyxdJYnIqrBl6WT/hS3KtklpnFia8Cq7pnWelGCJenkGC80uvpudKxnXEy57rTIoxdikqdIZluOI8PW2lwrmaaHyTujjUT2yPTtIrHB8bTQDbMahTrp8AidlCOlsDC5TzSS68Ul4desdFADdN2HlwG8WmiwQlD8dvRUK887w71WOk/l78zErDRTCSESo2sICjWzY4iiKpKJ3kUxg0ohijbu4pAEMrApok4cPskTC0inj+cC1r+FrNTZH5S6sYhclO5lfVCmsTIlaWOzT/pze1ra1UqKk0xjeSk2ySkxoXjykdpLCnsYMVLOsgm9yx/H7NnimTVOAeW6gjIfepPiITWaIhFL8nqL+mWeNPhrH2jkcZzICvUzOi78hx6TPFGflBOiF/TC1rVWmufS2omyoOVEMYDxlHhm+SmExLubRcCwSJwzCzw6iGJr9csYjWelf6KNwtOw9xlTyflWEuujP3ClMVIXJS8qZ7i2lx+DaVn2NGTPuHSSosCTFyfytJYNUR38kic9R+rgTDCPLvXUic1gD1P2pNVo6jG1X11bq++HswA49YoHazUPh5agamp8NIGOLFQ9tsyDaUo4zN3Sb775Ztx3333W/xcvXgwAeOKJJ7B06dKDqqenNjwKCgoKCgoKBwQrVqyoq8FTxcHS01MbHgUFBQUFhSMUh3toicMJasMzBegeAB4g2SUm2wAfHOfo4kQHsRmVo+y67TpPkoesyezhxeUz7eUmkSvDTZ5Zjtr5AUAPAFWmyZUGjGp9if5gU22OTMRsOuaXRSc6xuOjDqAyUzmhb45rFmG3iJvVvgRLghutdJNTTPcx6iT2uvrxxKlwGG6cDKCv0GxF+87qQje8kRWPpFZSavQRTXNccLBm+s/9C6VZZFo3SQSvWCAzPrXFRR40uSJ59FEncpopv4Cjep8L3e3CnTKVwPODvYc4hlR3JG6ld6TE66fRJ/XxUSytgbx4bfSmRGDQQxN547AcXowFhfrpCHHfynhN8wtF5a2016GZ6M2IqCB7ucQLwlvsHCYPNYrX5muQ+TMQlzrrRYrkTjHOnG6KdE80ayTK8aSkH5gKHCOF0e1Zqc8p0W1W+szoJmCw/K+fXphpLvGAGyXeO23IGGXppU8R7bgrI+PVE5VyxnPSP2MZiiNGwoNF7oc8LUgByeMhsUqe2yJI6ABcQCrlK0c4p/nW2CRtTKbJOywtz9KaiVZjxxpeRMhLyz0kbS810FikpEwbvd5M688EeWxxlHNalgqVNU2rIcBq5ae12B7jUJ6V6tjTe+tgxtI63Cmtwwlqw6OgoKCgoHCkQm14Jg214ZkCSmHA8Nm/KkwOkk0f24F+uo8Ou3FYBls0corA7srLDCz5SQ6fDkLzs/xbSVJ9huTnMBOu3WRQck75snEWTDhL5TI0uqcYIvl0OvtpOxg9KF/N2Xb5Ui5k5OvM7ZfPpAgdKE0VSRuEOnVnPib1NESrhK093x063UqfGZGI6pvSLXAZLpwMIKH7YFRk/cOk/74jI1/lM3wSgf3spjdQCzvzkj9LlhmeCBpZDFz9pNUzQzq+uIMGsEusVXzgluX9U0mZIM0tSSALBDxFZEukvcNaS3XSHAJjTZ+ELQgESK+GDu62huWg8igdjp0ZkYPlW+nw8Mxmuc7juHlMLEItIbEAlChchVny4DQAGxKtcLp3M0NWUKD8saj0W1yTuuXiUv9Ag0zWwqjk0aIycXWyyLlD8jk+EZf8RkTK4f58Iy7iLE0UimJbVixUrxdC+P8APDMxB3Mi263rvXmxhulkgn10cJ6Vfm+rzGdeoSPUP2t2ykHoGS3S/+wUEPLS+I7TSVyyjHE0eUY0IO9LwF3uN4/pBNKA21uCAcM2r0a3yjvia5M+MWPy7oefF7NOYjGHApFyHKSnVGyUcXGPyxzgNZTBmjysvcNWmmC/9E/JV87vqXRHMQQUDXtoifAOCpETlPIzraR9NE55wpVwFeov62EJNSwKCgoKCgpHKNQZnslDbXgUFBQUFBSOVChKa9JQG54pwHRUfkhPhUM/EDti071h6sqmh0MTLdfCeehQNFFjDqaS6EBxpk1M47sfurPqNm7/RabdiaqMRiGiIV/5NWv3kASL7cAeMU62g4LuCamH3iLP4wOQLDuv+yX/QEIOmvLhYYPM/q84JTK1h04fbs4JxdDsScNZaYQBh3X/4pBoxeSCQkulaHBY82dtotNK84HV8Thpv5DOiU60S7FFbOgO0hjhPpzeKIdOe4eE+mmgQ7/NjTIAncEEkC3/O0KUVpIOgRe3iB2/6WQqf3O7ldbokGoqIXUL02HdwYScUJ/ZKAekh7Jy/ayWLVbaTZPOzadCaU4XaWINcMjqii5/qy+FBp/UmcM08IHnmEf4hlxYeOCAS16OHB1QLzXLS1miuVQiDRnWQepPCDVTIHqRDzA7HfJ+dtAB7BjFjanOwbArh6dH5ljXm30U24CwMCbR6keJu35hUOZ8e0jmQ5DoyI19Evpk+jShtwYnZLx8IYq0Tg4FBdLhCbRK3YaGZYzaW8ttNKoTuNL8bFoWofA0qRvTmnwg3Dw7bqW9pBdUzNGiMyj10WI0l0jbp9AlbQm8KXVgDTGm8Jn+Zy0z632shIzwjpVDUmRp3o4fJ89lKt8n/hbQPUSlVYZOUVqHJ9SwKCgoKCgoHKHQTBOa+fbNNPty75EGteFRUFBQUFA4UqEorUlDbXimAEe+bP00i7W9BIjVsNFYjjo0k3+Ewkw0klcNeUp5J4g2IdNplsJSBPvlAUyHsfl2YqY9XLrpIp2eSpRgK10Bm4JZ08c/TF5kAbkh3yb14KexNssISfcvahMzfrxA2h2k5z6Up6jNLjHjB4jmYMqp3TsBR4WackOHUaFbVo6LF8z6EaEAZjUIZcNaQK8PSp6eJsnDUbkLGdLSSZJ+SLu0t5ijiRCUe7ePCXUSoIjSadLwafCLXX5zvNH6d7BX9FhmHS/hGLZNlzoUaTJqOaI807vFGKkgQZSBRiEGtjtjVpo9rR4fOM5KR7zSb2nyvJsdETrppLB4KnV5pD9N3Qv0A6eEt2K7LnOj1SkUSYLcHPuSUh/WdOKQCrlh8UgKd0g5TOFxGxtiQuXM6BaugjWRQm4Zoza/lBkiKq3NLfTWCd4BYCfwrsibGCwtsq5HyFuwhTjjBMVI2JCgEN2EkaxQXR1hWSQiPikzypRfQOZnjug5DzkaJkmfJ5OQOnR1CjU2UtF68sIBOMv9osFEQ4P0G9N/NoPBiIxRksbIF5J32ReQPsy6Jb9nl1RU90qhjjg1gNYrpvOZ8mcvLQ4VsXsRVR0epvIpwkldj1xvnMLKZMqlabUdDhUOMdSGR0FBQUFB4QiF8tKaPNSGR0FBQUFB4UiForQmDbXhmQJMZ+Vkf50IuoUG8lhK0Ol+MpHaopGzsB+JXeXFaQeGh+iIOs9NTBdTsYfCPrApN7TLbstNTHfA5azWoyy4BQAhEtqamEUihnFqQ4QFvii8AnktOUlMzOeWinAkZaYnYhSNel5IQjl4qRHs6TPTO2Slx4gKGSmGLUrrxYkeFCt8YjxP3igeKXMgTTEzSBewKSzmeqbM5rXKczcMCvWQJyE7g6LD+9qFlsqNCmXAnixMx/BzXSRIOCs6BoyW/9WOEwpg83ah3sIU3TtD1Bib/Q2KWK3pHEeE6k/CbsUACb5RP7AA45yoUFeRkExkF3G54yXp3HU5Ec3bNN6JfwLw0x2nweuV+rf7hbJx0nPdTimTI55zGA5HG4kcUrscJA5plOR6Jid9NTYmc6mtWepQILebJHl1HRcVCmxNUTyqfKaG8wA8OTEfi0l4sL8gdOSuvKTHC9I/TJm9tkWEImdPl/di87CIHM5ukf5nMU+O8B5PiVCkzyvz3+0lj60UeTyRJ2CkIkLoMZ1AAchP+JAzDRSyRC0RuEyzSeZqU6OMy9io9LPLS1xUnVA1nHalZOxyLbXXXF6vOPq5K0NzuHIswFmZ4qUQUNTLnlpVhHZS/iaifSUL8g17hvMx6hxjUDi0UBseBQUFBQWFIxSK0po81IZHQUFBQUHhSIWitCYNteGZAvSgCdNn306HN5OAWY/kZbMoC87xdfYGyLJjhs2sK+lIL9FSmphROVov3+udIC+wGLlZoeKlVamXZwIwKybYNJUVFAcgpMWyDhfRZra25eUZ7HnkJC7uhB4plL2idmVE6Oy5sRlWelF0l5XuoIBj33jjfCvN3kOzw6NwVaivwUwYhYrrhruOq9zAEEUMny4UyYyIeBIx/cRxoJwkquakqNDEosC1kcJCd0uf+IhWC3rlOsdsKlLE84rzBzK62xZ7y0veLjrRhT7qc428Cj1EBxRiPJ8oYjsJaxYKskR0BeNW+oymrVY6SipvPk3a5SM6Mq4Lr8CR66cFJ4BM+d9pIaELB8k7j+OgMYU0XpAyeY5xjKoEeclxnCmmEbnPvRT3jUUdczmp86w2oZDYc5AFGLMV8UBNM7E60W1dTxXJC4nouV0pmf9MHc7okvbuistcLaSlPm+WZPHobhbxxjzRcAUSCSwVyIOPvNXcA5JnOCm8enhG+b0zK/3a2DaBgmYgnZW2hClG3siojJ1rh/R/gjwcWYyUKWCtSfrTGCWPQqIjSx2yiGoT0kZeK7OtdY4LEP0U3lEu01MRjzUdFQ8z+qvoJgqM42fRFAbpTVrCrbW8wQ4UlIVn8nDsPYuCgoKCgoKCwpENZeFRUFBQUFA4UqEorUlDbXimAMNlAG4DrhTFgSHPHvYSMMgaSywWfKLnVTeuFl93UeidYoC8E8RJA26ixtgjgUUB9d305ophwFGNpRUFChUTrJPMv8kZtevNQlsl8jTL95AoHwmaDZXExJ31idl81EmeNWRXHUlLp7K3zrMjVCFC74B0hm444DGdWA5gLBVAvuJSd3rXNisP01uLm3dYaaZI1o1I/KnxfqEbQi0yINmUdKqzRLRiRtpuo41yRFFlpR+YamHPGu6TahyokunArqGYdb2nXQYmVxI7e4HoMD3MdB5ROW4pP7BJ6pOZKxSbQbHA3LOlnnmj9tIxWIzWvH5CQGKZzW0RbyNNd8McXo4PtbwAL032eEDmwOs5iWuWJBHCHHkSsRdVIit52EurSHNSJ1qnpVleIBbNcxLtMqdJaKwZQenz6V6hPtmjcG2+PGd0w4kC9RXH8xpOiqeSn4QNByZkvnncsnroFP8rGJUXNZOUecjlD4xJOd6wUEV58saKRqTPzeNJDJCeWxXAdJtOYAKI+XIoaLqN0srTWDBdVYxSnDUIPBTPKzdA1CRTWjFyMyU4h8kDkcaLxVGdJPzHax97b6WmlW/wVmJpVTcOPLUT08nLj14jFjl0p/b05GIR2oOBY4mW2hcoSktBQUFBQUHhqIey8CgoKCgoKBypMM3d4nm8jfuPEagNz1RQiTlVCok903SIkcxJgm1+CRMF/6hQAUkykWZbyVPBW/tYv5NM72nyQghQ+Rw3hiz+cBMd5qH4XABQ8gullY+ZyFcmPceLMck0rQ2TMJ2LPInCFONmjGLfkPeQl8zjIxNCVcwmb5dtoyKMxrG3xvKSn0363dG4lR5yCTUwJzpS9tJKA++buR56hWbgeExMi6zaOcNKp8fEtB5tEZrD1yj1SffLs7SQmNxLMSIuyVvKZO8nEid0kUhjR0gGh2M2sTBjV7jsKdPkzcAXEIqQqRzun/Fx6Tdfv+TJsYcLeW9ljuPgPySa2SZt9zrk3qhLrhvEJbDHVt6Q+fBCapaV7k0LBbkoNIwTAazJ9oCcXVAi97+dWaHJdqYlze3lGGR5iutUzFGMJ3afo/hiQ+Sp5w8LVdQelXFhQb83EiL2mCa+ZPWwCCqe3VamUAOuPHzUPy5y3/GwMGNevPm0OvwEe1rlaex8DVLnElGZTppj3A+egMzbdEbqHwqSxxl5kHUGynPPZbiAiXK/65qJEzvEg3LjuIwp01V5ehd0Envk+mgREkJ0U1xAilXniUjdim205gxI/Vmc0KxDKTnzJB5YeVRVeNCVAYySndIinUgbTUbTH5k2DuhVeb7y0josoSgtBQUFBQUFhaMeysIzFRgaYGhWRFwA8A2TRgtJruTFYAHTRfo0g7Kd9kzIvQ7SBdEMyZOW85ogGRrbl4ebdHHq7dZzTfb/Owv8ZaNBr3yR+MToAr+cx0R8Dj2DDuy50nRouUO+1Az6GvU3kiWE9Ew2D8pXIX9dzo5RJQg+p3w5zgmKZou3Ua4PFCLWsdyn+mdbOjx8AJX1avJ5sgDQVxl/KZdIiwY+su4lpC0cQqJIZWo7xZpki7ZMZW4ZlcE5bZoc7s0UpPyq/k/JcGJaTCbCRgotYZIFKdQs9Um3ylg46Ovb9NCXaVbq44iIlYlDRXT7ZEIs8ImeUtKQiZ8jqw6dj0aSRFLeGdtqpVsqYaW73WP45bBEFW/2inmSLUjdobiV3pmSl4AjhvOh9AmTLCc0B8ygtL0wKnVzRCVP35CMi9sj/XbW9C1WusMrY/Gmp8VKb0014d2Vf9ePNchzSfOHD6u7KRSLn0I/ZPPSn6y5lItLnQs7xZqnzSMLW6q2V4RO2jtGkaxkmuTnPnxy09zy8+HA3wSAouFEUQNyZF30U/gYTicp+nksIGaRLM1t7of+fukrBluJQ37phwRZhHxk0SpsF0ssh1PJdPA6W0lUuqAUKIeW4LgRwX7WMqOxy5KViSz71QgqjoMZLV15aU0aasOjoKCgoKBwhEIz9k3o8GCKJB5qKEpLQUFBQUFB4aiHsvBMAY6SBkdRQylEpuheoj5IA4fNoqwDwWbRklhdbRoSgUEynRJdxeBDc8wicOgKprc4PwB4x014KqPPoSWyIj8DB2nLeOP0PD4QSM/wDElFiu0F1AIfCmXzfp4onpd3SRyLd3X3WulGn1SiLyuc4V+3yIHYrtZxeEwnlgEolJzIV0JwJEbF7N/UJodR+ZCk4SfNEzrg2tJAQkeEdF6oohxRDw6S0y9F6POJkm4y4wd80lepkkyWE1r6rXRfvExd7UhF4fLIYAaoP5kWYUrOEZbyXSTv76L80XYpZzwlE5kjtjOKdCp0rkd0dSKaPMtLE/CCoNBAjHTJi/UAzgr04r2zXreuD+hCRe0sCc2xKS8UXodPxnFjUugkpmOY6hrNyBwwiNbsnivhGDiMSMIj49hJB8tPCgntGHBIe/PNRFNOCBfN4TnaG6UcjjhfoIPWSTo876Dx0ol21Nw8LjIWA8NC87l8RDHTgeHoE1J+8lzS4emV/onPlNLbmsp19phOIAc4HQacmoGJvPQPHxrn/ucD2DlqL+v26MQlt7YJRTg0IG1J7pSD+s4G0omiNapEh5ydvA46SB+N/uJVqXnr7HtVh4ecQJI95Igi0xwlP4VrSdIRhK7ydZ30zA44FKU1aRxSC8/tt9+Od77znQiHw2htbcX73/9+bNiwwZYnl8vh2muvRVNTE0KhED74wQ9icHDQlqevrw8XXXQRAoEAWltb8fnPfx6lUsmWZ+XKlTj55JPh9XoxZ84crFix4kA3T0FBQUFB4YCi6qW1Lz/HCg7phufJJ5/Etddei2effRaPPPIIisUizj//fKTT8tVx/fXX43//93/xq1/9Ck8++SR27dqFv/3bv7V+r+s6LrroIhQKBTzzzDO47777sGLFCtx8881Wnt7eXlx00UU4++yzsWbNGlx33XW4+uqr8dBDDx3U9iooKCgoKOxXVHV49uXnGMEhpbQefPBB2/9XrFiB1tZWvPTSS3jPe96DiYkJ/OAHP8DPfvYznHPOOQCAH/3oR1iwYAGeffZZnHHGGXj44Yexfv16PProo2hra8NJJ52Er3zlK/jCF76AW2+9FR6PB/feey9mzpyJO++8EwCwYMECPP3007jrrruwfPnySdfXldTgLGrIhWSCJGdKmj2WONwD01VMYzmJZmLdCNbSYfOqh7ym2CPMJqlOauwFsQLbaK/y7zR4K6beQkxCSzS9Rh4NrRTKgrzOWNOHy2WqD+QJwlofPvJA8ZE3x2icvCrGJP+miHhy7RqfbaV7mqUzTJ1oiJwP3kqHOJ2GpXtish4I6bd0kseTr0msghkK0zCSkrpxSAgOT2CSt4uL6cltFE6im+gJyp8kgaVhj9AKXqf0Q6FCsRV0B/pHheIJB4mKisu9DqLMmF7lCO8hX23asUT0R09E6J5V40IdDoUkbMFgTibawrBos7B31XE+oedmuCU0g1aZQBOGG6/mRccmZ1L/F6X8n7zxTivtIK8rB1Fv7JGXnOAXRZIO8ujh+TC9Qdrb6JN0k0c+wp6bkH4Yyws9FPHIWPhdRevfU7olfAnPqyjlT5POD9NAiXEp30Pec8WslGMEyG2SaC8zQ8s76XxlhBVEaVD6x2yT8j1Uhyr1Vh3PXaNR5GHYdHKiqyU9dJK814EG8szKURs5/ERJ0vmtMtaaiwaMaCyznxbIVnGH8uyU8gstzGlJ0jFOkdkr3bZ7iB3vOOUnbyteTz20vmdayNu2Uk2zdlQMhUOMw+oMz8RE+Y9PY2P5r+tLL72EYrGIZcuWWXnmz5+P6dOnY9WqVTjjjDOwatUqLFq0CG1t8hYvX74c11xzDdatW4fFixdj1apVtjKqea677rqa9cjn88jnyfUxUf4L73VocDo0QOM/blrNtJs2MLwhYe0zJ+dx1r5ORzGsMzd73EtlOnhRpzzGbrY80wFrw+OlentIVFB3arb8Vp465To1WlnIeOimtJcK8lCjvZTHoHLq5XHXue41HfBUnuGhZ/m0vdfBTV86tudSfh210zwnuB+81Ic65XHQvS7Ufq69PtImbi+3ka/zcBuonYef5aSzK7Z+pgNbGj3LSYchXJR26Lyz5okvf4wMh2zktMp1Q/da6fKz6EwU7artbaQNDz3VSf1WqGPE5qv15pXbkE2Cq057Ob89j9P619Bql8/nherNN2+d98hhm3v83nG6Nvh953tNnhs13tnqXKvWiec/z3Pbu1bnfdFsY0eLFtfH1hS6t06d3dQuTeP+kSS3vbqn230d9PLaSvfy2UVPvfXaUf137+Owv6CEByePw2bDYxgGrrvuOrzrXe/CO97xDgDAwMAAPB4PYrGYLW9bWxsGBgasPLzZqf6++ru3ypNIJJDNZuH3+22/u/3223HbbbftUcebFvYgEAjYLzbvke2Iw00LZ8h/3nEQH8xfQYE66bRYdWzRB1k5mr68QB+7n9MXy38aa+dB7fPI9eGrk2Y01rk+GaTf+tc3lE6290O9PpwMaht4AJZvGT2+dp6x2pcxVOc6YaDGtf51/1a320iKCovcdTLVQ2TvWWyITzH/XvD+0WV7z/RWqB2LFfDUuT4ZnDTF/Lr9v7f65pcTPP+XTqKcyRygmLn3LLb3nbFwEve21P/VTcfPmEQBk0Mmk8FH9ltpe4E6tDxpHDYbnmuvvRZr167F008/fairgi996Uu44YYbrP8nEgl0d3fjO7/vhcvjw/Bi+sKg3b07SdYe0skv0qKre4gCy0p+DhVhM52S2KAvLl+c8bn0tU1mV66PLdL6botEIVz+orllzgx89dWtyFfFDtlaRH8QvfwHjj5eCtS2LEm+GxFaJYlO8pGJm8XTtAx9XXZK58UCYvb3ueSvu5MojMGkNG5O4whcphOXji/F75seRaniMcIy/lmiFTJkkRjPyY5hoD9mpR1e8uQiSXwOT8BilPwl6Caak6XvS621bd7eIHn9DEp9mjrT+AJOwNfxKlKvSXubT5cD/By2gCOnD5K3y7yZQi2lCrKzSVLk62lRmXSdFK3+rOibVnpDrsNKTxTlg4GjdduFCoXqijhkTFOlEPQ3vgjn/K9hW0nqOa4LPbdyaK6VZmtAgMJw8NiNkZdZYbz2rtSZIDHQ2YmaeVJJuberXXiOaUHpH6aiEgXJn0qHrPEa205UY4u8rEzFMsXWEKZQJuT9l6e0TrQvfHKvIymTzxOvLYqqByS/GaT3lMwZDi+FIKl0lRcO3Oqfh5uTbyIPAyYJbILE97g+oHbxOsDp2CsU6X4WrSFUT7BXWpqsarSGshWa0/xuMqqvi9eh4aaFM3D7S1tRMEzbelqkjwg+XsBrK1tIqsZIPX8w3bQUJovDYsPzmc98Bn/84x/x1FNPoatLXJLb29tRKBQQj8dtVp7BwUG0t7dbeZ5//nlbeVUvLs6zu2fX4OAgIpHIHtYdAPB6vfB6vXtcL+gmjJKJPE1wPu9FFnDotI4U+TrdoPORF8pf4C8qoqIdzHWTGjPfy5v1enUA5MxOtSwuz3oe3c/u7rwpylOePLXNIB9sdj/X6HqO3FEdJpupSTGVZJ0dlHbSH/c85S86dMv9u+TQUarEf3JQY4oU/6xInBw/i8t0UJrbxZ3N50CY5rPNCerjEnYbEHqCVQfqn0LlegGGbay4zobG/SaVsPUP5be1V6udp0Txs+Akd2CHbNh0BylC018agw6UafRXRKODEZrpqfy+ANOk8k06j8FzgDrdXa8tPH/qBDVy0h9cZx3ltXydech9UiTemPPYxstk+obeC/pycNR5Vp5czrk+uq1d/B5J/9uUw+usQWad99RJ13ffLuRhIA8DBj1A4/eXr9vmeW3qLa/TusZrSJ028m6G11Cmn0zbu4ma2H3YC4aJvG7a1lNeN+ut77YNj7P6zINnNlGU1uRxSL20TNPEZz7zGfzud7/D448/jpkz7fbMU045BW63G4899ph1bcOGDejr68OSJUsAAEuWLMFrr72GoSGxpT/yyCOIRCJYuHChlYfLqOaplqGgoKCgoHBEQnlpTRqH1MJz7bXX4mc/+xn+8Ic/IBwOW2duotEo/H4/otEorrrqKtxwww1obGxEJBLBZz/7WSxZsgRnnHEGAOD888/HwoULcfnll+OOO+7AwMAAbrrpJlx77bWWlebTn/40vvOd7+DGG2/EJz7xCTz++OP45S9/iQceeGBK9U12OeD0Omx0FYsKcqwkg66z95JBngccaT09bc+IuwDgIeHBiZn09cZWBH4umV35i6e0m2Xf9lWg6CcHrgAAWThJREFUyTPrWXK4nVy/kljrbTFldDLXeymac25CKuIKktAfmaznNEssLY7Xk8jJvcc1DlvphVGx3iVKPuvwaF53oVjphHXDoqiYGBGvq6Z2oSeKFM+MT3+7NxFlc5wMfokO1nJ7NaO2CZ092hxjJNRGMdX8p4it3Nkpg58cCwGNQHIsiJYzhZbyE80Xz0o9ddtJdkluHpZDZ+yhNq9J+tNBn3ydJPbopsmxLLxOnkUTwqdJfYKU9tmsH0RfGg4MAWh0pNEaEO+tOAkPzpwuHzNFeskGS8Knbic3wtddFF+MhD5ZHK/YSdQPfdKPJ4XDCEVk3jZ4hWZq9crBLw9ZewrEZSYqfWJAg3eajCl7YOUprlyJvK6GcsQT01xiCsyZr2NRJDqpGCZLCMWbY8OJRvHXNPIcZEE/+Mv9o8MB+AGXV4cOAyaJIoJEC0tu+gPKcejYW4ro4NR0suowHUbtNVkEdULuLQXkXluMvxTlZ+9WWq+qxwWqh5R1b9kaVO+IgIe8txj+MfJIrURO15SX1mGJQ7rh+d73vgcAWLp0qe36j370I1x55ZUAgLvuugsOhwMf/OAHkc/nsXz5cnz3u9+18jqdTvzxj3/ENddcgyVLliAYDOKKK67Al7/8ZSvPzJkz8cADD+D666/H3Xffja6uLnz/+9+fkku6goKCgoLC4QZFaU0eh3TDY07ClObz+XDPPffgnnvuqZunp6cHf/rTn96ynKVLl2L16tVTrqOCgoKCgsJhC+WlNWkcFoeWjxSYTruJE7AffGMWIddS56QceUXYtHGypP9BrtIFEiq0nflj6Qq9dh5iBWxUFwCUggYcFT2LYthEsbL5DOyUAor0bKa0+DAzm5TZ+0Mj7wwW/fNExF5cJJG0wAZ5wPaGmJU+sVW8e9oD0jF+OhD72rh4DI1n/fCaDvx/AHYmo9YB0KBXDsQmyRSfKwiVwLGo/GGpZ448R5z0OaSRuKJN/I3ZSaLkHHnymiGRt7xL8nhIqCnilzrkKzRQoCGDZE76Ku+Ue5uDQp30J4UWMYtENZKo4DAJKiYoLlJTQMrZ5hKqiA8M/790q5U+NbrVSjc6awd/m+YWPiDmoFhgFUoo4CghTpPUYzugLs/dQS6P40XhJ4ZJ/LB3m1BagQahIM2XxAus0CBlxhYIldYclfq3B2W+tXjl+liBYk4V5CXj/jmuaRgYLf87SnGjmHZsiUiZfOh9JCnl54kCts83eb98A3RwOkoHpGnc+dCyM0XihFSmK0WH+duJ267m1xxABCiO+1A0DbvoF4uOeujdL9Y+JsqUE1O9ziTRauTV6EpIJk9c8hukG8ZepT4ZUtv76CbvqupRgCoT6R+wx+ACAJpiKIjep81Li+NqWef660k+KBxSqA2PgoKCgoLCEQpFaU0easOjoKCgoKBwpMIwyz/7cv8xArXhmQI0o/yTbyTdC/IecJLAlSfOKliS9G+gWEzkOeUmJsBJQj9sLmXzqotMr1wOm1q5zDyZY8u/c5Sl1KcBvmHNosVYoNAbpxvY8aKOem1gp5jWswuEcmLqykHeWJGY0A3ZxaRtQjF3Xh4QXaaGoOQPe4TuYe+bkLdQlunPAhFfzqK0WMa/sVE6JpESiiHiJ68cvzyLY2yNpGUQJpxyb54E7li0zXRxHA7UhKtFKB6d6D+Xk3Vmyv9qGuCieFhzGsSjrUDuelkSqeOYRC43Uar0rJ6oUE4c72kgK4PNsbEaKbZUhqioDZk5VtpJk2ZBcBddl/qXSn50AXg6PQchCtJWJO54jALQbc4IldafoboRbxFtESoqReOrzyaeISPlcyyzIonpTWTk+k6f0GEsehmieZgkWnDrRBgf9gKv7eqAw0c0LnkCTqTJ+4/isjU0SN/yWKdGxIOMqfRcp8y32Kvk1TiHPA3D0vYSexHS+lUkTy6OS1WMlB9WjeigFaq6O3uGaQAAMyl18A2THhTR/KUgeasSraZRfVxEb5k0h3OkluyZqE2N2WITErVvE4StTKuqs2y2Fcgb9rY46+gH8tqYlSmJcF/5Zr1wEDcR6gzPpHFIdXgUFBQUFBQUFA4GlIVnKqh81DhJzpy/MIoR+mphHRoKD1Gg2Di2g3VsQYmSrsko6/bQIUSSPLd9kdQ5LMdfNtV7qgHwTJccuA6QIDXFcrSFS+CyDLfUiaOB8/lqT0CsPaWiFFSiyKhej3xd8uFhtngEKZQAHxDlw7p94w1WwMJWf8pSxY26KTQ9odBCX/R0AHVXWqwHvdvlE86zY88vXwDwpOnAJ+mfOElvxCdSN0hRGI4SWYeC02SyeOkE5fCuENAJpHeF4J8u/ZkpSX18lJ+1dJwUGiNEB6H5wHaJDs0GSNtnXlgmxEyvNCDmlElQoM/pLreInvhIjZmtQ8MlMSPmKtahrOFBBx1mHtPFqsOKymGXfHI76MB8nqxbXP+NpugO5Sn6rjMmY5ceJcsJvbe6v3Yok9aAWAi5zzMUZsJVObTvchnIkhZNgSKMO+lwL0bkIPooHSznkBO2w7cTpCNE68vEPMnvJitzPix1c6bkXpM1c8i6YrPAVMLhmLYgpfa+sllp6LB0vlnqE3td6jMxp7ZZwZmrrWHFGl8uepVta1Sd+GLcP5y/uo5VjbDOfFm1nsXFeZ1lsBMHa5dNzC7XU6/TjgMBDft4hme/1eTwh9rwKCgoKCgoHKnYV7XkY0hpWVFaCgoKCgoKCgcEX/3qV3HmmWciEAjYYmJW8corr+DDH/4wuru74ff7sWDBAtx999175Fu5ciVOPvlkeL1ezJkzBytWrJhyXZSFZwowXIDmApxkXi3EiDognRU+1JZrkjSbV/k6h4cI9lOeRjIPN1KeHZJmvRwOIREZFHNyYoZ9b2s65bCe4abAeCynkZS2JXuobUTF2UJZkIneRVo3Gul18KHZTErswm6f2IU7KVr3rgnhAJmmafXLwdRO4gxPb9wKh+EGek9Elz8OvUKrJKhjRvPSYVvi0qnpv8hpyNwisZuzrH2hk2iOURk0G81H2iB8PTWD+ocONnsphEGWDmw7QkKdtMwYBwrlf8fyQkkw5bQxJdSbg/rc46UD5HRotikkVOCmEaF+msJyvaB3SgNikuwgMZR2l/Q/h59g/ZyYU8ZrhlsOWufdAYwDOC2wGXlH7VPdBQon0Uwn8fkg+jCNKYcjKZGuEdNSuYz0c2untCXml3EPEYUaccsY+Yk3TtK8mhEROs8RTALDi3BC+y7kiH5NFGXOT+SEQtUa5cD20Dh5DhBYh0fvkXqmMtJeihmLQkTG3RuUdIFoVna6cGQpFEWQQzxUC6/kK2hwGIAeoaCudAicw0PwIeQ80fkcyd2k98WmQcbOEdQuYlPt+mB1wjkwjcXHCzIVuSZnpWxPEjB1+7rMZXoptASHqLCFzKjkN3fT8zmQONzd0guFAj70oQ9hyZIl+MEPfrDH71966SW0trbiJz/5Cbq7u/HMM8/gU5/6FJxOJz7zmc8AAHp7e3HRRRfh05/+NH7605/isccew9VXX42Ojo4pRUxQGx4FBQUFBYUjFfvJSyuRSNgue71eKx7lvuC2224DgLoWmU984hO2/8+aNQurVq3Cb3/7W2vDc++992LmzJm48847AQALFizA008/jbvuumtKGx5FaSkoKCgoKBzj6O7uRjQatX5uv/32Q1aXiYkJNDaK9X3VqlVYtmyZLc/y5cuxatWqKZWrLDxTgOksezTxqfzW12RrPbaQPLZYz4ZMoXydpdDreh6QdxSHkLDRYZSf9TlYw2d36F6gau13pQG9UjZ7kRke0tlgz4Ugp+WB3gHxQClMI4n7kDS0QB5JHAk6n5OG5mMyLd/dtdlKhyiMcYBohYkSe1fF4DJcOBHAYzvnolDpkI6QfL0w5cH6LfkTxVaup0jHhj8LmFZoFXt33kWR08MUBT7CcSYkaZJXVJboFab8bJ5T7gJQKP/7Dgq38fxIj5V2k26PzyN1Y00e9swqUriN+a0SkTxOejIcSbxE3lhDxDewZg6n30hJyI/ZAfHwSlDMk0c2L8KXncBnX/kwLpv/jHWdo58zBRkvSN16h+UlcHO/EY3FXlGsAeWk9NCumORvIy0p0s8Zzsqkz5DuE/dtNilfw8d1ll/6wUwYOXopOYREjkNOjEn54agsBk7S4Sn6pG6ZPllIwn0UeXwuvag58g6r44tjkL6NTnpTTG+h6slV4T4MjwnDNHfTz6FCWV+MIpvn2mtTXeyBxetjvXUtTdHVOVyLLdSFWbu9KSq/yo46q91qlp9DDCqK1BZe9wK2YwdUz0oVDA73c4ChmSa0fTh4XL13+/btiETkvd4f1p23g2eeeQa/+MUv8MADD1jXBgYG0NbWZsvX1taGRCKBbDYLv9+/ezE1oSw8CgoKCgoKRyqM/fADIBKJ2H7easPzxS9+EZqmveXPG2+8MeWmrF27FpdccgluueUWnH/++VO+f29QFh4FBQUFBQWFSeNzn/scrrzyyrfMM2vWrCmVuX79epx77rn41Kc+hZtuusn2u/b2dgwODtquDQ4OIhKJTNq6A6gNz5RgOAHNCRSmi6m7fwaFlhiiyNvp2iKBLFXOJlI/idKx9Zm08FAMk/mZIglzlHbvmPwn3U51200i3QUR3KoKKgJ2eXb2HGOpdn4em7U5crqDvLQKKbFNB1rEAyhPHkklopAGE2J35ijes2PiHtafkTwtfilzNBcoh5ZAOWRAvvL54ncLxeOmcO/sScS0iF6Sr5twh7iOsGdQhigM5zShw7wknJhLSxvNtLTR5xc6iSktRrNPyhxJlWODFA2nLVL8tJC4nSSI7olPyORyUrvYA4upn02j4qXFXnIhl8x1B7nwsZdWkTiAgFPomHfH3pT66PISvJKQcCHvmb4J2LkI75m+CUWacDszMSvN4n5GHaqCw3CwN1/elL4NBqQtmm3cqXyinJh+Gk9J/XPDkva2yBi5yNMwVwnPkSu5kdBr13miN2alPe1STqqPIt2HyOttlCKGUxibTBd5iibIW8pLawQ7wAVo/lOZYFrdTzdUqefdhAc9wyRcSdQ2e2sWSUTVJupIdWOqnlg+mzes7qlNY7HQKnvJ8hrFax97dVWvVykt01k2drAXaoacFEPsGctrukwrK7SPTtcONPYXpTUVtLS0oKWlZe8ZJ4l169bhnHPOwRVXXIGvfvWre/x+yZIl+NOf/mS79sgjj2DJkiVTeo7a8CgoKCgoKBypOMxjafX19WFsbAx9fX3QdR1r1qwBAMyZMwehUAhr167FOeecg+XLl+OGG27AwMAAAMDpdFqbqk9/+tP4zne+gxtvvBGf+MQn8Pjjj+OXv/yl7ZzPZKA2PAoKCgoKCkcqDnOl5Ztvvhn33Xef9f/FixcDAJ544gksXboUv/71rzE8PIyf/OQn+MlPfmLl6+npwdatWwEAM2fOxAMPPIDrr78ed999N7q6uvD9739/Si7pgNrwTA1V6oeEvph+4jgwwZ1ynQW0mN6KbJGJlm2qHSuGI54Xw+TZQCZTk8y9bPFn0SymqoCy6bVKaekeQK9YodkbgmPKGPxOsGWbH8h5KB1rpujkmyRsu3e6UEX8zplUJnvBjOWF3yuQ7Ztpju0DjeVYWiHA4ynBrNAwHqJFWFAu6hV7N3tyZRopYnhCBjAdJ7E4J/MEgmyCXDvyYlt3NcizuL0GxU4qTBDN1yHXO4MJIFv+d0tK6KddSfKWIpc+B9VNp/JHk0J1meQl1xqTsdgyJOUHSGiRPeOipP7W5JLx5UjoHAMrqMm95wXXW+mcHsDAzg/gY02rrMj2ALA4sNVKD5bEdfDNbLuVbvTKi7J61zQrzfRWlCKPd0ZkfJkqbYxKWyKe2uGxM0Hpn0hX7TwJcunJ5QNAHgh68rY+4TEKzhPPOPbYGqW4Vw6mWSlGW3ir5HcnZBzTPbXnpGOC6LBxyZ+jCOb87tsXkuriUPXWKv/DNJZGc4mFRg2ixlwU/yu8XdrCwndZYkkMivMV3SjlxxfUFnvlWHU5mcI2ap7jF1aFXKvCg4UoUDDsa2XD6/KsiTm0/tLayulSxaFQ3229PZaxYsWKt1RFvvXWW3HrrbfutZylS5di9erV+1QXteFRUFBQUFA4QnG4Ky0fTlAbHgUFBQUFhSMVhzmldThBbXimgsrhMAfFomEvJY6BVWRxPvK0YktxMUBeBWQCLbI5lMzM054U2+nA6ZRJY08IuewhpXCm0gAg1yKeDKZDGKjGN+SByS5pJ7eBdOPgiRMd0yq0USAgFEZio9BYeqR2kBmOvRULClWRIaG8dEFcLHzkdRXzSP6/WbAWTsMF7JyPc6e/Cb3Cy5WISogXpQEDWfH2Kuok7FYUWiGTJU+rIpniXbXpA5O9ctiLLS71dzQxhyfJaJd4SDV5hY4Zq3gtZXUXRkn4risi+VkcTycPrBRRbM46NBwj4Be+dJziPQ34hD5jsUevo/aYBkhZc5ReiFaP0EONWhGdAF7OzsDqjNBVHK9qR1bmz86k0FtMXXE6R+NlUMyy198Q77Bop7wczeTltzMl5W/rF2HDWdOEL9kwLLxLJil9294qY+GtSJxlih4bVVegOcYikEESgWxtk3LGk/LiMsUzcRyJKOaYYxYYfo5nJ9eLRVn2OZaWMytpmyBhdX2p/OsddQCGaRMGZBHVfDPRWOTJxWsfU05M27NHleGVG1Kir2mn6qkOaWE1bWCqzpaudE/1KaZWqSPlKdAxApdME5s4Ib+/VpkHUXhQYfJQGx4FBQUFBYUjFJqx2/mrt3H/sQK14VFQUFBQUDhSoSitSUNteKYAh17+YdMvi2mxSdVNdBJ7BhQolkuOPBJY2M8lTiNoe1FsxQOnk6meGC23MAQ2Ya0shR5hc2z1/qqumjstsV8yrWL7Zu+GQoO8FN4x8qJqI0GwoNiaWaOsYZ50wMjOmJX2kODbRL/QJS0U12lW64iVZsG9MNm+c9QZfdlGuCoVXz/RjmLFtjyUknhMyaTQMeyp1NIkHelxSd04vpWnSQanWJRBa46KXX4gL7SIkedgaJL0Utv1YaF74kSZpZtlvBsqtF2DJ2u1CbDHBWNBxSR5k9lilk3IBIm1SXvZe6s5Ql5XDvn8M0jNLVkSKidPlFaSPJU6fELNMI3V5pbrWiVwnEcroc0rLw3H5Iq4hbJM+aRPhtMypgz2UGPhRxB9MzEm97a190l+6s+TZorSnIs+gz2N0s+JIHlmkadVoEJ3Oh0GUtnaEv1Mw42SUGSJBCpZwJP/LnHYqFKU+BNajzi2HQvxMTXGlLzpILHURhlT385yOV6HBnQCxZCJomFCj9BzmT4bkz7n9ZHrqdvqI/9his07JnlsMf5oXfLL8mBfQ8nTleNzsWBgtZzqOlg9/MtrJbO1bAlxMr3FdGHltTOOIavJkQS14VFQUFBQUDhScZgLDx5OUBseBQUFBQWFIxSHIrTEkQq14ZkKTJRP8LMTDnknsGmTaSmmsbwTlJ/1C8l0ymbU+FzyEGJ2hEymxBaArefGW4yufwjwOgHMK3s6WJQWmY65nb7hOp4gZB53u0nsjmzWyYyY/Rs6hLYoECXEMbbeJC+YCYoPtTA2YKUf2TnPSs+Ijlvp4WzQiqW1eUerFUursUloGjd57ngo7lWYRAg5flMpKPVM50kUjngFN9ETnoBQbzpREqW8DEg6LQOldYn93Ut0TJwCqWUrk2s0H0CK+jbiEY+qkkOux5qkP5Mp6cMAxfBKZ6QOnU0yMSeykr+nRfp2TkCoxhP926y0jyavTyOhQnKnCdZxW0mWgtgK4FT/NryD8gzp8tLs8oqX1g5fo5Xu80uave3YE4qRykt7edxHC0InndrQh1qo54nG3mdjVE42HwQyQKM3Y/PS8rukT0Yzkj8WkQVjdFTaaxh13juOLZWR9vr7iWKfXltU0KR31jsk9zIdrpFgZjWGX5Xx0sMGdNOweasaXnmAoyh1KBI15h2ovSCVQhRXi+qZ6aQ2UlwtDwkt8hpq8yQl7U9eo4mhtbxNq6+T7i0LsDKlxccUeE03eb2mqV1lax0HMZaWwuShNjwKCgoKCgpHKtSh5UlDbXimANNRtrLoAZLPTzpq5s2LhIdtt8/S6aw/YVNypy8GttLwlwdbinL8LPry8NChv5x8DJfLypajvwNlzaBipUkUBBtFConB2hdFOisa3EGHbIMULT4mVotcQj69+NBsoEHyFCiEhJ6QdMonn2er+mdYaaej9ks6OzJSPrScBuZ2D1oHfEsU+Trqk+dyiAEOOdHhlw7uDoqVgyNxZ0k/PkWHdcNuGfAhOlg7nI5ZaRdZgZhDd74q+ee8/w0rPVbRogm6inhjtNW63jltO9VBrE982Jgj0SdHpf+9FKE7npEx8tGB6pd2dlvp5pkyYbfThIq5pZxGmqQBmvizPGIdCtJ1o3LgPGm4sdMQy0aOhKkmKNJ6gg5L96ViVrp3s2j4gCyNja1i/kznpH/yg1Lm8e8UyyGPL7dlgsSnhshkO0GaToyOwAQwXv43Rgdlh3MyvlmPzDe2/Lhnk44NWQ5HEnJvLinjqJNmTqa79gFgo0HKdw1Q+JIgH2CmBgRlDjgS5Wc5qiaeqqWH1kG2Q+kUcsI9JgtYMUKaZX0c2ZzqSVHUPRN04Jm1zGjJ5bA9XAleWxn5mKT9lWH3OgEsKFvZNcO+nnponS000HVeoym9W/SNg4Mq87Av9x8jUBseBQUFBQWFIxTqDM/kUds8oaCgoKCgoKBwFEFZeKYA01H+0chUzJLqJQonwdc5OJvBW0yye7K5NCjyHzYTrJsoLVLhtz+LD+XRIUR9N8u7OyWPdxTkMB9TcU7SstBJzoRDViTmkD7JDjpgHSWNeOoArVD7QKmeJGEh0u7ojsStNEdFZ+qhwSOUSod3Ao4KTXJG41YYzMVV4Cbez1nHnsuhE8YpLgfTWCNET3DIAwbTKO4QRRsPSOcOD8q9vpOEgnHQYAZceevf49uFgmF9GA6HUaKDzRodUg1S5Hr+sGsJyXWO6O2LSv+xls44TfZmEoJirZ4+mkwcOT1GIia5UghBAH3FZozVobEYbqLqZoSFs40sEJqsQPVnbRwP6940Sz3XjcvLlI5IH76hy/VEUV6mRq/MtwJxzkybTmTLXEtvqgkJvfZ3Zb2xy1Loh+SY0GccfoWjqBs5eqdYq4fWBSMteVgPx5Glg8cU2Tz0mrQ3dVz5fXFW41MY5RgM7gSFnqHpr9EB42KYxWukcvlGqQ+Hh2Bu33bOnddQ+qvlpOMC1cPVAFDy1y6HtX2qU9i525LkjUuaKS1ex3k9LNLaHRis5D2YOjwm9vEMz36ryWEPteFRUFBQUFA4UqEOLU8aitJSUFBQUFBQOOqhLDxTgaP8406S9wBZ3tl7gOktNqmylgNHVPeII5BtG8qm0XreWDa6ivU2yFS7u9eCQ5fHcGgJpqsKdby08uTxpZFOSDEmD8+PS6U8YbGteyKkdeORQs0mKeed00QLZVF4p5Vmrx8fVahAYhwTuh+OGi4LBvGHEzRor8U7rfTmEQnhvKi930qH3BTGgiitTInD2gtCXqkne4SxDgyDPYmy5K02kBOeoKMSXiHoLKBAk4K1elgLKDEh9zL9wVSgk6K9M421c1hs9GfPedNKz/EOWun5YaHVojQZuefTNAEzFP4jbQofkDJcMAGEHDn4HKSVRC+Nk9rb7JK+6vaSl1xAvLc4LEWGuIcUcbzbA9LGBRFpy2BeJr3LJfO2KyAvKEeB5/4cLgjFOZQuv6weh45cTp47LTxR895dKXkueyAyjVXMyTw3KQQJh4cAeTkZNKbw0cgwjUXXmYpKzpMx1Vy7vU8uA6ZpwDPBNJn8OvqmlB9fWJs+K0WICh+V66yfU4hSW0h3KLJZ8jO1z+umLSQEU2N0jKA6HaqMo7MIOHX7WsfheZhKsx0jONSR0XfThntb9x8jOKQWnqeeegoXX3wxOjs7oWkafv/739t+b5ombr75ZnR0dMDv92PZsmXYuHGjLc/Y2Bg++tGPIhKJIBaL4aqrrkIqZf/r/uqrr+Ld7343fD4furu7cccddxzopikoKCgoKBxwVL209uXnWMEh3fCk02mceOKJuOeee2r+/o477sC3v/1t3HvvvXjuuecQDAaxfPly5HLyxf3Rj34U69atwyOPPII//vGPeOqpp/CpT33K+n0ikcD555+Pnp4evPTSS/jGN76BW2+9Ff/1X/91wNunoKCgoKCgcHjgkFJaF154IS688MKavzNNE9/61rdw00034ZJLLgEA/PjHP0ZbWxt+//vf47LLLsPrr7+OBx98EC+88AJOPfVUAMB//ud/4n3vex/+4z/+A52dnfjpT3+KQqGAH/7wh/B4PDj++OOxZs0afPOb37RtjBj5fB75vNASiUSZ5/GZGpymhkITeTP0yZ6RKSAvUVSklQaT5dtZvCpGeYjqCsXpep0wFmTdtgkScmRgbbdNfL4NMCqCX1qDVtOrIERl6VQnCiQOisCAEnkuOMhU7uiTm/U2aYQWFjtsmMzmDqI/tqaEcopS1OyhfJiuC0+4I9MAl+nEfADbU+0oVWzb7T7h6txkw53pF4ok2irXhyjGRiRE400dlacwEMGg1KGeNxl7ZrGHzpsjokYZ88m8Oz0s7nr9FcVKt+mAg2i1E0LDVnqXKyZ1nibl7yJ6K0CifCnyIGPaYl6buLLEqR8yQenzQYjdP+OQDxA3tatIk5T7gaYSvChgBEATCijSuAScMl5julBFI0RHFmme6EST8fUSRT8v0osYJApgOCNc8WmRrTXrnDVq05cZQ17oHo9Ylqc7i8A4cGpkACfHpK/G6KXMEMXWFpNxZJpy3Cfp0bTcm0rLdSdRb4VR4rcpZANsdAx54Y3WpucLbuqgyr3eyvextxplfD4JJxKtlj2ZIp7T4uRL8lpJdSOKPDIg9zrZ24vq5iamjpzwYHBzXfQO0nWNwvxUq+ytvK8urwbdAEDrtYv7rbjnvQBgkjerUXldtPxBlB5Uh5YnjcP2DE9vby8GBgawbNky61o0GsXpp5+OVatW4bLLLsOqVasQi8WszQ4ALFu2DA6HA8899xw+8IEPYNWqVXjPe94Dj0cWxOXLl+PrX/86xsfH0dDQgN1x++2347bbbtvj+k0LexAI7OYu271HtiMONy2ccWAfMGcSefidG6qba0p43/D5+6eg9N6z7BPYLZa96DfLvJ5d+fesXe/ft2fRptlm383VSTPGz7GStMbb0m8XI+u/XPd3wTrpA4L9NPeqOH7bh/dvgVXUFngG2utcZ7A68cypP/or0blvnaG2ogDQVuc6Y8YUK7Mf8S8n7L+HZzIZfGS/lbYXqA3PpHHYbngGBsoHCdva7G9JW1ub9buBgQG0trbafu9yudDY2GjLM3PmzD3KqP6u1obnS1/6Em644Qbr/4lEAt3d3fi39dvg9PlQiE7CwkPWEZuFhwOAsoWHPiD5S4I1IdiqY7PwUKiHSVt4YoDXoeFf58/Av63firyx56RnDQq99nlbFGKkfRGgYH9ROtjZTzL4ZOEJheUvq0YVXNgih2ODFPlvKhaeS8eX4k8tD9e08PCh5nHqpLG8/DkdIo2dnhCHlpB7Ryl/iQaknoXHQbu6ehaeIFl4PjR9tZXuz7bgrF3vx9Odv8dISfqzyy9125WLWelBqr/NwkMHxdnCEw3IWAQpIGnAKfkvannNSk9zixUoPEULD6NU8mFk/ZfRvPBmFEljJ0cH0e0WHkkX6KXJkIUnT9ezZOFJ0ouYpFAgfmrjqftg4eHD1abuxfHbPox1PffbtKDqWXh4btgsPIXJWHhkIbFZeCjkBMdCcIxKW+paeBrI5Osr3+uFA19pmIN/ndiIPAxoZDrRyORh0OFqNj/7BupYeMgYErBZeKgYtvCQ40e6Q9IGtdeVpXeQ1lObJbtq4XFo+D8nzcBXX91zHTTrWXhI/4fX4urBZj1f76tB4VDisN3wHEp4vV54vXv+hS9lTJi6afuDliHvAQr+DQ6x46Iz1Ow94MrQgkEmWFdOrie6aAGgF5112ULbKR4ObaIKHqpnp/1F1oqaJYKY00zkK4u7TyzryNAz2EOBN2rZAC2kMdrkEN1jdkkeF3kMFYkPawxI43K0+HMU7E5ardwu8pyiP0wx/0Q5lhYAtzsDrdLhIdq9sQdQgMT0mv3yh0BLy0aal65pvriVjtMf0zDRCrwpKpGnDG+iRijGVpBE/1Lk0fNcsstKLwqWBybqTSDtlMm1lTZ+HX7ZhKybEJ5gjOKXuVqlNR6/bCCnx8S0wZ5N/Eec+zBP17PEAQwUZHP1P9tPt9IXda610uzlZJa8mAnghXwXIlQ+x9JKkevO6+SWw33LmxP2pON3NUfjxen5Aem3FzMdlKf2JofFBps8Yv5LU79tH2/D8QAeHJiDBuKlPRQob0dK+ipEm8xmn5S5Ky4fZJkcUXUl+uNOm42iTu8d0ZdajtYRnfqKaFz29jI9dG+mslBoGtAAFJw6CjDg9sr7W6DYXt7tMi6FRqKAm6Ttnjh5bFE8r1xn7TiFNg8pWlsdZH1lpzRHmjarjbXjc1U/FEvVddBhIg/TtplhyoxCftnS7JFbnW7GwVTzUxaeSeOw1eFpby8vbIODg7brg4OD1u/a29sxNGS3QZdKJYyNjdny1CqDn6GgoKCgoHBEwtgPP8cIDtsNz8yZM9He3o7HHnvMupZIJPDcc89hyZIlAIAlS5YgHo/jpZdesvI8/vjjMAwDp59+upXnqaeeQrEo9shHHnkE8+bNq0lnKSgoKCgoHClQbumTxyGltFKpFDZt2mT9v7e3F2vWrEFjYyOmT5+O6667Dv/2b/+GuXPnYubMmfjXf/1XdHZ24v3vfz8AYMGCBbjgggvwyU9+Evfeey+KxSI+85nP4LLLLkNnZ9m75yMf+Qhuu+02XHXVVfjCF76AtWvX4u6778Zdd9015fo68+Xzpa469CxZz+HkPHSdY1UZRDkxChEyP9c5ERrcRd4/b9IZlnPFvtq0XkzI6e7d9ramadXXlQGqlnBiJGwxblhoi9vpHRE7crF2OCkYxOMbxPvnC2Kid5EnVAPRBGN5sV/3EGXDNEdvWjo14CxYlFa3bwxG5XxGj2fUyhNkAUONT/EK3hUUwb0inSqOk7taB1EwGeosprQ4VteEX9oyGJQDX3/dLmfMpjfKmZzjw7ukXZXPsIAjj3QpJvmpT4YLQm8Nj0u6sVlou4Bb2psnWmdXRurT7JP51OmPW+kmp1BvM1xyJipO51tiDrn3/8z+k5VO01mX4ZI8K1M51FYynTYxSdtXJ1EVJ4REiDJJc4D7n+OdpemcTEMdmiHikpfslXHyCvTIS9zsk7YzlRajAyX83OZACsiX/+3PCH3ZRa6Po0mZSym31DNGz+V3pCki78UAxV8rZol643Ms4zK+LJbKtHqmm6irouQxSVDUEy+31+PQgE7ATLphmgYKPukH55g8K98q644WkjF1DkkbiyQ8yGdgPBO1qS4+hGhSPflsoZ/O/9CQ2tZTPqfkrxj+vU4A88pndByG/cylrRzy8PLSdb/oViJdYaH5rKbC4YNDuuF58cUXcfbZZ1v/rx4UvuKKK7BixQrceOONSKfT+NSnPoV4PI6zzjoLDz74IHw+mZE//elP8ZnPfAbnnnsuHA4HPvjBD+Lb3/629ftoNIqHH34Y1157LU455RQ0Nzfj5ptvruuSrqCgoKCgcMRAneGZNA7phmfp0qUw36KzNU3Dl7/8ZXz5y/VdVhsbG/Gzn/3sLZ9zwgkn4C9/+cvbrqeCgoKCgsJhCcPc0w13qvcfI1BeWlOA7gfgtZtR3RR7ihx+4JmQSaR72QNL8rCnK5tL2QvKR2VmxXsZJrmM958lpnE2V+caxDzsTuxJn7kqrgaFCFCoWJhZMJHLYoqB61egY1DmKHVMm9h82X21o1EqPpwgf3rCaeFeK31qeKuVZu+edlfcSvtI2M2nlWDoPuwcuhDnh9bDUeEW2c05YZB7MqVz5Nq8oyBeTq+n5XD7G+MikzCWEvt4V4PUJ0cxtpr9QkOwh06iKH0V8Em7UgWifmhSeCvB0IqmE0GXUHKjxdrKNHM65DD/1lFpS0tQqBm/W/qtlSgb9iY7JUrjRVRUN8W06mT1SaK9inUoJCckTllB9+IVAH8XfQ1FohozRBvx2MUNP12X8WLKjKku9vbK13Et5/hc86cLjZikZ3k0aaOjTrvm+6VdpZIfGD8bFzSvQ0KT95ApzrMaJUwOU3Lsrs5yCuwarxPl5CKPyP4BeSH1ElE/7NlUkPq4KB4Wv+8lurcqw6FplWveyklXKkdvYWqYqLEJaZfO8byIevMMsSsUZUkTFcWeUG7ybqV6kmKBbS1myoyPCFS9tByV3/sHy0KqqenUFKoyM65M8Te9LvNWqyjL6rWZcoVDDLXhUVBQUFBQOFKhKK1JQ214pgDTUf7hyOYcOTwoH3hITqdDgmQRYYtQju7lrwf+yslKAG944nS9hSw2PF/piyTdSV9au51ZdicAV+XjzjcqVptsW21BMO8YlUXXW1+ir8t3U5UMeiDlT+Tk69ukgmaHR6z0IFkSGp1iIWmhcANsbYiTKFF/MQaH4caJAH49cSr0SseydWicTi6OFeTTkXVXfCRGlyKhv4hXTHR8ALgzIHVjITs+wJymcjh6e8IpfVIoyRf3vABZQip1dmu6TQywaNR2tNxGVp3OmFhpsmR96gjIZ3CChPiWNIqFbYw+rbs8ckD6ycxsK83WsL8My/V5UbEybU+L5SFJVqyTosM4G8C3ht4DjSxXTjLRx0nUqp8OV7dSWBDuc9bemaB7g6SVxNHh27wydq/Ep1np48IiSpWgk6xjeZk/AykyzRKOiyTw9wCeHJ+HKB38Zg0fPuTM9e/yyQKj0zx5c0JMvBMkPBgKkHCMTu+plw4kszBgUBYkIyX1cfCh5RYqM07eCwB8fS5oholcJy1saSnHmSHLj6/2eqKRdajQQuWQ/g8fhGbKhgUGPQl6FhmYkz2SdpMOmp9UTFIVlfxqtPRMJ5A3dtNNo3WZrUxs2R44jYRVK9NEP6i6g/u44TmYmkGHGIetW7qCgoKCgoKCwv6CsvAoKCgoKCgcqVCU1qShNjxTgKNYPuBG5yNtOg11zo/CwXFX2ErLWiNk7vWTMDTH52KNHH4undG0PYtjbJUa+AQyUAo5UT33mGsqm3IBQPdLpVxJMfuzbDvbBYcXk8YOxetBXm7QEnTolK4zN5Y3ak/F/mLMSre4mNISKmF9RrRTGlwZOCqVHchFrFhaLV6xUzNd4qWTmm7iHmMUt4spBj4kzPdG6DQ6h65g7IIMoIueFXfLAEaCMoA6dXSoMrAhRx6DdIj6vQ0baj63r01oJlsoFKK0AkQhuaj/N1JYjYWkBfRO33Yr/YOxM6X+RBstbhSdHNb5OT4q9Bzr7fgrQ9HhjWOATtZuzwhnwGEgfC7p896E6C+1BWR8CzRZV78mGkennLDFSnMsszgFaloUk3py/ZnWnBaI16xbhMJDtHnLdFubbwIpqs/2dMxKcwgJprdydDiZadACxTlw0kHlItGgngjNn20yV90UUoHDSTCNxeuRQdRYYGclSrpTA6YBuVkF5GHAOUQHkpuI+yFKy5WqTW3zOuMdYi0vqg/VgQ8nw1ubJmOqncOZ51ol7R+h+G6VKlcPLZf8QMnYba0jGBRGqN6B6qa15XaVigdRvtgwsU+01DHkpaUoLQUFBQUFBYWjHsrCo6CgoKCgcKTCNMo/+3L/MQK14ZkCPMlyeAlvXCZIskuMZB7ywGLzKutDsA4Eh5+wReglzQyWDolukedOzJLnMoPSsEn+s6uDTMtj9qF2H5eAt2LgM6blYFTcu9jkVwqRqZlMys4JKYs1MRw5el4LNTQk3jFsKg8F96QAAKDTHbfSrLcTdkiHcXiIFtJR0eEAKlGrjwsOWKEl3kyLfs4I6czMi4jbRoNLKIYOrgOlmW5gT7EYhbJnz6wJ8iBLkRvJbB9FJy8tsNIcSoPhr7TX78gj7JZ+KBBHynVjzR+mtDxEO64ekmjs18x5ykr3UfyThT6htIpUzqcb/2qlk6SjX9zdHbACZx2Te1H3YyeAc0OvWx51AFCI1eYVnFrtxblI/VBgXX9hO5Expf8TxANPcwtl6dOkDnqdtjCMJsnDekEl3Y/SDuDc6HpkaC3IRKUOrB3EY8RUZiPNSaZlN6fEfXM4K5zK0IRQvXpIxlr3cvwG6UNDY0qLKkreW5m5RVu9nMNuOE3T/u4Tbe2iBuc6hIJkTzEtLWPEnlxMJxlUf66bmyKtF6MUooK8t5gC8w9I/nSHlF+dbtWcml6m9UohKSe4g9rSxG5mlKTTAqMLy8/ScweRPFFneCYNteFRUFBQUFA4UqHO8Ewa6gyPgoKCgoKCwlEPZeGZAjSj/DO6SPaJXgnCjfQ0yks0E1vGi+R1xfQWi2ZxlHK24Kfba9NnbAYeOpm8o1g0q9HupeUyNZgVu2womIO78qBUUkz9TqI/dL32VHGT8FehWZ5RSAu94iVKSy8QBeMijyESBjwnIMJ3UU3yGPQVo4O126kzAJR0H54F8A+xtXBVeEM9KvfWC3mg1/nQYRJllGgIptjGdKHJ3syJ3Xx7TryNxgu1PbzmBIXe2uUST66XE6KedlpwJyIAEnoA4wWhydqjcSt9/+AZVrpEky5AgnvzguICeFxI0kyjvCf8htQhO8NKM1X0elYmOwsSMuqFdWDqJ2Ia6ALwXGaWTXiQKUumljgUCIdj4PIzulwPEVfMnoActqPHLy/xdI8IYHId2LMsrss4sregk2aKoXvRA2BzvhU60YsBLpNe0AlDxpT7J0WCh9NI8TThletMacWCsqgM5+idpbltFGjBoLAvWoain2dJ0K9KFVXoL0dRg8MAHERdlQJSTr5Z2suhK/SwXK9HSzmzRO1RtPTADsmf7aT8OaJrx2ktIo/WzDSqD3meVqdzVXjQdJkwDRO+kdrR2DlcFdNYTlqKirV1KA8sFKU1aagNj4KCgoKCwpEKE/u44dlvNTnsoSgtBQUFBQUFhaMeysIzBeQaAacXcFMkXptYF3lUudnrinuZo47zzprS7L3F9BZr87GooJcip5M13xZDxlmwD3WuFICpOYAOINkXQb7yhcBxdrQsm7jFzBvcKWmuB5uvS+QJkk+KXTgYE5O7gzqAPZiYWnI7pEy3xt5AUs+UKTblYR3QKwWsLwShOcvtZnqFPXqYkuCYUL/ZcpKVvmLus1baIHqFvaLcRFFxtO4Q0UmjeemsItFDvWnxuGkiT5yFAfGQyldiaeVNF3oCQiExvbKsaX3NtjCV0+amyUKY4ZG4Uc+l51jpRgos9GpGwkh3UGA3jgvGfcLeXhyLihHSTHQB6M03I2TKixWil4CpH6axhkiVk8UPS8TxcmR5Btd5iOgtrn9/XniRc2KvU31kTJnWZCQqrpaG6YCD6s/UmIMWAzdxJNzGKCmMclytCHnqhUnwcKhYOyK82yflG/3yrpWCJDSalrnNMf9K2fL88Tg0oBUoxgwUTcPmlWlwJHTyxDS8tc0HNhqLaCmb/ih5QqW7a4slsocXxzVkRz1/P3uEUTUrdatSWr4RDZpu95jNxyRd8nNbakeirx5l0A6m1URRWpOG2vAoKCgoKCgcqTAM2E8avp37jw0oSktBQUFBQUHhqIey8EwBJT9g+kSwCrDHUWHYNMs43guZWjlOFlNaHqLM2MTLMbzYjJqmOpBOma0Ou+u1Bfuc5dg4HYDpM2FW1DZ9O8krip7Nbc5IKCfoZBI3iQ4zOQ5OThqdzktlnREp9PiAxGDaUBTPplcKYt6vJxbnhNR5Y6ENmu5BM4AXMzMt4UEWJ2SPME5P94i3DtNYz43PstINHhEYfLZfvKgWtUoMJo7T1JeKoRZ8TrnONBYL9DHl56hcd8C0UWAnBCS+lZMUU5kinOETz6NZHvEI21aUcrYXhX5qJs52rmdA0l5JO6ieRcj4Jmlict8yTcPtmijEAABN7jTcdWgsLocpIe4rzpMn+pLFJKMkDsnlsHgjY3Fgq5WeRgKYMfK0ctC7naMXbsIVxiiAUwNb4CCOxF1HODFHL9uuksx/pro4Bly3W2hNFiHsDAkXlc0TjVuU8otNUqYrXvu6HiC6KlbuK63yfezIaXCYDhiNRM+NybP0MIkNEi2uFahMXiu81IlFinVVYI8tEtJ0y3UHCRj6RolabSGqq4neo/yeQotaZYHOtprIm6ZNNNbmmUVpzsMUWDFSORrgPIg00WFOaX31q1/FAw88gDVr1sDj8SAej9fNOzo6ihNPPBE7d+7E+Pg4YrGY9buVK1fihhtuwLp169Dd3Y2bbroJV1555ZTqoiw8CgoKCgoKRyqqG559+TmAKBQK+NCHPoRrrrlmr3mvuuoqnHDCCXtc7+3txUUXXYSzzz4ba9aswXXXXYerr74aDz300JTqoiw8CgoKCgoKxzgSCbuemdfrhdfrrZN78rjtttsAACtWrHjLfN/73vcQj8dx8803489//rPtd/feey9mzpyJO++8EwCwYMECPP3007jrrruwfPnySddFbXimANNVpnmYKjLJdOklkyoLD3JsLBYbZLB4FaOeRxgClCZbq+Ehcy/RUMZuzhuFBsOKo+PIOOCo2GirJlkAcCekrIBo1CHVTY8mjwmThMs0EjQz/dIZvpjQFmQVtlEncRJh25JvtdJMi9QThWOEHHmYFarDRmcQXeKxKYhJssUlL393q9AHLL53SnirleaYWRxL632Nr1ppFvfrzbdYaaZjXiSxwf4K3QMA3c6UlffMhk3W9U05iREWINv6NK9Qfhzn61ejp1npk8J9VnpnXmiUM0JS/kBJPJVYXLHRSS6ABPaAY884prG4/2MVob8ez4gt5tSugtTnrynhUBMFoTU7AjJGswNC1U1z1o6NxZ5ZTGONkFoc01h8L7eFQunBR/PHRy99oyOP0cq/Oo1vvVhjMaLzmpzinZekOc9zL6PJ9fOaxTvvybF5VprCZMGgWFRO8tjSG2Vc/FukzGwHeWtOuCvlaUAUMNwmDNOEa7C2R5iRlzYyze3bJvlLfsnDa1+uk8aLqHD/dhKxbJYB4DWRRf/46IAjx9QYeXtV6K0qPVUVVGTvVj52YFtP6S8nr/XVNVPP8+p2gLGfQkt0d3fbLt9yyy249dZb3365U8D69evx5S9/Gc899xy2bNmyx+9XrVqFZcuW2a4tX74c11133ZSeozY8CgoKCgoKRyhM07DOYL7d+wFg+/btiERkh7c/rDuTQT6fx4c//GF84xvfwPTp02tueAYGBtDW1ma71tbWhkQigWw2C7+/dtDl3aHO8CgoKCgoKBypMM2ylebt/lTO8EQiEdvPW214vvjFL0LTtLf8eeONN+rez/jSl76EBQsW4GMf+9h+6Y63grLwTAGaXv7xiUYb8s0UT4a8pdj8ySZSzmO4ifYhsyubY9lcyoKE0x8UbmznUtnd8r2dfxWKY8sHdhtqJyxKywgYMKpfCCWm5aSwrDiCQCcBLsNFplQSG9QCYjb3eMlrg6rQHKntndRCdInbJ15IGZPjWEn7gyQE1+5LwNB92AngrOAmOCqd5iPbt5ue5SG7v5vT9C3g1mp/F7jAooh7f5WypoxHJrDDSq+nGFssWshihg0VAcAGVwohh7Tl8ZH5Vvrv2l600s46ymcxt9BbTdTPM8IyqZ9KSplzfMJlMjWWNGTOcQwpFlTcnBM60iC3lhk+8YbLmxqCKFOCOr003PZTottqls8ikEwL8rMMGlO9Dp3EYozsrcZU16qxmVb6/BYRIewkb0GOk1UsBeAE8EahFcM0b3/cJ/HOPjr9eSvNYzFK1CHTmk6awxy/bIgC9I3lZVzyeZmTDqKYi2NSHwd5TmW7pM/Z+8mIla+bVS8tvRyCi2lyFiZlalijeHz5JnpfKAZfiWJmOZNSZ14fmWr3jpD4Zwt5ido8YylGGM0H3xDFCKt0g7PiaudOAoZePrpg5fFz+VKOi9qba5U8nkqMsDpT7ajB5z73ub16SM2aNestf1/F448/jtdeew2//vWvAQBmZQPW3NyMf/mXf8Ftt92G9vZ2DA4O2u4bHBxEJBKZtHUHUBseBQUFBQWFIxfmPp7heRteWi0tLWhpadl7xkngN7/5DbJZ+YB94YUX8IlPfAJ/+ctfMHv2bADAkiVL8Kc//cl23yOPPIIlS5ZM6VlqwzMFOHOA0wSypEPDFhgmCG36OXTA2G6xqW3VoY9VW7RenQ7Q9Z9Z26rD5e98j9zszNknteE25VCjoVkn95wkL59vki8X/qqyhcGgw3k6fU1rw/JsM0dWi4VxK10iC9LOUsxKv9cv1gaDDg8X6cWs9wHl1DSUNB07AbQ6TbisQ+UUDoPqr3MEdio/R1YLncOH0LN07P1gYpgPjtL1N8h68EpWDiqfGJCDxBvzwllvyHViUeXfTrK6fGbaY1b6kcQ7rPS5kXVWmiOeL42IdeL59GwrzXo1HC2dwZa0hFE7VIRBI8PWj2IdrRt3JQxEm2sCbleG8jtr5nfatIkoPIHNgkeaLTRGrB3krKOHE6fD53wwvqdDtIzYkhNzSJ0ZfIw+TKbZa2eutNIcosJNLy5HhGcLGx+iDtfxfngXWWLzuvT5zpGY/IKnLVsCSQMH5IzhHCuX49Q0oLG8VJgAjJDU2ZmVZ2mkn+OgMBZ6iMLNtEtbNDrkDLLqODnSeoQinqfJckVOE6wLpCUkD2vmsFWqmjYqjy+GgKIBW/9odNibw2R4dtEB+AbKXzGS2f4uHGgYxp5Ca1PBPpz/mQz6+vowNjaGvr4+6LqONWvWAADmzJmDUChkbWqqGBkpv2sLFiywdHg+/elP4zvf+Q5uvPFGfOITn8Djjz+OX/7yl3jggQemVBe14VFQUFBQUFA4ILj55ptx3333Wf9fvHgxAOCJJ57A0qVLJ1XGzJkz8cADD+D666/H3Xffja6uLnz/+9+fkks6oDY8CgoKCgoKRy4OAaU1FaxYsWKvGjyMpUuXWud4dr++evXqfaqL2vBMAXoltARHGGb5c5PMsd5hOvRKh5YjWyXP2EKiWcJ0OI7MyW4y63rlnCJybWT6pQN0fLgvwFHNOYwFyofqLFOvR0eVcNHJFGt66HCylyTf2V5v1qZ1WDreaJS0h/IPDkultkyXA67bCsIN7yR7MVMY8aJQD3NIg8XrKELT3egB8Gh6miXy4SEbc5pidLTYKDM+hEx1Jq0V1m+xafgQ6oVdCGtCbfChXy916GhJDqzO9Qqdsa1id484MzbNH64/RyR/I99Zs24b88LHFimqOOvqcAT2aS6hpTjMQTs9l8EHmFl3yFknuGFR92MA5VAh7noiVVOEcx8W/xanUHtGnbnNmkL8LNbYaUYJfQBmukfhctVuV716zqCwEZPJ3+SQOnuJ9toYkndqNCVjmgbRzcwN08FmW9TySHncq+OpmeUfJx1sLtH65RkjWryZFhSv5Am/Tro6FOW80EwaQVSme4xCYISonkyBjVB4CzpsbNA65ijVp6H1gAHdNG3hJ9gpo2mNtCs1nW6kYalqBBnZgxeQ0zQMmPtAae2LS/uRhqP8LLmCgoKCgoKCgrLwKCgoKCgoHLk4zCmtwwlqwzMFGC4TcJnwDdf2ZGIqqtBAOhBkMRwNsW4PeZnk2IzKeg9yb6619sQsRqmcjNSNpdZtejkAHA15OKoGvpzToqbY5GtSNPNSkLxm2GspSNodFBmZrOzQOyVPalToGJa4T5H+TI5cKVIlkbvv8Yt+y1MDc6w0Ry2f2zQCt+FED4D12S5L22VnLiZ5gkKBDZaEVuMwAUwzTYa64vAWTP2MEUU1WJQwDRyRvC8v2i8lotVmeIdteXoq/4Y8cu8moqjOjUmIgTdzHVb6WdKQafPLvTszUp83KHzD/JCE+Xi8sMBKz/SLpxKPEbedrzPtyB5tTW6ZHKWSH2cA+H38FAzrMtZhl9B/DeS9lSeuhSPC+53c/0KjsIcU143pyyyFb/CTi2RfVriWkZxQQu9sFF0gBs/hVD6MCwH83+H3wk0v8cujXVb6+Abp505f3EoP5GVObpgQWmokJXPpohnihbcxJXm6/EJBbo6Ly5btbxqFenH1S9uZVuc/oI54ud8cmgY0lL2bDNOEK8EEgYxvfqZ4sTmHpHxnm/RDahGtm+QdplHaMyxjVCBaHBSNHSWi2pMyvgZHYK/jpVVdf52ahJgwAdB0sK/dp9Fal5K68ZqrBw4BPWSYdm+7qeIY2vAoSktBQUFBQUHhqIey8CgoKCgoKBypME2gjlPA5O8/NqA2PFOBVv7JtZEHz5iYNm1R1CntYEaEFdizZAolr4LALpJOb6bwE+SR4BuUPJlO9qai6MeDZGYmTwgAMEpOEYkLiJeWQXPfzFP08wCJjCXIc2ywtsAX02wc9VjTKXwDUVpR8tBh7yH2iBnICwVzVrsEmBvMCXcXc2fhrNAefZlGlCqd73JI/V8aFxeLZp94J0WIRvHSoHEdmC6JEtVCWnc2uiTskDJ3Uf0HKAwze9+snxCxwZ1+yT/dW6aBnJppo9s4/Xp2mpUeLwmdFPFIHQaz0lebh4TyKCSFHtreEbPSQ/2SPn62hMNgcPnxgniflQyifkkEr8ErY+00XDgDwIZkK7alhTbqDIkXWMAlbcyUhJMYSElbvG4Zr46A0HYep1xnr6udaenbeEbq3BMTSmicwjS8s1kEIV+ZEFrKR1Raqih9WCqW6a1tqQbEAtIP727bbKXXTQjtyAKJaaJxQ26h2E7pEUHI3rSM3dyQULSrx6Vu3N5MQug2z06KwN7BLpcC1/CeHk+7O60Rs4rSSfIeaRTSAl30XufoeokXSyqUQtIUZ0rdHEPSt0ZAKhLYKPXMLpR56Nwl7WVPLk4H+8p18Do1YBrgTjpgGCZ0H+WnsBdajqLA0xGBUozWikoYHS0jtN6BhmmYMPeB0qrlAn604piitO655x7MmDEDPp8Pp59+Op5//vm936SgoKCgoHC4wjT2/ecYwTGz4fnFL36BG264AbfccgtefvllnHjiiVi+fDmGhob2frOCgoKCgoLCEY1jhtL65je/iU9+8pP4+Mc/DgC499578cADD+CHP/whvvjFL9ry5vN55PNikkwkyub1pj4HXB4HDJfsE11ZMm2SqbWeDpRngoTKghxLS2iQPMVm8Q1z9HK5zhbMyOtyb0as5CCtQLh32WMTOQpOeJwasASIbvcgX+Gy8o1S8ehGiqRMMXp6/jdupYfOiFnp5AyifojGKnnIFu6jRqSEMlhFUb9Pi2210jvyRMGQ14nPXdsUv6zrWaDi7XNGpA9mhfIJU/yjLXkRNmSPmDeJDogSTbNuUDyY/maWeMdMkFfRKxNCJzGVECDKYy5FJB8vCOW0ISHt2jko3GNQqoMnB6bjTA14ctsCnDVdqI2fb5V+e99M8dJyEqf6+kC3lW4OiofUcc1xK93QKdRDX1Im4KUnvWKl2VstTW33E23kDYhoHlOTnR551v1977TSx0fLnndt7jxOnradyhcK4w9vLrLSH5j3qpU+MyYUG9OIaydEdHHdw/Os9BnvW2ulj2sWTyv2Mhuk+bCVPMKO8wjVtTEuY31aZJeVZsqy5CoBSWCGP4VGn3A/c0hUsN8l87DFKdRVjOjUFyYkT2dDr5X+39WnW+nW02SuRh3y/m4bkfq0rpH+TMwi+nxcKKFpT8v1He+xknBX6DZv9V8XAFODl2LnhZ6R93RiEXH4FJuv6WXJPy7TFp5k7YBzHH6NvasCr0pbisKCQhuQ9cRFjFLDK+Q9S16y1TyeynMad2ko6EBwiGJ+RWReTcySe53MWA1JnmB/uaKlgobavnz7H4rSmjw08xhobaFQQCAQwK9//Wu8//3vt65fccUViMfj+MMf/mDLf+utt+K2227bo5yf/exnCAQCe1xXUFBQUFCoIpPJ4CMf+QgmJiYQiUT2fsPbQCKRQDQaxVJcApfm3vsNdVAyi1iJPxzQuh4uOCYsPCMjI9B1HW1tbbbrbW1teOONPSNDf+lLX8INN9xg/X9iYgLTp0/HHQ9vgNPjs0Un5yjkun/vFh53oraFh8lFtvCQjEhdCw8ZL5ClJnonJE0f5OV7CoDbqeH606bjP9b3iYWngSw8m8nCI1Ix6H5QCh45Vb4ikz10wI/0fFh23mbhoeYv6tlppU+JyrfRurR8rfdOSCV8rtoWnsumvQDoHkS3fBLbZ/zYOjEeok7amheLymBBvkx30UHWiEfyvz4kOicXzJD54qIBeS1BB1BtFh752p0dEh0bPty7JSnt2j4kgz9nmtCtw/EGfE47Hnea67Cka6N1/Ym+uVb6/J4NVprDHDy1U6IRNwXkoLXbJXWLkUVrRzJmpd/ZIod17RYeWWD9TjrET9aSEll42j0yZ369/WQrvSA6hmXDy/Boy6PoCIgFLEMWngc2LbTSF88VC1uQXg628LxOY7HpyRnSlvPEAtbqFasLH/weIotiX0bG4txm6duHhkWb6IzGrVaarYWlkt9qV4NP2j7LK2P6lwkZuw4vhziRsXt5RKxzF3W+ZqV/+Nf3St1OEcvVUE7qsHa79EOELTwzKDQORRvvXCXXd76LvS7K77VX0/B/umbg30Z6kTdNBHZQKBbS3Zo4niw8pKvT+Io8K36cZJmyhUcirtgsPOQHYNMvi22UQgvBWhYeDf+/C3vwnZV9KOgmgsMyn/NhqX9iBll4xCBnQ2Cg3FelQnluHgx7QgnFfdIdLKH2Wno04piw8OzatQvTpk3DM888gyVLlljXb7zxRjz55JN47rnn3vL+HTt2oLu7+y3zKCgoKCgoMLZv346urq69Z3wbyOVymDlzJgYGBvaeeS9ob29Hb28vfD7f3jMfwTgmLDzNzc1wOp0YHBy0XR8cHER7e3uduwSdnZ3Yvn07wuEwNOKtj3QkEgl0d3dj+/btR5Up82hs19HYJkC160jC0dgm4MC0yzRNJJNJdHbWDuK7P+Dz+dDb24tCoY65aQrweDxH/WYHOEY2PB6PB6eccgoee+wx6wyPYRh47LHH8JnPfGav9zscjgO2Sz8cEIlEjqoFrIqjsV1HY5sA1a4jCUdjm4D9365oNLr3TPsIn893TGxU9heOiQ0PANxwww244oorcOqpp+K0007Dt771LaTTactrS0FBQUFBQeHoxTGz4bn00ksxPDyMm2++GQMDAzjppJPw4IMP7nGQWUFBQUFBQeHowzGz4QGAz3zmM5OisI4VeL1e3HLLLfB6vXvPfAThaGzX0dgmQLXrSMLR2Cbg6G2Xwp44Jry0FBQUFBQUFI5tHDOhJRQUFBQUFBSOXagNj4KCgoKCgsJRD7XhUVBQUFBQUDjqoTY8CgoKCgoKCkc91IbnKMI999yDGTNmwOfz4fTTT8fzzz9fN+9///d/493vfjcaGhrQ0NCAZcuW7ZH/yiuvhKZptp8LLrjgQDdjD0ylXStWrNijzrsLc5mmiZtvvhkdHR3w+/1YtmwZNm7cWKfEA4eptGvp0qV7tEvTNFx00UVWnkM9Xk899RQuvvhidHZ2QtM0/P73v9/rPStXrsTJJ58Mr9eLOXPmYMWKFXvkmUo/HQhMtV2//e1vcd5556GlpQWRSARLlizBQw89ZMtz66237jFW8+fPr1Pi/sdU27Ry5cqa82/3sAZH2ljVemc0TcPxxx9v5TnUY6Ww/6A2PEcJfvGLX+CGG27ALbfcgpdffhknnngili9fjqGhoZr5V65ciQ9/+MN44oknsGrVKnR3d+P888/Hzp07bfkuuOAC9Pf3Wz/333//wWiOham2CygrpnKdt23bZvv9HXfcgW9/+9u499578dxzzyEYDGL58uXI5XJ1Stz/mGq7fvvb39ratHbtWjidTnzoQx+y5TuU45VOp3HiiSfinnvumVT+3t5eXHTRRTj77LOxZs0aXHfddbj66qttm4O3M/77G1Nt11NPPYXzzjsPf/rTn/DSSy/h7LPPxsUXX4zVq1fb8h1//PG2sXr66acPRPVrYqptqmLDhg22Ore2SmDdI3Gs7r77blt7tm/fjsbGxj3eq0M5Vgr7EabCUYHTTjvNvPbaa63/67pudnZ2mrfffvuk7i+VSmY4HDbvu+8+69oVV1xhXnLJJfu7qlPCVNv1ox/9yIxGo3XLMwzDbG9vN7/xjW9Y1+LxuOn1es37779/v9V7b9jX8brrrrvMcDhsplIp69rhMF5VADB/97vfvWWeG2+80Tz++ONt1y699FJz+fLl1v/3tZ/2NybTrlpYuHChedttt1n/v+WWW8wTTzxx/1VsHzCZNj3xxBMmAHN8fLxunqNhrH73u9+ZmqaZW7duta4dTmOlsG9QFp6jAIVCAS+99BKWLVtmXXM4HFi2bBlWrVo1qTIymQyKxSIaGxtt11euXInW1lbMmzcP11xzDUZHR/dr3d8Kb7ddqVQKPT096O7uxiWXXIJ169ZZv+vt7cXAwICtzGg0itNPP33SfbWv2B/j9YMf/ACXXXYZgsGg7fqhHK+pYtWqVbY+AIDly5dbfbA/+ulwgGEYSCaTe7xbGzduRGdnJ2bNmoWPfvSj6OvrO0Q1nDxOOukkdHR04LzzzsNf//pX6/rRMlY/+MEPsGzZMvT09NiuH4ljpbAn1IbnKMDIyAh0Xd8jTEZbW9seHHs9fOELX0BnZ6dtwbrgggvw4x//GI899hi+/vWv48knn8SFF14IXdf3a/3r4e20a968efjhD3+IP/zhD/jJT34CwzBw5plnYseOHQBg3bcvfbWv2Nfxev7557F27VpcffXVtuuHerymioGBgZp9kEgkkM1m98u8PhzwH//xH0ilUvj7v/9769rpp5+OFStW4MEHH8T3vvc99Pb24t3vfjeSyeQhrGl9dHR04N5778VvfvMb/OY3v0F3dzeWLl2Kl19+GcD+WYMONXbt2oU///nPe7xXR9pYKdTHMRVaQqE2vva1r+HnP/85Vq5caTvge9lll1npRYsW4YQTTsDs2bOxcuVKnHvuuYeiqnvFkiVLsGTJEuv/Z555JhYsWID/+3//L77yla8cwprtP/zgBz/AokWLcNppp9muH4njdbTjZz/7GW677Tb84Q9/sJ13ufDCC630CSecgNNPPx09PT345S9/iauuuupQVPUtMW/ePMybN8/6/5lnnonNmzfjrrvuwv/8z/8cwprtP9x3332IxWJ4//vfb7t+pI2VQn0oC89RgObmZjidTgwODtquDw4Oor29/S3v/Y//+A987Wtfw8MPP4wTTjjhLfPOmjULzc3N2LRp0z7XeTLYl3ZV4Xa7sXjxYqvO1fv2pcx9xb60K51O4+c///mkFtqDPV5TRXt7e80+iEQi8Pv9+2X8DyV+/vOf4+qrr8Yvf/nLPai73RGLxXDccccdtmNVC6eddppV3yN9rEzTxA9/+ENcfvnl8Hg8b5n3SBwrhTLUhucogMfjwSmnnILHHnvMumYYBh577DGbtWN33HHHHfjKV76CBx98EKeeeupen7Njxw6Mjo6io6Njv9R7b3i77WLouo7XXnvNqvPMmTPR3t5uKzORSOC5556bdJn7in1p169+9Svk83l87GMf2+tzDvZ4TRVLliyx9QEAPPLII1Yf7I/xP1S4//778fGPfxz333+/TTqgHlKpFDZv3nzYjlUtrFmzxqrvkTxWAPDkk09i06ZNk/qQOBLHSqGCQ31qWmH/4Oc//7np9XrNFStWmOvXrzc/9alPmbFYzBwYGDBN0zQvv/xy84tf/KKV/2tf+5rp8XjMX//612Z/f7/1k0wmTdM0zWQyaf7zP/+zuWrVKrO3t9d89NFHzZNPPtmcO3eumcvlDtt23XbbbeZDDz1kbt682XzppZfMyy67zPT5fOa6detsbY/FYuYf/vAH89VXXzUvueQSc+bMmWY2mz1s21XFWWedZV566aV7XD8cxiuZTJqrV682V69ebQIwv/nNb5qrV682t23bZpqmaX7xi180L7/8civ/li1bzEAgYH7+8583X3/9dfOee+4xnU6n+eCDD1p59tZPh2O7fvrTn5oul8u85557bO9WPB638nzuc58zV65cafb29pp//etfzWXLlpnNzc3m0NDQYdmmu+66y/z9739vbty40XzttdfMf/qnfzIdDof56KOPWnmOxLGq4mMf+5h5+umn1yzzUI+Vwv6D2vAcRfjP//xPc/r06abH4zFPO+0089lnn7V+9973vte84oorrP/39PSYAPb4ueWWW0zTNM1MJmOef/75ZktLi+l2u82enh7zk5/85EFdvKqYSruuu+46K29bW5v5vve9z3z55Zdt5RmGYf7rv/6r2dbWZnq9XvPcc881N2zYcLCaY2Eq7TJN03zjjTdMAObDDz+8R1mHw3hVXZd3/6m244orrjDf+9737nHPSSedZHo8HnPWrFnmj370oz3Kfat+OhiYarve+973vmV+0yy733d0dJgej8ecNm2aeemll5qbNm06bNv09a9/3Zw9e7bp8/nMxsZGc+nSpebjjz++R7lH2liZZlmWwu/3m//1X/9Vs8xDPVYK+w+aaZrmATYiKSgoKCgoKCgcUqgzPAoKCgoKCgpHPdSGR0FBQUFBQeGoh9rwKCgoKCgoKBz1UBseBQUFBQUFhaMeasOjoKCgoKCgcNRDbXgUFBQUFBQUjnqoDY+CgoKCgoLCUQ+14VFQUFBQUFA46qE2PAoKxyCuvPLKPaJCH0xcfvnl+Pd///dJ5b3ssstw5513HuAaKSgoHO1QSssKCkcZNE17y9/fcsstuP7662GaJmKx2MGpFOGVV17BOeecg23btiEUCu01/9q1a/Ge97wHvb29iEajB6GGCgoKRyPUhkdB4SjDwMCAlf7FL36Bm2++GRs2bLCuhUKhSW00DhSuvvpquFwu3HvvvZO+553vfCeuvPJKXHvttQewZgoKCkczFKWloHCUob293fqJRqPQNM12LRQK7UFpLV26FJ/97Gdx3XXXoaGhAW1tbfjv//5vpNNpfPzjH0c4HMacOXPw5z//2fastWvX4sILL0QoFEJbWxsuv/xyjIyM1K2bruv49a9/jYsvvth2/bvf/S7mzp0Ln8+HtrY2/N3f/Z3t9xdffDF+/vOf73vnKCgoHLNQGx4FBQUAwH333Yfm5mY8//zz+OxnP4trrrkGH/rQh3DmmWfi5Zdfxvnnn4/LL78cmUwGABCPx3HOOedg8eLFePHFF/Hggw9icHAQf//3f1/3Ga+++iomJiZw6qmnWtdefPFF/OM//iO+/OUvY8OGDXjwwQfxnve8x3bfaaedhueffx75fP7ANF5BQeGoh9rwKCgoAABOPPFE3HTTTZg7dy6+9KUvwefzobm5GZ/85Ccxd+5c3HzzzRgdHcWrr74KAPjOd76DxYsX49///d8xf/58LF68GD/84Q/xxBNP4M0336z5jG3btsHpdKK1tdW61tfXh2AwiL/5m79BT08PFi9ejH/8x3+03dfZ2YlCoWCj6xQUFBSmArXhUVBQAACccMIJVtrpdKKpqQmLFi2yrrW1tQEAhoaGAJQPHz/xxBPWmaBQKIT58+cDADZv3lzzGdlsFl6v13aw+rzzzkNPTw9mzZqFyy+/HD/96U8tK1IVfr8fAPa4rqCgoDBZqA2PgoICAMDtdtv+r2ma7Vp1k2IYBgAglUrh4osvxpo1a2w/Gzdu3IOSqqK5uRmZTAaFQsG6Fg6H8fLLL+P+++9HR0cHbr75Zpx44omIx+NWnrGxMQBAS0vLfmmrgoLCsQe14VFQUHhbOPnkk7Fu3TrMmDEDc+bMsf0Eg8Ga95x00kkAgPXr19uuu1wuLFu2DHfccQdeffVVbN26FY8//rj1+7Vr16KrqwvNzc0HrD0KCgpHN9SGR0FB4W3h2muvxdjYGD784Q/jhRdewObNm/HQQw/h4x//OHRdr3lPS0sLTj75ZDz99NPWtT/+8Y/49re/jTVr1mDbtm348Y9/DMMwMG/ePCvPX/7yF5x//vkHvE0KCgpHL9SGR0FB4W2hs7MTf/3rX6HrOs4//3wsWrQI1113HWKxGByO+kvL1VdfjZ/+9KfW/2OxGH7729/inHPOwYIFC3Dvvffi/vvvx/HHHw8AyOVy+P3vf49PfvKTB7xNCgoKRy+U8KCCgsJBRTabxbx58/CLX/wCS5Ys2Wv+733ve/jd736Hhx9++CDUTkFB4WiFsvAoKCgcVPj9fvz4xz9+S4FChtvtxn/+538e4FopKCgc7VAWHgUFBQUFBYWjHsrCo6CgoKCgoHDUQ214FBQUFBQUFI56qA2PgoKCgoKCwlEPteFRUFBQUFBQOOqhNjwKCgoKCgoKRz3UhkdBQUFBQUHhqIfa8CgoKCgoKCgc9VAbHgUFBQUFBYWjHmrDo6CgoKCgoHDU4/8P/OTjWYiMzx4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the first audio in the df (again for comparison)\n",
    "\n",
    "audio_data = df['AudioData'].loc[0]\n",
    "sr = df['SampleRates'].loc[0]\n",
    "\n",
    "plot_time_dimension(audio_data, sr)\n",
    "plot_spectrogram(audio_data, sr)\n",
    "plot_freq_dimension(audio_data, sr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "43a80fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all_audios(df) # (again for comparison)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88dcad61",
   "metadata": {},
   "source": [
    "### Duration analysis and normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5cd61ffb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_duration_histogram(df): \n",
    "    \"\"\"Plot histogram of audio tracks duration.\"\"\"\n",
    "    \n",
    "    recList = []\n",
    "    lenList = []\n",
    "\n",
    "    for idx, row in df.iterrows():\n",
    "        rec = row['AudioData']\n",
    "        sr = row['SampleRates']\n",
    "        recList.append(rec)\n",
    "        lenList.append(len(rec)/sr)\n",
    "\n",
    "    plt.xlabel('Seconds')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.grid(True)\n",
    "    hist_array = plt.hist(np.array(lenList), bins='auto', color='#0504aa', alpha=0.7, rwidth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d01ac99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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37Pba+FqdTclfaqVOc/GXOiX/qZU6GzfmhfCpv7Nz8uRJfffdd+rcubP69+8vq9WqrKws5/6cnBzl5ubKbj9zZcJut+vAgQMqLCx09snMzJTNZlNMTEyzzx8AAPger17ZeeSRR3TbbbcpOjpaR44c0Zw5c9S6dWvdeeedCgkJ0eTJk5WamqqOHTvKZrPpoYcekt1u1+DBgyVJcXFxiomJ0d13361FixYpPz9fjz/+uFJSUmq9cgMAAPyPV8POjz/+qDvvvFNHjx7VxRdfrBtvvFE7duzQxRdfLEl64YUX1KpVKyUmJqq8vFzx8fF65ZVXnMe3bt1aa9eu1ZQpU2S329WuXTslJydr/vz53ioJAAD4GK+GnTfeeOO8+4OCgpSWlqa0tLQ6+0RHR2vdunWenhoAADAJn3pmBwAAwNMIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIO34sIeFjb08BAIAmR9gBAACm5lbY+f777z09DwAAgCbhVti57LLLdMstt+j1119XWVmZp+eEZpSQ8DG3swAApuZW2Nm7d6/69Omj1NRURUZG6je/+Y127drl6bkBAAA0mlthp2/fvnrxxRd15MgRrVixQnl5ebrxxht19dVXa/Hixfr55589PU8AAAC3NOoB5TZt2mjs2LFavXq1Fi5cqG+//VaPPPKIunbtqokTJyovL89T8wQAAHBLo8LOp59+qgcffFCdO3fW4sWL9cgjj+i7775TZmamjhw5otGjR3tqngAAAG5p485BixcvVnp6unJycjRy5Ei99tprGjlypFq1OpOdunfvrpUrV+qSSy7x5FwBAAAazK2ws3TpUt17772aNGmSOnfuXGuf8PBwLV++vFGTAwAAaCy3ws7Bgwfr7RMQEKDk5GR3hgcAAPAYt57ZSU9P1+rVq2u0r169WqtWrWr0pAAAADzFrbDz9NNPq1OnTjXaw8PD9dRTTzV6UgAAAJ7iVtjJzc1V9+7da7RHR0crNze30ZMCAADwFLfCTnh4uD7//PMa7Z999pnCwsIaPSkAAABPcSvs3Hnnnfrd736nDz/8UJWVlaqsrNTmzZs1bdo0jR8/3tNzBAAAcJtb78ZasGCB/vWvfyk2NlZt2pwZoqqqShMnTuSZHQAA4FPcCjsBAQF68803tWDBAn322Wdq27atevfurejoaE/PDwAAoFHcCjvVLr/8cl1++eWemgsAAIDHuRV2KisrtXLlSmVlZamwsFBVVVUu+zdv3uyRyQEAADSWWw8oT5s2TdOmTVNlZaWuvvpqXXPNNS5f7njmmWdksVg0ffp0Z1tZWZlSUlIUFham9u3bKzExUQUFBS7H5ebmatSoUQoODlZ4eLgeffRRnT592q05+LqEhI+9PQUAAFoct67svPHGG3rrrbc0cuRIj0xi9+7devXVV9WnTx+X9hkzZuj999/X6tWrFRISoqlTp2rs2LH65JNPJJ25wjRq1ChFRkZq+/btysvL08SJE2W1WnlQGgAASHLzyk5AQIAuu+wyj0zg5MmTmjBhgv7617/qoosucrYXFxdr+fLlWrx4sYYMGaL+/fsrPT1d27dv144dOyRJGzdu1FdffaXXX39dffv21YgRI7RgwQKlpaWpoqLCI/MDAAAtm1tXdh5++GG9+OKL+vOf/yyLxdKoCaSkpGjUqFEaOnSonnzySWf7nj175HA4NHToUGfblVdeqW7duik7O1uDBw9Wdna2evfurYiICGef+Ph4TZkyRV9++aX69etX6znLy8tVXl7u3C4pKZEkORwOORwOl77V2+e2e4PVWuWch9X6v+ekHA6Hy/bZ7ef2ravdl+psav5SK3Wai7/UKflPrdTpubHrYzEMw2jo4GPGjNGHH36ojh076qqrrpLVanXZ//bbb1/QOG+88Yb+9Kc/affu3QoKCtLNN9+svn37asmSJcrIyNA999zjEkokaeDAgbrlllu0cOFCPfDAAzp8+LA2bNjg3F9aWqp27dpp3bp1GjFiRK3nnTt3rubNm1ejPSMjQ8HBwRc0dwAA4F2lpaVKSkpScXGxbDZbnf3curITGhqqMWPGuD05Sfrhhx80bdo0ZWZmKigoqFFjNdSsWbOUmprq3C4pKVHXrl0VFxdX45vlcDiUmZmpYcOG1Qh1zS0pKVsZGXbnv6tlZNhdts9uP7dvXe2rVg3wmTqbmi+taVOiTnPxlzol/6mVOhuv+s5MfdwKO+np6e4c5mLPnj0qLCzUtdde62yrrKzUtm3b9Oc//1kbNmxQRUWFioqKFBoa6uxTUFCgyMhISVJkZKR27drlMm71u7Wq+9QmMDBQgYGBNdqtVmudC3G+fc3F4WjlnIPD8b/HraxWq8v22e3n9q2rvbrNF+psLv5SK3Wai7/UKflPrdTZuDEvhFsPKEvS6dOntWnTJr366qs6ceKEJOnIkSM6efLkBR0fGxurAwcOaP/+/c6vAQMGaMKECc5/W61WZWVlOY/JyclRbm6u7PYzVybsdrsOHDigwsJCZ5/MzEzZbDbFxMS4WxoAADARt67sHD58WMOHD1dubq7Ky8s1bNgwdejQQQsXLlR5ebmWLVtW7xgdOnTQ1Vdf7dLWrl07hYWFOdsnT56s1NRUdezYUTabTQ899JDsdrsGDx4sSYqLi1NMTIzuvvtuLVq0SPn5+Xr88ceVkpJS65UbAADgf9z+o4IDBgzQ8ePH1bZtW2f7mDFjXK7ENNYLL7ygW2+9VYmJibrpppsUGRnp8vBz69attXbtWrVu3Vp2u1133XWXJk6cqPnz53tsDgAAoGVz68rORx99pO3btysgIMCl/ZJLLtFPP/3k9mS2bNnish0UFKS0tDSlpaXVeUx0dLTWrVvn9jkBAIC5uXVlp6qqSpWVlTXaf/zxR3Xo0KHRkwIAAPAUt8JOXFyclixZ4ty2WCw6efKk5syZ47GPkAAAAPAEt25jPf/884qPj1dMTIzKysqUlJSkgwcPqlOnTvr73//u6TkCAAC4za2w06VLF3322Wd644039Pnnn+vkyZOaPHmyJkyY4PLAMgAAgLe5FXYkqU2bNrrrrrs8ORcAAACPcyvsvPbaa+fdP3HiRLcmAwAA4GluhZ1p06a5bDscDpWWliogIEDBwcGEHQAA4DPcejfW8ePHXb5OnjypnJwc3XjjjTygDAAAfIrbn411rp49e+qZZ56pcdUHAADAmzwWdqQzDy0fOXLEk0MCAAA0ilvP7Lz77rsu24ZhKC8vT3/+8591ww03eGRiAAAAnuBW2ElISHDZtlgsuvjiizVkyBA9//zznpgXAACAR7gVdqqqqjw9DwAAgCbh0Wd2AAAAfI1bV3ZSU1MvuO/ixYvdOQUAAIBHuBV29u3bp3379snhcOiKK66QJH3zzTdq3bq1rr32Wmc/i8XimVkCAAC4ya2wc9ttt6lDhw5atWqVLrroIkln/tDgPffco1/+8pd6+OGHPTpJAAAAd7n1zM7zzz+vp59+2hl0JOmiiy7Sk08+ybuxAACAT3Er7JSUlOjnn3+u0f7zzz/rxIkTjZ4UAACAp7gVdsaMGaN77rlHb7/9tn788Uf9+OOP+n//7/9p8uTJGjt2rKfnCAAA4Da3ntlZtmyZHnnkESUlJcnhcJwZqE0bTZ48Wc8++6xHJwgAANAYboWd4OBgvfLKK3r22Wf13XffSZJ69Oihdu3aeXRyAAAAjdWoPyqYl5envLw89ezZU+3atZNhGJ6aFwAAgEe4FXaOHj2q2NhYXX755Ro5cqTy8vIkSZMnT+Zt5wAAwKe4FXZmzJghq9Wq3NxcBQcHO9vHjRun9evXe2xyAAAAjeVW2Nm4caMWLlyoLl26uLT37NlThw8f9sjE4B1JSdnengIAAB7lVtg5deqUyxWdaseOHVNgYGCjJwUAAOApboWdX/7yl3rttdec2xaLRVVVVVq0aJFuueUWj00OAACgsdx66/miRYsUGxurTz/9VBUVFXrsscf05Zdf6tixY/rkk088PUcAAAC3uXVl5+qrr9Y333yjG2+8UaNHj9apU6c0duxY7du3Tz169PD0HAEAANzW4Cs7DodDw4cP17Jly/THP/6xKeYEAADgMQ2+smO1WvX55583xVwAAAA8zq3bWHfddZeWL1/u6bkAAAB4nFsPKJ8+fVorVqzQpk2b1L9//xqfibV48WKPTA4AAKCxGhR2vv/+e11yySX64osvdO2110qSvvnmG5c+FovFc7MDAABopAaFnZ49eyovL08ffvihpDMfD/HSSy8pIiKiSSYHAADQWA16ZufcTzX/4IMPdOrUKY9OCAAAwJPcekC52rnhBwAAwNc0KOxYLJYaz+Q05hmdpUuXqk+fPrLZbLLZbLLb7frggw+c+8vKypSSkqKwsDC1b99eiYmJKigocBkjNzdXo0aNUnBwsMLDw/Xoo4/q9OnTbs8JAACYS4Oe2TEMQ5MmTXJ+2GdZWZl++9vf1ng31ttvv31B43Xp0kXPPPOMevbsKcMwtGrVKo0ePVr79u3TVVddpRkzZuj999/X6tWrFRISoqlTp2rs2LHOj6SorKzUqFGjFBkZqe3btysvL08TJ06U1WrVU0891ZDSAACASTUo7CQnJ7ts33XXXY06+W233eay/ac//UlLly7Vjh071KVLFy1fvlwZGRkaMmSIJCk9PV29evXSjh07NHjwYG3cuFFfffWVNm3apIiICPXt21cLFizQzJkzNXfuXAUEBNR63vLycpWXlzu3S0pKJJ3569AOh8Olb/X2ue3eYLVWOedhtVY52x0Oh8v22e3n9q2r/X9thk/U2pR8aU2bEnWai7/UKflPrdTpubHrYzF85MGbyspKrV69WsnJydq3b5/y8/MVGxur48ePKzQ01NkvOjpa06dP14wZMzR79my9++672r9/v3P/oUOHdOmll2rv3r3q169freeaO3eu5s2bV6M9IyNDwcHBni4NAAA0gdLSUiUlJam4uFg2m63Ofm79UUFPOnDggOx2u8rKytS+fXu98847iomJ0f79+xUQEOASdCQpIiJC+fn5kqT8/Pwab3uv3q7uU5tZs2YpNTXVuV1SUqKuXbsqLi6uxjfL4XAoMzNTw4YNk9VqbUypjZaUlK2MDLvz39UyMuwu22e3n9u3rvZVqwYoMzNTb74ZqlWrrvf43H2JL61pU6JOc/GXOiX/qdVf6kxO3q5x44qapM7qOzP18XrYueKKK7R//34VFxfrH//4h5KTk7V169YmPWdgYKDzuaOzWa3WOhfifPuai8PRSrffvvO/W/97ttxqtcrhqPmsefV8z91XW/v/2ixer7O5+MKaNgfqNBd/qVPyn1rNXqfDceaNTE1R54WO5/WwExAQoMsuu0yS1L9/f+3evVsvvviixo0bp4qKChUVFblc3SkoKFBkZKQkKTIyUrt27XIZr/rdWtV9AACAf2vU39lpClVVVSovL1f//v1ltVqVlZXl3JeTk6Pc3FzZ7Wduw9jtdh04cECFhYXOPpmZmbLZbIqJiWn2uQMAAN/j1Ss7s2bN0ogRI9StWzedOHFCGRkZ2rJlizZs2KCQkBBNnjxZqamp6tixo2w2mx566CHZ7XYNHjxYkhQXF6eYmBjdfffdWrRokfLz8/X4448rJSWl1ttUAADA/3g17BQWFmrixInKy8tTSEiI+vTpow0bNmjYsGGSpBdeeEGtWrVSYmKiysvLFR8fr1deecV5fOvWrbV27VpNmTJFdrtd7dq1U3JysubPn++tkgAAgI/xathZvnz5efcHBQUpLS1NaWlpdfaJjo7WunXrPD01AABgEj73zA4AAIAnEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXZMICHhY29PAQAAn0XYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYgSQpKSnb21MAAKBJEHYAAICpEXYAAICpEXYAAICpEXZQq4SEj5WQ8LG3pwEAQKMRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKl5New8/fTTuu6669ShQweFh4crISFBOTk5Ln3KysqUkpKisLAwtW/fXomJiSooKHDpk5ubq1GjRik4OFjh4eF69NFHdfr06eYsBQAA+Civhp2tW7cqJSVFO3bsUGZmphwOh+Li4nTq1ClnnxkzZui9997T6tWrtXXrVh05ckRjx4517q+srNSoUaNUUVGh7du3a9WqVVq5cqVmz57tjZIAAICPaePNk69fv95le+XKlQoPD9eePXt00003qbi4WMuXL1dGRoaGDBkiSUpPT1evXr20Y8cODR48WBs3btRXX32lTZs2KSIiQn379tWCBQs0c+ZMzZ07VwEBATXOW15ervLycud2SUmJJMnhcMjhcLj0rd4+t90brNaqBvWvnvO5x9XWbrUazv86HA7nPl+o29N8aU2bEnWai7/UKflPrf5SZ/Xvl6ao80LHtBiGYXj87G769ttv1bNnTx04cEBXX321Nm/erNjYWB0/flyhoaHOftHR0Zo+fbpmzJih2bNn691339X+/fud+w8dOqRLL71Ue/fuVb9+/WqcZ+7cuZo3b16N9oyMDAUHBzdFaQAAwMNKS0uVlJSk4uJi2Wy2Ovt59crO2aqqqjR9+nTdcMMNuvrqqyVJ+fn5CggIcAk6khQREaH8/Hxnn4iIiBr7q/fVZtasWUpNTXVul5SUqGvXroqLi6vxzXI4HMrMzNSwYcNktVobVWNjNfSTyTMy7LUeV1u71Wpo3LgivflmqFatut65r7qvmfjSmjYl6jQXf6lT8p9a/aXO5OTtGjeuqEnqrL4zUx+fCTspKSn64osv9PHHTf8RBYGBgQoMDKzRbrVa61yI8+1rLg5Hwx6xqp7vucfV3l5928oiq9Xq3OftmpuSL6xpc6BOc/GXOiX/qdXsdTocFklNU+eFjucTbz2fOnWq1q5dqw8//FBdunRxtkdGRqqiokJFRUUu/QsKChQZGensc+67s6q3q/sAAAD/5dWwYxiGpk6dqnfeeUebN29W9+7dXfb3799fVqtVWVlZzracnBzl5ubKbj9ze8Vut+vAgQMqLCx09snMzJTNZlNMTEzzFAIAAHyWV29jpaSkKCMjQ//85z/VoUMH5zM2ISEhatu2rUJCQjR58mSlpqaqY8eOstlseuihh2S32zV48GBJUlxcnGJiYnT33Xdr0aJFys/P1+OPP66UlJRab1UBAAD/4tWws3TpUknSzTff7NKenp6uSZMmSZJeeOEFtWrVSomJiSovL1d8fLxeeeUVZ9/WrVtr7dq1mjJliux2u9q1a6fk5GTNnz+/ucoAAAA+zKth50Le9R4UFKS0tDSlpaXV2Sc6Olrr1q3z5NQAAIBJ+MQDygAAAE2FsAMAAEyNsIPzSkho+r97BABAUyLsAAAAUyPs+DiurAAA0DiEHQAAYGqEHQAAYGo+80GgcOVLt6+q57JmzY1engkAAA3HlR0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB24JSHhYyUkfOztaQAAUC/CDgAAMDXCDgAAMDXCDhqM21cAgJaEsAMAAEyNsAMAAEzNq2Fn27Ztuu222xQVFSWLxaI1a9a47DcMQ7Nnz1bnzp3Vtm1bDR06VAcPHnTpc+zYMU2YMEE2m02hoaGaPHmyTp482YxVAAAAX+bVsHPq1Cldc801SktLq3X/okWL9NJLL2nZsmXauXOn2rVrp/j4eJWVlTn7TJgwQV9++aUyMzO1du1abdu2TQ888EBzlQAAAHxcG2+efMSIERoxYkSt+wzD0JIlS/T4449r9OjRkqTXXntNERERWrNmjcaPH6+vv/5a69ev1+7duzVgwABJ0ssvv6yRI0fqueeeU1RUVLPVAgAAfJNXw875HDp0SPn5+Ro6dKizLSQkRIMGDVJ2drbGjx+v7OxshYaGOoOOJA0dOlStWrXSzp07NWbMmFrHLi8vV3l5uXO7pKREkuRwOORwOFz6Vm+f297UrNYq579vv33bf9saNkb1nM8eq652q9Vw/tfhcNQ4pq7jmvv74gneWtPmRp3m4i91Sv5Tq7/UWf37pSnqvNAxLYZhGB4/uxssFoveeecdJSQkSJK2b9+uG264QUeOHFHnzp2d/e644w5ZLBa9+eabeuqpp7Rq1Srl5OS4jBUeHq558+ZpypQptZ5r7ty5mjdvXo32jIwMBQcHe64oAADQZEpLS5WUlKTi4mLZbLY6+/nslZ2mNGvWLKWmpjq3S0pK1LVrV8XFxdX4ZjkcDmVmZmrYsGGyNvTSSiMkJWU3eoyMDHutY9XWbrUaGjeuSG++GapVq66v9fy1HVfd1pJ4a02bG3Wai7/UKflPrf5SZ3Lydo0bV9QkdVbfmamPz4adyMhISVJBQYHLlZ2CggL17dvX2aewsNDluNOnT+vYsWPO42sTGBiowMDAGu1Wq7XOhTjfvqbgcDT+2fHq+Z47Vu3tVf9ts8hqtdZ6/tqOs1qtzj8yuGbNjY2ec3Nq7jX1Fuo0F3+pU/KfWs1ep8NhkdQ0dV7oeD77d3a6d++uyMhIZWVlOdtKSkq0c+dO2e1nribY7XYVFRVpz549zj6bN29WVVWVBg0a1OxzBgAAvserV3ZOnjypb7/91rl96NAh7d+/Xx07dlS3bt00ffp0Pfnkk+rZs6e6d++uJ554QlFRUc7nenr16qXhw4fr/vvv17Jly+RwODR16lSNHz+ed2IBAABJXg47n376qW655RbndvVzNMnJyVq5cqUee+wxnTp1Sg888ICKiop04403av369QoKCnIe87e//U1Tp05VbGysWrVqpcTERL300kvNXosn8dlTAAB4jlfDzs0336zzvRnMYrFo/vz5mj9/fp19OnbsqIyMjKaYHi4AwQwA4Ot89pkdAAAATyDsAAAAUyPsAAAAUyPsAAAAUyPsoEnw4DIAwFcQdgAAgKkRdlADV2UAAGZC2AEAAKZG2AEAAKbms596jpaJW2AAAF/DlR0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0fkJDwMe9iAgCgiRB2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2vIx3YQEA0LQIOwAAwNQIOwAAwNQIO2gW/OFEAIC3tPH2BNByEFYAAC0RV3bQrAhMAIDmRthBszvfLS3CEADA0wg7aDI8pwMA8AWEHQAAYGo8oOwlXPEAAKB5cGUHAACYGmEHAACYGmEHXsctPQBAUyLsAAAAU+MBZfiE+v7uzpo1NzbndAAAJkLY8SHczgEAwPO4jeUFhJrza8gfI+R7CQCoj2nCTlpami655BIFBQVp0KBB2rVrl7enhP/yRCAh1AAA3GWK21hvvvmmUlNTtWzZMg0aNEhLlixRfHy8cnJyFB4e7u3poQ4NDTBnP79z9rHnjsPzPQCAs5niys7ixYt1//3365577lFMTIyWLVum4OBgrVixwttTc96S4cqE9/D9BwD/1uKv7FRUVGjPnj2aNWuWs61Vq1YaOnSosrOzaz2mvLxc5eXlzu3i4mJJ0rFjx+RwOFz6OhwOlZaW6ujRo7JarW7M8ITzX0ePHq3R1pQSEj5oQLuh0tJSSVZJFrfOd6Y+z9VW13j1nae+uq1WQ2PHliopKVMOx5laly8fKEmaPLnm7c/qfbWp7n++Puc77txznz3OhY5d13HLlvVz62f33PEa6+zxPD225InXaMvgqTqbYg08jTU1m5NNVueJE2d+FxiGcf6ORgv3008/GZKM7du3u7Q/+uijxsCBA2s9Zs6cOYYkvvjiiy+++OLLBF8//PDDebNCi7+y445Zs2YpNTXVuV1VVaVjx44pLCxMFovrVY2SkhJ17dpVP/zwg2w2W3NPtdn4S52S/9RKnebiL3VK/lMrdTaeYRg6ceKEoqKiztuvxYedTp06qXXr1iooKHBpLygoUGRkZK3HBAYGKjAw0KUtNDT0vOex2Wym/mGs5i91Sv5TK3Wai7/UKflPrdTZOCEhIfX2afEPKAcEBKh///7KyspytlVVVSkrK0t2u92LMwMAAL6gxV/ZkaTU1FQlJydrwIABGjhwoJYsWaJTp07pnnvu8fbUAACAl5ki7IwbN04///yzZs+erfz8fPXt21fr169XREREo8cODAzUnDlzatz2Mht/qVPyn1qp01z8pU7Jf2qlzuZjMYz63q8FAADQcrX4Z3YAAADOh7ADAABMjbADAABMjbADAABMjbAjKS0tTZdccomCgoI0aNAg7dpV8/ORzrZ69WpdeeWVCgoKUu/evbVu3bpmmql7nn76aV133XXq0KGDwsPDlZCQoJycnPMes3LlSlksFpevoKCgZpqx++bOnVtj3ldeeeV5j2lp6ylJl1xySY06LRaLUlJSau3fUtZz27Ztuu222xQVFSWLxaI1a9a47DcMQ7Nnz1bnzp3Vtm1bDR06VAcPHqx33Ia+xpvD+Wp1OByaOXOmevfurXbt2ikqKkoTJ07UkSNHzjumOz//Ta2+NZ00aVKNOQ8fPrzecX1tTeurs7bXq8Vi0bPPPlvnmL64nhfy+6SsrEwpKSkKCwtT+/btlZiYWOMP/57L3df2hfL7sPPmm28qNTVVc+bM0d69e3XNNdcoPj5ehYWFtfbfvn277rzzTk2ePFn79u1TQkKCEhIS9MUXXzTzzC/c1q1blZKSoh07digzM1MOh0NxcXE6derUeY+z2WzKy8tzfh0+fLiZZtw4V111lcu8P/647k88b4nrKUm7d+92qTEzM1OSdPvtt9d5TEtYz1OnTumaa65RWlparfsXLVqkl156ScuWLdPOnTvVrl07xcfHq6ysrM4xG/oaby7nq7W0tFR79+7VE088ob179+rtt99WTk6Ofv3rX9c7bkN+/ptDfWsqScOHD3eZ89///vfzjumLa1pfnWfXl5eXpxUrVshisSgxMfG84/rael7I75MZM2bovffe0+rVq7V161YdOXJEY8eOPe+47ry2G8QTH8bZkg0cONBISUlxbldWVhpRUVHG008/XWv/O+64wxg1apRL26BBg4zf/OY3TTpPTyosLDQkGVu3bq2zT3p6uhESEtJ8k/KQOXPmGNdcc80F9zfDehqGYUybNs3o0aOHUVVVVev+lriekox33nnHuV1VVWVERkYazz77rLOtqKjICAwMNP7+97/XOU5DX+PecG6ttdm1a5chyTh8+HCdfRr689/caqszOTnZGD16dIPG8fU1vZD1HD16tDFkyJDz9vH19TSMmr9PioqKDKvVaqxevdrZ5+uvvzYkGdnZ2bWO4e5ruyH8+spORUWF9uzZo6FDhzrbWrVqpaFDhyo7O7vWY7Kzs136S1J8fHyd/X1RcXGxJKljx47n7Xfy5ElFR0era9euGj16tL788svmmF6jHTx4UFFRUbr00ks1YcIE5ebm1tnXDOtZUVGh119/Xffee2+ND7I9W0tdz2qHDh1Sfn6+y3qFhIRo0KBBda6XO69xX1VcXCyLxVLv5/g15OffV2zZskXh4eG64oorNGXKFB09erTOvmZY04KCAr3//vuaPHlyvX19fT3P/X2yZ88eORwOl/W58sor1a1btzrXx53XdkP5ddj597//rcrKyhp/aTkiIkL5+fm1HpOfn9+g/r6mqqpK06dP1w033KCrr766zn5XXHGFVqxYoX/+8596/fXXVVVVpeuvv14//vhjM8624QYNGqSVK1dq/fr1Wrp0qQ4dOqRf/vKXOnHiRK39W/p6StKaNWtUVFSkSZMm1dmnpa7n2arXpCHr5c5r3BeVlZVp5syZuvPOO8/7QYoN/fn3BcOHD9drr72mrKwsLVy4UFu3btWIESNUWVlZa38zrOmqVavUoUOHem/t+Pp61vb7JD8/XwEBATVCeX2/V6v7XOgxDWWKj4vAhUtJSdEXX3xR731fu93u8kGq119/vXr16qVXX31VCxYsaOppum3EiBHOf/fp00eDBg1SdHS03nrrrQv6v6iWaPny5RoxYoSioqLq7NNS1xNnHla+4447ZBiGli5det6+LfHnf/z48c5/9+7dW3369FGPHj20ZcsWxcbGenFmTWfFihWaMGFCvW8S8PX1vNDfJ77Ar6/sdOrUSa1bt67xlHhBQYEiIyNrPSYyMrJB/X3J1KlTtXbtWn344Yfq0qVLg461Wq3q16+fvv322yaaXdMIDQ3V5ZdfXue8W/J6StLhw4e1adMm3XfffQ06riWuZ/WaNGS93HmN+5LqoHP48GFlZmae96pOber7+fdFl156qTp16lTnnFv6mn700UfKyclp8GtW8q31rOv3SWRkpCoqKlRUVOTSv77fq9V9LvSYhvLrsBMQEKD+/fsrKyvL2VZVVaWsrCyX/ws+m91ud+kvSZmZmXX29wWGYWjq1Kl65513tHnzZnXv3r3BY1RWVurAgQPq3LlzE8yw6Zw8eVLfffddnfNuiet5tvT0dIWHh2vUqFENOq4lrmf37t0VGRnpsl4lJSXauXNnnevlzmvcV1QHnYMHD2rTpk0KCwtr8Bj1/fz7oh9//FFHjx6tc84teU2lM1di+/fvr2uuuabBx/rCetb3+6R///6yWq0u65OTk6Pc3Nw618ed17Y7E/drb7zxhhEYGGisXLnS+Oqrr4wHHnjACA0NNfLz8w3DMIy7777b+P3vf+/s/8knnxht2rQxnnvuOePrr7825syZY1itVuPAgQPeKqFeU6ZMMUJCQowtW7YYeXl5zq/S0lJnn3PrnDdvnrFhwwbju+++M/bs2WOMHz/eCAoKMr788ktvlHDBHn74YWPLli3GoUOHjE8++cQYOnSo0alTJ6OwsNAwDHOsZ7XKykqjW7duxsyZM2vsa6nreeLECWPfvn3Gvn37DEnG4sWLjX379jnfgfTMM88YoaGhxj//+U/j888/N0aPHm10797d+M9//uMcY8iQIcbLL7/s3K7vNe4t56u1oqLC+PWvf2106dLF2L9/v8vrtry83DnGubXW9/PvDeer88SJE8YjjzxiZGdnG4cOHTI2bdpkXHvttUbPnj2NsrIy5xgtYU3r+9k1DMMoLi42goODjaVLl9Y6RktYzwv5ffLb3/7W6Natm7F582bj008/Nex2u2G3213GueKKK4y3337buX0hr+3G8PuwYxiG8fLLLxvdunUzAgICjIEDBxo7duxw7vvVr35lJCcnu/R/6623jMsvv9wICAgwrrrqKuP9999v5hk3jKRav9LT0519zq1z+vTpzu9JRESEMXLkSGPv3r3NP/kGGjdunNG5c2cjICDA+MUvfmGMGzfO+Pbbb537zbCe1TZs2GBIMnJycmrsa6nr+eGHH9b6s1pdS1VVlfHEE08YERERRmBgoBEbG1uj/ujoaGPOnDkubed7jXvL+Wo9dOhQna/bDz/80DnGubXW9/PvDeers7S01IiLizMuvvhiw2q1GtHR0cb9999fI7S0hDWt72fXMAzj1VdfNdq2bWsUFRXVOkZLWM8L+X3yn//8x3jwwQeNiy66yAgODjbGjBlj5OXl1Rjn7GMu5LXdGJb/nhQAAMCU/PqZHQAAYH6EHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQC4QJMmTVJCQoK3pwGggQg7AJrdzz//rClTpqhbt24KDAxUZGSk4uPj9cknn3h7agBMqI23JwDA/yQmJqqiokKrVq3SpZdeqoKCAmVlZeno0aPenhoAE+LKDoBmVVRUpI8++kgLFy7ULbfcoujoaA0cOFCzZs3Sr3/9a2ef++67TxdffLFsNpuGDBmizz77zGWc9957T9ddd52CgoLUqVMnjRkzxrnv+PHjmjhxoi666CIFBwdrxIgROnjwoHP/ypUrFRoaqg0bNqhXr15q3769hg8frry8PGefyspKpaamKjQ0VGFhYXrsscd07kcJ/uMf/1Dv3r3Vtm1bhYWFaejQoTp16lRTfNsANAJhB0Czat++vdq3b681a9aovLy81j633367CgsL9cEHH2jPnj269tprFRsbq2PHjkmS3n//fY0ZM0YjR47Uvn37lJWVpYEDBzqPnzRpkj799FO9++67ys7OlmEYGjlypBwOh7NPaWmpnnvuOf3f//2ftm3bptzcXD3yyCPO/c8//7xWrlypFStW6OOPP9axY8f0zjvvOPfn5eXpzjvv1L333quvv/5aW7Zs0dixY2sEIgA+wGOfnw4AF+gf//iHcdFFFxlBQUHG9ddfb8yaNcv47LPPDMMwjI8++siw2WxGWVmZyzE9evQwXn31VcMwDMNutxsTJkyodexvvvnGkGR88sknzrZ///vfRtu2bY233nrLMAzDSE9PNyQZ3377rbNPWlqaERER4dzu3LmzsWjRIue2w+EwunTpYowePdowDMPYs2ePIcn417/+1YjvBIDmwJUdAM0uMTFRR44c0bvvvqvhw4dry5Ytuvbaa7Vy5Up99tlnOnnypMLCwpxXgdq3b69Dhw7pu+++kyTt379fsbGxtY799ddfq02bNho0aJCzLSwsTFdccYW+/vprZ1twcLB69Ojh3O7cubMKCwslScXFxcrLy3MZo02bNhowYIBz+5prrlFsbKx69+6t22+/XX/96191/Phxz3yDAHgUDygD8IqgoCANGzZMw4YN0xNPPKH77rtPc+bM0YMPPqjOnTtry5YtNY4JDQ2VJLVt27bR57darS7bFoulQbegWrdurczMTG3fvl0bN27Uyy+/rD/+8Y/auXOnunfv3uj5AfAcruwA8AkxMTE6deqUrr32WuXn56tNmza67LLLXL46deokSerTp4+ysrJqHadXr146ffq0du7c6Ww7evSocnJyFBMTc0FzCQkJUefOnV3GOH36tPbs2ePSz2Kx6IYbbtC8efO0b98+BQQEuDzXA8A3cGUHQLM6evSobr/9dt17773q06ePOnTooE8//VSLFi3S6NGjNXToUNntdiUkJGjRokW6/PLLdeTIEedDyQMGDNCcOXMUGxurHj16aPz48Tp9+rTWrVunmTNnqmfPnho9erTuv/9+vfrqq+rQoYN+//vf6xe/+IVGjx59wfOcNm2annnmGfXs2VNXXnmlFi9erKKiIuf+nTt3KisrS3FxcQoPD9fOnTv1888/q1evXk3wXQPQGIQdAM2qffv2GjRokF544QV99913cjgc6tq1q+6//3794Q9/kMVi0bp16/THP/5R99xzj37++WdFRkbqpptuUkREhCTp5ptv1urVq7VgwQI988wzstlsuummm5znSE9P17Rp03TrrbeqoqJCN910k9atW1fj1tX5PPzww8rLy1NycrJatWqle++9V2PGjFFxcbEkyWazadu2bVqyZIlKSkoUHR2t559/XiNGjPDsNwxAo1mMhtykBgAAaGF4ZgcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJgaYQcAAJja/wcQ/Ypqr5Ru9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_duration_histogram(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "274308df",
   "metadata": {},
   "outputs": [],
   "source": [
    "def standardise_length(df, seconds):\n",
    "    \"\"\"Trim all audio tracks to fixed length seconds. \"\"\"\n",
    "\n",
    "    recListAdj = []\n",
    "    lenListAdj = []\n",
    "\n",
    "    for idx, row in df.iterrows():\n",
    "        rec = row['AudioData']\n",
    "        sr = row['SampleRates']\n",
    "        rec, index = librosa.effects.trim(rec, top_db=30)\n",
    "        threshold = sr * seconds \n",
    "        if len(rec) > threshold:\n",
    "            rec = rec[:threshold]\n",
    "        recListAdj.append(rec)\n",
    "        lenListAdj.append(len(rec)/sr)\n",
    "    df['AudioData'] = recListAdj\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "829ba746",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = standardise_length(df, 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fe22d667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_duration_histogram(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73229156",
   "metadata": {},
   "source": [
    "### Spectrogram splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f9d560f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def split_spectrogram(audio, sr, n_blocks):\n",
    "    \"\"\"Split an audio track's spectrogra, in (n_blocks x n_blocks) equal blocks; \n",
    "    returns a list of all the blocks means, stdev, and the list of blocks themselves.\"\"\"\n",
    "    \n",
    "    frequencies, times, spec = spectrogram(audio, fs=sr)\n",
    "    times = (times * 1000).astype(int) #milliseconds\n",
    "    frequencies = frequencies.astype(int)\n",
    "        \n",
    "    \n",
    "    # Split the matrix into sub-arrays of size N along the first axis (rows)\n",
    "    rows_blocks = np.array_split(spec, n_blocks, axis=0)\n",
    "    #this is a list: each element of the list is a sub-matrix obtained slicing horizontally the spectrogram\n",
    "    # into n_blocks slices\n",
    "\n",
    "    # Split the sub_matrices into sub-arrays of size NxN along the second axis (columns)\n",
    "    blocks = [ np.array_split(block, n_blocks, axis=1) for block in rows_blocks]\n",
    "    #This is, instead, a list of lists: the first element is a list containing all the blocks obtained from \n",
    "    #the first vertical slice\n",
    "\n",
    "\n",
    "    # Initialize arrays to store the mean and standard deviation of each block\n",
    "    block_means = np.zeros((n_blocks,n_blocks))\n",
    "    block_stds = np.zeros((n_blocks,n_blocks))\n",
    "\n",
    "    # Iterate over blocks\n",
    "    for i in range(0, n_blocks):\n",
    "        for j in range(0, n_blocks):\n",
    "            # Compute mean and standard deviation of the block\n",
    "             #i is the index of the horizontal split  (indicating which column)\n",
    "            block = blocks[i][j] #j is the index of the vertical split (indicating which row)\n",
    "            mean = np.mean(block)\n",
    "            std = np.std(block)\n",
    "            block_means[i,j] = mean\n",
    "            block_stds[i,j] = std\n",
    "    return block_means.tolist(), block_stds.tolist(), blocks\n",
    "\n",
    "def plot_split_spectrogram(audio, sr, n_blocks):\n",
    "    \"\"\"Plot the audio track's split spectrogram (n_blocks x n_blocks).\"\"\"\n",
    "    \n",
    "    frequencies, times, spec = spectrogram(audio, fs=sr)\n",
    "    times = (times * 1000).astype(int) #milliseconds\n",
    "    frequencies = frequencies.astype(int)\n",
    "    fig, ax = plt.subplots(1,1)\n",
    "    plt.pcolormesh(times, frequencies, 10*np.log10(spec)) #changed the scale to dB\n",
    "    plt.colorbar()\n",
    "    time_bounds = list()\n",
    "    freq_bounds = list()\n",
    "    \n",
    "    #n_blocks is the same vertically and horizontally\n",
    "    #starting from vertical lines to create time bins:\n",
    "    v_step = int(times[len(times)-1] / n_blocks)\n",
    "    for v in range(0, times[len(times)-1], v_step):\n",
    "        ax.axvline(v, color = 'red')\n",
    "        time_bounds.append(v)\n",
    "        \n",
    "    #and then horizontal lines to create frequency bins:\n",
    "    h_step = int(frequencies[len(frequencies)-1]/n_blocks)\n",
    "    for h in range(0, frequencies[len(frequencies)-1], h_step):\n",
    "        ax.axhline(h, color = 'red')\n",
    "        freq_bounds.append(h)\n",
    "    plt.xlabel(\"Time (ms)\")\n",
    "    plt.xlim(left=0)\n",
    "    plt.ylabel(\"Frequency (Hz)\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3cb576cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding the statistics of the blocks into the dataframe\n",
    "\n",
    "def add_stats_df(df,sr,n_blocks):\n",
    "    \"\"\"Perform spectrogram splitting from scratch and add the blocks statistics to the DataFrame df.\"\"\"\n",
    "    \n",
    "    for i in range(0,n_blocks):\n",
    "        for j in range(0, n_blocks):\n",
    "            idx = j+n_blocks*i\n",
    "            col_name_mean = 'block_mean_'+str(idx)\n",
    "            col_name_dev = 'block_dev_'+str(idx)\n",
    "            df[col_name_mean] = ''\n",
    "            df[col_name_dev] = ''\n",
    "    all_means = list()\n",
    "    all_std_devs = list()\n",
    "\n",
    "\n",
    "    for index, row in df.iterrows():\n",
    "        audio = row['AudioData']\n",
    "        meanTemp, stdTemp, blocksTemps = split_spectrogram(audio, sr, n_blocks)\n",
    "        for i in range(0,n_blocks):\n",
    "            for j in range(0, n_blocks):\n",
    "                idx = j+n_blocks*i\n",
    "                col_name_mean = 'block_mean_'+str(idx)\n",
    "                col_name_dev = 'block_dev_'+str(idx)\n",
    "                df.loc[index, col_name_mean] = meanTemp[i][j]\n",
    "                df.loc[index, col_name_dev] = stdTemp[i][j]\n",
    "        \n",
    "   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d6477a",
   "metadata": {},
   "source": [
    "## Training setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c71117c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create dictionary to map the labels (str) to integers\n",
    "\n",
    "label_dictionary = {'increase volume': 0, 'decrease volume': 1, 'increase heat': 2, 'decrease heat':3, 'change language none':4,'activate music':5, 'deactivate lights':6 }\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "176bb14e",
   "metadata": {},
   "source": [
    "### n_blocks parameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5bdd28f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/k1/d0rvjnq127s1n3prqzn3314m0000gn/T/ipykernel_51786/2416293282.py:9: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  y = y.replace(label_dictionary)\n",
      "/var/folders/k1/d0rvjnq127s1n3prqzn3314m0000gn/T/ipykernel_51786/2416293282.py:9: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  y = y.replace(label_dictionary)\n",
      "/var/folders/k1/d0rvjnq127s1n3prqzn3314m0000gn/T/ipykernel_51786/2416293282.py:9: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  y = y.replace(label_dictionary)\n",
      "/var/folders/k1/d0rvjnq127s1n3prqzn3314m0000gn/T/ipykernel_51786/2416293282.py:9: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  y = y.replace(label_dictionary)\n",
      "/var/folders/k1/d0rvjnq127s1n3prqzn3314m0000gn/T/ipykernel_51786/2416293282.py:9: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  y = y.replace(label_dictionary)\n",
      "/var/folders/k1/d0rvjnq127s1n3prqzn3314m0000gn/T/ipykernel_51786/2416293282.py:9: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  y = y.replace(label_dictionary)\n",
      "/var/folders/k1/d0rvjnq127s1n3prqzn3314m0000gn/T/ipykernel_51786/2416293282.py:9: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  y = y.replace(label_dictionary)\n"
     ]
    }
   ],
   "source": [
    "df0 = df.copy()\n",
    "\n",
    "# produce different split spectrograms to compare results with varying n_blocks\n",
    "\n",
    "acc_basic_rf = list()\n",
    "for n in range(2,16,2):\n",
    "    add_stats_df(df0, 16000, n)\n",
    "    y = df0['action'] + ' ' + df0['object']\n",
    "    y = y.replace(label_dictionary)\n",
    "    X = df0.drop(['action', 'object', 'path', 'speakerId', 'AudioData'], axis = 1) \n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=31)\n",
    "    rf_basic = RandomForestClassifier()\n",
    "    rf_basic.fit(X_train,y_train)\n",
    "    acc_rf = accuracy_score(y_test, rf_basic.predict(X_test))\n",
    "    acc_basic_rf.append(acc_rf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9132264a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot varying n_blocks results\n",
    "\n",
    "fig, ax = plt.subplots(1,1)\n",
    "ax.plot(range(2,16,2), acc_basic_rf)\n",
    "plt.xlabel(\"n\")\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54a3163f",
   "metadata": {},
   "source": [
    "### Random forest's hyperparameters tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ba4b2f1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# random forest hyperparameters possible values:\n",
    "RF_params = {\n",
    "    'n_estimators' : [50, 100, 200, 300],\n",
    "    'criterion' : ['gini', 'entropy'],\n",
    "    'max_depth' : [10, 20, 50, 100, None],\n",
    "    'min_samples_split': [2, 5, 10, 20],\n",
    "    'min_samples_leaf': [1, 2, 5, 10]\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "41d68170",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/k1/d0rvjnq127s1n3prqzn3314m0000gn/T/ipykernel_51786/1156405708.py:7: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  y = y.replace(label_dictionary)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 300}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 300}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 100}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 300}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 20, 'n_estimators': 50}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 20, 'n_estimators': 100}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 20, 'n_estimators': 200}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 20, 'n_estimators': 300}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 300}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 300}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 100}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 300}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 20, 'n_estimators': 50}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 20, 'n_estimators': 100}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 20, 'n_estimators': 200}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 20, 'n_estimators': 300}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 5, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 5, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 5, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 5, 'min_samples_split': 2, 'n_estimators': 300}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 5, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 5, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 5, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 5, 'min_samples_split': 5, 'n_estimators': 300}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 5, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 5, 'min_samples_split': 10, 'n_estimators': 100}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 5, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "{'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 5, 'min_samples_split': 10, 'n_estimators': 300}\n",
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      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 5, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 5, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 5, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 5, 'min_samples_split': 5, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 5, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 5, 'min_samples_split': 10, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 5, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 5, 'min_samples_split': 10, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 5, 'min_samples_split': 20, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 5, 'min_samples_split': 20, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 5, 'min_samples_split': 20, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 5, 'min_samples_split': 20, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 5, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 10, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 10, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 20, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 20, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 20, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': 100, 'min_samples_leaf': 10, 'min_samples_split': 20, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 20, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 20, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 20, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 20, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 20, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 20, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 20, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 20, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 2, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 5, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 10, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 10, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 20, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 20, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 20, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 5, 'min_samples_split': 20, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 2, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 5, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 10, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 10, 'n_estimators': 300}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 20, 'n_estimators': 50}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 20, 'n_estimators': 100}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 20, 'n_estimators': 200}\n",
      "{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 20, 'n_estimators': 300}\n",
      "The best accuracy for  14  blocks is  0.7057331303906647  obtained with the configuration  {'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 300}\n"
     ]
    }
   ],
   "source": [
    "df1 = df.copy() #working with a new copy of the original df\n",
    "\n",
    "n=14 #experimentally (from ^)\n",
    "\n",
    "add_stats_df(df1, 16000, n)\n",
    "y = df1['action'] + ' ' + df1['object']\n",
    "y = y.replace(label_dictionary)\n",
    "X = df1.drop(['action', 'object', 'path', 'speakerId', 'AudioData'], axis = 1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=31)\n",
    "accuracies_rf = list()\n",
    "configs_rf = list()\n",
    "for config in ParameterGrid(RF_params):\n",
    "    print(config)\n",
    "    rf = RandomForestClassifier(**config)\n",
    "    rf.fit(X_train, y_train)\n",
    "    acc = accuracy_score(y_test, rf.predict(X_test))\n",
    "    accuracies_rf.append(acc)\n",
    "    configs_rf.append(config)\n",
    "max_val = max(accuracies_rf)\n",
    "max_idx = accuracies_rf.index(max_val)\n",
    "best_conf = configs_rf[max_idx]\n",
    "print('The best accuracy for ', n, ' blocks is ', max_val , ' obtained with the configuration ', best_conf)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e8ef3cb",
   "metadata": {},
   "source": [
    "### Load and preprocess test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "532d9b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** DATAFRAME INFO *****\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1455 entries, 0 to 1454\n",
      "Data columns (total 8 columns):\n",
      " #   Column                                 Non-Null Count  Dtype \n",
      "---  ------                                 --------------  ----- \n",
      " 0   Id                                     1455 non-null   int64 \n",
      " 1   path                                   1455 non-null   object\n",
      " 2   speakerId                              1455 non-null   object\n",
      " 3   Self-reported fluency level            1455 non-null   object\n",
      " 4   First Language spoken                  1455 non-null   object\n",
      " 5   Current language used for work/school  1455 non-null   object\n",
      " 6   gender                                 1455 non-null   object\n",
      " 7   ageRange                               1455 non-null   object\n",
      "dtypes: int64(1), object(7)\n",
      "memory usage: 91.1+ KB\n",
      "None\n",
      "\n",
      "\n",
      "***** HEAD *****\n",
      "\n",
      "   Id                                               path          speakerId  \\\n",
      "0   0  dsl_data/audio/speakers/NgQEvO2x7Vh3xy2xz/f53c...  NgQEvO2x7Vh3xy2xz   \n",
      "1   1  dsl_data/audio/speakers/k5bqyxx2lzIbrlg9/1d5f8...   k5bqyxx2lzIbrlg9   \n",
      "2   2  dsl_data/audio/speakers/7B4XmNppyrCK977p/1c0d5...   7B4XmNppyrCK977p   \n",
      "3   3  dsl_data/audio/speakers/k5bqyxx2lzIbrlg9/275c3...   k5bqyxx2lzIbrlg9   \n",
      "4   4  dsl_data/audio/speakers/V4ZbwLm9G5irobWn/b7c7a...   V4ZbwLm9G5irobWn   \n",
      "\n",
      "  Self-reported fluency level     First Language spoken  \\\n",
      "0                       native  English (United States)   \n",
      "1                       native  English (United States)   \n",
      "2                       native  English (United States)   \n",
      "3                       native  English (United States)   \n",
      "4                       native  English (United States)   \n",
      "\n",
      "  Current language used for work/school  gender ageRange  \n",
      "0               English (United States)    male    22-40  \n",
      "1               English (United States)    male    22-40  \n",
      "2               English (United States)    male    41-65  \n",
      "3               English (United States)    male    22-40  \n",
      "4               English (United States)  female    41-65  \n",
      "\n",
      "\n",
      " ***** SAMPLE RATES *****\n",
      "SampleRates\n",
      "16000    1455\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      " ***** Self-reported fluency level *****\n",
      "Self-reported fluency level \n",
      "native    1455\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      " ***** First Language spoken *****\n",
      "First Language spoken\n",
      "English (United States)    1455\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      " ***** Current language used for work/school *****\n",
      "Current language used for work/school\n",
      "English (United States)    1455\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      " ***** Gender *****\n",
      "gender\n",
      "female    773\n",
      "male      682\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      " ***** Age range *****\n",
      "ageRange\n",
      "22-40    907\n",
      "41-65    548\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "eval_df = load_dataframe(eval_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "68b19a07",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/k1/d0rvjnq127s1n3prqzn3314m0000gn/T/ipykernel_51786/609327417.py:5: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  y = y.replace(label_dictionary)\n"
     ]
    }
   ],
   "source": [
    "# X, y to train the classifiers\n",
    "\n",
    "add_stats_df(df, 16000, 14)\n",
    "y = df['action'] + ' ' + df['object']\n",
    "y = y.replace(label_dictionary)\n",
    "X = df.drop(['action', 'object', 'path', 'speakerId', 'AudioData', 'SampleRates'], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "decc4529",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample rates after resampling:\n",
      " SampleRates\n",
      "16000    1455\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/k1/d0rvjnq127s1n3prqzn3314m0000gn/T/ipykernel_51786/4153402632.py:93: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df[feat] = df[feat].replace(dic)\n"
     ]
    }
   ],
   "source": [
    "# eval_df must have the same features of the df used for the training of the classifiers\n",
    "\n",
    "target_rate = 16000\n",
    "edit_sample_rates(eval_df, target_rate) #bring all srs to 16kHz\n",
    "eval_df = normalise_audios(eval_df)\n",
    "eval_df = standardise_length(eval_df, 3)\n",
    "\n",
    "add_stats_df(eval_df, target_rate, 14)\n",
    "\n",
    "eval_X = eval_df.drop(['path', 'speakerId', 'AudioData', 'SampleRates'], axis = 1)\n",
    "\n",
    "eval_X = clean_df(eval_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e0e259f",
   "metadata": {},
   "source": [
    "## Train classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "18e7ac59",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(criterion='entropy', max_depth=100,min_samples_leaf=1, min_samples_split=5, n_estimators=300)\n",
    "rf.fit(X, y)\n",
    "eval_y_rf = rf.predict(eval_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41dfe05f",
   "metadata": {},
   "source": [
    "## Predict labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9960062a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/k1/d0rvjnq127s1n3prqzn3314m0000gn/T/ipykernel_51786/3007936967.py:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  solution_rf['Predicted'].replace(inverse_dict, inplace=True)\n"
     ]
    },
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>decreasevolume</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>increaseheat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>increaseheat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>deactivatelights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>decreasevolume</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted\n",
       "0    decreasevolume\n",
       "1      increaseheat\n",
       "2      increaseheat\n",
       "3  deactivatelights\n",
       "4    decreasevolume"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inverse_dict = {0: 'increasevolume', 1: 'decreasevolume',2: 'increaseheat',3: 'decreaseheat',4: 'changelanguagenone',5: 'activatemusic',6: 'deactivatelights'}\n",
    "\n",
    "solution_rf = pd.DataFrame(eval_y_rf, index = eval_X.index, columns = ['Predicted'])\n",
    "solution_rf['Predicted'].replace(inverse_dict, inplace=True)\n",
    "\n",
    "solution_rf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "64a03637",
   "metadata": {},
   "outputs": [],
   "source": [
    "solution_rf.to_csv('draft_solution_rf.csv', sep=',', index_label='Id')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1ae6f9d",
   "metadata": {},
   "source": [
    "## Baseline comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbad8ced",
   "metadata": {},
   "source": [
    "### Naive (not-tuned, default) Random Forest classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b13143a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_naive = RandomForestClassifier()\n",
    "rf_naive.fit(X, y)\n",
    "eval_y_rf_naive = rf_naive.predict(eval_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0bd6e5b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/k1/d0rvjnq127s1n3prqzn3314m0000gn/T/ipykernel_51786/50244429.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  solution_rf_naive['Predicted'].replace(inverse_dict, inplace=True)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>decreasevolume</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>increaseheat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>increasevolume</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>deactivatelights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>decreasevolume</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted\n",
       "0    decreasevolume\n",
       "1      increaseheat\n",
       "2    increasevolume\n",
       "3  deactivatelights\n",
       "4    decreasevolume"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solution_rf_naive = pd.DataFrame(eval_y_rf_naive, index = eval_X.index, columns = ['Predicted'])\n",
    "solution_rf_naive['Predicted'].replace(inverse_dict, inplace=True)\n",
    "\n",
    "solution_rf_naive.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ffe7c6eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "solution_rf_naive.to_csv('draft_solution_rf_naive.csv', sep=',', index_label='Id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1611515",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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