CLIP-16-Evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e7bd18d3",
   "metadata": {},
   "source": [
    "# Multi-modal Embedding using CLIP-16"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7e3f228",
   "metadata": {},
   "source": [
    "## Imports and Utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "78762744",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer, util, models, InputExample, losses\n",
    "from PIL import Image\n",
    "import glob\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "import pickle\n",
    "import zipfile\n",
    "from IPython.display import display\n",
    "from IPython.display import Image as IPImage\n",
    "import pandas as pd\n",
    "import os\n",
    "from tqdm.autonotebook import tqdm, trange\n",
    "import torch\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import image as mpimg\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report, confusion_matrix, f1_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9056b848",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_folder = 'images/'\n",
    "def show_image(img_id, img_folder=image_folder):\n",
    "    try:\n",
    "        image_link = img_folder + img_id + '.jpg'\n",
    "        plt.title(\"Image\")\n",
    "        plt.xlabel(\"X pixel scaling\")\n",
    "        plt.ylabel(\"Y pixels scaling\")\n",
    "\n",
    "        image = mpimg.imread(image_link)\n",
    "    except:\n",
    "        image_link = img_folder + img_id + '.png'\n",
    "        plt.title(\"Image\")\n",
    "        plt.xlabel(\"X pixel scaling\")\n",
    "        plt.ylabel(\"Y pixels scaling\")\n",
    "\n",
    "        image = mpimg.imread(image_link)\n",
    "    plt.imshow(image)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0415eab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "def set_seed_everywhere(seed, cuda):\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    random.seed(seed)\n",
    "    if cuda:\n",
    "        torch.cuda.manual_seed_all(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "366ad8a4",
   "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>index</th>\n",
       "      <th>id</th>\n",
       "      <th>skepticism</th>\n",
       "      <th>mainstream</th>\n",
       "      <th>label</th>\n",
       "      <th>text_NL</th>\n",
       "      <th>text_EN</th>\n",
       "      <th>split</th>\n",
       "      <th>available</th>\n",
       "      <th>target</th>\n",
       "      <th>unseen_images</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>956</td>\n",
       "      <td>327027443732983808</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>RT @USER : 3D waterkaart , hoe stroomt het wat...</td>\n",
       "      <td>RT @USER : 3D water map , how the water flows ...</td>\n",
       "      <td>test</td>\n",
       "      <td>True</td>\n",
       "      <td>3_327026285966667776</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1211</th>\n",
       "      <td>1211</td>\n",
       "      <td>375896131127218176</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>Kom naar de Wilhelminakade R'dam en klim boven...</td>\n",
       "      <td>Come to the Wilhelminakade R'dam and climb abo...</td>\n",
       "      <td>val</td>\n",
       "      <td>True</td>\n",
       "      <td>3_375896131013971968</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1212</th>\n",
       "      <td>1212</td>\n",
       "      <td>375899134915772416</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>RT @USER : Kom naar de Wilhelminakade R'dam en...</td>\n",
       "      <td>RT @USER : Come to the Wilhelminakade R'dam an...</td>\n",
       "      <td>val</td>\n",
       "      <td>True</td>\n",
       "      <td>3_375896131013971968</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1214</th>\n",
       "      <td>1214</td>\n",
       "      <td>375946829881278464</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>Scholieren klimmen met Jeugdwaterschap naar ni...</td>\n",
       "      <td>Students climb to the water level of their hou...</td>\n",
       "      <td>val</td>\n",
       "      <td>True</td>\n",
       "      <td>3_375946829747089408</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1215</th>\n",
       "      <td>1215</td>\n",
       "      <td>375946961951539200</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>RT @USER : Scholieren klimmen met Jeugdwatersc...</td>\n",
       "      <td>RT @USER : Students climb to the water level o...</td>\n",
       "      <td>train</td>\n",
       "      <td>True</td>\n",
       "      <td>3_375946829747089408</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                  id  skepticism  mainstream  label  \\\n",
       "956     956  327027443732983808       False        True      1   \n",
       "1211   1211  375896131127218176       False        True      1   \n",
       "1212   1212  375899134915772416       False        True      1   \n",
       "1214   1214  375946829881278464       False        True      1   \n",
       "1215   1215  375946961951539200       False        True      1   \n",
       "\n",
       "                                                text_NL  \\\n",
       "956   RT @USER : 3D waterkaart , hoe stroomt het wat...   \n",
       "1211  Kom naar de Wilhelminakade R'dam en klim boven...   \n",
       "1212  RT @USER : Kom naar de Wilhelminakade R'dam en...   \n",
       "1214  Scholieren klimmen met Jeugdwaterschap naar ni...   \n",
       "1215  RT @USER : Scholieren klimmen met Jeugdwatersc...   \n",
       "\n",
       "                                                text_EN  split  available  \\\n",
       "956   RT @USER : 3D water map , how the water flows ...   test       True   \n",
       "1211  Come to the Wilhelminakade R'dam and climb abo...    val       True   \n",
       "1212  RT @USER : Come to the Wilhelminakade R'dam an...    val       True   \n",
       "1214  Students climb to the water level of their hou...    val       True   \n",
       "1215  RT @USER : Students climb to the water level o...  train       True   \n",
       "\n",
       "                    target  unseen_images  \n",
       "956   3_327026285966667776              1  \n",
       "1211  3_375896131013971968              1  \n",
       "1212  3_375896131013971968              1  \n",
       "1214  3_375946829747089408              0  \n",
       "1215  3_375946829747089408              0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/tweets_subset.csv', index_col='Unnamed: 0').reset_index()\n",
    "df[df.available].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "44baebe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We use the original CLIP model for computing image embeddings and English text embeddings\n",
    "en_model = SentenceTransformer('clip-ViT-B-16')\n",
    "tokenizer = en_model._first_module().processor.tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c10f520a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def embed_documents(documents, embedding_model=en_model, convert_to_tensor = True, verbose = False):\n",
    "    \"\"\" Embed a list of n documents/words into an n-dimensional\n",
    "    matrix of embeddings\n",
    "\n",
    "    Arguments:\n",
    "        documents: A list of documents or words to be embedded\n",
    "        verbose: Controls the verbosity of the process\n",
    "\n",
    "    Returns:\n",
    "        Document/words embeddings with shape (n, m) with `n` documents/words\n",
    "        that each have an embeddings size of `m`\n",
    "    \"\"\"\n",
    "    truncated_docs = [truncate_document(doc) for doc in documents]\n",
    "    embeddings = embedding_model.encode(truncated_docs, show_progress_bar=verbose, convert_to_tensor=convert_to_tensor)\n",
    "    return embeddings\n",
    "\n",
    "def truncate_document(document, tokenizer=tokenizer):\n",
    "    tokens = tokenizer.encode(document)\n",
    "\n",
    "    if len(tokens) > 77:\n",
    "        # Skip the starting token, only include 75 tokens\n",
    "        truncated_tokens = tokens[1:76]\n",
    "        document = tokenizer.decode(truncated_tokens)\n",
    "\n",
    "        # Recursive call here, because the encode(decode()) can have different result\n",
    "        return truncate_document(document)\n",
    "\n",
    "    else:\n",
    "        return document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "918a44e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Token indices sequence length is longer than the specified maximum sequence length for this model (80 > 77). Running this sequence through the model will result in indexing errors\n"
     ]
    }
   ],
   "source": [
    "df['text_EN_trun'] = df['text_EN'].apply(truncate_document)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d195eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def embed_images(images, verbose=True, batch_size=32, embedding_model=en_model, convert_to_tensor=True):\n",
    "    if batch_size:\n",
    "        nr_iterations = int(np.ceil(len(images) / batch_size))\n",
    "\n",
    "        # Embed images per batch\n",
    "        embeddings = []\n",
    "        for i in tqdm(range(nr_iterations), disable=not verbose):\n",
    "            start_index = i * batch_size\n",
    "            end_index = (i * batch_size) + batch_size\n",
    "\n",
    "            images_to_embed = [Image.open(img_name_dict[image]) if (isinstance(image, str) and not str(image)=='nan') \n",
    "                               else (Image.new('RGB', (800, 600), color = (255,255,255)) if str(image)=='nan'\n",
    "                               else image) for image in images[start_index:end_index]]\n",
    "            img_emb = embedding_model.encode(images_to_embed, show_progress_bar=False, convert_to_tensor=convert_to_tensor)\n",
    "            embeddings.extend(img_emb.tolist())\n",
    "\n",
    "            # Close images\n",
    "            if isinstance(images[0], str):\n",
    "                for image in images_to_embed:\n",
    "                    image.close()\n",
    "        if not convert_to_tensor:\n",
    "            embeddings = np.array(embeddings)\n",
    "        else:\n",
    "            embeddings = torch.tensor(embeddings).to(device='cuda')\n",
    "    else:\n",
    "        images_to_embed = [Image.open(filepath) for filepath in images]\n",
    "        embeddings = embedding_model.encode(images_to_embed, show_progress_bar=False, convert_to_tensor=convert_to_tensor)\n",
    "    return embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "612ed879",
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_report(labels, preds, ids=[]):\n",
    "    if len(ids)==0:\n",
    "        print(metrics.classification_report(labels.cpu(), preds.cpu(), digits=4))\n",
    "        return metrics.classification_report(labels.cpu(), preds.cpu(), digits=4, output_dict=True)\n",
    "    else:\n",
    "        print(metrics.classification_report(labels[torch.tensor(list(ids))].cpu(), preds[torch.tensor(list(ids))].cpu(), digits=4))\n",
    "        return metrics.classification_report(labels[torch.tensor(list(ids))].cpu(), preds[torch.tensor(list(ids))].cpu(), digits=4, output_dict=True)\n",
    "\n",
    "def evaluate_dataset_F1(df, preds_all, device = 'cuda', val_set = 'val'):\n",
    "    preds_all = preds_all.float()\n",
    "    label_all = torch.tensor(np.array(df['skepticism'])).to(device).float()\n",
    "    train = df[df['split'] == 'train'].index\n",
    "    val = df[df['split'] == 'val'].index\n",
    "    test = df[df['split'] == 'test'].index\n",
    "    image = df[df['available']].index\n",
    "    unseen = df[df['unseen_images']==1].index\n",
    "    \n",
    "    if val_set=='val':\n",
    "        temp = val.copy()\n",
    "    elif val_set=='test':\n",
    "        temp = test.copy()\n",
    "    \n",
    "    a = temp.intersection(image)\n",
    "    b = temp.difference(image)\n",
    "    c = temp.intersection(image).intersection(unseen)    \n",
    "    \n",
    "    print('Overall Dataset:')\n",
    "    all_report = return_report(label_all, preds_all)\n",
    "    \n",
    "    print('Training Set:')\n",
    "    train_report = return_report(label_all, preds_all, train)\n",
    "    \n",
    "    print('Validation Set:')\n",
    "    val_report = return_report(label_all, preds_all, temp)\n",
    "    \n",
    "    print('Validation Set with both Images and Texts:')\n",
    "    val_img_report = return_report(label_all, preds_all, a)\n",
    "    \n",
    "    print('Validation Set with only Texts:')\n",
    "    val_txt_report = return_report(label_all, preds_all, b)\n",
    "    \n",
    "    print('Validation Set with unseen Images:')\n",
    "    val_unseen_report = return_report(label_all, preds_all, c)\n",
    "    \n",
    "    all_acc = ((label_all==preds_all).sum()/len(preds_all)).item()*100\n",
    "    train_acc = ((label_all[torch.tensor(list(train))]==preds_all[torch.tensor(list(train))]).sum()/len(train)).item()*100\n",
    "    val_acc = ((label_all[torch.tensor(list(temp))]==preds_all[torch.tensor(list(temp))]).sum()/len(temp)).item()*100\n",
    "    val_img_acc = ((label_all[torch.tensor(list((a)))]==preds_all[torch.tensor(list(a))]).sum()/len(a)).item()*100\n",
    "    val_txt_acc = ((label_all[torch.tensor(list((b)))]==preds_all[torch.tensor(list((b)))]).sum()/len(b)).item()*100\n",
    "    val_unseen = ((label_all[torch.tensor(list((c)))]==preds_all[torch.tensor(list((c)))]).sum()/len(c)).item()*100\n",
    "    \n",
    "    print('''\n",
    "    Overall Accuracy: {:.2f}\n",
    "    Training Set Accuracy: {:.2f}\n",
    "    Validation Set Accuracy: {:.2f}\n",
    "    Validation Set with both Images and Texts: {:.2f}\n",
    "    Validation Set with only Texts: {:.2f}\n",
    "    Validation Set with unseen Images: {:.2f}\n",
    "    '''.format(all_acc, train_acc, val_acc, val_img_acc, val_txt_acc, val_unseen))\n",
    "    \n",
    "    return all_report, train_report, val_report, val_img_report, val_txt_report, val_unseen_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "10dd7c2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_dataset(df, preds_all, device = 'cuda', val_set = 'val'):\n",
    "    preds_all = preds_all.float()\n",
    "    label_all = torch.tensor(np.array(df['skepticism'])).to(device).float()\n",
    "    #train = torch.tensor(np.array(df_all['split'] == 'train')).to(device).float()\n",
    "    train = df[df['split'] == 'train'].index\n",
    "    #val = torch.tensor(np.array(df_all['split'] == 'val')).to(device).float()\n",
    "    val = df[df['split'] == 'val'].index\n",
    "    #test = torch.tensor(np.array(df_all['split'] == 'test')).to(device).float()\n",
    "    test = df[df['split'] == 'test'].index\n",
    "    #image = torch.tensor(np.array(df_all['available'])).to(device).float()\n",
    "    image = df[df['available']].index\n",
    "    #unseen = torch.tensor(np.array(df_all['unseen_images'])).to(device).float()\n",
    "    unseen = df[df['unseen_images']==1].index\n",
    "    \n",
    "    if val_set=='val':\n",
    "        a = val.intersection(image)\n",
    "        b = val.difference(image)\n",
    "        c = val.intersection(image).intersection(unseen)\n",
    "        val_acc = ((label_all[torch.tensor(list(val))]==preds_all[torch.tensor(list(val))]).sum()/len(val)).item()*100\n",
    "            \n",
    "    elif val_set=='test':\n",
    "        a = test.intersection(image)\n",
    "        b = test.difference(image)\n",
    "        c = test.intersection(image).intersection(unseen)\n",
    "        val_acc = ((label_all[torch.tensor(list(test))]==preds_all[torch.tensor(list(test))]).sum()/len(test)).item()*100\n",
    "    \n",
    "    all_acc = ((label_all==preds_all).sum()/len(preds_all)).item()*100\n",
    "    train_acc = ((label_all[torch.tensor(list(train))]==preds_all[torch.tensor(list(train))]).sum()/len(train)).item()*100\n",
    "    \n",
    "    val_img_acc = ((label_all[torch.tensor(list((a)))]==preds_all[torch.tensor(list(a))]).sum()/len(a)).item()*100\n",
    "    val_txt_acc = ((label_all[torch.tensor(list((b)))]==preds_all[torch.tensor(list((b)))]).sum()/len(b)).item()*100\n",
    "    val_unseen = ((label_all[torch.tensor(list((c)))]==preds_all[torch.tensor(list((c)))]).sum()/len(c)).item()*100\n",
    "    \n",
    "    print('''\n",
    "    Overall Accuracy: {:.2f}\n",
    "    Training Set Accuracy: {:.2f}\n",
    "    Validation Set Accuracy: {:.2f}\n",
    "    Validation Set with both Images and Texts: {:.2f}\n",
    "    Validation Set with only Texts: {:.2f}\n",
    "    Validation Set with unseen Images: {:.2f}\n",
    "    '''.format(all_acc, train_acc, val_acc, val_img_acc, val_txt_acc, val_unseen))\n",
    "    \n",
    "    return all_acc, train_acc, val_acc, val_img_acc, val_txt_acc, val_unseen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "743df3fb",
   "metadata": {},
   "source": [
    "## Process Raw Data into Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ecee2fc",
   "metadata": {},
   "source": [
    "Two sentences to compute embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1c456e07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4cd32601f0cd40d494c1d76062f374ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Climate change is a very serious issue to pay attention to! We should care about climate change!', \n",
    "          'Climate change is not even a real problem to concern about! I do not believe in it!']\n",
    "lab_emb = en_model.encode(labels, convert_to_tensor=True)\n",
    "empty_img_emb = embed_images(['nan'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d40a5dba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 512])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "empty_img_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b680222",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt_emb = embed_documents(list(df['text_EN']), convert_to_tensor=True, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07e3c50d",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_emb = embed_images(list(df['target']), convert_to_tensor=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f011284b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('data/CLIP16/embeddings.pkl', \"wb\") as fOut:\n",
    "    pickle.dump({'sentences': list(df['text_EN_trun']), 'txt_emb': txt_emb, 'img_emb': img_emb, \n",
    "                 'lab_emb': lab_emb, 'empty_img_emb': empty_img_emb}, fOut, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "31a1cd0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/CLIP16/embeddings.pkl', \"rb\") as fIn:\n",
    "    stored_data = pickle.load(fIn)\n",
    "    #stored_sentences = stored_data['sentences']\n",
    "    txt_emb = stored_data['txt_emb']\n",
    "    img_emb = stored_data['img_emb']\n",
    "    lab_emb = stored_data['lab_emb']\n",
    "    empty_img_emb = stored_data['empty_img_emb']\n",
    "\n",
    "labs = {}\n",
    "labs['txt_emb'] = lab_emb\n",
    "labs['img_emb'] = torch.vstack([empty_img_emb] * lab_emb.shape[0])\n",
    "mul_emb = (img_emb + txt_emb)/2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d53de3d",
   "metadata": {},
   "source": [
    "## Zero-shot Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da062fc9",
   "metadata": {},
   "source": [
    "Using Cosine Similarity to make classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "990b44e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def zero_shot(df_all, model_name='CLIP16', merge = 'average', from_stored_embedding=False, pre_computed = True,\n",
    "             stored_data='', embedding_model = '', val_set='val'):\n",
    "    \n",
    "    if from_stored_embedding:\n",
    "        folder = 'model_storage/{}/embeddings.pkl'.format(model_name)\n",
    "        with open(folder, \"rb\") as fIn:\n",
    "            stored_data = pickle.load(fIn)\n",
    "            #stored_sentences = stored_data['sentences']\n",
    "            txt_emb = stored_data['txt_emb']\n",
    "            img_emb = stored_data['img_emb']\n",
    "            lab_emb = stored_data['lab_emb']\n",
    "    elif pre_computed:\n",
    "        stored_data = stored_data\n",
    "        #stored_sentences = stored_data['sentences']\n",
    "        txt_emb = stored_data['txt_emb']\n",
    "        img_emb = stored_data['img_emb']\n",
    "        lab_emb = stored_data['lab_emb']\n",
    "    else:\n",
    "        txt_emb = embed_documents(list(df_all['text_EN_trun']), embedding_model=embedding_model, convert_to_tensor=True, verbose=True)\n",
    "        img_emb = embed_images(list(df_all['target']), embedding_model=embedding_model, convert_to_tensor=True, verbose=True)\n",
    "        labels = ['Climate change is a very serious issue to pay attention to! We should care about climate change!', \n",
    "                  'Climate change is not even a real problem to concern about! I do not believe in it!']\n",
    "        lab_emb = embedding_model.encode(labels, convert_to_tensor=True)\n",
    "        with open('model_storage/CLIP/embeddings_re.pkl', \"wb\") as fOut:\n",
    "            pickle.dump({'sentences': list(df['text_EN_trun']), 'txt_emb': txt_emb, 'img_emb': img_emb, 'lab_emb': lab_emb}, fOut, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    \n",
    "    if merge == 'average':\n",
    "        mul_emb = (img_emb + txt_emb)/2\n",
    "    elif merge == 'max':\n",
    "        mul_emb = torch.max(img_emb, txt_emb)\n",
    "    else:\n",
    "        print(\"'Merger' input must be one of 'average' or 'max'\")\n",
    "    \n",
    "    TXT_LAB = util.cos_sim(lab_emb, txt_emb)\n",
    "    IMG_LAB = util.cos_sim(lab_emb, img_emb)\n",
    "    MUL_LAB = util.cos_sim(lab_emb, mul_emb)\n",
    "    \n",
    "    pred_TXT = torch.argmax(TXT_LAB[:2,:], dim=0).float()\n",
    "    pred_IMG = torch.argmax(IMG_LAB[:2,:], dim=0).float()\n",
    "    pred_MUL = torch.argmax(MUL_LAB[:2,:], dim=0).float()\n",
    "    \n",
    "    lab = torch.tensor(np.array(df_all['skepticism'])).to('cuda').float()\n",
    "    \n",
    "    set_seed_everywhere(42, cuda=True)\n",
    "    lab_rand = (torch.rand(len(lab))>0.5).float().to('cuda')\n",
    "    \n",
    "    print('Using textual embedding only:')\n",
    "    evaluate_dataset(df_all, pred_TXT, val_set=val_set)\n",
    "    \n",
    "    print('Using image embedding only:')\n",
    "    evaluate_dataset(df_all, pred_IMG, val_set=val_set)\n",
    "    \n",
    "    print('Using multi-modal embedding:')\n",
    "    evaluate_dataset(df_all, pred_MUL, val_set=val_set)\n",
    "    \n",
    "    print('Comparing to a random prediction:')\n",
    "    evaluate_dataset(df_all, lab_rand, val_set=val_set)\n",
    "    \n",
    "    print('Test set performance multi-modal:')\n",
    "    evaluate_dataset(df_all, pred_MUL, val_set='test')\n",
    "    \n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cb85005f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using textual embedding only:\n",
      "\n",
      "    Overall Accuracy: 46.66\n",
      "    Training Set Accuracy: 46.73\n",
      "    Validation Set Accuracy: 47.05\n",
      "    Validation Set with both Images and Texts: 43.39\n",
      "    Validation Set with only Texts: 47.72\n",
      "    Validation Set with unseen Images: 43.89\n",
      "    \n",
      "Using image embedding only:\n",
      "\n",
      "    Overall Accuracy: 44.89\n",
      "    Training Set Accuracy: 45.05\n",
      "    Validation Set Accuracy: 45.15\n",
      "    Validation Set with both Images and Texts: 59.74\n",
      "    Validation Set with only Texts: 42.47\n",
      "    Validation Set with unseen Images: 59.01\n",
      "    \n",
      "Using multi-modal embedding:\n",
      "\n",
      "    Overall Accuracy: 44.18\n",
      "    Training Set Accuracy: 44.27\n",
      "    Validation Set Accuracy: 44.50\n",
      "    Validation Set with both Images and Texts: 53.74\n",
      "    Validation Set with only Texts: 42.80\n",
      "    Validation Set with unseen Images: 54.66\n",
      "    \n",
      "Comparing to a random prediction:\n",
      "\n",
      "    Overall Accuracy: 49.64\n",
      "    Training Set Accuracy: 49.67\n",
      "    Validation Set Accuracy: 49.99\n",
      "    Validation Set with both Images and Texts: 48.09\n",
      "    Validation Set with only Texts: 50.34\n",
      "    Validation Set with unseen Images: 47.20\n",
      "    \n",
      "Test set performance multi-modal:\n",
      "\n",
      "    Overall Accuracy: 44.18\n",
      "    Training Set Accuracy: 44.27\n",
      "    Validation Set Accuracy: 43.46\n",
      "    Validation Set with both Images and Texts: 54.89\n",
      "    Validation Set with only Texts: 41.54\n",
      "    Validation Set with unseen Images: 55.53\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "zero_shot(df, stored_data = stored_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92f68085",
   "metadata": {},
   "source": [
    "## Training an additional MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "496fe10f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from argparse import Namespace\n",
    "args = Namespace(\n",
    "    # Data and Path information\n",
    "    model_state_file='MLP_classifier.pth',\n",
    "    save_dir='model_storage/CLIP16/',\n",
    "    result_dir='results/CLIP16/',\n",
    "    embedding_size=512,\n",
    "    early_stopping_criteria=20,\n",
    "    num_epochs=20,\n",
    "    seed=42,\n",
    "    \n",
    "    # Training hyper parameters\n",
    "    batch_size=32,\n",
    "    embedding_type='text',\n",
    "    fusion_type='average',\n",
    "    hidden_dim=128, \n",
    "    learning_rate=5e-5,\n",
    "    l2=1e-5,\n",
    "    dropout_p=0.1,\n",
    "    \n",
    "    # Runtime options\n",
    "    catch_keyboard_interrupt=True,\n",
    "    device= 'cuda',\n",
    "    cuda=True,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b8720053",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "class MLPClassifier(nn.Module):\n",
    "    \n",
    "    def __init__(self, embedding_size, embedding_type, fusion_type, hidden_dim, num_classes, dropout_p, \n",
    "                 pretrained_embeddings=None):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            embedding_size (int): size of the embedding vectors\n",
    "            embedding_type (str): type of embedding to be used, can be one of {'text', 'image', 'multi'}\n",
    "            fusion_type (str): type of fusing multimodal embeddings if embedding_type=='multi', can be one of \n",
    "                                {'average', 'concat', 'complex'}\n",
    "            hidden_dim (int): the size of the hidden dimension\n",
    "            num_classes (int): the number of classes in classification\n",
    "            dropout_p (float): a dropout parameter \n",
    "            pretrained_embeddings (numpy.array): previously trained embeddings\n",
    "            \n",
    "        \"\"\"\n",
    "        super(MLPClassifier, self).__init__()\n",
    "\n",
    "        self.embedding_type = embedding_type\n",
    "        self.fusion_type = fusion_type\n",
    "        self._dropout_p = dropout_p\n",
    "        self.dropout = nn.Dropout(dropout_p)\n",
    "        #self.prelu = nn.PReLU()\n",
    "        \n",
    "        self.fc1 = nn.Linear(embedding_size, hidden_dim)\n",
    "        self.fc2 = nn.Linear(hidden_dim, num_classes)\n",
    "        \n",
    "        self.fc_concat = nn.Linear(2*embedding_size, hidden_dim)\n",
    "        self.fc_complex = nn.Linear(5*embedding_size, 2*embedding_size)\n",
    "\n",
    "    def forward(self, x_in, apply_softmax=False):\n",
    "        \"\"\"The forward pass of the classifier\n",
    "        \n",
    "        Args:\n",
    "            x_in (dictionary): a dictionary of input data tensors with embeddings. \n",
    "                x_in.shape should be (batch, dataset._max_seq_length)\n",
    "            apply_softmax (bool): a flag for the softmax activation\n",
    "                should be false if used with the Cross Entropy losses\n",
    "        Returns:\n",
    "            the resulting tensor. tensor.shape should be (batch, num_classes)\n",
    "        \"\"\"\n",
    "        \n",
    "        # embed and permute so features are channels\n",
    "        x_embedded = x_in\n",
    "                \n",
    "        # logistic regression\n",
    "        if (not self.embedding_type=='multi') or self.fusion_type=='average':\n",
    "            intermediate_vector = F.relu(self.dropout(self.fc1(x_embedded)))\n",
    "            prediction_vector = self.fc2(intermediate_vector)\n",
    "        elif self.embedding_type=='multi' and self.fusion_type=='concat':\n",
    "            intermediate_vector = F.relu(self.dropout(self.fc_concat(x_embedded)))\n",
    "            prediction_vector = self.fc2(intermediate_vector)\n",
    "        elif self.embedding_type=='multi' and self.fusion_type=='complex':\n",
    "            first_vector = F.relu(self.dropout(self.fc_complex(x_embedded)))\n",
    "            intermediate_vector = F.relu(self.dropout(self.fc_concat(first_vector)))\n",
    "            prediction_vector = self.fc2(intermediate_vector)\n",
    "        \n",
    "        if apply_softmax:\n",
    "            prediction_vector = F.softmax(prediction_vector, dim=1)\n",
    "\n",
    "        return prediction_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4928e3f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomDataset(Dataset):\n",
    "    def __init__(self, df_all, embeddings):\n",
    "        self.df = df_all\n",
    "        self.labels = torch.tensor(np.array(self.df['skepticism'])).to(args.device).float()\n",
    "        measure_len = lambda context: len(context.split(\" \"))\n",
    "        self._max_seq_length = max(map(measure_len, self.df.text_EN_trun)) + 0\n",
    "        self.embeddings = embeddings\n",
    "        \n",
    "        train = torch.tensor(df[df['split'] == 'train'].index)\n",
    "        self.train_df = self.df[self.df.split=='train']\n",
    "        self.train_size = len(self.train_df)\n",
    "        self.train_emb = self.embeddings[train]\n",
    "        \n",
    "        val = torch.tensor(df[df['split'] == 'val'].index)\n",
    "        self.val_df = self.df[self.df.split=='val']\n",
    "        self.validation_size = len(self.val_df)\n",
    "        self.val_emb = self.embeddings[val]\n",
    "                \n",
    "        test = torch.tensor(df[df['split'] == 'test'].index)\n",
    "        self.test_df = self.df[self.df.split=='test']\n",
    "        self.test_size = len(self.test_df)\n",
    "        self.test_emb = self.embeddings[test]\n",
    "\n",
    "        self._lookup_dict = {'train': (self.train_df, self.train_size, self.train_emb),\n",
    "                             'val': (self.val_df, self.validation_size, self.val_emb),\n",
    "                             'test': (self.test_df, self.test_size, self.test_emb)}\n",
    "\n",
    "        self.set_split('train')\n",
    "            \n",
    "    def set_split(self, split=\"train\"):\n",
    "        \"\"\" selects the splits in the dataset using a column in the dataframe \n",
    "        \n",
    "        Args:\n",
    "            split (str): one of \"train\", \"val\", or \"test\"\n",
    "        \"\"\"\n",
    "        self._target_split = split\n",
    "        self._target_df, self._target_size, self._target_emb = self._lookup_dict[split]\n",
    "        \n",
    "    def get_num_batches(self, batch_size):\n",
    "        \"\"\"Given a batch size, return the number of batches in the dataset\n",
    "        \n",
    "        Args:\n",
    "            batch_size (int)\n",
    "        Returns:\n",
    "            number of batches in the dataset\n",
    "        \"\"\"\n",
    "        if len(self) % batch_size==0:\n",
    "            return len(self) // batch_size\n",
    "        else:\n",
    "            return len(self) // batch_size + 1\n",
    "\n",
    "    def __len__(self):\n",
    "        return self._target_size\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        emb = self._target_emb[idx].to(args.device)\n",
    "        row = self._target_df.iloc[idx]\n",
    "        label = torch.tensor(row[['mainstream', 'skepticism']].astype(int)).to(args.device)\n",
    "        \n",
    "        return {'emb': emb, \n",
    "                'label': label}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e98c3e81",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_train_state(args):\n",
    "    return {'stop_early': False,\n",
    "            'early_stopping_step': 0,\n",
    "            'early_stopping_best_1_acc_val': 0,\n",
    "            'learning_rate': args.learning_rate,\n",
    "            'epoch_index': 0,\n",
    "            'golden_loss': [],\n",
    "            'train_loss': [],\n",
    "            'train_1_acc': [],\n",
    "            'train_acc': [],\n",
    "            'val_loss': [],\n",
    "            'val_1_acc': [],\n",
    "            'val_acc': [],\n",
    "            'val_img_txt': [],\n",
    "            'val_txt_only': [],\n",
    "            'val_unseen_img': [],\n",
    "            'test_loss': -1,\n",
    "            'test_1_acc': -1,\n",
    "            'test_acc': -1,\n",
    "            'test_img_txt': -1,\n",
    "            'test_txt_only': -1,\n",
    "            'test_unseen_img': -1,\n",
    "            'model_filename': args.model_state_file}\n",
    "\n",
    "def update_train_state(args, model, train_state):\n",
    "    \"\"\"Handle the training state updates.\n",
    "\n",
    "    Components:\n",
    "     - Early Stopping: Prevent overfitting.\n",
    "     - Model Checkpoint: Model is saved if the model is better\n",
    "\n",
    "    :param args: main arguments\n",
    "    :param model: model to train\n",
    "    :param train_state: a dictionary representing the training state values\n",
    "    :returns:\n",
    "        a new train_state\n",
    "    \"\"\"\n",
    "\n",
    "    # Save one model at least\n",
    "    if train_state['epoch_index'] == 0:\n",
    "        torch.save(model.state_dict(), args.save_dir + args.embedding_type + \"_\" + args.fusion_type + \"_\" + args.model_state_file)\n",
    "        train_state['stop_early'] = False\n",
    "\n",
    "    # Save model if performance improved\n",
    "    elif train_state['epoch_index'] >= 1:\n",
    "        acc_tm1, acc_t = train_state['val_1_acc'][-2:]\n",
    "\n",
    "        # If accuracy worsened\n",
    "        if acc_t <= train_state['early_stopping_best_1_acc_val']:\n",
    "            # Update step\n",
    "            train_state['early_stopping_step'] += 1\n",
    "        # Loss decreased\n",
    "        else:\n",
    "            # Save the best model from sklearn\n",
    "            if acc_t > train_state['early_stopping_best_1_acc_val']:\n",
    "                train_state['early_stopping_best_1_acc_val'] = acc_t\n",
    "                torch.save(model.state_dict(), args.save_dir + args.embedding_type + \"_\" + args.fusion_type + \"_\" + args.model_state_file)\n",
    "                \n",
    "            # Reset early stopping step\n",
    "            train_state['early_stopping_step'] = 0\n",
    "\n",
    "        # Stop early ?\n",
    "        train_state['stop_early'] = \\\n",
    "            train_state['early_stopping_step'] >= args.early_stopping_criteria\n",
    "\n",
    "    return train_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "63db575f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cross_entropy(y_pred, y_target):\n",
    "    y_target = y_target.cpu().float()\n",
    "    y_pred = y_pred.cpu().float()\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    return criterion(y_target, y_pred)\n",
    "\n",
    "def compute_1_accuracy(y_pred, y_target):\n",
    "    y_target_indices = y_target.max(dim=1)[1]\n",
    "    y_pred_indices = y_pred.max(dim=1)[1]\n",
    "    n_correct = torch.eq(y_pred_indices, y_target_indices).sum().item()\n",
    "    return n_correct / len(y_pred_indices) * 100\n",
    "\n",
    "def compute_k_accuracy(y_pred, y_target, k=3):\n",
    "    y_pred_indices = y_pred.topk(k, dim=1)[1]\n",
    "    y_target_indices = y_target.max(dim=1)[1]\n",
    "    n_correct = torch.tensor([y_pred_indices[i] in y_target_indices[i] for i in range(len(y_pred))]).sum().item()\n",
    "    return n_correct / len(y_pred_indices) * 100\n",
    "\n",
    "def compute_k_jaccard_index(y_pred, y_target, k=3):\n",
    "    y_target_indices = y_target.topk(k, dim=1)[1]\n",
    "    y_pred_indices = y_pred.max(dim=1)[1]\n",
    "    jaccard = torch.tensor([len(np.intersect1d(y_target_indices[i], y_pred_indices[i]))/\n",
    "                            len(np.union1d(y_target_indices[i], y_pred_indices[i]))\n",
    "                            for i in range(len(y_pred))]).sum().item()\n",
    "    return jaccard / len(y_pred_indices)\n",
    "\n",
    "def compute_jaccard_index(y_pred, y_target, k=3, multilabel=False):\n",
    "    \n",
    "    threshold = 1.0/(k+1)\n",
    "    threshold_2 = 0.5\n",
    "    \n",
    "    if multilabel:\n",
    "        y_pred_indices = y_pred.gt(threshold_2)\n",
    "    else:\n",
    "        y_pred_indices = y_pred.gt(threshold)\n",
    "    \n",
    "    y_target_indices = y_target.gt(threshold)\n",
    "        \n",
    "    jaccard = ((y_target_indices*y_pred_indices).sum(axis=1)/((y_target_indices+y_pred_indices).sum(axis=1)+1e-8)).sum().item()\n",
    "    return jaccard / len(y_pred_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6414e4bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax_sensitive(T):\n",
    "    T = np.exp(T) - np.exp(0) + 1e-9\n",
    "    if len(T.shape)==1:\n",
    "        return T/T.sum()\n",
    "    return  T/(T.sum(axis=1).unsqueeze(1))\n",
    "\n",
    "def cross_entropy(pred, soft_targets):\n",
    "    logsoftmax = nn.LogSoftmax(dim=1)\n",
    "    return torch.mean(torch.sum(- soft_targets * logsoftmax(pred), 1))\n",
    "\n",
    "# convert a df to tensor to be used in pytorch\n",
    "def df_to_tensor(df):\n",
    "    device = args.device\n",
    "    return torch.from_numpy(df.values).float().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f9b7c48a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_embedding(embeddings, embedding_type, fusion_type):\n",
    "    '''\n",
    "    Args:\n",
    "        embeddings (dictionary): a dictionary of input data tensors with embeddings. \n",
    "        embedding_type (str): type of embedding to be used, can be one of {'text', 'image', 'multi'}\n",
    "        fusion_type (str): type of fusing multimodal embeddings if embedding_type=='multi', can be one of \n",
    "                        {'average', 'concat', 'complex'}'''\n",
    "    if embedding_type=='text':\n",
    "        x_embedded = embeddings['txt_emb']\n",
    "    elif embedding_type=='image':\n",
    "        x_embedded = embeddings['img_emb']\n",
    "    elif embedding_type=='multi':\n",
    "        if fusion_type=='average':\n",
    "            x_embedded = (embeddings['txt_emb']+embeddings['img_emb'])/2\n",
    "        elif fusion_type=='concat':\n",
    "            x_embedded = torch.hstack([embeddings['txt_emb'], embeddings['img_emb']])\n",
    "        elif fusion_type=='complex':\n",
    "            txt_emb = embeddings['txt_emb']\n",
    "            img_emb = embeddings['img_emb']\n",
    "            x_embedded = torch.hstack([txt_emb, img_emb, txt_emb+img_emb, txt_emb-img_emb, txt_emb*img_emb])\n",
    "        else:\n",
    "            print('fusion_type is not supported')\n",
    "    else:\n",
    "        print('embedding_type is not supported')\n",
    "        \n",
    "    return x_embedded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "72be7dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialization(df, stored_data):\n",
    "    set_seed_everywhere(args.seed, args.cuda)\n",
    "    embeddings = prepare_embedding(stored_data, args.embedding_type, args.fusion_type)\n",
    "    dataset = CustomDataset(df, embeddings)\n",
    "    \n",
    "    classifier = MLPClassifier(embedding_size=args.embedding_size, \n",
    "                        hidden_dim=args.hidden_dim, \n",
    "                        embedding_type=args.embedding_type, \n",
    "                        fusion_type=args.fusion_type,\n",
    "                        num_classes=len(labels), \n",
    "                        dropout_p=args.dropout_p).to(args.device)\n",
    "    \n",
    "    return embeddings, dataset, classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "789f15fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_train_state_evaluation(classifier, embeddings, train_state, df):\n",
    "    preds_all = classifier(embeddings).argmax(dim=1)\n",
    "    _, train_acc, val_acc, val_img_acc, val_txt_acc, val_unseen = evaluate_dataset(df, preds_all)\n",
    "    \n",
    "    train_state['train_acc'].append(train_acc)\n",
    "    train_state['val_acc'].append(val_acc)\n",
    "    train_state['val_img_txt'].append(val_img_acc)\n",
    "    train_state['val_txt_only'].append(val_txt_acc)\n",
    "    train_state['val_unseen_img'].append(val_unseen)\n",
    "    \n",
    "    return train_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fff070d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def training_loop(df, stored_data, labs=None, verbose=True):\n",
    "    \n",
    "    embeddings, dataset, classifier = initialization(df, stored_data)\n",
    "    if labs:\n",
    "        lab_embeddings = prepare_embedding(labs, args.embedding_type, args.fusion_type)\n",
    "    classifier = classifier.to(args.device)\n",
    "\n",
    "    loss_func = cross_entropy\n",
    "    optimizer = optim.Adam(classifier.parameters(), lr=args.learning_rate, weight_decay=args.l2)\n",
    "    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer,\n",
    "                                               mode='min', factor=0.5,\n",
    "                                               patience=1)\n",
    "    train_state = make_train_state(args)\n",
    "    \n",
    "    print('Using {} embedding with {} fusion method'.format(args.embedding_type, args.fusion_type))\n",
    "    \n",
    "    set_seed_everywhere(args.seed, cuda=True)\n",
    "    train_state = write_train_state_evaluation(classifier, embeddings, train_state, df)\n",
    "    \n",
    "    set_seed_everywhere(args.seed, cuda=True)\n",
    "    try:\n",
    "        for epoch_index in trange(args.num_epochs, desc=\"Epoch\", disable=not args.verbose):\n",
    "            train_state['epoch_index'] = epoch_index\n",
    "\n",
    "            # Iterate over training dataset\n",
    "\n",
    "            # setup: batch generator, set loss and acc to 0, set train mode on\n",
    "            dataset.set_split('train')\n",
    "            train_dataloader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True)\n",
    "            \n",
    "            running_loss = 0.0\n",
    "            running_1_acc = 0.0\n",
    "            classifier.train()\n",
    "\n",
    "            for batch_index, batch_dict in enumerate(train_dataloader):\n",
    "\n",
    "                # step 1. zero the gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # step 2. get the data compute fuzzy labels\n",
    "                X = batch_dict['emb']\n",
    "                y = batch_dict['label']\n",
    "\n",
    "                size = y.shape[0]\n",
    "\n",
    "                # step 3. compute the output\n",
    "                y_pred = classifier(X)\n",
    "\n",
    "                # step 4. compute the loss\n",
    "                loss = loss_func(y_pred, y)\n",
    "                loss_t = loss.item()\n",
    "\n",
    "                running_loss = (running_loss * batch_index * args.batch_size + loss_t * size)/(batch_index * args.batch_size + size)\n",
    "                \n",
    "                # step 5. use loss to produce gradients\n",
    "                loss.backward()\n",
    "\n",
    "                # step 6. use optimizer to take gradient step\n",
    "                optimizer.step()\n",
    "\n",
    "                # -----------------------------------------\n",
    "                # compute the accuracies\n",
    "                acc_1_t = compute_1_accuracy(y_pred, y)\n",
    "                running_1_acc = (running_1_acc * batch_index * args.batch_size + acc_1_t * size)/(batch_index * args.batch_size + size)\n",
    "            \n",
    "            if labs:\n",
    "                optimizer.zero_grad()\n",
    "                y_pred = classifier(lab_embeddings)\n",
    "                y = torch.tensor([[1,0], [0,1]]).to(args.device)\n",
    "                loss = loss_func(y_pred, y)\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "                train_state['golden_loss'].append(loss.item())\n",
    "            else:\n",
    "                train_state['golden_loss'].append('not implemented')\n",
    "            \n",
    "            train_state['train_loss'].append(running_loss)\n",
    "            train_state['train_1_acc'].append(running_1_acc)\n",
    "\n",
    "            # Iterate over val dataset\n",
    "\n",
    "            # setup: batch generator, set loss and acc to 0; set eval mode on\n",
    "\n",
    "            dataset.set_split('val')\n",
    "            val_dataloader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True)\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_1_acc = 0.0\n",
    "            classifier.eval()\n",
    "\n",
    "            for batch_index, batch_dict in enumerate(val_dataloader):\n",
    "\n",
    "                # step 2. get the data compute fuzzy labels\n",
    "                X = batch_dict['emb']\n",
    "                y = batch_dict['label']\n",
    "                size = y.shape[0]\n",
    "\n",
    "                # step 3. compute the output\n",
    "                y_pred = classifier(X)\n",
    "\n",
    "                # step 4. compute the loss\n",
    "                loss = loss_func(y_pred, y)\n",
    "                loss_t = loss.item()\n",
    "                running_loss = (running_loss * batch_index * args.batch_size + loss_t * size)/(batch_index * args.batch_size + size)\n",
    "\n",
    "                # -----------------------------------------\n",
    "                # compute the accuracies\n",
    "                acc_1_t = compute_1_accuracy(y_pred, y)\n",
    "                running_1_acc = (running_1_acc * batch_index * args.batch_size + acc_1_t * size)/(batch_index * args.batch_size + size)\n",
    "\n",
    "            train_state['val_loss'].append(running_loss)\n",
    "            train_state['val_1_acc'].append(running_1_acc)\n",
    "\n",
    "            train_state = write_train_state_evaluation(classifier, embeddings, train_state, df)\n",
    "            \n",
    "            print('Current Running Accuracy Training Set: {:.2f}, Validation Set: {:.2f}, Golden Set: {:.2f}'.format(train_state['train_1_acc'][-1], train_state['val_1_acc'][-1], train_state['golden_loss'][-1]))\n",
    "\n",
    "            train_state = update_train_state(args=args, model=classifier, train_state=train_state)\n",
    "\n",
    "            scheduler.step(train_state['val_loss'][-1])\n",
    "\n",
    "            if train_state['stop_early']:\n",
    "                break\n",
    "\n",
    "            with open(args.save_dir + args.embedding_type + \"_\" + args.fusion_type + \"_\" + 'train_state.p', 'wb') as fp:\n",
    "                pickle.dump(train_state, fp, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    \n",
    "    except KeyboardInterrupt:\n",
    "        print(\"Exiting loop\")\n",
    "        pass\n",
    "    \n",
    "    return train_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1a3c2e58",
   "metadata": {},
   "outputs": [],
   "source": [
    "def experiment(df, stored_data, labs = None, hyperdict={}):\n",
    "    emb_types = ['text', 'image', 'multi']\n",
    "    fusion_types = ['average', 'concat', 'complex']\n",
    "    combi = {(a,b) if a=='multi' else (a, fusion_types[0]) for a in emb_types for b in fusion_types}\n",
    "    \n",
    "    for key in combi:\n",
    "        print(key)\n",
    "        args.embedding_type = key[0]\n",
    "        args.fusion_type = key[1]\n",
    "        if not key in hyperdict:\n",
    "            train_state = training_loop(df, stored_data, labs=labs)\n",
    "            hyperdict[key] = train_state\n",
    "            \n",
    "        with open(args.save_dir+'hyperdict.p', 'wb') as fp:\n",
    "            pickle.dump(hyperdict,fp, protocol=pickle.HIGHEST_PROTOCOL)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0465b2d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def demonstration(hyperdict):\n",
    "    emb_types = ['text', 'image', 'multi']\n",
    "    fusion_types = ['average', 'concat', 'complex']\n",
    "    metrics = ['train_acc', 'val_acc', 'val_img_txt', 'val_txt_only', 'val_unseen_img']\n",
    "    combi = {(a,b) if a=='multi' else (a, fusion_types[0]) for a in emb_types for b in fusion_types}\n",
    "    \n",
    "    for m in metrics:\n",
    "        print(m)\n",
    "        for key in combi:\n",
    "            train_state = hyperdict[key]\n",
    "            pd.Series(train_state[m]).plot(label=str(key))\n",
    "            plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "76eee907",
   "metadata": {},
   "outputs": [],
   "source": [
    "def statistics(hyperdict, stored_data, df, labs=None, subfolder = ''):\n",
    "    emb_types = ['text', 'image', 'multi']\n",
    "    fusion_types = ['average', 'concat', 'complex']\n",
    "    combi = {(a,b) if a=='multi' else (a, fusion_types[0]) for a in emb_types for b in fusion_types}\n",
    "    train = df[df['split'] == 'train'].index\n",
    "    val = df[df['split'] == 'val'].index\n",
    "    results = {}\n",
    "    \n",
    "    for key in combi:\n",
    "        print(key)\n",
    "        print()\n",
    "        results[key] = {}\n",
    "        args.embedding_type = key[0]\n",
    "        args.fusion_type = key[1]\n",
    "        classifier = MLPClassifier(embedding_size=args.embedding_size, \n",
    "                        hidden_dim=args.hidden_dim, \n",
    "                        embedding_type=args.embedding_type, \n",
    "                        fusion_type=args.fusion_type,\n",
    "                        num_classes=len(labels), \n",
    "                        dropout_p=args.dropout_p).to(args.device)\n",
    "        classifier.load_state_dict(torch.load(args.save_dir + subfolder + args.embedding_type + \"_\" + args.fusion_type + \"_\" + args.model_state_file, map_location=torch.device('cpu')))\n",
    "        classifier.eval()\n",
    "        y = F.softmax(classifier(prepare_embedding(stored_data, args.embedding_type, args.fusion_type)), dim=1)\n",
    "        pd.DataFrame(y.cpu().detach().numpy()).to_csv(args.save_dir + subfolder + args.embedding_type + \"_\" + args.fusion_type + \"_\" +'prediction.csv')\n",
    "        \n",
    "        conf, pred = y.max(dim=1)\n",
    "        pd.Series(conf.cpu().detach().numpy()).hist()\n",
    "        \n",
    "        results[key]['performance']={}\n",
    "        \n",
    "        print('Validation Set:')\n",
    "        results[key]['performance']['val'] = evaluate_dataset(df, pred)\n",
    "        \n",
    "        print('Test Set:')\n",
    "        results[key]['performance']['test'] = evaluate_dataset(df, pred, val_set='test')\n",
    "        \n",
    "        if labs:\n",
    "            print('Prediction for Golden Sentences:')\n",
    "            print(F.softmax(classifier(prepare_embedding(labs, args.embedding_type, args.fusion_type)), dim=1).cpu().detach().numpy())\n",
    "            results[key]['golden'] = F.softmax(classifier(prepare_embedding(labs, args.embedding_type, args.fusion_type)), dim=1).cpu().detach().numpy()\n",
    "            print()\n",
    "        \n",
    "        print('Full Dataset:')\n",
    "        print(pd.Series(conf.cpu().detach().numpy()).describe())\n",
    "        print('Training Set:')\n",
    "        print(pd.Series(conf[train].cpu().detach().numpy()).describe())\n",
    "        print('Validation Set:')\n",
    "        print(pd.Series(conf[val].cpu().detach().numpy()).describe())\n",
    "        plt.show()\n",
    "        \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ae936ff3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def statistics_F1(hyperdict, stored_data, df, labs=None, subfolder = ''):\n",
    "    emb_types = ['text', 'image', 'multi']\n",
    "    fusion_types = ['average', 'concat', 'complex']\n",
    "    combi = {(a,b) if a=='multi' else (a, fusion_types[0]) for a in emb_types for b in fusion_types}\n",
    "    train = df[df['split'] == 'train'].index\n",
    "    val = df[df['split'] == 'val'].index\n",
    "    results = {}\n",
    "    \n",
    "    for key in combi:\n",
    "        print(key)\n",
    "        print()\n",
    "        results[key] = {}\n",
    "        args.embedding_type = key[0]\n",
    "        args.fusion_type = key[1]\n",
    "        classifier = MLPClassifier(embedding_size=args.embedding_size, \n",
    "                        hidden_dim=args.hidden_dim, \n",
    "                        embedding_type=args.embedding_type, \n",
    "                        fusion_type=args.fusion_type,\n",
    "                        num_classes=len(labels), \n",
    "                        dropout_p=args.dropout_p).to(args.device)\n",
    "        classifier.load_state_dict(torch.load(args.save_dir + subfolder + args.embedding_type + \"_\" + args.fusion_type + \"_\" + args.model_state_file, map_location=torch.device('cpu')))\n",
    "        classifier.eval()\n",
    "        y = F.softmax(classifier(prepare_embedding(stored_data, args.embedding_type, args.fusion_type)), dim=1)\n",
    "        #pd.DataFrame(y.cpu().detach().numpy()).to_csv(args.save_dir + subfolder + args.embedding_type + \"_\" + args.fusion_type + \"_\" +'prediction.csv')\n",
    "        \n",
    "        conf, pred = y.max(dim=1)\n",
    "        pd.Series(conf.cpu().detach().numpy()).hist()\n",
    "        \n",
    "        results[key]['performance']={}\n",
    "        \n",
    "        print('Validation Set:')\n",
    "        results[key]['performance']['val'] = evaluate_dataset_F1(df, pred)\n",
    "        \n",
    "        print('Test Set:')\n",
    "        results[key]['performance']['test'] = evaluate_dataset_F1(df, pred, val_set='test')\n",
    "        \n",
    "        if labs:\n",
    "            print('Prediction for Golden Sentences:')\n",
    "            print(F.softmax(classifier(prepare_embedding(labs, args.embedding_type, args.fusion_type)), dim=1).cpu().detach().numpy())\n",
    "            results[key]['golden'] = F.softmax(classifier(prepare_embedding(labs, args.embedding_type, args.fusion_type)), dim=1).cpu().detach().numpy()\n",
    "            print()\n",
    "        \n",
    "        print('Full Dataset:')\n",
    "        print(pd.Series(conf.cpu().detach().numpy()).describe())\n",
    "        print('Training Set:')\n",
    "        print(pd.Series(conf[train].cpu().detach().numpy()).describe())\n",
    "        print('Validation Set:')\n",
    "        print(pd.Series(conf[val].cpu().detach().numpy()).describe())\n",
    "        plt.show()\n",
    "        \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "230f3e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperdict = {}\n",
    "experiment(df, stored_data, hyperdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f1279b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperdict = {}\n",
    "experiment(df, stored_data, labs, hyperdict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a58b3434",
   "metadata": {},
   "source": [
    "## Performance on Trained Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "29309456",
   "metadata": {},
   "outputs": [],
   "source": [
    "perform_results = {}\n",
    "for o in ['O1', 'O2']:\n",
    "    perform_results[o] = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "52cde508",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(args.save_dir+'001/hyperdict.p', 'rb') as fp:\n",
    "    hyperdict = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "52c83fcf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_acc\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_acc\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_img_txt\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_txt_only\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_unseen_img\n"
     ]
    },
    {
     "data": {
      "image/png": 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HhK5jR3XnzwHxuPXvf952Zy3btIm0e2egVFTgNngQke+912wFviXGEn5L+Y2lR5ayI7v6A4Oroytjo8YyMXYiA0IGNChbYbKYyCrPqvfillGaQVZ5Fmar+bTn0KAh0DVQDU5OuriFuau3uTo2X7GzVbGSW5FbZ1qh6vVklGVQUbOYvFKAa4BtfDXHXBVguTs1X+CoKAp5+rxamYpa4y/LpMxUdsbzeDp71vsaqm7z1fm2+JSaBB/nIavFyj/fJ7H3L7U2oOvQUIZf3wkHBzUyNerNLH9nN5mHi3BycWDi/b0I6+DTsJNnH4Svr8Gam0ZOYgD5B3Tq9EsVJyeCHnkEv+m3oAFI/UcNRA78AsbqVRVEDlSnZbpdAa7n4KfWjJ3w12uQuKL6ti6T1KAjtJf9n09RIHOX+l7t/R+Uq+3Ol3i482ygOm3yxoDnuLjL1SiKQnZKCYmbMknalo2+zGQ7jafxBMHH1hNacoDY117EY/jwRg2jzFTGbatv40DeAcLcw/hy/Je4GbxZ89l+MpIKAeg0KITh18ZR/MXn5LzxBgAeo0cT/tqrzfYJ3ZCURNEvv9TpzOng44OlsLDO8c4xMbWCEafQ0GYZlz2V/rOB4/fdh2Iw4D5sGBHvvG33PXbMVjMbMzay7Mgy1qWtw1BZJK1Bw8DQgUxuP5kx7cbg5uRm1+e1WC3kVOTUWzNQdXHXW/RnPI+fi1+tupOa2ZNQj9B6M4JVzFYzJ8pP2J4/ozSjViYnsywTk9V0ysdXCXANwMPJgxPlJ+oNRk7mrfNWL+I1pqOqLurhHuF4OXud8sJe9b5VjbNqzDXfx6p/w9Pxc/E7bYDk6dzEKfhmIMHHecZYYWb1J/s5tj8PgMFT29NnbLs6P9wmg4UV7+0mPbEQR50DE2f0JLzTGYKAw3+gfD+dkiNGTuzyx1ym/nN7jr2YgPvvJ/e99ylZvRoA9+EXETZvHo7+lfUGxjJIWKlONSSvq566cHCGTpepUw0dLoaT0q6Zhws5kVJMYDtPgmO8cHRq5k/XaVvgr1fh8JrKGzRqgDT8MQjudsaHK0YjpX//jSH5KC7duuLWpw9at0b+IreY4PAf7NrxMbcZEjFpNNybV8bleSEcd5tIUulACguq/z3dvJ2JjdLg8f1ruGUlNrkgNK8ij1t+vYXU4lTifONYeOlCPBw92b4qha3Lj6Io4B3kyrg7uqPb/w8ZTzyJYjSi69qFyPffxyk4+Kye92TmvDyKV6ygaMkv6A8csN2u9fbGa/xlWMZdxB8ex7jEexDuB45RvnUr5du2YUhMtDXKquIUEaEGI/HxuA2IxykiosU/yenLTGQeLiQvo4yQGC/CO/rapsBK/vyT9AceRDGZ8Bg5kvD/exOtzj7pfEVRSMhPYOmRpaw8upJ8fb7tvvbe7ZnUfhITYicQ4t74Tqn2oigK+fr8eoOCqotuqan0jOfxdPK0XWBD3EMoM5XZMgDZ5dlYquq0TsFB40CQW1CtAKHmxTrEPcQ2zaIoCkWGonqDgqrbGlIz4+boVisQqDBX2M5zouwEZuXMGaOqMdcX4IS6N2/GqLlI8HEeKcnXs+Ld3eSll+HgpGXsrV1p3/fUSyJNRgurPthL2oF8HJ20jJ/Rk8gufvUfvO0zjN89QdY2D8oy1U9jThERBD/zNJ4jRwKVc6Lf/8CJuXNRDAYcAwMJe+1V3AcNqn2u4kzY+4MaiGRXX1Rw81enGnpeiyWoF5uXHmXnmur5Z62jhuBo9Zd2WAcfQtp746SzUzCS8o8adBytrNjWOKhjueg/ENjxtA9VFAX9nj3qVMCKlViKiqrvdHTEpVtX3OPjce3fH7d+/XDwPPOnjOP5GTz63dO45wbSrbwHHoXBWJTqLqSOWjOxPX3oNLw9nkc2ceKpWSgmk92Wwh4vOc5Nq24ityKXvkF9+XDsh7g4upCRVMiaz/ZTWmBA66hhyBUdiPPLVVfC5OfjGBysBj5dupzV81oNBkrXrlW3YP/nn7q7sU6ejMfIkewrSuCBtQ+Qr8/HR+fDS0NfYkTkCAAsRUWUb99hC0b0Bw5Un6fq/QsOVgOR/v1xi++Pc2ys3YOR8mIjGUmFZBwuJONQIXkZpdQsi/Dw1dFxYAgRSgqlzz0CJhOeYy8mfP58u/TQOFF2gpVHV7L0yFIOFx623e7n4sf4mPFMaj+JLn5dzpsVKsXG4jrTOTUzKAWGgjOew0nrVKcgtmb2JMgtCEet/fahKTOV1Qmmao4/T593xnNU1cqEe4TXu9ooxO3crpU5WxJ8nCeyU4tZ8d4eyouMuHo5M+HengTHnPk1m00Wfv1wH6n78nBw1HLZvT2I6lajI6fVgnXV0+R9/iV5Bz1RLBo0Tk743XE7AXfdVe98tP7QIdIffRTj4SOg0eB/110EPnB/3c2lFAWy9sKe79T6kDI1nV5kDua3sqfIrmgHQHhHHwqyyikvrt3fQqvVEBjlSVicD2FxPoR28EHn2ohfHIqiZmH+eg2Obaw8qaOahbno0TPWpZjS0ylatoyiJb9gTEmx3e4YGIhrnz5U7NuLOSOz9oO0Wlw6d8YtXv0U7tqvH46+vhgrzGQeKSIjqYDjhwrISilEq9QOrFzdtYT5ZBJV9j/aO/+Dk5OG3NKLyf1lO1DZBOyVVxqfaTmFxPxEbv31VkpMJYyMHMkbI9/AUeuIvtTE2i8PcnS3OjUU3TOAiy72JueR+2xNzMIXzLcFpWeiKAoVO3ZQtOQXin/9FWtJie0+l5491Q3Pxo+3FZX+lvIbT/3zFAaLAWets63vyY1dbuSRfo/g7FD7wm0pLaNi5041GNm6lYp9+8BUO73u4OdXKzOii4trdDfX0gIDGUkFasCRVEhBVnmdY3xD3PANdSc9sQBDefUnWs/iVKL9iun/4h24+Z391FW5qZw/jv3BsiPL2JS5yVYE6qx1ZlS7UUxuP5nBYYNx0l54F6tyU3mtJaFZZVm4O7nXulj7u/rbvTCyKfRmPZllmdXTT2WZuDi42KZkQt1Dz/lVQhaLFazg4CQFpzZtJfhI3pXDms/2YzZa8QtzZ8J9PfHyb3iazWKysvqTfRzdnYvWUcOld/UgpmcAGMsofe06TixJwFiiXtTdBg0i5Lnn0MXGnPac1ooKTsz7L4Xffw+Aa58+hL/+2qn3frCYIXkdSas38uf+fhgVNxy1pRSGL6QoJIWbrZ6EGILIKGlHRmk70kvaUWqsXSirwUqA2wnCPNII8zxGmGcaLo6nmZPVF0HuIfXvDs7Q5yYY9jD4tDv1e1VaSsnq1RT9spTyLVuqn9vFBc+xY/GeMgX3wYNsxZem9HTKKj+Bl2/diilVzeSYHN0o9O5AoU8HioK7U+wcBNT+BFquK6Jjtwg6dAkjLM4Hn2A39VNqdgLWpf8h63/7KEpRAw2/K8cSNOdNu7c/35a1jbvX3I3RamRq3FRmD56NRqNBURT2/pnOhh+TsJoV3H10jLk2Gutbz9Rp334qxtRUin5ZStHSpZiOVxcmO4aF4j1pMt5TJqOLrQ4AFUXhs32f8eaONwEYHjGcl4e+zId7PuSrg18B0MWvC68Of5Vo7+hTPq+1ooKK3bsp36r+m1Ts3o1iqD1vrvX2xrVbV5zCw3EKC1O/Kv/uGBQEDg6U5OlJP1SV2SigOLduzYJ/uDthcb62ANnNSw2MzCYLBz7/jYNrDpHn2xWl8uKi1Wpo192fTgNDiO7p3+BpxuMlx/ls32csT15eqw6hb1BfJrWfxCXRl5y2HkKIs2GoMLP6o724eDgz9rauds2iSfBxDlMUhV2/p7Hxp8OgQGRXP8bd2b1xn/4rWSxW1ny6nyM7ctBqNYyZ7In7e3dQfEj9pezg40Hws7PxGj++UT9gxb/+SuYzz2ItLUXr5UXoS3PwuuSSOseZjBb++e4QBzaomYJ8r1RWdvicUp2aSnVQFG4oLmFGQRHulT9mxZZAMozdyDB2JcPYjSJLWJ3z+jmmEuZ8gDCnfYQ5H8DdobD2AY4u0G86DH0IvOo+HkAxmyn791+KllTu4ll1odJocBs4EO/Jk/G85BIcPE79ibUqBX98dwbpB3MpLKkbJLhU5OBbeBjFfJgs7yMMHjeVHmOvwSms9rhqbfymUQjpV4Rvh3LocQ1c8hJ42qfmosofx/7g0T8fxapYubPHnTzY90HbfTlpJfz2yX4KT5Sj0UC/S9sRuXURRf+rsXHdrCdtwZilqIjiVb9S9MsvVOzcaTuP1s0Nz0svxXvKFNzi+9cJokxWEy9teomfktQVNjd0uYHH+z9u+0T49/G/efqfpyk0FOLq6Mqzg55lUvtJDXp9VqMR/d69tmCkfOdOlPLaWQsFKHcNotAnjkLfOIp8O6J3Oin41YB/sI7wLv6EdfInLM4HF/f6MwyFS5aQ+dTTYLXiMvlqisfdwaEtJ8hOrc766Nwc6dAviE6DQgmJrb8oMaUohY/3fsyK5BW2eoZIz0gmtZ/ExNiJRHpGNug9EKKxinMrWP7uHgoyy3DUOXDNrP74htiv4FyCj3OU1WLl728PsX+92jWz2/Bwhl8bh9bh7D/5Wi1Wfv9sP0nbc9AoFroeWEhw7nZ8p4wl8Om5DapVqI/x+HHS//Mf9Lv3AOAz7TqCn3jCVsmfmpLFqo/2Ysl3QMHK9vDf2B65GhcnHWNDBlNRls2agn0ABDl5MTNqEpf49ajzy7isFDLStWSka0k/rqUgv+574eNrJSy88itCwS2u7ym7seoTEyleuoyiVSux5OTabneOicFr0iS8J0445SqK8mIjmYcLSU8qJPM0KfiQSFf8ycbz+G5Kt6zG4chxTh61U3i4beWGc7t2ZD7zbPWW96+9jEf5atj6KaCAzgtGPwvxt4MdU7U/HvqR2f/OBuDJAU9yQ5cbbPcZ9WbWf3eIhH+zAAiL86a/215K/+8VADxGjMDn6qsoWrac0rVrUaqmPLRa3IcOVbdgHzP6lEtKiwxF/OfP/7A5azNajZaZ8TNtz2+xWG11FNllJ3hm47Nsy9oGwISYCTw58MlGL3VUTCb0CQnk7E0lM6WU7FwNuQYvDJra49NYLXiWpOJbmIRP0WG8i5JxtOhBo8ExMLB2xiS8+u/l27aTNXs2KAo+V19FyAsv2IKt/IwyEjdncWhLFqUF1dkYr0BXOg0ModPAELwDXTlccJiP9n7E6pTVWCuLt4eGDeX2HrfTP7i/3es4rFYFxdo8v961Wk3zbPkgmk3W0SJWvreHihIT7t7OTLivF4Ht7LtiRoKPc5Chwszqj/eRdiAfNDD0yg70GhPZ5F84Fbt2kfHU4+xyHk1WyEBQrIy4CLrfeHGTx6yYTOS89RZ5H38CgHPHODJn3sAfCSfw2d4JR8WJMqci1sZ9SbvOAUxqP6nWkr9/0v9h7ua5pJWoTa+Ghg3lqYFP0c7r1FMkFSVGW7FfxuFCco/XLvhraf7hHrb0e80UPMCRwiPcsPIGKC7lDoYxqbg95Vu31lssCeAUFkbkhx+gi4tTb0jfAcsfUZfrgroceOIbEN7PbuP/aM9HvL3zbQBeuegVxseOr3V/4uYs/lqciMlgQefuyOAeBhzeeLzOlIauUye8p0zBa+KEMxbGJmen8OyvL1KUV4G/KZjxgVPwNgZQkq+nJF9PRcmZl0bai4OjluAYT0LCnAn0qMDXkoNyIh1TRob6la7+XdGfeckogO/10wh+5pl6p8qsVoX0QwUc2pTF4Z05mA3VPwMVAXls8VrDEf9dGB0rGBkxkrt63kWPwB5n/dpMRgsleXr1K7/mnxWU5OkpKzY22/8drwAX+2/5IJrN4e3Z/L7wABaTlYBIDybM6IWHr/0brUnwcY4pzqtgxbt7yM8ow9FZy9jbuhHbu2n7qJgLCshZ8AaFP/wAgMZZ4ehFd5Bs6QsaGH1TF7oMaXqfBEVRSFj1LYbZr6It13Cg8w3kBfQBIDcwhYjJWiZ2vYxg9/qnDfRmPZ/t+4xP9n6CyWrCWevM7T1u5/Yetzeoy6C+zETWkSLSk9Q5+py00mb7NKfRQGA7T0LjfAivLIY9VQq+yFDEtBXTSCtJo39wfz665CNbQaCtWLKyZqRi715cu3cn4u236jbSslrUXXX/mAOGIkAD/W+DMc/apZeKoij8d8t/WZywGEetI++Ofpch4UNqHVN4opzfPt1PzjF1+qBrDxfCfngWjcWI98RJ6hbsnTvbzldRYjrpYlf9Z2FeGRZ96/1KcXTWEtreuzJY9CUo2vOMNRiKomApKFADkfTaQUnV3xWDAb9bbibwP/9p0AcGk8HC3+t3sn19Em4nAtFU5sasWgsh3dyJv6gjkd38bH186mMor36fiyvf49LKP4vz9OhLWy6Iq49XgAtXPxmPi8eFVwh7oVAUhR2rU9m0JBmA6B7+jL29G84u9lsdVJMEH+eQE0eLWfH+HiqKjbh5OzNhRs+zb48OKFYrRT8vIfv119UtswHvmHKCbhiLwzXv8tcPR23TOiNv6ES3i05RLHqmcZedYMXRFSw7sozDhYdpfyKacYduweroh8Zqpr3Dbkb/9y6cvBvWaTW1OJW5m+eyMUNdoRLpGcnTA59maPjQBo/JajBQsnU7hct/pXTtWqxl1V0AXXr2xGvCBDwvvhgH77N7fx0cNQ0qFjRbzdz7+71sytxEuEc4iycsxs/lFMudUetPcHA4/UWrNBt+e1bd0A7ALUCtBel1XZPb21sVK0/+/SSrUlapbdgv+bTOJ26L2cq/S46w+/fK1uwRHvQcHUF5sbHOp2uzyVrf09RidK4gOMgXv0BPPPxd8PRTv7wCXHD30Z1yqrHYUMzLm15iffp6AIaFD+OpgU/j4+LToNfq5Kxt0jTmqSgmExqnhl1kd2Xv4oM9H7AhfQMA7gYfJlquJyKrG2UnqlfLuHo6ERcfTEiMN6UFBltmSH2/KzDqT9/fAsDZxQFPfxc8/V1t77H6vfp3e69mALUv0ZIFOyjO1RPeyYdJD/Y+bRAlWofFbOWvxYkc3KjW5PUcHcHQq+LQNuN0mQQf54gjO7L5/fMDmE1W/MM9mHBfTzz9zr77oT7xEFkvvEDFDrWtss7bREi/ItymPQnDHoXKFQ3rv09i7zp1JcLw6zrSY2REg85f35I/jaKhX8Y4+qWNQ6NocdcZ6bLp//AqSsEpIoLwBfNx7dmzQedXFIXfUn/j1S2vkl2hLtEdGzWWJ+KfqDdzYi0vp2LXLsq2bqVi6zYq9uxBMVYv3XWKiMB7srrCwjkqqkFjsIdXtrzCVwe/qrd1epMdXQ8r/lPdtj1qKEyYD0Fn14OjisliYsYfM9iUuQkfnQ9fXPYFMd51Vz+l7M1VW7Of4VO1m7czXpUXOA8/F3brt7Es+ydKdfn0bt+d/46ae9YdNxVF4dvEb3l96+sYrUaCXIP47/D/Eh8Sf1bnawmKorDtxDY+3P0hm7M2A2rzqwmxE7ijxx3EeMegKAq5aaUkbsri0NasBk0/uXg41QkoqoI4Tz8XdG6tk3XIyyjlx1e2YzJY6DEyguHXnb6vjmhZ+jITv360j/TEAjQauOjahl8HmkKCj1amKAo7fzvGvz8fAaBdN3/G3Xn2qS5LaRm5775L/hdfgMWCxgkCuxXh18WM5sr3ofuVdZ5/w4+HbZ9ih17Vgd4X119nYbFa2HpiK8uOLGNN6ppaS/4Geg1l4IErMKapv+Di4oMZeX0nLIn7SP/PY5jS08HRkaBHHsbv1lsbvGS01FjKe7vfY/HBxVgUC26ObszoPYPrIiZj2rWnsrfDNir27wdz7U6BDgEBeI4aiffll+Pat2+LN1v6Oelnntv4HABvjHyDi6OaXltTh9kIm96FP18Bc4Xax2Tw/TBiJjiffWV6mamM21ffzv68/YS6h/LlZV/WG/SVFRrY+NNhSgsMdS98/i54+lZ/ojZZTMz+dzZLjywF4KauN/Gffv+xS4+DxPxEHvvrMVKKU9BqtNzd827u6nmXXRtKNZWiKPyb8S8f7vnQtteKo8aRKR2mcHuP20+5csVisZJ2IJ9Dm7MoyTfg6aer8R672t5ruzXkawZHd+ew8oO9oDQtyyrsqyinghXv7qYgqxwnnQOX3NGN6B4ts2+SBB8trNRYyi9HfmFExAhC3cL4e3Gibflpj5ERDLu6Q6NTwYqiYDx6lLJNm8j78CPMJ04A4BkDwd2zcAr0g2nfQOSAUz5+05JkdqxOBWDwFe3pO646O1BiLOHzfZ+z9MhSTpSfsN3ezrMdE9tPJN44ij3f51BRYsLRWcvw6zrReXCI7WJvKS4m87nnKfn1VwDchw0j7L/zGrU5WMLRrXz//Qu47UumS5pC9AnQnvTT6Bgaamvu5da/P87R0a3W3XFn9k5uW32bumNt7xnc2+ve5n3CwmOw6snqvWq8IuCyV6DzhLOeisnX53PzqptJLU6lg08HFl66EG9dIzcprFRkKOLhdQ+z7cQ2HDQOzBowi2s7X3tW5zqVclM587bMY8nhJYDaA+OV4a+0altxUP9//X38bz7a8xF7ctUVYU5aJ6bGTeX27rcT6nHu70tjD9tWprB5aTJaBw1THu5DWJxPaw+pTcs8UsTK9/egLzXh4atjwn09CYhouT1gJPhoYZ/s/YT/2/F/+Gj8mZ7+NPpUB9DAsKvj6DW6YWv2FasVQ9JhW0fH8m3bsORVt/F1CvEnpOsxPIKKIKAT3PC9uuPs6c6pKGxZfpRtK1IAGDg5hv7jY9ibs5fH/36c9FJ1EztPZ08ui76MSe0n0d23B5t/SWZXZdbEP8KDcXd0q3ct+Mmt2R0CAgh/9RXchwypcyyAOSfHVoRZvnUbhqSkOsdk+UBZ92j6XnIjAUNG4hxxbnyayirL4trl15Kvz2ds1FheH/F6y3VdTFwFK2dCUWXb+rhxMP7VM/77n0p6aTo3rbyJnIqcWm3YG+NY8THu++M+UopTcHdy5/URrzMsfNhZjachlicvZ86/cyg3l+Ot82bOkDmMajeq2Z7vVKyKlXXH1vHhng85mH8QAJ2Djqs7Xs30btNPWXh9oVIUhd8+3c/hbdm4ejpx1ZP9G9UsUdhP0tYT/LHoIBazlcB2nkyY0RN3H/uvaDkdCT5a2INrH2Rb0m4uS7gbv4oQrI4Wxt7Wlc59T33hVMxm9AmJtkCjYtu22vuLABqdDtdevfCIdsZX+R9aByvEjIBrvgBXnwaPb9vKo2xeehQAx/hCPnCcgxkz4R7hPNLvEUZFjsLZwZminHJ++2S/rWlSj1ERDJna/oxFmIakJNIffRRD0mG1NfuddxL4wP2Yc3NtgUb51q212plXcW7fHrf4/ii9uvCF8za+zlUzKd46bx7p+whXxF3R6q2VK8wV3LLqFg7mH6STbye+uOwLu+8gekbGcvj7Ndj4NlhNaqO14Y/BkAfBsfG/YGq1YY8YyRuj3mjwdMaOEzt4aN1DFBoKCXUP5Z0x79DRt/nn/I8VH+Pxvx/nQJ66t9D1na/n0f6PNmjVVFNZrBbWpK7hwz0f2vZccXV05bpO13Fzt5sJcG2ZtPa5yGS08NNr28lNKyUg0oOpj/U7p6eLLjSKorB9VYrtd3xMrwDG3tatVf4NJPhoYZd/cTXDttyIq9mTMqdCVnb5CK8wZ14b/pqtGFExGqnYv1+9EG/bSsX2HbVWawBo3Nxw69PHNs3gEumNdsN82L1YPaDvzTBhQZ1dZBvin+UH2b1cnQraEbYGn4uMPD/0eVv75kNbs/jz60RMegs6N0dG39ylUcuBrRUVnPjvKxR+9x0ARp0rzoaT2qRrNOg6dareHKx/v+oddCvtzN7JnE1zSCpQsyK9Anvx7KBn7VvU2QiKojDz75n8mvIrvjpfvp34LWEe9XdVbRE5iWpBaoq6GgTfaIgcqLaX944En0jwbgfeEeB0+mzG9hPbuXvN3RgsBq7ocAUvDHnhjFNay5OX89yG5zBZTXTz78Y7Y95p0QuvyWLi/3b8H4sOLAKgs19nXh3+ar3Fs2ej5r4d6WXV28fvy91HarE6henh5MG0ztO4qetN+Lo0fTn0haAkX88P87ZSUWKifd8gxt3Z7bzZ/O58ZjFZWfd1Aomb1GaBvS+OZPDUDs26ouV0JPhoQUWGIh56bQ7xx8fjHeJC9DQtz+9+moKSE3TJdOQOy2Bij+rVvSgqal+MtZ6euPXti9uAeDXY6NJFXc6XnwzrF6g7yForCy4vfkFtJ34W/6H/zfiXp/55itDkbgxNmQpArzGRDL2qA2ajlfXfHbItxwrt4M3Y27o1elVOVpGeD/46QupPS5mx7Xs8zHqsGi2u3bvhXrX5V7++ODRgaa7ZambxwcW8u+tdys3lOGgcuL7L9dzX+75Gd75sqo/3fMxbO9/CUePIx5d8TP+Q/i36/PVSFHWH4dVP2zb2q5dHcHVAYgtO2lX/XefBumPrePjPh7EqVm7vfjsP93v4FE+p8MHuD3hv93sAjGk3hnkXzWu1bb//Pv43z/zzDAWGAlwdXXl64NNMbj/5jBe8UmNpvdupV+20WnPr+pN5OntyU5ebuL7L9WddJ3MhyzxcyJI3dmK1KLYpXtF89GUmVn2wl4ykQjRaDcOv60j34a07TS3BRwvanLmZhR+sovuJ4fTo7kBnyy6KN/+Lfs8eHMy131oHH59axZO6Tp1s+2cAkJsEf7+uXlgq93wgZjiMnAVR9ddRnI7JauK9Xe/x6d5PUVDo4NOBB5ye4+BStT9I50EhnEgpVtuIa6D/+Gjix0c3qjj2eEE5H/x1hO+3HsdoUfs/DPbXoj+STIJ7CDeP6cqs8We3TDSrLIvXtr7Gb6m/ARDkGsTjAx5nXNS4FvlUte7YOh5a9xAKCs8OepZrOl3T7M/ZKPoiOPw7FKRCURoUpqlFqkVpYKrbGr4OV1/wjuQnTw+eN6tLs2e2m8BNnaepwYmrL2g0GC1Gnt/4PMuTlwNwa7dbebjfw60+HZZdns2s9bPYkqVuFjghdgIP9XmIfEN+neCianv0EmPJGc4Kbo5utbdu9wgj3COcoWFD8XCWjp6nc2BDBuu+TADgsnt6NLmZoqhf4Ylylr+7m6LsCpxcHLj0zu606+Z/5gc2Mwk+WtCi/YtI/jibIH0/Ohz+H+2Or7PdZ/RxZ3tYBfsiIadjII9eOZ8+IfW0zj5xQJ3P3/8ztn7IHS6G4TOh3cCzGld6aTpP/P0Eu3N2A3BNx2t4PP5xXBxd2L8+nT8XJ9qeyt3bmbG3dSO8U8NTyKl5Zby37gg/7jiOubLj6IBoPx4cE8fQDv4s35PJA9+om5AtuKYXU/ue/RrzDekbmLt5LsdK1ILLdp7t6B/Sn37B/egf3L9ZpkEOFxzmhpU3UG4u59pO1/LMoGfs/hzNRlGgPB8KawQlRZWBSWGaWriqr11f9LG3F2/5+QAwLzuXiWXlENKDwrGzeejQl+zI3oGDxoFnBj3DVR2vaoUXVT+L1cKn+z7lvZ3vYuHMzc9ArScKc68dXIS5h9m2RPdyrn9DuHOeoRSOb4GUDZC6ETJ2gLNH5VTcSVmvqmyYi/0zOOu/O8Sedcdx0jlw5cx+0oLdzjKSCln5wR4MZWY8/HRMvK/XOfMeS/DRgmatn0XYogi0jl3odvwnOnZzx7V/f9zj43GKiuJA/gFm/jWTYyXHcNA4MKP3DG7vfrvaByFztxp0HFxWfcJO49VCwibs77EmdQ3Pb3ieElMJnk6ezB4ym0uia+9Ke3BjJn9/m0hEZz9G39wZVw/nU5yttiM5pby79jC/7M7AUhl0DO3gzwOj4xgUWzvyfn11Iu+sO4yzo5bv7x5M70ifs35NBouBz/aqbdqNVmOt+0LdQ+kf3N8WkLTzbNeki0ehvpBpK6ZxvPQ48SHxfDj2Q1vr9AuGvqhGUJKGUpjKKzn/8rUlB0dF4Z0TOYSbzNwXEsgxJyc8HN2ZP2oBQ8Ian4FrViVZsPopdiYt46lAf447ORFgthBmNhNqNhOGI2FeUYQF9yS03XDCokfg3sBuqec8fREc2wQp/6jBRuau6mnahtJ5nxScRNYITqLAzb/RU71Wi5Vlb+/meEIBXgEuXPVk/wb/fhGnl7g5i7VfHsRqVgiK8mT8jJ64e7fsipbTkeCjBV3xyxVc9vMUTC7tGNlfT7c7xtc5psxUxpxNc1iRrPZrGODbhXklZoKS/qg+qOsUuOgxCG1Yt9D66M16Xt36Kj8cUvd76RnYk1eHv0q4R/3zgBaLtcFtkROzSnh7bRIr9mZS9RMzslMgD4yOo19U/RkTq1Xh7q+2s+bACYI8dSx7YBjBXmff4RXU/iQ7s3ey7cQ2tmdtZ3/eftu25FUCXQNtWZF+wf1o79O+wcGIyWri3jX3sjlrM+Ee4Xwz4Zs2U1RoVaw8uf5JVh1dhauDC05WM8WKmTCTmXeLDHQY+Rz0uRka2EyuWVnMsPUTWPsSGEtAo0XpfwfGLhPRpW9XL8bHNqn31eTkrmYTo4ZA1DAI73tWq4VaRVkeHNuovraUfyBrL3V2jvOOVLviRg2BdoPAbKieiitMq50Nqzh1fYuNk5tavHxyQXNVoOIRUu/Pg77UxA+vbKM4p4Lwjj5MeujCa8GuWBV1+4F8PY7OWvt3nC3Lg+LjENwdRaNl6/KjbK1sm9C+TyBjbu2Kk/O5tapIgo8WojfrGfT1QO7781mMOj8mXRdIu5H171KpKApLtyzg5YRFVKDga7HwUm4Bw2MrMx1NbJ99uOAwj//9OIcLD6NBw+09bmdG7xlN/sS+L72Id9Ye5tf9WbbbxnYN5oHRHegZ4XPGx5cazEx9bwOHTpTSK9KH7+4ahEsD9k9pqHJTObtydrH9xHa2ZW1jb+5eTNbabat9db70De5rC0Y6+nY8ZQfOeZvnsThhMa6Ornw1/qsWWUJ6LjFZTNy/9n7bHjw9vWL5v6wcAk7sVw+IiFdXXDUhSG6y49vU3YCz1OZehPdTxxTWu/ZxFjOc2Fs5DVE5FaEvrH2Mo4v6mqKGqBftiHhwbuFl1KdSklU97pQNkHOw7jF+sZXBxlCIHqoGBQ1lKK0xLXesds1QYRqUZp35HFqnyuCkKiipLm7OMwTz4/uZagv2EeEMn9Y6K9bOltVirWfPnRp/L9BjPamuz9nVsboT8El/evm74OLhdOoPQqf497a4R7DW+DSH0tTGen0uacfgy9ujaaUVLafTbMFHdHQ0qampdW6fMWMG7777LtOnT2fRokW17hs4cCCbNm1q6FOcV8HH3py9PLR4Gtfsew2Lg44bnuuPT9hJY1YUdVnkX69CynqOOjkyMzCABJ2ahry568083PdhnM5i+ax6eoWfkn7iv1v+i96ix9/Fn3kXzWNw2OAmvbZdaYW8/UcSfySoqyk0GhjfPZT7RnWg68mv8QxS88qY8u4GCstNTO0TzvxrejXbnLrerGdv7l41M3JiO7uzd6O31N4u3dPJkz7BfWzBSBf/Ljhpnfjx0I/M/nc2AG+OfJMxUWOaZYznunJTOS9uehFPJ0/+0/8/uGgcYctHsO5lMJaCRgsD7oZRT4FLC/4fLc+HP16A7YsARa1XuHg29L0FGtLO3WqF7APqL/fUyqmKspzax2id1GxI1QW93UDQtVCHyMK0yovPBvXik3+k7jGBnaszG1FDwasZO6ma9FCcflLmpMbfi9OrC+NP4ahhACsLngC0jOyyhW7djbVXXnlHgFPrrJgymyyU5htsAUVxXkWtXZrLCgyc6eqo0YC7jw6z0Yq+7Mx79Tg6aasDEk8znmTgqT+IZ9EmPMt24a4tQKOpftIKbTCrch4k09QVDRZGRCyn2+g46HE1eJ57De2aLfjIycnBYqn+Ydu3bx9jx45l3bp1jBw5kunTp3PixAk+//xz2zHOzs74+Z16x8+mDL61/XDoB1YtfIl++W8AcPv8i6q3YFcUOPIH/PUapFUGX1on6H09hsH3sSD5JxYnqP07uvl349Xhr9LOqxGfWlCnIF749wVWp6wGYGjYUF4a9lKT+i5sTcnnrT+SWJ+Uqw5ZA5N6hXH/qA7EBZ/9L+GNh3O56bMtWKwKT43vzF3D25/1uRrDZDGxP2+/LRjZmb2TMlPt/iqujq70DOzJ9hPbW651+vmoOANWP1VZGI2acr90LnSb2uSdd09LUWDXYljzLJRXdv3tfYO6/NyjCaspFEVdYVbzgl+SUfsYjRZCe0G7waBrjt9HinpBT9lQ3cG2+skhpLs6PRQ1RP1yP4eamVnM6vtVq6D5WO0iZ4uRbaVXsrn0RrSYmeL3PGHOB2qfxz2odt1J9EXQfjQ42G8Pn8zDhRzdk1src1FebDzj47SOGjx9T8pk1Njgz91XZ5tOMurNlOYbKM6roLRGtqS48jnLixrwfBozHq5GPP2c8QwNJONoBcW5epwdDVzq/RqRTtvVAzVaaD9G3fW684RWC+BO1mLTLg8//DDLly8nKSkJjUbD9OnTKSwsZMmSJWd7yvMq+Jjz7xyUz1bi7/AyoDDjvdHq7+BDv6qZjgx1oykcdGqDsKEPqf/JKq09tpbnNj5HkaEIdyd3nh30LBNiJzTouffk7GHm3zNJL03HUePIg30f5JZut5zV8kdFUfg3OY+3/khiU7I6D+yg1XBFn3DuG9WBmAD79NZYtDGF55fuR6OBz6bHM6pTkF3O2xhmq5nEgkS2ZanByPYT2yk2Ftvub/HW6eejw3/AysfUfjQAsaNg/OsQ0MH+z3XiAKx4FI79q34f2AUmLjirpednpChQkFIj9f2PWiPRUjQO6tRRVT1Ku0GN6mR8zrFaoSwbpeAYv/2Qz+EkZ1ycDVzd63u89AfVQOWkDwI27kHqp/te10JIz7MKbhVFIf1QIdtWHCX9UGG9xzjqHGyBRNXUSM3pEjcv57Ob3lAUtSFgavWUn6U4hxJLACWWIEosgZRYgylx7kSJth0lRm9KSzUo9SzY8vR3YeJ9vfDzMaiB/+5v1VVNVXRe0HUy9JoG7Ya0ak1WiwQfRqORsLAwHn30UZ566ikApk+fzpIlS3B2dsbHx4cRI0bw8ssvExTU8IvM+RR83LDiBsZ8doKKwKfQOVq4Y0ZlC+ysveoBjq7Q/zYY8sAp06NZZVk88fcTth0xL+9wObMGzDpl+26rYmXh/oW8veNtzIraIv214a/RI7D+WpPTURSFv5NyefuPJLalqr0/nBw0XNUvkhkj2xPpZ9+5b0VRmPXTXr7dmoanzpGf7xtKh6DWXSJmVawcLjzMtqxtFBmLuKXrLS3fOv18ZNLDhv+D9fPBYgAHZxj6MFz0qH0+hRlK4a//wr/vqal9J3cY+SQMuvesOvyetaLjaiCSvgMsZ/7kelbc/NTMSuSAlpviaWEmo4WfX99BzrES/CM8uPLxfjg5a6GioEa25JiaiTq4DMpzqx8c1FX9hN/jmgZNMymKQtqBfLatTCHziLqkXOugIS4+mIAIj1pZDBf309RgNIbVCtn7a9cX1XwNoP4fCe9XPW0WORB01b//rBYrpYUGNWtSmS1RFOh2UThuXietFso7Anu+UxtRFtbImHm3UwO2ntc1z4eBM2iR4OP777/n+uuv59ixY4SFqX0WvvvuOzw8PIiKiuLo0aM8++yzmM1mtm/fjk5Xf0W5wWDAYDDUGnxkZOQ5H3xYrBYGLR7EcwtDOdbhYby12dwYdLd6p7MHxN+hboPegLSw2Wrmoz0f8eGeD7EqVqK9onl9xOt1WornVuTy9D9P24oBL42+lOcGP4enc+N/YR3IKGbWz3vZnVaoDtlRy3Xxkdwzoj1hPs2XwjOardzwySa2phQQG+DOz/cNxdv1AlvG2pbkHYGVj6tTjKC2ex//OsSNPbvzKQocXAq/zlJrCgC6TIJL/6vWB4jzVq0W7H0CGXdn9/qzChYTHFmrXlgTVqrBLahTDTEj1E/4XSaCc+2MrKIopOzNY9uKo7b9qRwctXQdFkafS9o1umvzaVnMkLW7uqfKsY11eufg6AqR8dX1QxH97T89YrWq0/q7v4H9S8BQncUlIh56Xgvdr1QD3BbQIsHHuHHjcHZ2ZtmyZac8JjMzk6ioKL799lumTp1a7zGzZ8/mhRdeqHP7uR58JBcmc80Pk3nti54c6HYHIQ4HuTLiFRh4t/rp7Cz+sbdmbeXJ9U+SXZ6Ns9aZx+If47pO16HRaNiYsZGn1j9Fnj4PFwcXZg2cxRUdrmh01K4oCl9uSuWlFQcxmq24OGm5YWAUdw+PJaiJy2AbKrfUwOS3/yGjSM/wjoF8Pj0eh3Owcls0UFXAsOrJ6pqJswkY8pPV3XsPr1G/94lSA5mOl5z+ceK8UbMF+4BJMcRPOEML9opCOLBEnWqomnoDNRPWdQr0ug6l3TCS9+SxbVUKuWmlgFrY2W1EOH3GtrNPHwyzUZ1Gr6oNStusFl/X5OyhTpVVTZuF9QHHFuxvYqpQd8De/a3a+biqGFjrBB3HqUFb3CXNOqZmDz5SU1OJjY3lp59+YsqUKac9Ni4ujjvuuIMnnnii3vvP18zHiuQVfPrtE8xYM5jETtOI1m1hwi3h0PemJp23QF/Asxue5a/jfwEwOnI0Ud5RLNy3EAWFON84Xhv+Gu19Gl+wWVhuZOb/9vDbgRMAjOkcxH+v7EmgZ8v3OdifUcSV729Eb7Jy50UxPD2ha4uPQdiZoQT+/C9ser9xUyVmQ/UUjllv/ykccU6p1YL97h7E9mlg0XD+Udjzvfopv+AoVkXLYf0QtldMI9+oZt+ddA70GBlOrzHt6k5VNIapAo5vra79Ob5V/dmsycVbrbGIrpxGCell1yLZJinNhr3/U9+rqiXpoG6Z0P1KNRAJ72f3QvFmDz5mz57Nhx9+SFpaGo6Op36z8/LyCA8P56OPPuLmm29u0LnPl5qP+dvmc/yrz7jkwFiSYyfT2fUPxjx5PQR3a/K5FUVhccJi5m+bX6tnxbWdruWx/o/h4tj4DMXWlHwe+mYnGUV6nB20zBrfmelDolu1jfSKPZnct1itdZl/dS+u7Cdp9QvCif2w/NHqVV5BXdU+HFH1LP8+shZWPFa9rDRmBEyYDwFxLTde0eKqWrA76hy4qpEt2K1mC4d+/Zft6/IpLFMf56wpo6fbCnq1T8Wl3+XqBbYxK4MMJWo2o6rHRvp2OKlfEG4BapARXbn6KKjbudFw70xOHIA936qBW0lm9e3+HeCW5XZdrt2swYfVaiUmJoZp06bx3//+13Z7aWkps2fP5sorryQ0NJSUlBSeeuopjh07xsGDB/H0bFhdwvkSfNz52510W7iB2KIrSIscQx/P5Qz576t2jXwP5B3gyfVPkq/P5/nBzzM2qvHz6BarwrvrDvPm74ewKhAT4M7b0/rQPfzc2JVz/m+JvL32MM4OWr69exB927WNbqIXPKsVdi+G356t7qTZ+wYY+6J6USjOrFy2+5N6n0cwjJurXjTOx31VRKPUbMHu6e/C1bPO3ILdYraS8G8mO1anUpyrZiF0bo707llKD4fv0B1dXt1eXusIHcaqhaodLwWnkz6wVRSqHXBT/1GDjczddXuWeIZWN2+LGgoBHc/vn02rBY7+pU7LHFwG7oHw0G67vqZmDT5+++03xo0bR2JiIh07Vnd/rKio4PLLL2fnzp0UFhYSGhrKqFGjmDNnDpGRkac549kPvrUoisLw74bz6Ce5WD1uIitkEIMj/qTvMy/a/bmsihWL1XJWTciyivQ8/N1O2/LZqX3CefHy7njozpHUILVbsAd66lh2/zBCvFum9qSlpOWX4+3mhJdLGyysLc+H32fDjsrmgy4+ahHcrsW2tugMuKuyYdm5ERCfzGyxsj+j2LZrs72F+bgS3oxF3ucqfZmJH/6rtmAPi/Nh8sP1t2A3mywc3KAGHaUF6hS9q6cTvS9uR/cR4Ti7VP4+K8uFfT+qUw0ZO6tP4OKt9qJpN7i6biNrH3Va0/u0q+6pEj0UfGPO72DjdAwl6rLykMavkjwdaa/ezLLKsrjkh4v5fIGFpE73kOffg1G9D9D1nvtbe2g2axNO8J/vd1NQbsLN2YE5U7qfs9MapQYzV763kcQTJfSK8Oa7uwfbtQV7a8gu1vPLrgx+3HGchKwS3JwduHFQFHdeFNsqNTatLm2r2hL9xN7q28L7qz07Qnu13rhO40BGMT/tOM6SXRnklhrO/ICzpNXAzYOj+c8lHfFsYwFqfkYZ/3t1Gya9he7DwxlxffUKP5PRwoH1Gez4LdXWoMvN25k+Y9vR7aJwnHSn+R2RnVA91VC1aupk/h2qV6JEDanVg0mcHQk+mtm6Y+uY8/MDvPu+hW19/0OxVyyXXVpM7OWXt/bQMJgtvPprIp/+cxSAbmFevD2tD7GB58aWy6dyLK+cye/+Q2G5ict7h/HGtb3Pu23NK4wWfjuQxY870vknKYfKTX/RaLC1adY5apk2oB33jGh/wWV4zshihq0fq59Oe99Q2Rb93JozP1Gs55dd6fy0I52ErOpN6bxdnfB3t/8qAYuikJpXDkCQp45nJnZlUs/Q8+5nvymO7sll5ft7QIER13ei44Bg9v2Vzq7fj1FRotZdePjq6Dsuii5DQ3FszAcTq0UtGN39LeQmqitQqoINz5BmekVtlwQfzez93e+z9ft3ePxHK5sGP0e5Lpgr7ggirH/3Vh3X0dwyHvhmB/vS1bXetw6N5snLOqNzPD+yCBuP5HLTp2oL9lmXdebuES3Tgr0prFaFTUfz+HlHOiv3ZlJmrJ437hfly9S+4UzoEcqOYwW89cdhdlX1VXHQck18BPeMaE+ErzQ1a02nChqdHbRc3DWIqX0iGNEpEKdm2pV1fVIOz/2yn6O5arfPYR0CeHFKt3PyA4OiKGxKziezqIIpvcPttkR++68pbFqSjFarwcnVAUOZWrvhFeBC33FRdB4cioPjuRWoirok+GhmD619CN9vfufa9VbWX/QKJgcPpj0bj19463Un/HnncZ75eR9lRgu+bk68dlUvLu567m08dCZf/JvCc79UtmC/JZ5RnVu+BXtDHM4u5eedx1myM4P0wgrb7ZF+rlzRJ4KpfcKJDqjbBOmfw7m8/cdhtqSodTiOWg1X9o1gxqj2RPnbp429OLOGBI0Te4Thbc8t0k/DYLbw0V/JvLPuMAazFWcHLXePiOW+UR3OiSlIRVH481AOb/+RxI5jhQBc3CWYt6b1xs256TVkiqKw5tP9JG1TN7L0DnKl/2XRxA0IrrcORJybJPhoZuP+N45pX6YxIBHWjXgLNFpufXVY09aVn6Uyg5nnftnPjzuOAzAgxo//u643od7nZwGboig89fM+vtly7JxpwV4lv8zIst0Z/LTjOLuPV3cz9HRxZGLPUKb2jaB/lG+DUuabkvN4e20SGw6rG6U5aDVM6RXGjFEdzpnXeyE6XdA4tU8EV9QTNLakY3nlPLd0H38m5tjG9eLk7q0WhCuKwpoDJ3hn3WH2VP7MO1dmIIxmK93Dvfj0lniC7dCg0Gy0sOv3NLwDXWnfLwitNB4870jw0YyKDEUM+3YYb71vxq/MnX+GvgrAPe+ObPEIfX9GEQ8s3klybhlaDTw4Jo4HRsed991CjWYrN36ymS0p+cQEuLNkxtAW+wR6MoPZwrqEbH7ckc66hGzMlTl5B62GER0Dmdo3nIu7BJ/1p9PtqQW8vTbJdrHRaGBCj1DuH92BziEt+/NvtSoczillc3IeW1IKKCxvnr1MtBoNwV46InzdCPdxJdxXXe0R6u2CYzP8H7IFjTvTbdsJwNkFjS1BURRW78/ihWUHyCxSl5SO6xbMc5O6tdiqGKtVYdW+LN5em2SrfXF1cuCGge24a3gsaQUV3PXFNvLKjIR6u/DpLfF0DTv3fl+LliXBRzPakrmFGctv48v5Fsrcgtk84DmcnRXufGtMi41BURQWbUxh7soEjBYrIV4u/N91vRkY699iY2huuaUGpryzgfTCCi6KC+Dz6fHNcmGqj6Io7DhWyE87jrN8TyZFFdXNhrqHezG1TwSTeoXZddXKnuOFvL32MGsqu8+CesF5YHRcs/VksVgVDmYWs/loPluO5rHlaD4F5aYzP7CZOGg1hHi52AKSiMqgRP27G6HeLg0O8k4XNI7sGMjUvhGM6RJ0TkxpnEqZwcxbfyTx6T9HMVsVXJ0cePjiOG4bFtNs9ScWq8LyPRm8s/YwSdlq+3B3ZwduHhLNHcNi8Peo/pk/llfOrQu3cCSnDHdnB965vu85O00qWoYEH81o0f5F/LLkNV7+0kJJYDRbuz2Ol58jN80d3iLPX1Bm5PH/7eH3g+pF6uIuQbx2VS98m6ESv7Xtzyjiqvf/pcJk4Y5hMTwzsXlbsKfll/PTjnR+3nmclMoVCAAhXi5M6RPG1D4RdApp3rqeAxnFvLvuMCv3ZdpWyIzuHMQDozvQp4kN2EwWK/vSiyqDjXy2puRTojfXOsbFSUu/KF8GxvgT6eeKBvtnA4wWK1lFetILKjheWE56QQUZhfoG9dEI9NQR7lMZmPi6ElEjOAnzcSUxq4Sfdx5n2e76g8bJvcMI8Di/ljonZpXwzJK9bE1Rd57uGOzBnCnd7fphw2SxsmRnOu/9ecRW+Orp4sitQ2O4bWg0Pm71/34pqjBx71fb2XgkD60Gnp/UjVuGRNttXOL8IsFHM5q1fhb6H5dy169WSjp0YmvEgwRFeXL1rPhmf+7NyXk8/N0uMitbpD81vjO3tHKL9Oa2cm8mM75WW7C/fnUvrmpCr5IKo4X0wgrSCys4XqBe9NS/V5BeUEFWcfXeDa5ODlzWPYSpfSMY3N6/xaeykk6U8M66wyzbnWFbfXFRXAAPjI5jQEzDNi00mC3sTiuqnEbJZ3tqAeXG2l0cPXSO9I9Wg40BMX70CPe2zem3JKtVIbfUQFrlv4n6b1Nu+7dJL6yoM/YzCfFy4fI+4UztG07H4PN7q3pFUfjf9uPMW5VAfpk6HXZl3whmje/cpGDKaLbyv+3Hef+vw6TlqzUwPm5O3DEshpuHRDeoMZ7JYuWZn/fx3bY0AKYPiebZiV3P++lf0XgSfDSjK365ghHfJjJup0Jxn95s876Tdt38mPRA72Z7TotV4e21Sbz1RxJWBWID3HnrHGqR3twWrDnEW38knbEFe7HepF6oCmoEGJUXsuMFFeSVnb6GQaOBIe39mdongku7h+B+DnSCPZpbxrvrDvPzznQslVHIwBg/HhwTx5D2/rUCzwqjhR3HCth8NJ/NyXnsTCvEaK6dTfBxcyI+2o+BMX4MjPGnS6hni01nNYWiKBSUm2oFJcdrBSoVFFWYWj1obG6F5UZeXZ3IN1uOoShq/5GZl3ZiWny7RhVo6k0Wvt+Wxvt/HrHVlQR4OHPnRbHcOCiq0T/7iqLwwV/JvPKrumHcmM5BvDWtzznxf0i0HAk+monerGfQ4kE8/4WBzsehcOgQdjjdQMcBwYy9rekbytUnq0jPQ9/uZPNRdWnmlX0jeHFKtzb1n9pqVbj36+2s3q+2YH9mQhdySgwnXYDKKT5pCqE+HjrHemsKwn1cifZ3P2enr9Lyy3nvzyP8b3saJov6X7ZvOx+mDWhHcm4Zm5Pz2HO8yFbbUCXAw5mBMf4MjPVjQIwfHYM8L9hVBCV6E04O2nO6jsNedhwr4Jmf93EgU+3p0yvSh5cv737GDyQVRgtfb07lo7+TyS5Ru7YGe+m4e3h7pg1oh6tz0967lXszeeS7XRjMVrqGevHp9P7n7co70XgSfDSTfbn7uH75dSxcYMXVqJA7Zix7LJfTc3QEF13T8cwnaKQKo4Vxb/7Nsfxy3J0deOmK7lzR59xskd7cygxmrnx/Y62uk/XxdXOyBRMnr6aI9HXDy9XxvJ6myiis4MO/jvDN1rQ6WQ2AUG8XBsb4MaAy4IgNcD+vX684NbPFypebUpn/2yFKDWZbm/ZHL+lYZ7qk1GDmy39T+WR9si0DGObtwr2jOnB1vwi7Bmw7jxVw5xfbyC01Euyl49Nb4ttMlratk+Cjmfxw6Ac+WPkC73xgQaNVyLjkCg7qxzJgUgzxE2Ls/nxv/n6IN39PItTbhcV3DiKmFfsPnAvS8st5+LtdWBWl1iqIiBoBRlvJCGUX6/no72Q2H82nc4gnA2L8GBTrT4SvqwQbbUx2sZ45Kw6ybHcGAAEeOp6d2IXJvcIo1ptZtDGFzzYcpbByJVM7PzdmjGzP1L4RzVbfk5Zfzm0Lt5KUXYqrkwNvTevD2POw6aFoHAk+mslLm14iedk3PP6jFZ2PicSh95Jc1ofh13Wkx0j7ZiSOF5QzZv5fGMxW3r2+LxN6htr1/EKIC8s/Sbk898s+kitXq/SK9CE5u5QSgzodGRvgzn2jOjCld1iL1PkU603c9/UO1iflotHAMxO6ctvQC6dA3mpVOJRdwo7UQrqHe9Ezwqe1h9TqGnP9bhsfE+3kYP5BOla2YXDxMaF3VCN5Fw/7N8CatzIBg9nKwBg/xveQDZCEEKc3LC6AVQ9fxMd/J/P22sO2hmodgz24f3QcE3qEtmgBrpeLE59Nj+f5pftZvPkYc5YfICW3jOcndT0vipxPVtUXZ1NyHpsrl6pXZZOcHDTMm9qzSavx2hoJPhrIYrWQVJDE2Bw1UaTzMVGhqKsu7B18bDySy4q9mWg1MHtytwvmk4IQonnpHB24f3QcU3qH8+WmVPpE+jCuW0irFRk7OWh5+fLuxPi7M3fVQb7clEpaQTlvT+uDZwOW8bYmk8XK3vQiNierTfi2pRTYskhVXJ0ciPB1JSm7lMd+2E1KbhmPju14wRZ125MEHw2UWpxKhbmCaHXfIzXzYVb3M3Bxt99/IrPFygtLDwBw46AouoSeW1NPQohzX6SfG0+N79LawwBAo9Fw5/BY2vm78dC3O/kzMYerP/iXT6fHt1i7+IbQmyzsTiu0NeHbnlpAhal2bxnPqr44sdV9cRw0GhasOcQ76w7zzrrDpOSV8frVvdrEqqumkOCjgRLyE9AZFYIL1MyHs7cZQ4ka3doz+Fi85RiJJ0rwcXPi0bH2X0EjhBCtYVy3EL6/ezC3L9pGQlYJl7+7gU9v6d9qtRLlRjM7UgvZcjSPTUfz2XWKvjgDov1sBd1dQr3qnbp6bFwnovzdeOrnvSzfk0lGYQUf3dz/vOum25Ik+GighPwE2lVmPRxdLCiB7bFWbmxqr2mX/DIj8387BMB/Lul0ypbGQghxPuoZ4cOS+4Zy+8KtJGSVcM2H//LmtX24tHvz17WV6E1sSy1gc3I+m4/msbfevjg6tQFfrNqELy7Io8FTKFf3jyTC1417vtrOjmOFXPHeBj6fHk+HoHOvu67BbCG/zNiqPVgk+Gigg/kHic6urPfwNaEPiIfD4OCkxamJjXmqzP8tkaIKE11Cvbh+QDu7nFMIIc4l4T6u/HDPYO5fvJO/DuVw79fbmXVZZ+68KLZJ9W1Gs5XMourtEo6ftI1CRmEFJ8Uatr44VdMoTe2LM7i9Pz/NGMJtC7eSmlfOFe9t5IMb+zG0Q8BZn9Pe1ifl8Nwv+/FydeLne4e0Wn2KBB8NoCgKCfkJXFkZfLh4m6jw6QWAq52yHvszivhmyzEAZk+SfRGEEBcuTxcnPr2lPy8sO8CXm1KZuzKBo7nlvDil2yl37FX3ZqrbWr9qG4XsEgNnahzRzs/NFmwMjPFrlr447QM9+HnGUO76YhvbUgu45bMtvHxFd66Nb90PlCeK9cxZfoDlezIBCPLUkVZQTpR/6/SPkuCjAU6Un6DQUEh0jvq9zsdMoUcnQEFnh3oPRVF4YekBrApM7Blq190qhRDiXOTooOXFKd2IDnDnpRUH+GbLMY4XlHPToCgyCmsEGJV/zz/D3kyg7sqsNiB0s22fUPVnO383gjxdWuCVgZ+7M1/dMZAnftzDL7syeOLHvRzNLWfmuE4tnmkwW6ws+jeVN9ZUd8K9ZUg0j47t2KorjiT4aICE/AQ0ikJUjgZQ0PmY0LtEASl2KTZdtieTLSn5uDhpz5kKdSGEaG4ajYbbh8XQzs+NB7/ZyfqkXNYn5Z7yeE8XR1tAcfL2CRG+rvi5O58zrQlcnBx489reRPu7839/JPHBX0dIzStjwTW9m7yHTkNtTy3gmSX7OFi5B1Cfdj68dHl3uoW1frt7CT4a4GD+QQILwcVgRaNV0EW1Q29Ug46mBh/lRjPzVh4E4L6RHQg7h5aeCSFESxjbNZgf7hnMi8sOoDdb1KCiKmtRI8jwdj23e4OcTKPR8MjYjkT5u/HEj3tYtS+LjKJNfHxzv2bNwhSUGXnl1wS+3ZoGqLsfP3lZZ67tH3nO9CCR4KMBEvISiKpsLubsZUYT2Rd9qdrZrqk1H1VbWkf4unLn8Ngmj1UIIc5H3cO9+f6ewa09jGYxtW8E4T6u3P3VdnanFXLFuxv5bHo8nULsuxLGalX43/bjzFt1kILK7qtX94vgycs643+OLfs9/3rctoLEgkSiarRVJ6wP+jL1H7Ypy2yP5ZXz4d/JgLrvgTSlEUKIC9PAWH9+njGUmAB30gsruOr9jfx9KMdu5z+YWcw1H/7LzB/3UFBuolOwJz/cM5jXru51zgUeIMHHGRUZikgvTSeqxjJbwvtWBx9NmHZ5eeUBjGYrwzoEMK6b7PgohBAXspgAd366dwgDYvwoMZi5deFWvt6c2qRzlhrMvLT8ABPf/odtqQW4OTvw9PguLH9wGPHRfnYauf1J8HEGifmJAMTmqm+Vi48FQnrapl1c3M9u5mp9Ug6r95/AQavh+Uldz5kiKSGEEM3H192ZL28fwNQ+4VisCk//vI+XVxzAcnITkjNQFIWVezO5eP5ffPLPUSxWhfE9QvjjPyO4c3jsKZcsnyuk5uMMEvITcDEoBOarPf517aNA51Fj2qXxXUhNFisvLFP3b7l5cBRxwedeBzwhhBDNQ+fowPxrehEd4M6CNYf4eP1RUvPKefO63rg5n/mynJJbxnNL99umbdr5ufHClG6M6hTU3EO3Gwk+ziAhP4HIymk5RxcLju37AdTIfDR+2uWLf1M5nF2Kn7szD18s+7cIIURbo9FoeHBMHFH+bjz+wx5+O3CCaz/cxCe39CfYq/6VMHqThQ/+OsJ7fx7BaLbi7KDlnpHtmTGy/XlXMyjBxxkczD9ITM16j7A+ADUyH417C3NLDbz5u7p/y8xxnc67pWNCCCHsZ0rvcMJ9XLnzi23sTS/i8nc38Nn0+Do7mv99KIfnftlHSl45ABfFBfDilO7EBLROh9KmOrcnhVqZ3qznaNFR2lW1VfdRi03NJgtmo7r7YWMzH6+vTqREb6Z7uBdX94+0+5iFEEKcX/pH+7HkvqHEBrqTWaTnqvc3si5B3ck0q0jPfV/v4ObPtpCSV06Qp453ru/DF7cNOG8DD5DMx2kdKTyCRbHQPtcBMKPztUJwd/SlZgA0Wg3Org1/C/ceL+K7bWrTl9mTusn+LUIIIQCI8nfn53uHcvdX29iUnM/ti7ZyVb8IVuzJpMxoQauB6UNieGRsXKu2RbcXyXycxsH8g2gUhXYn1GJTl/ZR4OSCvnKPARd3xwavUlEUheeX7kNR4Io+4fQ/h5dACSGEaHnebk58cdtAruoXgVWB77cdp8xooW87H5Y9MIznJnW9IAIPkMzHaSXkJxBYCM5GBY1Wwbnr2RebLtmVzo5jhbg5O/DkZZ2bY7hCCCHOc86OWl67qidxQR78vDOd6UOiueYcaotuLxJ8nMbB/IO25mLO3iY0kZXBR5k67dLQ7qalBjPzViYAcP/oDqesZBZCCCE0Gg13j2jP3SPat/ZQmo1Mu5yCxWohqSCJKLXmBxdvM4T3BWh0d9N31x0mu8RAlL8btw+LaZbxCiGEEOcLCT5OIbUklQpzBbE5aqpL56dAUFegxrRLAzIfR3PL+HT9UQCendAVneP5tRZbCCGEsDcJPk4hIU+dJqkKPlzatwMHNdhoTM3HS8sPYLRYGdExkDFdzp/uc0IIIURzkZqPU6hqq+5X1Va9e1/bfQ2ddlmXmM0fCdk4ajU8J/u3CCGEEIBkPk4pIT+BdlVt1V0tOHYcaLuvurvpqYMPo9nKnMr9W24bFkP7QI/mG6wQQghxHpHgox6KopCQn2Bb6aKr7GxapSGZj4Ubj5KcW0aAh44HRndo3gELIYQQ5xEJPupxovwEBYYCoqtWuvgBAdUbwFWcoeA0u0TPW38cBuCJSztdME1hhBBCCHuQ4KMeifmJAHTMVVem6GIiQFu9SsVwhszHq78mUmow0yvShyv7RjTzaIUQQojzixSc1qOqrXpYVVv1br1s91ktVgzllU3G6gk+dh4r4H/bjwMwe1LXC64rnRBCCNFUkvmoR1Vbdaeqtuq9h9nuqwo8QN3bpSarVWH20v0AXNUvgj7tfFtkvEIIIcT5RIKPetQsNnX2NqFpF2+7r6rYVOfmiNah9tv3447j7D5ehIfOkZmXdmq5AQshhBDnEQk+TlJsLCa9NJ3oyuDDxU8DvtUt0auKTXUnTbkU60288qtaK/LgmA4Eecr+LUIIIUR9JPg4SXWxqTqloosOBW3123Sq7qZv/5FEbqmB2AB3pg+R/VuEEEKIU5Hg4yQH8w4CEH3CCoBLl2617q+vx8eRnFI+35ACwHOTuuLsKG+rEEIIcSpylTxJVVt174LKtur9Lqp1f1Xw4Vqjx8dXm1IxWxVGdQpkZCfZv0UIIYQ4HQk+TpJQcFJb9S4nBR/1TLusS1C7kV0bH9kygxRCCCHOYxJ81GCwGEguTK5uq+4PeNcOKKr3dVFrQpJzSknJK8fJQcOwuMAWHa8QQghxPpLgo4bDBYexKBbictS3xaVdEJy0E+3JmY91iWqaZECMHx466dkmhBBCnIkEHzUk5CcA0LFyTxddp851jrH1+agKPiqnXEZJrYcQQgjRIPJRvYaqtupBVW3V+wypc0xV5sPVw4lSg5nNR/MAGNVZgg8hhBCiISTzUUNCfgJBheBoQm2r3v+SOsdU13w48U9SLiaLQpS/G7EB7i08WiGEEOL81KjgIzo6Go1GU+frvvvuA0BRFGbPnk1YWBiurq6MHDmS/fv3N8vA7c1itXCo4BDtqtqq+4LGr/aOtIqiYCir3lSu5pSLRiMbyAkhhBAN0ajgY+vWrWRmZtq+1qxZA8DVV18NwKuvvsqCBQt455132Lp1KyEhIYwdO5aSkhL7j9zOjpUco8JcQYdsNYhwifSvc4xRb8FqrVwJ4+bIukQ1+BgtUy5CCCFEgzUq+AgMDCQkJMT2tXz5ctq3b8+IESNQFIU333yTp59+mqlTp9K9e3cWLVpEeXk5ixcvbq7x201VsWmX7MqVLnFxdY6pqvdwdNaSmFtGdokBN2cHBsb6tdxAhRBCiPPcWdd8GI1GvvrqK2677TY0Gg1Hjx4lKyuLSy6prpPQ6XSMGDGCjRs3nvI8BoOB4uLiWl+t4WC+2lY94kTlapZe8XWOqbnMtmrKZWiHAHSODi00SiGEEOL8d9bBx5IlSygsLGT69OkAZGVlARAcHFzruODgYNt99Zk3bx7e3t62r8jI1ukSmpCXgKtBwaNI/V436NI6x9QsNl2bKEtshRBCiLNx1sHHp59+ymWXXUZYWFit208uvFQU5bTFmLNmzaKoqMj2lZaWdrZDOmuKopBYkEhkVVt1N3CMaF/nuKrgw8HFgV1phQCM6ixdTYUQQojGOKs+H6mpqfz+++/89NNPtttCQkIANQMSGhpquz07O7tONqQmnU6HTqc7m2HYTXZ5Nvn6fOKr2qqHe9d7XNW0S5HFiqJAl1AvQr1dW2ycQgghxIXgrDIfn3/+OUFBQUyYMMF2W0xMDCEhIbYVMKDWhfz1118MGVK3Wde5pKrYtHtVsWmHmHqPq8p8ZFQYABgtWQ8hhBCi0Rqd+bBarXz++efccsstODpWP1yj0fDwww8zd+5c4uLiiIuLY+7cubi5uXH99dfbddD2VlVsGpOl9vDQ9ehX73FVmY+jxRXgJEtshRBCiLPR6ODj999/59ixY9x222117ps5cyYVFRXMmDGDgoICBg4cyG+//Yanp6ddBttcEvMT0SgK/jkKoMFl4MX1HleV+Si0WvBxc6J3pG8LjlIIIYS4MDQ6+LjkkktQFKXe+zQaDbNnz2b27NlNHVeLOph/kKBCcDBr0DiAc6fu9R5XFXzoNTCiYyAOWulqKoQQQjRWm9/bpdhYTHppOlFVxabB7mgc64/JqoKPco0iUy5CCCHEWWrzwUdifiJQXWyqi213ymPLio0AGLQKIzpKsakQQghxNtp88FG10qVjZuWGcd16nfLYisqC0/bhXvi4OTf/4IQQQogLkAQflcFHaI4VAF38yHqPMxstYFGnZgZ3kSkXIYQQ4my1+eDjYP5BXA0KrsWV0y7detZ7XFGh2tvDgsKoHqdumiaEEEKI02vTwYfBYuBo4VHaVbVV99Lh6Fv/8tktiepBRq3a2VQIIYQQZ6dNBx+HCw9jVsx0UveIQxcdespjtx/KA8DR1fG0e9UIIYQQ4vTadPCRkKfWe/TItADg0rVHvccpisL+lEIAvLxbdx8aIYQQ4nzXpoOPqrbq7U6owYeuz+B6jzucXUpFqbrMNjBANpITQgghmqJNBx9VbdW98yo3lOtWf+ZjXWI2Loo61eLhKUtshRBCiKZos8GHxWohsSCRoELQmjVoHLU4R0fXe+zahGxcrWrw4eLh1HKDFEIIIS5AbTb4OFZyjApzBXFVxaYRgfW2VS/Wm9iWUoBr5XY2OncJPoQQQoimaLPBR1Vzsd4ZlfUenbvUe9z6Q7mYrQr+Tmpg4iLBhxBCCNEkbT74aF9ZbOrSK77e49YmqKmRAGc16HCVaRchhBCiSdp88BGYq9Zy6Lp0q3OM1arw1yE1+HCrLDiVzIcQQgjRNG0y+FAUhYT8BFwNCs4llW3VO3Wsc9ye9CJyS4146BzBWJkhkcyHEEII0SRtMvjIqcghX59PVGWxqaOfZ71t1ddVTrlc1MEfY0Vl8CGZDyGEEKJJ2mTwUTXl0jersti0Y92sB6j9PQBGRPurN2hA51Z3RYwQQgghGq5NBh8H89TOpl0qgw+X7r3rHJNdomfP8SIA+od6A6BzdUTr0CbfMiGEEMJu2uSVtCrzEV7V46NL3WW2f1buYtsj3Bs3pbIDqky5CCGEEE3WZoMPjaLgnl8ZVHTqVOeYqnqPUZ2D0JeZ1OOk2FQIIYRosjYXfJQYSzheepygAtCYNWicHOu0VTearaxPygVgdM3gQzIfQgghRJO1ueDD1tm0qtg0NqpOW/VtqfmUGswEeDjTM9wbfalkPoQQQgh7abPBR6/MyuCjW886x1RNuYzoGIRWq6kOPiTzIYQQQjRZmw0+orLVneJcOneuc8xaW71HIIBMuwghhBB21GaDD9+qtuodaxebHssr50hOGQ5aDRfFnRR8yLSLEEII0WRtKvgwWowkFybjalBwLHUA6rZVX5twAoD+Ub54u6rBhmQ+hBBCCPtpU8FHUmESZsVMlywrAI6BAXXaqq+t7O8xunOQ7TYpOBVCCCHsp00FH4n5iQD0zzQDoOvStdb95UYzm5LzgJOCD8l8CCGEEHbTpoKPqrbqcSfUzMfJzcU2Hs7DaLYS4etKhyAPQN0BV1+mBisSfAghhBBN16aCj6pi0+CcymLTzrWDj7WVG8mN7hyERqMeY6wwo1grV8Z4yKZyQgghRFO1meDDqlhJLEhEoyi4FFS2Va+xzFZRlOqW6p3qTrk46hxwdHJowRELIYQQF6Y2E3zkVuTi6uBCRL4CZi0aZyeco6Js9ydklZBZpMfFScvg9v622ytsDcYk6yGEEELYQ5sJPoLcgvhz0Dw+OaAWlOriOtZqq17VWGxI+wBcamQ4pLupEEIIYV9tJvgA0GTuwqlADTh0JxWb/plYvYttTQZZ6SKEEELYVZsKPsjYib5QDT5cahSbFpYb2Z5aANReYgvYVrq4So8PIYQQwi7aVvCRvgNDoRpE6DpVF5v+dSgHqwKdgj0J93Gt9ZCKUiMgmQ8hhBDCXtpO8FFRiOVEMqayysxHjbbqVatcRlZuJFdTVeZDJ5kPIYQQwi7aTvCh0WDo/DAAjiEhOPj4AGCxKvx1qLKleqegOg+TglMhhBDCvtpO8OHijd5BzXbU3ExuV1oBBeUmvFwc6RflW+dhVX0+pOZDCCGEsI+2E3wAhgR1bxeXGvUe6xLUrMfwjoE4OtR9O2RfFyGEEMK+2lbwkagGHzUzH1X9PU5e5VJFdrQVQggh7KvNBB+K1Yo+KQmobqueVaTnQGYxGg2M6Fi32BQk8yGEEELYW5vpGW7OyVE7mup0trbq6yobi/WK8MHfQ1fnMSajBYupcgdcyXwIIYQQdtFmgg+n4GA6bt6EJS/P1la9oVMuWgcNTjrZVE4IIYSwhzYz7QKg0WhwDAgAwGC2sOFwLtCAeg93JzQaTcsMUgghhLjAtango6YtR/MpN1oI8tTRLcyr3mNs9R4y5SKEEELYTZsNPqqmXEZ1CjplVkOKTYUQQgj7a7PBR1VL9ZN3sa1JltkKIYQQ9tcmg4/knFJS8spxctAwLC7glMdJ5kMIIYSwvzYZfFRNuQyI8cNDd+oFP7KvixBCCGF/bTL4qOrvMaqejeRqksyHEEIIYX9tLvgoNZjZcjQfOPUS2yqy2kUIIYSwvzYXfPyTlIvJohDt70ZsoMdpj5WCUyGEEML+2lzw0ZBVLlVk2kUIIYSwv0YHH+np6dx44434+/vj5uZG79692b59u+3+6dOno9Foan0NGjTIroM+W4qi2Oo9zjTlAjULTttMF3ohhBCi2TXqqlpQUMDQoUMZNWoUq1atIigoiCNHjuDj41PruEsvvZTPP//c9r2zs7NdBttU+zOKyS4x4ObswIAYv9Mea7FYMeotgEy7CCGEEPbUqODjlVdeITIyslZgER0dXec4nU5HSEhIkwdnb1VLbId2CEDnePqN4gxlZvUvGtC5SfAhhBBC2Eujpl2WLl1K//79ufrqqwkKCqJPnz58/PHHdY77888/CQoKomPHjtx5551kZ2fbbcBNcTZTLjo3R7Ra2VROCCGEsJdGBR/Jycm8//77xMXFsXr1au655x4efPBBvvjiC9sxl112GV9//TVr165l/vz5bN26ldGjR2MwGOo9p8FgoLi4uNZXc8grNbArrRA4c38PAH2ZEZBiUyGEEMLeGjXtYrVa6d+/P3PnzgWgT58+7N+/n/fff5+bb74ZgGuvvdZ2fPfu3enfvz9RUVGsWLGCqVOn1jnnvHnzeOGFF5ryGhrE0UHL8xO7ciSnjBBvlzMery9Vp10k+BBCCCHsq1GZj9DQULp27Vrrti5dunDs2LHTPiYqKoqkpKR67581axZFRUW2r7S0tMYMqcG8XZ2YPjSGOZd3b9DxVctsXaXYVAghhLCrRmU+hg4dSmJiYq3bDh06RFRU1Ckfk5eXR1paGqGhofXer9Pp0Ol0jRlGi5AeH0IIIUTzaFTm45FHHmHTpk3MnTuXw4cPs3jxYj766CPuu+8+AEpLS3nsscf4999/SUlJ4c8//2TSpEkEBARwxRVXNMsLaC4VVQWnkvkQQggh7KpRwUd8fDw///wz33zzDd27d2fOnDm8+eab3HDDDQA4ODiwd+9epkyZQseOHbnlllvo2LEj//77L56ens3yApqLZD6EEEKI5tHo1p0TJ05k4sSJ9d7n6urK6tWrmzyoc0HVUlup+RBCCCHsq83t7dJQBsl8CCGEEM1Cgo9TkGkXIYQQonlI8HEKVQWnsq+LEEIIYV8SfNRDsSoy7SKEEEI0Ewk+6mGoMKMo6t8l+BBCCCHsS4KPelTVezjpHHBwkrdICCGEsCe5stajapmtZD2EEEII+5Pgox62lS5SbCqEEELYnQQf9ZDgQwghhGg+EnzUQ6ZdhBBCiOYjwUc9pMGYEEII0Xwk+KhHdeaj0VvfCCGEEOIMJPioR3XNh3Mrj0QIIYS48EjwUY/q4EMyH0IIIYS9SfBRD32pGZCaDyGEEKI5SPBRD32pEZDgQwghhGgOEnycRFEU9GWS+RBCCCGaiwQfJzEbrVjMVkCajAkhhBDNQYKPk1QVm2odNTjpHFp5NEIIIcSFR4KPk9TsbqrRaFp5NEIIIcSFR4KPk0hrdSGEEKJ5SfBxkqppF1ep9xBCCCGahQQfJ5F9XYQQQojmJcHHSaqCD51kPoQQQohmIcHHSSqk5kMIIYRoVhJ8nEQKToUQQojmJcHHSQxScCqEEEI0Kwk+TiIFp0IIIUTzkuDjJLaaD8l8CCGEEM1Cgo+TSOZDCCGEaF4SfNRgMVsx6S2AZD6EEEKI5iLBRw1VWQ+NBnSujq08GiGEEOLCJMFHDbYGY25OaLSyqZwQQgjRHCT4qEEvxaZCCCFEs5PgowYpNhVCCCGanwQfNUjmQwghhGh+EnzUUJ35kGJTIYQQorlI8FGD7OsihBBCND8JPmqwZT5k2kUIIYRoNhJ81KAvMwOS+RBCCCGakwQfNUjBqRBCCNH8JPioQZbaCiGEEM1Pgo8apOBUCCGEaH4SfFRSrAqGcpl2EUIIIZqbBB+VDBVmFEX9u2Q+hBBCiOYjwUelqikXJxcHHBzlbRFCCCGai1xlK0mxqRBCCNEyJPioVJX5cJV6DyGEEKJZSfBRSTIfQgghRMuQ4KNSVfChk+BDCCGEaFayfWsl6W4qhLAni8WCyWRq7WEIYVdOTk44ODg0+TwSfFSqkGkXIYQdKIpCVlYWhYWFrT0UIZqFj48PISEhaDSasz6HBB+VDFJwKoSwg6rAIygoCDc3tyb9ghbiXKIoCuXl5WRnZwMQGhp61ueS4KOSFJwKIZrKYrHYAg9/f//WHo4Qdufq6gpAdnY2QUFBZz0FIwWnlST4EEI0VVWNh5ubWyuPRIjmU/Xz3ZSaJgk+KlVIwakQwk5kqkVcyOzx893o4CM9PZ0bb7wRf39/3Nzc6N27N9u3b7fdrygKs2fPJiwsDFdXV0aOHMn+/fubPNDmpChKdeZDgg8hhBCiWTUq+CgoKGDo0KE4OTmxatUqDhw4wPz58/Hx8bEd8+qrr7JgwQLeeecdtm7dSkhICGPHjqWkpMTeY7cbk8GC1azuKifTLkKItigvL4+goCBSUlLqvX/hwoW1fteLC88777zD5MmTW+S5GhV8vPLKK0RGRvL5558zYMAAoqOjGTNmDO3btwfUDMKbb77J008/zdSpU+nevTuLFi2ivLycxYsXN8sLsIeqrIeDoxZHZ5mJEkK0PfPmzWPSpElER0cDkJKSUiu9fu2113Lo0KFWGl3jRUdH8+effzb4+D///NP22tuKk1/znXfeydatW/nnn3+a/bkbdaVdunQp/fv35+qrryYoKIg+ffrw8ccf2+4/evQoWVlZXHLJJbbbdDodI0aMYOPGjfWe02AwUFxcXOurpRnKzAC4uDvKXK0Qos2pqKjg008/5Y477jjlMa6urgQFBbXgqNqm1mxMp9PpuP7663n77beb/bkaFXwkJyfz/vvvExcXx+rVq7nnnnt48MEH+eKLLwB1fTtAcHBwrccFBwfb7jvZvHnz8Pb2tn1FRkaezetokopSIwAuHs4t/txCCNHaVq1ahaOjI4MHDz7lMSdPu8yePZvevXvz2Wef0a5dOzw8PLj33nuxWCy8+uqrhISEEBQUxMsvv1zrPAsWLKBHjx64u7sTGRnJjBkzKC0trXXMxx9/TGRkJG5ublxxxRUsWLCgzpTPsmXL6NevHy4uLsTGxvLCCy9gNpub/F5UOXLkCFOmTCE4OBgPDw/i4+P5/fffbffPmjWLQYMG1Xlcz549ef75523ff/7553Tp0gUXFxc6d+7Me++9Z7uvKrv0/fffM3LkSFxcXPjqq6/Iy8tj2rRpRERE4ObmRo8ePfjmm29qPU9JSQk33HAD7u7uhIaG8sYbbzBy5Egefvhh2zFGo5GZM2cSHh6Ou7s7AwcOPGM2aPLkySxZsoSKiopGvmON06jgw2q10rdvX+bOnUufPn24++67ufPOO3n//fdrHXdy9kBRlFNmFGbNmkVRUZHtKy0trZEvoemqi02l7YkQwn4URaHcaG6VL0VRGjzOv//+m/79+zf69R05coRVq1bx66+/8s033/DZZ58xYcIEjh8/zl9//cUrr7zCM888w6ZNm2yP0Wq1vPXWW+zbt49Fixaxdu1aZs6cabt/w4YN3HPPPTz00EPs2rWLsWPH1glgVq9ezY033siDDz7IgQMH+PDDD1m4cGGd45qitLSU8ePH8/vvv7Nz507GjRvHpEmTOHbsGAA33HADmzdv5siRI7bH7N+/n71793LDDTcAahD19NNP8/LLL3Pw4EHmzp3Ls88+y6JFi2o91xNPPMGDDz7IwYMHGTduHHq9nn79+rF8+XL27dvHXXfdxU033cTmzZttj3n00UfZsGEDS5cuZc2aNaxfv54dO3bUOu+tt97Khg0b+Pbbb9mzZw9XX301l156KUlJSad83f3798dkMrFly5Ymv4en06irbWhoKF27dq11W5cuXfjxxx8BCAkJAdQMSM3OZ9nZ2XWyIVV0Oh06na5Rg7Y3fWnVtIsUmwoh7KfCZKHrc6tb5bkPvDgON+eG/YpPSUkhLCys1m3R0dFnDGCsViufffYZnp6edO3alVGjRpGYmMjKlSvRarV06tSJV155hT///NOWJaj5yTwmJoY5c+Zw77332jICb7/9NpdddhmPPfYYAB07dmTjxo0sX77c9riXX36ZJ598kltuuQWA2NhY5syZw8yZM21Zh1MVzp7KyJEjaz2mV69e9OrVy/b9Sy+9xM8//8zSpUu5//776d69Oz179mTx4sU8++yzAHz99dfEx8fTsWNHAObMmcP8+fOZOnWq7fVWBUtVY696T6qOqVL1+gEeeOABfv31V3744QcGDhxISUkJixYtYvHixYwZMwZQMyw1/w2PHDnCN998w/Hjx223P/bYY/z66698/vnnzJ07t85rBnB3d8fHx4eUlBRGjBjRqPewMRqV+Rg6dCiJiYm1bjt06BBRUVGA+saGhISwZs0a2/1Go5G//vqLIUOG2GG4zUMajAkh2rKKigpcXFwa/bjo6Gg8PT1t3wcHB9O1a1e0Wm2t26racQOsW7eOsWPHEh4ejqenJzfffDN5eXmUlZUBkJiYyIABA2o9z8nfb9++nRdffBEPDw/b15133klmZibl5eWNfh31KSsrY+bMmXTt2hUfHx88PDxISEiwZT5AzX58/fXXgJrl+uabb2xZj5ycHNLS0rj99ttrjfOll16qlS0B6mSdLBYLL7/8Mj179sTf3x8PDw9+++0323MnJydjMplqvS/e3t506tTJ9v2OHTtQFIWOHTvWev6//vqrzvOfzNXV1W7v46k0KvPxyCOPMGTIEObOncs111zDli1b+Oijj/joo48Adbrl4YcfZu7cucTFxREXF8fcuXNxc3Pj+uuvb5YXYA+2HW0l+BBC2JGrkwMHXhzXas/dUAEBARQUFDT6OZycav/O1Gg09d5mtVoBSE1NZfz48dxzzz3MmTMHPz8//vnnH26//XZboWV90/QnZ2CsVisvvPBCnWwBcFZBVH0ef/xxVq9ezeuvv06HDh1wdXXlqquuwmg02o65/vrrefLJJ9mxYwcVFRWkpaVx3XXX2cYI6tTLwIEDa5375Jbk7u7utb6fP38+b7zxBm+++aatPubhhx+2PXfV+3G698lqteLg4MD27dvrPJ+Hh8dpX3t+fj6BgYGnPaapGhV8xMfH8/PPPzNr1ixefPFFYmJiePPNN22RHsDMmTOpqKhgxowZFBQUMHDgQH777bda0fG5RhqMCSGag0ajafDUR2vq06cPX331VbM/z7Zt2zCbzcyfP9+WHfn+++9rHdO5c+c69Qbbtm2r9X3fvn1JTEykQ4cOzTbW9evXM336dK644gpArQE5eYoiIiKC4cOH8/XXX1NRUcHFF19sKzEIDg4mPDyc5OTkWtfIhj73lClTuPHGGwE1kEhKSqJLly4AtG/fHicnJ7Zs2WJbpFFcXExSUpJtqqRPnz5YLBays7O56KKLGvzcR44cQa/X06dPn0aNubEa/b9i4sSJTJw48ZT3azQaZs+ezezZs5syrhYlwYcQoi0bN24cs2bNoqCgAF9f32Z7nvbt22M2m3n77beZNGkSGzZs4IMPPqh1zAMPPMDw4cNZsGABkyZNYu3ataxatarWp/znnnuOiRMnEhkZydVXX41Wq2XPnj3s3buXl156yS5j7dChAz/99BOTJk1Co9Hw7LPP2rIZNd1www3Mnj0bo9HIG2+8Ueu+2bNn8+CDD+Ll5cVll12GwWBg27ZtFBQU8Oijj572uX/88Uc2btyIr68vCxYsICsryxZ8eHp6csstt/D444/j5+dHUFAQzz//PFqt1vY+dezYkRtuuIGbb76Z+fPn06dPH3Jzc1m7di09evRg/Pjx9T73+vXriY2NtfXvai7SUQuZdhFCtG09evSgf//+dbIQ9ta7d28WLFjAK6+8Qvfu3fn666+ZN29erWOGDh3KBx98wIIFC+jVqxe//vorjzzySK3plHHjxrF8+XLWrFlDfHw8gwYNYsGCBbb6w/qMHDmS6dOnN3isb7zxBr6+vgwZMoRJkyYxbtw4+vbtW+e4q6++mry8PMrLy7n88str3XfHHXfwySefsHDhQnr06MGIESNYuHAhMTExp33uZ599lr59+zJu3DhGjhxJSEhInXMvWLCAwYMHM3HiRC6++GKGDh1qW9Jb5fPPP+fmm2/mP//5D506dWLy5Mls3rz5tC0tvvnmG+68884zv0FNpFEasx6rBRQXF+Pt7U1RURFeXl4t8pxfPL2Rkjw9V87sR0isd4s8pxDiwqPX6zl69CgxMTF2qz1oKStXruSxxx5j3759tQpGzwV33nknCQkJrF+//qzPER0dzezZsxsVgJxPysrKCA8PZ/78+dx+++1ndY59+/YxZswYDh06hLf3qa+Fp/o5b8z1+9yfjGwBetnRVgjRxo0fP56kpCTS09NbpdljTa+//jpjx47F3d2dVatWsWjRolrNuRorISHBtrLmQrFz504SEhIYMGAARUVFvPjiiwBMmTLlrM+ZkZHBF198cdrAw17afPBhMVsxGSyATLsIIdq2hx56qLWHAMCWLVt49dVXKSkpITY2lrfeeuu0rd/PpHPnzuzdu9eOIzw3vP766yQmJuLs7Ey/fv1Yv349AQEBZ32+mlujNLc2H3xUFZtqNKBzbfNvhxBCtLrmrj25EPTp04ft27e39jDO2rk1sdcKqqZcdO5OaLSyqZwQQgjR3CT4kJUuQgghRIuS4KNy2sVVik2FEEKIFiHBR1n1tIsQQgghmp8EH9LdVAghhGhREnxIzYcQQgjRoiT4KJWaDyGEyMvLIygoqM7mac0tJSUFjUbDrl27TnvcyJEjefjhh1tkTBeq5cuX06dPn3r3qGlpEnyUSeZDCCHmzZvHpEmTiI6OBqqDgpb2559/otFoKCwsrHX7Tz/9xJw5c2zfjxw5koULFzb4vK31elrTya954sSJaDQaFi9e3IqjUknwIcGHEKKNq6io4NNPP21SF9Hm5ufnh6enZ2sPo8lMJlOrPv+tt97K22+/3apjAAk+0JeZAXDxkO6mQoi2adWqVTg6OjJ48OBTHrNw4UJ8fHxYvnw5nTp1ws3NjauuuoqysjIWLVpEdHQ0vr6+PPDAA1gsFtvjNBoNS5YsqXUuHx+ferMWKSkpjBo1CgBfX180Go1tIzh7T7vk5eUxbdo0IiIicHNzo0ePHnzzzTe2+z/88EPCw8PrTFFMnjyZW265xfb9smXL6NevHy4uLsTGxvLCCy9gNptt92s0Gj744AOmTJmCu7s7L730EhaLhdtvv52YmBhcXV3p1KkT//d//1frecxmMw8++CA+Pj74+/vzxBNPcMstt9Ta3VZRFF599VViY2NxdXWlV69e/O9//zvt6548eTJbtmwhOTn5bN42u2nzV9yKUiMALu7OrTwSIcQFR1HAVN46z+3kpu4b0QB///03/fv3P+Nx5eXlvPXWW3z77beUlJQwdepUpk6dio+PDytXriQ5OZkrr7ySYcOGce211zZ6yJGRkfz4449ceeWVJCYm4uXlhaura6PP0xB6vZ5+/frxxBNP4OXlxYoVK7jpppuIjY1l4MCBXH311Tz44IOsW7eOMWPGAFBQUMDq1atZtmwZAKtXr+bGG2/krbfe4qKLLuLIkSPcddddADz//PO253r++eeZN28eb7zxBg4ODlitViIiIvj+++8JCAhg48aN3HXXXYSGhnLNNdcA8Morr/D111/z+eef06VLF/7v//6PJUuW2IIzgGeeeYaffvqJ999/n7i4OP7++29uvPFGAgMDGTFiRL2vOyoqiqCgINavX09sbGyzvLcN0aaDD6tVwVBelfmQaRchhJ2ZymFuWOs891MZ4OzeoENTUlIIC6s9zujoaBRFqXWbyWTi/fffp3379gBcddVVfPnll5w4cQIPDw+6du3KqFGjWLdu3VkFHw4ODvj5+QEQFBSEj4/PKY/9888/G3Xuk19PeHg4jz32mO37Bx54gF9//ZUffviBgQMH4ufnx6WXXsrixYttwccPP/yAn5+f7fuXX36ZJ5980pYJiY2NZc6cOcycObNW8HH99ddz22231RrPCy+8YPt7TEwMGzdu5Pvvv7cFH2+//TazZs3iiiuuAOCdd95h5cqVtseUlZWxYMEC1q5da8tYxcbG8s8///Dhhx8yYsSIev8Nq157SxcWn6xNBx/GcjNU/rvo3Nv0WyGEaMMqKipwcXE543Fubm62wAMgODiY6OhoPDw8at2WnZ3dLOO0J4vFwn//+1++++470tPTMRgMGAwG3N2rA7YbbriBu+66i/feew+dTsfXX3/Nddddh4ODAwDbt29n69atvPzyy7XOq9frKS8vx83NDaDerNIHH3zAJ598QmpqKhUVFRiNRnr37g1AUVERJ06cYMCAAbbjHRwc6Nevn20a6MCBA+j1esaOHVvrvEajkT59+pz2tbu6ulJe3koZuUpt+opbVWzq7OKAg0ObL38RQtibk5uagWit526ggIAACgoKznxKp9oZYo1GU+9tNeskNBpNvRmU1jZ//nzeeOMN3nzzTXr06IG7uzsPP/wwRqPRdsykSZOwWq2sWLGC+Ph41q9fz4IFC2z3W61WXnjhBaZOnVrn/DWDuZoBDai79j7yyCPMnz+fwYMH4+npyWuvvcbmzZtrHXfy6pya72PVe7xixQrCw8NrHafT6U772vPz8wkMDDztMc2tTQcfFaXS3VQI0Yw0mgZPfbSmPn368NVXXzXLuQMDA8nMzLR9n5SUdNpP3c7Oav1dzaLV5rB+/XqmTJnCjTfeCKgX86SkJLp06WI7xtXVlalTp/L1119z+PBhOnbsSL9+/Wz39+3bl8TERDp06NDo5x4yZAgzZsyw3XbkyBHb3729vQkODmbLli1cdNFFgPp+7Ny505Yd6dq1KzqdjmPHjp2yvqM+er2eI0eOnDE70tzadPAhy2yFEALGjRvHrFmzKCgowNfX167nHj16NO+88w6DBg3CarXyxBNP1MmW1BQVFYVGo2H58uWMHz8eV1fXWtM69tKhQwd+/PFHNm7ciK+vLwsWLCArK6tW8AHq1MukSZPYv3+/LVCp8txzzzFx4kQiIyO5+uqr0Wq17Nmzh7179/LSSy+d9rm/+OILVq9eTUxMDF9++SVbt24lJibGdswDDzzAvHnz6NChA507d+btt9+moKDAlg3x9PTkscce45FHHsFqtTJs2DCKi4vZuHEjHh4etVbk1LRp0yZ0Ot1pVza1hDY916CXzIcQQtCjRw/69+/P999/b/dzz58/n8jISIYPH87111/PY489ZquFqE94eDgvvPACTz75JMHBwdx///0Nep7p06czcuTIBo/r2WefpW/fvowbN46RI0cSEhJSaxlrldGjR+Pn50diYiLXX399rfvGjRvH8uXLWbNmDfHx8QwaNIgFCxYQFRV12ue+5557mDp1Ktdeey0DBw4kLy+vVhYE4IknnmDatGncfPPNDB48GA8PD8aNG1drOmfOnDk899xzzJs3jy5dujBu3DiWLVtWK4g52TfffMMNN9xw2n+DlqBR6iuFbUXFxcV4e3tTVFSEl5dXsz7XzjXH2PjjYToOCGbsbd2a9bmEEBc+vV7P0aNHiYmJaVAB57lk5cqVPPbYY+zbtw+t9vz7XDpy5EhGjhzJ7NmzW3sozcJqtdKlSxeuueaaWp1eGyMnJ4fOnTuzbdu20wYoZ3Kqn/PGXL9l2gWZdhFCiPHjx5OUlER6ejqRkZGtPZxGKSkp4ciRIyxfvry1h2I3qamp/Pbbb4wYMQKDwcA777zD0aNH62RfGuPo0aO89957TQo87KVtBx8y7SKEEDYPPfRQaw/hrHh6epKWltbaw7ArrVbLwoULeeyxx1AUhe7du/P777/XqUlpjAEDBtRavtua2nbwIZkPIYQQ56DIyEg2bNjQ2sNoNuffxJ4dSeZDCCGEaHltO/iQzIcQQgjR4tp28CGZDyGEEKLFtdngQ1EUyXwIIYQQraDNBh8mgwWrRW1xIpkPIYQQouW02eCjasrFwUmLk7NDK49GCCGEaDvabvAhUy5CCGGTl5dHUFAQKSkpLfq8KSkpaDQadu3addrjRo4cycMPP9wiY2pt0dHRvPnmm812/scee4wHH3yw2c7fEG03+JBiUyGEsJk3bx6TJk0iOjoaqA4KWtqff/6JRqOhsLCw1u0//fRTrbbiI0eOZOHChQ0+b2u9nnPBwoULa+17M3PmTD7//HOOHj3aamNqu8GHZD6EEAKAiooKPv30U+64447WHsop+fn54enp2drDuCAEBQVxySWX8MEHH7TaGCT4kOBDCNHGrVq1CkdHx9Nus75w4UJ8fHxYvnw5nTp1ws3NjauuuoqysjIWLVpEdHQ0vr6+PPDAA1gsFtvjNBoNS5YsqXUuHx+ferMWKSkpjBo1CgBfX180Gg3Tp08HmmfaZcOGDYwYMQI3Nzd8fX0ZN24cBQUFABgMBh588EGCgoJwcXFh2LBhbN261fbYqgzN6tWr6dOnD66urowePZrs7GxWrVpFly5d8PLyYtq0aZSXl9seN3LkSO6//37uv/9+fHx88Pf355lnnuF0e7wWFRVx1113ERQUhJeXF6NHj2b37t2AullcSEgIc+fOtR2/efNmnJ2d+e233055zsmTJ/PNN9+c9XvXVG22vbpMuwghmpuiKFSYK1rluV0dXRs8zfD333/Tv3//Mx5XXl7OW2+9xbfffktJSQlTp05l6tSp+Pj4sHLlSpKTk7nyyisZNmwY1157baPHHBkZyY8//siVV15JYmIiXl5euLq6Nvo8DbFr1y7GjBnDbbfdxltvvYWjoyPr1q2zBU4zZ87kxx9/ZNGiRURFRfHqq68ybtw4Dh8+jJ+fn+08s2fP5p133sHNzY1rrrmGa665Bp1Ox+LFiyktLeWKK67g7bff5oknnrA9ZtGiRdx+++1s3ryZbdu2cddddxEVFcWdd95ZZ5yKojBhwgT8/PxYuXIl3t7efPjhh4wZM4ZDhw4RGBjIZ599xuWXX84ll1xC586dufHGG5kxYwaXXHLJKV//gAEDSEtLIzU1laioKDu+sw3T5oMPVwk+hBDNpMJcwcDFA1vluTdfvxk3J7cGHZuSkkJYWFit26Kjo+t8GjeZTLz//vu0b98egKuuuoovv/z/9u48rIlr/QP4N0AChF1lF1lUcENEEMQN1FsQ3Fpal2IR1Gq1daui4q+26FOt1LrUpWL1UrFqte61oqJVUCxqUVFxo5RFuYpSl7LvOb8/vJlrIAlEIQnk/TwPz2Nmzpx5zxzivJw5M7MTT548gaGhIbp164bBgwcjMTHxtZIPbW1t7sRuYWEBU1NTmWWTkpIUqrtue1atWgVPT09s3ryZW9a9e3cAQGlpKWJiYhAXF4fAwEAAwLZt23D69GnExsZiwYIF3DbLly9H//79AQBTpkzB4sWLkZWVBScnJwAvj1FiYqJE8mFnZ4d169aBx+PBxcUF6enpWLdundTkIzExEenp6SgoKICuri4AYPXq1Thy5AgOHDiAadOmISgoCFOnTsWECRPQp08f6OnpITo6mqsjPDycG0ESs7W1BfCy71WRfNBlF7rsQgjRcOXl5dDT02uwnFAo5BIPALC0tISDgwMMDQ0llhUUFDRLnE1JPPIhTVZWFqqrq7mkAgD4fD68vLxw9+5dibI9e/bk/m1paQmhUMglHuJldY9H3759JUalfHx8kJmZKXG5Suzq1asoKSlB27ZtYWhoyP3k5OQgKyuLK7d69WrU1NRg37592L17d4P9KR5RevWSkDJp7sgHl3xo7CEghDQzfR19XA65rLJ9N1a7du24uQ7y8PmSf6zxeDypy0QikcRnaSMoqibvco443rqXrRhj9Za92v7GHA9FiUQiWFtbSx3peXVkKDs7G48ePYJIJML9+/clkiJpnj9/DgAwNzd/7djehMaeeStKawAAujTyQQhpJjwer9GXPlTJ3d0du3btapa6zc3NkZ+fz33OzMyU+9e2QCAAAKmjAE2pZ8+eOHPmDJYtW1ZvXadOnSAQCHDhwgWEhIQAeJkwXblypUkmvV66dKne586dO0Nbu/4DL3v37o3Hjx9DR0eHuw26rqqqKkyYMAHjxo1Dly5dMGXKFKSnp8PS0lJmDLdu3QKfz+cuNSmb5l52oQmnhBACAAgICMDt27cbNfqhqCFDhmDTpk24du0arly5gunTp9cbHXiVvb09eDwejh07hr///hslJSVNHhMALF68GKmpqfj4449x8+ZN3Lt3DzExMXj69CkMDAwwY8YMLFiwACdPnsSdO3cwdepUlJWVYcqUKW+877y8PMybNw8ZGRnYs2cPNm7ciDlz5kgt+69//Qs+Pj54++23kZCQgNzcXKSkpGDJkiW4cuUKAOCzzz5DYWEhNmzYgIULF6Jr164NxpmcnIyBAwc224Tehmhs8lFeShNOCSEEAFxdXeHp6Yl9+/Y1ed1r1qyBnZ0dBg0ahJCQEEREREAolD0aZGtri2XLliEyMhKWlpaYOXNmo/YTHh4u8SCthjg7O+PUqVO4ceMGvLy84OPjg19++QU6Oi8vCERHR+Pdd99FaGgoevfujb/++gsJCQkwMzNr9D5kmThxIsrLy+Hl5YVPPvkEs2bNwrRp06SW5fF4OH78OAYNGoTJkyfD2dkZ48ePR25uLiwtLZGUlIRvv/0WO3fuhLGxMbS0tLBz505cuHABMTExMmPYs2eP1AmuysJj8m4uVoGioiKYmJigsLAQxsbGzbKP2moRtsxKAgB8uHYgdIWUgBBC3lxFRQVycnLg6OjYqAmc6uT48eOIiIjArVu3oKXV8v4u9fPzg5+fH5YuXarqUOTy8/NDr169mvXx6Q2Jj4/HggULcPPmTS7ZUoSs33NFzt8aOedDPNmUp8WDQF8jDwEhhEgICgpCZmYmHj58CDs7O1WHo5Di4mJkZWXh2LFjqg6lRSgtLcX27dtfK/FoKhp55n31ThdNfdY/IYTUJWvegbozMjJCXl6eqsNoMcaOHavqEDQz+SgvoWd8EEIIUT5FH47WWrW8C3tNgO50IYQQQlRHM5MPeropIYQQojKUfBBCCCFEqTQ7+aDLLoQQQojSaWbyQRNOCSGEEJXRzOSDRj4IIYQQldHM5INGPgghRMKzZ89gYWGB3NxcVYdCXtOxY8fg7u7+Rm/RVRbNTD5owikhhEhYuXIlRo4cyb05NTc3V+IhjHFxcRKvcG8qsur18/NDXFxco+upG68mqNvmESNGgMfj4aefflJhVI2jUPKxdOlS8Hg8iR8rKytufXh4eL31ffv2bfKg3xQ954MQQv6nvLwcsbGx+PDDD1UdSotXXV2t0v1PmjQJGzduVGkMjaHwyEf37t2Rn5/P/aSnp0usHzZsmMT648ePN1mwTUEkYqgsrwFAIx+EEAIAJ06cgI6ODnx8fKSuT0pKwqRJk1BYWMj9YSl+gVtVVRUWLlwIW1tbGBgYwNvbm3uKZ0VFBbp37y7xxtacnByYmJhg27Ztcut9U8+ePcP777+P9u3bQygUwtXVFXv27OHWf//997C1ta13iWLUqFEICwvjPv/666/w8PCAnp4enJycsGzZMtTU1HDreTwetmzZgtGjR8PAwADLly9HbW0tpkyZAkdHR+jr68PFxQXr16+X2E9NTQ1mz54NU1NTtG3bFosWLUJYWBjefvttrgxjDKtWrYKTkxP09fXh5uaGAwcOyG33qFGj8McffyA7O/t1DpvSKPx4dR0dHYnRjrp0dXXlrle1yrJq4L/v8dUz0MinyxNClIQxBlZerpJ98/T1G30Z4vz58/D09JS5vl+/fvj222/xxRdfICMjAwBgaGgI4OVf2rm5udi7dy9sbGxw+PBhDBs2DOnp6ejcuTN2794Nb29vBAUFYeTIkQgNDcXgwYMxdepUVFVVyaz3TVVUVMDDwwOLFi2CsbEx4uPjERoaCicnJ3h7e2PMmDGYPXs2EhMTMXToUADAixcvkJCQgF9//RUAkJCQgA8++AAbNmzAwIEDkZWVxSVSUVFR3L6ioqKwcuVKrFu3Dtra2hCJRGjfvj327duHdu3aISUlBdOmTYO1tTX3XpWvv/4au3fvxvbt29G1a1esX78eR44cweDBg7l6lyxZgkOHDiEmJgadO3fG+fPn8cEHH8Dc3By+vr5S221vbw8LCwskJyfDycmpSY5lc1D47JuZmQkbGxvo6urC29sbX331lUQDk5KSYGFhAVNTU/j6+mLFihWwsLBo0qDfhPiSi0BfB1raGjnlhRCiJKy8HBm9PVSyb5drV8ETChtVNjc3FzY2NhLLHBwcwNjLv9QEAgFMTEzqXWrPysrCnj178J///IfbPiIiAidPnsT27dvx1VdfoVevXli+fDmmTp2K999/H1lZWThy5IjcegHF34HyarwAYGtri4iICO7zrFmzcPLkSezfvx/e3t5o06YNhg0bhp9++olLPvbv3482bdpwn1esWIHIyEhuJMTJyQlffvklFi5cKJF8hISEYPLkyRLxLFu2jPu3o6MjUlJSsG/fPi752LhxIxYvXox33nkHALBp0yaJKwWlpaVYu3Ytzp49y41IOTk54cKFC/j+++/h6+tbr82vtl3dJw4rlHx4e3vjxx9/hLOzM548eYLly5ejX79+uH37Ntq2bYvAwECMGTMG9vb2yMnJweeff44hQ4bg6tWr0NXVlVpnZWUlKisruc9FRUVv1qIGVJT+95ILzfcghBAAL+d86OnpKbzdtWvXwBiDs7OzxPLKykq0bduW+zx//nz88ssv2LhxI06cOIF27dq9ccwNqa2tRXR0NH7++Wc8fPiQO9cYGBhwZSZMmIBp06Zh8+bN0NXVxe7duzF+/Hhoa2sDAK5evYrU1FSsWLFCot6KigqUlZVB+N/kTtqo0ZYtW/Dvf/8b9+/fR3l5OaqqqtCrVy8AQGFhIZ48eQIvLy+uvLa2Njw8PLjLQHfu3EFFRQXeeustiXqrqqrg7u4ut+36+vooKytT4Ggpn0LJR2BgIPdvV1dX+Pj4oGPHjtixYwfmzZuHcePGcet79OgBT09P2NvbIz4+HsHBwVLrXLlypUSG2NwqSqoA0HwPQkjz4+nrw+XaVZXtu7HatWuHFy9eKLwPkUgEbW1tXL16lTthi716+aSgoAAZGRnQ1tZGZmYmhg0bpvC+FLVmzRqsW7cO3377LVxdXWFgYIC5c+eiqqqKKzNy5EiIRCLEx8ejT58+SE5Oxtq1ayXat2zZMqnnr1eTtVcTGgDYt28fPv30U6xZswY+Pj4wMjLCN998g8uXL0uUq3tZ7NVRDHESEh8fD1tbW4lysv6YF3v+/DnMzc3lllG1N5r0YGBgAFdXV2RmZkpdb21tDXt7e5nrAWDx4sWYN28e97moqAh2dnZvEpZcdJstIURZeDxeoy99qJK7uzt27dolt4xAIEBtbW297Wpra1FQUICBAwfK3Hby5Mno0aMHpk6diilTpmDo0KHo1q2bzHqbQnJyMkaPHo0PPvgAwMuTeWZmJrp27cqV0dfXR3BwMHbv3o2//voLzs7O8PD432Wy3r17IyMjA506dVJ43/369cPHH3/MLcvKyuL+bWJiAktLS/zxxx/ccautrUVaWho3OtKtWzfo6uriwYMHMud3SFNRUYGsrKwGR0dU7Y2Sj8rKSty9e1fmL92zZ8+Ql5cHa2trmXXo6uo2mMU1pYoS8WUXmmxKCCEAEBAQgMWLF+PFixcwMzOTWsbBwQElJSU4c+YM3NzcIBQK4ezsjAkTJmDixIlYs2YN3N3d8fTpU5w9exaurq4ICgrCd999h4sXL+LmzZuws7PDiRMnMGHCBFy+fBkCgUBqvcImSNg6deqEgwcPIiUlBWZmZli7di0eP34skXwALy+9jBw5Erdv3+YSFbEvvvgCI0aMgJ2dHcaMGQMtLS3cvHkT6enpWL58udx9//jjj0hISICjoyN27tyJ1NRUODo6cmVmzZqFlStXolOnTujSpQs2btyIFy9ecKMhRkZGiIiIwKeffgqRSIQBAwagqKgIKSkpMDQ0lLgj51WXLl2Crq6uzDuX1AZTwPz581lSUhLLzs5mly5dYiNGjGBGRkYsNzeXFRcXs/nz57OUlBSWk5PDEhMTmY+PD7O1tWVFRUWN3kdhYSEDwAoLCxUJrdFSDv3FNn10hiX//Gez1E8I0Vzl5eXszp07rLy8XNWhKKxv375sy5YtcstMnz6dtW3blgFgUVFRjDHGqqqq2BdffMEcHBwYn89nVlZW7J133mE3b95kd+/eZfr6+uynn37i6igsLGQODg5s4cKFcuutKywsjPn6+ja6Pc+ePWOjR49mhoaGzMLCgi1ZsoRNnDiRjR49WqJcTU0Ns7a2ZgBYVlZWvXpOnjzJ+vXrx/T19ZmxsTHz8vJiW7du5dYDYIcPH5bYpqKigoWHhzMTExNmamrKZsyYwSIjI5mbmxtXprq6ms2cOZMZGxszMzMztmjRIjZmzBg2fvx4roxIJGLr169nLi4ujM/nM3NzcxYQEMDOnTsns93Tpk1jH330UaOP0+uQ9XuuyPlboeRj3LhxzNramvH5fGZjY8OCg4PZ7du3GWOMlZWVMX9/f2Zubs74fD7r0KEDCwsLYw8ePFBkF82efJz98Q7b9NEZlhqf3Sz1E0I0V0tOPuLj41nXrl1ZbW2tqkORytfXV2Zi0hrU1tYyZ2dntmTJkteuo6CggLVp04ZlZzfv+a0pkg+Frj3s3btX5jp9fX0kJCS89giMsnB3u9CcD0II4QQFBSEzMxMPHz5s1nl3r6O4uBhZWVk4duyYqkNpMvfv38epU6fg6+uLyspKbNq0CTk5OQgJCXntOnNycrB582aJyzvqSuMmPognnOpS8kEIIRLmzJmj6hCkMjIyQl5enqrDaFJaWlqIi4tDREQEGGPo0aMHfvvtt3pzUhTh5eUlcfuuOtPY5IOe80EIIURV7Ozs8Pvvv6s6DJXRuEd8lv/3Caf6lHwQQgghKqFRyQdjDJUl9JwPQgghRJU0KvmorqiFSPTyCXKUfBBCCCGqoVHJh3i+hw5fCzoC7QZKE0IIIaQ5aGTyQZNNCSGEENXRqORDPNmUkg9CCCFEdTQq+aigyaaEECLVs2fPYGFhgdzcXKXuNzc3FzweD9evX5dbzs/PD3PnzlVKTC1Nnz59cOjQIVWHoRDNSj7ojbaEECLVypUrMXLkSDg4OAD4X1KgbElJSeDxePjnn38klh86dAhffvkl99nPzw9xcXGNrldV7XkTso5FeHg4li5dyn3+/PPPERkZCZFIpNwA3wAlH4QQouHKy8sRGxuLDz/8UNWhyNSmTRsYGRmpOgy1NHz4cBQWFraIV5yIaVbyQXM+CCGknhMnTkBHR0fua9jj4uJgamqKY8eOwcXFBUKhEO+99x5KS0uxY8cOODg4wMzMDLNmzUJtbS23HY/Hw5EjRyTqMjU1lTpqkZubi8GDBwMAzMzMwOPxEB4eDqB5Lrv8/vvv8PX1hVAohJmZGQICAvDixQsAQGVlJWbPng0LCwvo6elhwIABSE1N5bYVj0qcOXMGnp6eEAqF6NevHzIyMiT2cfToUXh6ekJPTw/t2rVDcHAwt27Xrl3w9PSEkZERrKysEBISgoKCggaPRV3a2toICgrCnj17mvLwNCvNSj5o5IMQokSMMVRX1qrkhzHW6DjPnz8PT0/PBsuVlZVhw4YN2Lt3L06ePImkpCQEBwfj+PHjOH78OHbu3ImtW7fiwIEDr3W87OzscPDgQQBARkYG8vPzsX79+teqqyHXr1/H0KFD0b17d1y8eBEXLlzAyJEjucRp4cKFOHjwIHbs2IFr166hU6dOCAgIwPPnzyXq+eyzz7BmzRpcuXIFOjo6mDx5MrcuPj4ewcHBGD58ONLS0rhERayqqgpffvklbty4gSNHjiAnJ4dLMBQ9Fl5eXkhOTm6qw9PsNOrdLjTyQQhRppoqEbbOOaeSfU9b7wu+buOeZ5SbmwsbGxuJZQ4ODvUSmOrqasTExKBjx44AgPfeew87d+7EkydPYGhoiG7dumHw4MFITEzEuHHjFI5ZW1sbbdq0AQBYWFjA1NRUZtmkpCSF6q7bnlWrVsHT0xObN2/mlnXv3h0AUFpaipiYGMTFxSEwMBAAsG3bNpw+fRqxsbFYsGABt82KFSvg6+sLAIiMjMTw4cNRUVEBPT09rFixAuPHj8eyZcu48m5ubty/X01UnJycsGHDBnh5eaGkpASGhoYyj4W0USNbW1s8ePAAIpEIWlrqP66g/hE2IRr5IISQ+srLy6Gnp9dgOaFQyCUeAGBpaQkHBwcYGhpKLBNfOlBn4pEPabKyslBdXY3+/ftzy/h8Pry8vHD37l2Jsj179uT+bW1tDQBc++XtAwDS0tIwevRo2Nvbw8jICH5+fgCABw8eKNwefX19iEQiVFZWKrytKmjWyAc9ZIwQokQ6Ai1MW++rsn03Vrt27bi5DvLw+ZL/d/J4PKnLXr3rgsfjSR1BUTV9fX2Z68Tx1r07hjFWb9mr7RevE7df3j5KS0vh7+8Pf39/7Nq1C+bm5njw4AECAgJQVVWlWGMAPH/+HEKhUO4+1YlmjXzQcz4IIUrE4/HA19VWyY8it5W6u7vjzp07zXIMzM3NkZ+fz33OzMxEWVmZzPICgQAAJCatNoeePXvizJkzUtd16tQJAoEAFy5c4JZVV1fjypUr6Nq1a5Ps4969e3j69Cmio6MxcOBAdOnSpd6IkSLH4tatW+jdu3ejY1M1jUk+aqprUVP1MhulkQ9CCPmfgIAA3L59u1GjH4oaMmQINm3ahGvXruHKlSuYPn16vdGSV9nb24PH4+HYsWP4+++/UVJS0uQxAcDixYuRmpqKjz/+GDdv3sS9e/cQExODp0+fwsDAADNmzMCCBQtw8uRJ3LlzB1OnTkVZWRmmTJnS6H1ERUVhz549iIqKwt27d5Geno5Vq1YBADp06ACBQICNGzciOzsbR48elXiOCaDYsUhOToa/v//rHQwV0JjkgzHAe5QT3IbaQaBHL5UjhBAxV1dXeHp6Yt++fU1e95o1a2BnZ4dBgwYhJCQEEREREAqFMsvb2tpi2bJliIyMhKWlJWbOnNmo/YSHh3NzJhrD2dkZp06dwo0bN+Dl5QUfHx/88ssv0NF5ORshOjoa7777LkJDQ9G7d2/89ddfSEhIgJmZWaP34efnh/379+Po0aPo1asXhgwZgsuXLwN4OSIUFxeH/fv3o1u3boiOjsbq1asltm/ssXj48CFSUlIwadKkRsemajymyP1YSlBUVAQTExMUFhbC2NhY1eEQQkijVVRUICcnB46Ojo2awKlOjh8/joiICNy6datF3C1Rl5+fH/z8/CSe/KkpFixYgMLCQmzdulUp+5P1e67I+VujJpwSQgiRLigoCJmZmXj48CHs7OxUHY5CiouLkZWVhWPHjqk6FJWwsLBARESEqsNQCCUfhBBCAABz5sxRdQivxcjICHl5eaoOQ2Vefe5IS9HyxtYIIYQQ0qJR8kEIIYQQpaLkgxBCCCFKRckHIYQ0sVef8ElIa9MUv9804ZQQQpqIQCCAlpYWHj16BHNzcwgEAoWeNEqIOmOMoaqqCn///Te0tLS4J7C+Dko+CCGkiWhpacHR0RH5+fl49OiRqsMhpFkIhUJ06NDhjZ4HQ8kHIYQ0IYFAgA4dOqCmpqbZ309CiLJpa2tDR0fnjUf0KPkghJAmJn7bq7x3mBCiyWjCKSGEEEKUipIPQgghhCgVJR+EEEIIUSq1m/MhfsluUVGRiiMhhBBCSGOJz9vi87g8apd8FBcXA0CLe6siIYQQQl6ex01MTOSW4bHGpChKJBKJ8OjRIxgZGTX5w3mKiopgZ2eHvLw8GBsbN2nd6qC1tw9o/W2k9rV8rb2Nrb19QOtvY3O1jzGG4uJi2NjYNPgMELUb+dDS0kL79u2bdR/Gxsat8hdKrLW3D2j9baT2tXytvY2tvX1A629jc7SvoREPMZpwSgghhBClouSDEEIIIUqlUcmHrq4uoqKioKurq+pQmkVrbx/Q+ttI7Wv5WnsbW3v7gNbfRnVon9pNOCWEEEJI66ZRIx+EEEIIUT1KPgghhBCiVJR8EEIIIUSpKPkghBBCiFK1uuRj8+bNcHR0hJ6eHjw8PJCcnCy3/Llz5+Dh4QE9PT04OTlhy5YtSopUMStXrkSfPn1gZGQECwsLvP3228jIyJC7TVJSEng8Xr2fe/fuKSlqxSxdurRerFZWVnK3aSn9BwAODg5S++OTTz6RWr4l9N/58+cxcuRI2NjYgMfj4ciRIxLrGWNYunQpbGxsoK+vDz8/P9y+fbvBeg8ePIhu3bpBV1cX3bp1w+HDh5upBfLJa191dTUWLVoEV1dXGBgYwMbGBhMnTsSjR4/k1hkXFye1XysqKpq5NfU11H/h4eH14uzbt2+D9apL/wENt1FaX/B4PHzzzTcy61SXPmzMeUFdv4OtKvn4+eefMXfuXHz22WdIS0vDwIEDERgYiAcPHkgtn5OTg6CgIAwcOBBpaWn4v//7P8yePRsHDx5UcuQNO3fuHD755BNcunQJp0+fRk1NDfz9/VFaWtrgthkZGcjPz+d+OnfurISIX0/37t0lYk1PT5dZtiX1HwCkpqZKtO306dMAgDFjxsjdTp37r7S0FG5ubti0aZPU9atWrcLatWuxadMmpKamwsrKCm+99Rb3DidpLl68iHHjxiE0NBQ3btxAaGgoxo4di8uXLzdXM2SS176ysjJcu3YNn3/+Oa5du4ZDhw7hzz//xKhRoxqs19jYWKJP8/Pzoaen1xxNkKuh/gOAYcOGScR5/PhxuXWqU/8BDbexbj/88MMP4PF4ePfdd+XWqw592Jjzgtp+B1kr4uXlxaZPny6xrEuXLiwyMlJq+YULF7IuXbpILPvoo49Y3759my3GplJQUMAAsHPnzsksk5iYyACwFy9eKC+wNxAVFcXc3NwaXb4l9x9jjM2ZM4d17NiRiUQiqetbWv8BYIcPH+Y+i0QiZmVlxaKjo7llFRUVzMTEhG3ZskVmPWPHjmXDhg2TWBYQEMDGjx/f5DErom77pPnjjz8YAHb//n2ZZbZv385MTEyaNrgmIK19YWFhbPTo0QrVo679x1jj+nD06NFsyJAhcsuoax/WPS+o83ew1Yx8VFVV4erVq/D395dY7u/vj5SUFKnbXLx4sV75gIAAXLlyBdXV1c0Wa1MoLCwEALRp06bBsu7u7rC2tsbQoUORmJjY3KG9kczMTNjY2MDR0RHjx49Hdna2zLItuf+qqqqwa9cuTJ48ucEXKLak/ntVTk4OHj9+LNFHurq68PX1lfmdBGT3q7xt1EVhYSF4PB5MTU3llispKYG9vT3at2+PESNGIC0tTTkBvoakpCRYWFjA2dkZU6dORUFBgdzyLbn/njx5gvj4eEyZMqXBsurYh3XPC+r8HWw1ycfTp09RW1sLS0tLieWWlpZ4/Pix1G0eP34stXxNTQ2ePn3abLG+KcYY5s2bhwEDBqBHjx4yy1lbW2Pr1q04ePAgDh06BBcXFwwdOhTnz59XYrSN5+3tjR9//BEJCQnYtm0bHj9+jH79+uHZs2dSy7fU/gOAI0eO4J9//kF4eLjMMi2t/+oSf+8U+U6Kt1N0G3VQUVGByMhIhISEyH1ZV5cuXRAXF4ejR49iz5490NPTQ//+/ZGZmanEaBsnMDAQu3fvxtmzZ7FmzRqkpqZiyJAhqKyslLlNS+0/ANixYweMjIwQHBwst5w69qG084I6fwfV7q22b6ruX5GMMbl/WUorL225Opk5cyZu3ryJCxcuyC3n4uICFxcX7rOPjw/y8vKwevVqDBo0qLnDVFhgYCD3b1dXV/j4+KBjx47YsWMH5s2bJ3Wblth/ABAbG4vAwEDY2NjILNPS+k8WRb+Tr7uNKlVXV2P8+PEQiUTYvHmz3LJ9+/aVmLTZv39/9O7dGxs3bsSGDRuaO1SFjBs3jvt3jx494OnpCXt7e8THx8s9Qbe0/hP74YcfMGHChAbnbqhjH8o7L6jjd7DVjHy0a9cO2tra9TKzgoKCehmcmJWVldTyOjo6aNu2bbPF+iZmzZqFo0ePIjExEe3bt1d4+759+6rlX1jSGBgYwNXVVWa8LbH/AOD+/fv47bff8OGHHyq8bUvqP/GdSop8J8XbKbqNKlVXV2Ps2LHIycnB6dOnFX5FuZaWFvr06dMi+tXa2hr29vZyY21p/SeWnJyMjIyM1/peqroPZZ0X1Pk72GqSD4FAAA8PD+4OArHTp0+jX79+Urfx8fGpV/7UqVPw9PQEn89vtlhfB2MMM2fOxKFDh3D27Fk4Ojq+Vj1paWmwtrZu4uiaR2VlJe7evSsz3pbUf6/avn07LCwsMHz4cIW3bUn95+joCCsrK4k+qqqqwrlz52R+JwHZ/SpvG1URJx6ZmZn47bffXivpZYzh+vXrLaJfnz17hry8PLmxtqT+e1VsbCw8PDzg5uam8Laq6sOGzgtq/R1ssqmramDv3r2Mz+ez2NhYdufOHTZ37lxmYGDAcnNzGWOMRUZGstDQUK58dnY2EwqF7NNPP2V37txhsbGxjM/nswMHDqiqCTLNmDGDmZiYsKSkJJafn8/9lJWVcWXqtm/dunXs8OHD7M8//2S3bt1ikZGRDAA7ePCgKprQoPnz57OkpCSWnZ3NLl26xEaMGMGMjIxaRf+J1dbWsg4dOrBFixbVW9cS+6+4uJilpaWxtLQ0BoCtXbuWpaWlcXd7REdHMxMTE3bo0CGWnp7O3n//fWZtbc2Kioq4OkJDQyXuSPv999+ZtrY2i46OZnfv3mXR0dFMR0eHXbp0Sa3aV11dzUaNGsXat2/Prl+/LvG9rKyslNm+pUuXspMnT7KsrCyWlpbGJk2axHR0dNjly5fVqn3FxcVs/vz5LCUlheXk5LDExETm4+PDbG1tW0z/Mdbw7yhjjBUWFjKhUMhiYmKk1qGufdiY84K6fgdbVfLBGGPfffcds7e3ZwKBgPXu3VviVtSwsDDm6+srUT4pKYm5u7szgUDAHBwcZP7yqRoAqT/bt2/nytRt39dff806duzI9PT0mJmZGRswYACLj49XfvCNNG7cOGZtbc34fD6zsbFhwcHB7Pbt29z6ltx/YgkJCQwAy8jIqLeuJfaf+Hbguj9hYWGMsZe3+kVFRTErKyumq6vLBg0axNLT0yXq8PX15cqL7d+/n7m4uDA+n8+6dOmisoRLXvtycnJkfi8TExO5Ouq2b+7cuaxDhw5MIBAwc3Nz5u/vz1JSUpTfOCa/fWVlZczf35+Zm5szPp/POnTowMLCwtiDBw8k6lDn/mOs4d9Rxhj7/vvvmb6+Pvvnn3+k1qGufdiY84K6fgd5/20AIYQQQohStJo5H4QQQghpGSj5IIQQQohSUfJBCCGEEKWi5IMQQgghSkXJByGEEEKUipIPQgghhCgVJR+EEEIIUSpKPgghhBCiVJR8EEIIIUSpKPkghBBCiFJR8kEIIYQQpaLkgxBCCCFK9f+YpENtyyO8yQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demonstration(hyperdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3e468adb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('image', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6133    0.9906    0.7575     29306\n",
      "         1.0     0.8606    0.0852    0.1550     20010\n",
      "\n",
      "    accuracy                         0.6232     49316\n",
      "   macro avg     0.7369    0.5379    0.4563     49316\n",
      "weighted avg     0.7136    0.6232    0.5131     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6133    0.9921    0.7580     20463\n",
      "         1.0     0.8864    0.0893    0.1623     14058\n",
      "\n",
      "    accuracy                         0.6245     34521\n",
      "   macro avg     0.7498    0.5407    0.4602     34521\n",
      "weighted avg     0.7245    0.6245    0.5154     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6096    0.9859    0.7534      4400\n",
      "         1.0     0.7794    0.0731    0.1336      2997\n",
      "\n",
      "    accuracy                         0.6161      7397\n",
      "   macro avg     0.6945    0.5295    0.4435      7397\n",
      "weighted avg     0.6784    0.6161    0.5023      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8562    0.9231    0.8884       806\n",
      "         1.0     0.7794    0.6366    0.7008       344\n",
      "\n",
      "    accuracy                         0.8374      1150\n",
      "   macro avg     0.8178    0.7799    0.7946      1150\n",
      "weighted avg     0.8332    0.8374    0.8323      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5753    1.0000    0.7304      3594\n",
      "         1.0     0.0000    0.0000    0.0000      2653\n",
      "\n",
      "    accuracy                         0.5753      6247\n",
      "   macro avg     0.2877    0.5000    0.3652      6247\n",
      "weighted avg     0.3310    0.5753    0.4202      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7709    0.8519    0.8094       324\n",
      "         1.0     0.6160    0.4843    0.5423       159\n",
      "\n",
      "    accuracy                         0.7308       483\n",
      "   macro avg     0.6935    0.6681    0.6758       483\n",
      "weighted avg     0.7199    0.7308    0.7214       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 62.32\n",
      "    Training Set Accuracy: 62.45\n",
      "    Validation Set Accuracy: 61.61\n",
      "    Validation Set with both Images and Texts: 83.74\n",
      "    Validation Set with only Texts: 57.53\n",
      "    Validation Set with unseen Images: 73.08\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6133    0.9906    0.7575     29306\n",
      "         1.0     0.8606    0.0852    0.1550     20010\n",
      "\n",
      "    accuracy                         0.6232     49316\n",
      "   macro avg     0.7369    0.5379    0.4563     49316\n",
      "weighted avg     0.7136    0.6232    0.5131     49316\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6133    0.9921    0.7580     20463\n",
      "         1.0     0.8864    0.0893    0.1623     14058\n",
      "\n",
      "    accuracy                         0.6245     34521\n",
      "   macro avg     0.7498    0.5407    0.4602     34521\n",
      "weighted avg     0.7245    0.6245    0.5154     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6169    0.9881    0.7596      4443\n",
      "         1.0     0.8121    0.0775    0.1415      2955\n",
      "\n",
      "    accuracy                         0.6244      7398\n",
      "   macro avg     0.7145    0.5328    0.4505      7398\n",
      "weighted avg     0.6949    0.6244    0.5127      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8478    0.9260    0.8852       716\n",
      "         1.0     0.8121    0.6580    0.7270       348\n",
      "\n",
      "    accuracy                         0.8383      1064\n",
      "   macro avg     0.8299    0.7920    0.8061      1064\n",
      "weighted avg     0.8361    0.8383    0.8334      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5884    1.0000    0.7409      3727\n",
      "         1.0     0.0000    0.0000    0.0000      2607\n",
      "\n",
      "    accuracy                         0.5884      6334\n",
      "   macro avg     0.2942    0.5000    0.3704      6334\n",
      "weighted avg     0.3462    0.5884    0.4359      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7453    0.8495    0.7940       279\n",
      "         1.0     0.6379    0.4774    0.5461       155\n",
      "\n",
      "    accuracy                         0.7166       434\n",
      "   macro avg     0.6916    0.6634    0.6700       434\n",
      "weighted avg     0.7069    0.7166    0.7055       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 62.32\n",
      "    Training Set Accuracy: 62.45\n",
      "    Validation Set Accuracy: 62.44\n",
      "    Validation Set with both Images and Texts: 83.83\n",
      "    Validation Set with only Texts: 58.84\n",
      "    Validation Set with unseen Images: 71.66\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.57997537 0.4200246 ]\n",
      " [0.57997537 0.4200246 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.618727\n",
      "std          0.108827\n",
      "min          0.500023\n",
      "25%          0.579975\n",
      "50%          0.579975\n",
      "75%          0.579975\n",
      "max          0.999822\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.618932\n",
      "std          0.109046\n",
      "min          0.500023\n",
      "25%          0.579975\n",
      "50%          0.579975\n",
      "75%          0.579975\n",
      "max          0.999822\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.620265\n",
      "std         0.110947\n",
      "min         0.500687\n",
      "25%         0.579975\n",
      "50%         0.579975\n",
      "75%         0.579975\n",
      "max         0.999716\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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EHQAAYFkEHQAAYFkEHQAAYFkEHQAAYFkEHQAAYFkEHQAAYFkEHQAAYFkEHQAAYFkEHQAAYFkEHQAAYFlBBZ1NmzbpvvvuU0JCghISEpSfn6/XX3/dPG8YhiorK5WRkaGYmBhNmjRJR48eDZjD5/Np0aJFSk5OVlxcnIqLi3XmzJmAmo6ODrndbjmdTjmdTrndbl24cCGg5tSpU5o5c6bi4uKUnJyssrIydXd3B3n5AADAyoIKOsOGDdMPf/hDHT58WIcPH9aDDz6ohx56yAwzq1at0rp167Rx40YdOnRIaWlpKigo0MWLF805ysvLtWvXLtXU1Kiurk6XLl1SUVGRenp6zJqSkhI1NzfL4/HI4/GoublZbrfbPN/T06MZM2bo8uXLqqurU01NjXbu3KklS5bcaT8AAICFDA6meObMmQHHy5cv16ZNm3TgwAGNGTNGGzZs0NNPP61Zs2ZJkl588UWlpqbq5Zdf1oIFC9TZ2amtW7dq+/btmjp1qiSpurpamZmZ2rt3rwoLC3X8+HF5PB4dOHBAubm5kqQtW7YoPz9fJ06cUFZWlrxer44dO6bTp08rIyNDkrR27VrNnTtXy5cvV0JCwh03BgAARL6ggs5H9fT06Gc/+5kuX76s/Px8nTx5Um1tbXK5XGaNw+HQxIkTVV9frwULFqipqUl+vz+gJiMjQ9nZ2aqvr1dhYaEaGhrkdDrNkCNJeXl5cjqdqq+vV1ZWlhoaGpSdnW2GHEkqLCyUz+dTU1OTJk+efMM1+3w++Xw+87irq0uS5Pf75ff7+9qKG7o2X3/PG2qOKGOglxAUx6AP1xtpfY40kXo/Rxr6HB70OXxC1etg5gs66Bw5ckT5+fn661//qs985jPatWuXxowZo/r6eklSampqQH1qaqreffddSVJbW5uio6OVmJjYq6atrc2sSUlJ6fW+KSkpATXXv09iYqKio6PNmhtZuXKlli1b1mvc6/UqNjb24y69T2pra0Myb6isemCgV9A3kdbnSEWfw4M+hwd9Dp/+7vWVK1duuzbooJOVlaXm5mZduHBBO3fu1Jw5c7R//37zvM1mC6g3DKPX2PWur7lRfV9qrrd06VItXrzYPO7q6lJmZqZcLle/f9zl9/tVW1urgoIC2e32fp07lLIr3xjoJQTFMcjQ98d9EHF9jjSRej9HGvocHvQ5fELV62ufyNyOoINOdHS0vvCFL0iSxo0bp0OHDulHP/qR/u3f/k3Sh7st6enpZn17e7u5+5KWlqbu7m51dHQE7Oq0t7dr/PjxZs3Zs2d7ve+5c+cC5mlsbAw439HRIb/f32un56McDoccDkevcbvdHrKbPZRzh4Kv59ah9JMq0vocqehzeNDn8KDP4dPfvQ5mrjv+d3QMw5DP59PIkSOVlpYWsD3V3d2t/fv3myEmJydHdrs9oKa1tVUtLS1mTX5+vjo7O3Xw4EGzprGxUZ2dnQE1LS0tam1tNWu8Xq8cDodycnLu9JIAAIBFBLWj89RTT2n69OnKzMzUxYsXVVNTo3379snj8chms6m8vFwrVqzQqFGjNGrUKK1YsUKxsbEqKSmRJDmdTs2bN09LlizR0KFDlZSUpIqKCo0dO9Z8Cmv06NGaNm2aSktLtXnzZknS/PnzVVRUpKysLEmSy+XSmDFj5Ha7tXr1ap0/f14VFRUqLS3liSsAAGAKKuicPXtWbrdbra2tcjqduu++++TxeFRQUCBJeuKJJ3T16lUtXLhQHR0dys3NldfrVXx8vDnH+vXrNXjwYM2ePVtXr17VlClTtG3bNkVFRZk1O3bsUFlZmfl0VnFxsTZu3Giej4qK0p49e7Rw4UJNmDBBMTExKikp0Zo1a+6oGQAAwFqCCjpbt2695XmbzabKykpVVlbetGbIkCGqqqpSVVXVTWuSkpJUXV19y/caPny4du/efcsaAADw6cbvugIAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJYVVNBZuXKlvvzlLys+Pl4pKSl6+OGHdeLEiYCauXPnymazBfzk5eUF1Ph8Pi1atEjJycmKi4tTcXGxzpw5E1DT0dEht9stp9Mpp9Mpt9utCxcuBNScOnVKM2fOVFxcnJKTk1VWVqbu7u5gLgkAAFhYUEFn//79evTRR3XgwAHV1tbqb3/7m1wuly5fvhxQN23aNLW2tpo/r732WsD58vJy7dq1SzU1Naqrq9OlS5dUVFSknp4es6akpETNzc3yeDzyeDxqbm6W2+02z/f09GjGjBm6fPmy6urqVFNTo507d2rJkiV96QMAALCgwcEUezyegOMXXnhBKSkpampq0le/+lVz3OFwKC0t7YZzdHZ2auvWrdq+fbumTp0qSaqurlZmZqb27t2rwsJCHT9+XB6PRwcOHFBubq4kacuWLcrPz9eJEyeUlZUlr9erY8eO6fTp08rIyJAkrV27VnPnztXy5cuVkJAQzKUBAAALCiroXK+zs1OSlJSUFDC+b98+paSk6K677tLEiRO1fPlypaSkSJKamprk9/vlcrnM+oyMDGVnZ6u+vl6FhYVqaGiQ0+k0Q44k5eXlyel0qr6+XllZWWpoaFB2drYZciSpsLBQPp9PTU1Nmjx5cq/1+nw++Xw+87irq0uS5Pf75ff776QVvVybr7/nDTVHlDHQSwiKY9CH6420PkeaSL2fIw19Dg/6HD6h6nUw8/U56BiGocWLF+srX/mKsrOzzfHp06frm9/8pkaMGKGTJ0/qmWee0YMPPqimpiY5HA61tbUpOjpaiYmJAfOlpqaqra1NktTW1mYGo49KSUkJqElNTQ04n5iYqOjoaLPmeitXrtSyZct6jXu9XsXGxgbXgNtUW1sbknlDZdUDA72Cvom0Pkcq+hwe9Dk86HP49Hevr1y5ctu1fQ46jz32mH73u9+prq4uYPyRRx4x/5ydna1x48ZpxIgR2rNnj2bNmnXT+QzDkM1mM48/+uc7qfmopUuXavHixeZxV1eXMjMz5XK5+v2jLr/fr9raWhUUFMhut/fr3KGUXfnGQC8hKI5Bhr4/7oOI63OkidT7OdLQ5/Cgz+ETql5f+0TmdvQp6CxatEivvvqq3nzzTQ0bNuyWtenp6RoxYoTefvttSVJaWpq6u7vV0dERsKvT3t6u8ePHmzVnz57tNde5c+fMXZy0tDQ1NjYGnO/o6JDf7++103ONw+GQw+HoNW6320N2s4dy7lDw9dw4JH7SRVqfIxV9Dg/6HB70OXz6u9fBzBXUU1eGYeixxx7TL37xC/3617/WyJEjP/Y177//vk6fPq309HRJUk5Ojux2e8A2Vmtrq1paWsygk5+fr87OTh08eNCsaWxsVGdnZ0BNS0uLWltbzRqv1yuHw6GcnJxgLgsAAFhUUDs6jz76qF5++WX96le/Unx8vPldGKfTqZiYGF26dEmVlZX6+te/rvT0dL3zzjt66qmnlJycrK997Wtm7bx587RkyRINHTpUSUlJqqio0NixY82nsEaPHq1p06aptLRUmzdvliTNnz9fRUVFysrKkiS5XC6NGTNGbrdbq1ev1vnz51VRUaHS0lKeuAIAAJKC3NHZtGmTOjs7NWnSJKWnp5s/r7zyiiQpKipKR44c0UMPPaR77rlHc+bM0T333KOGhgbFx8eb86xfv14PP/ywZs+erQkTJig2Nlb/8z//o6ioKLNmx44dGjt2rFwul1wul+677z5t377dPB8VFaU9e/ZoyJAhmjBhgmbPnq2HH35Ya9asudOeAAAAiwhqR8cwbv3ocUxMjN544+O/zDpkyBBVVVWpqqrqpjVJSUmqrq6+5TzDhw/X7t27P/b9AADApxO/6woAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFgWQQcAAFhWUEFn5cqV+vKXv6z4+HilpKTo4Ycf1okTJwJqDMNQZWWlMjIyFBMTo0mTJuno0aMBNT6fT4sWLVJycrLi4uJUXFysM2fOBNR0dHTI7XbL6XTK6XTK7XbrwoULATWnTp3SzJkzFRcXp+TkZJWVlam7uzuYSwIAABYWVNDZv3+/Hn30UR04cEC1tbX629/+JpfLpcuXL5s1q1at0rp167Rx40YdOnRIaWlpKigo0MWLF82a8vJy7dq1SzU1Naqrq9OlS5dUVFSknp4es6akpETNzc3yeDzyeDxqbm6W2+02z/f09GjGjBm6fPmy6urqVFNTo507d2rJkiV30g8AAGAhg4Mp9ng8AccvvPCCUlJS1NTUpK9+9asyDEMbNmzQ008/rVmzZkmSXnzxRaWmpurll1/WggUL1NnZqa1bt2r79u2aOnWqJKm6ulqZmZnau3evCgsLdfz4cXk8Hh04cEC5ubmSpC1btig/P18nTpxQVlaWvF6vjh07ptOnTysjI0OStHbtWs2dO1fLly9XQkLCHTcHAABEtqCCzvU6OzslSUlJSZKkkydPqq2tTS6Xy6xxOByaOHGi6uvrtWDBAjU1Ncnv9wfUZGRkKDs7W/X19SosLFRDQ4OcTqcZciQpLy9PTqdT9fX1ysrKUkNDg7Kzs82QI0mFhYXy+XxqamrS5MmTe63X5/PJ5/OZx11dXZIkv98vv99/J63o5dp8/T1vqDmijIFeQlAcgz5cb6T1OdJE6v0caehzeNDn8AlVr4OZr89BxzAMLV68WF/5yleUnZ0tSWpra5MkpaamBtSmpqbq3XffNWuio6OVmJjYq+ba69va2pSSktLrPVNSUgJqrn+fxMRERUdHmzXXW7lypZYtW9Zr3Ov1KjY29mOvuS9qa2tDMm+orHpgoFfQN5HW50hFn8ODPocHfQ6f/u71lStXbru2z0Hnscce0+9+9zvV1dX1Omez2QKODcPoNXa962tuVN+Xmo9aunSpFi9ebB53dXUpMzNTLper3z/q8vv9qq2tVUFBgex2e7/OHUrZlW8M9BKC4hhk6PvjPoi4PkeaSL2fIw19Dg/6HD6h6vW1T2RuR5+CzqJFi/Tqq6/qzTff1LBhw8zxtLQ0SR/utqSnp5vj7e3t5u5LWlqauru71dHREbCr097ervHjx5s1Z8+e7fW+586dC5insbEx4HxHR4f8fn+vnZ5rHA6HHA5Hr3G73R6ymz2Uc4eCr+fWgfSTKtL6HKnoc3jQ5/Cgz+HT370OZq6gnroyDEOPPfaYfvGLX+jXv/61Ro4cGXB+5MiRSktLC9ii6u7u1v79+80Qk5OTI7vdHlDT2tqqlpYWsyY/P1+dnZ06ePCgWdPY2KjOzs6AmpaWFrW2tpo1Xq9XDodDOTk5wVwWAACwqKB2dB599FG9/PLL+tWvfqX4+HjzuzBOp1MxMTGy2WwqLy/XihUrNGrUKI0aNUorVqxQbGysSkpKzNp58+ZpyZIlGjp0qJKSklRRUaGxY8eaT2GNHj1a06ZNU2lpqTZv3ixJmj9/voqKipSVlSVJcrlcGjNmjNxut1avXq3z58+roqJCpaWlPHEFAAAkBRl0Nm3aJEmaNGlSwPgLL7yguXPnSpKeeOIJXb16VQsXLlRHR4dyc3Pl9XoVHx9v1q9fv16DBw/W7NmzdfXqVU2ZMkXbtm1TVFSUWbNjxw6VlZWZT2cVFxdr48aN5vmoqCjt2bNHCxcu1IQJExQTE6OSkhKtWbMmqAYAAADrCiroGMbHP3pss9lUWVmpysrKm9YMGTJEVVVVqqqqumlNUlKSqqurb/lew4cP1+7duz92TQAA4NOJ33UFAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsK+ig8+abb2rmzJnKyMiQzWbTL3/5y4Dzc+fOlc1mC/jJy8sLqPH5fFq0aJGSk5MVFxen4uJinTlzJqCmo6NDbrdbTqdTTqdTbrdbFy5cCKg5deqUZs6cqbi4OCUnJ6usrEzd3d3BXhIAALCooIPO5cuX9aUvfUkbN268ac20adPU2tpq/rz22msB58vLy7Vr1y7V1NSorq5Oly5dUlFRkXp6esyakpISNTc3y+PxyOPxqLm5WW632zzf09OjGTNm6PLly6qrq1NNTY127typJUuWBHtJAADAogYH+4Lp06dr+vTpt6xxOBxKS0u74bnOzk5t3bpV27dv19SpUyVJ1dXVyszM1N69e1VYWKjjx4/L4/HowIEDys3NlSRt2bJF+fn5OnHihLKysuT1enXs2DGdPn1aGRkZkqS1a9dq7ty5Wr58uRISEoK9NAAAYDEh+Y7Ovn37lJKSonvuuUelpaVqb283zzU1Ncnv98vlcpljGRkZys7OVn19vSSpoaFBTqfTDDmSlJeXJ6fTGVCTnZ1thhxJKiwslM/nU1NTUyguCwAARJigd3Q+zvTp0/XNb35TI0aM0MmTJ/XMM8/owQcfVFNTkxwOh9ra2hQdHa3ExMSA16WmpqqtrU2S1NbWppSUlF5zp6SkBNSkpqYGnE9MTFR0dLRZcz2fzyefz2ced3V1SZL8fr/8fn/fL/oGrs3X3/OGmiPKGOglBMUx6MP1RlqfI02k3s+Rhj6HB30On1D1Opj5+j3oPPLII+afs7OzNW7cOI0YMUJ79uzRrFmzbvo6wzBks9nM44/++U5qPmrlypVatmxZr3Gv16vY2Nibru1O1NbWhmTeUFn1wECvoG8irc+Rij6HB30OD/ocPv3d6ytXrtx2bb8Hneulp6drxIgRevvttyVJaWlp6u7uVkdHR8CuTnt7u8aPH2/WnD17ttdc586dM3dx0tLS1NjYGHC+o6NDfr+/107PNUuXLtXixYvN466uLmVmZsrlcvX7d3r8fr9qa2tVUFAgu93er3OHUnblGwO9hKA4Bhn6/rgPIq7PkSZS7+dIQ5/Dgz6HT6h6fe0TmdsR8qDz/vvv6/Tp00pPT5ck5eTkyG63q7a2VrNnz5Yktba2qqWlRatWrZIk5efnq7OzUwcPHtQDD3y4xdDY2KjOzk4zDOXn52v58uVqbW015/Z6vXI4HMrJybnhWhwOhxwOR69xu90esps9lHOHgq/nxrthn3SR1udIRZ/Dgz6HB30On/7udTBzBR10Ll26pD/+8Y/m8cmTJ9Xc3KykpCQlJSWpsrJSX//615Wenq533nlHTz31lJKTk/W1r31NkuR0OjVv3jwtWbJEQ4cOVVJSkioqKjR27FjzKazRo0dr2rRpKi0t1ebNmyVJ8+fPV1FRkbKysiRJLpdLY8aMkdvt1urVq3X+/HlVVFSotLSUJ64AAICkPgSdw4cPa/LkyebxtY+C5syZo02bNunIkSN66aWXdOHCBaWnp2vy5Ml65ZVXFB8fb75m/fr1Gjx4sGbPnq2rV69qypQp2rZtm6KiosyaHTt2qKyszHw6q7i4OODf7omKitKePXu0cOFCTZgwQTExMSopKdGaNWuC7wIAALCkoIPOpEmTZBg3fzLnjTc+/jseQ4YMUVVVlaqqqm5ak5SUpOrq6lvOM3z4cO3evftj3w8AAHw68buuAACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZRF0AACAZQUddN58803NnDlTGRkZstls+uUvfxlw3jAMVVZWKiMjQzExMZo0aZKOHj0aUOPz+bRo0SIlJycrLi5OxcXFOnPmTEBNR0eH3G63nE6nnE6n3G63Lly4EFBz6tQpzZw5U3FxcUpOTlZZWZm6u7uDvSQAAGBRQQedy5cv60tf+pI2btx4w/OrVq3SunXrtHHjRh06dEhpaWkqKCjQxYsXzZry8nLt2rVLNTU1qqur06VLl1RUVKSenh6zpqSkRM3NzfJ4PPJ4PGpubpbb7TbP9/T0aMaMGbp8+bLq6upUU1OjnTt3asmSJcFeEgAAsKjBwb5g+vTpmj59+g3PGYahDRs26Omnn9asWbMkSS+++KJSU1P18ssva8GCBers7NTWrVu1fft2TZ06VZJUXV2tzMxM7d27V4WFhTp+/Lg8Ho8OHDig3NxcSdKWLVuUn5+vEydOKCsrS16vV8eOHdPp06eVkZEhSVq7dq3mzp2r5cuXKyEhoU8NAQAA1hF00LmVkydPqq2tTS6XyxxzOByaOHGi6uvrtWDBAjU1Ncnv9wfUZGRkKDs7W/X19SosLFRDQ4OcTqcZciQpLy9PTqdT9fX1ysrKUkNDg7Kzs82QI0mFhYXy+XxqamrS5MmTe63P5/PJ5/OZx11dXZIkv98vv9/fn60w5+vveUPNEWUM9BKC4hj04Xojrc+RJlLv50hDn8ODPodPqHodzHz9GnTa2tokSampqQHjqampevfdd82a6OhoJSYm9qq59vq2tjalpKT0mj8lJSWg5vr3SUxMVHR0tFlzvZUrV2rZsmW9xr1er2JjY2/nEoNWW1sbknlDZdUDA72Cvom0Pkcq+hwe9Dk86HP49Hevr1y5ctu1/Rp0rrHZbAHHhmH0Grve9TU3qu9LzUctXbpUixcvNo+7urqUmZkpl8vV7x91+f1+1dbWqqCgQHa7vV/nDqXsyjcGeglBcQwy9P1xH0RcnyNNpN7PkYY+hwd9Dp9Q9fraJzK3o1+DTlpamqQPd1vS09PN8fb2dnP3JS0tTd3d3ero6AjY1Wlvb9f48ePNmrNnz/aa/9y5cwHzNDY2Bpzv6OiQ3+/vtdNzjcPhkMPh6DVut9tDdrOHcu5Q8PXcOpB+UkVanyMVfQ4P+hwe9Dl8+rvXwczVr/+OzsiRI5WWlhawRdXd3a39+/ebISYnJ0d2uz2gprW1VS0tLWZNfn6+Ojs7dfDgQbOmsbFRnZ2dATUtLS1qbW01a7xerxwOh3JycvrzsgAAQIQKekfn0qVL+uMf/2genzx5Us3NzUpKStLw4cNVXl6uFStWaNSoURo1apRWrFih2NhYlZSUSJKcTqfmzZunJUuWaOjQoUpKSlJFRYXGjh1rPoU1evRoTZs2TaWlpdq8ebMkaf78+SoqKlJWVpYkyeVyacyYMXK73Vq9erXOnz+viooKlZaW8sQVAACQ1Iegc/jw4YAnmq5952XOnDnatm2bnnjiCV29elULFy5UR0eHcnNz5fV6FR8fb75m/fr1Gjx4sGbPnq2rV69qypQp2rZtm6KiosyaHTt2qKyszHw6q7i4OODf7omKitKePXu0cOFCTZgwQTExMSopKdGaNWuC7wIAALCkoIPOpEmTZBg3fwTZZrOpsrJSlZWVN60ZMmSIqqqqVFVVddOapKQkVVdX33Itw4cP1+7duz92zQAA4NOJ33UFAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsq9+DTmVlpWw2W8BPWlqaed4wDFVWViojI0MxMTGaNGmSjh49GjCHz+fTokWLlJycrLi4OBUXF+vMmTMBNR0dHXK73XI6nXI6nXK73bpw4UJ/Xw4AAIhgIdnRuffee9Xa2mr+HDlyxDy3atUqrVu3Ths3btShQ4eUlpamgoICXbx40awpLy/Xrl27VFNTo7q6Ol26dElFRUXq6ekxa0pKStTc3CyPxyOPx6Pm5ma53e5QXA4AAIhQg0My6eDBAbs41xiGoQ0bNujpp5/WrFmzJEkvvviiUlNT9fLLL2vBggXq7OzU1q1btX37dk2dOlWSVF1drczMTO3du1eFhYU6fvy4PB6PDhw4oNzcXEnSli1blJ+frxMnTigrKysUlwUAACJMSILO22+/rYyMDDkcDuXm5mrFihW6++67dfLkSbW1tcnlcpm1DodDEydOVH19vRYsWKCmpib5/f6AmoyMDGVnZ6u+vl6FhYVqaGiQ0+k0Q44k5eXlyel0qr6+/qZBx+fzyefzmcddXV2SJL/fL7/f3689uDZff88bao4oY6CXEBTHoA/XG2l9jjSRej9HGvocHvQ5fELV62Dm6/egk5ubq5deekn33HOPzp49qx/84AcaP368jh49qra2NklSampqwGtSU1P17rvvSpLa2toUHR2txMTEXjXXXt/W1qaUlJRe752SkmLW3MjKlSu1bNmyXuNer1exsbHBXehtqq2tDcm8obLqgYFeQd9EWp8jFX0OD/ocHvQ5fPq711euXLnt2n4POtOnTzf/PHbsWOXn5+vzn/+8XnzxReXl5UmSbDZbwGsMw+g1dr3ra25U/3HzLF26VIsXLzaPu7q6lJmZKZfLpYSEhFtfWJD8fr9qa2tVUFAgu93er3OHUnblGwO9hKA4Bhn6/rgPIq7PkSZS7+dIQ5/Dgz6HT6h6fe0TmdsRko+uPiouLk5jx47V22+/rYcffljShzsy6enpZk17e7u5y5OWlqbu7m51dHQE7Oq0t7dr/PjxZs3Zs2d7vde5c+d67RZ9lMPhkMPh6DVut9tDdrOHcu5Q8PXcOnB+UkVanyMVfQ4P+hwe9Dl8+rvXwcwV8n9Hx+fz6fjx40pPT9fIkSOVlpYWsIXV3d2t/fv3myEmJydHdrs9oKa1tVUtLS1mTX5+vjo7O3Xw4EGzprGxUZ2dnWYNAABAv+/oVFRUaObMmRo+fLja29v1gx/8QF1dXZozZ45sNpvKy8u1YsUKjRo1SqNGjdKKFSsUGxurkpISSZLT6dS8efO0ZMkSDR06VElJSaqoqNDYsWPNp7BGjx6tadOmqbS0VJs3b5YkzZ8/X0VFRTxxBQAATP0edM6cOaN/+qd/0nvvvae/+7u/U15eng4cOKARI0ZIkp544gldvXpVCxcuVEdHh3Jzc+X1ehUfH2/OsX79eg0ePFizZ8/W1atXNWXKFG3btk1RUVFmzY4dO1RWVmY+nVVcXKyNGzf29+UAAIAI1u9Bp6am5pbnbTabKisrVVlZedOaIUOGqKqqSlVVVTetSUpKUnV1dV+XCQAAPgX4XVcAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCy+v23lwMAgP73uSf3DPQSguaIMrTqgYFdAzs6AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsgg6AADAsvilngCAT6Xsyjfk67EN9DIQYuzoAAAAy2JHBwBwRz735J6BXkJQHFGGVj0w0KtAuBB0AOATho9UgP4T8UHn2Wef1erVq9Xa2qp7771XGzZs0D/+4z8O9LIAS4q0/wC/88MZA70EAAMsooPOK6+8ovLycj377LOaMGGCNm/erOnTp+vYsWMaPnz4QC9PUuT9hyFS0efQitStfj5SARDRX0Zet26d5s2bp29/+9saPXq0NmzYoMzMTG3atGmglwYAAD4BInZHp7u7W01NTXryyScDxl0ul+rr62/4Gp/PJ5/PZx53dnZKks6fPy+/39+v6/P7/bpy5YoG+wep5wN2GkJl8AeGrlz5gD6HGH0OD/ocHvQ5fK71+v3335fdbu+3eS9evChJMgzj49fQb+8aZu+99556enqUmpoaMJ6amqq2trYbvmblypVatmxZr/GRI0eGZI0Ij5KBXsCnBH0OD/ocHvQ5fELZ64sXL8rpdN6yJmKDzjU2W2AaNwyj19g1S5cu1eLFi83jDz74QOfPn9fQoUNv+pq+6urqUmZmpk6fPq2EhIR+nRv/H30OD/ocHvQ5POhz+ISq14Zh6OLFi8rIyPjY2ogNOsnJyYqKiuq1e9Pe3t5rl+cah8Mhh8MRMHbXXXeFaomSpISEBP6HFAb0OTzoc3jQ5/Cgz+ETil5/3E7ONRH7ZeTo6Gjl5OSotrY2YLy2tlbjx48foFUBAIBPkojd0ZGkxYsXy+12a9y4ccrPz9dzzz2nU6dO6Tvf+c5ALw0AAHwCRHTQeeSRR/T+++/rP/7jP9Ta2qrs7Gy99tprGjFixEAvTQ6HQ9/73vd6fVSG/kWfw4M+hwd9Dg/6HD6fhF7bjNt5NgsAACACRex3dAAAAD4OQQcAAFgWQQcAAFgWQQcAAFgWQaePnn32WY0cOVJDhgxRTk6Ofvvb3960dt++fbLZbL1+fv/734dxxZErmF5LH/5Os6efflojRoyQw+HQ5z//eT3//PNhWm3kCqbPc+fOveE9fe+994ZxxZEp2Pt5x44d+tKXvqTY2Filp6frX/7lX/T++++HabWRK9g+/+QnP9Ho0aMVExOjrKwsvfTSS2FaaeR68803NXPmTGVkZMhms+mXv/zlx75m//79ysnJ0ZAhQ3T33Xfrv/7rv0K/UANBq6mpMex2u7Flyxbj2LFjxuOPP27ExcUZ77777g3rf/Ob3xiSjBMnThitra3mz9/+9rcwrzzyBNtrwzCM4uJiIzc316itrTVOnjxpNDY2Gv/7v/8bxlVHnmD7fOHChYB7+fTp00ZSUpLxve99L7wLjzDB9vm3v/2tMWjQIONHP/qR8ec//9n47W9/a9x7773Gww8/HOaVR5Zg+/zss88a8fHxRk1NjfGnP/3J+O///m/jM5/5jPHqq6+GeeWR5bXXXjOefvppY+fOnYYkY9euXbes//Of/2zExsYajz/+uHHs2DFjy5Ytht1uN37+85+HdJ0EnT544IEHjO985zsBY1/84heNJ5988ob114JOR0dHGFZnLcH2+vXXXzecTqfx/vvvh2N5lhFsn6+3a9cuw2azGe+8804olmcZwfZ59erVxt133x0w9uMf/9gYNmxYyNZoBcH2OT8/36ioqAgYe/zxx40JEyaEbI1WcztB54knnjC++MUvBowtWLDAyMvLC+HKDIOProLU3d2tpqYmuVyugHGXy6X6+vpbvvb+++9Xenq6pkyZot/85jehXKYl9KXXr776qsaNG6dVq1bps5/9rO655x5VVFTo6tWr4VhyRLqTe/qarVu3aurUqZ+If6zzk6ovfR4/frzOnDmj1157TYZh6OzZs/r5z3+uGTNmhGPJEakvffb5fBoyZEjAWExMjA4ePCi/3x+ytX7aNDQ09Pp7KSws1OHDh0PaZ4JOkN577z319PT0+sWhqampvX7B6DXp6el67rnntHPnTv3iF79QVlaWpkyZojfffDMcS45Yfen1n//8Z9XV1amlpUW7du3Shg0b9POf/1yPPvpoOJYckfrS549qbW3V66+/rm9/+9uhWqIl9KXP48eP144dO/TII48oOjpaaWlpuuuuu1RVVRWOJUekvvS5sLBQP/3pT9XU1CTDMHT48GE9//zz8vv9eu+998Kx7E+Ftra2G/69/O1vfwtpnyP6V0AMJJvNFnBsGEavsWuysrKUlZVlHufn5+v06dNas2aNvvrVr4Z0nVYQTK8/+OAD2Ww27dixw/zNtuvWrdM3vvEN/eQnP1FMTEzI1xupgunzR23btk133XWXHn744RCtzFqC6fOxY8dUVlamf//3f1dhYaFaW1v13e9+V9/5zne0devWcCw3YgXT52eeeUZtbW3Ky8uTYRhKTU3V3LlztWrVKkVFRYVjuZ8aN/p7udF4f2JHJ0jJycmKiorq9f8ZtLe390qqt5KXl6e33367v5dnKX3pdXp6uj772c+aIUeSRo8eLcMwdObMmZCuN1LdyT1tGIaef/55ud1uRUdHh3KZEa8vfV65cqUmTJig7373u7rvvvtUWFioZ599Vs8//7xaW1vDseyI05c+x8TE6Pnnn9eVK1f0zjvv6NSpU/rc5z6n+Ph4JScnh2PZnwppaWk3/HsZPHiwhg4dGrL3JegEKTo6Wjk5OaqtrQ0Yr62t1fjx4297nrfeekvp6en9vTxL6UuvJ0yYoL/85S+6dOmSOfaHP/xBgwYN0rBhw0K63kh1J/f0/v379cc//lHz5s0L5RItoS99vnLligYNCvw/09d2GAx+TeEN3cn9bLfbNWzYMEVFRammpkZFRUW9+o++y8/P7/X34vV6NW7cONnt9tC9cUi/6mxR1x5d3Lp1q3Hs2DGjvLzciIuLM584efLJJw23223Wr1+/3ti1a5fxhz/8wWhpaTGefPJJQ5Kxc+fOgbqEiBFsry9evGgMGzbM+MY3vmEcPXrU2L9/vzFq1Cjj29/+9kBdQkQIts/X/PM//7ORm5sb7uVGrGD7/MILLxiDBw82nn32WeNPf/qTUVdXZ4wbN8544IEHBuoSIkKwfT5x4oSxfft24w9/+IPR2NhoPPLII0ZSUpJx8uTJAbqCyHDx4kXjrbfeMt566y1DkrFu3TrjrbfeMh/jv77P1x4v/9d//Vfj2LFjxtatW3m8/JPsJz/5iTFixAgjOjra+Id/+Adj//795rk5c+YYEydONI//8z//0/j85z9vDBkyxEhMTDS+8pWvGHv27BmAVUemYHptGIZx/PhxY+rUqUZMTIwxbNgwY/HixcaVK1fCvOrIE2yfL1y4YMTExBjPPfdcmFca2YLt849//GNjzJgxRkxMjJGenm5861vfMs6cORPmVUeeYPp87Ngx4+///u+NmJgYIyEhwXjooYeM3//+9wOw6shy7Z9Ouf5nzpw5hmHc+H7et2+fcf/99xvR0dHG5z73OWPTpk0hX6fNMNj/BAAA1sSHjwAAwLIIOgAAwLIIOgAAwLIIOgAAwLIIOgAAwLIIOgAAwLIIOgAAwLIIOgAAwLIIOgAAwLIIOgAAwLIIOgAAwLIIOgAAwLL+Hzn8AiPhV6uTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('multi', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7731    0.7618    0.7674     29306\n",
      "         1.0     0.6584    0.6725    0.6654     20010\n",
      "\n",
      "    accuracy                         0.7256     49316\n",
      "   macro avg     0.7158    0.7171    0.7164     49316\n",
      "weighted avg     0.7266    0.7256    0.7260     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7739    0.7623    0.7680     20463\n",
      "         1.0     0.6614    0.6758    0.6685     14058\n",
      "\n",
      "    accuracy                         0.7271     34521\n",
      "   macro avg     0.7176    0.7190    0.7183     34521\n",
      "weighted avg     0.7281    0.7271    0.7275     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7724    0.7634    0.7679      4400\n",
      "         1.0     0.6585    0.6697    0.6640      2997\n",
      "\n",
      "    accuracy                         0.7254      7397\n",
      "   macro avg     0.7154    0.7165    0.7159      7397\n",
      "weighted avg     0.7262    0.7254    0.7258      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8987    0.8809    0.8897       806\n",
      "         1.0     0.7333    0.7674    0.7500       344\n",
      "\n",
      "    accuracy                         0.8470      1150\n",
      "   macro avg     0.8160    0.8242    0.8199      1150\n",
      "weighted avg     0.8493    0.8470    0.8479      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7443    0.7371    0.7407      3594\n",
      "         1.0     0.6484    0.6570    0.6527      2653\n",
      "\n",
      "    accuracy                         0.7031      6247\n",
      "   macro avg     0.6964    0.6970    0.6967      6247\n",
      "weighted avg     0.7036    0.7031    0.7033      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8275    0.7994    0.8132       324\n",
      "         1.0     0.6176    0.6604    0.6383       159\n",
      "\n",
      "    accuracy                         0.7536       483\n",
      "   macro avg     0.7226    0.7299    0.7257       483\n",
      "weighted avg     0.7584    0.7536    0.7556       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.56\n",
      "    Training Set Accuracy: 72.71\n",
      "    Validation Set Accuracy: 72.54\n",
      "    Validation Set with both Images and Texts: 84.70\n",
      "    Validation Set with only Texts: 70.31\n",
      "    Validation Set with unseen Images: 75.36\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7731    0.7618    0.7674     29306\n",
      "         1.0     0.6584    0.6725    0.6654     20010\n",
      "\n",
      "    accuracy                         0.7256     49316\n",
      "   macro avg     0.7158    0.7171    0.7164     49316\n",
      "weighted avg     0.7266    0.7256    0.7260     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7739    0.7623    0.7680     20463\n",
      "         1.0     0.6614    0.6758    0.6685     14058\n",
      "\n",
      "    accuracy                         0.7271     34521\n",
      "   macro avg     0.7176    0.7190    0.7183     34521\n",
      "weighted avg     0.7281    0.7271    0.7275     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7700    0.7580    0.7640      4443\n",
      "         1.0     0.6445    0.6596    0.6519      2955\n",
      "\n",
      "    accuracy                         0.7187      7398\n",
      "   macro avg     0.7073    0.7088    0.7080      7398\n",
      "weighted avg     0.7199    0.7187    0.7192      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8766    0.8827    0.8796       716\n",
      "         1.0     0.7551    0.7443    0.7496       348\n",
      "\n",
      "    accuracy                         0.8374      1064\n",
      "   macro avg     0.8158    0.8135    0.8146      1064\n",
      "weighted avg     0.8368    0.8374    0.8371      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7490    0.7341    0.7415      3727\n",
      "         1.0     0.6304    0.6483    0.6392      2607\n",
      "\n",
      "    accuracy                         0.6988      6334\n",
      "   macro avg     0.6897    0.6912    0.6903      6334\n",
      "weighted avg     0.7002    0.6988    0.6994      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8094    0.8065    0.8079       279\n",
      "         1.0     0.6538    0.6581    0.6559       155\n",
      "\n",
      "    accuracy                         0.7535       434\n",
      "   macro avg     0.7316    0.7323    0.7319       434\n",
      "weighted avg     0.7538    0.7535    0.7536       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.56\n",
      "    Training Set Accuracy: 72.71\n",
      "    Validation Set Accuracy: 71.87\n",
      "    Validation Set with both Images and Texts: 83.74\n",
      "    Validation Set with only Texts: 69.88\n",
      "    Validation Set with unseen Images: 75.35\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.5018474  0.49815264]\n",
      " [0.34934747 0.6506525 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.710984\n",
      "std          0.158998\n",
      "min          0.500005\n",
      "25%          0.571649\n",
      "50%          0.668931\n",
      "75%          0.851788\n",
      "max          0.999964\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.710751\n",
      "std          0.158627\n",
      "min          0.500005\n",
      "25%          0.571719\n",
      "50%          0.669070\n",
      "75%          0.851206\n",
      "max          0.999964\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.711944\n",
      "std         0.159710\n",
      "min         0.500013\n",
      "25%         0.572643\n",
      "50%         0.669641\n",
      "75%         0.852611\n",
      "max         0.999922\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('multi', 'complex')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8503    0.7971    0.8229     29306\n",
      "         1.0     0.7278    0.7945    0.7597     20010\n",
      "\n",
      "    accuracy                         0.7961     49316\n",
      "   macro avg     0.7891    0.7958    0.7913     49316\n",
      "weighted avg     0.8006    0.7961    0.7972     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8702    0.8161    0.8423     20463\n",
      "         1.0     0.7545    0.8227    0.7872     14058\n",
      "\n",
      "    accuracy                         0.8188     34521\n",
      "   macro avg     0.8123    0.8194    0.8147     34521\n",
      "weighted avg     0.8231    0.8188    0.8198     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8037    0.7593    0.7809      4400\n",
      "         1.0     0.6731    0.7277    0.6994      2997\n",
      "\n",
      "    accuracy                         0.7465      7397\n",
      "   macro avg     0.7384    0.7435    0.7401      7397\n",
      "weighted avg     0.7508    0.7465    0.7479      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9288    0.9231    0.9259       806\n",
      "         1.0     0.8223    0.8343    0.8283       344\n",
      "\n",
      "    accuracy                         0.8965      1150\n",
      "   macro avg     0.8756    0.8787    0.8771      1150\n",
      "weighted avg     0.8970    0.8965    0.8967      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7738    0.7226    0.7473      3594\n",
      "         1.0     0.6551    0.7139    0.6833      2653\n",
      "\n",
      "    accuracy                         0.7189      6247\n",
      "   macro avg     0.7145    0.7183    0.7153      6247\n",
      "weighted avg     0.7234    0.7189    0.7201      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8403    0.8117    0.8257       324\n",
      "         1.0     0.6412    0.6855    0.6626       159\n",
      "\n",
      "    accuracy                         0.7702       483\n",
      "   macro avg     0.7407    0.7486    0.7442       483\n",
      "weighted avg     0.7747    0.7702    0.7720       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 79.61\n",
      "    Training Set Accuracy: 81.88\n",
      "    Validation Set Accuracy: 74.65\n",
      "    Validation Set with both Images and Texts: 89.65\n",
      "    Validation Set with only Texts: 71.89\n",
      "    Validation Set with unseen Images: 77.02\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8503    0.7971    0.8229     29306\n",
      "         1.0     0.7278    0.7945    0.7597     20010\n",
      "\n",
      "    accuracy                         0.7961     49316\n",
      "   macro avg     0.7891    0.7958    0.7913     49316\n",
      "weighted avg     0.8006    0.7961    0.7972     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8702    0.8161    0.8423     20463\n",
      "         1.0     0.7545    0.8227    0.7872     14058\n",
      "\n",
      "    accuracy                         0.8188     34521\n",
      "   macro avg     0.8123    0.8194    0.8147     34521\n",
      "weighted avg     0.8231    0.8188    0.8198     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8050    0.7472    0.7751      4443\n",
      "         1.0     0.6570    0.7279    0.6906      2955\n",
      "\n",
      "    accuracy                         0.7395      7398\n",
      "   macro avg     0.7310    0.7376    0.7329      7398\n",
      "weighted avg     0.7459    0.7395    0.7413      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9095    0.9260    0.9176       716\n",
      "         1.0     0.8418    0.8103    0.8258       348\n",
      "\n",
      "    accuracy                         0.8882      1064\n",
      "   macro avg     0.8756    0.8682    0.8717      1064\n",
      "weighted avg     0.8873    0.8882    0.8876      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7826    0.7129    0.7461      3727\n",
      "         1.0     0.6359    0.7169    0.6740      2607\n",
      "\n",
      "    accuracy                         0.7146      6334\n",
      "   macro avg     0.7093    0.7149    0.7101      6334\n",
      "weighted avg     0.7222    0.7146    0.7164      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7917    0.8172    0.8042       279\n",
      "         1.0     0.6507    0.6129    0.6312       155\n",
      "\n",
      "    accuracy                         0.7442       434\n",
      "   macro avg     0.7212    0.7151    0.7177       434\n",
      "weighted avg     0.7413    0.7442    0.7424       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 79.61\n",
      "    Training Set Accuracy: 81.88\n",
      "    Validation Set Accuracy: 73.95\n",
      "    Validation Set with both Images and Texts: 88.82\n",
      "    Validation Set with only Texts: 71.46\n",
      "    Validation Set with unseen Images: 74.42\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.6898286  0.31017143]\n",
      " [0.46219778 0.5378022 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.791894\n",
      "std          0.169947\n",
      "min          0.500002\n",
      "25%          0.634098\n",
      "50%          0.800131\n",
      "75%          0.973725\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.794450\n",
      "std          0.170035\n",
      "min          0.500002\n",
      "25%          0.636573\n",
      "50%          0.806124\n",
      "75%          0.976890\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.787024\n",
      "std         0.169903\n",
      "min         0.500020\n",
      "25%         0.629167\n",
      "50%         0.791478\n",
      "75%         0.966298\n",
      "max         1.000000\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('text', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7847    0.7759    0.7803     29306\n",
      "         1.0     0.6771    0.6883    0.6826     20010\n",
      "\n",
      "    accuracy                         0.7403     49316\n",
      "   macro avg     0.7309    0.7321    0.7315     49316\n",
      "weighted avg     0.7410    0.7403    0.7407     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7888    0.7797    0.7843     20463\n",
      "         1.0     0.6847    0.6962    0.6904     14058\n",
      "\n",
      "    accuracy                         0.7457     34521\n",
      "   macro avg     0.7368    0.7380    0.7373     34521\n",
      "weighted avg     0.7464    0.7457    0.7460     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7777    0.7711    0.7744      4400\n",
      "         1.0     0.6681    0.6763    0.6722      2997\n",
      "\n",
      "    accuracy                         0.7327      7397\n",
      "   macro avg     0.7229    0.7237    0.7233      7397\n",
      "weighted avg     0.7333    0.7327    0.7330      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8757    0.9094    0.8923       806\n",
      "         1.0     0.7668    0.6977    0.7306       344\n",
      "\n",
      "    accuracy                         0.8461      1150\n",
      "   macro avg     0.8213    0.8036    0.8114      1150\n",
      "weighted avg     0.8431    0.8461    0.8439      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7544    0.7401    0.7472      3594\n",
      "         1.0     0.6567    0.6736    0.6651      2653\n",
      "\n",
      "    accuracy                         0.7119      6247\n",
      "   macro avg     0.7056    0.7068    0.7061      6247\n",
      "weighted avg     0.7129    0.7119    0.7123      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8235    0.8642    0.8434       324\n",
      "         1.0     0.6923    0.6226    0.6556       159\n",
      "\n",
      "    accuracy                         0.7847       483\n",
      "   macro avg     0.7579    0.7434    0.7495       483\n",
      "weighted avg     0.7803    0.7847    0.7816       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 74.03\n",
      "    Training Set Accuracy: 74.57\n",
      "    Validation Set Accuracy: 73.27\n",
      "    Validation Set with both Images and Texts: 84.61\n",
      "    Validation Set with only Texts: 71.19\n",
      "    Validation Set with unseen Images: 78.47\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7847    0.7759    0.7803     29306\n",
      "         1.0     0.6771    0.6883    0.6826     20010\n",
      "\n",
      "    accuracy                         0.7403     49316\n",
      "   macro avg     0.7309    0.7321    0.7315     49316\n",
      "weighted avg     0.7410    0.7403    0.7407     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7888    0.7797    0.7843     20463\n",
      "         1.0     0.6847    0.6962    0.6904     14058\n",
      "\n",
      "    accuracy                         0.7457     34521\n",
      "   macro avg     0.7368    0.7380    0.7373     34521\n",
      "weighted avg     0.7464    0.7457    0.7460     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7727    0.7628    0.7677      4443\n",
      "         1.0     0.6501    0.6626    0.6563      2955\n",
      "\n",
      "    accuracy                         0.7228      7398\n",
      "   macro avg     0.7114    0.7127    0.7120      7398\n",
      "weighted avg     0.7237    0.7228    0.7232      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8506    0.9148    0.8816       716\n",
      "         1.0     0.7925    0.6695    0.7259       348\n",
      "\n",
      "    accuracy                         0.8346      1064\n",
      "   macro avg     0.8216    0.7922    0.8037      1064\n",
      "weighted avg     0.8316    0.8346    0.8306      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7561    0.7336    0.7447      3727\n",
      "         1.0     0.6347    0.6617    0.6479      2607\n",
      "\n",
      "    accuracy                         0.7040      6334\n",
      "   macro avg     0.6954    0.6976    0.6963      6334\n",
      "weighted avg     0.7061    0.7040    0.7048      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7895    0.8602    0.8233       279\n",
      "         1.0     0.7000    0.5871    0.6386       155\n",
      "\n",
      "    accuracy                         0.7627       434\n",
      "   macro avg     0.7447    0.7237    0.7310       434\n",
      "weighted avg     0.7575    0.7627    0.7574       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 74.03\n",
      "    Training Set Accuracy: 74.57\n",
      "    Validation Set Accuracy: 72.28\n",
      "    Validation Set with both Images and Texts: 83.46\n",
      "    Validation Set with only Texts: 70.40\n",
      "    Validation Set with unseen Images: 76.27\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.5274205  0.47257948]\n",
      " [0.34050614 0.65949386]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.721841\n",
      "std          0.158837\n",
      "min          0.500006\n",
      "25%          0.580954\n",
      "50%          0.687343\n",
      "75%          0.862697\n",
      "max          0.999909\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.721516\n",
      "std          0.158720\n",
      "min          0.500013\n",
      "25%          0.580638\n",
      "50%          0.687060\n",
      "75%          0.861773\n",
      "max          0.999909\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.722299\n",
      "std         0.159058\n",
      "min         0.500108\n",
      "25%         0.581283\n",
      "50%         0.687843\n",
      "75%         0.863685\n",
      "max         0.999722\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('multi', 'concat')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7975    0.7678    0.7824     29306\n",
      "         1.0     0.6775    0.7144    0.6955     20010\n",
      "\n",
      "    accuracy                         0.7462     49316\n",
      "   macro avg     0.7375    0.7411    0.7389     49316\n",
      "weighted avg     0.7488    0.7462    0.7471     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8032    0.7734    0.7880     20463\n",
      "         1.0     0.6871    0.7242    0.7052     14058\n",
      "\n",
      "    accuracy                         0.7534     34521\n",
      "   macro avg     0.7451    0.7488    0.7466     34521\n",
      "weighted avg     0.7559    0.7534    0.7543     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7859    0.7593    0.7724      4400\n",
      "         1.0     0.6634    0.6964    0.6795      2997\n",
      "\n",
      "    accuracy                         0.7338      7397\n",
      "   macro avg     0.7247    0.7278    0.7259      7397\n",
      "weighted avg     0.7363    0.7338    0.7347      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9026    0.8970    0.8998       806\n",
      "         1.0     0.7622    0.7733    0.7677       344\n",
      "\n",
      "    accuracy                         0.8600      1150\n",
      "   macro avg     0.8324    0.8351    0.8337      1150\n",
      "weighted avg     0.8606    0.8600    0.8603      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7588    0.7284    0.7433      3594\n",
      "         1.0     0.6511    0.6864    0.6683      2653\n",
      "\n",
      "    accuracy                         0.7106      6247\n",
      "   macro avg     0.7049    0.7074    0.7058      6247\n",
      "weighted avg     0.7131    0.7106    0.7114      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8228    0.8025    0.8125       324\n",
      "         1.0     0.6168    0.6478    0.6319       159\n",
      "\n",
      "    accuracy                         0.7516       483\n",
      "   macro avg     0.7198    0.7251    0.7222       483\n",
      "weighted avg     0.7550    0.7516    0.7530       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 74.62\n",
      "    Training Set Accuracy: 75.34\n",
      "    Validation Set Accuracy: 73.38\n",
      "    Validation Set with both Images and Texts: 86.00\n",
      "    Validation Set with only Texts: 71.06\n",
      "    Validation Set with unseen Images: 75.16\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7975    0.7678    0.7824     29306\n",
      "         1.0     0.6775    0.7144    0.6955     20010\n",
      "\n",
      "    accuracy                         0.7462     49316\n",
      "   macro avg     0.7375    0.7411    0.7389     49316\n",
      "weighted avg     0.7488    0.7462    0.7471     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8032    0.7734    0.7880     20463\n",
      "         1.0     0.6871    0.7242    0.7052     14058\n",
      "\n",
      "    accuracy                         0.7534     34521\n",
      "   macro avg     0.7451    0.7488    0.7466     34521\n",
      "weighted avg     0.7559    0.7534    0.7543     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7825    0.7506    0.7662      4443\n",
      "         1.0     0.6467    0.6863    0.6659      2955\n",
      "\n",
      "    accuracy                         0.7249      7398\n",
      "   macro avg     0.7146    0.7185    0.7161      7398\n",
      "weighted avg     0.7282    0.7249    0.7262      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8954    0.8966    0.8960       716\n",
      "         1.0     0.7867    0.7845    0.7856       348\n",
      "\n",
      "    accuracy                         0.8600      1064\n",
      "   macro avg     0.8411    0.8406    0.8408      1064\n",
      "weighted avg     0.8599    0.8600    0.8599      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7597    0.7226    0.7406      3727\n",
      "         1.0     0.6293    0.6732    0.6505      2607\n",
      "\n",
      "    accuracy                         0.7022      6334\n",
      "   macro avg     0.6945    0.6979    0.6956      6334\n",
      "weighted avg     0.7060    0.7022    0.7035      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8129    0.8100    0.8115       279\n",
      "         1.0     0.6603    0.6645    0.6624       155\n",
      "\n",
      "    accuracy                         0.7581       434\n",
      "   macro avg     0.7366    0.7373    0.7369       434\n",
      "weighted avg     0.7584    0.7581    0.7582       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 74.62\n",
      "    Training Set Accuracy: 75.34\n",
      "    Validation Set Accuracy: 72.49\n",
      "    Validation Set with both Images and Texts: 86.00\n",
      "    Validation Set with only Texts: 70.22\n",
      "    Validation Set with unseen Images: 75.81\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.5266492  0.4733508 ]\n",
      " [0.31821245 0.68178755]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.723583\n",
      "std          0.162650\n",
      "min          0.500000\n",
      "25%          0.579055\n",
      "50%          0.684630\n",
      "75%          0.873706\n",
      "max          0.999990\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.723444\n",
      "std          0.162520\n",
      "min          0.500000\n",
      "25%          0.578746\n",
      "50%          0.685332\n",
      "75%          0.873114\n",
      "max          0.999990\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.724401\n",
      "std         0.162882\n",
      "min         0.500018\n",
      "25%         0.580459\n",
      "50%         0.684430\n",
      "75%         0.873499\n",
      "max         0.999985\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perform_results['O1'] = statistics_F1(hyperdict, stored_data, df, labs, subfolder='001/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8b67f85f",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(args.save_dir+'002/hyperdict.p', 'rb') as fp:\n",
    "    hyperdict = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e3409605",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_acc\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pofqog/ISGxUldsqLbZSX2CkvsYGicOucM0NWNgkfQhyHzW3j2bXP8nHuxwCckXAGz5zzDN0juoe4ZEIIoY64s1W6KC+uHzDUkFFx2IbPozT5XrfLi8Go68DS1pHwIUQTco7m8NAPD5FfkY8GDb8Z/Bt+N/R3MneHEKLDOWrcgWBRP2RUlNhxO71Nvk+r1xCVYCU60UJ0opWoRAvRSVaiE63oDaFb5kHChxDHUBSFxTsXM3/dfFw+FwmWBOadM4/RKaNDXTQhRBdxIKeMnf89RFltyHDWeJo8V6OBiHhLvYBhJTpJ3Q6PNaMNYd+OprQofGRkZLB3794G+6dNm8Zf//pXpk6dyptvvhl0bPTo0fz8888nV0ohOkiZo4zHf3qclftXAjCu+ziePOtJYswxoS2YEKJLqDrqYPXHu9m9vqTBsfAYk1pzEQgYao1GZLwFnf7UWqyyReFj7dq1eL111Ttbt27loosu4vrrrw/su+SSS1i4cGHgtdFobINiCtH+fjn0C4+uepQSuzp3x/+M+B8m95ssc3cIIdqd1+1j44p9rF9WgMflQ6OB/menktYvlugkK1EJFgym0PTPaA8tCh8JCQlBr59++ml69erFuHHjAvtMJhPJycltUzohOsDOoztZvHMx/879NwoKmVGZ/OXcv9A3tm+oiyaEaANetw+3y4s5rHP21yrYUsqqD3OpPGwHIKV3FOfelEV894gQl6z9tLrPh8vl4p133uHBBx8M+stw5cqVJCYmEh0dzbhx43jqqadITExs8jpOpxOn0xl4XVlZ2doiCdFsLq+Lr/d+zfs73w/M2wFwbZ9rmTlypszdIcQpzF7l4lBeBUV7KijKq6BkbxVej48eA+MYckF30vrHdooazfISGz9+lMveLUcAsEYZOeva3vQZmdQpyteeNIqiND0O5zg+/PBDJk+ezL59+0hNTQXggw8+IDw8nPT0dPLz83nsscfweDysX78ek8nU6HVmzZrF7NmzG+yvqKggMjKyNUUTokmHqg/x0a6P+Dj3Y446jgKg1+i5MP1Cbu53M8OShoW4hEKIllB8CmVFNor2VHAor5yiPZWUF9uO+56YlDCGnN+dvqOT0YdgqKnb6WX9lwVs/HofPo+CVqdhyPlpjLg8A6P51B0HUllZSVRUVLN+v1sdPiZMmIDRaOTzzz9v8pxDhw6Rnp7O+++/z6RJkxo9p7Gaj7S0NAkfos0oisLPh37m/Z3vs3L/SnyKD4BEayLXZ13PtX2uJcGacIKrCCE6A7fLS0l+JYdqazWK9lTgtDUcCRKTEkZKryiSe0aR0isKNLBl5X52/HQoMDTVHGZg4DmpDBrXnfCYxv9AbkuKopC34TA//SuX6jL1dy9tQCzn3NCHmOSwdr9/e2tJ+GhVxNq7dy8rVqzgk08+Oe55KSkppKenk5ub2+Q5JpOpyVoRIU5GpauSz3Z/xgc5H1BQWRDYPyp5FDf1u4nxaeNlzg4hOrnqMmddrUZeBaWF1fh8wX8z6w1akjIjSa4NG8k9oxrt33HODVmMmtiTHT8dZPN3+6k64mD9l3vZ+NU+eo9IZMgFaSSmt88fvUcOVrPqg10cyCkHICLOzNnX9yFzSHy7NbEoioK3vBz3gYO49+/HfeAA7gMHcB3YD243Pf75z3a5b3O0KnwsXLiQxMRELr/88uOed+TIEQoLC0lJSWlV4YRojZyjObyf8z5f7PkCu0ftwBVmCOPKXldyU9+b6BndM8QlFEI0xuf1ceRATW3YUGs2qo46GpwXFm2qq9XoHUVc93B0uuYNNTVZ9Ay9sAdnnJ9GwaZSfv1mH4d2V7Drl2J2/VJMSq8ohlyQRuaQeLTNvObxOO0e1n6ez+aV+1F8CjqDlmET0hl2cY82afLxVlbWhYr9+xsEDV9NTeNv1OlQPB40+tA087T4rj6fj4ULF3L77bejr1fo6upqZs2axbXXXktKSgoFBQX86U9/Ij4+nmuuuaZNCy3Esdxet9qBNOd9NpZsDOzvHd2bm/rexBW9riDMcOpXawpxuvC6fRw9VMPhwipK91VxuLCa0gPVeI6ZrVOjgbju4aT0jCK5dxQpvaIJjzGddG2BVquhZ3YCPbMTKNlbyeZv95O7rphDeWrwiYg1M/i87gw4OxWTpeU/0IpPIWdNEas/2Y29yg1Az6EJnHVdbyLjLc2+jre6JhAk/KHCdaA2ZBw4gK8ZgzR0CfEYu3XH0K2b+uiuPodSi/t8fPXVV0yYMIGcnByysrIC++12O1dffTUbN26kvLyclJQUzjvvPJ588knS0tKaff2WtBkJUVRTpHYg3fUxRxxqj3G9Rs8F6RdwU9+bGJ40/LTvNS5EZ+dyeCjdX01pYW3IKKzi6MEafN6GPz9Gs46k2n4ayb2iSMqI7LBOmDUVTrZ+f4CtPxzAUa0GBoNJR78zUzjjvO5EJzZvFFzJ3kp+eH8XxflqMIhOsnLOjX3oMSCu0fMVjwf3/v049+Tj2pNX+7wHV0EB3vLyE95PFxuLoXt3DN1SMXbrVrvdDUO37hhSU9Cazc37Ak5Sh3Q4bS8SPsSJKIrCmqI1agfSwpV4FfUvpURLItf1vY7r+lwnHUiFCBF7tYvSfdUcLqxSazUKq2tXUW14rsmqJz4tgoS08NrnCKKTrSGfDtzj8rJrbTGbvink6MHaZgsNZAyOZ8gFaXTLim70jxp7tYuf/7OH7T8eBEUNLiMuz2DI+Wno9Fp8NTU48wtqA8YeXHvyceXvwVWwF8XtbrI8uqioeoGirubC2L07htRUtNbOMTWAhA9x2vD6vBxxHKHEVkJxTTEFlQV8uvvToA6kI5NHclPfmzivx3nSgVSc9hRFweP24XZ4cTk8uB1e3E4PLoc3sM/l8OJxedHqNOgNOvRGLXpj7XP914Z6+2ufm9t3QlEUqsucHN5XFzJKC6sCoziOFRZlJKFHRCBkxKeFExFn7tQ1k4qisH9nGZu+LQzMxQEQ1y2cIRd0p8/IJPQGHT6fwrYfDrDmsz2BkTc9e+oYnHAI/YE8XHv24NyzB09RUZP30pjNGDMzMWVmYuzVE1PPnhgzMzF0744uPLzdP2tbkPAhTgkur4tiW3EgWJTYSii2FQceJbYSDtsOB2o26rPqrVzZ60pu7HsjvWN6h6D0QrSe4lOoLndSXmSjutwZFB7cDg8uZ12QcDu9uOy1zw4vbqcXxdd+/9nWaDUNw4lBi96oxWDUoTNocTu9HC6sanKxs6gEixoyeoTXBo0IrJGda6kNxecDjwel/sPtAY8bxesNvFY8bvB4KC91sn2Lg925Hjy1/0kymxR6dfdw4KCPcrs6ajPcdpCsnPeJrshr9L66uLjagNELU89MjD3VoKFPSUGjPbXWZzlWuw+1FeJEql3VdUGiXrCo/+yf5OtEtBot8ZZ4kq3JJFoTGZ0ymom9JkoHUtHpuV1edenzYhtlRTbKi2rUVUqLbXhcvpO+vsGkw2DWYTTrMZrVbYNJ3dabdChetZbE41JrQtRt/2sfHrcXt8uH1+XF/2eo4lNqQ1DTy7T7abUaYlLC1GaTHrU1Gt3DMbaig2ZrKS4XniNH8JSW4jlciudIKV7/dmlp7bHD+MorUNzuQNDA1/LvPxVI0Fs4mHIW+7uNw0Es2/LU2la9u4ae+Z+TeugntBowpPfAlNmzXi1GT0w9M9FFR7ftF3CKkpoP0WY8Pg8r9q5g0bZFbDuyrVnvMelMJFoTSbImqc9hSSRZ6x6J1kTiLHHotZKTReekKAo15S7Ki2vUgFFso6zYRllRDdVHG2+CAPWHOyrRQkSsGUO98GA06zGYdLWv9cHPptrjZh0Gow5NG/WNUBQFn0fB7fIG1kHxPx8bVjwuH1qdhvju4cSmhqE3tP0MoYrXi/foUTU4+ENE6WG8pbUho97DV1HRdjfWatHo9erwU4MhsK3R6cCgR6Ov26foDRRZelNo6EuE2c2QLDcRfdIx9eqJIT0dbRdcVFVqPkSHsnvsfLr7U97c9iYHqg8E9kcYIgJhwh8s/EHD/4gyRXXqNl8h/DxuLxUl9tqAUS9oFNkCM2Y2xhSmJyYpjJhkK9HJVmKSrMQkhxERb252/4r2ptFo0Bk06AztXx6fzYa7uBhPURHuomI8xUW4i4rwFBWr+w8fxnv0aMtqJgwG9HFx6OPj0cfHo4v3byfUPsehi45GYzSqwUKnR2PQ14ULvR70+hY3e8iMQa0n4UO0WpmjjMU7F7N452LKneUARJuimdxvMjf0vYE4S+PDyoTojLweH9VlDqqOOKg6Wu+5drvyiKPRERug9pOIjDcTkxxGdJJVDRq1z5bwrvMXsK+mBndR/TBR+1x0KBAuml1TodGg8wcK/3NCPLr4ePRx6rY/bGij5I+YU42ED9FihVWFvLXtLT7d/SkOrzr7YLfwbkwdOJWrel+FRd/8CXSE6CguhycQJKqPHhMwjjioqXQ1GS78jBY9MbW1F2otRhjRyVaiEizo9J2jFqOlFJ8PxeUKfrjdKC4XvsA+d+1+9bWvuhp3vVCh1l4U46uqatY9tVYr+pQUDElJ6FOSMSQlo09OwpCcjD4xUa29iIlRmzvEaUnCh2i2bUe2sWjrIr7a+1VgcbYBcQO4Y9AdXNjjQumXIULG6/bhsLmpKXc2CBX+2oumRmbUpzNoiYg1ExFnVp/rbUcnWbFEGDrlX9iKouAtLa2bpCpvD669e/HV1DQdLGqf8Zz4e2kJbUQEhuQk9IFAkRJ4bUhOQp+ScsoMHRXtR34txHEpisLqg6tZuHUha4rWBPaf1e0s7hx4JyOTR3bK/xiLU5PX7cNR48ZR48Zpc+Oo8dS9rvHgsLlxVrtx1B5z1h5r7sgRk1VP+DGhov52Zw0XforXq86EmbcHV/4e9bl2DonmTLPdHBqDQe0b4X9u8DCgtVrr1Vak1NVaJCWjC5dRaOLEJHyIRrl9bpYXLGfh1oXsKtsFqNOWX5J5CVMHTqVvbN8Ql1CcShSfQnmJjZK9VRw9VIOj2q0Gh1aGiMZoNGCJMDYaKvzPHTkE9GT4HA5c+flB4cKVl4eroKDpmTC1Wgzdu6vDOnuqwzq1EZFojGqI0B4bIhoLF4bOHb7E6ePU+H+i6DA2t42Pcz/m7e1vc6jmEAAWvYXrsq7j1v63khIuKxSL4/MHjcP7qijZV8XhveoMmM2ZNwLUEGEKM2AOM2Cy6jH7t8OO2bYaAueZw/QYzfo2G3ra3hS3G19NDd7qajzFxTjz8nDl7cGZvwdX3h7cBw9CE7MgaEwmdSZMf8jo1RNjz14YM9LRmkwd/EmEaB0JHwKAUnsp7+14jw9yPqDSpVbfxppjuaX/LdzQ9waiTFEhLqHojBSfQsVhOyX7KinZe/ygoTNoie+uznhpjTIGhQhzuAGTtfOHCMXlwltTg6+6Gl91Nd6qKnzVNfhqal9XV6uvq+u/rnv436s4Gi4TfyxdVJQ6C2av2gmqevXE2KsXhpQU6YgpTnkSPrq4gooC3tz+Jp/t/gyXzwVAemQ6UwdOZWKviZh08peUUNUPGof3VqlreuyrwnWcoJHYI4KE9AgS0yOJSbai7STzWtSnKAre8nI8xcV4iotxl5TgKS6p3S5Wtw8fxldVpXbQbEMaiwV9TExdDUa9kKGLiZEmEHHakvDRBSmKwq+Hf+WtbW/xzb5vUGrHF54RfwZ3DrqT8Wnj0WnlL6uuzB80Du+romRvZYuCRkKPSGJTOkfQ8DmdeEpqg0RxcXCoKDmsBo6SkhaHCq3VijY8PPDQhYehDY+ofR2GLjwcbVh48OvwcLThEbXnhqMNC1MntxKiC5J/+V2I2+dmxd4VvL39bbaUbgnsH999PFMHTWVY4jD5S+sUpigKHpcvsNJp/WeXf8GyY/a7HV5c9Rcuq/fcWJeDQNNJjwgSetTWaKRYO2SmTkVR8NXY8FVW4K2sxFtegbeyAl9lJd6KSryVlXiOlAYChqe4GG8Lpt7WxcaiT0rCkJiIPikJfVIi+sREdS6KxER0UVFqaLBapdlDtB9FgZpSOLoHKg+A1wVeN/jc4PWor/3bPne9Y+7g8xrsc4PPU/daq4e7vgrZx5Tw0QVUOCv4OPdj3tvxHsW2YgCMWiNX9LqCW/vfKqvCngIcNW51ls1SOxWldqpK1e3qcicue124aMuVmnR6LXHdw0lM9weNCGJSwk46aPgcDrzl5XgrKo8JEpVqmKioxFtxzOtK9dGaOSk0JtMxoSIJQ22w0CcloU9MQp+Y0CXX4hAh4vNBdZEaMBo8CsDVvMnaTooutP/eJXycxvZV7uOdHe/w6e5PsXvsgNqJ9KZ+N3FDlkx/3pl43N7AFN6Vh+3qc6mdylI7VUccOG0t+NHVgNHUcFEyY73X6gJmwfuCFzDTY4k0tEnQcGzfgWPLZuxbtmLfshn33n0ndU0MBnRRUegiI9FFRqKNikQXGaXui40J1FToE9WQIVNvi5DweaGiEI7m1wsWtdtl+eA5XqdjDUR1h6g00JvUoKAzqLUVOgNoDepzYFtf+2yst13vmM54zHm1jxCS8HGaURSFdcXreGv7W3xf+H2gP0efmD7c2v9WLut5mXQiDQHFp1BT4aSytC5U1AUMBzXlTa9+6meJNBIZZyYy3kJkvPocEWvGZK1dBdWiPhtMupD82CpeL87deUFBw7krt/HaCp0uECAC4SEyUm3aCHpduy8ySt2OjERjsUiYEMEUBWxHoOoQVB5Sn6sOQeVBqCqCqoPgqACdCfRm9Qddbwa98ZjXJ3qu3daZgq/hbyap/yjbqzZvNEWjg+geENuz3iNTfY5OB4O5476/EJDwcZpwe918WfAlb29/mx1HdwT2n9PtHG4dcCtjUsbIf7A7WGWpnV1ri8nbUMLRQzX4PMdvE9GbdETFm4mIsxAVbyEi3hx4joyzYDB1nn4GiqLgPnBQDRqbt+DYsgX79u0oNluDc3Xx8VgGD8ZyxmDMg8/APHCAusKo/HsUzeGsrgsQVUXBgaKqSA0b1UVqX4jORmeEmMy6UFE/YESlhbz2IZQkfJziyh3lfLTrIxbvXMxh+2EAzDozV/a6kikDptAzShZ97ki2She715eQu7aIoj3B011rtBoiYk1qzUWcmcgEC5FxdSHDHN55Z5f0lJWpAWPzFuxbNuPYvAVvWVmD87RWK+ZBgwJBw3LGYPTJyZ32c4kQcTvAVqrWGNSU1ts+DNXF9QLGIXC2YNp4azxEpkBEvYf/tSVWDSgeB3ic6vOxrxs8N7avkfdYY+tCRUy9oBGZCjJysFESPk5Reyr28M72d/g87/PAyrIJlgRu7ncz12ddT7Q5OrQF7EJcdg97Nh0m95diCneWofhqazg00L1vDH1GJtG9bwzhMaZOMfxUURR1gTGnE8XpxFf7rDid+BxOFJezdnrvgkDNhnv//oYXMhgw9+2LefAgLLVBw5iZKSNBuqLjhQlbKdQcCd5uaYdKY3htmEhWf9AjkiEiNfh1eLLaBCJOCRI+TiGKovDzoZ95e/vbrDqwKrC/f2x/bh1wK5dkXIKhC1fjdSSP28u+rUfZtbaIgi1H8Lrr1iRJTI8ga1QyvYcnEhbdtv1rFEXBc+gQzrw8nLvzcO/fj8/pQHG6UBwOfC5nw22ns+6c2pDRGsbMTLVGY5DahGLq10+m8+6svG71x95trx1u6ax9dtUN3fQ467a9ruOcc8w+rxNcNfVCRivCBKidJ63xEJYAYXG12/G1weKYmgtTRNt/RyKkJHycApxeJ0v3LOXtHW+TW5YLgAYN49PGc+uAWxmRNEKqtTuAz6dwYFcZub8Uk7fxMC57XUfK6CQrWaOS6DMiiegk60nfS/H5cB84gHP3bly1QUNd/yMPXyP9KlpNo0FjNquLjpnNaEwmtCYjGqMJfXIyljPOwDJ4EOZBg9BFRrbdfUXrOKvVZomqIvW5/nZVEVSXqP0fbEc6vmyNholjt+PrQoY5Sl3IR3RJEj46MZvbxrs73uWdHe9w1HEUUBd5u7r31dzS/xZ6RPYIcQk7luJTqDrqQKPVYI0ydtjEViV7q8j9pZjcdcXYKus6tYVFm+gzMomskUnEp4W3KgAqHk/tEun+gLEb1+48nHv2NL3+h16PMSMdU6/eGNPT1ZkyTUa0JhMak7netqk2TDS9jV4vwTXUFAVsR9XQUD9AVBXXe659uKqbf12NDgxWtVOj3lQ7vNLYyKN2v76Rfbpj32eoG+UhYUKcBAkfnZDb6+bj3I95bdNrHHGof8EkWZOY3H8y1/a5tsss8mavdlGcX0lxQSUltc+B+S40YAk3YI00ERZlxBplxBqlbodFmQLb1igjekPL+yCUFdWwa20xub8UU3HYHthvsurpNTyRrJFJpPaObvYCaIrbjWvfvuCAkZeHKz+/yam9NQZD7ZofvTD27oWpV29MvXth7NEDjUGa104JzqrgoZ/HDgWtqg0cxxuSeSxDGEQkQXjtIyK53nOi2vchIlntYKkNfR8jIRoj4aMT8Sk+luUv45WNr7C/Wu3g1z28O9OGTuOSzEswaE/fHxyv28fh/VVq2KgNGpX1fvT9tHoN+NQmEHuVG3uVmyMHjn9tk1WPNbI2nEQbCYs0YQ2ElLpnl91D7toSdq0torSw7i9MvVFL5hnx9BmVTI8Bsej0J/4Puru4BNu6tdjXr8e2bj3OPXuanJ1TYzary6PXCximXr0wdO8ua390Vh5XXU1Fg6Gf9UZqtKSmwhJ7TIBIqvdcb1v6P4jTgPyXrRNQFIUfD/zIixteJKcsB4A4cxz3DrmXa/tce9p1IlUUhcpSO8X5lRTVho3S/VWNzoMRnWQlKTOSpIxIkjIjiesejlajwV7txlbppKbCha3C/+yipsIZ9Nrr8eG0eXDaPJQVNb+vhFarIW1gLFkjk8g4Ix6juen/qyiKgruwENvaddjWrcO2fj3ufQ1n8dRarRh798bUq5dag9GrF6bevTGkpqKRv1Dbh6KoHSXd9rqhk24HeOxqZ0p37bPHXrvf0fh5rhq1OaTqoFpzYSttfhlMkcGdKI8dCuqvuZCRGqILkfARYr+W/MqLG15kXfE6AMIN4dwx6A5u6X8LVsPJd1zsDBw1bkoK1NoMf62Go7phNbM53BAUNBLTIzGHNR68rJFGrJFG4rs3fV9FUXDaPGooqXRiK68XUiqdgbBSU+HC41RXa03pHUXWqGR6DUvAEt74j4Hi8+HM3Y1t/Trs69ZhW7sOz+HDwSdptZj79cMyYjjWESOwDBqEPiVF+lc0l8+rzkjpqFDnefBvN/qod9xVXRsc7HXzMNCGC97UpzXUG/6Z0nDop/+1Kbx97i/EKUzCR4jklefx4oYX+a7wO0Bd6O3mfjfzm8G/OeXn6Kg8YmfvliOBoFFe3LDGQavXkJAWEQgaSZmRRMa37bTZGo0Gc5gBc5iB2NSw457rcnjweZVGw47iduPYsUOt2Vi/Htv69fiOWS1VYzBgHjwY64gRWEcMx5KdjS5CqscDfF4oK4DSXXAkDxzlTQcIR0U7LaylAYOltsOkRZ2+Wl/vUf91/fP0JrXjZnhCcMCQPhVCtJqEjw52qPoQf/31r3y+53N8ig+tRstVva5i2tBpJIclh7p4J+VwYRUbv9rH7vUldRNt1YpKsARCRlJGFPHdw9EZOs9/uOs3q/gcDuybNtfVbPy6qcG04RqrFevQoXU1G2ecgdZ8eq/F0Cwel7quRWkOHM6Bwzvh8C41dHhbMb+IwaqOoqj/MEU23GeOAnMkGCOaCBUWdaSG1DwJ0SlI+OggZY4y/rHlH7y/833ctT3bL+xxIfdl30fP6FN3CnRFUTiQU8aGr/ZRuP1oYH9qn2hSs6JJzowiKSMSc3jH91tRFAXcbnwOBz67A8Vhx+dwoNjttfvs6mRcdgc+hx3PoSJs69dj37IF3MHNQrqoKCzDhwdqNsz9+3ftESduO5TmqgGj1B8yctTg4WtiBV69GeL7QFyfuqGZTYaJaDVMnGb9nYQQKgkf7czmtvHW9rdYtG0RNe4aAEYmj2T6sOmckXBGiEvXej6fwp6Nh9n41V5K9qpV5BoN9B6RRPZFPUjocfJNDoqi4C0vx33wIJ5Dh3AfPIS7qAhfdTU+hx3F7ggOE8fuczrB623VvfWJiWqNRm3Nhql3767ZKdRZpdZcHN4ZXJtRtpcm+1IYwyGhLyT0g/gs9Tmhr7qCp6xzIYRAwke7cXvdfLTrI/6++e+BCcL6x/Zn+rDpjE0de8p2PPS4vOz8uYiNX+8LDIXVG7T0PyuVoRemERlvafa1fE4nnqIi3P5gcegg7kOH8Bw8pO47dKjpibZaSqdDa7Gos3mazWgtZjRmC1qzGY3FjNZsUWs3srOxjhiOIS2t8/1vVFUMRVugaLP6XLxVrWkA0GjVSaU0WvUHXqNpZJ82+BG0z39eveNVRVB5nHHMlpi6YFE/aESmSvOGEOK4JHy0MZ/iY2n+Ul7Z+AoHqtX/cPeI6MF92fdxccbFaDWn5l/Pjho3W78/wObvCrFXqU0SpjA9Z4zvzuDzujcYGaIoCt6jRwOhIlBzcaju4S1t3nBFXUI8hpRUDCkpGJKT0UZFojVb6gKExVwbKhrZZ6kNGKdSE4nPC0d21wsaW9XtmpLQlCc8GRLq1WDE14aNsHgJGUKIVpHw0UYURWHVgVW8uOFFdpXtAiDeEs/vhvyOa/pcc8pOEFZ11MGmFYVs++lgYDhqRKyZoRel0f/MVAwmtRrdZ7dj37iRmp/XULPmZ5w7c5q1gJnGbMaQWhssUlPQp6TUBY3UFPTJyWiNp/H8B85qKN4GxVtqw8YWKN6uzi9xLI0W4npD8uC6R3yWuqaGzwuKL/gRtK922+drZF8T77XGqaHDEtPx34sQ4rQm4aMNVDgreOC7B1hfvB6ACEMEdw6+k8n9Jp+yc3UcOVDNxq/2kbu2GF/tyJW47uEMu7gHvYYnovV6sG/eSPnPa7D9/DP2TZtQjumkiUaDPiEBQ0oK+tTgUGFIUYOGLjq68zVvtAdFUZsx6jebFG2pbTZppO+EIQySBtYLGmdAYn8wnpr/noQQoj4JH23g7e1vs754PSadicn9J3PXoLtOyfVXFEXh0O5yNizfx96tdatidusbTfYF3UlQDmFb8zkHXvsZ24YNDfpj6JOTCRs9GuuYMVizh6ozd57OtRYnUl0CO7+AnKVwYH3TK41GpATXZiQNhthM6ZwphDhtSfhoA9uPbAfgweEPMrn/5Da7rtfjY9M3hRTnV2IO02OOMGIJN2AJNwS2zeEGLBFGDMbW/1ApPoX8TaVs+GovxfmV6k4NZPQ208dUgGXrR9jeXcfe6uB1KnSxsYSNGY119BjCxozG0KNH16jFOJ7yQti5BLZ/Bvv+S1CthkanNpMkD4bkQXVBIzwhZMUVQohQkPDRBvzrsfSP699m1zx6qIYVC7dzeF/zZnrUG7SYIwxYwmtDSe22uTasWCKCt00WPV6vj11ritn49b7ALKRajUIPTQHdtn6C6bs9eAF/5NBGRmIdNZKw0WOwjh6FqU+f9g8bzmq18+WR3epEVUfz1ams08ZA2ii102Oole6GHZ+pj4Mbg4+lZkP/K6HnOEgcoM6cKYQQXZyEj5N01HGUEps6CiErJuukr6f4FDav3M9//52H1+3DFKYn+6Ie+LwKjmo39mo39ioXjhp1RVd7tQufR8Hj9lF91En10ebNIqnRatBqwVu7mJvea6fb/u9J278So7t23g6rFeuI4WrYGDMac79+aHTt0BSgKOpKoKW76kJG6S71R71yfxNvelF9iusNaaPrHvFZ7T/ltaKow1x3fK7WcBzeUe+gBnqMhQFXQr8rIDqtfcsihBCnIAkfJynnqFrr0SOiB2GG468fciLVZQ6+eXMH+3eWqdccGMv5t/UnLMrU5HsURcHt8KqhpNqFo8odvF3jxlHlwl7lwnbUhqPGjdurRfEpeH1gcpSRtv9bUg/9hEGnYBk+TG1KGTUay+BBbTtE1W2vDRe56uNIbl3IqJ2ArVHWODVUxPeBmAx1gqvCNepkV/5akV/fVc81R6s1ImmjoccYSB3WNp00fT6138aOz9TQUZZfd0yrh8xz1RqOfperS6ILIYRoUovCR0ZGBnv37m2wf9q0afz1r39FURRmz57N66+/TllZGaNHj+avf/0rAwcObLMCdzb+YbV9Y/ue1HVy1xbz/eIcnDYPeoOWs67rzcBzu52wWUOj0WC06DFa9EQl1FXp+2pqsG/ahG39BuwbN2D/dRO+2vVJfBo9bkMYHlM4sX1SiLhyJNZRt2HJHorW1HTQaTZnNRzcUBcsSnepQaO8kCZnxdToILanGjDi+6hhI6522xrb+HtsR2H/Oij8GQp/Ubcd5ZD7lfoANRgkn1EbRmprRyJTm/c5vB7Yt1oNGzuWqMup++nN0OsCtYYja4IMRxVCiBZoUfhYu3Yt3nrTVW/dupWLLrqI66+/HoBnn32WBQsWsGjRIrKyspgzZw4XXXQROTk5RJymK3zuPLoTgL4xrQsfjho3P7y/i9y1xQAkpkdw4R0DiEluWS2Ku7gY+4YN2DZsxL5+PY6cnAZTi2sjIrBkD8U6bBiWYcOwDB6M1tJGfRB8Xsj/ATa9r9YOuBuuZAuoNRPxWbWP3nXbMRktX8fDGgtZF6sPAK9bHb5auEZ97FujBoaDG9THmlfV86J6qLUjPcaoYSRpYN3IEo8T9nyvfoacpcEjVIzhatDofyX0vlCWShdCiFbSKIrSxJ+iJzZ9+nSWLFlCbm4uAKmpqUyfPp2HH34YAKfTSVJSEs888wy//e1vm3XNyspKoqKiqKioIDIysrVF6zCTPptEblkuL5//MuPTxrfovYU7jvLNmzuoKXei0WoYcWk6wy/LQKc7fp8FxefDuXt3bdjYgH39BtwHGk6DbUhNxTJsGNbhatgw9e7d9n02DufApsWw+cPgqbij0tQOlv5aDP+zNa7jZsVUFKjYXxtEflafi7eqk2jVZwyH7iPU2ovd34Czsu6YJQb6Xq7WcGSOU1dIFUII0UBLfr9b3efD5XLxzjvv8OCDD6LRaNizZw9FRUVcfPHFgXNMJhPjxo1j9erVTYYPp9OJs95MmJWVlY2e1xk5vU7yy9W2/36x/Zr9Po/Ly38/zWPzt2pnyqhECxfeMYDkzMbnBvE5HDi2bMG2YSO2Deuxb/wV37Hfk1aLqW9frPXChiE5uXUf7ERqjsDWj2HTe8GjO8zRMOhaGDoZug0P/dTbGo3a4TM6DQZfp+5zVql9N/bV1o7sX6uGjT0r694Xngz9r1BrONLPAp10jRJCiLbU6v+qfvrpp5SXlzN16lQAioqKAEhKSgo6LykpqdF+In7z5s1j9uzZrS1GSOWV5+FRPESZokiyJp34DcDhfVV8/c9tlBWpzRKDzu3Gmdf2DkxTXp/n8GGKnppL1TffNFjiXWOxYBkypK4JZegQdOHt2AzgcUHucvh1sfrsXzZdq4c+F8OQm9UmCX0b9BlpT6YI6DlefYDaXFSyQw0iNYeh1/nQbUT7j5gRQogurNXh44033uDSSy8lNTW4896xHSQVRTlup8lHH32UBx98MPC6srKStLRTY3iif6RL35i+J+wY6vMpbFi+l7Wf5+PzKVgjjZx/W3/SB8U1OFdRFCq/WErxk0/iragA1MXVrMOGYx2WjWXYcMz9+rb/YmmKAgc2qM0qW/8F9rK6YylD1cAx+LrOMddGa2l1tRN+DQp1SYQQostoVfjYu3cvK1as4JNPPgnsS66t4i8qKiIlJSWwv6SkpEFtSH0mkwlTW4ywCAH/5GInGulScdjOioXbKdqjBole2QmMm9K3wUqwAJ7SUopmz6bq6xUAmAb0J+X/nsQ8cEDHzR5asR82f6B2Hi3dVbc/IgXOuEENHYltN6GaEEKIrqVV4WPhwoUkJiZy+eWXB/ZlZmaSnJzM119/TXZ2NqD2C/n+++955pln2qa0nYx/pEtT/T0URWHHT4dY9VEuHqcXo1nHuTdlkTU6udEgUblsGUWz/w9veTno9cRP+x3xd9/dMcvBO6vVIaWb3oP8VQSGxOot0H8iDLlJbaqQ9UaEEEKcpBaHD5/Px8KFC7n99tvR6+vertFomD59OnPnzqVPnz706dOHuXPnYrVamTy57dY76SwURWHX0do5PhoZZmurdPHdOzsp2FwKQGqfaC6Y2p/IuIZDWz1Hj1L0f09S9eWXAJj69SN13lzM/du5dsHnhYJVaj+OY4fHZpyjBo7+V4K58486EkIIcepocfhYsWIF+/bt484772xwbObMmdjtdqZNmxaYZOyrr746Lef4OFhzkCp3FXqtnp5RPYOO5W86zHfv7MRe5Uar1zDmyl4MuTANrbaR2o7lX1E0ezbeo0dBpyP+t78l/t7f1q0GW7gW9v6ozmHh89Q+u9UJsHz+fbXb/nManHfMcf+zowLsR+sKE9tLbVI54waISW/Pr08IIUQXdlLzfLSHU2Wej2/2fcP076bTL7YfH038CACXw8OPH+Wy46dDAMR1C+PCOwYS373hKBRPWRnFc56i8osvADD16UPK0/Ow+GeD9brhu6fgx+fb94OYo9ThsUNuhu4jQz88VgghxCmpQ+b56Or8TS7+xeQO7S5nxaLtVJY6QAPZF/Zg9JU90RkaDtms+uYbDj0xC29pKeh0xN39G+KnTUPrr+0o2wsf36XOQQGQdam6XojOAFqDOu+EVl+7bVC3/c+BbUMT+2rfp9WD3ggJ/WXiLCGEEB1Kwkcr1e9sejC3nE8XbEBRIDzWxIVTB9Atq+FaH97ycormzqXys88BMPbuReq8eVgGD647aft/4D/3gbMCTFFw1csw4KoO+UxCCCFER5Dw0Ur+Ybb9YvtR8FMpigLd+sZw6b2DMVkafq1V331H0eNP4Dl8GLRa4u66k/g//KFuITe3HZb/Cdb9U33dfSRc+4b0vRBCCHHakfDRCpWuSg5Uq+uYZMVksaZcnSY9fWBcg+DhraykeO48Kj79FABjZiap8+ZiGTq07qSSnfCvO6BkO6CBs6fDef/b8oXWhBBCiFOAhI9W8Pf3SAlLIcoURU15HgBhMcGThlX/8AOHHnscT3ExaDTE3nEHCfffh9Zc28dCUWDj27B0JnjsEJYAk15Xp/gWQgghTlMSPlohMLNp7fweNeXqwnjh0WoTireqiuJnnqHiXx8DYExPJ2XeXKzDhtVdxFEJS6arC7QB9DwPrvk7RDRvjRghhBDiVCXhoxUCa7rE9kVRlED4CIs2Uf3TTxz682N4Dh1Saztuu5WE6dPRWupNLnZgPfzrTigrAI0OLngMznxAFjMTQgjRJUj4aIX6I12cNg8etw+Aqpefo+rDxQAY0tJInfsU1pEj697o88HPf4UVs9RJvqJ6wHVvQNqojv4IQgghRMhI+Gght89NXm0fj74xfQO1HgavLRA8YqZMIfF/HkRrtda9saYU/n0v7P5afd3/SrjyZbBEd2TxhRBCiJCT8NFCBRUFuHwuwgxhdIvoxv5CdZl5o70MQ2oqKXPnEjZmdPCb8n+Aj++G6iLQm+GSeTD8DplNVAghRJck4aOF/E0ufWP6otVoqa6t+TA5y0n44/Tg4OH1wPfPwA9/ARSI7wvXL4SkgSEouRBCCNE5SPhooV1lwdOqV5fWAGBylWPJzq47sWI/fPwb2Pdf9fWw2+CSp8EY1qHlFUIIITobCR8tVL+zKUDFniIALDo3hm7dak/6Aj6dBo5yMEbAxBdg8HUhKK0QQgjR+Uj4aAFFUQLDbP3ho+pQORBGREo0Go8Tvn4cfvm7+obUYepoltieoSmwEEII0QlJ+GiBw/bDlDnL0Gq09IruBUBNpQsII6p7OLxxIRRtUU8+8z44/3F15VghhBBCBEj4aAF/k0tmZCZmvRnF58PuNoABYg69Cc6tYI1TZyrtc1GISyuEEEJ0ThI+WqD+zKYA9t35uA3hAMRG7IPuo+CGtyAyJWRlFEIIITo7mc+7BQJrutSGj6NrNgGg9bmx6Cvh3BkSPIQQQogTkPDRAoHOpjFqZ9OyLbsBMCvlaLRa6DEmZGUTQgghThUSPprJ5raxt3IvAFmx6hwfFXmHAAjTHoGUoWCOClXxhBBCiFOGhI9myi3PRUEh3hJPvCUeT1kZNRXq7KYRxiOQcXaISyiEEEKcGiR8NFODzqYbf8VpigYgwnAEMs8NVdGEEEKIU4qEj2YKhI8Yf/jYiNMYDUCYrkz6ewghhBDNJOGjmXaWBU+rbtu4IVDzER4fCaaIUBVNCCGEOKVI+GgGr89LblkuoDa7KC4Xji1bcZrUDqZhPTJDWTwhhBDilCLhoxkKqwqxe+yYdWbSI9Jx7NiBz+nEVVvzEdbnjNAWUAghhDiFSPhoBn+TS5+YPui0OmwbN+LRh+HTGgAI6zcqlMUTQgghTikSPpqhwUiXDRsDTS5mvQ19eGTIyiaEEEKcaiR8NEP9kS6KogR1Ng0LU0JYMiGEEOLUI+GjGQLTqsf2w33gAN7DpTjN0QCEx4WFsGRCCCHEqUfCxwkcdRylxF6CBg19Yvpg37gRAG+kOrQ2LDkhlMUTQgghTjkSPk7AX+uRFpFGmCEM24YNAHgi1H4eYbHhISubEEIIcSqS8HECjU2rDuCyqB1Ow6NNISmXEEIIcaqS8HECOWV1/T281dU4d+0CwKGPBiBMwocQQgjRIhI+TmDnUXWOj74xfbFv2gQ+H4YwDzXEARI+hBBCiJaS8HEcTq+T/Ip8QG12sW9QO5sa4304fGqfD2l2EUIIIVpGwsdx5JXn4VW8RJmiSLImBUa6kKAOr9XptZjC9CEsoRBCCHHqkfBxHIH5PWL6gc+nNrsAvpjaYbbRRjQaTcjKJ4QQQpyKJHwcR6C/R2xfnLm5+Gpq0Op9uMLiAenvIYQQQrSGhI/j8I906RvbN9DkYolzUxM5FJD+HkIIIURrSPhogqIoQWu62Go7m1riXdRY1Dk/pOZDCCGEaLkWh48DBw5wyy23EBcXh9VqZejQoaxfvz5wfOrUqWg0mqDHmDFj2rTQHeFA9QGq3dUYtAZ6RvWsq/mId1GjSQUkfAghhBCt0aKhGmVlZZx11lmcd955LFu2jMTERPLy8oiOjg4675JLLmHhwoWB10ajsU0K25H8TS69onvBkTLc+/cDCpYkDTWuMKBKwocQQgjRCi0KH8888wxpaWlBwSIjI6PBeSaTieTk5JMuXCjVb3LxT6luivag6zWK6l1uQPp8CCGEEK3RomaXzz77jBEjRnD99deTmJhIdnY2//jHPxqct3LlShITE8nKyuLuu++mpKSkzQrcUQLDbGP7Ya9dTM4S70JJP4eaCicgzS5CCCFEa7QofOzZs4dXX32VPn36sHz5cu69917uv/9+3nrrrcA5l156Ke+++y7ffvst8+fPZ+3atZx//vk4nc5Gr+l0OqmsrAx6dAb1R7rYavt7WONdOJLOxudRAAkfQgghRGu0qNnF5/MxYsQI5s6dC0B2djbbtm3j1Vdf5bbbbgPgxhtvDJw/aNAgRowYQXp6Ol988QWTJk1qcM158+Yxe/bsk/kMba7SVcmB6gMA9LH04ND27QBYkrVUWPoCm7BEGNDpZbCQEEII0VIt+vVMSUlhwIABQfv69+/Pvn37jvue9PR0cnNzGz3+6KOPUlFREXgUFha2pEjtYtdRdeXa1LBUjLmF4PGgM3sx9B9BTZXUegghhBAno0U1H2eddRY5OTlB+3bt2kV6enqT7zly5AiFhYWkpKQ0etxkMmEyda4fcn+TS1ZsVmB+D2u8C03mudSUS38PIYQQ4mS0qObjj3/8Iz///DNz585l9+7dvPfee7z++uv8/ve/B6C6upoZM2bw3//+l4KCAlauXMnEiROJj4/nmmuuaZcP0B7806r3i+2HfWNdZ1MyzqFawocQQghxUloUPkaOHMm///1vFi9ezKBBg3jyySd54YUXmDJlCgA6nY4tW7Zw1VVXkZWVxe23305WVhb//e9/iYiIaJcP0B4CI12i+2LfoE6gZk3WQrdhgZoPGWYrhBBCtE6L14O/4ooruOKKKxo9ZrFYWL58+UkXKpTcPje7y3cD0LvKiq2iCo1OwTx0BOgM0uwihBBCnCQZrnGM/Ip83D434YZwInMOAmCOdaHpdS6AhA8hhBDiJEn4OIa/ySUrJisws6m1tr8HEOjzIc0uQgghROtI+DhGYFr12L7Y1/4MgCVZB6lD8bi8OGs8gNR8CCGEEK0l4eMYO8vUkS4D9T1w7VMnGrNkD1X7e9ROq643aDFZW9xdRgghhBBI+AiiKEpggrHe+70AGCPc6AecBwT399BoNKEppBBCCHGKk/BRT4mthDJnGTqNjphdxQBY4t2QGdzfQ5pchBBCiNaT8FGPf2bTzKhM3LX9PazJWkgeAkBNmQuQ8CGEEEKcDAkf9QQ6m0b0xr5DbX6xDBkMOrV/h0wwJoQQQpw8CR/1+KdVH1YRg+L2ojP6MA6/IHBcml2EEEKIkyfho55dZWptR699avOKJb5ucjGQCcaEEEKItiDho5bNbWNv5V4AYrbkAmBJ1kLyGYFzqssdAITHSPgQQgghWkvCR61dZbtQUEgwx+Pdqvb9sAzqD1odAIpPwVYuHU6FEEKIkyXho5a/yWUE6XjKa0CjYBlb19/DXu3G51NAA9YoY6iKKYQQQpzyJHzU8nc2HV5sBcAc40bb7/zAcX9/D0uEEZ1OvjYhhBCiteRXtJZ/mG3mrlIArMkaSBoUOC4LygkhhBBtQ8IH4PV5yS1XO5lG7iwEwDKwT6C/B8hIFyGEEKKtSPgA9lXtw+6xE+0xoRwoB8By5vlB50j4EEIIIdqGhA/qplUfV5kMChjCPBiyLwk6p67ZRTqbCiGEECdD1oWnrr/HkH0+ACxJQOLAoHOk5kMIIURn5PMpuH0+PF4Fj0/B4/Xh8Sm4vf59vtr9tft8CooCozJjQ1ZmCR/UjXRJzysDwNIvE7TBlUISPoQQQjSXz6dgc3uxOT3UuLzUOD3UOD3YXF6qnR5sLg/VzmOOuzzYnF5qXHXn+sOE2x8iagOE16fgrj3mU1pePpNeS86cS9v+gzeThA9g19FdaHwK4QUVAFjHnNPgHAkfQgjRuSmKgk8Bn6LgU9S/7n21+7xeBafXi9Ptw+X11Xv24vT4cHl86nPtOXX7vIFjdY+6fS6PD/uxgcKlBodQ0mk16LUaDDotep26rdeq2wadFpM+tL0uunz4OGI/Qom9hPRSDRqXglbvw3T2VUHnuF1enDYPIENthRCiuRRFwebyUmZzUW5zU2ZzUWZzU25zUVbjptwevL/C5sLtVQLhwaeoNQi+eqFCqRcufEpw4FBaUQPQ3rQaCDPqsZp0hJn0hBn1hJl0tfv0hJt0WI16wozq8fr7LAYdBp0Wg06DXqcNhAmdVhPYZ9DWHtNpMGi1gdCh1WpC/dGPq8uHD39n07HFYUAFlkTQJA8KOqemTK310Bu1GC1d/isTQnQxiqJQ4/JS5XBT5fBQYXdTVtNIoKgNGf795TY3Lq8v1MUP0GjU5gajTovJoFO39VpMel3tc/1H3T5jk/vU11ajDqtRR7hJj9WoV59rA4bZoEWj6dxBIBS6/C/prqPqtOqDC9UqMktWt+P295B/REKIU4m/9qHK4aHK4aay9rnK4aGy9rkq8Fx3TqW97li109OqfgV+Rp2WaKuBGKsx8BwTZiDaaiTG6n9Wj5n0WrQaDRoNaDWa2gdoNBp0WnW7qePa+vu0ddsaTV0zhPw3vHPo8uFjZ5na2bT7XhsAlpFjG5wjs5sKIVpCURQcbl/tj7ubCrv6Q19pr/thV197cLi9eH0KXkVBURR121fXtOD11X9WmyG8ilLbHMExx+v2eX0KNS41UHhPJjnUo9dqiDDribTUBYegQHFMkIgJU/dZDDr50RdBunz4yDmaQ3S1gqXcByhYzp/U4BzpbCrE6cn/Y+/2Kri8PtxetQOhO7CtjixwenxUO9WwcGyI8NcgHBss3N7O1QFBVxscIsx6IkyGQIiIMOuJNBvqjpmP3WcgsvZZmhBEW+nS4cPpdZJfkc+I/ep/JEyxoMsY1uA8CR9CdAz/yAGbWx0tYHd5sbnU0QOBbbcXu8tDjdOL3V03ssDuUkctBAcIJbDtDxdur4Lb48NZ+7o9OylqNag/3hb1xzyy3rZ/v9WoQxtoUlA7Cuo0GnT1mg102uD9Go1/u26/VoO6ra17T5hRF7iP1D6IzqRLh4/d5bvxKl4GH9QCPiy9k9QeSceQ8CFE0xRFwe72Umn39xdopIag3r4qhwdb7RwGx4YHTxs1D5wMY+3oAoNei0Gndk406rWBmoFjQ0T92oNIS/D+MKP84AvRmC4dPvwzmw4qVHtjW4cNb/Q86fMhTlWNNSu4vT7cHnVGRP92/WOe2nNtLo8aGOzuBuHh2GaGtg4Neq0GS+0IAv+QwzCTDotRj9Wg7vcftxj1gdEG/lEIRp0uECDUMFEbKGrnNzDotLXhQhM4btRrpUOiEB2ky4cPg1shucgLaLCcd02j50nNh2hv/hEJ9UcfVNYbfVB1zOgD/8iEaqenLlDUDxie2uYFX/s2K9Sn02oCfQOO18wQYVZrBCy1waJ+kLAa9FiM6vBFIcTpq0uHj51Hd9KrCLQ+DTqLgmHwWQ3OUXwKtgoXAOExEj5E4/xNDxV2dY4D/3OlXZ1Iyd8kodYaNBziWO1suxEJJ+KvATAcUyPgf/bXAISZ6jodRlrUTofqc12IqB8urNLEIIRopi4bPhRFYVfZLi6o7Wxq7RmHRtvwry1blQufT0GjAWukrGh7uvN4fVQ6PJTbXJTb1RBRUS9MVNSGicC+eue0xWRK+sCIhMZHH0QGHTMQZtLVTnSkTp2shofgcGHU1U2pbNBJs4IQIvS6bPg4UH2Aanc1/WrDh2XIGY2e529ysUQa0eqkKrgjeH0KVY76P/bq7In+AOD0eINWb/QvruRftdHjq12EqbF9x6z86N/n9anzMlQ7PSdVdr1WQ7RV7WwYXTsXQpQlODT4OyjWhYq6bRmRIIToCrps+Mg5mgOKQr/9PkCDddwVjZ5XI51NW83l8am1Ava6KZf9QaIyECpqaw9qaxrKbWrnxlCv0RBh0hNlNRBlMRBd+xxlMQa2oy21+6wGoi3G2mdpehBCiObouuGjLIeUoxDm0KDRKZjHXNjoeV2xs6l/dsYqh5sqZ23HRn//hGNeV9e+Vve7a/er2zUnuapjmFFHtNVYrxZB/cE3G3TqCo3+BZe09VZtrL9Pq1H31198yb9PG7xYk7521EO01UikWY9earmEEKLddNnwsfPoTvoeUP+8NqdFoTE1Hi6qy0798KEoCuU2Nwcr7Bwqd3Cows7BCgfFFQ4q/CMonHVhotrhabOhkxqNWosQba1Xa2A1BoWJ+q/VfWpThYx4EEKI01OXDR85R3OY6O9sOrh/k+edCjUflQ43h8odweGi9vlQhfrscLe8M6RGA+G1Ix7CTXrCaztAhpvqdXoM7DfUnlv3OsaqDq/UdfKlnYUQQnSsLhk+Kl2VHKw5SF9/Z9OzL27y3FBPMOb0eCk8aq8LEvVqLg6Vq/ua20kyPtxIcpSZlCgLqVFmkqMsgYDgDxYRtcEi3KxO5qSV4CCEEKKNdcnwkXM0hzC7Qvcj6mvLOZc0eW5H1Xx4fQr7jtrIKaokp6iaXcVV7CyqpOCIrVnzP0RZDKREmUmNtgQ9p0RZSI02kxRpxmzQtetnEEIIIZqjy4aPrNr+HsZ4C/rY2CbPbevwoSgKxZVOcoqr2FVUxc6iKnYVV5FbUtVk00iYUaeGiWi1xiIlykJKtJnUKAvJUWZSo81YjV3yf0ohhBCnoC75i5VTllM3v8fAPk2e53J4cDnUERutaXapsLnZVVIbMIqqyCmqIqe4igq7u9HzTXotWUkRZCVF0C85gqxk9TkxwiTDN4UQQpw2umb4OLKdG2prPqxnntfkef5aD4NJh9HS9FelKAo7DlWx41AlOcW1IaOoiqJKR6Pn67QaMuPD6FsbNPomq48esVbpnCmEEOK01+LwceDAAR5++GGWLVuG3W4nKyuLN954g+HD1RVhFUVh9uzZvP7665SVlTF69Gj++te/MnDgwDYvfGu4fW7yj+ym90H1teWsxuf3gOY1ufy85wh/WZ7D+r1ljR7vFm0hKymcvsmR9E0Op29SJD0TwqT/hRBCiC6rReGjrKyMs846i/POO49ly5aRmJhIXl4e0dHRgXOeffZZFixYwKJFi8jKymLOnDlcdNFF5OTkEBER0dblb7H8iny6FXkxeUBr0WPs2bPJc48XPrbsr+DZ5TtZlVsKqE0m2T2i6ZsUEQgafZIiiDQb2ueDCCGEEKeoFoWPZ555hrS0NBYuXBjYl5GREdhWFIUXXniB//3f/2XSpEkAvPnmmyQlJfHee+/x29/+tm1KfRJyjuYEhtha+2U0upicX2PDbHeXVDH/q10s21oEqGt53DQqjfvO70NSpLkdSy6EEEKcHlo0heRnn33GiBEjuP7660lMTCQ7O5t//OMfgeP5+fkUFRVx8cV182aYTCbGjRvH6tWrG72m0+mksrIy6NGecg5vCcxsahl99nHPrSl3AWrNR+FRGzM+2sTFz//Asq1FaDQwKbsb3/7PeOZcPViChxBCCNFMLQofe/bs4dVXX6VPnz4sX76ce++9l/vvv5+33noLgKIitTYgKSkp6H1JSUmBY8eaN28eUVFRgUdaWlprPkez7Ty0tm5yseN0NoW6ZpfvC49y/vyV/Gv9fnwKXDwgiS8fOJcFNw6lR5y1XcsrhBBCnG5a1Ozi8/kYMWIEc+fOBSA7O5tt27bx6quvcttttwXOO3ZYqKIoTQ4VffTRR3nwwQcDrysrK9stgCiKwuEDBcRWg6LVYBk8uMlzK2xucveWowO+KSjFbVQ4u3c8Myb0ZWhadLuUTwghhOgKWhQ+UlJSGDBgQNC+/v378/HHHwOQnJwMqDUgKSkpgXNKSkoa1Ib4mUwmTE0s6tbWSmwlpOxTm1LMGcloLZYG59hcHhb+VMDfv89jcrmeCDQkp4Qz+5r+nNk7vkPKKYQQQpzOWhQ+zjrrLHJycoL27dq1i/T0dAAyMzNJTk7m66+/Jjs7GwCXy8X333/PM88800ZFbr2cko2BJpewUWOCjjk9Xt7/pZCXv91NabUTjQJhijpS5W+/GUlEjPTpEEIIIdpCi8LHH//4R84880zmzp3LDTfcwC+//MLrr7/O66+/DqjNLdOnT2fu3Ln06dOHPn36MHfuXKxWK5MnT26XD9ASOwu+retsOuZcQF1T5ZMN+3lhRS4Hyu0A9Ii18sCZPSl6Jw+NBsIijSErsxBCCHG6aVH4GDlyJP/+97959NFH+b//+z8yMzN54YUXmDJlSuCcmTNnYrfbmTZtWmCSsa+++qpTzPGxp3AjZ5Wo2+bsbJZtOcT8r3exu6QagMQIE/df0IcbRqRRtr+afwHWSCNaXYv65QohhBDiOFo8w+kVV1zBFVdc0eRxjUbDrFmzmDVr1smUq10484rQKuCKsXLth7lsOVABQLTVwO/G9eK2sRlYjOrMox21mq0QQgjR1XSZtV1sVYeIPqAuErc+PI0tByoIM+q465ye/OaczAYzkUr4EEIIIdpHlwkfOw4dYUSBCXCwOW4gd52dybTxvYgLbzxcNDa7qRBCCCFOXpcJH8N69efXEnWukf958HrSRg047vmBmo8YCR9CCCFEW+oy4cNTXExYhBXFrqH7sEEnPL9Gaj6EEEKIdtFlwochNZU+P67CW1qKRn/ijy19PoQQQoj20aXGkGo0GvQJCc06t1rChxBCCNEuulT4aC6Xw4PboY6MkfAhhBBCtC0JH43wN7kYzTqM5i7TMiWEEEJ0CAkfjagukyYXIYQQor1I+GiEdDYVQggh2o+0KTRCJhgTQpwsr9eL2+0OdTGEaFMGgwGdTnfS15Hw0Qip+RBCtJaiKBQVFVFeXh7qogjRLqKjo0lOTkaj0bT6GhI+GiHhQwjRWv7gkZiYiNVqPan/QAvRmSiKgs1mo6REXR4+JSWl1deS8NEICR9CiNbwer2B4BEXFxfq4gjR5iwWCwAlJSUkJia2uglGOpw2ItDnQ9Z1EUK0gL+Ph9VqDXFJhGg//n/fJ9OnScLHMXxeH/ZKFyA1H0KI1pGmFnE6a4t/3xI+jmGrdKEooNFqsEQYQ10cIYQQ4rQj4eMYgTVdooxotfLXixCiazhy5AiJiYkUFBQ0enzRokVER0d3aJlEx3rllVe48sorO+ReEj6OIZ1NhRBd0bx585g4cSIZGRkAFBQUBFWv33jjjezatStEpWu5jIwMVq5c2ezzV65cGfjsXcWxn/nuu+9m7dq1/Pjjj+1+bwkfx5DwIYToaux2O2+88Qa/+c1vmjzHYrGQmJjYgaXqmkI5MZ3JZGLy5Mm8/PLL7X4vCR/HkPAhhOhqli1bhl6vZ+zYsU2ec2yzy6xZsxg6dCj//Oc/6dGjB+Hh4fzud7/D6/Xy7LPPkpycTGJiIk899VTQdRYsWMDgwYMJCwsjLS2NadOmUV1dHXTOP/7xD9LS0rBarVxzzTUsWLCgQZPP559/zvDhwzGbzfTs2ZPZs2fj8XhO+rvwy8vL46qrriIpKYnw8HBGjhzJihUrAscfffRRxowZ0+B9Z5xxBk888UTg9cKFC+nfvz9ms5l+/frxt7/9LXDMX7v04YcfMn78eMxmM++88w5Hjhzh5ptvpnv37litVgYPHszixYuD7lNVVcWUKVMICwsjJSWF559/nvHjxzN9+vTAOS6Xi5kzZ9KtWzfCwsIYPXr0CWuDrrzySj799FPsdnsLv7GWkXk+jiFTqwsh2oqiKNjd3pDc22LQNXtUwg8//MCIESNafI+8vDyWLVvGl19+SV5eHtdddx35+flkZWXx/fffs3r1au68804uuOCCwA+1VqvlpZdeIiMjg/z8fKZNm8bMmTMDP8o//fQT9957L8888wxXXnklK1as4LHHHgu67/Lly7nlllt46aWXOOecc8jLy+Oee+4BCPrhPxnV1dVcdtllzJkzB7PZzJtvvsnEiRPJycmhR48eTJkyhaeffpq8vDx69eoFwLZt29iyZQv/+te/ADVEPfHEE7zyyitkZ2ezceNG7r77bsLCwrj99tsD93r44YeZP38+CxcuxGQy4XA4GD58OA8//DCRkZF88cUX3HrrrfTs2ZPRo0cD8OCDD/LTTz/x2WefkZSUxOOPP86GDRsYOnRo4Lp33HEHBQUFvP/++6SmpvLvf/+bSy65hC1bttCnT59GP/eIESNwu9388ssvjBs3rk2+y8ZoFEVR2u3qrVBZWUlUVBQVFRVERkZ2+P0/fX4DB3LKufCOAfQdndzh9xdCnLocDgf5+flkZmZiNpuxuTwMeHx5SMqy/f8mYDU27+/Lq6++mri4ON54440mz1m0aBHTp08PTBs/a9Ys/vKXv1BUVERERAQAl1xyCTk5OeTl5aHVqhXr/fr1Y+rUqTzyyCONXvejjz7id7/7HaWlpQDcdNNNVFdXs2TJksA5t9xyC0uWLAnc+9xzz+XSSy/l0UcfDZzzzjvvMHPmTA4ePNisz9waAwcO5He/+x1/+MMfABgyZAjXXXddIBz96U9/YsWKFfzyyy8A9OjRg2eeeYabb745cI05c+awdOlSVq9eTUFBAZmZmbzwwgs88MADx7335ZdfTv/+/XnuueeoqqoiLi6O9957j+uuuw6AiooKUlNTufvuu3nhhRfIy8ujT58+7N+/n9TU1MB1LrzwQkaNGsXcuXObvFdsbCzPP/98UECq79h/534t+f2Wmo9j1JTLHB9CiK7FbrcH/Yg0V0ZGRiB4ACQlJaHT6QLBw7/PPx03wHfffcfcuXPZvn07lZWVeDweHA4HNTU1hIWFkZOTwzXXXBN0n1GjRgWFkfXr17N27dqgJh2v14vD4cBms7XJJG81NTXMnj2bJUuWcPDgQTweD3a7nX379gXOmTJlCv/85z957LHHUBSFxYsXB5o9Dh8+TGFhIXfddRd333134D0ej4eoqKigex1b6+T1enn66af54IMPOHDgAE6nE6fTSVhYGAB79uzB7XYzatSowHuioqLo27dv4PWGDRtQFIWsrKygazudzhPOvmuxWLDZbM34llpPwkc9iqJIs4sQos1YDDq2/9+EkN27ueLj4ykrK2vxPQwGQ9BrjUbT6D6fzwfA3r17ueyyy7j33nt58skniY2N5ccff+Suu+4KdLRUFKVBc9GxFfQ+n4/Zs2czadKkBmVqTYhqzEMPPcTy5ct57rnn6N27NxaLheuuuw6XyxU4Z/LkyTzyyCNs2LABu91OYWEhN910U6CMoDa9+JtK/I6dktwfKvzmz5/P888/zwsvvBDoHzN9+vTAvf3fx/G+J5/Ph06nY/369Q3uFx4eftzPfvToURISEo57zsmS8FGPy+HF41TbZ6XmQwhxsjQaTbObPkIpOzubd955p93vs27dOjweD/Pnzw/Ujnz44YdB5/Tr1y/QbFH/ffUNGzaMnJwcevfu3W5lXbVqFVOnTg3UwlRXVzeYA6V79+6ce+65vPvuu9jtdi688EKSkpIAtcanW7du7NmzhylTprT43ldddRW33HILoAaJ3Nxc+vfvD0CvXr0wGAz88ssvpKWlAWqTR25ubqCfRnZ2Nl6vl5KSEs4555xm3zsvLw+Hw0F2dnaLytxSnf//FR2opkyt9TBa9BhMrVssRwghTjUTJkzg0UcfpaysjJiYmHa7T69evfB4PLz88stMnDiRn376iddeey3onPvuu49zzz2XBQsWMHHiRL799luWLVsW9Ff+448/zhVXXEFaWhrXX389Wq2WzZs3s2XLFubMmdMmZe3duzeffPIJEydORKPR8NhjjwVqM+qbMmUKs2bNwuVy8fzzzwcdmzVrFvfffz+RkZFceumlOJ1O1q1bR1lZGQ8++OBx7/3xxx+zevVqYmJiWLBgAUVFRYHwERERwe23385DDz1EbGwsiYmJPPHEE2i12sD3lJWVxZQpU7jtttuYP38+2dnZlJaW8u233zJ48GAuu+yyRu+9atUqevbsGehE215kqG09MsxWCNEVDR48mBEjRjSohWhrQ4cOZcGCBTzzzDMMGjSId999l3nz5gWdc9ZZZ/Haa6+xYMEChgwZwpdffskf//jHoOaUCRMmsGTJEr7++mtGjhzJmDFjWLBgAenp6U3ee/z48UydOrXZZX3++eeJiYnhzDPPZOLEiUyYMIFhw4Y1OO/666/nyJEj2Gw2rr766qBjv/nNb/h//+//sWjRIgYPHsy4ceNYtGgRmZmZx733Y489xrBhw5gwYQLjx48nOTm5wbUXLFjA2LFjueKKK7jwwgs566yzAkN6/RYuXMhtt93G//zP/9C3b1+uvPJK1qxZE6gtaczixYuD+qi0FxntUs+O1Yf49q0dpPWP4coH2rfKSQhx+mlqFMCpYOnSpcyYMYOtW7cGdRjtDO6++2527tzJqlWrWn2NjIwMZs2a1aIAciqpqamhW7duzJ8/n7vuuqtV19i6dSsXXHABu3btatAptj4Z7dLGAjUfMafWfzSEEOJkXXbZZeTm5nLgwIHj/mXcEZ577jkuuugiwsLCWLZsGW+++WbQ5FwttXPnTiIiIrjtttvasJShtXHjRnbu3MmoUaOoqKjg//7v/wC46qqrWn3NgwcP8tZbbx03eLQVCR/11MhIFyFEF3aiuSY6yi+//MKzzz5LVVUVPXv25KWXXjru1O8n0q9fP7Zs2dKGJewcnnvuOXJycjAajQwfPpxVq1YRHx/f6utdfPHFbVi645PwUU+19PkQQoiQa+++J6eD7Oxs1q9fH+pitFrnatgLMelwKoQQQrQ/CR/1yARjQgghRPuT8FHL6/Vhr5Kp1YUQQoj2JuGjlq3CBQpodRos4YYTv0EIIYQQrSLho5a/v4c1yohG27xlqIUQQgjRchI+alWXSX8PIYQQoiNI+KglI12EEF3ZkSNHSExMbLB4WnsrKChAo9Hw66+/Hve88ePHB5arF62zZMkSsrOzG12jpqNJ+Kgl4UMI0ZXNmzePiRMnkpGRAdSFgo62cuVKNBoN5eXlQfs/+eQTnnzyycDr8ePHs2jRomZfN1SfJ5SO/cxXXHEFGo2G9957L4SlUkn4qCUTjAkhuiq73c4bb7xxUrOItrfY2FgiIiJCXYyT5na7Q3r/O+64g5dffjmkZYAWho9Zs2ah0WiCHsnJyYHjU6dObXB8zJgxbV7o9iBTqwshuqply5ah1+sZO3Zsk+csWrSI6OholixZQt++fbFarVx33XXU1NTw5ptvkpGRQUxMDPfddx9erzfwPo1Gw6effhp0rejo6EZrLQoKCjjvvPMAiImJQaPRBBaCa+tmlyNHjnDzzTfTvXt3rFYrgwcPZvHixYHjf//73+nWrVuDJoorr7yS22+/PfD6888/Z/jw4ZjNZnr27Mns2bPxeDyB4xqNhtdee42rrrqKsLAw5syZg9fr5a677iIzMxOLxULfvn158cUXg+7j8Xi4//77iY6OJi4ujocffpjbb789aHVbRVF49tln6dmzJxaLhSFDhvCvf/3ruJ/7yiuv5JdffmHPnj2t+draTIunVx84cCArVqwIvNbpdEHHL7nkEhYuXBh4bTQaT6J4HUeaXYQQbU5RwG0Lzb0NVmhmM8MPP/zAiBEjTniezWbjpZde4v3336eqqopJkyYxadIkoqOjWbp0KXv27OHaa6/l7LPP5sYbb2xxkdPS0vj444+59tprycnJITIyEovF0uLrNIfD4WD48OE8/PDDREZG8sUXX3DrrbfSs2dPRo8ezfXXX8/999/Pd999xwUXXABAWVkZy5cv5/PPPwdg+fLl3HLLLbz00kucc8455OXlcc899wDwxBNPBO71xBNPMG/ePJ5//nl0Oh0+n4/u3bvz4YcfEh8fz+rVq7nnnntISUnhhhtuAOCZZ57h3XffZeHChfTv358XX3yRTz/9NBDOAP785z/zySef8Oqrr9KnTx9++OEHbrnlFhISEhg3blyjnzs9PZ3ExERWrVpFz5492+W7bY4Whw+9Xh9U23Esk8l03OOdkaIoEj6EEG3PbYO5qaG5958OgjGsWacWFBSQmhpczoyMDBRFCdrndrt59dVX6dWrFwDXXXcdb7/9NsXFxYSHhzNgwADOO+88vvvuu1aFD51OR2xsLACJiYlER0c3ee7KlStbdO1jP0+3bt2YMWNG4PV9993Hl19+yUcffcTo0aOJjY3lkksu4b333guEj48++ojY2NjA66eeeopHHnkkUBPSs2dPnnzySWbOnBkUPiZPnsydd94ZVJ7Zs2cHtjMzM1m9ejUffvhhIHy8/PLLPProo1xzzTUAvPLKKyxdujTwnpqaGhYsWMC3334bqLHq2bMnP/74I3//+98ZN25co/8b+j97R3csPlaLw0dubi6pqamYTCZGjx7N3Llzg9LTypUrA/9oxo0bx1NPPUViYmKT13M6nTidzsDrysrKlhbppDltHjxutWpNml2EEF2N3W7HbDaf8Dyr1RoIHgBJSUlkZGQQHh4etK+kpKRdytmWvF4vTz/9NB988AEHDhwI/BaFhdUFtilTpnDPPffwt7/9DZPJxLvvvstNN90UqPFfv349a9eu5amnngq6rsPhwGazYbVaARqtVXrttdf4f//v/7F3717sdjsul4uhQ4cCUFFRQXFxMaNGjQqcr9PpGD58eKAZaPv27TgcDi666KKg67pcLrKzs4/72S0WCzZbiGrkarUofIwePZq33nqLrKwsiouLmTNnDmeeeSbbtm0jLi6OSy+9lOuvv5709HTy8/N57LHHOP/881m/fj0mU+M/6vPmzQtKgKHgr/UwWfXojboTnC2EEM1ksKo1EKG6dzPFx8dTVlZ24ksagmd/1mg0je6r309Co9E0WoMSavPnz+f555/nhRdeYPDgwYSFhTF9+nRcLlfgnIkTJ+Lz+fjiiy8YOXIkq1atYsGCBYHjPp+P2bNnM2nSpAbXrx/m6gcaUFft/eMf/8j8+fMZO3YsERER/OUvf2HNmjVB5x07Oqf+9+j/jr/44gu6desWdF5Tv7d+R48eJSEh4bjntLcWhY9LL700sD148GDGjh1Lr169ePPNN3nwwQeDqtkGDRrEiBEjSE9P54svvmj0fxyARx99lAcffDDwurKykrS0tJZ+jpMiTS5CiHah0TS76SOUsrOzeeedd9rl2gkJCRw6dCjwOjc397h/dfv7CdbvtNoeVq1axVVXXcUtt9wCqD/mubm59O/fP3COxWJh0qRJvPvuu+zevZusrCyGDx8eOD5s2DBycnLo3bt3i+995plnMm3atMC+vLy8wHZUVBRJSUn88ssvnHPOOYD6fWzcuDFQOzJgwABMJhP79u1rsn9HYxwOB3l5eSesHWlvLW52qS8sLIzBgweTm5vb6PGUlBTS09ObPA5qQjtRSmtvspqtEKIrmzBhAo8++ihlZWXExMS06bXPP/98XnnlFcaMGYPP5+Phhx9uUFtSX3p6OhqNhiVLlnDZZZdhsViCmnXaSu/evfn4449ZvXo1MTExLFiwgKKioqDwAWrTy8SJE9m2bVsgqPg9/vjjXHHFFaSlpXH99dej1WrZvHkzW7ZsYc6cOce991tvvcXy5cvJzMzk7bffZu3atWRmZgbOue+++5g3bx69e/emX79+vPzyy5SVlQVqQyIiIpgxYwZ//OMf8fl8nH322VRWVrJ69WrCw8ODRuTU9/PPP2MymY47sqkjnNQ8H06nkx07dpCSktLo8SNHjlBYWNjk8c5Caj6EEF3Z4MGDGTFiBB9++GGbX3v+/PmkpaVx7rnnMnnyZGbMmBHoC9GYbt26MXv2bB555BGSkpL4wx/+0Kz7TJ06lfHjxze7XI899hjDhg1jwoQJjB8/nuTk5KBhrH7nn38+sbGx5OTkMHny5KBjEyZMYMmSJXz99deMHDmSMWPGsGDBAtLT049773vvvZdJkyZx4403Mnr0aI4cORJUCwLw8MMPc/PNN3PbbbcxduxYwsPDmTBhQlBzzpNPPsnjjz/OvHnz6N+/PxMmTODzzz8PCjHHWrx4MVOmTDnu/wYdQaM01hW2CTNmzGDixIn06NGDkpIS5syZw/fff8+WLVuIi4tj1qxZXHvttaSkpFBQUMCf/vQn9u3bx44dO5o9OUxlZSVRUVFUVFQQGRnZ6g/WEivf3cm2VQcZcVkGo68M3dAjIcSpzeFwkJ+fT2ZmZrM6cHYmS5cuZcaMGWzduhWt9tSbf3L8+PGMHz+eWbNmhboo7cLn89G/f39uuOGGoJleW+Lw4cP069ePdevWHTegnEhT/85b8vvdomaX/fv3c/PNN1NaWkpCQgJjxozh559/Jj09HbvdzpYtW3jrrbcoLy8nJSWF8847jw8++KDTz0onNR9CiK7usssuIzc3lwMHDnR4v7uTVVVVRV5eHkuWLAl1UdrM3r17+eqrrxg3bhxOp5NXXnmF/Pz8BrUvLZGfn8/f/va3kwoebaVF4eP9999v8pjFYmH58uUnXaBQkD4fQggBDzzwQKiL0CoREREUFhaGuhhtSqvVsmjRImbMmIGiKAwaNIgVK1Y06JPSEqNGjQoavhtKJ9Xh9HQhNR9CCCE6k7S0NH766adQF6PdnHoNe23M6/Fhr1LHnEv4EEIIIdpflw8fNRVqrYdWp8ES3vTwLyGEEEK0DQkf5epsdmHRJjTa5i3CJIQQQojWk/AhnU2FEEKIDiXhQzqbCiGEEB2qy4eP6jIHIOFDCCGE6ChdPnxIzYcQQqjLYSQmJlJQUNCh9y0oKECj0fDrr78e97zx48czffr0DilTqGVkZPDCCy+02/VnzJjB/fff327Xb44uHz5kgjEhhIB58+YxceJEMjIygLpQ0NFWrlyJRqOhvLw8aP8nn3wSNK34+PHjWbRoUbOvG6rP0xksWrQoaN2bmTNnsnDhQvLz80NWpi4fPqTmQwjR1dntdt544w1+85vfhLooTYqNje30S3WcKhITE7n44ot57bXXQlaGLh0+FEUJGmorhBBd0bJly9Dr9cddZn3RokVER0ezZMkS+vbti9Vq5brrrqOmpoY333yTjIwMYmJiuO+++/B6vYH3aTQaPv3006BrRUdHN1prUVBQwHnnnQdATEwMGo2GqVOnAu3T7PLTTz8xbtw4rFYrMTExTJgwgbKyMkBdtf3+++8nMTERs9nM2Wefzdq1awPv9dfQLF++nOzsbCwWC+effz4lJSUsW7aM/v37ExkZyc0334zNZgu8b/z48fzhD3/gD3/4A9HR0cTFxfHnP/+Z463xWlFRwT333ENiYiKRkZGcf/75bNq0CVAXi0tOTmbu3LmB89esWYPRaOSrr75q8ppXXnklixcvbvV3d7K69PTqzhoPXo8PgLBoY4hLI4Q43SiKgt1jD8m9LXpLs5sZfvjhB0aMGHHC82w2Gy+99BLvv/8+VVVVTJo0iUmTJhEdHc3SpUvZs2cP1157LWeffTY33nhji8uclpbGxx9/zLXXXktOTg6RkZFYLJYWX6c5fv31Vy644ALuvPNOXnrpJfR6Pd99910gOM2cOZOPP/6YN998k/T0dJ599lkmTJjA7t27iY2NDVxn1qxZvPLKK1itVm644QZuuOEGTCYT7733HtXV1VxzzTW8/PLLPPzww4H3vPnmm9x1112sWbOGdevWcc8995Cens7dd9/doJyKonD55ZcTGxvL0qVLiYqK4u9//zsXXHABu3btIiEhgX/+859cffXVXHzxxfTr149bbrmFadOmcfHFFzf5+UeNGkVhYSF79+4lPT29Db/Z5unS4cPf38McZkBv0IW4NEKI043dY2f0e6NDcu81k9dgNVibdW5BQQGpqalB+zIyMhr8Ne52u3n11Vfp1asXANdddx1vv/02xcXFhIeHM2DAAM477zy+++67VoUPnU4X+GFPTEwkOjq6yXNXrlzZomsf+3meffZZRowYwd/+9rfAvoEDBwJQU1PDq6++yqJFi7j00ksB+Mc//sHXX3/NG2+8wUMPPRR4z5w5czjrrLMAuOuuu3j00UfJy8ujZ8+egPodfffdd0HhIy0tjeeffx6NRkPfvn3ZsmULzz//fKPh47vvvmPLli2UlJRgMqk19M899xyffvop//rXv7jnnnu47LLLuPvuu5kyZQojR47EbDbz9NNPB64xderUQA2SX7du3QD1f/tQhI8u3ewi/T2EEELt82E2m094ntVqDQQPgKSkJDIyMggPDw/aV1JS0i7lbEv+mo/G5OXl4Xa7A6ECwGAwMGrUKHbs2BF07hlnnBHYTkpKwmq1BoKHf9+x38eYMWOCaqXGjh1Lbm5uUHOV3/r166muriYuLo7w8PDAIz8/n7y8vMB5zz33HB6Phw8//JB33333hP97+muU6jcJdaQuXfMh4UMI0Z4segtrJq8J2b2bKz4+PtDX4XgMhuD1rzQaTaP7fD5f0OvGalBC7XjNOf7yHttspShKg331P39zvo+W8vl8pKSkNFrTU79maM+ePRw8eBCfz8fevXuDQlFjjh49CkBCQkKry3YyunT4qBtmK/09hBBtT6PRNLvpI5Sys7N555132uXaCQkJHDp0KPA6Nzf3uH9tG43qf48bqwVoS2eccQbffPMNs2fPbnCsd+/eGI1GfvzxRyZPngyogWndunVt0un1559/bvC6T58+6HQNm/+HDRtGUVERer0+MAz6WC6XiylTpnDjjTfSr18/7rrrLrZs2UJSUlKTZdi6dSsGgyHQ1NTRpNkFqfkQQnRtEyZMYNu2bc2q/Wip888/n1deeYUNGzawbt067r333ga1A/Wlp6ej0WhYsmQJhw8fprq6us3LBPDoo4+ydu1apk2bxubNm9m5cyevvvoqpaWlhIWF8bvf/Y6HHnqIL7/8ku3bt3P33Xdjs9m46667TvrehYWFPPjgg+Tk5LB48WJefvllHnjggUbPvfDCCxk7dixXX301y5cvp6CggNWrV/PnP/+ZdevWAfC///u/VFRU8NJLLzFz5kz69+9/wnKuWrWKc845p9069J6IhA8kfAghurbBgwczYsQIPvzwwza/9vz580lLS+Pcc89l8uTJzJgxA6u16dqgbt26MXv2bB555BGSkpL4wx/+0Kz7TJ06NWgirRPJysriq6++YtOmTYwaNYqxY8fyn//8B71ebRB4+umnufbaa7n11lsZNmwYu3fvZvny5cTExDT7Hk257bbbsNvtjBo1it///vfcd9993HPPPY2eq9FoWLp0Keeeey533nknWVlZ3HTTTRQUFJCUlMTKlSt54YUXePvtt4mMjESr1fL222/z448/8uqrrzZZhsWLFzfawbWjaJTjDS4OgcrKSqKioqioqCAyMrJd7/X+nF84sr+ay39/BhmD49v1XkKI05/D4SA/P5/MzMxmdeDsTJYuXcqMGTPYunUrWu2p93fp+PHjGT9+PLNmzQp1UY5r/PjxDB06tF2nTz+RL774goceeojNmzcHwlZLNPXvvCW/3126z4e/5iM8Rmo+hBBd22WXXUZubi4HDhwgLS0t1MVpkaqqKvLy8liyZEmoi3JKqKmpYeHCha0KHm2ly4YPr9uHo1rtcS3NLkIIQZP9Djq7iIgICgsLQ12MU8YNN9wQ6iJ03fBRU6HWeuj0WsxhTXd+EkIIIdpKSydHO12deg17baQ60NnU2GVXOhRCCCFCocuGDxnpIoQQQoSGhA8JH0IIIUSH6rLho1rChxBCCBESXTZ8BIbZSvgQQgghOlSXDx9S8yGEEEJ0LAkfEj6EEIIjR46QmJhIQUFBqIsiWmnJkiVkZ2ef1Cq6HaVLhg9FUagpdwHS7CKEEADz5s1j4sSJgZVTCwoKgqYhWLRoUdAS7m2lqeuOHz+eRYsWNfs6x5a3Kzj2M19xxRVoNBree++9EJaqebpk+HDUuPF61GQYFiXhQwjRtdntdt544w1+85vfhLoopzy32x3S+99xxx28/PLLIS1Dc3TJ8FFdpja5WCIM6Axd8isQQoiAZcuWodfrGTt2bKPHV65cyR133EFFRQUajQaNRhNYwM3lcjFz5ky6detGWFgYo0ePDszi6XA4GDhwYNCKrfn5+URFRfGPf/zjuNc9WUeOHOHmm2+me/fuWK1WBg8ezOLFiwPH//73v9OtW7cGTRRXXnklt99+e+D1559/zvDhwzGbzfTs2ZPZs2fj8XgCxzUaDa+99hpXXXUVYWFhzJkzB6/Xy1133UVmZiYWi4W+ffvy4osvBt3H4/Fw//33Ex0dTVxcHA8//DC33347V199deAcRVF49tln6dmzJxaLhSFDhvCvf/3ruJ/7yiuv5JdffmHPnj2t+do6jtLJVFRUKIBSUVHRbvfI33xYeeW33yjvz1nTbvcQQnQ9drtd2b59u2K32xVFURSfz6d4a2pC8vD5fM0u9wMPPKBccsklQfvy8/MV/0+E0+lUXnjhBSUyMlI5dOiQcujQIaWqqkpRFEWZPHmycuaZZyo//PCDsnv3buUvf/mLYjKZlF27dimKoigbN25UjEaj8u9//1vxeDzKWWedpVx11VUnvO64ceOUhQsXNvsz1C+voijK/v37lb/85S/Kxo0blby8POWll15SdDqd8vPPPyuKoihHjhxRjEajsmLFisB7jh49qhiNRmX58uWKoijKl19+qURGRiqLFi1S8vLylK+++krJyMhQZs2aFXgPoCQmJipvvPGGkpeXpxQUFCgul0t5/PHHlV9++UXZs2eP8s477yhWq1X54IMPAu+bM2eOEhsbq3zyySfKjh07lHvvvVeJjIwMfDeKoih/+tOflH79+ilffvmlkpeXpyxcuFAxmUzKypUrG/3MfomJicqiRYua/d211LH/zv1a8vutURRFCWX4OVZLluRtrW2rDrDy3RzSB8dxxe+HtMs9hBBdz7FLjftsNnKGDQ9JWfpuWI/Wam3WuVdffTVxcXG88cYbTZ6zaNEipk+fTnl5eWBfXl4effr0Yf/+/aSmpgb2X3jhhYwaNYq5c+cC8Je//IVnn32Wm2++mY8++ogtW7YQHx/f5HXby+WXX07//v157rnnALjqqquIj48PfO7XX3+dJ554gv3796PT6Tj33HO59NJLefTRRwPXeOedd5g5cyYHDx4E1JqP6dOn8/zzzx/33r///e8pLi4O1FwkJyczY8YMZsyYAYDX66Vnz55kZ2fz6aefUlNTQ3x8PN9++21QjdRvfvMbbDbbcft1DBs2jKuuuoonnniiFd/SiR3779yvJb/fXXJhOZlgTAgh6tjt9qAfkebasGEDiqKQlZUVtN/pdBIXFxd4/T//8z/85z//4eWXX2bZsmWB4NGevF4vTz/9NB988AEHDhzA6XTidDoJCwsLnDNlyhTuuece/va3v2EymXj33Xe56aab0Ol0AKxfv561a9fy1FNPBV3X4XBgs9mw1oa7ESNGNLj/a6+9xv/7f/+PvXv3YrfbcblcDB06FICKigqKi4sZNWpU4HydTsfw4cMDzUDbt2/H4XBw0UUXBV3X5XKRnZ193M9usViw2Wwt+LY6XpcMHzLBmBCiI2gsFvpuWB+yezdXfHw8ZWVlLb6Hz+dDp9Oxfv36wA+2X3h4eGC7pKSEnJwcdDodubm5XHLJJS2+V0vNnz+f559/nhdeeIHBgwcTFhbG9OnTcblcgXMmTpyIz+fjiy++YOTIkaxatYoFCxYEfb7Zs2czadKkBtevH9bqBxqADz/8kD/+8Y/Mnz+fsWPHEhERwV/+8hfWrFkTdN6xo3PqN0T4Q8gXX3xBt27dgs4zmY7/23X06FESEhKOe06odenwITUfQoj2pNFo0DSz6SOUsrOzeeedd457jtFoxOv1Nnif1+ulpKSEc845p8n33nnnnQwaNIi7776bu+66iwsuuIABAwY0ed22sGrVKq666ipuueUWQP0xz83NpX///oFzLBYLkyZN4t1332X37t1kZWUxfHhdM9mwYcPIycmhd+/eLb73mWeeybRp0wL78vLyAttRUVEkJSXxyy+/BL43r9fLxo0bA7UjAwYMwGQysW/fPsaNG9fsezscDvLy8k5YOxJqEj6EEKKLmzBhAo8++ihlZWXExMQ0ek5GRgbV1dV88803DBkyBKvVSlZWFlOmTOG2225j/vz5ZGdnU1payrfffsvgwYO57LLL+Otf/8p///tfNm/eTFpaGsuWLWPKlCmsWbMGo9HY6HWtbRDYevfuzccff8zq1auJiYlhwYIFFBUVBYUPUJteJk6cyLZt2wJBxe/xxx/niiuuIC0tjeuvvx6tVsvmzZvZsmULc+bMOe6933rrLZYvX05mZiZvv/02a9euJTMzM3DOfffdx7x58+jduzf9+vXj5ZdfpqysLFAbEhERwYwZM/jjH/+Iz+fj7LPPprKyktWrVxMeHh40Iqe+n3/+GZPJ1OTIpU6jPXrCnoyOGO3yjwe/V1757TdK6f6qdruHEKLraWoUwKlgzJgxymuvvXbcc+69914lLi5OAZQnnnhCURQlMLIjIyNDMRgMSnJysnLNNdcomzdvVnbs2KFYLBblvffeC1yjoqJCycjIUGbOnHnc6x7r9ttvV8aNG9fsz3PkyBHlqquuUsLDw5XExETlz3/+s3LbbbcFjSZRFEXxeDxKSkqKAih5eXkNrvPll18qZ555pmKxWJTIyEhl1KhRyuuvvx44Dij//ve/g97jcDiUqVOnKlFRUUp0dLTyu9/9TnnkkUeUIUOGBM5xu93KH/7wByUyMlKJiYlRHn74YeX6669XbrrppsA5Pp9PefHFF5W+ffsqBoNBSUhIUCZMmKB8//33TX7ue+65R/ntb3/b7O+pNWS0Syt4XF7+fv/3ANw1/xzMYYY2v4cQomtqahTAqWDp0qXMmDGDrVu3otV2vvmPxo8fz/jx49tsHpDOxufz0b9/f2644QaefPLJVl3j8OHD9OvXj3Xr1gXVsrQ1Ge3SCjUVapOLzqDFZO1yH18IIRp12WWXkZuby4EDB0hLSwt1cYJUVVWRl5fHkiVLQl2UNrN3716++uorxo0bh9Pp5JVXXiE/P5/Jkye3+pr5+fn87W9/a9fg0VZaFG9nzZoVmIXO/0hOTg4cVxSFWbNmkZqaisViYfz48Wzbtq3NC30y6vf36GrrAAghxPE88MADnS54gNr/obCwMGgEzalOq9WyaNEiRo4cyVlnncWWLVtYsWJFgz4pLTFq1ChuvPHGNixl+2nxn/4DBw5kxYoVgdf1h1c9++yzLFiwgEWLFpGVlcWcOXO46KKLyMnJISIiom1KfJKqZZitEEKIEEtLS+Onn34KdTFCpsUNe3q9nuTk5MDDP5ZYURReeOEF/vd//5dJkyYxaNAg3nzzzRPOxNbRasrUMd4y0kUIIYQIjRaHj9zcXFJTU8nMzOSmm24KLF6Tn59PUVERF198ceBck8nEuHHjWL16dduV+CTJMFshhBAitFrU7DJ69GjeeustsrKyKC4uZs6cOZx55pls27aNoqIiAJKSkoLek5SUxN69e5u8pn/KW7/KysqWFKnFpNlFCCGECK0WhY9LL700sD148GDGjh1Lr169ePPNNxkzZgzQ+HSxx+vYOW/ePGbPnt2SYpwUqfkQQgghQuukBnOHhYUxePBgcnNzA6Ne/DUgfiUlJQ1qQ+p79NFHqaioCDwKCwtPpkgnJOFDCCGECK2TCh9Op5MdO3aQkpJCZmYmycnJfP3114HjLpeL77//njPPPLPJa5hMJiIjI4Me7UXxKYF5PsKije12HyGEEEI0rUXhY8aMGXz//ffk5+ezZs0arrvuOiorK7n99tvRaDRMnz6duXPn8u9//5utW7cydepUrFbrSU2a0pbs1W58XnVC17AoqfkQQgi/I0eOkJiYSEFBQYfet6CgAI1Gw6+//nrc88aPH8/06dM7pEynmpEjR/LJJ5+Euhgt0qLwsX//fm6++Wb69u3LpEmTMBqN/Pzzz6SnpwMwc+ZMpk+fzrRp0xgxYgQHDhzgq6++6jRzfPibXCwRBnT6zjd9sBBChMq8efOYOHEiGRkZQF0o6GgrV65Eo9FQXl4etP+TTz4JmnZ8/PjxLFq0qNnXDdXnORlNfRdTp04Nmmb+scce45FHHsHn83VsAU9Cizqcvv/++8c9rtFomDVrVqede1/6ewghREN2u5033niDpUuXhrooTYqNjQ11ETqtyy+/nLvvvpvly5cHDQzpzLrUn/8yzFYIIRpatmwZer3+uMuwL1q0iOjoaJYsWULfvn2xWq1cd9111NTU8Oabb5KRkUFMTAz33XcfXq838D6NRsOnn34adK3o6OhGay0KCgo477zzAIiJiUGj0TB16lSgfZpdfvrpJ8aNG4fVaiUmJoYJEyZQVlYGqH0a77//fhITEzGbzZx99tmsXbs28F5/rcQ333zDiBEjsFqtnHnmmeTk5ATd47PPPmPEiBGYzWbi4+OZNGlS4Ng777zDiBEjiIiIIDk5mcmTJ1NSUnLC7+JYOp2Oyy67jMWLF7fl19OuulT4kJoPIURHUhQFt9MbkkdLFiz/4YcfGDFixAnPs9lsvPTSS7z//vt8+eWXrFy5kkmTJrF06VKWLl3K22+/zeuvv86//vWvVn1faWlpfPzxxwDk5ORw6NAhXnzxxVZd60R+/fVXLrjgAgYOHMh///tffvzxRyZOnBgITjNnzuTjjz/mzTffZMOGDfTu3ZsJEyZw9OjRoOv87//+L/Pnz2fdunXo9XruvPPOwLEvvviCSZMmcfnll7Nx48ZAUPFzuVw8+eSTbNq0iU8//ZT8/PxAwGjpdzFq1ChWrVrVVl9Pu+tSy7pK+BBCdCSPy8frD3wfknvf8+I4DCbdiU9E/Ss7NTU1aF9GRkaDAON2u3n11Vfp1asXANdddx1vv/02xcXFhIeHM2DAAM477zy+++67Vi1wptPpAs0riYmJREdHN3nuypUrW3TtYz/Ps88+y4gRI/jb3/4W2Ddw4EAAampqePXVV1m0aFGgGeMf//gHX3/9NW+88QYPPfRQ4D1PPfUU48aNA+CRRx7h8ssvx+FwYDabeeqpp7jpppuC5rIaMmRIYLt+UOnZsycvvfQSo0aNorq6mvDw8Ca/i8Zqjbp168a+ffvw+XxotZ2/XqHzl7ANSfgQQoiG7HY7ZrP5hOdZrdZA8AB1BuuMjIyg1WaTkpICTQedmb/mozF5eXm43W7OOuuswD6DwcCoUaPYsWNH0LlnnHFGYDslJQUg8PmPdw+AjRs3ctVVV5Genk5ERATjx48HYN++fS3+PBaLBZ/PFzRjeGfWpWo+An0+YiR8CCHan96o5Z4Xx4Xs3s0VHx8f6OtwPAaDIei1RqNpdF/9URcajabRGpRQs1gsTR7zl7c5M3bX//z+Y/7Pf7x71NTUcPHFF3PxxRfzzjvvkJCQwL59+5gwYQIul6tlHwY4evQoVqv1uPfsTKTmQwgh2olGo8Fg0oXk0ZJhpdnZ2Wzfvr1dvoOEhAQOHToUeJ2bm4vNZmvyfKNRnQCyfqfV9nDGGWfwzTffNHqsd+/eGI1Gfvzxx8A+t9vNunXr6N+/f5vcY+fOnZSWlvL0009zzjnn0K9fvwY1Ri35LrZu3cqwYcOaXbZQ6zLhw+3y4rR5ABntIoQQ9U2YMIFt27Y1q/ajpc4//3xeeeUVNmzYwLp167j33nsb1JbUl56ejkajYcmSJRw+fJjq6uo2LxOoS3usXbuWadOmsXnzZnbu3Mmrr75KaWkpYWFh/O53v+Ohhx7iyy+/ZPv27dx9993YbDbuuuuuZt/jiSeeYPHixTzxxBPs2LGDLVu28OyzzwLQo0cPjEYjL7/8Mnv27OGzzz4LmscEWvZdrFq1KmhV+c6uy4QPxacw5uqenHF+d4yWLtXaJIQQxzV48GBGjBjBhx9+2ObXnj9/PmlpaZx77rlMnjyZGTNmYLVamzy/W7duzJ49m0ceeYSkpCT+8Ic/NOs+U6dODfSZaI6srCy++uorNm3axKhRoxg7diz/+c9/0OvV34enn36aa6+9lltvvZVhw4axe/duli9fTkxMTLPvMX78eD766CM+++wzhg4dyvnnn8+aNWsAtUZo0aJFfPTRRwwYMICnn36a5557Luj9zf0uDhw4wOrVq7njjjuaXbZQ0ygtGY/VASorK4mKiqKioqJd13kRQoi25nA4yM/PJzMzs1kdODuTpUuXMmPGDLZu3XpKjJY41vjx4xk/fnynneSyPT300ENUVFTw+uuvd8j9mvp33pLfb6kCEEIIwWWXXUZubi4HDhwgLS0t1MVpkaqqKvLy8liyZEmoixISiYmJzJgxI9TFaBEJH0IIIQB44IEHQl2EVomIiKCwsDDUxQiZ+vOOnCpOvbo1IYQQQpzSJHwIIYQQokNJ+BBCCCFEh5LwIYQQbaz+DJ9CnG7a4t+3dDgVQog2YjQa0Wq1HDx4kISEBIxGY4tmGhWiM1MUBZfLxeHDh9FqtYEZWFtDwocQQrQRrVZLZmYmhw4d4uDBg6EujhDtwmq10qNHj5OaD0bChxBCtCGj0UiPHj3weDztvj6JEB1Np9Oh1+tPukZPwocQQrQx/2qvx1vDRIiuTDqcCiGEEKJDSfgQQgghRIeS8CGE+P/t3X9M1PUfB/DnAcedMjhLxbuLQGIFQxlT1AB/0HSdUKaNFmCNsF8rpybilqzW4L/ol21lZjkEW61aAc6NluG8QxQxiqNIDVmcyibIdAkIA055ff/oe7cOPvc5DrkPn8+n12O7TT73er99vfa69z5vbp8PH8YYk5TsrvlwPWS3v79/hjNhjDHG2GS5ztuu87gY2W0+BgYGAEBxT1VkjDHG2D/ncYPBIBqjoclsUSQ0NjaGq1evIjw8fNr/OE9/fz/uv/9+dHV1ISIiYlrnlgO11weov0auT/nUXqPa6wPUX2Og6iMiDAwMwGw2+/wbILL75iMoKAhRUVEB/T8iIiJU+YFyUXt9gPpr5PqUT+01qr0+QP01BqI+X994uPAFp4wxxhiTFG8+GGOMMSap/9TmQ6fToaSkBDqdbqZTCQi11weov0auT/nUXqPa6wPUX6Mc6pPdBaeMMcYYU7f/1DcfjDHGGJt5vPlgjDHGmKR488EYY4wxSfHmgzHGGGOSUt3mY//+/YiNjYVer0dKSgoaGhpE4+vr65GSkgK9Xo8HHngABw4ckChT/7z99ttYvnw5wsPDERkZiSeffBLt7e2iY2w2GzQazYTXn3/+KVHW/iktLZ2Qq9FoFB2jlP4BwMKFCwX7sW3bNsF4JfTv5MmTeOKJJ2A2m6HRaHDkyBGP94kIpaWlMJvNmDVrFh555BGcO3fO57xVVVVITEyETqdDYmIiampqAlSBOLH6nE4n9uzZg6SkJISFhcFsNuO5557D1atXReesrKwU7Ovw8HCAq5nIV/+2bNkyIc/U1FSf88qlf4DvGoV6odFo8N5773mdUy49nMx5Qa5rUFWbj2+//RaFhYV48803YbfbsXr1amRlZeHKlSuC8Q6HA4899hhWr14Nu92ON954A6+99hqqqqokzty3+vp6bNu2DU1NTairq8Pt27dhsVgwODjoc2x7ezu6u7vdrwcffFCCjKdm0aJFHrm2tbV5jVVS/wCgubnZo7a6ujoAwNNPPy06Ts79GxwcRHJyMvbt2yf4/rvvvou9e/di3759aG5uhtFoxKOPPup+hpOQM2fOIDc3F/n5+fjtt9+Qn5+PnJwcnD17NlBleCVW39DQEFpaWvDWW2+hpaUF1dXVuHjxIjZu3Ohz3oiICI+ednd3Q6/XB6IEUb76BwCZmZkeef7www+ic8qpf4DvGsf34dChQ9BoNHjqqadE55VDDydzXpDtGiQVWbFiBb366qsexxISEqi4uFgw/vXXX6eEhASPY6+88gqlpqYGLMfp0tvbSwCovr7ea4zVaiUA9Pfff0uX2F0oKSmh5OTkSccruX9ERDt37qS4uDgaGxsTfF9p/QNANTU17p/HxsbIaDRSWVmZ+9jw8DAZDAY6cOCA13lycnIoMzPT49j69espLy9v2nP2x/j6hPz8888EgC5fvuw1pqKiggwGw/QmNw2E6isoKKBNmzb5NY9c+0c0uR5u2rSJ1q5dKxoj1x6OPy/IeQ2q5puP0dFR/Prrr7BYLB7HLRYLGhsbBcecOXNmQvz69evxyy+/wOl0BizX6dDX1wcAuPfee33GLlmyBCaTCevWrYPVag10anelo6MDZrMZsbGxyMvLQ2dnp9dYJfdvdHQUX375JV544QWfD1BUUv/+zeFwoKenx6NHOp0OGRkZXtck4L2vYmPkoq+vDxqNBnPmzBGNu3XrFmJiYhAVFYUNGzbAbrdLk+AU2Gw2REZG4qGHHsLLL7+M3t5e0Xgl9+/atWuora3Fiy++6DNWjj0cf16Q8xpUzebj+vXruHPnDhYsWOBxfMGCBejp6REc09PTIxh/+/ZtXL9+PWC53i0iQlFREVatWoXFixd7jTOZTPj8889RVVWF6upqxMfHY926dTh58qSE2U7eww8/jC+++ALHjh3DwYMH0dPTg/T0dNy4cUMwXqn9A4AjR47g5s2b2LJli9cYpfVvPNe682dNusb5O0YOhoeHUVxcjGeeeUb0YV0JCQmorKzE0aNH8fXXX0Ov12PlypXo6OiQMNvJycrKwldffYUTJ07ggw8+QHNzM9auXYuRkRGvY5TaPwA4fPgwwsPDkZ2dLRonxx4KnRfkvAZl91TbuzX+t0giEv3NUihe6LicbN++Hb///jtOnTolGhcfH4/4+Hj3z2lpaejq6sL777+PNWvWBDpNv2VlZbn/nZSUhLS0NMTFxeHw4cMoKioSHKPE/gFAeXk5srKyYDabvcYorX/e+LsmpzpmJjmdTuTl5WFsbAz79+8XjU1NTfW4aHPlypVYunQpPv74Y3z00UeBTtUvubm57n8vXrwYy5YtQ0xMDGpra0VP0Errn8uhQ4fw7LPP+rx2Q449FDsvyHENquabj3nz5iE4OHjCzqy3t3fCDs7FaDQKxoeEhGDu3LkBy/Vu7NixA0ePHoXVakVUVJTf41NTU2X5G5aQsLAwJCUlec1Xif0DgMuXL+P48eN46aWX/B6rpP657lTyZ026xvk7ZiY5nU7k5OTA4XCgrq7O70eUBwUFYfny5Yroq8lkQkxMjGiuSuufS0NDA9rb26e0Lme6h97OC3Jeg6rZfISGhiIlJcV9B4FLXV0d0tPTBcekpaVNiP/pp5+wbNkyaLXagOU6FUSE7du3o7q6GidOnEBsbOyU5rHb7TCZTNOcXWCMjIzgwoULXvNVUv/+raKiApGRkXj88cf9Hquk/sXGxsJoNHr0aHR0FPX19V7XJOC9r2JjZopr49HR0YHjx49PadNLRGhtbVVEX2/cuIGuri7RXJXUv38rLy9HSkoKkpOT/R47Uz30dV6Q9RqctktXZeCbb74hrVZL5eXldP78eSosLKSwsDC6dOkSEREVFxdTfn6+O76zs5Nmz55Nu3btovPnz1N5eTlptVr6/vvvZ6oEr7Zu3UoGg4FsNht1d3e7X0NDQ+6Y8fV9+OGHVFNTQxcvXqQ//viDiouLCQBVVVXNRAk+7d69m2w2G3V2dlJTUxNt2LCBwsPDVdE/lzt37lB0dDTt2bNnwntK7N/AwADZ7Xay2+0EgPbu3Ut2u919t0dZWRkZDAaqrq6mtrY22rx5M5lMJurv73fPkZ+f73FH2unTpyk4OJjKysrowoULVFZWRiEhIdTU1CSr+pxOJ23cuJGioqKotbXVY12OjIx4ra+0tJR+/PFH+uuvv8hut9Pzzz9PISEhdPbsWVnVNzAwQLt376bGxkZyOBxktVopLS2N7rvvPsX0j8j3Z5SIqK+vj2bPnk2ffvqp4Bxy7eFkzgtyXYOq2nwQEX3yyScUExNDoaGhtHTpUo9bUQsKCigjI8Mj3maz0ZIlSyg0NJQWLlzo9cM30wAIvioqKtwx4+t75513KC4ujvR6Pd1zzz20atUqqq2tlT75ScrNzSWTyURarZbMZjNlZ2fTuXPn3O8ruX8ux44dIwDU3t4+4T0l9s91O/D4V0FBARH9c6tfSUkJGY1G0ul0tGbNGmpra/OYIyMjwx3v8t1331F8fDxptVpKSEiYsQ2XWH0Oh8PrurRare45xtdXWFhI0dHRFBoaSvPnzyeLxUKNjY3SF0fi9Q0NDZHFYqH58+eTVqul6OhoKigooCtXrnjMIef+Efn+jBIRffbZZzRr1iy6efOm4Bxy7eFkzgtyXYOa/xfAGGOMMSYJ1VzzwRhjjDFl4M0HY4wxxiTFmw/GGGOMSYo3H4wxxhiTFG8+GGOMMSYp3nwwxhhjTFK8+WCMMcaYpHjzwRhjjDFJ8eaDMcYYY5LizQdjjDHGJMWbD8YYY4xJijcfjDHGGJPU/wD2WqZKHQ/0UQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_acc\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_img_txt\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_txt_only\n"
     ]
    },
    {
     "data": {
      "image/png": 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Rne19XhceTsjUCwmZPgPz8NM7vImiKLeCr1/fxbG8SgBOPy+JcTN7ozOceLBzOlwcOVThDSP5B0qxljXs0HvzoomYAmWeD0DCh+h6rE4rX2V+xQdpH7CraJf3+T5hfbiy/5VM7zWdoICgTixh8w5XHuaT9E/4KP0jCqvUYYIaNExInMCV/a7krB5nodO2b3VsXfX7dcRaYr39Ok7mjrLNcVdWYtu3T/3rfu9e7HvTsO/b1+hf9YpWh6P/aKpSTqc8JIVidyjFxQouZ8P/qs3BBmKSQ4hJDiYmOYTo5GACQ1v216zL6VbDSEVtKPHer/+z0om1ohp7lZMO6D/dYgajDpNFi8ngxqjYMDgqMFiL0ZcVojuah+5oDgHV5QRUl2NwVKA1mwg+5xxCZkwn6Iwzmp2bpL0oisLOH3P56aP9uJxuzCEBnHfDQHoOjmzXY5YdtXlrR/IPlOJ2KVzz0Fi/HkfChxB+cKDkAMv2LePz/Z9713YwaA2cn3w+V/a/kuExw0+qi6XT7eTHnB9ZlraM9Xm1kwvFBcZxWd/LmNV3FtGW6Dbv37NGSWFVIYcrD3O4Sr0VVhXW/qw8TLFd7VBn1pu983WY9eZWH8/lcnPkUDn56aXkZ5SQn1GK2+kmskcQUYlBRPaouSUGYQjomHCljvzIq+lkqYYMW1pakyM/CDDiGjCSquSaphNnCEXH3DirG1ZpGC16onsGE5NSGzaCwo0d+jvoditUVzmptrVtOviWsFc5sZZXYy2vpqrcod4vq3O/vBpruQOXs5Fqn2bo9dBnRDSDz+5JbErHr+EDYK2o5vs395K1XV3or+fgSM69YSCWkI5vCnW53H5vPpPwIUQbOVwOvsv+jmVpy9hUsMn7fGJQIlf0v4JL+lxChCmiE0voH4fKDvHhvg/5bP9nlNhLAHXtirN7ns2V/a9kTNwYn/+c3YqbY7Zj3gDhCRQ+AaPysHc+juZo0HBRr4ta3a+j2ubkcGYZeftLyN9fwuEDZTgdLbgAaSAsxkJkYhBRPdRbZI+gE75wu61W7OnpashI2+ftQFl/1ku3RovdGE51XB+qew7CHtGTKmMklU4TZWUuXI2cg96oI6ZnsE+NRmh0x49e6KoURcFhc1FVE0RqQ0nDkGItr8Za4fCpsYlMDGLwxAT6jYnFaOmY2o+cvcf4dsluKkur0eo1TPhdH4ad0+OU+k4lfAjRStll2Xy6/1M+Sf/Eu0S2VqPlrB5ncWX/K5mQMOGUHOppd9n5OutrlqUt49cjv3qfTwlJoX9EfwqrCr1BwzOS53iCA4KJtcSqt0D1Z4wlxvszPii+RQuLVZVVU5BR6g0bR7IrUNy+/10ZA/XE9w4jvk8ocT0t6KqtFB22UVRg41iBjaLDNqwVjZfbaNYREWskMtZMRJyJyDgT4dFGtT9DPYqjGvv+/TU1GWqNRvXBg3g6Xbg1OmymSKrM0ViD4qiO7Y0tNIEqfRiV1YZG+2Z46AxaonoEqc0nKcHE9AwhLM6CtgNHoXQJbhccXA8BFogdCnr/1Qa43QoFB0rZvS6P/VsKvYFPb9DSZ1QMgycmEpvaPrUhLpebjZ9nsvXrg6BAeJyF828eTHRS+w7d7QwSPoRogYLKAlZlreKrzK/YWbTT+3yUOYpZfWdxWd/LiA+K78QSdqy0Y2ksS1vG8gPLG63B0KAh0hzpEyY84cLzXIwlpk1DYz1t0vkZJeSnl5C3v5SSww3LEBRhJKFPGPGpQURoSzDl7cW2awe2nbuwp6c3OsdFtSGY8qBEKoJ6UBGYSEVQIlWWOJRG+rloFBeWqkKCKnIIqsglqDKXoIpcAqpLcWsNWE1RWM3RtbeQeGxBsVRpgmiuB6ZOryUkykRojIXQaHPtLcZMcIQJrZ+rv08qbhfs/BjWPA1H96nP6YwQPwwSR0GPmltYsjo85wTZKh3s21jArrV53s6eABEJgTW1IXF+64RZeqSKr/+zi8KDam3YoIkJnHlZXwzGjutj1ZEkfAjRhGO2Y3yT9Q0rMld4OzyCWssxNm4sl/W7jLN7nu2XlR5dZWWU/u9zSv/3PxSHQx0OmBCPISEBfbz605CQgD4qqtXzFLSnSkclX2d9TXl1uU+4iLJE+W0FTLdb4VheBXme/hrpJVSWNuyNH5EQSHyvEKKD7YSVZ6LN2K4Gjb17Gx/loNW26ALl1uiptMSqYSQwkYqgBCoCE3EYGu84rHPacB1n6LTeqCM0Sg0UteFCDRtBYcYOnU/jpOByws6P1NBRVLPYnDFU/Q6txQ23t0SpISRxFPQYCYkjwdT2JQUUReFwZhm71uayf3OhtwlPZ9DSd2QMgyYmEter7bUhab/k8+N7+3DYXRgtes6+dgC9R3TMvCGdRcKHEHWUVZfx/aHvWZm5kg35G7zDTkGd/+LC1As5P/l8osxRfjmedecuit9/j7IvV6BYW7DUtV6PIS7OG070CQnq/fgEDIkJGOLi0FpO/om2FEUh87ej7P4pj/z9pVRbfZtDPPMQxEZriHAfJjh/B+4927Ht2dPo56gNDcU8eDCmoUMxDRmMeehQ9LGxbb5YKIpCZUk1RbkVHM0ppyingqM5FZQcrvLO5RVg1hMWYybEW3th8YYNS0jAKdV+325cTtixTA0dx2rmyDGHw/g7YcytYAxWn8/ZDLmb1Z8FO8DdSNiM6l8TSEaqP2MGg67pqeCbYq9ykLbhMLvX5VKU61sbMujMBPqPbXltSLXVyY/vpbFvo7ogXkLfMM67cRDBER0/709Ha7fwkZKSwsGDBxs8f8cdd/B///d/KIrCww8/zKuvvkpxcTFjx47l//7v/xg8eHC7FF6IplQ5qliTs4YVmStYl7sOR53/uAZFDmJqylQuTL3QbxNZua1WylaspPj997HtqF2sydi3D2FXXkVAck8cuXk48vNx5OXhyM/DmZeP4/DhJqfDrksXFqbWkiTUhBJPQEnqgWnAgC5Vc1Kfoigc3FHExuWZHDlU2xnTYNQRk2gkylhGWGkGlozNOHZtb3SRMq3FgqkmaJiHqD8NPTqms56z2kXZURuWEHWCLQkYbeRywG/vw9pnoDhLfc4cARPugjFz1dDRFIdNDSA5m2oDSUnDaxF6MyQMr6kZqWmuCUlscXONtzZkXR77Nx32qQ3pMyKGQRMTiO8d2uTvQEFmKd+8vouyozY0Wg2jL0ph5NSU1vffsZXB9g8gcw0EBKo1PKYw9ac5rPHHAUF+aZY6Ee0WPo4cOYKrzn+UO3fu5Pzzz+eHH35g8uTJPPnkkzz66KMsXbqUfv368cgjj7BmzRrS0tIIbuG8+BI+2p/NaWPn0Z3sKtqFSWciyhxFlCWKaHM0UeaoLjUDZmtUu6pZl7uOrzK/YnXOau+MmQC9Q3szNVUNHMkhyX47pv3AAUo++ICSTz/DXaZOL6kxGAieMoXwq67EPHJksxcrxenEeeSIGkq84SQXR36+Gk7y8nBXVjb5flBrAAInjCfozIkEnnlmh00JfTyKonBo1zE2fnHA2+at10Of0EKiD28lYOdPKCXHGrxPYzRiGjjQJ2gEpKT4fe2RU4rbDcWZkLcNcreqP4/sUWsG+k9Vb1H9Oufi5KyG396DtYtqA4MlEibcDaNvAWMb58ipOAK5W2rCyCb1vO2NTPEaFKeGkKSxMPh3ENayyfXsVif7Nqh9Q4pyawNxeJyFwRMT1dqQILU2xO1W2FZnQbjgCBPn3zyY+N6tbBYq3AubXlNDWnXjKwU3SauvCSUtCCqmMPWWMFxt5vKTDmt2mTdvHsuXLyc9PR2AhIQE5s2bx/333w+A3W4nNjaWJ598kt///vd+L7xomVJ7Kb8W/srWwq1sPbyVXUW7fGoC6gs1hhJtjibSHEm0OdobSqIt6s8osxpUAg2Bnf5XoNPtZGP+RlZmreS7g9955+MA6BHUwxs4+ob19VtZFYeD8u++o/i996nasMH7vKFHD8KuvIKwWbPQR/pnwiBFUXCXl+PIz6c6J5eyg4c5llNG8ZFqSir02KxuIgt/JaZwC4aasGXs14/AiWcSNHEi5hEj2m0FzubKnLOnmA3/S+fwQTU46RQHiTmr6XnoWwIcdf5T1Wkx9U7GdPoozMOGYRo6FGPv3s2uotrtKQqUZvsGjbxfwV7a/PvCU2uDSM/xoGvnIabOavj1HVi7GEpr5jkJjK4JHTerf9H7k9sNRelqrYinhuTwblDq1SymTITTroKBF4Pp+NcYRVEozCpn17pc0jcd9s7BotNr6T0imr6jYvn1u0PkppUA0HdUDJOu6d/yIbwuJ6StUENH5pra56P6qeXU6sFaArZSsJX43reVqo+b+f+8SRot/K3o5Asf1dXVJCQk8Mc//pE///nPHDhwgN69e7N161aGDx/u3W7mzJmEhYXxxhtv+L3wonEFlQVsPbxVDRuFW9lfvL/Bsu6RpkhOjzkdt+LmqPUoR6xHOGo92uLhlKBOEuUJIt5QUhNQIkwRGHVGjDojAboA733PY5PeRIC2bW3kbsXNtsJtrMxcyTcHv+GYrfav5xhzDFNSpzA1ZSpDoob4NRw58vMpXraMko8+wnVEnSQIrZagSZMIv/oqAs88029/ndsqHRzLq6Qot0L9maf+tFc1/v1oNW5ibQeISVtFxLE9aGq+b43FQuCYMd4wEtCzp1/K1xhXeTkHVmxmy89lFNnUmk6tq5rEvDUkH/qGAEcFhkAnluhqzJHVmCIcGMMcaHWooxvihqh/iSUMh4QREN0fOnDm1S6rvKBe0NgGVUcbbqczQtxQSByhfobR/dX37PtKvai56nToNYZC3/Og31T1pzncf+V12mHb27DuWTUkAQTGwBn3wKib1KG0HaW6EvJ/UwNJ+teQtbb2Nb0ZBk5XL/Cpk1vUV6Ta6mTfpsPsWpvL0Wzfmgl1Qbh+DBgf17L/dyqOwNalsHkJlKlru6DRQv9pajNU6qSW1VQpCjisvmGkfjhp7DWA2386/v5boUPCx7Jly7jmmms4dOgQCQkJrF+/njPOOIPc3FwSEhK82916660cPHiQVatWNbofu92O3W73KXxSUpKEjxZSFIXM0ky2FG5h2+FtbC3cSm5FboPtkkOSGREzguExwxkZO5Kk4KQG/0AURaHUXsoR6xFvGDlSpf6sG1COVB1p0WRSLRGgrQkm+jrBRGfyBpb6jwHW563ncNVh7z7CjeGcn3w+U1OnMiJ2hF/n41DcbirXraP4/Q+oWL3aO6+DLjqKsMsuI/zyyzHU+X1vLYfdxbH8So7lVVCUV8mxvEqO5VY0OvIDQKPVEBZjJiIhkIiEIHR6Dfs2HvYZMmgxuemhZBG1YzmmvDSf9xuSexJ0xpkETjyTwDFj0Aa2/a9PV1kZVZu3ULVxI9m/5pLGYErC1NVztW4HCXlr6XN0JeHhR7DEVBM4MBnDhMugz/kN/3L3/GfoU1gLxJ9WG0YShkNEL7/+pdblVB2DvJrPJLcmaJQ3suKrVg8xg2qDRsIIiBnYdG2GvRwyflCDyL5VvuFFo4PkCdDvQrVWJLKNC6s5bLDtLTV0eC6mQXFw5jwYcUPHho6mlByC7cvUZo2i9Nrng2Jh6OVw2tVqCD4ORVEoPFjO7rW57NtcSESchfNvasGCcIqiBqFNr8GuT2sDoSVS/YxG3dTiZqGuqEPCx5QpUwgICOCLL74A8IaPvLw84uNr50aYO3cu2dnZfPXVV43uZ+HChTz88MMNnpfw0TiH28Heor1sLdzKlsNb2Fa4zTtDpYdWo2VAxABGxIxgRKwaOPw1ksOjylHlDSRHrEc4WlUbTo5aj1JiL6HaVY3dZcfutGN326l2VWNz2hrUwrSUsVph0CEFrQLOYAtDe09g4qCpjOo7mQCDf3uSO48do+Tjjyn5YBmOnBzv85axYwm/+iqCzz23VetCuJxuSg5X+dRiFOVVUnbU2uRaGcERJiIT1ZARkRBIZGIgYbGWBstiK4rCkUPl7F2fz75Nh31qR2LjDSQF5BK551scWzeAs/Y1jcGAeeRIgiaeSeCZEzH2a75pylVSQtXmzVRt2kTlpk3Y9+ylJDiVzNTpFIf3V/fpdpBa+SOnGz4lKqYAQ2IyDL0MBs+C2EGN71hRavsreC66+b823uZtDIWEeoEkrGend7RrE5dT7bOQ/UttCGusEyUaiB6gnqsnbMQOgbb+zrtd6gVw30pI+0rtG1JXVL/aINJjzPFrBBw22PoGrHuuNigFx8OZ98KI68HQ+qnz252iqJ/5b++pc4xY6/Q9ih2q1oYMvRyCY4+7K7dbQaOh+doOh1U9zsbX1N9tj8RRai3HoEva/n12Ie0ePg4ePEivXr345JNPmDlzJkCbm12k5qN5dpedbYXb1GaUw1vZfnS7T0dKAKPOyLDoYWrYiBnBaTGnEWjwc3uqnyiKgtPtVENJnZs3qNR7rrqkGNMvOwlev5OQXw+gczQyMkSjQRcSgi4sDF14uPrTc6v7OLz2eX1YGJp6fSEURcG6dSvF771P+apV3nkktCEhhP3uEsKuvBJjr14tPtfyYzbSNx1m/5ZCinIqcLsb/6dmDgkgMiFQDRg1QSMiPpAAc+v7PTgdLrK2F7FnfT7Zu4u8Q0T1Bi2pQ8NJCS4iKG0tVevW+YQqAH1MDIFnnqmGkfHjURSFqk2bqNq0maqNG7Hv24dnh6UhKWSmXMSxCDVQaBUnA4zfMir0I4LDjDBkFgy5VL1QtiUYeNrvvYFkKxRsB6et4baWyNrmmsRR0HOsf5sR/KksD/Z/B/u/hQM/qFXg9UX09g0accPa3imzJY5lqjUiaSvh4E9Qt+nVHA59p0D/C6H3ub59JBxW2LJUDR0VBepzwQkw8Y8w/LqT52LqrIb936hBJO2r2v4TGi30PketDek/rW01N8UHYfPrsPXN2rlLdEY1kI++Rf2OTyHtHj4WLlzIv//9b7Kzs9HXdAxTFIWEhATuvfdeFixYAKj9QmJiYqTDaRv9WvgrC9YsIL8y3+f5kIAQb63GiNgRDIoYhKG9O491IEdREcVf/8DR73+mZHcmdp2F6oBQqgNCcIbFEqS3ElW0E/PhfSiNDMtsKW1goE9AcR4uwJ6+3/u6aehQwq+6ipBpU9GaW/bXm63SQcbWQvZtPEze/hKfWo0Ak06txUhUQ4YncJiD26dDaGWJnbQNBexZn+8zW2hwhIn+4+Lo3dOFdscGKtatpWrDRhRbnQu7Vktjc4Jb+47kQPxkDmvVEKbFyQDz94yM+o6Q089SA0fSuPZpGnE51b/S6waSw7sa72wXM0jtVJk8AXqOg9Ae/i9PSzjt6pThGd+poaNwt+/rpjBIOVOdpyJxhNrM1JnByVaqltPTPFO3OUxrgJQz1H4ibiesfx4qapo/Q3rAxHvV0KFv2aq6XVLVMbU55Lf3IWdj7fMBwTB4Jgy7CpLPaP732+1Wg+XG19TP0fOfQGhPGH0TDL8eAttvBdvO1K7hw+12k5qaytVXX80TTzzh89qTTz7J448/zpIlS+jbty+PPfYYq1evlqG2reRW3CzdtZTntz6PS3ERaYpkbPxYRsaOZETMCHqF9Top1xlR3ArWCgdVZXYqS6upKq323q84XEZ5zlGqSuzYlADcuuP/BxYcaSJlSAQ9U43EhDmgvBRnSQmukhJcxTU/S0pwFRfX3i8pwVVa2uiFFUBjNhM6/SLCrrwK85CWzU/jqW3Yt7GAg7uKcNdZ+jyhbxj9xsTSc3Ck/1cgddjUmSH1RrV/RIAFDIEN1sTwzF2w9+d80jcdptpWW3uU0DeMAePj6TUkFOfOX6lYu47KdWu9IczYtw+WIX2wBurZWZTEIavaHq7BxYDAnxg1spKQMReqnePaMLnTCXPa4fDO2uaa7A2+bfkeoT0hebwaRHpOUDtjtkdTjaKoE2R5ajey1oKjbv8ojRo0+pwHfc5Vm44643NrCZdT/Tz3rVRrRYr2N9wmNEmt6Th99skdOhpTlKGGkO3vq31FPEKTYNiVatNMVN/a560l8Ou7sOk/cCyj9vne58DoudBvyinfgbpdw8fXX3/NlClTSEtLo1+/fj6veSYZ+/e//+0zydiQIcfvwNOWwp+KjtmO8Zd1f2Fd7joApqZM5e/j/05QQDtWu/qR4lbI2VdMXnqJGi5K7VSVVVNZqi6L3VTTQ2P0WheBoUYCo4IIDA3AEmrEFKinILOMnD3FPktqG0w6eg6KJHVYJMlDorzj7xsvoxt3WRmukhKc3mBSikarIejss9G14PfO7VbI3VfMvo2HObC10OeCHpkYSL8xcfQdHev/WQ0rjkD6KvVikPEDOBqZA0SrV0NIgMU3lARYcGqDOVDSn735fcg+GoVnPRK93k2fXjYGDnETn2zCVWGHgl8p2bOFjdljyLSPA0CDm/7xmYyckkjYqHO75gWn4ojaj+Lgz3BoPeRvbzjc0hxRE0RqakfiT2v70FN7hRoy9n+r3jwTaHkExdaGjV5ng+UkXRX56P7afiLV5WrnyNOu8esCcF2S2w2HflZDyK7PfOcSSRyl9g05sledFMwTNI0hcPo1atNK3YByipPp1U9Smws2c/+a+ym0FmLUGXlgzANc2vfSTp9LoyVsFQ72/JzPrrW5lBY2P6W4Ue8kwFqCobyQAHspxuoyAqrLCE6IIHzUYKLOHkdo/+RmF19y2F1k7zlG1o6jZO0owlpWOzpEo4G43qGkDIsidVgUYbEWv3yGiqJwNLuCtI0F7N902GdESlC4kX5jYuk3Jo7IRD8GRUVR/2NLW6H+p5+zCZ+2HGPNJEaOSt+2+hYod0WRZp3MXuvZlLpqR+yE6Arob/6BY45kMuwTPAWhX79qRl8xmrAe/u283O7sFernduhntQkkZzPU6zeF3qxOROVppukxpul+Foqi1rZ4ajcO/eLb9KM1qPvoc556ix18cnaIFQ05rOq/xd/eV7//+qE2ZpAaOIZd2b79dLooCR8nGZfbxX92/IeXfnsJt+ImNTSVZyY9Q7/wfsd/cyfyVOfvXKMuzOSpiTCYdPQ+PZrgKHW9i4DyQtw7NuFa/z2k70Cr1NRYaLVYRo8m+ILzCT7v/DbPzKm4FQ4fLCNr+1Gythf5zEYIEBptJmVYFCnDoojvE4qulSuIlh6xkr6pgH0bD1NcUFuFbrTo6T0yhv5jYonvHea/hcOc1WrHP08nwPojIOJPU9vd+18I8afXXtic1WoIqa5S/wKrrqz5WVXn+XqvV1eiVFeRfySQvXmp7D/SB4e77l+yCn2HRzDq4n5ExHfNTsyt5nKocz8cXK8Gh0M/+452AHX4afwwtWak53h1+GXetprA8V1tB0uP8JTasJEysVteeLqdikLY8RHsXa7Wbo2+We0P0o2DpoSPk8hR61EeWPsAG/LVmTIv7n0xfxn7lzYtS95Rqm1O9m1sONFOVFIQQ85KpO+IKFzpeyj7+hvKv/kGR3Z27ZsNBgLHjyPkggsIOvdc9OH+71xXVmQla3sRWTuOkptWjNtV+ytutOjpOTiSlGGRJA+ObHIWQmt5Nfu3FLJvYwEFB2qrWXUGLSlDo+g3JpbkwZHoDH7qe1N1DNK/Uau193/nW7WrM0KvSerwx34XQmiif47ZCIfdRca2QvZvKcRkMTB8Sk8iE07xC6nbrS7lfqgmjBz8uXZGzqYYLGrI8DSntHVuDCFOIRI+ThI/5/3Mg2sfpMhWhFlv5i9j/8LMPjM7u1gNeKb4Ltybz+5fjpCxz47DqaZ7LS4StXn0rPyNoCNpuIuK1A6ddWiMRoLOmkjw+ecTNHlyi/pU+Eu1zUn27mNqrciOImyVtdXjWq2G+L6hpAxVa0UCQ41k/naEfRsPk737mLd/ikYDif3D6Tcmjt7Do9s0BLZRR9PVmo19X6l/fSt1OsEGRqsd1PpNhd5n+38qatG80pyaPiM1t8I96iRefc5VA0fP8V2zv4sQnUjCRxfndDt5+beXeW37aygo9Anrw6JJi+gV1vI5JE6E4nLhrqzEXV6Os7gE17EinEXHan8WFeE8pv6sPlZCnrYnuTHjKQ3r492HpeowCXlriS/YgKGR2U61gYEETZpE8AUXEHTWxC6xJLzbrXD4QClZO46Sub2I4nzfzppancanliS6ZzD9xsTSd3QsgaF+uNC4nGpHyLSa0QN1e8SDuhx4/wvVOQUSRpzaM3mebNyuU36kghAnSsJHF3a48jD3r72fLYe3AHBZv8u4f/T9mPTNj4pQFAXFZsNdUYGrogJ3RaUaICorap+rrMJdoT52V1bgqqxUt/M+V4mrshKl6vhTo1tNkeQmnEl+3HgcAeowaY3iIqZyPynsJybUjiEyEl1kBPqISPSREeg8PyMj0YWG+n+Zd7dLbWcNjPLLolilR6rI2l5E5vaj5KeX4HYrhESZ6Dcmjn5jYgmP80Ntg8uhdlDbs1xdW6LBvAlnqmGj3xQI999qu0II0dEkfHRRa3PW8pd1f6HYXoxFb2HhhIVMTZ3a5PZuq5WChQsp/2E17oqKJuemaCuNwYAuIsIbILQRkRwx9SLTlkB+mRnPMMzAED2Dzohn8KSeBIZ1QFVz1TF1ToGi/WrTRFG6Oua+KANcdrVz18gbYeQcCIk/7u5awl7loKqs2m8jY6g4os7+uPl1KK8zSZw5oqY55UJ1/H8LVtUUQoiTgYSPLsbhdvDCthdYsnMJAAMjBvL0pKdJDmn6L11XWRnZt9+BdcsW3xc0GrSBgWiDgtAGBaqzdAYG1Tyu81xQENrA2ud03vfU3AIDvUutV5ba2b0uj93r8qgorp3qvuegCAaflUjK0Ei0rRwhclxOuzoZkzdg1Akb9UceNEWrh4Ez1Al8kid0jV7m+b/Bhn+rveBdNZ9lYIw6IVH/aZA0RqrvhRCnpNZcv7vo1HqnjryKPO5bcx/bj2wH4OoBV/OnUX/yrtDaGOfRoxy6ZS72vXvRBgeTuOgZjP0HqIHBYj6hZdvdbgV7lQPbMQdlR8vZsz6PzF+PejtXmoIMDJwQz+CJCYRGn2A/DbdbXWjKGzAy1FqMo+nqqqZKMzU5IYkQ2Ue9RfWFyL7qiILgeEj7Ejb+Rx2dsOtT9RYzWF2gadgVHd850+VUh9tt+LdaJo+EETDudnXRqFN9IiYhhGgFqfloR98f+p6//fQ3yqrLCDYE848z/sF5yec1+57qnFwO3XwTjoOH0EVF0fM/r2EaMKDRbV1ON7ZKB7aKmlulerPW3LdX+D62VTrUFU8b+cbje4cy+KxE+oyIObHhoxVH4JeX1IWaijLqTS1dT0AwRPVRg0VUTbjwhIyWBIiCHer6CTs+rDOzYCgMn61O9NPewx+rjqmreW78D5TVLNKm1athY+xt6qRVXaE2RgghOoA0u3Qyh8vB4i2LeXvP2wAMjRrKU2c9RY/g5he3su/fz6GbbsZZWIghMZGe/32d/IogDu4sqg0XdYKEw+Zqdn/NCTDpMAUH0HNgBEMmJZ74rJxlebD+Bdi8xHf2SI0OIlLr1WLUBI6gGP9cnK3FddZUOFD7fO9zYcyt0Pd8/zZ1HN4FG16B7ctqV1m1RMGoG9Upp0MSmn+/EEKcgiR8dKLssmzuW3Mfu4p2AXDDoBu4Z8Q9x1111rp9O9lzb8VVWoqxbx8SX32NLT9XsO3r5ic70mjAaDFgCjJgCqz56bkfqMccFFDzvB5TYACmIAPGQH2rZ/lsUvFB+Ok52PY2uGqmG08YAeP/oM7EGZ7il5EpLeJ2Q8b3sPFVdWSJp4onrKdaEzL8uravq+F2qcNjN7yiruPhETcUxt6uruZ6siwhLoQQ7UDCRydZlbWKhesXUuGoINQYyqNnPMqkpEnHfV/l+vVk33kXSlUVptOGEfvs//Hdxzkc2qV2vBwwPo6IhCDMdQNGzU+jWe+/ab1boygD1i5SF1PyrCnSczycdZ86iqOzmxuOZaojTba+VTu8VW+CIZfBmFsgYXjL9mMtVoPVxldrV7bUaNWOrmNvU8+5s89VCCG6AAkfHczhcvDkpif5IO0DAIbHDOeps54iLjDuuO8tW/U1efPnozgcBE6YgOUvT/DV0v2UHrGiN2g55/qB9B0d296n0HKHd6uhY9cntR1Ge02GsxZAyhmdWrRGVVfBzo/V8FCwvfb5HqPVJplBMxufqfJImtqB9Lf3avuTmMNhxA1qLUpYUseUXwghThISPjrYsrRl/POXfwJwy9Bb+MPpf0CvPf5AopKPPiL/7w+B203wlCnYr7uf797ah8PuIijCyLTbhhHdM7i9i98yedtgzTPqqA6PfhfCxPmQNLrzytVSiqKubLrxVXVZbM8qpJYoGHmD2lcjOEHtKLvhFbX5xiNmEIz9PQy9Ql2eXgghRAMy1LaD7S7aDcCNg2/knhH3tOg9Ra+/TuHTzwAQetll5IyZw6b/7AEgsV8YU+YOwRzcBYZnZm+ENU/X9KEA0MCgi2Hin9Q+HScLjUadYyNpDEx5DLa8AZv/qw4FXrsI1j0LQXHqY/UN6rwc425TFxCTphUhhPAbCR9+kFOhDrPsHXb8oZ2KonBk8WKKXvsPACE338qvQeeSuUJdNn3o2T0447I+/usQ2haKonaqXPM0ZK5Rn9NoYejlcOYfIabxob8njaAYmHQfnHlvzZwhr6nnW56nDtUdcZ06Z0h4SmeXVAghTkkSPvwgp1wNH0nBzfcDUFwuChY+TMmHHwJgvvN+1hwbyLGMo2j1GiZf05+BEzpxmKaiwP5v1dCRvUF9TquH065WL9Sn2rLhOr3a52PQTHXV0mMHIHUSGE/xJeSFEKKTSfg4QQ63g4LKAoBm5/FwV1eTt+B+yr/6CrRalLsf5dt9EdirKrGEBjD190OJ6xXaUcWuVzi3WgOw5ml1enAAnRFGXA9n3NM9OlfGDFRvQggh2p2EjxNUUFmAS3Fh1BmJMkc1uo27spKcu+6mcv16MBgo+f0itv2mQ1GcxKaGMPX3QztmwbYGBXOpU5OvXQSFar8VDBa18+WEuyD4+KN1hBBCiNaS8HGCPE0uiUGJaDUN+2m4SkrI/v1tWH/7DXdgCIeueJIDO9UhqgMnxDPp6v4nNp15a1QdU6ckP7wTCnbCwZ+gRO1rgjFEHXo67g4IjOyY8gghhOiWJHycIE9n08aaXByHC8m+5Wbs6fupjurJnsl/pijThVar4cwr+jJkUqJ/lm+vz+1SF3OrGzQO7/Rd2t3DHK4GjjG3gjnM/2URQggh6pHwcYI8NR89gnzDR/XBgxy66WYcubmUpYxi58CbsR11YQoycOHcIST2D/dPAawl6lojh3fWho3CPbVrjtQXngKxQ9RpwWOHQK9JYOwic4kIIYToFiR8nCBv+KhT82FLS+PQzbfgPHqUgiEz2RtzAYrVTVRSEFNvG0pIpLn1B3K7oTizYW1GaXbj2xsCIXZQTdAYArFD1Q6VppNj4jYhhBCnLgkfJ8jb7FJT81G1dSvZv78NZ4WV/WPuIMcyGNzQd1QMZ18/EENAK1ZXdbvV2TZ3faJOa+6obHy70J4QO7gmZNTUaoSngrYT5woRQgghmiDh4wRll6s1Dz2Ce1CxZg05d9+DzR3A7jP+TLE+FjQw/pLeDL+gZ+v6d9gr4LPbYc/ntc/pTWrtRd1mk9jB0ldDCCHESUXCxwkotZdSXl0OQNiaHWT/5SHKzInsHHEnNo2FALOeC24ZTPLgVo4eKT4I71+jNqtoDXDeQuh7PkT0VifGEkIIIU5iciU7AZ4mlwl5wRx94q/kx4whbeBs3OgIj7Mw7fZhhMW2ciGyrJ9g2XVQVQSB0XDl29BzXDuUXgghhOgcEj5OgKez6bjMAPb1nkVOj3MASBkWxfk3DiLA3MqPd/MSWDEf3E510bar3oXQpmdNFUIIIU5GEj5OgCd8BFed5g0eoy9KYfRFqWi0rejf4XLAVw/AJnWxOQbPgpn/J8u3CyGEOCVJ+DgBnmYXTbXap6N/fx1jZvRq3U4qi+DDG9RVVdHAOX9Vl6uXJdyFEEKcoiR8nICc8hy0bgXFGQBAaGIrJw47vAveu1qd4jwgCC79D/Sf2g4lFUIIIboOCR8nIKc8h6hScOgCAQhMaMWolj3L4ZNb1bk7wlPg6vdlVVUhhBDdgoSPNnK6neRX5jO4WMEREASAOaQFK9MqCqx5Bn54RH2cehZc/gZYItqxtEIIIUTXIeGjjQoqC3ApLnqU6Kg2qGujmIMMzb+puhI+uwN2f6Y+HvN7mPIo6I7zPiGEEOIUIuGjjTydTXtXBOIw1NR8BDcTIkqy4f2r1bVZtAa4aBGMvKEjiiqEEEJ0KRI+2sgzzDahxMTBaLXDqTkooPGND/4MH1wLVUfVicOueAuSx3dUUYUQQoguRcJHG3nWdAktC4BodQ03g6mRReO2LIUv54Pboa7HctV7EJbUsYUVQgghuhAJH23kGWarL1fn4zAH6n0XjnM5YNWfYeOr6uNBl8AlL0FAYMcXVgghhOhCJHy0UU5FDpFl4NKqYcIUWmekS9UxdeKwzDXq43P+ChPny8RhQgghBBI+2iynPIeUYwrVns6mnv4ehXvgvaugOEudOGzWqzDgos4rqBBCCNHFSPhog1J7KWXVZcQVg8MzzDY4APaugE/mQnUFhCWrE4fFDurk0gohhBBdi4SPNsityAUgtcxEtWeCsdLt8P7tgAIpE+GKN2XiMCGEEKIR2s4uwMnIM8y2Z5mhdo6PvG8BBcbcCtd9KsFDCCGEaIKEjzbwTDAWc8zlDR8mbRmMvgWmPS0zlgohhBDNkPDRBp5htsFHqmo7nGrLoKdMHCaEEEIcj4SPNsgpV4fZal1uHAE1HU61pRDdv5NLJoQQQnR9Ej7aIKcih/hjCgAOYwgAZm05RPbpzGIJIYQQJwUJH63kdDvJr8gnrhjcGj1OrTq5mDk8BAzmTi6dEEII0fVJ+GilgsoCnIqTxBINDoM6u6kGF8ZYWa9FCCGEaAkJH63kGemSXGb0djY1acvRxEh/DyGEEKIlJHy0kmeOj7hiBUdAnZEu0tlUCCGEaBEJH62UU56Dxq0QdtReZ5htKURJ+BBCCCFaQsJHK+VU5BBVM8zWaQoDaiYYi+7XuQUTQgghThISPlopp7x2mK0rPAYAs9EFptDOLJYQQghx0pDw0Uo5FTnEFav3nYHqHB+m4IBOLJEQQghxcpHw0Qpl1WWU2kuJL1ZrPpwB6rwe5vDgziyWEEIIcVKR8NEKueW5ACSVqgvHVXsmGIuM7LQyCSGEECcbCR+t4JnjI6FEA0A1NeEjLrHTyiSEEEKcbCR8tIJnmG14UTUANqWm2SUxtTOLJYQQQpxUJHy0Qk65OsxW51LAYMCu1MxwGh3TySUTQgghTh4SPlohuzybuJrOpkp8Ip6PT0a7CCGEEC0n4aMVcipyiD+m3lciwgAwGqrR6eRjFEIIIVpKrpot5HQ7ya/I99Z8uANrOpualc4slhBCCHHSkfDRQoerDuNUnCQUqyNd3AY1dJiDpMlFCCGEaA0JHy3kWc22R6kOAKfGBYApLKjTyiSEEEKcjFodPnJzc7n22muJjIzEYrFw+umns2XLFu/rc+bMQaPR+NzGjRvn10J3Bs8w28hjTgAcBgsA5vCQziyWEEIIcdLRt2bj4uJizjjjDM4++2xWrlxJTEwMGRkZhIWF+Wx34YUXsmTJEu/jgICTv2nCs5qtzqWg0euo1qlTqptlpIsQQgjRKq0KH08++SRJSUk+wSIlJaXBdkajkbi4uBMuXFeSU57j7WxqiA7GqtQsKhdk6MxiCSGEECedVjW7fP7554waNYrLL7+cmJgYhg8fzmuvvdZgu9WrVxMTE0O/fv2YO3cuhYWFTe7TbrdTVlbmc+uKcsprh9kGhGqxukMBqfkQQgghWqtV4ePAgQO8/PLL9O3bl1WrVnHbbbdx99138+abb3q3mTp1Ku+88w7ff/89ixYtYtOmTZxzzjnY7fZG9/n4448TGhrqvSUlJZ3YGbWTnIramo+AQBs2t1rzYZaaDyGEEKJVNIqitHiiioCAAEaNGsX69eu9z919991s2rSJn3/+udH35Ofnk5yczPvvv8+sWbMavG63232CSVlZGUlJSZSWlhIS0jU6c5ZXlzPhvQnc/6GLkfsV4sZY+SzoGarcEVzx59FE9wzu7CIKIYQQnaqsrIzQ0NAWXb9bVfMRHx/PoEGDfJ4bOHAghw4davY9ycnJpKenN/q60WgkJCTE59bVeIbZJpSoH5fBUumt+ZA+H0IIIUTrtCp8nHHGGaSlpfk8t2/fPpKTk5t8T1FREdnZ2cTHx7ethF1AToU6zDa62A2AEmjCXdNX1xws4UMIIYRojVaFj3vvvZdffvmFxx57jP379/Puu+/y6quv8oc//AGAiooK5s+fz88//0xWVharV69mxowZREVF8bvf/a5dTqAjeFaz1bsUNHotTqM6sZjBqENv0HVy6YQQQoiTS6uG2o4ePZpPP/2UBx98kH/84x+kpqby3HPPMXv2bAB0Oh07duzgzTffpKSkhPj4eM4++2w++OADgoNP3n4RPsNsI8zYUAOH1HoIIYQQrdeq8AEwffp0pk+f3uhrZrOZVatWnXChupq6q9kGBLuxusMBMMm6LkIIIUSrydouLVC35iPAVF5njg+p+RBCCCFaS8LHcbjcLvIq8ogvVh8HmCpkjg8hhBDiBEj4OI7DVYdxKk7iPOEj2InV0AMAszS7CCGEEK0m4eM4PKvZxpbUdDgNcmE1qMOGTdLsIoQQQrSahI/j8Kxmq3eBRqfFYHFhIwqQmg8hhBCiLSR8HIfPMNswPRotWN3qsGHp8yGEEEK0noSP41DDh3o/ILAaAGu1EZBmFyGEEKItJHwcR3Z5NvHHaobZmitQFLBa1dek2UUIIYRoPQkfx5FTUafmI8iJI7AnLocaRmSeDyGEEKL1JHw0o7y6nBJ7SW2fj2AXttBhAOj0WgxGWddFCCGEaC0JH83IrcitGWarPg4IcmINHACotR4ajabzCieEEEKcpCR8NMOzmq3BBRqdBoPFhdXcCwCTjHQRQggh2kTCRzN8htkGow6z1ScAYA6WzqZCCCFEW0j4aIZPZ9NAdYiLVRsDyBwfQgghRFtJ+GhGTnlO7TDbICeYQrFVqzUeMsxWCCGEaBsJH82oP8yWqP5YKxyATDAmhBBCtJWEjya43C5yK3J9htkS3Q9bTfiQZhchhBCibSR8NKGwqhCX0+EzzLZuzYd0OBVCCCHaRsJHEzyr2Rpc6igXg8UF0QOwlqvru0jNhxBCCNE2Ej6akF2eXdvkEuREowWi+0nNhxBCCHGCJHw0wWc12yAn6M24LD1w2FyATDImhBBCtJWEjyb4DLMNdkJUH6xVTgA0Wg1Gs74ziyeEEEKctCR8NKHBMNvoAVjLa4bZBhnQaGVdFyGEEKItJHw0wXdqdVfNSBfpbCqEEEKcKAkfjaiorqDEesx3mG10P2/Nh1kmGBNCCCHaTMJHI3IrcusMs1XUYbZR/etMMCYjXYQQQoi2kvDRCJ8mlyAnGr0eInrJHB9CCCGEH0j4aIRvZ1MXRPQCfUCddV2k5kMIIYRoKwkfjag7wZg6zLYfgNR8CCGEEH4g4aMRORU5xB9T7xuCnBDdH6C2z4fUfAghhBBtJuGjEbnluXVqPtQ1XYDaqdWl5kMIIYRoMwkf9bjcLnLLsuutZlvT7FIzz4dJhtoKIYQQbSbho57CqkLCSpz1htn2xe1yY69Up1eXobZCCCFE20n4qEcd6VJnmG14TwgIxFYTPNCAKVDWdRFCCCHaSsJHPb6r2bq8nU09I12MFj1anXxsQgghRFvJVbSeBsNsPeFDZjcVQggh/ELCRz0NhtnWn+NDOpsKIYQQJ0TCRz0Nh9nWm+NDaj6EEEKIEyLho54mh9mWyzBbIYQQwh8kfNRR6ahEc+QYBhegVTBER4IlApAJxoQQQgh/kfBRR055DvHHappcgpxoYvt7X7OWS7OLEEII4Q8SPuposJptTZMLgK1COpwKIYQQ/iDhow51jo+6w2wHeF+TobZCCCGEf0j4qCO7PLveara1NR/S4VQIIYTwDwkfddSdWj0g2AVRap8Pxa14p1eXmg8hhBDixEj4qCOvNKd2mG2ECYLjALBbnShuNZTIaBchhBDixEj4qOFyu7Dl59QOs03pCxoNUNvkEmDSoTPIRyaEEEKcCLmS1jhiPULUUbVTqSHQiSamYWdTU7A0uQghhBAnSsJHDXVBOfW+Mdjl09nUVi4TjAkhhBD+IuGjhs8w2yCnt7MpgNUzx4eEDyGEEOKESfioUXeCMUOw07ugHNTObirNLkIIIcSJk/BRw2dq9TAthPX0viY1H0IIIYT/SPio4bOabVJP0Oq8r8m6LkIIIYT/SPioUZl7qHaYba8BPq/Jui5CCCGE/0j4AKocVZjz1Q4f+kAXmtiBPq97h9pKs4sQQghxwiR84DvM1hTs9FnNFuo0u0iHUyGEEOKESfig3pouQb4jXRRFkQ6nQgghhB9J+MAzx4d63xDihoje3tccNhduZ826LlLzIYQQQpwwCR/UG2YbHw362pDh6e+hN2gxGHWNvl8IIYQQLSfhg3rDbHv18XnN0+RikpEuQgghhF9I+ADKc7IwuEDRKhh6DfZ5zSZzfAghhBB+1e3Dh1txQ04+ALogF5q4+sNsZY4PIYQQwp+6ffgorCokusgJgDmomWG2UvMhhBBC+EW3Dx91V7M1NhY+ZIIxIYQQwq8kfNRdzTYmGIxBPq/byqXZRQghhPCnVoeP3Nxcrr32WiIjI7FYLJx++uls2bLF+7qiKCxcuJCEhATMZjOTJ09m165dfi20P/kMs03q0eB1T82HNLsIIYQQ/tGq8FFcXMwZZ5yBwWBg5cqV7N69m0WLFhEWFubd5qmnnmLx4sW8+OKLbNq0ibi4OM4//3zKy8v9XXa/yCk9VDvMts+ABq9ba2o+pNlFCCGE8A99azZ+8sknSUpKYsmSJd7nUlJSvPcVReG5557jL3/5C7NmzQLgjTfeIDY2lnfffZff//73/im1H5XlHKgdZtv39Aave2s+ZHZTIYQQwi9aVfPx+eefM2rUKC6//HJiYmIYPnw4r732mvf1zMxMCgoKuOCCC7zPGY1GJk2axPr16xvdp91up6yszOfWkZzZueqdoIar2ULdZhep+RBCCCH8oVXh48CBA7z88sv07duXVatWcdttt3H33Xfz5ptvAlBQUABAbGysz/tiY2O9r9X3+OOPExoa6r0lJSW15TzapMpRRVCBGnZM9RaUA3BWu3DaXYB0OBVCCCH8pVXhw+12M2LECB577DGGDx/O73//e+bOncvLL7/ss51Go/F5rChKg+c8HnzwQUpLS7237OzsVp5C29VdzTYw3ACWCJ/XPbUeWp2GAHOrWqiEEEII0YRWhY/4+HgGDRrk89zAgQM5dOgQAHFxcQANajkKCwsb1IZ4GI1GQkJCfG4dxWc124SYBq/X7WzaVHgSQgghROu0KnycccYZpKWl+Ty3b98+kpOTAUhNTSUuLo5vvvnG+3p1dTU//vgjEyZM8ENx/avuBGMBKakNXrfJMFshhBDC71rVlnDvvfcyYcIEHnvsMa644go2btzIq6++yquvvgqozS3z5s3jscceo2/fvvTt25fHHnsMi8XCNddc0y4ncCJyyrI5rabmI6DfkAav1450kf4eQgghhL+0KnyMHj2aTz/9lAcffJB//OMfpKam8txzzzF79mzvNgsWLMBqtXLHHXdQXFzM2LFj+frrrwkODvZ74U9UaXYGAS5waxUM/Uc0eN3T7CIjXYQQQgj/aXUvyunTpzN9+vQmX9doNCxcuJCFCxeeSLk6RPXBgwAoQW40cYMbvO5d10Xm+BBCCCH8ptuu7eJW3OhzjwAQEKJAcHyDbWxS8yGEEEL4XbcNH0eqjhB9zAlAcEwINDKaRSYYE0IIIfyv24aP7PJs7zBbY4/ERrexltc0u8hoFyGEEMJvum34qDvBWEDvvo1uY62oaXaR0S5CCCGE33Tf8FGaTaxnmO2A4Y1uI/N8CCGEEP7XbcNH8aF9tcNsB4xp8LrL5cZepfYJkZoPIYQQwn+6bfiwH9gPgCtYQRPdu8HrnloPNGAMlPAhhBBC+Eu3DR/kFQKgjwgAra7By97OpoEGtFpZ10UIIYTwl24ZPqocVYQcsQIQFBfR6DbezqYyzFYIIYTwq24ZPnIrcr3DbIOSGy4oB2Ar96zrIp1NhRBCCH/qluHDZzXbPgMb3UZqPoQQQoj20T3DR+mh2mG2g0c3uo2s6yKEEEK0j24ZPooObK8dZjtwbKPbeJtdpOZDCCGE8KtuGT6sGXsBcIRo0ZgCG99GZjcVQggh2kW3DB/uXHWYrTba3OQ21nKZ3VQIIYRoD90ufLgVN8YjVQBYEmKb3K62z4fUfAghhBD+1O3Cx5GqI8TUjHQJ7dWvye1sMtpFCCGEaBfdLnzUXc3WPOD0RrdR3IosKieEEEK0k+4XPo7urR1mO2Rco9vYqhwoaj7BJDUfQgghhF91u/BxZN8mAlzg0oIhuU+j23g6mwaY9ej03e4jEkIIIdpVt7uyVh7YB0B1mA6NXt/oNtLfQwghhGg/3S58uPKOqHeig5rcxjvMVka6CCGEEH7X7cKHoWY1W1OPhCa38Q6zlc6mQgghhN91q/BhdVoJLXYDENZ3cNPblcvspkIIIUR76VbhI7c4wzvMNmTw+Ca3s8owWyGEEKLddKvwkZOzsXaY7YChTW7nneNDaj6EEEIIv+tW4eNw+ubaYbbx8U1u5212kdEuQgghhN91q/BRlrUfAGu4oclhtlB3XRdpdhFCCCH8rVuFD0d+EQDu2JBmt7NJzYcQQgjRbpr+8/8UoygK2qM2APQ9Eqmqdja5nafmA6O2ye2EEEKIk5nZoEOj0XTKsbtN+Ki0VhFUog6z/fhICO/+fVWj2wUocI/LDMCEZ3/E2TnfixBCCNGudv9jCpaAzokB3abZpaTskHeky15Tvya3s7jVtFGNIsFDCCGEaAfdpuYjIbof5WUBQDVL/nYZhh5JjW5XmFnGl8/+SkSEid0LJ3VsIYUQQogOYjboOu3Y3SZ8uIuL0ZnNuNxuQnomNTnaRbG5ALAEB3RadZQQQghxKus2V1d9VBT9NvyCq7y8ZcNsZXZTIYQQol10mz4fHrrg4GZfl3VdhBBCiPbV7cLH8dSu6yLhQwghhGgPEj7qsZV71nWRZhchhBCiPUj4qMdaoTa7mKTmQwghhGgXEj7qqV3RVmo+hBBCiPYg4aMea7n0+RBCCCHak4SPejzNLjLaRQghhGgfEj7qcFS7cFar67+YZZ4PIYQQol1I+KjDM8eHVq/BYOq8aWeFEEKIU5mEjzq8nU0DDZ22zLAQQghxqpPwUYens6lJRroIIYQQ7UbCRx3ezqYy0kUIIYRoNxI+6rDK7KZCCCFEu5PwUYdNaj6EEEKIdifho47amg8JH0IIIUR7kfBRh2dFW5PM8SGEEEK0GwkfdXjm+ZCaDyGEEKL96Du7AF2Jp+ZDZjcVQpwol8uFw+Ho7GII4VcGgwGd7sQn4ZTwUUftirZS8yGEaBtFUSgoKKCkpKSziyJEuwgLCyMuLu6EJuOU8FHD5XRTbXUCUvMhhGg7T/CIiYnBYrHIbMnilKEoClVVVRQWFgIQHx/f5n1J+KjhqfXQaDUYLfKxCCFaz+VyeYNHZGRkZxdHCL8zm80AFBYWEhMT0+YmGOlwWsMzu6kpUI9GK3+pCCFaz9PHw2KxdHJJhGg/nt/vE+nTJOGjhsxuKoTwF2lqEacyf/x+S/ioIeu6CCGEEB1DwkcN74q2Ej6EEN1QUVERMTExZGVlNfr60qVLCQsL69AyiY714osvcvHFF3fIsSR81LDJHB9CiG7s8ccfZ8aMGaSkpACQlZXlU71+5ZVXsm/fvk4qXeulpKSwevXqFm+/evVq77l3F/XPee7cuWzatIl169a1+7ElfNTwzG5qkjk+hBDdjNVq5fXXX+eWW25pchuz2UxMTEwHlqp76syJ6YxGI9dccw0vvPBCux9LwkcNmd1UCNFdrVy5Er1ez/jx45vcpn6zy8KFCzn99NP573//S8+ePQkKCuL222/H5XLx1FNPERcXR0xMDI8++qjPfhYvXszQoUMJDAwkKSmJO+64g4qKCp9tXnvtNZKSkrBYLPzud79j8eLFDZp8vvjiC0aOHInJZKJXr148/PDDOJ3OE/4sPDIyMpg5cyaxsbEEBQUxevRovv32W+/rDz74IOPGjWvwvmHDhvHQQw95Hy9ZsoSBAwdiMpkYMGAAL730kvc1T+3SsmXLmDx5MiaTibfffpuioiKuvvpqevTogcViYejQobz33ns+xykvL2f27NkEBgYSHx/Ps88+y+TJk5k3b553m+rqahYsWEBiYiKBgYGMHTv2uLVBF198MZ999hlWq7WVn1jryIQWNWRdFyGEvymKgtXh6pRjmw26Fo9KWLNmDaNGjWr1MTIyMli5ciVfffUVGRkZXHbZZWRmZtKvXz9+/PFH1q9fz0033cS5557rvVBrtVqef/55UlJSyMzM5I477mDBggXei/JPP/3EbbfdxpNPPsnFF1/Mt99+y9/+9jef465atYprr72W559/nokTJ5KRkcGtt94K4HPhPxEVFRVMmzaNRx55BJPJxBtvvMGMGTNIS0ujZ8+ezJ49myeeeIKMjAx69+4NwK5du9ixYwcfffQRoIaohx56iBdffJHhw4ezbds25s6dS2BgIDfccIP3WPfffz+LFi1iyZIlGI1GbDYbI0eO5P777yckJIQvv/yS6667jl69ejF27FgA/vjHP/LTTz/x+eefExsby9///ne2bt3K6aef7t3vjTfeSFZWFu+//z4JCQl8+umnXHjhhezYsYO+ffs2et6jRo3C4XCwceNGJk2a5JfPsjEaRVGUdtt7G5SVlREaGkppaSkhISEddtx3F/5CcUEVM+edTo8BER12XCHEqcNms5GZmUlqaiomk4mqaieD/r6qU8qy+x9TsAS07O/LSy65hMjISF5//fUmt1m6dCnz5s3zThu/cOFCnn76aQoKCggODgbgwgsvJC0tjYyMDLRatWJ9wIABzJkzhwceeKDR/X744YfcfvvtHD16FICrrrqKiooKli9f7t3m2muvZfny5d5jn3XWWUydOpUHH3zQu83bb7/NggULyMvLa9E5t8XgwYO5/fbbufPOOwE47bTTuOyyy7zh6M9//jPffvstGzduBKBnz548+eSTXH311d59PPLII6xYsYL169eTlZVFamoqzz33HPfcc0+zx77ooosYOHAgzzzzDOXl5URGRvLuu+9y2WWXAVBaWkpCQgJz587lueeeIyMjg759+5KTk0NCQoJ3P+eddx5jxozhsccea/JYERERPPvssz4Bqa76v+cerbl+t6rZZeHChWg0Gp9bXFyc9/U5c+Y0eL2xaqmuSOb5EEJ0V1ar1eci0lIpKSne4AEQGxvLoEGDvMHD85xnOm6AH374gfPPP5/ExESCg4O5/vrrKSoqorKyEoC0tDTGjBnjc5z6j7ds2cI//vEPgoKCvLe5c+eSn59PVVVVq8+jMZWVlSxYsIBBgwYRFhZGUFAQe/fu5dChQ95tZs+ezTvvvAOotVzvvfces2fPBuDIkSNkZ2dz8803+5TzkUceISMjw+dY9WudXC4Xjz76KMOGDSMyMpKgoCC+/vpr77EPHDiAw+Hw+VxCQ0Pp37+/9/HWrVtRFIV+/fr5HP/HH39scPz6zGaz3z7HprS62WXw4ME+7V71p1a98MILWbJkifdxQEDXv5i73Qq2KhlqK4TwL7NBx+5/TOm0Y7dUVFQUxcXFrT6GweD7/6VGo2n0ObfbDcDBgweZNm0at912G//85z+JiIhg3bp13Hzzzd6OloqiNGguql9B73a7efjhh5k1a1aDMrUlRDXmvvvuY9WqVTzzzDP06dMHs9nMZZddRnV1tXeba665hgceeICtW7ditVrJzs7mqquu8pYR1KYXT1OJR/3rZmBgoM/jRYsW8eyzz/Lcc895+8fMmzfPe2zP59Hc5+R2u9HpdGzZsqXB8YKCgpo992PHjhEdHd3sNieq1eFDr9f71HbUZzQam329K7JXOqDmO5PwIYTwF41G0+Kmj840fPhw3n777XY/zubNm3E6nSxatMhbO7Js2TKfbQYMGOBttqj7vrpGjBhBWloaffr0abeyrl27ljlz5vC73/0OUPuA1J8DpUePHpx11lm88847WK1WzjvvPGJjYwG1xicxMZEDBw54a0Nac+yZM2dy7bXXAmqQSE9PZ+DAgQD07t0bg8HAxo0bSUpKAtQmj/T0dG8/jeHDh+NyuSgsLGTixIktPnZGRgY2m43hw4e3qsyt1ep/Fenp6SQkJGA0Ghk7diyPPfYYvXr18r6+evVqYmJiCAsLY9KkSTz66KPNDs+y2+3Y7Xbv47KystYW6YR5mlyMFj06nQwAEkJ0L1OmTOHBBx+kuLiY8PDwdjtO7969cTqdvPDCC8yYMYOffvqJV155xWebu+66i7POOovFixczY8YMvv/+e1auXOnzV/7f//53pk+fTlJSEpdffjlarZbt27ezY8cOHnnkEb+UtU+fPnzyySfMmDEDjUbD3/72N29tRl2zZ89m4cKFVFdX8+yzz/q8tnDhQu6++25CQkKYOnUqdrudzZs3U1xczB//+Mdmj/3xxx+zfv16wsPDWbx4MQUFBd7wERwczA033MB9991HREQEMTExPPTQQ2i1Wu/n1K9fP2bPns3111/PokWLGD58OEePHuX7779n6NChTJs2rdFjr127ll69enk70baXVl1px44dy5tvvsmqVat47bXXKCgoYMKECRQVFQEwdepU3nnnHb7//nsWLVrEpk2bOOecc3zCRX2PP/44oaGh3psnxXUk79Tq0t9DCNENDR06lFGjRjWohfC3008/ncWLF/Pkk08yZMgQ3nnnHR5//HGfbc444wxeeeUVFi9ezGmnncZXX33Fvffe69OcMmXKFJYvX84333zD6NGjGTduHIsXLyY5ObnJY0+ePJk5c+a0uKzPPvss4eHhTJgwgRkzZjBlyhRGjBjRYLvLL7+coqIiqqqquOSSS3xeu+WWW/jPf/7D0qVLGTp0KJMmTWLp0qWkpqY2e+y//e1vjBgxgilTpjB58mTi4uIa7Hvx4sWMHz+e6dOnc95553HGGWd4h/R6LFmyhOuvv54//elP9O/fn4svvpgNGzY0e5197733mDt37vE/oBOlnICKigolNjZWWbRoUaOv5+XlKQaDQfn444+b3IfNZlNKS0u9t+zsbAVQSktLT6RorZK++bDy4u+/Uz5+anOHHVMIceqxWq3K7t27FavV2tlFabUvv/xSGThwoOJyuTq7KA3ccsstyplnnnlC+0hOTlaWLFninwJ1QRUVFUpoaKjyn//8p8372LFjhxITE6OUlJQ0u11Tv+elpaUtvn6fUGNkYGAgQ4cOJT09vdHX4+PjSU5ObvJ1UPuIGI3GEynGCbPV1HxIfw8hRHc1bdo00tPTyc3N7ZQa6LqeeeYZzj//fAIDA1m5ciVvvPGGz+RcrbV3717vyJpTxbZt29i7dy9jxoyhtLSUf/zjHwDMnDmzzfvMy8vjzTffJDQ01F/FbNIJhQ+73c6ePXua7MxSVFREdnY28fHxJ3KYdued3VSaXYQQ3djx5proKBs3buSpp56ivLycXr168fzzzzc79fvxDBgwgB07dvixhF3DM888Q1paGgEBAYwcOZK1a9cSFRXV5v1dcMEFfixd81oVPubPn8+MGTPo2bMnhYWFPPLII5SVlXHDDTdQUVHBwoULufTSS4mPjycrK4s///nPREVFeXsLd1Wyoq0QQnQd7d335FQwfPhwtmzZ0tnFaLNWhY+cnByuvvpqjh49SnR0NOPGjeOXX34hOTkZq9XKjh07ePPNNykpKSE+Pp6zzz6bDz74wGcSmq7I2+FUwocQQgjR7loVPt5///0mXzObzaxa1TnTCJ8omd1UCCGE6DgyqQW1HU6l5kMIIYRofxI+kJoPIYQQoiN1+/ChKAq2CulwKoQQQnSUbh8+7FVO3G51YRdzsIQPIYQQor11+/DhqfUwGHXoW7EKpBBCnEqKioqIiYlpsHhae8vKykKj0fDrr782u93kyZOZN29eh5TpVLV8+XKGDx/e6Bo1Ha3bhw9ruWddF6n1EEJ0X48//jgzZswgJSUFqA0FHW316tVoNBpKSkp8nv/kk0/45z//6X08efJkli5d2uL9dtb5dKb65zx9+nQ0Gg3vvvtuJ5ZKJeHD299DOpsKIbonq9XK66+/fkKziLa3iIiILj9nVEs4HI5OPf6NN97ICy+80KllAAkf3mYXqfkQQnRXK1euRK/XM378+Ca3Wbp0KWFhYSxfvpz+/ftjsVi47LLLqKys5I033iAlJYXw8HDuuusuXC6X930ajYbPPvvMZ19hYWGN1lpkZWVx9tlnAxAeHo5Go/GuROvvZpeioiKuvvpqevTogcViYejQobz33nve1//973+TmJjYoIni4osv5oYbbvA+/uKLLxg5ciQmk4levXrx8MMP43Q6va9rNBpeeeUVZs6cSWBgII888ggul4ubb76Z1NRUzGYz/fv351//+pfPcZxOJ3fffTdhYWFERkZy//33c8MNN/isbqsoCk899RS9evXCbDZz2mmn8dFHHzV73hdffDEbN27kwIEDbfnY/OaE1nY5FcjspkKIdqMo4KjqnGMbLNDCZoY1a9YwatSo425XVVXF888/z/vvv095eTmzZs1i1qxZhIWFsWLFCg4cOMCll17KmWeeyZVXXtnqIiclJfHxxx9z6aWXkpaWRkhICGazudX7aQmbzcbIkSO5//77CQkJ4csvv+S6666jV69ejB07lssvv5y7776bH374gXPPPReA4uJiVq1axRdffAHAqlWruPbaa3n++eeZOHEiGRkZ3HrrrQA89NBD3mM99NBDPP744zz77LPodDrcbjc9evRg2bJlREVFsX79em699Vbi4+O54oorAHjyySd55513WLJkCQMHDuRf//oXn332mTecAfz1r3/lk08+4eWXX6Zv376sWbOGa6+9lujoaCZNmtToeScnJxMTE8PatWvp1atXu3y2LSHhwzPHhzS7CCH8zVEFjyV0zrH/nAcBgS3aNCsri4QE33KmpKSgKIrPcw6Hg5dffpnevXsDcNlll/HWW29x+PBhgoKCGDRoEGeffTY//PBDm8KHTqcjIiICgJiYGMLCwprcdvXq1a3ad/3zSUxMZP78+d7Hd911F1999RUffvghY8eOJSIiggsvvJB3333XGz4+/PBDIiIivI8fffRRHnjgAW9NSK9evfjnP//JggULfMLHNddcw0033eRTnocffth7PzU1lfXr17Ns2TJv+HjhhRd48MEHvWujvfjii6xYscL7nsrKShYvXsz333/vrbHq1asX69at49///jeTJk1q9Dv0nHtHdyyuT8JHTc2HSZpdhBDdlNVqxWQyHXc7i8XiDR4AsbGxpKSkEBQU5PNcYWFhu5TTn1wuF0888QQffPABubm52O127HY7gYG1gW327NnceuutvPTSSxiNRt555x2uuuoqdDp1ZOSWLVvYtGkTjz76qM9+bTYbVVVVWCwWgEZrlV555RX+85//cPDgQaxWK9XV1Zx++ukAlJaWcvjwYcaMGePdXqfTMXLkSG8z0O7du7HZbJx//vk++62urmb48OHNnrvZbKaqqpNq5Gp0+/Bh89Z8SPgQQviZwaLWQHTWsVsoKiqK4uLi4+/S4Pv/pEajafS5uv0kNBpNozUonW3RokU8++yzPPfccwwdOpTAwEDmzZtHdXW1d5sZM2bgdrv58ssvGT16NGvXrmXx4sXe191uNw8//DCzZs1qsP+6Ya5uoAF11d57772XRYsWMX78eIKDg3n66afZsGGDz3b1R+fU/Rw9n/GXX35JYmKiz3ZGo7HZcz927BjR0dHNbtPeun348Ix2kWYXIYTfaTQtbvroTMOHD+ftt99ul31HR0eTn5/vfZyent7sX90BAer/xXU7rbaHtWvXMnPmTK699lpAvZinp6czcOBA7zZms5lZs2bxzjvvsH//fvr168fIkSO9r48YMYK0tDT69OnT6mNPmDCBO+64w/tcRkaG935oaCixsbFs3LiRiRMnAurnsW3bNm/tyKBBgzAajRw6dKjJ/h2NsdlsZGRkHLd2pL1J+CiXZhchRPc2ZcoUHnzwQYqLiwkPD/frvs855xxefPFFxo0bh9vt5v77729QW1JXcnIyGo2G5cuXM23aNMxms0+zjr/06dOHjz/+mPXr1xMeHs7ixYspKCjwCR+gNr3MmDGDXbt2eYOKx9///nemT59OUlISl19+OVqtlu3bt7Njxw4eeeSRZo/95ptvsmrVKlJTU3nrrbfYtGkTqamp3m3uuusuHn/8cfr06cOAAQN44YUXKC4u9taGBAcHM3/+fO69917cbjdnnnkmZWVlrF+/nqCgIJ8ROXX98ssvGI3GZkc2dYRuPdRWURSp+RBCdHtDhw5l1KhRLFu2zO/7XrRoEUlJSZx11llcc801zJ8/39sXojGJiYk8/PDDPPDAA8TGxnLnnXe26Dhz5sxh8uTJLS7X3/72N0aMGMGUKVOYPHkycXFxPsNYPc455xwiIiJIS0vjmmuu8XltypQpLF++nG+++YbRo0czbtw4Fi9eTHJycrPHvu2225g1axZXXnklY8eOpaioyKcWBOD+++/n6quv5vrrr2f8+PEEBQUxZcoUn+acf/7zn/z973/n8ccfZ+DAgUyZMoUvvvjCJ8TU99577zF79uxmv4OOoFEa6wrbicrKyggNDaW0tJSQkJB2PVa1zclr89YAMPe5swgwdfuKICHECbDZbGRmZpKamtqiDpxdyYoVK5g/fz47d+5Eqz35/i6dPHkykydPZuHChZ1dlHbhdrsZOHAgV1xxhc9Mr61x5MgRBgwYwObNm5sNKMfT1O95a67f3fpq65lgTKfXYjDKui5CiO5r2rRppKenk5ubS1JSUmcXp1XKy8vJyMhg+fLlnV0Uvzl48CBff/01kyZNwm638+KLL5KZmdmg9qU1MjMzeemll04oePhLtw4f3jk+gg3dbs5/IYSo75577unsIrRJcHAw2dnZnV0Mv9JqtSxdupT58+ejKApDhgzh22+/bdAnpTXGjBnjM3y3M3Xv8OGZ40OG2QohhOhCkpKS+Omnnzq7GO3m5GvY86Pamg/pbCqEEEJ0lG4dPryLyknNhxBCCNFhunX4qF1UTmo+hBBCiI7SzcOHWvMhE4wJIYQQHadbhw9buafmQ8KHEEII0VG6dfjwzm4qHU6FEEKIDtO9w4fUfAghBABFRUXExMSQlZXVocfNyspCo9Hw66+/Nrvd5MmTmTdvXoeUqbOlpKTw3HPPtdv+58+fz913391u+2+J7h0+PH0+JHwIIbq5xx9/nBkzZpCSkgLUhoKOtnr1ajQaDSUlJT7Pf/LJJz7Tik+ePJmlS5e2eL+ddT5dwdKlS33WvVmwYAFLliwhMzOz08rUbcOHy+HGYVOXbJZmFyFEd2a1Wnn99de55ZZbOrsoTYqIiCA4OLizi3FKiImJ4YILLuCVV17ptDJ02/DhGWar0Wowmrv1RK9CiG5u5cqV6PX6ZpdZX7p0KWFhYSxfvpz+/ftjsVi47LLLqKys5I033iAlJYXw8HDuuusuXC6X930ajYbPPvvMZ19hYWGN1lpkZWVx9tlnAxAeHo5Go2HOnDlA+zS7/PTTT0yaNAmLxUJ4eDhTpkyhuLgYALvdzt13301MTAwmk4kzzzyTTZs2ed/rqaFZtWoVw4cPx2w2c84551BYWMjKlSsZOHAgISEhXH311VRVVXnfN3nyZO68807uvPNOwsLCiIyM5K9//SvNrfFaWlrKrbfeSkxMDCEhIZxzzjn89ttvgLpYXFxcHI899ph3+w0bNhAQEMDXX3/d5D4vvvhi3nvvvTZ/dieq2151PbObmoIMaLTdsypOCNG+FEXB6rR2yrHNenOLmxnWrFnDqFGjjrtdVVUVzz//PO+//z7l5eXMmjWLWbNmERYWxooVKzhw4ACXXnopZ555JldeeWWry5yUlMTHH3/MpZdeSlpaGiEhIZjN5lbvpyV+/fVXzj33XG666Saef/559Ho9P/zwgzc4LViwgI8//pg33niD5ORknnrqKaZMmcL+/fuJiIjw7mfhwoW8+OKLWCwWrrjiCq644gqMRiPvvvsuFRUV/O53v+OFF17g/vvv977njTfe4Oabb2bDhg1s3ryZW2+9leTkZObOndugnIqicNFFFxEREcGKFSsIDQ3l3//+N+eeey779u0jOjqa//73v1xyySVccMEFDBgwgGuvvZY77riDCy64oMnzHzNmDNnZ2Rw8eJDk5GQ/frIt033DR4V0NhVCtC+r08rYd8d2yrE3XLMBi8HSom2zsrJISEjweS4lJaXBX+MOh4OXX36Z3r17A3DZZZfx1ltvcfjwYYKCghg0aBBnn302P/zwQ5vCh06n817YY2JiCAsLa3Lb1atXt2rf9c/nqaeeYtSoUbz00kve5wYPHgxAZWUlL7/8MkuXLmXq1KkAvPbaa3zzzTe8/vrr3Hfffd73PPLII5xxxhkA3HzzzTz44INkZGTQq1cvQP2MfvjhB5/wkZSUxLPPPotGo6F///7s2LGDZ599ttHw8cMPP7Bjxw4KCwsxGo0APPPMM3z22Wd89NFH3HrrrUybNo25c+cye/ZsRo8ejclk4oknnvDuY86cOd4aJI/ExERA/e47I3x032aXOivaCiFEd2a1WjGZTMfdzmKxeIMHQGxsLCkpKQQFBfk8V1hY2C7l9CdPzUdjMjIycDgc3lABYDAYGDNmDHv27PHZdtiwYd77sbGxWCwWb/DwPFf/8xg3bpxPrdT48eNJT0/3aa7y2LJlCxUVFURGRhIUFOS9ZWZmkpGR4d3umWeewel0smzZMt55553jfp+eGqW6TUIdqdvWfNSu6yKdTYUQ7cOsN7Phmg2dduyWioqK8vZ1aI7B4PvHmkajafQ5t9vt87ixGpTO1lxzjqe89ZutFEVp8Fzd82/J59Fabreb+Pj4Rmt66tYMHThwgLy8PNxuNwcPHvQJRY05duwYANHR0W0u24notuFD5vgQQrQ3jUbT4qaPzjR8+HDefvvtdtl3dHQ0+fn53sfp6enN/rUdEKD+QdhYLYA/DRs2jO+++46HH364wWt9+vQhICCAdevWcc011wBqYNq8ebNfOr3+8ssvDR737dsXnU7XYNsRI0ZQUFCAXq/3DoOur7q6mtmzZ3PllVcyYMAAbr75Znbs2EFsbGyTZdi5cycGg8Hb1NTRum+zS6VnXRep+RBCdG9Tpkxh165dLar9aK1zzjmHF198ka1bt7J582Zuu+22BrUDdSUnJ6PRaFi+fDlHjhyhoqLC72UCePDBB9m0aRN33HEH27dvZ+/evbz88sscPXqUwMBAbr/9du677z6++uordu/ezdy5c6mqquLmm28+4WNnZ2fzxz/+kbS0NN577z1eeOEF7rnnnka3Pe+88xg/fjyXXHIJq1atIisri/Xr1/PXv/6VzZs3A/CXv/yF0tJSnn/+eRYsWMDAgQOPW861a9cyceLEduvQezzdNnzYPH0+pOZDCNHNDR06lFGjRrFs2TK/73vRokUkJSVx1llncc011zB//nwslqZrgxITE3n44Yd54IEHiI2N5c4772zRcebMmeMzkdbx9OvXj6+//prffvuNMWPGMH78eP73v/+h16sNAk888QSXXnop1113HSNGjGD//v2sWrWK8PDwFh+jKddffz1Wq5UxY8bwhz/8gbvuuotbb7210W01Gg0rVqzgrLPO4qabbqJfv35cddVVZGVlERsby+rVq3nuued46623CAkJQavV8tZbb7Fu3TpefvnlJsvw3nvvNdrBtaNolOYGF3eCsrIyQkNDKS0tJSQkpN2O88kzW8jfX8qUuUPoMzKm3Y4jhOg+bDYbmZmZpKamtqgDZ1eyYsUK5s+fz86dO9FqT76/SydPnszkyZNZuHBhZxelWZMnT+b0009v1+nTj+fLL7/kvvvuY/v27d6w1RpN/Z635vrdjft8SM2HEEJ4TJs2jfT0dHJzc0lKSurs4rRKeXk5GRkZLF++vLOLclKorKxkyZIlbQoe/tJ9w0fNPB8mGWorhBAATfY76OqCg4PJzs7u7GKcNK644orOLkL3DB9ulxt7pROQobZCCCE6TmsnRztVnXwNe35gqwkeAKbAbpm/hBBCiE7TLcOHZ44PY6Aera5bfgRCCCFEp+mWV16rzG4qhBBCdJruGT48s5tKZ1MhhBCiw3XL8CHrugghhBCdp1uGD0/NhwyzFUIIITpe9wwfFTLBmBBC1FVUVERMTAxZWVmdXRTRRsuXL2f48OEntIpuR+me4aNcml2EEKKuxx9/nBkzZnhXTs3KyvJZPn7p0qU+S7j7S1P7nTx5MkuXLm3xfuqXtzuof87Tp09Ho9Hw7rvvdmKpWqZbhg9bpXQ4FUIID6vVyuuvv84tt9zS2UU56Tkcjk49/o033sgLL7zQqWVoiW4ZPqTmQwghaq1cuRK9Xs/48eMbfX316tXceOONlJaWotFo0Gg03gXcqqurWbBgAYmJiQQGBjJ27FjvLJ42m43Bgwf7rNiamZlJaGgor732WrP7PVFFRUVcffXV9OjRA4vFwtChQ3nvvfe8r//73/8mMTGxQRPFxRdfzA033OB9/MUXXzBy5EhMJhO9evXi4YcfxumsnahSo9HwyiuvMHPmTAIDA3nkkUdwuVzcfPPNpKamYjab6d+/P//61798juN0Orn77rsJCwsjMjKS+++/nxtuuIFLLrnEu42iKDz11FP06tULs9nMaaedxkcffdTseV988cVs3LiRAwcOtOVj6zhKF1NaWqoASmlpabsd4/X71iov/v47pfBQWbsdQwjR/VitVmX37t2K1WpVFEVR3G634qqs7JSb2+1ucbnvuece5cILL/R5LjMzU/FcIux2u/Lcc88pISEhSn5+vpKfn6+Ul5criqIo11xzjTJhwgRlzZo1yv79+5Wnn35aMRqNyr59+xRFUZRt27YpAQEByqeffqo4nU7ljDPOUGbOnHnc/U6aNElZsmRJi8+hbnkVRVFycnKUp59+Wtm2bZuSkZGhPP/884pOp1N++eUXRVEUpaioSAkICFC+/fZb73uOHTumBAQEKKtWrVIURVG++uorJSQkRFm6dKmSkZGhfP3110pKSoqycOFC73sAJSYmRnn99deVjIwMJSsrS6murlb+/ve/Kxs3blQOHDigvP3224rFYlE++OAD7/seeeQRJSIiQvnkk0+UPXv2KLfddpsSEhLi/WwURVH+/Oc/KwMGDFC++uorJSMjQ1myZIliNBqV1atXN3rOHjExMcrSpUtb/Nm1Vv3fc4/WXL81iqIonRl+6mvNkrxtobgVXr5zNYpb4YbHzyAo3Oj3Ywghuqf6S427q6pIGzGyU8rSf+sWtBZLi7a95JJLiIyM5PXXX29ym6VLlzJv3jxKSkq8z2VkZNC3b19ycnJISEjwPn/eeecxZswYHnvsMQCefvppnnrqKa6++mo+/PBDduzYQVRUVJP7bS8XXXQRAwcO5JlnngFg5syZREVFec/71Vdf5aGHHiInJwedTsdZZ53F1KlTefDBB737ePvtt1mwYAF5eXmAWvMxb948nn322WaP/Yc//IHDhw97ay7i4uKYP38+8+fPB8DlctGrVy+GDx/OZ599RmVlJVFRUXz//fc+NVK33HILVVVVzfbrGDFiBDNnzuShhx5qw6d0fPV/zz1ac/3udgub2K1OFLeat2S0ixBCqH0+6l5EWmrr1q0oikK/fv18nrfb7URGRnof/+lPf+J///sfL7zwAitXrvQGj/bkcrl44okn+OCDD8jNzcVut2O32wkMDPRuM3v2bG699VZeeukljEYj77zzDldddRU6nQ6ALVu2sGnTJh599FGf/dpsNqqqqrDUhLtRo0Y1OP4rr7zCf/7zHw4ePIjVaqW6uprTTz8dgNLSUg4fPsyYMWO82+t0OkaOHOltBtq9ezc2m43zzz/fZ7/V1dUMHz682XM3m81UVVW14tPqeN0ufHjm+Agw6dAZumWXFyFEB9GYzfTfuqXTjt1SUVFRFBcXt/oYbrcbnU7Hli1bvBdsj6CgIO/9wsJC0tLS0Ol0pKenc+GFF7b6WK21aNEinn32WZ577jmGDh1KYGAg8+bNo7q62rvNjBkzcLvdfPnll4wePZq1a9eyePFin/N7+OGHmTVrVoP91w1rdQMNwLJly7j33ntZtGgR48ePJzg4mKeffpoNGzb4bFd/dE7dhghPCPnyyy9JTEz02c5obL7G/tixY0RHRze7TWfrfuGjZo4PU7B0NhVCtC+NRoOmhU0fnWn48OG8/fbbzW4TEBCAy+Vq8D6Xy0VhYSETJ05s8r033XQTQ4YMYe7cudx8882ce+65DBo0qMn9+sPatWuZOXMm1157LaBezNPT0xk4cKB3G7PZzKxZs3jnnXfYv38//fr1Y+TI2mayESNGkJaWRp8+fVp97AkTJnDHHXd4n8vIyPDeDw0NJTY2lo0bN3o/N5fLxbZt27y1I4MGDcJoNHLo0CEmTZrU4mPbbDYyMjKOWzvS2bpd+LCVywRjQghR15QpU3jwwQcpLi4mPDy80W1SUlKoqKjgu+++47TTTsNisdCvXz9mz57N9ddfz6JFixg+fDhHjx7l+++/Z+jQoUybNo3/+7//4+eff2b79u0kJSWxcuVKZs+ezYYNGwgICGh0vxY/BLY+ffrw8ccfs379esLDw1m8eDEFBQU+4QPUppcZM2awa9cub1Dx+Pvf/8706dNJSkri8ssvR6vVsn37dnbs2MEjjzzS7LHffPNNVq1aRWpqKm+99RabNm0iNTXVu81dd93F448/Tp8+fRgwYAAvvPACxcXF3tqQ4OBg5s+fz7333ovb7ebMM8+krKyM9evXExQU5DMip65ffvkFo9HY5MilLqM9esKeiPYe7bJzTY7y4u+/U5a/+Gu77F8I0X01NQrgZDBu3DjllVdeaXab2267TYmMjFQA5aGHHlIURfGO7EhJSVEMBoMSFxen/O53v1O2b9+u7NmzRzGbzcq7777r3UdpaamSkpKiLFiwoNn91nfDDTcokyZNavH5FBUVKTNnzlSCgoKUmJgY5a9//aty/fXX+4wmURRFcTqdSnx8vAIoGRkZDfbz1VdfKRMmTFDMZrMSEhKijBkzRnn11Ve9rwPKp59+6vMem82mzJkzRwkNDVXCwsKU22+/XXnggQeU0047zbuNw+FQ7rzzTiUkJEQJDw9X7r//fuXyyy9XrrrqKu82brdb+de//qX0799fMRgMSnR0tDJlyhTlxx9/bPK8b731VuX3v/99iz+ntpDRLm2weUUWGz4/wIAJ8Zx7/cDjv0EIIVqoqVEAJ4MVK1Ywf/58du7ciVbb9frDTZ48mcmTJ/ttHpCuxu12M3DgQK644gr++c9/tmkfR44cYcCAAWzevNmnlsXfZLRLG1gramY3lWYXIYTwmjZtGunp6eTm5pKUlNTZxfFRXl5ORkYGy5cv7+yi+M3Bgwf5+uuvmTRpEna7nRdffJHMzEyuueaaNu8zMzOTl156qV2Dh790v/Ahs5sKIUSj7rnnns4uQqOCg4PJzs7u7GL4lVarZenSpcyfPx9FURgyZAjffvttgz4prTFmzBif4btdWbcLH7YKWddFCCFE50pKSuKnn37q7GJ0mq7XsNfOvENtpdlFCCGE6BTdLnzYasKHWeb5EEIIITpFtwofiqLU6fMhNR9CCCFEZ+hW4cNhd+FyqlPWSs2HEEII0TlaFT4WLlyoThdc5xYXF+d9XVEUFi5cSEJCAmazmcmTJ7Nr1y6/F7qtPLUeeoMWg1F3nK2FEEII0R5aXfMxePBg8vPzvbcdO3Z4X3vqqadYvHgxL774Ips2bSIuLo7zzz+f8vJyvxa6rTxzfJhkpIsQQgjRaVodPvR6PXFxcd6bZ+U8RVF47rnn+Mtf/sKsWbMYMmQIb7zxBlVVVbz77rt+L3hb2GSODyGEaFRRURExMTFkZWV16HGzsrLQaDT8+uuvzW43efJk5s2b1yFlOtmMHj2aTz75pLOL0SqtDh/p6ekkJCSQmprKVVddxYEDBwB1ZrWCggIuuOAC77ZGo5FJkyaxfv36Jvdnt9spKyvzubUXq8zxIYQQjXr88ceZMWMGKSkpQG0o6GirV69Go9FQUlLi8/wnn3ziM+345MmTWbp0aYv321nncyKa+izmzJnjM8383/72Nx544AHcbnfHFvAEtCp8jB071rtS32uvvUZBQQETJkygqKiIgoICAGJjY33eExsb632tMY8//jihoaHeW3tO6+vp8yFzfAghRC2r1crrr7/OLbfc0tlFaVJERATBwcGdXYwu6aKLLqK0tJRVq1Z1dlFarFXhY+rUqVx66aUMHTqU8847jy+//BKAN954w7tN/WSpKEqzafPBBx+ktLTUe2vPKXQ9E4xJs4sQQtRauXIler2+2WXYly5dSlhYGMuXL6d///5YLBYuu+wyKisreeONN0hJSSE8PJy77roLl8vlfZ9Go+Gzzz7z2VdYWFijtRZZWVmcffbZAISHh6PRaJgzZw7QPs0uP/30E5MmTcJisRAeHs6UKVMoLi4G1Fr5u+++m5iYGEwmE2eeeSabNm3yvtdTK/Hdd98xatQoLBYLEyZMIC0tzecYn3/+OaNGjcJkMhEVFcWsWbO8r7399tuMGjWK4OBg4uLiuOaaaygsLDzuZ1GfTqdj2rRpvPfee/78eNrVCQ21DQwMZOjQoaSnp3tHvdSv5SgsLGxQG1KX0WgkJCTE59ZebOXS7CKE6DiKouCwuzrl1poFy9esWcOoUaOOu11VVRXPP/8877//Pl999RWrV69m1qxZrFixghUrVvDWW2/x6quv8tFHH7Xp80pKSuLjjz8GIC0tjfz8fP71r3+1aV/H8+uvv3LuuecyePBgfv75Z9atW8eMGTO8wWnBggV8/PHHvPHGG2zdupU+ffowZcoUjh075rOfv/zlLyxatIjNmzej1+u56aabvK99+eWXzJo1i4suuoht27Z5g4pHdXU1//znP/ntt9/47LPPyMzM9AaM1n4WY8aMYe3atf76eNrdCa3tYrfb2bNnDxMnTiQ1NZW4uDi++eYbhg8fDqgf7I8//siTTz7pl8KeKKn5EEJ0JGe1m1fv+bFTjn3rvya1eEqBrKwsEhISfJ5LSUlpEGAcDgcvv/wyvXv3BuCyyy7jrbfe4vDhwwQFBTFo0CDOPvtsfvjhB6688spWl1mn0xEREQFATEwMYWFhTW67evXqVu27/vk89dRTjBo1ipdeesn73ODBgwGorKzk5ZdfZunSpUydOhWA1157jW+++YbXX3+d++67z/ueRx99lEmTJgHwwAMPcNFFF2Gz2TCZTDz66KNcddVVPPzww97tTzvtNO/9ukGlV69ePP/884wZM4aKigqCgoKa/CwaqzVKTEzk0KFDuN1utNquP4VXq0o4f/58fvzxRzIzM9mwYQOXXXYZZWVl3HDDDWg0GubNm8djjz3Gp59+ys6dO5kzZw4Wi+WElgj2J2tNzYf0+RBCiFpWqxWTyXTc7SwWizd4gNqnLyUlhaCgIJ/nPE0HXZmn5qMxGRkZOBwOzjjjDO9zBoOBMWPGsGfPHp9thw0b5r0fHx8P4D3/5o4BsG3bNmbOnElycjLBwcFMnjwZgEOHDrX6fMxmM263G7vd3ur3doZW1Xzk5ORw9dVXc/ToUaKjoxk3bhy//PILycnJgFpNZbVaueOOOyguLmbs2LF8/fXXXaaTkFXWdRFCdCB9gJZb/zWp047dUlFRUd6+Ds0xGHz/cNNoNI0+V3fUhUajabQGpbOZzeYmX/OUtyV9GOuev+c1z/k3d4zKykouuOACLrjgAt5++22io6M5dOgQU6ZMobq6unUnAxw7dgyLxdLsMbuSVtV8vP/+++Tl5VFdXU1ubi4ff/wxgwYN8r6u0WhYuHAh+fn52Gw2fvzxR4YMGeL3QrdVbbOL1HwIIdqfRqPBYNR1yq01w0qHDx/O7t272+UziI6OJj8/3/s4PT2dqqqqJrcPCFD/OKzbabU9DBs2jO+++67R1/r06UNAQADr1q3zPudwONi8eTMDBw70yzH27t3L0aNHeeKJJ5g4cSIDBgxoUGPUms9i586djBgxosVl62xdv2HIT5zVLpx29QuUDqdCCFFrypQp7Nq1q0W1H611zjnn8OKLL7J161Y2b97Mbbfd1qC2pK7k5GQ0Gg3Lly/nyJEjVFRU+L1MoI603LRpE3fccQfbt29n7969vPzyyxw9epTAwEBuv/127rvvPr766it2797N3Llzqaqq4uabb27xMR566CHee+89HnroIfbs2cOOHTt46qmnAOjZsycBAQG88MILHDhwgM8//9xnHhNo3Wexdu1an3m2urpuEz4UYOzFvTjtnCQCzCfUz1YIIU4pQ4cOZdSoUSxbtszv+160aBFJSUmcddZZXHPNNcyfPx+LxdLk9omJiTz88MM88MADxMbGcuedd7boOHPmzPH2mWiJfv368fXXX/Pbb78xZswYxo8fz//+9z/0evX68MQTT3DppZdy3XXXMWLECPbv38+qVasIDw9v8TEmT57Mhx9+yOeff87pp5/OOeecw4YNGwC1Rmjp0qV8+OGHDBo0iCeeeIJnnnnG5/0t/Sxyc3NZv349N954Y4vL1tk0SmvGY3WAsrIyQkNDKS0tbddht0II4W82m43MzExSU1Nb1IGzK1mxYgXz589n586dJ8VoifomT57M5MmTfWb+7C7uu+8+SktLefXVVzvkeE39nrfm+i1VAEIIIZg2bRrp6enk5ua260zT7aG8vJyMjAyWL1/e2UXpFDExMcyfP7+zi9EqEj6EEEIAcM8993R2EdokODi4XWfH7urqzjtysjj56taEEEIIcVKT8CGEEEKIDiXhQwghhBAdSsKHEEL4Wd0ZPoU41fjj91s6nAohhJ8EBASg1WrJy8sjOjqagICAVs00KkRXpigK1dXVHDlyBK1W652BtS0kfAghhJ9otVpSU1PJz88nLy+vs4sjRLuwWCz07NnzhOaDkfAhhBB+FBAQQM+ePXE6ne2+PokQHU2n06HX60+4Rk/ChxBC+Jlntdfm1jARojuTDqdCCCGE6FASPoQQQgjRoSR8CCGEEKJDdbk+H55FdsvKyjq5JEIIIYRoKc9123Mdb06XCx/l5eUAJ92qikIIIYRQr+OhoaHNbqNRWhJROpDb7SYvL4/g4GC/T85TVlZGUlIS2dnZhISE+HXfXcGpfn5w6p+jnN/J71Q/x1P9/ODUP8f2Oj9FUSgvLychIeG4c4B0uZoPrVZLjx492vUYISEhp+QvlMepfn5w6p+jnN/J71Q/x1P9/ODUP8f2OL/j1Xh4SIdTIYQQQnQoCR9CCCGE6FDdKnwYjUYeeughjEZjZxelXZzq5wen/jnK+Z38TvVzPNXPD079c+wK59flOpwKIYQQ4tTWrWo+hBBCCNH5JHwIIYQQokNJ+BBCCCFEh5LwIYQQQogOdcqFj5deeonU1FRMJhMjR45k7dq1zW7/448/MnLkSEwmE7169eKVV17poJK2zuOPP87o0aMJDg4mJiaGSy65hLS0tGbfs3r1ajQaTYPb3r17O6jUrbNw4cIGZY2Li2v2PSfL9weQkpLS6Pfxhz/8odHtT4bvb82aNcyYMYOEhAQ0Gg2fffaZz+uKorBw4UISEhIwm81MnjyZXbt2HXe/H3/8MYMGDcJoNDJo0CA+/fTTdjqD5jV3fg6Hg/vvv5+hQ4cSGBhIQkIC119/PXl5ec3uc+nSpY1+rzabrZ3PpqHjfX9z5sxpUM5x48Ydd79d5fuD459jY9+FRqPh6aefbnKfXeU7bMl1oav+GzylwscHH3zAvHnz+Mtf/sK2bduYOHEiU6dO5dChQ41un5mZybRp05g4cSLbtm3jz3/+M3fffTcff/xxB5f8+H788Uf+8Ic/8Msvv/DNN9/gdDq54IILqKysPO5709LSyM/P99769u3bASVum8GDB/uUdceOHU1uezJ9fwCbNm3yObdvvvkGgMsvv7zZ93Xl76+yspLTTjuNF198sdHXn3rqKRYvXsyLL77Ipk2biIuL4/zzz/eu4dSYn3/+mSuvvJLrrruO3377jeuuu44rrriCDRs2tNdpNKm586uqqmLr1q387W9/Y+vWrXzyySfs27ePiy+++Lj7DQkJ8flO8/PzMZlM7XEKzTre9wdw4YUX+pRzxYoVze6zK31/cPxzrP89/Pe//0Wj0XDppZc2u9+u8B225LrQZf8NKqeQMWPGKLfddpvPcwMGDFAeeOCBRrdfsGCBMmDAAJ/nfv/73yvjxo1rtzL6S2FhoQIoP/74Y5Pb/PDDDwqgFBcXd1zBTsBDDz2knHbaaS3e/mT+/hRFUe655x6ld+/eitvtbvT1k+37A5RPP/3U+9jtditxcXHKE0884X3OZrMpoaGhyiuvvNLkfq644grlwgsv9HluypQpylVXXeX3MrdG/fNrzMaNGxVAOXjwYJPbLFmyRAkNDfVv4fygsfO74YYblJkzZ7ZqP131+1OUln2HM2fOVM4555xmt+mq32H960JX/jd4ytR8VFdXs2XLFi644AKf5y+44ALWr1/f6Ht+/vnnBttPmTKFzZs343A42q2s/lBaWgpARETEcbcdPnw48fHxnHvuufzwww/tXbQTkp6eTkJCAqmpqVx11VUcOHCgyW1P5u+vurqat99+m5tuuum4CyieTN9fXZmZmRQUFPh8R0ajkUmTJjX5bxKa/l6be09XUVpaikajISwsrNntKioqSE5OpkePHkyfPp1t27Z1TAHbYPXq1cTExNCvXz/mzp1LYWFhs9ufzN/f4cOH+fLLL7n55puPu21X/A7rXxe68r/BUyZ8HD16FJfLRWxsrM/zsbGxFBQUNPqegoKCRrd3Op0cPXq03cp6ohRF4Y9//CNnnnkmQ4YMaXK7+Ph4Xn31VT7++GM++eQT+vfvz7nnnsuaNWs6sLQtN3bsWN58801WrVrFa6+9RkFBARMmTKCoqKjR7U/W7w/gs88+o6SkhDlz5jS5zcn2/dXn+XfXmn+Tnve19j1dgc1m44EHHuCaa65pdrGuAQMGsHTpUj7//HPee+89TCYTZ5xxBunp6R1Y2paZOnUq77zzDt9//z2LFi1i06ZNnHPOOdjt9ibfc7J+fwBvvPEGwcHBzJo1q9ntuuJ32Nh1oSv/G+xyq9qeqPp/RSqK0uxflo1t39jzXcmdd97J9u3bWbduXbPb9e/fn/79+3sfjx8/nuzsbJ555hnOOuus9i5mq02dOtV7f+jQoYwfP57evXvzxhtv8Mc//rHR95yM3x/A66+/ztSpU0lISGhym5Pt+2tKa/9NtvU9ncnhcHDVVVfhdrt56aWXmt123LhxPp02zzjjDEaMGMELL7zA888/395FbZUrr7zSe3/IkCGMGjWK5ORkvvzyy2Yv0Cfb9+fx3//+l9mzZx+370ZX/A6buy50xX+Dp0zNR1RUFDqdrkEyKywsbJDgPOLi4hrdXq/XExkZ2W5lPRF33XUXn3/+OT/88AM9evRo9fvHjRvXJf/CakxgYCBDhw5tsrwn4/cHcPDgQb799ltuueWWVr/3ZPr+PCOVWvNv0vO+1r6nMzkcDq644goyMzP55ptvWr1EuVarZfTo0SfF9xofH09ycnKzZT3Zvj+PtWvXkpaW1qZ/l539HTZ1XejK/wZPmfAREBDAyJEjvSMIPL755hsmTJjQ6HvGjx/fYPuvv/6aUaNGYTAY2q2sbaEoCnfeeSeffPIJ33//PampqW3az7Zt24iPj/dz6dqH3W5nz549TZb3ZPr+6lqyZAkxMTFcdNFFrX7vyfT9paamEhcX5/MdVVdX8+OPPzb5bxKa/l6be09n8QSP9PR0vv322zaFXkVR+PXXX0+K77WoqIjs7Oxmy3oyfX91vf7664wcOZLTTjut1e/trO/weNeFLv1v0G9dV7uA999/XzEYDMrrr7+u7N69W5k3b54SGBioZGVlKYqiKA888IBy3XXXebc/cOCAYrFYlHvvvVfZvXu38vrrrysGg0H56KOPOusUmnT77bcroaGhyurVq5X8/HzvraqqyrtN/fN79tlnlU8//VTZt2+fsnPnTuWBBx5QAOXjjz/ujFM4rj/96U/K6tWrlQMHDii//PKLMn36dCU4OPiU+P48XC6X0rNnT+X+++9v8NrJ+P2Vl5cr27ZtU7Zt26YAyuLFi5Vt27Z5R3s88cQTSmhoqPLJJ58oO3bsUK6++molPj5eKSsr8+7juuuu8xmR9tNPPyk6nU554oknlD179ihPPPGEotfrlV9++aVLnZ/D4VAuvvhipUePHsqvv/7q8+/Sbrc3eX4LFy5UvvrqKyUjI0PZtm2bcuONNyp6vV7ZsGFDlzq/8vJy5U9/+pOyfv16JfP/27dbHIWBMIzjXTHlwyGa8JEUEgRYcCSEuh4Ag+wVuAGaAxCC4gBoBKooLkAQqCacAAz2WbELCSy7i5otm/8vqerkDW/emfQRTJIojmN1Oh1VKpWXmZ/0+x6VpNPppHw+r+l0+rBGWmf4zHchrWfwX4UPSZpMJqpWq3JdV+12++YqahRFCoLgZv16vVar1ZLruqrVat9uvr/mOM7DZz6fX9fc9zcej1Wv15XNZlUoFNTtdrVcLu3/+CcNBgOVSiUZY1Qul9Xv97Xb7a7vX3l+F6vVSo7jaL/ff3n3ivO7XAe+f6IokvRx1W80GqlYLCqTyajX62m73d7UCILguv5isVio0WjIGKNms/lngeun/pIk+fZcxnF8rXHf33A4lO/7cl1XnucpDENtNhv7zenn/s7ns8IwlOd5MsbI931FUaTD4XBTI83zk37fo5I0m82Uy+V0PB4f1kjrDJ/5LqT1DL59NgAAAGDFv/nPBwAAeA2EDwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFa9AzkA0dixgc2nAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_unseen_img\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demonstration(hyperdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8219c79f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('image', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6129    0.9896    0.7570     29306\n",
      "         1.0     0.8473    0.0846    0.1539     20010\n",
      "\n",
      "    accuracy                         0.6224     49316\n",
      "   macro avg     0.7301    0.5371    0.4554     49316\n",
      "weighted avg     0.7080    0.6224    0.5123     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6128    0.9911    0.7573     20463\n",
      "         1.0     0.8717    0.0884    0.1606     14058\n",
      "\n",
      "    accuracy                         0.6235     34521\n",
      "   macro avg     0.7422    0.5397    0.4589     34521\n",
      "weighted avg     0.7182    0.6235    0.5143     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6099    0.9852    0.7534      4400\n",
      "         1.0     0.7751    0.0747    0.1363      2997\n",
      "\n",
      "    accuracy                         0.6163      7397\n",
      "   macro avg     0.6925    0.5300    0.4449      7397\n",
      "weighted avg     0.6768    0.6163    0.5034      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8606    0.9194    0.8890       806\n",
      "         1.0     0.7751    0.6512    0.7077       344\n",
      "\n",
      "    accuracy                         0.8391      1150\n",
      "   macro avg     0.8179    0.7853    0.7984      1150\n",
      "weighted avg     0.8350    0.8391    0.8348      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5753    1.0000    0.7304      3594\n",
      "         1.0     0.0000    0.0000    0.0000      2653\n",
      "\n",
      "    accuracy                         0.5753      6247\n",
      "   macro avg     0.2877    0.5000    0.3652      6247\n",
      "weighted avg     0.3310    0.5753    0.4202      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7812    0.8488    0.8136       324\n",
      "         1.0     0.6260    0.5157    0.5655       159\n",
      "\n",
      "    accuracy                         0.7391       483\n",
      "   macro avg     0.7036    0.6822    0.6896       483\n",
      "weighted avg     0.7301    0.7391    0.7319       483\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    Overall Accuracy: 62.24\n",
      "    Training Set Accuracy: 62.35\n",
      "    Validation Set Accuracy: 61.63\n",
      "    Validation Set with both Images and Texts: 83.91\n",
      "    Validation Set with only Texts: 57.53\n",
      "    Validation Set with unseen Images: 73.91\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6129    0.9896    0.7570     29306\n",
      "         1.0     0.8473    0.0846    0.1539     20010\n",
      "\n",
      "    accuracy                         0.6224     49316\n",
      "   macro avg     0.7301    0.5371    0.4554     49316\n",
      "weighted avg     0.7080    0.6224    0.5123     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6128    0.9911    0.7573     20463\n",
      "         1.0     0.8717    0.0884    0.1606     14058\n",
      "\n",
      "    accuracy                         0.6235     34521\n",
      "   macro avg     0.7422    0.5397    0.4589     34521\n",
      "weighted avg     0.7182    0.6235    0.5143     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6164    0.9872    0.7590      4443\n",
      "         1.0     0.7986    0.0765    0.1396      2955\n",
      "\n",
      "    accuracy                         0.6234      7398\n",
      "   macro avg     0.7075    0.5318    0.4493      7398\n",
      "weighted avg     0.6892    0.6234    0.5116      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8438    0.9204    0.8804       716\n",
      "         1.0     0.7986    0.6494    0.7163       348\n",
      "\n",
      "    accuracy                         0.8318      1064\n",
      "   macro avg     0.8212    0.7849    0.7984      1064\n",
      "weighted avg     0.8290    0.8318    0.8268      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5884    1.0000    0.7409      3727\n",
      "         1.0     0.0000    0.0000    0.0000      2607\n",
      "\n",
      "    accuracy                         0.5884      6334\n",
      "   macro avg     0.2942    0.5000    0.3704      6334\n",
      "weighted avg     0.3462    0.5884    0.4359      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7516    0.8459    0.7960       279\n",
      "         1.0     0.6417    0.4968    0.5600       155\n",
      "\n",
      "    accuracy                         0.7212       434\n",
      "   macro avg     0.6966    0.6713    0.6780       434\n",
      "weighted avg     0.7123    0.7212    0.7117       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 62.24\n",
      "    Training Set Accuracy: 62.35\n",
      "    Validation Set Accuracy: 62.34\n",
      "    Validation Set with both Images and Texts: 83.18\n",
      "    Validation Set with only Texts: 58.84\n",
      "    Validation Set with unseen Images: 72.12\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.5615057  0.43849432]\n",
      " [0.5615057  0.43849432]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.602325\n",
      "std          0.113375\n",
      "min          0.500235\n",
      "25%          0.561506\n",
      "50%          0.561506\n",
      "75%          0.561506\n",
      "max          0.999689\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.602449\n",
      "std          0.113492\n",
      "min          0.500235\n",
      "25%          0.561506\n",
      "50%          0.561506\n",
      "75%          0.561506\n",
      "max          0.999689\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.604088\n",
      "std         0.115678\n",
      "min         0.500325\n",
      "25%         0.561506\n",
      "50%         0.561506\n",
      "75%         0.561506\n",
      "max         0.999431\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('multi', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7854    0.7355    0.7596     29306\n",
      "         1.0     0.6456    0.7056    0.6743     20010\n",
      "\n",
      "    accuracy                         0.7234     49316\n",
      "   macro avg     0.7155    0.7206    0.7169     49316\n",
      "weighted avg     0.7287    0.7234    0.7250     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7857    0.7338    0.7589     20463\n",
      "         1.0     0.6465    0.7087    0.6762     14058\n",
      "\n",
      "    accuracy                         0.7236     34521\n",
      "   macro avg     0.7161    0.7213    0.7175     34521\n",
      "weighted avg     0.7290    0.7236    0.7252     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7844    0.7443    0.7638      4400\n",
      "         1.0     0.6508    0.6997    0.6744      2997\n",
      "\n",
      "    accuracy                         0.7262      7397\n",
      "   macro avg     0.7176    0.7220    0.7191      7397\n",
      "weighted avg     0.7303    0.7262    0.7276      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8943    0.8821    0.8882       806\n",
      "         1.0     0.7324    0.7558    0.7439       344\n",
      "\n",
      "    accuracy                         0.8443      1150\n",
      "   macro avg     0.8134    0.8190    0.8161      1150\n",
      "weighted avg     0.8459    0.8443    0.8450      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7586    0.7134    0.7353      3594\n",
      "         1.0     0.6407    0.6924    0.6656      2653\n",
      "\n",
      "    accuracy                         0.7045      6247\n",
      "   macro avg     0.6997    0.7029    0.7004      6247\n",
      "weighted avg     0.7085    0.7045    0.7057      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8296    0.7963    0.8126       324\n",
      "         1.0     0.6163    0.6667    0.6405       159\n",
      "\n",
      "    accuracy                         0.7536       483\n",
      "   macro avg     0.7229    0.7315    0.7265       483\n",
      "weighted avg     0.7594    0.7536    0.7559       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.34\n",
      "    Training Set Accuracy: 72.36\n",
      "    Validation Set Accuracy: 72.62\n",
      "    Validation Set with both Images and Texts: 84.43\n",
      "    Validation Set with only Texts: 70.45\n",
      "    Validation Set with unseen Images: 75.36\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7854    0.7355    0.7596     29306\n",
      "         1.0     0.6456    0.7056    0.6743     20010\n",
      "\n",
      "    accuracy                         0.7234     49316\n",
      "   macro avg     0.7155    0.7206    0.7169     49316\n",
      "weighted avg     0.7287    0.7234    0.7250     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7857    0.7338    0.7589     20463\n",
      "         1.0     0.6465    0.7087    0.6762     14058\n",
      "\n",
      "    accuracy                         0.7236     34521\n",
      "   macro avg     0.7161    0.7213    0.7175     34521\n",
      "weighted avg     0.7290    0.7236    0.7252     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7848    0.7344    0.7587      4443\n",
      "         1.0     0.6358    0.6971    0.6651      2955\n",
      "\n",
      "    accuracy                         0.7195      7398\n",
      "   macro avg     0.7103    0.7158    0.7119      7398\n",
      "weighted avg     0.7253    0.7195    0.7213      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8793    0.8855    0.8824       716\n",
      "         1.0     0.7609    0.7500    0.7554       348\n",
      "\n",
      "    accuracy                         0.8412      1064\n",
      "   macro avg     0.8201    0.8177    0.8189      1064\n",
      "weighted avg     0.8406    0.8412    0.8409      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7649    0.7054    0.7339      3727\n",
      "         1.0     0.6210    0.6901    0.6537      2607\n",
      "\n",
      "    accuracy                         0.6991      6334\n",
      "   macro avg     0.6929    0.6977    0.6938      6334\n",
      "weighted avg     0.7057    0.6991    0.7009      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8225    0.8136    0.8180       279\n",
      "         1.0     0.6709    0.6839    0.6773       155\n",
      "\n",
      "    accuracy                         0.7673       434\n",
      "   macro avg     0.7467    0.7487    0.7477       434\n",
      "weighted avg     0.7683    0.7673    0.7678       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.34\n",
      "    Training Set Accuracy: 72.36\n",
      "    Validation Set Accuracy: 71.95\n",
      "    Validation Set with both Images and Texts: 84.12\n",
      "    Validation Set with only Texts: 69.91\n",
      "    Validation Set with unseen Images: 76.73\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.57783115 0.42216882]\n",
      " [0.3210932  0.6789068 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.715359\n",
      "std          0.156193\n",
      "min          0.500006\n",
      "25%          0.579962\n",
      "50%          0.676378\n",
      "75%          0.853104\n",
      "max          0.999949\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.715156\n",
      "std          0.155730\n",
      "min          0.500006\n",
      "25%          0.580415\n",
      "50%          0.676620\n",
      "75%          0.852472\n",
      "max          0.999949\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.716225\n",
      "std         0.157144\n",
      "min         0.500090\n",
      "25%         0.579327\n",
      "50%         0.677747\n",
      "75%         0.854143\n",
      "max         0.999897\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('multi', 'complex')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8405    0.7873    0.8130     29306\n",
      "         1.0     0.7149    0.7811    0.7465     20010\n",
      "\n",
      "    accuracy                         0.7848     49316\n",
      "   macro avg     0.7777    0.7842    0.7798     49316\n",
      "weighted avg     0.7895    0.7848    0.7860     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8575    0.8020    0.8288     20463\n",
      "         1.0     0.7366    0.8059    0.7697     14058\n",
      "\n",
      "    accuracy                         0.8036     34521\n",
      "   macro avg     0.7970    0.8040    0.7992     34521\n",
      "weighted avg     0.8082    0.8036    0.8047     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8001    0.7605    0.7798      4400\n",
      "         1.0     0.6722    0.7211    0.6958      2997\n",
      "\n",
      "    accuracy                         0.7445      7397\n",
      "   macro avg     0.7361    0.7408    0.7378      7397\n",
      "weighted avg     0.7483    0.7445    0.7457      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9267    0.9256    0.9261       806\n",
      "         1.0     0.8261    0.8285    0.8273       344\n",
      "\n",
      "    accuracy                         0.8965      1150\n",
      "   macro avg     0.8764    0.8770    0.8767      1150\n",
      "weighted avg     0.8966    0.8965    0.8966      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7699    0.7234    0.7459      3594\n",
      "         1.0     0.6537    0.7071    0.6793      2653\n",
      "\n",
      "    accuracy                         0.7165      6247\n",
      "   macro avg     0.7118    0.7153    0.7126      6247\n",
      "weighted avg     0.7205    0.7165    0.7177      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8428    0.8272    0.8349       324\n",
      "         1.0     0.6606    0.6855    0.6728       159\n",
      "\n",
      "    accuracy                         0.7805       483\n",
      "   macro avg     0.7517    0.7563    0.7539       483\n",
      "weighted avg     0.7828    0.7805    0.7815       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 78.48\n",
      "    Training Set Accuracy: 80.36\n",
      "    Validation Set Accuracy: 74.45\n",
      "    Validation Set with both Images and Texts: 89.65\n",
      "    Validation Set with only Texts: 71.65\n",
      "    Validation Set with unseen Images: 78.05\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8405    0.7873    0.8130     29306\n",
      "         1.0     0.7149    0.7811    0.7465     20010\n",
      "\n",
      "    accuracy                         0.7848     49316\n",
      "   macro avg     0.7777    0.7842    0.7798     49316\n",
      "weighted avg     0.7895    0.7848    0.7860     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8575    0.8020    0.8288     20463\n",
      "         1.0     0.7366    0.8059    0.7697     14058\n",
      "\n",
      "    accuracy                         0.8036     34521\n",
      "   macro avg     0.7970    0.8040    0.7992     34521\n",
      "weighted avg     0.8082    0.8036    0.8047     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8025    0.7463    0.7734      4443\n",
      "         1.0     0.6549    0.7239    0.6877      2955\n",
      "\n",
      "    accuracy                         0.7374      7398\n",
      "   macro avg     0.7287    0.7351    0.7305      7398\n",
      "weighted avg     0.7436    0.7374    0.7392      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9086    0.9302    0.9193       716\n",
      "         1.0     0.8489    0.8075    0.8277       348\n",
      "\n",
      "    accuracy                         0.8900      1064\n",
      "   macro avg     0.8788    0.8688    0.8735      1064\n",
      "weighted avg     0.8891    0.8900    0.8893      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7796    0.7110    0.7438      3727\n",
      "         1.0     0.6330    0.7127    0.6705      2607\n",
      "\n",
      "    accuracy                         0.7117      6334\n",
      "   macro avg     0.7063    0.7119    0.7071      6334\n",
      "weighted avg     0.7193    0.7117    0.7136      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8000    0.8315    0.8155       279\n",
      "         1.0     0.6736    0.6258    0.6488       155\n",
      "\n",
      "    accuracy                         0.7581       434\n",
      "   macro avg     0.7368    0.7287    0.7321       434\n",
      "weighted avg     0.7549    0.7581    0.7560       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 78.48\n",
      "    Training Set Accuracy: 80.36\n",
      "    Validation Set Accuracy: 73.74\n",
      "    Validation Set with both Images and Texts: 89.00\n",
      "    Validation Set with only Texts: 71.17\n",
      "    Validation Set with unseen Images: 75.81\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.90251356 0.09748643]\n",
      " [0.20180789 0.7981921 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.780058\n",
      "std          0.170282\n",
      "min          0.500023\n",
      "25%          0.620004\n",
      "50%          0.778016\n",
      "75%          0.961846\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.782172\n",
      "std          0.170629\n",
      "min          0.500023\n",
      "25%          0.621423\n",
      "50%          0.781655\n",
      "75%          0.964924\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.776304\n",
      "std         0.169645\n",
      "min         0.500028\n",
      "25%         0.616351\n",
      "50%         0.773050\n",
      "75%         0.954564\n",
      "max         1.000000\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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155136v7779eIESOuqCEAAMAcIQedSzl8+LA6OjpUWFhoj7lcLt18881qamrSwoUL1draqkAgEFTj9XqVm5urpqYmFRUVqbm5WW632w45kjRp0iS53W41NTUpJydHzc3Nys3NtUOOJBUVFcnv96u1tVW33HLLoPX5/X75/X77uKenR5IUCAQUCATC2Qp7vnDPi2D0OTroc3TQ5+iI1T674qyhXkLIXMPeX3OkfsZejrAGnY6ODklSZmZm0HhmZqaOHDli18THx2vkyJGDagbO7+joUEZGxqD5MzIygmrOf56RI0cqPj7erjnfmjVrtGrVqkHj9fX1SkpKupxLDFlDQ0NE5kUw+hwd9Dk66HN0xFqfKycO9QquXLh73dvbe9m1YQ06AxwOR9CxZVmDxs53fs2F6q+k5q+tWLFC5eXl9nFPT4+ys7NVWFgY9pe6AoGAGhoaNGPGDDmdzrDOjQ/Q5+igz9FBn6MjVvucW/HiUC8hZK5hln4w4VzYez3wiszlCGvQ8Xg8kt7fbcnKyrLHOzs77d0Xj8ejvr4+dXV1Be3qdHZ2avLkyXbNiRMnBs1/8uTJoHlaWlqCHu/q6lIgEBi00zPA5XLJ5XINGnc6nRH7Zo/k3PgAfY4O+hwd9Dk6Yq3P/v5Lbxh8nIW716HMFdbfozN69Gh5PJ6gLaq+vj7t2LHDDjF5eXlyOp1BNe3t7dq3b59dU1BQoO7ubu3evduuaWlpUXd3d1DNvn371N7ebtfU19fL5XIpLy8vnJcFAABiVMg7OmfOnNEbb7xhHx8+fFhtbW1KTU3VqFGjVFZWptWrV2vMmDEaM2aMVq9eraSkJJWWlkqS3G635s2bp6VLlyotLU2pqalatmyZxo0bZ9+FNXbsWM2cOVPz58/Xhg0bJEkLFixQcXGxcnJyJEmFhYW64YYb5PP59OCDD+qdd97RsmXLNH/+fO64AgAAkq4g6OzduzfojqaB97zMnTtXmzdv1r333quzZ89q0aJF6urqUn5+vurr65WSkmKfs27dOg0fPlxz5szR2bNnNW3aNG3evFlxcXF2zdatW7VkyRL77qySkpKg390TFxen559/XosWLdKUKVOUmJio0tJSPfTQQ6F3AQAAGCnkoDN16lRZ1sVvcXM4HKqoqFBFRcVFaxISElRVVaWqqqqL1qSmpqqmpuaSaxk1apSee+65D10zAAD4ZOKzrgAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxwh503nvvPf37v/+7Ro8ercTERF1//fX6/ve/r3Pnztk1lmWpoqJCXq9XiYmJmjp1qvbv3x80j9/v1+LFi5Wenq7k5GSVlJTo+PHjQTVdXV3y+Xxyu91yu93y+Xw6depUuC8JAADEqLAHnQceeEA//vGPVV1drYMHD6qyslIPPvigqqqq7JrKykqtXbtW1dXV2rNnjzwej2bMmKHTp0/bNWVlZdq+fbtqa2vV2NioM2fOqLi4WP39/XZNaWmp2traVFdXp7q6OrW1tcnn84X7kgAAQIwaHu4Jm5ub9cUvflGzZs2SJF133XX66U9/qr1790p6fzdn/fr1WrlypW6//XZJ0lNPPaXMzExt27ZNCxcuVHd3tzZt2qQtW7Zo+vTpkqSamhplZ2frpZdeUlFRkQ4ePKi6ujrt2rVL+fn5kqSNGzeqoKBAhw4dUk5OTrgvDQAAxJiw7+h8/vOf13//93/r9ddflyT97//+rxobG/WP//iPkqTDhw+ro6NDhYWF9jkul0s333yzmpqaJEmtra0KBAJBNV6vV7m5uXZNc3Oz3G63HXIkadKkSXK73XYNAAD4ZAv7js53vvMddXd369Of/rTi4uLU39+v+++/X//yL/8iSero6JAkZWZmBp2XmZmpI0eO2DXx8fEaOXLkoJqB8zs6OpSRkTHo+TMyMuya8/n9fvn9fvu4p6dHkhQIBBQIBK7kci9qYL5wz4tg9Dk66HN00OfoiNU+u+KsoV5CyFzD3l9zpH7GXo6wB52f/exnqqmp0bZt23TjjTeqra1NZWVl8nq9mjt3rl3ncDiCzrMsa9DY+c6vuVD9peZZs2aNVq1aNWi8vr5eSUlJl3zuK9XQ0BCReRGMPkcHfY4O+hwdsdbnyolDvYIrF+5e9/b2XnZt2IPOPffco+XLl+srX/mKJGncuHE6cuSI1qxZo7lz58rj8Uh6f0cmKyvLPq+zs9Pe5fF4POrr61NXV1fQrk5nZ6cmT55s15w4cWLQ8588eXLQbtGAFStWqLy83D7u6elRdna2CgsLNWLEiI945cECgYAaGho0Y8YMOZ3OsM6ND9Dn6KDP0UGfoyNW+5xb8eJQLyFkrmGWfjDhXNh7PfCKzOUIe9Dp7e3VsGHBb/2Ji4uzby8fPXq0PB6PGhoaNH78eElSX1+fduzYoQceeECSlJeXJ6fTqYaGBs2ZM0eS1N7ern379qmyslKSVFBQoO7ubu3evVsTJ74fc1taWtTd3W2HofO5XC65XK5B406nM2Lf7JGcGx+gz9FBn6ODPkdHrPXZ33/pVz0+zsLd61DmCnvQmT17tu6//36NGjVKN954o1577TWtXbtW//qv/yrp/ZebysrKtHr1ao0ZM0ZjxozR6tWrlZSUpNLSUkmS2+3WvHnztHTpUqWlpSk1NVXLli3TuHHj7Luwxo4dq5kzZ2r+/PnasGGDJGnBggUqLi7mjisAACApAkGnqqpK9913nxYtWqTOzk55vV4tXLhQ3/3ud+2ae++9V2fPntWiRYvU1dWl/Px81dfXKyUlxa5Zt26dhg8frjlz5ujs2bOaNm2aNm/erLi4OLtm69atWrJkiX13VklJiaqrq8N9SQAAIEaFPeikpKRo/fr1Wr9+/UVrHA6HKioqVFFRcdGahIQEVVVVBf2iwfOlpqaqpqbmI6wWAACYjM+6AgAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGNFJOj86U9/0le/+lWlpaUpKSlJn/3sZ9Xa2mo/blmWKioq5PV6lZiYqKlTp2r//v1Bc/j9fi1evFjp6elKTk5WSUmJjh8/HlTT1dUln88nt9stt9stn8+nU6dOReKSAABADAp70Onq6tKUKVPkdDr1q1/9SgcOHNDDDz+sq666yq6prKzU2rVrVV1drT179sjj8WjGjBk6ffq0XVNWVqbt27ertrZWjY2NOnPmjIqLi9Xf32/XlJaWqq2tTXV1daqrq1NbW5t8Pl+4LwkAAMSo4eGe8IEHHlB2draefPJJe+y6666z/9uyLK1fv14rV67U7bffLkl66qmnlJmZqW3btmnhwoXq7u7Wpk2btGXLFk2fPl2SVFNTo+zsbL300ksqKirSwYMHVVdXp127dik/P1+StHHjRhUUFOjQoUPKyckJ96UBAIAYE/ag8+yzz6qoqEj//M//rB07dujqq6/WokWLNH/+fEnS4cOH1dHRocLCQvscl8ulm2++WU1NTVq4cKFaW1sVCASCarxer3Jzc9XU1KSioiI1NzfL7XbbIUeSJk2aJLfbraampgsGHb/fL7/fbx/39PRIkgKBgAKBQFj7MDBfuOdFMPocHfQ5OuhzdMRqn11x1lAvIWSuYe+vOVI/Yy9H2IPOm2++qccee0zl5eX6t3/7N+3evVtLliyRy+XS1772NXV0dEiSMjMzg87LzMzUkSNHJEkdHR2Kj4/XyJEjB9UMnN/R0aGMjIxBz5+RkWHXnG/NmjVatWrVoPH6+nolJSWFfrGXoaGhISLzIhh9jg76HB30OTpirc+VE4d6BVcu3L3u7e297NqwB51z585pwoQJWr16tSRp/Pjx2r9/vx577DF97Wtfs+scDkfQeZZlDRo73/k1F6q/1DwrVqxQeXm5fdzT06Ps7GwVFhZqxIgRH35xIQgEAmpoaNCMGTPkdDrDOjc+QJ+jgz5HB32Ojljtc27Fi0O9hJC5hln6wYRzYe/1wCsylyPsQScrK0s33HBD0NjYsWP19NNPS5I8Ho+k93dksrKy7JrOzk57l8fj8aivr09dXV1BuzqdnZ2aPHmyXXPixIlBz3/y5MlBu0UDXC6XXC7XoHGn0xmxb/ZIzo0P0OfooM/RQZ+jI9b67O+/9GbAx1m4ex3KXGG/62rKlCk6dOhQ0Njrr7+ua6+9VpI0evRoeTyeoG2svr4+7dixww4xeXl5cjqdQTXt7e3at2+fXVNQUKDu7m7t3r3brmlpaVF3d7ddAwAAPtnCvqPz7W9/W5MnT9bq1as1Z84c7d69W48//rgef/xxSe+/3FRWVqbVq1drzJgxGjNmjFavXq2kpCSVlpZKktxut+bNm6elS5cqLS1NqampWrZsmcaNG2ffhTV27FjNnDlT8+fP14YNGyRJCxYsUHFxMXdcAQAASREIOp/73Oe0fft2rVixQt///vc1evRorV+/XnfccYddc++99+rs2bNatGiRurq6lJ+fr/r6eqWkpNg169at0/DhwzVnzhydPXtW06ZN0+bNmxUXF2fXbN26VUuWLLHvziopKVF1dXW4LwkAAMSosAcdSSouLlZxcfFFH3c4HKqoqFBFRcVFaxISElRVVaWqqqqL1qSmpqqmpuajLBUAABiMz7oCAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMNbwoV6A6XIrXpS/3zHUy7hsb/2/WUO9BAAAwoYdHQAAYCx2dBDkuuXPD/USQuKKs1Q5cahXAQD4uGJHBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMxUdAwAh8eCoA4ELY0QEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgrIgHnTVr1sjhcKisrMwesyxLFRUV8nq9SkxM1NSpU7V///6g8/x+vxYvXqz09HQlJyerpKREx48fD6rp6uqSz+eT2+2W2+2Wz+fTqVOnIn1JAAAgRkQ06OzZs0ePP/64PvOZzwSNV1ZWau3ataqurtaePXvk8Xg0Y8YMnT592q4pKyvT9u3bVVtbq8bGRp05c0bFxcXq7++3a0pLS9XW1qa6ujrV1dWpra1NPp8vkpcEAABiSMSCzpkzZ3THHXdo48aNGjlypD1uWZbWr1+vlStX6vbbb1dubq6eeuop9fb2atu2bZKk7u5ubdq0SQ8//LCmT5+u8ePHq6amRr/73e/00ksvSZIOHjyouro6/eQnP1FBQYEKCgq0ceNGPffcczp06FCkLgsAAMSQ4ZGa+K677tKsWbM0ffp0/cd//Ic9fvjwYXV0dKiwsNAec7lcuvnmm9XU1KSFCxeqtbVVgUAgqMbr9So3N1dNTU0qKipSc3Oz3G638vPz7ZpJkybJ7XarqalJOTk5g9bk9/vl9/vt456eHklSIBBQIBAI6/UPzOcaZoV1XgQb6G+s9Tln5XNDvYSQuIZZ+sEEhf3vCYIN9Jc+R1as9tkVF1v/zkkf/NscqZ+xlyMiQae2tlavvvqq9uzZM+ixjo4OSVJmZmbQeGZmpo4cOWLXxMfHB+0EDdQMnN/R0aGMjIxB82dkZNg151uzZo1WrVo1aLy+vl5JSUmXcWWh+8GEcxGZF8Hoc3Q0NDQM9RI+EehzdMRanysnDvUKrly4e93b23vZtWEPOseOHdPdd9+t+vp6JSQkXLTO4XAEHVuWNWjsfOfXXKj+UvOsWLFC5eXl9nFPT4+ys7NVWFioESNGXPK5QxUIBNTQ0KD79g6T/9ylrwtX7v2dhnP0OcIG+jxjxgw5nc6hXo6xBv7doM+RFat9zq14caiXELJI/dsx8IrM5Qh70GltbVVnZ6fy8vLssf7+fu3cuVPV1dX2+2c6OjqUlZVl13R2dtq7PB6PR319ferq6gra1ens7NTkyZPtmhMnTgx6/pMnTw7aLRrgcrnkcrkGjTudzoh9s/vPOeTv5wdwpNHn6Ijk3xV8gD5HR6z1OZb/jQt3r0OZK+xvRp42bZp+97vfqa2tzf6aMGGC7rjjDrW1ten666+Xx+MJ2sbq6+vTjh077BCTl5cnp9MZVNPe3q59+/bZNQUFBeru7tbu3bvtmpaWFnV3d9s1AADgky3sOzopKSnKzc0NGktOTlZaWpo9XlZWptWrV2vMmDEaM2aMVq9eraSkJJWWlkqS3G635s2bp6VLlyotLU2pqalatmyZxo0bp+nTp0uSxo4dq5kzZ2r+/PnasGGDJGnBggUqLi6+4BuRAQDAJ0/E7rq6lHvvvVdnz57VokWL1NXVpfz8fNXX1yslJcWuWbdunYYPH645c+bo7NmzmjZtmjZv3qy4uDi7ZuvWrVqyZIl9d1ZJSYmqq6ujfj0AAODjKSpB5ze/+U3QscPhUEVFhSoqKi56TkJCgqqqqlRVVXXRmtTUVNXU1IRplQAAwDR81hUAADAWQQcAABiLoAMAAIw1JG9GBhCbcitejKnf5fHW/5s11EsAMMTY0QEAAMYi6AAAAGPx0hUA4BMp1l6KxZVhRwcAABiLoAMAAIxF0AEAAMbiPToAjHXd8ueHegkhccVZqpw41KsAzMKODgAAMBZBBwAAGIugAwAAjEXQAQAAxuLNyACAj4Q3fePjjKADAB8z/MZeIHx46QoAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABgr7EFnzZo1+tznPqeUlBRlZGTotttu06FDh4JqLMtSRUWFvF6vEhMTNXXqVO3fvz+oxu/3a/HixUpPT1dycrJKSkp0/PjxoJquri75fD653W653W75fD6dOnUq3JcEAABiVNiDzo4dO3TXXXdp165damho0HvvvafCwkK9++67dk1lZaXWrl2r6upq7dmzRx6PRzNmzNDp06ftmrKyMm3fvl21tbVqbGzUmTNnVFxcrP7+frumtLRUbW1tqqurU11dndra2uTz+cJ9SQAAIEYND/eEdXV1QcdPPvmkMjIy1Nraqi984QuyLEvr16/XypUrdfvtt0uSnnrqKWVmZmrbtm1auHChuru7tWnTJm3ZskXTp0+XJNXU1Cg7O1svvfSSioqKdPDgQdXV1WnXrl3Kz8+XJG3cuFEFBQU6dOiQcnJywn1pAAAgxoQ96Jyvu7tbkpSamipJOnz4sDo6OlRYWGjXuFwu3XzzzWpqatLChQvV2tqqQCAQVOP1epWbm6umpiYVFRWpublZbrfbDjmSNGnSJLndbjU1NV0w6Pj9fvn9fvu4p6dHkhQIBBQIBMJ63QPzuYZZYZ0XwQb6S58jiz5HB32ODvocPQM9jtTP2MsR0aBjWZbKy8v1+c9/Xrm5uZKkjo4OSVJmZmZQbWZmpo4cOWLXxMfHa+TIkYNqBs7v6OhQRkbGoOfMyMiwa863Zs0arVq1atB4fX29kpKSQry6y/ODCeciMi+C0efooM/RQZ+jgz5HT0NDQ1jn6+3tvezaiAadb33rW/rtb3+rxsbGQY85HI6gY8uyBo2d7/yaC9Vfap4VK1aovLzcPu7p6VF2drYKCws1YsSISz53qAKBgBoaGnTf3mHyn7v0deHKuYZZ+sGEc/Q5wuhzdNDn6KDP0TPQ6xkzZsjpdIZt3oFXZC5HxILO4sWL9eyzz2rnzp265ppr7HGPxyPp/R2ZrKwse7yzs9Pe5fF4POrr61NXV1fQrk5nZ6cmT55s15w4cWLQ8548eXLQbtEAl8sll8s1aNzpdIb1D+Cv+c855O/nL1Kk0efooM/RQZ+jgz5HT7h/zoYyV9jvurIsS9/61rf0i1/8Qr/+9a81evTooMdHjx4tj8cTtI3V19enHTt22CEmLy9PTqczqKa9vV379u2zawoKCtTd3a3du3fbNS0tLeru7rZrAADAJ1vYd3Tuuusubdu2Tf/1X/+llJQU+/0ybrdbiYmJcjgcKisr0+rVqzVmzBiNGTNGq1evVlJSkkpLS+3aefPmaenSpUpLS1NqaqqWLVumcePG2XdhjR07VjNnztT8+fO1YcMGSdKCBQtUXFzMHVcAAEBSBILOY489JkmaOnVq0PiTTz6pO++8U5J077336uzZs1q0aJG6urqUn5+v+vp6paSk2PXr1q3T8OHDNWfOHJ09e1bTpk3T5s2bFRcXZ9ds3bpVS5Ysse/OKikpUXV1dbgvCQAAxKiwBx3L+vDb9RwOhyoqKlRRUXHRmoSEBFVVVamqquqiNampqaqpqbmSZQIAgE8APusKAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGCvmg86jjz6q0aNHKyEhQXl5eXrllVeGekkAAOBjIqaDzs9+9jOVlZVp5cqVeu211/QP//APuvXWW3X06NGhXhoAAPgYiOmgs3btWs2bN0/f+MY3NHbsWK1fv17Z2dl67LHHhnppAADgY2D4UC/gSvX19am1tVXLly8PGi8sLFRTU9MFz/H7/fL7/fZxd3e3JOmdd95RIBAI6/oCgYB6e3s1PDBM/eccYZ0bHxh+zlJv7zn6HGH0OTroc3TQ5+gZ6PXbb78tp9MZtnlPnz4tSbIs68PXELZnjbK//OUv6u/vV2ZmZtB4ZmamOjo6LnjOmjVrtGrVqkHjo0ePjsgaER2lQ72ATwj6HB30OTroc/REstenT5+W2+2+ZE3MBp0BDkdwGrcsa9DYgBUrVqi8vNw+PnfunN555x2lpaVd9Jwr1dPTo+zsbB07dkwjRowI69z4AH2ODvocHfQ5Ouhz9ESq15Zl6fTp0/J6vR9aG7NBJz09XXFxcYN2bzo7Owft8gxwuVxyuVxBY1dddVWklihJGjFiBH+RooA+Rwd9jg76HB30OXoi0esP28kZELNvRo6Pj1deXp4aGhqCxhsaGjR58uQhWhUAAPg4idkdHUkqLy+Xz+fThAkTVFBQoMcff1xHjx7VN7/5zaFeGgAA+BiI6aDz5S9/WW+//ba+//3vq729Xbm5uXrhhRd07bXXDvXS5HK59L3vfW/QS2UIL/ocHfQ5OuhzdNDn6Pk49NphXc69WQAAADEoZt+jAwAA8GEIOgAAwFgEHQAAYCyCDgAAMBZB5wo9+uijGj16tBISEpSXl6dXXnnlorW/+c1v5HA4Bn39/ve/j+KKY1covZbe/0yzlStX6tprr5XL5dKnPvUpPfHEE1FabewKpc933nnnBb+nb7zxxiiuODaF+v28detW3XTTTUpKSlJWVpa+/vWv6+23347SamNXqH1+5JFHNHbsWCUmJionJ0f/+Z//GaWVxq6dO3dq9uzZ8nq9cjgceuaZZz70nB07digvL08JCQm6/vrr9eMf/zjyC7UQstraWsvpdFobN260Dhw4YN19991WcnKydeTIkQvWv/zyy5Yk69ChQ1Z7e7v99d5770V55bEn1F5blmWVlJRY+fn5VkNDg3X48GGrpaXF+p//+Z8orjr2hNrnU6dOBX0vHzt2zEpNTbW+973vRXfhMSbUPr/yyivWsGHDrB/+8IfWm2++ab3yyivWjTfeaN12221RXnlsCbXPjz76qJWSkmLV1tZaf/zjH62f/vSn1t/8zd9Yzz77bJRXHlteeOEFa+XKldbTTz9tSbK2b99+yfo333zTSkpKsu6++27rwIED1saNGy2n02n9/Oc/j+g6CTpXYOLEidY3v/nNoLFPf/rT1vLlyy9YPxB0urq6orA6s4Ta61/96leW2+223n777Wgszxih9vl827dvtxwOh/XWW29FYnnGCLXPDz74oHX99dcHjf3oRz+yrrnmmoit0QSh9rmgoMBatmxZ0Njdd99tTZkyJWJrNM3lBJ17773X+vSnPx00tnDhQmvSpEkRXJll8dJViPr6+tTa2qrCwsKg8cLCQjU1NV3y3PHjxysrK0vTpk3Tyy+/HMllGuFKev3ss89qwoQJqqys1NVXX62/+7u/07Jly3T27NloLDkmfZTv6QGbNm3S9OnTPxa/rPPj6kr6PHnyZB0/flwvvPCCLMvSiRMn9POf/1yzZs2KxpJj0pX02e/3KyEhIWgsMTFRu3fvViAQiNhaP2mam5sH/bkUFRVp7969Ee0zQSdEf/nLX9Tf3z/og0MzMzMHfcDogKysLD3++ON6+umn9Ytf/EI5OTmaNm2adu7cGY0lx6wr6fWbb76pxsZG7du3T9u3b9f69ev185//XHfddVc0lhyTrqTPf629vV2/+tWv9I1vfCNSSzTClfR58uTJ2rp1q7785S8rPj5eHo9HV111laqqqqKx5Jh0JX0uKirST37yE7W2tsqyLO3du1dPPPGEAoGA/vKXv0Rj2Z8IHR0dF/xzee+99yLa55j+CIih5HA4go4tyxo0NiAnJ0c5OTn2cUFBgY4dO6aHHnpIX/jCFyK6ThOE0utz587J4XBo69at9ifbrl27Vl/60pf0yCOPKDExMeLrjVWh9Pmvbd68WVdddZVuu+22CK3MLKH0+cCBA1qyZIm++93vqqioSO3t7brnnnv0zW9+U5s2bYrGcmNWKH2+77771NHRoUmTJsmyLGVmZurOO+9UZWWl4uLiorHcT4wL/blcaDyc2NEJUXp6uuLi4gb9n0FnZ+egpHopkyZN0h/+8IdwL88oV9LrrKwsXX311XbIkaSxY8fKsiwdP348ouuNVR/le9qyLD3xxBPy+XyKj4+P5DJj3pX0ec2aNZoyZYruuecefeYzn1FRUZEeffRRPfHEE2pvb4/GsmPOlfQ5MTFRTzzxhHp7e/XWW2/p6NGjuu6665SSkqL09PRoLPsTwePxXPDPZfjw4UpLS4vY8xJ0QhQfH6+8vDw1NDQEjTc0NGjy5MmXPc9rr72mrKyscC/PKFfS6ylTpujPf/6zzpw5Y4+9/vrrGjZsmK655pqIrjdWfZTv6R07duiNN97QvHnzIrlEI1xJn3t7ezVsWPA/0wM7DBYfU3hBH+X72el06pprrlFcXJxqa2tVXFw8qP+4cgUFBYP+XOrr6zVhwgQ5nc7IPXFE3+psqIFbFzdt2mQdOHDAKisrs5KTk+07TpYvX275fD67ft26ddb27dut119/3dq3b5+1fPlyS5L19NNPD9UlxIxQe3369Gnrmmuusb70pS9Z+/fvt3bs2GGNGTPG+sY3vjFUlxATQu3zgK9+9atWfn5+tJcbs0Lt85NPPmkNHz7cevTRR60//vGPVmNjozVhwgRr4sSJQ3UJMSHUPh86dMjasmWL9frrr1stLS3Wl7/8ZSs1NdU6fPjwEF1BbDh9+rT12muvWa+99polyVq7dq312muv2bfxn9/ngdvLv/3tb1sHDhywNm3axO3lH2ePPPKIde2111rx8fHW3//931s7duywH5s7d651880328cPPPCA9alPfcpKSEiwRo4caX3+85+3nn/++SFYdWwKpdeWZVkHDx60pk+fbiUmJlrXXHONVV5ebvX29kZ51bEn1D6fOnXKSkxMtB5//PEorzS2hdrnH/3oR9YNN9xgJSYmWllZWdYdd9xhHT9+PMqrjj2h9PnAgQPWZz/7WSsxMdEaMWKE9cUvftH6/e9/PwSrji0Dvzrl/K+5c+dalnXh7+ff/OY31vjx4634+Hjruuuusx577LGIr9NhWex/AgAAM/HiIwAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADG+v8ASZ5SkGrQ41EAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('text', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8100    0.7273    0.7664     29306\n",
      "         1.0     0.6526    0.7501    0.6979     20010\n",
      "\n",
      "    accuracy                         0.7366     49316\n",
      "   macro avg     0.7313    0.7387    0.7322     49316\n",
      "weighted avg     0.7461    0.7366    0.7386     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8131    0.7303    0.7695     20463\n",
      "         1.0     0.6581    0.7556    0.7035     14058\n",
      "\n",
      "    accuracy                         0.7406     34521\n",
      "   macro avg     0.7356    0.7429    0.7365     34521\n",
      "weighted avg     0.7499    0.7406    0.7426     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8056    0.7264    0.7640      4400\n",
      "         1.0     0.6490    0.7427    0.6927      2997\n",
      "\n",
      "    accuracy                         0.7330      7397\n",
      "   macro avg     0.7273    0.7346    0.7283      7397\n",
      "weighted avg     0.7422    0.7330    0.7351      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8864    0.8908    0.8886       806\n",
      "         1.0     0.7412    0.7326    0.7368       344\n",
      "\n",
      "    accuracy                         0.8435      1150\n",
      "   macro avg     0.8138    0.8117    0.8127      1150\n",
      "weighted avg     0.8430    0.8435    0.8432      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7849    0.6895    0.7341      3594\n",
      "         1.0     0.6388    0.7441    0.6874      2653\n",
      "\n",
      "    accuracy                         0.7127      6247\n",
      "   macro avg     0.7119    0.7168    0.7108      6247\n",
      "weighted avg     0.7229    0.7127    0.7143      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8328    0.8302    0.8315       324\n",
      "         1.0     0.6562    0.6604    0.6583       159\n",
      "\n",
      "    accuracy                         0.7743       483\n",
      "   macro avg     0.7445    0.7453    0.7449       483\n",
      "weighted avg     0.7747    0.7743    0.7745       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 73.66\n",
      "    Training Set Accuracy: 74.06\n",
      "    Validation Set Accuracy: 73.30\n",
      "    Validation Set with both Images and Texts: 84.35\n",
      "    Validation Set with only Texts: 71.27\n",
      "    Validation Set with unseen Images: 77.43\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8100    0.7273    0.7664     29306\n",
      "         1.0     0.6526    0.7501    0.6979     20010\n",
      "\n",
      "    accuracy                         0.7366     49316\n",
      "   macro avg     0.7313    0.7387    0.7322     49316\n",
      "weighted avg     0.7461    0.7366    0.7386     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8131    0.7303    0.7695     20463\n",
      "         1.0     0.6581    0.7556    0.7035     14058\n",
      "\n",
      "    accuracy                         0.7406     34521\n",
      "   macro avg     0.7356    0.7429    0.7365     34521\n",
      "weighted avg     0.7499    0.7406    0.7426     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7999    0.7146    0.7549      4443\n",
      "         1.0     0.6302    0.7313    0.6770      2955\n",
      "\n",
      "    accuracy                         0.7213      7398\n",
      "   macro avg     0.7151    0.7230    0.7159      7398\n",
      "weighted avg     0.7322    0.7213    0.7238      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8622    0.8911    0.8764       716\n",
      "         1.0     0.7593    0.7069    0.7321       348\n",
      "\n",
      "    accuracy                         0.8308      1064\n",
      "   macro avg     0.8107    0.7990    0.8043      1064\n",
      "weighted avg     0.8285    0.8308    0.8292      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7857    0.6807    0.7294      3727\n",
      "         1.0     0.6167    0.7346    0.6705      2607\n",
      "\n",
      "    accuracy                         0.7029      6334\n",
      "   macro avg     0.7012    0.7076    0.7000      6334\n",
      "weighted avg     0.7162    0.7029    0.7052      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8077    0.8280    0.8177       279\n",
      "         1.0     0.6757    0.6452    0.6601       155\n",
      "\n",
      "    accuracy                         0.7627       434\n",
      "   macro avg     0.7417    0.7366    0.7389       434\n",
      "weighted avg     0.7605    0.7627    0.7614       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 73.66\n",
      "    Training Set Accuracy: 74.06\n",
      "    Validation Set Accuracy: 72.13\n",
      "    Validation Set with both Images and Texts: 83.08\n",
      "    Validation Set with only Texts: 70.29\n",
      "    Validation Set with unseen Images: 76.27\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.6908437  0.30915633]\n",
      " [0.28509027 0.71490973]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.725055\n",
      "std          0.154364\n",
      "min          0.500000\n",
      "25%          0.591344\n",
      "50%          0.695476\n",
      "75%          0.857792\n",
      "max          0.999892\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.724771\n",
      "std          0.154190\n",
      "min          0.500000\n",
      "25%          0.590955\n",
      "50%          0.695708\n",
      "75%          0.857140\n",
      "max          0.999892\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.725510\n",
      "std         0.154674\n",
      "min         0.500080\n",
      "25%         0.592104\n",
      "50%         0.696269\n",
      "75%         0.858160\n",
      "max         0.999668\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('multi', 'concat')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8004    0.7641    0.7818     29306\n",
      "         1.0     0.6761    0.7209    0.6978     20010\n",
      "\n",
      "    accuracy                         0.7466     49316\n",
      "   macro avg     0.7382    0.7425    0.7398     49316\n",
      "weighted avg     0.7499    0.7466    0.7477     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8059    0.7689    0.7870     20463\n",
      "         1.0     0.6847    0.7305    0.7068     14058\n",
      "\n",
      "    accuracy                         0.7533     34521\n",
      "   macro avg     0.7453    0.7497    0.7469     34521\n",
      "weighted avg     0.7566    0.7533    0.7543     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7877    0.7570    0.7720      4400\n",
      "         1.0     0.6626    0.7004    0.6809      2997\n",
      "\n",
      "    accuracy                         0.7341      7397\n",
      "   macro avg     0.7251    0.7287    0.7265      7397\n",
      "weighted avg     0.7370    0.7341    0.7351      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9025    0.8958    0.8991       806\n",
      "         1.0     0.7600    0.7733    0.7666       344\n",
      "\n",
      "    accuracy                         0.8591      1150\n",
      "   macro avg     0.8313    0.8345    0.8328      1150\n",
      "weighted avg     0.8599    0.8591    0.8595      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7609    0.7259    0.7430      3594\n",
      "         1.0     0.6505    0.6909    0.6701      2653\n",
      "\n",
      "    accuracy                         0.7111      6247\n",
      "   macro avg     0.7057    0.7084    0.7065      6247\n",
      "weighted avg     0.7140    0.7111    0.7120      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8233    0.8056    0.8144       324\n",
      "         1.0     0.6205    0.6478    0.6338       159\n",
      "\n",
      "    accuracy                         0.7536       483\n",
      "   macro avg     0.7219    0.7267    0.7241       483\n",
      "weighted avg     0.7566    0.7536    0.7549       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 74.66\n",
      "    Training Set Accuracy: 75.33\n",
      "    Validation Set Accuracy: 73.41\n",
      "    Validation Set with both Images and Texts: 85.91\n",
      "    Validation Set with only Texts: 71.11\n",
      "    Validation Set with unseen Images: 75.36\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8004    0.7641    0.7818     29306\n",
      "         1.0     0.6761    0.7209    0.6978     20010\n",
      "\n",
      "    accuracy                         0.7466     49316\n",
      "   macro avg     0.7382    0.7425    0.7398     49316\n",
      "weighted avg     0.7499    0.7466    0.7477     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8059    0.7689    0.7870     20463\n",
      "         1.0     0.6847    0.7305    0.7068     14058\n",
      "\n",
      "    accuracy                         0.7533     34521\n",
      "   macro avg     0.7453    0.7497    0.7469     34521\n",
      "weighted avg     0.7566    0.7533    0.7543     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7876    0.7493    0.7679      4443\n",
      "         1.0     0.6487    0.6961    0.6716      2955\n",
      "\n",
      "    accuracy                         0.7280      7398\n",
      "   macro avg     0.7181    0.7227    0.7197      7398\n",
      "weighted avg     0.7321    0.7280    0.7294      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8923    0.9022    0.8972       716\n",
      "         1.0     0.7941    0.7759    0.7849       348\n",
      "\n",
      "    accuracy                         0.8609      1064\n",
      "   macro avg     0.8432    0.8390    0.8411      1064\n",
      "weighted avg     0.8602    0.8609    0.8605      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7659    0.7199    0.7422      3727\n",
      "         1.0     0.6312    0.6855    0.6572      2607\n",
      "\n",
      "    accuracy                         0.7057      6334\n",
      "   macro avg     0.6986    0.7027    0.6997      6334\n",
      "weighted avg     0.7105    0.7057    0.7072      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8092    0.8208    0.8149       279\n",
      "         1.0     0.6689    0.6516    0.6601       155\n",
      "\n",
      "    accuracy                         0.7604       434\n",
      "   macro avg     0.7390    0.7362    0.7375       434\n",
      "weighted avg     0.7591    0.7604    0.7597       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 74.66\n",
      "    Training Set Accuracy: 75.33\n",
      "    Validation Set Accuracy: 72.80\n",
      "    Validation Set with both Images and Texts: 86.09\n",
      "    Validation Set with only Texts: 70.57\n",
      "    Validation Set with unseen Images: 76.04\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.6527988  0.34720126]\n",
      " [0.25418857 0.7458114 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.722678\n",
      "std          0.162547\n",
      "min          0.500014\n",
      "25%          0.577989\n",
      "50%          0.683955\n",
      "75%          0.872173\n",
      "max          0.999992\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.722554\n",
      "std          0.162441\n",
      "min          0.500023\n",
      "25%          0.577594\n",
      "50%          0.684398\n",
      "75%          0.871363\n",
      "max          0.999992\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.723498\n",
      "std         0.162794\n",
      "min         0.500014\n",
      "25%         0.578972\n",
      "50%         0.683980\n",
      "75%         0.872883\n",
      "max         0.999986\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perform_results['O2'] = statistics_F1(hyperdict, stored_data, df, labs, subfolder='002/')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9967be3",
   "metadata": {},
   "source": [
    "## Zero-shot Classification with Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1df6e275",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = {}\n",
    "for v in ['O1', 'O2']:\n",
    "    embeddings[v] = {}\n",
    "    for l in ['L2', 'L1', 'L0']:\n",
    "        embeddings[v][l] = {}\n",
    "        for t in ['val', 'test']:\n",
    "            embeddings[v][l][t] = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b022ae3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'O1': {'L2': {'val': {}, 'test': {}},\n",
       "  'L1': {'val': {}, 'test': {}},\n",
       "  'L0': {'val': {}, 'test': {}}},\n",
       " 'O2': {'L2': {'val': {}, 'test': {}},\n",
       "  'L1': {'val': {}, 'test': {}},\n",
       "  'L0': {'val': {}, 'test': {}}}}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e44c55c",
   "metadata": {},
   "source": [
    "### Option 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dbb347b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Namespace(model_state_file='MLP_classifier.pth', save_dir='model_storage/CLIP16/', embedding_size=512, early_stopping_criteria=20, num_epochs=20, seed=42, batch_size=32, embedding_type='multi', fusion_type='concat', hidden_dim=128, learning_rate=5e-05, l2=1e-05, dropout_p=0.1, catch_keyboard_interrupt=True, device='cuda', cuda=True, verbose=True)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2afe1375",
   "metadata": {},
   "outputs": [],
   "source": [
    "args.embedding_type = 'multi'\n",
    "args.fusion_type = 'complex'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "72541c20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (fc1): Linear(in_features=512, out_features=128, bias=True)\n",
       "  (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
       "  (fc_concat): Linear(in_features=1024, out_features=128, bias=True)\n",
       "  (fc_complex): Linear(in_features=2560, out_features=1024, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = MLPClassifier(embedding_size=args.embedding_size, \n",
    "                        hidden_dim=args.hidden_dim, \n",
    "                        embedding_type=args.embedding_type, \n",
    "                        fusion_type=args.fusion_type,\n",
    "                        num_classes=len(labels), \n",
    "                        dropout_p=args.dropout_p).to(args.device)\n",
    "classifier.load_state_dict(torch.load(args.save_dir + '001/' + args.embedding_type + \"_\" + args.fusion_type + \"_\" + args.model_state_file, map_location=torch.device('cpu')))\n",
    "classifier.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3961b71e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 128])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_emb = F.relu(classifier.dropout(classifier.fc_concat(F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(stored_data, args.embedding_type, args.fusion_type)))))))\n",
    "new_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2fc1bfcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "labs = {}\n",
    "labs['txt_emb'] = stored_data['lab_emb']\n",
    "empty_img_emb = stored_data['empty_img_emb']\n",
    "labs['img_emb'] = torch.vstack([empty_img_emb] * labs['txt_emb'].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6bf457bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 512])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labs['img_emb'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e01ffae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 128])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_lab_emb = F.relu(classifier.dropout(classifier.fc_concat(F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(labs, args.embedding_type, args.fusion_type)))))))\n",
    "new_lab_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3583acd6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero-shot using new multi-modal embedding:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8847    0.7367    0.8040     29306\n",
      "         1.0     0.6903    0.8594    0.7656     20010\n",
      "\n",
      "    accuracy                         0.7865     49316\n",
      "   macro avg     0.7875    0.7981    0.7848     49316\n",
      "weighted avg     0.8058    0.7865    0.7884     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9048    0.7543    0.8227     20463\n",
      "         1.0     0.7121    0.8845    0.7890     14058\n",
      "\n",
      "    accuracy                         0.8073     34521\n",
      "   macro avg     0.8084    0.8194    0.8058     34521\n",
      "weighted avg     0.8263    0.8073    0.8090     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8387    0.6998    0.7630      4400\n",
      "         1.0     0.6455    0.8025    0.7155      2997\n",
      "\n",
      "    accuracy                         0.7414      7397\n",
      "   macro avg     0.7421    0.7511    0.7392      7397\n",
      "weighted avg     0.7604    0.7414    0.7437      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9355    0.8995    0.9171       806\n",
      "         1.0     0.7840    0.8547    0.8178       344\n",
      "\n",
      "    accuracy                         0.8861      1150\n",
      "   macro avg     0.8597    0.8771    0.8675      1150\n",
      "weighted avg     0.8902    0.8861    0.8874      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8128    0.6550    0.7254      3594\n",
      "         1.0     0.6300    0.7957    0.7032      2653\n",
      "\n",
      "    accuracy                         0.7147      6247\n",
      "   macro avg     0.7214    0.7253    0.7143      6247\n",
      "weighted avg     0.7352    0.7147    0.7160      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8464    0.7654    0.8039       324\n",
      "         1.0     0.6000    0.7170    0.6533       159\n",
      "\n",
      "    accuracy                         0.7495       483\n",
      "   macro avg     0.7232    0.7412    0.7286       483\n",
      "weighted avg     0.7653    0.7495    0.7543       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 78.65\n",
      "    Training Set Accuracy: 80.73\n",
      "    Validation Set Accuracy: 74.14\n",
      "    Validation Set with both Images and Texts: 88.61\n",
      "    Validation Set with only Texts: 71.47\n",
      "    Validation Set with unseen Images: 74.95\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8847    0.7367    0.8040     29306\n",
      "         1.0     0.6903    0.8594    0.7656     20010\n",
      "\n",
      "    accuracy                         0.7865     49316\n",
      "   macro avg     0.7875    0.7981    0.7848     49316\n",
      "weighted avg     0.8058    0.7865    0.7884     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9048    0.7543    0.8227     20463\n",
      "         1.0     0.7121    0.8845    0.7890     14058\n",
      "\n",
      "    accuracy                         0.8073     34521\n",
      "   macro avg     0.8084    0.8194    0.8058     34521\n",
      "weighted avg     0.8263    0.8073    0.8090     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8375    0.6923    0.7580      4443\n",
      "         1.0     0.6330    0.7980    0.7060      2955\n",
      "\n",
      "    accuracy                         0.7345      7398\n",
      "   macro avg     0.7352    0.7451    0.7320      7398\n",
      "weighted avg     0.7558    0.7345    0.7372      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9362    0.9022    0.9189       716\n",
      "         1.0     0.8128    0.8736    0.8421       348\n",
      "\n",
      "    accuracy                         0.8929      1064\n",
      "   macro avg     0.8745    0.8879    0.8805      1064\n",
      "weighted avg     0.8959    0.8929    0.8938      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8146    0.6520    0.7243      3727\n",
      "         1.0     0.6130    0.7879    0.6895      2607\n",
      "\n",
      "    accuracy                         0.7079      6334\n",
      "   macro avg     0.7138    0.7199    0.7069      6334\n",
      "weighted avg     0.7316    0.7079    0.7100      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8386    0.7634    0.7992       279\n",
      "         1.0     0.6333    0.7355    0.6806       155\n",
      "\n",
      "    accuracy                         0.7535       434\n",
      "   macro avg     0.7360    0.7495    0.7399       434\n",
      "weighted avg     0.7653    0.7535    0.7569       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 78.65\n",
      "    Training Set Accuracy: 80.73\n",
      "    Validation Set Accuracy: 73.45\n",
      "    Validation Set with both Images and Texts: 89.29\n",
      "    Validation Set with only Texts: 70.79\n",
      "    Validation Set with unseen Images: 75.35\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "MUL_LAB = util.cos_sim(new_lab_emb, new_emb)\n",
    "pred_MUL = torch.argmax(MUL_LAB[:2,:], dim=0).float()\n",
    "lab = torch.tensor(np.array(df['skepticism'])).to('cuda').float()\n",
    "print('Zero-shot using new multi-modal embedding:')\n",
    "embeddings['O1']['L2']['val'] = evaluate_dataset_F1(df, pred_MUL)\n",
    "print('Zero-shot using new multi-modal embedding (test):')\n",
    "embeddings['O1']['L2']['test'] = evaluate_dataset_F1(df, pred_MUL, val_set='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "88e2ed53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 1024])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_emb = F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(stored_data, args.embedding_type, args.fusion_type))))\n",
    "new_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "109bc18d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 1024])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_lab_emb = F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(labs, args.embedding_type, args.fusion_type))))\n",
    "new_lab_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b3d4a62d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero-shot using new multi-modal embedding:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6573    0.8805    0.7527     29306\n",
      "         1.0     0.6519    0.3277    0.4361     20010\n",
      "\n",
      "    accuracy                         0.6562     49316\n",
      "   macro avg     0.6546    0.6041    0.5944     49316\n",
      "weighted avg     0.6551    0.6562    0.6243     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6562    0.8777    0.7510     20463\n",
      "         1.0     0.6501    0.3308    0.4385     14058\n",
      "\n",
      "    accuracy                         0.6550     34521\n",
      "   macro avg     0.6532    0.6042    0.5947     34521\n",
      "weighted avg     0.6537    0.6550    0.6237     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6571    0.8864    0.7547      4400\n",
      "         1.0     0.6580    0.3210    0.4315      2997\n",
      "\n",
      "    accuracy                         0.6573      7397\n",
      "   macro avg     0.6576    0.6037    0.5931      7397\n",
      "weighted avg     0.6575    0.6573    0.6238      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7680    0.9777    0.8603       806\n",
      "         1.0     0.8548    0.3081    0.4530       344\n",
      "\n",
      "    accuracy                         0.7774      1150\n",
      "   macro avg     0.8114    0.6429    0.6566      1150\n",
      "weighted avg     0.7940    0.7774    0.7384      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6339    0.8659    0.7320      3594\n",
      "         1.0     0.6398    0.3227    0.4290      2653\n",
      "\n",
      "    accuracy                         0.6352      6247\n",
      "   macro avg     0.6368    0.5943    0.5805      6247\n",
      "weighted avg     0.6364    0.6352    0.6033      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7387    0.9599    0.8349       324\n",
      "         1.0     0.7903    0.3082    0.4434       159\n",
      "\n",
      "    accuracy                         0.7453       483\n",
      "   macro avg     0.7645    0.6340    0.6392       483\n",
      "weighted avg     0.7557    0.7453    0.7060       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 65.62\n",
      "    Training Set Accuracy: 65.50\n",
      "    Validation Set Accuracy: 65.73\n",
      "    Validation Set with both Images and Texts: 77.74\n",
      "    Validation Set with only Texts: 63.52\n",
      "    Validation Set with unseen Images: 74.53\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6573    0.8805    0.7527     29306\n",
      "         1.0     0.6519    0.3277    0.4361     20010\n",
      "\n",
      "    accuracy                         0.6562     49316\n",
      "   macro avg     0.6546    0.6041    0.5944     49316\n",
      "weighted avg     0.6551    0.6562    0.6243     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6562    0.8777    0.7510     20463\n",
      "         1.0     0.6501    0.3308    0.4385     14058\n",
      "\n",
      "    accuracy                         0.6550     34521\n",
      "   macro avg     0.6532    0.6042    0.5947     34521\n",
      "weighted avg     0.6537    0.6550    0.6237     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6625    0.8879    0.7588      4443\n",
      "         1.0     0.6549    0.3198    0.4297      2955\n",
      "\n",
      "    accuracy                         0.6610      7398\n",
      "   macro avg     0.6587    0.6039    0.5943      7398\n",
      "weighted avg     0.6594    0.6610    0.6274      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7468    0.9763    0.8462       716\n",
      "         1.0     0.8672    0.3190    0.4664       348\n",
      "\n",
      "    accuracy                         0.7613      1064\n",
      "   macro avg     0.8070    0.6476    0.6563      1064\n",
      "weighted avg     0.7862    0.7613    0.7220      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6467    0.8709    0.7423      3727\n",
      "         1.0     0.6342    0.3199    0.4253      2607\n",
      "\n",
      "    accuracy                         0.6441      6334\n",
      "   macro avg     0.6405    0.5954    0.5838      6334\n",
      "weighted avg     0.6416    0.6441    0.6118      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6917    0.9570    0.8030       279\n",
      "         1.0     0.7500    0.2323    0.3547       155\n",
      "\n",
      "    accuracy                         0.6982       434\n",
      "   macro avg     0.7209    0.5946    0.5788       434\n",
      "weighted avg     0.7125    0.6982    0.6429       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 65.62\n",
      "    Training Set Accuracy: 65.50\n",
      "    Validation Set Accuracy: 66.10\n",
      "    Validation Set with both Images and Texts: 76.13\n",
      "    Validation Set with only Texts: 64.41\n",
      "    Validation Set with unseen Images: 69.82\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "MUL_LAB = util.cos_sim(new_lab_emb, new_emb)\n",
    "pred_MUL = torch.argmax(MUL_LAB[:2,:], dim=0).float()\n",
    "lab = torch.tensor(np.array(df['skepticism'])).to('cuda').float()\n",
    "print('Zero-shot using new multi-modal embedding:')\n",
    "embeddings['O1']['L1']['val'] = evaluate_dataset_F1(df, pred_MUL)\n",
    "print('Zero-shot using new multi-modal embedding (test):')\n",
    "embeddings['O1']['L1']['test'] = evaluate_dataset_F1(df, pred_MUL, val_set='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f491e654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 2560])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_emb = prepare_embedding(stored_data, args.embedding_type, args.fusion_type)\n",
    "new_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a727b55c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2560])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_lab_emb = prepare_embedding(labs, args.embedding_type, args.fusion_type)\n",
    "new_lab_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ddbe5e58",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero-shot using new multi-modal embedding:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5965    0.0559    0.1022     29306\n",
      "         1.0     0.4059    0.9446    0.5678     20010\n",
      "\n",
      "    accuracy                         0.4165     49316\n",
      "   macro avg     0.5012    0.5003    0.3350     49316\n",
      "weighted avg     0.5192    0.4165    0.2911     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5963    0.0557    0.1018     20463\n",
      "         1.0     0.4074    0.9452    0.5694     14058\n",
      "\n",
      "    accuracy                         0.4179     34521\n",
      "   macro avg     0.5019    0.5004    0.3356     34521\n",
      "weighted avg     0.5194    0.4179    0.2922     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5826    0.0577    0.1050      4400\n",
      "         1.0     0.4044    0.9393    0.5654      2997\n",
      "\n",
      "    accuracy                         0.4149      7397\n",
      "   macro avg     0.4935    0.4985    0.3352      7397\n",
      "weighted avg     0.5104    0.4149    0.2916      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8500    0.0211    0.0412       806\n",
      "         1.0     0.3018    0.9913    0.4627       344\n",
      "\n",
      "    accuracy                         0.3113      1150\n",
      "   macro avg     0.5759    0.5062    0.2519      1150\n",
      "weighted avg     0.6860    0.3113    0.1673      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5697    0.0659    0.1182      3594\n",
      "         1.0     0.4243    0.9325    0.5832      2653\n",
      "\n",
      "    accuracy                         0.4340      6247\n",
      "   macro avg     0.4970    0.4992    0.3507      6247\n",
      "weighted avg     0.5080    0.4340    0.3157      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7500    0.0278    0.0536       324\n",
      "         1.0     0.3312    0.9811    0.4952       159\n",
      "\n",
      "    accuracy                         0.3416       483\n",
      "   macro avg     0.5406    0.5045    0.2744       483\n",
      "weighted avg     0.6121    0.3416    0.1990       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 41.65\n",
      "    Training Set Accuracy: 41.79\n",
      "    Validation Set Accuracy: 41.49\n",
      "    Validation Set with both Images and Texts: 31.13\n",
      "    Validation Set with only Texts: 43.40\n",
      "    Validation Set with unseen Images: 34.16\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5965    0.0559    0.1022     29306\n",
      "         1.0     0.4059    0.9446    0.5678     20010\n",
      "\n",
      "    accuracy                         0.4165     49316\n",
      "   macro avg     0.5012    0.5003    0.3350     49316\n",
      "weighted avg     0.5192    0.4165    0.2911     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5963    0.0557    0.1018     20463\n",
      "         1.0     0.4074    0.9452    0.5694     14058\n",
      "\n",
      "    accuracy                         0.4179     34521\n",
      "   macro avg     0.5019    0.5004    0.3356     34521\n",
      "weighted avg     0.5194    0.4179    0.2922     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6125    0.0551    0.1012      4443\n",
      "         1.0     0.4001    0.9475    0.5626      2955\n",
      "\n",
      "    accuracy                         0.4116      7398\n",
      "   macro avg     0.5063    0.5013    0.3319      7398\n",
      "weighted avg     0.5277    0.4116    0.2855      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7778    0.0196    0.0381       716\n",
      "         1.0     0.3289    0.9885    0.4935       348\n",
      "\n",
      "    accuracy                         0.3365      1064\n",
      "   macro avg     0.5533    0.5040    0.2658      1064\n",
      "weighted avg     0.6310    0.3365    0.1871      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6047    0.0620    0.1124      3727\n",
      "         1.0     0.4126    0.9421    0.5739      2607\n",
      "\n",
      "    accuracy                         0.4242      6334\n",
      "   macro avg     0.5087    0.5020    0.3432      6334\n",
      "weighted avg     0.5257    0.4242    0.3024      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7500    0.0215    0.0418       279\n",
      "         1.0     0.3592    0.9871    0.5267       155\n",
      "\n",
      "    accuracy                         0.3664       434\n",
      "   macro avg     0.5546    0.5043    0.2842       434\n",
      "weighted avg     0.6104    0.3664    0.2150       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 41.65\n",
      "    Training Set Accuracy: 41.79\n",
      "    Validation Set Accuracy: 41.16\n",
      "    Validation Set with both Images and Texts: 33.65\n",
      "    Validation Set with only Texts: 42.42\n",
      "    Validation Set with unseen Images: 36.64\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "MUL_LAB = util.cos_sim(new_lab_emb, new_emb)\n",
    "pred_MUL = torch.argmax(MUL_LAB[:2,:], dim=0).float()\n",
    "lab = torch.tensor(np.array(df['skepticism'])).to('cuda').float()\n",
    "print('Zero-shot using new multi-modal embedding:')\n",
    "embeddings['O1']['L0']['val'] = evaluate_dataset_F1(df, pred_MUL)\n",
    "print('Zero-shot using new multi-modal embedding (test):')\n",
    "embeddings['O1']['L0']['test'] = evaluate_dataset_F1(df, pred_MUL, val_set='test')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba260af9",
   "metadata": {},
   "source": [
    "### Option 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "87ab7f77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (fc1): Linear(in_features=512, out_features=128, bias=True)\n",
       "  (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
       "  (fc_concat): Linear(in_features=1024, out_features=128, bias=True)\n",
       "  (fc_complex): Linear(in_features=2560, out_features=1024, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = MLPClassifier(embedding_size=args.embedding_size, \n",
    "                        hidden_dim=args.hidden_dim, \n",
    "                        embedding_type=args.embedding_type, \n",
    "                        fusion_type=args.fusion_type,\n",
    "                        num_classes=len(labels), \n",
    "                        dropout_p=args.dropout_p).to(args.device)\n",
    "classifier.load_state_dict(torch.load(args.save_dir + '002/' + args.embedding_type + \"_\" + args.fusion_type + \"_\" + args.model_state_file,map_location=torch.device('cpu')))\n",
    "classifier.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "913f8c7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 128])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_emb = F.relu(classifier.dropout(classifier.fc_concat(F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(stored_data, args.embedding_type, args.fusion_type)))))))\n",
    "new_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bb70907b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 128])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_lab_emb = F.relu(classifier.dropout(classifier.fc_concat(F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(labs, args.embedding_type, args.fusion_type)))))))\n",
    "new_lab_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "dfbc8057",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero-shot using new multi-modal embedding:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8722    0.7325    0.7962     29306\n",
      "         1.0     0.6826    0.8428    0.7543     20010\n",
      "\n",
      "    accuracy                         0.7772     49316\n",
      "   macro avg     0.7774    0.7876    0.7753     49316\n",
      "weighted avg     0.7953    0.7772    0.7792     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8892    0.7484    0.8127     20463\n",
      "         1.0     0.7024    0.8643    0.7749     14058\n",
      "\n",
      "    accuracy                         0.7956     34521\n",
      "   macro avg     0.7958    0.8063    0.7938     34521\n",
      "weighted avg     0.8131    0.7956    0.7973     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8325    0.7002    0.7606      4400\n",
      "         1.0     0.6431    0.7931    0.7103      2997\n",
      "\n",
      "    accuracy                         0.7379      7397\n",
      "   macro avg     0.7378    0.7467    0.7355      7397\n",
      "weighted avg     0.7558    0.7379    0.7402      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9369    0.9020    0.9191       806\n",
      "         1.0     0.7888    0.8576    0.8217       344\n",
      "\n",
      "    accuracy                         0.8887      1150\n",
      "   macro avg     0.8628    0.8798    0.8704      1150\n",
      "weighted avg     0.8926    0.8887    0.8900      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8048    0.6550    0.7222      3594\n",
      "         1.0     0.6267    0.7848    0.6969      2653\n",
      "\n",
      "    accuracy                         0.7101      6247\n",
      "   macro avg     0.7158    0.7199    0.7096      6247\n",
      "weighted avg     0.7292    0.7101    0.7115      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8514    0.7778    0.8129       324\n",
      "         1.0     0.6150    0.7233    0.6647       159\n",
      "\n",
      "    accuracy                         0.7598       483\n",
      "   macro avg     0.7332    0.7505    0.7388       483\n",
      "weighted avg     0.7735    0.7598    0.7641       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 77.72\n",
      "    Training Set Accuracy: 79.56\n",
      "    Validation Set Accuracy: 73.79\n",
      "    Validation Set with both Images and Texts: 88.87\n",
      "    Validation Set with only Texts: 71.01\n",
      "    Validation Set with unseen Images: 75.98\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8722    0.7325    0.7962     29306\n",
      "         1.0     0.6826    0.8428    0.7543     20010\n",
      "\n",
      "    accuracy                         0.7772     49316\n",
      "   macro avg     0.7774    0.7876    0.7753     49316\n",
      "weighted avg     0.7953    0.7772    0.7792     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8892    0.7484    0.8127     20463\n",
      "         1.0     0.7024    0.8643    0.7749     14058\n",
      "\n",
      "    accuracy                         0.7956     34521\n",
      "   macro avg     0.7958    0.8063    0.7938     34521\n",
      "weighted avg     0.8131    0.7956    0.7973     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8325    0.6912    0.7553      4443\n",
      "         1.0     0.6301    0.7909    0.7014      2955\n",
      "\n",
      "    accuracy                         0.7310      7398\n",
      "   macro avg     0.7313    0.7410    0.7283      7398\n",
      "weighted avg     0.7516    0.7310    0.7338      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9255    0.9022    0.9137       716\n",
      "         1.0     0.8087    0.8506    0.8291       348\n",
      "\n",
      "    accuracy                         0.8853      1064\n",
      "   macro avg     0.8671    0.8764    0.8714      1064\n",
      "weighted avg     0.8873    0.8853    0.8861      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8108    0.6507    0.7219      3727\n",
      "         1.0     0.6105    0.7829    0.6861      2607\n",
      "\n",
      "    accuracy                         0.7051      6334\n",
      "   macro avg     0.7106    0.7168    0.7040      6334\n",
      "weighted avg     0.7284    0.7051    0.7072      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8238    0.7706    0.7963       279\n",
      "         1.0     0.6301    0.7032    0.6646       155\n",
      "\n",
      "    accuracy                         0.7465       434\n",
      "   macro avg     0.7269    0.7369    0.7305       434\n",
      "weighted avg     0.7546    0.7465    0.7493       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 77.72\n",
      "    Training Set Accuracy: 79.56\n",
      "    Validation Set Accuracy: 73.10\n",
      "    Validation Set with both Images and Texts: 88.53\n",
      "    Validation Set with only Texts: 70.51\n",
      "    Validation Set with unseen Images: 74.65\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "MUL_LAB = util.cos_sim(new_lab_emb, new_emb)\n",
    "pred_MUL = torch.argmax(MUL_LAB[:2,:], dim=0).float()\n",
    "lab = torch.tensor(np.array(df['skepticism'])).to('cuda').float()\n",
    "print('Zero-shot using new multi-modal embedding:')\n",
    "embeddings['O2']['L2']['val'] = evaluate_dataset_F1(df, pred_MUL)\n",
    "print('Zero-shot using new multi-modal embedding (test):')\n",
    "embeddings['O2']['L2']['test'] = evaluate_dataset_F1(df, pred_MUL, val_set='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d6aa36c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 1024])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_emb = F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(stored_data, args.embedding_type, args.fusion_type))))\n",
    "new_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ffcaaf9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 1024])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_lab_emb = F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(labs, args.embedding_type, args.fusion_type))))\n",
    "new_lab_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "aec96d26",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero-shot using new multi-modal embedding:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7702    0.7581    0.7641     29306\n",
      "         1.0     0.6537    0.6688    0.6612     20010\n",
      "\n",
      "    accuracy                         0.7219     49316\n",
      "   macro avg     0.7120    0.7135    0.7127     49316\n",
      "weighted avg     0.7230    0.7219    0.7224     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7750    0.7595    0.7672     20463\n",
      "         1.0     0.6598    0.6790    0.6693     14058\n",
      "\n",
      "    accuracy                         0.7267     34521\n",
      "   macro avg     0.7174    0.7193    0.7182     34521\n",
      "weighted avg     0.7281    0.7267    0.7273     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7607    0.7614    0.7610      4400\n",
      "         1.0     0.6492    0.6483    0.6487      2997\n",
      "\n",
      "    accuracy                         0.7156      7397\n",
      "   macro avg     0.7049    0.7048    0.7049      7397\n",
      "weighted avg     0.7155    0.7156    0.7155      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8943    0.9132    0.9036       806\n",
      "         1.0     0.7859    0.7471    0.7660       344\n",
      "\n",
      "    accuracy                         0.8635      1150\n",
      "   macro avg     0.8401    0.8301    0.8348      1150\n",
      "weighted avg     0.8619    0.8635    0.8625      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7300    0.7273    0.7286      3594\n",
      "         1.0     0.6324    0.6355    0.6340      2653\n",
      "\n",
      "    accuracy                         0.6883      6247\n",
      "   macro avg     0.6812    0.6814    0.6813      6247\n",
      "weighted avg     0.6885    0.6883    0.6884      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8223    0.8426    0.8323       324\n",
      "         1.0     0.6623    0.6289    0.6452       159\n",
      "\n",
      "    accuracy                         0.7723       483\n",
      "   macro avg     0.7423    0.7358    0.7387       483\n",
      "weighted avg     0.7696    0.7723    0.7707       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.19\n",
      "    Training Set Accuracy: 72.67\n",
      "    Validation Set Accuracy: 71.56\n",
      "    Validation Set with both Images and Texts: 86.35\n",
      "    Validation Set with only Texts: 68.83\n",
      "    Validation Set with unseen Images: 77.23\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7702    0.7581    0.7641     29306\n",
      "         1.0     0.6537    0.6688    0.6612     20010\n",
      "\n",
      "    accuracy                         0.7219     49316\n",
      "   macro avg     0.7120    0.7135    0.7127     49316\n",
      "weighted avg     0.7230    0.7219    0.7224     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7750    0.7595    0.7672     20463\n",
      "         1.0     0.6598    0.6790    0.6693     14058\n",
      "\n",
      "    accuracy                         0.7267     34521\n",
      "   macro avg     0.7174    0.7193    0.7182     34521\n",
      "weighted avg     0.7281    0.7267    0.7273     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7580    0.7488    0.7534      4443\n",
      "         1.0     0.6291    0.6406    0.6348      2955\n",
      "\n",
      "    accuracy                         0.7056      7398\n",
      "   macro avg     0.6936    0.6947    0.6941      7398\n",
      "weighted avg     0.7065    0.7056    0.7060      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8583    0.9134    0.8850       716\n",
      "         1.0     0.7947    0.6897    0.7385       348\n",
      "\n",
      "    accuracy                         0.8402      1064\n",
      "   macro avg     0.8265    0.8015    0.8117      1064\n",
      "weighted avg     0.8375    0.8402    0.8371      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7370    0.7172    0.7270      3727\n",
      "         1.0     0.6106    0.6341    0.6221      2607\n",
      "\n",
      "    accuracy                         0.6830      6334\n",
      "   macro avg     0.6738    0.6756    0.6745      6334\n",
      "weighted avg     0.6850    0.6830    0.6838      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7796    0.8495    0.8130       279\n",
      "         1.0     0.6769    0.5677    0.6175       155\n",
      "\n",
      "    accuracy                         0.7488       434\n",
      "   macro avg     0.7283    0.7086    0.7153       434\n",
      "weighted avg     0.7429    0.7488    0.7432       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.19\n",
      "    Training Set Accuracy: 72.67\n",
      "    Validation Set Accuracy: 70.56\n",
      "    Validation Set with both Images and Texts: 84.02\n",
      "    Validation Set with only Texts: 68.30\n",
      "    Validation Set with unseen Images: 74.88\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "MUL_LAB = util.cos_sim(new_lab_emb, new_emb)\n",
    "pred_MUL = torch.argmax(MUL_LAB[:2,:], dim=0).float()\n",
    "lab = torch.tensor(np.array(df['skepticism'])).to('cuda').float()\n",
    "print('Zero-shot using new multi-modal embedding:')\n",
    "embeddings['O2']['L1']['val'] = evaluate_dataset_F1(df, pred_MUL)\n",
    "print('Zero-shot using new multi-modal embedding (test):')\n",
    "embeddings['O2']['L1']['test'] = evaluate_dataset_F1(df, pred_MUL, val_set='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8bbf9085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 2560])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_emb = prepare_embedding(stored_data, args.embedding_type, args.fusion_type)\n",
    "new_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fe5505c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2560])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_lab_emb = prepare_embedding(labs, args.embedding_type, args.fusion_type)\n",
    "new_lab_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "68d0e2e6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero-shot using new multi-modal embedding:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5965    0.0559    0.1022     29306\n",
      "         1.0     0.4059    0.9446    0.5678     20010\n",
      "\n",
      "    accuracy                         0.4165     49316\n",
      "   macro avg     0.5012    0.5003    0.3350     49316\n",
      "weighted avg     0.5192    0.4165    0.2911     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5963    0.0557    0.1018     20463\n",
      "         1.0     0.4074    0.9452    0.5694     14058\n",
      "\n",
      "    accuracy                         0.4179     34521\n",
      "   macro avg     0.5019    0.5004    0.3356     34521\n",
      "weighted avg     0.5194    0.4179    0.2922     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5826    0.0577    0.1050      4400\n",
      "         1.0     0.4044    0.9393    0.5654      2997\n",
      "\n",
      "    accuracy                         0.4149      7397\n",
      "   macro avg     0.4935    0.4985    0.3352      7397\n",
      "weighted avg     0.5104    0.4149    0.2916      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8500    0.0211    0.0412       806\n",
      "         1.0     0.3018    0.9913    0.4627       344\n",
      "\n",
      "    accuracy                         0.3113      1150\n",
      "   macro avg     0.5759    0.5062    0.2519      1150\n",
      "weighted avg     0.6860    0.3113    0.1673      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5697    0.0659    0.1182      3594\n",
      "         1.0     0.4243    0.9325    0.5832      2653\n",
      "\n",
      "    accuracy                         0.4340      6247\n",
      "   macro avg     0.4970    0.4992    0.3507      6247\n",
      "weighted avg     0.5080    0.4340    0.3157      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7500    0.0278    0.0536       324\n",
      "         1.0     0.3312    0.9811    0.4952       159\n",
      "\n",
      "    accuracy                         0.3416       483\n",
      "   macro avg     0.5406    0.5045    0.2744       483\n",
      "weighted avg     0.6121    0.3416    0.1990       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 41.65\n",
      "    Training Set Accuracy: 41.79\n",
      "    Validation Set Accuracy: 41.49\n",
      "    Validation Set with both Images and Texts: 31.13\n",
      "    Validation Set with only Texts: 43.40\n",
      "    Validation Set with unseen Images: 34.16\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5965    0.0559    0.1022     29306\n",
      "         1.0     0.4059    0.9446    0.5678     20010\n",
      "\n",
      "    accuracy                         0.4165     49316\n",
      "   macro avg     0.5012    0.5003    0.3350     49316\n",
      "weighted avg     0.5192    0.4165    0.2911     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5963    0.0557    0.1018     20463\n",
      "         1.0     0.4074    0.9452    0.5694     14058\n",
      "\n",
      "    accuracy                         0.4179     34521\n",
      "   macro avg     0.5019    0.5004    0.3356     34521\n",
      "weighted avg     0.5194    0.4179    0.2922     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6125    0.0551    0.1012      4443\n",
      "         1.0     0.4001    0.9475    0.5626      2955\n",
      "\n",
      "    accuracy                         0.4116      7398\n",
      "   macro avg     0.5063    0.5013    0.3319      7398\n",
      "weighted avg     0.5277    0.4116    0.2855      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7778    0.0196    0.0381       716\n",
      "         1.0     0.3289    0.9885    0.4935       348\n",
      "\n",
      "    accuracy                         0.3365      1064\n",
      "   macro avg     0.5533    0.5040    0.2658      1064\n",
      "weighted avg     0.6310    0.3365    0.1871      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6047    0.0620    0.1124      3727\n",
      "         1.0     0.4126    0.9421    0.5739      2607\n",
      "\n",
      "    accuracy                         0.4242      6334\n",
      "   macro avg     0.5087    0.5020    0.3432      6334\n",
      "weighted avg     0.5257    0.4242    0.3024      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7500    0.0215    0.0418       279\n",
      "         1.0     0.3592    0.9871    0.5267       155\n",
      "\n",
      "    accuracy                         0.3664       434\n",
      "   macro avg     0.5546    0.5043    0.2842       434\n",
      "weighted avg     0.6104    0.3664    0.2150       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 41.65\n",
      "    Training Set Accuracy: 41.79\n",
      "    Validation Set Accuracy: 41.16\n",
      "    Validation Set with both Images and Texts: 33.65\n",
      "    Validation Set with only Texts: 42.42\n",
      "    Validation Set with unseen Images: 36.64\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "MUL_LAB = util.cos_sim(new_lab_emb, new_emb)\n",
    "pred_MUL = torch.argmax(MUL_LAB[:2,:], dim=0).float()\n",
    "lab = torch.tensor(np.array(df['skepticism'])).to('cuda').float()\n",
    "print('Zero-shot using new multi-modal embedding:')\n",
    "embeddings['O2']['L0']['val'] = evaluate_dataset_F1(df, pred_MUL)\n",
    "print('Zero-shot using new multi-modal embedding (test):')\n",
    "embeddings['O2']['L0']['test'] = evaluate_dataset_F1(df, pred_MUL, val_set='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b46a0293",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {}\n",
    "results['fusion'] = perform_results\n",
    "results['new_embedding'] = embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "23074568",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(args.result_dir + 'results_F1.pkl', \"wb\") as fOut:\n",
    "    pickle.dump(results, fOut, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df26a1ad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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