CLIP-ML_EN-Evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e7bd18d3",
   "metadata": {},
   "source": [
    "# Multi-modal Embedding using Multilingual CLIP (English)"
   ]
  },
  {
   "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": 5,
   "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": 5,
     "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": 6,
   "id": "44baebe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We use the original CLIP model for computing image embeddings and English text embeddings\n",
    "txt_model = SentenceTransformer('clip-ViT-B-32-multilingual-v1')\n",
    "img_model = SentenceTransformer('clip-ViT-B-32')\n",
    "txt_tokenizer = txt_model.tokenizer\n",
    "img_tokenizer = img_model._first_module().processor.tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c10f520a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def embed_documents(documents, embedding_model=txt_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=txt_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": 8,
   "id": "a5a051b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['text_EN_trun'] = df['text_EN'].apply(truncate_document)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3d195eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def embed_images(images, verbose=True, batch_size=32, embedding_model=img_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": 10,
   "id": "54bd8cdf",
   "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": 11,
   "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": "071147c7",
   "metadata": {},
   "source": [
    "## Process Raw Data into Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ecee2fc",
   "metadata": {},
   "source": [
    "Two sentences to compute embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "66eca625",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11d358ff0fdf4fab8d905a2f6083c42a",
       "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 = txt_model.encode(labels, convert_to_tensor=True)\n",
    "empty_img_emb = embed_images(['nan'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3e0ecac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 512])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "empty_img_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84c4e0e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt_emb = embed_documents(list(df['text_EN']), convert_to_tensor=True, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8780d38e",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_emb = embed_images(list(df['target']), convert_to_tensor=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b7b2003",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('data/CLIP_ML_EN/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": 16,
   "id": "96befd9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/CLIP_ML_EN/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": 17,
   "id": "990b44e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def zero_shot(df_all, model_name='CLIP_ML_EN', 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": 18,
   "id": "cb85005f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using textual embedding only:\n",
      "\n",
      "    Overall Accuracy: 61.75\n",
      "    Training Set Accuracy: 61.87\n",
      "    Validation Set Accuracy: 62.00\n",
      "    Validation Set with both Images and Texts: 69.39\n",
      "    Validation Set with only Texts: 60.64\n",
      "    Validation Set with unseen Images: 64.18\n",
      "    \n",
      "Using image embedding only:\n",
      "\n",
      "    Overall Accuracy: 44.69\n",
      "    Training Set Accuracy: 44.82\n",
      "    Validation Set Accuracy: 45.17\n",
      "    Validation Set with both Images and Texts: 59.83\n",
      "    Validation Set with only Texts: 42.47\n",
      "    Validation Set with unseen Images: 61.28\n",
      "    \n",
      "Using multi-modal embedding:\n",
      "\n",
      "    Overall Accuracy: 49.91\n",
      "    Training Set Accuracy: 49.93\n",
      "    Validation Set Accuracy: 50.62\n",
      "    Validation Set with both Images and Texts: 68.70\n",
      "    Validation Set with only Texts: 47.29\n",
      "    Validation Set with unseen Images: 67.49\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: 49.91\n",
      "    Training Set Accuracy: 49.93\n",
      "    Validation Set Accuracy: 49.09\n",
      "    Validation Set with both Images and Texts: 66.82\n",
      "    Validation Set with only Texts: 46.12\n",
      "    Validation Set with unseen Images: 64.98\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": 19,
   "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/CLIP_ML_EN/',\n",
    "    result_dir='results/CLIP_ML_EN/',\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": 20,
   "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": 21,
   "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": 22,
   "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": 23,
   "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": 24,
   "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": 25,
   "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": 26,
   "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": 27,
   "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": 28,
   "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": 29,
   "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": 30,
   "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": 31,
   "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": 36,
   "id": "731aeca7",
   "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": "d332249a",
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperdict = {}\n",
    "experiment(df, stored_data, hyperdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74dacbeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperdict = {}\n",
    "experiment(df, stored_data, labs, hyperdict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efffe68d",
   "metadata": {},
   "source": [
    "## Performance on Trained Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f6295688",
   "metadata": {},
   "outputs": [],
   "source": [
    "perform_results = {}\n",
    "for o in ['O1', 'O2']:\n",
    "    perform_results[o] = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 35,
   "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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c/tFmjllsBPvpeee6fozsHNXgryNaCFshbPtE7V4pUKdXozVA7yvV1orYXqd/bo8Hdn0Dvz0HFnXbAqJ7wAUzodP5zX9mjKLAgZ/LQsdW9ZghQO1OGnYfBJy8EGFLI+FDiCZ0MKuIC/79B4oCi+8dQc82Ib6uEnklDmb9uJcFW9MBiA32Y+ZlPZjQI7bBXuOn3Rk8+NUOrE43HSID+O9NA+gQJTv2tkr5qbDxfdjyETjU8Uf4h8PAsvEcQQ33d4fTpr7WqlfAZlGPtR+lzoyJ79twr9NQFEVtuVn5AqSXrathMKufy/D7ISDSt/VrQBI+hGhC936xjUU7jjG+ewzv3zjA19WpYvWBHJ74fhepuerOuhN6xDDz0p7Ehvid9jkVReGt3w7y2s/7AXWQ69vX9CPEXIeWFY8bsv6EgCgIjG7+v1ZF7dI2quM5/lwIStkOxJFd1IGSfa5u3HEZpXmw+rWymTEO9VivaTDuSQhLarzXrStFUceqrHwBjm5Sj+n9YdBtMOx+CDz7WgglfAjRRPYfL2LC62qrx5L7RtI9vvn9zdqcbt789QDv/3EIl0ch0KTnkQu7ct3gxHov1251uHn42x0s3pkBwM3Dk3hiYre67TSbnwrzb634h9g/DKK7Q3Q3db2B8tstuM+72XCUqAt3OW3qYlJarXpd5aKrdFtTVk5XTblqLkfLQkf5f0uADmPV8Rwdz2vQtSNOKT8VVpTPjAF0Rhh0B4z8h2/+lhQFDq1Qp8yWj3fR+6utQMPvV0P3WUrChxBN5O+fbeXHXRlc2COW927o7+vq1OqvzEIeX7CLbUcKAOjbLpQXJvfinNi6/X+WabFx+8eb2ZVuQa/V8NzlPblmULu6vfie72HhfWC3qGsXeJwVv5RPFBijhhBvMOkG0eeASdYKqZGiQPZfcPAX9ZK6tqI1oDHpjNDrShh6N8T0aPzXq03GDvj5aTi0Ur1vCoGRD6kDOZtiZoyiwOHfYcULkLZePab3gwG3wPAHIOjsX2RPwoc4Kx3MKmbZnkyGdYxokjU0TuWvzEIufH0VAD89MLLOX+K+5PYofL4hlZd+2kex3YVeq+GOUR2477zOte63sj2tgDs+3kxWkZ3wACNzruvH4Gp26j2J0wo/PQ5b5qr32w6CKf9Vf/3l7Fe7YCpfLEdqPldIOzWElAeTqHMgqmvLn3J5umwW9Yv24C/qmILC9KqPB7dVxxMonpovHrf6pVnluPuE+0pZuUrHzBHQ/yZ13EJz+yV/0syYtjDuCeh9VePNjDn8hxo6jqxV7+tMaugY8UDDjndp5iR8iLOG1eFmya4Mvtx0hE0p+QAEGHV8fddQesT7dmDn3z7dwtLdmVzcK453rmtZS0BnWmw8s3A3y/YcByAxwszsK3oxvNPJg99+2J7Ow9/uxOHy0DUmiP/eNICE8FrWZiiX9Sd8ewtk7QU0MOJBGPtP0NUyNsReBNn71Odk/VV2/ScUZ1ZfXqOFsPZlgaQbJA6HDmPOzrEkHg9k7qwIG2kbKtbNAPULL2mEOvOj0/kQ2fns/BzqwuOGnV/Db7OgUF17htB2amhqaI5SyNmn3taZoP909W+98qJprYSED9Hi7U638OWmI/yw7RhFdhcAOq2GmCATxyw2YoJNfP/34cSF+OZX755jFi5+czUaDSx7YBRdYlpml8CyPZk888MeMgttAEzu14YnL+5OeIARj0fhleX7eHdlMgDnd4vm9av7Emg6xX6UiqKuYrn0UXBZISAaJr8PHceefkVL89RuBW8o+VO9bc07uWyb/jDm8ZYx/fJUSnIgeYUaOJJ/VTcZqyyic0XYSBxW+4JdrZHTBhv/A3+8qnb5NRadUV0afuRD6nLorZSED9EiFdqcLNx+jC83HWF3eqH3eEK4P1cPbMfU/m3xM+iYOmctB7KK6RYXzDd3DT31l2EjuOPjzSzfe5xJfeJ565pmOL2vHopsTl5Zto+P16eiKBBmNvDYRefwy59Z/LxXbRn525iOPDy+K9pTDVC1WWDR/bDnO/V+x3FwxX8ap2leUaA4C7LLumwyd8HuBWrgAWgzAMY+rg6AbCkhxO1SB4qWj904to0qO5UaA9WFujqdp16aw6yOZsblcZFjzeFY8TEySjLUiyWFYksa8aZQEkwRJPhFkOAXSbQxGK2mAQbHxvVpVd0rNZHwIVoMRVHYkprPl5vS+HFnhne3VqNOy/geMVwzqB1DO0RU+dJLyyvlinfXklNsZ0zXKP5744C6zbZoILvTLVzyltrq8fODo5rtlvb1te1IPo8v2MVfmUXeY0a9lpem9OKKvm1PfYKjm9VuloJU0OrVzcCG3de0Mx+Ks2DNG7Dpw4oQ0nYQjHlMDULNMYQUZqitGgd+VmdJ2E74hR7TqyxsnA8Jg326cVhzUOosJbMkk4ySDI6VHCOjWA0Yx4qPkVmSyfHS47grd0fVwqg10iaoDQlBCVUubYPa0jawLUZd6/6s60vCh2j28kocLNh6lC83pXEwq9h7vFN0IFcPTGByv7a1bki2Pa2Aq99fh83p4foh7Xjusp5Ntpz3bR9t4pc/s7js3HjeuLplt3qcyOn28N9Vh3n9l/0E+xv4zw396Xeqwb0eD6x7C379F3hcat/61LnQ1odrnhQdV0PI5g/BpXYpkTBY7Y5pDmNCLOmwZwHs+hYytld9zC9U7aLqdIEamFrR2AFFUciz5ZFZknlSsChvxSiwF5zyPHqNnpiAGOIC4ogPjCc2IJYAQwDHio+RVpRGWlEaGcUZuBRXjefQoCEmIEYNI4FtTwonISbfjDlze9xYXdaTLqWu0qrHnCeXsbqs2Fw2rC4rGo2GD8Z/0KB1k/AhmiWPR2FNcg5fbkpj+Z5MnG71T8/foOOS3nFcPSiBfu3C6hwiftqdyd8+24KiwJMXd+O2kR0as/oA7DxawKVvr0GrgZ8fGk3Hs3RFT4vViVGnxd94itkBxVnw3V3qL3eA7pfDpDfAP7Sxq1g3RZmw+nXY/L+KLdrbDVVDSPtRTRtCSvPUzdV2fQupa6joTtFAm34VYzfi+4Gu6bsSfSWjOIM1x9awJn0NGzI2UOQsOuVzAgwB3mARFxB30u1I/0h0p5jZ4vK4yCzJ9IaRo0VHvbfTitIodZXW+vxgY7A3jAQYAvAoHjyKBwUFt+JWbytKxTGPGw8Vx8pvuxV3xbFKz1cUBZfHpQYGt80bKByehplCrdfq2XbDtgY5VzkJH6JZybTY+GZzGl9tTuNovtV7vFebEK4elMClfeJPe9+R/646xKwf/0SjgTnX9efCno3b73rz3I2s2JfN5L5teO2qcxv1tZq95BXw3Z1QfFxdz+Cil9RBd75uVahOYQas/jdsmVcRQhKHq90x7Uc13uvai2HfUtj9rTqGw1Ppl3a7odBrKnS77Kxc7bImdredLce3sCZdDRzJluQqj2vQEOUfRWxgLPEBZYEiMM4bLOIC4wg2Nu53Q3kLTE3BJNeW26ivXxdajRZ/vT9+Oj/89f74G/zV60oXs9580rHyi5/ej7EJYxu0xVjCh/A5l9vDin3ZfLnxCCv2ZeEp+ysL8tNz+bltuGpgQoPsgaIoCk//sIdP1qfiZ9Dy5R1DOTch9IzPW51tR/K54t216LQafnloNO0jAxrldZo9t1NdMnrVa4CiLgI2ba461bW5Kzym1nvrRxWLcCWOUAemJo1omNdwOdSWoF3fqMHDWekXdGwv6DkVek6B0ISGeb0WILUwldXpq1mTvoZNmZuwuW3ex7QaLb0jezO8zXCGxw+na3jXZj/WotRZytHio95g4nA70Gg0aDVatGjVa422yjGNRoNOo6tyXIN6rdPoTipbftxP73dyiDD4Y9Qam93O0RI+hE8oisLOoxZ+3JXB99vSySqyex8blBTOVQMTmNgr7tRN+fXkcnu4/ePNrNiXTWSgke/uHl63dSjq6ab/beT3/dlM7d+WV6b1afDztwgFR+DbW9XltQH63wwTZre8KZ6WdHVfkK0fV4SQpJHqOiSJw+p/Po9bXVV01zdq14qtoOKxsPbqniO9pqqLorUCpc5SNmVuUgPHsTWkFaVVeTzaP1oNG22GMyRuiM/GT4iGJeFDNBlFUdhx1MKSXRks2ZVRpVslIsDIlP5tuXJAAp2iG3dsRLHdxZXvrWNvRiGdogOZ/7dhhPg33BbyW1LzmTJHbfX47R+jSYxoha0ee3+AhfeqszFMIXDpG9DjCl/XyivXmotBZ6hfk7zlKKx6FbZ+oi75DupU1jGPQ+LQ2p+rKOpU2N3z1UtRRsVjgTFq60avqeoYjmb2C7WhKYrCwYKDrElfw+pjq9l6fCvO8s8TdXxBv+h+DG8znBFtRtA5tHOz+9UuzpyED9GoygPHjzuPsWRXJukFFYHD36BjXLdoLukVx3ndYjDqm26aZabFxuXvrCGz0MawjhHMu3lQg73+DR9uYNWBHK4akMBLU3s3yDlbDKcVlj2hzhwBdf2MqR/6dI2JAlsBe3P3sid3D7tzdrMndw/HS4+j1+gZ224sUzpPYUjckFMOOqw44RE1hGz7tGJcRocxMOaf0G5w1bLZ+9UxHLu+hbxK4xX8QqDbpWorR9KIxlvKu5kodBSy/th61hxbw+r01WSVZlV5vE1gG0a0GcHw+OEMihtEgKEVBvZWRsKHaHCKorA9raCshaNq4DAbdYw7J5qLe8Uxpmt0g3er1MeeYxaufG8dJQ430/q35eWpvc/4F9amlDymvbcOvVbDihljGqVLp9nK3gff3AxZe9T7Ix6EsU/UvkR6Ayt2FPNn3p/sydnD7tzd7MnZw9HioyeV06BBqbQgV1xAHFd0uoIrOl9BbEAdByLnp6ohZPtnFSGk4zh1t9bje9VulcydFeX1/tD1IrWFo9P5oDedyVttMKXOUvbn78fqsuLyuHArblweFy7Fhdvjrnqs0u0Tj7k9bpwe50mPHy06yo7sHVXW0zDpTAyMHegNHInBidK60cpI+BANojxw/Lgzg6W7qw8cl/SOY3QX3waOE634K4tbP9qER4EZ47twz7jOZ3S+az9Yz9rkXK4ZlMALk1tJq4eiwLZPYMkjZUukR6krlXY6r1Ff1uqysi9vX5UWjRRLSpVQUa5dUDt6RPagR0QPekb2pFt4N44WH2XBgQUsSl5EoUNdJVeDhuFthjO181RGJYzCoK1DcMpPgT9ege2fV90/BdQF1DqOU1s4ul7k89123R43BwsOsjtnN7tydrErZxcHCw7iqWnX4AbUPqQ9w+PVrpT+Mf3x0/s1+muK5kvChzhtiqKwLa2AJTUEjvO6xXBxr9hmFzhO9Mn6VJ76fjcAb1x9Lped2+a0zrPhUC5Xvb8eg05t9Wgbdpa1eigKWPPVbgfLUbCkqdeZO9WdOgE6jFWDRwNvCe50O9mfv79K0EguSK52dcq4gDh6Rvake0R3ekT0oHtE91oHKdrddn5J/YX5B+azKXOT93iEXwSXdbqMyZ0nkxiceOpK5h2GVa/A3kXqlvG9pqprmQQ0wgZldaAoCsdLj6shI1sNGnty92B1WU8qG+UfRahfKHqNHr1Wveg0OvVaq/MeLz9W5fHqjml1GLQGdBodIaYQBscNpk3g6f1/Jc5OEj5EvVQOHEt2ZXDMUjENriJwxDGma1St2643N7MW7+W/qw9j1Gn57PbBDEwKr/c5rn5/HesP5XHd4HY8f0WvRqhlI3O7oOiYGigK0srCRVql+0fBWVL9cxtwiXSP4uGw5TC7cnaxO2c3u3N2sz9/f5VBieUi/CLoGdnT26rRI6IHEf6n/2WfWpjKdwe+4/uD31dZn2FAzACmdJnC+e3Ob7a/2IsdxezJ3cOunF3szN7J7pzdZFuzTypn1pvpGdmTXpG96BXZi56RPYkJaNiwKMSpSPgQp1QeOH7cmcHSEwJHQFngmNgCA0dlHo/C3z7bwrI9xwk1G/ju7uH1WptjbXIO136wAaNOy8qHxxAf6psddGvk8ajBwZJeESrKA0X57aJjUJfm94Bodd2JkLYQkqBe2o+CmO6nVbXs0mx25qhflruyd7E7dzcl1YScEFMIPSPKWjQie9AzoifR5uhGGSvg9Dj54+gfzN8/nzXH1ni7JYKNwVzS4RImd55M13DfTYV1epwcyD/A7pzd3qBxyHLopC4nnUZH57DO3qDRK7IX7UPa131wrRCNRMKHqNGxAivfbUvn2y1HOZxT8WVQHjgu7h3H6C4tN3CcyOpwc/X769hx1EJShJnv7h5OWC17xpRTFIWr/rOejSl53Dg0kX9d1vP0KuC0qst75x5QBzB63OoiXR5X1Yv3mFud8uk97jq5bHn5asZBVEtrKAsVbdV9V8oDRmhZyAhuA4bT/+Vf6iz1dp2UjznILMk8qZy/3p/uEd3pFdnLGzTaBLbxyaDEzJJMvjv4Hd8d+I6Mkoopsr0iezG582Quan9Ro8zOcLqd5NpyybHmeC+HLIfYnbObvbl7sbvtJz0nPiCeXlEVQaNbRDf89c0sCAuBhA9xAqvDzfK9mXy75SirD+ZQ/l/cbNRxQXe1heNsChwnyi6yc/k7a0gvsDIwKYxPbh18yve65mAO1/13A0a9lj8eHktsyGl8Odss8MW1kLr6NGteR34hFa0VIW0rQkV5wAiIbrCdZes6uFGr0dIxtGOVX+cdQzui1zavPUvcHjfrM9Yz/8B8VqStwFU2w8Vf789F7S9icufJ9I6sfcaUR/FQYC/wholca9Vw4b1vy8Fit9R4HoAgQ5DafRJV0X0S6R/ZoO9ZNCxPaSkeqxWN0aheDAY0TbmTczMi4UOgKApbj+Tz7ZajLN6RQZG9Yk+JIR3Cmdo/gYt6xhJgal5fBo3lwPEiJs9ZS5HNxaV94nn9qnPRaqv/QlEUhanvrWNLaj7ThyXx7KU96v+CxVnw6WTI3AWmYBj5EBgD1bUftAZ1PIVWr24gVn5ba1Af11V6vPJFZzj5+XoTmBpnAbf6DG6MMcfQO6q3d9xBj4gemA0ta3BurjWXRcmLmH9gPimFKd7jnUI7cVnHyzDqjGqYsOWSXZrtDRa5ttw6b+EO6o6rEf4RRPpHEukfSVxAnDdsJAYnotW0ni8uxe3Gvn8/pVu3Yt26DdvevRg7diB06lQCR4xAo2+e/z4pikLppk0UfP0NRcuWoThPGLuk16shpDyMGA1oDAa0RiMag/GEx6q5Lr9tMqI1B6ANMKMNCEAbEIAuMNB723sxm9HofP/jUcJHK1ZTt0rbMH+m9GvL1P5tW9c6FZWsOZjDTf/biMujcO+4TvxjfPX9+3/sz+bG/23EpNfyxyNjiQmuZ6tHfip8cjnkHVKnqF4/H+Ka73Lsdred9KL0ik20io9ypPAIf+X9Ve3gxgBDAD0jelb5hR5tjvZBzRuHoihszdrKggMLWJayrNqukOqEmcKqhIryi/eYn3o/2BTcqgJGZe7iEqw7tmPdth3r1q1Yd+zAU1L9gGd9TAyhUyYTMnkKxrbNY1aNKz8fy/c/UPD11zgOH/Z1darQ+PuXhRE1qOjMAWirCyqVygRPnCgby5WT8FF/NqebZXtO7lbxN+iY2CuOqf3bMrh9eI2/9FuTrzen8ci36iJRL0/tzZUDqm7upSgKV7y7lu1pBdwyvD1PT6rngMvje9UWj6IMCGkHN34PER0bqPanz2K3nLQzZ/klqzSr2nU0QB3c2CWsi7dFo3dUb5KCk1rN4MZCRyFLDi3h96O/46/3rz5Y+EUS7h9et/VDWhnnsWOUbt2GdetWSrdtw75vnzpQuhJtQAD+ffrg368fft27U7phA5YffsBdUKAW0GgIGDqU0CunETRuHBpj0246pygK1s2byS9v5XCoewFpzGZCLr6Y0Kuuwq97NxSXC8XhUC9OZ72uPQ4HOJ14TnzMZle7dUpKKl2KcZeU4ClRj+NyneId1MBgoNuunacuVw8SPloBtVuloKxb5ViVbpXB7cOZ2r8tF/WKI7CVdKvUxyvL9vH2ioPotRo+umUQwztV9Kmv2JfFzXM34WdQWz2ig+rR6pG2ET6bpm4qFtUNblhAiX8IK9JWUGgvxKQzYdKb8NP5qbcr3TfqjOrxSvfrMz7Co3jIKs06KViUB47yBbdqEmAIICEogYSgBNoGtSUhKIGOIR1lcKOoM8XlwrZvH9at27Bu20rp1m24Mk8eeGyIj8e/Xz/8+/XF3K8fps6dT+oy8DgcFP/yCwXffkvJ2nXe47qwMEIuv5zQqVMwdWzcUO8uKMDyww/kf/0NjuSKZfRN3bsRduVVBF9yMbrAxt2z6lQURVHDS5VwUoKnuBhPSUlZSCm/VA0xKAoJ781p0Po0WvhISkoiNTX1pON3330377zzDoqiMHPmTN5//33y8/MZPHgw77zzDj161L3PXMJH7TIsVhZsTWf+lqMcqtSt0ibUn6n92zKlX1vaRZyl3SoFabDmDWjTH/pcfdqbdXk8Cvd/tZ1FO44R5Kdnwd+G0TkmCEVRuPydNew4auH2ke154uJ6tHoc+AW+vgGcpShtB7BrwkwWpP3CksNLqh0jURd6jR6T3lQRVHQm/PSVgorOhIJCenE66UXpODyOWs8X6R95UsAov4SZwmQpbFEv7uJirNt3lLVqbMW2Yyee0tKqhXQ6/M45B/9+/TD364t/v34YYuq3/ogjLY2C+fOxLPgOV1bF/jH+/foROm0awRdOQOvfMAFZURSsW7eS/9VXFP1UqZXD35/giycSdtVV+PXsKf+v1KDRwkd2djZud8XAqt27d3PBBRewYsUKxowZw0svvcTzzz/PvHnz6NKlC7NmzeKPP/5g3759BAXVbQliCR8nq61b5aJesUzrn3B2d6soCmz9CJY9CY4i9ViHsXDpm+rU0dNgc7q5/r8b2JyaT9swf767ezg7jxZw60eb8TfoWPXoWCID67hPx65v4bs7seBhcVJf5gcHc8BS8UspKTiJLmFdsLvt2Nw2HG4HNpcNu9tecXGpj1W36FZd6TV64gLjqoSK8pDRNrBtixsAKpof2/79FC5aRPGq1dj37z+5CyUwEP9zz/W2avj36oU2oGGmLCsuF8V/rKLg228p/v13KPsu0gYGEjzpEsKmTcOv++mtS+O2WMpaOb7GcbBSK8c55xB21ZUET5rk81aOlqDJul0eeOABFi9ezIEDBwCIj4/ngQce4NFHHwXAbrcTExPDSy+9xJ133tnglW8N3llxkPd+T6bIVtGtMqisW2Via+hWKUhTt3E/tEK9H9MTcg+Cy6bOHjn/WRhw62lNJc0rcTD53TWk5JbSp20ILo/CnmOF3Dm6A49f1K1O51A2vM+mFU8xPyiAXwIDcZSNnTDpTIxPHM/kzpPpH9O/zr+UPIqnShhxuB3Y3LZq79vddjyKxxs44gLimt1UVtHyOTMzKfzxRywLF6ljNioxtGlTpVXD1KlTk8y6cB7PwvLddxR8+y3OoxWbDPp1707otKkEX3IJulP84FUUBeu2bRR89RWFPy1DsasDizX+/gRPvEht5ejVS1o56qFJwofD4SA+Pp6HHnqIf/7znxw6dIiOHTuydetW+vbt6y132WWXERoaykcffVTteex2O3Z7xWjywsJCEhISJHwAX248wmMLdgFqt8qU/m2Z0q8NiRGtYGtqRYEt82D5U2prh95PXep7yN/U/TYW3gNHyvqCE0eorSCnMbDzcE4JV7y7hoJStcXBbNSx+tFxhJ9iIbKc0my+//khvsvexBFDxUDDrmFdmdJlChPbT6x17xEhmjN3URFFy5djWbSY0g0b8Da3GgwEjhpF8EUXYR44EEOMb2c5KR4PpRs2UPDNtxT9/LN3yqvGz4/gCy8k9Mpp+PftWyVAqK0cCyn45mvsBw56j5u6diX0qisJmTTplMFFVK8+4eO0fyZ9//33FBQUMH36dAAyywYWxZzQnxcTE1PtOJFyL7zwAjNnzjzdapy1tqcV8PQP6jbm95/XmfvP63z2dqucqOBIWWvHSvV+wmC47B2ILNudNrITTF8Cm/4LvzyrLuI1Zzic9xQMvktdC6OO2kcG8MGNA7jugw043B5uGpZUY/Bwe9ysObaG+fu/5fe0lbhRwGAgQKNnYucrmNJ5Ct0jussvJdEiKQ4HxatXY1m4iOLffvOOdwDwH9CfkEsmEXzhBHShob6r5Ak0Wi0BQ4cSMHQorvx8ChcuJP+bb3AcTMby/fdYvv8eY8eOhE6dil+3c7B89z2FP/1U0crh50fwxImEXXUlfr1rX0xONKzTbvmYMGECRqORRYsWAbB27VqGDx/OsWPHiIuL85a7/fbbSUtL46effqr2PNLycbKcYjuT3lpNhsXG+O4xvHd9/9YRPBQFtswta+0oVls7znu69kCRn6IGlfIdWNsOVINKVP326Ph9fza//nmcRy4856SurGPFx7xLcR8vPe493sdmZ0rSRUwY94KMpxAtUnnXg2XhQoqW/oTbUrECq7FjR0ImTSL4kkuazTobdaEoCtbt2yn45lsKly5FsZ484NvUpUtFK0cr/Z5pDI3e8pGamsovv/zCggULvMdiY2MBtQWkcvjIyso6qTWkMpPJhMlUx4F9rYDT7eHvn20lw2KjY1QAr17Zp3UEj5NaO4aUtXZ0qv15YUlw48KKAalHN8F7I2DMYzDsfnUF0ToY3SWK0V2ivPedbicr0lYw/8B81h1b510HIxQdkyz5TC6x0enit6D3lafxZoXwLfuhQ1gWLqRw0WKc6ene47qoSEIuvoSQSydh6tatRbYEaDQazH37Yu7bl5h/Pk7hj0so+OYbHEeOEDRuHKFXXYn/uee2yPd2Njmt8DF37lyio6O5+OKLvcfat29PbGwsP//8s3fMh8Ph4Pfff+ell15qmNq2Ai8u/YsNh/MINOn5zw0DCPI7yxcuUhTY/D/4+emy1g7/staOO+vefaLRQP/p0Ol8WPQAHPwZfv0X7P1BDTCxvepcnUOWQ3x34DsWJi8kz5bnPT44uj9TM5IZd2QnRr0/XPkJdBlfv/cqUBQFV3Y2+rAwNIaz/G+7mXFmZVG4ZAmFixZj27PHe1xrNhM0fjwhl07CPHhws1imu6HoAgMJu+pKwq6SHwnNTb3Dh8fjYe7cudx0003oK627r9FoeOCBB5g9ezadO3emc+fOzJ49G7PZzLXXXtuglT5b/bA9nQ9Xq0v2vjKtD52iz/KpXfmp6sDR8i6TdkPVsHC6K4KGtIXrvoGdX8HSRyFjB7w/Bkb+A0bOAH3VsRxWl5UjhUc4XHiYFEsK646tY2vWVu/jUf5RXN7pcq6IH0nCgrsha6+6idu1X0O7Iaf5plsv+6HDZD77LKUbN6Lx88Ove3f8e/fGv3cv/Hr3wdAmXn6NNjB3cQnFv/6CZeEiStatq5gaq9cTOGIEIZdOInDs2AZbJ0OIuqp3+Pjll184cuQIt9xyy0mPPfLII1itVu6++27vImPLly+v8xofrdneY4U8Ol9d6vbvYztyYc9YH9eoEXk86tiOyq0d5z8Dg+48891XNRp1AbIOY+HHh1D+Wszx1a+Qsu87Us69ksMaFymFKaRYUsgoyThpWXGtRsuoNqOY3HkyI9uORF9wBD6+HApSITAWblgAMaex0Vwrpjgc5Pz3v+TOec87G0Gx2dS9PbZWhD1dRAT+vXrh17sX/r374N+rJ7oQmTFUX4rTScnatVgWLqLo119RbDbvY/7nnkvwpZMIvugi9GFhPqylaO1kefVmoKDUwaS3V5OWZ2VUlyjmTh+I7mwd53HiANEzbe0oY3VZSS1MJcWSwuHCwxy2qK0ZqQXJlNay8mewMZj2Ie1JCk6ic1hnLky6kJiAsjFKmbvgk8lQkgVh7eGG7yC8/RnVs7Up3bKFjKef8S5PHTByJLFPP4XidGHduQPbzl1Yd+7Etm8fnLgzKGBMSsK/T2/8evVWr7t2bfK9PVoCRVGw7dyJZeEiCpcswZ2f733MmJRE8KRLCJk0CWO701uUT4i6kL1dWhC3R+HmeZv4Y382CeH+LLpnBKHms/AfV48HNn8IPz8DzpKy1o5nYdAddW7tKN+/5LDlsLf1ovx2RklGjc/TabQkYCSpOI8kp4v2pnCShv2DpC4Ta15WPHUtfH412C0Q00vdmTaofstCt2buwkKyXnmVgq+/BtRWjZh/Pl7jLpoeux37n39i3bkTa1kgcR45clI5jcGAqXs3/MvCiH/v3hjatWu13TWOlBQsixZjWbwIZ2rF56WLiCB44kRCJl0iC2WJJiPhowUp3+TMz6Blwd+G0z3+LHzP+Snwwz2Qskq9nzgcLn2r2tYORVHIseaQWpjKkaIj6nXhEVKLUkkrTMPmtp30nHIhphDaB7cnKSTJ25qRFJJEQmACBp0B/voRFj8ExZmARp3Ce95TYDxh0bZ9P8E3N6mrqLYbCtd8Cf6hDfZxnM0URaFo2TIyn38ed3YOAKHTphL9j3/Ue30IV34+tl27ysKI2kri3em0El1ICH69e+Pfq5e62ubAAWjP4hl0rrw8CpcsxbJoIbYdFbuSavz9CTr/fEIunUTA0KFo9LLarWhaEj5aiGV7Mrnzky0AvHH1uVx2bsuZS18nJ7Z2GMxw/rMoA24j32lRQ0VhqjdolN8vdZXWeEq9Rk/boLZqwAhur4aMkCSSgpMI86tDH7Y1X52Su/1T9X5YkhqE2o9S72//An74Oyhu6HIhTJ0LRlnDoy6c6elk/us5dd8NwNi+PXH/mol54MAGOb+iKDjT0rDu2Il1105sO3Zi+/PPKothgbpwlHnQQAJHjCRw1EgMiYkt/pe/x2ql6NffsCxaSMnqNd59TdBqCRg+nJBLJxE0blyD7aMixOmQ8NECHMwq5vJ31lBsd3HL8PY8Pen0NkRqtvIOY1l4N0eObSbVoOdIVAdS255Lqi2XI4VHKHIW1fhUrUZLfEA8icGJtAtup14HqdfxgfENs3/JwV9g4f1QWLYvxIBb1E3qfnlWvd/7arjsbdDJdNBTUdxu8j/9lKw33kQpLQWDgcg77iDizjvQNvL4DMXhwLZvf1nLyE5K1m/Adfx4lTKGhAQCR44gYMRIAgYPajFf0IrLRcn6DRQuWkTRzz9X2THWr1cvQiZdog4cjYqq5SxCNB0JH81ckc3JZe+s4VB2CYPbh/PpbYMx6M5wlkcz4fa4eeOnu/g+cy35tbwnDRpiA2LVcBFUKWQEt6voJmlstkL45Rl1nZHKhtwN458/85k3rYBt714ynnrau26E/4D+xM2cianjmQ0gPl2KomA/cICSVaspXrWK0i1bqg5kNRgw9+/vDSOmLp2bVauIoijY9u6lcOEiLEt+9HZdARjatiXk0kkEXzIJUwcZ+CyaHwkfzZjHo3DXp1tYvvc4scF+LLp3BFFBZ0f/tNPj5Mkfb2ZJ3g7vsWi/cNqFdKhoxSgLGglBCfjp/XxY20oO/6HOwMlPgXFPqmuCNKMvpObIU1pK9ltvk/fxx+B2ow0KIvrhGYROnYqmGYU2T0kJJRs2UrJ6FcV/rKqyAyqAPjqagJEjCBw5ioBhQ3221Lbj6FEKFy/GsnARjkOHvMd1oaEET7yI4EmTZFVO0exJ+GjG3llxkP9btg+jTsvXdw3l3IRQX1epQdjddh7+6TZW5GxHryjMDOzB+ZM+xGxqIQuluexQnAWhCb6uSbNX/McfZD47E+exYwAET7yImMcfb/bN/4qi4ExNpXjVaopXr6J0w8Yqa2Cg0+Hfp4+3VcSvR/czDlKKoqBYrbgLC3FbLN6Lp7AQt0U9Vrp5M9YtW7zP0ZhMBJ03juBJkwgcPlymFosWQ8JHM7VyXxY3z9uEosCLk3tx9aCzY859qbOU+5ffzvqcnRg9Cv82JDLqmh/qvK+KaBlcOTkcn/0ChUuWAKCPjyP26acJGjPGtxU7TR67ndLNm71dNOVrkZTThYURMGIEgSNHYB4yBI1Od3KAKLCowaLQgsdi8QaK8rDhsVi8C6vVSqMhYOgQgi+ZRND4C9AFtpDQLkQlEj6aoSO5pUx6ezUWq5NrBrXjhcl132+kOSt0FPL35XewPXcP/h4Pb7tCGXTT8pOnr4oWS/F4KJg/n6z/ewVPYSFotYTfeCNR997TYgZv1oUzPZ3i1WsoWb2KkrXr8JSUNNzJDQZ0wcHqJSQEbYh6rQsOwdgugaAJEzDUsgGnEC2BhI9mptThYvK7a/krs4hzE0L56s4hmPQtf/OmPFsedy2/nT/z9xPk9jCnVEef6b9CYPNufhd1Zz90iMynn6F082YATN27Efev5/DveXYvMa84nVi3b/d20dj3/gmAtlKA0IUEoy0LEOX3dSEhapmQUO99XXAwGrNZxmuIs56Ej2ZEURQe+Go7P2w/RmSgkUX3jiAupOVv4nS85Dh3/Hw7hyyHCXe7eT/fTtfpy894mXTRPHgcDnLf/4Dc//wHxelE4+9P1H33EX7D9a1y8SqPzYbGYDirdnwVoqHV5/u79f0r0sT+tyaFH7YfQ6/V8M61/c6K4HG06Ci3Lb+N9OJ0YlwuPsi20P667yV4NAOKx4OntBRPSUm1F3dxcaX7NZQrLlbHK5R1OwSMGkns089gbHuWLYJXD1q/ZjIzS4izhISPRrQuOZfZS9Tm2icu7sbgDhE+rtGZO1RwiNuX306WNYsEp5P/ZmYTP+UjSBjk66q1Kq78fPK/+ILilb/jKSxUg0VJibrIVwPRRUQQ+8Q/CbroIukyEEI0KAkfjeRYgZV7Pt+K26Nw+bnxTB+W5OsqnbE/c//kzp/vJN+eTyeHg/czs4ga/xJ0u8TXVWs1HEfTyfvoIwq+/RbFaq25oE6HNiCg7GJGGxCALiCw0rHqLmZ0gRVlDAkJZ/UeKUII35Hw0QhsTjd/+3QLuSUOuscF88Lk3i3+l+P2rO3c/cvdFDmL6GF38F5mFqFD7oHBd/i6aq2C7c8/yf3wfxQuXerd18PUvRvh19+AMaGtGhgqBQeNydTi/+aEEGcvCR+N4NmFe9hx1EKo2cB/buiPv7FlD1Jbd2wd96+4H6vLSj+7k3cyjhPYYzKcP9PXVTurKYpC6bp15P73Q0rWrvUeDxg2jIjbbsU8dKgEDCFEiyTho4F9vuEIX25KQ6OBN6/uS0J4y94R9bcjvzHj9xk4PU6GOzz8OyMT/3bD4fI5svdJI1FcLgqXLSP3ww+9UzzRagm+6CIibr0Fv+5n2SaEQohWR8JHA9p6JJ9nFu4GYMb4rozq0rLXu1hyaAn/XP1P3Iqb8106Xko/gjHqHLj6U9DLWICG5iktpWDBd+TNnYszPR1Qt4cPnTqV8Ok3YWzb1sc1FEKIhiHho4FkFdn426dbcLoVLuwRy91jWva002/2f8Nz655DQeFSJYCZaX+iD4yF674F/zBfV++s4srLI/+zz8n/7DPcBQWAurR32PXXEXbttejD5PMWQpxdJHw0AKfbwz2fbeN4oZ1O0YG8cmWfFt0X/9Gej3hl8ysAXGWI4Z/7N6E1BsJ138jGaw3IkZZG3tx5FCxY4N3gzJCQQPjN0wm94gq0/i1/TRghhKiOhI8G8PXmNDam5BFo0vOfG/oTaGqZH6uiKMzZMYc5O+YAcEtgFx7Y9QsarR6u/Bjievu4hmcH6+495P3vQwp/WgYeDwB+PXoQcdutBF1wQatcQVQI0brIv3INYEdaAQDThyXRMapl7kapKAqvbH6Fj/d+DMD90cO4bcOX6oOT3oRO5/mwdi2foiiUrF5D7v8+pHTdeu/xgJEjibj1VsyDB7Xo1jIhhKgPCR8NIDlbXYa6S2yQj2tyetweN8+tf475B+YD8FjipVz3+7vqg2OfgL7X+bB2LZuiKBQtXUrO+x9g/+sv9aBOR/DFE4m45Rb8zjnHtxUUQggfkPBxhhRF4WBWMQCdGrrVw1oAX14HWXuh8wXQ9SLodD6YGi7kOD1Onlj1BEtTlqLVaHn2nOlcsfwlUDzQ70YY9XCDvVZr47HZyHzmWSw//ACAxmwmbNpUwm+8EUOb1rtPihBCSPg4Q7klDixWJxoNdIgKaLgTl+bBJ5dDxg71/s6v1IvOCO1HqUGk60QIjj/tl7C77cxYOYOVR1ei1+p58dwHmbB0Jris0OkCuPjfIF0Bp8WZkcHRe+7FtmcPaLVE3nUn4TfeiC401NdVE0IIn5PwcYbKWz3ahvnjZ2iglUxLcuHjy+D4LjBHwMRX4NhW+GsJ5CXDwV/Uy4//gLhz4ZyL1SAS06NOYcHtcbMrZxdvb3ubDZkbMOlMvDbkWUYteQpKcyCuD0ybBzr58zgdpZs2cfT+B3Dn5aELDaXNv18jYOhQX1dLCCGaDfl2OUPJ2Q3c5VKcrQaPrD0QEA03LYTobtBzMoyfBdn7Yd+PsG8ppG2EjO3qZcXzENpODSFdJ0LiMNAZvKfNt+Wz5tgaVh1dxZpja7DYLQCY9WbeHvUKA396FvIOqee49hswtcyBs76kKAr5n33O8RdfBJcLU7dutH3rrVa9Fb0QQlRHwscZKm/5aJBZLkXH4eNLIfsvCIyFmxZBVJeqZaK6qJcRD0JxFuz/SQ0iySug4AhseA82vIfHL4S/OgxnVVgUq+zZ7Mzdg4LiPU2QMYjh8cO5tcd0zvn5eUjfDH6hcN18CIo58/fSCBRFwZWZiS4iAq3R6OvqVOGx28mc+S8sCxYAEHzxxcTNek7W6hBCiGpI+DhD5TNdOkWfYfgozICPJkHuAQiKh+mLIeIUq6QGRquDQvvdCI5SivYvZf2fX7EqdxerDBpyrLuh0q7rXU2RjEw8j5EdJtI7qjd6jQ6WzFBbUnQmuObLk8OOjymKgm3nTgqXL6fo519wHjmCPjqa6H88RPCkSWiawf4yzsxMjt53P7adO0GrJXrGDMJvni5TZ4UQogYSPs5QcnnLx5mED8tRNXjkHYKQBLWrJbzDKZ+mKAqHLIdYdXQVf6T/wbbj23ApLvBX/7P6a3QMdSiMtGQzotRGrPsI/LUVYpep40RcNtj0X0ADk9+HxOYxLkFxu7Fu3Urh8p8p+vlnXJmZVR53ZWVx7NHHyPv0M2Iefxxzv74+qimUbtnC0fvux52biy4khPjXXiVw+HCf1UcIIVoCCR9noMTuIr1AbVo47TEfBUdg3iVQkKqOt7hpMYQl1ljc6rKyKXMTfxz9g1VHV3Gs5FiVx5OCkxjZdiQj24ykf0x/jDoj5ByAfUvU7pkj6yFzp3opN2E29Lj89OrfQBSnk5KNGyla/jNFv/6KOyfH+5jWbCZwzGiCLrgA85AhFHzzLbnvvYdt1y5Sr72W4IsvJvofD2GIP/2ZP/Wur6JQ8OWXZD4/Wx3f0bUrbd9+C2OCLD8vhBCnolEURTl1saZTWFhISEgIFouF4OBgX1enVrvTLVzy1mrCA4xsfeqC+p8gPwXmTQLLEQhrr47xqGbvlLSiNG/rxqaMTTg8Du9jRq2RgXEDGdlmJKPajCIh+BRffiU5FeNEUlbDwFvhvKfrX/cG4HE4KFmzhqLlP1P822+4LRbvY9rgYILGjiVowngChg1D6+dX5bmu7Gyy3ngDy/wFoChoTCYibr2FiNtuQ2s2N3q9M//1LyzfqouyBU+8iLhZsxr9dYUQojmrz/e3hI8z8P22dB74ajuDksL5+q56dlnkJsNHl0LhUQjvqI7xOGHNDo/i4eHfH2Z56vIqx+MD4hnZdiSj2o5iYOxA/PUtZ1Cjx2ql+I9VFC1fTvHKlXhKSryP6cLDCTrvPILGjydg8CA0dRhUat2zh6wXXqR082aARh8P4jx+nKP33YdtR9n4joceJPzWW2V8hxCi1avP97d0u5yB8mm29R7vkXMQProEijIgsova4hEUe1KxHw7+wPLU5eg0OvrF9GNUm1GMbDuSDiEdWtSXnbu4mOIVK9XAsWqVdwdXUMNC0AUXEDR+POYB/dHo6rdWin+PHrT75GOKlv9M1ssv40xPV8eDfPY5MY8/hrlvw40HKd26laP33487OwdtSAhtXn2VwBEyvkMIIepLwscZqJhmW4+VTbP3qYNLi49DVDd1cGlg9EnFCmwFvLblNQAe7P8gN/W4qUHq3FTcBQUU/fobRcuXU7J2LYrT6X3M0KYNQePHEzT+Avz79DnjFgqNRkPwhPEEjhlN3kcfq+NBdu4k9Zqy8SAz/oEhLu6MXiP/y6/IfP55cDoxdemiju9o1+6MzimEEK2VhI8z4F1grK4tH8f3qut4lGRDTE+48QcIiKy26L+3/psCewGdwzpzbbdrG6rKjU5xOMic9TwF8+eD2+09bmzf3hs4/Lp3b5SWG63JROQdtxN6xeXe8SCFP/5I0a+/EnHLLUTcdmu9x2V4HA6Oz3qegq+/BiBowgTiZz+PNqABl9IXQohWRsLHaXK5PRzOUccr1GmBscxd6sqlpbkQ21sNHubwaotuy9rGggPqYlVPDXkKg9ZQbbnmxl1YyNF776N0wwYATOecQ9D4CwgePx5Tp05NVg99VBTxs2YRds013vEgOe++S8G339ZrPIgzK4v0++7Hun07aDREPfggEbff1qK6vIQQojmS8HGa0vKtON0KfgYtbUJPMeDz2HZ1kzhrPsT3hRu+A/+waos6PU7+te5fAEzuPJm+0b5bw6I+nOnpHLnzThwHk9GazbR543UCR470aZ3OZDxI6bZtpN93P67sbLTBwbR55f8IHDWqCWsvhBBnL98vD9lClY/36BAZiFZbyy/h9C1qV4s1H9oMgBu+rzF4AHy29zMOFhwk1BTKg/0ebOBaNw7r7j0cvvpqHAeT0cfEkPj5Zz4PHuXKx4N0WPIjUQ89hNZs9o4HSf/HDJwZGSc9J/+bb0i98SZc2dmYOnei/TdfS/AQQogGJOHjNJWHj1rHe6Rtgo8vB5sFEoaUtXiE1lg8sySTd3e8C8BD/R8i1K/mss1F0YoVpN5wA+7sHExdu5L01Zf4nXOOr6t1kvLxIB2X/UTI1Cmg0VD4448kXzSR7DffwlNaiuJwkPHss2Q+9TQ4nQRdcAGJX3yJMbHmRd+EEELUn3S7nCbvNNuaxnscWQ+fTgVHESQOh2u/PuVOsS9ufBGry0q/6H5c1umyhq5yg8v7/HOOz3oePB4Chg+nzRuvowts3rvh1jgeZP589NHR2HbtUsd33H8/EXfeIeM7hBCiEUj4OE21tnykrIbPrgRnCbQfpW7YZqx9dsQfR//g1yO/otfoeXLIk2g1zbdRSvF4yHrlVfL+9z8AQqZOIe6ZZ9AYWsbAWKh+PIjr+HG0QUHq+I7Ro31dRSGEOGtJ+DgNiqLUPM320O/w+VXgskKHsXD152CsfXqn1WVl9obZANzQ/QY6h3VulHo3BI/NxrFHH6No2TIAoh64n4g772yRLQSV1wfJ/+QTrLv3EP3A/RiTknxdNSGEOKtJ+DgN2UV2imwutBpIiqwULA7+Cl9eq+4W2+kCuOpTMPjVfKIyH+z8gPTidGIDYrmrz12NWPMz48rP5+jdf8e6bRsYDMTPfp6QSZN8Xa0zpjWZiLjtNl9XQwghWg0JH6fhYFmrR7twMyZ92XLg+5fDV9eD2w5dLoQrPwa96ZTnOlRwiLl75gLw2KDHMBua5+ZkjtRUjtxxB87UI2iDg2n79lsEDBrk62oJIYRogSR8nIbkrBMGm6Zvha+uA7cDzrkEps4F/ak3RVMUhVkbZuHyuBjddjTjEsY1ZrVPW+nWbRy9+27cBQUY2rQh4f3/YOrY0dfVEkII0UI131GNzVhytrqyqXe8x1+L1eDRfjRMm1en4AGw+NBiNmVuwk/nx+ODH2+W4yYKf1rGkenTcRcU4NezJ0lffSnBQwghxBmR8HEaDp7Y8lGUqV53GA26us34sNgtvLL5FQDu7HMnbQLbNHg9z4SiKOR++D/SH3gAxeEgcNw4Ej/+CH1k9XvRCCGEEHUl3S6nwRs+ok8IH4GxdT7Hm1vfJM+WR4eQDtzUvXntWKu4XByfPZv8z78AIOz664l5/LF6b3cvhBBCVEfCRz0V211kFtoA6HRiy0dQTJ3OsSt7F9/s/waAJ4c8iaGOrSVNwVNaSvpD/6B45UrQaIh+9BHCb7qpWXYJCSGEaJkkfNRT+WDTyEATIeay0FBcHj7iTvl8l8fFc+ufQ0Hh0o6XMjB2YGNVtd5c2dmk3fU3bHv2oDGZiP+/lwkeP97X1RJCCHGWkfBRTxWLi5WtWOpyQGmuersO3S5f7fuKP/P+JNgYzEP9H2qsatab/eBB0u64E+exY+jCwkiY8y7+557r62oJIYQ4C0n4qKeTBpsWH1evtQYwh9f63KzSLN7a9hYA9/e7nwj/iFO+nv3gQWx796KLiEAfGYU+KhJdaCgabcONFS5Zv4Gj996Lp6gIY2IiCR+8j7FduwY7vxBCCFGZhI96OmlZ9fLwERQLpxgX8fKmlylxltA7sjdTu0w95Ws509NJufoaPMXFVR/Q6dBHRKCPjEQXFYk+MlINJpGR6L33I9FFRqENMNc6XsOycCHHnngSnE78+/Wj7Ttvow8LO2XdhBBCiNMl4aOeTp5mm6FeB9Y+2HRt+lqWpSxDq9HWaeM4xe3m2KOP4SkuRh8Xhy4oCFdODu78fHC7cWVl4crKOmV9Nf7+3jBSHk50ZbedaWnkfvBfAIIuupD4F19Eazr1qqxCCCHEmZDwUQ9Ot4fU3FKgUsuHd6ZLzeM97G47z294HoBrz7mWbhHdTvlaefPmUbp5M1qzmcSP5nm7QRSnE1dePq6cbNw5ObhycnBll13n5KjHy+57SkpQrFacaWk409JqfK2I228j6sEHG7QrRwghhKiJhI96SM0txeVRMBt1xIWUbRhXudulBh/u+pAjRUeI9o/m7+f+/ZSvY/vrL7JefwOAmH8+XmX8hcZgwBATjSEm+pTn8ZSW4srNLQsn2WrLSaWw4ikuJuSKKwidfMUpzyWEEEI0FAkf9VC5y8U7jsLb7VJ9+EgtTOW/u9SujYcHPUygMbDW1/DY7Rx7+GFwOgk87zxCpkw57fpqzWaMZjPGhITTPocQQgjR0KSdvR7KB5t2jAqoOFhUc8uHoig8v/55nB4nw+OHMyFxwilfI/vfr2M/cBBdRARx/5opi3sJIYQ460j4qIfyBca84z2g0gJjJ4ePZSnLWJexDqPWyD8H//OUQaJk/Xry5s0DIO75WegjTj0VVwghhGhp6h0+0tPTuf7664mIiMBsNnPuueeyZcsW7+PTp09Ho9FUuQwZMqRBK+0rJ02zhRoHnBY5inh508sA3Nb7NtoF175uhtti4dhjjwMQetVVBI0Z0zCVFkIIIZqZeo35yM/PZ/jw4YwdO5alS5cSHR1NcnIyoaGhVcpdeOGFzJ0713vfaKzbFvPNmaIoJGeXAJWm2bqdUJKj3j5hzMfb294m25pNYnAit/a89ZTnz3xuFq7MTAyJ7Yh55OEGrbsQQgjRnNQrfLz00kskJCRUCRZJSUknlTOZTMTG1n2H15bgeKGdYrsLnVZDYkTZmI/iLEABrR7MFV0ke3P38uW+LwF4YvATGHW1hy/Ljz9SuHgx6HS0eflltAEBtZYXQgghWrJ6dbssXLiQAQMGMG3aNKKjo+nbty8ffPDBSeVWrlxJdHQ0Xbp04fbbbyerlsWw7HY7hYWFVS7NUflMl8RwM0Z92cdWPt4jMAbK1shwe9w8t+45PIqHi5IuYmj80FrP68zIIHPmvwCIvPNO/Pv0aZw3IIQQQjQT9Qofhw4dYs6cOXTu3Jlly5Zx1113cd999/Hxxx97y1x00UV89tln/Pbbb7z66qts2rSJcePGYbfbqz3nCy+8QEhIiPeS0EynhR7MKgKgY5XxHmUzXSqtbvrt/m/ZnbubQEMgDw+svftE8Xg49s9/4iksxK9XLyL/dleD11sIIYRoburV7eLxeBgwYACzZ88GoG/fvuzZs4c5c+Zw4403AnDVVVd5y/fs2ZMBAwaQmJjIjz/+yOTJk0865+OPP85DD1Xs7lpYWNgsA8hJ4z2gYo2PssGmOdYc3tiqLg52T997iDJH1XrO/E8+oXTdejR+fsS//BIag6HhKy6EEEI0M/Vq+YiLi6N79+5VjnXr1o0jR47U+pzExEQOHDhQ7eMmk4ng4OAql+boYLXTbKuu8fHq5lcpchbRLbwbV3e9utbz2fbvJ+vV1wCIefQRTO3bN3ylhRBCiGaoXuFj+PDh7Nu3r8qx/fv3k5iYWONzcnNzSUtLIy4u7vRq2ExUv8BYxeqmGzM2svjQYjRoeHro0+i0uhrP5XE4OPbIoygOBwGjRxF6de1BRQghhDib1Ct8PPjgg6xfv57Zs2dz8OBBPv/8c95//33+/nd1v5Li4mJmzJjBunXrSElJYeXKlUyaNInIyEiuuKLl7h9SaHOSVaSOWaluzIcjIJLn1j8HwJVdr6RnZM9az5fz1lvY//oLXWgo8bNmySqmQgghWpV6hY+BAwfy3Xff8cUXX9CzZ0+ee+45Xn/9da677joAdDodu3bt4rLLLqNLly7cdNNNdOnShXXr1hEUFNQob6AplK9sGhNsItiv0riMstku862ppBSmEOEXwX397qv1XKWbNpH73w8BiH3uX+ijah8XIoQQQpxt6r2x3CWXXMIll1xS7WP+/v4sW7bsjCvV3FTeUK6KstVNlxf8CcDNPW8m2FjzmBV3cTHHHn0MFIWQyZMJvuCCxqmwEEII0YzJ3i51UD7TpcpgU7cLSrIp0GrZmr8fgPPanVfreY7Peh7nsWMY2rYl5p//bLT6CiGEEM2ZhI86qLbloyQbFA+/m8148NAlrAttg9rWeI7CZcuxfP89aLXEv/QiukBZxVQIIUTrJOGjDqrdUK5svMeK4FAAxiaMrfH5zqwsMp95BoCI227D3L9/41RUCCGEaAEkfJyC3eXmSF4pcOICY8exaTSsNaof4bh246p9vqIoZDzxJO6CAkzduxF1z98bvc5CCCFEcybh4xRSc0txexQCTXpigk0VDxRlsN7PD6sGYgNi6Rberdrn53/xBSWrVqExmWjz8stozoIdfoUQQogzIeHjFJKzKhYXq7IeR/FxVgT4A2qXS3VrddgPHSbr5f8DIPof/8DUqVPjV1gIIYRo5iR8nIJ3sGl01Wm27sIMVporwseJFKeTY488gmKzETBsGGHXX9f4lRVCCCFaAAkfp1DtYFNgZ+Fh8nQ6grQmBsQOOOl5OXPmYNu9G21ICHEvzEajlY9aCCGEAAkfp3Qwu/oFxlbY1X1dRoZ1w6Ctuhtt6bZt5Lz3HwDinn0GQ0xME9RUCCGEaBkkfNTC41FIzjp5gTFFUfhNYwVgbPywqs8pKVFXMfV4CL50EsEXXdR0FRZCCCFaAAkftcgotGF1utFrNbQLN3uPHy44SKpOg0FRGJFYdYn04y++hPPIEfRxccQ++WRTV1kIIYRo9iR81KJ8sGlSZAAGXcVH9duhJQAMstoIDE3yHi/67TcKvvkGNBriX3wRXXDN+7wIIYQQrZWEj1pUnmZb2Yq0FQCMc+lBp+7N58rJIePJpwAIv/lmAgYPasKaCiGEEC2HhI9aHKxmpkt2aTY7LckAjDFEAGWrmD71NO68PExduhD1wP1NX1khhBCihZDwUYvkajaUK2/16GWzEx0UD0Dhj0soXrECjcFA/P/9H1pZxVQIIYSokYSPWlS3xoe3y6XUCkGxAOR/8gkAEXfeiV/XLk1cSyGEEKJlkfBRg4JSBznFDqCi5aPEWcKGjA0AjC0thcBYbPv2Yd2xA/R6wq6+ymf1FUIIIVoKCR81KG/1iAvxI8CkDipdnb4ap8dJO4x0cLogKIaCr74GIOi889BHRvqsvkIIIURLIeGjBtUtLubtcnHr0AAeYwSWhQsBCL1yWpPXUQghhGiJJHzU4MRl1Z0eJ38c/QOAscXqY4WbU/AUF2NISCBg6FDfVFQIIYRoYSR81ODE3Wy3HN9CkaOIcFMYfQqOA1Dw02oAQqdNk43jhBBCiDqSb8waJGdXXWBsxRG1y2V03BB0Hhe2AgPWXXtBryf0ist9VU0hhBCixZHwUQ2b001aXimgjvlQFIXf0n4DYGxYdwAKjoQDEDRuHPqoKN9UVAghhGiBJHxUIyW3BI8CQX56ogJN/JX3F5klmfjr/RlqjMbj0mBJVmfAhF55pY9rK4QQQrQsEj6qUT7eo1N0IBqNxjvLZWjcUPyseRSm+eGxKxjatCFgmAw0FUIIIepDwkc1vNNsy2a6/HZE7XIZ124cFGVQkGwGZKCpEEIIcTrkm7Ma3mm20YGkF6ezL38fWo2WUW1HYT9wEGuOCbQaQiZf4eOaCiGEEC2PhI9qlG8o1ykqkJVpKwHoG92XML8w8v/4E4Cgvh0xREf7qIZCCCFEyyXh4wQej8KhnIqWD2+XS8I4PDYblh05AIROHO2zOgohhBAtmYSPE6QXWLE5PRh1WoLNDrYc3wLA2HZjKVq+HI9NwWB2ETB8pI9rKoQQQrRMEj5OUD7eIynSzNqM1bgVN51CO5EQlEB+2SZyoR1L0YTG+bKaQgghRIsl4eMEyZWm2Xo3kms3DntyMtYtW0CjENK+FAJjfFlNIYQQosWS8HGC8mXVEyONrE5X924ZlzCOgq/VVo/AeBuGiFDQm3xVRSGEEKJFk/BxgvIFxjT+B7C6rESbozknsCMF3/8AQFjHUgiSLhchhBDidEn4OEFytrrAWLpjMwBjE8ZS/PPPeCwW9JEhBMTaIUi6XIQQQojTJeGjkrwSB3klDsDD9ty1QFmXS/lA0+Gd0GiBwFjfVVIIIYRo4SR8VFI+3iMmKos8Wy6BhkD6lEZSunkzaLWE9glTCwZJ+BBCCCFOl97XFWhOysd7BIbvoxQY2WYkxfO/U4+NGYNBV6AWlPAhhBDiDCiKgtvlwWlz47C5cdpdOGxuHFYXTrsbp82N0+7G41bweDwoHgW3W0FxK2XHyi5uBcXt8d72eNQybreC4lHwVH6s/JhHQavVMO3xgT57/xI+KikPH1b9DvDAuNiRWL6fDUDoldMgWb0t02yFEKJ6Ho+Cy+7G6VC/PF0ON067p5pjFY85HW5cZfcVQKPRoNGARlt2XXYfrQaNRoPWe7tSWY2morxWA2XHtFqgchmNBo2Wqtfe16p43sllTihfXlarwe304LS5ykKEGiAcdjdOm0sNF2W3HTZ3WdhQj3s8is/+O+n0vu34kPBRSXJ2MRpjNkWeY+i1evr+6SCvoAB9bCyBI0fC9ky1oMx2EaJFUxQFl8ODw+rCbnVVuXZYXdhLK90u+yWq02vRm3QYjDr0Ri0Gkw69UYfBpMNgrPRYDWV89Y99+S9sl8PjvXY53bidHlxOD26HB5fLg8tR6ZhTLeNyeLzHKj+nIjx4KkKEw43Lrr6GqB+9UYvRT4/BT6dem3QY/dS/JZ1Oi0anQavToNWecK3TotFWvq8GMF3ZtVanrVJeU6mcVifho9k4mFWMIXAvAINiB2H7ZCEAoVOnotFqoag8fEjLhxB1oSgKthInRbk2ivPsFOXZKLHYgaq/ONGAtuxXa11+paJR/0E98Veq21U1UDhKy27bKgcK9ZdpU//q1Go1ZcFEDSp6oxpSdAYNikf9rLzXCqCUNaGX3VYUUDyVr8tuKwooVC1bdh63Uw0W+OAHtkZDjUHsxNBW/lnojeqXacX7Ud9blc/GU/G+K79fj6JApc/vpLKe8nOp50apep6qZcs/8+ofq3pcQafXYvTXYzTpMPjpMPhV3K4SKvx0GE3l99VyBpNO/VtuZSR8lLE63KQXWPFvtweAC7W9Kd34tjrQdMpksBWAW/1HU2a7CKFyuz2U5NspzrdRlGujqCxgFOXZKC67djma7y9hjQaMZj0mf7365eGnx2Quu+1fcdxg0uFxe6r82i/vKqjSteAsu29343SoXQ3lIcfjUcrCjy/fMOgNWvQG9YteZ9CiN2jRGXTqcaNWbeExlt03aNGV3S4v632sPECYyo55A4UOvUk9j0bT+r5URd1I+ChzKKcYtEXo/NMAOHd9NjYgcNQoDHFxkPWXWtAvFAx+PqunEOU8HvWXrbf/+cT+6AbgsLnUMJFrozjfXhYwKoJFSYFd/fV5CuYQI0HhfgSF+xEQYkKjpeqvV4+Cp/xXvOfUv0hPahEou9bqNFUChOmEEFH1tvpl2dhfkG6X54RxDlXDi9vtKRuboKkyjkBz4tiDk8YiVGr5OXHMQ1k5nb4sNJQFBq1OI4FANAsSPsokZ5egD/oTNAp9QrrjfG85AKFXXqkWKMpQr2Wmi6gjxaN4+88rD7TzDrKr9IXk/RVd9mu5vA/dafecMDivjn3rJ34pVf5Cq9JdUd2gPUCjwV7ixF7qOuX71Oo1BIX5ERjuR1CEH0FhJvU6vOxYmB86Q+ud1a/Tq60ABBh8XRUhmg0JH2UOZhWjLxvvMTmzLe78nehjYggcNVItUHxcvZbw0WJ43B7cbrV1wONWB92VXzwupdrbJw62q3K/fHCe04O77H7FIL6y+5XKe1y+G8nu7Scvv3MGTGa9GiIqXQLDKwKGOcioBhohhKgjCR9l9h/PQRdwEIDuq46iAKFTpqDRl31E5S0fMt6j0XncHkoLnZRY7JQU2Cm12CmxOCguu+20u3G71PnrbqcaMDzeYKGUBQpPnboDmopWr8FwwmA7vVFbdXbECbMmTipbeWCeSVt1BkV1g+FqGKR3ctlqBtF5FIz+eoLC/TD6yz8TQoiGJf+qlPnTsglNoItuxVEoW3aCRkPo1CkVBYqk5eNMlc98KCmwU1Lg8IaLkgI1XKjXdqyFjkYJDjq9Fq1e420G15Xd1uoqblfuH68YZKdDZ6x6X3/i/fLbxrLyVZ6v9fm0NiGEaE4kfABuj0K2Zys64Or94UAGAaNGYoiPryhUXD7NVsJHTVwONwVZVvIzSyjKtXnDhDdsFNrr3BWh0WowBxsJCDESEGpSLyEmzCFGjH76iuBwQpBQw4QGnaHqbe9gPiGEED4n4QNIzS1EG/AnepfCOeuPABB21VVVC5Wv8dHKVzdVFIWSAgf5x0soyCyl4Hgp+cdLKcgspSjfVqfhBf5BBswhJgJDTQSEGDGXBYuAUPWYOcSIf5CxVc59F0KI1kDCB7D80Ho0OiuD9pjQWIrQR0cTOGpU1UJFrWt1U6fDTcHxUu8lP7PittPurvF5JrOe0BgzIdH+BIb6ERBq9AaLgFAT5mCjz5f1FUII4VsSPoDVx1YCcNFOI1BK6NRKA01BXZDgLFzdVG3FsHuDhdqCUUL+8VKK8+w1Pk+j1RAc6UdYbAChMWbCYsyExqrXfoEG6d4QQghRq1YfPhRFYX/RemLyFLqmFKgDTadMqVrIXgiusmUJW+BsF4/bQ2GOjbyMEvIzS8jPKFVvHy/FVVsrRoCesJgAb7AIjTETFmsmONJfWi+EEEKctlYfPvbn78eq5HDFdvXXesDIERjatKlaqHymiykEjOYmrmHduZ0eCrLKgkVGCfmZ6u2CrNIaB3pqtRqCo/xPasEIjTXjH2hs4ncghBCiNWj14eO3I7+hcyuM2amGj7DyFU0r865u2jy6XBw2l9pNklFCXkYp+Zkl5GWUUJhtrXGKqt6gJTTWTHhcAGGxAep1nJngKH90Mg1UCCFEE2r14WN5yq8MOKAQanWji4oicPTokwv5aHVTp91NbnoxecdKvF0meRkltY7HMPrrCasUMsLi1NtB4X6yCqUQQohmoVWHj4ziDA5a9nHVNrW5IHTKZDSGavZfaILVTUssdnKOFpOTVlR2XUxBVmmNU1f9gwxVWjDC4gIIjw3AHGKUAZ9CCCGatVYdPn5L+43ofIU+KQoKGkKnTq2+oHd10zPvdvF4FCxZpd6AkZNWRPbRYqyFjmrLm4ONRLQJIDwusErI8AuUTaqEEEK0TK06fKxIW8F5O9SdQY937UP3tm2rL1h8emt8OB1qt0lOWrG3VSM3vRiXo5rdSDUQFmMmsm0gkQlBRLYNJKJtIAEhpnq9phBCCNHctdrwYbFb2J6+mVt3qv0atvGTai5ch9VNSwsdlbpM1OuC46XVDgDVG7VEtKkIGZEJgUTEB2Iw6c7kLQkhhBAtQqsNH6vSV9HngJPQEsgzBRE14fyaC9eyumlhjpX1PxziwKbj1T7VP9hIVEIgkW2DiEwIJLJtICHRZlk6XAghRKtV7/CRnp7Oo48+ytKlS7FarXTp0oUPP/yQ/v37A+qiXTNnzuT9998nPz+fwYMH884779CjR48Gr/yZWHFkBeeXDTRdnjiQB+JCay5czWwXW7GTzUtS2PX7UTzusgGrMWZvwChv1ZBuEyGEEKKqeoWP/Px8hg8fztixY1m6dCnR0dEkJycTGhrqLfPyyy/z2muvMW/ePLp06cKsWbO44IIL2LdvH0FBQQ1d/9PicDv4a/cf3HJYDQ0bzhlBWEANC2rZi8BRrN4OjMHlcLNzxVG2/JSKw+oCIKFbGEOv6ERUu+bx/oQQQojmrF7h46WXXiIhIYG5c+d6jyUlJXlvK4rC66+/zhNPPMHkyZMB+Oijj4iJieHzzz/nzjvvbJhan6ENGRsYtrUELbA1qjPBHZJqLlw208VjCGbfliI2LtpBcb66zkZE20CGTe5Iu+4RjV9pIYQQ4ixRr6UtFy5cyIABA5g2bRrR0dH07duXDz74wPv44cOHyczMZPz48d5jJpOJ0aNHs3bt2mrPabfbKSwsrHJpbCsP/8rYHWqrx5KkoXSMDqyxrFJ4jFR7X77OepHfPv6T4nw7gWEmzp/ejav+OVCChxBCCFFP9Qofhw4dYs6cOXTu3Jlly5Zx1113cd999/Hxxx8DkJmpDsyMiak6KyQmJsb72IleeOEFQkJCvJeEhITTeR915lE85P66jLASKA4IYH1cDzpGBVRbNvtIEQs/t7I4/2ly7XGYzHqGTe7Edf8aQtchcbJiqBBCCHEa6tXt4vF4GDBgALNnzwagb9++7Nmzhzlz5nDjjTd6y524wqaiKDWuuvn444/z0EMPee8XFhY2agDZnbObQRstAKztPAy3VkenE1o+CnOsbFh4iP0bjwMGtDjp3e4A/e+/E78AWdxLCCGEOBP1Ch9xcXF07969yrFu3boxf/58AGJj1dkgmZmZxMVVTEvNyso6qTWknMlkwmRquhkh67b+wIhDapfLtzEDAOgYpYYPW4mTzUtT2LXyqHcX2C4JGQy2PkNw/6tAgocQQghxxurV7TJ8+HD27dtX5dj+/ftJTEwEoH379sTGxvLzzz97H3c4HPz+++8MGzasAap75pw//IQWsPTqSJp/BP4GHTEBRrYuT+XTp9ax45c0PC6FtueEceU/B3LBOSsI1mc3+aZyQgghxNmqXi0fDz74IMOGDWP27NlceeWVbNy4kffff5/3338fULtbHnjgAWbPnk3nzp3p3Lkzs2fPxmw2c+211zbKG6iPlLxD9N2YB0Dp+dPQHIKRBn++eHZDxQyWNuoMloTu4WpX0W/lq5tK+BBCCCEaQr3Cx8CBA/nuu+94/PHH+de//kX79u15/fXXue6667xlHnnkEaxWK3fffbd3kbHly5c3izU+dnz/IV2KoTTQwL6APtxYlEO0xU0xbgLDTAy+tANdBsdWXX3Uu7rpmW8qJ4QQQgjQKEp1u4/4TmFhISEhIVgsFoKDgxv03IuuGEJMWhB7h0ynxKWOSVEMGoZd0oHeY9uiN1azt8oLCWAvhHs2Q2TnBq2PEEIIcbaoz/d3q9nbJXXnTuzK5WzqPwBcWjwa2Gp0ccV13eg3qIbZNY4SNXhArZvKCSGEEKLu6jXgtCUzhoaRFdcPNFo6D4zhiwgnK/yddG0XWvOTyrtcDAFg8n23kRBCCHE2aDUtH3HtEjhvuoGwWDOEGTk2OwWtBpIizTU/qfJ4jxrWKRFCCCFE/bSa8AHQdbA6Y2XtwRwA2oWbMemrGedRrlhmugghhBANrdV0u1SWnK3uUlu+uFiNyjaVkzU+hBBCiIbTKsPHwSw1fJy4rPpJijLUawkfQgghRINpleEjObsEoNbdbAEolpYPIYQQoqG1yvBR3vJx6m4XGfMhhBBCNLRWFz6K7S4yC20AdKpr+JDVTYUQQogG0+rCR3JZq0dkoIkQ8yl2qS2f7RIUV3s5IYQQQtRZqwsfFYNNA2ov6LSCzaLeltVNhRBCiAbT6sJH3afZlrV66P3AL6SRayWEEEK0Hq0ufNR5mm3lmS6yuqkQQgjRYFpd+Kh7y0fZGh8y00UIIYRoUK0qfDjdHlJzS4G6LDAma3wIIYQQjaFVhY/U3FJcHgWzUUdciF/thWV1UyGEEKJRtKrwUXlxMc2pxnGUj/mQmS5CCCFEg2pV4aN8vMcpu1yg0gJjssaHEEII0ZBaV/jwtnycYo0PkNVNhRBCiEbSqsLHwfq0fMjqpkIIIUSjaDXhQ1GUSi0fpwgfThtY89XbMuZDCCGEaFCtJnxkFdkpcbjRaTUkRpyi26V8sKnOBP5hjV85IYQQohXR+7oCTSUm2I+dz44nLa8Uo/4Umcu7ummMrG4qhBBCNLBW0/IBEOxnoEd8HfZpkdVNhRBCiEbTqsJHnRVVavkQQgghRIOS8FEdmekihBBCNBoJH9UpX+NDZroIIYQQDU7CR3VkdVMhhBCi0Uj4qI6sbiqEEEI0Ggkf1Skf8yGzXYQQQogGJ+HjRC4HlOaqt6XbRQghhGhwrWaRsTorX2BMawBzuG/rIoRosdxuN06n09fVEKJBGQwGdDrdGZ9HwseJvKubxsrqpkKIelMUhczMTAoKCnxdFSEaRWhoKLGxsWjO4DtSwseJvKubymBTIUT9lQeP6OhozGbzGf0DLURzoigKpaWlZGVlARAXd/pDEyR8nMg700UGmwoh6sftdnuDR0REhK+rI0SD8/f3ByArK4vo6OjT7oKRAacnqtztIoQQ9VA+xsNsNvu4JkI0nvK/7zMZ0yTh40SyqZwQ4gxJV4s4mzXE37eEjxMVScuHEEII0ZgkfJxIxnwIIYQQjUrCx4mKZVM5IUTrk5ubS3R0NCkpKU36uikpKWg0GrZv315ruTFjxvDAAw80SZ18LSkpiddff73Rzj9jxgzuu+++Rjt/XUj4qMzthJIc9basbiqEaEVeeOEFJk2aRFJSElARCpraypUr0Wg0J62TsmDBAp577jnv/TFjxjBv3rw6n9dX76c5mDdvHmPGjPHef+SRR5g7dy6HDx/2WZ0kfFRWnAUooNWDWabJCSFaB6vVyocffshtt93m66rUKDw8nKCgIF9X46wQHR3N+PHjee+993xWBwkflZV3uQREg1Y+GiHEmVEUhVKHyycXRVHqXM+lS5ei1+sZOnRojWXmzZtHaGgoixcvpmvXrpjNZqZOnUpJSQkfffQRSUlJhIWFce+99+J2u73P02g0fP/991XOFRoaWm2rRUpKCmPHjgUgLCwMjUbD9OnTgcbpdlmzZg2jR4/GbDYTFhbGhAkTyM/PB8But3PfffcRHR2Nn58fI0aMYNOmTd7nlrfQLFu2jL59++Lv78+4cePIyspi6dKldOvWjeDgYK655hpKS0u9zxszZgz33HMP99xzD6GhoURERPDkk0/W+t/LYrFwxx13EB0dTXBwMOPGjWPHjh0AZGdnExsby+zZs73lN2zYgNFoZPny5TWe89JLL+WLL7447c/uTMkiY5XJTBchRAOyOt10f3qZT157778mYDbW7Z/4P/74gwEDBpyyXGlpKW+++SZffvklRUVFTJ48mcmTJxMaGsqSJUs4dOgQU6ZMYcSIEVx11VX1rnNCQgLz589nypQp7Nu3j+DgYO+iVg1t+/btnHfeedxyyy28+eab6PV6VqxY4Q1OjzzyCPPnz+ejjz4iMTGRl19+mQkTJnDw4EHCwyv2/Xr22Wd5++23MZvNXHnllVx55ZWYTCY+//xziouLueKKK3jrrbd49NFHvc/56KOPuPXWW9mwYQObN2/mjjvuIDExkdtvv/2keiqKwsUXX0x4eDhLliwhJCSE//znP5x33nns37+fqKgo/ve//3H55Zczfvx4zjnnHK6//nruvvtuxo8fX+P7HzRoEGlpaaSmppKYmNiAn2zdSPiorHyNDwkfQohWJCUlhfj4+CrHkpKSTvo17nQ6mTNnDh07dgRg6tSpfPLJJxw/fpzAwEC6d+/O2LFjWbFixWmFD51O5/1ij46OJjQ0tMayK1eurNe5T3w/L7/8MgMGDODdd9/1HuvRowcAJSUlzJkzh3nz5nHRRRcB8MEHH/Dzzz/z4Ycf8vDDD3ufM2vWLIYPHw7ArbfeyuOPP05ycjIdOnQA1M9oxYoVVcJHQkIC//73v9FoNHTt2pVdu3bx73//u9rwsWLFCnbt2kVWVhYmkwmAV155he+//55vv/2WO+64g4kTJ3L77bdz3XXXMXDgQPz8/HjxxRe955g+fbq3BalcmzZtAPW/vYQPX5PVTYUQDcjfoGPvvyb47LXrymq14ufnd8pyZrPZGzwAYmJiSEpKIjAwsMqx8r0/mrPt27czbdq0ah9LTk7G6XR6QwWou7kOGjSIP//8s0rZ3r17e2/HxMRgNpu9waP82MaNG6s8Z8iQIVUGvw4dOpRXX30Vt9t90nLlW7Zsobi4+KTl+q1WK8nJyd77r7zyCj179uTrr79m8+bNp/zvWd6iVLlLqClJ+KhMVjcVQjQgjUZT564PX4qMjPSOdaiNwWCocl+j0VR7zOPxVLlfXQuKr9XWnVNe3xNnxyiKctKxyu+/Lp9HfXk8HuLi4qpt6ancMnTo0CGOHTuGx+MhNTW1SiiqTl5eHgBRUVGnXbczIaMqK/OO+ZA1PoQQrUffvn3Zu3dvo5w7KiqKjIwM7/0DBw7U+mvbaDQCVBm02hh69+7Nr7/+Wu1jnTp1wmg0snr1au8xp9PJ5s2b6dat2xm/9vr160+637lz52o3aevXrx+ZmZno9Xo6depU5RIZGQmAw+Hguuuu46qrrmLWrFnceuutHD9+vNY67N69G4PB4O1qamoSPiorn+0ia3wIIVqRCRMmsGfPnjq1ftTXuHHjePvtt9m6dSubN2/mrrvuOql1oLLExEQ0Gg2LFy8mOzub4uLiBq8TwOOPP86mTZu4++672blzJ3/99Rdz5swhJyeHgIAA/va3v/Hwww/z008/sXfvXm6//XZKS0u59dZbz/i109LSeOihh9i3bx9ffPEFb731Fvfff3+1Zc8//3yGDh3K5ZdfzrJly0hJSWHt2rU8+eSTbN68GYAnnngCi8XCm2++ySOPPEK3bt1OWc9Vq1YxcuTIRhvQeyoSPiorktVNhRCtT69evRgwYABff/11g5/71VdfJSEhgVGjRnHttdcyY8aMWnf9bdOmDTNnzuSxxx4jJiaGe+65p06vM3369CoLaZ1Kly5dWL58OTt27GDQoEEMHTqUH374Ab1e7SZ78cUXmTJlCjfccAP9+vXj4MGDLFu2jLCwsDq/Rk1uvPFGrFYrgwYN4u9//zv33nsvd9xxR7VlNRoNS5YsYdSoUdxyyy106dKFq6++mpSUFGJiYli5ciWvv/46n3zyCcHBwWi1Wj755BNWr17NnDlzaqzDF198Ue0A16aiUeozGbwJFBYWEhISgsViITg4uOle2O2CWVGgeOAf+6XrRQhRbzabjcOHD9O+ffs6DeBsTpYsWcKMGTPYvXs32ha4ztGYMWMYM2YMzz77rK+rUqsxY8Zw7rnnNury6afy448/8vDDD7Nz505v2KqPmv7O6/P93fxHQjWVkmw1eGi0EBDp69oIIUSTmjhxIgcOHCA9PZ2EhARfV6deioqKSE5OZvHixb6uSotQUlLC3LlzTyt4NBQJH+WqrG5a9ylqQghxtqhp3EFzFxQURFpamq+r0WJceeWVvq6ChA8vWd1UCCFEI6vv4mhnq5bXsddYZHVTIYQQoklI+ChXvrqpzHQRQgghGpWEj3Lelg9Z40MIIYRoTBI+ysnqpkIIIUSTkPBRTlY3FUIIIZqEhI9ysrqpEEII0SQkfAB43FBctgW0zHYRQrRCubm5REdHk5KS0qSvm5KSgkajYfv27bWWGzNmDA888ECT1KmlGThwIAsWLPB1NeqlXuHj2WefRaPRVLnExlZ8WU+fPv2kx4cMGdLglW5wpbmguAGNusiYEEK0Mi+88AKTJk0iKSkJqAgFTW3lypVoNBoKCgqqHF+wYAHPPfec9/6YMWOYN29enc/rq/dzJmr6LKZPn15lGfmnnnqKxx57DI/H07QVPAP1bvno0aMHGRkZ3suuXbuqPH7hhRdWeXzJkiUNVtlGUz7TJSAKdLLumhCidbFarXz44Yfcdtttvq5KjcLDwwkKCvJ1NZqliy++GIvFwrJly3xdlTqrd/jQ6/XExsZ6L1FRUVUeN5lMVR4PDw+v9Xx2u53CwsIqlyYnq5sKIRqDooCjxDeXeuwZunTpUvR6PUOHDq2xzLx58wgNDWXx4sV07doVs9nM1KlTKSkp4aOPPiIpKYmwsDDuvfde3G6393kajYbvv/++yrlCQ0OrbbVISUlh7NixAISFhaHRaJg+fTrQON0ua9asYfTo0ZjNZsLCwpgwYQL5+fmA+t103333ER0djZ+fHyNGjGDTpk3e55a3Svz6668MGDAAs9nMsGHD2LdvX5XXWLhwIQMGDMDPz4/IyEgmT57sfezTTz9lwIABBAUFERsby7XXXktWVtYpP4sT6XQ6Jk6cyBdffNGQH0+jqvfP/AMHDhAfH4/JZGLw4MHMnj2bDh06eB9fuXIl0dHRhIaGMnr0aJ5//nmio2vuynjhhReYOXPm6dW+ocjqpkKIxuAshdnxvnntfx4DY0Cdiv7xxx8MGDDglOVKS0t58803+fLLLykqKmLy5MlMnjyZ0NBQlixZwqFDh5gyZQojRozgqquuqneVExISmD9/PlOmTGHfvn0EBwfj7+9f7/PUxfbt2znvvPO45ZZbePPNN9Hr9axYscIbnB555BHmz5/PRx99RGJiIi+//DITJkzg4MGDVX5UP/HEE7z66qtERUVx1113ccstt7BmzRpA3T128uTJPPHEE3zyySc4HA5+/PFH73MdDgfPPfccXbt2JSsriwcffJDp06ezZMmSen8WgwYN4uWXX26Uz6ox1Ct8DB48mI8//pguXbpw/PhxZs2axbBhw9izZw8RERFcdNFFTJs2jcTERA4fPsxTTz3FuHHj2LJlCyaTqdpzPv744zz00EPe+4WFhU2/o6KsbiqEaMVSUlKIj68akpKSklBOaD1xOp3MmTOHjh07AjB16lQ++eQTjh8/TmBgIN27d2fs2LGsWLHitMKHTqfzfrGX/4itSX33SDnx/bz88ssMGDCAd99913usR48egLrr65w5c5g3bx4XXXQRAB988AE///wzH374IQ8//LD3Oc8//zyjR48G4LHHHuPiiy/GZrPh5+fH888/z9VXX13lB3afPn28t2+55Rbv7Q4dOvDmm28yaNAgiouLCQwMrPGzqK7VqE2bNhw5cgSPx4NW2/znktQrfJT/RwDo1asXQ4cOpWPHjnz00Uc89NBDVf7YevbsyYABA0hMTPSmv+qYTKYag0mTKZI1PoQQjcBgVlsgfPXadWS1WvHz8ztlObPZ7A0eADExMSQlJREYGFjlWHnXQXO2fft2pk2bVu1jycnJOJ1Ohg8f7j1mMBgYNGgQf/75Z5WyvXv39t6Oi1O/Q7KysmjXrh3bt2/n9ttvr7EO27Zt49lnn2X79u3k5eV5B4weOXKE7t271+v9+Pv74/F4sNvtjdZa1JDOaHRlQEAAvXr14sCBA9U+HhcXR2JiYo2PNxve8CEtH0KIBqTR1Lnrw5ciIyO9Yx1qYzAYqtzXaDTVHqs860Kj0VTbguJrtX1Bl9f3xNkxiqKcdKzy+y9/rPz91/YaJSUljB8/nvHjx/Ppp58SFRXFkSNHmDBhAg6Ho35vBsjLy8NsNreI4AFnuM6H3W7nzz//9Ka9E+Xm5pKWllbj481G+eqmgTLmQwjR+vTt25e9e/c2yrmjoqLIyMjw3j9w4AClpaU1ljcajQBVBq02ht69e/Prr79W+1inTp0wGo2sXr3ae8zpdLJ582a6devWIK/x119/kZOTw4svvsjIkSM555xzTmoxqs9nsXv3bvr161fnuvlavcLHjBkz+P333zl8+DAbNmxg6tSpFBYWctNNN1FcXMyMGTNYt24dKSkprFy5kkmTJhEZGckVV1zRWPVvGNLtIoRoxSZMmMCePXvq1PpRX+PGjePtt99m69atbN68mbvuuuuk1pLKEhMT0Wg0LF68mOzsbIqLixu8TqCON9y0aRN33303O3fu5K+//mLOnDnk5OQQEBDA3/72Nx5++GF++ukn9u7dy+23305paSm33nprnV/jmWee4YsvvuCZZ57hzz//ZNeuXd5Boe3atcNoNPLWW29x6NAhFi5cWGUdE6jfZ7Fq1SrGjx9/eh+GD9QrfBw9epRrrrmGrl27MnnyZIxGI+vXrycxMRGdTseuXbu47LLL6NKlCzfddBNdunRh3bp1zXtutsdTMeBUul2EEK1Qr169GDBgAF9//XWDn/vVV18lISGBUaNGce211zJjxgzM5prHo7Rp04aZM2fy2GOPERMTwz333FOn15k+fTpjxoypc726dOnC8uXL2bFjB4MGDWLo0KH88MMP6PXqaIQXX3yRKVOmcMMNN9CvXz8OHjzIsmXLCAsLq/NrjBkzhm+++YaFCxdy7rnnMm7cODZs2ACoLULz5s3jm2++oXv37rz44ou88sorVZ5f188iPT2dtWvXcvPNN9e5br6mUU7sjPOxwsJCQkJCsFgsBAcHN/4LluTA/5UNoHoqB3Q1J3IhhKiNzWbj8OHDtG/fvk4DOJuTJUuWMGPGDHbv3t0iZkucaMyYMYwZM6bKyp+txcMPP4zFYuH9999vkter6e+8Pt/fspxn+Rof5kgJHkKIVmvixIkcOHCA9PT0pl/u4AwVFRWRnJzM4sWLfV0Vn4iOjmbGjBm+rka9SPiQ1U2FEAKA+++/39dVOC1BQUGkpaX5uho+U3ndkZai5bWtNTRZ3VQIIYRoUhI+ZJqtEEII0aQkfEi3ixBCCNGkJHxIt4sQQgjRpCR8yKZyQgghRJOS8OHtdpHVTYUQQoim0LrDh6JUDDiV1U2FEEKIJtG6w4c1H9xluwdKt4sQohXLzc0lOjqalJQUX1dFnKbFixfTt2/fKrsKN1etO3yUDzb1Dwe9ybd1EUIIH3rhhReYNGkSSUlJAKSkpFTZPn7evHmEhoY2+OvWdN4xY8Ywb968Op/nxPq2Bie+50suuQSNRsPnn3/uw1rVTSsPH+VdLjLTRQjRelmtVj788ENuu+02X1elxXM6nT59/Ztvvpm33nrLp3Woi9YdPopljQ8hRONRFIVSZ6lPLvXZM3Tp0qXo9XqGDh1a7eMrV67k5ptvxmKxoNFo0Gg03g3cHA4HjzzyCG3atCEgIIDBgwezcuVKQN2ArEePHtxxxx3ecx0+fJiQkBA++OCDWs97pnJzc7nmmmto27YtZrOZXr168cUXX3gf/89//kObNm1O6qK49NJLuemmm7z3Fy1aRP/+/fHz86NDhw7MnDkTl8vlfVyj0fDee+9x2WWXERAQwKxZs3C73dx66620b98ef39/unbtyhtvvFHldVwuF/fddx+hoaFERETw6KOPctNNN3H55Zd7yyiKwssvv0yHDh3w9/enT58+fPvtt7W+70svvZSNGzdy6NCh0/nYmkzr3tulvNtFVjcVQjQCq8vK4M8H++S1N1y7AbOh5q3rK/vjjz8YMGBAjY8PGzaM119/naeffpp9+/YBEBgYCKi/tFNSUvjyyy+Jj4/nu+++48ILL2TXrl107tyZzz77jMGDBzNx4kQmTZrEDTfcwNixY7n99ttxOBw1nvdM2Ww2+vfvz6OPPkpwcDA//vgjN9xwAx06dGDw4MFMmzaN++67jxUrVnDeeecBkJ+fz7Jly1i0aBEAy5Yt4/rrr+fNN99k5MiRJCcne4PUM888432tZ555hhdeeIF///vf6HQ6PB4Pbdu25euvvyYyMpK1a9dyxx13EBcXx5VXXgnASy+9xGeffcbcuXPp1q0bb7zxBt9//z1jx471nvfJJ59kwYIFzJkzh86dO/PHH39w/fXXExUVxejRo6t934mJiURHR7Nq1So6dOjQIJ9lY2jl4aO85UMGmwohWq+UlBTi4+OrHEtKSvK2nhiNRkJCQtBoNMTGVvxYS05O5osvvuDo0aPe58+YMYOffvqJuXPnMnv2bM4991xmzZrF7bffzjXXXENycjLff/99recFvK0ndVW5vgBt2rSpstPrvffey08//cQ333zD4MGDCQ8P58ILL+Tzzz/3ho9vvvmG8PBw7/3nn3+exx57zNsS0qFDB5577jkeeeSRKuHj2muv5ZZbbqlSn5kzZ3pvt2/fnrVr1/L11197w8dbb73F448/zhVXXAHA22+/zZIlS7zPKSkp4bXXXuO3337ztkh16NCB1atX85///IfRo0ef9J4rv/fmPnC4lYeP8tVNZY0PIUTD89f7s+HaDT577bqyWq34+fnV+zW2bt2Koih06dKlynG73U5ERIT3/j/+8Q9++OEH3nrrLZYuXUpkZGS9X6u+3G43L774Il999RXp6enY7XbsdjsBAQHeMtdddx133HEH7777LiaTic8++4yrr74anU4HwJYtW9i0aRPPP/98lfPabDZKS0sxm9WWpepajd577z3++9//kpqaitVqxeFwcO655wJgsVg4fvw4gwYN8pbX6XT079/f2w20d+9ebDYbF1xwQZXzOhwO+vbtW+t79/f3p7S0tB6fVtNr3eFDVjcVQjQijUZT564PX4qMjCQ/P7/ez/N4POh0OrZs2eL9wi5XufskKyuLffv2odPpOHDgABdeeOEZ1/lUXn31Vf7973/z+uuv06tXLwICAnjggQdwOBzeMpMmTcLj8fDjjz8ycOBAVq1axWuvvVbl/c2cOZPJkyefdP7KYa1yoAH4+uuvefDBB3n11VcZOnQoQUFB/N///R8bNlQNoifOzqncilEeQn788UfatGlTpZzJVPvszLy8PKKiomot42utO3x4Z7tIy4cQovXq27cvn376aa1ljEYjbrf7pOe53W6ysrIYOXJkjc+95ZZb6NmzJ7fffju33nor5513Ht27d6/xvA1h1apVXHbZZVx//fWA+mV+4MABunXr5i3j7+/P5MmT+eyzzzh48CBdunShf//+3sf79evHvn376NSpU71fe9iwYdx9993eY8nJyd7bISEhxMTEsHHjRu/n5na72bZtm7d1pHv37phMJo4cOVLj+I7q2Gw2kpOTT9k64mutN3woSqXwIS0fQojWa8KECTz++OPk5+cTFhZWbZmkpCSKi4v59ddf6dOnD2azmS5dunDddddx44038uqrr9K3b19ycnL47bff6NWrFxMnTuSdd95h3bp17Ny5k4SEBJYuXcp1113Hhg0bMBqN1Z63vDvjTHTq1In58+ezdu1awsLCeO2118jMzKwSPkDtepk0aRJ79uzxBpVyTz/9NJdccgkJCQlMmzYNrVbLzp072bVrF7Nmzar1tT/++GOWLVtG+/bt+eSTT9i0aRPt27f3lrn33nt54YUX6NSpE+eccw5vvfUW+fn53taQoKAgZsyYwYMPPojH42HEiBEUFhaydu1aAgMDq8zIqWz9+vWYTKYaZy41G0ozY7FYFECxWCyN+0KleYryTLB6cVgb97WEEK2C1WpV9u7dq1itLe/flCFDhijvvfderWXuuusuJSIiQgGUZ555RlEURXE4HMrTTz+tJCUlKQaDQYmNjVWuuOIKZefOncqff/6p+Pv7K59//rn3HBaLRUlKSlIeeeSRWs97optuukkZPXp0nd9Pbm6uctlllymBgYFKdHS08uSTTyo33nijctlll1Up53K5lLi4OAVQkpOTTzrPTz/9pAwbNkzx9/dXgoODlUGDBinvv/++93FA+e6776o8x2azKdOnT1dCQkKU0NBQ5W9/+5vy2GOPKX369PGWcTqdyj333KMEBwcrYWFhyqOPPqpMmzZNufrqq71lPB6P8sYbbyhdu3ZVDAaDEhUVpUyYMEH5/fffa3zfd9xxh3LnnXfW+XM6HTX9ndfn+1ujKPWYDN4ECgsLCQkJwWKxEBwc3HgvlPUnvDsE/ELhsdTGex0hRKths9k4fPgw7du3P60BnL60ZMkSZsyYwe7du9Fqm98SUGPGjGHMmDENtg5Ic+PxeOjWrRtXXnklzz333GmdIzs7m3POOYfNmzdXaWVpaDX9ndfn+7v1drvI6qZCCOE1ceJEDhw4QHp6OgkJCb6uThVFRUUkJyezePFiX1elwaSmprJ8+XJGjx6N3W7n7bff5vDhw1x77bWnfc7Dhw/z7rvvNmrwaCitN3zI6qZCCFHF/fff7+sqVCsoKIi0tDRfV6NBabVa5s2bx4wZM1AUhZ49e/LLL7+cNCalPgYNGlRl+m5z1nrDh6xuKoQQwkcSEhJYs2aNr6vhM82vY6+pyOqmQgghhE+03vBRLGt8CCGEEL7QesNH+YBTWd1UCCGEaFISPmTAqRBCCNGkWmf4qLK6qYQPIYQQoim1zvBhLwSXVb0ts12EEEKIJtU6w0f5TBdTCBib/46TQgjR2HJzc4mOjiYlJaVJXzclJQWNRsP27dtrLTdmzBgeeOCBJqnT2Wrx4sX07dvXu2OuL7XS8FG2xodMsxVCCABeeOEFJk2aRFJSElARCpraypUr0Wg0FBQUVDm+YMGCKsuOjxkzhnnz5tX5vL56P7504nu+5JJL0Gg0fP755z6slap1ho/y1U1lposQQmC1Wvnwww+57bbbfF2VGoWHhxMUFOTrapwxp9Pp09e/+eabeeutt3xaB2it4cPb8iFrfAghGo+iKHhKS31yqc+eoUuXLkWv19e6Dfu8efMIDQ1l8eLFdO3aFbPZzNSpUykpKeGjjz4iKSmJsLAw7r33Xtxut/d5Go2G77//vsq5QkNDq221SElJYezYsQCEhYWh0WiYPn060PDdLrm5uVxzzTW0bdsWs9lMr169+OKLL7yP/+c//6FNmzYndVFceumlVbazX7RoEf3798fPz48OHTowc+ZMXC6X93GNRsN7773HZZddRkBAALNmzcLtdnPrrbfSvn17/P396dq1K2+88UaV13G5XNx3332EhoYSERHBo48+yk033cTll1/uLaMoCi+//DIdOnTA39+fPn368O2339b6vi+99FI2btzIoUOHTudjazCtc3l1Wd1UCNEEFKuVff36++S1u27dgsZctzFtf/zxBwMGDDhludLSUt58802+/PJLioqKmDx5MpMnTyY0NJQlS5Zw6NAhpkyZwogRI7jqqqvqXeeEhATmz5/PlClT2LdvH8HBwfj7+9f7PHVhs9no378/jz76KMHBwfz444/ccMMNdOjQgcGDBzNt2jTuu+8+VqxYwXnnnQdAfn4+y5YtY9GiRQAsW7aM66+/njfffJORI0eSnJzMHXfcwf+3d+dRTZzrH8C/ASGsUlEgRLaAC1i5CKiAG1xtRa1o9VpFULFWrVq9xaWKvVXw1ApaCVa51i4KWq1VK3pasVo8BUWpSoW2FC3SEJWqSLUKKIQt7+8Pb+ZnIASCkAR4PufknGTmnXfJmznz5J13ZgAgOjqaKys6OhqxsbFISEiAoaEh5HI5HBwccPjwYfTq1QtZWVlYuHAh7O3tMX36dADA5s2bceDAASQlJcHDwwMfffQRjh8/zgVnAPDee+8hJSUFH3/8Mfr27Ytz585h1qxZsLGxQWBgoMp2Ozs7w9bWFpmZmXB1dW2X77YlumbwQXc3JYQQzo0bNyAUCpWWubi4NBo9qa2txccffww3NzcAwLRp0/DFF1/g3r17sLCwwIABA/DPf/4T6enprQo+DA0NYW1tDQCwtbXFCy+80GTajIwMjfJu2J7evXtj1apV3Odly5bh1KlTOHLkCPz8/GBtbY1x48bhyy+/5IKPI0eOwNramvv8wQcfICoqihsJcXV1xfvvv4/Vq1crBR9hYWGYN2+eUn02bNjAvReJRMjKysLhw4e54GPHjh1Yu3YtpkyZAgBITEzEyZMnuW2ePHkCsViMH374gRuxcnV1xfnz5/HJJ58gMDBQZR8q2q7ticUNdc3gg+5uSgjRAp6pKfrnXNFZ2S1VVVUFExOTZtOZmZlxgQcA2NnZwcXFBRYWFkrLSktLNausDtTX1yMuLg6HDh3C7du3UV1djerqapibm3NpwsPDsXDhQuzcuRN8Ph8HDhxAaGgoDA0NAQBXrlxBdnY2PvjgA6V8ZTIZKisrYfa/kSdVo0q7du3C559/jps3b6Kqqgo1NTUYNGgQAKCsrAz37t1TekKtoaEhfH19udNAV69ehUwmw8svv6yUb01NDby9vdW23dTUFJWVlRp8W22vawcfdIMxQkg74vF4LT71oUu9evXCw4cPm01nZGSk9JnH46lc9uw8CR6Pp3IERdfi4+ORkJCAbdu2wdPTE+bm5oiMjERNTQ2XJiQkBHK5HKmpqRgyZAgyMzMhFou59XK5HBs2bMDUqVMb5f9sMPdsQAMAhw8fxvLlyxEfH4+AgABYWlriww8/xKVLl5TSNbw659nvUfEdp6amonfv3krp+Hy+2rb//fffsLGxUZumvXXx4INOuxBCiLe3N/bv398uedvY2ODu3bvc58LCQrX/uo2NjQFAadJqe8jMzMTkyZMxa9YsAE8P5oWFhfDw8ODSmJqaYurUqThw4AD++OMP9OvXD76+/z+Hx8fHBwUFBejTp4/GZQ8bNgxLlizhlkkkEu69lZUV7OzscPnyZYwcORLA0+8jNzeXGx0ZMGAA+Hw+bt261eT8DlVkMhkkEkmzoyPtresFH9UVQO2Tp+/ptAshhCA4OBhr167Fw4cP0aNHjzbNe/To0UhMTIS/vz/kcjnWrFnTaLTkWc7OzuDxeDhx4gQmTJgAU1NTpdM6baVPnz44evQosrKy0KNHD4jFYpSUlCgFH8DTUy8hISHIz8/nAhWF9evXY+LEiXB0dMRrr70GAwMD/Prrr8jLy8PGjRvVlr1v3z6cPn0aIpEIX3zxBbKzsyESibg0y5YtQ2xsLPr06QN3d3fs2LEDDx8+5EZDLC0tsWrVKixfvhxyuRwjRoxAeXk5srKyYGFhoXRFzrMuXrwIPp+v9sombeh6l9oqrnQxtgD4bf+DJoSQjsbT0xODBw/G4cOH2zzv+Ph4ODo6YtSoUQgLC8OqVau4uRCq9O7dGxs2bEBUVBTs7OywdOnSFpUzd+5cBAUFtbhe69atg4+PD4KDgxEUFASBQKB0GavC6NGjYW1tjYKCAoSFhSmtCw4OxokTJ5CWloYhQ4bA398fYrEYzs7OastetGgRpk6dihkzZsDPzw8PHjxQGgUBgDVr1mDmzJmYM2cOAgICYGFhgeDgYKXTOe+//z7Wr1+P2NhYeHh4IDg4GN9++61SENPQwYMHER4errYPtIHHNLkYXAvKy8thZWWFsrIydO/eve0LkGYCeycCPfsAy3QzEYwQ0jnJZDJIpVKIRKIWTeDUJydPnsSqVavw22+/wcCg4/0vDQoKQlBQEGJiYnRdlXYhl8vh4eGB6dOnK93pVRN//fUX3N3d8dNPP6kNUJrT1O9ck+N31zvtwt3dlCabEkKIwoQJE1BYWIjbt2/D0dFR19XRSEVFBSQSCU6cOKHrqrSZmzdv4vvvv0dgYCCqq6uRmJgIqVTaaPRFE1KpFDt37nyuwKOtdL3gg650IYQQld5++21dV6FVLC0tUVxcrOtqtCkDAwMkJydj1apVYIxh4MCBOHPmTKM5KZoYOnSo0uW7utQFgw/FrdUp+CCEEKKfHB0dceHCBV1Xo910vBN7z4seKkcIIYToVNcLPugeH4QQQohOdeHgg0Y+CCGEEF3oesGH4rQLjXwQQgghOtG1go+aJ0B1+dP3NOeDEEII0YmuFXwoTrkYmQF8S93WhRBCCOmiumbwYSkAGjwtkBBCurIHDx7A1tYWN27cULk+OTkZL7zwglbrRLQrMTERkyZN0kpZXSv4ePy/4IPubkoIIUpiY2MREhICFxcXAMCNGzeUHuk+Y8YMXL9+XUe105yLiwsyMjJanD4jI4Nre1fRsM0LFixAdnY2zp8/3+5ld63gQ/FQObrBGCGEcKqqqrB7927Mnz+/yTSmpqawtbXVYq26ptraWp2VzefzERYWhh07drR7WV0s+KC7mxJCtIcxhtrqep28NHlm6HfffYdu3bqpfcx6w9MuMTExGDRoEPbs2QMnJydYWFhg8eLFqK+vx5YtWyAQCGBra4sPPvhAKR+xWAxPT0+Ym5vD0dERS5YswePHj5XSfPbZZ3B0dISZmRmmTJkCsVjc6JTPt99+C19fX5iYmMDV1RUbNmxAXV1di9vcHIlEgsmTJ8POzg4WFhYYMmQIzpw5w61fu3Yt/P39G233j3/8A9HR0dznpKQkeHh4wMTEBO7u7ti5cye3TjG6dPjwYQQFBcHExAT79+/HgwcPMHPmTDg4OMDMzAyenp44ePCgUjkVFRUIDw+Hubk57O3tkZCQgKCgIERGRnJpampqsHr1avTu3Rvm5ubw8/NrdjRo0qRJOH78OKqqqjT8xjTTtW6vTnc3JYRoUV2NHJ++fVYnZS/8KBBGfMMWpT137hwGDx6scRkSiQTfffcdTp06BYlEgmnTpkEqlaJfv344e/YssrKyMG/ePIwZM4Y7UBsYGGD79u1wcXGBVCrFkiVLsHr1au6gfOHCBSxatAibN2/GpEmTcObMGaxbt06p3NOnT2PWrFnYvn07Ro4cCYlEgoULFwKA0oH/eTx+/BgTJkzAxo0bYWJigr179yIkJAQFBQVwcnJCeHg44uLiIJFI4ObmBgDIz89HXl4evv76awBPg6jo6GgkJibC29sbubm5WLBgAczNzREREcGVtWbNGsTHxyMpKQl8Ph8ymQy+vr5Ys2YNunfvjtTUVMyePRuurq7w8/MDAKxYsQIXLlzAN998Azs7O6xfvx45OTkYNGgQl+/rr7+OGzdu4KuvvoJQKMSxY8cwbtw45OXloW/fvirbPXjwYNTW1uLy5csIDAxsk+9SFR7TJDzWAk0eyauxvSGA9Bww5VPAa0bb5k0I6fIaPmq8trq+QwQfr776Knr27Indu3c3mSY5ORmRkZF49OgRgKcjHx9++CFKSkpgafn06sFx48ahoKAAEokEBgZPB9bd3d0xd+5cREVFqcz3yJEjWLx4Me7fvw8ACA0NxePHj5WeUDtr1iycOHGCK3vUqFEYP3481q5dy6XZv38/Vq9ejTt37rSoza3x4osvYvHixVi6dCkAwMvLC9OmTeOCo3fffRdnzpzB5cuXAQBOTk7YvHkzZs6cyeWxceNGnDx5EllZWbhx4wZEIhG2bdvW7EP9XnnlFXh4eGDr1q2oqKhAz5498eWXX2LatGkAgLKyMgiFQixYsADbtm2DRCJB37598eeff0IoFHL5vPTSSxg6dCg2bdrUZFnW1tZISEhQCpCe1fB3rqDJ8btrjXxwcz5o5IMQ0v66GRtg4Uft9++xubJbqqqqSukg0lIuLi5c4AEAdnZ2MDQ05AIPxbLS0lLuc3p6OjZt2oSrV6+ivLwcdXV1kMlkePLkCczNzVFQUIApU6YolTN06FClYOTKlSvIzs5WOqVTX18PmUyGyspKmJmZadyWhp48eYINGzbgxIkTuHPnDurq6lBVVYVbt25xacLDw7Fnzx6sW7cOjDEcPHiQO+3x119/obi4GG+88QYWLFjAbVNXVwcrKyulshqOOtXX1yMuLg6HDh3C7du3UV1djerqapibmwMAioqKUFtbq/SEWisrK/Tv35/7nJOTA8YY+vXrp5R3dXU1evbsqbbtpqamqKysbMG31HpdK/igq10IIVrE4/FaPPqgS7169cLDhw813s7IyEjpM4/HU7lMLpcDAG7evIkJEyZg0aJFeP/992FtbY3z58/jjTfe4CZaMsaUrrJRLHuWXC7Hhg0bMHXq1EZ1ak0Qpco777yD06dPY+vWrejTpw9MTU0xbdo01NTUcGnCwsIQFRWFnJwcVFVVobi4GKGhoVwdgaenXhSnShQMDZV/E4qgQiE+Ph4JCQnYtm0bNz8mMjKSK1vxfaj7nuRyOQwNDXHlypVG5VlYWKht+99//w0bGxu1aZ5X1wk+aqsAWdnT9zThlBBCON7e3ti/f3+7l/PTTz+hrq4O8fHx3OjI4cOHldK4u7tzpy2e3e5ZPj4+KCgoQJ8+fdqtrpmZmZg7dy43CvP48eNG90BxcHDAqFGjcODAAVRVVeGll16Cnd3TkXU7Ozv07t0bRUVFCA8P17jsyZMnY9asWQCeBhKFhYXw8PAAALi5ucHIyAiXL1+Go6MjgKenPAoLC7l5Gt7e3qivr0dpaSlGjhzZ4rIlEglkMhm8vb01qrOmNLraJSYmBjweT+klEPz/gZwxhpiYGAiFQpiamiIoKAj5+fltXulWYXJgzHrAbzFgYtV8ekII6SKCg4ORn5/fqtEPTbi5uaGurg47duxAUVERvvjiC+zatUspzbJly3Dy5EmIxWIUFhbik08+wXfffaf0L3/9+vXYt28fYmJikJ+fj2vXruHQoUN477332qyuffr0QUpKCn7++Wf88ssvCAsL40YznhUeHo6vvvoKR44c4YIFhZiYGMTGxuKjjz7C9evXkZeXh6SkJIjF4mbLTktLQ1ZWFq5du4Y333wTJSUl3HpLS0tERETgnXfeQXp6OvLz8zFv3jwYGBhw31O/fv0QHh6OOXPmICUlBVKpFNnZ2di8eTNOnjzZZNmZmZlwdXXlJtG2F40vtX3xxRdx9+5d7pWXl8et27JlC8RiMRITE5GdnQ2BQICXX34ZFRUVbVrpVjE2B0auBMbH0d1NCSHkGZ6enhg8eHCjUYi2NmjQIIjFYmzevBkDBw7EgQMHEBsbq5Rm+PDh2LVrF8RiMby8vHDq1CksX75c6XRKcHAwTpw4gbS0NAwZMgT+/v4Qi8VwdnZusuygoCDMnTu3xXVNSEhAjx49MGzYMISEhCA4OBg+Pj6N0r322mt48OABKisr8eqrryqtmz9/Pj7//HMkJyfD09MTgYGBSE5OhkgkUlv2unXr4OPjg+DgYAQFBUEgEDTKWywWIyAgABMnTsRLL72E4cOHc5f0KiQlJWHOnDlYuXIl+vfvj0mTJuHSpUvcaIkqBw8eVJqj0m6YBqKjo5mXl5fKdXK5nAkEAhYXF8ctk8lkzMrKiu3atavJPGUyGSsrK+NexcXFDAArKyvTpGqEEKJzVVVV7OrVq6yqqkrXVdFYamoq8/DwYPX19bquSiPz589nI0aMeK48nJ2dWVJSUttUSA89fvyYWVlZsc8//7zVeeTl5TFbW1v26NEjtema+p2XlZW1+Pit8chHYWEhhEIhRCIRQkNDUVRUBACQSqUoKSnB2LFjubR8Ph+BgYHIyspqMr/Y2FhYWVlxL3URGSGEkPYxYcIEvPnmm7h9+7auq4KtW7fil19+wR9//IEdO3Zg7969TV722RK///47LC0tMWfOnDaspW7l5ubi4MGDkEgkyMnJ4eaVTJ48udV53rlzB/v27Wt0NU570GjCqZ+fH/bt24d+/frh3r172LhxI4YNG4b8/HzufJRiso2CnZ0dbt682WSea9euxYoVK7jP5eXlFIAQQogONHevCW25fPkytmzZgoqKCri6umL79u1qb/3eHHd3d6UpAp3F1q1bUVBQAGNjY/j6+iIzMxO9evVqdX7PDh60N42Cj/Hjx3PvPT09ERAQADc3N+zdu5e7e52qS38aLnsWn88Hn8/XpBqEEEI6sfaee9IZeHt748qVK7quRqs917NdzM3N4enpicLCQu6ql2dn5AJAaWlpo9EQQgghhHRdzxV8VFdX49q1a7C3t4dIJIJAIEBaWhq3vqamBmfPnsWwYcOeu6KEENJRqLokk5DOoi1+3xqddlm1ahVCQkLg5OSE0tJSbNy4EeXl5YiIiACPx0NkZCQ2bdqEvn37om/fvti0aRPMzMwQFhb23BUlhBB9Z2xsDAMDA9y5cwc2NjYwNjZWe9qZkI6EMYaamhr89ddfMDAwgLGxcavz0ij4+PPPPzFz5kzcv38fNjY28Pf3x8WLF7lrq1evXo2qqiosWbIEDx8+hJ+fH77//nule/8TQkhnZWBgAJFIhLt377brA84I0SUzMzM4OTkpPcNHU13rqbaEEKIFjDHU1dWhvr5e11UhpE0ZGhqiW7duKkf06Km2hBCiQ4oHrDV8yBoh5KnnmnBKCCGEEKIpCj4IIYQQolUUfBBCCCFEq/Ruzodi/mt5ebmOa0IIIYSQllIct1tyHYveBR8VFRUAQM93IYQQQjqgioqKZh9Op3eX2srlcty5cweWlpZtfnMexUPriouLO+VlvJ29fUDnbyO1r+Pr7G3s7O0DOn8b26t9jDFUVFRAKBQ2ew8QvRv5MDAwgIODQ7uW0b179075g1Lo7O0DOn8bqX0dX2dvY2dvH9D529ge7WtuxEOBJpwSQgghRKso+CCEEEKIVnWp4IPP5yM6Ohp8Pl/XVWkXnb19QOdvI7Wv4+vsbezs7QM6fxv1oX16N+GUEEIIIZ1blxr5IIQQQojuUfBBCCGEEK2i4IMQQgghWkXBByGEEEK0ioIPQgghhGhVpws+du7cCZFIBBMTE/j6+iIzM1Nt+rNnz8LX1xcmJiZwdXXFrl27tFRTzcTGxmLIkCGwtLSEra0tXn31VRQUFKjdJiMjAzwer9Hr999/11KtNRMTE9OorgKBQO02HaX/AMDFxUVlf7z11lsq03eE/jt37hxCQkIgFArB4/Fw/PhxpfWMMcTExEAoFMLU1BRBQUHIz89vNt+jR49iwIAB4PP5GDBgAI4dO9ZOLVBPXftqa2uxZs0aeHp6wtzcHEKhEHPmzMGdO3fU5pmcnKyyX2UyWTu3prHm+m/u3LmN6unv799svvrSf0DzbVTVFzweDx9++GGTeepLH7bkuKCv+2CnCj4OHTqEyMhI/Oc//0Fubi5GjhyJ8ePH49atWyrTS6VSTJgwASNHjkRubi7effdd/Pvf/8bRo0e1XPPmnT17Fm+99RYuXryItLQ01NXVYezYsXjy5Emz2xYUFODu3bvcq2/fvlqoceu8+OKLSnXNy8trMm1H6j8AyM7OVmpbWloaAOC1115Tu50+99+TJ0/g5eWFxMREleu3bNkCsViMxMREZGdnQyAQ4OWXX+YeIKnKjz/+iBkzZmD27Nn45ZdfMHv2bEyfPh2XLl1qr2Y0SV37KisrkZOTg3Xr1iEnJwcpKSm4fv06Jk2a1Gy+3bt3V+rTu3fvwsTEpD2aoFZz/QcA48aNU6rnyZMn1eapT/0HNN/Ghv2wZ88e8Hg8/Otf/1Kbrz70YUuOC3q7D7JOZOjQoWzRokVKy9zd3VlUVJTK9KtXr2bu7u5Ky958803m7+/fbnVsK6WlpQwAO3v2bJNp0tPTGQD28OFD7VXsOURHRzMvL68Wp+/I/ccYY2+//TZzc3Njcrlc5fqO1n8A2LFjx7jPcrmcCQQCFhcXxy2TyWTMysqK7dq1q8l8pk+fzsaNG6e0LDg4mIWGhrZ5nTXRsH2qXL58mQFgN2/ebDJNUlISs7KyatvKtQFV7YuIiGCTJ0/WKB997T/GWtaHkydPZqNHj1abRl/7sOFxQZ/3wU4z8lFTU4MrV65g7NixSsvHjh2LrKwsldv8+OOPjdIHBwfjp59+Qm1tbbvVtS2UlZUBAKytrZtN6+3tDXt7e4wZMwbp6entXbXnUlhYCKFQCJFIhNDQUBQVFTWZtiP3X01NDfbv34958+Y1+/TmjtR/z5JKpSgpKVHqIz6fj8DAwCb3SaDpflW3jb4oKysDj8fDCy+8oDbd48eP4ezsDAcHB0ycOBG5ubnaqWArZGRkwNbWFv369cOCBQtQWlqqNn1H7r979+4hNTUVb7zxRrNp9bEPGx4X9Hkf7DTBx/3791FfXw87Ozul5XZ2digpKVG5TUlJicr0dXV1uH//frvV9XkxxrBixQqMGDECAwcObDKdvb09Pv30Uxw9ehQpKSno378/xowZg3Pnzmmxti3n5+eHffv24fTp0/jss89QUlKCYcOG4cGDByrTd9T+A4Djx4/j0aNHmDt3bpNpOlr/NaTY7zTZJxXbabqNPpDJZIiKikJYWJjaJ4W6u7sjOTkZ33zzDQ4ePAgTExMMHz4chYWFWqxty4wfPx4HDhzADz/8gPj4eGRnZ2P06NGorq5ucpuO2n8AsHfvXlhaWmLq1Klq0+ljH6o6LujzPtitzXLSEw3/RTLG1P6zVJVe1XJ9snTpUvz66684f/682nT9+/dH//79uc8BAQEoLi7G1q1bMWrUqPaupsbGjx/Pvff09ERAQADc3Nywd+9erFixQuU2HbH/AGD37t0YP348hEJhk2k6Wv81RdN9srXb6FJtbS1CQ0Mhl8uxc+dOtWn9/f2VJm0OHz4cPj4+2LFjB7Zv397eVdXIjBkzuPcDBw7E4MGD4ezsjNTUVLUH6I7Wfwp79uxBeHh4s3M39LEP1R0X9HEf7DQjH7169YKhoWGjyKy0tLRRBKcgEAhUpu/WrRt69uzZbnV9HsuWLcM333yD9PR0ODg4aLy9v7+/Xv7DUsXc3Byenp5N1rcj9h8A3Lx5E2fOnMH8+fM13rYj9Z/iSiVN9knFdppuo0u1tbWYPn06pFIp0tLS1I56qGJgYIAhQ4Z0iH61t7eHs7Oz2rp2tP5TyMzMREFBQav2S133YVPHBX3eBztN8GFsbAxfX1/uCgKFtLQ0DBs2TOU2AQEBjdJ///33GDx4MIyMjNqtrq3BGMPSpUuRkpKCH374ASKRqFX55Obmwt7evo1r1z6qq6tx7dq1JuvbkfrvWUlJSbC1tcUrr7yi8bYdqf9EIhEEAoFSH9XU1ODs2bNN7pNA0/2qbhtdUQQehYWFOHPmTKuCXsYYfv755w7Rrw8ePEBxcbHaunak/nvW7t274evrCy8vL4231VUfNndc0Ot9sM2mruqBr776ihkZGbHdu3ezq1evssjISGZubs5u3LjBGGMsKiqKzZ49m0tfVFTEzMzM2PLly9nVq1fZ7t27mZGREfv666911YQmLV68mFlZWbGMjAx29+5d7lVZWcmladi+hIQEduzYMXb9+nX222+/saioKAaAHT16VBdNaNbKlStZRkYGKyoqYhcvXmQTJ05klpaWnaL/FOrr65mTkxNbs2ZNo3Udsf8qKipYbm4uy83NZQCYWCxmubm53NUecXFxzMrKiqWkpLC8vDw2c+ZMZm9vz8rLy7k8Zs+erXRF2oULF5ihoSGLi4tj165dY3Fxcaxbt27s4sWLetW+2tpaNmnSJObg4MB+/vlnpf2yurq6yfbFxMSwU6dOMYlEwnJzc9nrr7/OunXrxi5duqRX7auoqGArV65kWVlZTCqVsvT0dBYQEMB69+7dYfqPseZ/o4wxVlZWxszMzNjHH3+sMg997cOWHBf0dR/sVMEHY4z997//Zc7OzszY2Jj5+PgoXYoaERHBAgMDldJnZGQwb29vZmxszFxcXJr88ekaAJWvpKQkLk3D9m3evJm5ubkxExMT1qNHDzZixAiWmpqq/cq30IwZM5i9vT0zMjJiQqGQTZ06leXn53PrO3L/KZw+fZoBYAUFBY3WdcT+U1wO3PAVERHBGHt6qV90dDQTCASMz+ezUaNGsby8PKU8AgMDufQKR44cYf3792dGRkbM3d1dZwGXuvZJpdIm98v09HQuj4bti4yMZE5OTszY2JjZ2NiwsWPHsqysLO03jqlvX2VlJRs7diyzsbFhRkZGzMnJiUVERLBbt24p5aHP/cdY879Rxhj75JNPmKmpKXv06JHKPPS1D1tyXNDXfZD3vwYQQgghhGhFp5nzQQghhJCOgYIPQgghhGgVBR+EEEII0SoKPgghhBCiVRR8EEIIIUSrKPgghBBCiFZR8EEIIYQQraLggxBCCCFaRcEHIYQQQrSKgg9CCCGEaBUFH4QQQgjRqv8DyIxTo1cOURcAAAAASUVORK5CYII=",
      "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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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demonstration(hyperdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3e468adb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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     0.8095    0.7691    0.7888     29306\n",
      "         1.0     0.6849    0.7349    0.7090     20010\n",
      "\n",
      "    accuracy                         0.7552     49316\n",
      "   macro avg     0.7472    0.7520    0.7489     49316\n",
      "weighted avg     0.7589    0.7552    0.7564     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8194    0.7763    0.7973     20463\n",
      "         1.0     0.6975    0.7509    0.7232     14058\n",
      "\n",
      "    accuracy                         0.7660     34521\n",
      "   macro avg     0.7585    0.7636    0.7603     34521\n",
      "weighted avg     0.7698    0.7660    0.7671     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7942    0.7570    0.7752      4400\n",
      "         1.0     0.6663    0.7120    0.6884      2997\n",
      "\n",
      "    accuracy                         0.7388      7397\n",
      "   macro avg     0.7302    0.7345    0.7318      7397\n",
      "weighted avg     0.7424    0.7388    0.7400      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9312    0.9231    0.9271       806\n",
      "         1.0     0.8234    0.8401    0.8317       344\n",
      "\n",
      "    accuracy                         0.8983      1150\n",
      "   macro avg     0.8773    0.8816    0.8794      1150\n",
      "weighted avg     0.8989    0.8983    0.8986      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7620    0.7198    0.7403      3594\n",
      "         1.0     0.6469    0.6954    0.6703      2653\n",
      "\n",
      "    accuracy                         0.7095      6247\n",
      "   macro avg     0.7045    0.7076    0.7053      6247\n",
      "weighted avg     0.7131    0.7095    0.7106      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8349    0.8117    0.8232       324\n",
      "         1.0     0.6369    0.6730    0.6544       159\n",
      "\n",
      "    accuracy                         0.7660       483\n",
      "   macro avg     0.7359    0.7423    0.7388       483\n",
      "weighted avg     0.7697    0.7660    0.7676       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 75.52\n",
      "    Training Set Accuracy: 76.60\n",
      "    Validation Set Accuracy: 73.88\n",
      "    Validation Set with both Images and Texts: 89.83\n",
      "    Validation Set with only Texts: 70.95\n",
      "    Validation Set with unseen Images: 76.60\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8095    0.7691    0.7888     29306\n",
      "         1.0     0.6849    0.7349    0.7090     20010\n",
      "\n",
      "    accuracy                         0.7552     49316\n",
      "   macro avg     0.7472    0.7520    0.7489     49316\n",
      "weighted avg     0.7589    0.7552    0.7564     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8194    0.7763    0.7973     20463\n",
      "         1.0     0.6975    0.7509    0.7232     14058\n",
      "\n",
      "    accuracy                         0.7660     34521\n",
      "   macro avg     0.7585    0.7636    0.7603     34521\n",
      "weighted avg     0.7698    0.7660    0.7671     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7795    0.7479    0.7634      4443\n",
      "         1.0     0.6427    0.6819    0.6617      2955\n",
      "\n",
      "    accuracy                         0.7215      7398\n",
      "   macro avg     0.7111    0.7149    0.7126      7398\n",
      "weighted avg     0.7249    0.7215    0.7228      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9053    0.9218    0.9135       716\n",
      "         1.0     0.8328    0.8017    0.8170       348\n",
      "\n",
      "    accuracy                         0.8825      1064\n",
      "   macro avg     0.8691    0.8618    0.8652      1064\n",
      "weighted avg     0.8816    0.8825    0.8819      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7535    0.7145    0.7335      3727\n",
      "         1.0     0.6200    0.6659    0.6421      2607\n",
      "\n",
      "    accuracy                         0.6945      6334\n",
      "   macro avg     0.6868    0.6902    0.6878      6334\n",
      "weighted avg     0.6986    0.6945    0.6959      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7740    0.8100    0.7916       279\n",
      "         1.0     0.6268    0.5742    0.5993       155\n",
      "\n",
      "    accuracy                         0.7258       434\n",
      "   macro avg     0.7004    0.6921    0.6955       434\n",
      "weighted avg     0.7214    0.7258    0.7229       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 75.52\n",
      "    Training Set Accuracy: 76.60\n",
      "    Validation Set Accuracy: 72.15\n",
      "    Validation Set with both Images and Texts: 88.25\n",
      "    Validation Set with only Texts: 69.45\n",
      "    Validation Set with unseen Images: 72.58\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.4779617 0.5220383]\n",
      " [0.3636306 0.6363694]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.753395\n",
      "std          0.175571\n",
      "min          0.500005\n",
      "25%          0.587136\n",
      "50%          0.728071\n",
      "75%          0.941199\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.754859\n",
      "std          0.176014\n",
      "min          0.500010\n",
      "25%          0.588078\n",
      "50%          0.729900\n",
      "75%          0.944226\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.751292\n",
      "std         0.174910\n",
      "min         0.500038\n",
      "25%         0.586165\n",
      "50%         0.723571\n",
      "75%         0.937486\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": [
      "('multi', 'concat')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7685    0.7820    0.7752     29306\n",
      "         1.0     0.6723    0.6551    0.6636     20010\n",
      "\n",
      "    accuracy                         0.7305     49316\n",
      "   macro avg     0.7204    0.7185    0.7194     49316\n",
      "weighted avg     0.7295    0.7305    0.7299     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7717    0.7839    0.7778     20463\n",
      "         1.0     0.6780    0.6625    0.6702     14058\n",
      "\n",
      "    accuracy                         0.7345     34521\n",
      "   macro avg     0.7249    0.7232    0.7240     34521\n",
      "weighted avg     0.7336    0.7345    0.7339     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7659    0.7777    0.7718      4400\n",
      "         1.0     0.6661    0.6510    0.6585      2997\n",
      "\n",
      "    accuracy                         0.7264      7397\n",
      "   macro avg     0.7160    0.7144    0.7151      7397\n",
      "weighted avg     0.7255    0.7264    0.7259      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8984    0.9218    0.9100       806\n",
      "         1.0     0.8050    0.7558    0.7796       344\n",
      "\n",
      "    accuracy                         0.8722      1150\n",
      "   macro avg     0.8517    0.8388    0.8448      1150\n",
      "weighted avg     0.8705    0.8722    0.8710      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7358    0.7454    0.7406      3594\n",
      "         1.0     0.6489    0.6374    0.6431      2653\n",
      "\n",
      "    accuracy                         0.6995      6247\n",
      "   macro avg     0.6923    0.6914    0.6918      6247\n",
      "weighted avg     0.6989    0.6995    0.6992      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8258    0.8488    0.8371       324\n",
      "         1.0     0.6733    0.6352    0.6537       159\n",
      "\n",
      "    accuracy                         0.7785       483\n",
      "   macro avg     0.7496    0.7420    0.7454       483\n",
      "weighted avg     0.7756    0.7785    0.7768       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 73.05\n",
      "    Training Set Accuracy: 73.45\n",
      "    Validation Set Accuracy: 72.64\n",
      "    Validation Set with both Images and Texts: 87.22\n",
      "    Validation Set with only Texts: 69.95\n",
      "    Validation Set with unseen Images: 77.85\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7685    0.7820    0.7752     29306\n",
      "         1.0     0.6723    0.6551    0.6636     20010\n",
      "\n",
      "    accuracy                         0.7305     49316\n",
      "   macro avg     0.7204    0.7185    0.7194     49316\n",
      "weighted avg     0.7295    0.7305    0.7299     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7717    0.7839    0.7778     20463\n",
      "         1.0     0.6780    0.6625    0.6702     14058\n",
      "\n",
      "    accuracy                         0.7345     34521\n",
      "   macro avg     0.7249    0.7232    0.7240     34521\n",
      "weighted avg     0.7336    0.7345    0.7339     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7567    0.7776    0.7670      4443\n",
      "         1.0     0.6511    0.6240    0.6373      2955\n",
      "\n",
      "    accuracy                         0.7163      7398\n",
      "   macro avg     0.7039    0.7008    0.7022      7398\n",
      "weighted avg     0.7145    0.7163    0.7152      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8705    0.9204    0.8948       716\n",
      "         1.0     0.8143    0.7184    0.7634       348\n",
      "\n",
      "    accuracy                         0.8543      1064\n",
      "   macro avg     0.8424    0.8194    0.8291      1064\n",
      "weighted avg     0.8522    0.8543    0.8518      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7341    0.7502    0.7420      3727\n",
      "         1.0     0.6313    0.6114    0.6212      2607\n",
      "\n",
      "    accuracy                         0.6931      6334\n",
      "   macro avg     0.6827    0.6808    0.6816      6334\n",
      "weighted avg     0.6918    0.6931    0.6923      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7730    0.8423    0.8062       279\n",
      "         1.0     0.6615    0.5548    0.6035       155\n",
      "\n",
      "    accuracy                         0.7396       434\n",
      "   macro avg     0.7173    0.6986    0.7048       434\n",
      "weighted avg     0.7332    0.7396    0.7338       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 73.05\n",
      "    Training Set Accuracy: 73.45\n",
      "    Validation Set Accuracy: 71.63\n",
      "    Validation Set with both Images and Texts: 85.43\n",
      "    Validation Set with only Texts: 69.31\n",
      "    Validation Set with unseen Images: 73.96\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.44617143 0.55382854]\n",
      " [0.31496337 0.68503666]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.714354\n",
      "std          0.158757\n",
      "min          0.500007\n",
      "25%          0.574875\n",
      "50%          0.675443\n",
      "75%          0.854074\n",
      "max          0.999998\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.714723\n",
      "std          0.158723\n",
      "min          0.500007\n",
      "25%          0.575213\n",
      "50%          0.676296\n",
      "75%          0.854284\n",
      "max          0.999998\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.712993\n",
      "std         0.158959\n",
      "min         0.500068\n",
      "25%         0.573516\n",
      "50%         0.671682\n",
      "75%         0.851168\n",
      "max         0.999998\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     0.7681    0.7485    0.7582     29306\n",
      "         1.0     0.6450    0.6691    0.6568     20010\n",
      "\n",
      "    accuracy                         0.7163     49316\n",
      "   macro avg     0.7065    0.7088    0.7075     49316\n",
      "weighted avg     0.7181    0.7163    0.7170     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7692    0.7486    0.7588     20463\n",
      "         1.0     0.6478    0.6731    0.6602     14058\n",
      "\n",
      "    accuracy                         0.7179     34521\n",
      "   macro avg     0.7085    0.7109    0.7095     34521\n",
      "weighted avg     0.7198    0.7179    0.7186     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7678    0.7484    0.7580      4400\n",
      "         1.0     0.6438    0.6677    0.6555      2997\n",
      "\n",
      "    accuracy                         0.7157      7397\n",
      "   macro avg     0.7058    0.7080    0.7067      7397\n",
      "weighted avg     0.7176    0.7157    0.7165      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8548    0.8983    0.8760       806\n",
      "         1.0     0.7294    0.6424    0.6832       344\n",
      "\n",
      "    accuracy                         0.8217      1150\n",
      "   macro avg     0.7921    0.7704    0.7796      1150\n",
      "weighted avg     0.8173    0.8217    0.8183      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7464    0.7148    0.7302      3594\n",
      "         1.0     0.6346    0.6709    0.6523      2653\n",
      "\n",
      "    accuracy                         0.6962      6247\n",
      "   macro avg     0.6905    0.6929    0.6912      6247\n",
      "weighted avg     0.6989    0.6962    0.6971      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8166    0.8519    0.8338       324\n",
      "         1.0     0.6690    0.6101    0.6382       159\n",
      "\n",
      "    accuracy                         0.7723       483\n",
      "   macro avg     0.7428    0.7310    0.7360       483\n",
      "weighted avg     0.7680    0.7723    0.7694       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 71.63\n",
      "    Training Set Accuracy: 71.79\n",
      "    Validation Set Accuracy: 71.57\n",
      "    Validation Set with both Images and Texts: 82.17\n",
      "    Validation Set with only Texts: 69.62\n",
      "    Validation Set with unseen Images: 77.23\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7681    0.7485    0.7582     29306\n",
      "         1.0     0.6450    0.6691    0.6568     20010\n",
      "\n",
      "    accuracy                         0.7163     49316\n",
      "   macro avg     0.7065    0.7088    0.7075     49316\n",
      "weighted avg     0.7181    0.7163    0.7170     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7692    0.7486    0.7588     20463\n",
      "         1.0     0.6478    0.6731    0.6602     14058\n",
      "\n",
      "    accuracy                         0.7179     34521\n",
      "   macro avg     0.7085    0.7109    0.7095     34521\n",
      "weighted avg     0.7198    0.7179    0.7186     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7633    0.7484    0.7558      4443\n",
      "         1.0     0.6325    0.6511    0.6417      2955\n",
      "\n",
      "    accuracy                         0.7095      7398\n",
      "   macro avg     0.6979    0.6997    0.6987      7398\n",
      "weighted avg     0.7111    0.7095    0.7102      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8295    0.9106    0.8682       716\n",
      "         1.0     0.7698    0.6149    0.6837       348\n",
      "\n",
      "    accuracy                         0.8139      1064\n",
      "   macro avg     0.7997    0.7628    0.7759      1064\n",
      "weighted avg     0.8100    0.8139    0.8078      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7487    0.7172    0.7326      3727\n",
      "         1.0     0.6187    0.6559    0.6368      2607\n",
      "\n",
      "    accuracy                         0.6920      6334\n",
      "   macro avg     0.6837    0.6866    0.6847      6334\n",
      "weighted avg     0.6952    0.6920    0.6932      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7735    0.8566    0.8129       279\n",
      "         1.0     0.6800    0.5484    0.6071       155\n",
      "\n",
      "    accuracy                         0.7465       434\n",
      "   macro avg     0.7267    0.7025    0.7100       434\n",
      "weighted avg     0.7401    0.7465    0.7394       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 71.63\n",
      "    Training Set Accuracy: 71.79\n",
      "    Validation Set Accuracy: 70.95\n",
      "    Validation Set with both Images and Texts: 81.39\n",
      "    Validation Set with only Texts: 69.20\n",
      "    Validation Set with unseen Images: 74.65\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.44009095 0.55990905]\n",
      " [0.30889452 0.6911055 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.699589\n",
      "std          0.147097\n",
      "min          0.500002\n",
      "25%          0.573507\n",
      "50%          0.665394\n",
      "75%          0.813013\n",
      "max          0.999791\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.699696\n",
      "std          0.146930\n",
      "min          0.500002\n",
      "25%          0.573614\n",
      "50%          0.666149\n",
      "75%          0.811907\n",
      "max          0.999683\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.698521\n",
      "std         0.147099\n",
      "min         0.500027\n",
      "25%         0.573609\n",
      "50%         0.659428\n",
      "75%         0.812605\n",
      "max         0.999346\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', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8104    0.6518    0.7225     29306\n",
      "         1.0     0.6037    0.7767    0.6794     20010\n",
      "\n",
      "    accuracy                         0.7025     49316\n",
      "   macro avg     0.7071    0.7143    0.7010     49316\n",
      "weighted avg     0.7266    0.7025    0.7050     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8106    0.6510    0.7221     20463\n",
      "         1.0     0.6052    0.7786    0.6810     14058\n",
      "\n",
      "    accuracy                         0.7030     34521\n",
      "   macro avg     0.7079    0.7148    0.7016     34521\n",
      "weighted avg     0.7270    0.7030    0.7054     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8123    0.6550    0.7252      4400\n",
      "         1.0     0.6056    0.7778    0.6810      2997\n",
      "\n",
      "    accuracy                         0.7047      7397\n",
      "   macro avg     0.7090    0.7164    0.7031      7397\n",
      "weighted avg     0.7286    0.7047    0.7073      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8765    0.8896    0.8830       806\n",
      "         1.0     0.7319    0.7064    0.7189       344\n",
      "\n",
      "    accuracy                         0.8348      1150\n",
      "   macro avg     0.8042    0.7980    0.8010      1150\n",
      "weighted avg     0.8333    0.8348    0.8339      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7930    0.6024    0.6847      3594\n",
      "         1.0     0.5937    0.7870    0.6768      2653\n",
      "\n",
      "    accuracy                         0.6808      6247\n",
      "   macro avg     0.6934    0.6947    0.6808      6247\n",
      "weighted avg     0.7084    0.6808    0.6814      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8241    0.8241    0.8241       324\n",
      "         1.0     0.6415    0.6415    0.6415       159\n",
      "\n",
      "    accuracy                         0.7640       483\n",
      "   macro avg     0.7328    0.7328    0.7328       483\n",
      "weighted avg     0.7640    0.7640    0.7640       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 70.25\n",
      "    Training Set Accuracy: 70.30\n",
      "    Validation Set Accuracy: 70.47\n",
      "    Validation Set with both Images and Texts: 83.48\n",
      "    Validation Set with only Texts: 68.08\n",
      "    Validation Set with unseen Images: 76.40\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8104    0.6518    0.7225     29306\n",
      "         1.0     0.6037    0.7767    0.6794     20010\n",
      "\n",
      "    accuracy                         0.7025     49316\n",
      "   macro avg     0.7071    0.7143    0.7010     49316\n",
      "weighted avg     0.7266    0.7025    0.7050     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8106    0.6510    0.7221     20463\n",
      "         1.0     0.6052    0.7786    0.6810     14058\n",
      "\n",
      "    accuracy                         0.7030     34521\n",
      "   macro avg     0.7079    0.7148    0.7016     34521\n",
      "weighted avg     0.7270    0.7030    0.7054     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8077    0.6525    0.7219      4443\n",
      "         1.0     0.5946    0.7665    0.6697      2955\n",
      "\n",
      "    accuracy                         0.6980      7398\n",
      "   macro avg     0.7012    0.7095    0.6958      7398\n",
      "weighted avg     0.7226    0.6980    0.7010      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8524    0.8953    0.8733       716\n",
      "         1.0     0.7596    0.6810    0.7182       348\n",
      "\n",
      "    accuracy                         0.8252      1064\n",
      "   macro avg     0.8060    0.7881    0.7957      1064\n",
      "weighted avg     0.8220    0.8252    0.8226      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7959    0.6058    0.6880      3727\n",
      "         1.0     0.5799    0.7779    0.6645      2607\n",
      "\n",
      "    accuracy                         0.6767      6334\n",
      "   macro avg     0.6879    0.6919    0.6762      6334\n",
      "weighted avg     0.7070    0.6767    0.6783      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7738    0.8459    0.8082       279\n",
      "         1.0     0.6667    0.5548    0.6056       155\n",
      "\n",
      "    accuracy                         0.7419       434\n",
      "   macro avg     0.7202    0.7004    0.7069       434\n",
      "weighted avg     0.7355    0.7419    0.7359       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 70.25\n",
      "    Training Set Accuracy: 70.30\n",
      "    Validation Set Accuracy: 69.80\n",
      "    Validation Set with both Images and Texts: 82.52\n",
      "    Validation Set with only Texts: 67.67\n",
      "    Validation Set with unseen Images: 74.19\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.43473825 0.5652617 ]\n",
      " [0.29022086 0.70977914]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.687557\n",
      "std          0.139115\n",
      "min          0.500008\n",
      "25%          0.576752\n",
      "50%          0.652928\n",
      "75%          0.771906\n",
      "max          0.999989\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.687998\n",
      "std          0.139043\n",
      "min          0.500008\n",
      "25%          0.577412\n",
      "50%          0.653309\n",
      "75%          0.771271\n",
      "max          0.999989\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.687188\n",
      "std         0.140345\n",
      "min         0.500024\n",
      "25%         0.574541\n",
      "50%         0.652053\n",
      "75%         0.776504\n",
      "max         0.999946\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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s7dy50779zJkz8vl8ysvLs8dcLpfmzp2r5uZmSVJLS4sCgUBQTWpqqmbMmGHXHDx4UG632w4wkjRnzhy53W67BgAA3NxC2ol55ZVXtG3bNpWVlekb3/iGjhw5otWrV8vlcunzn/+8fD6fJCk5OTnofsnJyTp79qwkyefzKSYmRhMnThxSM3h/n8+npKSkIY+flJRk11zO7/fL7/fbx11dXZKkQCCgQCAQymm+o8H5XOOsEZ0XwQb7S5/Da7C/I/06QbDB/tLn8KPXkRGuPocyX0ghZmBgQLNnz1ZFRYUkadasWTpx4oS2bdumz3/+83adw+EIup9lWUPGLnd5zZXqrzXPpk2btHHjxiHjjY2Nio+Pv+ZjD9ejswfCMi+C0efI8Hq9o72EmwJ9jhx6HRkj3eeenp7rrg0pxKSkpOiuu+4KGps2bZqefvppSZLH45H09k5KSkqKXdPe3m7vzng8HvX19amjoyNoN6a9vV05OTl2zfnz54c8/oULF4bs8gxav369ysrK7OOuri6lpaUpLy9PEyZMCOU031EgEJDX69Ujx8bJP3DtcIbhc42z9OjsAfocZoN9zs3NldPpHO3ljFmD3zfoc/jR68gIV58H30m5HiGFmHvuuUenT58OGnv55Zd16623SpLS09Pl8Xjk9Xo1a9YsSVJfX5+ampr02GOPSZIyMzPldDrl9Xq1ZMkSSVJbW5uOHz+uyspKSVJ2drY6Ozt15MgR3X333ZKkw4cPq7Oz0w46l3O5XHK5XEPGnU5n2J7E/gGH/P38cA03+hwZ4Xyt4K/oc+TQ68gY6T6HMldIIearX/2qcnJyVFFRoSVLlujIkSPasWOHduzYIentt4BKS0tVUVGhjIwMZWRkqKKiQvHx8SoqKpIkud1uLVu2TGvWrNGkSZOUmJiotWvXaubMmfbVStOmTdPChQu1fPlybd++XZK0YsUKFRQUcGUSAACQFGKI+chHPqL6+nqtX79e3/72t5Wenq6tW7fqs5/9rF2zbt069fb2auXKlero6FBWVpYaGxuVkJBg12zZskXR0dFasmSJent7NX/+fO3atUtRUVF2zd69e7V69Wr7KqbCwkJVV1ff6PkCAIAxIqQQI0kFBQUqKCi46u0Oh0Pl5eUqLy+/ak1sbKyqqqpUVVV11ZrExETV1taGujwAAHCT4LOTAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjhRRiysvL5XA4gr48Ho99u2VZKi8vV2pqquLi4jRv3jydOHEiaA6/369Vq1Zp8uTJGj9+vAoLC3Xu3Lmgmo6ODhUXF8vtdsvtdqu4uFgXL14c/lkCAIAxJ+SdmOnTp6utrc3+eumll+zbKisrtXnzZlVXV+vo0aPyeDzKzc1Vd3e3XVNaWqr6+nrV1dXpwIEDunTpkgoKCtTf32/XFBUVqbW1VQ0NDWpoaFBra6uKi4tv8FQBAMBYEh3yHaKjg3ZfBlmWpa1bt2rDhg1avHixJGn37t1KTk7Wvn37VFJSos7OTtXU1GjPnj1asGCBJKm2tlZpaWnav3+/8vPzderUKTU0NOjQoUPKysqSJO3cuVPZ2dk6ffq0pk6deiPnCwAAxoiQd2J+//vfKzU1Venp6fr0pz+tV155RZJ05swZ+Xw+5eXl2bUul0tz585Vc3OzJKmlpUWBQCCoJjU1VTNmzLBrDh48KLfbbQcYSZozZ47cbrddAwAAENJOTFZWln7605/qAx/4gM6fP6/vfOc7ysnJ0YkTJ+Tz+SRJycnJQfdJTk7W2bNnJUk+n08xMTGaOHHikJrB+/t8PiUlJQ157KSkJLvmSvx+v/x+v33c1dUlSQoEAgoEAqGc5jsanM81zhrReRFssL/0ObwG+zvSrxMEG+wvfQ4/eh0Z4epzKPOFFGIWLVpk/3vmzJnKzs7WHXfcod27d2vOnDmSJIfDEXQfy7KGjF3u8por1b/TPJs2bdLGjRuHjDc2Nio+Pv6ajz9cj84eCMu8CEafI8Pr9Y72Em4K9Dly6HVkjHSfe3p6rrs25N+J+Vvjx4/XzJkz9fvf/17333+/pLd3UlJSUuya9vZ2e3fG4/Gor69PHR0dQbsx7e3tysnJsWvOnz8/5LEuXLgwZJfnb61fv15lZWX2cVdXl9LS0pSXl6cJEybcyGkOEQgE5PV69cixcfIPXDugYfhc4yw9OnuAPofZYJ9zc3PldDpHezlj1uD3DfocfvQ6MsLV58F3Uq7HDYUYv9+vU6dO6WMf+5jS09Pl8Xjk9Xo1a9YsSVJfX5+ampr02GOPSZIyMzPldDrl9Xq1ZMkSSVJbW5uOHz+uyspKSVJ2drY6Ozt15MgR3X333ZKkw4cPq7Oz0w46V+JyueRyuYaMO53OsD2J/QMO+fv54Rpu9DkywvlawV/R58ih15Ex0n0OZa6QQszatWt133336f3vf7/a29v1ne98R11dXVq6dKkcDodKS0tVUVGhjIwMZWRkqKKiQvHx8SoqKpIkud1uLVu2TGvWrNGkSZOUmJiotWvXaubMmfbVStOmTdPChQu1fPlybd++XZK0YsUKFRQUcGUSAACwhRRizp07p8985jN644039N73vldz5szRoUOHdOutt0qS1q1bp97eXq1cuVIdHR3KyspSY2OjEhIS7Dm2bNmi6OhoLVmyRL29vZo/f7527dqlqKgou2bv3r1avXq1fRVTYWGhqqurR+J8AQDAGBFSiKmrq7vm7Q6HQ+Xl5SovL79qTWxsrKqqqlRVVXXVmsTERNXW1oayNAAAcJPhs5MAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYKSQPjsJwNg1o/zf5e93jPYyrtur37t3tJcAYJSxEwMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRbijEbNq0SQ6HQ6WlpfaYZVkqLy9Xamqq4uLiNG/ePJ04cSLofn6/X6tWrdLkyZM1fvx4FRYW6ty5c0E1HR0dKi4ultvtltvtVnFxsS5evHgjywUAAGPIsEPM0aNHtWPHDn3wgx8MGq+srNTmzZtVXV2to0ePyuPxKDc3V93d3XZNaWmp6uvrVVdXpwMHDujSpUsqKChQf3+/XVNUVKTW1lY1NDSooaFBra2tKi4uHu5yAQDAGDOsEHPp0iV99rOf1c6dOzVx4kR73LIsbd26VRs2bNDixYs1Y8YM7d69Wz09Pdq3b58kqbOzUzU1NfrBD36gBQsWaNasWaqtrdVLL72k/fv3S5JOnTqlhoYG/eQnP1F2drays7O1c+dO/du//ZtOnz49AqcNAABMN6wQ8+CDD+ree+/VggULgsbPnDkjn8+nvLw8e8zlcmnu3Llqbm6WJLW0tCgQCATVpKamasaMGXbNwYMH5Xa7lZWVZdfMmTNHbrfbrgEAADe36FDvUFdXp//6r//S0aNHh9zm8/kkScnJyUHjycnJOnv2rF0TExMTtIMzWDN4f5/Pp6SkpCHzJyUl2TWX8/v98vv99nFXV5ckKRAIKBAIXO/pXZfB+VzjrBGdF8EG+0ufw8vUPo/06zrcBtdr2rpNRK8jI1x9DmW+kELM66+/roceekiNjY2KjY29ap3D4Qg6tixryNjlLq+5Uv215tm0aZM2btw4ZLyxsVHx8fHXfOzhenT2QFjmRTD6HBmm9fnZZ58d7SUMi9frHe0l3DTodWSMdJ97enquuzakENPS0qL29nZlZmbaY/39/Xr++edVXV1t/76Kz+dTSkqKXdPe3m7vzng8HvX19amjoyNoN6a9vV05OTl2zfnz54c8/oULF4bs8gxav369ysrK7OOuri6lpaUpLy9PEyZMCOU031EgEJDX69Ujx8bJP3DtcIbhc42z9OjsAfocZqb2+Xh5/mgvISSD3zdyc3PldDpHezljGr2OjHD1efCdlOsRUoiZP3++XnrppaCxL3zhC7rzzjv19a9/Xbfffrs8Ho+8Xq9mzZolSerr61NTU5Mee+wxSVJmZqacTqe8Xq+WLFkiSWpra9Px48dVWVkpScrOzlZnZ6eOHDmiu+++W5J0+PBhdXZ22kHnci6XSy6Xa8i40+kM25PYP+CQv9+cb/qmos+RYVqfTf3hFM7vSQhGryNjpPscylwhhZiEhATNmDEjaGz8+PGaNGmSPV5aWqqKigplZGQoIyNDFRUVio+PV1FRkSTJ7XZr2bJlWrNmjSZNmqTExEStXbtWM2fOtH9ReNq0aVq4cKGWL1+u7du3S5JWrFihgoICTZ06NZQlAwCAMSrkX+x9J+vWrVNvb69Wrlypjo4OZWVlqbGxUQkJCXbNli1bFB0drSVLlqi3t1fz58/Xrl27FBUVZdfs3btXq1evtq9iKiwsVHV19UgvFwAAGOqGQ8xzzz0XdOxwOFReXq7y8vKr3ic2NlZVVVWqqqq6ak1iYqJqa2tvdHkAAGCM4rOTAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjjfhf7AUAAKG77eFfj/YSQuKKslR59+iugZ0YAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkaJDKd62bZu2bdumV199VZI0ffp0ffOb39SiRYskSZZlaePGjdqxY4c6OjqUlZWlxx9/XNOnT7fn8Pv9Wrt2rX72s5+pt7dX8+fP1xNPPKEpU6bYNR0dHVq9erWeeeYZSVJhYaGqqqp0yy233ODpAhgrbnv416O9hJC4oixV3j3aqwDGlpB2YqZMmaLvfe97OnbsmI4dO6Z/+qd/0sc//nGdOHFCklRZWanNmzerurpaR48elcfjUW5urrq7u+05SktLVV9fr7q6Oh04cECXLl1SQUGB+vv77ZqioiK1traqoaFBDQ0Nam1tVXFx8QidMgAAGAtC2om57777go6/+93vatu2bTp06JDuuusubd26VRs2bNDixYslSbt371ZycrL27dunkpISdXZ2qqamRnv27NGCBQskSbW1tUpLS9P+/fuVn5+vU6dOqaGhQYcOHVJWVpYkaefOncrOztbp06c1derUkThvAABguJBCzN/q7+/Xv/zLv+gvf/mLsrOzdebMGfl8PuXl5dk1LpdLc+fOVXNzs0pKStTS0qJAIBBUk5qaqhkzZqi5uVn5+fk6ePCg3G63HWAkac6cOXK73Wpubr5qiPH7/fL7/fZxV1eXJCkQCCgQCAz3NK9ocD7XOGtE50Wwwf7S5/Ciz5Ex2N+R/n6EoQZ7bFqvXVFmvQbD9ZwOZb6QQ8xLL72k7Oxs/d///Z/e8573qL6+XnfddZeam5slScnJyUH1ycnJOnv2rCTJ5/MpJiZGEydOHFLj8/nsmqSkpCGPm5SUZNdcyaZNm7Rx48Yh442NjYqPjw/tJK/To7MHwjIvgtHnyKDPkeH1ekd7CTcN03pt6u9MjXSfe3p6rrs25BAzdepUtba26uLFi3r66ae1dOlSNTU12bc7HI6gesuyhoxd7vKaK9W/0zzr169XWVmZfdzV1aW0tDTl5eVpwoQJ73heoQgEAvJ6vXrk2Dj5B659bhg+1zhLj84eoM9hRp8jY7DPubm5cjqdo72cMW3we7RpvZ5R/u+jvYSQhOs5PfhOyvUIOcTExMTo7/7u7yRJs2fP1tGjR/XDH/5QX//61yW9vZOSkpJi17e3t9u7Mx6PR319fero6AjajWlvb1dOTo5dc/78+SGPe+HChSG7PH/L5XLJ5XINGXc6nWF7EvsHHPL3800/3OhzZNDnyAjn9yQEM63Xpr7+RrrPocx1w38nxrIs+f1+paeny+PxBG0r9fX1qampyQ4omZmZcjqdQTVtbW06fvy4XZOdna3Ozk4dOXLErjl8+LA6OzvtGgAAgJB2Yr7xjW9o0aJFSktLU3d3t+rq6vTcc8+poaFBDodDpaWlqqioUEZGhjIyMlRRUaH4+HgVFRVJktxut5YtW6Y1a9Zo0qRJSkxM1Nq1azVz5kz7aqVp06Zp4cKFWr58ubZv3y5JWrFihQoKCrgyCQAA2EIKMefPn1dxcbHa2trkdrv1wQ9+UA0NDcrNzZUkrVu3Tr29vVq5cqX9x+4aGxuVkJBgz7FlyxZFR0dryZIl9h+727Vrl6KiouyavXv3avXq1fZVTIWFhaqurh6J8wUAAGNESCGmpqbmmrc7HA6Vl5ervLz8qjWxsbGqqqpSVVXVVWsSExNVW1sbytIAAMBNhs9OAgAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEaKHu0FAMDNZEb5v8vf7xjtZVy3V79372gvAbgqdmIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJq5MAAFd128O/Hu0lhOz3j+aN9hIQIYQYAMCYMqP831V5t3mXsyN0vJ0EAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADBSSCFm06ZN+shHPqKEhAQlJSXp/vvv1+nTp4NqLMtSeXm5UlNTFRcXp3nz5unEiRNBNX6/X6tWrdLkyZM1fvx4FRYW6ty5c0E1HR0dKi4ultvtltvtVnFxsS5evDi8swQAAGNOSCGmqalJDz74oA4dOiSv16u33npLeXl5+stf/mLXVFZWavPmzaqurtbRo0fl8XiUm5ur7u5uu6a0tFT19fWqq6vTgQMHdOnSJRUUFKi/v9+uKSoqUmtrqxoaGtTQ0KDW1lYVFxePwCkDAICxIKS/2NvQ0BB0/NRTTykpKUktLS36h3/4B1mWpa1bt2rDhg1avHixJGn37t1KTk7Wvn37VFJSos7OTtXU1GjPnj1asGCBJKm2tlZpaWnav3+/8vPzderUKTU0NOjQoUPKysqSJO3cuVPZ2dk6ffq0pk6dOhLnDgAADHZDHzvQ2dkpSUpMTJQknTlzRj6fT3l5f/3cCpfLpblz56q5uVklJSVqaWlRIBAIqklNTdWMGTPU3Nys/Px8HTx4UG632w4wkjRnzhy53W41NzdfMcT4/X75/X77uKurS5IUCAQUCARu5DSHGJzPNc4a0XkRbLC/9Dm86HNk0OfIodeRMdjfcP2MvR7DDjGWZamsrEwf/ehHNWPGDEmSz+eTJCUnJwfVJicn6+zZs3ZNTEyMJk6cOKRm8P4+n09JSUlDHjMpKcmuudymTZu0cePGIeONjY2Kj48P8eyuz6OzB8IyL4LR58igz5FBnyOHXkeG1+sd0fl6enquu3bYIeYrX/mK/ud//kcHDhwYcpvDEfyBW5ZlDRm73OU1V6q/1jzr169XWVmZfdzV1aW0tDTl5eVpwoQJ13zsUAUCAXm9Xj1ybJz8A3y4WLi4xll6dPYAfQ4z+hwZ9Dly6HVkDPY5NzdXTqdzxOYdfCflegwrxKxatUrPPPOMnn/+eU2ZMsUe93g8kt7eSUlJSbHH29vb7d0Zj8ejvr4+dXR0BO3GtLe3Kycnx645f/78kMe9cOHCkF2eQS6XSy6Xa8i40+kc0eb+Lf+Ag09IjQD6HBn0OTLoc+TQ68gY6Z+zocwV0tVJlmXpK1/5in75y1/qP/7jP5Senh50e3p6ujweT9DWUl9fn5qamuyAkpmZKafTGVTT1tam48eP2zXZ2dnq7OzUkSNH7JrDhw+rs7PTrgEAADe3kHZiHnzwQe3bt0//+q//qoSEBPv3U9xut+Li4uRwOFRaWqqKigplZGQoIyNDFRUVio+PV1FRkV27bNkyrVmzRpMmTVJiYqLWrl2rmTNn2lcrTZs2TQsXLtTy5cu1fft2SdKKFStUUFDAlUkAAEBSiCFm27ZtkqR58+YFjT/11FN64IEHJEnr1q1Tb2+vVq5cqY6ODmVlZamxsVEJCQl2/ZYtWxQdHa0lS5aot7dX8+fP165duxQVFWXX7N27V6tXr7avYiosLFR1dfVwzhEAAIxBIYUYy3rny9UcDofKy8tVXl5+1ZrY2FhVVVWpqqrqqjWJiYmqra0NZXkAAOAmwmcnAQAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGCjnEPP/887rvvvuUmpoqh8OhX/3qV0G3W5al8vJypaamKi4uTvPmzdOJEyeCavx+v1atWqXJkydr/PjxKiws1Llz54JqOjo6VFxcLLfbLbfbreLiYl28eDHkEwQAAGNTyCHmL3/5iz70oQ+purr6irdXVlZq8+bNqq6u1tGjR+XxeJSbm6vu7m67prS0VPX19aqrq9OBAwd06dIlFRQUqL+/364pKipSa2urGhoa1NDQoNbWVhUXFw/jFAEAwFgUHeodFi1apEWLFl3xNsuytHXrVm3YsEGLFy+WJO3evVvJycnat2+fSkpK1NnZqZqaGu3Zs0cLFiyQJNXW1iotLU379+9Xfn6+Tp06pYaGBh06dEhZWVmSpJ07dyo7O1unT5/W1KlTh3u+AABgjAg5xFzLmTNn5PP5lJeXZ4+5XC7NnTtXzc3NKikpUUtLiwKBQFBNamqqZsyYoebmZuXn5+vgwYNyu912gJGkOXPmyO12q7m5+Yohxu/3y+/328ddXV2SpEAgoEAgMJKnac/nGmeN6LwINthf+hxe9Dky6HPk0OvIGOxvuH7GXo8RDTE+n0+SlJycHDSenJyss2fP2jUxMTGaOHHikJrB+/t8PiUlJQ2ZPykpya653KZNm7Rx48Yh442NjYqPjw/9ZK7Do7MHwjIvgtHnyKDPkUGfI4deR4bX6x3R+Xp6eq67dkRDzCCHwxF0bFnWkLHLXV5zpfprzbN+/XqVlZXZx11dXUpLS1NeXp4mTJgQyvLfUSAQkNfr1SPHxsk/cO3zwvC5xll6dPYAfQ4z+hwZ9Dly6HVkDPY5NzdXTqdzxOYdfCfleoxoiPF4PJLe3klJSUmxx9vb2+3dGY/Ho76+PnV0dATtxrS3tysnJ8euOX/+/JD5L1y4MGSXZ5DL5ZLL5Roy7nQ6R7S5f8s/4JC/nxdIuNHnyKDPkUGfI4deR8ZI/5wNZa4R/Tsx6enp8ng8QVtLfX19ampqsgNKZmamnE5nUE1bW5uOHz9u12RnZ6uzs1NHjhyxaw4fPqzOzk67BgAA3NxC3om5dOmS/vCHP9jHZ86cUWtrqxITE/X+979fpaWlqqioUEZGhjIyMlRRUaH4+HgVFRVJktxut5YtW6Y1a9Zo0qRJSkxM1Nq1azVz5kz7aqVp06Zp4cKFWr58ubZv3y5JWrFihQoKCrgyCQAASBpGiDl27Jj+8R//0T4e/D2UpUuXateuXVq3bp16e3u1cuVKdXR0KCsrS42NjUpISLDvs2XLFkVHR2vJkiXq7e3V/PnztWvXLkVFRdk1e/fu1erVq+2rmAoLC6/6t2kAAMDNJ+QQM2/ePFnW1S9bczgcKi8vV3l5+VVrYmNjVVVVpaqqqqvWJCYmqra2NtTlAQCAmwSfnQQAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAjEWIAAICRCDEAAMBIhBgAAGAkQgwAADASIQYAABiJEAMAAIxEiAEAAEYixAAAACMRYgAAgJEIMQAAwEiEGAAAYCRCDAAAMBIhBgAAGOldH2KeeOIJpaenKzY2VpmZmXrhhRdGe0kAAOBd4F0dYn7+85+rtLRUGzZs0IsvvqiPfexjWrRokV577bXRXhoAABhl7+oQs3nzZi1btkxf+tKXNG3aNG3dulVpaWnatm3baC8NAACMsujRXsDV9PX1qaWlRQ8//HDQeF5enpqbm4fU+/1++f1++7izs1OS9Oc//1mBQGBE1xYIBNTT06PowDj1DzhGdG78VfSApZ6eAfocZvQ5Muhz5NDryBjs85tvvimn0zli83Z3d0uSLMt65zWM2KOOsDfeeEP9/f1KTk4OGk9OTpbP5xtSv2nTJm3cuHHIeHp6etjWiPArGu0F3CToc2TQ58ih15ERzj53d3fL7XZfs+ZdG2IGORzBKdqyrCFjkrR+/XqVlZXZxwMDA/rzn/+sSZMmXbH+RnR1dSktLU2vv/66JkyYMKJz46/oc2TQ58igz5FDryMjXH22LEvd3d1KTU19x9p3bYiZPHmyoqKihuy6tLe3D9mdkSSXyyWXyxU0dsstt4RziZowYQIvkAigz5FBnyODPkcOvY6McPT5nXZgBr1rf7E3JiZGmZmZ8nq9QeNer1c5OTmjtCoAAPBu8a7diZGksrIyFRcXa/bs2crOztaOHTv02muv6ctf/vJoLw0AAIyyd3WI+dSnPqU333xT3/72t9XW1qYZM2bo2Wef1a233jqq63K5XPrWt7415O0rjCz6HBn0OTLoc+TQ68h4N/TZYV3PNUwAAADvMu/a34kBAAC4FkIMAAAwEiEGAAAYiRADAACMRIi5iieeeELp6emKjY1VZmamXnjhhavWPvfcc3I4HEO+fve730VwxWYKpc/S25+RtWHDBt16661yuVy644479OSTT0ZoteYKpc8PPPDAFZ/P06dPj+CKzRTq83nv3r360Ic+pPj4eKWkpOgLX/iC3nzzzQit1lyh9vnxxx/XtGnTFBcXp6lTp+qnP/1phFZqrueff1733XefUlNT5XA49Ktf/eod79PU1KTMzEzFxsbq9ttv149//OPwL9TCEHV1dZbT6bR27txpnTx50nrooYes8ePHW2fPnr1i/W9/+1tLknX69Gmrra3N/nrrrbcivHKzhNpny7KswsJCKysry/J6vdaZM2esw4cPW//5n/8ZwVWbJ9Q+X7x4Meh5/Prrr1uJiYnWt771rcgu3DCh9vmFF16wxo0bZ/3whz+0XnnlFeuFF16wpk+fbt1///0RXrlZQu3zE088YSUkJFh1dXXWH//4R+tnP/uZ9Z73vMd65plnIrxyszz77LPWhg0brKefftqSZNXX11+z/pVXXrHi4+Othx56yDp58qS1c+dOy+l0Wr/4xS/Cuk5CzBXcfffd1pe//OWgsTvvvNN6+OGHr1g/GGI6OjoisLqxI9Q+/+Y3v7Hcbrf15ptvRmJ5Y0aofb5cfX295XA4rFdffTUcyxszQu3z97//fev2228PGvvRj35kTZkyJWxrHAtC7XN2dra1du3aoLGHHnrIuueee8K2xrHmekLMunXrrDvvvDNorKSkxJozZ04YV2ZZvJ10mb6+PrW0tCgvLy9oPC8vT83Nzde876xZs5SSkqL58+frt7/9bTiXabzh9PmZZ57R7NmzVVlZqfe97336wAc+oLVr16q3tzcSSzbSjTyfB9XU1GjBggWj/kcm382G0+ecnBydO3dOzz77rCzL0vnz5/WLX/xC9957bySWbKTh9Nnv9ys2NjZoLC4uTkeOHFEgEAjbWm82Bw8eHPL/kp+fr2PHjoW1z4SYy7zxxhvq7+8f8iGTycnJQz6MclBKSop27Nihp59+Wr/85S81depUzZ8/X88//3wklmyk4fT5lVde0YEDB3T8+HHV19dr69at+sUvfqEHH3wwEks20nD6/Lfa2tr0m9/8Rl/60pfCtcQxYTh9zsnJ0d69e/WpT31KMTEx8ng8uuWWW1RVVRWJJRtpOH3Oz8/XT37yE7W0tMiyLB07dkxPPvmkAoGA3njjjUgs+6bg8/mu+P/y1ltvhbXP7+qPHRhNDocj6NiyrCFjg6ZOnaqpU6fax9nZ2Xr99df1z//8z/qHf/iHsK7TdKH0eWBgQA6HQ3v37rU/4XTz5s36xCc+occff1xxcXFhX6+pQunz39q1a5duueUW3X///WFa2dgSSp9Pnjyp1atX65vf/Kby8/PV1tamr33ta/ryl7+smpqaSCzXWKH0+ZFHHpHP59OcOXNkWZaSk5P1wAMPqLKyUlFRUZFY7k3jSv8vVxofSezEXGby5MmKiooakurb29uHpMxrmTNnjn7/+9+P9PLGjOH0OSUlRe973/uCPqJ92rRpsixL586dC+t6TXUjz2fLsvTkk0+quLhYMTEx4Vym8YbT502bNumee+7R1772NX3wgx9Ufn6+nnjiCT355JNqa2uLxLKNM5w+x8XF6cknn1RPT49effVVvfbaa7rtttuUkJCgyZMnR2LZNwWPx3PF/5fo6GhNmjQpbI9LiLlMTEyMMjMz5fV6g8a9Xq9ycnKue54XX3xRKSkpI728MWM4fb7nnnv0pz/9SZcuXbLHXn75ZY0bN05TpkwJ63pNdSPP56amJv3hD3/QsmXLwrnEMWE4fe7p6dG4ccHfggd3Biw+0u6KbuT57HQ6NWXKFEVFRamurk4FBQVD+o/hy87OHvL/0tjYqNmzZ8vpdIbvgcP6a8OGGryEr6amxjp58qRVWlpqjR8/3r464+GHH7aKi4vt+i1btlj19fXWyy+/bB0/ftx6+OGHLUnW008/PVqnYIRQ+9zd3W1NmTLF+sQnPmGdOHHCampqsjIyMqwvfelLo3UKRgi1z4M+97nPWVlZWZFerrFC7fNTTz1lRUdHW0888YT1xz/+0Tpw4IA1e/Zs6+677x6tUzBCqH0+ffq0tWfPHuvll1+2Dh8+bH3qU5+yEhMTrTNnzozSGZihu7vbevHFF60XX3zRkmRt3rzZevHFF+1L2S/v8+Al1l/96letkydPWjU1NVxiPZoef/xx69Zbb7ViYmKsv//7v7eamprs25YuXWrNnTvXPn7sscesO+64w4qNjbUmTpxoffSjH7V+/etfj8KqzRNKny3Lsk6dOmUtWLDAiouLs6ZMmWKVlZVZPT09EV61eULt88WLF624uDhrx44dEV6p2ULt849+9CPrrrvusuLi4qyUlBTrs5/9rHXu3LkIr9o8ofT55MmT1oc//GErLi7OmjBhgvXxj3/c+t3vfjcKqzbL4J8Oufxr6dKllmVd+fn83HPPWbNmzbJiYmKs2267zdq2bVvY1+mwLPYtAQCAeXhDEAAAGIkQAwAAjESIAQAARiLEAAAAIxFiAACAkQgxAADASIQYAABgJEIMAAAwEiEGAAAYiRADAACMRIgBAABGIsQAAAAj/X83s3u+NicubgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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     0.6135    0.9902    0.7576     29306\n",
      "         1.0     0.8573    0.0865    0.1571     20010\n",
      "\n",
      "    accuracy                         0.6235     49316\n",
      "   macro avg     0.7354    0.5383    0.4573     49316\n",
      "weighted avg     0.7124    0.6235    0.5139     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6135    0.9922    0.7582     20463\n",
      "         1.0     0.8883    0.0900    0.1634     14058\n",
      "\n",
      "    accuracy                         0.6248     34521\n",
      "   macro avg     0.7509    0.5411    0.4608     34521\n",
      "weighted avg     0.7254    0.6248    0.5160     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6110    0.9852    0.7542      4400\n",
      "         1.0     0.7848    0.0791    0.1437      2997\n",
      "\n",
      "    accuracy                         0.6181      7397\n",
      "   macro avg     0.6979    0.5322    0.4490      7397\n",
      "weighted avg     0.6814    0.6181    0.5069      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8738    0.9194    0.8960       806\n",
      "         1.0     0.7848    0.6890    0.7337       344\n",
      "\n",
      "    accuracy                         0.8504      1150\n",
      "   macro avg     0.8293    0.8042    0.8149      1150\n",
      "weighted avg     0.8472    0.8504    0.8475      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     0.7867    0.8426    0.8137       324\n",
      "         1.0     0.6250    0.5346    0.5763       159\n",
      "\n",
      "    accuracy                         0.7412       483\n",
      "   macro avg     0.7059    0.6886    0.6950       483\n",
      "weighted avg     0.7335    0.7412    0.7355       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: 62.35\n",
      "    Training Set Accuracy: 62.48\n",
      "    Validation Set Accuracy: 61.81\n",
      "    Validation Set with both Images and Texts: 85.04\n",
      "    Validation Set with only Texts: 57.53\n",
      "    Validation Set with unseen Images: 74.12\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6135    0.9902    0.7576     29306\n",
      "         1.0     0.8573    0.0865    0.1571     20010\n",
      "\n",
      "    accuracy                         0.6235     49316\n",
      "   macro avg     0.7354    0.5383    0.4573     49316\n",
      "weighted avg     0.7124    0.6235    0.5139     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6135    0.9922    0.7582     20463\n",
      "         1.0     0.8883    0.0900    0.1634     14058\n",
      "\n",
      "    accuracy                         0.6248     34521\n",
      "   macro avg     0.7509    0.5411    0.4608     34521\n",
      "weighted avg     0.7254    0.6248    0.5160     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6162    0.9856    0.7583      4443\n",
      "         1.0     0.7808    0.0772    0.1404      2955\n",
      "\n",
      "    accuracy                         0.6227      7398\n",
      "   macro avg     0.6985    0.5314    0.4494      7398\n",
      "weighted avg     0.6820    0.6227    0.5115      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8446    0.9106    0.8763       716\n",
      "         1.0     0.7808    0.6552    0.7125       348\n",
      "\n",
      "    accuracy                         0.8271      1064\n",
      "   macro avg     0.8127    0.7829    0.7944      1064\n",
      "weighted avg     0.8237    0.8271    0.8228      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     0.7397    0.8351    0.7845       279\n",
      "         1.0     0.6134    0.4710    0.5328       155\n",
      "\n",
      "    accuracy                         0.7051       434\n",
      "   macro avg     0.6766    0.6530    0.6587       434\n",
      "weighted avg     0.6946    0.7051    0.6946       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 62.35\n",
      "    Training Set Accuracy: 62.48\n",
      "    Validation Set Accuracy: 62.27\n",
      "    Validation Set with both Images and Texts: 82.71\n",
      "    Validation Set with only Texts: 58.84\n",
      "    Validation Set with unseen Images: 70.51\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.5761644 0.4238356]\n",
      " [0.5761644 0.4238356]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.615672\n",
      "std          0.109957\n",
      "min          0.500452\n",
      "25%          0.576164\n",
      "50%          0.576164\n",
      "75%          0.576164\n",
      "max          0.999909\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.615973\n",
      "std          0.110503\n",
      "min          0.500452\n",
      "25%          0.576164\n",
      "50%          0.576164\n",
      "75%          0.576164\n",
      "max          0.999909\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.617014\n",
      "std         0.111269\n",
      "min         0.500815\n",
      "25%         0.576164\n",
      "50%         0.576164\n",
      "75%         0.576164\n",
      "max         0.999483\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"
    }
   ],
   "source": [
    "perform_results['O1'] = statistics_F1(hyperdict, stored_data, df, labs, subfolder='001/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 39,
   "id": "e3409605",
   "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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W+kTe/CUBi03Bz82Z56+L44q+wa3yeHmlZnXIpjqMHEovwmJTuH1EBLeOiLhoai3WH8vmyeX7ySo2o9XArHHdeezyaAz61u0ZUBSFhOzS6tqNbHYmF2Cx1f63c3PWMSbanwm9AonvFUiw14UNRymKwuqDmTz34xF7j8/QSB+emdKHvmEt9IemqkKdGVATOAqSHG/3iVJnCERfri7/7SQr0HZoVotaq1Pz8y5MAbeAcw+NufpIrU17a2ymjd4VHjvQog8l4aMTO55Vwt+W7eNAuvop5Y99g3nu2r5tOtRxMSsqr2L+D4f4ek86AD2D3Fl444DzflOuqLKSU2Imu6SC7GIz2SXm2u9LzGQXm8kqriCvrNLhft0D3JhQXbsxNNIXZ33L/4GuqLLy3qaTvLXhBBVVNjQatdZn9qRe5/f7UJhSO5RycqNj967WCSJH184S8O/Rck9E1FNqtpBdXFH9u1ZzUb/Pqf69KzJVMbK7Hw/Edyc6SGpjRB2KovZkuXq36GklfHRCVpvC+5tPsvCn41RabXi5OvF/U/twTf9Q2XitAWsOZfLUNwfILa1Er9Xw4MQe/GVCD5x0WhRFodhksf8xz64TLnJKzdUhQw0XJRVNK5Qz6LWM7O6nBo5egXT1a7ti0NOFJl5cdZTv96m1Fx4ueh65LJo7RkXidLYhHWsVpG6H49UrPOYccbzdI1Tt2eg5WR1WMVzYVOnOzmZTKCivtIeJuqE254xQW95AMXhjNBr1Q8hfJvSgT2jHXLxKdAwSPjqZkzmlzP5yH7tTCgG4LCaQBdPiCGzF2SSXgrxSM09/d5CVBzIBCPN2RaOBnBKzww665+Ks1xLoYai+uBBQ87Vn7ffdA9zbvch1R3I+8384xMF0dfy3e4AbT1/dm/i6xcc2G5xcD3s/UQNH3bFijVbdKj76crWHI6hPxy/aa4TFauNETimH0tXhxBPZpVRZm/470RzllVayi83klpodhuPOxc1ZR6CnCwHuBgI86//+6bQaPvwtmTWHsuz3mRgTyIMTezCoa/vUOzWmosrKT4ezWHs4iy4+rtwwuMsFr/sj2p6Ej07CZlP48LdkXlx9lIoqGx4GPU9P6c0Ng7tIb0cTKYrCD/sz+Od3Byk8YyaPp4ve/sc9sIE/7oGeBgI8XPB00XeY19tqU/hyZyqvrDlmHwq6LCaQeeM8CT/1tRo66i5CZfSDHpergaP7RDA2vM9OR1ZRZeVYZolDzdLRzJJmBdCW5OvmTKCHofr3rOEwG+hhwK2JtVTHMkv4z/oTrNh/mppsM7qHHw9OiGZEN992/d09fLqYZTtT+WZPusO0coBhkb7cODScK+OCMTpfHHVj4uwkfHQCqfnlzPlqP7+dzANgTA9/XprejzDvS2eTotZSZasiz5RHrimXXFMuOaYcUouySMzLwaDXYnTW4eqsQ9+ChXJ9/fsyOXIyeu3F8Ue0uKKK//x0iIzflzNds54x2oNoNdV/Cly8IO5G6HcThA26pHZGLamo4khGicMU8oTsUvsOynV5GPT0CjXg75+KxiWFcPcooj0G46pv2foJg15nDxf+7oazD4VdgKTcMt7ecIKvd6fbe1iGRPjw4MQejO8Z0KQQklGaweb0zWSXZxPtE02sbyxdPLqg1TS9zUWmKr7fd5plO1LttWmgzsy6ZkAoxzNL2Hg8xx6U3A16pvQP5aah4fTv4tVuYUlRFMqqysgx5ZBryiWrLJv9GWkcykpDg55AYyBhHoGEewXR3TeEnv6heBia//fYZjJhPnYM0+HDVKWno/P2Rh8Q4HDReXtflB94JHxcwhRF4fMdqTy34jBllVZcnXT8/apYbhve9aL8ZWxL5VXl9jCRY8oht7w2XNT8m2fKo6CiAKU5e320kC7uXbg77m6u6X4NTu25tXbWIdj9kbr3R521GLZY+7DS6XIGTb6N64b2QNvBl2qvmYVVMxPr8Oli+2qwZ/J1c7av5BvsV0oR+zlYsJ2dWTuoqjNtWKfR0T+gP2O7jGVcl3FEe0d3uP93aQXlvLvxJF/sTLWvbBwX5sWDE3tweWyQw8+9ylbF3uy9bE7fzOa0zZwoPFHvfO5O7sT4xhDjG0Nvv97E+MYQ5RXlELQVRWHbyXyW7Uxl5YEMe6+Sk07D5b2DuHFIOGOjA+zbA2QUmVi+K41lO9MctoGICfbgxiHhXDcwDJ8W2r3aptjIr8iv/TBSnuPwd6PmuryKPEwNraFxNlYjesUTF60P7npffAx++Lv6E+weQFfPYLo7exKRU4QxORXzkaNUHDlM5ckkdfjzbJyc0Pv71waSul8HBKAPqP7ezw+NU9v9rZHwcYnKLKrgieX72Xhc3YxoWKQvr9zQjwg/t3ZuWeuz2qwkFydzLP8YGWUZDf5xKLeUn/tE1fQaPb6uvgS4BhDgGoCfqx/eBu9mfYJrKpPFxI8nf6TArO5nE2QM4s99/8z10dfjom+jupyKIji4XA0dp3fXXu8RijJgBr95XcFTG8rsb879u3jxzDV9LrragLPZnVLApuM5HExX16BpbP2ZUC8XhwXxooNdSDcdUt9g0zdzqviUw/Fh7mEMDBzI4bzDnCw66XBbkDFIDSJh4xgeMhyj00WwqmwTZRVX8P6mk3yyPQVTlVrA2jPInT+N8cXonciW9M38dvo3SqpK7PfRarQMCBhAhGcECQUJHC84TqWtst65DToDPX16EuHRk+KiQPaecCM92wsU9Y0wOtCdm4aqIeJsM69sNoVtSXl8sSOVVQcz7WHJWafl8j5B3DQknDE9/BsMypXWytq/E+Vn/L0w5aiBwpRHXkUeVqXpBbyK1YBi8cBm8UCveBHmEYiT3kZRVT7l1gIqlUJs2mI0WsdzepUpRGUqRGVCVJZCVJZCUGHDj1HsbiA3zJ+q0BAitJ74lFdgy83BkpODtbCROzVEo0Hn49NoOPG4/HI0+pbrjZXwcYlRFIVv9qQz7/tDFFdYcNZrmTO5F38eHdXxNxJrQKW1khOFJziSd4Qj+eoloSChSZ86XPWu+Lv6E+AaoP5rVP8987rWChqNKa8qZ3nCchYfXEyOSQ2Pfi5+3NHnDm7sdSNuTq0QIBUFTm2FPR/BoW9rp8Zq9dDrjzDwT9DjMvuwSqXFxpKtSbzx8wlKzeosnmkDw3jijzGtuhT+hUorKOeFlUfshcN1Rfm70SfUkz6hXvQNU//1dXMmsyzT/ml+W8Y2h98tvUbP4KDBjO0ylrFhY4nyirL3bqSVpPFr+q9sTt/M9oztmOvsFuukdWJI0BDGdRnH2C5jifCMaP0n3wJySky8smEdqxLXY3U5jM413eF2H4MPY8LGMLbLWEaFjsLLUDtjpspWRVJREkfzj3Ik7wiH8w5zrOAYZVX1e5gURYunrgv9A3szOrw/sX6xxPjGNPl3v6i8im/3pvH5zgSO5aaj0Zeg0Zfg7VFBdIiNQJ9KyqwF9qBRXNnAwlqN0KDBx8WHANcAfFz8sFS6U1DsQkq2juIyoz1sKBYPYoL8iO8VyMSYQAZ1rb8IoKIoVKalkbbzd3L37sJy7BiuSSm4FjW8EWKWNyQHaTgZpCEpGJKCNBS5O/5dt1WE4K3pRx/v4YwJjiPOCFGaCnSFeVhycrBk52DJzVW/rrnk5YHlLLPx9Hpi9u9D04LDyxI+LiE5JWae+uYAPx1WK9b7h3vz6g396RF4aVSCl1eVc6zgmD1oHM0/yonCE1ga2LPAVe9KL59ehHuE42+sDRM1wSLAGHD+b+Ll+VCSoS6cZPRr0ToHa3ExaHXo3N0wW818d+I7PjjwAafL1KmvXgYvbou9jRmxM/B0boHf+ZJM2Psp7PnYccdO/14w6Hbod/NZt+fOLqngldXH+HJXGgBGZx1/mdCDu8ZENXtTwdZUXmnhnQ2JvLvpJGaLDa0GrowLYVBXH/qGeREb4oGHi/pJ22KzsC9nH5vT1N6N4wXHHc4V4BpgDxsjQkbg7nzu/18Vlgp2ZO5gc/pmNqVtIr3U8U27q0dXNYiEjWVw8GAMuotnrZ0icxFbT29lc9pmtpzeQn6F41LoVlMYltIYfDT9eGBUPDcNiTjnzz4xp5RlO1L5ancq+eYMdC6n0bqk4+OTA85plFvrhwENGrp6diXWN9Y+dKOgNDr0kWvKpcLa+IrKZ3LSOtX74OHn6mfv8fQ3+uPn4kdhiQu/JuTzy9FsdiTnU2WtfVt0ddIxuoc/E2ICmNArkNA6dXWK1UrlqVNUHD5CxeHD6uXIEWxFDawGq9XiHBWFS+/euMTGVv8bQ6GTloTc0yQWZJBSmEVGaTY55Tnkm3MpqErFpDkFGqXOY7piKe2JtSyGri4D6BfaxR6we4d64uWq/s4rNhvWwsLacFITSqpDimKpInzRoia/lk0h4eMSkF1SwQebk/ho2ynKK6046TQ8+oee3DuuW4tuc9+WisxFasDIO8rh/MMcyTvCqeJTDdZfeDp7EusXS29fdQw51i+Wrh5d0V1oKKjZxvrMXSPrbmOt0VWv3njmio0N7EFylpU7LQUF5Pz73xQu+xKNszOekyfhdf31GIcOxaJY+PHkj/z3wH/t3fxuTm7cEnMLt/e+HV+XZs4qsVrUBcB2f6j+W9ON7OQGfafBoD+pG4M1oz5hX2oh8384ZJ/C3dXXyFNXxTKpd1C71jnUzFBasPKIfWhlRDdfnpnSh9iQ2r8ZeaY8tpzeYn+DLamsHT7QoKFfQD97OIjxjbmg56QoCknFSWq4SdvMruxdDgHaVe/K8JDhjA1TA06Ie8h5P9b5tu94wXF7b8/enL3Y6mzw5+7kzsjQkYzrMo5B/iNYvb+M9zYlkVuq9uwEehiYNa4bM4Z3dZh5Ul5pYcX+DJbtSGXnqQL79f7uzlw/qAs3DAmnR6A7iqKQVZ7l0Jt5JO8IWeW104Cbw93JHX9Xf3xd/KiqdOd0nhPpuU4o1b0THk6+XNW7F7cNiyE2pP7aJhVVVn47mceGo9n8ciyb1HzHXtUofzfie6lhY1iULy5OOpTKSsyJidUhozpsHDuGUt7AcK+TE4boHo5Bo1cvtMbmD8vlmfJYkbCBdckbOVy4g0qltgdFUTTYTOFYynphKe2FrSKUrr7u9vql3tVDiwEebRN8JXx0YKcLTby7MZHPd6Tai7L6dfHipev7OfxhvdjllOdwJF/thq3pkq35pH+mQNdAYvxiiPWNVS9+sYS4tcBeJIoCBcmOSwpn7K+/c2cNVx91l8/mFKO6eNdbYlpxCaBgawo5X23AVlr/D5NT1654T7sWr6nXoPX346eUdbx38AN7MZ+rzoXp0dczs/efCDQ23kMBQMEpdVhl32fqhl41ugxTezn6XHdeO78qioJFsaAoCt/vO83Lq4+RXay+EY3u4cdTV/UmOqjte98OpRfz3I+H2ZmsvtGFervy9ytjmNQnCA0ajhccZ1PaJjanb+Zg7kGHYOtl8GJ06GjGdRnHqNBR+Li0Xj1LaWUp2zO2syl9E5vTNtuH2mr08O5hDz5xAXGtMgRY0zNT83pklzv+3vfw7qGGoS5jGRA4ACetY2FiRZWVL3ak8s7GRHvI83Vz5q4xUQyJ8OHbven8sC/DPkSn1UB8r0BuGhrOxJjAJs3aya/I52jeUXsgOV5wHCetU6PDpjX1WQ3V1qTklfPlrlS+3JlGZnFt70j/Ll7cODScYZG+bDuZx/pjOWxNzKWiqjZ8Oeu0DO/ma191OMKooeLYMSqOqCHDfPgI5oQElKoGNtZ0dcWlVy81YPRRw4ahRw80zi2/V43FZuFA7gE2p21mfcpGThQ59uDZLB5YS3tiKY3BUhYNNvXDUaCHwb4Tee/qIUh1XaOW/RAh4aMDOpVXxtsbElm+O83e5TewqzcPT4wmvlfTpsG1N5tiY33qet7f/z6H8g41eEwX9y7E+tWGjBjfGPxd/Vvgwa2Qm1A/aJgb6P7UaNUhiJB+tbs8BsepSw1bLVCWU3/b75LM2t06a7YFt9YvtCvNNJC125PKYvUPucG7iuBBRWh0CoWJRopTXLFZqv8oaxTcQ8x4dyvHGFrBRndX3vP25JBB/ZTipChMKynlz0XFhFmaUBBn9If+N8PA2yEwpskvXZWtipOFJ2uDYv4RjuUfa1YB78Uq1jeWMWFjGNdlHHH+cRfec3YeFEXhWMExNQSkbWZ/7n6HXoe24qJzqe196TKWUPfQJt2v0mLj691pvLUh0WHmSY0IPyM3Dgnn+kFdLnhfopZgtSlsOp7DFztSWXckq9GF20K8XJgQE8jELq4MrMiGhNqwUZnU8IwTraenY29G71icIyPR6NpnODKrLMteg/Tb6d8c/s9q0KGv7EZpYQ8sJTHYKgOB2vcRb6MTm+ZMwNOl5WbDSPjoQBKySnhrQyLf7U23z2sf2c2Phyb2YGR3vw4ROqw2K2uS1/D+gfftn961Gi1RnlH2gNHbrze9fHu1TE2DfSvpOhslZR5sZCtpZwis3ko6pHor6cDe4HyBsxIUBUwF9iBSmXCErMXfU7o3WX1YVy0Bw/V4hxegqaoNQDaLhuIUFwpPGjHl1naF6lyseEWa8OpWxq4gJ9719mSPi/qHXK8oXF1axl2FxUSeWUCm0UKPP6iBo+cV59wZ1GQxcbzguMOnzYSCBIfppB2Zm5MbI0NGMrbLWMaEjSHQGHjuO7WxwopCdTgofTNb0rdQaC5stcfq4t7FXgA7NHjoBdWdWKw2VuzP4K0NJ0grMDG5TzA3DglneJTvRTstO7fUzDe70/liZypJuWWM99cy2bmIfqZMPFJPUnH4MFVpaQ3eVxfgf0bQ6INT2MW7XUWltZLd2bvZnKbWICUXJzvc7qkPxJs4TEU9ST0diq/Rne1//0OLtkHCRwdw6HQR/1l/glUHM6n5CcT3CuDBCT0YEtkxVpGsslWxInEFHxz4L6dK1JoJd42eWxQPbis14au0wn9SmwUKT6n/nsnJTQ0Ywf1qw0ZADLTimhrW0jLy3n2X/CVL1C5ZvR7fW2fg/8AD6Lyqx5qrTGCpXyRnTkqm8LsfKfphJdb82vFy14H98Zp6FScGBvBe0hdsy9oJqIFucpcJ3B17Gz29u6sH6wyNBqniymKO5R9zGPpKKk5q8FO3h5MHMX5qwV/N8FfAWYZ8tp3M55U1RzmRrY4/9wzy4IkrYhgS2XJDGVtO5PLKmmP26b+xIZ48eUUMA7p61zu2ct8BSpd+gpKZjZN/APpAx0WZaqYa6gIC0LZCd/j5sik2h1qUlqTVaHF3cr9o3ywBbGVl6vBGdQ2F+dgxbBVNLyhtDmtREdbc3AZvc+rSRQ0ZNcMmsbE4BV58wbU5UotT1WLo9E3syNjhMCXaWetMnN9gFk58sfn1ZWch4eMitjulgEW/nOCXo7Xjr1f0UTd9iuvSATZ9slowZx3g28Of8L+MTZy2qb0NXlYrtxeXcEtxCZ7N2J/ivLn6Og6bhAwA325ttn23YrNR9P335Ly6EEuOOp7vNno0QX+fi6F79+adq6qK0o0bKfxqOaWbNtm7e7VubnheeSU5l/XjPct6NqZvst9nYvhEZvWbRR//PgDkmnLtAaOmmC+ttOFPdL4uvvWKebu4N39JfovVxifbU1i49rh9aeyr4kKYe2UMXXzOv2cpKbeM51Yc5ufq/yN+bs48PrkXNwwJd5harigK5dt/J/eddyjftq3J59d5eaEPDEBXb+2DAPT+1f8GBqB1c7uo37g7GmthoX1YoyZsVCYnQ1u+BTUy48T+QeESZbKY7PU/m9I2kVGWgY/Bh/U3rm/RoUgJHxeZmtX9Fq1PYMsJdTl0rQam9A/lgfge9Aq+SLe7rqqA7EP2+onyjL18WZ7MUg9XcqoXpvGzWJlZVMyN5VUY7cMb/auDQCuNg3pHgFeXdtvUzLR/P5nPP0/Fvv2AWkAa9OSTuE+Iv+A3q6qsbIq+/ZbC5cupSqmdgWOI7oH5j2P5qGsqK/I22gsp+/j1Iac8h2xTw0W0oW6h9oBRU2cT4FpbQ2QzmRzXBsjJtU/HcwoOxuu6a3EODz9rm/PLKlm49hifbk/Bpqg7+N47vjv3j+/erM30SiqqWPTLCf63JYkqq4Jeq2HmqEge/kO0w7i0oiiUbdpE7tvvYNq7V71Sr8fr2ql4xMdjyc93mFJYc7Hm5DZYMNgYjatrw6tH+vs79KzofHxadK2Ejk5RFCzZOVQcUaeemo8coeLQYapON1xwrg8KsgcBQysGAa2LC4YePc5rxsmlRFEUThadJL00nXFdxrXouSV8XCQURWHj8RwW/XLCPg1Nr9UwbVAY98f3IMr/IlqZ1FwCmQdqCzUz9kHOUVCslGg0fO7pwUdeHhRUF1YFKzru9IjhuqgrcQkbDP49W3V442JQlZ1NzmuvU/TNNwBojUb87r8P3zvuaPGufEVRKN+xg8KvvqJkzU8o5urFrJyc0I4dxto4hQ9cd2LRqL0kGjREeEaoIcMnht5OXelm8cFYbG44XFRfbGUNLzdel3HECLyvvx6PSZejNTReM3Ako5j5Pxxi20l1zYgQLxfmXhnLlH5nn7lksyl8tTuNl1cfs0/tjO8VwNNX96Z7nZ1NFZuNknXryH3nHcyHj6jP29kZ7+nT8bv7LpxCz15AqSgKtqIix9cjN7fBNRBspQ0vCNUgnQ69n1/9pa0v8iGflqAoClVpaVQcOlzbq3HkSOPDG1271ivY1Pv5tXGrRWuR8NHObDaFtUeyWPTLCfvGSc56LTcNCefe8d2a1SV9KPcQh/IO0dOnJz19erbc8s1leer0zPRdatCouxhVtUKtlo/9AvnU3YUS1De5cGMwd/e7lyk9prbv/iQtqKrSSvqxAgqzynF1d8LoacDo5YyblwGDmx6lqoqCDz8k9623sVXP6fe69loC/vpYm4wLW4uLKf7xRwq/Wk7FodpZRJqgAPJH9MQDFzyKLSj5Bef3Cd/Fpd6bpc7PF9POXZT99pu9W1zr6YnX1VfjPf16XHr3bvBciqKw6mAmz/94hPRCdUhuaKQPz0zpQ9+w+p9od6cUMP/7Q+xLU/+fRPm78fTVsUyMCao9p8VC8arV5L77DpUn1N9TjdGIz8034zvzDpwCAykvriT1SD6KohAe64ub14Wta2DvEWoknNh7U/LzmzVsoPPyQndGOHEKC1PfjM9zHYi2olgsVCYlVYeM2qBhK2mgZkWrxdC9W3VvRqw9cOg8LtJeXtEiJHy0E6tNYcX+0/xn/QmOZ6mfnFyddNw2oiv3jO1GYDOXqC6vKucPX/7BvreCVqMl0jPSYQOnGN8Yh+WOz6kkE7a+CTv/B1VnTJvz7AIh/cgN7MmH1jw+z96OqXo1wW5e3bin3z1cEXnFRbMz64UozjWRfCCPUwfzSD9egLWq4amPWi04VxbhXF6Ac2URRnc9fmOH4NWzK0YvA25ezmpY8XRC2waLv1UcPUrhV8sp+uGHhldRrKPR2gb/Ol+fo7ahMi2doq+/pvCbb7BkZNivd+ndG6/p1+N19dXoGvh/WlFl5b1NJ3lrwwkqqmxoNHDz0HBmT+qFn7uBrOIKXlx1lG/2qKuCuhv0PHxZD2aOisJZr76OSmUlRT/8QO5771F1Sh2C0rq743P7bfjcdjv5xXpOHczl1ME8sk85vgEGdPUgoq8fEX39CIz0bLXZGEpVVfUwTy6WnOz6vSrNGfKpqUeo0yvgEhvbLvUItspKzMcTaodODh9RF9RqoBhU4+SEoVcvx4LNnj3RusoO252NhI82pigKX+1S58HXVOZ7GPTcMSqSO8dE4Xueuy9+nfA1z2x9Bg9nD1x0LvUWKqoR5h5mX564Zmy/3kyFwhTY8m91Y7Ga/ShC+quLUIX0h+D+ZFLF/w7+j68TvrbvWRHjG8OsfrO4rOtlbboXSkuzWmxknCjk1EE1cBRkOgYvdx8DQVGemMstlBdXUpZvwlzRjLUYNNh7Tdy8nDF6OWP0MmD0VHtQ/MPd8Q5suU+1NrOZknXrMO3ajc7bq37PRQt38StWK2Vbf6Nw+XJKfv4Zqt9INQYDHpMm4X399RiHDa1X+3C60MSLq47y/T51vN/DRc/V/UL4bu9pyivVtUtuGNyFx6/oRaCHi/25FS5fTt5//4vltBp4dN7euN/2Z0r6TyI1sZyUQ3mYShzfzAO6eqDRUC+IuLg70bWPLxF9/eja2w8Xt7bvsWtwyKf6Upl8iorDh+2Fy2dyCgurDSPVPQkt2eNmn3FSZ+jEfOJEg/uCaI1GtSejTkAydO/epjuniotXq4WPyMhITp06Ve/6Bx54gP/85z/MnDmTpUuXOtw2fPhwtjWjEr0jho+vdqUx+8t9gLpwy12jo/jTqEj7Gvvn69Yfb2V/7n4eG/wYd/a9k1xTLkfyjtgXgjrbjAZ/V381kLgG0/v0IWIS1hNWaVaXmAkfDuPmqJuKaTSkFqfywcEP+C7xO/uS0P0C+nFvv3sZGza2w1b8lxWaOXVIDRupR/KpqqhdqEuj1RDS3cv+6dg3VP30by0uJvc//yH/k0+xWaHK6INh2gycLruKCrOGsqJKyosrKS8yq18XmSkvqUJpwgwfr0BX++OFRfugc+qYYc5SUEDx999T+NVyzAkJ9uudwsPxvn4aXtddh1NQkMN9diTnM+/7Qxw6Xbu/x6Cu3jwzpQ/9w70BsJWXU/DFMvL+94HaUwCYuvTGFD+DbOeuZCaXOrzOTi46usb60rX6Na0ZaikvriSl+ueecjifSlPtm6hGA8HdvOja14/IOD/8wi6eqaiWnJx6s0GavgZFb5zCws75XBxmnFSHjcZmnOi8vR0Cj0vv3jhHREhxrWhUq4WPnJwcrNbaP+AHDx7k8ssvZ/369cTHxzNz5kyysrJYvHix/RhnZ2d8fZs+j7gjho8HPtnF2gNpTB0UwfypfXEzXPiwxLH8Y0z/YTp6rZ5109fh59pwUVaz1nJAS6xXD2LDRhDjF0uwMZjlCctZmbTSfvyw4GHM6jeLYcHDLpo/yk1lsylkJRXbu+JzUx2LBl09nIjo40dEnD/hsT4YjLXh0GYyUfT9D+S8/jrWArU42P2yywia8zjOEWffnVSxKZhKqygvrgkklXW+NlNaYCYnpQRbnc2q9M5ausT42sOIh2/7rwzZXIqiUHHgAIVfLaf4xx9ri1e1WtzHjsVr+vV4xMfbPxVbbQrLdqay8kAG0waFce0A9c3SWlJCwSefkL9kKeYSEwXePSkIH0p+0ADKzY6zZXxC3OyvWUh3L3T6s78RWq02sk4WcepgHskH8sg/7Vhg6+blrJ4vzp8uMT44u7TdkKKiKFRWWNFoaPRxrUVFVBw5WqeY8zCVJ8+y+mbdHonoaKoyMhyWCG/KjJOaoRN9SAtscdAMNpuCuawKg5vTRbtomTi7Nht2efTRR1mxYgUJCQloNBpmzpxJYWEh33777fmeskOGj4efe5XXqp7F4h6CIfYKiJ4EUePA+fxnszy/7Xk+P/Y5kyIm8Wr8q02/4+m9mDa9zPGktRxxduaowZnDHv6c0FioUhrfXnlM2Bhm9ZvFwMCB593m9lBRWmXv3Ug5nIe5rM5z1EBgmJEuXfWEelfgZcvDmnvuWR/O3bsT9Pe5uI8e3WLtrDRZSDtaYA9GZUWOS7P7hdW+qQZ382qT+pGWZCsvp3jNTxQu/wrTzl3263V+fnhNnYr39OsxdOvmcB9LQQH5H35I+peryTFEkufXhwKfniia2jdivZOWsBgfNTT29cPT/8LqCEryK+xDb2lH87FU1r6Ja3UaQqO97T8H7yDjeb352mwKFaVVlBWZKS+qtP/r0GNWrF5nqa418gxwJSDcnYCuHviHexAQ7oHRs+FhM5vJhPnYMUx1prE2tu9IQ9p6xomlymp//g6vSXGlw+tjKqlEUcDgpqdrb7VnqmtvP1zcZUino2iT8FFZWUloaCh//etf+fvf/w7AzJkz+fbbb3F2dsbb25vx48fz/PPPE9iM8cmOFj4yikx888osHtB/73iDzgCRY9QgEn05+DV94SmTxcRlyy6jpKqE9y5/j5GhI899p5TtsOkVOLG2+goN9L4Gxv4NQvpTZa0isSjRYev6U8WnGBg4kHv63UMfvz5Nf9LtSFEUcpKLSNqRRsrhfHKyLA49xk5U4l+Zgl/eIXxSd+BUXtD4yc6g8/XF/7578bnlllYdw1YUhdy0UvVN8EAeWUlFDs/B2VVP197VNQp9/Bp9E7pYmU8mUfT1cgq//c5hyqXrwIFqCBk4hIQPV5G8N4tcz56YjI5DNJ7+LkT09VeHp3p6o2/GWiHNYamycjqhkFMH8kg+mEdxjuPy/J4BdYbJenoD1AkRtW+gZ76Rmpo4DHcubl7OahjpqoaRgK4euPsYGgxEtTuu1s5CMZ84gVNQoH1YpqZWoyVmnCiKQqXJ0vAwZHGlw9fm8sY/9JyLRgNBUZ723wf/8ItnmEzU1ybhY9myZcyYMYOUlBRCq+fXf/HFF7i7uxMREUFSUhJPP/00FouFXbt2YWhkfQCz2Yy5Zg2D6saHh4d3mPCx8kAGRV/czy369dD7WnUr9oQ1aoFnXX49aoNIxGjQNz4V8LsT3/GPLf8gzD2MldNWNl7oqSiQtEkNHcmb1es0Woi7Acb8tVmbi12MbDYb+YdOkbnjBNmJueTlWim0elKpd9xN1b00Db+8Q/jlH8KzOAntGUNONbM+7ItCNTT7ox1XtKworSLliBpEUg7lU1Hm+Ak2MMLDPjQQ2NUDzQV2SVurbJQVN/BpvO4bR3EllqombGZ3NgooliqUykqUKgs1uwXbdAZsdXZQ1WoUQnv6EBHnf0E9DheqMKu8ulckl/SEQmyW2j+NWq0GW3MCRU0Bcs2MKHvxsXOdomR1Sre10kZOWgk5KSXkppaSk1JCYXZ5g5srG9z0ahCpDiM1hcwX+jtRw2ZTMJVU1uupKC8yU1ZcO6RYt9emKXR6LUZPZ/sUdvVfZ4dp7UYvZ1yMTmSdKuZU9Uy0vHTHoVOjl3P10Kkf4TG+OLt2/Jl3l5I2CR+TJ0/G2dmZH374odFjMjIyiIiI4PPPP2fatGkNHjNv3jzmz59f7/qOEj6eW3GYodsfYrJuJ1z1Kgy9Ww0Fucch4Sf1cmqr414kTm7QLV4NItGTwCvM4Zy3r7ydvTl7eWTQI9wdd3f9B1UUSFirho6039XrtE4wYAaMeVRdXbSDsVZZyNmdQOaeJLJPFpJfoFCkeGPR1+9m11nN+BQcI7DqFEHO+Xj4GRtd4Enn79+hFnay2RSyk4vtQwM5KY4zN1w9nOhaPQQRHutrn7lRUz9QXtOtXRMu6nxd093vMDTVTgy2MrpGu9P9D30uyjeRyorqYbJDeaQczKO0QP2ApNVr7DOY7P96OTt87eZlwNXjwqZeV1ZYyEsrJSe1hJzqQFJwuqzBAORk0OHfxb1OD4k7PiFu6Oo8fkNDHw193dxeG2dXfW2oqnn+1YGi7tcGo/68AmVpQe0wWerRAizm2kCs1WoIifYioo8/EXF++AS3T2gVtVo9fJw6dYpu3brx9ddfM3Xq1LMeGx0dzd13380TTzzR4O0dvefj+re3MifjMYZrj8INS9Spq2eqKIaTG6rDyFp1O/a6gvpWB5HJJLj7MG3FDeg1etbesNZxu3mbDY6uUENHprq0N3oXGHQHjH5YXXK8A6gqqyDz96Nk7U8h51Qx+cU6ijU+2HT1Q4LGZsHDmo+Ph4WALm4E9elCyKBInAP8OkXVfc2MnZSDeaQ0MGPHN9SNqgpLsz+JnvkmWrtmSe2bqJOh5Yc7FEVBYyrHK9IfbQf5+SmKQmmBGSeD7rzfRFuCtcpG3ulSe+9ITmoJeWmlDf7ctXoNviFuWKtszR/60ICrxxk9E2f8jtQEDadWGhJriLXKxukT6jDZqUN5FGY5Tpf38HOpHSbr5dOmbROq5oSP8/q4sXjxYgIDA7nqqqvOelxeXh6pqamEhIQ0eozBYGh0SOZiV2mxcSC9CF9t9adT10Zm9bh4qvUXva9Rey0y99cGkbQdkHVQvfz6GssDg8DNwHjPbvjX/E2xWuDQ17D5VXXJc1B7T4beBSMfBI+ghh/3ImAuKOH0b0fJOphGbnoZ+aVOlOp8ULR6wF29VP8W6qxmPJVCfL1sBER4EjwggqAhPdEbO95MkJbi5m2g9+hQeo8OVdcqSSyqrhXJpSCznLw0x25pZxedYxe/l+Obh33l1nZ8E1V/7h2HRqO5KGYj6Zy0BEZ4EhhR+0fdZrVRkFVuDyS51T0llSZLvdleOr22gV6a+kMfru5ts2Bec+mctITH+hIe68sYoinMVofJUg7mkX68kJK8Cg5uTOfgxnR0TlrCevoQGdcyhcqi5TW758NmsxEVFcUtt9zCiy++aL++tLSUefPmcf311xMSEkJycjJ///vfSUlJ4ciRI3g0scipIxWc7kstZOp/trDL5X78KIL7tkBw3+adpDwfTvwMCT9RcWIdlwW4UqzT8XZmNmNMZggbBKYCyD+pHm/wguH3woj7wdhyWyG3FKvVxqEPfyFpVyYFJgNleh+1DuUMeks53poifH01BET5EDI4Cv8BPdA5XVzd7xez4lwTuWmluLg72d9EWqO3QnQsiqJQnFtB/ulSnAw6e7ho38DZuqrMVtKOFdiDec0wWQ2fYCNdYnxx9zXUBq9O8Lq0tVbt+Vi3bh0pKSnceeedDtfrdDoOHDjAhx9+SGFhISEhIUyYMIEvvviiycGjo9mdUoAGG95U93ycTxgw+kK/G6DfDaw98T3FW54iVGdkpFc0mA6oe68AGP1gxAMw7B5wufi2f7ZabRz6bh+7V5+iTOsJhEJ1TaGhqgQvfQl+/noCe/gRMjQan95dO0y3+8XK099VPtGJejQaDV4BrngFdJ7fDSeDjqh+/kT180dRepKfUWYvWs1ILKIgs7zeqsY16hbDnlm7U/e6C63jEY6aHT4mTZpEQ50lrq6urFmzpkUa1VHsSSnEg3J01ZuuNTrs0kRfJSwHYFrcnej63wvFGZD4szpU03faBa0b0losVVYOrUtk94oTlFtdQOuJvqqMHv7FhA0II2RYNF7dw859IiGEaAEajQa/UHf8Qt0ZNDkCc3kVqUcKyEoqanAasNVioyS/gpL8+vvWOJ5YrYWpO6RZM5ypd26dUKLTa/ENdcM3xO2cC+p1NNLHfQH2pBbgo6keV3V2B6fzHxc+WXiS3dm70Wq0XNvjWvVKzxAYeNuFN7QVVJmtHNqYyu4VCZgqdYALzpXFRLumMnTOtbh1C2/vJgohBAajEz0GB9JjcP31ppo8C6hYXQDNVFyJqbiSvIZXvW81Wr0aqALC3dVF6Lp64NfFvUMX1Ur4OE85JWZS800MPFexaRMtr+71GNdlHEFuF3EBqcnCgQ1p7F19EnWSkg5DRQHdK/Yy6MGr8BpzbTu3UAghmkbvpGvS8KV9/ZMzVmWtWSfHYmnGJpTNUFVhJTdNLSDOSSmpnnqvbrao0YB3sBsBXd3ta7/4h7s7bBtxMZPwcZ72pKgrZ/b1tkI5F1T8abaa+T5RXSH1hp43tETzWlxFaRX7fkll/88pVJrV/2guphyicjbT79bR+N30DBq9/DoJIS49Wq0GNy+1WJU27tStKSDOTS2pnmKtrv9iKq6kIKOMgowyjm/Psh/v6e+iBpE6K+NejKsky7vFedqTWghAnK/lgsPHz6d+ptBcSJAxiNGhLbefSEsoKzKzb10qBzam2ffBMJZlEJm6ll6XRRP07+fQeXu3byOFEOISVbeAuPug2qGjsiJz7fTqFDWQlORVUJyrXhL35NiPNVYv1W/vIenqjoevS7vO8pHwcZ5qej56elRvEGY8/42Zvkr4CoBp0dPQaS+OMbyS/Ar2rE3h8K+nsVYvYuRekkrkqdVERLsQ/N7fcenZs51bKYQQnZOblwG3OAORcbULUVaUVTmEkdzUEgqyyikvqlRn/xzIsx9rcNNz+3OjMLTT6sISPs6DxWpjX2oRAOEu1RXS5xk+kouS2ZG5A61Gy3U9GlgdtY0V5ZSze00KR3/LsG8B71mcRGTyKoKNRQTPewL3yy6TefFCCHGRcXFzokuML11ianviKyss5KWX1Rm2KSH/dBl6vbbdggdI+Dgvx7JKMFVZ8TDo8dWUYC7Wk/nODownFuF7+23NGob4OuFrQN3SPsS98ZVgW1t+Rhm7VieT8HuWfYdV78LjRCavxrcylYB778V35h1oO+hqtEII0Rk5u+gJ6e5FSPfa9aGsVTZKC81nuVfrk/BxHvakFAIwoKs3GlM+eYfdKU/OoPzYf8hfvBifGbfgO3Mmen//s56n0lrJtye+BWB69PRWbnXDclJL2LUqWR0frA4dfoVHiUhaiXdRIl5TryHgr+/iFHTxzsARQgjRdDonbbsvQifh4zzsrq73GBjujZKSR0m6ur6HU5cuVKWlkfffD8j/6GO8b7wRv7vuxCk4uMHz/JL6CwXmAgJdAxnbZWybtR+gtMDMxs+Okbw/135dYNlxuh79Bs+SFFzi4gj6+6cYBw5s03YJIYS49En4OA97q3s+Bkb4ULYuG1uVFp23B91Xr6J002Zy33mHiv37KfjoIwo+/xzv667D7567cQ53nKO1/Li6tse10dei17bdjyI3rZQVi/ZRVmhGo4EQ2ym67PwY97LT6Pz9CZz7Al7XTu0Uu8YKIYRoexI+mqmgrJKTuWUADOjiTcmxUkCLx9jhaPR6PCZOwH1CPOW//Ubu2+9QvmMHhcuWUbh8OV5XX43fvbMwdOtGanEq2zK2oUHDtOhpbdb+lMN5rH7vIFUVVjz0Jnpvfw23knRwcsLv7rvwu+8+dO4da9dRIYQQHYuEj2bam1YIQDd/N7wNWhKS1es9Lr/cfoxGo8Ft1CjcRo2ifNcuct95l7LNmyn67juKvv8ejysm89NY9Q1+VNgowtxbf+8TW1kZez/9jW27QEGLd+Fx4g6+h5PFhHt8PEFPPoFzZGSrt0MIIYSQ8NFMe06p9R4DunpTvm0zVrMWrbMNtzHxDR5vHDyYru+/h+nAQXLffYfSdT9Tsmo1o1aBcw8NXR8a0irtVBSFyuRkSjdupHTTJg5mBZAcPhmAoKwd9E7+Eo8xw/G5dQbuY9u23kQIIUTnJuGjmWpWNh3U1YeSHz8DwKOLBY3R86z3c43rS/iiRVQcO86h1+bjsmE3Q04o8MirpIz6Df/778M4dOgFtc1mNlP++w574KhKScGm0XGk121khQ8DIMYzjWEzRuE2/FGZNiuEEKJdSPhoBptNqS027eJJyYZfAfCIbvputi69evL+9R6c7KljztEehG05QdnWrZRt3YrrkMH433c/bqNHNXkRr6rTpyndtInSDRsp27YNpaJ2W+gqF08OD3uEPG0wGg2Mn9GLPmMnNv0JCyGEEK1AwkcznMgppcRsweisIyLnFKk5+Wj1Ntx6nn09j7rSStLYenorip+GXq++SVCRlrz/vk/R8q8x7dxF6t134xIXh//99+E+YUK9EKJUVVG+Zw9lmzZRunEj5oQTDrfrg4JwHz8ehoxj/S5XCrIqcDLouGJWX7r2Of8l4IUQQoiWIuGjGWr2c+nXxYvyn9cB4B5qRuvR9Df1rxO+RkFhRMgIwj3CwQNC5s3D//77yf/fYgq++IKKAwdIe+AvGHr1wv++e3EdNJiyLVso3bSJsi1bsJWU1J5Qq8V14EDcx4/Hffw4DD17kpNSwor/7MdUXIGbt4GrH+yHfxePFn0thBBCiPMl4aMZalY2HRjuTfFrawHw6GJq8r4uFpuldkXTno4rmjoFBRE090n8Zt1D/tIPKfjkE8zHjpH+2F/rnUfn44P7uLG4jx+P2+jR6Lxql81N3p/Lmv8exFJpwy/Mnasf7Ie7T9OHhYQQQojWJuGjGWrCxzBbHlUpKWicdLiHmMHoe/Y7VtuUtokcUw6+Lr5MDG+49kLv50fgXx/D7647yf/4Ywo+/AhrUREuffvaezdc+vZtcAGwAxvS2PzFcRQFwmN9uGJWHM7tuHGQEEII0RB5Z2qi4ooqjmerwx1RR3ZgAtx6+qF1Sm1yz8dXx78CYGqPqTjpnM56rM7Li4C//AW/e+5BqahA59n4bBrFprD1m0T2rk0BIHZUCONv7YVOJyuUCiGEuPhI+Gii/alFao+Cryu2jb8A4NnLTb3R9dw9HxmlGfyars6OuT76+iY/rtbZGZydG73dUmll3ZLDJO7OAWD4NVEM/mOkbHkvhBDioiXho4lqik0nuJrUGSZ6Pe7hNsiiScMu35z4BgWFYcHDiPCMaJE2mUorWfnWATJPFqHVaZj4p1h6DW94EzshhBDiYiHho4lqdrIdnXEAALcRI9ApB9UbzzHsYrFZ+Drha6B+oen5KswuZ8Wb+yjKMWEw6vnjvXGE9fJpkXMLIYQQrUnCRxMoimJf2bTLge0AeEy6HFI2AmBz8SF5bw5GT2f8wtxxMugc7r8lfQtZ5Vl4G7y5rOtlF9yezJNF/Pif/VSUVeHh68LVD/bHN9Ttgs8rhBBCtAUJH02QnFdOYXkVXcyFaBOOglaLx8SJ8FY+AHt2aNm2Wu0R0WjAO8hIQFcP/MM9COjqwdcnvwPgmu7X4KxrvH6jKRJ3Z7N28WGsVTYCunpw1V/64eYly6QLIYToOCR8NEFNvcd1pQmAulmc3t0ZbFUoioZDv6uzYJxd9VSaLBRkllOQWc7x37MA6MnVBBtGEFXSlZ05yfiHuxPQ1aNZoUFRFPb9nMqW5SdAgch+/ky6q0+9XhYhhBDiYifhowlq6j2Gpe4DwGPSJCjPAyDdOoiSfDPOrnr+/NJozCYLOSkl5KaWkpNawskT6VDijKfZn6yD5WQdPGk/r9HTubqHRA0jAeEeePi51JupYrMp/LosgQMb0gCIGx/GmJt6otXKjBYhhBAdj4SPJtiTUohPRTF+p44B4HH5H8CUDsARs7pNffTQIPTOOvTOOtziDETG+WO1Wfnj138jr6iQJ6OeoYc1rjqYlFCQVU55cSWnDuZx6mCe/bEMRr0aRsLVYRu/MHe2f3+S5P25AIy6vgcD/hAuU2mFEEJ0WBI+zqG80sLRzBKuyDiIRlFw6d8Pp+BgSDiA2WYksXQAoC7sdaatp7eSUZaBp9GTKeMuw0Vfu8x5ldlKXnopOSkl6iW1hPzTZZjLLaQfKyT9WKHDuXR6LX/4c296DA5szacrhBBCtDoJH+dwIK0Iq01hQtYhADwnTVJvKM8noWIsVsUJ31A3AiPqb9y2PGE5oBaa1g0eAE4GHcHdvAjuVrsvi9ViI/90GTmpJeRWB5LctFJc3Z25/K4+hHT3QgghhOjoJHycw+6UQjwqy4jNUotNPS6/XL2hPI8jJnXabOyokHrDIDnlOWxI3QA0fUVTnV6r1n509YDR6nU2m4JGgwyzCCGEuGTI5h/nsCelgBEZh9AqNgwxMTh37QpAXoaJ7KpotBpbg6uKfnviW6yKlQEBA+jh0+O8H1+r1UjwEEIIcUmR8HEWNYuLjT6truHhMely+21HEtSN3iLDCnH1cFy7w6bY7EMuLbWiqRBCCHGpkPBxFmkFJsryixiUcxwAz+ohF6vFxrG0MABiY0z17rctYxvppel4OHkwKXJS2zVYCCGE6AAkfJzFntRChmYdwclmxTkqCuce6vBJ8oFcKqpcMGrz6RrtWu9+Xx3/CoCru1+Nq77+7UIIIURnJuHjLPakFDDm9H5AXVispvbiyNYMAGJc16N1c9zMLdeUy/qU9UDTC02FEEKIzkTCx1kcTMxiSNZRoHaWS1mhmZTqRcFiXX+pt6Ptdye+w6JY6Offj16+vdq2wUIIIUQHIOGjERVVVlz27sDFWoUmJBSXPr0BOLotA0WBEOcjeOtPO4QPm2Lj64SvASk0FUIIIRoj4aMRh04XMyJNHXLxnnw5Go0GRVHsQy6xLuvUA42+9vvsyNxBSkkKbk5uTI6c3OZtFkIIIToCCR+N2JuYzfDMw0DtqqYZiUUUZZvQO2vo7rIV9C7gZLTfp6bQ9KqoqzDWuV4IIYQQtSR8NCJ38xbcLBWYvXxxHTAAqC00je7thLO2Alx9oboINb8in3Upam+IDLkIIYQQjZPw0Qjvnb+qX4wZj0arpbLCwold2QDExlSot9Wp9/j+xPdYbBb6+PUh1i+2rZsrhBBCdBjNCh+RkZFoNJp6l7/85S+AuiLovHnzCA0NxdXVlfj4eA4dOtQqDW9NGXml9D+1D4DwqVcCcGJXNhazFe8gI8E++eqB1fUeiqLIiqZCCCFEEzUrfOzYsYOMjAz7Ze3atQDccMMNALz88sssXLiQRYsWsWPHDoKDg7n88sspKSlp+Za3oqNrN+FVWU6pixu+o0ao19UUmo4KQWNSp9rWhI8dmTtILk7GVe/KH6P+2C5tFkIIITqKZoWPgIAAgoOD7ZcVK1bQvXt3xo8fj6IovP766zz11FNMmzaNvn37snTpUsrLy/n0009bq/2tomytWruR2XcYGr2egswyMhKL0Gg19BoRDOU1PR9+VForWfD7AgCu6nYVbk5u7dVsIYQQokM475qPyspKPv74Y+688040Gg1JSUlkZmYyaVLtXiYGg4Hx48ezdevWRs9jNpspLi52uLQnxWYjYK/aXqcJlwFw9De11yOijy9uXgYor+75cPVl0d5FnCg8ga+LLw8OeLBd2iyEEEJ0JOcdPr799lsKCwuZOXMmAJmZmQAEBQU5HBcUFGS/rSELFizAy8vLfgkPDz/fJrWIkj178SwrokzvQo8rJmCz2jj6m9r+2FGh6kHV4WMXZpYcXALAMyOfwc/Vr6FTCiGEEKKO8w4fH3zwAX/84x8JDQ11uL5m/5MaiqLUu66uuXPnUlRUZL+kpqaeb5NaRMq3KwDYE9aHbiE+pBzKp7y4ElcPJyLiqsOFKZ8yjYanstajoDC1+1Qmdp3Yjq0WQgghOg79+dzp1KlTrFu3jq+//tp+XXBwMKD2gISEhNivz87OrtcbUpfBYMBgMJxPM1qcoihY1v+CDsgZOAqtVmNf26Pn8GB0+uqsVp7PK74+pFcWEuoWypPDnmy/RgshhBAdzHn1fCxevJjAwECuuuoq+3VRUVEEBwfbZ8CAWheyceNGRo0adeEtbQPmI0cw5GZRoXPCc+xYyosrSd6fC0DsyNpAtbGqgOWe7gA8N+Y53J3d26W9QgghREfU7J4Pm83G4sWLueOOO9Dra++u0Wh49NFHeeGFF4iOjiY6OpoXXngBo9HIjBkzWrTRraX4p58A2BkYQ7/oYI7/nonNphAY6YlfmBowCkz5PFOdNW7vNpWhwUPbq7lCCCFEh9Ts8LFu3TpSUlK488476902Z84cTCYTDzzwAAUFBQwfPpyffvoJDw+PFmlsaytcrYaPLaFx3NDFi5WfnQTUtT1AHZZ5dus88nQ6uldW8sjgx9qtrUIIIURH1ezwMWnSJBRFafA2jUbDvHnzmDdv3oW2q82ZExOxJidRpdGRFzcUc1YFBRll6Jy0RA9Va1ZWnFzB2rT16BWF5/NKMLj6nuOsQgghhDiT7O1SraR6yGVPYDSxPUI4svU0AN0HBWBw1ZNZlsmC7epiYvcWFtHHydO+qZwQQgghmk7CR7Xin9RC2S2h/RgQ6kXCjixAXdvDptj4x5Z/UFJVQpx7V+4uLHbYVE4IIYQQTSfhA6hMTcV85AhWjZZtwX0ILVGorLDi6e9CWLQ3nx39jO0Z23HRufB8+NXqWJWrT3s3WwghhOiQJHwAJdW9Hgf8umHz9KLgUAEAMSNDSCpJ4rVdrwHw2ODHiFKqXzLp+RBCCCHOi4QPoKR6bZJfQ+MYFujB6eOFoIEewwN4avNTmK1mRoaM5OaYm2v3dZHwIYQQQpyXTh8+qrKyMO3dC8BvIX3pV+kEQHisL5+nf8TBvIN4OHnwf6P/D61GWyd8yEwXIYQQ4nx0+vBRsnYdAIlB3Shw8cL1dAUAHnFW3tv3HgB/H/F3gt3U5eMpz1f/lZ4PIYQQ4ryc194ul5KaIZefA/rQ1aLFUlaFs1HPG/nPY1EsTIqYxFVRtcvI23s+ZI0PIYQQ4rx06p4PS34+5Tt2ALA1pC8jNOoGd6UR6ZwoTcDf1Z9/jPiH4668Jun5EEIIIS5Epw4fJT//DDYbReHdKXL1o0u5ev33+g8BmD9qPj4uZ0yptQ+7yFRbIYQQ4nx07vBRPeSyJ3IgsVU6tDYocs8i1y2N6T2nM67LuPp3ktkuQgghxAXptOHDWlxM2W/bAPjOI5q+lWr5ywH/X+ni0YXHhzxe/06V5WBRC1IlfAghhBDnp9MWnJZu2ABVVRAZRb7Wn2CrFqvGwgn/Xbw39m2MTsb6d6rp9dA6gbN7m7ZXCCGEuFR02p6PmiGXrAGj6GuxAZDse4AZA29iYODAhu9Ut9hUNpUTQgghzkunDB+28nJKN/8KwPaQfvSuUl+Gkm5p/GXAXxq/oywwJoQQQlywThk+SjdtRqmowCk8nH1lGbhaXSh1LuRvU+/FWefc+B1lgTEhhBDignXK8FEz5GIZO4LwIisAxt42Yvxjzn5He/iQng8hhBDifHW68GGrrFSLTYH3jScJL+oJwK3XXnHuO8vqpkIIIcQF63Tho2zLFmxlZVT6epBrDkSDlhIvG77BTZi9IqubCiGEEBes04WPmo3k1neroFf2CAB8+wQ27c5ScCqEEEJcsE4VPpSqKnVJdeBwRBReZn/MKAwaFdq0E8jqpkIIIcQF61Tho3zHDmxFRRQZwUOnLp1+3GAjLrKJ+7TIbBchhBDignWq8JH0/ecA7OjpSlTeAABMXVww6HVNO0FN+JCCUyGEEOK8dZrwUWYupeznX9Rv+lyPxqohV2ujR0wzgoRJptoKIYQQF6rThI/M43vRV9kod9EQqL8MgIPOVgZGNHHIpbIcqsrVryV8CCGEEOet02ws1z1uDMVbfuX4lh3kfl2ODYVDzhYGdm1i+Kjp9dDqweDZeg0VQgghLnGdpucDwNPNF3NJdwAS9TbcvQyEerk07c7lsqmcEEII0RI6VfiwWm0c25YBwAGDhUFdfdA0NUjI6qZCCCFEi+hU4ePUgTxMJVVUOWlI0tsY2NW76XeW1U2FEEKIFtGpwseRrWqvxzEXGzYNTa/3gDrDLs24jxBCCCHq6TTho6zIzKmD6tDJdszotRriwryafgJZ3VQIIYRoEZ0mfOh0WoZdHYmxhwf5OoXYEE9cnZu4uBjI6qZCCCFEC+k04cPF3YkhV0aRFuMG0Lx6D5CCUyGEEKKFdJrwUWN3SgFwHuFDCk6FEEKIFtGpwofZYuVQejEAg5pTbAp1aj6k50MIIYS4EJ0qfBw+XUyl1YavmzNdfY3Nu7PUfAghhBAtolOFj90phQAMDPdu+uJiNcplUzkhhBCiJXSq8LGnut5jUFM3k6tRVQFVZerXUnAqhBBCXJBOFj4KAbXno1lqik01OnBpxtogQgghhKin04SP7OIK0gtNaDTQr7nho26xqWwqJ4QQQlwQfXs3oK14GZ34fNYIknLLcDc082nL6qZCCCFEi+k04cOg1zGimx8jup1HgJCZLkIIIUSLafawS3p6Orfddht+fn4YjUYGDBjArl277LfPnDkTjUbjcBkxYkSLNrrN2Vc3lU3lhBBCiAvVrJ6PgoICRo8ezYQJE1i1ahWBgYEkJibi7e3tcNwVV1zB4sWL7d87Ozu3SGPbjUmdJSM9H0IIIcSFa1b4eOmllwgPD3cIFpGRkfWOMxgMBAcHX3DjLhqyuqkQQgjRYpo17PL9998zZMgQbrjhBgIDAxk4cCDvv/9+veM2bNhAYGAgPXv25J577iE7O7vRc5rNZoqLix0uFx2p+RBCCCFaTLPCx8mTJ3n77beJjo5mzZo13HfffTz88MN8+OGH9mP++Mc/8sknn/DLL7/w6quvsmPHDiZOnIjZbG7wnAsWLMDLy8t+CQ8Pv7Bn1BpktosQQgjRYjSKoihNPdjZ2ZkhQ4awdetW+3UPP/wwO3bs4LfffmvwPhkZGURERPD5558zbdq0erebzWaHYFJcXEx4eDhFRUV4eno257m0nnfHQ8ZeuOUL6HVFe7dGCCGEuOgUFxfj5eXVpPfvZvV8hISE0Lt3b4frYmNjSUlJOet9IiIiSEhIaPB2g8GAp6enw+WiY5JhFyGEEKKlNCt8jB49mmPHjjlcd/z4cSIiIhq9T15eHqmpqYSEhJxfCy8GsqmcEEII0WKaFT4ee+wxtm3bxgsvvMCJEyf49NNPee+99/jLX/4CQGlpKbNnz+a3334jOTmZDRs2MGXKFPz9/bnuuuta5Qm0OosZKkvVryV8CCGEEBesWeFj6NChfPPNN3z22Wf07duXZ599ltdff51bb70VAJ1Ox4EDB5g6dSo9e/bkjjvuoGfPnvz22294eHi0yhNodTW9HhotGGRTOSGEEOJCNXt59auvvpqrr766wdtcXV1Zs2bNBTfqomJf3dQXtJ1mHz4hhBCi1ci76blIsakQQgjRoiR8nIusbiqEEEK0KAkf5yKrmwohhBAtSsLHudSED9nRVgghhGgREj7ORZZWF0IIIVqUhI9zkYJTIYQQokVJ+DgXKTgVQgghWpSEj3ORglMhhBCiRUn4OJe6i4wJIYQQ4oJJ+DgX6fkQQgghWpSEj7OxVEJlifq11HwIIYQQLULCx9mY6mwq5+Ldrk0RQgghLhUSPs6m7gJjsqmcEEII0SLkHfVspNhUCCGEaHESPs5GVjcVQgghWpyEj7OR1U2FEEKIFifh42zsPR+yqZwQQgjRUiR8nE15gfqv9HwIIYQQLUbCx9lIwakQQgjR4iR8nI0UnAohhBAtTsLH2UjBqRBCCNHiJHycjb3nQ4ZdhBBCiJYi4eNspOBUCCGEaHESPhpjrQJzkfq1FJwKIYQQLUbCR2Nq9nVBA67e7dkSIYQQ4pIi4aMxprqbyunaty1CCCHEJUTCR2Ok2FQIIYRoFRI+GlMu02yFEEKI1iDhozGyuqkQQgjRKiR8NEYWGBNCCCFahYSPxtiHXaTnQwghhGhJEj4aIwWnQgghRKuQ8NEYKTgVQgghWoWEj8ZIwakQQgjRKiR8NEYKToUQQohWIeGjMfaaDwkfQgghREuS8NEQqwUqqjeVk4JTIYQQokVJ+GiIqaD6Cw24eLdnS4QQQohLjoSPhtQMubh4gU7fvm0RQgghLjESPhoixaZCCCFEq5Hw0RBZYEwIIYRoNc0OH+np6dx22234+flhNBoZMGAAu3btst+uKArz5s0jNDQUV1dX4uPjOXToUIs2utXJTBchhBCi1TQrfBQUFDB69GicnJxYtWoVhw8f5tVXX8Xb29t+zMsvv8zChQtZtGgRO3bsIDg4mMsvv5ySkpKWbnvrkdVNhRBCiFbTrGrKl156ifDwcBYvXmy/LjIy0v61oii8/vrrPPXUU0ybNg2ApUuXEhQUxKeffsq9997bMq1ubfbVTX3atx1CCCHEJahZPR/ff/89Q4YM4YYbbiAwMJCBAwfy/vvv229PSkoiMzOTSZMm2a8zGAyMHz+erVu3tlyrW1vNVFvp+RBCCCFaXLPCx8mTJ3n77beJjo5mzZo13HfffTz88MN8+OGHAGRmZgIQFBTkcL+goCD7bWcym80UFxc7XNqdFJwKIYQQraZZwy42m40hQ4bwwgsvADBw4EAOHTrE22+/zZ/+9Cf7cRqNxuF+iqLUu67GggULmD9/fnPb3bqk4FQIIYRoNc3q+QgJCaF3794O18XGxpKSkgJAcHAwQL1ejuzs7Hq9ITXmzp1LUVGR/ZKamtqcJrUOKTgVQgghWk2zwsfo0aM5duyYw3XHjx8nIiICgKioKIKDg1m7dq399srKSjZu3MioUaMaPKfBYMDT09Ph0u7sBacy7CKEEEK0tGYNuzz22GOMGjWKF154gRtvvJHff/+d9957j/feew9Qh1seffRRXnjhBaKjo4mOjuaFF17AaDQyY8aMVnkCLc5hUznp+RBCCCFaWrPCx9ChQ/nmm2+YO3cu//d//0dUVBSvv/46t956q/2YOXPmYDKZeOCBBygoKGD48OH89NNPeHh4tHjjW0VFIaCoX8tUWyGEEKLFaRRFUdq7EXUVFxfj5eVFUVFR+wzB5ByD/wxTN5V7MqXtH18IIYTogJrz/i17u5xJik2FEEKIViXh40xSbCqEEEK0KgkfZzJJz4cQQgjRmiR8nElWNxVCCCFalYSPM8nqpkIIIUSrkvBxpvKaTeWk50MIIYRoDRI+ziQFp0IIIUSrkvBxJik4FUIIIVqVhI8zScGpEEII0aokfJxJCk6FEEKIViXhoy6bFUyF6tcSPoQQQohWIeGjLlMhsqmcEEII0bokfNRVU2xq8AKdU/u2RQghhLhESfioy17vIb0eQgghRGuR8FGXFJsKIYQQrU7CR13lssaHEEII0dokfNQlq5sKIYQQrU7CR12yuqkQQgjR6iR81CUFp0IIIUSrk/BRl31HW+n5EEIIIVqLhI+6ZLaLEEII0eokfNQlBadCCCFEq5PwUZcUnAohhBCtTsJHDZsNTDU1H9LzIYQQQrQWCR81KgpBsalfy7CLEEII0WokfNSoWd3U2QP0zu3bFiGEEOISJuGjhn2mi/R6CCGEEK1JwkcNKTYVQggh2oS+vRtw0ZCeDyFEC7JarVRVVbV3M4RoUU5OTuh0ugs+j4SPGrKjrRCiBSiKQmZmJoWFhe3dFCFahbe3N8HBwWg0mvM+h4SPGrLAmBCiBdQEj8DAQIxG4wX9gRbiYqIoCuXl5WRnZwMQEhJy3ueS8FFDllYXQlwgq9VqDx5+fvK3RFx6XF1dAcjOziYwMPC8h2Ck4LSGLDAmhLhANTUeRqOxnVsiROup+f2+kJomCR81pOBUCNFCZKhFXMpa4vdbwkcNKTgVQggh2oSEjxpScCqE6MTy8vIIDAwkOTm5TR83OTkZjUbD3r17z3pcfHw8jz76aJu0qb1FRkby+uuvt9r5Z8+ezcMPP9xq528KCR9Qvamc9HwIITqvBQsWMGXKFCIjI4HaUNDWNmzYgEajqTdV+euvv+bZZ5+1fx8fH8+SJUuafN72ej4XgyVLlhAfH2//fs6cOSxevJikpKR2a5OEDwBzUe2mclLzIYToZEwmEx988AF33313ezelUb6+vnh4eLR3My4JgYGBTJo0iXfeeafd2iDhA+psKucOekP7tkUIIdrYqlWr0Ov1jBw5stFjlixZgre3NytWrKBXr14YjUamT59OWVkZS5cuJTIyEh8fHx566CGsVqv9fhqNhm+//dbhXN7e3g32WiQnJzNhwgQAfHx80Gg0zJw5E2idYZctW7Ywfvx4jEYjPj4+TJ48mYICdeaj2Wzm4YcfJjAwEBcXF8aMGcOOHTvs963poVmzZg0DBw7E1dWViRMnkp2dzapVq4iNjcXT05NbbrmF8vJy+/3i4+N58MEHefDBB/H29sbPz49//OMfKIrSaDuLioqYNWsWgYGBeHp6MnHiRPbt2wdATk4OwcHBvPDCC/bjt2/fjrOzMz/99FOj57zmmmv47LPPzvu1u1CyzgfUKTaVXg8hRMtRFAVTlfXcB7YCVyddk4cZNm3axJAhQ855XHl5OW+88Qaff/45JSUlTJs2jWnTpuHt7c3KlSs5efIk119/PWPGjOGmm25qdpvDw8NZvnw5119/PceOHcPT09O+rkRL27t3L5dddhl33nknb7zxBnq9nvXr19uD05w5c1i+fDlLly4lIiKCl19+mcmTJ3PixAl8fWvfK+bNm8eiRYswGo3ceOON3HjjjRgMBj799FNKS0u57rrrePPNN3niiSfs91m6dCl33XUX27dvZ+fOncyaNYuIiAjuueeeeu1UFIWrrroKX19fVq5ciZeXF++++y6XXXYZx48fJyAggP/9739ce+21TJo0iZiYGG677TYeeOABJk2a1OjzHzZsGKmpqZw6dYqIiIgWfGWbRsIHSLGpEKJVmKqs9P7nmnZ57MP/Nxmjc9P+xCcnJxMaGupwXWRkZL1P41VVVbz99tt0794dgOnTp/PRRx+RlZWFu7s7vXv3ZsKECaxfv/68wodOp7O/sQcGBuLt7d3osRs2bGjWuc98Pi+//DJDhgzhrbfesl/Xp08fAMrKynj77bdZsmQJf/zjHwF4//33Wbt2LR988AGPP/64/T7PPfcco0ePBuCuu+5i7ty5JCYm0q1bN0B9jdavX+8QPsLDw3nttdfQaDT06tWLAwcO8NprrzUYPtavX8+BAwfIzs7GYFB75v/1r3/x7bff8tVXXzFr1iyuvPJK7rnnHm699VaGDh2Ki4sLL774ov0cM2fOtPcg1QgLCwPUn317hA8ZdgFZ3VQI0amZTCZcXFzOeZzRaLQHD4CgoCAiIyNxd3d3uK5m+e2LWU3PR0MSExOpqqqyhwpQN1QbNmwYR44ccTi2X79+9q+DgoIwGo324FFz3Zmvx4gRIxx6pUaOHElCQoLDcFWNXbt2UVpaip+fH+7u7vZLUlISiYmJ9uP+9a9/YbFYWLZsGZ988sk5f541PUp1h4TaUrN6PubNm8f8+fMdrgsKCiIzMxNQ09XSpUsdbh8+fDjbtm27wGa2MpnpIoRoBa5OOg7/3+R2e+ym8vf3t9c6nI2Tk5PD9xqNpsHrbDabw/cN9aC0t7MN59S098xhK0VR6l1X9/k35fVoLpvNRkhISIM9PXV7hk6ePMnp06ex2WycOnXKIRQ1JD9ffd8LCAg477ZdiGYPu/Tp04d169bZvz9zXfcrrriCxYsX2793dna+gOa1EVndVAjRCjQaTZOHPtrTwIED+fjjj1vl3AEBAWRkZNi/T0hIOOun7Zr3jIZ6AVpSv379+Pnnn+t9oAbo0aMHzs7O/Prrr8yYMQNQA9POnTtbpOj1zA/k27ZtIzo6usF9UgYNGkRmZiZ6vd4+DfpMlZWV3Hrrrdx0003ExMRw1113ceDAAYKCghptw8GDB3FycrIPNbW1Zg+76PV6goOD7ZczU5PBYHC4vW5hzkVLVjcVQnRikydP5tChQ03q/WiuiRMnsmjRInbv3s3OnTu577776vUO1BUREYFGo2HFihXk5ORQWlra4m0CmDt3Ljt27OCBBx5g//79HD16lLfffpvc3Fzc3Ny4//77efzxx1m9ejWHDx/mnnvuoby8nLvuuuuCHzs1NZW//vWvHDt2jM8++4w333yTRx55pMFj//CHPzBy5EiuvfZa1qxZQ3JyMlu3buUf//gHO3fuBOCpp56iqKiIN954gzlz5hAbG3vOdm7evJmxY8e2WkHvuTQ7fCQkJBAaGkpUVBQ333wzJ0+edLh9w4YNBAYG0rNnT+65555zjv2ZzWaKi4sdLm3OXnDq0/aPLYQQ7SwuLo4hQ4awbNmyFj/3q6++Snh4OOPGjWPGjBnMnj37rBvvhYWFMX/+fJ588kmCgoJ48MEHm/Q4M2fOdFhI61x69uzJTz/9xL59+xg2bBgjR47ku+++Q69Xe6pefPFFrr/+em6//XYGDRrEiRMnWLNmDT4+F/4+8ac//QmTycSwYcP4y1/+wkMPPcSsWbMaPFaj0bBy5UrGjRvHnXfeSc+ePbn55ptJTk4mKCiIDRs28Prrr/PRRx/h6emJVqvlo48+4tdff+Xtt99utA2fffZZgwWubUWjnG1y8RlWrVpFeXk5PXv2JCsri+eee46jR49y6NAh/Pz8+OKLL3B3dyciIoKkpCSefvppLBYLu3btslfpnqmhOhJQ5zV7enqe/zNrjv/9EVK2wvTF0Hda2zymEOKSU1FRQVJSElFRUU0q4LyYrFy5ktmzZ3Pw4EG02o43FyE+Pp74+HjmzZvX3k05q/j4eAYMGNCqy6efy48//sjjjz/O/v377WGrORr7PS8uLsbLy6tJ79/NetSaKUegJuWRI0fSvXt3li5dyl//+leHqVV9+/ZlyJAhRERE8OOPPzJtWsNv6nPnzuWvf/2rQ+PDw8Ob06wLJwWnQohO7sorryQhIYH09PS2/xt8gUpKSkhMTGTFihXt3ZQOoaysjMWLF59X8GgpF/TIbm5uxMXFkZCQ0ODtISEhRERENHo7qDUijfWKtBkpOBVCiEbrDi52Hh4epKamtnczOowbb7yxvZtwYeHDbDZz5MgRxo4d2+DteXl5pKamEhISciEP07oURQpOhRBCtInmLo52qWrWwN7s2bPZuHEjSUlJbN++nenTp1NcXMwdd9xBaWkps2fP5rfffiM5OZkNGzYwZcoU/P39ue6661qr/ReuogiU6ildssKpEEII0eqa1fORlpbGLbfcQm5uLgEBAYwYMYJt27YRERGByWTiwIEDfPjhhxQWFhISEsKECRP44osvLu6dCGuGXJzcwKljFYgJIYQQHVGzwsfnn3/e6G2urq6sWdM+exhcEFP1vHYZchFCCCHaRMebT9XS7MWmssaHEEII0RYkfEixqRBCCNGmJHzYVzeVYlMhhBCiLUj4kAXGhBCCvLw8AgMDSU5ObtPHTU5ORqPRsHfv3rMeFx8f3yKbul2Khg4dytdff93ezWgWCR/2mg8JH0KIzmvBggVMmTLFvnNqTShoaxs2bECj0VBYWOhw/ddff82zzz5r/z4+Pp4lS5Y0+bzt9XwuRGOvxcyZMx2WkX/66ad58sknsdlsbdvACyDhQ1Y3FUJ0ciaTiQ8++IC77767vZvSKF9f34t72YZ2dNVVV1FUVNShZpxK+CivmWor4UMI0TmtWrUKvV7PyJEjGz1myZIleHt7s2LFCnr16oXRaGT69OmUlZWxdOlSIiMj8fHx4aGHHsJqtdrvp9Fo+Pbbbx3O5e3t3WCvRXJyMhMmTADAx8cHjUbDzJkzgdYZdtmyZQvjx4/HaDTi4+PD5MmTKShQ3xPMZjMPP/wwgYGBuLi4MGbMGHbs2GG/b02vxM8//8yQIUMwGo2MGjWKY8eOOTzG999/z5AhQ3BxccHf399hn7OPP/6YIUOG4OHhQXBwMDNmzLDvBH+21+JMOp2OK6+8ks8++6wlX55WJeFDCk6FEK1FUaCyrH0uTd+wnE2bNjFkyJBzHldeXs4bb7zB559/zurVq9mwYQPTpk1j5cqVrFy5ko8++oj33nuPr7766rxervDwcJYvXw7AsWPHyMjI4N///vd5netc9u7dy2WXXUafPn347bff+PXXX5kyZYo9OM2ZM4fly5ezdOlSdu/eTY8ePZg8eTL5+fkO53nqqad49dVX2blzJ3q9njvvvNN+W82mqldddRV79uyxB5UalZWVPPvss+zbt49vv/2WpKQke8Bo7msxbNgwNm/e3FIvT6trvy3tLhZScCqEaC1V5fBCaPs89t9Pg7Nbkw5NTk4mNNSxnZGRkShnBJiqqirefvttunfvDsD06dP56KOPyMrKwt3dnd69ezNhwgTWr1/vsMt5U+l0Onx91Q+CgYGBeHt7N3psc/dIOfP5vPzyywwZMoS33nrLfl2fPn0AddfXt99+myVLlth3c3///fdZu3YtH3zwAY8//rj9Ps8//zzjx48H4Mknn+Sqq66ioqICFxcXnn/+eW6++Wbmz59vP75///72r+sGlW7duvHGG28wbNgwSktLcXd3b/S1aKjXKCwsjJSUFGw2G1rtxd+vcPG3sDUpihScCiE6PZPJhIvLubeXMBqN9uABEBQURGRkJO7u7g7X1QwdXMxqej4akpiYSFVVFaNHj7Zf5+TkxLBhwzhy5IjDsf369bN/XbOJas3zP9tjAOzZs4epU6cSERGBh4cH8fHxAKSkpDT7+bi6umKz2TCbzc2+b3vo3D0f5mKwWdSvpeZDCNHSnIxqD0R7PXYT+fv722sdznpKJyeH7zUaTYPX1Z11odFoGuxBaW+urq6N3lbT3jNnxyiKUu+6us+/5raa53+2xygrK2PSpElMmjSJjz/+mICAAFJSUpg8eTKVlZXNezJAfn4+RqPxrI95MencPR81q5s6GcGpY/zAhBAdiEajDn20x6UZ00oHDhzI4cOHW+UlCAgIICMjw/59QkIC5eXljR7v7OwM4FC02hr69evHzz//3OBtPXr0wNnZmV9//dV+XVVVFTt37iQ2NrZFHuPo0aPk5uby4osvMnbsWGJiYur1GDXntTh48CCDBg1qctvam4QPkGJTIUSnNnnyZA4dOtSk3o/mmjhxIosWLWL37t3s3LmT++67r15vSV0RERFoNBpWrFhBTk4OpaWlLd4mgLlz57Jjxw4eeOAB9u/fz9GjR3n77bfJzc3Fzc2N+++/n8cff5zVq1dz+PBh7rnnHsrLy7nrrrua/BjPPPMMn332Gc888wxHjhzhwIEDvPzyywB07doVZ2dn3nzzTU6ePMn333/vsI4JNO+12Lx5M5MmTTq/F6MddO7wYS82lfAhhOi84uLiGDJkCMuWLWvxc7/66quEh4czbtw4ZsyYwezZszEaGx8SCgsLY/78+Tz55JMEBQXx4IMPNulxZs6caa+ZaIqePXvy008/sW/fPoYNG8bIkSP57rvv0OvVaoQXX3yR66+/nttvv51BgwZx4sQJ1qxZg49P0zchjY+P58svv+T7779nwIABTJw4ke3btwNqj9CSJUv48ssv6d27Ny+++CL/+te/HO7f1NciPT2drVu38uc//7nJbWtvGuXMwbh2VlxcjJeXF0VFRXh6erbug+37HL65F7rFw5++a93HEkJc8ioqKkhKSiIqKqpJBZwXk5UrVzJ79mwOHjzYIWZLnCk+Pp74+HiHlT87i8cff5yioiLee++9Nnm8xn7Pm/P+3bkLTmWmixBCAHDllVeSkJBAeno64eHh7d2cZikpKSExMZEVK1a0d1PaRWBgILNnz27vZjRLJw8fssaHEELUeOSRR9q7CefFw8OD1NTU9m5Gu6m77khH0fH61lqSrG4qhBBCtLnOHT5kdVMhhBCizXXu8FEus12EEEKIttbJw0dNwamEDyGEEKKtdPLwIcMuQgghRFvrvOGj7qZyUnAqhBBCtJnOGz4qS8FWvbmR9HwIIYQQbabzho+aXg+9Czg3ffdHIYS4FOXl5REYGEhycnJ7N0WcpxUrVjBw4ECHXYUvVhI+pNdDCCFYsGABU6ZMITIyEoDk5GSH7eOXLFmCt7d3iz9uY+eNj49nyZIlTT7Pme3tDM58zldffTUajYZPP/20HVvVNJ04fFTv3igzXYQQnZzJZOKDDz7g7rvvbu+mdHhVVVXt+vh//vOfefPNN9u1DU3RicOHFJsKIQTAqlWr0Ov1jBw5ssHbN2zYwJ///GeKiorQaDRoNBr7Bm6VlZXMmTOHsLAw3NzcGD58OBs2bADUDcj69OnDrFmz7OdKSkrCy8uL999//6znvVB5eXnccsstdOnSBaPRSFxcHJ999pn99nfffZewsLB6QxTXXHMNd9xxh/37H374gcGDB+Pi4kK3bt2YP38+FovFfrtGo+Gdd95h6tSpuLm58dxzz2G1WrnrrruIiorC1dWVXr168e9//9vhcSwWCw8//DDe3t74+fnxxBNPcMcdd3Dttdfaj1EUhZdffplu3brh6upK//79+eqrr876vK+55hp+//13Tp48eT4vW9tRLjJFRUUKoBQVFbXuA/32lqI846koy2a27uMIIToNk8mkHD58WDGZTIqiKIrNZlPKKsva5WKz2Zrc7kceeUS54oorHK5LSkpSat4izGaz8vrrryuenp5KRkaGkpGRoZSUlCiKoigzZsxQRo0apWzatEk5ceKE8sorrygGg0E5fvy4oiiKsmfPHsXZ2Vn55ptvFIvFoowePVqZOnXqOc87fvx4ZfHixU1+DnXbqyiKkpaWprzyyivKnj17lMTEROWNN95QdDqdsm3bNkVRFCUvL09xdnZW1q1bZ79Pfn6+4uzsrKxZs0ZRFEVZvXq14unpqSxZskRJTExUfvrpJyUyMlKZN2+e/T6AEhgYqHzwwQdKYmKikpycrFRWVir//Oc/ld9//105efKk8vHHHytGo1H54osv7Pd77rnnFF9fX+Xrr79Wjhw5otx3332Kp6en/bVRFEX5+9//rsTExCirV69WEhMTlcWLFysGg0HZsGFDg8+5RmBgoLJkyZImv3bNdebveY3mvH933o3lZIExIUQrM1lMDP90eLs89vYZ2zE6Na2YPjk5mdDQUIfrIiMjURQFAGdnZ7y8vNBoNAQHB9uPSUxM5LPPPiMtLc1+/9mzZ7N69WoWL17MCy+8wIABA3juuee45557uOWWW0hMTOTbb78963kBe+9JU9VtL0BYWJjDTq8PPfQQq1ev5ssvv2T48OH4+vpyxRVX8Omnn3LZZZcB8OWXX+Lr62v//vnnn+fJJ5+094R069aNZ599ljlz5vDMM8/Yzz1jxgzuvPNOh/bMnz/f/nVUVBRbt25l2bJl3HjjjQC8+eabzJ07l+uuuw6ARYsWsXLlSvt9ysrKWLhwIb/88ou9R6pbt278+uuvvPvuu4wfP77ec6773C/2wmEJH1JwKoTo5EwmEy4uLs2+3+7du1EUhZ49ezpcbzab8fOr/dv6t7/9je+++44333yTVatW4e/vf8FtPher1cqLL77IF198QXp6OmazGbPZjJubm/2YW2+9lVmzZvHWW29hMBj45JNPuPnmm9HpdADs2rWLHTt28Pzzzzuct6KigvLycoxGNdwNGTKk3uO/8847/Pe//+XUqVOYTCYqKysZMGAAAEVFRWRlZTFs2DD78TqdjsGDB9uHgQ4fPkxFRQWXX365w3krKysZOHDgWZ+7q6sr5eXlzXi12l4nDh+yuqkQonW56l3ZPmN7uz12U/n7+1NQUNDsx7DZbOh0Onbt2mV/w67h7u5u/zo7O5tjx46h0+lISEjgiiuuaPZjNderr77Ka6+9xuuvv05cXBxubm48+uijVFZW2o+ZMmUKNpuNH3/8kaFDh7J582YWLlzo8Pzmz5/PtGnT6p2/blirG2gAli1bxmOPPcarr77KyJEj8fDw4JVXXmH7dsffhTNn59TtxagJIT/++CNhYWEOxxkMhrM+9/z8fAICAs56THvrxOFDCk6FEK1Lo9E0eeijPQ0cOJCPP/74rMc4OztjtVrr3c9qtZKdnc3YsWMbve+dd95J3759ueeee7jrrru47LLL6N27d6PnbQmbN29m6tSp3HbbbYD6Zp6QkEBsbKz9GFdXV6ZNm8Ynn3zCiRMn6NmzJ4MHD7bfPmjQII4dO0aPHj2a/dijRo3igQcesF+XmJho/9rLy4ugoCB+//13++tmtVrZs2ePvXekd+/eGAwGUlJSGD9+fJMfu6KigsTExHP2jrS3zhs+TDLVVgghACZPnszcuXMpKCjAx8enwWMiIyMpLS3l559/pn///hiNRnr27Mmtt97Kn/70J1599VUGDhxIbm4uv/zyC3FxcVx55ZX85z//4bfffmP//v2Eh4ezatUqbr31VrZv346zs3OD560ZzrgQPXr0YPny5WzduhUfHx8WLlxIZmamQ/gAdehlypQpHDp0yB5Uavzzn//k6quvJjw8nBtuuAGtVsv+/fs5cOAAzz333Fkf+8MPP2TNmjVERUXx0UcfsWPHDqKiouzHPPTQQyxYsIAePXoQExPDm2++SUFBgb03xMPDg9mzZ/PYY49hs9kYM2YMxcXFbN26FXd3d4cZOXVt27YNg8HQ6Myli0YrFMJekDab7fKvXupsl/Tdrfs4QohOo7FZAB3BiBEjlHfeeeesx9x3332Kn5+fAijPPPOMoiiKfWZHZGSk4uTkpAQHByvXXXedsn//fuXIkSOKq6ur8umnn9rPUVRUpERGRipz5sw563nPdMcddyjjx49v8vPJy8tTpk6dqri7uyuBgYHKP/7xD+VPf/qTw2wSRVEUi8WihISEKICSmJhY7zyrV69WRo0apbi6uiqenp7KsGHDlPfee89+O6B88803DvepqKhQZs6cqXh5eSne3t7K/fffrzz55JNK//797cdUVVUpDz74oOLp6an4+PgoTzzxhHLDDTcoN998s/0Ym82m/Pvf/1Z69eqlODk5KQEBAcrkyZOVjRs3Nvq8Z82apdx7771Nfp3OR0vMdtEoSgOlsu2ouLgYLy8vioqK8PT0bJ0HURR4LhCslfDoAfDu2jqPI4ToVCoqKkhKSiIqKuq8Cjjb08qVK5k9ezYHDx5Eq734loCKj48nPj6+xdYBudjYbDZiY2O58cYbefbZZ8/rHDk5OcTExLBz506HXpaW1tjveXPevzvnsEtlmRo8QApOhRACuPLKK0lISCA9PZ3w8PD2bo6DkpISEhMTWbFiRXs3pcWcOnWKn376ifHjx2M2m1m0aBFJSUnMmDHjvM+ZlJTEW2+91arBo6V0zvBRU2yqM0AHKAYTQoi28Mgjj7R3Exrk4eFBampqezejRWm1WpYsWcLs2bNRFIW+ffuybt26ejUpzTFs2DCH6bsXs84ZPkx1ptl2so2IhBBCtL/w8HC2bNnS3s1oNxffwF5bkNVNhRBCiHbTScNHTc+HhA8hhBCirXXy8CHFpkIIIURba1b4mDdvnn3b45pL3c2AFEVh3rx5hIaG4urqSnx8PIcOHWrxRl8wWd1UCCGEaDfN7vno06cPGRkZ9suBAwfst7388sssXLiQRYsWsWPHDoKDg7n88sspKSlp0UZfMJP0fAghhBDtpdnhQ6/XExwcbL/UbF6jKAqvv/46Tz31FNOmTaNv374sXbqU8vJyPv300xZv+AWRglMhhBCi3TQ7fCQkJBAaGkpUVBQ333wzJ0+eBNTFTTIzM5k0aZL9WIPBwPjx49m6dWuj5zObzRQXFztcWp3UfAghhIO8vDwCAwNJTk5u08dNTk5Go9Gwd+/esx4XHx/Po48+2iZtulStWLGCgQMH2nfMbU/NCh/Dhw+3b5bz/vvvk5mZyahRo8jLyyMzMxOAoKAgh/sEBQXZb2vIggUL8PLysl/aZGU9me0ihBAOFixYwJQpU4iMjARqQ0Fb27BhAxqNhsLCQofrv/76a4dlx+Pj41myZEmTz9tez6c9nfmcr776ajQazUUxGtGs8PHHP/6R66+/nri4OP7whz/w448/ArB06VL7MWf+cBVFOesPfO7cuRQVFdkvbbKKnRScCiGEnclk4oMPPuDuu+9u76Y0ytfXFw8Pj/ZuxgWrqqpq18f/85//zJtvvtmubYALnGrr5uZGXFwcCQkJ9lkvZ/ZyZGdn1+sNqctgMODp6elwaVWKIgWnQghRx6pVq9Dr9Wfdhn3JkiV4e3uzYsUKevXqhdFoZPr06ZSVlbF06VIiIyPx8fHhoYcewmq12u+n0Wj49ttvHc7l7e3dYK9FcnIyEyZMAMDHxweNRsPMmTOBlh92ycvL45ZbbqFLly4YjUbi4uL47LPP7Le/++67hIWF1RuiuOaaaxy2s//hhx8YPHgwLi4udOvWjfnz52OxWOy3azQa3nnnHaZOnYqbmxvPPfccVquVu+66i6ioKFxdXenVqxf//ve/HR7HYrHw8MMP4+3tjZ+fH0888QR33HEH1157rf0YRVF4+eWX6datG66urvTv35+vvvrqrM/7mmuu4ffff7eXTLSXC1pe3Ww2c+TIEcaOHUtUVBTBwcGsXbuWgQMHAlBZWcnGjRt56aWXWqSxLaKqHCwV6tcy7CKEaEWKoqCYTO3y2BpX1yYPM2zatIkhQ4ac87jy8nLeeOMNPv/8c0pKSpg2bRrTpk3D29ublStXcvLkSa6//nrGjBnDTTfd1Ow2h4eHs3z5cq6//nqOHTuGp6cnrq6uzT5PU1RUVDB48GCeeOIJPD09+fHHH7n99tvp1q0bw4cP54YbbuDhhx9m/fr1XHbZZQAUFBSwZs0afvjhBwDWrFnDbbfdxhtvvMHYsWNJTExk1qxZADzzzDP2x3rmmWdYsGABr732GjqdDpvNRpcuXVi2bBn+/v5s3bqVWbNmERISwo033gjASy+9xCeffMLixYuJjY3l3//+N99++609nAH84x//4Ouvv+btt98mOjqaTZs2cdtttxEQEMD48eMbfN4REREEBgayefNmunXr1iqvbVM0K3zMnj2bKVOm0LVrV7Kzs3nuuecoLi7mjjvuQKPR8Oijj/LCCy8QHR1NdHQ0L7zwAkaj8YJ26WtxNfUeOmdwdm/ftgghLmmKycSxQYPb5bF77d6Fxti0jTOTk5MJDQ11uC4yMhJFURyuq6qq4u2336Z79+4ATJ8+nY8++oisrCzc3d3p3bs3EyZMYP369ecVPnQ6Hb6+6ofCwMBAvL29Gz12w4YNzTr3mc8nLCyM2bNn279/6KGHWL16NV9++SXDhw/H19eXK664gk8//dQePr788kt8fX3t3z///PM8+eST9p6Qbt268eyzzzJnzhyH8DFjxgzuvPNOh/bMnz/f/nVUVBRbt25l2bJl9vDx5ptvMnfuXK677joAFi1axMqVK+33KSsrY+HChfzyyy/2Hqtu3brx66+/8u677zJ+/PgGf4Y1z72tC4vP1KzwkZaWxi233EJubi4BAQGMGDGCbdu2ERERAcCcOXMwmUw88MADFBQUMHz4cH766aeLa5zOPs1WNpUTQghQaz5cXFzOeZzRaLQHD1AnFERGRuLu7u5wXXZ2dqu0syVZrVZefPFFvvjiC9LT0zGbzZjNZtzc3OzH3HrrrcyaNYu33noLg8HAJ598ws0334xOpwNg165d7Nixg+eff97hvBUVFZSXl2OsDn8N9Sq98847/Pe//+XUqVOYTCYqKysZMGAAAEVFRWRlZTnsUKvT6Rg8eLB9GOjw4cNUVFRw+eWXO5y3srLSPvrQGFdXV8rLy5vxarW8ZoWPzz///Ky3azQa5s2bx7x58y6kTa1Lik2FEG1E4+pKr9272u2xm8rf35+CgoJzHufk5OT4GBpNg9fVrZPQaDQN9qC0t1dffZXXXnuN119/nbi4ONzc3Hj00UeprKy0HzNlyhRsNhs//vgjQ4cOZfPmzSxcuNB+u81mY/78+UybNq3e+euGubqBBmDZsmU89thjvPrqq4wcORIPDw9eeeUVtm/f7nBcQxM46j42wI8//khYWJjDcQaD4azPPT8/375GV3u5oJqPDslU/R9M6j2EEK1Mo9E0eeijPQ0cOJCPP/64Vc4dEBBARkaG/fuEhISzfup2dnYGcChabQ2bN29m6tSp3HbbbYD6Zp6QkEBsbKz9GFdXV6ZNm8Ynn3zCiRMn6NmzJ4MH1w6jDRo0iGPHjtGjR49mP/aoUaN44IEH7NclJibav/by8iIoKIjff/+dsWPHAurrsWfPHnvvSO/evTEYDKSkpDRa39GQiooKEhMTz9k70to6X/iQ1U2FEMLB5MmTmTt3LgUFBfj4+LTouSdOnMiiRYsYMWIENpuNJ554ol5vSV0RERFoNBpWrFjBlVdeiaurq8OwTkvp0aMHy5cvZ+vWrfj4+LBw4UIyMzMdwgeoQy9Tpkzh0KFD9qBS45///CdXX3014eHh3HDDDWi1Wvbv38+BAwd47rnnzvrYNWtmRUVF8dFHH7Fjxw6ioqLsxzz00EMsWLCAHj16EBMTw5tvvklBQYG9N8TDw4PZs2fz2GOPYbPZGDNmDMXFxWzduhV3d3eHGTl1bdu2DYPBcNaZTW2h8+1qK6ubCiGEg7i4OIYMGcKyZcta/Nyvvvoq4eHhjBs3jhkzZjB79mx7LURDwsLCmD9/Pk8++SRBQUE8+OCDTXqcmTNnEh8f3+R2Pf300wwaNIjJkycTHx9PcHCwwzTWGhMnTsTX15djx47VmzwxefJkVqxYwdq1axk6dCgjRoxg4cKF9jrIxtx3331MmzaNm266ieHDh5OXl+fQCwLwxBNPcMstt/CnP/2JkSNH4u7uzuTJkx2Gc5599ln++c9/smDBAmJjY5k8eTI//PCDQ4g502effcatt9561p9BW9AoDZXCtqPi4mK8vLwoKipqnTU/fpwNO96HsbPhsqdb/vxCiE6roqKCpKQkoqKimlTAeTFZuXIls2fP5uDBg2i1He9zaXx8PPHx8Rd3zeEFsNlsxMbGcuONNzqs9NocOTk5xMTEsHPnzrMGlHNp7Pe8Oe/fnXjYRXo+hBCixpVXXklCQgLp6elts81FCyopKSExMZEVK1a0d1NazKlTp/jpp58YP348ZrOZRYsWkZSUdEFLVyQlJfHWW29dUPBoKZ0vfMjqpkII0aBHHnmkvZtwXjw8PNpma442pNVqWbJkCbNnz0ZRFPr27cu6devq1aQ0x7Bhwxym77anzhc+pOBUCCHERS48PJwtW7a0dzNaTccb2LtQ5TLVVgghhGhPnTB8yCJjQgghRHvqXOGjshws1Zs8Sc2HEEII0S46V/ioKTbVOoHhItpvRgghhOhEOlf4qFtsKpvKCSGEEO2ik4UPmWYrhBBCtLdOFj6k2FQIIRqSl5dHYGAgycnJDd6+ZMkSvL2927RNom0tWrSIa665pk0eq5OFj5qeDwkfQghR14IFC5gyZQqRkZEAJCcnO2zpftNNN3H8+PF2al3zRUZGsmHDhiYfv2HDBvtz7yzOfM733HMPO3bs4Ndff231x+5c4UNWNxVCiHpMJhMffPABd999d6PHuLq6EhgY2Iat6pyqqqra7bENBgMzZszgzTffbPXH6lzhQ1Y3FUKIelatWoVerz/rNutnDrvMmzePAQMG8L///Y+uXbvi7u7O/fffj9Vq5eWXXyY4OJjAwECef/55h/MsXLiQuLg43NzcCA8P54EHHqC0tNThmPfff5/w8HCMRiPXXXcdCxcurDfk88MPPzB48GBcXFzo1q0b8+fPx2KxXPBrUSMxMZGpU6cSFBSEu7s7Q4cOZd26dfbb586dy4gRI+rdr1+/fjzzzDP27xcvXkxsbCwuLi7ExMTw1ltv2W+r6V1atmwZ8fHxuLi48PHHH5OXl8ctt9xCly5dMBqNxMXF8dlnnzk8TklJCbfeeitubm6EhITw2muvER8fz6OPPmo/prKykjlz5hAWFoabmxvDhw8/Z2/QNddcw7fffovJZGrmK9Y8nSx8SM+HEKLtKIpCldnaLpfmbFi+adMmhgwZ0uznl5iYyKpVq1i9ejWfffYZ//vf/7jqqqtIS0tj48aNvPTSS/zjH/9g27Zt9vtotVreeOMNDh48yNKlS/nll1+YM2eO/fYtW7Zw33338cgjj7B3714uv/zyegFmzZo13HbbbTz88MMcPnyYd999lyVLltQ77kKUlpZy5ZVXsm7dOvbs2cPkyZOZMmUKKSkpANx6661s376dxMRE+30OHTrEgQMHuPXWWwE1RD311FM8//zzHDlyhBdeeIGnn36apUuXOjzWE088wcMPP8yRI0eYPHkyFRUVDB48mBUrVnDw4EFmzZrF7bffzvbt2+33+etf/8qWLVv4/vvvWbt2LZs3b2b37t0O5/3zn//Mli1b+Pzzz9m/fz833HADV1xxBQkJCY0+7yFDhlBVVcXvv/9+wa/h2WiU5vyGtoHmbMnbbB9eCyfXw7XvwIBbWvbcQohO78ytxqvMVt57ZGO7tGXWv8fjZNA16dhrr70WPz8/Pvjgg0aPWbJkCY8++iiFhYWA2vPxyiuvkJmZiYeHum7SFVdcwbFjx0hMTESrVT/bxsTEMHPmTJ588skGz/vll19y//33k5ubC8DNN99MaWmpww61t912GytWrLA/9rhx4/jjH//I3Llz7cd8/PHHzJkzh9OnTzfpOZ+PPn36cP/99/Pggw8C0L9/f6ZPn87TTz8NwN///nfWrVtnf+Pu2rUrL730ErfcUvt+89xzz7Fy5Uq2bt1KcnIyUVFRvP766+fc1O+qq64iNjaWf/3rX5SUlODn58enn37K9OnTASgqKiI0NJR77rmH119/ncTERKKjo0lLSyM0NNR+nj/84Q8MGzaMF154odHH8vX15bXXXuOOO+5o8PYzf89r/H97dx7U5PXuAfwblrAVKFZWkUWsUCtDARcQBa8LCAWxtirVsrTorb0uRa2VtpeKbaeihdCr1tIFQUfH2hZ0HNfisIjiwhIrLiAjVBmBohYB2QPn/uEvmQaSQDAJSXw+M8yQ5JzznoeHd94nJ++bV57j9/N1YznRxy608kEIIUIdHR1iB5GhcnJyEhUeAGBtbQ1dXV1R4SF8rrGxUfQ4Ly8PX3/9NW7evImWlhYIBAJ0dnaira0NJiYmqKysxBtvvCG2nalTp4oVI6WlpSguLhZb6ejt7UVnZyfa29thbGwsdyz9tbW1YevWrTh+/Djq6uogEAjQ0dEhWvkAnq5+7N27FwkJCWCM4dChQ6KPPR48eIDa2lrExsZi5cqVoj4CgQDm5uZi2+q/6tTb24ukpCQcPnwY9+/fR1dXF7q6umBiYgIAqK6uRk9Pj9gdas3NzeHq6ip6XFZWBsYYJkyYIDZ2V1cXXnpJ9jHQyMgI7e3tQ/grDd/zVXx0CG8qR8UHIUT59Lg6+O//CxixbQ/V6NGj0dTUJPc29PX1xR5zOByJz/X19QEA7t69i5CQEKxatQpffvklRo0ahfPnzyM2NlZ0oiVjTOwqG+Fz/9bX14etW7di0aJFA+Y0nCJKkk2bNuHMmTNITk7G+PHjYWRkhLfeegvd3d2iNsuWLUN8fDzKysrQ0dGB2tpaREREiOYIPP3oZdq0aWJj6+qKr0gJiwqhlJQUpKam4ttvvxWdHxMXFyfatvDvIevv1NfXB11dXZSWlg7Y3gsvvCAz9n/++QeWlpYy2zyr56v4EK18WIzsPAghzwUOhzPkjz5GkqenJw4cOKD07ZSUlEAgECAlJUW0OvLrr7+KtXFzcxtwvkFJSYnYYy8vL1RWVmL8+PFKm2thYSFiYmJEqzBPnjwZ8B0o9vb28Pf3x8GDB9HR0YG5c+fC2toawNMVnzFjxqC6ulp0Dog82w4PD8c777wD4GkhUVVVhVdeeQUA4OLiAn19fVy5cgVjx44F8PQjj6qqKgQEPC12PT090dvbi8bGRsycOXPI275z5w46Ozvh6ekp15zl9fwUHz0dQM9/lpFo5YMQQkSCgoLwySefoKmpCRYWyntz5uLiAoFAgF27diEsLAwXLlxAWlqaWJu1a9fC398fPB4PYWFhyM3NxalTp8Te5X/++ecIDQ3F2LFjsXjxYujo6ODatWsoLy/HV199pZC5jh8/HtnZ2QgLCwOHw0FCQoJoNePfli9fjsTERHR3dyM1NVXstcTERKxbtw5mZmYIDg5GV1cXSkpK0NTUhA0bNsjcdlZWFoqKimBhYQEej4eGhgZR8WFqaoro6Ghs2rQJo0aNgpWVFbZs2QIdHR3R32nChAlYvnw5oqKikJKSAk9PTzx8+BC5ublwd3dHSEiIxG0XFhZi3LhxcHFxGe6fbkien6tdGAP+638Bn/8BDBR8IishhGgwd3d3TJ48ecAqhKK99tpr4PF42L59OyZNmoSDBw9i27ZtYm38/PyQlpYGHo8HDw8PnD59GuvXrxf7OCUoKAjHjx9HTk4OpkyZAh8fH/B4PDg6Okrd9qxZsxATEzPkuaampsLCwgLTp09HWFgYgoKC4OXlNaDd4sWL8ejRI7S3t2PhwoVir61YsQI///wzMjMz4e7ujoCAAGRmZsLZ2VnmthMSEuDl5YWgoCDMmjULNjY2A8bm8Xjw9fVFaGgo5s6dCz8/P9ElvUIZGRmIiorCxo0b4erqigULFuDy5cui1RJJDh06JHaOirI8X1e7EEKIEkm7CkATnDx5Eh999BGuX78udsKoOli5ciUqKipQWFg47DGcnJyQmJgoVwGiSdra2jBmzBikpKQgNjZ2WGNcv34dc+bMwe3btwecFPtvdLULIYQQhQgJCUFVVRXu378v852xKiQnJ2PevHkwMTHBqVOnsG/fPrEv55JXRUUFTE1NERUVpcBZjiw+n4+KigpMnToVzc3N+OKLLwAA4eHhwx6zrq4O+/fvl1l4KAoVH4QQQgBg0O+aUJUrV65gx44daG1txbhx47Bz506ZX/0+GDc3N5SXlytwhuohOTkZlZWV4HK58Pb2RmFhIUaPHj3s8QIDAxU4O9mo+CCEEKJWlH3uiTbw9PREaWnpSE9j2NTrgz1CCCGEaD0qPgghhBCiUlR8EEKIgkn6PghCtIUi/r/pnA9CCFEQLpcLHR0d1NXVwdLSElwud8BXYBOiqRhj6O7uxoMHD6CjowMulzvssaj4IIQQBdHR0YGzszPq6+uVendVQkaSsbExHBwcnun7YKj4IIQQBeJyuXBwcIBAIEBvb+9IT4cQhdLV1YWent4zr+hR8UEIIQomvLtr/zu8EkKeohNOCSGEEKJSVHwQQgghRKWo+CCEEEKISqndOR/Cm+y2tLSM8EwIIYQQMlTC47bwOC6L2hUfra2tADDid1UkhBBCiPxaW1sHvTMuhw2lRFGhvr4+1NXVwdTUVOFfztPS0oKxY8eitrYWZmZmCh1bHWh7fID2x0jxaT5tj1Hb4wO0P0ZlxccYQ2trK+zs7Ab9DhC1W/nQ0dGBvb29UrdhZmamlf9QQtoeH6D9MVJ8mk/bY9T2+ADtj1EZ8Q224iFEJ5wSQgghRKWo+CCEEEKISj1XxYeBgQG2bNkCAwODkZ6KUmh7fID2x0jxaT5tj1Hb4wO0P0Z1iE/tTjglhBBCiHZ7rlY+CCGEEDLyqPgghBBCiEpR8UEIIYQQlaLigxBCCCEqpXXFx549e+Ds7AxDQ0N4e3ujsLBQZvuCggJ4e3vD0NAQ48aNQ1pamopmKp9t27ZhypQpMDU1hZWVFRYuXIjKykqZffLz88HhcAb8VFRUqGjW8klMTBwwVxsbG5l9NCV/AODk5CQxH6tXr5bYXhPyd+7cOYSFhcHOzg4cDgdHjx4Ve50xhsTERNjZ2cHIyAizZs3CjRs3Bh03KysLEydOhIGBASZOnIgjR44oKQLZZMXX09ODzZs3w93dHSYmJrCzs0NUVBTq6upkjpmZmSkxr52dnUqOZqDB8hcTEzNgnj4+PoOOqy75AwaPUVIuOBwOvvnmG6ljqksOh3JcUNd9UKuKj8OHDyMuLg6fffYZ+Hw+Zs6cieDgYNy7d09i+5qaGoSEhGDmzJng8/n49NNPsW7dOmRlZal45oMrKCjA6tWrcenSJeTk5EAgECAwMBBtbW2D9q2srER9fb3o5+WXX1bBjIfn1VdfFZtreXm51LaalD8AKC4uFostJycHALB48WKZ/dQ5f21tbfDw8MDu3bslvr5jxw7weDzs3r0bxcXFsLGxwbx580T3cJLk4sWLWLp0KSIjI/Hnn38iMjISS5YsweXLl5UVhlSy4mtvb0dZWRkSEhJQVlaG7Oxs3L59GwsWLBh0XDMzM7Gc1tfXw9DQUBkhyDRY/gBg/vz5YvM8efKkzDHVKX/A4DH2z8PevXvB4XDw5ptvyhxXHXI4lOOC2u6DTItMnTqVrVq1Suw5Nzc3Fh8fL7H9xx9/zNzc3MSee//995mPj4/S5qgojY2NDAArKCiQ2iYvL48BYE1NTaqb2DPYsmUL8/DwGHJ7Tc4fY4x9+OGHzMXFhfX19Ul8XdPyB4AdOXJE9Livr4/Z2NiwpKQk0XOdnZ3M3NycpaWlSR1nyZIlbP78+WLPBQUFsYiICIXPWR7945PkypUrDAC7e/eu1DYZGRnM3NxcsZNTAEnxRUdHs/DwcLnGUdf8MTa0HIaHh7PZs2fLbKOuOex/XFDnfVBrVj66u7tRWlqKwMBAsecDAwNRVFQksc/FixcHtA8KCkJJSQl6enqUNldFaG5uBgCMGjVq0Laenp6wtbXFnDlzkJeXp+ypPZOqqirY2dnB2dkZERERqK6ultpWk/PX3d2NAwcO4L333hv0BoqalL9/q6mpQUNDg1iODAwMEBAQIHWfBKTnVVYfddHc3AwOh4MXX3xRZrsnT57A0dER9vb2CA0NBZ/PV80EhyE/Px9WVlaYMGECVq5cicbGRpntNTl/f//9N06cOIHY2NhB26pjDvsfF9R5H9Sa4uPhw4fo7e2FtbW12PPW1tZoaGiQ2KehoUFie4FAgIcPHyptrs+KMYYNGzZgxowZmDRpktR2tra2+PHHH5GVlYXs7Gy4urpizpw5OHfunApnO3TTpk3D/v37cebMGfz0009oaGjA9OnT8ejRI4ntNTV/AHD06FE8fvwYMTExUttoWv76E+538uyTwn7y9lEHnZ2diI+Px7Jly2TerMvNzQ2ZmZk4duwYDh06BENDQ/j5+aGqqkqFsx2a4OBgHDx4ELm5uUhJSUFxcTFmz56Nrq4uqX00NX8AsG/fPpiammLRokUy26ljDiUdF9R5H1S7u9o+q/7vIhljMt9ZSmov6Xl1smbNGly7dg3nz5+X2c7V1RWurq6ix76+vqitrUVycjL8/f2VPU25BQcHi353d3eHr68vXFxcsG/fPmzYsEFiH03MHwCkp6cjODgYdnZ2UttoWv6kkXefHG6fkdTT04OIiAj09fVhz549Mtv6+PiInbTp5+cHLy8v7Nq1Czt37lT2VOWydOlS0e+TJk3C5MmT4ejoiBMnTsg8QGta/oT27t2L5cuXD3ruhjrmUNZxQR33Qa1Z+Rg9ejR0dXUHVGaNjY0DKjghGxsbie319PTw0ksvKW2uz2Lt2rU4duwY8vLyYG9vL3d/Hx8ftXyHJYmJiQnc3d2lzlcT8wcAd+/exdmzZ7FixQq5+2pS/oRXKsmzTwr7ydtnJPX09GDJkiWoqalBTk6O3Lco19HRwZQpUzQir7a2tnB0dJQ5V03Ln1BhYSEqKyuHtV+OdA6lHRfUeR/UmuKDy+XC29tbdAWBUE5ODqZPny6xj6+v74D2f/zxByZPngx9fX2lzXU4GGNYs2YNsrOzkZubC2dn52GNw+fzYWtrq+DZKUdXVxdu3boldb6alL9/y8jIgJWVFV5//XW5+2pS/pydnWFjYyOWo+7ubhQUFEjdJwHpeZXVZ6QIC4+qqiqcPXt2WEUvYwxXr17ViLw+evQItbW1MueqSfn7t/T0dHh7e8PDw0PuviOVw8GOC2q9Dyrs1FU18MsvvzB9fX2Wnp7Obt68yeLi4piJiQn766+/GGOMxcfHs8jISFH76upqZmxszNavX89u3rzJ0tPTmb6+Pvv9999HKgSpPvjgA2Zubs7y8/NZfX296Ke9vV3Upn98qamp7MiRI+z27dvs+vXrLD4+ngFgWVlZIxHCoDZu3Mjy8/NZdXU1u3TpEgsNDWWmpqZakT+h3t5e5uDgwDZv3jzgNU3MX2trK+Pz+YzP5zMAjMfjMT6fL7raIykpiZmbm7Ps7GxWXl7O3n77bWZra8taWlpEY0RGRopdkXbhwgWmq6vLkpKS2K1bt1hSUhLT09Njly5dUqv4enp62IIFC5i9vT27evWq2H7Z1dUlNb7ExER2+vRpdufOHcbn89m7777L9PT02OXLl9UqvtbWVrZx40ZWVFTEampqWF5eHvP19WVjxozRmPwxNvj/KGOMNTc3M2NjY/b9999LHENdcziU44K67oNaVXwwxth3333HHB0dGZfLZV5eXmKXokZHR7OAgACx9vn5+czT05NxuVzm5OQk9Z9vpAGQ+JORkSFq0z++7du3MxcXF2ZoaMgsLCzYjBkz2IkTJ1Q/+SFaunQps7W1Zfr6+szOzo4tWrSI3bhxQ/S6JudP6MyZMwwAq6ysHPCaJuZPeDlw/5/o6GjG2NNL/bZs2cJsbGyYgYEB8/f3Z+Xl5WJjBAQEiNoL/fbbb8zV1ZXp6+szNze3ESu4ZMVXU1Mjdb/My8sTjdE/vri4OObg4MC4XC6ztLRkgYGBrKioSPXBMdnxtbe3s8DAQGZpacn09fWZg4MDi46OZvfu3RMbQ53zx9jg/6OMMfbDDz8wIyMj9vjxY4ljqGsOh3JcUNd9kPOfAAghhBBCVEJrzvkghBBCiGag4oMQQgghKkXFByGEEEJUiooPQgghhKgUFR+EEEIIUSkqPgghhBCiUlR8EEIIIUSlqPgghBBCiEpR8UEIIYQQlaLigxBCCCEqRcUHIYQQQlSKig9CCCGEqNT/A3N5B635TCRuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demonstration(hyperdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6cc6c9d1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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     0.8132    0.7665    0.7892     29306\n",
      "         1.0     0.6846    0.7422    0.7122     20010\n",
      "\n",
      "    accuracy                         0.7566     49316\n",
      "   macro avg     0.7489    0.7543    0.7507     49316\n",
      "weighted avg     0.7610    0.7566    0.7579     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8232    0.7736    0.7976     20463\n",
      "         1.0     0.6970    0.7581    0.7263     14058\n",
      "\n",
      "    accuracy                         0.7673     34521\n",
      "   macro avg     0.7601    0.7659    0.7620     34521\n",
      "weighted avg     0.7718    0.7673    0.7686     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7980    0.7541    0.7754      4400\n",
      "         1.0     0.6659    0.7197    0.6918      2997\n",
      "\n",
      "    accuracy                         0.7402      7397\n",
      "   macro avg     0.7320    0.7369    0.7336      7397\n",
      "weighted avg     0.7445    0.7402    0.7415      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9323    0.9231    0.9277       806\n",
      "         1.0     0.8239    0.8430    0.8333       344\n",
      "\n",
      "    accuracy                         0.8991      1150\n",
      "   macro avg     0.8781    0.8831    0.8805      1150\n",
      "weighted avg     0.8999    0.8991    0.8995      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7661    0.7162    0.7403      3594\n",
      "         1.0     0.6467    0.7037    0.6740      2653\n",
      "\n",
      "    accuracy                         0.7109      6247\n",
      "   macro avg     0.7064    0.7100    0.7072      6247\n",
      "weighted avg     0.7154    0.7109    0.7121      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8376    0.8117    0.8245       324\n",
      "         1.0     0.6391    0.6792    0.6585       159\n",
      "\n",
      "    accuracy                         0.7681       483\n",
      "   macro avg     0.7383    0.7455    0.7415       483\n",
      "weighted avg     0.7722    0.7681    0.7698       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 75.66\n",
      "    Training Set Accuracy: 76.73\n",
      "    Validation Set Accuracy: 74.02\n",
      "    Validation Set with both Images and Texts: 89.91\n",
      "    Validation Set with only Texts: 71.09\n",
      "    Validation Set with unseen Images: 76.81\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8132    0.7665    0.7892     29306\n",
      "         1.0     0.6846    0.7422    0.7122     20010\n",
      "\n",
      "    accuracy                         0.7566     49316\n",
      "   macro avg     0.7489    0.7543    0.7507     49316\n",
      "weighted avg     0.7610    0.7566    0.7579     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8232    0.7736    0.7976     20463\n",
      "         1.0     0.6970    0.7581    0.7263     14058\n",
      "\n",
      "    accuracy                         0.7673     34521\n",
      "   macro avg     0.7601    0.7659    0.7620     34521\n",
      "weighted avg     0.7718    0.7673    0.7686     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7829    0.7461    0.7641      4443\n",
      "         1.0     0.6435    0.6890    0.6655      2955\n",
      "\n",
      "    accuracy                         0.7233      7398\n",
      "   macro avg     0.7132    0.7176    0.7148      7398\n",
      "weighted avg     0.7272    0.7233    0.7247      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9072    0.9148    0.9110       716\n",
      "         1.0     0.8216    0.8075    0.8145       348\n",
      "\n",
      "    accuracy                         0.8797      1064\n",
      "   macro avg     0.8644    0.8611    0.8627      1064\n",
      "weighted avg     0.8792    0.8797    0.8794      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7574    0.7137    0.7349      3727\n",
      "         1.0     0.6219    0.6732    0.6465      2607\n",
      "\n",
      "    accuracy                         0.6970      6334\n",
      "   macro avg     0.6897    0.6934    0.6907      6334\n",
      "weighted avg     0.7016    0.6970    0.6985      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7719    0.7885    0.7801       279\n",
      "         1.0     0.6040    0.5806    0.5921       155\n",
      "\n",
      "    accuracy                         0.7143       434\n",
      "   macro avg     0.6880    0.6846    0.6861       434\n",
      "weighted avg     0.7120    0.7143    0.7130       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 75.66\n",
      "    Training Set Accuracy: 76.73\n",
      "    Validation Set Accuracy: 72.33\n",
      "    Validation Set with both Images and Texts: 87.97\n",
      "    Validation Set with only Texts: 69.70\n",
      "    Validation Set with unseen Images: 71.43\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.68260735 0.31739265]\n",
      " [0.16515654 0.83484346]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.752215\n",
      "std          0.175657\n",
      "min          0.500000\n",
      "25%          0.586537\n",
      "50%          0.724608\n",
      "75%          0.939551\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.753700\n",
      "std          0.176089\n",
      "min          0.500000\n",
      "25%          0.586994\n",
      "50%          0.726556\n",
      "75%          0.943206\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.749760\n",
      "std         0.175308\n",
      "min         0.500016\n",
      "25%         0.586122\n",
      "50%         0.721308\n",
      "75%         0.935395\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": [
      "('multi', 'concat')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7784    0.7534    0.7657     29306\n",
      "         1.0     0.6550    0.6858    0.6701     20010\n",
      "\n",
      "    accuracy                         0.7260     49316\n",
      "   macro avg     0.7167    0.7196    0.7179     49316\n",
      "weighted avg     0.7283    0.7260    0.7269     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7806    0.7542    0.7672     20463\n",
      "         1.0     0.6590    0.6915    0.6749     14058\n",
      "\n",
      "    accuracy                         0.7287     34521\n",
      "   macro avg     0.7198    0.7228    0.7210     34521\n",
      "weighted avg     0.7311    0.7287    0.7296     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7770    0.7539    0.7653      4400\n",
      "         1.0     0.6538    0.6823    0.6678      2997\n",
      "\n",
      "    accuracy                         0.7249      7397\n",
      "   macro avg     0.7154    0.7181    0.7165      7397\n",
      "weighted avg     0.7271    0.7249    0.7258      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9200    0.8846    0.9020       806\n",
      "         1.0     0.7520    0.8198    0.7844       344\n",
      "\n",
      "    accuracy                         0.8652      1150\n",
      "   macro avg     0.8360    0.8522    0.8432      1150\n",
      "weighted avg     0.8697    0.8652    0.8668      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7453    0.7245    0.7348      3594\n",
      "         1.0     0.6404    0.6645    0.6522      2653\n",
      "\n",
      "    accuracy                         0.6991      6247\n",
      "   macro avg     0.6928    0.6945    0.6935      6247\n",
      "weighted avg     0.7007    0.6991    0.6997      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8529    0.8056    0.8286       324\n",
      "         1.0     0.6441    0.7170    0.6786       159\n",
      "\n",
      "    accuracy                         0.7764       483\n",
      "   macro avg     0.7485    0.7613    0.7536       483\n",
      "weighted avg     0.7842    0.7764    0.7792       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.60\n",
      "    Training Set Accuracy: 72.87\n",
      "    Validation Set Accuracy: 72.49\n",
      "    Validation Set with both Images and Texts: 86.52\n",
      "    Validation Set with only Texts: 69.91\n",
      "    Validation Set with unseen Images: 77.64\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7784    0.7534    0.7657     29306\n",
      "         1.0     0.6550    0.6858    0.6701     20010\n",
      "\n",
      "    accuracy                         0.7260     49316\n",
      "   macro avg     0.7167    0.7196    0.7179     49316\n",
      "weighted avg     0.7283    0.7260    0.7269     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7806    0.7542    0.7672     20463\n",
      "         1.0     0.6590    0.6915    0.6749     14058\n",
      "\n",
      "    accuracy                         0.7287     34521\n",
      "   macro avg     0.7198    0.7228    0.7210     34521\n",
      "weighted avg     0.7311    0.7287    0.7296     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7694    0.7493    0.7592      4443\n",
      "         1.0     0.6373    0.6623    0.6495      2955\n",
      "\n",
      "    accuracy                         0.7145      7398\n",
      "   macro avg     0.7033    0.7058    0.7043      7398\n",
      "weighted avg     0.7166    0.7145    0.7154      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8914    0.8827    0.8870       716\n",
      "         1.0     0.7634    0.7787    0.7710       348\n",
      "\n",
      "    accuracy                         0.8487      1064\n",
      "   macro avg     0.8274    0.8307    0.8290      1064\n",
      "weighted avg     0.8495    0.8487    0.8491      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7454    0.7236    0.7344      3727\n",
      "         1.0     0.6208    0.6467    0.6335      2607\n",
      "\n",
      "    accuracy                         0.6920      6334\n",
      "   macro avg     0.6831    0.6852    0.6839      6334\n",
      "weighted avg     0.6941    0.6920    0.6928      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8139    0.7993    0.8065       279\n",
      "         1.0     0.6500    0.6710    0.6603       155\n",
      "\n",
      "    accuracy                         0.7535       434\n",
      "   macro avg     0.7319    0.7351    0.7334       434\n",
      "weighted avg     0.7553    0.7535    0.7543       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.60\n",
      "    Training Set Accuracy: 72.87\n",
      "    Validation Set Accuracy: 71.45\n",
      "    Validation Set with both Images and Texts: 84.87\n",
      "    Validation Set with only Texts: 69.20\n",
      "    Validation Set with unseen Images: 75.35\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.5802266 0.4197734]\n",
      " [0.2997922 0.7002078]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.699037\n",
      "std          0.153508\n",
      "min          0.500003\n",
      "25%          0.567476\n",
      "50%          0.655975\n",
      "75%          0.822803\n",
      "max          0.999993\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.699353\n",
      "std          0.153527\n",
      "min          0.500003\n",
      "25%          0.567971\n",
      "50%          0.656492\n",
      "75%          0.823408\n",
      "max          0.999993\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.698020\n",
      "std         0.153465\n",
      "min         0.500013\n",
      "25%         0.567268\n",
      "50%         0.650714\n",
      "75%         0.820769\n",
      "max         0.999987\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     0.7561    0.7753    0.7656     29306\n",
      "         1.0     0.6582    0.6338    0.6458     20010\n",
      "\n",
      "    accuracy                         0.7179     49316\n",
      "   macro avg     0.7072    0.7045    0.7057     49316\n",
      "weighted avg     0.7164    0.7179    0.7170     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7577    0.7754    0.7664     20463\n",
      "         1.0     0.6615    0.6391    0.6501     14058\n",
      "\n",
      "    accuracy                         0.7199     34521\n",
      "   macro avg     0.7096    0.7072    0.7083     34521\n",
      "weighted avg     0.7185    0.7199    0.7190     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7553    0.7730    0.7640      4400\n",
      "         1.0     0.6548    0.6323    0.6434      2997\n",
      "\n",
      "    accuracy                         0.7160      7397\n",
      "   macro avg     0.7050    0.7026    0.7037      7397\n",
      "weighted avg     0.7146    0.7160    0.7151      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8444    0.9020    0.8722       806\n",
      "         1.0     0.7266    0.6105    0.6635       344\n",
      "\n",
      "    accuracy                         0.8148      1150\n",
      "   macro avg     0.7855    0.7562    0.7679      1150\n",
      "weighted avg     0.8092    0.8148    0.8098      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7342    0.7440    0.7391      3594\n",
      "         1.0     0.6468    0.6351    0.6409      2653\n",
      "\n",
      "    accuracy                         0.6978      6247\n",
      "   macro avg     0.6905    0.6896    0.6900      6247\n",
      "weighted avg     0.6971    0.6978    0.6974      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7989    0.8580    0.8274       324\n",
      "         1.0     0.6593    0.5597    0.6054       159\n",
      "\n",
      "    accuracy                         0.7598       483\n",
      "   macro avg     0.7291    0.7089    0.7164       483\n",
      "weighted avg     0.7529    0.7598    0.7543       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 71.79\n",
      "    Training Set Accuracy: 71.99\n",
      "    Validation Set Accuracy: 71.60\n",
      "    Validation Set with both Images and Texts: 81.48\n",
      "    Validation Set with only Texts: 69.78\n",
      "    Validation Set with unseen Images: 75.98\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7561    0.7753    0.7656     29306\n",
      "         1.0     0.6582    0.6338    0.6458     20010\n",
      "\n",
      "    accuracy                         0.7179     49316\n",
      "   macro avg     0.7072    0.7045    0.7057     49316\n",
      "weighted avg     0.7164    0.7179    0.7170     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7577    0.7754    0.7664     20463\n",
      "         1.0     0.6615    0.6391    0.6501     14058\n",
      "\n",
      "    accuracy                         0.7199     34521\n",
      "   macro avg     0.7096    0.7072    0.7083     34521\n",
      "weighted avg     0.7185    0.7199    0.7190     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7498    0.7772    0.7633      4443\n",
      "         1.0     0.6455    0.6102    0.6273      2955\n",
      "\n",
      "    accuracy                         0.7105      7398\n",
      "   macro avg     0.6977    0.6937    0.6953      7398\n",
      "weighted avg     0.7082    0.7105    0.7090      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8206    0.9134    0.8645       716\n",
      "         1.0     0.7678    0.5891    0.6667       348\n",
      "\n",
      "    accuracy                         0.8073      1064\n",
      "   macro avg     0.7942    0.7512    0.7656      1064\n",
      "weighted avg     0.8033    0.8073    0.7998      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7350    0.7510    0.7429      3727\n",
      "         1.0     0.6326    0.6130    0.6226      2607\n",
      "\n",
      "    accuracy                         0.6942      6334\n",
      "   macro avg     0.6838    0.6820    0.6828      6334\n",
      "weighted avg     0.6929    0.6942    0.6934      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7595    0.8602    0.8067       279\n",
      "         1.0     0.6695    0.5097    0.5788       155\n",
      "\n",
      "    accuracy                         0.7350       434\n",
      "   macro avg     0.7145    0.6849    0.6927       434\n",
      "weighted avg     0.7274    0.7350    0.7253       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 71.79\n",
      "    Training Set Accuracy: 71.99\n",
      "    Validation Set Accuracy: 71.05\n",
      "    Validation Set with both Images and Texts: 80.73\n",
      "    Validation Set with only Texts: 69.42\n",
      "    Validation Set with unseen Images: 73.50\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.6077533  0.3922467 ]\n",
      " [0.23787908 0.76212084]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.705455\n",
      "std          0.151007\n",
      "min          0.500010\n",
      "25%          0.574038\n",
      "50%          0.670001\n",
      "75%          0.829015\n",
      "max          0.999847\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.705492\n",
      "std          0.150795\n",
      "min          0.500010\n",
      "25%          0.574578\n",
      "50%          0.670394\n",
      "75%          0.827989\n",
      "max          0.999751\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.704431\n",
      "std         0.151214\n",
      "min         0.500020\n",
      "25%         0.573078\n",
      "50%         0.667109\n",
      "75%         0.829927\n",
      "max         0.999567\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', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7463    0.7648    0.7554     29306\n",
      "         1.0     0.6425    0.6191    0.6306     20010\n",
      "\n",
      "    accuracy                         0.7057     49316\n",
      "   macro avg     0.6944    0.6920    0.6930     49316\n",
      "weighted avg     0.7042    0.7057    0.7048     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7459    0.7636    0.7546     20463\n",
      "         1.0     0.6436    0.6213    0.6322     14058\n",
      "\n",
      "    accuracy                         0.7057     34521\n",
      "   macro avg     0.6947    0.6925    0.6934     34521\n",
      "weighted avg     0.7042    0.7057    0.7048     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7467    0.7693    0.7579      4400\n",
      "         1.0     0.6456    0.6170    0.6310      2997\n",
      "\n",
      "    accuracy                         0.7076      7397\n",
      "   macro avg     0.6962    0.6931    0.6944      7397\n",
      "weighted avg     0.7058    0.7076    0.7064      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8852    0.8710    0.8780       806\n",
      "         1.0     0.7087    0.7355    0.7218       344\n",
      "\n",
      "    accuracy                         0.8304      1150\n",
      "   macro avg     0.7970    0.8032    0.7999      1150\n",
      "weighted avg     0.8324    0.8304    0.8313      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7174    0.7465    0.7317      3594\n",
      "         1.0     0.6366    0.6016    0.6186      2653\n",
      "\n",
      "    accuracy                         0.6850      6247\n",
      "   macro avg     0.6770    0.6741    0.6751      6247\n",
      "weighted avg     0.6831    0.6850    0.6836      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8333    0.8025    0.8176       324\n",
      "         1.0     0.6257    0.6730    0.6485       159\n",
      "\n",
      "    accuracy                         0.7598       483\n",
      "   macro avg     0.7295    0.7377    0.7330       483\n",
      "weighted avg     0.7650    0.7598    0.7619       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 70.57\n",
      "    Training Set Accuracy: 70.57\n",
      "    Validation Set Accuracy: 70.76\n",
      "    Validation Set with both Images and Texts: 83.04\n",
      "    Validation Set with only Texts: 68.50\n",
      "    Validation Set with unseen Images: 75.98\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7463    0.7648    0.7554     29306\n",
      "         1.0     0.6425    0.6191    0.6306     20010\n",
      "\n",
      "    accuracy                         0.7057     49316\n",
      "   macro avg     0.6944    0.6920    0.6930     49316\n",
      "weighted avg     0.7042    0.7057    0.7048     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7459    0.7636    0.7546     20463\n",
      "         1.0     0.6436    0.6213    0.6322     14058\n",
      "\n",
      "    accuracy                         0.7057     34521\n",
      "   macro avg     0.6947    0.6925    0.6934     34521\n",
      "weighted avg     0.7042    0.7057    0.7048     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7476    0.7659    0.7566      4443\n",
      "         1.0     0.6346    0.6112    0.6227      2955\n",
      "\n",
      "    accuracy                         0.7041      7398\n",
      "   macro avg     0.6911    0.6885    0.6896      7398\n",
      "weighted avg     0.7024    0.7041    0.7031      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8607    0.8799    0.8702       716\n",
      "         1.0     0.7410    0.7069    0.7235       348\n",
      "\n",
      "    accuracy                         0.8233      1064\n",
      "   macro avg     0.8008    0.7934    0.7968      1064\n",
      "weighted avg     0.8215    0.8233    0.8222      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7259    0.7440    0.7349      3727\n",
      "         1.0     0.6205    0.5984    0.6093      2607\n",
      "\n",
      "    accuracy                         0.6841      6334\n",
      "   macro avg     0.6732    0.6712    0.6721      6334\n",
      "weighted avg     0.6825    0.6841    0.6832      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7877    0.8244    0.8056       279\n",
      "         1.0     0.6549    0.6000    0.6263       155\n",
      "\n",
      "    accuracy                         0.7442       434\n",
      "   macro avg     0.7213    0.7122    0.7159       434\n",
      "weighted avg     0.7403    0.7442    0.7416       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 70.57\n",
      "    Training Set Accuracy: 70.57\n",
      "    Validation Set Accuracy: 70.41\n",
      "    Validation Set with both Images and Texts: 82.33\n",
      "    Validation Set with only Texts: 68.41\n",
      "    Validation Set with unseen Images: 74.42\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.5318737  0.46812633]\n",
      " [0.32734817 0.6726518 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.678349\n",
      "std          0.145687\n",
      "min          0.500010\n",
      "25%          0.558404\n",
      "50%          0.631541\n",
      "75%          0.782772\n",
      "max          0.999979\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.678518\n",
      "std          0.145698\n",
      "min          0.500010\n",
      "25%          0.558649\n",
      "50%          0.632078\n",
      "75%          0.782576\n",
      "max          0.999979\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.678392\n",
      "std         0.146655\n",
      "min         0.500011\n",
      "25%         0.556959\n",
      "50%         0.629427\n",
      "75%         0.784662\n",
      "max         0.999902\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": [
      "('image', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6139    0.9880    0.7572     29306\n",
      "         1.0     0.8363    0.0899    0.1623     20010\n",
      "\n",
      "    accuracy                         0.6236     49316\n",
      "   macro avg     0.7251    0.5389    0.4598     49316\n",
      "weighted avg     0.7041    0.6236    0.5158     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6139    0.9900    0.7578     20463\n",
      "         1.0     0.8652    0.0936    0.1689     14058\n",
      "\n",
      "    accuracy                         0.6250     34521\n",
      "   macro avg     0.7395    0.5418    0.4634     34521\n",
      "weighted avg     0.7162    0.6250    0.5180     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6113    0.9832    0.7539      4400\n",
      "         1.0     0.7688    0.0821    0.1483      2997\n",
      "\n",
      "    accuracy                         0.6181      7397\n",
      "   macro avg     0.6900    0.5326    0.4511      7397\n",
      "weighted avg     0.6751    0.6181    0.5085      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8819    0.9082    0.8949       806\n",
      "         1.0     0.7688    0.7151    0.7410       344\n",
      "\n",
      "    accuracy                         0.8504      1150\n",
      "   macro avg     0.8253    0.8117    0.8179      1150\n",
      "weighted avg     0.8481    0.8504    0.8488      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     0.7994    0.8241    0.8116       324\n",
      "         1.0     0.6174    0.5786    0.5974       159\n",
      "\n",
      "    accuracy                         0.7433       483\n",
      "   macro avg     0.7084    0.7013    0.7045       483\n",
      "weighted avg     0.7395    0.7433    0.7411       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 62.36\n",
      "    Training Set Accuracy: 62.50\n",
      "    Validation Set Accuracy: 61.81\n",
      "    Validation Set with both Images and Texts: 85.04\n",
      "    Validation Set with only Texts: 57.53\n",
      "    Validation Set with unseen Images: 74.33\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6139    0.9880    0.7572     29306\n",
      "         1.0     0.8363    0.0899    0.1623     20010\n",
      "\n",
      "    accuracy                         0.6236     49316\n",
      "   macro avg     0.7251    0.5389    0.4598     49316\n",
      "weighted avg     0.7041    0.6236    0.5158     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     0.6139    0.9900    0.7578     20463\n",
      "         1.0     0.8652    0.0936    0.1689     14058\n",
      "\n",
      "    accuracy                         0.6250     34521\n",
      "   macro avg     0.7395    0.5418    0.4634     34521\n",
      "weighted avg     0.7162    0.6250    0.5180     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6164    0.9836    0.7579      4443\n",
      "         1.0     0.7638    0.0799    0.1446      2955\n",
      "\n",
      "    accuracy                         0.6226      7398\n",
      "   macro avg     0.6901    0.5317    0.4512      7398\n",
      "weighted avg     0.6753    0.6226    0.5129      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8517    0.8980    0.8742       716\n",
      "         1.0     0.7638    0.6782    0.7184       348\n",
      "\n",
      "    accuracy                         0.8261      1064\n",
      "   macro avg     0.8077    0.7881    0.7963      1064\n",
      "weighted avg     0.8229    0.8261    0.8233      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     0.7450    0.8065    0.7745       279\n",
      "         1.0     0.5909    0.5032    0.5436       155\n",
      "\n",
      "    accuracy                         0.6982       434\n",
      "   macro avg     0.6680    0.6548    0.6590       434\n",
      "weighted avg     0.6900    0.6982    0.6920       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 62.36\n",
      "    Training Set Accuracy: 62.50\n",
      "    Validation Set Accuracy: 62.26\n",
      "    Validation Set with both Images and Texts: 82.61\n",
      "    Validation Set with only Texts: 58.84\n",
      "    Validation Set with unseen Images: 69.82\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.5750071  0.42499295]\n",
      " [0.5750071  0.42499295]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.613620\n",
      "std          0.108093\n",
      "min          0.500067\n",
      "25%          0.575007\n",
      "50%          0.575007\n",
      "75%          0.575007\n",
      "max          0.999864\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.613930\n",
      "std          0.108709\n",
      "min          0.500067\n",
      "25%          0.575007\n",
      "50%          0.575007\n",
      "75%          0.575007\n",
      "max          0.999864\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.614779\n",
      "std         0.109175\n",
      "min         0.500218\n",
      "25%         0.575007\n",
      "50%         0.575007\n",
      "75%         0.575007\n",
      "max         0.999332\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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IOgAAwLIIOgAAwLIIOgAAwLIIOgAAwLIIOgAAwLIIOgAAwLIIOgAAwLIIOgAAwLIIOgAAwLIIOgAAwLKCCjqbNm3Sfffdp4SEBCUkJMjlcun11183zxuGocrKSmVkZCgmJkaTJ0/W0aNHA+bw+XxavHixkpOTFRcXp6KiIp05cyagprOzUx6PR06nU06nUx6PRxcuXAioOXXqlGbNmqW4uDglJyertLRUPT09QV4+AACwsqCCzogRI/TDH/5Qhw4d0qFDh/TQQw/p4YcfNsPMqlWrtG7dOm3cuFEHDx5UWlqa8vPzdfHiRXOOsrIy1dbWqqamRg0NDbp06ZIKCwvV29tr1hQXF6u1tVV1dXWqq6tTa2urPB6Peb63t1czZ87U5cuX1dDQoJqaGu3cuVPl5eV32g8AAGAhQ4MpnjVrVsDx8uXLtWnTJu3fv1/jxo3Thg0b9PTTT2v27NmSpBdffFGpqal6+eWXtXDhQnV1dWnr1q3avn27pk2bJkmqrq5WZmam9uzZo4KCAh0/flx1dXXav3+/cnNzJUlbtmyRy+XSiRMnlJWVJa/Xq2PHjun06dPKyMiQJK1du1bz5s3T8uXLlZCQcMeNAQAAkS+ooPNRvb29+tnPfqbLly/L5XLp5MmTam9vl9vtNmscDocefPBBNTY2auHChWppaZHf7w+oycjIUHZ2thobG1VQUKCmpiY5nU4z5EhSXl6enE6nGhsblZWVpaamJmVnZ5shR5IKCgrk8/nU0tKiKVOm3HDNPp9PPp/PPO7u7pYk+f1++f3+/rbihq7NN9DzhpojyhjsJQTFMeTD9UZanyNNpN7PkYY+hwd9Dp9Q9TqY+YIOOocPH5bL5dJf/vIXfeYzn1Ftba3GjRunxsZGSVJqampAfWpqqt59911JUnt7u6Kjo5WYmNinpr293axJSUnp874pKSkBNde/T2JioqKjo82aG1m5cqWWLVvWZ9zr9So2NvbjLr1f6uvrQzJvqKx6YLBX0D+R1udIRZ/Dgz6HB30On4Hu9ZUrV267Nuigk5WVpdbWVl24cEE7d+7U3LlztW/fPvO8zWYLqDcMo8/Y9a6vuVF9f2qut3TpUi1ZssQ87u7uVmZmptxu94B/3OX3+1VfX6/8/HzZ7fYBnTuUsivfGOwlBMUxxND3J3wQcX2ONJF6P0ca+hwe9Dl8QtXra5/I3I6gg050dLS+8IUvSJImTJiggwcP6kc/+pH+5V/+RdKHuy3p6elmfUdHh7n7kpaWpp6eHnV2dgbs6nR0dGjixIlmzdmzZ/u877lz5wLmaW5uDjjf2dkpv9/fZ6fnoxwOhxwOR59xu90esps9lHOHgq/31qH0kyrS+hyp6HN40OfwoM/hM9C9DmauO/53dAzDkM/n0+jRo5WWlhawPdXT06N9+/aZISYnJ0d2uz2gpq2tTUeOHDFrXC6Xurq6dODAAbOmublZXV1dATVHjhxRW1ubWeP1euVwOJSTk3OnlwQAACwiqB2dp556SjNmzFBmZqYuXryompoa7d27V3V1dbLZbCorK9OKFSs0ZswYjRkzRitWrFBsbKyKi4slSU6nU/Pnz1d5ebmGDx+upKQkVVRUaPz48eZTWGPHjtX06dNVUlKizZs3S5IWLFigwsJCZWVlSZLcbrfGjRsnj8ej1atX6/z586qoqFBJSQlPXAEAAFNQQefs2bPyeDxqa2uT0+nUfffdp7q6OuXn50uSnnjiCV29elWLFi1SZ2encnNz5fV6FR8fb86xfv16DR06VHPmzNHVq1c1depUbdu2TVFRUWbNjh07VFpaaj6dVVRUpI0bN5rno6KitHv3bi1atEiTJk1STEyMiouLtWbNmjtqBgAAsJaggs7WrVtved5ms6myslKVlZU3rRk2bJiqqqpUVVV105qkpCRVV1ff8r1GjhypXbt23bIGAAB8uvG7rgAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGURdAAAgGUFFXRWrlypL3/5y4qPj1dKSooeeeQRnThxIqBm3rx5stlsAT95eXkBNT6fT4sXL1ZycrLi4uJUVFSkM2fOBNR0dnbK4/HI6XTK6XTK4/HowoULATWnTp3SrFmzFBcXp+TkZJWWlqqnpyeYSwIAABYWVNDZt2+fHnvsMe3fv1/19fX661//KrfbrcuXLwfUTZ8+XW1tbebPa6+9FnC+rKxMtbW1qqmpUUNDgy5duqTCwkL19vaaNcXFxWptbVVdXZ3q6urU2toqj8djnu/t7dXMmTN1+fJlNTQ0qKamRjt37lR5eXl/+gAAACxoaDDFdXV1AccvvPCCUlJS1NLSoq9+9avmuMPhUFpa2g3n6Orq0tatW7V9+3ZNmzZNklRdXa3MzEzt2bNHBQUFOn78uOrq6rR//37l5uZKkrZs2SKXy6UTJ04oKytLXq9Xx44d0+nTp5WRkSFJWrt2rebNm6fly5crISEhmEsDAAAWdEff0enq6pIkJSUlBYzv3btXKSkpuueee1RSUqKOjg7zXEtLi/x+v9xutzmWkZGh7OxsNTY2SpKamprkdDrNkCNJeXl5cjqdATXZ2dlmyJGkgoIC+Xw+tbS03MllAQAAiwhqR+ejDMPQkiVL9JWvfEXZ2dnm+IwZM/TNb35To0aN0smTJ/XMM8/ooYceUktLixwOh9rb2xUdHa3ExMSA+VJTU9Xe3i5Jam9vV0pKSp/3TElJCahJTU0NOJ+YmKjo6Giz5no+n08+n8887u7uliT5/X75/f5+dOHmrs030POGmiPKGOwlBMUx5MP1RlqfI02k3s+Rhj6HB30On1D1Opj5+h10vvvd7+q3v/2tGhoaAsYfffRR88/Z2dmaMGGCRo0apd27d2v27Nk3nc8wDNlsNvP4o3++k5qPWrlypZYtW9Zn3Ov1KjY29qZruxP19fUhmTdUVj0w2Cvon0jrc6Siz+FBn8ODPofPQPf6ypUrt13br6CzePFivfrqq3rzzTc1YsSIW9amp6dr1KhRevvttyVJaWlp6unpUWdnZ8CuTkdHhyZOnGjWnD17ts9c586dM3dx0tLS1NzcHHC+s7NTfr+/z07PNUuXLtWSJUvM4+7ubmVmZsrtdg/4d3r8fr/q6+uVn58vu90+oHOHUnblG4O9hKA4hhj6/oQPIq7PkSZS7+dIQ5/Dgz6HT6h6fe0TmdsRVNAxDEOLFy9WbW2t9u7dq9GjR3/sa95//32dPn1a6enpkqScnBzZ7XbV19drzpw5kqS2tjYdOXJEq1atkiS5XC51dXXpwIEDeuCBD7cYmpub1dXVZYYhl8ul5cuXq62tzZzb6/XK4XAoJyfnhmtxOBxyOBx9xu12e8hu9lDOHQq+3hvvhn3SRVqfIxV9Dg/6HB70OXwGutfBzBVU0Hnsscf08ssv61e/+pXi4+PN78I4nU7FxMTo0qVLqqys1Ne//nWlp6frnXfe0VNPPaXk5GR97WtfM2vnz5+v8vJyDR8+XElJSaqoqND48ePNp7DGjh2r6dOnq6SkRJs3b5YkLViwQIWFhcrKypIkud1ujRs3Th6PR6tXr9b58+dVUVGhkpISnrgCAACSgnzqatOmTerq6tLkyZOVnp5u/rzyyiuSpKioKB0+fFgPP/yw7rnnHs2dO1f33HOPmpqaFB8fb86zfv16PfLII5ozZ44mTZqk2NhY/fd//7eioqLMmh07dmj8+PFyu91yu9267777tH37dvN8VFSUdu/erWHDhmnSpEmaM2eOHnnkEa1Zs+ZOewIAACwi6I+ubiUmJkZvvPHx3/EYNmyYqqqqVFVVddOapKQkVVdX33KekSNHateuXR/7fgAA4NOJ33UFAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsK6igs3LlSn35y19WfHy8UlJS9Mgjj+jEiRMBNYZhqLKyUhkZGYqJidHkyZN19OjRgBqfz6fFixcrOTlZcXFxKioq0pkzZwJqOjs75fF45HQ65XQ65fF4dOHChYCaU6dOadasWYqLi1NycrJKS0vV09MTzCUBAAALCyro7Nu3T4899pj279+v+vp6/fWvf5Xb7dbly5fNmlWrVmndunXauHGjDh48qLS0NOXn5+vixYtmTVlZmWpra1VTU6OGhgZdunRJhYWF6u3tNWuKi4vV2tqquro61dXVqbW1VR6Pxzzf29urmTNn6vLly2poaFBNTY127typ8vLyO+kHAACwkKHBFNfV1QUcv/DCC0pJSVFLS4u++tWvyjAMbdiwQU8//bRmz54tSXrxxReVmpqql19+WQsXLlRXV5e2bt2q7du3a9q0aZKk6upqZWZmas+ePSooKNDx48dVV1en/fv3Kzc3V5K0ZcsWuVwunThxQllZWfJ6vTp27JhOnz6tjIwMSdLatWs1b948LV++XAkJCXfcHAAAENmCCjrX6+rqkiQlJSVJkk6ePKn29na53W6zxuFw6MEHH1RjY6MWLlyolpYW+f3+gJqMjAxlZ2ersbFRBQUFampqktPpNEOOJOXl5cnpdKqxsVFZWVlqampSdna2GXIkqaCgQD6fTy0tLZoyZUqf9fp8Pvl8PvO4u7tbkuT3++X3+++kFX1cm2+g5w01R5Qx2EsIimPIh+uNtD5Hmki9nyMNfQ4P+hw+oep1MPP1O+gYhqElS5boK1/5irKzsyVJ7e3tkqTU1NSA2tTUVL377rtmTXR0tBITE/vUXHt9e3u7UlJS+rxnSkpKQM3175OYmKjo6Giz5norV67UsmXL+ox7vV7FxsZ+7DX3R319fUjmDZVVDwz2Cvon0vocqehzeNDn8KDP4TPQvb5y5cpt1/Y76Hz3u9/Vb3/7WzU0NPQ5Z7PZAo4Nw+gzdr3ra25U35+aj1q6dKmWLFliHnd3dyszM1Nut3vAP+ry+/2qr69Xfn6+7Hb7gM4dStmVbwz2EoLiGGLo+xM+iLg+R5pIvZ8jDX0OD/ocPqHq9bVPZG5Hv4LO4sWL9eqrr+rNN9/UiBEjzPG0tDRJH+62pKenm+MdHR3m7ktaWpp6enrU2dkZsKvT0dGhiRMnmjVnz57t877nzp0LmKe5uTngfGdnp/x+f5+dnmscDoccDkefcbvdHrKbPZRzh4Kv99aB9JMq0vocqehzeNDn8KDP4TPQvQ5mrqCeujIMQ9/97nf1i1/8Qv/zP/+j0aNHB5wfPXq00tLSAraoenp6tG/fPjPE5OTkyG63B9S0tbXpyJEjZo3L5VJXV5cOHDhg1jQ3N6urqyug5siRI2prazNrvF6vHA6HcnJygrksAABgUUHt6Dz22GN6+eWX9atf/Urx8fHmd2GcTqdiYmJks9lUVlamFStWaMyYMRozZoxWrFih2NhYFRcXm7Xz589XeXm5hg8frqSkJFVUVGj8+PHmU1hjx47V9OnTVVJSos2bN0uSFixYoMLCQmVlZUmS3G63xo0bJ4/Ho9WrV+v8+fOqqKhQSUkJT1wBAABJQQadTZs2SZImT54cMP7CCy9o3rx5kqQnnnhCV69e1aJFi9TZ2anc3Fx5vV7Fx8eb9evXr9fQoUM1Z84cXb16VVOnTtW2bdsUFRVl1uzYsUOlpaXm01lFRUXauHGjeT4qKkq7d+/WokWLNGnSJMXExKi4uFhr1qwJqgEAAMC6ggo6hvHxjx7bbDZVVlaqsrLypjXDhg1TVVWVqqqqblqTlJSk6urqW77XyJEjtWvXro9dEwAA+HTid10BAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLCjrovPnmm5o1a5YyMjJks9n0y1/+MuD8vHnzZLPZAn7y8vICanw+nxYvXqzk5GTFxcWpqKhIZ86cCajp7OyUx+OR0+mU0+mUx+PRhQsXAmpOnTqlWbNmKS4uTsnJySotLVVPT0+wlwQAACwq6KBz+fJlfelLX9LGjRtvWjN9+nS1tbWZP6+99lrA+bKyMtXW1qqmpkYNDQ26dOmSCgsL1dvba9YUFxertbVVdXV1qqurU2trqzwej3m+t7dXM2fO1OXLl9XQ0KCamhrt3LlT5eXlwV4SAACwqKHBvmDGjBmaMWPGLWscDofS0tJueK6rq0tbt27V9u3bNW3aNElSdXW1MjMztWfPHhUUFOj48eOqq6vT/v37lZubK0nasmWLXC6XTpw4oaysLHm9Xh07dkynT59WRkaGJGnt2rWaN2+eli9froSEhGAvDQAAWExIvqOzd+9epaSk6J577lFJSYk6OjrMcy0tLfL7/XK73eZYRkaGsrOz1djYKElqamqS0+k0Q44k5eXlyel0BtRkZ2ebIUeSCgoK5PP51NLSEorLAgAAESboHZ2PM2PGDH3zm9/UqFGjdPLkST3zzDN66KGH1NLSIofDofb2dkVHRysxMTHgdampqWpvb5cktbe3KyUlpc/cKSkpATWpqakB5xMTExUdHW3WXM/n88nn85nH3d3dkiS/3y+/39//i76Ba/MN9Lyh5ogyBnsJQXEM+XC9kdbnSBOp93Okoc/hQZ/DJ1S9Dma+AQ86jz76qPnn7OxsTZgwQaNGjdLu3bs1e/bsm77OMAzZbDbz+KN/vpOaj1q5cqWWLVvWZ9zr9So2Nvama7sT9fX1IZk3VFY9MNgr6J9I63Okos/hQZ/Dgz6Hz0D3+sqVK7ddO+BB53rp6ekaNWqU3n77bUlSWlqaenp61NnZGbCr09HRoYkTJ5o1Z8+e7TPXuXPnzF2ctLQ0NTc3B5zv7OyU3+/vs9NzzdKlS7VkyRLzuLu7W5mZmXK73QP+nR6/36/6+nrl5+fLbrcP6NyhlF35xmAvISiOIYa+P+GDiOtzpInU+znS0OfwoM/hE6peX/tE5naEPOi8//77On36tNLT0yVJOTk5stvtqq+v15w5cyRJbW1tOnLkiFatWiVJcrlc6urq0oEDB/TAAx9uMTQ3N6urq8sMQy6XS8uXL1dbW5s5t9frlcPhUE5Ozg3X4nA45HA4+ozb7faQ3eyhnDsUfL033g37pIu0Pkcq+hwe9Dk86HP4DHSvg5kr6KBz6dIl/eEPfzCPT548qdbWViUlJSkpKUmVlZX6+te/rvT0dL3zzjt66qmnlJycrK997WuSJKfTqfnz56u8vFzDhw9XUlKSKioqNH78ePMprLFjx2r69OkqKSnR5s2bJUkLFixQYWGhsrKyJElut1vjxo2Tx+PR6tWrdf78eVVUVKikpIQnrgAAgKR+BJ1Dhw5pypQp5vG1j4Lmzp2rTZs26fDhw3rppZd04cIFpaena8qUKXrllVcUHx9vvmb9+vUaOnSo5syZo6tXr2rq1Knatm2boqKizJodO3aotLTUfDqrqKgo4N/uiYqK0u7du7Vo0SJNmjRJMTExKi4u1po1a4LvAgAAsKSgg87kyZNlGDd/MueNNz7+Ox7Dhg1TVVWVqqqqblqTlJSk6urqW84zcuRI7dq162PfDwAAfDrxu64AAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlEXQAAIBlBR103nzzTc2aNUsZGRmy2Wz65S9/GXDeMAxVVlYqIyNDMTExmjx5so4ePRpQ4/P5tHjxYiUnJysuLk5FRUU6c+ZMQE1nZ6c8Ho+cTqecTqc8Ho8uXLgQUHPq1CnNmjVLcXFxSk5OVmlpqXp6eoK9JAAAYFFBB53Lly/rS1/6kjZu3HjD86tWrdK6deu0ceNGHTx4UGlpacrPz9fFixfNmrKyMtXW1qqmpkYNDQ26dOmSCgsL1dvba9YUFxertbVVdXV1qqurU2trqzwej3m+t7dXM2fO1OXLl9XQ0KCamhrt3LlT5eXlwV4SAACwqKHBvmDGjBmaMWPGDc8ZhqENGzbo6aef1uzZsyVJL774olJTU/Xyyy9r4cKF6urq0tatW7V9+3ZNmzZNklRdXa3MzEzt2bNHBQUFOn78uOrq6rR//37l5uZKkrZs2SKXy6UTJ04oKytLXq9Xx44d0+nTp5WRkSFJWrt2rebNm6fly5crISGhXw0BAADWEXTQuZWTJ0+qvb1dbrfbHHM4HHrwwQfV2NiohQsXqqWlRX6/P6AmIyND2dnZamxsVEFBgZqamuR0Os2QI0l5eXlyOp1qbGxUVlaWmpqalJ2dbYYcSSooKJDP51NLS4umTJnSZ30+n08+n8887u7uliT5/X75/f6BbIU530DPG2qOKGOwlxAUx5AP1xtpfY40kXo/Rxr6HB70OXxC1etg5hvQoNPe3i5JSk1NDRhPTU3Vu+++a9ZER0crMTGxT82117e3tyslJaXP/CkpKQE1179PYmKioqOjzZrrrVy5UsuWLesz7vV6FRsbezuXGLT6+vqQzBsqqx4Y7BX0T6T1OVLR5/Cgz+FBn8NnoHt95cqV264d0KBzjc1mCzg2DKPP2PWur7lRfX9qPmrp0qVasmSJedzd3a3MzEy53e4B/6jL7/ervr5e+fn5stvtAzp3KGVXvjHYSwiKY4ih70/4IOL6HGki9X6ONPQ5POhz+ISq19c+kbkdAxp00tLSJH2425Kenm6Od3R0mLsvaWlp6unpUWdnZ8CuTkdHhyZOnGjWnD17ts/8586dC5inubk54HxnZ6f8fn+fnZ5rHA6HHA5Hn3G73R6ymz2Uc4eCr/fWgfSTKtL6HKnoc3jQ5/Cgz+Ez0L0OZq4B/Xd0Ro8erbS0tIAtqp6eHu3bt88MMTk5ObLb7QE1bW1tOnLkiFnjcrnU1dWlAwcOmDXNzc3q6uoKqDly5Ija2trMGq/XK4fDoZycnIG8LAAAEKGC3tG5dOmS/vCHP5jHJ0+eVGtrq5KSkjRy5EiVlZVpxYoVGjNmjMaMGaMVK1YoNjZWxcXFkiSn06n58+ervLxcw4cPV1JSkioqKjR+/HjzKayxY8dq+vTpKikp0ebNmyVJCxYsUGFhobKysiRJbrdb48aNk8fj0erVq3X+/HlVVFSopKSEJ64AAICkfgSdQ4cOBTzRdO07L3PnztW2bdv0xBNP6OrVq1q0aJE6OzuVm5srr9er+Ph48zXr16/X0KFDNWfOHF29elVTp07Vtm3bFBUVZdbs2LFDpaWl5tNZRUVFAf92T1RUlHbv3q1FixZp0qRJiomJUXFxsdasWRN8FwAAgCUFHXQmT54sw7j5I8g2m02VlZWqrKy8ac2wYcNUVVWlqqqqm9YkJSWpurr6lmsZOXKkdu3a9bFrBgAAn078risAAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZBB0AAGBZAx50KisrZbPZAn7S0tLM84ZhqLKyUhkZGYqJidHkyZN19OjRgDl8Pp8WL16s5ORkxcXFqaioSGfOnAmo6ezslMfjkdPplNPplMfj0YULFwb6cgAAQAQLyY7Ovffeq7a2NvPn8OHD5rlVq1Zp3bp12rhxow4ePKi0tDTl5+fr4sWLZk1ZWZlqa2tVU1OjhoYGXbp0SYWFhert7TVriouL1draqrq6OtXV1am1tVUejycUlwMAACLU0JBMOnRowC7ONYZhaMOGDXr66ac1e/ZsSdKLL76o1NRUvfzyy1q4cKG6urq0detWbd++XdOmTZMkVVdXKzMzU3v27FFBQYGOHz+uuro67d+/X7m5uZKkLVu2yOVy6cSJE8rKygrFZQEAgAgTkqDz9ttvKyMjQw6HQ7m5uVqxYoXuvvtunTx5Uu3t7XK73Watw+HQgw8+qMbGRi1cuFAtLS3y+/0BNRkZGcrOzlZjY6MKCgrU1NQkp9NphhxJysvLk9PpVGNj402Djs/nk8/nM4+7u7slSX6/X36/f0B7cG2+gZ431BxRxmAvISiOIR+uN9L6HGki9X6ONPQ5POhz+ISq18HMN+BBJzc3Vy+99JLuuecenT17Vj/4wQ80ceJEHT16VO3t7ZKk1NTUgNekpqbq3XfflSS1t7crOjpaiYmJfWquvb69vV0pKSl93jslJcWsuZGVK1dq2bJlfca9Xq9iY2ODu9DbVF9fH5J5Q2XVA4O9gv6JtD5HKvocHvQ5POhz+Ax0r69cuXLbtQMedGbMmGH+efz48XK5XPr85z+vF198UXl5eZIkm80W8BrDMPqMXe/6mhvVf9w8S5cu1ZIlS8zj7u5uZWZmyu12KyEh4dYXFiS/36/6+nrl5+fLbrcP6NyhlF35xmAvISiOIYa+P+GDiOtzpInU+znS0OfwoM/hE6peX/tE5naE5KOrj4qLi9P48eP19ttv65FHHpH04Y5Menq6WdPR0WHu8qSlpamnp0ednZ0BuzodHR2aOHGiWXP27Nk+73Xu3Lk+u0Uf5XA45HA4+ozb7faQ3eyhnDsUfL23DpyfVJHW50hFn8ODPocHfQ6fge51MHOF/N/R8fl8On78uNLT0zV69GilpaUFbGH19PRo3759ZojJycmR3W4PqGlra9ORI0fMGpfLpa6uLh04cMCsaW5uVldXl1kDAAAw4Ds6FRUVmjVrlkaOHKmOjg794Ac/UHd3t+bOnSubzaaysjKtWLFCY8aM0ZgxY7RixQrFxsaquLhYkuR0OjV//nyVl5dr+PDhSkpKUkVFhcaPH28+hTV27FhNnz5dJSUl2rx5syRpwYIFKiws5IkrAABgGvCgc+bMGf3DP/yD3nvvPf3N3/yN8vLytH//fo0aNUqS9MQTT+jq1atatGiROjs7lZubK6/Xq/j4eHOO9evXa+jQoZozZ46uXr2qqVOnatu2bYqKijJrduzYodLSUvPprKKiIm3cuHGgLwcAAESwAQ86NTU1tzxvs9lUWVmpysrKm9YMGzZMVVVVqqqqumlNUlKSqqur+7tMAADwKcDvugIAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJY1dLAXAAAAPt7nntw92EsImiPK0KoHBncN7OgAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADLIugAAADL4ldAAAA+lbIr35Cv1zbYy0CIsaMDAAAsix0dAPiEYachtD4Jv2gS4cOODgAAsKyI39F59tlntXr1arW1tenee+/Vhg0b9Pd///eDvSzAkthpCC12GoCBF9FB55VXXlFZWZmeffZZTZo0SZs3b9aMGTN07NgxjRw5crCXJ4n/MIQLfQ4t/gMMIFJF9EdX69at0/z58/Xtb39bY8eO1YYNG5SZmalNmzYN9tIAAMAnQMTu6PT09KilpUVPPvlkwLjb7VZjY+MNX+Pz+eTz+czjrq4uSdL58+fl9/sHdH1+v19XrlzRUP8Q9X7ATkOoDP3A0JUrH9DnEKPP4UGfw4M+h8+1Xr///vuy2+0DNu/FixclSYZhfPwaBuxdw+y9995Tb2+vUlNTA8ZTU1PV3t5+w9esXLlSy5Yt6zM+evTokKwR4VE82Av4lKDP4UGfw4M+h08oe33x4kU5nc5b1kRs0LnGZgtM44Zh9Bm7ZunSpVqyZIl5/MEHH+j8+fMaPnz4TV/TX93d3crMzNTp06eVkJAwoHPj/6PP4UGfw4M+hwd9Dp9Q9dowDF28eFEZGRkfWxuxQSc5OVlRUVF9dm86Ojr67PJc43A45HA4AsbuuuuuUC1RkpSQkMD/kMKAPocHfQ4P+hwe9Dl8QtHrj9vJuSZiv4wcHR2tnJwc1dfXB4zX19dr4sSJg7QqAADwSRKxOzqStGTJEnk8Hk2YMEEul0vPPfecTp06pe985zuDvTQAAPAJENFB59FHH9X777+vf//3f1dbW5uys7P12muvadSoUYO9NDkcDv3bv/1bn4/KMLDoc3jQ5/Cgz+FBn8Pnk9Brm3E7z2YBAABEoIj9jg4AAMDHIegAAADLIugAAADLIugAAADLIuj007PPPqvRo0dr2LBhysnJ0W9+85ub1u7du1c2m63Pz+9+97swrjhyBdNr6cPfafb0009r1KhRcjgc+vznP6/nn38+TKuNXMH0ed68eTe8p++9994wrjgyBXs/79ixQ1/60pcUGxur9PR0/dM//ZPef//9MK02cgXb55/85CcaO3asYmJilJWVpZdeeilMK41cb775pmbNmqWMjAzZbDb98pe//NjX7Nu3Tzk5ORo2bJjuvvtu/ed//mfoF2ogaDU1NYbdbje2bNliHDt2zHj88ceNuLg44913371h/a9//WtDknHixAmjra3N/PnrX/8a5pVHnmB7bRiGUVRUZOTm5hr19fXGyZMnjebmZuN///d/w7jqyBNsny9cuBBwL58+fdpISkoy/u3f/i28C48wwfb5N7/5jTFkyBDjRz/6kfGnP/3J+M1vfmPce++9xiOPPBLmlUeWYPv87LPPGvHx8UZNTY3xxz/+0fiv//ov4zOf+Yzx6quvhnnlkeW1114znn76aWPnzp2GJKO2tvaW9X/605+M2NhY4/HHHzeOHTtmbNmyxbDb7cbPf/7zkK6ToNMPDzzwgPGd73wnYOyLX/yi8eSTT96w/lrQ6ezsDMPqrCXYXr/++uuG0+k03n///XAszzKC7fP1amtrDZvNZrzzzjuhWJ5lBNvn1atXG3fffXfA2I9//GNjxIgRIVujFQTbZ5fLZVRUVASMPf7448akSZNCtkaruZ2g88QTTxhf/OIXA8YWLlxo5OXlhXBlhsFHV0Hq6elRS0uL3G53wLjb7VZjY+MtX3v//fcrPT1dU6dO1a9//etQLtMS+tPrV199VRMmTNCqVav02c9+Vvfcc48qKip09erVcCw5It3JPX3N1q1bNW3atE/EP9b5SdWfPk+cOFFnzpzRa6+9JsMwdPbsWf385z/XzJkzw7HkiNSfPvt8Pg0bNixgLCYmRgcOHJDf7w/ZWj9tmpqa+vy9FBQU6NChQyHtM0EnSO+99556e3v7/OLQ1NTUPr9g9Jr09HQ999xz2rlzp37xi18oKytLU6dO1ZtvvhmOJUes/vT6T3/6kxoaGnTkyBHV1tZqw4YN+vnPf67HHnssHEuOSP3p80e1tbXp9ddf17e//e1QLdES+tPniRMnaseOHXr00UcVHR2ttLQ03XXXXaqqqgrHkiNSf/pcUFCgn/70p2ppaZFhGDp06JCef/55+f1+vffee+FY9qdCe3v7Df9e/vrXv4a0zxH9KyAGk81mCzg2DKPP2DVZWVnKysoyj10ul06fPq01a9boq1/9akjXaQXB9PqDDz6QzWbTjh07zN9su27dOn3jG9/QT37yE8XExIR8vZEqmD5/1LZt23TXXXfpkUceCdHKrCWYPh87dkylpaX613/9VxUUFKitrU3f+9739J3vfEdbt24Nx3IjVjB9fuaZZ9Te3q68vDwZhqHU1FTNmzdPq1atUlRUVDiW+6lxo7+XG40PJHZ0gpScnKyoqKg+/59BR0dHn6R6K3l5eXr77bcHenmW0p9ep6en67Of/awZciRp7NixMgxDZ86cCel6I9Wd3NOGYej555+Xx+NRdHR0KJcZ8frT55UrV2rSpEn63ve+p/vuu08FBQV69tln9fzzz6utrS0cy444/elzTEyMnn/+eV25ckXvvPOOTp06pc997nOKj49XcnJyOJb9qZCWlnbDv5ehQ4dq+PDhIXtfgk6QoqOjlZOTo/r6+oDx+vp6TZw48bbneeutt5Senj7Qy7OU/vR60qRJ+vOf/6xLly6ZY7///e81ZMgQjRgxIqTrjVR3ck/v27dPf/jDHzR//vxQLtES+tPnK1euaMiQwP8zfW2HweDXFN7QndzPdrtdI0aMUFRUlGpqalRYWNin/+g/l8vV5+/F6/VqwoQJstvtoXvjkH7V2aKuPbq4detW49ixY0ZZWZkRFxdnPnHy5JNPGh6Px6xfv369UVtba/z+9783jhw5Yjz55JOGJGPnzp2DdQkRI9heX7x40RgxYoTxjW98wzh69Kixb98+Y8yYMca3v/3twbqEiBBsn6/5x3/8RyM3Nzfcy41Ywfb5hRdeMIYOHWo8++yzxh//+EejoaHBmDBhgvHAAw8M1iVEhGD7fOLECWP79u3G73//e6O5udl49NFHjaSkJOPkyZODdAWR4eLFi8Zbb71lvPXWW4YkY926dcZbb71lPsZ/fZ+vPV7+z//8z8axY8eMrVu38nj5J9lPfvITY9SoUUZ0dLTxd3/3d8a+ffvMc3PnzjUefPBB8/g//uM/jM9//vPGsGHDjMTEROMrX/mKsXv37kFYdWQKpteGYRjHjx83pk2bZsTExBgjRowwlixZYly5ciXMq448wfb5woULRkxMjPHcc8+FeaWRLdg+//jHPzbGjRtnxMTEGOnp6ca3vvUt48yZM2FedeQJps/Hjh0z/vZv/9aIiYkxEhISjIcfftj43e9+NwirjizX/umU63/mzp1rGMaN7+e9e/ca999/vxEdHW187nOfMzZt2hTyddoMg/1PAABgTXz4CAAALIugAwAALIugAwAALIugAwAALIugAwAALIugAwAALIugAwAALIugAwAALIugAwAALIugAwAALIugAwAALIugAwAALOv/AVXS/1fCC8SwAAAAAElFTkSuQmCC",
      "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": 41,
   "id": "1a2ed3cc",
   "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": 42,
   "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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e44c55c",
   "metadata": {},
   "source": [
    "### Option 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dbb347b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Namespace(model_state_file='MLP_classifier.pth', save_dir='model_storage/CLIP_ML_EN/', result_dir='results/CLIP_ML_EN/', embedding_size=512, early_stopping_criteria=20, num_epochs=20, seed=42, batch_size=32, embedding_type='image', fusion_type='average', 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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2afe1375",
   "metadata": {},
   "outputs": [],
   "source": [
    "args.embedding_type = 'multi'\n",
    "args.fusion_type = 'complex'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 45,
     "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": 46,
   "id": "3961b71e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 128])"
      ]
     },
     "execution_count": 46,
     "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": 47,
   "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": 48,
   "id": "6bf457bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 512])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labs['img_emb'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e01ffae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 128])"
      ]
     },
     "execution_count": 49,
     "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": 50,
   "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     0.7936    0.7076    0.7481     29306\n",
      "         1.0     0.6304    0.7304    0.6767     20010\n",
      "\n",
      "    accuracy                         0.7168     49316\n",
      "   macro avg     0.7120    0.7190    0.7124     49316\n",
      "weighted avg     0.7274    0.7168    0.7191     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8019    0.7198    0.7586     20463\n",
      "         1.0     0.6450    0.7412    0.6898     14058\n",
      "\n",
      "    accuracy                         0.7285     34521\n",
      "   macro avg     0.7235    0.7305    0.7242     34521\n",
      "weighted avg     0.7380    0.7285    0.7306     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7772    0.6811    0.7260      4400\n",
      "         1.0     0.6038    0.7134    0.6540      2997\n",
      "\n",
      "    accuracy                         0.6942      7397\n",
      "   macro avg     0.6905    0.6973    0.6900      7397\n",
      "weighted avg     0.7070    0.6942    0.6968      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9581    0.7382    0.8339       806\n",
      "         1.0     0.6011    0.9244    0.7285       344\n",
      "\n",
      "    accuracy                         0.7939      1150\n",
      "   macro avg     0.7796    0.8313    0.7812      1150\n",
      "weighted avg     0.8513    0.7939    0.8024      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7425    0.6683    0.7035      3594\n",
      "         1.0     0.6042    0.6860    0.6425      2653\n",
      "\n",
      "    accuracy                         0.6758      6247\n",
      "   macro avg     0.6734    0.6772    0.6730      6247\n",
      "weighted avg     0.6838    0.6758    0.6776      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8756    0.5648    0.6867       324\n",
      "         1.0     0.4854    0.8365    0.6143       159\n",
      "\n",
      "    accuracy                         0.6542       483\n",
      "   macro avg     0.6805    0.7006    0.6505       483\n",
      "weighted avg     0.7471    0.6542    0.6629       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 71.68\n",
      "    Training Set Accuracy: 72.85\n",
      "    Validation Set Accuracy: 69.42\n",
      "    Validation Set with both Images and Texts: 79.39\n",
      "    Validation Set with only Texts: 67.58\n",
      "    Validation Set with unseen Images: 65.42\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7936    0.7076    0.7481     29306\n",
      "         1.0     0.6304    0.7304    0.6767     20010\n",
      "\n",
      "    accuracy                         0.7168     49316\n",
      "   macro avg     0.7120    0.7190    0.7124     49316\n",
      "weighted avg     0.7274    0.7168    0.7191     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8019    0.7198    0.7586     20463\n",
      "         1.0     0.6450    0.7412    0.6898     14058\n",
      "\n",
      "    accuracy                         0.7285     34521\n",
      "   macro avg     0.7235    0.7305    0.7242     34521\n",
      "weighted avg     0.7380    0.7285    0.7306     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7704    0.6775    0.7210      4443\n",
      "         1.0     0.5895    0.6964    0.6385      2955\n",
      "\n",
      "    accuracy                         0.6851      7398\n",
      "   macro avg     0.6800    0.6870    0.6797      7398\n",
      "weighted avg     0.6982    0.6851    0.6880      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9365    0.7416    0.8277       716\n",
      "         1.0     0.6278    0.8966    0.7385       348\n",
      "\n",
      "    accuracy                         0.7923      1064\n",
      "   macro avg     0.7821    0.8191    0.7831      1064\n",
      "weighted avg     0.8355    0.7923    0.7985      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7422    0.6651    0.7016      3727\n",
      "         1.0     0.5832    0.6697    0.6235      2607\n",
      "\n",
      "    accuracy                         0.6670      6334\n",
      "   macro avg     0.6627    0.6674    0.6625      6334\n",
      "weighted avg     0.6768    0.6670    0.6694      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8043    0.5305    0.6393       279\n",
      "         1.0     0.4760    0.7677    0.5877       155\n",
      "\n",
      "    accuracy                         0.6152       434\n",
      "   macro avg     0.6402    0.6491    0.6135       434\n",
      "weighted avg     0.6871    0.6152    0.6209       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 71.68\n",
      "    Training Set Accuracy: 72.85\n",
      "    Validation Set Accuracy: 68.51\n",
      "    Validation Set with both Images and Texts: 79.23\n",
      "    Validation Set with only Texts: 66.70\n",
      "    Validation Set with unseen Images: 61.52\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": 51,
   "id": "88e2ed53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 1024])"
      ]
     },
     "execution_count": 51,
     "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": 52,
   "id": "109bc18d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 1024])"
      ]
     },
     "execution_count": 52,
     "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": 53,
   "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     0.6707    0.8618    0.7543     29306\n",
      "         1.0     0.6526    0.3803    0.4805     20010\n",
      "\n",
      "    accuracy                         0.6664     49316\n",
      "   macro avg     0.6617    0.6210    0.6174     49316\n",
      "weighted avg     0.6634    0.6664    0.6432     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6702    0.8609    0.7537     20463\n",
      "         1.0     0.6544    0.3833    0.4835     14058\n",
      "\n",
      "    accuracy                         0.6664     34521\n",
      "   macro avg     0.6623    0.6221    0.6186     34521\n",
      "weighted avg     0.6638    0.6664    0.6436     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6715    0.8675    0.7570      4400\n",
      "         1.0     0.6597    0.3770    0.4798      2997\n",
      "\n",
      "    accuracy                         0.6688      7397\n",
      "   macro avg     0.6656    0.6223    0.6184      7397\n",
      "weighted avg     0.6667    0.6688    0.6447      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8121    0.9330    0.8684       806\n",
      "         1.0     0.7589    0.4942    0.5986       344\n",
      "\n",
      "    accuracy                         0.8017      1150\n",
      "   macro avg     0.7855    0.7136    0.7335      1150\n",
      "weighted avg     0.7962    0.8017    0.7877      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6442    0.8528    0.7340      3594\n",
      "         1.0     0.6447    0.3619    0.4635      2653\n",
      "\n",
      "    accuracy                         0.6443      6247\n",
      "   macro avg     0.6445    0.6073    0.5988      6247\n",
      "weighted avg     0.6444    0.6443    0.6191      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7688    0.8827    0.8218       324\n",
      "         1.0     0.6577    0.4591    0.5407       159\n",
      "\n",
      "    accuracy                         0.7433       483\n",
      "   macro avg     0.7132    0.6709    0.6813       483\n",
      "weighted avg     0.7322    0.7433    0.7293       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 66.64\n",
      "    Training Set Accuracy: 66.64\n",
      "    Validation Set Accuracy: 66.88\n",
      "    Validation Set with both Images and Texts: 80.17\n",
      "    Validation Set with only Texts: 64.43\n",
      "    Validation Set with unseen Images: 74.33\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6707    0.8618    0.7543     29306\n",
      "         1.0     0.6526    0.3803    0.4805     20010\n",
      "\n",
      "    accuracy                         0.6664     49316\n",
      "   macro avg     0.6617    0.6210    0.6174     49316\n",
      "weighted avg     0.6634    0.6664    0.6432     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6702    0.8609    0.7537     20463\n",
      "         1.0     0.6544    0.3833    0.4835     14058\n",
      "\n",
      "    accuracy                         0.6664     34521\n",
      "   macro avg     0.6623    0.6221    0.6186     34521\n",
      "weighted avg     0.6638    0.6664    0.6436     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6721    0.8602    0.7546      4443\n",
      "         1.0     0.6371    0.3689    0.4672      2955\n",
      "\n",
      "    accuracy                         0.6640      7398\n",
      "   macro avg     0.6546    0.6145    0.6109      7398\n",
      "weighted avg     0.6581    0.6640    0.6398      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7877    0.9330    0.8542       716\n",
      "         1.0     0.7778    0.4828    0.5957       348\n",
      "\n",
      "    accuracy                         0.7857      1064\n",
      "   macro avg     0.7828    0.7079    0.7250      1064\n",
      "weighted avg     0.7845    0.7857    0.7697      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6518    0.8463    0.7364      3727\n",
      "         1.0     0.6167    0.3537    0.4495      2607\n",
      "\n",
      "    accuracy                         0.6435      6334\n",
      "   macro avg     0.6343    0.6000    0.5930      6334\n",
      "weighted avg     0.6374    0.6435    0.6183      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7341    0.8710    0.7967       279\n",
      "         1.0     0.6505    0.4323    0.5194       155\n",
      "\n",
      "    accuracy                         0.7143       434\n",
      "   macro avg     0.6923    0.6516    0.6581       434\n",
      "weighted avg     0.7043    0.7143    0.6977       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 66.64\n",
      "    Training Set Accuracy: 66.64\n",
      "    Validation Set Accuracy: 66.40\n",
      "    Validation Set with both Images and Texts: 78.57\n",
      "    Validation Set with only Texts: 64.35\n",
      "    Validation Set with unseen Images: 71.43\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": 54,
   "id": "f491e654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 2560])"
      ]
     },
     "execution_count": 54,
     "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": 55,
   "id": "a727b55c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2560])"
      ]
     },
     "execution_count": 55,
     "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": 56,
   "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     0.6278    0.8635    0.7270     29306\n",
      "         1.0     0.5560    0.2504    0.3453     20010\n",
      "\n",
      "    accuracy                         0.6147     49316\n",
      "   macro avg     0.5919    0.5569    0.5362     49316\n",
      "weighted avg     0.5987    0.6147    0.5721     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6267    0.8634    0.7262     20463\n",
      "         1.0     0.5582    0.2513    0.3466     14058\n",
      "\n",
      "    accuracy                         0.6141     34521\n",
      "   macro avg     0.5924    0.5573    0.5364     34521\n",
      "weighted avg     0.5988    0.6141    0.5716     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6275    0.8595    0.7254      4400\n",
      "         1.0     0.5489    0.2509    0.3444      2997\n",
      "\n",
      "    accuracy                         0.6130      7397\n",
      "   macro avg     0.5882    0.5552    0.5349      7397\n",
      "weighted avg     0.5957    0.6130    0.5710      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7161    0.9702    0.8240       806\n",
      "         1.0     0.5862    0.0988    0.1692       344\n",
      "\n",
      "    accuracy                         0.7096      1150\n",
      "   macro avg     0.6512    0.5345    0.4966      1150\n",
      "weighted avg     0.6773    0.7096    0.6281      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6079    0.8347    0.7035      3594\n",
      "         1.0     0.5473    0.2706    0.3622      2653\n",
      "\n",
      "    accuracy                         0.5952      6247\n",
      "   macro avg     0.5776    0.5527    0.5328      6247\n",
      "weighted avg     0.5821    0.5952    0.5585      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6861    0.9444    0.7948       324\n",
      "         1.0     0.5135    0.1195    0.1939       159\n",
      "\n",
      "    accuracy                         0.6729       483\n",
      "   macro avg     0.5998    0.5320    0.4943       483\n",
      "weighted avg     0.6293    0.6729    0.5970       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 61.47\n",
      "    Training Set Accuracy: 61.41\n",
      "    Validation Set Accuracy: 61.30\n",
      "    Validation Set with both Images and Texts: 70.96\n",
      "    Validation Set with only Texts: 59.52\n",
      "    Validation Set with unseen Images: 67.29\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6278    0.8635    0.7270     29306\n",
      "         1.0     0.5560    0.2504    0.3453     20010\n",
      "\n",
      "    accuracy                         0.6147     49316\n",
      "   macro avg     0.5919    0.5569    0.5362     49316\n",
      "weighted avg     0.5987    0.6147    0.5721     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6267    0.8634    0.7262     20463\n",
      "         1.0     0.5582    0.2513    0.3466     14058\n",
      "\n",
      "    accuracy                         0.6141     34521\n",
      "   macro avg     0.5924    0.5573    0.5364     34521\n",
      "weighted avg     0.5988    0.6141    0.5716     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6336    0.8679    0.7325      4443\n",
      "         1.0     0.5526    0.2453    0.3398      2955\n",
      "\n",
      "    accuracy                         0.6192      7398\n",
      "   macro avg     0.5931    0.5566    0.5361      7398\n",
      "weighted avg     0.6012    0.6192    0.5756      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6968    0.9595    0.8073       716\n",
      "         1.0     0.6282    0.1408    0.2300       348\n",
      "\n",
      "    accuracy                         0.6917      1064\n",
      "   macro avg     0.6625    0.5502    0.5187      1064\n",
      "weighted avg     0.6743    0.6917    0.6185      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6214    0.8503    0.7180      3727\n",
      "         1.0     0.5478    0.2593    0.3520      2607\n",
      "\n",
      "    accuracy                         0.6070      6334\n",
      "   macro avg     0.5846    0.5548    0.5350      6334\n",
      "weighted avg     0.5911    0.6070    0.5674      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6778    0.9427    0.7886       279\n",
      "         1.0     0.6522    0.1935    0.2985       155\n",
      "\n",
      "    accuracy                         0.6751       434\n",
      "   macro avg     0.6650    0.5681    0.5436       434\n",
      "weighted avg     0.6687    0.6751    0.6136       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 61.47\n",
      "    Training Set Accuracy: 61.41\n",
      "    Validation Set Accuracy: 61.92\n",
      "    Validation Set with both Images and Texts: 69.17\n",
      "    Validation Set with only Texts: 60.70\n",
      "    Validation Set with unseen Images: 67.51\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": 57,
   "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": 57,
     "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": 58,
   "id": "913f8c7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 128])"
      ]
     },
     "execution_count": 58,
     "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": 59,
   "id": "bb70907b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 128])"
      ]
     },
     "execution_count": 59,
     "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": 60,
   "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     0.8377    0.6640    0.7408     29306\n",
      "         1.0     0.6226    0.8116    0.7046     20010\n",
      "\n",
      "    accuracy                         0.7239     49316\n",
      "   macro avg     0.7301    0.7378    0.7227     49316\n",
      "weighted avg     0.7504    0.7239    0.7261     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8471    0.6764    0.7522     20463\n",
      "         1.0     0.6358    0.8222    0.7171     14058\n",
      "\n",
      "    accuracy                         0.7358     34521\n",
      "   macro avg     0.7414    0.7493    0.7346     34521\n",
      "weighted avg     0.7610    0.7358    0.7379     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8239    0.6382    0.7193      4400\n",
      "         1.0     0.6009    0.7998    0.6862      2997\n",
      "\n",
      "    accuracy                         0.7037      7397\n",
      "   macro avg     0.7124    0.7190    0.7027      7397\n",
      "weighted avg     0.7336    0.7037    0.7059      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9598    0.8002    0.8728       806\n",
      "         1.0     0.6632    0.9215    0.7713       344\n",
      "\n",
      "    accuracy                         0.8365      1150\n",
      "   macro avg     0.8115    0.8609    0.8220      1150\n",
      "weighted avg     0.8711    0.8365    0.8424      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7906    0.6018    0.6834      3594\n",
      "         1.0     0.5924    0.7840    0.6749      2653\n",
      "\n",
      "    accuracy                         0.6792      6247\n",
      "   macro avg     0.6915    0.6929    0.6791      6247\n",
      "weighted avg     0.7064    0.6792    0.6798      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8826    0.6265    0.7329       324\n",
      "         1.0     0.5217    0.8302    0.6408       159\n",
      "\n",
      "    accuracy                         0.6936       483\n",
      "   macro avg     0.7022    0.7284    0.6868       483\n",
      "weighted avg     0.7638    0.6936    0.7025       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.39\n",
      "    Training Set Accuracy: 73.58\n",
      "    Validation Set Accuracy: 70.37\n",
      "    Validation Set with both Images and Texts: 83.65\n",
      "    Validation Set with only Texts: 67.92\n",
      "    Validation Set with unseen Images: 69.36\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8377    0.6640    0.7408     29306\n",
      "         1.0     0.6226    0.8116    0.7046     20010\n",
      "\n",
      "    accuracy                         0.7239     49316\n",
      "   macro avg     0.7301    0.7378    0.7227     49316\n",
      "weighted avg     0.7504    0.7239    0.7261     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8471    0.6764    0.7522     20463\n",
      "         1.0     0.6358    0.8222    0.7171     14058\n",
      "\n",
      "    accuracy                         0.7358     34521\n",
      "   macro avg     0.7414    0.7493    0.7346     34521\n",
      "weighted avg     0.7610    0.7358    0.7379     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8073    0.6327    0.7094      4443\n",
      "         1.0     0.5832    0.7729    0.6648      2955\n",
      "\n",
      "    accuracy                         0.6887      7398\n",
      "   macro avg     0.6953    0.7028    0.6871      7398\n",
      "weighted avg     0.7178    0.6887    0.6916      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9436    0.7947    0.8628       716\n",
      "         1.0     0.6811    0.9023    0.7763       348\n",
      "\n",
      "    accuracy                         0.8299      1064\n",
      "   macro avg     0.8124    0.8485    0.8195      1064\n",
      "weighted avg     0.8578    0.8299    0.8345      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7787    0.6016    0.6788      3727\n",
      "         1.0     0.5702    0.7557    0.6500      2607\n",
      "\n",
      "    accuracy                         0.6650      6334\n",
      "   macro avg     0.6745    0.6786    0.6644      6334\n",
      "weighted avg     0.6929    0.6650    0.6669      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8238    0.5699    0.6737       279\n",
      "         1.0     0.5021    0.7806    0.6111       155\n",
      "\n",
      "    accuracy                         0.6452       434\n",
      "   macro avg     0.6630    0.6753    0.6424       434\n",
      "weighted avg     0.7089    0.6452    0.6514       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.39\n",
      "    Training Set Accuracy: 73.58\n",
      "    Validation Set Accuracy: 68.87\n",
      "    Validation Set with both Images and Texts: 82.99\n",
      "    Validation Set with only Texts: 66.50\n",
      "    Validation Set with unseen Images: 64.52\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": 61,
   "id": "d6aa36c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 1024])"
      ]
     },
     "execution_count": 61,
     "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": 62,
   "id": "ffcaaf9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 1024])"
      ]
     },
     "execution_count": 62,
     "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": 63,
   "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     0.7417    0.7845    0.7625     29306\n",
      "         1.0     0.6553    0.5998    0.6263     20010\n",
      "\n",
      "    accuracy                         0.7096     49316\n",
      "   macro avg     0.6985    0.6921    0.6944     49316\n",
      "weighted avg     0.7066    0.7096    0.7072     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7437    0.7869    0.7647     20463\n",
      "         1.0     0.6611    0.6051    0.6319     14058\n",
      "\n",
      "    accuracy                         0.7129     34521\n",
      "   macro avg     0.7024    0.6960    0.6983     34521\n",
      "weighted avg     0.7101    0.7129    0.7106     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7361    0.7816    0.7582      4400\n",
      "         1.0     0.6473    0.5886    0.6166      2997\n",
      "\n",
      "    accuracy                         0.7034      7397\n",
      "   macro avg     0.6917    0.6851    0.6874      7397\n",
      "weighted avg     0.7001    0.7034    0.7008      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8963    0.8362    0.8652       806\n",
      "         1.0     0.6683    0.7733    0.7170       344\n",
      "\n",
      "    accuracy                         0.8174      1150\n",
      "   macro avg     0.7823    0.8047    0.7911      1150\n",
      "weighted avg     0.8281    0.8174    0.8209      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7054    0.7693    0.7360      3594\n",
      "         1.0     0.6437    0.5646    0.6016      2653\n",
      "\n",
      "    accuracy                         0.6824      6247\n",
      "   macro avg     0.6746    0.6670    0.6688      6247\n",
      "weighted avg     0.6792    0.6824    0.6789      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8503    0.7716    0.8091       324\n",
      "         1.0     0.6085    0.7233    0.6609       159\n",
      "\n",
      "    accuracy                         0.7557       483\n",
      "   macro avg     0.7294    0.7474    0.7350       483\n",
      "weighted avg     0.7707    0.7557    0.7603       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 70.96\n",
      "    Training Set Accuracy: 71.29\n",
      "    Validation Set Accuracy: 70.34\n",
      "    Validation Set with both Images and Texts: 81.74\n",
      "    Validation Set with only Texts: 68.24\n",
      "    Validation Set with unseen Images: 75.57\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7417    0.7845    0.7625     29306\n",
      "         1.0     0.6553    0.5998    0.6263     20010\n",
      "\n",
      "    accuracy                         0.7096     49316\n",
      "   macro avg     0.6985    0.6921    0.6944     49316\n",
      "weighted avg     0.7066    0.7096    0.7072     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7437    0.7869    0.7647     20463\n",
      "         1.0     0.6611    0.6051    0.6319     14058\n",
      "\n",
      "    accuracy                         0.7129     34521\n",
      "   macro avg     0.7024    0.6960    0.6983     34521\n",
      "weighted avg     0.7101    0.7129    0.7106     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7380    0.7765    0.7567      4443\n",
      "         1.0     0.6353    0.5854    0.6094      2955\n",
      "\n",
      "    accuracy                         0.7002      7398\n",
      "   macro avg     0.6866    0.6810    0.6831      7398\n",
      "weighted avg     0.6970    0.7002    0.6979      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8791    0.8324    0.8551       716\n",
      "         1.0     0.6891    0.7644    0.7248       348\n",
      "\n",
      "    accuracy                         0.8102      1064\n",
      "   macro avg     0.7841    0.7984    0.7899      1064\n",
      "weighted avg     0.8169    0.8102    0.8125      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7140    0.7658    0.7390      3727\n",
      "         1.0     0.6264    0.5616    0.5922      2607\n",
      "\n",
      "    accuracy                         0.6817      6334\n",
      "   macro avg     0.6702    0.6637    0.6656      6334\n",
      "weighted avg     0.6780    0.6817    0.6786      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8123    0.7599    0.7852       279\n",
      "         1.0     0.6127    0.6839    0.6463       155\n",
      "\n",
      "    accuracy                         0.7327       434\n",
      "   macro avg     0.7125    0.7219    0.7158       434\n",
      "weighted avg     0.7410    0.7327    0.7356       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 70.96\n",
      "    Training Set Accuracy: 71.29\n",
      "    Validation Set Accuracy: 70.02\n",
      "    Validation Set with both Images and Texts: 81.02\n",
      "    Validation Set with only Texts: 68.17\n",
      "    Validation Set with unseen Images: 73.27\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": 64,
   "id": "8bbf9085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 2560])"
      ]
     },
     "execution_count": 64,
     "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": 65,
   "id": "fe5505c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2560])"
      ]
     },
     "execution_count": 65,
     "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": 66,
   "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     0.6278    0.8635    0.7270     29306\n",
      "         1.0     0.5560    0.2504    0.3453     20010\n",
      "\n",
      "    accuracy                         0.6147     49316\n",
      "   macro avg     0.5919    0.5569    0.5362     49316\n",
      "weighted avg     0.5987    0.6147    0.5721     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6267    0.8634    0.7262     20463\n",
      "         1.0     0.5582    0.2513    0.3466     14058\n",
      "\n",
      "    accuracy                         0.6141     34521\n",
      "   macro avg     0.5924    0.5573    0.5364     34521\n",
      "weighted avg     0.5988    0.6141    0.5716     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6275    0.8595    0.7254      4400\n",
      "         1.0     0.5489    0.2509    0.3444      2997\n",
      "\n",
      "    accuracy                         0.6130      7397\n",
      "   macro avg     0.5882    0.5552    0.5349      7397\n",
      "weighted avg     0.5957    0.6130    0.5710      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7161    0.9702    0.8240       806\n",
      "         1.0     0.5862    0.0988    0.1692       344\n",
      "\n",
      "    accuracy                         0.7096      1150\n",
      "   macro avg     0.6512    0.5345    0.4966      1150\n",
      "weighted avg     0.6773    0.7096    0.6281      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6079    0.8347    0.7035      3594\n",
      "         1.0     0.5473    0.2706    0.3622      2653\n",
      "\n",
      "    accuracy                         0.5952      6247\n",
      "   macro avg     0.5776    0.5527    0.5328      6247\n",
      "weighted avg     0.5821    0.5952    0.5585      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6861    0.9444    0.7948       324\n",
      "         1.0     0.5135    0.1195    0.1939       159\n",
      "\n",
      "    accuracy                         0.6729       483\n",
      "   macro avg     0.5998    0.5320    0.4943       483\n",
      "weighted avg     0.6293    0.6729    0.5970       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 61.47\n",
      "    Training Set Accuracy: 61.41\n",
      "    Validation Set Accuracy: 61.30\n",
      "    Validation Set with both Images and Texts: 70.96\n",
      "    Validation Set with only Texts: 59.52\n",
      "    Validation Set with unseen Images: 67.29\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6278    0.8635    0.7270     29306\n",
      "         1.0     0.5560    0.2504    0.3453     20010\n",
      "\n",
      "    accuracy                         0.6147     49316\n",
      "   macro avg     0.5919    0.5569    0.5362     49316\n",
      "weighted avg     0.5987    0.6147    0.5721     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6267    0.8634    0.7262     20463\n",
      "         1.0     0.5582    0.2513    0.3466     14058\n",
      "\n",
      "    accuracy                         0.6141     34521\n",
      "   macro avg     0.5924    0.5573    0.5364     34521\n",
      "weighted avg     0.5988    0.6141    0.5716     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6336    0.8679    0.7325      4443\n",
      "         1.0     0.5526    0.2453    0.3398      2955\n",
      "\n",
      "    accuracy                         0.6192      7398\n",
      "   macro avg     0.5931    0.5566    0.5361      7398\n",
      "weighted avg     0.6012    0.6192    0.5756      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6968    0.9595    0.8073       716\n",
      "         1.0     0.6282    0.1408    0.2300       348\n",
      "\n",
      "    accuracy                         0.6917      1064\n",
      "   macro avg     0.6625    0.5502    0.5187      1064\n",
      "weighted avg     0.6743    0.6917    0.6185      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6214    0.8503    0.7180      3727\n",
      "         1.0     0.5478    0.2593    0.3520      2607\n",
      "\n",
      "    accuracy                         0.6070      6334\n",
      "   macro avg     0.5846    0.5548    0.5350      6334\n",
      "weighted avg     0.5911    0.6070    0.5674      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6778    0.9427    0.7886       279\n",
      "         1.0     0.6522    0.1935    0.2985       155\n",
      "\n",
      "    accuracy                         0.6751       434\n",
      "   macro avg     0.6650    0.5681    0.5436       434\n",
      "weighted avg     0.6687    0.6751    0.6136       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 61.47\n",
      "    Training Set Accuracy: 61.41\n",
      "    Validation Set Accuracy: 61.92\n",
      "    Validation Set with both Images and Texts: 69.17\n",
      "    Validation Set with only Texts: 60.70\n",
      "    Validation Set with unseen Images: 67.51\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": 67,
   "id": "b46a0293",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {}\n",
    "results['fusion'] = perform_results\n",
    "results['new_embedding'] = embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "474c0a22",
   "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": "3640628f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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